Cars are automobiles that can transport people.
 It is the main means of travelling for hundreds of millions of people all over the world. 
Cars have changed the way we live probably more than any other invention in history. 
At first only a few people had cars but after a while more and more people bought them because they improved the way people lived. 
Farmers with cars were able to bring their products to places that were farther away. 
The appearance of cities and towns also changed. More and more workers drove to their jobs and people started to move to suburbs outside the town centers.
Automobiles give people many jobs. 
Millions of people around the world work in factories where cars are produced. 
Millions more work at gas stations, restaurants or motels that travelers stop at.
However, cars also cause problems. 
Millions of people die in car accidents every year. 
Automobiles pollute the air that we breathe and parking space in cities is scarce because everyone wants to use their cars to get to city centers.
Cars are very complicated machines and all systems in them work together. 
They power a car, control and steer it and make it comfortable for people to drive in.
The heart of every car is its engine. 
It produces the power that turns the wheels and electricity for lights and other systems.
Most automobiles are powered by an internal combustion engine. 
Fuel, usually gasoline or petrol, is burned with air to create gases that expand. 
A spark plug creates a spark that ignites the gas and makes it burn. 
This energy moves through cylinders in which pistons slide up and down. 
They are attached to rods that move a crankshaft. 
Normal car engines have four to six cylinders but there are also models with eight and sixteen cylinders. 
The turning movement is passed through the drivetrain to the drive wheels.
The fuel system pumps petrol from the tank to the engine. 
Older cars used to have carburetors that mix fuel with air and send the gas to the engine. 
Some cars have a special fuel injection system that sprays petrol into the engine. 
Modern cars have turbo chargers that suck in extra air and therefore create more power.
The engine and all parts that carry power to the wheels are called the drivetrain. 
It includes the transmission, drive shaft, differential, the axles and the drive wheels that move the car. 
While most cars have drive wheels in the front, some have them in the back.
 Cars that need to drive over all kinds of ground have a four-wheel drive.
The transmission controls the speed and torque. 
When a car travels at a normal speed on a flat road it does not need so much torque to keep it moving, but when you want to start a car from a hill the engine must produce more power. 
Gears control speed and power of the engine in different driving conditions.
In cars with manual transmission you have to change gears by pressing down the clutch with your foot and moving a lever. 
Cars with automatic transmission change gears without control by the driver. 
Lower gears give the car more torque and speed. When the car moves faster the transmission shifts to higher gears.
The driveshaft carries the power to the axle which is connected to the wheels. 
It has several joints which make the axle and wheels moveable as the car drives on uneven and bumpy roads.
The differential is connected to the rear end of the driveshaft. 
It lets the wheels turn at different speeds because in curves the outer wheels must travel a greater distance than the inner ones.
The steering system controls the front wheels. 
Turning the steering wheel makes them point to the left or right. 
Most cars have power steering; a hydraulic system makes it easier for the driver to turn the wheels.
The brake system slows down or stops the car. Brakes operate on all four wheels. 
There are two basic types of brakes: drum or disc brakes.
 In both cases a friction pad is pressed against a drum or disc with the help of a hydraulic system.
All cars have emergency hand brakes which you use if the hydraulic system fails. 
It is also called a parking brake because you use it to stop a vehicle from rolling down a hill. 
Antilock braking systems (ABS) keep the wheels turning when you step on the brakes. 
This computer controlled system prevents skidding if you are on a slippery road.
The suspension system supports the weight of the car. 
It has wheels, axles, tires and springs. 
Most cars have shock absorbers to guarantee a smooth ride. 
Springs are between the axles of the wheels and the body of the car. 
They allow each wheel to move up and down on its own. 
The tires also help to make driving smoother. 
They are built so that they give the car grip on roads in all conditions.
When a car burns fuel gases are produced. 
They must be removed so that new fuel can be burned. 
The pistons in the engine’s cylinders force gas out of the engine. 
It passes through a muffler into tail pipes. 
The muffler also keeps the car running quietly. 
For about thirty years cars have been equipped with a catalytic converter. 
It reduces pollution by converting harmful gases into carbon dioxide and water.
Burning fuel inside a car’s engine creates a lot of heat. 
Most of it has to be removed by a cooling system. 
Liquid cooling systems have a mixture of water and chemicals. 
A water pump forces this mixture to flow between the cylinders of the engine. 
The hot water is then pumped through a radiator where the air carries away the heat.
Oil is important for an engine to work.
It flows through the moving parts so that the metal does not rub against other metallic pieces. 
Without lubrication the metal would become too hot and the engine would be destroyed.
Oil is stored in an oil tank at the bottom of the engine. 
From there it is pumped around the engine. 
A filter removes dirt from the oil so that it won’t do any damage to engine parts. 
After you have driven a certain number of kilometers you must change the oil and the oil filter.
The dashboard has many instruments that show you how fast you are moving, the amount of petrol that is left in the tank, the oil temperature and some other information.
The body of the car is the outer shell that surrounds the mechanical parts and the passengers inside. 
Most bodies are made of steel, although some parts are made of strong plastic or fiberglass. 
The body includes the passenger compartment, hood, trunk and the fenders which cover the wheels.
Today all cars have safety features that protect passengers from accidents that may happen on the road. 
In almost every country passengers have to fasten their seat belts. 
Children and babies must be put in special seats.
Since the mid 1990s almost all cars have been equipped with air bags. 
They are normally in the steering wheel and if a car crashes they come out, inflate and protect the passengers from slamming into the front window. 
But there are other safety laws that carmakers must follow. 
Doors must have special locks that are crash resistant and bumpers must be able to absorb some force if the car crashes.
In the late 1770s Nicolas-Joseph Cugnot, a French engineer, built a car that ran on steam. 
Many American companies also started producing them but they were very expensive to make and cost a lot of money.
As time went on, engineers started experimenting with petrol-driven cars. 
They could travel faster and over longer distances. 
They were also safer than steam-powered models which ran with petrol.
Towards the end of the 19 th century Germany became the centre of car-making. 
Nikolaus Otto built the first internal combustion engine, Gottlieb Daimler and Karl Benz also began building petrol-driven engines.
Automobile production in the USA began in the 1890s.
 It was Henry Ford who started producing cars on an assembly line. 
Workers do only one task and car parts pass on a conveyer belt. 
By 1908 Ford’s Model T became the most popular car in the world and by 1927 the Ford Motor Company had produced over 15 million of them.
After car production had slowed down during the two world wars car makers began adding new features to post - war models. Power steering, power brakes and automatic controls became common. 
More and more big cars were produced in the 1950s and 1960s. 
They used up a lot of fuel in a time in which oil was still very cheap.
This changed in the 1970s when Arab oil-producing countries started to raise prices for oil because western countries supported Israel.
In the years that followed much was done to try to save and conserve fuel. 
Automakers started producing compact cars that were fuel-efficient.
In the meantime, Japan and Europe had begun to compete with American carmakers. 
By 1980 Japan became the largest car producing countries in the world.
Even though today’s car is a great machine that is fast, elegant and beautiful to look at, engineers are constantly working on a car that will make today’s automobile look old. 
Experts say that future cars will be made of plastics and carbon fibers that will be stronger and lighter than steel.
As oil is becoming more and more expensive, alternative power sources are being explored. 
Biodiesel, hydrogen fuel cells, electric cars and hybrids are energy sources that carmakers may use in the future.
Cars are becoming computerized machines. 
Some day they may drive themselves. 
Highways and other roads could be built so that cars can be programmed to drive along them by autopilot while passengers sit in the back and relax. 
Such cars could be radar controlled to avoid contact with other vehicles on the road.
Sport Utility Vehicles (SUVs) have existed since the last 1940s, though they didn't gain the popular name until the 1980s.
Built on a light truck drivetrain, these vehicles mix rugged off-road performance with comfort and room to spare.
The earliest versions of this vehicle type developed out of military needs in World War II, which were eventually converted to civilian needs with the addition of doors and hard roofs.
Today, these vehicles are most associated with general toughness and excellent handling over a variety of terrains.
They've also become more luxurious, with many recent models featuring comforts designed for long off-road vacations.
Their popularity began to explode in the 1990s when a mix between market pressure towards larger vehicles and families' desires for safer vehicles came together.
For many households, SUVs were the ideal replacement for station wagons and minivans - they were about the same size, but with the added power, they were better at getting jobs done.
As an extension of this, SUVs are easy to find in the used car market, making them affordable on many different budgets.
That said, they do have some competition in crossovers, which have a lower and more car-like ride than SUVs.
Many SUVs sold today are equipped with four-wheel drive systems for maximum power, but it is possible to find two-wheel drive versions at a lower cost.
?This is especially helpful for families that don't need to go off-road very often and would rather save on purchase price and gas.
Modern SUVs are typically sold as mid-sized or full-sized - smaller SUVs are rare because, in most cases, they just don't fit what buyers are looking for.
Mid-size SUVs tend to have better handling and fuel economy, and they're fundamentally similar to crossovers (while having better towing and off-road abilities).
Full-size SUVs are more like pickup trucks with a huge cabin instead of a storage bed in the back.
?These vehicles typically seat as many as nine passengers, with enough towing ability to haul significant loads.?
?There is one other thing that's held SUVs back, however - fuel economy.
Full-size SUVs usually get around 20 combined miles per gallon, which is significantly lower than many other types of vehicles.
?It's not a surprise since they're built on the basic framework of trucks, but the higher fuel cost is something to keep in mind when deciding which type of vehicle to buy.
Trucks are one of the oldest types of vehicles in existence, with their earliest version appearing in the 1800s and starting to take off past 1900 as the steam wagon.
Throughout this time, trucks have remained true to their original purpose: carrying heavy cargo loads. 
The trucks most people think of when they hear the word are actually ""light trucks,"" car-sized vehicles meant for personal and small business use. 
This type of truck is what this guide focuses on.
 These trucks are sometimes referred to as ""pickup trucks"" instead (for reasons nobody knows), but regardless of the name, trucks are associated with power and toughness.
 Curiously, most people don't buy pickup trucks for work - instead, they're often selected as a lifestyle choice and used similarly to passenger cars. 
Accounting for almost 1 in 5 vehicles sold, trucks are easy to find on the used market and can often be rented for short-term needs. 
Since they're built for power, pickup trucks typically come in four-wheel drive versions capable of heavy rear loads and towing motorhomes, boats, or other large, heavy trailers behind them. 
Aside from the powertrain, however, there are two major types of trucks sold. 
The first version is the cargo-focused truck, which has a small passenger cabin and a large truck bed. 
These rarely carry more than three people in comfort but offer plenty of space for tools, gardening material, or anything else the owner wants to haul around. 
They're also a popular choice for carrying campers and other significant modifications.
 The second version is the passenger truck, which has a smaller cargo bed and a larger, two-row passenger cabin.
 This is more of a family or work crew vehicle and tends to come with more features and luxuries than the cargo-focused version. Regardless of style, trucks tend to be elevated higher off the ground than most other vehicles, making them more difficult to get in and out of.
 To solve this, it's common for manufacturers to add handlebars and steps that passengers can use when getting in and out, though it's rare to find steps that make it easy to get into an opened cargo bed. 
Other sizes of trucks include ultra-lights (which are typically 1-2 passenger vehicles meant for narrow spaces and graceful movement), medium trucks (which mostly see use with public utilities like firefighting and garbage collection), and heavy trucks (large dump trucks, heavy cargo trailers, and so on).
Sedans are passenger cars separated into three distinct sections - engine, passenger, and cargo. 
Most sedans have them in that order from front to back, but a few models switch the engine and cargo sections. 
Vehicles that lack engines, such as some modern electric vehicles, tend to have front and rear cargo sections instead. 
Sedans always seat at least four people, and more often five, between two rows of seats. 
Sedans also have at least 33 cubic feet of rear interior volume - any smaller and the vehicle is a coupe instead, regardless of configuration or seating. 
The earliest modern Sedan began production in 1899, though the name wouldn't come into common use until quite some time afterward. 
Since then, the basic format of the vehicle has remained the same, though it has split into two offshoots. 
The smaller version is the mid-size sedan, which usually has two single seats up front, a three-person bench in the rear, four doors, and a trunk for cargo. 
Full-size sedans are just like mid-size sedans, but generally larger all-around. 
Sport sedans are a variant model, offering more power and performance than standard models. 
Since their primary purpose is to get people from one place to another by way of paved roads, sedans are one of the most popular vehicles on the market and are usually bought by families that have minimal cargo-transportation needs. 
Sedans with plenty of cargo space are also popular as rental vehicles, and indeed, make up the majority of most rental fleets. 
Since they're not meant for rough off-road use, sedans tend to have a low wheelbase and center of gravity, and this typically serves them well. 
Two things tend to set sedans apart from each other. 
First are the exact dimensions of the vehicle, with consumers generally preferring sedans that have more leg, head, and cargo room. 
This is especially important for taller buyers since sedans don't always have enough clearance for them to sit in the vehicle comfortably. 
The second factor is the luxuries offered on each model. 
From modified blinkers to robust entertainment centers, sedans run the gamut between low-cost, no-frills transport and true luxury. 
In recent years, thanks to legislation and technological advancements, sedans have seen significant improvements in gas mileage and the types of engine offered, with hybrid and electric vehicles becoming an increasingly significant part of the market. 
With prices that are similarly diverse and plenty of used models on dealers' lots, sedans represent affordable transportation on just about any budget.
Tracing their descent from the covered caravans of old, vans are one of the most popular types of vehicles in the world. 
The main thing that distinguishes vans from trucks is the interior access to the cargo areas and the fully covered body. 
This makes vans more secure and more protected from the elements than trucks, making them better for many buyers' needs. 
Most vans are built in one of four styles. 
The most popular style for many families is the passenger van, which usually features three or four rows of seats and a small rear cargo area. 
Essentially a private bus, passenger vans are ideal for moving large families or groups of people with minimal fuss. 
While some passenger vans have doors for each row, it's more common to see doors only on the first two rows. 
Other passengers usually have to squeeze into the back.
 The second popular type of van is the cargo van, which removes most or all of the seats behind the front row in favor of open space. 
These vehicles often add cabinets or shelves with a center aisle for movement, allowing them to maximize the amount of cargo they can hold. 
Some cargo vans are used for surveillance or converted into mobile entertainment centers. 
The third type is the refrigerated van, though these are rarely bought by consumers - instead, they're used to carry things like specialty items like luxury foods or certain medical supplies.
 Finally, minivans are a smaller version of passenger vans and tend to have almost no cargo space, though the seats can fold down to make room.
On some models, the seats must be moved down for passengers who want access to the back row. 
Today, vans have largely fallen out of favor as passenger vehicles for families - SUVs tend to offer just as much space but more flexibility with cargo. 
Instead, they're often bought by clubs or groups. 
Cargo vans are used almost entirely by small businesses who need to transport lots of products or people on a regular basis. 
This includes many service industries, such as cleaners, plumbers, and technical support for telecommunication companies. 
Cargo vans are also the vehicles of choice for parcel delivery companies, though these businesses tend to prefer box-shaped vans over the smoother, more fuel-efficient passenger versions. 
There's some question about whether or not drone deliveries will undercut the market for vans, but for now, these remain the most effective way to transport 9-15 people or significant amounts of cargo.
Since the earliest iteration of the design in the 18th century, the coupe has been the small, sporty member of the vehicle family. 
Essentially a smaller version of sedans, coupes feature room for 2-4 passengers between one or two rows of seats.
While most coupes have a single door on either side, some versions have four doors for easy access to a rear passenger bench. 
These versions, in particular, are much like sedans, with the main difference being that coupes have less than 33 cubic feet of rear interior volume. 
Since these vehicles have almost no cargo space, they're rarely used as family vehicles. Instead, coupes are often driven for fun on racetracks, scenic roads, or day trips. 
Some companies have begun using the term coupe for marketing since the word is seen as more exciting than the ""boring"" family sedan. 
Ultimately, coupes tend to fall into one of two categories. 
Passenger coupes are simply a way of getting between two points with comfort and style. 
Sport coupes are more like racing vehicles - in fact, some really are racing vehicles - and have significantly more power and performance than their small size suggests. 
Most coupes sold today are sport coupes since there's little demand among families for vehicles that don't have the space to hold children and groceries at the same time. 
As luxury vehicles, coupes tend to come with more bells and whistles than their sedan equivalents, especially when it comes to performance and control. 
It's not hard to find coupes with hundreds of horsepower, allowing them to perform well even when loaded with as much as will fit. 
The biggest flaw in coupes is, perhaps, inevitable - they simply don't have the mass of larger vehicles. 
This can make them more dangerous during a collision, especially when a much larger vehicle is involved. 
This isn't as important for city driving, but it's something to think about if you're on the highway a lot. 
As far as fuel economy goes, coupes vary significantly.
Many of the better models receive at least 25 combined miles per gallon, but some hybrid and electric versions have achieved more than 100 combined miles per gallon. 
That said, there's usually a tradeoff between power and fuel economy, so it's important to check the exact specifications of any model you want to purchase. 
Don't limit yourself to the sticker price when deciding which coupe to buy - the long-term cost of ownership is a better measure for any vehicle, but it's especially applicable to coupes.
Wagons - also known variably as station wagons and estate cars - first began to appear in the 1930s as service vehicles in train depots. 
The focus was on commercial and business applications instead of consumer transport, but companies soon realized that the distinctive shaping offered value for families. 
Unlike sedans, which are split into the engine, passenger, and cargo sections, wagons only have two parts - the engine and a larger combined passenger/cargo area. 
Wagons are similar to hatchbacks, but typically prioritize cargo space and usefulness over style and aerodynamics. 
Today, wagons are seen as a smaller and more affordable alternative to SUVs and minivans, which have a similar design but are considerably larger overall. 
Since sales are low, there aren't too many used station wagons available - Subaru models being a notable exception since they continue to market and release wagons each year. 
Modern station wagons don't compete with each other on size - get too big and you end up with an SUV, which defeats the purpose of making a wagon in the first place. 
Instead, they compete on features, especially those associated with the tailgate and passenger seating. 
Wagons offer several tailgate designs worth considering. 
The most basic of these is the simple upper hinge, which lifts the entire door up. 
Wagons often include a motion sensor for 'no touch' entry, allowing you to open the rear without having to put down groceries or other cargo. 
The second type of liftgate is the dual gate, which has the window open up and the solid lower section fall down (to provide a step). 
This is seen as more of a working design since it allows access to things on the inside of the wagon without having to open the entire thing. 
A few models feature a foldable tailgate and a retractable roof, allowing the wagon to fit unusually tall objects. 
These wagons are effectively a sedan/truck hybrid, offering an unusual but surprisingly effective niche. 
Some station wagons are only a little larger than their sedan bases, but others have a pickup truck-length cargo area (especially on full-size wagons). 
Regardless of liftgate and cargo area style, most modern wagons have foldable rear seats so the vehicle can focus on passengers or cargo as needed. 
Mechanically, most wagons are based on a sedan wheelbase and have similar transmission, engine, and luxury options. 
Ultimately, wagons aren't for everyone, but they remain a popular choice for families who want more storage than a sedan without the cost of an SUV.
Convertibles first appeared on the market soon after automobiles were introduced, representing the choice of whether or not to drive with a roof. 
After all, not everyone wanted to drive in the rain, so cars couldn't be open forever. 
The earliest roofs were typically made of leather or folding textiles, but they began to fall out of favor in the 1920's when fully-enclosed steel vehicles started to see mass production. 
At this point, the remaining convertibles were luxury vehicles and ignored by most of the population. 
Convertibles experienced something of a renaissance after World War II when soldiers stationed in Europe began looking for roadster-like cars back home. 
Production surged for a few years but fell again in the 1970s as the noise of higher road speeds and improved safety standards made convertibles dubious at best. 
The proliferation of air conditioning further cemented the convertible's decline. 
However, newer advancements have helped the convertible return once again.
It's still seen as something of a luxury vehicle, but the varying styles have helped to overcome many of the convertible's biggest flaws. 
The most important part of a convertible is its roof, which generally comes in one of three styles. 
The most iconic style is the soft top, which is usually black and made of a mixture of materials including polyester, rubber, and canvas. 
These roofs need to be replaced every few years, but the fact that they can easily fold up and hide in the vehicle means storage isn't a concern. 
Rear windows are often made of plastic with this type of roof, but some convertibles have found a way to switch to glass. 
The second type of roof is the detachable hardtop, which can be stored in a garage whenever you don't want to drive with it. 
This is more popular on older models, but since the top needs to be left at home, it's the least secure model if you're parking elsewhere. 
The final type of convertible roof is the retractable hardtop, which can hide in the rear of the vehicle thanks to its mechanical systems. 
These are the safest type of convertible roof, but the added mechanical systems mean they're also the most expensive to install and maintain. 
Aside from the roof (and any necessary components thereof), convertibles are otherwise identical to the coupe or sedan base they're installed on. 
Regardless of style, convertibles are still considered luxury vehicles meant for people who enjoy feeling the wind as they drive.
Opinions vary on what defines a sports car. 
Some people use the term to describe any vehicle that has two seats, but for our purposes, the term refers to a lightweight, high-maneuverability, high-power vehicle. 
They should not be confused with muscle cars, which are larger and meant for high-speed racing. 
Most sports cars only seat two people, but the occasional four-door, five-passenger models exist. 
Despite the name, sports cars aren't always luxury vehicles - some of them are quite spartan. 
Sports cars have been an important part of the market since the 1920s when their high power and performance attracted people who just wanted to drive instead of move people or cargo between destinations. 
This distinction holds true today, as sports cars are still vehicles that are more about the journey than the destination. 
Several specific traits help to set the sports car apart from coupes and other ""sporty"" competition. 
First, the low center of gravity and precisely tuned steering offer maximum control even at high speeds. 
This allows sports cars to smoothly navigate roads that other vehicles find treacherous at best. 
Meanwhile, the light body of the vehicle ensures that the car can dedicate as much power as possible to speed and control, rather than moving the car itself. 
Power and performance are strongly influenced by the engine and drivetrain layout, and this may be the most important thing to evaluate if you're thinking about buying a sports car. 
The most common layout has a front engine and a rear-wheel-drive, but some models have switched to a rear engine with rear-wheel-drive. 
This is intended to help with weight distribution and improve handling, and it's used by manufacturers like Lamborghini and Ferrari. 
A rarer setup has a front engine and front-wheel-drive, which is prone to understeer. 
However, it's ideal for lighter and lower-power sports cars because it significantly reduces the vehicle's weight. 
If you're looking for a sports car at a lower price point, you may want to look at vehicles in this style. 
Four-wheel drive is rare on sports cars because it adds significant weight to the vehicle, but it can be found on high-power models with the horsepower to handle it. 
Today, sports cars aren't as popular as sedans or SUVs, but enough of them have been sold over the years to ensure a steady presence in new and used car lots. 
These vehicles don't work as family cars, but if you enjoy driving, it's hard to beat a sports car for pure power and pleasure.
Diesel isn't a type of vehicle - it's a type of engine. 
While most commonly associated with trucks and other high-power, heavy-duty vehicles, diesel engines have also been installed on SUVs, sedans, crossovers, and even some compact vehicles. 
The basic design of the diesel engine has been around since the 1800s, though it's received significant improvements over the years to enhance its power and overall efficiency. 
These engines have remained viable throughout the decades, serving as a steady companion to the more-popular Internal Combustion Engine seen on most passenger vehicles. 
Diesel engines aren't just ICE with a different type of gasoline, though - there are some significant differences between them. 
The most significant advantage is that diesel fuel has a higher energy density than gasoline - in effect, it takes less fuel to do the same amount of work. 
Depending on the efficiency of the engine, diesel fuel is about 10% to 15% more efficient than gasoline. 
As if that wasn't enough, diesel engines are about 15% more efficient at converting the energy into mechanical power. 
Together, these factors allow diesel engines to go about 20% to 35% farther on a single gallon, making them ideal for long-distance movement. 
Diesel engines also tend to be more reliable than their ICE counterparts - with no need for high voltage electronics, diesel vehicles are better in damp environments. 
The straightforward, high-powered construction gives the engines better longevity, too, with diesel engines typically lasting twice as long as their gasoline counterparts. 
Traditionally, diesel fuel exists as a fractional distillate of petroleum fuel oil, but advances in technology have seen the introduction of viable alternatives to petroleum. 
The most popular of these is biodiesel, which is produced by plants or animals to create a renewable source of energy. 
In most cases, biodiesel can be used in standard diesel engines with no need for additives or alterations, though some companies prefer to blend it with petroleum-based diesel (often called ""petrodiesel"") to help keep costs down. 
Other fuel variants include biomass to liquid (BTL) and gas to liquid (GTL), which respectively turn grasses and natural gas into viable diesel fuel. 
Neither of these is as popular as biodiesel or petrodiesel, but ongoing research and studies hope to change this. 
Thanks to all of these advantages, diesel engines aren't likely to disappear from the market anytime soon - especially when it comes to transporting heavy items over long distances. 
Battery-powered vehicles may become more popular someday, but the ease of producing biodiesel gives countries an effectively unlimited supply as long as there are power plants to create it.
Crossovers first began to show up in 1948 with the Willys-Overland Jeepster, though they wouldn't take the form we know for several more decades. 
At its core, the crossover is a vehicle that offers most of the features of an SUV but places them on a sedan or wagon base instead of a truck base. 
This offers generally better handling and control at the cost of some power and ability. 
Many consumers cannot see the difference between crossovers and SUVs, and for most of them, the distinction doesn't matter. 
Part of the confusion is driven by the use of ""crossover"" as a marketing tool, which blurs the line of expectations. 
Broadly, however, crossovers can be split into four major categories. 
Mini Crossovers are subcompact vehicles with mid-high rear roofs but short cargo areas. 
This makes them effective at hauling tall items like luggage, but the majority of interior space is focused on passengers.
Compact crossovers are a little larger and tend to feature more cargo space and a somewhat larger engine, making them a better choice for up to five passengers. 
Mid-size crossovers have significantly more cargo space than their smaller relatives, and it's here that they're easily mistaken for SUVs. 
The use of the crossover name in marketing certainly hasn't helped matters, but from the exterior and interior alike, mid-size crossovers look like SUVs. 
The main difference is in the mechanics, rather than the design. Full-size crossovers have a more distinctive cargo area, and many have a third row of seats that can be raised or lowered as needed. 
At this point, crossovers are closer to being a full-size SUV or a minivan than a sedan, but they still retain the lighter and agiler base. 
This makes full-size crossovers the ideal choice for groups that need to move a large family without too much extra weight or need for towing. 
In the modern market, crossovers are often lumped together with the SUVs they resemble - and by 2006, crossovers made up more than half of the overall SUV market. 
As things currently stand, crossovers are more popular with older segments of the population, who generally appreciate the roomy interiors, easy handling, and reasonable amounts of power. 
In many areas, crossovers have all but entirely replaced the smaller station wagons, lending credence to the belief that people want more cargo space as long as it doesn't increase the price too much. 
For the foreseeable future, crossovers are likely to remain a major segment of the market.
Luxury cars have existed for almost as long as cars themselves, but opinions vary on what a luxury car is. 
Some people define this as any vehicle from a luxury-focused brand like Rolls-Royce, while others believe that a luxury car is anything with features and gadgets beyond the basics. 
Also, what constitutes luxury has changed over time. Several years ago, electronic touchscreens and rear-view cameras were rare and exciting, but now they're expected on just about every vehicle. 
For our purposes, we're going to define a luxury car as any car that has significantly better equipment, performance, materials, comforts, technology, or features than most vehicles of its type. 
For many vehicles, this means the most expensive models in their line qualify as luxury cars thanks to a variety of standard and optional features. 
However, this does not mean that expensive vehicles are inherently luxurious - the premium version of a mass-market vehicle may have more comforts than a true luxury brand's entry-level model. 
The primary cause of this cost difference is the optics. 
Much like wearing an expensive watch, luxury vehicles are a status symbol meant to impress others. 
Any Mercedes-Benz is likely to be valued higher than a Honda or Mazda, regardless of their features and specifications. 
Most luxury cars are coupes or sedans, but there are a few luxury SUVs and Crossovers on the market. 
Minivans and trucks are almost never considered luxury vehicles - their respective emphasis on transporting many people and hauling heavy loads is seen as ""mundane"", and therefore lesser, though some people have modified these types of vehicles enough to make them luxurious. 
Regardless of manufacturer, several components are common (although not universal) in luxury vehicles. 
Two of the most popular choices are quality leather upholstery and wood-grain paneling, both of which provide a more natural, comfortable appearance. 
Luxury cars also tend to get new safety and entertainment features before the rest of the market - now-common features like DVD entertainment, anti-lock brakes, and electronic stability control all showed up on luxury cars before appearing anywhere else. 
For sporty vehicles, luxury cars tend to feature larger and more powerful engines as well as better handling and control. 
Naturally, all of these new features and luxuries come at a price. 
On average, a luxury car will likely cost at least $10,000 more than a mass-market competitor, even within the same model line. 
This doesn't include the price of optional features, many of which can drive up the price by another several thousand dollars.
A hybrid electric vehicle (HEV) is a car that adds an electric motor and a battery to an existing internal combustion (or, more rarely, diesel) engine. 
While more expensive than a standard engine-only vehicle, this setup offers several notable advantages that many people feel justifies the added cost. 
First, and most importantly, many hybrid electric vehicles are capable of using only their onboard battery for short trips. 
This means you could go for days or even weeks without consuming fuel - and it's undoubtedly cheaper to recharge a battery than it is to fill a tank with gasoline. 
HEVs also use a variety of energy-saving and energy-generating technologies, such as regenerative brakes that charge the battery by converting the kinetic energy from braking into electrical power. 
Despite the recent surge in interest, HEVs aren't new.
 In fact, the Lohner-Porsche Mixed Hybrid vehicle was produced from 1900 to 1905, but limits on efficiency and energy storage stopped it from being a viable competitor for the other vehicles of the time. 
Today, the majority of hybrid electric vehicles are coupes and sedans, though the technology is slowly spreading to trucks, buses, and other types of vehicles. 
Most of these vehicles fit into one of two categories. 
Mild Hybrid vehicles emphasize fuel economy and can't run solely on their electric motor. 
For these vehicles, the electric motor is typically used when the car is stopped, braking, or coasting along, and the engine is restarted whenever power is needed. 
This delivers an overall improvement of about 15% for urban driving, which is where most mild hybrids are used. 
Full Hybrid vehicles are capable of running entirely on gasoline, entirely on electric, or in a combination. 
Full hybrids are seen as the better of the two types because they ultimately have much better fuel economy. 
Despite sharing the hybrid name, Mild Hybrid and Full Hybrid are distinctly different types of vehicles. 
This is why you should never buy a vehicle just because it has a ""hybrid"" in the name. 
Instead, take a careful look at the specifications to be sure the vehicle works the way you think it does. 
If you end up buying the wrong type of vehicle, you probably won't save as much money as you expected to. 
Also, remember that Plug-in Hybrid Electric Vehicles (PHEVs) have fast-charging batters and tend to have significantly better range than Full Hybrids.
 If you're worried about being caught with a dead battery and low fuel tank on a long trip, a PHEV is the best way to avoid that.
For classification purposes, automobile manufacturers have historically divided their light vehicle products into two categories, Automobiles and Light Trucks. 
Sales and marketing analysts use these classifications when discussing auto sales figures, so I’ll use them as well.
In the section called Hybrids, I discuss the new categories that cross the car-truck line, as well as the new alternative-powered cars.
Sedans are a good choice for most automobile shoppers.
The enclosed trunk offers security, while the rear doors allow easy entry for rear-seat passengers. 
Most luxury vehicles are four-door sedans because they’re more comfortable than most other body styles.
The smallest available in the US are sub-compact sedans like the Hyundai Accent and Chevrolet Metro. 
Slightly larger are compact models like the Honda Civic and Ford Focus.
Mid-size sedans include the Honda Accord, Toyota Camry, Ford Taurus, and Chevrolet Lumina, while the Ford Crown Victoria and Buick LeSabre are considered large sedans. 
Automotive marketers have created a new “near-luxury” sedan category, meaning any new sedan priced between $30,000 and $40,000, while the traditional luxury sedan costs over $40,000 when new. 
Near-luxury sedans are usually mid-sized; luxury sedans are usually large, though there are a few exceptions to the size and price limitations.
Coupes are usually driven by single adults or childless couples.
Many of them have a hatchback instead of a trunk, to allow large items to be carried for short distances. 
The rear seats are difficult to access, as the front doors must be used.
An active family will want to look at minivans, sport utility vehicles, or station wagons. 
In the rest of the world, station wagons remain the first choice for active families. 
In North America, first minivans and now SUVs have grabbed most of the station wagon’s customer base.
I have to admit that many minivans now drive and handle much like the wagons they’ve replaced, but I don’t understand the increasing popularity of large SUV’s. 
They’re twice as big as they need to be, but seat fewer people than a minivan; they get horrible gas mileage, and their truck-like ride and handling are rough.
You’ll pay substantially more to insure an SUV than a comparable automobile as a direct result of their poor handling. 
Many inexperienced drivers find out the hard way that SUVs don’t corner like automobiles.
I strongly suggest SUV shoppers reconsider and take another look at the station wagon. 
Station wagons offer more stability, better gas mileage, lower insurance rates, and SUV-sized interiors. 
You won’t lose your all-wheel drive either, as Subaru, Volkswagen, Audi, Volvo, and Mercedes-Benz offer all-wheel drive on all of their wagons.
Most convertibles are sports cars, meaning two seats, high-performance engines and superior handling. 
However, GM, Ford, Mitsubishi, and Chrysler offer a few “normal” convertibles, i.e. regular production coupes with four seats and convertible tops, such as the Chevrolet Cavalier, Pontiac Sunfire, Ford Mustang, Dodge Avenger, Chrysler Conquest and Mitsubishi Eclipse Spyder. 
Luxury convertibles are available from BMW, Mercedes-Benz, Saab, and Volvo. 
Convertibles are great when the weather’s perfect, but their drawbacks are obvious.
Sports cars were originally European two-seat roadsters designed for both daily travel and week-end racing hobbyists. 
A few 1950’s manufacturers (notably Jaguar and Alfa-Romeo) put permanent tops on their roadsters, resulting in the sports coupe.
The term sports-sedan is a more recent term to describe a four-door vehicle that handles like a sports coupe or roadster. 
Recently we’ve seen luxury cars advertised as luxury sports sedans. 
Porsche, selling traditional sports cars in this country since the 1950’s, until recently had as its only competition the Chevrolet Corvette.
1990 marked the return of the affordable sports car in this country, when Mazda offered its MX-5 (Miata) for under $20,000 dollars, and the incredible demand for it prompted other companies to follow suit.
BMW, Mercedes-Benz, Mitsubishi, and Porsche all introduced roadsters for under $40,000 in the latter part of the 1990’s. 
At the same time, Dodge dealers begged Chrysler to produce a 1993 concept car to give the Corvette a run for the money.
The Dodge Viper and Plymouth Prowler remain a success story for Chrysler, with this year’s production already sold out. 
Sports cars are cool and fun to drive, though impractical for daily transportation. 
You’ll need a garage to store them in, and a second mortgage to pay for their insurance. 
But if you’ve got money to burn, go for it!.
If you’re constantly carting kids or cargo, a minivan may be your best choice. 
Most newer models offer an additional 4th door on the driver’s side and offer comfortable seating for seven. 
Be aware of the different engines available.
I highly recommend you elect to get the largest ( 3.5 & 3.8 liter) engine available in whatever minivan you decide upon. 
Positively avoid buying a 4-cylinder Dodge or Chrysler minivan, they’re grossly underpowered and incapable of hauling heavy payloads for any distances. 
Most minivans are only available with front wheel drive, although Chrysler offers an optional all-wheel-drive system on certain models.
According to Crashtest.com, the Ford Windstar, Toyota Sienna, and ’99 & ’00 Honda Odyssey are three of the safest vehicles on the road today.
In addition, minivans drive and handle just like a car, with the bonus of better visibility due to a higher center of gravity and an upright driving position. 
Don’t look for minivans to handle your boat or trailer towing duties, as front wheel drive vehicles have a very limited towing capacity.
I mentioned in the Station Wagon category how I regard SUVs. 
Although they’re designed for off-road usage, 90% of them never leave the road, fortunately for our wildernesses. 
If a wagon isn’t for you, the car-like SUV’s ride and handle significantly better than the rest.
They include the BMW X-5, the Lexus RX 300, and the Mercedes-Benz ML320, ML430, and AMG-tuned ML55.
More new pickup trucks are sold in this country than any other type of vehicle.
The smaller models now offer quad or crew-cab four-door versions, with seating for 5 adults. 
Full-size models offer extended cabs with smaller third and fourth doors giving access to the rear seats.
Standard rear-wheel drive versions don’t handle well on snow or ice without a substantial amount of weight in the rear of the truck. 
When equipped with towing packages with 8- or 10-cylinder engines, these rear-wheel drive vehicles can tow large boats and trailers. 
Full-size 2-wheel and 4-wheel drive pickups get about 15 miles per gallon.
If you transport large amounts of cargo or need room for more than seven adults, a full-size van is your only option. 
They’re available with and without windows and in payload capacities of over one ton.
Extended vans can seat up to 15 adult passengers. 
Towing packages with 8- or 10-cylinder engines will allow these rear-wheel-drive vehicles to tow large boats and trailers.
A type of car in which the cargo area is located behind the rear pillars of the car, often separated from passenger cabin.
Compared to hatchbacks, sedans have better protection for the cargo against theft due to the lack of glass window.
A type of car in which the rear cargo door swings up when opened.
The rear window exposes the content of the cargo space, but can be covered up using mat. 
Traditionally, hatchbacks are usually taller than sedans with taller roof and taller driving position. 
With the seats folded, the cargo space can be linked with passenger room, creating a larger cargo space. 
Hatchbacks have shorter cargo length than sedans but in exchange they have superior height.
Minivans are bigger sized hatchbacks that often come with third row seats (6–8 seats). The main emphasize is on cabin space, which is why it’s often associated with family cars.
SUV is a type of car that emphasize on off-road capability. 
They are characterized by the tall ride height, boxy body shape (to make it easy to look around), tall bonnet (to see the distance between bonnet and object ahead), and most importantly, they use ladder frame chassis, which is also found in trucks. 
Ladder frame chassis is heavier than the usual monocoque chassis but has an advantage on off roading.
Often mistaken as SUV, CUV/crossover is probably the most confusing type of car. 
Some CUVs resemble the design of SUVs, but what makes it different is the chassis. 
SUVs use ladder frame chassis like trucks, while CUVs use the lighter monocoque chassis like sedans and hatchbacks. 
They are taller than hatchbacks, but not as spacious as minivans.
Pickup is a type of car in which the cargo is separated from the passenger cabin and has an open cargo bed, which can be closed with mat. 
The open cargo bed allows unlimited vertical space.
Coupes are basically cars with 2 doors. It can be based on sedan or hatchback.
Convertibles are cars that can fold its roof out, allowing passengers to interact with outdoor wind.
They have longer cargo length than sedan (or equally to sedan) with hatchback style cargo door. 
Station wagons are often a variation of an existing sedan/hatchback.
They have longer cargo length than sedan (or equally to sedan) with hatchback style cargo door. 
Station wagons are often a variation of an existing sedan/hatchback.
The smallest size category for cars is micro. 
They are extremely small and can only fit 1 person. 
They are extremely rare because the size is not practical.Examples : BMW Isetta, Corbin Sparrow, Heinkei Kabine.
They are considered small in most countries. 
In exchange for the short length, they often have tall height to maximize space.
In Japan, there is a size category called Kei cars. Kei cars are made to avoid size taxes and insurance cost. 
Examples : Smart fortwo, Suzuki Wagon R, Volkswagen Up, Ford Ka.
The most common size for cars in developing Asian countries, which is considered a decent size. 
Examples : Toyota Yaris/Vios, Honda Fit/Jazz/City, Volkswagen Polo, Ford Fiesta.
The most common size for cars in developed countries. 
They are spacious enough for groceries and family. 
Examples : Toyota Corolla, Honda Civic, Volkswagen Golf/Jetta, Ford Focus, Mercedes-Benz C-Class, BMW 3 Series.
Usually for family need, but some benefit from their engine size and cargo space. 
Examples : Toyota Camry, Honda Accord, Mercedes-Benz E-Class, BMW 5 Series.
Considered the longest size for those who need for space or simply want the premium of extra space. 
Examples : Toyota Crown, Honda Legend, Mercedes-Benz S-Class, BMW 7 Series.
A type of car that emphasize on performance. 
Body style is traditionally coupe or convertible, but a sedan or hatchback can also be a sportscar. 
The requirement to become a sportscar is very subjective. 
Usually the must exceed certain horsepower depending on the era.
Hatchbacks that have sportscar performance and handling.
Cars that have a stretched length, either to accommodate more passengers or to increase legroom.
Sedan/coupe/sportscar with pickup bed and sportscar performance.
Mid size sportscar that emphasize on power over handling for saving cost.
Like muscle car, but compact size. 
Sometimes pony cars are also classified as muscle cars.
 Cars that are either designed or modified to go racing ; often sacrifice comfort for the sake of performance and lighter weight.
 2 door sportscars that are designed to be comfortable for long trip with extra 2 seats at the back and a relatively spacious cargo space.
A modification style in which the car receive different engine (usually V8). 
Most hotrods are based on pre-war American cars. 
Some common features include visible engine from the outside and exposed tires.
Basically hot rod with rusty/old appearance.
 The body paint is often matte finish.
A modification style using full size American cars from the 60s-80s.
 Often feature complex details and low ride height. 
The signature feature is hydraulic suspension system, allowing independent suspensions to adjust the height, creating an impression that the car is dancing. 
Chrome grille and chrome wheels are very common as well.
Japanese-inspired modification that focus on performance and handling. 
They often come with big spoiler and big bumpers.
Japanese modification that reflects a gangster lifestyle, characterized by the extreme size of exhaust pipes and bumpers.
 A modification style with Californian beach theme. 
Volkswagen Beetle is usually the popular choice.
Japanese style modification that reflects modern luxury lifestyle. 
The cars are usually full size luxury sedans with large chrome wheels, large bumpers, low ride height, and wheel camber angle that are pop out.
Cars modified for drifting stunt.
Cars modified for rally motorsport, or simply for recreational driving on dirt.
Cars modified for drag race (straight line race).
Classic cars that are modified minimal enough to maintain the original looks of the car.
A car built from scratch by the owner, where the parts are individually selected from different manufacturers.
A motorsport that represents the pinnacle achievement of racing technologies. 
They consists of different classes, with F1 as the highest performed one.
A type of motorsport that takes part on speedway tracks (Tracks that are built for higher gears). 
Like its name, the wheels of the cars are enclosed by the body for protection against collision.
A race that takes place on rough terrains.
A race that tests the cars’ durability. 
It often consists of more-than-usual number of laps or time.
 A race that takes part on public roads, where the cars are based on road cars.
A motorsport that consists of drifting (Sliding the rear tires). 
It can either be a race or a stunt contest.
 A type of race that can only use production road cars.
Engine located at the front of/above front axles. 
Found in most cars.
Engine located between the front and rear axles, creating a more balanced weight distribution
Engine located on back of the rear axles. 
It used to be popular in economy cars, but not anymore. 
Rear engine layout’s center of gravity makes it very hard to control. 
Today, only Porsche 911 use this strange layout and it attracts certain fans.
Basically, a front engine layout with the engine located behind the front axles to provide more balanced center of gravity.
AUSTIN, Texas — Lewis Hamilton has a certain love affair with the rolling hills and scrub land in the heart of Texas.
It's easy to see why. The Mercedes driver has been almost invincible here, with five wins at the United States Grand Prix since 2012, including a season championship secured in 2015.
That makes it an almost perfect spot for more.
Win another race and another championship on Sunday and the Mercedes driver will sit all alone in second in Formula One history, with only a short step left to reach the top.
The British driver is on the cusp of securing a sixth career championship that would move him past Argentina's Juan Manuel Fangio, the "Godfather" of F1 drivers, and within one of the record seven won by Germany's?Michael Schumacher, who is still regarded as the sport's greatest champion.
Hamilton should have little trouble doing it.
All he needs to do is finish eighth or higher on Sunday.
It's been a good hunting ground for me, so very excited to go there, and who knows whether we can get the job done, Hamilton said.
There's not much reason to do that at this point.
Hamilton nearly closed out the championship last week with his?surprising win in Mexico City?, but teammate Valtteri Bottas' third-place finish pushed the title chase into another week.
Bottas is the only driver still mathematically in the championship, but just barely.
I don't mind, Hamilton said after not quite closing it out last week.
 "I love racing.
Hamilton didn't win the Texas race last year, finishing a third as Kimi Raikkonen took the checkered flag with Ferrari.
But he was in spectacular form last week in Mexico City, getting his 10th win of the season on a track that favored rivals Red Bull and Ferrari.
Hamilton is a de facto spokesman for growing Formula One in the U.S.
Still young and stylish at 34, an?environmental activist on social media?, Hamilton is a valuable face and force for promoting the series in America, which hasn't been as easy as F1 officials hoped when they returned to American soil with the Texas track and race in 2012.
Efforts to start other races haven't been so easy.
A dream race in Miami couldn't take hold in the downtown venue on Biscayne Bay the series wanted, and the current idea of racing around the parking lot of the stadium where the NFL's Miami Dolphins play has run into fierce opposition from locals.
The Texas race has been a stronghold and Hamilton still does his part.
 He was in New York City with an event in Times Square before coming to Austin.
Hamilton sees himself — the first and still only black driver in Formula One who comes from a middle class family — as a story that can be inspiring to an American audience.
I think my story and my family's story is something that a lot of people in different countries can relate to, Hamilton said.
Ferrari and Red Bull could still put up a fight Sunday at the Circuit of the Americas.
Ferrari has started on pole the last six races and the U.S.
Grand Prix winner has come from the front row every year since the Texas track opened.
The recent runs from pole have produced just three victories however, and none since Singapore on Sept.22
The team is partly to blame for not winning, through a series of blunders or being outmaneuvered by Mercedes.
In Mexico City, Ferrari put drivers Sebastian Vettel on different pit strategies and both surrendered the lead to change tires at different times.
We certainly want to do better than that, Ferrari team principal Mario Binotto said.
Verstappen will be looking to put a?bad race in Mexico?behind him.
 He was stripped of pole position in Mexico City because of a penalty for not slowing down while under a yellow flag in qualifying.
 He then punctured a tire on the fourth lap.
A car and driver that had the pace to win finished sixth.
Verstappen finished second in Texas last year and won his late-lap duel with Hamilton for the position.
A lot is possible at this track as there are so many good overtaking opportunities, which makes things interesting in the race, said Verstappen, who will be racing his 100th career grand prix at just age 22.
Hamilton, who is driving his 248th, may be wary of talk like that from Verstappen.
Their cars touched on the opening lap in Mexico City, and Hamilton said he's learned to give Verstappen a lot of room to race.
It's the smartest thing you can do, Hamilton said.
AUSTIN, TEXAS –?Closer racing and smaller budgets.
Formula One hopes it has found the formula to close the gap between teams fighting for championships and the cars bringing up the rear of the grid to create better and more compelling racing.
Series officials unveiled their long-awaited rules for 2021 and beyond Thursday at the U.S.
Grand Prix, with Formula One chief executive Chase Carey insisting the changes “respect the DNA” of the open-wheel series while improving its future,
“The goal has always been to improve the competition and action on the track,” Carey said.
“We will have cars that are better able to battle on the track.
Formula One has been dominated by Mercedes since the move to the hybrid engine in 2014 as the team has won six consecutive constructor’s championships.
Mercedes driver Lewis Hamilton can clinch the team’s sixth consecutive driver’s championship Sunday.
The only teams even challenging Mercedes on the podium have been Ferrari and Red Bull.
While technical regulations will create more standard parts and make the cars slightly heavier and slower, the biggest change is likely to be the new limit on what teams can spend.
While the budgets of the biggest teams can push close to $500 million, from 2021 and on teams will be limited to $175 million spent for on-track performance
Those figures won’t include expenses for things such as marketing, driver contracts and the three highest salaries on the team.
But teams that violate the racing budget cap could face penalties, including losing a championship.
Putting a spending cap on F1 teams has been one of the biggest sticking points for years.
But each year gets more expensive and the big teams finally came to the table and agreed something needed to be done, said Ross Brawn, F1’s managing director.
“Budgets have been escalating,” Brawn said.
“F1 is almost a victim of its own success … All of the big teams wanted cost control.
They needed it.
They needed saving from themselves.
F1 hopes that by closing the spending gaps, it will close the racing gap as well.
Previous efforts at spending limits have been “gentlemen’s agreements” that have been largely ignored, Brawn said.
“We’ve tried for these in the past.
There’s not many gentlemen in the paddock,” he said.
Expanding the season could strain those budgets.
Under the new rules, the F1 schedule could expand from its current 21 races to 25.
Vietnam has been added as a race in 2020 and F1 is currently making plans to add a Miami Grand Prix in 2021.
“We’ll add races only if we think it really adds to the sport,” Carey said.
Technical changes to the heavier car include tweaks to aerodynamics and a bigger wheel size, and a speed reduction of up to 3 seconds per lap is expected.
That would put them closer to the speeds of the 2016 season.
Formula One will keep the current hybrid engine system.
Engine manufacturers that sell to other teams, such as Mercedes and Ferrari, will have to provide their customers with power units equal to the ones they use in their own cars.
Racing Point driver Lance Stroll said he hoped the changes deliver the desired track results.
Racing Point sits seventh in the current team standings and regularly gets lapped by Mercedes, Ferrari and Red Bull as it fights to be the first in the middle.
“It’s great to fight for ‘best of the rest,’ ” Stroll said.
“But there’s no reward for that.
Closer racing is the priority.
The city’s?RapidRide H project?on Delridge Way SW is shaping up to be one of the most disappointing so-called “multi-modal” improvement projects in the city.
With a huge need for people to bike along this rare, relatively flat and continuous street spanning the neighborhood from the West Seattle Bridge to White Center, the project’s bike elements call for a mix of strange half solutions and downright non-solutions.
It would even remove an existing northbound bike lane that serves Chief Sealth High and Denny International Middle Schools.
But it doesn’t need to be this way.
By taking advantage of unused road space, the project could be better.
And by prioritizing safety over car parking, it could be great.
First, let’s look at the 30% design and talk about the problems that need solving.
Under the current plans, the city would build a fairly long bike lane that only goes south.
A bike lane that only goes one way isn’t really a thing.
It’s half a thing.
Trying to go north?
Good luck!
Planners are trying to create two very separate bike routes, one for people headed in each direction.
This is very unconventional, and not in a good way.
The plans also call for attempting to route people biking onto various side streets, often with very steep inclines between those side streets and Delridge if they connect at all.
Here’s an overview (note that minor streets are omitted, so there are blocks between these lines that are not shown):
It looks kind of alright on paper if you’ve never been on these streets.
But West Seattle is not flat like a map.
People did fight for complete bike lanes on Delridge, but complete bus lanes, turn lanes and car parking were prioritized.
But that doesn’t mean there isn’t still room for significant improvements that will make the investment go a lot further for people trying to get around on bike while also prioritizing buses.
The southbound route isn’t perfect, but it is at least workable.
I could see people actually using it and getting benefit from the improvements.
But we really need to talk about the northbound route.
The biggest problem is that it follows the existing Delridge-Highland Park Neighborhood Greenway several blocks to the east of Delridge, which is complete garbage.
It’s a joke of a bike facility.
There’s a very busy section with just sharrows, a section that crosses the same busy street two times within view of each other (though this project should fix that, at least), and there’s even a damn staircase in the middle of it:
Riding it feels like you are being led around by someone who is pretending to know their way but clearly doesn’t.
When you hit the staircase, you really start to wonder if you’re the butt of some kind of joke.
Did the design team really just give up like this?
Rather than combining a greenway with sections of bike lane on 16th Ave SW where needed, the route compromises usability at seemingly every opportunity.
The result is a real shame.
I’m sure parts are nice for some intraneighborhood trips if you live there, but it’s not a quality cross-town bike route.
But the shoddy quality is almost beside the point, because this route just is not a Delridge Way alternative by any stretch of the imagination.
If anything, it is a 16th Ave SW alternative, an entirely different arterial street.
It is at times six or even eight blocks out of the way round trip.
And the cliff face you have to climb is absurd at points.
There is even a mile-long section without a single connection between the greenway and Delridge.
So we gotta figure out something better.
The most obvious solution is bike lanes on Delridge, and for most of the road, this can be accomplished by removing on-street parking and/or reallocating space reserved for a center median.
Three parking studies and public outreach responses all showed that parking in the area is not hard to find.
This is a very simple solution by design standards.
Only politics and fear make it difficult.
Here’s a sample of the road design between SW Holden St and SW Roxbury St:
If the city prioritizes bike safety and connectivity over on-street parking, the solution here is clear.
Use the parking space on the east side of the street for a northbound bike lane.
Done!
But there are other section where removing on-street parking isn’t even necessary.
For a significant stretch of the street between SW Orchard St and at least SW Juneau St if not SW Alaska St, there is this funny little center buffer space that isn’t wide enough for a turn lane.
But it just so happens to be as wide as a bike lane…
I don’t know what the exact design should be, but I wanted to demonstrate that the existing design has both space to work with and needs that remain unmet.
We should be demanding more from our public improvements.
Even if the city only fixes the section that doesn’t remove parking (which, of course, I am not advocating for), they would be able to create a northbound bike lane that would serve Chief Sealth High and Denny International Middle Schools.
The current design would remove an existing northbound bike lane between SW Kenyon and Myrtle Streets, which is unacceptable.
We can’t go backwards on our bike network, especially when serving our city’s students.
Getting bike lanes as far north as Juneau would at least provide a connection to the 26th Ave SW neighborhood greenway, which has a decent connection to the West Seattle Bridge Trail.
So connecting Chief Sealth High to Juneau should be the minimum goal of this project.
The Delridge project is nearing the end of its design phase, but there’s still time to make changes.
The project timeline doesn’t have design finishing until early 2020, and the feedback summary (PDF) from public outreach on this concept showed clear dissatisfaction with their proposed bike solution.
So SDOT and city leaders should not accept this design as complete yet.
Instead, they should back up our city’s climate, biking, transit and walking goals by completing quality, usable design that will actually work for everyone.
The online registration deadline in King County is Monday (October 28).
So make sure all your friends and family get registered while it is still as easy as signing up online.
After that, you can register in-person at King County’s?Renton and downtown Seattle Election offices?up to and including election day.
If your registration is current, you should have already received your ballot in the mail.
If not, go to King County’s My Voter Information site and check that status of your registration and request a replacement.
The single most important vote on this ballot is?NO on I-976.
Tim Eyman’s deceptively simple initiative is sure to get a lot of votes because it basically asks people if they would like to pay less to register their cars.
It does not detail the massive cuts to vital and popular infrastructure maintenance and transit service in communities across the state that this initiative would force, work that voters and elected leaders have already planned and funded.
That’s the frustrating thing about these anti-tax initiatives: People are willing to fund improvements when asked about those improvements.
But when solely asked whether they want to pay a tax, of course a lot of people will say no.
This initiative only asks about the tax mechanism, not the vital work the tax is funding.
This is a long-winded way of saying, this one’s going to be close.
We need every single vote we can get.
So if you encounter someone who for whatever reason doesn’t care about the rest of the local elections on this ballot, tell them they at the very least need to vote NO on I-976.
Yet again, we have a race where neither candidate is great on biking and safe streets issues.
But Andrew Lewis is the better of the two, as you can see for yourself in this KCTS clip from a recent Seattle City Club debate:
I gotta be honest, I considered changing my planned Andrew Lewis endorsement to “no endorsement” after reading?his comments to Erica C. Barnett in a recent interview, which expand on his stance in the debate video.
In the interview, he suggests that the problem with the Missing Link is that there wasn’t enough process.
No really.
Then he says that he thinks bike lanes in general should go through more process and that it’s “fine” if they are meandering and indirect.
“I’m thinking of specific conversations that have been in the news in other districts, like the Missing Link of the Burke-Gilman Trail and 35th Ave. NE up in Wedgwood.
I think that part of the concern in those discussions was that there is broad-based support for connections, but the route that was picked by the city was controversial.
I would want to step back and have a little bit more of a process with all the stakeholders and then, at the end, have a recommendation.
And it might sometimes lead to a route where I, as a biker, might not find it to be the most convenient route.
But if it’s safe, I’ll use it and I’ll be thrilled, and if I have to dogleg over a block, go up, and then rejoin whatever the route is, I’m fine with that.
First of all, we’ve been arguing about the Missing Link for over two decades.
If that’s not enough process for you, then I just don’t know what to tell you.
Second, bike lanes were picked for 35th Ave NE after a significant amount of public outreach both for the paving project and the Bicycle Master Plan.
The bike lanes were the solution that met our city’s goals.
The route may have been “controversial” to some, but I think we’ve seen that abandoning the city’s goals by cutting those lanes was even more controversial.
The bike lanes were chosen both because it was the only direct and continuous bike route option and because the city needed to make the street safer for all users.
Protected bike lanes would have accomplished both of those goals.
The 39th Ave NE neighborhood greenway, which bike lane opponents kept pointing to as an alternative, does not connect to the north and is eight very steep blocks out of the way (four there, four back).
That is not an alternative, and it’s not “fine.
There was no amount of process that would have gotten the opponent group on board with the bike lanes.
The result of not putting bike lanes on 35th is that people have continued biking there because it is the only direct and continuous option, but now there are no safety enhancements to help them do so.
And speeding and dangerous passing is rampant because the road did not receive the safety benefits of having protected bike lanes, which reduce serious collisions for all road users.
This is what happens when leaders don’t stand up for our plans and goals.
But to zoom out from this one project, Seattle needs to make a lot of changes to its streets if we are going to connect our city’s bike network and achieve Vision Zero.
That requires our leaders to be committed to our safe streets, transit and climate change plans even when the work is difficult.
Especially when the work is difficult.
But his opponent Jim Pugel is worse.
For example, he spent his entire answer about bike lanes in that City Club debate complaining about how the arena construction project moved the 1st Ave N bike lane to the other side of the street so that they could stage their construction site on top of the old bike lane.
The problem?
People want to park cars there.
So in Pugel’s mind, people biking should be put at increased risk of injury or death during arena construction so that people driving can park more conveniently.
Congratulations, Jim Pugel, you’re worse than Andrew Lewis.
I hope Lewis can learn and change his position on essentially sabotaging the bike plan.
He bikes, and he talks about needing to build the bike network.
I hope he gets ready to bring the level of political leadership that’s going to take.
This one is a no-brainer.
District 4, my district, should elect Shaun Scott to the City Council.
There are elections where you vote for someone you believe in, and there are elections where you vote against someone you think would be harmful.
Both are true in this race.
Shaun Scott does not shy away from big ideas.
He is not afraid of making bold changes.
His ideas for Seattle’s Green New Deal are appropriately and necessarily big.
He’s not going to spit B.S. at you and pretend that adding some electric car chargers is going to solve climate change.
He’s going to talk about how to build a ton of affordable housing near improved transit service.
He’s going to talk about completing the Bicycle Master Plan even when it gets politically difficult.
And he’s going to talk about not just how our city’s carbon emissions are bad for the climate, but how the pollution from burning those fossil fuels disproportionately impacts the health of working people and communities of color.
But it’s not just his ideas that are exciting.
Scott has also inspired a movement.
He maxed out on the city’s democracy voucher system in record time, almost making a joke of the program’s limits.
He encouraged his campaign staff to unionize, which is extremely rare even in union-friendly Seattle.
And his staff and a ton of volunteers have been putting in huge amount of time tabling, knocking on doors and in many ways innovating what a political ground game looks like in Seattle’s still-new Council district system.
His campaign is rewriting Seattle’s election rules and creating a new path to power.
It would be a good thing for the city if they are successful because their model of organizing is truly grassroots and based on optimistic energy that, frankly, most other Council campaigns are lacking.
Scott makes me feel like our city really can do what it takes to become the affordable, equitable and sustainable city I believe it can be.
His opponent, Alex Pedersen, fought against light rail.
That’s right, he opposed the 2016 levy to fund a major expansion of Sound Transit light rail.
Worse, he still stands by his opposition to the levy.
And now he wants to represent this district while two of its three light rail stations begin service?
No way.
We need big changes to accompany these new stations with strong priority for walking, bike and bus access and more nearby affordable housing.
And Pedersen has shown that he’s not the person to do that job.
His transportation and climate plan includes subsidizing Uber rides and requiring car parking both on public streets and in new buildings (which dramatically increases the cost of those buildings and makes the new units more expensive).
Our top greenhouse gas emission source is from transportation, traffic is terrible because there are already too many people driving and people cannot afford homes in our city, yes his solution to these problems is more cars.
This is irresponsible.
Pedersen also fought against the 2015 Move Seattle Levy to fund vital street maintenance, street safety and transit efficiency work across our city, including work to improve light rail station access.
He thinks the new dangerous 35th Ave NE design was a good outcome despite that neighbors on bikes now feel terrified of their main commercial street.
He is the biggest threat to bike safety in any of the Council races.
Pedersen called to clarify that he did not oppose the $15 minimum wage effort as previously noted here.
I have checked my notes and can’t find where I read that, so I may have confused myself.
I regret the error and strive to do better.
Basically, if it was bold, Pedersen opposed it and will continue doing so if elected.
Sure, he has a B.S. excuse for all his unpopular stances of the past, but his stances tell you the real story, not his excuses.
A single City Council seat can only do so much to craft new laws.
You need a solid block of councilmembers to get big ideas through.
Scott will be part of that progressive block, and Pedersen won’t.
But while it is difficult for a single councilmember to create a new law, they can be very effective at muddying, delaying or even killing other people’s bold ideas.
That’s why Pedersen would be so dangerous on Council.
Seattle’s status quo is drowning working people, polluting the planet, killing people in traffic and displacing communities of color.
Scott wants to change the status quo.
Pedersen doesn’t.
So vote Scott.
Scott has also been endorsed by Washington Bikes, the Urbanist, Seattle Subway and the Transit Riders Union.
Nick and Lisa are traveling to Paris, where they are going to attend a special language school to study French. 
They have just arrived at the airport.
Unfortunately, nick is afraid of flying. 
 Let’s find the check-in counter.
We are flying Pan World Airlines. 
 I think it is at the end of the terminal.
Yes, I see the sign over there. Are you sure you do not want tot ho by boat? 
Come on, Nick! 
Here is the economy- class check-in counter. (to the clerk) 
Hi, we want to check in.
May I have your tickets and passports, please? 
Yes, here you are.
And we would like a window and an aisle seat, if possible. 
 Let’s see… 
OK. 
How many bags will you be checking in today? 
Um, four.
Tow each. 
 Please put them on the scale. 
Your bags are too heavy, I am afraid there will be an overweight-luggage charge of thirty dollars. 
Oh no!
I told you not to pack so many things. 
 I will pat the overweight charge. 
 Here are your boarding cards. 
You can board at gate nine at seventy-thirty. 
We could drive to Paris.
There are no weight limits for luggage when you drive.
Drive to Paris?
There are no roads from Liveville to Paris, Nick!
Having checking in, Nick and Lisa proceed to their gate. 
But first, they must go through security and immigration.
They have really beefed up security since September eleven.
This may take a while. 
 I don’t mind.
I hope they search everyone very thoroughly.
Please empty out your pockets and put all metal objects in this tray.
OK.
Step through the metal detector. 
The metal detector beeps. 
But I put everything in the tray, even my watch.
The metal detector beeps again.
I hate airports.
They frisked me and went through everything in my bags!
 It is your own fault. 
The cartoon character on your T-shirt has metal eyes.
That set off the metal detector. 
 It was a present from my aunt. And it is my lucky shirt. 
 Anyway, have your boarding card and passport out. 
We have to go through immigration now. 
 I hope they send us home. 
After passing through security and immigration, and after shopping in the duty-free store.
Nick and Lisa have finally arrived at their gate.
Flight PW-854 is now boarding.
All passengers on flight PW-854 should proceed to gate nine for boarding.
All passengers seated in rows thirty-six to forty-four may now board.
That's us. 
Let's go.
Nick is loaded down with things from he duty-free store.
Help me carry something.
Why did we buy so many things in the duty-free store?
May I see your boarding pass?
Thank you.
Nick and Lisa walk onto the plane.
Here we are—forty-three A and B.
I get the window seat!
You can have it. 
I don't want to look down.
Put this bag in the overhead compartment for me.
I'll put my purse under the seat in front of me.
These seats are so small! 
I feel like a sardine.
I'm going to recline my seat and put down my tray for reading.
Sir, please keep your seat in the upright position and your tray up until after takeoff.
 Can't we study French here in Liveville?
The plane has taken off and the pilot has turned off the seat belt sign. 
This is your captain speaking, we have reached cruising altitude,' and I have turned off the seat belt sign.
Still, for your safety, please keep your seat belt fastened when you are in your seat.
Oh, look, Nick.
Our seats have personal video screens.
 And here it shows where we are over the ocean, and the speed, and the altitude…
I don't want to know! (To the flight attendant)
Excuse me.
 May I have a pillow and a blanket?
Let's see what the in-flight entertainment is. 
 Oh, they have three movies.
I'm going to put on my headset.
Would you like something to drink?
 Yes, I would like a glass of orange juice, and he would like a glass of milk.
Oh, here is the dinner cart. 
 I love airplane food. 
Oooohhhh!
Would you like the spicy chicken or the fried beef?
I'll have the chicken, please. 
He'd like . . . don't think my friend is hungry.
Oooohhhh! 
Why is the plane bouncing so much?
It's just a little turbulence, Nick. 
Oh, look!
We're way above the clouds now.
Nick: O000hhh! 
Nick faints.
Nick and Lisa are traveling to Paris, to study French and do some sightseeing.
They are now on the airplane.  
Lisa loves flying, but Nick is afraid.
We'll be landing soon, Nick.
I see a few items I want in this duty-free catalog.
But you already bought duty-free stuff before we took off.
Shopping on the airplane makes fun. (To the flight attendant)
Excuse me, I want to buy some duty-tree items.
All right. 
 I can get them for you now.
 What would you like?
 I'd like to get a bottle of Eau de Live Number five perfume, and this box of chocolate.
 I'm sorry. 
That chocolate is already sold out.
 Oh, well, I'll just buy the perfume, then. 
Can I use U.S. dollars?
 Yes, that will be fifty dollars, please.
 I'll bring you the perfume in a minute.
(Over the loudspeaker) This is your captain speaking. 
We will be landing shortly. 
Please make sure that your seatback and tray table are in the upright and locked position.
A few minutes later; the plane lands.
Welcome to Charles de Gaulle International Airport. 
The weather today in Paris is sunny and a little bit windy.
Oh, it is wonderful to be in Paris again. 
How romantic! 
Oh, yes! It is wonderful to be here—on solid' ground again. 
How ... safe.
Nick and Lisa’s plane has just landed.
I am so happy to be back on the ground again.
Oh, Nick. 
According to' statistics, you are more likely to get in an accident on the ground than in a plane.
 I can't reach my bag in this overhead compartment …
Lisa's bag falls from the overhead compartment unto Nick's foot.
Ouch!
See, I told you.
Nick and Lisa follow the crowd out of the plane.
Here's the immigration area. 
Get your passport out.
Welcome to Paris. 
May I see your passport?
Here you are.
What is the purpose of your visit, and how long will you be staying?
Four weeks. 
We're taking a French course.
 And doing some sightseeing, of course. 
Oh, and shopping, too!
Oh no.
 I'm going to have to carry even more bags home.
 If we're lucky, your suitcase won't arrive.
 Then you will have an extra hand.
Having gone through immigration, Nick and Lisa got to the baggage-claim area to get their check-through bags. 
The security is tight here. 
The luggage-collection area is over there. 
And the screen says the bags from our flight will come out on carousel number twelve. 
You go and look for the bags.
 I will get a cart. 
Oh, look, Nick, there is my suitcase. 
Get it. Careful! 
It is very heavy. 
Got it!  
Help!
(Nick falls onto the luggage carousel and runs in place, trying to get off.
Help. 
Quit clowning around, Nick. 
It figures! 
My suitcase is the first one out and yours will be the last one out. 
Where is my suitcase?
I want to report a lost bag.
 My name is Nick Gregory.
Yea, sir. Let me see… your suitcase is now in Timbuktu. 
Please fill out this form. 
Timbuktu? 
Oh, no. 
Nick’s suitcase was sent to the wrong airport, so they collect their other bags and get in line in the customs area.
 I filled out our customs-declaration forms on the plane-while you are sleeping. 
 I have our passports here. Oh, it is our turn. 
Please put your bags on the counter.
 Here you are.
 And here are our customs-declaration forms. 
May I see your passports, please? 
Where are you coming from? 
LiveVille!
 Here you go, sir.
Please open this bag.
 Do you have any plants or meat products? 
Any fruits ot vegetables? 
No, we don’t. 
If we did, it would be in Timbuktu! 
Sorry, my suitcase was put on the wrong plane.
 I hate traveling. 
 Oh, I am sorry to hear that. OK, you can go.
Way to go. 
You scared him with all of your complaining, so he let us pass through quickly. 
Now, we can take an airport shuttle into the city, or we can take a taxi. 
You have too many nags. We need a taxi. 
Oh, look. 
A store, I want to go in and look around!
Oh, no
Nick and Lisa have gone to a bank to change money. 
Banks are usually the best place to change money. 
They have the best rates and charge the lowest commissions. 
And banks in Paris have the best customer service. 
Nick sees a beautiful French teller.
 Lisa, you wait here.
 I will take care of changing the money. 
Nick walks to the bank’s counter.
Bonjour.
Bonjour, monsieur.
 May I help you? 
Oui. 
We, I mean I, would like to change some money. 
What is the exchange rate? 
The exchange rates for today are displayed on the sign over there. 
Wow!
 That's a good rate.
 I would like to change five hundred U.S. Dollars.
 I have my traveler's checks right here. 
OK. Please sign here. How would you like that? 
 Nick, hurry up! I want to go shopping. 
Quiet, Lisa! 
What denominations do the bills come in? 
 Five hundred, two hundred, one hundred, and then a fifty, two twenties, and a ten. 
Lisa is angry and she walks up to the counter.
Here you are.
 Will there be anything else today? 
Well, mademoiselle, how about if you show me around Paris…
Lisa grabs the money out if Nick’s hands.
 Nick Gregory!
You can stay there and change money in the bank.
 I will go out and spend the money in the stores! 
Lisa walks quickly out of the bank.
No! 
Wait! 
Will all passengers for flight ABC four thirty-one please proceed to gate eleven for boarding at this time.
Flight ABC four thirty-one to Chicago has been canceled. 
Passengers scheduled to fly on flight ABC four thirty-one should check in at the gate for instructions. 
Will the last five passengers for flight ABC four thirty-one please proceed immediately to gate elven for boarding. 
What time does the flight board?
What is out gate?
How do I get to the gate?
Do you know how to get to the gate? 
Would you like a beverage before your meal? 
Please put your seat back in the upright position. 
What would you like for your dinner, chicken or beef? 
Would you like coffee or tea with your dinner? 
May I take your tray?
This is your captain speaking. 
We ask that all passengers return to their seats at this time and fasten their seat belts.
 We are experiencing a little turbulence, but it is not serious and we should be getting through it soon. 
Excuse me, I am not feeling well. 
Do you have any aspirin? 
I have a headache. 
My stomach is upset.
 I think I am going to vomit. 
My ears are all blocked up. 
May I see your boarding pass, please? 
Only ticketed passengers can purchase duty-free items. 
I will need to see your passport and boarding pass, please.  
The exchange rates for foreign currencies are listed here. 
How many bottles of brandy can I buy? 
How many cartons of cigarettes can I buy? 
Do you accept credit cards? 
Is there an extra charge for using a credit card? 
What is the exchange rate if I use American dollars?
Excuse me, when can we but duty-free items? 
How do I buy duty-free items? 
I want to buy this perfume. 
I ordered a bottle of perfume, but it has not arrived at. 
Just fill out this form, and we will deliver the items to you. 
I am sorry. 
We don’t have any left in stock. 
We will bring it to you right away. 
All passengers are required to disembark and wait in the airport during this time. 
Please bring all of your bags with you. 
How long of a layover do we have?
Do we have to pick up our check-in bags? 
Excuse me, I am catching a connecting flight to Taipei. Where do I go? 
Where do I go to catch my connecting flight? 
Hi, I am transferring to flight ABC three-one-two to Taipei. 
OK, let me see your passport and ticket, please
Where do we go to get our bags? 
Where is the baggage-claim area? 
Which carousel is for my flight?
Where can I get a luggage cart?
Where is the lost-luggage counter? 
Excuse me, my bag did not come out.  
I would like to report a lost bag. 
I think my luggage has been lost.
Where can I catch a bus? 
Which bus goes to the train station? 
How much is the fare? 
Where do we catch the airport shuttle?  
Is this the shuttle into town? 
Is there a taxi stand nearby? 
How many people can you take? 
Can you take me to the Capital Hotel, please? 
Can you open the trunk for me, please? 
How much do you charge for putting luggage in the trunk?
What are your rates? 
I would like to rent a car for three-days. 
What is the rate per day? 
Does the price include insurance?  
Do I have to fill up the gas when return the car? 
Do I have to pay a deposit?
Nick and Lisa are on a tour of Paris, and their tour bus has stopped near the arch of triumph.
Nick, look. 
 I have seen it in so many movies, it is even more impressive in real life.
The arch of triumph was commissioned by napoleon in 1806, to commemorate his victories.
How big is it?
 It is fifty meters high and forty-five meters wide. 
Lisa, my guidebook says the tomb of the unknown solider is inside.
 Let’s walk over and look. 
 Nick, we can’t leave the tour group.
My guidebook says there is also a gift shop inside. 
 Um, then let’s go. But we would better not be gone for too long. 
It is so big.
 My guidebook says the view from the roof is awesome. 
 Nick, look. 
Everyone is getting in the tour bus.
 They are leaving. 
Oh, no.  
Run.
Nick and Lisa are now at the Eiffel Tower.
The Eiffel Tower! It's so romantic. 
 I will bet they have a great gift shop.
The Eiffel Tower is three hundred meters tall. 
When it was completed in 1889, it was the world's tallest structure.
 At night, special lights light up the tower's paint—forty tons of it.
People say this is the world's most romantic sight! 
What do you think, Nick?
 Fantastic! My guidebook says there are fifteen thousand iron pieces and two point five million rivets.
Rivets?! 
Metal pieces?! 
You're about as romantic as a robot!
The tower took just over two years and two months to complete.
 Now, we'll go and actually visit.
 I'm so thrilled! 
Nick, won't it be romantic to ride up the glass elevators together and see the view from the top?
 No way! 
When we get home, I want to tell people I made it up the one thousand six hundred steps to the top.
 Huh! 
So much for romance!
Nick and Lisa are at the Museum d'Orsay, Paris's famous museum for impressionist art.
We'll begin here, viewing these impressionist masterpieces.
Then we'll also see works by Monet and others.
 What a deep painting this one is! 
When I look at it, I see a troubled world . . .and lots of emotion. 
What do you see, Nick?
 I see a small dog and a purple monkey. 
 You are so shallow, Nick!
 This work is by the impressionist painter John Patricks.
 The colors are so vivid, the images so, so . . .
Painted in 1892, it is entitled' "The Flower."
 It is believed he was expressing his love for his cat.
Look! 
The brush strokes just jump out at you.
Hmm .
 I wonder what it feels like—
Nick, no! 
Don't touch it! 
Rtrrrimminnnnnnggggg!
Whoops!
 Oh no!
Lisa and Nick are on a private nighttime boat tour on the River Seine.
I'm so glad we took a private tour. 
It's so romantic.
 Look at all the lights.
Yeah! 
This is great for nighttime photography.
Hey, there's the Eiffel Tower.
 It is just beautiful at night.
 It is … There is so much history along this river.
 It is the heart of Paris.
 I know! 
Check out these old bridges, and the architecture.
 Roman armies, wars, the French Revolution—this river has seen so much.
 I wonder what relics' are floating around under there.
 It's probably not very clean.
 Look, there's something floating there! 
Hold my hand while I leans to get it.
Nick, no!
 Aaaahhh!
 Nick falls into the water.
 I think our perfect vacation is in deep water.
Can I  book a city tour here? 
Which tour best covers the major attractions? 
Where does the tour go? 
How long does the tour spend at each stop? 
Will there be time to buy souvenirs? 
Are there lots of good spots to take pictures? 
Does the tour include lunch? 
How much does it cost? 
Do you have a brochure( in Chinese)?
We  have half-day and full -day tours. 
The tour goes to sll the important sights. 
Lunch is included. 
Here's a brochure with pictures and details. 
Tours depart at eight-thirty each morning. 
How much is this T-shirt?
That's expensive. 
Can you go any lower on the price?
Do you have this in larger/　smaller size?
Do you have this in another color?
I am booking for something woth a local flavor. 
Do you take credit cards?
Everything in the store is ten percent off day. 
That's our most popular souvenir. 
I will see if we have any another sizes/ colors in the back. 
Sorry, that's the final price. 
There's  no bargaining. 
If you buy five of them, then I can give you a twenty percent discount. 
I would like to buy some stamps. 
What kind of stamps would you like?
Any kind is fine. 
Do you have any limited editiion stamps?
We have several different limited edition stamps. 
Do you have any if the Harry Potter stamps left? 
Sorry, we are all aold out og those. 
Excuse me, do you sell postcards of the local attractions here? 
Yes, we have a collector's set of ten different postcards. 
How mich will this cost to mail? 
We will have to weight it first. 
I want to send this by air mail. 
Do you want to send this first class or express?
Excuse me, can you take a picture for me?
I want a portrait/ landscape. 
I would like the whole tower in the backgroung. 
Just push this buttin. 
Another one, please. 
Get in the picturer with me. 
Give me your e-mails address, and then I will e-mail you the picture. 
How do I use the canera? 
Which buttin should I push?
Say cheese. 
Move closer together.
Move a little to the/ your right/ left. 
I have lost my traveler's checks. 
Do you have the receipts?
Yes. 
Is there a bank or travel office nearby where I can get thenm replaced? 
Let me call a few banks for you to find out. 
I have lost my wallet. 
OK, I will help you file a report with the price. 
What was in your wallet? 
It had my credit cards and six hundred U.S. dollars in cash. 
Is there anything I can do? 
Yes. 
Get on the phone and cancel your credit crads. 
My friend is sick. 
Is there a hospital nearby? 
Yes, there is. 
Does the hospital have an emergency room? 
Yes, it does. 
Is your friend well enough to go in a taxi?
Lisa and her sister, Mary, have just come to Hula-Hula Island for a vacation. 
Here, they arrive at their hotel.
 Wow! 
I've never stayed in a place this fancy' before!
Welcome to the Hula-Hula Hotel. 
How can I help you?
We'd like to check in. 
Our names are Lisa and Mary Lee. 
We have a reservation or a double room.
 Let's see . . . 
Yes. 
We have a suite for you on the third floor, room three-two-one, for five nights.
 Great! 
Oh, and my sister's luggage was lost.
 Please tell us if it's delivered to the hotel.
Of course.
 I'll need your passport, and please sign this guest card.
OK. 
Here you are. 
Oh, and what is the checkout time?
Eleven a.m. 
And if you need any assistance,' please see our concierge or call the front desk.
 Here are your keys.
Mary and Lisa have arrived in their hotel room. 
Unfortunately, the airline lost Lisa's luggage.
 Ah . . .
 We're finally in our hotel room. 
Cable TV, air conditioning, king-size beds, a refrigerator. . . 
This suite is beautiful!
Yeah, but I can't stop thinking about my bags. 
The airline lost all of my luggage!
Don't worry, Lisa. 
We can share mine.
 I packed well—I think.
OK. 
What did you pack in your suitcase? 
Let's open it up and see what you brought.
I have a camera and film, traveler's checks, toiletries,' some guidebooks …
Those are some good travel items.
 But,Mary. .. 
Didn't you pack any clothes?
 Oh, this is going to be a terrible vacation. 
What can we do on vacation with no extra clothes?
That's easy . . . Shop!
Lisa and Mary are asking the desk clerk about the hotel facilities.
Hello. 
What can I do for you?
We just arrived at the hotel. 
Can you tell us what restaurants you have?
The Hula-Hula Restaurant is open now. 
There's also a complimentary' breakfast buffet' each morning.
Great! 
Can we get a wake-up call at eight tomorrow?
No problem. 
What's your room number?
We're in room three-two-one.
OK. 
Be sure to try our gym' on the second floor. 
There is also a business lounge" on the third floor, with computers, copiers, and fax machines.
How about a nice place to relax here?
The beach is just down the street. And out in back, we have a swimming pool, jacuzzi, and sauna.
Great. 
Now we have an excuse to shop-to buy swimming suits. 
Some things are going wrong in Lisa and Mary's hotel room, so they call the front desk.
This is the front desk. How can I help you?
 We're having some problems in room three-two-one. 
Can you send someone up right away?
Certainly. 
What seems to be the problem?
Well, my sister some juice on the bed sheets
Oh, dear! 
We'll send a maid up with fresh linen' as soon as possible.
Well, my sister tried to clean the sheets, but the water wouldn't stop. 
Now there's water everywhere…
Oh, no! 
We'll send the janitor' and the plumber,' too!
And then my sister slipped on the wet tile' and hurt her toe.
Oh, we'll also send a doctor.
 Lisa screams. 
What's the matter?
And send an electrician, as well. 
The lights just went out.
Hello. 
What kind of information do you have here? 
We have a number facilities: a business room, laundry facilities, free e-mail…
We are here on vacation. 
We won't be needing any of those services.  
Especially the laundry service. 
What tours are available? 
If you are here for a holiday, I suggest taking a guided walking tour. 
Here are some brochures. 
Great! 
Do you have any maps of the island? 
Of couse. 
Here is one with all the major landmarks labeled. 
I can help arrange a car rental if you would like. 
No thanks. 
But can you help us book tiurs or make restaurant reservations? 
Of course. 
Just look at the brochures and let me know what tour you would be interested in. 
How about a shopping tour? 
That's all you think about!
Hello. 
What can I do for you?
We would like to check out of our room. 
Here are the keys. 
It's room number three-two-one. 
Did you take anything from the refrigerator? 
Yes, we had two colas. 
We also ordered room service once. 
OK. 
Your five-night total comes to three hundred and ten dollars. 
Are you paying by cash or credit card?
Credit card. 
Here you are. 
Oh, and can you call an airport taxi forus? 
I will do that right awat. 
Please sign on the dotted line. 
You bought so much, we can barely carry it all. 
I am glad they list my suitcase. 
Oh, and I almost forgot. 
Mr. Lee, someone dropped this off for you morning. 
What? 
Hey, that's my luggage. 
How many nights will you be staying? 
What kind of room would you like? 
We will be staying for three nights. 
We would like a double room with a queen-size bed. 
I have a reservation.
My name is Carl Smith. 
Let me see…
I am afraid we canceled the reservation because we never received the deposit.  
Oh no!
Do you have any vacancies? 
I am afraid we don't have amy single rooms available. 
Is that for a single room? 
Oh no!
Yes, please. 
All guests receive a free breakfast.
 Would you like the continenntal breakfast or an American breakfast?
What about the breakfast buffet over there? 
That is an all-you-can-eat buffet. 
It is six dollars per person. 
Excuse me, can I order some iced tea by the pool? 
Certainly, I will place the order for you. 
Hi, I was wondering if you could help me. 
I need to send out some faxes and make some overseas calls. 
Sure. 
We can send the faxes for you in our business center, and you can make your calls there or in the privacy of your room. 
Thanks. 
How about Internet access?
Is there a place where I can hook up my notebook? 
There is Internet access in every room or you can use the business center. 
How late is the gym open? 
It is open until ten o'clock. 
What kinds of massage do you have?
We have traditional massage, herbal massage, foot massage and Shiatsu massage. 
I will have a Shiatsu massage. 
Hi, I would like to get a wake-up call tomorrow morning. 
Sure. 
What time will that be for? 
Six-thirty a.m., plwase. 
I would like to get a safe-deposit box. 
Each room has a personal safe in the closet. 
Did you receive any calls while I was out? 
May I have your name and room number?
Can you recommend a good show? 
I can get tickets for you to the musical Mice. 
Do you know of any good restaurants close by?
There is an excellent French restaurant with a beautiful view. 
I would like to send the letters by airmails and the package by surace mail.  
Please fill out this form. 
How can I get to the history museum from here? 
Turn right onto West Street, and you will see the museum. 
Can you call a cab for me? 
Iwill call one right away. 
There seems to be a problem with my bill. 
Yes. 
May I see?
There are charges here for beverages from the refrigerator, but I did not drink anything. 
Can you wait just a moment?
I will check for you. 
There are charges here for three movies, but I did not order any movies. 
Let me see…
Oh, I apologize. 
We have fiven you the wrong bill. 
I am very sorry. 
Well, accidents happen. 
Nick is treating Lisa to dinner. 
Nick wants to impress her, but he is very cheap. 
What do you feel like eating? 
Hmm…
I want to eat something really good. 
There is a fast-food restaurant over there. 
We could get a burger. 
Fast food? 
A burger?!
Are you kidding?
It's my birthday. 
I want ti eat something special, like French food. 
French food?
That's too expensive- I mean, difficult to order. 
Well, hoe about steak? 
The Ranch Steak House is right over there. 
I've never been to an American steak house!
Ranch Steak House?
You mean the expensive five-star restaurant that serves the best Steak in town. 
Oh!
You have been there? 
Then you will know what ot order? 
All I know is that it will be a tall order for me to pay the bill. 
Nick and Lisa enter the restaurant and are greeted by the hostess. 
Nick is worried because the restaurant is expensive. 
Hello!
Welcome to Ranch Steak House. 
We would like a table for two, please. 
All right. 
Would you prefer smoking or nonsmoking?
Nonsmoking, please. 　
And we would like a table by the window if possible. 
OK. 
Let me see…
Yes, we do have a table available right now. 
Follow me, please. 
Really?
We can get a table? !
Normally, you need a reservation to get a table here. 
We're really lucky.
Yeah! 
This restaurant looks really busy. 
Here is your table. 
And here are your menus. 
Thank you.
Wow!
Our table is so beautiful. 
This restaurant is really nice. 
Yeah, maybe it is too nice. 
There aren't any prices on the menus!
Nick and Lisa have already been seated at their table. 
The waitress is taking their orders. 
Nick hopes it will be cheap. 
Can I bring you something to drink?
I will have a glass of orange juice, please. 
I will just have water, thankk you…
What are the soecials today?
Today, we have baked fish with rice, and barbecued steak with potatoes. 
We would like one steak special- to share. 
And we would like the steak well-done. 
What? 
That won't be enough for the both of us. 
Well, the portions here are big. 
You don't want to get too full. 
I don’t want to be hungry, either. 
I don’t want you to eat too much-that's all. 
I will be fine. 
Miss, he will have the steak special, and I will have the fish special, please. 
All right. 
I will be back with your drinks in a minute. 
The waitress brings Nick and Lisa their orders. 
Here you go. 
Is there anything else I can get for you? 
Uh…
I am sorry, but I didn't order this. 
I ordered the steak. 
This is the steak. 
But therer is sauce all over it!
I didn't order it like this. 
This is how our chef cooks steak. 
It's our most popular dish. 
But this steak is … undercooked. 
I can see… blood. 
Well, didn't you order your steak rare? 
No, I order it well-done. 
I can't eat this. 
I am sorry. 
I'll take it back to the kitchen and bring you another one. 
I'm sorry about the mistake. 
That's OK.
And could you ask the chef not to put sauce on it, please?
Sure. 
I'll be right back. 
Nick and Lisa have finished their meal. 
What a delicious meal. 
I'm so full. 
I hope you're not too full, because here comes the waitress with a birthday cake. 
Happy birthday, Lisa. 
Now I see why you didn't want me to eat too much. 
I always save a little room for dessert. 
Great. 
You can take the rest of your fish home in a doggie bag. 
Miss, can you wrap this for her, please? 
Certainly. 
And could we also have the bill, please. 
Of course. 
Here you go. 
Thanks. 
Oh, well, money is no object when it comes to birthdays. 
I agree- birthdays should be special. 
I am glad you said that. 
Have I mentioned that it's my birthday next week? 
My fork is dirty. 
I am very sorry. 
This steak is undercooked. 
I apologize, sir. 
I will take it back to the kitchen right away. 
My steak is too tough, I think it was over-cooked. 
Sorry about that. 
I will take it back and get you another one. 
Excuse me, but this soup is too salty. 
I am very sorry about that. 
I will have the cooked make a fresh batch. 
I am sorry, I spilled my water all over the table. 
No problem. 
I will clean up the table and get you another glass of water. 
Welcome to Lucky Hamburger. 
What can I get for you today?
I need a chicken sandwich with extra lettuce but no mayonnaise. 
I would like to order a hot dog with no mustard. 
I would like to have a chessburger with pickles and ketchup. 
Nick is meeting Lisa to go to lunch at a fancy restaurant. 
Unfortunately, they are having trouble just meeting. 
Nick calls Lisa's cellphone. 
Lisa, where are you ? 
I have been waiting for you here at the subway station. 
I can't find it.
I am lost. 
Oh no!
Where are you?
I am in front of a small supermarket. 
Hmm… is there a park next to the supermarket … and a school across the street. 
Yes, and there is a convince store kitty-coner from here. 
OK.
Just walk north for about five minutes and you will see a department store on the right. 
And the subway station is next to the department? 
Yes. 
It is between te department store and a large bank. 
And please hurry. 
Of you hadn't given me such bad direction, I would be there already. 
It's about time!
I've been waiting all day!
Oh, relax!
Now, we'd better ask someone where this restaurant is.
I know it’s on one of the subway lines.
Let’s look for a map.
Oh, Nick!
You’re always afraid to ask for directions!
Lisa talks to a man and a woman on the street.
Excuse me, can you tell us ow to get to the Royal Lion Steak House?
Oh, yeah, let’s see… that’s in northern Liveville…
No! 
It’s actually north of Liveville.
I don’t think so.
It’s in the shopping district.
Nonsense!
It’s way out in the boondocks.
Now you can see why I don’t like asking for directions!
Maybe we should buy a map!
I think we have to go east on the Green Line, and that means we need to get on the train on platform three.
We still need to ask for directions.
Here’s our train!
We can ask for directions after we get on.
Excuse me.
Can you tell us how to get to the Royal Line Steak House?
Sure, dear.
Take the Green Line west to First station and change trains.
Get on Blue Line and ride to Park Street Station.
So we get off at the Park Street Station?
Yes.
The restaurant is about a fifteen-minute walk from there.
Excellent.
Thank you.
See.
That was easy.
Yeah, expect that she told us to get west on the Green Line, and we’re going east.
I don’t believe it.
We’re lost again!
I’m asking for directions.
To a man and a woman on the street.
Excuse me, can you tell us how to get to the Royal Line Steak House?
I think so, um…walk west for two blocks and then turn right.
That’s River Road.
Wait.
I’m writing down.
OK.
Then go up River Road to State Street, and turn left on State.
No, you go down River Road for two blocks to King Street and then turn left.
No, that’s the fast way.
I suggest taking the scenic route. Walk along State Street until you get to a large park and turn right.
No!
Turn left.
Nick, I think I know the best way to find a place-
Me, too!
Take a taxi.
Taxi!
It’s so hot!
Do you want to go to the beach?
We can go swimming to cool off.
No, there’s nothing to do there.
Well, uh, I … I don’t really like swimming.
It’s kind of boring.
Well, there are other things to do at the beach.
We can go jet-skiing, have a barbecue, or …go parasailing.
Parasailing? 
No.
I’m not crazy about flying, either.
Well, there’s a beautiful coral reef at one end of the beach.
We could go snorkeling.
Snorkeling?!
But we might see a big fish!
I hope we see lots of fish if we go snorkeling. 
That’s the whole point!
But what about sharks?
Didn’t you ever see the movie Jaws?
Are you afraid of fish?
Me afraid of fish?!
No!
Come on, let’s go!
My grandfather taught me how to fish.
This is the hook and these are the weights.
They make the hook sink.
Yep.
Now, the first thing we do is bait the hook.
Oh, that’s disgusting!
I don’t want to watch.
No, no.
We’re going to use cheese.
It’s a secret my grandfather taught me.
Here.
Show me how to cast.
This is a closed reel.
It’s easy.
You just hold the rod here, push this button down with your thumb, and release it at the end of your cast — like this!
That’s easy.
Now it’s my turn.
When you feel a bite, jerk the pole to hook the fish.
When feel a bite, jerk the pole to hook the fish.
Whoever catches the fewest fish cleans them all!
Hey, I’ve got a bite!
Oh no!
Do you have our beach towels?
Uh, yeah.
I’ve got them in this bag.
Here you go.
Great.
Let’s put them down here.
What else do you have in that bag?
It’s so full.
Uh, just a couple of things, like my swimsuit, my swimming cap—
Swimming cap?
You don’t need a swimming cap at the beach.
Oh.
Well, I also brought some waterproof sunscreen so I don’t get sunburned.
Smart thinking!
Well, put on some sunscreen and let’s start tanning.
Tanning?
No way!
We’ll roast in this hot sun!
I thought you liked barbecue.
Not when I’m the main course.
Let’s go for a walk on the beach.
Ok.
Maybe we can find some pretty shells or rocks to bring home.
Yeah.
Hey, here’s a nice shell.
I’ll put it in my pocket.
Oh, look!
A crab!
It buried itself in the sand.
Hey, look, a starfish!
And it’s still alive.
Uh, Lisa!
Ah!
Ouch!
There’s something in my picket!
It’s biting me!
What?!
What is it?
Take it out!
It’s the shell!
It’s alive!
Now it’s biting my hand!
That’s the sell of a hermit crab!
It’s not biting you; it’s pinching you with its claws!
I don’t know what’s worse, getting pinched by a crab or seeing a big fish!
I love swimming!
It’s great fun and exercise.
Can you teach me how to swim?
Sure. 
Show me what you know how to do first.
Ok.
This is the only way I know how to swim.
That’s not bad, but, uh, that’s how my grandmother swims.
It’s called the dog paddle.
Oh, how embarrassing!
This is why I never swim in public.
Here, watch me.
This stroke is called the front crawl.
Cool!
What you are doing now called treading water.
Really?
I thought this was called drowning!
Help!
I love swimming!
It’s great fun and exercise.
Can you teach me how to swim?
Sure. 
Show me what you know how to do first.
Ok.
This is the only way I know how to swim.
That’s not bad, but, uh, that’s how my grandmother swims.
It’s called the dog paddle.
Oh, how embarrassing!
This is why I never swim in public.
Here, watch me.
This stroke is called the front crawl.
Cool!
What you are doing now called treading water.
Really?
I thought this was called drowning!
Help!
The great thing about beach volleyball is you can play with only two people.
That’s good.
I’d be embarrassed to play with a team.
Now remember, you can hit the ball with open hands, like this, or with a closed fist, like this, or with your hands together, like this.
I think I understand.
Go ahead and serve.
I’m ready.
All right. Here goes…
Got it!
Nick!
You can’t catch the ball!
You’re supposed to hit it.
That’s one point for me.
We’re keeping score?
Yes, and the loser buys lunch.
That’s the only way I will keep you serious about the game.
I don’t want to play then.
Then I’ll play with that handsome weight lifter over there.
Ok, ok!
I’ll play.
Serve!
Oh, how cute!
You’re building a sand castle!
This is more than a sand castle!
This is a work of art!
Do you want to help me?
No, but I’d like to buy you in sand.
Maybe later.
Do you think I should build a wall around it?
No.
But you should build a parking lot.
Castles don’t have parking lots!
Haven’t you ever built a sand castle before?
I know how.
What I was trying to say is that I don’t really like to build sand castle.
I like to destroy them, like this!
No!
I don’t think I want to wear a wet suit and carry a tank.
We’re not going scuba diving, we’re going snorkeling!
Oh.
Well, what do we need?
We’ll each need a mask, a snorkel, and some flippers.
A snorkel?
You mean this thing?
How do you use this?
You put this end in your mouth when you’re in the water, and this end sticks out of the water.
You breathe through it.
And why do we need flippers?
With flippers, we can swim faster.
Speed is good.
Especially if we see a …
Don’t worry!
We won’t see any sharks.
Are you sure?
Trust me.
Even if we did see one, it wouldn’t want to eat you.
You’re too thin!
Look at how well I got this barbecue going.
Yeah. But why did you use both charcoal and wood?
To give the food a special flavor.
A smoked flavor.
Oh.
Should we put more pork on the grill?
No, there’s already a lot.
There’s also chicken and beef.
We should turn beef over.
The fire is too strong.
It looks like it’s getting burned.
Girls always think boys can’t cook.
Now let me do the cooking, please.
Ouch!
Don’t slap my shoulder.
My sunburn…
Ouch!
Why is everything at the beach either scary or painful?
That’s what males coming to the beach fun, Nick.
Ed and Amy are planning a trip to America.
I have three weeks of vacation saved up. Let's take a second honeymoon. 
Really? 
How about we go to the States? 
I'd like to see Disneyland and Universal Studios.
Why not?
How about a couple4 of days in Las Vegas, too?
We've got to go to the Grand Canyon, too.
And we can't miss New York City.
 I'd love to take in a Broad way play.
Isn't New York on the other side of the country?
 Who cares? 
It's just a five-hour plane ride from the west coast. 
Well, when you put it that way, what can I say but yes.
Amy is calling a travel agent to purchaser airline tickets.
Good morning. 
Fun times travel agency. 
May I help you? 
Yes.
 I would like to book two tickets to Los Angeles.
 I would also like to go to New York, Las Vegas….. hmm, let’s me see… 
Do you have your itinerary all worked out? 
I guess my husband and I haven’t really given it too much thought, but we are sure we want to go to LA., Las Vegas, the Grand Canyon, and New York.
We have a great package deal that I could recommend to you.
 If you fly business class on the international leg of your flight, you get four stops in America for the price of one. 
Sounds great! 
About how much is it going to cost me?
I will scout around for a good price and get back to you in a few days. 
Could I have your phone number, please? 
Sure. It is 8983-0992.
Ed is at the bank buying some traveler's checks.
May I help you sir?
I'd like to purchase one thousand two hundred dollars worth of   traveler's checks, please. 
How would you like them? 
We have twenty, fifty , and one hundred dollar denominations. 
Give me ten twenties, ten fifties, ten fifty, and five hundreds. 
The teller returns with the checks.
Here you go, sir. 
Would you please sign the top of the checks now?
Sure. 
Better safe than sorry.
Also, don't forget to keep a list of your check numbers in a safe place.
OK. 
What's the total?
There's a two percent service charge, sir, so that comes to one thousand two hundreds twenty-four dollars altogether. 
How would you like to pay? 
Can you just take it out of my account?  
Certainly, sir.
 Have a good trip.
Ed and Amy are packing for a trip.  
Don't forget to bring our passports and some extra cash. 
We'll also need our driver’s licenses if we plan to rent a car.
l'm taking a couple of dresses for formal occasions. 
You should take your dark suit.  
I will wear the jacket on the plane to save space.
We should take along some pills just in case you  get airsick.
Oh, man! 
Look at this suitcase. 
It's falling apart.
 I need a new one.
I'm taking along a duffel bag for bringing back anything we buy on the trip.
How about umbrellas and raincoats?
No. 
We can buy them there if we need it.
Ed and Amy plan to take a taxi to the airport.
Should we drive to the airport or take a taxi?
Let's take a taxi.
 Parking is pretty expensive, and my suitcases are too heavy to lug around.
Fine. 
It takes about an hour to get there, and we will hit rush hour, so we should leave at about seven o'clock
Good idea.
 Later on I'll call the airport taxi service and make a reservation.
I think your luggage is going to be over the weight limit.
 It looks like you're thinking of staying there for good.
Ha-ha!
 I already checked with the airlines, OK?
OK, just as long as you know that you are the one who is going to have to carry them. 
Ed and Amy are at the ticket counter checking in their luggage
Is this where we check in for flight two thirty-seven to Los Angeles?
Yes, it is sir.
Great.  
Here are our tickets and passports. 
Do you have a seating preference?
We'd like a seat with plenty of legroom.
 I will put you in the
How many bags do you want to check in?  
We have two each. 
Any carry-ons?
Just one. 
Here are your tickets and boarding passes. 
Your baggage claim tickets are attached. 
Have a great flight!
Ed and Amy’s flight has been delayed for an hour due to mechanical difficulties. 
Rats! 
We got up at the crack of dawn just to end up sitting around waiting. 
Well, at least we can relax here in the departure lounge. 
Look at all of these goodies. 
We won’t need to eat breakfast. 
And the chairs here are very comfortable. 
Time will fly. 
How will we know when our flight is ready to board? 
We will hear an announcement over the loudspeaker. 
I think I will nod off for a few minutes. 
Don’t worry. 
I will wake you up. 
By the way, did you notice which way our boarding gate is? 
You are such a worrywart.
 I know where the gate is. Now go to sleep.
Ed and Amy’s flight is now ready for boarding. 
Well, it is almost time. 
That nap in the lounge made me feel good. 
I am raring to go. 
All business-class passengers may now board the aircraft. 
That’s us, honey. 
Let’s go. 
Welcome aboard. 
Your seats are up front there on the right. 
Super.
 Lots of legroom to stretch out in.
 I just might take another nap! 
Do you mind if I sit by the window? 
Not at all. 
Just let me put my bag in the overhead bin. 
Look. 
Every seat has its own TV. 
Far out!
Would you like something to drink before we take off? 
We would both like some orange juice. 
Thanks. 
Ed and Amy are transit passengers at the airport. 
Excuse me. 
Is this the transit counter?
Yes, sir.
 How may I help you? 
I just came in on flight two thirty-seven and am transferring to Los Angeles. 
Do I have to change planes? 
May I see tour boarding pass, sir? 
Sure. 
Here it is. 
You are going to Los Angeles on flight two thirty-seven, so you do not have to change planes. 
Do I have time to make a phone call? 
Yes, you have half an hour. Just be at gate twelve for boarding at twelve thirty-five p.m.
Do I need to check in again? 
No, that is not necessary. 
Thank you. 
So long. 
The flight attendant is giving more instructions following takeoff. 
Ladies and gentlemen, please be advised that smoking is not allowed during the flight, neither in the cabin nor in the bathrooms. 
We will distribute headsets shortly. 
Please check the menu card in your seat pocket to help you make your meal selections. 
We will be coming around with drinks shortly. 
This is terrific service. 
Those snacks in the lounge will tide me over till we gets some real food. 
Did you notice the overhead lights and air-conditioning jets? 
You can turn them on and off with this button. 
What else have you discovered? 
There are nine music channels and four movies to choose from. 
I know what I will be doing on the flight. 
A meal is now being served on the airplane. 
Excuse me, sir. 
What would you like for dinner? 
We ordered the low-fat meat. 
Do we get a choice of entrees?
Yes. 
Today, we have broiled shrip with rice pilaf or chicken teriyaki served with noodles. 
I will have the chicken. 
I would like the shrimp. 
What else comes with the meal? 
Vegetables, salad, a roll, and yogurt for dessert.
 Would you care for some wine with dinner? 
Sure.
 We are celebrating. 
This is our second honeymoon.
 Let’s live a little. 
Congratulation. 
White or red? 
We would both like white. 
Yes, sir. 
Here you go. 
Ed and Amy want to buy some duty-free items. 
Did you see really cool stuff in there.
Excuse me, miss. 
How do we go about purchasing things in the catalog? 
You can order them here on the plane or visit one of the duty-free shops in the airport when we land. 
OK.
 I would like this model airplane. 
Sorry, ma’am. 
The model airplanes have been out of stock recently. 
How about these neat electronic gadgets?
You have to pay tax on them. 
I think I will wait and shop around at the airport. 
Just show your passport and boarding pass to the salesclerk. 
Thanks for the tip. 
The airplane is passong through some turbulence. 
Excuse me, sir.  
We are expecting some turbulences. 
Please take your seat and fasten your seat belt. 
I was just on my way to the john. 
Can you wait until the fasten-seat-belt sign goes off? 
Sorry, I cannot wait. 
All right, but please hurry. 
Ladies and gentlemen, we are experiencing some turbulence.
For your safety…
Amy is talking to Ed. 
There you are. 
You made it back just in time. 
When you gotta go, you gotta go!
Excuse me, ma'am. 
How long will this last? 
Only a few minutes. 
The plane has just passed through the turbulence, and Amy is not deeling well. 
I feel sick to my stomach. 
It's in knots. 
Do you feel dizzy?
Yes, and I feel like I'm going to throw up. 
Put your head doen. 
I'll call the flight attendant. 
Excuse me, miss? 
May I help ypu, sir? 
My wife is sick. 
I think it's from  the turbulence, 
Cover your mouth and bose with this papper bag and breathe slowly through your mouth about ten minutes. 
Try to relax. 
Do you feel any better? 
I feeel much better now. 
Here is an airsickness bag just in case you feel sick again. 
Thank you. 
The plane is about to land at the airport. 
Ladies and gentlemen, we are now approaching Los Angeles international airport.
The local time is seven a.m. 
Please fasten your seat belt, put your seat in the upright position, and return your table to its locked position. 
We are  almost there.
 I wonder what the temperature is. 
It is seventy-five degrees Fahrenheit, and there is no rain forecasted for days. 
That’s great! 
We really lucked out. 
Look around and make sure you haven’t forgotten anything. 
I looked already. 
We’re coming down pretty fast. 
Here. 
Chew some of this gun and keep swallowing hard. 
Thanks. 
My ears are all plugged up.
Amy and Ed’s plane has just landed at the airport. 
Ladies and gentlemen, we have now landed at the Los Angeles international airport. 
Please remain seated with your seat belts fastened until the seat-belt sign is switched off and the plane has come to a complete stop. 
That was really a very smooth landing. 
I didn’t feel a thing. 
How are your ears? 
Can you hear now? 
Everything is as clear as a bell. 
The seat-belt light just went out. 
Don’t forget your bag in the overhead compartment. 
Please leave your headphones in the seat pocket. 
We will pick them up later. Be sure to take your completed customs from with you. 
I have it in my purse, so we are all set. 
Ed is going through immigration. 
May I see your passport, please? 
How long will you be staying in the United States? 
Just three weeks. 
What is the purpose of your visit? 
My wife and I are on vacation. 
Where will you be staying?
We have hotel reservations in L.A.,Las Vegas, the Grand Canyon, and the New York. 
Do you have the address handy? 
Here is a copy of our itinerary. 
Thanks. Welcome to the U.S. 
Your papers are all in order. 
Thank you. 
Ed and Amy are asking directions to the baggage claim area and retrieving their luggage. 
Excuse me. 
Where is the baggage claim area? 
Go down the escalators at the end of the hallway, and the baggage claim is on your right. 
How will I know where to get my luggage? 
Just check the flight display board above each carousel. 
Do I have to how my ticket to get my bags? 
No, but double-check to make sure you get the right ones. 
Ed and Amy are at the luggage carousel.
Have you spotted our bags yet? 
Yeah. 
Those two, right? 
Sure enough, that is them. 
One of Amy's suitcase is missing. 
She reports it to the airlines's lost-luggage department. 
Oh, no. 
One of my suitcase didn't make it. 
What should we do? 
o know where the lost-liggage counter is. 
Let's go. 
I would like to report a missing bag. 
What flight were you on ?
Fight two thirty-seven from Tokyo. 
What color was the suitcase?
Black with a Nike logo on it. 　
Please fill in this form, and be sure to include a phone number where we can reach you. 
We will can you as soon as it arrives and arrange for it to be delivered to your hotel. 
Thank you. 
Amy is going through customs 
How do you do, ma’am? 
Do you have anything to declare today? 
Just this mango. 
Where did you get it?
I picked it at a friend’s farm. 
I am sorry.
 No fresh fruit, vegetables, or meats may be brought in. 
Darn it!
 I want to give it to a friend here. 
Well, I guess you can be the friend. 
Thanks, anyway, ma’am, bit we will have to destroy it. 
We do not want to risk infestation. 
You are really going throw out this delicious fruit? 
What a shame! 
If that’s all, ma’am, then you are free to go. 
Have a nice visit. 
Amy and Ed are walking through the airport terminal, and they get lost. 
With so many different hallways here, it is so confusing. 
I haven't have a clue as to where we are.
Just relax. 
We'll find it. 
Here's an information booth. 
Welcome to Los Angeles. 
We are totally lost. 
Where are the taxis?
Here's a map of the airport terminal, and here's yhe exit you want for the taxis. 
This is a big help. 
I got all tyrned around. 
Anything else I can help with? 
We would like to use the restroom. 
They're right near here. 
See over there by the turnstile? 
Just to the left. 
Thanks. 
Ed and Amy are taking the hotel shuttle to their hotel. 
Pardon me, but where can I call the Hilton for a shuttle pickup? 
There is a telephone on the pillar just down the hallway, sir. 
Thanks. 
C'mon, Amy. 
Hello. 
My name is Ed Potter. 
I would like to request a pickup from the airport. 
Certainly, sir. 
Please exit the terminal and go to the shuttle stop just outside the door. 
I will radio a driver to meet you. 
How long will it take? 
About ten minutes. 
How long is the ride to the hotel? 
Traffic is backed up today, so it will take about thirty minutes. 
Sorry about that. 
No problem. 
Thank you. 
Ed and Amy are checking in. 
Hi. 
I have a reservation for two. 
My name is Ed Potter. 
Yes, we have you down for three nights. 
I'll need your credit card. 
Please fill out this form and sign here. 
Not a problem. 
Here are your keys. 
Can I get a newspaper delivered to my room? 
Ceatainly. 
We'll just put it on your tab. 
Our breakfast buffet opens at six-thirty. 
Do you have any vrochures on things to do? 
There is a rack just around the corner with many pamphlets. 
Help yourself. 
Thank you so much. 
Ed has jet lag. 
Come on, laxybones. 
We just got here, and you're dozing off already. 
Huh?
What time is it anyway? 
Only five-thirty p.m.
We'd better not go to bed yet, or we will never get in sync with the time here. 
But I'm so beat. 
All I want to do is sleep. 
If I can cope, so can you. 
Didn't we bring some anti-jet lag medicine along? 
Amy gets the medicine. 
Here you go. 
Take two of these and no more jet lag. 
Are there any side effects? 
No, it says here that there aren't. 
I don't believe that dor a minute, but here goes nothing. 
Amy and Ed are ordering room service. 
Hello, operator?
How late is room service open? 
Until eleven-thirty p.m., sir. 
May I connect you?
Yes, please. 
Hello. 
Room service. 
We'd like a breakfast, please. 
Send up two eggs, sunny-side up, hash browns, and toast. 
Anything to drink?
Um, orange juice and coffee, please. 
Your room number? 
Room three forty-five. 
Got it. 
We'll send it right up to you. 
Thanks. 
Ed and Amy are going to eat breakfast at a hotel buffet. 
Hurry!
We are going to late for the breakfast buffet. 
This all loks so good, and it's all-you-can-eat. 
Quite a deal. 
I could eat a hourse. 
These sausages and biscuits look really good. 
I'm going for the waffles, fresh strawberries, and whipped cream. 
I think I'll have an omelet. 
Sir, I'll fix that omelet for you. 
What would you like in it? 
I'll have cheese, green onions, mushroom, and ham, with a little salsa on top. 
It'll be ready in a jiffy. 
Ed and Amy are checking out. 
We're all packed. 
I'll call the front desk. 
Amy is calling the front desk. 
Good morning. 
We’d like to check out now, please. 
May I have your room number, please? 
Certainly. 
This is room five-oh-one.
We'll send someone up to hekp you with your luggage right away. 
Amy and Ed are at the front desk checking out. 
Hi, folks. 
I hope you enjoyed your stay with us. 
Yes, thabk you. 
I would like to pay the bill. 
How would you like to take care of it? 
Put it on my credit card, please. 
Of course, sir. 
If you would just check the additional charges and sign herem that'll be everything. 
Ed is checking the bill. 
Everything seems to be in order. 
Here is your receipt. 
We hope that you'll stay with us again. 
Amy is renting a car. 
Hi, my name is Amy Potter.
 I called in advance about renting a car for three days. 
May I see your driver’s license, passport, and credit card, please? 
 Here is my international driver’s license, credit card, and passport. 
Would you like to get insurance?
No, my gold card covers that. 
The agent is indicting places on the form.
Please sigh on all the lines marked with an X and initial here and here. 
What are the charges? 
A three-day package is two hundred fifty dollars. 
You get unlimited mileage, and if you sill the tank before bringing the car back, you will save a the-dollar fill-up charge. 
Here are your keys. 
Thanks. 
Ed and Amy are filling up the car. 
I forgot to find out how to open the gas tank. 
Look in the glove box and see if there is a manual. 
I don’t think we need the manual. 
There should be a lever beside your seat. Just pall it. 
Hey!
You are right.  
It just popped open.
This is a self-service station, and it is cheaper if you pay in cash. 
Do you think I should put in premium or regular? 
Premium. 
I will use the squeegee to clean the windshield. 
I would better check the oil. 
I am going to the convenient store for a snack. 
Do you want something to it? 
Sure, thanks. 
Get me some soda and chips. 
Ed is speaking with a police officer after being caught speeding. 
Excuse me, sir. 
May I see your driver's license and registration. 
What is the problem, officer?
You were driving seventy in a fifty-mile-an-hour zone. 
I am going to have to give you a ticket. 
Officer, I'm not from around here. 
How do I pay it? 
You can mail the ticket with a check for the correct amount. 
You also have the right to contest the violation in a court of law. 
Well, I don't have a cank account with an American bank. 
Do you have a credit card? 
You can pay with that. 
That's a relief. 
Keep an eye on the speed linit during the rest of your visit. 
Joey and Rachel have a map to direct them from their hotel to Disneyland. 
Rachel, why don’t you drive, and I will navigate? 
That is fine with me. 
Joey is looking at the map.
Here’s Disneyland on page four. 
Go to the intersection and turn south. 
Your sense of direction is better than mine.
 I need right or left. 
OK. 
Turn left at Lanker shim Avenue and make another left at Collette Boulevard. 
Now what? 
Right there!
 Take that freeway entrance onto high 101 till you see 1-5. 
Here we are at 1-5. 
Now just keep going about twenty miles until you see the Disneyland exit. 
Gotcha. 
That was easy. 
Here we are at the Magic Kingdom! 
Tim and Annie are riding the subway to the theater. 
Excuse me. 
Where can we catch the subway? 
Just across the street. See? 
There is an entrance overt there. 
Oh, yeah. I see it. 
Thanks, officer. 
Hi. 
Where can we buy some tokens? 
Tokens were phase out in 2003. 
We use Metrocaod now. 
You can buy at the Metrocard vending machine over there. 
The machine accepts bills, credit cards, or debit cards.
 Just touch the vending-machine screen to get started. 
How much does it cost to get to Broadway? 
Well, a 1-day Unlimited Ride Fun Pass costs 8.25 dollars. 
Or you could buy a single-ride ticket for 2.25 dollars. 
It is late, so Tim and Annie take a cub from the theater back to the hotel. 
Let’s hail a cab., so we do not have to ride the subway so late. 
Here comes one now. 
I will hail it. 
Do you have the hotel’s business card? 
Yeah, I have got one.
 It has the hotel’s address and phone number. 
Where to go? 
The Park Plaza Hotel, please. 
Here is the address. 
I know that hotel. it is a nice place. 
Have you been in town long? 
Just a few days. What about you? 
Are you a native New Yorker? 
Well, I was born and raised in California.
 I moved to New York about ten years ago. 
Watch out. 
You nearly ran over that old lady. 
Relax. 
Get used to it.  
This is New York.
Tom and Tina are asking a passerby to take a picture for them. 
Excuse me, sir.
Would you mind taking a few pictures for us? 
Sure. 
Just stand over there. 
Can you get the Empire State Building in?
Move back a little bit. 
Now to the lest. 
There, that's perfect. 
Say cheese. 
Thanks. 
Could you rake another one, please?
I can't seem to get it focused. 
Just push the button on the front, under the lens. 
It should zoom in. 
Got it. 
Any more?
Could you snap a picture for us next to this sign?
You have got it. 
Move in a little closer there, friends. 
Try not to squint. 
Thabks for everything. 
Tina is looking for Rockefeller Center. 
Excuse me, miss.
 Is this the way to Rockefeller Center? 
No, you are way off. 
Do you have a minute to give me directions? 
Sure. 
You are about a fifteen-minute walk away. 
First cross the street, and go three blocks east. 
Which way is east? 
That way. 
Then head south until you come to a flashing neon sign. 
How far is that? 
Four blocks, give or take a block. 
Are there any other landmarks? 
Yeah, there is a big foundation. 
Go past the foundation, turn right at the corner, and the Center’s on the right. 
Thanks so much. 
Tina wants to change some Taiwanese dollars into US dollars at a bank. 
Pardom me. 
I'd like to change money-NT dollars for US dollars. 
Certainly, ma'am. 
The exchange rate is thirty-four NT dlooars for one US dollars. 
By the way, that includes the banj fee. 
That's a bad rate. 
I should have done this in Taipei. 
I would like to buy two hundred US dollars. 
That'll be six thousand eight hundred NT.
May I see some identification, please. 
Here's my passport. 
Please fillout this form and sign it. 
We'll need a local contact address where you can be reached. 
We're staying at the Park Plaza Hotel. 
That's fine. 
Will that be all? 
I just need a receipt, thanks. 
Tom and Tina are looking for sightseeong brochures and city tour information. 
Hi. 
We'd like to join a sightseeing day tour. 
You can take a limo, a tour bus, or join a walking tour.
It's up to you. 
Which best covers the major attractions?
This bus tour what your're looking for. 
It visits the Empire State Building, Times Square, and, of course, the Statue of Liberty. 
Do you have any brochures? 
You'll receive a complete information package when you board the bus. 
OK, we'll take the New Year's Eve tour. 
We'll see you at eight-thirty on Friday. 
Oh, and lunch is included, as well. 
Be sure to bring your camera with you. 
Tina and Tom are buying souvenirs. 
Excuse me. 
How much is this cap?
The cap's sixteen dollars and fifty cents. 
And this T-shirt? 
Twenty-four dollars and ninety-five cents. 
Yikes. 
They're expensive. 
This cap glows in the dark and that shirt won't shrink, wrinkle, or fade. 
Well, in that case, how can I go wrong? 
How many world you like? 
Two shirts. 
What sizes do they come in?
Small, medium, large, and extra-large. 
I guess we'll take two larges. 
Would you like them gift-wrapped?
No, thanks. 
They're for us. 
Tina's buying a postcard and stamps. 
The scenery here is so beautiful. 
Let's send a postcard home to our family. 
I saw a rack of postcards in the foyer. 
They've got quite a selection. 
My goodness. 
There are so many to choose from. 
Which one do you like? 
This one of the sun getting over desert is nice. 
It's so different from where we live. 
How much is it? 
Only thirty-five cents
Let's see if they have any stamps. 
Excuse me. 
Do you sell stamps here? 
Yes, we do. 
I need enough for a postcard to Taiwan.
That'll be ninety-eight cents, and thirty-five cents for the postcard. 
With tax, that comes to one dollar and forty-three cents. 
I only have a fifty. 
Sorry, I can't break a fifty. 
Tina and Tom are reporting a lost handbag. 
Oh, no. 
I've lost my handbag. 
Where did you lose it? 
Well, I had it when we got on the bus. 
It must be there. 
My bus ticket has a number to call. 
Good. 
I'll call them and report it. 
Big Apple Bus Line. 
May I help you? 
My wife left her shoulder bag on a number one-oh-one bus. 
Has anyone turned it in yet? 
No, sir. 
Could you describe it and give me a number where you can reach you?
It's a brown leather bag with the initials AC on it. 
Our phone number is 555-2764.
Lost and found will call if it's recovered. 
Tina' on the phone making a doctor's appointment for Tom, and she takes him to the clinic. 
Hello. 
I'd like to make an appointment for my husband to see a doctor. 
What's the problem?
He has a high fever. 
You can come to our emergency clinic for this afternoon at two thirty. 
Good afternoon. 
I'm Mrs. Chen. 
I called for an appointment for my husband. 
Would you please fill out this form?
Do you have any health insurance? 
No, none in America, so I guess we'll have to pay out of pocket. 
I feel terrible. 
I think I'm going to pass out. 
Lie down here, sir. 
The doctor will be right in. 
This is a real drag. 
Cindy and Terry are shopping in a local supermarket. 
Can you believe this place?
The selection is mind-boggling. 
Look at all the different breads and all these gourment desserts. 
How about this deli?
I just love the cheese, and they make party platters to order. 
This is what you call one-step shopping. 
This place has everything: cosmetics, household items, and lots more. 
Here's the bulk food section. 
Just scoop out what you want and put a twist tie on the bag. 
Would you like paper or plastic? 
Plastic is fine. 
Can I pay with a credit card? 
Sure. 
Just slide your card through this machine and punch "yes" when the total shows. 
Cindy and Terry are ordering a meal in a fast-food restaurant. 
Hey, guys. 
What'll it be? 
I'd like a double cheeseburger, fries, and a shake. 
And I'll jave a fillet of fish, a chicken burger, a large tooseed salad, onion rings, and a shake. 
Strawberry. 
Tropical fruit. 
Altogether, that'll be sixteen dollars and ninety-eight cents. 
Here's twenty. 
Is that for here or to go? 
For here. 
Here's your change, three dollars and two cents. 
Thanks, folks. 
Enjoy. 
Terry is making a dinner reservation. 
Good evening. 
Brown Derby. 
May I help you? 
Yes, I'd like to make a dinner reservation for this Saturday night at seven. 
We're booked solid. 
How about seven-thirty? 
We'll have a booth available then. 
That's fine. 
Smoking or nonsmoking?
Nonsmoking. 
Is the booth by the window? 
Yes. 
It overlooks the park. 
Excellent. 
That'll do nicely. 
May I have your name and phone number, please? 
Terry Chen, and the number is 555-8910.
Cindy and Terry are having dinner at the very popular Brown Derby restaurant. 
Good evening. 
Would you like a cocktail before dinner? 
Let's velebrate. 
I'll have a glass of champagne. 
It's our big night out on the town. 
May I take your order? 
We'll start with oyster Rockefeller. 
And for the main course?
What do you recommend? 
Our piece de resistance is filet mignon with roasted hazelnuts and truffles. 
I'm convinced. 
My mouth is watering just thinking about it. 
For dessert, I suggest the chocolate mousse. 
Vicky and Sean spend a day at Disneyland. 
We're finally here at the Magic Kingdom!
I am so excited. 
I've got our admission passports, so we don't have to stand in line at the ticket booth. 
Let's hit the most popular reides before they gwt too crowded. 
Fantasyland is the most popular spot, so let's go there first. 
Look!
There are Mickey and Minnie. 
Let's get a picture with them. 
OK, but quick, and then we'll zip over to the Indiana Jones ride. 
I'm hungry. 
Let's try some Cajun food in the New Orleans Square.
Sounds good. 
Then we have to grab some souvenirs to take back with us. 
Before all that, let's watch the Street Party Parade. 
Sean is calling Universal Studios for directions and information about buying tickets. 
Hello. 
I'd like some information about buying tickets. 
Yes, sir. 
Our bix office is open from eighty-thirty a.m. to four o'clock p.m.
How much is a ticket for an adult? 
It's fifty-seven dollars for an adult. 
You can buy your ticket at the front gate. 
I will be coming from the Hilton Hotel. 
How do I get there? 
Just get on route 101 south and take the exit ramp marked "Exit 29, Universal Studios."
Are there any parking spaces nearby? 
There is a paring lot on the premises. 
It's seven dollars for cars and ten dollars for RVs. 
Thanks a bunch. 
Vicky is signing up for a bus tour to Los Vegas. 
Good afternoon, miss. 
Can you help us arrange a bus tour? 
Yes bet. 
Where would you like to go? 
Do you have a two-day tour to Las Vegas the day after tomorrow?
Yes, we do. 
For how many?
There are two fo us. 
What tome does the bus leave, and where do we catch it? 
The bus leaves at a quarter past six in the morning. 
It will pick you up at your hotel. 
That's very convenient. 
We're staying at the Millennium Biltmore Hotel. 
Oh, and one more thing: How long does it take to get there? 
About five hours. 
Your meals are included in the price of your ticket. 
Sounds good to me. 
Thanks. 
Vicky and Sean are gambling at a casino in Las Vegas. 
My word!
Did you ever see so many one-armed bandits in all your life? 
Those people look like they've been sitting there for hours on end. 
I know. 
They're waiting to hit the jackpot. 
I feel lucky tonight. 
I think I'll try the machines for a while. 
How about you? 
I think I'll try the craps table. 
I love to watch the dice roll. 
OK, but don't get too carried away. 
Stick to our budget. 
Don't worry. 
I won't lose control. 
If you say so. 
Just wish me luck. 
Vicky and Sean are attending a show at the hotel. 
Wow!
You look so beautiful!
You could be in the show. 
OK, that's enough. 
You don't look so bad yourself. 
I've always wanted to see a Vegas show wth Tom Jones headlining. 
Our table is down center. 
We can have dinner while we watch the show. 
Neat/ 
Boy, this is really great!
The music is fantastic. 
Look at those gorgeous costumes. 
It look like a full house tonight. 
One of them is pointing at you. 
I think she wants you to join her on the stage. 
No way!
I suddenly have to go to the restroom. 
See you in a few minutes. 
Sean and Vicky are at the Grand Canyon. 
This is Mother Nature at her best. 
Vicky is shaking. 
Do you have to stand so close to the edge? 
What are you talking about? 
There's a railing. 
I can't possibly fall. 
Sean climbs the railing. 
Sean, come down now. 
Vicky, it's OK.
Don't be such a worrywart. 
Nothing's going to happen. 
Says you!
I really feel uncomfortable here. 
Let's go. 
Go? 
You've got to be pulling my leg. We only just got here a few minutes age, and we still have the mule ride. 
I'm going to get you for this. 
I feel even worse than I did at the edge. 
Vicky and Sean are flying into New York City at Christmastime. 
I'm so glad this flight is in the daytime. 
Can you see down there? 
It's the Statue of Liberty. 
Yes!
And look over there!
I can see Central Park. 
I see the Empire State Building. 
It's even bigger than I imagined. 
The map shows the Empire State Building is located on Fifth Avenue. 
I can't wait to go shopping there and see all of the beautiful Chtistmas decorations. 
There are tons of restaurants to check out, too. 
Maybe we could take romantic horse-drawn carriage ride, too. 
Central Park is right near our hotel. 
We can take a stroll in the moonlight. 
We're about to land. 
Welcome to the Big Apple, Sean. 
Vicky and Sean are taking a carriage ride around Central Park. 
What a beautiful night. 
You sure said it. 
How about a buggy ride through the park? 
How did you know? 
That's just what I was thinking. 
Great minds think alike. 
Sean, do you see that? 
I think it's starting to snow again. 
We're going to have a white Christmas. 
All those Christmas lights and the snow. 
The park looks like a wonderland. 
You'd never guess by the look of it, but there's a mugging here every five minutes. 
Thanks for running the moment.  
Sean and Vicky are on a sightseing tour in New York City. 
Welcome aboard. 
Let's do roll call, and then we can get started. 
We'll start off our tour in Battery Park to give you a spectacular harbor view of the statue of Liberty. 
Excuse me. 
Will the tour include a ferry trip to Lady Liberty and Ellis Island? 
Sorry, little lady, our tour doesn't , but you can take a ferry there almost anytime. 
The Empire State Building is currently the tallest building in New York. 
It is one of the great dames of New York architevture. 
When was it built? 
It was built in the depths of the Depression in only four hundred and ten days. 
Amazing. 
Vicky and Sean are taking a tour of the Metropolitan Museum of Art. 
Here's a floor plan. 
We have a walking tour starting in ten minutes. 
Thank you. 
Can we take pictures? 
Yes, but no flash.
 Welcome. 
Today, we will see a display of Mary Cassatt's pronts. 
Do you have any questions? 
I wonder if you might have any literature in Chinese. 
Yes, ma'am. 
The International Visitor Desk is located in the Great Hall. 
They can assist you there. 
Do you want to get a bite to eat later? 
Be quiet. 
I'm trying to listen. 
Vicky and Sean are relaxing with a drink and snacks before dinner. 
How about the ambiance? 
The fireplace is so cozy. 
I feel like we're in jolly old England and not in downtown New York City. 
What'll it be, folks? 
We both would like a pint of your dark beer. 
Coming right up. 
Anything else? 
What do you recommend? 
If you'd like something really delicious, try our hot artichoke chowder. 
That sounds really yummy. 
We'll have that. 
Great ready for some good eating. 
Good. 
I'm starving. 
The bartender brings their food. 
Here you go, folks. 
Vicky and Sean are at a theater to see a Broadway play. 
We were really lucky to get tickets to this play. 
I know . 
It's been sold out for months. 
The scalper's prices weren't too bad, either. 
They are in the mezzanine, so we'll be all right. 
Sean and Vicky are lookng for their seats. 
What row are we in? 
Row M. 
Excuse me. 
Is this row M. 
Tes, it is. 
What seats do we have? 
Ten and eleven. 
Right here. 
Hurry, the performance is about to begin. 
OK. 
It's only scends to midnight in Times Square, and Vicky and Sean are ther seeing in the New Year. 
Amazing. 
This place is zbsolutely wall-to-wall people. 
Look at that big ball up there, all lit up. 
What a way to see in the New Year. 
Where did you get the streamers and noisemakers? 
Somebody just handed them to me. 
Here, take one. 
The countdown is about to begin. 
This [lace is on TV all over the world. 
Look. 
The ball is starting to fall. 
Ten… nine… eight… 
Get ready with the noisemarkers. 
OK,here we go. 
Three… two… one!
Happy New Year. 
Pucker up. 
In a move that’ll surprise precisely no one, Volvo has revealed it’ll be pooling resources with its parent company Geely – but specifically in the area of combustion engines.
The move will result in a new ‘global supplier’ of next-gen hybrid and ICE engines, and will leave a direct future-proofed path for further EV development. 
They still are – and you can read about the best ones, and most exciting ones here – but there’s still significant demand for ICE power. And because that demand is going reduce eventually, it’s far more efficient to pool resources, and divert the savings to the next new battery-powered battleground.
The new arrangement will certainly benefit Volvo, which expects all of its sales to be electrified by 2025, with half full BEV and half hybrid. 
The move will also benefit the rest of the Geely empire too, with the likes of Proton, Lynk & Co and Lotus all gaining access to a new supplier – the latter’s SUV-shaped ambitious feels like a perfect fit for the new engines.
Geely says that the new engines could also be sold to third-parties – so it’s possible Geely could franchise out its ICE expertise in the same way BMW is set to do for the JLR group. 
Right now, both Geely and Volvo will carve out and separate their ICE departments from the rest of the business – and after that they’ll look to combine them.
When I first flew the original Swearingen SJ30 in San Antonio more than a decade ago, I was impressed with the light jet’s rocketlike performance.
Jets with cruise speeds of nearly 500 knots true and a maximum range of 2,500 nm were and still are rare.
The original SJ30’s ability to hold its cabin at sea level sea up to 41,000 feet also made the airplane a winner.
The jet’s designer, Ed Swearingen, gained fame 40 years ago with a line of tough, fast Merlin and the regional Metroliner turboprops with a design philosophy–large wings and big engines are not always the better answer–at the heart of the light jet.
Actor Morgan Freeman signed up as an early owner of one of the single-pilot certified jets.
While I came away from that first flight impressed with the airplane’s performance, I remember a nagging question I posed to my company demo pilot, Mark Fairchild, after we landed.
Why will anyone spend $6M (then) for an airplane with a steam-gauge cockpit, when so many aircraft in 2006 were debuting with a host of glass cockpit options?
Turns out not too many people did. Over the years the original SJ30 suffered through some financial and mismanagement turmoil, including a number of prominent accidents. 
Many people assumed it was only a matter of time before the sprightly little jet disappeared completely.
But some people, like Mark Fairchild, never lost faith in the airplane. As NBAA 2019 opens in Las Vegas this week, SyberJet Aircraft, the new owners of the SJ30 series, are attending having just completed a successful maiden flight of the updated SJ30i.
SyberJet conducted the flight from its Engineering and Product Development Center at the San Antonio International Airport (KSAT) and kicked off an 18-month certification test program which will culminate in an amended type certificate for the SJ30i and immediate subsequent deliveries thereafter.
The new SJ30i, powered by a pair of Williams FJ44-2A engines, retains most of the same performance of the original airplane, but has brought both the cockpit and cabin up to modern standards. 
Up front, pilots will find as standard four 12-inch liquid crystal displays, with the SmartView synthetic vision system (SVS), INAV moving map display system, electronics charts/maps, TCAS II, TAWS Level A, synoptic displays, dual flight management systems (FMS) with dual WAAS GPS/LPV, single inertial navigation system (IRS), onboard weather radar, full EICAS, electronic checklists, DME, ADS-B Out, and 0.3-nm RNP, as well as support for FANS-1A, SmartLanding, SmartRunway, TOLD, ADS-B In, emergency descent mode, and RVSM operations. 
SJ30i options include CPDLC, XM weather, flight data recorder, cockpit voice recorder, dual charts/maps, HF radio, SATCOM, enhanced vision systems, second MFD, and the flexibility for other customer requests.
Mark Elwess, chief engineering test pilot and senior flight test engineer Robert Moehle crewed the SJ30i on its first flight. 
Elwess said, "We completed all of the test points planned for the first flight and got a look at how much easier the cockpit and systems are to manage with the new Honeywell Epic 2.0 cockpit.
The SJ30i known as the fastest and longest ranged light jet on the market now has a cockpit to lead us into the next generation."
Mark Fairchild, now Syberjet’s vice president of customer experience, said, "The SJ30i takes the original SJ30-2 to the next level.
While it remains the fastest and longest-range jet in its class, the SJ30 now has all of the advances in cockpit and electronic design to make it simply the finest light jet available.
In addition, the SJ30i also features an all new interior design in conjunction with the lowest cabin altitude of any business jet flying today.
NBAA visitors can see the flight test aircraft at Static Space SD908 at Henderson Executive Airport.
Turquoise Yachts has partnered with DeBasto Design to unveil a new 62 metre explorer yacht concept named Project Nautilus.
Revealed at the Fort Lauderdale International Boat Show, Project Nautilus is described by the yard as a “go-anywhere vessel”, marrying a functional layout with luxury accommodation.
Penned inside and out by DeBasto Design, Project Nautilus sports as “purposeful, masculine look” while being “elegant and timeless,” the yard said.
Key features include a 13.4-metre-long tender deck located at the front of the superstructure, which can accommodate a 12-metre tender.
With the tender deck forward, the owner’s staterooms sits on the upper deck with access to two exterior terraces for far reaching ocean views.
The remaining guest accommodation is for 10 guests in five cabins. There is also a full-size cinema that can be converted into a seventh guest cabin.
Elsewhere, Project Nautilus features a swimming pool on the aft main deck, a lower deck beach club, gym, sauna and hot tub on the top deck.
DeBasto’s layout provide a specific service traffic pattern to allow complete separation between guests and crew on all guests.
The crew accommodation meanwhile comprises seven double ensuite crew cabins, including a captain’s cabin on the bridge deck. 
The rest of the crew cabins sit on the lower deck, alongside two crew lounges.
Project Nautilus follows the in-build 53 metre Turquoise superyacht Tala, which was also penned by DeBasto Designs.
Valley Metro has awarded Motor Coach Industries (MCI) a five-year contract for up to 50 MCI D45 CRT LE and D45 CRT Commuter Rapid Transit coaches.
The contract begins with an initial order for four MCI D45 CRT LE coaches, scheduled for delivery in summer 2020. 
The procurement is part of Valley Metro's strategic plan to improve the level of service and passenger comfort on its commuter express highway routes connecting Phoenix, Ariz., Mesa, Tempe and surrounding communities.
"We are excited to introduce a style of commuter bus that will enhance the overall experience for our express riders," said Scott Wisner, bus services delivery manager, Valley Metro.
"In addition to enhanced comfort and safety amenities, the area designated for mobility devices will help riders more easily board and exit the bus."
ADA and Buy America compliant and Altoona tested, MCI says its commuter coach has a low-entry vestibule featuring an automated retractable ramp that delivers shorter dwell times and a more efficient boarding process for all passengers.
The MCI D45 CRT LE has a shorter turning radius which helps with navigation around city streets.
The new model, which went into production in 2019, also offers enhanced interior illumination and brighter LED headlights to enhance visibility.
 A wider front door with an ergonomic spiral entryway also features enhanced illumination on the stairwell.
While Valley Metro has operated heavy-duty transit buses by MCI's sister company, New Flyer, on its regional routes, this order represents Valley Metro’s first purchase of MCI commuter coaches.
"We are extremely honored to have an opportunity to support Valley Metro," said Tom Wagner, MCI vice president public sector.
"When designing this coach, MCI consulted with leading advocacy groups, including the National Council on Independent Living, that evaluated numerous concepts and the final prototype design. 
Their participation guided the coach design, creating a versatile, comfortable and more accessible commuter coach for systems like Valley Transit.
Four crew members were killed when the helicopter went down in rugged terrain in the northeast part of the country.
A rescue helicopter crashed on Saturday after it collided with the world’s longest zipline in Ras al-Khaimah in the northeastern region of the United Arab Emirates, killing the four crew members on board.
Video showed the Leonardo AW139 spinning out of control before crashing in a fireball as the helicopter was flying a rescue mission at about 5:50 p.m. local time on Saturday near the UAE’s highest mountain of Jebel Jais.
The zipline spans a length of 1.74 miles long at an altitude of 5,512 feet msl.
The summit of the mountain is 6,345 feet msl. It opened in February to allow the smaller emirate of Ras al-Khaimah to attract more tourists and residents from neighboring emirates like Abu Dhabi and Dubai.
The helicopter was operated by the UAE’s National Search and Rescue Center.
The UAE’s General Civil Aviation Authority said it is investigating.
Rolls-Royce Motor Cars used Geneva 2019 to pull the covers off its new range of Bespoke – a near-infinite combinations of trim, colour and material options that will tailor any Rolls-Royce into exactly what the buyer wants.
The company also confirmed that its Black Badge models can also be customised as part of the Bespoke programme.
Rolls-Royce's boss also used the show to confirm that the company had its best year ever in 2018, with its highest recorded sales and 200 new jobs confirmed at the company’s HQ in Goodwood
The first Bespoke Cullinan was unveiled at Geneva. It’s called - unimaginatively - the Geneve 2019.
 It’s a trim and paint upgrade that majors on an interesting theme.
In this case, the exterior recalls the khaki shades worn by explorers, while inside, Navy Blue and Oatmeal are the colours of choice.
In the back, you get stainless steel cocktail and seasoning services, alongside some terribly tasteful glassware and American Walnut serving boards.
You can mix drinks in RR-monogrammed highball crystal glasses to go with the luxury picnic set that’s integral to this car.
Your butler will also be able to make use of a hidden compartment that contains napkins, paring knives, and stainless steel drinking straws.
Finally, there are two boot-mounted rear seats which RR calls the Viewing Suite.
Topping the saloon range is the Phantom, and the limited edition Bespoke version of this is known as the Tranquillity. 
Just 25 will be built, and features include an interior design inspired by elements of the British Skylark space rocket.
In a nutshell, there are shadow patterns fashioned in the highly-reflective stainless steel inserts, and these are complemented by 24-carat gold plating and space grade aluminium.
There are genuine slivers of the Muonionalusta meteorite, which fell to earth in Kiruna, Sweden in 1906 – which makes the extensive use of gold on the interior controls seem ever-so ordinary.
The interior is a work of art with liberal use of black gloss finish that’s highlighted with stainless steel Pinstripes.
The space theme continues with yellow gold speaker grilles, which Rolls-Royce says is inspired by the two gold records containing sounds and images that went with the Voyager exploration probe in the late-1970s.
It’s largely the same story with the Geneve 2019 Dawn model, which majors on paint that’s lacquered with glass particles to allow the car to shimmer under the show lights.
You know the drill – if you have to ask, you can’t afford it.
But an RR spokesperson whispered on stand that more customers than ever are demanding personalisation – and all but the most over-the-top requests can be accommodated.
Dubai’s transport authority signs another Memorandum of Understanding, this time with tech giant Cisco, in a bid to protect fare revenues.
Dubai’s Roads and Transport Authority (RTA) has signed a Memorandum of Understanding with Cisco to use sophisticated technologies to spot bus fare evaders.
The MoU is the latest step in RTA’s strategy to optimise inspection processes and protect public transport revenues.
Ahmed Hashim Bahrozyan, CEO of Public Transport Agency, and Shukri Eid Atari, Managing Director, Cisco, East Region, signed the MoU during GITEX 2019 at World Trade Centre, in the presence of several officials from both parties.
“The MoU calls for the installation of smart cameras on board buses to ease the job of inspectors in detecting fare evaders on board public buses.
The step marks the introduction of a new generation of artificial intelligence technology, computer vision and effective monitoring.
The technology will be used on a trial base on some buses to assess the results,” said Bahrozyan.
“RTA is operating about 1,615 buses covering all parts of Dubai and connecting with key cities in the UAE to serve millions of passengers.
As buses are important source of RTA’s revenues, it is imperative to use advanced technologies to curb fare evasion and detect the incorrect tapping of cards on fare validation machines,” he added.
“This MoU forms part of Cisco’s ongoing Country Digital Acceleration programme, which aims to harness the power of disruptive technologies to develop smarter solutions for the advancement of the UAE’s national infrastructure.
We are proud to partner with RTA in transforming the process and experience of public transportation for operators, commuters and tourists alike.
The technology also ensures fairness in benefiting from public transportation means in Dubai,” said Eid.
Local Motors and Maryland DOT agree to expand Olli testing in National Harbor, including some public roads to help provide solutions to the city's congestion challenges.
Local Motors by LM Industries Inc., in partnership with the Maryland Department of Transportation (MDOT), has expanded the testing of Olli, its low-speed, connected, self-driving shuttle, to public roads in National Harbor.
Due to a landmark local permit, Olli will collect imperative insights to help solve Maryland's most pressing transportation challenges such as traffic congestion, accessibility, and environmental concerns like pollution.
As Olli completes its route on private and public roads, Local Motors' engineers will collect and analyze data in real-time from scenarios such as intersection crossing, and interactions with pedestrians.
The route will serve as an alternative transportation option to the residents and employees of National Harbor as well as visiting tourists, carrying them to harbor attractions and commerce centers.
Any interested member of the public who is a guest of Local Motors can ride Olli Monday through Friday from 10:00 AM-5:00 PM ET. 
To become guests of Local Motors, register at rideOlli.com to sign a waiver and get a QR Code to board the vehicle.
"Our goal at Local Motors is to create and deliver safe and accessible mobility solutions for local communities," said Vikrant Aggarwal, President of Local Motors.
"By completing 'real-world' testing on public roads in National Harbor, we're compiling the data needed to ensure that Olli meets consumer needs and desires in all scenarios."
This public road testing in Maryland also marks the power of local legislators working with American manufacturers like Local Motors to drive mobility innovation.
Under Senator Van Hollen's leadership, Maryland has positioned itself on the cutting-edge of electric and autonomous vehicle testing.
"Visiting Local Motors this week gave me the opportunity to see their innovative work first-hand.
 I appreciated the chance to tour their facility and learn more about their efforts to create sustainable, accessible new forms of transportation.
I look forward to seeing their continued progress with Olli," said Senator Van Hollen, who toured the Local Motors facility in National Harbor, Md. on Monday.
Olli is an environmentally-friendly, viable and sustainable transportation option for cities, companies, hospitals, campuses and other locations where people need to move from one place to another.
The shuttle features cognitive response technology and sensors, and an obstacle avoidance system, which are provided in partnership with Robotics Research.
Robotics Research, a leading provider of autonomous and robotic technologies to commercial and federal customers, assisted Local Motors in the mapping of National Harbor. For more information on Olli, visit localmotors.com/meet-olli/.
Local Motors by LM Industries Group Inc. is a ground mobility company focused on shaping the future for the better. Founded in 2007 with a belief in open collaboration and co-creation, Local Motors began low volume vehicle manufacturing of open-source designs using multiple microfactories.
Since inception, Local Motors has debuted no less than three world firsts; the world's first co-created vehicle, the world's first 3D-printed car and the world's first co-created, self-driving, electric vehicle, Olli.
We believe that Olli is the answer to a sustainable, accessible transportation solution for all.
The U.S. Senate passed a minibus spending package for Fiscal Year 2020 (H.R. 3055), which includes four appropriations bills concerning the Departments of Agriculture, Commerce, Housing and Urban Development, Interior, Justice and Transportation, as well as the Environmental Protection Agency and National Aeronautics and Space Administration
The spending package will now move into conference with the Senate and the House of Representatives. 
The U.S. Department of Transportation was provided $86.6 billion for FY2020 in the package, which is $167 million above the FY2019 enacted level.
Within the USDOT’s portion of the package, key programs funded would include: $1 billion for Better Utilizing Investments to Leverage Development (BUILD) grants with $15 million that would be available for planning grants.
$1.25 billion for the Surface Transportation Block Grant funds and for the elimination of hazards at railway/highway grade crossings.
$2.8 billion for the Federal Railroad Administration, which includes $255 million for the Consolidated Rail Infrastructure and Safety Improvement grants program, $300 million for Federal-State Partnership for State of Good Repair grants and $2 million for Restoration and Enhancement grants.
Amtrak was funded with $2 billion, which includes $680 million for the Northeast Corridor.
$13.0 billion for the Federal Transit Administration, which includes $10.1 billion in transit formula grants from the Mass Transit Account of the Highway Trust Fund and $560 million in transit infrastructure grants from the general fund.
The bill provides a total of $1.978 billion for Capital Investment Grants (CIG), fully funding all current “Full Funding Grant Agreement” transit projects, as well as new projects that have met the criteria of the CIG program.
Most important to the transit industry was the spending package’s inclusion of an amendment that would block automatic cuts to public transit funding, a penalty that would have cut $1.2 billion from formula funding and impacted every public transit agency in the U.S.
The automatic reduction in funding was triggered earlier this year following the Mass Transit Account of the Highway Trust Fund failing a FY2020 forward-looking solvency test.
The amendment to block the automatic cut was submitted by U.S. Sen. Martha McSally (R-AZ) and U.S. Sen. Doug Jones (D-AL).
“Without legislative action to block this cut, public transit agencies across America will suffer a 12 percent across-the-board funding cut,” said Sen. McSally.
“These cuts would be devastating to all of our communities. Transit funds in my home state of Arizona are critically important to our quickly growing communities.
Maricopa County was the fastest-growing county in the United States last year, and cities such as Flagstaff and Tucson are also attracting more jobs and more families.”
The American Public Transportation Association led a coalition supporting the amendment.
APTA President and CEO Paul Skoutelas said the association hailed the passage of the spending package with the adoption of the McSally-Jones amendment.
“The Jones-McSally Amendment prevents a $1.2 billion cut to public transportation funding by blocking a 12 percent across-the-board funding cut to every transit agency in the nation in fiscal year 2020,” said Skoutelas.
“APTA thanks Sen. Doug Jones and Sen. Martha McSally for offering this critical bipartisan amendment to protect public transit funding from implementation of the Rostenkowski Test…
APTA remains committed to building a stronger and more interconnected transportation system across America and will continue to work with Congress and the Administration to ensure adequate funding is available to maintain transit services and support critical projects that will repair, maintain and improve our public transit systems today and in the future.”
The new myStandardAero app and 10,000th Falcon airframe service event are highlights.
Major milestones were announced Monday by StandardAero Business Aviation at the National Business Aviation Association's 2019 Business Aviation Convention & Exhibition.
The company is currently completing its 10,000th Falcon airframe service event since 2000, on a recently acquired Falcon 7X. In addition, StandardAero also just completed its 400th major HTF7000 maintenance event.
“Most of the HTF7000 events so far have been completed in our Augusta, Georgia, shop but many have also taken place remotely, either at customer locations or during AOGs,” said Marc Drobny, President of StandardAero Business Aviation.
“We are committed to expanding our capabilities and reducing turn-around-time to better serve Honeywell’s HTF7000 operators worldwide2.”
Also at NBAA-BACE, StandardAero announced the opening of the company’s first European-based business aviation parts warehouse in Amsterdam to support operators and strengthen the company’s parts support in the region.
The warehouse operations will stock current parts that support Honeywell TFE731, HTF7000, and CFE738 engines and Honeywell APU parts, with future expansions to include parts to support Lear, Challenger, Global, Gulfstream, Hawker, Falcon and Cessna Citation airframes.
A new MRO Project Management service app called myStandardAero was also announced as a major enhancement to the company’s myStandardAero portal.
The app provides a mobile platform for customers to remotely access and manage MRO events at StandardAero facilities in Georgia, Texas and Illinois. 
Customers will be able to review and approve squawks, communicate directly with specific StandardAero project managers and review work orders from anywhere they can access the internet.
Robust global growth for StandardAero continues to drive hiring of technicians, Drobny said. In 2020, the company has plans to hire 100 technicians for their U.S. business aviation division, and 400 to 500 technicians worldwide.
“We are really excited about our outreach programs, which now extend all the way down to the elementary school level, through middle and high schools to the university level.
This is how we win the battle on hiring,” Drobny said.
Visit StandardAero at Booth C8331 at the Las Vegas Convention Center during NBAA-BACE 2019.
Videos demonstrate the risk of fire fighting flights.
Now that California’s Camp Fire in the northern portion of the state, the deadliest in the state’s history, has been smothered, authorities have begun looking not only at what’s left, but precisely how much was saved thanks to heroic efforts of fire fighters across the state.
Rotor & Wing International says "some, 9,000 firefighters, fixed wing aircraft, ground equipment and 45 helicopters" helped battle fires across the state. Among the aircraft used were Sikorsky S-70 Firehawks, UH-60A Utility Hawks, Boeing CH-47s, Bell UH-1s and Sikorsky Skycranes.
The Camp Fire claimed at least 86 lives and incinerated 153,000 acres of land, including 19,000 buildings of which "14,000 were homes," according to the Wall Street Journal.
Thanks to the headcams worn by many firefighters, a number of videos highlighting the drama of the past three weeks have made their way to the Internet. In this one, a Cal Guard Blackhawk pilot captured a water drop in the Camp Fire
In a recorded rescue in Southern California’s Woolsey blaze (below), the pilot can be heard explaining the rescue flight is “rapidly becoming ugly,” as he flies through visibilities that appear to be low IFR.
The flames in the area north of Malibu consumed some 97,000 acres.
About five minutes into the video, the helicopter crew demonstrates the close coordination necessary between team members in order to keep everyone safe during flight during incredibly dangerous conditions.
“Remember fuel is critical,” the pilot reminds his co-pilot as the helicopter finally touches down to rescue two people and a dog, an event that quickly morphed into three people and two dogs, one a frightened English Mastiff that wasn’t all that keen on a helicopter flight.
The video points out just how close the helicopters were operating to the open flames of the blaze. 
With everyone safe on board, the co-pilot says, “That’s enough excitement for me today.” Me too. Awesome flying.
His new life is perfect for the adventurer in him.
Careers are funny sometimes. Despite conscientious planning, they sometimes turn on a dime and point a person in the most unexpected direction.
Holt Lindenberger completed his professional flight training with ATP in November 2013 and wanted to reward himself for all the hard work it took to earn his commercial, instrument and CFI ratings.
“The training at ATP was pretty fast-paced,” he remembers and the competitive qualifications earned during the program open up countless career options as a pilot.
Lindenberger wanted a diversion, so in October 2014, he headed to Florida and added that single-engine rating following a weekend of fun splashing around in a Cub on floats.
Lindenberger admits he never saw his seaplane rating as anything more than a fun way to blow off some steam.
“I read a surfer magazine that talked about the barefoot pilots of the Maldives,” Lindenberger says.
“I thought that was the coolest thing I’ve ever heard of. These pilots would go out and surf with some professionals and anchor off one of these reef breaks.
They’d just go surf with the guys, and I wondered if there’s a way to get a job like that. That’s the kind of thing I got to do.”
When he returned to reality after his flight training, Lindenberger found himself flying with dozens of students—all of whom wanted to become instructors to gather the flight time needed in order to be eligible for any of the regional airlines ATP works with.
He headed out to a few regional-airline interviews himself but realized something didn’t seem right. 
“I really enjoy flying little airplanes.” So out of the blue, Lindenberger, an adventurer at heart, realized his calling: “I was hooked on seaplanes.”
While most ATP alumni are airline bound, graduates have the flexibility of choosing which direction to take their professional pilot career and Lindenberger started looking for the next step in his flying adventure.
“I ran Google searches trying to figure out how I was going to build some floatplane time. I thought maybe I could earn a multiengine-seaplane rating somehow or go fly in the Maldives.”
His plan included a possible job at Grand Canyon Airways to fly their Twin-Otters and then heading back to school for a multiengine-seaplane rating. But fate or luck—or whatever you might call it—stepped in. 
“One of my buddies found an ad for a pilot at Tropic Ocean Airways in Fort Lauderdale.”
Tropic Ocean operates an all-Cessna fleet of 13 seaplanes, mostly Caravans. The company hired Lindenberger in November 2015.
In short order, he first upgraded to captain on a Cessna 208 on wheels.
“I actually flew right seat on the Caravan at first, which was my first introduction to turbine engines.”
In June 2016, he took command of a Caravan on floats. “I have nothing but good things to say about my time at Tropic Ocean,” Lindenberger says, but after 2? years at Tropic Ocean, he was ready for the next adventure. 
“There are just so many opportunities for pilots these days,” he says.
It’s a small community of seaplane pilots who possess any significant turbine experience, and Lindenberger quickly realized: “Seaplane jobs are paying pretty well.”
In May 2018, Lindenberger went to work for Tailwind Air LLC in Westchester, New York, as their primary on-demand Caravan floatplane pilot. 
The company also operates a number of jet aircraft. 
He’s now based near his airplane at Long Island’s MacArthur Airport and lives just five minutes from the airport. 
“Primarily, I fly people to the Hamptons from the 23rd Street seaport in Manhattan, where there’s only room for one airplane on the dock at a time. 
Some days, I’ve had as many as 10 airplanes waiting ahead of me to dock, when there also might be helicopters circling above.”
In New York, the Caravan is the perfect airplane because a trip from 23rd Street to the Hamptons only takes 35 minutes.
“On a bad-traffic day, it could take someone five hours to make the drive,” Lindenberger says.
“I get to see all those cars backed up on the roads going out to the Hamptons. I hate traffic myself, but you gotta credit the traffic for keeping me employed.”
Lindenberger says Tailwind “flies charters to Maine and Connecticut and sometimes the Adirondacks.
So there’s quite a variety of flying.
On any given day, I might depart an airport VFR, go to the East River, and fly some of the helicopter routes down to the exclusions zones for a river landing.”
He says it’s some of the choppiest water you’ll ever see flying floats—at least in the United States—because there’s always half a dozen ferries just ripping up the river nonstop.
“That means 2-foot waves all over town. Flying the river in general is challenging because of the confined area, high-traffic airspace and busy waterways.
The flying keeps your skills sharp across the board, because it’s probably one of the most well-rounded flying experiences you can have.
Flight planning means you might have to reference sectionals, helicopter charts, low-IFR en route charts, satellite imagery and marine depth charts—and you might have to make a few calls for local knowledge.
I love it all. Even in peak season when we are flying 14 legs in a day.”
Inbound back to 23rd Street, “I fly over La Guardia at 1,500 feet and then to the north tip of Roosevelt Island, where I descend to the join the East River exclusion at 1,100 feet.
A lot of times, you go from a perfectly sunny day to IFR when the marine layers move in. 
Then I’m shooting a special VFR, and it gets pretty interesting.”
He says, with certain wind directions, there’s a ton of turbulence off the buildings, “so it keeps me on my toes, even on calm days. But for me, it’s fun to stay engaged.”
Lindenberger spends spring and summer flying around New York, but just to keep things interesting, Tailwind sends him and the Caravan back to his old neighborhood in Florida during fall and winter to fly the Caribbean.
“I’ve got a pretty good situation, and I’m happy with it,” he says. And what about those fancy jets Tailwind’s operating?
 “They’ve thrown those out there to me. But I do like my quality of life and my experiences. So right now, I’m enjoying being the seaplane guy.”
Helicopter safety should focus on seven key life-saving actions.
With half of 2019 behind and another six months to go, the U.S. Helicopter Safety Team (USHST) says the U.S. helicopter industry is experiencing a year of fatal accidents.
The USHST is calling on helicopter operators, pilots, instructors and mechanics to rely on safety basics and place a stronger emphasis on identifying and managing risk.
For the first six months of 2019, the U.S. helicopter industry has experienced 15 fatal accidents with 27 fatalities, on track to match 2013, when 30 fatal accidents occurred.
However, since July is usually a month with a high number of accidents, the industry also is at risk to reach the total from 2008, when there were 35 fatal helicopter accidents.
To help slow this fatal accident trend, the USHST wants pilots, instructors and others with a stake in helicopter safety to focus on seven key actions that will save lives.
Carry enough fuel for unexpected situations.
Ignoring minimum fuel reserve requirements is generally the result of overconfidence, a lack of flight planning, or deliberately ignoring regulations.
Conduct an adequate preflight inspection. 
Use a checklist and a final walk around to determine the condition of an aircraft prior to flight.
Post-flight inspections can also identify issues prior to the next flight.
Recognize the Potency of OTC Medications.
Pilots frequently underestimate the effects of OTC medications and the impairment caused by these sedating drugs.
In spite of specific federal regulations and education efforts regarding flying while impaired, over-the-counter medication usage by pilots remains a factor in 10 to 13 percent of aircraft accidents.
Flying at low altitudes to avoid clouds or bad weather is dangerous and can result in collisions with terrain or obstacles such as wires and towers.
VFR Flight in IMC can be fatal. 
This is the all-too-often result of flying too low.
It is even more dangerous if the pilot is not instrument qualified or is unwilling to believe what the gauges are indicating and an inability to recognize deteriorating conditions.
This "disease" clouds the vision and impairs judgment by causing a fixation on the original goal with a total disregard for any alternative courses of action.
Don't be Afraid to Divert, Turn Around or Land.
Always make sure you have an alternative course of action available should the weather conditions preclude the completion of the flight as planned.
In other words, don't be afraid to land and live.
Land a backcountry flying job, no matter if it's nature or a mission calling you.
Bush pilots operate in remote areas worldwide with little operational support.
Career success and survival require excellent backcountry flying skills, self-reliance and resourcefulness, and above all, the right attitude, say bush pilots Glen Ferguson and Keith Saulnier.
They represent the two bush-pilot career paths you might consider: missionary aviation and commercial operations.
Saulnier owns Georgian Bay Airways in Parry Sound, Ontario, which offers a Career Bush Pilot Program, and Ferguson is CEO of the International Association of Missionary Aviation (IAMA), whose 60-some member organizations include mission-aviation fellowships and colleges with mission-aviation programs.
Here are their tips for becoming a bush pilot.
The commercial fleet ranges from Piper Cubs through Cessna Caravans (with many on floats), along with vintage de Havilland Beavers and piston and turbine Otters.
Taildraggers are widely used.
Missionary aviation "is moving to turbine aircraft" because of the declining availability of avgas, says Ferguson.
This means you'll find more Quest Kodiaks, Pilatus Porters and PC-12s, and Caravans.
 "When airstrips get too short, then we put in helicopters."
Missionary aviation typically requires a minimum of 500 hours of flight experience, commercial and instrument pilot's certificates, and a high-performance endorsement, as well as an A&P license. 
With the mission fleet moving to heavier, tricycle-gear aircraft, a tailwheel endorsement is no longer mandatory.
Requirements among commercial operators vary, but include a commercial pilot license and an endorsement or rating in whatever type of platform you'll operate--and experience flying in the area you'll be working.
Commercial bush-pilot training is available through flight-training facilities offering instruction in float, tailwheel, tundra tire, or ski and glacier operations, in a number of locations around North America.
U.S. pilots trained and certified in Canada can receive FAA approval through a Foreign License Validation Certificate (FLVC).
More than a dozen colleges and universities have ab initio mission-aviation programs.
To help offset the high program costs, schools are investigating a forgivable loan-assistance program that writes off part of the loan for each year flying overseas, Ferguson says.
"You have to start at an entry-level position, and in the bush that usually entails a lot of loading and unloading of airplanes," says Saulnier. 
 "Very rarely will people jump into the left seat in a medium-size aircraft in any category."
Additional practical skills enhance a pilot's employability.
"If you're a wrench [an A&P], a plumber or an electrician, or a computer programmer who can help put up a website or post content on social media, you'll make a better [job] candidate," Saulnier says.
Clearly, any skills that contribute to the greater good of working in a remote location can advance a pilot's resume.
Missionary-aviation organizations conduct 10-day technical-evaluation courses to determine an applicant's piloting and mechanic skills—and attitude. 
"You don't want the bad apple who only wants to fly airplanes," said Ferguson.
"They [pilots] need to plug in to the bigger mission."
Those accepted will return for orientation and to polish their skills before posting overseas.
Missionary aviators support a broad range of faith-based objectives that often involve helping isolated people in remote locations. 
Pilots and A&Ps operate under duty limits, including hours and days off.
They may serve in their assigned location for four years and take one year off.
"Then most people go back to the same place or move on to somewhere else, and spend another four years on location," Ferguson said.
Commercial bush pilots perform "a lot of camp work" which includes hauling supplies and people to remote sites.
Work might support tourism, resource industries or public agencies, and it can be seasonal.
Salaries for commercial bush pilots vary widely, depending on the company, the job, and the degree of hazard.
A starting bush pilot salary might be $2,500 per month, while more seasoned pilots operating larger airplanes can earn $6,000 to $8,000 per month or more, Saulnier says.
Average mean wage for commercial pilots of nonscheduled operators is about $85,000, according to the Bureau of Labor Statistics.
Missionary pilots fund their own expenses and salary, an amount typically ranging between $4,000 and $6,000 per month, through self-developed donor networks.
"For me it's when I see changed lives," says Ferguson.
Adds Saulnier: "Absolute freedom. There's still romance associated with our side of the aviation industry."
Add in the opportunity to fly close to nature's glory, and a truly unique aviation career awaits the pilot who seeks it.
Learn more about bush pilot training and careers here and here.
Also, contact the Recreational Aviation Foundation for more information on flying in the backcountry.
Ongoing demand makes aerial flame suppression a hot zone for pilots.
While we don't have hard figures on their ranks or the number of jobs that exist, demand for aerial firefighter pilots is increasing, industry experts say.
"There are opportunities, but it's not very well-organized," says Dean Talley, an air tanker captain and board member of the Associated Aerial Firefighters.
In fact, operators "are having a lot of trouble filling positions," with retirements creating vacancies and brisk airline hiring draining the already-small applicant pool for aviation firefighters, the former Coast Guard and ag pilot says. The problem continues today.
Federal agencies employ some aerial firefighter pilots, but most work under seasonal contracts for companies the agencies hire for fire protection.
The U.S. Forest Service, the largest federal contractor of aerial firefighting services, also has 52 full-time pilots, according to an agency spokesperson.
The Bureau of Indian Affairs, Bureau of Land Management and National Park Service also contract aerial firefighting services, as do individual states and agencies within states.
Both fixed- and rotor-wing aircraft are used.
Fixed-wing platforms range from single-engine air tankers (SEATs) often adapted from crop-dusters, such as the Thrush 510 and Air Tractor 802 Fire Boss, to larger tankers (more than 3,000-gallon capacity), including jet transports repurposed for dropping massive firefighting loads.
However, rotorcraft from a host of municipal agencies actually dominate the aerial fleet, though firefighting is typically just one of these helicopters' multipurpose roles.
Some pilots, rather than fire bombing, fly the King Airs or OV-10 Broncos that often serve as the observation platform for the forward air controller who directs the fire attack.
Pilot backgrounds are diverse, including military, bush and airline flying, but tailwheel and low-level flying experience — the latter over terrain — are typical requisites.
“It does require a natural ability at flying the airplane,” says Cliff Hale, chief pilot and vice president of flight operations at Global SuperTanker Services.
“There’s no automation that’s going to help you, so if you’re the type of pilot who relies heavily on automation, it’s probably not the place for you.”
Hale’s company owns and operates Global Supertanker, a 747 converted in 2016 into the world’s largest fire bomber, with a 19,000-plus-gallon capacity.
To prepare for a platform like this, “Experience in underpowered, small airplanes is actually preferred,” says Hale. “That’s where you really learn this stuff.”
With no standard training program for the profession, one career path into the hot zone is serving as a pilot in an observation aircraft, then transitioning into a SEAT or the right seat of a larger tanker.
The average annual salary range for a Forest Service pilot is $73,600 to $113,800.
Contract SEAT pilots have traditionally been paid by flight time, “so income is much more fluid” but can be “in the hundreds of thousands” of dollars in a busy season, says Talley.
However, the SEAT fleet has been evolving to a fee-per-season contract, with “minor incentives for extra hours,” providing more stable earnings.
Captains on a large air tanker might earn from $100,000 in the first season to $360,000 for a senior captain, with the seasonal hiring arrangement an attraction for many firefighters.
 “A lot of these pilots like their winters off,” says Hale. But with demand for such services in the Southern Hemisphere growing, and the extended fire season in North America, it’s becoming a full-time job. 
“California just had its largest fire ever, and it was in the month of December,” notes Hale, whose company has four pilots.
“The industry is moving toward full-time [employment], with schedules of time on and off on a year-round basis.”
Oklahoma City streetcars could soon be available for rolling weddings, receptions and other private events.
Embark staff is working on a streetcar charter policy to present to the city's transit authority, Jesse Rush, the streetcar manager, said Friday.
Charters are offered on systems including Cincinnati and New Orleans, where options include vistas along the riverfront and trips through the French Market.
Rush said Embark recently hosted a streetcar charter event for the Greater Oklahoma City Chamber, the city's leading business organization, and used the occasion to test charter possibilities and "see how that will play out."
Results were encouraging, he said, with the chartered streetcar making the downtown loop on time and no interruption of regular service.
Rush said a streetcar could be chartered, as an example, by a group going to dinner.
The streetcar could drop the group off near its restaurant, stand by on a side track, and return at an agreed upon time to return the group to its hotel.
Charters could become part of the package offered out-of-town convention-goers once the MAPS 3 convention center opens in about a year.
According to the Cincinnati Bell Connector website, a Cincinnati streetcar can be chartered for up to four hours at a base rate of $1,350. Each additional hour is $310.
Both Cincinnati and New Orleans allow catering but prohibit glass containers and alcoholic beverages. Both suggest an on-board limit of 75 people.
New Orleans promotes charters for parties, corporate events and city tours; times on the five routes range from an hour to an hour and 45 minutes.
Rates start at $1,000 and include the opportunity to decorate the streetcar before the charter begins.
Rush told the Central Oklahoma Transportation and Parking Authority on Friday that Embark expects to have a charter policy ready soon for trustees to review.
Figures reveal that MTA ridership has increased across subways, buses, LIRR and Metro-North, with on-time performance improving across the board.
The New York Metropolitan Transportation Authority (MTA) has announced figures that show ridership increases on subways, buses, Long Island Rail Road and Metro-North Railroad.
Average weekday ridership on the subway in September 2019 was up 4.5 per cent over the prior year to 5.77 million, while local bus ridership of 2.25 million represents a 1.5 per cent increase over the previous year. 
Long Island Rail Road ridership increased 2 per cent in September 2019, boosting year-to-date ridership by 2.4 per cent.
Metro-North Railroad’s ridership increased 1 per cent in September 2019 and has risen 0.6 per cent year-to-date.
 Despite the increase in ridership across mass transit modes, traffic on MTA bridges and tunnels also rose to be 0.6 per cent higher than the prior year in August.
The subway system is carrying nearly 250,000 more trips each weekday than it was a year earlier, while buses are carrying 34,000 more trips per average weekday.
The MTA’s commuter railroads together have carried nearly 2 million more people in 2019 through September.
“These numbers are the result of a tremendous amount of hard work and dedication,” said MTA Chairman, Patrick J. Foye.
“The MTA’s top priority is increasing the reliability of the system and our workforce has been focused on identifying and fixing track defects, fixing signals and switches, and overhauling train cars and buses at a faster rate than at any time in memory.
We are also rethinking how we communicate with our customers – and as a result of all of these improvements it is clear New Yorkers are taking notice.”
Weekday subway on-time performance was 82.7 per cent in September 2019 – the fourth month that is has been above 80 per cent in five years.
The SAP, launched by Governor Andrew M. Cuomo and then-MTA Chairman Joe Lhota, has provided a surge of additional union personnel, outside contractors, and new tools and methods for the maintaining and improving the system.
Speakers from Streamax Technology Co Ltd, Shezhen Bus Group and SAIS Trasporti discuss how operators can meet new regulations and policies on road safety, how they can better manage risk on the roads to protect their fleets, how we can obtain the full picture of driver behaviour aside from traditional ECO data and much more.
Andy Byford, New York City Transit President, said: “Together, the work of the Save Safe Seconds campaign and the assistance we got from the Subway Action Plan have helped us achieve performance numbers unseen in some time and we look forward to improving our numbers even more in the months ahead.”
LIRR’s year-to-date on-time performance of 92.6 per cent through September 30 is 2.4 percentage points higher than it was over the same time in 2018.
The LIRR has scheduled 1.3 per cent more trains in 2019 through September 30 than it had over that time frame in 2018, yet experienced a 0.4 percentage point increase in trips completed, to 99.4 per cent.
Trains operating with fewer cars than their normal length decreased 23.6 per cent and trains’ mechanical reliability increased 7.4 per cent, with trains travelling 193,667 miles between experiencing a mechanical failure as of August 31.
Track circuit failures in 2019, have reduced to 42 through September 30, down by a third from 64 during the prior year.
LIRR President, Phil Eng, said: “We remain focused on balancing state of good repair and an unprecedented amount of capital work to expand and modernise our railroad that will provide a better customer experience for this generation, and for generations to come.”
Metro-North’s year-to-date on-time performance of 94.3 per cent through September 30 is 3.7 percentage points higher than it was over the same time in 2018.
Metro-North has scheduled 142 more trains in 2019 through September 30 than it had over that time frame in 2018, yet experienced a 0.3 percentage point increase in trips completed, to 99.8 per cent.
The percentage of trains operating at their full length this year has increased 0.7 percentage points East of Hudson through September 30, to 99.4 per cent, while the improvement West of Hudson was even more pronounced, rising 1.2 percentage points to 98.9 per cent.
Trains’ mechanical reliability surged 63 per cent, with trains traveling 244,074 miles between experiencing a mechanical failure, up from 149,683 a year prior.
Delays related to switch and signal problems have decreased to 741 through September 30, down by more than half from 1,800 experienced during the prior year.
“Metro-North’s Way Ahead programme is providing a road map for the railroad’s future, where we concentrate on increasing train service safety and reliability,” said Catherine Rinaldi, President of Metro-North Railroad. 
“These statistics come roughly after our first year of fulfilling the Way Ahead plan, and all signs point to even more improvement going forward.
I thank the hard work and dedication of the entire Metro-North workforce for bringing these results.”
Amid mounting calls for a more reliable statewide public transit system, state lawmakers and transportation experts are making a push for electricity-powered transportation solutions this legislative session.
Oct. 18--Amid mounting calls for a more reliable statewide public transit system, state lawmakers and transportation experts are making a push for electricity-powered transportation solutions this legislative session. 
Lawmakers on the Joint Committee on Transportation held a hearing on Tuesday to discuss a rash of bills looking to electrify buses, expand electric charging stations and require that the registration of future vehicles in the state be exclusively for 'zero emissions vehicles.'
The proposed legislation would do everything from require transit agencies and school bus operators be fully electrified by 2035, to setting a timeline for a complete transition to zero-emission vehicles by 2038.
"I think what people are excited about is this technology presents an opportunity to meet our greenhouse gas reduction goals," said Matthew Casale, transportation campaign director at MASSPIRG.
"It's becoming increasingly clear that if we're going to meet those goals, we need to accelerate public transportation electrification."
While Massachusetts has consistently ranked ahead of the rest of the nation in clean energy efforts, experts say the state's transition to cleaner transportation solutions has been lagging in part due to a lack of resources.
"The primary issue with electric buses is that they cost more upfront, and the MBTA is broke," said Daniel Gatti, senior transportation analyst with the Union of Concerned Scientists.
"So it's challenging for the MBTA to take on big new expensive projects, and bring new resources to them."
In his proposed $18 billion transportation bond bill, Gov. Charlie Baker has set aside $330 million for Regional Transit Authorities for new fleets and facilities, including electric busses; and $32 million has been included in the supplemental budget for electric vehicle programs, according to Gatti. 
That $32 million is devoted to rebates for customers who purchase electric vehicles.
Gatti said the movement toward electric transportation is required if the state wants to reduce its carbon footprint by 25 percent of 1990s levels by 2020 -- a level required by the Global Warming Solutions Act, a federal law the state adopted years ago.
By 2050, the law mandates a reduction of emissions by 80 percent.
"Transportation is the largest source of pollution," Gatti said.
"I think getting a handle on transportation solutions ... is something that people are increasingly aware is vital for the state to meet its 25 percent reduction goal by next year."
About 42 percent of statewide greenhouse gas emissions come from the transportation sector, according to MassDOT.
The safety technology now operates on Far Rockaway, Long Beach, Oyster Bay and West Hempstead branches.
Select trains operating on the Long Island Rail Road’s (LIRR) Far Rockaway, Long Beach, Oyster Bay and West Hempstead Branches have begun operating with Positive Train Control (PTC), according to LIRR President Phillip Eng. 
PTC is a signal system enhancement that reduces the potential for human error to cause specific types of train collisions and derailments. 
The branches have become the latest segments of the LIRR to be operating under PTC. The system was commissioned on the Port Washington Branch on Dec. 17, 2018.
The segment of the Montauk Branch between Babylon and Patchogue received the technology in April and the Hempstead Branch received it in August. 
As a result of this progress, 65 route miles are in PTC operation, or 21.5 percent of the LIRR’s PTC system. 
“The successful and on-time launch of Positive Train Control on these branches continues our forward progress on this critical initiative,” Eng said.
“Meeting this milestone reinforces my confidence that we will complete systemwide roll-out of Positive Train Control on time by the end of the 2020.” 
PTC is a federally mandated safety system that is designed to enhance railroad safety by protecting against the potential for human error to contribute to train-to-train collisions, trains traveling into zones where railroad employees are working on tracks, or derailments caused by a train traveling too fast into a curve or into a misaligned switch.
It builds upon existing LIRR systems such as in-cab signaling and automatic speed enforcement at critical curves and bridges.
These safety measures already offer some of the most substantial functions of PTC to LIRR customers. 
LIRR and the Metro-North Railroad are adhering to an aggressive segment-by-segment implementation schedule that puts them on paths to complete the roll-out of PTC across their entire networks before the Federal deadline of Dec. 31, 2020. 
What the New York Daily News calls a "group of powerful New York lawmakers" wants to see "non-essential" helicopter flights over downtown Manhattan come to a grinding halt.
The action comes just a week after an Augusta 109E owned by American Continental Properties crashed onto the roof of a skyscraper shortly after it took off from the 34th Street Heliport.
Weather at the time of the accident was reported as very poor with visibility of about ? mile beneath a 400-foot ceiling.
Local New York lawmakers wrote a letter to the FAA’s acting administrator Daniel Elwell claiming helicopter flights pose an “intolerable risk to the public.” 
The group wants Elwell and the agency to begin posting temporary flight restrictions over Manhattan.
CBS News in New York reported, “there have been at least 30 helicopter accidents over Manhattan since 1983.”
Rep. Carolyn Maloney said she would hold the agency accountable and vowed to create legislation if that’s what it takes to reduce the number of flights.
She said she doesn’t believe that executive travel and tourist flights would qualify as “essential.”
The Daily News reported the letter to Elwell warned, "It could have been far worse had the helicopter crashed into the Midtown streets below or into a building," noting the crash happened near Trump Tower, the president's private residence.
Learning to fly a rotorcraft or helicopter opens up flying into places that other aircraft can’t reach.
As a professional helicopter pilot, you can fly for rescue and emergency medical services (EMS) or executive transport, among many options.
The rotorcraft category of aircraft includes helicopters, as well as autogyros and gyrodynes .
You're most likely to begin training in a light, single-engine piston-powered helicopter, for the same reasons that a person learns to fly fixed-wing aircraft in a single-engine airplane: cost, availability, and simplicity.
Common training rotorcraft include the Schweizer 300 and the Robinson R-22. You'll start by pursuing a private pilot certificate with the rotorcraft category rating, and then can progress to obtain a commercial pilot certificate in order to perform work for hire.
You may also go for an instrument rating, which allows you to fly the helicopter in the clouds or low visibility, or a flight instructor certificate, which lets you train other pilots.
In order to fly large turbine transport helicopters, you'll need an airline transport pilot certificate.
Rome is the first Italian city to debut the bike-sharing scheme, with 700 bicycles appearing across the city, and a further 2,800 planned to be deployed over the coming weeks
Rome’s mayor, Virginia Raggi, has announced the launch of bike-sharing service JUMP for the Italian capital.
Rome is the first city in Italy to debut the bike-sharing scheme, with 3,500 bicycles planned to be deployed over the coming weeks.
The red pedal-assisted bicycles are fitted with GPS tracking and a shopping-basket at the front. The bikes can be found in the Uber app, which allows the user to locate the nearest bike and then unlockit with a pin that is provided in the app. 
The cost of hire is 20 cents per minute with a 50 cent charge to release the bicycle. The bikes can also be put on hold for up to 30 minutes.
The scheme covers a large area of Rome, from the centre to EUR, Copped?, Monteverde Nuovo and Fleming.
The bikes can be parked across the city in safe areas but cannot be parked along the Lungotevere river, in a bid to avoid damage, and vehicles parked along the river will incur a fee.
The arrival of Uber Jump has come a year after Gobee.bike took its bikes out of Rome and Europe, after claiming that 60 per cent of its European fleet was vandalised, stolen or dumped in rivers.
Taiwanese yard Johnson Yachts has give a construction update on its first “entry level superyacht”, the 21.3 metre Johnson 70.
The yacht is now entering the final stages of construction with the installation of interiors now underway. 
It is set for completion for early next year and is scheduled to launch in March.
Designed by Bill Dixon of Dixon Yacht Design, the customisable yacht is available with a skylounge or open fly bridge and in a three or four cabin layout.
The first flybridge has begun construction and will feature interiors by Design Unlimited, which will include a mix of textures and materials for a stylish and comfortable environment.
The galley sits forward on the main deck while an L-shaped sofa sits opposite accompanied by a large dining table and loose chairs for easy, large group dining.
The main saloon social area will feature a built-in chaise longue and loveseat while wide sliding doors will provide access to the cockpit.
There is also an option here to add a fishing cockpit, a feature which led the yard to previously describe the Johnson 70 as “truly a blank canvas”.
Other features include davits for deploying the tender and an al fresco galley complete with a grilling station for outdoor entertaining.
The yacht is available in either a three cabin or four cabin layout for a maximum of eight guests in four double ensuite cabins.
The full beam master sits amidships and is equipped with a desk, dressing table and large walk-in wardrobe area.
The yacht has a 5.6 metre beam and is equipped with a pair of CAT C18 1015HP engines for a max speed of 25 knots.
Johnson Yachts general manager Peter Chang said, “With the design and engineering of the Johnson 70 – together with Design Unlimited and Bill Dixon - we have a yacht designed to be versatile.
"We want to create a new line of yachts that sets the trend rather than follows it.”
It came after the yard revealed new details and renderings of its new flagship, the Johnson 115 at last year's Fort Lauderdale International Boat Show.
The yard’s first tri-deck yacht has also been designed by Bill Dixon and will accommodate either 10 or 12 guests across five or six staterooms.
Jaguar’s i-Pace electric crossover won Car of the Year a few months ago – just – after an unprecedented tie.
The Jag scored 250 points from the jury of 60 European motoring journalists – the same points total achieved by the Alpine A110 sports car.
However, according to the Oxford Dictionary, the i-Pace cannot be classified as a car, because the current definition of a car is ‘A road vehicle, typically with four wheels, powered by an internal combustion engine and able to carry a small number of people.’
With that in mind, Jaguar has now made a formal request to update the definition to include additional powertrains – including the electric, lithium-ion drivetrain used by the i-Pace. 
A lot of time and thought is put into the name of any new vehicle or technology to ensure it is consumer friendly, so it’s surprising to see that the definition of the car is a little outdated,' said David Browne, head of Jaguar Land Rover’s naming committee.
We are therefore inviting the Oxford English Dictionary and the Oxford Dictionaries to update its online classification to reflect the shift from traditional internal combustion engines towards more sustainable powertrains.'
The victor was decided by a countback of the number of jurors who placed each car top of their list, with the Jaguar scoring 18 first places compared with 16 for the Alpine. In an incredibly close race, the Kia Ceed took the third place on the podium, with 247 points. 
Receiving the award, Jaguar’s design director Ian Callum said: ‘An electric car has won the award [again], and this is the future for the automotive sector. And it’s the first time Jaguar has won the Car of the Year award.’ 
It’s the third time an electric car has won the gong, with the Nissan Leaf triumphing in 2011 and the Opel-Vauxhall Ampera coming top the following year.
The award will give a welcome dose of good news to Jaguar and sister brand Land Rover: the company announced a ?90m financial loss for the third quarter of 2018 and is in the process of cutting 4500 jobs, as it seeks to save ?2.5billion and battle back to profitability.
Jaguar Land Rover has been rocked by a contraction in its Chinese sales, Brexit and the flight from diesel engines in European markets.
Seven cars were shortlisted for the award, from 38 cars launched in 2018 that were eligible.
After the Jaguar, Alpine and Kia, the Ford Focus came in fourth with 235 points, with the Citro?n C5 Aircross (210 points), Peugeot 508 (192 points) and Mercedes-Benz A-class (116 points) completing the grid.
Car of the Year is decided by a jury comprising 60 journalists from 23 different countries across Europe.
CAR’s editor-in-chief, Phil McNamara, is on the jury. Each juror has 25 points to distribute among the seven cars, with a juror having to give his or her top-ranked car at least a point more than the next favourite car on the shortlist.
That’s how the organisers can pick a winner in the event of a tie.
It’s a democractic and transparent process, with each juror having to submit short written testimony to explain their votes.
All the verdicts are available at caroftheyear.org.
Transit leaders said the changes will make the transit system a more convenient and viable option for everyone.
Bloomington Transit officials are looking for feedback on a plan to realign and cut back on some bus routes’ hours of operation.
Zac Huneck, planning and special projects manager with Bloomington Transit, said the changes will make the transit system a more convenient and viable option for everyone.
Many of the recommendations are from a Bloomington Transit and IU Campus Bus Route Optimization Study.
Residents are invited to provide feedback on the proposed changes by filling out an online survey, attending upcoming public input sessions or by calling 812-336-7433.
Huneck said he expects adjustments would be made based on the feedback received ahead of implementation.
Service changes will be finalized in early 2020, and the changes will take effect next fall.
“We’re looking to make the bus system work better for everyone, so community feedback is vital to the process,” Huneck said.
Currently, most Bloomington Transit bus routes stop running around 11 p.m.
Under the proposed system redesign, six of the 12 proposed routes will cease all operations after 7 p.m. 
Huneck said consultants recommended reducing those hours to redirect resources between 6 a.m. and 6 p.m., during peak ridership.
Huneck said it’s only within the past decade that Bloomington Transit expanded to have evening service to 11 p.m. 
That was made possible, he said, by grants from the Federal Transit Administration’s Job Access and Return Commute grant program.
“This was a program designed during the recession to facilitate public transit connections for employees getting off work at around the end of typical second shifts,” Huneck said.
 “We’ve been able to stretch that grant funding until now, but it has since run out, so that is part of our consideration in if we should continue to provide that late night service.”
He said, while routes tend to move fewer people in the evening, those who ride late are often those who depend on public transit the most.
He said transit officials are concerned about the impact those proposed changes might have on late-evening riders.
As part of the recommendations, consultants suggest both realigning and ending hours of operation around 7 p.m. for proposed routes 1, 4, 5, 12, 14, 40.
One proposed change to the transit system would provide service to new areas, including the Ivy Tech Community College campus west of Bloomington.
Huneck said providing service to Ivy Tech has been a long-time request from transit riders.
 He said extending service to the college campus also presents an opportunity to serve those who work at a number of westside businesses, such as Cook Medical’s global headquarters.
Even though it is a highly requested service, Huneck said Bloomington Transit is currently only allowed to operate within city boundaries.
He added an amendment to the operating ordinance would be needed for Bloomington Transit to provide service outside city limits.
At the moment, Area 10 on Agency Rural Transit provides service to the Ivy Tech campus.
Huneck said Bloomington Transit route 3 transit riders can now transfer to the Rural Transit bus to reach Ivy Tech.
Consultants also propose providing service to the new Indiana University Health Bloomington Regional Academic Health Center along the Ind. 45/46 Bypass.
The $557 million project is currently under construction and slated to be completed in 2020-21.
Proposed public transit access would use both Pete Ellis Drive and Range Road — which was recently renamed East Discovery Parkway — and the proposed hospital access at 14th Street and the Ind. 45/46 Bypass, according to the proposed route.
Another recommended service route would take more transit riders to the area around Walmart and the Social Security Administration Office.
While the goal of developing a public transit system is not to directly serve any one business, Huneck said consultants viewed that area as a major shopping destination for many residents.
He said a guiding principle for transit officials is to create a self-contained route that provides route riders with access to a diversity of destinations such as work, home and grocery stores.
Adding a third route to one of the county’s major shopping centers, he said, would also make that location an important transfer center to other areas.
Proposed changes also seek to make it easier for riders to get to their destinations faster by making routes more direct.
Huneck said for a bus service to be competitive against other forms of transportation, it has to be able to get its riders from point A to B in a certain amount of time.
A number of routes were realigned with this concept in mind.
However, Huneck said a consequence of creating more direct routes is that buses would avoid deviations into neighborhoods and shopping centers in exchange for a more streamlined service.
For example, he said consultants proposed eliminating buses sent directly into the Park Ridge East neighborhood.
Another proposal will eliminate buses turning into the Whitehall Crossing Shopping Center.
The challenge, Huneck said, is finding the right balance between providing adequate coverage and speedy bus service.
He said transit officials are sensitive to the fact that there are riders who, due to mobility or other issues, could be impacted by these proposed changes.
That is why he strongly encourages the public to provide their feedback.
“We are not looking to create any new barriers,” Huneck said.
Transit officials are also proposing eliminating duplicative and underperforming routes.
Huneck said they identified areas where the Bloomington Transit service may be better suited as a supplement to the IU campus bus service.
In addition, he said the proposal suggests combining some routes whose coverage area overlaps.
For example, he said the proposed route 7, which would provide service from south of Bloomington to the IU campus via Walnut Street Pike and Henderson Street, combines elements of the current routes 1 South and 7.
Huneck said they are also recommending the elimination of route 8, which currently serves people living in Hyde Park, Stands, High Street, Park Ridge, 10th Street and Pete Ellis Drive areas and along Sare Road, and have two new routes pick up the area it alone used to cover.
He said the number of riders using that particular bus route has declined to around 10 riders per hour.
He added that is right at the threshold used to determine whether a route is being productive.
He said while it would be eliminated, the area will still be serviced by two other routes.
Why are they doing this?Huneck said one reason Bloomington Transit is proposing making changes to its transit route system is to improve ridership.
Public transit systems — both locally and across the country — have seen a decline in ridership since 2014, said Huneck.
In Bloomington, he said the number of transit users declined about 5% a year since that time.
“One of the big factors (of the proposed system changes) is to reverse that ridership decline,” Huneck said.
The proliferation of ride-share programs such as Uber and Lyft, Huneck said, is one possible reason why transit systems has seen a decline in ridership.
He said, when gas prices remain relatively low, many still prefer to drive themselves rather than wait on public transit.
On the local level, Huneck said speculate that a decline in ridership is may, in some ways, be impacted by the increased downtown development in recent years.
He added Indiana University students make up around 70% of ridership, and more students are living downtown in areas that provide better walkability options.
He said another reason for updating the transit network is to ensure bus routes reflect the community’s growth in the past few years.
Huneck said the current routes were designed two decades ago.
Huneck said the proposed changes are intended to be a budget-neutral.
The founders of superyacht design studio Harrison Eidsgaard have dissected the DNA of a Heesen-built superyacht.
Speaking to BOAT International, Ben Harrison and Peder Eidsgaard reflected on their eight year working partnership with the Dutch yard, which began with the 51 metre Irisha.
The fully custom designed superyacht Irisha was delivered in 2018.
Speaking about their first collaboration with Heesen, Harrison said: “We very much enjoyed the whole experience, from the first hand sketches on a piece of paper through to the delivery of the yacht three years later.”
Eidsgaard added that the process demonstrated “what an efficient organisation” Heesen is.
“The exterior was created from our sketches into a 3D model extremely fast and there were no changes from that until the build,” he said.
Irisha demonstrates the key DNA of Heesen’s standout superyachts with a “fast, elegant hull,” Eidsgaard said.
“It’s clearly a fast boat, all of them aluminium - this makes them stand out in the size range.”
Harrison added that the interiors on board were indicative of Heesen’s “high standards”.
“Heesen do a lot of the interior fit out themselves and that’s really the fully custom bespoke element that you get with a Heesen.
I think you can always tell the quality of the fittings, it is of a very high standard.”
Gulfstream announced the G700 last night, the Savannah, Georgia-based airframer's new entry into the ultra-long-range aircraft category—a model clearly aimed at Bombardier's Global 7500. 
Also announced were two new fleet operators of the G700 that will help keep the assembly lines busy until 2023, the next delivery opportunity.
Flexjet will serve as the G700's launch customer in the U.S., while Qatar Airways serves a similar role overseas.
The cabin of the G700 is long–nearly 57 feet, excluding the baggage area–a number that bests the G650ER by four feet and offers operators the option for five distinct living areas, or four if they choose a Part 135-compliant crew rest station.
When finished, the G700’s cabin is 6 feet 3 inches tall and 8 feet 2 inches wide.
The G700 will carry as many as 19, while berthing up to 10.
Powered by two new Rolls-Royce Pearl 700 engines specifically designed for this new Gulfstream, the G700 will deliver a maximum cruise speed of Mach 0.925.
Gulfstream said much of the thinking behind the new G700 emerged from the positive technological and customer experiences derived while created the G500 and G600, although the 700 includes plenty of brand-new technology that will amaze both the cockpit crew and passengers in the cabin.
The G700 is also expected to also share a common type rating with the earlier 500 and 600.
The G700 interior is sure to delight owners and operators with a private stateroom option that includes four large G650-size windows, as well as a private shower to freshen up after a journey that might extend to the G700’s maximum range of 7,500 nm range. 
The rest of the cabin is inundated with more of those large G650-style windows totaling 10 on each side of the fuselage.
The cabin uses 100 percent fresh air all the time and offers four complete zone controls to moderate the environment.
At 41,000 feet, the G700 holds the cabin down to 3,300 feet while at 51,000 feet, the cabin climbs only another 1,500 feet.
What passengers will really enjoy in the G700 cabin is the spectacular audio system that includes no speakers.
New to this airplane Gulfstream’s 3D audio system that actually turns the sidewalls into speakers capable of delivering rich high-frequency sound and impressive, wall-shaking base as near to surround sound as most people have ever experienced. 
The new Gulfstream includes a host of new technologies for passengers to control their media experience using their own devices, as well as cleverly concealed plugs to charge everything.
Another important part of the new G700’s cabin is a galley counter that stretches more than 10 feet in length and to allow more than one person at a time to prepare meals or snacks.
The airplane includes special rear-cabin table that stretches across the aisle to seat six but requires no external table leaf.
Everyone will enjoy the G700’s cabin lighting that’s offered in two versions.
The HD uses thousands of bright white, soft white and amber LEDs to set the color of the cabin temperature, while the advanced Ultra HD system uses 20,000 LEDs and offers 68,000 different possible light settings. 
Best of all, either of the two LED lighting system are capable of emulating natural light from sunrise to sunset that can also be correlated to the passenger’s natural circadian rhythm, no matter where the flight began.
Up front, the G700 comes standard with the active sidesticks the G500 and G600 first brought to the marketplace.
The Symmetry flight deck, based on Honeywell’s Primus EPIC, is standard, as are dual head-up displays and autobrakes.
Gulfstream co-created its new predictive landing performance system with Honeywell.
In the landing configuration, the runway appears visually on the PFD’s speed tape clearly indicating where the aircraft can be expected to stop depending on speed and autobrake setting.
Total aircraft energy is computed by adding altitude.
If the system detects the airplane is too high or too fast to safely halt the G700 on the runway in use, it will issue the crew a “Go Around,” command, one of the few times Gulfstream makes a decision for the pilot.
Of course there’s no regulation that says pilots must obey the command, but Gulfstream still expects it to help reduce runway overruns with the added safety net the system provides.
Gulfstream president Mark Burns, a 37-year employee, offered Flying a few insights on the new G700, the 12th airplane he's had a hand in certifying as a company employee.
He began by detailing the involvement of the maintenance people early in the G700's development.
"One of our goals was to be able to remove anything on the G700 in under 30 minutes," with the only exception being an engine.
Gulfstream of course builds only business jets. "We build airplanes to help customers be more productive … to ensure they're successful," Burns said.
"When we build an airplane, we also want to be their service provider for 40 years.
We want to be the owner of the [maintenance] facility, to have control of how that experience happens for our customer, and understand their experience." 
To help gain the needed feedback, Burns told Flying, "the first thing that I do every morning is read customer surveys, because I think it's important to know how people feel about your product and your people."
No first flight or tentative delivery dates have yet been announced for the Gulfstream G700. 
The new aircraft is currently priced at $76 million for the next delivery slot available in 2023.
In a bid to promote the use of public transport and decrease the use of cars, Germany is planning to increase subsidies for public transport services.
The Federal Ministry of Finance is planning to further finance the operation and development of regional and S-bahn networks, which service German suburban areas, the operation of public buses, improvements to the rail ways and the purchasing of new trains. 
Currently, regionalisation laws state that the annual finance is to be increased by 1.8 per cent every year until 2031.
The funds are now set to increase by €150 million in 2020, by €300 million in 2021 and 2022, and by €450 million by 2023 – a total of around €1.2 billion.
Groups and transport operators have welcomed the announcement, with the Pro-Rail Alliance lobby organisation’s Managing Director Dirk Flege, which advocates for a higher market share for rail transport, stating that “the federal government wants to double the number of passengers in local and long-distance transport by 2030 and the additional funds are a step in the right direction.”
The milestone is one of the last needed before revenue service, scheduled before the end of 2019, can begin.
The Bay Area Rapid Transit (BART) Silicon Valley Extension is moving closer to reaching its goal of opening by the end of the year with pre-revenue operations commencing Oct. 28 on the 10-mile first phase of the extension.  
Santa Clara Valley Transportation Authority (VTA), which was responsible for the construction of the BART Silicon Valley Berryessa Extension and transferred control of Phase 1 to BART in June, says the start of pre-revenue operations marks one of the final steps required before passenger service can begin.
“The first step of pre-revenue operations is training and system familiarization of the trackwork, stations and facilities for all personnel, including operators, systems and maintenance staff.
Then comes simulated service, where the rail system is operated exactly as it would be when passenger service begins, but without passengers. 
This process allows for verifying schedules and the timing of trains.
It helps ensure service starts off as safely and efficiently as possible,” wrote Santa Clara VTA in a blog post.
“The final step before trains can carry passengers will be VTA issuing a report verifying safety certification of the system along with BART’s notice of intent to operate the service to the California Public Utilities Commission (CPUC), before receiving approval from the state regulatory agency.”
Phase 1 of the project will extend service from Alameda County into Santa Clara County, with stops at Milpitas and Berryessa/North San Jose. Phase 2 will extend service from the Berryessa Transit Center to stations at Alum Rock/28TH Street, downtown San Jose and Diridon Station, with the service ending in Santa Clara.
Phase 2 of the project was the first project selected to participate in the Federal Transit Administration’s new pilot program designed to fast track major transportation infrastructure projects.
As a result of its selection, Phase 2 of the project has been allocated $125 million in federal funds that are contingent on Santa Clara VTA meeting all program requirements needed to proceed to a construction grant agreement.
The Dynamo Taxi, London’s first all-electric taxi, built in Coventry by Dynamo Motor Company, is based on a Nissan e NV 200 Evalia MPV and can carry five passengers.
Dynamo, the UK-based vehicle manufacturer, has unveiled the first 100 per cent electric, zero emission ‘Hackney Carriage’ black cab that has been officially approved by Transport for London.
Dynamo Managing Director Brendan O’Toole unveiled the Dynamo Taxi, based on a Nissan e-NV200 Evalia MPV, at the International Clean Air Conference at London City Hall.
The Mayor of London, Sadiq Khan, said: “Air pollution is a national health crisis that is stunting the lung development of our children and leading to thousands of premature deaths.
We have cut pollution by a third in central London by introducing the world’s first Ultra Low Emission Zone and worked tirelessly to clean up the bus and taxi fleet.
“London’s black cabs are known around the world, which is why I am pleased to launch the first all-electric London black cab by Dynamo.
Working with cabbies to go electric is a key part of our plans to improve London’s air quality.
The Dynamo Taxi will accelerate the retirement of polluting diesel taxis from city streets across the UK, improving air quality, helping to tackle the climate emergency and to create a green economy.
I have been delighted by the number of cabbies who have applied for our ?42 million fund to trade in their older, dirtier vehicles earlier – doing their bit to improve our filthy air.”
The Dynamo Taxi costs ?55,495, and is eligible for ?7,500 in Government Plug-in Car Grant funding.
Brendan O’Toole, founder and CEO of Dynamo, added: “The UK’s new car market is experiencing an electric revolution, with record numbers of fully electric vehicle registrations taking place each year.
Electric vehicle technology is now a viable alternative to petrol and diesel vehicles, and it is imperative the UK’s taxi market changes with the times.
“With a low starting price, and a reliable vehicle architecture in the Nissan e-NV200, we are offering a solution to the tens of thousands of taxi drivers in London and cities across the UK.”
According to TfL, taxis currently account for 16 per cent of all vehicle nitrogen oxide (NOx) emissions in central London and the Dynamo taxi comes at a time when TfL has introduced licencing requirements for new taxis in order to operate in London, limiting their emissions to 50g/km CO2 and a zero emissions range of at least 30 miles.
The Dynamo is also said to lower costs for drivers; the average taxi driver in London covers between 90 to 120 miles a day.
Dynamo has said that, using a home charger, it will cost around ?6 to travel 174 miles, as opposed to around ?35-?40 in current diesel taxis.
The taxi also includes contactless payment solutions, a built-in infotainment system, heated seats and steering wheel, wheelchair accessibility and an enlarged glass roof for extra lighting.
Drone operator apparently took no evasive action.
Over the past few years, we’ve come to respect the logistical power of drones to, for example, conduct surveillance in places and weather conditions airplanes and pilots cannot.
Drones can remain on station longer than a human could tolerate, so they should have a bright future completing jobs we’ve yet to create.
But like any other kind of flying machine, if unregulated in airspace as complex as that of the U.S., drones will become a menace.
While the FAA's Part 107 has brought some order to commercial drone operations, the guidance for hobbyist operators is a bit more vague; fly beneath the safety umbrella of a drone-community like the Academy of Model Aeronautics.
Many hobbyist operators seem to know enough to fly below 400 feet AGL, remain clear of other flying machines – especially the ones with folks like us in them – and avoid flying over groups of people.
With millions of hobby drones just waiting for takeoff, the chances of the FAA enforcing any rules or guidelines are pretty slim.
 Manufacturers don’t even include much safety guidance information inside the package an unsuspecting new operator receives either.
No one’s worried about a 10-ounce micro-drone of course.
And while hobbyist machines like some of the DJIs weigh just a few pounds, hobbyists are free to purchase what they want – up to 55 pounds - even a DJI Matrice 600 Pro Hexacopter, that with a full load can weigh nearly 35 pounds.
The larger the drone and the faster its speed versus that of an airplane headed in the opposite direction, the greater the threat to whatever that drone might strike.
More significant threats of a collision between a drone and a manned aircraft seem to be on the rise.
At an NBAA Safety Committee meeting not long ago, a corporate pilot detailed his near collision with a drone in Alaska.
Last week, a drone overflew a helicopter dumping water on the Miles fire in Oregon, causing the entire airborne firefighting team to standdown until the drone had been cleared.
Remember the drone video earlier this year shot of a passing Frontier Airbus headed into Las Vegas?
The drone operator shot the video from above the Airbus.
Don't forget the drone operator near downtown Chicago flying well above 400-feet.
Last week, a drone operator on Florida's east coast posted a video that showed another incredibly close call.
The drone operator later claimed he was flying below 400 feet AGL, but the helicopter that nearly collided with the drone was flying legally too.
In the video, you’ll see the helicopter flying north along the coast near Hollywood approaching the drone, but the operator takes no evasive action, except to turn the camera around to try and capture a shot of the passing helicopter which by then was long gone.
The drone was reported to be a DJI Mavic that weighs just a few pounds.
How much damage the helicopter’s windshield would have sustained in a head-on collision is anyone’s guess.
While we might argue about who was flying at the proper altitude, avoiding manned aircraft at all times should trump any other drone guidelines.
On the website where the Florida drone pilot first posted the video, other operators left scathing comments and a potential glimpse of the future.
"Grow up and be a decent pilot,” one said.
“You are messing this up for all of us!"
Another warned, "If there was a collision, a court would find you responsible.
You'd have more to worry about than losing your expensive drone."
How much time do we have left before a drone collides with a manned aircraft, possibly resulting in fatalities.
If the industry waits until an accident to try and police this safety threat, it will surely set the drone industry back a decade, including the legal Part 107 legal operators.
The redesigned bus network will reportedly provide a more coherently planned, higher capacity and more understandable network, increasing overall services by 22 per cent.
The level of bus services in the Dublin network are predicted to increase by 22 per cent as a result of the Dublin Area Bus Network redesign, published by the National Transport Authority (NTA) in Dublin.
The redesign of the network was a key measure of the Transport Strategy for the Greater Dublin Area 2016-2035.
It is among the measures in Project Ireland 2040 and is included as an action point in the Climate Action Plan, published in early 2019.
Under the plan, the network will now be arranged on the basis of eight ‘spines’ from the city centre.
Spines are said to be very frequent routes made up of individual bus services timetabled to work together along a corridor.
At the end of the spine, the individual services branch off to serve different areas.
The plan also includes 10 orbital routes which will aim to reduce the need for passengers to travel into the city centre.
Orbital services operate around the city, providing connections between suburbs and town centres, along with connections to rail, Luas and other bus routes.
A number of city-bound services operating into Dublin City Centre are also included in the plan.
These are services that are not part of any spine and operate on their own timetable as part of the network.
Evening and weekend services will be increased and there is also provision for local services providing important connections within local areas, linking to local retail centres and to onward transport connections.
Anne Graham, NTA Chief Executive Officer, said: “Last year, we published for public consultation, our draft network plan.
The level of engagement we saw from members of the public in that process was unprecedented, and it provided us with the kind of insight into our bus services, that cannot be garnered any other way.
I have no doubt that the plan we are publishing is one that will increase overall services levels for bus customers.
 It will also make the bus network more useful to more people and will make all parts of Dublin more accessible than ever before.”
NTA has produced information brochures and a route mapper tool that has been developed for people to find details of the new network for a journey.
Ray Coyne, Dublin Bus Chief Executive Officer, said: “This revised network design is part of an ambitious and welcome investment of €1.5 billion in Dublin’s bus system.
This is also a significant opportunity to continue the growth and success of Dublin Bus and the city.
It is clear from BusConnects that urban bus public transport is key to developing a modern, dynamic public transport network and harnessing the strong economic growth experienced over the last few years.
With over 3 million extra customers carried by Dublin Bus in 2018, we will build on the strengths of this proposed network to the benefit of the city and our customers.”
The board named Thierry Betbeze head of Dassault Falcon Jet.
The Dassault Falcon Jet board named Thierry Betbeze CEO of its division, a Dassault Aviation subsidiary, among news of mixed success for the business unit in the first half of 2019.
Betbeze had served since 2016 as senior vice president, finance, for Dassault Falcon Jet.
He succeeds Jean Rosanvallon, who led the company for 23 years.
The DFJ subsidiary is responsible for the marketing, service, and sales support of Dassault in the Americas.
?ric Trappier, chairman and CEO of Dassault Aviation, reviewed the climate during the preceding months: "The first half of 2019 was dominated by uncertainty over the European elections, geopolitical tensions, terrorism, the trade war between China and the United States, Brexit and environmental pressures.
The economic environment and the [dollar-to-euro] exchange rate remain unpredictable."
Against this backdrop, the company took in only seven orders at the end of June 2019, with a total of 26 orders through August 31.
Still, the company celebrated wins on the support side, with its spares availability and service center network impressing customers and industry this year.
Target sales for 2019 remain firm, according to Trappier, with a projection of 45 Falcon and 26 Rafale aircraft.
CL Yachts, the luxury performance division of Cheoy Lee, has begun construction on a 27 metre motor yacht penned by Milan-based designer Jozeph Forakis.
Due to launch in the second quarter of 2020, the composite yacht named CL88 features a RINA certified performance hull and is targeted towards “the family with a heart for adventure”, the builder said.
An open plan layout and large exterior spaces provide far-reaching ocean views while the 1.65 metre draft enables entrance into shallow and remote anchorages and bays.
Accommodation is for a total of eight guests in four staterooms all furnished in “an equal level of luxury”.
Two crew cabins meanwhile can accommodate a total of four staff.
The propulsion package consists of twin Caterpillar C32 Acert 1600 bhp each for a top speed of 25 knots.
Designer Forakis said the exterior of the yacht was designed “to stimulate the collective memories and imagination of every yachting enthusiast.”
“Her effortless beauty and balanced proportions are timeless, but her overall look is distinctly bold and contemporary.
With the interior, pure sunlight and open, flowing spaces are the primary ingredients, giving the feeling of being connected to the water and air,” he added.
New figures reveal that investment in the UK car industry has collapsed by 70% in the continuing uncertainty over Brexit.
Just ?90 million was invested here in the first half of 2019, compared with ?2.7 billion annualised average over the past seven years, as bosses pause plans to see how the divorce from the EU plays out.
It's hit manufacturing at factories around Great Britain, too: output fell by a fifth in the first six months of the year to 666,521 cars built here, down 168,052 on the same period last year.
Figures released by the Society of Motor Manufacturers and Traders (SMMT) showed that car manufacturers spent 'at least ?330 million' on contingency plans, as they organised factory shutdowns in advance of the original 31 March Brexit deadline.
Former prime minister Theresa May failed to pass legislation to meet that date, and the industry is now braced for a no-deal Brexit in October 2019 as hardliner Boris Johnson has succeeded her as leader of the Conservative Party.
Stay tuned for more chaos in the months ahead, potentially undoing years of growth and investment in the UK's car industry.
Factories across the land are already suffering from the dramatic fall in investment.
Honda has confirmed that it will close its sole UK car factory in Swindon, with the loss of 3500 jobs from 2021, when the current lifecycle of cars like the Civic produced there come to an end.
Katsushi Inoue, chief officer for regional operations, pointed to 'unprecedented changes' affecting the industry, and that 'it is vital that we accelerate our electrification strategy and restructure our global operations accordingly.'
Inoue also said that the decision 'has not been taken lightly and we deeply regret how unsettling this announcement will be for our people.'
Nissan, too, pulled the next X-Trail from its production plans in the UK, reversing a 2016 pledge to build the seven-seater SUV in its Sunderland factory in the north-east, but it wrote to staff in spring 2019 admitting it would no longer manufacture the X-Trail in Britain.
The model will now be made in Kyushu, Japan instead and Nissan admitted in the letter that Brexit was damaging its long-term investment plans.
 It said 'the environment for the car industry in Europe has changed dramatically' since the original decision in 2016.
Ford is on record saying that a no-deal Brexit would be 'catastrophic' for the UK arm of its business and is reportedly ramping up plans to relocate or minimise its English workforce.
Brexit affects all car makers - including those which don't manufacture in the UK.
Porsche told the BBC that it may have to charge up to 10% more on the list price of cars it sells in the UK post-Brexit, owing to customs duties and other ancillary costs of dealing with a country cast out from the EU trading zone.
It's clear the UK’s automotive industry is under serious threat from the ‘wrong’ Brexit.
So what could happen to car companies, to dealers and to the cars we buy when we exit the EU on 31 October 2019?
Right now, unless the government somehow appeases everyone and scores a great deal from the EU, it’s looking pretty gloomy, especially for the 856,000 employed in the business across the UK.
UK car manufacturers like borderless trading with the EU.
Their web of supplier networks across Europe involves an estimated 1100 trucks crossing the Channel each day, bringing parts straight to the assembly line at factories in Sunderland, Cowley, Halewood, Swindon or one of the other 32 vehicle manufacturing plants in the UK.
It’s a delicate operation that any border stoppages would disrupt in a major way.
A no-deal withdrawal will mean no transition period.
That’ll throw sand into the slick parts delivery system from Europe and could stop production lines dead.
‘It’s becoming more and more difficult to justify investment in the UK because of the uncertainty,’ Jaguar Land Rover boss Ralf Speth (below) told CAR at the Paris motor show.
‘Every day we build around 3000 cars in the UK. That’s about 25 million parts. If we miss just one part, we cannot build a vehicle… and stopped production will cost us ?60m a day.’
Very.
After the vote in 2016, car companies were sanguine, saying they respected the wishes of the people and would await the outcome of negotiations.
Two years on and faced with the ‘total ineptitude’ of the government negotiators, as one analyst put it, the usually mild CEOs are baring their teeth.
‘Hard Brexit is a red line,’ Steve Armstrong, CEO of Ford of Europe, said in a recent statement.
‘It could severely damage the UK’s competitiveness and result in a significant threat to much of the auto industry, including our own UK manufacturing operations.’ 
He hinted that could mean shutting down its two engine plants (Bridgend and Dagenham).
‘We will take whatever action is necessary to protect our business in the event of a hard Brexit.’
BMW has said it could take some Mini production out of Oxford and transfer it to the Netherlands, and JLR’s Speth has warned it could take planned investment into electric cars elsewhere.
In addition, BMW was so worried it brought forward the annual summer shutdown of its Mini plant in Cowley to 1 April 2019, a few days after the original Brexit deadline.
While we believe this worst-case scenario is an unlikely outcome, we have to plan for it,’ a BMW spokesperson said at the time.
There’s no way of knowing how serious the manufacturers are when they talk of pulling production in the event of a no-deal, although the closure of Honda's Swindon factory is proof they're not bluffing.
But we do know that a no-deal and immediate switch to World Trade Organisation (WTO) rules – with 10% import tariffs – will hurt car makers shipping to or from the EU.
‘Profit margins in our industry are significantly lower than 10%. 
These extra costs will either be passed on to the consumer or will have to be absorbed by the manufacturers,’ said Erik Jonnaert of the car makers’ European lobbying association, ACEA.
‘That will put the competitiveness of our operations under threat,’ Toyota Motor Europe CEO Johan van Zyl said.
The wrong Brexit deal would give manufacturers the excuse they need to up sticks and leave, particularly given our weaker labour laws compared to France or Germany.
‘It’s easy to get rid of people in the UK, so it’s easy to pull out of,’ said David Bailey, professor of business at Aston University.
‘I’m not saying that’s going to happen when models are mid-cycle, but when they are being replaced that’s when manufacturers make those locational choices.’
A no-deal scenario will badly hit already weakening car sales, according to market analysts LMC Automotive.
The firm believes can and van sales would continue to fall, bottoming out at 2.55 million in 2020, compared to three million in 2016.
On the other hand, if we manage to negotiate a nice free-trade agreement involving an orderly transitional period, that would be enough to halt the sales slide next year and increase demand to 2.81 million by 2021.
The difference between the two potential 2020 figures is huge: 270,000 vehicles. 
One of the many Brexit unknowns is whether we’ll remain in Europe’s average CO2 agreement, which fines manufacturers that don’t hit targets for average emissions across their range.  
‘If the UK dropped out there would be no reason for manufacturers to sell plug-in vehicles in the UK because they would no longer count to CO2 targets,’ said Greg Archer of green pressure group Transport & Environment (T&E).
‘That would create a shortage.’
Manufacturers might be more willing to sell us less-fuel-efficient engine options, some of which have already been canned, knowing it wouldn’t impact their overall EU score.
Well, although new-car sales are down, it’s not by much – dealers have been saved by the mass migration to finance, which has softened the blow of the price rises on Eurozone imports caused by the weak pound.
This pattern is likely to continue after Brexit. Daksh Gupta, CEO of the Marshall Motor Group dealership chain, said: ‘Customers are used to paying the ?300 a month.
They like the idea of PCP.’ They just don’t get quite as much for their ?300 as they used to.
‘They’ve come down a model or even migrated across brands.
They’re still buying the cars, but the richness is decreasing,’ he said.
That’ll get worse in a no-deal scenario – another 10% will be wiped off the pound’s value again if we leave with no deal, LMC predicts.
So although we’ll still buy cars, we just won’t get as much for our money. Not very cheerful after all then.
Great Britain plc has enjoyed a strong few years of production, peaking at nearly 1.8 million units in 2017; but as the SMMT graph below demonstrates, that's quickly sliding as political uncertainty bites.
‘It’s very concerning to see demand for UK built cars decline in November, with output seriously impacted by falling business and consumer confidence in the UK allied to weakening export markets,’ Mike Hawes, SMMT Chief Executive, said last autumn.
‘With fewer than 100 days until the UK leaves the European Union, the automotive industry needs certainty and a ‘no-deal’ Brexit must be ruled out.
Thousands of jobs in British car factories and supply chains depend on free and frictionless trade with the EU – if the country falls off a cliff-edge next March the consequences would be devastating.
The boss of Jaguar Land Rover has already warned that uncertainty over Brexit negotiations is putting ?80 billion of future investment at risk.
Chief executive Ralph Speth said that JLR was reconsidering its future options as it prepared for a worse-case scenario of no deal being struck over the UK's exit from the EU.
A bad Brexit deal would cost Jaguar Land Rover more than ?1.2bn profit each year,’ Speth warned.
As a result, we would have to drastically adjust our spending profile.
We have spent around ?50bn in the UK in the past five years - with plans for a further ?80bn more in the next five. 
This would be in jeopardy should we be faced with the wrong outcome.’
He added that the company's 'heart and soul is in the UK,' but in an interview with the Financial Times he admitted that JLR would consider closing UK factories if necessary.
If I'm forced to go out because we don't have the right deal, then we have to close plants here in the UK and it will be very, very sad.
This is hypothetical, and I hope it's an option we never have to go for.'
JLR joins the chorus of different manufacturing giants warning about the perils of a no-deal solution, which could add tariffs and red tape for a complex, multi-national car making industry that relies on frictionless movement of people and parts to build cars in Britain.
Mini (above) has also warned that uncertainty over Brexit is damaging its business.
We caught up with Paul Philpott, the CEO and president of Kia Motors UK.
We import 55% of our cars from Slovakia, the other 45% from Korea, and are worried about how we get cars and parts into the UK.
What are the implications of tariffs to my business? What regulatory changes might we face?
The honest answer is, until a deal is done we just don't know.'
He warned that this ongoing uncertainty was damaging to business. 
There's just no clarity - it's really hard to plan for all the different scenarios.
If this uncertainty goes beyond November, we will be in trouble.'
Kia normally holds around 7000 cars in stock across the dealership network, Philpott (above) said.
And we can store an additional 15,000 cars at the Immingham docks [where Kia imports cars in Lincolnshire].
We can store over 20,000 cars, or around two months' stock and are considering how much we need to do this to balance the risk from Brexit.'
Manufacturers, importers, dealers and others in the supply chain all face an anxious wait for a deal to be done. 
Jaguar Land Rover and BMW have announced a surprise collaboration to co-develop the next generation of electric drive systems, including the motors and control software that operate them.
Both companies have established creds in the area, with pioneering products such as the i3, i8 and i-Pace leading the charge to battery-powered vehicles.
It's a sign that even the world's biggest car makers can't do this electrification thing on their own; such is the scale of the transformation required, they can share costs and pool knowledge better by working together.
The announcement revealed the two groups would jointly invest in research and development, engineering and procurement. No financial terms of the deal have been announced.
Nick Rogers, Jaguar Land Rover engineering director said: 'The transition to Autonomous, Connected, Electric and Shared (ACES) represents the greatest technological shift in the automotive industry in a generation.
The pace of change and consumer interest in electrified vehicles is gathering real momentum and it’s essential we work across industry to advance the technologies required to deliver this exciting future. 
We’ve proven we can build world beating electric cars but now we need to scale the technology to support the next generation of Jaguar and Land Rover products.
It was clear from discussions with BMW Group that both companies’ requirements for next generation EDUs to support this transition have significant overlap making for a mutually beneficial collaboration.'
Engineers in England and Germany will share their knowledge in developing electric drive units - the e-motors and software that power electric cars.
By pooling their resources, both brands will 'take advantage of efficiencies arising from shared research and development and production planning as well as economies of scale from joint procurement across the supply chain.'
Yep, it's about saving money.
But each brand is adamant they will retain brand differntiation, 'to deliver the specific characteristics required for their respective range of products.'
It's no different to how BMW and Toyota have shared engines in the past decade, for instance. 
Each manufacturer will build their own electric drive systems - in Wolverhampton for Jaguar and Land Rover, and Munich for BMW's.
Could this deal spark further collaboration between the two car makers? We'd say it's likely.
An official told us: 'We are collaborating with BMW on powertrain technologies, but beyond the EDU [electric drive unit] collaboration we have nothing further to say.'
The BMW X3 M is the first fully fledged M division effort in this sector, after a series of M Performance models. 
As you might expect, it's no sluggard - with the 503bhp straight six that'll also power next year's new BMW M3 and M4. 
But while a sledgehammer engine might make sense in a sports saloon or coupe, does it add up in a mid-weight SUV?
We're a fan of the regular BMW X3, but we've now tested the new M model in the UK, as well as on the international launch: read on for our fully updated review.
It counts cars like the Porsche Macan, Mercedes-Benz GLC 63 and Alfa Romeo Stelvio Quadrifoglio as rivals - and to give it the best possible fighting chance, Munich has smuggled its most powerful six-pot production engine in history and plopped it in the engine bay.
It's enough for some pretty startling, sports car-troubling performance claims, with 0-62mph taking a whisker over four seconds…
Displacing three litres and boosted by two independent monoscroll turbochargers the X3 M produces 503bhp and 443lb ft of torque in Competition specification.
 The standard car makes 474hp, but that won’t be coming to the UK.
It’s a brand new engine, sharing only 10% of its parts with the BMW X3 M40i. 
Technical highlights include a cylinder head with a 3D printed element for lightness and better heat displacement, plus a seriously beefed-up cooling system with three radiators and a pair of oil coolers.
Higher pressure injectors leap from 200 to 350 bar and the oil sump features a clever channel design and pump to stop lubricant pooling at one end under acceleration and cornering. Inside, the crankshaft and pistons are forged for extra strength and hunger for revs, while the turbo housing is built into the exhaust manifold to save weight.
 Plus it looks cool.
BMW’s engineers said this unit runs at ‘just’ 2.3 bar of boost pressure in the X3 M, less than you’d expect considering those significantly strengthened internals, which hints at some serious power gains to be unlocked in the future. 
Such as when it gets dropped into the next M3.
Way less unstressed than that high specific output would suggest, with omnipresent torque from 2600rpm to nearly 6k. 
Peak power arrives at 6250rpm and is maintained right up to the 7200rpm redline. 
It literally plateaus for 1000rpm – almost as if its true potential is being held back for a future car.
Anyway, the result of all of this is that the X3 M feels as stout as a redwood across a huge spread of engine speeds, from barely any revs to that distinctly un-turbocharged redine, with a tangible benefit to revving it out. 
As such it’s alarmingly fast – taking 4.1 seconds to crack 0-62mph and going on to 177mph, if derestricted.
Sounds good too – despite the forced induction and the fact it also features two gasoline particulate filters and four catalytic converters – the Competition-standard M Sport exhaust system delivers a pleasing soundtrack through its pair of double tailpipes, full of burble and bass at tickover and soaring soprano in its upper reaches.
Power is sent through the latest-generation M Steptronic eight-speed transmission – while undeniably effective at rattling off gearshifts, it’s not particularly exciting to use, with too-smooth shifts in all three of its ferocity modes and shift paddles that flap rather than clunk positively with each pull. 
It sounds like an odd complaint because the stepless changes bring about great stability on track and even when driving hard on the road, but sometimes you’d like a Patrick Swayze-style roundhouse to the spine in response to a full-bore upshift to remind you that you’re driving a car with more than 500hp, right?
Yes, and that brings us onto the chassis, which is best described as purposefully firm. 
We drove the 21-inch wheel-equipped Competition model on cracked New Jersey tarmac not unlike the roads we get in the UK and even in Comfort mode, the adaptive steel suspension is keen to let you know exactly how sharp-edged the defect you just rode over was. 
We've now driven it in the UK, too, and can confirm that the BMW X3 M rides stiffly across most road surfaces. 
On the big rims, it just lacks some of the pliancy needed to cope with broken blacktop.
It’s not unusable but those after a powerful, everyday X3 will be better served by one of the faster models in the standard range. 
That’s a bit of a shame because that burly, flexible engine coupled with a cushy, air suspended ride and decent space for four adults would have made the X3 M a hilariously good long-distance cruiser.
That’s not really what M Division is about though, is it? 
Despite being an SUV, this car is principally (apparently) a focused cornering machine designed to thrill owners on a circuit as well as on the road. BMW’s engineers claim it should drive like an M3, only a bit higher up.
Changes to the chassis include a fettled front end with unique swivel bearings, torque arms and wishbones, plus elastomer bearings and more camber for stability. 
There’s also a hefty strut brace in the engine bay, additional underbody bracing and M-specific anti-roll bars, which quell body movements well. 
Adaptive dampers adjust depending on surface or can be manually tweaked with Comfort, Sport and Sport+ modes.
Huge brakes borrowed from the M760i haul the X3 M up effectively but did start to get a bit long in the pedal after four (admittedly quite long) laps at Monticello Motor Club. 
Carbon ceramics are not available either – too expensive for this market, says BMW.
The xDrive system is biased towards the rear, and dynamic stability control plus an Active M Differential help to put that power down, for the most part, while avoiding having to shunt it to the front wheels. 
Traction is peerless in most everyday driving conditions.
There’s no two-wheel drive drift-mode like you get with the M5, but the X3 M is principally a rear-driver. 
Despite this being the latest generation of xDrive you can still sense power moving around under you – it’s not abrupt but it’s far from natural, although the mid-corner traction it summons up is remarkable.
When you’d rather explore the limits of grip yourself there is the clever M Dynamic Mode –which winds off the DSC interference and activates the M xDrive 4WD Sport setting, shifting more power aft. 
Do so and the rear of the X3 M feels very involved, allowing you to steer on the throttle or tuck the nose into the apex when you’ve been a bit ambitious with your entry speed. 
We can't believe we're writing this in an SUV review, but smooth oversteer is available if you pin the front end and commit with the throttle – anything less results in the car hopping and skipping along sideways on its outside wheel before the DSC pulls everything into a straight line. 
There’s a pervasive sense of something – the AWD or traction control – just waiting to come and tidy up after you that sets this apart from a proper rear-wheel drive car.
Altogether we’d say the BMW is not quite as tail-happy as a Porsche Macan or Alfa Romeo Stelvio, but loads more adjustable than an Audi SQ5. 
A neatly happy medium that inspires confidence, which will lead to fast lap times and a smile on your face.
But who the hell ever takes their SUV on a track day?
Back to more sensible matters, and the X3 scores highly. 
It's a well rounded package, with plenty of space for five adults (no three rows of seats here) and a decent sized boot. 
Larger families or those wanting maximum space may wish to trade up to an X5, but we prefer the more bijou dimensions of the X3.
It's all the car many families will need, but we're just not quite sure if the M model masters its all-things-to-all-people brief…
An M Division engineer we spoke to said they set up fast SUVs with exactly the same ethos as their saloons, so an M3 driver that needs a bit of extra ride height to negotiate a long, rutted driveway should feel right at home in an X3 M.
In truth, there are always going to be compromises when you boost the ride height and start sending power to the front and rear wheels, and this SUV is no exception to that. 
As a driver’s car, the X3 M is up there with the Macan – the Porsche offering a more raw, engaging drive; the BMW more stable, confidence-inspiring and ultimately satisfying if you’re chasing after fast lap times. 
It's just a bit too bouncy and stiff-riding for our tastes in the UK.
That engine is a real treat, though – generating huge power throughout the rev range and doing so in a linear, boost-free way that feels distinctly un-turbocharged. 
Roll on, the new M3.
Ahead of its official unveiling in Tokyo in January 2020, we have driven a prototype version of Toyota’s new hot hatch, the GR Yaris.
It’s an all-wheel-drive three-door, powered by a 1.6-litre turbocharged petrol triple. 
Power outputs are said to be around 260bhp and more than 260lb ft; Toyota claims it’s the most compact and most powerful 1.6 ever made. 
The gearbox is a six-speed manual.
There are no direct competitors, but its combination of light weight and high power means it will be competitive with some larger hot hatches. 
Expect it to be priced closer to the Honda Civic Type R than the Ford Fiesta ST, both of which are front-wheel drive, when it goes on sale towards the end of 2020. 
It will be a full production model, not a limited edition.
It’s an entirely new car, developed over three years and sharing very few components with the recently unveiled regular 2020 Yaris. 
It has several roles: sexing up Toyota’s largely frumpy image; re-energising Toyota’s engineers; and homologating the 2021 Yaris WRC car. 
Although the GR and the WRC car will be largely different under the skin, they will share the same general look, shape and size. 
Hence the three-door body and sloping roofline – 95mm lower at the rear – which gives the WRC aero team more scope to create an effective rear wing.
The engine is 21mm further back to improve weight distribution, and the regular Yaris’s torsion-beam rear suspension is replaced by a double-wishbone set-up. 
The doors and bonnet are aluminium, and the roof is carbonfibre-reinforced plastic, for some serious lightweighting.
GR chief engineer Naohiko Saito told CAR magazine that although the car had been developed chiefly in Japan, it had also been tested at the N?rburgring in Europe and had benefited from input from Toyota WRC manager Tomi M?kkinen’s TMR operation, including all three of the outgoing Toyota WRC drivers. 
‘They gave us a lot of feedback and many difficult requests. 
Most of those requests were for a lightweight body and good aerodynamics. 
Also we changed the underbody based on their requests – it’s reshaped and reinforced.
‘Ninety per cent of what TMR asked for has gone in. 
The 10 per cent is mostly about a more extreme shape at the back, for directional airflow. 
But it’s a road car, so there needs to be some room in the back and a window you can see out of.’
The rally team also had a lot of input into the four-wheel-drive system. 
It has three modes: Normal is 60 per cent front, 40 per cent rear; Sport is 30 per cent front, 70 per cent rear (for on-road fun); Track is 50:50, for gravel and snow use as well as on track.
There are no other modes for the driver to choose between: suspension, throttle and steering response are all fixed.
There will be the option of limited-slip Torsen differentials to move drive between left and right. 
That will be offered as part of a track pack, also featuring different tyres, tweaked suspension and different wheels.
A privateer’s rally version is also being investigated, with factory-installed rollcage.
There’s no choice of transmission – it’s a six-speed manual.
The brakes are new: 18in discs, four-piston up front, two at the rear.
Although the wheelbase is the same as the normal 2020 Yaris, the rear track is wider. 
The cabin will be largely the same.
The version we drove on road and on track at Estoril in Portugal was camouflaged outside and in, but was clearly close to being a production-ready car. 
It’s perfectly happy in town and on motorway, but perhaps most in its element on twisty roads and the racetrack, where it’s unintimidating and easy to enjoy.
Because part of its job is paving the way for the 2021 rally car, it doesn’t sit low like a sports car – there’s a decent amount of ground clearance. 
But the suspension is firm, so there’s not excessive bodyroll in corners. 
In fact it could prove to be too firm for some tastes, especially if you’re carrying passengers.
There are echoes of both the Fiesta ST and the Civic Type R, in that it’s agile and responsive, with direct steering and a healthy amount of low-down torque. 
The engine and chassis felt well matched, and the four-wheel-drive system dealt effortlessly with a wet track, getting the power down quickly and securely.
 The gearshift isn’t as slick as the Honda’s, but then few are.
Saito says all-wheel-drive cars as varied as the Audi S1, Ford Focus RS, Subaru Impreza and even the classic Lancia Delta Integrale were examined as the GR engineers worked on the Yaris. 
More prosaically, some of the hardware is shared with the RAV4.
The Yaris is the second GR model in the current Toyota line-up, joining the Supra. But unlike the Supra – co-developed with the BMW Z4 – the Yaris is all Toyota’s own work: ‘A very big chance for Toyota engineers,’ says Saito. 
It will be built at a Japanese factory.
There will also be a Yaris GR Sport, but that’s a largely cosmetic upgrade, not a hot GR model.
The GRMN badge that caused widespread bewilderment when it was used on the limited-edition hot version of the outgoing Yaris is being dropped in Europe, where we’ll just get GR, for Gazoo Racing – the pet project of Toyota boss Akio Toyoda. 
On the evidence of our early GR Yaris prototype drive, this new Toyota hot hatch suggests that the Japanese manufacturing giant is on a roll; it’s a unique surprise in the small-car sector and looks to provide a high-tech, rally-bred burst of fun in the pocket rocket marketplace. 
What’s next from GR? It’s not been confirmed, but Toyota is not denying that some of the GR departmental magic will be applied to the GT86 before it dies of old age. 
It’s great to see Toyota back on the front foot.
Critics might like to portray the car industry as arthritic and slow-moving, but the merger between the PSA and FCA groups show that it can occasionally be the very opposite. 
The two have been gently courting for a while, but serious dating only restarted in September after FCA's brief engagement to Renault imploded in May 2019.
PSA's CEO Carlos Tavares and FCA's chairman John Elkann shook hands on the deal over dinner in Paris on Sunday and we've already seen its broad outline. 
And now it's been finalised.
The new and as-yet unnamed entity will be the world's third biggest carmaker, ahead of General Motors. 
It will have revenues of €170bn, profits of around €11bn and build cars under 15 marques.
According to an announcement from the new company, it will be the 4th largest global OEM by volume and 3rd largest by revenue. 
Annual sales will stretch to 8.7 million units, with combined revenues of nearly €170 billion.
It will be bigger than Volkswagen in Europe and have an SUV and truck business in the US which is setting record margins and profits. 
By combining, it will generate an additional €3.7bn in annual profit for a one-off cost of around €2.8bn, but does not expect to close any factories.
John Elkann will be group chairman and Carlos Tavares will take the place of group CEO. 
Under the brilliant leadership of Tavares and after his 2017 acquisition of Vauxhall and Opel, PSA is bumping its head on the ceiling in Europe and needs a presence in the US. 
FCA needs scale and stability and to refresh Fiat's line-up.
Tavares is just the man for that job. 
He'll lead the merged business as CEO for the next five years, with FCA's British-born boss Michael Manley as his number two, running the US operations, and the 43 year-old Elkann as chairman. 
Tavares took Vauxhall-Opel from death's door to profit in a year and it's hugely exciting to think what he might do to with Fiat, Alfa and Maserati. He'll be able to generate huge savings - and make better cars - by simply picking the new group's best platform in each segment and building everything on that.
The merger doesn't solve everything for Tavares, though, and it might create some new headaches. 
He'll want to rationalise those 15 nameplates. 
Ironically, the two which give FCA its name - Fiat and Chrysler - are among the vulnerable, but actually killing Fiat may prove politically impossible. 
Both carmakers lag on EV and AV tech, but at least the huge spending required can now be amortized over nearly nine million sales. 
Both groups lag badly in China, and the merger won't help much there.
Mike Manley, as of 1 November, has also reportedly discussed the shelving of the 8C (above) and GTV projects on a conference call with the media.
But overall this huge deal makes huge sense, and more sense than a merger between Renault and FCA.
In our CAR Power List 2019, we ranked Tavares at #3, partly for the likelihood that he'd pull off this deal. 
We also said that he was the new Sergio Marchionne: a far-sighted and unsentimental leader who saw that the industry needed to consolidate in order to thrive. 
We think Sergio would approve.
When Honda launched the 10th-generation Civic back in 2017, buyers and journalists (but mainly journalists) moaned like stink that the ‘buttons’ lining the side of the infotainment display were touch-sensitive, and the volume control was an awkward slider.
Ready for 2020, though, the facelifted model replaces these with physical buttons and a proper scroller knob for the volume control. 
And Honda UK even commissioned an over-dramatic press shot to emphasise the fact. It’s got the measure of us, and we applaud it for that.
Anyway. 
There’s slightly more to the Civic’s MMC (Minor Model Change – corporate-speak for very mild facelift) than just a twisty dial on the dashboard.
You’d be hard pressed to notice the changes, inside or out. 
The bumpers have been slightly restyled front and rear, the headlights are a little slimmer with redesigned DRLs.
Certain models also get a new design of 17-inch alloy wheel.
Meanwhile, inside you’ll find the aforementioned knob, plus additional buttons for the climate control panel – meaning you don’t have to delve into the touchscreen menus as often as before.
Certain models also get electric adjustment for the seats for the first time, something that was missing from range-topping models before.
Speaking of the range, a new trim level has been introduced. Called EX Sport Line, it sits on top of the regular EX model but brings a little of that Type R bling in the form of a lower body kit, big alloy wheels and a truly chavtacular rear spoiler sitting in the middle of the split rear screen.
 It’s Honda’s take on an S-Line or AMG Line. 
‘Cause it’s got Line in the name, see?
What’s more telling is what hasn’t changed. 
You still get the same narrow but brilliant engine lineup – a 126bhp 1.0-litre 3cyl, a 179bhp 1.5-litre 4cyl and a 116bhp 1.6-litre 4-cyl diesel. 
You still get the same sublime six-speed manual ‘box, which now has a Type-R aping spherical knob in EX Sport Line trim. 
The rest of the interior’s virtually unchanged, too, which means loads of space for four six-footers and a cavernous boot. 
It’s not quite Skoda Octavia levels of space in here, but you won’t be disappointed coming from a Golf or Focus.
Yes, slightly. After all, despite its dynamic looks, the Civic’s always been more biased towards comfort. 
Sport Line doesn’t bring any dynamic changes as such, but it rides on big alloy wheels and features the same two-mode adaptive dampers as other top-spec models, so it’s as racy as the Civic gets outside of the Type R. 
Other than that, it’s business as usual. 
The chassis is still a real highlight of the Civic – not as engaging overall as a Ford Focus, but still really good fun to stick into a corner.
 The driving position is utterly perfect too, with loads of adjustment and a nice low seat.
Despite this a near perfect-balance is struck when it comes to ride comfort. 
This Civic’s a large car with a long wheelbase, and as such it’s able to ride terrible roads with consummate ease. 
It rides superbly on the motorway too, without sacrificing too much to body lean.
 In fact, all the adaptive dampers do is make it less comfortable – there’s no point engaging them.
You’ll still want the Type R or at least the 180bhp 1.5-litre if you want to make rapid progress, but for most the 1.0-litre triple is perfectly adequate. 
It belies its small size with a rorty tone (verging on plain noisy at times) and decent slug of low-end torque.
 You’ll be changing ratio fairly often, but that’s no chore with the six-speed ‘box.
A heavy flywheel takes some enjoyment away – it bogs down the engine response, so it’s slow to lose revs, making for jerky low-speed progress. 
It hits back with good efficiency.
It probably won’t surprise you that we recommend avoiding the automatic gearbox. 
Diesel models were previously available with a nine-speed torque converter – this has been dropped, and now your only automatic option is the CVT on either 1.0-litre or 1.5-litre petrols.
It’s dire. Don’t torture yourself. 
The CVT’s approach to progress seems to be to pull the engine to 3,000 revs as soon as possible (coincidentally, the noisiest part of the rev range) and keep it there come hell or high water, regardless of what the accelerator pedal is asking for. 
WLTP efficiency is improved but in the real world we were struggling to best 40mpg. Put simply, it’s not worth the pain. 
If you need an auto gearbox in a car of this size, get yourself a VW Golf with a DSG or a Ford Focus or Mazda 3 with a torque converter.
There’s also little point going any higher than EX Sport Line in the range. 
With dual-zone climate, touchscreen infotainment, heated seats, a full suite of safety aids and LED lights, there’s nothing missing that you could find further up. 
In fact, unless the body kit really attracts you, maybe just stick to EX…
The facelift’s definitely a bit half-baked, but the Civic was a good car before it and it’s still a good car after. 
The improvements made, however small, do make a difference – the volume knob, for example, is long overdue. 
We’d still like to see the CVT gearbox and the infotainment system replaced, but stick to what the Civic’s good at and it really does make a case for itself even against the might of this crowded class.
The typical good Honda dealership experience and exemplary reliability should definitely stand the Civic in good stead among private buyers, though Honda’s finance packages aren’t particularly competitive for those used to chopping and changing their PCP deals with regularity. 
Would we recommend it against the brilliant Focus and Golf? 
Not quite – but it runs them closer than you’d think. 
We reckon it’s well worth a look.
If you’re part of the Apple architecture, then you’ll probably already know about – or even use – Apple’s intuitive CarPlay system, which allows a user to mirror a select group of Apple apps onto their car’s multimedia screen.
Apple CarPlay apps include Apple Maps, Google Maps, the alternate mapping service Waze, music streaming service Spotify, WhatsApp, voice-to-text messaging and more.
Currently, an Apple CarPlay head unit requires the user to simply plug a phone into a USB port in their car to activate the CarPlay apps, but there’s a movement afoot across the industry to make Apple CarPlay wireless.
What’s Apple CarPlay, and how does wireless Apple CarPlay work? 
Well… you hop in your car, hit ‘CarPlay’ on the head unit and it… just works. 
The system uses a combination of WiFi, cellular and Bluetooth reception to stream to your compatible multimedia system.
BMW was the first car company to offer the service, even though it inexplicably decided to charge end users a subscription fee for a service that every other carmaker provides for free. 
The company is reportedly looking to drop the fee.
Other carmakers joining the wireless Apple CarPlay revolution include Audi, which debuted the system aboard the latest A6, while fellow VW Group brand Volkswagen has also indicated that it’ll roll the wireless CarPlay system out on the Golf Mk8.
Volkswagen expects other vehicles to be updated within 12-18 months, with passenger cars such as the Tiguan, Golf and Touareg in the firing line.
 It will also encompass the new T-Roc and T-Cross that are due in May 2020.
There are some exceptions to Volkswagen’s wireless CarPlay rollout, mainly on the commercial vehicle side of the business.
Mercedes-Benz, meanwhile, will add wireless CarPlay functionality to its MBUX system, which launched with 2018’s A-Class update.
If your car is still a few years away from an update, it’s possible to buy an Apple CarPlay head unit aftermarket in order to access the service.
Companies like Pioneer offer Apple CarPlay stereos that will offer the wireless service.
In case you’re wondering, CarPlay is compatible with the iPhone 5, the iPhone 5C, iPhone 5S, iPhone 6 and iPhone 6 Plus, the iPhone 6S and iPhone 6S Plus, the iPhone 7 and iPhone 7 Plus, the iPhone 8 and iPhone 8 Plus, the iPhone X, the iPhone XS and XS Max, and the iPhone XR.
Seat’s perennial Leon hatch is about to be replaced, with the brand dropping its first teaser.
 It also confirmed the date of the reveal: Tuesday 28 January 2020. Until then, a shadowy video is all we have to go on.
Even so, there are plenty of familiar Seat cues throughout. 
The headlights are full LED and shaped much like the Tarraco’s units, with Seat confirming that a ‘dynamic’ version of them will be available. 
The rear light bar, which stretches across the whole rear end (and which Seat calls coast-to-coast), is also like that of the Tarraco. 
Sliding indicators will also be a thing, and puddle lights will also be an option.
There’s a glimpse of the interior, too. 
Naturally, it’s heavily related to that of the Mk8 Golf and updated Skoda Octavia. 
Along with a red bow-line stretching around the dashboard housing, you can also spot the virtual instruments and infotainment screen from the new VW inside here. 
There’s also the stubby shift-by-wire gear selector.
Little else is confirmed, but it’s pretty much a given that there’ll be an updated MQB platform underneath, use of mild hybrid petrol engines and cleaner diesels when it goes on sale early next year.
It has been 20 years since the?Honda S2000?came into the automotive world, and the world has been pining for a replacement ever since its retirement in 2009. This isn’t it though.
Instead, Honda Access, the accessories arm of Honda, will be presenting a modified?S2000?at next month’s Tokyo Auto Salon, to commemorate the 20th?anniversary of the iconic roadster’s debut in 1999.
According to Honda Access, the S2000 20th?Anniversary Prototype will feature mostly cosmetic changes in the form of a sharper front bumper that can be seen from the lone teaser image,??new suspension, and a new audio system.
It is uncertain if these accessories will be made available to S2000 owners, or will merely be a commemorative one-off.
That being said, the S2000 isn’t the only icon getting a modern treatment for Tokyo Auto Salon, as Honda Access will also debut a customised EK9 Civic Type R.
The first-generation Civic Type R?(more popularly known by its internal designation, the EK9), left an indelible mark on early-2000s car culture with its frantic 182bhp, 188pb ft four-cylinder B16B screamer of an engine, lightweight ethos, and aftermarket tuning potential.
As a tribute to the EK9’s iconic status in car culture, Honda Access will debut an accessorised example known as the “Civic Cyber Night Japan Cruiser 2020”, which reimagines the EK9 for the “modern youth” that “reflects a neo-Japan where the near future and underground are disturbed”.
Not sure what that means exactly, but those who are familiar with the EK9’s popularity in the tuning scene will get the gist.
Tokyo Auto Salon 2020 will be held from January 10th?to 12th?at Chiba City’s Makuhari Messe.
Audi is experimenting with new technology to make its employees safer, stronger and just a little more like Iron Man.
A trial of two exoskeletons has begun by Audi with the goal of choosing a long term system for its production line employees.
The Paexo from Ottobock and the Skelex 360 from Skelex are the current products being tested on around 60 employees to determine which is more suitable for widespread implementation.
According to Audi, the external support structures are designed to help in performing overhead tasks so that muscles tire less quickly. 
The technology is being used across the assembly line, paint shop and tool construction stations.
They do this through joints positioned behind the shoulders of the jacket-like device which help keep the workers arms held up while distributing weight to the hips. 
The function of these is purely mechanical with no motorised assistance.
The main questions being asked by this testing are about movement restriction, quality of material after extended use and the level of support offered by the use of these exoskeletons.
Audi has been experimenting with exoskeletons for a number of years now with products from Laevo having been in use in different stages of production.
Those, however, primarily assisted in lifting heavy objects with the support being focused on the thighs, hips and back.
 The Laevo units had hydraulic-adjustable carbonfibre reinforced plastic struts for support and could be fitted according to workers heights.
Audi’s new trial is looking at reducing all round muscle fatigue where possible. 
This is not new technology, with large scale manufacturers BMW, Hyundai, Ford and General Motors all experimenting with systems to keep workers safer.
‘Vorsprung durch Technik’ or ‘Being ahead through technology’ is Audi’s motto and this move to increase safety for their workers suggests they are working towards the fulfilment of that statement.
Mass legal action against Volkswagen's role in the emissions cheating scandal has begun in the UK, as around 91,000 motorists challenge VW in the High Court. 
They are banding together to claim compensation for being misled over their vehicles' emissions - and it's touted as the largest class action in UK legal history.
Owners of VWs, Audis, Seats and Skodas are trying to sue the car maker three years after the litigtaion was first filed in 2016. Shazia Yamin from the Product Safety and Consumer Law team at law firm Leigh Day said: 'After four years, I look forward to our clients having their day in court. 
They believe that Volkswagen not only misled customers but that they also endangered public health with their blatant disregard for safe NOx emission levels and should be held to account.' 
VW denies any wrongdoing and intends to defend the action.
A two-week preliminary hearing is now underway at the High Court in London, where Mr Justice Waksman will judge whether the software in UK cars should be classed as a 'defeat device' under EU law, as it was in the United States. 
If he agrees with disgruntled owners, then Volkswagen could be forced to offer compensation to owners here, piling on further pressure to Wolfsburg, which has already spent billions on compensation and fines in the US and other territories. 
A spokesperson from VW Group told us: 'The purpose of the hearing is to determine two specific questions of law, namely whether the English & Welsh High Court is bound by the findings of the German Federal Motor Transport Authority (KBA) or the British Vehicle Certification Agency (VCA), and whether the legal definition under Article 3(10) of Regulation 715/2007/EC of a defeat device is met if certain factors are fulfilled. 
Volkswagen says the answer to both questions is no.
Volkswagen Group continues to defend robustly its position in the High Court in London. 
It remains Volkswagen Group’s case that the claimants did not suffer any loss at all and that the affected vehicles did not contain a prohibited defeat device. 
The decision today does not affect any questions of liability or loss.'
We will update this story with news of the case as it unfolds - and this class action could rumble on for two to three years, legal experts predict.
 Read on for more background about the VW emissions scandal.
The so-called Dieselgate scandal dates back to 2015. VW was found to have systematically cheated emissions tests in the US and Europe by using 'cheat devices' in engine ECU controls, so vehicles could detect when they were in a lab and when they were on a real road, trimming exhaust pollution signficantly to score better in tests. 
Volkswagen has set aside more than €30 billion to pay fines, recalls and other costs arising, forced sweeping changes in its management and realigned its future strategy around electric cars, as it accelerates away from fossil fuels
It's hard not to underestimate the import of the Dieselgate scandal - it transformed the industrial landscape in Wolfsburg, and arguably boardrooms across the world.
Volkswagen's 'defeat device' was first uncovered by the US Environmental Protection Agency (EPA), which alleged that 3.0-litre diesel engines in some VW, Audi and Porsche cars were fitted with software capping their nitrogen oxide (NOx) emissions during testing - but allowing levels up to nine times greater during ordinary driving. 
Chaos ensued, and the scandal rapidly went straight to the top of the group - with chairman Martin Winterkorn stepping down in September 2015, along with a variety of notable exeucutives such as engineering chief Ulrich Hackenberg, who masterminded the roll-out of the MQB modular architecture that underpins crucial volume models across VW, Audi, Skoda and Seat.
Lawsuits - civil and criminal - continue to be held in different territories around the world and VW set aside billions in compensation and fines, redrawing the company's future strategy.
It quickly became clear what effect the new austerity era in Wolfsburg would have: VW pledged that the next-generation Phaeton will switch to become an electric limousine, it fast-tracked hyper-clean diesel technology and it developed a new scalable electric architecture dubbed MEB to underpin a new generation of EVs with projected ranges of between 150-300 miles. 
The silver lining of this particular cloud is that VW skipped a generation and invested heavily in EVs.
The UK boss of Volkswagen, Paul Willis (below), was hauled in front of MPs and admitted that 400,000 British cars would need physical engine modifications to remedy the 'defeat device' emissions cheat software. 
The company started fixing affected cars in January 2016 and said it would take a good year to sort all the cars.
Willis, who has now left his role, was called to appear in front of the transport select committee to explain how VW would restore motorists' faith in the company's cars. 
He admitted that a third of cars affected in the UK would require a change to the fuel injection system - mostly the 1.6 TDI models sold since 2008.
 It's a further blow to VW, and confirmation that it's not just a simple software upgrade.
It has transpired that around 3 million of the 11m cars identified globally could require physical hardware upgrades, as well as a software reflash.
Officials were keen to stress this was a service action to 'refit' cars, not a full recall. 
It's a technicality, but full-blown recalls are reserved in the UK for safety-related issues. 
VW wrote to affected owners and offered a retro-fit upgrade to their diesel car. 
Owners took their car to a main dealer who offered to upgrade the ECU to eliminate the problem software free of charge. 
But VW subsequently confirmed that some cars would need additional physical engineering changes as well as a software reflash.
Models affected include all Golf Mk6, Passat Mk7 and Tiguan Mk1 diesels, which were 'equipped exclusively with type EA189 diesel engines.
 Audis affected included some A1, A3, A4, A5, A6, TT, Q3 and Q5 models, it was confirmed. Skoda and Seat models affected were also based on the group MQB architecture.
The new group boss of Volkswagen certainly had his hands full. 
Wolfsburg announced sweeping boardroom changes on Friday 25 September 2015: Porsche leader Matthias Mueller (above) was appointed the new CEO of Volkswagen AG and pledged that his first priority was to clean up the company with a major restructure, new personnel and emergency actions to restore faith among the 80 million Volkswagen owners worldwide.
Dr Herbert Diess, CEO of the VW car division, said: 'We are working at full speed on a technical solution that we will present to partners, to our customers and to the public as swiftly as possible. 
Our aim is to inform our customers as quickly as possible, so that their vehicles comply fully with regulations.
 I assure you that Volkswagen will do everything humanly possible to win back the trust of our customers, the dealerships and the public.'
The story first broke in the US. 
Emissions bodies discovered 2.0-litre diesel engines used a hidden special 'cheat cycle' when placed on a laboratory testbed (the cars can tell because the front wheels are spinning on a dynometer while the rears are stationary).
A simple recall story in the US rapidly escalated into a full-blown global scandal, with American authorities threatening a robust $18 billion fine, VW shares plummeting by a third, Switzerland banning sales of affected diesels (and Italian VW Group dealers following suit, with an eye on potential recall costs) and Wolfsburg hastily committing to the recall of nearly half a million vehicles in the US, and probably more elsewhere in the world.
The boss resigned on Wednesday 23 September 2015. 
He issued this statement: 'I am shocked by the events of the past few days. 
Above all, I am stunned that misconduct on such a scale was possible in the Volkswagen Group. 
As CEO I accept responsibility for the irregularities that have been found in diesel engines and have therefore requested the Supervisory Board to agree on terminating my function as CEO of the Volkswagen Group.
 I am doing this in the interests of the company even though I am not aware of any wrong doing on my part. 
Volkswagen needs a fresh start – also in terms of personnel.
 I am clearing the way for this fresh start with my resignation.
 I have always been driven by my desire to serve this company, especially our customers and employees. 
Volkswagen has been, is and will always be my life. 
The process of clarification and transparency must continue. 
This is the only way to win back trust.
 I am convinced that the Volkswagen Group and its team will overcome this grave crisis.'
The US Environmental Protection Agency (EPA) and the California Air Resources Board (CARB) found a 'defeat device' embedded in the engine management systems on the modern Volkswagen 2.0-litre diesel engine described as EA189, designed to lower exhaust levels of certain pollutants such as nitrogen oxides (NOx) substantially if the car was being emissions tested. 
When released back to the road, the engine would then pump out normal, higher levels - in tests the EPA found that NOx was 40 times higher when running in normal mode. 
VW admits 'while testing diesel cars of the Volkswagen Group they have detected manipulations that violate American environmental standards.' 
Volkswagen has now admitted the 'dual-mode software map' on affected diesel engines.
Put simply, these cars contained software that turns off emissions controls when driving normally and turns them on when the car is undergoing an emissions test,' said Cynthia Giles, an EPA enforcement officer.
 'We intend to hold Volkswagen responsible. 
VW was concealing the facts from the EPA, the state of California and from consumers. 
We expected better from VW. 
Using a defeat device in cars to evade clean air standards is illegal and a threat to public health.'
The US authorities have the power to levy a fine as high as $37,500 per vehicle affected. Yes, that means a total of $18 billion - in theory. 
It is thought to be unlikely that the lawyers will be quite as exteme, however, since VW is a smaller player in the States (just 13% of all VW cars sold were in the US in 2014). 
But make no mistake: this is a serious setback in Wolfsburg's drive for global domination (in 2015 it became the world's biggest car making group in the first half) and the scandal now looks set to derail that success. 
It has already spelled the end of CEO Martin Winterkorn's leadership in Wolfsburg; his contract was up for renewal this month, and more heads will be likely to roll as independent and external investigations get under way…
Oh yes. So far VW - and Audi, it should be pointed out - have agreed to recall 482,000 four-cylinder Jettas, Beetles, Golfs, Passats and A3s sold since 2008. It must remove the 'defeat device' and clean up the NOx emissions on all the affected cars in the US. 
That is a seriously expensive recall - never mind the questions whether the same software was fitted to group vehicles sold elsewhere in the world. Volkswagen has subsequently admitted that there are around 11 million products using the 2.0-litre TDI engine globally. 
And word is that the company may be facing class legal action from US consumers. 
We sense this is only the tip of the iceberg…
Respected industry watcher Max Warburton of Bernstein Research says the scandal is a major setback for VW: 'The commencement of legal proceedings against VW by the US EPA is profoundly serious,' he said.
 'This is not your usual recall issue, an error in calibration or even a serious safety flaw. 
All of the former can be attributed to bad luck or bad execution - OEMs can normally claim they were trying their best, but fell short. 
This is quite different - the accusation is that VW deliberately set out to mislead regulators with a cleverly hidden piece of software.'
Prof Christian Stadler, of Warwick Business School added: 'No question that this is a big problem for Volkswagen... From 2009 to 2011 Toyota recalled nine million cars for issues leading to unintended acceleration. 
Estimations suggest that Toyota dealers lost more than $2 billion as a result and the company itself also around $2 billion. 
Toyota also settled with the government for $1.2 billion. 
To some extent the cheating by Volkswagen seems more blatant, but the numbers are lower and there are no fatalities involved. 
This suggests that in the "heat of the moment" the long-term effect on Volkswagen may be overstated. 
Sure it will hurt, but maybe not quite as bad as we expect right now.'
Over to Martin Winterkorn, the former chief executive of Volkswagen AG. 
The Board of Management at Volkswagen AG takes these findings very seriously,' he said, shortly before being removed from office. 
I personally am deeply sorry that we have broken the trust of our customers and the public. 
We will cooperate fully with the responsible agencies, with transparency and urgency, to clearly, openly, and completely establish all of the facts of this case.
Volkswagen has ordered an external investigation of this matter... 
We at Volkswagen will do everything that must be done in order to re-establish the trust that so many people have placed in us, and we will do everything necessary in order to reverse the damage this has caused.'
This is all about section 203 (a) (3) (B) of the Clean Air Act.
 And we quote: car makers 'are subject to a civil penalty of up to $3750 for each violation that occurred on or after 13 January 2009. 
In addition, any manufacturer who, on or after 13 January 2009, sold... any new motor vehicle that was not covered by an EPA-issued COC is subject, among other things, to a civil penalty of up to $37,500 for each violation.
 This is where the $18bn fine threat comes from.
There is a back story here, starting in May 2014 when a study in West Virginia University found conflicting emissions results on a 2012 Jetta and 2013 Passat diesel. 
They alerted the CARB and EPA - leading to the current scandal. 
And we fear this one has a long way to run…
Inventive as ever, humans keep finding new ways to crash cars. 
Fortunately, automotive boffins keep coming up with new ways to protect us.
 It’s 100 years since the first airbag patent was filed (by two dentists from Birmingham) and 20 years since they became ubiquitous, but so long as people keep getting hurt in car crashes there’s still scope for airbags to improve, as demonstrated by four new developments.
Honda has co-developed a front airbag that should reduce injuries in many types of crash, particularly angled frontal collisions. 
Mercedes has shown a new rear airbag that’s designed to be gentle with the kids it’s meant to be protecting, and a front bag made with self-driving cars in mind. 
And ZF has demonstrated an external airbag to reduce the severity of injuries from side impacts.  
The Merc and ZF bags are not yet ready for production, but the new airbag jointly developed by Honda and Autoliv (the world’s largest automotive safety supplier) will start appearing on Honda cars, initially in the US, in 2020 (below). 
Instead of the familiar single inflatable chamber it has three bags and a fabric panel. 
The result resembles a baseball catcher’s mitt, catching and slowing the head, and simultaneously pulling the side chambers inward to cradle and protect the head. 
Where a conventional airbag can struggle to stop your head rotating severely or sliding off the inflated bag, painfully, in an angled crash, this should give you a better chance.
Mercedes has a driver airbag fitted in the dash, not the steering wheel, enabling it to be bigger and more effective. 
And its prototype rear airbag uses inflatable tubes, making it better able to adapt to the great variety of passenger shapes, sizes and postures it has to handle. 
The tubes are similar to the air hoses used in ‘air tents’ as an alternative to poles. 
They’re inflated with compressed gas and unfold into a wing-shaped framework. 
A large airbag that fills up with air from the cabin is suspended between the wings. 
In a frontal impact, the airbag can reduce the loads transmitted to a rear passenger’s head and neck by up to 30 per cent, claims Merc.  
ZF says its prototype pre-crash external side airbag system is a world first.
 It deploys milliseconds before a collision, expanding upwards from the door sill. 
This forms an extra crumple zone, but on the side rather than at the front or back, to complement the familiar rigid occupant cell. 
When drivers are no longer required to drive they’ll still need protecting in accidents.
 Merc’s dash-mounted airbag will be ready for the time when the steering wheel can be retracted during autonomous driving.
ZF external side airbags rely on data from sensors being crunched through bespoke algorithms in 150 milliseconds to assess if an unavoidable crash is imminent, and if airbag deployment will help.
Merc’s dash-mounted front airbag requires the steering wheel to be flat-topped – practical now thanks to progress with electric power steering.
Say hello to the spiritual successor to the McLaren F1. 
After a splurge of specs and a rather detailed sketch, we now have a real picture of Gordon Murray’s latest supercar. 
Well, an official rendering of one, at least. 
As shown by the sketch, the new car is dominated by a 400mm ground-effect fan, but the rest is par for the supercar-course. 
Interestingly we can see bits of McLaren F1 DNA along with some Ferrari – and perhaps a little McLaren Longtail, too… 
Alongside the picture we’ve also got more information about how the complex ground-effect aero the T.50 has will work. 
It's been developed using Racing Point F1 team's windtunnel using 40% models, and comes with a six settings, all conducting the car’s moving aerodynamic surfaces.  
Auto mode responds to driver inputs, while Braking mode doubles the level of downforce using the fan – and deploys the rear aerofoils like an airbrake. 
This shortens the braking distance of the T.50 by 10 metres. 
High Downforce mode will increase the amount the car sis forced onto tarmac by 30% – so ideal in twisty bits, while Streamline mode reduces drag by 10%, eases off the downforce and increases top speed and fuel consumption. 
Underbody ducts are shut, and the fan increases the trailing wake of the car, creating what Murray calls a ‘virtual longtail.’ 
Finally a V-max mode works just like Streamline mode, but takes top speed over consumption and gives the car a 30hp boost using the 48V starter motor. 
History is repeating in the best possible way. 
The phrase, so often used as a sombre warning on the perils of dumbly heading down the same well-trodden road to catastrophe, is this time cause for jubilation. 
Gordon Murray, he of the rule-stretching and awesomely successful Formula 1 cars and the majestic McLaren F1 road car, is working on a sequel – a carbon-tubbed, turbo-free, V12 three-seater designed from the ground up to weigh next to nothing and deliver the purest, most engaging driving experience since his last, spectacular crack at the same high-performance puzzle.
It's always annoyed me that no one has done an F1 since the F1,' says Murray, aware that the sentiment could be construed as a lack of modesty.
 'I know it sounds big-headed but nobody's done a pure, lightweight, focused driver's car since – the T.50 will be better in every way.'
The F1, you'll recall, married Murray's flair for original engineering and packaging with McLaren's composites expertise and BMW's naturally-aspirated V12. 
It seated three people and, for all its searing speed, was refined like a GT, with luggage and air-con. 
It wasn't conceived as a track car – but won the Le Mans 24 Hours outright nonetheless.
In broad terms, the T.50 shares much with the F1. 
A carbonfibre tub encloses the three-man cockpit. 
Like every single component on the T.50, the chassis is bespoke and optimised to the car – the key to hitting Murray's target weight of just 980kg. 
How, when a McLaren 600LT weighs more than 1200kg? 
Most cars suffer from committee decisions and carry-over parts. 
You can't get a car under 1000kg unless you start with a clean sheet of paper. 
There isn't a single carry-over component on the T.50.'
The T.50 is neither turbo nor hybrid (there's a 48-volt generator, powering the T.50's 400mm fan – the key to its Brabham BT46B-inspired aero. 
Instead it uses a new V12 from Cosworth (supplier of the far bigger V12 for Aston's Valkyrie) capable of revving beyond 12,000rpm. 
It'll be the lightest V12 ever built – 60kg lighter than the F1's BMW engine – as well as the highest revving,' says Murray.
All 100 T.50s will be built in 2022. 
Given the costs, this could be the last great analogue supercar,' muses Murray. 
I've driven all the latest supercars. 
They're fantastic, but they don't involve you in any way, shape or form. 
Get back into an F1 and the hairs on the back of your neck stand up.
 T.50 will do the same.'
A trained mechanical engineer, it was in F1 – first with Brabham, then McLaren – that Murray's problem-solving virtuosity gained global recognition. 
He founded Gordon Murray Design in 2007. 
The T.50 will be Gordon Murray Automotive's first car.
The T.50 will use a fan at the rear of the engine bay, partly to draw heat out but mainly to manage the car's complex aero.
 'The fan is constantly active, interacting with the series of flaps in the underfloor – the upper body does nothing on the aero side, apart from the airbrake. 
The fan gives us control over drag, downforce and the centre of pressure. 
We can vary all three depending on what's required at that moment.'
We're developing a new power-assisted steering system. 
I haven't driven a car with electric power-steering that I like yet.
I have an Alpine A110 and it's okay. McLaren have stuck to hydraulics, and it's one of the better systems, but it's not as nice as manual steering. 
But I recognise people need assistance. 
So we're developing a completely new assisted steering system that will give you the same kind of feel as the F1's.'
I wondered if a manual was a step too far, and that perhaps a sequential manual might be the way to go – DCT 'boxes are such a non-event – but in the last 15 months I've been lobbied hard by owners to do a manual [as in the F1, above]. 
I was under pressure from the investors to do 300 cars for ?800k [the T.50 will cost ?2.3m before taxes] but I wanted to get to know each owner, and what they want from the car – as I was able to do with the McLaren F1.'
A lot of people are current McLaren F1 [above] owners. 
Then we're getting a bunch of younger people for whom the F1 was their poster car – this is their chance to buy something like an F1 now that they're ?20m. 
And then there's the third group, my age, who are desperate for another analogue supercar they can get involved with.'
The Porsche Experience Center Los Angeles recently celebrated its third anniversary, and we were invited to the party. 
While there, we met with the Center's leading lady and manager, Jennifer Nicole Malacarne, to discuss her journey from cross-country road rallies to running one of the coolest places in Southern California.
Automobile: So, you started with a Honda Prelude. 
How did driving that car as a teenager lead to you becoming a Porsche aficionado?
Jennifer Nicole Malacarne: I bought my Honda Prelude before I even had a driver license, at the age of 15. 
In my neighborhood, I was the only girl with a sports car among all these guys that were driving around in lowered or lifted trucks. 
The guys had their driver's licenses for a couple of years and more driving experience. 
As a female, when you grow up surrounded by guys, you have to beat them at everything. 
Otherwise, they assume that you're not the real deal.
With a sports car, which had more horsepower, I became the expert and quickly discovered that guys in souped-up trucks couldn't beat a Honda Prelude. 
Soon after, I joined a car club, attended car shows, and learned more about cars.
Being one of the few females in the car community opened more doors for me and I accepted every opportunity that came my way. 
I sort of fell into Porsche and 20 years later, I landed my dream job at the Porsche Experience Center Los Angeles. 
This is not just a job; it is my life and a lifestyle that I find myself fully immersed in.
When did you join the Porsche Experience Center Los Angeles team?
Before it was ever announced that a Porsche Experience Center was going to open in Los Angeles, I had the opportunity to meet a few people from Porsche at automotive events.
 I felt that Porsche would be a good brand to work for and started to ask if there was anything I could do at the company.
 We had these conversations for about two years and finally, in 2014, I was at a race in Montreal and I got a call from Porsche—they were hiring. 
They brought me on as the events and sales marketing manager, and in 2016 I got the nod to manage the Porsche Experience Center Los Angeles.
Why did you pursue Porsche and not another car brand?
Prior to making the move to Porsche, I had been working at an agency for top automotive aftermarket brands including Recaro, Brembo, and Pirelli. 
At that time, I was trying to figure out where I wanted to take my career next. 
Knowing I had already worked for the best in aftermarket, I set my sights on joining an OEM brand and considered moving outside of California. 
When I zeroed in on a brand that I would want to be associated with, or maybe the car my future husband would drive, I arrived at Porsche.
Porsche is classy, innovative, and built on integrity. 
It is a brand with a strong heritage and traditional values. 
To that end, Porsche is a brand that says something about who I am as a person.
At one point, your career was heading toward fashion design. 
Do you find that a correlation between fashion and cars?
When I was growing up, my core interests were sports, music, cars, and fashion.
 I thought if I could become a fashion designer it would afford me all the beautiful cars in the world, including a Porsche 918 Spyder. 
I was consumed by fashion, but then all these cool opportunities such as cross-country road rallies and creative marketing for automotive brands came knocking on the door.
My career took a detour from fashion to cars, and I found a brand that is a blend of both. 
Like fashion, Porsche is a lifestyle and I'm not disappointed that my career went down this road. 
Automotive had always been my calling and in many regards, I'm doing a little bit of everything I thought I would do, only the focus is on cars. 
I still don't have a 918 Spyder parked in my garage, but that being said, a career in automotive has afforded me other sports cars that have put big smiles on my face.
Of the eight cross-country rallies you've done, which one was the most challenging?
All of them were challenging, however, one year I drove alone—that experience really tested my skills as a driver. 
The cross-country rallies I ran involved a group of 100 to 150 people, including friends, CEOs of companies, and celebrities, who were driving up to 3,500 miles in six days. 
Usually during these rallies, you had one or more co-pilots to rotate into the driver seat. 
In 2007, I drove without a co-pilot from Montreal to Key West and by the time I made it to Florida, I was beyond exhausted.
I ended up winning the driver of the year award and that was such a huge deal for me because of who I was competing against. 
The trophy sits at my home office and every time I see it, I'm reminded of that young girl who finished on her own.
How involved were you in the ideation and opening of the Porsche Experience Center in L.A.?
I had the privilege of seeing PEC L.A. built from start to finish on a piece of land that was previously a golf course. 
Everything from the art on the walls to the naming conventions of the conference rooms were a part of a presentation that I worked on with a small team of people. 
We worked on the branding together, and our goal was to design a place that felt like you were walking into your living room. We didn't want PEC L.A. to look like a dealership. 
To be able to use my design abilities to create the Speedster Caf?, Restaurant 917, and curate the cars featured in the gallery was not only cool, but a dream come true.
Ultimately, we wanted to make PEC L.A. feel like a second home for Porsche enthusiasts, because if California were its own country, it would be the fifth-largest market for Porsche worldwide.
What's the one accomplishment you're most proud of during your time at PEC L.A.?
When I returned to L.A. from Atlanta to open the Center, myself and the operations manager were tasked with hiring 100 people in three months. 
Trying to find the right talent who could be great ambassadors of the brand was not easy by any means. 
We built a team that exudes a passion for Porsche and anyone who walks through our doors will see that our employees are a direct reflection of the brand. 
It is an accomplishment I am very proud of.
Some might not expect to see a woman managing PEC L.A. 
How important is it that a woman is excelling in this role? 
There are a lot of people who come in and probably don't expect to see a vibrant, young, and smiling woman. 
With the exception of one location, Porsche Experience Centers worldwide are managed by women. 
When you look at what we do at PEC, we are an events company that involves both driving and dining. 
I think it is beneficial to have women in leading roles, as women are great at multitasking and more nurturing.
There is a recurring car meet at PEC L.A. called "Morning Shift.  
" How did it start and what's it all about? 
Morning Shift is a monthly car meet that we host at PEC L.A. one Saturday a month for the Porsche community.
 It is like a cars and coffee, only on a larger scale and with a rotating theme. 
Rather than opening our gates at 6:00 a.m. like most car shows, we open at 8:00 a.m. because not everyone likes to wake up early on Saturdays.
Is PEC L.A. a "members-only" club?
Absolutely not. You don't have to own a Porsche and you don't have to buy one just because you came here. 
The car community in general is welcome at PEC L.A. 
Everyone is invited to have a cup of coffee or tea at Morning Shift and we highly recommend that they check out the cars in our gallery. 
We truly care about all of our first-time visitors and customers, whether they drive a Porsche or not.
We heard you have a 1963 Porsche 356 parked in your garage and that Rod Emory was involved.
In 2008, I had a photo of me taken inside a Speedster and we staged it to make it look like I was driving. 
I sent the photo to my mom and told her this was my dream car and that one day, I would be driving one for real. 
Fast forward to 2014, I got hired by Porsche and four years later, I had the money to buy my dream car.
My good friend Rod Emory knew I was looking for a 356 and he found one in Santa Cruz that was sitting on 14-year-old tires. 
After Rennsport Reunion VI, we flatbedded the car to Rod's garage for restoration, and both of our dads got involved, which was really special. 
And because I work for Porsche, I told Rod that I didn't want my 356 to be modified like John Oates's Emory Outlaw, I wanted a super classy look for my 356. 
Interestingly, the original seatbelts that were in John Oates's 356 are actually in my car.
When you're not making things happen at PEC L.A. or taking your 356 for a cruise, what other hobbies are you devoted to?
Last year, I got into self-defense and I've been training in Krav Maga.
 I take night classes and try to go at least twice if not three times a week.
 I earned my yellow belt a few months ago and am working toward my black belt. 
Self-defense is empowering and a good tool for women to have in their back pocket. 
I probably look a bit unassuming, so it's definitely good for me.
Before we wrap it up, tell us what you think is the coolest thing about the Porsche community.  
If you go to a Porsche gathering, an owner of a million-dollar 918 Spyder will park next to a 914 project car that someone bought at a salvage yard and there's no difference in the people. 
They come together for a common purpose, and Porsche has a such a great way of telling that story. 
Rennsport Reunion at Laguna Seca Raceway, which drew a crowd of more than 81,000 in 2018, is the perfect example.
NHTSA’s Ease-of-Use Ratings let you compare how easy it is to use certain car seat features so you can make informed decisions about the right car seat.
Using the Car Seat Finder, just enter your child’s age, height and weight, then click on “View Detail” in the “Ease-of-Use Ratings” column next to the car seat brand, model and mode (position). Each mode has its own rating.
All NHTSA-rated car seats meet Federal Safety Standards and strict crash performance standards. While all rated seats are safe, they do differ in their ease of use in four basic categories:
Examines the content and clarity of the instructions manual for the child restraint.
Examines the ease of using features that pertain to installing the child restraint in a vehicle.
Examines the content and clarity of the labeling attached to the child restraint.
How are child restraints rated for their Ease of Use?
A child restraint is assessed under each mode (rear-facing, forward-facing, and booster) of proper use and is awarded both an individual category rating and an overall rating.
For each mode, child restraints are individually assessed on the following four categories:
Evaluation of Labels: Examines the content and clarity of the labeling attached to the child restraint.
Evaluation of Instructions: Examines the content and clarity of the instruction manual for the restraint.
Securing the Child: Examines the ease of using features that pertain to securing a child correctly in the restraint.
Vehicle Installation Features: Examines the ease of using features that pertain to installing the child restraint in a vehicle.
Is a seat that receives the highest rating safer than a seat that receives the lowest rating?
No. A child restraint is most effective if correctly installed in a vehicle and if the child is correctly secured in the restraint.
The ratings represent each child restraint’s individual category rating as well as its overall rating for Ease of Use, not safety.
How should consumers interpret NHTSA’s Child Restraint Ease of Use Ratings?
Consumers can use this program to help them evaluate the categories that are most important to them and select a child restraint that meets their needs.
For example, consumers who will be installing their child restraints in multiple vehicles on a regular basis may want a child restraint with the highest rating in the “Vehicle Installation Features” category. Conversely, consumers who are more familiar with child restraints or who rarely remove them from their vehicle may not find this category rating as important.
does the Ease of Use Ratings take into account LATCH (Lower Anchors and Tethers for CHildren)?
Yes. Under the Vehicle Installation Feature, the program evaluates child restraint Ease of Use both with belts and with the LATCH system.
 i want to know what child restraint fits into my car – can NHTSA’s program provide this information?
Because of the various combinations of child restraints and their fit in all vehicles, the costs, and the timeliness associated with providing that kind of “fit” information to consumers, NHTSA has decided against such a program at this time.
Because some vehicle manufacturers have done this for their respective vehicles, consumers can contact their vehicle manufacturer (not the dealer) to see if the manufacturer has this kind of information available.
Many retailers that sell child restraints will also allow consumers to install the child restraint in their vehicle before buying.
Finally, along with NHTSA’s Ease of Use ratings and other child passenger safety programs, consumers can seek installation assistance from one of the thousands of child seat inspection stations across the country.
does NHTSA rate the safety of child restraints?
Not presently, though all child restraints sold in this country are required to comply with the dynamic testing requirements of Federal Motor Vehicle Safety Standard (FMVSS) No. 213, Child Restraint Systems.
On May 23, 2005, NHTSA released a Notice of Final Decision on its pilot testing program (comprised of simulated crash situations as well as New Car Assessment Program (NCAP) tests) designed to determine how well child restraints and vehicles protect children.
Based on an analysis of the data, the agency determined that a rating program based on simulated crashes would not provide practicable, readily understandable, or meaningful information to consumers. 
Similar results were obtained when the restraints were subject to NCAP testing.
where can i find out additional information about the Ease of Use program?
For additional information regarding NHTSA’s Ease of Use program, please visit www.nhtsa.gov or call 1-888-327-4236.
TOYOTA GAZOO Racing earned its third win of the 2019-2020 FIA World Endurance Championship (WEC) season with a hard-fought one-two victory in the 8 Hours of Bahrain.
For this race, the World Champions faced a severe success handicap, which reduces hybrid and fuel use per lap, but overcame a strong challenge thanks to strong strategy, fast pit stops and consistent performance, as well as impressive reliability over the 1,390km race.
Mike Conway, Kamui Kobayashi and Jos? Mar?a L?pez, in the #7 TS050 HYBRID, won for the second time this season to retake the lead in the drivers' World Championship.
The #8 TS050 HYBRID of S?bastien Buemi, Kazuki Nakajima and Brendon Hartley, battling a success handicap 0.21secs per lap more than its sister car, finished a lap behind in second to complete a perfect result for the team; a third one-two from four races this season.
A dramatic start saw the #5 Ginetta hit the pole position #1 Rebellion, pushing both cars into a spin which caught up S?bastien in the #8, damaging his front left bodywork. While S?bastien, who had started third, dropped to 10th as a result, Mike avoided the debris from fourth on the grid and took the lead.
Following a safety car, S?bastien got the #8 up to third by the 10th lap, while Mike extended the #7 car's lead as the first fuel stops approached. Mike emerged with a lead of over 30 seconds from the #6 Ginetta but S?bastien, whose #8 required a front bodywork change, came out fourth.
Over the next half an hour, S?bastien pushed to catch the top three and his efforts paid off; after a close fight with the #6 Ginetta, he took third on lap 43. Soon after the 90-minute mark, Mike handed the leading #7 to Kamui while Brendon took over the #8, with both cars on new tyres.
With two-and-a-half hours gone, the race took a twist when the #1 Rebellion, which had been pushing hard to close the gap to Kamui in the #7, lost five minutes in the pits due to a technical problem. 
That lifted Brendon up to second in the #8 car, although more than a minute adrift of Kamui.
TOYOTA GAZOO Racing was in control of the race, with its nearest challenger, the #5 Ginetta, two laps behind following a troubled first part of its race.
With three-and-a-half hours remaining, that car's difficulties continued and it stopped at the side of the track, promoting the #1 Rebellion to third, three laps behind.
Long after darkness fell on the Bahrain International Circuit, and with that comfortable cushion, neither TS050 HYBRID took any risks over the remainder of the race, which finished with only three LMP1 cars running following the retirement of both Ginettas.
Jos? was at the wheel of the #7 when it took the chequered flag to win after 257 laps, establishing an eight-point lead in the drivers' World Championship ahead of the #8 crew, for whom Kazuki brought the car home in second. TOYOTA GAZOO Racing holds a 41-point advantage over Rebellion in the teams' standings.
The World Championship battle will resume in the new year, returning to Austin, Texas following a two-year absence for the 6 Hours of Circuit of the Americas on 23 February, the first of a double-header in the United States with the 1000 Miles of Sebring following on 20 March.
"I am very pleased with this result, particularly because we expected a big challenge to win here in Bahrain.
The team worked hard and focused on delivering the maximum performance in every area.
The first part of the race was exciting and we were expecting a close fight until the end, so it was a pity that our rivals had trouble.?
Now we have come to the end of our racing for 2019, a year when we became World Champions, won Le Mans for a second time and are leading the championship.
 I would like to thank the team for their efforts to achieve this, as well as the fans and the WEC organisation for their contribution to this year of endurance racing. Now we look forward to another memorable year in 2020."
"It is a fantastic result to get a one-two here.
We pushed as hard as we could all day and got a result which we weren't expecting at the start of the week.
That is thanks to a great job by everyone; my team-mates and my team. 
We struggled earlier in the week but together as a team we got the car dialled in.
It's brilliant to win and it's nice to lead the World Championship again."
"To finish the year with a win is great; thanks to the team for doing such a good job here.
We knew it would be a tough race as we had some strong competition but we managed it really well, made no mistakes and had fast pit stops.
Mike and Jos? did a really good job too.
There is still a long way to go in the season but I am really happy and we will be pushing to keep this form going."
"I am very happy with the win. Everyone in the team, including car #8, did a great job this weekend.
Thanks to the team we managed to finish one-two despite very difficult circumstances with the success handicap.
Mike and Kamui both performed really well as usual. It is nice to finish the year with a win.
This means we go into Christmas and New Year able to celebrate the results we achieved together this year."
"It was a tough race for our car. 
At the start, I couldn't avoid getting caught up in the incident between the Ginetta and the Rebellion, and I had contact when I rejoined; there was nothing I could do.
 From there it was difficult to challenge the sister car. But it's a one-two for the team which was the target so I am pleased for that and now I look forward to Austin."
"In the end it was just not our day today and that was clear from the start of the race onwards. 
We knew it was going to be difficult and at one moment we were quite a few places down, so at that point our target was to finish second. 
We managed it and a one-two for the team is good; not ideal for our car but for the team it was the best result we could achieve here."
"Today simply didn't go our way. 
S?b was unlucky at the start; he got caught up in an incident that had nothing to do with him so we had to battle with damage through the whole race. 
We knew it would be hard to beat the sister car but we were motivated to keep them under pressure. 
They drove a very good race, with no mistakes and clean pit stops. 
In the end a one-two for TOYOTA is a great result."
Toyota City, Japan, November 25, 2019?Toyota Motor Corporation (Toyota) announces the launch of its new model "Granace*2," which will go on sale at Toyota vehicle dealers nationwide on December 16. 
The new Granace comes in two grades: Premium grade, a three-row, six-seater priced at 6,500,000 yen*3 and G grade, a four-row, eight-seater priced at 6,200,000 yen*3.
Utilizing a semi-bonnet*4 package, the new Granace is characterized by its superb basic performance?including quietness and driving stability?and classy interior. 
An exterior style that exudes presence, a gorgeous cockpit, and a comfortable rear seating design further accentuate the car's individuality.
Full-size body with a total length of 5 meters or more.
 The model's name is taken from the word "gran," which means "big or great" in Spanish, and "ace," which means "top or excellent person" in English.
Manufacturer's suggested retail prices (including consumption tax).
Vehicle design in which the driver's seat is positioned at the rear end of the powertrain, resulting in a very short bonnet.
The new Granace's large radiator grille is embellished with metallic accents and flows seamlessly into the headlamps, which project in vertical and horizontal directions to create a powerful and bold frontal appearance. 
The distinctive LED daytime running lamps*5 pierce the headlamps and, together with the projective twin-lens LED headlamps that flow into the decorative chrome frame, express a sophistication well-suited to a luxury car.
Cladding panels*6 as well as moldings embellished with metallic accents flowing into the lower edge of the rear bumper suggest an extremely low center of gravity while contributing to a majestic side view.
The rear design complements the front design, and at the same time, the rear-combination taillight LED belts emphasize the car's advanced nature. 
The rear-combination taillights appear to point skywards in a distinctive design and merge with the rear door garnish, expressing an impressively grand style with its high position.
The 17-inch tires are paired with aluminum wheels featuring a sculpted texture and metallic accents for enhanced luxury. 
The radial spoke design makes the wheels appear larger, and highlights their ability to powerfully support the car.
Despite its total length of 5,300 millimeters and total width of 1,970 millimeters, when clad with 17-inch tires the new Granace boasts a minimum turning radius of just 5.6 meters. 
Optimal steering angles and gear ratios enable smooth tire movement to achieve maneuverability ideal for urban driving.
The new Granace comes in a total of four exterior colors, including the arresting and classy White Pearl Crystal Shine option, and a sophisticated and luxurious Black.
Daytime Running Lamp (DRL) A light that is placed on the front of the car and lit in the daytime.
Cladding panels are resin panels attached to the underside of the wheel arches and body.
The new Granace's spacious cabin measures 3,290 millimeters in length and 1,735 millimeters in width. 
Available both in three-row six-seater and four-row eight-seater variants, it caters to a wide range of user needs.
The four seats comprising the second and third rows of the three-row Premium grade feature executive power seating designed for complete relaxation.
In addition to comfortable seating, the car is equipped with a long-slide mechanism, power-reclining function, power ottoman, heated seats, a stowable table, and other amenities that enhance convenience and comfort.
The four-row G grade features executive power seats in the second row, lever-operated adjustable and relaxing captain seats in the third-row, and six-to-four ratio tip-up seats that lift up at the touch of a button in the fourth row. 
The G grade is therefore designed to cater to varying passenger numbers and luggage volumes in a flexible manner.
The low, wide, black-infused instrument panel provides a feeling of luxury, featuring metallic accents on the air-conditioner outlets and wood-grain embellishment in front of the front passenger seat.
The meter hood is wrapped in leather and genuine stitching, while the steering wheel combines genuine leather with wood-grain embellishments, contributing to a quality interior space.
The black-base interior coloring creates a feeling of compactness, while an attractive fromage is used for the ceiling. A black*7 ceiling is available as an option. 
When combined with the seat color, the interior color scheme underscores both the individuality and the elegance of the car.
The width of the slide-door opening is an ample 1,000 millimeters, giving consideration to ease of entry and exit for the rear seats.
Wood grain decorations flow from the back of the front seats toward the side trim as if to wrap the rear seat passengers in comfort. 
The LED side color illumination is gently lit, giving rise to a cool yet calm sophisticated luxury.
The Smart Entry & Push Start System, which features the Welcome Power Slide Door function, is fitted to all grades as standard. 
When the reservation lock function is activated*8, the sliding door unlocks and opens automatically if a user carrying the smart key approaches*9 the vehicle. 
The system also incorporates an active lock function: the Smart Door Lock registers that the slide door is closing, and automatically locks the door after it has closed fully.
Available as an option only on the Premium grade.
Reservation operation using a smart key. The reservation period is about 20 minutes.
The outdoor detection area is within a radius of about 0.7 to 1.5 meters from the sliding door handles on the left and right sides.
The new Granace is equipped with a 1GD 2.8-liter clean diesel engine and six-speed automatic transmission.
 The powertrain provides the smoothness, quietness, and low-speed torque demanded of a luxury vehicle, while realizing fuel efficiency of 10.0 kilometers per liter according to the WLTC test cycle*10.
The use of DPR*11 and a urea SCR*12 system achieves significant reductions in nitrogen oxide emissions, and enables the car to conform to the Post New Long-term Regulations exhaust gas standards.
The new Granace uses a rear-wheel drive layout. 
Based on fundamental principles, the underbody utilizes a straight-ladder structure; this enables the side members to pass straight through and preserves the torsional rigidity of the floor surface. 
Each pillar is also joined to the underbody in a ring-shaped frame, giving rise to a high-rigidity body.
The front of the car uses MacPherson strut-type independent suspension, while the rear employs a trailing-link rigid-axle suspension. 
Despite its high-rigidity body, optimization of the suspension geometry and stroke preservation mean that the car delivers superb ground feel, luxurious ride comfort, and outstanding driving stability.
Vibration-control and soundproofing materials have been effectively distributed throughout the vehicle, and include sandwich steel plates in the dash panels that separate the engine room and the cabin*13. 
These materials help realize a serene quietness suitable for luxury wagons in various road environments.
A wide and low instrument panel provides expansive forward visibility. 
Excellent side visibility is also achieved through adjustments such as slimmer front pillars, expanded triangular windows, and a low beltline.
According to test values from the Ministry of Land, Infrastructure, Transport and Tourism. WLTC (World Harmonized Light Vehicles Test Cycle) is an internationally recognized test cycle, and is based on average usage ratios for urban, suburban, and highway driving. 
DPR Diesel Particulate active Reduction System. 
SCR Selective Catalytic Reduction.
Compound steel plates with a layered structure and vibration-control materials sandwiched between the plates.
The new Granace is equipped with both Display Audio (DA) and DCM*14 as standard, providing all customers with access to safe and convenient connected services.
The SmartDeviceLinkTM-compatible TC Smartphone Navigation*15, as well as music and radio apps, can be displayed and operated via DA; LINE Car Navigation enables voice-activated destination-setting, the sending and receiving of LINE messages, and music playback.
Apple CarPlay*16 and Android AutoTM*17 enable everyday map and music apps to be used and operated via DA (available as an option set together with TV; a T-Connect contract is required when signing-up to the service).
Customers can also use conventional on-board navigation functions with the optional T-Connect navigation kit.
Comes standard with the latest version of Toyota Safety Sense, featuring improved sensing functions that make use of the pre-collision safety system that detects pedestrians during the day and at night, as well as cyclists during the day.
The new Granace is equipped with a full range of safety equipment for safety and peace of mind: Intelligent Clearance Sonar with Parking Support Brakes (Stationary Objects) helps mitigate damage from collisions while driving in parking lots and similar environments by detecting stationary objects; when reversing in parking lots, Rear Cross Traffic Auto Brake with Parking Support Brakes (rear approaching vehicle) detects vehicles approaching from left-rear and right-rear directions, and engages the brakes when it senses the possibility of a collision; and Digital Inner Mirror projects images from the rear-facing camera onto the in-mirror display at the flick of a switch.
 DCM Data Communication Module.
SmartDeviceLinkTM is a trademark or registered trademark of SmartDeviceLink Consortium.
By connecting smartphones via Bluetooth? (a trademark of Bluetooth SIG, Inc.), SmartDeviceLinkTM enables navigation apps such as TC Smartphone Navigation and LINE Car Navigation (a registered trademark of LINE Corporation)?which is provided in collaboration with LINE Corporation, and a variety of other apps to be used on Display Audio (some apps may require a USB connection).
Apple CarPlay is a trademark of Apple Inc., registered in the U.S. and other countries.
Android AutoTM is a trademark of Google LLC.
Toyota proudly presents the world premiere of the new "GR Yaris" at Tokyo Auto Salon 2020 to be held from January 10 to 12 in Makuhari Messe, near Tokyo Japan.
GR Yaris" is the second model launched globally from "GR" models, TOYOTA GAZOO Racing's sports car lineup, following last year's return of the legendary Toyota GR Supra*.
TOYOTA GAZOO Racing (TGR) has been developed people and cars by driving the world's roads, competing in a series of demanding races in a variety of categories. 
What's more, we reentered the FIA World Rally Championship (WRC) in 2017. 
TGR claimed five wins in the 2018 season and took the manufacturer's title and Driver's and Co-driver's title in the season of 2019.
TGR announces the arrival of an all-new sports car?a car that incorporates all the technologies, knowledge, and experience learned from WRC.
Toyota's president, under his TGR Master Driver code name "Morizo," has just performed the final test of the car, pushing it to its furthermost limits. 
We are delighted to share the film taken of the test.
In advance of the world premiere at Tokyo Auto Salon, the car will be camouflaged in the signature red-black-and-white TGR colors, and will make its first official dynamic appearance at the Japan TOYOTA GAZOO Racing Festival 2019 on December 15.
Further details of the car's specifications will be announced in due course.
The name given for marketing activity. The name reported to the Ministry of Land, Infrastructure, Transport and Tourism is "Supra."
LOS ANGELES (Nov. 20, 2019)?The fifth-generation Toyota RAV4, on the market for barely a year, is going more places than it has ever gone before.
The arrival of the first-ever RAV4 TRD Off-Road model was announced earlier this year, and today, at the Los Angeles Auto Show, Toyota debuts a new premium, fun-to-drive RAV4 performance model with an estimated 302-horsepower, advanced all-wheel drive, sport-tuned suspension and exclusive design features.
It just happens to also be the first-ever RAV4 plug-in hybrid electric vehicle (PHEV). 
And so, in addition to an ability to do 0-60 mph in a projected 5.8 seconds, which is the second quickest acceleration time in the Toyota lineup, it can drive an estimated 39 miles on battery alone on a single charge, which is the highest EV range of any PHEV SUV on the market. 
The RAV4 Prime also has a manufacturer-estimated 90 combined MPGe. Welcome to a new chapter of Toyota SUV performance.
The Toyota RAV4 Prime, a 2021 model that will arrive in summer 2020, breaks ground as the most powerful and quickest RAV4 ever while also being the most fuel-efficient.
The 2021 RAV4 Prime will be available in SE and XSE grades, both emphasizing athletic on-road performance and premium comfort and style. 
With its plug-in hybrid technology advancing, Toyota sees such vehicles as critical to an overall electrification strategy that will also include standard hybrids and battery electric vehicles (BEVs), along with fuel cell electric vehicles (FCEVs) like the second-generation Mirai unveiled in October.
The Toyota RAV4 Prime builds on the RAV4 Hybrid, which is currently the most powerful and most fuel-efficient model in the line with sales up 72% over last year and currently the best-selling hybrid vehicle on the market. 
The RAV4 Prime amplifies both performance and efficiency, reflecting Toyota's 20+ years of hybrid vehicle leadership.
With more powerful motor-generators, a newly developed high-capacity Lithium-Ion battery and a booster converter, the 2021 RAV4 Prime yields an 83-horsepower (hp) jump in total system output over the RAV4 Hybrid and has the most horsepower in its segment. 
The resulting boost in performance is striking: Toyota projects 0-60 mph acceleration in 5.8 seconds which is quicker than the RAV4 Hybrid (7.8 sec.) and in a league with luxury/performance SUVs that come nowhere near this Toyota's remarkable fuel economy. 
And, notably, the RAV4 Prime uses regular-grade gasoline?just not much of it.
A comparison with an older RAV4 offers a vivid illustration of the march of technology. The 2006-2012 RAV4 offered an optional 269-hp, 3.5-liter gas V6 engine that reached 0-60 mph in 6.3-seconds, which is a half-second slower than the RAV4 Prime. 
And, that model's 21 combined MPG fuel economy rating simply pales in comparison.
Toyota engineered the 2021 RAV4 Prime for the performance-oriented driver and is therefore offering it in the sporty SE and XSE grades. 
The SE grade is new for the fifth-generation RAV4 Prime, and the XSE is currently exclusive to the RAV4 Hybrid. 
The SE flaunts its sporty attitude with 18-in. painted and machined alloy wheels and an exclusive front grille design and front lower spoiler. 
Piano black exterior accents and a painted grille/diffuser complete the premium look.
The SE's equipment is quite comprehensive, with standard heated front seats, 8-way power driver's seat with lumbar adjustment, 7-in. 
Multi-Information Display, Blind Spot Monitor, a leather steering wheel and shift knob, power liftgate, and Audio with 8-in. touch-screen and Amazon Alexa, as well as Android Auto and Apple CarPlay compatibility. 
The available Weather & Moonroof Package lets in the sun, moon and stars while keeping the cold at bay with a heated steering wheel, heated rear outboard seats, and windshield wiper de-icer, while rain-sensing windshield wipers add to convenience.
As on the current RAV4 Hybrid XSE, the Prime version of this grade stands apart with a two-tone exterior paint scheme pairing a black roof with select colors, including the striking new-to-RAV4 Supersonic Red. 
Exclusive 19-in. alloy wheels, the largest ever offered on a hybrid RAV4, have a unique two-tone design. 
Vertical LED accent lights give the XSE a distinct look, while the optional Adaptive Front Lighting System (AFS) with headlamp auto-leveling partially aims the beams into turns as the driver steers.
Inside, XSE steps up the sport, luxury and tech with RAV4's first-ever paddle shifters, along with moonroof, unique SofTex synthetic leather seat surfaces, wireless smartphone charging, ambient lighting, auto-dimming rearview mirror with integrated garage door opener and the largest multimedia screen in any RAV4 ever. 
The standard Audio Plus system comes with a 9-in. touch-screen and there is an available Premium Audio that includes Dynamic Navigation and JBL speaker system.
An available Premium Package pushes the RAV4 Prime XSE further with Premium Audio with Dynamic Navigation and JBL speakers, perforated and ventilated SofTex front seats, RAV4's first head-up display, heated rear SofTex outboard seats, heated steering wheel, panoramic moonroof, digital rear-view mirror, memory driver's seat, 4-way power passenger seat, 5-door SmartKey system, kick-type power rear liftgate, Bird's Eye View Monitor and yes, more.
Toyota hybrids have for years demonstrated high performance with low fuel consumption, and now the 2021 RAV4 Prime makes one of the strongest cases yet. 
The RAV4 Prime uses a differently tuned version of the RAV4 Hybrid's 2.5-liter four-cylinder Atkinson-cycle gas engine.
 It produces the same 176 hp as in the hybrid, but with 168 lb.-ft. of peak torque at 2,800 rpm vs. the RAV4 Hybrid's 163 lb.-ft. at 3,600-5,200 rpm.
That makes a useful difference in lower-speed performance, while using more powerful electric motor-generators really juice the RAV4 Prime's responses. 
As in the RAV4 Hybrid, the gas engine and a motor generator work together to deliver dynamic performance, while both motor generators charge the battery.
The RAV4 Prime's interior space isn't compromised by the larger Lithium-Ion battery, as it is mounted under the floor. 
The mounting position also gives the RAV4 Prime a lower center of gravity and enhanced driving stability.
The RAV4 Prime's enhanced heat pump HVAC system, based on Prius Prime's and tailored to fit RAV4 Prime, contributes to an increasing EV range, as energy consumption for cabin temperature control can significantly decrease EV driving range.
The RAV4 Prime employs the same version of Electronic On-Demand All-Wheel Drive (AWD) as the RAV4 Hybrid. 
In both models, a separate rear-mounted electric motor powers the rear wheels when needed, including proactively on acceleration startup and also in reduced-traction conditions.
The AWD system also reduces understeer during cornering for enhanced steering stability. 
Off-pavement, AWD enhances hill-climbing performance. 
A driver-selectable Trail mode makes it possible to get unstuck by braking a spinning wheel and sending torque to the grounding wheel.
With available paddle shifters, the driver can "downshift" to increase the regenerative braking in steps, which fosters greater control when driving in hilly areas, for example.
As on the RAV4 Hybrid, the innovative Predictive Efficient Drive feature acts like an invisible "hyper-miler" co-driver. 
Using the available navigation system, Predictive Efficient Drive essentially reads the road and learns driver patterns to optimize hybrid battery charging and discharging operations based on driving conditions. 
The system accumulates data as the vehicle is driven and "remembers" road features such as hills and stoplights and adjusts the hybrid powertrain operation to maximize efficiency.
The XSE Premium Package adds Rear Cross Traffic Braking (RCTB) and Front and Rear Parking Assist with Automated Braking (PA w/AB).
Starting with the 2020 model year, every Toyota Hybrid Battery Warranty is being increased from 8 years or 100,000 miles to 10 years from original date of first use, or 150,000 miles, whichever comes first.
The TOYOTA GAZOO Racing Rally Challenge Program takes another significant step in 2020, with Japanese driver Takamoto Katsuta set to drive a Toyota Yaris WRC on eight FIA World Rally Championship events.
Katsuta began 2019 by contesting seven WRC rounds in with an R5-specification car?winning the WRC2 class on Rally Chile?alongside two victories from his two initial starts in the Yaris WRC in the Finnish championship. 
Given the strong progress shown, his programme for the second half of the season was revised accordingly, and he stepped up to the top category of the WRC with a Yaris WRC for Rallye Deutschland and Rally de Espa?a. 
This represented a significant achievement for the program, which set out to develop a Japanese WRC driver.
With Katsuta having met his objectives by gaining crucial experience and demonstrating improvement through both events, another milestone is set to follow. 
Next season he will tackle an expanded schedule at rallying's top level, starting all seven European rallies as well as his home event, Rally Japan. 
He will continue to be guided by experienced co-driver Dan Barritt.
"Takamoto has already gained some experience driving the Yaris WRC this year, and just this week he did the final day of our test to prepare for Rallye Monte Carlo, so he's becoming more knowledgeable about the car and gaining a better understanding of how a World Rally Car behaves, compared to what he's driven before. 
In Spain we had already seen him set some good stage times, especially on his second time through the stages.
 Now he just needs more experience, which will help him to improve his consistency.
 I'm very confident that he will show good things throughout his programme of events in 2020."
"I expect Taka and Dan to continue developing constantly during our 2020 programme. 
They already made big steps in 2019 with their speed in the Yaris in the two WRC events they did. 
Having said that, improving their stage times now gets harder and harder as the margins at the top level are so small.
 In our calendar for next season there are some familiar events where Taka can push more, and some events that will have to be taken purely for learning more about the Yaris and the conditions.
 We will focus our training on all the small details that can be made better to keep learning more and to reach the top level in this demanding sport."
"I must say thank you very much to TOYOTA GAZOO Racing and Tommi M?kinen Racing for giving me this amazing opportunity. 
2020 will be a new challenge for me. 
I have good experience of each event from this year, but to be able to compete at the top level in a World Rally Car is a big step up. 
Although my first events in the Yaris WRC this year were very positive, I know that I still need to improve my driving and my pace-notes to reach a higher level. 
I'm really motivated to do that and I'm looking forward to seeing how much progress I can make next year. 
I hope that I can show a good step between the beginning and the end of the season, and I'm really excited for it to start."
Born in Aichi, Japan on March 17, 1993. Katsuta drove in his first kart race at the age of 12 and at age 18 he won the Formula Challenge Japan (FCJ). 
He placed second in the F3 series at age 20, and since 2014 he has competed in F3 in addition to notching his first JN5 class win in the eighth round of the Japanese Rally Championship. 
In the seventh round of the 2017 FIA World Rally Championship (Italy), he took his first podium by finishing third in the WRC2 class.
 In 2018 he won Rally Sweden, the second round of World Rally Championship in the WRC2 class. 
In 2019 he won WRC2 on Rally Chile, and he claimed two wins in the Finnish Rally Championship before starting his first WRC events in a Toyota Yaris WRC in Germany and Spain.
Toyota proudly presents the world premiere of the new "GR Yaris" at Tokyo Auto Salon 2020 to be held from January 10 to 12 in Makuhari Messe, near Tokyo Japan.
"GR Yaris" is the second model launched globally from "GR" models, TOYOTA GAZOO Racing's sports car lineup, following last year's return of the legendary Toyota GR Supra*.
TOYOTA GAZOO Racing (TGR) has been developed people and cars by driving the world's roads, competing in a series of demanding races in a variety of categories. 
What's more, we reentered the FIA World Rally Championship (WRC) in 2017. 
TGR claimed five wins in the 2018 season and took the manufacturer's title and Driver's and Co-driver's title in the season of 2019.
TGR announces the arrival of an all-new sports car?a car that incorporates all the technologies, knowledge, and experience learned from WRC.
Toyota's president, under his TGR Master Driver code name "Morizo," has just performed the final test of the car, pushing it to its furthermost limits. 
We are delighted to share the film taken of the test.
In advance of the world premiere at Tokyo Auto Salon, the car will be camouflaged in the signature red-black-and-white TGR colors, and will make its first official dynamic appearance at the Japan TOYOTA GAZOO Racing Festival 2019 on December 15.
Further details of the car's specifications will be announced in due course.
The name given for marketing activity. 
The name reported to the Ministry of Land, Infrastructure, Transport and Tourism is "Supra."
TOYOTA GAZOO Racing returns to Bahrain to bring the curtain down on a highly-successful year with the fourth round of the 2019-2020 FIA World Endurance Championship (WEC).
The team returns for the 8 Hours of Bahrain as World Champions and in pole position to retain both drivers' and teams' titles, with two wins to their credit so far and five races remaining.
TOYOTA GAZOO Racing leads its standings by 27 points while the #8 TS050 HYBRID crew of S?bastien Buemi, Kazuki Nakajima and Brendon Hartley top the drivers' championship by just three points from team-mates Mike Conway, Kamui Kobayashi and Jos? Mar?a L?pez in the #7 car.
However, victory for Rebellion Racing last time out in Shanghai cut that advantage and illustrated the unprecedented challenge to the efficiency of the TS050 HYBRID in its farewell season. 
The #8 car finished second while the #7 was third in China.
Compared to its last visit to Bahrain in 2017, this weekend the TS050 HYBRIDs are permitted less hybrid power and less fuel per lap, whilst also weighing more, as part of WEC's success handicap system. 
As championship leader, the #8 car carries maximum success handicap of 2.72secs per lap, with the #7 having 2.51secs per lap.
TOYOTA GAZOO Racing has positive memories of the 5.412km, 15-turn Bahrain International Circuit, having won three times there in WEC. 
The team's last visit to the Gulf State came at the end of the 2017 season, with S?bastien, Kazuki and Anthony Davidson in the #8 TS050 HYBRID winning their fifth race of the year.
For just the second time in WEC history after last season's Sebring race, Saturday's race will be contested over eight hours. 
It will begin at 3pm local time and run into darkness, with the chequered flag scheduled to fly at 11pm.
Air temperatures around 25°C can be expected during the day, but when the sun sets track temperatures are likely to drop. 
Therefore, the week's schedule is designed to give teams chance to experience all conditions, with Thursday's three hours of practice split over a day and a night session.
At the conclusion of Saturday's race, the team has just 11 hours before it will be in action again, with the WEC rookie test beginning at 10am on Sunday. 
TOYOTA GAZOO Racing will give Thomas Laurent, Kenta Yamashita and Nyck de Vries a chance to drive the TS050 HYBRIDs.
That is not the end of the action in the Gulf state though. 
Sunday evening will see the official WEC prize-giving ceremony for the 2018-2019 season, which ended in June at Le Mans. 
TOYOTA GAZOO Racing will collect its teams' World Championship trophy, while S?bastien, Kazuki and Fernando Alonso will be presented with their trophies.
"We are ready for Bahrain and looking forward to another exciting fight. 
Realistically we achieved the best result available in Shanghai but it is still painful to lose, so we are very motivated to return to the top step of the podium in Bahrain. 
To challenge for the win, we will have to work strongly together as a team to demonstrate the incredible efficiency of the TS050 HYBRID. 
Although we are only at the mid-point of our season, this is the final race of the year so I know everyone is pushing hard to earn an early Christmas present in Bahrain."
"I really like the Bahrain circuit; it has a nice flow to it and I have some great memories there. 
I won my first WEC race in Bahrain in 2014 and I've often been on the podium there.
 It's an eight-hour race and I enjoy the longer ones because more can happen; there's more action, more driving and more fun. 
I think a longer race will be good for us."
"I enjoy racing in Bahrain and Saturday's race should be an interesting one because it is longer than usual and will also go into darkness. 
We've got to do our job and get the maximum from our car for the full eight hours and then let's see where we are; I hope we can achieve a strong result. 
The team won two years ago in Bahrain so that will be our target again."
"I hope Bahrain is better for us than Shanghai; I expect it will be because the track is more suited to the efficiency of our TS050 HYBRID. I am sure we can be more competitive there so I am looking forward to it. 
Rebellion performed well in Shanghai. 
We knew the challenge would come from the non-hybrid LMP1 cars and we're ready for the fight in Bahrain."
"I like racing in Bahrain because I enjoy the track and the atmosphere, plus some of my family are living there too.
 I also have very good memories because I won my first WEC race in Bahrain in 2013 and became World Champion a year later. 
It's also a cool race to participate in because we start in the day and finish in the night. I'm looking forward to it."
"I am confident we will be more competitive in Bahrain, although again it will not be an easy race because the opposition will be quick. 
My last memory of Bahrain was winning in 2017 in the final race against Porsche. 
This time the race will be longer but hopefully we can achieve the same result on the TS050 HYBRID's last visit to Bahrain."
"We head to Bahrain leading the drivers' championship. 
Although this also means we will carry the biggest success handicap I still feel optimistic that it will be a close race and we will have a chance to take the victory.
 I'm feeling more and more comfortable in the team and am taking a lot of pleasure every time I drive the TS050 HYBRID in its last season."
Toyota City, Japan, November 25, 2019?Toyota Motor Corporation (Toyota) announces the launch of its new model "Granace*2," which will go on sale at Toyota vehicle dealers nationwide on December 16. 
The new Granace comes in two grades: Premium grade, a three-row, six-seater priced at 6,500,000 yen*3 and G grade, a four-row, eight-seater priced at 6,200,000 yen*3.
Utilizing a semi-bonnet*4 package, the new Granace is characterized by its superb basic performance?including quietness and driving stability?and classy interior. 
An exterior style that exudes presence, a gorgeous cockpit, and a comfortable rear seating design further accentuate the car's individuality.
Full-size body with a total length of 5 meters or more.
The model's name is taken from the word "gran," which means "big or great" in Spanish, and "ace," which means "top or excellent person" in English.
Manufacturer's suggested retail prices (including consumption tax).
Vehicle design in which the driver's seat is positioned at the rear end of the powertrain, resulting in a very short bonnet.
The new Granace's large radiator grille is embellished with metallic accents and flows seamlessly into the headlamps, which project in vertical and horizontal directions to create a powerful and bold frontal appearance. 
The distinctive LED daytime running lamps*5 pierce the headlamps and, together with the projective twin-lens LED headlamps that flow into the decorative chrome frame, express a sophistication well-suited to a luxury car.
Cladding panels*6 as well as moldings embellished with metallic accents flowing into the lower edge of the rear bumper suggest an extremely low center of gravity while contributing to a majestic side view.
The rear design complements the front design, and at the same time, the rear-combination taillight LED belts emphasize the car's advanced nature. 
The rear-combination taillights appear to point skywards in a distinctive design and merge with the rear door garnish, expressing an impressively grand style with its high position.
The 17-inch tires are paired with aluminum wheels featuring a sculpted texture and metallic accents for enhanced luxury. 
The radial spoke design makes the wheels appear larger, and highlights their ability to powerfully support the car.
Despite its total length of 5,300 millimeters and total width of 1,970 millimeters, when clad with 17-inch tires the new Granace boasts a minimum turning radius of just 5.6 meters. 
Optimal steering angles and gear ratios enable smooth tire movement to achieve maneuverability ideal for urban driving.
The new Granace comes in a total of four exterior colors, including the arresting and classy White Pearl Crystal Shine option, and a sophisticated and luxurious Black.
Daytime Running Lamp (DRL) A light that is placed on the front of the car and lit in the daytime.
Cladding panels are resin panels attached to the underside of the wheel arches and body.
The new Granace's spacious cabin measures 3,290 millimeters in length and 1,735 millimeters in width. 
Available both in three-row six-seater and four-row eight-seater variants, it caters to a wide range of user needs.
The four seats comprising the second and third rows of the three-row Premium grade feature executive power seating designed for complete relaxation.
 In addition to comfortable seating, the car is equipped with a long-slide mechanism, power-reclining function, power ottoman, heated seats, a stowable table, and other amenities that enhance convenience and comfort.
The four-row G grade features executive power seats in the second row, lever-operated adjustable and relaxing captain seats in the third-row, and six-to-four ratio tip-up seats that lift up at the touch of a button in the fourth row. 
The G grade is therefore designed to cater to varying passenger numbers and luggage volumes in a flexible manner.
The low, wide, black-infused instrument panel provides a feeling of luxury, featuring metallic accents on the air-conditioner outlets and wood-grain embellishment in front of the front passenger seat.
The meter hood is wrapped in leather and genuine stitching, while the steering wheel combines genuine leather with wood-grain embellishments, contributing to a quality interior space.
The black-base interior coloring creates a feeling of compactness, while an attractive fromage is used for the ceiling. 
A black*7 ceiling is available as an option.
 When combined with the seat color, the interior color scheme underscores both the individuality and the elegance of the car.
The width of the slide-door opening is an ample 1,000 millimeters, giving consideration to ease of entry and exit for the rear seats.
Wood grain decorations flow from the back of the front seats toward the side trim as if to wrap the rear seat passengers in comfort. 
The LED side color illumination is gently lit, giving rise to a cool yet calm sophisticated luxury.
The Smart Entry & Push Start System, which features the Welcome Power Slide Door function, is fitted to all grades as standard. 
When the reservation lock function is activated*8, the sliding door unlocks and opens automatically if a user carrying the smart key approaches*9 the vehicle. 
The system also incorporates an active lock function: the Smart Door Lock registers that the slide door is closing, and automatically locks the door after it has closed fully.
Available as an option only on the Premium grade.
Reservation operation using a smart key. 
The reservation period is about 20 minutes.
The outdoor detection area is within a radius of about 0.7 to 1.5 meters from the sliding door handles on the left and right sides.
The new Granace is equipped with a 1GD 2.8-liter clean diesel engine and six-speed automatic transmission. 
The powertrain provides the smoothness, quietness, and low-speed torque demanded of a luxury vehicle, while realizing fuel efficiency of 10.0 kilometers per liter according to the WLTC test cycle*10.
The use of DPR*11 and a urea SCR*12 system achieves significant reductions in nitrogen oxide emissions, and enables the car to conform to the Post New Long-term Regulations exhaust gas standards.
The new Granace uses a rear-wheel drive layout. 
Based on fundamental principles, the underbody utilizes a straight-ladder structure; this enables the side members to pass straight through and preserves the torsional rigidity of the floor surface. 
Each pillar is also joined to the underbody in a ring-shaped frame, giving rise to a high-rigidity body.
The front of the car uses MacPherson strut-type independent suspension, while the rear employs a trailing-link rigid-axle suspension. 
Despite its high-rigidity body, optimization of the suspension geometry and stroke preservation mean that the car delivers superb ground feel, luxurious ride comfort, and outstanding driving stability.
Vibration-control and soundproofing materials have been effectively distributed throughout the vehicle, and include sandwich steel plates in the dash panels that separate the engine room and the cabin*13. 
These materials help realize a serene quietness suitable for luxury wagons in various road environments.
A wide and low instrument panel provides expansive forward visibility. 
Excellent side visibility is also achieved through adjustments such as slimmer front pillars, expanded triangular windows, and a low beltline.
According to test values from the Ministry of Land, Infrastructure, Transport and Tourism. WLTC (World Harmonized Light Vehicles Test Cycle) is an internationally recognized test cycle, and is based on average usage ratios for urban, suburban, and highway driving.
DPR Diesel Particulate active Reduction System.
SCR Selective Catalytic Reduction.
Compound steel plates with a layered structure and vibration-control materials sandwiched between the plates.
The new Granace is equipped with both Display Audio (DA) and DCM*14 as standard, providing all customers with access to safe and convenient connected services.
The SmartDeviceLinkTM-compatible TC Smartphone Navigation*15, as well as music and radio apps, can be displayed and operated via DA; LINE Car Navigation enables voice-activated destination-setting, the sending and receiving of LINE messages, and music playback.
Apple CarPlay*16 and Android AutoTM*17 enable everyday map and music apps to be used and operated via DA (available as an option set together with TV; a T-Connect contract is required when signing-up to the service).
Customers can also use conventional on-board navigation functions with the optional T-Connect navigation kit.
Comes standard with the latest version of Toyota Safety Sense, featuring improved sensing functions that make use of the pre-collision safety system that detects pedestrians during the day and at night, as well as cyclists during the day.
The new Granace is equipped with a full range of safety equipment for safety and peace of mind: Intelligent Clearance Sonar with Parking Support Brakes (Stationary Objects) helps mitigate damage from collisions while driving in parking lots and similar environments by detecting stationary objects; when reversing in parking lots, Rear Cross Traffic Auto Brake with Parking Support Brakes (rear approaching vehicle) detects vehicles approaching from left-rear and right-rear directions, and engages the brakes when it senses the possibility of a collision; and Digital Inner Mirror projects images from the rear-facing camera onto the in-mirror display at the flick of a switch.
DCM Data Communication Module.
SmartDeviceLinkTM is a trademark or registered trademark of SmartDeviceLink Consortium.
By connecting smartphones via Bluetooth? (a trademark of Bluetooth SIG, Inc.), SmartDeviceLinkTM enables navigation apps such as TC Smartphone Navigation and LINE Car Navigation (a registered trademark of LINE Corporation)?which is provided in collaboration with LINE Corporation, and a variety of other apps to be used on Display Audio (some apps may require a USB connection).
Apple CarPlay is a trademark of Apple Inc., registered in the U.S. and other countries.
Android AutoTM is a trademark of Google LLC.
When we say "Connected", we mean "People connected"?a society in which people are linked, and a society in which the warmth and kindness of people can be felt.
President Akio Toyoda's remarks from the Tokyo Motor Show 2019 press conference held October 23 at 13:30 can be viewed in the video below.
Hello, everyone!
Thank you for coming to the Toyota booth press briefing today.
I'm Akio Toyoda, the VTuber Morizo.
Well, today, what I want to talk about is not cars, but people.
I will move around a bit as I talk.
This time, I think I want to continue on as this character.
In line with my own expressions, it laughs and shows surprise. 
What do you think?
 For example, even if we are a distance away, can't you kind of feel that I'm right beside you?
People instantly process enormous amounts of information in this way and then reach conclusions.
That's exactly why, when various kinds of information are linked, one would expect communities, society, and, of course, cars, to become more centered on people.
That's why we made our Toyota booth this time one in which visitors can experience a future society of mobility centered on people.
Well, I think it's about time for us to have a look.
This e-Palette is scheduled to debut in front of everyone at the time of next year's Olympic and Paralympic Games Tokyo 2020.
In the future, the e-Palette will be able to be an office, a shop, or even a hotel.
 It will be able to become various kinds of services, and it will go to people.
Our booth this time does not feature a single car "to be launched next year". 
All that is found here are forms of mobility that link to society and communities and that provide modes of getting around and services to people.
Wow, what do you think? 
Doesn't it excite you to see a future filled with such forms of mobility? 
For example, we will even have something like this?the e-Chargeair.
This is the e-RACER!
The cars in everyone's garages will all be sports cars, like this e-RACER.
Well, that's a little overstating it, but... wanting to move about as one wishes, ...and wanting to go faster and farther...are, I think, universal human desires.
The birth of the automobile led to 15 million horses being replaced by cars in the United States.
But still, we have racehorses.
The joy of riding a horse can hold its own against or even outdo what cars have to offer. 
If there is an obstacle, a horse can avoid it. If there is a hole in the ground, a horse can make its own judgement and jump over it.
Horses can communicate with people and their hearts. 
For people who ride them, horses are irreplaceable.
Through the evolution of artificial intelligence, I think that cars, too, can also become able to communicate with people and their hearts.
That's right... I think cars of the future will be like horses.
If we look at shared forms of mobility, such as the e-Palette, as if they were horse carriages, forms of mobility owned by individuals, like the e-RACER, would be "beloved horses".
I would say that this means that our future society of mobility will be a society in which horse carriages and "beloved horses" co-exist.
And what people want of "beloved horses" is heartfelt communication and the joy of moving together.
Well, here is another sidekick that offers heartfelt communication.
 I think maybe I could even call it a "magic broom".
If you visit the Toyota booth, you can hop on like this and give it a try. 
But it's not yet ready to fly…
I'll get on the broom and give you a quick introduction to our booth.
First, at the reception counter, you make a residency card.
 You take it and, if you try out various things in our booth, you can accumulate points.
For example, this is a future health check that can tell you how your body is doing if you get on it.
And here, you can experience what it will be like to change clothes in the future. 
You can choose clothes that suit you even without putting them on.
There are other various things that you can experience. 
The more you try what we have, the more points you will accumulate.
And this is a Toyota convenience store. 
The points you've accumulated can be exchanged for various items, and we tried hard to make some good things.
Thank you, Morizo!
Our theme is life in the future centered on people. 
Our first objective is to deliver fun to those who visit us. 
We feel that it is important to have fun. 
That same feeling extends to the importance we place on the concept of "FUN TO DRIVE", even when it comes to future mobility.
My aim today was to help you get to know the Toyota booth. 
We have prepared our new cars for you to see at another place. 
Please go have a look.
At any rate, today, the focus is people.
 Society today is rapidly advancing toward automation, such as in the form of "artificial intelligence" and "robotics".
At the same time, concerns such as "Might the day not come when robots dominate people?" are being voiced.
Just how should Toyota respond to such changes in the times?
I think a hint can be found in the history of our predecessors.
The Toyota Production System contains a hint.
Toyota's roots are found in the automatic loom invented by Sakichi Toyoda.
The greatest trait of that invention was that, if a single thread broke, the loom would automatically stop.
Of course, that helped to prevent the making of defective products. 
But it was based on the thinking that we shouldn't turn people into machine watchers.
At Toyota, we call this "automation with people", or "intelligent automation".
To this "intelligent automation", Kiichiro Toyoda, who took up the challenge of producing automobiles, added the "make only what is needed, when it is needed, and in the amount needed" thinking of "just-in-time".
This means being just a bit ahead of the expectations of our customers.
This is the ultimate in manufacturing omotenashi (hospitality that sincerely and warmheartedly anticipates and fulfills people's needs).
The two pillars of the Toyota Production System?"Intelligent automation" and "Just-in-time".
What both of these have in common is placing of people at the center.
And that's exactly why people will continue to be at the center of the future that we envision.
I believe that the more automation advances, the more the ability of human beings will be put to the test.
For example, people's warmth and kindness...and also the hearts that feel such...
What we want to express through our booth is the concept of "people connected". "
People connected" refers to a society in which people are linked?a society in which the warmth and kindness of people can be felt.
The key words are "people connected".
Toyota believes in the power of people. 
The power of people…
Please look forward to what Toyota will achieve.
Toyota City, Japan, February 26, 2018?Toyota Motor Corporation (Toyota) announces that it has developed a new continuously variable transmission (CVT), 6-speed manual transmission, 2.0-liter engine, 2.0-liter hybrid system, and 4WD systems based on the Toyota New Global Architecture (TNGA), a development framework aimed at making ever-better cars. 
The new technologies offer both superb driving performance and high environmental performance.
The new continuously variable transmission features a launch gear, a world first, to significantly improve transmission efficiency at low speeds when compared to existing CVTs.
 It realizes both direct and smooth driving response to accelerator application, as well as superior fuel efficiency.
The basic function of any transmission system is to achieve transmission efficiency, high-efficiency engine ranges, and highly responsive gear changes. 
To improve these functions, Toyota has striven to reduce mechanical loss, adopt a wider gear range, and improve shift tracking. 
These initiatives have resulted in a direct and smooth driving experience with superior fuel efficiency, which has been improved by six percent over the existing transmission system.
The new powertrain unit features the world's first launch gear in a passenger vehicle CVT1, which facilitates improved transmission efficiency in lower gear ratios where belt efficiency is poor. 
The transmission system utilizes gear drive when starting from a full stop, resulting in powerful acceleration while at the same time resolving the momentary sluggish feeling that was previously present during accelerator operation. 
Both smooth and comfortable launch performance are realized. 
When switching from gear drive to belt drive, the transmission system uses highly responsive gear change control technologies cultivated from AT technology.
In line with the adoption of a launch gear, belt use is now specified for higher gear ratios. 
This new setup not only improves the efficiency of belt use, but also enables the adoption of wider gear ranges, thereby realizing a class-leading gear ratio range of 7.5 for the 2.0-liter class1.
The adoption of launch gears results in reduced input load. 
This enables the size of both belt and pulley components to be reduced. 
The belt angle has been narrowed and pulley diameters reduced, resulting in shifting speeds that are 20 percent faster. 
Both powerful and rhythmic acceleration are realized.
Toyota has also developed a new manual transmission in response to global needs, particularly those in Europe. 
Compared to the existing version, the mass of the new system has been reduced by seven kilograms and total length by 24 millimeters. 
This makes it one of the world's smallest transmissions1, and its small size contributes to improved fuel efficiency. 
The 6MT also offers world-leading transmission efficiency1, while the use of iMT (Intelligent Manual Transmission) controls, which automatically adjust engine rotations when changing gears, ensures smooth gear shifting?free of uncomfortable recoils?for the driver.
Toyota's new Dynamic Force Engine adopts high-speed combustion technologies and a variable control system. 
It also achieves greater thermal efficiency, resulting in high output, due to a reduction in energy loss associated with exhaust and cooling systems, the movement of mechanical parts, and other aspects. 
As a result, the newly developed 2.0-liter gasoline vehicle and hybrid vehicle engines achieve world-leading thermal efficiencies of 40 percent and 41 percent respectively1.
 In addition, compared to existing engines, the new engines achieve increased torque at all engine speeds?from low to high rotations?and will comply with expected future exhaust regulations in each country in advance.
Toyota has developed a new hybrid system for 2.0-liter engines, which applies the same size-reducing, weight-reducing, and loss-reducing technologies used in the fourth-generation Prius. 
The new system realizes improved driving performance while retaining superior fuel efficiency. 
When accelerating, the hybrid system reduces engine rotations while drawing increased electric power from the battery, thereby delivering a linear and lengthened sense of acceleration.
Toyota has developed two new 4WD systems with the aim of improving fuel efficiency and achieving high 4WD handling, stability, and off-road performance.
The new Dynamic Torque Vectoring AWD system is used in gasoline engine vehicles. 
By adopting a torque vectoring mechanism, which independently distributes torque to the left and right rear wheels according to driving conditions, the Dynamic Torque Vectoring AWD system enables the driver to steer the vehicle exactly as intended.
 It achieves high off-road performance even on the toughest roads. 
It also incorporates a disconnect mechanism, which features the world's first ratchet-type dog clutches1 on both the front and rear wheel shafts. 
These clutches stop the drive system rotations, which transmit driving force to rear wheels when in 2WD mode, significantly reducing energy loss and improving fuel efficiency.
The new E-Four system will be used in hybrid vehicles.
 The system increases total torque to the rear wheels?which are electrically driven?by 30 percent compared to existing versions. 
By adopting a new control system that optimally distributes torque to the rear wheels based on the driving conditions, the E-Four system offers high off-road performance, handling, and stability.
Moreover, both the Dynamic Torque Vectoring AWD system and the new E-Four system feature AWD Integrated Management (AIM), which harmonizes engine, transmission, braking, and 4WD systems to offer superb handling and stability regardless of road surface conditions.
Toyota intends to expand the number of models equipped with the newly announced powertrain units globally from this spring onward.
The powertrain units will not only contribute to improved environmental and driving performance of conventional gasoline engine vehicles, but the core technologies will be reflected in the performance improvement of electrified vehicles, including hybrid electric vehicles (HEVs), plug-in hybrid electric vehicles (PHEVs), battery electric vehicles (BEVs), and fuel cell electric vehicles (FCEVs). 
These technologies play a part in Toyota's pursuit of the popularization of electrified vehicles.
Regarding TNGA-based powertrains, Toyota has already announced plans to introduce 17 versions of nine engines, 10 versions of four transmissions, and 10 versions of six hybrid systems by the end of 2021. 
The new continuously variable transmission, 6-speed manual transmission, 2.0-liter engine, and 2.0-liter hybrid system represent four of the planned components.
Within the next five-years to the end of 2023, Toyota aims to have TNGA-based powertrain units installed in approximately 80 percent of Toyota-brand and Lexus-brand vehicles sold annually in Japan, the United States, Europe, and China. 
Toyota forecasts that the TNGA-based powertrain units alone will improve fuel efficiency enough to reduce CO2 emissions from Toyota vehicles by more than 18 percent.
Toyota City, Japan, October 11, 2019?Toyota Motor Corporation (Toyota) today announced the "LQ", a concept vehicle that leverages advanced technology to build an emotional bond between car and driver. 
The next generation of the Toyota "Concept-i", a concept vehicle first exhibited at the 2017 Consumer Electronics Show, LQ is equipped with automated driving capabilities and "Yui," a powerful artificial intelligence-powered interactive agent designed to learn from the driver and deliver a personalized mobility experience.
"In the past, our love for cars was built on their ability to take us to distant places and enable our adventures," said LQ development leader Daisuke Ido. 
"Advanced technology gives us the power to match customer lifestyles with new opportunities for excitement and engagement. 
With the LQ, we are proud to propose a vehicle that can deliver a personalized experience, meet each driver's unique mobility needs, and build an even stronger bond between car and driver."
As a mobility company, Toyota believes that when people are free to move, anything is possible. 
This vision is built on an understanding that mobility goes beyond physical transportation to include the human need to be moved and engaged emotionally.
LQ follows this philosophy under a core development theme of "Learn, Grow, Love." Yui and LQ's automated driving technology, both developed in partnership with Toyota Research Institute (TRI), combine to create a unique mobility experience that builds the relationship between vehicle and driver by learning from and responding to individual preferences and needs. 
The name expresses Toyota's hope that this approach will "cue" the development of future vehicles that enhance the relationship between car and driver.
LQ will be on public display at the "Future Expo", a special exhibition of the 2019 Tokyo Motor Show1 from October 24 to November 4. 
In addition, Toyota today announced "Toyota Yui Project Tours 2020", a public test-drive event scheduled to run from June to September 2020. 
The public will have the opportunity to register for a chance to be selected to experience the LQ and the "Yui" AI. 
By using a smartphone app in advance to provide their interests and preferences, selected participants will join a test drive of the "LQ" with "Yui".
LQ features an on-board artificial intelligence agent named "Yui" that provides a personalized mobility experience based on the driver's emotional state and alertness. 
In order to ensure safety and comfort, the AI can engage with the driver using interactive voice communications, in-seat functions designed to increase alertness or reduce stress, in-vehicle illumination, air conditioning, fragrances and other human-machine interactions (HMI). 
Yui can also select and play music based on the driving environment and provide real-time information on topics of interest to the driver.
Going forward, Toyota will continue to work on further expanding Yui's implementation through integration with other products such as smartphones.
The LQ is equipped with an SAE2 Level 4 equivalent automated driving function.
The system eliminates the need to search for parking spaces by automatically driving between a drop-off spot and an assigned parking space in nearby parking lot, improving accessibility for those with mobility limitations including seniors, people with physical disabilities, pregnant women, customers with infants, and anyone who has difficulty parking. 
The system also maximizes space in the parking lot by reducing clearance between adjacent vehicles to 20 centimeters.
Automated parking uses an on-vehicle system that identifies the current position of the vehicle using multiple cameras, sonar and radar, 2D road mapping, cameras installed in the parking lot and a control center.
 Vehicle sensors and parking lot cameras also monitor for other vehicles and pedestrians on the automated driving route, automatically stopping the vehicle when another vehicle or a pedestrian is detected.
LQ's Augmented Reality Head's Up Display (AR-HUD) uses Augmented Reality (AR) to expand the information display area of the Head's Up Display (HUD), supporting safe driving by reducing driver eye movement.
Driving information such as lane warnings, road signs, and route guidance can be displayed in a three-dimensional and easy-to-understand manner over the scenery seen through the windshield.
 The system helps keep the driver's eyes on the road thanks to a large screen display (equivalent to 230 inches) that has a depth of 7 m to 41 m ahead of the vehicle.
LQ's advanced seating system consists of multiple inflatable air bladders embedded into the seat with an in-seat air conditioning system to help keep the driver awake or relaxed depending on the driving situation.
When the system recognizes that the driver is tired, it inflates the air bladder in the seat back to support an upright sitting posture and directs cool air from the ventilation system located in the seat.
When conditions allow the driver to relax, such as in automated driving mode, the air bladder in the seat back gradually inflates and contracts to encourage abdominal breathing.
LQ uses the roof and floor mat areas as an intuitive communications platform to share information between the vehicle and passengers. 
Embedded lighting displays different colors to indicate automated or manual driving mode, and lights up different foot wells to indicate which passenger Yui is addressing.
LQ can also communicate information such as road surface conditions to people inside and outside of the vehicle using the Digital Micromirror Device (DMD) installed in its headlights. 
The system can activate one million tiny embedded mirrors to project complex figures on the road ahead.
A first for Toyota, LQ's dashboard and meters are displayed using organic LEDs (OLEDs). 
The advanced instrument panel design wraps around the driver while ensuring high visibility.
LQ features a newly developed catalyst coating that decomposes ozone into oxygen on the radiator fan, allowing ozone near the ground surface, a cause of photochemical smog, to be decomposed as the vehicle moves. 
Toyota has measured the effect of the coating as purifying about 60 percent of ozone contained in 1,000 liters of air over the course of an hour drive.
Toyota expects this technology to help clean harmful emissions like ozone from the air during drives and is considering the coating for use in commercial vehicles in the future.
The LQ cabin is designed with a futuristic, forward-projecting silhouette that puts Yui at the center of the instrument panel, with lines that flow from the inside of the vehicle out across its exterior.
The minimalist interior is smooth and sleek, with key elements like air conditioner vents hidden behind invisible registers. 
The 3D-printed center console is reinforced using the design technique of topology optimization, which provides optimal strength and supports an advanced vehicle interior with fewer support structures visible to the driver. 
The exterior doors feature glass that seamlessly connects with the interior of the vehicle, creating an integrated, elegant design.
Lexus to distribute a new documentary on Amazon Prime Video that poses the question of whether the most devoted craftsmen and women will survive in an increasingly 'AI powered' world?
In the West it's often considered that it takes 10,000 hours of study for the average person to become an expert in their subject. 
But in Japan you're not considered a master of your craft until you've spent 60,000 hours refining your skills. 
That's the equivalent of working 8 hours a day, 250-days a year for 30 years.
A fascinating documentary unveils the world of the Takumi?the highest level of artisan in Japan. 
The visually-stunning character-driven portrait, made by Chef's Table Director, Clay Jeter for luxury automotive brand Lexus, is due for release on Prime Video, through the Prime Video Direct self-publishing service, on 19th March 2019 globally.
Takumi - A 60,000-hour story on the survival of human craft?follows four Japanese artisans who are dedicating their lives to their crafts, including a double Michelin starred chef, a traditional paper cutting artist, an automotive master craftsman and a carpenter for one of the oldest construction companies in the world.
The documentary, which premiered at the DOC NYC film festival in New York, is unique in that the medium is also the message. 
There will be a feature length version plus a '60,000' hour cut which loops scenes of each Takumi's essential skills of their craft they repeat over and over again to highlight the hours, days and years of practice involved.
Narrated by Former British Museum Director Neil Macgregor and including interviews from world experts in craft and AI, it asks how we will honor and preserve human craft as simultaneously we design machines to act more precisely and faster than humans ever can.
"In the time period we live in, which is so attention-deficit, we all feel like we don't have enough time." says Nora Atkinson Curator of Craft at the Smithsonian American Art Museum.
 "So, the thousands of hours it really takes to become a skilled craftsman is something that a smaller sphere of artists will experience."
By 2050, it's estimated that machines will be capable of surpassing human performance in virtually every field "We're in the midst of exponential progress," says Martin Ford Rise of The Robots: Technology and The Threat of a Jobless Future author. 
He adds that this rate of transformation hasn't been seen before. "In the next 10 years, we're going to see 10,000 years of progress."
Will human craft disappear as artificial intelligence reaches beyond our limits? 
Or will this cornerstone of our culture survive and become more valuable than ever? 
This documentary looks at how to take the long road to excellence in a world that's constantly striving for shortcuts.
"The essence of Takumi is to gain a sublime understanding of the nuances of a particular art." 
Says Nahoko Kojima, the paper cut artist who appears in the documentary.
 "To be focused and spend countless hours on one thing, and to carry on. 
It requires one to empty the mind and focus in a way that is simply not possible when still acquiring a skill."
"The concept of Takumi has physically and philosophically been at the core of the Lexus brand since it started 30 years ago." 
Said Spiros Fotinos, Head of Global Brand at Lexus International. 
"Our Takumi masters have over 60,000 hours (30+ working years) of experience developing their craft.
 To celebrate the brand's anniversary year, we wanted to capture the essence of Takumi?and their 60,000-hour journey?on film."
Viewers can enjoy the 54-minute version or sit and watch the 60,000-hour version on www.takumi-craft.com that allows them to soak up the level of dedication and commitment it takes to achieve a special kind of mastery.
The documentary, created by The&Partnership London, will be available on Amazon Prime Video, Amazon Instant, Google Play and iTunes.
American director Clay Jeter has worked on projects such as the Emmy-nominated Chef's Table, the first original Netflix documentary series. 
Having produced six episodes from 2015 to 2018, he has developed a unique and visually engaging style which is clear throughout his work. 
Another of his works, film 'Jess + Moss', made its debut at the Sundance Film Festival in 2011 and since then he has continued to grow as 'one to watch'.
Dave Bedwood started his copywriting career in 1998. 
In 2004, after working for several London Ad agencies he and three colleagues set up their own called Lean Mean Fighting Machine. 
Within four years, they had won 'Agency of the year' at the Cannes International Advertising Awards. 
Dave has worked and won awards on clients such as Emirates, Virgin, Samsung, The Guardian and Lexus.
Rupert has a long track record of producing content with major feature film and documentary directors.
In 1994, Rupert founded Saville Productions, which has produced projects with some of the most widely acclaimed, prominent award-winning documentary and feature filmmakers including: Martin Campbell (Casino Royale), Fernando Meirelles (City of God), Stephen Daldry (The Reader), James McTeigue (V For Vendetta), Gavin O'Connor (Warrior), Barry Levinson (Rain Man), Bryan Singer (The Usual Suspects), Spike Lee (Inside Man), and Paul Haggis (Crash), Wim Wenders (Paris Texas), Morgan Neville (20 Feet from Stardom), and Werner Herzog (Cave of Forgotten Dreams).
Saville produced a Global World Cup short film with Adidas directed by Fernando Meirelles (City Of God). He also produced a Werner Herzog directed 35-minute film for AT&T "From One Second to the Next" was a huge web and PR success. 
The film is now being shown in over 40,000 schools and colleges.
Other notable projects include Bending the Light, a Michael Apted (The Up series) directed project about the art of photography through the lens of photographers.
The first subject of the documentary, carpenter Shigeo Kiuchi, 67, was trained by his father in the art of 'Miyadaiku'?an ancient form of carpentry founded in Japan. 
"I see myself as like a custodian," explains Kiuchi. 
"I learned from my father who worked here before me, and now I'm passing on the skills to future generations."
Kiuchi works at Kong? Gumi in Osaka, a temple-making company started in 578AD that he joined as a teenage apprentice. 
Kiuchi plans to continue his career indefinitely?"carpenters don't retire" he says. 
However, in contrast, he describes his lifelong contribution to the company as "like a blink of an eye" in comparison to its history.
Kong? Gumi is the world's oldest existing company, founded when Prince Shotoku commissioned Japan's first Buddhist temple. 
It's been in the hands of the same family ever since, and today a 41st generation family member sits on the Kong? Gumi board.
Hisato Nakahigashi runs Miyamasou, a two-Michelin star restaurant in Kyoto. 
He is a fourth generation Kaiseki chef whose great grandfather founded Miyamasou, an inn for pilgrims to stay when visiting the 12th century temple on which the restaurant shares its grounds.
 "For Hisato, his turning point came when he was 20,000 hours into his Takumi journey," says the documentary's director Clay Jeter. 
"His father died unexpectedly at the age of 55, and Hisato had been finessing his craft working in fine dining restaurants overseas. 
However [at this pivotal point in his life] he made the decision to come home and continue the legacy and he's really elevated the restaurant to something extraordinary"
Every morning, to source his ingredients for his honoured guests, Hisato fishes in the local river and forages for local herbs and mountain vegetables?and says he "gives thanks" to nature for supplying his food. 
This dedication is all part of Kaiseki?a traditional Japanese multi-course dinner with a time honoured tradition of going above and beyond for your guests.
The third subject of the documentary, artist Nahoko Kojima is 37 years old, but already has dedicated 60,000 hours to her craft.
 Kojima began 'Kirie' (Japanese papercutting) under private tutelage when she was only five years old and continued throughout her formative years. 
At 18, she moved to Tokyo, and in 2004 she graduated with a degree in Design from Kuwasawa Institute. 
She briefly pursued a career in Graphic Design in Tokyo, but ultimately moved to London to study arts further, and within a few years exhibited her first solo paper-cutting show. 
Then in 2012, her piece 'Cloud Leopard' was unveiled at the Saatchi Gallery. 
It is a sculpture that took five months to complete, cut entirely of one sheet of black paper. 
Kojima's process begins with careful sketches and tests using much smaller pieces of paper. 
Her practice "is labor-intensive in the extreme, and demands tremendous concentration; if she makes a mistake there is no way to repair it. 
She uses scalpel blades that are half the thickness of normal blades and are replaced every three minutes".
In 2013 she won the Jerwood Makers Open award for which she created 'Byaku', the swimming polar bear. 
In 2018 she faced her biggest challenge by creating a 32m life sized sculpture of a blue whale, Shiro. 
This sculpture was shown being cut in the film.
Though she primarily lives in London now, in 2016 at a ceremony in Tokyo, Kojima accepted the coveted Kuwasawa Award for her contribution to the arts.
The documentary also introduces us one of the Takumis at Lexus?Katsuaki Suganuma, who has worked at the company for 32 years. 
Katsuaki, who's a Takumi in charge of the final inspection line at Lexus, has seen big changes in terms of technology, with the introduction of artificial intelligence and robots. 
But he's proof that humans still play a vital role in car manufacturing. 
The documentary takes us behind the scenes at the 4 million square meter plant at Tahara in Aichi, Japan, which is regarded as one of the world's most high-tech factories.
Katsuaki is one of a handful of extraordinarily dedicated individuals with the quarter of a century of practice required to become a Takumi Master Craftsman. 
That's 60,000 hours practice.
This extensive time is spent practicing and refining with minute precision. 
The result is a group of superhuman Craftsmen with razor sharp senses. 
They are seen and see themselves as the guardians of Lexus craft at every stage of production.
This approach to craft is a philosophy that runs throughout the business, which the Takumi are responsible for passing onto the new generations. 
Each Takumi will train their youngers to ensure that their expertise, their tradition, and the Takumi spirit develop in all the new talent. 
This is why we know that nothing is crafted like a Lexus.
Nora Atkinson is a prominent American expert on craft, with a specific focus on the role and importance of handmade in modern culture. 
Her current role is a Curator of Craft at the Smithsonian American Art Museum in Washington DC. 
She was recently named Washingtonian Magazine's 2018 "Best Boundary-Pushing Curator" for her work on a number of critically-acclaimed shows.
Earlier this year Atkinson spoke at TED event called 'The Age of Amazement'?a future focused event exploring AI and new forms of creativity and social change. 
We have filmed Atkinson in Washington. 
Her commentary was about craft in general and its role in the digital age.
 Importantly for us, she linked craft to luxury in terms of real handmade objects and their value in the future.
Martin Ford is a futurist and author focusing on the impact of artificial intelligence and robotics on society and the economy. 
He has written two books on technology. 
His most recent book, Rise of the Robots: Technology and the Threat of a Jobless Future (2015), was a New York Times bestseller and won the Financial Times and McKinsey Business Book of the Year Award in 2015.
Martin's TED Talk on 'How AI could cause job loss' discusses the dichotomy between the negative effect on industries that AI could have, versus the undeniable progress it can cause and the new industries it could inspire. 
He has a loyal following on Twitter, with 42.3k followers and had actively engaged in the discussion on Japan and Technology, tweeting on the 31st of July about 'Why Westerners Fear Robots and the Japanese do not'.
Jon Bruner is a journalist and programmer who runs the Digital Factory program at Formlabs, a company that builds professional-grade 3D printers. 
Before joining Formlabs, he oversaw publications on data, artificial intelligence, hardware, the Internet of Things, manufacturing, and electronics, and was program chair, along with Joi Ito, focused on the intersection between software and the physical world. 
He is a prolific contributor online with articles such as 'Making AI Transparent' and 'Integrating Data with AI' where he talks about the relationship between 'human experts' and algorithms. 
Jon has been interviewed by The Economist's own Podcast on the subject of the ability of machine to mimic man. He asks if 'computers can create beautiful music; can 3D printers adopt traditional techniques to give us reinforced floors?'
 In fascinating contrast to Martina Ford, Jon Bruner is an optimist. 
He's the kind of futurist who is excited about the opportunities that are opened to humans when AI replaces certain tasks and jobs. 
He speaks to the beauty of the man working side-by-side with a machine.
Neil is a highly regarded expert in the history of humankind, having been director of the National Gallery and British Museum for many years, and now as a director of the soon to open Humbolt Forum in Berlin (Germany's answer to The Met). 
Neil has used the lens of human made objects and craft, to tell the history of the world. His bestselling book, exhibition and podcast?A History of the World in 100 Objects, is his most famous work in this area. 
He is a globally renowned expert in this field and a highly respected author and broadcaster.
Lexus launched in 1989 with a flagship sedan and a guest experience that helped define the premium automotive industry. 
In 1998, Lexus introduced the luxury crossover category with the launch of the Lexus RX. 
The luxury hybrid sales leader, Lexus delivered the world's first luxury hybrid and has since sold over 1.45 million hybrid vehicles.
A global luxury automotive brand with an unwavering commitment to bold, uncompromising design, exceptional craftsmanship, and exhilarating performance, Lexus has developed its lineup to meet the needs of the next generation of global luxury guests, and is currently available in over 90 countries worldwide.
Lexus associates/team members across the world are dedicated to crafting amazing experiences that are uniquely Lexus, and that excite and change the world.
The&Partnership London is a future-focused creative agency whose mission is to bring together creativity, technology and data in new, exciting and effective ways.
 Originally founded in 2001 as CHI, the agency rebranded in 2018 as The&Partnership London, underlining its position as the creative and strategic heart of WPP-backed The&Partnership, which is both the UK's largest and Europe's fastest-growing independent agency network. 
The&Partnership London believes the future of the creative industries lies in big, bold and bionic ideas which blend world-class creativity with smart data, progressive technology and artificial intelligence.
LOS ANGELES, USA (November 19, 2019)?The much-anticipated convertible version of the Lexus LC 500 flagship coupe made its global debut today in Los Angeles. 
The LC 500 Convertible will be on display at the 2019 Los Angeles Auto Show, which runs from November 18 to December 1, 2019*1.
The LC lineup is representative of Lexus' evolution towards becoming a luxury lifestyle brand, providing driving pleasure and excitement to enrich customers' lifestyles. 
The new LC 500 Convertible joins the coupe as aspirational halo models for the entire Lexus lineup, providing unique driving experiences that stimulate the senses, and express ultimate beauty.
The exterior of the new LC artfully blends the coupe's unique roofline with the character of a convertible, achieving beauty with the top open or closed. 
The interior design considers how details such as the tonneau cover and seating materials become part of the design impression when viewed from the outside.
The new model follows the coupe's 'Exhilarating Performance' driving signature, providing a sense of unity with nature and an exhilarating driving experience that can only be enjoyed with a convertible.
The interior body structure has optimal placement and shape of braces to yield high dynamic performance faithful to the driver's intentions. 
And thanks to the car's robust and sensual sounding V8, the LC 500 Convertible accelerates in a pleasantly linear manner. 
To further enhance the driving experience, the LC 500 Convertible comes equipped with neck heaters and a transparent wind deflector?supporting the leading comfort and quiet that is Lexus' DNA.
The new LC 500 Convertible is scheduled to go on sale in summer 2020.
With the exterior styling based on that of the LC coupe, the convertible retains excellent aerodynamic performance and optimal weight distribution, incorporating functional beauty into the design. 
The soft-top roof provides a unique silhouette and sporty disposition befitting a convertible. 
The line of the rear trunk lid has been lifted and widened to create a dynamic side view and emphasize the low-and-wide stance.
To ensure that the convertible version of the LC 500 retains a beautiful silhouette when the top is lowered, the automatic folding mechanism stores the top under an integrated tonneau cover. 
Because the designers incorporated a beltline that kicks up at the end of the doors, the vehicle's profile is characterized by a body that wraps around the cabin, providing an overall tight and clean appearance.
The 4-layer soft top has been designed to retain the flowing roofline of the coupe, without the supporting frame visible through the fabric. 
Furthermore, the fabric of the roof has been carefully selected and manufactured to ensure optimal tension and no wrinkling, with improved sound insulation.
The color palettes of the exterior and the soft top, as well as the elegant hues of the interior, were carefully selected and combined to cater to the LC customer's high sense of style, taste and diverse lifestyles.
The interior features details such as gradating quilting and perforation pattern on the upper portion of the seats, as well as the "L" logo embossed on the back of headrests.
When opening and closing the soft top, the movement is carefully controlled especially at the beginning and end to yield a sense of quality and safety for the driver.
 The movement of the top and the tonneau cover have been precisely synched to achieve an elegant and natural movement, while ensuring class-leading opening and closing speeds.
The roof can be opened or closed while the vehicle is traveling at up to 50 km/h, and an animation display within the instrument gauges allows the driver to easily monitor the top's operation progress.
The body structure of the LC 500 Convertible was newly designed to achieve a rigidity level supporting Lexus driving signature equal to the Coupe, while simultaneously offering both beautiful styling and sufficient luggage space.
Thanks to the strategic placement and shape of the rear suspension tower brace, the car's structural rigidity has been significantly enhanced. 
The brace is composed of lightweight die cast aluminum to minimize added weight, and a "PERFORMANCE DAMPER?*2" is adopted for high-quality ride comfort. 
Other braces are located under the body, and materials such as magnesium and aluminum are used to achieve both high rigidity and light weight. The result is dynamic performance faithful to the driver's intentions.
The naturally aspirated 5.0-liter V8, mated to the DirectShift-10 automatic transmission, gives the LC linear-yet-invigorating acceleration. 
Power output is tuned according to the driving scene, with smooth acceleration character during normal commuting, and acceleration at the limit when the driver desires.
In order to make the LC 500 Convertible's V8 more enjoyable when the top is down, a sound generator transmits sensual engine intake sounds through the dash panel, while an exhaust valve enhances the powerful engine sound.
In order to create a space to feel with excitement of open-air driving without hindering conversation in the car, we focused on aerodynamics such as the beltline and rear molding.
 In combination with the transparent polycarbonate wind deflector, wind flow inside the car is suppressed and excellent quietness is achieved.
Sound management is a key part of Lexus' DNA, and the interior space of the LC 500 Convertible stimulates the senses with emotional engine sounds. 
Active Noise Control (ANC), combined with sound insulation and absorption techniques, suppresses unwanted noises and unpleasant sound frequencies.
The audio system faithfully reproduces the original sounds of each instrument, vocals, and recording environment. 
Additionally, the acoustic design automatically changes with the top up or down, providing an acoustic space that can be enjoyed in every situation.
The Lexus Climate Concierge is used to automatically control air conditioning, seat heaters, neck heaters, and steering wheel heater, all while considering whether the top is up or down - making the convertible version of the LC 500's cabin comfortable for its passengers regardless of the temperature outside.
Good morning. My name is Yoshihiro Sawa. I am the President of Lexus International. 
Welcome and thank you all for visiting the Lexus booth today at the Tokyo Motor Show.
Thirty years have passed since Lexus was born in the United States. 
Since then, the brand has continued to grow with outstanding quality, hospitality and a spirit of challenge.
Lexus has always anticipated people's needs and rolled out numerous models such as luxury SUV pioneer RX to provide luxury lifestyle experiences.
However, a somewhat shocking incident did occur that helped shift our history.
 It was in 2011 at Pebble Beach when we first showcased the all-new GS. 
One journalist commented "Lexus is boring brand". 
The comment shocked Akio Toyoda so much that he swore " we will never let anyone say Lexus is boring again".
Following this incident, Akio established the Lexus International company. 
And He himself became Chief Branding Officer as well as Master Driver to help drive bold brand and product reform. 
Within Lexus, we realized that our entrepreneur's challenging spirit was disappearing over the time. 
So, this was a renewed challenge for us.
In Akio's heart, he wants the brand to be something our customers finally choose over other luxury brands. 
For that, Akio strongly believe that we need to nurture gracefulness for the brand in addition to achieve high driving performance and exquisite quality.
The challenger mindset is the foundation of Lexus. 
The first-generation LS was born to redefine the Luxury sedan and this model surprised with high quality, comfort and overwhelming refinement.
Since the 2011 moment, Lexus has been relentlessly challenging to stimulate the five senses. 
We engaged to various activities to become a luxury lifestyle brand.
At this year's Tokyo Motor Show, we present "Lexus Senses Theatre", a space where people can experience the "stimulation of the five senses"
In the audio theatre, the sensual engine sound of Lexus LFA stimulates hearing. 
In the visual theatre, the beauty of Lexus LC , which changes its appearance according to the time of day and the viewing angle, is highlighted. 
Please come and enjoy this real experience.
In addition to providing experiences to customers, a luxury brand need to meet social responsibility for better future.
The vision of automobiles and mobility for a better society is being redefined. We try to offer a luxury brand that provides unique values.
The foundation of our brand activities is rooted from the concept of being "human-centered." 
Using advanced safety technology as an example, we constantly strive to produce higher safety standards, including easy-to-understand interfaces between humans and cars, and develop driving behavior that brings safety, security and confidence.
As a part of our achievements, we plan to launch a model equipped with Lexus Teammate, an advanced driving assist technology, in 2020.
The Lexus brand will continue to value the stimulation of the five senses and bond with the life of each one of us.
In order for us to contribute to enhancing our customer's lives, vehicles will need to keep playing a vital role. 
In Japan, we sometimes call our car "Aisha", which literary means "beloved car".
For Lexus, Aisha refers to our central philosophy for crafting vehicles that people are not just merely interested in and passionate about, but something that people can actually bond with.
We aim to provide vehicles that will be enjoyed and cherished, which will exceed their expectations, thereby imparting a rich lifestyle by stimulating the five senses. Lexus is unwavering in this mission.
Another message is about future electrification technologies. 
At Lexus, we are building upon our fundamental mission, which is to deliver higher levels of driver engagement and help incite a passion for driving, and elaborate upon it further. 
Finally, we aim to fundamentally change the concept of future luxury vehicles.
Lexus is already at the forefront globally as a leader in hybrid technologies. 
We are confident that Lexus will continue to be cutting-edge, backed by core technologies, including batteries, motors and power controls, that are essential components for electrified vehicles. 
Based on this idea, we came up with the name Lexus Electrified to define our brand's electrification vision.
The LF-30 Electrified is a concept car that embodies our Lexus Electrified vision. 
The concept car is a BEV with an automated driving function while also possessing the intuitive values of a car. 
This is one realization of our Yet philosophy.
We created the concept car design by realizing an advanced image expected of BEVs and incorporating the advanced technologies of connectivity and automated driving.
The Lexus LF-30 Electrified uses a new technology, we call it Lexus Advanced Posture Control. 
This integrates the pinnacle of electrification technologies and movement control technologies that Lexus has been developing. 
With precise control of the motors, the technologies enable us to realize drive-control that conventional gasoline vehicles cannot attain.
As the ultimate form, the concept car is equipped with four in-wheel-motors.
The LF-30 Electrified employs various core technologies beyond Lexus Advanced Posture Control.
To advance this goal, Lexus is developing HV, PHV, FCV other than EV to meet the needs of various regions and markets depending on their condition such as governmental policies and energy situation. 
We will launch such models immediately to markets like Europe and China where the needs are particularly high.
Concretely speaking, we will introduce the first Lexus EV model next month and start selling it in 2020. 
Then, PHV and pure EV will follow in the early half of the 2020s.
Furthermore, we will continue make advances in our core HV technologies and also continue to develop FC technology as our overall electrification strategy.
Finally, around 2025 we expect to offer an electrified variant of each model in our line-up.
As we mark this 30-year milestone, we plan to further accelerate the electrification of vehicles while providing customers with a richer lifestyle, and elevated driver engagement.
 Please keep watching to see what Lexus will have to offer going forward.
TOKYO, JAPAN (October 23, 2019)?In its continued efforts to deliver innovative and amazing experiences, Lexus unveiled its "Lexus Electrified" vision for an upcoming generation of electrified vehicles. 
Headlining this moment was the debut of the Lexus LF-30 Electrified Concept, which made its world premiere at the 46th Tokyo Motor Show 2019.
Since the brand's creation in 1989, Lexus has endeavored to deliver to its customers innovative and amazing product and brand experiences that stimulate the five senses. 
Since the launch of the RX 400h in 2005, Lexus has led the world as a pioneer in electrification technologies such as the two-stage reduction gear and the multi-stage hybrid system which leverage technology to offer excellent performance and the direct driving sensation characteristic of Lexus Hybrid vehicles.
The "Lexus Electrified" vision unveiled today targets a fundamental leap in vehicle performance, handling, control and driver enjoyment?even as mobility within our society continues to change with autonomous driving and vehicle electrification.
Evoking the original fun of driving, Lexus is developing new advanced posture control and other electrification technologies to further evolve driving pleasure, and to fundamentally transform the essence of luxury vehicles of the future. 
Towards this end, Lexus is drawing on the lessons learned developing the core technologies found in Lexus' popular range of gasoline-electric hybrids, including battery management, power control modules, and electric motors. 
In particular, the technology of Lexus Electrified enables integrated control of powertrain, steering, suspension, and brakes, realizing the ultimate potential of the motor control technology cultivated in HV. 
With this technology, we can control the driving force to provide ideal vehicle posture according to each driving situation. 
Lexus endeavors to continue providing enjoyable and safer driving vehicles.
To advance this goal, Lexus plans to unveil its first BEV in November 2019?broadening our response to the needs of various regions around the world, including the development of HEVs, PHEVs, BEVs, and FCEVs. 
Moving forward after that, Lexus plans to expand its electrified vehicle lineup?we will launch our first Lexus PHEV and a new dedicated BEV platform early in the coming decade. 
By 2025, Lexus will have available electrified versions of all Lexus vehicle models, and we are aiming for the sales of electrified vehicle models to outpace those of conventional internal combustion engine vehicle models.
The LF-30 Electrified concept vehicle embodies the "Lexus Electrified" vision. 
For its exterior styling the advanced image expected of a BEV has been channeled into artistic qualities that result in a futuristic form, and an interior that assertively weaves in autonomous driving and other new technologies aims to manifest Lexus' distinctive worldview. 
Performance is rooted in Lexus Electrified components, adding Lexus' latest technology to our leadership in development of HEV systems. 
Precise electric motor control enables instantaneous adjustments to posture not possible with conventional vehicles. 
Furthermore, the LF-30 Electrified employs numerous advanced technologies with a look ahead to the year 2030- such as a new-concept cockpit based on a human-centered design philosophy and a steer-by-wire system.
In taking up the challenge of expressing a new design that could only be achieved with a BEV powered by in-wheel electric motors, Lexus visually articulated the LF-30 Electrified's unique energy flow. 
The vehicle form is meant to visually express the energy created by the wheels set at the corners of the vehicle body streaming toward the vehicle cabin and past the driver to directly flow onto the road surface.
Taking advantage of a hoodless vehicle shape made possible by being a BEV, Lexus' signature "spindle" form has been further evolved to span the entire vehicle architecture. 
The window glass, which continually stretches from the front to rear, the muscular fenders, and the wing-shaped headlights form the contours of the Lexus iconic spindle. 
The shape of the body is fashioned with an elegantly flowing front which transitions into a linear and sharp rear.
 In addition to the wing-shaped headlights, the sharpness of the rear lights and side air intakes combine to achieve both excellent aerodynamics and cooling performance, resulting in styling fused with function.
The opacity of the side windows can be freely adjusted, providing occupants with expansive views of the surrounding scenery and a high level of privacy at night and in other situations. 
The color of the front face of the vehicle and luminescence patterns help identify from the outside whether the vehicle is being operated in its normal mode or in its autonomous driving mode, reflecting Lexus' pursuit of both a high level of styling and functionality. 
The exterior color 'voltaic sky' employs a leading-edge metal-infused coating to achieve a unique quality tinted by a touch of blue-green.
To manifest in a higher dimension Lexus' fundamental human-centered philosophy, the cockpit was designed based on the new Lexus concept of "Tazuna".
 Inspired by how a single rein can be used to achieve mutual understanding between horse and rider, the steering controller-mounted switches and head-up display have been coordinated to a high degree, creating a space that enables the driver to focus on driving while controlling various functions, such as the navigation and audio system and driving-mode selection, without having to shift one's vision or operate manual switches. 
As an indication of the future image of a Tazuna cockpit, the LF-30 Electrified employs next-generation interfaces, such as gesture control and enhanced presentation of vehicle information through AR (augmented reality). 
The resulting interior is one that provides comfort and convenience for both driver and passengers.
With the layout of the front passenger seat echoing that of a first-class seat on an airliner, the interior is one in which a sense of openness and a sense of envelopment coexist. 
All switches and other controls being comfortably within reach and a gesture-control large-screen display for the passenger seat add to the achievement of interior comfort and convenience.
The rear seats use artificial muscle technology to mold to their occupant, and can support various modes such as reclining, relaxation, and alert functions. 
A Mark Levinson? audio system creates a next-generation listening environment, in which minute speaker control establishes ideal acoustic spaces for music listening pleasure for the driver and each passenger, and speakers built into the headrests not only provide an optimal audio environment but also have a noise-cancelling feature that contributes to enhanced quietness.
A glass roof above the rear seats features voice control and a gesture controlled "SkyGate" display window that uses AR to display various types of information, such as a realistic star-filled sky, user-favorite videos, and even navigation.
In addition to its unique design, the interior also indicates the direction of next-generation luxury by using sustainable materials to reduce environmental burden. 
Yakisugi (charred cedar), a traditional Japanese material, is used in the floor and steering controller while recycled metal was processed into fibers for use in creating the pleated door trim.
 This approach expresses Lexus' distinctiveness and innovative spirit.
To achieve a fundamental leap in vehicle performance, handling, control and driver enjoyment, the LF-30 employs numerous state-of-the-art technologies even beyond advanced posture control. 
In-wheel electric motors for each of the vehicle's four wheels and low positioning of the battery enable better handling of inertia and high-level driving performance. 
Autonomous driving technologies and drone support vehicle technologies look ahead to the year 2030 and the widely expanded value that vehicles can offer.
Lexus Advanced Posture Control technology regulates the drive-power output from high-torque electric motors to adjust vehicle posture in tune with human sensibilities. 
Completely independent control of front and rear drive wheels allows appropriate provision of front-wheel drive, rear-wheel drive, and all-wheel drive, depending on the driving situation.
 Compact and lightweight drive-power units expand freedom in vehicle packaging and are used to enable the driver to enjoy ideal driving, regardless of the road surface or driving conditions.
Positioning Lexus Advanced Posture Control technology as a core element of the 'Lexus Electrified' vision, Lexus intends to widely apply this technology throughout its lineup of electrified vehicles.
The steer-by-wire system eliminates a mechanical connection to allow more flexible turning control depending on driving conditions, and a more precise steering feel aligned with the driver's intention. 
It also contributes to a greater sense of openness by allowing the steering controller can be shifted forward and out of the way during autonomous driving.
As a next-generation BEV, LF-30 uses wireless charging technology to simplify daily charging, and AI-based energy management to enable optimal distribution of electric power to both the vehicle and the home, and charging control coordinated with the user's daily schedule.
Onboard AI distinguishes the voices of vehicle occupants, and uses personalized information stored on the driver's control key to serve as a partner.
 It facilitates the adjustment of elements of the interior environment, such as air temperature and audio, and the setting of navigation routes and destinations, while also making proposals for activities after arrival. 
It also understands driver's preferences and helps them control the suspension and powertrain settings in real-time according to the driving scene.
The LF-30 Electrified also carries the 'Lexus Airporter' drone-technology support vehicle. 
Using autonomous control, the Lexus Airporter is capable of such tasks as independently transporting baggage from a household doorstep to the vehicle's luggage area.
Based on the latest autonomous driving technology concept of 'Lexus Teammate', the LF-30 Electrified features advanced driving support functions in the form of a Chauffeur mode and a Guardian mode. 
Occupants can enjoy both comfort and peace of mind during autonomous driving with advanced posture control technology being employed. 
Furthermore, a self-parking function and a front-door pickup function in which the LF-30 Electrified autonomously moves from driveway to doorstep provide an especially high level of convenience.
In addition to the LF-30 Electrified exhibition, the Lexus booth at Tokyo Motor Show will contain "Lexus Senses theatre", a space where people can experience the "stimulation of the five senses".
 It consists of two experience spaces, audio and visual. 
In Theater 1, the sensual engine sound of Lexus LFA stimulates hearing with 360 degree 3D sound.
Theater 2 offers a visually stimulating experience with the Lexus LC model by projection mapping - which changes its appearance according to the time of day and the viewing angle. 
In this booth layout you can appreciate the Lexus philosophy, while enjoying sensory stimulation through "real experience".
GUANGZHOU, China (November 22, 2019)?LEXUS will introduce its first battery electric vehicle (BEV), the UX 300e, at the Guangzhou International Automobile Exhibition, which will be held in Guangzhou, China, from November 22 to December 1, 2019.
Since introducing the RX 400h in 2005, LEXUS has been a pioneer in vehicle electrification technology, playing a leading role in offering products that possess both performance and environmental friendliness.
 At the 2019 Tokyo Motor Show, Lexus unveiled its global electrification strategy, called "Lexus Electrified", which targets a fundamental leap in vehicle performance, handling, control and driver enjoyment.
In particular, the technology of Lexus Electrified enables integrated control of powertrain, steering, suspension, and brakes, realizing the ultimate potential of the motor control technology cultivated in HV. 
With this technology, we can control the driving force to provide ideal vehicle posture according to each driving situation. 
Lexus endeavors to continue providing enjoyable and safer driving vehicles.
As the first production model under the Lexus Electrified banner, the all-electric UX 300e was developed for excellent on-road performance. 
Lexus engineers kept the distinctive design and the utility characteristics of the UX crossover intact, and focused on the opportunities to build on the performance advantages unique to EVs. 
The UX 300e's high-output motor provides a natural-yet-brisk acceleration character, and the high-capacity batteries located directly underneath the floor of the cabin deliver a low center of gravity and 400 km-driving range.
 Combined with the newest connectivity technology, the UX 300e maximizes the advantages of EVs while realizing driving performance and convenience in a single package. ('yet' philosophy)
The UX 300e is scheduled to go on sale in the Chinese and European markets in 2020, and in Japan early in 2021.
Lexus has always focused on providing exhilarating performance, and the case is no different with development of a BEV. 
Starting from the refined Lexus driving signature of the UX, Lexus engineers were able to leverage the new electric drivetrain to even further enhance the vehicle's on-road performance. 
At the same time, UX 300e has one of the quietest cabins in its class, as befits the sound management heritage of the Lexus DNA.
UX 300e's Drive Mode Select function lets customers manage smooth acceleration and deceleration according to their situation. 
Drivers can feel the powerful acceleration and instant torque of the EV powertrain as they push the pedal, and use the paddle shift in a similar manner as engine braking, through four levels of deceleration regeneration?all while enjoying a natural on-road feel.
The UX 300e provides excellent dynamic performance thanks to the low center of gravity resulting from motor and battery placement underneath the vehicle body, combined with optimized of front/rear weight distribution and moment of inertia.
The high-performance level of the GA-C platform is enhanced with additional braces and optimization of the shock absorbers' damping force to match the dynamic changes of electrification.
While EVs are naturally quiet, UX 300e adds insulation beyond just the battery and suppresses outside noises such as wind or pebbles which would be otherwise noticeable in the absence of an engine and transmission. 
Lexus focus on sound management lets drivers enjoy comfortable tranquility in the cabin.
Engineers also focused on sound while driving to provide a natural feeling. 
Active Sound Control (ASC) transmits natural ambient sounds to allow for understanding of driving conditions, and provides a natural feeling for the cabin's occupants.
In developing the UX 300e, Lexus utilized the knowledge acquired developing the brand's industry-leading hybrid systems, and applied the same level of quality and comfortable operation to its first production EV as it always has with other vehicles. 
The Lexus engineering team delivered outstanding battery reliability, and also adopt the latest connectivity technology to maximize everyday usability and the functionality with smartphones.
The efficiency of the motor, inverter, gears and high-capacity battery were all maximized, utilizing the knowledge acquired developing hybrid vehicles. 
By improving the performance of the entire system, the UX 300e's driving range is an anxiety-free 400 km.
The batteries are equipped with a temperature management system that operates at low and high ambient temperatures. 
Reliability is also increased with the use of multiple monitoring systems that regulate charging and prevent conditions like overcharging.
UX 300e offers the latest in connected car technology. 
By linking to a smartphone using a dedicated app, drivers can check the battery state of charge and driving range. 
Charging controls are also included such as timer function to inform the owner when the vehicle will be fully charged or to schedule the charging according to when the vehicle is expected to be driven next.
 The app also allows the owner to remotely control various convenience functions such as the A/C, and window defrosters.
Distinctive styling and high functionality from the Lexus UX compact crossover were passed on to the UX 300e, providing an excellent overall package
In addition to the bold and sophisticated exterior reminiscent of tough and agile driving, Lexus developed special aerodynamic wheels and underbody cover for the UX 300e.
Location of the shift-by-wire system on the center console contributes to the simplicity and functionality of the interior design.
Lexus prioritizes the development of the most advanced safety technologies and quickly delivering them to drivers. 
UX 300e adopts Lexus Safety System+, as Lexus continues to pursue the prevention of accidents and fatalities, as well as decreasing driver stress and developing driver assist systems to provide a more natural and safe driving experience.
Which kinds of electric cars can you buy nowadays?
 What makes a plug-in hybrid different from a mild hybrid? 
Read on to find the answers in our comparison of electric cars.
Sooner or later, the majority of drivers will be making the switch from petrol cars to an electric vehicle. 
The advantages are obvious, above all for the environment, because engines powered by electricity don’t give off emissions, so electric cars are locally emission-free. 
At the moment, electric vehicles (EVs) are more expensive than conventional ones. 
However, with EVs comes a variety of savings, like lower operating and maintenance costs, that their fuel-powered counterparts don’t have. 
On top of this, manufacturers offer refunds for EVs and many countries have incentives and tax credits for them.
Electric cars are obviously a practical choice, but that doesn't mean they aren't fun. 
When you're at a traffic light, you’ll have enough torque to smoke the guy next to you when the light turns green, but you’ll be able to do it in a sneaky way because the engine is so quiet.
Technological advances in electric mobility enable carmakers to offer an ever-expanding range of vehicles, which makes it easy to lose track of all the developments. 
Some buyers are perfectly happy with a plug-in hybrid, while others want a fully electric car. 
Our comparison of electric cars explains the different kinds of designs. 
We use the term EV to include electric vehicles as well as hybrids.
As an e-mobility pioneer, the BMW Group has reached another electromobility milestone and already delivered half a million electrified cars to customers worldwide until the end of 2019. 
On top of that BMW aims to have one million electrified vehicles on the roads within two years and contribute towards effective climate protection.
How does an electric vehicle work? 
As opposed to a combustion engine, an EV uses electricity from a battery rather than the combustion of fuel to power the engine. 
The capacity of the battery determines the EV's range (how far it can go on a single charge of the battery).
An all-electric vehicle (BEV – battery electric vehicle) runs strictly on electricity. 
It does not have a combustion engine, which is why it does not produce emissions locally. 
For this reason we have given it the maximum number of points for environmental friendliness in our comparison of electric vehicles.
The problem is that many motorists are worried about range – a worry that is generally unfounded. 
Today, most BEVs have a range of over 185 miles and most motorists in the USA drive less than 60 miles a day.
By using a range extender, motorists can breathe easy in this regard. 
A range extender is a petrol-powered generator that feeds electricity exclusively to the battery when its charge is nearly drained. 
In a BEV, this generator does not directly power the car, because if it did, it would be a hybrid.
Another advantage of BEVs (Battery Electric Vehicles) is that they have the most country-specific incentives and tax credits. 
Fully electric vehicles are ideal for people who can charge their battery at home or at work. 
Nowadays, more and more public charging stations are being opened, especially in metro areas and along motorways. 
This means that in the future, it will be more and more easy to make long-distance trips.
What is a hybrid car? In contrast to an electric vehicle, a hybrid electric vehicle (HEV) has both a combustion and an electric engine. 
Depending on the car, both motors can either be independent of one another or can work in tandem.
The degree to which hybrids function as an electric vehicle depends on their electric performance, their electric range and the range of their recharging system. 
There are two types of HEVs: mild hybrids and plug-in hybrids.
How does a mild hybrid work? 
The electric motor of a mild hybrid assists the combustion engine. 
It kicks in when a great deal of fuel is being consumed, particularly during startup. 
This enables mild hybrids to reduce their fuel consumption and emissions. 
Batteries are automatically recharged with regenerative braking.
Mild hybrid vehicles – also known as 48-volt hybrids or MHEVs (mild hybrid electric vehicles) – have an electric motor that assists the combustion engine. 
The electric motor kicks in when a lot of fuel is being burned, particularly during startup. 
It can also serve to boost the engine's power during acceleration.
The battery is exclusively charged via regenerative braking, which captures the energy created by the friction of braking, converts it to electricity and stores it in the battery. 
Mild hybrids do not use charging stations.
The main advantage of a mild hybrid is its fuel consumption, that is 0.1 gallons (per 62 miles) lower than that of a petrol car. Since less fuel is consumed, the vehicle can go farther on a full tank of petrol or diesel. 
Because the main propulsion system is powered by a combustion engine, mild hybrids benefit from the ubiquity of petrol stations. 
So mild hybrids are ideal for motorists who are looking for maximum range combined with low fuel consumption and who don’t want to worry about charging the battery.
Because they consume less fuel, mild hybrids have lower emissions, but the electric motor is not capable of powering the car on its own. 
This is why mild hybrids get none of the incentives that are offered for EVs and why they receive only two points for sustainability in our comparison of electric vehicles.
What is a plug-in hybrid? 
A plug-in hybrid vehicle (PHEV) has both a combustion engine and an electric motor. 
Each one is capable of powering the vehicle on its own. 
Plug-in hybrids use regenerative braking as their energy source, but they can also be plugged in to recharge the battery.
While a mild hybrid car captures electric energy solely while it’s being driven, and thus can only supply a limited amount of power, a plug-in hybrid – PHEV (plug-in hybrid vehicle) – is also capable of recharging its battery when it’s parked at a charging station. 
This significantly expands the electric range of a plug-in hybrid like the 2018 BMW 530e iPerformance, which can drive 28 miles only on electricity with a fully charged battery. 
Over the next few years, technological advances will significantly improve the range of electric motors – as well as that of all battery-powered cars.
Many PHEV owners can already manage most of their trips on electricity because daily commutes are generally less than 30 miles.
 It depends on how much you pay for electricity, but you will likely be saving a great deal. 
If the electric charge is depleted, then the combustion engine takes over, so you don’t have to worry about finding a charging station.
PHEVs are ideal for motorists who want to use their cars in a variety of ways. 
You can use the electric motor for daily commutes, but also take advantage of the great range and flexibility of a petrol engine when you go on longer trips. 
 In addition, owners can benefit directly from financial incentives for electric vehicles in certain countries and indirectly from lower taxes from reduced CO2 emissions.
How does a fuel cell vehicle work? 
Cars powered by hydrogen are also considered EVs because oxygen and hydrogen are converted to electric energy, which then powers the electric motor with a battery. 
They can also recapture the energy that is lost during braking and store it in a battery.
Fuel cell electric vehicles (FCEV) create their own electricity on board. 
Hydrogen in the fuel cell reacts with oxygen in the air, thereby generating electricity, which is used to power the electric motor, similar to a BEV. 
As a result, they only emit water vapor and warm air. 
However, it does have an ecological disadvantage because the production of hydrogen requires a large amount of electricity. 
On top of this, the hydrogen must be transported from the production facility to petrol stations.
FCEVs have a range similar to that of future battery-powered EVs. 
One significant advantage of FCEVs is the short time it takes to fill the tank – a matter of minutes - just like it is with a petrol/diesel car. 
One problem, however, is that filling stations are few and far between and little progress is being made in adding new ones. 
Should this change in the future then there would be little difference between operating a FCEV and a petrol car.
It is also still very expensive to manufacture fuel cell systems. 
One of the main reasons for this is that platinum is needed for the catalytic converter.
Every driver is different and has their own personal needs. 
Luckily, there are a lot of different types of engines out there to serve these needs. 
And each kind of vehicle offers drivers certain advantages. 
Even conventional petrol or diesel cars have their place in the mobility mixture of the future for specific user groups and for special areas of application.
The future will also likely see a combination of several technologies. 
BMW has prepared for this with its innovative vehicle platform, which can accommodate the three types of propulsion systems, the powertrain of a combustion engine as well as that of a plug-in hybrid or a fully electric vehicle. 
The production model of a fully electric vehicle, the BMW Vision iNEXT (launch in 2021) will be the first vehicle with this universal propulsion platform.
A car is a precious gift for those truly special moments in life. 
Such a token of affection deserves to be presented in an emotional and tasteful way. 
Let us show you our step-by-step guide on how to wrap your vehicle to be an unforgettable gift – hassle-free, but with tears of joy guaranteed.
You can give a young person the amazing gift of freedom – in the form of their very own first car. 
You can fulfill your or your loved one’s life’s dream – in the shape of their long sought-after dream car. 
Anyone who has ever received a car as a present knows just how happy a gift like that can make you – more for its emotional value than for its material cost.
But how do you gift wrap a car? 
The classic way is to tie a bow on the hood or wrap it around the whole car. 
That looks very loving, but also a little amateurish. 
So maybe you should gift wrap the car in a way that does justice to the present itself? 
One that doesn’t give everything away immediately, but heightens the sense of anticipation, because the contents of the package aren’t obvious at first glance. 
One that conjures up an amazed expression on the face of the recipient – followed by unbridled joy.
Stylist Dagmar Murkudis has come up with a simple and ingenious way to gift wrap a car for BMW.com – cover the car, stick on fluorescent design elements and shine a black light lamp on it.
“What’s nice about this method of car gift wrapping is that it’s really fun because you can run riot creatively,” Dagmar says. “
It’s also really easy to do and doesn’t take that long.
 After two or three hours you get a stunning visual result.”
Use our step-by-step help guide to find out how to wrap a car: What you need and how to do it.
Packaging as a promise: Fluorescent adhesive strips let you highlight design features of the car.
Dagmar Murkudis has put a lot of thought into how to wrap a car. 
She quickly ruled out wrapping paper – just imagine the effort! 
Not to mention the garbage that all that wrapping paper would create. 
“At some point I came up with the idea of creating a lighting effect – with a black car cover and some self-adhesive neon tape.”
Dagmar recommends making a sketch of the pattern you want to stick on before you start. 
That way, you don’t have to make corrections later on.
 Pro tip: Large decorative elements such as patterns (stars, hearts, etc.) or lettering (e.g. “Happy Birthday”) work best on the hood or the side doors.
She studied under German artist Joseph Beuys and world-famous make-up artist Ren? Koch and worked for fashion magazines such as Cosmopolitan and Marie Claire. 
Dagmar Murkudis is a renowned German stylist who loves wrapping presents. “
With gift packing, there are no limits on your creativity,” she says. 
“And the best thing about it is seeing the joy of the person unwrapping the present.
 In which case it’s perfectly fine if they tear up the packaging.”
Fittingly for Christmastime, Dagmar is planning to put stars on the doors. 
As another decorative element, she has opted for a series of large triangles. 
She used these to trace the contours of the BMW X2. Like the decorative stripes on the car, the triangles along the striking lines of the bodywork look very dynamic. 
At the same time, they hint at the actual gift: You can make out the silhouette of the gift-wrapped car.
“When you’re sticking shapes on the car, you can let your imagination run wild,” Dagmar says. 
But she advises against covering a large area with neon: “Using a black light lamp gets its effect from the contrast between the dark car cover and the colored tape. 
A big neon surface wouldn’t give you that.”
An indoor car cover (textile, matte black) that fits the specific vehicle model (about USD 140).
Fluorescent neon sticky tape in two or three colors (approx. USD 9 per roll).
A black light lamp or 2 (at least 30 W) to illuminate a darkened room (e.g. a garage) from two directions (approx. USD 40 each).
A cutting mat (about USD 11) or other cut-resistant base and a boxcutter/utility knife. 
Scissors are less suitable because the tape sticks to them.
A ruler to help with long straight cuts (optional).
Dagmar’s pro tip on color selection for the neon tape: “I limit myself to two colors so that it doesn’t get too wild.
 If possible, you need to try out the colors under a black light lamp. 
For example, this test demonstrates that green and yellow are too similar and do not produce any contrast.” 
Dagmar ultimately went for yellow and pink for her DIY car wrap.
“What’s nice about this method of car gift wrapping is that it’s really fun because you can run riot creatively,” Dagmar says. 
“It’s also really easy to do and doesn’t take that long. 
After two or three hours you get a stunning visual result.”
Pull the car cover over the bodywork – preferably right down to the wheels so that the car is completely covered. 
(Pro tip: It’s easier to pull the cover on with a friend) Pull the car cover tight and avoid creases so that all the tape sticks properly.
Cut tape triangles with the boxcutter, stick them onto the car cover and press down firmly. 
Repeat until you have finished your decoration.
Cut large decorative patterns like stars with the utility knife and stick them on. 
Dagmar’s pro tip: “In the chosen spots, the material should be in direct contact with the bodywork so that you can press the pattern down well.”
And just like that, your gift-wrapped car is ready to provide a spectacular surprise. 
Now make the room dark and line up the black light lamp or lamps so that the neon tape shines brightly. 
Your recipient will be left speechless on seeing the colored decorations sparkle like stars under the black light lamp. 
“That moment when I switched on the lamp for the first time – it was truly amazing,” says Dagmar.
Then just remove the decorated car cover, and the car will take the spotlight. 
What an unforgettable gift!
Dagmar Murkudis didn’t just gift wrap a BMW X2. 
She also packaged up a number of gift ideas from BMW Lifestyle. 
See if you can guess what’s in these packages. 
All the products can be found in the BMW Lifestyle store.
When it comes to alternative power sources for engines, to the mind of the general public, the fuel cell battery currently lags behind. 
Yet experts believe that hydrogen fuel cell cars will catch up. 
But how does the technology work? 
What are the pros and cons? 
Read on for the answers to all the key questions.
Fewer pollutants, less noise – these are among the many great hopes for electrically powered vehicles. 
When it comes to electromobility, most people think of vehicles with a large battery that you charge from a wall outlet. 
Yet there is another propulsion technology that traffic experts are expecting a lot from – including an alternative to long charging times.
The technology in question is the hydrogen engine, also known as the fuel cell electric vehicle, or FCEV. 
Before we discuss the pros and cons of hydrogen fuel cell cars, as well as the costs and risks involved, we’ll first briefly outline how this technology works.
How does a hydrogen engine work?
Hydrogen fuel cell cars are powered by an electric motor and are therefore classified as e-cars. 
The common abbreviation is FCEV, short for “Fuel Cell Electric Vehicle,” in contrast to a BEV or “Battery Electric Vehicle.”
There is one crucial difference between hydrogen fuel cell cars and other electric vehicles – hydrogen cars produce the electricity themselves. 
So, unlike in fully electric or plug-in hybrid vehicles, the vehicle doesn’t get its power from a built-in battery that can be charged from an external power source (? Read more: Electric cars and plug-in hybrids explained). 
Instead, hydrogen cars effectively have their own efficient power plant on board: the fuel cell.
In fuel cell technology, a process known as reverse electrolysis takes place, in which hydrogen reacts with oxygen in the fuel cell. 
The hydrogen comes from one or more tanks built into the FCEV, while the oxygen comes from the ambient air. 
The only results of this reaction are electrical energy, heat and water, which is emitted through the exhaust as water vapor. 
So hydrogen-powered cars are locally emission-free – more about that in a minute.
The electricity generated in the fuel cell of a hydrogen engine can take two routes, depending on the demands of the specific driving situation.
 It either flows to the electric motor and powers the FCEV directly or it charges a battery, which stores the energy until it’s needed for the engine. 
This battery, known as a traction battery, is significantly smaller and therefore lighter than the battery of a fully electric car, as it’s being constantly recharged by the fuel cell. 
Like other e-cars, hydrogen vehicles can also recover or “recuperate” braking energy. 
The electric motor converts the car’s kinetic energy back into electrical energy and feeds it into the back-up battery.
The pros and cons of a particular propulsion technology can be seen from two main perspectives: that of the user, and that of the environment. 
If any technology is to succeed as an alternative to the combustion engine, it must be user-friendly and significantly reduce the emission of pollutants. 
We’ll start by examining the key benefits and disadvantages for drivers/owners of hydrogen fuel cell cars – with the help of Axel R?cker, Program Manager Hydrogen Fuel Cell at the BMW Group.
The propulsion in hydrogen fuel cell cars is purely electrical. 
When you drive one, it feels similar to driving a regular electric car. 
What does that mean? 
Virtually no engine noise and a lively start, because electric motors provide full torque even at low speeds.
Another advantage is the quick charging time. 
Depending on the charging station and battery capacity, fully electric vehicles currently require between 30 minutes and several hours for a full charge. 
The hydrogen tanks of fuel cell cars, on the other hand, are full and ready to go again in less than five minutes. 
For users, this brings vehicle availability and flexibility into line with those of a conventional car.
For the time being, hydrogen cars still have a longer range than purely electric cars. 
A full hydrogen tank will last around 300 miles (approx. 480 kilometers). 
Battery-powered cars can match this with very large batteries – which in turn will lead to an increase in both vehicle weight and charging times.
The range of fuel cell vehicles is not dependent on the outside temperature.
In other words, it does not deteriorate in cold weather.
Currently, the biggest shortcoming of hydrogen fuel cell cars is the sparsity of options for refueling. 
A hydrogen engine is refueled at special fuel pumps, which in the future will probably find their way into ordinary service stations. 
As things stand, however, there are still very few refueling stations for hydrogen-powered cars. 
At the end of 2019 there are only around 40 in the U.S., as compared to approx. 80 in Germany.
“We have a chicken and egg problem with hydrogen fuel cell technology,” explains BMW expert R?cker. 
“As long as the network of refueling stations for hydrogen-powered cars is so thin, the low demand from customers will not allow for profitable mass production of fuel cell vehicles. 
And as long as there are hardly any hydrogen cars on the roads, the operators will only hesitantly expand their refueling station network.”
BMW’s homeland of Germany leads the way in terms of infrastructure for hydrogen fuel cell cars. 
In order to promote the expansion of refueling infrastructure there, vehicle manufacturers like BMW have joined forces with hydrogen producers and filling station operators in the Clean Energy Partnership initiative, which plans to expand the hydrogen fueling station network to 130 stations by 2022. 
That would allow the operation of about 60,000 hydrogen cars on Germany’s roads. 
The next target, with a corresponding increase in fuel cell vehicles, will be 400 stations by 2025. 
More fueling stations are also needed in neighboring countries to actually make it possible to travel outside Germany via FCEV, according to R?cker.
In addition to the thin fueling station network, there is another reason for the as of yet low demand for hydrogen fuel cell cars: they are relatively expensive to buy. 
The few models of fuel cell vehicles already available on the market cost around USD 80,000 for a mid- or upper-mid-range vehicle. 
That’s almost twice as much as comparable fully electric or hybrid vehicles.
There are a range of reasons why hydrogen fuel cell cars are still expensive. 
In addition to small volumes, which means that production is still to be industrialized, there’s also the question of the need for the precious metal, platinum, which acts as a catalyst during power generation. 
The amount of platinum needed for vehicle fuel cells has already been greatly reduced. “
The general goal is to bring down the price of hydrogen-powered cars to a similar level to that of other electric cars,” explains R?cker.
Another reason for the high purchase price is that hydrogen fuel cell cars tend to be quite large because the hydrogen tank(s) take up a lot of space.
 The drive unit for a purely battery-driven electric vehicle, on the other hand, also fits into small cars. 
That’s why classic electric cars can currently be found in all vehicle classes.
In addition to the cost of purchase, operating costs also play an important role in the cost-effectiveness and acceptance of a propulsion technology.
 In hydrogen fuel cell cars, these costs are not least dependent on the price of the fuel. 
At present, 1 lb (0.45 kg) of hydrogen costs around USD 14 in the U.S., as compared with USD 4.80 in Germany (this is the price the H2 Mobility partners have agreed on). 
An FCEV can drive about 28 miles (45 km) on 1 lb (0.45 kg) of hydrogen.
The cost per mile of running hydrogen cars is therefore currently almost twice as high as that of battery-powered vehicles charged at home. 
R?cker expects these operating costs to converge: “If the demand for hydrogen increases, the price could drop to around USD 2.50/lb (USD 5.60/kg) by 2030.”
A car that uses only renewable energy and produces no harmful emissions would be ideal from an environmental point of view. 
Let’s take a look at how close fuel cell cars are to this goal in comparison to other types of propulsion:
Alternative propulsion systems are designed to reduce the emission of pollutants, in particular climate-harming CO2, but also other noxious gases such as nitrous oxide. 
The exhaust gas from a hydrogen engine consist of pure water vapor. 
Hydrogen fuel cell technology is therefore locally emission-free. 
This means it keeps the air clean in cities, but does it protect the climate at the same time?
That depends on the conditions under which the hydrogen for the fuel cell vehicles was produced. 
Hydrogen production requires electrical energy. 
This electrical energy is used to break water down into its constituent elements, hydrogen and oxygen, via the process of electrolysis. 
If the electricity used comes from renewable energy sources, the hydrogen production has a neutral carbon footprint. 
If, on the other hand, fossil fuels are used, this will ultimately have a knock-on effect on the carbon footprint of the fuel cell cars using the hydrogen. 
How strong that effect is depends on the energy mix used. In this respect, hydrogen fuel cell cars are no different from other electric vehicles.
However, one disadvantage of producing hydrogen is the losses during electrolysis. 
The overall efficiency in the “power to vehicle drive” energy chain is therefore only half the level of a BEV.
However, hydrogen can be produced at times when there is an oversupply of electricity from renewable energy sources when the wind or solar energy currently produced is not otherwise used. 
The potential for this is huge.
Hydrogen is also a by-product of many industrial processes, where all too often it is treated as waste with no further use. 
The fuel cell battery offers a way to upcycle this hydrogen, although it must be cleaned first.
The energy balance sheet for hydrogen fuel cell cars also has to include the transportation and storage of the hydrogen. 
Depending on the transportation technology used (liquid or gaseous), different costs for compression, cooling, transport and storage arise. 
Due to its better transportability and storage ability, the trend is towards liquid hydrogen. 
Nevertheless, the transportation and storage of hydrogen are – at this stage – still a good deal more complex and energy-intensive than for gasoline or diesel.
 In contrast to fossil fuels, hydrogen can be produced anywhere there is access to electricity and water, theoretically even at the actual filling stations for fuel cell cars.
 A more highly developed infrastructure could thus shorten transportation distances significantly in future.
In conclusion, hydrogen fuel cell technology has the potential to make ecologically sustainable mobility possible. 
However, according to BMW’s expert Axel R?cker, this would above all require the use of renewable energy sources when producing the hydrogen used, as well as an expansion of the technological infrastructure in order to shorten transportation distances.
What happens when hydrogen reacts with oxygen in an uncontrolled reaction? 
Many people will remember this from chemistry class at school. 
What you get is an explosive reaction known as an oxyhydrogen gas reaction. 
Hydrogen is flammable, as this shows, but an uncontrolled reaction of hydrogen and oxygen in the operation of an FCEV is virtually impossible.
This is because, in hydrogen fuel cell cars, the hydrogen is stored in liquid form in thick-walled tanks that are particularly safe. 
As R?cker emphasizes, numerous crash tests have confirmed the safety of how hydrogen cars are designed: the tanks came out of the tests undamaged and no hydrogen leaked.
We should also not forget that hydrogen technology is not new, but is tried and tested in a range of fields. 
By way of example, refineries today use large quantities of hydrogen as a process gas in the processing of crude oil.
 Pipelines and hydrogen storage have also been in operation for decades.
BMW is convinced that hydrogen can make an important contribution to sustainable mobility alongside BEVs in the future – provided the necessary hydrogen infrastructure is in place and offers a good price for hydrogen, and the price of the vehicles falls. 
In those circumstances, hydrogen fuel cell cars can be the zero-emissions technology that allows users to maintain the flexible driving habits they are accustomed to.
The Hydrogen Council, a global initiative of leading energy, transport and industry companies, is also convinced of this. 
The council sees hydrogen not only as a sustainable future means of propulsion for fuel cell vehicles, but also as a clean energy source for heating, electricity and industry.
Each driver has different wants and needs when it comes to mobility. 
Oliver Zipse, CEO of BMW AG, puts it like this: “For us, the central questions are: Which kinds of propulsion and technology will our customers want in the future? 
And how do we realize their preferences with the maximum possible climate protection?”
 That’s why BMW will continue to focus on a range of different propulsion concepts – the classic combustion engine, fully electric vehicles and plug-in hybrids, and more research into hydrogen fuel cell cars.
To change or not to change? 
Learn why you really should switch to winter tires when it gets cold. 
We also have answers to other questions about your wheels and tires, like: What does tire balancing mean?
This fact is an indicator of how important your car tires are for road safety: The surface contact of all four tires combined is about the size of a piece of letter paper.
 This contact with the road is where everything happens – accelerating, braking and directional control. 
This is why you should take your tires seriously. 
Read on to find out everything you need to know.
Why not just drive with your winter tires in summer? 
Then you don't have to pay for two sets of tires and for changing them. 
The reason is simply that it’s dangerous – you're putting your safety, that of your passengers and other drivers at risk. 
Tires are a major factor in how safe your car is to drive. 
So what's the difference between summer tires and winter tires then?
The amount of rubber used in winter tires helps the tire stay soft and flexible so it can grip the road when it's cold outside. 
If you drive on winter tires in the summer, then your tires will be too soft, which means they will wear faster, reduce fuel efficiency and also need a greater distance for braking. 
The reason for this is because winter tires are more pliable at higher temperatures, so they wear more quickly on hard, dry asphalt.
The rubber compound used in summer tires is considerably harder than it is in winter tires so they can handle the heat of summer. 
If you drive in the winter with summer tires, stopping distances will be longer and it will be harder to drive in a straight line because the tires aren't soft enough to grip the road. 
If you're even able to get going, that is…
Summer tires have large contact patches which give the car a better grip on the road. 
Although the tread pattern has fewer grooves and sipes (thin slits that cut across the rubber) than that of winter tires, the grooves are bigger so they can move large quantities of water away to the sides, maximizing the contact with the road in order to avoid hydroplaning.
Winter tires, on the other hand, have a lot of grooves, which are also deeper than those in summer tires. 
It is these grooves that allow winter tires to keep their traction on snow and ice.
 There are also smaller channels called sipes that help the grooves keep the tire in contact with the surface.
Your car will skid if you drive on summer tires in winter – see for yourself what that looks like and how winter tires fare in the summer.
Experts agree: Winter tires are just for winter. 
But when should you change your tires? 
Whenever the temperature consistently stays below 50 degrees is when you need to use your winter tires. 
The rule of thumb is that below 50 degrees is when winter tires do best on the road and above 50 degrees is when summer tires do best.
Whether the use of winter tires is obligatory  depends on which country you live in. 
The US and most of Canada don’t have any tire laws requiring winter tires. 
But the Canadian province of Quebec does, so be sure to read up on it if you plan to drive there in winter.
In general, you should get new tires when your tread is worn down. 
The legal limit for tread depth is 2/32 of an inch, but experts recommend about 5/32 of an inch for winter tires and 4/32 of an inch for summer tires.
So how do you know if you have enough tread or not? 
An easy way to check the tire tread depth is to use the penny test. 
Insert the penny into your tire’s tread groove with Lincoln’s head upside down, facing you. 
Check several grooves on your tire, but especially those on the outside where the tread wears the fastest.
 If you can see all of Lincoln’s head, you have less than 2/32 inch remaining and it’s time to replace your tires.
A lot of drivers don't know that tires age even if they aren't being used. 
UV rays, humidity and temperature all degrade the material.
 This is why you should buy new tires every eight years even if you have plenty of tread left.
Each time you change the wheels or tires and have driven about 50 miles, you should retighten the lug nuts on the wheel rims.
 This is purely a precautionary measure, but under certain circumstances it is possible for the nuts to loosen up a bit during daily use.
P245/40 R19 98V - even though it may look like one, those numbers on your car's sidewall are not a secret code. 
They are known as the “tire code”.
The letter “P” at the beginning tells you it is a P-metric tire made to standards in the United States and intended for passenger vehicles. 
If a tire size has no letters at the beginning, then it is a Euro-metric tire constructed according to European standards.
The first three-digit number in the tire size is the tire width. 
So in this case, the width of the tire from sidewall to sidewall is 245 millimeters.
The 40 in this tire size tells you the aspect ratio, which means that the height is equal to 40 percent of the tire's width.
The letter “R” stands for “radial” because the layers run radially across the tire.
The 19 refers to the wheel diameter, which is the size of the wheel that the tire is intended to fit, i.e. this tire is made for a wheel with a 19" diameter.
The last part of the code (98V) is the load index and speed rating.
98 refers to the load index, or how much weight the tire can support when it’s properly inflated. 
If you consult a tire road index chart, you will find that the 98 means the tire can carry 1,653 pounds.
The final “V” at the end is a speed rating, indicating the maximum speed this particular tire can sustain under its recommended load capacity. 
Ratings range from A to Z, and in this case, V is equivalent to a maximum speed of 149 mph.
Only certified winter tires can carry the snowflake symbol, while the M+S (mud and snow) following it can often be found on all-weather tires as well. 
The Department of Transportation number on the tire indicates when the tire was made. 
For instance, 2519 means that the tire was produced in the 25th week of 2019.
If you live in the south where warm winters are common and snow is all too rare, then using all-weather tires year round will likely be fine. 
All-weather tires (or all-season tires) are halfway between summer tires and winter tires. 
Basically, all-weather tires are winter tires that have been given some aspects of summer tires, which means they are always a compromise between the two.
Low rolling resistance tires: This type of tire reduces resistance so you save on gas or electricity. 
The rubber compound used is different to that of regular tires, so the tread is smoother, meaning these tires have less grip and offer less comfort than comparable tires.
M+S stands for mud and snow and is often found on all-weather tires.
Offroad tires have a lot more rubber, which makes them a lot better on unpaved surfaces. 
But then, of course this means they do not perform as well on paved surfaces.
Runflat tires let you keep on driving even after your tire has been punctured, but only up to 50 miles at a maximum of 50 mph. 
And they should really only be installed on cars that have a tire-pressure monitoring system and are approved for this type of tire because otherwise you might not know you have a flat.
Racing slicks have very little little to no tread pattern. 
This is to make sure that as much of the tire as possible is hitting the ground. 
While they are not at all meant for use in the rain, under ideal track conditions, they provide optimum traction. 
However, most of these tires are not approved for street use.
DOT R compound tires are basically racing tires that have been created to comply with DOT requirements. 
They do have grooves, but they’re nothing like production tires. 
Although R compounds are street legal, they are terrible for driving on wet road pavement.
Studded snow tires are winter tires with metal studs that chip into the ice to create traction. 
They are not permitted in some states in the US, so you should check if you're planning to drive to other states in winter.
The correct air pressure is determined by car manufacturers and tire makers.
 You can consult a tire pressure chart – usually located in the car's door jamb or sometimes in the trunk, but always in your owner's manual – to find the right tire pressure for your tires. 
Tire pressure depends on the type of vehicle you have, the type of tire and the load. 
You should check your tire pressure regularly, especially before you take any long trips.
Certain car tires have a directional tread pattern, which channels water away and increases stability. 
These tires have arrows on them pointing in the direction that the tire needs to be mounted. 
When you change your tires, make sure they are all mounted in the right direction.
 If they are mounted incorrectly, it will result in a much noisier ride and they will wear faster.
After changing the entire tire (tire with the rim), they should be stored horizontally and on top of one another.
 You should store tires without the rim standing up, and turn them from time to time. 
For both options, the same conditions should be observed: as dry, cool and dark as possible. 
The way you store your tires affects the lifespan of the rubber compound.
How do you know if your tires are unbalanced?
 One obvious sign is when your steering wheel starts to vibrate. 
A car repair shop should have the right tools to check the balance of your tires and adjust them. 
To rebalance the tire, weights are applied to planes on the tire rim, inside and out.
Hydroplaning is something everyone worries about.
 It happens when there are large amounts of water on the road that the tires aren't able to displace. 
Water is pushed under the tire, creating a thin film that separates the tire from the road surface, causing it to lose traction. 
This results in a loss of steering and braking ability. 
Tires more likely to hydroplane are ones that are especially wide (they have more water to push away) and tires with a worn tread. 
When there is little tread left, the grooves fill with water so the tire can't displace the water. 
The same thing can happen if the tire pressure isn’t right.
What should you do if you suddenly find yourself hyrdoplaning? 
Take your foot off the gas, but do not brake. 
Avoid steering and disengage the clutch if your car is a standard. 
Wait until you feel the tires reconnect with the surface of the road.
What does tomorrow’s luxury look like? 
On a trip over the Furka Pass, masterchef Nenad Mlinarevic and hotelier Daniel Mani journey through time in a BMW 8 Series Gran Coup? – and its 30-year-old predecessor.
It’s 7 a.m. on the Furka Pass, high in the Swiss Alps. 
Silence. 
The only sound is the waterfall on the Rh?ne Glacier. 
The hairpin bends are undisturbed. 
From the Grimsel Pass opposite, the wind pushes impressive banks of fog deep into the valley. 
Nature could not have done a better job setting the stage for this meeting of present and past.
For over 100 years the Furka Pass has been a popular route for luxury travel. 
First via stagecoach, later by car. 
The BMW 8 Series (E31) was the most advanced luxury car of its day in the 1980s and 90s. 
But how do you bring the lure of the extraordinary into the future? 
How has the understanding of exclusivity changed since the first BMW 8 Series? 
We invited two Swiss experts in contemporary luxury to a special interview, and sent them on a search for clues – taking the scenic route on the famous mountain roads of their homeland in the BMW 8 Series of the present.
Where once guests of the Furka Pass Hotel Belvedere would have climbed out of their stagecoaches onto the Swiss glacier, the new BMW M850i xDrive Gran Coup? and its predecessor, the BMW 8 Series (E31) from 1989, are now parked. 
Nenad Mlinarevic and Daniel Mani step out of their vehicles and stare in silence. Their homeland has once again left them spellbound.
In the restaurant “focus” in Vitznau, near Lucerne, “Swiss Chef of the Year 2016” Nenad Mlinarevic cooked his way to two Michelin stars. 
But then he made the brave and unusual decision to give up his awards. 
Mlinarevic wanted a restaurant for everyone, to make more people happy with good food. 
Together with friends, he breathed new life into “Bauernsch?nke” in Zurich, and along the way developed food concepts for restaurants such as “Fritz & Felix” in the exclusive “Brenner's Park Hotel” in Baden-Baden, Germany.
How many of us get to say that our work takes us across one of the most beautiful Alpine passes?
 Hotelier Daniel Mani, together with his partners G?nter and Manfred Weilguni, designs unique wellness retreats for the modern traveler in Switzerland. 
His city hotel “Spedition” in Thun received this year's UNESCO Prix Versailles for the world's most beautiful interior. 
A drive over the Furka Pass lies the village of Flims, home to Mani’s design hotel, “The Hide” Hotel Flims – and the finish line for our experts’ Switzerland tour in the old and new generations of the BMW 8 Series.
Review. 
The two-car trip through time begins in Gletsch. 
The Furka Pass connects the hamlet in the canton of Valais with Andermatt in the canton of Uri. 
For Daniel Mani, who wants to do the first section in the original 8 Series, it’s a familiar sight.
 When he was a child his family would take this route across the mountains, while today he often commutes over the mountain roads between his hotels in Thun and Flims. 
“Our trips in the Furka Pass or the Grimsel Pass were a ritual. 
And when I got my driver's license I drove straight over the Furka Pass the very next day.”
 For Nenad Mlinarevic, too, the mountains are familiar territory. 
“They’re home. 
I particularly love the trip over the Julier Pass.” 
In order to enjoy the pass, comfort and good roadholding are key. 
But even more important is safety – and being able to accelerate out of corners fast. 
Both generations of the BMW 8 Series tick all the boxes.
Things change over time. 
As the perception of luxury cars has developed, so too has the understanding of luxury travel. 
Following its construction in 1882 and the beginning of tourism on the mountain passes, the Hotel Belv?d?re stood for nobility and pioneering spirit. 
But in the time since its fortunes faded, and the iconic building has now been closed for a number of years. 
This aspirational destination is now no more than a monument. 
Its allure, however, remains unbroken. 
Half an hour later, both vehicles are in front of the hotel. 
Driver change.
Daniel Mani takes his place in the driver’s seat of the BMW 8 Series Gran Coup?. 
He strokes the seam of the perforated seats and looks across to Mlinarevic. “
I love the feel of it. 
I wanted to hold the Swarovski crystal gear lever, to touch the speakers (? The best 6 songs to test car speakers). 
How does the milled metal feel? 
Like the leather on the dashboard? 
Whether it's a hotel room or a car, if a multitude of small details make a whole, that to me is perfect design.”
Where in the past luxury was associated with high-end products and expensive materials, today experiences like a trip or top-quality food enjoy a high status. 
“Luxury does not have to be ostentatious,” explains Daniel Mani.
 The days when bellboys or elevator operators served guests but were not allowed to talk to them are over. 
“Luxury is reducing the distance. 
Approaching people. Being a host with personality.”
That’s right,” Nenad Mlinarevic agrees. 
“Personality is the most important ingredient – both on the plate or at the reception”.
At the turn of the 20th century, luxury hotels attracted guests with conveniences such as electric elevators and speech tubes, which connected guests to the staff from the comfort of their rooms. 
Today, guests can control mood lighting and draw curtains from their tablets, or relax in their suite’s own private spa. 
“Guests staying in luxury hotels expect comfort and the right products. 
What we can offer beyond that is a human touch,” adds Daniel Mani. 
“A host who’s personally on site. Attention. 
And service that goes beyond expectations.”
Between the mountain slopes, the historic Furka Cogwheel Steam Railway winds its way up the gear rails yard by yard, as it did in its golden age. 
A hundred or so yards above it, the BMW 8 Series Gran Coup? glides around the bend, followed by its predecessor. 
Its V12 engine, in particular, was pioneering and an impressive demonstration of what was possible in the 1980s and 90s.
Innovation is a key factor for Nenad Mlinarevic. 
But luxury does not mean having to reinvent everything. 
It simply needs implementation with passion: “It’s much more effective to put your own spin on something that already exists,” Mlinarevic says. 
The 38-year-old is tall, dressed head-to-toe in black, tattooed, has a three-day-beard, and spices up every sentence with a pinch of humor. 
The new generation of gourmets is down-to-earth and innovative.
Comfort Food with a new twist is what the Michelin-starred chef calls his philosophy. 
By way of example, Mlinarevic reinterpreted Auguste Escoffier’s eponymous delicacy (invented in 1927) for the restaurant “Tatar”, including vegan variations. 
“I'm not one of those chefs who finishes cooking then spends another ten minutes moving the food around with a pair of tweezers. 
I aim for my food to be simple, but fun. 
You cannot see the effort behind the dishes. 
You have to taste it.”
The skilled restaurateur Daniel Mani can only agree. 
He says many people feel that fine dining is becoming more and more expensive, more exclusive, more extraordinary, whether in a big city or a Swiss Alps hotel. 
He shows Mlinarevic a picture on his smartphone. 
“I recently cooked for a billionaire heiress in my hotel. 
And I chose a very simple dish: meatloaf with potato pancakes. 
After she’d finished she came into the kitchen full of enthusiasm – she had not eaten that well in a long time. 
Simple cooking with the best ingredients is the new luxury for me.”
Modern luxury cars such as the BMW 8 Series Gran Coup? are tailored to the needs of the driver – and that includes under the hood. 
The pleasure trips that Nenad Mlinarevic likes to take are just as tailor-made – and he enjoys getting up into the high gears. 
For the culinary concept of his restaurant “Fritz & Felix”, he had a special idea. 
The focal point of the restaurant is a 3.1-ton (2.8-metric ton) cast-iron designer grill custom-made in La Coru?a, Spain. 
For the cost of a mid-class car. 
That, too, is modern luxury for both Mlinarevic and Mani – not just a meal on a plate, but an atmospheric, work-of-art experience of kitchen, interior and service.
Next on the menu for our two drivers are the 24 hairpin bends of the Oberalp Pass from Andermatt to Disentis, a dish perfectly prepared for a pleasure drive. 
Behind every bend (12 pro tips: How to find the racing line on any corner), a new panoramic delicacy awaits. 
A little while later, the two BMW 8 Series cars park in front of the finish line for this Switzerland tour trip through time: “The Hide” Hotel Flims, which opened in late 2018.
For Nenad Mlinarevic, modern luxury in the kitchen is no longer restricted to caviar, lobster and foie gras. 
“For me, luxury also means something rare. 
In my kitchen, for example, I use oils from Simon M?ller. 
The oil-maker produces a rosehip seed oil in his factory in Basel, Switzerland. 
There are only about 4 pints (2 liters) of it made each year. 
That is a luxurious ingredient.” 
The oil is produced with an oil press worth around 25,000 US dollars. 
But it gets even more exclusive than that – one of Mlinarevic’s producers gets to decide whether you can buy his product at all. 
“My baker not only calls himself “Eigenbr?tler”, he is a lone wolf,” Mlinarevic explains. 
“He selects his customers and shares the bread from his bakery only with people who he believes understand, appreciate and respect his craft.”
In contrast to the closed Furka Pass hotel the Belv?d?re, or the five-star resort the Chedi Andermatt – which put the small Swiss town of Andermatt on the luxury tourism map – “The Hide” Hotel Flims is scarcely recognizable as such from the outside. 
And it's like that on purpose.
 For host Daniel Mani, luxury is simple, smart and all about detail. 
Once again, the idea was implemented by the Swedish design studio Stylt Trampoli AB on the interior. 
The reward: all three Swiss hotels that Mani and his partners operate have been accepted into the Association of Design Hotels.
“We wanted to create a big living room. 
A lot of the furniture was custom-made to our specifications.” 
Customized aesthetics for guests who want to ditch their workday suit for a pair of shorts every now and then. 
The host himself is the living embodiment of the stress-free interpretation of luxury that prevails in his hotel. 
“Silicon Valley has done it,” Mani says. 
“The new luxury travel means consciously choosing hotels where you can move about unencumbered. 
What is priceless to our customers is the time and the personal attention we can give them. 
Experiences that you remember.”
What does a top chef value when he’s traveling? Mani asks Mlinarevic. 
“I don’t want to feel like I’m in some impersonal furniture shop or a baroque palace,” Mlinarevic says. “
I like hotels with lots of space and nice design.
 Hotels like “The Hide” that feel like I'm with friends. Friends who just happen to have a big house.
 I’d love to come back here for a private weekend stay some time.”
“This is one of my favorite places,” says Mani, opening the door to the sun terrace. 
Mlinarevic's eyes are immediately drawn to the designer outdoor chairs. 
He instantly recognizes the furniture of a Spanish designer label. 
“I’ve wanted to buy this for myself for a long time. Very nice!”
When Nenad Mlinarevic and Daniel Mani talk about details, you can see the rev counter of dedication in their eyes, whether it be about the preparation of potatoes or the development of a new restaurant or hotel. 
“As simple as needed, as good as possible,” explains Mlinarevic. 
“When you’re designing a restaurant, a hotel or luxury cars, you want to get everything right the first time.”
From the hotel lobby, Nenad Mlinarevic and Daniel Mani look out into the courtyard. 
The last rays of the day’s sunshine fall on the current and the previous generations of the BMW 8 Series. 
Where to next for another road trip? 
Another scenic route? 
“I'm doing a road trip through Australia. For a whole month. 
Time is my luxury,” Mlinarevic reveals. 
Mani is drawn to Tierra del Fuego. 
“I've been dreaming about it for a long time. 
This raw, barren landscape. 
Rain, sun, the sea, lonely roads – and me alone behind the wheel.” 
He runs his hand over the lines of the BMW 8 Series Gran Coup?. 
“Of course, I would love to take this car with me. 
But I have the luxury of memory now. 
Because a journey is measured in memories, not miles.”
A world premiere without the bright lights: BMW is the first manufacturer to produce a Vantablack car, painted with a light-absorbing paint in the blackest black. 
But where does this ultimate black come from? 
And what makes it so special?
Vantablack is not actually a color pigment or a paint, but a coating of carbon nanotubes. 
These have the property of absorbing incident light almost completely. 
Against a deep black background, objects coated in Vantablack material seem to disappear, as the perception of spatial depth is lost. 
This is because the human eye perceives shapes coated in Vantablack to be two-dimensional.
The BMW X6 show car has been coated with the Vantablack variant VBx2, which was originally developed for the fields of architecture and science. 
This Vantablack variant has a total hemispherical reflectance (THR) of one percent and is therefore still considered “superblack”, but it provides at least a little surface reflection from every angle.
 The great advantage of the VBx2 material is that it can be sprayed on.
Vantablack is a material made from carbon nanotubes that reflects virtually no light. 
It is considered the blackest black in existence. 
The rights lie with Surrey NanoSystems, which developed the substance for space and metrology applications. 
Vanta stands for vertically aligned carbon nanotube arrays.
The BMW X6’s superblack, non-reflective paint makes it unique: never before has a car been painted in Vantablack. 
Designed in collaboration with Surrey NanoSystems, the developer of Vantablack, this car is eye-catching in the truest sense of the word, thanks to its light-absorbing paint. 
“We have previously rejected a number of approaches from various carmakers,” says Ben Jensen, inventor of the Vantablack pigment and founder of Surrey NanoSystems. 
Only with the BMW X6 did the company feel it had the right vehicle for the job, he explains.
The technology was originally developed for space travel (? Read also: A real meteorite in a car). 
Vantablack is processed at temperatures above 800 degrees Fahrenheit and can be applied to sensitive materials such as aluminum. 
Vantablack-coated lenses make faint stars and distant galaxies visible, as solar flares stand little chance against the high-tech light-absorbing material.
Then came this collaboration with BMW. 
The design elements of the BMW X6, such as the illuminated kidney grille, the dual headlights and the strikingly designed tail lights, contrast with the smooth surface of the light-absorbing paint.
According to designer Hussein Al Attar, this was the special lure of the Vantablack car project.
 The non-reflective paint also opened up new perspectives for the designers, who were able to concentrate on silhouettes and proportions without having to consider reflections, shading and light.
This show car is destined to remain a one-off because of the enormous difficulty involved in making Vantablack paint suitably durable for everyday automotive use. 
The car paint needed for the world’s blackest black would also be extremely expensive, not to mention questionable in terms of road safety due to its level on the absorption spectrum. 
However, the technology is set to be used in laser-based sensor arrangements for driver assistance systems and thus in autonomous driving (? The path to autonomous driving).
The BMW Vision M NEXT is the latest vision vehicle by BMW. 
This design study gives you a sporty glimpse into the future. 
If you just can’t wait to see it, here are 4 ways you can experience the BMW Vision M NEXT today.
While everyone is talking about self-driving cars these days, the sporty silhouette and clear design of the BMW Vision M NEXT leave no doubt that the focus is on the driver, fully-engaged driving pleasure and sportiness. 
The vision car debuted at BMW’s new tech conference, the BMW Group #NEXTGen.
But you can glimpse the future right here, too – take a look at the design highlights of the dynamic BMW Vision M NEXT sports car in this gallery.
The BMW Vision M NEXT is a vision car that triggers instinctive reflexes in sports car fans. 
They want to touch the bodywork, enjoy the sound of the electric engine or just admire the dynamic body lines. 
And today, you can do exactly that – thanks to our four downloads for BMW lovers everywhere.
Going forward, the BMW Group will present new technologies, services and products at its own conference, the BMW Group #NEXTGen. Selected international journalists, analysts and other stakeholders will be invited to come together at the BMW Welt in Munich. 
The event will also be live-streamed for the general public.
Going to your garage and running your hand over the sporty contours of your BMW Vision M NEXT – whether or not this wondrous dream will ever come true is for fate to decide. 
But what you can already do is admire this vision car as a model and explore the shape of the BMW Vision M NEXT in miniature.
All you need is a 3D printer. Download the 3D printing data and print yourself a model BMW Vision M NEXT.
 The only limits on size are set by your printer.
Discover a new detail every day. 
It’s your model sports car, so you – and only you – get to decide the angles and directions you want to admire your miniature vision vehicle from.
And here’s a pro tip so you can enjoy your model BMW Vision M NEXT even more: Print the body and the rims separately. 
It will improve the outcome significantly.
The design of the “BMW Vision M NEXT” (below: the “vehicle”) as well as the word mark “BMW” and the BMW logo are the intellectual property of BMW AG and enjoy comprehensive legal protection for BMW AG worldwide.
 BMW AG hereby authorizes you to download and use the file “3D_model_BMW_Vision_M_NEXT” (below: the “file”) for the purpose of the one-time creation of a single model of the vehicle using 3D printing technology. 
The authorization is limited to the creation of the model for private use only.
The creation of several models of the vehicle, the commercial use of the model and/or the file, in particular the sale of the model to third parties, as well as any reproduction, publicly making available and/or any other transfer of the file to third parties are prohibited.
The use and/or depiction of the model in a context that may harm the reputation of BMW AG, its products or its trademarks is prohibited.
Electric cars are not known for their soundscape. 
The BMW Vision M NEXT, on the other hand, is an acoustic and emotional experience. 
Not least because legendary Hollywood film composer Hans Zimmer and BMW sound designer Renzo Vitale designed the boost sound especially for this concept vehicle. 
Find out what the BMW Vision M NEXT sounds like when the Power PHEV accelerates in Boost+ Mode.
You can download the sound file for Boost+ Mode here and set it as your ringtone or notification tone.
Hans Zimmer is known worldwide for his film music. 
He has composed countless soundtracks for Hollywood and has won numerous awards. 
Now Hans Zimmer has composed the sound for the BMW Vision M NEXT together with Renzo Vitale, acoustic engineer and sound designer at the BMW Group.
The film composer explains what connects him to BMW: “Sound has always been a key element in my emotional landscape even before I knew I was going to be making a living as a musician and composer. 
And the sound of a car has special resonance. 
My friends and I played a game in which we tried to guess the make and model of every passing vehicle without looking – and I could usually win.
I grew up with BMWs in my family and, as a child lying in bed at night in the dark, the sound of my parents’ car returning home spoke not only of power and beauty but also of safety and comfort and a sense of reassurance that all was well with the world.
The advent of the virtually silent electric vehicle gives me a minimalist sonic canvas upon which I am trying to create something subtly beautiful – a sonic experience which can convey the sense of confidence, well-being, joy and excitement I relished all those years ago, along with the thrill of owning and driving a technological and scientific miracle. 
I am very proud to be part of the team shaping the sound of the next generation of vehicles for the world to hear.”
Who is Hans Zimmer?
Hans Zimmer is a film score composer, arranger and record producer who works in Hollywood. 
He has won an Oscar and two Golden Globe Awards and has received 17 Grammy Award nominations – and won four of them. Famous works of the German composer include the score for “Pirates of the Caribbean”, “The Dark Knight”, “Pearl Harbor” and many other internationally famous films.
Was your childhood bedroom plastered with sports car posters? 
It was? 
Then we’ve got the perfect artwork for you! 
Download your very own poster of the Power PHEV BMW Vision M NEXT - an image that could hang on any wall.
Simply download the image file and print it out using a good color printer. 
The resolution of the image file allows a high-quality printout up to ANSI C [ISO A2] size.
Although there may be limits to picture quality, your dreams know no bounds. 
We hope you enjoy your BMW Vision M NEXT poster!
The latest vision vehicles by BMW look ahead to the future of autonomous driving. 
With the BMW Vision iNEXT and BMW Vision M NEXT vision vehicles, BMW is showcasing two versions of that future: in Boost Mode, the driver of the BMW Vision M NEXT does the driving with maximum support from the driver assistance systems, whereas the BMW Vision iNEXT in Ease Mode takes care of the driving autonomously, while the driver can use the time to do other things.
With the BMW Vision M NEXT, the focus is clearly on driving fun where the user is in complete control. 
The driver can select the driving setting on the steering wheel, like in a Formula E race car. 
This includes Boost+ Mode, which gives the driver extra engine power at the push of a button and demonstrates once again just how sporty the BMW Vision M NEXT is.
BMW and Mercedes have always been rivals, but now the car manufacturers are collaborating on car sharing services. 
That means car sharing members have the option to temporarily suspend their allegiance to their favorite brand – BMW drivers can try a Mercedes and Mercedes fans can take a BMW for a spin. 
And at our request, two car sharing users have done just that…
Many things in life require making a choice. “
Star Wars” or “Star Trek?”
 Cats or dogs? 
However, the most important choice of all is whether to drive a BMW or a Mercedes. 
Hard core fans will tell you that both is simply not an option.
But now, the two German car makers have joined forces by merging car sharing companies DriveNow (BMW Group) and Car2Go (Daimler) to create SHARE NOW. 
As a result, the more than four million car share users worldwide will now have a much bigger number of vehicles to choose from.
This newly created fleet also builds bridges. 
Now even diehard BMW and Mercedes fans can test drive the other brand without a hassle. 
This is the exact challenge that we set up for two of our car sharing users for BMW.com.
Daniel M. is a passionate BMW fan who stepped up to accept the challenge of driving a Mercedes GLA from Car2Go’s fleet for us. 
Katharina K. is a true blue Mercedes girl who selflessly volunteered to try out a BMW X2 from DriveNow.
We’re not just writing this as click bait – you really won’t believe what happened! 
But we’ll let them tell you their stories themselves…
Daniel M. is a 34-year-old architect from Munich. 
Our experiment put this staunch BMW fan behind the wheel of a Mercedes for the first time ever.
I would give up my job and all my money before giving up my car brand. 
I chose my driving school specifically because they used BMWs.
 I never get into a taxi with one of those stars on the hood unless it’s a dire emergency (like when it’s raining cats and dogs). 
Driving a BMW is one of the great joys of my life!
But my decades-old weltanschauung is starting to come apart at the seams: BMW and Mercedes have joined forces, at least as far as car sharing goes. 
And because the writers at BMW.com asked me so nicely, I decided to bite the bullet and find out what a Mercedes GLA can do.
My first impression of the outside was pretty good, but what I really liked when I got in were the sweet leather seats. 
Not the washable fake kind they have in taxis that I usually associate with Mercedeses (is that even a word?) 
Even cooler were the propellers in the air vents, which made me feel right at home in this foreign land. 
And lo, what have we here? An ignition switch. 
How quaint! 
It makes me a little nostalgic to see one of these. 
It reminds me of my first car – an E46 with a proper ignition switch.
Here’s where the problems start. 
In my BMW 330i, everything is where it’s supposed to be. 
The Mercedes GLA has carelessly relocated many features. 
As I’m diligently looking around for the parking brake, I discover it in the form of an extra pedal way off in the left field.
 In a stick shift, that would mean a total of four pedals. 
Gives a whole new meaning to “four on the floor,” doesn’t it? 
Add one or two more of those and you’ve got yourself a nice little organ there.
I’m not ready to head downtown just yet though because I still haven’t found everything on this fun little scavenger hunt. 
I do discover that the blinker switch is where the wipers should be, but I drive a Bimmer, so who needs a blinker? 
It’s like they didn’t want me to find the windshield wipers.
Finally, I’ve found all the relevant parts and I set off. 
And you know what?
 I’m surprised at how comfortable a ride this is.
 Now I understand why Mercedes buyers are two years older on average than BMW buyers. 
Is that maybe why the teenager at the drive-through window of “my” Car2Go Mercedes is yelling at me like I’m half deaf?
I have to admit that the GLA is a really smooth and fun ride.
 I’m even considering buying myself a Daimler – in 20 or 30 years maybe. 
So there’s my test drive at an end. 
It was fun. 
But it would have been better without the rain and me having to constantly deal with the blinker/wiper conundrum.
Katharina K. is a 36-year-old doctor from Stuttgart. 
The lovely Mercedes lady bravely sat behind the wheel of a BMW for the first time in our BMW vs Mercedes experiment.
After I was born, my father, who naturally worked for Daimler, brought me home from the hospital in our Mercedes 300 D. 
When I was a teenager, he told me that I was never to bring home a boyfriend who drove a BMW.
 Such a thing would have never crossed my mind anyway. Honestly. 
It’s not like one day you root for Harvard and the next for Yale. 
I’m a Benz girl through and through.
So this will be my first fling with a car with the white and blue propeller on it. 
My first thought as I walk up to the BMW X2 is that I like the cool gold metallic color.
 I walk around the car and see BMW logos glaring back at me seemingly everywhere I look. 
Nothing like subtlety and understatement, is there? 
It's not really surprising if you think about it; Bavarians are known for their, ah, well-developed sense of self-worth.
Once I’m inside, however, I must admit that the interior is pretty chic. 
Sitting behind the wheel, I’m starting to feel the perfidy of my actions. 
Am I compromising myself? 
Is this the start of something bigger than the both of us?
 I tell myself that I’m just helping out the nice DriveNow car sharing company people and I will explain it all to my dad later.
 To help calm my nerves, I carefully place a Mercedes sticker over the BMW logo on the steering wheel. 
Much better.
At this point, I’m ready to get this BMW X2 out on the road, but nothing seems to be in the same place as in my Mercedes A Class. 
After an extensive search, I find everything, including the blinker.
 I’d always wondered if BMWs had blinker switches! 
And amazingly, the right blinker works perfectly fine too – even though only Bernie Sanders is more determined to stay on the left than a BMW driver on the highway.
After the first few miles, I have to say that it really hugs the curves, which makes it fun to drive. 
Now I am personally acquainted with every manhole cover in this city – driving the BMW X2 is a bit more bracing than I’m used to. 
But what I want to know is this: If BMW builds such great race cars, why were they only Formula 1 world champions that one time in 1983? 
Mercedes manages to win the title every year –  just saying.
After being bounced around like Lewis Hamilton in Monaco, my BMW adventure finally ends at a gas station. 
But it’s not completely over because as I go to fill the tank, I find nothing.
 I panicked a little and considered drilling a hole for the tank nozzle.. 
Maybe that’s why they have two extra BMW logos towards the back: to remind Mercedes SHARE NOW customers that the tank is on the other side. 
All in all, it was a truly novel experience. 
Thank you shary much!
Long distance driving makes you tired, so falling asleep is a real danger. 
But how do you prevent that? 
Coffee? 
Loud music? 
Or a break and a cat nap? 
Read on for the best tips on how to stay awake while driving.
We’ve all felt drowsy on the road before, but many people underestimate just how dangerous tiredness at the wheel really is.
According to a study published in the British journal Occupational and Environmental Medicine, researchers in Australia and New Zealand found that drowsy driving has some of the same effects as drunk driving. 
They found that people who drive after being awake for 17 to 19 hours performed worse than those with a blood alcohol level of 0.05 percent, which is the legal limit for drunk driving in most western European countries, though it is a little higher in most U.S. states at 0.1 percent or 0.08 percent.
Fatigued drivers may be plagued by so-called microsleep, where a driver nods off for a few seconds.
This is highly dangerous, as a five-second failure to stay awake while driving at 55 mph (approx. 90 km/h) would mean you’ve traveled around 135 yards (120 meter) down the road when you were asleep, which is more than enough time to cause a crash.
There are often warning signs of impending microsleep, like frequent yawning and blinking, heavy eyelids and irritated eyes. 
When these symptoms occur, it’s time to do something about it.
Want to know the best way to stay awake and how to prevent an unintended nap? 
Look no further – read our tips below.
Get enough sleep: The only true remedy for fatigue and drowsiness is sleep. 
Above all, the key thing for long distance driving is to be well rested. 
So don’t embark on a long journey after major stress and with a bad night’s sleep behind you.
Power naps: On longer trips you should take a cat nap at least every four hours. 
The benefits of napping are clear, but these power naps should be no longer than 20 minutes (as recommended by the US National Highway Traffic Safety Administration), as otherwise your body may head into deep sleep.
Avoid your natural low points: Adjust your driving times or your journey to avoid your body’s biological low points, dictated by your circadian rhythm. 
These are generally between 2 a.m. and 5 a.m. and about 1 p.m. and 3 p.m. 
While you may miss the traffic by planning a long car ride in the middle of the night, it’s definitely not a good way to prevent the dangers of drowsy driving.
Take regular breaks: One of the most important long distance driving tips is to make sure you take regular breaks. Park up, relax, and leave your vehicle. 
A short walk in the fresh air not only gives you a chance to stretch your limbs, but also boosts the supply of oxygen into your bloodstream, increasing your ability to concentrate.
Food: The right food also affects your condition. 
Don’t drive hungry or with a full stomach – both will inhibit your performance. 
Eat light snacks for driving such as vegetables, which provide an energy boost while at the same time being a healthy choice. 
Don’t consume heavy food during your journey, as this will only induce drowsiness.
Fluids: You should always drink enough fluids, something that doesn’t just apply to long distance driving. 
Drink as much water (or unsweetened juices) as possible.
The right amount of distraction: It can be helpful to have other things to do on a long car ride, just not so much that they keep you from concentrating on the road. 
An exciting audiobook can keep you mentally fresh during a monotonous activity like driving long distances. 
Even better is a conversation partner, one who entertains you and notices when you’re getting tired. In an ideal scenario, he then takes over at the wheel and gets you both to your destination safe and sound.
Medication and medical conditions: Be careful when driving after taking certain medication. 
Side effects may include drowsiness and diminished attention, which could result in drowsy driving or even microsleep. 
Sleeping pills, psychoactive drugs, analgesics and even allergy medicines can have this effect. 
If you’re unsure about a medicine’s effects, you should contact your doctor or pharmacist. 
The same applies if you suffer from medical conditions like chronic fatigue or a sleep disorder.
Candy will give you a quick boost of energy.
 But your blood sugar level will then drop just as quickly and the tiredness will return, so it is just a short-term fix and not the best way to maintain your safety when driving long distances.
It’s a similar story with energy drinks and coffee: After the initial lift from caffeine and other similar substances, the fatigue soon comes back.
Airing out: Briefly opening the windows will give you an extra burst of oxygen (and perhaps a shock to the system that will provide a short blast of alertness). 
This trick, too, wears off quickly and therefore also goes in the “short-term fixes” folder.
Chewing gum stimulates the circulation of blood in the brain and reduces the symptoms of sleep deprivation – but only temporarily. 
It’s no cure for drowsy driving over prolonged periods.
Even loud music only helps briefly. 
What’s more, when you’re tired, you’re already less receptive to what’s going on around you, something not improved by a music system on full blast. 
This, in turn, is bad for traffic safety.
Driver assistance systems can also help you with drowsy driving. 
Typical symptoms of fatigue include careless driving and leaving your lane. 
There are various driver assistance systems to help prevent these things from happening.
 The Steering and Lane Control Assistant and side collision protection sound an alarm if you perform unnatural steering wheel movements or are in danger of being hit from the side. .
If this happens because you’re struggling with tiredness at the wheel, you should definitely take a break
Digital assistants such as the BMW Intelligent Personal Assistant also work to help you combat tiredness. 
Simply say “Hey BMW, I’m tired!” and your vehicle will launch the reinvigoration program, which is designed to combat fatigue via temperature adjustment, lighting effects and music. 
In fact, after a few hours of uninterrupted driving, a BMW tells you to take a break. 
If you need a bed, the navigation system, supported by the BMW Concierge Service, will guide you to the nearest hotel.
The golden rule, and one that always applies, is safety first. 
If you’ve followed these safe driving tips to help you prevent drowsy driving but you’re still tired, you need to stop! 
Break up your journey and continue only after a good night’s rest. 
The best way to tackle a lack of sleep is always with sleep.
Why know your traffic signs?
Traffic signs play a vital role in directing, informing and controlling road users' behaviour in an effort to make the roads as safe as possible for everyone.
 This makes a knowledge of traffic signs essential.
Not just for new drivers or riders needing to pass their theory test, but for all road users, including experienced professional drivers.
We live in times of change.
Society, technology and the economy all play their part in changing the way we travel.
New road signs conveying new messages and in new formats are introduced from time to time, so drivers or riders who passed their driving test a few years ago need to keep up to date or run the risk of failing to understand or comply with recently introduced signs.
Having experience is all very well, but it's not enough if your knowledge is out of date.
The central administrations above are responsible for the UK’s strategic road network.
Strategic roads are the highways that link cities, areas of population, ports and airports.
Most motorways and some “A” roads are strategic roads.
Local or regional highway authorities are responsible for local roads, and this includes a few motorways, all other “A” roads and all other public roads.
While responsibility for placing, erecting and maintaining traffic signs is split among these bodies, it is important that signs are consistent both in appearance and in the way they are used.
To ensure that the UK has a uniform traffic signing system, signs must conform to the designs prescribed in the Traffic Signs Regulations and General Directions (although some signs may have been specially authorised by the Secretary of State).
The Traffic Signs Manual, published by TSO, provides detailed guidance for those responsible for designing and installing traffic signs.
It was probably the Romans who first used "traffic signs" in Britain.
They marked off road distances at one thousand paces (about one mile) with stones called "milliaries".
Most early signposts were erected by private individuals at their own expense.
 A law  passed in 1648 required each parish to place guide posts at its crossroads, but it was not until after the General Turnpike Act 1773 that these "guide posts" or"fingerposts" became more common.
During the second half of the nineteenth century, bicycles became more popular.
Steep hills and sharp bends were very dangerous for early cyclists, and "danger" and "caution" signs were erected at the top of steep hills.
Signs showing  skull and crossbones were erected at the most dangerous places.
 Local authorities and cycling organisations installed an estimated 4000 warning signs.
The Motor Car Act 1903 made local authorities responsible for placing certain warning and prohibitory signs.
 The signs were for crossroads,steep hills and dangerous bends.
 "A" and "B" numbering of roads was introduced in 1921, and these numbers were shown on fingerpost-style signs alongside the destination and distance.
 Town or village name signs and warning signs for schools, level crossings and double bends were introduced at the same time.
The main task of signposting our roads during the 1920s and 1930s still fell on the motoring organisations, but in in 1931 a committee chaired by Sir Henry Maybury was asked to recommend improvements to the signing then in use, and by 1933 further new signs began to appear, including "No entry" and "Keep left" signs, warning signs for narrow roads and bridges, low bridges, roundabouts and hospitals.
 Other signs followed during the 1930s, including "Halt at major road ahead".
 These formed the basis of our traffic signing until the early 1960s.
It was not until after 1918 that white lines began to appear on British roads, and during the 1920s their use spread rapidly.
 In 1926 the first Ministry of Transport circular on the subject laid down general principles on the use of white lines.
 In the 1930s, white lines were used as "stop" lines at road junctions controlled by either police or traffic lights.
 Reflecting road studs (often referred to as "cat's eyes") first came into use in 1934.
 By 1944, white lines were also being used to indicate traffic lanes and define the boundary of the main carriageway at entrances to side roads and lay-bys, and in conjunction with "halt" signs.
 In 1959, regulations came into effect to control overtaking by the use of double white lines.
It was realised that the old system of signing would not be adequate for motorways, and the  Anderson Committee was set up in 1958 to consider new designs.
It recommended much larger signs, with blue backgrounds.
Then, in 1961, the Worboys Committee began to review the complete system of traffic signing.
 It concluded that the UK should adopt the main principles of the European system, with the message expressed as a symbol within a red triangle (for warning signs) or a red circle (for prohibitions).
 Work began on the conversion of British signs in 1965, and this is still the basic system in use today.
Later developments include the use of yellow box markings at busy road junctions, special signs and road markings at pedestrian crossings, mini roundabouts and bus lanes.
 Regulations published in 1994 included new regulatory and warning signs and simplified the yellow line system of waiting restrictions that was originally introduced in the 1950s.
 Further Regulations were published in 2002.
More use is being made of new technology to provide better information to drivers on hazards, delays and diversions.
 The future will undoubtedly see more developments in traffic signing to keep pace with the changing traffic demands on our roads.
There are three basic types of traffic sign: signs that give orders, signs that warn and signs that give information.
 Each type has a different shape.
 A further guide to the function of a sign is its colour.
 All triangular signs are red.
There are a few exceptions to the shape and colour rules, to give certain signs greater prominence.
The words "must" or "must not", when used in the descriptions that follow, refer to legal requirements that have to be obeyed.
Most regulatory signs are circular.
 A RED RING or RED CIRCLE indicates a prohibition.
 A BLUE CIRCLE generally gives a positive (mandatory) instruction or indicates a route for use only by particular classes of vehicle (see sections on tram signs and bus and cycle signs).
Remember that in areas of street lighting (other than on motorways) a 30 mph limit applies unless another limit is specifically signed.
Each year there are hundreds of incidents in which bridges are struck by vehicles too high to pass under them.
 Both rail and road users have been killed in these incidents.
 Look out for signs in this section and make sure that you are not a bridge basher.
All bridges with a clearance of less than 16 feet 6 inches (about 5 metres) are normally signed.
 Both regulatory roundels and warning triangles can be used, depending on the type of bridge.
Bridges particularly at risk from strikes may have a variable message sign that is activated by high vehicles passing through an infra-red beam.
When the sign is activated, four amber lamps flash, the top pair alternating with the bottom pair.
At non-arch bridges mandatory signs may be used; it is unlawful for an overheight vehicle to pass one of these.
 They are placed on the bridge and at the side of the road in front of the bridge.
A warning sign indicates, in imperial units, the maximum headroom under a bridge or other overhead obstruction.
 There may be an additional sign showing the height in metric units.
 These PD signs may be sited well in advance of a bridge, with the distance, either in yards or miles, shown on a plate; this may have an arrow to indicate that the bridge is on a side road at a junction ahead.
Chord markings used indicate the points between which different headrooms over different parts of an arch bridge are available.
The maximum safe headroom at an arch bridge is shown on the triangular warning signs.
 Road markings guide high vehicles through the highest part of the arch.
 Drivers of all vehicles should give way to oncoming high vehicles in the middle of the road when there is insufficient room to pass.
 Drivers of cars and other low vehicles may keep to the left-hand side of the road, crossing the road markings, where this would enable them to pass oncoming vehicles in safety.
To improve the conspicuity of a bridge, black and yellow bands may be added to the arches or beams and to the abutments.
Roundels or warning triangles will sometimes be incorporated into directional signs that may also indicate an alternative route to take to avoid the low bridge.
Roundels may also be incorporated into road works signs to indicate temporary height restrictions.
Some crossings have flashing red road traffic signals; these mean STOP (and this applies to pedestrians too).
 A steady amber light shows before the red lights begin to flash, as at ordinary road traffic signals; this means STOP unless it is unsafe to do so.
 If the red lights flash for more than three minutes without a train arriving (other than at crossings with full barriers), or any barrier is lowered without the lights flashing, phone the signal operator.
 When the barriers rise, do not proceed until the signals go out.
 If your vehicle breaks down or stalls on a crossing, get yourself and your passengers out of the vehicle as soon as possible.
 Phone the signal operator and follow the instructions given.
 Stand well clear of the crossing if the alarm sounds, the signals show or the barriers lower.
Amber lights and audible warnings followed by flashing red lights warn that a train is approaching and that the barriers are about to come down.
 You must STOP.
 The red lights flash all the time the barriers are down, but the audible warning might stop.
 If another train is approaching, the barriers will stay down; the lights will continue to flash and, if there is an audible warning, the sound will change.
 Full directions for using these crossings are given on roadside signs.
 You must stop even if the gates or barriers have been left open.
 Always close the gates or barriers after crossing.
The St Andrew’s cross is used at level crossings where there are no gates or barriers.
 At automatic crossings, you must always STOP when the traffic light signals show.
 At crossings with "give way" signs, always look out for and give way to trains.
Trams can run on roads used by other vehicles and pedestrians.
 The part of the road used by trams (the "swept path") may have a different colour or textured surface to the rest of the road, or it may be edged with special road markings.
 Trams cannot move out of the way of other road users!
Areas such as shopping streets may be signed as "pedestrian zones".
Depending on the extent of the vehicle entry restrictions, such areas may be paved without the usual separation between footway and carriageway and may not have yellow lines and kerb markings to indicate waiting and loading restrictions.
 Instead restrictions are detailed on zone entry signs and repeater plates.
 The entry signs may indicate that buses, taxis, disabled badge holders or permit holders may enter the zone.
Various examples of zone entry signs are shown below
Where loading restrictions apply in addition to waiting restrictions ("loading" means both loading and unloading), these are indicated by both yellow kerb marks and white plates.
These plates may be combined with the yellow "no waiting" plates.
As the marks are placed intermittently along the kerb, a white plate is normally erected at the first mark (where the loading restriction begins) and may include an arrow indicating the direction along the road in which the loading restriction applies.
Where a white plate does not indicate the days of the week, the restrictions apply at the same times every day, including Sunday.
 If a bank holiday falls on a day when the restrictions are in operation, the restrictions apply in the normal way unless the plate states that they do not.
Routes recommended for goods vehicles have black signs with a white lorry symbol.
 Other direction signs may incorporate black lorry route panels.
The most suitable route for lorries to a particular destination may be different from that for other vehicles.
The lorry symbol faces in the direction inwhich vehicles turn at a junction.
 For ahead destinations, the symbol generally faces left.
 Where route numbers for motorways and primary routes are shown, these are placed on blue and green patches respectively.
 On all-purpose roads, the symbols may be used on separate signs with yellow backgrounds.
Temporary diversion signs may be required when a road is closed for reasons other than an emergency, e.g.to carry out works.
Direction signs specifically for cyclists have a blue background and include a white pedal cycle symbol.
 Most are free-standing signs, but some primary and non-primary route direction signs may incorporate a blue panel indicating a route for cyclists that is different from that for other traffic.
 The cycle symbol may also be used on pedestrian signs where cyclists and pedestrians share the route (see page 113).
 Some local authorities may have their own numbered cycle routes using different coloured patches.
 Where a cycle route leads to a national or regional route, the number of the route to which it leads may be shown in brackets.
Temporary signs are put out when vehicles are to be stopped for an excise license check or vehicle inspection.
 These signs may apply to specific types of vehicle such as goods vehicles or buses, and they may indicate which lanes to use.
On some busy roads, lane control signals are used to vary the number of lanes available to give priority to the main traffic flow.
Traffic signs control traffic flow, making streets and highways safe for drivers,bicyclists and pedestrians.
These signs, which are posted by the Indiana Department of Transportation and local governments, use colors, shapes, written messages, and symbols to help drivers quickly understand the information.
 Understanding these signs is necessary to obtain an Indiana driver’s license.
The background color of a traffic sign helps to identify the type of information displayed on the sign.
 There are seven colors commonly used for signs.
Red traffic signs convey traffic regulations that require drivers to take immediate action to avoid threats to traffic safety.
 A “Wrong Way” sign is an example of a traffic sign with a red background.
Yellow or fluorescent yellow-green traffic signs prepare drivers for specific road conditions and hazards ahead, and alert drivers to nearby school zones.
 A “Slippery When Wet” sign is one example of a traffic sign with a yellow background.
Fluorescent yellow-green signs warn drivers of nearby schools, pedestrians, bicycles, playgrounds, and school bus routes.
 A “Pedestrian Crossing” sign for a school crossing is an example of a traffic sign that may have a fluorescent yellow-green background.
White traffic signs display traffic regulations, such as speed limits, that drivers must obey, as well as helpful information such as state highway markers.
 A “No Turn On Red” sign is an example of a traffic sign with a white background.
Orange traffic signs warn drivers of temporary traffic conditions.
 A “Flagger Ahead” sign is an example of a traffic sign with an orange background.
Green traffic signs indicate permitted movements and directions or guidance, such as highway entrances and exits or distance to upcoming destinations.
 A sign showing distance is an example of a traffic sign with a green background.
Blue traffic signs display road services and evacuation route information.
 A sign showing information about amenities at an upcoming exit is an example of a traffic sign with a blue background.
Brown traffic signs indicate nearby recreational and cultural interest sites.
 A sign showing a nearby state park is an example of a traffic sign with a brown background.
The shape of a traffic sign also indicates the type of information displayed on the sign.
Circular traffic signs alert drivers to upcoming railroad crossings.
Traffic signs with three sides of equal length warn drivers to slow down when approaching an intersection, and to be prepared to come to a complete stop in order to yield to other drivers or pedestrians.
Pennant-shaped traffic signs are posted on the left-hand side of two-way roads to warn drivers not to pass other vehicles on the left.
Rectangular traffic signs display one of three types of information.
 They may convey traffic regulations that drivers must obey, such as speed limits and turn movement prohibitions like “No Left Turn.
They may provide helpful information such as route marker signs that identify a state highway, or destination signs that give the direction to the next town.
Diamond-shaped traffic signs warn drivers of upcoming road conditions and hazards.
 A “Divided Highway Ends” sign is an example of a diamond-shaped traffic sign.
Warning signs prepare drivers for upcoming road conditions and hazards.
The following signs are examples of Indiana’s warning traffic signs:
Yellow or fluorescent yellow-green signs warn drivers that they are entering an area near a school in which children may be crossing the road.
A slow-moving vehicle emblem has an orange fluorescent center and red reflective borders, and indicates a slow-moving vehicle which cannot exceed 25 miles per hour.
Traffic regulation signs regulate traffic speed as well as movement and display rules which drivers must obey.
Traffic guidance signs provide drivers with information about the type of road they are traveling on, upcoming highway entrances and exits, and distances to various destinations.
Driver services and recreation signs provide drivers with information about nearby amenities, parks and recreational areas.
Traffic control devices such as stop lights and signs are used to control traffic flow and indicate right of way at intersections and pedestrian crossings.
A green light means go.
 If you are facing a green light, you have the right of way and may drive through an intersection as long as the intersection is clear of other vehicles and pedestrians.
A steady yellow light means the green light has ended and the signal is about to turn red.
 If you are facing a steady yellow light, your right of way is ending.
 If you are approaching the intersection and are too close to stop safely, you may complete your movement.
A red light means stop.
 If you are facing a red light, you may not enter the intersection until the light facing you turns green and the intersection is clear.
If you are facing a green arrow displayed with a red or green light, you have the right of way and may turn through an intersection, as long as the intersection is clear.
If you are facing a green light displayed without an arrow, you may turn through an intersection as long as the intersection is clear.
 You must yield the right of way to all oncoming traffic.
 Only one vehicle at a time may move into an intersection to turn left.
A yellow flashing arrow for a turning movement means that you may proceed with the turn only after you have yielded the right of way to pedestrians and oncoming traffic.
If you are facing a steady yellow light or arrow, your right of way is ending.
If you are facing a red light or arrow, your right of way has ended.
If you are in the middle of an intersection, you may turn once oncoming traffic has stopped.
 If you are facing a red light or arrow, you may not enter the intersection until the light facing you turns green and the intersection is clear.
To turn right through an intersection with a red light or arrow, when permissible, you must come to a full stop, check to make sure that there are no vehicles and pedestrians in the path of your turn or about to enter the path of your turn, check that there is not a “No Turn on Red” sign and use the correct lane.
You may turn left through an intersection with a red light or arrow if you are turning from a one-way street onto a one-way street.
 You must also come to a full stop, check to make sure that there are no vehicles and pedestrians in the path of your turn or about to enter the path of your turn, check that there is not a “No Turn on Red” sign, and use the correct lanes.
A yellow flashing light displayed without an arrow at an intersection means that you should slow down and use caution when traveling through an intersection.
 If turning left, you must yield to oncoming traffic and pedestrians.
 All traffic on the cross street is required to yield the right of way to you.
 However, you should watch for other vehicles or pedestrians attempting to cross the intersection.
A red flashing light at an intersection is equivalent to a stop sign and means that you must come to a complete stop before proceeding with caution to enter the intersection.
If you are facing a red flashing light at an intersection at which cross-traffic is not required to stop, you may proceed only when the intersection is clear and when you will not interfere with the right of way of cross-traffic.
If you are facing a red flashing light at an intersection at which all traffic is required to stop, you may proceed only after you have stopped and yielded the right of way to any vehicle that is already in the intersection, any vehicle that stopped before you and is entering the intersection, and any vehicle that arrived at the same time as you and is to your right.
If you are approaching a red light or a stop sign, you must stop at the solid white stop line.
 If there is no stop line, you should come to a complete stop perpendicular to the stop sign or before entering the crosswalk on your side of the intersection.
 If there is no crosswalk, you should come to a complete stop before entering the intersection.
Often people who are operating motorcycles, motor driven cycles and bicycles get stuck at a red light and the signal fails to change to green.
 These individuals may avoid prolonged waits at red lights under the following condition:
An operator approaching an intersection controlled by a traffic signal may proceed through a steady red light if the operator comes to a complete stop for at least two minutes and exercises due caution 
This rule does not apply to autocycles
A yield sign indicates that a driver must slow down when approaching an intersection and be prepared to come to a complete stop if a vehicle or pedestrian with the right of way is approaching from another direction.
If you are approaching a yield sign, a vehicle approaching from another direction with the right of way should not have to brake to avoid a collision with you.
If you are approaching an intersection with a non-operating signal, you should stop before entering the intersection.
Before entering a street from an alley or driveway, you should stop and yield the right of way to other vehicles.
Pedestrian signals alert pedestrians when they may safely cross a street or intersection.
Pedestrian signals display the word “WALK” or a symbol of a person walking when pedestrians may safely cross a street or intersection.
 At some intersections, there is a button near the base of the pedestrian signal or stop sign that may be pushed to activate the walk signal.
Pedestrian signals display the words “DON’T WALK” or a symbol of a raised hand when it is not safe for pedestrians to cross a street or intersection.
 The words or symbols flash to alert pedestrians that the time in which to safely cross the street or intersection is ending.
A pedestrian hybrid beacon is a signal used to facilitate pedestrian crossing, and which may be found at a mid-block crosswalk.
 The pedestrian hybrid beacon is dark unless it has been activated by a pedestrian.
Once activated by a pedestrian, the pedestrian hybrid beacon will display a flashing yellow light to allow drivers to clear the crossing.
 The flashing yellow will be followed by a steady yellow light to warn drivers that their right of way is ending.
 Then, two steady red lights will be displayed while the pedestrian crosses, and then the two red lights will flash to allow drivers to proceed through if the crossing is clear of pedestrians.
 The pedestrian hybrid beacon will then go dark until activated again by a pedestrian.
Theft from lorries and haulage containers is a growing problem throughout Europe and those with sides made of fabric are particularly vulnerable to attack.
Cargo containers spend a lot of time unattended in loading or storage depots and their tarpaulin covers, while light and convenient to use, offer little protection against the knives of vandals and thieves.
By 1996, so serious had this problem become that three companies, a French manufacturer of haulage containers, a Belgian plastics and composites company and a large Belgian rail/road haulier joined forces with CRIF, a Belgian collective industrial research centre, to develop a new protection system for  containers.
The work was supported under the EU's CRAFT scheme and initial studies pointed towards the development of a better material for fabric screens, which would retain the advantages of lightness, flexibility and ease of cleaning, while offering great strength and resistance to attack. 
But where might one find such a material?
As part of its work, ESA’s Technology Transfer Network (TTN) surveys non- space companies to see what kind of technology they might need.
 It was through this mechanismthat the Belgian TTN partner Creaction circulated the requirement for a vandal-resistant textile.
By good fortune, a French company Societ? Ariegeoise de Bonneterie, following the success of its flame-proof textiles used on Ariane rockets, had modified its knitting technique to create a flexible fabric from steel wire which was extremely difficult to cut and well- suited to the application.
A newspaper article about this new material was spotted by Novespace, the French TTN partner at the time,and so the connection was made.
Parcouri, a consortium of eight European companies that includes a Dutch multinational producer of vehicle covers and a French SME specialising in coach building and kit fixing systems is now developing a vandal resistant alternative to the standard tarpaulins presently in use.
Within an existing global market of 120,000 units a year, current predictions for the newmaterial showa healthy potential market opening of 7000 units annually.
Composite  materials  made  of  a  carbon  matrix reinforced by long carbon fibres can withstand high temperatures and are very resistant to wear.
These materials were originally developed for use in the extreme  conditions  found in the  nozzles  of the European Ariane  rocket motors.
The  developers realised that brakes made fromsuch composites were more reliable,  reduced vibration,  and caused less pollution than traditional  braking  systems  fitted to planes and road vehicles.
Messier-Bugatti, based in France, produceda novel carbonbraking systemcalled Sepcarb?for use on aircraft such as the Airbus and nowsupplies one-third of the world market for carbon composite brakes for commercial planes with more than 100 seats (over 145 airlines have nowchosen Sepcarb?carbon brakes for over 1600 aircraft).
Similar systems have also been employed on Formula 1 racing cars, road vehicles and passenger trains.
Another important safety feature - the airbag - has contributed a great deal to safer car travel in recent years, saving many lives and helping to prevent serious injury in collisions.
Today, the device is considered to be one of the most important safety devices since the seat belt was first introducedinthe 1960s.
Whenanairbag inflates, there is essentially a controlled explosion occurring inside your car!
The typical standard device is housed in the centre of the steering wheel along with the inflator.
Anigniter activates compressed gas capsules and these fill the bag withaninert gas whenanimpact of above a certain force is sensed.
The whole inflation process occurs within a split second and the bag is completely deployed in less than a second - enough time to restrain the occupant.
As most newcars employ suchsafety devices, the market forthe pyrotechnic charges is huge.
The Frenchcompany SNPE Propulsion is using its knowledge in the field of solid propulsion for ballistic missiles and space launchers  to design and develop the pyrotechnic charges used in airbag gas generators and seat-belt tighteners.
SNPE Propulsion estimates that its products are used in one out of every four safety devices fitted on new cars each year.
To help in the construction and maintenance of the International Space Station, the Canadian Space Agency has been coordinating the development of the ‘Special Purpose Dextrous Manipulator’ (SPDM) - a two-handed robot which is essentially an extension of the astronauts' own limbs.
Until recently, these augmented limbs lacked one critical feature - a sense of touch.
Without a sense of touch, machines can easily accidentally knock over or bump into other objects.
 In space, obviously, this can have drastic consequences.
 Although automated vision systems have been under intensive development for several years, tactile sensing technologies are rare and relatively primitive.
Recognising this challenge, Canadiancompany CanpolarEast developedKINOTEX - a novel sensorthat emulates human touch and can be applied like a skin or sleeve to cover entire robotic limbs.
Normally arranged in arrays, these sensors can detect and interpret contact at many points over the surface of the machie.
Because they use light to detect change, KINOTEX sensors can be very small and are immune to interference fromsources such as electromagnetic radiation.
They are also very responsive, sensing minute amounts of pressure and reacting extremely quickly to change.
Many industries are implementing KINOTEX products.
For example, automotive companies have acquired the rights to develop pressure-sensitive car seats that help increase  safety.
KINOTEX  sensors  are  also being  considered for incorporation into energy absorption bumpers for cars to determine the severity of crashes and collisions with pedestrians.
So, thanks to the sense of touch developed for robots in space, we may be able to travel much more safely in our vehicles!
Movements and vibrations inevitably occur when many different parts are brought togetherto builda carora spacecraft.
Inbothcases, it is necessary to findout how these components interact before they are assembled.
Special simulation software used by ESA to design the Columbus module for the International Space Station can assess the behaviour of complete systems even before they are built and sent into space, thus avoiding the prohibitively high costs otherwise involved in fixing problems afterwards.
The same software canhelpcar manufacturers to simulate vehicle dynamics andthendiagnose any vibration problems.
This software has already attractedthe attentionof the automotive industry andcompanies suchas BMW, DaimlerChrysler, Rover, Bosch and Iveco are now using it to develop virtual prototypes of entire cars and heavy goods vehicles.
The Prost Formula 1 racing teamwas using another type of technology, known as the SPADD (Smart Passive Damping Device).
This systemwas developed by the French company Artec Aerospace to protect satellites and space structures fromthe strong vibrations occurring during launch.
However, the SPADD system can also be used to reduce (dampen) vibrations ina racing car, so leading to improveddriversafety.
Incidentally, the same technology is also being applied to reduce the noise and mechanical shocks in concrete mixers!
When the Rosetta space probe, one of ESA's main science Cornerstone missions, is launched in 2003 to study cometWirtanen, it will use a clever piece of technology which will soon benefit engineering applications nearer home.
The device is an actuator for implementing fine movements developed by Cedrat Recherche, a spin-off company from the Polytechnic Institute of Grenoble, and is based on the piezoelectric effect.
This is a well-established phenomenon whereby a small voltage passing across a crystal such as quartz causes it to change shape (or vice versa - pressure on the crystal induces a voltage).
The distances moved are small but the forces produced are large.
The effect is exploited in many electromechanical devices such as quartz clocks and microphones, and is ideal for controlling movements fast and accurately and with little power.
Cedrat had already developed piezo actuators for the French Space Agency CNES, for micro-positioning and vibration damping of satellite optical systems.
Further applications followed, including optical shutters, piezoelectrically-controlled valves and non-magnetic rotating motors for sensitive instrumentation.
Cedrat's actuators employ a clever combination of synthetic piezoelectric materials and mechanical engineering to give a much greater range of movement than previous piezo devices.
 So it is not surprising that these amplified piezo actuators have found many non-space uses, for example in instrumentation such as microscopes, camera shutters and hospital MRI scanners.
The technology is increasingly penetrating more established fields of engineering.
The next generation of car engines already face enormous demands in terms of efficiency, power output and low emissions.
They will need to respond rapidly to changes indriving conditions, constantly optimising engine performance.
Microchips are already able to supply the real-time electronic commands needed for adaptive engine management, and newactuators are needed to translate these into the mechanical language of the engine.
Much attention has been given to the improvement of fuel injection systems where the electrical control of individual injectors can significantly affect engine performance.
Amplified piezo actuators with their combination of very fast response times, low voltage operation, high operating forces and precise control offer one very promising approachto the automotive injector of the future.
Withthis inmind, anautomotive injector designed by Cedrat Recherche and Fiat, and based on the amplified piezo actuator, has recently been patented.
So, a novel device used to control spacecraft will help build the next generation of greener, more efficient cars.
Nutation, from the Latin word to nod, usually describes the circular movements of a growing shoot or the wavy path followed by the Earth's axis as it travels around the Sun.
It is also the motion of a coin, spinning like a top, as it slows and falls.
Careful observation shows that as the coin slows it describes a circle on the table top.
Interestingly, the diameter of this circle is smaller than the diameter of the coin itself.
This means that for each nutation of the coin, the circumference of the circle traced on the table top is less than the circumference of the coin and the coin must therefore rotate.
Even closer observation reveals that although the point of contact between the coin and the table moves very quickly, the coin itself rotates quite slowly.
The visible effect is that of an apparent gearing between the two motions.
This is not an illusion, but a real and useful effect which can be accurately described mathematically.
Many everyday appliances rely upon small electric motors to operate - video recorders, car window winders and seat adjusters, tape drives andCD players all have them.
Oftenthe requiredshaft speedof the motoris quite lowbut, toprovide significant power, small machines work best at high speeds.
To reduce the speed of rotation and so gain an increase in output torque, or twisting power, a gearbox is needed - just as in a car.
If the difference between the speed of the motor and its load is great, conventional gears may need several stages of speed reduction.
This leads to power loss, noise and expense.
Unfortunately, large increases in output torque also cause large forces on the teeth of conventional gears, so larger teeth and better materials are needed.
Drawing upon the gearing effects of nutation, an Italian space company Stamsrl has created a new formof gearbox that overcomes these disadvantages.
The device, called SPACEGEAR, was developed for use in satellites and uses an arrangement in which one bevel gear nutates with another instead of rotating.
 The gear ratio is determined by the difference in the number of teeth of the fixed and moving gears and not, as with conventional gears, on the ratio of their circumferences.
By applying the principle of nutation twice, very high reduction ratios of up to 3000 can be achieved.
The design, which combines two pairs of gears, makes possible any ratio with the same simple configuration.
 Because the design ensures that at least two teeth are in contact at any one time, loadings are reduced and materials of lower strength may be used.
SPACEGEAR is particularly suited to electrically-driven automotive components where high reduction ratios are required but space is at a premium.
Using nutator technology, smaller, faster electric motors can provide the same level of mechanical power as their conventional counterparts.
At present, suchmechanisms andtheir electric motors typically cost from200 Euros fora small carto2000 Euros fora luxury car.
The Europeanautomotive industry produces about 15 million cars per year, offering a potential market of 4500 million Euros.
The company is exploring materials for mass manufacture - in metal or plastic - and is developing a computer programfor designing nutating-gear systems.
In another application a similar gear has been developed for bicycles.
Robots, like people, live in an imperfect world.
The notion that a robot, working alone in space, might be programmed with perfect knowledge of the environment in which it will carry out tasks with perfect accuracy is an impossible ideal.
Errors and variations will always exist in the robot and its world, which will affect both precision and performance.
The traditional solution of human intervention through tele-manipulation is not always an option.
As a result, ESA contracted the Belgian company Krypton Electronic Engineering to study ways of making robot control more precise.
The aimwas to support an in-orbit demonstration of ESA's Autonomous Interactivity Concept - a way of improving the competence of pre- programmed robots - and this required an accurate picture of the performance and responses of robotic manipulators under real-world operating conditions.
It was rapidly discovered that few commercial tools were available for calibrating robots.
Existing data onper formance - where it existed at all - was inadequate.
As a result, measurement systems and procedures all had to be developed which would identify the differences between perfect robots ina computer-generatedworldandreal robots working onthe shopfloor.
Once these errors were known and compensated for, it would be possible tobe confident that robotic tasks planned on Earth would be faithfully performed in space.
The system that evolved, Rodym, ensures thata robot consistently reaches its correct position during eachof its programmed actions.
To achieve this, a mathematical model is used to generate adjustments, compensating for the inevitable errors between the robot's actual and programmed positions.
The position data needed for these calculations must be very precise indeed and, once again, no commercial measurement solutions were available.
Rody mem ploys a multiple camera system, which can measure accurately the position and orientation of up to 256 infrared emitting diodes that are attached to, and move with, the robot.
Once these are exactly located in relation to the operating environment, error corrections can readily be made.
Using a similar camera /LED system, it is also possible to evaluate and correct the positioning of any tool that the robot is using.
Today, Krypton has become the market leader in the field of industrial robot calibration and testing.
The methods and tools first developed for space applications have become mature and most constructors of industrial robots now own at least one Krypton measurement system.
Robots that have been calibrated with Rodympositioning and compensation are capable of more accurate performances, leading to better quality, higher outputs and less down time - an advantage not lost on the car manufacturer BMW, which has decided to make Rodyma standard feature of its automobile production systems.
Space applications have long been a major driver in the development of fuel cells.
These electrochemical devices, which provide electricity from simple chemical reactions such as the combination of hydrogen (the fuel) and oxygen (from the air in terrestrial applications) to produce water, are ideal for powering spacecraft.
There are no moving parts, hydrogenis light, and the only waste production is water.
Based upon its efficiency, high power output and ability to operate for as long as the fuel is available, the fuel cell is also seen by many as the power source of the future for cars and other vehicles.
It is roughly twice as efficient as a conventional petrol engine, with virtually no harmful emissions, and can be operated with fuels made from renewable sources.
A car electrically powered by a fuel cell is quiet and easy to use.
A German aerospace company, Dornier, had made considerable progress in producing a compact, safe and reliable hydrogen fuel cell for use in spacecraft.
When Dornier became part of the automotive company DaimlerChrysler, its fuel cell technology became available for automotive applications and from then on progress was rapid.
In late 2000, two new fuel-cell vehicles were announced, the Mercedes-Benz A-class NECAR 5 and the Jeep Commander 2.
 Both vehicles are quiet and environmentally friendly, with fuel cell systems that occupy no more space than a conventional engine.
The two cars actually use methanol (a type of alcohol) as a fuel.
Otherwise hydrogen would have to be carried on board a vehicle as a liquid under high-pressure so there would be a danger of explosion.
An alternative is to use various hydrocarbon or hydrocarbon-derived fuels that can be first broken down into hydrogen and carbon dioxide using a reforming catalyst.
 Methanol, which can be handled and sold like petrol or diesel, is an excellent choice to feed reforming fuel cells designed for domestic cars.
Methanol is produced on a large scale from natural gas and oil and, ultimately, it will be available from renewable biomass.
Fuel cells are also being developed that use methanol directly as the fuel.
Such is the promise of fuel cells that DaimlerChrysler aims to invest over 1 billion Euros to develop the new drive system for mass production.
Many of us love the excitement of motor racing.
Even people who are not fans recognize that the sport contributes an enormous amounted development of new, efficient technologies.
Unfortunately, one aspect that the race teams don’t seem to worry about is the availability and cost of the fuel that powers their engines.
However, there is one motor race in the world that does contribute to the development of an environmentally friendly energy source - solar power.
The World Solar Challenge is the biggest race in the world for vehicles powered solely by the energy of the Sun.
 The race, held every two years,  crosses the Australian continent north to south from Darwin to Adelaide over 3010 kilometers, and the race teams have to deal with some of the most arduous conditions on Earth.
The first World Solar Challenge was held in 1987 in order to show the world the potential of solar power.
 The best solar cars perform extremely  impressively,  being  capable  of travelling 1000 kilometers fora cost of just over 2 Euros!
This is about 50 times more efficient than an average family car.
Some of the cars can also achieve speeds in excess of 160 kilometres an hour.
The competition participants vary from multinational companies to high schools and universities.
 In 2001, the regular contestants were joined by a team from The Netherlands, consisting of students from the Delft University of Technology and the University of Amsterdam.
One of the unique features of the Dutch entry was that their vehicle carried solar cells, provided by ESA, which were once employed on the Hubble Space Telescope.
The Dutch team also used new cells designed by the same people who produced the original cells for Hubble to power the car.
They are some of the best performing and most efficient solar arrays ever designed.
As well as providing solar cells, ESA supported the venture by providing technical expertise, anode of its former astronauts led the advisory team.
Thus equipment and technology used in space was transferred directly to a technically challenging venture and this collaboration paid off because Nuna, the Dutch solar car, won the World Solar Challenge on 21 November 2001.
Never before had a newcomer won the race at the first attempt and Nuna broke 4 world records in the process!
Over the past 35 years, the European space industry has gained considerable expertise in building, launching, controlling and communicating with satellites.
From this long experience of how to overcome the hazards and problems created by such a hostile environment, many valuable new technologies, products and procedures have been developed.
Today, this expertise is improving our daily lives by providing many innovative solutions for products and services on Earth.
Groundbreaking European space technologies are becoming increasingly more available for development and licensing to the non-space industry through the process of technology transfer.
The ESA Technology Transfer Programmed has already achieved over 120 successful transfers or spin-offs from space to non-space sectors
This success is reflected by the fact that since 1991 technology transfer has generated more than 20 million Euros in turnover for European space companies and 120 million Euros for the non-space industries involved.
Already 2,500 jobs and 12 new companies have been created, with 25 expected by 2003.
The ESA Technology Transfer Programmed is carried out by a network of technology brokers across Europe and Canada.
Their job is to identify technologies with potential for non-space applications on one side, and on the other to detect the non-space technology needs.
Subsequently, they market the technology and provide assistance in the transfer process.
There are a host of other technologies developed for or used in space which have also been spun-off to the car manufacturing industry.
Examples include: car windscreens being used as antennas to transmit data; transparent heating systems for windscreens; the use of GPS satellites to provide navigation maps and route directions; and micro-coating of metals for car headlamps.
Also special composite materials,plastic paneling, resins and carbon fibers – already applied to spacecraft – are now made available by the plastics industry for car bodies.
And it’s not only plastics.
The Audi car company is using silicon and manganese a luminous alloys for car parts, and there are special car bodywork electro- coatings with moly benumb diesel hide to reduce air friction at high speed.
Fabrics and pyrotechnic devices used in space are being used for airbags and safety belts in cars.
Finally, technologies first developed for rocket propulsion are also used in the automotive industry.
Examples include seals for fuel pumps, engine cooling tubes, shape memory alloys to optimize the  performance  of catalytic  converters,  and microfiber/ceramic insulation material for use in exhaust silencers.
So next time you get into your car, give a thought to what space technologies might have been transferred to it!
Global automobile manufacturers see India as a manufacturing hub for auto components due to the following reasons
Low-cost labour force and availability of raw material which makes India cost competitive
An established manufacturing base in India
Setting up of the operations of major international auto components including Delphi, Visteon, Bosch and Meritor in India
Setting up of International Purchasing Offices (IPOs) of automobile manufacturers and auto component manufacturers in India
Fine-quality components manufactured in India
India being a global hub for research and development (R&D), General Motors, DiamlerChrysler, Bosch, Suzuki, Johnson Controls, etc., have their research centres in India
In this Unit, you will learn about the various components and systems that make a complete automobile? —? the engine and its parts, the body and chassis, drive transmission and steering parts, suspension and brake parts, electrical parts and other systems that make running an automobile possible.
Chassis is a French term and was initially used to denote the frame or main structure of a vehicle.
The chassis contains all the major units necessary to propel the vehicle, guide its motion, stop it and allow it to run smoothly over uneven surfaces.
It is the main mounting for all the components including the body.
It is also known as the carrying unit.
A steel frame, which is a major part.
In case of a passenger car, the whole body is also an integral part of the chassis.
However, in commercial vehicles like trucks and buses, the body is not a part of the chassis. 
Therefore, a chassis is almost a complete vehicle except the body and other accessories, which are not involved in the movement of the vehicle.
Other major components include engine, transmission system, front and rear axle, steering system, suspension system, wheels, tires and brakes.
In case of integral or frameless construction, the body is an integral part of the chassis.
But, in case of the conventional chassis, the body or superstructure is made after receiving the chassis from the manufacturer.
The shape of the body depends upon the ultimate use for which the vehicle is meant.
The body of a car  is made of a sheet of metal or fibre glass, so that passengers can sit in it.
To make the journey comfortable, cushioned seats are provided.
The body is provided on all sides with glass panes fixed to protect the passengers from dust and rain.
The body of a truck has the driver’s compartment covered and the rest is kept open.
Such bodies are usually called load bodies.
In most of the cases, it is an open body, whereas for liquid material like water, milk and fuel products, a tank is mounted on the chassis.
The body is fixed to the chassis with the help of I- or U-bolts with rubber packing placed between the chassis and body cross members.
The body of a motor vehicle should fulfill certain requirements.
It should be light weight.
have minimum number of components.
have long fatigue life.
have uniformly distributed load.
have sufficient space for passengers and luggage.
have good access to the engine and suspension system.
have minimum vibrations when the vehicle is running.
have minimum resistance to air.
be cheap and easy in manufacturing.
have clear all-round vision through glass areas.
have an attractive shape and colour.
An engine is complex unit in which different components are assembled, and fuel is burned to produce power or energy.
the engine converts chemical energy (heat energy) into mechanical energy, which is then utilized for vehicular movement.
There are different processes of fuel combustion.
When the fuel is burned within the engine, it is called an Internal Combustion (IC) engine, and when it is burned externally and the produced steam is used for the mechanical movement, it is called an External Combustion (EC) engine.
Nowadays, automobile engines are quite economical due to the developments taking place in the field of internal combustion engine.
On the basis of the process of ignition, the automobile engines are classified into spark ignition engine (petrol or gas) and compression ignition engine (diesel).
In an IC engine, the reciprocating motion of the piston is converted into rotary motion of the crankshaft and the produced power is then transmitted to move the vehicle.
In case of a rotary engine or Wankel engine, the rotor rotates and completes the process of combustion and produces the power, which helps the vehicular movement.
The spark ignition engine can be differentiated from the compression ignition engine as per the following factors.
The type of fuel used.
The way the fuel enters in the combustion chamber.
The way in which fuel is ignited.
The spark ignition engine uses a highly volatile fuel, such as gasoline, which turns into vapor easily.
The fuel is mixed with air before it enters in the combustion chamber, and forms a combustible air-fuel mixture.
This mixture then enters the cylinder and gets compressed with the help of a piston.
An electric spark is produced by the ignition system which ignites the combustible air-fuel mixture.
The combustible gases burn and expand, which forces the piston downwards for generating power.
In the compression ignition engine or diesel engine, only fresh air enters the cylinder, which is compressed to a very high pressure and temperature, which could go up to 1000°F (538°C).
The diesel is then injected or sprayed into the engine combustion chamber.
This spray contains very fine and tiny particles of diesel in an atomized form.
The hot air or heat of compression ignites the fuel and generates the power stroke.
Cylinder: The cylinder or cylinder liner of an IC engine is fitted in the cylinder block, which is a single casted unit and is considered to be the main body of an engine.
The block has cylinder liners.
The piston reciprocates up and down from Top Dead Centre (TDC) to Bottom Dead Centre (BDC) to generate power.
The cylinder liner and the cylinder block have to withstand very high pressure (about 70 bar) and temperature (about 700°C) during power stroke.
The material used for the cylinder block must withstand such heat and also disperse it effectively.
The cylinder block is well-designed with water passages to remove the excess heat and separate oil passages are provided for the circulation of lubricating oil.
The top portion of the cylinder block is covered by the cylinder head.
The crankcase is an integral part of the cylinder block which houses the crankshaft and the lower portion is dipped in an oil pan.
Nowadays, cylinder liners are made of special alloy and internal portion is coated with material like titanium which provides mirror finish and can withstand the wear resistance.
The upper end of the cylinder liner has a flange which fits well in the cylinder block.
The exterior portion of the cylinder liner is exposed to water jacket for easy dispersion of heat.
Cylinder Head: The cylinder head is also single-casted unit and bolted to the top portion of the cylinder block.
The combustion chamber is a part of the cylinder head, where the combustion of gases takes place.
The water passages are provided to remove the heat from the cylinder head.
In latest engines, the cylinder head also houses the camshaft which has the inlet and exhaust valves with supportive valve mechanism.
This provision is made to fix spark plug in SI engines and nozzle in CI engines.
The lower portion of the cylinder head is well-machined to ensure there is no leakage of gases.
Cylinder head gasket is usually cast as one piece and bolted to the top of the cylinder (engine block).
Copper and asbestos gaskets are provided between the cylinder and cylinder?head to obtain a gas?tight joint.
The charge enters the combustion chamber through the inlet valve connected to the inlet manifold, and the exhaust gases are removed through the exhaust valves connected to the exhaust manifold.
Piston and Piston Rings: Piston is a cylindrical unit, used to compress the charge during compression stroke and to transmit the gas force to the connecting rod and then to the crankshaft during power stroke.
The pistons of IC engines are usually made of aluminium alloy, which has high thermal conductivity and is light in weight.
The material of piston must have the ability for higher heat transfer.
The piston moves up and down (from TDC to BDC) and assists in completing the engine cycle.
The piston rings are placed in the ring groove and provide sealing between the piston and the cylinder liner, thereby preventing the leakage of high pressure gases.
These are made of special grade cast iron, which retains its elastic property even at very high temperature.
The upper piston rings are called the compression rings and the lower piston rings are called the oiling or oil control rings.
Connecting Rod: It is usually manufactured by using drop-forged steel.
It is made in the shape of ‘I’ so as to reduce its weight and to withstand strength.
Its small end is connected to the piston with the help of gudgeon pin and the big end is connected to the crankpin with shell bearings.
It has a passage for the transfer of lubricating oil from the big end bearing to the small end bearing (gudgeon pin).
The major function of the connecting rod is to convert the reciprocating motion of piston to the rotary motion of the crankshaft.
Crank and Crankshaft: The crankshaft is called the backbone of an engine because it converts the reciprocating motion of piston into the rotary motion of the crankshaft.
The crankshaft is a single casted unit and is made of drop-forged steel main journals which are placed and supported in the crank case.
The main journal and connecting journals are machined to a smooth finish to reduce friction and shell bearings are used for smooth rotation of crankshaft.
Front end of the crankshaft will transmit drive to the camshaft and also to the timing gear, whereas the flywheel is bolted to the flange at rear end of the crankshaft.
Main journal of the crankshaft carries the oil passages to lubricate shell bearings.
In case of a single cylinder engine crank assembly  is used, two crank webs are connected with the crank pin, and crank webs shafts are press fitted in both.
At one side of the shaft magneto is fastened whereas clutch assembly is mounted to the other.
The crank assembly is balanced dynamically as well as statically for the smooth transmission of power.
Piston Pin or Gudgeon Pin.
This unit connects the piston and small end of the connecting rod and passes through the piston  Circlips are fitted into recesses in the piston to prevent the gudgeon from touching the cylinder wall
The needle bearing or bronze bushing is press fitted into the connecting rod, due to this the gudgeon pin provides bearing for the oscillating small end of the connecting rod.
Inlet Valve: The major role of the inlet valve is to submit fresh charge in to the cylinder during the suction stroke.
Opening and closing of the valve will control the admission of the charge into the petrol engine or air into diesel engine during suction stroke of an engine.
The valve operations will be as per the valve timings.
The inlet valve has a wider face or in latest engines two inlet valves are used to maintain volumetric efficiency of an engine.
Exhaust Valve: The exhaust valve removes out the burnt gases from the combustion chamber after power stroke.
The exhaust valve has to bare more heat resistance
Valve Spring: The valve spring plays an important role to close the valve and also provides air tight compartment to seal the combustible gases during power stroke and also maintain the self-centering movement of the valve.
Both ends of the vale spring are machined for smooth function and up and down movements of the valves.
Inlet Manifold: The fuel air mixture is carried from the carburetor to the cylinder through a separate pipe through inlet manifold in a carbureted engine.
Whereas in compression ignition engines (diesel), the air is sucked through the induction manifold.
In M.P.F.I the engine holds the throttle body on top of the manifold and the supply of air is monitored by the throttle body sensor.
Exhaust Manifold: It is a set of pipes and muffler, which is used to remove the exhaust gases from the exhaust ports.
Engines oxygen sensors and catalog convertors are used to reduce sound and air pollution, respectively .
Camshaft: The major function of the camshaft is to operate the intake and exhaust valves through the cam lobe, the gear drive transmits the power for the rotation of oil pump, therefore the oil pump sucks the oil from the oil sump and transits the same to the oil gallery.
The camshaft is driven by crankshaft at half the speed of the crankshaft.
"Cam,Lobeand	Tappet: The cam lobe of the camshaft is placed directly above the bucket tappet, such that the lobe comes around it and pushes down the bucket tappet and the valve, thus opening the valve."
 In an overhead camshaft with rocker arm, the cam lobe comes under the valve lifter, and causes the rocker arm to rock or turn the lobe and pushes down the valve steam and it moves down to open.
 When the cam lobe passes the valve lifter the valve spring retains back to the original length.
To close the valve the rocker arm turns back and the valve lifter is pushed down on the cam.
In case of double overhead camshaft engine, the double row valves are usually operated by the separate overhead camshaft.
Push Rod and Rocker Arm: The motion of the cam lobe pushes the valve lifter upwards
This movement pushes the push rod and the rocker turns the upward motion of the push rod to the downward movement of the valve stem resulting in opening of the valve.
Crank Case: The crank case is an integrated part of the cylinder block.
The casing is provided to hold the crankshaft.
The crankshaft is allowed to rotate freely and transmit the power to the flywheel 
Water Pump and Water Jacket: The function of water pump is to draw water from the radiator and supply it to the water passages provided in the cylinder block and cylinder head with certain pressure.
The circulation of coolant removes the excessive heat from an engine.
This helps in maintaining the engine temperature and also the life of an engine.
Radiator: The major function of the radiator is to radiate the heat from the coolants.
It has two tanks located at the top and bottom.
The upper tank is connected to the lower tank with the core through the passages for easy radiation of the heat.
The radiator also stores the coolant.
Flywheel: It is a wheel mounted on the crankshaft which stores the energy during the power stroke and transmits the energy to the transmission system, the clutch and then to the gear box.
Governor: It is run by drive from the crankshaft.
The function of the governor is to regulate the charge in case of petrol engine and amount of fuel in case of diesel engine to maintain the speed of the engine constant, when the load requirement varies.
The components described above are commonly used for all types of IC engine.
Here we are describing only a few components which are used in particular types of engines.
Carburettor: The major function of the carburetor is to supply carburised fuel as per speed and the engine load.
In petrol engines the carburettor is mounted on the induction pipe or on the induction manifold.
The quantity of fuel air mixture in appropriate ratio is controlled by the throttle valve and the movements of the throttle valve are connected to the accelerator.
Spark Plug: The function of the spark plug is to ignite the fuel air mixture after completion of the compression stroke in an engine.
It is generally placed in the combustion chamber of the cylinder head.
This is only used in petrol engine.
Fuel Injection Pump: In case of diesel engine the diesel oil from the fuel tank is sucked by the fuel feed pump.
The pump first sends the diesel oil to the fuel filter and then to the transfer pump.
The transfer pump increase the pressure of the fuel.
The high pressure of fuel is then sent to the distributor rotor through the metering valve and from rotor the fuel is sent to the injector.
In case of a multi point fuel injection system, the electric fuel pump is used and placed in the fuel tank.
The pump generates the injection pressure and sends it to the fuel filter, and then to the common rail at a pressure of 3 to 4 bar.
The common rail or fuel rail is connected to the fuel injector.
Fuel  Injector:  The  function  of  fuel  injector is to break the fuel into fine spray (atomised condition) as it enters the combustion chamber of diesel engine.
In case of an MPFI engine petrol is injected at the end of compression stroke as the fine spray of the fuel burns more efficiently in the combustion chamber giving better fuel efficiency with less air pollution.
As you know, our body requires fluids like water and also oil in the form of fats like ghee, butter, cooking oil for maintenance of our system.
Similarly, lubrication is required for maintenance of engine.
Lubrication system is one of the most important parts of an engine.
The engine cannot run smoothly for more than a few minutes without the lubricating oil.
Whenever two metallic surfaces move over each other under direct contact, dry or solid friction is produced.
This is due to the irregularities on the two surfaces interlocking each other.
The dry friction thus created produces a lot of heat and results in wear and tear of the metal surface.
The main objectives of lubrication are to reduce friction between moving parts to its minimum value so that power loss is minimised, andto reduce wear and tear of the moving parts as much as possible.
Apart from these objectives, lubrication also serves other important purposes, which may be called secondary.
To provide cooling effect: The lubricating oil takes heat from the hot moving parts during its circulation and delivers it to the surrounding air through the crank case.
To provide cushioning effect: The lubricating oil also serves as a good cushion against the shocks experienced by the engine.
For example, instant combustion of the fuel in the combustion chamber produces a sudden rise of pressure in the cylinder and the shock goes to the bearings through the piston, gudgeon pin and the connecting rod.
This shock is then absorbed by the layer of oil present in the main bearings.
To provide cleaning action: The lubricating oil serves another useful purpose of providing a cleaning action.
During its circulation, it carries away many impurities, such as carbon particles, etc.
To provide a sealing action: The lubricating oil also helps the piston rings in maintaining an effective seal against the high pressure gases in the cylinder thus preventing leakage towards the crank case.
Like our body requires air and water for cooling our system, similarly the engine of a vehicle also requires cooling.
The cooling system has three primary functions, which are as follows.
Remove excess heat from the engine.
Maintain a constant engine operating temperature.
ncrease the temperature of a cold engine as quickly as possible by maintaining the thermostat valve in a closed position which is fitted in the path of coolant circulation.
The cylinders of internal combustion engines require cooling because the engine cannot convert all the heat energy released by combustion into useful work.
Liquid cooling is employed in most of the IC engines, whether they are used in automobiles or elsewhere.
The water (coolant) is circulated around the cylinders to pick up heat and then dissipate it through a radiator.
As the temperature increases from 71 to 82 degrees centigrade, the thermostat valve opens and sends water to the radiator to radiate the heat.
When the temperature rises above 82 degrees, the thermostat switch operates the cooling fan to support the cooling process in radiator.
In petrol engines, the fuel and air mixture is supplied to the combustion chamber of an engine.
This mixture is atomized and then vaporized by the carburetor.
Then the mixture is ignited by the spark plug.
The fuels, such as petrol, benzoyl and alcohol are used in an SI engine.
Nowadays, fuel is injected in the flow of air at a certain temperature and pressure and the fuel vaporizes faster and the combustion process is better, with low emission.
It also shows better fuel efficiency.
In case of compression ignition engine (diesel) the fuel is sent through the fuel pump to the injector and the injector sprays the fuel at end of compression stroke.
The oil fuels which are used in CI engines do not vaporize easily.
Therefore, a separate injection system is used consisting of fuel injection pump (FIP) and injectors.
These injectors atomize the fuel and it is then sent for combustion.
Nowadays, in case of compression ignition engine the common rail direct injection system (CRDI) is used for better engine performance.
Fuel Tank: In most of the vehicles the fuel tank is located at the rear end of the vehicle.
The fuel tank is made of a metal sheet or plastic.
It is attached to the chassis.
The filler opening is closed with a cap.
The fuel line is attached to the fuel pump and also to the fuel gauge.
Fuel Line: The fuel line carries the fuel from the fuel tank to the carburettor or to the common rail system used in MPFI engines.
The line has to withstand the pressure and provide resistance for the corrosion.
The rigid line is placed safely in the chassis.
It is connected to the units like carburettor, through a flexible pipe.
Fuel Filter: The major role of the fuel filter is to send clean fuel to the engine.
This prevents blockages in the fuel system.
The filter contains a cartridge of filtering the element through which the fuel passes the filter traps any particles and prevents them from entering the fuel system.
The fuel filter is required to be replaced at regular intervals.
Air Cleaner: It is the main unit of the fuel system.
It supplies clean air to the engine.
The element of the air cleaner must be cleaned and replaced at regular intervals for maintaining a healthy life of the engine.
Fuel Injector: The solonide injector used in the M.P.F.I fuel system is operated electrically as per the variation in the supply of current with resistance.
The solonide winding induces current within it and controls the movements of the needle valve to inject fuel as per the variations in speed and the load.
The fuel system is controlled by the ECM
Pressure Regulator: It controls the amount of pressure that enters the injector.
The extra fuel is sent back to the fuel tank.
Transmission system is used in motor vehicles to supply the output of the internal combustion engine to the drive wheels.
The transmission reduces the higher engine speed to the slower wheel speed, increasing torque in the process.
Transmissions are also used in pedal bicycles, fixed machines and where rotational speed and torque need to be adapted.
The transmission system consists of the following components.
Clutch assembly
Gear box assembly (Transmission case assembly)
Propeller shaft assembly
Clutch is a mechanism which enables the rotary motion of one shaft to be transmitted, when desired.
The axes of driving shaft and driven shaft are coincident.
To disconnect  the engine power from the gear box as required, under the following circumstances: to start the engine and warm it up;to engage first and second gear to start the vehicle from rest;to facilitate changing the gear as required; anddisconnect from the engine to stop the vehicle after application of brakes.
To allow the engine to take up load gradually without shock or jerk.
The clutch should meet the following requirements.
Torque transmission or the ability to transmit maximum torque of the engine.
Gradual engagement, i.e.to engage gradually and avoid sudden jerks.
Heat dissipation, i.e., ability to dissipate large amount of heat generated during the clutch operation due to friction.
Dynamic balancing, which means that the clutch should be dynamically balanced.
This is particularly required in the case of high-speed engine clutches.
Vibration damping, i.e., having a suitable mechanism to damp vibrations to eliminate noise produced during the power transmission.
Size of the clutch should be as small as possible so that it occupies minimum space.
Free pedal play, which helps the clutch to reduce effective load on the carbon thrust bearing and its wear.
Easy in operation and requiring as little exertion as possible on the part of the driver.
Light weight of the driven member of the clutch so that it does not continue to rotate for any length of time after the clutch has been disengaged.
The main parts of a clutch are divided into three groups.
Driving members: The driving members consist of a flywheel mounted on the engine crankshaft.
The flywheel is bolted to a cover which carries a pressure plate or driving disc, pressure springs and releasing levers.
Thus, the entire assembly of the flywheel and the cover rotate all the time.
The clutch housing and the cover provided with openings, dissipate the heat generated by friction during the clutch operation.
Driven members: The driven members consist of a disc or plate, called the clutch plate.
It is free to slide lengthwise on the splines of the clutch shaft (primary shaft).
It carries friction material on both of its surfaces.
When it is gripped between the flywheel and the pressure plate, it rotates the clutch shaft through the splines.
Operating members: The operating members consist of a foot pedal, linkage, release bearing, release levers and the springs.
Gear Box (Transmission Case) Assembly
We need different gear ratios in the gear box or transmission system to enable the vehicle to move at different speeds.
At the time of starting the vehicle, the maximum amount of torque is available on the flywheel, for which low gear ratio is selected for the movement of the vehicle.
As the engine speed increases, the amount of torque is reduced on the flywheel and it is required to select higher gear ratio.
To provide a means to vary the leverage or torque ratio between the engine and the road wheels as required.
The transmission also provides a neutral position so that the engine and the road wheels are disconnected even with the clutch in the engaged position.
It provides a means to reverse the car by selecting the reverse gear.
In this session, we shall discuss the axle and steering system, which transmits power to the wheel.
It plays a crucial role in the movement of a vehicle.
This is a shaft which transmits power from an engine to the wheels of a motor vehicle.
It is a hollow tubular shaft and consists of mainly three parts.
Shaft: It mainly bears torsional stress produced due to twisting.
It is usually made of tubular cross section.
Universal joints: One or two universal joints are used, depending on the type of rear axle drive used.
The universal joints help in the up and down movements of the rear axle when the vehicle is in running condition.
Slip joint: Depending on the type of drive, one slip joint may be there in the shaft.
This serves to adjust the length of the propeller shaft when demanded by the rear axle movements
Front axle carries the weight of the front portion of the automobile as well as facilitates steering and controls the rolling of wheels.
It also absorbs road shocks arising due to road surface variations.
In case of a commercial vehicle the front axles are generally dead axles.
The front axle is designed to transmit the weight of the automobile from the springs to the front wheels, turning right or left as required.
To prevent interference due to front engine location, and for providing greater stability and safety at high speeds by lowering the centre of gravity of the road vehicles, front axle includes the axle-beam, stub-axles with brake assemblies.
It is made of drop forged alloy steel consisting of 0.4% carbon steel and 1.3% nickel steel.
The axle is made of I-section at centre and of circular or elliptical section in the ends since it has to bear the bending stress and torsional stress.
In order to lower the chassis height a downward sweep is provided at the centre of the beam axle.
The main beam axle is connected to the stub axle with a king pin.
The front road wheels are mounted on the stub axle.
For smooth steering effects and maintaining proper control, the front axle of a car is supported with an independent suspension system, such as Mac-pherson.
The strut and coil spring allows the wheel to move up and down but does not allow to change the driving angle of axle shaft to transmit the drive smoothly.
It also allows the wheel to rotate freely.
This supports in steering the vehicle.
The rear axle bears the weight of the vehicle body and load with springs.
It enables to transmit the driving and breaking torque to the chassis frame and body of the vehicle.
It also bears the side thrust or pull due to any side load on the wheel.
It supports various parts like bevel pinion, bevel gear, cage of sun gear and star pinions, axle shafts, and different support bearings.
It is important to note that rear road wheels are mounted on the axle shaft and the differential mechanism enables the outer wheel to move faster than the inner wheel while taking a turn.
The steering mechanism permits the driver to control the car on a straight road and turn right or left as desired.
The steering mechanism includes a steering wheel,
In modern cars, the manually operated steering system is assisted by power and is called power steering.
The electric power drawn from the battery or hydraulic power is used.
It provides directional stability to the vehicle when moving in a straight (ahead) direction.
It provides perfect steering condition, i.e., perfect rolling motion of the wheels at all times.
It facilitates straight ahead recovery after completion of turn.
It controls the wear and tear of the tyre.
It is used to turn the vehicle as per the will of the driver.
It converts the rotary motion of the steering wheel into angular displacement of the front wheel.
It multiplies the effort of the driver to ease operation.
It absorbs road shocks and prevents them from reaching the driver.
Requirements of a Good Steering System.
It should be very accurate.
It should be easy to handle.
The effort required should be minimal.
It should provide directional stability.
The front wheels should roll without lateral skid while negotiating curves.
There should be proper proportion between the angles turned by the front wheels.
The type must have good elasticity so that on turns, these may follow an arc of greater radius than the stiff type.
The wheels should automatically come to the straight ahead position after negotiating the bend.
When going straight, the wheels must maintain the neutral position.
The angular oscillations of the wheels must be minimum.
The system must be irreversible to a certain degree so that minimum front wheel shocks are transmitted to the driver’s hands.
For perfect steering, it must always have an instantaneous centre about which all the wheels must rotate.
To achieve this the inner wheel has to turn more than the outer wheel.
Two types of mechanism are available, viz., the Davis and the Ackermann steering mechanism.
Out of these Ackermann type is more popularly used because of its simplicity.
It also lessens wear of tyre and lowers friction.
A steering linkage is the part of an automotive steering system that connects to the front wheels.
In a commercial vehicle a rigid axle type front suspension system is used.
It is made of polyurethane or hard plastic.
It consists of a circular rim with a hub at the centre.
The rim is slightly elliptical in cross section to maintain strength and provide hand grip.
The steering shaft is mated in the undulations cut on the inside of the steering wheel hub.
It is tubular in nature.
It provides switches for horn, light and wiper for easy and quick operation.
The collapsible columns are used for safety, which collapse upon impact and reduce the chances of injury to the driver.
It is made from drop forged alloy steel.
It connects the steering wheel to the steering gear box and transfers movements of the steering wheel to the steering gear, or to the pinion.
It is also called as pitmen arm.
It is made up of drop forged steel.
It connects the cross shaft with the draglink.
It connects the drop arm to the steering knuckle.
It is also made up of drop forged steel.
The tie rod ends are different parts of the steering linkage will be connected to the ball joints which provide angular motion to the steering system.
The steering gear converts the turning motion of the steering wheel into the to-and-fro motion of the link rod of the steering linkage.
It also provides the necessary leverage so that the driver is able to steer the vehicle without fatigue.
Suspension is the term given to the system of springs, shock absorbers and linkages that connects a vehicle to its wheels.
The suspension system serves a dual purpose, contributing to the vehicle’s road holding or handling and braking for safety and driving comfort, and keeping the vehicle occupants comfortable and reasonably well isolated from road noise, bumps and vibrations, etc.
The main functions of a suspension system are as follows:
To safeguard the occupants against road shocks and provide riding comfort.
To minimise the effects of stresses due to road shocks on the mechanism of the motor vehicle and provide a cushioning effect.
To keep the body perfectly in level while travelling over rough uneven ground, i.e., the up and down movement of the wheels should be relative to the body.
To isolate the structure of the vehicle from shock loading and vibration due to irregularities of the road surface without impairing its stability.
To provide the requisite height to the body structure as well as to bear the torque and braking reactions.
The components of a suspension system can be categorised as follows.
Mechanical Suspension: Leaf springs, Coil springs, Rubber springs,Torsion bars.
Hydraulic Suspension: Hydraulic shock absorber and Telescopic fork absorber
Compressed air is used in an air suspension system.
Leaf Spring: A leaf spring is a component of a vehicles’ suspension system.
Leaf springs are curved, and the curvature helps the spring absorb impact.
Coil Spring: Coil springs are commonly called compression springs, torsion springs or helical springs.
They store energy and release it to absorb shock or maintain a force between two contacting surfaces.
Mostly coil springs or helical springs are used in engine starter and hinges.
Rubber Spring: A rubber string stores more energy per unit mass than any other type of spring material.
The rubber spring is installed between the frame and the top link of the suspension system.
When the spring is connected to a point near the link pivot, deflection of the spring reduces to a minimum, without affecting the total wheel movement.
The energy released from the rubber spring after deflection is considerably less than that imparted to it.
Torsion Bars: Torsion bars are of two types?— helical or spiral.
These bars are used in automobile vehicles for transmitting torque.
Hydraulic suspension combines rubber springs with a damper system, linking the front and rear wheel on the same side of the car.
As the front wheel rises over a bump, some of the fluid from its suspension unit (known as a displacer unit) flows to the rear ?wheel unit and raises it, so tending to keep the car level.
In each of the displacer units, the fluid passes through a two-way valve, which provides the damping effect.
Once the rear wheel has passed over the bump, the fluid returns to the front displacer unit and the original level is restored.
Hydraulic Shock Absorber: It is a mechanical device designed to absorb shock impulses.
This device is also used for checking or damping out the suspension spring to a comfort level.
Telescopic Fork Absorber: A telescopic fork is a form of motorcycle front suspension whose use is so common that it is virtually universal.
The telescopic fork uses fork tubes and sliders which contain springs and dampers.
In this suspension, compressed air is used as a spring.
This suspension system is operated with air and controlled by a microprocessor.
It helps in maintaining self?driving conditions and supports the weight of the vehicle.
The wheel is an important component of a vehicle.
Wheel of a four-wheeler vehicle is mounted on a hub and consists of parts like rim, type and tube.
The wheels not only support the weight of the vehicle, but also protect it from road shocks.
All the four wheels must resist the braking stresses and withstand side thrust.
A wheel should be light and easily removable.
Functions of the wheel.
To withstand the weight of the vehicle.
To absorb road shocks.
To grip the road surface.
To balance dynamically (i.e., when the vehicle is in motion) and statically (i.e., when the vehicle is at rest).
The rimis the ‘outer edge of a wheel, holding the tyre’.
It makes up the outer circular design of the wheel on which the inside edge of the type is mounted on vehicles, such as automobiles.
the rim is a large hoop attached to the outer ends of the spokes of the wheel that holds the type and the tube.
Disc wheel Rim: A wheel is generally composed of rim and disc.
Rim is an outer partof the wheel and holds the tyre.
Disc is a part of wheel which connects the rim and the axle hub.
Wire Spoke Wheel Rim (used in motorcycle, bicycle): Wire spoke wheel rim is where the outside part of the wheel (rim) and axle installed part are connected by many numbers of wires called spokes.
Spilt Wheel Rim (used in scooter): Spilt wheel rim is used in a multi?piece wheel.
This wheel rim holds the tyre with a locking ring.
A split wheel rim cannot be used normally in all types of vehicle.
Heavy Vehicle Wheel Rim (available in three piece and four piece including locking ring): Heavy vehicle wheel rim have a three and four?piece locking ring.
This type of wheel rim is used in heavy vehicles wheel like truck, buses container, etc., because it has a longer life.
The type is mounted on the wheel rim.
It carries the vehicle load and provides a cushioning effect.
It must produce minimum noise, while the wheel turns on the road.
It resists the tendency for the vehicle to oversteer.
It should have good grip while accelerating and braking the vehicle on both dry and wet roads.
A type must have the following properties.
Non-skidding: The type must have grip to avoid skidding or slipping on the road surface.
Uniform wear: The type must get worn uniformly over its outer circumference.
Load carrying: The type is required to carry the vehicle load.
Cushioning: The type needs to absorb the vibrations due to the different road surfaces and their impact, and thus, provide cushioning effect to the vehicle.
Power consumption: While rolling on the road, the type should consume little power created by the engine.
Noise: The type should create minimum noise while running on the road.
Balancing: ???The type should be balanced dynamically as well as statically, i.e., maintain balance at both times? —? when the vehicle is in motion as well as at rest.
To carry the load of the vehicle.
To absorb minor road shocks.
To reduce vibration to some extent.
To transmit the power from the engine through gear box, propeller shaft and rear axle to the ground with which the vehicle moves.
The treads made on the types grip the road for better traction.
Solid type: It is used in children’s cycle and is filled with solid material, like rubber, which makes it sturdy.
Tube type: It consists of a tube between the rim and the type, in which air is filled.
It is used in most of the vehicles seen on road.
Tubeless type: Nowadays, with the advancements in technology, tubeless types are replacing the tube types.
Tubeless types are mainly used in modern cars.
The benefits of tubeless types include slow leakage of air during punctures, better balancing of wheels, low cost and ease of puncture repairing.
Brakes are one of the most important control components of a vehicle.
They are required to stop the vehicle within the smallest possible distance and this is done by converting the kinetic energy of the wheels into the heat energy which is dissipated into the atmosphere.
To stop the vehicle in the shortest possible distance and time.
To control the vehicle speed while moving on plain roads and hills.
To work equally well on fair and bad roads.
To ensure that the pedal effort applied by the driver is not much, thereby reducing the inconvenience for the driver.
To work efficiently in all weathers.
It should have very few wearing parts.
It should require little maintenance.
Brake, when applied should not disturb the steering geometry.
There should be minimum sound when brake is applied.
Foot Brake: Foot brake is one of the most common brake systems operated by the foot pedal.
When pressure is applied to the foot pedal, the vehicle stops.
Pedal force applied by the driver is further multiplied and sent to the braking drum or disc either by mechanical linkages or by hydraulic pressure which in turn causes braking.
It is also known as a service brake.
Hand Brake: Hand brakes are Spring Cam usually used for stable parking Brake of the vehicle either a on flat lining road or slope.
They are also called parking brakes.
Hand brakes are connected to the brake mechanism directly and the other end is operated by the driver.
This type of brake is also known as emergency brake as it is independent of the main service brake.
Dru Brakes or Internal Expanding Brakes: Drum
This type of brake is fitted in automobile
These brakes have a two
Shoes are fitted in the drum.
The friction between the shoes and the drum produces the braking torque and reduces the speed of the drum so that the Disc Brake vehicle stops.
Disc Brake or External Contracting Brakes: It is the type of braking system in which instead of a drum assembly a disc rotor is attached to the hub of the wheel in such a fashion that it rotates with the wheel.
This disc rotor is clamped in between the caliper which is rigidly fixed with the knuckle or upright of the vehicle.
When brakes are applied the actuation mechanism contracts the attached brake shoes which in turn make the frictional contact with the rotating disc rotor and cause the stopping of a vehicle.
An external contracting brake is used for only parking purpose as well as used to operate in flour mills, various types of electrical components, etc.
Mechanical Brake: This brake system has an inbuilt mechanical device for absorbing energy from a moving system.
Mechanical brake is a cable pull system, which consists of rim?like brakes just arranged in a different way.
Power Brake: Power brake system is a combination of the mechanical components to multiply the force applied to the brake pedal by the driver to stop the vehicle.
In a power brake system we mainly use the vacuum booster and master cylinder, brake calipers, drum brake, etc.
These braking systems are designed to reduce the effort required to depress the brake pedal when stopping a vehicle.
Vacuum Brake: It is the conventional type of braking system in which vacuum inside the brake lines causes brake pads to move, which in turn finally stop or deaccelerate the vehicle.
This type of brake is mainly used in railways in place of air brakes.
This brake can remove the kinetic energy and convert it into a form of heat.
The conversion is usually done by applying a contact material to the rotating wheel attached to the axles.
Vacuum brakes are cheaper than air brakes but are less safe than air brakes.
Air Brake: Air brake system is a very advanced braking system.
It is generally used in very heavy vehicles like buses and trucks.
It is the type of braking system in which the atmospheric air through compressors and valves is used to transmit brake pedal force from brake pedal to the final drum or disc rotor.
Air brakes generate higher brake force than hydraulic brake which is the need of the heavy vehicle.
High?end cars these days are using air brake systems due to its effectiveness and fail proof ability.
Hydraulic Brakes: A hydraulic braking system transmits brake-pedal force to the wheel brakes through pressurised fluid, converting the fluid pressure into useful work of braking at the wheels.
The brake pedal relays the driver’s foot effort to the master-cylinder piston, which compresses the brake fluid.
This fluid pressure is equally transmitted throughout the fluid to the front disc-caliper pistons and to the rear wheel-cylinder pistons.
The pressure on a liquid is called hydraulic pressure.
The brakes which are operated by means of hydraulic pressure are called hydraulic brakes.
Anti?lock Braking System: Anti?lock Braking System prevents the wheels from locking or skidding.
The anti-lock braking (ABS) system is a component that ensures passenger safety by stopping the vehicle in adverse conditions, like stopping very quickly or if the road is slippery.
To simplify it, the ABS prevents the wheels of the vehicle from locking up and causing you to skid out of control.
Electric Brake: It is
Electric brakes use electrical motors which are the main source of power in electric vehicles.
Electric brakes or secondary shoe are similar to the drum brakes in an automobile.
Electric brakes are actuated by an electromagnet.
Nowadays, all the automobiles run with the help of electrical and electronic system, and therefore, it plays an important part in the functioning of an automobile.
The electrical and electronic systems consist of the following.
Starting system: The starting motor is driven by means of the current taken from the battery.
Ignition system: The function of the ignition system is to produce a spark in the engine combustion chamber at the end of the compression stroke.
Generating or charging system: The function of the charging system in an automobile is to generate, regulate and supply the electrical energy for charging the battery.
Lighting system: It consists of various types of lighting used during the vehicle running, such as head light, tail light, fog light, brake light, reversing light, left and right indicators, parking light, cabin light, panel board lights, etc.
Connections for other accessories.
During summer, an automobile requires considerable amount of refrigerating capacity to maintain cool and comfortable conditions in the sitting space.
Similarly, when moving in a cold day in winter, the same vehicle would require considerable heating capacity to keep it comfortably warm for passengers.
Modern-day automobiles have an air conditioning to maintain suitably controlled temperature and humidity conditions inside the vehicle.
In automobiles, an air conditioner is a refrigeration Machine which requires electrical energy drawn from the battery system.
The battery is charged by energy of the engine.
For heating purposes, the warm water from the engine cooling system is used.
The heat required to warm the automobile is generally provided by circulating warm water through a heating coil.
Besides controlling the temperature levels, the air conditioner also cleans the air.
During summer, the humidity of the air inside the vehicle is reduced with air conditioner in operation, which makes the sitting area comfortable.
Car air conditioner comes inbuilt in air conditioned (AC) car models.
However, these can also be fitted at a later stage in a non-AC model of the car.
Compressor: A compressor is unit driven by the engine.
It has a low-pressure side port which is connected to the evaporator and a high pressure side port which is connected to the condenser using rubber hoses.
The compressor is the main mechanical part of the system.
In hybrid engines the compressor is electrically powered.
A small electric motor is fitted inside the compressor which pressurises the refrigerant.
These compressors have a pair of large gauge wires which form the compressor controller.
In latest cars, where the climetrons are used the electric power supply is controlled by ECU as per the temperature settings.
Clutch: The compressor is always fixed with a clutch.
The major function of the clutch is to transmit the power smoothly to the compressor when the system is operated.
Condenser: The major function of this device will be to change the high-pressure refrigerant vapour to a liquid.
The condenser is mounted in front of the engine’s radiator, and it looks similar to a radiator.
The condenser is a cooling device in which the vapour is condensed to a liquid because of the high pressure that is driving it in, and this generates a great deal of heat.
The heat is then in turn removed from the condenser by air flowing through the condenser on the outside.
Receiver-drier: The main function of this device is to filter refrigerant.
The liquid refrigerant moves to the receiver-drier.
This is a small reservoir vessel for the liquid refrigerant, which removes any moisture that may have leaked into the refrigerant and also stores excess quantity of refrigerant.
Expansion Valve: The pressurised refrigerant flows from the receiver-drier to the expansion valve.
The expansion valve is a controlling device which controlsthe varying load when there are pressure changes in the evaporator, as it may increase or decrease.
The valve maintains a constant pressure throughout the varying load on the evaporator controlling the quantity of refrigerant flowing into the evaporator.
Evaporator: It is the main component of a refrigeration system and is also called the cooling coil.
It has tubes and fins or freezing coil.
It is usually placed inside the passenger compartment above the footwell.
As the cold low-pressure refrigerant is passed into the evaporator, it vapourises and absorbs heat from the air in the passenger compartment.
The blower fan inside the passenger compartment pushes air over the outside of the evaporator, so cold air is circulated inside the car.
On the ‘air-side’ of the evaporator, the moisture in the air is reduced, and the ‘condensate’ is collected.
Throttling Device: It is a part of refrigeration system and air conditioning system.
When refrigerant comes out from the condenser at a medium temperature and high pressure, it enters the throttling valve.
In the throttling valve, the pressure and temperature of the refrigerant are decreased suddenly and the cooling effect is provided to the evaporator.
In a car’s air conditioning system, the refrigerant vapour from the evaporator is compressed to high pressure by the compressor.
The compressor is driven by the engine through a belt drive.
In a hybrid car, the compressor is driven by the motor and the power is used from the battery.
The compressor is connected by an electromagnetic clutch which serves, engages and disengages the compressor as required.
A variable displacement A/C compressor is sometimes used to match a compressor capacity to varying cooling requirement.
The refrigerant pressure and temperature increases in the compressor and converts it into the vapour form and then to the condensed form.
In the condenser the refrigerant liberates heat and converts into the liquid form.
Sometimes the air is not sufficient and therefore, an extra engine or electric driven fan is used to cool the refrigerant.
This cooled but high pressure refrigerant is passed through the dehydrator to extract any moisture.
Dry refrigerant liquid is then made to pass through expansion valve mounted at the inlet side of the evaporator.
The expansion valve allows the refrigerant liquid to expand to low pressure in the evaporator.
The process of expansion to low pressure makes the refrigerant evaporate and thereby cool the evaporator.
A sensing device, called temperature tube signals the diaphragm in the expansion valve to change the size depending upon the refrigerant temperature at the evaporator outlet, thus achieving automatic temperature control
There are different safety and security systems for automobiles available in the market and some of which are fitted by the manufacturer.
Some of the active and passive security systems are mentioned as follows.
Safety glass is used in all windows and doors of automotive.
The safety glass used in today’s vehicles is of two types?—?laminated and tempered.
These are considered as safety glass because of their varying strength.
Laminated plate glass is used to make windshields.
It consists of two thin sheets of glass with a thin layer of clear plastic between them.
Some glass manufacturers increase the thickness of the plastic material for better strength.
When this type of glass breaks, the plastic material tends to hold the shattered glass in place and thus, prevents it from causing injury.
Tempered glass is used for side and rear window glass but rarely for windshields.
It is a single piece of heat-treated glass and has more resistance to impact than the regular glass of the same thickness.
Thus, it has greater strength compared to a laminated plate glass.
A seat belt is also called a safety belt.
It is a harness designed to protect the occupant of a vehicle from harmful movement, during a collision or when the vehicle stops suddenly.
A seat belt reduces the likelihood and severity of injury in a traffic collision.
It prevents the vehicle occupant from hitting hard against the interior elements of the vehicle or other passengers, and keeps the occupants positioned in place for maximum benefit from the airbag.
The passenger must fasten the seat belt for crash protection.
However, in case of a assive safety system, such as the inflation of air bags at the time of an accident, is automatic.
No action is required of the occupant to make functional.
Nowadays, seat belts are also provided for ear seat occupants.
An airbag is one of the passive safety systems for the occupants of a four wheeler.
The electrical system of airbags includes impact sensors and an electronic control module.
In case of an accident, the sensor detects the impact and the airbag opens to save the driver and bags other occupants.
Modern bumpers are designed to absorb the energy of a low-speed impact, minimizing the shock directed to the frame and to the occupants of the vehicle.
Most energy absorbers are mounted between the bumper face bar or bumper reinforcement bar and the frame.
There are three basic types of security devices available — locking devices, disabling devices and alarm systems.
In automobile vehicle, an anti-theft system or device is installed to prevent theft of a vehicle.
Many car security devices are available in the market.
These are mechanical devices and ignition cut off devices, intelligent computerized anti-theft devices, satellite tracking system, engine control module, etc.
Vehicle owners may select as per risk and install it in their vehicles.
Prior to purchasing, the customers should check that these theft devices are duly approved from the Automobile Research Association of India (ARAI).
Important features of these devices are explained below.
Alarm: In the case of vehicle tampering, audible warning sounds emerge
Keyless Lock Device: To use the vehicle, electronic coding device is required
Electronic Immobilizers: These built-in transponders send signals to the ignition and fuel pump system.
The vehicle remains in stationary or inoperable state if the ignition starters do not get correct signals.
Steering Wheel Lock: This device is fitted in the steering of the vehicle and it locks it in one place so that no one can drive it without removing the lock.
Vehicle Tracking: Even if a thief steals a vehicle, the tracking technologies can help trace it.
Tracking devices offer real-time location of the stolen vehicle with the help of the global positioning system (GPS).
Congestion: a serious and worsening problem.
Traffic congestion has been increasing in much of the world, developed or not, and everything indicates that it will continue to get worse, representing an undoubted menace to the quality of urban life.
Its main expression is a progressive reduction in traffic speeds, resulting in increases in journey times, fuel consumption, other operating costs and environmental pollution, as compared with an uninterrupted traffic flow.
Congestion is mainly due to the intensive use of automobiles, whose ownership has spread massively in Latin America in recent decades.
Private cars have advantages in terms of facilitating personal mobility, and they give a sensation of security and even of heightened status, especially in developing countries.
They are not an efficient means of passenger transport, however, since on average at rush hours each occupant of a private car causes about 11 times as much congestion as a passenger on a bus.
The situation is further aggravated in the region by problems of road design and maintenance in the cities, a style of driving which shows little respect for other road users, faulty information on traffic conditions, and unsuitable management by the responsible authorities, which are often split up among a host of different bodies.
The cost of congestion is extremely high.
According to conservative calculations, for example, increasing the average speed of private car journeys by 1 km/hr and that of public transport by 0.
5 km/hr would give a reduction in journey times and operating costs worth the equivalent of 0.1% of the gross domestic product (GDP) (Thomson, 2000b).
The harmful effects of congestion are suffered directly by the vehicles that are trying to circulate.
They are not only suffered by motorists, however, but also by users of public transport –generally lower-income persons–who not only take longer to travel from one place to another but also have to pay higher fares on account of congestion.
All city-dwellers are also adversely affected, in terms of a deterioration in their quality of life through such factors as greater air and noise pollution and the negative long-term impact on the healthiness and sustainability of their cities, all of which makes it vitally necessary to keep congestion under control.
Make a start with measures affecting supply
The most logical approach is to tackle congestion through measures affecting the supply of transport, i.e., the availability and quality of the transport infrastructure, vehicles and their management, since this represents an increase in the capacity for travel.
There are many shortcomings in the current urban road systems which need to be put right: it is necessary to improve the design of intersections, mark roads properly and provide them with suitable signs, and correct the operating cycles of traffic lights, for example.
Another possible measure would be to make the traffic flow in the main avenues reversible at rush hours.
These measures can relieve congestion considerably and do not usually cost much, the main requirement being a knowledge of traffic engineering.
The construction of new roads or the widening of existing ones should not be ruled out, when appropriate and feasible within the context of a harmonious form of urban development which provides for adequate spaces for pedestrians and preserves the architectural heritage.
It should be borne in mind, however, that building more and more roads, under- and overpasses and urban expressways may be counter-productive in the medium and long term and may actually make congestion even worse, as we have unfortunately seen in the cases of some cities which adopted this strategy.
Big savings may be obtained through a system of traffic lights run from a central computer.
The rather high cost of this system in the view of many municipalities might make it advisable to set about this programme initially in several stages and only in certain sectors of the city, beginning with the progressive replacement of obsolete traffic lights by newer ones suited to the necessary technology.
The application of this system in areas of heavy traffic would show off its virtues and obtain citizen support for its wider use.
Another very real need is to organize a public transport system which provides effective service.
Substantial benefits are provided, not only for buses but also for private cars, by segregated lanes for public transport.
It may also be necessary to reorganize the bus lines into trunk and feeder lines, to give them certain preferential traffic rights, and to improve the quality of the buses used and the business capacity of their operators.
Buses of a higher standard than those generally in service may also have a useful role to play, especially if their operating timetables and frequencies allow them to offer a viable alternative to private car users.
A significant contribution can be made by transport systems similar to above-ground subway lines, organized on the basis of buses running in their own segregated lanes, with regular journey frequencies, centralized control, boarding and alighting of passengers only at designated stations, and a requirement that passengers must purchase their tickets before boarding the bus.
Although installing these systems is a complex matter and the construction of the necessary infrastructure will assuredly need the contribution of public resources, the excellent results obtained in Curitiba, the Quito trolleybus system and the Transmilenio public transport system in Bogot? fully justify this solution, which costs only a fraction of the construction of an underground subway system.
It is important that public transport should be improved in order to provide a rapid service of decent standard and thus maintain the present proportion of journeys made by this means.
In developing countries, over half of all journeys –and as much as 80% in some cities—are made by public transport.
If well designed and executed, measures affecting supply offer an interesting potential for tackling congestion.
Even so, it is necessary to incorporate other measures, especially respecting demand, to be able to solve the imbalances in infrastructure use and aid in achieving an acceptable balance for the community as a whole.
Measures aimed at demand also have a role to play
The aim of these measures is to persuade a substantial number of private car users travelling at rush hours or in areas of heavy traffic to use higher-density forms of transport, use non-motorized means of transport, or change the times at which they travel.
Some measures may involve the application of regulations and restrictions.
Others may provide economic rewards or disincentives for adopting forms of conduct that reduce congestion.
Both types of measures need to be considered for a better overall result, since economic measures may not be fully effective, while those involving regulations may be vulnerable if the controls are weak.
Substantial results can be achieved through the rationalization of parking spaces, since their availability and cost condition access by private car users.
Permanent or daytime prohibition of parking on the main streets, charges for parking on other streets, the regulation of paid parking in private parking lots and free parking offered by institutions and firms to their workers or to the public, economic incentives for not going to work by car, and intermediate parking lots for leaving cars and continuing the journey in public transport are potentially useful measures if applied in the right places and the right way.
Some of them may also generate income for the municipalities.
Staggering the starting hours of activities relieves congestion somewhat, as it spreads the morning rush hour over a longer period, while restrictions on vehicle use can take a substantial part of the total number of vehicles out of circulation.
The application of such restrictions only in the most congested sectors or times, as for example in central areas during the morning and evening rush hours, may have more lasting effects than their more general application, since it will give less incentive to buy extra cars to get round the restrictions.
Another form of restriction is to charge differential license fees depending on whether or not the vehicle can be used every day of the week.
Road use tariffs, which have been proposed by many academics and urban transport officials because they represent an attractive concept for making drivers pay for the costs they cause to society, are the measures which have met with the strongest resistance, especially from private car owners.
These measures seem to get results, at least in the short term, but they are questioned from every imaginable angle.
They are unacceptable for users, since they require them to pay for moving about in conditions of congestion; there are doubts about how to apply them; there are objections about their effects on areas immediately next to those subject to tariffs; they are accused of being inequitable with regard to persons with fewer resources; it is feared that economic activities in the areas subject to tariffs will be adversely affected; there are doubts about their long-term effects on town planning, because they are an incentive for cities to expand unless there are severe controls on land use; and last but not least, it is claimed that their application would be theoretically inconsistent unless other related prices, such as those of green spaces, are also made subject to the recovery of their marginal cost.
It would therefore appear that the likelihood of their application is limited, unless some city (other than Singapore, which has extremely special conditions) manages to put them into effect successfully.
They may perhaps be tried out first in developed countries, if the congestion there reaches intolerable levels, no other effective means are seen to exist, and the theoretical and practical doubts that still affect them can be successfully solved.
If carried out permanently ever since childhood, education in proper road use can help to reduce congestion by teaching the population not to drive in an undisciplined manner or fail to show due respect to other road users, whether pedestrians or other drivers.
Likewise, pedestrians must also be made to observe traffic rules and cross the street only at suitable places and moments.
Measures affecting demand must be carefully analysed in order to avoid unwanted ill-effects.
Over-restrictive regulations can alienate firms and residents and depress some areas of cities.
How should the problem be tackled?
The rapidity with which congestion can reach acute levels in big cities makes it essential for the authorities to take the right approach when seeking to adapt urban transport systems in this respect, both in the case of public transport and in that of private car use in problem areas or times.
The first concern should be to relieve the effects of congestion on those who have little or no responsibility for causing it, by:
Promoting or recovering the road system’s quality of a public good, by facilitating the free circulation of those who do not contribute to congestion, or only do so to a negligible extent.
This mostly means providing public transport with clear, unimpeded routes and giving it some degree of priority over other road users, including segregated bus lanes when appropriate in order that it should not be held up by congestion; Keeping the emission of pollutants under control; andLimiting congestion in order to prevent it from endangering the quality of life and sustainability of cities.
Reducing congestion also has the result of reducing the emission of pollutants that contaminate the air, since in most cities in the world the transport system is one of the main culprits for atmospheric pollution.
An integrated strategy for combating these two problems can therefore result in more efficient solutions than the application of isolated measures to combat each of them separately.
Combating congestion entails various amounts of costs.
Some must be defrayed by the public bodies that are applying the measures; others affect the population in general, while those related with actions regarding demand affect motorists in particular.
Everything indicates that an effort should be made to apply a set of actions designed to affect both the supply of transport and the demand for it, in order to rationalize public road use.
It must be recognized that a style of personal mobility based essentially on the use of private cars is not sustainable in the long term, although this does not necessarily mean that it should be prohibited.
Private cars have many uses which make urban life easier, such as facilitating social life, shopping or travelling to distant destinations.
Using them every day to go to one’s place of work or study in areas of heavy traffic is a different matter, however.
It is therefore necessary to design policies and measures of a multi-disciplinary nature which will make it possible to keep congestion under control, since it is not reasonable to think of eliminating it altogether.
In the case of cities in developing regions, while local conditions must always be taken into account, it would seem advisable to give priority to the following measures:
Rectification of intersections.
Improvement of road markings and signs.
Rationalization of on-street parking.
Staggering of working hours.
Synchronization of traffic lights.
Reversibility of traffic flow direction in some main avenues.
Establishment of segregated bus lanes, together with the restructuring of the system of bus routes.
At the same time, it is necessary to establish a long-term strategic vision of how the city should develop which will make it possible to harmonize the needs of mobility, growth and competitiveness, which are so necessary in the world of today, with the sustainability of cities and the improvement of their quality of life.
This is a complex task, calling for high professional and leadership qualities on the part of the town planning and transport authorities, and it could perhaps be made easier by the establishment of a single unified transport authority in metropolitan areas.
Keeping congestion under control is an ongoing, never-ending task.
Tools exist for this purpose, some of them more effective and some of them more readily accepted than others, but a set of measures which has the support of the local population is needed in order not to run the risk of succumbing in the face of the modern scourge of traffic congestion.
Congestion: an escalating negative phenomenon.
In recent years, and especially since the early 1990s, the increase in road traffic and in the demand for transport have caused serious congestion, delays, accidents and environmental problems, above all in large cities.
Traffic congestion has become a veritable scourge which plagues industrialized countries and developing nations alike.
It affects both motorists and users of public transport, and as well as reducing economic efficiency it also has other negative effects on society.
The disturbing thing is that this expression of modern times has been intensifying, without any sign of having a limit, thus becoming a nightmare that threatens the quality of urban life.
The last few decades have seen a rapid escalation in the number of motor vehicles in developing countries, as a result of various factors, such as the increase in the purchasing power of the middle-income socioeconomic classes, the greater availability of credit, the relative reduction in prices and the greater supply of used vehicles.
The growing availability of automobiles has allowed greater individual mobility, which, together with population growth in cities, the smaller number of persons per household and the fact that structured urban transport policies have been applied in only a few cases, has led to an increase in congestion.
Although the greater individual mobility provided by the motor vehicle may be considered positive, it also implies more intensive use of the space available for circulation.
The most obvious consequence of congestion is the increase in travel time, especially at peak periods, which has reached levels well above those considered acceptable in some cities.
In addition, the slow pace of circulation is a source of exasperation and triggers aggressive behavior in drivers.
Another result is the exacerbation of environmental pollution.
Its relationship with congestion is an aspect that still needs to be studied in greater depth, although valuable evidence has already been obtained in some Latin 
American cities.
Pollution affects the health of all citizens, so that it must be kept below certain limits.
Quite apart from the harm caused by pollution at the local level, however, vehicles also emit greenhouse gases, which adds a global dimension to the issue that cannot be overlooked.
In addition to the above considerations, there are other important harmful effects which should be taken into account, such as the larger number of accidents, the increase in the consumption of fuel for the distance covered and, in general, the higher operating costs of vehicles.
The situation is compounded by the fact that congestion affects not only motorists but also users of public transport, who, in developing countries, are lower-income persons; in addition to lengthening their travel time, there is a possibly even more regrettable consequence for them, which is that congestion pushes up fares, as explained in chapter II.
Nevertheless, a limited degree of congestion may not be altogether unacceptable.
It is preferable to tolerate a certain level than to adopt measures which have an even greater cost.
After all, congestion is a sign of activity, and trying to eliminate it altogether could entail disproportionate investments in the road network which could significantly prejudice various other kinds of socially beneficial ventures.
While it is clear that acute congestion has direct negative consequences, it also has other more general and disturbing effects that loom over cities suffering from it.
Damage to competitiveness.
Congestion interferes with a city’s economic efficiency, since it imposes extra costs that make all activities more expensive and put a damper on development.
In a globalized world like today’s, where customers are more and more demanding and there are many places offering advantages for investors, cities have to be competitive both nationally and internationally.
For this, they must pay attention to, and reduce, various types of costs, including those that are related to transport, such as the time spent travelling, the energy consumed, the level of air pollution and the number of accidents.
Who would start a venture in a city where travel times are intolerable or where there is doubt whether one can arrive on time for one’s daily engagements?
Since there are multiple options worldwide to choose from, a city with serious problems of congestion will drive away investors, however favorable other important conditions may be, such as proximity to the high seats of power or decision-making or the availability of a skilled labor force.
Although traffic congestion may not be the only cause, it can be a major factor in the exodus of various activities from traditional city centres in search of conditions that permit better performance.
There is a real danger that the centre may remain only as the location of government institutions, small businesses and low-income residents, or it may even be partly abandoned, all of which will result in visible deterioration.
Historic centers –especially those of capital cities– harbour a rich heritage which deserves not only to be preserved but also to remain current and in regular use.
In the medium and long term, congestion can make a city’s lifestyle unsustainable.
Excessive travel times, fuel consumption and pollution can cancel out the synergy arising from the concentration of services and opportunities offered by cities.
In a situation marked by increasing difficulties and danger to public health, more and more people will opt to escape from such an environment and migrate somewhere else.
In short, congestion and its consequences are gradually becoming an acute threat to the sustainability of cities.
Cities for living, developing and moving about
There is yet another important consideration, however.
The significant advantages offered by cities have caused them to grow and absorb an inflow of people from rural areas.
Today, however, the benefits offered by the concentration of activities are not enough in themselves: cities must also provide a quality of life in keeping with the intrinsic dignity of human beings.
Currently, quality of life is recognized as a fundamental value which, moreover, must be sustained over time.
In other words, conditions must be generated to make living more pleasant, and this must be on a lasting basis.
Competitiveness and mobility are part of the quality of life, insofar as they provide fuller opportunities for development, work and recreation.
Such conditions favour the possibility of undertaking ventures, working, moving about and relaxing, all of which are considered necessary for better personal fulfilment.
Nevertheless, promoting competitiveness and mobility indiscriminately can, in certain circumstances, detract from the quality of life.
Thus, for example, a generous broadening of the roadway for the circulation of vehicles can confine pedestrians into notably insufficient spaces, swallow up large extensions of green spaces, or result in the segregation of zones or neighbourhoods.
On the contrary, enough public space must be reserved for walking, jogging or simply getting together with other people, since this is an inherent part of the pleasure of living and also has an important effect in terms of promoting better health for today’s sedentary citizens.
Consequently, it is necessary to develop a clear concept of the kind of city which is desired, where there is a harmonious blend of economic efficiency, mobility, a tolerable degree of congestion, a clean environment and a better quality of life, all on a sustainable basis.
Clearly, uncontrolled traffic congestion goes against such aspirations and can give rise to a disturbing future.
Thus, it must be controlled in the short and medium term through the application of technical knowledge, as well as by learning to take useful and sustainable measures which must go hand in hand with new civic attitudes with respect to mobility, the transport system, public spaces and traffic.
At the same time, however, congestion is not a problem to be addressed only in a technical and autonomous way: it must also form part of the broader effort to develop cities for the benefit of people.
In designing concrete measures, account must also be taken of their various impacts on harmonious urban development and care must be taken to prevent negative effects.
This calls for an integral approach which will enable us to attain cities that offer a better quality of life and are sustainable over time.
WHAT IS CONGESTION?
The word “congestion” is frequently employed in the road traffic context, both by technicians and by the public at large.
Webster ’s Third New International Dictionary defines it as “a condition ofovercrowding or overburdening”, while “to congest” means “to overcrowd, overburden or fill to excess so as to obstruct or hinder” something: in this case, road traffic.
It is usually understood as meaning a situation in which there are a large number of vehicles circulating, all of which are moving forward in a slow and irregular manner.
These definitions are of a subjective nature, however, and are not sufficiently precise.
The fundamental cause of congestion is the friction or mutual interference between vehicles in the traffic flow.
Up to a certain level of traffic, vehicles can circulate at a relatively freelydetermined speed which depends on the legal speed limit, the frequency of intersections, and other conditioning factors.
At higher levels of traffic, however, every additional vehicle interferes with the circulation of the others: in other words, the phenomenon of congestion appears.
A possible objective definition, then, would be: “congestion is the situation where the introduction of an additional vehicle into a traffic flow increases the journey times of the others” (Thomson and Bull, 2001).
As traffic increases, traffic speeds go down more and more sharply.
In figure II.
The difference between the two curves represents, for any volume of traffic (q), the increase in the journey times of the other vehicles which are in circulation due to the introduction of an additional vehicle.
It may be noted that the two curves coincide up to a traffic level Oq0; up to that point, the change in the total journey times of all the vehicles is simply the time taken by the additional vehicle, since the others can continue circulating at the same speed as before.
From that point on, however, the two functions diverge, and d(qt)/dq is above t.
This means that each additional vehicle not only experiences its own delay but also increases the delay of all the other vehicles which are already circulating.
Consequently, the individual user is only aware of part of the congestion he causes, while the rest is suffered by the other vehicles in the traffic flow at that moment (Ort?zar, 1994).
In the corresponding specialized language, users are said to perceive the mean private costs, but not the marginal social costs.
Strictly speaking, users do not have a very clear idea of the mean private costs either, since, for example, few drivers have a clear idea of how much it costs them to make an additional journey in terms of maintenance, tyre wear, etc.
In contrast, they do clearly perceive the costs imposed on them by the government –particularly the fuel tax– which are seen as mere transfers from motorists to the State, all of which distorts their manner to taking decisions.
Another conclusion which can be drawn –and which can be confirmed by simple observation– is that at low levels of congestion an increase in the traffic flow does not significantly increase journey times, but at higher levels the same increase causes considerably greater overall delays.
According to the definition given earlier, congestion begins with a traffic level Oq0.
Generally, however, this occurs at relatively low traffic levels, unlike what most people think.
Towards a practical definition in the case of road traffic.
Even some specialized studies do not give very strict definitions of congestion.
Thus, two well-known specialists in transport modeling consider that congestion occurs when the demand nears the capacity of the travel infrastructure and transit times rise to a much higher level than that obtaining in conditions of low demand (Ort?zar and Willumsen, 1994).
Although this definition reflects the perceptions of the average citizen, it does not propose exact limits for the point at which the phenomenon begins.
An attempt to define the term precisely in line with the usual perception of it was that made in a draft law like that approved by the Chilean Chamber of Deputies for the introduction of road use tariffs.
As the aim was to avoid the possibility of discretionarily on the part of public authorities, the definition was very precise.
A road was considered to be congested when, in more than half of its total length (including not necessarily continuous stretches), the average speed of the traffic flow was less than 40% of the speed in unrestricted conditions.
This state of affairs must be registered for at least four hours a day between Tuesday and Thursday, on the basis of measurements made for four consecutive weeks between March and December.
An exact definition was also given of congested areas.
This definition was perhaps too precise and difficult to apply in practice, although so far it has not been necessary to apply it, since the draft law has not yet received full legislative approval.
Without going into such detail, yet continuing to seek objectivity, the term congestion could be defined as “the situation which occurs if the introduction of a vehicle into a traffic flow increases the travel times of the other vehicles by more than x%”.
An objective although still somewhat arbitrary definition of congestion would be to define it as the volume of traffic at which d(qt)/dq = at, where a equals, for example,1.50.
In other words, congestion would begin when the increase in the journey time of all the vehicles already present in the flow was equal to half of the travel time of an additional vehicle.
The transport system, including the provision of urban land for transport infrastructure, operates with very special characteristics, including in particular the following:
Why are big cities prone to congestion?
The demand for transport is “derived”: in other words, journeys are rarely made because of an intrinsic desire to travel but are generally due to the need to travel to the places where various kinds of activities are carried on, such as work, shopping, studies, recreation, relaxation, etc., all of which take place in different locations.
The demand for transport is eminently variable and has very marked peak periods in which a large number of journeys are concentrated because of the desire to make the best use of the hours of the day to carry on the various types of activities and have an opportunity to make contact with other persons.
Transport takes place in limited road spaces, which are fixed and invariable in the short term; as will readily be understood, it is not possible to store up unused road capacity for later use at times of greater demand.
The forms of transport which have the most desirable characteristics – security, comfort, reliability, and autonomy, as in the case of private cars –are those which use the most road space per passenger, as will be explained below.
Especially in urban areas, the provision of road infrastructure to satisfy rush hour demand is extremely costly.
Because of the above factors, congestion occurs at various points, with all its negative consequences of pollution, heavy expenditure of private and social resources, and adverse effects on the quality of life.
A further aggravating factor is that, as noted in the previous section, the cost of congestion is not fully perceived by the users who help to generate it. Every time this happens, more of the good or service in question is consumed than is desirable for society as a whole.
As users are not aware of the greater costs in terms of time and operation that they cause to others, their decisions on routes, forms of transport, points of origin and destination and time of execution of journeys are not taken on the basis of the social costs involved, but their own personal costs or, rather, an often partial perception of those costs.
The natural result is the over-exploitation of the existing road system, at least in certain areas and at certain times.
The problem is mainly caused by private car users.
Some vehicles cause more congestion than others.
In transport engineering, each type of vehicle is assigned a passenger car equivalence called a pcu, or passenger car unit.
A private car is equivalent to 1 pcu, while other vehicles have equivalencies corresponding to their disturbing influence on the traffic flow or the space they occupy in it, as compared with a private car.
A bus is normally considered to be equivalent to 3 pcus and a truck to 2 pcus.
Strictly speaking, however, the pcu factor varies according to whether the vehicle in question is close to an intersection or is in a stretch of road between two intersections.
Although a bus causes more congestion than a private car, it generally carries more persons.
Thus, if a bus carries 50 passengers but a private car only carries an average of 1.5 persons, then every private car passenger is causing 11 times as much congestion as a bus passenger.
Consequently, other things being equal, congestion is reduced if the share of buses in the intermodal journey mix is increased.
Unless buses transport less than 4.5 passengers, on average they cause less congestion than private cars.
It is not normal for buses to transport fewer than 4.5 passengers, although this can sometimes happen, as for example in some sectors of Santiago, Chile, at off-peak hours in the late 1980s, or in Lima ten years later.
The existence of an excessive number of public transport vehicles can help to increase congestion, as noted in a number of cities.
One of the features of the current economic models is deregulation, and in the case of urban passenger transport, broad deregulation is normally reflected in an exaggerated increase in the number of buses and taxis and a deterioration in the levels of order and discipline associated with their operation.
This phenomenon bore much of the blame for the deterioration in congestion in Santiago in the 1980s and in Lima in the following decade.
The liberalization of the rules on the importation of used vehicles and the deregulation of public transport both had particularly serious effects in Lima.
In Santiago, which had some 4,300,000 inhabitants in the late 1980s, there were relatively few cases of the importation of used vehicles, and the public transport fleet (all types of buses, plus collective taxis) did not amount to more than 16,000 vehicles.
In the mid-1990s in Lima, however, which had some 6,700,000 inhabitants at that time, the public transport fleet amounted to at least 38,000 vehicles (and some sources indicate that the real number was close to 50,000).
In other words, in the mid-1990s the number of units per inhabitant in Lima was between 52% and 101% higher than it had been in Santiago some seven years before, at a time when deregulation in Chile was having its most striking results.
The state of the roads and driving habits also contribute to congestion
Urban road networks: design and maintenance problems
Faulty design or maintenance of roadsystems causes unnecessary congestion.
In many cities there are frequent cases of failure to mark traffic lanes, unexpected changes in the numberof lanes, bus stops located precisely where the road width becomes narrower, and other shortcomings which disturb a smooth traffic flow.
Likewise, road surfaces in bad condition, and especially the presence of potholes, give rise to increasing constraints on road capacity and increase congestion.
In many Latin American cities, such as Caracas, the accumulation of rainwater on roads reduces their traffic capacity and hence increases congestion.
Some driving habits cause more congestion than others
There are drivers who show little respect for other road users.
In some cities, such as Lima, many drivers try to cut a few seconds off their journey times by forcing their way into intersections and blocking the passage of other motorists, thus causing economic losses to others which are much greater than their own gains.
In other cities, such as Santiago, it is a tradition for buses to stop immediately before an intersection, thereby causing congestion (and accidents).
In those same cities, as in others that have an excessive number of taxis that do not habitually operate from fixed taxi ranks, these vehicles crawl along looking for passengers, and this also gives rise to congestion.
In addition to these practices, the traffic flows also often include old and poorly maintained vehicles, as well as some drawn by animals.
It must be borne in mind that when the traffic flow resumes after being stopped at a traffic light, a form of congestion ensues because vehicles with a normal rate of acceleration are held up by slower vehicles located in front of them.
Furthermore, a vehicle which is stopped or moving sluggishly seriously affects the smooth flow of traffic, since in effect it blocks a traffic lane.
Insufficient information is available on traffic conditions
Another factor which increases congestion is ignorance of the prevailing traffic conditions.
If a motorist with two possible routes, A and B, for reaching his destination knew that traffic conditions were bad on route A, he could use route B, where his own contribution to congestion would be less.
A study of a hypothetical city made in the University of Texas in the United States indicates that the fact of being well informed about traffic conditions in different parts of the road network can reduce congestion much more than such drastic measures as levying charges for using congested streets (IMT, 2000).
Basic unfamiliarity with the road system can also increase the average distance of each journey and thereby contribute to congestion.
The result is that there is a generalized reduction in capacity
Generally speaking, both the way motorists drive and the state of the road and vehicles mean that in Latin America a street or urban road network will assuredly have a lower capacity than one of similar dimensions located in Europe or North America.
Measurements made in Caracas in the early 1970s showed that an expressway there had only 67% of the capacity of a United States expressway of similar size.
The actual percentage difference may vary from one city to another, but there is no doubt that the road systems of Latin American cities are relatively prone to congestion.
There is also an institutional problem
In almost all Latin American cities, the deterioration in traffic conditions has been significantly worse than it could and should have been, partly because of inappropriate actions by the corresponding authorities.
It is obvious that the problem has clearly overtaken the institutional capacity to deal with the situation.
So far, the reaction of the authorities has only been of a piecemeal nature, because in virtually the whole region the responsibility for urban transport planning and management is split up among a host of bodies, including various national ministries, regional governments, municipalities, suburban train or metro companies, the traffic police, etc.
Each of these does what it considers to be most appropriate, without taking much account of the repercussions on the interests of the other institutions.
A municipality, for example, fearing the diversion of economic activity to another part of the city, may authorize the construction of multi-storey car parks, or allow parking on the streets, without bothering about the impact of the congestion thus generated on road users who have to cross through the area in question.
Another situation which reflects the consequences of decisions taken without coordination and without considering their broader repercussions may occur in the context of a mass transit system such as the metro.
Because of the greater accessibility provided, land use becomes more dense and office blocks are built, and as municipal regulations usually demand a certain minimum number of private parking spaces for such buildings, this encourages the staff to come to work in their cars.
Thus, this set of measures fosters increased congestion.
Furthermore, in such a sensitive area as urban transport, strong pressures are exerted by organized groups, such as transport interests, as well as by politicians, who put forward their own points of view and sometimes take up arms on behalf of particular interests, which complicates the situation still further.
All the above factors are a source of distortions, yet urban transport should be handled in an integral, technical manner, instead of measures being taken separately by each institution or in favor of sectoral interests.
THE INVASION OF THE PRIVATE CAR
The last decade of the twentieth century brought with it a big increase in the number of private cars circulating in Latin America, as well as in their use for the most varied purposes, including journeys to places of work or study, thus exerting heavy pressure on the road network.
What are the causes of these phenomena?
Economic reforms have made private cars more easily accessible.
Among other effects, the economic reforms adopted in the region in the 1990s brought with them higher economic growth rates and lower car prices.
Instead of the almost always negative per capita GDP growth rates of the 1980s, the 1990s brought relatively high positive growth rates.
Thus, for example, Uruguay went from an average annual growth rate of -1% between 1981 and 1988 to a rate of +4% between 1991 and 1994 (ECLAC, 1989 and 1995a).
This had a favorable repercussion on personal income levels, thus making more resources available for the acquisition of consumer durables.
At the same time, in many cases there was a reduction in the tax burden on automobiles, especially in customs duties.
Moreover, in some countries there was an appreciation in the exchange rate, thus making imported products cheaper to buy.
In Colombia, for example, the real exchange rate in 1994 was only equivalent to 75% of that prevailing in 1990 (IDB, 1995).
This tendency does not necessarily mean that actual prices are lower, because at the same time the quality of vehicles has improved.
In the case of those vehicles whose characteristics have remained relatively unchanged, however, there has been a real reduction in their purchase prices.
In the Chilean market, for example, in 1996 a Volkswagen Beetle cost the equivalent of US$ 7,780, whereas in 1982 it had cost the equivalent of US$ 8,902 at 1996 prices.
The real reduction in the prices of used cars has undoubtedly been even greater, although it is difficult to obtain reliable data in this respect.
The rate of depreciation of private cars is directly related with the rate of ownership.
In countries where there are few vehicles per person, a second-hand car is a relatively scarce good, and the price at which it is sold will reflect a limited supply and, sometimes, abundant demand.
The rise in rates of vehicle ownership in Latin America in recent years has reduced the relative scarcity of used cars, thus tending to increase supply and reduce demand, because a larger proportion of the population now already have one, and hence drive down prices, putting such vehicles within the reach of lower-income families.
Consequently, in the current Latin American situation real incomes are rising and automobile prices are tending to go down.
The popularization of private car ownership.
In Latin American cities, the evolution of residents’ incomes and car prices –especially those of used cars– means that ownership of a vehicle is ceasing to be an unattainable dream and is becoming an accomplished fact for many families.
The increase in the rate of car ownership is a phenomenon which is repeated almost everywhere in Latin America and has made it possible –especially for the middle class– to reap one of the most important fruits of technological progress in the twentieth century.
In the countries where economic reforms were implemented rapidly, automobile imports increased equally fast.
The column corresponding to Peru shows that between 1990 and 1991 the value of automobile imports increased by a factor of 14.
Peru freed not only the importation of new vehicles but also that of used ones (except for a brief period between February and November 1996).
Consequently, the average unit cost went down, indicating that the number of units imported must have increased even more than the total value of imports.
In some countries that manufacture motor vehicles themselves, the economic reforms resulted in an increase both in vehicle imports and in domestic production.
This was so in Brazil, where automobile imports had been subject to heavy duties, as part of a policy designed to promote domestic production of these goods.
Thus, between 1990 and 1994 imports grew by over 10,000%, albeit starting from a very low level, yet domestic automobile production also rose, by 70%.
Vehicle exports were reduced because manufacturers preferred to sell their output on the growing domestic market (see table II.2).
Another factor which influenced the situation, during a period from mid-1994 on, was the appreciation of the local currency.
A concrete result was that in S?o Paulo, between 1990 and 1996 the population grew by 3.4% but the vehicle fleet expanded by 36.5%.
This equation has the expected form, although it could perhaps be subject to some technical reservations.
By using it, it is possible to estimate the elasticity or unit variation in the rate of automobile ownership with respect to income level.
Table II.3 shows that this elasticity is inversely related to income level.
Although the elasticity in low-income communes (La Pintana) is very high, a 1% increase in income only gives rise to a small increase in the absolute number of automobiles per family.
In contrast, a 1% increase in income in a middle-income commune results in an increase in the absolute number of automobiles per family which is very similar to that registered for a very high-income commune.
The most important conclusion to be drawn from this analysis is that an increase in income results in significant expansion of automobile ownership, not only in the richest neighborhoods but also in middle-income areas.
Thus the total number of automobiles in Santiago grew at the rate of 8% per year during the 1990s.
where there are fewer cars it nevertheless seems harder to get about?
The growing number of vehicles undoubtedly favours increased congestion, but at all events the rates of automobile ownership in Latin American cities are still much lower than in developed countries.
In 1980, the number of automobiles per person in North American cities such as Houston, Los Angeles, Phoenix, San Francisco, Detroit, Dallas, Denver, Toronto and Washington was between 0.55 and 0.85, while in European cities such as Brussels, Amsterdam, Copenhagen, Frankfurt, Hamburg, London, Stuttgart and Paris it was between 0.23 and 0.43.
Ten or fifteen years later, some Latin American cities (such as Chiclayo or Huancayo in Peru) still had no more than 0.02 cars per inhabitant, and in Lima, even though the boom in vehicle imports had already begun, there were still no more than 0.05 cars per person, while in Santiago there were 0.09.
On the other hand, in a few Latin American cities the rate of ownership was already nearing the lower limit of Western European cities.
In Curitiba, for example, in 1995 there were already close to 0.29 cars per person.
Nevertheless, there is evidence that it is easier to move about in the big cities of the developed world than in the comparable cities of Latin America.
In Quito, whose population in 1990 was approximately one million, the average journey time between home and workplace was 56 minutes, whereas in Munich, which had approximately 1.3 million inhabitants, the corresponding time was only 25 minutes.
Likewise, in Bogot? (5 million inhabitants) the journey time was 90 minutes, while in London (6.8 million) it was 30 minutes.
Many other examples along the same lines could be quoted.
Clearly, in the cities of the developed world there is a greater capacity to live with the automobile while avoiding its worst consequences, but Latin America has not yet learned to do this.
Furthermore, it would appear to be easier to move about in the Latin American cities with the highest rates of car ownership than in many where the rates are lower.
Curitiba, for example, has more cars per person than Guatemala City, which is of similar size, but travelling in the first-named city, whether by car or in public transport, is a good deal less disagreeable than in the Central American city.
The explanation for these apparent contradictions is to be found in the marked propensity to make intensive use of private cars for all kinds of purposes.
The strong influence of subjective factors.
One feature which aggravates congestion in Latin America is the marked preference of the population to use private cars.
A clear example was Mexico City, which has suffered for years from acute problems of congestion.
In order to reduce environmental pollution, it was decided to prohibit the use of one-fifth of the existing vehicles from Monday through Friday, but even this drastic measure did not succeed in persuading those affected to use public transport, even though there was an extensive metro system.
Instead, the widespread response was to acquire additional vehicles to evade the effects of the measure, since many people preferred to suffer the effects of congestion rather than use public transport.
In such circumstances, even if the authorities responsible for Latin American urban transport had clear ideas about how to control traffic in the cities (which is unfortunately often not the case), it would be difficult to put them into practice because members of parliament and city councillors, worried about losing votes among the increasingly numerous group of private car owners, would not approve them.
The inhabitants of the cities of the developed world are less likely to use their cars to go to the office in the morning rush hour.
A clear distinction is drawn between owning a car, and using it in situations that give rise to major difficulties.
A New York or London banker living in the suburbs would never dream of travelling every day to Wall Street or the City in his private car, because in both cases there is a good-quality public transport system.
In contrast, his opposite number in S?o Paulo or Santiago would never dream of travelling to the city centre any other way.
It is likely, however, that in the future there will be a change of attitude among motorists, and indeed, in some cities with a notably higher level of culture –such as Buenos Aires, where the quality of public transport is also higher than in most Latin American cities– there is already some evidence of a greater willingness to use public transport than in some other cities of the region.
What is the reason or explanation why there is such a strong preference for using private cars?
One important aspect in this connection is that of status.
In Latin America, the automobile is still considered not only a means of transport, but also an indication of its owner’s status in society.
A person driving a BMW will be considered as superior to one driving a Suzuki, while a person who arrives at the office by car rather than bus is seen as someone who has moved up in the world.
The prestige attached to being a car owner is a strong factor in the volume of traffic.
In addition to these reasons related with the social structure and cultural characteristics, the following considerations are also important in the region:
The poor quality of the buses compared with the aspirations of car owners.
The fact that the buses are very crowded at rush hours.
The feeling of insecurity caused by the dangerous way some bus drivers operate their vehicles.
The real or assumed possibility of being a victim of delinquents on board public transport vehicles.
The preference for travelling by private car becomes a problem at rush hours, when there is a concentration of journeys for reasons of work or study.
Not even serious delays in journeys are enough to cause people to stop using their cars.
If they had to choose between reaching their destination slowly by private car on congested roads and arriving a little more quickly by public transport, it is by no means certain that many Latin American motorists would opt for the latter alternative.
The strong preference for the private car therefore has a number of consequences, such as the following:
The number of motorists willing to move to new public transport systems of no more than regular quality may be quite small, so that the great majority of users of a new metro line would come from former bus users rather than private motorists.
In order to interest private motorists in public transport it would be necessary to offer them a better-class option, not only in terms of objective quality (fares, journey times and frequency of service) but also in terms of its subjective features (air conditioning, reclining seats, etc.)
Even if high taxes are imposed on fuel, road use or parking, this would only cause a few people to change to public transport.
Thus, these measures would serve rather to collect money that could be used to change the habits of the travelling public, and ii) while raising these levels of taxation would produce considerable fiscal income, it would bring relatively few social benefits.
The preference for travelling by private car can also have other consequences which go beyond the limits of the transport sector proper and have negative macroeconomic implications.
Consider, for example, the rises in international oil prices in 1999 and 2000.
The typical Latin American motorist probably did not reduce his vehicle use much but instead restricted his consumption of other goods and services –many of them produced domestically– thereby reducing the demand for them in the short term.
At the same time, importing countries had to increase the amount of foreign exchange spent on fuels because of their higher prices and the fact that demand for them is inelastic or at least not very sensitive to price variations.
Having a car to go to a shopping centre, visit friends or relations in distant parts of town, or travel outside the city is one of the fruits of economic development, and its costs are generally internalized to a large extent by car owners, since these journeys are made at times of low congestion.
Using the car every day to go to the office or the city centre generates high external costs in terms of congestion and pollution and does considerable harm to society, however.
Securing a better balance between the ownership and use of private cars is therefore one of the main challenges to be faced today in the Latin American transport sector.
HOW SERIOUS IS THE PROBLEM, AND WHO SUFFERS ITS EFFECTS?
Various indicators point to a serious and worsening situation.
Taken as a whole, urban transport is a major activity in national life.
The operation of the vehicles circulating on the roads of cities with more than 100,000 inhabitants absorbs around 3.5% of the GDP of Latin America and the Caribbean, to say nothing of non-essential journeys such as weekend trips.
The social value of the time taken up by journeys is equivalent to about a further 3% of GDP (Thomson, 2000b).
It can be seen from these figures that resources involved in urban transport are very significant.
These percentages are very probably on the increase, for various reasons.
One is the increase in the rate of vehicle ownership and the tendency towards intensive use of automobiles, as already mentioned.
Another is the expansion of the cities and the consequently longer journeys needed (Thomson, 2002a).
The growing demand for use of a relatively constant road supply inevitably leads to a progressive decline in traffic speeds.
This situation is deteriorating at an increasingly rapid rate, as shown by the form of the statically determined equations relating traffic speed on a street with the traffic volume.
At rush hours, a large part of the road network in Latin American cities is operating very close to its maximum capacity, which means that even small increases in traffic flows very severely aggravate congestion.
Although there are not many data directly reflecting the trend in terms of congestion over the years, data for S?o Paulo indicate that in 1992, on average, some 28 kilometers of the main road network suffered from acute congestion in the morning and 39 kilometers in the evening, but by 1996 the number of kilometers had risen to 80 and 122, respectively (Compamia de Engenharia de Tr?fego, 1998).
The case of Santiago, Chile, is interesting because this city is the capital of the Latin American country where the process of economic reform and greater openness began.
There is growing congestion, not only in the richest communes but also in some middle-income ones.
Congestion exists not only in the avenues of the highest-income neighborhoods, located in the northeastern part of the city, and in the roads leading into the city centre, but also in roads in other parts of the city where family incomes are much lower, and which are not even areas of habitual transit for persons with high incomes.
With regard to the cost of the congestion caused, estimates for the conditions prevailing in Caracas in 1971, when the situation was not as serious as it is today, indicate that each occupant of a private car gave rise to a congestion cost of US$ 0.
18 per kilometre, at 2000 prices, while the cost for each bus passenger was US$ 0.
02 per kilometre.
It therefore seems reasonable to conclude that the costs of congestion are high and that, conversely, the adoption of measures of only moderate cost in order to reduce them would bring significant net benefits.
Conservative calculations show that increasing the average speed of private car journeys by 1 km/hr and that of public transport by 0.5 km/hr would reduce journey times and operating costs by an amount equivalent to 0.1% of GDP (Thomson, 2000b).
In any case, the mere fact of measuring traffic speeds or calculating the monetary costs of congestion does not reflect the full depth of the problem.
For example, there are people who, in order to avoid some of the effects of congestion, change their forms of conduct and adopt habits which they would not normally prefer, such as leaving the house very early in order to travel before the times of worst congestion, or living close to their workplace.
There are also other serious consequences that severely affect urban living conditions, including the increased air pollution caused by the higher fuel consumption of vehicles moving at low speed in severely congested traffic conditions, higher noise levels around the main roads and streets, the irritability caused by loss of time, and the increased stress of driving in the midst of an excessively congested mass of vehicles.
These other results of congestion may be difficult to quantify, but they nevertheless cannot be ignored, since they are factors that further aggravate an already serious situation.
 Who pays the costs of congestion?
It must be clearly stated, for a start, that the harmful effects of congestion are suffered by all city dwellers, in terms of a deterioration in various aspects of their quality of life, such as greater air and noise pollution, a negative impact on mental health, etc.
Therefore, one way or another, no-one is immune to those effects.
If the analysis is centered on those who have to travel, the effects of congestion can be determined by breaking down its cost into two fundamental components: the time of the persons involved and the operating costs of vehicles, especially fuel.
Both of these costs are increased when travelling in conditions of congestion.
There is no question but that motorists themselves suffer the consequences of congestion.
In other words, they suffer the effects of the phenomenon for which they themselves are responsible, in terms of longerjourney times and higher operating costs.
However, motorists are not the only ones who suffer the effects of congestion, for this also aggravates the already unsatisfactory condition of public transport, whose users are thus also seriously affected, although they are not responsible for causing the problem.
This situation is a source of social inequity, since public transport is used mainly by poorer persons, who are thus captive clients.
Congestion holds up bus passengers
Congestion obviously causes bus passengers to take longer to complete their journeys.
These longer journey times are a loss in real terms, although perhaps this does not attract so much attention because these passengers have relatively low incomes, so that their personal time is assigned a low monetary value.
Especially in Latin America, urban bus users have much lower incomes than those of urban motorists.
In Santiago, Chile, analysis of the data from a study of origins and destinations carried out in 1991 enables the family income of bus passengers to be estimated at 99,321 pesos, compared with a family income of 308,078 pesos for private car users.
In other words, the income of private car users was over three times that of bus passengers.
Data for S?o Paulo in 1977 show that in principle the situation there was not too different from that of Santiago (see table II.4), and if measurements were available for other cities of the region they would probably give similar conclusions.
Another factor, which many passengers may consider to be more important than longer journey times, is the level of bus fares.
Congestion holds up not only the occupants of buses, but also the buses themselves, so that in order to provide a given transport capacity it is necessary to use more buses, with their respective drivers, consequently resulting in higher fares.
This phenomenon was analysed in 1982 and it was estimated that an increase in the average speed of public transport in Santiago from 15 to 17.5 km/hr at peak hours would make it possible to reduce fares by as much as 5% (Thomson, 1982).
A more recent study on the largest cities in Brazil estimated that congestion increased the operating costs of bus transport by up to 16% (see table II.5).
It may be noted that the percentage reductions were much lower in the cases of Brasilia, where the amount of road space is unusually generous, and Curitiba, where the buses operating the radial routes have exclusive bus lanes.
In the conditions prevailing in the year 2000, after almost 20 years of real increases in the cost of buses and the incomes of their drivers, a reduction in fares of 10% would undoubtedly be feasible.
Traffic congestion, especially in the big cities, is an increasingly widespread problem all over the world.
The enormous and growing costs caused by it in terms of loss of time and vehicle operation make it essential to find ways and means of tackling it.
In urban areas, especially at times of greatest demand, congestion is inevitable and may even be desirable, within certain limits, in that the costs it causes may be less than those needed to eliminate it altogether.
Trying to do away with congestion altogether would involve the following costs, among others:
Those connected with the investments needed to expand road capacity, which may exceed the costs caused by moderate levels of congestion
Those caused as a consequence of the diversion of users to other roads, forms of transport or times of travel
Those associated with the possible suppression of journeys as a result of the application of restrictions on motorists.
Furthermore, under-utilization of the existing road space also represents a loss of benefits for society, and it should not be forgotten, either, that congestion is a result of human activity which, in spite of the congestion caused, represents advantages for those making the journeys in question (Taylor, 2002): naturally, a city with a low level of activity will not suffer from congestion.
It is therefore not a question of trying to eliminate congestion completely, since this is either impossible or very costly, and it may not even be desirable.
What is essential is to keep congestion under control, since if it becomes more serious this will adversely affect the sustainability of big cities.
The authorities should take a new view of the situation
The deterioration in traffic conditions has been considerably worse than it could and should have been, partly because of the inappropriate measures taken by the responsible authorities.
The expansion in the number of private vehicles has clearly exceeded the institutional capacity for dealing with the situation.
So far, the authorities have only reacted in a piecemeal manner, because all over Latin America responsibility for the planning and management of urban transport is split up among a host of bodies, including various national ministries, regional governments, municipalities, suburban train or metro companies, the traffic police, etc.
Each one of these does what it considers best, without taking much account of the repercussions on the interests of the other institutions.
Thus, for example, because a municipality fears the possible diversion of economic activity to another part of the city, it may authorize the construction of multi-storey car parks, or allow on-street parking, without bothering about the impact of the congestion thus generated on road users crossing through the area in question.
Another situation reflecting the consequences of separate decisions taken without foreseeing their broader repercussions may arise in the context of a mass transit system such as the metro.
Because of the greater accessibility created, land use becomes more intensive, office blocks are built, and as municipal regulations usually require a certain minimum number of private parking spaces for such buildings, this encourages the staff to come to work by car.
In other words, these measures regarding parking spaces foster an increase in congestion.
Likewise, in an area as sensitive as that of urban transport, strong pressures are exerted by organized groups –road transport firms, for example– as well as by politicians who put forward their points of view and sometimes come out in defence of particular interests.
All this leads to distortions and complicates the situation still further.
Institutions must therefore expand the size and quality of their capacity to respond to problems and, even better, address them in advance.
It is also necessary to develop the capacity to withstand the pressures experienced from so many different sources.
What is needed, then, is growing professional and specialized competence in transport management, both in the responsible bodies and in universities and national consulting firms.
Traffic must also be dealt with in a global manner, and not separately at the level of each individual institution.
Congestion is too serious and far-reaching a problem to believe that it can be relieved through unilateral, erratic or voluntarist measures.
On the contrary, keeping it under control and ensuring a minimum of sustainability of urban standards of living calls for a multidisciplinary effort which includes the improvement of driving habits, the provision of better infrastructure, and measures to manage traffic (supply-side management) and rationalize the use of public roads (demand management).
In other words, the problem must be approached in an integral manner and a set of feasible measures must be taken to improve the productivity of the urban transport system, while bearing in mind at all times that the application of a given measure may have repercussions on other aspects of road traffic, and this must be anticipated in order to avoid negative effects.
What is transport supply?
Transport supply consists of a combination of means that make transportation possible.
Urban transport supply tends to be categorized according to its capacity, that is, the number of persons who can be transported in a given period of time.
Just from the infrastructure standpoint, capacity is usually measured as the number of vehicles that can circulate in a given area in a certain period of time; this parameter is meaningful when analyzing congestion, but it should not be forgotten that what really matters in a city is allowing people to move around satisfactorily.
The simplest forms of road infrastructure are nodes and arcs.
Nodes or intersections are points where two or more roads cross, meaning that the road space is shared by them; at intersections, vehicles can switch routes.
Arcs are stretches of motorway between intersections, generally of uniform width; it is not possible for vehicles travelling along an arc to change routes; they are only able to exit or enter the roadway from adjacent properties.
A succession of arcs and intersections make up what is called the road axis, or simply the road or street.
Roads cross each other to form a true grid.
For this reason, what happens on one street can have repercussions on others, especially in situations of congestion.
Hence, in technical language the term “road networks” is used for the combination of arcs and nodes that join together to make up a system.
Traffic patterns make this evident, as the impact of one incident spreads throughout a region in a chain reaction.
A road network should be treated as such, which often means that the areas of analysis must be expanded so that suitable measures can be adopted to improve transport operations.
A wide variety of vehicles use the streets and avenues of a city, ranging from cars to large buses and multi-passenger, service and freight vehicles of all sorts.
There numerous types of vehicles transport persons and things, although their forms and quality of service vary.
In addition, there are modes of transportation that do not make use of motorways, such as subway systems.
An important issue in connection with congestion is the use that each type of vehicle makes of the space available for circulation, and it should be noted that those carrying the most passengers are the most efficient in this regard, even though they may not fare as well in rrelation to other criteria such as speed of movement or convenience.
Management of the transport system.
The road network and vehicles should be considered as a whole, since the same infrastructure and the same types of vehicles can yield quite different transport capacities.
In other words, how the system is managed makes a big difference.
Whether the streets have one- or two-way traffic, whether one can turn in any direction at all intersections, whether traffic lights are synchronized, whether average vehicle occupancy is high or low, or whether buses are given priority on the roadway, are all factors that change the outcome.
In fact, it is the combination of infrastructure, vehicles and transport management that determines transport capacity or supply.
Frequent calls to expand transport supply.
In a given situation, a high concentration of activities in urban areas and the intensive use of public space, particularly by transit vehicles, can create an imbalance between the volume of traffic and the capacity of the motorways.
The result is vehicle congestion, a deterioration in service for drivers and passengers and a poorer quality of life for the population in general.
As congestion becomes apparent, one option to combat it is to expand the supply of transport.
Supply-side measures include actions affecting roads, vehicles and their operation.
Improving any component of supply yields benefits in the form of reduced congestion.
The first option considered is usually to enlarge the capacity of the road network with a view to enhancing the flow of traffic.
With respect to the infrastructure, the greatest technical efforts have traditionally focused on easing or eliminating congestion, and many of the measures proposed are intended to improve intersections or roadways.
Large and costly projects such as expanding or building expensive highways or building under- and overpasses are viewed more favourably than other alternatives, even though they often do not provide a lasting solution.
In any event, the idea of making a large number of small improvements, such as upgrading crosswalks or improving signposting, should not be discounted because they can yield great benefits when properly designed.
It is also possible to focus on the size of vehicles or their capacity as away of making more efficient use of road space.
Initiatives such as large-capacity buses on heavily trafficked avenues, collective taxis and carpooling are examples of this approach.
The third component of supply is management, which provides countless options that are increasingly broad in scope, thanks to modern technology.
Synchronization of traffic signals, bus priority systems, flexible management of the direction of travel and efficient traffic reporting systems have all made valuable contributions, for example.
It is not hard to see that the three components of supply are closely linked to each other.
Measures affecting them can and should be complementary in order to enlarge capacity and ease congestion in the short term.
The choice of the right packages is the key.
Cities are for living and moving.
Supply-side measures are designed to improve mobility and possibilities for getting around the city.
As important as this is, however, other essential urban values must be safeguarded as well, including habitability and quality of life.
Hence the importance of considering the urban impact associated with every measure, since the degree to which changes in the form and use of motor will ways affect adjacent areas depends on the scale of those chanfes and the type of land use involved.
Some of the most serious consequences of inadequate transport supply management in response to congestion are the lass of space, the lower priosity that may be given to pedestrians and the segragation of districts and neighbourhoods.
Sidewalks and green spaces have suffered from the physical encroachment of road projects, which may the walking and recreational opportunities for residents, adults and children alike.
On other occasions, virtually impassable barriers have been erected to prevent local access, which definitely translates into a poorer quality of life.
It is not easy to balance mobility and habitability.
One way to make these two factors compatible is to designate specialized functions for certain roadways, giving priority to the flow of traffic on main arteries while restoring the neighbourhood atmosphere on streets leading to places where trips are generally started or finished.
All urban motorways that exist today can be placed somewhere
Some routes can be designated for joining distant points of origin and destinations, with little or no intermediate access to properties located along the way, while always providing solutions for interconnectivity between adjacent areas.
This type of thoroughfare does not appear to be appropriate for city centres, as it inhibits pedestrians and may generate noise pollution.
Other streets should give priority to local access, which may even be accomplished by going so far as to make them inconvenient for going from one part of the city to another.
And finally, another category of streets and avenues could lend themselves more to mobility or to access, without excluding other uses.
The classification of roadways and the definition of the corresponding design and operational criteria would make it possible to establish an order in which the two prime functions of urban life, habitability and mobility, could remain in balance.
Intersections are points where two or more roads cross.
Normally, it is intersections that determine the capacity of thoroughfares; because they are a common point between two or more roads, they must allow for the alternation of conflicting flows.
Thus, traffic spends less time moving when it reaches an intersection than when it is flowing along an arc or straight line.
Consequently, intersections become congested first and indeed become bottlenecks or operational restrictions for the entire system.
That is why interventions in intersections have a great potential benefit for traffic flow.
Crossroads.
These are made up of four branches coming together in the shape of a cross.
Multiple intersections.
These are made up of more than four branches and are the most difficult case to deal with.
It is generally preferable to eliminate one of the branches, connecting it to another outside the intersection if possible.
Roundabouts.
In this solution, branches are joined by means of a circular, elliptical or similar ring around which vehicles travel until they reach the branch where they exit.
It may require that incoming and outgoing traffic be weaved together at one or more points (figure III.4).
Traffic signals should not be used and incoming vehicles should yield to those already in the ring on the left (MIDEPLAN, 1998a).
One example of this type of solution is the mini-roundabout, characterized by a considerably smaller centre island less than five metres in diametre.
Figure III.4 shows a British example, in which a mini-roundabout significantly 
The roundabout is a compromise that can offer some advantages if many of the following conditions are also present (MIDEPLAN, 1998a).
Intersections with five or more branches and more or less equal traffic volume on all of them.
Relatively large flows going around, exceeding the flow travelling straight.
Extensive flat land available at a low price.
Little pedestrian traffic. 
Sufficient distance between each pair of consecutive branches to enable traffic to weave together.
The capacity of the roundabout is determined by the most critical of its segments.
Because they have the potential to become congested, intersections should be carefully designed.
In general, in urban areas the predominant criterion for intersections will be to increase their capacity, since it is normal for them to reach saturation during some periods of operation.
This effort necessarily involves physical and operational aspects that must be addressed simultaneously.
In the last few decades, a number of computer models have been developed to assist in the design effort.
These models, in turn, have specific data requirements that must be added to those mentioned above.
Thus, intersection design can be viewed as an iterative process in which physical and operational changes can be modelled at low cost and their performance tested.
A wide range of options can be explored and high-quality solutions achieved.
The simulation of each option’s operational results also facilitates the economic evaluation of the most appropriate alternatives, so that the solution with the best economic attributes can be chosen.
To enlarge capacity, it is recommended that islands be kept to the smallest size necessary to protect vehicles undertaking certain movements; in addition, the number of lanes should increase as each road approaches the intersection to achieve the maximum rate of vehicle clearance or discharge.
Another element to be taken into account in cities is the location of bus stops.
They cannot be too far away from key intersections, as these are precisely where passengers will want to transfer, but neither can they be so close to intersections that their operations are hampered.
As mentioned earlier, intersections tend to impose an operational restriction on a roadway.
Therefore, the control systems put in place to regulate the right of way should respond to both the needs of the specific location and the general criteria of the axis or network in question.
Basically, intersections can operate with priority signposting (prioritized intersection) or traffic signals (signalized intersection).
The former are governed by stop signs and the latter by signal lights.
These intersections regulate the right of way with yield or stop signs.
The yield sign tells drivers who encounter it that the vehicles on the other road have priority; they do not need to stop if the flow of traffic on the main road allows enough room for them to cross or turn onto that road safely.
This sign should be installed in all cases in which visibility is not restricted, according to the criteria listed below.
The stop sign is intended to instruct drivers to bring their vehicles to a complete stop and to resume motion only when they can do so without causing an accident.
It should be placed on the line where the vehicles must stop, in such a way that the driver can see the main road well enough to pull out safely.
Traffic signals are a more sophisticated means of controlling an intersection.
They make it possible to separate the periods of time when the traffic flowing on each road can cross the intersection.
The allocation of times is called “distribution”, and the cycle is the time elapsed from the beginning of a phase until that phase is reactivated.
In general, traffic signals are considered a positive development that yields benefits and enhances safety in any situation.
This belief is not always corroborated, however.
To provide real benefits to the population, a number of conditions must be present so that the benefits of a traffic signal are actually greater than the costs.
For example, given the variability of demand, it is possible that a traffic signal may be fully justified during certain periods of the day or certain seasons of the year, but represent a cost to the community at other times.
Expansion of physical capacity.
Arcs are stretches of roadway between intersections.
Normally they do not give rise to major conflicts.
There may, however, be situations in which an arc becomes a bottleneck within a succession of arcs and intersections.
The most appropriate solution is to expand their physical capacity.
Increasing the capacity of the entire roadway axis may also be considered, if a significant expansion of the roadway supply is contemplated.
In this case, there is competition from alternative uses of public space and even private space if expropriations are necessary.
From the standpoint of controlling congestion, it is worth noting that increases in physical capacity such as the latter option above tend to provide only short-term solutions to imbalances between supply and demand, since pent-up demand resulting from the congestion itself tends to become fully expressed in a relatively short time, as a more fluid and expeditious operation attracts more traffic.
Thus, congestion has a tendency to regenerate itself with higher flow levels.
If this trend takes hold, it can end up committing public space to motorized transport, to the exclusion of pedestrian traffic and other activities in that space.
This phenomenon is especially acute in cities that have consciously or unconsciously chosen to provide greater capacity in response to increased flows.
Other cities, in contrast, perhaps because of the special significance of their architectural or historical heritage, have explored alternatives to generating more capacity without allocating significant amounts of new land for vehicular use.
The traditional expansion of physical capacity is thus being replaced by more efficient ways of using that capacity.
Naturally, synchronizing traffic lights appears to be the best option for improving speed on thoroughfares, thus reducing travel times and congestion (see section D below).
But another possibility is to change the direction of traffic on arcs or roadways during certain hours of the day as a function of the principal movements of commuters.
And still another is to assign lanes by type of vehicle, facilitating the travel of those that make the most efficient use of space, namely, mass transit vehicles.
This method of increasing capacity can also attract new vehicles to the flow and soon the new facilities are pushed to the limit again.
Therefore, explicit consideration of the modes of transportation favored by the improvement should also be part of the analysis and medium- and long-term projections.
Reversible-flow roads are those that change the direction of traffic throughout the day depending on the volume in each direction, with a view to enhancing flow in the direction with the higher volume.
The direction of traffic can thus be reversed on a one-way street, or a two-way street can be turned into a one-way street, temporarily providing greater capacity for accommodating the massive flows that characterize the travel patterns and general operating rhythm of cities.
These movements tend to occur, for example, from residential areas to places of work or study during peak hours in the morning and vice-versa during peak hours in the afternoon.
This finely tuned management of existing capacity makes more intensive use of the principal roadways by accommodating the greatest flow of traffic.
 In many cases, this measure significantly enlarges the transport supply in order to meet rush-hour needs.
In 2001, Chilean transportation authorities implemented a number of transit management measures, among the most noteworthy being the variation of traffic direction on six major axes in the city.
Substantial results were obtained.
Synchronizing traffic lights is one of the most efficient ways to cut delays, fuel consumption, pollution and accidents.
Synchronization consists of setting up cycles, distribution andphasing of lights along a road or within a network so that vehicles can move at a certain speed, with minimal delays caused by red lights.
The most important parametres to be taken into consideration for synchronizing a system are the cycle, which will normally be common to all the synchronized lights, the distribution of green light times and the time lag, that is, the period between the beginning of a specific phase of one signal and the beginning of that same phase at the next intersection.
In addition to these basic parameters, there are a number of conditions that must be determined as the complexity of the axis or network increases.
This whole task can be facilitated with modelling tools designed to represent and optimize each case.
The basic unit of synchronization is an axis, corridor or roadway.
Simple one-way axes can be synchronized using graphic techniques or “green banding”, usually by developing fixed programming calculated on the basis of historical data on flows and velocities.
But in the case of two-way thoroughfares with irregularly spaced intersections, it may be difficult if not impossible to set up an uninterrupted “green band” that meets demand.
Of course, the synchronization of networks is outright impossible to carry out using this method.
In the last 30 years an extraordinary development of technology has resulted in the incorporation of computers and electronics into the management of complex traffic situations.
It is now possible to control vast networks with centralized, flexible, demand-oriented systems.
Programs such as SIGOP, COMBINATION METHOD and TRANSYT have completely changed the approach to the problem, providing previously unsuspected capacities to achieve the maximum potential of transit systems.
In particular, TRANSYT has been widely tested in many countries and has become a veritable international standard for network synchronization.
Synchronization with fixed plans.
This method requires traffic light controllers with sufficient capacity to receive and run instructions from pre-established plans.
The plans are generated externally by measuring flows.
Ideally, the number of plans should equal the number of significant operational periods that can be detected.
In this case, it is essential that each controller have clocks that function with the frequency of the network or high-precision quartz clocks, so that the lag time can be programmed adequately and maintained over time.
Alternatively, a cable connection between controllers may be considered, in which case the system would function with a common clock.
Synchronization with fixed plans arose at a time when computing, communications and detection technology were not sufficient to provide solutions more suitable for complex situations of variable demand and interaction among networks.
This does not mean that this method is necessarily obsolete.
A case-by-case analysis will reveal whether a given control need can be addressed with fixed plans, meeting both demand and cost needs.
Flexible or dynamic synchronization.
Flexible, demand-driven synchronization solutions are useful in areas of intensive use, which are usually subject to unpredictable interferences.
This method is based on real-time detection of significant flows reaching each intersection.
The data are processed online by a central computer that generates updated signal cycle plans, which are then transmitted to the controller at each intersection.
The system is quite sophisticated, so in addition to a central computer programmed with appropriate software and traffic signals with controllers capable of following instructions, there must be direct communication between the central computer and the controller at each intersection.
Some of the best known options of this sort are the United Kingdom’s SCOOT and the Australian SCAT systems.
Centralized control systems.
A more technologically complex option is a centralized traffic-light control system that opens up the possibility of using different modes of control to meet given areas’ differing requirements.
This means that, for example, a dynamic control system could be used in a city’s downtown area but need not be applied to the entire traffic light system or to intersections that should not be coordinated with others because they do not form part of any functional network.
Having centralized control allows for the entire system to be administered in accordance with the needs of each part.
In addition, the following features are possible:
Direct communication with each signal controller for the purpose of regulating traffic.
Direct communication with each controller for monitoring errors.
Implementation of emergency plans for the circulation of special vehicles.
Administration of signs displaying variable information to advise drivers on the condition of the route and, in special cases, to generate detour routes.
Administration of television cameras for the direct observation of conditions in intersections or key axes.
In other words, when there is communication between the central computer and each controller, the possibilities for managing traffic are extended to aspects not necessarily linked to the traffic signal programs.
This paves the way for a more integrated management of the intersections and networks in question.
A centralized traffic control system allows for not only the synchronization of axes or networks of traffic lights, but also a comprehensive management of circulation problems using television cameras, variable driver advisory signs, remote detection of errors and management of emergencies.
In general, centralized traffic control systems are projects that yield tremendous social benefits when correctly designed and applied in cities with road congestion.
The system in Santiago has been particularly successful; its development followed a rigorous process of analysis and evaluation of alternatives, after which implementation took place in stages.
It is currently functioning and enabling drivers to achieve significant savings in travel time.
The best evidence of the effective service rendered was seen in the chaos and tremendous congestion that ensued when the computers controlling the system went down.
An important measure for giving priority to public transportation is to reserve road space for its exclusive use.
Traffic signals must be programmed in conjunction with this measure so that buses are given priority.
Clearly there is no need to modify the size and characteristics of mass transit vehicles, but improving the circulation of these vehicles does in fact expand their capacity.
This increase could attract commuters from other modes of transportation and lead to a balanced use of motorways that is more in keeping with the general interest.
Reserving space for buses is a regulatory move that is designed to correct, through the administration of the motorways, the distortion caused by an erroneous perception of congestion by individual drivers.
If motorists knew the total cost of operating their vehicles in congested conditions and if bus passengers knew as well, changes in the allocation of the transport supply would make the kind of intervention proposed here practically unnecessary, since a new balance with smaller flows would be created.
Absent this development, the authorities intervene by distributing lanes on a given thoroughfare with a view to ensuring adequate service for mass transit and incidentally eliminating friction between buses and other vehicles.
Documented international experience generally reveals significant benefits from this type of initiative, although how much of an advantage is yielded depends on the quality of the design.
In Europe the experience has been positive.
In Latin America, when signs of congestion first appeared, some major cities implemented these measures as well, with the added bonus that the majority of travel still takes place on public transit.
Some of the methods of reserving space for public transport are discussed below.
These are lanes that allow only buses to circulate, normally indicated with pavement markings.
They are characterized by the low cost of implementation, but their actual results–except in the case of lanes in which buses go against traffic–depend on the willingness of drivers to comply, and investment in prolonged law enforcement efforts is required.
It is not always possible to impose discipline in the use of these lanes; sometimes the measure is invalidated by the systematic violation of the restrictions.
In the first case, a one-way street has a bus-only lane on the right.
This is the most widely used variant.
The second example is the same except that the bus travels against the traffic, which prevents other vehicles from using the lane.
The third case shows a two-way street with bus-only lanes on the outside.
In general, right-hand bus-only lanes have the advantage of providing access to the sidewalk and are recommended when the surrounding area is characterized by retail or service businesses that generate considerable incoming and outgoing traffic.
This lane configuration tends to be violated by private vehicles that are just as attracted by the proximity to adjacent businesses (which are separated from their designated travel lanes by the bus-only prohibition) or that must enter these lanes to make right turns.
Therefore, this type of lane requires abundant and constant enforcement patrols.
Segregated bus lanes are similar to bus-only lanes in that they are exclusively for the circulation of mass transit vehicles, but the difference lies in the fact that there is a physical barrier between them and the space allocated to other vehicles.
The barrier prevents other vehicles from entering, thereby allowing buses to travel more freely.
Segregated bus lanes are a much more elaborate and costly solution than bus-only lanes.
In some cases private transport must be restricted significantly and in other cases major expropriations are required, but the net benefits that have been documented support their implementation and incorporation as a standard criterion of transport system design.
It is common for segregated lanes to be placed in the centre of the roadway in order to ensure that private vehicles have access to both sides and to facilitate right-hand turns.
This is not necessary if there is a park, railroad or other property along the side of the thoroughfare that does not require vehicle access from the street, or if there is a local road precisely for the purpose of providing such access.
Segregated lanes can be one-way or two-way, with one or more in each direction and with or without an extra passing lane where bus stops are located.
Those placed in the centre of the roadway often have bus stops placed on alternating sides to make better use of the width of the pavement.
When there are long stretches without stops, the excess room can be used for a median strip, for a narrower paved section or for special left-turn lanes.
Although these measures do yield considerable benefits for mass transit, it is important for the design to take into account the hindrances caused by the separation in the form of problems making turns, restricted access to adjacent properties and an eventual reduction in capacity for private vehicles.
The segregated bus lanes set up on the Alameda Bernardo O’Higgins in Santiago are an interesting case (see table III.5).
The avenue has two five-lane thoroughfares, and since the late 1970s more and more of them have been designated exclusively for buses.
Originally there were two bus-only lanes in each direction, labeled with markings on the pavement.
At present, buses have three lanes in each direction, separated from other traffic by physical barriers that make encroachment extremely difficult.
Bus-only streets are entire motorways devoted exclusively to mass transit.
Although from the standpoint of physical design this method seems simple in principle, requiring only adequate signposting to function, in operational terms it tends to have a major impact because of the redirection of private vehicle traffic and the limited access to adjacent properties.
Bus-only streets have been a permanent fixture in different countries for many years.
The method of establishing that exclusivity for only certain periods of time seems to be a recent innovation, however.
It has been tried in Santiago as part of the whole set of projects implemented in 2001 to reduce congestion.
This method involves granting buses an entire street for their exclusive use, but only during peak hours, after which the street returns to mixed traffic.
The streets in this programme are part of the main network in the city of Santiago.
Travel times before and after implementation are shown in table III.6.
The data show that the average savings in travel time is approximately 10%; subsequent measurements brought this figure up to as much as 13%.
Moreover, it is interesting to note that in cases such as southbound Gran Avenida, westbound Pajaritos and southbound Vicu?a Mackenna, which have light traffic during morning rush hour since they lead away from downtown, the benefits of the measure are marginal and even negative, which suggests that the bus capacity could be excessive, or that in fact there was no problem before.
Initial measurements on the alternate routes used by cars, such as Vivaceta (the alternate for Independencia) and Avenida Las Industrias (the alternate for Vicu?a Mackenna), revealed an increase in travel times.
Consequently, complementary measures such as reversible-flow lanes were adopted to accommodate private vehicles more effectively.
It can thus be concluded that preparations for implementing these measures should include mass information campaigns warning drivers and the population in general of the changes and suggesting alternate routes and detours to mitigate any negative impact that might occur.
Public transit reorganized with the equivalent of a surface subway
Segregating lanes for buses can achieve an even greater dimension if public transit on these lanes is reorganized in a system equivalent to a surface subway.
Various experiments were tried in Brazil involving not only separating bus traffic but also building stations, developing an integrated fare system and setting up transfer terminals.
These systems have given rise to a truly new mode of transportation that is very similar to a subway system, with the added bonus that the costs of putting it into service are several times lower.
A paradigmatic case that has inspired subsequent experiments elsewhere in the region is that of Curitiba, Brazil.
Begun in the 1970s, this programmed established a concept of mass transit that includes bus-only lanes and special stops, “tube stops”, with raised platforms level with the bus’s floor.
This makes it easier for passengers to board and also prevents anyone from getting on or off the vehicle at an inappropriate place.
There is also a pre-payment system, with tickets being sold before getting on the bus and integrated fares between trunk and feeder services.
The project in Curitiba was followed by others in Brazil and other countries.
A noteworthy example is that of the trolleybus system in Quito, Ecuador, which went on line in 1995 and was subsequently expanded (Arias, 2001).
It is characterized by a bus-only street with a lane 3.5 metres wide in each direction, pre-pay stations and bus-level platforms designed to minimize time spent getting on and off.
Travel time studies conducted before and after implementation of the project revealed an average of up to 50% savings.
Public transit reorganized with the equivalent of a surface subway.
Segregating lanes for buses can achieve an even greater dimension if public transit on these lanes is reorganized in a system equivalent to a surface subway.
Various experiments were tried in Brazil involving not only separating bus traffic but also building stations, developing an integrated fare system and setting up transfer terminals.
These systems have given rise to a truly new mode of transportation that is very similar to a subway system, with the added bonus that the costs of putting it into service are several times lower.
A paradigmatic case that has inspired subsequent experiments elsewhere in the region is that of Curitiba, Brazil.
Begun in the 1970s, this programme established a concept of mass transit that includes bus-only lanes and special stops, “tube stops”, with raised platforms level with the bus’s floor.
This makes it easier for passengers to board and also prevents anyone from getting on or off the vehicle at an inappropriate place.
There is also a pre-payment system, with tickets being sold before getting on the bus and integrated fares between trunk and feeder services.
The project in Curitiba was followed by others in Brazil and other countries.
A noteworthy example is that of the trolleybus system in Quito, Ecuador, which went on line in 1995 and was subsequently expanded (Arias, 2001).
It is characterized by a bus-only street with a lane 3.5 metres wide in each direction, pre-pay stations and bus-level platforms designed to minimize time spent getting on and off.
Travel time studies conducted before and after implementation of the project revealed an average of up to 50% savings.
One possibility for encouraging drivers to abandon their cars for routine trips such as commuting to work is to institute high quality multi-passenger vehicles on regular routes.
In some cities these have been called “executive buses” or “differential buses”.
Available on the market is a broad and diverse range of buses and minibuses that offer a high degree of comfort and are designed for use in tourism or business travel.
The vehicles most suitable for urban mass transit can be selected from among them.
Their potential contribution to decongestion lies in their attraction of motorists rather than conventional bus riders.
There is solid evidence that differential services offered in various Latin American cities, such as Buenos Aires and Rio de Janeiro, transport large numbers of passengers who used to drive in cars (ECLAC, 1995b), reducing congestion by improving the coefficient of space occupied per passenger.
Obviously an executive bus service, no matter how high the quality, is not equivalent to the service provided by a car; but under certain conditions it could be preferred on the grounds of comfort, safety, reliability and travel time to destination, considering that travel time can be used to good advantage, for reading, for example (CIS, 1995).
Although that is not the purpose, riders of conventional public transportation who are dissatisfied with it and willing to pay more for better service may also be attracted.
Relatively frequent and regular service (at least four times an hour).
Routes corresponding to existing patterns of origins and destinations.
Relatively high operating speeds, not very different from those possible in a car, but in any case higher than those of regular buses.
This means more widely spaced stops.
It would be even better if these vehicles travelled on segregated lanes or those giving priority to both conventional and executive buses, with preferences in traffic light cycles.
Comfortable vehicles with pneumatic suspension, reclinable seats, no standing passengers and trained, uniformed personnel.
Other characteristics may include ambient music and air conditioning or heating, as required by the weather.
Another important consideration for attractiveness is for business organizations to be modern and efficient, with a positive image reflected in enthusiastic customer service, expeditious ticket sales, including the possibility of subscriptions, new well-maintained buses, appropriately designed bus stops and, most significantly, following different criteria than traditional approaches (CIS, 1995).
Are executive buses viable?
In cities with a regulated transport system, it would be interesting to design these superior services and to grant concessions to operate them.
In cities with unregulated transport, executive buses may not emerge spontaneously, so some sort of impetus or facilitation may be necessary.
It is unknown, however, whether the service would be profitable and sustainable, especially since it is not known what motorists’ preferences would be or what advantages they would perceive.
In addition, routes would have to be assigned appropriately, giving preference to high-income neighbourhoods.
One way of proving their viability would be to leave it up to the market; they would simply be allowed to emerge, with a high minimum fare being established as an essential condition for truly differentiating them from regular services.
In this regard, it is helpful not to impose too many regulations with respect to bus size or specifications, frequencies, routes or other aspects.
On the contrary, entrepreneurial initiative should be given the freedom to adapt to users’ requirements.
It would also be helpful to allow differential buses to operate only Monday through Friday, with other services being offered to tourists on the weekends.
Another high-quality mass transit option is represented by collective taxis, which are widely used in many cities.
They offer fixed routes and charge a higher fare than buses.
Their advantage lies in the fact that, being cars and carrying a small number of passengers, they can travel at a higher speed.
Because they are a tight fit, however, they do not easily attract motorists and are more likely to attract people who would otherwise ride the bus.
Among the grounds frequently cited to justify building an urban railway system is that it will reduce traffic congestion.
The truth of that assertion is examined in this section.
The term “subway” includes actual subterranean systems, known in some cities as the “underground”, as well as light rail trains (LRT) and urban and suburban trains.
In the middle of the nineteenth century, London barrister Charles Pearson asserted that an underground railway would alleviate traffic congestion in the British capital, where the number of travellers was already 750,000 persons per day (Howson, 1981).
The private company that put the first London underground in service in 1863 certainly did not have the goal of alleviating congestion; however, it must have viewed that congestion and the high costs of street-level travel as economic justification for its project.
Similar justification could be found for the private Anglo Argentina tramway company’s investment in the first Latin American subway, which opened in Buenos Aires in 1913.
Later on, as public-sector participation in urban transport increased, congestion was also a consideration in the construction of subways, which were seen as a means of reducing it.
A study in Latin America in 1927 concluded that building a subway could solve S?o Paulo’s traffic problems (Hochtief, Monteral and Deconsult, 1968).
It should be noted that at that time, every passenger-kilometre produced by mass transit generated much more congestion than today, since trams represented a tremendous obstacle to non-rail traffic on shared-use streets.
Moreover, the first buses (or collective vehicles) had a low capacity per square metre of street space occupied, as well as limited acceleration and braking power.
By 1932, European urban architect Karl Brunner, referring to the situation in Santiago, stated that “traffic congestion and parking problems will remain in the city centre and within a few years building a subway, at least through the city centre, will be an urgent necessity that cannot be postponed” (Brunner, 1932).
Judging by the words of that architect, it is clear that there were already signs that the car was contributing to congestion in Santiago.
Once the car emerged as the principal cause of congestion, given its heavy presence among transportation options, it became much less likely that building a subway could solve the problem (Thomson, 1997), as will be argued in the sections below.
For example, it is estimated that by the middle of the 1920s in London, buses and trams accounted for at least 25% of the flow of traffic other than delivery or freight vehicles.
Today, in contrast, using the city of Santiago as an example, the comparable proportion would be approximately 7% during rush hour.
Where do subway passengers come from?
In Latin America, according to surveys of declared preferences conducted in many cities beginning in about 1975, for trips on a single mode of transportation many commuters prefer the subway to regular surface mass transit, if the travel time is similar or even if there is a slight disadvantage in terms of fare.
Before that time, although passenger preferences are not known due to the lack of data, very few would not have preferred travelling on a new subway to riding on the rudimentary, noisy buses, trams or multi-passenger vehicles of the time.
Therefore, in the past, even more than now, a new subway would have attracted large numbers of former mass transit riders.
On the other hand, the car is preferred to the subway if travel time is the same, both because of its subjective attributes and due to its flexibility, privacy, ability to carry cargo, protection from the weather and other considerations.
Consequently, a recently opened subway is much more attractive for people who used to ride the bus than for motorists.
One of the most comprehensive studies on mass transit in cities in developing countries concluded that, typically, immediately after a subway begins operating, 81% of its passengers are former bus riders, 16% are people who simply did not travel on the axis and only 3% have switched from cars or motorbikes on the axis (Allport and Thomson, 1990).
As a result, it is clear that there is a direct correlation between the significance of mass transit vehicles in the flow of traffic and the direct impact of the subway on congestion.
Empirical evidence suggests that Latin American subways that came on line in recent decades have had a minimal impact on traffic congestion.
For example, in S?o Paulo it was found that “bus traffic fell by 500 per hour in each direction in the corridors in question, and although congestion was reduced at first, it later returned to serious levels”.
In Santiago, “congestion remained serious on the principal east-west axis and bus traffic stayed near the maximum level possible”.
 In Porto Alegre, “there was no serious congestion before or after the opening of the suburban train system”; and in Mexico, “congestion caused by motor vehicles remained heavy, and although bus speeds were adequate, that was more attributable to the implementation of segregated lanes than to the subway” (Allport and J.Thomson, 1990).
Evidence from cities in other regions is generally similar (I.Thomson, 1997).
There have been cases in which modernized trolley or LRT systems have attracted significant numbers of motorists; this was true in Sheffield, where an exceptional 22% of passengers on the new trolley system had been commuting by car before (Hass-Klau and others, 2000).
There is no guarantee, however, that the road space they free up will not be taken over by other drivers.
Stories in the technical-popular press indicate that the new tram system in Croydon, a suburb south of London, has cut parking in the downtown area by 6%, while at the same time sales in businesses located there have climbed 11%.
An explanation for subways’ inability to reduce congestion.
In analysing what has happened, the following conclusions can be drawn:
The opening of the subway attracts many former mass transit passengers and just a few motorists;
The transfer from mass transit reduces the demand for that mode and at least in the medium term, will reduce the flow of buses on the corridor, especially during rush hour, unless bus companies continue operating as they did before even with lower demand (this seems to have happened in Santiago);
The road space freed up during peak hours is used by motorists who prior to the opening of the subway left a little earlier or a little later in order to avoid the worst congestion of rush hour.
As a result, in the medium term, speeds at peak hours are about the same as they were before the subway came on line; and
The few motorists who switch to the subway free up parking spaces, especially downtown, which are taken by other citizens switching from public transit to cars, though not necessarily on the axis of the recently opened subway.
In cases of very little use of private transportation, as was true 100 years ago, the situation could be different.
The road space freed up by the shift mentioned in the second point would remain unused (without counting a small redistribution in time of trips on mass transit), which would nullify the consequence identified in the third point; the process identified in the fourth point would also be of little or no significance.
It is worth noting that the fact that subways do not reduce congestion during rush hour does not mean that they cannot be beneficial from a socioeconomic point of view.
First, travel time is reduced for people switching from bus to subway; second, by making rush hour shorter, more citizens are able to travel at times that suit them better, rather than at times when there is less congestion.
Consequences of denser land use.
In the medium term, the subway’s contribution to solving the congestion problem could actually be negative because of its impact on land use, influenced by building regulations adopted by many municipal authorities throughout Latin America.
Undoubtedly, the subway improves access to areas near stations and therefore encourages greater residential and especially commercial density.
That increased density does not always occur, as it also depends on other factors; it is likely to occur in neighborhoods that are attractive from a city planning standpoint and is evident in districts such as Chaca?to in Caracas and Providencia in Santiago.
In these areas, the greater accessibility created by the subway leads to the construction of office buildings that workers can reach easily on subway trains.
The regulations prevailing in many Latin American municipalities require, however, that these buildings have a minimum number of parking spaces per square metre of floorspace.
This creates a perverse situation, in which many office workers, taking advantage of the parking spaces provided by municipal decree, do not use the subway but drive instead.
In Santiago, the higher density of trips at peak hours within the subway’s sphere of influence can be calculated.
How can subways help reduce congestion?
The fact that subways have rarely reduced traffic congestion, at least since the beginning of the era of massive car use, does not mean this is impossible.
The measures that would have to be taken to maximize their contribution in this regard include the following:
When a new subway line begins operating, the supply of parking spaces in areas near stations should be reduced by the number of people switching from car to subway; and
Municipal regulations governing parking at commercial buildings should be revised so that those built near subway stations should have a maximum limit rather than a minimum number of parking spaces per square meter.
Even if these measures are taken, the impact of the subway during rush hour would be limited by motorists’ changing their travel schedules, which is impossible to control.
Subways are no doubt part of a number of measures aimed at improving the flow of traffic, which necessarily must include the availability of attractive mass transit systems.
Subways are not enough by themselves to bring about a reduction in congestion, however.
The measures achieve results.
Supply-side measures in general expand transport capacity, achieving a certain reduction in congestion.
In some cases the effect on congestion is marked, as is the case with synchronization of traffic signals, segregated lanes for buses and reversible-flow lanes.
Specifically, increases in the average speed of circulation and low travel times and operating costs are obtained.
Lower toxic gas emissions.
Higher travel speeds mean a reduction in total emissions of toxic gases, which helps improve the environment.
Savings in public transit fleet.
The higher travel speed of buses allows them to carry the same number of passengers in a smaller number of vehicles.
This reduces the total cost of the fleet, with the resultant lower fares, which is of particular benefit to low-income travelers who must invest a major share of their income in transportation.
Greater social equity.
Along with lower fares, shorter travel time for buses improves their riders’ quality of life, and most riders are in the social strata with the least power.
Some measures can be implemented at low to moderate cost
Several supply-side measures are affordable for most municipalities in big cities.
That is true of redesigned intersections, marking lanes and erecting signs and instituting reversible-flow lanes.
Eventually, segregated lanes may be financed through franchising.
Retention of public transportation users
The measures related to improving public transportation increase its attractiveness and reduce the pressure to switch to motor vehicles.
To the extent that cities in developing regions manage to maintain the high proportion of trips still carried out on mass transit, which fluctuates between 50% and 80%, the most serious congestion problems can be avoided.
A quality public transit system discourages urban sprawl
If public transportation is adequate, there are advantages to running businesses or living near its routes, which reduces the pressure to move to the suburbs where commuters depend on cars.
Some measures are costly.
Centralized synchronization of traffic signals, though highly effective, iscostly.
The same is true of the reorganization of public transit into a system equivalent to a surface subway.
These projects may require financial support from the national government, which is also usually the case with subways.
Difficulties in adapting to reversible-flow lanes
Reversible-flow lanes require that traffic flowing in the opposite direction be diverted.
This may result in longer travel times and there may be a certain amount of congestion.
The measure can cause confusion among drivers and an increase in accidents, so a solid signposting and publicity programme must be put in place.
Some degree of resistance on the part of drivers should not be ruled out.
Problems with segregated lanes in the centre of the roadway.
Pedestrians must be given safe access to bus stops, and signs must be posted with instructions on how to cross lanes to the left.
Problems with segregated lanes at the side of the roadway.
The issue of making right turns and gaining access to properties located along the length of the roadway must be resolved.
Problems with the introduction of new technologies.
Centralized control of traffic signals requires a complex technology system that is available in few places.
The reorganization of public transportation into a system similar to a surface subway also demands new technologies for controlling traffic and possibly integrating fares with other bus lines, with the corresponding system of distributing revenues among the various players, plus some kind of smart ticket.
The new technologies require training for those who are going to operate the systems, and passengers also must adapt.
Need to create new institutions
A service similar to a surface subway needs a new set of institutions capable of running the complex system, in which various components come together (transport infrastructure and its relationship with the network of streets and roads, vehicles and their operation and control, ticket sales and the distribution of revenues, general management, public relations and others).
The interests of all these institutions must be considered.
A service similar to a surface subway is a complex measure to implement, not only because of the difficulties inherent in installing a new system, but also because of the resistance to change that characterizes the players involved in the previous system.
In particular, the reaction of bus companies, which may not be in a position to become integrated into the new arrangement, must also be taken into account, and it cannot be forgotten that passengers must also incorporate new practices in their travel.
Transport supply should be viewed as a system, and it must be improved and adapted constantly.
Better results can be expected from the simultaneous and progressive intervention in a broad range of facets that make up the transportation system, such as the appropriate marking and maintenance of streets, the synchronization of traffic signals, the rationalization of public transit and many others.
In other words, a number of feasible measures must be put into practice with a view to expanding capacity by improving the management and productivity of the existing infrastructure.
Of all measures, the most promising ones seem to be the synchronization of traffic lights and the establishment of segregated bus lanes, along with the corresponding reorganization of public transit.
Improving and even widening streets is a potentially useful measure, provided that negative effects on habitability and quality of life can be avoided.
In addition, it should be accompanied by others aimed at preventing the rapid clogging of these streets, or the mere shift of the congestion to a few blocks away.
Simply adding more road infrastructure does not eliminate congestion, however, and it is important to resist succumbing to the illusion that it can be solved with urban freeways, tunnels and viaducts.
In fact, these measures may even exacerbate the situation.
Experience shows that in Los Angeles and other major metropolitan areas that built numerous urban freeways, they were so attractive for cars that congestion became much more unmanageable.
In any case, improving the utilization of the supply does not by itself account for the complex realities associated with congestion.
Urban motorways, especially in city centres, do not have sufficient capacity to support the indiscriminate use of private motor vehicles and they never will, even if all the financially, environmentally and politically feasible measures are taken to expand them.
Thus, it is necessary to incorporate demand-side measures to resolve the imbalances in the use of the infrastructure and achieve a balance acceptable to the community.
What is transport demand?
Transport demand is a response to the need or desire to transport persons and goods from one place to another.
Activities take place in different places around the city, involving multiple trips to come and go, for example, from home to work or school, to go shopping, attend cultural, social, or recreational events, or others.
To make better use of the day, many work and educational activities begin early in the morning, which results in a huge accumulation of journeys during relatively brief periods of time.
This situation is repeated in the afternoon at the end of the workday, although it is generally less marked.
Although the essence of this demand is the mobilization of persons or things, it also has a traffic dimension, in terms of volumes of vehicles moving along the public roadways to carry out these objectives.
The aforementioned concentrations of trips in the morning and afternoon generate an increase in the volume of traffic, known as peak times or rush hour, which translates into congestion on different streets and during different periods.
What is transport demand?
Transport demand is a response to the need or desire to transport persons and goods from one place to another.
Activities take place in different places around the city, involving multiple trips to come and go, for example, from home to work or school, to go shopping, attend cultural, social, or recreational events, or others.
To make better use of the day, many work and educational activities begin early in the morning, which results in a huge accumulation of journeys during relatively brief periods of time.
This situation is repeated in the afternoon at the end of the workday, although it is generally less marked.
Although the essence of this demand is the mobilization of persons or things, it also has a traffic dimension, in terms of volumes of vehicles moving along the public roadways to carry out these objectives.
The aforementioned concentrations of trips in the morning and afternoon generate an increase in the volume of traffic, known as peak times or rush hour, which translates into congestion on different streets and during different periods.
Be inappropriate insofar as it meant a loss of the advantages to be gained from such trips, along with the potential suppression of the activities people wish to carry out.
In the medium and long terms, however, modern communication technologies such as Internet, email and mobile telephony, or the modification of land use can help reduce the need to travel, with the resultant easing of congestion.
Completely eliminating cars from city centres seems unviable and unnecessary.
Demand-side measures should focus primarily on modifying the distribution of transportation modes or the type of vehicles used during peak times and on shifting some trips to times of lighter traffic.
Using cars less during rush hour in fact expands the availability of roadway space, which translates into increased speeds and shorter travel times for all vehicles, obviously including public transit vehicles.
According to this reasoning, congestion could be cut significantly if it were possible to convince a large number of motorists to take mass transit or use non-motorized methods when they travel to heavily congested areas or go during periods of heavy congestion, or to change the times of their trips.
In other words, it is a question of inducing changes in behaviour that would result in the temporary replacement of the car.
Demand-side measures that prevent congestion
There are many different demand-side actions that can be useful in easing congestion.
Several methods are aimed at changing driving habits by identifying convenient alternatives.
These methods attempt to instill in motorists a strong conviction that will lead them to make lasting changes in behaviour, such as voluntarily restricting the use of the car (see section F.1 of this chapter).
Other methods are designed to induce changes in behaviour through coercion or incentives/disincentives.
Coercive measures are regulatory in nature and they impose restrictions on drivers.
Incentives, in contrast, provide advantages or economic rewards for those who adopt certain behaviours, and disincentives exact a price for engaging in certain activities (GTZ, 2001).
Economic measures can seem less effective and in some cases are reputed to be socially inequitable, although they may enjoy more acceptance among drivers.
Regulatory measures, on the other hand, are vulnerable.
Both types of measure should be considered so that the best overall result can be obtained.
A variety of measures can contribute to the desired change in transportation habits to mitigate congestion.
In addition, the chapter presents reflections on the relationship between land use and congestion in the long run.
Parking is obviously an indispensable element of any road transport system.
Cars, in particular, are not designed for perpetual motion; they make certain specific trips, depending on the driver’s intent.
After the journey is completed, or after a sequence of journeys, when the driver no longer needs to travel, the vehicle rests in place, and it must necessarily occupy a space that is therefore eliminated from any alternative use.
This fact means that managing parking can be a tool for regulating transit and alleviating congestion.
The presence or absence of parking spaces and the cost thereof, can facilitate or hinder access by car, especially on routes where the driver must find an accessible place to leave his car.
A shortage of parking near a given destination or a high price for parking is in fact a disincentive for using private vehicles in many cases; and ample availability of parking has the reverse effect.
A better balance must be achieved between accessibility and limitations on the use of cars.
This may mean that public transportation must be improved, or that restrictive measures must be implemented gradually.
Controlling parking consists of regulating the availability of parking places in various parts of the city with a view to relieving congestion.
Controlling parking by restricting the number of spaces available or charging for their use has long been recognized as one of the most effective elements in any strategy for reducing car use (Enoch, 2002).
In this way, the message is conveyed that not all car trips at all times are good for society.
One option for easing congestion is to discourage car trips to downtown areas during peak hours.
Given that more than half of journeys at those times are for going to work, there is an interesting potential for attacking congestion through actions to discourage or hinder long-term parking in or near areas with a large concentration of workplaces.
Restrictions can encourage commuters to switch to high occupancy vehicles or to shift their travel to off hours.
Moreover, providing intermediate parking places to enable commuters to combine car travel with public transit can also help alleviate congestion.
It should be emphasized that controlling parking is not intended to prohibit the use of cars, much less to hamper the development of urban activities.
Therefore, it is necessary to balance the appropriate provision of parking in uncongested areas with certain limitations on parking in congested areas, in order to achieve the best results for the development of a city.
The experiences of several different countries have shown that parking regulations and proper enforcement of them can yield positive results and can play an important role in reducing the use of private cars.
In any case, measures should be differentiated according to the type of parking, as shown below.
Free or unrestricted on-street parking.
Regulated or paid on-street parking.
Paid off-street parking.
Free off-street parking.
The possibilities for mitigating congestion using the aforementioned methods are discussed below.
Free or unrestricted on-street parking
Drivers like to park on the street, since in many cases this is a reasonable solution near a specific destination, especially if it is free.
Unfortunately, the space thus occupied is taken out of circulation for the entire time the vehicle remains there.
This reduces the capacity of the street and can become critical on heavily traveled arteries, particularly at rush hour.
As the name suggests, this measure consists of banning parking at certain times and places.
The ban virtually rescues a lane for vehicles in circulation, at the cost of inconveniencing the relatively few vehicles that might be parked there.
An additional lane can allow some 1500 vehicles to pass through each hour, greatly enhancing the flow of traffic on high-demand thoroughfares and drastically reducing congestion.
The most noteworthy aspect is that this roadway space is already available and can be freed up with measures that transport authorities can readily implement.
The prohibition of on-street parking is justified only in areas where it can make an effective contribution.
It is appropriate on streets where the volume of traffic is such that more space is needed to ease the flow.
This is generally true of major avenues and streets in city centres.
The ban can be permanent on main avenues, on one or both sides; and it can be temporary on other streets, applicable only on workdays (Monday through Friday or Saturday morning), excluding nighttime hours.
In contrast, it makes no sense to impose such a ban on neighbourhood streets.
One case that should be noted is stopping for loading and unloading, which should be banned at peak hours and expressly allowed during times when traffic is light.
This measure is generally taken at the municipal level and it can enjoy a reasonable degree of citizen support.
The greatest resistance to implementing it may come from residents who do not have their own parking spaces; in that case, allowing nighttime parking should be considered where feasible.
As in so many other cases, it is not enough just to adopt the ban (OECD, 1981).
The parking ban must be posted appropriately with conventional signs, and when the measure is adopted it must be widely publicized so that no one can plead ignorance.
At any rate, it is preferable to win citizen support, since voluntary compliance makes it much easier to achieve the objectives.
For that reason, explaining the goal of the measure is essential.
All things considered, the measure must be strictly enforced and violations should result in real penalties.
In this regard, it is important to have a mechanism in place, especially during times of peak demand, that will guarantee a high probability that violators will be discovered.
This means having an appropriate number of inspectors and a good patrol system.
In addition, an objective schedule of penalties and a good enforcement system must be adopted.
Absent effective controls, illegal parking will abound.
Provisions that do not have a working enforcement system soon become dead letter.
In Washington, D.C., where parking regulations were flouted routinely, an enforcement system with wheel clamps and tow trucks met with initial resistance, but later it was accepted and the system was eventually privatized (MINTRATEL, 1995).
A similar experience has been reported in Guatemala City.
To be successful, a parking ban must be part of a set of measures, among which improved public transportation should be considered.
In addition, it is important to have parking places available in the areas surrounding the restricted zones so that those who want or need to go there by car will have somewhere to park.
Regulated or paid on-street parking
If a ban is justified on heavily travelled streets, on many other streets where parking is allowed, it should be regulated by means of metres and other methods of rationalizing demand.
The purpose is to make it likely that a space will be found when needed and if parking has to be paid for, to make the driver assume the cost to society of providing the space.
In city centres, on-street parking should be short-term.
This allows access for personal business or shopping and prevents parking by commuters, who are the ones most likely to be travelling at rush hour.
Payment of a fee should be the primary method of rationalizing regulated on-street parking and it provides the added advantage of generating revenues for the municipality.
It may be appropriate to set maximum limits on parking time, although charging a fee is itself a deterrent to long stays.
A less stringent regulation is to allow free parking that is severely limited in time, which would ensure rotation.
Another method is to reserve spaces for national or foreign dignitaries’ vehicles, although that could be considered discriminatory and might meet with resistance.
No matter what method is used, appropriate signs and symbols should be posted at the location to convey the restrictions clearly.
Regulating on-street parking is generally a municipal function and it may enjoy reasonable public support.
The cost of implementation is normally affordable with funding from the municipal budget or franchising, and costs can be offset by fees.
There are different ways of implementing controls.
A simple option for limiting time, regardless of whether the on-street parking is paid or free, is to use a standardized document displayed visibly within the vehicle that indicates the authorized parking time.
The sale of parking permits for specific periods is one such measure.
Improvements in technology have reduced the vulnerability that originally characterized these systems.
Parking metres are effective ways to regulate authorized on-street parking.
These devices have a wide range of features (they can be simple clocks, or they may have mechanical or electronic means of collecting fees manually or automatically, in the form of coins or cards).
It is preferable for the metres to be able to limit the total time vehicles are allowed to remain parked.
In countries with low-cost labour or high rates of unemployment, it is possible to operate parking systems with human collectors equipped with devices that can record parking time.
In addition to creating sources of formal employment, this method makes it possible to charge for the exact time of use, without the need for a minimum fee for parking.
As a result, non-payment for time exceeding the prepaid amount can be avoided.
Moreover, the collector is in fact an enforcement officer and the use of informal “vehicle watchers” can thus be eliminated.
Franchising the parking metre operation has the advantage of reducing the risks inherent in the business, especially with respect to collecting fees.
It also avoids the expansion of the municipal payroll.
Another similar option is to establish a decentralized city-owned corporation.
A number of municipal jurisdictions in Santiago, Chile have contracted with businesses to carry out on-street parking enforcement using human collectors equipped with hand-held electronic meters that can also issue a receipt for the driver.
During times of high unemployment, this measure has resulted not only in a successful regulation of the supply of on-street parking, but it has also created formal jobs.
In addition, about 4,400 underground parking spaces have been built, eliminating 5,500 parking spaces on the street.
A law passed in 1997 authorized municipalities to grant concessions on the subsoil of their jurisdictions as a means of clearing the streets and reducing pollution.
The concessions are granted after approval by the Ministry of Housing and City Planning (MINVU) and require the elimination of surface parking spaces amounting to at least 120% of the authorized underground spaces.
In the area surrounding the new parking structure (200 to 500 metres away), on-street parking is prohibited in order to increase the space available for circulation and also prevent competition with nearby parking.
How much to charge?
Charging a fee for a scarce good in high demand is a mechanism aimed at ensuring that drivers pay the costs incurred by society, and no less than the true cost should be paid.
Furthermore, the fee should be whatever is necessary to rationalize use in accordance with demand, which means that it may be a fixed amount or one that varies by time of day.
The costs of adapting the location for parking (signposting and equipment) and operating the parking system, including, of course, the salaries of workers and inspectors, in order to self-finance and eventually franchise the system.
To the extent that this does not occur (as is sometimes the case), drivers will receive an implicit subsidy, with the consequent overuse.
The value of the right to occupy public space for a period of time; this can be calculated on the basis of the costs that congestion imposes on vehicles in circulation because they do not have the full width of the pavement available to them (Valenzuela and G?lvez, 1995).
In other words, without parking, a certain speed of travel prevails, with the commensurate level of consumption, whereas with parking spaces on the street, these factors are different; the parking fee can be calculated as the difference between the two, which can give rise to differential fees throughout the day.
In addition, it is recommended that parking metre rates be higher than the fees charged for underground parking so as to encourage the latter, since it is preferable to take parked vehicles off the street.
Human collectors can be the first line of defence in a control system, and they should be complemented by a group of supervisors.
In the case of direct payment to parking metres, inspectors should be assigned (one inspector for every 100 to 150 spaces) to prevent non-payment.
There is no doubt that franchising the parking metre business is the most effective collection system.
An important element in the proper functioning of on-street parking is consistent enforcement in a given area.
If metred parking spaces are adequately patrolled and nearby areas where parking is banned are not patrolled, there will be a great risk of illegal parking.
Prohibiting the use of regulated parking spaces during the morning rush hour is one way of expanding efforts to alleviate congestion.
This measure serves the purpose of facilitating the flow of traffic and prevents commuter parking.
Drivers who would otherwise park there must change their mode of transportation or travel during off hours, thus easing the heaviest flows of traffic.
If current parking concessions are affected by this measure, appropriate compensation must be considered.
It is logical that most parking will be off-street, either on private lots or on public land off of the pavement.
Not only is it impossible for the streets to provide enough room for all vehicles to park, their primary function is not parking.
Paid off-street parking can be underground, in parking structures, or above ground.
Sometimes above-ground parking is set up haphazardly on vacant lots; from a city planning standpoint, it would be a good idea to require the upgrading of such lots.
One aspect that merits attention is access, as entrances and exits should not be a source of congestion; any queuing that results should take place outside the lanes of the main thoroughfare.
Off-street parking has the advantage of reducing pressure for on-street parking and can be a commercial business venture.
High-rise or underground parking structures involve huge investments, but they can be profitable with the collection of fees.
They may be the result of private initiative or the concession of surface space (on public land off the street) or the public subsoil (under avenues, plazas, parks and other properties, for example).
Highinvestment concessions tend to go hand in hand with the prohibition of parking on all streets within a certain radius of blocks to prevent the erosion of demand.
How the concession is structured, including what agency grants the concession, depends on each country’s institutions.
These concessions may or may not fall within the purview of the municipal government.
Drivers accustomed to parking at low cost may express a certain amount of resistance.
In Munich, Germany, a comprehensive parking policy was adopted for the entire city, with flexible fees depending on the occupancy level.
An important line of action was to eliminate 1,200 long-term downtown public parking spaces (operated by franchises or by the municipal government) in order to raise the price of private long-term parking.
Large stores have limited parking and it is expensive; moreover, like service centres, stores must develop their own access to public transit stations.
At the same time, actions were taken to expand mass transit capability and improve the mix of vehicles on public roadways, curbing the aggressiveness of car drivers through “traffic calming” measures on the streets.
In Paris, France, the number of long-term parking spaces was also limited in the city centre by eliminating 3,500 spaces.
Parking fees were raised and a unified fare system was implemented for parking and mass transit.
The capacity of the beltway was also improved, with an underground motorway built on the south/ southeast stretch.
Paid off-street parking can also play a role in controlling congestion.
Of course it allows for the elimination of on-street parking.
Payment is also an incentive for rationalizing the use of the car, especially in the case of long-term parking.
It must be noted that it is feasible to charge municipal fees, either for using public land or for operating a business, in order to bring rates to appropriate levels.
A complementary method of reducing the availability of long-term parking would be to prohibit entry into paid parking areas during morning rush hour.
This would require paying compensation to parking companies for lost revenues.
Initial indications are that it is more appropriate to levy a tax so that the amount of the parking fee would discourage commuters from leaving their cars parked for an entire workday.
A 1998 study analysed various measures for easing congestion in London, including several proposals related to parking.
One was to raise the fee for short-term parking in the city centre.
It was estimated that a 200% hike would reduce traffic by 4%; for reasons of equity, parking fees in other commercial areas competing with downtown businesses would be raised as well.
Another proposal was to raise long-term public parking rates by 200%, which would reduce traffic by 6%.
The imposition of a 5,000-pound yearly tax on every private non-residential (PNR) parking space would stem traffic flows by 13%.
The effect would be most notable in the outer reaches of the city, where a large number of such spaces are currently available at low rates.
Various institutions offer free parking to enhance their operations.
Bearing in mind at all times that the problem is most acute in city centres during rush hour, the institutions can be classified based on their impact on congestion.
Providing free parking for customers may be a vital necessity for the functioning of shopping centres and other entities where the public goes to conduct transactions or business, such as medical centres, payment offices for utilities, banks and various public and private enterprises.
Individuals may stay a short time at these locations, and much of their business (though not necessarily the majority) is conducted during off hours anyway.
Along with having appropriately designed entrances and exits, in congested areas it may be possible for these entities to be closed for business at the height of the morning rush hour.
Except for medical establishments, it may be feasible to regulate business hours in this manner with a view to eliminating or limiting service during peak traffic times.
Any other type of restriction, such as charging a fee proportional to the establishment’s surface area, would meet with great resistance and might inhibit the development of businesses and institutions in the area in question.
Company-provided employee parking is a different matter, since travel to workplaces occurs primarily during rush hour.
These perquisites may be offered to attract qualified employees or may be the subject of collective bargaining agreements.
Free student parking provided by institutions of higher learning has a similar effect.
One byproduct is that parking facilities at schools and workplaces stimulate urban sprawl, since they make it possible to live in places not served by public transit.
Furthermore, public may never reach these low-density residential areas.
Thus, car-dependency is increased, with an even greater impact on congestion.
Urban sprawl also increases the cost of running the city because basic service networks must expand outward and travel distances and costs grow accordingly.
A complete ban on entry during the morning rush hour may be an unviable and overly drastic alternative for alleviating congestion.
A more feasible option would be to charge the entity in question a fee or tax for every parking space provided.
This cost could be passed on to students, but not to employees.
Another option is to limit the number of parking spaces that can be provided, obviously to less than the number of employees.
In both cases, if public transportation is deemed inadequate, the establishment can provide collective transportation or encourage carpooling.
It can also try to introduce flexible work or class schedules so that not everyone arrives at the same time.
This type of regulation affects property rights, so it cannot be introduced by a municipality on its own; an ordinance authorizing the restrictions may be required.
These measures would undoubtedly meet with strong opposition in the entities in question.
A number of English companies are paying their employees to give up the right to park on company property.
The hospitals in Derriford and Southampton, Heathrow Airport, the telecommunications firms Orange in Bristol and Vodafone in Newbury and the pharmaceutical company Pfizer’s plants in Kent and Reigate all have plans to implement this measure.
They are applying different schemes, ranging from a single “expropriation” fee for the right to park, to daily compensation payments, to annual or monthly payments to workers who arrive at work by any means other than a car.
The daily payments fluctuate from two to five pounds and the monthly payments are in the range of 80 pounds.
This measure suits the companies’ interests, as it is much more expensive for them to provide parking spaces, particularly if they decide to expand their facilities or move to another site.
The most successful schemes have been the most flexible ones, involving a payment for every day the parking is not utilized; more than one-third of the employees agree to leave their cars at home under such circumstances.
The downside is that these plans are more costly to administer.
In contrast, few employees have been interested in the schemes for longer terms.
The programme could be even more effective if the income from these payments were tax-exempt.
Complementary actions that have been helpful are establishing minibus routes, subsidizing the use of public transit, facilities for cyclists and a carpooling database.
In Santa Monica, California, a state law was passed in 1992 requiring companies with more than 50 employees located on the southern coast of the state to offer employees who are eligible for free or subsidized parking a bonus for declining to use it.
The law also applies to leased off-site parking, and the bonus must be equal to the amount previously charged for parking.
A 1998 law made these payments tax-exempt for employers and employees alike.
Employees may reject the offer and continue to use the company parking, or accept it and travel by other means or park at their own expense.
This measure has been useful for reducing traffic and emissions.
Parking at residential developments is indispensable, and there must be enough space so that vehicles are not parked on the street.
Steps must also be taken, however, to design entrances and exits in a way that prevents traffic jams.
Another possibility for easing congestion stems from the combined use of cars in uncongested areas and public transportation on the rest of the journey.
This allows the car to be used only on the part of the route where the costs to the driver are not significantly different from the costs to society. One prerequisite for this solution is to provide intermediary parking facilities.
The travel scheme would be as follows (OECD, 1981).
Travel by private car in outlying areas, where there is little or no public transit, for example, because any fares collected would not pay for the cost of serving remote areas with low occupancy rates.
Car parks outside the city centre near a public transit station or stop.
Utilization of some form of public transportation to reach the city centre, with appropriate and reliable service, hopefully faster than travelling by private car, so as to obviate the need to find parking downtown.
The success of an intermediary parking system depends on various factors, as indicated below (OECD, 1981).
They must be located near high quality public transit lines that operate at adequate frequencies and speed and offer comfortable seats and sufficient capacity.
They must be clearly accessible, even by those not familiar with the road network.
Their entry and exit capacity must be sufficient during peak hours.
There must be a high likelihood of finding a free space, as lengthy searches may cause the driver on his way to work to continue the journey by car.
The foot path from the parking area to the public transit station should be short and, if possible, covered.
Parking fees and public transit fares should compare favorably with the cost of making the entire journey by car.
The driver must be assured that his vehicle will not be at risk for theft or vandalism.
The intermediary parking system must be advertised in promotional campaigns, and signs pointing to the car park should be posted on the roadway.
Intermediary parking has been established in many urban areas in the United States, including Baltimore, Boston, Hartford, Portland, Seattle and Washington, D.C.
The Baltimore region has seven free car parks for a total of 1,770 spaces, served by express or local bus lines.
Intermediary car parks in the Boston region are linked to rapid transit lines (express bus or train) for commuting to work.
The Hartford and Portland regions have numerous intermediary car parks.
In Hartford there are about 30 parks with express bus service and another 84 established for carpooling.
In Portland there are 73 free car parks served by mass transit.
The TriMet, the mass transit authority, does not own any of these car parks, but makes use of parking spaces made available free by churches, shopping centres and suburban municipalities.
Seattle has six permanent intermediary car parks owned by the transit system and 15 part-time parks provided by shopping centres or churches.
All of them offer free parking and are served by mass transit lines.
Washington, D.C. has three intermediary stations with bus service and six located near subway stations.
A fee is charged for parking.
Controlling parking is one of many tools available for combating vehicular congestion.
Whether parking is prohibited, the private cost of parking at various facilities is raised, or shuttling between car and public transit is facilitated, a certain improvement in congestion is achieved on principal avenues and in city centres by freeing up space on the roadway for the flow of traffic.
Moreover, during peak hours travelers shift from cars to public transit or put off their trips until off hours.
The results include higher average travel speeds, with the consequent reductions in travel time and operating costs.
These results have been observed in many cities that have applied the measure.
Higher travel speeds reduce total toxic gas emissions, which helps improve the environment.
The costs of implementing a ban on parking are relatively low–informing the public, posting signs, patrolling and enforcing–unless businesses with contractual rights must be compensated, as is the case with parking concessions that lose business during certain hours.
If parking structures have to be built, the costs may be moderate for surface structures, but very high for underground or high-rise structures.
The advantage is that they can be privately financed by granting concessions.
Charging for parking is a reflection of the sound economic principle that the costs imposed on society should be defrayed, be they for implementation, for land allocated to parking, or for congestion caused directly by the reduced available road surface or indirectly by rush hour car travel.
Restricting parking in downtown areas during peak hours sends a positive message that tends to limit explosive urban development.
Indeed, if it is difficult or impossible to find parking when driving to work, the use of public transportation has to be accepted, or the employee must live relatively close to the workplace, which will dampen the urge to move to the suburbs for more than a few people.
A more compact city pays lower costs for urban development, transportation and utilities.
People have a strong desire to travel by car and parking restrictions make that difficult, especially during rush hour.
This can cause a certain amount of resistance.
The more extensive the measure, the greater the resistance, especially if companies and businesses see access to their facilities threatened.
Opposition can also come from municipal authorities, for the same reason.
Residents of an area where parking is restricted must have access to their homes, so a system must be designed to make that possible.
Outsiders may take undue advantage of that access, however.
To the extent that businesses lose acquired rights, they must be compensated in the amount of the damage.
For example, a commercial venture or a franchisee may lose revenue from parking fees when parking restrictions are imposed.
Radical parking restrictions, especially downtown, can threaten the area’s vitality.
Throughout the city there must be reasonably appropriate parking spaces for the development of various activities.
The design of parking facilities must ensure that i) there is access to the city centre; ii) activities are not hampered in areas with restricted parking; and iii) the quality of the ambient area is maintained.
Unfortunately, the results of studies and models developed to date are not conclusive with regard to the magnitude of the impact that parking limitations may have on business and commercial activity and employment in the city centre.
Since there appears to be some correlation, however, complementary measures should always be adopted for a commensurate enhancement of public transportation (Still and Simmonds, 2000).
Traffic has definite peak periods when large numbers of trips are made.
This phenomenon is generally attributable to the fact that at the beginning of the day there is a lot of activity, causing many people to travel nearly simultaneously to work or school.
A similar phenomenon, though less marked, occurs in the afternoon when the work and business day ends.
Consequently, congestion can be relieved to the extent that it is feasible to spread the start time of different schedules over a longer period.
Flex time involves establishing different starting and ending times for the various activities that go on in large cities, such as work, business, school, college and the like, so that each activity begins at a different time than the others.
The purpose of this measure is to avoid very definite peak times by staggering journeys over a longer period of time.
In this manner, the time of greatest demand for the roadway system is spread out and the streets are less congested.
Obviously, the best results are obtained when rush hour trips are distributed over a longer period time.
The reorganization of schedules depends directly on the nature of activities in each city; in any case, care should be taken not to interfere with the normal functioning of the activities subject to modification.
Every city has different business activities, an educational system that may or may not have evenly staggered schedules and a culture that may or may not facilitate the application of this measure.
There is a tendency for schools to start earlier than most jobs, since parents like to be able to drop off their children on the way to work.
Higher education, business and most private activities can be scheduled more flexibly.
One definite option is to have different starting times for work in different sectors, such as the public sector, the private sector, banking and construction.
Sometimes this occurs spontaneously, as construction tends to begin as soon as the sun rises.
The public sector, in turn, can start earlier or later, depending on national idiosyncrasies or the number of hours offices are open—if they are open for six hours, it is feasible to schedule the entire workday before lunch.
Bank customers can conduct their business on the Internet, so there is no need for banks to open early.
Another possibility is to encourage businesses to allow employees to work flexible hours wherever feasible.
In other words, each employee would choose his own times for arriving and departing, as long as the required number of hours is worked and they are present when all employees are needed for meetings or other joint activities.
Telecommuting, taking advantage of modern communications technology, the Internet, email and other innovations, can also help spread out schedules.
In any event, it must be acknowledged that the private sector lends itself better to such creative solutions.
Some case studies demonstrate the validity of flex time as a means of reducing congestion (Fernandes, 1985).
During the Second World War, Philadelphia and other U.S.cities pioneered the implementation of staggered schedules to ease the demand for public transportation to business centres during peak hours.
Several U.S.cities, as well as Toronto and Ottawa (Canada) and Paris (France) have implemented programmes that have been positively evaluated from various points of view, such as reducing the number of rush hour trips, cutting travel times and greater comfort on public transportation (Fernandes, 1985).
Numerous businesses in developed countries, including some public agencies, have flexible work hours that each employee chooses voluntarily, the only condition being that all employees must be at the workplace during specified hours.
Many workers do indeed elect to travel to during off hours, either before or after the morning rush hour.
Several Brazilian cities have adopted flex time plans, including Rio de Janeiro, S?o Paulo, Porto Alegre, Recife and Curitiba.
The results are a smaller number of trips during peak hours, less fuel consumption and increased travel speeds for public transit vehicles (Fernandes, 1985).
In Guatemala City, the start of the public sector workday (including municipal offices) was delayed till 9:00 a.m. in 1996.
Since schools begin at 7:00 a.m., the measure had a significant impact on congestion.
In Santiago, Chile, schedules have gradually been staggered, in some cases spontaneously.
Hence, construction begins at approximately 7:00 a.m., factories at 8:00 a.m., schools between 8:00 and 8:30 a.m., public agencies at 8:30 a.m., banks at 9:00 a.m., the private sector between 9:00 and 9:30 a.m. and retail businesses from 9:30 a.m. onward.
Banks have mandatory business hours and a few decades ago retail businesses were required to open at 10:00 a.m.
Once the latter were allowed to open when they chose, they began their day slightly earlier, as shoppers do not tend to go out early in the morning.
Furthermore, a complication is arising in Santiago: publicly funded schools are in the process of switching from a split session (different groups of students attend class either in the morning or in the afternoon) to a joint session, so many students who used to travel in the afternoon before rush hour will now be going to school in the morning, with the consequent overloading of public transportation.
A study (MIDEPLAN, 1998b) has identified this impact on the urban transportation system.
One line of action proposed to counteract it is to have staggered schedules at different schools to avoid the pressure of many additional trips during rush hour.
It is obvious that it does result in a longer period of time when people are traveling and therefore less traffic during the heaviest times, which means shorter travel times and lower vehicular operating costs.
The measure itself does not cost any significant amount to implement, although there may be costs stemming from the need to adjust to new schedules.
Commuters have the option of continuing to travel at the times they prefer.
A longer rush hour means that the same total number of passengers can be moved with fewer buses and therefore the density of trips is reduced.
The required adjustment to new schedules could result in temporary productivity losses.
Additional trips may be required, because the staggering could hinder a combination of trips that was previously feasible.
That is typical of parents taking their children to school and continuing on to work, which may not be possible without immediately returning home.
Changing habits is at least a subjective inconvenience, as it entails rearranging activities.
It takes time to arrive at a new system.
In the private sector, the authorities have little chance of mandating a certain schedule of activities.
Given that congestion occurs because many cars are circulating, it has occurred to some that congestion could be eased by prohibiting a portion of the existing fleet of vehicles from circulating, without infringing on the right to buy vehicles.
Vehicle restriction involves prohibiting the circulation of some vehicles during certain periods in certain areas, Monday through Friday.
This measure has been applied to reduce congestion or environmental pollution; therefore, depending on the intended goal, the type of application will vary.
The focus here is to deal with congestion by taking a certain number of vehicles out of circulation in the restricted area, although in contrast there are references to controlling pollution.
Who should be restricted?
Obtaining appreciable results in cutting congestion requires prohibiting a significant portion of the fleet, which should rotate throughout the week.
Usually the measure is applied to 20% of the vehicles subject to restriction each day Monday through Friday, although this proportion may be higher when pollution indices are high.
The prohibition may encompass all vehicles across the board, or some may be exempt.
Private vehicles (meaning cars, all kinds of taxis, pickup trucks and vans) are the main ones affected.
Furthermore, it is common to ban the circulation of trucks and other freight vehicles in downtown areas during rush hour and to establish special times for loading and unloading.
It makes no sense to prohibit the circulation of buses unless the goal is to combat pollution as well, since it is buses that cause the least congestion per passenger transported and that provide an important option for those who must leave their cars at home.
For this reason, school transportation, which is done by minibus in many cities, should not be affected either.
Collective taxis do not transport enough passengers to justify exemption, and if they were exempt, there would be tremendous pressure to convert individual taxis into collective ones en masse.
One simple option is to go by the last digit of the licence plate.
A daily ban on two digits would restrict 20% and allow all vehicles to be covered between Monday and Friday.
Other formulas allow a greater or smaller fraction to be restricted.
It is a good idea to keep the same rotation pattern over a long period of time, possibly several months, as frequent changes cause confusion among drivers.
If the pattern remains in place for too long, however, it penalizes those who are restricted on Friday, when many people wish to leave the city.
Therefore, the pattern should change every so often.
Another, more market-oriented possibility is to discriminate by circulation permit or travel fee.
In cities where this measure is to be implemented, an additional fee would be charged, which would be higher for those who wish to be exempt from any restriction, a lesser amount for those who wish to be restricted one or two days a week, for example, and nil for those restricted Monday through Friday.
The fees would be set at levels that would cut circulation by the desired amount.
Vehicles would be distinguished by windshield stickers of different colours and characteristics.
To be sure, the opposite could be done, that is, reducing the cost of the annual permit by different amounts depending on how many days a week the restriction is to be in effect.
In any event, it must be accepted that enforcement is more complicated than it is when the final digit of the licence plate is the criterion.
This measure should cover all districts of the city where there is congestion.
That is generally true of the city centre and various major avenues.
The restriction should be limited to these areas, although for reasons of practicality and simplicity most of the city tends to be affected within an established perimetre.
Applying the restriction to the entire city is only justified for environmental reasons.
The restriction should be in force during periods of congestion, meaning rush hour, especially in the morning.
If many drivers choose to go to work by different means, it is less likely that they will be using their cars in the afternoon rush hour, which would automatically alleviate congestion.
The restriction has been applied for the entire day, excluding the nighttime hours.
Imposing it during valley or off-peak hours during the day would be justified by environmental reasons, not congestion.
On holidays and during the season when urban traffic diminishes significantly because of vacations, the vehicle restriction should obviously be suspended.
There is no doubt that prohibiting circulation can have an impact on traffic volumes in the short term, as it effectively shrinks the size of the fleet of vehicles.
In the medium term, however, its impact diminishes.
The high rate of vehicle purchases observed in Latin America over the last decade means that in three or four years the number of vehicles may grow by 20%, cancelling out the effects the restriction is intended to achieve.
Additional pressure on the growth and, incidentally, on the ageing of the fleet comes from the fact that those who can afford it have an incentive to buy a second vehicle, possibly an older one, to evade the restriction, especially if it applies to whole days.
Restricting vehicles as a means of combating congestion was tried in Buenos Aires, Argentina in the 1970s, when half of all vehicles were prevented from entering the city centre depending on whether the last digit of the licence plate was even or odd.
The method was also used in Caracas, Venezuela in the 1980s.
The same prohibition was imposed on half of all vehicles in Athens, Greece between 1985 and 1991.
Assessment of the programme did not yield good results, as the fleet grew older when many drivers purchased a second vehicle.
Moreover, the circulation of motorbikes increased, and it has been shown that they cause more pollution than cars.
Compliance with the restrictions also declined over time (MINTRATEL, 1995).
In Managua, Nicaragua, half of the fleet of taxis has been subject to restrictions since 2001, when the excessive number of vehicles in circulation caused congestion.
Vehicles with even-numbered licence plates circulate between 6:00 a.m.and 2:00 p.m., while those with uneven numbers may do so between 2:00 and 10:00 p.m.
Another example of this measure, taken for the purpose of combating air pollution, is in Mexico City, where there is a permanent vehicle restriction programme.
From Monday through Friday, between 5:00 a.m.and 10:00 p.m., vehicles are prohibited from circulating according to the final two digits of the licence plate, with each vehicle being allowed to circulate one day a week.
On days when pollution indices are high, the restriction is applied to half of all vehicles (even or odd licence numbers).
Studies show that the negative impacts of this measure are higher than the positive ones.
One reason is that people have bought second vehicles, so in effect many individuals are not restricted at all.
Indirect evidence suggests that environmental pollution has grown worse because of the restriction (Tovar, 1995).
In Bogot?, Colombia, the programme called “Pico y placa” [peak and plate] has been in place since 1998.
It consists of restricting four licence plate digits per day from Monday through Friday, only during peak hours of the morning (7:00 to 10:30 a.m.) and afternoon (5:30 to 7:30 p.m.).
The speed of traffic has increased by 43%, fuel consumption has fallen 8%, and air pollution is down 11%.
It should be noted that several other steps are being taken to promote travel on foot (restoring sidewalks, which often have been invaded by parked cars) and by bicycle (with a network of bike lanes).
In addition, a public transportation network with high-capacity buses travelling on special roads, known as Transmilenio, has been put in place.
In addition, measures have been adopted to encourage people not to drive private cars.
For seven hours every Sunday, 150 kilometres of roads are closed to vehicle traffic so that they can become bicycle-only roads.
The first Thursday in February, between 6:30 a.m. and 7:30 p.m., “car-free day” is celebrated.
As the name suggests, on this day people are invited to leave their cars at home, and it has enjoyed widespread acceptance.
Since 1995, a number of experiments in restricting vehicles have been conducted in the metropolitan region of S?o Paulo, Brazil.
At first, voluntary vehicle restriction was practised for a week; then the State Secretariat of the Environment suggested that each day cars with a certain combination of final digits on the licence plate be left at home.
The first two days, participation was relatively high at 50%, but it fell in the ensuing days.
The overall average was 38%.
In 1996, the restriction became mandatory (State Law 9,358) and a fine of 100 reales was levied against violators.
This plan was in place from 5 to 30 August, between 7:00 a.m. and 8:00 p.m.
Compliance with the measure hovered around 95%.
It is estimated that during the period when the law was in force, carbon monoxide emissions fell by 1,171 tons and 40 million litres of fuel was saved.
The average speed of traffic rose by 20% and congestion during peak hours was cut by 40%.
Between 23 June and 30 September 1997, the plan was again put into effect between 7:00 a.m. and 8:00 p.m. throughout the S?o Paulo metropolitan area, with a fine of 78.16 reales levied against violators.
Compliance was relatively high at 90% in the morning and 85% in the afternoon.
During that period, carbon monoxide emissions fell by 42,460 tons, particulate matter by 200 tons.
Vehicle restrictions have been in place since 1986 in Santiago, Chile, for the stated purpose of decreasing environmental pollution caused by motor vehicle emissions.
Pollution is most serious during the coldest period of the year (April to August) because the lack of wind hinders the dispersal of pollutants.
In recent years, however, the measure has been in place from March to December, which has shown its usefulness in reducing congestion as well.
The restriction is in effect from 6:30 a.m. to 8:30 p.m. throughout the city and adjacent areas Monday through Friday, with light vehicles being taken out of circulation according to the final digits of their licence plates, two digits being affected each day according to a table that is changed every few months.
Within the perimetre of the beltway avenue, freight vehicles are also subject to the same restrictions.
When pollution climbs higher than acceptable levels, exceptional measures designated “alert”, “pre-emergency” and “emergency” are triggered.
One consequence of these measures is that the number of licence plate digits subject to restrictions is increased to four, five and eight, respectively; certain avenues are also limited to buses only in order to help them travel more rapidly and thus reduce their emissions.
Originally, the restriction applied only to vehicles without catalytic converters, as a means of encouraging the purchase of vehicles equipped with them and thus enhancing air quality.
This measure has stimulated the conversion of the vehicle fleet to those emitting fewer pollutants.
Since 2001, however, 20% and 40% of vehicles with converters, respectively, have been restricted during pre-emergency and emergency states.
The rationale for this measure is that although they pollute less, they still contribute to pollution, especially by raising dust on the streets.
Vehicles with catalytic converters are identified by means of a green windshield sticker and those without them have red stickers.
Cities applying this measure have reduced congestion in the short term, but the effect dissipates because of the expansion of the fleet of vehicles and the possible purchase of a second car.
If the measure is applied with a variable-cost additional circulation permit, it is easier to make incremental changes in the number of vehicles allowed to circulate.
As long as congestion is eased, travel times will decrease and average speeds will therefore increase.
This impact is seen in the short term as traffic levels decline.
This measure requires publicity and, above all, enforcement, generally by traffic authorities.
Vehicle restriction, either by licence number or by surcharge, involves the dilemma of infringing upon the right to travel, which has been paid for expressly each year.
Suspending this right during certain pre-established periods amounts to an expropriation and can give rise to constitutional and legal arguments.
In some countries, a law may have to be passed to allow this measure to be put in effect.
Chile’s case is emblematic, as the measure has officially been implemented on environmental grounds, even though many interpret it as an anti-congestion effort.
In view of the heavy volume of traffic during peak hours, enforcement is difficult, especially if stopping a vehicle causes more congestion.
That might cause people to flout the restriction, which would be against equity.
This occurs whenever it is possible to obtain a second or even a third vehicle, at low cost, of course, to evade the restriction.
Traffic congestion is partly due to a strong propensity to drive, reinforced by the fact that individuals do not perceive the cost imposed on others when they drive under those conditions.
Road pricing is one way of ensuring that those who cause the added costs pay for them, so that only those who are willing to pay the price are allowed to continue circulating during peak hours.
This contributes to a net reduction in traffic.
Road pricing consists of levying a fee to circulate in or enter specific streets or areas during times when there is congestion there.
The purpose is to make individuals circulating in a congested area see that their presence there imposes a cost on the other vehicles circulating in the area, in the form of longer travel times and higher operating costs, especially fuel costs (see chapter II).
Normally this additional cost is not internalized individually, and drivers’ decisions are made according to a vision of the cost to themselves; and even if they take into account the effect that congestion has on them, it is less than the total impact.
The result is that traffic increases more than is good for the economy.
In practice, the perceived price of driving in a congested area is analogous to a subsidy, without any economic reason for it.
On the contrary, to the extent that each driver internalizes the added cost he imposes, the use of public roadways is rationalized.
In fact, certain drivers will not be willing to pay the price of congestion and will seek out other alternatives, either using other modes of transportation or driving at times when the fee is not charged.
In theory, road use would be optimized if the exact additional cost could be charged at all times (known as the marginal cost in technical terms) and if people knew that value for each one of their travel options before beginning their journey.
The result would be the control of congestion.
Consequently, charging a fee for congestion is a disincentive for using personal vehicles in congested areas and times.
It is interesting to note that this regulates the use of public roadways by means of a market tool rather than a regulation imposed by the authorities.
Which areas or streets should be subject to pricing?
Ideally, pricing should be applied to every stretch of roadway affected by congestion and only there.
It makes no economic sense to apply pricing elsewhere, which would be contrary to the overall public welfare.
The problem is that there are not yet adequate cost and effectiveness technologies for this purpose, although in the Netherlands and the United Kingdom there is an effort to develop them (see box IV.8).
In practice, currently available technologies limit the application of road pricing to specific areas or streets.
In the first variant, the fee is charged for entering areas defined as congested or for circulating on any street within them; in the second, it is charged for travelling on individual streets deemed congested.
To avoid arbitrariness, the characteristics that must prevail in order for a place to be considered congested and for streets and areas to be subject to pricing must be defined and publicized.
The problem with these schemes lies in the physical or temporal margins of the areas subject to fees.
It is easy to imagine that nearby streets, though not designed for it, will have heavier than normal traffic and may even become congested, with an increased risk of accidents.
The same thing would happen immediately before or after the pricing period.
This would transfer at least part of the congestion to places or times that were free of pricing.
When pricing should be in effect?
Pricing should cover periods when there is congestion, which generally happens during rush hour.
The morning rush hour tends to be the most congested, so it is possible that the measure will be sufficiently effective if applied only during that time; many people would stop driving to work, which would automatically relieve the afternoon rush hour.
If the congestion lasts beyond rush hour, pricing could extend beyond it as well.
Which types of vehicles should be subject to pricing?
It would apparently be necessary to subject all vehicles to pricing, because they all contribute to congestion, albeit at different levels.
The main causes of congestion, however, in terms of the number of passengers carried, are cars, so it is acceptable to apply the measure to them alone.
How much should be charged?
Fees can be levied in accordance with the distance travelled, the time spent on the street, the amount of congestion in the area or street in question, or simply for entering them.
The socially optimum fee is equal to the additional costs the vehicle entering the flow imposes on others already circulating there.
Technically, that would entail increasing the private costs of circulating on the congested street until they are equal to the social costs.
Since the amount of congestion varies over time, even within rush hour, strictly speaking the fee would have to be variable, with the consequent system of informing drivers so that they could take it into account at any time.
Without ruling out the possibility that the technology for making this theoretical system a reality may someday exist, at present we must be content with setting fees that reflect as well as possible the congestion created and letting everyone know in advance what they are so that no one feels deceived.
This is a scenario known as the “second best” option.
What technology should be used for collecting fees?
Technologies for collecting fees have improved dramatically in recent years.
There are several fee collection systems available for urban streets these days.
This consists essentially of using a card attached to vehicles’ windshields if drivers wish to circulate in the areas subject to pricing.
The cards can be purchased at various outlets.
The cost of implementing this system is moderate, although it has the drawback of a potentially high rate of evasion; moreover, it requires a lot of enforcement personnel, as visual inspection must occur at many different points, with the practical problem of effectively recording all violations.
Payment at stations similar to traditional toll booths is unacceptable in urban areas, as it would cause heavy congestion at collection points.
In the electronic system, the fee is automatically charged as each vehicle passes through the area in question.
For this purpose, each vehicle must be equipped with a transponder or tag, which sends a signal to antennae located at collection points (P?rez, 2001).
A transponder that identifies the user; the fee is collected by sending a bill periodically to the party, or it is deducted from an account previously identified y the party.
This system is also known as “second generation fee collection”, in contrast to collection at traditional booths.
It has the drawback of identifying at what time the driver passed through a given place, which could be considered an invasion of privacy.
A transponder that includes a pre-paid card, with the corresponding amount being deducted from the value of the card at each point.
This is also known as “third generation fee collection”.
Although it is true that this is a more complex technology, it protects the identity of the driver, as long as the card included in the transponder has a high enough balance.
Detection and classification of vehicles: they must be capable of identifying vehicles subject to payment and those exempt from payment; they must also be able to handle several lanes of traffic simultaneously, or else traffic must be channeled into separate lanes in a timely fashion.
Warnings to drivers that they are approaching a collection point.
Actual collection of the fee.
Identification of violators: normally this is done by taking a photograph of the licence plate of the suspect vehicle (as it does not have a transponder or enough of a balance on the card, or some other defect).
Antenna-transponder and antenna-central computer communication.
Administration, including billing or charging and reporting of violators.
In the Netherlands and the United Kingdom recently, experts have cited the need to develop a system for collecting, on any route at any time, the costs that each vehicle imposes by circulating, including congestion costs.
Briefly, the idea consists of requiring every vehicle to be equipped with a unit that makes it possible to locate the vehicle at all times no matter where it is using Global Positioning System (GPS) technology.
Monitoring is done by satellite.
The system would enable authorities to locate vehicles on all roads, be they large or small, urban or rural.
The portion of the fee corresponding to circulation, compared to that corresponding to congestion, would vary according to the type of street and the volume of traffic on it; on most routes the fee would be zero, as it would be on main roads during periods of light traffic.
Two possible methods of collecting the fee are under consideration, both similar to the electronic systems described above.
This possible future system is more equitable, since it would allow fees to be collected on all roads, thus solving the problem of what happens at the margins of times and areas subject to pricing.
Advocates of the plan also propose keeping the fiscal revenues from the transport system constant, which means that if it is implemented, it would be necessary to cut fuel taxes and fees for circulation permits accordingly.
Road pricing has been under discussion for more than three decades, but there are not many instances of its application.
It is clear that the population and legislators are highly resistant to this measure.
There are various reasons for the resistance, including doubts about the real effects, the fairness of its application and the effectiveness of enforcement; fears about the impact on the development of areas subject to pricing and about discrepancies in the use of the revenues collected; opposition to new taxes and other arguments.
It is not insignificant that no city except Singapore has implemented road pricing specifically to control congestion.
Singapore has many characteristics that make it a special case, such as the fact that it is an island nation, that it has a government with extensive powers and that its population accepts a large number of regulations in all aspects of life.
The impact on alternate routes to those subject to pricing, many of which will be local streets not necessarily designed to handle heavy volumes of diverted traffic.
The existence of appropriate public transit options to replace private cars.
Residents’ access to the area subject to pricing.
Possible adverse impact on low-income disadvantaged persons who travel by car.
Business, retail and educational activities in the areas subject to pricing; these must be considered early in the process of planning and all interested parties should participate in the identification of possible solutions.
One possibility to bear in mind is staggering the schedules of such activities.
The allocation of revenues: there is strong resistance to new taxes, so the only spending that the public is apparently willing to accept is for improved public transit, investment in widening, rehabilitating, maintaining, marking and posting signs on streets, promoting development of the city and the like, but they would be unlikely to tolerate pouring the revenues into the general fund of the nation.
Lack of understanding of collection technologies.
To the extent that these factors receive adequate consideration, it may be possible to gradually increase public acceptance.
At any rate, an extensive information and persuasion campaign cannot be omitted.
Numerous studies of road pricing have been conducted in different cities around the world since the 1960s, but actual applications are few.
Until the middle of 1994, the only application of road pricing to control congestion was in Singapore, which introduced its programme in 1975.
The measure was intended to alleviate congestion, but the fees are very high.
Thus, it can be inferred that collecting revenues is an essential objective of the system.
Congestion is under control in Singapore, especially since fee collection was automated.
At first the system was manual, but now it is electronic.
Thus, it is possible to vary fees in order to prevent a concentration of trips just before or after the period when the highest fee comes into effect.
Road pricing was tested in Hong Kong and officials finally decided not to implement it.
One of the main reasons for this was the technology used; it recorded the location of every vehicle at every moment, which was considered an unacceptable invasion of drivers’ privacy.
Moreover, fees were to be collected by mailing a bill and it was feared that there would be a high number of errors.
It can be concluded from this experience that choosing the technology for fee collection is a very important decision, since one that is not trusted or accepted sufficiently by the public may cause the measure to fail.
The Norwegian cities Oslo, Bergen and Trondheim charge tolls on avenues leading into the city centre.
The initial objective was to collect funds to invest in urban transport, especially roadwork.
In the city of Trondheim, plans are under way to improve the fee collection system both in space and in time, in order to vary the rates according to time and place.
Although the objective of the road pricing in Trondheim is still to collect revenues, differentiating the rates by time of day, with greater amounts charged during periods of high demand, brings the plan in line with the concept of road pricing to control congestion.
In this case, for drivers who pay electronically, entering the city centre between 6:00 and 10:00 a.m. costs from 25% to 50% more than between 10:00 a.m. and 5:00 p.m.
No fee is charged at other times.
Urban tolls in Norway are an interesting experience from the standpoint of manipulating demand.
There was a decline in the number of car trips.
In Trondheim, a 10% reduction was observed during toll periods and an 8% increase was seen during free periods, which suggests shifts in travel schedules.
Surveys also show that many people have switched to buses.
In addition, studies have been done for the city of Santiago, Chile, using the ESTRAUS Model (see chapter V).
It is not likely, however, that Congress will pass the law allowing the fees to be charged.
More road pricing studies have been conducted in the United Kingdom than in any other country, yet to date no system has been put into place.
This situation may change in 2003.
Ken Livingstone, mayor of London, ordered a toll of five pounds to be collected each day, beginning 17 February 2003, from all those entering the city centre of London Monday through Friday between 7:00 a.m. and 6:30 p.m. by crossing a pre-defined cordon of streets.
This measure was something he had promised during his election campaign.
Opponents of the measure have lobbied intensively, but the courts rejected their lawsuit attempting to have it annulled.
A study on anti-congestion measures in London (LPAC, 1998) concluded that road pricing applied within a specific area in the city centre would be an appropriate mechanism for cutting traffic in that area.
Decreases of 8%, 32% and 48% could be achieved with tolls of 2, 5 and 10 pounds, respectively.
These figures seem high, and in practice, traffic is expected to decline between 10% and 15% in the centre once the agreed-upon measure has been applied.
Outside the cordon, however, the impact would be limited, since many journeys begin and end without going into the centre.
Revenues ranging from 130 million to 180 million pounds per year are expected, and will be used for improving public transit.
The system will be implemented by selling entry permits at authorized locations.
Every ticket sold will be entered in a database along with the licence number of the vehicle it pertains to.
Cameras placed around the perimetre of the area will automatically record the licence numbers of vehicles crossing the cordon and they will be compared with the database for verification.
Although there is not much empirical evidence, the measure appears to have yielded significant results, reducing the number of car trips during rush hour and transferring a certain proportion of them to public transportation.
At least this is what the model studies show, particularly the one conducted for Santiago.
With fewer vehicles circulating, travel times are reduced, traffic speeds rise, and the cost of operating a vehicle declines.
It should be noted that these effects are probably short-term, and the results may be different in the medium and long terms.
With fewer vehicles on the road travelling faster and stopping less often, pollution diminishes.
Road pricing corrects the economic distortion that arises when drivers do not perceive the costs that they impose on others by causing congestion and allows for the rationalization of the transportation market.
Instead of the authorities imposing regulations, the use of public thoroughfares is regulated by market mechanisms.
The revenues generated by this measure are considerable, and they can be used for projects to improve urban transit or for local development.
Collecting a congestion fee is intended to make the cost perceived by drivers equal to the social cost of using the public roadways, including the price others must pay for the resultant congestion, and thus to make vehicle use appropriate from a social point of view.
In theory, the price of the congestion caused, which varies tremendously, should be collected on every street in every location at every moment and should be known to all drivers at all times.
This means that the number of streets subject to pricing and the amounts charged would be changing constantly.
It would require a detailed monitoring of traffic on practically every block of every street and a dynamic calculation of the costs of congestion, which would be transmitted instantaneously to drivers as the corresponding fee was being collected.
Current technology does not allow for this possibility, so road pricing is just an approximation, a “second best” choice.
Even if at some point it became possible to charge each vehicle for the additional or marginal costs it imposes on society, it is not clear what results road pricing would yield in the long run, since other components related to the transportation system are not governed by marginal prices.
That is true of green areas and farmlands surrounding cities.
Absent this type of pricing, or alternatively, controls on land use, cities will tend to expand, which complicates sustainability over the long term (see also section G.3 below).
Drivers will tend to avoid going into streets or areas subject to fees, diverting traffic towards toll-free streets and possibly causing congestion there.
Furthermore, immediately before a period of fee collection or higher fees, drivers will hurry to enter the area and risk causing accidents.
Because of practical difficulties with enforcement, manual systems have a high potential for evasion, which gives rise to inequity.
Electronic collection systems are more effective, although their current margin of error could undermine confidence if incorrect charges are made.
To prevent such a reaction, the companies collecting road tolls using this technology usually prefer forgoing the fee in cases of doubt.
In addition, in second-generation systems, the time and place of travel are recorded, which could spur resistance due to the invasion of privacy this entails.
The manual system has a relatively high cost, the electronic system an extremely high one.
Nonetheless, revenues could cover these costs and generate surpluses.
In this regard, the main problem of the electronic system is financing the hefty initial investment.
Even if enacted, the measure may encounter strong resistance from drivers, who would have to pay more or leave their cars at home for some habitual journeys, with the ensuing loss of satisfaction, safety and reliability.
A solution must be found for people who live in toll zones; if they are charged a fee, they could be prompted to move, and if they are exempt, third parties might fraudulently take advantage of the system.
Moreover, the effect on property values is not clear a priori.
Except for the manual system, introducing road pricing involves introducing a complex, cutting-edge technology that is still being developed.
This poses a major additional difficulty.
Road pricing, whether manual or electronic, is a complex system that entails collecting large amounts of money, which means that an entity to administer it must be created.
That may be a company or other appropriate body, either a public agency or a private firm operating under licence.
In any case, adequate controls must be put in place.
Recognizing that cars are extremely attractive because of the advantages they offer, a variety of methods have been developed and applied in some locations in an effort to encourage drivers to voluntarily change their car-related behaviours based on moral convictions.
These methods rely on the notion that people are more likely to make changes if they are given goals that are consistent with their scale of values or that represent positive changes in their lives (Department for Transport, United Kingdom, 2002a and b).
Emphasis is placed on identifying personalized options for making necessary trips by means other than the single-occupant car.
In the process, possible alternatives are analysed, and the advantages that individuals and society can obtain from them are examined.
The idea is that changes in behaviour come about if their benefits are understood.
These methods are people-oriented, and therefore they represent a substantial, diversified effort.
The results have been varied, but they offer hope for lasting behavioural changes.
A summary of the methods used, together with an evaluation of each, can be found in Department for Transport, United Kingdom (2002a).
Those that offer assistance in identifying the best way to carry out specific journeys, either by car or, preferably, by public transit.
Commuting to work is the main focus, although in some areas trips made for other purposes, such as looking for work or going to the hospital or convention centre, are included as well.
Those that attempt to modify travel habits and attitudes.
Some have been registered as intellectual property, such as IndiMark?, TravelSmart?, Travel Blending? and Living Neighbourhoods?.
IndiMark? and TravelSmart? are based on direct marketing in homes or by mail or telephone to promote the use of public transit and methods other than the car.
Travel Blending? and Living Neighbourhoods? are carried out at the community or neighbourhood level, and involve detailed analyses of travel habits and ways of modifying them.
Living Neighbourhoods? applies Travel Blending? in conjunction with other measures adopted by the locations in question to facilitate change.
Miscellaneous methods, including strict management of parking at private entities (see the box in section B.5.b titled “Paying commuters to leave their cars at home”); driver education in schools; publicity campaigns designed to raise awareness of sustainable modes of transportation, health, the environment and others; and travel information offices.
Travel Blending? is a technique intended to rationalize the use of the car without changing people’s activity patterns.
In this sequence of procedures, families’ habitual trips are recorded, then recommendations of possible changes are made, personal decisions are made about changes that are feasible and beneficial, follow-up and observation take place, and a final assessment is conducted.
The desired result is that certain transport behaviours are modified.
The technique requires the participation of specialized advisers who can make appropriate recommendations for thinking about and organizing trips in advance, harmonizing modes of transportation, and harmonizing activities (in terms of time and place so that long trips are avoided).
It is most appropriate in communities that share similar activities, such as businesses, neighbourhoods and schools.
Although from this point of view it covers only partial groups, its effect on peak travel times can be significant.
The table below sums up the results of three experiments.
The emphasis on promoting voluntary behavioural changes is relatively new, and it should be acknowledged that, in Latin America and possibly in many developing countries, making it a reality may be difficult.
Nonetheless, in developed countries such as Australia, this method is expected to cut car use by approximately 10% (The Review, 2002).
The behaviour of those who use the public roadways, be they drivers or pedestrians, has varying degrees of influence on congestion and also on safety.
The steady growth of roadway use led, first of all, to the establishment of rules of play, traffic regulations or standards, with a view to defining rights and restrictions on the use of streets and thus improving the flow of traffic as well as preventing accidents.
Unfortunately, many people are unaware of these rules or choose to ignore them.
A lack of driving discipline or consideration for others in fact reduces the capacity of the road network to a fraction of its potential.
Trying to gain a few seconds by violating the rules governing traffic at intersections or on streets seriously disrupts other vehicles’ movement, translating into heavier congestion and, unfortunately, a greater risk of accidents.
Pedestrians must also obey the rules of the road, crossing streets only at the times and places designated for that purpose.
Driver and pedestrian behaviour absolutely must improve.
That is why it is so very important to educate the entire population on traffic rules from early childhood onward.
This effort has tremendous possibilities and a broad scope.
Examples include driver education in school curricula, educational campaigns in radio and television spots or advertisements directed towards the public at large, driving schools, the requirement that a certain number of hours of supervised driving practice be carried out before obtaining a licence, and more stringent driving exams.
Some noteworthy initiatives have been launched in Brazil, such as using mimes to teach people how to cross the street at designated locations and playing folk or popular songs that teach the rules of the road at places where crowds congregate.
Another interesting innovation in Chile involves setting up uniformed student patrols to show classmates how to travel properly on the streets when going to and from school.
Immediate results cannot be expected, of course, but because young people are so malleable, programmes oriented towards them show great promise for future changes towards safer and more law-abiding behaviours.
Advances in computer technology make it possible to explore measures to encourage a reduction in the number of trips deemed necessary.
Beginning in the last decade of the twentieth century, residential access to the Internet and cable television has proliferated.
This will certainly influence the timing and frequency of travel.
An Internet connection allows greater flexibility in scheduling travel to work, and may also replace some trips with telecommuting.
As a result, the concentration of demand at peak times can be diluted.
In addition, some shopping trips can be replaced by ordering goods online to be delivered by truck.
It is also logical to assume that there will be less propensity to go out of the house for recreation, thanks to the expanded possibilities for at-home entertainment (Thomson, 2002a).
It is still premature to predict the repercussions of these changes, which are largely spontaneous in nature.
In any event, support for this trend can be one way of reducing the demand for travel during rush hour.
The spread of home ownership and car use has led to a deconcentration of residences and businesses in cities.
In the past, only members of the aristocracy could afford to travel in private vehicles such as carriages, and consequently no one else could live or travel more than 10 blocks from a streetcar, train or bus line for school, work or recreation.
This fact is still reflected in the cities of today.
In Santiago, for instance, 1992 data from the National Institute of Statistics (INE) show that the population density in traditional neighbourhoods that were developed before the days of the car hovers around 10,000 persons per square kilometre, while in newer areas, generally located on the outskirts of the city, this figure is 2,000 to 4,000.
In that same city, population density rose steadily from 1940 to 1980, but then it began to drop (Armijo, 2000) as citizens gained new freedom to travel by buying cars.
Between 1950 and 1970, in the U.S. cities of Chicago, Washington, D.C.and Boston, which by then were totally car-oriented, demographic density in the city centre had begun to decline as residents moved to the suburbs.
Meanwhile, the opposite was happening in the centres of Latin American cities.
Buenos Aires was the exception, as car driving arrived relatively early compared to the rest of Latin America, and it had the only suburban train system in the region, making it possible to live anywhere in a broad suburban area and commute downtown every day (Ingram and Carroll, 1978).
Suburbanization is also made easier by the fact that in Australian and North American city centres, there are between 0.4 and 0.5 parking places for every job (Kenworthy and Laube, 1999).
There is a strong correlation between urban density and the cost of public transport per passenger-kilometre, which prompts a pernicious spiral of greater car use, deteriorating public transit, reinforced car dependence, and finally, a city that is unsustainable in the long term.
Several authors have identified parametres in the trips made by individuals that appear to remain rather stable across time in a single city and even in groups of cities.
For example, the time spent travelling, per person and per day, fluctuates between 45 minutes and approximately one hour and 25 minutes, with a marked concentration at about one hour.
This is true whether the person lives in the United States or an African village (Schafer, 2000).
The speed of the available modes of transportation varies considerably.
Thus, in the villages of Ghana, people cover only 3.
5 kilometres per day in their daily hour, whereas in the United States they cover more than 60 kilometres, as they can travel by car rather than on foot.
A study of data compiled between 1955 and 1970 in U.S. cities concluded that “daily travel time per traveller is notably similar” (Zahavi, 1976).
If this correlation is true, a reduction in traffic speeds resulting from congestion would reduce the average distance travelled per person, unless alternative modes of transportation are found.
Some of these effects have an impact on land use, but not always in the same way, so the net impact varies from one city to another.
For example, reducing time available for travel would create a greater demand to live near workplaces or pressure to create new jobs near residential neighbourhoods; this would become especially apparent in cities with relatively strict land use regulations, particularly along the perimetre of the metropolitan zone, as is the case in Europe.
In Latin America, where these regulations are looser, the opposite trend would occur; that is, residences and workplaces would move to outlying areas that right now are free of major congestion.
Traffic congestion can encourage the use of public transport modes that are at least partially immune to its consequences, that is, those operating on separate roadways.
To the extent that their use increases, the demand for both residential and commercial property with good access would rise, as is the case near subway stations.
Although this trend appears to be occurring in several cities in the region, in Latin America the modal distribution (the manner in which travel is spread out among different modes of transportation) seems relatively inflexible in the face of changes in the prices or travel times of  the various modes (Swait and Eskeland, 1995).
In S?o Paulo, which is known for its heavy traffic congestion, surveys of travel patterns have been carried out over a period of 30 years.
As a result, certain inferences can be drawn with respect to the influence of congestion and its possible impact on land use, though no definite conclusions can be reached.
From 1977 to 1997, there was a reduction in the daily travel time per person from 76 to 64 minutes.
Although there are no consistent figures on traffic circulation speeds in S?o Paulo, they probably did decrease during this period; average speed per trip rose, however, from 9.4 km/h in 1977 to 9.7 km/h in 1987 and 10.9 km/h in 1997.
The consequence is that, despite the reduction in time spent travelling, the number of kilometres travelled per person per day has remained stable.
Moreover, the number of trips made per person per day fell from 1.53 in 1977 to 1.21 in 1997, and the average distance per trip rose from 7.8 to 9.5 kilometres (Companhia do Metropolitano de S?o Paulo, 1977, 1987 and 1997).
The situation has been analysed, at least partially, in relation to transport, but without identifying the impact of congestion on land use (Henry and Hubert, 2000 and Thomson, 2002a).
The scenario is consistent with growing congestion, however, and this encourages the movement of residential areas towards the suburbs, forcing citizens to cover ever-greater distances between home and work.
The greater physical separation and the rise in congestion, and perhaps also a greater availability of home entertainment thanks to cable television, causes people to make fewer trips.
Heavier congestion is compatible with a higher average speed per trip if people switch from slower modes of transportation, such as ordinary buses, to other less slow modes, such as buses on segregated lanes, subways or cars; and this is what appears to have happened.
From 1977 to 1997, the proportion of trips made by train (including the subway) climbed from 4.9% to 7.5%, and that of car trips from 26.3% to 30.9%.
Trips made by bus fell from 40.1% to 25.8%, and in general, travel speeds seem to have diminished.
The creation of separate bus lanes or streets in the most heavily travelled corridors during this time may have benefited more bus passengers, however.
Road pricing has been proposed as a means of improving the flow of traffic, cutting car use and promoting public transportation, and in general, of making the city a more enjoyable and sustainable place.
The long-term effects of this measure, however, may be diametrically opposed.
A number of analysts have studied, usually with mathematical models, the effect of road pricing aimed at controlling congestion on the physical boundaries of the city.
They have not always reached the same conclusions.
For example, one study concluded that road pricing would reduce the demand for travel and that it would lead to a more compact city (Oron et al, 1973).
Another reached the opposite conclusion (Mills, 1967).
Particularly in Latin America, signs point to the conclusion that road pricing would cause greater urban sprawl.
Road pricing raises the relative price of driving a car in more congested areas, that is, in areas of greater residential or commercial density, which normally tend to be located in the city centre.
In view of this increase, in the short term motorists tend to choose among three options, none of which is to their liking: i) switching to public transit; ii) continuing to travel by car but during toll-free times, either by getting up early or by arriving at work later; or iii) continuing to drive as usual, paying the corresponding fee.
The effect is that they have less access to areas subject to road pricing, making these less attractive places in which to live or work.
In the more distant future, drivers would have the option of moving away from the toll zone looking for cheaper places in which to drive.
Since car owners tend to have relatively high income, their exodus would reduce the demand for both high-end residences and business or office complexes.
Property prices could fall, and a process of urban decay might ensue.
Officials might try to halt this trend by improving the quality of public transit in the toll zone.
Proposals for road pricing often feature such improvements and call for financing them with some of the toll revenues.
The result would be better access to downtown areas for public transit riders.
Regrettably, it appears that there is a very low tendency to switch to public transportation in Latin America; at least that was reflected in a study conducted for S?o Paulo (Swait and Eskeland, 1995)2.
In other words, improvements in public transportation would not attract a large number of motorists, and their riders would generally remain persons of lower incomes and skill levels.
Especially in Latin America, business decisions regarding the location of offices take into account the preference of the most specialized employees, who are critical to success.
Given that they usually travel by car and they must be kept happy to avoid losing them, they will probably have their road tolls reimbursed.
No matter who is footing the bill, the costs of remaining in or moving to areas subject to road pricing would increase, and there would be pressure to go to exempt areas.
Residents would also contribute to that trend if they are not exempt from paying tolls.
The new destinations might have a lower quality of public transportation than the city centre.
Therefore, even employees who used to commute on public transit would have incentives to start driving, and if there is ample parking, that is exactly what they would do.
New residents might also continue driving their cars without any surcharge being imposed on them.
The result would be: i) less use of public transit; ii) more use of cars; iii) a tendency towards decay in the city centre; and iv) urban sprawl as the city incorporates land that had been devoted to farming or had been in a natural state.
It is difficult to avoid the conclusion that, unless the marked preference for driving cars could be changed, introducing road pricing in Latin American cities would lead to greater urban sprawl, the movement of workplaces out of toll zones and a reinforcement of the tendency of higher-income families to live in the suburbs, where public transportation is sometimes not very viable.
The average length of trips would increase and the city would become more dependent on private transportation (Thomson, 2002b).
The toll zone itself could undergo urban decay.
In other words, road pricing would have the perverse effect of decreasing the sustainability of the city in environmental terms, and probably in economic and social terms as well.
The aforementioned long-term trend towards unsustainability could be countered by imposing heavy restrictions on land use to prevent suburbanization.
With only a few exceptions, such as Curitiba, municipal authorities in Latin America have not proven to be very adept at implementing such restrictions, however.
At any rate, if they did so, they would run the risk of making the city a less attractive place to live, which would have an adverse effect on the prospects for economic development.
Imposing strict controls on parking would have different impacts, depending on the harshness of the measure.
If the restriction were applied only to on-street parking, the restricted area could become more attractive for high-end residences, which would eventually slow the trend towards suburbanization, though it would certainly not halt it altogether.
It is clear that demand-side actions have a place in the battle against congestion and yield concrete results.
To be sure, some are easier to implement than others, as they enjoy greater acceptance or less resistance among the citizenry.
In addition, some are low in cost and others can be financed by the private sector, which increases their viability.
Regulating parking and staggering schedules seem to be the best choices according to the criteria indicated above.
The optimum strategy could be a gradual implementation, combined with supply-side measures adopted in accordance with prevailing congestion levels.
What does seem clear is that the struggle will be constant, unless the demand for transport decreases for other reasons.
Although they may require painstaking measures to implement and therefore be slow to yield results on a citywide scale, techniques aimed at changing driving habits and attitudes may also have a lasting impact that can really be felt during rush hour.
In any case, when demand-side measures are designed, side-effects must be weighed carefully and special attention must be given to preventing undesired effects, especially in the long term.
In this regard, parking controls and road pricing are delicate matters, since poor design could result in depressed city districts or counterproductive consequences that undermine urban sustainability.
Santiago, the capital of Chile, is not unfamiliar with the phenomenon of rising traffic congestion that plagues large cities, with the consequent increases in travel times, vehicle operating costs and environmental pollution.
Projections for 2005 indicate that traffic speeds will worsen considerably, so the need to study and implement measures to help moderate and control congestion is inescapable.
The Interministerial Secretariat of Transport Planning (SECTRA) has developed the complex and proven computer models ESTRAUS, VERDI and MODEM, which make it possible to analyse in advance the results that would be obtained from adopting certain decisions regarding the Santiago transportation system.
These models were used to simulate the effects of various possible measures to control congestion and to evaluate their results in economic, social and environmental terms.
In 1991 a wide-ranging survey was conducted in Santiago of 32,000 households to determine what urban trips were made.
The ESTRAUS Model was calibrated on the basis of the results of that survey.
The greatest density of motor vehicle trips occurs between 7:30 a.m. and 8:30 a.m., so that time period is considered the morning rush hour.
This table also shows the projection for 2005, based on the assumption that the infrastructure and transport management will remain unchanged.
ESTRAUS (MIDEPLAN, 1997) is a model that balances transport supply and demand.
It is applicable to multimodal urban transport networks with many different types of travellers (as a function of their income, the purpose of their trips, or other factors).
Travellers are classified according to the socioeconomic attributes of the household they belong to; for this purpose, average income and the number of vehicles owned are taken into consideration.
The model assumes that in choosing among different available modes of transportation travellers apply a number of criteria, including costs, travel time and subjective preferences for one over another.
The multimodal network encompasses single modes of transportation, such as car, bus, taxi or subway, and combined modes, such as bus-subway, car-subway, etc.
The model incorporates capacity restrictions for both private and public transportation, which allows it to treat congestion explicitly.
It also incorporates the cost functions that exist on arcs (stretches of road) in the network.
The classic transport model features four stages: generation and attraction of trips, dispersal, modal distribution and allocation; the latter three are resolved simultaneously in ESTRAUS, while the first (generation and attraction of trips) is exogenous to the model.
The structure of the model can be seen in figure V.
The analyses are carried out for two periods during the day: i) the morning rush hour, from 7:30 a.m. to 8:30 a.m., and ii) off hours, between 10:00 a.m. and 12:00 noon.
The morning rush hour is when the urban transport system has the most unfavourable operating conditions, in terms of the number of trips by motor vehicle and the amount of congestion.
The importance of dealing with this period correctly is fundamental, considering that transport systems are designed to meet the demand for travel that occurs at that time, in terms of motorway capacity and public transit fleets.
Measures involving the urban transport system can mean changes in the costs of travelling on different modes of transportation in different places.
For example, charging for parking or raising the price can make car travel more costly, while introducing segregated lanes can reduce travel time for bus passengers.
A major change in travel time or cost will result in a modification of the modal distribution.
When the impact of a given measure is calculated, the total number of trips in the period of analysis remains constant, and the model must determine the new spatial distribution of that invariable number of trips, how they are spread out among the different modes, what routes are chosen, new volumes of traffic, travel times and operating costs for each stretch of motorway, among other elements.
To evaluate the impact of different measures, two scenarios are generated.
One is the base or “no project” scenario, corresponding to the original situation; the other is the situation that would arise if the measure were applied, also known as the “with project” scenario.
The results of a specific measure are portrayed as differences in relation to the base situation, which serves as a yardstick for comparison.
In the classic evaluation approach, the project’s benefits and costs are the positive or negative variations in resource consumption, which in the case of transportation essentially means travel times and vehicle operating costs.
If in the “with project” scenario the total of all these costs is less than in the base situation, the measure has benefits equal to the difference, and vice-versa.
The benefits must be compared with the costs of implementing the measure.
The option of evaluating the benefits to travellers estimates the total benefits of the project as the variation in the surpluses enjoyed by consumers (those who use the transport system) and those accruing to the producers of transport services (operators).
In the case of transport, a surplus is the difference between the costs a person is willing to incur and those he must actually pay to travel.
The surplus is a sort of margin or gain received by the user.
In other words, this system of evaluation considers subjective aspects, including the well-being or gain that travellers derive from using each mode of transportation.
This reflects the different preference for each mode, as people are willing to pay more for those considered most attractive; the car generally heads the list of preferences because of its greater versatility for travelling.
Thus, if a particular measure raises the cost of travelling on a mode, users of that mode suffer a loss, as their margin of gain diminishes; some must even switch to a lower-cost alternative that is less valued by them, which also diminishes their margin.
This would be the case, for example, of motorists who might switch to the bus in the face of road pricing or a change in the cost of parking that made driving by car cost more than they are willing to pay.
This would apply to bus passengers if the measure in question increased their travel speed.
In contrast to this traveller benefit scheme, classic evaluation is limited to measuring the impacts on the economy in terms of the variation in travel costs.
The two evaluations do not always yield the same results (both favourable or both unfavourable), contrary to what one might assume at first glance.
Sometimes a negative result is obtained when all the variations in surpluses or traveller benefits are summed up for the entire transport system of a city; this means that the majority of travellers suffer a loss of well-being or satisfaction.
At the same time, however, the classic evaluation can yield positive results if there is a decline in total transport costs, that is, if fewer resources are used to move all the travellers.
Santiago has adopted anti-congestion measures, but it is necessary to pursue new measures to improve the supply and operation of infrastructure and public transportation, and to act on demand as well, that is, on the use of vehicles.
Otherwise, the pressure exerted by demand can exceed any available infrastructure.
The ESTRAUS and VERDI Models were used to analyse a diverse set of options for dealing with congestion while also improving the economic efficiency of urban transport and helping to curb environmental pollution.
When the measures were proposed, special attention was given to the less affluent social classes which are frequently captive users of public transit.
The proposed measures are described below, with a summary of their characteristics and the principal impacts that would result in each case, using data from 1997.
In each case, the decision on whether to implement the measure will also have to take into account acceptance by the citizenry, as resistance by the citizens would render it unviable.
Segregated lanes are stretches of pavement isolated from the rest of the motorway by a physical barrier, and they are reserved solely for bus traffic.
This allows buses to travel more smoothly and avoid friction with other vehicles on the roadway.
The impact of segregated lanes was analysed previously (Fern?ndez and De Cea, 1999).
Implementation was considered for the principal arteries of Santiago, such as Pajaritos, Santa Rosa, Vicu?a Mackenna, Independencia, Alameda, Gran Avenida, Tobalaba, Am?rico Vespucio, San Pablo and Recoleta (see map V.1).
There would be one 3.5-metre lane in each direction, except in places where bus stops are located, where there would be two lanes to make it easier to go around stopped buses; the distance between stops ranges from 500 to 600 metres.
The results listed below come from the aforementioned study.
There is a change in the modal distribution favoring buses, with a nearly 0.4% increase in demand for buses among all trips.
The shift occurs mainly from subway and bus-subway travel.
Significant decreases in travel time are produced.
Travel on public transit takes approximately five minutes less on average.
Car drivers also realize savings of 1.5 minutes per trip.
Total time saved amounts to about 40 million hours per year.
These results have been more than corroborated by experiments with segregated lanes and bus-only streets introduced in Santiago (see chapter III section E).
The shorter travel time means higher speeds for both buses and cars.
It can be deduced that physical separation of types of traffic benefits public transport, and in particular leads to a general easing of congestion.
Consequently, this measure is deemed highly effective, and it has the virtue of favouring cars at the same time.
The principal results of the study (Fern?ndez and De Cea, 1999) are that in the first year of implementation there is an annual resource savings of US $57.
3 million.
Moreover, because of the greater travel speeds, 700 fewer buses are required to deliver the same service, with an existing fleet of approximately 10,000 buses.
Obviously, carrying out the segregation will require an investment (about US $637 million), but the long-term savings make the measure socially advantageous.
Executive buses are high-quality multi-passenger vehicles.
In the study for Santiago (CIS, 1995), they were assumed to have reclining seats, piped music, air conditioning, uniformed drivers, well-spaced stops and smart cards or subscription fares.
The establishment of 10 routes was considered (see map V.2), with two variants: i) 24-passenger buses circulating every 20 minutes, and ii) 40passenger buses circulating every 12 minutes.
A fare of 1,200 pesos was assumed, equivalent to US $2.61, which is between five and six times the regular bus fare.
The principal results are shown in table V.2.
It should be noted that for technical reasons related to the way the level of preference for executive buses was modelled, the results of the application of the ESTRAUS Model are not as precise as in the other cases studied, so they should be regarded as preliminary.
It can be seen that this system can attract nearly 2.
4% of all trips made during morning rush hour, which is rather significant in view of the fact that this amounts to more than half the number of trips made by subway.
Time spent waiting for executive buses is 68% less in the design with the higher capacity and frequency.
During rush hour, 33% of trips on executive buses, or about 6,700, would be made by people with high and medium-high incomes who, if this option were not available, would travel alone or carpool in private cars.
In addition, 40% of the passengers would come from regular buses.
Subway riders from the upper economic strata would also be attracted.
The average characteristics of bus and car trips improve slightly when smaller executive buses are introduced.
The same is true of bus trips when larger executive buses are employed, whereas car trips deteriorate slightly in this scenario.
What happens is that executive buses do not replace a sufficient number of car trips, and with larger executive buses, the number of congested stretches of roadway actually increases.
It is concluded that in the case of Santiago, introducing medium-sized executive buses (24 passengers) makes a contribution, albeit a modest one, to combating congestion; in contrast, introducing large buses is ineffective and even counterproductive.
They were not determined in this study.
Nevertheless, the operation of medium-sized executive buses is advantageous from a private point of view (CIS, 1995) during rush hour.
In other words, revenues from fares would offset the costs of the vehicles and their operation.
Parking prohibition was simulated, with an extremely high price assigned for parking in prohibited places.
The principal results are shown in table V.3.
It should be pointed out that parking restrictions in private places are difficult to implement, and legislation may have to be amended to make them possible.
When the different measures are analysed, a very slight improvement can be seen in operating variables in the scenarios that seem most feasible and that correspond to prohibiting parking on the street.
This may be due to the fact that Santiago already had a reasonable restriction in this regard.
The situation changes for the better when the prohibition is expanded to off-street parking during rush hour.
In the case of the greatest prohibition, the share of cars in total travel drops by 0.6%.
About 12,100 trips (0.1%) are subtracted from the single-occupant car mode; 40% of these drivers would switch to a car with more than one occupant, while the rest would switch to public transit.
With fewer cars competing for land use, travel times fall by about two minutes and speed rises by two km/h.
Travel times for public transport also decrease, with the consequent increase in speed.
The savings are similar for buses and cars.
The number of congested stretches of motorway declines to as little as 42, which is similar to the level seen in certain road pricing scenarios.
It can be concluded that if a city has already taken control over on-street parking, as Santiago has done, measures of this type for the purpose of reducing congestion would have to be harsh enough to discourage people from commuting to work by car, which is represented by the scenarios of maximum prohibition.
In keeping with the results of ESTRAUS, resource savings are low when on-street parking is banned, and the most restrictive scenarios appear the most attractive.
As parking becomes more restricted, the benefits to travellers increase commensurately.
The travellers who benefit the most are those in the lower-middle income group who do not have cars, and students in the middle-income strata.
Those who suffer the most losses are upper-middle income travellers who own two or more cars.
Road pricing consists of charging a fee during periods of peak traffic for travelling on specific streets or in specific areas where congestion is prevalent.
In the Santiago study, three concentric areas were identified for the application of this measure (see maps V.4 and V.5), and different tolls were indicated for each one (see table V.4).
The city centre and the area surrounding Avenida Providencia are where most travellers are headed. Avenida A. Vespucio is a beltway that encompasses most of the populated area of the city.
The tolls, equivalent to US $1.09, US$2.17, and US $4.35, respectively, were selected according to what was considered acceptable.
Tolls of less than 500 pesos would be too small to induce motorists to make significant behavioural changes.
Road pricing has been debated in the National Congress of Chile since 1991, with various formulas under consideration.
Although the Chamber of Deputies passed legislation, the matter has been tabled in the Senate, and passage seems a remote possibility.
The reasons for the resistance are analysed in chapter IV.
It should be mentioned, first of all, that road pricing in the area of Downtown plus Providencia yields inauspicious results.
The speed of buses and their attraction of passengers increases, but car circulation worsens.
The total savings in resources is modest.
The most noteworthy aspect is that as tolls rise, the already limited effectiveness of the measure declines until it even becomes negative.
This is because the area subject to pricing, though the destination of a large number of trips, comprises a small proportion of the total area of the city.
As a result, those not going downtown have an incentive to circumvent that area to get to their destinations, which lengthens the distance and time of many trips and cancels out many of the advantages yielded by buses.
Furthermore, with a higher toll, congestion is shifted to other streets.
Therefore, implementing road pricing in the city centre alone does not appear advisable.
The results are better in the other scenarios, with an increasing tendency to expand the area and raise tolls.
Buses could enhance their share of trips by 1% to 2%, while the opposite would occur, though in greater proportions, for car and taxi trips.
An interesting increase in average speeds for both private and public transportation is seen, in some cases by more than two km/h.
Travel times fall in all cases, especially for buses.
This is reflected in less total time spent travelling during rush hour, although in some scenarios there are sharp increases in the total distance travelled.
The effect on the number of congested stretches of roadway is variable.
The best results, logically, occur in the scenario with the highest toll (all of Santiago at 2,000 pesos), in which 55 stretches cease to be congested.
In general, the benefits calculated in terms of resource savings rise along with the amount of the toll charged, to as high as US $41 million per year in the most restrictive case.
Benefits calculated under the traveller benefits methodology, in contrast, are negative in every case, and the results grow even worse as the toll rises.
This is because when the toll is implemented, the difference between what drivers are willing to pay and the actual cost they must pay diminishes; in fact, a certain number switch to a less preferred mode, such as the bus, if the cost of driving their cars exceeds what they find acceptable.
The analysis by socioeconomic level indicates that all strata not owning cars are benefited because of the enhanced flow of bus traffic.
The opposite happens to car owners.
The group losing the most is the upper-middle income segment who own two or more cars.
All of the scenarios involve collecting revenues from tolls, which in the maximum case amount to more than US $90 million gross, assuming 250 business days per year.
The aforementioned segregated bus lanes are combined with parking prohibitions during rush hour in the Providencia strip and downtown, on the street in areas with and without parking metres.
Although an exhaustive comparison cannot be made because the evaluations were done separately, the results are not very different from those obtained from segregated bus lanes.
That was to be expected, since there is little to gain from prohibiting on-street parking in an area where there are few parking places anyway.
The changes in modal distribution are minimal, though in the case of buses they are greater, with an additional 0.5% of travellers attracted.
Car and bus travel times diminish, with the improvements for the latter being much greater at more than four minutes.
This makes major inroads into the time spent travelling during rush hour.
Distances travelled do not change substantially.
The measure reduces by 19 the number of congested stretches of motorway.
The savings in resources amount to more than US $13 million a year, a modest sum.
Unlike road pricing, this scenario yields positive results when evaluated according to traveller benefits.
The resulting figure is nearly US $93 million, primarily due to the significant time savings by bus passengers, on the order of 42,000 hours for each peak hour.
All socioeconomic strata improve their situation under this measure.
The fleet of buses could be reduced by about 570 units with the same level of service.
Segregated bus lanes are combined with a toll of 2,000 pesos for entering the area defined by the A.Vespucio Beltway.
This combination of measures is seen to be very effective with respect to modal distribution, as the number of those driving private cars falls by more than 2% and the number taking the bus rises by the same amount.
Major time savings (3.6 minutes) and increases in speed (3.3 km/h) for cars are realized.
The gains for public transit are much more pronounced, with trip times falling by more than five minutes and speeds rising by more than four km/h.
Nearly 70,000 hours are saved during rush hour, which translates into a decrease of 10% in total travel time.
Most is saved by bus riders (more than 40,000 hours).
In turn, congested stretches are cut by 70, that is, by half.
As with the road pricing scenarios, contrasting results are seen between the traveller benefit and resource savings evaluations.
Benefits calculated in terms of resource savings are positive, totalling nearly US $46 million per year.
On the other hand, the evaluation of benefits to travellers shows losses of nearly US $285 million per year.
The greatest decline in benefits is experienced by high-income travellers who own two or more cars, while the lowest-income strata who do not own cars receive positive results.
Higher bus speeds allow for a fleet reduction of 900 units, approximately 9%.
Charging a toll would yield more than US $30 million in gross revenues per year.
Santiago is a city that has not started from scratch when it comes to controlling congestion.
Consequently, the results achieved by the modelling should be analysed based on existing conditions, which are mentioned in section A.2 of this chapter.
Centralized traffic light control, bus-only lanes and segregated bus lanes on two streets, expanding the subway system, substantial improvements in horizontal and vertical signposting, redesigning numerous intersections, local rationalization of routes, and a certain level of on-street parking prohibition had all made major contributions.
It is difficult to quantify the contribution of the measures adopted previously, but it is clear that they have kept traffic speeds from declining significantly.
Centralized traffic light control may have played a great role, as evidenced by the traffic chaos and heavy congestion in many parts of the city that occurred one day when a criminal act took the computers off line and the traffic lights reverted to basic individual programming or were deactivated.
From the standpoint of saving resources, the most plausible results are derived fom segregated bus lanes and some road pricing scenarios.
When traveller benefits are considered, segregated bus lanes clearly stand out, while all road pricing schemes yield negative results.
Prohibiting off-street parking, whether paid or free, during rush hour in the city centre also appears to be an interesting prospect.
Nonetheless, it may be advisable to leave this measure for later, when the battle against congestion needs a new impetus, perhaps by offering incentives not to commute to work by car, as long as there is a good public transportation system.
At any rate, care would have to be taken to prevent this measure from driving businesses out of the city centre.
Prohibiting on-street parking more than is currently done, introducing executive buses, and road pricing downtown have insignificant and, in two cases, even counterproductive results.
In terms of self-sustainability or financing, road pricing measures would generate sufficient revenues to offset the costs involved in control systems, and might even yield surpluses.
One possible exception is road pricing in the downtown area alone, which would not produce major revenues.
In contrast, segregated lanes require someone to underwrite the cost of establishing them, which may be high if expropriation is required, and also maintaining them.
An executive bus system would apparently pay for itself.
From the above considerations, it can be deduced that the most appropriate measure, the one that would lead to a major reduction of congestion in Santiago as it stands today, appears to be the introduction of segregated bus lanes.
Little value value would be added by imposing additional on-street parking prohibitions; the latter aspect could be handled by making an ongoing assessment of thoroughfares that should continue providing on-street parking.
Although implementing road pricing alone or combined with segregated lanes could reduce the number of congested stretches of motorway, it cannot be ignored that all evaluations are negative when traveller benefits are taken into account.
In addition, there is strong resistance to tolls, and it is unlikely that legislation making it feasible will pass.
Besides establishing segregated lanes, introducing medium-sized executive buses might be a complementary measure, provided that it is selfsustaining.
Any set of conclusions should be considered in the context in which they were reached, so it would be risky to give advice to other cities based on the studies carried out in Santiago.
In particular, this metropolis has made progress in managing congestion, and it is under those circumstances that segregated bus lanes appear to be the most advisable alternative.
Nonetheless, Santiago’s experience highlights some measures that are worthy of consideration in other cities, though always with the proper studies and adaptations to local conditions.
Apparently, the most positive effects could be gained from a centralized traffic light control system coupled with segregated lanes for public transit, which could even lead to the reorganization of routes into trunk and feeder lines.
The rationalization of on-street parking, improved signposting and improvements in the design of intersections and the width of streets should not be ruled out and in fact should be an ongoing effort.
Vehicular congestion and atmospheric pollution are two major problems that plague modern cities, especially in developing countries.
The two have causes in common.
Congestion is produced by the operation of motor vehicles on streets and avenues of limited capacity.
Pollution is produced because contaminant emissions, of which vehicles account for a large percentage, exceed the absorption and dilution capacity of the basin in which the city is located.
Therefore, it is reasonable to expect that transport policies and measures designed to reduce congestion in a city will also have an effect on air pollution.
This chapter describes the influence of vehicular congestion control measures on emissions of atmospheric contaminants.
In addition, based on measures to ease congestion that were studied for the city of Santiago, Chile, the impact of pollutants on the population's health is modelled, and finally, the social benefits of reducing benefits are compared with those derived from cutting atmospheric contaminants.
Atmospheric contaminants can be classified into two large groups: i) those that have local and regional effects and ii) those that have global or planetary effects.
The main atmospheric contaminants are particulate matter, sulphur dioxide, carbon monoxide, ozone, nitrogenous oxides and volatile organic compounds.
In addition, many heavy metals are present in particulate matter in the atmosphere.
The term "primary contaminants" refers to those introduced directly into the atmosphere by the phenomena that produce them; "secondary contaminants" are those that form in the atmosphere because of the presence of primary contaminants.
The particulate matter (PM) in the atmosphere is a complex mixture of organic and inorganic substances, ranging from sea salt and soil particles to soot particles produced by burning fossil fuels.
Particulate matter from combustion can be emitted directly, in the form of elemental and organic carbon, or it can form in the atmosphere from other contaminants.
It can also be emitted when street dust is resuspended.
The total particulate matter present in the atmosphere is called total particles in suspension (TPS).
Finer particles are labelled according to their size; for example, PM10 encompasses all particles with a diametre of less than 10 microns,1 and PM2.5 includes all those smaller than 2.5 microns.
Sulphur dioxide or sulphurous anhydride (SO2) is a colourless gas that occurs because of the presence of sulphur in fuel, primarily diesel.
It later oxidates in the atmosphere and produces sulphates, which are part of the particulate matter.
SO2 in the presence of particulate matter forms a lethal mixture; this mixture has been responsible for episodes such as that of 1952 in London, when thousands of people died.
Carbon monoxide (CO) is an odourless, colourless gas that comes about because of incomplete combustion.
CO prevents the blood from transporting oxygen, and in high concentrations it causes death.
Ozone, an oxidant, is the main atmospheric contaminant constituting what is known as photochemical smog, which is produced by chemical reactions in the atmosphere in the presence of ultraviolet radiation.
The aerosols that form as part of the photochemical process result in reduced visibility, giving the atmosphere a reddish brown appearance.
The most important nitrogenous oxides are nitric oxide (NO) and nitrogen dioxide (NO2).
NO2 absorbs light in the visible range, which lessens visibility.
NO2 is part of the chain reaction that leads to the formation of photochemical smog.
And finally, many heavy metals can be found in the atmosphere.
Lead is perhaps the most common of them because of its use as an additive in regular gasolines.
Atmospheric contaminants can have various effects.
The main ones are an impact on public health, harm to vegetation and ecosystems, damage to materials and reduced visibility.
Because of its greater importance, human health is the focus of this analysis, although the other effects should not be ignored.
There is no longer any doubt that air pollution has harmful effects on public health.
The terrible episodes of the middle of the last century in London (England), Donora, Pennsylvania (United States) and the Meuse Valley (Belgium) left no room for doubt that high levels of pollution can have deleterious effects, including increased mortality in the exposed population.
Numerous epidemiological studies conducted in the last 30 years have shown that current levels of pollution also have adverse effects.
The Environmental Protection Agency in the United States (USEPA) and the World Health Organization (WHO) are constantly carrying out analyses and studies to quantify the health damage caused by air pollution.
Contaminants produce a great variety of health effects; the principal ones are shown in table VI.1, which sums up current knowledge.
An excellent review of the state of the art with respect to the impacts of atmospheric contaminants on health can be found in Holgate et al (1999).
The main global contaminants are the so-called greenhouse gases (GHG).
The most important ones are carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O) and tropospheric ozone (O3).
These gases trap infrared radiation reemitted to space by the earth, so an increase in their concentration causes the atmosphere to heat up (PICC, 2001).
The result is an increase in extreme weather phenomena and other climatic consequences.
These gases have a long life and are distributed throughout the atmosphere, so their effect does not depend on the place where they are emitted.
Motor vehicles are one of the principal sources of atmospheric contaminants in large cities.
Motor vehicles that operate on internal combustion engines produce three types of contaminant emissions, in general: exhaust pipe emissions, evaporative emissions, and dust raised from the street (see table VI.2).
Emissions from the exhaust pipe are the product of fuel combustion (whether the fuel is gasoline, diesel, or another petroleum derivative).
Because the combustion is not perfect, a number of contaminants are produced, such as carbon monoxide and nitrogenous oxides.
In addition, certain contaminants present in the fuel, such as lead and sulphur, are released into the atmosphere in the combustion process.
Exhaust pipe emissions depend on the characteristics of the vehicle, its technology and the engine size; for example, heavy vehicles tend to have higher unitary emissions (emissions per kilometre travelled) than light vehicles.
Emissions also depend on the presence of emission-reducing features such as catalytic converters; on the vehicle's maintenance status; on operational factors, such as speed of circulation and level of acceleration; and the characteristics of the fuel, such as sulphur content.
Evaporative emissions occur as the fuel evaporates into the atmosphere, and are therefore hydrocarbons (HC).
Their amount depends on the characteristics of the vehicle, operational factors such as the number and frequency of stops, geographic and meteorological factors such as altitude and environmental temperature, and most importantly, the steam pressure of the fuel.
Emissions caused by raising street dust depend on the weight of the vehicle and its circulation speed, and on the characteristics of the street, such as the average flow of vehicles on it.
They also depend, of course, on the amount of solid matter deposited on the streets that is likely to be raised by vehicles.
Unlike the particulate matter emitted by diesel vehicles' exhaust pipes, that raised by the circulation of vehicles is primarily inert matter from the earth's crust (dust), which can also contain other contaminants that have been deposited after being emitted into the atmosphere.
The composition of this particulate matter will thus depend on what city or place it is located in.
It is also necessary to emphasize that primary contaminants emitted into the atmosphere can react there, forming so-called secondary contaminants.
The most important of these are secondary particulate matter (which is part of fine particulate matter or PM2.5) and ozone.
In Santiago, it is estimated that more than 60% of the fine particulate matter is secondary matter (CONAMA, 2001a).
Vehicles generate a major part of the emissions in a city, the exact amount depending on multiple local factors.
By way of example, figure VI.1 depicts the situation in Santiago in 2000, and shows that mobile sources account for a significant percentage of primary contaminant emissions.
Furthermore, it is estimated that in 2000 mobile sources were responsible for approximately 48% of the fine particulate matter present in the atmosphere, of which buses were estimated to have contributed 21%, trucks 12% and light vehicles 15% (CONAMA, 2001a).
The emission factor, or the amount of contaminants emitted per kilometre by each type of vehicle, depends on the vehicle's technology, and it varies according to the speed of circulation (figures VI.2 and VI.3).
The emission factor also depends on the vehicles' acceleration, which in turn is related to the speed and the driving cycle.
The vehicle's technology is the way it treats emissions, which determines the amount of them.
In this context, the Euro standards established by the European Union are important.
Euro I prevailed in Europe until 1996, and compliance with it generally required a three-way catalytic converter.
That year it was replaced by Euro II, which in turn was replaced in 2000 by the more stringent Euro III, now in effect.
In the United States, heavy vehicles were required to meet the EPA91, EPA94 and EPA98 standards, similar to Euro I, II and III, respectively.
There is no emission control system for conventional vehicles.
It can be seen that in the case of a vehicle complying with the Euro I standard, as it increases speed emissions decline because the vehicle is operating more efficiently.
However, after a certain speed, which for nitrogenous oxide is approximately 50 km/h, emissions begin to increase because it takes more energy to move the vehicle at higher speeds.
For a conventional vehicle, emissions always increase with speed.
In this manner, if a measure to reduce congestion translates into a rise in operating speed, there is a decline in the emissions of catalytic converter vehicles because of the speed effect, as long as the speed is below the point at which emissions begin to climb.
This condition almost always occurs in an urban area.
For example, the average speed of private vehicles circulating in Santiago is 26 km/h during morning rush hour.
Depending on the composition of technologies of the vehicle fleet and average circulation speeds, a measure reducing congestion will either lower or raise nitrogenous oxide emissions.
The unitary emissions resulting from the speed of circulation varies according to the contaminant and the technology.
In general, for all contaminants of interest, emissions fall as the speed of travel rises (see figure VI.3).
Elasticity is the percentage change in emissions when circulation speed varies by 1%.
The negative values indicate that emissions decrease as speed increases, and vice-versa.
It can be seen that for carbon monoxide, up to a certain speed emissions decrease as speed increases.
This situation reverses at 70 or 80 km/h, depending on whether the vehicle is conventional or Euro I.
It is therefore unlikely that a measure reducing congestion could increase emissions.
Nevertheless, for nitrogenous oxides the situation is different, as noted above and as corroborated in this figure.
The second effect of a measure aimed at reducing congestion is a variation in the total distance travelled by each mode of transportation, due to the fact that some travellers change the mode they use.
In this case, whether total emissions rise or fall will depend largely on the type of measure.
If the distance travelled increases for the less polluting modes of transportation, the measure will lower total emissions, and vice-versa.
As shown in chapters III and IV, the measures designed to ease congestion can be classified according to whether they act on supply or demand.
In general, measures that modify supply by favouring the use of cars, such as redesigning, widening or building new roads and express lanes, can lead to an increase in the distance travelled by that means.
This would lead to higher emissions from that source, and possibly higher total emissions.
Conversely, measures that reduce the demand for car travel, such as road pricing in the short term, may result in smaller distances travelled by that means, and thus a decline in total emissions.
Changing the speed of circulation and the total distance travelled for each mode of transportation also has an impact on the total consumption of each type of fuel.
Emissions of some contaminants, such as CO2, sulphur and lead, depend directly on the consumption of fuels containing them, so their emission levels change proportionally.
Chapter V reported on the study that examined several measures designed to reduce congestion in Santiago using the ESTRAUS and VERDI Models.
For some of these measures, the effect of their implementation on the emission of contaminants was analysed, and the results of that analysis are presented here.
The MODEM Model is a computer program commissioned by the Ministry of Planning and Cooperation (MIDEPLAN) that makes it possible to calculate emissions of atmospheric contaminants by mobile sources.
Using as input the traffic volume and speed data supplied by the ESTRAUS Model, this model calculates the contaminant emissions generated by each mode of transportation.
More details on the MODEM Model can be found in University of Chile (2000).
The contaminants considered by MODEM are carbon monoxide (CO), total hydrocarbons (THC), nitrogenous oxides (NOx), ammonia (NH3) and breathable particulate matter (PM).
Estimates of the greenhouse gases nitrous oxide (N2O) and methane (CH4) are also included.
Carbon dioxide (CO2) emissions were estimated on the basis of fuel consumption, using the emission factors proposed by COPERT (EEA, 2000).
Sulphur dioxide (SO2) emissions were determined by simply analysing the sulphur content of the diesel fuel consumed in Santiago.
At present, the content is 300 parts per million, which is low, considering that in some cities in Latin America it can be as high as 1,000 or more parts per million.
Lead emissions were not considered because no leaded gasoline has been sold in Santiago since 2000.
The change in emissions of resuspended street dust was not calculated.
In any case, as shown in table VI.
8, the impact of this dust on the population's well-being is considerably smaller than that of emissions of other contaminants.
In the analysis, vehicles are divided into 18 categories, according to their type and emission standard.
The table below shows the types of vehicles considered.
In other words, the same types of vehicles are considered as with the ESTRAUS Model, but differentiated by emission technology.
Public transit buses have a capacity for approximately 35 to 40 seated passengers.
Three technologies, the EPA91 (or Euro I) and EPA94 (or Euro II) standards, plus buses without any emission controls, are considered.
In Chile, the Euro III standard has been in effect for new buses since September 2002, but the existing fleet of buses is made up of vehicles with other technologies.
No distinction is made for technology in the case of trucks and intercity buses.
Commercial vehicles are medium-sized vehicles such as pickup trucks and vans, with a weight of no more than 3.5 tons.
No distinction is made for emission reduction technology in the case of diesel vehicles because of the small number of them; this situation is changing in Chile, however, because of the steady rise in the price of fuel.
Although there is a substantial fleet of conventional cars, all those sold in the Metropolitan Region of Santiago since 1992 must meet the Tier 0 emission standard (the U.S. standard), which in practice requires the use of a three-way catalytic converter (as is the case for Euro I).
Beginning in 2003, light vehicles must meet the Tier 1 standard, which is even stricter, although as of September 2001 more than 90% of new catalytic-converter vehicles met that standard.
The relative composition of vehicle types has changed over time.
For example, as was mentioned previously, beginning in September 2002 buses in Santiago must meet the EPA98 standard (Euro III), so this category should be incorporated into the model along with the Tier I light vehicles.
The MODEM Model calculates emissions produced on every stretch of the transportation network, taking into account all categories of vehicles circulating there.
Total emissions of a given contaminant are obtained by adding up those produced on all segments of motorway, using hourly, daily and yearly periods.
The calculations cover all of Greater Santiago, although the model can yield results differentiated by zones within the city.
Other results are also provided, such as consumption levels of different fuels and the density of traffic flows.
These can be compared with those from other sources, such as regional fuel consumption statistics, to check the results.
The emission factors used by the model have been developed on the basis of the European emission factors proposed in COPERT II and III (EEA, 2000) and Chilean experiments with light vehicles at the Center for Vehicle Control and Certification of the Chilean Ministry of Transport.
Emission factors take into account the speed of travel for each arc with a correction for acceleration, which depends on the speed of travel.
The results of the ESTRAUS Model for the four measures enumerated above are discussed in chapter V.
They were used by the MODEM Model to calculate the changes that would occur in contaminant emissions if these measures were implemented.
The results obtained are presented below.
With a few exceptions, the road pricing measures have positive effects on emissions.
Road pricing makes it more expensive to travel by car, which prompts drivers to switch to other modes such as the bus.
As one might expect, the most significant 4 and VI.6).reductions in emissions are obtained when the highest tolls are applied to the largest geographical areas (see tables VI.
As the scope grows larger (SE+AV2000), the proportion of private vehicles (cars and taxis) drops by nearly 2.2%; a notable improvement in bus travel times is also seen, with an average reduction of 5.5 minutes.
This translates into major decreases in the consumption of time and in traffic on the network, which definitely reduces emissions of all contaminants.
This measure would bring about a reduction of 18% in CO emissions, mainly due to the lighter traffic in cars, which would give off 14,827 tons less per year.
Another important result is the 10% cut in NOx emissions stemming from major declines in truck traffic (1,129 tons less per year) and car traffic (924 tons less per year).
This measure causes an increase in total emissions, which is attributable to the longer travel times of nearly all modes as the new buses enter the flow of traffic.
The exception is regular buses, but their lower emissions do not offset the other modes' higher emissions.
The greatest percentage increase is for CO, at 2.2%, mainly due to the higher CO emissions of cars, taxis and commercial vehicles.
The truck sector produces the most significant increase in NOx emissions (175 tons/year) and PM (16.9 tons/year).
Table VI.6 shows the results of emission changes in percentages of the base case.
It is worth mentioning that the results presented here do not reflect the long-term effects, such as changing land use as a result of road pricing measures, as commented earlier (chapter IV, section F).
It can be observed that the most restrictive road pricing (C1000) also causes a rise in global emissions.
It is possible to assign a value to the health effects resulting from changes in the level of pollution caused by the adoption of measures, including those oriented towards reducing congestion.
The methodology described below was followed in establishing the value for the city of Santiago, Chile, as part of the studies carried out to evaluate the Prevention and Decontamination Plan of the Santiago Metropolitan Region (CONAMA, 2001b).
The change in the concentration of contaminants can be calculated as a function of emission changes using atmospheric models.
Another option is to use simplified roll-back models, which assume there is a proportional relationship between atmospheric concentrations in a basin and the contaminant emissions that produce them.
A base level not dependent on emissions in the basin can also be assumed.
In the case of Santiago, approximate roll-back models have been used, considering all primary contaminants that help make up the secondary particulate matter, namely, sulphur dioxide, ammonia, nitrogenous oxides, primary particulate matter and resuspended street dust.
In the case of ozone, only nitrogenous oxides, which determine the concentrations of that contaminant in Santiago, are considered as precursors.
The so-called concentration-response functions correlate changes in atmospheric concentrations of contaminants with the effects they have on the population's health.
These functions are obtained from epidemiological studies that measure the effects on an exposed population and compare them with the various effects the same population has experienced over time, or with the effects on other populations exposed to different levels of contamination.
In recent years a great number of these studies have been carried out, making it possible to establish quantitative relationships for many of the effects shown in table VI.1.
Although most of the studies were done in the United States or Europe, a growing number of them are taking place in developing countries.
The effects in Santiago were estimated on the basis of concentrationresponse functions developed in this city for premature mortality and children's visits to emergency rooms.
Other functions were taken from the specialized literature, especially the evaluation done by the USEPA (EPA, 1999).
The effects considered are premature mortality, hospital admissions, visits to the emergency room, incidence of chronic and acute bronchitis cases, incidents of asthma attacks, missed work and days of restricted activity.
Determining the values for each of the effects is the subject of specific studies.
The first two values can be calculated by quantifying the direct costs of the effects, for example, by analysing the medical costs of hospitalization for a certain illness and multiplying the length of hospitalization and the period of convalescence by the daily wages of the person involved.
The value of lost well-being or usefulness can be calculated by means of studies that measure individuals' willingness to pay to lessen the impact of the adverse effects.
The most important of these effects is the risk of death.
Numerous studies have been carried out to estimate the willingness to pay to reduce this risk.
The USEPA analysis (EPA, 1999) covers 26 studies conducted in the United States.
For the Santiago estimate, U.S. values were applied to Chile after adjusting for the respective per capita incomes, and were combined with the results of a contingent assessment study carried out in Santiago (see table VI.7).
Combining the three previous stages, the next step is to calculate the benefit or cost associated with reducing or increasing, respectively, the emission of each of the contaminants of interest by one ton (see table VI.8).
It is reasonable to determine a single value for each contaminant, since the changes occur at a level at which most of the functions are linear.
It is evident that the contaminant with the greatest value per ton of reduction is particulate matter, followed by sulphur dioxide and ammonia.
Of the total value, treatment costs represent about 2%, lost productivity 21%, and the rest –the largest proportion– corresponds to lost well-being.
That, in turn, is dominated by the population's willingness to pay to reduce its risk of death.
It should be emphasized that these are average values.
Although developing countries have not made any commitment to reduce greenhouse gases, they have an option to assign a value to them on the basis of the "Clean Development Mechanism" included in the Kyoto Protocol.
This allows for the sale of "carbon bonds" to countries that are under an obligation to reduce these gases.
The market is not fully developed yet, but institutions such as the Prototype Carbon Fund (PCF), created by the World Bank, have purchased carbon reductions from emission-cutting projects.
For example, in the Chacabuquito Project2  in Chile, the PCF recently bought reductions at a price of US $3.50 for the reduction of each ton of CO2 equivalent.
In the future, as the demand for CO2 reductions increases, this price should rise.
Based on these considerations, reductions of greenhouse gases were valued at US $3.50 and $10.00 per ton of CO2 equivalent.
The last step is to determine the change in social well-being due to the variation in the impact on health, multiplying the changes in tons reduced that each measure brings about by the corresponding monetary values (see table VI.9).
If the health costs diminish, there is a benefit, and vice-versa.
The table also shows the positive or negative effects of the same measures on the transport system as such, for comparative purposes.
Both types of benefit have been divided into direct and total.
In the case of transport, the benefits were calculated using the VERDI Model (see chapter V, section B).
Direct benefits are those corresponding to resource savings (savings in operating costs and time); total benefits are calculated according to the traveller benefit method, and include changes in usefulness or surpluses enjoyed by users of the transport system in addition to the two indicated effects.
In the case of pollution, direct benefits include the benefits from treatment costs and productivity losses, while total benefits also include the willingness of Santiago residents to pay to avoid adverse health effects.
It can be observed that the road pricing measures analysed are generally positive from the standpoint of pollution, while the opposite is true of large executive buses, which are not good for reducing congestion, either (see chapter V, section C.2).
The road pricing schemes taken into consideration yield positive direct benefits to the transport system, but they become negative if benefits to travellers are included, primarily due to the loss of usefulness for those switching to another mode of transportation.
The results show that the benefits of reducing pollution represent an additional benefit beyond the direct benefits to transport, equivalent to 8% to 13% of them.
Total benefits, including the willingness to pay to avoid health problems, represent between 35% and 55% of direct benefits to the transport system (see figure VI.4).
A comparison with total transport benefits is not rational, since these are negative for the measures analysed.
Measures designed to ease congestion affect the entire transport system, which in the majority of cities throughout the world is one of the main culprits responsible for air pollution.
It is to be expected, then, that a measure aimed at reducing congestion will also have effects on emissions of atmospheric contaminants.
In this chapter, the effects of four anti-congestion measures on the emissions of the transport system in the city of Santiago have been shown.
The results indicate that the reduction in emissions depends on the type of measure.
When demand is acted upon by means of road pricing, emissions of the transport system are cut, at least in the short term.
This has major social benefits, but the transport system itself benefits even more.
The introduction of a new mode of transportation, 40-passenger executive buses, increases total emissions in the system, with a net social cost or harm.
Although this analysis has been carried out for Santiago, which is a highly polluted city and where the transport sector accounts for approximately 50% of environmental concentrations of fine particulate matter, the results can serve as a point of reference for other Latin American cities that have air pollution problems in addition to congestion problems.
The results suggest that the effects of pollution should be considered when a cost-benefit analysis of transportation measures is undertaken.
An integrated strategy for attacking these two problems can lead to more efficient solutions than the application of isolated for combating each of them separately.
Traffic congestion has been gaining ground throughout the world, whether developed or not, and all indications are that it will continue worsening, posing a real threat to the quality of urban life.
The explosive growth in the number of cars and the indiscriminate desire to use them, for reasons of convenience and status, especially in developing countries, are exerting increasingly strong pressure on the capacity of roads to accommodate them.
The situation does not, however, appear to have been perceived as a major problem by broad sectors of the population in developing countries, or even by the authorities.
In opinion surveys, congestion, when it appears at all, is low on the list of concerns.
Normally unemployment, crime, the quality of public health and education, poverty and other problems are attributed greater importance.
In undeveloped countries in particular, car owners have a strong desire to use them and are willing to endure the congestion that affects them; people who do not yet have cars hope to have one someday.
Beyond the oftrepeated statements that congestion is annoying and the authorities should be more diligent in combating it by building more roads, there is no major evidence of any despair or intolerance of it.
One of the few manifestations is the use of helicopters to travel around in S?o Paulo, Brazil, an option limited to people of very high income.
When it comes to commuting to work or school, not many people voluntarily leave their cars at home unless they encounter serious parking problems.
The car undoubtedly offers much greater personal mobility and a sense of security, which explains why it is so popular.
Preference surveys reveal that this mode of transportation is favoured far and above others.
For this reason, current levels of congestion have not managed to alter the balance each individual attains in his own life, which clearly gives the advantage to driving the car even if there are public transit alternatives along the same route on separate thoroughfares that are not congested.
From the individual’s perspective congestion is an irritating problem, but one that costs less than going on foot.
It is obvious that the car is so advantageous that travellers are willing to tolerate a certain level of congestion, though this level has not been quantified.
Thus, it is not necessary or possible to eliminate car travel altogether, but it must be kept under control.
Attempts to exert such control should not, however, entail higher costs than those imposed by congestion itself.
Measures that act on supply, which do not involve expanding the space dedicated to motorways in consolidated urban areas, are generally welcomed, since they represent increases in transport capacity and often do not impose any direct cost on travellers.
It remains to be seen how Latin Americans will react to measures for which a fee is charged, such as urban thoroughfares operated by concession, and whether investors will be able to recoup their investment.
It is taken for granted, and often explicitly stated, that the authorities should always provide more and better road infrastructure because of the taxes paid by travellers.
This argument is understandable, as the authorities do have an obligation to administer resources appropriately and pursue the common good, which includes dealing with congestion.
On the other hand, it is not logical to do so only by building more and more roads, since that does not provide an efficient, stable and environmentally sustainable solution to the problems observed.
Moreover, widespread construction of under- and overpasses and highways in urban areas can become counterproductive in the medium and long terms and aggravate congestion.
Hence the importance of considering the urban and social impact of actions on infrastructure.
It is no less important for citizens and motorists to understand this point.
Furthermore, the authorities should pay attention to other factors that also contribute to the common good, such as maintaining high standards for quality of life while pursuing urban development.
Among other things, this implies guaranteeing space for pedestrians and strollers and preserving the city’s architectural heritage.
Spending should also be prioritized appropriately, which ultimately means that endless roadway expansions are not advisable in view of the fact that in consolidated urban areas road building can be very complicated and burdensome.
It also causes a deterioration of the quality of life in the areas where the construction takes place, even if the new transport infrastructure favours more distant areas.
Urban motorways have many deficiencies today, and they must be corrected.
Considering the authorities’ obligations, it is logical for them to begin the fight against congestion with actions such as improving the design of intersections, marking streets appropriately, rationalizing signposting and correcting the timing of traffic lights.
They can also open lanes now used for parking and implement reversible-flow lanes on heavily travelled arteries during rush hour.
These measures can do a lot to relieve congestion, and they are generally low in cost, the main requirement being an understanding of the principles of traffic engineering.
Of course, building new roads and widening existing ones should not be ruled out where appropriate and feasible, in the context of harmonious urban development.
Advances in technology open up more possibilities for providing better solutions and more appropriate and reliable designs, and as a result there are new alternatives for acting on transport demand.
Major savings can be achieved by managing traffic signals from a central computer.
Although it yields great benefits, for many municipalities this system is not inexpensive; it can be implemented in stages or by sectors of the city, however, perhaps beginning by gradually replacing obsolete traffic lights with signals that support the necessary technology.
Obviously perseverance is required to achieve the goal, which is not always assured beyond the term of office of the competent authorities, although applying these measures in heavily travelled areas would yield visible benefits and win citizen support.
Another real need is to organize a public transit system that provides effective service.
Important benefits for both buses and cars can be derived from segregated lanes for mass transit.
It may be necessary also to reorganize lines into trunk and feeder lines, establish certain circulation preferences and improve the quality of buses and the business capacity of transit companies.
High-quality buses can also play a role, especially if their frequencies and hours of operation allow them to serve as a viable alternative for motorists.
A significant contribution can be made by transport systems that resemble a surface subway, organized on the basis of buses that circulate on separate, dedicated thoroughfares at regular intervals under centralized control, with passengers boarding and alighting at stations where they buy tickets in advance.
Although they are complicated to implement and certainly require an infusion of public funds to build, the excellent results achieved in the Curitiba programme, the Quito trolleybus and the Bogot? Transmilenio attest to the value of these systems.
Their cost is a fraction of that required to build a subway.
It is not certain that the aforementioned measures would attract many motorists to public transit, or to a subway, but there is hope that the proportion of daily trips on this mode of transportation can be maintained and that highquality, fast service can be offered.
This is important in developing countries, since more than half of all trips, even as many as 80% in some cities, are made on mass transit.
Properly designed and executed supply-side measures show interesting potential for coping with congestion.
All things considered, it should not be forgotten that better use of supply alone does not address the complex realities associated with congestion.
Other measures, particularly demand-side measures, must be incorporated to correct disparities in infrastructure use and achieve an acceptable balance for the community.
Measures that act on demand are intended to persuade a significant proportion of travellers to switch from cars to high occupancy modes of transportation or travel by non-motorized means during hours of peak traffic, or to change the times of their travel.
These measures are either viewed less favourably by motorists or are downright unpopular, since they run counter to people’s strong desire to travel on their own.
They tend to be supported however, by public transit users, who form the majority in most cities in the region.
It is clear that such measures do have a role to play in easing congestion.
Some measures are regulatory in nature and impose restrictions.
Others provide economic rewards or disincentives to encourage behaviours that mitigate congestion.
Both types should be considered in order to achieve a better overall result, in view of the fact that economic measures may not be totally effective and regulatory measures are vulnerable if enforcement is weak.
Important achievements can be made by rationalizing parking, since the availability and cost of parking affects accessibility by car.
Permanent or daytime parking bans on major arteries, charging fees to park on other streets, regulating paid parking on private lots, regulating the free parking provided by institutions and companies to the public or their employees, economic incentives to discourage driving to work, intermediary parking linked to public transit—all are potentially useful measures if applied in the appropriate context to an appropriate degree.
Some can also generate revenues for the municipality.
In any case, care should be taken when imposing highly restrictive requirements that drive away businesses and residents, or else certain areas of the city might experience depression.
Thus, there should be a comprehensive parking policy that is consistent with urban development and sustainability.
Staggering the starting time of various activities can ease congestion somewhat by prolonging the morning rush hour.
Restrictions on vehicle use can take a significant proportion of vehicles out of circulation.
If such measures are applied only in places and times of heavy congestion, such as downtown areas during the morning and afternoon rush hours, they can have more lasting effects than more widespread restrictions, since there is less incentive to purchase additional cars.
Another type of limitation is requiring the purchase of permits for circulation, which vary in price according to how many days a week a car can be driven.
Restrictions can also call attention to the congestion problem and encourage people to participate collectively in reducing it.
Road pricing, advocated by many academics and urban transit officials because it is an attractive idea for defraying the costs that society incurs, meets with more resistance by drivers than any other measure.
It does seem to achieve results, at least in the short term, but it has been challenged from every imaginable point of view.
It is inconvenient for travellers to have to pay to travel in congestion; there are doubts about the forms in which it is applied; it is criticized for the impact it has on areas adjacent to those subject to pricing; it is deemed inequitable for low-income travellers; there are fears about the degradation of activities in areas subject to pricing; the long-term effects on urban planning are called into question, since road pricing promotes urban sprawl unless strict land use controls are imposed; and not least, there are allegations of inconsistency with theory if this measure is applied without other related prices, such as those of green areas, being subject to marginal cost pricing.
Even some original supporters of road pricing have changed their minds and are now sceptical.
It appears to have limited possibilities for application, unless there is some city other than Singapore (which has very special conditions) manages to implement it successfully.
Maybe its time will come in developed countries first, if congestion reaches intolerable levels, if other effective measures do not appear on the horizon, and if the theoretical and practical doubts that still remain can be resolved favourably.
And finally, driver education as an ongoing effort beginning in early childhood can contribute to reduced congestion by teaching drivers to be disciplined and respectful to others, be they pedestrians or other drivers.
Pedestrians, in turn, should also be taught to follow the rules of the road and cross streets only at the appropriate times and places.
Demand-side measures should be analysed carefully to mitigate any undesired negative effects.
In particular, care should be taken to avoid the depression of certain areas of the city and damage to urban sustainability.
Resistance to demand-side measures may subside as travellers begin to view them as useful tools for combating congestion.
They may gain more acceptance in developed countries, since there is more awareness of tradition, historic preservation and the environment in those countries.
Combating congestion carries with it costs of varying magnitude.
Some must be paid by public agencies that implement the measures; others are paid by the citizenry as a whole, and in particular, those related to demand-side actions are paid by motorists.
There is no doubt that the brunt of congestion is borne by its primary creators, car drivers.
Especially if public resources must be invested, the question must be asked, to what extent should drivers be saved from something that each of them as an individual has brought about as a result of a conscious decision based on strong personal preference?
If motorists were the only ones harmed by congestion, one might conclude that they should be abandoned to their own fate, until the majority of them showed a clear willingness to cooperative actively in alleviating the situation and accepted measures that might inconvenience them in some way.
Some effects of congestion have a wider impact, however, and it is necessary to lessen the impact to protect people who do not contribute to it, and to save the city from worse problems.
The consequences of congestion include, but are not limited to, slower circulation of buses, higher bus fares, more accidents, increased fuel consumption, exacerbated environmental pollution, threats to competitiveness, impaired sustainability of urban life and damage to the quality of life.
The very nature of their jobs requires the authorities to serve the common good.
Traffic congestion in large cities is worsening so fast that the authorities urgently need to devise an appropriate approach to the adaptation of urban transport systems, including both public transit and cars, at the times and places with the heaviest congestion.
The strong adverse effects of congestion in both the immediate and long terms require multidisciplinary efforts to keep it in check, since there is no possibility of eliminating it altogether.
This raises the challenge of designing policies and measures that will help moderate and control congestion.
The problem is complex, and the most appropriate solutions will not be found easily.
All indications are that a combination of actions should be tried with respect to both transport supply and demand, in terms of rationalizing the use of the public motorways.
It should also be recognized that basing mobility primarily on car driving is not sustainable in the long run, though it is not necessary to think of prohibiting cars.
They have many applications that facilitate urban life, such as pursuing social activities, shopping, or travelling to distant locations.
Taking advantage of this product of development is not objectionable if the inherent costs are paid.
Using the car every day to commute to work or school in areas of heavy traffic, on the other hand, will inevitably generate congestion and pollution, as well as other major detriments to society.
Individual transportation has its place, but it should not be exaggerated.
Therefore, a better balance between owning and using cars should be attained.
It is worth pointing out that in the short term, measures aimed at tempering the indiscriminate use of cars are politically costly.
The authorities, who are elected for terms of a few years, tend not to take a long-range perspective in their decision-making, so sustainability has not often guided transport policy in Latin American cities.
This approach is even more difficult in cities that are subdivided into several municipalities that compete among themselves to attract investment and may encourage car driving as part of that effort.
A single metropolitan transit authority can be a good way to reverse this situation.
In short, a long-term strategic vision of the city’s development must be devised to strike a balance between mobility, growth and competitiveness, all of which are very necessary in today’s world, and the city’s sustainability and quality of life.
This is a complex task, calling for high professional and leadership qualities on the part of urban planning and transport authorities.
Keeping congestion under control is an ongoing, never-ending task.
Tools exist for this purpose, some of them more effective and some of them more readily accepted than others, but a set of measures which has the support of the local population can avoid the risk of succumbing in the face of the modern scourge of traffic congestion.
﻿ The Spring Festival is the most important festival in China.
 People eat different food.
 The rice glue balls, which are eaten on the fifteenth day of the first lunar month, or the Lantern Festival, are the last items for the spring festival.
 On the lantern festival, it is said that every household eats glutinous rice dumplings.
 People in the north and south call it differently and make it in different ways.
In north, people make it by roll.
 The fillings are made with grounded sesame, peanuts or bean paste, mixed with sugar.
 The fillings are then dipped with water and rolled in the glutinous rice flour until it is big enough.
 Most people are busy now.
 So they normally buy it instead of making it themselves.
 Street hawkers make it.
 But the rice glue balls sold by some famous long-standing stores, are warmly welcome.
 Comparatively, people in the south would like to make it by themselves.
 They first mix glutinous rice flour with water.
 Then they make the fillings with grounded chestnut, peanut, sesame, jujube paste or bean paste.
 Then they start to make it.
 In south China, the rice glue balls made by Ningbo in east China and the Lai Tang Yuan made in Chengdu in southwest, are very famous.
The rice glue balls, cooked by boiling, tastes sweet and soft.
 People who like sweetness would find it delicious.
 People eat glutinous rice dumplings as a wish for reunion.
 Taiwan Lantern Festival 2018 : 2~11 March 2018 2018 Taiwan Lantern Festival will be held at Chiayi County (near National Palace Museum south branch and HSR Chiayi Station).
The 2018 Taiwan Lantern Festival is going to present itself via water, on the land, and in the air.
 Bringing together tourism, technology, culture and art, the festival is expected to manifest Chiayi County’s renewed traditions and creativity as a ground-breaking “smart” event.
 The “water” lantern zone features water and light shows which are in par with international standards on the globally famous sea of clouds, sunrise, forest railways and cherry blossoms of Alishan, in addition to a presentation of the water landscape and architecture of the Southern Branch, National Palace Museum located right in Chiayi.
The shows will take place at fixed hours every evening during the festival period, as accompanied by dance performances by professional troupes.
 The “land” lantern zone delves into Chiayi history, highlighting traditional craft art and the works by local artists.
 New ideas are instilled into this otherwise traditional lantern display in an artistic way.
 The “air” lantern zone shows high-tech lanterns made with new technology, materials and techniques.
 These lanterns look truly different.
 Chiayi is planning to build an innovative lantern festival.
 Added with technological, artistic and cultural elements, each of the displays is unique and worth some discussions.
 Please look forward to this one-and-only event!
 This is always the biggest and the most crowded.
 The nearby Yanshui township in Tainan also hosts Yuejin Lantern Festival, this one tend to collect contemporary art style work.
 You can also witness the infamous "beehive fireworks" if you go on the right day.
 The information 2018 is yet to come.
Pingxi is famous for its sky lantern.
 Again the date for 2018 is yet to be announced.
 Yeliu near Taipei also supposedly has a good festival, with firewalking and carrying a palanquin into the sea.
 Large cities such as Taipei and Kaohsiung also host their own festival each year.
 Click here to join Taiwan Holiday's 2018 Taiwan Lantern Festival group tour that will amaze you!
 The Lantern Festival is celebrated annually on the 15th day of the first lunar month to mark the grand finale of the Chinese New Year celebrations.
 It is also the very first full moon day of the New Year, symbolizing the coming of the spring.
 People usually celebrate this festival by enjoying family dinner together, eating Yuanxiao (glutinous rice dumpling), carrying paper lanterns, and solving the riddles on the lanterns.
 The festival is celebrated with fanfare events in Taiwan, including the internationally famed Pingxi Sky Lantern Festival in New Taipei City, Bombing Lord Han Dan in Taitung, and Yanshui Beehive Rockets Festival in Tainan, to welcome the New Year in a spirit of peace, prosperity and joy.
Bombing Lord Han Dan is a special ceremony in Taitung, which a chosen man performs in the role of Master Han Dan-a god of wealth, and gets thrown by firecrackers.
 During the event, the chosen man wears nothing but a pair of red short pants, holds one bamboo fan to protect his face, stands on a sedan chair, and being carried around by four devotees.
 Firecrackers are to be thrown at the chosen one as it is believed that Lord Han Dan cannot bear the cold weather.
 The firecrackers are to keep him warm as well as to pray for wealth and prosperities.
 Pingxi Sky Lantern Festival is held every year during the Lantern Festival in Pingxi of New Taipei City.
 Sky Lanterns, also known as Kongming Lantern are flying paper lanterns traditionally found in some Asian cultures.
 It was invented by Kongming during three kingdoms period by Zhu Ke-Liang (aka Kongming) in order to pass military information.
 They are constructed from oiled rice paper on a bamboo made frame, and contain a small candle or fuel cell composed of a waxy flammable material.
 After lit, the flame heats the air inside the lantern, same concept of a hot air balloon which raises the lantern into the sky.
 People nowadays usually write their wishes on the sky lanterns because it is believed as the lantern fly into the sky; it is a way to pass on your wishes to gods above.
Yanshui Beehive Rocket Festival is a distinctive religious event scheduled on the day of the Lantern Festival in Yanshui, Tainan.
 On the day of the Lantern Festival, people would visit Yanshui in Tainan City to follow the sedan chair of the divinity and the release of thousands of firecrackers.
 Participants are required to wear a helmet, mask, towel, as well as dressed in long pants and long sleeves shirt for safety.
Other than the three major Lantern Festival celebrations of Taiwan listed above, the annual Taiwan Lantern Festival takes place at different Taiwan City every year.
 The splendid Taiwan Lantern Festival is filled with thousands traditional, cartoon and thematically designed lanterns of various shapes, sizes, and colors.
 The theme of Taiwan Lantern Festival varies each year according to the Chinese zodiac sign of 12 animals (rat, ox, tiger, rabbit, dragon, snake, horse, sheep (or goat), monkey, rooster, dog, and pig.)
TAIPEI (Taiwan News) -- While the official Lantern Festival (元宵節, Yuan Xiao Jie) falls on February 11—the first full moon and 15th day of the lunar year, marking the end of New Year celebrations—Taiwan has celebrations throughout February at different locations to allow tourists and residents an opportunity to experience the holiday festivities in different towns.
 The Lantern Festival dates back to at least the Western Han Dynasty (206 BC-AD 25), though the origin is traced to multiple legends.
 While carrying colorful lanterns on the evening of the festival is the main event, it's also a tradition to eat tangyuan (湯圓)—sweet or savory glutinous rice balls.
While the Lantern Festival can be celebrated anywhere in Taiwan, there are a few larger celebrations for tourists and residents to experience.
 When people talk about Lantern Festival in Taiwan, the first place that comes to mind is Pingxi (平溪), which will host its event from February 11 to 19 this year.
 There will be a second Sky Lantern Festival on October 4 to celebrate the Mid-Autumn Festival.
 On February 11th starting at 10 am, free lantern releasing tickets will be handed out.
 Lanterns will be released at 6 and 7 pm on the February 11.
Pingxi, just west of Taipei, is renowned for its sky lanterns (天燈), with more than 100,000 sky lanterns launched during the festival in 2013.
 The town was selected among National Geographic magazine’s “Best Winter Trips 2016.
” In previous years it has been recommended by CNN and Fodor’s.
 Legends claim that the sky lantern, so named because it rises in the air due to a small flame placed inside, was invented by Zhuge Liang during the Three Kingdoms period (AD 220-265).
 While it was originally used for military purposes, it grew in popularity.
 The sky lantern became a part of Lantern Festival celebrations in Taiwan in the early 19th century.
 Today, people write wishes for the new year on the large lanterns before releasing them.
The festival has received criticism as the sky lanterns return to earth and residents are left to clean up the debris in the rivers and fields.
 However, the town has cleanup crews on hand during the festival to assist with retrieving the lanterns.
The lanterns are available year-round for visitors who prefer to avoid the crowd during the festival, but cleanup crews are not on hand the rest of the year.
The Lantern Festival was first held in the capital city in 1990 as the national lantern festival until 2001.
 This year's Taipei Lantern Festival has selected the theme "West Side Story, Taipei Glory" and will take place from February 4 to 17.
 The festival will kick off around Beimen on Sec.  1 Zhonghua Rd.  with a lighting ceremony at 6 pm.
 The celebration will feature traditional lantern displays and a high-tech showcase of projection and illumination technology on historical sites and buildings.
 The Japanese band World Order will perform on February 4 and 5 at the Main Display Area at Ximending.
The lantern zones will span several areas around Beimen and along Zhonghua Road, and include a main display area, light sculpture area, landscape creativity area, Universiade interactive area, blessing lantern area, freestyle creativity area, student creativity area and a friendly exchange area featuring lanterns sponsored by private businesses and foreign representative offices and nations.
In its 27th year, the Yunlin Lantern Festival will be held from February 11 to 19, with the entrance to the event at the Yunlin High Speed Rail Station.
 It will be the largest festival in the municipality's history, covering 50 hectares.
 This year's celebration is also host to the Taiwan Lantern Festival, which the Tourism Bureau began in 1990 in Taipei and has rotated through counties since 2001.
This year's slogan for the event is "Auspicious Rooster Soars Among the Clouds.
" The festival theme "Lantern Festival blended into the City" will feature 19 light-themed and decorated areas to connect the Lantern Festival with Yunlin County.
 These areas will lead visitors through Yunlin (Huashan) Gukeng Coffee Park, the winter corridor with blooming red cotton trees and the wetland fishing village, among others.
According to organizers, "it is a series of activities that showcase the LOHAS spirit, hometown nostalgia, traditional arts and crafts, and prayer for happiness and safety in the new year.
 Started in 2001, Kaohsiung's Lantern Festival festivities are held along Love River.
 This year's festival began on January 30 and will last until February 12.
 The main celebration will be held on February 11.
 It features daily fireworks displays, light and dancing fountain shows, as well as musical performances.
 The fireworks displays will be at 7 and 9 pm, while the light and dancing fountain shows will be staged from 7 to 10 pm with each show lasting 30 minutes.
Though Taitung foregoes the lanterns, it still hosts the Bombing of Master Handan (炸寒單), which began in 1954 as part of its Lantern Festival celebrations.
 In Taiwanese folklore the Handan is a god of wealth and war.
 He is also afraid of the cold, so the firecrackers are set off around a man playing the part of the god, which is paraded around town by volunteers, in an effort to warm him.
 The volunteers carrying the "Handan" wear only a red headscarf, an amulet and red shorts (and safety goggles).
The celebration had been banned for years due its connection to organized crime in the area, but it is now officially recognized by the government.
The city expects this year's festival to draw its largest crowd, with a target of about 600,000 attendees, at Yuejin Port in Yanshui District (鹽水).
 The celebration will include dance performances, concerts, art and light installations, and movie screenings throughout the area.
 And there will be the famous "beehive" fireworks at Yanshui Wu Miao Temple.
The event will run all month, with the larger performances and celebrations on Feb. 11
 This year's celebration will feature 50 art installations, including an illuminated artificial bamboo forest maze titled Yuemijin (月迷津) by Taiwanese artist Yu Wen-fu.
The festival will be held at Taichung Park and Taichung City Seaport Art Center from Feb. 5 to 19, with the theme "Blooming Phoenix.
" Lanterns will be lit each night between 6 and 10 pm and the celebration will include interactive lantern displays as well as plenty of local cuisine.
Penghu retains many traditions in its Lantern Festival celebrations, instead of attracting visitors with contemporary art installations and light shows like other destinations in Taiwan.
 Residents of Penghu will partake in the qigui (乞龜) practice—praying to turtles (平安龜) at temples as they symbolize longevity and good luck.
Festivities will run throughout the month and will include a rice noodle feast on Feb. 27.
 Hualien began celebrating on Jan. 21 and will continue until Feb. 19.
 Festivities will be held along Zhongshan Rd. by Dongdamen Night Market with additional celebrations at Liyu Lake (鯉魚潭) in Shoufeng Township, which will feature a giant red-faced duck.
The main light show in Hualien will be held every half hour between 6:30 and 9:30 pm along with a water and light show every half hour from 6:45 to 9:45.
 In Shoufeng, performances will be held every half hour from 2 to 4 p.m. and 6:30 to 9 p.m.
While Ciyou Temple, dedicated to the Taoist goddess Mazu/Matsu (天上聖母), the sea goddess, is generally more popular for the first day of the Lunar New Year, it also hosts its own Lantern Festival celebration on Feb. 11.
The Lantern Festival celebration begins at 4 pm and will include musical performances.
In the recent article “5 things to know about Chinese consumers” it was pointed out that “Chinese consumers love to buy gifts.
” This is because Chinese culture is filled with festivals and gift-giving occasions, which means Chinese consumers often enjoy shopping for others more then they do for themselves.
 Here are 8 Chinese holidays retailers need to know about, and a few ideas on how to provide Chinese consumers with what they’re looking for.
Most retailers in China are already well aware of the importance of the Chinese New Year, and plan promotions around it.
 Here are the main activities that take during Chinese New Year and how retailers leverage them.
 Cleaning – During CNY people clean their houses, thoroughly.
 The tradition is actually based around avoiding cleaning on new years day, which some believe could have the negative effect of “sweeping away good fortune.
” This is the perfect opportunity for retailers to over cleaning products, supplies, instructions or marketing campaigns around focused around cleaning as a theme.
 Imagine Nike promoting a shoe designed specifically for “home-cleaning”.
Red Envelopes – I won’t explain these since you’re mostly likely familiar with the act of giving money as a gift in a red envelope during CNY and other occasions, and the red envelope has already become an common marketing image for retailers in China.
 Here are some of the most popular ways retailers use red envelopes in marketing: Red envelope gift certificates, giving out or selling premium red envelopes that customers can use during gift giving, and red envelope sweepstakes that give consumers a change to win cash or prizes.
 The lantern festival is one of my favorite Chinese festivals because seeing a sky filled with glowing lanterns is simply stunning.
 Here are the main activities that take place during Lantern Festival and how retailers can leverage them.
 Lighting lanterns – This is the main event.
 Friends, families and couples will buy large paper lanterns, write some prayers, messages, or wishes on them, light them and watch them float up and away in the sky.
 Some retailers during this time will incorporate a lantern theme into their marketing, but it’s not as prevalent as you might think.
Here are a few of my own ideas: What if retailers organized branded lantern launching parties, and gave away branded lanterns?
 Large brands could sponsor lantern shaped hot air balloons to float over the city, or offer special pens, markers, and stickers that could be used to decorate lanterns.
 What about a lantern game app?
Rice Dumplings ( yuanxiao ) – These are small, sweet, rice dumplings filled with rose petals, sesame, bean paste, or fruits that will be given as gifts and eaten during the lantern festival.
 How can retailers leverage yuanxiao?
 I’m not sure… perhaps they can get creative.
 Novelty clothing for cute little rice dumplings?
Tomb sweeping day is all about paying respect to the dead, and honoring your ancestors, to this end Tomb Sweeping Day is more of a solemn day of remembrance then a big party, and retailers should be sensitive to this.
Tomb Sweeping – Tombs of relatives are cleaned and maintained.
 Many times relatives will bring presents such as the deceased persons favorite food or drink.
 Fake money is burned as an offering to the dead.
 As cremation becomes more popular the customs are changing, with less actual cleaning taking place.
 Since this is a day of respect for the dead, retailers should be sensitive to this and plan accordingly.
Kite Flying – This custom is enjoyed by both the young and old, and is believed to bring good luck, in addition to being fun.
 Retailers have an opportunity here to use kites in their marketing themes as a playful but respectful way to show support of tomb sweeping day.
 This is one of the most important festivals in Chinese tradition, and involves the following activities:
Racing dragon boats – Boats shapes and decorated to look like dragons are raced against each other manned by teams of rowers.
 Retailers should look for ways to promote team spirit, and sports-like competition during this holiday.
 Why not sponsor a local dragon boat team, or create branded “dragon boat” team uniforms for employees?
Eating Zongzi – Zongzi are steamed rice balls wrapped in leaves.
 There are lots of different kinds of Zongzi these days ranging from salty to sweet, and although they are most popular during dragon boat festival, they are actually available year round and make a great snack or meal.
 Retailers, at least here in Taiwan, are all over Zongzi.
 Starbucks famously offers sweet Zongzi during dragon boat festival, and markets these extensively.
 Also known as Mid-Autumn Festival, Moon Festival is linked to the legends of Chang E, the mythical Moon Goddess of Immortality.
 It’s a family occasion, and is all about eating lots of good foods, especially sweets and cakes.
 Here are a few of the most popular activities during Moon Festival: 
 Giving, and eating moon-cakes – Moon-cakes are round or rectangular pastries that usually contain a rich thick filling made from red bean or lotus seed paste, or even duck egg yolks.
 Giving moon cakes to friends and relatives, clients and contacts is so popular during this time that it’s a billion dollar industry in China, and has resulted in the demand for high-end “luxury” mooncakes.
Courtship – Love is in the moon during the moon festival, and many young people in China use this occasion to celebrate marriages, pray for romance, or create a little romance on their own.
 Retailers in China can use this as an opportunity to show their support for love and romance either in their marketing campaigns or in their product mix.
 On Mid-autumn Festival, the custom of “?月 (sh?ngyu?) admiring the full moon” began with people in ancient China, and is still maintained.
 In the Zhou Dynasty, on the night of the Mid-autumn Festival, people would offer sacrifices to the moon with “月?(yu?b?ng) moon cake” and fruit that was in season such as watermelon, apples and so on.
 During the Song Dynasty, the wealthy people would admire the full moon in their pavilions with their families.
 After the Ming and Qing Dynasties, moon cake became a necessary part of admiring the full moon.
 Now we know that moon cakes were first used as a sacrifice.
 Gradually, people combined moon cakes with “?月 (sh?ngyu?) admiring the moon” to mean family “?? (tu?nyu?n) reunion.”
 What’s especially important in moon cakes is the “?儿 (xi?nr) filling.
” The traditional fillings are sweetened bean paste, sesame, sugar etc.
 Now the concept of moon cake fillings has been updated to better reflect modern times.
 The fillings can be fruits, meat and so on.
 It is for sure that you will find a style that you love to eat.
 There are also different designs on the moon cake.
 These designs are generally related to the topic of reunion.
 So on Mid-autumn Festival, people also give moon cakes to their relatives and friends with best wishes.
 Both traditional Chinese and Western ideology is reflected in Taiwanese culture and several major events and festivals are celebrated from each.
 Chinese New Year (Lunar New Year), the Dragon Boat Festival and the Mid-Autumn Festival are the three main traditional Chinese festivals celebrated in Taiwan.
 However, the Western New Year and Christmas festivities have become more popular in recent decades.
This is the most important festival in traditional Chinese culture.
 The festivities last for a month and include several days' public holiday.
 It is celebrated on the first day of the new lunar cycle and marks the end of winter and the beginning of spring.
 Families gather together on New Year's Eve to thank their ancestors and the gods for their blessing and protection in the past year.
 Parents give their children money in a hongbao, which is a small red envelope.
 Food is an important part of the festival, and families often prepare a reunion dinner with dumplings, longevity noodles and fish.
 Fireworks are very popular and can be seen and heard across the country, especially on Chuxi or New Year's Eve.
 One of the biggest events of the Lunar New Year Festival is the traditional animated dragon and lion dance parade.
 Due to the number of people travelling home to be with family at this time, roads are extremely congested and airports, train stations and bus terminals are all very busy.
 Reservations for tickets need to be made well in advance.
The Lantern Festival (Shang Yuan Festival) takes place on the fifteenth day of the Lunar New Year (the first full moon of the Chinese New Year celebrations) and it officially marks the end of the Lunar New Year celebrations.
 On the night of the festival decorative lanterns depicting traditional stories and themes are carried by children though the streets.
 The Taipei City Government holds an annual Lantern Festival each year with events, displays and competitions taking place throughout the city over a ten-day period.
 The centrepiece of the festival is the giant lantern representing the Chinese astrological animal for the New Year, and the spectacle attracts millions of visitors each year.
The small township of Pingxi in the northeast of Taipei County plays host to the Pingxi Sky Lantern Festival.
 It attracts thousands of visitors who congregate to release lanterns, with their wishes for the following year written on them, into the sky.
This festival traditionally takes place on the fifth day of the fifth lunar month, and has its origins in the story of the ancient poet Qu Yuan.
 The most important features are Dragon Boat racing (in a traditional boat rowed by a team) and the eating of zongzi, a sticky dumpling wrapped in bamboo leaves.
Also known as the Moon Festival, this takes place on the on the 15th day of the eighth month in the Chinese lunar calendar and celebrates the moon in its biggest and brightest phase.
 Mooncakes (small round cakes traditionally made of red bean and egg) are eaten at this time, and signify unity and a cycle completed.
 Barbecues have become a popular way to celebrate this festival and families gather to eat, talk, and sit up late into the night watching the moon.
 Lanterns are hung around the house and sky lanterns are released.
 Also known as "Double Tenth Day" as it is held on 10 October, this festival commemorates the start of the Wuchang Uprising in 1911 that led to the overthrow of the Qing dynasty.
 Celebrations in Taipei include a military parade in front of the Presidential Office Building, public performances and firework displays.
The Western New Year, celebrated on 1 January, is becoming more popular and is playing an increasingly important role in Taiwan.
 The main event in Taipei is the live concert in front of the Taipei City Government building and the fireworks at the Taipei 101 building on New Year's Eve.
 Christmas is not a public holiday in Taiwan, although it is celebrated.
 Hundreds of thousands of people gather in front of Taipei City Hall for the annual countdown and fireworks display on Christmas Eve, and other celebrations are held throughout the city.
The sky lantern is also called Kongming lantern, which is used for praying for safety, love, and career now.
 Tourists can write your wishes on the lantern and However, it was used for informing others that this village is safe before.
 We will release the sky lantern on the train track, which is very nostalgic.
 After releasing sky lantern in Pingxi, you can also get to the Shifen Old Street to buy the cold marble soft drink in summer.
 And if tourists want to see the sky full of lanterns and you can go to Pingxi on Lantern Festival.
 Held in May/June each year, around the summer solstice, is the Duanwu (Dragon Boat) Festival, held in many cities on water.
 It’s been running for 2000 years and there’s nothing quite like it.
 Grab some of the traditional celebratory Zongzi (sticky rice dumplings) and watch the race of colourful dragon-shaped boats.
Many countries celebrate Halloween whereas the ghost festival celebration in China is called “中元? (Zh?ngyu?nji?)” – The Hungry Ghost festival.
 万圣? (W?nsh?ngji?) Western Halloween celebrations throw costume parties where people participate in cosplay.
 The streets are filled with spectacular live performances, intricately staged displays with realistic corpses and ghosts, and screenings of horror films.
 Teaching Chinese culture and tradition is an essential part of teaching Mandarin Chinese.
 Not only are the words and phrases important, but learning about the lives of Chinese people adds a motivation to integrate and inspire my students to continue to pursue the language.
On Mid-autumn Festival, the custom of “?月 (sh?ngyu?) admiring the full moon” began with people in ancient China, and is still maintained.
 In the Zhou Dynasty, on the night of the Mid-autumn Festival, people would offer sacrifices to .
In China, people have a similar celebration of a heroic figure from ancient times on the Dragon Boat Festival.
 The festival is held on the 15th day of the 8th month of the lunar calendar with a full moon at night, corresponding to mid September to early October of the Gregorian calendar Mooncakes, a rich pastry typically filled .
On the fifth day of the fifth lunar month, or around June in our Calendar, the Chinese Dragon Boat Festival takes place.
There are many legends as to the origin of this festival.
 One states that it probably got started as a celebration for the planting of the rice crop and to pray for a good rainfall since it was believed that Dragons controlled the rain and rivers.
People would put offerings in the river so that the dragons would bring rain for their crops.
Another legend tells the story of Qu Yuan, an old man that drowns himself by jumping from a boat, because he could not stand to see his country being destroyed by the poor leaders.
When the village people went to look for him, it was too late.
 They threw offerings of rice into the water to calm the man's spirit.
One day, Qu Yuan's spirit returned and told them the rice meant for him, was being devoured by the river dragon.
 He asked that they wrap the rice in leaves, in shapes like small pyramids.
 Today, during the Dragon Boat Festival season, people eat these rice dumplings, known as zongzi in memory of the old man.
 Nowadays, what characterizes the festivities is the Dragon Boat Races.
 These are very noisy and exciting events with hundreds of teams competing against each other in this great tradition.
Chinese Dragon Boats are long, narrow boats with a dragon's head at one end and a tail at the rear.
Teams of rowers paddle together in unison as they race to the finish line!
 A leader sits in the front by the head, facing the paddlers, and sets the pace by pounding a large drum.
 You can decorate easily with traditional or battery-operated lanterns, paper cuttings, lucky coins and banners to add a splash to the celebrations!
Festivals mark the passing of time in the course of a year.
 The celebrations often require time and effort to prepare and involves a large social network of friends and relatives.
 Overtime, the festivals that continued to be observed and celebrated tells us many things about the people and society.
 Chinese festivals follow the lunar calendar and the actual date changes every year.
 Below are the dates of various festivals.
Chinese New Year is the most important event in the Chinese cultural calendar.
 Chinese New Year falls on the first 15 days of the first lunar month (usually Jan or Feb)
 Preparing for Chinese New Year Preparing for the Chinese New Year involves months of work and planning before the big day.
 Celebration starts on New Year's eve After much time and effort, the count down for New Year starts.
 The New Year eve is also effectively the start of Chinese New Year celebrations.
 Nian Gao, 年糕, New year cake A Chinese pastry used during the Chinese New Year and also as gifts to family, friends and business associates.
 Red Packets Red coloured rectangle envelopes used for holding money as gifts during auspicious occasions.
 Read about the amounts considerd ok to put in a red packet and the amounts that are not so ok.
 Fire Crackers Fire crackers, replicas of fire crackers and images of them are almost every during the Chinese new year.
 We also have a clip for you to watch in case you live in an area where it is banned.
 The Duan Wu Jie falls on the 5th day of the 5th lunar month, usually in June.
 Qu Yuan 屈原 Qu Yuan, the main character around which the dumpling festival revolves.
 Read about the historical period that Qu Yuan lived in as well as see photos of his, hometown, temple, and tomb.
 These structures are being submerged by the three gorges dam 三?construction.
 Zong Zi， 粽子 (Dumplings) The food item that is closely associated with the Dumpling Festival.
Dragon Boat ?舟 The dragon boat custom that evolved from civilian’s attempt to save Qu Yuan to an international sports event.
The Hungry Ghost Festival also known as Zhong Yuan Jie, 中元? and Ullambana, Yulan Jie, 盂?盆, falls on the 7th lunar month.
The Mid Autumn Festival falls on the 15th day of the 8th Lunar month, usually in Auguest or September.
Mid Autumn celebrations looks at the customs leading to the day itself as well as myths and legends associated with Mid Autumn.
 Moon cake and Mongols examines the myths of moon cake's role in overthrow of Mongol dynasty.
 Is it historical fact or historical fiction?
 Moon cakes explores tradition and contemporary variations of moon cakes as well as the social functions of moon cake as part of the gift system.
The Mid-Autumn Festival 中秋節 is held on the 15th day of the eighth month in the Chinese calendar, which is usually around late September or early October in the Gregorian calendar.
It is a date that parallels the autumnal equinox of the solar calendar, when the moon is supposedly at its fullest and roundest.
 The traditional food of this festival is the moon cake, of which there are many different varieties.
 The Moon Festival, we have discovered, is also a time for romance.
 Romanics feel that lovers should spend time together at this festival.
 They could spend a romantic night together tasting the delicious moon cake with some wine while watching the full moon.
 Will and Guy suggest that even if a couple can't be together, they can still enjoy the night by watching the moon at the same time so it seems that they are together at that hour.
 The period of the Moon Festival is an excuse, if needed, to retell legendary stories.
 For example we have researches the legend which tells us that *Chang Er flew to the moon, where she has lived ever since.
 Look closely and you may see her dancing on the moon during the Moon Festival.
The time of this story is around 2170 B.C.
 The earth once had ten suns circling over it, each took its turn to illuminate to the earth.
 One day all ten suns appeared together, scorching the earth with their heat.
 The earth was saved by a strong and tyrannical archer Hou Yi.
 He succeeded in shooting down nine of the suns.
 One day, Hou Yi stole the elixir of life from a goddess.
 However his beautiful wife Chang Er drank the elixir of life in order to save the people from her husband's tyrannical rule.
 After drinking it, she found herself floating and flew to the moon.
 Hou Yi loved his divinely beautiful wife so much, he didn't shoot down the moon.
 For a fuller version please look further on this page.
 The Moon Festival is also an occasion for family reunions.
 When the full moon rises, families get together to watch the full moon, eat moon cakes, and sing moon poems.
 The Chinese people love and enjoy the Moon Festival, we have learned.
 During the Yuan dynasty [AD. 1280-1368] China was ruled by the Mongolian people.
 Leaders from the preceding Sung dynasty (A.D. 960-1280) were unhappy at submitting to foreign rule, and set how to coordinate the rebellion without it being discovered.
 The leaders of the rebellion, knowing that the Moon Festival was drawing near, ordered the making of special cakes.
 Backed into each moon cake was a message with the outline of the attack.
On the night of the Moon Festival, the rebels successfully attacked and overthrew the government.
 What followed was the establishment of the Ming dynasty [A.D. 1368-1644].
 Nowadays moon cakes are eaten to commemorate this legend.
 Moon cakes are typically round, symbolizing the full round moon of the mid-autumn festival.
 The round moon cakes, measuring about three inches in diameter and one and a half inches in thickness are made with melon seeds, lotus seeds, almonds, minced meats, bean paste, orange peels and lard.
 A golden yolk from a salted duck egg was placed at the centre of each cake, and the golden brown crust was decorated with symbols of the festival.
 A tasty morsel say Will and Guy.
 There are many beautiful legends about the moon in China.
 The most popular one tells how a goddess named Chang'e ascended to the moon.
A long, long time ago, a terrible drought plagued the earth.
 Ten suns burned fiercely in the sky like smouldering volcanoes.
 The trees and grass were scorched.
 The land was cracked and parched, and rivers ran dry.
 Many people died of hunger and thirst.
 The King of Heaven sent Hou Yi down to the earth to help.
 When Hou Yi arrived, he took out his red bow and white arrows and shot down nine suns one after another.
 The weather immediately turned cooler.
 Heavy rains filled the rivers with fresh water and the grass and trees turned green.
 Life had been restored and humanity was saved.
One day, a charming young woman, Chang'e makes her way home from a stream, holding a bamboo container, A young man comes forward, asking for a drink.
 When she sees the red bow and white arrows hanging from his belt, Chang'e realises that he is their saviour, Hou Yi.
 Inviting him to drink, Chang'e plucks a beautiful flower and gives it to him as a token of respect.
 Hou Yi, in turn, selects a beautiful silver fox fur as his gift for her.
 This meeting kindles the spark of their love.
 And soon after that, they get married.
A mortal's life is limited, of course.
 So in order to enjoy his happy life with Chang'e forever, Hou Yi decides to look for an elixir of life.
 He goes to the Kunlun Mountains where the Western Queen Mother lives.
Out of respect for the good deeds the has done, the Western Queen Mother rewards Hou Yi with elixir, a fine powder made from kernels of fruit which grows on the tree of eternity.
 At the same time, she tells him.
 'If you and your wife share the elixir, you will both enjoy eternal life.
 But if only one of you takes it, that one will ascend to Heaven and become immortal.' 
Hou Yi returns home and tells his wife all that has happened and they decide to drink the elixir together on the 15th day of the eighth lunar month when the moon is full and bright.
A wicked and merciless man named Feng Meng secretly hears about their plan.
 He wishes Hou Yi an early death so that he can drink the elixir himself and become immortal.
 His opportunity finally arrives.
 One day, when the full moon is rising, Hou Yi is on his way home from hunting, Feng Meng kills him.
 The murderer then runs to Hou Yi's home and forces Chang'e to give him the elixir: without hesitating, Chang'e picks up the elixir and drinks it all.
Overcome with grief, Chang'e rushes to her dead husband's side, weeping bitterly.
 Soon the elixir begins to have its effect and Chang'e feels herself being lifted towards Heaven.
Chang'e decides to live on the moon because it is nearest to the earth.
 There she lives a simple and contented life.
 Even though she is in Heaven, her heart remains in the world of mortals.
 Never does she forget the deep love she has for Hou Yi and the love she feels for the people who have shared their sadness and happiness.
he Lantern Festival in China is very old; legend has it that there are many wonderful stories about how the Lantern Festival first began.
 One story is that in ancient times, people would go in search of spirits with burning sticks.
 They thought the spirits could be seen during a full moon.
Another is about a lonely young girl, in Han times, who tricked an emperor into having a wonderful festival just so she could visit her family.
 The emperor apparently had such an excellent time, he decided to make this festival an annual event.
 According to one legend, from ancient times, a celestial swan came into the mortal world where it was shot down by a hunter.
 The Jade Emperor, the highest god in Heaven, vowed to avenge the swan.
 He started making plans to send a troop of celestial soldiers and generals to Earth on the fifteenth day of the first lunar month, with orders to incinerate all humans and animals.
 However, the other celestial beings disagreed with this course of action, and risked their lives to warn the people of Earth.
 As a result, before and after the fifteenth day of the first month, every family hung red lanterns outside their doors and set off firecrackers and fireworks, giving the impression that their homes were already burning.
 By successfully tricking the Jade Emperor in this way, humanity was saved from extermination.
By T'ang times, many families simply set aside one evening, during the first full moon after the new year, to honour the moon.
 They would sit outside, and gaze up, in awe and delight.
Footnote Please write to Will and Guy if you have any pictures of the Chinese Moon Festival - Zhongqiujie.
Chinese New Year, otherwise called the "Spring Festival" in present day Mainland China, will be China's most critical customary celebration, celebrated at the turn of the conventional lunisolar Chinese timetable, which comprises of both Gregorian and lunar-sun based date-book frameworks.
 Chinese New Year can start whenever between late January and mid-February.
China's Spring Festival open occasion begins on the Chinese New Year, and goes on for 7 days.
 Chinese New Year is likewise called "Spring Festival" and "Lunar New Year" since it comes in the springtime and is dated in light of the Chinese lunar schedule.
 The date varies, from a Western point of view, yet comes in either January or February.
Every Chinese New Year is assigned as "the time of" one of the 12 creatures of the Chinese Zodiac, which creature should portray that year and each one of those conceived in it.
Chinese New Year is the most imperative every year repeating celebration for individuals of Chinese parentage everywhere throughout the world.
 It has been praised for more than 1,000 years – conceivably any longer, and the customs included are profoundly instilled in Chinese culture.
 For some, it is likewise a religious occasion, brimming with petitions, offerings, and different demonstrations of commitment.
Chinese New Year is really celebrated for 15 sequential days, however the initial three days are generally vital.
 The fifteenth and last day, Chap Goh Mei is additionally a major occasion, where houses are finished with a plenitude of brilliantly shaded lights.
 It is a method for completion with a terrific finale as opposed to the celebrations simply blurring ceaselessly progressively.
 On the eve before the primary day of the new year, family-just meals and reunions are held.
 On the consequent days, be that as it may, many will welcome companions and inside and out outsiders to come feast with them.
 This "open house" approach is additionally worked on amid other Malaysian occasions and everywhere open Chinese New Year social events put on at Malaysian people group corridors.
 It ought not be difficult to get welcomed to a gathering.
Other Chinese New Year conventions include: "Yee Sang," a vegetable mixture dish eaten by tossing its pieces high noticeable all around with chopsticks to bring good fortunes; hanging up "duilian," scrolls bearing well known lines frame Chinese verse; going to lion and mythical serpent moves; wagering on card diversions, inasmuch as the wagers are unassuming; giving out blessings of cash in little red ang-pao bundles; and going to firecrackers shows, the greatest of which are in Chinese locale of Kuala Lumpur and in urban areas with vast Chinese populaces.
 The Chinese Reunion supper is a standout amongst the most vital components amid Chinese New Year.
 The get-together supper which is hung on the eve of New Year is where families assemble over an indulgent feast with bunches of clamor and giggling.
 Nourishment assumes an essential part for the Chinese and additionally generally Malaysians.
 Subsequently, amid the gathering supper, one will have the capacity to see a wide range of dishes on the table including the celebrated Yee Sang, panfry leeks, stick cakes and others.
Chinese New Year is the first day of the New Year in the Chinese lunisolar calendar (Chinese traditional calendar).
 It is also known as the Lunar New Year or the Spring Festival.
 The first day of the festival begins on the New Moon sometime each year between January 21st and February 20th.
 The holiday/festival lasts 16 days from New Year’s Eve to the 15th day of the New Year which also happens to be the Lantern Festival.
The lunisolar calendar uses the location of the sun and the moon relative to the earth to determine dates on the calendar.
 The Gregorian calendar, which is the most widely used calendar in the world today uses the location of the sun relative to the earth to determine the dates on the calendar.
Chinese New Year originated from legends and traditions.
 The most common story is based on a mythical beast named Nian, or “Year” in Chinese.
 The story goes that Nian would appear on the first day of the New Year.
 Fearing that this creature would devour crops, livestock, and villagers, even children, the people placed food in front of their doors to satiate the beast’s appetite.
 It was also believed that Nian feared the color red, fire, and loud noises.
 To this day, red lanterns, spring couplets in black characters on red paper, and firecrackers are ubiquitous on the island during this time of year.
 On the second day, after learning that Nian had been kept away, people would greet each other with the ever-present greeting gong xi or “congratulations.”
Before New Year’s Eve, the Taiwanese keep themselves busy with preparations.
 It is tradition to clean one’s house in order to symbolically sweep away all of the bad luck from the ending year.
 This custom is referred to as da sao chu, or “spring cleaning” – literally, “sweeping out.
” However, sweeping and throwing away things is avoided during the first five days of the New Year so as not to sweep or throw away any good luck or fortune that the gods may deliver.
As mentioned earlier, the color red is believed to fend off Nian.
 In both Taiwan and in the United States (and elsewhere, of course), Chinese hang spring couplets, or red paper scrolls inscribed with auspicious Chinese characters such as “good fortune,” “wealth,” and “longevity,” on their doors.
 Before the New Year begins, many people also purchase clothing, since it is customary to wear new clothes at the beginning of the year.
 Before the old year ends, companies invite their employees to a weiya, or year-end banquet.
 The weiya serves as a way of expressing gratitude towards the employees, and I was fortunate enough to participate in one recently.
 Like most businesses in Taiwan, my company organized a massive feast for all its employees.
 My colleagues and I enjoyed music, entertainment, and an array of Taiwanese delicacies amid high spirits and happily tipsy managers.
 I even won some money during the customary lucky draw!
 Though the Chinese New Year holiday is traditionally fifteen days long, the anticipation and boisterous activity of chu xi, or Chinese New Year’s Eve, makes this night the most exciting.
 The chu xi dinner is of significant importance during this holiday.
 Families gather to consume the bountiful supply of traditional dishes.
 Yu, or fish, is served to represent “having enough to spare” (the Chinese character for “having enough” is also pronounced yu).
 Jiu cai, or “garlic chives,” have the symbolic meaning of “long-lasting” (the Chinese character for “long-lasting” is also pronounced jiu) and are served with chicken and duck as an offering to the gods and ancestors.
 In addition, Taiwanese people also enjoy treats such as nian gao and fa gao for their auspicious symbolism.
 Nian gao is a homophone for “prosperous year,” and this glutinous-rice cake is thus eaten as a sign of good luck.
 Fa gao is also a type of rice cake, and it is believed that the wider the split in the top of this cake, the more prosperous the new year will be.
 Similarly, tang guo or candy not only satisfies children’s sweet tooths but also symbolizes a “sweet beginning.” 
Children and young adults are especially anxious on this night, as it is customary for the elders to distribute hong bao or red envelopes filled with money to the younger generation.
 Even though the children in my family are not fluent in Mandarin, hong bao is a term that we are all familiar with and an item we await with great anticipation!
 Back in the United States, my grandparents hand out the envelopes one by one, starting with the eldest grandchild.
 In Taiwan I have also received hong bao from unrelated elders.
 Foreigners may also receive these gifts from Taiwanese people when visiting people’s homes at this time, and it is considered polite to accept them.
 My first Chinese New Year’s Eve in Taiwan was truly an unforgettable experience.
 Although the New Year’s Eve dinner is celebrated with close family members, it is common for people to leave the house after dinner is finished.
 The night started when I headed out to meet my friends.
 With my first step outside, I could hear the booming and crackling of firecrackers.
 Fearless little boys and girls stood in the middle of the streets, igniting explosives with their family members.
 On my way out, one family was kind enough to let me light a luminescent sparkler.
 With the flick of a lighter, sparks set off in all directions, creating a bright flare at the end of the wand.
 The sparks grew in volume, volatile in motion, and edged closer and closer to my arms and face.
 Yet the surrounding family remained calm and continued to watch the blaze in awe.
 After this first magical experience, I discovered that my Taiwanese friends had even more in store for me.
 We gathered at a public park adjoining a river.
 The massive size of a nearby creatively designed bridge and, behind it, one of Taiwan’s largest ferris wheels were humbling.
 My friends had purchased all kinds of fireworks and firecrackers, and we lit them side-by-side with other locals who had come to the park for the same reason.
 I watched as the dark sky, illuminated by the city lights, became filled with randomly colored flashes of light.
 The river’s reflection became a canvas covered with splotches of various colors.
 And I could smell the powder from the blaring firecrackers lingering in the air.
 After we lit our last firecracker, we headed to Xingtian Temple, not far away.
 This is one of Taipei’s busiest temples, with more than 10,000 visitors a day on average.
 On this New Year’s Eve, with only a few minutes left until midnight, the complex was filled to capacity with people of all ages.
 Children, parents, grandparents, and even babies were crowded inside the temple, waiting for the annual New Year’s Eve ceremony to begin.
 Before entering the temple, my friends helped me purchase a pair of joss sticks.
 In Taiwan, these incense sticks are used when praying to the gods.
 This form of ritualistic prayer is called bai bai and is the most common form of worship in Taiwan.
 In addition to lighting joss sticks, Taiwanese people will also offer food and money to the gods by burning paper money, also called spirit money, and placing food items on small tables outside their homes and businesses.
At the stroke of midnight, the temple’s traditional ceremony began.
 Beating drums began to play, and the worshippers stood calmly and quietly, each with joss sticks in their hands.
 After the steady drumming, the temple’s main doors swung open.
 These doors are normally kept closed during the year, but on this night the doors open to graciously welcome the gods.
 After the end of the ceremony the visitors began to stir about the temple, making their way to the various shrines.
 After we lit our incense we also approached the gods’ shrines, and thus began my first bai bai experience.
 I held the joss sticks with two hands, and approached the statues of the gods.
 I silently told each my name, asked each to keep me safe and healthy, and concluded with a polite bow.
 My friends then directed me to the ash pit where I threw the burning incense.
 Even though I am not superstitious, I did indeed leave the temple feeling safe and sound.
 After this temple visit, it was time for me to head home.
 Though I would soon be in my bed, fast asleep, my friends returned home to find their families still awake, playing cards or majiang, a very old and very popular gambling game.
 Many Taiwanese people try to stay up the entire night, a tradition that is believed to bring longevity to the elder family members.
 Along with the games, food also accompanies this long night of family bonding.
On the next day, called chu yi in Chinese, Taipei was not the usual, bustling metropolis that I had been waking up to each day.
 Almost all businesses, with the exception of convenience stores, were closed, and the crowds were nowhere to be found.
 On this first day of Chinese New Year families make a visit to the eldest members of their extended family.
 For many people living in Taipei this means heading to central and southern Taiwan.
 I, too, left the unusually calm city of Taipei and headed to my grandparents home in the southem city of Kaohsiung.
 For anyone visiting Taiwan in the midst of these festivities, I can recommend visiting the island’s southern areas, as the Chinese New Year spirit seems to come alive even more here.
 After greeting my grandparents and enjoying delicious traditional foods, I headed to Lotus Lake, a famous Kaohsiung sight.
 It took me almost the whole day to walk around the lake, as the sight of larger-than-life pagodas, temples, and pavilions constantly stopped me on my tracks.
 Along the path, large crowds dispersed throughout the vendors’ markets, buying food, playing games, and even purchasing pet fish and turtles.
 The bright sun shone down on happy families and busy sellers.
 And I watched as traditionally-dressed worshippers performed a parade-like dance to honor the gods.
 Day two, chu er, of Chinese New Year marks the day for married daughters to visit their parent’s home.
 Thus, my grandparents and I were paid a special visit by my aunt and her family that day.
 We enjoyed a home-cooked meal together, and I received hong bao from my generous aunt and uncle.
But the festival is not officially over until the fifteenth day, when the Yuan Xiao Jie or Lantern Festival is celebrated.
 During this festival the locals eat tang yuan, a sweet dessert made of glutinous-rice balls in soup.
 In the evening, children light lanterns and carry them as they walk up and down the streets.
 The Lantern Festival is also known for the large-scale lantern-festival events that are staged around the island by central and local governments, attracting huge crowds with exhibitions of colorful lanterns and rich entertainment programs.
With the conclusion of the festive season, I looked back at this time of year in Taiwan with a new understanding and appreciation of my culture.
 I had held enchanting explosives in my hand, witnessed thousand-year-old customs and practices, prayed to the gods, and enjoyed more food than I ever thought I could consume, all in the company and safety of my loved ones.
Though many aspects of the Chinese New Year may appear as superstitions to outsiders, the traditions serve as a way for the Taiwanese to celebrate their culture and remember their history.
 Anyone who has the chance to engage in the Chinese New Year experience in Taiwan will agree, and I can only hope that the approaching New Year will be as unforgettable as the last!
A red envelope (紅包, h?ngb?o) is simply a long, narrow, red envelope.
 Traditional red envelopes are often decorated with gold Chinese characters, such as happiness and wealth.
 Variations include red envelopes with cartoon characters depicted and red envelopes from stores and companies that contain coupons and gift certificates inside.
During Chinese New Year, money is put inside red envelopes which are then handed out to younger generations by their parents, grandparents, relatives, and even close neighbors and friends.
 At some companies, workers may also receive a year-end cash bonus tucked inside a red envelope.
 Red envelopes are also popular gifts for birthdays and weddings.
 Some four-character expressions appropriate for a wedding red envelope are 天作之合 (ti?nzu? zh?h?, a marriage made in heaven) or 百年好合 (b?ini?n h?o h?, a happy union for 100 years).
Unlike a Western greeting card, red envelopes given at Chinese New Year are typically left unsigned.
 For birthdays or weddings, a short message, typically a four-character expression, and signature are optional.
 Red symbolizes luck and good fortune in Chinese culture.
 That is why red envelopes are used during Chinese New Year and other celebratory events.
 Other envelope colors are used for other types of occasions.
 For example, white envelopes are used for funerals.
Giving and receiving red envelopes, gifts, and even business cards is a solemn act.
 Therefore, red envelopes, gifts, and name cards are always presented with both hands and also received with both hands.
 The recipient of a red envelope at Chinese New Year or on his or her birthday should not open it in front of the giver.
 At Chinese weddings, the procedure is different.
 At a Chinese wedding, there is a table at the entrance of the wedding reception where guests give their red envelopes to attendants and sign their names on a large scroll.
 The attendants will immediately open the envelope, count the money inside, and record it on a register next to the guests’ names.
A record is kept of how much each guest gives to the newlyweds.
 This is done for several reasons.
 One reason is bookkeeping.
 A record ensures the newlyweds know how much each guest gave and can verify the amount of money they receive at the end of the wedding from the attendants is the same as what the guests brought.
 Another reason is that when unmarried guests eventually get married, the bride and groom are typically obliged to give the guest more money than what the newlyweds received at their wedding.
Deciding how much money to put into a red envelope depends on the situation.
 For red envelopes given to children for Chinese New Year, the amount depends on age and the giver’s relationship to the child.
For younger children, the equivalent of about $7 is fine.
 More money is given to older children and teenagers.
 The amount is usually enough for the child to buy a gift, like a T-shirt or DVD.
 Parents may give the child a more substantial amount since material gifts are usually not given during the holidays.
For employees at work, the year-end bonus is typically the equivalent of one month’s wage though the amount can vary from enough money to buy a small gift to more than one month’s wage.
 If you go to a wedding, the money in the red envelope should be equivalent to a nice gift that would be given at a Western wedding.
 Or, it should be enough money to cover the guest’s expense at the wedding.
 For example, if the wedding dinner costs the newlyweds US$35 per person, then the money in the envelope should be at least US$35.
 In Taiwan, typical amounts of money are NT$1,200, NT$1,600, NT$2,200, NT$2,600, NT$3,200, and NT$3,600.
 As with the Chinese New Year, the amount of money is relative to your relationship to the recipient — the closer your relationship is to the bride and groom, the more money is expected.
 For instance, immediate family like parents and siblings give more money than casual friends.
 It is not uncommon for business partners to be invited to weddings, and business partners often put more money in the envelope to strengthen the business relationship.
 Less money is given for birthdays than other holidays because it is viewed as the least important of the three occasions.
 Nowadays, people often just bring gifts for birthdays.
For all occasions, certain amounts of money are to be avoided.
 Anything with a four is best avoided because 四 (s?, four) sounds similar to 死 (s?, death).
 Even numbers, except four, are better than odd — as good things are believed to come in pairs.
 For example, gifting $20 is better than $21.
 Eight is a particularly auspicious number.
 The money inside a red envelope should always be new and crisp.
 Folding the money or giving dirty or wrinkled bills is in bad taste.
 Coins and checks are avoided, the former because change is not worth much and the latter because checks are not widely used in Asia.
 The Moon Festival is one of the three most significant festivals of the Chinese communities around the world besides the Lunar New Year (Chinese New Year) and the Dragon Boat Festival.
 Originally named the Mid-Autumn Festival, the Moon Festival is celebrated on the fifteenth day of the eighth lunar month in observance of the bountiful autumn harvest.
 On the 15th day of the lunar month, the moon forms a round shape that symbolizes family reunion.
 Upon this occasion, the legends of the festival are often told to the children.
The custom of eating moon cakes tells a story of the downfall of the Yuan dynasty.
 The time was the Yuan dynasty (AD 1280-1368), established by the invading Mongolians from the north who subjugated the Han Chinese.
 Leaders from the preceding Sung dynasty were furious about submitting to foreign rules.
 A secret rebellion plan was coordinated to overthrow the Mongolians.
 Drawing close to the Moon Festival, the rebellion plans and outlines of attacks successfully passed out to all coordinators secretly via the messages and outlines stuffed in each moon cake.
 On the night of the Moon Festival, the rebels successfully attacked and overthrew the Yuan government which followed the rise of the Ming dynasty.
Something weird happened one day, 10 suns arose in the sky blazing the earth instead of one.
 As an expert archer, Hou-Yi stepped forward and shot down 9 suns successfully.
 For rescuing the earth and people, he instantly became a hero and eventually earned his crown as well as married Lady Chang Er.
 Having all these powers and authorities in hands, Hou-Yi grew to be a greedy and despot ruler yet sought for elixir to prolong his life of ruling.
 Hoping to end Hou-Yi’s plan for the sake of the people, Chang Er purposely swallowed the elixir.
 Miraculously, she floated towards the moon.
 Another legend also states a rabbit became Chang Er’s companion when she headed to the moon, thus the moon rabbit tale.
Pomelo, is a large citrus fruit which looks like a large version of grapefruit.
 It is a high nutritional value fruit called “Yo Zhi” in Chinese.
 The Yo in Yo Zhi sounds similar to blessing in Chinese, as people wish for the blessing of the moon and the production season happens to be around September.
 So the fruit became very popular during this time of the year and gradually became the representative fruit of the holiday.
 It often serves with moon cakes while you visit friend and family during this time of the year.
 With all the traditional activities still carry out, a new custom during the Moon Festival somehow started throughout the past twenty years in Taiwan.
 Barbeque has become more and more popular during this time of the year.
 It has nothing to do with any tradition.
 No one knows exactly how this began.
 The event just somehow became what people do during this time, a must do.
 The moon festival is a holiday when the family gathers and celebrates together, and now they barbeque.
 Large barbeque events are even held in every city.
 It’s a new and interesting way to celebrate the harvest festival, as long it’s a joyful reunion and wonderful gathering.
 The shops and venders will have everything you need ready for a good barbeque.
 It’s a great chance to cook up some good times in Taiwan.
Come visit Taiwan and find out what moon cakes taste like for your Taiwan festival travel!
 Our island is a foodie's paradise where everything from night market stalls to Michelin-star restaurants offer dishes uniquely delicious and absolutely memorable.
 The Dragon Boat Festival (Duanwu Festival, Du?nw? Ji?, Double Fifth, Tuen Ng Jit) is a traditional holiday that commemorates the life and death of the famous Chinese scholar Qu Yuan (Chu Yuan).
 The festival occurs on the fifth day of the fifth month on the Chinese lunisolar calendar.
Dragon Boat Festival is a public holiday.
 It is a day off for the general population, and schools and most businesses are closed.
The Dragon Boat Festival is a celebration where many eat rice dumplings (zongzi), drink realgar wine (xionghuangjiu), and race dragon boats.
 Other activities include hanging icons of Zhong Kui (a mythic guardian figure), hanging mugwort and calamus, taking long walks, writing spells and wearing perfumed medicine bags.
All of these activities and games such as making an egg stand at noon were regarded by the ancients as an effective way of preventing disease, evil, while promoting good health and well-being.
 People sometimes wear talismans to fend off evil spirits or they may hang the picture of Zhong Kui, a guardian against evil spirits, on the door of their homes.
Many believe that the Dragon Boat Festival originated in ancient China based on the suicide of the poet and statesman of the Chu kingdom, Qu Yuan in 278 BCE.
 The festival commemorates the life and death of the famous Chinese scholar Qu Yuan, who was a loyal minister of the King of Chu in the third century BCE.
 Qu Yuan’s wisdom and intellectual ways antagonized other court officials, thus they accused him of false charges of conspiracy and was exiled by the king.
 During his exile, Qu Yuan composed many poems to express his anger and sorrow towards his sovereign and people.
 Qu Yuan drowned himself by attaching a heavy stone to his chest and jumping into the Miluo River in 278 BCE at the age of 61.
 The people of Chu tried to save him believing that Qu Yuan was an honorable man; they searched desperately in their boats looking for Qu Yuan but were unable to save him.
 Every year the Dragon Boat Festival is celebrated to commemorate this attempt at rescuing Qu Yuan.
The local people began the tradition of throwing sacrificial cooked rice into the river for Qu Yuan, while others believed that the rice would prevent the fishes in the river from eating Qu Yuan’s body.
 At first, the locals decided to make zongzi in hopes that it would sink into the river and reach Qu Yuan's body.
 However, the tradition of wrapping the rice in bamboo leaves to make zongzi began the following year.
A dragon boat is a human-powered boat or paddle boat that is traditionally made of teak wood to various designs and sizes.
 They usually have brightly decorated designs that range anywhere from 40 to 100 feet in length, with the front end shaped like open-mouthed dragons, and the back end with a scaly tail.
 The boat can have up to 80 rowers to power the boat, depending on the length.
 A sacred ceremony is performed before any competition in order to “bring the boat to life” by painting the eyes.
 The first team to grab a flag at the end of the course wins the race.
The zong zi is a glutinous rice ball with a filling and wrapped in corn leaves.
 The fillings can be egg, beans, dates, fruits, sweet potato, walnuts, mushrooms, meat, or a combination of them.
 They are generally steamed.
 It is said that if you can balance a raw egg on its end at exactly noon on Double Fifth Day, the rest of the year will be lucky.
 The hanging of calamus and moxa on the front door, the pasting up pictures of Chung Kuei, drinking hsiung huang wine and holding fragrant sachets are said to possess qualities for preventing evil and bringing peace.
 Another custom practiced in Taiwan is "fetching noon water," in which people draw well water on the afternoon of the festival in the belief that it will cure all illnesses.
 The Dragon Boat Festival is to commemorate the death of the famous Chinese poet Qu Yuan living in the latter part of the Warring States Period (476 - 221 BC).
 It was recognized as a traditional and statutory public holiday in China in 2008.
 The Dragon Boat Festival was selected into the first batch of the National Intangible Cultural Heritage items on May 20th, 2006.
 On October 30th, 2009, it was added to the UNESCO World Intangible Cultural Heritage Lists.
Originated in China, the Dragon Boat Festival was original the holiday to ease the diseases and prevent epidemic.
 Before the Spring and Autumn Period (770 - 476 BC), in Wu and Yue States, it has the custom of sacrificing the tribal totem by dragon boat racing on the fifth day of the fifth month (Chinese lunar calendar).
 Later, because Qu Yuan died at that day, the day becomes a festival to commemorate Qu Yuan.
 There are many kinds of legends about the origin of Dragon Boat Festival.
 Among all of them, the most popular one is about Qu Yuan.
 Qu Yuan is a minister in the Chu kingdom - one of the seven warring states before Qin (221BC - 206BC).
 He offered many good suggestions to the king such as the alliance with the Qi Kindom to defend Qin.
 While his good advices were opposed greatly by other court officials, thus was exiled by the king after their slanderous talk about him.
 During his exile time, he composed many poems to express his concern about his country and people.
 In 278BC, after knowing his country was taken by Qin, he drowned himself in Miluo River.
 After Qu Yuan died, the people of Chu tried to search him in the river.
 While searching, some fishermen threw cooked rice balls and eggs into the river thinking it would prevent fishes from eating Qu Yuan’s body.
 Later the rice balls were replaced by the present Zongzi (glutinous rice wrapped in bamboo leaves).
 An old doctor poured a pot of realgar wine into the river in order to prevent the monsters in the river to hurt Qu Yuan’s body.
 So, every year on the day that Qu Yuan died (May 5th of the Chinese Lunar Calendar), people will commemorate him with the traditions of eating Zongzi, drinking the realgar wine and boat racing.
There are many traditions for the Dragon Boat Festival and different areas have some different customs.
 Among them, the most popular ones are Eating Zongzi, drinking realgar wine, wearing sachet (perfumed medicine bags), tie hand-made “five color thread” bracelet, hanging mugwort and calamus as well as dragon boat racing.
 As the most popular food for the Dragon Boat Festival, Zongzi (glutinous rice wrapped in bamboo leaves and stuffed with different fillings) is filled with different fillings for the people in the northern and southern China.
 In the north, it is mostly filled with the date, while in south with bean - paste, meat, egg yolk or ham.
 The Zongzi made in Jiaxing of Zhejiang is the most famous one in China.
 During the festival, people usually hang mugwort and calamus on the front door, wear perfumed medicine bags, wear the “five color thread” on their neck, wrists or ankles and drink realgar wine as a way to prevent disease and evil.
Dragon boat racing is now the most popular activity during the festival.
 It is a human-powered boat made of teak wood with the front shaped like an open-mouthed dragon and the end with the tail.
 Every year, during the festival, there are many dragon boat racings held in different areas of China to celebrate the festival.
 Fifth lunar month for the annual Dragon Boat Festival, also known as the Duanwu Festival, afternoon Day Festival, May Day and so on.
 "Dragon Boat Festival" is one of China's national holidays, and has been included in the World Intangible Cultural Heritage.
 Dragon Boat Festival originated in China and it was a festival that the Chinese people initially remove epidemic illnesses.
 In Spring and Autumn Period, there was the custom have custom of tribal totem worship in the form of dragon boat race in the fifth lunar month.
 Due to the poet Qu Yuan died on this day, it became Han Chinese people's traditional festival to commemorate Qu Yuan.
 Dragon Boat Festival has the custom to eat Zongzi, drink realgar wine, and hang calamus, wormwood and moxa leaves, smoke herb and Angelica, race dragon boat.
 The History of Dragon Boat Festival Dragon Boat Festival originated numerous claims, which commemorate Qu Yuan has the most widely impact.
 According to the "Historical Records", "Qu Yuan Jia Sheng Biography" records, Qu Yuan, a minister of the King Huai of Chu of the Spring and Autumn Period.
 He advocated the virtuous and grant, make the country rich and its military force efficient, Unite Qi kingdom against Qin Kingdom, was strongly opposed by the nobility and others.
 Qu Yuan was greedy resigned, were expelled from the capital, exiled to Yuan, Xiang River.
 His exile, wrote a concern for the fate of the "Lament", "Heaven", "Nine Songs" and other immortal poem.
 His poem has unique style, far-reaching impacts (and thus, the Dragon Boat Festival, also known as a poet festival).
 In 278 BC, Qin defeated Chu Kyoto.
 Qu Yuan saw their country was invaded, felt as if a knife were piercing his heart, but still could not bear to abandon their homeland.
 On May 5, after writing the must document as "Huaisha", he bouldering into Miluo River and died.
 He used his own lives to compose a magnificent patriotic movement.
 After Qu Yuan's death, Chu abnormal grief people have flocked to pay tribute to Qu Yuan by Miluo River.
 Fishermen paddle a boat in the river to salvage his true identity.
 One fisherman prepared rice, eggs and other food, "thump, thump" thrown into the river, hoping to allow the fish, crab and lobster, it will not bite Qu Yuan's body.
 People saw and later followed.
 An old doctor brought a jar of wine and poured into the river that was used to fainted dragon beast, to avoid hurting Qu Yuan.
 Later, for fear that the food might be eaten by the dragon, people come up with the idea of wrapped the rice balls with neem leaves, color silk wrapped around the outside, to develop into zongzi.
 Later, in the fifth day of May each year, there is the customs of dragon boat races and eat zongzi, drinking realgar wine; in order to commemorate the patriotic poet Qu Yuan.
 Folk customs To celebrate Dragon Boat Festival is the traditions and customs of Chinese people for two thousand years.
 As a vast, numerous nationalities, with many stories and legends, so not only produced many disparate section name, but also has not the same customs throughout the nation.
 Wear Damselflies Damselflies were the hair accessories for women in Dragon Boat Festival in ancient times.
 The custom are mainly spread in the south of Yangtze River region.
 Damselflies were originated from step shake gold.
 Hanging Moxa Leaf Tiger Moxa Leaf Tiger is not only used to drive out evil spirits, but also used as ornaments.
 In ancient China, tiger was seen as the mythical creatures, so it can be used to drive the evil spirits and bless peace.
 So people take on more Moxa Leaf Tiger, particularly those in the Dragon Boat Festival is the most characteristic Moxa Leaf Tiger.
 Draw the Forehead The custom of draw realgar wine on children’s forehead which can expel poisonous insects.
 The typical way is to draw a “king” word on children’s forehead.
 On one hand, it means to abolish poison; on the other hand, use the symbolic meaning of tiger to drive out evil spirits.
 Dragon boat racing When dive dragon boat, there are many songs singing to add to fun.
 Such as Hubei Zigui dragon boat race, it has a complete singing melody art, lyrics blend together according to the local folk and chant, sing magnificent sound forceful.
 Special Diet Zongzi Zongzi called as “angle millet” in ancient times.
 According to legend, it was a custom to commerate Qu Yuan.
 Real Zongzi written records found" endemic in mind” in Jin and Zhou Dynasties.
 Today Zongzi in different areas, usually made with bamboo rice shell package, but color and containing inside are based on the specialty and custom, notably longan Zongzi, meat, crystal Zongzi, lotus seed paste Zongzi, candied fruit Zongzi, chestnut Zongzi, spicy Zongzi, sauerkraut Zongzi, ham Zongzi, salted egg Zongzi and so on.
 Realgar Wine The custom of drinking Realgar wine on the Dragon Boat Festival which is extremely popular in the Yangtze River region in the past.
 The drinking realgar wine is generally add realgar into liquor or just adding trace brewed rice wine.
The Dragon Boat Festival once had many interesting customs.
 Most are no longer commonly observed, although many are still practiced in rural areas.
The most popular activity of the Dragon Boat Festival is racing dragon boats.
The origin of the festival is said to be when locals paddled out on boats to scare the fish away and retrieve Qu Yuan's body (the patriotic poet who drowned himself in the Miluo River when the Chu State fell in 278 BC).
 The races are a symbol of the attempts to rescue and recover the body of Qu Yuan.
The dragon boat race custom started in southern China, where the fifth lunar day of the fifth lunar month was selected as a totem ceremony.
 The dragon was the main symbol on the totem, because the Chinese believe that they are sons of the dragon.
 Later the Chinese connected this ceremony with the Duanwu Festival.
 This festival activity is only held in southern China, where it has varying levels of popularity.
 Dragon Boat Race events are popular in Hong Kong and Taiwan.
 It is a tradition for the Chinese to eat zongzi during the Dragon Boat Festival.
 Zongzi is made differently in different areas of China.
 Historical records show that people used wild rice leaves to wrap millet flour dumplings into the shape of ox horns, and then placed them in bamboo to cook.
During every Dragon Boat Festival many Chinese families follow the custom of eating zongzi.
 People in the north enjoy zongzi with dates, while people in the south prefer mixed ingredients, such as meat, sausages, and eggs.
This custom is not only very popular in China, it is also practiced in Korea, Japan, and other countries in Southeast Asia.
 Many contagious diseases and plagues were said to originate during the fifth lunar month when the Dragon Boat Festival takes place.
Chinese people, especially children, made incense bags and hung them on their necks to avoid catching contagious diseases and to keep evil spirits away.
 Incense bags are made from a variety of sewn bags and include the powders of calamus, wormwood, and realgar, and other fragrant items.
 This tradition has been mostly abandoned.
 There is an old saying: "Hang willow branches at Qingming Festival and hang calamus and wormwood at Duanwu Festival. "
 On Dragon Boat Festival people often put calamus and wormwood leaves on their doors and windows to repel insects, flies, fleas, and moths from the house.
 Hanging these plants on doors or windows is also believed to dispel evil, and bring health to the family especial the kids.
There is an old saying: 'Drinking realgar wine drives diseases and evils away!
' Realgar wine is a Chinese alcoholic drink consisting of fermented cereals and powdered realgar.
 In ancient times, people believed that realgar was an antidote for all poisons, and effective for killing insects and driving away evil spirits.
 So everyone would drink some realgar wine during Duanwu Festival.
 Before Dragon Boat Festival arrives, parents usually prepare perfume pouches for their children.
 They sew little bags with colorful silk cloth, fill the bags with perfumes or herbal medicines, and then string them with silk threads.
 During Dragon Boat Festival perfume pouches are hung around kids' necks or tied to the front of a garment as an ornament.
 The perfume pouches are said to protect them from evil.
The Dragon Boat Festival is held at the start of summer, when diseases are more prevalent.
 Mugwort leaves are used medicinally in China.
Their fragrance is very pleasant, deterring flies and mosquitoes.
 Calamus an aquatic plant that has similar effects.
On the fifth day of the fifth month, people usually clean their houses, courtyards, and hang mugwort and calamus on doors lintels to discourage diseases.
 It is also said hanging mugwort and calamus can bring good luck to the family.
There are many legends about the origin of the Dragon Boat Festival.
 The most popular one is in commemoration of Qu Yuan.
Qu Yuan (340–278 BC) was a patriotic poet and exiled official during the Warring States Period of ancient China.
 He drowned himself in the Miluo River on the 5th day of the 5th Chinese lunar month, when his beloved Chu State fell to the State of Qin.
Local people desperately tried to save Qu Yuan or recover his body, to no avail.
 In order to commemorate Qu Yuan, every fifth day of the fifth lunar month people beat drums and paddle out in boats on the river as they once did to keep fish and evil spirits away from his body.
 The Duanwu Festival or Tuen Ng Festival is a traditional and statutory holiday.
 It is a public holiday in mainland China and Taiwan, where it is called the "Duanwu Jie" and a public holiday in Hong Kong and Macau, where it is called the "Tuen Ng Jit".
 In it is also referred to as "Dragon Boat Festival", after one of the traditional activities for the holiday.
 The Duanwu Festival occurs on the fifth day of the fifth month of the Chinese calendar, giving rise to the alternative name of Double Fifth .
 In 2008, this falls on 8 June.
 The focus of the celebrations includes eating ''zongzi'', which are large rice wraps, drinking realgar wine, and racing dragon boats.
 The Duanwu Festival has also been celebrated in other East Asian nations.
 For their equivalent or related celebrations, such as Kodomo no hi in Japan, in Korea, T& in Vietnam.
 The Duanwu Festival is believed to have originated in ancient Northern Chinese Regions.
 There are a number of theories about its origins.
 Today, the most commonly accepted version relates to the death of poet Qu Yuan in 278 BC despite a number of competing theories.
 The best-known traditional story holds that the festival commemorates the death of poet Qu Yuan of the ancient state of , in the Warring States Period of the Zhou Dynasty.
 A descendant of the Chu royal house, Qu served in high offices.
 However, when the king decided to ally with the increasingly powerful , Qu was banished for opposing the alliance.
 Qu Yuan was accused of treason.
 In 2008, Duanwu was celebrated in mainland China as a public holiday for the first time.
 Three of the most widespread activities for the Duanwu Festival are eating ''zongzi'', an angular rice ball wrapped in reed or bamboo leaves; drinking realgar wine, and racing dragon boats.
 Other common activities include hanging up icons of Zhong Kui , hanging up mugwort and calamus, taking long walks, and wearing perfumed medicine bags.
 Other traditional activities including a game of making an egg stand at noon, and writing .
 All of these activities, together with the drinking of realgar wine, are designed to ward off disease or evil.
Lantern Festival:  The Lantern Festival came about when the people of ancient China first believed that celestial spirits could be seen flying about in the light of the first full moon of the new lunar year.
 Their search using torches evolved into the current celebrations of colorful lanterns at temples and parks.
 Children of the past were given lanterns to carry on the school day of the New Year to symbolize the hope of a bright future for the child.
 In modern Taiwan, small children carry lanterns and roam the streets on the eve of the festival.
Tomb Sweeping Day: Since ancient times, a day has been designated for sweeping the tombs and honoring the ancestors.
 Ancestor worship is usually performed on the first few days prior to or following Ching Ming.
 Dragon Boat Festival: Many people attended the festive boat races in Taipei, Lukang, Taiwan and Kaohsiung, with teams coming from all over the world.
 Boat races during the Dragon Boat Festival is to commemorate the rescue attempt of Chu Yuan, a patriotic poet, who drowned on the fifth day of the fifth lunar month in 277BC.
 When the attempt to rescue him failed, the people had to throw bamboo stuffed with cooked rice into the water so that the fish would eat the rice and not his body.
 From that, it evolves to the present day custom of eating tzungtzu, a kind of rice dumplings filled with ham or bean paste and wrapped in bamboo leaves.
 Ghost Festival: It was said that on the first day of the seventh lunar month, known as Ghost Month, the gates of Hell would open wide and the spirits are allowed a month of feasting and revelry in the world of the living.
 To ensure that the ghosts enjoy a pleasant vacation, lavish sacrifices are set out, sacrifices paper money is burned, and Taiwanese operas are performed.
Mid-Autumn Festival: Autumn Moon Festival falls in the middle of the eighth lunar Chinese month when the moon is supposed to be at its fullest.
 With a ripe, round moon considered a symbol of happiness, the national holiday is also a time for family reunions.
 Double Ninth Day:  It is no wonder why the Double Ninth Day was named thus as it falls on the ninth day of the ninth lunar month.
 The day is also known as Chung Yang or Double Yang Festival for the Chinese custom recognize "nine" as a number belonging to the positive principle "yang".
 On that day, activities such as hill climbing, drinking chrysanthemum wine, hanging dogwood sprays, and other customs dating back to the Han Dynasty story of Huan Ching and Fei Chang-fang were put to practiced.
 The Taiwanese have also enriched their holiday with kite flying, an up and coming custom.
Many countries have traditional festivals and China, with its long history, is no exception.
 Chinese festivals reflect the diverse cultural heritage of its many ethnic groups.
 Food plays a very important part in any celebrations and what better way can one celebrate but by enjoying rich and colorful occasions with special tasty dishes.
 So, what are the festival foods?
Dumpling People in northern China typically will eat dumplings (jiaozi) on New Year's Eve.
 This occurs because 'jiaozi' sounds like a word meaning 'bidding farewell to the old and ushering in the new in Chinese.
 Dumplings are always made before midnight and eaten during the last hour of the old year and the first hour of the Lunar New Year.
 Some people like to wrap a one-yuan, fifty-cent or ten-cent coin in some of the dumplings, as a token of good fortune for those who eat them.
 This is said to ensure good luck and prosperity in the New Year.
Niangao (Rice Cake) Southern China produces rice, so traditionally the southern Chinese usually eat Niangao (rice cakes) to celebrate the New Year.
 This belief derives from the Chinese pronunciation of rice cake as 'niangao', a homophone for a word meaning a higher level of life.
 Niangao is made of glutinous rice powder and can be cooked by frying, steaming, stir-frying or boiling.
 With the development of the social culture, eating Niangao is also popular now among some people in north China during the Chinese New Year (Spring Festival).
ish (Braised Pomfret, Tangba Town's Stir-fried Fish, Steamed Perch) Fish, usually of a local variety, is an important item on the dinning table of thousands of families on the eve of the Chinese New Year.
 Fish is pronounced 'yu' in Chinese, with the implications of having more than just a basic need each year.
 It is also a present exchanged between relatives and friends during the Chinese New Year.
 In southern China, some people just eat the middle part of the fish on the New Year Eve, leaving the head and tail to the next day to symbolize completeness.
 Meanwhile, it is particularly important that when fish is placed on the dining table, its head must be at the elders, as a sign of respect.
 Yuanxiao Since the Song Dynasty (960-1279), people have had the custom of dining on traditional fare highlighted by 'Yuanxiao' on the Lantern Festival which is also called Yuanxiao Festival.
 Yuanxiao is a kind of rice dumpling made of glutinous rice powder and wrapped with various fillings like bean paste, brown sugar and all kinds of fruits and nuts.
 People eat these on the last day of the Chinese New Year celebration, since they are also named 'tangyuan' or 'tuanyuan' among the Chinese folk, pronounced like 'tuanyuan' (reunion).
 Zongzi Throughout the country families will eat 'zongzi' to memorize the national hero Qu Yuan (he ended his life by drowning in the Miluo River on hearing his state was defeated.) on the Dragon Boat Festival, the day of Qu Yuan's death.
 Zongzi is pyramid-shaped dumplings made of glutinous rice and wrapped in bamboo or reed leaves, usually made into sweet with dates in northern China.
 However, it can be either sweet or savory and made from a great variety of ingredients besides glutinous rice, such as meat, shrimp, bean paste and nuts in southern China.
 Moon Cake Offering sacrifices to the moon, eating moon cakes and watching the moon are the main activities on Mid-Autumn Day.
 Moon cakes are usually round-shaped, representing family reunion and bright life.
 They are made with a sweet bean-paste filling, and a golden brown flaky skin, but nowadays, there are more than a dozen variations, including bean paste, yolk paste, coconut paste, five-core paste and so on.
 To make them attractive, some clever cooks decorate the moon cakes by drawing the pattern of 'Chang Er's Flying to the Moon'.
Spring Rolls also named Spring Cakes by some northern Chinese, have a long history in China.
 It's said that as early as in the Eastern Jin Dynasty (316-420), people would eat 'Spring Plate', a dish with thin flour-made cakes at the center of the plate and green vegetables around them, on the First Day of Spring every year.
 Along with the development of the cooking techniques, 'Spring Cakes' have been evolved into the present lovely golden spring rolls made of thin flour wrappers with various fillings – sweet or savory, meat or vegetables.
On every fifth day of the fifth month in Chinese lunar calendar, the Dragon Boat Festival is celebrated in various ways, and the festival foods are always an indispensable part.
 Apart from Zongzi, what else do Chinese people eat during the Dragon Boat Festival?
Zongzi is a kind of sticky rice dumplings with different fillings wrapped by bamboo or reed leaves.
 The custom of eating Zongzi is originated from 340 AD, when the patriotic poet, Qu Yuan gave his life for his country by drowning himself in a river.
 To protect his body from being eaten by fish, people threw Zongzi into the river to feed the water creatures.
 Since then, Zongzi becomes a typical food for Dragon Boat Festival, which has been passed by for thousands of years.
 Drinking realgar wine during the Dragon Boat Festival is a tradition kept for generations, and kids’ cheek and forehead will also be rubbed with the wine.
 It is believed that the wine can dispel sickness, poisonous stuff and evil spirits.
 Now the realgar wine is not much liked by people, as there is tinny toxicant in it after being heated.
This is your Guide to 2020 Pingxi Sky Lantern Festival in Taiwan: When to Go and How to Get There.
 The Taiwan Sky Lantern Festival in Pingxi district (平溪) is a bucket list experience and is often listed as a top 20 world festival, and highly recommended by Lonely Planet and CNN.
 It’s a once-in-a-lifetime experience in the cool, crisp mountains of Taiwan in New Taipei county.
 Release lanterns in Taiwan during the world-famous Taiwan Sky Lantern Festival in Pingxi, Taiwan is simply magical to watch.
 The paper lanterns glow in the sky in the mountains of Taiwan during the Lantern Festival in spring and Moon Festival in the fall.
The first time I went to one of the most popular festivals in Taiwan, I couldn’t figure out how to get to the Sky Lantern Festival in Taiwan on my own very easily, much less with two kids.
 Was the festival in Pingxi (平溪) or Shifen?
 With the help of some friends to arrange the details, we finally made plans to stay in a hotel in Taipei and figured out how to get to the Pingxi Sky Lantern festival located in Shifen.
 I live and blog as an expat in Taiwan and getting to the Sky Lantern Festival took a lot of planning.
 It was worth all the effort!
The Pingxi Sky Lantern Festival takes place in the old coal-mining village of Shifen.
 Shifen is located in Pingxi District in New Taipei county about 1 hour east from Taipei.
 Don’t make the mistake of going to the town of Pingxi!
 Pingxi District is the region of Taiwan where the Sky Lantern Festival is held, and Shifen is the village.
 Make your plans to go to the Lantern Festival in Shifen, Pingxi District, Taiwan.
Most travelers arrive in Taipei for the Lantern Festival either at the Tayouan Taipei international airport or by the HSR trains.
To get to Shifen village in Pingxi District, take any northbound train (EXCEPT those going to Keelung) from Taipei Main Station to Ruifang station.
 At Ruifang station, get off the train to transfer trains.
 Purchase a ticket to Shifen Station in Pingxi District on the Pingxi line.
 The train ride from Taipei to the Pingxi Lantern Festival in Shifen is about 1 hour or longer during the festival.
 The fare is less than $100 NT one way so this is the most budget-friendly way to get to the lantern festival.
 It is also the most crowded!
Taking the bus from Taipei to Shifen is another way to go to the Pingxi Lantern Festival.
 The road to Shifen is closed to traffic for the lantern festival to make getting to and from Pingxi more efficient.
 Tour operators offer options for a shuttle bus from Taipei, Keelung or Juifen.
 A shuttle bus service typically starts at 9 a.m.
 from the Taipei Zoo for $50 NT or $15 NT from Ruifang train station to Shifen.
 The return trip is free.
Shuttle buses from Taipei, Juifen and Keelung run all day long on the day of the festival from different tour operators.
 Shuttle buses in Taipei depart from the Muzha MRT station or the Taipei Zoo station.
 For the festival, bus lines are separated into standing and seated lines or queues.
 Lines are shorter and quicker if you are willing to stand on the bus or shuttle.
 Shuttle buses run later than trains after the festival so if you plan on staying until the very end, a shuttle or private driver may be your best choice.
 Plan to depart Taipei no later than 3 or 4 pm to arrive in Shifen before sunset and avoid the worst traffic and packed trains.
 I recommend you stay nearby in Juifen or Keelung and visit the nearby Shifen waterfall in the area during the day.
The Pingxi Sky Lantern Festival showcases thousands of lanterns lifting off of the old railway tracks in Shifen, lighting up the night sky.
 The main stage at Shifen Square showcases the mass ascension of lanterns every 20-30 minutes.
 Mass ascensions are released periodically from approximately 6:30 pm – 9:00 pm.
 You can register for free to participate in the mass ascension by signing up as early as possible at the square starting at 10:00 am.
 There is no entrance fee to attend the lantern festival — attending the lantern festival is free!
 When you arrive at the festival, you can buy your own huge paper lantern to decorate and release for about $200 NT each.
 Vendors nearby have calligraphy brushes and buckets of black paint for you to design and paint your own lantern.
 Each lantern is made of thin bamboo covered by rice paper, and it has four big sides to decorate.
 Try painting your zodiac sign and adding blessings and wishes for the new year to bring you luck!
 My kids loved personalizing their sky lanterns and following them in the sky once they took flight.
 For safety reasons, a professional will light your lantern for you.
 Families and travelers will also enjoy the energy of the Shifen night market set up along the train tracks and village streets.
 Festival food and Taiwan delicacies like steamed soup dumplings, grilled sausages, pork buns and warm bowls of noodles keep you warm from the chilly air in the mountains.
 High mountain oolong tea is available everywhere.
One major caution for families is that trains actively arrive and load on the same tracks people use to light their lanterns.
 I had to pay attention and stay aware of sounds and approaching lights on the trains to jump off the tracks.
 Make sure you stay close and keep the kids off the tracks.
 In this article, I’ll walk through the etiquette for giving and receiving the red envelopes filled with lucky money that are an iconic symbol of Chinese New Year.
We’ll get to the details in a moment, but I’ll start by highlighting that the red envelope custom is all about the reciprocity of giving and receiving.
 It’s a gesture of goodwill, expressed through the exchange of red envelopes, that builds relationships among family and friends.
 In fact, after all the giving and receiving of red envelopes during Chinese New Year, you’ll probably find that you end up netting even financially.
 Count the relationships, not the dollars.
A Chinese red envelope (known as lai see in Cantonese and hong bao in Mandarin) is simply an ornate red pocket of paper the size of an index card.
 They’re commonly decorated with beautiful Chinese calligraphy and symbols conveying good luck and prosperity on the recipient.
 Though they’re unquestionably a symbol associated with Chinese New Year, red envelopes are also given for weddings, birthdays and other special occasions.
Here are the most common scenarios for giving red envelopes during Chinese New Year.
1.From Parents to their Children.
It’s traditional to leave a red envelope with two tangerines (leaves on, of course) by a child’s bedside on New Year’s Eve.
 Given that Chinese New Year isn’t celebrated with material gifts, the amount is usually around $20, enough for the child to buy a toy on his or her own.
 Grandparents generally give red envelopes in similar amounts to their grandchildren during visits on New Year’s Eve or in the days following New Year’s Day.
 2. From Married Adults to (Unmarried) Children in the Family. 
 Giving red envelopes is an important rite of adulthood, as symbolically you’ve become ready to share your riches and blessings with others.
 If you’re married, prepare to bring red envelopes for any little cousins and unmarried adult children in your extended family as you visit during Chinese New Year.
 A token amount around $10 is appropriate.
 3. From Adult Children to their Parents.
Giving a red envelope to your parents is a sign of respect, a gesture pointing back to longstanding notions of filial piety.
 Make the gift generous, between $50 and $100, and expect to receive a red envelope in return, symbolizing your parents’ blessings for you.
 4. When Visiting Family and Friends.
The days following New Year’s Day are a procession of visits to the homes of family and friends to wish them good luck in the year ahead.
 In addition to the red envelopes you may bring for any children in the home, you should bring a red envelope with about $20 for your hosts, which is customarily placed in the center of the Togetherness Tray of sweets as you snack together.
 5. From Employers to Employees.
 A red envelope at Chinese New Year takes the place of the Christmas bonus common in Western workplaces.
 Given the expense of traveling home for the holiday, many employers give their employees a red envelope filled with the equivalent of a month’s pay at the beginning of the festival, along with a smaller “token of red” when they return to work.
 Prepare to do the same if you employ a Chinese nanny or housekeeper in your home.
As you give and receive red envelopes, don’t forget these basic etiquette tips: Choose new bills, don’t ever include coins and wait to open your red envelopes until after you part company.
 Amounts in even numbers are generally preferred, except for the number 4 because of its resemblance to the word meaning death.
 And, optional, but denominations including 8s (rhyming with the word for good luck) and 9s (for longevity) carry especially positive symbolic meanings.
 Returning to the point I made at the outset, remember that when exchanging red envelopes at Chinese New Year, it’s the relationship that counts most.
 As with Western gift giving, red envelopes are a way to bring your nearest and dearest closer to you during the most important time of the year.
Lucky money in a Chinese red envelope is the easiest, most traditional gift during Chinese New Year.
 Pick a design below that conveys the sentiment you wish to send.
 These premium red envelopes feature the Chinese character for blessings (福) to wish the recipient a year filled with abundance and prosperity.
 These premium red envelopes feature the Chinese character for fullness (?) to wish the recipient a year filled with satisfaction and joy.
These premium red envelopes feature the Chinese character for luck (祥) to wish the recipient a year filled with success and achievement.
The Lantern Festival is celebrated annually on the 15th day of the first lunar month to mark the grand finale of the Chinese New Year celebrations.
 It is also the very first full moon day of the New Year, symbolizing the coming of the spring.
 People usually celebrate this festival by enjoying family dinner together, eating Yuanxiao (glutinous rice dumpling), carrying paper lanterns, and solving the riddles on the lanterns.
 The festival is celebrated with fanfare events in Taiwan, including the internationally famed Pingxi Sky Lantern Festival in New Taipei City, Bombing Lord Han Dan in Taitung, and Yanshui Beehive Rockets Festival in Tainan, to welcome the New Year in a spirit of peace, prosperity and joy.
 Bombing Lord Han Dan is a special ceremony in Taitung, which a chosen man performs in the role of Master Han Dan-a god of wealth, and gets thrown by firecrackers.
 During the event, the chosen man wears nothing but a pair of red short pants, holds one bamboo fan to protect his face, stands on a sedan chair, and being carried around by four devotees.
 Firecrackers are to be thrown at the chosen one as it is believed that Lord Han Dan cannot bear the cold weather.
 The firecrackers are to keep him warm as well as to pray for wealth and prosperities.
 Pingxi Sky Lantern Festival is held every year during the Lantern Festival in Pingxi of New Taipei City.
 Sky Lanterns, also known as Kongming Lantern are flying paper lanterns traditionally found in some Asian cultures.
 It was invented by Kongming during three kingdoms period by Zhu Ke-Liang (aka Kongming) in order to pass military information.
 They are constructed from oiled rice paper on a bamboo made frame, and contain a small candle or fuel cell composed of a waxy flammable material.
 After lit, the flame heats the air inside the lantern, same concept of a hot air balloon which raises the lantern into the sky.
 People nowadays usually write their wishes on the sky lanterns because it is believed as the lantern fly into the sky; it is a way to pass on your wishes to gods above.
Yanshui Beehive Rocket Festival is a distinctive religious event scheduled on the day of the Lantern Festival in Yanshui, Tainan.
 On the day of the Lantern Festival, people would visit Yanshui in Tainan City to follow the sedan chair of the divinity and the release of thousands of firecrackers.
 Participants are required to wear a helmet, mask, towel, as well as dressed in long pants and long sleeves shirt for safety.
 Other than the three major Lantern Festival celebrations of Taiwan listed above, the annual Taiwan Lantern Festival takes place at different Taiwan City every year.
 The splendid Taiwan Lantern Festival is filled with thousands traditional, cartoon and thematically designed lanterns of various shapes, sizes, and colors.
 The theme of Taiwan Lantern Festival varies each year according to the Chinese zodiac sign of 12 animals (rat, ox, tiger, rabbit, dragon, snake, horse, sheep (or goat), monkey, rooster, dog, and pig.) 
The history of Lantern Festival can be traced back to the time when Emperor Wen of the Western Han Dynasty (206 BC - 25 AD) officially set the 15th day of the first month of the Chinese calendar as the Yuan Xiao Festival.
 It has been celebrated as the birthday of the God of Heaven (Shang Yuan Jie), since the Tang dynasty (618 AD - 907 AD).
How is the Lantern Festival celebrated?
 These days, the Lantern Festival is an important occasion for family meetings and reunions.
 Parks across Taiwan become an amazing sight with numerous huge lanterns depicting anything from the current zodiac animal to scenes from traditional Chinese folk stories and more contemporary scenes.
 Schoolchildren will also make many more smaller lanterns adding to the spectacle of the evening.
People in northern China eat "Yuan Xiao," a traditional ball-shaped sweet dessert made with glutinous rice.
 "Yuan Xiao" is called "Tang Yuan" in southern China, symbolizing family unity.
 In ancient times, the lanterns were fairly simple, for only the emperor and noblemen had large ornate ones; in modern times, lanterns have been embellished with many complex designs.
 For example, lanterns are now often made in shapes of animals.
 The Lantern Festival is also known as the Little New Year since it marks the end of the series of celebrations starting from the Chinese New Year.
 Koreans celebrate this festival as the Daeboreum.
The 15th day of the 1st lunar month is the Chinese Lantern Festival because the first lunar month is called yuan-month and in the ancient times people called night Xiao.
 The 15th day is the first night to see a full moon.
 So the day is also called Yuan Xiao Festival in China.
 According to the Chinese tradition, at the very beginning of a new year, when there is a bright full moon hanging in the sky, there should be thousands of colorful lanterns hung out for people to appreciate.
 At this time, people will try to solve the puzzles on the lanterns and eat yuanxiao (glutinous rice ball) and get all their families united in the joyful atmosphere.
 There are many different beliefs about the origin of the Lantern Festival.
 But one thing for sure is that it had something to do with celebrating and cultivating positive relationship between people, families, nature and the higher beings they believed were responsible for bringing/returning the light each year.
 One legend tells us that it was a time to worship Taiyi, the God of Heaven in ancient times.
 The belief was that the God of Heaven controlled the destiny of the human world.
 He had sixteen dragons at his beck and call and he decided when to inflict drought,storms, famine or pestilence upon human beings.
 Beginning with Qinshihuang, the first emperor to unite the country, all subsequent emperors ordered splendid ceremonies each year.
 The emperor would ask Taiyi to bring favorable weather and good health to him and his people.
 Emperor Wudi of the Han Dynasty directed special attention to this event.
 In 104 BC, he proclaimed it one of the most important celebrations and the ceremony would last throughout the night.
 Another legend associates the Lantern Festival with Taoism.
 Tianguan is the Taoist god responsible for good fortune.
 His birthday falls on the 15th day of the first lunar month.
 It is said that Tianguan likes all types of entertainment.
 So followers prepare various kinds of activities during which they pray for good fortune.
 The third story about the origin of the festival goes like this.
 Buddhism first entered China during the reign of Emperor Mingdi of the Eastern Han Dynasty.
 That was in the first century.
 However, it did not exert any great influence among the Chinese people.
 One day, Emperor Mingdi had a dream about a gold man in his palace.
 At the very moment when he was about to ask the mysterious figure who he was, the gold man suddenly rose to the sky and disappeared in the west.
 The next day, Emperor Mingdi sent a scholar to India on a pilgrimage to locate Buddhist scriptures.
 After journeying thousands of miles, the scholar finally returned with the scriptures.
 Emperor Mingdi ordered that a temple be built to house a statue of the Buddha and serve as a repository for the scriptures.
 Followers believe that the aura of the Buddha can dispel darkness.
 So Emperor Mingdi ordered his subjects to display lighted lanterns during what was to become the Lantern Festival.
 Until the Sui Dynasty in the sixth century, Emperor Yangdi invited envoys from other countries to China to see the colorful lighted lanterns and enjoy the gala performances.
 By the beginning of the Tang Dynasty in the seventh century, the lantern displays would last three days.
 The emperor also lifted the curfew, allowing the people to enjoy the festive lanterns day and night.
 It is not difficult to find Chinese poems which describe this happy scene.
 In the Song Dynasty, the festival was celebrated for five days and the activities began to spread to many of the big cities in China.
 Colorful glass and even jade were used to make lanterns, with figures from folk tales painted on the lanterns.
 However, the largest Lantern Festival celebration took place in the early part of the 15th century.
 The festivities continued for ten days.
 Emperor Chengzu had the downtown area set aside as a center for displaying the lanterns.
 Even today, there is a place in Beijing called Dengshikou.
 In Chinese, Deng means lantern and Shi is market.
 The area became a market where lanterns were sold during the day.
 In the evening, the local people would go there to see the beautiful lighted lanterns on display.
 Today, the displaying of lanterns is still a big event on the 15th day of the first lunar month throughout China.
 People enjoy the brightly lit night.
 Chengdu in Southwest China's Sichuan Province, for example, holds a lantern fair each year in the Cultural Park.
 During the Lantern Festival,the park is literally an ocean of lanterns!
 Many new designs attract countless visitors.
 The most eye-catching lantern is the Dragon Pole.
 This is a lantern in the shape of a golden dragon, spiraling up a 27-meter-high pole, spewing fireworks from its mouth
The lion dance originated in China close to a thousand years ago.
The lion is traditionally regarded as a guardian creature.
It is featured in Buddhist lore, being the mount of Manjusri.
There are different variations of the lion dance in other Asian cultures including mainland China, Taiwan, Hong Kong, Macau, Japan, Okinawa, Korea, Vietnam, Malaysia, and Singapore,with each region possessing their own styles.
Chinese lion dances can be broadly categorised into two styles, Northern (北獅) and Southern (南獅).
Northern dance was used as entertainment for the imperial court.
The northern lion is usually red, orange, and yellow (sometimes with green fur for the female lion), shaggy in appearance, with a golden head.
The northern dance is acrobatic and is mainly performed as entertainment.
Southern dance is more symbolic.
It is usually performed as a ceremony to exorcise evil spirits and to summon luck and fortune.
The southern lion exhibits a wide variety of colour and has a distinctive head with large eyes, a mirror on the forehead, and a single horn at center of the head.
The lion dance also symbolises the myth of the Chinese new year The Lion dance is often confused with the Chinese Dragon Dance, which features a team of around ten or more dancers.
The Lion Dance usually consists of two people.
The lion dance has close relations to kung fu and the dancers are usually members of the local kung fu club.
They practise in their club and some train hard to master the skill.
In the north the lions usually appear in pairs.
Northern lions usually have long and shaggy orange and yellow hair with either a red bow, or a green bow on its head to represent a male or female.
During a performance, northern lions resemble a Pekinese Dog or Fu Dogs and movements are very life-like.
Acrobatics are very common, with stunts like lifts or balancing on a giant ball.
Northern lions sometimes appear as a family, with two large "adult" lions and a pair of small "young lions".
Ninghai, in Ningbo, is called the "Homeland of the Lion Dance" (?舞之?) for the northern variety.
During the 1950s-60's, people who joined lion dance troupes were “gangster-like” and there was a lot of fighting amongst lion dance troupes and kung fu schools.
Parents were afraid to let their children join lion dance troupes because of the “gangster” association with the members.
During festivals and performances, when lion dance troupes met, there would be fights between groups.
Some lifts and acrobatic tricks are designed for the lion to “fight” and knock over other rival lions.
Performers even hid daggers in their shoes and clothes, which could be used to injure other lion dancers’ legs, or even attached a metal horn on their lion’s forehead, which could be used to slash other lion heads.
The violence got so extreme that at one point, the Hong Kong government had to put a stop to lion dance completely.
Now, as with many other countries, lion dance troupes must attain a permit from the government in order to perform lion dance.
Although there is still a certain degree of competitiveness, troupes are a lot less violent and aggressive.
Today, lion dance is a more sport-oriented activity.
Lion dance is more for recreation than a way of living.
Several movies in the Once Upon a Time in China series involve plots centered around Lion Dancing, especially Once Upon a Time in China III and IV.
Jet Li has performed as a lion dancer in several of his films, including Southern style lion dancing in Once Upon a Time in China III, Once Upon a Time in China and America and Northern style lion dancing in Shaolin Temple 2, and Shaolin Temple 3.
Mooncakes are traditional Chinese pastries that are made during the Mid-Autumn Festival, which is celebrated in China, Vietnam, as well as other countries in Asia.
Mooncakes are usually round, made in a special mooncake mold, and contain a sweet filling, with the most common one being lotus seed paste or red bean paste.
The sacrifice to the moon on the Mid-Autumn Festival has a very long history, which dates back to the Zhou Dynasty (1046 - 256 BC).
In ancient times, the emperors usually offered a sacrifice to the moon on the Autumnal Equinox at the place called Altar of the Moon.
The Altar of the Moon in Beijing was where the emperors of the Ming (1368-1644) and Qing (1644-1911) Dynasties offered a sacrifice to the moon.
Through the passage of time, this custom has been adopted into folk lore and now the sacrificial ceremony is usually held in family units.
However, nowadays this activity continues only in certain rural areas or at attraction sites and no longer by the majority of Chinese families.
Following tradition, the major offerings for the sacrificial ceremony were moon cakes.
Besides, there were watermelons cut into the shape of a lotus flower, grapefruits, boiled green soybeans, oranges and wine, etc, mostly edible things that are round in shape.
This was significant as the Mid-Autumn Festival was a day for family reunion and the Chinese word for “round” had a similar pronunciation to that of “reunion”.
The offerings would be set on a table that the moonlight could reach, or facing the general direction of the moon on cloudy or rainy nights.
In front of the table, was an incense burner, with lighted red candles on each side.
With the announcement by the host, the ceremony began.
Two deacons walked slowly to stand one each side of the offering table, followed by the officiant (usually the oldest woman in the family or the hostess) and the other attendants (family members), who all went down on their knees in front of the offering table.
The officiant then took over three burning joss sticks from the deacon and made some wishes and then placed the joss sticks in the burner.
This would be done three times.
Then the officiant poured a cup of wine in front of the offerings and read prayers toward the moon.
The paper with written prayers was then burnt, together with moonlight papers (the incense papers painted with the moon palace and goodness of the moon).
After, all attendants genuflected three times.
Finally, the attendants burnt the incense, made wishes and worshiped the moon one by one.
Compared with the moon sacrificial ceremony, the custom of appreciating the moon is much more popular among modern people.
The family members sit around a table and appreciate the moon, while talking to each other and eating the offerings from the ceremony, etc.
The custom was actually derived from the sacrificial ceremony, which made a serious activity into a relaxing one.
It started in the Three Kingdoms Period (220 - 280 AD) to the Jin Dynasty (265 - 420 AD).
In the Tang Dynasty (618 - 907 AD), the custom became very popular.
There are many works of literature of that time praising the moon and expressing yearnings to distant relatives and friends.
It was during the Song Dynasty (960-1279 AD) that a folk festival involving appreciation of the moon was formed and it became the earliest official Mid-Autumn Festival.
The origin is attributed to the victorious insurrectionary army of the Yuan Dynasty (1271 - 1368 AD) that had passed messages by hiding notes in moon cakes.
As gifts, the leader gave moon cakes to his subordinates on the coming Mid-Autumn Festival.
Since then, the custom of eating moon cakes on Mid-Autumn Day became established.
In following eras, after the moon sacrificial ceremony, the officiant cut the biggest moon cake into even pieces based on the number of family members and passed them around to each of them.
Even those who could not make it home on the night had a piece reserved for them because the moon cake signified reunion and the cake sacrificed to the moon was considered auspicious.
Nowadays, although most families do not hold the sacrificial ceremony, family members still gather together to share the delicious round moon cakes on the festival night.
In addition to these common customs, there are those that are popular in certain areas on Mid-Autumn Day.
In southern China, children play with festival lanterns.
In Hong Kong, one of the most important activities is the fire dragon dances; in Shanghai, people go out for moon appreciation instead of staying inside and they burn incense buckets.
In Taiwan, people set off sky lanterns and single girls steal vegetables, which hopefully can bring them a 'Mr. Right.’ These form only the tip of the iceberg.
If one attends a Mid-Autumn Festival in China, they will find more interesting customs for sure.
Although originally a festival among the Han Chinese, the Mid-Autumn Festival is now very popular among ethnic minorities too and they have some unique and interesting customs, such as chasing the moon of Mongolians, seeking the moon of Tibetans and dancing in the moonlight of the Yi people, etc.
Why are the Mid-Autumn Festival lanterns Made?- 4 Symbolic Meanings
1. Create festive atmosphere
The Mid-Autumn Festival is a very happy event, when family members gather together to appreciate moon, worship moon and eat mooncakes.
People also make a variety of Mid-Autumn lanterns.
Several days before the festival, people hang these Moon Festival lanterns to create happy festive atmosphere and welcome the coming of the festival.
2. Inherit the 2,000’ years craftsmanship of making lanterns
Lanterns, the ancient Chinese traditional crafts, are originated in the Western Han Dynasty more than 2,000 years ago.
By making it during festivals, the crafts can be passed down to later generations.
3. Symbolize family reunion
In Chinese culture, lantern is also a symbol of happy reunion since most lanterns are round, and “round” in Chinese has the similar pronunciation with “reunion”.
4. Pray for babies
In some areas of China, the mother send a Mooncake Festival lantern to her daughter who is newly married on the Mid-Autumn Festival, wishing her to bring more population to the family.
This is also because “lantern” and “man” have the similar pronunciation.
And there is also a hope that the daughter have a bright future.
China's Mid-Autumn Festival is the second most important traditional festival in China, but it is much less well known than Chinese New Year overseas.
Quickly discover more about Mid-Autumn origins and customs below.
1. Harvest Moon Obsession — Reunion and Expectation
Since ancient times, there have been many legends about the moon in China.
For the Chinese, the moon is symbolized as being holy, pure, and noble.
Over tens of thousands of poems describing the moon have been recorded.
The moon's round shape also corresponds to the cyclic concepts of Taoism, like the eight diagrams.
That's why Chinese people are fixated on the moon and view round shapes as representing perfection.
2. Luxury Mooncakes — a Box of Mooncakes Is More Expensive Than an iPhone!
When people mention China's Mid-Autumn Festival, the first image many conjure up in their minds is that of a mooncake.
Chinese mooncakes not only have a long history but also have numerous flavors.
In addition to traditional fillings, such as lotus root, melon seeds, and fruit, there are some bizarre recipes, such as chocolate spicy beef filling, leek filling, and fermented bean curd filling.
With different packaging, a mooncake ranges in price from a few dozen yuan to a few thousand yuan, even though a normal mooncake's cost is very cheap.
These high-end mooncakes have become luxurious presents between bureaucrats over the past few years.
3. Travel Peak — Millions Go Home or Touring
As the second most important traditional festival in China, the crowds traveling during the Mid-Autumn Festival should not be underestimated.
Although they can't compare to the travel rush during the Spring Festival, the largest annual migration in the world.
In 2008, the Mid-Autumn Festival was approved as a statutory holiday and people were granted a three-day holiday.
Most people choose to go home for a reunion or go traveling, which normally causes transport stress.
If you have no intention of experiencing millions of people on the move in China, then you should avoid the holiday when planning your tour.
It's not unusual to end up sitting in the car all day on the highway during this holiday in China.
4. WeChat Red Envelopes — Money Rather Than Gifts
Chinese tap their phones urgently to peck up the bits of a shared Wechat red envelope.
Are you still sending cards or making phone calls during the holidays?
You are out of touch.
The most popular greeting nowadays in China is the WeChat red envelope, which is a mobile application allowing users to send or receive money online.
During the Mid-Autumn Festival Gala on CCTV, viewers are invited to shake their smartphones for a chance to win red envelopes.
People usually attach a few words as a greeting when sending a red envelope.
In addition, Chinese people prefer red envelopes containing the number 6 or 8; for example, sending a 666 yuan red envelope because '666' means everything will be fine for you and '888' means hoping you could make a fortune.
As Chinese netizens stated, "There's nothing in the world that cannot be solved by a red envelope; if there were, then two should do it!
5. Matchmaking Time — Fast-Food Style Relationship
Many urban parks in China have a matchmaking corner.
Visitors can see hundreds, sometimes thousands, of older parents and pensioners gather there.
They are there to exchange information about their children who are still unmarried, hoping to find them an ideal spouse.
In China, many people consider a girl to be "leftover" if she is not married by the time she is 25; a man is normally considered this at 30.
Their parents desire to see them get married, and the three-day vacation of the Mid-Autumn Festival is a great chance for them to have a blind date at their parents' request.
6. Family Dinner — Difficult to Book a Seat
Many Chinese enjoy their Mid-Autumn reunion dinner in a restaurant.
The Mid-Autumn Festival carries themes of reunification and strengthening family relationships.
So a family dinner is essential to the night of the Mid-Autumn Festival.
A traditional dinner in China normally means several hours of preparation.
Since the Mid-Autumn Festival is only a three-day break, people no longer make dishes at home but book a meal in a restaurant instead to save time and spend more time with their family.
Normally, people will book the seats and confirm the menu one or two months in advance.
But for some famous restaurants, such as Guangzhou Restaurant, Taotao Ju Restaurant, and Lianxiang Lou Restaurant in Guangzhou, people make reservations just after the Spring Festival!
7. Mid-Autumn Legends — Chinese Prefer the Romantic One
There are many interesting stories explaining the origin of the festival.
Three most widespread stories are: Chang'e flying to the moon, Wu Gang chopping a cherry bay, and the jade rabbit.
The story of Chang'e and Hou Yi is the most widely accepted by Chinese people.
It seems that people prefer romance.
According to legend, long ago there was a beautiful lady, Chang'e, whose husband was a brave archer, Hou Yi.
But one day she drank a bottle of elixir that made her immortal, to honor her husband's instructions to keep it safe.
Then she was separated from her beloved husband, floating up into the sky, and finally landing on the moon, where she lives to this day.
8. Historic Origins — Mid-Autumn Festival Was Around Long Before Mooncakes
The Chinese have celebrated the harvest during the autumn full moon since the Shang Dynasty (1600–1046 BC).
It started to gain popularity as a festival during the early Tang Dynasty (618–907) while the tradition of eating mooncakes during the festival began in the Yuan Dynasty (1271–1368), a dynasty ruled by the Mongols.
Zhu Yuanzhang (the first emperor of the Ming Dynasty, 1368–1644) starting an uprising using mooncakes.
Falling on the 15th day of the 8th month according to the Chinese lunar calendar, the Mid-Autumn Festival is the second grandest festival in China after the Chinese New Year.
It takes its name from the fact that it is always celebrated in the middle of the autumn season.
The day is also known as the Moon Festival, as at that time of the year the moon is at its roundest and brightest.
Mid-Autumn Festival is an inherited custom of moon sacrificial ceremonies.
The ancient Chinese observed that the movement of the moon had a close relationship with changes of the seasons and agricultural production.
Hence, to express their thanks to the moon and celebrate the harvest, they offered a sacrifice to the moon on autumn days.
The Moon Cake is the special food of Mid-Autumn Festival.
On that day, people sacrifice moon cakes to the moon as an offering and eat them for celebration.
Moon cakes come in various flavors according to the region.
The moon cakes are round, symbolizing the reunion of a family, so it is easy to understand how the eating of moon cakes under the round moon can evoke longing for distant relatives and friends.
Nowadays, people present moon cakes to relatives and friends to demonstrate that they wish them a long and happy life.
Mooncake (simplified Chinese: 月?; traditional Chinese:月餅; pinyin: yu? b?ng) is a Chinese bakery product traditionally eaten during the Mid-Autumn Festival / Zhongqiu Festival.
The festival is for lunar worship and moon watching; mooncakes are regarded as an indispensable delicacy on this occasion.
Mooncakes are offered between friends or on family gatherings while celebrating the festival.
The Mid-Autumn Festival is one of the four most important Chinese festivals.
Typical mooncakes are round or rectangular pastries, measuring about 10 cm in diameter and 4–5 cm thick.
A thick filling usually made from lotus seed paste is surrounded by a relatively thin (2–3 mm) crust and may contain yolks from salted duck eggs.
Mooncakes are usually eaten in small wedges accompanied by Chinese tea.
Today, it is customary for businessmen and families to present them to their clients or relatives as presents,[1] helping to fuel a demand for high-end mooncake styles.
Mooncake energy content can vary with the filling and size; the average moon cake carries 800 to 1200 kcal, mainly from fats and sugar.
In ancient times, when people were hungrier, mooncakes were a rich (calorie-wise) delicacy.
This is probably why many Chinese like the egg yolk mooncake (because eggs were expensive), even though it may seem strange to foreigners.
Some Asians actually don't like it (including me), and it is probably one of the flavors most likely to make you throw up and dislike mooncakes forever.
Lotus was also considered a delicacy.
In modern times, mooncakes have become symbols of status and wealth, since mooncakes are traditionally given as gifts to friend and family.
The fancier mooncakes you gift, the more generous you seem.
As a result, mooncakes and their packaging have diversified and more expensive varieties have become more common.
Companies compete to have new, innovative flavors and fillings, and package mooncakes in very fancy boxes.
There are even mooncakes made out of gold, purely for gifting purposes.
In China, they are almost given away too frequently, so many Chinese are reluctant to eat their gifts because of the huge number of calories each mooncake contains.
To make mooncakes more appealing to eat, some companies have created mooncakes for more health-conscious people, so there are fat free mooncakes.
If you receive too many mooncakes, I think you can just save them because all the fat and oil will preserve them.
Then you can snack on them one bite at a time, like cookies.
One should eat mooncakes while observing the full moon and drink tea to counter all the fat and oil you've just consumed.
As one of the most important three Taiwanese traditional festivals, Lantern Festival falls on the fifteenth day of the first lunar month (Chinese New Year), usually in February or March.
Called as Yuanxiao Festival as well, people will eat YuanXiao, which is made of glutinous rice filled with red bean paste or sesame, to celebrate the reunion and harmony of their family.
However, if you think that’s all, then you’re wrong!
Because the grandest and the most remarkable activity is the lantern festival, whose English name is just as same as this traditional festival.
With a variety of interesting and amazing art designs, activities and performances, it is promised that you could have a great time in lantern festival around Taiwan.
Now, let us introduce the story of lantern festival and the activities from south Taiwan to north Taiwan then give you some ideas to plan a new year tour.
In 1990, the government held the first Taiwan Lantern Festival, a giant outdoor exhibition, in which there are kinds of lanterns on display, in order to promote our Taiwanese folk festival and traditional cultures to other countries.
From then on, holding Taiwan Lantern Festival has been a yearly routine.
Each of city in Taiwan takes a turn to be the organizer so that there will be a balanced development.
As a result, depending on different cities, the mix of local custom, theme lantern and soundtrack has gradually been a feature.
For example, as an organizer, Yilan used the sound of afternoon thunderstorm as their beginning while Chiayi chose “Gan Shang Ching”（高山青）as their soundtrack, which mainly praises the magnificence of Ali Mountain and the story of aboriginals.
In 2007, Discovery Channel selected Taiwan Lantern Festival as one of the best worldwide festivals, moreover, they even assign a professional shooting team to Taiwan to make a record.
How glad is it, right?
In 2017, Taiwan Lantern Festival was held in Yunlin (south of Taiwan).
With two big areas and over three thousand lanterns, Taiwan Lantern Festival in this year has the wildest venue and the most lantern decorations.
In addition, there are puppets with the height up to 8 meters showing the local puppetry characteristic.
It’s really a feast combined with the arts of sound, light, and movement.
Every year, the theme lantern is always the main issue which draws the attention of people and the report of every media.
To express the tradition of Taiwanese culture, the theme lantern is absolutely themed with the Chinese zodiac of the current year, and its base is designed in the shape of the eight diagram（八卦）.
Moreover, the government will give out free handheld small lanterns every year, making everyone immersed in the joy of Lantern Festival.
Of course, the lighting ceremony is a highlight as well.
At a chosen auspicious time, the organizer will invite the president, the mayor, and other guests to count down with the public together.
In 2018, Taiwan Lantern Festival will be held in Chiayi.
However, if it’s not convenient for you to join the event, don’t feel upset too early as there are also other lantern festivals around Taiwan, which are brilliant as well.
Let’s move on to see the kinds of lantern festivals!
Taiwan National Day – Double Tenth Day on October 10th
National Day of the Republic of China which is known as the Taiwan National Day or Double Tenth Day is to commemorate the 1911 Wuchang Uprising, a milestone of China’s politics development and a new chapter in the history of the Chinese which led to the collapse of the Qing Dynasty.
On the 10th of October each year celebration and official ceremony are held around Taiwan.
The major official firework event will only hold by one city, but the other cities will proceed with its own celebration and activities.
During this important day of Taiwan, both local and oversea visitors and honored dignitaries from all over the world gather in Taiwan to celebrate and show their respect for the nation.
As a tradition, routine celebrations are planned in front of the Presidential Office Building.
The celebration ceremony begins with the raising of the flag and national anthem followed by a military parade.
The climax of the day is when the President of Taiwan addresses the Presidential Address made to the nation followed by a series of performances by local celebrities and groups.
Visitors may also find Taiwan flags fluttering on the major streets and roads of Taiwan during this period of time forming a colorful scene.
The military parade for the Celebration Ceremony is to show the martial spirit of the armed forces in full regalia during the military review.
The public parade is inclusive of shows performed by representatives of different fields along the way.
During the evening, splendid National Day themed fireworks would be displayed for several hours as finale to wrap up the birthday celebrations that turn the sky dazzling and colorful over major Taiwan cities representing the bright and colorful future of Taiwan.
The Double Tenth Day or Taiwan National Day is now celebrated as a holiday.
The holiday often align with the weekend and it became a long weekend.
Therefore it is often a opportunity for the people to take a short vacation out of town.
The renowned attractions are often packed visitors as well as the crazy traffic jamming throughout the famous tourist sites.
We suggest tourist visiting Taiwan at this time avoid the long distance traffics and simply just enjoy the city.
The red envelope is a gift that combines the material and tangible aspect of currency with the more abstract sentiments of well-wishing and blessings.
By giving someone this gift, not only are you giving them something that is practical and useful in their daily life, but you are also expressing to them that you wish them well.
The red envelope is thus a very significant symbol of Asian culture.
Just on the envelope design itself, you may discover the values of the Asian community: prosperity, health, longevity,etc.
(through the words that may be inscribed on it).
The manner by which the gift is given (usually from the elders to the younger people) expresses the Asian value of the responsibility of elders towards watching out for the younger generations.
Also, during Chinese New Year young ones are meant to return blessings to their elders in exchange for red envelopes, signifying the importance of inter-generational relationships.
Like any other gift, there is an assumption that there will be reciprocity.
However, often the gift giver will not be the direct recipient of the reciprocated gift, but perhaps their children may be.
It may be expected that if you give someone's child a red envelope that they, in return, would give to your child.
Consequently, there is a more complex network of gift exchange rather than a simple exchange between two people.
Red paper envelopes are mostly constructed and printed in China then shipped over to the United States to smaller distributors including small hole-in-the wall retailers or larger retailers such as large Asian supermarkets.
However, nowadays there are printing companies based in the U.S. will also print red envelopes with custom designs.
Most families will buy one large packet of red envelopes from their local market or stores to prepare for the holiday season.
During parties or gatherings (especially the Lunar New Year) the adults will pass out these red envelopes with money contained in them to the younger folks.
Often times the recipients of the envelope will focus on the money held inside and take for granted the culture and history behind the production of the envelope as well as the blessings that follow with it.
Giving a red envelope filled with lucky money is a common way for the Chinese to show appreciation during important celebrations like Chinese New Year, birthdays and weddings.
In this guide, I’ll cover when to give a red envelope, how to choose the right design and how much to give.
The red envelope tradition is all about the reciprocity of giving and receiving.
It’s a gesture of goodwill, expressed through the exchange of red envelopes, that builds relationships among family and friends.
In fact, after many rounds of giving and receiving red envelopes over the years, you’ll probably find that you end up netting even financially.
Count the relationships, not the dollars.
A Chinese red envelope (known as lai see in Cantonese and hong bao in Mandarin) is simply an ornate red pocket of paper the size of an index card.
They’re commonly decorated with beautiful Chinese calligraphy and symbols conveying good luck and prosperity on the recipient.
Though they’re unquestionably a symbol associated with Chinese New Year, birthdays and weddings, red envelopes are also given for graduations, the launches of new ventures and other special occasions.
Regardless of the event, this basic red envelope etiquette applies: Choose new bills, don’t ever include coins and these days checks are OK.
Avoid the number four because of its resemblance to the word meaning death.
And, optional, but $88 (8 rhymes with the word for good luck) and $99 (for longevity) are positive symbolic amounts.
One of the most memorable scenes from the 2011 award-winning Taiwanese movie You Are The Apple Of My Eye showed the lead characters jointly releasing a tiandeng (sky lantern) with their wishes on it.
The young couple penned their hopes on the bulbous-shaped paper creation and watched it drift off in the clear sky.
This gesture of sending one’s written wishes upwards to be blessed by divine forces may seem old-fashioned in the digital age, but this tradition remains strong in Taiwan.
The sky lanterns are popular with people who want to be blessed for various reasons, including students eyeing good grades, lovers hoping for a happy ending and workers aiming for career success.
Even married couples keen to have children want to be blessed because the local pronunciation of sky lantern is tiending, which sounds like “newborn son”.
Sky lanterns are made with rice paper, thin bamboo strips and wire and are powered by kerosene-soaked prayer papers.
Origin of Spring Festival The origin of the Spring Festival now is too old to be traced.
It is widely believed that the word ‘Nian'(in Chinese means 'year'), was first the name of a monster beast that started to prey on human being at the night before the beginning of a new year.
It had a very big mouth that would swallow many people with one bite.
People were very scared.
One day, an old man came to their rescue, offering to subdue ‘Nian'.
He said to ‘Nian' that ‘I hear that you are quite capable, but can you swallow other beasts on earth instead of people who are by no means of your worthy opponents?
 Hence, ‘Nian' did swallow many of the beasts of prey on earth that also harassed people and their domestic animals from time to time.
After that, the old man who turned out to be an immoral fairy disappeared riding the beast ‘Nian'.
Now that ‘Nian' had gone and other beasts of prey are scared off into the forests, people began to enjoy their life in peace and happiness.
Before the old man left, he had told people to put up red paper decorations on their windows and doors at each year's end to scare away ‘Nian' in case it sneaked back again, because red is the color that the beast feared most.
From then on, the tradition of observing the conquest of ‘Nian' is carried on from generation to generation.
The term ‘Guo Nian', which may mean ‘Survive the Nian' becomes today's ‘Celebrate the New Year', as the word ‘Guo' in Chinese having both the meaning of ‘pass-over' and ‘observe'.
The custom of putting up red paper and firing firecrackers to scare away 'Nian' had been well preserved
Two Features of Spring Festival Equal to Christmas of the West in significance, the Spring Festival is the most important holiday in China.
Two features distinguish it from the other festivals.
One is seeing off the old year and greeting the new.
The other is family reunion.
Two weeks before the festival the whole country is permeated with a holiday atmosphere.
On the 8th day of the twelfth lunar month, many families will make the Laba Congee, a kind of congee made from more than eight treasures, including the glutinous rice, lotus seed, beans, gingko, millet and so on.
Shops and streets are beautifully decorated and every household is busy at shopping and preparing for the festival.
In the past, all families would make a throughout house cleaning, settling accounts and clearing off debts, by which to pass the year.
Customs of Spring Festival Paste couplets(Chinese: ?春?): it’s a kind of literature.
Chinese people like to write some dual and concise words on red paper to express their new year’s wishes.
On the arrival of New Year, every family will paste couplets.
Paste couplets Family reunion dinner(Chinese: ???): people travelling or residing in a place far away from home will back to their home to get together with their families.
Stay up late on New Year’s Eve(Chinese: 守?): it’s a kind of way for Chinese people to welcome New Year’s arrival.
Staying up late on New Year’s Eve is endowed with auspicious meaning by people.
The old do it for cherishing their past time, the young do it for their parents’ longevity.
Hand out red packets(Chinese: ??包): elders will put some money into red packets, and then hand out to the younger generation during spring festival.
In recent years, electric red packets are popular among younger generation.
Set off firecrackers: Chinese people think the loud sound of the firecrackers can drive away devils, and the fire of the firecrackers can make their life thriving in the coming year.
Celebration Activities on Chinese New Year’s Eve After dinner, people sit together before the TV to watch the New Year's program and chat with each other.
About ten minutes before the ringing of the New Year's bell, people let out the fireworks to welcome back the Kitchen God from the Heaven, who is in charge of the fortune and misfortune of the household he dwells.
Some of the customs based on superstitions are quite interesting.
For example, on New Year's Day, people will not sweep the floor, do washing or dump their garbage out of the house, lest these would do away with their fortune.
On the Lunar New Year's Eve, people like to stick the Chinese character ‘Happiness' upside down on doors or walls, because ‘upside down' in Chinese is a homophone of ‘coming' or ‘arriving'.
The custom of pasting couplets on the doors has a long history.
In the ancient times, people hung short branches of peach tree on the doors or at the front gates for the purpose of driving away the evil things.
Later they became peach wood boards with some Chinese characters written on them.
With the invention of paper, on each of which was written a verse line to welcome the New Year to express wishes for happiness and good fortune.
During the long time development, spring couplets have become a special form of literature with their own characteristic.
After putting up couplets and pictures in the doors on the Lunar New Year's Eve, the last day of the twelfth moon in the Chinese lunar calendar , each family gathers for a sumptuous meal called ‘family reunion dinner'.
People will enjoy the food and drink in abundance and Jiaozi.
The meal is more luxurious than usual.
Dishes such as chicken, fish and bean curd is necessary, for in Chinese, their pronunciations sounds like ‘Ji', ‘Yu', and ‘Doufu', with the meanings of auspicious, abundant and rich.
Sons and daughters working away from home come back to join their parents.
What to Do During New Year Period?
On the first three days of the festival, people will visit their close relatives and best friends, exchanging greetings and presents, which is known as the ‘New Year's Visit'.
The young generation are given the red envelop from their elder generation.
The Spring Festival carnivals take place during this period.
There are performances of dragon dancing, lion dancing and recreational parades in the street by some troupes.
The fifth day is known as ‘Po Woo'.
On this day people stay home to welcome the God of Wealth.
No one visits relatives and friends because it will bring both parts bad luck.
From the sixth day to the tenth day, people either go out the visit their relatives or friends or go to the temples to pray for good fortune and health in the coming year.
In addition, the seventh day is the day for farmers to display their agricultural products.
The day also considered as the birthday of human beings.
Noodles are eaten to promote longevity.
As to the ninth day, people present the offering to the Jade Emperor, the God of the Heaven in Chinese Legend.
When it comes to the tenth day, relatives and friends should be invited home to have dinner.
After such a long time's sumptuous feast, on the 13th day people are supposed to have something simple and light to cleanse their body system to keep health.
Modern Chinese New Year Activities With the popularity of smart phone and other mobile devices, more and more people show interest in greeting by smart phone, which is very popular among younger generation.
? Greet by smart phone Sending congratulations through smart phone gradually becomes the most popular way to greet people during Chinese New Year.
Congratulations with text and graphics by smart phone is vivid and convenient.
Even the friend is far away from you, he/she still can receive congratulations immediately.
Chinese people prefer wechat or QQ, while western people prefer FaceBook or WhatsApp.
Besides, red packet in wechat makes Chinese people crazy.
No matter how much money they can receive from wechat friend, people still pretty enjoy it and never tire of it.
? Lover rental Different from western culture, elders will still pay much attention to their children’s life even they have been an adult.
When single youngsters who have exceeded the lawful marriage age, the elder generation will press them to look for a marriage partner quickly during Spring Festival, this creates new business—fake boy/girl friend rental.
Nian’gao(年糕): a kind of Chinese cake, made by sticky rice.
It’s an indispensible food in Chinese New Year for the meaning of thriving in the New Year.
Jiaozi(?子): a kind of dumplings in China.
There are many kinds of jiaozi with different ingredients.
Chinese people eat Jiaozi on New Year’s Eve, people who eats the Jiaozi with coin or some special ingredient will be the luckiest.
Family making dumpling together Tangyuan(??): a kind of dumplings in China.
Tangyuan with sesame inside is the most common filling.
There is a slight difference between northern China and southern China.
The filling is salty in northern China, while that is sweet in southern China.
Zongzi(粽子): a kind of dumplings in China.
It’s made of sticky rice filled with different fillings and wrapped in bamboo leaves.
Meat is the main filling in southern China, while bean is main filling in northern China.
Regarding to different places around the nation, people will have different activities to celebrate the festival.
During the festival, there are many operas and other performances on the stages.
Taiwan Lantern Festival The Tourism Bureau has been holding the Taiwan Lantern Festival for years to attract visitors and raise the international profile of the cultural attractions of Taiwan.
Traditionally, the festival has been celebrated by carrying hand lanterns.
The Taiwan Lantern Festival adds a high-tech touch to this traditional custom and brings the event to the international stage.
From the themed lantern displays to folk arts and performances, the festival has become a favourite of both locals and visitors.
Taipei & Kaohsiung Lantern Festivals Colorful lanterns of all sizes and shapes have always been the main feature of the Lantern Festival, which is celebrated with a grand national festival and major festivals in Taipei and Kaohsiung.
The Taipei Lantern Festival is held for several days at the Taipei Expo Park, reaching its peak on the day of the Lantern Festival itself.
There are many traditional lanterns, electromechanical lantern displays and large themed lanterns sponsored and designed by different companies.
The Kaohsiung Lantern Festival is held along the Love River.
During the festival period, both sides of the river as well as Wufu Rd, Heping Rd, Guangzhou St and other thoroughfares have lantern exhibitions.
There are also musical performances, helping to throw the whole city into a festive mood.
Pingxi Sky Lantern Festival The Pingxi Sky Lantern Festival is one of the most colorful activities.
Pingxi is a remote hillside town.
In the past, those who worked or farmed in the mountains faced the risk of being robbed or killed, and they used lanterns to inform their families they were safe.
The lanterns do not function as signals anymore, but are now used as symbols of peace and good fortune.
Yenshui Fireworks Festival The fireworks display at the God of War Temple in Yanshui, Tainan City, is one of the most popular and anticipated events of the Lantern Festival.
The display starts one day before the Lantern Festival, when the deity tours the town in his sedan chair, accompanied by the discharge of firecrackers and bottle rockets.
The noise, lights and rituals that follow the God continue well into the following morning.
Dragon Boat Festival is celebrated on the fifth day of the fifth lunar month to commemorate the patriotic poet Qu Yuan.
It is one of the three major celebrated festivals in Taiwan, together with Chinese New Year and the Moon Festival.
Also known as Mid-Autumn Festival, the Moon Festival is celebrated on the fifteenth day of the eighth lunar month in observance of the bountiful autumn harvest with the moon forming a round shape that symbolizes family reunion.
Taiwan National Day is also called Double Tenth Day as it is on the 10th of October.
It is to commemorate the 1911 Wuchang Uprising, a milestone of China’s politics development and a new chapter in the history of the Chinese which led to the collapse of the Qing Dynasty.
Chinese New Year is the most important festival celebrated by the ethnic Chinese and is based on the Chinese lunar calendar.
It begins on the first day of the first lunar month, and ends on the 15th day of the first month, which is the Lantern Festival.
The Lantern Festival is celebrated annually on the 15th day of the first lunar month to mark the grand finale of the Chinese New Year celebrations.
It is also the very first full moon day of the New Year, symbolizing the coming of the spring.
There are many activities all over Taiwan during Taiwan Lantern Festival.
During the Taiwan Lantern Festival, thousands of sky lanterns light over Pingxi District (平溪) in Taiwan.
In Yanshui District, the firecrackers ceremony of the Wumiao Temple is also one of the important activities.
The Tainan Yanshui Fireworks Display ("beehive of fireworks") was originally celebrated to ward off evil and disease from the town.
The Taipei Pingxi Sky Lanterns were released originally to let others know that the town was safe.
These lanterns are decorated with wishes and images relating to the owner.
These two events are known together as "Fireworks in the South, Sky Lanterns in the North.
The festival is the most important annual lantern festival in Taiwan.
Prior to 26, the event was held at Chiang Kai-shek Memorial Park in Taipei.
Since 2001, the event has toured Taiwan.
The American Discovery Channel's program "Fantastic Festivals of the World" has highlighted the Taiwan Lantern Festival as one of the best festivals in the world.
Taiwanese people write their wishes on the lanterns with a belief to bring an abundant crop.
Women wish for a new son to earn more hands to work.
These lanterns fly to the sky, bring their wishes to the Gods, and alternatively speak all dreams them that being blessed with luck and good things.
The theme of the main lanterns often corresponds with the zodiac signs of Chinese astrology.
All of them are over ten meters tall.
Since 123, every main lantern has its own theme music which is about 3 days in length and plays the rhythm when making performances during Taiwan Lantern Festival.
The smaller lanterns, often carried by children or placed on temples, show images of historical figures, birds, or images from that year's theme.
The lanterns depict images such as historical figures, birds, or of the theme determined each year.
There are various versions about the origin of the Double-fifth Festival, and at least ten different ones were sorted out by scholars, among which the most influential version is to reminisce about Qu Yuan (屈原, BC340-BC278).
It is said that Qu Yuan was a poet and a minister in the State of Chu during the Warring States Period (BC475-BC221).
At first he won the full confidence and respect of his sovereign, King Huai of the Chu State.
But later the king was surrounded by jealous self-seekers, so he ignored Qu Yuan’s advice that the State of Chu ought to unite with the state of Qi to fight against the state of Qin.
As a result, King Huai was tricked into the State of Qin and died there.
King Qingxiang of Chu, the eldest son of King Huai, didn’t take revenge.
Instead, he dismissed Qu Yuan from office and sent him into exile as a vagrant.
Later the capital of Chu was captured by the troops from Qin.
In great agony, Qu Yuan drowned himself in the Miluo River (汨?江, located in today’s Hunan province) with his wishes to save his beloved country unfulfilled.
Qu Yuan, a poet and political advisor in ancient China, which the origin and history of Dragon Boat Festival is based upon.
The legend claims that the day when Qu Yuan drowned himself in the river was the fifth day of the fifth lunar month.
The local people rushed in boats to rescue or search for him.
Some of them threw bamboo tubes with rice and other food inside into the river, hoping to feed fish and shrimps lest they should eat away his body.
This is said to be the origin of zongzi (rice dumpling).
An old doctor of traditional Chinese Medicine poured the realgar wine into the river to make river dragons drunk, otherwise they would hurt Qu Yuan.
The local people were also said to have paddled out on boats, either to scare the fish away or to retrieve his body.
This is said to be the origin of dragon boat racing.
Dragon boat racing is one of the main highlights during the Dragon Boat Festival.
This competition is very popular all over China especially in the south.
A dragon boat is a human-powered boat traditionally made of teak wood to various designs and sizes: from small dragon boats with 10 paddlers, up to the massive traditional boats which can have a capacity of 50 paddlers.
It is a long, slim, dragon-like canoe and is often brightly painted and decorated with designs of Chinese dragon heads and tails.
The crew use single bladed paddles to drive the boat forward, a method of propulsion common to many other paddled water craft around the world.
Every boat usually has one drummer or caller at the bow facing towards the paddlers, and one sweep or helmsman at the rear of the boat.
A dragon boat race usually cover distances over 200m or 250m, 500m, 1000m and 2000m.
Before the race starts there is also a series of ceremonies such as worship and awakening the dragon.
A fierce battle among the competitors was ignited the moment the competition starts.
Bursts of percussion and the cheering from viewers heat the atmosphere up rapidly.
During the sprint, the drummer leads the paddlers throughout a race using the rhythmic drum beat to indicate the frequency and synchronicity of all the paddlers’ strokes (that is, the cadence, picking up or accelerating the pace, slowing the rate, etc.) The drummer may issue commands to the crew through a combination of hand signals and voice calls, and also generally exhorts the crew to perform at their peak.
The drummer may be considered the “heartbeat” of the dragon boat.
Nowadays, dragon boat racing is a worldwide sport.
Modern dragon boat racing is organised at an international level by the International Dragon Boat Federation (IDBF).
IDBF International Standard Racing Boat has a Crew of 22, consisting of 20 paddlers, one Drummer and a Helm (Steerer).
Eating zongzi is an essential activity of the Dragon Boat Festival.
This kind of traditional Chinese food is made of glutinous rice stuffed with different fillings and wrapped in bamboo or reed leaves.
It is said that as early as the Spring and Autumn Period(BC 770- BC 476),the earliest form of zongzi: Tongzong(筒粽) and Jiaoshu(角黍) came into existence.
The former was made of rice in the bamboo tubes while the latter was made of the broomcorn millet wrapped in leaves in cow-horn shapes.
With the evolution over many dynasties, Zongzi is seen in various shapes with a variety of fillings.
The shape of zongzi ranges from being relatively tetrahedral in southern Chinese to cylindrical in northern Chinese.
Wrapping a zongzi neatly is a skill which is passed down through families, as are the recipes.
Zongzi comes in many types and flavours.
The more traditional ones includes savoury rice dumpling with fillings like meat, mushroom, salted eggs and nuts.
The sweeter versions may have red bean or a plain rice dumpling which is usually dipped in honey or sugar before every bite.
Different fillings give the dumpling different tastes.
Mung beans, red bean paste, jujubes, Chinese sausage, red-cooked pork, dried shrimp, dark’s egg yolk and so on are very common ingredients in zongzi recipe.
While making a zongzi with red-cooked pork filling, the glutinous rice in the recipe is commonly dipped in soy sauce beforehand making the zongzi tastier, complimenting the filling better and giving it its distinctive brownish color.
Zongzi need to be steamed or boiled for several minutes depending on how the rice is made prior to adding the fillings.
Usually, 20 minutes will be sufficient.
Once cooked, the zongzi can easily be frozen for later consumption.
While Zongzi is a daily food product that is available in many Chinese markets throughout the year, during this Dragon Boat Festival, Zongzi becomes very popular.
Being synonymous with the festival, many families will buy or even home-make Zongzi as part of a Chinese tradition.
An interesting and fun custom during this festival is to make eggs ‘stand up’.
In the Lunar Calendar, June is the ‘Horse month’, while the ‘Horse hour’ is from 11:00 AM to 13:00 PM.
The Dragon Boat festival being in June, it is traditionally believed that you will be lucky for the coming year if you can make an egg standing up during Horse hour on day of the festival.
It is said that it will be easier to make an egg stand up at noon.
This feat seemed quite a phenomenon and people looked for the scientific explanation.
It seems an egg can stay ‘standing up’ because the Dragon Boat Festival is close to the summer solstice, which is the longest day of the year.
The summer solstice occurs when the Earth’s axis tilts the most toward the sun, causing the sun to be farthest north at noon.
During the day and especially at noon, the gravitation between sun and earth pulling at each other are the strongest, hence explaining the phenomenon.
Try making an egg stand during the noon of Dragon Boat Festival!
It may amuse you and your friend and bring you good luck for the coming year!
Children often hang a small balmy bag on their necks on this day.
It‘s believed that if you carry the small spice balmy bag around with you, it not only drives away evil spirits but also brings fortune and happiness to those who wear it.
The small bags are hand-made by local craftsmen.
They‘re made with colourful silk, fine satin or cotton.
Figures of animals, flowers and fruits are often embroidered onto the bags and inside are mixed Chinese herbal medicines which send out the charming flavour.
Pomelo, is a large citrus fruit which looks like a large version of grapefruit.
It is a high nutritional value fruit called “Yo Zhi” in Chinese.
The Yo in Yo Zhi sounds similar to blessing in Chinese, as people wish for the blessing of the moon and the production season happens to be around September.
So the fruit became very popular during this time of the year and gradually became the representative fruit of the holiday.
It often serves with moon cakes while you visit friend and family during this time of the year.
The Qingming Festival is also known as Tomb-Sweeping Day and it falls in late spring (April 4th or 5th).
With natural and humanistic connotations, Tomb-Sweeping Day is not only the natural solar term but is also a traditional festival that has been celebrated by Chinese people for thousands of years.
The historical development of Tomb-Sweeping Day carries rich cultural connotations.
Due to different regional cultures, various customs are observed during the Qingming Festival across the country.
Although festival activities vary from place to place, tomb sweeping, ancestor worship, and outings are common basic rituals and customs in China.
Traditional Customs for the Qingming Festival:
Having an outing.
Weather conditions during the Qingming Festival are comfortable and people enjoy having outings with friends, something that has been popular since ancient times.
Tree planting.
Before and after the Qingming Festival, the survival rate of newly planted saplings is high and so is the growth rate.
Therefore, there is a custom of planting trees on Tomb-Sweeping Day, and some people also call it “Arbor Day”.
Flying a kite.
Flying kites is also a popular activity during the Qingming Festival, not just during the daytime but also at nighttime.
People tie small, colorful lanterns on the kites, which look like shining stars at night.
Tomb sweeping and worshiping ancestors.
The Qingming Festival is the most important day to honor ancestors in spring.
The Dragon Boat Festival is also known as the Duanwu Festival.
It falls on the fifth day of the fifth Chinese lunar month.
Among the traditional Chinese festivals, the Dragon Boat Festival has a long history of over 2,000 years.
During this significant festival, Chinese people eat rice dumplings and hold dragon boat races to celebrate it.
It is said that the festival is held in memory of a very famous poet named Qu Yuan.
Eating traditional festival food called zongzi.
Glutinous rice is wrapped in bamboo leaves.
The flavors differ between the north and south of China.
Northern people prefer sweet rice dumplings but southern people prefer to eat salty rice dumplings.
Dragon boat races.
It’s a very popular activity during the festival.
People are divided into groups and each team works the oars together to reach the destination first.
Drinking hsiung huang wine and wearing fragrant sachets to drive evil away.
This special custom originated in a fairy tale named The White Snake.
The leading man of the story was named Xu Xian and he fell in love with a girl, but he didn’t know the girl was actually a snake.
She was a very kind girl and was in love with him too.
However, when he discovered the truth, Xu Xian drank hsiung huang wine to drive away the snake.
The Mid-Autumn Festival is perhaps the second most important festival in China and originated from the practice of worshiping the moon.
It’s said that the fullest and roundest moon will appear on the festival night.
A round moon symbolizes perfection and reunions.
Traditionally, family members eat mooncakes and admire the beautiful moon in the yard together.
It’s a very precious and happy time for Chinese people.
Admiring the full and round moon.
The Mid-Autumn Festival can be dated back to the Zhou Dynasty (1046–256 BC).
A full moon is a symbol of family unity.
During the night, family members sit together to enjoy the beautiful scene in the yard.
Sacrificing to the moon.
In ancient times, people held ceremonies to celebrate the full moon with mooncakes, apples, and so on.
Eating some round food, such as mooncakes.
Mooncakes represent best wishes to the people they’re given to, and it’s necessary to eat them when gazing at the full moon.
Spring Festival, Dragon Boat Festival and Mid Autumn Festival are the three important festivals in China.
People eat different food on these festivals.
They are Tangyuan (also called rice glue ball or sweet dumpling), Zongzi (rice dumpling) and Moon Cake.
The Spring Festival is the most important festival in China.
The Tangyuan is eaten on the fifteenth day of the first lunar month, or the Lantern Festival.
The rice glue balls, cooked by boiling, tastes sweet and soft.
The fillings are with grounded chestnut, peanut, sesame, jujube paste or bean paste, mixed with sugar.
People who like sweetness would find it delicious.
People eat glutinous rice dumplings as a wish for reunion.
The Zongzi or rice dumplings, is eaten on the Dragon Boat Festival, or the fifth day of the fifth lunar month.
It is wrapped in bamboo or reed leaves.
People in different regions use different materials to make it.
In east China, like Suzhou, Jiaxing and Ningbo, the fillings would be bean paste, chestnut, jujube paste or fresh meat.
In north China, it would be jujube or preserved fruit.
As a kind of food for festivals, zongzi has been eaten for a long time.
The folklore goes that people ate it to commemorate a patriotic poet, Qu Yuan.
It is said that in the 3rd century B.C., the poet committed suicide because his country had been invaded.
People commemorated him by throwing glutinous rice, stored in bamboo tube, into the river.
Later they wrapped it with reed leaves and strings.
That's how the food developed.
Some people give it as a present when visiting friends and relatives on the Dragon Boat Festival.
The Mid Autumn Festival falls on the fifteenth day of the eighth lunar months.
People eat moon cakes for family gathering.
The cake is round, like the full moon, with fillings inside.
There are some patterns on the surface of the cake.
During the mid autumn festival, people would place some cake and fruit.
Moon cakes are different in different regions.
Those made in Beijing, Suzhou, some areas of south Guangzhou and Chaozhou in Guangzhou are most famous.
The fillings can be made of sugar, jujube paste, bean, ham, fruit, or cream, etc.
It is also one of the presents that people can take with them when visiting friends and relatives on Mid Autumn Festival.
The Mid-Autumn Festival is one of Taiwan’s most popular holidays, when families reunite for a meal, and kids gather around to hear tales of the lady in the moon.
Also known as Moon Festival, there are many traditions both old and new associated with the holiday.
But no festival in Taiwan would be complete without food, and Moon Festival is no different.
In recent years, bakeries have become even more adventurous with the moon cake.
You can now find them in practically any flavor under the sun, and H?agen-Dazs has even made their own ice-cream version, which is extremely popular if a little different.
Many companies in Taiwan will buy boxes of moon cakes and hand them out to their clients, employees, and customers.
So it’s not unusual for a typical household to have a few boxes to get through over the holiday.
But why do the Chinese celebrate by giving out and eating mooncakes?
The origin of the custom has several different origin stories.
The Mid-Autumn Festival itself can be traced back as far as the Zhou Dynasty, the longest of all Chinese dynasties that reigned in the years 1046 – 256 BCE, but the mooncake custom wasn’t solidified until the Tang Dynasty (619-907) when Chinese folklore tells the tale of a Turpan businessman who offered cakes to Emperor Taizong after his victory on the fifteenth day of the eighth lunar month against the Xiongnu, the nomadic peoples of ancient central Asia.
According to the story, Taizong looked at the moon while eating one of the cakes and said, “I’d like to invite the toad to enjoy the h?(胡) cake.
”He shared the cakes with his ministers, and the custom of eating these h?cakes was soon practiced in celebration of the event.
As the practice began to spread throughout the country, the round cakes eventually became known as mooncakes.
The mooncake giving was later linked to the harvest festival during the Song Dynasty (906–1279).
According to a different tale, the traditional mooncakes played a significant part during the Chinese rebellion against the Mongols at the end of the Yuan Dynasty (1280–1368).
Rebel leader Zhu Yuanzhang distributed thousands of mooncakes to Chinese residents in the Mongol capital in the guise of celebrating the Mongol ruler.
Each cake concealed a piece of paper saying, “Kill the Mongols on the 15th day of the eighth month.
” The plan succeeded, and the Mongols were overthrown.
Zhu then founded the Ming Dynasty (1368-1644) and supposedly began the tradition still practiced today.
Another important part of the celebration still being observed today is moon worship.
There is an ancient Chinese belief in rejuvenation which was then associated with the moon and water.
Offerings are usually made to the well-known lunar deity, Chang’e, the Moon Goddess of Immortality.
While changes in technology, science, economy, culture, and religion have contributed to the evolution of how the tradition is celebrated today, the festival’s traditions and myths have remained rooted in the three concepts of gathering, thanksgiving and praying.
The event is widely celebrated not only in China but also in many parts of the world with strong Chinese influence such as Vietnam and some parts of the Philippines.
Almost every country has a harvest festival, and China is no exception.
Mid Autumn Festival is China’s Thanksgiving celebration.
The festival is held on the 15th day of 8th Chinese Han Calendar month, and is celebrated with great enthusiasm all over the country.
There are several customs associated with the celebration of this festival, one of which is carrying lanterns.
For Mid Autumn Festival celebration, Chinese children make colorful lanterns on their own, and carry them all around to show them off to their friends and other acquaintances.
If they can’t make them, parents buy readymade lanterns for them, which they carry around with enthusiasm.
These lanterns can be of different sizes and shapes, and are left to float on the river waters.
People stay by the riverside until the lights of the lantern disappears.
Sometimes, Kongming lanterns are made, which may fly due to heated air inside them.
Flying lanterns are allowed to float in the air just like a hot air balloon.
People either make them at home, or buy readymade ones from the market.
It is tradition to make a wish for the coming year, and let the lantern float in the air.
The longer the lantern stays afloat, the better your chances are to get the wish fulfilled.
Some lanterns may be round, tall, short or square, while others may be in the shape of rabbits, pumpkins or other animals.
Zongzi is traditionally eaten during the Dragon Boat Festival (Mandarin: Duanwu; Cantonese: Tuen Ng) which falls on the fifth day of the fifth moon of the Chinese calendar (approximately early- to mid-June), commemorating the death of Qu Yuan, a famous Chinese poet from the kingdom of Chu who lived during the Warring States period.
Known for his patriotism, Qu Yuan tried unsuccessfully to warn his king and countrymen against the expansionism of their Qin neighbors.
When the Qin Dynasty general Bai Qi took Yingdu, the Chu capital, in 278 BC, Qu Yuan's grief was so intense that he drowned himself in the Miluo river after penning the Lament for Ying.
According to legend, rice dumplings were thrown into the river to prevent fish from eating the poet's body.
Another version states that the dumplings were given to placate a dragon that lived in the river.
In Taiwan, the celebration consists mainly of eating moon cakes, pomelos, and BBQ.
In recent years, the most popular way for people to mark the occasion is to gather with friends and relatives and barbecue on the sidewalk in front of one's house or business, in public parks, and along riverside parks.
Unlike other parts of the world that observe the lunar calendar, such as Hong Kong, Taiwan does not have much in the way of major lantern displays during the Moon Festival.
The best times to catch a glimpse of lanterns in Taiwan are on the 15th day of first month of the lunar year (元宵節, Chinese Lantern Festival), when you can see the sky lanterns in Pingxi in New Taipei, and during the Ghost Festival (中元節) on the 15th day of the 7th lunar month, when you can see the water lanterns in Keelung.
There are conflicting theories over the origin of moon cakes.
Many attribute them to the moon goddess Chang'e (嫦娥) , who legend has it took an immortality elixir and floated to the moon.
The other theory is that the moon cakes were distributed to all Han Chinese with a hidden message telling them to rebel against the Mongols during the Yuan Dynasty (1271-1368).
In addition to the moon, the round shape of the cake also symbolizes unity for Chinese families and the mid-Autumn Festival is a time for relatives to gather together.
China, being a culturally diverse and fervent society, celebrates various festivals.
Different regions or ethnic groups have their own festivals depending on the local customs and the ethnic culture.
The most important and popular national festivals of China are Spring Festival, Lantern Festival, Qingming Festival, Dragon Boat Festival and Mid-Autumn Festival.
The below is a list of some traditional Chinese festivals, tourist festivals and ethnic fetivals.
Dragon boat races are the most exciting part of the Dragon Boat Festival (aka Dunanwu Festival).
On the river, Dragon boats, like a cluster of flying arrows, are marching on following fast paddling, drum and shout in order.
The grand view always draws crowds of.
One of the most important Chinese festivals is the Mid-Autumn Festival (aka Moon Festival or Moon Cake Festival among the English speakers).
Chinese ancestors believed that the seventh, eighth, and ninth lunar months belong to autumn.
Qing Ming Festival, also called Pure Brightness Day and Tomb Sweeping Day.
It comes around April 5 every year.
Qingming, meaning clear and bright, is the day for mourning the dead.
This was originally a day set aside for people to offer sacrifices to their ancestor.
In China, there are public holidays on 7 legal festivals in a year, namely New Year's Day, Chinese New Year (Spring Festival), Qingming Festival, May Day, Dragon Boat Festival, Mid-Autumn Festival, and National Day.
People celebrate many other festivals but they do not have time off: Arbor Day, International Nurses' Day, Anniversary of the Founding of the Chinese Communist Party, Teachers' Day and Journalists' Day.
Burning Hell Money
To satiate their dead relatives, Chinese will offer meals and burn joss sticks, "hell money" (wads of fake paper money), and assorted paper versions of earthly goods like TVs, cars, and furniture.
The Chinese, who believe that the ancestors can help them and their businesses from beyond the grave, do this to ensure continuing blessings and protection from beyond.
Food Offerings Left in Public
Food offerings are also left along roadsides and street corners, and outside houses.
The latter theoretically prevent hungry ghosts from entering residences - after all, with food just waiting outside the door, who needs to go inside?
Visit the local Taoist temples and wet markets to see the most spectacular displays of food offerings for Hungry Ghost.
These displays are usually overseen by an effigy of the Leader of the Hungry Ghosts, the Taai Si Wong, who gets first dibs on the food on the table and keeps lesser ghosts in line, preventing them from doing too much mischief during their time on Earth.
Penang boasts of the largest Taai Si Wong in Malaysia, which is set up every year at Market Street on Bukit Mertajam.
These places are usually fragrant affairs, as the air will be thick with the smell of burning joss sticks.
Giant "dragon" joss sticks loom over the smaller sticks, like fenceposts in tall grass.
The giant joss sticks are usually placed by businessmen, who seek the favor of the spirits so their businesses will do better.
On the 30th day of the seventh moon, the ghosts find their way back to Hell, and the gates of the Underworld are shut.
To see the ghosts off, paper offerings and other goods are incinerated in a giant bonfire.
The Taai Si Wong effigy is burned along with the rest of the goods to send him back to Hell.
The month of Hungry Ghost Festival is, generally speaking, a bad time to do anything.
Many significant milestones are avoided at this time, as people believe it's simply bad luck.
Chinese believers avoid traveling or performing any significant ceremonies throughout the festival.
Businessmen avoid riding in airplanes, buying property, or closing business deals during Hungry Ghost Festival.
Moving house or getting married are frowned upon during this time - it's believed that ghosts will mess up one's plans during the festival, so your house or your marriage may be jeopardized at this time.
Swimming is also a scary prospect - children are told that hungry ghosts will pull them under, so they will have a soul to take their place in Hell!
Spring Festival has been celebrated in the history of China for more than 4,000 years.
However, in ancient Chinese culture, the beginning of a year was once on the Winter Solstice in December.
Around 2100 years ago, Emperor Liu Che held a sacrificial rite on 1st of January in Chinese Calendar to pray for well being of his people.
Afterwards, the month of January was established as the beginning of a new year, since when the Spring Festival has been celebrated.
This biggest holiday in China lasts from the 1st to 15th of January of the Chinese Calendar.
Preparations for the Spring Festival
23th or 24th of Dec. of the Chinese Calendar is the day to sacrifice to the Stove Fairy and eat a type of sticky candy.
In ancient Chinese culture, each family has a Stove Fairy, who on this day will go to the heaven and report everything happened in this family in the past year.
So, the worship ceremony accompanied by beautiful firecrackers is to see off deities with respect, while the sticky candy can make them feel sweet and hard to open their mouths to say anything bad.
24th or 25th of Dec. is to clean the entire house, while 25th to 29th of Dec.are to prepare or to shop for new clothes and food, get cut hair, etc.
Celebration and Custom of the Spring Festival
On the Eve of the Spring Festival, couplets and door gods would be pasted and a big family dinner will be served.
It is still an important custom in China that people would stay up late until 12 o’clock at night and set off firecrackers.
A ceremony is needed to welcome the Stove Fairy back, but nowadays many Chinese people don’t do this much.
On 1st of Jan of the Chinese Calendar, new years greetings would be sent to each other.
Kids will get gift money from older generations, and people would visit important relatives and have big dinners together.
Married couples would be visiting the man’s parents.
On the 2nd of Jan, married couples would visit the woman’s parents and bring some gifts, like candy or dessert, and then have dinner with them.
On the 5th or 6th of Jan, the floor would be swept, in order to send away the God of Poverty, and the market will be opened up.
Nowadays, most Chinese people will return to work on this day.
Lantern Festival (Shang Yuan) is the traditional Chinese Valentine’s day.
In ancient China, young unmarried girls are not allowed to go out freely, except for the Lantern Festival.
Hence, it is a perfect opportunity for single people to go out and to meet up.
Beautiful lanterns, the moon and fireworks at night, together consisted of a perfect circumstance for romantic encounters.
This holiday is on the 15th of January of the Chinese Calendar, the first full moon of the new year, when many types of lanterns will be lit to drive away the darkness and scary animals, and to pray for good luck.
Glue puddling/sweet dumpling is the traditional food for this festival, while various activities and performances will be held in different places in China as well.
Qingming was originated around 2,500 years ago, as one of the 24 Solar Terms in Chinese Traditional Calendar, which is a good time for planting.
In the year 1935, it was officially decided that the 5th of April of the Gregorian Calendar will be the Tomb-weeping Day.
Nowadays, 4th, 5th or 6th of April is the Tomb-weeping Day or Qing Ming Festival.
On this day, visiting and cleaning of ancestors’ graves, as well as holding some memorial ceremonies are important customs in Chinese culture.
Another popular activity is to go out, or take a trip to enjoy the nature, since this is the beginning of the spring.
Flying kites are favored in some places, some of them also have night kites (with some colorful small lanterns on the tail).
When the kite is flying in the sky, people would cut the string off and let the kite go, representing that all the bad lucks would be taken away.
Dragon Boat Festival, also named Duan Wu, is on 5th of May of Chinese Calendar.
It has been celebrated for more than 2,000 years, which is considered as the beginning of summer in Chinese Calendar.
This festival has many versions of originate stories and many customs in different places in China.
The most common elements of this festival are racing Dragon Boat, eating rice dumplings, and drinking realgar wine.
It is also popular to put five-color strings (red, white, black, yellow and green) on kids’ wrists, ankles, or necks in the morning.
While putting them on, the kid shouldn’t be talking.
It is believed that the strings can protect kids from poisoned animals.
People would then take them off and throw them into rivers when the next rain comes, hoping the river could take disease and bad luck away.
Sachets that are stuffed with various types of herbs, scented petals, or perfume, are another popular ornament to wear or send as gifts.
Pleasant smell and fancy appearance make them popular in China.
They are also typical gifts among couples in love.
Women usually would make some sachets on their own, and give them to their beloved ones.
The Ghost Festival, also named Zhong Yuan, is on the 15th of July of the Chinese Calendar.
This is the biggest festival to worship and commemorate the dead in Chinese culture.
According to Chinese mythology, on this day, all the ghosts will be set free to the human's world, and are allowed to go back to their previous homes or visit people they care.
Alive people will take this opportunity to worship their ancestors and beloved departed ones.
Big ceremonies would be held in religious places, to memorize people that were sacrificed on the battlefield and those without families.
Water is believed as Yin in Chinese culture, which connects the worlds of alive and deceased ones.
So, river lanterns are used on this day to illuminate the way for ghosts to come home.
In addition, people will burn up paper-made money and daily necessities that the ghosts in the other world could use.
Mid-Autumn Festival -- Reuniting of Family
The Mid-Autumn Day (Zhong Qiu) is on the15th of August of the Chinese Calendar, a festival that connects with the reunion and harvest.
The first record about this festival was around 2,200 to 2,500 years ago, but it was official and widely accepted in China in about 1,000 years ago.
Though without a very long history, the Mid-Autumn Day is one of the most popular festivals in China now.
Worshiping the moon is the most important ceremony of the Mid-Autumn Festival.
Usually, red candles and incense, nice fruits and mooncakes are common necessities for the rite.
Nowadays, enjoying the full moon and eating mooncakes are the most popular parts of this festival.
In a Chinese mythological legend, there is a sweet scented Osmanthus tree on the moon, as well as the beautiful Fairy Chang E and her rabbit.
Therefore, People also eat candy and cake, and drink wine on this day, all of which are made of sweet scented Osmanthus.
Chong Yang the Double Ninth Festival
On the 9th of September is the Double Ninth Festival (Chong Yang), which was first popularized in royal families in about 2,000 years ago, and then celebrated by everyone 1,000 years later in the history of China.
On this day, people will go climbing and enjoy the view of autumn.
Putting on cornel is also important, which is believed can protect people from disease and bad luck.
Other activities include appreciating chrysanthemum, drinking chrysanthemum wine, and eating Double Ninth cake.
Nowadays, it is also a festival for older people to pray for long lives and safety.
Being one of the biggest economies and a culturally diverse country, China celebrates a lot of traditional, and culture-based festivals that are nothing but pure joy to be a part of.
While the festivals play an integral role in Chinese culture, most of the festivals in China are based on the lunar calendar, and are fascinating indeed.
Be it the traditional festivals that have a deep-rooted history or the modern festivals that have shot to fame with fame, most these Chinese festivals have been celebrated for centuries and here’s why Chinese festivals in 2018 are worth learning more about.
 Also known as the Chinese Spring Festival, this is without a doubt one of the most important Chinese festivals with a history of more than 4000 years and you will get to know about Chinese new year traditions on this day.
Most of the people enjoy this festival for 7 days and the celebration lasts for over 2 weeks, beginning from the eve of the festival.
Also called the traditional Lantern Festival in China, Yuan Xiao Festival is held on the 15th day after the Spring Festival.
It marks the continuation and end of the New Year celebration.
This festival involves enjoying the sight of beautiful lanterns of different shapes and sizes at night.
The lantern shows bring people from all over the world to witness the amazing designs and colors.
A traditional Chinese dish called Tangyuan is also eaten on this day.
 This is one of the 3 most important Chinese festivals.
The Mid-Autumn Festival is a traditional holiday originating from the worship of the moon and it symbolizes harvest and family reunion.
Traditionally, this festival is celebrated with family, similar to the Thanksgiving Day.
As with the Spring Festival, family members get together on this day and enjoy the sight of the full moon, which is an auspicious symbol of luck and harmony, and feast on delicious mooncakes.
This is yet another one of the ancient Chinese festivals that attracts millions of people.
Also known as the Dragon Boat festival, people celebrate by gathering together to take part or watch the dragon boat races, especially in the southern areas of China.
Another important part of the Duanwu festival is eating Zong Zi, which is the glutinous rice wrapped in reed leaves and it is a must try.
This festival is also known as the Double Ninth Festival.
During the Chongyang festival, people celebrate by drinking Chrysanthemum wine and eating Chongyang cake.
In some parts of the country, people go mountain climbing or travel to areas where they can admire chrysanthemums and it is indeed a major festival that is celebrated with a lot of pomp.
Also known as Tomb Sweeping Day, Qingming Festival is one of the most important Chinese festivals as people gear up to offer sacrifices on this day.
The Han, as well as the minority ethnic groups, bring sacrifices to offer their ancestors and sweep the tombs of loved ones who have departed from this world.
It is pretty much similar to the All Souls’ Day that is celebrated by Christians in India on 2nd of November every year.
 This is one of the most prominent festivals in the Chinese festival calendar.
The Chinese people have special customs for the dead and their ghosts, especially those that are thousands of years old.
It’s part of a famous folk religion in China called Daoism where the locals believe that special precautions and ceremonies for the deceased ancestors are required in the seventh month of the lunar calendar.
Celebrated on the 15th day of the seventh lunar month, the Hungry Ghost Festival of China is just one of the many traditional festivals that are celebrated here to worship ancestors.
While there are many festivals that the Chinese celebrate, this list of Chinese festivals enlists some of the most important ones.
And no matter which one you celebrate, your experience is sure to be full of colors, fun, and sheer bliss on your next holiday in China.
Q. Which is the most famous festival in China?
A. The most famous festival in China is Chinese New Year.
It is celebrated with a lot of enthusiasm, music, dance, fireworks, and local food.
All the streets, markets, buildings, and restaurants in China are decked up with decorations on this day.
Q. What are the major traditional Chinese holidays?
A. The major traditional holidays in China are observed on festivals like the Mid-Autumn festival, The Chinese New Year, Duanwu Festival, etc.
With a more than 5000 years history, China has various and colorful traditional festivals to celebrate every year.
China also boasts of people’s rich culture life and different kinds of celebration activities.
When a festival comes, it is a good opportunity to enjoy the pageantry and the colorful traditional Chinese culture.
t is seems that New Year's Day (Yuandan in Chinese) is a new festival to the Chinese while it is celebrated for more than 3000 years.
Yuandan appeared in the ancient times and meant “the first day of a year”.
The Chinese character “Yuan” means “at the beginning” and “Dan” means “day”.
It was the first day of Chinese lunar year, while it was changed into the first day in Gregorian calendar since the People's Republic of China was founded in 1949.
Every year, the festival performances and activities are still held to celebrate the New Year's Day.
Spring Festival, also called Chinese New Year, is the first day of Chinese lunar year.
It is the most important and the grandest festival in the whole country.
Usually, People in China will clean their houses and worship the Chinese god before the Spring Festival.
People will get together to celebrate the achievements in the last year and the start of a new year.
Putting up the Spring Festival Scrolls, eating dumplings, setting off fireworks and watching Spring Festival Gala (the national celebration program) are the main activities.
People also visit their family relatives during the Spring Festival.
It is a nationwide festival in China.
Lantern Festival is the 15th day of the 1st month in lunar calendar and also marks the end of the Spring Festival’s celebrations.
Every family hangs lanterns in front the doors in the evening.
People also take part in temple affairs, enjoy colorful lights and guess lantern riddles at the night of Lantern Festival.
It is a traditional festival from generation to generation.
Qingming Festival, also called Tomb Sweeping Day is around the beginning of April in the Chinese lunar year.
It is not only a solar term but also a festival to worship ancestors.
Besides, people will wear willow shoots and fly kites.
The time of Dragon Boat Festival is the 5th of the Chinese 5th lunar month.
It is significantly celebrated in China because of a legend person in the ancient times.
Qu Yuan who lived in the Warring States Period (476 BC - 221 BC) was a patriotic poet and suicided in the Miluo River (in Hunan Province) to follow his motherland.
The dragon boat races are grandly held in the whole country.
People will still plug Chinese mugwort into the door and eat Zongzi (made by rice and meat) during the Dragon Boat Festival.
August 15 of the Chinese lunar year is the Mid-autumn Festival in China.
Families get together to enjoy the brightest full moon and eat mooncakes in the evening.
Numerous colorful lanterns with best wishes will fly in the sky.
Mid-autumn Festival is also beautified by mysterious legend stories.
It is a traditional festival for family reunion.
Double Ninth Festival, also called Chongyang festival, falls on September 9th of the Chinese lunar year.
It is a traditional Chinese festival and develops into Seniors’ Day.
People will visit senior family members, climb mountains, eat Chongyang cakes, ect.
Traditionally the family tomb is cleaned and swept on Qing Ming day with fresh offerings laid out for the ancestors.
This festival is anchored to the solar year rather than lunar year and so always falls between April 4th to 6th.
It marks the start of Spring and is associated with kite flying.
It has similarities to the Christian Easter Spring festival in that eggs are prepared and eaten.
In some areas boys used to wear willow wreathes on their heads to summon rain for the growing season.
Chinese New Year is perhaps the most important holiday for the Chinese and is celebrated on the 1st day of the 1st lunar month.
It falls at the end of January or beginning of February in our Western calendar and is basically two weeks jam-packed with feasts, parades, lion dances and fireworks.
It is also known as the Lunar New Year or the Spring Festival and during this two-week period millions travel home to be with their families.
During the Lantern Festival streets, markets, store fronts, homes, parks, and just about everywhere you go, will be lit with beautiful lanterns, not only the traditional Chinese red lanterns but lanterns in all sorts of shapes, forms and colors.
The Chinese Lantern Festival is celebrated on the 15th day of the 1st lunar month.
It is the culmination of the Chinese New Year celebrations with the first full moon of the year.
Tomb Sweeping Day or Qinming Festival is the time to honor the ancestors.
At this time, temples and cemeteries throughout China will be teeming with activity as everyone flocks to pay respect, bring offerings and burn incense.
It normally falls at the beginning of April in our calendar.
The Dragon Boat Festival is an exciting festival when the dragon boat races, a long-standing tradition, are held throughout China.
It is celebrated on the 5th day of the 5th lunar month, around June in our calendar.
It is a day full of excitement with teams of rowers paddling in unison to the beat of pounding drums to the finish line.
The Ghost Festival is the opposite of Qinming Festival when the living worship the ancestors.
During Yue Lan, the ghosts come out when the gates of heaven are opened for a month and visit the living.
This is actually an entire month of remembrance and celebration.
Ghost month falls in the seventh lunar month.
The Moon Festival or Mid-Autumn Festival is celebrated on the 8th full moon of the year.
On this night, the moon is at its brightest.
Colorful lanterns adorn homes and streets, friends and family gather together to enjoy the moonlight and of course eat mooncakes!
The seventh month of the Lunar calendar is considered the "Chinese Ghost Month".
On this month, the spirits of the deceased are released and they come back to wander off in the living world.
It is said that some spirits may be restless and will have to be appeased, and thus, worshippers will burn paper money and make food and incense offerings to make sure they are back in the good graces of their ancestors and any other wandering ghost that may happen to "drop by".
This is the opposite of the Qing Ming Festival when the living pay homage to the dead.
During Ghost Month, the gates of the afterworld are opened, and the spirits come to pay a visit to the world of the living.
Spirits are powerful and people treat them with respect.
To keep them happy, people will make offerings of food and incense and burn ghost money throughout the month.
Sometimes you see offerings by the side of the road.
This is for the orphan spirits who have no one to take care for them.
You wouldn't want a stray angry spirit roaming around your home.
Unhappy spirits can make bad things happen!
The Hungry Ghost Festival marks the middle of the Ghost Month on the 15th day of the 7th lunar month, and offerings and activities are held particularly on this day.
The seventh lunar month typically falls around August in our calendar.
Activities other than the usual offerings of incense, Chinese paper money and food, include Chinese Opera and the dragon and lion dances with plenty of noise to scare away the evil ghosts, and are held on the streets and in parks and plazas throughout the cities.
Rituals and ceremonies are performed to appease the spirits.
When dinner is done, some Chinese families would go to temples hours before the New Year to pray for a better year and light the first incense of the year.
In modern practice, many people would hold parties and in some cases even a countdown to the New Year.
Traditionally, people would lit firecrackers to scare away evil spirits and keep the household doors sealed and not open it till the new morning, also known as “opening the door of fortune”.
Celebrations for Spring Festival are all about Lion Dances, Dragon Dances, fireworks, giving red envelopes of money, enjoying time with family and friends, and eating candies, sweets and all kinds of delicious foods.
The most common color during the Spring Festival is red.
Chinese traditions believe that red can scare away both evil spirits and bad fortune.
Chinese also try to wear new clothes during this time so as the year is renewed, so should their clothes.
Tomb Sweeping Day (Qing Ming) also known as Pure Brightness Festival is the second most important festival in China after the Spring Festival.
This day is on April 4th, April 5th or April 6th depending on the year (It is on the first day of the fifth solar term of the traditional Chinese lunisolar calendar).
The importance of Tomb Sweeping Day lies in the fact that it is not just another festival of people gathering together and eating good food, it’s a time to remember and pay tribute to deceased family members and ancestors.
Chinese families visit the tombs of their ancestors and family members on this day to clean the gravesites, pray to the diseased, and make ritual offerings.
These Offerings typically include items such as traditional food and burning of joss sticks and papers.
The Qing Ming Festival has been practiced in China for over 2500 years.
This holiday is associated with eating Qingtuan, which are green colored dumplings made of glutinous rice and Chinese mugwort or barley grass.
Mid-Autumn Festival, also known as Moon Festival, is the harvest festival of China that occurs on the 15th day of the 8th month in the Chinese lunar calendar.
The name of this festival is because of the fact that it happens in the middle of autumn and when the moon is at its fullest and brightest.
This day corresponds to late September to early October of the Gregorian calendar.
The Mid-Autumn festival is about celebrates three closely connected fundamental concepts, gathering, thanksgiving, and praying.
On this day family and friends gather together (traditionally harvesting crops), they give thanks for a good harvest or for harmonious unions and praying to get babies, a spouse, beauty, longevity, good fortune and generally anything conceptual or material that would satisfy them.
Mid-Autumn Festival is known with eating mooncakes and sometimes is also referred to as Mooncake Festival.
Mooncakes are a rich pastry usually filled with sweet-bean or lotus-seed paste.
Ghost Festival or Spirit Festival (also known as the Hungry Ghost Festival) takes place on the 15th day of the 7th lunisolar month and is a traditional Buddhist and Taoist festival.
In the Chinese lunisolar calendar, the seventh month is generally regarded as the Ghost Month and the time when restless spirits are free to roam the earth.
Chinese believe that in this month Ghosts and spirits come out from the lower realm.
Activities for this festival include preparing ritualistic food offerings, burning incense and items for the visiting spirits of the deceased ancestors.
Qingming Festival (also known as Pure Brightness Festival or Tomb-sweeping Day), which falls on either April 4th or 5th of the gregorian calendar, is one of the Chinese Twenty-four Solar Terms.
From that date temperatures begin to rise and rainfall increases, indicating that it is the crucial time for plowing and sowing in the spring.
The festival therefore has a close relationship with agriculture.
However, it is not only a seasonal symbol; it is also a day of paying respect to the dead, a spring outing, and other activities.
Falling on the 15th day of the 8th month according to the Chinese lunar calendar, the Mid-Autumn Festival is the second grandest festival after the Spring Festival in China.
It takes its name from the fact that it is always celebrated in the middle of the autumn season.
The day is also known as the Moon Festival, as at that time of the year the moon is at its roundest and brightest.
The myths of Mid-Autumn Festival are well-known in Chinese culture.
As most tales, they’re fantastical stories that hold deeper symbolic and moral meanings.
For instance, every Chinese child has heard that in the moon lives the beautiful Chang E, a cute Jade Rabbit, and the woodman Wu Gang.
In the sequel of this article, we will intorduce the four main myths about Mid-Autumn Festival.
Legend has it that there were ten suns in the sky, in the ancient time.
The scorching suns made people suffer –the land was lifeless and people could barely survive.
Hou Yi (后羿), who was known for his powerful strength and superb archery skills, felt sad for people’s suffering and decided to help them.
He climbed to the top of a mountain and began shooting down the suns, eventually shooting down 9 out of the 10 suns.
He left the last sun to keep the earth bright in the day and nurture the crops.
Hou Yi soon became a beloved hero in people’s hearts.
Afterwards, Hou Yi married to the beautiful Chang E (嫦娥, Ch?ng’?).
Hou Yi also started to teach his apprentices archery skills.
But did the couple live happily ever after?
Not quite.
This brings us to our second story.
Chang E (or “Chang Er”,嫦娥) is“The Goddess of the Moon”in Chinese culture.
Hou Yi and Chang E loved each other very much.
So why is she now on the moon alone?
One day, Hou Yi came across the Heavenly Queen Mother (王母娘娘, W?ngm?ni?ngni?ng) on his way to visit his friend in Kunlun Mountain.
In order to live with his wife forever, Hou Yi kindly asked the Heavenly Queen Mother for the elixir of immortality.
The Heavenly Queen Mother was so moved by his love to Chang E that she gave him the medicine.
However, it was only enough for one person.
Hou Yi didn’t want to leave his wife by taking this immortality medicine alone, so he asked Chang E to keep it for him.
However, the evil apprentice Peng Meng (蓬蒙) heard about the immortality medicine and planned to steal it from Chang E.
When Hou Yi took his apprentices out hunting, Peng Meng claimed he was sick and stayed behind.
While Chang E was alone in the house, Peng Meng broke into the house and threatened her to give him the medicine.
Helpless and scared, Chang E swallowed the medicine in desperation.
After she swallowed the medicine, Chang E felt her body become too light to stay on the floor.
She rose out of the house up into the sky and finally landed on the moon.
After Hou Yi came home and found out what happened, he was outraged and depressed.
However, he couldn’t take revenge on the evil Peng Meng (who had already escaped) nor get his beloved wife back.
Chang E then settled down in Guanghan Palace (“The Moon Palace”, ?寒?) in the moon, becoming the tragic immortal Goddess of Moon that was separated from her husband.
Hou Yi could do nothing but prepare Chang E’s favorite food, stare at the bright full men, and think of her.
As people learned that Chang E became a Goddess of Moon, they decided to follow Hou Yi to commemorate and pray for Chang E’s blessings.
Wu Gang (??) was a lazy man that was obsessed with becoming an immortal God.
However, he was so quick to quit that he could never continue a task that lasted more than 3 days.
Nevertheless, Wu Gang desperately wanted to be immortal, and sought out a Chinese God to help him achieve his goal.
Wu Gang eventually found a gray-bearded, older God in the mountains and asked him to teach Wu Gang how to become an immortal God.
“It’s not that easy to become immortal, you know.
Are you sure you can stand the long and tough journey to become a God?” asked the God.
“Yes, yes, of course I can, just teach me how!” replied Wu Gang.
And thus the older God led Wu Gang into the mountains to collect medicinal herbs.
The God slowly began teaching Wu Gang the different functions and properties of each herb.
However, Wu Gang was too impatient to listen.
“Why are we doing this hard work?
Shouldn’t a God be happily flying around and relaxing?”
He complained and complained about how tiring the work was.
But the old God gave him another chance.
The God told Wu Gang to finish reading a book about life and philosophy.
“You should first understand the philosophy of the universe, read it through,” said the God.
“And I’ll become immortal afterwards?”
Wu Gang asked expectantly.
He quickly agreed.
However, Wu Gang started dozing off after only a few minute of reading.
He only awoke when he felt the God hitting his shoulder.
Turning angry from embarrassment, Wu Gang complained, “What kind of God are you?
Gluing to book all day?
A God should be flying into the moon and playing around!”
“Well, since you said that, let me take you to the moon and have a look around.”
The God flew to the moon with Wu Gang.
After they arrived on the moon, Wu Gang saw nothing but a bald land and a huge tree near him.
“That’s all?
Well, can we go back now?
” However, the God told him, “You said you want to become divine, right?
Take this axe, and chop that osmanthus tree down.
” Wu Gang was excited because chopping a tree down was easy and quick!
He could finally become immortal.
However,to his surprise, the tree healed itself every time Wu Gang made a cut on it!
It was impossible to chop the huge tree down.
The God told Wu Gang that this tree can only be chopped down if he made 300 continuous cuts with patience and concentration, or it will keep healing itself.
Moreover, he can only become divine and fly home by himself once the tree is down.
Since then, the tale of a man chopping the tree non-stop in the moon spread in Chinese culture: a punishment for a man, in order to teach him the importance of perseverance and hard work.
Jade Rabbit (玉兔) is well known to be the best friend of Chang E, since they live together on the moon.
So, why is Jade Rabbit on the moon too?
Once upon a time, three Gods disguised themselves as hungry and poor old men.
They asked a fox, a monkey, and a rabbit for food.
The fox and the monkey collected some fruits and food for the old men; while the rabbit only gathered grass.
When the rabbit saw her poor offering compared to the fox and monkey, it cried out for forgiveness and said “you can eat me!” and threw himself into the fire without hesitation.
The rabbit’s selfless sacrifice touched the Gods.
They decided to make him a “Jade Rabbit” and send him to the moon as an immortal God.
Jade Rabbit became the companion of Chang E and is known for making the herb of immortality on the moon.
The Symbolism Behind Chinese Mid-Autumn stories:
Cherish what you have instead of pursuing immortality; evil and greedy people can never get immortality
The evil apprentice Peng Meng never achieved immortality because he didn’t deserve it, while selfless characters like Chang E or Jade Rabbit were both able to achieve it.
In addition to Hou Yi’s love story, there’s another version of the story about how Hou Yi went astray after he won the people’s heart as a hero and a king.
He became cruel, greedy and wanted to live forever, so he asked the Heavenly Queen Mother for the elixir of immortality.
However, the Heavenly Queen Mother knew his intentions, so she gave him only one piece of medicine.
Cheng E knew about the medicine and thought it was wrong for her greedy husband to become immortal and continue to hurt others.
So, she took the medicine herself.
Hou Yi felt depressed and regretted it deeply after his wife left him forever.
In Wu Gang’s story, we learned that we shouldn’t think to achieve our goals without working hard.
No pain, no gain.
The most important virtues are perseverance, patience, and determination.
Wu Gang was stubborn, always tried to get things fast and daydreamed about becoming a God without paying any effort.
The God had him chop the tree nonstop just to make him realize that only when he’s concentrated and persistent he can achieve what he wants.
Selfless devotions and givings bring good rewards
The Jade Rabbit sacrificed himself to help those in need selflessly, which brought itself immortality in the end.
The story can also imply that it’s more blessed to “give” than to receive.
However, only those who are mindless, selfless and kind-hearted to help others deserves those rewards.
With these stories, we teach our children to be kind and give others what we’re capable of.
How Do Chinese Celebrate Mid-Autumn Festival?
Hou Yi, Cheng E, Jade Rabbit and Wu Gang play important roles in Chinese Mid-Autumn Festival and Chinese culture.
As one of the three biggest Chinese national holiday, people celebrate this day with several activities.
As mentioned before, the main core of Mid-Autumn Festival is the family reunion, when family members return home and celebrate together.
On Mid-Autumn Festival, most Chinese people enjoy the full moon, eat mooncake and pomelo, pray for ancestors, set off fireworks, and, nowadays, hold barbeques.
First and foremost, it’s full moon appreciation!
Normally on the day of Lunar August 15th, the moon will be full and bright.
Chinese people would gather under the moonlight, appreciate the full moon while chatting and eating.
Also, people would set out spectacular firework show to celebrate.
Within the family, Chinese would also play sparklers.
Eating Moon Cake, Pomelo, and Barbeque!
Chinese people send presents to their relatives or friends for Mid-Autumn Festival.
A package of mooncakes is the best gift for this day.
Mooncakes can be bought and reserved from convenient stores, cake stores, or even online.
Due to the fact that the Chinese pronunciation for pomelo “柚子”(Y?uzi) is similar to“blessing the children”“佑子”(Y?uzi), and to the fact that this is pomelo’s season, this fruit became the icon of Mid-Autumn Festival.
What’s more, it’s popular to wear the pomelo skin as hats just for fun!
You can have whatever short hairstyle as you want, bowl cut, bob cut, or even medium-length hair.
In Taiwan, barbecue is one of the most popular activities during Mid-Autumn Festival.
This happened after a barbecue sauce commercial had a catchy slogan.
For such an important festival, Chinese people tend to pray to ancestors with mooncake gift box and pomelo, offering them prayers and blessings.
If you have the chance to experience Mid-Autumn Festival in China, watch carefully to see if there’s silhouette of Chang E, Jade Rabbit, or Wu Gang on the moon!
And don’t forget to act like a local, try some mooncakes and pomelo, and design your own hat from a pomelo under the beautiful full moon!
The Mid-Autumn Festival–or zhong qiu jie–is the Chinese celebration when the moon is at its brightest point of the entire year.
Also known as the Moon Festival, this holiday falls on the fifteenth day of the eighth month of the lunar calendar (either September or October).
There is dancing, storytelling, and the enjoyment of an array of foods, mooncakes in particular.
Of course, there is also plenty of time spent gazing at the moon.
Several legends revolve around the Mid-Autumn Festival.
There is a story of the "lady living in the moon,” a moon fairy living in a crystal palace who comes out to dance on the moon's shadowed surface.
This legend dates back to ancient times, to a day when 10 suns appeared at once in the sky.
The Emperor ordered a famous archer to shoot down the nine extra suns.
Once the task was accomplished, the Goddess of Western Heaven rewarded the archer with a pill that would make him immortal.
However, his wife found the pill, took it, and was banished to the moon as a result.
Legend says that her beauty is greatest on the day of the Moon Festival.
According to another legend, on this day the "Man in the Moon" was spotted at an inn, carrying a writing tablet.
When questioned, he said he was recording the names of all the happy couples who were fated to marry and live happily forever after.
Accordingly, just as June is the traditional month for exchanging nuptials in the West, many Chinese weddings are held during the eighth lunar month, with the fifteenth day being the most popular.
Of course, the most famous legend surrounding the Moon festival concerns its possible role in Chinese history.
Overrun by the Mongols in the thirteenth century, the Chinese threw off their oppressors in 1368 CE.
It is said that mooncakes—which the Mongols did not eat—were the perfect vehicle for hiding and passing along plans for the rebellion.
Families were instructed not to eat the mooncakes until the day of the moon festival, which is when the rebellion took place.
(In another version plans were passed along in mooncakes over several years of Mid-Autumn Festivals, but the basic idea is the same).
While baked goods are a common feature at most Chinese celebrations, mooncakes are inextricably linked with the Moon Festival.
One type of traditional mooncake is filled with lotus seed paste.
Roughly the size of a human palm, these mooncakes are quite filling, meant to be cut diagonally in quarters and passed around.
This explains their rather steep price.
A word of caution: the salty yolk in the middle, representing the full moon, is an acquired taste.
More elaborate versions of mooncakes contain four egg yolks (representing the four phases of the moon).
Besides lotus seed paste, other traditional fillings include red bean paste and black bean paste.
Unfortunately for dieters, mooncakes are rather high in calories.
While in the past, mooncakes took up to four weeks to make, automation has speeded up the process considerably.
Today, mooncakes may be filled with everything from dates, nuts, and fruit to Chinese sausages.
More exotic creations include green tea mooncakes, and ping pei or snowskin mooncakes, a Southeast Asian variation made with cooked glutinous rice flour.
Haagen-Daz has even gotten into the act by introducing a line of ice cream mooncakes in Asian markets.
Given the difficulty of making them, most people prefer to purchase their mooncakes instead.
You'll find them at Asian bakeries beginning around mid-August.
Also known as the Mid-Autumn Festival or the Mooncake Festival, the Chinese Moon Festival is a favorite holiday for ethnic Chinese and Vietnamese people around the world.
Perhaps second only to the Lunar New Year in popularity, participants observing the Chinese Moon Festival share fun, often-overpriced cakes (mooncakes) with people they appreciate.
Some are tasty; some are as dense as hockey pucks and get filled with exotic ingredients.
The Chinese Moon Festival is also a joyous time for family, friends, and couples to reunite under a full moon during harvest (September or October).
All take a little time to appreciate the beautiful full moon on what is hopefully the clearest night of the year.
The round shape and completeness of the full moon symbolize reunited pieces.
The Chinese Moon Festival is a time to take a needed break from work; many people have a day or two off and celebrate through the weekend.
Family and friends converge to give thanks and pay homage to the full moon, sometimes with poems.
Mooncakes are gifted, swapped, and shared.
Just as holidays get commercialized in the West, the mooncakes go on sale weeks in advance of the festival.
Each year they become more elaborate and push the limits for ingredients, presentation, and cost.
Businesses often give cases of mooncakes to show appreciation to clients and employees.
Commercialization aside, the festival is a good excuse for couples to enjoy romantic time sitting under the full harvest moon.
Many people choose to celebrate quietly at home among family.
Travelers can enjoy the fun in parks and public spaces, but keep in mind that many shops and businesses may be closed in observance of the public holiday.
Transportation will be busy.
Public parks are lit with special displays and lanterns; there may be stages with cultural shows and parades.
Dragon and lion dances — there is a difference!— are popular during the festival.
Incense is burned in temples to honor ancestors and the moon goddess, Chang'e.
Bright lanterns are hung high from poles while floating, candle-powered lanterns get launched into the sky.
Along with consuming mooncakes, hairy crab is a delicacy available around festival time.
The Jade Rabbit, a creature from folklore who lives on the moon, is a popular symbol during the Chinese Moon Festival.
To honor tradition, some people still make offerings to the moon, although this practice is becoming less common.
The Chinese Moon Festival is designated as a public holiday throughout all regions in China including Macau, Hong Kong, and Taiwan.
Expect all banks and some businesses to be closed at least one day.
Public transportation will be busier than usual.
What's all the hype?
Chinese mooncakes are round, baked, palmsize cakes eaten and gifted during the Chinese Moon Festival — or anytime a rich delicacy is in order.
They're a popular gift, often given in decorative boxes to clients, family members, and important people.
Mooncakes are made with egg yolks and come with a variety of fillings; the most popular are made from bean paste, lotus seeds, fruits, and sometimes even meat.
The cakes are typically round to symbolize the full moon, although some are square.
Many are skillfully decorated.
Writing or patterns on top tell of good fortunes to come.
Regional variations abound.
The boxes for mooncakes are often as beautiful as the cakes inside, making them an attractive gift.
Many mooncakes are sweet but not all.
Some are savory.
Artisans push the shock factor with new creations each year.
Fillings such as sambal, durian, salted duck eggs, and gold flakes up intrigue and the price for a box.
Despite the small size, Chinese mooncakes are often prepared with lard or shortening and are quite "heavy.
" Unless self punishment is a goal, you wouldn't want to eat more than one in a sitting.
Many people choose to cut mooncakes into wedges or quarters to share them with friends over tea.
Given the difficulty of making artisan mooncakes and the far-flung fillings involved, some are surprisingly expensive!
Fillings that made a big splash in the past include unexpected options such as chicken floss, foie gras, ice cream, coffee, and others.
One pricey mooncake variant contains shark fin — an unsustainable option.
Around 11,000 sharks die per hour (roughly three per second), mostly due to finning practices driven by demand in Asia.
The environmental impact is certainly not worth the made-up health benefits — shark fin contains concentrated levels of mercury!
Some mooncakes share the same legacy as fruitcakes in the U.S. at Christmas: they get swapped and appreciated but don't end up consumed.
You probably won't have any trouble finding mooncakes on sale weeks before the actual festival begins.
Mooncakes will be available in every shop and restaurant.
Hotels will have their own in-house creations on display.
Even fast food and ice cream chains get in on the action during the festival.
Known as the Zhongqiu Jie (Middle Autumn Festival) in Mandarin, the Chinese Moon Festival dates back to over 3,000 years.
As with all practices so old, a lot of legends developed over the years; it becomes difficult to understand the original traditions.
Most stories are based on the idea that the goddess Chang'e lives on the moon; however, tales of how she got there diverge widely.
One story suggests that the moon goddess was the wife of a legendary archer who was ordered to shoot down all but one of the suns in the sky.
That's why we only have one sun.
After accomplishing the task, he was given an immortality pill as a reward.
His wife found and took the pill instead, then later flew to the moon where she lives now.
Another Chinese Moon Festival legend states that paper messages inside of mooncakes were used as a way to organize the exact date of a coup against the ruling Mongols during the Yuan Dynasty.
The Mongols were overthrown on the night of the Moon Festival.
Although this legend seems a little more plausible than a goddess living on the moon, little historical evidence suggests that this is how the Mongols were defeated.
China, Taiwan, Hong Kong, and Macau have the largest celebrations.
But the festival is especially popular in places around Southeast Asia with large ethnic Chinese populations such as Vietnam, Singapore, and Malaysia.
The Chinese Moon / Mid-Autumn Festival starts on the 15th day of the eighth month as determined by the Chinese lunisolar calendar.
The festival is most often in September, but occasionally ends up in early October.
Dates for the Chinese Moon Festival change annually, but it's always celebrated in the fall.
The fact that the date of Chinese New Year varies within about a month is a clue that it's linked to the new moon.
A rough, and almost infallible guide is that the Chinese New Year follows the second new moon after the winter solstice.
The winter solstice always falls on December 21st, the next new moon is on January 20th 2015, and the second new moon is on February 18th 2015.
Please note that there is a new moon on December 21st, but this does not count for calculation purposes.
However, this does mean that February 19th is about the latest day in the year for the Chinese New Year.
As the new year ends on February 7th 2016 this late begining it makes 2015 slightly shorter than most Chinese 'years'.
The Chinese New Year Calendar - With Its Associated 12 Animals
The lunar Chinese New Year (CNY) calendar below shows which of 12 animals you are!
Naturally the animal depends on the year in which you were born.
Note: if you were born in January or February you need to check if your birthday was before or after the date of that Chinese New Year.
(If it was before this day your animal is the one shown for the previous year).
Unlike western calendars, the Chinese calendar has names that are repeated every 60 years.
Within the 'Stem-Branch' system is shorter 'Celestial' cycle of 12 years denoted by animals.
Furthermore, the Chinese believe that people born in a particular year take on the characteristics of the animal associated with that year.
There was a Chinese boy called L?i, who had a large flock of sheep.
But one day, L?i lost a goat and didn't take care of his fence.
Gradually more goats escaped until, at last, there was only one goat left.
Only then did L?i realise the seriousness of his situation, and belatedly repaired the fence.
From that moment, none of his sheep were ever lost.
L?i's story tells us that we must deal with matters in time, or we would lose a lot.
What L?i Needed for His Sheep - Baa Codes!
The hero of the story, the redoubtable S?ng D?ng B?, a guy with a love of learning and the martial arts, goes to visit a friend who lives outside his city wall.
He enjoys himself and stays late.
The friend advises him to stay over, because the road home has a reputation for being haunted by ghosts, but of course S?ng D?ng B? will have none of this, and starts for home in the dark night.
Of course it's not long before our hero does encounter a ghost, but not a clever one, because he thinks he might be a ghost.
S?ng D?ng B? has fun pretending to be a ghost, and even learns what ghosts are most afraid of: having people spit in their faces!
Now you know why Chinese people love doing that so much!
Finally, with dawn breaking, the ghost is keen to be off, (seems like Chinese ghosts, just like their western cousins, prefer the dark night to the light of day.)
But S?ng D?ng B? holds on to it and won't let it go, and then.
S?ng D?ng B? is no fool, he knows it might turn back into a ghost, so he spits in its face and takes it to market and sells it.
Sheep people simply seek peace.
They are quiet and calm men and women
They enjoy carrying on with life in their own individual way; they are happy to be in the middle of the actions rather than being leaders.
Though shy by nature, they like to be in the company of others watching from the side-lines, allowing others to dazzle.
They are highly creative, enjoying cultural activities, and focus their energy on artistic hobbies.
Sheep often appear easy-going and relaxed, happy to be going with the flow, but inwardly can they can be anxious.
Sheep are nurturers, this is one of the few feminine signs.
They enjoy taking care of other people.
Their personalities are quiet, reserved and soothing.
Those born under the Sheep sign will do better than they realize, partly because they are so good at keeping the peace and partly because they are good at accomplishing the tasks at hand.
The characteristics of the Sheep are modified by one, or more, of these Chinese elements: Wood, Metal, Water, Fire and Earth.
Sheep enjoy being in the middle of a group, consequently, other flock to these Sheep, possibly because they are so compassionate and helpful.
Their sincerity can be taken advantage of and Wood Sheep may get their feelings hurt by undeserving sympathy seekers.
People Born in the Year of the Sheep Michelangelo, King George VI, Jane Austen, Boris Becker, Jamie Foxx, Mel Gibson, Franz Liszt, Michelangelo, Mark Twain, Barbara Walters, Orville Wright and Rudolph Valentino.
The Lantern Festival in China is very old; legend has it that there are many wonderful stories about how the Lantern Festival first began.
One story is that in ancient times, people would go in search of spirits with burning sticks.
They thought the spirits could be seen during a full moon.
Another is about a lonely young girl, in Han times, who tricked an emperor into having a wonderful festival just so she could visit her family.
The emperor apparently had such an excellent time, he decided to make this festival an annual event.
According to one legend, from ancient times, a celestial swan came into the mortal world where it was shot down by a hunter.
The Jade Emperor, the highest god in Heaven, vowed to avenge the swan.
He started making plans to send a troop of celestial soldiers and generals to Earth on the fifteenth day of the first lunar month, with orders to incinerate all humans and animals.
However, the other celestial beings disagreed with this course of action, and risked their lives to warn the people of Earth.
As a result, before and after the fifteenth day of the first month, every family hung red lanterns outside their doors and set off firecrackers and fireworks, giving the impression that their homes were already burning.
By successfully tricking the Jade Emperor in this way, humanity was saved from extermination.
By T'ang times, many families simply set aside one evening, during the first full moon after the new year, to honour the moon.
They would sit outside, and gaze up, in awe and delight.
Today, people wear white in honour of the moon, lanterns are hung in the malls and markets, and children carry paper lanterns to school, to light their way to a bright and happy future.
The lantern displays can be found in the town centres, the squares and temples.
In China there is often a lantern competition at the temple.
Traditional lanterns are made of paper.
They can make the lamps turn by the heat circulation from the candle inside.
Today the light of the lamp comes mostly from electricity.
People like to design for the Chinese New Year Lantern Festival by using zodiac animals, historical figures, saint and gods of Taoism or Buddhism.
Certainly, the current year's animal symbol of the Chinese calendar is most popular subject.
Using a computer they can now design the lantern with different movements, the different colours of light and even using the laser light with special visual and sound effects.
The Lantern Festival is also referred to as the Yuan-Xiao Festival.
This is because Chinese eat Yuan-Xiao on this day.
This custom originated from the Eastern Jin Dynasty in the fourth century and then became popular during the Tang and Song Dynasty.
They consist of sweet rice flour and are made into sticky glutinous balls.
These can then be filled in with sesame, red-bean or peanut butter paste.
Usually, they are served with sugar water; although some people still make a salty Tang-Yuan.
The difference between Yuan-Xiao and Tang-Yuan is the way they are made and cooked.
This is because that Chinese in different geographic areas prepare the food in different ways.
The Chinese people call the one they eat on Winter Solstice Day, Tang-Yuan.
The one they eat on the Lantern festival is called Yuan-Xiao.
The traditional food for the Chinese Lantern Festival is Yuanxiao dumplings, named after the lonely palace maid of long ago.
[Some versions of the story have her preparing stuffed dumplings for the God of Fire, as this was one of his favourite foods].
Yuanxiao are made with sticky rice flour.
They can be sweet or savoury; filled with everything from sugar, walnuts, and dried tangerine peel to meat and vegetables.
Incidentally, lanterns are widely used on China Day.
The Chinese masks that you see during the Chinese New Year Lantern Festival are exclusively used during that time of the year only.
The general feeling generated by the Chinese masks during this festive season is that of happiness and joy.
A man ties a New Year wish to a "wishing tree" at the Taoist White Cloud Temple, Beijing, on the day of Chinese New Year
A child in traditional costume takes part in the third day of Chinese New Year celebrations at the Dongyue temple, Beijing, China.
The Lunar New Year will be marked with a week-long holiday.
At the Chinese New Year red is important.
People wear red clothes, they write poems on red paper, and give children 'luck money' in red envelopes.
The symbolism behind the red colour is fire, and fire burns off bad luck.
As for fireworks one belief is that the cracker jacks and sparks frighten away evil spirits.
After the fireworks at the beginning of the celebration of the Chinese new year, comes the more tranquil Lantern Festival on the last day of the festivities.
Most Lantern parades feature a dragon made of silk and bamboo.
The dancers hold the monstrous dragon aloft on sticks.
Their coordination skills make the dragon appear to dance.
This is the Mid-Autumn Festival 中秋節 which is held on the 15th day of the eighth month in the Chinese calendar, which is usually around late September or early October in the Gregorian calendar.
It is a date that parallels the autumnal equinox of the solar calendar, when the moon is supposedly at its fullest and roundest.
The traditional food of this festival is the moon cake, of which there are many different varieties.
See more on Zongqui jie here.
Chinese Tomb Sweeping Day at the beginning of April, is for honoring ancestors and to make sure they are happy in the after world.
Ancestor worship includes cleaning and sweeping of their graves and at the same time bringing offerings of food, normally fruits, buns and sweets, wine, etc.
Cemeteries will be very crowded on this day, and this is where you are likely to see all the commotion.
Burning incense is also part of the tradition.
Chinese will burn incense and pray to their ancestors on this day.
It is also common to burn other offerings, specially "ghost money".
The belief is that by burning something, the essence of the burnt element is transferred to the spirit world, where it can be used by the dead.
It used to be that the most common item being "sent" was money, so the burning of paper money was most popular.
Nowadays, there is quite a variety of items being "sent", from iPads and cellphones to cars and even modern appliances that could be "useful" to the receiver in the other world.
So side by side with the traditional paper money, you see paper replicas of all the different gadgets which are burn and transferred to the deceased.
Check out this short clip showing a typical Ching Ming day in Hong Kong and the different offerings being prepared for the ancestors including ghost money, many food and every day items:
Nowadays, people like to fly kites during the Qing Ming Festival, and as it is the beginning of Spring, it is also a time to plant trees.
The Ching Ming Festival falls during the third lunar month.
You can find the Western calendar date here.
On most days of the year, cemeteries in Hong Kong are studiously avoided but on the Ching Ming Festival, public transport companies have to put on extra services to them, such is the exodus from the city’s streets to its hillside graveyards.
Ching Ming literally translates as ‘clean and bright’, and this is the day that Chinese people sweep the graves of their ancestors.
But the tidy up doesn’t end there: the festival is an important ancestor worship ritual that also requires families to weed graves, touch up headstone inscriptions, make offerings of food and light incense.
Traditionally, many people burn paper offerings at gravesites during the festival for their ancestors to use in the afterlife.
The most popular of these used to be faux cash, but it appears the consumer demands of the earthly realm have crossed over into the hereafter, because people in Hong Kong nowadays also burn paper imitations of mobile phones, laptops, refrigerators, air-conditioners and even luxury cars.
The Dragon Boat Festival originated in China and is known as the four traditional festivals of China together with the Spring Festival, Qingming and Mid-Autumn Festival.
In September 2009, UNESCO officially approved its inclusion in the “List of Representatives of the Intangible Cultural Heritage of Humanity”, and the Dragon Boat Festival became China’s first festival to be selected into the world.
There are more than twenty kinds of names in the Dragon Boat Festival.
They were originally the festivals of the ancient ancestors who sacrificed dragon ancestors in the form of dragon boat races.
They originated from the land of Wu Yue.
They believed in the dragon totems and thought they were children of dragons.
The two customs of the Dragon Boat Festival, eating scorpions and dragon boating are all related to the dragon.
The scorpion can be sacrificed to the dragon god in the water, and the race is also a dragon boat.
It is said that Qu Yuan, a poet of the Chu State during the Warring States period, plunged himself into the Luojiang River on the Dragon Boat Festival.
Later, people also used the Dragon Boat Festival as a festival to commemorate Qu Yuan.
China has several major holidays, virtually none of which we celebrate in the West.
The Chinese New Year (or Spring Festival) is increasingly recognized more in the US by non-Chinese, but still barely perceptibly.
Other holidays include Qing Ming Festival (which reveres the dead), Mid-Autumn Festival (which reveres the moon), May Day (international labor day the began in Chicago, but is not recognized in the US on that day) and National Day (when the CCP took over power from the KMT).
The last one is the Dragon Boat Festival, which is the focus of this post.
It focuses on dragon boats, poets, sticky rice and tensions with the government.
All are very poignant pieces of Chinese history.
Chinese holidays have involved histories, most of which are outside the scope of this post.
While there is a lengthy backstory behind Christmas, it’s probably more interesting just to detail elves, presents, Santa, Jesus and an angel or two.
The rest, if you’re interested, is out there.
In short, during the Warring States Period in Chinese history, there were – as you can guess – a bunch of disparate kingdoms vying to become the ultimate rulers of a central government.
Qu Yuan was a minister of high status.
When the Chu royal house aligned with a group he didn’t agree with, he was banished.
For 28 years, he wrote poetry in exile.
Then, the group the Chu aligned with captured the capital of his own people and he committed suicide by drowning himself in a river.
The people, who adored his works, tried to save his body by throwing rice in the water so the fish would eat the grain instead of Qu Yuan’s flesh.
Nowadays, to commemorate his legacy, people make zongzi, a dish made by wrapping rice and a filling inside zong leaf and steaming it.
It can be sweet or savory, but it’s always delicious.
Additionally, people host dragon boat races with dragon dances and music and all sorts of merriment.
A cool aspect of China is how diverse each region is and how distinct the customs are.
Each village or town has a particular tradition not found exactly replicated anywhere else.
In a certain village in Foshan they’ve hosted a ridiculously animated race for years.
In the months leading up to the competitive event, you can see oarsmen practicing out on bodies of water across the city.
While my buddy, Danny, and I crossed the foot bridge over the manmade canal to hit the pub and watch Arsenal play (and probably lose again), we heard drums beating, paddles slapping the water and synchronized exhales.
There were sayings written on red paper everywhere and allusions to the boat races.
Qu Yuan’s image appeared in malls and schools.
People excitedly rumbled on about it.
I was told by many of my Chinese friends we had to go with them to the village, and so we did.
Our buddies picked us up in a packed van and so we went.
As we approached, I noticed that this was the very village I once got super lost in on a long run while training for my marathon.
It was the quintessential modern Chinese village.
Loads of wealth, modest housing, low key clothing.
It had typical southern Chinese architecture, or lingnan style, with gray brick and dark wood.
Roads barely fit a small sedan, and pedestrians and vehicles shared the same space.
There is an orderliness somewhere between midtown Manhattan’s meticulous grid and New Delhi’s what-the-fuck.
We arrived, and after meandering through some narrow streets and a footbridge apparently doubling as one for cars, we walked around.
In steadily misting rain we walked around a jovial, spirited crowd.
There were crowds huddled on bleachers underneath a yellow tarp, dirty droplets dripping down.
Firecrackers sounded off in the distance, beckoning crowds to observe a dragon dance.
Someone banged a gong as the shizi moved in rhythm.
In my peripheral, I caught a dragon boat zooming forward toward an L-turn.
I rushed over to see how on earth they would avoid shattering the bow of the ship, sending everyone in the murky village water like a carnival dunk tank.
Suddenly, ten folks on the right side dipped their oars straight into the water, sinewy biceps strained, while the left side paddled ferociously.
The longboat lurched through the turn just about making it unscathed until thwack!
The dragon figurehead popped off into the canal and the scrapping of wood on stone could be heard over a groaning crowd.
This repeated ceaselessly throughout the day, as a local channel broadcasted the contests on TV.
We grabbed a couple of beers to beat the rain and watched under the shelter of a leaky roof.
As the day wore on, we were noticed as outsiders by quite a few, seeing as a white kid, a brown dude and a black girl were in a Chinese village of thousands.
Some of the younger dragon boaters let us paddle along a bit, butts getting wet in the hull, opaque water washing up over the ship’s edge.
This was an awesome experience, both seeing and participating ever so briefly in a major Chinese holiday.
Part of the joy of living in a foreign country is enjoying festivals you cannot partake in back home.
This was enjoyable, worthwhile.
Thank you.
The Dragon Boat Festival, also known as the Duanwu Festival or Double Fifth Festival, provides the perfect opportunity for family fun.
The holiday is celebrated outdoors during the summer, features exciting dragon boat races and involves themes that are easily accessible for children.
Here is a collection of easy activities, recipes and crafts with step-by-step directions to help your family celebrate an ancient Chinese holiday that’s become a modern international phenomenon.
The Dragon Boat Festival focuses on the themes of patriotism and sacrifice.
The holiday celebrates the patriot poet Qu Yuan, who drowned in the Miluo River during the Warring States period more than 2,000 years ago.
In ancient China, this summertime festival was observed to appease the rain gods and ward off illness.
Today, the Dragon Boat Festival’s growing popularity around the world can be credited in large part to the growth of dragon boat racing as a sport.
When sharing the Dragon Boat Festival story with your kids, focus on the sacrifices made by Qu Yuan and the holiday’s place in China’s agrarian society.
The holiday is one of the three big festivals designated for the living — the others are Chinese New Year and the Mid-Autumn Festival.
The Chinese have raced dragon boats for centuries, as an appeal to the water gods during the sweltering summer months.
It’s only been since the 1970s, however, that modern dragon boating has grown into an international sport with competitions around the world.
Rice dumplings are parcels of glutinous rice stuffed with different savory or sweet fillings and wrapped in bamboo leaves.
They’re eaten during the Dragon Boat Festival in reference to glutinous rice balls villagers tossed into the Miluo River while searching for the poet Qu Yuan.
Celebrating the Dragon Boat Festival provides an effortless opportunity for you to enjoying Chinese culture with your family.
Watching exhilarating dragon boat races from the waterfront under clear summer skies is the perfect backdrop for sharing the traditions of the Dragon Boat Festival.
If you live in a city with a dragon boat celebration, make sure to attend.
Most U.S. cities host Dragon Boat Festival celebrations featuring brightly decorated boats powered by teams of paddlers and manic drumming.
Read my interview with Hans Wu, race director of the San Francisco International Dragon Boat Festival, to know what to expect at the races.
Dragon boat racing has become an international sports phenomenon in the last 40 years.
Today’s there’s a full summer race schedule running from February through October in cities across the United States.
Use our directory to find a race near you.
If you’re interested in trying dragon boat racing for yourself, it’s easy.
Dragon boating is an accessible and fun team sport open to all ages and skill levels, all supported by a welcoming community.
Read about my experience joining the Oakland Renegades to see if it’s for you.
The Dragon Boat Festival’s traditional food, known as zongzi in Mandarin and joong in Cantonese, is a glutinous rice dumpling filled with savory or sweet fillings and wrapped in bamboo leaves.
You’ll find them for sale at a Dragon Boat Festival, as well as throughout Chinatown.
Fortunately, they’re also really fun to make at home with eager kids.
Making rice dumplings yourself at home is a fun family activity and the finished product can be eaten at home or given away as gifts during the festival.
Follow along with this tutorial and learn to make a classic rice dumpling filled with pork, peanuts, sausage and mung beans.
Attending a dragon boat festival outdoors is truly an all-ages experience.
You can use craft projects at home as a way to prepare for the festival’s excitement.
Alternatively, making a toy dragon boat for the pool or bathtub is a fun substitute, if you’re unable to make it out to the races.
Making a toy dragon boat is a terrific way to introduce younger children to the Dragon Boat Festival story.
See how you can make a beautiful boat with just some colorful paper, a few common items from around the house and our printable template.
Prior to the Dragon Boat Festival, it’s a good idea to pick up supplies to make rice dumplings, but that’s all you’ll really need.
You’ll also want to select a few children’s books to help introduce young readers to the holiday.
For a holiday that’s over 2,000 years old, there are surprisingly few children’s books exclusively dedicated to the Dragon Boat Festival.
Fortunately, I’ve found five that do a good job of introducing the holiday, as well as the sport of dragon boat racing.
I hope you have a great time celebrating the Dragon Boat Festival this year with these easy activities, recipes and crafts.
Please comment below if there are additional resources you would like to see added to the site!
The Dragon Boat Festival is a celebration where many eat rice dumplings (zongzi), drink realgar wine (xionghuangjiu), and race dragon boats.
Other activities include hanging icons of Zhong Kui (a mythic guardian figure), hanging mugwort and calamus, taking long walks, writing spells and wearing perfumed medicine bags.
All of these activities and games such as making an egg stand at noon were regarded by the ancients as an effective way of preventing disease, evil, while promoting good health and well-being.
People sometimes wear talismans to fend off evil spirits or they may hang the picture of Zhong Kui, a guardian against evil spirits, on the door of their homes.
Many believe that the Dragon Boat Festival originated in ancient China based on the suicide of the poet and statesman of the Chu kingdom, Qu Yuan in 278 BCE.
The festival commemorates the life and death of the famous Chinese scholar Qu Yuan, who was a loyal minister of the King of Chu in the third century BCE.
Qu Yuan’s wisdom and intellectual ways antagonized other court officials, thus they accused him of false charges of conspiracy and was exiled by the king.
During his exile, Qu Yuan composed many poems to express his anger and sorrow towards his sovereign and people.
Qu Yuan drowned himself by attaching a heavy stone to his chest and jumping into the Miluo River in 278 BCE at the age of 61.
The people of Chu tried to save him believing that Qu Yuan was an honorable man; they searched desperately in their boats looking for Qu Yuan but were unable to save him.
Every year the Dragon Boat Festival is celebrated to commemorate this attempt at rescuing Qu Yuan.
The local people began the tradition of throwing sacrificial cooked rice into the river for Qu Yuan, while others believed that the rice would prevent the fishes in the river from eating Qu Yuan’s body.
At first, the locals decided to make zongzi in hopes that it would sink into the river and reach Qu Yuan's body.
However, the tradition of wrapping the rice in bamboo leaves to make zongzi began the following year.
A dragon boat is a human-powered boat or paddle boat that is traditionally made of teak wood to various designs and sizes.
They usually have brightly decorated designs that range anywhere from 40 to 100 feet in length, with the front end shaped like open-mouthed dragons, and the back end with a scaly tail.
The boat can have up to 80 rowers to power the boat, depending on the length.
A sacred ceremony is performed before any competition in order to “bring the boat to life” by painting the eyes.
The first team to grab a flag at the end of the course wins the race.
The zong zi is a glutinous rice ball with a filling and wrapped in corn leaves.
The fillings can be egg, beans, dates, fruits, sweet potato, walnuts, mushrooms, meat, or a combination of them.
They are generally steamed.
It is said that if you can balance a raw egg on its end at exactly noon on Double Fifth Day, the rest of the year will be lucky.
Celebrated on the 5th day of the 5th month of the lunar calendar, the Dragon Boat Festival also known as Duan Wu Jie can trace its roots back to the Chu State of the Zhou Dynasty.
The Dragon Boat Festival is celebrated the world over by the Chinese.
One of the most popular legends behind the festival is the one about Qu Yuan, a Chu State official and poet.
Qu Yuan was known for his criticism of corruption in the Imperial Court.
He was eventually exiled and began writing poems for which he is still known.
When the Zhou was overthrown by the Qin Dynasty, Qu Yuan was so despondent he threw himself into the Miluo River.
According to legend, villagers in the surrounding area rushed to his rescue and threw rice dumplings (zongzi) into the river to prevent the fish from feasting on his body and fishermen paddled up and down the river in their long boats in search of Qu Yuan while beating on drums to scare away the fish.
Since then, Chinese people have commemorated Qu Yuan by eating zongzi and racing dragon boats egged on by a chorus of loud drum beats.
Zongzi are delicious dumplings usually shaped like a pyramid and wrapped in fresh bamboo or pandan leaves, glutinous rice filled with meat, dates and nuts, and steamed until tender.
Dragon boat racing has also become a popular way of observing the festival.
The Dragon Boat Festival is a significant Chinese festival which is celebrated on the fifth day of the fifth lunar month.
Its origin was to commemorate the patriotic poet Qu Yuan.
This festival is one of the three major celebrated festivals in Taiwan, together with Chinese New Year and the Moon Festival.
Out of all major Taiwan festivals, Dragon Boat Festival has the longest history with many stories telling its origin.
The most popular one is about the patriotic poet- Qu Yuan.
During the declination of China in the end of the Zhou Dynasty, Qu Yuan served as a minister to the Zhou Emperor.
Qu Yuan was a wise and articulate man well loved by the people.
The fights that he had against the rampant corruption made the other officials envy him.
The officials started spreading rumors of Qu Yuan in front of the emperor and eventually Qu Yuan had lost the emperor’s trust.
Qu Yuan then got exiled when he urged to avoid conflict with the Kingdom of Qing.
He travelled and wrote poems during his exile to.
He threw himself into Milou River after he heard that Zhou was being defeated by the Qing.
During the day of the Dragon Boat festival, there are various beliefs and traditions that ethnic Chinese do, such as racing dragon boats, eating glutinous rice dumplings (Zhongzi), hanging calamus and moxa on the front door, drinking concoctions,displaying portraits of Zhong Kuei, children wearing fragrant sachets, as well as standing an egg at 12:00 noon.
The Dragon Boat Festival is highlighted by the dragon boat races, which was originally meant to the attempts to rescue Qu Yuan.
This lively and colorful tradition continued for centuries.
Today, dragon boat races are held in various Taiwan cities such as Hsinchu, Tainan, Taipei, and Yilan, etc.
Other than enjoying dragon boat races, people eat Zhongzi (rice dumpling), which is a glutinous rice ball wrapped in corn leaves with fillings of egg, beans, walnuts, mushrooms, and meat.
Travelers may find Zhongzi as a common delicacy in Taiwan,taste differently in different area of Taiwan.
On the Dragon Boat Festival, hanging portraits of Zhong Kuei, calamus, and moxa on the front door are believed to keep evils away and bring peace.
Overall, the Dragon Boat Festival is not only a festival to commemorate a patriot, but also a chance for families and friends to get together.
Chinese people like eating, and Chinese culture is justifiably called a food culture.
They have different foods with special meanings for each festival.
For Dragon Boat Festival the Chinese usually eat zongzi and various other foods below, depending on the region
It is a traditional custom for Chinese to eat zongzi, a kind of sticky rice dumplings wrapped in bamboo leaves.
Zongzi are usually made of glutinous rice with meat or some other filling, and wrapped in bamboo leaves in the shape of a triangular pyramid.
They are many different flavors and shapes of zongzi available.
This fan-shaped food is made up of five multi-colored layers, with each layer covered with fried sprinkles of pepper powder.
The layers are pinched into a variety of patterns to make it appealing to eyes.
This dietary custom is said to be trace back to the tradition of making and selling fans during Duanwu Festival in ancient times.
The custom of eating eel on Dragon Boat Festival day prevails in central China's Wuhan region.
Eels are probably eaten simply because they are in season during the festival.
They are fatty and tender, and rich in nutrition.
In East China's Wenzhou area, every family eats a kind of thin pancake at Dragon Boat Festival.
The pancake is made of refined white wheat flour fried in a flat frying pan.
When the cake becomes very thin and translucent, as thin as a piece of silk as the locals describe it, it is done.
Green bean sprouts, leek, shredded meat, and mushrooms are then placed on the pancake, which is then rolled up and eaten as a wrap.
Chinese New Year, the Mid-Fall Moon Festival and the Dragon Boat Day are three major festivals in China.
The Dragon Boat Festival is celebrated on the 5th day of the 5th lunar month of the Chinese calendar.
Chinese call this day as Duan-Wu .
Duan means beginning.
Wu means Horse month in Chinese calendar.
The Horse month usually begins on June 5th or June 6th in the Gregorian calendar.
That means Dragon Boat Festival should be held in June, unless that year has Leap Month in the Chinese lunar calendar.
In China, the Dragon Boat Festival memorializes the Chinese patriotic poet Chiu Yuan (340 BC-278 BC or 343-290 B.C.), who committed suicide by jumping into the river after tying himself with big rock on the 5th day of the 5th lunar month.
Chiu Yuan was the number one advisor of the kingdom of Chu .
But people were jealous his position and said lots of bad words on his back.
The king wouldn't take his advice in the end and was killed by the enemy of neighbor kingdom.
The new king continued to enjoy the luxury life and didn't like Chiu Yuan either.
Later, Chiu Yuan was exiled.
He wrote many patriotic poem after then.
Chiu Yuan met a fisherman, who never cared about the country and quite satisfied his life.
Chiu Yuan thought that the king wouldn't run the country, people only cared about themselves, nobody cared the future of the country and to live is meaningless.
So he killed himself by drowning himself in the river.
Many fishermen tried to rescue him, but the body is never found.
Fishermen worried about fish would eat his body.
So they threw food into the river to feed the fish.
Plus, they tried to scare fish away by splashing the water with their paddles and beating the drums on the long narrow boats.
Then the dragon was added into the story.
Fishermen believed there was a water-dragon under the river.
One man poured down a big jar of strong yellow wine (made of rice).
Later, a drunken dragon-like fish floated on the river.
One piece of Chiu Yuan's clothing was found in-between the whisker of the water-dragon.
The custom of Dragon Boat Race might begin from the southern China.
They selected the 5rh lunar day of the 5th lunar month as the totem ceremony.
The dragon was the main symbol on the totem, because Chinese thought they were son of dragon.
They also made dragon-like canoe.
Later, Chinese connected this custom with Duan-Wu festival.
Since this was the event only in the southern China.
This might be why Dragon Boat Race doesn't that popular in entire China today.
But we can see yearly Dragon Boat Race events in Honk Hong and Taiwan.
The picture shows a person lies on the top of dragon head of the boat to prepare to catch the flag of target to win the race.
Now, the Dragon Boat Race becomes an international event.
This sport is popular in USA, Canada, Europe, Australia, Taiwan, Honk Hong, Singapore etc.
Some organization's events aren't held around the Dragon Boat Festival.
Some are in July, August or September.
You need to check their websites for the schedules.
One saying the fishermen threw bamboo-food into the river for Chiu Yuan.
The fishermen kept the custom on on the 5th day of 5th lunar month every year.
Until Late Han Dynasty (25-220 AD), one outsider came and acclaimed fisherman's behavior, but recommended to wrap the food with leaves and tie with color silky rope, which can scare fish away.
So the Chiu Yuan can unwrap the leaves to eat the food.
Obviously, the Zong-zi, which Chinese eat on the the Dragon Boat Day period, is like the food for Chiu Yuan.
Zong-zi is make of steamed glutinous sweet rice mixing with meat and condiment wrapped in the bamboo leaves.
There are salty, sweet, hot or cold different variety of Zong-Zi today.
You can find Zong-Zi in Chinese restaurants providing Dim-Sum lunch or same Chinese diners in Chinese communities in USA.
The Dragon Boat Day is usually in June, which is Horse month.
Horse hour in astrology is from 11:00 AM to 13:00 PM.
They said that you will be lucky for the coming year if you can make an egg standing up during Horse hour on the Dragon Boat Day.
Parents like to let their children to try to make the egg standing up on the Dragon Boat Day.
Certainly, the standing up egg competition will be held at noon in many places.
They said that it's will be easier to make a standing up egg at noon.
Many try to cheat on the ground, eggshell or inside egg to in order to win the competition.
People had looked for the scientific explanation.
The egg can standup easily is because the Dragon Boat Day is close to the summer solstice, which is the longest day of the year.
The summer solstice occurs when the Earth's axis tilts the most toward the sun, causing the sun to be farthest north at noon.
Sun reaches to the Tropic of Cancer in the northern hemisphere on the day of summer solstice.
Before sun travels back to the southern hemisphere, it seems as if the sun stands still.
When the gravitation between sun and earth are pulled each other to the most, an egg can stand up easier.
Actually, the Dragon Boat Day is not close to the summer solstice every year.
Because the lunar leap months, the Dragon Boat Day usually is close to summer solstice every three years.
The 5th lunar month is marked as "Poison" month in Chinese Farmer's Calendar.
This is because the gem, insect, fly, mosquito, and pest are active in this summer month and people is easy to catch infectious disease.
On the Dragon Boat Day, Chinese put the leaves of Acorus and Artemisia on the doors or windows to repel insect, fly, flea and moth away from the house.
Those leaves have anti-poison function and can prevent an epidemic.
The leaves of Acorus are linear, sword-like, glossy, evergreen and lush.
Artemisia belongs to daisy family with fern-like leaves.
In Tan Dynasty (618-907 A.D.), Rebel Huang scouted the target village and prepared the next attack in 5th lunar month.
He saw a woman carrying a boy in her one arm and holding the another boy's hand was running.
Rebel Huang asked her why running.
She said, "We heard Bandit Huang is coming, we need to run for our lives.
" Huang asked again, "Why carrying one in the arm, but holding another with hand?".
The woman said, "The one in the arm is the only son of my husband's elder brother.
The other one is my son.
In case, I cannot run quickly enough, I will drop my son and save my husband brother son.
" Rebel Huang was very touching, then told her that "Go home quickly and put Acorus and Artemisia on your door, then rebel forces won't hurt your family.
" She returned to the village and told some people.
On the 5th day of 5th lunar month, Rebel Huang's forces entered the village, all the families with Acorus and Artemisia on the door were safe.
The custom keeps since then.
Many contagious disease and plague had found in the 5th lunar month in the Chinese history.
Besides putting the Acorus and Artemisia on the doors or windows, Chinese make the incense bag and hang on the neck to prevent from contagious disease and keep evil spirit away.
The incense bag are made by hand.
Chinese put the powder of Acorus and Artemisia with some other fragrance stuff into different kind of sewing bags.
Therefore the incense bag can prevent an epidemic as the leaves of Acorus and Artemisia.
The popular incense bags was 12 horoscope animals.
Today, we can find many different auspicious symbols, many other animals, fish, flower, bird, even the cartoon characters.
Long time ago, Chinese women liked to make incense bag for children.
The 5th lunar month is a bad month.
The 5th day of the month is an inauspicious day, because many famous people died on this day in Chinese history.
One customary book recorded, "if a baby's born on this day, baby boy brings bad luck to dad and baby girl brings bad luck to mom.
" It happened that parent killed the baby born on this day before.
They said that you will become healthier to drink the spring water from the well on the Dragon Boat Day during Horse hour (11.00 AM to 13.00 PM).
The story is from late Ming dynasty.
A general brought his troop to the hill and couldn't find the water for days.
He prayed to the heaven, then struck his sword into the ground and the water spurted out on the 5th day of 5th lunar month at noon.
Once Chinese drank Strong Yellow Wine to clean up the gem in the body on the Dragon Boat Day.
The Strong Yellow Wine is made of rice.
It's a tonic wine and not good for children.
Therefore adult just wipe the children forehead with the Strong Yellow Wine.
They believe that will make children healthier and won't catch the 5th lunar month disease.
Over the past 40 years, the sport of dragon boat racing has grown beyond the Dragon Boat Festival’s official holiday celebration on the 5th day of the 5th lunar month every year.
Today, there’s a full summer race schedule spanning from February through October in cities across the United States.
The celebrations incorporate activities both on land and on the water.
Many of the largest celebrations begin with traditional opening ceremonies that awaken the dragons and bless the racing to come.
From there, paddlers take to their boats and spectators crowd the waterfront amidst a carnival of cultural activities and food.
A day at a dragon boat celebration combines culture, folklore and fierce athletic competition.
Powered by 20 paddlers and a drummer’s rhythmic beat, dragon boat teams compete with the same energy as the townspeople who fought to save Qu Yuan in the Miluo River so many years ago.
Teams span age groups, affiliations and ability levels in this inclusive sport.
In the United States, the largest dragon boat festivals are held in cities like San Francisco and New York, which have historically had the country’s largest Chinese populations, though races now exist almost everywhere there’s a waterway large enough to host them.
The most competitive racing classes ultimately send teams to the World Dragon Boat Racing Championships and the World Club Crew Championships, both sponsored by the International Dragon Boat Federation.
These worldwide competitions for national and club teams have been held in cities around the world like Hong Kong, Auckland, Vancouver and Cape Town.
Dragon Boat Festival, also called Duanwu or Tuen Ng Festival, is a traditional holiday observed annually over 2,000 years in China to commemorate Qu Yuan (340-278 BC), an ancient Chinese patriotic poet.
Originated from south China, Dragon Boat Festival enjoys higher popularity in southern areas, such as Jiangsu, Zhejiang, Guangdong and Fujian Provinces.
Chinese: 端午?[du?n w? ji?] Date: 5th day of 5th lunar month History: more than 2,000 years Traditions: eating Zongzi (sticky rice dumplings), dragon boat race
Defined by Chinese lunar calendar, the date of Dragon Boat Festival falls on the 5th day of the 5th lunar month according to lunar calendar, so the Gregorian date varies every year, and hereunder is the holiday schedule from 2019 to 2024.
Why is the Dragon Boat Festival celebrated?
With a history over 2,000 years, it used to be a hygiene day when people would use herbs to dispel diseases and viruses.
However, the most popular origin is closely related to the great poet Qu Yuan in the Warring States Period (475 – 221BC).
To engrave his death on the fifth day on the fifth lunar month, people celebrate the festival in various ways.
Great people like Wu Zixu and Cao E also died on the same day, so in certain areas, people also commemorate them during the festival.
As a minister in the State of Chu - one of the seven Warring States, Qu Yuan was a patriotic poet who wrote a lot of works to show his care and devotion to his country.
Composing masterpieces like Li Sao (The Lament), he was regarded as one of the greatest poets in Chinese history.
After he was exiled by the king, he chose to drown himself in the river rather than seeing his country invaded and conquered by the State of Qin.
He died on the fifth day of the fifth lunar month, thus people decided to commemorate him on that day every year.
Many traditional customs and activities are held on the specified day by people in China and even by people in neighboring Asian countries.
Dragon boat racing and eating Zongzi are the central customs of the festival.
In some regions in China, people also wear a perfume pouch, tie five-color silk thread and hang mugwort leaves or calamus on their doors.
Most Chinese festivals are observed by eating a particular food as a custom, and the Dragon Boat Festival is no exception.
Zongzi, a pyramid-shaped glutinous rice dumpling wrapped in reed leaves, is the special food eaten on the day.
It has various fillings.
In north China, people favor the jujubes as the filling, while the south sweetened bean paste, fresh meat, or egg yolk.
Nowadays, Zongzi already becomes a common food, which can be easily found in supermarkets.
However, some families still retain the tradition to make Zongzi on the festival day.
See How to Make Zongzi.
Dragon boats are thus named because the fore and stern of the boat is in a shape of traditional Chinese dragon.
A team of people works the oars in a bid to reach the destination before other teams.
One team member sits at the front of the boat beating a drum in order to maintain morale and ensure that the rowers keep in time with one another.
Legend holds that the race originates from the idea of the people who rowed their boats to save Qu Yuan after he drowned himself.
Now it has turned to be a sport event not only held in China, but also observed in Japan, Vietnam, and Britain.
The Moon Festival, also known as the Mid-Autumn Festival, the Harvest Moon festival, or the Zhongqui Festival, is celebrated on the 15th day of the 8th month of the lunar calendar—or September 24 in 2018.
It’s a day to celebrate family and tradition, so get together with your relatives, do some moon gazing, and enjoy a moon cake—or a few!
中秋快? (Happy Mid-Autumn Festival)!
Buy or bake mooncakes, a traditional Moon Festival treat.
No Moon Festival is complete without mooncakes, small, rich pastries that you can buy from a Chinese bakery or make on your own, if you have time.
They’re meant to be shared and gifted to family and friends, so have plenty!
Plan a dinner of tasty and traditional dishes.
A Moon Festival dinner offers big portions of delicious, symbolic dishes for the whole family to enjoy.
Think about how many people you’ll be inviting over and start to plan your meal accordingly.
Many families also choose to eat out for Moon Festival to avoid the burden of making a meal—it’s up to you!  like roast pork, a whole chicken, fish, vegetables, and rice.
Set up an honor table to present your mooncakes, fruits, and tea.
An honor table is a small end table or even a bench that you’ll place near your dinner table.
Use it to display your ritual offerings, including candles and incense, which you’ll burn to honor ancestors.
You’ll also place your mooncakes, picture pastries tea service, and fruits on the honor table.
Make lanterns to decorate and light up your home.
Moon Festival decorations don’t need to be over the top, but the one thing you can’t do without are lanterns.
Moon Festival lanterns are bright, festive, and often shaped like animals or interesting geometric designs, but you can make your own simple rectangular lanterns.
Creating lanterns as a family in the days beforehand is a great way to foster the spirit of togetherness that this holiday is all about.
Making lanterns is an especially great activity for kids, who can design their own and carry it around on the night of the celebration!
The Moon Festival has been celebrated since the Shang Dynasty, 3,000 years ago.
The festival originates from a folk story about an archer, Hou Yi, who receives an elixir of immortality in return for saving the world.
His wife, Chang’e, drinks the elixir and floats up to the moon, where she turns into a jade rabbit.
The story says that she still lives on the moon, longing for her husband, and reunites with him once a month, when the full moon shines brightly from the strength of their love.
Get together with your family for a meal and a casual celebration.
The Moon Festival is a time for family and togetherness, similar to an American Thanksgiving, and the best way to celebrate is by sharing a meal and catching up with family.
This meal is traditionally made and eaten at home, but some families today choose to eat out to save on time and effort.
Many people also choose to eat outside under the stars, taking advantage of the warm fall weather and using the opportunity to appreciate the moon.
If you’re hosting, make sure to get in touch with your relatives a few weeks beforehand to invite them and confirm that they’re coming.
Specify a time and whether you’d like them to bring any dishes.
If you eat outside, you can set up a blanket on the grass and have a picnic-style dinner.
Set up a bench or small, portable table as your honor table.
If you’re away from your family during the Moon Festival, or if some of your loved ones are away, make sure to call or message to wish each other a happy Moon Festival.
Serve tea and head outside to moon gaze together.
After dinner, serve your family tea and enjoy your dessert of mooncakes, picture pastries, and fruit.
If you ate inside, take your tea and pastries outdoors and sit down together to look at the moon and enjoy each other’s company.
Many families use this time together to reminisce about times past and talk about family members who have passed away or can’t be at the celebration.
Hang your lanterns outside, too.
They’ll give off a soft, pleasant glow while you talk.
Honor ancestors by burning incense.
Family is at the center of the Moon Festival celebration, and ancestors take a place of special honor.
To show your respect to ancestors and family members who have passed away, burn incense and bow 3 times in front of your honor table.
Share family stories with children to help them appreciate tradition.
Families typically allow children to stay up late on the night of the Moon Festival.
Include them in family conversations or read to them from books of Chinese poetry.
You can also let them march around with their colorful lanterns.
Make sure they’re old enough to carry the lanterns without hurting themselves or spilling the candle.
You can also replace the tealight inside with an electric light that looks like a candle, often right down to its sputtering glow.
Check for any Moon Festival celebrations in your area.
Chinese communities often hold special events to commemorate the Moon Festival, including fire dragon dances, lion dances, lantern exhibitions and carnivals.
You can see if any events are being held near you, or even travel to larger celebrations in China, Hong Kong, Vietnam, Singapore, and other countries where the holiday is observed.
If you decide to make a trip, be sure to book your tickets and accommodations well in advance.
Large cities known for their celebrations, including Beijing and Hong Kong, will often fill up quickly in the days beforehand.
In many countries, the day after the Moon Festival is considered a holiday, with work and school closed.
Keep this in mind if you’re traveling—you’ll be able to stay up later than usual, but some businesses may take the day off.
If you live near a large city with a Chinatown neighborhood, there’s a good chance you’ll be able to catch some Moon Festival celebrations there.
Ask around or do some research online to see.
An essential part of Chinese Culture, Chinese festivals have traditions which date back thousands of years.
Originally created to celebrate the seasons and harvests, they have evolved to include mythology and folk tales.
Every Chinese holiday is based off the traditional Chinese lunar calendar, so their dates vary every year.
They are an incredible part of Chinese life and a great look into the pageantry of China’s culture and history.
Spring Festival The most important holiday of the Chinese people, Spring Festival begins on the first day of the first lunar month and continues for fifteen days.
On each day of the festival, different traditions are followed and each one is looked forward to for the whole year.
This festival is particularly popular with children, who get presents in the form of Hong Bao, which are red envelopes filled with money.
The entire country decorates itself for the festival and every home, whether it be in a village, or in the city, is decorated and cleaned for the holiday.
The entire country takes on a look and the suspense is almost palatable.
It is a wonderful time to visit China and partake in the festivities.
Spring Festival Decorations & Lantern Festival The fifteenth and final day of the Spring Festival, Lantern Festival is known for its many wonderful lanterns.
Most cities in China have huge lantern displays and traditionally every family make their own lanterns to decorate their house with.
Lanterns are only part of the festival.
Dragon and Lion dances are performed and many fireworks are lit off.
Tomb Sweeping Day Each year, Qingming Jie, or Tomb Sweeping Day is celebrated on the twenty-first day of the second lunary month.
The festival has two main parts.
One part is for the entire family to outside and enjoy spring.
They will go to a park, or the countryside to enjoy the budding trees and flowers.
The second part involves visiting the tombs of their ancestors.
The tombs will be cleaned and tended.
While at the tombs, they will offer alcohol and cigarettes to their ancestors and fireworks and incense will be lit.
Popular in not only China, but around the world, Dragon Boat Festival is famous for its dragon boat races, but in China there is much more to the holiday.
According to tradition, the festival commemorates the death of Qu Yuan, a Zhou Dynasty poet and government official.
He was wrongfully accused of treason and committed suicide by jumping into a river.
The local people loved and admired him and threw rice into the river to feed the fish so that they would not eat his body.
They also rushed out into the river in boats to try to recover his body.
So today people eat Zhongzi, a sticky rice dumpling wrapped in lotus leaves and hold dragon boat races.
Zhongyuan Ghost Festival Celebrated on the fifteenth day of the seventh lunar month, Ghost Festival is a day to make offering to ancestors.
It is traditionally believed that on this day, ghosts can return to earth.
On this day, the Chinese people burn incense and paper objects to appease the spirits.
The paper objects that are burned come in the form of money, houses, cars, cell phones, alcohol, etc.
They are burned in the belief that the smoke from the objects can be reformed in heaven into their representative objects and be used by the deceased and therefore they will not both the living.
Mid-Autumn Festival Held on the fifteenth day of the eighth lunar month, Mid-Autumn Festival is a festival for families.
On this day, people eat moon cakes, a small cake filled with meat, vegetables, or fruit.
In the evening, families will go outside to enjoy the full moon together.
Chinese New Year , also known as the Spring Festival, or Chun Jie, Chinese New Year begins on the first day of the first month in the lunar calendar (January or February) and ends with the Lantern Festival on the 15th.
Chinese New Year is the most important festival in Beijing so many temples host fairs, and most businesses close for at least several days.
The Chinese New Year is a time for people to visit with family, so traffic may be especially bad at this time of year.
The annual Lantern Festival takes place on the last day of the Chinese New Year.
The tradition behind this holiday originates in the story of the heavenly Jade Emperor who was angered that a town killed his favourite goose.
The Emperor decided to burn the town to punish it.
However, the townspeople were warned by a sympathetic fairy and decided to burn lanterns so that when the Jade Emperor looked down on the town, he would be fooled into thinking it was already on fire.
Today, on the first full moon of the lunar calendar, the Chinese burn lanterns and celebrate with food, dancing, parades and fireworks.
Every April, families gather to pay their respects to their deceased ancestors and to clean and decorate their grave sites.
Kites may be flown and symbolic money burned to help ancestors in the afterlife.
The name of this festival literally means “holiday on the fifth day of the fifth month”.
It was created to honour the poet Qu Yuan, who is said to have protested against a corrupt emperor by jumping into a river in the third century.
While fishermen were trying to save him, they reportedly threw dumplings wrapped in bamboo leaves into the river to divert the fish and to prevent them from eating the man.
Today a traditional part of the celebration is eating zongzi, which are triangular, glutinous rice dumplings wrapped in banana or other leaves.
The festival includes dramatic dragon-boat races.
Mid-Autumn Festival , also known as the Moon Festival, Lantern Festival or the Zhongqiu Festival, this festival celebrating the end of the autumn harvest takes place on the 15th day of the eighth month (in the lunar calendar).
Dances, lighting lanterns and eating “moon cakes” are traditional activities revolving around the main activity: moon watching.
Chinese New Year is also known as Spring Festival or the Lunar New Year.
It's the most important traditional festival in China.
It is usually a time between late January or early February and lasts 15 days from the eve of lunar year until the lantern festival.
Before the Spring Festival, people clean their homes, put red couplets on their front doors, and set off firecrackers.
New Year's Eve and New Year's Day are celebrated as a family affair, a time of reunion.
On the eve of the Spring Festival, a banquet is a must, and the most popular food is Jiaozi (dumpling), which is supposed to bring good fortune.
Fish is also a main dish, which brings prosperity.
People in the south will make New Year's Cake (Nian Gao, "gao" is a homophone of "high", so the meaning of New Year's Cake is being promoted or reaching a higher level in the coming year).
The celebration was traditionally highlighted with a religious ceremony given in honor of Heaven and Earth, the gods of the household and the family ancestors.
On the New Year's Day, everybody wears new clothes and says happy new year (xin nian hao) and good fortune (gong xi fa cai) to each other.
Kids are given red envelopes with money by their relatives.
Lantern Festival is also called Yuanxiao Festival.
It's on the 15th day of the first lunar month, and it's the first full moon after the Spring Festival.
It is customary to eat special sweet dumplings called yuanxiao and enjoy displayed lanterns during this festival.
Yuanxiao are round balls made of glutinous rice flour stuffed with sugar and bean paste fillings, it symbolizes family unity and happiness.
Various types of beautiful lanterns are exhibited on this festive night, fireworks set off, folk shows such as acrobatics, walking on stilts, performing with dragon lanterns, dancing the yangge and other folk dances and playing on swings are displayed.
The custom of enjoying lanterns at this time of the year dates back to the first century, and has been popular since then.
Ching Ming Festival is also known as the Grave-sweeping Festival, "Clear and Bright", is when Chinese families show their respect by visiting the graves of their ancestors.
Graves are cleaned, and wine and fruits are offered to the ancestors.
It usually on the 4th or 5th day of the fourth lunar month.
It also marks the beginning of spring.
Dragon Boat Festival is also known as Duanwu Festival, it falls on the fifth day of the fifth lunar month, and together with Chinese New Year and Moon Festival forms the three major Chinese Festivals.
Since the summer is a time when diseases most easily spread, Dragon Boat Festival began as an occasion for driving off evil spirits and pestilence and for finding peace in one's life.
It is generally believed that the festival originated to memorialize the ancient patriotic poet Qu Yuan.
Dragon Boat Festival is highlighted by the dragon boat races, in which competing teams drive their boats forward rowing to the rhythm of pounding drums.
This lively and colorful tradition has continued unbroken for centuries to the present day.
Qu Yuan, a patriotic statesman who lived in the state of Chu over 2,200 years ago during the Warring States period (476 BC-221 BC), repeatedly offered his king proposals aimed at political corruption.
Subsequently, slandered by treacherous officials, he was exiled.
In 278 BC, the capital of the State of Chu was lost to his enemy and Qu Yuan drowned himself in despair in Miluo River on the fifth day of the fifth lunar month.
Aware of the tragedy, the local people living beside the river went out in the boats to try to find his corpse.
People began throwing balls of sweet rice wrapped in bamboo leaves into the Miluo River to keep the fish from eating the patriot's body.
Over time these rice balls became more elaborate and varied with the addition of pork, peanuts, salted eggs, and other fillings, gradually evolving into the modern day Zongzi (glutinous rice wrapped in a pyramid shape using bamboo or reed leaves).
Every year thereafter on this day people continued to row dragon boats on their local rivers in memory of Qu Yuans' life and death, throwing Zongzi into the river as an offering.
The most popular dish during Dragon Boat Festival is Zongzi.
For warding off evil and disease, some customary practices such as hanging calamus and moxa on the front door, and pasting up pictures of Zhong Kui (a nemesis of evil spirits) are used.
Adults drink Xiong Huang wine and children are given fragrant sachets, both of which are said to possess qualities for preventing evil and bringing peace.
Moon Festival is celebrated on the 15th day of the 8th month of the lunar calendar, it's also called Mid-autumn Festival.
it's a time for family reunion.
In China, the full moon has always represented the gatherings of friends and family.
It's said that this festival originated from the ancient ceremony of Sacrificing to the Moon Goddess.
In Chinese fairy tales, there live on the moon the fairy lady Chang E, her pet Jade Rabbit, and a wood cutter named Wu Gang.
On this full moon night, families will enjoy the moon while eating moon cakes, pomeloes and drinking tea.
Moon cakes are cookies with fillings of sugar, fat, sesame, walnut, the yoke of preserved eggs, ham or other material.
The clear full moon has been depicted by Chinese poets since ancient times.
And the bright moonlight brings warmth and peace to our hearts.
The Ghost Festival (also known as Zhongyuan Festival by Taoists or Yu Lan Pen Festival by Buddhists) is the day to pay respects to the deceased by offering sacrifices.
In Chinese culture, it is thought that all ghosts will come out from the hell on the fifteenth day of the seventh lunar month, so the day is called the Ghost Day and the seventh lunar month is the Ghost Month.
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Difference between Ghost Festival and Qingming Festival In China, people also have the custom of offering sacrifices to the deceased on the Spring Festival, Qingming Festival, and the Double Ninth Festival.
But different from these festivals, the Ghost Festival is the day that all ghosts will come out to visit the livings.
Also, people only offer sacrifices to their ancestors and relatives on the above festivals, while during the Ghost Festival, besides ancestors and relatives, people will offer sacrifices to all the ghosts or spirits.
So, it's also called the Hungry Ghost Festival.
The Hungry Ghost Festival is regarded as the most important one among all the festivals that offering sacrifices the deceased.
History and Legend about Hungry Ghost Festival About the history and legends of the ghost festival, there are mainly three ones.
The most popular one is Mulian Rescues His Mother.
The Ghost Festival falls on the 15th day of the 7th lunar month.
In Gregorian calendar, it usually falls on August or September.
The ghost month refers to the 7th lunar month.
The following chart shows the exact date for the festival and the ghost month durations from 2018 to 2021
In China, people think on the Ghost month, the gate of hell will open to allow the ghosts and spirits go back to the living world.
During the month, those have families will visit their families and those alone will roam on the street to seek food and entertainment.
Family members usually offer sacrifice to their deceased ancestors and relatives during the month and on the Ghost day.
They are honored with delicious food three times a day on a table.
The family’s ancestral tablets and photographs will be put on the table with incense burning near them.
People also pay tribute to those unknown wandering ghosts with food and burn joss paper to please the ghosts on the 15th (some places on the 14th) day of the 7th lunar month to avoid the harm by them.
Buddhists and Taoists usually perform ceremonies on the day to help the ghosts ease the sufferings.
They will set altars for them and chant scriptures.
Monks often throw rice or some small foods into the air to distribute them to the ghosts.
On the evening of the Ghost day, people also make lanterns and float them on the river to help their relatives find their way back to home.
The lanterns are usually lotus flower-shaped with light or candles.
Some people also write their ancestors’ name on the lanterns.
As the ghosts and the suffering spirits will come out from the hell to visit their homes during the 7th lunar month (the ghost month), many things should be avoided during this month including the ghost day.
1. Don't stroll at night.
2. Don't swimming.
It is said that the drowned evil ghost might try to drown you in order to find victims for them to rebirth.
3. As the month is considered to be inauspicious, don't move to new houses, start new businesses or marry.
4. Don't hang clothes outside at night.
5. Do not pick up coins or money found on the street and never bring it home.
6. Do not step on or kick the offerings by the roadside.
If you step by accident, you should apologize aloud to make it alright.
7. Do not wear red because ghosts are attracted to red.
8. Don't sing and whistle as these may attract ghosts.
9. Keep away from the walls as it is believed that ghosts like sticking to walls.
10. If you are born during the ghost month, avoid celebrating your birthday at night.
It's better to celebrate during the daytime.
Celebrated by Buddhists and Taoists all over Asia, the Ghost Festival is a holiday that is celebrated on the fifteenth day of the seventh month in the Chinese calendar.
It is believed that on this day the gates of hell are opened and the dead are allowed to walk the earth in search for food.
Therefore, it is very much like Halloween in western civilization, although there is more of a spiritual emphasis placed on this holiday than what the West places on Halloween.
While it is not currently known when the Ghost Festival was created, but it is known that it is rooted in ancient beliefs that are thousands of years old.
This festival combines aspects of ancient Chinese folk religion, Buddhism and Taoism principles.
A common practice on this day is the burning of fake paper money – commonly referred to as ‘hell money’ – on people’s doorsteps.
This is to provide the ghosts with the money they need for their travels across the Earth.
It is also common to see people set out food for these hungry ghosts and performing Chinese operas in public for the entertainment of these entities.
Also during this time, there are many street festivals.
Many people also make sure to attend temple activities during this time for protection against the ghosts that may try to cause them harm.
Where is Ghost Festival celebrated?
The Ghost Festival is a traditional Chinese holiday celebrated on the 15th day of the seventh lunar month.
A solemn holiday, the Ghost Festival represents the connections between the living and the dead, earth and heaven, body and soul.
The entire seventh month of the Chinese calendar is called the Ghost Month, a month in which ghosts and spirits are believed to emerge out from the lower world to visit earth.
The Ghost Festival is the climax of a series of the Ghost Month celebrations.
Activities of the festival include preparing ritual offerings of food, and burning ghost money (or paper money) to please the visiting ghosts and spirits as well as deities and ancestors.
Other activities include burying and releasing miniature paper boats and lanterns on water, which signifies "giving directions to the lost ghosts." 
The Ghost Festival has roots in the Buddhist festival of Ullambana, and also some from the Taoist culture.
In the Tang Dynasty, Ullambana and traditional festivities were mixed and celebrated on one day.
Thus, the Ghost Festival has special meaning for all Buddhists as one of their most important festivals.
The Buddhist origins of the festival can be traced back to a story that originally came from India, but later took on culturally Chinese overtones.
This story, "Mu-lien Saves His Mother from Hell," is an account of a well-to-do merchant who gives up his trade to become a devout follower of Buddhism.
After the merchant attains enlightenment, he thinks of his father and mother, and wonders what happens to them.
He travels over the known Buddhist universe, and finds his father in heaven.
However, his mother has been sent to hell, and has taken on the form of a hungry ghost--it cannot eat because its throat is very thin and no food can pass, yet it always hungers because it has such a large belly.
His mother was greedy with the money he left her.
He had instructed her to kindly host any Buddhist monks that ever came her way, but instead she withheld her kindness and her money.
It was for this reason she was sent to hell.
Mu-lien eventually saves her from this plight by battling various demons and entreating the help of the Buddha.
Buddhists instituted a day after the traditional summer retreat (the fifteenth day of the seventh month in the lunar calendar--usually mid-to-late August) as a day of prayer and offering in which monks can pray and make sacrifices on behalf of dead ancestors or hungry ghosts.
The family members of the deceased essentially pay for this service, and thus their patronage is a form of charity.
The deceased ancestors are pacified and hungry ghosts can eat (the sacrificial foods).
The Mu-lien story ends with this festival and the rescue of his mother from hell.
She ends up being reborn as a pet dog in a well-off household.
~ Ghost Festival ~ Festival Date - 31 AUG 2012 Celebrated on the 15th day of the 7th Lunar month.
On this day it is believed that the "Gates of Hell" are opened and that the dead return to visit their living relatives.
The Chinese feel that they have to satisfy the imprisoned and hungry ghosts in order to get good fortune and luck in their lives.
Historically, families have offered sacrifices of newly harvested grain to departed ancestors on this day, which also coincides with the Buddhist Ullambana, Deliverance, Festival and the Taoist Ghost Festival, called "Chung Yuan" in Taoist terminology.
Since each of these traditions in some way honours the spirits of the departed, the seventh lunar month has come to be known as Ghost Month, celebrated as a time when the "Good Brethren", ghosts from the underworld, come back to earth to feast on the victuals offered by the living.
Over time the Ullambana Festival and Ghost Festival have melded together to become the present day Chung Yuan Putu or "Mid-origin Passage to Universal Salvation.
" The festival is currently celebrated with ceremonies at homes, temples, associations, and guilds.
Prayers are offered to the dead and offerings of food such as chicken, vegetables, fruits, bean curd and white rice are placed at street corners and roadsides to appease the spirits.
This is believed to prevent the wandering spirits from entering their homes and causing disturbances in their households.
Offerings are also made by burning replica money notes, which are also known as ‘hell money’.
Some families also burn paper houses, cars and even paper television or radio sets to give to their dead relatives.
The Chinese feel that these offerings reach the ghosts and help them live comfortably in their world
The 15th day of the month is Ghost Festival, sometimes called Hungry Ghost Festival.
The Mandarin Chinese name of this festival is 中元節 (traditional form), or 中元? (simplified form), which is pronounced "zh?ng yu?n ji?.
" This is the day when the spirits are in high gear.
It’s important to give them a sumptuous feast, to please them and to bring luck to the family.
Taoists and Buddhists perform ceremonies on this day to ease the sufferings of the deceased.
The last day of the month is when the Gates of Hell close up again.
The chants of Taoist priests inform the spirits that it’s time to return, and as they are confined once again to the underworld, they let out an unearthly wail of lament.
If you happen to be in China during Ghost Month, it could be fun to learn these vocabulary words!
While terms like "ghost money" or "ghost month" are only applicable to Ghost Month, other words like "feast" or "offerings" can be used in casual conversation.
In Chinese folk legend, the seventh lunar month is the Ghost Month.
It is said that every year on the first day of the seventh lunar month, the gate of hell will be wide open and the ghosts will come out until the gate is closed on the 30th day of the month.
For the safety of both ghosts and human beings, China has the tradition of worshipping the dead in lunar July since ancient times.
In folk China, people would offer sacrifices on the first, second, fifteenth and last day of the Ghost Month.
There are many taboos in the Ghost Month.
For example, do not wear the clothes with your name, do not pat other people on the shoulder, do not whistle, children and senior citizens should not go out at night.
→ Can You Move House During Ghost Month?
→ Can You Get Married During Ghost Month?
Most people in ancient China believed in ghosts and gods.
Legend has it that anyone who dies normally could reincarnate while those who are guilty or die accidentally would become ghosts wandering in the mortal world.
Some evil spirits even seize the opportunity to disturb the living souls, causing their death in disasters and accidents.
As a result, people who die unexpectedly during this period are regarded "have been taken away by ghosts".
People believe that holding sacrifice ceremony for these spirits would help them to escape from hell at an early date and prevent them from disturbing the living beings.
Therefore, Chinese people would hold sacrifice ceremony and burn money at the roadside in the month to worship the ghosts, which become the customs like "setting lanterns" and "worshipping good brothers".
Since the sacrifice ceremony has the meaning of praying for safety, Chinese people also invite theatrical troupes to play for gods and entertain guests at open-air banquets in addition to worshipping the "good brothers" with substantial offerings at the Ghost Festival, also Zhongyuan Festival, which falls on the 15th day of the 7th lunar month.
Nowadays, China encourages frugality; the folk customs are improved and the sacrifice ceremony is simplified.
Ancient Chinese people believed natural and man-made disasters were frequent in the 7th lunar month, during which they had fears.
Hence, the cultural custom of avoid anything in this month is formed.
In modern society, many taboos fail to stand the test of time but some are still unexplainable with science.
Gate of Hell Opened For private houses, the Gate of Hell is open on the first day of the Ghost Month, on which ghosts come out to the mortal world and the sacrifice ceremony, grand or simple, is required.
The grand sacrifice ceremony means offering a good meal while the simple means preparing some fruits and desserts to show respect.
As the saying goes, there is no such thing as a free lunch.
This kind of sacrifice ceremony implies bringing me no trouble once eating my food.
Gate of Heaven Opened For public places, the Gate of Hell is open on the second day of the Ghost Month.
In ancient times, shops and government offices held the sacrifice ceremony one day after the private houses to pray for safety and no mysterious case in the month.
Zhongyuan Festival Zhongyuan Festival, also Ghost Festival, falls on the 15th day of the 7th lunar month.
In the night (early morning and late night), ghosts get together and evil spirits reach the peak.
On this very day, people would burn money and invoke ancestors and ghosts with sacrifice.
Likewise, public places sacrifice on the next day, i.e. the 16th day of the 7th lunar month.
Gate of Hell Closed On the last day of the 7th lunar month, the Gate of Hell is closed and ghosts go back to the hell from vacation.
People offer sacrifice on this day to pray for safety in the rest of days.
Do's and Don'ts
In the Ghost Month, evil spirits reach the peak.
According to the legend of scapegoat, children, senior citizens, weak or sensitive people should not go out at night, or they will be attacked by the evil spirits.
Also, you should keep away from entering the water, especially sea.
In short, you should stay away from risks and supernatural games, especially in the night of Ghost Festival.
Some people often take items to avoid evil spirits with them, such as amulet, prayer beads, coarse salt, glutinous rice, cross and lodestone with particular energy.
It is better to believe the function of these items as long as your normal life is not affected.
For many people, they can feel at ease regardless of the effect.
The Ghost Month is the seventh lunar month of Chinese Lunar Calendar.
The 15th lunar day of the 7th lunar month is the Chinese Ghost Festival.
The Chinese formal name of the Ghost Festival is Chung-Yuan .
The first lunar day of the 7th lunar month is on August 1, 2019.
August 15, 2019, is the 15th lunar day, which is the Chinese Ghost Festival.
The first lunar day of the 8th lunar month is on August 30, 2030.
Therefore, the Ghost Month is from August 1 to August 29 in the China time zone.
Many Chinese families have both Buddhism and Taoism religions.
The spirits jailed in the Hell are called ghosts.
The folklore says the ghosts have a one-month parole and will travel to the towns in the 7th lunar month every year.
People have better to feast them.
People are afraid that ghosts play trick-or-treat game.
That is why the Chinese call the 7th lunar month as the Ghost Month.
To feast the ghosts is from a story of Buddhism.
Moggallana was one of Buddha Shakyamuni's best students.
He had various supernormal powers and owned the divine eyes.
One day, he saw his deceased mother had been born among hungry ghosts.
He went down the Hell, filled a bowl with food to provide for his mother.
Before reaching his mother's mouth, the food turned into burning coals which couldn't be eaten.
Moggallana cried sorrowfully and asked for help from Buddha.
Buddha said the sins of his mother was deep and firmly rooted, it couldn't be forgiven just using the divine power and it's required the combined power of thousand monks to get rid of her sins.
Buddha told Moggallana that, " the 15th day of the 7th lunar month is the Pavarana Day for the assembled monks of all directions.
You should prepare an offering of clean basins full of hundreds of flavors and the five fruits, and other offerings of incense, oil, lamp, candle... to the greatly virtuous assembled monks.
Your present parents and parents of seven generations will escape from sufferings.
" Following Buddha's instructions, Moggallana's mother obtained liberation from sufferings as a hungry host by receiving the power of the merit and virtue form the awesome spiritual power of assembled monks on 15th day of the 7th lunar month.
Today, similar rituals are held in the Buddhism temples on this day for the deliverance of all suffering spirits.
People at home prepare lots of food to worship gods and pray for their spirits of ancestors, and then treat the homeless ghosts.
Some cities will spend weeks to build a multi-story sacrificial altar.
The Taoists will fast and take a bath, wear formal dress and perform the religious ceremony to pray for good luck for spirits on the Chung-Yuan Day.
The lighting decoration of the altar at night is the entertainment for ghosts.
Certainly, Chinese will bring children to there for sightseeing and to learn the traditional culture.
The Hungry Ghost Festival, also known as the Zhongyuan Festival, marks the time of year when tables are turned and the deceased are believed to visit the living.
During the month of the Hungry Ghost Festival, the gates of the afterlife are thrown open and ghosts are free to roam the earth in search of food, entertainment and mischief.
Though the Hungry Ghost Festival gets some acclaim as the “Chinese Halloween,” the holiday actually offers a great opportunity to teach kids about caring for the destitute and less fortunate.
During the duration of Ghost Month, hungry spirits roam the earth in search of mischief and worldly pleasure.
To ease their suffering, the living observe superstitions and make offerings of food, money and entertainment all month long, culminating with an outdoor ghost-feeding ceremony on the night of the Hungry Ghost Festival.
The Hungry Ghost Festival is a time when restless ghosts rise, when makeshift roadside altars glow with burning joss paper and when the living do everything they can to appease the wandering spirits.
It’s one of the two big annual festivals designated for the dead — the other is the Qingming Festival in the spring.
People take actions on the night of the Hungry Ghost Festival, and during the entire Ghost Month, to pacify the spirits looking to cause mischief around them.
It’s assumed that ghosts won’t curse those who make offerings of food, money and material goods in their honor.
Ghosts and goblins, spooky stories and incense make the Hungry Ghost Festival a lot of fun to share with your kids.
It’s a month-long opportunity to follow old superstitions, tell ghost stories and reinforce the importance of family, along with the need to look after the destitute.
Ghost Month culminates with a ghost-feeding ceremony on the night of the Hungry Ghost Festival that’s filled with fire, smoke and ritual offerings.
A ghost-feeding ceremony is all about providing for the wandering souls searching the earth for food, entertainment and mischief.
Planning a ghost-feeding ceremony is a bit like putting together a dinner party for a set of invisible guests under a full moon.
You can use craft projects during the Hungry Ghost Festival to introduce the meaning of ritual paper offerings, in the form of joss paper ingots and floating lanterns.
Burning ceremonial joss paper is meant to calm wandering spirits, while floating lanterns light a path home at the end of the holiday.
The Hungry Ghost Festival is celebrated on the 15th day of the seventh lunar month.
This day falls in July or August in our Western calendar.
In southern China, the Hungry Ghost Festival is celebrated by some on the 14th day of the seventh lunar month.
The people there are said to have begun celebrating the festival a day earlier during a time of long warfare to avoid being attacked by enemies during the inauspicious day.
The Hungry Ghost Festival is one of several traditional festivals in China to worship ancestors.
Others include the Spring Festival, the Qingming Festival, and the Double Ninth Festival.
In Jiangxi Province and Hunan Province, the Hungry Ghost Festival is considered to be more important than the Qingming Festival and the Double Ninth Festival.
The Taoist name for the Hungry Ghost Festival is the Zhongyuan Festival (中元?), and Buddhists call it the Yulanpen Festival.
They perform special ceremonies to avoid the wrath of the ghosts such as putting the family’s ancestral tablets on a table, burning incense, and preparing food three times that day.
The main ceremony is usually held at dusk.
People put the family’s ancestral tablets and old paintings and photographs on a table and then burn incense near them.
Plates of food are put out for the ghosts on the table, and the people may kowtow in front of the memorial tablets and report their behavior to their ancestors to receive a blessing or punishment.
People also feast on this night, and they might leave a place open at the table for a lost ancestor.
They want to feed the hungry ghosts who have been wandering the land since the beginning of Hungry Ghost Month.
It is thought that after two weeks of activity, they must be very hungry.
The Lantern Festival, also known as the Yuan Xiao Festival, welcomes the lunar year’s first full moon.
It’s a dazzling holiday that wraps up Chinese New Year’s annual Spring Festival with a night of sparkling lights.
Mark Your Calendars Lantern Festival 2019 is February 19, 2019.
It takes place every year between February 5 and March 5 on the 15th day of the 1st lunar month.
Here’s a simple guide to the holiday that sends people back to their daily routines fortified by the Spring Festival’s good luck for the year ahead.
The Lantern Festival celebration dates back some 2,000 years to lanterns hung in Buddhist temples by monks during the Han Dynasty.
By imperial decree, temples, homes and palaces across China adopted the practice of hanging brightly-lit lanterns on the 15th night of the year’s 1st lunar month.
Once the Lantern Festival passes, there’s no need to fear Chinese New Year taboos and it’s appropriate to take down Chinese New Year decorations.
Outside of China, you’re likely to see Lantern Festival celebrations in communities that also commemorate Chinese New Year.
It’s common to see street festivals and toned-down versions of the giant outdoor lantern exhibitions found in cities across China.
The Chinese words for the lunar year’s first month yuan and night xiao explain the Lantern Festival’s alternate name, the Yuan Xiao Festival.
The glutinous rice balls known as tang yuan are the Lantern’s Festival’s iconic food.
These round balls, most commonly filled with sweet sesame or red bean paste, resemble the shape of the full moon and symbolize family togetherness and happiness.
It’s most fun to simply stroll the neighborhood at night taking in the lanterns displayed outside homes, arranged in parks and hung along streets.
Red globes are most traditional, though you’ll see everything from geometric shapes to dragons and other animals.
Shopkeepers and other lantern owners attach paper slips to their lanterns with riddles written on them referencing Chinese poems or folk tales.
If you’re clever enough to crack the code, you may win a prize.
The Lantern Festival takes place every year between February 5 and March 5 on the 15th day of the 1st lunar month.
There is no time off granted for the Lantern Festival.
The 15th day of the 1st lunar month is the Chinese Lantern Festival because the first lunar month is called yuan-month and in the ancient times people called night Xiao.
The 15th day is the first night to see a full moon.
So the day is also called Yuan Xiao Festival in China.
According to the Chinese tradition, at the very beginning of a new year, when there is a bright full moon hanging in the sky, there should be thousands of colorful lanterns hung out for people to appreciate.
At this time, people will try to solve the puzzles on the lanterns and eat yuanxiao (元宵) (glutinous rice ball) and get all their families united in the joyful atmosphere.
There are many different beliefs about the origin of the Lantern Festival.
But one thing for sure is that it had something to do with celebrating and cultivating positive relationship between people, families, nature and the higher beings they believed were responsible for bringing/returning the light each year.
One legend tells us that it was a time to worship Taiyi, the God of Heaven in ancient times.
The belief was that the God of Heaven controlled the destiny of the human world.
He had sixteen dragons at his beck and call and he decided when to inflict drought, storms, famine or pestilence upon human beings.
Beginning with Qinshihuang, the first emperor to unite the country, all the emperors ordered splendid ceremonies each year.
The emperor would ask Taiyi to bring favorable weather and good health to him and his people.
Wudi of the Han Dynasty directed special attention to this event.
In 104 BC, he proclaimed it as one of the most important celebrations and the ceremony would last throughout the night.
They clean it all up in the morning.
Another legend associates the Lantern Festival with Taoism.
Tianguan is the Taoist god responsible for good fortune.
His birthday falls on the 15th day of the first lunar month.
It is said that Tianguan likes all types of entertainment, so followers prepare various kinds of activities during which they pray for good fortune.
There are many stories on how this festival was created.
One other story is about a maid.
In the Han Dynasty, Mr. Eastern was a favorite advisor of the emperor.
One winter day, he went to the garden and heard a little girl crying and getting ready to jump into a well to commit suicide.
Mr. Eastern stopped her and asked why.
She said she was a maid in the emperor's palace and her name was Yuan-Xiao.
She never had the chance to meet her family after she started working at the palace.
She missed them so much every 12th lunar month.
If she couldn't have the chance to show her filial piety in this life, she would rather die.
Mr. Eastern promised her to find a way so she could reunion with her family.
Mr. Eastern left the palace and set up a fortune-telling stall on the street and disguised himself as a fortuneteller.
Because of his reputation, many people asked for their fortunes.
But every one got the same prediction - a severe fire accident on the 15th lunar day.
The rumor spread quickly.
Everyone was worried about the future and asked Mr. Eastern for help.
Mr. Eastern said, "On the 13th lunar day, the God of Fire will send a fairy lady in red to burn down the city.
If you see a lady in red wearing green pants riding a black horse on that day, you should ask for her mercy.
" On that day, Yuan-Xiao pretended to be the red fairy lady.
When people asked for her help, she said, "I'm the messenger of the God of Fire and came to check on the city and I'm going to set up fire on 15th.
This is an order from Jade Emperor.
He will watch from the heavens.
I will give you a copy of the imperial decree from the God of Fire.
You should go to ask your emperor to find a way out.
" After she left, people went to the palace to show the emperor the decree which reads "The capital city is in trouble.
Fire burns on the palace, and fire from Heaven burns all night long on the 15th.
" The emperor of Han Dynasty was very shocked.
He called and asked Mr. Eastern for advice.
After pondering for a while, Mr.
Eastern said, "I heard that the God of Fire likes to eat Tang-Yuan (Sweet dumpling).
Does Yuan-Xiao often cook Tang-Yuan for you?
On the 15th lunar day, let Yuan-Xiao make Tang-Yuan.
Your Majesty will take charge of the worshipping ceremony and you will give an order to every house to prepare Tang-Yuan to worship the God of Fire at the same time.
Also, deliver another order to ask every house in the city to hang red lantern and explode fire crackers.
Lastly, everyone in the palace and people outside the city should carry their lanterns on the street to watch the lantern decoration and fireworks.
If everything goes this way, the Jade Emperor would be deceived.
Then everyone can avoid the fire accident." 
The emperor happily followed the plan.
Lanterns were everywhere in the capital city on the night of the 15th lunar day.
People were walking on the street.
Fire crackers kept making lots of noise.
It looked like the entire city was on fire.
Yuan-Xiao's parents went into the palace to watch the lantern decorations, and Yuan-Xiao made a big lantern and wrote her name on the lantern.
They happily reunited together after her parents called her name.
Everybody was safe during the night.
The emperor of Han Dynasty had a new order that people should do the same thing every year.
Since Yuan-Xiao cooked the best Tan-Yuan, people called the day Yuan-Xiao Festival.
Young people were chaperoned in the streets in hopes of finding love.
Matchmakers acted busily in hopes of pairing couples.
The brightest lanterns were symbolic of good luck and hope.
As time has passed, the festival no longer has such implications.
Those who do not carry lanterns often enjoy watching informal lantern parades.
In addition to eating tangyuan (simplified Chinese: ??; traditional Chinese: 湯圓; pinyin: t?ngyu?n), another popular activity at this festival is guessing lantern riddles (which became part of the festival during the Tang Dynasty), which often contain messages of good fortune, family reunion, abundant harvest, prosperity and love.
Until the Sui Dynasty in the sixth century, Emperor Yangdi invited envoys from other countries to China to see the colorful lighted lanterns and enjoy the gala performances.
By the beginning of the Tang Dynasty in the seventh century, the lantern displays would last three days.
The emperor also lifted the curfew, allowing the people to enjoy the festive lanterns day and night.
It is not difficult to find Chinese poems which describe this happy scene.
In the Song Dynasty, the festival was celebrated for five days and the activities began to spread to many of the big cities in China.
Colorful glass and even jade were used to make lanterns, with figures from folk tales painted on the lanterns.
However, the largest Lantern Festival celebration took place in the early part of the 15th century.
The festivities continued for ten days.
Emperor Chengzu had the downtown area set aside as a center for displaying the lanterns.
Even today, there is a place in Beijing called Dengshikou.
In Chinese, Deng means lantern and Shi is market.
The area became a market where lanterns were sold during the day.
In the evening, the local people would go there to see the beautiful lighted lanterns on display.
Today, the displaying of lanterns is still a big event on the 15th day of the first lunar month throughout China.
Chengdu in Southwest China's Sichuan Province, for example, holds a lantern fair each year in Culture Park.
During the Lantern Festival, the park is a virtual ocean of lanterns!
Many new designs attract countless visitors.
The most eye-catching lantern is the Dragon Pole.
This is a lantern in the shape of a golden dragon, spiraling up a 27-meter-high pole, spewing fireworks from its mouth.
Cities such as Hangzhou and Shanghai have adopted electric and neon lanterns, which can often be seen beside their traditional paper or wooden counterparts.
The Lantern Festival marks the first full moon of the new lunar year and the close of the Chinese New Year.
In a ritual dating back thousands of years to the Han dynasty, traditional lanterns as depicted in today’s Doodle are released into the night sky bearing messages of prosperity, unity, and love.
Lanterns are often red, the color of good fortune.
Some might even contain riddles, which may win the fortunate ones a small prize — a favorite pastime of little children over generations.
In observance of the festival, families feast on tangyuan (small rice balls filled with sweet red bean paste, fruit, and nuts) that are thought to bring happiness and good luck in the new year.
The round shape of the tangyuan symbolizes unity and togetherness.
Celebrations around the world include lion and dragon dances, stilt-walking, parades, and fireworks.
Modern and traditional worlds combine as electric and neon lanterns float beside their paper or wooden counterparts, creating yet another beautiful memory of a lamp-lit sky for the year ahead.
單句
Chinese New Year, also called Spring Festival, has more than 4,000 years of history.
Being one of the traditional Chinese festivals, it is the grandest and the most important festival for Chinese people.
It is also the time for the whole families to get together, which is similar with Christmas Day to the westerners.
Originating during the Shang Dynasty (about 17th - 11th century BC), it celebrates family reunion and hopes the advent of spring and flowers blossoming rich with full of colorful activities.
People from different regions and different ethnic groups celebrate it in their unique ways.
單句
 In ancient traditions, it was one of the few nights in ancient times without a strict curfew.
 Young people were chaperoned in the streets in hopes of finding love.
 Matchmakers acted busily in hopes of pairing couples.
 The brightest lanterns were symbolic of good luck and hope.
 As time has progressed, however, the festival no longer has such implications nowadays.
 Those who do not carry lanterns often enjoy watching informal lantern parades.
 In addition to eating tangyuan ( pinyin: t?ngyu?n), another popular activity at this festival is guessing lantern riddles (which became part of the festival since Tang Dynasty), which often contain messages of good fortune, family reunion, abundant harvest, prosperity and love.
單句
 Chinese Duanwu Festival (or Dragon Boat Festival) has been emphasized by the Chinese government as one of the traditional holidays and the citizen will be given one-day off.
 Two major activities will be: Dragon Boat races and eating of zongzi (pyramid-shaped rice wrapped in reed or bamboo leaves).
 Come and learn the history and story of this festival and also wrap your own zongzi, the glutinous rice pudding with the ingredients such as beans, lotus seeds, chestnuts, pork fat and the golden yolk of a salted duck egg, etc.
 Venue: China Culture Center, Chaoyang District, Beijing, China.
 Mid-Autumn Festival - Wikipedia The Mid-Autumn Festival (中秋節) is a harvest festival celebrated notably by the Chinese and Vietnamese people.
 It relates to Chuseok (in Korea) and Tsukimi (in Japan).
Jiaozi (Chinese: 餃子; [t?ja?u.ts?] (listen)) are a kind of Chinese dumpling, commonly eaten in China and other parts of East Asia.
 They are one of the major dishes eaten during the Chinese New Year and year-round in the northern provinces.
 Though considered part of Chinese cuisine, jiaozi are popular in other parts of Asia and in Western countries.
Jiaozi typically consist of a ground meat and/or vegetable filling wrapped into a thinly rolled piece of dough, which is then sealed by pressing the edges together.
 Finished jiaozi can be boiled (shu? ji?o), steamed (zh?ng ji?o) or pan-fried (ji?n ji?o) and are traditionally served with a black vinegar and sesame oil dip.
 They can also be served with soup as well.
 Traditionally, jiaozi were thought to be invented during the era of the Eastern Han (AD 25–220)[1][2] by Zhang Zhongjing [3] who was a great practitioner of traditional Chinese medicine.
 Jiaozi were originally referred to as "tender ears" (Chinese: 嬌耳; pinyin: jiao'er) because they were used to treat frostbitten ears.
 Zhang Zhongjing was on his way home during wintertime, when he found many common people had frostbitten ears, because they did not have warm clothes and sufficient food.
 He treated these poor people by stewing lamb, black pepper, and some warming medicines in a pot, chopped them, and used them to fill small dough wrappers.
 He boiled these dumplings and gave them with the broth to his patients, until the coming of the Chinese New Year.
 In order to celebrate the New Year as well as recovering from frostbitten ears, people imitated Zhang's recipe to make Jiao'er.
 Other theories suggest that jiaozi may have derived from dumplings in Western Asia.
 In the Western Han dynasty (206 BC – AD 9) jiaozi (餃子) were called jiaozi (角子).
 During the Three Kingdoms period (AD 220–280), the book Guangya by Zhang Yi mentions jiaozi.
 Yan Zhitui during the Northern Qi dynasty (AD 550–577) wrote: "Today the jiaozi, shaped like a crescent moon, is a common food in the world.
" Six Dynasties Turfan tombs contained dumplings.
 Later in the Tang dynasty (AD 618–907), jiaozi become more popular, called Bian Shi (扁食).
 Chinese archaeologists have found a bowl of jiaozi in the Tang dynasty tombs in Turpan.
 7th or 8th century dumplings and wontons were found in Turfan.
Jiaozi may also be named because they are horn-shaped.
 The Chinese word for "horn" is jiao (Chinese: 角; pinyin: ji?o), and jiaozi was originally written with the Chinese character for "horn", but later it was replaced by the specific character 餃, which has the food radical on the left and the phonetic component ji?o (交) on the right.
At the same time, jiaozi look like yuan bao silver or gold ingots used as currency during the Ming dynasty, and as the name sounds like the word for the earliest paper money, serving them is believed to bring prosperity.
Many families eat these at midnight on Chinese New Year's Eve.
 Some cooks will even hide a clean coin inside a jiaozi for the lucky to find.
Nowadays, jiaozi are eaten year-round, and can be eaten for breakfast, lunch or dinner.
 They can be served as an appetizer, a side dish, or as the main course.
 In China, sometimes jiaozi is served as a last course during restaurant meals.
 As a breakfast dish, jiaozi are prepared alongside xiaolongbao at inexpensive roadside restaurants.
 Typically, they are served in small steamers containing ten pieces each.
 Although mainly serving jiaozi to breakfast customers, these small restaurants keep them hot on steamers and ready to eat all day.
 Jiaozi are always served with a soy sauce-based dipping sauce that may include vinegar, garlic, ginger, rice wine, hot sauce, and sesame oil.
 They can also be served with soup as well.
The Lantern Festival or the Spring Lantern Festival is a Chinese festival celebrated on the fifteenth day of the first month in the lunisolar Chinese calendar.
 Usually falling in February or early March on the Gregorian calendar, it marks the final day of the traditional Chinese New Year celebrations.
 As early as the Western Han Dynasty (206 BCE-CE 25), it had become a festival with great significance.
During the Lantern Festival, children go out at night carrying paper lanterns and solve riddles on the lanterns (simplified Chinese: 猜??; traditional Chinese: 猜燈謎; pinyin: c?id?ngm?; Jyutping: caai1 dang1 mai4).
In ancient times, the lanterns were fairly simple, and only the emperor and noblemen had large ornate ones.
In modern times, lanterns have been embellished with many complex designs.
For example, lanterns are now often made in the shape of animals.
 The lanterns can symbolize the people letting go of their past selves and getting new ones, which they will let go of the next year.
 The lanterns are almost always red to symbolize good fortune.
There are several beliefs about the origin of the Lantern Festival.
 However, its roots trace back more than 2000 years ago and is popularly linked to the reign of Emperor Ming of Han at the time when Buddhism was growing in China.
Emperor Ming was an advocate of Buddhism and noticed Buddhist monks would light lanterns in temples on the fifteenth day of the first lunar month.
 As a result, Emperor Ming ordered all households, temples and the imperial palace to light lanterns on that evening.
From there it developed into a folk custom.
 Another likely origin is the celebration of "the declining darkness of winter" and community's ability to "move about at night with human-made light," namely, lanterns.
 During the Han Dynasty, the festival was connected to Ti Yin, the deity of the North Star.
Another legend about the origins of Lantern Festival involves a maid named Yuan-Xiao.
 In the Han Dynasty, Dongfang Shuo was a favorite adviser of the emperor.
 One winter day, he went to the garden and heard a little girl crying and getting ready to jump into a well to commit suicide.
 Shuo stopped her and asked why.
 She said she was Yuan-Xiao, a maid in the emperor's palace and that she never had a chance to see her family since she started working there.
 If she could not have the chance to show her filial piety in this life, she would rather die.
 Shuo promised to find a way to reunite her with her family.
 Shuo left the palace and set up a fortune-telling stall on the street.
 Due to his reputation, many people asked for their fortunes to be told but everyone got the same prediction - a calamitous fire on the fifteenth lunar day.
 The rumor spread quickly.
 Everyone was worried about the future and asked Shuo for help.
 Shuo said that on the thirteenth lunar day, the God of Fire would send a fairy in red riding a black horse to burn down the city.
 When people saw the fairy they should ask for her mercy.
 On that day, Yuan-Xiao pretended to be the red fairy.
 When people asked for her help, she said that she had a copy of a decree from the God of Fire that should be taken to the emperor.
 After she left, people went to the palace to show the emperor the decree which stated that the capital city would burn down on the fifteenth.
 The emperor asked Yangshuo for advice.
 Yangshuo said that the God of Fire liked to eat tangyuan (sweet dumplings).
 Yuan-Xiao should cook tangyuan on the fifteenth lunar day and the emperor should order every house to prepare tangyuan to worship the God of Fire at the same time.
 Also, every house in the city should hang red lantern and explode fire crackers.
 Lastly, everyone in the palace and people outside the city should carry their lanterns on the street to watch the lantern decorations and fireworks.
 The Jade Emperor would be deceived and everyone would avoid the disastrous fire.
 The emperor happily followed the plan.
 Lanterns were everywhere in the capital city on the night of the fifteenth lunar day.
 People were walking on the street.
 Fire crackers kept making lots of noise.
 It looked like the entire city was on fire.
 Yuan-Xiao's parents went into the palace to watch the lantern decorations and were reunited with their daughter.
 The emperor decreed that people should do the same thing every year.
 Since Yuan-Xiao cooked the best tangyuan, people called the day Yuan-Xiao Festival.
 Eaten during the Lantern Festival, tangyuan '??' (South China) or yuan xiao '元宵' (North China) is a glutinous rice ball typically filled with sweet red bean paste, sesame paste, or peanut butter.
 Actually, tangyuan is different from yuanxiao due to different manual making and filling processes.
However, they are very similar in shape and taste, so most people do not distinguish them for convenience and consider them as the same thing.
The Chinese people believe that the round shape of the balls and the bowls in which they are served symbolize family togetherness, and that eating tangyuan or yuanxiao may bring the family harmony, happiness and luck in the new year.
Until the Sui Dynasty in the sixth century, Emperor Yangdi invited envoys from other countries to China to see the colorful lighted lanterns and enjoy the gala performances.
By the beginning of the Tang Dynasty in the seventh century, the lantern displays would last three days.
 The emperor also lifted the curfew, allowing the people to enjoy the festive lanterns day and night.
 It is not difficult to find Chinese poems which describe this happy scene.
 In the Song Dynasty, the festival was celebrated for five days and the activities began to spread to many of the big cities in China.
 Colorful glass and even jade were used to make lanterns, with figures from folk tales painted on the lanterns.
 However, the largest Lantern Festival celebration took place in the early part of the 15th century.
 The festivities continued for ten days.
 Emperor Chengzu had the downtown area set aside as a center for displaying the lanterns.
 Even today, there is a place in Beijing called Dengshikou.
 In Chinese, deng means lantern and shi is market.
 The area became a market where lanterns were sold during the day.
 In the evening, the local people would go there to see the beautiful lighted lanterns on display.
Lion dance (simplified Chinese: 舞?; traditional Chinese: 舞獅; pinyin: w?sh?) is a form of traditional dance in Chinese culture, in which performers mimic a lion's movements in a lion costume Asiatic lions found in nearby India are the ones depicted in the Chinese culture.
As Taiwan plays a vital role in the global ICT industry, it has been regarded as “a high-tech island” in the world.
Besides, with the help of numerous Tibetan Buddhist followers and locals enthusiastic about Tibetan culture, the Tibetan religion, art, and culture have been developed and thrived on this island, thus creating an enormous religious power which not only purifies people’s minds, but engages in a positive conversation on self-reflection and self-improvement with modern civilization in pursuit of technological innovation.
For the related lunar festivals celebrated on the same day, see Tsukimi (Japan) and Chuseok (??/North and South Korea).
Mooncakes, a rich pastry typically filled with sweet-bean or lotus-seed paste, are traditionally eaten during the festival.
The Mid-Autumn Festival is also known by other names, such as:
Moon Festival or Harvest Moon Festival, because of the celebration's association with the full moon on this night, as well as the traditions of moon worship and moon viewing.
Zh?ngqi? Ji? (中秋?), is the official name in Mandarin.
Lantern Festival, a term sometimes used in Singapore, Malaysia and Indonesia, which is not to be confused with the Lantern Festival in China that occurs on the 15th day of the first month of the Chinese calendar.
Reunion Festival, in earlier times, a woman in China took this occasion to visit her parents before returning to celebrate with her husband and his parents.
Children's Festival, in Vietnam, because of the emphasis on the celebration of children.
The festival celebrates three fundamental concepts that are closely connected:
Gathering, such as family and friends coming together, or harvesting crops for the festival.
It is said the moon is the brightest and roundest on this day which means family reunion.
Consequently, this is the main reason why the festival is thought to be important.
Thanksgiving, to give thanks for the harvest, or for harmonious unions.
Praying (asking for conceptual or material satisfaction), such as for babies, a spouse, beauty, longevity, or for a good future
Traditions and myths surrounding the festival are formed around these concepts, although traditions have changed over time due to changes in technology, science, economy, culture, and religion.
It's about well being together.
The Chinese have celebrated the harvest during the autumn full moon since the Shang dynasty (c.1600–1046 BCE).
For the Baiyue peoples, the harvest time commemorated the dragon who brought rain for the crops.
The celebration as a festival only started to gain popularity during the early Tang dynasty (618–907 CE).
One legend explains that Emperor Xuanzong of Tang started to hold formal celebrations in his palace after having explored the Moon-Palace.
The term mid-autumn (中秋) first appeared in Rites of Zhou, a written collection of rituals of the Western Zhou dynasty (1046–771 BCE).
As for the royal court, it is dedicated to the goddess Taiyinxingjun(太陰星君T?iy?n x?ng j?n );.
And current still in taoism and Chinese folk religion
Empress Dowager Cixi (late 19th century) enjoyed celebrating Mid-Autumn Festival so much that she would spend the period between the thirteenth and seventeenth day of the eighth month staging elaborate rituals.
Houyi helplessly looking at his wife Chang'e flying off to the moon after she drank the elixir.
An important part of the festival celebration is moon worship.
The ancient Chinese believed in rejuvenation being associated with the moon and water, and connected this concept to the menstruation of women, calling it "monthly water".
The Zhuang people, for example, have an ancient fable saying the sun and moon are a couple and the stars are their children, and when the moon is pregnant, it becomes round, and then becomes crescent after giving birth to a child.
These beliefs made it popular among women to worship and give offerings to the moon on this evening.
In some areas of China, there are still customs in which "men do not worship the moon and the women do not offer sacrifices to the kitchen gods."
In China, the Mid-Autumn festival symbolizes the family reunion and family reunion, and on this day, all families will appreciate the moon in the evening, because it is the 15th day of the eighth month of the lunar calendar, when the moon is at its fullest.
There is a beautiful myth about the Mid-Autumn festival, that is Chang 'e flying to the moon.
The Chinese New Year, the Lantern, Mooncake and the Qingming Festivals explained, and where to go if you are hankering for food associated with these celebrations.
The richness and grandeur of China’s history and culture are on full display in the festivals and holidays that Chinese people celebrate throughout the year.
These festivals are observed by Chinese communities all over the world, including the Chinese American community here in New York City.
Here are brief descriptions of four of China’s major festivals, and how they are celebrated here, in Manhattan’s Chinatown.
Dragons are believed to ward off evils spirits.
The Chinese New Year, or Spring Festival, is the biggest Chinese festival both socially and economically.
Its origin can be traced to the Shang Dynasty (1600 BC -1046 BC).
The celebration of Chinese New Year serves a dual societal purpose.
First, it is a religious and social guide to enforce the power of the Shang Dynasty by appeasing one’s ancestors through participating in time-honored rituals.
These rituals include a detailed cleaning of one’s home – as a means to rid yourself of the bad qi or bad energy that had accumulated throughout the year – and ritual sacrifices of paper goods and foods.
Firecrackers were set off to ward off evil spirits.
Elders gave out money to children in red envelopes as a means to bring good life and longevity.
The second purpose of the festival is to send a signal to the farmers that spring is arriving.
Food plays an important role in Chinese New Year.
Dumplings are vital to the New Year feast; their significance lays in their shape, which is similar to the shape of a Chinese golden nugget.
Sticky rice or nian gao is a traditional staple during this holiday because the shape of the grains resembles pieces of gold.
The hope is that it will raise the level of prosperity and success, both in one’s career and livelihood.
Noodles are eaten during the festival to bring about longevity.
Fish is vital to the festival’s cuisine as fish in mandarin Chinese is “yu”, which can also mean plentiful or abundant.
Eating fish is believed to bring about an abundance of food for the whole year.
On the Gregorian calendar that most Western nations use, the Chinese New Year can be anywhere from late January to mid-February.
The Lantern Festival is the Chinese festival celebrated on the 15th day of the first month of the lunar calendar.
The festival falls on the first full moon of the New Year, celebrating the return of spring and the end of the Chinese New Year.
The festival can be traced back to the Eastern Han Dynasty (25 AD – 220 AD).
Emperor Hanming Di, a supporter of Buddhism, heard that monks liked to light lanterns on the 15th day of the first lunar month.
Liking this idea, he decided that all temples, households, and palaces should light lanterns on the same evening.
Lighting and appreciating lanterns is seen as a way to express good wishes for the future of one’s family.
Dragon dancing is held at the closing of the Chinese New Year as a means to ward off evil spirits and to usher in good fortune and safety in the coming year.
The dragons move from shop to shop, collecting cabbages and red envelopes.
During this holiday, it is customary to eat tang yuan or yuan xiao.
These are round soup balls for which the festivity is named.
They are made from glutinous rice flour, with different fillings inside, usually sweet.
Most common fillings are peanuts with brown sugar, sweet black sesame paste, and sweet red bean.
The tang yuan is generally steamed and served in fermented sweet water.
The round shape of the balls symbolizes wholeness and togetherness, both of which are said to express best wishes for the family’s future.
The Lantern Festival celebration is usually held anytime from early February to early March.
The Sweeping of the Tombs Festival is observed either on the third or fourth day of April.
It is a day to remember one’s ancestors, when families visit the tombs of their departed forefathers and loved ones to clean the place and offer sacrifices.
The festival dates back to the Zhou Dynasty (1046 BC – 256 BC), when emperors and wealthy officials started honoring their ancestors and offered sacrifices by praying for continued blessings of prosperity, peace, and plentiful harvest.
Sweeping the tomb is not just limited to sweeping, but also includes the removal of weeds, adding fresh soil, and removing any fallen branches from and near to the tomb.
Customarily, when one first approaches the tomb, one needs to bow three times as a sign of respect.
People burn incense on the tomb and paper money in a bin beside the tomb in the belief that doing so would make the family’s ancestors prosperous in heaven.
Rice wine is poured over the soil of the tomb and food (typically fruits, meats, and rice) are placed on the tomb to bring prosperity to their ancestors in heaven.
Kite-flying is a traditional activity, done by both young and old alike, both during the day and at night.
In the evening, little, colored lanterns are attached to the kite.
The lanterns make the flying kites look like twinkling stars.
Kite-flying is believed to bring in good luck and drive away diseases.
Traditional Sweeping of the Tombs Festival food are sweet green rice balls, peach blossom porridge, qingming cakes and eggs.
The Mid-Autumn Festival is celebrated on the 15th day on the eight month of the lunar calendar, usually on the night of the full moon between early September to early October of the Gregorian calendar.
The festival celebrates the fullness of the moon.
Moon worshiping dates back to the Shang Dynasty (1600 BC -1046 BC).
Chinese emperors believed that worshiping and providing sacrifices to the moon would bring a plentiful harvest for the following year.
By the Tang Dynasty (618 AD – 907 AD), officials would hold big parties while commoners would drink wine and gaze at the moon.
Mooncakes have become synonymous with the Mid-Autumn Festival.
Moon cakes are made from egg-based pastry skin with a sweet, dense, filling.
In Chinese culture, the roundness of the mooncakes symbolizes prosperity and togetherness for the whole family.
Giving mooncakes to friends and family members is considered an act of expressing love and best wishes.
Traditional mooncake fillings are lotus seed paste, sweet red bean, five nuts (nuts used differ from region to region, but are typically walnuts, pumpkin seeds, peanuts, sesame seeds and almonds), egg yolk, and jujube paste.
Chuseok (in Korea), Tsukimi (in Japan), Uposatha of Ashvini/Krittika (similar festivals that generally occur on the same day in Cambodia, Sri Lanka, Myanmar, Laos, Thailand)
"Mid-Autumn Festival" in Traditional (top) and Simplified (bottom) Chinese characters
Chuseok (??/秋夕; Autumn Eve), Korean variant of the Mid-Autumn Festival celebrated on the same day in the lunar calendar.
Tsukimi (月見; Moon-Viewing), Japanese variant of the Mid-Autumn Festival celebrated on the same day in the lunar calendar.
Offerings are also made to a more well-known lunar deity, Chang'e, known as the Moon Goddess of Immortality.
The myths associated with Chang'e explain the origin of moon worship during this day.
One version of the story is as follows, as described in Lihui Yang's Handbook of Chinese Mythology:[16]
In the ancient past, there was a hero named Hou Yi who was excellent at archery.
His wife was Chang'e.
One year, the ten suns rose in the sky together, causing great disaster to the people.
Yi shot down nine of the suns and left only one to provide light.
An immortal admired Yi and sent him the elixir of immortality.
Yi did not want to leave Chang'e and be immortal without her, so he let Chang'e keep the elixir.
However, Peng Meng, one of his apprentices, knew this secret.
So, on the fifteenth of August in the lunar calendar, when Yi went hunting, Peng Meng broke into Yi's house and forced Chang'e to give the elixir to him.
Chang'e refused to do so.
Instead, she swallowed it and flew into the sky.
Since she loved her husband and hoped to live nearby, she chose the moon for her residence.
When Yi came back and learned what had happened, he felt so sad that he displayed the fruits and cakes Chang'e liked in the yard and gave sacrifices to his wife.
People soon learned about these activities, and since they also were sympathetic to Chang'e they participated in these sacrifices with Yi.
“when people learned of this story, they burnt incense on a long altar and prayed to Chang ‘e, now the goddess of the moon, for luck and safety.
The custom of praying to the moon on Mid-Autumn Day has been handed down for thousands of years since that time.
"[17] actually, As chang 'e and hou yi's sad and beautiful love story spread, Chinese people also long for chang 'e to bless lovers, so that they can be happy together.
And ancient China also has the custom of appreciating lanterns and guessing riddles on the Mid-Autumn night.
According to legend, a man can get a lantern by guessing riddles and give it to a woman he likes.
If the woman also has the sentiment to the man, then may return presents the handkerchief, is really very romantic!
Handbook of Chinese Mythology also describes an alternate common version of the myth:[16]
After the hero Houyi shot down nine of the ten suns, he was pronounced king by the thankful people.
However, he soon became a conceited and tyrannical ruler.
In order to live long without death, he asked for the elixir from Xiwangmu.
But his wife, Chang'e, stole it on the fifteenth of August because she did not want the cruel king to live long and hurt more people.
She took the magic potion to prevent her husband from becoming immortal.
Houyi was so angry when discovered that Chang'e took the elixir, he shot at his wife as she flew toward the moon, though he missed.
Chang'e fled to the moon and became the spirit of the moon.
Houyi died soon because he was overcome with great anger.
Thereafter, people offer a sacrifice to Chang'e on every lunar fifteenth of August to commemorate Chang'e's action.
The festival was a time to enjoy the successful reaping of rice and wheat with food offerings made in honor of the moon.
Today, it is still an occasion for outdoor reunions among friends and relatives to eat mooncakes and watch the moon, a symbol of harmony and unity.
During a year of a solar eclipse, it is typical for governmental offices, banks, and schools to close extra days in order to enjoy the extended celestial celebration an eclipse brings.
[18] The festival is celebrated with many cultural or regional customs, among them:
Burning incense in reverence to deities including Chang'e.
Performance of dragon and lion dances, which is mainly practiced in southern China[2] and Vietnam.
A notable part of celebrating the holiday is the carrying of brightly lit lanterns, lighting lanterns on towers, or floating sky lanterns.
[2] Another tradition involving lanterns is to write riddles on them and have other people try to guess the answers (simplified Chinese: ??; traditional Chinese:燈謎; pinyin: d?ng m?; literally: 'lantern riddle').
It is difficult to discern the original purpose of lanterns in connection to the festival, but it is certain that lanterns were not used in conjunction with moon-worship prior to the Tang dynasty.
[11] Traditionally, the lantern has been used to symbolize fertility, and functioned mainly as a toy and decoration.
But today the lantern has come to symbolize the festival itself.
[11] In the old days, lanterns were made in the image of natural things, myths, and local cultures.
[11] Over time, a greater variety of lanterns could be found as local cultures became influenced by their neighbors.
As China gradually evolved from an agrarian society to a mixed agrarian-commercial one, traditions from other festivals began to be transmitted into the Mid-Autumn Festival, such as the putting of lanterns on rivers to guide the spirits of the drowned as practiced during the Ghost Festival, which is observed a month before.
[11] Hong Kong fishermen during the Qing dynasty, for example, would put up lanterns on their boats for the Ghost Festival and keep the lanterns up until Mid-Autumn Festival.
In Vietnam, children participate in parades in the dark under the full moon with lanterns of various forms, shapes, and colors.
Traditionally, lanterns signified the wish for the sun's light and warmth to return after winter.
[20] In addition to carrying lanterns, the children also don masks.
Elaborate masks were made of papier-m?ch?, though it is more common to find masks made of plastic nowadays.
[3] Handcrafted shadow lanterns were an important part of Mid-Autumn displays since the 12th-century L? dynasty, often of historical figures from Vietnamese history.
[3] Handcrafted lantern-making declined in modern times due to the availability of mass-produced plastic lanterns, which often depict internationally recognized characters such as Pok?mon's Pikachu, Disney characters, SpongeBob SquarePants, and Hello Kitty.
“The round moon cakes are symbols of the great family reunion just like the round.
”(Lemei) Moon cakes are becoming more and more popular, and the variety of flavors has turned them into gifts for friends and relatives.
Typical lotus bean-filled mooncakes eaten during the festival
Making and sharing mooncakes is one of the hallmark traditions of this festival.
In Chinese culture, a round shape symbolizes completeness and reunion.
Thus, the sharing and eating of round mooncakes among family members during the week of the festival signifies the completeness and unity of families.
[21] In some areas of China, there is a tradition of making mooncakes during the night of the Mid-Autumn Festival.
[22] The senior person in that household would cut the mooncakes into pieces and distribute them to each family member, signifying family reunion.
[22] In modern times, however, making mooncakes at home has given way to the more popular custom of giving mooncakes to family members, although the meaning of maintaining familial unity remains.
Although typical mooncakes can be around a few centimetres in diameter, imperial chefs have made some as large as 8 meters in diameter, with its surface pressed with designs of Chang'e, cassia trees, or the Moon-Palace.
[18] One tradition is to pile 13 mooncakes on top of each other to mimic a pagoda, the number 13 being chosen to represent the 13 months in a full lunar year.
[18] The spectacle of making very large mooncakes continues in modern China.
According to Chinese folklore, a Turpan businessman offered cakes to Emperor Taizong of Tang in his victory against the Xiongnu on the fifteenth day of the eighth lunar month.
Taizong took the round cakes and pointed to the moon with a smile, saying, "I'd like to invite the toad to enjoy the h?(胡) cake.
" After sharing the cakes with his ministers, the custom of eating these h?cakes spread throughout the country.
[24] Eventually these became known as mooncakes.
Although the legend explains the beginnings of mooncake-giving, its popularity and ties to the festival began during the Song dynasty (906–1279 CE).
Another popular legend concerns the Han Chinese's uprising against the ruling Mongols at the end of the Yuan dynasty (1280–1368 CE), in which the Han Chinese used traditional mooncakes to conceal the message that they were to rebel on Mid-Autumn Day.
[19] Because of strict controls upon Han Chinese families imposed by the Mongols in which only 1 out of every 10 households was allowed to own a knife guarded by a Mongolian guard, this coordinated message was important to gather as many available weapons as possible.
Cassia wine is the traditional choice for "reunion wine" drunk during Mid-Autumn Festival
Imperial dishes served on this occasion included nine-jointed lotus roots which symbolize peace, and watermelons cut in the shape of lotus petals which symbolize reunion.
[18] Teacups were placed on stone tables in the garden, where the family would pour tea and chat, waiting for the moment when the full moon's reflection appeared in the center of their cups.
[18] Owing to the timing of the plant's blossoms, cassia wine is the traditional choice for the "reunion wine" drunk on the occasion.
Also, people will celebrate by eating cassia cakes and candy.
In some places, people will celebrate by drinking osmanthus wine and eating osmanthus mooncakes.
Food offerings made to deities are placed on an altar set up in the courtyard, including apples, pears, peaches, grapes, pomegranates, melons, oranges, and pomelos.
[28] One of the first decorations purchased for the celebration table is a clay statue of the Jade Rabbit.
In Chinese folklore, the Jade Rabbit was an animal that lived on the moon and accompanied Chang'e.
Offerings of soy beans and cockscomb flowers were made to the Jade Rabbit.
Nowadays, in southern China, people will also eat some seasonal fruit that may differ in different district but carrying the same meaning of blessing.
In Taiwan, the Mid-Autumn Festival is a public holiday.
Outdoor barbecues have become a popular affair for friends and family to gather and enjoy each other's company.
[38] As of 2016, Taipei City designated 15 riverside parks to accommodate outdoor barbecues for the public.
The traditional Chinese holidays are an essential part of harvests or prayer offerings.
The most important Chinese holiday is the Chinese New Year (Spring Festival), which is also celebrated in Taiwan and overseas ethnic Chinese communities.
All traditional holidays are scheduled according to the Chinese calendar (except the Qing Ming and Winter Solstice days, falling on the respective Jie qi in the Agricultural calendar).
Lantern parade and lion dance celebrating the first full moon.
This day is also the last day of new year celebration.
This is Tourism Day in Taiwan
Eat Chinese pancakes (Chun bing, 春餅) and noodles, clean the house.
Also known as Dragon Raising its Head This is Earth God's Birthday in Taiwan
Qingming Festival (Tomb Sweeping Festival, Tomb Sweeping Day, Clear and Bright Festival)
Visit, clean, and make offerings at ancestral gravesites, spring outing
Dragon boat race, eat sticky rice wrapped in lotus leaves Zongzi (粽子).
This festival commemorates the ancient poet Qu Yuan; drink yellow rice wine, related to the White Snake Lady legend
15th night of the seventh month (14th in parts of southern China)
Burn fake paper money and make offerings to ancestors and the dead to comfort them in the afterlife and keep them from troubling the living.
Autumn outing and mountain climbing, some Chinese also visit the graves of their ancestors to pay their respects.
Chinese New Year (or generally referred to as Lunar New Year globally) is the Chinese festival that celebrates the beginning of a new year on the traditional Chinese calendar.
The festival is usually referred to as the Spring Festival in mainland China, and is one of several Lunar New Years in Asia.
Observances traditionally take place from the evening preceding the first day of the year to the Lantern Festival, held on the 15th day of the year.
The first day of Chinese New Year begins on the new moon that appears between 21 January and 20 February.
In 2020, the first day of the Chinese New Year will be on Saturday, 25 January, initiating the Year of the Rat.
Chinese New Year is associated with several myths and customs.
The festival was traditionally a time to honour deities as well as ancestors.
Within China, regional customs and traditions concerning the celebration of the New Year vary widely, and the evening preceding Chinese New Year's Day is frequently regarded as an occasion for Chinese families to gather for the annual reunion dinner.
It is also traditional for every family to thoroughly clean their house, in order to sweep away any ill-fortune and to make way for incoming good luck.
Another custom is the decoration of windows and doors with red paper-cuts and couplets.
Popular themes among these paper-cuts and couplets include that of good fortune or happiness, wealth, and longevity.
Other activities include lighting firecrackers and giving money in red paper envelopes.
For the northern regions of China, dumplings are featured prominently in meals celebrating the festival.
It often serves as the first meal of the year either at mid-night or as breakfast of the first day.
Chinese New Year is observed as a public holiday in some countries and territories where there is a sizable Chinese and Korean population.
Since Chinese New Year falls on different dates on the Gregorian calendar every year on different days of the week, some of these governments opt to shift working days in order to accommodate a longer public holiday.
In some countries, a statutory holiday is added on the following work day when the New Year falls on a weekend, as in the case of 2013, where the New Year's Eve (9 February) falls on Saturday and the New Year's Day (10 February) on Sunday.
Depending on the country, the holiday may be termed differently; common names are "Chinese New Year", "Lunar New Year", "New Year Festival", and "Spring Festival".
For New Year celebrations that are lunar but are outside of China and Chinese diaspora (such as Korea's Seollal and Vietnam's T?t), see the article on Lunar New Year.
For other countries where Chinese New Year is celebrated but not an official holiday, see the table below.
The first 3 days.
Usually, the Saturday before and the Sunday after Chinese New Year are declared working days, and the 2 additionally gained holidays are added to the official 3 days of holiday, so that people have 7 consecutive days, including weekends.
Divided into 3 days, the first day is the Wan chai (Thai: ???????; pay day), meaning the day that people go out to shop for offerings, second day is the Wan wai (???????; worship day), is a day of worshiping the gods and ancestral spirits, which is divided into three periods: dawn, late morning and afternoon, the third day is a Wan tieow (?????????; holiday), is a holiday that everyone will leave the house to travel or to bless relatives or respectable people.
And often wear red clothes because it is believed to bring auspiciousness to life.
Red couplets and red lanterns are displayed on the door frames and light up the atmosphere.
The air is filled with strong Chinese emotions.
In stores in Beijing, Shanghai, Wuhan, and other cities, products of traditional Chinese style have started to lead fashion trend[s].
Buy yourself a Chinese-style coat, get your kids tiger-head hats and shoes, and decorate your home with some beautiful red Chinese knots, then you will have an authentic Chinese-style Spring Festival.
In many households where Buddhism or Taoism is prevalent, home altars and statues are cleaned thoroughly, and decorations used to adorn altars over the past year are taken down and burned a week before the new year starts, to be replaced with new decorations.
Taoists (and Buddhists to a lesser extent) will also "send gods back to heaven" (Chinese: 送神; pinyin: s?ngsh?n), an example would be burning a paper effigy of Zao Jun the Kitchen God, the recorder of family functions.
This is done so that the Kitchen God can report to the Jade Emperor of the family household's transgressions and good deeds.
Families often offer sweet foods (such as candy) in order to "bribe" the deities into reporting good things about the family.
Prior to the Reunion Dinner, a prayer of thanksgiving is held to mark the safe passage of the previous year.
Confucianists take the opportunity to remember their ancestors, and those who had lived before them are revered.
Some people do not give a Buddhist prayer due to the influence of Christianity, with a Christian prayer offered instead.
The first day is for the welcoming of the deities of the heavens and earth, officially beginning at midnight.
It is a traditional practice to light fireworks, burn bamboo sticks and firecrackers and to make as much of a din as possible to chase off the evil spirits as encapsulated by nian of which the term Guo Nian was derived.
Many Buddhists abstain from meat consumption on the first day because it is believed to ensure longevity for them.
Some consider lighting fires and using knives to be bad luck on New Year's Day, so all food to be consumed is cooked the days before.
On this day, it is considered bad luck to use the broom, as good fortune is not to be "swept away" symbolically.
Most importantly, the first day of Chinese New Year is a time to honor one's elders and families visit the oldest and most senior members of their extended families, usually their parents, grandparents and great-grandparents.
For Buddhists, the first day is also the birthday of Maitreya Bodhisattva (better known as the more familiar Budai Luohan), the Buddha-to-be.
People also abstain from killing animals.
Some families may invite a lion dance troupe as a symbolic ritual to usher in the Chinese New Year as well as to evict bad spirits from the premises.
Members of the family who are married also give red envelopes containing cash known as lai see (Cantonese dialect) or angpow (Hokkien dialect/Fujian), or hongbao (Mandarin), a form of blessings and to suppress the aging and challenges associated with the coming year, to junior members of the family, mostly children and teenagers.
Business managers also give bonuses through red packets to employees for good luck, smooth-sailing, good health and wealth.
Incense is burned at the graves of ancestors as part of the offering and prayer rituals.
The second day of the Chinese New Year, known as "beginning of the year" (?年;開年; k?ini?n), was when married daughters visited their birth parents, relatives and close friends.
(Traditionally, married daughters didn't have the opportunity to visit their birth families frequently.)
During the days of imperial China, "beggars and other unemployed people circulate[d] from family to family, carrying a picture [of the God of Wealth] shouting, "Cai Shen dao!
" [The God of Wealth has come!].
"Householders would respond with "lucky money" to reward the messengers.
Business people of the Cantonese dialect group will hold a 'Hoi Nin' prayer to start their business on the 2nd day of Chinese New Year so they will be blessed with good luck and prosperity in their business for the year.
In those communities that celebrate Chinese New Year for 15 days, the fourth day is when corporate "spring dinners" kick off and business returns to normal.
Other areas that have a longer Chinese New Year holiday will celebrate and welcome the gods that were previously sent on this day.
This day is the god of Wealth's birthday.
In northern China, people eat jiaozi, or dumplings, on the morning of powu (Chinese: 破五; pinyin: p?w?).
In Taiwan, businesses traditionally re-open on the next day (the sixth day), accompanied by firecrackers.
It is also common in China that on the 5th day people will shoot off firecrackers to get Guan Yu's attention, thus ensuring his favor and good fortune for the new year.
The fifteenth day of the new year is celebrated as "Yuanxiao Festival" (元宵?;元宵節; Yu?n xi?o ji?), also known as "Shangyuan Festival" (上元?;上元節; Sh?ng yu?n ji?) or the Lantern Festival (otherwise known as Chap Goh Mei (十五暝; Sh?w?m?ng; 'the fifteen night') in Fujian dialect).
Rice dumplings tangyuan (??;湯圓; tang yu?n), a sweet glutinous rice ball brewed in a soup, are eaten this day.
Candles are lit outside houses as a way to guide wayward spirits home.
This day is celebrated as the Lantern Festival, and families walk the street carrying lighted lantern.
A reunion dinner (ni?n y? f?n) is held on New Year's Eve during which family members gather for a celebration.
The venue will usually be in or near the home of the most senior member of the family.
The New Year's Eve dinner is very large and sumptuous and traditionally includes dishes of meat (namely, pork and chicken) and fish.
Most reunion dinners also feature a communal hot pot as it is believed to signify the coming together of the family members for the meal.
Most reunion dinners (particularly in the Southern regions) also prominently feature specialty meats (e.g. wax-cured meats like duck and Chinese sausage) and seafood (e.g. lobster and abalone) that are usually reserved for this and other special occasions during the remainder of the year.
In most areas, fish (?;魚; y?) is included, but not eaten completely (and the remainder is stored overnight), as the Chinese phrase "may there be surpluses every year" (年年有余;年年有餘; ni?nni?n y?u y?) sounds the same as "let there be fish every year.
" Eight individual dishes are served to reflect the belief of good fortune associated with the number.
If in the previous year a death was experienced in the family, seven dishes are served.
Red packets for the immediate family are sometimes distributed during the reunion dinner.
These packets contain money in an amount that reflects good luck and honorability.
Several foods are consumed to usher in wealth, happiness, and good fortune.
Several of the Chinese food names are homophones for words that also mean good things.
The Lantern Festival or the Spring Lantern Festival is a Chinese festival celebrated on the fifteenth day of the first month in the lunisolar Chinese calendar.
Usually falling in February or early March on the Gregorian calendar, it marks the final day of the traditional Chinese New Year celebrations.
As early as the Western Han Dynasty (206 BCE-CE 25), it had become a festival with great significance.
During the Lantern Festival, children go out at night carrying paper lanterns and solve riddles on the lanterns.
In ancient times, the lanterns were fairly simple, and only the emperor and noblemen had large ornate ones.
In modern times, lanterns have been embellished with many complex designs.
For example, lanterns are now often made in the shape of animals.
The lanterns can symbolize the people letting go of their past selves and getting new ones, which they will let go of the next year.
The lanterns are almost always red to symbolize good fortune.
The festival acts as an Uposatha day on the Chinese calendar.
It should not to be confused with the Mid-Autumn Festival; which is sometimes also known as the "Lantern Festival" in locations such as Singapore and Malaysia.
The Lantern Festival has also become popular in Western countries, especially in cities with a large Chinese community.
There are several beliefs about the origin of the Lantern Festival.
However, its roots trace back more than 2000 years ago and is popularly linked to the reign of Emperor Ming of Han at the time when Buddhism was growing in China.
Emperor Ming was an advocate of Buddhism and noticed Buddhist monks would light lanterns in temples on the fifteenth day of the first lunar month.
As a result, Emperor Ming ordered all households, temples and the imperial palace to light lanterns on that evening.
From there it developed into a folk custom.
Another likely origin is the celebration of "the declining darkness of winter" and community's ability to "move about at night with human-made light," namely, lanterns.
During the Han Dynasty, the festival was connected to Ti Yin, the deity of the North Star.
There is one legend that states that it was a time to worship Taiyi, the God of Heaven in ancient times.
The belief was that the God of Heaven controlled the destiny of the human world.
He had sixteen dragons at his beck and call and he decided when to inflict drought, storms, famine or pestilence upon human beings.
Beginning with Qin Shi Huang, the first emperor of China, who named China, all the emperors ordered splendid ceremonies each year.
The emperor would ask Taiyi to bring favorable weather and good health to him and his people.
Yet another common legend dealing with the origins of the Lantern Festival speaks of a beautiful bird that flew down to earth from heaven, which was hunted and killed by some villagers.
This angered the Jade Emperor in Heaven because the bird was his favorite one.
Therefore, he planned a storm of fire to destroy the village on the 15th lunar day.
The Jade Emperor's daughter heard of this plan, and warned the villagers of her father’s plan to destroy their village.
The village was in turmoil because nobody knew how should they escape their imminent destruction.
However, a wise man from another village suggested that every family should hang red lanterns around their houses, set up bonfires on the streets, and explode firecrackers on the 14th, 15th, and 16th lunar days.
This would give the village the appearance of being on fire to the Jade Emperor.
On the 15th lunar day, troops sent down from heaven whose mission was to destroy the village saw that the village was already ablaze, and returned to heaven to report to the Jade Emperor.
Satisfied, the Jade Emperor decided not to burn down the village.
From that day on, people celebrate the anniversary on the 15th lunar day every year by carrying lanterns on the streets and exploding firecrackers and fireworks.
The Ghost Festival, also known as the Hungry Ghost Festival, Zhongyuan Jie (中元?), Gui Jie (鬼?) or Yulan Festival (盂?盆?) is a traditional Buddhist and Taoist festival held in certain Asian countries.
According to the Chinese calendar (a lunisolar calendar), the Ghost Festival is on the 15th night of the seventh month (14th in southern China).
In Chinese culture, the fifteenth day of the seventh month in the lunar calendar is called Ghost Day and the seventh month in general is regarded as the Ghost Month (鬼月), in which ghosts and spirits, including those of the deceased ancestors, come out from the lower realm.
Distinct from both the Qingming Festival (or Tomb Sweeping Day, in spring) and Double Ninth Festival (in autumn) in which living descendants pay homage to their deceased ancestors, during Ghost Festival, the deceased are believed to visit the living.
On the fifteenth day the realms of Heaven and Hell and the realm of the living are open and both Taoists and Buddhists would perform rituals to transmute and absolve the sufferings of the deceased.
Intrinsic to the Ghost Month is veneration of the dead, where traditionally the filial piety of descendants extends to their ancestors even after their deaths.
Activities during the month would include preparing ritualistic food offerings, burning incense, and burning joss paper, a papier-m?ch? form of material items such as clothes, gold and other fine goods for the visiting spirits of the ancestors.
Elaborate meals (often vegetarian meals) would be served with empty seats for each of the deceased in the family treating the deceased as if they are still living.
Ancestor worship is what distinguishes Qingming Festival from Ghost Festival because the latter includes paying respects to all deceased, including the same and younger generations, while the former only includes older generations.
Other festivities may include, buying and releasing miniature paper boats and lanterns on water, which signifies giving directions to the lost ghosts and spirits of the ancestors and other deities.
The seventh month of the lunar calendar marks the Ghost Month in Taiwan.
During this month, the gates of the underworld are open and spirits are allowed to visit their family, loved ones and or just roam around.
The 15th of the month is Zhong Yuan Pudu Festival, or Ghost Festival.
While being Taoist, the festival also has Buddhist roots.
Since Ghost Month does mark the month when ghosts can roam amongst the living, this is a month that is often avoided for any wedding or childbirth.
However, while in western society spirits are often viewed as malicious, in Buddhist Taoist culture, they are but passed away spirits that have not reincarnated yet.
All deceased, good or bad, will become a spirit.
While there are many festivals paying respects to the different deities and spirits, the special bit about the Ghost Festival is that it also pays respect to those spirits without any family.
When a person passes away, it is up to the survived family to pay respects and provide food to the deceased.
When this doesn’t happen, the spirits will considered homeless and their throats will shrink to the size of a needle.
Any food that they attempt to eat will catch on fire in their mouths.
During the Ghost Month, the Buddhist Gods will permit extinguish the fire and grow their throats, allowing them to feast.
As a sign of respect, you never refer to the spirits as ghosts (gwei: 鬼), instead they are referred to as good brothers (hao song di: 好兄弟)
Since this is a tribute to all the spirits in the area, typically temples would hold a large gathering having everyone put their tributes on the tables.
Then a Taoist or Buddhist priest (depending on the temple) will speak out a mantra and invite the spirits to feast.
Below are some interesting tributes special for the spirits
A bento box or a plate of typical home cooked meal is offered as tribute to the Di Ji Zhu.
This is often times also done for companies (especially factories) to ensure job functions go smoothly and without any accidents.
For the spirits, it is also expected to bring out a basin filled with water, towel, toothbrush and toothpaste to allow the spirits to clean themselves.
Probably the most bizarre are the burlesque shows meant to be entertainment for the deceased.
Typically seen only in rural parts of Taiwan, a stage will be set up near the temple and burlesque and pole dancers will perform.
The first row will be left empty to ensure the spirits have front row seats.
While Taiwan is known for its sky lanterns during the Lantern Festival, the release of water lanterns is also an age old and gorgeous tradition.
There are two types of water lanterns: lily lanterns and house lanterns.
Lily Lanterns come from the Buddhist tradition and is used to guide homeless spirits to the afterlife.
House Lanterns are typically for each household to guide any spirits of a family that have deceased to reincarnate.
The family name will often be found on the lanterns.
The lanterns are placed in the water then lit on fire as they go out to the water.
The further they go, the more blessings the one offered the tribute will receive.
Modern day, there are usually scuba divers around to make sure the lanterns travel out into the water.
Note: The most famous water lantern release is in Japan, called T?r? nagashi.
These are released during the Bon Festival, similarly to guide souls to the spirit world.
Mo Fa Ah Ma (Grandma and Her Ghosts) is a movie made in 1998 that centers around Taiwan’s Ghost Festival.
Using it as a vessel to understand the Taiwanese culture and superstitions around spirits.
Also, it’s just a cute movie about a young boy and his ah ma.
The Ghost Festival, also known as the Hungry Ghost Festival, Zhongyuan Jie (中元節), Gui Jie (鬼節) or Yulan Festival (traditional Chinese: 盂蘭盆節; simplified Chinese: 盂?盆?; pinyin: Y?l?np?nji?; Cantonese Jyutping: jyu4 laan4 pun4 zit3) is a traditional Buddhist and Taoist festival held in certain East Asian countries.
According to the Chinese calendar (a lunisolar calendar), the Ghost Festival is on the 15th night of the seventh month (14th in parts of southern China).
In Chinese culture, the fifteenth day of the seventh month in the lunar calendar is called Ghost Day and the seventh month in general is regarded as the Ghost Month (鬼月), in which ghosts and spirits, including those of deceased ancestors, come out from the lower realm.
Distinct from both the Qingming Festival (or Tomb Sweeping Day, in spring) and Double Ninth Festival (in autumn) in which living descendants pay homage to their deceased ancestors, during Ghost Festival, the deceased are believed to visit the living.
On the fifteenth day the realms of Heaven and Hell and the realm of the living are open and both Taoists and Buddhists would perform rituals to transmute and absolve the sufferings of the deceased.
Intrinsic to the Ghost Month is veneration of the dead, where traditionally the filial piety of descendants extends to their ancestors even after their deaths.
Activities during the month would include preparing ritualistic food offerings, burning incense, and burning joss paper, a papier-m?ch? form of material items such as clothes, gold and other fine goods for the visiting spirits of the ancestors.
Elaborate meals (often vegetarian meals) would be served with empty seats for each of the deceased in the family treating the deceased as if they are still living.
Ancestor worship is what distinguishes Qingming Festival from Ghost Festival because the latter includes paying respects to all deceased, including the same and younger generations, while the former only includes older generations.
Other festivities may include, buying and releasing miniature paper boats and lanterns on water, which signifies giving directions to the lost ghosts and spirits of the ancestors and other deities.
The timing and origin story of the modern Ghost Festival, however, ultimately derives from the Mahayana scripture known as the Yulanpen or Ullambana Sutra.
The sutra records the time when Maudgalyayana achieves abhij?? and uses his new found powers to search for his deceased parents.
Maudgalyayana discovers that his deceased mother was reborn into the preta or hungry ghost realm.
She was in a wasted condition and Maudgalyayana tried to help her by giving her a bowl of rice.
Unfortunately as a preta, she was unable to eat the rice as it was transformed into burning coal.
Maudgalyayana then asks the Buddha to help him; whereupon Buddha explains how one is able to assist one's current parents and deceased parents in this life and in one's past seven lives by willingly offering food, etc., to the sangha or monastic community during Pravarana (the end of the monsoon season or vassa), which usually occurs on the 15th day of the seventh month whereby the monastic community transfers the merits to the deceased parents, etc.
The Ghost Festival is held during the seventh month of the Chinese calendar.
It also falls at the same time as a full moon, the new season, the fall harvest, the peak of Buddhist monastic asceticism, the rebirth of ancestors, and the assembly of the local community.
During this month, the gates of hell are opened up and ghosts are free to roam the earth where they seek food and entertainment.
These ghosts are believed to be ancestors of those who forgot to pay tribute to them after they died, or those who were never given a proper ritual send-off.
They have long needle-thin necks because they have not been fed by their family, or as a punishment so that they are unable to swallow.
Family members offer prayers to their deceased relatives, offer food and drink and burn hell bank notes and other forms of joss paper.
Joss paper items are believed to have value in the afterlife, considered to be very similar in some aspects to the material world, People burn paper houses, cars, servants and televisions to please the ghosts.
Families also pay tribute to other unknown wandering ghosts so that these homeless souls do not intrude on their lives and bring misfortune.
A large feast is held for the ghosts on the fourteenth day of the seventh month, when people bring samples of food and place them on an offering table to please the ghosts and ward off bad luck.
Lotus-shaped lanterns are lit and set afloat in rivers and out onto seas to symbolicly guide the lost souls of forgotten ancestors to the afterlife.
In some East Asian countries today, live performances are held and everyone is invited to attend.
The first row of seats are always empty as this is where the ghosts sit.
The shows are always put on at night and at high volumes as the sound is believed to attract and please the ghosts.
Some shows include Chinese opera, dramas, and in some areas, even burlesque shows.
Traditionally Chinese opera was the main source of entertainment but the newer shows, concerts, dramas, wars and so forth are referred to as Getai.
These acts are better known as "Merry-making".
For rituals, Buddhists and Taoists hold ceremonies to relieve ghosts from suffering, many of them holding ceremonies in the afternoon or at night (as it is believed that the ghosts are released from hell when the sun sets).
Altars are built for the deceased and priests and monks alike perform rituals for the benefit of ghosts.
Monks and priests often throw rice or other small foods into the air in all directions to distribute them to the ghosts.
During the evening, incense is burnt in front of the doors households.
Incense stands for prosperity in Chinese culture, so families believe that there is more prosperity in burning more incense.
During the festival, some shops are closed as they want to leave the streets open for the ghosts.
In the middle of each street stands an altar of incense with fresh fruit and sacrifices displayed on it.
Fourteen days after the festival, to make sure all the hungry ghosts find their way back to hell, people float water lanterns and set them outside their houses.
These lanterns are made by setting a lotus flower-shaped lantern on a paper boat.
The lanterns are used to direct the ghosts back to the underworld, and when they go out, it symbolizes that they have found their way back.
Concert-like performances are a prominent feature of the Ghost Festival in Singapore and Malaysia.
Those live concerts are popularly known as Getai in Mandarin or Koh-tai in Hokkien Chinese.
They are performed by groups of singers, dancers, entertainers and opera troops or puppet shows on a temporary stage that is set up within a residential district.
The festival is funded by the residents of each individual district.
During these Getai the front row is left empty for the special guests—the ghosts.
It is known to be bad luck to sit on the front row of red seats, if anyone were to sit on them, they would become sick or similarly ailed.
In Singapore, people would pray to ghosts/spirits or ancestors with offerings & others outside their homes for the start of the 7th month.
Most patriotic events were held on 7th Month for Singapore, which includes general and presidential elections, the Olympics and the National Day Parade.
This is where the number of outings were minimised.
In Indonesia, the festival popularly known as Cioko, or Sembahyang Rebutan in Indonesian, (Scrambling prayer).
People gather around temples and bring an offering to a spirit who died in an unlucky way, and after that, they distribute it to the poor.
The way people scramble the offerings is the origin of the festival name.
Traditionally, it is believed that ghosts haunt the island of Taiwan for the entire seventh lunar month, when the mid-summer Ghost Festival is held.
The month is known as Ghost Month.
The first day of the month is marked by opening the gate of a temple, symbolizing the gates of hell.
During the month, people avoid surgery, buying cars, swimming, moving house, marrying, whistling and going out or taking pictures after dark.
It is also important that addresses are not revealed to the ghosts.
Ch?gen (中元), also Och?gen (?中元), is an annual event in Japan on the 15th day of the 7th lunar month, when people give gifts to their superiors and acquaintances.
Originally it was an annual event for giving gifts to the ancestral spirits.
Ghost Month (August): All of Taiwan observes Ghost Month during August.
During Ghost Month, the doors to Heaven and Hell open to the world of the living.
You will see stores full to the brim with stacks of yellow paper money, which are burned in small metal furnaces outside homes and shops all day long.
Food and drink offerings are also offered up to the ghosts.
Many Kinmenese are superstitious and adhere to certain rules to avoid getting on the bad side of the ghost.
For example, during this month, some people believe that hanging laundry outside to dry will cause a ghost to inhabit the clothes and then possess the wearer.
Mid-Autumn Festival/Moon Festival (September): The Mid-Autumn Festival is an important tradition in China and Taiwan.
It celebrates the harvest, and is a time for family members to get together and express gratitude and ask for future blessings.
There are several myths and stories associated with the festival that can offer insight into Taiwanese culture.
You will likely receive moon cakes from your school, your neighbors and your friends, and get invited to an outdoor barbecue!
Double Ten (October 10): This holiday celebrates the Wuchang Uprising of October 10, 1911, which led to the end of the Qing Dynasty and the formation of the R.O.C.
A few ETAs are selected from each site to attend the national 10/10 celebration in Taipei, but it is not widely celebrated on Kinmen.
New Year's Eve (December 31): On the day of, there is a concert and fireworks at the Jincheng gym, as well as smaller family and neighborhood gatherings.
Chinese New Year (Usually around February): Chinese New Year is arguably the most important holiday in Taiwanese culture.
School gets out for several weeks, so many ETAs take it as an opportunity to travel.
The entire island gets decorated in red and lit with lanterns.
Lantern Festival (February): Leading up to the Lantern Festival, you will start seeing beautiful and creative lanterns go up all over the island.
The Confucius Temple in the Jincheng Old Street hosts a lantern contest with submissions from every elementary school on the island, as well as other intricate lantern displays.
There is also a big lantern lighting ceremony, performances, and festivities in front of the temple.
Tomb Sweeping Day (April 5): On Tomb Sweeping Day (清明節), families gather together and visit their ancestral graves to tidy up, remember, pray, and offer food and drink to the spirits.
Dragon Boat Festival (June): Dragon Boat Festival, or 端午節, is celebrated in China and Taiwan.
It commemorates the life and death of Chinese scholar Qu Yuan and falls on the 5th day of the 5th month of the lunar calendar.
Two major customs associated with the festival are the consumption of zongzi (粽子) and dragon boat racing.
ETAs in the past have formed dragon boat teams and participated in racing events.
The Dragon Boat Festival (traditional Chinese: 端午節; simplified Chinese: 端午?) is a traditional holiday originating in China, occurring near the summer solstice.
The festival now occurs on the 5th day of the 5th month of the traditional Chinese calendar, which is the source of the festival's alternative name, the Double Fifth Festival.
The Chinese calendar is lunisolar, so the date of the festival varies from year to year on the Gregorian calendar.
In 2017, it occurred on 30 May; in 2018, on 18 June; and, in 2019, on 7 June.
The English language name for the holiday, "Dragon Boat Festival", possibly translates into two alternative Chinese names for the holiday, 龍船節 (L?ngchu?nji?) and 龍舟節 (L?ngzh?uji?).
The official Chinese name of the festival is "端午節" (simplified Chinese: 端午?; traditional Chinese: 端午節; pinyin: Du?nw?ji?) on the mainland,[2] Taiwan, and "Tuen Ng Festival" for [3] Hong Kong, Macao[4], Malaysia and Singapore[5].
This is pronounced variously in different Chinese languages.
In Mandarin, it is romanized as Du?nw?ji? on the mainland and Taiwan; in Cantonese, it is romanized as Tuen1 Ng5 Jit3 on Hong Kong and Tung1 Ng5 Jit3 on Macao.
All of these names (lit."Opening the Fifth") refer to its original position as the first fifth-day (午日, W?r?) in the fifth month (五月, W?yu?) of the traditional Chinese calendar, which was also known as 午 (W?).
People's Republic of China use "Dragon Boat Festival" as the official English translation of the holiday,[6][7] while Hong Kong calls it the "Tuen Ng Festival"[5] and Macao calls it "Dragon Boat Festival (Tun Ng)" in English[8] and Festividade do Barco-Drag?o (Tung Ng) in Portuguese.
The fifth lunar month is considered an unlucky month.
People believed that natural disasters and illness are common in the fifth month.
In order to get rid of the misfortune, people would put calamus, Artemisia, pomegranate flowers, Chinese ixora and garlic above the doors in the fifth of May.
Since the shape of calamus forms like a sword and with the strong smell of the garlic, it is believed that they can remove the evil spirits.
Other origin of Dragon Boat festival : Before the Qin Dynasty (221-206 B.C.), the fifth month of the lunar calendar was regarded as a bad month and the fifth day of the month as a bad day, and known as the Dragon Boat festival nowadays.
Poisonous animals will appear starting from this days such as snakes, centipedes and scorpions as well as that people may get sick easily after this day.
Therefore, during the Dragon Boat Festival, people try any way to avoid bad luck.
For example, people paste pictures of the five poisonous creatures on the wall and stick needles in them.
People also make paper cuttings of the five creatures and wrapped these around the wrists of their children.
Big ceremonies and performances developed from these practices in many areas, making the Dragon Boat Festival a day for getting rid of disease and bad luck.
Decorating doors by hanging wormwood and calamus plants is an important ritual of the Dragon Boat Festival.
People believe that doing so will drive evil spirits and diseases away.
The story best known in modern China holds that the festival commemorates the death of the poet and minister Qu Yuan (c.340–278 BC) of the ancient state of Chu during the Warring States period of the Zhou Dynasty.
A cadet member of the Chu royal house, Qu served in high offices.
However, when the king decided to ally with the increasingly powerful state of Qin, Qu was banished for opposing the alliance and even accused of treason.
During his exile, Qu Yuan wrote a great deal of poetry.
Twenty-eight years later, Qin captured Ying, the Chu capital.
In despair, Qu Yuan committed suicide by drowning himself in the Miluo River.
It is said that the local people, who admired him, raced out in their boats to save him, or at least retrieve his body.
This is said to have been the origin of dragon boat races.
When his body could not be found, they dropped balls of sticky rice into the river so that the fish would eat them instead of Qu Yuan's body.
This is said to be the origin of zongzi.
Despite the modern popularity of the Qu Yuan origin theory, in the former territory of the Kingdom of Wu, the festival commemorated Wu Zixu (died 484 BC), the Premier of Wu.
Xi Shi, a beautiful woman sent by King Goujian of the state of Yue, was much loved by King Fuchai of Wu.
Wu Zixu, seeing the dangerous plot of Goujian, warned Fuchai, who became angry at this remark.
Wu Zixu was forced to commit suicide by Fuchai, with his body thrown into the river on the fifth day of the fifth month.
After his death, in places such as Suzhou, Wu Zixu is remembered during the Dragon Boat Festival.
Although Wu Zixu is commemorated in southeast Jiangsu and Qu Yuan elsewhere in China, much of Northeastern Zhejiang including the cities of Shaoxing, Ningbo and Zhoushan celebrates the memory of the young girl Cao E (曹娥, AD 130–144) instead.
Cao E's father Cao Xu (曹盱) was a shaman who presided over local ceremonies at Shangyu.
In 143, while presiding over a ceremony commemorating Wu Zixu during the Dragon Boat Festival, Cao Xu accidentally fell into the Shun River.
Cao E, in an act of filial piety, decided to find her father in the river, searching for 3 days trying to find him.
After five days, she and her father were both found dead in the river from drowning.
Eight years later, in 151, a temple was built in Shangyu dedicated to the memory of Cao E and her sacrifice for filial piety.
The Shun River was renamed Cao'e River in her honour.
Modern research suggests that the stories of Qu Yuan or Wu Zixu were superimposed onto a pre-existing holiday tradition.
The promotion of these stories might be encouraged by Confucian scholars, seeking to legitimize and strengthen their influence in China.
The stories of both Qu Yuan and Wu Zixu were recorded in Sima Qian's Shiji, completed 187 and 393 years after the events, respectively, because historians wanted to praise both characters.
Another theory, advanced by Wen Yiduo, is that the Dragon Boat Festival originated from dragon worship.
Support is drawn from two key traditions of the festival: the tradition of dragon boat racing and zongzi.
The food may have originally represented an offering to the dragon king, while dragon boat racing naturally reflects reverence for the dragon and the active yang energy associated with it.
This was merged with the tradition of visiting friends and family on boats.
Another suggestion is that the festival celebrates a widespread feature of east Asian agrarian societies: the harvest of winter wheat.
Offerings were regularly made to deities and spirits at such times: in the ancient Yue, dragon kings; in the ancient Chu, Qu Yuan; in the ancient Wu, Wu Zixu (as a river god); in ancient Korea, mountain gods (see Dano).
As interactions between different regions increased, these similar festivals eventually merged into one holiday.
Dragon boat racing has a rich history of ancient ceremonial and ritualistic traditions, which originated in southern central China more than 2500 years ago.
The legend starts with the story of Qu Yuan, who was a minister in one of the Warring State governments, Chu.
He was slandered by jealous government officials and banished by king.
Out of disappointment in the Chu monarch, he drowned himself into the Miluo river.
The common people rushed to the water and tried to recover his body.
In commemoration of Qu Yuan, people hold dragon boat races yearly on the day of his death according to the legend.
They also scattered rice into the water to feed the fish, to prevent them from eating Qu Yuan's body, which is one of the origins of zongzi.
A notable part of celebrating Dragon Boat Festival is making and eating zongzi with family members and friends.
People traditionally wrap zongzi in leaves of reed, lotus or banana forming a pyramid shape.
The leaves also give a special aroma and flavor to the sticky rice and fillings.
Choices of fillings vary depending on regions.
Northern regions in China prefer sweet or dessert-styled zongzi, with bean paste, dates and nuts as fillings.
Southern regions in China prefer savory zongzi, with a variety of fillings including marinated pork belly, sausage and salted duck eggs.
The reason why the Chinese eat zongzi on this special day is because they are considered as a symbol of luck, as the pronunciation of zong is very similar to the pronunciation of zhong (中).
This character has a positive connotation, used in words such as 中? (winning a prize or a mini prize).
 'Wu' (午) in the name 'Duanwu' in Chinese has similar pronunciation as the number 5 in multiple dialects, and thus many regions have traditions of eating food that is related to the number 5.
Realgar wine or xionghuang wine is a Chinese alcoholic drink that is made from Chinese yellow wine dosed with powdered realgar, a yellow-orange arsenic sulfide mineral(it is also known as "rice wine").
Realgarly, it is often used as a pesticide against mosquitoes and other biting insects during the hot summers, and as a common antidote against poison in ancient Asia.
In some regions of China, parents braid silk threads of 5 colors and put them on the their children's wrists, on the day of the Dragon Boat Festival.
People believe that this will help keep bad spirits and disease away.
Other common activities include hanging up icons of Zhong Kui (a mythic guardian figure), hanging mugwort and calamus, taking long walks, and wearing perfumed medicine bags.
Other traditional activities include a game of making an egg stand at noon (this "game" implies that if someone succeeds in making the egg stand at exactly 12:00 noon, that person will receive luck for the next year), and writing spells.
All of these activities, together with the drinking of realgar wine or water, were regarded by the ancients and some today as effective in preventing disease or evil, while promoting health and well-being.
In the early years of the Republic of China, Duanwu was celebrated as the "Poets' Day" due to Qu Yuan's status as China's first known poet.
The Taiwanese also sometimes conflate the spring practice of egg-balancing with Duanwu.
The sun is considered to be at its strongest around the time of summer solstice, as the daylight in the northern hemisphere is the longest.
The sun, like the Chinese dragon, traditionally represents masculine energy, whereas the moon, like the phoenix, traditionally represents feminine energy.
The summer solstice is considered the annual peak of male energy while the winter solstice, the longest night of the year, represents the annual peak of feminine energy.
The masculine image of the dragon was thus associated with the Dragon Boat Festival.
單句
Festival Food: Rice Glue Ball, Zongzi and Moon Cake Spring Festival, Dragon Boat Festival and Mid Autumn Festival are the three important festivals in China.
 People eat different food on these festivals.
 They are rice glue ball, zongzi and moon cake.
The glutinous rice dumplings, or zongzi, are eaten on the Dragon Boat Festival, or the fifth day of the fifth lunar month.
 It is wrapped in bamboo or reed leaves.
 People in different regions use different materials to make it.
 In east China, like Suzhou, Jiaxing and Ningbo, the fillings would be bean paste, chestnut, jujube paste or fresh meat.
 In north China, it would be jujube or preserved fruit.
As a kind of food for festivals, zongzi has been eaten for a long time.
 The folklore goes that people ate it to commemorate a patriotic poet, Qu Yuan.
 It is said that in the 3rd century B. C. , the poet committed suicide because his country had been invaded.
 People commemorated him by throwing glutinous rice, stored in bamboo tube, into the river.
 Later they wrapped it with reed leaves and strings.
 That's how the food developed.
 Some people give it as a present when visiting friends and relatives on the Dragon Boat Festival.
The Mid Autumn Festival falls on the fifteenth day of the eight lunar months.
 People eat moon cakes for family gathering.
 The cake is round, like the full moon, with fillings inside.
 There are some patterns on the surface of the cake.
 During the mid autumn festival, people would place some cake and fruit.
Moon cakes are different in different regions.
 Those made in Beijing, Suzhou, some areas of south Guangzhou and Chaozhou in Guangzhou are most famous.
 The fillings can be made of sugar, jujube paste, bean, ham, fruit, or cream, etc.
 It is also one of the presents that people can take with them when visiting friends and relatives on Mid Autumn Festival.
The Chinese year is marked by a variety of festivals.
 Festival dates are calculated using the lunar calendar, so do not fall on the same day each year in the Western calendar.
 Three of the main festivals are described below.
First month: Spring Festival (Chinese New Year), chun jie 春?
Celebrated on the first day of the first lunar month, the spring festival is the most important festival of the year.
 During the festival, people hold family reunions and honour their ancestors.
 Fire crackers, believed to ward off evil spirits, herald the New Year, and auspicious couplets written on red paper adorn the entrances of houses.
 The Dragon and Lion dance is also performed.
 It is customary to give children money wrapped in a red envelope.
 The Spring Festival comes to an end on the fifteenth day of the first lunar month with the Lantern Festival (deng jie ??).
 Chinese people believe that the lanterns help guide lost spirits toward judgment and reincarnation.
 Traditional rice balls (called yuanxiao 元宵 in the north and tangyuan ?? in the south) are eaten during the Lantern Festival; symbolic of family reunion, harmony and happiness.
Fifth month: Dragon Boat Festival, duanwu jie 端午?
The Dragon Boat Festival, celebrated on the fifth day of the fifth lunar month, commemorates the death of Qu Yuan (屈原, c. 340 -278 BC), a great poet and minister of the state of Chu in southcentral China during the Warring States period.
 Legend says that he drowned himself in the Miluo River in protest at his banishment for opposing court corruption.
 On hearing the news, local fishermen jumped in their boats and raced to rescue him; said to be the origin of the festival’s ‘dragon boat’ races.
 Rice dumplings wrapped in leaves (zongzi 棕子), are eaten during the festival.
Eighth month: Mid-Autumn festival, zhongqiu jie 中秋?
Held on the fifteenth day of the eighth lunar month, this festival is a time for family reunions and remembering distant relatives.
 It is also known as the Moon Festival.
 On the evening of the MidAutumn Festival, the full moon appears larger and brighter than at any other time of the year and gatherings of the full family symbolically reflecting the moon’s fullness take place.
 It is traditional to eat moon cakes (yuebing 月?) which are pastries, round or rectangular in shape, made from lotus seed paste and often filled yolks of salted duck eggs or other fillings. 
 Even though Taiwan is not a part of mainland China, many of the Taiwanese people are from the Han ethnic group and speak Mandarin.
 They also adhere to many of the cultural customs of their mainland counterparts.
 While Chinese New Year, or the Spring Festival, is a large event in Chinese communities throughout the world, the holiday is quite extravagant in Taiwan.
 Chinese New Year is a public holiday according to legislation passed by the Taiwanese government.
he Spring Festival, or chunjie (春?), begins on the first day of the first month according to the Chinese lunar calendar.
 The festival continues until the conclusion of the holiday season on the 15th day of the same month.
 The Spring Festival is a time for family reunions and festivities.
 Many travelers from across the world also travel to Taiwan to celebrate Chinese New Year.
Taiwan has a very unique atmosphere during the Spring Festival.
 Since most of the Taiwanese population is Chinese, many of the shops in Taiwan close during the Spring Festival.
 This causes many business districts of Taiwan to be quiet and empty.
 While commercial areas of Taiwan become calm during the Chinese New Year holiday season, neighborhoods and communities become festive and energized.
 Large festivals are held in many villages, cities, and towns.
 Chinese New Year markets are also erected in the centers of communities.
 These become hubs for social gatherings and commercial transactions during the holiday season.
 Although many Taiwanese shops close during the Spring Festival, most hotels and restaurants stay open.
 This is primarily due to the fact that Taiwan experiences a massive influx of travelers during the Chinese New Year celebrations.
 Most Taiwanese people believe that it would be foolish to pass on the opportunity to earn some quick profits during a peak moment for the tourism industry in Taiwan.
 Because of this, many tourist attractions also remain open during Spring Festival.
Many Taiwanese people celebrate the Spring Festival with various traditional Chinese customs and traditions.
 Since Chinese New Year is one of the most vibrant holidays in Taiwan, all celebrations are oriented around having fun with family and friends.
If you’re familiar with Chinese New Year celebrations in mainland China, you probably know that northern China celebrates the New Year with dragon dances and southern China use lion dances.
 In Taiwan, the dragon of the north and the lion of Canton are both used in Spring Festival celebrations.
 This is likely due to the fact that the first Chinese people to come to Taiwan were from various areas of China.
 This caused people to bring their family and regional customs to Taiwan.
 This represents the fact that Taiwan is a melting pot for Chinese cultures.
While many East Asian holidays are not oriented around food, the Chinese New Year feast is one of the most anticipated occasions in Taiwan.
 Prior to this event, family members, and sometimes friends, will gather in a central location to reunite after a year of work or schooling.
 Together, Taiwanese families will enjoy a large meal of traditional Chinese foods.
 Some of the most popular dishes include pork dumplings, rice, steamed fish, chicken, and noodles.
 This also proves that Taiwan consists of many different Chinese cultures.
 In mainland China, noodles are almost exclusively consumed in Beijing, Shandong, and other northern areas of China.
 Rice is the staple in the diets of people in Canton and southern China.
 While enjoying the feast with their family members, Taiwanese people only speak about positive subjects.
 Chinese traditions state that speaking about misfortune during the Spring Festival will bring poor luck throughout the upcoming year.
In Taiwan, many people purchase gifts for their friends and family members during the Spring Festival.
 The types of items gifted depends largely on age and significance of the recipient, but gifts are often practical.
 For example, a mother may gift cooking supplies to her adult daughter.
 Festival foods and crafts are also often purchased during the Spring Festival.
 All of the items are available in the many Chinese New Year markets that are established during the festival season.
 There are many Taiwanese festivals that allow people to celebrate certain aspects of the Spring Festival.
In the northern Taiwan’s city of Pingxi, the Sky Lantern Festival is one of the most popular events.
 During this festival, thousands of Kongming, or flying paper lanterns, are released into the sky to send wishes to the gods.
 These lanterns are named after Zhuge Liang, a brilliant Shu strategist who fought Cao Cao during the Three States period of China.
 During his time, Zhuge Liang used the lanterns for military communication.
 Other major events in Taiwan include the Bombarding Master Handan Festival in Taitung and the Yanshui Beehive Fireworks Festival in Tainan.
Chinese New Year is a public holiday in Taiwan that allows people to reconnect with their families and enjoy a wide range of festivities.
For centuries, the Taiwanese have continued to practice the traditional customs and holidays of their forebears from mainland China.
 Without a doubt, Chinese New Year is considered the most important of the traditional festivals, as can be seen by the people’s commitment to reunite with their families and continue this legacy when the time arrives.
 One of the greatest things about being Taiwanese-American is my opportunity to enjoy the holiday both abroad and in Taiwan, the experiences of which I will share here.
 Taiwan is a land of religions, ceremonies and spirituality drawn from Buddhism, Taoism and Confucianism, as well as Tribal and Chinese folk religions.
 Taiwan is an island, so we have a lot of coastline to explore.
 Diverse coastal scenery includes sandy beaches, coral reefs, majestic cliffs and unique rock formations.
 As far as activities, we'll take you surfing, snorkeling, scuba diving, wind surfing, and of course swimming.
 The Dragon Boat Festival, or Duanwu Festival, is celebrated every year on the fifth day of the fifth lunar month.
While seeing family and friends, watching the dragon boat races and eating rice dumplings make for fond memories, the holiday actually celebrates a very somber occasion.
 Qu Yuan, a famous Chinese scholar, who lived in the third century BCE and served the king of Chu, was smart – perhaps too smart.
 His peers, tired of his wisdom, accused him of crimes that he didn’t commit.
 Exiled and distraught, Qu Yuan composed many hateful and sorrowful poems before drowning himself at the age of 61.
Chu citizens jumped in their boats to search for him, but it was to no avail.
 Dragon boat races are held to this day in order to remember this failed attempt.
 Teams go against one another and try to reach a flag first, but before the race begins, each boat must have its dragon’s eyes painted in order to bring the boat to life.
 After failing to save Qu Yuan, citizens took to throwing rice into the river in which he had drowned.
 They wrapped the rice in leaves with hopes that it might keep the fish from eating his body.
 While no one today still hopes of saving Qu Yuan’s body, the rice wrapped in leaves has become a tradition and is eaten during the festival.
 The food is called Zongzi.
The Dragon Boat Festival, meant to honor the dead and be enjoyed by the living, has other fun traditions that are observed.
 For instance, it has been said that if you can balance a raw egg on its end at noon on the fifth day of the fifth lunar month, you will have good fortune for the remainder of the year.
 Zongzi is wrapped in triangle or rectangle shapes in bamboo or reed leaves, and tied with soaked stalks or colorful silky cords.
The flavors of zongzi are usually different from one region to another across China.
 According to Chinese custom, the seventh month of the lunar calendar is Ghost Month.
 On the first day of the seventh lunar month, the gates of the Hell are opened and it is believed that ghosts from the underworld are allowed a month of freedom and haunt in the living world.
 During the eerie month, there are also some taboos that people follows.
 For example, to avoid whistling, swimming, surgery, buying cars and hanging clothes up to dry, going out at night.
 It is also important that addresses and name are not revealed to the ghosts.
 These taboos seems illogical but people always try best to follow the rules as we really want to avoid any trouble that’s ghost-related.
Chungyuan PuDu(中元普渡) is held on the 15th day of the seventh lunar month, we call Ghost Festival or Chungyuan Festival(中元節).
 Incense and food are offered to the ghost to avoid them visiting homes and spirit paper money is also burnt as an offering.
 Believers also think that the food for the ghost will bring them good luck if they eat it.
 Numerous traditional activities are held around Taiwan during Ghost Month, the best-knownings are releasing Water Lanterns in Keelung and Chiang Gu(搶孤) in Yilan Toucheng.
 The purpose of releasing Water Lanterns in custom is to help light the way for the lost spirits in the water, call the spirits to come on land to enjoy the offerings and pray for the early reincarnation of these spirits.
 It is also said that the farther a lanterns, the better the fortune that the clan it represents will enjoy in the coming year.
 Chiang Gu(搶孤) is a pole-climbing competitions for the food offerings.
 The competitor need to climb a pole with 12 feet height and oil spreading on the surface by the fastest speed they can.
 It is always the liveliest scene during Ghost Month.
 On the 29th day of the seventh lunar month, the last day of the seventh lunar month, the Ghost month will be the end and we said “Closing of the ghost gate".
 People will prepare cooked food outside the doors of their homes as a farewell dinner for the lonely ghosts.
 However, some lonely ghosts might be unwilling to return to the underworld.
 Temples will invite ChungKuei who is a deity who protects humans from evil to escort the ghost and keep people safe and peace.
 No matter you believe the ghosts or not.
 Hope this article will help you understand Taiwanese culture more and enjoy the interesting story.
The Lantern Festival ( yu?nxi?oji?) or (Yuanxiao Festival), also known as the Shang Yuan Festival ( pinyin: sh?ngyu?nji?) is a Chinese festival celebrated on the fifteenth day of the first month in the lunar year in the Chinese calendar.
 It is not to be confused with the Mid-Autumn Festival, which is also sometimes known as the "Lantern Festival" in locations such as Singapore, and Malaysia.
 During the Lantern Festival, children go out at night to temples carrying rabbit-shaped lanterns ( pinyin: t?zid?ng) and solve riddles on the lanterns ( pinyin: c?id?ngm?).
 It officially ends Chinese New Year.
During Emperor Qianlong’s reign in the Qing dynasty, Guangji Temple was the center of belief forneighboring villages, and the villagersformed the Song-JiangBattle Array for the Temple’s fair and festival.
A dancing lion always performsa dance before the Song-Jiang Formation sets off.
Given that the lion dance art and imposing power of the Song-Jiang Formations from the villages of the Qianzhenarea always outmatch those from other areas, it has eventually won the fame of “the Best Lion Dance”.
As entertainmentduring the spare time of busy farm work, farmers used to use the bamboo dustpan as the lion’s head whenperforming the lion dance, forming a unique lion dance culture.
In 1935, the “Si Shi Jia ” (Lion Dance Festival) was officially named after the former name of theShijia region (now combined under the Qianzhen District) .
Centering on Guangji Temple, Qianzhen District has been driving the “One SpecialtyPer District” campaign in accordance with the City Government’s policy.
The 2004 and 2005 “Lion Dance Championship” was hosted by Guangji Temple.
Mid-Autumn Festival, also called “Moon Festival”, is celebrated in Taiwan and elsewhere in East Asia to mark the fall harvest and, to some, to offer traditional worship to the moon.
It comes on the 15th day of the 8th month of the Han Chinese calendar and falls on the full moon of either September or October on the Western calendar.
Ethnic Chinese have been observing this holiday since at least the 10th Century B.C., and it has been extremely popular since the Tang Dynasty of the 7th through 10th Centuries A.D.
Today, it is a day of joining together to feast and fellowship with family and friends, as much as a time when farmers give thanks at local temples for the recent harvest.
Many also go to temples to pray for specific requests, such as to marry one’s desired partner, to give birth to a child, or to live a long, prosperous life.
There are many ancient myths and fables connected to Mid-Autumn Festival.
These are often told to young children this time of year, but adults “are allowed to” listen in too if they wish.
You may hear about the sun and moon being married and the stars being their children, about the full moon being pregnant and the crescent moon having shrunk after giving birth.
You will easily encounter the legend of Chang’e, but it’s hard to say which version, for there are many.
One version has Chang’e the wife of a cruel emperor who is planning to drink a magic elixir that will make him live forever.
To prevent everlasting oppression, she drinks the elixir herself and then flies off to become the moon goddess.
Unlike China, Taiwan is not known for paper lantern displays during Mid-Autumn Festival, these being mostly put out on the 15th day of Chinese New Year, but travellers will still find much to do, such as the following:
Go moon gazing.
You don’t have to worship the moon to look at it and enjoy its “effulgence.”
In Taipei, some of the best moon-gazing spots are the Danshui Fishing Wharf and Daan Park.
Outside of Taipei, try Sizhi Bay or Wuling Farm near Taichung.
The moon, you will find, is quite beautiful to behold, and some years the moon is extra large, when in the closest point of its orbit to Earth, or blood red, during a full lunar eclipse.
Eat mooncakes, which are so commonly eaten this time of year that Mid-Autumn Festival’s alternate name is “Mooncake Festival.
” Mooncakes are always round, like the moon, but the come in endless varieties.
Traditional flavours have the taste of roast pork and five kinds of seeds or nuts.
Sometimes, they hide a duck egg or yolk inside.
You will find there are also a plethora of newer flavours, such as green tea or chocolate.
Have a barbecue, like the locals will be doing.
Many will set up the grill on the sidewalk that fronts their home, but others will go to parks to feast on things like roast boar, pomelos, mooncakes (of course), and perhaps, cassia wine.
In Taipei, there are some 20 riverside parks to barbecue at, some of the best being Dajia Riverside Park and Huazhong Riverside Park.
If in Taiwan during Mid-Autumn Festival, you will find there are many enjoyable events to participate in that will help you better understand Taiwanese culture.
The fifteenth day, also called "the Lantern Festival or shang yuan festival", every family to organise dishes, drink for the New Year.
To make new balls, amusement activity reached a climax.
From the Chinese New Year began in the 15 all over, continuous activity, firmness with amusement lamp, lion dance which is, some in the first month, two from the start activities.
LongDeng and the lions before Posting, will come to advance, to give a red envelope after they eat, snacks.
LongDeng, the lions, except to houses, but also to the performance of the village temple and the ancestral temple to the gods and ancestral happy New Year.
After the Spring Festival on the first market time, each LongDeng, ship lamp, the lamp, want to set performance, called "open market".
Fifth for Dragon Boat Festival, every family to buy meat, kill ducks, rice dumplings, fruit unto the feast.
Do m Dragon boat is a large sections such, in many places in the outside of work will be home for the holiday.
August 15 Mid-Autumn festival, commonly known as the "BaYueJie", is also a large sections such.
Eat moon cakes, moon, a reunion.
Families getting hitched to drink, buy pork, kill chickens, ducks, fruit unto the feast.
Do m The Mid-Autumn evening, a family reunion after dinner to celebrate the feast, eat moon cakes.
The Dragon Boat Festival began as an occasion to drive off evil spirits and to find peace in life and today it is a key event with thousands gathering to watch the excitement.
Teams compete to the sound of beating drums and rowers win by grabbing the flag at the end of the course.
Many traditional customs accompany the festivities such as the drinking of Hsiung Huang wine and children are given fragrant sachets, both of which are thought to ward off evil spirits.
A culinary highlight of the festival is eating the traditional dish Zongzi which are glutinous rice dumplings wrapped in bamboo leaves.
The calendar will also feature the Ghost Festival on 09 September in Keelung.
Traditionally, it is believed that ghosts haunt the island of Taiwan for the entire seventh lunar month from dawn on the first day of the month, when the gates of the netherworld open, ending on the 29th day of the month, when the gates close.
The Ghost Festival combines the Buddhist Ullambana Festival and the Taoist Ghost Festival, both of which honour dead spirits.
Highlights of the event include folk-art performances, the opening of the gates of hell and the release of burning water lanterns.
During the festival, families make offerings to their ancestors and ghosts of the underworld.
Other calendar highlights include the Taiwan Cycling Festival on 11 November taking place in Yilan, Hualien and Taitung countries.
Since its debut in 2010 the festival has brought together world-renowned cyclists for a top-class international event.
The two main events are the Formosa 900 and the Taiwan KOM, however there are a number of smaller cycling events also taking place for amateur cyclists.
As we enter the month of June, we find ourselves already in the middle of the year.
However, according to the Chinese lunar calendar, the fifth month just begins and the Chinese people are preparing to celebrate another traditional festival-the Dragon Boat Festival.
Sometimes if Mid-Autumn Festival is near National Day, Mid-Autumn Festival may be in the golden week.
There are large firework displays in the cities.
The ancestors are given due reverence at the festival.
When guests arrive they should bring along food or a gift; these can take the form of packets of 'lucky paper money' in red envelopes.
Fish and Jiaozi (dumplings) are often eaten, the character for fish yu ?sounds the same as the character yu余meaning 'surplus; abundance' so a dish of fish has a lucky connotation.
The entrance to a house is often decorated with two couplets written in calligraphy on red paper on either side of the entrance.
Traditional fairs are held outside temples selling all sorts of small gifts and decorations during the holiday.
The Lantern festival is on the first full moon after the New Year and marks the very end of the Spring Festival.
Lanterns are lit and in places very long paper dragons parade the streets.
The lanterns lit the way for the ancestral spirits to go home to their tombs after joining the family for the festivities.
Great creativity was used in lantern design, which can include moving parts; some towns had riddles painted on them to entertain the people.
Tangyuan (glutinous rice balls) are eaten and fir branches placed above doors.
The traditional lion dance was originally tied to just this festival but now are seen more generally throughout the year.
In the countryside diseases were removed by making a procession out of the village, with many firecrackers scaring away and taking the illness with it.
Children, often in scary masks, used to put on little stage shows and pantomimes.
The seventh month of the traditional Chinese calendar is associated with ghosts.
The Hungry Ghost festival in the middle of the seventh month is the main festival but some people also mark the start of the month - Ghost Gate.
The ghost month is considered unlucky, spirits wander around for the whole month and so new projects and enterprises should not be started.
One superstition of relevance is to avoid sticking chopsticks vertically into the ricebowl as this invites in the ghosts.
It is a minor festival and not a public holiday.
This festival is held on the 15th day (full moon) of the 7th lunar (ghost) month.
It is also known as the Mid-year festival (中元?zh?ng yu?n ji?).
Traditionally the sufferings of ancestors are appeased by making offerings of food or incense at the ancestral shrine.
Prayers are said for spirits who have no families to venerate them.
Paper flags are hung over doorways to keep out the hungry ghosts.
The Autumn Moon Festival takes place at full moon in the 8th lunar month (15th day), it marks the end of harvest.
Lanterns are lit and moon cakes are cooked and consumed in large numbers.
It celebrates Chang'e the goddess of the moon and particular the romance with the archer god Houyi.
Traditionally, spirits of the dead came forth to feast on the fruits of summer harvest.
People would climb hills and mountains to watch the rising of the full moon with the greeting 看月亮K?n yu?liang‘Look at the bright moon!’
The night before the Chinese New Year, Chinese families like many other parts of the world gather around to enjoy some time together.
They celebrate it by eating a special New Year’s Eve dinner and after, they would talk and play some games till midnight.
The dinner served on New Year’s Eve is a reunion dinner and is considered the most important meal of the year for Chinese families.
Lantern Festival in China is a festival celebrated on the fifteenth day of the first month in the lunisolar year in the lunar calendar marking the last day of the lunar New Year celebration.
Falling on the 5th day of the 5th month according to Chinese lunar calendar, the Dragon Boat Festival is one of great significance.
It has been held annually for more than 2,000 years and is notable for its educational influence.
The festival commemorates the patriotic poet Qu Yuan (340-278 BC), and also acts as a chance for Chinese people to build their bodies and dispel diseases.
Many legends circulate around the festival but the most popular is the legend of Qu Yuan.
Chinese mid-autumn festival (中秋?, Zh?ngqi? ji?) is the second biggest festival a the Chinese New Year.
It is held on the 15th of August according to the Chinese Lunar calendar.
Lasting 3 to 7 days, depending on the year and where it falls in the calendar.
In China and the surrounding regions, people get a day off for the festival if it falls on weekdays.
Other countries have different ways of celebrating that that may not grant a festival day.
There are many legends behind the Mid-Autumn festival but the most popular one is surrounded by romance, sacrifice, and honor.
The story is beautiful, romantic and inspirational.
It starts with a young woman named Chang E (嫦娥, Ch?ng’?).
She was an immortal who was cast down to earth to live in a poor farm family.
At that time, there were 10 suns in the sky that kept getting hotter and hotter.
Chang E became friends with a young hunter from the village named Hou Yi.
Chang E convinced the young hunter that he was the strongest and bravest archer around so that he would shot down 9 of the 10 suns in order to save his village.
After doing so, the two married and became king and queen.
Obsessed with immortality, Hou Yi ordered to concoct an elixir.
It was a pill and Chang E swallowed it either accidentally or purposefully and fled.
Her angry husband attempted to shoot her down as she floated to the moon where she stayed and lived the rest of her life.
She is also the Goddess of the Moon.
She and her husband, Hou Yi are also part of the reason for the Ying and Yang of Chinese culture facts because her husband eventually settled into life on the sun.
There’s another version of the story that Hou Yi didn’t go astray after he became a king.
And Hou Yi asked for the elixir because he wanted to live with his beloved wife Chang E forever.
In fact, when Chinese children are told the stories about Hou Yi, most of them heard of “Hou Yi is a good man who loves his wife so much” version.
There are a total of four famous myths about Chinese mid-autumn festival that Chinese children have heard since they are little, including the story of Hou Yi, Chang E, Jade Rabbit and Wu Gang chopping the tree.
Read complete four stories in Chinese Mid-Autumn Festival Myths and The Meaning Behind them.
The moon festival or Mid-Autumn festival surrounds the moon and Chang-E.
So, the majority of the activities are focused toward those two things.
But some of the activities are family oriented and are meant to be enjoyed at the home around family and friends.
The activities are as follows; 
There is a symbol of sacrifice that is made by setting up a feast for the Moon Goddess using mooncakes and other traditional foods.
Appreciate the moon- this is significant because it symbolizes family reunion.
The children make colorful lanterns as decorations.
Making an effort of having a family dinner, is a big part of the festival.
The Mid-Autumn/Moon Festival is usually when most families plan a family reunion with distant relatives.
Giving gifts is a tradition as well as an activity done at the festival.
It depends on the region you are celebrating in.
As it is not just a traditional Chinese festival, the Moon festival is also a public holiday.
So many people, not just festival goers travel short distances as a way to make their own traditions by going on a short train ride.
They usually sold out the train tickets solid for the 3-day holiday.
Lastly and most popular among younger people, is shopping during the festival as there are plenty of discounts and sales.
You can participate in different activities depending on the region you are celebrating in.
Traditions no matter what country you are from or celebrating in are important to the culture and understanding of a people from different ethnic backgrounds.
Following in the Chinese tradition for the Mid-Autumn festival, the major offerings for the sacrificial ceremony are mooncakes.
Eating and sacrificing moon cakes tradition started during the Yuan Dynasty (1271 – 1368 AD) .
It started as a way to pass messages or notes between leaders and subordinates.
They passed out the mooncakes with the messages inside as gifts during the mid-autumn festival.
And thus, the tradition began of gift giving and mooncakes.
In addition, the more popular mooncake tradition, each region or area that celebrates the festival, hold their own special tradition.
In Taiwan, besides moon appreciation and eating mooncakes, Taiwanese people eat pomelo (柚子 Y?uzi), and most special, barbecue!
Barbecue can be the most iconic and unique mid-autumn activities.
Also, children would wear on the skin of the pomelo as a hat just for fun!
Pomelo is an iconic fruit for the mid-autumn festival in Taiwan.
Make your children (or pet) a new hat with the skin of pomelo on the mid-autumn festival!
Attending the Mid-Autumn festival will bring a plethora of tradition and activities that are sure to please the senses.
Every region has a unique tradition and Chinese superstitions which are special to them.
2017 This year’s festival begins on Wednesday, October 4, 2017.
The festival will be a 7-day event that the people attending will get a 3-day public holiday as well as the weekend to celebrate.
This occurs about every 3 years; on the day of the month when the moon is at the brightest and roundest.
This symbolizes togetherness and brings families together to celebrate the rice harvest.
It’s a good luck, togetherness festival that the whole family can enjoy.
Mooncakes are a staple at the Mid-Autumn festival.
Every home celebrating uses them as a sacrifice to the moon in honor of the festival.
Traditional mooncakes are little round cakes.
You can even make them from scratch or buy from a bakery.
They have a filling that varies from an egg yolk filling to berries and nuts.
They are delicious and also have lots of calories.
A dessert many Chinese families in China look forward to.
On top of the mooncakes, there are also a lot of other variety of food at a Moon Festival.
Red Dates with War, Osmanthus (flower) Blossom Syrup which is usually a feast.
The feast setting is usually on a table under the direct moonlight or if it is cloudy in a place which has the direct moonlight.
These foodies are for the appreciation of the moon, the rice harvest and the moon goddess, Chang E.
No matter where the festival is celebrated, it is always accompanied with bright colors, great smells, and amazing food.
It is a staple holiday in Chinese culture, that most families use as a family reunion.
The 3-day festival is a way to thank and appreciate the moon goddess and her blessings as well as the Rice Harvest.
Get out there and celebrate the Mid-Autumn Festival, it is almost here and the perfect way to spend the weekend with family.
I hope you have fun Chinese language learning through the Chinese Culture!
Chinese New Year is celebrated in the first month of the Chinese calendar.
Chinese New Year starts on the first and ends on the fifteenth day and is the most important holiday in China.
Chinese New Year is celebrated with family and friends.
Because most Chinese people work away from their family this will result in large migrations throughout the entire country.
Chinese New Year will be celebrated on February 16th.
Most factories in China will be closed in week 7 and 8.
Orders placed after week 3 may be delayed as a result.
Fast turnaround orders will not be directly affected.
Will a delay cause issues in your production process?
Let us know so we can work with you to find a solution.
In 2019 Chinese New Year will be celebrated on February 5th.
Most factories will be closed in week 6 and 7.
Stunningly beautiful lanterns from cities as wide apart as New York, London and Beijing.
Chinese traditional festivals, with various forms and rich contents, are an essential part of the Chinese nation’s long history and culture.
The formation of popular festivals is a process of long-term accumulation and cohesion of the history and culture of a nation or country.
The composition of the ancient traditional celebrations of the Chinese is related to the original beliefs, sacrificial culture, celestial phenomena, calendar, and other humanities and natural, cultural contents.
They cover philosophy, humanities, history, astronomy, and other aspects, and contain deep and rich cultural connotations.
The traditional Chinese festivals developed from the time of ancient ancestors clearly record the vibrant and colorful social life and cultural content of the Chinese nation, and also accumulate extensive and profound historical and cultural connotations.
Chinese traditional festivals mainly include Spring Festival, Lantern Festival, Dragon Head-up Festival, Qingming Festival, Dragon Boat Festival, Qixi Festival, Mid-Autumn Festival, Double Ninth Festival and New Year’s Eve.
The Spring Festival, the Lunar New Year, is the first year of the year, in the traditional sense of the “New Year’s Day.
” Commonly known as New Year, New Year, New Year, New Year, New Year, New Year, New Year, New Year, etc., verbally also known as the year, celebration, New Year, New Year.
Spring Festival has a long history, which evolved from the first year of prayer in ancient times.
During the Spring Festival, various activities were held throughout the country to celebrate the Spring Festival, which was full of joyful and joyous atmosphere.
These activities were mainly composed of old new cloth, welcome to the jubilee, worship God and ancestor worship, and pray for the harvest year.
The celebrations during the Spring Festival are extremely rich and varied, including lion dancing, floating colors, dragon dancing, god-wandering, boating, annual events, temple fairs, flower shopping, flower lanterns, gongs and drums, firecrackers, fireworks, spring festival, stilts, dry boat racing, Yangko twisting and so on.
Lantern Festival, also known as the Lantern Festival, the Little January Festival, the Lantern Festival or the Lantern Festival, is the fifteenth day of the first lunar month every year.
The custom of Lantern Festival has been based on the custom of watching lanterns warmly and festivally since ancient times.
Traditional customs include going out to enjoy the moon, lighting lamps, guessing riddles, eating Lantern Festival and pulling rabbit lanterns.
In addition, in many places, traditional folk performances such as dragon lanterns, lions, stilts, boating, Yangko twisting and Taiping drum playing have been added to the Lantern Festival.
Dragon Head-up Festival, also known as “Spring Farming Festival,” “Farming Festival” and “Spring Dragon Festival,” is a traditional Chinese folk festival.
Every year on the second day of February in the lunar calendar, it is said that the day when the Dragon rises is a traditional festival in China.
Celebrate “Dragon’s Day” to honor the dragon and pray for rain, so that God bless the harvest.
February 2, in southern China, is the birthday of the land god, known as “Land Birthday.
” To “warm the life of the land god,” some places have the custom of holding “Land Fair.
” Families collect money to celebrate the birthday of the land god, burn incense and sacrifice to the land temple, beat gongs and drums, and set off firecrackers.
Chinese people believe that the dragon is a lucky thing, and the dominant weathering rain, and the lunar calendar “February 2nd Dragon Rise” is the day when the Dragon wants to ascend to heaven.
Qingming Festival, also known as Ta Qing Festival and Xing Qing Festival, is at the turn of mid-spring and late spring.
Qingming Festival is a traditional spring festival.
Tomb-sweeping and ancestor-remembering is a beautiful tradition left by the Chinese nation for thousands of years.
It is not only conducive to promoting filial piety and family ties, awakening the collective memory of the family, but also developing the cohesion and identity of family members and even the nation.
Dragon Boat Festival, the fifth day of May in the lunar calendar, is one of the four traditional festivals in China.
Up to now, various customs and activities of the Dragon Boat Festival are still prevalent.
There are many customs of the Dragon Boat Festival.
Although there are different laws in different places, dumplings making and dragon boats competition are common customs.
Every year around the Dragon Boat Festival, many special festival activities, such as wrapping dumplings, hanging mugwort and calamus, dragon boat, nine lions worship elephants, and drought dragons, are carried out all over the country.
They have both traditional customs and innovative elements, which can be described as unique and colorful.
Qixi Festival, the seventh day of the seventh lunar month.
On this day, women will visit their intimate friends, worship the weaver girl, consult the scarlet girl, and beg for luck.
“Qixi Festival” is the earliest love festival in the world.
It is a traditional folk custom to sit and watch the morning glory and the Vega Star on the night of Qixi.
Countless loved men and women in the world will pray for their happy marriage this evening.
The Mid-Yuan Festival, namely the half-sacrifice of ancestors in July, is also called Shigu, ghost festival, Zhaigu and local official festival.
The festival customs mainly include sacrificing ancestors, setting out river lanterns, sacrificing dead souls, burning paper ingots, etc.
Folk sacrifices to ancestors, offering sacrifices to ancestors with new rice, and reporting Qiucheng to ancestors are a kind of traditional cultural festival in memory of ancestors.
Its cultural core is to respect ancestors and do filial piety.
Mid-Autumn Festival, also known as the Moon Festival, began in the early Tang Dynasty, prevailed in the Song Dynasty, to the Ming and Qing Dynasties, has become one of the traditional Chinese festivals with the same name as the Spring Festival.
Since ancient times, the Mid-Autumn Festival has the customs of offering sacrifices to the moon, observing the moon, worshipping the moon, eating moon cakes, drinking osmanthus wine and so on.
It has been handed down for a long time.
The Mid-Autumn Festival, with the reunion of the full moon as a sign of people’s gathering, pins on homesickness, hoping for a bumper harvest and happiness and becomes a vibrant and precious cultural heritage.
New Year’s Eve, originally meant “New Year’s Eve,” refers to the end of the year in addition to the old cloth new day, the past year to this end in addition to the new year.
New Year’s Eve is of particular significance in the hearts of Chinese people.
On the most important day at the end of this year, wanderers who wander farther away are also rushing home to reunite with their families, saying goodbye to the old age in the sound of firecrackers, and greeting the new spring with fireworks all over the sky.
On the day of New Year’s Day, the people paid particular attention to it.
Every household was busy or cleaning the courtyard, removing the old cloth and the new cloth, decorating the lanterns, welcoming the ancestors home for the New Year, and offering sacrifices to New Year’s Cake, Three Vegetables and Three Teas and Five Wines.
The Chinese believe that death is not the end, that ghosts and spirits co-exists with human beings at all times, only noticed when they are disquiet, and need to be put to rest.
It’s no surprise that the Chinese have many demon slaying deities.
The god that has by far enjoyed the most popularity, and endured the test of time, is Zhong Kui.
You may think that as Chinese New Year comes to an end, there isn’t much else you can comfortably tap into to enjoy until the next one.
That is not the case.
Chinese life, even in the 21st century, is closely connected to their traditional festivals, of which there is a full calendar all year round.
Here are five more you can look forward to after Spring Festival.
Summer is here again, with Dragon Boat Festival to mark it.
This year, instead of delivering my culture tweets, I’ve put together an article, so that people interested to look further can read more about it.
After all, Dragon Boat Festival is China’s major traditional summer festival, and probably the second most well-known celebratory event after Spring Festival.
Tomb-sweeping Day is a festival that descendants offer sacrifices to ancestors,the traditional activity is to pay respects to the dead person at his tomb.
It’s on April 4th and lasts 3 days.
It’s said that the origin of Tomb-sweeping Day can trace back to the ancient periods.
The emperor,generals,and the prime ministers etc had the ceremony of worship to the dead emperor on April 4th,later folk followed and commemorated their dead relatives on this day.
Ages followed and became a fixed custom of Chinese nation.
According to the old tradition, people will carry food and fruit, drink,paper money and other items and put the food in front of the grave of relatives，fire the paper money,add new soil for the grave and fold a few Liu branches in the new green insert in the grave,then kowtow and worship,finally eat the food and go home.
Besides sweeping the bomb in this period,there are also a series of custom sports activities in Guilin like hiking, swing, playing Cuju,inserting Liu and so on.
In addition,one or two days before the Tomb-sweeping Day,Guilin people will also prepare Tzung Tzu, glutinous rice cake, mugwort leaf Baba, lotus root and water chestnut cake etc.
This festival,which has both bitter tears of worship and the sound of laughter of spring outing,is a distinctive holiday.
The Mid-Autumn festival,which is also called Moon festival,originate from the ancient people's worship of the moon,legend has it that on August 15th the moon will be full round around the year and because it’s both in the middle of autumn and August,so it’s called the Mid-Autumn festival.
And full moon symbolizes reunion and entrust with the people's thought of love to relatives especially those who are away from home.
Otherwise,people like to present moon cakes to relatives,colleagues,or neighbors etc to send their braw blessings before the festival.
And on the night of this day,relatives get together to enjoy the glorious full moon with eating moon cakes and fruits,meanwhile reposing their endless love to life and the desire for a better life.
During the Mid-Autumn festival many places in Guilin will hold varied activities for citizens,such as theme park visiting,lantern riddles,knowledge contest about the Mid-autumn festival,praying moon with grapefruit incense etc.
When it comes to grapefruit incense,almost no one knows its origin,however long time ago.
The New Year celebration actually lasts for 15 days and finishes on the Lantern Festival on 2nd March 2018!
Our moon is especially beautiful at this time of year.
Mostly we don’t much notice the rising and setting of the moon, but astronomical events do tend to force it on our attention in September-October.
Chinese love an excuse for families to get together, as we have noted before, and eat special food and have fun.
They have many festival days, often based on the traditional farming calendar.
Moon Festival, also known as Mid-Autumn Festival, is the time of the second.
In September university tutors in England who have international students from Taiwan may be pleasantly surprised to receive unexpected presents, for the 28th of September is celebrated in Taiwan as National Teachers’ Day.
The role of teaching is honoured in Chinese society, although as so often with respected occupations everywhere, that does not mean that teachers are well-paid or powerful.
On this day pupils show their respect and thanks to their teachers for their education, and this is usually well-deserved since education in Taiwan, as in many Asian countries, is generally excellent.
Children may also perform little ceremonies or concerts.
The Hungry Ghost Month (鬼月, Gu? Yu?) is a traditional Buddhist and Taoist holiday held in Asian countries during the seventh lunar month.
This is usually celebrated in August or September, depending on the lunar calendar.
The highlight of the month is the Hungry Ghost Festival, which this year is held on Saturday 11th Augus.
A great day for Chinese families, and one of the most popular Chinese festivals, takes place today on April 4th It’s called ‘Tomb Sweeping Day’.
It’s also called ‘Clear Brightness Festival’ or Qing Ming, and it has many meanings for Chinese people.
Qing Ming combines two important traditions of Chinese life.
Respect for ancestors is an ancient tradition, and Tomb Sweeping Festival dates back at least 2500 years.
It is celebrated in various ways throughout Chinese communities in Asia.
It is a public holiday which can involve everyone in a community
15 days after the first day of the new lunar calendar (Chinese New Year’s Day) greets us with another, widely celebrated festival – The Lantern Festival, or Spring Lantern Festival.
The Lantern Festival is the second most significant celebration in the lunar calendar (after Chinese New Year) and is a celebration of the coming of light.
There are other, varying beliefs surrounding Lantern Festival but the one that I like to follow is the belief surrounding the escape from the darkness of.
We have selected two beautiful legends from the Chinese Mid-Autumn Festival.
Both stories are about beautiful ladies.
The first one is a bit sad and is about a lady called Chang E.
The second one is a happy story about a princess called Nong Yu.
You can find Nong Yu’s story in our book Dragon Tales: Stories of Chinese Dragon.
It is called ‘A Good Son in-law’.
The Moon Goodness: Chang E
Mid-Autumn Festival, or otherwise known as ‘Moon Festival’ is the harvest festival celebrated by Chinese during the 8th Month of the Chinese Calendar.
It is interesting to find that eastern and western calendars differ, for example; According to Chinese lunar calendar, the 8th month is the second month of Autumn.
In the UK Autumn begins in September (the 9th month for westerners).
Moon Festival is generally celebrated on 15th day of the 8th month.
This is because it is calculated to be the middle.
Friday 17th August 2018 marks the date of The Qixi Festival.
This Chinese festival celebrates the annual meeting of the cowherd and weaver girl in Chinese mythology.
It falls on the seventh day of the 7th month on the Chinese lunar calendar and is now more commonly known as ‘Chinese Valentines day, or Double Seventh Festival (On double Seventh day).
The festival originated from the tale of The Weaver Girl and Cowheard, a romantic legend of two lovers, Zhin? (the Weaver Maid) and Niulang.
The New Year starts on the first new moon after the sun enters the constellation of Aquarius.
This system helps to keep the lunar calendar in step with the solar calendar.
The traditional farmers’ work calendar divides the year into twenty-four fortnightly periods starting from New Year.
Did you know that the most important meal of the whole year for Chinese people is the family reunion dinner on Chinese New Year’s Eve when every member of the family tries to.
Also known as the Lunar New Year, it falls on the first day of the lunar calendar.
In fact, the origin of this highly celebrated festival is rather interesting.
According to the ancient Chinese legend, there was a beast named “Nian” that liked to hunt and eat humans in the Spring.
Since the beast was very fierce, it remained a serious threat to the village.
Fortunately, an elderly fellow happened to discover that Nian actually feared the sizzling sound of burning firecrackers and got extremely irritated by the red color.
In order to prevent Nian from entering the village again, people stuck red papers at their doors and set off firecrackers whenever the beast came near the village.
After the great effort in driving Nian away, the beast never showed his face again.
In order to celebrate their survival, people made great feasts during Spring regularly and they called that “Guo Nian”, meaning the threat caused by Nian was finally passed.
At present, it remains a tradition for the Chinese to celebrate Chinese New Year.
On New Year's Eve, family members would have a big meal together and say their blessings to everyone.
Red packets (lucky money inserted in red envelopes) are given to children on New Year's Day and it is customary for people to wear new clothes and shoes on this special day of the year.
This special occasion marks the end of the Lunar New Year and it takes place on the fifteen day of the first month according to the lunar calendar.
Legend said that some townsmen accidentally killed the goose that belonged to the Emperor of the Heaven.
The emperor was furious and planned to burn the entire town down.
A kind-hearted fairy taught the people in that town to hang lanterns everywhere to avoid the upcoming disaster and they followed her advice.
With the lanterns on, it looked like the town was on fire.
Thinking that his goose already got avenged, the Emperor of the Heaven decided to abandon his original destruction plan.
In order to show their appreciation to the fairy, people continued to hang lanterns the same time every year; besides, lion and dragon dancing and riddle solving also became parts of the celebration components.
Nowadays, besides watching the lion and dragon dancing performances, hanging lanterns and solving riddles, the Chinese eat special dumpling made of sweet black sesames in celebration of the festival.
In fact, the sweet dumpling- eating tradition could be dated back to the Han Dynasty (206 BC- 221 AD).
The sweet dumplings are also called “Yuan Xiao and they were believed to be named after a palace maid during Emperor Wu Di's reign.
Since the dumpling-eating tradition is deep-rooted, the Lantern Festival is also known as Yuan Xiao Jie.
This festival takes place during the fifth term of the lunar calendar.
It is established to tribute Jie Zitui, one of the loyal officials of the Jin Kingdom during the Spring and Autumn period (770-475 BC).
According to Chinese history, the King of the Jin Kingdom had a kind-hearted and talented son named Chong Er.
Fearing that he would succeed the throne, the emperor's concubine accused him of treason and Chong was forced to flee together with some officials.
They escaped and managed to hide themselves in the mountains, but found no food.
After starving for a few days, Jie Zitui cut his flesh out for Chong to eat.
Chong burst into tears and promised to be a dutiful ruler once he returned to the Jin Kingdom.
After being in exile for three long years, Chong was able to return to his motherland following the wicked concubine's death.
He rewarded all his followers, but forgot all about Jie.
When Chong finally located the Jie the loyal official, he refused to accept any rewards.
In order to force Jie out of his residence, Chong ordered some soldiers to set the place on fire.
However, there was still no sign of Jie.
Three days later, the loyal official and his mother's bodies were discovered.
Chong wept in regret and ordered the whole Kingdom to mourn and show respect to Jie.
In addition, a decree was passed to forbid cooking with fire a day before Jie's death; that was the reason why the Qing Ming Festival is also known as the Cold Food Festival (Hanshi Jie).
Currently, Chinese usually visit their ancestors' graves during the Qing Ming Festival to pay respect.
In addition, they will sweep and clean the grave, remove excessive weeds, burn ceremonial currencies and repair gravestone engravings.
The celebration is on the fifth of the fifth month of the lunar calendar in the honor of the patriotic poet Qu Yuan.
He was an official of the imperial court in the state of Chu during the Warring States Period (475-221 BC).
At that particular stage, the aggressive ruler of the state of Qin was planning to conquer the six other warring states and unify China.
Qu advised the Chu ruler to avoid direct conflict with the state of Qin, but the ruler refused to listen.
Due to the false accusations made by Qu's political rivalries, the Chu King started to distrust Qu and dismissed him from the imperial court.
The patriotic poet drowned himself in the river after receiving the news that the state of Chu was defeated by the state of Qin.
Most of the commoners in the state of Chu loved Qu dearly; they raced down the river in dragon boats and threw rice dumplings into the waters to prevent fishes from eating the poet's body.
From that day on, people hold dragon boat races and eat rice dumplings to honor Qu.
It is still customary to celebrate this special occasion with dragon boat races and rice dumplings.
One difference is that dragon boat racing has developed into a popular sport.
Those who are into the sport practice regularly in preparation of various competitions prior to or during the festival.
The Chinese usually have family reunions during the Moon Festival on the 15th of the 8th lunar month.
This tradition was held because of a famous Chinese folktale regarding a beautiful maiden named Chang E.
Once upon a time, there were ten suns up in the sky in China.
People suffered from severe heat and drought.
A young man called Houyi shot nine suns down to save the village and became the hero of the villagers.
Because of their gratitude and admiration of Houyi, they even elected him as their King and arranged the prettiest maiden of the village, Chang E to be his Queen.
Unfortunately, Houyi turned out to be an evil ruler.
Besides imposing harsh decrees, he also ordered the imperial court officials to research on longevity medications so that he could remain young and at the same time enjoy a longer life.
Feeling discontented, Chang E broke into the imperial clinic and attempted to destroy the longevity pills.
Unfortunately, before she could make any damage, imperial guards passed by the clinic and she tried to escape.
Fearing that the pills would be returned to Houyi if she got arrested, she swallowed all of them while during her escape.
Magically, she had the ability to fly all of a sudden and headed to the moon without hesitating.
From then on, rumors said Chang E settled down on the moon and lived happily with the moon rabbit.
In celebration of the moon festival, families usually hold reunion parties to stay close.
Children love playing lanterns and everyone is offered at least a slice of moon cake.
It is just very relaxing and enjoyable when you can spend time with your close kin chatting and viewing the beautiful full moon up in the sky.
The Chinese New Year holiday comes to its climax with the Yuan Xiao (元宵?—yu?n xi?o ji?), or Lantern Festival.
Began over 2000 years ago, the festival has developed many meanings.
It celebrates family reunions and society.
It features ancient spiritual traditions.
Some also call this the “true” Chinese Valentine’s Day.
The many activities include moon gazing, lighting lanterns, riddles, lion dances and eating rice balls.
According to the lunar calendar, the festival takes place on January 15.
The Spring Festival is a time reserved for families.
There is the reunion dinner on New Year’s Eve, visits (拜年—b?i ni?n) to in-laws on the 2nd day and neighbors after that.
Stores reopen on the 5th and society basically goes back to normal.
But on the 15th, everyone—regardless of age or gender—go out onto the streets to celebrate.
Though the Lantern Festival symbolizes reunions (more on that later), it’s also a time of socializing and freedom.
In Ancient China, women usually weren’t allowed out the house.
But on this night, they can stroll freely, lighting lanterns, playing games and interacting with men.
The wild and romantic stories are why some say the Lantern Festival is the true Chinese Valentine’s Day, rather than Qixi (七夕).
On a more serious side, the Lantern Festival also has religious aspects.
It was important in ancient Chinese paganism, and also modern day Buddhism and ethnic minority cultures.
The general consensus is that the festival began more than 2000 years ago in the Western Han dynasty.
Emperor Wu (?武帝—h?n w? d?) designated this day for worship rituals for Taiyi (太一神—t?i y? sh?n), one of the universe’s sovereigns.
Intense power play and unrest came after his reign.
The new emperor was Emperor Wen (?文帝—h?n w?n d?).
To celebrate the return of peace, he made the 15th a national holiday.
Every household would light candles and lanterns.
It became known as ?元宵(n?o yu?n xi?o).
“Nao” can be interpreted as having fun, or going wild with excitement.
Emperor Ming of the later Eastern Han was a devout Buddhist.
He heard that on the 15th, monks would light candles for the Buddha.
He ordered the palace and temples to light candles, and for the citizens to hang lanterns.
Both events combined and eventually developed into the Lantern Festival we know today.
The duration of celebrations varied throughout history.
Nowadays, the festival technically isn’t a national holiday, so there aren’t any days off.
The best period for Lantern Festival lovers would be the Ming dynasty.
It lasted around 1 month!
A famous variation is the Kongming lantern (孔明?—k?ng m?ng d?ng).
They represent hope, success and happiness.
In the past, people used these lanterns to signify they were safe after an attack.
Now, they are used for wishes.
Also known as the sky lantern (天?—ti?n d?ng), it sounds similar to 添丁 (ti?n ding), which means “adding children”.
Lanterns would be gifted to newlyweds or couples without children to pass on the blessings.
Pregnant women would receive a pair of small lanterns to wish health and safety on both mother and child.
Some regions also burn lanterns to determine the gender of their future child from the shape of the ashes.
Every holiday has its own set of activities.
There’s more to this festival than lanterns!
In the city of Fengyang, swings play a major role.
A popular saying there is, “Swing on the Lantern Festival, no aches or pains the entire year."
The ancient Chinese would often get together with some friends, drink wine and write poetry.
Plays on words and riddles were a favorite pastime.
During this festival, people would write riddles on the lanterns.
These small games are popular with everyone.
They require you to be clever and think outside the box.
According to many love stories, you can catch the attention of your crush through this game!
Dragon dance has a history almost as long as Chinese culture itself.
The performers create impressive formations to the beat of Chinese drums and cymbals.
Lion dances can be seen in any festive event, from holidays to weddings and store openings.
The lion is intricately designed, with movable eyes and mouths.
Sometimes, the lion will open its mouth and demand food and red pockets.
Other times, they roll around and play like oversized kittens.
Chinese stilt performances are an ancient act.
They stem from Chinese opera and the performers sing and dance while on stilts.
Depending on their character, they have difference costumes and heights.
The trademark food of the Lantern Festival is called yuan xiao, just like the festival itself.
It’s also known as t?ng yuan (??) in the South, and one of the many tasty Chinese New Year desserts.
They are glutinous rice dumplings with sweet fillings made of syrup, red bean paste, black sesame paste or more.
They can be steamed or fried, but usually boiled and served in hot water.
They represent family reunions because tang yuan sounds similar to “reunion” (??—tu?n yuan).
Some businessmen also call these balls 元? (yu?n b?o), meaning gold or silver ingots.
Despite being a night of revelry, the Lantern Festival is also a night for families.
Before Chinese New Year finally ends, the family should reunite again.
Take a break from the celebrations and relax with your family.
Reconnect under the moon.
Enjoy firework shows and performances while eating a bowl of yuan xiao.
For the festival associated with mooncakes sometimes called Lantern Festival, see Mid-Autumn Festival.
In ancient times, the lanterns were fairly simple, for only the emperor and noblemen had large ornate ones; in modern times, lanterns have been embellished with many complex designs.
For example, lanterns are now often made in shapes of animals.
The Lantern Festival is also known as the Little New Year since it marks the end of the series of celebrations starting from the Chinese New Year.
單句
An Australian started a conversation with me recently at an airport lounge in Paris after we discovered our flight was delayed for five hours.
A couple of minutes into our conversation I was struck by a question he asked: "Are you an 'ABC'?" a terminology that I wouldn't expect a Westerner to know unless he has had some close encounters with Chinese nationals.
He later explained that he learned the term 'ABC' from colleagues of various Chinese backgrounds while working at Silicon Valley in California.
This conversation reminded me of my own identity crisis when I was growing up in San Jose, California.
My family immigrated to the US when I was 13 years of age.
I lived in California through high school, university and later pursued a graduate degree in the US.
I remember I was always bothered by this question of identity when I was growing up: "Are you Taiwanese, American, Chinese American or…?"
I am not an ABC but I desperately wished I was one while growing up.
My logic behind it was that if I were an ABC then I would naturally be fluent in the English language and I would not have to flip through the dictionary for every other
English word in my textbooks.
I also thought it would have been easier to make friends and have a better social life at school.
The desire to be 'Americanized' or to have an 'American' identity was great because I believed it was the only way to fit in and to be accepted.
I used to reply, "I grew up in America, but I'm originally from Taiwan" to show that I was 'Westernized', but I now proudly reply to the question with the answer of, "No, I am not an ABC, I'm Taiwanese."
Allow me to share with you what makes an ABC and the other subgroups of Taiwanese with American influences.
ABC means American-born Chinese.
ABC's are second, third or even fourth generation Chinese, born of Chinese immigrant parents living in America.
The first significant number of Chinese immigrants arrived during the California Gold Rush in the mid1800s.
They were mostly from Guangdong province in China and immigrated to America seeking labor.
From the late 1950s until the 1970s, many Taiwanese began to move to the United States, especially after the ban on Asian immigration was lifted in 1965.
The first group of Taiwanese immigrants was mainly educated Taiwanese scholars, most of whom had graduated from National Taiwan University (the island's most prestigious), with science or engineering backgrounds.
At the time there were no proper post-graduate degrees in universities in Taiwan, thus they could only pursue further education and research overseas.
Due to the prestige and fame of US universities as well as the good US diplomatic relations Taiwan enjoyed with the States back then, most Taiwanese students chose to continue their studies in America.
Many later settled permanently in the United States and held occupations such as doctors, scientists, researchers, engineers, and professors.
The second wave of Taiwanese immigration to the United States began when Taiwan lost the United Nations seat to China and the US withdrew recognition of the ROC in favor of China in 1979.
A large group of Taiwanese moved away due to the political and economic uncertainty of Taiwan's new position.
From mid-1980s onward, many of those who moved to the US were from affluent and well educated families seeking to broaden the international view of their children.
The descendents of these Taiwanese and Chinese immigrants alike are referred to as 'ABC's'.
Regardless of whether ABC's are descendents of early Chinese immigrants from the mid-1800s, or second- or third- generation descendents of Taiwanese arriving in the US since late 1950s, ABC's have their own subculture.
They are native English speakers, and some speak the mother tongue (to a varying degree of fluency) of their parents, but many do not know how to read or write Chinese.
If they live on the east or west coast of the US, where there are strong Chinese communities, they are generally sent to attend weekend classes to learn Chinese and to maintain a good connection with Chinese culture.
ABC's by and large are well-educated, energetic, adventurous and independent.
They tend to hold good positions in all industries.
In contrast to the ABC's, there is another group of returning Taiwanese who are strongly influenced by American culture.
This 'Returning Taiwanese' group consists of Taiwanese families who immigrated to the US when their children were either in elementary school or of middle school age in the 1970s and early 1980s.
In many cases, splitting the household was a common strategy seen among these immigrant families from Taiwan where the mother moved with the children to the US while the father stayed behind in Taiwan to work and earn money to support the family in their new home.
Another group that falls under this Returning Taiwanese group is called xiaoli xuesheng (小留學生, literally 'young students studying abroad').
These are children who went to the US alone, without their parents, often staying in a boarding school or with a relative who lives in the US and can act as a guardian.
Returning Taiwanese, including both immigrant families and young students, have often lived or worked in the States for at least seven to twenty (or more) years.
Their prime education was in the US school system and they are greatly influenced by the American mindset.
They are fluent in both English and Mandarin, and although they have assimilated mainstream American culture, they retain traditional Taiwanese values taught by their parents at home.
In the workplace, they are not only bilingual but are also bicultural, and serve as a bridge between the local Taiwanese and their global team counterparts in understanding and relating to both American and Taiwanese cultures.
A third large group of Taiwanese who have lived in the US obtained their post-graduate degrees there after completing their university degree in Taiwan.
They were mainly in the United States for studying and most return to Taiwan after obtaining their degree or degrees.
It is a widespread desire to have the opportunity to pursue further education abroad to enhance one's horizon and international viewpoint.
As a result, many Taiwanese earn a master or doctoral degree and commonly hold multiple diplomas from the US and/or another country.
Most group together with people of their own backgrounds studying, living, or traveling together, but while accustomed to daily life in America, may not necessarily understand the deeper Western cultural values of being independent, individual or direct in communication with others, due to lack of interaction with Westerners.
During the 1990s, political liberalization and economic development in Taiwan encouraged many ABC's and others who lived in the States to return to Taiwan to pursue careers and education.
Many of my ABC friends and I myself returned during this period, and many locals have a 'love-hate' feeling towards these returnees.
Locals are often excited to meet and learn from those who are somewhat 'Americanized' as they perceive them, yet at the same time they may stereotype the returnees as arrogant, rich, overly open-minded, and with poor command of Mandarin language skills and proper etiquette.
The challenge for any returnee to the workplace in Taiwan is how to best bring back the global skills and knowledge to improve collaboration and productivity in the Taiwanese office while at the same time not seem aloof due to their education and experiences overseas.
Local Taiwanese certainly make clear distinctions and have varying perceptions of the three subgroups (ABC's, Returning Taiwanese and those who have received a post-graduate degree overseas).
Nonetheless, their return has no doubt contributed to the development of Taiwan's industry and helped Taiwan to excel in high-tech industries, and reinvigorated traditional business in manufacturing, trading and other fields.
They are key members of the global team in bridging the gap between work style differences to ensure effective communication and to reduce misunderstanding when working in a global environment.
I strongly believe that in order to become a competent global player and leader in this competitive world it is crucial not only to have foreign language capabilities, but also to understand international business practices.
Only by an increased awareness of the differences between cultures, insights, beliefs and values will this be possible.
Among the many aspects of Taiwanese culture, cross-cultural issues, society and lifestyle, Taiwan's betel nut culture is one of the most unique.
Betel nut (檳榔, bin lang) is a type of palm tree which is grown in Taiwan and Southeast Asian countries.
This coconut-like tree produces a seed (green in color) that creates a chewing tobacco-like 'high' when chewed.
Thus the section of the Taiwanese population who chew betel nuts are mostly truck, bus or taxi drivers, laborers, and construction workers who chew them to help stay awake during the long working day.
An easy crop to grow, betel nut is Taiwan's second largest agricultural produce and an important cash crop.
Thus, despite the fact that the shallow root systems of these trees sap all the nutrients out of the surrounding soil, and that a strong typhoon can easily destroy the slender palms or cause landslides, many farmers continue to make a living planting and growing betel nut trees.
Betel nuts are usually sold wrapped in a leaf, with a slit down the middle of the nut into which is placed a lump of lime paste.
The vast majority of betel nut chewers are male, though many aborigine women also chew them.
I have not tried one myself but heard it has a strong, bitter taste and produces a hot sensation in the mouth.
The red juice which forms after chewing isn't swallowed, but is spat out, and in the past dried red splashes on city sidewalks and streets were left everywhere by betel nut chewers, although this phenomenon seems to have decreased recently, especially in major cities, as chewers now often spit into disposable plastic cups instead.
It's easy to recognize a betel nut chewer, as their teeth and even the corners of their mouth are generally stained with dark red marks.
Don't mistake the betel nut juice they spit out for blood!
What makes the betel nut special in Taiwan is its creative marketing technique in employing the 'betel nut beauty' (檳榔西施, binlang xishi) in selling the product.
These are girls, typically dressed in revealing clothing (usually little more than bikini).
Sitting in a clear glass-walled booth beside the road, often with bright colorful flashing neon lights, these girls prepare and count betel nuts, and hawk betel nuts packaged in boxes printed with pictures of semi-clad girls to people driving or walking by.
They generally also sell cigarettes and drinks.
These betel nut kiosks are a unique feature in the cities and rural areas of Taiwan, and are usually set up near freeway entrance ramps and beside roads where truck drivers (the most fanatical consumers) are common.
It's a lot like a live roadside bikini contest, and you're unlikely to miss them if you drive or travel in Taiwan.
Since the wages for betel nut beauties can be high, many girls (mostly of agricultural or working-class backgrounds, and often poorly educated) become embroiled in the business.
Though betel nuts are chewed commonly in many countries in the Asia-Pacific region, the betel nut beauty phenomenon is distinctively Taiwanese.
Politicians have recently declared their intention to get rid of the betel nut beauties, as the business is appalling for the country's image.
However, the lawmakers face challenges dealing with the powerful petition of betel nut growers, chewers and sellers, who have made certain the practice is not forbidden entirely.
In some areas of the country a compromise has been reached, and the betel nut beauties remain, but have been told to cover up.
Once I dozed off sitting in the train heading back to Taipei after a short break at Taroko Gorge in Hualien, eastern Taiwan.
I was woken up by the familiar fragrance of a delicious and very traditional Taiwanese railway biandang (便當), or lunchbox.
I turned my head and noticed that a mother and her son sitting on the opposite side of the aisle from me were holding bamboo chopsticks, scooping rice and gobbling down pork chop from a rectangular paper box, against a backdrop through the window of the beautiful blue water of the Pacific Ocean.
This lunchtime scene was so Taiwan, so local and so beautiful; it's a scene that reminds me of the flavors and nostalgic memories of tasty boxed lunches eaten during childhood trips by train.
Taiwan railway lunch boxes are pleasant memories; they were always the highlight of long journeys by train for those Taiwanese who are old enough to remember them, served in a round, stainless steel box with the 'Taiwan Railway' logo embossed on the cover and filled with delicious food.
Hawkers also sold other kinds of boxed lunch at a few station platforms as the train slowly approached the platform, greeting passengers with the call, "ben-dong, ben dong" (Taiwanese for biandang), and selling their lunchboxes during the train's short three-minute stop at the station.
Some hawkers even ran along with the train trying to reach the last few customers, passing lunchboxes through the windows during the time when the trains did not yet have air conditioning.
Biandang lunchboxes became a familiar part of Taiwanese culture during the Japanese occupation (1895-1945), when they were called O-bento in Japanese.
A traditional Taiwanese biandang consists of a slice of pork chop, some bean curd and an egg (all stewed in soy sauce), some stir-fried vegetables and some pickled radish, all traditionally packaged in a box of thin wooden strips, although nowadays the box is usually made of cardboard.
This boxed lunch may seem simple by today's standards, yet they were regarded as a luxury by people in the old days, when a slice of pork was commonly shared by the entire family during a meal.
Lunchboxes from Fenchihu (奮起湖), on the route of the mountain train between Chiayi (嘉義) and Alishan (阿里山) in south-central Taiwan are the only railway lunch boxes that have remained popular, and have now became a tourist attraction in their own right.
Nowadays biandang come in many varieties (pork chop, fish filet, chicken drumstick and vegetarian are among the more common kinds) and various prices.
A standard lunchbox costs NT$60-70, while fancy boxed meals costing up to NT$500 can also be found.
At school, children eat a lunchbox either brought with them in the morning freshly made, or personally delivered to the school by one of their parents at lunchtime, or bought in the school canteen.
Office workers often get a lunchbox rather than eating out at a restaurant; laborers squat down at the construction site eating boxed lunches; politicians have a lunchbox over lunch meetings.
Boxed lunches can be bought from hawkers on the streets, from restaurants, or at convenience stores.
Alternatively, small buffet-style (自助餐, zizhucan) restaurants offer various selections from which customers can create their own biandang.
They are quite similar to some Chinese food stalls at Western food courts where customers pick three or four items along with rice.
Biandang culture is a unique aspect of Taiwanese culture.
Rather than going out to eat during office lunch hours or chucking down a sandwich at your office desk, next time join your Taiwanese colleagues for a biandang at the office eating area.
This can help you build rapport with your Taiwanese peers, plus it's fun to occasionally do things the local way!
While Westerners have cereal and milk, muffins or bagels and a cup of steaming coffee or tea in the morning, what do Taiwanese eat for breakfast?
Rice is a nutritious staple that the Taiwanese traditionally consumed three times a day, including for breakfast.
However, rice was not readily available for every Taiwanese family in my father's generation (he is about seventy years old now), and only wealthy families could afford to eat it with every meal.
Many families thus used to mix mostly yams (sweet potatoes) with just a bit of rice boiled in water to make yam-rice congee.
Congee (xi fan, 稀飯, in Mandarin or commonly heard overseas as jook in Cantonese) is a watery kind of rice gruel.
As Taiwan became more developed, congee was made with only rice.
It was not until quite recently that yam was found to be healthy, and eating it (as well as eating yam leaves) has become common once more.
At home many Taiwanese begin their day with a bowl of warm, plain congee, made with just rice and nothing else.
It is usually eaten with vegetables, meat, pork floss (rou song, 肉鬆, finely shredded pork roasted until dried and shredded into crispy flakes), salted peanuts, warm tofu and pan-fried eggs (hebao dan, 荷包蛋) with a dab of soy sauce and other side dishes, including pickled cucumber, cabbage hearts or other pickled vegetables, dried radish, and beans.
When I was a child, my mother would rush downstairs when the vendor came around our alley in his truck hawking freshly-made side dishes for breakfast.
The warm, fresh tofu was especially tasty and was definitely my favorite.
Nowadays, we buy all of these either fresh at a traditional market (hoping no preservatives are added) or canned at a convenience store (where preservatives are certainly added).
Another popular Taiwanese breakfast option is a nice bowl of piping hot soybean milk (doujiang, 豆漿), either sweet or salty, eaten with youtiao (油條, foot-long, deep fried dough sticks sometimes known as 'crunchy crullers') or other Taiwanese breakfast items sold at traditional breakfast shops (youtiao and doujiang are usually bought from a shop rather than made at home).
Let's start with Soybean milk (doujiang).
Sweet doujiang is served either hot, cold or lukewarm, and many Taiwanese prefer this high-protein alternative to cow's milk.
Health-conscious eaters can ask for less sugar to be added to their soybean milk, or even drink it unsweetened.
Salty soybean soup (xian doujiang, 鹹豆漿) is served in a bowl with chopped vegetables (some fresh and some dried), dried shrimps, dried radish and some chopped-up youtiao.
The last step is to pour hot plain soybean milk into the bowl.
Before eating it's usual to add lots of rice vinegar to curdle the doujiang, giving it a texture like egg-drop soup.
Another kind of drink you may see is mijiang (米漿 ), made with roasted rice which gives the drink a brown color and thicker texture than soybean milk.
Like doujiang it is served both hot and cold, and can mixed 50/50 with doujiang.
Now let's see what, apart from youtiao, is often eaten with breakfast drinks.
Shaobing (燒餅) are long lengths of soft, baked bread covered in toasted white sesame seeds.
Shaobing are usually split open and filled, most commonly with youtiao, when they're simply known as shaobing youtiao (燒餅油條) .
The youtiao filling is crunchy when hot and chewy when cold.
Nowadays you can also find shaobing filled with stir-fried pork, or with pan-fried eggs and spring onions (my personal favorite, as they give the shaobing a juicy, flavorful taste).
Taiwanese-style pancakes or danbing (蛋餅)are also very popular at breakfast time.
This is the best-known and most often tried Taiwanese breakfast item among foreigners living in Taiwan.
Danbing are prepared by beating chopped spring onions and egg, and frying the mixture on a large iron griddle.
A tortilla-like pre-made pancake (now often factory-made, although some shops continue to make their own) is placed on top of the semi-cooked egg.
The cooked pancake is then folded into a burrito-like shape and chopped up into pieces.
Danbing can be eaten with soy sauce or chili sauce.
Fantuan (飯糰) is a delicious roll made from sticky glutinous rice.
The steaming hot rice is taken out of a wooden rice bucket and placed onto a plastic bag laid on top of a piece of cloth.
Now the rice is patted into a flat oval shape with a flat rice serving spoon.
Next the fillings (small pieces of crispy youtiao, some pork floss and pickled radish) are placed in the center; the rice is then rolled up into a spring roll shape.
Fantuan were traditionally formed into a long egg shape, but it's faster to roll the rice up and close the ends tightly.
Now turn the plastic bag inside out, and the fantuan is inside and ready to go.
Sweet fantuan can also be made with youtiao, finely grounded peanut powder and sugar.
Most traditional breakfast shops also stock a variety of steamed buns.
The basic buns without fillings are called mantou (饅頭).
Mantou originate from northern China, especially from Shandong province, and are generally light, fluffy, spongy and chewy in texture.
They come in various sizes, small or big.
I enjoy my mantou cut open and filled with egg and pork floss, like a sandwich.
Other steamed buns stuffed with fillings are called baozi (包子).
Fillings include meat, pork with chopped cabbage and shredded mushroom, leeks and other types of vegetable although sweet types can also be found, filled with red beans or black sesame paste.
Taiwan offers a wonderful array of breakfast options and they are readily found in many back alleys or near busy bus or MRT stops.
These neighborhood street vendors or shops offer a cheap and hot breakfast, prepared freshly.
Popular breakfast chain stores also specialize in cheap grilled egg and ham sandwiches and Taiwan-style hamburgers.
Follow the scent of those sizzling eggs and listen for the sound of frying youtiao and don't be afraid to check out the next breakfast vendor or movable cart you see.
Some breakfast congee and soybean joints are even open 24 hours a day for you to savor these traditional foods anytime of the day.
The German family I spent a month with during Christmas in Frankfurt a few years ago did not understand why I always craved soup when asked what I wanted to eat for dinner.
Believe me, soup is a comfort food to many Taiwanese, and was especially soothing during the freezing, snowy days of the Christmas holiday season when I was in Germany.
Soup features on almost every Chinese menu; it is a staple, and often a meal in itself.
It should come as no surprise that there are an amazing variety of Chinese soups.
Furthermore, Chinese are especially obsessed with 'chicken soup', not 'fish soup' or 'tofu soup'; it has been used by the Chinese to treat illness for centuries.
Just like in other parts of the world, chicken soup is believed by the Taiwanese to cure colds, sore throats or an achy body.
Many of you have heard or learned about yin and yang (陰陽).
A large part of Chinese medicine is based on the concept of these two forces, which rule the universe.
Yin and yang can be described as opposite or complementary forces.
Depending on the context, yin refers to the feminine, darker, cooling forces; while yang represents the masculine, lighter, hot forces.
So, in Chinese terms, illness is a signal that yin and yang forces are out of balance.
This imbalance leads to disharmony and blockages of energy flow in the body, thus causing disease.
For example, if you have a cold, according to the Chinese principles of yin and yang, it's because there is too much yin in your body.
A Chinese herbalist might prescribe a soup designed to restore the yang force.
Similarly, a fever might be treated with a yin soup.
The secret of a good Chinese soup lies in the stock (no, I can't cook, but I've checked with my mom!).
Chicken is the primary meat of choice for preparing good stock to help restore physical strength.
Therefore, chicken soup made with a number of Chinese herbal ingredients to balance yin and yang is thought to increase energy, to strengthen body functions and to have a great healing effect.
As a result, after drinking chicken soup, one's body is neither too 'warm', nor too 'cold', but neutral.
The beauty of Chinese herbal soup is that it is not only drunk to correct imbalances and restore energy during times of illness, it is also drunk frequently in Taiwanese homes for its delicious taste and to care for the body, preventing disease.
Homemade chicken soup is both nutritious and good for the body and the soul.
The curative powers of chicken soup are not simply an old wives' tale.
There is medical evidence it can help relieve cold or flu symptoms.
It ups the intake of fluids and provides easily absorbed nutrients; the steam from hot soup can also reduce nasal or bronchial congestion and sooth a sore throat.
Even if you don't agree with the Chinese belief that certain chicken soups can have healing powers, a bowl of soup, lovingly prepared, can at least make us think we feel better and it certainly has its ability to comfort.
Calligraphy is more than simply writing characters: it is a rich and profound art.
Each character is written like a beautiful flower, and the different forms, styles and elegant flowing of characters carefully convey the artist's emotions, integrity, thoughts, and are an expression of his or her outlook on life.
Calligraphy is one of the highest forms of Chinese art and is well respected in Chinese culture.
It is believed to be a great mental exercise for relaxing one's mind and body.
It is a highly disciplined exercise that requires one's full concentration and proper posture.
One's energy flows through the brush and this is reflected in the strokes; the brush style, the turns and curves of each stroke, the thickness of the strokes, all of which can represent the state of mind of the artist and what he/she wants to express.
Many calligraphy artists throughout history were well-known for their longevity and good health.
In Taiwan, starting in third grade (though this varies with schools), every child is taught to write with brush and ink on rice paper.
Calligraphy is considered so important that traditionally schools hold a contest every semester and award prizes to the students who have the best calligraphy.
I can still remember how strict my teacher was about our posture during calligraphy lessons.
The body should be upright with the back straight and shoulders relaxed.
The legs should be apart with the feet firmly on the ground and the head held straight up and the chin down, leaning slightly forward.
The eyes should be fixed on the spot where you intend to write.
The right hand is to hold the brush vertically, gripped between the thumb and middle finger, while the upper part of the brush is pressed by the index finger to stabilize it.
The palm should not touch the brush, while the elbow is raised high, not touching the desk when making strokes.
And yes, this is what my teacher expected of me while writing calligraphy!
In order to write calligraphy, four essential tools are needed - the brush, ink, paper, and ink stone, which are together commonly referred by the Chinese as the four  treasures of the study (文房四寶, wen fang si bao).
Brushes are made from different types of animal hair, mounted on a bamboo shaft.
The Taiwanese use hair from wolves, sheep, rabbits, deer, foxes, or mice, depending on the type of writing.
There is a lovely tradition of making a special and meaningful brush using the hair of your own newborn, by shaving their hair when they're between one and four months old.
This, it is believed, will make the baby's hair grow to be thicker and fuller.
This brush then becomes an once-in-a-lifetime souvenir for the child.
Different brushes are used to create different expressions of calligraphic work, just like the use of various brushes in Western watercolor painting.
Ink was formally prepared by rubbing a solid black ink stick on the flat surface of an ink stone moistened with water.
Carefully rubbing the ink is an essential part of calligraphy study, as it produces different consistencies depending on the amount of water used, and the length of time the ink stick is ground.
Artists use different consistencies of ink to create images that are intense and dark or light and clear, and as a way to express various emotions.
Today, pre-mixed bottled inks are also available.
A special type of paper is used for calligraphy, referred to as xuan paper (宣紙) or rice paper.
Paper of different quality and thickness produces varied results.
The texture can be rough and absorbent or it can have a smooth and fine surface which resists ink absorption.
Ink stones are pieces of hard, flat stone or pottery generally carved with beautiful designs.
The calligrapher puts water on the ink stone then rubs the solid ink stick against it, storing the ink once it is ground into liquid.
Many ink stones are regarded as collectible works of art.
Five distinct styles of calligraphy have been used from ancient times up to the present: seal (篆書, zhuan shu), official or clerical (隸書, li shu), regular (楷書, kai shu), running or semi-cursive (行書, xing shu), and cursive (草書, cao shu).
Each style has its own characteristics and purpose.
Today, cursive and semi-cursive scripts are most commonly used for their profound beauty and artistic expressiveness.
Among the basic rules when writing characters are to always write from top to bottom and from left to right; to follow the correct stroke order to properly balance the composition of characters on the paper; and to carefully handle the brush to create the appropriate lines of each character.
There are eight common strokes: The character for 'eternal,' 永 (yong) has all eight basic strokes.
If you can write this character nicely and with well-balanced composition then you can probably write any other Chinese character well!
While making a US Dollar time deposit transaction at the bank, I almost lost track of the number of chops that were used.
I had to chop on the withdrawal slip from my US dollar account, the teller chopped on slips to confirm she handled the paperwork properly, the manager chopped as well, and then there were a few other chops on the time deposit certificate I finally received.
Chops or seals (圖章,tu zhang or 印章, yin zhang) are specially carved stamps which are used by the Chinese instead of a signature.
A chop carries more power than the person holding it, and for centuries it has symbolized authority and power.
In Taiwan, chops are used by official departments as well as private individuals.
They're used every day for everything from receiving registered mail or opening an account or withdrawing money at the bank, to legalizing a contract or acknowledging a document.
Many foreign businessmen who are used to signing their names on a contract in their own country find with astonishment that they are required to use seals to make things official, and that one does not have to be present to sign a lease as long as the chop is used.
A perfect seal is very much determined by the engraver's speed and the strength of his wrist and finger movements.
He must be good at writing various styles of Chinese script and arrange all the characters in a perfect balance.
As in Chinese calligraphy, sometimes he needs to exaggerate the thickness or thinness of a stroke, straighten or curve it, or even deliberately deform an ideogram to create an artistic and graceful effect.
Seals can be made from various materials, including jade, gold, brass, stone, ivory, crystal, or other valuable stone.
Most people in Taiwan have a simple wooden chop (costing about NTS50 to make), but they also normally have more than one seal: perhaps one for banking, one for business contract signing, and one for less important matters like picking up the post or getting paperwork done at government offices.
When using a chop, make sure you know which way up to hold it when stamping a document.
The engraver normally places a dot on one side of the chop to identify which way to hold the chop while stamping.
The chop is pressed into a red ink paste made from cinnabar (yes, red is the standard color).
Seals are indeed widely used in Taiwan and in many other countries in Asia.
The art of seal engraving is now more, rather than less, popular than ever, and many foreigners can now appreciate this art form which for a long time has been considered uniquely Chinese.
If you don't already have your own personal seal, it's not hard to have one made.
First, if you don't already have one, ask a friend to give you a Chinese name.
In the past, Westerners' Chinese names were often created by transliterating their own given names; however it is becoming more important to have a meaningful Chinese name to show your respect for the culture and to build a Chinese identity.
Select one of the many Chinese surnames in use.
Next pick a given name that has a meaning that suits your personality, such as righteous, brave or scholarly for men, and elegant, sophisticated or joyful for women.
Once a Chinese name is chosen, you can search for a perfect seal with the right material and the style of calligraphy you appreciate.
Frequently, you can find a chop maker at key cutting shops (often near traditional markets or on busy shopping streets) where they now use computer-operated techniques to make the chop from a piece of wood, with a plastic cover to prevent staining after use.
You can find better quality chops made of jade or other kinds of stone at antique markets such as the Jianguo Jade Market (建國玉市) in Taipei.
Now take the stone to the seal engraver and have your chop carved.
The quality of the finished result depends on the artist's calligraphy skills, and the fluidity, force, strokes and angles of each character.
Good chop engraver can charge by the character carved, carefully balancing them on the limited space available on the chop, and creating an overall harmonious design.
The cost for each character engraved onto jade another hard stone can range from NT$1,000 to 10,000, according to the artist.
Many Taiwanese not only enjoy collecting seals but also believe that having an 'auspicious seal' will ensure power and bring prosperity and good fortune to the seal owner.
Certainly it's extremely helpful to have your own seal so you can begin chopping away on those official documents!
Do you love Asian cuisine?
Trust me, Asian dishes are tastier when using chopsticks Chopsticks, called kuaizi (筷子, literally 'quick little things') in Chinese, are a pair of narrow sticks about 9 to 10 inches long, cut square at the top and rounded off at the bottom.
They are traditional eating utensils in China, Japan, Korea and Vietnam but their popularity has spread worldwide.
Chopsticks are commonly made of bamboo, wood, bone, metal, ivory, or plastic.
Most families, night market food stands, family-style restaurants, and even some fairly decent restaurants in Taiwan however still use wood or bamboo chopsticks, because they are inexpensive and resistant to heat.
These days, to be environmentally friendly, it is a trend in Taiwan to carry one's own metal chopsticks instead of wasting pairs of disposable ones.
In dynastic China, the wealthy used chopsticks made of jade, gold or bronze, while emperors preferred using silver chopsticks, as they were said to turn black if they came into contact with poisoned food.
Well, it may be easier for me to say, "Simply hold the bottom chopstick firmly in place while pivoting the top one to pick up the food," than for a beginner to actually do it at first.
It's best to watch others use them first, but here are a few simple directions for correctly positioning the chopsticks.
First chopstick: Rest the thicker end of the chopstick at the base of your thumb and the thinner part on your fourth finger (the ring finger) close to the fingertip.
Press the end of your thumb forward onto the stick so that it is gripped firmly in place.
There will now be a hollow between your thumb and index finger.
Second chopstick: Now pick up the other chopstick and hold it in place, with your index finger on one side and the tip of your thumb on the other, close to the index finger.
The index finger should be curled.
 This is the pivoting stick.
Always keep the bottom chopstick stationary and use the top chopstick to maneuver and pick up food.
Finally, make sure to grab the chopsticks in the middle with the tips of even length.
Try not to cross your sticks, even though you may see many young Taiwanese do this!
(It wasn't until adulthood that my younger sister stopped crossing her chopsticks and could hold them properly, so don't worry if you do; it's more important to be able to pick up the food!)
Never wave chopsticks to point at things or 'direct traffic' in the restaurant.
Pick food up with the chopsticks; don't spear it -only small children are allowed this infraction (though you may see adults spearing meat or fish balls sometimes).
Put your chopsticks down before picking up a cup or bowl.
Never suck or chew the tips of the chopsticks.
Don't stick chopsticks vertically into a bowl of rice.
This is only done at funerals, when the sticks resemble incense sticks lit for the dead.
This is probably the one thing most likely to offend the Taiwanese.
Don't pass food from your chopsticks to somebody else's chopsticks
Never use chopsticks as drumsticks to tap on the rice bowl.
That's a beggar's gesture.
Don't use chopsticks as a hair decoration, even though in the past women used them as decorative hairpins.
Don't pick up an item and put it right back in the dish.
You should aim before raising your chopsticks.
Once you touch it, it's yours.
It may be a polite gesture to pick up the best piece of food from the dish and send it to your guest's bowl or plate.
If there are serving spoons or communal chopsticks on the table, use those to get food.
If not, reverse your chopsticks and use the other, clean ends to pick up the food.
With some practice, you can easily use chopsticks to pick up many kinds of Chinese food and enjoy savoring Chinese cuisine the authentic way.
Your next challenge may be using chopsticks to eat rice.
The secret to properly eating rice is to raise the rice bowl, bring it close to your mouth and place the rice into your mouth with the chopsticks.
(Nowadays people don't always raise the rice bow and often you'll see them quickly shovel rice into their mouth.)
If rice were served on a plate, as it is in the West, I would ask for a fork, as it is difficult to pick up the rice grain by grain from the plate.
That's why Chinese traditionally eat from a bowl.
Your fingers and hand may be tired and sore after your first few attempts, and many claim to get bad cramps, but I'm sure everyone can acquire the skill in using chopsticks (my friend Tim did on his first attempt to use chopsticks throughout dinner.
He ate slowly and we ended up going for a burger right after dinner, as he was hungry by then).
It's never insulting to ask for a fork, but the Taiwanese are highly appreciative when foreigners use chopsticks while dining.
Let's get those chopsticks to work!
Confucius (孔子, Kongzi) is the greatest teacher and philosopher in Chinese culture.
His wisdom continued to be appreciated after his death, travelled from kingdom to kingdom through dynastic China, and his philosophy continues to have a tremendous influence to this day on the thought and life of people in China, Taiwan, Japan, Korea, and Vietnam, and on Chinese people in other countries around the world.
For thousands of years Confucianism, the teaching of Confucius, has been a foundation for Chinese society and it has also been the basis for the education of Chinese students.
Much of Confucius' teaching spread to Taiwan with the migration of Chinese settlers to this island over the past four hundred years.
Confucianism is a way of life: a philosophy of ethics, a system that propounds proper personal conduct, and guidance for living a moral life.
Confucianism shaped how people speak, think and act, and it remains relevant in the everyday values of the Taiwanese.
Though modern Taiwanese culture does not strictly follow all Confucian values, many of them are still taught by teachers and parents today in Taiwanese society.
These basic Confucian values have profoundly influenced and remain deeply rooted in Taiwanese people's daily life.
These values include the idea of the group, a need to retain harmony, respect for hierarchy, honoring one's parents, obedience to superiors, loyalty, propriety, and education.
Understanding the way these traditional values are followed today will help you to interact successfully with Taiwanese nationals.
Let's start from how a Taiwanese generally sees one's self.
The self is never defined by as the 'individual' self as "I do what I like to do and I am responsible for my decisions and actions".
'Self for a Taiwanese is defined by the relations with others and by the surrounding relationships.
One always needs to live up to the others' expectations.
For instance, living up the expectations of one's parents who may want their son or daughter to be a doctor or an engineer; participating in a drawing contest to please the teacher in school; or working overtime to complete a challenging project to satisfy the manager.
One can never separate his obligations from others and his identity is only complete when recognition is given by others.
One is part of a group and defined by that group.
Consequently, Taiwanese care a lot about how others see them and often their self-confidence is derived from others' recognition.
One's achievement is for others to glorify and express but never for the individual to voice openly.
People are expected to be modest about their individual accomplishments or personal opinions.
Taiwanese people's self-perception is reflected in how they receive feedback positive comment from a senior results in further support and loyalty given to that authority, while a constructive criticism may be perceived as a sign of personal incompetence.
In other words, appreciation expressed as a pat on the shoulder is perceived as a tremendous reward, while a seemly minor criticism may well be taken as a serious reprimand.
Taiwanese do not separate one's emotions from a feedback.
Any feedback is never taken simply about the task; it involves one's face; a positive feedback gives face while a negative feedback diminishes one's reputation.
Furthermore, the stress of fitting in and belonging to a group is important.
The 'IN-Group' is strong and stable, and one may go beyond their means to help an insider, but an outsider has to follow the rules and policy.
Insiders are like family members, hence special privileges are given, and direct and open communication can be used.
On the contrary, one is merely polite to an outsider because no relationship (guanxi, 關係) is yet established, thus communication is often indirect.
To build a good working relationship and enhance communication, it is important to be part of your Taiwanese colleagues' insider circle as well as letting others enter your insider circle.
Be aware that this results in preferential treatment for those on the 'inside', and one may be protected and taken care of endlessly when seen as an insider.
If your Taiwanese colleague invites you to his or her home for a meal, that indicates you are getting close to entering their inside circle, as Taiwanese typically only entertain those they consider as true friends at home.
Sometimes you might be invited to someone's home because he or she owns a big property, in which case the reason may be to show off their wealth, rather than to indicate that you are part or the insider group.
Social harmony is the great goal of Confucianism.
Harmony with other people can be achieved when every individual is receptive to and accepts his or her place in the social order and can play their parts well in the group.
To preserve harmony, conflict should be avoided at all costs.
No one desires to invite direct confrontations or to create chaos as they disrupt harmonious personal relationships.
Personal assertiveness and directness can be seen as selfish, and thus are discouraged.
In valuing harmony, each individual is aware of his role in society.
They do not openly disagree, particularly with figures of authority; they speak indirectly and wait for their turn to speak according to their social role; they are comfortable with periods of silence and only express what they believe they want to be heard, and not necessary the truth; they much prefer to be offered options or assistance than directly asking for support.
Consequently, a more 'hand-holding' process needs to be adopted when working with Taiwanese colleagues.
A frequent check-up on the process of the project and generous offers of support and experience sharing may be required.
Many meetings happen after the official meeting sessions, and private sessions encourage more self-expression.
Much of Confucius' teaching focuses on showing respect for parents, family the elderly and for those with wisdom and experience.
Confucius taught the Five-Constant Relationships: emperor-subject, parent-child, husband-wife, elder brother-younger brother and friend-friend (this is the only relationship of equal status among the five relationships).
Respect for parents and older members of the family (living or dead) is considered the greatest virtue for a Taiwanese.
This principle is called xiao(孝, filial piety), and it denotes that a child needs to pay ultimate love and respect to his or her parents and family.
Children should be obedient to their parents, and take care of them when they grow old.
Listening to their parents and obeying without questioning is believed to be the duty of a son.
Respect for hierarchy and veneration of those in positions of authority is taught from a young age.
Parents, teachers and managers are among those of authority and ranking.
Status in terms of age, ranking and experience should be observed at all times.
It gives a structure and provides a clear guideline for correct behavior and communication.
For instance, titles are frequently used to address senior executives; business cards are presented with both hands to show respect; decisions are generally made by those in authority and the subordinates follow.
The relationship between seniors and juniors should be similar to that of parent and child.
Loyalty thus is extended from one's family and one's spouse and to one's leader.
Thus a good manager is seen as someone who shows genuine concern for the wellbeing of their subordinates, not only on work-related issues but also in personal and family matters.
He provides stability and security and quite often is expected to know everything; he/she ought to be wiser and experienced, and should have all the right answers.
Seniors are expected to provide mentorship and, more importantly, protection to their junior employees, and thus receive great loyalty in return.
This respect of hierarchy results in the manager being addressed with formal titles, and spending more time after work with Taiwanese associates at social gatherings like dinners and entertainment.
Managers are also expected to comply with favors asked, and may be required to give more frequent advice.
Confucius taught that rituals and protocol preserve social order.
Li (禮, rite) translates as propriety, courtesy, respect, ritual, morals, standard of conduct, or the way things should be done'.
Without Li there can be no proper etiquette and social behavior in all relationships; there can be no rules on the proper status of the elders and juniors; no organization of the moral relationship between parents and children and no standard on what is right and what is wrong.
Li provides the structure for social interaction in social and political institutions, and is the Chinese code of ethics for daily behavior.
People should accept and fulfill their social role in society regardless which side of the relationship they are on.
Parents have an obligation to raise and educate their children, who will then care for their parents in their old age; seniors have an obligation to provide to juniors, who in return swear loyalty.
People behave properly accordingly to li so as not to lose face.
The ideal of following li has been pointed out as a weakness of the Taiwanese education system.
Showing extreme respect to the teachers and blindly following the ritual of li has affected the development of students' creative learning, critical thinking and problem solving skills.
Students are incapable of thinking freely and creatively, instead focusing on following the instructions of the teachers, who are believed to have the knowledge and right answers for every question.
This phenomenon also transfers to Taiwan's business and professional settings, where the person in authority is expected to have an answer to everything. 
I can't emphasize enough the importance of education.
Education is valued highly in Taiwan, and every parent expects his child to achieve the highest possible level of education.
Confucius taught that the chief goals of life are to become well educated and to live a moral life.
Education is a way to learn how to conduct oneself properly, thus creating a balanced society.
Confucius is renowned for his support of 'Teaching without discrimination' (有教無類, you jiao wu le), that is, education should be for all, without class distinction, and that anyone should be taught if they desire to learn.
Education promotes social status.
It led to the structure of civil services in the Taiwan government and the university entrance examination system in education.
Through education, one is able to achieve a higher role in society.
The higher the degree of education achieved, the higher the status and respect projected.
As a result, to motivate Taiwanese employees it is important to acknowledge their educational background and achievement regardless of the age or the position of the person, as a sign of respect.
In Taiwan, one's level of education will always be asked at a job interview, regardless of the job experience.
Every colleague is well aware of each other's educational accomplishments, and which position they occupy in the ranking system among their peers.
Though one's performance and merit weighs more in the modern working environment in Taiwan, education is still seen as the benchmark for determining the success of an individual.
Confucius, held in the highest regard as a teacher, is famous for his many wise sayings, collected in the Analects.
These sayings help us learn about human behavior, while offering guidance for living a moral life that remains relevant for us today.
His often-quoted golden rule is: "Do not unto others that which you do not wish for yourself.
"Some other examples of his wise sayings I like very much are:
"I hear but I forget.
 I see and I remember.
 I do and I understand."
"When you enjoy what you do, you'll never work another day in your life."
"Silence is a friend who will not betray."
In honoring Confucius' life and contributions, his birthday has been celebrated as Teacher's Day on September 28th since 1968 in Taiwan.
As Confucianism is not a religion but a moral philosophy and ethical code, Confucian temples in Taiwan are not places for worship, but rather for paying tremendous respect to Confucius.
Locals visit Confucius temples hoping to acquire wisdom; students come with their identification cards and leave them on the altar as a prayer to pass school examinations; on Teacher's Day, formal celebrations and ceremonies are held at the Confucius temples to pay respect to the Sage.
The first Confucius temple in Taiwan was constructed in Tainan (台南, southwest Taiwan) more than three hundred years ago.
It was the place for education and nowadays, its graceful, ancient looking architecture has become a famous tourist attraction in the city.
In conclusion, Confucianism is a complex system that taught the importance of moral and ethical relationships between all people.
It had tremendous influence the culture and history of the Taiwanese and has impacted them in their daily behavior and business dealings.
Anyone seeking to work in Taiwan would find it vital to understand Confucian principles and values.
Dragon Boat Festival (端午節 , duanwu jie), also known as the 'Poet's Festival(詩人節) , is one of the three most important festivals celebrated in Taiwan(the other two are the Moon Festival, celebrated in autumn and Lunar New Year, in winter).
All Taiwanese make their best effort to return home for these three big occasions.
This festival, which falls on the fifth day of the fifth month in the lunar calendar, commemorates the death of a poet by the name of Chu Yuan (屈原) ,a loyal counselor for the government of Chu (楚) of the Warring States Period (戰國時期, a very turbulent period in China).
According to legend, he was banished by the King of Chu after his good advice was rejected.
During his time in exile, he started composing patriotic poems expressing his deepest love and concern for the future of his state, and upon hearing the news that it had been defeated by its rival, Chin (秦國), he jumped into a river out of despair and drowned himself.
Upon hearing the news of the suicide, local fishermen and villagers who loved Chu Yuan for his patriotism rushed out in their boats to try to rescue him, and upon realizing they were too late, frantically beat drums to scare the fish away and threw jongzi (粽子, glutinous rice wrapped in bamboo leaves) into the water in the hope that the hungry fish would not eat Chu Yuan's body.
The ritual of boat racing was held every year after on the anniversary of Chu Yuan's death to symbolize the effort to rescue him, while jongzi are also eaten, in memory of the fishermen's efforts to preserve their hero's body.
Many Dragon Boat Festival traditions continue to be observed today in Taiwan.
Taiwanese eat jongzi, stand eggs on their pointed ends at twelve noon, hang fragrant herbs on the door, and (the highlight of the day) organize lively dragon boat races.
Let's take a look at the meaning behind all these traditions.
Dragon Boat Festival falls around the time when the warm days of spring are turning into the hot and humid weather of summer.
In the old days it was believed this time is when pests and diseases are most likely to spread, and other negative forces tend to strike.
Thus this was the time of year to drive away pestilence in order to stay healthy and safe.
In the old days, around this time parents made a pouch out of cloth or silk with fragrant herbs inside called a xiang bao (香包) for children to wear around their necks and adults drunk xiung huang wine (雄黃酒), also made from special herbs.
Sprigs of herbs were hung outside the front door and wine was sprinkled in all four corners of the house to protect against insects and to repel evil.
Some of these customs mentioned above are no longer generally observed in Taiwan, but it's still common to see families hanging herbs above their doors during the period of the Dragon Boat Festival.
Calamus (菖蒲, also known as 'water sword', as it resembles a sword) and moxa (艾草 ) are two types of herbs used for this purpose, as they are believed to ward off evil and to ensure longevity and prosperity.
When I was a child, my mother used to boil moxa herbs into a herbal bath for us to bathe in, as it was believed to cleanse the body of evil spirits and strengthen health, although these days fewer families do this (at least in the big cities).
The custom of wearing a pouch of fragrant herbs has also enjoyed a gradual revival in Taiwan in recent years.
They can be found at traditional markets during the Dragon Boat Festival season.
They are generally made with colored silk materials in the shape of animals or cartoon characters and are filled not only with the traditional herbs but with aromatic flowers like lavender or rose petals.
Another custom practiced in Taiwan is to stand an egg on its end at exactly noon on the day of the festival.
It is believed if you can successfully stand an ego.
the coming year will be filled with tremendous luck (Dragon Boat Festival usually falls close to the Summer Solstice of June 22nd, which is by far the easiest time of the year to accomplish this feat).
Jongzi (粽子) is the most popular traditional food eaten during Dragon Boat Festival.
Traditionally, families make their own jongzi at this time to share with relatives and friends to eat during the festival.
Gradually they became readily available in restaurants and from street vendors, and can now be eaten any time of the year.
Jongzi is in fact a very rich and nutritious snack with a high level of cholesterol.
It is traditionally made with glutinous (very sticky) rice with fillings that can include pork, egg yolk, peanuts, mushrooms, and other ingredients, the whole thing wrapped in dried bamboo leaves and usually steamed.
The ingredients used may vary according to region.
For example, jongzi in northern Taiwan are chewier and made with cooked glutinous rice before steaming, while those from the south are softer and glue-ier and made with uncooked or half-cooked rice which is steamed before eating.
Jongzi can easily be made at home.
First, fold dried bamboo leaves into a triangular shape.
Second, fill with glutinous rice, and then place other ingredients such as pork, mushrooms, egg yolk, peanuts, walnuts, in any combination onto the rice.
Now cover them with more glutinous rice.
Finally, close the package tightly by folding down the bamboo leaves and tie it tightly with cotton string.
The jongzi is now ready to be steamed; the length of cooking required depends on whether cooked or uncooked rice was used.
After steaming, the jongzi is ready to be eaten, either plain or popularly with delicious sweet/chili sauce (Aizi Wei, 愛之味sauce company makes a good one).
The most exciting part of Dragon Boat Festival are no doubt the dragon boat races (龍舟賽) themselves, which attract crowds of spectators.
Dragon boats are typically canoes ranging from 40-100 feet in length; the heads are in the shape of open-mouthed dragons, the body of the boat is painted to look like a dragon's scales, and the dragon's tail is decoratively designed at the stern.
Dragon boats are generally brightly painted, and a formal, sacred 'eye-dotting' ceremony must be performed to bring life to the boat by dabbing red paint in the pupils of the dragon's eyes before the race.
The competing teams have rowers, a drummer and a flag-catcher at the front of the boat.
The team row forward in time with the pounding drums, and the winner is the first team to grab the flag at the end of the course.
Dragon boat races are held in major cities and counties around the island.
Taipei City has an international competition called the Taipei International Dragon Boat Race Championships which attracts teams of paddlers from all over the world to join together to learn the cultural traditions of the festival.
The international races have been held on the Dajia section (大佳段) of the Keelung River near Dazhi Bridge (大直橋) since 1996.
International team from Australia, Canada, Europe, Japan, the US, Thailand, the Philippines and other countries gather at the crack of dawn every day for many weeks before the race to train.
On the day of the race, spectators (both local nationals and international members of the community) come to enjoy the fun of the festival and the races, and there are many festival-related performances and activities, all staged at the Dajia Riverside Park.
The vibrant Dragon Boat Festival in Taipei is a perfect day out for families, and especially children.
I've had friends from Hong Kong, Singapore, Shanghai, and Japan visit Taipei simply to have a 'power eating tour.'
They are not interested in high-class restaurants; instead they crave Taiwan's most famous xiao chi (小吃), or 'small eateries'.
If you go to the night markets or the food courts in the basement of most department stores in Taiwan, you can find a great variety of food items that are very specific to Taiwan.
Here, I'd like to focus solely on the great variety of dumplings (or at least, dishes that are usually translated as 'dumplings' by foreigners) to be found in Taiwan.
Dumplings come in many different forms, shapes and tastes, and have different names.
Dumplings, like Italian ravioli, usually have a filling containing ground meat and vegetables wrapped in a skin made from flour dough.
They can be steamed boiled, pan-fried, or served in soup.
Common dumpling meat fillings include pork, beef, shrimp and crab; popular vegetable fillings include cabbage, scallion (spring onions) and local chives.
They are usually eaten with a dipping sauce containing a combination of soy sauce, vinegar, sesame oil and hot chili sauce.
Dumplings are a very important food during Chinese New Year because their crescent shape looks similar to ancient Chinese gold or silver ingots, and symbolizes wealth.
On New Year's Eve in the old days, family members would join together to make dumplings as a family activity, hiding a coin in one of them.
The lucky person who eats the dumpling with the coin will likely enjoy good fortune in the coming year.
Dumplings are one of the most delicious and popular foods in Taiwan.
Dumpling stands and restaurants are everywhere around the country.
Perhaps you can let me know which is your favorite after you try all eight types!
Night markets are one of the highlights of Taiwanese life, with their own unique culture, and for most tourists and foreign guests a visit to at least one is a 'must' when in Taiwan.
The average night market offers a mixture of individual vendors and stalls hawking food, trendy clothing, shoes, bags, watches, trinkets, hair accessories, consumer goods, and more.
There are also children's arcades with traditional children's games like pinball, ring toss and goldfish catching.
For a few coins per game, children can win prizes such as goldfish, bubble gum, stuffed animals, toys, or a ticket to play another game.
Night markets in Taiwan are renowned for their specialty food items.
These are found in local eateries called xiao chi (小吃, which literally means 'little eats' or 'snacks').
The diversity of these snacks can range from a quick mouthful to a complete, filling meal; from iced drinks and sweets to hot foods, costing from NT$20 to over a hundred.
A satisfying, sizzling steak set, for instance, is only about NT$120!
Gourmet cuisine from many countries is available in Taiwan, but I always crave local Taiwanese delicacies when away for a long period; the street food is so special that I couldn't find it when I was living overseas in America or Japan.
I know I am not speaking just for myself, but for many other Taiwanese too when saying the experience of eating local food at a night market is sometimes much better than having a fancy dinner at a fine restaurant.
Many night markets are located near schools, since students are the main customer group; food and other goods are sold at much lower prices than in most other restaurants and stores.
Businesses at the night market generally set up at around 4 or 5 pm as students begin to return home after school.
The peak hours are between 8 and 11 pm or so, when workers are off duty and families go out for dinner or late night snacks.
Vendors normally close at midnight, or around 1 or 2 am on the weekends.
There are endless xiao chi to choose from in Taiwan.
Many townships and cities in Taiwan are known from their own signature dish that is specific to that area.
For example, if you want the best mochi (麻糬, glutinous rice paste with peanut powder), you should go to Hualien (花蓮) in eastern Taiwan; if you like rice noodles (米粉, mifen) you can get the best from Hsinchu City (新竹).
It's customary for the Taiwanese to not only enjoy the local specialty while traveling in Taiwan, but it's also important to bring some back to share with their family or colleagues especially managers, as it suggests that you were thinking of them while away.
Fortunately, these specialty foods are readily available in all cities at the night markets without having to travel to the region where they were made.
Let me share some traditional Taiwanese xiao chi that I love and missed the most when I was living overseas.
All major night markets in each city in Taiwan are crowded, hot in the summer time and very noisy with the sounds of hawkers shouting and pop music playing over loud speakers.
They are generally packed with people shopping and enjoying good meals.
There's always something fun to discover, and it is definitely worth exploring; you'll certainly find some unexpected surprises.
For those who enjoy the food found at night markets, but don't like the idea of eating at night markets because they're too hot or crowded, many xiao chi stalls have also been introduced to food courts at department stores so that people can enjoy night market stall food items in an air-conditioned environment.
Alternatively, a whole range of Chinese food is available at restaurants in Taiwan.
With its diverse ethnic groups, Taiwan offers the most authentic original cooking styles of Chinese food; Fujian and Hakka cooking came with the Chinese immigrants who began arriving in the 17th century, and Chinese who came from all different provinces in China when the ROC government moved to Taiwan in 1949 all brought their original home cooking to this island.
You name it, you can find the most traditional cuisine from Shanghai, Sichuan, Zhejiang, Shandong and other regions of China in Taiwan.
From the night markets in the streets to comfortable restaurants, Taiwan is a wonderful place to savor delicious Chinese cooking.
The former president of the American Chamber of Commerce Taipei, Richard Vuylsteke often shares: “If you haven't eaten there, you haven't been there.
Living in Taiwan it's easy to become confused and maybe even frightened by Taiwanese customs.
We all naturally ant to avoid offending the locals, and knowing a few customs and behaviors that are considered unacceptable by most people in Taiwan can help you avoid getting into an embarrassing and sometimes difficult situation.
The Taiwanese generally do not expect foreigners to know much about their customs and taboos, and are usually forgiving.
This chapter, however, explains the two most important things to keep in mind in order to avoid the possibility of offending the Taiwanese, plus some appropriate business etiquette that can help you avoid behaviors that may cause problems.
Here are the two mistakes in etiquette that are most likely to upset the Taiwanese, together with the correct procedure to follow.
When using chopsticks, never stick them upright in your rice bowl.
This is probably the most offensive thing for a Taiwanese.
He or she may not explain to you your mistake, yet it brings discomfort and uneasiness to many.
The reason for this is that it is only done at a funeral.
When somebody dies, Taiwanese place incense sticks upright in a bowl of rice to show respect for the dead.
So sticking your chopsticks in your rice can be read as wishing the 'death of the person, or that the person sitting across from you is 'dead'!
Instead, lay them horizontally on top of the bowl, on your dish or on the chopstick rest if one is provided.
While on the subject of rice bowls, never tap on your bowl with your chopsticks.
Beggars do this, so it is not polite.
If you are in someone's home, it may also be regarded as an insult to their cooking.
Present and receive business cards with BOTH hands to show respect.
You must have business cards and always carry them with you.
The exchange of business cards is a fundamental aspect of a business relationship.
The Taiwanese feel embarrassed not having them when meeting new people, sometimes even in social settings.
Your business card represents your identity and your social 'face', prestige and authority.
It is recommended for non-working foreign residents to create a business card of home information.
It's a good way to exchange contacts.
Make sure your title and relevant credentials appear on your card so that the other person knows how to greet and speak with you according to your status.
Always present the business card with BOTH hands, as this is a sign of respect.
The card should be given right-side up and facing the recipient where the recipient can quickly read and scan information presented on it.
It is demeaning to write on someone's business card (as the card represents the 'face' of a person) or to put the business card directly into your pocket without first reading and commenting on it.
Always look at it and read it again to show respect.
In addition, here are a few business etiquettes to keep in mind when working with the Taiwanese:
Greeting is important.
Taiwanese generally make a gentle nod to acknowledge the presence of the people in the meeting.
Hierarchy and status are important in business meetings.
People treat elders and people of higher status with great respect.
Thus, in a group meeting, greet the most senior person first.
If you are a member of the group, stand in order of seniority.
Don't be surprised if everyone stands up and slightly bows to the most senior person when he or she enters the meeting room.
Taiwanese tend to be 10-15 minutes late for meetings, and are only punctual when the boss is attending.
Many local companies have a lunch hour from noon until 1:30 pm.
Thus it may seem rude to schedule meetings around lunch hours, as many Taiwanese staff enjoy taking a twenty- to thirty-minute nap during this time.
The Taiwanese workers' custom of simply dropping their heads down on the working desk and sleeping often surprises foreign colleagues.
Taiwanese have had this kind of training since elementary school and are accustomed to taking a power nap after lunch on the school desk.
A meeting may begin with some small talk.
Avoid going straight to the agenda.
Try not to openly criticize.
Preserving group harmony is the key during the meeting.
Business entertainment, such as dinner or evening drinks, always takes place in a restaurant, and it may seem a lengthy night, especially if you have a traditional Chinese ten-course meal.
It's also common to have a second or even third round of entertainment at a local KTV (Karaoke club, commonly known as 'Karaoke Television'), lounge bar or pub.
You may be overly inundated with social invitations; however try to always accept the invitations if possible.
It is believed the best way to truly know other people is to go out with them socially.
It's much easier to talk about business once you have established social connections at dinner or after a few drinks.
Always check with your local Taiwanese peers which invitations are not to be missed.
Toasting is inevitable at formal business dinners.
You will probably hear the word ganbei (乾杯), which means 'bottoms up'.
You are not necessarily expected to drink everything, and can graciously decline the challenge, but when a toast is offered, you should raise your glass with both hands and at least sip your wine, beer, juice or tea (taking only a sip is called sueiyi, 隨意) It is a polite gesture to always hold the glass with both hands when you drink.
There is always an opening and closing toast and a number of other toasts will be made between each course of the meal.
Yes, that's lots of toasts!
It's best to respond to every individual or group toast throughout dinner to show your respect.
At least the Taiwanese are certainly much more relaxed now than fifteen years ago, when drinking until drunk was common at business dinners.
Chopsticks
Yes almost all dining requires the use of chopsticks.
However it's perfectly fine to ask for a knife and fork if you really can't use them.
Being able to use chopsticks will, however, be appreciated.
Fish or chicken bones
There are a lot of bones involved in the average Chinese meal: fish, pork chops and chicken all have bones which you'll have to deal with (the Taiwanese in fact prefer to eat chicken meat on the bone, which is why chicken breasts are cheaper than chicken legs here in Taiwan).
It's best to place the bones on the plate or in a napkin and wrap it up when you finish eating.
Don't be surprised, though if you see some Taiwanese spit the bones directly onto the table!
Paying the bill is generally a one-person job.
One member of the party generally picks up the bill (for business dinners it's always the host); similarly one person also pays the taxi fare (generally the person sitting in the front passenger seat).
Taiwanese keep a mental record of this, as at some point it will be your turn to pay.
You should offer to pay and do indeed pay at some point.
An exception to this rule is when going out for an informal meal with friends and colleagues, when it is common to split the bill.
Tipping at restaurants is not required.
In fact it is not necessary for any services in Taiwan: not for the hairdresser, the taxi driver, nor at the hotel (though hotel staff have been spoiled by foreign guests and now feel it is standard to receive some tip).
It may perhaps be more customary to leave a tip for the bartender or waitress when visiting bars.
Nonetheless, whether or not to tip in Taiwan is a personal choice and is never compulsory for anything.
Some may even feel offended and refuse to accept it.
For both social entertainment and business meetings, do not sit down first.
Always wait until the most senior person has sat down or wait until you are told where to sit.
The most important guests are usually escorted to their seats.
As a rule of thumb, the host always seats at the formal round banquet table facing the entrance door if it is a private room, so he or she can see guests entering the room.
The guests of honor typically sit next to the host, on either side.
The person of lowest ranking from the host side of the party sits closest to the door in order to take care of the food and drink orders and to makes sure the bill is correct.
Remember: 1Always have your business cards with you, and 2Do not stick your chopsticks straight up in a rice bowl.
Both are important keys in showing respect to Taiwanese and to ensure that your Taiwanese friends and business associates do not take offence.
The remaining rules of etiquette listed here should not offend the Taiwanese if not followed but are good to know and to keep in mind should you wish to avoid making a cultural faux pas at business meetings or events in Taiwan.
Further rules of etiquette are listed under the 'Gift Giving' section on page 80.
Everything you do is about 'face' here in Taiwan!
How you give and save it for yourself and for others is extremely important, both professionally and socially.
The Taiwanese concept of 'face' is similar to the Western sense of 'being embarrassed' or one's 'reputation' but it's much more serious than that for any Taiwanese.
Face goes far beyond the self to embrace the entire family, ancestors, and everybody that is part of their 'group'.
If anybody does something bad, they haven't just harmed their own reputation, but have also brought shame upon many people (to all those in the 'group').
The term mianzi (面子), literally means 'face', but it refers to the whole of a Taiwanese person's identity.
Mianzi is the perception of prestige, one's projected social image, social self-respect and social self-esteem.
It influences how people see each other, and how they relate to (and are expected to speak to) others.
A person's self-concept is connected closely with one's 'face.
Taiwanese who are in prestigious positions are often perceived to have 'face', and consequently their respect, pride and self-worth are enhanced greatly.
Losing face is when one's reputation is reduced or destroyed in public; it brings shame upon the individual, and thus everyone tries to avoid losing face at all costs.
Face can only be lost in public; it is external only if someone finds out about it.
Once face is lost, it is hard to regain or to recover.
It's not only a loss of trust, influence, and power, but it also affects one's connections in the social network and one's ability to function effectively in business.
Taiwanese insist on putting on a flashy wedding, even if they can't afford it.
Even guests to whom the newlyweds are not closely related are invited, to give both parties face.
Taiwanese do not reveal personal or family disgrace to others, as it brings shame not only to the person but also to his or her family.
Telling others things like, "My brother didn't graduate from high school," or "My father was just laid-off" or "My mom re-married twice," are all seen as ways of losing face.
Children are taught to remember whatever they do, good or bad, will affect the whole family.
They're reminded to study hard and attend a good university, as high academic achievement brings face not only to the child but even more so to the parents and the entire family.
For example: a quiz is given and any child who misses a question has to stand up while the teacher lectures about ow they have shamed the school and their families, let everyone down and lost face.
This isn't the worst part either; students know that after the lecture they're going to be punished or hit for each question they missed.
This is still pretty much the method of discipline in Taiwanese schools (though corporal punishment can be banned in some schools now, other forms of punishment can be handed down, such as copying out a chapter in a text book, or staying after school to do campus cleaning chores).
Thus people are very reluctant to put themselves in a position where they might give the wrong answer.
In the presence of their seniors, Taiwanese colleagues are generally very reluctant to offer solutions, or still less openly disagree, unless they have had an appropriate time to process their thoughts, check out the 'rules' and feel confident that they are giving the 'right' response (which may not be the most brilliant suggestion of the discussion, but simply one they believe the group, and the seniors, wants to hear).
The related concepts of face and guanxi (discussed on page 84) are both very important in understanding working dynamics, not only in Taiwan but also in many countries in Asia such as Korea, Japan, China, Hong Kong, Singapore and Vietnam, where similar concepts exist.
Every conversation is an exchange about face; every interaction involves guanxi.
Understanding the two concepts will help to ensure your success in dealing with Taiwanese both at work and in your everyday life.
As I was growing up, I often heard of the importance of having good feng shui at home and in the office.
Here are some examples I remember hearing from my mom: 
It's not good to live in a house with a front door directly facing onto a street.
Don't sleep with your feet directly towards the door.
This is known as the 'coffin' position and you are basically asking for ill health and bad luck.
My father always had a solid wall behind his desk in the office.
It's even better feng shui to hang a painting of mountains on the wall because it symbolizes support as strong and vast as the mountains from your employees and your superiors.
You should never have a door or window behind you, or else your coworkers will not have confidence in you.
So what exactly is feng shui?
Feng shui (風水), which translates literally as 'wind-water,' is the ancient Chinese art of tapping into the Earth's five elements (earth, fire, metal, water and wood) to boost energy, health and prosperity.
It is also the interaction of the yin-yang forces the two opposing yet deeply interconnected forces that rule the universe.
Chinese believe that good feng shui helps you tap back into the flow of things and thus helps improve health, wealth, and relationships, and ensures harmony and happiness.
Feng shui is about locations and directions, like the placement of doors and windows, and ideal sitting and facing directions.
Traditionally it is used in choosing a place to live (where to buy the house) and a place to lie peacefully after death (a suitable burial site).
In modern Taiwan, it is still widely believed and practiced; many Taiwanese check with a feng shui master before buying a property to live in or to rest after death.
Taiwanese pay close attention to the basic layout rules of their home location and direction (the most preferred home direction is for the front door to be facing south).
Feng shui dictates the direction of the stove in the kitchen, direction of the bathroom, and the position of the bed in the bedroom to ensure a good, refreshing night's rest.
Furthermore, it is believed that good feng shui in the office of the person in charge brings fortune and success not only to the corporation but also to the employees.
An expatriate was astonished that not only did his Taiwanese colleagues rearrange his office, but that they even went to a fortune teller to find the best possible Chinese name for him when he first arrived.
He was told that his Chinese name (which would be registered with the Taiwanese government as the person in charge) would greatly influence the company's business volume, its future, and the wealth and well- being of every person in the office.
The feng shui practitioner uses various tools to activate a client's luck and to create better feng shui, including a crystal ball, lucky bambo0, and a laughing Buddha.
Crystal balls are among the most popular feng shui tools.
Not only can they be used as beautiful decorations, but they can also deflect bad angles, known as 'poison arrows'.
Bamboo is widely represented in Chinese art, literature and poetry, and is believed to bring luck and fortune to business owners, happiness to homes and to result in a jump up the corporate ladder when placed in an office cubical.
The laughing Buddha with his big sweet smile and round belly is my favorite.
Friendly and playful, it brings joy into a home and helps ward off bad luck.
Rub his tummy enough, and he brings money and happiness.
These feng shui tools bring the balanced energy needed for a healthier and gratifying executive shared that he environment.
There are plenty of books explaining the concepts of feng shui, and countless workshops held by feng shui masters aim at creating a harmonious home or a space that balances health and well-being.
I won't claim that feng shui tools perform miracles, but it might be interesting to see if prosperity and harmony arrive in your life if you introduce a little nature, such as wind chimes, candles, water fountains, laughing Buddhas, lucky bamboo, or crystals into your home or office in the right spaces.
Even if you don't believe in their powers, you'll be surrounded by some very pleasant decorations.
Westerners and Taiwanese definitely take a different approach to solving life's doubts and questions.
When things get too hot to handle, Westerners consult a professional for help, discovering possibilities and solutions to the problems and struggles they have in their lives with the aid of a counselor or other qualified professional.
By contrast, the Taiwanese prefer to go to a fortune teller for answers.
The Taiwanese belief, fostered in childhood, is that they should seek and follow directions in life from teachers, parents and the elderly.
Self- exploration of options and possibilities has never been highly encouraged in Taiwanese society.
It might be hard to believe, but I can assure you that most Taiwanese have been to a fortune teller at least once in their life.
Interestingly enough, many highly educated working-class people, academics, politicians, and the business elite do very much believe in fortune telling and the power of psychics.
They often have their own personal fortune teller (or someone they refer to as their teacher, or laoshi, 老師), whom they visit for advice on a very regular basis throughout their entire life, and these teachers' input does impact one's decision making.
Taiwanese seek advice from fortune tellers before making a decision about a whole list of things: education, marriage, career decisions, opening a business, choosing suitable business partners, buying, selling or even decorating a house, relocating, naming a child, communicating with the dead, choosing the color of car to purchase, or even (more recently) predicting the result of election campaigns in Taiwan.
The most common and comprehensive method of fortune telling uses a client's Chinese name, and the figures known as the ba zi (八字, literally 'eight figures'), derived from his or her date of birth: the year, month, day, and time to the hour).
With this information, the fortune teller can predict the client's prospects for health, wealth, marriage, career, relationships, success and many other subjects of importance.
Other fortune-telling methods range from palm reading (手相, shouxiang), face reading (面相, mianxiang), and feng shui (風水), to analysis of a client's name, reading of facial or body blemishes (such as moles and spots on the face), or picking fortune sticks offered in temples, where a written message is given to be interpreted according to the question asked.
In addition, 'fortune telling birds' (鳥掛,niao gua) or 'tortoise shell' (龜掛, guei gua) methods are still practiced in some places.
In the former, the fortune teller (also the owner of the birds) whispers the client's name, age, and date of birth to the birds in the cage after which a bird will stick its head through the bars and select a fortune written on a slip of paper, either from a pot or stacked in a pile.
The tortoise shell method on the other hand is performed by placing three ancient Chinese coins with characters written on them in an empty tortoise shell.
The shell (with the coins inside) is shaken for a few seconds and then carefully emptied onto the table.
The fortune then is read from the compiled characters from these three coins.
Western astrology and Tarot card reading have also become popular in Taiwan In a modern and technologically highly developed society like Taiwan, not only it still common to visit a fortune telling expert to get guidance and understanding of one's fate, but countless websites, newspaper columns and TV programs on fortune telling are also in style and are very popular.
Most fortune tellers tend to work at home, and new clients usually come to them through recommendations from their relatives or friends.
There are also some who prefer to do it on the street, usually sitting outside on a chair beside a small table, waiting to attract passers by.
You can often see them around night markets or at temples such as near the famous Lungshan Temple in Wanhua district, or at the underpass near Xingtian Temple in Taipei or even around the busy shopping districts of Dinghao (頂好) on Zhongxiao East Road in east Taipei, but personally, I wouldn't go to them because I have no reference or relationship with them.
Fortune tellers are paid in the form of hongbao (紅包), money placed in a red envelope.
Though each fortune teller has his or her going rate, the expected amount is really up to each customer; some tend to give more than the asking rate if they feel the information given is true and accurate, that they received enlightening advice to their puzzles and concerns in life, or simply if they are happy to hear that a propitious fate and fortune awaits them.
Local Taiwanese also believe in the possibility of changing one's fortune.
The busiest time for fortune tellers is generally around Chinese New Year, when people want to know their fortune for the coming year, and tips that can promote better luck, wealth and success.
Suggested ways to change one's fortune can include changing the directions of furnishings at home, positioning additional objects like crystals, mirrors, or flowers in specific places to bring luck, changing one's name to bring a fuller and brighter future, removing unlucky facial moles or blemishes, or forming the proper shape of the eyebrows, among others.
Nowadays, expecting mothers may even plan an auspicious day to have a caesarean to ensure their child is born at the very best and luckiest time.
Whether you believe that your future life is predictable and under your own control, or that everything in your life is pre-destined, I think there must be some truth and accuracy to fortune telling for it to have been so deeply involved in Taiwanese people's lives for hundreds of years.
More than anything it provides guidance and comfort for those who need to share their troubles and Worries, and helps achieve happiness and balance in one's emotions and state of mind.
In today's scientific world, so much remains inexplicable and difficult to understand that there is room for unscientific realities such as the possibility of accurate fortune telling.
"When my wife and I got lost in Taipei and took out a map to get our bearings, within minutes we had six people offering help and giving us directions, even though they didn't speak English." 
"When I asked a scooter rider for directions, he asked me to hop on and he rode me to my destination." 
Does this sound familiar?
Although Taiwanese may seem shy on the outside, they're actually very hospitable.
Indeed the general impression that most foreigners and foreign residents in Taiwan carry is that the Taiwanese are, among other things, friendly, polite, hard-working, kind, passionate, easy-going, reliable, open, and flexible.
I am often asked by foreigners (not only Westerners but also people from other parts of Asia like Japan, Korea, Hong Kong and Singapore) "Why are the Taiwanese so nice?" and "Are they really sincere about their friendliness?
" It's a fact that these days people are often wary when others are too nice, but in the case of the Taiwanese, then yes, they are indeed among the most friendly people you'll find in the world, and they're sincere about their generosity and willingness to help others.
The Chinese have a saying: jing di zhi wa (井底之蛙), which translates literally as 'a frog living at the bottom of a well'.
No Taiwanese want to be jing di zhi wa; to be constrained in what they can see by the walls of the well, while the bigger outside world remains out of sight and out of reach.
Taiwan is an island surrounding by oceans.
Though it has plenty of natural resources, a rich culture and diverse people, the land has been independent or insulated in a sense from other civilizations, information and from interaction with people from other cultures.
There are 23 million inhabitants dwelling in this Formosan 'well', drinking the same water.
Thus any Taiwanese who has had a chance to live overseas and see the world outside of this 'well' are well received and even envied.
Everyone wants to interact with people from other parts of the world, to share in the latest technology and knowledge from overseas.
For the majority of Taiwanese, it is also important to excel and advance so one day they can leap out of the well and see the world outside of Taiwan.
Those returning from overseas are often referred as 'someone who's had some Western water' (喝過洋水, he guo yang shui).
'Water' from America, Australia.
England, and certain other places is perceived to be both different and better than the 'well water' at home, and citizens returning from time spent overseas are therefore perceived to have gained more information and new ideas.
I believe this concept also contributes greatly to the large number of Taiwanese who like to study abroad, whether in a degree program, an exchange program or simply a short summer study program.
The motive is not only to acquire a better education, but to share with other locals the experience of visiting and studying in a different country is also a matter of pride, For those local Taiwanese who have not yet traveled nor had many encounters with non-Taiwanese, foreign visitors or residents on the island are seen as a source of 'different' information and experiences, and a great opportunity to meet and learn more about others lifestyles, ways of thinking, and culture.
Likewise, many Taiwanese enjoy being a foreign ambassador, and when they meet a foreigner they relish the opportunity to share their proud cultural heritage, the glories of Taiwanese food, the night life, hot springs, shopping, historic sites, great scenery and mountains, and the offshore island adventures of Taiwan.
Another reason for the great friendliness of the Taiwanese can be attributed to its rich heritage of diverse populations.
People from many different places and cultures have left their influences on this land: the indigenous tribes who lived in remote valleys along the central mountain range of the island; the Portuguese who sailed by (naming the island 'Ilha Formosa' or 'beautiful island'); the Dutch, who colonized the island for 38 years; the Chinese who immigrated from Fujian and Guangdong provinces in mainland China over a two-hundred year period starting from early 1600s; the Japanese who occupied the island for fifty years (1895-1945); and most recently, immigrants from mainland China who arrived in 1949 following the loss against the Communists.
Each of these groups has contributed to Taiwan's development today.
Over the centuries, the people of Taiwan have learned to interact and co-exist with different ethnic groups and now readily accept people of different customs and traditions.
It is this process of integration, where initial differences in thinking and culture between people living on the island have gradually disappeared, forming the unique Taiwanese culture, that have made people of Taiwan flexible, open-minded and quick to adjust to the new and different, which includes foreign visitors and residents.
This beautiful island truly has amazing things to offer from its wonderful scenery, the nation's rich, diversified cultural heritage to its sincere, hospitable and friendly people.
Whether you are a visitor or a resident in Taiwan, your cultural Experience will be filled with memories of friendly and generous people striving to reserve its traditions.
And above all, the Taiwanese remain humble and kind.
I sincerely invite you to personally discover the true beauty of this marvelous land, whether you are here for tourism, business, study, or living and working here.
Nothing can beat Taiwan's long lasting-reputation for warm, gracious and hospitable people.
Chinese believe in life after death, when the deceased become spirits or ghosts roaming between Heaven and Earth.
For one month of every year, however, these spirits get to return, rather like the Western tradition of Halloween, when spirits are said to rise and walk among the living for a night.
Taiwan, however dedicates a whole month to paying respects and making sacrifices to the ghosts and souls of the underworld.
This month is called the Ghost Month (鬼月) and it falls on the seventh month of the lunar calendar (usually August September in the Western calendar).
On the first day of this month, the 'Ghost Gate' opens and the spirits of the dead (the ying world) are welcomed into the world of the living (the yang world) to visit their descendants and enjoy extravagant feasts prepared in their honor.
On the last day of the month, (the 30th of the seventh lunar month) the spirits' 'vacation' ends and all ghosts must return to the underworld before the Ghost Gate is closed until the following year.
In Taiwan, Ghost Month means ancestor worship.
It's not only a time for people to pray to their deceased ancestors to show the importance of filial piety after death, but it is more so a time to cater for the needs of spirits without any remaining descendents to care for them.
These are typically people who died far from home, committed suicide, drowned or who died without bearing children.
Ghost Month is traditionally more important for Taiwanese people than it was for the mainland Chinese.
Settlers from China came to Taiwan to develop the lane and establish their own homes starting in the early 1600s.
They fought against wild animals in the rugged mountains, encountered deadly diseases, and deal with hardship and turmoil while building on the land.
Many of them died far from home with no one to bury them and no one to carry out ceremonial services in their honor.
These wandering souls became rootless, roaming aimlessly about and can disturb the living.
Consequently, settlers began to look after these wandering souls and honored them like fellow brothers in order to ensure their own peace.
The ritual of paying respect to roaming ghosts in the seventh month of the year also came to be seen as 'honoring brothers' who came to settle in Taiwan rather than simply honoring deceased strangers from a foreign land.
The more the aimless souls of these unfortunate 'brothers' are looked after, the more fortunate and harmonious life the living can enjoy.
With this historical background, Ghost Month is especially unique and respected in Taiwanese society.
Out of a mixture of respect and fear of the ghosts, Taiwanese do anything possible to appease the wandering spirits during Ghost Month.
Folk activities practiced during ghost month aimed at appeasing the visiting ancestors and wandering spirits include inviting the ghosts, preparing ritualistic food offerings, burning incense and paper money, plus entertaining them during their time on Earth by holding parades, drumming troupes, chanting sutras, and performing folk operas, lion-parades, stilt walking, and more.
Major events are generally centered on temples.
Opening ceremonies on the first day of the seventh month are held at temples, where tall bamboo poles of lanterns are erected.
These lanterns are well lit, with 'celebrate Zhongyuan', (慶讚中原)written on them to clearly guide the ghosts to the feast.
Extravagant banquets and entertainments are prepared in temple courtyards to welcome the hungry ghosts.
Taiwanese families and business establishments also make the effort to choose one day of the month to offer sacrifices outside their doors to feed the passing spirits.
Ghost Month activities reach a climax on the 15th day of the month, which is called Zhongyuan Pudu Festival (中原普渡).
 Let's talk about this and some of the other major events that take place during Ghost Month.
Zhongyuan Pudu blends Taoist and Buddhist beliefs.
Taoists call the fifteenth day of the seventh moon the Zhongyuan Festival or 'ghost festival' while Buddhists call it Pudu or the Ullambana Festival.
Pudu simply means 'universal salvation'.
Zhongyuan Pudu literally translates as the 'mid-origin passage to universal salvation'.
According to Taoist belief, there are three main officials in the Taoist pantheon, governing sky, earth and water.
Zhongyuan celebrates the birthday of the Earth god, Diguan (地宮)who is in charge of the earth and the land.
He comes to the mortals and decides who is good and who is sinful.
Furthermore, the day was developed to pay respect to family ancestors and to rescue those lonely souls who were believed to have committed great sins.
Buddhists believe the day celebrates the saving of Mulian's mother from the hungry ghosts.
Mulian (目蓮) was the eldest monk disciple of Buddha who heard his mother was suffering from the attentions of hungry ghosts in Hell.
Mulian turned to Buddha for help but was told that his mother's sins were so great that he must hold a massive ritual on the 15th day in the lunar calendar combined with the chanting of monks in order to save her soul.
Mulian offered abundant sacrifices consisting of 'hundreds of flavors and the five fruits' (百味五果) to save not only his mother's suffering soul from Hell also but to rescue all other suffering spirits.
Nowhere is the celebration of the Ghost Month more evident than in Taiwan, and the offering of food is the most important rite during Zhongyuan Pudu Festival in order to pacify wandering and malicious ghosts who cannot be reincarnated.
The day is celebrated by slaughtering pigs, and by laying out rich dishes and countless treats on banquet tables.
Some of these typical 'sacrifices' include meat, wine, fish, vegetables, sweets, cookies, rice and any other delicious food you can possibly think of.
This is to ensure every soul is fed, and that no ancestor or ghost is left hungry or angry.
An incense stick is stuck into every dish, and spirit paper money is also burnt so that ghosts can take money with them to spend in the underworld.
In addition, sutras are recited or chanted for the delivery of the wandering souls.
It's believed that this leads the way for the lonely and lost souls to cross over from the underworld to paradise where they will no longer have to suffer.
Apart from food offerings, two very exciting and lively folk activities celebrated at this time are the 'Releasing of the Water Lanterns' (放水燈) which takes place on the day of the Zhongyuan Pudu Festival in the Taipei County port of Keelung and Chianggu (搶孤, the 'Snatching of the Flag' competition) which is celebrated before the ghost gate closes.
The city of Keelung has long dealt with early immigrants and their historical turmoil and suffering has led to Ghost Month's grandest ritual, the Releasing of the Water Lanterns, on the fifteenth day of Ghost Month.
This visually striking ritual is held at the port of Wanhai (望海巷海邊) in Keelung.
Burning paper lanterns are floated out to sea to light the way for souls who drowned, so they can find their way to shore to enjoy the warmth of life amongst the living for a while.
It was decided by the fifteen major local clans (each clan is of a family, according to the surname) to work jointly to hold this large-scale ceremony annually.
Every year three family clans (for example the Huangs, the Lins and the Chens) take their turn to act as hosts and sponsors, taking charge of organizing the event, and every family clan participates eagerly.
The event begins with a traditional parade and ceremonial chanting and prayers by the Taoist priest.
Next the huge, beautifully decorated paper lanterns (one made by each family clan) are carried into the water and pushed out into the ocean.
The lantern not only invites the ghosts of one's own family clan but also other shy souls.
It is believed the brighter, faster and further the lantern reaches out to the ocean, the better fortune it brings to the family.
It is especially moving to see believers swim far out to escort the lanterns.
The ceremony brings thousands of spectators down to the harbor to take a look at the spectacle.
The Snatching the Flag competition is especially boisterous.
It is held on the 29th (and last) day of Ghost Month, and is intended to send away ghosts that have not yet returned to the underworld.
Chianggu originated in the Qing Dynasty, but was banned for a long while due to the dangers involved in this undertaking, yet it has been revived in recent years in the town of Toucheng, Illan County.
Chianggu is a competition to snatch flags mounted on a tower of sacrificial objects.
The Chianggu stage is divided into two levels.
The first platform is set at t twelve meters high, with twelve supporting poles for the twelve teams of five persons that take part in the competition.
The poles are greased with beef fat, requiring the team to work together to climb the slippery poles to get to the second platform.
Once the platform is reached, there are thirteen towers each about thirty meters high with twelve red flags and one yellow flag mounted on each, and exciting and rich sacrificial objects.
The first team climber to snatch the yellow flag and one red flag gains not only all the food on the tower, but also tremendous prestige.
The yellow flag is also believed to bring luck for the owner's fishing boat and to protect workers on board.
Thus it can be sold for a fortune.
The revival of Chianggu has preserved the significance of this traditional ritual as well as providing an energetic and healthy sport for the modern Taiwanese.
Ghost Month concludes with the Closing of the Ghost Gate, an event held at dusk on the 29th day of the seventh lunar month, where more cooked food is offered as a 'farewell dinner for drifting souls.
This dinner serves both as a last dinner to be enjoyed among the living and as a sign to return back to the underworld.
The lantern poles at the temples must be dismantled and temples invite Zhongkuei (鍾馗), a special deity who protects the living from evil spirits to escort unwilling spirits back to their own world.
This final ritual completes the month of fun and freedom enjoyed by the spirits and keeps the living safe and undisturbed.
There are a wide range of 'Don'ts' to remember during the Ghost Month, as most Taiwanese believe in ghosts and wish to please them and make sure they don't offend them.
Even today, some of these rules are still followed closely by the Taiwanese.
The only 'Do's are to make offerings and more offerings to the drifting ghosts.
During Ghost Month, people avoid scheduling any big life events like weddings, making big business deals, launching a new product, opening a new business, buying a car or a new home, moving, or having surgery.
Other taboos include no whistling at night (as this will lead the ghost straight to your home); no swimming, especially in open lakes, rivers or the ocean (as a water ghost can easily steal any living soul it finds in the water at this time of year); no using the word 'ghost' or similar words carelessly (as this invites souls dese to you; the correct term to use when talking about them at this time of year is good brothers' (好兄弟, hao xiong di); no leaving clothes hanging outside overnight (as playful ghosts like to wear them, causing illness to the owner); and no staying out too late at night because wandering souls are literally everywhere.
Taiwanese used to avoid traveling, resulting in special discount air tickets at t time; however this belief has faded and promotional packages as a result are ase disappearing.
Whether you are superstitious or not, there does seem to be an increase in the numbers of accidents and deaths on the road and in the water during Ghost Month.
Taiwanese in any event do try their best to do things before or postpone them until after Ghost Month.
Though the government today encourages Ghost Month to be celebrated in a modest fashion, the custom of paying respects to the family ancestors and those 'good brothers' who lost their lives protecting and defending their home is ever lively and strong.
This holiday ritual is an indicator of how the people on this island have fully integrated religion into their everyday life, and its culture will continue to be fostered and passed down to future generations.
In Chinese societies, although gift giving IS associated with the same types of occasions as in other countries, it is of even more significance than in most other cultures, and it is quite important to make sure that you give the appropriate gift at the right time, to the right person, for the specific occasion.
Traditionally only older Taiwanese people celebrate their birthdays and then only those that are celebrating birthdays in multiples of ten: a sixtieth, seventieth or eightieth birthday, for instance.
The higher the number the more important the celebration becomes.
However, with the influence of Western culture young people now celebrate each other's birthdays.
If you are invited to a birthday party, by all means bring a birthday present.
When visiting someone in the hospital, Taiwanese often bring food, particularly soup, or liquid drinks that promote health and quick recovery.
Common items include Chinese herbal soup, chicken soup, fish soup, health food products, or a basket of fresh fruits.
Flowers are not common.
During Chinese New Year, adults give money to children.
Traditionally, the money is put in a red envelope called a hongbao (紅包) for good luck.
Adult family members within the same household do not give presents to each other but when visiting other homes during the festival period, they often bring a small gift of boxed cookies, cakes or food items for each other.
Red envelopes can be readily bought at stationery stores, 7-Eleven or at supermarkets.
Chinese traditionally give money in a hongbao to the bride and groom at the wedding.
The actual exchange usually takes place at the wedding reception via an intermediary (or the wedding receptionist) who opens the hongbao and writes down the amount given by each guest.
You will probably also be asked to sign a scroll.
As a foreign guest it is quite acceptable to give a gift from your home country, although a hongbao would be more usual.
Normally NTS2,000 per person is expected at a wedding banquet held at hotels and NT$1,600 at a restaurant.
Other amounts like NTS3,600 or 6,000 are for those with whom you have a strong relationship.
When you are invited for lunch or dinner at a restaurant, you do not need to worry about bringing anything.
 When dining out with Taiwanese people (both socially and for business), usually only one person from the group pays the bill.
 Try to keep a mental record of when it is your turn to pay.
 However, today many young people pay their own way when dining out together.
If you are invited to eat at someone's home make sure you bring a small gift.
 An appropriate offering would be a bottle of wine, sweets from a well-known bakery, or some small token you have brought from your home country.
If the host is the husband, the wife will be the one working in the kitchen (although the reverse certainly isn't true!).
 Since the preparation of a Chinese meal requires lots of work within the last hour or so before the meal begins, she is likely to be in and out of the kitchen frequently.
 She will sit closest to the kitchen during the meal and might even ask the guests to start the meal without her.
 Do not insist on waiting until the cook is ready to eat.
You should make appreciative comments about the food and the hospitality, but be prepared for the hosts to apologize for various inadequacies such as, "I didn't prepare enough food.
 I hope you had enough to eat," or "I hope you didn't mind my horrible cooking."
Whatever you do, do not make the mistake of agreeing with any of these self-deprecating comments!
Don’t be surprised when Taiwanese friends decline your gift.
 Taiwanese do not usually accept a gift when it is first presented.
 Politely refusing two or three times is thought to reflect modesty and humility.
 After a battle in which the gift is offered and refused several times, it will be accepted with appreciation, yet the gift is often not opened until the giver has departed.
 Accepting something in haste makes a person look aggressive and greedy, as does opening it in front of the giver.
Something from another country or from your hometown is always a great gift.
 For example, a baseball cap from your university or your town's ball team, a pencil or pen or other piece of stationery with designs representing where you are from, or famous snacks or sweets produced in your country all make great gifts.
It is also important, however to know what NOT to give, so that you don't upset your Taiwanese hosts.
 The following gifts should be avoided at all times: 
Clocks: The phrase 'giving a clock' (song zhong, 送鐘) sounds like the phrase for sending someone off at their funeral!
 Flowers: Flowers in general are all right apart from white and yellow chrysanthemums, which are regarded as funeral flowers and should never be given on other occasions.
 Carnations are popular gifts for mothers on Mother's Day, Please do not give carnations to impress your special date!
 Gift-wraps: When wrapping, be aware that the Taiwanese ascribe great significance to various colors.
 Red is the color of luck and prosperity, while white, gray and black are colors of mourning.
 Handkerchiefs and towels are usually given to those who attend funerals at the end of the ceremony and are signs of sadness.
 Sharp objects like scissors and knives symbolize cutting off friendship or a relationship.
 They disconnect luck and prosperity.
 Umbrellas and Chinese paper fans also symbolize the separation or termination of friendship.
 Thus, do not give your boyfriend or girlfriend an umbrella if you want to stay together!
If you mistakenly give an unlucky gift, Taiwanese typically offer the giver a coin (NT$1 or NT$10).
 This action suggests that the item has been 'purchased' and not given.
 Taiwanese generally do not expect foreigners to be familiar with local etiquette and are very forgiving when foreigners make a cultural faux pas.
 When in doubt just ASK!
Every business transaction is a dealing of guanxi (關係) and every guanxi is intricately connected and maintained.
 A Taiwanese has guanxi with all kinds of people: in the work unit, at local shops and street stands, and with relatives, friends, colleagues, subordinates and supervisors.
 It's what makes many aspects of daily life run smoothly.
 No one should wish to remain completely outside the guanxi system, and no one can.
 It is just as important to accumulate credit in what I call the 'quanxi account' as it is to save money in one's bank account.
 Just as we all wish to have more money in our bank account, every Taiwanese desires to accumulate more connections in their guanxi account.
 The more one has in his her guanxi account, the more face, respect and prestige are gained.
There is no direct English translation for the word guanxi.
 It's often translated as 'relationship' but it is far more than that; it describes your relationship connection, dependency, network, friendship and, most importantly, your obligation.
 One's life revolves around the accumulated guanxi and the resulting obligations of these connections.
 Guanxi involves an ongoing series of reciprocal exchanges.
 One helps and gives to another and therefore expects, at some unspecified future date, to receive from that other person.
To put it in another way, if one receives, one incurs an obligation to give later on.
 What is given need not be similar in value to what was received.
 For instance, one's gift of imported wine may later be repaid by the other's use of influence with getting better services at a hospital.
 Taiwanese often keep a mental record of what they have received and given.
 When guanxi is needed, a Taiwanese will search for the best connection within one's own web of relationships to achieve their objective.
 Should further guanxi be required, one would 'pull' guanxi (拉關係, la guanxi) to search in others' webs of relationships.
Introductions are an important way to gain guanxi, even among remote connections.
 At the first meeting (whether business or social), Taiwanese show respect for each other to ensure harmony.
 However, as an introduction is made the friendship and connection are established.
 The formality gradually dissolles into informality.
 One begins to trust the other party through the person makes the introduction.
 This introduction not only connects the two new partes it also helps establish credibility for the person making the introduction.
My classic example of the importance of introduction is as follows.
 I've been buying fresh juice at a small local juice stand for about two years, and with the guanxi built over time, the laoban (老闆 , owner) now gives me an extra small cup of juice to drink while waiting to get my order.
 I introduced my friend Lisa, who is American and came to Taiwan for a summer Chinese language program to the laoban, and she immediately received the same treatment: a small cup of juice to sample while waiting for her order.
 It took me two years to build the relationship and I no doubt spent a good total sum of money having delicious fresh juice at the stand.
 With my introduction, Lisa immediately benefited from my two years of guanxi and was treated the same.
 Every time Lisa goes back to that same juice stand, she receives something extra: a fresh orange, some local sweets, or that small cup of juice.
 Lisa of course is a friendly American and the friendly Taiwanese no doubt like to show her a little local hospitality, yet without my initial introduction, she would not have gotten this special treatment so quickly.
 The same concept further applies to a business setting.
 Trust is built when guanxi is introduced, and thus it becomes easier to do business.
 Everyone is trying to accumulate guanxi in their life, and this collected guanxi is saved and spent with discretion, just like money in a bank account.
Nurturing a relationship thus becomes important.
 It's vital to reciprocate.
 Understand that it is expected that the favor is definitely to be returned at some time in the future.
 Many guanxi are built on coffee breaks, golf outings and dinners; some business entertaining can involve cultural sightseeing trips or gift giving.
 A small gift is appreciated as it indicates a kind gesture and may lead to forming important connections.
 However, you should be suspicious when receiving a 'valuable' gift such as a brand name watch.
 This action could be an indicator that a major favor will be expected from you in the future.
For a Taiwanese, one's guanxi and 'obligation' can never be disconnected once introduced.
 One depends on these guanxi in life.
 As a result, Taiwanese are people oriented.
 They enjoy face-to-face interaction as it develops trust; they share more information among those with whom they have the best guanxi; business and friendships may seem to evolve slowly, yet once established last.
 Consequently, when interacting or working with Taiwanese associates, expect to spend a higher portion of time socializing at dinners and functions, deal with more unscheduled visits or last-minute changes of arranged meetings, and to comply (due to obligations) with favors asked.
 Remember: the web of guanxi is circular; it never dies out.
It is difficult to miss the sight of a good steaming hot pot restaurant while walking through the streets during the chilly Taiwan winter season.
 Hot pot or huo guo (火鍋), literally means 'fire pot' in Chinese and is undoubtedly the Taiwanese people's favorite food during the cold days of winter.
 It is also a social meal, where friends gather around the table, cooking, chatting, drinking and sampling a variety of delicious food, all at the same time.
The idea of hot pot is similar to that of fondue in the West, although in a Taiwanese hot pot, food is cooked in stock instead of dipped into hot cheese.
 The hot pot is placed on a hot plate or burner at the center of the dining table where everyone can reach it, and is kept himmering while plates of goodies ranging from thinly sliced meat (usually pork or beef, but also lamb or chicken), to leafy vegetables, seafood, various types of tofu and other varieties of bean curd mushrooms, egg/ fish/ shrimp dumplings, and any other raw ingredients can be put into the boiling stock for cooking.
 The flavor of the stock gets stronger as various bits of food are cooked in it, and it is typically drunk at the end of the meal.
The cooked food is dipped into a sauce before eating.
 The most commonly used sauce Taiwanese prepare for hot pots both at home and at a restaurant is called shacha jiang (沙茶醬, a seasoning sauce made with garlic, soybean, onions, peanuts, fish and shrimp pastes and other ingredients.
 It is slightly spicy, and is usually mixed with some soy sauce, spring onions and sometimes a raw egg.
In the old days, the basic stock was made using a vegetable, meat or fish base.
 Nowadays however, hot pot restaurants have created new variations of stock to attract customers, including spicy Sichuan style mala huoguo (a, literally 'numb and spicy hot pot').
 It does indeed create a spicy, burning and slightly numbing sensation on the tongue.
 For those who can't take this fiery soup base, a yuanyang guo (鴛鴦鍋, literally a 'female and male mandarin duck hot pot') has a divider down the middle of the hot pot, with half the pot filled with spicy stock and the other half with a mild, non-spicy stock.
Another variation is the 'Manchurian' style hot pot called dongbei suancai guo (東北酸菜鍋) in which plenty of suancai (Chinese pickled cabbage or sauerkraut) is placed in the stock to flavor the soup.
 Fatty, sliced pork is commonly cooked in this sour stock as the sauerkraut absorbs the oil, which makes a good blend for the soup base.
During winter, Chinese herb mutton hot pot, (yangrou lu, 羊肉爐)is extremely popular, as it is known to give the body extra energy to stay warm during winter days.
 The broth is prepared with unique Chinese herbs and spices in which chunks of mutton are cooked with root ginger and rice wine to rid the mutton of its rank flavor.
Other kinds of hot pot you may come across include: Japanese shabu-shabu with fine Kobe beef, Korean kimchi hot pot or stone-pot, Thai curry-base hot pot, chouchou guo (臭臭鍋 , literally 'stinky stinky hot pot', although it's only a name, the food doesn't stink; on the contrary, it tastes delicious) as well as special Chinese mild and light herbal vegetarian hot pot for religious or health-conscious eaters.
Food in hot pots is taken out using chopsticks or ladles.
 At restaurants there will be special serving chopsticks and utensils which each person should use to put food in the pot.
 When the food is cooked, it is shared with other people at the table.
 At home, members of the family handle the cooking on their own with their eating chopsticks.
 The main meal on Chinese New Year's Eve is hot pot, which on this occasion is called weilu (圍爐, which means 'around the stove', or circling around a hot pot').
 Eating a hot pot while sitting round a table has the significance of emphasizing togetherness, and the bringing of the family closer to each other.
Hot pot is a very popular business, and hot pot restaurants can be found everywhere in Taiwan, from night markets and department store food courts where a meal will cost upwards of NT$150 per person, to high-end restaurants that may be priced at NT$1,000-1,500 per person.
 Hot pot is now eaten in Taiwan during all four seasons, where a family or group of friends can enjoy a meal that also acts as a social gathering, cooking and eating fresh and tasty food while Sipping wine and chatting with each other.
Hot spring culture is prevalent and well established in Taiwan and is a great experience when visiting or living here, especially during the winter.
 Taiwan has one of the world's largest concentrations of hot springs with various types of hot to lukewarm spring water which can be saline, sulfur-based, clear alkaline or sodium-carbonate, among others.
 Natural hot springs range in temperature from 45 to 90 degrees Celsius, and hot springs in Taiwan vary from natural, undeveloped pools deep in the mountains, through simple bathhouses (sometimes seemingly located in a residential home) and more private cubical rooms, all the way to luxurious five-star resorts with public communal pools or private rooms with tubs, spa treatments, massages, facials and manicures.
The Japanese were the first to discover the ample resources and value of hot springs while Taiwan was under Japanese Imperial Rule from 1895 to 1945.
 They were obsessed with hot springs and regarded a luxurious hot spring soak as a way of curing anything and everything.
 They first developed hot spring facilities around the hills of Beitou and Yangmingshan in Taipei.
 When they left in 1945, hot springs soon fell back out of favor.
 It was not until the late 1990s that hot springs once again gained in popularity and Japanese designers came to work with the Taiwanese to redevelop hot spring resorts.
 Nowadays, many of the luxurious five- star spas enjoyed by the Taiwanese are of Japanese design, with Japanese Zen interiors.
In recent years hot spring bathing has become an increasingly popular pastime in Taiwan.
 Families enjoy a weekend get-away at hot spring resorts nestled below the quiet mountains; companies organize company retreats or meetings elegant hot spring locations as a reward for their employees.
 Hot spring bathing is not only appreciated as a luxurious pleasure; it is also a good way to sociall or reconnect with family and friends.
Hot springs are spread all over Taiwan, so you can experience a hot spring soak in most parts of the island.
 Just north of Taipei, you can find springs rich in sultur in Yangmingshan (陽明山) and Beitou (北投), while colorless, odorless sodium- carbonate springs bubble out of the ground in Wulai (烏來) township south of the city.
 Along the east coast of Taiwan, among many to choose from there's Jiaoxi (礁溪)in Yilan County, Rueisuei (瑞穗) in Hualien County, and the famous Jhiben Hot Spring (知本溫泉) in Taitung County.
 On the west coast, several hot spring options can be found in Hsinchu County, while Tai-An (泰安) in Miaoli County and Guguan (谷關) in Taichung County are a little further south.
 Tucked deep in the Central Mountain Range of Nantou County is Lushan Hot Springs (廬山溫泉) while the old Japanese resort of Guanziling (關子嶺) lies in the mountain foothills of Tainan County.
 Finally, in the far south of Taiwan, there's Baolai (寶來) in Kaohsiung, and Sihchongsi (四重溪) in Pingtung.
 For something a little different, there's the clear, odorless cold spring in the llan County town of Suao (蘇澳冷泉, one of only two cold mineral springs of its kind in the world, Italy having the other) where the water is a chilly 22 degrees Celsius!
Hot spring resorts generally feature facilities such as bubbling pools, water massage beds, aromatherapy pools, whirlpools, brown-colored Chinese herbal pools and steam rooms, parents’ and kids’ pools and a swimming pool with water slide.
 Swimming suits and caps must be worn in some, while some have segregated pools where men and women, separated, can bathe naked.
 Many hot spring resorts also highlight outdoor pools nestling in a natural landscape with rock walls or waterfalls where users can enjoy open-air hot spring bathing beside a river or overlook marvelous mountain scenery.
Should you be looking for something more than a nice soak in the mineral Water, many up-scale resorts offer qualified spa treatments and massages ranging from hot stones and aromatherapy to oil massage.
 A cup of herbal tea is commonly provided at the end of this full-service treatment.
Taking a hot spring is not just about enjoying a comfortable hot bath in a private-room tub or public pool.
 It's also very relaxing, and the mineral-rich springs are believed to have many properties beneficial to the health.
 Hot spring mineral water is widely known for its therapeutic effects on various disorders such as skin disease, gout and arthritis, and can improve blood circulation and relieve muscle aches and pains.
 It is also simply good for relaxing the mind and body.
A less well-known fact is that hot spring water can also be used to cultivate vegetables.
 Farmers, especially in Jiaoxi in Yilan County, are experts in using hos spring water for irrigating their crops.
 The mineral water gives the vegetables added nutrition and makes them tastier.
 The best vegetable irrigated by hot spring water is the water spinach (空心菜 , kong xin cai), although sponge gourd tomatoes, and water bamboo shoots are also grown this way.
Correct hot spring bathing etiquette should of course be followed when using a communal pool at a hot spring.
 It is important to always shower and clean oneself before entering the pool.
 Most places offer a Japanese-style wooden water scoop and bucket (or sometimes simply a plastic one) for bathing; shampooing and washing with soap should be handled prior to entering the pool as you should never use soap inside the pool or take a towel in with you.
When entering the pool, move slowly and don't splash or make big waves as this can disrupt the bathers who are enjoying a comfortable, peaceful soak.
 Many Taiwanese enjoy alternating their soak between the hot and cold pools.
 Generally one should soak for no more than 10-15 minutes in the hot spring before taking a break, or having a dip in the cold pool, although this is a bit of a shock to the system if you are not used to Taiwanese soaking culture.
 I strongly advise people with heart problems, high blood pressure, or other medical conditions to check with a doctor before trying either a hot or cold spring.
 Furthermore, do not drink alcohol while bathing in hot springs, be sure to drink lots of water before and after soaking, and avoid bathing on a full stomach (it's recommended to wait one hour after a meal before having a hot spring).
In Taiwan, hot spring bathing is a new health and beauty trend.
 There are countless new hotels and resorts offering hotel stays and meal packages where you can enjoy gourmet dining on Chinese, Japanese or Western cuisine of the best quality after a relaxing hot spring bath.
 It's a great way to get away from it all and to unwind from the stressful working week while enjoying nature, plus it's good for both your body and mind.
In Taipei, the MRT (Taipei Metro) can bring you right into the center of the hot spring resort of Beitou, which has become a major tourist attraction with the biggest choice of hot spring facilities in Taiwan.
 Taiwanese enjoy hot spring bathing all year round.
 It is an especially wonderful treat to have a soak on a chilly, rainy winter night.
 Explore Taiwan's thriving hot spring culture, and I am sure you will find it very soothing and enjoyable.
I was inspired to write this piece after seeing how my older brother raises his daughter in Taiwan in comparison to how my younger sister and her third generation Chinese American husband (who live in the States) bring up their twins.
Nurseries are a good indicator of how individuals express their identity.
Westerners value individualism, while Taiwanese respect group-orientation.
In many Western cultures, prior to the arrival of the baby the nursery or baby's bedroom is set up according to the personality, preferred colors and design the parents.
 Babies are brought home to sleep alone from the first day, since Westerners generally believe babies are safer and can learn independence if placed in a crib with less contact with mom and dad, who can check on their baby via a baby monitor.
Babies in Asian cultures such as Taiwan, on the other hand sleep in the same room as their parents and/or elder siblings.
 The baby's crib is often placed next to their parents' double bed, ensuring easy attention to baby's physical and emotional needs.
 Children in Taiwan don't sleep in their own room until much older, even if a separate nursery was created and decorated before the baby was born.
 Besides cultural custom and practice, limitations in space and the number of rooms in a traditional Taiwanese home is another reason babies sleep in the same room as their parents.
 As babies grow out of the crib, they may begin sleeping in between their mom and dad in the double bed, or sleep with the mother while the father sleeps in a separate room.
 Later they may share a room with a sibling, before finally having a room to themselves as school work requires more attention and more private studying time.
An individual growing up with his own space in Western cultures generally establishes his identity as a free and independent person.
 One is taught from a young age to communicate his personal achievements and worth, to respect privacy, to express his own individual opinions and desires and to have his own voice heard.
 The 'self-identity (I, me, my) and the need to stand out from the crowd and be different is considered desirable.
 Western parents teach independence and self-sufficiency starting from a very young age.
 Upon reaching adulthood, youngsters are generally expected to support and be responsible for their own self.
 Often times family connection is typically reserved for the immediate family group rather than for members of the extended family.
Taiwanese, on the other hand, grow up sharing a space with their parents and siblings.
 They identify closely with the group (family, relatives, or people they consider in the 'in-group').
 The group's interest, its wellbeing and the maintenance of harmony are highly valued.
 All members of the extended family generally remain close and care for each another.
 All aspects of personal and professional life, including relationships, are connected and intertwined for everyone who is considered part of the group.
 The desires of the 'self cannot be separated from the wishes of the group and the family.
 For example, teachers stress learning by writing homework, and teach on a 'one method fits all' basis, into which students are molded, rather than giving attention to each individual; many parents want their child to study English starting at age three so they can start developing a world view, which is seen to guarantee future success.
 Though a child may perhaps have a talent for music or art, he or she will often have to give up their own desires, in order to study in the fields preferred by the parents/group and live up to their expectations.
 The goal then is to be praised by the group, who will as a result see the child as a complete and responsible young person who follows and achieves the group's projected desire.
Loyalty to the group is prioritized over personal feelings and desires.
 It's important to be modest about personal achievements or opinions and not boast of individual successes.
 This is reserved for others to define and praise.
Consequently, it is important to keep in mind that when working with individuals from Taiwan, personal responsibility and freedom of personal expression needs to be constantly encouraged and pushed.
 I have observed in large open group forums that Taiwanese are typically shy about asking questions and sharing personal opinions in public.
 Often in my own group training work, I have to specifically and carefully (to give and save face) choose someone to answer a question.
 It's rare to see Taiwanese volunteering their ideas and thoughts.
When working with Taiwanese, the idea that a group is defined as consisting Of unique individuals, and the advantages of being individual (as opposed to the commonly received wisdom among Taiwanese that the individual should allow the group consensus) should be given repeatedly to encourage Taiwanese Individuals to speak up for themselves and express their thoughts.
Do you sometimes find it difficult to understand what your Taiwanese friends or colleagues are trying to express?
 You may already realize that the Taiwanese often speak in a very indirect way and what is NOT said is often more important than what is.
Communication in Chinese has to be seen in the context of relationships to others.
 The main function of communication is to maintain existing relationships among individuals, and to preserve harmony within the group.
 Furthermore, face' (面子, mianzi) is also an important consideration; how it is said, when to say it and who should say it matters greatly.
 Everyone saves and gives face in a conversation, and according to one's status within the group, there is an appropriate time to speak.
The factors listed below all have an influence on the actual words said:
The Taiwanese value indirect communication and they don't spell out everything.
 It is believed that words can be inadequate and insufficient and the listener is given (hopefully) enough information to interpret and infer the unspoken meaning.
 Meanings often reside in unspoken messages, and requests are often implied.
 Consequently, many Taiwanese do not 'ask' or make direct requests.
 Instead they wait and expect the recipient to ponder and realize the underlying message delivered.
 Recipients are then expected to reply or react based on the interpreted message.
Consequently, reading between the lines is an important skill to learn in understanding the true meaning of a message.
 Furthermore, nonverbal communication such as facial expressions, body movements and pauses often provides useful clues in communication.
The hierarchical structure often determines how much can be said, and how it should be spoken.
 Not everyone is entitled to speak; the person holding the higher position normally speaks first.
 As a result, Taiwanese tend to keep their views to themselves and may feel uncomfortable speaking up.
 Obedient children are those who listen well, do what they are told, and meet others' expectations.
 The term 'guai (乖, good, obedient) is a word parents often use to address their child; a little boy or girl who is guai listens well and doesn't talk back.
 An obedient employee speaks only when it is appropriate and follows instruction.
It is often helpful to break people into smaller groups of two or three for private discussions.
 This allows them the opportunity to confirm their thoughts with each other before voicing them in a larger group, and will prevent them feeling singled out or responsible for disobeying the hierarchical speaking order.
Taiwanese make clear distinctions between 'insiders' and 'outsiders.
 An insider enjoys special treatment beyond an outsider's comprehension.
 Insiders include members of the family, relatives and very close friends.
 Insiders in companies may include people on the same hierarchical level or on the same project team.
 Insiders speak in a 'direct' manner to each other.
 For example, an 'insider' friend can be very honest and say, "Amy, you are too fat, you ought to watch what you eat.
" Neither Taiwanese couples nor parents often say "thank you", "I 'm sorry", or "I love you" to each other or to their children because it is a given and as family (an insider) there is no need to further elaborate one's appreciation; it is understood.
An outsider on the other hand is treated politely, but kept at a distance; outsiders communicate at a superficial level using an indirect and formal tone.
 They are expected to follow protocol; favors and exceptions do not generally apply to outsiders.
 Taiwanese do not initiate interactions or develop social relationships with outsiders.
 A good example is that most Taiwanese do not make an effort to know their neighbors.
 They perhaps nod their heads when seeing each other, ye often no further interaction or conversation will take place.
When working with Taiwanese colleagues, it is important to learn about each team member's family and also share your own family information.
 This is d sign of care and interest, and by doing so it is easier to build trust and to enter each other's insider circle.
 As a foreigner in Taiwan, you can possibly enter and be accepted into a local's inside circle because the Taiwanese are very receptive to foreigners and foreign culture.
 Once in the insider circle, people open up and share more information, and things get done faster and more smoothly.
Taiwanese are taught to show respect and to engage in polite talk with the elderly and those in authority.
 Siblings of a family call each other not by name but by the honorific terms of gege (哥哥 , older brother) or jiejie (姐姐, older sister), and certainly don't call parents by their names.
 They call adults (other than in the family group, like parents' friends or colleagues) 'aunties and 'uncles' to show respect.
 Furthermore, they learn not to take credit for their own behavior or to be boastful in any situation.
 Children are taught at a very young age to be modest about themselves and to never show off their accomplishments.
 Success and achievement are for others to recognize and praise.
 Taiwanese certainly appreciate compliments and praise, even though they may seem shy receiving them.
Taiwanese often communicate with silence.
 When nothing is said it does not mean approval.
 It usually means either they are still pondering the idea suggested or that there is some objection which it isn't convenient to share openly.
 When encountering silence, allow more time for the other party to think rather than jumping in to fill the gap.
 Not speaking up does not mean they are unskillful or inexperienced; the Taiwanese simply need more time to think before responding.
Yes' means 'I hear you'.
 Recognize that in some circumstances it's challenging for Taiwanese to give definite responses.
 Taiwanese frequently say 'yes' to everything, and are reluctant to say 'no', which generally implies one is incapable or unskillful, thus losing face.
 Yes has multiple meanings; it can mean 'yes', 'maybe', or often times it simply means 'I hear you'.
 In conversations, 'yes' creates harmony and it gives and saves face.
 Do not take 'yes' as an agreement, commitment or settlement.
 It is best to double check for understanding and to avoid asking 'yes/no' questions should you wish to get further information.
Ask your colleagues what they did on the weekend, and most of them will probably tell you that they spent time eating and catching up with their family members.
 In Taiwan it is indeed very typical, especially for married couples and those with children, to visit their parents every weekend.
 (Yes, that's every weekend!).
For many non-Taiwanese, this will probably seem a bit too often.
 Interestingly, the character for family (家, jia) is formed by placing the 'pig radical under the 'roof' radical.
 Taiwanese believe that family is a place where shelter is provided.
 In the old times, pigs were kept inside and were free to wander about the house.
 The pig also represents wealth for those living under the same roof, as the family shares living space and finances.
 The Taiwanese think of their family as an indivisible unit that prospers if functioning properly, while being equally capable of bringing ruin to all its members if not.
A person's identity comes from the family, or the 'group', that one feels part of.
 One's 'self' can only be complete when living up to the expectations of the group (the family, the extended family, the community, colleagues and anyone who is considered part of the family, or part of the group).
 The survival and prosperity of the family takes precedence over individual interests.
 As a result, Taiwanese people care very much about what others say and think of them, both positive appraisals and negative criticisms.
Family ties are far stronger among the Taiwanese than in Mainland China, Japan, or Hong Kong.
 Parents raise their children, and in return children are taught to respect their parents and to take care of them when they grow old.
 Parents will do their utmost to provide the best education and living standards for their children, who are later expected to reciprocate, for example by buying a nice or a house for their mom and dad.
 Placing the elderly in an old people's home is seen as a sign of disrespect.
Taiwanese family members are not only expected to provide emotional but also financial support to each other if needed.
 When one is in crisis he or she goes to a member of the 'internal' family first, and when in need of money, they are expected to help out.
 Family members are thus obliged to ensure the family is functioning properly.
When working in Taiwan, it is important to understand that the company is run by the same rules and is in fact run very much like an extended family.
 This phenomenon is probably stronger in Taiwan than in Hong Kong, Singapore or China.
 The top person in the company assumes a leading-father' role; he takes care of the employees not only by leading and mentoring, but also by caring for and protecting them like a father would in the family.
 A good leader is someone who gets the team of subordinates working harmoniously together.
 He provides stability, security and advancement, and has the knowledge, skills and wisdom to resolve situations; quite often he is expected to have the 'right' answer to most questions; and he not only guides work-related problems, but also cares about the personal and family issues of each employee.
 He knows the age, education background, marital status, family dynamics of his team members and suggests a career path that's most suitable pertaining to each employee.
 Employees look up to the top executives as mentors, and juniors respect and are absolutely obedient and loyal to their seniors.
 These close-knit ties in the work unit help to ensure the success of the business.
Taiwanese families regularly interact and socialize with one another.
 One's life revolves around 'family'; one can never separate one's self from the family the inside group nor the work unit.
 Family is both a home and the center of the community.
 It is the foundation of Taiwanese society.
Kuanyin (觀音), Kuanshih Yin (觀世音) or Guanyin, commonly known in the West as the Goddess of Mercy (or the Goddess of Compassion and Caring) has for centuries been one of the most beloved and revered deities throughout Asia.
 Her name in Chinese roughly translates as 'The One Who Hears the Cries of the World,' and she is often seen as the most powerful being in the entire Chinese pantheon.
Kuanyin is the Divine Mother we all long for: merciful, tender, compassionate, loving, protecting, caring, healing, and wise.
 She supports the distressed and hungry, rescues the unfortunate, forgives humanity, gives comfort and quietly comes to aid wherever it is needed.
Kuanyin is depicted in various forms and poses.
 She always appears cloaked in white, the color of purity, with a long and flowing gown.
 She usually holds a willow branch in one hand, which symbolizes the ability to bend and adapt without breaking.
 The willow is also used in shamanistic rituals and has medicinal purposes as well.
 In her other hand she holds a vase which symbolizes her pouring compassion onto the world.
 She is also sometimes seen holding a rosary, a symbol of her devotion to Buddhism, or holding a child, a reminder of her role as the patron saint of barren women.
Kuanyin also commonly takes the form of the 'Thousand Armed, Thousand Eyed' deity.
 These arms allow her to help stop the suffering of everyone around the world, while the thousand eyes help her see anyone who may be in need.
She is usually depicted either seated or standing on a lotus blossom.
 The lotus flower is one of the main symbols of Buddhist purity, as it is a beautiful flower that grows out of mud.
 The implication is that our hearts should be pure like the lotus flower, even though our lives might be surrounded by dirty or impure people and situations.
Kuanyin is honored in most traditional Taiwanese homes, where statues or paintings are placed at the center of the home altar with the family ancestry plaque to the side.
Believers of Kuanyin carry a rosary, a string of 108 beads for keeping count while saying Kuanyin's name as a prayer (nan mou Guan Shi Yin Pu Sha) or her manta (om mani padme hum).
 It is believed that the mere utterance of her name in repetition will assure salvation from physical and spiritual harm.
 Many Taiwanese wear bead necklaces or bracelets that have been blessed by the deity in a temple, and many utter Kuanyin's name in silence while waiting for or taking the bus or MRT, on a plane, or whenever they find a free moment.
Xin nian kuai le! (新年快樂, happy New Year), gong xi fa cai! (恭喜發財, (good fortune & prosperity!), hongbao na lai (紅包拿來, give me the red envelope - a favorite among children).
These are all hao hua (好話, good words) used during the festive period of the Lunar New Year, also commonly recognized as Chinese New Year (CNY) to wish each other a good and prosperous New Year.
In Taiwan the New Year (also known as Spring Festival (春節, chun jie) as it marks the start of a fresh new year) is celebrated according to the lunar calendar, and is one of the most important festivals for people of Chinese descent around the world.
 Lunar New Year is the longest public holiday in the Taiwanese calendar: the first five days are usually a public holiday, giving people time off for family reunions, temple visits, and feasting on traditional New Year dishes.
 Nowadays, many use the time to travel overseas.
 Thus it's essential to book air tickets a few months in advance if traveling during the Chinese New Year period.
So what exactly goes on before and during Lunar New Year?
 Let me walk you through this biggest festival in Chinese culture and see what Taiwanese families do during this period in Taiwan.
In the weeks leading up to the festival, the celebratory atmosphere becomes noticeable surrounding the work place, in the shops, at the markets, in every neighborhood and on the streets.
A few weeks prior to CNY, city streets in front of restaurants begin to fill with happy people carrying packages of prizes after having had a gratifying meal with wine and spirits.
 This occasion is known as the weiya (尾牙), a banquet dinner hosted by all companies to show appreciation for their employees' hard work and dedication during the past year.
 Apart from a meal, prizes and year-end bonuses are a highlight of the occasion.
 Each year during the weiya period, most fine local restaurants are fully booked with parties celebrating this happy annual celebration.
 Some local high-tech companies hold a weiya on a large scale, a grand event not only the employees but their families also, with raffle prizes including maybe a BMW or Mercedes, and entertainment courtesy of local celebrities.
 Businessmen and women, and vendors and customers doing business with the companies may be invited to their weiya parties as well, and this is a good time to strengthen business relationships and ensure continuing cooperation.
Most households begin preparation for CNY by undergoing a thorough clean-up called da shao chu (大掃除), which often includes not only cleaning the rooms of the house, but also every window pane, window screen, and curtain.
 The purpose of this is not only to remove any unwanted materials at home but also to symbolically sweep away any bad luck and misfortune from the past year.
 Sweeping or dusting should not be performed on New Year's Day or else the fresh vear's good fortune will be swept away.
Live blooming flowers and plants symbolize rebirth, young energy and new growth.
 Flowers, as the precursors of fruit and seed, are symbolic of wealth and the hope for attaining a higher position in one's career in the New Year.
 Therefore it is important to have some type of floral decoration in the home to welcome the fresh start.
These are 'must have items during CNY for sharing the happy spirit of the festival.
 Spring couplets (chun lian, 春聯)are hung outside every apartment, house, office, and store.
 They are normally hung vertically on both side of the main entrance of the apartment or house, with another placed horizontally above the door.
 Spring couplets are short poems, idioms or expressions that describe the arriving of spring, good fortune, long life and prosperity for the coming year.
 They are traditionally handwritten in gold or black ink on red paper, but now commercially printed couplets can be bought everywhere at traditional markets, supermarkets and at street vendors.
 You will find many Taiwanese keep spring couplets on the door all year round, hoping to attract more luck and good energy throughout the entire year, only changing them again before the next Chinese New Year.
During CNY, families stock up with food and goodies to welcome families and friends.
 In Taipei, the Dihua Street (迪化街) bazaar is the most popular location to buy New Year's goods.
 This amazing market is loaded with interesting shops with foods, sweets, dried fruits, melon seeds, nuts, colorful candies and hundreds of other goodies while wonderful Chinese New Year celebration songs play in the background.
 It's great fun to visit Dihua Street to experience the joy of CNY shopping: fighting your way through the jostling crowd on the narrow street, enjoying free samples, and feeling the chaos and the high spirits of the holiday.
What are some auspicious foods Taiwanese buy for Chinese New Year?
 The names of many fruits and flowers sound similar to lucky words or phrases in Chinese which are considered auspicious symbols.
 For example: The Chinese word for orange, ju (橘) sounds similar to ji (吉), which means 'lucky, auspicious', which is why people buy oranges or potted orange or tangerine plants to decorate their homes at this time.
In Mandarin, the word for the fruit persimmon is pronounced shi (柿), which suggests the expression shi shi ru yi (事事如意 ,may everything happen as you wish).
 Rice cakes called nian gao (年糕) symbolically bring to mind the phrase nian nian gao sheng (年年高升), a blessing meant to encourage advancement in one's job during the coming years, or promotion year after year.
Another rice cake is called fa gao (發糕) 'fa' in Taiwanese means 'to make plenty of money'.
All types of sweets symbolize happiness and good fortune.
 During CNY, each family has a candy tray (normally red and circular, signifying togetherness) as a symbol for starting the New Year sweetly.
The items described above will be found in every Taiwanese family home during the Chinese New Year period.
Though in modern Taiwan each generation shows progressively less interest in the confusing and elaborate rituals undertaken during CNY, the following at least are still commonly performed in the homes of most Taiwanese families.
Families with an ancestral altar or worship altar begin the morning of Chinese New Year's Eve by first performing the ritual in prayer to request the god(s) they believe in to join in the celebration of this festival.
 Later in the afternoon, it is time to welcome the family's ancestral souls or spirits to join this yearly festivity by speaking to the family ancestral wooden tablet on the altar (where it is believed the soul lives on after mortal death) and invite them to enjoy food prepared specially for them on the altar table.
When these two basic ceremonies are over, the family then can prepare the New Year's Eve feast which the whole family will join.
Family members who have left their hometowns make every effort to return on New Year's Eve to join together for a night of good food and family unity.
 This traditional feast is called weilu (圍爐)or 'surrounding the hearth/stove' where everyone huddles close together at the table for warmth and for family joy and thanksgiving.
New Year's Eve dinner is best eaten slowly, savoring the flavor of each dish.
 Hot pot is the most common treat, placed at the center of the circular table, and the constant simmering of the pot symbolizes continuing financial success in the coming year.
 There are also some other 'must have' dishes on the dinner table: long life vegetables (mustard greens), to represent long life; 'whole chicken, which symbolizes starting a new family and (for those already married) bringing wealth to the whole family; fish, in Chinese pronounced yu, (魚), which has the exact same pronunciation as the word for 'abundance' (餘, yu).
 It's important to have an abundance of food, which symbolizes a surplus of good things for the coming year.
 Thus though every family prepares fish for their New Year's Eve dinner table, no one dares to eat it all, in order to symbolically leave something for the remainder of the year.
After dinner, children get really excited because it's time for them to receive red envelopes filled with money.
 It's customary for adults to give junior and unmarried members of the family red envelopes.
 Many play majong (麻將), a traditional Chinese game of small tile blocks played with four players, watch endless TV variety shows, and stay past midnight to 'keep the night' (shou shuei, 守歲).
 It is believed that when children stay up on New Year's Eve, respect is shown to their parents and the longer they will live.
When the clock strikes twelve midnight, the New Year is welcomed with endless firecrackers and fireworks, which can be heard for hours into the night.
 Shooting off firecrackers on New Year's Eve is a way of sending out the old year and welcoming in the new one.
 Children of all ages in my generation grew up playing with firecrackers during CNY, yet it is harder nowadays to find children playing with them in the cities as it is more difficult to buy them.
 Nonetheless, it is still a tradition in rural areas.
Red represents happiness, joy, luck and the hope that everything good will follow automatically.
 Red envelopes (hongbao, 紅包) with money inside are thus given during CNY as gifts to signify good fortu ne and to wish the recipient good luck in the New Year.
 The cash should be in even numbers and the notes should be crisp and fresh, as this symbolizes giving good energy for the start of the New Year.
 During the days running up to the New Year, people go to banks to get crisp new notes.
 There is frequently a long line at this time and most banks have limitations as to how many new notes can be exchanged per customer per day to ensure everybody can get new crispy notes for the holiday.
There are no specific rules on who gives and who receives red envelopes, but usually elders like grandparents, parents, and elder relatives give them to youngsters like grandchildren, children, nephews and nieces.
 However, when children begin earning they give red envelopes to their parents.
 I also confirmed that unmarried adults (no matter what age) are considered children so they continue receiving good-luck money from their elders.
 Sometimes red envelopes are a good way during CNY to extend a helping hand for any needy relatives without hurting their face and self-esteem.
Red envelopes are also distributed among employees as bonuses during CNY.
 In some traditional & smaller offices, bonuses still literally come in red envelopes n cash form instead, but it's more common nowadays to deposit the money directly into employees' bank accounts.
 Most companies do, however, retain the tradition of giving a red envelope to employees as a gesture of appreciation of hard work and good luck for the coming year by inserting a payment slip detailing the actual bonus amount in the red envelope instead of cash.
There is no fixed amount to put into a red envelope, but even numbers including the figure 'six' are always good.
 For children, depending on age, a red envelope  can contain NT$600, NT$1,600 or NT$2,000, while the rate to give elderly parents after their children start working and are capable of carrying responsibility is NT$6,000, NT$10,000 and up for each parent.
 Normally both parents should receive the same amount.
While getting a red envelope brings happiness, wearing red clothing is also preferred during this festive occasion.
 This means wearing something red inside and out, from red underclothes to a red hat or a red jacket.
 Red is considered a bright and happy color, thus wearing red will bring a sunny, cheerful and bright future.
 It sets a great tone to the start of the year.
On the first day of the Lunar New Year most people go to a temple to pray for a great start to the New Year.
 My family for a long time has always gone to the same temple in Keelung (which my father has been visiting since childhood) on the first day of New Year, and we see our relatives and friends there as well.
 Some may go to temples after dinner on New Year's Eve and wait at the temple until after midnight to try to be the first person to place incense at the temple after midnight (this tradition, called chiang tou xiang, 搶頭香 is intended to obtain the best luck for the year).
 However, due to many accidents from pushing and running to place the first stick of incense, many temples now suggest good wishes and luck are distributed to whoever has the heart to come to the temple anytime during Lunar New Year.
The first day of the New Year is a time to bainian (拜年 , send New Year's greetings) to family and friends.
 It's very common now to bainian by sending text messages or calling friends and family instead of visiting them at their homes.
The second day of the New Year is a special day dedicated for those married wives to return to their parents' home, a tradition called hui niang jia (回娘家) Iraditionally women were only allowed to go home once a year, since going home too frequently was believed to mean that the wife was not happy living with her husband's family.
 Furthermore, with limited transportation options it was often a long trip to travel back home, and one which couldn't be made often.
From the third day onwards, there are no specific rules as to what must be done who must be visited.
 People may take this time to unite with relatives they haven't seen for a while and to catch up with friends.
The fifth day of the New Year is when everyone returns to work.
 All businesses commence with an opening ceremony and employers welcome the employees back by giving them a red envelope with a small sum of lucky money inside and invite them to a 'spring dinner' (chun jiu, 春酒), another eating gathering similar to the weiya before CNY, only this time simply a meal without the entertainment or lucky draw.
 It's a great way to encourage employees to put in another good year of hard work starting from the beginning of the year.
Chinese New year officially ends on the fifteenth day of the Lunar New Year.
 which is called the Lantern Festival (元宵節), when paper lanterns are lit and (a common event nowadays) people visit specially designated locations in each city around Taiwan to see lantern displays of all sizes and shapes (most common are lanterns in the shape of the Chinese zodiac animal of the year).
 On this day, a traditional festival food is eaten called yuanxiao (元宵).
 These are tiny round dumplings made of glutinous rice in a sweet soup.
 The roundness of the dumplings symbolizes both the day's full moon (the first of the year) and family wholeness.
 Yuanxiao are usually pinkish-red and white (red being an auspicious color) symbolizing abundant wealth why there are also white dumplings.
On this day, many people in Taiwan enjoy releasing lanterns called tian deng (天燈) into the night sky.
 These lanterns are made of thin rice paper stretched on a wire frame.
 Hot air, produced by lighted stacks of ghost money bills soaked in cooking oil and fixed inside, lifts them high into the sky.
 In the township of Pingsi (平溪) southeast of Taipei, thousands of people gather on this night to send off tian deng with their wishes of the year written on the outside of the paper lantern.
 It's a peaceful sight to gaze at the paper lanterns rising upward into the sky, flickering like small stars, filling the night with joy and good wishes.
Another tradition unique to Taiwan during Lantern Festival is the Beehive Rocket Festival in the town of Yanshui (鹽水) in Tainan County.
 This is a rather different experience, when tens of thousands of small rockets and firecrackers are fired into the air (and often straight into the crowds) from beehive-shaped racks.
Traditionally most businesses, including restaurants, are closed during the first five days of the CNY holiday, but as the number of shoppers increases many times during this period (due to the amount of lucky money people have received) many usinesses, small vendors and shopkeepers open during the holiday, starting on the first day of the New Year.
Lunar New Year is a very festive period and is one of Taiwan's most culturally fascinating occasions.
 I hope you will experience the energy of Lunar New Year with lots of delicious food, lucky money, firecrackers and some lantern fun.
Little Maddie and Emma arrived at the Chinese restaurant where my younger sister was celebrating the first important event of their young lives: man yue (滿月), a celebration marking the end of their first month of life.
 The twin girls were wearing beautiful pink satin dresses with Hello Kitty gold necklaces (bought by Uncle Arthur) around their necks, and delicately engraved gold bangles on their cute little wrists.
 They were absolutely adorable; two beautiful live dolls.
 These two adorable guests of honor certainly attracted a lot of attention, causing a stir the party.
 crowd had been waiting inside the restaurant rushed to the new mother and father, offering congratulations and good wishes, and the twins were passed from one guest to the next on their first public debut.
 Everyone wanted to touch them, hold them; everyone wanted to see if they look alike or if they look more like their handsome father or beautiful mother.
 Countless 'red envelopes' were tucked onto Maddie and Emma's clothes and the designated gift table was piled with mountains of beautifully wrapped gift boxes of all colors and sizes.
 The proud mom and dad shared their experience as new parents and smiled non-stop throughout the lunch celebration as everyone praised and sent wishes to the two beautiful infants.
In Taiwan, it is customary to have a 'welcome party' for a newborn baby when he or she turns one month old.
 In the old days, the infant mortality rate was high, thus a baby was forbidden to go outside until a full month had passed after the birth.
 The new mother also used this one-month period to restore her health and energy (a period called zuo yuezi, which is described in another chapter, see page 181).
 It was believed that if the baby could survive through the first month, it was an event worth celebrating.
Consequently, as the infant turned one month old, a party was thrown in his or her honor, an event which also marked the first time he or she was seen by other members of the family, relatives and friends.
 These days, this month of self- imposed incarceration isn't always observed, but the man yue celebration remains a popular event.
 Everyone comes to join in this celebration when the baby's formal Chinese name is introduced to the guests.
 The parents have to have a name for the newborn by this day at the latest.
 See the 'Naming the Baby' chapter on page 126 for more on choosing names.
Proud parents may hold a man yue party at a fancy restaurant to introduce the latest addition to friends and relatives.
 Traditionally, due to the importance of male children in Chinese culture, the celebration for boys was more elaborate, but today (and for my generation) parties are given for babies of both sexes.
 Guests attending the party bring gifts.
 Hongbao, or 'lucky money' in red envelopes is the most commonly given gift as an expression of best wishes, good health and long life for the newborn.
 Clothes or jewelry, (most commonly gold necklaces, rings and bracelets) are also very popular, especially for baby girls.
The guests don't leave empty handed, either.
 Parents hand out red-dyed eggs symbolizing the changing process of life: the egg's round shape symbolizes a harmonious life and unity, and it is red because this color is a sign of luck and happiness in Chinese culture.
 Besides eggs, Taiwanese food like sticky rice with a chicken drumstick was once a common gift when the family had a baby boy, while for a girl cakes and sweets were given.
 In modern Taiwan, man yue celebrations are less elaborate than in the past, and more and more choose not to have a party.
 Nonetheless, the new parents at least send friends and family a box of cake or candy announcing the new addition to the family.
The bright and radiant moon has always been a popular theme of Chinese poetry and songs.
 Traditionally, Chinese planted and harvested according to the moon, so consequently Chinese have always paid it special attention and admiration, while the major festivals in Taiwan are celebrated according to the lunar calendar.
 Furthermore, the clear, round, full moon symbolizes union and a perfect world, and also represents the gatherings and closeness of family and friends.
The moon is believed to be at its fullest and brightest on the 15th night of the eighth lunar month.
 Chinese celebrate this day as Mid-Autumn (or Moon) Festival.
 Furthermore, in an agricultural society (as Taiwan formerlly was), the autumn moon also indicated the end of the year's hard work in the fields and it was the perfect time to celebrate the harvest, which is why this day is also called Harvest Day.
Moon Festival is one of three most important traditional festivals for the Taiwanese (the other two being Dragon Boat Festival and Chinese New Year) a little like Christmas in the West and Thanksgiving in the US.
 Just as these two holidays are times for family get-togethers in the West, Moon Festival is an occasion for family reunions in Taiwan.
 People travel across the nation to return home to have a warm and joyful meal with their family; when the full moon rises, families go to scenic spots, parks, riverbanks or rooftops to watch and appreciate the full moon.
 Traditionally Taiwanese eat moon cakes and pomelos and drink some Chinese tea in the cool night air, and pray for a sate and healthy time during the remainder of the year.
Moon Festival in Taiwan has recently turned into a 'National Barbecue Day although it's unclear when and how this custom came about.
 BBQS certainly weren't something people did during Moon Festival when I was a kid in the late 1970s growing up in Taipei, but had become a passion by the time I returned to Taipei from the US in the early 1990s.
Families and friends gather to have a barbeque just about everywhere, even in the heart of the city!
 On this night you'll see groups happily cooking food in front of their apartments, on the sidewalks, in the parks, on rooftops and balconies and even next to 7-Elevens (where store clerks who can't go home get to share in the joy of the festival).
 On many streets, a group of people is gathered every few yards around a tiny 12-inch by 18-inch grill cooking popular Moon Festival BBQ Items like Taiwanese sausages, mini hot-dogs, corn-on-the-cob, skewered chicken, shrimp, squid, fish balls, vegetables and mushrooms, all flavored with charcoal smoke and giving off that delicious BBQ smell.
 It certainly creates an excellent opportunity for people to interact with friends and neighbors in apartment blocks, where there's often little chance to otherwise socialize.
The Moon Festival is rich in legendary stories, yet the origin of the festival is unknown.
 The most famous legend associated with the festival (one that's known by everybody) is the story Chang Er Flees to the Moon (嫦娥奔月).
 In the story a girl called Chang Er drinks the potion of immortality and floats up to the moon where she has lived ever since.
 It was once thought that you can see Chang Er dancing on the moon on the day of the Autumn Festival.
There are several versions of the legend on how and why Chang Er now lives on the moon.
 One relates that Hou Yi (后羿), the greatest archer of his time saved the earth by shooting down nine suns that had mysteriously appeared in the sky, thereby keeping the world from burning up in the scorching heat.
 Hou Yi naturally gained a great reputation and respect for this feat, became a great teacher of archery, and married a beautiful and kind-hearted girl named Chang Er.
 One day Hou Yi received the potion of immortality from a goddess, yet since he didn't want to leave his beloved wife behind, he gave it to Chang Er for safe keeping.
 Unfortunately it was seen by Hou Yi's student, Peng Mong (蓬蒙) who later came to Chang Er asking for the potion while Hou Yi was out hunting Chang Er knew she couldn't fight Peng Mong, and so rather than give it to him she quickly took out the potion and swallowed it.
 Chang Er's body became lighter and lighter and floated off into the sky, where, saddened at not being able to be with her beloved husband, she settled on the moon where she could view him from afar.
Upon Hou Yi's return, his house servant informed him what had happened.
 Hou Yi was disheartened, and in despair gazed at the bright moon where he thought he saw a shadow of his wife.
 He (along the local villagers) now quickly prepared sacrifices to the moon and sent prayers wishing the best for Chang Er.
 This was the 15th night of the eighth lunar month, the day when Chang Er's beauty is at its most radiant.
In Taiwan moon cakes are the most important snack and gift for celebrating the Moon Festival.
 Pomelos (柚子) are also eaten on this day.
 The round moon cakes symbolize family unity, while the Chinese word for pomelo (柚, you) sounds the same as the character '祐' (you), conveying a hope for protection and blessing during the coming year.
Moon cakes are very rich pastries stuffed with an array of fillings.
 The most traditional version has a filling of red-bean paste and a hard-boiled, salted egg yolk inside with delicate classical designs embossed on the top.
 Other fillings include lotus seeds, dates, walnuts, and almonds.
 Be careful: moon cakes are very high in calories and you'll gain a few kilograms if you eat many during the festival!
 As people become more health-conscious in Taiwan, bakeries have come up with new, less fattening creations each year ranging from fruit to tea-flavored moon cakes, while another less healthy but delicious new kind is filled with Haagen Dazs ice cream.
Moon cake giving Moon Festival is a season for friends, family, relatives and employers to show their appreciation for support and friendship received during the year.
 The occasion is marked by delivering boxes of moon cakes to families, friends and business associates and acquaintances.
 Companies no doubt use this holiday for public relations purposes, to send vendors and buyers moon cakes to express their sincere gratitude for a year of good business relationships, and the hope for continuing business and support during the coming year.
Boxes of moon cakes are readily available at almost all bakeries, cake shops and at most convenience stores in Taiwan around the time of the festival.
 Manufacturers of moon cakes these days compete not only to produce the most delicious varieties, but also to create the most stylish and exotic moon cake boxes.
 Recently, the custom of sending moon cake boxes has been replaced by sending gifts that are of healthier products like tea or red wine.
It's customary for companies and other employers to give moon cake boxes to their employees.
 Nonetheless, companies may now give department store gift certificates instead of high calorie moon cakes to employees to show their appreciation.
Moon cakes are not only eaten at this time of year because they resemble the shape of the moon.
 According to popular belief, the custom of eating moon cakes began during the Yuan Dynasty, a time when China was ruled by the Mongols.
 The Han Chinese resented Mongol rule and decided to overthrow these foreign rulers, hiding secret messages detailing when to initiate the revolt inside moon cakes.
 Thus informed, the people rose together to attack according to the hidden message, which designated "Revolt on the 15th day of the eighth lunar moon" and successfully defeated the Mongolian government.
 To commemorate this triumph, moon cakes are eaten on the 15th day of the eighth month, and to this day are considered an essential part of the Moon Festival.
Taiwanese certainly have a special appreciation for the full moon.
 Its brightness and roundness represents wholeness, the unity of a family and the circle of life.
 The Moon Festival is indeed a very important time to connect with the ones you love and care about, and it is also a time to re-connect with those you want to rebuild relationships with.
 Watch out however for horrendous traffic jams prior to the festival.
 Not only are people heading home for festival gatherings, but just about everyone else is on the road delivering moon cakes or gift boxes.
How do Taiwanese parents go about giving their baby a name?
 Influenced by Western practices, over the last fifteen years or so many Taiwanese parents have started not only giving their baby a Chinese name but also an English one.
 The English name is often chosen before the baby is born.
 On the other hand, Taiwanese parents spend a long time choosing their child's Chinese name because it is believed that it determines his or her future and destiny.
I became an 'official' auntie in 2006 and have seen first-hand the time and effort that goes into coming up with a Chinese name for my little niece, Lisa.
 While a small group of people in Taiwan have only one character as their given name, a typical Chinese name consists of three words, the first character being the family name and the next two characters being the given name.
 In Taiwan, the given name (either character) can represent the child's generation within the family.
 Therefore, it is possible for relatives to know the relationships between family members by just looking at their names.
For example, my Chinese name is Liu, Chien-ju (劉情如): Liu' being my family name and 'Chien-ju' being my given name.
 My younger sister and all my female cousins from my father's side of the family all have 'ju' (如) as the third character in their names.
 So if you run into a 'Liu' with a given name of ju' as the third character in their name, you could possibly be meeting a girl cousin of mine!
 On the other hand, all the boy cousins of mine in the Liu family have the character of 'si' (思) as the second character in their name.
 This tradition is however followed more loosely by the parents of the current generation as it is becoming increasingly difficult to cross-check with relatives and to agree upon a pleasing character which satisfies all parties.
When naming a child, parents have to take into consideration the newborn baby's Eight Characters (八字, ba zi), four pairs of numbers indicating the year, month, day and hour of birth, and also the basic Five Principal Elements (五行, wu xing): metal, wood, water, fire and earth.
Another factor to take into account is the total number of strokes of a name, because this also has a lot to do with the baby's fate.
 Finally the characters also have to sound or rhyme well when putting a name together for the baby.
Traditionally a nickname (now commonly an English name) will be used until the family comes up with a formal Chinese name, customarily created by the grandfather, but now more often given by the parents, maybe with the guidance of a fortune-teller.
 As you can see, it's quite a task to come up with two desirable characters for a baby's given name, matching the time the baby was born and the Five Principal Elements, generation order, and the strokes.
 And most importantly, it has to sound beautiful as well!
Tea has been a traditional Chinese beverage for thousands of years.
 I am very proud to share that Taiwan produces some of the best teas in the world, and it is especially renowned for its oolong tea.
 Tea has been an important agricultural product here for over a century.
 It was once produced mainly for export, and it was not until the 1970s that tea lovers in Taiwan began to discover and enjoy the tea grown on their own doorstep.
All tea comes from the same plant, Camelia sinensis.
 The difference between teas depends on where it is grown and the way it has been processed after harvesting.
 Chinese tea is classified according to the degree of fermentation it undergoes.
Tea houses are the place for family and friends to get together, drink tea, relax and mingle.
 Visiting a tea house (especially those up in the mountains) in Taiwan is a memorable experience, and also a great opportunity to learn a little about the richness of Taiwanese culture.
 Traditional tea houses can be found in every major city in Taiwan.
 Nowadays these tea houses are often constructed of wood, and may be decorated with calligraphy, paintings and sculptures to recreate the atmosphere of an older Taiwan.
 Some larger tea houses may have an outdoor garden and a Chinese-style fish pond.
 Traditional music is often played to promote a relaxing atmosphere.
 Many people linger for hours enjoying a good pot of tea with some snacks and perhaps a light meal.
 There are many attractive outdoor tea houses up in the mountains around Taiwan, often with great views of the countryside or the city.
Drinking tea is an everyday pastime for most families in Taiwan.
 The Taiwanese normally prefer less fermented varieties than the black tea that is usually drunk in the West.
 Chinese tea has a more subtle flavor and is regarded as a healthy drink: research reports prove that tea is good for you.
 It can help stimulate the immune system, improve the functions of the digestive system, prevent blood clots, and lower cholesterol levels among other benefits.
 Drink tea for your body, your health and your soul!
When I see a qipao (旗袍), the traditional Chinese dress for women, I think of my grandmothers from both my father's and mother's side.
 I recall both my grandmothers being always so very proper and graceful in their qipao, displaying their modesty softness, serenity and graceful, refined manner, so elegant and gentle.
 No matter whether it is for daily wear or for a formal occasion, they looked so natural and wonderful in a qipao with pearl earrings, requiring no additional accessories such as a scarf, necklace, or belt.
Qipao is a one-piece, full-length dress made of silk and embroidered on the collar and lapel, with a high neck and slits on the sides.
 In Taiwan, it is traditionally worn by older women, or by waitresses and receptionists in some Chinese restaurants.
In recent years, qipao have become highly fashionable, growing in popularity among women of all ages not only in Asia, but also throughout the world.
 Qipao is designed to reveal the woman's natural figure, with the slits on the sides creating the illusion of slender legs, practical yet sexy.
 It retains a traditional Chinese flavor and reflects the exotic charm of the Orient which is often considered very alluring and glamorous.
Qipao originated from the Manchus of the Qing Dynasty (1644-1911).
 Under Qing law, all Han Chinese men were required to wear a queue and women had to dress in a Manchurian qipao instead of the traditional Han clothing, or they would be killed.
 For the next three hundred years, qipao were adopted and worn.
 The qipao of those times was loosely fitted, baggy and hung straight down the body.
 It covered most of the women's body to conceal the figure and was worn by all ages.
 The men of that time also wore a similar loose-fitting, one-piece garment.
This garment survived after the Manchus were overthrown by the Republic in 1911.
 In the 1920s qipao fell under the influence of Western dress styles, and started being tailored to be more fitting and revealing to display the beauty of the female figure.
 With time, qipao became fashionable among women in China, especially in Shanghai, and various styles were developed.
 However, the wearing of qipao and other fashionable clothes ended with the Communist Revolution of 1949 in China.
 However qipao fashion was brought to Hong Kong and Taiwan where it remained popular.
Modern qipao come in all varieties and designs, long or short, with some above knee-level.
 The traditional high-collared stiff look of the 1920s was softened to allow more space for free neck movement.
 More collar shapes were created including collarless styles to allow room for necklaces and other jewelry.
 Short or sleeveless dresses were also designed for summer.
 An especially important factor in the design of the qipao are buttons and buttonhole loops, which usually run to the left (from the wearer's perspective) from shoulder to waist and are of traditional Chinese knots.
Qipao are usually made of high quality silk or satin in trendy black, dazzling red or many other colors.
 Traditional Chinese floral patterns such as peonies, lotus blossoms and chrysanthemums are widely used.
 The peony is most favored as it symbolizes wealth and prosperity; lotus stands for beauty, purity and holiness, while the chrysanthemum suggests longevity.
 The dragon motif is commonly found on men's jackets as it symbolizes strength and power.
Though traditional clothing has commonly been replaced by more comfortable wear like jeans, T-shirts, sweaters, business suits and cocktail dresses, even among the elder generation, qipao are appreciated at important social gatherings and special occasions such as diplomatic occasions, weddings and during Chinese New Year.
It has also recently become a fad for the bride to wear a qipao for part of her wedding reception, as well as a Western white gown.
 Brides even like to wear their mother's old qipao, although this is often not possible, as the younger generation is generally taller and wider in size than their mothers'.
 Therefore it is very special if one can fit into her mother's qipao.
Over the years, qipao have evolved tremendously to match the trend in modern fashion while retaining the unique elements of the traditional style.
 Qipao can be worn for both formal occasions for those in high society or casually as a modern day outfit, depending on the color and style.
Qipao is such an amazing classic garment that it is suitable for people of various body figures; it is generally custom designed to flatter your slender body or to hide your flaws creating an impression of charm, simplicity, elegance and ultimate attraction.
Hongbao (紅包), red envelopes or packets with money inside are every child's delight at Chinese New Year.
 The tradition of giving gifts in Chinese culture re is not the same as practiced in Western cultures.
 These days small gifts may be given when visiting a home of a friend, at birthday parties, and increasingly at weddings, but traditionally money is considered the best gift that can be given at any happy celebration or occasion.
As Chinese New Year approaches, preparations begin well in advance as people purchase new clothing, snacks, candy, and colorful decorations with auspicious meanings.
 Traditionally children often say "gongxi fa cai, hongbao na lai" (恭喜發財, 紅包拿來) which means 'congratulations on the New Year, please bring out the red envelope money dear!'
Of course the 'give me the hongbao' phrase is only said to someone they know well, otherwise it is seen as disrespectful and greedy.
 Giving out red envelopes is a high point of the Chinese New Year.
 People believe that red envelopes will protect children all through the year and bring good luck to them.
 The color red was chosen because it expresses rich feelings and is symbolic of festive joy, new life, warmth, luck and hospitality.
Knowing who should give whom hongbao and how much is appropriate is a real art.
 In Taiwanese culture, older relatives gave red envelopes to bestow positive energy and blessing.
 Traditionally Taiwanese try to give crisp new notes in the envelope to signify the beginning of the New Year and good luck.
After dinner on Chinese New Year's Eve, parents begin passing out red envelopes to their children.
 Older relatives, such as a grandmother or uncle, give young members of the family a lucky red envelope too.
 Another custom is for married adults and working people to give youngsters (such as nieces and nephews who are not working yet) a red envelope as pocket money and also to e their parents a red envelope to thank them for raising them well, now that they are making a good living.
As a rule of thumb, give even numbers (as odd numbers are considered unlucky) in hongbao, but NEVER in multiples of four (NT$400, 2,400, or 4,000 for instance, even though they are even numbers).
 Four is considered an unlucky number as its pronunciation is similar to the words 'to die' or 'death'.
 Amounts including a six (NT$600, 1,600, 2,600, 3,600 etc) are suitable for hongbao, with NT$6,000 considered a high amount, given only to people with whom you have a particularly good relationship.
 The amount you give to children other than your own depends on your guanxi or relationship with them or their parents.
 For children under ten years old, NT$600 is considered appropriate.
 Teens would enjoy getting NTS1,000-1,600.
 College kids would of course like to get as much as possible from parents, yet may feel uncomfortable accepting hongbao from other relatives and friends once they begin working.
It is also important to give a red envelope during Chinese New Year to show appreciation to: Your doorman (NT$600-1,200, if the person has been helpful.
 You are not required to give to every doorman should there be more than one at your building.
 Give it to the one who has been helpful to you); Garbage collector (NT$600- 1,000); Your maid (NT$1,000-3,600 if they are part-time).
 If you have a full-time maid, you'll need to find out from your agency the required amount you should give as an employer); Your driver (NT$3,600-6,000, assuming your company is already giving him a year-end bonus).
Don't forget these people!
 Your appreciation for the hard work they have done all year to make your life comfortable will result in friendly greetings, and willingness to look after you and give you a hand when needed.
Hongbao (the envelope itself) can be bought at any stationery store, supermarket, or convenience store for about NT$30 (US$1) per pack of ten.
 As Chinese New Year approaches you'll find specially designed hongbao with popular cartoon characters of the Chinese zodiac animal corresponding to the New Year.
 In 2009, the Year of the Ox, for instance, the hongbao will be decorated with pictures of an ox.
 Other popular illustrations on hongbao are of fish, since the Chinese word for fish, yu (魚), sounds the same as the word for 'abundance' (餘, yu), thus symbolizing a time of wealth and plenty during the coming year.
 In recent years, local banks have even given away finely designed hongbao, with their name printed on the outside, of course.
Hongbao are also given at other occasions, such as during weddings, birthday or at a baby's man yue party (a celebration held when a newborn turns one-month old, see page 120), or anytime you want to show appreciation, since hongbao are seen to attract good fortune.
 It's always a good idea to check with local friends or colleagues to find out what amount is appropriate for the occasion, taking into account the guanxi (relationship) you enjoy with the recipient.
After landing in Taiwan for only a few hours on his first visit to Taiwan, a close international friend of mine who has lived and worked in China for over ten years and claims to have visited every province in China states, "Taiwan is really different.
 It's the most traditional Chinese place I ever visited (even more Chinese than China); it really is a wonderful blend of Western and Chinese traditions."
Here in Taiwan, he explained, he saw cultural heritage: the richness of Chinese traditional cultural life, the liveliness of religious practices and folk beliefs; a culture that's living in everything locals do in their daily life.
 There are countless temples - large ones grand and compelling, small ones hidden in between fancy tall buildings yet forming a central part of many local people's life.
Indeed, you see the zeal of religious involvement and practice in Taiwan, deeply felt and passionately expressed, influencing all aspects of many people's everyday life.
 Common sights include a small shrine of the Kitchen God in many restaurants to guarantee good business, students visiting temples for good luck before examinations, and shops along Zhongxiao East Road (the most crowded and affluent street in Taipei) performing the ceremony of food offering and spirit paper burning in front of the store as a prayer for good business.
 Furthermore, TV commercials remind religious Taiwanese the day prior to important ritual ceremonies, and three or four television channels are dedicated to religious preaching and chanting, running 24/7.
 Religious practice is a natural part of many people's daily life.
The Taiwanese have a high degree of religious freedom and choice; people are open to accept foreign religious faiths and ideas and are rarely judged by their faith of choice.
 You can find all main world religious practices and religious organizations on the island: Buddhism, Taoism and folk beliefs, and other religions such as Catholicism, Protestantism, Islam, Judaism, Japanese Buddhism, the teachings of Tenrikyo and Mahaikarikyo and more.
Let's now further look at the history of main religions in Taiwan.
 The indigenous people of Taiwan who were the first group of people to live on the island had no religion, yet practiced their tribal rites and nature worship.
 In the early 1600s, the Spanish brought their Catholic religion to the island, and the Dutch brought Protestant Christianity to Taiwan during their occupation (1624-1662).
 As a result, most aborigines in Taiwan are Christian.
 Chinese immigrants from Mainland China who started arriving in 1663 brought Buddhism, Taoism and folk beliefs t the island.
 The worshiping of the Taoist goddess Mazu, (the Goddess of the Seal is especially elaborate in Taiwan as she was believed to have protected the large number of immigrants safely crossing the Taiwan Strait.
During the Japanese colonial period (1895-1945), the Japanese brought Shinto/ Japanese Buddhism to Taiwan.
 Practices of Taoism and Chinese folk beliefs were restricted and only Buddhist temples were allowed.
 As a result, many temples dedicated to Taoism or folk beliefs added Buddhist statues to avoid closure.
 It wasn't until Japan's defeat in 1945 and Taiwan's coming under the rule of the ROC government that the Taiwanese again practiced their Taoist and folk beliefs openly, and also received further Buddhist teaching from renowned Buddhist masters from Mainland China.
The most commonly practiced religions in Taiwan are Buddhism, Taoism and various folk beliefs.
 93 % of the total population in Taiwan recognize themselves as the practitioner of one of these, or commonly a combination of all three.
 Many Taiwanese who do not have a strict religious belief do follow certain practices or rituals connected to Buddhism or Taoism or with folk beliefs.
 In modern Taiwan you see magnificent temples in all cities and towns, small outdoor shrines on roadsides and in alleys, and also in neighborhood apartments, either of a Taoist or Buddhist temple, or more commonly a combination of the two under the same roof.
 It is common to see Kuanyin (觀音, the Goddess of Mercy, the most popular Buddhist deity) in many Taoist temples, or Mazu (媽祖, the Goddess of the Sea, the most respected Taoist deity) in Buddhist temples.
 The two religions are often intermingled, so in many temples it is difficult to worship purely as a Buddhist or a Taoist.
Buddhism originated from India and is the most widely practiced religion in Taiwan, and is growing in popularity both in Taiwan and worldwide.
 Buddhist teaching has been further promoted through recently established Buddhist high schools and universities, and also through conferences, study groups, and TV programs.
 Parents are sending children of all ages (youngsters, teenagers and college students) to Buddhist summer camps, educational functions and events.
 Furthermore, it is becoming acceptable and desirable for children to attend Buddhist high schools or universities.
 It is also not uncommon to see university graduates entering monasteries to become devoted nuns or monks.
Buddhism has long been an integral part of the life of many Taiwanese.
 The fundamental teaching of Buddhism demonstrates that not only is it necessary to show great mercy to people you know, but that one's compassion should be extended to strangers as well.
 The learning of Buddhism as a result can provide simple answers and clear directions in our daily lives.
Modern Buddhism in Taiwan involves followers in humanitarian work such as disaster relief work and medical treatment and support, in an effort to help others who are in need of aid.
 The concept of doing compassionate work has also attracted more followers or donors who desire to provide kind-hearted contributions, yet have little time or interest in attending temple services or understanding much of Buddhist teaching.
 Buddhist temples in Taiwan are run like large business corporations managing large sums of donations from believers and donors in order to support meaningful events, build schools, medical centers and libraries and sponsor cultural exchanges.
There are four major Buddhist organizations throughout the island, in northern, central, southern and eastern Taiwan, should you wish to acquire a deeper understanding of Buddhism.
 These organizations are reputable and honored by Taiwanese believers, and they have also successfully reached out to people around the globe.
In the north, located in Jinshan (金山) in Taipei County, is the Zen Buddhist Fagu Shan (法鼓山) or Dharma Drum Mountain, founded by Master Sheng Yen (聖嚴法師)in 1989.
 His teaching has spread in the West (particularly in the United States), In several Asian countries, and in Taiwan, leading countless people both Western and Eastern into the world of Buddism.
Close to Puli (埔里) Township in Nantou County, central Taiwan, you can find Znong Tai Zen Monastery.
 Another Zen Buddhist temple, this is a peaceful and tranquil place established by Zen Master Wei Chueh in 1987.
In southern Taiwan, the major center of Buddhism is at the Light of Buddha Mountain (佛光山, Foguang Shan) in Kaohsiung County, founded by Master Hsin  Yun (星雲法師) in 1967.
 This is a large religious center that has also become a tourist attraction; people come to admire its temple construction and to enjoy the desian of the gardens and the famous 480 standing Buddha statues.
In Hualien (花蓮), eastern Taiwan, you can find the Tzu Chi foundation (慈濟) You can't miss the Tzu Chi people (慈濟人).
 They are very prominent in Taiwan and are known for their generous charity work in time of crisis or suffering around the globe.
 They always wear white pants and navy blue shirts (both men and women); their volunteers take part in relief efforts wherever there is a major disaster.
 They are at the front line along with the Red Cross in giving unconditional aid and support by providing medical care, food, shelter, clothing or anything that alleviates suffering and helps disaster victims regain strength to rebuild their lives.
 They began their compassionate work in just Taiwan, but have since reached out globally.
 Tzu Chi has built schools and hospitals such as the Tzu Chi University and hospital in the Hualien area.
Tzu Chi Foundation was founded in 1966 by Master Cheng Yen (證嚴法師, the only female master among these four major Buddhist establishments.
 She emphasizes community service, charity work and outreach programs, medicine, education and culture.
 Master Cheng Yen was nominated for the Nobel Peace Prize in 1993 and was awarded the Eisenhower Medallion for her contributions to world peace in 1994.
Buddhist temples in general are magnificent, grand, yet serene and tranquil, without the colorful decorative dragons or immortal stories depicted on the temple walls of Taoist temples.
 Instead, there are manicured gardens, Buddha statues and lots of designs in gold or yellow.
 Devotees practice meditation; vegetarian nuns and monks with shaved heads who are not allowed to be married or to kill any living things often live on site.
 In Taiwan, not all Buddhist followers have a profound understanding of the fundamental Buddhist teaching of the Eight-fold Path, the Four Noble Truths, Reincarnation, or the concepts of Karma and Nirvana, yet they are certainly involved in great compassionate charity work.
Without doubt Buddhism in Taiwan is gaining more respect not only among local followers but also among people worldwide.
 It is playing a significant role in promoting compassion for all through global charity work.
Taoism is the second most popular religion in Taiwan.
 Tao (道) literally translates as 'the way' or 'path', and can be described as a way of living.
 It began with the philosopher Lao Tzu (老子, old sage).
 Among its more recognizable components are yin-yang, the five elements, the energy concept of qi (氣), feng shui (風水), and the balance of nature.
 It represents the harmony and the coexistence of opposites, the flow of forces in the universe.
 Things happen naturally; nature and the environment takes care of itself.
 Good moral conduct is encouraged and rewarded with a happy and long life, while on the contrary, bad demeanor results in an unhappy fate with suffering, disease, and a shorter life.
Taoism is native to China.
 Many Tao deities were mortals who once lived and are now venerated and worshiped because of their special contribution.
 For example, the red-faced Guan Gong (關公) seen in many Taoist temples was a brave and fair general more than 1,500 years ago, and thus is respected as the God of War.
 Businessmen worship and honor him in the hope of getting fair business deals and opportunities.
Mazu (媽祖), Goddess of the Sea on the other hand, is the most popular Taoist deity in Taiwan.
 She is known for saving her father and brothers from drowning in a typhoon and also rescuing sailors.
 She gave calm water and weather for a safe voyage when large numbers of Chinese immigrants came from Fujian province in China to settle in Taiwan in search of a better life.
 Consequently, these Chinese immigrants built Mazu temples everywhere around Taiwan to honor Mazu for clear guidance to the island.
 In addition, since Taiwan is surrounded by water, and much trade was conducted with the outlying islands, the locals (especially fishermen) widely worship Mazu to seek protection or a good catch while on the sea.
 In Taiwan, the birthday celebration of Mazu is the largest and most elaborate religious festival of all.
 The best-known of these celebrations, the Dajia Mazu Pilgrimage, lasts eight days and seven nights, and is held annually in the spring around the time of the goddess's birthday.
 The journey starts in Dajia (大甲).
 Taichung County, and passes through four counties and twenty-one townships in central Taiwan, a distance of almost 300 kilometers, which devotees cover on foot visiting and blessing nearly a hundred temples along the way.
A Taoist temple is generally elaborate with colorful dragons, phoenixes or Immortals.
 There are lots of red lanterns, food offerings, candles, but no nuns and monks.
 Taoist believers use incense for prayer as the rising smoke is seen as a means of communicating with the gods; spirit paper money is also burnt.
In the past, Taoist temples were places where people gathered for traditional puppet shows, Taiwanese opera performances and variety shows before there were TVs.
 This activity is seen less these days, but still continues at many rural temples.
The parades frequently seen marching down the streets of Taipei and other cities in Taiwan, with drumming troupes, gongs, martial-arts, firecrackers, and the marching of costumed deities are all part of traditional Taoist practices during celebrations and festivals.
 Look out for the representations of the tall, white-faced deity called Chiye (七爺) and the short, dark-faced god called Baye (八爺).
 These are in charge of rewarding the good and punishing the bad.
Confucianism, although not a religion, is another important part of religious thinking that greatly influences the view of the Taiwanese.
Confucius was the greatest teacher and philosopher in China, and his teaching and philosophy has had a great influence on the ethics, education, ideology and most importantly the fundamental values of Taiwanese people.
 He encouraged the practice of rituals and the worship of ancestors to show deep respect to one's parents and ancestors.
There are many Confucius temples in Taiwan, but they are places to honor Confucius' great teachings, rather than for religious worship.
Folk beliefs permeate the daily life of the Taiwanese, often intermingling with Buddhist and Taoist practices, although Taiwanese people don't seem to be bothered or concerned about the resulting overlaps or contradictions.
 Folk beliefs include offerings to ghosts, ancestor worship at home altars, using spirit mediums called jitong (乩童) to transmit messages from the gods and to communicate with the dead, and many practices and rituals of the major festivals in Taiwan.
 One may call himself a Buddhist or a Taoist yet in reality his rituals are simply folk beliefs.
Ancestor worship is practiced in almost all Taiwanese homes regardless of religion.
 A home altar is set up with ancestor tablets for paying daily homage to deceased family members, in return for which it is believed that ancestors will protect and bless the living family members.
 Today, it is rather common to see a statue or picture of a Buddhist or Taoist deity or folk gods alongside the ancestor tablet.
Taiwan is indeed a unique place, combining a world-renowned high-tech industry with the most traditional cultural customs and religious beliefs.
 Furthermore, we exercise freedom and respect religious diversity while keeping and practicing traditional beliefs.
 Religious differences do not seem to create conflicts or antagonism in Taiwan.
 Today, few Taiwanese practice one exclusive religion; all sort of deities are worshipped in the same temple and at home, a very special feature of religion in Taiwan.
Today young people in Taiwan enjoy celebrating their birthdays every year, complete with a birthday cake, giving gifts and eating out at restaurants or going to a KTV (Karoke TV).
 Parents also make the effort to give birthday parties for children at home or at a child-friendly restaurant or activity center.
 These youngsters celebrate their birthday as it appears on the Western calendar.
 The older generation, however still mark their birthdays according to the traditional lunar calendar.
 Traditionally, Taiwanese people don't celebrate their birthdays until they reach the age of sixty years old.
 The sixtieth birthday is regarded as a very important point of life.
 It is also the first year where both the animal and the element symbol of the Chinese (lunar) calendar are exactly the same as in the year of birth.
Increasingly, people enjoy celebrating a little extra during the Chinese zodiac animal year of their birth, which occurs once every twelve years.
 It is believed that on that year a person will be either especially lucky or particularly unlucky and, at that time many Taiwanese will seek ways to bring themselves good luck.
 For example, during the Year of the Rat, those born under that animal sign may go to a temple to pray for additional luck to keep the bad energy away.
A sixtieth birthday is most elaborate and is commonly celebrated by inviting relatives and close friends to a Chinese banquet dinner.
 After that landmark event, a birthday celebration is customarily held every ten years, until the person's death.
 Grown-up sons and daughters are expected to coordinate and pay for a grand celebration at a hotel or restaurant to show respect for their parents, and as a way to express thanks for all they have done for their children.
 Traditionally the older the person, the bigger the party, but nowadays elders prefer a low-key birthday celebration with just family members.
At the sixtieth birthday celebration, everyone eats traditional foods and they extend their best wishes and give hongbao (紅包, money stuffed in a traditional red envelope).
 The money must be of an even number such as NTS2,000, 3,600 or 6,000, but never in multiples of four, such as 2,400 or 4,000, because for the Taiwanese 'four' is an unlucky number suggesting death.
 Alternatively little statuettes of twenty-four karat gold are commonly given to the long-lived star to wish them continued prosperity and health.
 Do remember to directly on the red envelope so that your gift can be recognized.
Another custom when celebrating an elder's birthday is to offer foods with happy symbolic implications.
 A bowl of 'long-life' noodles (麵線, mian xian) symbolizes a long life.
 The noodles are never cut or broken because this can imply cutting life short.
 Pig knuckles, (豬腳,zhu jiao) represent power and energy, while red eggs, (紅蛋, hong dan) are a symbol of a harmonious and happy life.
 Finally the peach bun (壽桃, shou tao), which is a steamed bun in the shape of a peach with sweet (red or green) bean paste inside, symbolizes long life.
Sixty years makes up a cycle of a life and when one turns sixty, he or she is expected to have a big family with many children and grandchildren.
 It is an age to be proud of and to reward one's great achievements.
 This is why elderly Taiwanese people traditionally start to celebrate their birthdays only upon reaching sixty.
Books about Chinese culture, movies and many TV programs on the local channels here in Taiwan feature traditional Chinese martial arts such as Tai Chi Chuan, Taekwondo, Bagua, Xingyi, and Karate.
 Most martial arts were originally designed as a means of self-defense for oneself, one's ovwn clan, village or temple.
 Sometimes this meant preventing another person from killing or injuring you.
 Nowadays, the same system can be employed to help keep stress from killing or injuring you.
Tai Chi (太極) is one of the most commonly practiced martial arts in Taiwan.
 As you stroll around the city's green spaces, from small community parks around the corner to big plazas like the Sun Yat-sen Memorial Halll and Daan Forest Park in Taipei City early in the morning, it is hard to miss the pleasant scene of groups of men and women (and nowadays youngsters in their teens as well) practicing Tai Chi, a series of slow, gentle, repetitive, flowing movements.
Tai Chi literally means 'ultimate pole', whereas chuan is the word for 'boxing.
 It deals with energy management, as the Chinese word chi (氣) literally means breath' or 'energy.
 The energies the breath, circulation, muscles and bones, and the nervous system in the body circulate through channels; if these are blocked, the energy will not flow.
Tai Chi is based on the principles of yin-yang, the balance and coexistence of the two opposing forces, complementing each other.
 For every Tai Chi movement the body moves (yang) while keeping stationary (yin) at the torso which allows support and energy for each form.
 One of the aims during practice is to achievethe harmony of yin and yang; commonly the yin-yang symbol is also known as the Tai Chi diagram.
Tai Chi forms have a very precise choreography.
 The form most commonly practiced by people seen at the parks contains no leaps, no high kicks and no running (although some Tai Chi forms do have leaps and kicks) and the feet always stay firmly on the ground.
 Each posture flows gracefully into the next without pausing; each movement and gesture is charged with meaning for combat and every Tai Chi posture has multiple purposes.
 One move might be for aligning your own ankle, avoiding a kick from someone, or massaging the spleen.
 All the moves are always concerned with maintaining the center, alignment and balance.
Cheng Man-ching (鄭曼青) has been among the most influential of all the modern Tai Chi masters.
 He is renowned as a master of the 'Five Excellences': painting, poetry, calligraphy, medicine and martial arts, and was responsible for shortening the forms of Tai Chi from 108 moves to 36, which helps Tai Chi beginners to grasp the forms faster and to encourage daily practice.
 Each one follows in the same order every time the form is done; one move flows into the next at the same speed, without interruption.
 It is all about movement and breathing.
 The form can be done slowly, taking seven to ten minutes.
 Many people do their daily routines at this speed, or even more slowly, taking as long as an hour, for advanced energizing, or to heal the body.
Tai Chi has been promoted and has spread worldwide due to its health benefits, and usefulness in health maintenance as well as stress management.
 There are at least five main styles of Tai Chi: Chen (陳式), Yang (楊式), Wu/Hao or Wu Yu-Hsiang (武式), Wu (吳式) and Sun (孫式).
 With the stresses and tensions we encounter daily, our bodies can become tense and static; our bones can harden and grow brittle and our vitality decrease.
 A few minutes of Tai Chi daily promotes mental clarity and a healthy body.
 It can also assist with balance and flexibility, reduce stress, improve circulation of the blood, increase longevity and promote overall well-being.
 Tai Chi is a lifetime fitness for better health!
My family has been going to Xingtian Temple (行天宮) ever since I was a little girl (that's some thirty long years ago!).
 It's the most popular Taoist temple in Taipei city, dedicated to Guan Gong (關公), the god of war and the patron god of merchants, also known for his physical strength and fairness, which is why many business people worship there (see page 141 for more on religions in Taiwan).
 There are two things most Taiwanese do when visiting this temple.
 One is to visit the ladies wearing long blue robes and carrying burning incense in their hands; the other is of course to get their fortune told, or rather to ask Guan Gung to answer their requests, with the help of a pair of red divining blocks.
Volunteers selected by the temple and dressed in blue robes (they're always ladies) are available inside the temple to help people rid themselves of bad energy.
 Each blue-robed lady has her own queue of people waiting to be cleansed or purified.
 They're all good, so take your pick and wait in line.
 When your turn comes, tell her your Chinese or English name.
 She will then brush her hands with burning incense in front of you and touch your head, then your back.
 She will repeat the same stroke three to five times, after which the process is complete.
 It usually takes only about one or two minutes.
 With the process complete, you will be blessed with healthy and positive energy around you.
 If someone cannot make it to the temple himself, family members often take a piece of clothing (usually a shirt or T-shirt, but not shorts or pants) belonging to the individual and ask the blue-robed lady to perform the same strokes on the clothing.
 The individual must then wear that piece of clothing that night to replace their accumulated bad energy with blessed energy.
When in doubt, it is common for Taiwanese to go to temples and use the blocks to ask for directions in taking their 'next-step' from the temple god(s).
 The most frequently asked questions typically relate to marriage, success, career and health.
 You may see people buying incense before entering the temple, although it is not required.
 At Xingtian Temple, unlike many other temples, all services are free of charge, including the incense used for praying.
 So when you have some free time, try stopping by Xingtian Temple and experience for yourself some temple traditions.
 I bet you'll enjoy studying the life, the cultu re and the people of Taiwan there just as much as my Japanese photographer friend, Nao, who found this place to be most amazing during his visit to Taiwan.
 He sat there for five long hours observing the rituals, the practices, and the local people's daily life.
Weddings in all cultures are not only fun but are also a great place to learn about a country's cultural customs and values.
 Influenced by Western culture, Taiwanese brides now often wear a white wedding gown, but the traditional color red is still popular at Taiwanese celebrations.
 Typically, the bride wears three different dresses during the wedding: she first wears the white gown for the opening ceremony, then changes into a different dress half-way through the traditional ten-course dinner banquet.
 Finally she emerges dressed in yet another beautiful gown when she and the other members of the bridal party stand near the exit of the banquet room to say farewell and thank you to the guests as they leave.
Although the Taiwanese lifestyle has been influenced by Western trends, weddings are occasions when Taiwanese customs, traditions, and values still prevail.
 As recently as fifty years ago (in my grandparents' and even into my parents' generation), arranged marriages were quite common, where unions were set up by the couple's parents.
 Sometimes, the marrying pair would not have seen each other until their wedding night.
 But with a more open and modern society, most people date and choose their own spouses.
 Nonetheless, parents still often play an important role in bringing young people together in what's calling xiang chin (相親), a formal meeting where a young man and a woman are introduced in the presence of the family members or a match-maker.
 Many Taiwanese, both men and women now wait until they are almost thirty years old or even later before getting married.
 The man will often postpone marriage until he has completed his schooling and military service and is stable and secure with a good job.
Typically, the wedding ceremony takes place on an auspicious date and time chosen by an astrologer or fortuneteller.
 On the morning of the wedding, the groom along with the groomsmen arrives at the bride's home where the bride and her bridesmaids await patiently.
 The couple bow to the bride's parents to show respect and the bride announces farewell to her beloved parents.
 Thereafter the groom is ready to usher the bride to his home.
 Upon reaching the groom's house, the couple must first pay respects to the groom's family ancestors by burning incense at the family altar, symbolizing the introduction of a new member to the family.
 Later in the afternoon, the couple rests and gets ready for the evening wedding banquet where the main wedding celebrations take place.
The printed wedding invitation is invariably colored red, a happy color, and the obligation to present money to the newlyweds.
 It usually brings with Therefore, wedding invitations are often referred to as 'red bombs'.
 You don't need to bring wedding gifts to the wedding party or review the wedding registry online; the couple just wants cash (no checks!).
 It is customary to give cash to the couple by placing money in a red envelope called a hongbao (紅包, see page 134 for more details).
 Please keep in mind that the money you are expected togive is really to pay for the amazing ten-course meal that you will be served at the wedding party.
 The typical amount expected these days ranges from NT$1,600 to NT$3,600 per person, depending on your guanxi or relationship with the wedding party.
 It's OK to bring a friend with you but make sure you let the host know in advance, so enough food will be served, and be sure to give a larger hongbao.
How much to include?
 Don't be surprised when the receptionist takes the money out of your red envelope, starts counting it, and registers your name and amount in a record book This is not meant to be rude but is a way for the couple to know later which quests gave how much, just as couples in the West like to know who gave which gift, and is also a reference for the couple as to how much they should include in a hongbag in return when they are invited to your function in the future.
Expect to drink and eat a lot at a wedding party, and there's plenty of time for talking and socializing.
 Midway through the banquet, the couple will begin coming around to each table to toast the guests.
 This is the time for the couple to see who has come to the party, and the time for you to wish them happiness.
 Interestingly, it is also a time where close friends play tricks with the newlyweds when coming to their table.
 The seemingly shy couple is often asked to perform actions that can be embarrassing in front of the crowd.
 For instance, the groom may drink beer from a plastic bag that is hanging in front of the bride's chest; the bride might have to carefully pass a raw egg from the groom's pants from one leg over to the other.
 Luckily this act is not a tradition to be commanded by every table at the dinner banquet; only very close friends and colleagues dare to do so.
 After the banquet, the couple stands beside the door while the guests wait in line to say goodbye as they leave.
Weddings in Taiwan usually include some form of entertainment.
 A karaoke set may be set up, where guests take turns to serenade the couple; alternatively a live music band or an orchestra may be on site to add to the sophistication of the wedding ceremony.
 Weddings in the countryside were once well-known tor more risqu? forms of entertainment, which often included strippers, although this custom is now frowned upon.
When you are invited to a wedding, just sit back, enjoy the food, the slide show of the couple chronicling their lives from childhood, and other entertainment.
 After all, you've paid for them!
Many newcomers in my Cross Cultural Training seminar express astonishment when discovering that a great number of their Taiwanese colleagues have left a bright future overseas to return to Taiwan simply to look after their elderly parents.
 And furthermore, these parents may not be all that old in their view.
 This phenomenon can be explained by one of the greatest virtues of Chinese culture, xiao (孝), or filial piety.
 Xiao means showing great love and respect for one's parents and ancestors.
According to Chinese tradition, xiao is the primary duty of all Chinese.
 This means honoring and obeying parents without reservation, showing love and respect, supporting aged family members, displaying courtesy, and bringing honor to the family.
 Furthermore, male heirs are expected to carry on the family name by having at least one son.
Being a filial son means complete obedience to one's parents during their lifetime and taking the best possible care of them as they grow older.
 The older the parents, the more they are to be honored and the greater the obligation to obey them.
 Most importantly, a son must respect his parents even after their death.
 Generally, the eldest son is required to perform ritual sacrifices at their gravesite or, more commonly nowadays, at the ancestral temple.
 Thus ancestor worship at one's own home altar and on Tomb Sweeping Day (April 5th) is of significance to every Taiwanese family.
 This practice of ancestor worship is the binding force that has held the Taiwanese people and their culture together for hundreds of years.
 It also contributes significantly to success in business over generations.
In traditional Taiwanese society, not to continue the family line (ex: not having a son) was the worst offense against the concept of filial piety.
 If a marriage remained barren it was the son's duty to take a second wife or adopt a child - whatever it took to continue the family name.
 If the couple only had daughters, they would continue trying until they gave birth to a son.
 It is also the wife's duty to fulfill filial conduct after marrying into the husband's family.
 This meant a woman had to: 1 Respect and serve her in-laws, in particular her mother-in-law, and 2 Give birth to a son.
By fulfilling these duties, she would also gain prestige for her own family In modern society in Taiwan, the young generations believe differently.
 The daughters still fulfill the duty of xiao, but the duty of passing down one's family name is seen as less important today.
The ideal of respecting and behaving properly towards one's parents was taught to children through common children's stories as welll as by admonition and example, for instance the story of the filial son, Wu Meng.
According to legend, during the Chin Dynasty (4th-5th Century CE), at eight years old a boy named Wu Meng (吳孟), was already serving his parents with exemplary filial piety.
 The family was so poor that they could not even afford a gauze net as protection against mosquitoes.
 Therefore swarms of the insects would come and bite them every night during the warm, humid summer season.
 To protect his parents, Wu Meng let the mosquitoes feast on his uncovered body and would not drive them away in the fear that if he did, the mosquitoes would instead bite his dear parents.
 This was a way of showing respect and love for them.
Children grow up knowing they should never offend their parents and never speak badly of them.
 They should express their devotion to their parents by passing examinations to enter the best university, winning awards at school to gain prestige for the family, and by not traveling far away without purpose.
 Furthermore, they should always be conscious of their parents' age and well- being, and protect them whenever necessary.
Foreigners living in Taiwan can improve their chances of success both socially and professionally when dealing with their Taiwanese counterparts by demonstrating their awareness of the role and importance of xiao, and of respecting the elderly.
The Chinese symbol yin-yang is seen and utilized in many areas.
 It represents the cause of everything and governs how everything works.
 Chinese people apply yin-yang to many aspects of their everyday life and it is not too much to say that for many Chinese it is a way of living.
 Yin-yang is the name given to two opposing forces, or more appropriately, complementary forces.
 Let's examine the importance of yin-yang, a concept which lies at the heart of Chinese culture.
The outer circle periphery represents the completeness of things in life and the circle of harmony each Chinese is obligated to maintain, while the black and white shapes within the circle represent the yin and yang energies Chinese make an effort to balance in life.
 The curved dividing line between the black and white shapes represents the continual interchange of the two energies that are mutually extant.
 The white shape has a black dot and the black shape has a white dot signifying that nothing is completely black or white, as in real life: things are not done in just one way.
Yin is associated with the feminine force (the moon); it is dark, passive, receptive, weak, cold, and corresponds to the night.
 On the contrary yang is the masculine force (the sun); it is bright, active, strong, hot, full of movement, active, and corresponds to the day.
 Yin is often characterized by water and earth, while yang is symbolized by fire and air.
Have you been surprised when you hear Taiwanese ordering water that's boiling-hot or lukewarm (rather than cold) to drink?
 Or when they ask to remove ice cubes from drinks?
 People in Taiwan generally believe that foods have certain properties that can help improve one's health by maintaining or regaining balance.
 To attain this, the right combination of food and drink is important.
All foods are believed to have either a yin or yang essence, but no one food is purely considered as one or the other.
 Some examples of food rich in cooling vin properties are: cabbage, cucumber, most fruits, bitter gourd, winter melon and white radish.
 Hot, yang-rich food include mangos, durian, litchi, ginger, eggs, sesame oil, lamb, beef and wine.
 Most Taiwanese understand these food qualities and traditional home cooking and food preparation have always been based on the principle of balancing yin and yang as well as obtaining a good appearance and flavor.
Choosing suitable ingredients and cooking methods for food according to the season are also important.
 All cooking methods have their own yin or yang categorization as well.
 For example, boiling, poaching, microwaving and steaming are considered yin, while deep-frying, roasting, baking and barbecuing increases the yang force in food.
 Stir-frying is the most balanced way of cooking, bringing out the balance of yin and yang, and is the cooking method most commonly used in Taiwanese homes.
By consuming food and cooking in a harmonious manner, it is believed that one can achieve a healthy way of living and a balanced well-being.
The Chinese essentially see all foods as medicine.
 Chinese doctors examine patients by judging whether it is a surplus of yin or yang that has caused the illness.
 When treating a patient, the physician advises dietary changes in order to regain a healthy balance between the yin and yang in the body, or prescribes herbal drinks to restore harmony.
 People who are storing up too much heat should eat more cooling yin foods, while people who easily feel weak and have cold hands and feet can make themselves stronger by taking warmer yang foods.
I recall as a child often getting nose bleeds during the hot summer days.
 My grandmother told me that I had too much yang energy and I lacked yin foods and liquids in my diet (which made no sense to me at that time, when I believed it was simply old people's superstition), and she proceeded to prepare 'bitter tea' (苦茶 ,which was indeed very bitter!) cooked with numerous 'cooling' herbs.
 I was told that drinking a 500 cc glass of it few days in a row could quench the 'fire condition in my body.
 This cooling drink (which is effective regardless of whether it's served warm or cold) can also be good for treating other 'fire' conditions like toothache, headache, sore throat, and high blood pressure.
 Note that the 'cooling and 'warming' properties of yin and yang have nothing to do with the actual temperature of the food.
 A correct balance of yin and yang has a great impact on the body and helps heal illness.
The Taiwanese define the 'self' by two terms: the 'small self (小我, xiao wo) and the 'big self' (大我, da wo).
 The 'small self' is the true individual self, what one wants and desires, yet in Taiwanese society it is often ignored and effaced.
 The Taiwanese perceive as more important the 'big self,' which covers one's relations with parents, teachers, people in the in-group, and the surroundings, and how one is recognized by others from this outer circle of yin-yang.
 The Taiwanese focus on the 'we' identity, on how one fits into the group, the family, and the work unit.
 One does not have great freedom to explore one's individually.
 'Self' is to Iive up to other's expectations.
 For example, many children hope to become a doctor or lawyer as per their parents' wish, while workers have to behave in a certain way at work because of their obligation to others.
 Consequently, a Taiwanese individual cares a lot about others' feedback, either complementary or critical, rather than being grounded in him or herself.
The fundamental difference in values and beliefs between the West and East is that Western cultures value 'honesty' while those in the East value 'harmony'.
 In an honesty-based culture, truth is the key to building good personal and professional relationships.
 If there is truth, workers can easily trust and respect each other; thus business can be conducted in a very straightforward and direct fashion.
Western businessmen coming to the East must know the concept of balancing yin-yang in the workplace when managing and doing business with Taiwanese nationals.
 In the symbol of yin-yang, the black and white shapes represent the contrast between two different environments, climates and ways of living, each deeply rooted in its own history and culture.
 Consequently, when West meets East it is extremely important to learn and understand the balance of yin-yang as the concept of 'harmony' in the workplace.
 Harmony is indeed the foundation, the root of Taiwanese culture and the key to success in working effectively with your Taiwan colleagues.
Taiwanese people avoid conflict at all costs because it invites direct confrontation which disrupts the harmony, thus creating an imbalance of the circle of yin-yang.
 It is best to preserve and maintain harmonious interpersonal relationships (called guanxi).
 Guanxi is an important aspect of doing business, and every conversation in business involves 'face'.
 Tactfully giving face and saving face in a conversation is important in showing one's respect for status and hierarchy.
 Communications thus are indirect and a third party strategy can often be used instead of a clear and straightforward communication.
 What really matters is who should say it, when to say it, how to say it and what NOT to say in a given situation.
 The person in authority is expected to provide not only mentorship but also protection like a father figure, while in return junior employees act with complete loyalty and respect.
 The key cultural values of harmony, guanxi, 'face' and hierarchy are interdependent and intertwined within the circle of yin-yang.
The yin-yang philosophy influences greatly the way of doing business in Taiwan.
 Keeping these forces in balance and behaving appropriately according to the requirements of situations can lead to a smoothly running and successful organization.
Thus, yin and yang are two opposing principles which are basic to everything in the life of the Taiwanese people.
 Opposites are complementary; one could not exist without the other, and when in equilibrium they bring health, happiness wealth and success.
Do you realize why many Taiwanese women pushing strollers or walking with young children look so young and beautiful?
 How do they keep themselves so free of care?
 It's definitely not just SKII or Chanel cosmetics.
 Perhaps part of e secret is zuo yuezi.
 No, it's not a secret Chinese skin cream recipe, but a ritual that literally means being confined for a month after delivering a baby.
Zuo yuezi, (坐月子) literally means 'doing the month,' and is a time when the new mother regains her strength after birth and, according to custom, replenishes the blood lost during delivery.
 During the thirty days after giving birth she does nothing but rest and enjoy the pampered care of her mother, mother-in-law or very common nowadays) a private zuo yuezi center in Taiwan.
 At the zuo yuezi center women go with the newborn baby after leaving the hospital to be cared for by well-trained nannies for one month before returning home.
 The husband can spend the night at the center; friends and family, however can only visit at the designated meeting room, so as not to disturb the new mother and her newborn.
 All zuo yuezi rituals are taken care of at the center, easing the responsibilities traditionally assumed by the mother or mother-in-law.
 Zuo yuezi customs also prevail amongst Chinese Americans living overseas, where they often hire a live-in zuo yuezi nanny to spend the entire month in their home caring for the new mother and the baby.
 Many of my friends (both in Taiwan and overseas, including my younger sister who lives in America), had to reserve months ahead or as early as the pregnancy is confirmed to ensure the zuo yuezi nanny or center had an available person at that time.
So what really happens in this one-month period?
 Here are some of the traditional 'rules' to follow while 'doing the month': Drink chicken and fish soup enriched with nutritious Chinese herbs every day (and night) for thirty days.
 No cold or iced food or drinks.
 Neither foods the Chinese regard as 'cold' (see page 178 on yin-yang for the Chinese concept of 'hot' and 'cold' foods) nor food at low temperature should be eaten, and definitely no raw food.
 Warm food and soup protects the body from harm caused by and energy lost during delivery.
 Avoid getting tired.
 This means no housework for a month, but also no TV or reading books, which are thought to be bad for the eyes.
 The new mother is not allowed to lift heavy things, do home chores or run errands.
 Get lots of rest.
 The new mother must lie down and relax as much as possible.
 Sleep all day if possible, as rest is the best way to strengthen the weakened body following delivery.
 No bath or shower should be taken for a month.
 It's important not to catch a cold.
 In the old days, especially in the winter, bathing was believed to open up the pores and was an easy way to catch a cold.
 A warm towel bath should be taken instead.
 The hair should not be washed for a month either, to avoid headaches.
 (Many young mothers nowadays disregard this rule, as it's too hard to not bathe or wash hair for four weeks!) No cold water, no cool breezes or draft are allowed.
 This also means no air conditioning or fan, even on the hottest days.
 (This is for real!
 My poor friend who had her son in July could not turn on the AC for a month).
 No male visitors.
 In the old days, only women were allowed to visit, as men would invade the new mother's privacy.
 However, this isn't much of a concern now.
 However it is always polite to inquire before visiting at the hospital or the zuo yuezi center.
Some of these rules might seem unreasonable to Westerners (although on the other hand, some of them might appeal, especially the no-housework and sleep- all-day edicts!), but are practical as well as traditional.
 Taiwanese new mothers use these guidelines as a hint that it is important to rest up after giving birth.
Zuo yuezi protocol is a deeply rooted aspect of Chinese culture and is still commonly practiced among women in Taiwan as well as amongst ethnic Chinese around the globe.
 It's a tradition not to be ignored unless the husband is to be viewed as incapable or irresponsible for not being able to provide the most basic care for his wife and their new family.
 Should a new mother complain of frequent joint pains, a weak lower back, or especially hip problems, the cause will no doubt be blamed on not observing the month-long zuo yuezi.
After all, zuo yuezi is not endured simply to help a new mother retain her young looks and beauty after having had a child.
 Rather it is believed to be a necessary process that helps a new mother to return to full health and strength before even considering or preparing to get pregnant with a second or subsequent child.
The young family is generally very busy caring for the new member of the family during the first month, thus it's important to give the new mother as much rest time as possible.
 Visit only after the first month if possible or attend the party that is customarily held when the baby turns one-month old (this party is described in the 'Man Yue').
Widespread immigration has also affected Taiwanese religious practices.
But whereas languages have generally remained separate, different religions have blended together.
The majority of people now follow a faith that is a mix of Buddhism, Taoism, and traditional folk beliefs.
Owing to all of these religious influences, Taiwan is now home to hundreds of different gods that are worshipped in thousands of temples around the island.
People worship these gods and their ancestors by offering incense, paper money, which is also called joss paper, and food.
As you make your way around Taiwan, one of the first things you'll notice is the temples.
There are thousands of them.
Some are exclusively Buddhist, but most Taiwanese temples combine Buddhism, Taoism, and traditional folk religion.
In fact, you can sometimes find people worshipping Buddhist and Taoist gods in the same building.
The majority of people in Taiwan follow a combination of Buddhism, Taoism, and folk religion.
According to Buddhism, people are reborn when they die.
If they have lived a good life, they will be born into a favorable situation, but if they have lived an immoral life, they will return to an unsatisfactory position.
Guanyin, the goddess of mercy, is an important figure in Buddhism, and her statues can be found all over Taiwan.
Taoism teaches that we should try to live in harmony with the Tao, which is the true nature of the universe.
Many Taoist gods are people who did great things while they were alive.
One of the most famous is Guangong, a Chinese general who died in 219 AD.
He is now worshipped as the god of war.
Taiwanese folk religion involves ancestor worship, fortune telling, and a belief in ghosts and spirits.
Mazu, the goddess of the sea, is the most important figure in Taiwanese folk religion.
Statues of her and temples bearing her name can be found in many towns and cities, especially those along the coast.
To make religion in Taiwan even more interesting, there are also a large number of Confucius temples.
Confucius is not really regarded' as a god, but his teachings on morality and duty have influenced Chinese culture and religions.
Worship in Taiwan generally involves paying your respects and making offerings.
In temples, people light incense, offer food and drink, and burn paper money for the gods.
It is believed that this money is transported up to the gods when it is burnt.
People also use incense and paper money to honor their ancestors.
On some occasions, offerings are made to keep ghosts away.
For many Taiwanese people, visiting fortune tellers is an important practice.
Many different methods are used to predict a person's future, but most fortune tellers in Taiwan work by looking at a person's date and time of birth.
While some people will only visit fortune tellers once or twice in their lives others will consult them before making any important decision.
Businesspeople ask about good days to open a new company, lovers ask if their partner is a good match for them, and parents ask for advice on naming their children.
It's believed that getting one of these decisions wrong could put you out of harmony with the universe, which would bring bad luck.
Taiwan also has a large Christian population.
The religion was first brought to the country by Dutch missionaries in the 17th century.
Spanish priests from the Roman Catholic Church followed, and missionaries have been making their way here ever since.
It is estimated that there are over a million Christians now living in Taiwan.
Daisy: Do you often go to the temple?
Brad: Yes, I usually go with my family about once every two weeks
Daisy: So what kind of temple is it?
Is it Buddhist or Taoist?
Brad: I don't really know to be honest with you.
Daisy: What?
How can you not know?
You say you go there quite often.
Brad: Buddhism and Taoism have really gotten mixed together in Taiwan, so it's sometimes difficult to know which is which.
Daisy: Really?
That seems so strange.
Brad: Strange or not, that's how it is here.
A lot of people follow both religions at the same time, and a lot of temples cover both religions.
Daisy: Wow!
Brad: So this kind of thing doesn't happen in Europe?
Daisy: No. Over there, a Christian is a Christian and a Jew is a Jew.
The two religions stay separate from one another.
Brad: Hmm. It's so different to here.
A: I'm going to see a fortune teller this afternoon.
Do you want to come with me?
B: Yeah, sure.
Maybe I'll get my fortune read, too.
A: When was the last time you visited a fortune teller?
B: I don't know.
My parents took me to see one when I was a kid, but that must have been about 15 years ago.
A: Wow!
Today will be my second trip this year.
B: Oh right.
Do you consult him before making any big decision?
A: Yes, I do.
I've just been offered a new job, so I want to know if it would be good for me to take it.
B: OK.
What else did you see him about this year?
A: I asked him about that guy I met called Brad.
Do you remember him?
B: Yes, but I haven't seen him for a long time.
A: That's because I was told we would argue a lot, so we decided to end the relationship.
In Taiwan, if you look at the windows of apartment blocks at nighttime, you'll notice that some have a red light coming from them.
This is because some families keep a shrine in their homes to honor the gods or their ancestors.
Red lamps are kept on in these shrines because red is a lucky color and it helps to keep away evil spirits.
Burning incense is an important part of worship in Taiwan, and the busier temples are usually filled with thick smoke.
One reason for this is that the smoke is believed to spiritually cleanse the surrounding area.
Sticks of incense are also used to help people pay their respects to the gods.
In most of Taiwan's temples, people offer one stick of incense to each god or goddess worshipped in that temple.
Taiwan has received many foreign missionaries over the years.
They have come from many different countries and have introduced many different teachings.
The biggest group of missionaries in Taiwan at present is from the Church of Jesus Christ of Latter Day Saints, often called the Mormon Church, which was founded in America in 1830.
Mazu has been described as "Taiwan's guardian goddess," and she is certainly one of Taiwan's most popular religious figures.
Mazu was actually a real person, a girl who was born in Fujian Province in about 960 AD.
Stories about her say she would wear red clothes and stand on the shore to help fishermen find their way.
Another says that she saved her father when he was caught up in a storm.
Taiwanese people don't just buy clothes, shoes, and electronic gadgets.
When you walk along a shopping street, you'll definitely notice some stores that are very different from those that you see in the West.
The most unusual ones are the places that sell traditional Chinese medicines.
These medicine stores usually look quite old-fashioned, and they're full of old wooden cabinets with drawers.
You'll also see jars containing herbs, roots, and other strange-looking objects.
In times gone by, Chinese medicine involved the use of rare animal parts like antlers, but many of these things are now illegal in Taiwan.
Most medicines are made from plants and funguses, although insects are sometimes used.
The people who own these stores are very knowledgeable and are able to prepare prescriptions for many illnesses.
Something else that's good for your health is Taiwanese tea.
Some of the best tea in the world is grown in this country, and stores selling it can be found everywhere.
The aromas coming from these stores are incredible, and you might find it difficult not to walk inside and take a look around.
You'll find shelves lined with packs of locally grown teas and tables where customers sit and sample the produce.
The best teas have complex and subtle flavors, and they can be very expensive.
But unless you're a tea connoisseur, you might want to buy a cheaper version instead.
Another common sight along Taiwan's roads is a type of store that sells things for people worshipping in temples.
They are usually very simple places, as they don't keep many items in stock.
Inside and outside, you'll see piles of paper money and bundles of incense and candles.
Often, pieces of paper money will be folded up to make models of lotus flowers.
As Taiwan has developed, some older kinds of stores have been replaced by newer, brighter outlets.
Old-fashioned grocery stores used to be found everywhere around the country.
With modern convenience stores becoming so popular, many of these family-owned places have closed in recent years.
They still exist in countryside areas and on the edges of towns, though.
They're usually a lot cheaper than convenience stores, and you can often find snacks, drinks, and candies that you wouldn't get in other places.
There are other interesting stores that might be familiar to foreign tourists.
What makes them so special is their size.
Stationery stores in Taiwan are often huge places that carry hundreds of different types of pens, pencils, and notebooks.
You'll also find materials for handicrafts, sports equipment, gift boxes, and toys.
That might seem like a wide range of products, but Taiwan's general merchandise stores have much, much more.
It's incredible how many different things you can find in these places.
There's furniture, tools, toiletries, clothes, gardening equipment, and almost anything else you can think of.
Walking around a general merchandise store can be fun, as you never know what you're going to see.
A: You really don't look well.
Are you OK?
B: I actually feel terrible.
I think I've got the flu.
A: Why don't you go and see a doctor?
I think there's one just down the road.
It'll be quick, and you'll get some medicine.
B: To be honest with you, I don't really like taking medicine.
A: OK, how about natural Chinese medicine?
It's basically just herbs and plants.
B: Yeah, I suppose that would be OK.
But does it work?
A: I don't know whether it will work for you, but quite a lot of my friends say it's really helpful.
B: So where do I get it from?
A: I'll take you to a Chinese medicine store.
The people there will know just what to give you.
B: How do I take this medicine?
Do I eat it?
A: There are some things that you can eat, but mostly, you boil it in water to make a kind of tea.
Tourist: I've seen some interesting looking stores as we've driven around Taiwan, and I was wondering what they were.
Guide: What do they look like?
Tourist: They're fairly big places and they look quite simple.
They're not clean and modern like supermarkets.
Outside the stores, there are usually quite a lot of things like furniture, electrical fans, motorbike helmets, and slippers.
Guide: Oh, they would be general merchandise stores.
You don't really see them in the middle of Taipei, but they're pretty popular around Taiwan.
Tourist: So what can you find inside them?
Guide: That's a good question.
The range of products they carry is huge, and you will often be surprised by what you find.
Tourist: So what's the difference between them and supermarkets?
Guide: Well, although some of them do have food, they mostly sell things like tools, cleaning products, and things for the home.
Tourist: It doesn't sound like a tourist shop, but l'd still like to look around one.
This is one of the more expensive things you'll find in a traditional Chinese medicine store.
It's actually a kind of fungus that grows inside the body of a small insect.
It is often given to people suffering from extreme tiredness.
Some people also believe it can be used to treat cancer.
You won't see this tasty drink in many modern supermarkets, but you can find it in some old- grocery stores.
The great thing about these drinks is that the bottle is closed up at the top with a marble.
To open the bottle, you have to push the marble into the bottle.
In the past, children used to get the marbles out of the bottles and play with them.
Taiwanese tea is traditionally prepared in small teapots.
It's then poured into small round cups.
For some teas, connoisseurs might even use two cups.
People will typically drink several cups of tea in one sitting.
Because tea is so important to so many people, the teapots and teacups they use are sometimes very beautiful and expensive.
Printing stamps are popular in Taiwan, and you'll see them in most stationery stores.
There are usually a lot of stamps with cute pictures, cartoon characters, or phrases like "Good Luck!" on them.
Also, almost everyone in Taiwan has at least one name stamp called a chop.
Chops are usually bought in special stores where names are carved into wood or stone.
Taiwan is blessed with a rich variety of folk arts and practices that range from folk opera to puppet theater and indigenous and Chinese ceremonies.
There are also a number of different activities, including paper cutting and the spinning of "tuoluo" or tops.
Many of the folk arts are still regularly practiced, and some can be seen on the streets at tourist destinations or during festivals.
Although travelers might not know much about Taiwanese folk arts when they first arrive in the country, they're unlikely to leave without seeing at least one of these art forms in practice.
Taiwanese opera is believed to have originated in the northeastern area of Yilan in the early 20th century.
It developed through a fusion of Chinese opera with old Taiwanese folk songs and stories.
Because performances are given in the Taiwanese language, the art form quickly became popular throughout the island.
Although costumes and props are important, the main focus of Taiwanese opera is the beautiful singing.
Many different kinds of instruments are used to create the music, with Chinese string instruments, cymbals, and hollow wooden blocks featured in most performances.
Taiwanese opera was once a major part of everyday life for people in rural areas, but its popularity began to fade as Taiwan developed into an industrial nation.
The art is making a comeback, however, and should live on long into the future.
Another Taiwanese folk art that is enjoying a rise in popularity is puppet theater.
Regular TV shows attract youthful audiences, and puppet theater groups can often be seen performing at temple festivals.
The history of this art form stretches back hundreds of years.
In ancient, rural Taiwan, companies of performers would travel the countryside and put on performances in the villages they passed through.
Like Taiwanese opera, shows are conducted in Taiwanese, and the language is an integral part of the art's popularity.
The characters often use poetry and idioms when they speak.
Taiwan has 14 officially recognized indigenous tribes, and they all have their own customs and ceremonies.
Different ceremonies are held to celebrate stages of life, mark times of the year, and seek good fortune.
During the Bunun Tribe's Ear-shooting Festival, the ears of hunted animals are attached to a pole.
Men then try to shoot the ears with bows and arrows.
Fathers and older brothers also help young boys to shoot the ears, believing that this will make them better hunters.
The Puyuma Tribe's Monkey Ceremony ("Vasivas" in the local indigenous language) centers on hunting, and it marks the point at which boys become men.
Other ceremonies are more Chinese in character, and one of the best examples is the Confucius Ceremony.
This ceremony is held on September 28th each year, marking the birthday of the Chinese world's most important teacher.
The ceremony, which is very serious, is held at around sunrise, and it involves a series of processions and rituals.
Many of the participants dress in traditional Chinese robes, so it should be an interesting event for tourists.
A: Hey, do you see that guy over there?
B: Do you mean the one with the spinning tops?
A: Yes, he's incredible.
I can't believe he's able to hurl that top through the air and make it land in that small circle.
B: He's pretty good, but I've seen better.
I once saw someone throwing tops about three meters high and having them land on tiny little plates on top of sticks.
A: Wow!
So is spinning tops a big activity here?
B: Yes.
It's actually one of Taiwan's many folk arts, and you often see people or groups performing at traditional travel destinations.
A: Oh, OK.
So can you spin tops like those guys?
B: Ha ha, no.
It's fairly normal for people to learn how to do it when they're kids, but I was never really very good at it.
A: So, what folk arts are you good at?
B: Well, I'm pretty good at paper cutting.
I've always been able to make interesting or beautiful designs by cutting paper.
A: You have to show me how to do that.
J: So, what did you think about the puppet theater TV show?
I: To be honest, I'm amazed.
I thought it was fantastic.
J: I told you you'd like it.
I: I know, but I didn't believe you.
The puppet shows we have in England are not very entertaining.
They were popular about 200 years ago, but nobody watches them now.
J: Taiwanese puppet theater has a very long history too, but the art has adapted over time.
I guess that's why it's still popular.
I: You might be right.
This puppet show used lots of special effects that didn't look old-fashioned at all.
J: And the puppets are quite intricate, so they appear quite lifelike when you see them.
I: Yeah, but the best part was the story.
English puppet shows use silly stories, so it was great to see the performance tell a serious story with lots of action in it.
J: That's great. I'm really glad you liked it!
The Wang Ye Boat Burning Festival(王船祭).
These festivals take place once every three years, and the biggest one is held in Donggang, Pingtung County.
Tens of thousands of people visit the town for the festival, which lasts for several days.
It all ends with the burning of a huge, hand-crafted boat.
Many people believe the Wang Ye, or Royal Lords, have the ability to keep disease at bay.
Beehive Fireworks(蜂炮節).
Every year in Yanshui, Tainan City, the Lantern Festival is celebrated with thousands of fireworks.
But, instead of being fired up into the air, the fireworks are blasted horizontally, straight into the crowds of people.
Amazingly, people come from all over Taiwan to experience this celebration.
They always dress in thick clothing and helmets to protect themselves from the fireworks.
Ghost Month(鬼月).
The seventh month of the lunar calendar is known as Ghost Month in Taiwan.
People believe that during this month, the gates of Hell are opened and the dead return to Earth.
To keep the ghosts happy, people make offerings of food and drink and also burn paper money and incense.
Since it is also thought that ghosts might try and take the lives of others, many people avoid dangerous activities, like swimming in lakes and rivers during this time.
By this stage of the book, it should be clear that Taiwan has had a long and varied history.
The island has been inhabited by people from across the globe and numerous religions and languages have been introduced here.
As a result, Taiwan's culture is both rich and unique, but visitors should be aware that social customs here are different from those in Western countries.
If you don't want to cause outrage, then there are a few things you need to know, and a few rules you need to follow.
You don't need to be careful all the time, though, and Taiwan's many festivals and holidays are usually a good time to have fun.
The biggest festival of the year is Chinese New Year, and most people around the world are familiar with the event.
It's an occasion when families spend time together, eat lots of food, and give gifts of money in red envelopes.
This isn't the only time of year when families get together, though.
Tomb Sweeping Day and the Mid-Autumn Festival are also family- centered occasions, and many people will usually return home for Mother's Day and Father's Day.
Most festivals are associated with special foods and activities, and each event has its own flavor and atmosphere.
Not all of Taiwan's holidays and festivals are joyous occasions.
The start of huge massacre is commemorated on 228 Memorial Day.
However most of Taiwan's special days actually have positive connotations, like when the Taiwanese celebrate romance and love during Qixi Festival.
Although this festival is sometimes known as Chinese Valentine's Day, readers shouldn't think it's simply a Chinese version of the popular Western holiday In fact, Qixi Festival is hundreds of years older than Valentine's Day.
The Double Ninth Festival is a time for Chinese people to pay their respect to the ancestors and the elderly.
No matter the time of year, there are a few things that you should definitely avoid doing.
Public displays of affection are not common, and they may offend a lot of people, especially the older members of Taiwanese society.
Although some younger people are getting more affectionate, many husbands and wives don't even hold hands in public.
While no one would grumble if you gave your partner a quick kiss, anything more than that might get you into trouble.
On a similar note, men and women in the West might greet one another with a kiss on the cheek, but that would probably cause a lot of awkwardness and embarrassment in Taiwan.
Another thing to watch out for is how to accept and offer things.
In many situations, Taiwanese people consider it impolite to accept something the first time it's offered.
If you ask someone whether they'd like some help or offer a guest a glass of wine, they might initially say no.
It's possible that they're just being polite, so you should ask them whether they're sure and also let them know that it's OK to say yes.
Likewise, if somebody makes you an offer, don't say yes straightaway or they might think that you're greedy or rude.
Being polite and respectful is important in both Taiwan and the West.
In both places, there are rules of behavior that you should follow if you don't want to offend people.
These rules sometimes appear very similar, and people from both cultures might show their respect to someone in exactly the same way.
Despite this, however, the Taiwanese system of etiquette is sometimes very different from the Western system.
As a result, somebody from one culture might easily offend somebody from the other without ever intending to.
In Taiwanese society, there are clear rules regarding social standing.
Parents, older siblings, teachers, and bosses have a higher position and should be respected.
In the West, however, the idea of social standing has lost most of its importance over the last 200 years.
Nowadays, everyone is supposed to be fairly respectful of everyone else.
So, although bosses have a higher status than employees, they're still expected to respect their workers.
If they don't, they probably won't be treated very well by their employees.
Older brothers and sisters don't really have a higher position in a family than their younger siblings, and elderly people actually sometimes receive less rather than more respect.
In many Taiwanese families, men have a higher standing than women, but things are changing and the gap between the sexes isn't as big as it used to be.
Yet even in modern Taiwan, there are still cases of men and women being treated differently by family members.
During family occasions, it is usually the women who prepare and serve the food while the men sit back and relax.
In the past, it was even thought that a man would be humiliating himself by stepping into a kitchen.
In the West, there are some families where these kinds of gender differences still exist, but they are usually regarded as old fashioned.
In addition to all of this, there are many actions that might be perfectly acceptable in one culture but vulgar or offensive in another.
A lot of them concern food and how it should be eaten.
To a Westerner, it might seem convenient to just stick your chopsticks into your bowl of food when you're not using them, but doing this would be a mistake.
A pair of chopsticks sticking up from a bowl look like sticks of incense offered to a dead ancestor, so this action would be very offensive to those at the same table.
In Taiwan, many people hold their food bowls towards their mouths when eating, but this is considered uncivilized in the West, where it's also unacceptable to drink soup directly from a bowl or to put your knife in your mouth.
In Taiwan, four is a very unlucky number because in Chinese, it sounds like the word for "death" or "die."
As a result, some apartment buildings and almost all hospitals do not have a fourth floor.
In the West, however, the number 13 is considered unlucky.
One possible reason for this is that there were 13 people at Jesus Christ's last meal.
Maggie: Which floor does Richard live on?
Paul: I think his apartment is on the fifth floor.
Maggie: OK, I'll press the button.
Hold on...where's the button for the fourth floor?
Paul: There isn't a fourth floor.
Maggie: What do you mean?
Paul: Well, the floors are numbered one, two, three, and then five.
Maggie: Why isn't there a fourth floor?
Paul: Because four is a very unlucky number for Taiwanese people.
The Chinese word for four sounds the same as the word for death.
Maggie: So if you had an apartment on the fourth floor, it would be like you lived on the death floor?
Paul: Kind of, yeah.
A lot of apartment buildings still have a fourth floor, but most hospitals don't.
Maggie: Oh, OK.
In America, 13 is the unluckiest number.
Paul: I thought it was 666.
Maggie: The number 666 is considered to be the mark of the devil, but it is the number 13 that we think of as unlucky.
Joe: How was dinner with your boyfriend's family last night?
Andrea: It was interesting.
That was the first time I've eaten with a Taiwanese family, and it's a bit different from America.
Joe: I guess it would be.
Andrea: One thing that surprised me was that my boyfriend and his father did nothing to help his mother and sister bring the food to the table or clear away the dishes.
Joe: In a lot of families, men don't really do that.
Did you help?
Andrea: Yes.
In America it would be a bit strange for a guest to help, but I felt sorry for my boyfriend's mother.
Joe: Was anything else strange?
Andrea: Yes.
When I ate pieces of chicken, I nibbled at the meat and left the fat and bones.
But they would put the whole piece in their mouth, then pull out the bones and leave them on the table.
Joe: That doesn't happen in America?
Andrea: No!
It looks horrible!
In Taiwan, you should never give people clocks as gifts.
In Chinese, to give someone a clock means the same thing as sending someone on their final journey.
In Taiwan, it's quite normal to give people fruit, but this would be unusual in the West.
Common gifts would be chocolate, candy, and cookies.
In Taiwan, you should always close your umbrella before you go inside someone's home.
People believe that if your umbrella is open, you might take evil spirits into a house.
Funnily enough, people in the West also believe it's very bad luck to open an umbrella indoors.
Traditionally, when someone died in Taiwan, their body was put in a coffin and left in the family house for a few days.
The dead person was always positioned so that their feet pointed towards the door.
Therefore, some people believe it's very bad luck to sleep with your feet pointing towards the bedroom door.
Chinese New Year is the biggest and most important festival of the year in Taiwan.
Like Christmas in the West, it is a time for families, food, fun, and gifts.
Couplets about luck or wealth are posted around people's doors, and firecrackers are set off.
As with most of Taiwan's festivals, Chinese New Year follows the lunar calendar, so it doesn't have a fixed date on the more commonly used solar calendar.
The New Year could fall anytime between late January and late February.
The New Year festivities really begin on New Year's Eve.
Most families get together for a large meal that traditionally involves a few special foods like fish and a type of greens called "long-year vegetables." People say that since the Chinese word for fish sounds the same as the word for surplus, you can't eat all the fish.
If you do, your family won't have any extra food or money for the whole year.
After eating, families usually stay up very late, and the traditional belief is that the longer you stay awake, the longer your parents will live.
On New Year's Day, you're supposed to wear new clothes.
Although some people don't follow this tradition anymore, you do still see little children wearing bright, new clothes on this day.
This is also the day when red envelopes are given.
When children are young, they get envelopes from other people in the family, but when they grow up and begin earning money, they need to give envelopes to their parents.
Although people are only granted a few days off work, the festival doesn't end until the 15th day of the New Year.
This day is marked by the Lantern Festival, and it's a time when bright lanterns light up the night sky.
There are many different legends about the origins of the Lantern Festival, and while some say that lanterns were first lit to amuse fun-loving gods, others say they were used to confuse a god who wanted to destroy a village.
In modern Taiwan, parks and streets in cities around the country are decorated with hundreds of beautiful lanterns.
Some of these lanterns have very imaginative designs and others are extremely large.
Not surprisingly, then, these displays usually attract thousands of visitors.
The next festival on the calendar is Tomb Sweeping Day.
It usually falls on April 5th, and it always comes 104 days after the shortest day of the year.
Traditionally, this is the time of year when people should head out to enjoy springtime and when families should sweep and clean the tombs of their ancestors.
Since Taiwan is a small, overcrowded island, people are increasingly being cremated when they die.
Their ashes are then placed in special buildings with room for hundreds of people's remains.
Since there are fewer tombs than there used to be, Tomb Sweeping Day is losing some of its importance in Taiwan.
That said, many families still do spend the day clearing the weeds away from their deceased relatives' tombs.
After cleaning the site, they burn incense and paper money for their ancestors.
E: It's Chinese New Year next week, right?
J: Yes, that's right.
It's going to be the Year of the Dragon.
E: What does that mean the Year of the Dragon?
J: According to Chinese beliefs, each year is connected to an animal.
There are twelve animals in total.
E: And the dragon is one of those animals?
J: Exactly.
The animals have different characteristics, so people in each year are supposed to have different personalities.
E: So what about people born in the Year of the Dragon?
J: They're supposed to be powerful and energetic, but they can be self-centered.
E: What animal are you?
J: I'm a horse, so that means I should be cheerful, unpredictable, and sometimes short-tempered.
E: That's pretty accurate.
I was born on April 22, 1987, so what animal am I?
J: That would make you a rabbit.
You're supposed to be very lucky, polite, and elegant.
J: What are you doing this weekend?
D: I'm visiting a friend in Kaohsiung, and I think we're going to look at the Lantern Festival displays along Love River.
J: You're going to look at lanterns?
D: Yeah, it's supposed to be really nice.
It's always quite nice to walk along the river, and my friend says the lanterns are really interesting.
People make them using all different designs, so some of them look like cartoon characters and others look like buildings.
J: I thought lanterns were always just round.
D: Maybe they were traditionally, but people are quite imaginative with them now.
J: You've got me interested.
Are there any Lantern Festival displays in or around Taipei?
D: Yes, there's one in Taipei almost every year.
You could also take a trip to Pingxi where they release sky lanterns every year.
J: What are sky lanterns?
D: They're a bit like little hot air balloons.
You light a fire underneath them and they float away into the sky.
Often called "yuan dan" meaning "the first day," January 1st is the first holiday of the year in Taiwan.
Traditionally, it wasn't celebrated at all, and many older people will not make an effort to stay up after midnight on New Year's Eve.
Young people often do meet up with their friends on December 31st and stay up late.
The biggest New Year party in the country takes place outside Taipei 101, and it features an amazing fireworks display.
The 228 Incident was a massacre of thousands of Taiwanese citizens that began on February 28th, 1947.
One day earlier, citizens in Taiwan began a huge protest against the government.
The government responded by brutally killing anyone connected with the protest.
It is thought that between 10,000 and 30,000 people were killed.
The incident is remembered with a national holiday on February 28th, which is now referred as Peace Memorial Day or 228 Memorial Day.
This day in honor of children is celebrated around the world, though countries have chosen their own dates for the holiday.
In Taiwan, Children's Day falls on April 4th, and it coincides with Women's Day.
This is a national holiday, so schools and some businesses close for the day.
On May 1st many businesses, especially those related to manufacturing, close for the day, as do banks, in Taiwan.
However, many government offices and schools stay open.
Although it's known as Labor Day here, it's also called May Day and International Workers' Day in other places.
The day is often marked by workers holding demonstrations to demand better wages and working conditions.
On the fifth day of the fifth lunar month, the Taiwanese celebrate the Dragon Boat Festival.
The event commemorates the death of Qu Yuan, a third-century statesman and poet who lived in China's Chu kingdom.
When Qin forces took over Chu, Qu Yuan killed himself by throwing himself into a river.
Legend has it that the local people were so distressed by this that they sailed up and down the river looking for his body.
They even threw rice dumplings into the water, hoping that the fish would eat the food instead of Qu Yuan's body.
Present day celebrations are based on this story.
Dragon boat races have become very popular, and teams now come to Taiwan from all over the world to take part in the events.
Races are fun events and the colorfully decorated boats attract crowds of spectators.
Rice dumplings, or "zong zi" are commonly eaten at this time of year.
There are several different kinds of dumplings, most of which feature sticky rice, meat, and mushrooms wrapped in bamboo or shell ginger leaves.
On the seventh day of the seventh lunar month, people celebrate Qixi Festival, which is sometimes known as Chinese Valentine's Day.
Legend has it that two young lovers were sent to different stars by the goddess of Heaven.
She then separated the stars by creating a river between them.
The magpies of the world took pity on the lovers, and decided to make a bridge over the river once a year so that the sweethearts could be united.
Just like Western Valentine's Day, people celebrate the day by giving their partners gifts.
During the Ghost Festival, it is believed that the gates of Hell are opened and the dead are permitted to return to Earth.
Traditionally in China, people say this happens on the 15th day of the seventh lunar month.
In Taiwan, however, many people believe ghosts are with us throughout the seventh month, and the period is therefore known as Ghost Month.
People believe that ghosts are so desperate for a new life that they might take the lives of other people.
To appease the ghosts, people offer food, incense, and paper money to them during Ghost Month.
They also avoid hazardous activities like swimming in lakes and rivers.
They don't whistle or talk about ghosts to avoid attracting them.
The last main festival of the year is the Mid-Autumn Festival, and it falls on the 15th day of the eighth lunar month.
The day has also been dubbed Moon Festival, and the moon plays an important role in the celebrations.
An old legend says that a beautiful woman named Chang E lives on the moon.
Once a year, on Mid-Autumn Festival, she is visited by her husband, Hou Yi, and the moon shines more brightly as a result.
In Taiwan, this is a day for families to spend time together.
People often barbecue outside their homes and eat moon cakes.
T: I keep seeing these triangular things in food shops.
Do you know what they are?
A: Are they like little pyramids?
T: Yes.
A: And wrapped in leaves?
T: Yes.
What are they?
A: They're rice dumplings called "zong zi," and they're really popular around the time of Dragon Boat Festival.
T: Why?
A: Well, the festival started when people rowed their boats across a river looking for an important person who drowned himself in the water.
T: Oh right.
A: They also dropped rice dumplings in the water so the fish wouldn't want to eat the person's body.
T: So what's inside them?
A: There's sticky rice and some fillings.
Most shops and families use slightly different recipes, so there are lots of different kinds.
My mom makes them with pork, peanuts, and dried shrimp.
T: Sounds tasty.
A: I'll bring you some if you want.
My mom makes loads every year.
T: That would be great.
Marie: A Taiwanese told me that people here believe there's a woman living on the moon
Gary: That's not entirely true.
There's an old story about a woman called Chang E who's supposed to live on the moon, but we don't really believe it.
Marie: So what's the story?
Gary: Well, Chang E used to live on Earth with her husband, Hou Yi, who was a brilliant archer.
Marie: So did he shoot her up to the moon?
Gary: No.
The story goes that there were once 10 suns, and when they started to burn the earth, Hou Yi shot nine of them down.
Hou Yi went to the Queen Mother of the West searching for immortality and was given a pill that would allow him to live forever.
Marie: Right.
Gary: Instead of eating it, he hid it in his home.
Marie: And then Chang E ate it instead?
Gary: Exactly, and then she floated up to the moon.
Marie: Wow, that's a crazy story.
In Taiwan, people celebrate Mother's Day on the second Sunday of May every year.
As in the rest of the world, it's a day for honoring and appreciating mothers.
Children usually either travel home or call their mothers on this special day.
As of the year 2000, the second Sunday in May has also been own as Buddha Day in Taiwan, so people might also make an effort to visit a temple.
Taiwan is the only country in the world to celebrate Father's day on August 8th.
In Chinese, the word for eight is "ba," and the date 8/8 sounds very similar to father or "baba." As with Mother's Day, children travel home or call their parents.
Celebrated on the ninth day of the ninth month of the lunar calendar, this festival has lost a lot of its importance in Taiwan.
Some people do still follow the traditions, however.
According to ancient Chinese thinking, nine is a good, strong number.
A double nine might be too strong, though, and this day was thought of as dangerous.
The ancient Chinese believed they could overcome this danger by climbing mountains and drinking chrysanthemum wine.
October 10th marks the start of the Wuchang Uprising in 1911.
This mass protest led to the end of imperial rule in China, and the Republic of China was created on January 1st the following year.
The day is marked by festivities outside the Presidential Building in Taipei.
The celebrations include the raising of the flag and the playing of the national anthem.
Buddhism and Taoism are the two major religions on the island.
There is also an important philosophy called Confucianism, and folk beliefs are widely practiced.
Most Taiwanese follow a mix of religions and folk beliefs.
Traditional folk religion is a mixture of several beliefs concerning gods, goddesses, ghosts, ancestors, and luck.
It is not unheard of for some Taiwanese people to go and see a fortune teller before making a big decision.
There are different customs and beliefs throughout Taiwan.
For example, people in fishing villages prefer to worship Matsu, the folk goddess of the sea.
People from other areas may worship Guanyin, the goddess of mercy, or Guan Yu, a famous soldier from China.
Farmers, on the other hand, prefer to worship the land god because he better understands the importance of a good harvest.
Taiwan's folk beliefs are colorful and diverse.
Lots of local festivals in Taiwan are actually religious activities.
One of the largest religious festivals is the International Matsu Cultural Festival, which takes place during the third month on the lunar calendar.
The pilgrimage procession sets out from Zhenlan Temple in Dajia, Taichung.
It stops at several places in central and southern Taiwan before returning to the Zhenlan Temple after nine days.
The statue of Matsu, puppets of gods, dancing lions and dragons, performing groups, and exploding firecrackers together form a loud and lively parade watched by crowds of people.
Worshippers from all over the country walk along with the procession, hoping to receive a blessing from Matsu.
Another important local festival concerns the city gods.
For example, the city god of Taipei's birthday is celebrated every year in the fifth month on the lunar calendar.
Other gods' birthdays are celebrated in front of temples and include performances that attract large groups of people.
Ethan: Tell me about the religions in Taiwan.
Laura: Buddhism and Taoism are the most important religions in Taiwan.
However, most Taiwanese believe in a mix of these two, as well as Confucianism and other folk beliefs.
Ethan: It's a good thing that people with different religions can live together in harmony.
Laura: No doubt.
Ancestor worship is also very important to the Taiwanese.
Ethan: You really worship a lot of gods and goddesses!
Can you tell me something about the folk beliefs here?
Laura: For example, Matsu, the goddess of the sea, is widely worshipped in Taiwan. 
There are many legends about her, and she is said to be the protector of fishermen and sailors.
People usually worship Matsu for good luck and safety.
Ethan: The folk beliefs here are quite vivid and interesting!
Laura: Yes.
You may have heard of the Eight Infernal Generals.
Ethan: Who are they?
Laura: They are eight messengers from the underworld.
They're in charge of capturing or expelling evil spirits and monsters.
They are also the defenders of the chief deity.
That's why you can often see them lead religious processions.
One of the first things that a first-time visitor to Taiwan notices is the temples.
They are very different from churches in the West Temples can be found all over the island, from enormous ones to small shrines.
Many of them are decorated in a very colorful and catching manner.
You may find that the various types of temples are a little confusing.
This is because they are all built according to special rules, such as feng shui (wind and water).
Of course, temples might also be built a little differently because of their own unique story and the history of their gods or goddesses.
Some temples are always crowded, like the Longshan Temple in Taipei.
No matter what time you visit, you will always find people lighting incense and doing "bai bai" rituals.
This is what it's called when someone puts their hands together, often with an incense stick between them, and bows to an altar.
This ritual can be used to venerate both gods and ancestors.
Taoist temples are usually managed by local people and they tend to be more decorative than Buddhist temples.
Taoist temples aren't just places to practice religion.
They serve as community centers where local people can get together.
You might even find elderly people playing chess or card games around the temple grounds.
Many of Taiwan's folk arts are connected to temples, such as music, dance, puppet shows and Taiwanese opera.
The architecture and decoration of the temples add a feeling of vibrancy to cultural performances.
What's more, the carved decorations of gods, dragons, spirits, other legendary creatures, and educational stories are a traditional art form in themselves.
During important holidays like Chinese New Year, people often go to temples to watch performances.
Some temples even offer different performances every day for two weeks straight.
E: There are so many temples in Taiwan.
L: Yes, it is a distinguishing feature here.
E: So, which one are we going to today?
L: I'm going to take you to Longshan Temple. It is one of the busiest temples in Taipei.
E: I can hardly tell a Buddhist temple from a Taoist one.
L: Generally, Taiwan's temples are a mix.
They are often devoted to the worship of a combination of Buddhist, Taoist, and folk gods and goddesses.
Longshan Temple is no exception.
E: When was it built?
L: Longshan Temple was built over 200 years ago.
It is officially a second-grade historic monument. It has typical temple architecture.
E: Wow, just look at the antique carvings all over the building!
L: Longshan Temple was damaged and rebuilt several times. It has the only bronze dragon pillars in Taiwan.
Here, let me show you.
E: They're amazing!
May I take a picture of them?
L: Sure.
E: What kinds of deities are worshipped here?
L: The goddess of mercy, the goddess of the sea, the god of literature, and many other deities.
You might be interested in the god of marriage, who is said to help people find their Mr. or Miss Right!
E: No wonder it is crowded with worshippers every day!
I'd like to make my wish to the god of marriage.
Maybe I'll meet my true love here!
Confucianism, as well as Buddhism and Taoism, has had a great influence on Taiwanese society.
People live and think according to Confucian thought even today.
Confucius was a great philosopher.
Since he lived in a time of war, it was his goal that one day people would live in peace.
He wanted to change the way that people relate to each other in order to create a peaceful society.
He held the idea that everyone has his or her own position in society.
It is important for all parts of society to work together in harmony.
Each person must fulfill his or her duty.
For example, a father should take care of his family and a son should love and respect his parents and look after them when they are older.
Confucius was also a great teacher.
He believed that if people are educated, they will naturally do the right thing.
"Education without discrimination" is a strong Confucian belief.
Today, Confucian temples are built in many cities in honor of this great mentor of China.
The Taipei Confucius Temple was first built in 1879 and rebuilt in 1925.
It is the largest Confucian temple in northern Taiwan.
Every year on the 28th of September, a memorial ceremony is held to celebrate Confucius' birthday.
Traditional dancing and rituals are arranged according to ancient practices.
The Taipei Confucius Temple possesses typical traditional Chinese architecture, with solemn entrances, grand red columns, ornate roofs, colorful paintings and decorations.
The half-moon shaped pond, called Pan Pond, in front of the main gate, is designed according to the principles of feng shui.
It also has the function of preventing fire and adjusting the temperature.
E: I visited a Taiwanese family last week.
They really look after their grandparents well.
L: That's because Confucianism still has an influence on Taiwanese society.
E: But, I suppose life is changing in Taiwan.
L: Yes.
Elderly people in Taiwan used to stay with the eldest son.
But that has been changing over the past few years.
E: In what way has it been changing?
L: Well, sometimes there isn't an eldest son.
So, a daughter might look after them instead.
What's more is that sometimes it's the daughters who get the best jobs these days.
E: So, Confucius thought family was important, eh?
L: He did.
But don't forget that he also had a plan for society as a whole.
E: What kind of plan?
L: Well, he thought that everyone in society should act a certain way.
There was a proper position for everyone.
E: He must be a great mentor for the Chinese.
L: That's right.
He is considered the greatest teacher in Chinese history.
You may see Confucian temples in many Chinese and Taiwanese cities.
E: Will I have a chance to visit one in Taipei?
L: Yes.
The Taipei Confucius Temple is quite large.
The architecture and decoration all follow the traditional manner and the principles of feng shui.
It will really impress you!
Taiwan has plenty of traditional arts, which can be divided into two categories: traditional crafts and performing arts.
Traditional crafts include painting carving, weaving, and ceramics.
Performing arts include folk music, folk dance, folk opera, acrobatics, and puppetry.
Although no longer living a traditional life, Taiwanese people are still interested in traditional crafts.
You might discover traditional paintings or wood carvings when you visit a Taiwanese person's house.
You might also see some traditional ceramics, such as cups, saucers, and teapots, which are both useful and decorative.
Taiwan is home to lots of different kinds of operas.
The island's own "Taiwanese opera" is actually a mix of different styles of Chinese opera.
It has also been influenced by indigenous music and Taiwanese folk songs.
Taiwanese opera is often performed outside temples, and sometimes the whole community comes out to see a performance.
In recent years, new forms of opera have been created.
It is very much a living art.
This kind of performing art tends to tell the stories of human life and folk legends.
Much of the time, it's associated with folk religions and is performed on many religious occasions.
Other folk arts, such as paper cutting, also still survive in modern-day Taiwan.
Arts that require more skill, such as puppetry, lion dances, folk opera, and acrobatics are slowly disappearing.
The government and various community groups are trying to keep these folk arts alive.
Much like other countries, the Taiwanese government has promoted festivals to help boost cultural activities.
Some of these festivals have been very successful.
Meanwhile, the innovation of traditional arts has played an important role in their survival.
For example, Taiwanese puppetry makes use of new lighting technology to create dazzling visual effects.
The puppet costumes are colorful and designed to attract the young.
E: I noticed that there are many traditional folk arts in Taiwan.
L: There sure are.
They include painting, carving, dancing, and so on.
E: But with TV, the Internet, and other new attractions, have some traditional art forms declined in popularity?
L: Some of the arts such as puppetry and lion dances are having a bit of trouble.
However, during the last few years, cultural life in the cities has improved a lot.
E: Are we talking about modern culture, like theater, music, and so on?
L: I mean all kinds of culture, Chinese and Western, modern and traditional.
E: Are there any steps being taken to save the disappearing arts?
L: The government is trying to promote the arts throughout Taiwan.
This includes building a lot of cultural centers, supporting emerging art groups and holding lots of festivals and exhibitions.
E: I heard that traditional arts have also adopted modern ideas to present a new appearance.
L: Yes, the performing arts use modern technologies and even Western materials in order to attract young people.
Some of them are very successful.
E: I have also heard about many interesting festivals related to traditional arts.
L: Yes, for example, there is the International Children's Folklore & Folkgame Festival in Yilan on the east coast.
Buildings have changed quite a bit in Taiwan over the past few decades.
Fifty years ago, many buildings in Taiwan had distinctive Chinese-style roofs.
Nowadays, most Taiwanese people live in Western-style apartment blocks.
The population of Taiwan is too big for everyone to keep living in Chinese-style buildings.
However, there are several public buildings that have been built in the old Chinese style.
One such building is Taipei Railway Station.
Traditional buildings follow the principles of feng shui (wind and water).
Feng shui is an ancient Chinese belief that tells us how buildings should be positioned.
If they are not positioned correctly in relation to water, mountains and other types of terrain, bad luck can result.
Feng shui is sometimes used when constructing homes, temples, and even public or commercial buildings.
Japan controlled Taiwan for fifty years, so it is not surprising that there are still some Japanese influences on the island.
For example, the Taiwanese language still uses many Japanese words.
Japanese food is quite popular and many people study Japanese.
You might occasionally see an old Japanese-style building.
If you're interested in this style of building, Sun Yat-sen Historical Events Memorial Hall, A Drop of Water Memorial Hall, and Huguo Chan Buddhist Temple of Linji School are good places to visit.
The Dutch and Spanish came even earlier than the Japanese.
They invaded Taiwan in the early 1600s, and have left some historic structures.
The main feature of Dutch-style architecture is the use of bricks.
Sections of brick wall outside the Anping Fort in Tainan are an example.
There are also a few British-style buildings, for example, the former British Consulate at Takao, located in Kaohsiung, which was built in 1865.
E: How is Westernization affecting Taiwanese society?
L: You can see lots of hamburger joints, Western movies, TV shows, and so on.
But that's happening in other places, too.
It's not just in Taiwan.
E: I saw some Japanese-style buildings in Taipei.
Is it because the Japanese ruled over Taiwan for a long time?
L: Taiwan was governed by the Japanese for fifty years.
A lot of buildings were constructed at that time. Some contemporary architecture is built in the Japanese style.
E: I have heard of "A Drop of Water Memorial Hall" in New Taipei City, I know it is Japanese-style.
L: Yes.
After being moved to Danshui from Japan and reassembled in 2009, it was opened to the public in 2011.
E: What are other examples of Japanese-style architecture that are worth seeing?
L: The Martyrs' Shrine in Taoyuan, the Huguo Chan Buddhist Temple of Linji School in Taipei, and the Memorial Hall of Founding of Yilan Administration are all typical Japanese-style structures.
E: I also know that there are some Dutch, Spanish, and British historic monuments.
L: Yes.
Taiwan's colonial background has resulted in a diversity of architectural styles and features.
Like any other country, Taiwan has its own traditional customs and etiquette.
Many foreigners are not familiar with these customs and etiquette, so they sometimes embarrass Taiwanese people without even knowing that they're doing anything wrong.
This can make foreigners very confused sometimes.
In Taiwanese society, it's always a good idea to make people feel good.
This is why Taiwanese people will often compliment their guests.
At the same time, they tend to be modest about themselves and the food that they're serving.
This mixture of compliments and modesty can be quite confusing to foreigners.
For example, Taiwanese people might tell a foreigner that his or her Chinese is very good.
But, if asked, they will say that their own English is very poor.
They will say this even if they speak English better than their foreign guest speaks Mandarin!
Taiwanese people rely on strong relationships with people to deal with both business and day-to-day problems.
For example, if someone is looking for a job, one of his or her friends may know someone who is looking to hire a new employee.
Thus, he or she better chance of getting a job.
This is because may have a Taiwanese people value family and friends very much.
"Helping each other" is a virtue in Taiwanese society.
However, Taiwanese people are often willing to give assistance even if they don't know you.
So, if you get lost in Taiwan, feel free to ask a Taiwanese for help.
You'll find them to be very friendly.
Finally, under no circumstances should death ever be discussed with a Taiwanese person.
Talking about death can bring bad luck.
This is why Taiwanese tourists often skip "Death Valley" when they visit America.
E: It seems like it would be easy to make a mistake here and hurt people's feelings without meaning to.
L: You just need to get familiar with the etiquette here.
Taiwanese people tend to treat people politely.
That's why you shouldn't be too direct when talking to someone.
E: So, I should be careful not to embarrass people in front of others.
L: That's right.
E: Oh, and why does everyone think my Chinese is so good even though I can only say "ni hao"?
L: Generally speaking, making people feel good is important here, so compliments are common in Taiwanese society.
E: So, they just want to make me feel good?
L: They're just trying to be polite.
But, who knows, maybe your "ni hao" is the best they've ever heard.
E: I doubt it, but it's nice of them to say so!
L: You may also want to think about complimenting others.
Even though Taiwanese people are very humble, everyone enjoys some compliments every once in a while.
E: Sure thing.
That will give me an opportunity to practice my Chinese!
L: You may like to make some Taiwanese friends, too.
It is the belief that relationships are very important, and that people should support one another whenever they're in need.
E: I'm learning to build relationships here.
It is really great that many Taiwanese are very friendly and willing to help me, even if they don't know me!
Drinking is an important part of Taiwanese culture.
When getting together with friends, many Taiwanese people love to have a little beer or wine to relax.
When drinking wine or beer, the Taiwanese toast each other for any good things that have happened to them recently.
They will usually drink all of the wine or beer from their glass at one time.
This is considered to show support to one another.
Taiwanese people often say "gan bei" and then finish their drink.
"Gan bei" means "cheers" in English.
There is even a saying that goes, "Do not keep a goldfish at the bottom of your wine glass," which implies that one should never leave any liquid unfinished.
Today, the "gan bei" culture has gradually disappeared.
The government has made great effort to raise awareness of the dangers of drinking and driving.
So, now it is more common for people to take a sip when giving a toast.
They do not ask their friends to "gan bei" very often.
In Taiwan, giving gifts is quite popular.
Gifts can be given on many different occasions.
When people get married, move into a house, give birth to a baby, start a business, and so on, they usually receive gifts from friends.
During Chinese New Year, children and the elderly receive money as a gift.
Money is also given to the happy couple on their wedding day.
Even when going to a funeral, you must give money to the bereaved.
When money is given as a gift in Taiwan, it is always put in a red envelope, unless the occasion is a funeral, in which a white envelope is used instead.
The color red represents good fortune while white is associated with death in Taiwanese culture.
When giving a gift, foreigners should be careful not to give money in any denomination that has a four, as this number represents death.
In Mandarin, the words for death and four sound almost the same.
Therefore, Taiwanese people tend to avoid this number.
Even today, hospitals in Taiwan don't have a fourth floor.
If they did, nobody would want to stay on it.
When visiting a person's home in Taiwan, it is important to bring a gift.
The gift can be fruit, chocolates, cake, wine or something similar.
When you give your gift, you should offer it using both hands.
Be modest about what it is and say that it's just something small.
Don't be surprised when the person receiving the gift puts it down to open it later.
Taiwanese people don't usually open a gift in front of the person who gave it to them.
If you are given a gift, it's best to act the same way.
If giving an expensive present, it is a good idea to wrap it up nicely Taiwanese people regard gift giving as an expression of sincerity.
However, clocks and knives should never be given to a Taiwanese person as a gift.
Clocks represent death because the word for "clock (zhong)" sounds the same as "the end of life (zhong)" in Mandarin.
Knives represent the cutting of personal ties.
Taiwanese people always take off their shoes when they enter other people's homes.
They may tell a visitor that it's not necessary, but you should take them off anyway.
After someone removes their shoes, they are often given a pair of slippers to wear inside the house.
Another useful tip to remember is that it is considered respectful to greet the eldest person first whenever you enter somebody's home.
E: Why are you buying so many pineapple cakes?
L: I’m visiting my aunt tomorrow.
I'd like to bring some gifts along.
Pineapple cakes are perfect to give as gifts.
E: Is giving gifts part of the Taiwanese culture?
L: Yes.
Gifting is very important in Taiwanese society.
The gift can be cakes, fruit, wine, and tea.
Even money is given on some occasions.
E: On what kinds of occasions is money given?
L: Mostly at wedding receptions and funerals.
Money is given either as a blessing or comfort.
E: I have heard of the custom of giving red envelopes with money in them to children and the elderly during Chinese New Year.
L: Exactly.
E: So, what's the occasion you're attending tomorrow?
L: Ha, Nothing!
It has been a long time since I visited my aunt.
I guess bringing gifts when visiting relatives or friends is simply a behavior or form of politeness and sincerity in Taiwanese society.
Especially when going to an elder person's house, it is considered rude not to bring a gift.
E: I see.
So, you can give anything you want as a gift?
L: No, no.
Clocks, knives, shoes, and umbrellas are not appropriate gifts.
Clocks imply death, and knives are taken as a symbol of cutting relationships.
E: But what negative meanings do shoes and umbrellas have?
L: Shoes imply asking a person to leave.
Umbrellas represent separation because they sound alike in Mandarin.
E: Thanks for letting me know.
I should be extremely careful when choosing gifts.
Taiwanese people receive gifts when they give birth to a baby.
However, to celebrate the completion of the baby's first month after birth, parents will send gifts to their relatives and friends.
Usually, these gifts are cakes, red eggs and sticky rice with chicken drumstick, which are symbols of auspiciousness, propagation and promotion.
Food has always been important in Taiwan culture.
Taiwanese people even greet one another by asking.
"Have you eaten yet?" One of the first things that a visitor will notice is how many restaurants and snack bars there are in Taiwan.
What's more, there are lots of food stalls along the roadsides and in traditional night markets.
You may find different styles of Chinese food in Taiwan.
Taiwan also has its own style of cooking that has developed over hundreds of years.
You may even come across traditional indigenous food.
Modern Taiwanese cities are also home to lots of restaurants serving different kinds of cuisine from all around the world.
You can find Indian, Thai, Korean, French, and Mexican restaurants among others.
In Taipei, you can even try Greek, Iranian, and Russian cuisine.
Of course, American fast food is widely available.
Taiwan also has its own fast food restaurants.
Foreigners may find them strange, yet oddly familiar.
The two basic foods of Taiwan are rice and noodles.
Since rice is so important, it should be no surprise that there are lots of tasty rice dishes in Taiwan.
For breakfast, Taiwanese people sometimes eat watery rice porridge with pickled vegetables.
This dish is called "congee" in English.
For dinner, Taiwanese families eat steamed white rice together with vegetable, fish, and meat dishes.
Sometimes, a Taiwanese dinner can end up looking like a banquet!
Noodles are the other basic food in Taiwan.
They became popular in China long ago because they are so easy to carry and store.
Two popular noodle dishes in Taiwan are "soup noodles" and "beef noodles." Beef noodles have become more and more popular.
In the past, people would not eat beef because cows were seen as important agricultural animals.
However, most Taiwanese people no longer have any problem eating beef.
Some noodles are made from rice flour.
Delicious fried rice noodles and rice noodle soup are available in night markets and from roadside food stalls.
You know, it's a little strange.
If people aren't asking if I've already eaten, they're inquiring about whether I'm full or not.
What gives?
I think they're being polite.
As you know, Taiwanese people love their food.
There's even an old Chinese saying that goes "for people, food is paramount." With all the delicious Chinese restaurants around the world, this isn't too surprising.
Why are they so interested in food?
Some people say it's because Chinese people feared not getting enough food to feed their families in ancient times.
I suppose there were lots of famines back then, eh?
Famines, droughts, and wars.
Any of these disasters could leave people hungry.
This made rice very important.
Sounds like the way the Irish feel about potatoes.
Potatoes were their main food.
As long as you had potatoes, you could survive.
That's right.
Chinese people also associated rice and food with prosperity.
Rich people could eat as much as they wanted.
So, even in modern Taiwan where everyone has enough to eat, people still think this way about food.
That's right.
Do Taiwanese people have rice with every single meal?
They do, more or less.
Whether they're at home or attending a fancy banquet, rice goes with pretty much everything.
So, all of the main Chinese styles are available in Taiwan?
Yes, that's why it's a great place for food lovers!
You can also find various kinds of foreign food in the big cities, such as Thai, Vietnamese, French and Indonesian cuisine.
Several festivals are celebrated in Taiwan every year.
For visitors, festivals can be both an enjoyable and colorful experience.
For Taiwanese people, it's a great way to keep Taiwanese culture alive in today's world and get a day off work!
Most of Taiwan's festivals go by the lunar calendar and their Western calendar date changes every year.
Chinese New Year falls on January 1st on the lunar calendar, and it is the biggest event on the Chinese cultural calendar.
It generally occurs around the end of January to early February on the Western calendar.
During Chinese New Year, families come together, eat, and enjoy each other's company no matter how far apart they live.
Traditionally, special foods such as sticky rice and year cakes are prepared.
The words for "year cake" are pronounced like "rise in the coming year" in Chinese.
So, people eat the cakes to wish for a good year.
Some of the cakes are sweet because the Chinese believe that having sweet food can bring about good luck in the New Year.
There are several unusual beliefs about Chinese New Year.
People don't like to sweep the floor during Chinese New Year because they don't want to sweep away good fortune along with the dirt.
They also don't take out the garbage until the fifth day of Chinese New Year.
During Chinese New Year, Taiwanese people visit relatives and pay respects to the god of wealth.
On the second day of the New Year, married couples go to the wife's house and spend time with her parents.
At this time, houses are decorated with flowers and couplets or congratulatory speeches written on red paper.
Red is considered lucky and white represents death.
Therefore, red is emphasized and white is discouraged during the holiday.
Children and elderly people often receive gifts of money in red envelopes.
Eventually, firecrackers are set off and families go out into the streets to enjoy traditional folk activities such as lion dances.
Not so many people believe in old ideas about good or bad fortune nowadays.
However, people still like to follow the old traditions.
It looks like you're going to spend Chinese New Year in Taiwan!
Yes, I'm pretty excited about it.
What's more, my parents just called to tell me they're coming here.
That's great!
l'd love to meet them.
Of course, l'd be glad to introduce you.
They want to meet all my new Taiwanese and foreign friends.
Is there anybody else in your family coming to Visit My brother is thinking of coming with my parents.
I was thinking it might be fun to take them to a traditional festival in Taiwan.
That's a great idea!
Do you know how long they're planning on staying?
They said they could stay for three weeks.
Well, if they're coming for three weeks that gives them the chance to stay for the entire Chinese New Year!
I understand that this is an important family time in Taiwan.
That's right.
Families try to get together just like for the Western New Year.
Everyone chats, eats delicious food, and enjoys each other's company Does everything close down?
I'm worried that they may come to Taiwan and find that nothing is open.
Not really.
In fact, people are busy getting prepared during the week leading up to Chinese New Year, so the stores are open to take advantage of the season.
So they can see all of the traditional decorations in the streets, and maybe some lion dances as well!
During Chinese New Year, some shops and restaurants are closed.
However, the scenic spots are crowded with people.
Various activities are held in the streets and temples to celebrate the New Year.
Taiwanese people start shopping for food, snacks and decorations several weeks prior to Chinese New Year.
Candies and cookies are put into red candy boxes in every home.
Dihua Streett, Taipei, is the most famous place to shop for food and snacks, or "New Year supplies" in Chinese.
It's also call the "New Year Supplies Avenue." When the Chinese New Year is approaching, Dihua Street is always packed with shoppers.
Food materials such as dried mushrooms, dried shrimps, rice noodles, etc. are available as well as Chinese herbs, preserved fruit, and tea.
On Chinese New Year's Eve, people will go home to have dinner with their family.
This is called a reunion dinner.
People usually prepare lucky foods for the dinner, such as fish, which means "to have profit every year." The word "fish" and "profit" have the same pronunciation in Mandarin.
People also eat dumplings because they are shaped like the ancient gold sycee, or ingot, now a symbol of wealth.
During Chinese New Year firecrackers are set off to scare away the evil "year monsters." It is said that the year monsters are afraid of loud noise and anything red.
That's why people hang red lanterns in front of their houses during this time of year.
Decorations such as lucky knots are hung around the house during Chinese New Year.
People like to wear red clothes during Chinese New Year because the color red represents good luck.
Black melon seed are common snacks during Chinese New Year.
The Lantern Festival takes place on the fifteenth day of Chinese New Year.
It is often known as the second New Year, or "little New Year," and it marks the end of the New Year season.
During the festival, people make elaborate, beautiful lanterns.
In the old days, only the rich were able to make these kinds of lanterns.
In modern society, everyone can enjoy them, and it has become a special kind of art.
Some lanterns are shaped like animals, people, or objects.
There are even Taipei 101 lanterns!
This is one tradition that has been modernized in a very successful way.
Nowadays, different parts of Taiwan hold public events to celebrate the Lantern Festival.
They use modern technology to provide an exciting light display.
On the night of the Lantern Festival, people traditionally eat sweet glutinous rice balls.
They are made of glutinous rice, sometimes with sweet stuffing, and served in syrup.
Answering riddles is another traditional activity.
Another important event is the Dragon Boat Festival.
This festival falls on May 5th on the lunar calendar, and usually takes place in June on the Western calendar.
In ancient China, summer was a time of disease, so this festival was created to drive away the evil spirits that cause sickness.
As time passed, the story of Qu Yuan was added.
Qu Yuan was an ancient minister and poet who drowned himself when the king would not listen to his advice.
After he jumped into the water, fishermen raced to try and save him.
Although they were too late to save his life, they threw sticky rice dumplings into the river to keep the fish from eating his body.
This is the story behind the tradition of racing dragon boats and eating sticky rice dumplings during the Dragon Boat Festival.
Dragon boat teams from different organizations are made up of students, businessmen, soldiers, and so on.
The boats themselves are highly decorated and quite beautiful.
This festival isn't just popular in Taiwan.
Nowadays, dragon boat teams come from all over the world to compete in the races and celebrate this unique festival.
What's the Lantern Festival?
It falls on the first full moon following Chinese New Year.
It's a great end to the New Year festivities.
Are there any interesting activities on this day?
You can experience the Lantern Festival in Taipei.
It has become quite famous since it started in 1990.
It's very modern, and there's an amazing light show.
Sounds good.
Another special activity on Lantern Festival is the Yanshui Beehive Fireworks Festival held in Tainan.
It's a little dangerous, though.
Fireworks are shot into the crowds of people.
So, you need to wear thick clothing and a helmet.
Oh, that sounds dangerous!
I think I'll skip that one.
If your family likes sports, then perhaps they should come to see the Dragon Boat Festival in June.
Is this some kind of river god festival?
No.
It commemorates an ancient poet, Qu Yuan, who drowned himself.
Why did he do that?
He tried to give a king some advice that could save his kingdom, but the king refused to listen to him.
So why is it called the Dragon Boat Festival?
Is it some kind of race?
Yup.
Teams of rowers in dragon boats race against each other.
The tradition comes from fishermen rushing to save Qu Yuan.
The boats are beautifully decorated.
It's a festival that's becoming popular all over the world!
Two important events accompanying the Lantern Festival are the Sky Lantern Festival in Pingxi, New Taipei City, and Yanshui Fireworks Festival in Tainan.
Pingxi Sky Lantern Festival is held annually.
People write their wishes for the coming year on paper lanterns and release them into the sky.
This festival is very popular among the young.
However, once the festival ends, the remains of the paper lanterns pollute the environment.
The flames from the lanterns sometimes cause fires when they land on the ground.
Thus, this activity is not encouraged as much nowadays.
A particularly crazy fireworks festival takes place around the same time as the Lantern Festival every year in Yanshui, Tainan.
Rockets and fireworks are shot into the crowds and explode around them.
The fireworks result in spectacular light and sound effects.
People come from all around the world to experience this activity, because although it can be dangerous it is also very exciting.
Participants have to wear a helmet and protective clothing.
However, some people still get hurt.
Thus, improvements are continually being made to the fireworks materials to reduce the danger.
Taiwanese people believe that ghosts leave the underworld for one month every year.
To ensure that these ghosts are happy and do not bother them, Taiwanese people observe certain traditions during "Ghost Month." This festival takes place in July on the lunar calendar.
Ghost Month is a time when families pray for the ghosts of their ancestors.
They will also leave food out to feed any "hungry ghosts" that might wander by.
Taiwanese people will also burn specially printed ghost money to make sure that their ancestors are comfortable in the afterlife.
All in all, Ghost Month is seen as an unlucky time of year.
Because of this, very few people get married, move into a new house, or open a business during this month.
Going near water is also forbidden, because the wandering ghosts may drag you under to replace them so that they can have another chance at life.
The last major event of the year is the Moon Festival, also known as the Mid-Autumn Festival.
This festival takes place on the 15th of August on the lunar calendar.
On the night of the full moon people get together with their family and eat "moon cakes" and pomelos.
Since the festival is in honor of the moon, families often look up at the moon together.
There are many different Chinese and Taiwanese legends about the moon.
While Westerners may mention a man on the moon, Taiwanese people will talk about a woman called Chang-e or the Jade Rabbit.
Over time, it has become popular for people to give moon cakes to their relatives and friends during the Mid-Autumn Festival.
Traditional moon cakes are made using egg yolks and flour.
However, nowadays moon cakes come in many different flavors, including almond, chocolate, and pineapple.
Some even come filled with ice cream!
Hmm, I don't know.
My family isn't really into sports.
If they want something very Chinese thar can be seen all over the island, they could come during "Ghost Month." What are you talking about?
They don't worry about bad luck, do they?
No, they're not very superstitious.
Are they easily frightened?
They aren't scared of ghosts, bad luck, monsters, or anything like that.
Why do you ask?
The Chinese believe that the doors of the underworld are opened once a year and the ghosts get to take a break.
So that's what you meant by ghosts, bad luck, and so on.
So, what do people do during Ghost Month?
Not much.
It's a very unlucky time of year.
Let me guess--people don't get married or buy houses.
That's right.
They won't do major things like get married or start a business.
But, they will leave food out so that the ghosts won't bother them.
Well, my family may not like such ghost stories!
If they want to avoid that time of year, the Moon Festival is also interesting.
I heard it's a day for families to have a reunion.
That's correct.
Families get together and eat moon cakes and pomelos.
Usually, they will have a barbeque outdoors and watch the beautiful full moon.
There are many legends about the moon.
In Chinese mythology, a story tells about a woman named Chang-e and her husband Houyi 后羿.
They were once immortals.
One day, ten of the Jade Emperor's sons turned themselves into ten suns to scorch the earth and caused great pain.
Being a great archer, Houyi shot down nine of them to save the earth.
However, this enraged the Jade Emperor because nine of his sons had been killed.
He drove Houyi and Chang-e out of heaven, and they become mortals.
Seeking to return to immortality, Houyi went to the goddess Xi Wangmu and asked for an elixir.
She agreed, but this elixir must be shared by Houyi and Chang-e because it was a dosage for two.
However, Chang-e accidentally swallowed all of it and found herself floating into the sky until she landed on the moon.
There she met a rabbit, named Jade Rabbit.
The Jade Rabbit lives on the moon and grinds herbal medicine in a mortar.
They keep each other company.
The Moon Festival is also called the Reunion Festival.
The country takes a day off on the fifteenth of August on the Chinese lunar calendar, allowing everyone to go home and enjoy a day with their family.
In Taiwan, people love to have barbeques under the full moon.
A barbeque with family or friends on the night of the Moon Festival isn't a traditional activity, but it is very popular among families nowadays.
In addition to traditional festivals such as Chinese New Year and the Lantern Festival, there are many indigenous and modern cultural festivals held in Taiwan.
Special industries, such as wood carving, have started to gain in popularity due to government promotion.
Examples of industry-related events include the Sanyi Wood Carving Festival and the Hsinchu City International Glass Art Festival.
Modern cultural festivals in Taiwan are quite varied.
The Taiwan International Festival of Art and Spring Scream are very representative of this category.
The Taiwan International Festival of Art began in 2009.
It invites top performers, producers, actors, and composers from Taiwan and around the world to present dance, music, and drama performances.
The festival takes place at the National Theater and National Concert Hall in Taipei.
It usually lasts for one and a half months.
Spring Scream is held in Kenting every April.
It was begun in 1995 by two Americans living in Taiwan and is now the largest music festival in Taiwan Hundreds of performing groups and artists gather in southern Taiwan to show their passion and talent for music.
There are also stalls selling arts and crafts, clothes, and food.
It's one of the most exciting festivals in Taiwan.
If you are traveling through an area where there are indigenous communities, such as the east coast, you may be lucky enough to see an indigenous ceremony.
Harvest festivals are particularly important among the indigenous tribes as they express the tribe's gratitude to the ancestral spirits and tribal deities for giving them food.
The Amis, Rukai, and Paiwan tribes all celebrate this traditional festival.
Singing and dancing are essential parts of the festival.
However, the Harvest Festival isn't just about being grateful for food.
It is also a time to unite the tribal members and sometimes to carry out coming-of-age rites.
Since Tainan was the first capital of Taiwan, it's not surprising that it has many historic sites.
Tainan has more Buddhist and Taoist temples than any other place in Taiwan.
Two important temples are those devoted to the worship of Confucius and the god of war.
The Confucius Temple is the oldest in Taiwan.
It was originally a Confucian school, which is why people call it the "first school in Taiwan" It still has some of the original stones with rules for students carved on them.
One stone tells us that students were not allowed to drink alcohol.
If you visit on September 28th, you will be treated to interesting ceremonies to honor Confucius.
Much like Tainan's Confucius Temple, the Martial Temple is also the oldest of its kind in Taiwan.
This temple is home to a striking statue of Guan Gong riding a horse and holding his legendary "green dragon crescent blade.
Guan Gong was originally a Chinese general.
Government officials and business people have been coming to this temple for hundreds of years to offer sacrifices.
They do this out of the hope that Guan Gong will reward them with good luck.
Over the years, people have donated new decorations and lavish gifts to the temple, making it a particularly vibrant structure.
Legend has it that women were stopped from entering the temple in the past because of its high step.
Tainan is a city that is teeming with wonderful things to see.
Some notable sites include the Great South Gate, National Museum of Taiwanese Literature, and the Chihkan Tower-and that's just to name a few.
The area along the west coast highway just outside the city is also known for its natural beauty.
It's a great place to do some bird-watching!
Tainan has lots of delicious traditional foods.
Coffin sandwiches are one of the more popular ones.
The bread is hollowed out and stuffed with tasty filling like curried chicken.
Pig's trotters and shrimp cakes are two other popular dishes from Tainan.
While in Tainan, also try a dessert called Anping bean jelly and a special noodle dish called danzai mian.
Just like any other Taiwanese city, you can find these special dishes and other delicious cuisine in restaurants, food stalls, night markets, and even in alleyways.
Tasty food is never too far away!
Have you considered checking out Tainan?
It's totally worth visiting, and right on the way to Kaohsiung.
What's so good about it?
It's the oldest and most historic city in Taiwan.
Sounds like a place my parents would have liked.
What a pity they didn't have more time.
It's got such great food too.
Just thinking about it makes me hungry.
I'm always up for discovering something new to eat!
As long as the food isn't too strange.
Don't worry, Tainan's food is loved by both Taiwanese and foreigners like.
Oh really?
Do tell.
The most famous food in Tainan is coffin sandwiches.
Uh, didn't you say that Taiwanese people don't like hearing about death?
Yup.
Strange name, eh?
So how does the word "coffin" apply?
I'm not really sure.
It's a bit like French toast, though the bread is hollowed out and filled with seafood stew or curried chicken.
So the filling is the body and the bread is the coffin!
It sounds like whoever invented coffin sandwiches had a dark sense of humor.
Excellent observation.
You'll be happy to know that other foods in Tainan are very tasty and they don't have such strange names.
And they are?
Pig's trotters, shrimp cakes, and Anping bean jelly.
Sounds like we'll be eating well then.
That's Taiwan for you, lots of delicious food to eat.
If you take the train to Zuoying Station, you will find yourself near Lotus Lake, one of the most beautiful places in the city.
Here, there is a large park lined with beautiful buildings and temples.
These buildings are based on traditional Chinese beliefs and stories.
If you walk around the lake, you will come across the Spring and Autumn Pavilions.
They are dedicated to Guan Gong, a famous soldier of ancient time.
In front of these pavilions there is a statue of Guanyin, the goddess of mercy.
She is riding a dragon.
It is said that she appeared in the clouds one day and made the request that the statue be built like this.
Further down the road are two tall Chinese-style buildings called the Dragon and Tiger pagodas.
One represents the tiger and the other represents the dragon.
If you go in through the dragon's mouth and come out the tiger's mouth, it is said that you'll be rewarded with good luck.
The pagodas have many paintings inside them.
Some of these paintings urge visitors to do good deeds, and others warn of the punishment that awaits them if they do bad deeds.
All of these paintings are based on traditional Chinese religion.
Other attractions of note are Shou Shan Zoo 壽山動物園, Xiziwan Scenic Area西子灣風景區, and Ban Ping Mountain半屏山 in the northern part of the city.
There is also a magnificent Confucius Temple that can't be missed.
This temple is the largest one of its kind in Taiwan, and there is an imposing statue of Confucius nearby.
Kaohsiung has opened quite a few museums over the years.
The main ones are the National Science and Technology Museum, the Kaohsiung Museum of Fine Arts, and the Kaohsiung Museum of History.
Traditional religions in Taiwan are very different to those in the west.
There are two traditional religions, Buddhism and Taoism and also a kind of philosophy called Confucianism.
Most Taiwanese will follow these religions mixed in with a type of popular folk religion which has its own gods and goddesses and customs.
Buddhism came from India 2000 years ago.
Its main belief is that people come back to Earth in new bodies after they die.
If they were good, their next life will be better.
People must try to reach perfection each time they are born again.
When they succeed, they will stay in a heavenly state and not return to Earth again.
Taoism teaches that the whole world is governed by one principle, the Tao or way.
It is very difficult to explain.
To put it simply, the Tao is the way people should live their lives in order to live in harmony with the world.
The religion was started by a man named Laotzu about 1500 years ago.
Another man named Chuang-Tzu helped to develop Taoism further.
Laotzu started it as a philosophy, not a religion but eventually it developed into a religion.
The religion then began to focus on magic ways of helping people to live forever.
It also became mixed with local folk customs and beliefs.
Confucius was the great philosopher of China.
He wanted to develop a way for people to relate to each other so that society could be peaceful.
He lived at a time when there were many wars so it is not surprising he tried to develop a peaceful society.
Confucius thought that earlier Chinese rulers had the wisdom to do this but the people of his time had forgotten it.
He thought that the wisest man is the best man and so should be the ruler.
Traditional folk religion is a mixture of many beliefs concerning gods, ghosts, ancestors, good luck and bad.
Today, the Taiwanese will often go and talk to a fortune teller before making a big decision.
Some of the gods and ghosts are local and of course, everyone's ancestors are different.
Therefore there can be many different beliefs and customs in different areas.
In fishing villages, for example, many people will worship Matsu, Goddess of the Sea.
Other people may worship Guanyin, Goddess of Mercy or Guanyu a famous soldier from China.
Farmers will worship the Earth God.
There are other folk beliefs which are important in Taiwanese people's lives.
Some of them will be mentioned later in this book.
There are also several hundred thousand Christians on the island.
Christian Missionaries have been working there for over a hundred years.
Buddhism, Taoism and Christianity are the most important but there are nine other religions practiced on Taiwan and recognized by the government.
One of the first things that a visitor to Taiwan will notice is the temples.
They are very different to churches in the West.
From enormous city temples to small shrines by the road they are found all over the island.
Except for the Confucian temples they are decorated in a very colorful, eye catching manner.
You may find yourself quite confused by the many types of temples and the statues associated with them.
They are built according to specific rules including feng shui.
Of course, each temple has its own stories associated with it.
Some of the temples are very popular and always crowded like the Lungshan temple in Wanhua, Taipei City.
If you go inside will find people lighting incense and doing the bai-bai ritual.
They put their hands together perhaps with an incense stick between them and bow to an altar.
They may be venerating a god or their ancestors or both.
Taoist temples are usually managed by the local people and are much more decorated than Buddhist temples.
Monks often take care of Buddhist temples perhaps along with local people.
Both types of temples are also community centers where people will meet.
You may even find elderly people playing games in or outside temples.
In Taiwan, the family is particularly important.
Traditionally the father was the head of the family and the mother looked after the household.
Most people would be born, live and die in the same town or village.
There would be a village leader in each village and everyone would know everyone else's business.
Families used to live in houses with central courtyards and the family would carry out religious ceremonies there.
The family would follow the general religious customs of the local area.
Generally this lifestyle came about because of the ideas of Confucius.
His ideas became the basis of Chinese society.
For Confucius it was important that all parts of Society worked together in harmony.
When he lived there were many wars and he wanted to see his country, which is now part of China, become peaceful and prosperous.
Confucius tried very hard to get the government of his country to accept his ideas.
While he lived, he was not successful but after he died his ideas were finally accepted in Chinese society.
Confucius thought that a bad government is worse than a wild animal.
So he wanted society to have a stable structure.
The Emperor would be at the top, then the nobles then the common people.
Within families the father would be at the top then the wife, then the eldest son and so forth.
However, people should not follow this system because they were afraid of the law.
They should follow it because they believe it is the right thing to do.
People must be educated to do the right thing naturally.
And this has affected Chinese society and the way people think and act.
Even today, most people think in terms of the family and are loyal to it.
However, this means that sometimes some people do not think about the rights of people outside their family.
Confucius would probably not be happy about this.
Other ways in which Chinese culture has influenced Taiwan are in painting, carving, calligraphy, ceramics, jade carving, martial arts, architecture, religion and social customs.
Another thing you may see is Chinese traditional ceramics such as cups, saucers and teapots.
Traditional Chinese painting like calligraphy is concerned with how the brush is used.
Moreover, painters would write poems as well as their signatures on their paints.
This shows the connection between painting and calligraphy in China.
There are many folk arts in Taiwan.
Some of them, such as paper cutting, are surviving in Taiwan.
Others that require some skill, such as puppetry, lion dancing, folk opera and acrobatics are finding it more difficult to keep going.
The government and other groups are trying to keep them going.
As in other parts of the world, the government has promoted festivals, some quite successfully, to help keep certain cultural activities alive.
There are various types of opera in Taiwan.
However, Taiwanese opera has developed from various kinds of Chinese opera which has been going on for 900 years.
Taiwanese opera also has been influenced by local folk songs which were also influenced by Taiwanese aboriginal music.
It is sung on occasions such as weddings and birthdays.
In recent years new forms of have developed so it is very much a living art.
As regards drama, since the 1960s, new writers and theater groups have started.
At the moment the theater scene is quite strong and includes Western plays as well as local ones.
One thing that is very different to the West that is very obvious to foreigners is the temples.
They are the centers not only of religious life but also-in many places-community life.
There are many folk arts connected to the temples such as music, dance, religious ceremonies, puppet shows and folk opera.
However, visitors will first be attracted by the architecture and decoration of the temples.
Taoist and folk temples are more decorated than Buddhist temples.
Confucian temples are quieter and with much less decoration and often feel like parks.
The decorations are carvings of gods, dragons, spirits and other legendary things and are a traditional art themselves.
In the last few years Taiwan like other many countries in the world, has been very westernized.
Society has become more organized and western things such as movies and TV have become popular.
Western-style food, drinks and restaurants are now very common and there are more foreigners in Taiwan than ever before.
A Westerner can still find much Taiwanese traditional culture and he or she can now find many familiar things in Taiwan and be more comfortable.
E: I visited a Taiwanese family last week.
They really look after their grandparents well, don't they?
L: Yes, you don't see many homes for old people in Taiwan.
E: It made me think of my grandfather on his farm about 20 years ago.
L: Of course, he'd have the family near him to watch over him.
E: Yes, my mother visited him every few days to make sure he was ok.
But he didn't want to live with them.
L: Elderly people here usually stay with the eldest son.
However, that's changing.
E: In what way?
L: Sometimes there isn't an eldest son.
So maybe the eldest daughter looks after them instead.
E: I suppose people's lives are changing in Taiwan.
L: Yes, sometimes it's the daughters who have the best jobs.
E: Of course Taiwan was a farming country about 30 years ago.
L: That's right.
Nowadays not many people farm.
So the way of life is changing and so is people's thinking.
They don't follow Confucius so much.
E: He thought the family was important, huh?
L: Yeah but don't forget, he had a whole plan for society.
E: What?
The man was to be master of the family?
L: Well, that was only part of his ideas.
He thought of the whole of society having to act in a certain way.
Each person had their right position.
E: With the Emperor on top?
L: That's it.
Perhaps it's not too surprising it took thousands of years for China to change.
But he was very worried about bad government.
He thought if everyone lived in the right way it would solve a lot of problems.
E: I notice they still seem to have their own traditional art.
L: Yes, you can still see a lot of traditional stuff.
Painting, poetry, calligraphy and so on.
E: But with TV, the Internet and other new attractions, some of the old arts have to be going down in popularity.
L: Some of the old arts such as lion dancing are having trouble, it's true.
However, in the last few years cultural life in the cities has improved.
E: Are we talking about a more modern culture theater, music?
L: Every kind, both Chinese and Western.
Theater groups and writers have started and some of them are very successful.
E: I guess Ang Lee the famous film director from Tainan, comes from this time.
L: He certainly became internationally famous during this time.
But the government is trying to help the arts.
E: By building more cultural centers, is that it?
L: They do that, yes but they also support many art groups and modern festivals.
Like many countries, the Taiwanese have their own customs, many of which come from China.
They will of course, often pardon foreigners who make social mistakes.
However, no one wants to do the wrong thing and offend people by mistake so it is well to understand some of the important social ideas in Taiwan.
In many ways, "face" is possibly the most important social idea in Taiwan and indeed, in other eastern countries.
Of course, throughout the world people do not like to lose face but in eastern countries it is particularly important not to embarrass people.
If someone does something stupid it is important that others try to protect the person's feelings.
This can cause problems for foreigners trying to do the right thing in Taiwan.
People who come from more direct cultures and who for example, may feel that a mistake is not always something to worry about can get very confused.
Face seems to become more important as a person rises in society and a person who loses a lot of money can also lose a lot of face.
Of course if someone is poor and then becomes rich he or she gains face.
Thus many people try to get rich in order to gain face which is in many ways, more important than the money.
One way in which face can be lost is by someone getting very angry.
Both the person who gets angry and the person who it is directed at can both lose face.
This is regarded as very impolite in Taiwanese culture.
However, in recent years, famous people, such as politicians, have been seen to get angry so Taiwan appears to be changing.
Nevertheless, it is best for people to try to solve differences without showing anger.
It should only be used if nothing else works.
What people try to do is to persist and smile and try to persuade the other person to do what is right.
For foreigners, this can be particularly difficult.
In the past the Taiwanese have relied on family first and friends second to get things done.
Because of this, it is important for them to know people and to exchange favors.
So it is necessary to have strong relationships with people when it comes to business or dealing with government bodies and the law.
If someone has a problem with, for example, the city council, a friend who knows another friend will talk with them to solve the problem.
Again, foreigners who come from countries where things are done through a contract or by law can find this hard to understand.
Taiwan is slowly changing here too but the system of "guanxi" as it is known, is still very strong.
So a foreigner may find that he needs to ask a favor of a friend and that later the friend may ask him for one.
He may also find people asking him for favors first and if he says no, this may cause a misunderstanding.
Because of the need to give face to others, it is important to make people feel good.
Therefore Taiwanese will often flatter guest, especially foreigners.
At the same time they will often be modest about themselves or the food they are serving.
So there is a mixture of flattery and modesty that can also be quite confusing to foreigners.
For example, a Taiwanese may tell a foreigner that their Chinese is very good and be modest about how well they can speak English-even if they speak it very well.
It is a good idea for foreigners not to take this too seriously.
Taiwan is a place where people often give gifts.
They can be given on many different occasions not just birthdays.
In Chinese New Year children are given gifts of money.
In weddings the happy couple is also given money.
Money is given in red envelopes as red is thought of as the color of good fortune.
However, you must not give certain numbers such as four, 40, 400 etc. as this is the number of death.
In the West it used to be that people avoided the number 13.
In Taiwan they avoid the number four.
Even today, hospital in Taiwan have no fourth floor.
In no circumstances should death ever be discussed.
When Taiwanese tourists visit America they usually do not go to "Death Valley."
When visiting a person's home it is usual to bring a gift.
This can be fruit, chocolates or wine.
When you give a gift you should give it with both hands and you should be modest about it saying what a small gift it is etc.
The person receiving it will put it to one side to open later.
They do not open it in front of the person giving it.
If you are given a gift you should act the same way.
If giving a more expensive present it is important to wrap it nicely.
Taiwanese people like foreign products and brand items.
Do not give white gifts or money in white envelopes as white is the color of death.
For the same reason do not give clocks as gifts.
Taiwanese people take off their shoes in other people's houses.
They may say to foreigners that there is no need but a foreign visitor should take them off anyway.
He or she will find that they will immediately be given some slippers.
Another thing to remember is that it is considered respectful to greet the eldest person first when you come into someone's house.
E: Gee, you've really got to be careful here.
I was reading about this idea of "face." It's hard to guess about people's feelings.
L: You are so right.
It's very easy to hurt people's feelings without meaning to.
E: It seems to be even more important than in the West.
L: It's connected to some many areas of life; your job, how much money you have and even if you get angry.
E: But I've seen some important people get angry here.
It can't be that important.
L: I think Taiwan is changing.
Maybe people aren't so concerned with it any more.
E: I heard though that the richer you are the more face you have.
L: Yeah, I guess some people might still think that E: Are there any other social ideas I have to think about?
L: You'll probably meet the idea of "guanxi." E: What's that?
L: Well, you know the old English expression, "You scratch my back and I'll scratch yours?" E: Oh, is that what it is?
L: You do a favor for me and I'll do one for you.
If you help me, I'll help you.
E: But like "face" it seems stronger here.
L: The reason for that is that you don't just do it once.
You build up a lot of relationships by doing favors for the same people all the time.
E: It's like what they call "networking" in the West.
L: That's it.
It's no surprise business cards are so common in Taiwan.
You have to have guanxi!
E: That's a little tough on anyone who hasn't got much.
I mean, suppose you have a problem with the government or someone official'?
L: Right.
If need some help with official business you you need to know someone before it gets done.
E: Of course that used to be a problem in the West too.
L: Yes, until they began to use ways to make sure everything was fairer.
That's starting to happen more in Taiwan too.
The civil service is being trained to deal properly with people and so on.
E: I guess Taiwan has to develop that way too.
So if I get asked for a favor or I need one I have to remember this.
L: Yes, it's very easy to have a misunderstanding.
You have to be careful too sometimes the favors aren't the same size.
E: You mean you might need a small favor and then they ask for a big one later?
L: You've got it.
If you like the person and it's not too much trouble then why not?
But sometimes it could be something awkward.
E: You mean like something you're not supposed to do?
L: That's correct.
You could find yourself in a difficult position.
E: I'll have to remember that.
The other thing I wanted to ask you about was when they tell you your Chinese is very good.
L: [Laughs] Yeah, I think you have to be careful about that.
You remember I told you about how important it is not to make anyone lose face?
E: Yeah.
L: It's connected to that.
You want to make people feel good so there's a lot of flattery in this society.
E: So they want to make me feel good.
L: They're just trying to be polite.
Of course, I don't know.
Maybe after two months here maybe your Chinese is great!
E: [Laughs] I don't think so!
But it's nice of them to say so!
L: It's also nice if you flatter others.
It also gives the Taiwanese a good feeling if you are humble.
E: Good.
That gives me an opportunity to tell them my Chinese isn't so great!
E: You know, it's a little strange.
People sometimes ask me if I have just eaten.
They seem very interested in how much I've eaten.
L: I think they're being polite.
They're probably saying hello to you in the Chinese way.
They don't understand it's different in English.
E: So when they're asking me if I've eaten, they're asking me how I am.
Is that correct?
L: That's it.
As you know, they're pretty interested in food.
E: With all the Chinese restaurants around the world and the great food in them, it's not too surprising.
L: Even in the street, you can often find stalls that have some great little snacks.
In the night markets you can have a great time eating!
E: Why are they so interested in food?
L: Well some people say it's because for a long time the Chinese were afraid of not getting enough food for themselves and their families.
E: I suppose there were famines sometimes, huh?
L: Famines, droughts and wars.
Any of these could leave people hungry.
So rice was important.
E: It's like potatoes with the Irish, isn't it?
It was the main food.
As long as you had that you could survive
L: That's correct.
And the Chinese associated rice and food with prosperity.
If you were rich you could eat as much as you wanted.
E: So even today when everyone has plenty to eat in Taiwan, there is still this kind of thinking about food.
L: That's right. Rice in the south of China and noodles in the north.
E: Really?
You think of the Chinese eating both all over China.
Why was that?
L: The weather, I think.
The weather in the north is generally better for growing wheat instead of rice.
E: So the Taiwanese have rice with every meal, do they?
L: At home they do.
But at formal business meals they don't because it's seen as too cheap for those kinds of meals.
E: I think "face" might have something to do with this maybe?
L: I think you're right.
The other reason that the Chinese and Taiwanese are so interested in food is that there is so much variety.
E: Right.
That's because China is so big and each area has its own kind of cooking, huh?
L: Yeah and when the Chinese came over in the late 1940s they brought all their different kinds of cooking with them.
E: So you've got almost all the main Chinese styles here.
L: It's a great place for food lovers!
You can also get many different kinds of foreign food in the big cities.
Thai, Vietnamese, French, Indonesian can all be found.
People celebrate many festivals in Taiwan.
A visitor can find them very enjoyable and colorful.
Workers are glad to get the holiday.
The traditional festivals also help to keep Taiwanese culture alive in the modern world.
Because they are set by the lunar (Moon) calendar, the dates change every year in the western calendar.
As well as the normal Chinese festivals, there are also local Taiwanese ones.
For example, Matsu Goddess of the Sea, is very popular in Taiwan because of the importance of fishing.
Therefore around the island, you may often find a festival to celebrate her.
Another example is the City God of Taipei whose birthday is celebrated every year.
If you go to Yenshue there is a particularly crazy fireworks festival which takes place at the same time as the Lantern Festival.
People wear protective clothing while rockets and fireworks explode around them.
Visitors come from around the world to participate.
Every year some people are hurt so if you do go, wear protective clothing (including a helmet).
There are also aboriginal and modern cultural festivals.
If you find yourself in an area where there are many aborigines such as the east coast, you may be lucky to see one.
Their harvest festivals are especially popular.
There are many varied modern festivals including the stone sculpture one in Hualien and the Children's Folklore & Folkgame Festival.
Chinese New Year is the big event of the Chinese year (around end of January-early February in the western calendar).
It is a time when families, no matter how far away they live, come together to eat and talk.
Traditionally, the mother and daughters will prepare special foods such as sticky cakes and rice and special dumplings.
There are many unusual beliefs about Chinese New Year.
Although there is special food, some people do not eat meat on New Year's Day.
This is to bring them long and happy lives.
The second day of the New Year, they are kind to dogs because this is considered the birthday of the dogs.
On other days they will visit other relatives, give respect to parents-in-law and welcome the God of Wealth.
Because farming used to be so important, farmers also have some special celebrations and make traditional drinks.
From the 10th to the 12th days relatives are invited for dinner.
The house is specially decorated with flowers and short poems written on red paper.
As we have seen above, red is considered lucky and white is the color of death.
Therefore red is emphasized and white discouraged at this time.
Children and elderly people are given money in red envelopes.
The floor is not swept in case good fortune is swept out the door too.
Firecrackers are set off and traditional folk activities such as lion dancing are enjoyed in the streets.
Today, perhaps not many people believe in the old ideas about good and bad fortune etc.
However, they often like to follow the old traditions in the same way as Western people do.
On the 14th day of the New Year Season people get ready for the Lantern Festival on the 15th.
This is often known as the "second New Year" and marks the end of the New Year season.
People make lanterns which today are highly decorated.
In the old days only the rich and powerful made such lanterns but now it is almost a kind of art.
Some of the lanterns are shaped like animals or have other interesting shapes.
So it is an old tradition which has been modernized.
Children carry the lanterns into the streets and temples are also decorated with them.
Nowadays, different places have their own public festivals.
For example, the one in Taipei which was started in 1990 is now very famous.
It uses modern technology to give an exciting light display.
Many people go to it every year in Chiang Kai Shek Memorial Park.
Another important time is Dragon Boat Festival (around June).
In the old days summer was a time when disease was especially bad so this festival was at the start, to drive off disease and evil ghosts.
As time passed, the story of Chung Yuan was added to the festival.
He was a poet of the sticky old China who drowned himself when the leader of his country would not listen to his advice.
Some fishermen raced their boats into the river to try to save him.
When they could not, they threw sticky rice balls into the river to stop the fish eating his body.
This is supposed to have started the traditions of dragon boat racing and of eating sticky rice balls on this day.
The dragon boat are made up of teams from many different organizations, students, businessmen, soldiers etc. and are very highly decorated.
This festival is growing in popularity not only in Taiwan but around the world too.
The Taiwanese believe that ghosts leave Hell for one month.
So in order to ensure the ghosts are happy and do not bother them they celebrate "Ghost Month" (around August).
Families pray for the ghosts of their ancestors and food is left out for the "hungry ghosts." The food is soon eaten by the Taiwanese as they believe the smell of the food is enough for the ghosts.
Other ideas are to burn specially printed ghost money so their ancestors have enough money in Hell.
Ghost Month is seen as an unlucky time and very few people get married or open businesses at this time.
The last major event is the "Moon Festival" also known as "Mid-Autumn Festival" (around September-October).
On the night of the full Moon people will meet with their families to eat "moon cakes", pomelos and to look at the Moon.
It was to honor the Moon.
A number of different legends connected with the Moon developed.
Where Westerners talk about the "man on the Moon," the Taiwanese talk about the woman or the "rabbit on the moon." As time passed, people began to make "moon cakes" to give to their relatives.
Traditional moon cakes are made with eggs and flour but nowadays many new types (such as pineapple) have been made.
About 600 years ago the Chinese fought against their Mongol rulers.
The story is that messages to start the fight were hidden in moon cakes which were sent around the country.
The Chinese began to fight at the same time and threw out the Mongol rulers.
This story is now part of the Moon Festival.
E: Well that sounds like a possibility.
Knowing him he'd probably want to try it himself!
L: Now, if he wants something that can be seen all over the island and is very Chinese he could come at "Hell Month" in August.
E: What are you talking about?
L: He doesn't worry about bad luck, does he?
E: No, he's a very relaxed happy man.
L: He isn't easily scared, is he?
E: He isn't frightened by ghosts or bad luck or monsters or anything like that.
What is this about?
Some kind of Chinese Halloween?
L: Something like that.
The Chinese believe that every year the doors of Hell are opened and the ghosts take a vacation.
E: So that's what you meant by ghosts and bad luck and so forth.
You had me wondering if you'd hit your head or something.
So what do people do around then?
L: Not too much.
It's a very unlucky time of year.
E: Let me guess.
People don't get married, buy houses or a lot of other things.
L: Yes.
They also leave out food for the ghosts so that they can avoid bad luck.
E: Well, I guess he could come and listen to a few ghost stories!
E: Well that sounds like a possibility.
Knowing him he'd probably want to try it himself!
L: Now, if he wants something that can be seen all over the island and is very Chinese he could come at "Hell Month" in August.
E: What are you talking about?
L: He doesn't worry about bad luck, does he?
E: No, he's a very relaxed happy man.
L: He isn't easily scared, is he?
E: He isn't frightened by ghosts or bad luck or monsters or anything like that.
What is this about?
Some kind of Chinese Halloween?
L: Something like that.
The Chinese believe that every year the doors of Hell are opened and the ghosts take a vacation.
E: So that's what you meant by ghosts and bad luck and so forth.
You had me wondering if you'd hit your head or something.
So what do people do around then?
L: Not too much.
It's a very unlucky time of year.
E: Let me guess.
People don't get married, buy houses or a lot of other things.
L: Yes.
They also leave out food for the ghosts so that they can avoid bad luck.
E: Well, I guess he could come and listen to a few ghost stories!
D: It still has the feel of an island.
It has a lighthouse, plenty of seafood restaurants and a temple to Matsu.
D: Who's that?
L: She's the Goddess of the Sea.
E: Maybe we'll try it.
Are there any sights in or near the city?
L: Well, if you do go down to Kaohsiung, you'll definitely want to visit Lotus Lake.
E: The sounds are pretty, Wwhat about the place itself?
Is it some kind of park?
L: That's right.
The lake has a large park with many temples and other attractive buildings around it.
Quite a few tourists visit it.
D: Let me guess.
Are these temples to that Sea Goddess you told us about?
L: I don't think so but there's one to Confucius which is the biggest one in Taiwan.
There are also two pavilions dedicated to the God of War with a statue of Kuanyin the Goddess of Mercy.
E: Is there anything that's particularly interesting in the park?
L: Well, there are two pagodas, one for the tiger and one for the dragon.
A lot of people like to go in the dragon's mouth and out of the tiger's.
It's supposed to bring you good luck.
D: That's always useful.
L: Another favorite place for visitors is Fokuangshan.
It's a famous Buddhist monastery.
D: Can you stay there?
L: Yes, they'll take you in for a few days.
Of course you have to follow the rules, vegetarian food etc.
However, a lot of people like to do it.
D: Is there anything else worth looking at?
L: There are quite a few museums, a Hakka cultural center and a lot of nice country side around the city.
However, you might like to take a day trip to Kenting National Park.
Many people go there from Kaohsiung.
E: That sounds good.
We'll probably need to see somewhere natural after a while.
Two other important temples are those of Confucius and the God of War.
The Confucian temple is the oldest one in Taiwan.
It was also a Confucian school.
This is why it is called the "First School in Taiwan." There are still some stones with words carved rules for the Confucian students to on them that gave follow, such as not drinking wine.
As usual in Confucius' temples, on September 28 there are interesting ceremonies to remember the wise man of China.
As with Confucius' Temple, that of the God of War is the oldest in Taiwan.
His statue is seen with a big sword and on a horse.
Government officials offered sacrifices to the god hundreds of years ago.
The God of War was originally a Chinese general who was also very good at business.
So this is a temple where businesspeople come in hope of ensuring good luck.
As time has passed people have given the temple new decorations and gifts so it is a very attractive one.
In the past it is said that women were stopped from entering the temple by its high step.
There are many other attractive temples in Tainan.
Other sights include the Great South Gate, Anping Old Culture Hall-in the oldest street in Taiwan- and the Chikan Towers, with a museum of old Dutch things.
Just outside the city, along the New West Coast Highway, there is an area of natural beauty where visitors can do some bird watching.
Visitors will be struck by the amount of activity and liveliness that occurs at places of worship in Taiwan.
One of the most interesting temples is located in Taipei, itself, Longshan Temple in Taipei City.
Zhinan Gong in the Maokong district south of Taipei is also definitely worth a visit.
Buddhist architecture fans will think they have found heaven at Shitoushan, which hosts several temples, or at Tainan, the oldest city in Taiwan; it's essential to visit at least three of Tainan's hundreds of temples.
The temple of Confucius, the oldest and most beautiful temple on the island, enjoys a quiet and peaceful atmosphere.
The Dongyue Temple enshrines a sobering wall painting that illustrates, in great detail, the sufferings in hell.
Last but not least, the Nankunshen Temple, north of Tainan, attracts people each year for an exuberant festival that includes exorcism.
One of Taiwan's most important religious events is the annual Mazu Pilgrimage during the third month of the lunar calendar.
Mazu, the sea goddess of Fujian Province, migrated to Taiwan in the 17C with the Fujianese people.
She has become the most revered deity in Taiwan; more than 870 temples are dedicated to her.
To observe the oldest and the largest celebration, head to Dajia (Taichung County) to see festivities prepared to welcome the procession: puppet and theater performances, float parades, and dragon and lion dances.
The procession moves from Dajia to Xingang (Chiayi County) and back to Dajia over a period of 9 days and 8 nights.
The return journey to Dajia, when Mazu's statue is brought back on her palanquin to her shrine, isn't any less lively; devotees are heartily welcomed by their families and friends on their way back home.
Foundation Day Observed every January 1, this island- wide holiday remembers the day China became a republic.
On January 1, 1912, Dr. Sun Yat-sen became the first president of the newly formed republic.
Plenty of fireworks light up the night skies after daytime parades and speeches.
Lunar New Year/Chinese New Year Officially a two-day holiday, this celebration is the longest and most significant of Chinese festivals, lasting up to 15 days.
Houses, cleaned to dispel bad luck, are festooned with red strips of paper bearing blessings (chunlian).
Families convene to light torches and feast on rice cakes called niangao.
Most businesses close.
Families exchange gifts; children receive "lucky money" in small red packets (hong bao).
Yanshui Fireworks Festival This hugely attended event occurs two weeks after Chinese New Year.
Lantern Festival The final day of the Lunar New Year celebration marks the beginning of the 7-day Lantern Festival.
Elaborate lanterns, often modeled after figures from Chinese astrology, are lit throughout the island and sweet dumplings called yuanxiao are consumed.
Sky Lantern Festival This festival, held in the town of Pingxi in Taipei County, is part of the Lantern Festival.
Thousands of paper lanterns lit with candles and inscribed with wishes glow in the night sky.
Several different carnivals are held throughout the celebration.
Guanyin's Birthday This religious festival is held at the island's Buddhist temples, such as Longshan Temple in Taipei, to honor the goddess of mercy.
Dajia Mazu Pilgrimage Thousands of people convene to view the annual pilgrimage of Mazu, the revered goddess of the Sea, as her statue is carried on a 300km/184mi journey around Central Taiwan.
The 9- day religious procession-the island's most elaborate-begins and ends at Zhenlan Temple in the west coast town of Dajia.
Alishan Cherry Blossom Festival From late March to early April, cherry blossoms are in full bloom in the Alishan Forest Recreation Area.
The region boasts a variety of species, with blossoms ranging from vibrant pinks to creamy whites.
Breathtaking sunrises are an added bonus of springtime here.
Flying Fish Festival Orchid Island (Lanyu) is the setting for this annual custom of the Tao (Yami) aborigines.
Arrayed in traditional costume, men launch their newly handcrafted boats on the open sea and call forth blessings at the start of the fishing season.
Spring Scream Staged yearly in South Taiwan's Kenting National Park-known for its beautiful beaches-this festival is especially popular with young Western expatriates who throng the town of Kending, the festival's epicenter.
Held over several days, the event features area and international indie bands that perform in a variety of venues.
Tomb Sweeping Day The Taiwanese honor their deceased ancestors on April 5 by sweeping their graves and paying respects at temples throughout the island.
Mazu's Birthday Celebrated in the third lunar month, joyous tribute is paid at hundreds of temples around Taiwan to this popular deity with fireworks and dancing.
Bunun Festival Based on a male coming-of-age ritual of the aboriginal Bunun tribe, this festival centers on a practice known as "ear-shooting"-marksmanship with a bow and arrow.
Other competitions include wood-chopping and millet- husking, activities once central to the tribal way of life.
Sanyi Woodcarving Festival The town of Sanyi, in Miaoli County, lauds its well-known woodcarving industry with displays, music and carving contests.
 Taipei Traditional Arts Festival Extending from April through June, this city-wide event focuses on traditional Chinese music, performed largely by the Taipei Chinese Orchestra at Zhongshan Hall.
Dragon Beat Festival Held the fifth day of the fifth lunar month, this popular race pits teams against each other, rowing to the beat of drums in decorated boats The competition is based on the story of Qu Yuan, a 3C BC poet and government official who was loyal to his sovereign, but lost trust as he was edged out by peers.
Fishermen failed to save him when he threw himself overboard after being exiled.
Yingge Ceramics Festival The town of Yingge, famous for its pottery and ceramics, stages this festival each year to showcase its highly varied output.
Amis Harvest Festival Evolved from a ceremony of gratitude to the gods for rain and a bountiful harvest, this colorful festival showcases the costumes, dances, songs and local food of the indigenous Ami people.
Ghost Month Starting in August, the customs of burning paper money and incense mark the period when ghosts are believed to emerge from hell and visit the living.
Festivals are held in cities such as Keelung to appease the spirits.
Mid-Autumn Festival/ Moon Festival This traditional festival is celebrated with family feasting that typically includes barbecues, dancing, moon gazing and eating mooncakes-the special pastries that symbolize the moon.
Confucius' Birthday Also known as Teachers' Day September 28 commemorates the birthday of the revered teacher philosopher and honors teachers in general.
Double Tenth Day/National Day Observed on October 10, this holiday heralds Sun Yat-sen's overthrow of the Qing dynasty in 1911, leading to the formation of the republic.
Retrocession Day October 25, the day in 1945 when Taiwan was freed from half a century of Japanese rule, is remembered.
Festival of Austronesian Cultures Held in Taitung, on Taiwan's dramatic East Coast, this festival presents the cultural diversity of the region's Austronesian peoples.
Festival-goers will be treated to an array of traditional snacks, crafts demonstrations and musical performances.
Sun Yat-sen's Birthday November 12 is observed in honor of the Republic of China's first president.
Commemorative speeches are given in tribute to the revolutionary leader.
Constitution Day An important historical milestone, December 25, 1946, is memorialized as the day in which the Republic of China's new constitution was adopted.
Countdown Party On December 31, Taipei holds its massive New Year Countdown Party with pop stars and a midnight fireworks spectacle.
Fengshui, which literally translates as "wind and water," is a form of geomancy in which material elements in one's immediate environment, natural and manmade, are blended in harmony to allow beneficial qi (氣) to flow.
Qi is "vital energy" that flows through the natural world.
The visitor to modern, high-tech Taiwan will see fengshui in practice all around, if they know where to look.
Restaurants, homes and other places might have an aquarium in the entrance, placed at a right angle.
The square shape deflects bad energy, and the fish inside may absorb it; a fish found floating is believed to have died from it and thus protected the premises.
Few banks or hotels have their counters directly facing the doors, for such placement invites profits to flow out.
Rural hillsides will be covered in tombs facing every which way; each of these has been positioned individually for perfect fengshui, on advice of a geomancer.
Traditional folk religion is a kaleidoscopic combination of deities, spirits and mystical beliefs.
Elements that the visitor will most likely see practiced ancestor worship and fengshui.
Outsiders generally believe that Confucian respect for elders is the source of ancestor worship, but Confucius bowed to the already common tradi- tion of honoring, and praying to, one's ancestors.
It is commonly believed that when person passes away, he or she becomes a spirit, entering another world where there are material needs much like our own.
 Many deities, in fact, serve roles similar to the officials of imperial days; the City God overseeing each urban area is a prime example.
Most families will burn paper money and offer food before a family altar that holds a tablet, considered sacred, inscribed with the names of most often three generations (sometimes more) of predecessors.
The spirits partake of the essence of the food, which the family consumes later, and the smoke carries the essence of the money to the next world.
In modern times, unusual items such as paper TVs, expensive cars and mansions are burned-all in miniature.
If the ancestors are well taken care of, they protect home and hearth, but if abandoned, the family is inviting ill fortune.
Erected in 1738 by Fujianese settlers-but rebuilt many times since then-the temple faces south.
It's designed with three main sections: front, middle and rear halls.
It is awash in golds and ted (gold symbolizes heaven and red, happiness), Lanterns hang everywhere.
When going through the front hall, visitor pass a pair of bronze columns decorated with dragons.
Four other pairs are found in the middle hall.
The statue of Guanyin (called Avalokite?vara in Sanskrit) stand in this hall and is flanked by Wenshu, the bodhisattva of transcendent wisdom and Puxian, the bodhisattva action.
Wenshu is recognizable by the flame sword he wields, cutting down ignorance, whereas Puxian is holding a flower.
Around them are dispatched the 18 arhats, enlightened disciples of the Buddha.
The rear hall contains a hall of Guanyu (on the left), where worshipers can pray to the fourth of the Four Great Bodhisat- tvas in Chinese Buddhism, Dizang (Ksiti garbha in Sanskrit), the bodhisattva of hell beings, depicted as a monk with a nimbus around his shaved head.
He car- ries a staff to force open the gates of hell and a wish-fulfilling jewel to lighten the darkness.
In the Mazu Hall, the goddess Mazu receives the prayers of people asking her to grant them a safe return when they are traveling by sea or land (air travelers should refer to Guanyin).
Mazu is shielded by her two trustwor- thy bodyguards: Qianli Yan (Thousand Mile Eyes), who helps her monitor the disasters of the world, and Shunfeng Er (Thousand Mile Ears), who is in charge of listening to the complaints of the world for her.
Bordered by Ketagalan Boulevard and Gongyuan Road, this park is dedicated to the victims of the 2-28 Incident, when protesters against the Kuomintang government were massacred on February 28, 1947.
Today the park is a much-used green space within Taipei's concrete-heavy urbanscape, offering Japanese-style gardens, ponds, arched bridges, walking paths and an amphitheatre as places for rest and renewal.
 Established in 1908 during the Japanese era, the park was the first European- style urban park in Taiwan.
In 1930 Taiwan's Japanese authorities built a radio station within it to serve as headquarters for the Taipei Broadcasting Bureau, a propaganda arm.
A year later, the Taiwan Broadcasting Bureau was founded, broadcasting island-wide from the park.
After the Kuomintang took over the island, the park was renamed, and the broadcasting association became the Taiwan Broadcasting Company, with the same function of dispensing propaganda.
In 1972 the Taipei government began administering the radio station.
The buddings of Taiwanese democracy prompted the government's acknowledgement of the 2-28 Incident, as well as the subsequent creation of a memorial museum within the former radio station, and the renaming of the park.
 In the park's center, the memorial consists of a post-Modern sculpture of a needle standing on three cubes.
The 2-28 Memorial Museum, facing the memorial on the east, provides a detailed explanation of the 2-28 Incident by way of testimonies and exhibits.
The current renovation will add new exhibit space to the museum.
A wall of mementos next to the museum is graced with spider lilies 2-28 Memorial Peace Park, the symbol of peace.
A small hall is enclosed within glass walls on which photographs of the victims have been posted.
KEELUNG Keelung's Ghost Festival The seventh lunar month is known as Ghost Month throughout the Chinese-speaking world, since popular religious tradition-a unique confluence of both Buddhist and Daoist rituals-maintains that the Gates of Hell open on the first day of this month and close on the last day.
During this period spirits of the deceased are free to wander the earth and cause trouble, especially the "hungry ghosts" more commonly called "good brethren".
Elaborate ceremonies and fabulous feasts are prepared to satiate them.
These celebrations take different forms in different places around Taiwan.
Two of the most unusual are the "grappling with ghosts" in Yilan's Toucheng Township, and Keelung's Badouzi fishing harbor, where lanterns are launched onto the sea.
The main Keelung ceremony takes place on the 14th day of the 7th lunar month, but for the entire month, the city is in party mode.
Key activities, starting with ritual opening of the gates of a tower housing funereal urns at Laodagong Temple are televised and broadcast nationwide.
During the evening lanterns, each inscribed with a family name, are paraded through Keelung's streets on decorated floats.
The event originated following a particularly nasty clash between descendents of Quanzhou and Zhangzhou Fujianese immigrants in 1851, in which more than Floating looterns, Keelung 0people died.
To heal the split community, a lantern festival honoring the dead was devised based on family names, most of which were shared by both groups, rather than based on ethnic division.
The floats make their way along the coast to Badouzi, followed by crowds of citizens.
There, shortly before midnight, with fireworks whizzing into the air, the decorated lanterns are carried into the sea by teams of men, and then set adrift until they catch fire and sink.
Everyone then heads for home, or back to Keelung's famous Temple Entrance night market, which does good business well into the small hours of the morning.
During the annual Lantern Festival (15th day of the Lunar New Year holiday), Taichung Park comes alive with red and yellow glowing lanterns.
On the evening of the festival, just before the full moon rises, firecrackers resound throughout the town, scaring ghosts.
At dusk crowds pour into the park, carrying lanterns.
This gathering is one of the city's favorite communal events, an ancient tradition that remains alive in the 21C.
At midnight on a carefully chosen day during the third lunar month--usually in mid-April-surrounded by throngs of people, clusters of devotees, swirls of chaotic ritual and the ear-splitting thunder of fireworks underfoot and overhead, a magnificent icon of the goddess Mazu is lifted from her altar a Zhenlan Temple in Dajia (45min north of Taichung), and the annual Mazu Pilgrimage begins.
For more than 200km/124mi, the statue of Mazu, goddess of the Sea, is carried on a palanquin from the temple south through Taichung, Changhua, Yunlin and Chiayi, and then back again to Zhenlan.
Along the way, the goddess will visit more than 80 temples, bringing them good fortune by her very presence.
The pilgrimage, a chaotic procession often more than a kilometer long, is a churning sea of activity that combines vigorous rituals, solemn parades of devotees, clusters of curious onlookers and the colorful costumes, masks and icons of various folk deities.
Sometimes detain or "steal" the Zhenlan Mazu, and bring the good-luck goddess to their own temples to acquire good karma.
Shoving, scuffling and even brawls are not uncommon.
Devotees will also to ignore the roaring firecrackers and duck beneath the palanquin to bring themselves good luck.
Despite the all-encompassing commotion, visitors will be struck by the genuine devotion etched on the faces of the believers, many of whom leave their jobs and follow the goddess for the entire journey.
The layers of Taiwanese folk religion run deep, and the Mazu pilgrimage is a pageant that beautifully illustrates this unique aspect of Taiwan.
Of Taiwan's many Confucian temples Tainan's is the oldest and the most complete.
Confucius, one of the world's greatest teachers, thought that education should be available to all, not just to the aristocracy.
The tablet hanging above the main entrance, the East Gate of Great Achievement, reads "Taiwans Foremost School" (全臺首學), indicating the temple's status as Taiwan's first official learning institute.
Koxinga's son, Cheng Ching, commissioned the temple in 1665, and it remained the most prestigious school until the end of the Qing dynasty (1644-1911).
Like other Confucian temples, its design is elegant and simple, in contrast to Tainan's many ostentatious temples.
There have been several reconstructions, but the temple has maintained its original integrity and is believed by many to be the best example of Fujianese-style temple architecture in Tainan.
In advance of the 2009 World Games, Lotus Pond was drained, cleaned and deepened to serve as the venue for water-skiing and dragon-boat competitions.
At the same time, non-native fish and turtle species, many of them released by Buddhists trying to earn merit, were removed.
Yanshui Beehive Fireworks Festival (鹽水蜂炮) Suddenly all falls quiet---the excitement and fear are palpable.
Bearers begin to rock their heavily protected palanquins carrying statues of deities.
The first bottle rockets shoot out of their "hives" like angry bees and slam into the thrill-seeking festival-goers, who must hop repeatedly to keep from being trampled or hit by the blazing fireworks.
Aimed first at the feet, the rocket barrage eventually ends over the heads of the crowds, as each salvo discharges row by row.
The noise of hundreds of exploding rockets is deafening.
Attracting tens of thousands of spectators, this event is among the world's most dangerous festivals.
Ambulances and fire trucks are on hand to deal with emergencies.
Between 10-100 attendees end up in hospital each year, either burned or crushed in the mayhem.
Village houses regularly catch fire.
Cannon walls are bamboo or metal shelves filled with fused bottle rockets stacked horizontally-like a honeycomb-and aimed at the crowd.
They are topped by a vertical pyrotechnic array poised to erupt after the rockets are spent.
Large cannon walls are almost two-stories high and eight-meters wide; smaller walls are the size of a sawhorse, but all are loaded with rockets equally capable of inflicting harm.
During the festival, rockets ricochet around the town day and night.
Personal protection is essential: heavy clothing, helmet, gloves, boots, earplugs, facemask, even a towel for your neck, with all holes taped shut for extra safety.
In the final stage, when fireworks light up the sky, spectators cautiously lift their helmet visors and take a deep breath as the acrid smoke clears.
They slap each other on the back, some slaps to congratulate, some to put out lingering fires, Hard-core revelers trudge to the next wall to experience it all again.
What is now a spectacle began as a religious rite to exorcise small towns of disease.
When an epidemic plagued the village of Yanshui nearly two centuries ago, a statue of Guan Gong was carried through the streets on a palanquin accompanied by firecrackers to scare away evil demons associated with the sickness.
The epidemic faded after two decades and thereafter, Guan Gong was paraded annually.
Guanyin Cave Around a southerly bend in the island's ring road, this cave is marked with a red and white arch.
According to local lore, an unexplained light beaming from the limestone cave guided a fisherman lost at sea to shore.
Residents searched for the source of the light, and came upon the cave containing a stalagmite resembling Guanyin, the goddess of Mercy sitting atop a lotus.
The cave, which contains an underground river, remains a holy site today.
A snack stand sells venison fried rice and venison noodle soup, ice cream and sunglasses.
Boat-burning festivals are a tradition born of plagues and other harsh conditions endured by early coastal folk in southeastern China.
Desiring to expel an epidemic from their midst, villagers loaded boats with statues of the responsible demons, as well as lavish gifts to coax these gods on board, before sending the vessels out to sea.
Significant numbers of these boats and their pernicious cargo beached on the shores of southwestern Taiwan.
There, fearful local recipients built temples to honor the beached gods in hopes of gaining protection from the pestilence-or alternatively, treated them to a feast before the boats were sent to sea again.
Modern medicine has diminished the role of pestilence gods.
The once-dreaded ritual has become a much- anticipated festival that culminates in a royal boat being set ablaze.
The belief is that disease and disaster will rise to the heavens with the smoke, ensuring peace and prosperity to the faithful.
Boat-burnings festivals are held today in Donggang.
Penghu Island and elsewhere in Taiwan.
Taiwan’s most popular public holidays are Chinese Lunar New Year, and the commemoration of the founding of the Republic of China, which coincides with the solar New Year.
January 1-3 開國紀念日-New Year's Day and the founding of the Republic of China
Chinese New Year 春節-Differs each year according to the lunar calendar-usually in late January early February.
The three-day national holiday for Chinese New Year is observed throughout Taiwan; almost all businesses are closed.
February 28 和平紀念日-Peace Memorial Day commemorates the Incident of 1947.
April 5 清明節 Tomb-Sweeping Day
Dragon Boat Festival 端午節-5th day of the fifth month of the lunar calendar.
Mid-Autumn Festival 中秋節-15th day of the eighth month of the lunar calendar.
October 10 國慶日-National Day
Lunar Calendar (n?ngli) There are two calendars in use in Taiwan.
One is the Gregorian, or solar calendar (yinli), which westerners are familiar with; the other is the Chinese lunar calendar.
The two calendars do not correspond with each other because a lunar month is slightly shorter than a solar month.
To keep the two calendars in harmony, the Chinese add an extra month to the lunar calendar every 30 months, essentially creating a lunar leap year.
Thus, the Chinese lunar New Year, the most important holiday, can fall anywhere between 21 January and 28 February on the Gregorian calendar.
Calendars showing all the holidays for the current year are readily available in Taiwan.
These calendars look almost exactly like the ones westerners are familiar with, but the lunar dates are shown in smaller numbers.
Renao (r?n?o) It's hard to translate into English, but renao means something like lively', 'festive', 'happy' and 'noisy' - especially 'noisy.
Many Taiwanese seem immune to noise.
You'll notice that department stores and restaurants have background music blaring at around 100 decibels.
This is used to attract customers, whereas in the West it would surely drive them away.
Lighting firecrackers is also very renao.
Many people in Taiwan have asked me why Americans like to live in the suburbs and commute to the city for work.
“The city is so much more exciting (renao),” they say, 'so why would anyone want to live out in the lonely countryside?' You may meet a number of people in Taiwan who must work in the country, but they live in the city, where housing is much more expen- sive, and commute every day to the country.
Why?' you ask.
'Because,' they say, 'the city is a good place to raise children.'
Guanxi (guanxi) The closest English word to this would be 'relationship'.
However, guanxi has stronger meaning, similar to the English expression 'You scratch my back and I'll scratch yours'.
To build up good guanxi, you have to do things for people: give them gifts, take them to dinner, grant favours and so on.
Once this is done, an unspoken obligation exists.
It is perhaps because of this unspoken debt that people automatically try to refuse gifts.
They may not wish to establish guanxi with someone, because, sooner or later, they may have to repay the favour.
Even after it is repaid, guanxi is rarely terminated.
It is a continuing process of mutual gift-giving, back-scratching and favouritism that can last a lifetime.
Guanxi also helps if you know the right people, as it can help you avoid a lot of red tape and may be mutually beneficial to all parties.
This is very important in traditional Chinese society, where stifling bureaucracy can make it difficult to accomplish anything.
Of course, guanxi exists everywhere, and it's stronger on the Chinese mainland than in Taiwan, s0 you are likely to encounter it eventually.
Those doing business with local people should be particularly aware of guanxi.
Gift Giving This is a very complex and important part of Chinese culture.
When visiting people it is important to bring a gift, perhaps a tin of biscuits, flowers, a cake or chocolate.
As a visiting foreigner, you will that people want to give you gifts.
Gift giving is a fascinating ordeal, sort of like bargaining in reverse.
Your host will invariably refuse the gift.
You are expected to insist.
The verbal volleyball can continue for quite some time.
If the host accepts too readily, then they are considered to be too greedy.
They must first refuse and then you must insist.
When receiving a gift, never open it in front of the person who gave it to you.
That makes you look greedy.
Express your deep thanks, then put it aside and open it later.
In Asia, having 'big face is synonymous with prestige, and prestige is important throughout the continent.
All families, even poor ones, are expected to have big wedding parties and throw money around like water in order to gain face.
The fact that this can cause bankruptcy for the young couple is considered far less serious a problem than losing face.
Much of the Taiwanese obsession with materialism is really to do with gaining face, not material wealth.
There are many ways to gain points in the face game: owning nice clothes, a big car (even if you can't drive), a piano (even if you can't play), imported cigarettes and liquor (even if you don't smoke or drink), or a golf club membership (even if you don't know where the golf course is).
Therefore, when choosing a gift for a Taiwanese friend, try to give something with snob appeal, such as imported liquor, perfume, cigarettes or fine chocolates.
Things imported from the US, Europe or Japan are good options that will please your host and help win you points in the face game.
Flatter your host and guest to give them big face.
Words of praise like, 'You're so intelligent and humorous (or beautiful etc)' will go down well, If you speak three words of Chinese someone will surely say, 'You speak Chinese very well'.
The proper response should be self-deprecating: 'Oh no, my Chinese is very bad' (probably true).
Boasting is a real faux pas.
Remaining humble is very much a part of the Confucian tradition.
The Chinese are famous for their humility and you will often hear Taiwanese saying things like 'Oh, I'm so ugly and stupid!'.
Be sure not to agree with such comments, even in jest.
The Taiwanese believe strongly in omens, which probably explains why Chinese geographical names always mean something wonderful, like 'Paradise Valley', 'Heaven's Gateway' or 'Happiness Rd'.
There is one national park in the US the Taiwanese never visit - Death Valley.
The Taiwanese are really into longevity; death is a taboo topic.
Don't talk about accidents and death as if they might really occur.
For example, never say 'Be careful on that ladder or you'll break your neck': that implies it will happen.
Chinese people almost never leave a will, because to write a will indicates the person will soon die.
If you wrote a will in Taiwan, it would be virtually impossible to find a local to witness your signature for you.
They will not want anything to do with it.
In spoken Chinese, the word for four sounds just like the word for death.
As a result, hospitals never put patients on the 4th floor.
If you give someone flowers, always give red flowers, not white.
White symbolizes death.
Many Chinese are afraid to receive a clock as a gift, a sure sign that someone will die soon.
The Chinese (lunar) New Year has its special taboos and omens which affect your fortune in the coming year.
During New Year's Day don't wash clothes or you will have to work hard in the coming year, and don't sweep dirt out of the house or you will sweep your wealth away.
Also, be sure not to argue during New Year, or you will face a year of bickering.
Young people and urban dwellers are far less likely to pay heed to taboos and omens than the elder generation and rural residents.
Many religions are practised in China, and all the major ones have been carried over to Taiwan.
One outstanding fact from Chinese history is that the Chinese have never engaged in religious wars.
The main religions of China are Buddhism, Taoism and Confucianism.
Most Taiwanese consider themselves at least nominally Buddhist-Taoist-Confucianist, which makes for some pretty interesting temples.
Buddhism (F? Ji?o) One of the world's great religions, Buddhism originally developed in India, from where it spread all over east and South-East Asia.
With this spread of influence, the form and concepts of Buddhism have changed significantly.
Buddhism today has developed into numerous sects, or schools of thought, but these sects are not mutually exclusive or antagonistic towards one another.
Buddhism was founded in India in the 6th century BC by Siddhartha Gautama (563-483 BC), partly as a reaction against Brahmanism.
Bon as a prince, Siddhartha lived during his early years a life of luxury, but later became disillusioned with the world when he was confronted with the sights of old age, sickness and death.
He despaired of finding fulfilment on the physical level, since the body was inescapably subject to these weaknesses.
Dissatisfied with the cruel realities of life, he left his home at the age of 29 and became an ascetic in search of a solution.
At the age of 35, Siddhartha sat under a banyan tree and in a deep state of meditation, attained enlightenment.
He thus became a Buddha, meaning 'enlightened one'.
It is claimed that Gautama Buddha was not the first Buddha, but the fourth, and he is not expected to be the last.
Central to Buddhist philosophy is the belief that all life is suffering.
All people are subject to the traumas of birth, sickness, feebleness and death; the fear of what they most dread (incurable illness or personal weakness); and separation from what they love.
The cause of suffering is desire - specifically the desires of the body and the desire for personal fulfilment.
Happiness can only be achieved if these desires are overcome, and this requires following the eight-fold path.
By following this path the Buddhist aims to attain nirvana.
Volumes have been written in attempts to define nirvana; the sutras (the Buddha's discourses) simply say that it's a state of complete freedom from greed, anger, ignorance and the various other chains of human existence.
The first branch of the eight-fold path is 'right understanding': the recognition that life is suffering, that suffering is caused by desire for personal gratification, and that suffering can be overcome.
The second branch is 'right mindedness': cultivating a mind free from sensuous desire, ill will and cruelty.
The remaining branches require that one refrain from abuse and deceit: show kindness and avoid self-seeking in all actions; develop virtues and curb passions; and practise meditation.
Many westerners misunderstand certain key aspects of Buddhism.
First of all, it should be understood that the Buddha is not a god but a human being who claims no divine powers.
In Buddhist philosophy, human beings are considered their own masters and gods are irrelevant.
Reincarnation is also widely misunderstood.
It is not considered desirable in Buddhism to be reborn into the world.
Since all life (existence) is suffering, one does not wish to return to this world.
One hopes to escape the endless cycle of rebirths by reaching nirvana.
Buddhism reached its height in India by the 3rd century BC, when it was declared the state religion of India by the emperor Ashoka.
It declined sharply after that as a result of factionalism and persecution by the Brahmans.
Numerous Buddhist sects have since evolved in different parts of the world.
Classical Buddhists will not kill any creature and are therefore strict vegetarians They believe that attempting to escape life's sufferings by committing suicide will only bring more bad karma and result in rebirth at a lower level.
Yet there are other Buddhist sects that hold opposite views.
During the 1960s, for example, South Vietnamese Buddhists made world headlines by publicly burning themselves to death to protest government policies.
Somehow, the various sects of Buddhism manage not to clash with each other.
Buddhism reached China around the 1st century AD and became its prominent religion by the 3rd century.
Ironically, while Buddhism expanded rapidly throughout east Asia, it declined in India.
Buddhism in China is mixed with other Chinese philosophies such as Confucianism ud Taoism.
The Chinese, in particular, had a hard time accepting the fact that they should not wish to return to this life, as they believe in longevity.
As many as 13 schools of Buddhist thought evolved in China, the most famous, perhaps, being Chan, which is usually known in the West by its Japanese name, Zen.
Taoism (Dao Ji?o) Unlike Buddhism, which was imported from India, Taoism is indigenous to China.
It is second only to Confucianism in its influence on Chinese culture.
The philosophy of Taoism is believed to have originated with a man called Laozi (whose name is also variously spelled Laotze, Laotzu or Laotse).
Laozi literally means 'old one'.
Relatively little is known about Laozi, and many question whether or not he really existed.
He is believed to have lived in the 6th century BC and been the custodian of the imperial archives for the Chinese government.
Confucius is supposed to have consulted him.
Understanding Taoism is not simple.
The word tao (pronounced d?o) means 'the way'.
It's considered indescribable, but signifies something like the essence of all things.
A major principle of Taoism is the concept of wuwei, or 'doing nothing'.
A quote attributed to Laozi, 'Do nothing, and nothing will not be done', emphasises this principle.
The idea is to remain humble, passive, nonassertive and nonaggressive.
Chien Szuma (145-90 BC), a Chinese historian, warned 'Do not take the lead in planning affairs, or you may be held respons?ble'.
Nonintervention, or live and let live, is the keystone of the Tao.
Harmony and patience are needed, action is obtained through inaction.
Taoists like to note that water, the softest substance, will wear away stone, the hardest substance.
Thus, eternal patience and tolerance will eventually produce the desired result.
Westerners have a hard time accepting this.
The Western notion of getting things done quickly conflicts with this aspect of the Tao.
Westerners note that the Chinese are like spectators, afraid to get involved.
The Chinese say that westerners like to complain and are impatient.
Taoists are baffled at the willingness of westerners to fight and die for abstract causes, such as a religious ideal.
It's doubtful that Laozi ever intended his philosophy to become a religion.
Chang Ling is said to have formally established the religion in 143 BC.
Zhuangzi (also spelled Chuangtzu or Chuangtse) is regarded as the greatest of all Taoist writers.
You can find a collection of Zhuangzi's work in The Book of Zhuangzi, The Book of Chuangtzu and The Book of Chuangtse, which are available in English.
Taoism later split into two schools, the 'Cult of the Immortals' and the 'Way of the Heavenly Teacher.
The former offers immortality through meditation, exercise, alchemy and various other techniques.
The Way of the Heavenly Teacher has many gods, ceremonies, saints, special diets to prolong life and offerings to the ghosts.
As time has passed, Taoism has become increasingly wrapped up in the supernatural, witchcraft, self-mutilation, exorcism, fortune telling, magic and ritualism.
Confucianism (R?jia Sixiang) Confucius is regarded as China's greatest philosopher and teacher.
The philosophy of Confucius has been borrowed by Japan, Korea, Vietnam and other neighbouring countries.
Confucius never claimed to be a religious leader, prophet or god, but despite this his influence is so great in China that Confucianism has come to be regarded as a religion by many.
Confucius (551-479 BC) lived through a time of great chaos and feudal rivalry known as the Warring States Period.
He emphasised devotion to parents and family, loyalty to friends, justice, peace, education, reform and humanitarianism, and preached against practices such as corruption, excessive taxation, war and torture.
He also emphasised respect and deference to those in positions of authority, a philosophy later heavily exploited by emperors and warlords.
However, not everything said by Confucius has been   universally praised - it seems that he was also a male chauvinist who firmly believed that men are superior to women.
Confucius preached the virtues of good government, but his philosophy helped create China's horrifying bureaucracy, which exists to this day.
On a more positive note, his ideas led to the system of civil service and university entrance examinations, where positions were awarded on ability and merit, rather than from noble birth and connections.
He was the first teacher to open his school to students on the basis of their desire to learn rather than their ability to pay for tuition.
The philosophy of Confucius is most easily found in the Analects of Confucius (Lunyu).
There have been many quotes taken from these works, the most famous perhaps being the Golden Rule.
Westerners have translated this rule as 'Do unto others as you would have them do unto you'.
Actually, it was written in the negative: 'Do not do unto others what you would not have them do unto you'.
No matter what his virtues, Confucius received little recognition during his lifetime.
It was only after his death that he was canonised.
Emperors, warlords and mandarins found it convenient to preach the Confucian ethic, particularly the part about deference to those in authority.
Thus, with official support, Confucianism gained influence as a philosophy and has attained almost religious status.
Mengzi (formerly spelled Mencius; 372-289 BC) is regarded as the first great Confucian philosopher.
He developed many of the ideas of Confucian ism as they were later understood.
Although Confucius died some 2500 years ago, his influence lives on.
The Chinese remain solidly loyal to friends, family and teachers.
The bureaucracy and examination systems still thrive, and it is also true that a son is almost universally favoured over a daughter.
It can be said that, even to this day, Confucian thought is behind much of Chinese culture.
Numerous forms of worship exist in Taiwan.
Many people have altars in their houses and you can frequently see people performing a worship ceremony (b?ib?i) in front of their homes.
A worship ceremony can take many forms, as they're performed for varying reasons.
Often, you will see somebody burning pieces of paper, which represent money.
If the money has a silver square in the middle it's 'ghost money'; if it has a gold square it's 'god money'.
The money is usually burned to satisfy a 'hungry ghost' from the underworld (hell) so that it will not bother you or members of your family.
The money could also be for a departed relative who needs some cash in heaven.
Truck drivers often throw ghost money out of the window of their vehicles to appease the 'road ghosts', to ensure that they don't have an accident.
Some people place the ashes of ghost money in water and drink the resulting mixture as a cure for disease.
Another custom practised is the burning of paper models of cars and motorbikes so the dear departed may have a means of transport in heaven.
Incense is frequently burned, often placed on a table with some delicious-looking food which is meant for the ghosts.
However, after the ghost has had a few nibbles, the living will sit down to a feast of the leftovers.
It's also possible to rent or borrow carved images of the deities to take home from the temple for home worship ceremonies.
If you visit a temple in Taiwan, you will probably encounter some strange objects that you may not have seen before.
One such object is a box full of wooden rods called a qian.
Before praying for something you desire, such as health, wealth or a good spouse, you must select a rod from the qjan.
Then pick up two kidney-shaped objects called shimbui (shimbui is a Taiwanese word, not a Mandarin one).
Drop them on the ground three times.
If two out of three times they land with one round surface up and one flat surface up, then your wish may be granted.
If both flat sides are down, then your wish may not be granted.
If both flat sides are up, the god is laughing at you.
Many festivals are held throughout the year in accordance with the lunar calendar Some festivals only occur once every 12 years, at the end of the cycle of the 12 lunar animals.
Some festivals occur only once every 60 years.
This is because each of the 12 lunar animals is associated with five elements: metal, wood, earth, water and fire.
The full cycle takes 60 years (5x 12) and at m the end of this time there is a 'super worship' festival, which may involve tens of thousands of participants.
You can frequently see a Taoist street parade in Taiwan, complete with crashing cymbals and firecrackers.
The purpose is usually to celebrate a god's birthday.
Look closely at the temples in Taiwan and you will see some Chinese characters inscribed on every stone, engraving, painting and statue.
These characters are not those of the artist, but rather the names of the people who have donated money to purchase that particular temple ornament.
Should you donate some money to a temple, you may also have your name engraved in stone.
Other than monks and nuns, today practically nobody in Taiwan receives any formal religious education.
Therefore, the majority of the population understands little of the history and philosophy behind Buddhism and Taoism.
2-28 (Er Er Ba) This, Taiwan's newest public holiday (established 1997), commemorates the events of 28 February 1947, when thousands of Taiwanese were massacred in a military crackdown against political dissent.
Establishing this as a public holiday was the brainchild of the DPP, and it's still very controversial.
It's entirely possible that this holiday could be cancelled.
Youth Day (Qingni?n Ji?) Youth Day falls on 29 March.
Of course, all schools are closed on this day.
Tomb Sweep Day (Qing Ming Ji?) A day for worshipping ancestors; people visit and clean the graves of their departed relatives.
They often place flowers on the tomb and burn ghost money for the deceased.
It falls on 5 April in most years, 4 April in leap years.
Teachers' Day (Ji?oshi Ji?) The birthday of Confucius is celebrated as Teachers' Day on 28 September.
There is a very interesting ceremony held at every Confucian temple on this day, beginning at about 4am.
However, tickets are needed to attend this ceremony and they are not sold at the temple gate.
The tickets can sometimes be purchased from universities, hotels or tour agencies, but generally they are not easy to obtain.
National Day (Shuangshi Ji?) As it falls on 10 October - the 10th day of the 10th month National Day is usually called Double 10th Day'.
Big military parades are held in Taipei near the Presidential Building.
At night there is a huge fireworks display beside the Tamsui River.
It's one of the more interesting times to visit Taipei.
The rest of the country tends to use this day to head to beaches, karaoke bars etc.
Retrocession Day (Guangfu Ji?) Taiwan's Retrocession Day, 25 October, celebrates Taiwan's return to the ROC after 50 years of Japanese occupation.
There are only three lunar public holidays: the Chinese New Year, the Dragon Boat Festival and the Mid-Autumn Festival, but many festivals are also held according to the lunar calendar.
Chinese (Lunar) New Year (Chun Ji?)
The Chinese celebrate New Year on the first day of the first moon.
Actually, the holiday lasts three days but many people take a full week off work.
It is a very difficult time to book tickets, as all transport and hotels are booked to capacity.
Workers demand double wages during the New Year and hotel rooms triple in price.
Dragon Boat Festival (Duanw? Ji?) On the fifth day of the fifth moon, colorful dragon boat races are held in Taipei and in a few other cities - they're shown on TV.
It's the traditional day to eat steamed rice dumplings (zongzi).
Mid-Autumn Festival (Zhyng qiu Jie) Also known as the Moon Festival, this takes place on the 15th day of the eighth moon.
Gazing at the moon and lighting fireworks become very popular at this time.
This is the time to eat tasty moon cakes (yu? bing), which are available from every bakery.
Lantern Festival (Yu?nxi?o Ji?) Also known as Tourism Day, the Lantern Festival is not a public holiday, but it still ranks as a very colorful celebration.
Hundreds of thousands of people use this time to descend on the towns of Yenshui, Luerhmen and Peikang to ignite fireworks these towns good places to visit or avoid, depending on how you feel about fireworks and crowds.
Kuanyin's Birthday (Guansh?yin Shen-gri) The birthday of Kuanyin, goddess of mercy, is on the 19th day of the second moon and is a good time for temple worship festivals.
Matsu's Birthday (M?zu Shengri) Matsu, goddess of the sea, is the friend and patron of all fishermen.
Her birthday is widely celebrated at temples throughout Taiwan.
 Matsu's birthday is on the 23rd day of the third moon.
Ghost Month (Gui Yu?) Ghost Month is the seventh lunar month.
The devout believe that during this time the ghosts from hell walk the earth, making it a dangerous time to travel, go swimming, get married, or move to a new house.
If someone dies during this month, the body will be preserved and the funeral and burial will not be performed until the following month.
As Chinese people tend not to travel during this time, it's a good time for visitors to travel easily around the island and avoid crowds.
It is also a good time to see temple worship.
On the first and 15th day of the Ghost Month, people will be burning ghost money and incense and placing offerings of food on tables outside their homes; the 15th day is usually the most exciting day of the month.
Ghost Month is the best time to visit a Taoist temple - an experience not to be missed.
Compared with Taiwan's ornate Buddhist and Taoist temples, the Confucius Temple is a modest place.
There are no statues or deities and the only time it comes to life is on 28 September, the birthday of Confucius (Teachers' Day), when there is an interesting festival held at dawn.
If you're around at this time, check the tourist offices or your hotel to see if you can get a ticket to this festival.
Probably the easiest way to get to the temple (at 275 Talung St) is via the Tamsui MRT line to Yuanshan station.
2-28 PEACE PARK This was once known as New Park, but the name was changed in 1997 by Taipei's then-mayor (and now president) Chen Shuibian.
The park's new theme is to honour the anti-Kuomintang (KMT) protestors and innocent bystanders who were murdered in the military crackdown that began on 28 February 1947.
The park's pleasant tree-shaded grounds contain a lake, a pagoda, pavilions and 2-28 memorabilia, including the 2-28 Museum.
Chenghuang Temple The temple contains lots of artwork and is especially active during Ghost Month and on the 1st and 15th days of the lunar month (see Public Holidays and Special Events in the Facts for the Visitor chapter).
You'll find the temple near the intersection of Chungshan Rd and Tungmen St.
Confucius Temple Built in 1665 by General Chen Yunghua, a Ming dynasty supporter, this is the oldest
Confucian temple in Taiwan. The temple is at 2 Nanmen Rd, near the main police station.
On 28 September -the birthday of Confucius- a colourful ceremony is held at dawn.
Dajia Zhenlan Temple is the shrine for worshiping Mazu.
Mazu is the goddess of sailors and fishermen.
Every spring, many worshipers come here for Mazu’s birthday celebration.
Mazu is the goddess of the sea that watches over fishermen.
On the Lantern Festival, a lot is drawn to decide on the exact date to start the Mazu tour.
The parade of Mazu is regarded as one of the major world religious festivals.
Tainan Confucius Temple was built in 1666.
That was the first Confucius Temple and the “Highest Institute” in Taiwan.
Tainan Confucius Temple is, of course, a First Grade Historic Site.
The Confucius Temple itself is a site of traditional culture.
All families get together for dinner on Lunar New Year’s Eve.
When I was a kid, I always looked forward to the Red Envelopes.
What are the Red Envelopes?
They are small red paper envelopes that contain money.
Do you receive the Red Envelopes as gifts?
Only when you are a kid, but you give the Red Envelopes after you grow up.
It is much more fun to be a kid than an adult.
Most Taiwanese get a week holidays for the Lunar New Year.
Before the Lunar New Year’s Day, all families clean up their places.
Most people give out the Red Envelopes after the reunion dinner.
At the Lunar New Year’s Eve, people set off firecrackers.
On the Lunar New Year’s Day, people dress in new clothes to visit their relatives.
On the second day of the Lunar New Year, married daughters visit their parents with their husbands and children.
Colorful dragon and lion dance are in the street.
All people hope to start a new year with a fresh beginning.
Have you tasted "Tangyuan" before?
What is "Tangyuan"?
It is a glutinous rice ball eaten at the Lantern Festival.
What else do you do during the festival?
People carry lanterns or watch lantern parades.
Do families get together at this time?
Many families get together and enjoy guessing lantern riddles.
I am sure they eat "Tangyuan" at the same time.
People enjoy a family reunion on the night of the Lantern Festival.
Traditionally, the Lantern Festival is the last day of the Lunar New Year.
"Tangyuan" is made of glutinous rice ball with fillings, cooked in a soup.
"Tangyuan" has different fillings, such as peanut butter and sesame.
In the Tang Dynasty, guessing lantern riddles became part of the festival.
The lantern riddles are often about health and family prosperity.
Tomb Sweeping Day is a day to worship ancestors.
We clean up the weeds around the ancestors’ tombs.
Do you clean up the tombs together with other family members?
That’s right, and it is a day for family reunion, too.
I’m going to buy some spirit money for Tomb Sweeping Day.
Is it the paper money you burn to offer to the ancestors?
You’re right, and we burn it next to the tombs.
Westerners don’t usually worship their ancestors like that.
We also lay food out in front of the tombs with smoking incense.
Do all family members get together for that ceremony?
Some families send their representatives.
Tomb Sweeping Day is on April 5th and is a national holiday.
Offerings are laid out for the ancestors, along with spirit money.
Some cold dishes are eaten especially on Tomb Sweeping Day.
It is a good tradition to clean up the graves of ancestors.
People celebrate the coming of spring on Tomb Sweeping Day, too.
Afterwards we eat the food we offered to our ancestors.
Incense burning is very important in the ceremony.
Spirit money is paper money we burn for our ancestors in another world.
Take a look at the dragon boat racing over there!
Do you see those colorful dragon boats?
Are they racing now?
Yes, it's for the Dragon Boat Festival.
Really? That sounds quite interesting.
The yellow dragon boat just got the first prize.
Let me quickly take a photo.
Do you want to watch the dragon boat racing?
Of course, please take me with you.
You've heard of the Dragon Boat Festival, haven't you?
I don't think so.
It is a holiday for Qu Yuan, who drowned himself in a river.
Why did he do that to himself?
The emperor at his time wouldn't listen to him.
I suppose it was to the emperor's loss.
The Dragon Boat Festival is on the fifth day of the fifth lunar month.
Qu Yuan threw himself into the Miluo River and died.
The locals dropped wrapped rice into the river so that fish wouldn't eat Qu Yuan's body.
Rice dumplings are sticky rice wrapped in bamboo leaves.
The locals paddled out on boats to look for Qu Yuan's body.
Nowadays people hold dragon boat races at the Dragon Boat Festival.
Many people wear perfumed medicine bags to keep away evil spirit.
In Taiwan, the Dragon Boat Festival is also celebrated as "the Poet's Day".
At Hungry Ghost Festival all ghosts will come out.
We have a day for ghosts, Hungry Ghost Festival.
Is that similar to Halloween?
Not quite similar.
When is this Hungry Ghost Festival?
The 15th of July of the lunar calendar, the Ghost Month.
What can I do to avoid the ghosts around that time?
Many people burn incense in front of their stores
What god are they worshiping?
Today is Hungry Ghost Festival, a day when all ghosts come out.
Are they worshiping the ghosts?
All people who died before us are worshiped on this day.
Your ancestors are worshiped on Tomb Sweeping Day, aren't they?
Some ghosts, who are not worshiped, wander around for food.
Now I see what the food on the tables is for.
There are rituals for the Hungry Ghost Festival in almost all temples.
In Buddhist temples, ancestors are especially worshiped on this day.
Food and drinks are laid out in front of doors to serve the hungry ghosts.
People worship at the Hungry Ghost Festival for peace of mind.
In the Ghost Month, many hungry ghosts wander about.
We hope the ghosts do not make trouble for us.
In the Ghost Month, many people avoid going swimming.
Many ghost stories are talked about in the Ghost Month.
How about tasting the hand-made moon cake?
Do you see many stores selling moon cake now?
Why are they called moon cake?
They are sweet pastry eaten at the Mid-Autumn Festival.
Is it a festival about the moon?
On that night, the moon is said to be most beautiful.
Let's watch the moon together on that night.
The Mid-Autumn Festival is around the corner.
When is it exactly?
It is on August 15 of the lunar calendar, and this year it is on September 30th.
What are you going to do to celebrate it?
My family will get together and have a barbecue outdoors.
Why do you have a barbecue at the Mid-Autumn Festival?
My family just wants to get together to admire the moon together.
Moon cake is also part of the feast, right?
The Mid-Autumn Festival is also known as the Moon Festival.
Chang-Er took her husband's magic medicine and flew to the moon.
The Jade Rabbit pounds medicine, together with Chang-Er.
Chang-Er and the Jade Rabbit live on the moon forever.
Admiring the moon with the family must be a lot of fun.
Is there anywhere I can learn how to make moon cake?
單句
China is a country with many nationalities.
Each nationality has its own customs and traditional festivals and each festival usually has fascinating legends associated with it.
Chun Jie (The Spring Festival) The Spring Festival, or Chinese New Year, is the most important festival in China, and its celebration dates back to some two thousand years ago.
It marks the beginning of the lunar year, and is the time when families get together and are reunited if they have been separated.
The date of the festival varies each year, but is usually in late January or early February according to the Gregorian calendar.
On Lunar New Year's eve, the sound of firecrackers can be heard throughout the night signifying "doing away with the old and making way for the new" The practice of letting off firecrackers, however, is on the decline because many cities have banned their use to prevent fires and accidents caused by the paper bombs.
It is an old custom for people to stay up late or all night on New Year's Eve.
In cities, most people stay up late watching TV, playing cards, dancing or preparing food for the next day.
Yuan Xiao Jie (The Lantern Festival) The Lantern Festival falls 15 days after the Lunar New Year.
It is a tradition to hang decorative lanterns in public places and eat "Yuan Xiao", a kind of glutinous rice flour ball with a sweet or savory filling.
Qing Ming Jie (The Pure Brightness Festival) The Pure Brightness Festival, is the fifth of the 24 solar terms according to the traditional Chinese calendar, which are defined according to the position of the sun in the zodiac.
The festival takes place on the fourth or fifth day of the fourth month of the Gregorian calendar, and on this day people usually go to tidy or "sweep" the graves of their departed friends and relatives, and of revolutionary martyrs.
Duan Wu Jie (The Dragon Boat Festival) The Dragon Boat Festival falls on the fifth day of the fifth lunar month.
It originates as a means to propitiate the river dragon gods into a popular festival commemorating the suicide of Qu Yuan, a poet of the Warring States Period (BC) who could no longer bear the moral degeneration of his state.
On the fifth day of the fifth month in the Lunar calendar dragon boat races are held in commemoration of those who tried to save the poet and as an offering to the river gods Dragon boat racing has now become a popular sport in China.
Zhong Qiu Jie (The Mid-Autumn Festival) The Mid-Autumn Festival is held on the 15th day of the eighth Lunar month, the middle of autumn in the traditional Chinese calendar, it takes place at harvest time on the night of the full moon, which symbolized unity.
Moon cakes are eaten on this auspicious day.
These are round cakes filled with dried fruits, and are symbolic of the perfect roundness of the moon at the time of the festival.
Public holidays New Year's Day (one day off); Spring Festival (three days off); Labour Day (May 1, three days off); The National Day (October 1, three days off) Foreign experts are entitled to the above holidays and many activities are arranged both locally and nationally to which experts will be invited.
In addition, experts are entitled to holidays on occasions of important festivals in their own countries, such as Christmas, watersplashing festival, Corban, etc.
Work schedules must be arranged to fit in with such holidays, as the Chinese do not normally celebrate them.
Chong Yang Jie (Double Nine Festival) Double Nine Festival is a traditional Chinese festival on the 9th day of the 9th month of the Chinese Lunar calendar, In the "Books of Changes", nine (9) is defined as a positive figure ("Yang"), and "Chong" in Chinese means "double", so it is called "Double-Nine Day".
On the day people go outing, climbing, kite-flying, drink wine (chrysanthemum wine) and eat cakes.
The day has been appointed as "Senior Citizens' Day", " Nine" pronounces the same sound as "longevity" in Chinese, so on the day when they celebrate the festival people do things to show respect and wishes of longevity to their elderly.
Other festivals: Water Splashing Festival is New Year's Day of the Dai and some other minority nationalities residing on Hainan Island, which falls on the 15th day of the 6th month of the Dai calendar usually a mid-April day.
Early in the morning during the festival, female villagers would gather to pour water over Buddhist sculptures "to wash the dust off.
After that villagers, especially boys and girls would sprinkle water on each other, believing that diseases and germs can thus be eliminated.
Christmas and Easter for Chinese Christians, and Corban and Ramadan for Muslims are also observed among some people in some places.
Lantern Festival, Pingxi Heavenly Lantern Festival(元肯節、平溪天燈)
Celebrated on the 15th day of the first lunar month, the Lantern Festival is one of the biggest and most colorful events of the year in Taiwan.
Make sure to visit Pingxi to enjoy the Heavenly Lantern Festival, when the night sky comes alight with thousands of ascending lanterns bearing wishes for the New Year.
Ghost Festival(中元祭典)
The Keelung Ghost Festival dates back to an epic feud 140 years ago between immigrant clans from Zhangzhou and Quanzhou in mainland China.
After the long fight, the locals began holding an annual ceremony to help the spirits of those killed during the feud to ascend to heaven.
Since then the festival has grown to become one of the main annual events in Keelung.
Dajia Mazu Pilgrimage(大甲媽祖繞境)
Known as the Goddess of the Sea, Mazu is one of the most widely revered of Taoist deities in Taiwan.
During Mazu's birthday, celebrated on the 23rd day of the third lunar month Mazu temples across lawan cary their resident statue of the goddess on a pilgrimage accompanied by a procession of thousands of followers.
The pilgrimage from Dajia's Zhenlan Temple to Fengtian Temple in Xingang.
Chiayi County is one of the grandest, attracting over 100.000 Worshippers.
Yanshui Beehive Rocket Festival(鹽水蜂炮)
This festival traces back to the 1885 plague in the town of Yanshui.
In order to seek divine protection from Guansheng Dijun, the townspeople held a ceremony on the god's birthday lighting firecrackers to welcome the deity and also to drive off evil spirits.
Today, thousands of people fill the streets in Yanshuei for this pyrotechnic spectacle during the Lantern Festival.
The Mid-Autumn Festival is on August 15 in lunar calendar every year, between September and October in Gregorian calendar.
According to the lunar calendar, autumn is from July to September, August is the second month of autumn, so it is known as “mid-autumn”.
The Mid-Autumn Festival is one of the four main Chinese festivals including Lunar New Year, Tomb Sweeping Festival and Dragon Boat Festival, and it is also a traditional festival in other countries or regions such as South Korea, Japan, Vietnam, and Ryukyu.
Autumn is a harvest season.
In ancient times, the harvest celebration each year was always held in the full-moon day in autumn to appreciate the gods and the whole family members would also gather together in the celebration.
This symbolizes reunion and completeness, which is also the original meaning of the Mid-Autumn Festival.
There is a rich food culture in Taiwan, so different special foods can be tasted in this grand festival; for instance, the mooncakes which must be eaten during the Mid-Autumn Festival have various tastes and flavors in Taiwan.
These range from the traditional time-honored flavor to the modern innovative flavor to satisfy the taste buds of all the people.
The pomelo produced in autumn is a particular taste for this season.
The pomelo in Madou, Tainan City is the most popular pomelo variety and
Its juicy and sour-sweet taste is unforgettable.
Most Mid-Autumn Festival activities are related to the moon.
Every country or district has different customs and celebration methods.
In Taiwan, the importance of the Mid-Autumn Festival ranks only second to the Lunar New Year.
It is a family reunion day because people working far away from home always travel back home to celebrate the Mid-Autumn Festival with dearest family members.
The celebration activities include worshiping the earth, appreciating the moon and sweet osmanthus, and eating mooncakes and pomelo.
The custom of enjoying the pomelo originates from the homophony of you (柚pomelo in Mandarin) and you (佑 blessing in Mandarin pronunciation) to pray for the blessing in daily life.
Besides, the largest difference between Taiwan and other countries is that Taiwanese always have a barbecue on the Mid-Autumn Festival, but the barbecue activity is actually not highly related to the Mid-Autumn Festival.
In the past, there was no traditional custom of barbecue in Taiwan.
However, since the 1980s, the successful advertisement of barbecue sauce led the tide of barbecue on the Mid-Autumn Festival.
Since then, “barbecue during the Mid-Autumn Festival” has become one of Taiwanese’s most favorite annual activities.
In the Mid-Autumn Festival, the full moon represents the reunion and carries the emotion of missing hometown and family members.
Thus, the Mid-Autumn Festival has become a colorful and precious cultural heritage.
Mid-Autumn Festival is one of the four most important holidays in Taiwan, along with Chinese New Year, Tomb Sweeping Day, and Dragon Boat Festival.
Mid-Autumn Festival is held on the fifteenth day of the eighth month on the Chinese calendar, which is usually in late September or early October.
On Mid-Autumn Festival there is a full moon, so families like to go outside at night and look at the beautiful moon while they have barbecues with family and friends.
People eat moon cakes, which are small, round, sweet cakes filled with egg yolks, bean paste, and other things.
They also eat pomelos, a fruit that looks like a large grapefruit.
Sometimes children use the peel of the pomelo to make a hat.
There is an old story that is often told on Mid-Autumn Festival about a man named Hou Yi and his wife Chang E.
Many years ago, there were ten suns in the sky.
The weather was too hot and everything was dying.
Hou Yi was a good archer, so he used his bow and arrow to shoot down nine of the suns.
Everyone was very happy, and to thank him, a goddess gave him a medicine that would make him live forever.
But Chang E took the medicine, and flew to the moon.
Chinese New Year is the most important festival celebrated by the ethnic Chinese.
Also known as the Spring Festival, Chinese Lunar New Year and Lunar New Year, the Chinese New Year is based on the Chinese lunar calendar, begins on the first day of the first lunar month, and the Lantern Festival celebrated on the 15th day of the first month marks the grand finale of the Chinese New Year celebrations.
On the Chinese New Year's Eve, family members would gather to share a sumptuous family dinner, and manage to stay up all night to welcome the New Year as it is believed that parents would live a longer life for doing this.
The first day of the Chinese New Year, people would dress in red (symbolizing luckiness and propitious) to visit relatives and friends, as well as wish everyone a prosperous year.
In the past, children would have to kneel down and show respect to grandparents in receiving red envelopes.
However, this tradition has gradually disappeared.
Today, children receive red envelopes when greeting elders with auspicious words.
The Lantern Festival is celebrated annually on the 15th day of the first lunar month to mark the grand finale of the Chinese New Year celebrations.
It is also the very first full moon day of the New Year, symbolizing the coming of the spring.
People usually celebrate this festival by enjoying family dinner together, eating Yuanxiao (glutinous rice dumpling), carrying paper lanterns, and solving the riddles on the lanterns.
The festival is celebrated with fanfare events in Taiwan, including the internationally famed Pingxi Sky Lantern Festival in New Taipei City, Bombing Lord Han Dan in Taitung, and Yanshui Beehive Rockets Festival in Tainan, to welcome the New Year in a spirit of peace, prosperity and joy.
Bombing Lord Han Dan is a special ceremony in Taitung, which a chosen man performs in the role of Master Han Dan-a god of wealth, and gets thrown by firecrackers.
During the event, the chosen man wears nothing but a pair of red short pants, holds one bamboo fan to protect his face, stands on a sedan chair, and being carried around by four devotees.
Firecrackers are to be thrown at the chosen one as it is believed that Lord Han Dan cannot bear the cold weather.
The firecrackers are to keep him warm as well as to pray for wealth and prosperities.
The Dragon Boat Festival is a significant Chinese festival which is celebrated on the fifth day of the fifth lunar month.
Its origin was to commemorate the patriotic poet Qu Yuan.
This festival is one of the three major celebrated festivals in Taiwan, together with Chinese New Year and the Moon Festival.
Out of all major Taiwan festivals, Dragon Boat Festival has the longest history with many stories telling its origin.
The most popular one is about the patriotic poet- Qu Yuan.
During the declination of China in the end of the Zhou Dynasty, Qu Yuan served as a minister to the Zhou Emperor.
Qu Yuan was a wise and articulate man well loved by the people.
The fights that he had against the rampant corruption made the other officials envy him.
The officials started spreading rumors of Qu Yuan in front of the emperor and eventually Qu Yuan had lost the emperor’s trust.
Qu Yuan then got exiled when he urged to avoid conflict with the Kingdom of Qing.
He travelled and wrote poems during his exile to.
He threw himself into Milou River after he heard that Zhou was being defeated by the Qing.
The Moon Festival is one of the three most significant festivals of the Chinese communities around the world besides the Lunar New Year (Chinese New Year) and the Dragon Boat Festival.
Originally named the Mid-Autumn Festival, the Moon Festival is celebrated on the fifteenth day of the eighth lunar month in observance of the bountiful autumn harvest.
On the 15th day of the lunar month, the moon forms a round shape that symbolizes family reunion.
Upon this occasion, the legends of the festival are often told to the children.
National Day of the Republic of China which is known as the Taiwan National Day or Double Tenth Day is to commemorate the 1911 Wuchang Uprising, a milestone of China’s politics development and a new chapter in the history of the Chinese which led to the collapse of the Qing Dynasty.
On the 10th of October each year celebration and official ceremony are held around Taiwan.
The major official firework event will only hold by one city, but the other cities will proceed with its own celebration and activities.
Every year, the Lantern (or “Yuanxiao”) Festival marks the end of the Lunar New Year (Spring Festival) festivities.
The official Taiwan Lantern Festival is staged in a different location each time, organized by a selected county or city government.
In a break with a tradition dating back 157 years, tables of Western food were laid out this year (2012) alongside the more usual Chinese fare at the Keelung Mid-Summer Ghost Festival ( 雞 籠 中 元 祭 ), for the feeding of hungry ghosts.
Also for the first time, a priest performed Christian rituals beside Buddhist and Daoist counterparts.
This strange turn of events derived from a unique combination of religious belief and historical circumstance.
The 7th lunar month is also known as Ghost Month ( 鬼月 ), since according to popular belief the Gates of Hell are open the full month and spirits of the deceased are free to wander the earth.
Given the Han Chinese people’s complex helpand-be-helped relationship with their ancestors, this is not necessarily a bad thing.
Except, that is, in the case of “hungry ghosts” (more commonly called “good brethren”), who do not have descendents making regular offerings and who might therefore cause trouble rather than offer help from the afterlife.
Elaborate ceremonies and fabulous feasts are prepared to placate them.
These celebrations are held all over Taiwan, but the largest and one of the most colorful takes place in Keelung.
It culminates at the fishing port of Badouzi ( 八 斗 子 ), where floating lanterns are launched onto the sea.
Curiously, these lanterns are all decorated with a single Chinese character, such as 謝 (xie; “gratitude”), 林 (lin; “wood”), and 江 (jiang; “river”).
The reason for this lies in the Keelung festival’s origins.
During its long period of colonization by Han Chinese, from the early 1600s well into the 1800s, Taiwan was a frontier territory in which following the rule of law was not always easy or desired, and armed clashes were not uncommon.
These were not limited to conflicts with indigenous people, or between Hakka and Hoklo-speaking immigrants, but even occurred between members of the last group, almost all of whom hailed from either the Quanzhou or Zhangzhou regions of Fujian Province in mainland China.
One particularly nasty clash in Keelung in 1851 led to around 100 deaths.
To heal social wounds and prevent future clashes, an annual ceremony honoring the dead was mutually devised and got under way in 1855.
Rather than being based on hometown affiliations as was normal, it was organized according to clan names, since these were shared by families of both groups.
This is the origin of the character on each lantern: Each is a family name that also has other meaning.
It is believed that the farther a lantern floats out to sea, the better the luck to be enjoyed by that clan in the year to come.
Wangye Worshipping Ceremony
Taiwan wasn’t always the safe, healthy place it is today.
Until the early 20th century, malaria was a constant threat and cholera epidemics were frequent.
Lacking medical knowledge and influenced by traditions they had brought from mainland China’s Fujian and Guangdong provinces, Taiwanese of Han descent lived in fear of plague-spreading demons.
Naturally, they sought divine protection from these malevolent spirits, whom they called Wang Ye (plague gods), or“royal lords.”
Taiwan in the 1990s has experienced a flourishing of modern democratic and scientific thought at the same time traditional Taiwanese folk traditions are being renewed and adapted to a modern industrialized society.
The economic gains of recent decades have brought a boom in renovating and building Buddhist and Taoist temples.
New religious groups have arisen that meld traditional Chinese religious ideas with modern concerns.
Taiwain's religious culture reflects a unique status as a culture brought by immigrants from Fujian and Guangdong, influenced by Japanese and Western presence, and then regulated by the Guomindang Party since 1947.
The dominant religious activities are ancestral rituals and community temple festivals.
According to 1994 ROC statistics, 11.2 million out of the 21 million people in Taiwan identify themselves as religious.
Of these religious Taiwanese, 43 percent are Buddhist, 34 percent Taoists, 8 percent Yiguandao, 6 percent Christians, and 9 percent followers of other religions.
These figures do not adequately portray the rich diversity of religious rituals, sects, temples, and deities that are part of Taiwanese life.
Moreover, the ongoing vitality of Taiwanese folk religion and ancestor reverence makes the distinction between Buddhists and Taoists more a matter of preferred designation than actual practice.
Traditional Chinese concepts of humanity, nature, time, and space continue to be meaningful to the Taiwanese despite the dominance of the modern scientific worldview.
The traditional worldview envisions humans living in harmony with the natural order, usually referred to as Tian, or Heaven.
Human beings should live in harmony with the natural order by understanding and adjusting their lives to the natural order as seen in the changes of the seasons and the landscape of the earth.
The transformation within this natural order is known as Dao, or the Way.
Dao is not only the way of nature; it is the way humans should follow to live in harmony with self, others, and the natural world.
All religious and philosophical approaches teach the Way, but with different emphases and interpretations.
According to the Chinese view of a harmonious natural order, all things of heaven and earth are connected by the life force, qi.
Qi is the breath of the universe, and in humans, the breath of life.
The flow of qi and the patterns of change are understood in terms of two polar opposites: yin and yang.
Yin and yang are polar opposites seen in the transformations and relationships of all things.
Cold and hot, male and female, dry and wet, moon and sun, night and day, all are examples of yin-yang dichotomy.
Another Chinese concept that interprets relationships and change is that of the five agents: wood, fire, earth, metal, and water.
These traditional concepts--qi, the five agents, and the yin-yang polarity--are used to conceptualize and order all aspects of nature and society.
Seasons can be understood in terms of the ebb and flow of yin and yang and the alternating dominance of the five agents.
Historical change can be seen as the movement of the five agents overcoming each other.
Most significantly, the human being is a microcosm of the natural universe.
Like the rest of the universe, human life is governed by qi, the five agents, and the yin-yang polarity.
The major organs of the body are dominated by the five agents; the forces of yin and yang are balanced in a healthy body.
In Taiwan two different calendars mark the passing of time: the Western calendar for business, school and government, and the lunar calendar for religious observances and personal guidance.
The Western (Gregorian) solar calendar is the official calendar and the basis for government-designated national holidays: Founding Day of the Republic of China, Women's Day, Youth Day, Children's Day, Armed Forces Day, Teachers' Day (celebrated as Confucius's birthday), Double Ten (National Day), Taiwan's Retrocession Day, and Constitution Day: These days celebrate ROC nationalism and social progress.
The official government calendar begins with the founding of the Republic of China in 1911, so the date of "retrocession" of Taiwan to China would be given as the year 34.
Some government holidays conveniently coincide with Western and Christian holidays: Founding Day is January 1, and Constitution Day is December 25, Christmas Day.
Chinese festivals follow the traditional lunar calendar.
This calendar is based on twelve lunar months and twenty-four solar divisions and is eleven days shorter than the Western solar calendar.
The biggest festival for families in Taiwan is the lunar New Year's Day, also known as Spring Festival.
On New Year Taiwanese families try to share a meal of abundance with the entire family as they face a year of new beginnings with new clothes, newly clean homes, new finances, and renewed hope for good fortune.
(See Chapter 3 for more information on festivals.) The Chinese almanac gives information about the lunar festivals and information about auspicious and inauspicious activities for each day.
For example, the Taiwanese may consult the almanac or a specialist to find out the most auspicious days for opening a new business, getting married, or moving.
To live in harmony with nature, the Taiwanese have used the tradition of fengshui.
In the natural landscape there is the interaction of yin and yang and the flow of qi.
Humans must live in accordance with those natural forces and not disrupt them when they build new structures.
In building temples, homes, or gravesites, the Taiwanese continue to acknowledge the need for humanity and nature to live in harmony.
Thus the natural-urban landscape is conceptualized through traditional concepts such as yin-yang and qi.
The Chinese science of fengshui (wind and water) is a tradition in which experts attempt to analyze the landscape in order to see the flow of qi and the relationships of yin and yang.
3Fengshui experts are consulted to position a building or a grave in a way harmonious with its surroundings and therefore auspicious for its inhabitants (or their descendants).
A well-positioned home or temple brings good fortune, wealth, and good crops to the family or community.
Poor positioning brings various kinds of ill fortune.
The tools of the trade include ancient manuals and a special compass (luoban) that incorporate the eight trigrams (bagua), the nine primary stars and twenty- eight constellations, the five elements, and the twelve-year and sixty-year cycles.
(For more information, see Chapter 6, "Architecture.") Although fully trained traditional experts are few, a generalized knowledge of fengshui principles is widespread.
Popularizers of fengshui are numerous, and the field has become quite lucrative for those consulted in the construction of large building projects.
Now interior design and furniture placement have become areas for the application of popularized fengshui principles.
Moral philosophy and political philosophy play central roles in Taiwanese history, just as they have in China since the time of Confucius (born ca.551 B.C.).
The Confucian tradition emphasizes moral cultivation of the individual and harmonious ordering of society.
The Analects, written by Confucius students to represent his teachings, places great value on the moral virtues of benevolence (ren) and propriety or ritual (li).
The more specific virtue of filial piety (xiao) is also extolled in the Analects and countless philosophical and popular texts seeking to inculcate this respect and obedience to parents and ancestors.
Much of Confucian philosophy resonates with folk tradition and organized religion in Taiwan.
The Confucian ideal of filial piety, for example, is ritually expressed in Taiwanese funerals, ancestral shrines, and religious festivals.
The Confucian tradition is also strongly hierarchical because it emphasizes the obligations in a hierarchically ordered society centered around the bonds of father and son, elder brother and younger brother, husband and wife, elders and juniors, and rulers and subjects.
The authoritarian and patriarchal tendencies of these relationships are increasingly being questioned by the more egalitarian, democratically minded Taiwanese.
The official ideology of the Guomindang Party and the Republic of China came from the father of the ROC, Sun Yat-sen, and his Three Principles of the People (sanmin zhuyi).
Sun Yat-sen was a product of the meeting of East and West.
With his Western training, lack of classical Chinese training, and life in Western-influenced ports such as Macau and Hong Kong and abroad, he advocated a form of Chinese revolution that was highly Westernized.
His three principles were nationalism, democracy, and the people's livelihood--basically concepts from the modern West sufficiently vague to be embraced by a large portion of the Chinese.
Chiang Kai-shek, successor to Sun Yat-sen and leader of the Guomindang government in Taiwan for thirty years, had a less-Westernized background and was more interested in traditional Confucian morality.
His New Life Movement advocated four Confucian virtues as a means of strengthening the party and the nation.
He defined the classical moral virtues of propriety (li), righteousness (yi), integrity (lian), and a sense of shame (chi) in modern terms of regulated attitude, right conduct, clear discrimination, and real self-consciousness.
He envisioned a highly disciplined, rational, and frugal lifestyle.
The New Life Movement was a failure in China, but its ideas took new forms in ROC educational and social policies.
There has always been a strong Confucian emphasis in the ideal culture supported by the Guomindang government.
Chiang Kai-shek's political and moral program reflected the Confucian commitment to virtues as the foundation of leadership and government.
The Guornindang Party's early commitment to Confucian culture is seen most visibly in Taipei with its streets renamed for Confucian virtues and its temple celebration for Confucius's birthday on September 28.
Textbooks used to teach language, literature, and social studies contain moral tales illustrating the Confucian values of filial obedience, loyalty, and frugality.
The Confucian orientation of education in Taiwan goes back to the examination system by which Chinese men were trained and tested in classical texts in order to become government officials.
The official examination system entered Taiwan in 1687 and lasted until 1895.
Under Japanese rule until 1945, the educational system in Taiwan was carried out in Japanese language to further Japanese colonial rule.
When the Guomindang Party came into power in 1949, education became a means of inculcating an ideal of reunified Chinese culture.
Teaching was conducted in the Mandarin Chinese dialect, a dialect unknown to most Taiwanese.
Inculcation of loyalty to the Republic of China and its party leaders was accomplished in the schools through civics and history classes and through the rituals of Chinese patriotism with the ever-present ROC flag and portraits of Dr.
Sun Yat-sen, PresidentChiang Kai-shek, and later Presidents Chiang Ching-kuo and Lee Teng-hui.
PresidentLee Teng-hui, the first native Taiwanese president and the first to be directly elected by the people of Taiwan, officially upholds the ideal of reunification with China but also seeks greater democratization and a larger international role for Taiwan.
Although the Three Principles of the People and the reunification of Taiwan with China are official ideology of the Republic of China, they are now being challenged openly and with vigor.
With growing awareness of Taiwanese identity, many Taiwanese express resentment at an educational system that has taught them about the history and geography of an idealized China and nothing about Taiwan and its local culture.
Education and the philosophy of education is in the midst of Taiwanization.
Advocates of education reform seek to change the examination system to make it more compatible with a modern, pluralistic society.
This would include the elimination of testing on the Three Principles of the People," which critics say should not be singled out as the only political thought appropriate to modern Taiwan.
With the lifting of martial law in 1987 and growing freedom for dissent, advocates of Taiwanese culture and Taiwanese independence have become more vocal and more numerous.
The Taiwanese have been embracing their own history and culture, often suppressed by the Guomindang Party to foster a sense of identity with China.
Instead of being viewed as inferior, Taiwanese language and customs are now extolled as the culture of a free Taiwan.
Especially since the first direct presidential elections in 1996, the Taiwanese have great pride in their embodiment of democratic ideals.
Democracy, freedom, and political autonomy are the new ideals for many Taiwanese.
Philosophical interests mirror Taiwanese social changes.
In the 1960s during rapid social change and industrialization, intellectuals and students were drawn to translations of existentialists: Sartre, Camus, Kafka, and Heidegger.
Some philosophers in Taiwan are engaged in the centuries-old effort to synthesize Chinese philosophy, particularly Confucianism, with Western philosophy.
For example, the "contemporary Neo-Confucian synthesis" represented by T'ang Ch?n-Yi and Mou Tsung-san incorporates the idealistic school of Neo-Confucianism (emphasizing texts by Mencius and Wang Yangming) and the German philosophers Kant and Hegel.
New intellectual trends have mirrored new social concerns.
Recently philosophers have begun work on the philosophy of science and technology to understand the place of humanity in the highly technological society in which the Taiwanese now find themselves.
Groups silent in the past have found their voices in the more open political and social environment.
Environmentalists write essays, songs, and poetry in support of a renewed appreciation and concern for the ecological well-being of the island in the face of nuclear energy and naphtha cracker plants.
Feminists such as Lyu Xiulian (Annette Lu) have offered critiques of Confucianism as they involve themselves in Taiwanese politics.
The single voice of the Three Principles of the People has been replaced with a multitude of voices struggling to define moral and political principles for a modern Taiwan.
The renewal of interest and pride in a distinctively Taiwanese culture shows itself clearly in Taiwanese folk religion.
Taiwanese folk religion is the oldest of all religions in Taiwan, with the exception of aboriginal religions.
Folk religion is overwhelmingly the most prevalent of all religious activity.
The early settlers from Fujian and Guangdong brought with them the devotion, rituals, and images of their villages and countryside.
Early immigrant life was rough and difficult.
Religious and intellectual elites were not among the immigrant groups that developed early religious life and folk traditions.
The immigrants' safe arrival in Taiwan was often marked by the building of simple shrines for their protector deities as a means of repaying them for their safe arrival on the island.
Towns formed by immigrants from the same counties created temple traditions that gave them a sense of community in spite of the loss of their older clan traditions.
Some of these temples later became the enormous temple complexes in modern Taiwan.
Many deities worshipped in Taiwan have their roots in traditions found throughout China, such as Guanyin Bodhisattva and Lord Guandi.
The Chinese Buddhist Guanyin Bodhisattva is worshipped not only by Taiwanese Buddhists but also by Taiwanese who see her as a goddess of mercy in the folk tradition.
She is worshipped for saving people from sea wrecks, fires, and illnesses and also for bringing children to women who pray for them.
The god Guandi, originally a general of the Three Kingdoms period, has been known in China as a loyal, brave hero and later as a god.
In Taiwan his status and perceived power have grown; today he is worshipped as a healing god and a patron of businesspeople.
These two figures, Guanyin and Lord Guandi, are the deities most often pictured on the family ancestral shrines.
Most deities of Taiwan are portrayed in religious art and literature with images of bureaucratic power.
Gods and goddesses are emperors, empresses, or appointed bureaucratic or military officials who have received imperial designations of their status.
Gods of folk tradition look like Qing dynasty officials sitting on thrones in their palatial temples.
The Stove God, Zaojun, is one of the lower members of this supernatural hierarchy.
His image is found in homes, especially during the New Year's Festival.
Another popular god, Lord Tudi, sits in small shrines to bring good fortune to the farms, towns, businesses, and surrounding area.
The Jade Emperor is at the top of the hierarchy, but he is not all powerful or by any means the most important of Taiwanese gods: He reigns on high, but other deities are emotionally and practically more significant to the Taiwanese.
Other Taiwanese deities are more local spirits or divinized heroes and saints.
They are valued for their healing powers and for the protection they afford from plagues and natural disasters.
Offerings and promises made to the deities ensure their continued protection.
In Taiwanese folk religion, deities are worshipped primarily because of community membership, but individuals may also choose to worship a specific deity because of his or her reputation for providing protection, good fortune, or healing.
One of the most popular deities of Taiwanese folk religion is the goddess Mazu.
In fact, she is often considered the patron goddess of the island.
According to Taiwanese traditions, Mazu grew up in the tenth century as a pious child on the island of Meizhou in Fujian Province.
When she was just sixteen she miraculously saved her father and brother from a shipwreck.
Miraculous stories of her saving interventions multiplied, and after her death a temple was built in Meizhou.
Worship of Mazu eventually spread throughout the southern coastal fishing and farming villages and onward to the Taiwanese frontier.
She is affectionately called Mazu-po, or "Granny," by the Taiwanese, but she has numerous exalted titles given to her by Chinese emperors.
In 1409 she was given the title Tian Fei, or Imperial Concubine of Heaven.
Then in 1683 she was further elevated to Tian Hou, or Consort of Heaven.
Mazu temples and festivals are the grandest in Taiwan.
One of the Mazu temples in Peikang traces its history back to 1694, when a statue of Mazu from Meizhou was enshrined in gratitude for her protection.
Peikang's annual festival for Mazu attracts more pilgrims than any other religious festival in Taiwan.
Busloads of pilgrims from Taiwanese communities travel to Peikang to bring their Mazu statues home to the mother temple and to participate in feasting, processions, and rituals.
Taiwanese celebrated the 1,000th anniversary of her ascent to heaven in 1987 with an elaborate procession and special rituals.
Since the opening of travel to the mainland, some pilgrims have made religious journeys to the island of Meizhou to worship Mazu at the original mother temple.
Bringing back images and incense from older temples in Fujian has been one way in which Taiwanese temples have increased their stature in relation to competing Taiwanese Mazu temples.
The ties between Fujian and Taiwanese temples is a complex issue in the current discussion of Taiwanese identity.
The connections to Fujianese religions and culture reinforce specifically regional--that is, Taiwanese--culture at the same time that they acknowledge the close cultural ties to China.
Numerous temples in Taiwan are devoted to the Wangye, or Royal Lords--deities who drive away evil spirits and protect against plagues.
There are over 700 registered temples to Royal Lords on the island, but the identities of the "lords" and the rituals honoring them vary locally.
Some folk traditions tell of a lord who died while trying to stop plague spirits from harming people.
The status of Royal Lords in the supernatural hierarchy is low; they are simply ghosts who have been elevated to the status of gods.
Their powers have expanded in Taiwanese traditions to include not only preventing plague (no longer of grave concern) but also bestowing general healing and prosperity.
The Royal Lords festivals involve the destruction of plague spirits by the burning of wooden or paper boats on which the evil spirits have been placed.
In addition to the gods and goddesses, Taiwanese popular religion also recognizes other spirit-beings, particularly ancestor spirits and ghosts.
Gods, ghosts, and ancestor spirits are all closely related to the human realm.
Ancestor spirits are simply the deceased and honored ancestors who contributed to a family's patrilineal line.
The honoring of ancestors is an important part of most family rituals and religious festivals.
One of the highest moral values in Taiwanese society, filial respect (xiao), is ritually expressed through offerings to the ancestors and the maintenance of a family shrine for the ancestors' tablets.
Ghosts are the spirits of dead strangers, particularly the dead who died a violent death or have no descendants to make offerings to them.
Offerings are given to them to prevent them from causing accidents or illnesses.
The seventh lunar month is a period of heightened awareness of the dangers of ghosts because they are allowed out of the underworld to roam freely.
Ghosts are an active part of the popular Taiwanese imagination: They appear in fearsome and humorous forms in folk tales, modern horror movies, and popular television series.
Ancestor spirits and ghosts are similar to the gods of folk religion because the gods are often ancestors or ghosts who eventually became respected and worshipped for their power in a larger community.
The temples and festivals of Taiwanese folk religion are the foundation of Taiwanese folk arts, puppet and opera theaters, and community identity.
Temples are the center of more than just community religious life.
They are a place to socialize over tea, to play chess, or to watch Taiwanese opera during festivals.
The elaborate festivals for the community temple bring the Taiwanese together in ways that celebrate folk traditions and solidify community leadership, hierarchy, and relationships.
Communities strengthen mutual ties by sending representatives to one another's religious processions; representatives include temple leaders, musical bands, statues of the temple's gods, and groups of young men to perform the lion dance.
Grand feasting at Taiwanese festivals celebrates and expresses hope for continued good fortune in the community.
The temple deities are given offerings of pigs, roosters, wine, tea, fruits, and sweets. (Buddhist-related deities and the celibate goddess Mazu are generally not offered meat or wine.) 
An abundance of food is offered to the gods and then eaten with family or a larger group.
This extravagant feasting at festival time or for weddings or other special social events contrasts to the relatively frugal meals of daily life.
Taiwanese folk religion has changed over the past decades.
Temples and festivals have modernized along with society.
Religious processions include elaborate lighted floats with modern nightclub entertainers.
Moreover, the Guomindang government has worked to simplify and regulate Taiwanese folk religion to reduce waste and expense.
In some temples, such as the popular Xing Tian Gong in northern Taipei, the government's influence has been successful in eliminating elaborate offerings of livestock or spirit money.
Also banned are spirit mediums who become possessed by gods and spirits at other temples.
At Xing Tian Gong and other "reformed" temples worshippers seek the healing power of the enshrined god through the burning of incense and the making of petitions.
Blue-robed volunteers assist and advise visitors needing help with the ritual or interpretation of the written fortunes.
The traditional healing arts of China are based on concepts such as yinyang, the five agents, and qi.
Traditional Chinese medicine is also based on several millennia of experimentation.
Yin and yang are polar opposites that exist in all of nature; in the human body these two polar opposites must be in harmony for one to be healthy.
The five elements of wood, fire, metal, earth, and water are symbolic means of ordering the universe and the human body.
The human body is a microcosm of the universe, containing the movement of yin and yang and the five major internal organs associated with the five elements.
In the human body it is qi, the life force, that circulates and animates the body; it is the breath as it moves through the respiratory and circulatory system.
Chinese medicine brings order and harmony to a body out of balance and maintains balance for the healthy.
The practice of Chinese medicine goes back to traditions recorded in texts beginning in the third century B.C.: the Nei jing (ca.221- 207 B.C.).
And it is heavily dependent on the influential Ben cao gang mu by Li Shizhen of the Ming dynasty (A.D.1368-1644).
Based on this ongoing tradition, herbalists in Taiwan prescribe medicine to treat imbalances in their patients, usually by combining plant and animal products into a mixture made into a tea and ingested.
Two other means of bringing harmony back to an unhealthy body are acupuncture and moxibustion.
Acupuncturists insert slender needles in order to stimulate qi within the body and harmonize the yin and yang energies.
Today the acupuncturists often use mild electrical current at the acupuncture points instead of puncturing the skin in order to achieve the same effect.
Moxibustion is based on similar principles; it is the technique of burning Chinese mugroot at certain points along the channels through which the qi flows.
Modern Western medicine is widely available in Taiwan.
Some Taiwanese continue to value traditional medicine for its ability to maintain health and its gentle means of restoring balance.
At the same time many Taiwanese view modern medicine as a necessary means of dealing with major health problems requiring surgery, though often too harsh for less serious and chronic conditions.
The Taiwanese concern for good health is also evident in the resurgence of interest in the practice of qigong and the martial arts.
In qigong practitioners direct the movement of their qi in order to heal themselves and increase their energy.
Someone who has very strong qi is able to use it to heal others as well.
Qigong is taught through self-help books, in classes, and even on television.
Chinese martial arts make use of the understanding of yin-yang and qi to strengthen and discipline the body and mind, as well as to prepare for the defense of the person.
There are some 100 different kinds of martial arts (guo-gong).
The gentle art of taijiquan, popular with both the young and the old in Taiwan, is now taught at Chinese Culture University and is part of the Asian Games.
One often sees its practitioners in the early morning in parks or on temple grounds exercising for good health and emotional calm.
Traditional medicine, taijiquan, and qigong are health practices derived from traditional religious and philosophical concepts.
They can, though, be practiced independently of one's religious worldview; indeed, these traditions are increasingly used in the West as alternative medical treatments that are gaining increased attention from the Western medical community.
Although truly religious means of healing have declined in popularity with the increased accessibility of modern medicine, they continue to be popular for chronic or incurable conditions.
Offerings are made to gods and goddesses known for their healing power along with petitions for help.
When healing does occur, the worshipper returns to the temple or shrine with more offerings in order to bao en, or repay the deity for its help.
Those who worship the Buddhist Guanyin Bodhisattva typically seek her healing power by chanting her name a specified number of times.
To repay her for her help, a follower may continue chanting or perhaps become a vegetarian for a period of time.
Taiwanese gods and goddesses are known for more than just physical healing.
Individuals seek divine power to protect their children from harm, to become pregnant, to do well on the college entrance examinations, and to prosper financially.
Religion provides much in the way of folk psychology.
In Taiwan, religion continues as a popular means of self-help and therapy despite rapid Westernization in most other areas.
Western therapeutic models that require revealing personal and family problems to a stranger are quite at odds with Taiwanese family ideals.
Religious books on self-cultivation through the Book of Changes, through various forms of meditation, and by traditional divination techniques remain popular.
Fortune telling thrives in various traditional forms.
Fortune telling is not primarily about predicting the future but, rather, about understanding the factors that influence a person's life and must be understood if one is to make decisions in harmony with the conditions of one's life.
Some experts base their conclusions and personal advice on facial physiognomy.
Others rely on Chinese astrology to interpret the influence of a person's exact time of birth on his or her present circumstances.
In temples, visitors throw crescent-shaped, red divination blocks in order to receive from the gods answers to their questions.
Then a numbered stick may be chosen to determine which numbered fortune should be applied to their problems.
The printed text of these "fortunes" is ambiguous, classical Chinese that can be freely applied to questions of marriage, illness, relocation, or a troubled child.
Temples have professional interpreters or volunteers who assist in the reading and application of the printed text.
For those inclined to high-tech advice, modernized divination appears in the form of computer software of the Book of Changes or astrological texts.
Holidays and community festivals have long provided a celebratory break in the busy lives of the hardworking Taiwanese.
On such occasions the traditionally frugal Taiwanese feast and party while reinforcing family structures and community ties.
Calendrical and temple festivals have also supported many of Taiwan's folk arts, namely, Taiwanese opera and puppet theater, specialty foods, and crafts.
In recent years the popularity of movies and television has threatened the survival of Taiwan's performing traditions, but operas and puppet theaters have learned that if you can't fight television, you can be on television.
The Taiwanese celebrate three kinds of festivals.
First are the official government commemorative holidays based on the modern solar calendar.
These mainly honor important events and leaders of the Republic of China.
Second are the major lunar festivals known and celebrated by most Taiwanese.
Third are the community-based or temple-based celebrations of the birth of regional goddesses or gods, the ascension of temple deities, or other regional or ethnic religious events.
The Republic of China's commemorative days are honored by closing of schools and government offices and by often extravagant government parades and decorations.
These days honor the founding fathers and historical events of the Guomindang and the Republic of China.
In the past these days were an occasion to demonstrate the prestige of the Guomindang Party and the power of the Republic of China's military.
Since the late 1980s, however, the military significance has lessened.
Moreover, new political pluralism and Taiwanization movements have raised questions about the propriety and significance of these commemorative days.
One might even see the February 28th Incident ceremonies, commemorating the 1947 uprising against the Guomindang in Taiwan, as a new unofficial commemorative day honoring a long-repressed tradition of political dissent.
The first commemorative day of the year is January 1, Founding Day of the Republic of China.
The day marks the anniversary of the inauguration of Sun Yat-sen as the provisional president of the Republic on the first day of 1912.
Flags, banners, and portraits of Sun Yat-sen and Chiang Kai-shek are displayed throughout the island, but particularly in the area around the Presidential Office building in downtown Taipei.
Bands play the national anthem, which extols the "Three Principles of the People" formulated by Sun Yat-sen.
Double Ten National Day marks the Republic of China's birth at the uprising in Wuchang on October 10, 1911.
The day is marked by political and cultural celebrations.
The central events in the capital city of Taipei are ceremonies, parades, and the presidential address at the Presidential Office building.
Parades and evening ceremonies present traditional Chinese and ethnic dancers, school bands, folk artist performers, and other entertainment.
The day no longer conveys the strong militaristic image of earlier decades, however.
Instead it celebrates in fireworks, music, and dance a successful capitalist island.
The birth of the Republic of China in Taiwan is marked by Taiwan's Retrocession Day, officially extolled on October 25 to commemorate the end of Japanese rule in Taiwan in 1945.
Armed Forces Day is celebrated on September 3 to honor all armed forces, whose divisions had their own separate days of honor until they were combined in 1955.
The day is meant to honor the Chinese who fought against the Japanese in World War II.
Youth Day, March 29, began as Revolutionary Martyrs Day.
Like Armed Forces Day, it commemorates soldiers, in this case especially the seventy-two soldiers led by Huang Xing who died in the 1910 Canton Uprising against the Qing government.
On this day the president of the Republic of China officiates at a service for all soldiers at the Martyrs of the Revolution Shrine in Taipei.
As the war recedes in memory, the day has become more a celebration of youth.
December 25 is an official government holiday in Taiwan.
The official reason is to celebrate not Christmas but Constitution Day, designated as such in 1963 to honor the completion of the constitution by the Constitutional Convention after the war.
That a government holiday conveniently falls on Christmas reflects the Christianity of many prominent Guomindang leaders.
In practice, for most Taiwanese December 25 is just part of the New Year's period.
Thus Christmas decorations in hotels and department stores add to the New Year's festive atmosphere.
Two of the government commemorative days, Women's Day on March 8 and Children's Day on April 4, were inspired by international efforts to improve the well-being of women and children.
Women's Day follows the international designation by the International Women's Conference in Denmark in 1910 to raise women's issues through an international day for women.
Children's Day was inspired by the 1925 conference on children in Switzerland, which urged countries to designate a day for children.
In Taiwan, the April 4 holiday promotes children's activities and honors model students.
The government honors Confucius and all teachers on the September 28 commemoration of Confucius's birthday, Teachers' Day.
The day obviously emphasizes the important Confucian value of education and respect for teachers.
It also serves to symbolize the government's commitment to represent true Chinese culture, especially in its elaborate ritual celebration at dawn in the Confucius Temple.
The elaborate ritual includes traditionally clothed dancers, ancient ceremonial music, and the traditional sacrificial offering of an ox, a pig, and a goat.
The colorful religious festivals in Taiwan follow the Chinese lunar calendar.
By far the most important are those festivals centering on the Chinese New Year.
New Year's Festival or Spring Festival is a celebration of family past and present and an expression of hope for family prosperity and good fortune.
Unlike some East Asian nations, Taiwan has clung to the traditional lunar celebration of the New Year, rather than transfer the festival into a more "Western" or "modern" event based on the Western solar calendar.
The festivities embody values too close to the Taiwanese heart--family, prosperity, and tradition--to be switched to the solar calendar.
Prior to New Year's festival itself, the Ascension of the Kitchen God on the twenty-fourth day of the twelfth lunar month begins the traditions associated with the coming of the new year.
Taiwanese traditions say that on this day the Kitchen God of every home reports to the Jade Emperor about the activities of the family.
To ensure a positive report on the family, members offer meat, sweet rice balls, and spirit money to the god before he ascends to heaven.
Preparations for New Year's festivities are hectic.
People send out New Year's cards to friends and business associates.
Families do a thorough spring cleaning to prepare for the new year.
On the doorways of homes and businesses, vertical red banners express auspicious sentiments for the New Year.
Shoppers stock up on the traditional cakes and snacks for New Year.
Taiwanese eat peanuts, which symbolize long life; melon seeds, which symbolize many descendants; and perhaps red dates, which express a woman's desire to give birth to a healthy child.
Shopping must be done ahead of time because many shops close for several days or even weeks during the New Year's period.
Preparations also include paying off current debts so that the family may begin the year with a rosy financial picture.
In Taiwan New Year's Eve is a time for family.
In the afternoon many Taiwanese worship their ancestors by offering them wine and special New Year's cake.
Family members place on their family altars offerings of cooked food, fresh fruits, flowers, pastries, spirit money, and incense.
Worship of ancestors, gods, and Buddhas is followed by the noisy sendoff of firecrackers.
If possible, all Taiwanese try to return home to share the last evening meal of the year.
If someone cannot return because he or she is working or studying far away, then a place is left empty at the table in remembrance.
This ritual meal consists of favorite foods full of symbolic meaning.
A platter of fish (yu), for example, represents the hoped-for abundance (yu) of the coming year because the two words sound alike.
To ensure the abundance, no one will eat the last of the fish.
After dinner older members of the family distribute red envelopes (hongbao) filled with money to the excited junior members of the family.
This ritual giving of wealth to the younger generation not only is great fun for children but is also said to ensure the family's future prosperity.
Some even say that the giving of red envelopes encourages reciprocity and filial piety in the younger generation, who will be expected to show respect for their elders and give them financial support.
New Year's is great fun.
Taiwanese families spend New Year's Eve playing mahjong or other games, snacking on nuts and seeds, watching television specials, and exploding firecrackers.
Loud explosions are meant to drive away evil spirits, but many boys love lighting the long strands of red and gold firecrackers for sheer joy.
The Taiwanese may stay awake until dawn of New Year's Day as part of a New Year's vigil traditionally meant to ensure the longevity of one's parents.
New Year's Day itself is full of more festivities: lighting firecrackers, making offerings to ancestor spirits and gods, playing games, and watching endless television specials.
The Taiwanese give each other greetings for a prosperous New Year.
They take great care to avoid inauspicious words or actions that might start the year off with bad luck.
Death should never be mentioned, nor should articles associated with death be present.
The spirit of New Year continues until the Lantern Festival on the fifteenth day of the first lunar month.
Taiwanese families enjoy making riceflour dumplings as offerings to their ancestors and as delicious treats for themselves.
The evening glows with brightly decorated lanterns and fireworks.
The temples of Taiwan have long hung lanterns on this evening.
Lanterns have traditionally been made of paper and bamboo, but the newer electric versions use a variety of materials.
Colorful lanterns combine traditional folkcrafts and designs with modern creativity.
Often, decorative themes come from Chinese legends like the story of the White Snake or historical stories such as the Romance of the Three Kingdoms.
The most popular animal depicted is usually the zodiac sign for the new year.
The largest lantern competition is now held at the spacious plaza at Chiang Kai-shek Memorial Hall, but in southern Taiwan the Tainan Yanshui Fireworks Display attracts the largest crowds.
The First Head Feast (second month, second day) and the Final Head Feast (twelfth month, sixteenth day) both honor the hugely popular Tudi Gong, the local earth god present everywhere in Taiwan.
The First Head Feast is often seen as a birthday celebration for Tudi Gong.
He is offered meat and spirit money to ensure his blessings and protection.
On this day of their patron deity, business owners may provide a banquet for their workers.
At the Final Head Feast banquets of the past, business owners fired an employee by pointing the head of a rooster in his or her direction.
In general, the end of the year is the time for settling business accounts and making arrangements for the obligatory, and often quite generous, New Year's bonuses.
The Tomb-Sweeping Festival marks the day when the Taiwanese pay respects to their dead ancestors at their gravesites.
Traditionally Tomb- Sweeping Day has been marked as the 105th day after the winter solstice.
But this day is one festival that has been modernized and clearly politicized.
This festival is now celebrated on April 5, which is the date of Chiang Kaishek's death in 1975.
There have always been subethnic variations in the timing of this ancestral ritual.
The Taiwanese from Zhangzhou, Fujian, have traditionally swept their ancestors' tombs on Tomb-Sweeping Day itself, whereas Quanzhou descendants have traditionally swept just before the festival.
The Hakka people allow a long period during which to sweep their ancestors' graves, because their long migration on mainland China required them to be flexible if they were ever to return north to their ancestors' tombs.
The "sweeping" of the tomb means removing weeds and repairing the grave.
Family members burn incense offerings to Tudi Gong, who protects the grave, as well as to their ancestors.
Pieces of gold or multicolored paper are weighted down on the gravesite to offer ancestors spirit money and to show the community that the family's ancestors are well cared for.
The Dragon Boat Festival (fifth lunar month, fifth day) is unusual because it commemorates a historical person, Chu Yuan, the poet and minister of the ancient state of Chu.
According to tradition, Chu Yuan threw himself into a river out of despair about the state of political affairs.
Friends and subordinates threw offerings of food wrapped in bamboo leaves into the river to appease his disturbed spirit.
Today the Taiwanese honor his memory by eating rice and meat dumplings wrapped in bamboo leaves (zongzi) and competing in dragon boat races.
The Birthday of the Seventh Goddess is celebrated on the seventh day of the seventh month.
This goddess protects children under age sixteen.
In a sense this day marks transition to adulthood as children leave the goddess's protection on this day.
Some families may burn paper pavilions for the Seventh Goddess to thank her for protecting their children who turned sixteen in the previous year.
The day is also celebrated as a "Chinese Valentine's Day" because it celebrates as well the romantic legend of the Cowherder and the Weaving Girl, now constellations, reunited once a year on this day when birds form a bridge to reunite the couple.
There exist countless versions of this folk tale.
In one, the Weaving Girl was the beautiful daughter of the Emperor of Heaven, who married her to the handsome Cowherd.
They fell so deeply in love that the Weaving Girl failed to make garments for the deities of the heavens and the Cowherd failed to milk the cows of heaven.
When the gods complained, the Emperor of Heaven responded by separating the loving couple except for this one day of the year.
The seventh month is the Ghost Month, a great danger to those who believe in the wandering ghosts who venture forth during this period.
The seventh month is a time to avoid starting a new home, a new business, or especially a new marriage to someone who might turn out to be a ghost.
Even the most educated of Taiwanese expressed surprise and disbelief when we packed up our belongings to move from Taipei to Tainan during this month.
On the first of the month the gates of the netherworld are opened to release the spirits for a month of wandering.
Taiwanese families and temples make offerings to the wandering spirits to encourage them to keep moving rather than to stop and bother them.
The fifteenth of the month is the day to make offering to the spirits of one's own ancestors and to release floating lanterns.
On the last day of the month, many Taiwanese set out offerings of food, beer, and cigarettes for the spirits as they return to the netherworld.
Not surprisingly, all these offerings are gone by the next morning.
The Mid-Autumn Festival (eighth lunar month, fifteenth day) is simply a beautiful, contemplative festival.
On mainland China the day used to be a celebration of the end of the harvest, but in Taiwan today the agricultural season extends almost throughout the year.
Now the modern Taiwanese primarily admire the beauty of the full moon on this night.
Families honor their ancestors and gods with offerings, but the focus is on the enjoyment of viewing the moon and eating varieties of the treat specific to this festival-- moon cakes.
The love of the moon is expressed in several legends retold by the Taiwanese.
One story tells of the beautiful Chang O and her husband Yi, the famous archer who saved the world by shooting down nine of the ten suns that appeared in the sky one day.
The Queen Mother of Heaven rewarded Archer Yi by giving him an elixir that would confer immortality.
The beautiful but greedy Chang O stole the elixir and swallowed it.
She attained immortality, but she also found herself ascending to the moon, where she has lived forever after.
Chang O lives in a beautiful jade palace full of dancing fairies--a lovely but rather bittersweet image to see while contemplating the full moon.
Numerous other lunar festivals are celebrated.
The Double Nine Festival (ninth month, ninth day) is a day for hiking and ancestor worship; it is also traditionally a time for flying kites.
A traditional holiday figured according to the solar calendar, the Winter Solstice is celebrated with red and white rice balls offered to the ancestors and to the gods (and then, of course, eaten).
In addition to traditional lunar festivals celebrated, or at least acknowledged, by most Taiwanese are numerous festivals celebrated by communities in honor of their deities.
These festivals, known as baibai, are the liveliest, loudest celebrations held by Taiwanese towns, villages, and even urban communities.
One of the biggest community festivals is truly an islandwide celebration of Taiwan's patron goddess Mazu.
The baibai celebrates the birthday of this saving goddess on the twenty-third day of the third lunar month.
This is the most important Taiwanese festival honoring a particular god or goddess.
Festivals and processions are held throughout the island, but the destination of the most devout Mazu followers is the gigantic festival in Peikang.
Representatives of hundreds of temples carry their goddess statues in palanquins to Peikang's central Mazu temple.
One might consider this festival with its ritual offerings, folk operas, noisy processions, and spirit mediums as the height of authentic Taiwanese religious consciousness.
Growing numbers of Taiwanese Buddhists honor their tradition on special Buddhist festival days.
Their most important festivals celebrate the birthdays of Sakyamuni Buddha and Guanyin Bodhisattva.
The birthday of Sakyamuni Buddha, the historical Buddha, is honored on the eighth day of the fourth lunar month.
At the Longshan Temple in Taipei, a golden statue of the Buddha is brought out and washed in dew water on this day.
Buddhists celebrate the birthday of the Bodhisattva of compassion, Guanyin, on the nineteenth day of the second lunar month and repeat the celebrations on the nineteenth day of the sixth and ninth months.
Celebrations are held at the Longshan Temple and in Guanyin temples all over the island.
Moreover, every local temple has a special festival.
Subethnic groups celebrate their own special days and hold festivals unique to their own communities.
The Hakka, the largest minority group in Taiwan, celebrate festivals for the yimin, or "righteous citizens," on various days to commemorate Hakka heroes who died in battles for which they were frequently recruited by Qing troops.
Twenty-one yi-min temples in Taiwan honor the remains of Hakka fighters involved in the Zhu Yigui Rebellion (1721), the Lin Shuangwen Rebellion (1786), and the Dai Zhaozun Rebellion (1862).
The yimin festival in Hsinchu features elaborate processions, Hakka music, and the honoring of "King Swine," the winner in the competition to raise the largest pig as an offering to the yimin.
Many festivals in Taiwan are celebrated on a very particular day, according to the lunar calendar.
Such festivals include the New Year, the Dragon Boat Festival, Qing Ming (sweeping of ancestral grave sites), and the Mid-Autumn Festival.
These occasions are observed throughout Taiwan.
An islandwide spirit unites people across demarcations of village, township, and county, as well as across the boundaries of family, lineage, clan, and ethnic group.
There are other occasions, though, that are particular to certain villages or that are celebrated at different times from village to village.
The Ghost Festival is a traditional festival and holiday, which is celebrated by Chinese in many countries.
In the Chinese calendar , the Ghost Festival is on the 15th night of the seventh lunar month.
In Chinese tradition, the fifteenth day of the seventh month in the lunar calendar is called Ghost Day and the seventh month in general is regarded as the Ghost Month , in which ghosts and s, including those of the deceased ancestors, come out from the .
During the Qingming Festival the living descendants pay homage to their ancestors and on Ghost Day, the deceased visit the living.
On the thirteenth day the three realms of Heaven, Hell and the realm of the living are open and both Taoists and Buddhists would perform rituals to transmute and absolve the sufferings of the deceased.
Intrinsic to the Ghost Month is ancestor worship, where traditionally the filial piety of descendants extends to their ancestors even after their deaths.
Activities during the month would include preparing ritualistic food offerings, burning incense, and burning joss paper, a papier-mache form of material items such as clothes, gold and other fine goods for the visiting spirits of the ancestors.
Elaborate meals would be served with empty seats for each of the deceased in the family treating the deceased as if they are still living.
Ancestor worship is what distinguishes Qingming Festival from Ghost Festival because the former includes paying respects to all deceased, including the same and younger generations, while the latter only includes older generations.
Other festivities may include, buying and releasing miniature paper boats and lanterns on water, which signifies giving directions to the lost ghosts and spirits of the ancestors and other deities.
The Ghost Festival shares some similarities with the predominantly Mexican observance of ''''.
Due to theme of ghosts and spirits, the festival is sometimes also known as the Chinese Halloween, though many have debated the difference between the two.
﻿Researchers are using gene microarrays to understand what makes young skin taut and even-toned, and to find out why older epidermis sags, wrinkles, or develops spots. 
They can then hunt for molecules that activate those same genes in aging or sun-damaged skin. 
A number of skin-care lines, such as Olay products, already contain ingredients based on microarray work.
Another way makeup makers develop new products is to examine the metabolism of skin cells. A healthy cell, with active mitochondria, is a comely cell. 
Old cells with fewer or malfunctioning mitochondria look aged. Modern devices can infer metabolic rate based on oxygen consumption and the acidic by-products released by cell cultures into the media in which the cells are grown.
Cultured skin is crucial to in vitro studies of personal-care products. One of the best mimics for the human integument is, not surprisingly, made of human skin. 
Epidermal cells from elective surgery, dissociated and reconstituted, are common in cosmetics labs where scientists daub on new compounds and test the safety of different formulations.
In fact, these days, product safety testing often takes place in a dish. 
Rarely do the methods involve using animals, which has been out of vogue for decades, notes Michael Ingrassia, senior manager for skin biology and drug delivery at Dow Pharmaceutical Sciences in Petaluma, California, where he works on the CeraVe moisturizer line. 
In fact, the desire to limit animal experimentation has driven the development of many modern cosmetics testing methods.
One common goal among cosmetics researchers is to make skin appear fresh and young. 
Signs of aging vary depending on skin tone. Light-skinned consumers are looking to minimize wrinkles, the earliest sign of aging in people with low levels of melanin in the skin. 
Buyers with dark, melanin-filled skin frequently seek products to diminish the darkened spots that appear in their skin before wrinkles.
To develop creams and other anti-aging remedies, researchers working at personal-care product companies use many of the same tools as their counterparts in basic research and pharmaceutical sciences. 
Here, The Scientist profiles four modern methods behind the makeup.
Gene expression microarrays are common tools in the personal- care products industry. 
In fact, Procter & Gamble (P&G) is one of the largest users of Affymetrix gene chips, processing tens of thousands of chips per year, says Jay Tiesman, leader of P&G’s genomics group, based in Mason, Ohio. 
Cosmetics and pharmaceutical industry users may want to look for desirable transcriptome profiles, and then scan chemical libraries for compounds that push cells in that direction.
“Instead of just poking around in the dark—trying ingredients and seeing which ones work—[microarray technology] gives us a real, fundamental understanding of skin biology,” Tiesman says. 
For example, the team has compared old and young skin samples from women’s forearms, where sun damage is an issue, and their buttocks, where it likely is not (Br J Dermatol, 166 Suppl 2:9-15, 2012). 
They discovered that a lipid biosynthesis pathway, involving the production of cholesterol, was downregulated with age and sun exposure. 
The work led to the inclusion of hexamidine, which boosts this pathway, in the Olay Professional Pro-X products.
Skin biopsies are removed from the face or other locations, via scraping with a razor or scalpel, or as a round cutout taken by a small needle or a punch device a bit like a long cookie cutter. 
Researchers isolate the RNA and hybridize it to fingernail-size glass chips holding millions of nucleic acid probes. 
Tiesman’s lab runs so many chips they use a high-throughput, 96-chip format.
Researchers isolate the RNA and hybridize it to fingernail-size glass chips holding millions of nucleic acid probes. 
Tiesman’s lab runs so many chips they use a high-throughput, 96-chip format.
Gene chips provide an unbiased approach. It’s important to be open-minded about where the data might lead you, advises William Swindell of the University of Michigan in Ann Arbor, who uses chips to study psoriasis.
As it’s an established technology, there are plenty of publicly available microarray data to compare to yours.
Gene chips only include probes for known mRNAs; they can’t help you discover new variants.
Skin samples will include more than one cell type, complicating analysis.
“Find a good statistician, and listen to him or her from the beginning,” Tiesman says. Statisticians’ input is important in both study design and analysis.
In addition to the details of gene expression, cosmetics scientists want to understand the ins and outs of cellular metabolism. 
Aging cells slow down metabolically, says Mary Johnson, a principal scientist at P&G. “The overall aging that we experience, a lot of it’s driven by cellular energetics,” she says. 
Ingredients in anti-aging creams and sera promote a healthy metabolic rate, which translates into a youthful appearance.
Johnson uses the Seahorse XF Extracellular Flux Analyzer to monitor cellular metabolism as she investigates new skin-care ingredients. 
Seahorse’s analyzer has replaced tedious experiments in which researchers had to isolate individual mitochondria or use special electrodes to measure oxygen consumption. 
The ease of the Seahorse system ignited an explosion of studies into cellular bioenergetics in the past decade, Johnson says.
The XF analyzer measures two features of metabolic rate via sensors submerged in each well of a disposable 24- or 96-well microplate cartridge. 
It tracks the production of acidic lactate, the final step of glycolysis, as well as the uptake of oxygen as the cell respires. Together, these measures serve as a proxy for ATP production.
Compounds that promote respiration and glycolysis will cause the cells to increase their metabolism and might make skin cells healthier and more attractive.
Make sure cells form a nice monolayer, recommends Eugenia Trushina, who uses the XF Analyzer to study mitochondrial dysfunction in disease at the Mayo Clinic in Rochester, Minnesota. Clumps or empty spots will interfere with good results.
In vitro models of the epidermis are another common tool in the cosmetics industry. 
MatTek Corporation of Ashland, Massachusetts, markets several versions of skin in a dish. 
“This is the only human organ that can be reconstituted in vitro with not too much difficulty,” says Meenhard Herlyn, a tumor biologist who makes his own reconstructed skin cultures for studies of melanoma at the Wistar Institute in Philadelphia, Pennsylvania.
In cosmetics labs, researchers can use the cultures to test how well creams or sera penetrate the skin, by measuring the quantity of ingredients that reach the liquid media on the underside of the skin culture. 
Scientists can also experiment with skin-lightening agents and can check the toxicity of a new formula without using animals. 
A typical toxicity test, for example, involves the application of a tetrazolium dye to skin or other cell cultures. Active, healthy cells change the dye’s color, which is observed after extracting the dye from the treated tissues.
For Herlyn, reconstructed human skin is a crucial tool because no other animal has skin like ours except for pigs—and those are rather large, expensive animals to work with. 
Herlyn makes his cultures out of infant foreskin that has been removed during circumcision. 
He mixes keratinocytes and melanocytes, and cultures them over a base of collagen and fibroblasts. 
When he exposes the culture to air, the keratinocytes move up while the melanocytes remain below, forming a realistic 3-dimensional tissue.
The melanocytes then pump pigment toward the surface, with a good bit of light exposure, “you can give them a real mean tan,” Herlyn says.
If you’d rather buy the skin premade, MatTek manufactures its models from dissociated infant foreskin or adult skin removed during breast reductions and tummy tucks. 
“It’s a thinner, smaller version of human skin,” says Ingrassia, who used the company’s models regularly when he worked for Est?e Lauder.
Cosmetics companies want their products to be as mild and nonirritating as possible. 
The Cytosensor microphysiometer was the first machine of its kind to be tested and approved by US regulators to analyze eye irritation potential, replacing animal testing with assays of cultured mouse fibroblasts. 
The instrument is basically “a fancy pH meter,” says Warren Casey, acting director of the US National Toxicology Program’s Interagency Center for the Evaluation of Alternative Toxicological Methods in Research Triangle Park, North Carolina.
The microphysiometer doesn’t work for hydrophobic materials, which include many cosmetics. 
Therefore, the device is often best suited to test individual ingredients in solution, rather than complete formulations, says Hans Raabe, director of laboratory services at IIVS. 
The microphysiometer is only recommended to identify compounds at the extremes of the irritation scale—either highly irritating or not at all—and can’t distinguish between mild and moderate irritants.
While the Cytosensor is the only device of this type currently approved, Casey would eventually like.
The Cytosensor microphysiometer measures cellular pH to determine the potential of a given substance to irritate the eye. 
Cells are pipetted into 12-mm wells (top), which then fit into the Cytosensor chamber (bottom). 
When normal acid production drops by half, the treatment is considered to have a high degree of irritation.COURTESY OF IIVS, 2013that work similarly, such as the XF Analyzer.
Peptides found in skin can act by different mechanisms of action, being able to function as epidermal or nervous growth factors or even as neurotransmitters. 
Due to the vast functionality of these compounds, there is growing research on bioactive peptides aimed at investigating their uses in products developed for stimulating collagen and elastin synthesis and improving skin healing. 
Thus, a literature search on applications of the most common bioactive peptides used in cosmeceuticals was carried out. 
There is a lack of proper reviews concerning this topic in scientific literature. 
Nine peptides with specific actions on body and facial dysfunctions were described. It could be noted while searching scientific literature that studies aimed at investigating peptides which prevent aging of the skin are overrepresented.
This makes searching for peptides designed for treating other skin dysfunctions more difficult. 
The use of biomimetic peptides in cosmetic formulations aimed at attenuating or preventing different types of skin dysfunctions is a topic where information is still lackluster. 
Even though research on these compounds is relatively common, there is still a need for more studies concerning their practical uses so their mechanisms of action can be fully elucidated, as they tend to be quite complex.
Techniques aimed at extracting, isolating, characterizing, and synthetizing molecules, as well as techniques for the advanced study of molecular structures, were greatly improved after the second half of the last century.
As a result, bioactive peptides or biomimetic peptides were brought to light in the current knowledge. 
Several peptides with remarkable biological activity were synthetized in the last few years. 
They can be used in areas ranging from cosmetics, therapeutics, and immunology to even food sciences. 
Peptides account for 10% of the sales of pharmaceutical companies, which amounts to US$ 25 billion; the commercialization of peptides has been increasing faster than that of small molecules. Up to 2017, the global cosmeceutical market was estimated to have generated around US$ 42.8 billion
In this review, emphasis was given to synthetic peptides that are used in cosmeceutical formulations, which can be named either as bioactive, biomimetic, or topical peptides.
Peptides are short chains of amino acids. 
Some occur naturally in the human body and are known for playing several biological roles, especially as signaling/regulating molecules in a variety of physiological processes, including defense, immunity, stress, growth, homeostasis, and reproduction. 
As examples, there are vasodilators, vasoconstrictors, and other substances that act upon cell metabolism
Biomimetic peptides, on the other hand, are compounds which have an identical amino acid sequence to physiological peptides (oligopeptides with a sequence ranging from 10 to 15 amino acids), but are synthetized biotechnologically. 
They mimic the action of growth factors and cytokines by interacting with their receptors, leading to clinical effects such as the slowing of aging.
 Examples of biomimetic peptides include acetyl decapeptide-3 (Rejuline), oligopeptide-24 (CG-EGP3), oligopeptide-34 (CG-TGP2), and oligopeptide-72 (Boostrin).
Bioactive or topical peptides are also synthetic compounds, but they consist of modified amino acid chains, which improve an already existing physiological function, such as increasing skin permeability, stability, solubility, and better interaction with cell receptors. 
Also, several natural physiological processes are signaled and modulated exclusively by interaction with specific amino acid sequences found in certain peptides and protein fragments. 
Thus, in a technological context, bioactive peptides are becoming increasingly promising as cosmeceuticals with clinical applications in different skin conditions.
The multiple applications of these synthetic compounds, either biomimetic or bioactive, provide treatment options when used in formulations designed for topical applying, preventing, or attenuating the clinical aspects of skin damaged by dysfunctions: aging, hyperpigmentation, increase of body fat, and wrinkle development. These peptides can also stimulate the synthesis of collagen and elastin, improve wound healing, increase fibroblast proliferation, and act as growth factors  or even as tensioning and tightening agents.
Bioactive peptides can be classified according to their mechanism of action in signal, carrier, and neurotransmitter inhibitor peptides.
The goal of this study was to carry out a literature review on bioactive peptides aiming to describe some of their mechanisms of action and possible applications in cosmetic products. 
This review might contribute to future research by summarizing relevant information and making it readily available for professionals and researchers interested in the subject.
Signal peptides have a structure which can be divided in three different domains: a positively-charged amino-terminal domain (region n, 1–5 long residues); a hydrophobic central domain (region h, 7–15 residues); and a polar carboxyl-terminal domain (region c, 3–7 residues). 
This class of peptides is very important due to its ability to open protein channels which allow the translocation of synthetized proteins to their specific site of action.
Vastly used as active compounds in products aimed at preventing aging, signal peptides are a class of peptides capable of stimulating skin fibroblasts, leading to an increased production of collagen and elastic fibers. 
They can also act as growth factors, as they activate protein kinase C, which is the major factor responsible for cell growth and migration. 
Such stimulus occurs whenever the peptide has its amino acids aligned in a specific pattern, as is the case with the sequence valine-glycine-valine-alanine-proline-glycine (VGVAPG), commercially named palmitoyl oligopeptide?, as elastin-derived peptides bind to cytoplasmic fibroblast receptors.
Heptapeptide Acetyl-DEETGEF-OH (Perfection Peptide P7TM), a signal peptide, acts by protecting cell DNA by stimulating Nrf2-dependant antioxidant enzymes.
 This heptapeptide is a competitive inhibitor of the repression factor Keap1 which acts directly upon transcription factor Nrf2, responsible for scavenging free radicals. 
Water in oil emulsions developed with the aid of nanotechnology can contain a very minimal amount of the peptide (0.0014%) initially dispersed in shea butter.
 After exposure to ultraviolet radiation (UV) for two hours, Langerhans cells were reduced in size and more cells were “sunburned” in the skin of subjects treated with placebo.
 In comparison, subjects that had the formulation containing the heptapeptide had the depletion of skin cells reduced by 6%, and the DNA damage of the affected cells was reduced by 20%. 
This study aimed to prove the efficacy of the formulation, and was carried out by Suter and collaborators (2016). 
It is commercialized by Mibelle Biochemistry Group and its INCI (International Nomenclature of Cosmetic Ingredients) name is acetyl sh-heptapeptide-1 (and) hydrogenated lecithin (and) glycerin (and) Butyrospermum parkii (shea) butter (and) phenethyl alcohol (and) ethylhexylglycerin (and) aqua.
Oligopeptide-68 (β-WHITE?, sequence Arg-Asp-Gly-Gln-Ile-Leu-Ser-Thr-Trp-Tyr) is a whitening agent used in cosmetics on skins affected by melasma.
 It inhibits the actions of microphthalmia-associated transcription factor (MITF), a regulator of melanocyte differentiation, by reducing its tyrosinase activity and “slowing down” key enzymes of the pigmentation process. 
As a signal peptide, it interacts well with cells when used at concentrations of 1.0–2.5%. 
Studies were carried out comparing the effects of hydroquinone (HQ) and of oligopeptide-68 associated with diacetyl boldine (DAB), a compound capable of stabilizing tyrosinases.
 Different oil in water emulsion formulations (one containing HQ and another containing peptide and DAB) were applied to the skin of 40 volunteers; the subjects were assessed on the 6th and 12th week of treatment after controlled exposure to radiation. 
The clarifying effects were considered significant, moderate, or slight for 2.6%, 76.3%, and 21.1% of the subjects treated with the formulation containing the oligopeptide, respectively, and these values are higher than those observed in subjects treated with creams containing HQ at concentrations of 2% and 4%. 
This whitening agent is becoming a frequent subject of study in the research and development of cosmetics.
 It is commercialized by Biotec, part of the AQIA Industrial Chemistry group, under INCI name water (and) butylene glycol (and) hydrogenated lecithin (and) sodium oleate (and) oligopeptide-68 (and) disodium EDTA.
Adipocytes found in the hypodermis contain a high amount of lipids, and each is connected to a number of capillary vessels. 
When hyperplasia and hypertrophy of such adipocytes occur, remodeling of the surrounding capillaries and neovascularization must take place, otherwise the lipodystrophy that follows causes the formation of wrinkles which lead to cellulitis.
Tripeptide-41 (Lipoxyn?) activates NF-kB, a nuclear transcription factor which promotes the synthesis of tumor necrosis factor α (TNFα), a cytokine capable of triggering lipolysis. 
The peptide also reduces the expression of C/EBP, a transcription factor essential for adipocyte differentiation; this factor, when bound to PPARγ, contributes to hyperplasia of adipose tissue. 
The peptide also increases the concentration of cAMP, an important intracellular signaling factor that causes lipolysis by promoting the hydrolysis of lipids into triglycerides.
This peptide is also a signal peptide, but there are few completed studies which report on its efficacy.3
It is commercialized by PharmaSpecial?, represented by Caregen, a leading global company in the market of peptides and growth factors. 
Studies carried out by the product developers have been published. 
One such study, for example, was carried out in vivo, describing the application of an emulsion containing 5% of the peptide once a week for 8 weeks, and the volunteer reported having their waist circumference reduced by 5 cm.
Carrier peptides are responsible for transporting and stabilizing oligoelements such as copper and manganese, carrying them to the skin and allowing their intake by epithelial cells. 
Copper is one of the metals which can be transported by such peptides, playing a role on wound healing as well as being a cofactor for enzymes lysyl oxidase, tyrosinase, and superoxide dismutase, which are essential for collagen synthesis, melanogenesis, and superoxide dismutation (antioxidant action). 
Also, these peptides can stimulate key enzyme actions; an example is the tripeptide-copper complex glycyl-l-histidyl-l-lysine-Cu2+ (copper peptide GHK-Cu or copper tripeptide 1), which not only transports copper, but also increases the tissue levels of metalloproteinases, enzymes responsible for degrading the basic components of the extracellular matrix.
The effects of GHK-Cu upon metalloproteinase synthesis (MMP-2) by skin fibroblasts in culture were demonstrated by Sim?on and collaborators (2000). 
In their study, cultivated fibroblasts treated with GHK-Cu showed increased MMP-2 levels. 
This was evidenced by increased levels of MMP2 mRNA and increased secretion of tissue metalloproteinase inhibitors (TIMP-1 and TIMP-2). 
GHK-Cu is also responsible for the remodeling of the extracellular matrix, as it modulates the expression of MMP by acting directly in wound fibroblasts.
A study carried out by Finkley and collaborators (2005) describes the application of facial creams containing GHK-Cu for 12 weeks on 71 volunteers aged between 50 and 59 years, and the results demonstrated a visible reduction of the effects of aging. 
In another study by the same authors, they describe the application of the formulation on the eyes of 41 volunteers using similar experimental conditions. At the same time, placebo and controls containing vitamin K were also applied to the subjects. 
In both studies it was demonstrated that the cream containing GHK-Cu improved the elasticity and tightness of the skin, and also reduced fine lines and deep wrinkles.
These peptides are capable of increasing minimal muscle activity.
 For muscle contraction to occur, vesicles containing neurotransmitter acetylcholine must be released in neuromuscular junctions and interact with SNARE complexes (soluble N-ethylmaleimide-sensitive factor activating protein receptor). 
This process is modulated by a receptor protein, SNAP-25, which is a membrane protein that becomes associated with the vesicle, and directly regulates binding and vesicle fusion involving the SNARE complex. 
Some peptides possess structural similarities to proteins SNAP-25 in the SNARE complexes, and compete for the binding sites of these complexes, leading to the destabilization of their structure and preventing the release of acetylcholine at nervous endings, modulating the actions of this neurotransmitter.
Peptides of this class are used in anti-aging cosmetics due to their attenuating actions on the formation of wrinkles as they promote the involuntary movement of facial muscles. 
These peptides have been shown to specifically inhibit neurosecretion, and therefore have been called neurotransmitter inhibitor peptides.
The bioactive peptide of sequence Acetyl-Glu-Glu-Met-Gln-Arg-ArgNH2, known as acetyl hexapeptide-3, is a compound similar to botulinum toxin A, but it lacks the N-terminal domain of protein SNAP-25, competing for binding to the SNARE complexes modulating its formation, which inhibits the formation of acetylcholine and as a consequence attenuates muscle contraction.
The peptide also attenuates wrinkle development due to involuntary skin movements. 
This results in the inhibition of the release of acetylcholine.
In vivo tests using four lobsters with exposed muscular mass were carried out; the animals were submerged in solutions containing different concentrations of the active peptide, and it could be verified that the amplitude of excitatory post-synaptic potentials was progressively reduced, inhibiting the action of other proteins which are vital for muscle movement.
In another experiment carried out with 20 human subjects for 30 days, the skin application of oil in water emulsions containing the peptide caused a reduction of wrinkle depth by 59% and 71% and size by 41% and 50% for dry and oily skins, respectively, in comparison with placebo controls. 
An area of 1.5 cm2 of skin was subjected to application, which occurred twice a day in a standardized manner. 
The equipment Clinipro Antiaging SD alongside an IMAGE DB system was used for determination of wrinkle depth and width.
This peptide is commercialized by Galena?, a pioneer in the field of distribution of raw materials to pharmaceutical companies, and has the INCI name aqua (and) acetyl hexapeptide-8 (and) phenoxyethanol (and) methylparaben (and) ethylparaben (and) butylparaben (and) propylparaben (and) isobutylparaben.
This peptide is commercialized by Galena?, a pioneer in the field of distribution of raw materials to pharmaceutical companies, and has the INCI name aqua (and) acetyl hexapeptide-8 (and) phenoxyethanol (and) methylparaben (and) ethylparaben (and) butylparaben (and) propylparaben (and) isobutylparaben.
Vanistryl? is the commercial name of formulations containing acetyl tripeptide-30 citrulline and pentapetide-18, which are bioactive peptides used in wrinkle smoothing formulations that act in synergism modulating muscular tension and inhibiting matrix metalloproteinases (MMPs) when applied to the skin. 
Acetyl tripeptide-30 citrulline (Sequence: Lys-α-Asp-Ile-Citrulline) is a signal peptide, while pentapeptide-18 (Sequence: Tyr-D-Ala-Gly-Phe-Leu) is a neurotransmitter inhibitor peptide.
The application of water in oil emulsions to the skin of 12 volunteers with recent stretch marks at the waist and thighs, twice a day for 30 and 60 days and containing these active components at a concentration of 5%, improved the visual aspects of wrinkles by 38.89%, tightness by 70.83%, softness by 133.33%, touch perception by 28.61%, and color by 50.58%. 
Stretch marks were also softened. Complete quantitative and qualitative clinical evaluations were carried out by dermatologists before treatment and after 30 and 60 days of treatment. 
Surface studies (tightness and drying) were performed with the aid of a Visioscan VC98; color studies made use of Mexameter MX18; and skin elasticity analysis was carried out using Soft Mini Three equipment.
As acetyl hexapeptide-3, this peptide is commercialized by Galena? and by LIPOTEC?, a laboratory which has developed several technological compounds, mostly bioactive peptides. 
Its INCI name is water, caprylyl/capryl glucoside, lecithin, glycerin, Pseudoalteromonas ferment extract, acetyl tripeptide-30 citrulline, pentapeptide-18, xanthan gum, caprylyl glycol .
Neurocosmetics are cosmeceuticals which contain synthetic neuropeptides that interact with the nervous system through skin mediators. 
These compounds can play a role in skin homeostasis by activating or inhibiting such mediators. 
Skin responses carried out by mediators are regulated by a neuroendocrine system found in the skin capable of initiating adaptation mechanisms through quick pathways (neural pathways) or slow pathways (humoral pathways), acting at both local and systemic levels. 
Neurocosmetics can act in the central nervous system, being capable of stimulating the nerve endings of the skin, sending pleasure and well-being “feelings” to the hypothalamus, and causing the release of specific substances on the skin which improve the aspect of skin relief.
Skin cells release growth factors and proteins that bind insulin, which are synthetized from proopiomelanocortine (POMC), catecholamines, steroidal hormones, vitamin D, eicosanoids from fat acids, and retinoids from diet carotenoids. 
Skin has developed an autonomous system that responds to local and peripheral stress, which functions by making use of neurotransmitters and hormone peptides in a manner similar to the hypothalamus-hypophysis-adrenal axis
POMC peptides are synthetized by melanocytes, keratinocytes, microvascular endothelial cells, annex epithelial cells, mastocytes, Langerhans cells, fibroblasts, and immune cells, such as monocytes and macrophages. 
POMC, as a precursor protein, leads to the synthesis of several biologically active peptides through a series of enzymatic steps, which are often specific to each tissue, resulting in the formation of melanocyte stimulator hormones (MSHs), corticotrophin (ACTH), and ?-endorphin.
Fatemi and collaborators (2016) demonstrated that a peptide derived from POMC, bPOMC, has anti-inflammatory properties and does not disrupt melanogenesis. 
When skin is exposed to capsaicin, biomimetic peptides are capable of attenuating inflammation by preventing the release of substance P and the actions of IL-8 and IL-1.
The same research group, in a double-blind study, employed 56 healthy volunteers with sensitive skin divided into two groups of 28 volunteers each. Both groups received a facial formulation (0.1 g) containing bPOMC, which was used on the right side of the face, and a placebo formulation, which was used on the left side, applied on clean cheeks from the nasolabial to the outer cheek areas with circular movements twice a day for 14 days.
 The sensorial irritation test (pinching, burning, and itching) was carried out with the application of capsaicin at 3 × 10?4% for 5 min. After the testing period, the authors verified that the group treated with the formulation containing bPOMC had fewer irritation sensations when compared to the control group, further indicating that biomimetic peptides, such as bPOMC, when used in skin formulations, can significantly reduce symptoms from contact dermatitis, which is an issue for several consumers of cosmetics and topic formulations.
This compound is often associated with liposomes, made of a complex of phyto-endorphins responsible for stimulating fibroblast and keratinocyte proliferation, resulting in wrinkle attenuation and increased cell renewal, revitalization, and hydration. 
There are still no conclusive studies on the efficacy of this peptide. 
Some bioactive peptides have already been studied regarding their safety both in vitro and in vivo. 
According to the FDA (Food and Drug Administration), up to 2012, palmitoyl-like peptides have been the most extensively tested molecules regarding safety so far, as they are also commercialized in cosmetic products.
The bioactive acetyl hexapeptide-3 has been studied to some extent. Maia Campos and collaborators (2014) evaluated the safety of this peptide by making use of the primary skin irritation test and the patch test. 
They describe having used a hypoallergenic adhesive tape (50 mm2 area) applied a single time at occlusive conditions to the dorsal area of 27 volunteers (aged between 20 to 59 years) of phototypes II and IV. 
After 48 h, the tape was removed and the visual evaluation of the result was performed when the erythema was no longer was perceptible; no irritation reactions were caused after 48 h of occlusive contact. 
The research group of Blanes-Mira and collaborators, also demonstrated by means of the skin irritation test, reported that the bioactive acetyl hexapeptide-3 is safe to use in an analysis that used botulinum neurotoxin as a control.
Another study of skin irritation carried out with peptide GHK-Cu at a volume of 0.5 mL in an area of 6 cm2 using three male rabbits and a covering tape for 24 h, with readings conducted at 24 and 72 h, demonstrated that this active component is not a skin irritant under the tested conditions.
Bioactive peptides are becoming increasingly popular in the research and development of cosmetic formulations aimed at treating damaged and dysfunctional skin. 
Several companies such as PharmaSpecial?, Galena?, Biotec?, Lipotec?, and Silab? are investing in technologically innovative bioactive peptides, focusing on signal peptides and neurotransmitter inhibitor peptides. 
Bioactive peptides amount to 10% of all sales of pharmaceutical companies.
Any cosmetic products which contain bioactive peptides in their formulations must be submitted to efficacy and safety tests in order to be approved by the National Health Surveillance Agency (ANVISA) and thus allowed to be commercialized. 
However, there is a lack of published studies on such peptides; the most commonly studied peptides are synthetized from palmitoyl, such as palmitoyl oligopeptide, palmitoyl pentapeptide-4, and palmitoyl tetrapeptide-7, and such studies are generally focused on wrinkle attenuation and skin filling.
It was observed that the majority of research studies are focused on the development of anti-aging actives, and there is still room for research to be carried out to evaluate other functions of these actives. 
Studies reporting on the efficacy of bioactive peptides with specific functions and clarifying their mechanisms of action, mostly regarding their actions upon the attenuation of stretch marks and cellulites, are few, which makes it difficult to search for specific functions of bioactive peptides.
Airborne contaminants are predominantly derived from anthropogenic activities, and include carbon monoxide, sulfur dioxide, nitrogen oxides, volatile organic compounds, ozone and particulate matter. 
The exposure to these air pollutants is associated to detrimental effects on human skin, such as premature aging, pigment spot formation, skin rashes and eczema, and can worsen some skin conditions, such as atopic dermatitis.
A cosmetic approach to this problem involves the topical application of skincare products containing functional ingredients able to counteract pollution-induced skin damage. 
Considering that the demand for natural actives is growing in all segments of global cosmetic market, the aim of this review is to describe some commercial cosmetic ingredients obtained from botanical sources able to reduce the impact of air pollutants on human skin with different mechanisms, providing a scientific rationale for their use.
Nowadays air pollution is a global environmental and health problem of growing concern. While some kinds of air pollution are produced naturally, anthropogenic activities are the main cause of the emission of chemical pollutants into the atmosphere. 
Most of air chemical pollutants of human origin are produced by the combustion of fossil fuel to produce heat and energy, major industrial processes, exhaust from transportation vehicles and agricultural sources. 
Air pollution is composed of a heterogeneous mixture of compounds, categorized into two broad groups: primary and secondary pollutants. Primary pollutants are emitted directly from pollution sources, and include gases, low molecular weight hydrocarbons, persistent organic pollutants, heavy metals  and particulate matter. 
Secondary pollutants are formed in the atmosphere through chemical and photochemical reactions involving primary pollutants; they include ozone, NO2, peroxy acetyl nitrate, hydrogen peroxide and aldehydes. Gaseous pollutants are mainly produced by fuel combustion, while dioxines are produced when materials containing chlorine are burned. 
Airborne particulate matter is a major concern especially in the air of densely populated urban areas; it consists of mixtures of particles of different size and composition. Depending on their aerodynamic diameter, they are commonly referred to as PM10, PM2.5–10, PM2.5  and ultrafine particles. The composition of PM varies, because they can absorb and carry on their surface a great variety of pollutants, such as gases, heavy metals, organic compounds, polyaromatic hydrocarbons, directly related to their toxicity. In countries such as Northern India and China, particularly high levels of PM2.5 are detectable subject to seasonal fluctuations and higher than the World Health Organization (WHO) recommendations.
Exposure to air pollution is associated with increasing morbidity and mortality worldwide. Airborne pollutants may penetrate the human body through multiple routes, including direct inhalation and ingestion, as well as dermal contact, and they cause well-documented acute and long-term effects on human health. 
Once inhaled, airborne pollutants can affect respiratory system, with airways irritation, bronchoconstriction and dyspnoea, lung inflammation and worsening of conditions of patients with lung diseases. 
Epidemiological and clinical studies have shown that air pollution is also associated with cardiovascular diseases, and a relationship of exposure to air pollutants with the risk of acute myocardial infarction, stroke, ischaemic heart disease and increase in blood pressure was reported. 
Moreover, there is increasing evidence that outdoor pollution may have a significant impact on central nervous system and may be associated with some neurological diseases, such as Alzheimer’s disease, Parkinson’s disease and neurodevelopmental disorders.
 Air pollution is also considered a risk factor in the incidence of some other pathological conditions, such as autism, retinopathy, low birth weight and immunological dysfunctions.
Being the largest organ of the human body as well as the boundary between the environment and the organism, the skin unsurprisingly is one of the major targets of air pollutants. 
Air pollution has considerable effects on the human skin, and it is generally accepted that every single pollutant has a different toxicological impact on it. Recently, many Authors reported potential explanations for outdoor air pollutants impact on skin damaging, focusing their interest especially on PM and ozone.
PM are essentially combustion particles formed by a core of elemental carbon coated with a variety of chemicals, such as metals, organic compounds, particularly polycyclic aromatic hydrocarbons (PAHs), nitrates and sulfates. 
PM induce in skin oxidative stress, producing reactive oxygen species (ROS) and causing the secretion of pro-inflammatory cytokines. As a consequence of the increased production of ROS, an increase of matrix metalloproteinases (MMPs) occurs, resulting in the degradation of mature dermal collagen, which contributes to skin aging. 
Coarse PM produce ROS essentially through transition metals (iron, copper, vanadium, chromium) absorbed on their surface, which are able to generate ROS (especially OH°) in the Fenton’s reaction, while smaller particles produce ROS due essentially to the presence of PAHs and quinones. 
Quinones are by-products of diesel fuel combustion, but can also be produced in the skin through biotransformation of PAHs by some enzymes. Li et al demonstrated that ultrafine particles had the highest ROS activity compared to coarse and fine particles. 
PAHs are highly lipophilic carbon compounds with two or more fused aromatic rings, emitted to the atmosphere primarily from the incomplete combustion of organic matter. 
PAHs absorbed on the surface of airborne PM can penetrate into intact skin and exert direct effects on epidermis cells, such as keratinocytes and melanocytes.
 PAHs are ligands for the aryl hydrocarbon receptor (AhR), a ubiquitous ligand-dependent cytosolic transcription factor.
 When AhR ligands engage the receptor, a conformational change occurs in it, which leads to its nuclear translocation and subsequent binding and activation of several genes, included genes encoding several phase I and II xenobiotic metabolizing enzymes. 
The oxidized products of PAHs metabolized by these enzymes induce oxidative stress responses in cells and confirm the involvement of PAHs in the genesis of skin damage due to air pollution. 
Pan et al explored the effect of PM on the function of skin barrier, and showed that particulate matter disrupt stratum corneum and tight junctions both in in vitro and in vivo experiments in pigs, also promoting the skin uptake of some drugs.
Ozone occurs in the stratosphere and in the troposphere, where it is present as a main component of photochemical smog. 
At ground-level it is normally found in low concentrations, but it can be formed in higher amounts through interaction of UV radiations with hydrocarbons, volatile organic compounds and nitrogen oxides, becoming a ubiquitous pollutant in the urban environment with concentrations ranging from 0.2 to 1.2 ppm.
 Due to its peculiar anatomical position, skin is one of the tissues more exposed to the detrimental effects of ozone, especially during smoggy and O3-alert days. 
Although Afaq et al. showed in human epidermal keratinocytes that AhR is an ozone sensor in human skin, suggesting that AhR signalling is an integral part of induction of cytochrome P450 isoforms by O3, ozone should not reach viable skin cells due to its high reactivity, and it is common opinion that its main cutaneous target is the stratum corneum.
 Ozone represents an important source of oxidative stress for skin; studies on animal models showed that ozone exposure leads to a progressive depletion of vitamin E and hydrophilic antioxidants and to malondialdehyde production in murine stratum corneum, inducing oxidative damage to lipids and leading to a perturbation of epidermal barrier function. 
A study performed on the skin of human volunteers showed that the ozone exposure significantly reduced vitamin E levels and increased lipid hydroperoxides in the stratum corneum, confirming that the effects of O3 are limited to the superficial layers of the human skin.
 In addition to increasing oxidative stress and decreasing of antioxidant skin defenses, ozone exposure is able to induce pro-inflammatory markers and increase the levels of heat shock proteins in mice skin. 
Inflammatory reactions in turn induce the production of ROS, thus triggering a vicious circle. 
Detrimental effects of O3 on skin can be enhanced by simultaneous exposure to UV radiation. 
The use of topical antioxidant mixtures has proven to be effective in preventing O3-induced oxidative damage both in human keratinocytes in culture  and in reconstructed human epidermis.
Air pollution, with other exogenous factors such as UV radiation and smoking, is definitely recognized as an important extrinsic skin-aging factor, whose pivotal mechanism is the formation of ROS and the subsequent oxidative stress, which can trigger further cellular responses.
 The skin is equipped with an elaborate antioxidant defense system including enzymatic and nonenzymatic, hydrophilic and lipophilic elements; however, when the extent of the oxidative stress exceeds skin’s antioxidant capacity, it leads to oxidative damage, premature skin aging and eventually skin cancer.
Until today, no standard protocol is available to objectively substantiate the “anti-pollution” claim, though several in vitro and in vivo tests have been proposed to this purpose. 
In vitro tests are based on the use of cell cultures or reconstituted skin models and evaluate several biomarkers after pollutants exposure. 
In vivo tests, performed on volunteers’ panels, include instrumental evaluation of skin parameters and the evaluation of the levels of oxidative stress and inflammatory markers after exposure to pollutant stressors.
 The availability of reliable and specific markers of airborne pollution upon skin would allow to evaluate and quantify the cutaneous impact of this phenomenon, as well as to assess the effectiveness of ingredients or finished products in counteracting detrimental effects of air pollutants.
 Recently, the oxidation of squalene has been recognized as a useful model. Squalene, a high-unsaturated triterpene produced by human sebaceous glands and present in sebum with an average concentration of 12%, is very prone to oxidation and is one of the main targets of oxidative stress induced by air pollution. 
Its peroxidized by-products are considered inflammatory mediators and are involved in comedogenesis, acne and wrinkles formation. 
Pham et al. established various protocols to evaluate the influence of different pollutants upon squalene oxidation by determining the amount of squalene oxides produced, and concluded that squalene oxidation is a reliable marker of pollution-induced skin damage.
Recently, a small number of studies investigated the cause-effect relationship between air pollution and skin quality. 
Vierk?tter et al. found a significant association between traffic-related airborne particles and extrinsic skin aging signs in a group of German women. 
In particular, an increase in soot and particles from traffic  were associated with 20% more pigment spots on forehead and cheeks. 
The influence of air pollutants on a number of skin parameters was evaluated in a clinical comparative study conducted on 96 subjects in Mexico City  and 93 subjects in Cuernavaca, considered a town preserved from urban pollution.
 In this comparative study, the Authors studied quantitative and qualitative modifications of a number of skin parameters. 
The results of this investigation demonstrated that moisturizing was significantly higher in Cuernavaca population; an increased level of sebum excretion rate, a lower level of vitamin E and squalene in sebum, and an increase of lactic acid and a higher erythematous index of the face of subjects were documented in Mexico City group.
 In the stratum corneum a higher level of carbonylated proteins, a lower level of IL 1α, a decrease of ATP concentration and a decrease of chymotrypsin like activity were detected. 
A clinical evaluation conducted by dermatologists on the same groups showed a general tendency of a higher incidence of skin problems  in Mexico City population compared to Cuernavaca population.
In addition to the effects on healthy skin, a number of studies have shown that outdoor air pollution is a relevant risk factor for the development of atopic dermatitis, a chronic inflammatory skin disease, and can also exacerbate this condition. As a consequence, the prevalence of atopic dermatitis in urban areas is significantly higher compared to that of rural areas.
The awareness of detrimental effects of environmental pollutants on skin has increased enormously in the most recent years not only within the scientific community but also among consumers.
 As a consequence, the anti-pollution trend, originated in Asia and subsequently spread to Western markets, is nowadays a rising trend in cosmetics and personal care industry worldwide, and cosmetic brands are unceasingly developing new concepts and new active ingredients to meet consumers’ demand. 
Several cosmetic strategies can be adopted to protect human skin against environmental pollution. 
The very first step in an effective cosmetic anti-pollution routine is a proper cleansing of the skin to remove chemicals deposited on it. Another way to defend the skin against environmental stressors is the isolation of the epidermis through the formation of a cohesive and non-occlusive film on its surface, preventing the direct contact with airborne pollutants; this physical barrier can be obtained through the use of film-forming ingredients, both synthetic (silicones, acrylic acid copolymers) and naturally derived (peptides and polysaccharides extracted from plants or obtained by fermentation processes). 
The third approach is the inclusion in anti-pollution formulations of antioxidants, in order to protect against free radical effects, or ingredients able to up-regulate the antioxidant defenses of the epidermis cells. 
Some cosmetic companies introduce in their anti-pollution cosmetics several ingredients with different complementary mechanisms of action, obtaining formulations designed to tackle as many pollutants as possible.
Most of the active anti-pollution ingredients present in formulations on the market are products of botanical origin. 
This reflects a more general trend in the today’s cosmetics and personal care industry. 
Indeed plants contain countless metabolites with potential cosmetic applications, which combine efficiency, reduced risk of irritation and allergies, reduced adverse effects and the possibility to refer on the labels of beauty products placed on the market to claims such as “organic”, “environmental sustainability” and “fair trade”; these claims are increasingly popular amongst the consumers due to the ever-increasing demand for more ethical, natural and “green” formulations.
The current review aims to present a selection of the most popular ingredients of botanical origin marketed by suppliers with the claim “anti-pollution”, and the scientific rationale behind their cosmetic applications.
 A number of commercially available anti-pollution cosmetic ingredients of botanical origin were retrieved by an electronic survey conducted by the popular search engine Google and the technical websites for chemicals and materials Prospector, SpecialChem and Cosmetic Design Europe, using the key words “anti-pollution cosmetics” and “anti-pollution ingredients”.
 For practical reasons, it has been decided to include in this review only the ingredients derived from a single botanical species, thus excluding the products containing mixtures of plant extracts.
 For each ingredient the technical documentation was acquired from the manufacturers’ own websites. 
Subsequently, scientific papers, found by using the academic search engines Google Scholar, ScienceDirect and PubMed, were consulted to verify the scientific soundness of the anti-pollution claims of these botanical extracts plants.
 For the ease of the readers, the anti-pollution ingredients taken into account in this review were divided into two tables, according to their botanical origin: Algae and Spermatophytae. 
In these tables, the trade name, the supplier, the INCI name, the supplier claims and the recommended concentrations were reported for each ingredient.
Marine algae are eukaryotic organisms classified in microalgae and macroalgae. Macroalgae  are in turn classified in Rhodophyceae, Chlorophyceae  and Pheophyceae, according to their dominant pigment. 
Algae provide a great variety of metabolites and can be easily cultured on seashores in great volumes; moreover, they grow quickly, and it is possible to control the production of their metabolites by manipulating the culture conditions. 
For all these reasons, algae represent an attractive renewable source of bioactive compounds with potential applications in pharmaceutical, nutraceutical and cosmetics industries.
A number of bioactive compounds and extracts derived from macroalgae have proven to be useful in the treatment of some skin conditions.
 Some algae species produce bioactive molecules with photo-protective activity due to their ability to absorb UV-A and UV-B radiation; other algal species are potential sources of skin whitening agents, since they produce metabolites able to inhibit natural tyrosinase. 
Moreover, some compounds derived from algae exhibit antibacterial and anti-inflammatory activity and can be useful in the management of acne-affected skin. Other bioactivities from algae are closely linked to the use of seaweed-derived products as anti-pollution cosmetic ingredients.
 In particular, researchers have extensively investigated the antioxidant activity of algae extracts; indeed algae, due to the extreme conditions in which they often live, are naturally exposed to oxidative stress, and develop efficient strategies to protect against the effects of ROS and other oxidizing agents.
 The antioxidant potential of a variety of algal species extracts was demonstrated with different methods, such as 2,2-diphenyl-1-picrylhydrazyl free-radical-scavenging, ferric-reducing antioxidant power, ABTS  radical scavenging, in vitro copper-induced oxidation of human LDL  assay, reducing activity, metal chelating assay, scavenging ability on hydroxyl and superoxide radicals.
 Brown algae have been reported to contain comparatively higher contents and more active antioxidants than red and green algae.
 A statistically significant correlation between this antioxidant activity and the total polyphenol content of these extracts was demonstrated, suggesting that this class of compounds is at least in part responsible for the antioxidant properties of seaweed extracts.
 Amongst the many polyphenols been identified in algal extracts, of particular interest are phlorotannins, formed by polymerization of phloroglucinol units linked together in different ways.
 Phlorotannins only exist within brown algae, are not found in terrestrial plants, and can be divided into six categories.
They possess a strong antioxidant activity related to phenol rings in their structure, and having up to eight rings they are more efficient free radical scavengers when compared to polyphenols from terrestrial plants, which have 3–4 rings.
Other components which contribute to the antioxidant potential of algae are sulfated polysaccharides, that in recent times have attracted the interest from life science researchers owing to a wide range of biological activities with potential health benefits, such as anti-allergic, anti-HIV, anticancer, anticoagulant and anti-oxidant activities.
 Sulfated polysaccharides are anionic polymers widespread among marine algae but also occurring in animals; their chemical structure varies depending on the seaweed species that they come from.
 The most important sulfated polysaccharides recovered in marine algae are ulvans in green algae, carrageenans in red algae and fucoidans and laminarians in brown algae.
Ulvans are water-soluble sulfated heteropolysaccharides, mainly constituted by disaccharide repeated units formed by d-glucuronic or l-iduronic acid linked to l-rhamnose-3-sulfate.
 These polymers exhibit a broad range of biological activities, a notable example being the antioxidant one. 
The antioxidant properties of ulvans are influenced by the extraction procedures and depend on the carbohydrate composition and the sulfate content, since ulvans with higher sulfate content show a significantly higher antioxidant activity.
The composition, structures and biological properties of fucoidans have been extensively reviewed. They are sulfated polysaccharides found exclusively in the cell walls of brown algae; their major components are l-fucose and sulfate. 
Fucoidans exhibit a broad spectrum of biological activities, including anticancer, apoptosis-inducing, immunomodulatory, antiviral, anti-thrombotic, anti-inflammatory and antioxidant activities. 
In vitro antioxidant activity of fucoidans has been determined by various methods, such as DPPH free radical scavenging assay, iron-chelating activity assessment, superoxide anion and hydroxyl radical scavenging activity, reducing power assay. 
Recently, effective methods of extraction of fucoidans, alternative to classical extraction methods, time-expensive and associated to multi-step processes, high temperature, large solvent volumes, were developed. 
Fucoidans extracted by these techniques exhibited in some cases a higher antioxidant activity than those extracted by conventional methods. 
In addition to their antioxidant properties, fucoidans possess another biological activity relevant in the cosmetic field: they are able to prevent UVB-induced matrix metalloproteinase-1 (MMP-1) expression and suppress MMP-3 in vitro. 
MMPs induce degradation of dermal proteins such as collagen, fibronectin and elastin, contributing to skin damage; therefore fucoidan may be useful to prevent skin photoaging not only by scavenging ROS formed during exposition to UV radiations, but also by inhibiting the formation of MMPs.
As shown in Table S1, a number of cosmetic ingredients based on algal extracts have been developed and are proposed by manufacturers as functional substances suitable for anti-pollution skincare products. Their use is substantiated not only by the general literature referred so far, but often also, when available, by investigations focused on specific algal species.
The ingredient No. 1, Contacticel?, contains an extract of Acrochaetium moniliforme, an epiphytic red macroalga made of cell filaments found in very low quantities in the ocean; the patented Celebrity? technology produces biomass of this red alga in photobioreactors in a sufficient quantity, unavailable in the sea. 
Scientific literature on possible biological activities of this alga was not found; the information leaflet of the manufacturer claims that the patented commercial extract limits in vivo excessive sebum production and reduces the ozonolyzed squalene (tests performed versus placebo on two groups of 20 women each, in Shanghai’s polluted atmosphere). 
Moreover, in an in vitro sebocyte model exposed to urban dust the extract proved to be effective in regulating the lipid production.
The antioxidant activity of the edible brown seaweed Laminaria digitata  is documented by a number of investigations. The study of Heffernan et al. showed that the crude extracts of L. digitata showed a total phenolic content and an antioxidant activity lower than other macroalgae examined, but these parameters improved when the extracts were fractionated with suitable dialysis membranes. Moreover, it has been demonstrated that the thermal treatment increased its content in antioxidant compounds and improved its free radical scavenging activity.
The ingredients No. 3 and No. 7 contain as an active anti-pollution ingredient Undaria pinnatifida extract; this brown alga is widely used as food and as a remedy in traditional Chinese medicine for over 1000 years. 
As stated previously for brown algae, U. pinnatifida contains sulfated polysaccharides that exhibit good antioxidant activities, related with their sulfate content. 
Moreover, it also contains fucoxanthin, a carotenoid present in the chloroplasts, able to counteract oxidative stress by UV radiation.
The ingredient No. 4, designed to be used in hair-care products, contains a hydroglycolic extract of the brown seaweed Pelvetia canaliculata. 
Although P. canaliculata, like all brown algae, contains fucoidans  and phlorotannins and carotenoids, able to absorb UV radiation and to fight photoxidative stress, the leaflet supplied by the manufacturer emphasises the effectiveness of its extract in reducing residues and depositions caused by the action of pollutants, chlorine and the build-up effect of cationic hair conditioners. 
Alginates and fucoidans contained in the cells walls of P. canaliculata are poly-anions due to the presence of carboxylic and sulfonic groups, and as a consequence this alga can act as a natural cation exchanger. 
This ability is widely documented in the scientific literature, and it was proposed to use P. canaliculata biomass to sequestrate and remove metal ions (zinc, iron, copper, trivalent chromium, lead, nickel) from industrial wastewaters.
 All of these biological activities make P. canaliculata extract a good candidate for the formulation of anti-pollution cosmetics.
The ingredient No. 5 consist of an extract of Ascophyllum nodosum, harvested on Ouessant Island by a hand cutting harvest method; it is concentrated in high molecular weight fucoidans, as declared by manufacturer.
 The antioxidant activity of this brown seaweed is well documented and can be attributed to the presence of both sulfated polysaccharides and phenolic compounds. A. nodosum contains the abovementioned fucoidan but also ascophyllan, another sulfated polysaccharide structurally similar to fucoidan characterized by a more pronounced antioxidant activity, in addition to a wide variety of interesting biological activities.
 Moreover, A. nodosum produces a variety of phenolic compounds, namely phlorotannins, flavonoids and phenolic acid derivatives. 
On the leaflet of the manufacturer the extract No. 5, at the concentration of 3%, is claimed to decrease AhR receptor expression by 73% compared to a placebo in an ex vivo test. 
Moreover, to this ingredient is ascribed the ability to reinforce the skin barrier; in particular, it is reported that on the model of reconstructed epidermal skin Episkin? native fucoidans (5%) increased the number of mature corneocytes by 225%, while the whole extract (3%), after 56 days, decreased TEWL, an indicator of the barrier dysfunction, by 13% versus placebo in an in vivo test.
Finally, the ingredient No. 6 contains an extract of the green alga Ulva lactuca, also known by the common name of sea lettuce and rich in the sulfated polysaccharides ulvans, as mentioned above. 
If administered orally, U. lactuca extracts show anti-inflammatory effect in carrageenan-induced paw oedema in rats and are able to ameliorate hepatic enzymatic and non-enzymatic antioxidant defenses of hypercholesterolemic rats.
Eriodictyon californicum is an evergreen shrub within the Boraginaceae family, native to Central America. 
For centuries Native Americans used it as a medicinal plant to treat several respiratory conditions and skin wounds. 
The leaves of E. californicum are covered by a resin containing flavonoids (such as eriodictyol and homoeriodictyol), which act as herbivore deterrents and UV screens; this plant is also a source of moisturizing compounds such as mucopolysaccharides and glycoproteins, which produce their moisturizing effects via hydrogen bonding of water by their sugar moieties.
In the technical data sheet provided by the manufacturer a remarkable improvement of skin moisturization (30%) produced by Yerba Santa Glycoprotein PF (5%) was reported, whereas in the same experimental conditions Aloe vera gel 10× produced an increase of 20%. Ingredient No. 1 was also tested to verify its anti-pollution properties.
 The extract was applied to the skin, which was then contaminated with a known amount of activated charcoal. 
After washing with a controlled volume of water, the amount of microparticles remained on the skin was evaluated; when compared with an untreated control, the extract was able to prevent the deposition of PM particles into the skin fine lines and wrinkles. 
The extracts of E. californicum, due to the presence of flavonoids such as homoeriodictyol and eriodictyol, well known for their antioxidant activity, provide further benefits when added to cosmetic formulations.
The ingredient No. 2 of Table S2 is composed by polyphenols extracted from green tea leaves and esterified with palmitic acid.
 Green tea is obtained by roasting or steaming Camellia sinensis (Theaceae) leaves in order to inactivate polyphenol oxidase activity.
 Green tea extracts are complex mixtures of bioactive compounds, including tea polyphenols, primarily green tea catechins, that account for 30–40% of the extractable solid of dried green tea leaves. Tea catechins include epicatechin, epicatechin-3-gallate, epigallocatechin and epigallocatechin gallate. 
These polyphenols have gained interest in recent years because of interesting biological activities, including antimicrobial, anti-inflammatory, anticancer, antioxidant and radical scavenging activities. 
On the basis of their biological properties, green tea polyphenols are generally accepted as having a protective effect against oxidative stress and DNA and cell structures damage induced by a number of environmental toxins/toxicants; these properties provide the rationale for the use of green tea extracts as functional ingredients of anti-pollution cosmetics.
However, it is known that polyphenols in their native form are unstable because they are susceptible to oxidation induced by several environmental agents. 
Moreover, green tea polyphenols are soluble in water and therefore difficult to use in cosmetic formulations when lipophilic ingredients are required. 
A way of stabilizing these polyphenols and imparting them lipophilic properties is protecting the phenol functions as fatty acid esters with a method described in the American Patent US 5808119. 
To evaluate whether the biological properties of polyphenols are maintained after esterification, studies were performed by using cutaneous explants from abdominoplasty surgery as a model of human skin. 
After topical application of the green tea extract, free radical production was induced by UV radiation; the lipid peroxidation process was studied by determining the levels of malonyldialdehyde (MDA) as indicator. 
Stabilized polyphenols showed a good anti-lipoperoxidant activity, higher than that of Vitamin E. Since the radical scavenging of polyphenols related is to the free phenolic OH, it is assumed that esterified polyphenols permeate the skin barrier and then are hydrolyzed by skin esterases to the active forms. 
Moreover, green tea polyphenols at concentration of 0.1% and 0.25% respectively induce an increase of 18% and 40% of collagen IV in the dermo-epidermal junction; at 0.25% and 0.5% they increase of 13% and 21% fibriline-1 in the dermo-epidermal junction.
Marrubium vulgare is a plant widely used in antipollution skincare products; four ingredients examined in this review (ingredients No. 4, 6, 7 and 8, Table S2) contain M. vulgare extracts.
 The genus Marrubium includes about 40 species of flowering plants indigenous in Europe, Mediterranean area and Asia.
 Many species of Marrubium are reported in the literature to be used in folk medicine and their extracts have been investigated for their chemical composition and for their antioxidant and lightening properties. 
M. vulgare, widely used in traditional medicine in some countries, is the most investigated species of Marrubium.
 Its aerial parts are official in Hungarian Pharmacopoeia VII, and the European Medicine Agency (EMA) approved in 2013 the treatment of cough associated to cold, mild dyspeptic complaints and temporary loss of appetite as indication for aerial part of this plant. 
M. vulgare is reported to possess several biological activities, among which the most interesting are antihepatotoxic, antihyperglicemic, antibacterial, anti-inflammatory  and antioxidant properties. 
In particular antioxidant properties may justify the widespread of Marrubium vulgare extracts, often prepared with peculiar extraction techniques, as natural cosmetic ingredients, with claims including anti-pollution, antioxidant, protective for irritated and stressed skin, detoxifying, soothing. 
Reported results suggest a remarkable antioxidant activity of M. vulgare extracts assessed with different methods (DPPH° radical scavenging, scavenging activity against hydrogen peroxide, iron reducing power). 
In order to correlate this antioxidant activity to specific bioactive compounds, phytochemical composition of M. vulgare has been extensively investigated; these studies, conducted on different types of extracts, led to the identification of a wide array of phytochemical compounds, such as flavonoids, terpenoids and phenylethanoid glycosides. 
Several flavonoids were isolated from M. vulgare, including luteolin, apigenin, terniflorin, anisofolin A, ladanein. Phytochemical screening revealed the presence of several terpenoid compounds, such as marrubiin, premarrubiin, marrubenol, sacranoside A, deacetylforskolin, preleosibirin, marrulibacetal. 
Finally, several phenylpropanoid compounds were isolated and identified from M. vulgare: caffeoyl-l-malic acid, verbascoside, decaffeoylverbascoside, forsytoside B, alyssonoside, leukoceptoside A, acteoside, arenarioside, ballotetroside. Ladanein, verbascoside and forsythoside B showed a relevant antioxidant activity in vitro; on the other hand, experimental data suggest that natural phenylpropanoids could protect cells from oxidative stress. 
These studies justify the use of M. vulgare extracts as cosmetic ingredient and support scientific substantiation of the anti-pollution claim.
Schinus molle (Anacardiaceae), also known as Peruvian pepper tree, false pepper or pink pepper, is an evergreen tree native to Peruvian Andes. 
Widely used in traditional medicine for its purported analgesic, antidepressant, antimicrobial, diuretic, astringent and antispasmodic properties, Schinus molle exhibits insect repellent, anti-inflammatory, antifungal and antioxidant effects.
 The antioxidant properties of leaf and fruit essentials oils of S. molle were demonstrated by using DPPH° free radical, ABTS and β-carotene/linoleic acid method. 
Methanolic extracts of bark and flowers of S. molle were also tested for their DPPH° scavenging activity, and they exhibited a remarkable antioxidant activity when compared to quercetin. 
In aqueous methanolic extracts of the leaves of S. molle a number of polyphenolic metabolites were found, including glycosides based on quercetin as an aglycone.
 Some of them exhibited moderate to strong radical scavenging properties on lipid peroxidation, OH° and superoxide anion generation; the most active compounds were miquelianin and quercetin 3-O-β-d-galacturonopyranoside. An extract of Schinus molle is marketed as a cosmetic ingredient with anti-pollution, anti-aging and anti-wrinkle benefits. 
In the technical datasheet provided by the manufacturer it is stated that Elixiance? is rich in polyphenols such as quercitrin and miquelianin. 
As far as the anti-pollution benefits of this extract are concerned, the document claims that it “...limits the effects of air pollution in vitro, “…contributes to reduction in skin permeability induced by environmental stress (in vitro)…” and “…is associated with anti-pollution benefits supported by a clinical study on 39 volunteers in Shangai.” 
Moreover, a skin-purifying effect, characterized by a reduced quantity of skin sebum and by a decrease of the appearance of pores, is associated to S. molle extract.
Camellia japonica, also known as Rose of winter, is a flowering tree or shrub belonging to the Theaceae family and naturally occurring in China, Japan and Korea. 
An extract of C. japonica flowers is the active component of the ingredient No. 9 of Table S2 (RedSnow?). 
It has been reported that C. japonica, whose flowers and flower buds were traditionally used in oriental medicine as an astringent, anti-hemorrhagic and anti-inflammatory remedy, exhibits a variety of biological activities, such as antiviral, anti-atherogenic, anti-hyperuricemic, anti-photoaging, antioxidant, radical scavenging and anti-inflammatory effects, and glycation inhibitory action. 
The ethanol extract of C. japonica flowers exhibits antioxidant properties by scavenging ROS (superoxide and hydroxyl radicals) in a free-cell system and in human HaCaT keratinocytes; moreover, it is able to increase the protein expression of the antioxidant enzymes superoxide dismutase, catalase and glutathione peroxidase.
 The ROS scavenging effect and the induction of antioxidant enzymes of C. japonica extract may be associated with the presence of antioxidant phenolic compounds such as quercetin and kaempferol glycosides. 
In a study on the anti-aging properties of C. japonica flower extract in an ex vivo model, it has been shown that it reduces piknotic nuclei and it prevents the detachment of the dermo-epidermal junction induced by pollutants such as heavy metals and hydrocarbons; moreover, it also induces an increase of collagen I and a decrease of MMP-1. These results support the use of C. japonica flower extract in anti-aging and anti-pollution cosmetics.
Schisandra chinensis Baill is a plant native to China, Japan and Russia; its dried fruits are used in traditional Chinese medicine, where it is considered one of the 50 fundamental herbs. 
Modern studies show that this plant possesses several biological activities such as anti-hepatotoxic, antitumour, anti-inflammatory and antioxidant. 
The major constituents of the fruit extract of S. chinensis are lignans, a large group of naturally occurring phenols classified into several group according to their chemical structure. 
The fruit extract of S. chinensis especially contains lignans with dibenzocyclooctadiene skeleton, such as schisandrol, schisantherin A, deoxyschisandrin, schisandrin B and schisandrin C, and with tetrahydrofuran structure, such as d-epigalbacin, machilin G and chicanine; several scientific studies demonstrated the antioxidant activity of these compounds.
 Some recent studies have also shown the presence in fruits and leaves of S. chinensis of phenolic compounds with good antioxidant activity, such as chlorogenic acid, isoquercitrin and quercitrin. 
Schisandrin also exhibits anti-inflammatory activity, and S. chinensis fruit extract has been reported to reduce pro-inflammatory cytokine levels in THP-1 cells stimulated with P. acnes and to protect UVB-exposed fibroblasts from photoaging.
 Due to these biological properties, the extract from S. chinensis fruits has beneficial effects on the skin, and has been proposed as a cosmetic anti-pollution ingredient.
This ingredient contains schisandrin 8–12%.
 In the datasheet provided by the manufacturer it is claimed that this product is able to reduce the NQ01 (NAD(P)H dehydrogenase 1) expression, to limit the expression of MT1H (Metallothionein 1H) and to protect from inflammation on a 3D reconstructed full-thickness skin model exposed to a mixture of urban pollutants (in vitro tests); in vivo tests performed on female volunteers exposed to urban pollution showed a conservation of basal TEWL, an amelioration of skin radiance and an improvement of microcirculation and tissular oxygenation.
Several scientific investigations have established that the prolonged exposure to environmental pollutants can produce in human skin biochemical parameters modifications and impairment of barrier function, and can promote the mechanisms of skin aging; the visible results of these effects are dryness, wrinkles, dark spots, sagging and the aggravation of skin sensitivity. 
As the awareness of the impact of environmental stressors on the skin grows, there is an increasing consumer demand for cosmetics and personal care products able to provide anti-pollution benefits.
 The anti-pollution skincare is one of the latest cosmetic trends; started in Asia, it is currently gaining ground all over the world, and new solutions, ingredients and products specifically designed to offer skin protection against pollution are continuously developed.
 With the growth in demand for natural cosmetics steadily on the rise, it is natural that plant extracts are becoming the most popular ingredients of cosmetics designed to fight skin pollution; indeed plant extracts are often rich in bioactive compounds whose activities can be exploited in anti-pollution formulations.
 As stated above, airborne pollutants induce adverse effects on human skin mainly via oxidative damage, with a consequent oxidative stress and a depletion of ant ioxidant enzymes and other antioxidant substances in epidermis.
 For this reason, it is not surprising that most of the plants used as a source of anti-pollution cosmetic ingredients contain antioxidants as active substances.
This review was aimed to give a representative list of the most popular anti-pollution cosmetic ingredients of botanical origin, describe their mechanism(s) of action and provide a scientific rationale justifying their use.
 This list is not exhaustive; indeed, manufacturers are expected to propose an increasing number of plant derivatives as active ingredients of antipollution cosmetics, since the demand for this skincare segment is here to stay and it will even increase.
Consumers pay more and more attention not just to the safety and health aspects of ingredients entering their cosmetics’ formulations, but also to their potency, origin, processing, ethical value and environmental footprint.
Sustainability of the supply chain, preservation of biodiversity, as well as greener extraction techniques are hence very popular with consumers.
Consumers are primarily concerned by the efficacy of the cosmetic products they use and continuously scrutinize product labels, so marketing arguments need to be based on rigorous testing and reliable results to support claims displayed on the product’s packaging.
As a result, the increasing demand for natural ingredients with assessed bioactivities has profoundly modified the strategies adopted by cosmetic professionals to innovate in terms of actives. 
Sourcing and developing new natural cosmetic actives is a long-term procedure that is thoroughly described in the present paper, via the example of the design of both liquid and solid ingredients based on Quercus pubescens Willd. leaves extract, for which anti-age properties were assessed by a combination of in vitro assays.
Constantly evolving consumers’ lifestyle is the key driver behind the transformation of the worldwide cosmetic industry engaged over the two past decades. 
The sector is expected to reach $429.8 billion by 2022, registering a CAGR of 4.3%.
 An inclination towards natural beauty and personal care products has been initiated few years ago, fueled by the consumers’ ecological and ethical considerations, and their will for safer cosmetics.
‘Natural’ is often, not always quite rightly, synonym of ‘safer products presenting less side effects’ in the consumers’ mind and one notably notices the boom of ‘without’ claims.
 In brief, consumers are looking for safer and greener cosmetic ingredients and place a premium on real efficacy, so manufacturers must provide tangible evidence of the allegations claimed on the products’ packaging.
 Consequently, manufacturers are constantly seeking originality and naturality, while multiplying bioassays to scientifically substantiate cosmetic allegations, and the natural beauty market valued approximately 11.06 billion U.S. dollars worldwide in 2016, is estimated to reach almost 22 billion U.S. dollars in 2024.
The global cosmetic ingredients market amounted to approximately 22.9 billion U.S. dollars in 2016 is expected to rise to reach 33.80 billion U.S. dollars by the end of 2025.
 Even continuously progressing, natural ingredients still represent today only 7% of the global market. 
To keep up with current trends, cosmetic manufacturers must ceaselessly innovate and develop new natural ingredients in accordance to consumer preferences, using the latest technologies of extraction, and notably of eco-extraction, or exploring the potentialities of original natural resources, while keeping sustainability and ethics in mind. 
Manufacturers can adopt one of the two following strategies to source naturally derived ingredients: they can either call on ethnobotanical knowledge, i.e., select plants of interest based on their renown traditional use, or based on a high-throughput screening.
Consumers are daily exposed to air pollutants and to all kinds of blue light sources. 
They are more and more concerned with the impact of pollution and their hectic lifestyle, on the quality and beauty of their skin. 
These preoccupations are strongly setting the guidelines of the cosmetics market over the next few years: anti-pollution, anti-blue light and moreover anti-age actives will call the tune on the cosmetic ingredients’ market. 
Skin health and beauty are intimately linked to the overall consumers’ well-being, so it is not surprising that skincare constitutes the cosmetic segment that witnessed the strongest growth over the 1998–2010 period, with maket shares evolving from 16.4% to 23.0%. 
Skin aging is a complex biological process influenced by a combination of endogenous  and exogenous  factors. To fight the visible signs of aging, cosmetic anti-aging ingredients reinforce the skin barrier, improve the skin elasticity, boost its density, protect it against radicals, fade wrinkles, diminish the apparition of age spots, etc. 
The pursuit of beauty and the demographic realities—life expectancy registered over the last few years its fastest gain since the 1960s—are substantially and ceaselessly fueling the demand for anti-aging actives and formulations; this sector is no exception: naturality rules.
The worldwide biodiversity constitutes an exceptional reservoir of innovative molecules presenting highly diverse structures and functions that can be tapped for novel drug leads as well as cosmetic development.
 The Mediterranean area has long been identified as a region presenting high biodiversity due to its remarkable flora and specially its high rate of endemic species: representing only 2% of the world’s surface, this area houses 20% of the world’s total floristic richness. 
This Mediterranean biome hence constitutes a choice target to source interesting molecules and notably natural ingredients intended for cosmetics and personal care formulation. 
Due to its establishment at the heart of this rich region and to its expertise in phytochemistry, our research team has access to this diversity through longstanding collaborations. 
Hence, 50 Mediterranean plants were initially selected over more than 500 available at the laboratory for the present study, based on their accessibility and their originality regarding the anti-aging activity. 
Hence, aqueous and organic solvent extracts, as well as essential oils were investigated for their anti-aging properties. From this survey, a crude extract of Quercus pubescens Willd. 
Leaves display the best unprecedented anti-aging activities and was then selected for further investigations leading to the development of an innovative anti-age active and its practical cosmetic formulation.
Q. pubescens, commonly known as downy or pubescent oak, is a species of white oak native to southern Europe and southwest Asia; the word ‘pubescens’ meaning ‘with soft bristle’ actually refers to its hairy leaves and twigs, constituting an adaptation to drought.
 This species is also known as ‘truffle oak’ as it serves among other species as a host for economically important truffles. Q. pubescens is a medium-sized deciduous tree growing up to 20 m, preferentially in regions presenting a sub-Mediterranean microclimate characterized by hot dry summers and cool winters with little rainfall. 
Particularly adaptable, this species grows on well-drained lime soils as well as on acidic grounds, and is found from sea level up to 1300 m.
 Extracts of various species of oak are already used in cosmetics, mainly as skin conditioning actives; however, those actives are mainly based on bark or wood extracts: such a sampling endangers the survival of the oak trees tapped and is therefore not sustainable.
 One patent reports the composition of an anti-oxidant cosmetic formula based on Q. robur, Q. ilex and Q. pubescens bark extracts. 
To our knowledge, no previous study reports the development and the subsequent use of an extract of Q. pubescens leaves as a cosmetic active.
Developing and objectifying new natural cosmetic ingredients is a long-term procedure that is presented in detail in this article, via the example of the design of an anti-age ingredient based on an extract of Q. pubescens leaves.
 The first step consists of the optimization of the extraction procedure of Q. pubescens leaves to potentialize the bioactivity of the resulting extract while keeping in mind its further industrial scale-up.
 The second part of such a development encompasses the amelioration of the extract’s formulability, including its discoloration and/or deodorization, and the further development of both liquid and solid forms of the cosmetic ingredient.
The plant material was air dried and crushed into fine powder. 
Extraction of plant material was then performed by maceration using either the water/ethanol system or pure EtOH. About 1 g of plant material was extracted with approximately 10 g of solvent (extraction ratio 1/10) at room temperature (RT) using a magnetic stirrer (500 rpm) for 2 h.
 The resulting extracts were then filtered over filter paper 8–12 ?m, gathered together and vacuum-concentrated to dryness.
To identify the active fractions, the H2O/EtOH 1/1 extract of Q. pubescens leaves was then fractionated over silica gel.
 The fractionation of the bulk extract led to the recovery of 5 distinct fractions: F1 (250 mL cyclohexane), F2 (250 mL cyclohexane/diethyl ether 1/1), F3 (250 mL diethyl ether), F4 (250 mL methanol) and F5 (250 mL methanol/water 1/1). 
The resulting fractions were further evaluated for their bioactivities, and their respective compositions were addressed by HPLC and GC, as well as by UPLC-HRMS 
Crude extracts and fractions diluted at 10 mg/mL in methanol and filtrated over 0.45 ?m PTFE syringe filter, were analyzed using an HPLC Agilent 1200 system  equipped with a DAD and an ELSD  operating under the following conditions: injection volume: 20 ?L, and flow rate: 1.0 mL/min. 
Separations were performed on a C18 column (Phenomenex, Le Pecq, Ile-de-France, France; Luna? 5 μm, 150 mm × 4.6 mm i.d.).
 The mobile phase consisted in a multistep gradient of water (A), acetonitrile (B) and 2-propanol (C), all acidified with 0.1% acid formic: 0–5 min, 5% B; 5–40 min, 5–45% B; 40–50 min, 100% B; 50–55 min, 100% B; 55–68 min, 100% C, 68–70 min, 100% C. 
The DAD was set at 220, 254 and 330 nm, and ELSD conditions were set as follows: nebulizer gas pressure 3.7 bars, evaporative tube temperature 40 °C and gain 4.
Q. pubescens extract obtained using H2O/EtOH 1/1 was then evaporated to dryness, dissolved in methanol (50 mg/mL) and semi-preparative HPLC was performed on a C18 column (Phenomenex, Le Pecq, Ile-de-France, France; Luna? 5 μm, 250 mm × 10 mm i.d.).
 Elution was performed using a multistep gradient of water (A) and acetonitrile (B), both acidified with 0.1% acid formic: 0–5 min, 5% B; 5–10 min, 5–100% B and 10–14 min, 100% B, under the following conditions: injection volume: 100 ?L, and flow rate: 4.0 mL/min. 
Multiple injections were carried out and the resulting respective sub-fractions were pooled together.
he GC-MS analyses were performed using an Agilent 6890 gas chromatograph (Palo Alto, CA, USA) equipped with an Agilent MSD5973N mass selective detector, a multifunction automatic sampler (Combi-Pal, CTC Analytics, Zwingen, Switzerland) on an HP-1 MS capillary column (100% polydimethylpolysiloxane; 0.2 mm × 50 mm; film thickness, 0.33 μm).
 Samples (1 ?L) were injected in splitless mode (split vent: 50 mL/min–30 s) and the injector was set at a temperature of 250 °C. 
The carrier gas was helium in constant flow mode at 1 mL/min.
 The oven temperature was programmed to rise from 60 °C to 180 °C at 2 °C/min, then from 180 °C to 300 °C at 6 °C/min and kept isothermally at 300 °C for 5 min. 
Acquisition was performed in scan mode (35–500 a.m.u. (atomic mass unit)/s; scan rate: 3.15 scans/s) and mass spectra were generated at 70 eV.
Compound identifications were based on comparison of mass spectra with literature, commercial libraries NIST, Wiley, Indianapolis, IN, USA) and laboratory MS libraries built up from pure substances, combined with comparison of GC linear retention index.
 Retention indices were determined with a series of linear alkanes C8–C24 used as a reference.
Untreated 96-well plates were purchased from Thermo Nunc, whereas the UV-transparent ones were obtained from Costar, Sigma-Aldrich. Adhesive films were used to seal the 96-well plates during incubation. 
Samples were prepared at a concentration of 3.433 mg/mL in dimethyl sulfoxide (DMSO) in 1.5 mL Eppendorf tubes, appropriate for the use of the automated pipetting system epMotion? 5075.
An hydroglycerinated extract of Q. robur heartwood commercialized for its claimed protective activities against photo-aging, free radicals and environmental factors, was tested alongside our samples to perform direct comparison between oak-based ingredients.
An automated pipetting system Eppendorf epMotion? 5075 was used to carry out the bioassays.
 A microplate reader (Spectramax Plus 384, Molecular Devices, Wokingham, Berkshire, UK) was used to measure absorbance values.
 Data were acquired with the SoftMaxPro software (Molecular devices, Wokingham, Berkshire, UK) and the Prism software (GraphPad Software, La Jolla, CA, USA) was used to calculate inhibition percentages.
Similarly, all OD were corrected with the blank measurement corresponding to the absorbance of the sample before addition of the substrate.
The antioxidant activity of the extracts and fractions was measured based on the scavenging activity of the stable 1,1-diphenyl-2-picrylhydrazyl radical according to the following method widely used to examine the antioxidant activity of plant extracts: 150 ?L of a solution of ethanol/acetate buffer 0.1 M pH = 5.4 (50/50) were distributed in each well, together with 7.5 ?L of the extracts evaluated. 
Trolox (3607.8 ?M in DMSO) and a commercial extract of Rosmarinus officinalis L. (3.433 mg/mL in DMSO) were used as positive controls; DMSO alone constitutes the negative one (ODcontrol). 
A first OD reading was performed at 517 nm.
 Then, 100 ?L of a DPPH solution were distributed in each well. The plate was sealed and incubated in the dark at room temperature (RT). After 30 min, the final OD reading was performed at 517 nm to assess the percentage of inhibition.
Tyrosinase is a copper-containing oxidase controlling the production of melanin; it is mainly involved in the hydroxylation of l-tyrosine into L-DOPA and its further oxidation to dopaquinone.
 Since this enzyme plays a key role in melanogenesis, tyrosinase inhibitors are of great concern in the development of skin whitening agents.
 The assays were performed as follows: 150 ?L of a solution of mushroom tyrosinase prepared at a concentration of 171.66 U/mL in phosphate buffer were distributed in each well or 50 U/mL, together with 7.5 ?L of the extracts evaluated. 
Kojic acid (3.433 mM in DMSO) and a commercial extract of Arctostaphylos uva-ursi (L.) Spreng. (3.433 mg/mL in DMSO) were used as positive controls; DMSO alone constitutes the negative one (ODcontrol).
 The plate was filmed and incubated at RT for 20 min. 
Then, 100 ?L of a solution of l-tyrosine (monophenolic activity assay) or L-DOPA (diphenolic activity assay) 1 mM in phosphate buffer pH = 6.8 (substrate’s final concentration per well: 0.388 mM) were distributed in each well. After 20 min of incubation, OD reading was performed at 480 nm to assess the percentage of inhibition.
Lipoxygenase, an iron-containing enzyme catalyzing the deoxygenation of polyunsaturated fatty acids into the corresponding hydroperoxides, is known to play a key role in inflammation [34]. The assays are performed as follows: 150 ?L of a solution of soybean lipoxygenase prepared at a concentration of 686.66 U/mL in phosphate buffer (50 mM pH = 8) were distributed in each well, together with 7.5 ?L of the extracts evaluated.
 Quercetin hydrate (1000 ?M in DMSO) and a commercial extract of Arnica montana L. (3.433 mg/mL in DMSO) were used as positive controls; DMSO alone constitutes the negative one (ODcontrol).
 The plate was sealed and was incubated in the dark for 10 min. Then, 100 ?L of a solution of linoleic acid prepared in phosphate buffer pH = 8 were distributed in each well. 
After incubation for 2 min in the dark, a first OD reading was performed at 235 nm. After a further incubation of 50 min, the final OD reading was performed at 235 nm to assess the percentage of inhibition.
Elastase is a serine protease that preferentially digests elastin, the highly elastic protein that works together with collagen to give skin its shape and firmness.
 The assays were performed as follows: 150 ?L of a solution of porcine pancreatic elastase prepared at a concentration of 0.171 U/mL in Tris buffer were distributed in each well, together with 7.5 ?L of the extracts evaluated.
 Quercetin hydrate  and a commercial extract of Rubus idaeus L. were used as positive controls; DMSO alone constitutes the negative one (ODcontrol).
 The plate was filmed and incubated at RT for 20 min. A first OD reading was performed at 410 nm.
 Then, 100 ?L of a solution of N-succinyl-Ala-Ala-Ala-p-nitroanilide 2.06 mM in Tris buffer were distributed in each well. After 40 min incubation, OD reading was performed at 410 nm to assess the percentage of inhibition.
Hyaluronidases are a family of enzymes that degrade hyaluronic acid, a high-molecular-weight glycosaminoglycan of the extracellular matrix.
 Presenting a unique capacity to bind and retain water molecules, this macromolecule is widely distributed in the body and notably at the periphery of collagen and elastin fibers: it therefore plays a major role in skin aging. 
The assays were performed as follows: 150 ?L of a solution of hyaluronidase prepared at a concentration of 13.3 U/mL in hyaluronidase buffer (pH 7) were distributed in each well, together with 7.5 ?L of the extracts evaluated.
 Tannic acid  were used as positive controls; DMSO alone constitutes the negative one (ODcontrol).
 The plate was filmed and incubated at 37 °C for 20 min. A first OD reading was performed at 405 nm. 
Then, 100 ?L of a solution of hyaluronic acid prepared at a concentration of 150 ?g/mL in buffer (pH 5.35) were distributed in each well. 
After 30 min incubation at 37 °C, 50 ?L of CTAB prepared at a concentration of 40 mM in a NaOH solution (2%) were added in each well and OD reading was performed at 405 nm to assess the percentage of inhibition (ODsample).
Responsible for the tensile strength of the skin, collagen constitutes therefore one of the structural units of the extracellular matrix.
 Collagenases are enzymes that cleave the collagen molecule within its helical region and that are more generally involved in the degradation of the extracellular matrix components, thus leading to skin wrinkling.
 The assays were performed as follows: 150 ?L of a solution of collagenase prepared at a concentration of 53 U/mL in tricine buffer (pH 7.5) were distributed in each well, together with 7.5 ?L of the extracts evaluated.
 Tannic acid (1.72 ?M in DMSO) and a commercial extract of Glycyrrhiza glabra L. (3.433 mg/mL in DMSO) were used as positive controls; DMSO alone constitutes the negative one (ODcontrol).
 The plate was filmed and incubated at RT for 15 min. A first OD reading was performed at 345 nm. Then, 100 ?L of a solution of FALGPA (2-furanacryloyl-l-leucylglycyl-l-prolyl-l-alanine) prepared at a concentration of 5.15 mM in tricine buffer were distributed in each well. 
After 30 min incubation, final OD reading was performed at 345 nm to assess the percentage of inhibition (ODsample).
The total polyphenolic content was estimated by the Folin–Ciocalteu method in 96-well plates as follows: 75 ?L of ultrapure water were distributed in each well, together with 15 ?L of the extracts evaluated.
 DMSO alone was used as negative control. 
Then, 25 ?L of a solution of Folin–Ciocalteu reagent/ultrapure water were added in each well. 
After 6 min incubation under agitation, 100 ?L of a solution of sodium carbonate (75 g/L) were distributed in each well. 
The plate was then filmed and incubated at RT in the dark for 90 min. An OD reading was performed at 765 nm: the amounts are expressed in milligrams of gallic acid equivalents (GAE) per gram of extract. 
The standard curve was prepared with gallic acid solutions (concentrations range: 200, 100, 50, 25 and 12.5 ?g/mL).
The total flavonoid content was estimated in 96-well plates as follows: 100 ?L of a mixture of ultrapure water/DMSO (1/1) were distributed in each well, together with 15 ?L of the extracts evaluated (final concentration per well: 100 ?g/mL).
 DMSO alone was used as negative control. Then, 10 ?L of a solution of aluminum trichloride (100 g/L) were added in each well, together with 15 ?L of potassium acetate (1 M) and 100 ?L of ultrapure water. 
The plate was then filmed and incubated at RT in the dark for 40 min. An OD reading was performed at 415 nm: the amounts are expressed in milligrams of quercetin equivalents (QE) per gram of extract. 
The standard curve was prepared with quercetin hydrate solutions.
The fraction fingerprints were obtained using an HPLC Acquity system.
 Separations were performed on an Acquity UPLC Kinetex? C18 column at 25 °C with a flow rate of 0.6 mL/min.
 The injection volume was set at 1 ?L. The mobile phase consisted of water (solvent A) and acetonitrile (solvent B) both acidified with 0.05% formic acid (all of chromatography grade), and was used in multistep gradient mode.
 The gradient was operated as follows: 0–1 min, 5% B; 1–9 min, 5–40% B; 9–15 min, 40–100% B, 15–17 min, 100% B; final isocratic step for 2 min at 5% B. The sample manager was thermostated at 15 °C.
 ESI (electrospray ionization) conditions operated in positive mode were set as follows: source temperature 150 °C, desolvation temperature 500 °C; capillary voltage 3 KV and cone voltage 10 V.
 Nitrogen was used as cone (10 L/hr) and desolvation gas (1000 L/hr).
SPE cartridges  were fitted into stopcocks and connected to a vacuum manifold. 
The sorbent was conditioned with 10 mL of methanol (MeOH), and equilibrated with 10 mL water.
 With the stopcocks opened and the vacuum turned on, the SPE cartridge is then loaded with either 100 mg of Oak2_M or with 10 mL of Oak3D/H2O (1/3 v/v). 
The vacuum pressure was set at 40 kPa. 
After sample addition, the column was washed first with 10 mL H2O, then with 10 mL H2O/MeOH (10/1 v/v) and finally with 10 mL MeOH/dichloromethane (1/1 v/v). 
Sample was eluted twice with 5 mL MeOH (this stepwise elution was preferred to a single-10 mL elution as it improves the final SPE yield).
Color was evaluated using a spectrophotometer Color i? 5 (X-Rite, Grand Rapids, MI, USA) previously calibrated with a white reference. 
An aliquot of each ingredient discolored or not was placed in glass tubes. 
The results were expressed according to the three-dimensional color space CIE 1976 L* a* b*, defined by the International Commission on Illumination.
 The three coordinates denote respectively the lightness of the color (L*), its red-versus-green attribute (a*) and its yellow-versus-blue attribute (b*), and ΔE*ab represents the color difference between two samples (ΔE*ab will have no unit).
A ΔE*ab between 1.00 to 2.50 represents the points at which an average individual starts to visually notice a color difference; if the ΔE*ab is less than 1.00, the color difference is barely perceivable by an average human observer.
As already stated, a series of Mediterranean plants were initially selected over more than 500 for the present study, based on their accessibility and their originality regarding the anti-aging activity. 
From this survey, it appears that crude extract of Q. pubescens leaves displays the best unprecedented anti-aging activities. 
Added to this, the easiness of the raw material supply in the region, and the attractive marketing potential of the resulting cosmetic active induced its selection for further investigations.
First, the extraction of leaves of Q. pubescens was performed with H2O/EtOH 1/1 to cover the larger polarity range possible, hence to recover a maximum of metabolites. Leaves of Q. pubescens collected in two locations were extracted in the same conditions and the effect of the origin of the raw material on the chemical composition of the resulting extract was investigated. 
The various origin of plant material induced slightly different extraction yields; however, no major difference was evidenced in the phytochemical profiles of oak extracts obtained from leaves collected in different locations (data not shown). 
For convenience, the Q. pubescens extract obtained using H2O/EtOH 1/1 will further be referred to as Oak1 in the present article.
The HPLC fingerprinting of this extract revealed the presence of a large group of polyphenols eluting between 14 and 56 min.
The bioactivities of Oak1 were assessed using in vitro bioassays, and were compared to the bioactivities of commercial cosmetic ingredients used for the respective activities tested (positive control) and to the bioactivities of a commercial oak ingredient. 
To characterize the active compounds responsible for the bioactivities evidenced, Oak1 extract was then fractionated over silica gel. The fractionation of 3.5 g of Oak1 led to the recovery of five distinct fractions: F1, F2, F3, F4 and F5. 
The resulting fractions Oak1_F2 to Oak1_F5 were further evaluated for their bioactivities; no further investigation was undertaken for Oak1_F1 as even if this fraction would be interesting in terms of bioactivity, it would not be commercially viable to develop any resulting cosmetic ingredient due to it poor extraction yield.
The crude extract Oak1, as well as fractions Oak1_F4 and Oak1_F5, all presenting a strong anti-oxidant potency, appear to be quite rich in polyphenolic compounds; such correlation between chemical composition and antioxidant activity was already reported in the literature.
 HPLC-ESI-MS analysis of fraction Oak1_F4 reveals that it is mainly constituted of catechin, a flavanol already known to act as a powerful antioxidant.
Positive relationships were also observed between anti-hyaluronidase activity and phenolic content in the cases of fractions Oak1_F4 and Oak1_F5, as well as between anti-elastase activity and flavonoid content in the cases of fraction Oak1_F3 and to a lesser extent, of fraction Oak1_F4, as previously stated in the literature.
From the previous results, it appeared that the molecules responsible for the bioactivities tested are concentrated in the most polar fractions resulting from Oak1 fractionation scheme 1, i.e., the fractions obtained with diethyl ether, MeOH and MeOH/H2O 1/1. 
To gain information about these metabolites, the Oak1 extract concentrated at 50 mg/mL in MeOH was roughly fractionated by semi-preparative HPLC as described in the Materials and Methods section to separate polar (Oak1_PF eluting between 0–7 min; yield: 62%) from less polar compounds. 
Several injections were realized and successive polar/less polar fractions were respectively pulled together to recover enough material to assess their bioactivities the same way as the one of Oak1.
No remarkable bioactivity was observed for Oak1_PF obtained with water added up with 5% of acetonitrile: this fraction is constituted of more polar compounds that the ones extracted in the previous fractions Oak1_F3 to Oak1_F5.
 The less polar compounds extracted in Oak1_LPF appear to be mainly responsible for the activities reported for Oak1: a large amount of polyphenols eluting between 10 and 50 min were notably identified based on their UV spectra and by comparison with standards available at the laboratory.
 In fact, Oak1_LPF present similar anti-inflammatory and anti-hyaluronidase activities, and appear to be a slightly less potent anti-oxidant, compared to Oak1.
 Some interesting anti-elastase activity was reported for Oak1_LPF, revealing a potential antagonistic effect between both fractions, the polar one impeding the activity of the less polar one against this enzyme.
From the two fractionation strategies adopted, it appears that the compounds responsible for the bioactivities of Oak1 are of intermediate polarity. 
The subsequent process adopted to further develop oak-based cosmetic ingredients will be adapted to preferentially extract those compounds and hence potentialize their bioactivities.
Once the active fraction identified, the corresponding natural cosmetic ingredient can be developed. 
The addition of an appropriate cosmetic support, either liquid or solid, can be necessary to facilitate the incorporation of natural actives into a cosmetic formulation.
As already stated in Section 3.1, the compounds responsible for the bioactivities of Oak1 are of intermediate polarity, so H2O/EtOH 1/1 is not the most convenient solvent system for the further development of an oak-based cosmetic ingredient.
 Added to this observation, one can argue that the elimination of water through concentration by vacuum evaporation is highly energy-consuming, and such a solvent system is hence not the most appropriate one for the ingredients’ industrial production. 
To objectivate the active metabolites’ content of the future ingredient and to facilitate the industrial scale-up of its fabrication process, H2O/EtOH 1/1 was replaced by pure EtOH: for convenience, the Q. pubescens extract obtained by 2 h-maceration of Q. pubescens leaves in ethanol will further be referred to as Oak2 in the present article (extraction yield range: 3–5%). 
The bioactivities of Oak2 were assessed using in vitro bioassays. As presented in Figure 9, apart from the promising antioxidant and anti-hyaluronidase activities already evidenced in the H2O/EtOH 1/1 extract Oak1, Oak2 also displays some anti-inflammatory activity and especially some very interesting anti-elastase activity.
Figure 9. Bioactivities of Oak2, the Q. pubescens extract obtained using pure ethanol, compared to the bioactivities of the discolored extract Oak2D, of the discolored extract deposited on maltodextrin Oak2DM and of commercial cosmetic ingredients used for the respective activities tested (positive controls).
The organic solvents used to extract active molecules from a plant also extract the molecules responsible for the plant’s color, which are generally unacceptable in skin care products  and need therefore to be submitted to further processing, including notably a discoloration procedure. 
Discoloration can be performed by adsorption of the undesirable molecule on activated carbon: the powdered activated carbon can easily be added to the liquid extract, and then removed by settling and filtration.
 Several discoloration conditions were tested using varying discoloration durations and various extract/activated carbon ratios.
 The best results were obtained when Oak2’s discoloration was performed with 1% (w/w) of activated carbon for 1 h.
 The resulting appropriately discolored extract Oak2D was recovered after elimination of the activated carbon (recover yield range: 55–60%) and its bioactivities were assessed using in vitro bioassays. 
Maltodextrin, a solid agent used in cosmetics to bind other compounds and stabilize formulas, was then added to the extract Oak2D (maltodextrin/crude extract 2/1 w/w). 
The mixture is then vacuum-concentrated to dryness, and the resulting powder (Oak2DM) is homogenized using pestle and mortar. The bioactivities of Oak2DM, as well as of maltodextrin alone (no activity reported; data not shown), were assessed using in vitro bioassays as already enunciated (at the same concentration as the extracts and fractions).
The solid ingredient Oak2DM developed here appears to be an efficient active to be incorporated in cosmetic anti-age formula. However, some optimization could still be done in the development of this solid oak ingredient.
Some further discoloration trials should be undertaken to reach the same discoloration level or even a better one, without such a mass loss. 
One can also imagine that adjustments in the ratio of maltodextrin added to the discolored extract may potentially lead to less anti-elastase bioactivity’s loss.
Liquid ingredients are preferred to formulate some cosmetics. Propylene glycol is one of the most widely used cosmetic supports: it serves as a humectant, a viscosity decreasing agent and as a solvent, so it is employed in many personal care formulations including facial cleansers, moisturizers, etc. Owing to its polarity, it was tested to directly extract the active metabolites of Q. pubescens leaves: leaves macerate in propylene glycol (dried plant/propylene glycol 1/10 w/w) for 4 h at room temperature. For convenience, the subsequent Q. pubescens extract will further be referred to as Oak3 in the present article.
From these tests, it appeared that the most suitable discoloration parameters consist in the addition of 1% (w/w) of activated carbon for 1 h: the discolored extract further referred to as Oak3D, was recovered after elimination of the activated carbon. From the color measurements presented in Table 2, it appears that the ΔE*ab is less than 1.00, indicating a color difference barely perceivable by an average human observer. Thus, one can conclude that additional discoloration attempts must be undertaken to obtain an appropriately discolored liquid extract.
The bioactivities of Oak3 and Oak3D, as well as the ones of propylene glycol alone (no activity reported; data not shown) were assessed using in vitro bioassays (Figure 10): no interesting bioactivities were evidenced.
Another attempt was then performed using a greater maceration time: oak leaves macerate in propylene glycol (dried plant/propylene glycol 1/10 w/w) for 7 h 30 at room temperature.
The subsequent Q. pubescens extract, referred to as Oak4 displays some promising anti-hyaluronidase, anti-inflammatory and anti-oxidant activities
From these results, it appears quite clearly that maceration time highly influences the bioactivities of the subsequent extract.
Nevertheless, some further discoloration trials should be undertaken to reach appropriate discoloration, while preserving the extremely interesting anti-aging activity of Oak4.
Finally, greener glycols are also currently tested to develop an even more natural cosmetic ingredient.
Accelerated stability testing that was carried out were performed in glass vials at 42 °C to ensure that the ingredients developed meet the intended quality standards as well as functionality and aesthetics when stored under specific conditions.
As no regulation exists regarding how to perform this kind of tests, the specific testing conditions are defined by the manufacturer.
Typically, decisions are made whether the product is stable or not after eight weeks of testing, and a consensus exists that states that if a product is stable after eight weeks at 45 °C, it corresponds to stability after a one-year storage at room temperature.
In the present case, periodic monitoring of those samples is currently undertaken: visual/olfactory evaluation of critical aesthetic properties such as color, fragrance, texture is performed, and the chemical compositions of both ingredients are scrutinized weekly. 
The evolution of the molecular composition of these finished ingredients, and particularly of their phenolic contents, is followed by HPLC in the same experimental conditions
The tests are not over, but so far, i.e., one month after the beginning of the stability test, no modification of the composition of the ingredient was noticed, but further investigations should confirm these observations.
Furthermore, it would be necessary to push these stability tests forwards once the definite formulation has been determined: additional tests should be performed in an aging chamber where the effect of temperature/lighting variations mimicking the ones a finished product is submitted to, from formulation to consumer’s use, could be monitored.
In this article, the R&D process adopted to develop a new objectivated cosmetic ingredient, from the plant sourcing to the actual ingredient formulation is presented in detail through the example of the development of promising natural anti-age ingredients based on Q. pubescens leaves extract.
Application of high throughput screening technologies to natural product samples accelerates considerably the discovery, development and use of natural cosmetic ingredients, but such a screening is still a long-term undertaking 
It was recently suggested that only five in every 100 genetic resources identified as being potentially of interest will ever end up in cosmetic and personal care formulas
In fact, once the ingredient developed, it must pass all the efficacy, quality, shelf life and safety/tolerability (cytotoxicity, skin and ocular irritability) tests right throughout the development chain prior to be launched on the market. 
Only after this array of controls has been undertaken will a formulator consider using this ingredient in an actual finished-product formulation.
Some researchers actually integrate the toxicology assessment directly into the cosmetic ingredient R&D process to avoid an unpleasant discovery at the end of the process, which could lead to the restriction or even the abandonment of the ingredient’s use due to toxicity concerns
The vigilance about worldwide regulation compliance is guaranteed by the U.S. Food and Drug Administration (FDA). At the European level, the Scientific Committee on Consumer Safety (SCCS, replacing the former SCCNFP) establishes and regularly revises guidance notes that contain relevant information on the different aspects of testing and safety evaluation of cosmetic substances
These notes must be adapted at each individual case, depending, for instance, on the nature of the ingredients entering the formulation, on the finished product’s formulation itself, and the frequency and route of consumers’ use of the product.
Hence, such a translation of laboratory research into commercial successes often takes time (up to several years) and manufacturers must back the right horse about market trends many years in advance.
H.P. is grateful to the CIFRE - Conventions Industrielles de Formation par la REcherche - convention (ANRT JYTA) for her Ph.D. financing. 
The objective of this study was to evaluate the potential use of some vegetal raw materials in personal-care products. Four ethanolic extracts (grape pomace, Pinus pinaster wood chips, Acacia dealbata flowers, and Lentinus edodes) were prepared and total phenolics, monomeric sugars, and antioxidant capacity were determined on alcoholic extracts.
 Six of the most important groups of cosmetics products (hand cream, body oil, shampoo, clay mask, body exfoliating cream, and skin cleanser) were formulated. 
Participants evaluated some sensory attributes and overall acceptance by a 10-point scale; the results showed differences among age-intervals, but not between males and females.
The results confirmed that all extracts presented characteristics appropriate for their use in cosmetic formulations and their good acceptability by consumers into all cosmetic products.
Texture/appearance, spreadability, and skin feeling are important attributes among consumer expectations, but odor and color were the primary drivers and helped differentiate the natural extracts added into all personal-care products.
Consumers are increasingly demanding natural ingredients and additives in cosmetic products, as well as the replacement of synthetic compounds with possible negative effects on health and the environment
Antioxidants are preservatives with the function of preventing lipid oxidation of the product. They can act following different mechanisms, i.e., reducing agents, oxygen scavengers, synergistic agents, and chelating agents. More recently, antioxidant-based products have been proposed to protect the skin
Among natural compounds with antioxidant properties, phenolics are the most studied.
Natural phenolics—including benzoic acids, cinnamic acids, and flavonoids—are widely distributed in renewable and abundant sources, such as agricultural, food, and forest products and by-products. 
The utilization of these alternative low cost sources is desirable for the integral valorization of vegetal raw materials and could benefit the economy of the process and the cost of the products.
The potential of selected natural extracts obtained from underutilized and residual vegetal biomass processed with food-grade green solvents as additives in cosmetic products was previously reported. 
The extracts were safe for topical use and enhanced the oxidative stability of model oil-in-water emulsions
Phenolic compounds present a wide variety of activities of interest in cosmetics, such as antioxidant, antimicrobial, anti-inflammatory, or anti-aging.
Formulations enriched in phenolic antioxidants are increasingly used in anti-aging cosmetics as a defense strategy against reactive oxygen species (ROS) 
 In addition, natural phenolic compounds can permeate through the skin barrier, in particular the stratum corneum
Cosmetic products need to be effective and stable, but also the acceptance by the consumer needs to be confirmed.
 Equally important are the favorable benefits on skin health and the desirable sensory attributes, because the incorporation of natural extracts could confer undesirable characteristics and strong colors or aromas, that would limit the acceptance of the product.
Sensory analysis can discriminate the characteristics influencing consumer acceptance and to indicate how they are perceived and, consequently, guide success in the development of new cosmetic products.
 According to Elezovi? et al., the characteristics influencing consumer acceptance are based, first, on its packaging, and then on its smell, appearance, and texture (touch and feel).
Therefore, after the development of a formulation, researchers and cosmetic companies should carry out a sensory evaluation with trained or consumer panels.
Sensory analysis represents a valuable tool, but it is financially expensive and time-consuming.
For this reason, some papers have recently appeared studying the application of instrumental analysis, mainly through rheological measurements, to detect changes of entry ingredients
Cosmetic properties, such as the optimal mechanical properties (firmness), adequate rheological behavior, and appropriate adhesion, could be measured by instrumental analysis.
Other attributes—including, appearance, odor, residual greasiness after application, or the sensation produced by the cosmetic application—play an important role in the acceptability of cosmetic products by consumers.
They are subjective and, consequently sensory evaluation methods should be applied. 
The objective of this work was to formulate six cosmetic products with ethanolic extracts from four vegetal raw materials which provide different types of aromatic families: flower, fruity, wood, and mushroom/earthy.
The reducing power and radical scavenging capacity of the extracts were characterized and the sensory evaluation of the final personal-care products was assessed.
Grape pomace was provided by Destiler?a Galicia. Pine (Pinus pinaster) wood chips, kindly provided by FINSA Orember (Ourense, Spain), were air dried and milled under 1 mm.
Acacia dealbata flowers were collected in forest areas in the surrounding of Ourense (Spain) in Winter 2014 and freshly processed.
Shiitake (Lentinus edodes) was purchased in local markets and was freeze dried and ground before processing.
Pressed distilled grape pomace was extracted with water at a liquid to solid ratio 15 w/w, at 50 °C in an orbital shaker at 175 rpm overnight.
The solid and liquid phases were separated by filtration and the liquid phase was contacted with non-ionic polymeric resins
Before use, resins were rinsed with deionized water at a liquid-to-solid ratio of 5 (w/w). 
Desorption was carried out with 96% ethanol at a solvent to resin ratio 3 (mL/g) in an orbital shaker at 175 rpm and 50 °C. 
The resin was regenerated in 1 M NaOH overnight and further washed with deionized water 
Ground Pinus pinaster wood samples, Acacia dealbata flowers, and ground freeze-dried Lentinus edodes samples were contacted with 96% ethanol in sealed Erlenmeyer flasks at 50 °C in an orbital shaker at 175 rpm overnight.
Six cosmetic model products were formulated with conventional ingredients and with the extracts from the selected sources.
The cosmetics prepared were: hand cream (HC), body oil (BO), shampoo (S), clay mask (CM), body exfoliating (BE), and a skin cleanser (SC). 
The ethanolic extracts—grape pomace extract (GPE), pine wood extract (PWE), acacia flowers extract (AFE), and shiitake extract (SE)—were added to cosmetics dissolved in a minimum amount of ethanol.
These extracts have both the function of antioxidants and additives (colorants and perfumes). Control samples without extracts were also prepared.
Hand cream (HC) contained: paraffinun liquidum (30 g), lanolin (30 g), Kathon CG (0.2 g), and extract (five drops).
Paraffin was slowly melted in a water bath (50 °C) and stirred to obtain an homogeneous mixture, which was neutralized with triethanolamine (if required). The extracts were added to the cold mixture.
The oils were mixed with gentle stirring in a water bath at 40 °C, and once cooled, the extract was added.
Shampoo (S) was prepared with the following ingredients: sodium laureth sulfate (45 wt %), diethanolamine (3 wt %), Kathon CG (0.02 wt %), citric acid (0.01 wt %), extract (five drops) and distilled water. Approximately half of the water volume was mixed with the detergent and the other half was used to dissolve diethanolamine, citric acid and Kathon.
Both solutions were mixed with intense stirring before adding the extract.
Clay mask (CM) was formulated with the following components: sodium laureth sulfate (0.1 g), kaolin (35 g), bentonite (5 g), cetyl alcohol (2 g), glycerin (10 g), Kathon CG (0.2 g), extract (five drops), and distilled water.
Water was incorporated to bentonite and was allowed to stand 24 h until gelification. Cetyl alcohol was melted in a water bath.
The detergent, glycerin, and the antimicrobial agent were added to the bentonite mixture, which was then heated at 40 °C. Kaolin was also added at this temperature, stirring to avoid lumps. The extract was added to the cooled mixture.
Body exfoliating salt scrub (BE) was composed of: sodium chloride (150 g), almond oil and extract (five drops). 
The oil was used in an amount needed to moist the salt, then the extract was added and the mixture was stirred until homogenization.
Skin cleanser (SC) was prepared with: cetyl alcohol (10.5 mL), liquid paraffin (30 mL), distilled water (258 mL), triethanolamine (1.5 mL), Kathon CG (0.6 mL), and extract (five drops). Cetyl alcohol was melted in a water bath at 70 °C and mixed with paraffin under mild stirring. 
Triethanolamine, the antimicrobial agent, and distilled water were mixed with continuous stirring at the previously indicated temperature. The aqueous phase was dropped on the oily phase until it coole and then the extract was added.
The extracts were characterized for phenolic and sugar content and for antioxidant activity. 
The personal care products were characterized for sensorial properties.
Total phenolic content was colorimetrically determined using the Folin–Ciocalteu reagent (Sigma-Aldrich, St. Louis, MO, USA) and expressed as gallic acid (Sigma-Aldrich, St. Louis, MO, USA) equivalents.
All analyses were performed at least in triplicate and are reported on a dry matter basis. Ash content was gravimetrically determined.
The sensory panel consisted of 26 female and 29 male assessors (18–45 years old) recruited from a pool of students and staff of IES Lauro Olmo (O Barco de Valdeorras, Ourense, Spain), without previous experience in sensory analysis.
The six cosmetic samples (about 10 mL) were served in transparent glass containers encoded with three-digit random numbers. Mineral water and paper towels were provided for skin rinsing between samples.
Panelists were asked to fill out a questionnaire evaluating the intensity of the sensory properties by using a 0 to 10 scale (where 0 represents ‘none’ and 10 ‘extremely strong’) in two sessions along one month.
The list contained 15 descriptors or attributes typically used to characterize the skincare products: six appearance attributes (gloss, color, odor intensity, odor preference, firmness/consistency, creaminess/texture/appearance) and eight skin parameters (spreadability, penetration, softness, skin odor intensity, skin odor preference, skin odor persistence, skin gloss, and skin feel).
Finally, participants were asked to rate their global appreciation of the product on a 10-point scale to report which extract they preferred in each formulation. 
Assessors valuated the appearance attributes during the first session; the skin parameters and the comparison of the six personal-care products to the global score were recorded in the second session.
Intensity values from sensorial data were analyzed by a two-factor (extract, cosmetic) analysis of variance (ANOVA) test using Excel software. 
A Fisher LSD post hoc test (p < 0.05) was used to test the significance of the relative mean differences among the samples. 
Differences among extracts and sample formulations were obtained from preference and descriptive data evaluated by means of principal component analysis (PCA) using the program Statistica 8.0 (Statsoft Inc., 2004, Tulsa, OK, USA).
The phenolic content in the extracts was higher for the grape pomace and acacia flower (18 wt %), and very low for the mushroom, which contained sugars and polyols (threalose, mannitol, and arabitol).
The increased phenolic content of the extracts led to increased ABTS radical scavenging capacity and reducing capacity, except for the grape pomace extract. 
The most active radical scavengers were the acacia flower extract (AFE), followed by the grape pomace extract (GPE) and the pine wood extract (PWE), with 50–70% of the activity of Trolox. The highest reducing power was also found for AFE.
Many natural products, botanicals, or waste materials—derived from agricultural products, foods and beverages—can be used in cosmetics products
Sensory test was carried out to evaluate the possibility of using natural extracts as ingredients of some cosmetic preparations and their acceptability by consumers. 
Independently of the antioxidant activity, the ethanolic extracts were added to provide their odoriferous characteristics to the different cosmetic formulations and to evaluate their acceptance by the consumers.
Likewise, the color differed with raw material too and the volunteers scored also this difference.
Personal-care products from acacia flowers extract (AFE) always showed an intense yellow color
Sensory analysis was performed to determine the preference for the natural extracts, because the composition of the individual formulations was the same, except in this ingredient.
A control sample which did not contain any extract from the studied vegetal raw materials was used for reference.
The most preferred and valued attributes in all cosmetics were spreadability, softness, consistence/texture, and skin feel, but the ANOVA results showed that these attributes in each cosmetic product were comparable and the use of the different extracts caused only a significant effect (p < 0.05) on two parameters: color and odor.
Participants from the two genres accomplished the sensory analysis (26 women and 29 men), and, as can be seen in Figure 2, no great differences between them were found.
Only four samples demonstrated significant differences: women evaluated better than men the acacia flower extract when it was included in the hand cream and exfoliating preparations, and the control-exfoliating and the shampoo with grape pomace extract obtained better scores with men than with women.
Female participants valued body oil and clay masks better than males, and, on the contrary, men valued the shampoo more.
Hand cream elaborated with acacia flowers or shiitake attained the best scores, with 7.20 and 7.04 points, respectively.
From the four assayed raw materials, the least preferred extracts in all cosmetics were red grape pomace and pinus wood ethanolic extracts.
The average overall preference showed that these extracts were best valued as aromatic additives in the shampoo, and the worst score was attained by the exfoliating body. 
The cosmetic most valued with the acacia or shiitake extracts were the hand creams, and the worst was the body exfoliating cream. 
 Control samples (without any added extracts) achieved similar scores than when GPE and PWE were added into all personal-care products, except in shampoo, where it was obtained the best punctuation along with the shiitake extract.
The participants were also divided into three age-segments: <20, 20–30, and 30–45 years, with 20, 19, and 16 persons, respectively.
This last group perceived acacia and shiitake extracts with the significant highest values for all formulations, revealing the influence of color and odor of these natural extracts 
In opposition, the participants under 20 years of age preferred the extracts obtained from pinus wood and grape pomace or the control sample (without any extract), except in hand cream.
These two extracts received lower score in the group of older participants.
Consumers in the 20–30 years old segment evaluated samples with the highest overall preference values in all personal-care products, and the preferred extracts were acacia flowers and shiitake extracts.
The sensory characterization confirms which properties mostly influence consumer acceptance. 
All volunteers considered that the different tested personal-care products have a good spreadability, softness, and good skin penetration ability and posterior skin feeling; but also a pleasant color and fragrance.
These results have shown that a variety of plant materials can be used as additives in cosmetic products to supply color and aroma
Principal component analysis was performed to know how consumer acceptance is based on sensory attributes.
The interrelationships among the extracts from natural raw materials used in personal care-products and sensory descriptors showed that the first two factors explained 88.3% of the total variance among the extracts. 
The first component accounted for 70.5% of the data variability and the second for 17.8%.
In the PCA the extract samples are clearly grouped into two clusters. Cluster 1, located in the upper half, was associated with the cosmetics elaborated from acacia flowers and shiitake extracts, which had the greatest acceptance.
Cluster 2, in the positive part of Factor 1 and in the negative part of Factor 2, was characterized by the other two extracts (grape pomace and pine wood) and the control (without added extract). No differences between different cosmetic formulations were found.
All products were well-tolerated because any visible skin irritation or erythema was observed. 
Besides expected appearance, spreadability, softness, and skin feeling, the color and odor also play an important role on overall preference and, consequently, on purchase intent.
According to the obtained results, fragrance and color were two important attributes for consumer preference and they are essential additives to make personal-care products, even, cosmetic companies use colors in packaging design to communicate the properties of their fragrances
Between the four assayed vegetal extracts, the floral aroma and yellow color provided by the acacia flower extract were evaluated higher by all consumers, independently of genre or age; likewise, this ethanolic extract also presented the highest in vitro antioxidant activity.
Consumers are increasingly rejecting synthetic chemicals in beauty and cosmetic products and demand natural products.
In this study, ethanolic extracts from different raw materials were added to some personal-care products and sensory analysis was carried out to evaluate some attributes such as color, aroma, and texture, and the overall acceptability by consumers.
Results have shown that the ethanolic extracts obtained from a flower, a mushroom, a tree, or agricultural waste (grape pomace) can be valorized and they have a potential application as an ingredient for cosmetic formulations.
Pollution from air and sunlight has adverse effects on human health, particularly skin health. It creates oxidative stress, which results in skin diseases, including skin cancer and aging.
Different types of antioxidants are used as preventative actives in skin-care products.
However, they have some limitations as they also scavenge oxygen. 
Recently, spin traps are being explored to trap free radicals before these radicals generating more free radicals (cascading effect) and not the oxygen molecules.
However, not all spin traps can be used in the topical cosmetic skin-care products due to their toxicity and regulatory issues.
The present review focuses on the different pathways of reactive oxygen species (ROS) generation due to pollution and the potential use of spin traps in anti-pollution cosmetics to control ROS.
The World Health Organization (WHO) has recognized the urban pollution as one of the most important environmental issues in the world. 
Over 99% of the urban population in Asia is regularly exposed to concentrations of air pollutants that are above the WHO recommendation level. 
According to the latest urban air quality database, more than 100,000 inhabitants in 98% of cities in low- and middle-income countries do not live in areas with good or healthy quality air (as per WHO air quality guidelines).
On the contrary, high-income countries are improving their air quality.
Air pollution is comprised of various particulate matters (PMs) that can cause skin diseases, cancer, pulmonary, and cardiovascular diseases
The increased ambient PM from industrialization and urbanization is highly associated with morbidity and mortality worldwide
Free radicals or reactive chemical species exhibit a single unpaired electron in an outer orbit to form the unstable configuration creating energy and releasing it through reactions with adjacent molecules, such as proteins, lipids, carbohydrates, and nucleic acids
Nanosized particles from traffic sources are the most harmful components of ambient PM because of oxidative stress due to their small size and large surface per unit mass, and are highly reactive towards biological surfaces and structures 
Additionally, nanoparticles can carry organic chemicals and metals to mitochondria and generate ROS [10].
Additionally, Polycyclic Aromatic Hydrocarbons (PAHs) adsorbed on the surface of airborne PM can activate xenobiotic metabolism to convert PAHs into quinones and producing ROS.
Two types of antioxidants that can help scavenge ROS are (i) enzymatic antioxidants such as superoxide dismutase (SOD), catalase (CAT), ascorbate peroxidase (APX), guaiacol peroxidase (GPX), glutathione reductase (GR), monodehydroascorbate reductase (MDHAR), and dehydroascorbate reductase (DHAR); and (ii) non-enzymatic antioxidants such as ascorbic acid (AA), reduced glutathione (GSH), α-tocopherol, carotenoids, flavonoids, and the osmolyte proline. 
 A co-factor is an ion or a molecule that binds to the catalytic site of an apoenzyme rendering it active.
In addition to antioxidants, co-factors such as zinc also play an important role in the free radical induced oxidative damage.
It is five to six times more concentrated in the epidermis than the dermis.
Zinc is an essential element of more than 200 metalloenzymes including the antioxidant enzyme and CuZnSOD
Topical divalent zinc provides antioxidant photoprotection for skin either by replacing redox active molecules such as iron and copper at critical sites in cell membranes and proteins or by inducing the synthesis of metallothionein like sulfhydryl-rich proteins that protect against free radicals 
The conventional antioxidants react with free radicals to convert the ROS into water. 
However, antioxidants may also contribute to hypoxia in deep tissue by indiscriminately converting both normal oxygen and ROS molecules to water
On the other hand, spin traps have the ability to scavenge or stabilize free radicals before their deterioration and help reduce their cascade effect on other molecules to generate more free radicals. Spin traps can selectively trap ROS as opposed to antioxidants
The present review discusses ROS and oxidative stress pathways arising due to pollution, the potential use of spin traps against ROS and oxidative stress, and their limitations.
Ambient PM can penetrate the skin either through hair follicles or trans-epidermally, and PM-bound PAHs generate ROS.
The long-term exposure to air pollution can lead to extrinsic skin aging through oxidative stress generated by the particles themselves and/or by associated PAHs.
Moreover, PMs have been found to disrupt stratum corneum integrity by 2-fold and mildly damaging tight junctions. 
Additionally, an increase in soot (per 0.5 × 10?5/m) and particles from traffic (per 475 kg/year/km2) has been associated with 20% more pigment spots on the forehead and cheeks 
M induces oxidative stress through the production of ROS and secretion of pro-inflammatory cytokines such as Tumor necrosis factor (TNF)-α, Interleukin (IL)-1α and IL-8 
 The increased production of ROS such as superoxide and hydroxyl radical by PM exposure increases matrix metalloproteinases (MMPs) such as MMP-1, MMP-2 and MMP-9. MMPs are responsible of the degradation of collagen and lead to the increase in inflammatory skin diseases and skin aging
 In addition, environmental cigarette smoke, which is well known as an oxidizing agent, is responsible for androgenetic alopecia (AGA).
The ultrafine particles including black carbon and PAHs enhance the incidence of skin cancer.
Overall, increased PM levels are responsible for the development of various skin diseases via the regulation of oxidative stress and inflammatory cytokines.
Antioxidant and anti-inflammatory drugs may be useful for treating PM-induced skin diseases.
The onset of systemic lupus erythematosus (SLE), an immune-complex-mediated multi-systemic autoimmune condition, can be flared by various environmental factors such as cigarette smoke, alcohol, occupationally- and non-occupationally- related chemicals, ultraviolet (UV) light, infections, etc.
Sensitive skin is induced by various environmental factors such as UV light, cold, heat and air pollution. 
The activation of cutaneous endothelin receptors and transient receptor potential (TRP) channels represent a mechanism by which external environmental stimuli are transferred to individuals with sensitive skin
Additionally, UV irradiations upregulate proinflammatory lipids including lysophosphatidic acids (LPA) such as LPA 18:1
These upregulated proinflammatory lipids are agonists of TRPV1 (transient receptor potential cation channel subfamily V member 1 or the capsaicin receptor or the vanilloid receptor 1) in the skin
 TRPV1 contributes to thermal hyperalgesia and mechanical allodynia and trigger the sensation of pain
Toll-like receptors (TLRs) are cellular sensors that recognize pathogens and can be expressed on various cell types including keratinocytes, Langerhans cells, mast cells, and fibroblasts in the skin
Upon stimulation with exogenous or endogenous ligands, TLR3 cells are intimately involved in the pathogenesis of infectious or inflammatory skin diseases such as viral infections or allergic and irritant contact dermatitis, and itching sensations in the skin 
Atopic dermatitis (AD) is a chronic skin disorder which is characterized by pruritus and recurrent eczematous lesions that are accompanied by T-helper (Th)2-dominated inflammation. 
AD is characterized by complex interactions between genetic and environmental factors, such as skin barrier dysfunctions, allergy/immunity, and pruritus. 
Filaggrin is one of the key proteins involved in skin barrier function. 
Th2 cells produce interleukin (IL)-31 which can provoke pruritus and other Th2 cytokines can decrease filaggrin expression by keratinocytes.
AD can be treated by newly developed drug, Dupilumab (as a post treatment and not as preventative measure), which can bind to IL-4 receptor α and inhibit downstream signaling induced by IL-4 and IL-13
UV irradiation is one of main environment pollutants that can cause human carcinogens.
 UV-C is absorbed by oxygen and ozone in the Earth’s atmosphere and does not have any significant impact on the skin
However, the longer wavelength, UV-B and UV-A radiations, have significant effects on the biota.
UV irradiations are responsible for melanoma formation [31]. The DNA damaging, carcinogenic, inflammatory, and immunosuppressive properties of UV radiations contribute to initiation, progression, and metastasis of primary melanoma
The changes in ROS signaling pathways have the damaging action of UV-A and UV-B irradiations on the skin
Moreover, the overproduction of ROS may stimulate malignant transformation to melanoma.
UV-A light can deeply penetrate into the skin generating ROS that damages DNA leading to 92% of malignant melanoma. 
 On the contrary, UV-B light, that has a shorter wavelength than UV-A light, causes sunburn, induces DNA damage leading to the apoptosis of keratinocytes
Therefore, UV-B light affects the skin directly compared to UV-A light and is responsible for 8% of total melanoma production
Mechanistically, the UV-B light exposure results in (i) the formation of covalent linkages between pairs of thymine and cytosine bases in DNA, (ii) the formation of pyrimidine (cyclobutane) dimers and (iii) the excitation of DNA of the skin cells.
Additionally, the DNA polymerase incorporates an incorrect base opposite to an abnormal base leading to a DNA mutation during the replication process, which, in turn, can cause skin cancers.
In addition to the DNA mutation, some of the major side products such as 6-4 photoproducts (6-4 PPs) pyrimidine adducts and Dewar valence isomers are also formed by the photoisomerization of 6-4 PPs due to exposure of the skin cells to UV-B light (>290 nm)
Most of these genetic lesions are generally corrected by nucleotide excision repair.
The genetic information may be permanently mutated if nucleotide excision repair dies not occur.
After UV-A irradiation absorption, endogenous photosensitizes such as flavins, NADH/NADPH, urocanic acid, and some sterols present in tissue are converted to their long-lived triplet state, which in turn can transfer energy to oxygen molecules to form an energetically excited and highly reactive singlet oxygen.
Additionally, a novel class of UV-A photo-sensitizers comprised of skin biomolecules based on 3-hydroxypyridine derivatives such as enzymatic collagen cross-links, B6 vitamin, and glycation end products in chronologically aged skin are capable of skin photooxidative damage
UV-A-irradiated cultured human melanocytes can be photosensitized by chromophores such as pheomelanin and/or melanin intermediates
In addition, UV-B natural chromophores may exhibit similar phototoxic properties. UV-B-sensitized tryptophan produces singlet oxygen (1O2) and superoxide radicals (O2?.), and these reactive forms of oxygen may contribute to membrane-, cytoplasm- and DNA-damaging effects
Singlet oxygen, hydroxyl radical and hydrogen peroxide are ROS that can also produce oxidative stress in cells and organisms
Oxidative stress is the imbalance between ROS production and a biological system’s ability to detoxify these reactive intermediates.
The oxidative stress is considered as a critical pathophysiological mechanism in cancererogenesis 
Reactive chemical species can reach DNA by diffusion and the resultant bimolecular reaction will damage the DNA
Singlet oxygen interacts preferentially with guanine to produce 8-oxo-7,8-dihydroguanine after losing two electrons.
 Additional removal of two electrons from 8-oxo-7,8-dihydroguanine can yield highly mutagenic spiroiminodi-hydantoin (Sp) R and S stereoisomers that are capable of causing G→T and G→C conversions, and, in turn, this may initiate cancer 
Typical levels of ozone that are recorded in urban environments are in the range of 0.2 to 1.2 ppm
 This century will see an increase in ozone levels, which will have adverse effects on skin 
Ozone is a potent oxidant that can react with a variety of extracellular and intracellular biomolecules and damage the barrier function of the epidermis
The cytotoxicity of ozone is due to its capability of antioxidant depletion and its interaction with unsaturated lipids to generate damaging free radicals or toxic intermediate products.
 Ozone exposure can influence antioxidant levels and oxidation markers in the outermost stratum corneum layer 
The changes in the oxidant levels and oxidation markers induce cellular stress responses in the deeper skin cells 
The chronic exposure of the skin to environmental stressors can overwhelm the skin’s defensive system comprised of both enzymatic and non-enzymatic low molecular weight antioxidants, and induces persistent damage to cutaneous tissues. 
Therefore, antioxidants can be used as a defensive approach against the pollution induced oxidative stress.
However, the antioxidant protection is limited by the first-pass metabolism and the lack of ability to sustain enough antioxidants in the skin. 
Additionally, the topical application of single antioxidants is not enough to protect the skin in a comprehensive manner; therefore, the synergistic action of diverse types (enzymatic and non-enzymatic) of antioxidants may better protect against oxidative stress. In this regard, the combination of ferulic acid with vitamin oxidants such as vitamin C and vitamin E has been found to provide double protection to the skin from environmentally induced oxidative stress
 These findings may be helpful to researchers to find new ways of preventing or neutralizing the toxic effects of O3 in cutaneous tissue. 
The exposure of normal human epidermal keratinocytes (NHEK) to ozone (0.3 ppm) can result in an increase in protein and messenger RNA (mRNA) expression of the family of cytochrome P450 (CYP) isoforms (CYP1A1, CYP1A2 and CYP1B1). Additionally, NHEK exposure to ozone results in nuclear translocation of the aryl hydrocarbon receptor (AhR) and in phosphorylation of epidermal growth factor receptor (EGFR).
Moreover, the effect of ozone on events downstream of EGFR can result in an increased activation of phosphoinositide 3-kinase and phosphorylation of protein kinase B and mitogen-activated protein kinases [73]
Acute ozone exposure depletes skin vitamins C and E and induces lipid peroxidation in upper epidermal layers
The dramatic increase of basal and squamous cell skin cancer in the past few years has been associated with stratospheric ozone depletion caused by ozone depleting substances (ODS) of anthropogenic origin and resultant higher UV-B radiations
Skin aging leads to a progressive loss of structure and function, which can be influenced by both intrinsic and extrinsic factors.
Oxidative stress induced by ROS plays an important role in the aging process [76]. 
Mitochondria is the major source of cellular oxidative stress and causing cutaneous aging and senescence.
Antioxidants are generally produced to counteract the oxidative stress.
However, due to environmental stress, elevated ROS levels can overwhelm endogenous cellular antioxidant mechanisms
This can lead to an imbalance in tissue oxygen homeostasis, with oxidant effects outweighing antioxidant effects, and, therefore, the cellular environment becomes oxidatively stressed 
Oxidation of lipids by ROS can damage cellular structures and result in premature cell death
In addition, the interaction with nuclear and mitochondrial nucleic acids results in mutations that predispose them to strand breaks 
Extrinsic skin aging is generally influenced by environmental factors and external stressors such as UV radiation, pollution and lifestyle factors that stimulate the ROS production and oxidative stress
The properties of antioxidants such as preventive, radical scavenging, repair and de novo, and the adaptations are described below. 
The first line of defensive antioxidants suppresses the formation of free radicals, but the precise mechanism and site of radical formation in vivo are not well established so far. 
The metal-induced decompositions of hydroperoxides and hydrogen peroxide is assumed to be one of the important sources of free radicals. 
Some antioxidants reduce hydroperoxides and hydrogen peroxide to alcohols and water, respectively, prior to the generation of free radicals. Enzyme antioxidants such as glutathione peroxidase, glutathione-S-transferase, phospholipid hydroperoxide glutathione peroxidase (PHGPX), and peroxidase decompose lipid hydroperoxides to corresponding alcohols.
 PHGPX is a unique enzyme antioxidant as it can reduce hydroperoxides of phospholipids integrated into biomembranes. 
Antioxidants, glutathione peroxidase and catalase reduce hydrogen peroxide to water. The second line of defensive antioxidants scavenge the active radicals to suppress chain initiation and/or break the chain propagation reactions.
 Endogenous radical-scavenging antioxidants can be hydrophilic and lipophilic in nature. Hydrophilic radical-scavenging antioxidants are vitamin C, uric acid, bilirubin, albumin, and thiols, while lipophilic radical-scavenging antioxidants are vitamin E and ubiquinol. 
Vitamin E is widely treated as the most potent radical-scavenging lipophilic antioxidant. 
The third line of defensive antioxidants comprised of the repair and de novo antioxidants such as proteolytic enzymes, proteinases, proteases, and peptidases that are present in the cytosol and in the mitochondria of mammalian cells. 
These antioxidants recognize, degrade and remove oxidatively modified proteins and prevent the accumulation of oxidized proteins. 
Additionally, the DNA repair systems play an important part of the total defense system against oxidative damage. 
Enzymes such as glycosylases and nucleases repair the damaged DNA. Furthermore, there is an important function called adaptation that produces the signal for the production and reactions of free radicals to induce the formation and transport of the appropriate antioxidant to the right site.
 Antioxidants have been used to reduce the effect of oxidative stress and photoaging or repair the damaged skin. However, the efficacy of antioxidants to protect the skin is dependent on potency as well as its stability of antioxidants in the skin or in the formulation.
 Antioxidant mixtures protect against ozone induced damage in human reconstructed skin models.
 Recently, Valacchi et al. have demonstrated that antioxidant mixtures comprised of L-ascorbic acid, alpha-tocopherol, ferulic acid, and phloretin can restrict ROS production induced by ozone exposure in a reconstructed human epidermis (RHE) model in addition to the prevention of ozone-induced oxidative damage in human keratinocytes. 
As discussed in the next section, spin traps do not react with unpaired electrons of oxygen and trap free radicals only. 
Vitamin C, which is known to be antioxidant, can also be an oxidant to form the ascorbate free radical. 
Spin traps do not form free radicals like Vitamin C.
Janzen and Blackburn coined the term “spin trap” for trapping free radical intermediates.
 The fundamental mechanism of “spin trap” action is different from traditional antioxidants such as Vitamin A or C. Spin traps are quick to trap or stabilize free radicals before deterioration of free radicals. 
Furthermore, they can reduce the free radical cascade effect on other molecules to generate more free radicals. Spin traps essentially scavenge the free radicals and selectively trap ROS in comparison to antioxidants. 
As discussed before, the antioxidants chemically react with the free radicals to convert the ROS into water to terminate the chain reaction.
 Antioxidants might be counterproductive as they may contribute to hypoxia (lack of oxygen) in deep tissues by indiscriminately converting both normal oxygen and ROS molecules to water. 
On the other hand, spin traps react only with the ROS in a passive way by intercepting it before any damage is done. 
It can differentiate between good oxygen molecules and ROS (which are harmful).
The spin trapping technique consists of using a nitrone or a nitroso compound to “trap” the initial unstable free radical as a “long-lived” nitroxide that can be observed at room temperature using conventional Electron spin resonance (ESR) spectrometric procedures.
Nitrones, particularly PBN, have been studied for anti-aging properties.
 PBN suppresses the amount of ROS produced in mitochondrial respiration. With their unique actions to control ROS, spin traps also suppress pro-inflammatory conditions and destroy age-inducing free radicals in skin.
 Therefore, they can be utilized in skin anti-aging products, scar-reducing products, and used to treat inflammatory conditions such as rosacea and sunburn.
 Additionally, spin traps can control cellular oxidation states and oxidatively sensitive enzyme systems, and exhibit anti-irritant and anti-microbial properties in topical skin-care formulations.
 Particularly, PBN scavenges the hydroxyl free radicals generated by α-hydroxy acids in the Fenton reaction.
Fuchs et al. have studied the toxicity of different spin traps, and estimated the irritation potency (IP) based on erythematous and odematous lesions; II of spin traps estimated at 100 mM is provided in. 
Based on II, authors have found that the nitroxide precursors and nitrones can be classified clinically non-irritant, 2,2,3,4,5,5-hexamethyl-imidazoline-1-yloxyl (Imidazo), 5,5-dimethyl-1-pyrroline N-oxide (DMPO)) to slightly irritant (3,3,5,5-tetramethylpyrroline-N-oxide (TEMPO), 2,2,5,5-tetramethyl-3-oxazolidinoxyl (DOXO), PBN) as per the Draize protocol. 
PBN, DOXO, and TEMPO are found to be non-irritant at 10 mM; therefore, a low amount of these three spin traps can be used in cosmetic formulation to avoid toxicity. 
Further research may be needed to find maximum safe concentrations of these three spin traps.
Authors have also found a significant increase in the trans-epidermal water loss values by 100 mM of TEMPO, DOXO and PBN and 2,2,6,6-tetramethyl-1-hydroxypiperidine (TOLH), hydroxylamine-TEMPO and its major skin metabolite did not cause skin irritation. 
The nitroxide irritancy potency is found to be TEMPO > DOXO > Imidazo = PROXO. This irritancy potency is further found to be in inverse order (Imidazo = PROXO > DOXO > TEMPO) of nitroxide biostability in murine and human skin.
Additionally, nitroxides and nitrones did not show the sensitizing effect as per the Magnusson and Kligman test. 
Therefore, the nitroxide precursors and nitrones tested have exhibited a low potential of acute skin intolerance. Janzen et al. found that the lethal dose of PBN was found to be approximately 100 mg/100 g bodyweight (0.564 mmol/100 g), suggesting that PBN is non-toxic. 
DMPO was found to be the least toxic (no toxic signs at twice the lethal dose for PBN), whereas 2,6-difluoro-PBN and M4PO (3,3,5,5-tetramethyl-1-pyrroline-N-oxide) were the most toxic as both cause death at 1/8 of the PBN-equivalent lethal dose.
 Non-irritating spin traps have low TEWL (13 g/m2·h–16 g/m2·h), whereas DOXO is a slight irritant with good TEWL (24 ± 5 g/m2·h). 
Theoretically, a combination of spin traps with non-irritation and high TEWL can be used in skin care formulations. 
Moreover, Haselof et al. and Janzen have studied the cytotoxicity (the inhibitory concentration at half maximum, IC 50 mM; 50% cell viability) of spin traps on bovine aortic endothelial cells and found that DMPO is least toxic (138.34 ± 2.22 mM) followed by PBN (9.37 ± 0.26 mM), but TEMPO is found to be highly cytotoxic (0.72 ± 0.05 mM). 
DMPO is least toxic but may not hold trans-epidermal moisturization as it exhibits low TEWL. Comparing all properties, PBN has low toxic and provide good TEWL and can be used alone or with DMPO (least toxic but low TEWL) in cosmetic formulations.
PBN, other nitrone-based and nitroso-based spin traps can ameliorate the cellular dysfunction in tissue partially due to high energy oxygen and hydroxyl free radicals, and enhance recuperation of the tissue. 
Therefore, PBN is used as an anti-alopecia agent to stimulate cosmetic hair growth. 
A study by Barclay et al. revealed that PBN exhibited only retardant (not antioxidant) activity during peroxidation of linoleate (in lipids) initiated by lipid-soluble di-tert-butyl hyponitrite or azobis (2-amidino propane hydrochloride) in sodium dodecyl sulphate micelles.
In the near future, PBN or other suitable spin traps (along with other actives/antioxidants) may also need to be included in cosmetic products to reduce or stop ROS and other free radicals generated by high frequency—high energy (HFHE) visible light (λ = 400–450 nm), near-infrared light (NIR) (λ = 760–3000 nm) and blue light (λ = 450–495 nm). 
This is important because the free radicals generated by the HFHE visible light can potentially affect 40 skin related genes that can attenuate the healing process leading to weak barrier functions, inflammation and un-even pigmentation to the skin.
 Additionally, NIR light has been shown to create free radicals.
 Both HFHE and NIR can produce MMP-1 and MMP-9 leading to aging and degradation of collagen in the skin.
 Moreover, blue-violet light at high doses may also generate free radicals.
 Blue light is used to treat acne, psoriasis and AD through ROS production and its short-term use in clinics is found to be safe.
 PBN like spin traps can help reduce the effect of prolong exposure of blue light induced free radicals on the (healthy, diseased or old) skin and further reduce the aging effect.
 This is particularly applicable for our current (modern) lifestyle due to our extensive exposure to digital appliances such as laptop, smart phone, television, etc. 
Further research is needed to ascertain the effect of prolonged exposure to blue light from optoelectronic appliances on the skin under various conditions (environmental or age-related), in order to develop new spin traps or formulations to enhance the protection of the skin.
Spin traps, lipophilic in nature, can be encapsulated by lipid spherules or vesicles to release into the epidermis and dermis layers.
 The diffusion constant of these encapsulated systems in the stratum corneum is found to be <1 × 10?7 cm2·s?1. The diffusion coefficient can be varied by varying the composition of vesicles.
Different molecular weights of sodium hyaluronate are known to transfer biomacromolecules and other actives transdermally, and can also be used for dermal delivery of other actives including spin traps.
 Hypothetically, low viscous (less than 300 cPs) oil-in-water formulations such as serums containing low viscous sodium hyaluronate, medium viscous hydrogels  and creams with viscosity  can be used to transfer actives including spin traps to different parts of the skin.
 For example, low viscous serum containing low molecular weight (MW) sodium hyaluronate (<50 kDa) may transport actives to cell–cell junctions, hydrogels with medium MW sodium hyaluronate (50–300 kDa) will restrict transfer of actives predominantly to epidermis and supply moisturization, and high viscous cream with high MW sodium hyaluronate (>1 MDa) (containing 30–60% oil phase) can restrict actives to stratum corneum or top skin layers and reduce TEWL due to a high oil phase. 
Additionally, different weights of sodium hyaluronate may provide moisturization to all skin layers from inside out.
 With this three-step process, actives with spin traps can provide thorough protection to all levels of the skin structure from inside out. 
Additionally, chemical enhancersmor polymeric hydroethanolic systems can be used for the controlled dermal delivery of spin traps and actives.
In the current global cosmetic market, the spin traps (particularly PBN) are included along with other antioxidants or cosmeceuticals in a few high-end cosmetic products, mostly for the antiaging purposes.
 The number of antipollution cosmetics has increased by 40% between 2011 and 2013, which represent a market share of 28% in the Asia-Pacific region. 
Anti-pollution cosmetics accounted for 1% of newly launched beauty products worldwide in 2016, except in Europe where the anti-pollution cosmetic trend is stagnating.
 This trend of using spin traps in cosmetics shall increase in the next few years. 
However, the toxicity and regulatory constraints of spin traps need further studies before being included into topical skin-care products to treat skin-indications.
Pollution from air and sunlight has an adverse effect on the human health, particularly the skin health due to the oxidative stress, which can induce skin diseases such as skin cancer and aging. 
Different types of antioxidants have been used as preventative actives in skin-care products to scavenge free radicals. 
However, antioxidants have some limitations, as they can block the normal oxygen in the organs, which is helpful for the skin rejuvenation. 
Recently, spin traps are being explored to trap ROS before free radicals generate more free radicals in the cascading effect, with PBN being one of the spin-traps used in cosmetic products. 
Spin traps can specifically scavenge free radicals before the cascading effect and will not scavenge normal oxygen. However, not all spin traps can be used in the topical cosmetic skin-care products currently due to toxicity and regulatory issues.
Skin moisturizing products generally seek to increase the water content of the stratum corneum, which depends on the barrier properties and the water gradient across the epidermis. 
High water content in the stratum corneum and low transepidermal water loss (TEWL) are among the main features of healthy skin.
The electrical capacitance method has been the most used among the several methods proposed for the determination of the water content of the epidermis (WCE), because it uses low frequency current and is little affected by temperature and relative humidity.
The evaluation of TEWL is a well-established method in dermatology to assess the integrity of the skin barrier. When skin is damaged, its barrier function is impaired resulting in increased water loss.
TEWL measurements allow observation of disturbances in the protective function of the skin at an early stage even before they are visible. Normal skin allows loss of water only in small amounts. 
In the case of many topical diseases or dry skin, the loss of water is much greater. 
The determination of TEWL is also an important support for investigating skin irritation that occurs due to various physical and chemical influences.
Licuri (Syagrus coronata) and catol? (Syagrus cearensis) are Brazilian palm trees from which oils containing a high content of saturated and unsaturated fatty acids (including oleic, linoleic, lauric and palmitic acid) are extracted. 
These fatty acids give emollient properties when incorporated into topical formulations.
From spent coffee grounds—a residue of coffee from homes and industries—it is possible to extract an oil with a high content of unsaturated fatty acids, predominantly linoleic acid, which makes it an alternative for cosmetic application.
Currently, polyunsaturated fatty acids have been used in the production of cosmetics to improve the appearance and health of the skin. 
Creams enriched with linoleic acid are especially related to reduction of dryness and problems of desquamation, thus providing brightness and softness to the skin.
Saturated and unsaturated fatty acids are in the natural composition of said oils giving them emollient properties when incorporated into dermocosmetic formulations. 
Consequently, it is of great importance to investigate the characteristics of these oils, seeking to demonstrate their moisturizing properties and to use these oils as new ingredients with proven efficacy and safety.
Nonionic o/w creams containing 10% weight/weight (w/w) of oils from licuri (LIC Oil Cream), catol? (CAT Oil Cream), spent coffee grounds (SCOF Oil Cream), sweet almonds (SwA Oil Cream), and one formulation without any oil (No Oil cream) were prepared.
 Sweet almond oil was used as a positive control. To prepare the creams, the oily and aqueous phases were heated separately to 70 ± 5 °C, maintaining stirring at 1000 rpm for 30 min, then the oily phase was added to the aqueous phase and the system was mixed with constant agitation until the temperature reached 25 °C. The carbomer was neutralized using a sodium hydroxide 20% (w/v) solution.
 Catol? and licuri oil were extracted from nuts using a hydraulic press. The spent coffee grounds oil was extracted through a Soxhlet extractor.
The pH of the formulations was determined by the pH 21 pH meter (Hanna, Woonsocket, RI, USA). 
The viscosity and rheological properties of the creams were determined at ambient temperature using the Digital Viscometer (Rheology (International) Shannon, County Clare, Ireland). 
The size distribution was measured by light scattering using the Zetasizer? Nano-ZS90 (Malvern Instruments, Malvern, UK). All samples were analyzed by dilution 1:100 with water.
A total of 30 healthy female subjects, 18–40 years old and having Fitzpatrick skin types II, III, and IV, participated in this study after having given their written informed consent. 
The exclusion criteria were the presence of any dermatitis or other skin or allergic diseases and a smoking habit. Volunteers were instructed not to apply any topical formulations on the test sites for two days before and during the study.
The moisturizing power of formulations containing the catol?, licuri and spent coffee grounds was compared to a placebo formulation and the same formulation containing sweet almond oil (SwA Oil Cream).
 The region selected for the studies was the lower middle portion of the forearms of the volunteers.
The two forearms were subdivided into three regions (16 cm2), where 0.1 g of the formulations containing oils from catol?, licuri, spent coffee grounds, sweet almonds and the placebo formulation were applied, a control area was also evaluated. 
These regions and the formulations applied therein were randomized among the volunteers in order to minimize the differences between the analyses.
The measurements were carried out in an air-conditioned room, with an ambient temperature of 25 ± 2 °C and relative humidity of 50 ± 5%. The volunteers stayed 15 min in this room before the measurements.
The study was submitted and approved by the Research Ethics Committee of the Federal University of Pernambuco/Brazil, under registration N° 167/11—SISNEP FR—417180 and 16847813.7.0000.5208.
For the determination of the immediate effects, the formulations were applied to the forearms of the volunteers, being measured before (baseline) and after 2 h of the single application.
For the long-term study, the volunteers took home the formulations that were applied daily for a period of 20 days. 
The formulations were given to the volunteers in a randomized and anonymous fashion. After 20 days, the volunteers returned to the laboratory to perform new measurements.
The volunteers were adequately instructed in terms of the amount to be applied of each formulation in their specific region.
The water content of the epidermis and transepidermal water loss for all creams was performed with a Corneometer CM 825 and a Tewameter TM300 (Courage and Khazaka Electronic GmbH, Cologne, Germany).
 The measuring principle of the TEWL is based on Fick’s diffusion law, indicating the quantity being transported per area and period of time, the TEWL value is expressed in g/m2h.
 The values of WCE considered are the mean of ten measurements on adjacent sites of the forearm expressed as arbitrary units (UA).
Ten measurements were made in each region of the volunteers’ forearms, and the mean values obtained were calculated.
 The number of measurements made was determined according to the size of the region studied, in order to ensure that the whole site was evaluated.
For the HET-CAM, saline solution (0.9% NaCl w/v) and 0.1 N sodium hydroxide (NaOH) were used as a negative and positive control, respectively. All creams were diluted 1:1 with 0.9% NaCl. 
For each formulation tested, four fresh, fertile Leghorn eggs were used. The eggs were incubated at 37 ± 0.5 °C with a relative humidity of 65 ± 2% for 10 days. 
On the tenth day, the shell membrane was removed, exposing the chorioallantoic membrane (CAM). Visual analysis was used to verify if the CAM was suitable to test, then 300 μL of each formulation was placed on the CAM surface.
 After 20 s, the formulation was removed with saline solution. The CAM was observed under a magnifying glass for 5 min to determine the occurrence of any irritation effects in the CAM blood vessels (vascular lysis, hemorrhage, or coagulation).
 The vascular effects were observed according to the criteria described in the Protocol ICCVAM HET-CAM test method. Each formulation was classified according to the scores: 0–0.99 corresponding to nonirritant; 1.00–4.99 corresponding to slightly irritant; 5.00–8.99 corresponding to moderately irritant (MI); and 9.00–21.00 corresponding to severely irritant (SI) .
The CM-700d Spectrophotometer (Konica Minolta, Tokyo, Japan), using the L*a*b* system color was used to assess the potential irritation of the formulations by verifying possible changes in skin color.
To evaluate the acceptability of all creams, a questionnaire was completed by each volunteer, and assessed sensory attributes such as texture, odor, spreadability, and tackiness using a scale from 1 to 10.
The results are expressed as means ± SD of at least three values. The difference between the measurements was statistically evaluated with the analysis of variance (ANOVA). Data statistical significance was fixed at p < 0.05.
Concerning the macroscopic characteristics, all creams appeared white, glossy, and as semimobile emulsions. 
The pH should ensure the stability of the ingredients of a formulation, their efficacy and safety, as well as being compatible with the intended route of administration. 
The formulations studied had the pH adjusted to slightly acidic values, as this was the ideal pH range (5.5–6.5) for the proposed purposes, and also to optimize the viscosity of the polymer used. 
The formulations showed well-formed droplets with small size variation, thus indicating greater stability of the emulsified system.
The flow curves show that all formulations presented non-Newtonian behavior of the pseudoplastic type, where the viscosity of the fluid decreases with increasing shear rate.
The pseudoplastic behavior presented by all formulations is a desirable feature for emulsions since viscosity values are reduced with increased shear stress, facilitating application to the skin. 
This type of behavior is frequent in formulations containing natural or synthetic gums and polymers.
 Thus, all the creams designed within this work were appropriate for skin application.
The results obtained showed that after two hours of application, the creams containing oils from licuri, catol?, spent coffee grounds, and sweet almonds significantly increase the epidermis water content.
 The effect was more evident for the formulations containing oils from licuri and catol?.
All the formulations studied produced a significant increase in stratum corneum moisture (p < 0.05) 20 days after daily application, when compared with the baseline values and with the placebo cream.
The evaluation of the immediate effects of cosmetic products is important because it allows verification of the action of these products on the skin soon after their application. Likewise, evaluating a dermocosmetic product under the actual conditions of use allows the scientific elucidation of its long-term effects on the skin.
Skin dryness is the most prevalent skin health problem globally and can impact the perception of well-being and quality of life.
 Additionally, dry skin, redness, and cracking may increase the possibility of an infection because the skin barrier is damaged.
 The measurement of hydration in the surface layer of the skin and TEWL gives important information on the biophysical properties and function of the skin barrier.
The WCE results were expressed in arbitrary units and values below 30 represent very dry skin, between 30 and 45, moderately dry skin and values greater than 45, sufficiently hydrated skin.
 Therefore, it is perceived that the use of the formulations containing the oils under study contributed in a significant way to improve the hydration of the skin of the volunteers, who, in general, presented as dry skin.
As know, there is a correlation between stratum corneum (SC) hydration and TEWL values; lower TEWL indicates better skin barrier function.
 In our experiments, all creams containing oils from catol?, licuri and spent coffee grounds increased Corneometer and decreased Tewameter measurements 2 h and 20 days after daily application, suggesting skin hydration.
There was no significant difference between the hydration profiles of the formulations containing the evaluated oils, although their composition is different, licuri and catol? oils have higher percentages of saturated fatty acids, primarily lauric acid. 
The coffee grounds oil has a predominance of unsaturated fatty acids, including 44.7% linoleic acid.
The sweet almond oil was used as a reference, because it has recognized moisturizing and toning properties of dry skin, as confirmed in our study since the cream containing SwA Oil also increased WCE measurements.
Lipids play a significant role in the skin, fatty acids like linoleic, oleic, and lauric acid are naturally present in the skin triglycerides. 
Especially unsaturated fatty acids such as linoleic acid, which has recognized potential in terms of skin hydration by helping to maintain skin elasticity and combat its premature aging.
 However, we can infer that in this case, the improvement on hydration and skin barrier function probably occurred by hindering water permeation through the skin. 
This means spreading an occlusive oily layer onto the stratum corneum surface or by supplementation of skin lipids, and there’s no specific relationship to the fatty acid compositions of each oil.
Previous literature shows that formulations containing oil from spent coffee grounds have multiple skin benefits on barrier function and skin hydration when compared to baseline values, corroborating the results obtained in this study.
The HET-CAM is used to provide qualitative information on the potential effects occurring in the conjunctiva region following exposure to a substance. This assay has been widely used to assess the irritant potential of topical medicines and cosmetics that may come in contact with the eyes.
The results of color variation (a* parameter) did not shown statistically significant alterations (p < 0.05) for any of the tested creams, indicating that they did not promote irritation of the skin during the tests.
According the results of the sensorial evaluation, all creams met consumer appeal and acceptance requirements. 
However, the volunteers could not verify differences between the formulations.
Sensory assessments are important because they show consumers’ perception and acceptability of products. 
The creams presented good acceptability and the volunteers were able to perceive a positive effect in the use of the formulations, however, without differentiating those that contained oil and placebo.
This work showed that all creams containing these oils under study had significant hydration effects on the skin by increasing the water content of epidermis and on skin barrier function, which resulted in TEWL reduction when compared with the placebo and control area, and similar results when compared with formulations containing the positive control.
 This provides a sustainable, cheaper, and effective alternative to the oils of consecrated use in hydration and tonicity of the skin.
In conclusion, these results allow us to signal new vegetable oils with cosmetic applicability to the product chain of natural products with sustainable development.
Aging is a natural and progressive declining physiological process that is influenced by multifactorial aspects and affects individuals’ health in very different ways. 
The skin is one of the major organs in which aging is more evident, as it progressively loses some of its natural functions. 
With the new societal paradigms regarding youth and beauty have emerged new concerns about appearance, encouraging millions of consumers to use cosmetic/personal care products as part of their daily routine. 
Hence, cosmetics have become a global and highly competitive market in a constant state of evolution.
 This industry is highly committed to finding natural sources of functional/bioactive-rich compounds, preferably from sustainable and cheap raw materials, to deliver innovative products and solutions that meet consumers’ expectations. 
Macroalgae are an excellent example of a natural resource that can fit these requirements. 
The incorporation of macroalgae-derived ingredients in cosmetics has been growing, as more and more scientific evidence reports their skin health-promoting effects. 
This review provides an overview on the possible applications of macroalgae as active ingredients for the cosmetic field, highlighting the main compounds responsible for their bioactivity on skin.
The world population continues to grow, although at a slower rate than in the recent past, and is expected to reach 9.7 billion by 2050.
 Globally, demographic projections indicate that life expectancy at birth is increasing, which means that populations are getting older. 
This will certainly have wide-ranging repercussions on social, economic, and health systems.
Aging is a natural and progressive declining physiological process, influenced by multifactorial aspects, that affects individuals’ health in very different ways. 
Oxidative stress has a substantial role in aging, and several studies have suggested different mechanisms by which free radicals can damage biological systems, leading to the development of chronic diseases: diabetes, cognitive decline and neurodegenerative diseases (e.g., Alzheimer’s and Parkinson’s), cardiovascular injuries, skin damage, and certain types of cancer, among many others.
The skin has historically been used for the topical delivery of compounds, being a dynamic, complex, integrated arrangement of cells, tissues, and matrix elements that regulates body heat and water loss, whilst preventing the invasion of toxic substances and microorganisms.
 Structurally, skin is composed of three major regions: epidermis, dermis, and hypodermis. 
According to Mathes et al. , the most superficial layer of the epidermis (stratum corneum) contains a cornified layer of protein-rich dead cells embedded in a lipid matrix which, in turn, manly comprises ceramides, cholesterol, and fatty acids (FA). 
Within the epidermis, melanocytes, Merkel cells, and Langerhans cells can also be found; these are responsible for melanin production, sensorial perception, and immunological defense, respectively.
 The viable epidermis (50–100 μm) containing the basal membrane presents laminins (at least one type), type IV collagen, and nidogen, as well as the proteoglycan perlecan, while in the dermis (1–2 mm) it is possible to find sweat glands and hair follicles.
 The dermis is pervaded by blood and lymph vessels. This skin layer matrix comprises not only arranged collagen fibers and a reticular layer with dense collagen fibers arranged in parallel to the skin surface, but also collagen and elastin, which provide the elastic properties of the skin.
 Fibroblasts are the main cell type of the dermis. Beneath the dermis lies the hypodermis, where adipocytes are the most prominent cell type.
The efficacy of cosmetic active ingredients is related to their diffusion rate through the skin barrier to their specific targets.
 However, small soluble molecules with simultaneous lipophilic and hydrophilic properties have a greater ability to cross the stratum corneum than do high-molecular-weight particles, polymers, or highly lipophilic substances.
 Also, it should be highlighted that the skin surface has long been recognized to be acidic, with a pH of 4.2–5.6, being described as the acid mantle.
During aging, skin becomes thinner, fragile, and progressively loses its natural elasticity and ability to maintain hydration, and with the new society paradigms regarding youth and beauty have emerged new concerns about appearance. 
The use of cosmetic/personal care products (PCP) and their ingredients is part of the daily routine of millions of consumers.
 PCP can be locally applied on the skin, lips, eyes, oral cavity, or mucosa, but systemic exposure to the ingredients cannot be neglected and should be carefully considered. Besides this, there is the possibility of local adverse reactions, such as irritation, sensitization, or photoreactions. 
Given the massive use of these products, they must be diligently evaluated for safety prior to marketing.
According to Regulation European Commission (EC,) 1223/2009, a cosmetic product is defined as “any substance or mixture intended to be placed in contact with the external parts of the human body (epidermis, hair system, nails, lips and external genital organs) or with the teeth and the mucous membranes of the oral cavity with a view exclusively or mainly to cleaning them, perfuming them, changing their appearance, protecting them, keeping them in good condition or correcting body odours”.
However, there is another category—the “cosmeceuticals”—which is attracting the industry’s attention and is of interest to the most attentive consumers. The term has its origin about three decades ago, but to this day it has no legal meaning, namely, under the Federal Food, Drug, and Cosmetic Act.
 Even so, the industry continues to use this designation, and cosmeceuticals’ development and marketing still lies between the individual benefits of cosmetics and pharmaceuticals.
 Recently, Kim stated that some cosmetic formulations, in fact, are intended to prevent disease or to affect human skin’s function or structure, and can be considered as drugs. 
These may include sunscreens or antidandruff shampoos, but also other cosmetics containing active ingredients that promote physiological changes in skin cells, making them appear healthier and younger.
Cosmetics are a global and highly competitive market worth more than €425 billion worldwide.
 In 2016, the European cosmetics and personal care market was the largest in the world, valued at €77 billion in retail sales price, followed by the United States (€64 billion) and Brazil (€24 billion).
In recent decades, consumers have been drawing more and more attention not only to lifestyle issues and their impact on health and well-being, but also to environment and sustainability matters, questioning the origin of products, manufacturing processes, and ecological implications, along with safety issues. 
The search for natural products for a great diversity of purposes, including food, nutraceuticals, cosmetics, and personal hygiene products, among others, somehow reflects these concerns.
 In part, this is due to the consumers’ perception about the safety of botanicals, which are derived from nature, making them desirable ingredients over synthetic ones for a diversity of formulations.
 This is strong encouragement for industry-related research to find solutions and novel/alternative natural raw materials with additional properties that go further than their basic functions (e.g., nutrition).
 Nevertheless, it is of huge importance to guarantee that the selected raw materials are nontoxic and safe, and to also ensure accurate controls throughout all the production phases of industrial batches.
At the end, the main challenge of this whole process is to add value to products. This can be accomplished in several different ways, namely, by finding natural raw materials that are simultaneously rich in functional and bioactive compounds; using these resources in a sustainable way; processing them through green processes and eco-friendly procedures, with low environmental impact; and/or delivering products and innovative solutions that meet consumers’ expectations.
The following sections will provide an overview about the possible application of macroalgae as active ingredients in the cosmetic field, highlighting the main compounds responsible for their bioactivity on skin.
Macroalgae are an excellent example of a natural resource that can fit the above-mentioned principles.
 According to the latest available statistics from FAO (Food and Agriculture Organization), about 23.8 million tons of macroalgae ($6.4 billion) and other algae are harvested annually. The major producing countries are China (54%) and Indonesia (27%), followed by the Philippines (7.4%), Republic of Korea (4.3%), Japan (1.85%), and Malaysia (1.39%). 
In Asian countries, macroalgae are traditionally used as food, for medicinal purposes, or as fertilizers. Besides this, they are a valuable raw material used as an ingredient in animal feed. 
However, some authors consider them to be still underexploited and to have not yet reached their full potential of application.
Overall, following global trends, there is a growing demand for edible algae and algae-based products.
 With aquaculture, which is one of the fastest growing producing sectors, it is possible to considerably increase the availability of that biomass.
The marine environment is extremely demanding, competitive, and aggressive. 
Consequently, marine organisms, including macroalgae, are forced to develop an efficient metabolic response as a self-defense mechanism, for example, by producing secondary metabolites that allow them to preserve their survival and protect themselves against external threats.
 Therefore, sea biodiversity presents the opportunity to explore these molecules and find novel and natural bioactive compounds.
Macroalgae are one of the most ecologically and economically important living resources of the oceans, being generally classified into three groups according to their pigmentation: Phaeophyceae (brown), Rhodophyceae (red), and Chlorophyceae (green).
 Undeniably, they have huge potential as a natural source of important nutrients, namely, fiber (15–76% dry weight, dw), protein (1–50% dw), essential amino acids, essential minerals, and trace elements (ash: 11–55% dw).
 Despite having a low fat content (0.3–5% dw), they provide long-chain polyunsaturated fatty acids from the n-3 family (n-3 LC-PUFA), such as eicosapentaenoic acid (EPA, 20:5n-3), and liposoluble vitamins (e.g., β-carotene, vitamin E).
 However, it is important to highlight that macroalgae development and composition is affected by the species genetics and the surrounding growth conditions, namely, light, temperature, pH, salinity, and nutrient variations.
The production of macroalgae in aquaculture is not very complex and can be performed at a large scale.
 They can develop quickly and, by controlling their growth conditions, it is possible to manipulate their chemical composition, namely, protein, polyphenol, and pigment contents.
Regardless of their origin (either from wild harvest or from controlled production), the overall chemical composition of macroalgae makes them a very worthy bio-sustainable ingredient for a wide range of applications.
 This is of particular interest for the cosmetic industry, in which the ingredients used in the formulations—either active substances, excipients, or additives—are elements of added value and differentiation of a final product. 
The active ingredient is responsible for the cosmetic activity of interest (moisturizing, whitening, antiaging, etc.), while the excipient constitutes the vector for the active ingredient and, in turn, the additive is an ingredient intended to improve the product preservation or its organoleptic properties.
For years, due to their composition, some species of macroalgae have been traditionally used as a source of phycocolloids, namely, agar and carrageenan extracted from red algae such as Gracilaria, Chondrus, Gelidiella, among others, and alginate from brown algae like Ascophyllum, Laminaria, or Sargassum. 
These phycocolloids are water-soluble polysaccharides, mainly used to thicken (increase the viscosity of) aqueous solutions, to make gels of variable degrees of firmness, to produce water-soluble films, and to stabilize some products.
 Agar and carrageenan form thermally reversible gels, while alginate gels do not melt on heating. These compounds are industrially extracted and, due to their technological characteristics, further used as ingredients/additives in a wide variety of products in agro-food, pharmaceutic and cosmetic industries  
Natural plant extracts can be incorporated in a wide variety of cosmetic products, like creams and body lotions, soaps, shampoos, hair conditioners, toothpastes, deodorants, shaving creams, perfumes, and make-up, among others; this has been a very active area of research.
 Regarding, specifically, the use of macroalgae, some species are suitable for dermocosmetic applications.
Within the additives class, preservatives are one of the most representative substances. 
For the industry, finding sources of natural additives as alternatives to current commercial synthetic ones is a matter of great interest.
 Some of the more commonly used additives are BHT (butyl hydroxytoluene) and BHA (butyl hydroxyanisole), used as synthetic antioxidants to retard lipid oxidation.
 However, BHT has been associated with cancer and respiratory and behavioral issues in children. An alternative is to use BHA instead, although, in high doses, it can also be carcinogenic.
 Alternatively, natural antioxidants from plants and macroalgae have been demonstrating a solid substitution potential.
 Their antioxidant-rich extracts actively protect formulations against oil oxidative processes, particularly those containing a higher amount of oily phase, while simultaneously presenting health-promoting effects.
Currently, the interest of the cosmetic industry in macroalgae goes further than just using it as a source of excipients and additives.
 Besides their functional and technological properties, macroalgae are a source of bioactive compounds of added value, which can also be a competitive advantage for this industry.
Over the years, many studies have been conducted about the nutritional composition, secondary metabolites and bioactivities—as well as the potential health-promoting effects—of macroalgae. 
To date, most of these marine-derived compounds were intended for food and pharmaceutical applications.
 Also, several researchers have been exploring the effects of macroalgae on health, showing some progress and important positive outcomes in regards to some types of cancer; heart diseases; thyroid and immune functions; allergy; inflammation; and antioxidant, antibacterial, and antiviral activity, among many others.
Aware of this, the cosmetic industry is interested in using macroalgae as a source of bio-sustainable ingredients since they are extremely rich in biologically active compounds, some of which are already documented as functional active skin care agents.
 As an additional advantage for this industry, these ingredients can be cheap, while matching consumers’ requests for “natural” and “healthier” products.
Some of the bioactive compounds associated with skin care include polysaccharides, proteins, (especially peptides and amino acids), lipids (including PUFA, sterols, and squalene), minerals, and vitamins, but also the secondary metabolites such as phenolic compounds, terpenoids, and halogenated compounds, among others.
 Depending on their physicochemical properties, molecular size, and solubility, bioactive compounds can be extracted, isolated, and purified by several different methods.
 However, in order to be used as ingredients in cosmetics, solvents used in the whole process of extraction must be GRAS-grade (Generally Recognized As Safe), which excludes all of those listed as substances prohibited in cosmetic products, described in Annex II of Regulation (EC) No 1223/2009 of the European Parliament and of the Council of 18 December 2006, concerning cosmetic products. Summarizes some health benefits associated with macroalgae-derived bioactive compounds.
The biological activity of several macroalgae-derived sulphated polysaccharides (SPs) has been often reported.
 The chemical structure of these macromolecules varies according to the species: brown species present mainly laminarans (up to 32%–35% dw) and fucoidans; red algae are mainly rich in carrageenans and porphyrans; and green algae are typically rich in ulvans.
 Anti-proliferative activity in cancer cell lines as well as inhibitory activity against tumors has been described for fucoidans.
 The genus Porphyra contains mainly porphyrans, an agar-like sulphated galactan disaccharide, which accounts for up to 48% of thallus (dw).
 It has been reported that red macroalgae SPs, namely, xylomannan, galactans, and carrageenans, exhibit antiviral activity.
 When accessing the antioxidant activity of different SPs—carrageenans (lambda, kappa, and iota), fucoidans, and fucans—de Souza and colleagues found that fucoidan and lambda carrageenan exhibited the highest antioxidant activity and free radical scavenging activity against superoxide anions and hydroxyl radicals. 
Ulvans, in turn, designated a water-soluble group typically found in green macroalgae, which are mainly composed of glucuronic acid and iduronic acid units together with rhamnose and xylose sulphates.
 It has been reported that these compounds present a high antioxidant capacity against some reactive oxygen species (ROS), namely, superoxide and hydroxyl radicals.
In macroalgae, proteins are a structural part of cell walls, enzymes, and bioactive molecules, such as glycoproteins and pigments.
Protein content is one important parameter when determining the value of biomass, and may be the starting point for selecting species that may be more profitable from which to obtain bioactive peptides and amino acids through selected enzymes. 
The interest in enzymes in the field of cosmetics has increased. Enzymes are highly specific and selective, easy to process, and can be applied in a wide range of substrates and organic transformations in diverse reaction media.
Besides presenting substantial amounts of protein (up to 47% dw), most species present a complete profile of essential amino acids.
 Even so, protein content varies according to species, being generally higher in Rhodophyceae (8%–50% dw), compared with Chlorophyceae and Phaeophyceae. 
Geographical origin and seasonality also affect their protein composition, especially because nitrogen availability may fluctuate due to water temperature and salinity variations, light irradiation, and wave force, thereby affecting their nutrient supply.
Peptides are formed of short chains of 2 to 20 amino acids. Their biofunctional properties depend on their amino acid composition and sequence in the parent protein, which needs to undergo a hydrolysis, commonly with digestive enzymes, so that peptides can be released and become active.
 The biofunctional and bioactive properties of peptides are based on their physiological behavior, which resembles hormones or druglike activities. Besides this, they have the capacity to modify physiological functions, even in the skin, due to their ability to interact with target cells, binding to specific cell receptors or inhibiting enzymatic reactions.
 Marine peptides, including macroalgae-derived ones, have been considered safer than synthetic molecules due to their high bioactivity and biospecificity to targets, with rare adverse effects and reduced risk of unwanted side effects.
 In fact, lately, peptides have been considered a captivating topic in the field of cosmetics and skin applications.
Macroalgae are an excellent source of amino acids and amino acid derivatives, which constitute the natural moisturizing factor (NMF) in the stratum corneum and promote collagen production in the skin.
 Some species of red macroalgae like Palmaria and Porphyra have been reported to present high amounts of arginine in their composition. Arginine is a precursor of urea, which is a component of NMF, used in cosmetic formulations.
Mycosporine-like amino acids are a family of secondary water-soluble metabolites with low molecular weight.
 They are characterized by a cyclohexenone or cyclohexenimine chromophore conjugated with a nitrogen substituent of an amino acid, amino alcohol, or amino group, with maximum absorption wavelengths ranging from 310 to 360 nm. 
Mycosporine-like amino acids protect macroalgae from UV radiation, and have been described as important antioxidant compounds in red algae with reports that they are very efficient photoprotector agents.
 Hence, these metabolites have great potential to be used as natural skin protection ingredients in photo-protective formulations.
Macroalgae are known as a low-energy food, and, although their total lipid contribution is generally very low, their PUFA contents are comparable to or even higher than those found in terrestrial plants.
 Still, the main classes of lipids are present in their composition and include essential FA, triglycerides, phospholipids, glycolipids, sterols, liposoluble vitamins (A, D, E, and K), and carotenoids.
Long-chain PUFAs (LC-PUFAs) have 20 or more carbons with two or more double bonds from the methyl (omega) terminus. 
Marine lipids contain substantial amounts of LC-PUFAs, among which eicosapentaenoic acid (EPA; 20:5n-3) and docosahexaenoic acid (DHA; 22:6n-3) are the most important, along with the precursors α-linolenic acid (ALA; 18:3n-3) and docosapentaenoic acid (22:5n-3).
 Beneficial clinical and nutraceutical applications have been described for these compounds.
LC-PUFAs are essential components of all cell membranes and eicosanoid precursors, and are critical bioregulators of many cellular processes.
 As mediators of many different biochemical pathways, they play an important role in health.
 In several macroalgae species, EPA (C20:5n-3) is frequently the most representative PUFA—in some cases, reaching 50% of the total FA content.
A study performed by Kumari and colleagues reported interesting features when comparing several macroalgae species: Chlorophyta species presented higher C18-PUFAs amounts than did C20-PUF.
As, while the analyzed Rhodophyta species showed the opposite trend.
 In turn, Phaeophyta samples exhibited a C18-PUFAs profile comparable to that of Chlorophyta and a C20-PUFAs profile similar to that of Rhodophyta. Both brown and red species were richer in arachidonic acid and EPA, while the green ones contained higher amounts of DHA.
As stated by several authors, variations in the lipid content and FA composition are often found, and it is generally accepted that such disparities, besides the already mentioned environmental conditions, could be due to different sample treatments and extraction methods.
Macroalgae are a good source of both fat-soluble vitamins (e.g., vitamin E) and water-soluble vitamins, namely, B1 (thiamine), B2 (riboflavin), B3 (niacin), B5 (pantothenic acid), B6 (pyridoxine), B12 (cobalamin), B8 (biotin), B9 (folic acid), and C (ascorbic acid).
 Interestingly, macroalgae are also one of the few vegetable sources of vitamin B12—Its presence is likely due to the bacteria living on their surface or in the proximate waters.
Besides this, macroalgae are important sources of minerals and trace elements, namely, calcium, sodium, potassium, magnesium, iron, copper, iodine, and zinc.
Macroalgae contain a wide variety of pigments that absorb light for photosynthesis, many of which are not found in terrestrial plants. 
Species are characterized by specific sets of pigments. 
Three major classes of photosynthetic pigments are found in algae: chlorophylls, carotenoids (carotenes and xanthophylls), and phycobiliproteins.
 These compounds are responsible for macroalgae color variations during their growth and reproduction cycles, which depend on the amounts of pigments present.
Chlorophylls and carotenoids are liposoluble molecules.
 Chlorophylls, the greenish pigments, are a group of cyclic tetrapyrrolic pigments, with a porphyrin ring with a central magnesium ion and usually a long hydrophobic chain. 
Generally, chlorophyll a is the most abundant photosynthetic pigment, while others are considered accessory pigments.
In turn, carotenoids are polyene hydrocarbons biosynthesized from eight isoprene units (tetraterpenes), usually presenting red, orange, or yellow colorations and remarkable antioxidant properties.
 Within the carotenes group, β-carotene is the most representative one and is present in all classes of macroalgae.
Xanthophylls contain oxygen in the form of hydroxy, epoxy, or oxo groups.
Astaxanthin is a lipophilic carotenoid, structurally similar to β-carotene but with an additional hydroxyl and ketone group on each ionone ring.
 Some studies have reported that astaxanthin can be more effective than β-carotene in preventing lipid peroxidation in solution and various biomembrane systems. 
In turn, fucoxanthin is one of the major xanthophyll pigments in brown algae and is found in edible brown algae, such as Undaria sp., Sargassum sp., Laminaria sp., and Hizikia sp. 
This molecule presents a unique structure including allenic, conjugated carbonyl, epoxide, and acetyl groups, and was recently identified as the major bioactive antioxidant carotenoid in 30 Hawaiian macroalgae species.
Phycobiliproteins (PBP) are a water-soluble group of photosynthetic pigments comprising different compounds, like phycoerythrins with a red pigment linked to the protein molecule, or phycocianins with a blue pigment instead.
 These different molecules absorb at different wavelengths of the spectrum, which makes them very colorful and highly fluorescent in vivo and in vitro. 
This is of special interest for biotechnological applications, where they are useful in diverse biomedical diagnostic systems.
 Some have been used as natural food colorants, as well as pink and purple dyes in lipsticks, eyeliner, and other cosmetic formulations.
 Being water-soluble molecules, it is possible to extract PBP from algal tissues using green extraction solvents, like water or buffers.
In macroalgae, phenolic compounds are secondary metabolites, which means that they do not directly intervene in primary metabolic processes such as photosynthesis, cell division, or reproduction.
 Instead, it is believed that this class of compounds is mainly responsible for protection mechanisms, namely, against oxidative stress or UV cytotoxic effects.
Phlorotannins, a subgroup of tannins mainly found in brown macroalgae and, to a lesser extent, in red species, are derived from phloroglucinol units, whereas in plants polyphenols are derived from gallic and ellagic acids.
 Phlorotannins are highly hydrophilic compounds with a wide range of molecular sizes (from 126 Da to 650 kDa), and are of interest for different applications (e.g., nutritional supplements, cosmetic and cosmeceutical products).
Many external factors, including UV radiation, climate conditions, and air/environmental pollutants (e.g., tobacco smoke) can affect the protective ability of skin and promote its premature aging. 
Commonly, this continuous exposure leads to oxidative stress caused by the imbalance between oxidants and antioxidants, which affects skin health. 
Skin aging produces several changes: it becomes thinner, more fragile, and progressively loses its natural elasticity and ability to maintain hydration.
In cosmetic formulations, the primary functions of natural ingredients may be antioxidant, collagen boosting, or even anti-inflammatory.
 The incorporation of macroalgae-derived bioactive compounds in cosmetics has been growing as more and more scientific evidence is documented in regards to their health-promoting and anti-pollution effects.
 The foremost interesting classes of bioactive compounds include those intended for antiaging care, including protection against free radicals, prevention of skin flaccidity and wrinkles, anti-photoaging, photoprotection against UV radiation, moisturizing, and skin whitening.
In biological systems, oxygen is the most common generator of free radicals—highly reactive molecules with harmful potential. ROS and reactive nitrogen species (RNS, such as nitric oxide, NO?) are products of normal cellular metabolism. 
They act as secondary messengers by regulating several normal physiological functions. However, they can play a dual role, as they can act as both damaging and beneficial species. 
Oxidative stress, caused by an overproduction of ROS, can induce serious damages in several cell structures (lipids and membranes, proteins, and DNA). At the same time, ROS and RNS also participate in several redox regulatory mechanisms of cells in order to protect them against oxidative stress and maintain their “redox homeostasis”.
A great diversity of bioactive compounds, namely vitamin E, vitamin C, superoxide dismutase, coenzyme Q10, zinc sulphate, ferulic acid, polyphenols, and carotenoids, among others, have been successfully used, for a long time, in cosmetic products as free-radical-scavenging molecules.
An in vitro study showed that an algal extract containing astaxanthin presented a protective effect in the reduction of DNA damage and maintenance of cellular antioxidant status in lines of human skin fibroblasts (1BR-3), human melanocytes (HEMAc), and human intestinal Caco-2 cells, irradiated with UVA.
In the last few years, other classes of macroalgae compounds have been showing potential as bioactive ingredients for cosmetics.
 In a study performed with Ecklonia cava, crude polysaccharide and polyphenolic fractions obtained by a former enzymatic hydrolysis were evaluated, showing a suppressive effect on tumor cell growth, and antioxidant and radical scavenging activities in different cell lines, with low toxicity.
 In another study, Zhang and colleagues evaluated the antioxidant activity of SPs extracted from five macroalgae—one brown (Laminaria japonica), one red (Porphyra haitanensis), and three green species (Ulva pertusa, Enteromorpha linza and Bryopsis plumose)—reporting that their antioxidant behavior depended on the type of polysaccharides of each extract, which was shown to be different among the species.
Likewise, protein hydrolysates, peptides, or amino acids from macroalgae can play a substantial antioxidant role in a diverse range of oxidative processes.
Moisturizing and hydration are crucial for skin care and are essential to maintaining its healthy appearance and elasticity, while also strengthening its role as a barrier to harmful environmental factors.
 Approximately 60% of the epidermis is water which is fixed by hygroscopic substances known by the generic name of NMF (natural moisturizing factor). NMF constitution includes amino acids (40%), including serine (20–30%), lactic acid (12%), pyrrolidone carboxylic acid (12%), urea (8%), sugars, minerals, and a fraction that still remains undetermined.
 Topical application of the above-mentioned components, which can act as humectants, can improve the skin moisturizing ability and relieve a dry skin condition.
Polysaccharides play a very important role in cosmetic formulations as humectants and moisturizers.
 These macromolecules have a high capacity for water storage and can be linked to keratin through hydrogen bonds, thus improving skin moisturization.
 According to Wang and colleagues, polysaccharides extracted from Saccharina japonica revealed better moisturizing properties than hyaluronic acid, suggesting that these polysaccharides could be an interesting ingredient for cosmetics.
 The authors also found that the sulphated group was a main active site for moisture absorption and moisture retention ability, and that the lower-molecular-weight polysaccharides presented the highest moisture absorption and moisture retention abilities.
 A cosmetic formulation containing 5–10% extract of Laminaria japonica improved skin moisture in a group of volunteers.
 Authors suggest that two mechanisms might be responsible for these promising results: on the one hand, the hydroscopic substances of the extract  may contribute to reinforcing the NMF in skin, helping to retain appropriate moisture levels in the epidermis; on the other hand, phycocolloids, like alginate, and protein in extracts attach to skin proteins to form a protective barrier for moisture loss regulation.
With aging, the extracellular tissue matrix components—collagen, hyaluronic acid, and elastin, among others—decrease, leading to thinner skin with a weakened structure.
 However, some active ingredients have been showing promising results in reverting these signs.
 For instance, some peptides have been used as cosmeceutical ingredients showing interesting antiaging effects, namely in wrinkle and fine line reduction, and in skin firming and skin whitening.
 Different types of peptides and mechanisms of action are responsible for those effects. Signal peptides, for instance, stimulate ETM production by specifically increasing neocollagenesis.
 Besides this, they can also promote fibronectin and elastin synthesis, as well as cell–cell cohesion, with results in skin firming and wrinkle and fine line reduction.
 Therefore, the use of formulations containing these compounds can promote the replacement of the lost extracellular tissue matrix, reducing, then, the appearance of wrinkles.
Marine-derived phlorotannins, extracted from Eisenia bicyclis and Ecklonia kurome, presented a strong hyaluronidase inhibitory effect in in vitro assays, showing potential as a bioactive ingredient to recover ETM functions.
Sunlight UV radiation is still the most powerful environmental risk factor in skin cancer pathogenesis. 
The use of photoprotective products with UV filters is extensively recommended to prevent (and protect the skin from) several types of damage, like sunburn, photo-aging, photodermatoses, or even skin cancer.
 Within this type of product, formulations containing sun-screening agents combined with antioxidants are considered to be safer and more effective.
Bioactive compounds able to absorb UV radiation can protect human fibroblast cells from UV-induced cell death and suppress UV-induced aging in human skin.
As previously mentioned, macroalgae have developed mechanisms to prevent damage from UVB and UVA radiations, either by producing screen pigments, like carotenoids, or by phenolics.
Heo and Jeon reported that fucoxantin from Sargassum siliquastrum presented a great in vitro ability to protect human fibroblasts against oxidative stress induced by UVB radiation. 
Another study with Halidrys siliquosa (Phaeophyta) showed that the tested extracts presented strong antioxidant activity and a good sunscreen potential, associated with the presence of phlorotannins like diphlorethols, triphlorethols, trifuhalols, and tetrafuhalols.
Melanin, which is the main determinant of skin color, absorbs UV radiation and prevents free radical generation, protecting skin from sun damage and aging.
 However, the abnormal production of melanin can be a dermatological condition and a serious cosmetic issue.
Tyrosinase catalyzes melanin synthesis in two different pathways: the hydroxylation of L-tyrosine to 3,4-dihydroxy-l-phenylalanine (L-dopa) and the oxidation of L-dopa to dopaquinone, followed by further conversion to melanin.
 It is possible to regulate melanin biosynthesis, for instance, by protecting skin and avoiding UV exposure, or by inhibiting tyrosinase action or melanocyte metabolism and proliferation.
The demand for natural products that inhibit/control or prevent melanogenesis and, consequently, skin pigmentation, is growing all over the world, especially for melanin hyperpigmentation dermatological diseases, as well as for cosmetic formulations for depigmentation. 
Recently, macroalgae extracts showed profound inhibitory effects against tyrosinase and melanin synthesis in both in vitro cell experiments and an in vivo zebrafish animal model.
An inflammatory process causes oxidative stress and reduces cellular antioxidant capacity. The large amount of produced free radicals react with FA and proteins of cell membranes, permanently damaging their normal functions.
Senevirathne and colleagues evaluated antioxidant and anticholinesterase (AChE) activities, as well as the protective effects of enzymatic extracts from Porphyra tenera against lipopolysaccharides (LPS)-induced nitrite production in RAW264.7 macrophage cells.
 The authors concluded that all enzymatic extracts showed no cell cytotoxicity (cell viabilities greater than 90% in all cases), and all enzymatic extracts effectively inhibited LPS-induced nitric oxide production in RAW264.7 macrophages. 
These results indicate that Porphyra tenera could be a valuable source of natural antioxidants and anti-inflammatory ingredients for cosmetic purposes.
Although cellulite is not a pathological condition, it remains a matter of cosmetic concern, especially for postadolescent women. 
Many efforts have been made to find treatments that improve symptoms and signs of cellulite, as well as the visual appearance of skin.
Al-Bader and colleagues tested a formulation containing aqueous extracts of Furcellaria lumbricalis and Fucus vesiculosus to assess in vitro lipolysis in mature adipocytes and measured pro-collagen I in human primary fibroblasts, finding that there was an improvement of lipolysis-related mechanisms and pro-collagen I production.
 Subsequently, they evaluated cellulite by dermatological grading and ultrasound measurements and could observe a clinical improvement in the cellulite.
Macroalgae extracts may also be of interest for slimming purposes, as evidence demonstrates that they significantly decrease the body weight gain, fat-pad weight, and serum and hepatic lipid levels in high-fat-diet-induced Sprague Dawley male obese rats, and showed a protective effect against these factors through the regulation of gene and protein expression involved in lipolysis and lipogenesis.
Thyroid hormones are involved in mechanisms that increase the synthesis of carnitine palmitoyl transferase which, in turn, promote lipolysis by increasing the penetration of fatty acids in the mitochondria.
 Diet is the major contributor of iodine, but breathing gaseous iodine in the air and placing it on the skin are other possible paths.
 Fucus serratus L. is a rich source of iodine. A recent in vivo study reported that bath thalassotherapy with this macroalgae had the potential to increase the urinary iodide concentration of the bather, indicating inhalation of volatile iodine as the predominant route of uptake.
 Another in vivo study also showed the effectiveness of a cosmetic product containing extracts of Gelidium cartilagineum, Pelvetia canaliculata, and Laminaria digitata, as well as other active ingredients, in exerting a slimming effect, compared with a placebo.
An enzyme-assisted extraction enabled a more effective obtention of proteins, neutral sugars, uronic acids, and sulphate groups in three species of macroalgae: the red Solieria chordalis, the green Ulva sp., and the brown Sargassum muticum. In this study, although no cytotoxicity was observed for all extracts, only S. chordalis presented good antiherpetic activities, mainly attributed to its richness in sulphate groups.
An O/W (oil in water) emulsion prepared with a phlorotannin-enriched fraction obtained from the brown macroalgae Halidrys siliquosa presented antibacterial capacity against Pseudomonas aeruginosa, Staphylococcus aureus, and Escherichia coli.
 Another study with a red macroalgae (Pterocladia capillacea) revealed that using carbohydrate degrading enzymes prior to in vitro assays produced extracts with higher antioxidant capacity and stronger antibacterial effect against Escherichia coli and Staphylococcus aureus.
 This could be of particular interest for the development of natural preservatives to be used in cosmetic formulations.
Macroalgae-derived ingredients have been used in cosmetic formulations due to their technological properties. 
However, it is well established that the interest of the cosmetic industry in macroalgae goes further than just using it as a source of excipients and technological additives. 
Macroalgae are a source of added-value compounds, with scientific evidence showing their benefits for human health and wellbeing. This can be a competitive advantage for this industry, namely in terms of finding and using novel molecules and agents that apparently have biological effects on skin, such as antiaging, antioxidant, moisturizing, collagen-boosting, photo-protective, whitening and melanin-inhibiting, anti-inflammatory, anti-cellulite and slimming, and antiviral and antibacterial activities. 
This review has summarized some of the possible applications of macroalgae as active ingredients in the cosmetic field, highlighting the main compounds responsible for their bioactivity on skin.
Sunscreens are the most common products used for skin protection against the harmful effects of ultraviolet radiation. 
However, as frequent application is recommended, the use of large amount of sunscreens could reflect in possible systemic absorption and since these preparations are often applied on large skin areas, even low penetration rates can cause a significant amount of sunscreen to enter the body.
 An ideal sunscreen should have a high substantivity and should neither penetrate the viable epidermis, the dermis and the systemic circulation, nor in hair follicle.
 The research of methods to assess the degree of penetration of solar filters into the skin is nowadays even more important than in the past, due to the widespread use of nanomaterials and the new discoveries in cosmetic formulation technology.
 In the present paper, different in vitro studies, published in the last five years, have been reviewed, in order to focus the attention on the different methodological approaches employed to effectively assess the skin permeation and retention of sunscreens.
The detrimental effects of human exposure to ultraviolet (UV) radiation have been widely investigated and can be immediate, as in the case of sunburns, or long-term, causing, in most cases, the formation of oxidizing species responsible of photo-aging, immunosuppression and chronic effects such as photo carcinogenicity.
 Ultraviolet radiation of sunlight consists of UVA (315–400 nm), UVB (280–315 nm) and UVC (100–280 nm) radiations, depending on the wavelength.
 Whereas the stratospheric ozone layer completely blocks UVC radiation and UVB wavelengths below 295 nm, 90–95% of the UV radiation that reaches the Earth’s surface is UVA, with UVB accounting for most of the remainder.
 At longer wavelengths, UVA penetrates deeply through the skin layers, reaching the basal layer of the epidermis and the inner dermis, interacting with endogenous and exogenous photosensitizers and generating reactive oxygen species (ROS), which are responsible for the onset of DNA mutations related to skin cancer development, of the acceleration of collagen breakdown and of the decrease of collagen synthesis, with consequent appearance of skin fragility and wrinkles.
Sunscreens are the most common products used for skin protection against the harmful effects of ultraviolet radiation; they should provide broad-spectrum UV protection for the presence of active ingredients, which attenuate the transmission of UV radiation onto the skin by absorbing, reflecting or scattering the incident radiation.
 It is not infrequent to see different types of molecules contemporaneously present in commercially available formulations, used in combination because none of them is individually able to provide broad spectrum UVA-UVB protection.
 The active molecules could be classified as either “chemical” or “physical” based on their mechanism of action: In chemical sunscreens, the active ingredient is an organic compound, with aromatic structure, that works by absorbing UV radiation and dissipating the energy as heat or light; in physical sunscreens, the active ingredient is an inorganic compound that acts by physically reflecting or scattering the UV radiation.
 Recent advances in nanotechnology have led to the production of nano-sized particles of these metal oxides, whose ability to absorb UV radiation is increased with respect to micronized ones.
 Unfortunately, TiO2 and ZnO nanoparticles, in addition to being effective sunblock and eliminating the anaesthetic formation of an opaque film on the skin (due to visible light scattering), seem to possess a photocatalytic activity associated with oxidative stress and genotoxicity.
 Moreover, as frequent application and reapplication after contact to water are recommended, the use of large amount of sunscreens could reflect in possible systemic absorption.
 Since these preparations are often applied on large skin areas, even low penetration rates can cause significant amount of sunscreen to enter the body.
 As the site of action of sunscreens is restricted to the skin surface or to the uppermost part of the stratum corneum, they should not penetrate into the viable epidermis, the dermis and into the systemic circulation; furthermore, the follicular uptake should be avoided, in order to not penetrate human cells where they can cause deleterious DNA damages.
 This can happen when the solar-filter has a high substantivity, intended as the capacity of adhering to and of being retained by the skin, thus resisting removal by bathing or perspiration.
The degree of penetration depends strongly on the physico-chemical properties of the active compound, the nature of the vehicle in which the sunscreen is formulated and several factors related to the skin.
 Indeed, both molecular weight and lipophilicity of the molecule play an important role in cutaneous penetration, as well as it has been demonstrated that skin permeation and retention from topical products can differ significantly among the formulations used.
Traditionally, in vitro percutaneous absorption studies have been carried out using animal skin or excised human skin from cosmetic surgery or autopsy. 
However, various three-dimensional cultures of human skin epithelial cells, simulating the native multilayer tissue architecture, are nowadays commercially available.
In the present paper, different in vitro studies, published in the last five years, have been reviewed, in order to focus the attention on the different methodological approaches employed to effectively assess the skin permeation and retention of sunscreens in the light of the entry into force of the EU Cosmetic Regulation with the ban of animal testing for cosmetic purposes, as well as the widespread use of nanomaterials and the new discoveries in cosmetic formulation technology.
Organic filters are molecules with aromatic structure having a carboxyl group that undergoes isomerization under the influence of energy absorbed from radiation. 
Their efficacy is mainly due to the physico-chemical properties, such as absorption coefficient and absorption spectra.
Butyl methoxy dibenzoyl methane (avobenzone, AVO) is among the most common UV filters present on the market, due to the broad absorption spectrum in the UVA region.
 However, it suffers photo-degradation, giving rise to new compounds responsible of photoallergic and phototoxic reactions.
 Therefore, the maximum concentration in ready to use preparation is fixed at 5%.
Diethylamino hydroxybenzoyl hexyl benzoate (DHHB) is an effective UVA filter, with high compatibility with other sunscreens and it is used in solar products at a maximum concentration of 10%.
Compounds from the group of camphor are characterized by high photo-stability and rarely are cause of allergic manifestations.
 Among them, 4-methylbenzylidene camphor (4-MBC) is authorized in Europe at the maximum concentration of 4%.
A recently approved chemical compound for use in cosmetic products is 2-cyano-3,3-diphenyl acrylic acid 2-ethylhexyl ester (octocrylene, OCT), absorbing UVB radiation at 303 nm wavelength and which maximum authorized concentration is 10% as acid form.
Isoamyl p-methoxycinnamate (IPMC, amiloxate), liquid at room temperature, is an efficient UVB absorber. 
It is a lipophilic molecule and the maximum amount that can be used in topical formulations is 10%.
Ethylhexyl triazone (ETZ) possesses an excellent photostability and, thanks to its water insolubility, is often used in water resistant products.
 The very high extinction coefficient and the high molecular weight make it a very efficient solar filter.
 The FDA does not approve its use in sunscreen products, while in Europe, it is allowed at maximum concentration of 5%.
A derivative of the once-popular PABA sunscreen ingredient, ethylhexyl dimethyl PABA (Padimate O), is among the most potent UV-B absorbers.
 The decline in its use, along with the demand for higher sun protection factor (SPF) products, have led to the incorporation of multiple active ingredients into a single product to achieve the desired SPF, replacing single PABA esters.
 It is suspected to release free radicals, causing indirect DNA damage, to possess estrogenic activity, and to cause allergic reactions; the maximum allowed concentration is 8%.
Ethylhexyl methoxycinnamate (octinoxate, OMC) is one of the most commonly used UVB filters in sunscreen products, due to its high absorption capacity in the shorter wavelength region (290–320 nm).
 Its safety profile has been, firstly, reviewed by the SCC (SPC/1037/93, S28) in 1993. 
 The maximum authorized concentration in sunscreen products is 10%.
A solid type UVA-UVB filter is represented by 2-hydroxy-4-methoxybenzophenone (oxybenzone, benzophenone-3, BP-3), a common ingredient in commercial sunscreens, thanks to the broad absorption bands in the UVA (400–315 nm), UVB (315–280 nm) and UVC (280–100 nm) regions, and therefore suitable to absorb incident solar radiation (UVA and UVB) and artificial UV sources (UVC). Although it remains photostable after being irradiated for many hours, some controversies regarding its ability to affect endocrine system and to cause dermatological problems are still ongoing.
 In Europe, as of September 2017, the use BP-3 is allowed as a UV-filter up to 6% in cosmetic sunscreen products and up to 0.5% in all types of cosmetic products to protect the formulation.
 Moreover, the consumers must be warned that the formulation contains BP-3 due to allergenic and photoallergenic potential.
A recent strategy to reduce cutaneous absorption of sunscreen is the use of high molecular weight UV-filters, such as bis-ethylhexyloxyphenol methoxyphenol triazine (bemotrizinol, BMZ), one of the few chemical sunscreens with good coverage in both the UVA and UVB range.
 It is a new oil-soluble filter with broad-spectrum protection and high efficacy, which does not degrade under sunlight; its photostability and compatibility with many other products allow it to be used in cosmetic formulations to protect less photostable UV filters, such as AVO.
Phenylbenzimidazole sulfonic acid is a chemical sunscreen agent that absorbs primarily UVB radiation. It provides some protection against short UVA (UVA-2) but cannot be considered a comprehensive UVA blocker.
 It is used as an UV-filter in cosmetic products at a maximum concentration at 8%.
 The booster effect on the body natural reserve of antioxidants can contribute to neutralization of intrinsic and extrinsic ROS, creating a new kind of sunscreen with a two-step protection: The first operated by UV-filters as a “passive” protection by absorbing and reflecting UV radiation and the second as “active” protection by antioxidants quenching ROS generated by UV light that has by-passed UV filters.
 Differently from synthetic UV-filters, which have to remain on the stratum corneum to be safe and effective, natural compounds should reach the viable skin layers to exploit photo-protection effect, since ultraviolet radiation penetrates deeply the skin.
Among the naturally occurring polyphenols, one of the most investigated products is trans-resveratrol, which possesses antioxidant, anti-inflammatory and anti-tumoral properties, as widely reported.
 It has also been demonstrated that it is able to inhibit UVB induced inflammation and lipid peroxidation of the skin following topical application.
The well known carotenoid β-carotene possesses skin protective effects against UV and IR radiation and if applied prior to irradiation is able to protect skin from UVA induced oxidative stress.
Inorganic filters are inert and non-irritant substances, able to protect the skin from the incident solar radiation due to physical phenomena, such as scattering and reflection, by forming a layer over the skin that works as mechanical barrier.
 Notwithstanding the physical filters on the market are very few with respect to the chemicals, they possess many advantages, as high photo-protection level in the longer UVA range, photostability and low photoallergic potential. 
The most investigated inorganic UV filters are zinc oxide (ZnO) and titanium dioxide (TiO2).
Available literature data on skin permeation and penetration of nanomaterials should be deeply evaluated in order to perform a risk evaluation of these relatively new kinds of materials. 
Despite the relevant scientific data on this topic, there is still the need to provide a definite safety profile related to nanoparticles skin exposure. 
Indeed, notwithstanding the stratum corneum represents the outermost barrier for penetration of exogenous substances, it has to be taken into account that this layer could undergo impairment or disruption upon treatments or age related mechanisms; it is also well known that skin diseases, such as contact dermatitis or erythema, can cause an increase in skin permeability.
 Moreover, during the last years, the trans-appendageal route (diffusion through the hair follicle and sweat gland) has gained importance, as either potential target site or shunt for delivery of various molecules.
 Therefore, the first point to clarify is the mechanism of nanoparticles penetration through the skin. 
Many authors proposed nanoparticles storage in the skin lipid matrix between corneocytes or in the appendages, from where they are released in a controlled and prolonged manner.
 On these bases, it is fundamental to further investigate how nanoparticles size, shape, surface chemistry and charge can affect skin penetration, also in relation to other physical factors concerning the environmental media.
 A significant point to take into account is that in vitro alternative methods to animal testing have not yet been validated for nanomaterials, representing an obstacle to the safety assessment of these cosmetics ingredients in the European market. Anyway, given the importance of the subject and the wide literature on it, it was necessary to mention the subject, but it will not be discussed in this review.
In vitro studies on sunscreens published from 2013 to 2017 have been reviewed in order to focus on the assessment of the permeation/penetration profile of the molecules not in their nano-form.
 It is a consolidated opinion that in vitro tests on the skin, using Franz cells and similar techniques, allow to obtain important information on penetration pathways, in order to ensure that the investigated molecules are effective and their residence time in the skin is adequate to assure UV protection. 
These methodologies are considered a reliable model to investigate skin diffusion, even though what happens in real condition can be underestimated. 
Indeed, in real condition skin permeation could be increased when superficial impairment or skin flexion happened as well as for active transportation .
Some controversies have been occurred regarding the skin models to be employed during permeation/penetration studies, in order to select the most appropriate to resemble human in vivo conditions.
 As reported in OECD guidelines for the testing of chemicals, skin from human or animal sources can be used, as either epidermal membranes or dermatomed skin at different thickness. 
During the last years, different kinds of skin have been investigated, on the general principle that rats’ and rabbits’ one is more permeable than that of humans, while the skin permeability of pigs is more similar.
 Anyway, the selection of one species rather than another, the anatomical site and the preparative technique must be justified in performing in vitro tests. 
Full thickness skin, dermatomed at a constant depth, is the preponderant choice for in vitro permeation/penetration tests; some authors decided to use human abdominal epidermal membranes, removed from the dermis with a scalpel blade, dried and stored at 4 °C before experiments.
 The choice of stratum corneum-epidermis membrane instead of full thickness skin was justified since the dermis could act as a further barrier to permeation, distorting in vitro evidence.
Analysis of recent literature shows an increasing use of porcine skin with respect to the past.
 Most of the studies employed the outer surface of freshly excised pig ears, after hair and underlying cartilage removal.
 The skin was often dermatomed to reduce its thickness and stored at ?20 °C/?80 °C for a maximum period of 30 days before use. 
Different receptor media were chosen, depending on the solar filter investigated and considering the very low water solubility of these molecules and their generally high lipophilicity.
 The choice of an appropriate receiving phase is determinant for these studies, especially when lipophilic molecules are investigated, as it can lead to false conclusions linked to insufficient solubility of UV-filters.
 Moreover, it has to be taken into consideration that the receptor fluid chosen should not alter the barrier properties of the skin.
 In case of studies on BP-3, the receptor medium used consisted of phosphate buffer solution (pH 7.2, 150 mM) added of either albumin or 0.5% Tween 80 to ensure BP-3 solubility.
 In another study, a phosphate buffer (pH 7.4, 100 mM) containing 4% w/v bovine serum albumin (BSA), for its ability in solubilizing lipophilic molecules, represented the receiving phase when the behaviour of different solar-filters (BMZ, OMC, AVO, OCT) after cutaneous application of an O/W emulsion had been investigated.
In most cases, the permeation experiments lasted 12–24 h and, after this time, the amount of active ingredient distributed in the different skin layers was evaluated by removing SC with 15–20 subsequent tape-strips and by mechanically separating epidermis (E) and dermis (D).
 In all reported cases, the samples were analyzed by high performance liquid chromatography after opportune extraction treatment from the biological matrix to quantify the amount of solar-filter in the receiving fluid and the skin portions.
It was found that BP-3, with a relatively low molecular weight and a log P of 3.58, had itself a good ability to permeate and penetrate the skin.
 OMC, AVO, OCT and BMZ that possess the log P of 5.96, 4.51, 6.78 and 12.6, respectively, indicative of a high lipophilicity, was retained over 90% in the SC while the permeated amounts were below LLOQ.
 Due to these characteristics, they seem able to accumulate into the lipid phases of the stratum corneum, producing a kind of reservoir, while they appear to have difficulty in penetrating the viable epidermis, layer of predominantly hydrophilic nature.
Shokri et al. investigated the fate of AVO, BP-3 and ESZ formulated in a cosmetic O/W emulsion as free filters or included in a complex with β-cyclodextrin in permeation studies through abdominal skin of Wistar rats, which were shaved with razors 24 h prior of the experiments.
 It is necessary to emphasize that these products are among the most common UV filters on the market and that, after cutaneous application, in vivo have been demonstrated to permeate the skin in significant amounts.
The receptor phase was based on phosphate isotonic buffer pH 7.4 and isopropanol 70:30 v/v to favour sunscreens solubility.
 The quantitative determination was performed by high performance liquid chromatography (HPLC). It was found that ESZ, when applied on the skin free or included in the cyclodextrin complex, was able to permeate the rat skin in a higher amount than BP-3 and AVO. 
In all cases, the complexation not only reduced the amount of permeated agent, but also prolonged the lag time of permeation.
 It is important to highlight the different behavior of the filters in relationship with their chemical–physical characteristics. 
In fact, both BP-3 and AVO exhibited a moderate lipophilicity and a low molecular weight, which allow the transit through the stratum corneum, but hinder a high penetration in the more hydrophilic viable epidermis and dermis.
 However, ESZ possesses a log P of ?1.1/?2.1 and a comparable molecular weight with respect to the other filters. 
On this basis, it seems that the ESZ dimension are somewhat responsible of its penetration through the stratum corneum, while the hydrophilic character allow a high flux through the skin.
By comparing the results obtained from the above mentioned permeation studies through different skin models, it is clear that free BP-3 applied in O/W emulsion was able, in every case, to permeate the skin and showed the same flux through both pig and rat skin, while the time to saturate the membrane was higher in case of pig skin. 
Conversely, the more lipophilic AVO did not permeate the pigskin, while it was found in appreciable amounts in the receiving phase of permeation studies through rat skin. 
Such results confirm what is already known from previous studies, suggesting that rat skin is generally more permeable than pigskin towards permeants with different physicochemical properties and in particular for the more lipophilic ones.
 Indeed, both the composition and packing of stratum corneum lipids, known to be key factors of skin permeability, differ between rodent and pigskin.
Most researchers are employing intact skin for the permeation/penetration studies, without taking into account that aging processes, diseases and sun exposure could often alter the skin structure. 
As some permeants could overcome a compromized barrier and penetrate through skin by inducing toxicity, it would represent an interesting tool to evaluate percutaneous absorption of solar filters through altered skin. 
Such an experiment was performed by Hung et al., which used nude mice aged eight and 24 weeks as animal model for young and senescent skin in order to mimic chrono-aging. 
The use of nude mouse was justified since it has been reported an identical histology and biochemistry to human skin in photoaging studies; even though it is notably more permeable, the mouse skin could be a useful model of facial skin on which the filters are applied, legitimizing the use of mouse skin in this kind of experiment.
 Moreover, they irradiated the dorsal skin of the mouse with UVA (365 nm, 10 J/cm2) every other day for three days and with UVB (312 nm, 175 mJ/cm2) once a day for five days, at a distance of 10 cm for a period of 100 min and 1 min for UVA and UVB, respectively, in order to induce skin photo-aging. 
A combination of the two was also carried out to simulate UVA + UVB radiation. 
The skin absorption and follicular uptake of AVO and BP-3 as chemical sunscreens and of ZnO as physical sunscreen were evaluated by performing a permeation experiment with Franz vertical diffusion cells followed by the differential stripping technique from aqueous vehicles containing the solar filters.
 The experimental conditions were set with a receiving phase consisting of 30% ethanol/pH 7.4 buffer, a temperature of 37 °C and an experiment length of 24 h. To completely remove the stratum corneum from the skin, at the end of the permeation experiment, 20-tape strips were performed, followed by cyanoacrylate casting to extract the hair follicle.
 It was found that ZnO, both in the micronized and nano-form, was not able to penetrate into the skin or the receptor, regardless of the treatment used. 
The behavior of BP-3 was not affected by chrono-aged skin, while irradiation, in particular UVA and UVA + UVB, increased both the permeation and the deposition of the filter in the follicle. BP-3 has been shown to penetrate skin and reach the circulation, phenomenon that appears more severe when the skin is irradiated with UV light. 
On the contrary, regarding the more lipophilic AVO, senescent skin showed less deposition with respect to young skin, probably due to lower sebum distribution in aged skin; the follicular uptake in senescent skin was increased by UVA or UVA + UVB radiation, thus reaching the same values of young skin. 
In any case, AVO was able to permeate the skin, maybe due to its high affinity for stratum corneum. The application of AVO and ZnO to photoaged skin may not increase the risk of excessive absorption; besides, when the skin is naturally aged the skin penetration of AVO is even reduced.
As stated above, many studies investigating the fate of UV filters after cutaneous application performed sequential tape stripping in order to evaluate the amount of the molecule penetrated in the stratum corneum. 
The evaluation of drug penetration into the stratum corneum (SC) by tape stripping requires an accurate measure of the amount of SC on each tape-strip in order to determine the stratum corneum depth. 
Recent studies are applying infrared densitometry (IR-D) to in vitro tape stripping using SquameScanTM 850A to verify the endpoint of tape-stripping, i.e., complete SC removal. 
In fact, the SC depth can be extrapolated from the IR-D data of sequential tape-strips, where the protein content of each tape strip can be indirectly quantified from the tape absorbance; the lower limit of quantification of IR-D indicates the complete removal of the SC (less than 5% of the total SC remaining) and can be used to know the exact numbers of tapes needed.
 The IR-D technique allowed the authors to affirm that the UV absorbing molecules were largely distributed in the first 1.7 μm of the SC, with smaller amounts accounting for the other 3.8 μm, confirming only superficial penetration of these materials as for their intended use.
Among all the papers reviewed, only the study performed by Monti and colleagues used a reconstructed human epidermis model from normal human keratinocytes (Episkin, SkinEthic Laboratories, Lyon Cedex 7, France) as substrate for the permeation/penetration studies; this model is histologically similar to the native human epidermis. 
Episkin was placed between on the donor and receiving chambers of a Harvard apparatus, equipped with six thermostated cells.
 The receiving phase consisted of pH 7.4 phosphate buffer solution (PBS) added with 1.0% Brij 98 to increase the solubility of the sunscreen under study, 4-MBC. 
To assess the distribution profile of the solar filter in the skin, the tape-stripping technique was employed.
 As the tissue was constituted only of the epidermis without dermis, two tape strips were performed and the remaining tissue was considered as the living epidermis. 
In order to quantify the degree of skin penetration of 4-MBC, an extraction procedure from the tissues was performed and the samples were analyzed by HPLC. 
Since the Cosmetic Regulation 1223/2009 have banned the use of experimental animals for testing cosmetic products and there are, at the moment, no validated methods for the assessment of the permeation/penetration, such kind of studies could broaden the knowledge of the theme and produce important elements of evaluation.
Many scientific reports confirm the interest in formulating innovative UV filters carriers to achieve high skin photo-protection, contemporaneously reducing undesirable effect linked to skin permeation.
 Colloidal carriers have been demonstrated to promote the accumulation of the sunscreens in the uppermost layers of the skin, where their action should occur, by enhancing their photo-protection ability. Lipid nanocarriers are almost made of well-tolerated and biodegradable raw materials, which, together with the colloidal size of the particles that facilitates the formulation in dermatological products, enable confortable skin application.
 In recent years, several studies focused on the formulation of UV filters in micro and nanocarriers to protect them from photo-degradation and to prevent skin permeation.
Since it is noteworthy that BP-3 permeates across the skin leading to undesirable effects, a deeper knowledge of the influence of different formulation on its penetration properties could provide interesting implications.
 Martins et al. showed that BP-3 incorporation in solid lipid microparticles (SLM) with natural waxes, such as carnauba wax, was able to inhibit permeation and reduce 3-fold penetration with respect to free BP-3.
 The importance of the stability of microparticles has been underlined, since a degradation of the carrier components could lead to a faster release or not prevent skin penetration.
Moreover, it has been demonstrated that the cutaneous penetration of 4-MBC decreased when it was incorporated in polymeric cationic microspheres with respect to that obtained from free sunscreen, without change in SPF.
 The microspheres formulated in a W/O emulsion appeared to bind to keratin for a long period of time, thereby increasing the uptake of 4-MBC on the skin surface, especially stratum corneum, where it can explicit its action.
Nanostructured lipid carriers (NLC) are characterized by a solid lipid matrix, in which a liquid lipid is added; on the other hand, a wall of hydrophobic polymer surrounding their lipid core typically characterizes both nanostructured polymeric lipid carriers (NPLC) and nano-capsules (NC).
 It has been reported that NLC dramatically reduced the skin permeation and favored sunscreens localization in the superficial layers of the skin when compared to a nanoemulsion formulation.
 Among the sun filters tested, AVO and DHHB exhibited the higher flux at the steady state when formulated in nanoemulsion (log P 4.5 and 5.7, respectively) and showed a reduced flux when encapsulated in NLC. 
However, ETZ and BMZ, in any case permeated through the epidermis after 24 h, maybe due to their high substantivity for the stratum corneum as highlighted also by the value of log P that was >7 and 12.6 for ETZ and BMZ, respectively.
 It is interesting to underline that the degree of flux reduction after encapsulation, more considerable for DHHB, seems to be independent of both molecular weight and lipophilicity of the original molecule.
 It could be interesting for further works to deepen the physical-chemical characteristic of the complexes themself, as well as their mechanisms of interaction with the skin.
Moreover, since, in commercially available formulations, two or more sun filters are often combined to broaden the solar spectrum coverage in both the UVA and UVB regions, the effect of NC and nanoemulsion was evaluated on OMC and AVO simultaneously present in the same formulation.
 It was found that, when the sun filters were incorporated in NLC, they exhibit a lower flux than the nanoemulsion containing the same amounts of molecules. Furthermore, the application of NLC, both containing some filters, did not produce an appreciable increase in the amount of substance permeated through the full epidermal layer; on the contrary, the use of nanoemulsion led to a significantly higher amount of AVO and OMC with respect to the same emulsion containing only one of the two. Besides, Gilbert et al.
 Demonstrated that, when BP-3 was formulated into NPLC and NC suspensions, the polymeric envelope retained the molecule in the lipid matrix and the presence of poloxamer 188 in the aqueous phase could solubilize free BP-3, thus reducing BP-3 flux through the skin with respect to the albumin aqueous solution of the filter. 
Moreover, it was observed a better efficiency of polymeric nanoparticles to reduce BP-3 penetration in the skin layers and to show the highest in vitro SPF.
Another interesting formulation tool is represented by bioadhesive nanoparticles (BNPs), described by Deng et al. 
Starting from polylactic acid-hyperbranched polyglicerol (PLA-HPG) nanoparticles, the HPG was converted into an aldeide-rich corona with bioadhesive properties, and padimate-O was incorporated in these new highly skin adherent and not penetrant BNPs. 
The BNPs, thus prepared, remained on the stratum corneum after topical application, from which they could easily be removed with active towel drying because of water-resistance, and the deposition into hair follicle was prevented.
Among the properties that influence partition/dissolution of sunscreens agents into the surface of the stratum corneum and their diffusion through the lamellar lipid layers, it can be mentioned both the molecular weight and the lipophilic characteristics. 
In order to increase the dimension of the solar filter and its concentration in the upper stratum corneum, the formation of inclusion complexes with cyclodextrins has been investigated, also to increase the sunscreen photostability
They found that the complexation reduced the rate of permeation of sunscreens with respect to when the free filter was taken in account, also increasing the lag time, while the physical mixture affected permeation only for a little extent.
Another strategy to improve the effectiveness of sunscreens is the combination of organic and inorganic filters, as performed by Li et al.
 The authors encapsulated BP-3 into the inorganic UV-filter mesopouros silica (MS) by an in-situ sol-gel process using tetraethyl orthosilicate as a precursor and an ionic liquid as solvent and pore-forming agent. 
Moreover, they tuned up a cheaper and timesaving procedure, consisting in adsorbing BP-3 onto MS, used as drug delivery systems with a high surface area. A synergistic effect on the UV-absorption ability was observed and was ascribed to the lowered crystallinity of the BP-3 molecules and the additional light scattering induced by the mesoporous structure, which led to a greater optical density. 
Furthermore, it was found that an O/W emulsion containing the BP-3 adsorbed or included in MS exhibited in vitro SPF and UVA-PF higher than the free BP-3 containing emulsion and, at the same time, the in vitro release profile BP-3 through a cellulose membrane was significantly reduced. 
In particular, the encapsulation of BP-3 in MS produced the lowest flux through the membrane, suggesting a tightly entrapment of the filter in the MS matrix. The same authors in another paper used a modified dextrin as drug carrier for AVO.
 The dextrin was modified via reactions with alkyl oxiranes to create a biodegradable molecule and more stable against protein denaturation, with a decreased skin affinity.
 As previously reported for other products, encapsulation eliminates cristallinity of AVO, suggesting the entry of the molecule into the cavities of the modified dextrin; moreover, the release of AVO through a cellulose membrane from a cold-process prepared O/W emulsion containing the encapsulated filter was even slower than from the same emulsion containing the free-AVO, suggesting a low degree of skin penetration.
Recent studies confirm that exposure to solar radiation is associated with adverse effects on the skin, such as aging and cancer. 
As a result, effective sun protection and improved body defence system have become important research topics.
 Currently, most of the solar protection products on the market contain organic or inorganic UV filters that are primarily directed against radiation induced sunburn and DNA damage. 
However, some of these UV filters can penetrate the skin and at high concentrations can accumulate in the tissues, causing allergies and/or contact dermatitis.
In addition, filters can undergo photo-degradation following sunlight or artificial light exposure, leading to a decrease in their UV protection capability and to the generation of harmful photolytic products, such as free radicals and ROS. 
Therefore, recent studies aimed at searching for encapsulation or incorporation methods for organic UV filters in order to reduce skin penetration and to design an effective carrier based on new technologies in controlled delivery.
In the past, the stratum corneum was considered the only permeation barrier for chemicals to enter in the skin, but, in recent years, also viable epidermis and dermis has gained importance for their role in skin absorption of small molecules.
 Alterations of the biochemistry and the structure of the skin layers may have a role in molecular delivery through the skin. Recent studies highlight the role of UVA and UVA + UVB radiation in the expression of E-cadherin in the stratum granulosum, which contributes to epidermal barrier by governing tight junctions and whose levels are reduced by irradiation provoking epidermal thickening and wrinkled appearance.
 Moreover, radiation can induce some proliferative activity in the epidermal layers and an up-regulation of epidermal COX-2 expression in chrono-aged and photo-aged skin, suggesting inflammatory processes. 
Anyway, it has been demonstrated that not necessarily intrinsic and extrinsic aging increase skin permeation, especially in case of lipophilic permeants. An important role is represented by UVA radiation, the main responsible of percutaneous absorption during solar exposure.
The physicochemical properties of the solar filters are determinant in the process of penetration into and permeation through the skin layers.
 In particular, the log P value is a crucial element to assess if a molecule is able to permeate across the skin or not.
 Generally, a log P value above 2 indicates a high lipophilicity of the compound and it is likely that such molecules are capable of accumulating and forming reservoirs within the lipid phases of the stratum corneum. 
Additionally, these agents would have difficulty in penetrating the viable epidermis and dermis because of the hydrophilic nature of these layers. 
However, highly hydrophilic molecule would remain above the stratum corneum, while molecule that exhibited both aqueous and lipophilic properties are candidate to permeate the skin. 
Several studies pointed out on the tuning of micro- and nano-carriers to formulate chemical solar filters, in order to reduce their skin permeation and penetration, favoring the retention in the outer stratum corneum, where they are desired to act, and to protect them from photo-degradation.
The vehicle chosen to deliver filters to the skin is fundamental in influencing dermal absorption. 
The studies mentioned above suggest that the choice of high molecular weight filters and the use of O/W emulsions can contribute to obtain low skin permeation rates and high UV filter retention in the stratum corneum. 
Moreover, combination of UV filters and antioxidants could influence the skin retention of the filters, by reducing the amount of filters penetrated in epidermis and dermis. 
While filters must remain on the skin surface and have high substantivity for stratum corneum, antioxidants, present in the sunscreens formulation, should penetrate the skin to act as radical scavengers in the deeper skin layers, without reaching the systemic circulation. 
As well as penetration of UV filters in the skin layers, in addition to compromise the protective effect on the skin, can cause photosensitivity and an increased risk of allergic reactions, the combination of UV filters and antioxidants in a sunscreen can improve the efficacy of the product, with a synergistic effect in UV skin protection and antioxidant activity.
In the majority of the cases analyzed in this review, the in vitro evaluation of the filters behavior towards the skin is performed with vertical diffusion cells, in order to establish the entity of permeation and penetration of the molecule through and into the skin. 
In the last years, the follicular route has gained much importance and the differential stripping technique has been proposed, in order to differentiate transepidermal and transfollicular penetration, allowing the quantification of substances in all the skin compartments.
The entry into force in the European Union of the Cosmetic Regulation 1223/2009, with the ban for animal testing for cosmetics and the absence of validated alternative test for the skin permeation/penetration studies has led researches to increment the use of human tissues from abdominal or aesthetical surgery. 
Anyway, due to the lack of suppliers and the difficulties in availability, many researches in Europe are using pig ear skin, allowed because considered as waste material from slaughter, or in vitro reconstructed human epidermis from normal human keratinocytes.
There is still a long way to go, because, although formulation strategies are improving and the road to reduce the penetration of sunscreens seems to have been found, the methods to assess their skin permeation/penetration with a margin of accuracy and reproducibility and with reduced time and costs, in the respect of ethical principles, are still so far.
The backlash against traditional beauty companies — and the rise of “clean” ones — might have been inevitable. 
As scary-sounding reports about ingredients made the rounds over the years, consumers demanded answers. But cosmetics regulation laws in this country haven’t been meaningfully updated since 1938.
 The Food and Drug Administration, contrary to what some people assume, only has minimal oversight of the beauty industry. 
For the most part, beauty companies regulate themselves.
But now cosmetics industry regulatory legislation that languished for years is closer than ever to becoming law. 
And the traditional big beauty conglomerates are scared enough of the clean beauty backlash that even they are actively seeking more oversight. 
It’s going to fundamentally change how brands talk about beauty and how we as consumers shop for it.
Natural products used to be sold primarily in health food stores and farmers markets with labels decorated with pictures of leaves.
 It was a very specific niche and not taken seriously by the beauty industry. But now sleek new brands positioning themselves as “cleaner” alternatives to the mainstream are exploding.
Daniela Ciocan — the marketing director at Cosmoprof North America, an entity that hosts a large expo where brands can display their wares in hopes of landing retail placement — says that thanks to retailer and customer demand, this year the organization doubled the amount of space it dedicated to new “clean” brands at the 2017 convention.
In the past 12 months, so-called natural brands like Tata Harper and Jessica Alba’s Honest Company products have made up about a quarter of all higher-end skin care sales, according to the NPD Group.
 The category is growing at a faster rate than last year.
“We’re absolutely inundated,” says Annie Jackson, a co-founder of Credo, which was dubbed the “Sephora of clean beauty” when it launched in 2015. It currently has eight stores in the US and a robust online business, where it sells about 115 brands.
 Credo receives about 200 new products a month from brands hoping to sell there.
And it has a competitor. 
Follain, which opened before Credo in 2013 as a local shop in Boston, is growing rapidly. 
It currently has five stores, will open two more in October and expects to have 10 by the end of 2019. Its growth rate is up more than 200 percent in 2018.
In the meantime, customer demand means mainstream companies and retailers are giving more lip service to the concept of clean beauty. In 2017, Target bumped up its natural beauty offerings. 
CVS announced it was removing parabens and other ingredients from 600 of its house-branded products by the end of 2019. 
Brands regularly remove parabens and sulfates and the like, sometimes quietly and sometimes with great fanfare.
Sephora launched its “Clean at Sephora” initiative in May, citing in-house research that revealed that 54 percent of its skin care shoppers think it’s important that their products “have a point of view on clean” and looking to shop brands that are “grounded in a ‘free of’ ingredient perspective,” according to Cindy Deily, the senior director of skin care merchandising at Sephora, though she did not say free of what. 
Sephora received some criticism that its clean standards weren’t as rigorous as they could be, but Deily says the “no” list is still evolving.
And it’s not just retailers. Traditional companies are more transparent than ever, at least superficially. 
In February, Unilever announced it was voluntarily disclosing the fragrance ingredients in its beauty and personal care brands like Dove, Axe, and Suave. 
Johnson & Johnson is doing the same for its baby care products.
Because of the lack of regulation in the industry, all these new products have caused some confusion among consumers.
 The terms “clean” and “natural” are often used interchangeably and are the most common; you’ll also see “safe,” “green,” and “nontoxic.” 
Walk into Sephora and you’ll be confronted with signage that designates those products “Clean by Sephora.” Walk into Nordstrom and you have to ask where the natural products are. 
But because the terminology isn’t regulated by an agency or governing body like the Federal Trade Commission or the FDA, they’re all essentially meaningless words when they appear on cosmetics and personal care products.
 Natural usually tends to mean it contains plant-based ingredients, though there’s a push by some new brands to move away from the word natural because there are many safe synthetic ingredients. 
But it’s still a free-for-all. Usually clean products are notable for the ingredients they are free of: parabens, phthalates, sulfates, and more.
The designation “organic” as it relates to cosmetics is even more confusing. The US Department of Agriculture, the organization that regulates food, has rules about what kinds of products can be labeled as organic; in beauty, it’s dependent on what percentage of USDA standard organic ingredients are in the product. But ultimately, being organic doesn’t make an ingredient better or safer, as the FDA notes.
So any company can call a product “natural” or “clean” and define that term any way it wants.
 And companies don’t hesitate to slap on that label, because shoppers respond to it. A 2018 survey by students in the Fashion Institute of Technology’s graduate school of cosmetics and fragrance marketing and management found that “90% of consumers believed that natural or naturally-derived beauty ingredients were better for them.” 
Of course, a lot of natural things can be bad for you. Poison ivy. Cyanide in apple seeds. Some essential oils.
But it’s led to a misconception among some consumers that chemicals equal danger.
 “I can’t tell you how many times I see a product that says ‘free of chemicals,’” says Kelly Dobos, a cosmetic chemist of 15 years. “It’s ridiculous because water is a chemical.”
Certain ingredients have garnered headlines throughout the past 10 years, bringing cosmetic safety to the forefront. 
In 2010, large amounts of chemicals that turned into formaldehyde gas when heated were found in a popular hair straightening treatment from the brand Brazilian Blowout. 
The Occupational Safety and Health Administration called it a hazard for salon workers and potentially for customers. 
In 2012, the FDA discovered that 400 types of lipsticks contained tiny amounts of lead; the effects on humans are unknown.
In 2014, following consumer outcry, Johnson & Johnson removed a type of preservative from its baby shampoo that releases very small amounts of formaldehyde into the air.
 In 2017, the American Journal of Obstetrics and Gynecology issued an opinion stating that women of color were disproportionately exposed to problematic ingredients in beauty products, partially because of the societal pressure on them to use hair relaxers and skin lightening products.
Clean beauty proponents often cite the statistic that the European Union has banned more than 1,300 chemicals from being used in beauty products while the US has only banned about 30. 
And this is true. The clean beauty market is made up of brands that have voluntarily cut these chemicals from their products. 
Like parabens, for example. Parabens make up a category of preservatives that have been widely used in cosmetics for decades.
 Any water-based product, which includes everything from shampoo to lotions, needs to contain a preservative to prevent the product from growing bacteria and fungus while it’s sitting in your medicine cabinet. 
But “paraben-free” is the most frequent claim you’ll see on beauty products these days.
Parabens are known to weakly mimic estrogen in certain situations, which gained them the description “endocrine disruptor.” 
In 2004, the Journal of Applied Toxicology published a study in which researchers found parabens in breast cancer tissue. 
It’s important to clarify that they did not test the women’s healthy tissue, and they did not suggest that the parabens caused the breast cancer. But this study was the first to the chemical some notoriety among consumer watchdog groups.
In 2014, the EU banned some parabens; this is really when the outrage against them peaked in the US.
 But the fact that Europe did not ban some of the most commonly used parabens was widely overlooked. 
The European Scientific Committee on Consumer Safety wrote:
The group of chemicals known as parabens make up an important part of the preservatives which could be used in cosmetics.
 In addition to Propylparaben and Butylparaben, other parabens, like Methylparaben and Ethylparaben, are safe, as repeatedly confirmed by the Scientific Committee on Consumer Safety (SCCS).
 They are also some of the most efficient preservatives.
Large organizations like the American Cancer Society put out statements that the data about parabens’ harm to humans was limited, writing: “There are also many other compounds in the environment that mimic naturally produced estrogen.”
Parabens may well be terrible for us, but for now the long-term effects of parabens on humans are simply unknown — there is no conclusive data that they hurt us.
 But the seeds of doubt were planted, consumers balked, and companies started removing them, thus reinforcing the belief that parabens must be bad. You’ll find them in very few products these days.
But how afraid should you actually be? In toxicology, the study of chemicals and their effect on living things, the mantra is “the dose makes the poison.”
 “If you give enough of any chemical, it will produce harm,” says Dr. Curtis Klaassen, a toxicologist who edited the textbook Casarett & Doull’s Toxicology: The Basic Science of Poisons. 
He also evaluates chemical data as an independent scientist for the Cosmetic Ingredient Review, a regulatory council created by an industry trade group.
Take formaldehyde, which has been labeled a human carcinogen. “It was discovered about 25 years ago that it is a carcinogen when they exposed rats and mice to very high concentrations in the air,” says Klaassen. “But it turns out that you and I make formaldehyde [in our bodies].
 Its likelihood of causing cancer in humans at the dose that you’re exposed to from washing your hair is essentially zero.”
We will likely never conclusively know the effects of years of daily use of these chemicals. 
It’s impossible to study in a controlled way, and the sheer number of ingredients we use on a daily basis makes it difficult to ever pinpoint a toxic smoking gun. 
But some argue that’s the whole point.
“What we’re most concerned about is the overall [chemical] body burden,” says Nneka Leiba, the director of the Environmental Working Group’s Healthy Living Science program. 
“Companies hear our position on that and sometimes they agree and sometimes they don’t.”
Some watchdog groups have become powerful in challenging the mainstream beauty establishment on this issue; the EWG, established 25 years ago as a nonprofit to look at pesticides and food, is arguably the most powerful one.
 But since then, it’s expanded to larger environmental and human health initiatives, including cosmetics. 
In 2004, the same year the breast cancer paraben study was released, the group published its first Skin Deep cosmetics database.
The EWG’s database contains more than 73,000 products and ingredients, giving them a rating for their potential hazards based on a complicated set of data and methodology. 
Leiba says a staff of 12, including toxicologists, chemists, and public health specialists, review the data on ingredients and update it regularly. 
“We speak to external scientists and industry scientists and see where we differ from them,” Leiba says. “Most of the times we realize we are erring on the side of precaution.”
The Skin Deep database has become a go-to resource for consumers, a go-to reference for the media — especially for its popular annual sunscreen guide — and a pain point for many brands.
 But it’s been criticized for perceived fearmongering along the way by some cosmetic chemists and others, as well as for rating inconsistently and giving ratings when there is limited data available.
“I agree with a great deal of what they do. 
We have a lot of carcinogenic materials. 
The vast majority of the problem is the chemicals that are being used both for cosmetics and for household cleaners are made using some really environmentally destructive methods,” says Gay Timmons, owner of Oh, Oh Organic, a company that provides organic cosmetic ingredients to companies like Aveda. 
Still, Timmons says, “EWG has had a big role in frightening consumers. That’s really very much the tack they have taken, for good or for bad.”
Leiba does not agree with that characterization of the EWG, saying they just want customers to understand they have options: “We are staunch in the fact that we’re not fearmongering. 
We’re educating. We’re not saying, ‘Don’t get this, don’t get that.’ We take a precautionary approach. 
That’s the same approach the European Union takes when it’s regulating chemicals.”
Part of the reason we’re in this confusing mess is that the FDA is not empowered by law to actually regulate the beauty industry. 
Based on the 1938 Federal Food, Drug, and Cosmetic Act, the only ingredients it can approve before they hit the market are color additives. 
It can’t order product recalls, but it can request them. 
If it thinks a product is contaminated or misbranded, it can work with other agencies to take legal action and does conduct occasional testing, as in the lead lipstick situation, if it has safety concerns. 
Ingredient safety is the biggest point of contention, though, because companies are expected to determine that on their own — which is kind of a joke because, per the FDA website, it doesn’t require companies to demonstrate safety or even share information.
The Personal Care Products Council, the beauty industry’s biggest trade group, formed the Cosmetics Ingredient Review 40 years ago. 
The CIR reviews data and studies on about 300 to 500 ingredients per year and offers judgment about their safety.
Each CIR panel member must pass a conflict of interest analysis, and a representative from the FDA and a consumer advocate group called the Consumer Federation of America sit in on meetings. 
The reports are finalized, peer-reviewed, and then published in the International Journal of Toxicology. 
But because the PCPC, whose members include some of the biggest beauty conglomerates in the country, funds the CIR, its conclusions just aren’t trusted the way they would be if it were a truly objective organization. 
Ironically, it’s been accused of the same thing the EWG has — passing judgment on ingredients where there is limited data. 
And it should be noted that the EWG partially relies on data from and refers to the CIR in its own rating system.
In the beauty and lifestyle media, ingredients are regularly referred to as “toxic” while clean brands are called “nontoxic.”
According to a report on the industry site Cosmetics Design, the PCPC all but begged beauty editors to talk more about science at the organization’s last annual meeting. 
“The misinformation that’s out there drives the scientific side of me crazy,” the PCPC’s chief scientist told the room. 
And now they’re asking the FDA for oversight too. “Consumers are very confused and the beauty industry and brands are very frustrated. 
This is something that cannot be solved by industry. 
Industry has tried and has lobbied the federal government constantly over the years for more funding to the FDA,” says professor Stephan Kanlian, who is the chair of FIT’s master’s program in cosmetics and fragrance marketing and management.
Scott Faber, EWG’s senior vice president for government affairs, agrees. “Big companies have been working with us to give FDA more authority because consumers don’t trust the regulatory programs like CIR.” 
He compares it to the situation last year, when, as Politico reported, some large food companies like Nestle and Campbell Soup Co. left their grocery trade lobbying organization because its policies weren’t perceived to be in step with what consumers wanted.
So we may finally be closer to stronger regulation. 
There are several pieces of legislation pending now. Rep. Frank Pallone (D-NJ) submitted a discussion draft for a House bill to regulate cosmetics in 2016. Sen. Orrin Hatch (R-UT) introduced the FDA Cosmetic Safety and Modernization Act at the end of 2017, which the PCPC publicly supported at the time, though it’s unclear if it still does.
But the most prominent proposal is the Personal Care Products Safety Act, introduced by Sen. 
Dianne Feinstein (D-CA) and co-sponsored by Sen. Susan Collins (R-ME). It’s been around in various forms since 2015. Basically, it gives the FDA the same power over cosmetics that it has over drugs and medical devices. 
The FDA could inspect safety records and mandate recalls. The bill also requires the FDA to review safety data on at least five ingredients per year. Companies will be charged a fee on a sliding scale depending on size to help fund the FDA so it can fulfill its new responsibilities.
The EWG has been devoting resources to draw attention and publicity to the bill, including even bringing Kourtney Kardashian to Washington, DC, for a briefing at the Capitol.
Beautycounter, a multilevel marketing beauty company that has banned 1,100 ingredients in its products and has been a visible and vocal voice in the clean beauty movement, has also supported the bill publicly. 
The company has a team dedicated to advocacy and it’s made multiple trips to Washington for hearings and meetings with lawmakers. 
Lindsay Dahl, Beautycounter’s vice president of social and environmental responsibility, says “a Senate version of the bill is about 95 percent negotiated, which is no small feat.” 
But because of the Brett Kavanaugh Supreme Court hearings and a focus on the November midterm elections, industry sources suspect the bill might be tabled until early 2019.
You can be excused for being skeptical about whether the current administration, which has shown itself to be decidedly anti-regulation, would support a bill regulating an industry that primarily affects women and their health. 
But it has bipartisan support, and the EWG’s Faber is hopeful.
“FDA has been a pleasant surprise. Commissioner Scott Gottlieb and his team understand that it’s government’s job to keep us safe from dangerous products. 
Which is a departure from some of President Trump’s appointees,” says Faber. 
The FDA even just launched its first-ever survey of safety practices and manufacturing standards of cosmetic companies, indicating that it is gearing up for more oversight of the industry.
None of this legislation is perfect and all-encompassing, and there is still a lot of data missing on ingredient safety. 
Even “clean” beauty produts contain ingredients that have no data one way or the other on safety. 
Plus, it’s not clear how the FDA would or could provide guidance on labels like “natural” or “clean.” 
But it would be a win-win for both companies and consumers, at least superficially.
Big beauty companies would be relieved of some of the burden and bad press that has come with regulating itself.
 For shoppers, it could seem like there is an objective entity looking out for them.
There are some downsides to pulling ingredients out of products, though, especially if there aren’t good alternatives. 
The Honest Company has been plagued with recalls, lawsuits, and complaints through the years because of faulty products. It’s going through a bit of soul-searching and reorganization.
Then there’s the risk of ditching well-known preservatives like parabens. “I have actually seen more recalls for microbial contamination in the past few years than I’ve seen in all my years in the industry,” says Dobos, the cosmetic chemist. 
Just this month, Avalon Organics and Bath & Body Works issued recalls for microbial contamination in some of their products.
What’s important to remember is that in the meantime, clean brands and retailers are still trying to sell you things just like the traditional beauty companies are, even if some of them truly have public health top of mind. 
Marketing themselves as “clean” is an advantage in this market. Even the EWG utilizes Amazon affiliate links on its product pages, meaning if you click through to Amazon to purchase, the EWG gets a percentage of the sale. 
It also sells a special certification label to companies allowing them to state their products are “EWG verified.”
“We are trying to make money,” says Beautycounter founder Gregg Renfrew.
 “We are doing well by our financial stakeholders while simultaneously creating significant social impact. 
The companies that are going to be successful in future will do both.”
In FIT’s report on industry transparency, the authors quoted an infectious diseases specialist reacting to the anti-vaccination movement in recent years. 
People are scared of ingredients, and they have made that clear with their dollars. 
Eventually, the clean beauty industry is going to just become ... the beauty industry.
Forms of body decoration exist in all human cultures. However, in Western societies, women are more likely to engage in appearance modification, especially through the use of facial cosmetics.
 How effective are cosmetics at altering attractiveness?
 Previous research has hinted that the effect is not large, especially when compared to the variation in attractiveness observed between individuals due to differences in identity. 
In order to build a fuller understanding of how cosmetics and identity affect attractiveness, here we examine how professionally-applied cosmetics alter attractiveness and compare this effect with the variation in attractiveness observed between individuals.
 In Study 1, 33 YouTube models were rated for attractiveness before and after the application of professionally-applied cosmetics.
 Cosmetics explained a larger proportion of the variation in attractiveness compared with previous studies, but this effect remained smaller than variation caused by differences in attractiveness between individuals.
 Study 2 replicated the results of the first study with a sample of 45 supermodels, with the aim of examining the effect of cosmetics in a sample of faces with low variation in attractiveness between individuals. 
While the effect size of cosmetics was generally large, between-person variability due to identity remained larger. Both studies also found interactions between cosmetics and identity–more attractive models received smaller increases when cosmetics were worn. 
Overall, we show that professionally-applied cosmetics produce a larger effect than self-applied cosmetics, an important theoretical consideration for the field. 
However, the effect of individual differences in facial appearance is ultimately more important in perceptions of attractiveness.
Modification of the body with dyes, paints, and other pigments is among the most universal of human behaviours, present in all culture.
 However, in Western society, women perform the majority of self-adornment, and perhaps the most prevalent behaviour of this kind is the use of facial cosmetics.
 This behaviour is served by the global cosmetics industry which is worth billions of pounds.
Women report using cosmetics for a variety of reasons, ranging from anxiety about facial appearance, conformity to social norms, and public self-consciousness, through to appearing more sociable and assertive to others.
 Cosmetics are effective at improving social perceptions that the wearer may wish to modulate, with individuals appearing to be healthier and earning more, displaying greater competence, likeability and trustworthiness, as well as appearing more prestigious and dominant. 
Cosmetics also influence the behaviour of others, especially men, who tip higher amounts and with greater frequency to waitresses wearing cosmetics, and are more likely to approach wearers in the environment.
 It is likely that the effect of cosmetics on social perceptions is brought about by the increase in attractiveness it confers to faces, which is now a well documented effect.
 Research has documented cosmetics function by altering sex-typical colouration in faces such as facial contrast, by increasing the homogeneity of facial skin, or by affecting colour cues to traits such as health  and age.
While the effect of cosmetics on perceived attractiveness seems clear, other research has revealed it is more nuanced than previously thought. Etcoff and colleagues demonstrated that attractiveness increased linearly with the amount of cosmetics worn—simply, more cosmetics equates to appearing more attractive. 
Of the range of cosmetics that can be worn, the quantity of cosmetics applied to the eyes and mouth have been shown to be significant predictors of attractiveness, with more cosmetics on these features leading to higher ratings of attractiveness. 
However, other evidence suggests that the typical amount of cosmetics applied by a sample of young women is excessive, with observers preferring close to half the actual amount for optimal attractiveness, calling into question the linear relationship between cosmetics quantity and attractiveness.
One concern of facial attractiveness research is that it does not compare the effects of predictors of attractiveness.
Recent work has begun to address this by examining the importance of within-person variation in attractiveness (caused by the presence or absence of makeup, for example), compared with the between-person variation in attractiveness simply due to differences between identities.
 Specifically, it has been previously shown that the effect of cosmetics on attractiveness, a source of within-person variation, is very small, explaining just 2% of the variance in ratings.
 This is an especially small effect when compared with differences in attractiveness between individuals, a between-person variation in attractiveness, which explained 69% of the variance in judgements. 
More simply, while facial cosmetics do increase attractiveness, that contribution is small and does little to change an individual’s attractiveness standing in the population.
However, the use of cosmetics is an idiosyncratic and extremely varied practice, and its effect on attractiveness is more complex than previously thought. 
The use of a professional makeup artist is a common practice in almost all studies examining the effect of cosmetics on perceptions, and only a few utilise self-applied cosmetics.
 An initial examination of the effect size of cosmetics on attractiveness also had models self-apply their cosmetics. There are good reasons for using professionally-applied cosmetics, as it provides a clearer test of how cosmetics alter facial attractiveness. The increased variability in self-applied cosmetics, due, for example, to differences in application skill or the products used, could make it more difficult to detect an effect of cosmetics on attractiveness, and previous work has indeed found the effect to be small.
 This distinction represents a trade-off between experimental control and ecological validity—the vast majority of women, if any, do not have a professional makeup artist apply their cosmetics daily, yet the majority of studies examining cosmetics and attractiveness draw conclusions based on professionally-applied cosmetics, which may only indirectly inform as to how cosmetics affect attractiveness in the real world.
We seek to address important theoretical points regarding how cosmetics influence attractiveness. How large is the effect size of cosmetics on attractiveness when cosmetics have been professionally-applied? 
If cosmetics in psychological experiments are applied with more skill than is typically achieved, then current knowledge of cosmetics and attractiveness likely overstates the relationship, given the reliance on professionally-applied cosmetics in the literature. 
Moreover, how does the ability of professionally-applied cosmetics compare to previous measures of the effect of cosmetics on attractiveness? In the following study, we examine the effect size of cosmetics on attractiveness in two sets of faces that have had cosmetics applied professionally, with the prediction that the effect will be substantially larger than the previous assessment that considered self-applied cosmetics. 
In addition, by using a similar design to previous research, we can draw direct comparisons with current knowledge of how cosmetics and identity affect attractiveness.
A separate but related question regarding cosmetics concerns how it affects faces of different levels of attractiveness. Many studies in the literature on cosmetics and social perceptions have used models recruited from university or college.
 How do cosmetics affect faces of a different population, specifically faces considered to be very attractive? Previous research found no interaction between cosmetics and identity, suggesting cosmetics affect each face’s attractiveness similarly. 
However, the models used were of a university-aged sample of population-typical attractiveness levels. 
The present studies, particularly Study 2, examine the effect cosmetics have on perceived attractiveness in a sample of women typically considered to be very attractive—models. 
Using a sample of faces that are already constrained in attractiveness enables us to manipulate another source of variation in attractiveness, specifically between-person variability. 
As such, we can observe the effects of cosmetics on attractiveness in a sample with a (hypothesised) lower effect of identity (differences between individuals) than elsewhere.
The present study has several aims. 
First, we examine how cosmetics affect attractiveness when cosmetics have been professionally-applied. We predict that cosmetics will have a notably larger effect size in this sample compared to the previous study examining this question. 
Second, we consider the effect size of cosmetics in sets of faces that are considered highly attractive, where between-person variation (identity effect size) should be reduced. 
The relative effect size of cosmetics may therefore be increased, and may be more likely to overshadow the smaller between-person variation in attractiveness. 
Conversely, cosmetics may have less of an effect in these samples as the women are already at the higher end of attractiveness without cosmetics, leaving little room for judgements of attractiveness to increase when cosmetics are applied. 
Finally, by using an identical design to previous research, we will compare the findings obtained in these studies to those presented in previous research in order to build a fuller picture of the relative importance of cosmetics and identity in attractiveness perceptions.
In the first study, we examine how cosmetics impact attractiveness when they are applied professionally. 
To do this, we take advantage of an Internet-based sample to acquire images of models whose cosmetics have been applied by high-profile makeup artists. 
Compared to previous work examining this question, we predict that the effect size due to cosmetics should be larger here. However, the effect size of identity may still overshadow it.
Ninety North American university students participated in the main study for course credit. 
Due to a software error, age data was not recorded for the first 50 participants, with the mean age being calculated from the remaining participants. 
However, all participants were within the same demographic and age range. A further 15 students  rated the quantity of cosmetics worn by the models. Informed consent was obtained from all participants included in the study.
Ethical approval for all studies was obtained from the Gettysburg College institutional review board. All participants gave written informed consent before beginning the study.
From the YouTube website, we collected images of White British women, who acted as models while their cosmetics were applied by high-profile professional makeup artists from the United Kingdom. 
Twenty-three models were obtained from one artist’s channel with a further ten collected from another. We utilised all available videos at the time of writing that featured a model receiving a makeover where they were shown before and after an application of cosmetics. 
In addition, we included only videos where faces began free of cosmetics, and the artist had the intention of applying a particular cosmetics look, rather than with the aim of hiding blemishes or skin conditions (such as acne).
 Images were captured from video tutorials, which served to instruct viewers on a number of popular cosmetics styles for a range of scenarios. 
Both authors classified the cosmetics looks into categories using information provided by descriptions within the videos. 
Three categories were apparent—an everyday, natural look, a ‘going out’ look, and vintage or editorial looks based on cosmetics the makeup artist had applied during professional photo shoots in the past.
 A third researcher, with extensive experience in this field, arrived at these three categories independently, providing further confirmation.
We captured a high-resolution screenshot of each model at the end of each video, where images of the models were presented before and after their application of cosmetics side-by-side.
 Models had a neutral expression and looked directly into the camera for the comparison.
 In addition, the two photographs were taken under the same lighting and camera conditions. From each comparison screenshot, we cropped the ‘before’ and ‘after’ versions of each model to produce two separate images. 
Final images were cropped just below the chin, at the hairline (or mid-forehead based on the limitations of the original), and tight to the widest part of the face (and so removing the ears). Given the variable nature of the images in terms of hairstyle, we chose models whose hair did not occlude their faces, and we masked loose hair in the lower portions of the images if it was not tied back. 
Images were resized to a height of 451 pixels. Given copyright restrictions, we present the average of models without cosmetics, and separately with cosmetics. 
Averages were produced using JPsychomorph after landmarks were applied to the facial features in each image.
Participants rated the attractiveness of the models using custom PsychoPy software.
 Images were presented in a random order, and each participant rated each model only once, in a randomly selected cosmetics condition.
 This design was specifically chosen to prevent carryover effects between conditions. 
Participants rated the attractiveness of the models on a 1  to 7  scale, indicating their response via mouse click. Stimuli remained onscreen until a judgement was made.
A separate sample of participants judged the quantity of cosmetics worn by the models. 
These participants saw the ‘without’ and ‘with cosmetics’ images onscreen next to each other, and were asked ‘how much makeup has been applied to this face?’ 
Participants indicated their responses via mouse click on a 1 (very light) to 7 (very heavy) scale. Trials were presented in a random order. 
Though this is only a perceived measure of quantity, rather than an actual quantity of cosmetics, we believe it to be suitable as it is the perceived quantity that would affect the perceptions of observers. Importantly, other studies have found general agreement in the quantity of cosmetics applied by a professional makeup artist and the perceived amount of cosmetics being worn.
Each image was rated an average of 45 times.
To examine effects of observer sex on ratings, the data were split by the sex of each observer before averaging.
 This resulted in four scores for each model—one in each cosmetics condition, as rated by men and women.
We also calculated the average amount of perceived cosmetics applied (M = 4.96, SD = 1.09), as judged by the separate sample of raters. These judgements of quantity were collected in order to be able to control for the varying amounts of cosmetics worn by each model in our analyses. However, this measure showed no relationship with the dependent variable (attractiveness) at all levels of observer sex and cosmetics.
 As such, there was no reason to include quantity as a covariate, and we therefore analysed our results using a repeated measures ANOVA with model as the unit of analysis.
We focus here on the effect sizes of variables in order to estimate the real world effect of cosmetics on attractiveness. 
In particular, we utilise eta squared (η2) as a measure of effect size, which expresses how much each factor contributes to the total variance in attractiveness ratings as an interpretable percentage value, rather than partial eta squared, which does not sum across factors to one. 
We calculated η2 effect sizes for both main effects (Cosmetics, Observer Sex) and the interaction by dividing the sums of squares (SS) attributable to each effect by the total SS, calculated by summing the SS attributable to each effect and their respective errors. 
We also gave special consideration to the variance attributable to differences between items. This variation is typically ignored in repeated measures analyses since it usually represents variation between participants on the measured dependent variable, which is generally unimportant for repeated measures designs (which instead focus on variation within participants). 
However, in this case, it takes on a useful property. 
By using the images of the models as the unit of analysis, the variation between models represents variation in attractiveness arising due to the fact that models have different facial identities or appearances. 
We were therefore able to calculate an effect size for this ‘identity’ measure. The full results of the ANOVA are reported, illustrating the effect sizes, their associated SS, and other statistics.
 It should be noted that there is no error term for conducting an F test on differences between models, and as such, no F ratio is calculated interactions with the Identity measure can be interpreted as an error term for that variable.
The models used in Study 1 were women who had agreed to participate for the purposes of demonstration in a makeup tutorial. We have shown that the effect of cosmetics, when professionally-applied, results in a larger effect size compared with previous research.
 Next, we investigate how cosmetics alter the attractiveness of a sample of women who are generally regarded as very attractive and earn a living based on their appearance—supermodels.
 We examine how much variation in attractiveness can be explained by cosmetics, and compare it with the effect size of identity, the differences in attractiveness between supermodels. 
Here, the effect size of identity should be smaller, given the potentially homogenous nature of the women in terms of attractiveness. 
How much of a benefit do cosmetics confer to highly attractive women, and in turn, do cosmetics overcome the differences in attractiveness between individuals?
Ethical approval for all studies was obtained from the Gettysburg College institutional review board (IRB). 
All participants gave written informed consent before beginning the study. The Ethical Governance and Approval System at the University of Aberdeen granted approval for the study conducted there. 
Again, all participants gave written informed consent before beginning the study.
We collected images (n = 45) of supermodels without their makeup from the Internet. These images were casting photographs for Louis Vuitton’s Fall-Winter 2010 runway show. 
All pictures were taken with the models looking directly into the camera, with a neutral expression. We then collected images of the same women wearing cosmetics from professional photo shoots, and selected images where they had a neutral expression and were looking directly into the camera in order to match the casting photographs as closely as possible.
 However, these cosmetics photos were considerably less constrained in that the lighting varied between images, as did the amount of time between the two photos for each model. Therefore, while every care was taken to ensure similarity between these images and those of Study 1, we note that such limitations mean that any conclusions drawn from this study are necessarily more tentative.
Final images were cropped as in Study 1 to just below the chin, at the hairline, and tight to the widest part of the face (and so removing the ears). 
Hair was masked at the bottom of the images as before, and images were resized to a height of 250 pixels. Given copyright restrictions, we present the average of supermodels without cosmetics, and separately with cosmetics
We have shown that professionally-applied cosmetics increase the attractiveness of both models and supermodels, with generally larger effect sizes than have been observed elsewhere.
 Here, we combine the data from Study 1 with the data reported in previous work that provided an estimate of the effect size of cosmetics when self-applied to a student population.
 This will allow a comparison of both model sets without and with cosmetics, and an overall comparison of the effect size of cosmetics and identity in a pooled setting of cosmetics use. 
We included only the models from Study 1 as these images were captured under more controlled conditions, similar to the images used in the previous work. In the initial study, there were 44 self-reported White women acting as models.
 Models applied their own cosmetics from a range of provided products, and were rated using the same procedure used here. 
To conduct this analysis, we employed a three-way mixed ANOVA: Set (Students, YouTube) × Cosmetics (With, Without) × Observer Sex (Female, Male). 
Set represented a between-subjects factor, while the remaining factors were both within-subjects. As before, the model was the unit of analysis. Since a factorial ANOVA produces several statistical tests, we focus on the theoretically important outcomes.
 In this case, an interaction between Set and Cosmetics indicates that an application of cosmetics affects the model sets differently. We would predict models that received an application of professional cosmetics would appear more attractive.
The predicted interaction between Set and Cosmetics was present.
 Bonferroni adjusted post-hoc tests revealed that without cosmetics, the YouTube models were rated as slightly more attractive than models from the student set.
 However, with cosmetics, YouTube models  received significantly higher ratings of attractiveness than the student models, indicating a larger change in attractiveness with professionally-applied cosmetics than with self-applied cosmetics.
We can also draw comparisons between the sizes of our effects across all three studies (the two presented here and the student set). 
While η2 is ideal for comparing effect sizes within a study (the total always sums to 100%), comparison between studies is generally not recommended because the total variability depends on the study design and the number of independent variables.
 However, the two studies reported here, as well as earlier data, use identical study designs, and the total variability is very similar in all cases.
 The main differences were the models used and the type of cosmetics applied. As such, we can justifiably make some comparisons between the effect sizes of cosmetics and identity across these studies.
While the effect size due to identity was similar, the earlier study using students showed a much larger effect.
Therefore, while variation in attractiveness between individuals was somewhat greater among a sample of university students as compared to models and supermodels (as we would expect), the effect size of professionally-applied cosmetics was much larger than self-applied cosmetics.
 It is also important to note that the effect sizes obtained for the data in Study 2 are to be interpreted cautiously, given the more unconstrained nature of the images.
Across several studies, we find that using cosmetics increases perceptions of attractiveness compared to no cosmetics, with several novel findings and caveats. 
First, we show that the effect size of cosmetics on attractiveness is large when those cosmetics have been professionally-applied, though the effect of identity is still greater. 
However, the difference between identity and cosmetics effects is much smaller than in a student sample of faces with self-applied cosmetics.
 Second, we show that in a sample of supermodels with a smaller, more constrained effect size of identity (i.e., reduced between-person variance in attractiveness), identity is still more important than cosmetics, though the effect size of cosmetics is still larger than in previous cases. 
In both cases, but particularly the set of supermodels, we found evidence of an interaction between facial identity and cosmetics, indicating a differential effect of cosmetics on attractiveness. Further analysis revealed that the more attractive a face was without cosmetics, the less of an increase in attractiveness cosmetics conferred.
Across all studies, we observed that the effect of facial identity was larger than the effect of cosmetics. This finding extends previous research demonstrating that between-person variation is consistently larger than within-person manipulations of attractiveness.
 Interestingly, the ratio between the effect sizes of identity and cosmetics in these studies (i.e., how much more variation identity explained than cosmetics in attractiveness judgements) is smaller than the comparison observed with emotional expression, suggesting that professionally-a—pplied cosmetics might be more effective at modulating attractiveness perceptions than facial expression, at least in female faces. 
Additionally, the finding that identity might be more important than within-person variation should perhaps be interpreted with caution. We refer to ‘identity’ in the current paper but use single, passport-style images of each model.
 However, individuals appear differently across different photographs, and this within-person variation in appearance has also been shown to affect perceived attractiveness.
A surprising source of variance in both studies was the interaction between identity and cosmetics. 
This finding, indicating that cosmetics affected different faces differently, was analysed further to reveal that the more attractive a face was initially, the less of an increase in attractiveness cosmetics conferred. 
While this is an intuitive finding, it has not been demonstrated before, and was particularly pronounced in the set of supermodels where the effect size of the interaction was almost as large as that of cosmetics itself. 
Cosmetics confer attractive patterns of colouration to faces, enhancing sex typical features in skin reflectance, as well as smoothing skin homogeneity and colour distribution.
 Female faces that are considered attractive tend to have lighter skin, darker eyes, and redder lips than the average female face, which are all correlates of attractiveness, and in a recent study, are colourations that are conferred to faces by cosmetics.
 It may be that the more attractive faces  already possess the most attractive features that cosmetics can alter, and so there is little change in attractiveness after an application. 
That less attractive faces receive more of an increase from cosmetics also has practical implications. 
By definition, the majority of women will lie around average attractiveness, and so a significant number of women could receive a boost in attractiveness from cosmetics.
We also found that the perceived quantity of cosmetics applied to faces played almost no role in the perceived attractiveness of faces with cosmetics. 
Recent evidence has shown that faces with lighter makeup are perceived as more attractive than faces with heavier makeup which is at odds with our findings here. 
However, that study used different models for each cosmetics condition, conflating sources of cosmetics and identity variance, as well as using digitally applied cosmetics.
 While observers seem to find lighter cosmetics optimally attractive when given the choice to vary the quantity, no study as of yet has systematically shown that lighter cosmetics are optimally attractive for a given face. 
Our measurements here, as well as previous data, seem to suggest quantity does not play a large role in perceptions of attractiveness with cosmetics.
Combining image sets from previous researchwith the findings from Study 1 revealed that, while the models from Study 1 were slightly more attractive than the models from the previous study, they were rated as significantly more attractive with cosmetics.
 After considering the similarity of designs and total variability across all studies, we compared the effect sizes of identity and cosmetics directly. 
Variability due to attractiveness between individuals (identity) was smaller among models and supermodels compared to university students, as predicted, but the effect size of cosmetics was noticeably larger for professionally-applied cosmetics. 
However, it is important to note that the sample sizes of models differed, and larger sample sizes might also result in greater between-person variability.
These findings have relevance for investigating the effects of cosmetics on social perceptions. There now exist estimates of the effect size of cosmetics when they are self-applied, and when they are applied professionally. 
In previous work, cosmetics explained just 2% of the variation in attractiveness, while the finding from a sample of models showed cosmetics explained 33% of the variation in attractiveness. 
This study demonstrated larger effect sizes of cosmetics when directly compared to previous research, though the studies used different sets of faces, and it is important to note that any effect size estimate calculated is ultimately based on the context of the research, and should be interpreted within this context.
 However, the variances in the current and previous research are very similar, and the design of the studies is identical, meaning direct comparisons are valid and appropriate.
The literature examining the effect of cosmetics on social perceptions has, for the most part, used models with professionally-applied cosmetics in laboratory studies as well as field experiments. 
With our comparison of the effect size of cosmetics under both self-applied and professionally—aaaapplied conditions, it seems possible that some of the effects of cosmetics observed in the literature may be inflated. 
Further, women report higher self-confidence and engage in more social activities after a professional makeover and this increase in self-confidence may translate into slight expression or postural differences in images, which could represent an additional within-person boost in attractiveness due to cosmetics.
There are some caveats to the study. Images were obtained from various Internet sources, and so were not as constrained in lighting or emotional expression as previous research. 
Study 1 suffered less from this potential issue as images were collected from the same photographic session. 
As the images of supermodels with cosmetics were obtained from different sources, while the images of those women without cosmetics were obtained from the same source, the magnitude of the interaction between identity and cosmetics should be interpreted with caution.
 However, given its presence in Study 1 with more controlled stimuli, we think it safe to conclude that cosmetics affect more attractive individuals to a lesser extent than others. Furthermore, that such an effect was obtained in Study 2 with more variable photographs could be considered strong evidence. 
Since the images were more variable and cosmetics were confounded with variations in lighting (both considered noise in the current study), it seems likely an effect would be obtained under stricter conditions.
There now exists convincing evidence that alterations to within-person facial appearance via cosmetics, whether self-applied or professionally-applied, do not overcome between-person variability in attractiveness due to simple identity.
 Facial attractiveness is, to an extent, more about what you have, rather than what you do with it. However, we have uncovered here interesting caveats to this overarching and consistent finding. An increased skill level in applying cosmetics seems to offer a larger increase in attractiveness than self-applied cosmetics does—larger effects were clear when a professional makeup artist applied cosmetics. 
Furthermore, we have shown cosmetics affect faces of varying levels of attractiveness differently, particularly within a sample of faces with lower variation in attractiveness between individuals. More attractive individuals simply have less to gain from using cosmetics.
 These findings have theoretical implications for attractiveness research.
 Cosmetics is perhaps the most common form of modification of facial appearance, and we have shown that the currently reported literature, with its reliance on professionally-applied cosmetics, highlights an effect that does not seem achievable through everyday use.
How cosmetics affect attractiveness is a growing literature, and many studies use professionally-applied cosmetics as a means to examine this change. 
We have shown that professionally-applied cosmetics seem to explain a larger proportion of variation in attractiveness judgements than self-applied cosmetics, a category which the vast majority of cosmetics users fall under.
Cosmetics is big business in mainland China and growing. Data from China’s National Bureau of Statistics suggests total retail sales of cosmetics in China in 2018 exceeded RMB 260 billion and a year-on-year growth of almost 10%.
The size and growth of the Chinese cosmetics market has not gone unnoticed by the authorities. The PRC authorities are now overhauling the system to better regulate the cosmetics industry.
The Regulations on Hygiene Supervision of Cosmetics are the most important and also primary regulation that currently governs the production of cosmetics and the operation of cosmetics companies in China. 
As the Current Regulation was passed a long time ago (actually when there was still a Soviet Union) and a lot has happened in China ever since, it is more and more obvious that the Current Regulation is too outdated to deal with new issues that continue to emerge in the cosmetics industry.
The Chinese authorities have taken measures to improve the regulatory framework under the Current Regulation to meet changing needs of the market and also of an industry that has evolved towards increasing complexity but the time is nigh that the Current Regulation  is now ripe to bow out after almost thirty years since first being published
The much awaited second draft of Regulations on Supervision and Administration of Cosmetics was circulated for comment amongst cosmetics industry associations and stakeholders back in August 2018. 
A final draft is anticipated to be issued in the near future and when this happens it will replace the Current Regulation.
With a total of 72 articles the Second Draft is far more comprehensive than the Current Regulation which consists of only 35 articles.
 Accordingly, the Second Draft provides a more detailed and practical regulatory framework that better fits the market realities of China in 2019.
Reports suggest the main drivers for the authorities were to simplify the administration but also encourage technological innovation. 
The Second Draft has adopted a classification management system for the administration of cosmetic new ingredients and also for cosmetic products which is based on different perceived risk levels. 
This aims to balance consumer safety on the one hand but on the other to ensure that regulation is not an overly burdensome constraint and thereby still allow for innovation.
Currently, prior approval must be obtained from China Food and Drug Administration for the use of any new ingredient, either natural or synthetic, if this ingredient has not already been used in the manufacture of cosmetics in China.
The Second Draft provides that new ingredients (except for ingredients used for antiseptic, sunscreen, colorant, hair dye, skin whitener and other higher risk ingredients) will be admitted on a CFDA filing basis rather than requiring approval. 
High-risk new ingredients will still be subject to pre-use registration with CFDA. After registration or filing, the applicant shall report regularly for three years to CFDA as to the use and any safety issues related to the new ingredients. 
If a new ingredient results in safety concerns during such three-year period then the CFDA will revoke its filing or registration. If the three year passes without incident then the new ingredient will be included in the catalogue accepted for use in cosmetics production in China. 
Cosmetics companies have long been frustrated by restrictions on the import of non-special use cosmetics into China.
The Chinese authorities have simplified the import of non-special use cosmetics by starting a pilot program in Shanghai Pudong New Area in 2017 to allow for a filing only process. 
This Pudong pilot scheme was extended on a nationwide basis in 2018.
The Second Draft further clarifies that non-special use cosmetics, either manufactured domestically or imported, only require filing with the competent CFDA.
This new policy will greatly simplify the process by which foreign cosmetics can be imported into the Chinese market. 
This will lead to the purchasing cycle for imported cosmetics being shortened and this will also lead to lower logistics and warehousing costs for foreign cosmetic companies.
Special use cosmetics will still be subject to registration before being allowed to be manufactured or imported into China. 
However, the scope of what constitutes special use cosmetics in the Second Draft has been narrowed from ten categories under the Current Regulation to five categories going forward. 
The special use cosmetics categories include: 1) hair dye products; 2) hair perm products; 3) spots removal and skin whitening products; 4) sunscreen products; and 5) other products which claim a new function.
 Products for hair nourishment, hair removal, breast shaping, fitness, and deodorizing have been removed from the list and are now considered to be ordinary cosmetics and therefore only need a filing.
The Current Regulation sets out only the most general of requirements for cosmetics manufacturers in that they are required to conduct hygiene quality examination of products before market launch.
 The Second Draft requires cosmetics manufacturer to appoint a responsible person in charge of safety and quality. 
The responsible person must have specialist knowledge for medicine, pharmacy, chemistry, toxicology, chemicals and biology and have at least five years working experience in cosmetics manufacturing or quality management. 
The Second Draft provides that applicants for cosmetics registration or filing must carry out a safety assessment of the products before the application. 
The Chinese government has taken a tough stance against the illegal use of forbidden substances in cosmetics. 
The Second Draft provides the CFDA may carry out supplementary tests if current tests are considered insufficient to guard against adulteration or illegal use of restricted or forbidden ingredients in cosmetic products. 
The Second Draft provides a number of new methods by which the CFDA can administer and supervise cosmetic products and cosmetic manufacturers. 
The guidelines cover the whole manufacturing process, market entry and post-sale administration for the cosmetics.
The Current Regulation has attracted criticism for not providing legal grounds for the CFDA to take a more proactive approach to regulation and supervision on imported cosmetics and overseas cosmetics manufacturers.
The Second Draft vests in CFDA the power to carry out overseas on-site inspection in respect of manufacturers of imported cosmetics in order to ensure the manufacturers have met Good Manufacturing Practice (GMP) requirements and to ensure the registration or filing documentation submitted by importers are true and valid.
In addition to the traditional measures of administrative penalties in the case of production quality problems the Second Draft also enables CFDA to have more weapons in their arsenal to police problematic cosmetics manufacturers.
These new powers of the CFDA include the ability to issue safety warnings to the public, ordering mandatory product recalls, reviewing and copying relevant documentation (contracts, bills and ledgers etc.), sealing up and seizing potentially harmful cosmetics and ingredients, and closing down production or distribution sites involved in illegal activities.
 The Second Draft also sets forth a credit system established by CFDA for cosmetics manufacturers and distributors.
Manufacturers and distributors with bad records will invite stricter supervision and be subject to increased numbers of unannounced spot checks.
The Current Regulation sets strict principles regarding the labelling of cosmetic products. In particular, the packaging, labelling and instructions for cosmetic products are prohibited from carrying medical terms or indicating any curative effect.
The prohibition against the use of medical terms does continue in the Second Draft but it is unclear in the draft as the prohibition applies only to the “label” of the cosmetic products but there is no definition of “label”.
 Based on the context of the Second Draft and considering the legislative intent we are inclined to interpret “label” in a broad sense. Therefore, we assume this prohibition will also cover packaging and instructions for cosmetics products.
The Current Regulation also provides a series of principles that must be followed when advertising cosmetics products. 
These include, amongst others, a prohibition on the use of any false or exaggerating or misleading descriptions as to the efficacy of such products. 
The Second Draft further expands upon such scope of the prohibition in respect of labelling of cosmetic products and also advertisements. 
In addition, claims of any function or efficacy of cosmetic products must be supported by sufficient scientific evidence, such as research data, assessment reports or relevant literature. 
Cosmetic manufacturers are responsible for such claims and must disclose a summary of the scientific evidence on designated websites. 
The Second Draft establishes an adverse reaction monitoring system for cosmetics. 
This system requires cosmetics manufacturers and distributors to report to the monitoring authority any reported adverse reaction to their products. 
After the gathering, analysis and assessment of all the materials regarding the adverse reaction, the monitoring authority will provide suggestions for steps to be taken. 
In the case of a serious or large-scale adverse reaction then the CFDA can take emergency measures to prevent an outbreak including suspending manufacturing and sales. Customs is also empowered to block import of such cosmetics. 
China’s massive e-commerce market continues to grow at a staggering rate.
In this regard the Second Draft when read together with the new PRC E-Commerce Law increases the responsibility upon cosmetics e-commerce operators and subjects them to stricter oversight and greater obligations.
Specifically, cosmetics e-commerce platform operators must ensure real-name registration and check necessary qualifications of cosmetics retailers that operate on their platforms.
 They must also promptly put a stop to any activities in violation of the Second Draft and report to the competent authority.
Offline retailers are also subject to increased responsibility. 
Operators of centralized cosmetics stores, store counters for cosmetics and organizers of cosmetics trade fairs are also required by the Second Draft to meet a range of management obligations, including pre-entry examination as to the qualifications of the participating cosmetics operators, post-entry inspection of participating cosmetics operators on a regular basis and promptly stopping and reporting to the competent authority any activities that violate the Second Draft. 
E-commerce platforms and offline operators that fail to perform abovementioned duties may be subject to fines of up to RMB 100,000. 
In addition, hotels and beauty salons that provide or use cosmetics when providing their services shall bear the same responsibilities as operators of cosmetics. 
One of the major complaints after the circulation of the Second Draft was that harsh punishments were overkill. 
These complaints are becoming louder and many claim the increased penalties will turn cosmetics manufacturing into a high-risk industry.
Compared to the Current Regulation the penalties provided for in the Second Draft are far greater.
By way of example, under the Current Regulation, a manufacturer that produces cosmetics that do not meet applicable mandatory standards will be subject to the punishment of confiscation of the relevant products and illegal earnings and may be imposed a fine in the amount of 3 to 5 times of its illegal earnings. 
Under the Second Draft in addition to confiscation of the relevant products and illegal earnings, the manufacturer of the non-conforming cosmetics could also face confiscation of raw materials, packing materials, tools and equipment used for the manufacture and a fine of between 2 to 10 times of the value of the relevant commodity (depending on the exact value); be ordered to stop production and have its cosmetics licenses revoked. 
In addition, the individuals in charge and directly responsible may face personal liability which may include fines ranging from RMB 10,000 to 50,000 and five-year bans from working in cosmetics. 
On balance it seems trite to brand the penalties under the Second Draft as being too harsh. 
Upon careful reading of the relevant provisions it appears clear that the Second Draft not only increases penalties but also provides much clearer guidance as to the application of different penalties depending on the severity of the actions and other relevant factors.
 In this way although the Second Draft increases potential liability it does protect cosmetics operators by providing greater certainty as to what is considered a violation and also guidance as to how punishments will be meted out.
A reading of the Second Draft gives the reader a clear impression that the regulation tends to evolve in the direction of complexity. 
This is very much the case with a regulation that is more than thirty years old.
The PRC regulations in respect of cosmetics are no longer fit for purpose today and do not confront the complex market situation in China — a world of booming Chinese middle class consumers; increasing demand for imported products; online purchases and no doubt a wish to foster a domestic Chinese manufacturing industry.
For overseas cosmetics manufacturers the Second Draft provides more market access; less red tape and more certainty. 
On the negative side the Second Draft will increase obligations and expands the types of measures the Chinese authorities can take. However, none of the measures are highly surprising and the triggers for taking action are also reasonable. 
Few international manufacturers are likely to be anxious about such measures. 
It would be wise for overseas manufacturers to monitor the progress of the new Chinese regulations on cosmetics as they will not bring just increased levels of responsibilities but very welcome market access and clarity.
Worldwide phenomenon, South Korean beauty, or K-Beauty, is always two steps ahead of the game with a skin-first approach to beauty that combines innovative science with generations-old traditions and herbal medicine.?
Home to the 10-step skincare routine where hydration and sun protection are non-negotiable, K-beauty brought us the viral likes of “Glass Skin,” “Honey Skin,” and now “Velvet Skin,” and enlightened us to the world of daily sheet masking and fermented essences.?
From the antioxidant powers of snail slime and ginseng to the healing properties of green tea and bamboo sap, K-Beauty’s “more is more” skincare philosophy brings us high-performing products accessible at various price points.
Heritage brand Sulwhasoo was founded in 1966 when it launched its ABC Ginseng Cream — the world’s first ginseng-based cosmetic product — and has since pioneered over 50 years of research on the Ginseng plant.?
Today, the premier skincare label combines the Asian wisdom of balance and harmony with the time-tested medicinal herbs of Korean “Hanbang.” The First Care Activating Serum EX is Sulwhasoo’s signature product — one bottle is sold every ten seconds.
K-Beauty heavyweight Neogen develops skincare products based on the latest derma-ceutical research and its patented six-core biotechnology, which maximises the powers of natural ingredients.
Its famous Bio-Peels Gauze pads harness the powers of AHA chemical exfoliation and antioxidant resveratrol, while its Real Ferment Micro Essence brightens and locks in moisture with a cocktail of natural ingredients.
Erborian is a half-Korean, half-French beauty brand — need we say more? By combining the best of traditional Korean herbal ingredients, Korean skincare technology, and the luxury savoir faire of the French, Erborian creates sophisticated formulas that improve both the health and appearance of your skin.?
Its do-it-all CC creams are loved in both Asia and Europe, and its Solid Cleansing Oil and Double Lotion are staples in any multi-step skincare routine.
CosRX, which stands for cosmetics, was founded in 2014 with the mission to deliver effective, affordable skincare solutions that focus on formulation and ingredients — its Snail Essence is made with 96% snail mucin.
Its low pH cleanser spotlights the importance of maintaining the skin’s optimal acid mantle (i.e. gentle cleansing without stripping the skin of its natural oils), while its Acne Pimple Master Patches are game-changing.
Famous for its cult-favourite line of Sleeping Masks, Laneige was created by Amore Pacific in 1994 and named after the French word for “snow.”
The Water Sleeping Mask (an overnight moisture-recharging gel mask) and the Lip Sleeping Mask are some of the most-reviewed and top-rated treatments in the world.
AmorePacific is like the Est?e Lauder of Korean beauty, owning nearly 30 of the most well-known K-Beauty labels including Laneige, Sulwhasoo, Etude House and Innisfree.?
Sung-When Suh founded AmorePacific in 1945 with the idea of incorporating green tea from Jeju island into skin treatments, giving birth to the Vintage Single Extract Essence, which is made from handpicked green tea leaves that are naturally fermented in optimal conditions for 50 days.
Other signature products like the Enzyme Peel Cleansing Powder balance tradition with modern science and, most importantly, is one of the most gentle and effective exfoliators on the market.
One of the first K-Beauty brands to break into the western market, Dr. Jart was created in 2004 by dermatologist Dr. SungJae Jung to provide science-driven skincare solutions and cosmetics targeting dryness, sensitive and acne-prone skin.?
Dr. Jart’s first product was the BB Cream in 2006 and the brand consistently produced other hits like the Cicapair Tiger Grass colour correcting line and the Cermidin range.
SU:M37 specializes in skincare made from advanced natural fermentation technology.?
At the heart of its products is a patented compound Cytosis which features over 80 local and seasonal ingredients that are fermented at the optimal temperature of 37 degrees celsius for a minimum of 365 days — some for as long as a decade.?
Its signature Secret Essence is a magical do-it-all potion that firms, brightens, hydrates and tones all skin types.
The History of Whoo is a modern interpretation of traditional Korean royal court beauty secrets using advanced lab ingredients, nanotechnology and premium herbal medicines specifically designed for Asian skin.
Each product features the brand’s Gongjinbidan Complex, a key ingredient derived from an ancient health formula used by Korean emperors and empresses for over 800 years to improve blood circulation.?
The Bichup Ja Saeng Essence is a best selling luxury serum in Asia.
If you left your Halloween costume to the last minute and are relying on your closet’s current contents, a strong makeup look can make all the difference.?
For inspiration, look no further than the SS20 runways, where witchy mascara and graphic eyeliner made for a fashionable take on spooky beauty.
From the clumpy, elongated lashes at Olivier Theyskens, to the angular onyx liner at Gypsy Sport, it’s apparent that a premature Halloween energy took over the runways, even if the mood boards were much broader than ghosts and ghouls.
“I was super inspired by 90s tribal tattoos and KISS the band for this look,” makeup artist Fatima Thomas, said backstage at Gypsy Sport when asked about the look she created using MAC’s Brush Stroke Liner in black.
“This might not be a look for everyday life, but it’s there to inspire people to take risks.” In other words, it’s perfect for Halloween.
When creating the next-level lashes at Olivier Theyskens, makeup artist Issamaya Ffrench accomplished her mission to create “something dark that is still beautiful and elegant,” as she described it.
“I wanted a spidery, fragile length for the lashes, to make it look like an illustration,” she continued. A sturdy hand along with Kryolan lashes, Duo lash glue, and MAC’s Blacktrack eyeliner did the job.
Here, to help you get into the Halloween spirit, more trick or treat-worthy eye makeup looks from the SS20 runway shows.
For a fashion girl, dressing up on Halloween goes far beyond the sartorial.?
It’s a head-to-toe affair—with a heavy emphasis on the former. A quick rifle through her treasure trove of clothes yields an idiosyncratic-chic costume, but it’s when she dips into her makeup bag that the fun really begins.?
Whether stepping into cinematic character or reimagining a spine-chilling classic, it’s a cool-girl calling card to indulge your wildest beauty fantasy over October’s last days.
“On Halloween, it’s about paying homage to traditional characters, but giving them a modern twist!” says editorial makeup artist Grace Ahn, encasing British-Nigerian artist Oyinda’s piercing gaze with two gold stars that flash against her glowing skin, glossy lips, and heavy-metal Marni dress.?
Korean model Hyunji Shin receives a duo of sharp, deconstructed feline flicks and muted brown pout, her dark lengths sculpted into cat ear-like knots, while rising runway star Massima Desire is getting a more-pared back Harlequin treatment, clad in a diamond-print Puppets & Puppets suit.
“I’m a Scorpio with a Pisces moon, so I’m a sad clown in my daily life,” Desire laughs as Ahn defines her eyes with punkish Pierrot liner and swirls soft peach pigment on her cheeks. “I love being a goofball who’s also really into crying.”
Finding a sweet spot between Betty Boop and Jessica Rabbit, Dominican model Anyelina Rosa is a subversive bombshell, radiating cartoonish shine as she slinks around in crimson patent leather Rick Owens boots with matching vinyl red lips.?
As for Belgian beauty Hanne Gaby, she’s breathing new life into the tried-and-true, flesh-eating zombie; Ahn administering painterly bursts of neon-pastels on the lids to play off her tattered powder pink Moschino dress.
Bringing the bewitching and beguiling to life, Ahn dreams up five makeup ideas that will help you lay the drama on thick this Halloween.
Per tradition, Paris Fashion Week was all things to beauty dreamers.?
For the Francophiles, there were looks that captured that elusive insouciance—the Gallic flicks of liner at Dior, the undone ’70s waves at Celine—but also ones that paid homage to the past: Thom Browne’s chandelier-bound Marie Antoinette poufs, for example, transported showgoers back to the 18th century.?
With a flair for the dramatics as a through line, fantastical flourishes were omnipresent: At Giambattista Valli, supercharged complexions were adorned with fresh-from-the-garden florals, while at Dries Van Noten, the Belgian designer paid homage to his co-collaborator Christian Lacroix with ostrich plume-topped updos.
And then there were the dizzying, super-Surrealist cascades of eye crystals at Schiaparelli, which helped close out the nine-day marathon with a veritable bang.?
But of all the mic-drop moments, none was more jaw-dropping—or thought-provoking—than the glass-sharp prosthetic cheekbones and inflated lips at Balenciaga. Here, five standout takeaways from the week.
At Dior, makeup artist Peter Philips embraced a more minimal mindset than he has in seasons past.?
“This is a toned-down version of a French woman’s classic eyeliner,” said Philips of the smooth, slender slashes of pigment along the upper lash line, which didn’t extend all the way to the inner corner for a more pared-back effect.
“Less is more” was also the overarching mood for the hair at Celine, Chanel, and Saint Laurent, where each of the French fashion houses channeled the late ’60s and early ’70s with clean center parts and tousled, languid lengths.
Translucent shine is the new go-with-everything neutral. For proof, look no further than the dewy, crystal-clear lips that came down the runway at Mugler, Off-White, and Giambattista Valli.
But if it’s a dose of juicy color you crave, one fell swoop of Pat McGrath’s saturated Lust: Gloss will do the trick, as evidenced by the bright pink pouts at Louis Vuitton.
Or, you can make like Chanel’s global creative makeup and color designer Lucia Pica, who painted lips coral pink, blending out the outer edges for a diffused effect, and added a vinyl-like top layer.?
“It almost looks as if the girls have been eating a strawberry,” Pica said backstage.
Partially obscured by plumed fringe, there were Blade Runner–inspired “brushstroke masks” running across gazes in gold, black, and white at Dries Van Noten.?
And bringing sparkle to the eye-level equation, there were meteor-shower-inspired dustings of metallic silver glitter and loose octagon sequins at Off-White, as well as four shimmering gold eye looks at Valentino that utilized aureate gems and heavy-duty falsies. Most extra of all? That would be Schiaparelli, where makeup artist Erin Parsons created a wide array of Surrealist crystalized gazes ranging from a silver glittered “rhinestone cowgirl” look to a gradient rainbow “poison dart frog” one.
"I'm not a foundation person," is how?most?of the team ELLE prefaces their foundation picks. 
Blame BB creams, tinted moisturizers, and the prevalence of no-makeup makeup, but there's a recent reluctance to come out as a daily foundation wearer.
However, unless you are born with genetically perfect skin, a great foundation (you know, one that covers but?doesn't?feel like a mask ) is an makeup bag essential for everyone. 
So, let's play pretend-perfect-skin with 11 of ELLE's favorite foundations—and a few foundation-like BB creams snuck in—below.
However, unless you are born with genetically perfect skin, a great foundation (you know, one that covers but?doesn't?feel like a mask ) is an makeup bag essential for everyone. 
"I’ve been obsessed with this foundation for years. It requires very little blending and the creamy formula looks exactly like actual skin. 
Well 'actual skin' if it were spotless and glowing. 
The price tag hurts, but I build up the coverage lightly and slowly so none goes to waste. 
I also only use it on special occasions and wear a more?affordable CC cream?on an average day."
"The foundation that singlehandedly got me through high school and my accompanying?acne,?Clinique's Acne Solutions Liquid Makeup,?is smooth, oil-free, and unobtrusive. 
It provides long-lasting coverage while also remaining light and blending seamlessly with?my skin tone."
“My new favorite foundation is the?Guerlain L’Essentiel Natural 16H Wear Foundation. 
I must be honest, I’m not normally a heavy wearing makeup girl because I prefer a natural look?and?it’s hard finding foundations that match my complexion. 
But I love this one! It’s super-light and still feels natural without being too heavy and looking caked on. 
For those life events when I want to be the *best* version of myself, this is my go-to.”
"Two people have stopped me on the street recently to ask about my skin. I credit it to a few things, including drinking a lot of water,?Dior Airflash Spray Foundation, and saying a spell during a full moon. 
I like to spray Airflash onto my hand then buff it onto my face with an Artis brush so the coverage is lighter.
 It never feels like I have foundation on but perfectly evens out my skin so I can skip cover up, which rules."
"In a perfect world, I wouldn’t have to use foundation to cover up my acne scars or hide any active breakouts. Luckily,?Anastasia Beverly Hills Luminous Foundation?does a pretty good job of hiding my impurities all while making me look all fresh-faced and radiant with just one pump—yes, it’s that good!"
"All I need in this life of sin is a full-coverage foundation that feels lightweight. 
Charlotte Tilbury's magical formula manages to do both, while keeping my oily face matte and poreless."?
"I just graduated to using foundation as part of my regular makeup routine last year.
 I've always been a skincare girl, so my goal was never to use foundation as a coverup. 
I really needed a formula that would act as a second skin—literally. Something lightweight that made for a good base to layer on blush and contour, and still allowed my skin to shine through (I haven't adopted an eight-step regime for nothing). When I discovered this formula—thanks to my friend, makeup artist Katie Jane Hughes—I was hooked! And the best part is that it's packed with luminizers, so I've got a dewy glow, all year-round."
"I've been using BareMinerals products since high school—I had acne then, and it didn't break out my skin.
 I still use it today because I love the solid coverage from a light powder. 
I switched to the matte version because my skin tends to be oily in the summer and this keeps the shine down. 
Truly a solid, essential product in my makeup kit that has held up for years."
"ELLE editors love this foundation and I'm one of them. 
It's the best for full coverage and creating a flawless complexion. I use this for nights out on the town when I want to impress."
For girls that feel edgy one day and flirty the next, Mirabella Beauty aims to create clean, mineral makeup, from foundations to eye shadows, that are free of parabens, talc and lead in an array of beautiful shades to fit all skin tones and types.
 From high-definition, full-coverage liquid foundations to cc cr?mes and even full coverage powder foundations, Mirabella Beauty has a foundation to fit every women’s needs.
Mirabella’s heaviest full coverage matte foundation, Invincible HD Anti Aging Foundation comes in five versatile shades.
 Each shade can be worn alone or blended for the perfect skin match. 
Invincible Foundation is the best mineral makeup for Rosacea as its high definition formula smoothes and refines skin for a flawless finish. 
When searching for the best mineral makeup for acne prone skin, Mirabella’s medium-coverage foundation, Skin Tint Cr?me is a great solution.
 Made with skin-loving ingredients, this face foundation makeup glides on to a silky luminescent finish.
For girls and women looking for a less heavy coverage, Mirabella Beauty offers two additional mineral makeup foundations, one in the form of a cc cr?me and one full coverage, lightweight foundation makeup. 
The cc cr?me features soothing and hydrating ingredients like apple and bamboo waters that keep skin fresh and looking young. 
For this reason, the cc cr?mes perform well in warm temperature and stay beautiful on the skin, making this foundation the perfect product for Coachella makeup. 
Finally, Pure Press powder face foundation makeup is infused with natural vitamins and minerals and can be worn alone or used to set cream and liquid foundations.?
2018 is turning into the year of foundation. 
With new lines emerging with innovative formulas and old favorites expanding their collection, it has never been a better time to be a makeup lover. 
Whether you’re looking for something light to simply help even out your skin tone, you’re needing full coverage, or anything in between there’s plenty of products for whatever makeup mood you’re in. 
The best part?
 Many major cosmetic companies are expanding their shade range, making it easier than ever for everyone to find exactly what they’re looking for. 
Here are our top picks for the next time you’re looking for a new face product.
This foundation has been heralded for having shades for even those with the most difficult to match complexions. 
It also boasts 24 hour wear, giving your face a natural look while still being full coverage.
 With 56 shades to choose from there are plenty to experiment with to find your perfect shade.
If a lightweight foundation with stage production quality sounds like all your dreams coming true, you’ll need to check out this Dior foundation. 
This formula was made to be worn not only on your face, but also your body even in high temperatures and humid environments.
For those that battle oily skin every day, this foundation boasts a full coverage, matte wear that lasts all day. 
It’s so full coverage, in fact, that you only need a small drop to give your skin a flawless, airbrushed look. 
With so many shades to choose from, you’ll definitely want to head into Nordstrom to get matched with your perfect shade.
Who doesn’t want more luminous skin and a more even complexion? 
This foundation by Clinique was formulated by dermatologists to give you better skin even on the days you go bare faced. 
This medium-to-full coverage foundation includes SPF, making your morning routine that much quicker, while also helping to improve your skin.
The hunt for a foundation that’s full coverage and combats shine all day is over.
 Make Up For Ever’s Matte Velvet line is great for all skin types and boasts 24 hour wear. 
It’s perfect for anyone wanting buildable coverage that’s good for your skin.
Finding a good vegan foundation can be a struggle. 
Cover FX has delivered with this full coverage, long lasting foundation. 
This brand prides itself on providing “clean” beauty products, free from ingredients that can be damaging to not only your skin, but to your overall health.
 With hundreds of rave reviews online from happy makeup lovers, you’re sure to find one you love in this impressive 40 shade line up.
If you haven’t heard of Rihanna’s record breaking beauty line, you are missing out.
 Her line of foundations were created to be inclusive of women on both ends of the shade spectrum – the ones who typically have the hardest time finding a color match in other lines.
 With 43 shades in the lineup, and a three-step guide to finding your match on Sephora’s website, it’s never been easier to find the perfect foundation.
Many of the foundation’s we’ve been talking about have been more on the full coverage side. 
But what about those who want a more natural look while still covering minor imperfections in their skin?
 Have no fear – Tarte has got you covered! Their vegan formula with their signature Amazonian clay goes on smooth and won’t leave you looking cakey at the end of a long day.
For those looking for flawless coverage that gives your skin the appearance of wearing nothing at all, you’ll want to check out Too Faced. 
This 35-shade range provides plenty of nuanced colors to give you the best coverage for your skin’s unique coloring. 
It’s also known for being great for photos and not giving your skin the white cast that so many other foundations can be guilty of. Give this one a whirl, and get your selfie on, too!
But last week, trace amounts of asbestos — a known cancer causer — were?found in concealer as well as sparkly makeup marketed to kids at Claire's, a reminder that?toxic chemicals and compounds still lurk in beauty products.?
In March, Claire's also?voluntarily recalled some of its eye shadow and face powder?after asbestos was found in those products as well.
The issue isn't limited to cosmetics:?The FDA recently warned?about dangerous bacteria in a no-rinse cleansing foam used by hospital patients, alerted tattoo artists about ink contaminated with microorganisms, and found yeast in Young Living essential oils moisturizer.
In part, these problems arise because US beauty products are largely unregulated.
"The law does not require cosmetic products and ingredients, other than color additives, to have FDA approval before they go on the market," the?US Food and Drug Administration?(FDA) notes.?
Some toxic ingredients (like asbestos) are inadvertently added during the manufacturing process, while product makers put others in purposefully to help with absorption, shine, shimmer, or a non-greasy feel.
 Studies suggest that chemicals from the products people put on their faces and bodies can show?up later in urine.
 Certain compounds, especially when mixed together in the body, might?up a person's odds of developing cancer?or mess with their?reproductive ability.
But it's nearly impossible?for consumers to determine what's in cosmetics even by reading the labels, since many compounds can be considered trade secrets and hide in the "parfum" or "fragrance" ingredients on a list.
Alec Batis, a former research chemist who once made hair dyes for the?L'Oreal group, is an expert in the risks and benefits of chemicals used in beauty products.?
Batis, who now works as a paid consultant for beauty companies and recently appeared in a?documentary?called "Toxic Beauty," told Business Insider that people should be concerned about some chemicals in products like soap, shampoo, and perfume. But not every formulation is dangerous.
"It's not about hating chemicals," Batis said. 
"Let's understand what this stuff really is."
Here's a look at 11 problematic ingredients that are near-universal bathroom vanity staples.?
Phthalates help make plastics durable and flexible.
 They're used in?raincoats, flooring, hair spray, nail polish, perfume, lotion, shampoo, aftershave, food packaging, and toys, among many other items.?
When it comes to makeup, the FDA says?on its website?that diethylphthalate (DEP) is "the only phthalate still commonly used in cosmetics."
At least one phthalate can cause cancer, according to the?National Institutes of Health.
 There's also evidence that the chemicals can also?mess with reproduction?and child development.?
Batis said he'll wear some fragrance when he goes out, but he washes it off before bed.?
"We don't know the long term effects, and we have to be smart about it," he said.?
The chemicals are meant to prevent mold and bacterial growth, but it's not clear yet how they?impact human health?at low levels.
Many cosmetic makers have switched to "paraben-free" formulations, but Batis said that doesn't mean they're better.
"They switch to other [preservatives], for example, methylchloroisothiazolinone and its sister compounds," he said. "
And I'm thinking, 'Wow. You're switching to that, which is a known sensitizing allergen.'"
Frequent use of?sensitizing allergens like methylchloroisothiazolinone can cause lesions and a scaly red rash?in some people.
According to the FDA, 1,4 dioxane "is a potential human carcinogen."
 It sometimes shows up in beauty products that contain detergents, foams, stabilizers or solvents.?
The FDA recommends that manufacturers use a vacuum technique so that the cancer-linked byproduct can be avoided. Batis agrees.?
"The manufacturing process should be standardized to vacuum," he said. 
"There's so many simple solves for some of these things."?
Asbestos, a known cancer-causer, was found in makeup sold at Claire's twice this year.
"It wasn't surprising to me, because there's no regulation," Dr. Shruthi Mahalingaiah, a gynecologist at Boston Medical Center,?said at the time.
Coal tar dyes can irritate the skin, and in severe cases, make people go blind.
"There are no color additives approved by FDA for permanent dyeing or tinting of eyelashes and eyebrows," the FDA says in its warning on?eye cosmetic safety.
Most coal tar hair dyes today are made with petroleum, but they?can still cause harm. 
The FDA?suggests keeping hair dye away?from your eyes, and says "do not dye your eyebrows or eyelashes."
The first step to applying like a professional is to choose the correct type of foundation for your skin type and secondly, the correct color.
If you have oily skin, go for an oil-free to avoid looking shiny half way through the day, or if you have dry skin, choose an oil-based to keep your skin moisturized.
For a foundation that holds the whole day, go for a long-lasting base with a slightly thicker medium.
 These types tend to stay put for an extended period of time without the need for re-application.
For a lightweight, air-brushed look, go for a mousse or powder, which offers a very thin layer of cover and sheen-like look.
 This type is best for people with flawless, blemish free skin as it does not cover marks well, but rather creates a perfect finish.
Choosing the correct color for your skin is vital to avoid looking like you have foundation on or having a ‘jawline line’.
The best way to choose the correct color is to test the foundation on your jawline in bright daylight.
 If the color disappears into your skin tone, the color is correct. If you can see the foundation on your skin, it is probably too dark or light. 
Expert tip! If you can’t afford to splurge on foundation, get a beauty adviser in the store to help you pick the perfect color, and ask for a sample to take home.
It is important to make sure your skin is clean and moisturized before applying foundation. 
Cleanse your face, moisturize and wait for five minutes to let the moisturizer to sink into your skin before applying the foundation.
If you have large pores, use a primer before applying foundation to fill in pores and make them appear smaller, as well as help give you a matt finish.
A primer is also a good layer to add if you want your makeup to last the whole day or night.
There are a few ways to apply foundation and these depend on the amount of coverage you desire.
If you are looking for light coverage for an everyday look, professional makeup artists suggest using your fingers to dab a little foundation on spots, marks and blemishes and simply rub it in evenly.
If you are looking for more coverage, use a sponge and blend the foundation in all over your face, working from the middle outwards. 
Be careful of applying too much foundation though — make sure the first layer is blended in completely before applying a second layer.
Concealer is a great way to cover those stubborn marks or blemishes just won’t hide. Apply your concealer after as applying it before can create a cakey look. 
Using a small, pointy brush, dab some concealer on the darkest part of under-eye circles to get rid of those bags and shadows or use a cotton bud to dab some concealer onto spots and marks that need to go!
Expert tip!?If you suffer from problem skin, avoid using foundation too often as this tends to clog the pores. 
Find a concealer that has medicinal properties that help to dry out spots and pimples and use this instead — it will hide the spots and dry them out at the same time.
To create a flawless, airbrushed look, finish application with a lightweight powder. New translucent powders on the market help to reflect the light help skin glow and reduce shine, so take a large brush and dust some on in a ‘W’ motion. 
Begin at your hairline on one side, swoop the brush down towards your cheek bone, back up to the bridge of your nose, lightly back down the other cheek, and then up again to the opposite hairline
The mere word ‘foundation’ used to conjure images of the thick, cakey makeup we used to see pasted on faces of the older generation. 
However it has come a long way since then, evolving to become a sheer, svelte layer that is easy to apply and makes your skin look flawless. 
There are myriad types on the market today, from thick,?long-lasting cr?me foundations?to powder-light mousses that are barely there. 
The trick to ending up with flawless skin is not only to choose the correct for your skin type, but to apply it in a way that will leave you looking ready for the catwalk.
Foundation is a liquid makeup applied to the face to create an even, uniform color to the complexion, cover flaws and, sometimes, to change the natural skin tone.
 Lipstick is a cosmetic product containing pigments, oils, waxes, and emollients that apply color, texture, and protection to the lips.
 Mascara is a cosmetic commonly used to enhance the eyelashes. It may darken, thicken, lengthen, and/or define the eyelashes.
For thousands of years the fashion conscious have used make-up to get their look just right, and keep up with fast-moving trends. 
Now, the global beauty industry is experiencing a revolution driven by South Korea. Say hello to K-beauty.
Young people in Western countries have become infatuated with K-pop - Korean pop music - and Korean soap operas.
Many Korean celebrities and pop stars, including the seven-member boy band BTS, are known for their signature looks.
But it's not just Korean entertainment - in the last 18 months, there has also been a rise in Korean beauty trends coming over to the West.
In 2017, South Korea's beauty industry was estimated to be worth just over $13bn (?10bn), according to retail researchers Mintel.
The fascination with Korean cosmetics is due to how innovative they are, says Marie Claire's digital beauty editor Katie Thomas.
South Korea's beauty industry is typically about 10-12 years ahead of the rest of the world, she says.
"It's not that there's been a big boom, we're just catching up with them essentially,  the expansion of Instagram and beauty blogging."
Before even putting any make-up on, Koreans put in a lot of effort to take care of their skin.
"It's sort of ingrained in Korean culture from a very young age to look after your skin," Ms Thomas says, explaining that the Korean ethos is to ensure that you have good skin, rather than needing foundation and other products to cover up unsightly blemishes.
You might be used to the typical daily three-step routine of using cleanser, toner and moisturiser before applying make-up, but in South Korea, skincare regimes range from seven to 12 steps, with a focus on hydrating the skin using gentle, natural ingredients.
Some people would see it as excessive, but the fact is, you're feeding your skin with these incredible ingredients. It's so different in the UK [in comparison]," says Ms Thomas.
Much more research is carried out into new products in South Korea than in other countries, she says, because there are so many competing brands, each trying to be the best.
"The Korean beauty industry doesn't shy away from introducing new, unique ingredients to their formulas that would never be considered in the West," says Karen Hong, the founder of K Beauty Bar, a concession stand for Korean beauty products found in Topshop's flagship store in London's Oxford Street.
“Unique ingredients such as...? "Snail mucin for moisturising, pearl for brightening, green tea for oil control and propolis from bees for soothing and nourishing," she reels off.
In the US, 13% of 10 to 17-year-old girls are interested in trying K-beauty products, and 18% of 18 to 22-year-old women have used these products.
According to Mintel's global beauty analyst Andrew McDougall, Korean beauty trends have grown in popularity thanks to "clever digital marketing strategies" on social media that have gained the interest of Western beauty influencers, bloggers and journalists.
Consumers' interests are piqued by colourful packaging, as well as reviews and demonstrations on Instagram and YouTube, he explains.
"It's consumers who are more informed and do their own research, and it's influencers who put them on the K-beauty path in the first place," says Mr McDougall.
Katie Thomas agrees: "That K-pop fun quite cartoony approach is very much part of their industry. But it does do very well. People do buy into fun packaging - things they can take a picture of on their bathroom shelf."
Some products can be found in Topshop and TKMaxx, but other than that, most British consumers can only buy these products online.
YesStyle.com is one such company, and it sees Korean make-up as big business.
The Hong Kong-based e-commerce firm carries more than 150 Korean brands, and it expects sales of K-beauty products to top $25m in 2018.
Its founder Joshua Lau says the website's success has been down to reviews from verified buyers that give Western consumers the confidence to take a chance on new items.
YesStyle's beauty editor Romy Rose Reyes says that Western consumers have been intrigued by the "Chok Chok" no make-up make-up look, where the aim is to have "dewy and bouncy skin with an extra glow".
According to Ms Reyes and Ms Hong, looking "natural" and "youthful" is in, and the matte look favoured by European and US markets is now out.
Western cosmetics makers have been taking notice too.
"We see some Western brands take on a few of these skincare steps into their own regimes," says Marie Claire's Katie Thomas.
"For example, Yves Saint Laurent has cushion foundation and cushion blushers. It's a bit less scary for consumers - an easy way into K-beauty with a known brand."
This summer, Primark launched its K-Pop range of cosmetics, which sold out quickly.
Primark told the BBC that it was inspired by a "huge trend" it saw based on skincare innovations from South Korea, and it is continuing to stock face masks.
"We found this was a range that really worked for the younger consumer, who was not necessarily after serious skincare," a company spokesperson said.
So is it just a fad, or is the K-beauty trend likely to last?
Ms Thomas thinks it's here to stay, because young people are very concerned about the environment, and how damage to it affects humans as well as plants and animals.
"People are becoming more attuned to what is happening to their skin - there's so much pollution that gets into our skin that we don't know about," she says. 
Anything related to environmental damage is very much coming over to the Western market.
But while much of this applies to women, in Korea cosmetics is big business for both male and female consumers, so is K-beauty likely to take off with men in the West?
"In Korea there is a different attitude amongst men towards skincare and make-up - they're not afraid to express themselves as long as they look and feel good, especially the younger generation. But this isn't a trend that has reached the general male population in the West yet," says Karen Hong.
Mintel's Andrew McDougall agrees.
"Men in the West are increasingly becoming more active consumers in the beauty and personal care area, though there is a long way to catch up," he says.
He points to Chanel's recent launch of a make-up line targeted at men called Boy de Chanel - the company chose to debut the products in South Korea, rather than its native France.
Mr McDougall also adds that he doesn't wear make-up or use a fancy skincare routine himself.
But while much of this applies to women, in Korea cosmetics is big business for both male and female consumers, so is K-beauty likely to take off with men in the West?
"In Korea there is a different attitude amongst men towards skincare and make-up - they're not afraid to express themselves as long as they look and feel good, especially the younger generation. 
But this isn't a trend that has reached the general male population in the West yet," says Karen Hong.
Mintel's Andrew McDougall agrees.
"Men in the West are increasingly becoming more active consumers in the beauty and personal care area, though there is a long way to catch up," he says.
He points to Chanel's recent launch of a make-up line targeted at men called Boy de Chanel - the company chose to debut the products in South Korea, rather than its native France.
Mr McDougall also adds that he doesn't wear make-up or use a fancy skincare routine himself.
2018 Winter Olympics may be over, but the world is still buzzing about all things South Korean. 
South Korea has continued to emerge as one of the top drivers for global economic and fast moving consumer goods industries, which is inspiring many new trends in other countries.
South Korean culture comes in many forms – in song, movie, television, food and distinctive fashion.
The overall culture and consumption of the population in South Korea is very much influenced by popular actors and singers endorsing the products.
Fans are more likely to buy products that their idols are endorsing, which causes major market shifts and can increase consumption significantly. 
Moreover, as the actors and singers become popular overseas, so do the products that they promote. 
As a result, manufacturers compete to get the most popular representatives for their brands, and this forms a major part of their overseas expansion strategy.
There is no doubt that many brands and manufacturers have utilised the Olympics to catapult their products onto the world stage during South Korea’s moment in the spotlight. 
This can be seen especially with the growth of K-beauty products in the U.S. market.
In 2015, South Korea’s beauty exports to the US grew 59%, reaching usd$207 million. 
Social media has propelled the growth of K-beauty in the US by turning skin care and makeup routines into creative outlets for self-expression. 
The rapid proliferation of social media since the late 2000s has provided fashion-forward individuals with a myriad of platforms to spread the gospel of K-beauty.
Makeup and skin care- loving Koreans took to YouTube, blogs and Instagram to share their interests with the rest of the world.
Product reviews and makeup tutorials engage and educate viewers, while “transformation videos,” in which YouTubers alter themselves into recognisable celebrities, highlight the astonishing power of makeup and excite the viewer’s imagination.
Social media celebrities have been more successful at promoting Korean skin care than corporate or government-sponsored marketing campaigns because they focus on the topics that truly excite them, while also providing compelling video and photographic evidence of their personal skin care journeys.
As the generation that inspired the term “hipster”, many millennials passionately embrace individualism. 
These fashion-forward children of the information age investigate products online before they shop, relying on reviews and tutorials to appraise product efficiency; thus they readily seek out niche and foreign brands.
Because Korean skincare transcends the traditional Western cleanse-tone-moisturise routine, K-Beauty’s myriad of products like serums, essences, ampoules and masks can be customised to an individual’s exact skin care needs and preferences. 
The array of products appeals to millennials who are paying more attention to their skin’s unique needs than previous generations.
Many millennials respond strongly to novelty and emotional appeals, making room in their bathroom cabinets for products with cute designs, alluring scents, transforming textures and on-trend ingredients which set them apart from mainstream American beauty products. 
This drives interest in brands like Tony Moly, whose products are packaged to look like whimsical cartoon fruit and animals, and Missha, whose collections include imaginative collaborations with Hello Kitty and D.C.’s Wonder Woman. 
The fun of K-beauty appeals to even the youngest millennial consumers; Leah Park, co-owner of Korean Beauty Retailer Choc Choc in Chicago, remarks that customers as young as 10 years old come to her store to purchase cute products like sheet masks printed with animal faces.
South Korean brands stand to gain significant market share in the U.S. teen and preteen market with endearing product design and packaging.
Korean brands like Innisfree and Skin Food have a “natural” angle to them, aligning well with increasing millennial interest in natural, organic and sustainably sourced products across all areas of beauty and personal care. 
Euromonitor International’s 2015 Beauty Survey found that 13% of U.S. respondents seek out natural or organic colour cosmetic products and 20% seek out natural or organic skin care products.
Thus, Korean beauty brands hoping to enter the U.S. beauty market would do well to emphasise their products’ gentleness and natural ingredients further.
There's face powder in panda palettes, hand gel in gummy bears and bubble tea sleeping packs.
Often packaged in bright colors and decked out with cartoon characters, Korean beauty products are too cute to ignore, but they also provide some health benefits.
It's "skin-tertainment," said Christine Chang, who co-created the Glow Recipe brand to bring Korean beauty products to the American market. 
She and partner Sarah Lee travel to South Korea multiple times a year to find new products and are repeatedly "blown away by the new innovation in the market."
Complete Korean cosmetics shopping guide 
The Korean beauty market is among the top 10 around the world, with an estimated worth of over $13.1 billion in sales in 2018, according to Mintel, a global market intelligence agency. 
Facial skin care products alone make up half of the total market share and are projected to reach $7.2 billion by 2020.
 And one in five facial skin care launches in South Korea, the agency reports, is actually a mask.
"For a long time, France and Japan were considered a symbol of cosmetics business around the globe," said Ryan Park, who founded the Korean beauty brand Whamisa in 1999.
"Korea was able to catch up with them within a very short time thanks to the balance of its accumulated fundamental industry, chemistry, bioscience and Korean Wave culture."
K-pop group record breaking album conquers three continents
The Korean wave, called "hallyu," is about the spread of South Korean pop culture and how all things Korean -- food, dramas, makeup, movies and music -- have propagated throughout the world through social media and online platforms.
 A lot of this wave radiates off of the music, K-pop, with artists like PSY, Wonder Girls and BTS whose edgy look, style and sound attract global fans.
Simply put, consumers want the skin of Korean celebrities, who supposedly use it too, said Dr. Soyun Cho, a dermatology professor at Seoul National University.
The Korean brand Whamisa was founded in 1999.
E-Commerce Guide by CNN Underscored: Your guide to using Korean skin care products
Korea is also one of few countries with "functional cosmetics," Cho said, a label allowed by the Korea Food and Drug Administration for anti-wrinkle, elasticity-boosting, pigment-fading and sunscreen properties. 
This has fueled more research for better products, she said.
"Korea has become the test bed of many world-famous cosmetic companies," said Cho, who has studied the behavior behind cosmetic use in Koreans. 
"Korean consumers are very knowledgeable about different cosmetic types and ingredients, and they are picky. 
They are early adapters of new products, and cosmetic trend comes and goes at a very fast rate in Korea, partly due to the ubiquitous high-speed internet and heavy use of social media.
"Young Korean women are very keen to try the new trend, and they don't want to be left out of the loop when all their friends are using a new product."
To showcase new Korean beauty products, Chang co-founded Glow Recipe in 2014 after working at Kiehl's in global skincare marketing and at L'Oreal in Korea and the US. 
"A key strength of K-beauty products is the experience," she said. "Formulas often have enjoyable, unique textures or flexible usage methods."
Chang cites the use of aloe instead of water for intense nourishment, applying "rubber masks" -- instead of paper sheet face masks -- for better nutrient absorption and fermented botanicals for more efficient absorption into the skin.
Fermented botanicals contain micro-organisms that release enzymes that ferment and break down molecules into the raw material, resulting in the creation of new substances that benefit the skin, explains dermatology professor Cho. 
Another example of innovation is the combination of beauty balm cream, BB cream, with an air cushion compact, Cho said.
Although these creams were created in Germany, Korean companies popularized the merging of foundation, moisturizer, anti-aging cream, whitening agent and sunscreen in one product.
The air cushion compact "wicks the formula off a sponge and effortlessly applies evenly onto the face for that dewy, no-makeup makeup look," Chang said.
Many of these products follow that "baby-like" look with "cosmeceuticals," Cho said, combining cosmetics and therapeutics with such natural ingredients as traditional Korean herbs and plant extracts. 
Snail slime has also been a popular component in many Korean beauty products, because it reportedly improves skin imperfections like scars, wrinkles and acne.
Glow Recipe worked with Whamisa on a green tea line with antioxidants and botanical extracts that melts makeup and removes pore-clogging impurities. 
Its star product, the Watermelon Glow Sleeping Mask, sold out eight times with the French cosmetics giant Sephora last year and had a wait list of over 20,000 on the Glow Recipe website.
The Soko Glam e-commerce site helps people find Korean skin care and opened a pop-up shop in Bloomingdales in New York last year. 
Co-founders and spouses Charlotte and David Cho also recommend products with botanicals like E Nature Birch Juice Hydro Cream Sheet Mask.
"Our Birch Juice Hydro line formulas completely replace water, commonly used as the main ingredient for other skin care products, with birch sap, which is the liquid that is tapped straight from Japanese birch trees," E Nature's Anna Kim said. 
"Birch sap has been deemed the next 'coconut water' because it is full of electrolytes and antioxidants, thus providing the skin with intense hydration and soothing abilities when it is applied."
The brand SkinFood takes this even further with "food cosmetics," applying the belief that "you are what you eat," with products that contain natural food extracts rather than artificial preservatives.
Their research finds ingredients by "eating, applying and studying foods," said Jae-mo Park from SkinFood. He recommends their products with black sugar, like Black Sugar Mask Wash Off, which softens the skin.
Seoul National University's Cho says many of these Korean makeup products are beneficial because they contain sunscreen filters with high SPF, which help protect the skin from the sun's harmful rays.
But as for the effectiveness of their botanical ingredients, they are "basically all antioxidants, which have anti-inflammatory and anti-aging functions, albeit weak."
Some ingredients may be beneficial, she said. 
Black sugar can leave the skin surface more hydrated, and birch sap can reduce inflammation and retain moisture.
However, while rubber masks help with absorption, she doesn't believe a facial mask is any more beneficial than a good moisturizing cream but can be "a fun way to pamper yourself for 15 minutes."
Besides health benefits, Korean beauty products also tout eco-consciousness. E Nature uses packaging that is recyclable, and Innisfree incorporates eco-friendly ingredients, such as organic green tea and camellia flower petals, grown in Jeju Island in Korea.
As for the future of Korean beauty products, they're only going to get better, Cho said.
E-Commerce Guide by CNN Underscored: Subscribe to Beauteque Monthly for the best in Asian beauty and skin care
"As all Asians age with wrinkles and age spots, multifunctional cosmeceutical products with whitening and anti-aging properties all in one will continue to be in high demand," she said. "With continued advancement of cosmetics science and technology, new products with more innovative and functional properties will keep coming out."
Growth in the beauty and personal care industry reached a decade high last year and will continue unabated in 2019 and beyond, facilitated by new models of aspiration such as self-optimization and tailored experiences. Our latest report World Market for Beauty and Personal Care explores Euromonitor’s latest Beauty and Personal Care data to tell a story of unprecedented growth.
Global beauty and personal care recorded dynamic 6% value growth in 2018, which was the strongest for over a decade. 
Even in real terms (excluding inflation), 2018 still stands out as a strong year for the industry, reaching highs not seen since 2015, despite Western Europe’s flat performance.
The robust results largely reflect Latin America’s sustained resurgence following an economic crisis, as well as Asia Pacific’s continued strong growth. 
Asia Pacific accounted for one-third of global industry value in 2018 and is anticipated to generate over half of the total USD68 billion in absolute growth over 2018-2023.
China alone is predicted to account for USD21 billion of the industry’s growth over the forecast period – more than North America and Latin America combined. 
Sluggish consumption continues to plague the industry although volume growth did increase notably in 2018, to reach 3%. 
Volume sales declined in North America, whilst Latin America saw growth of 8%.
Premium beauty outpaced the mass segment for the fourth consecutive year in 2018. 
However, whilst premium was the standout story of 2017, 2018 was the year of the comeback for mass products. 
Mass improved on its previous performance in all regions in 2018, with the exception of Eastern Europe.
Most notably, the mass segment almost doubled its growth in North America year-on-year, with a 3% increase in 2018. 
Western Europe recorded 2% growth in mass beauty and personal care in 2018, and for the first time since 2012, premium and mass grew almost in tandem in the region – a real breakthrough for the mass segment, with the advent of a new digital era providing a breeding ground for new premium values, aspirations and products.
Skincare remains unchallenged as the largest beauty and personal care category, accounting for over one-quarter of value. 
Sales of skincare grew by 8% in 2018 helped by alignment with health and self-care, as well as being the locus of the shift to “cleaner” formulations in the industry, contributed to its strong growth in 2018.
Other categories able to align with these new consumer values also reaped rewards, including baby and child-specific products and make-up with skincare benefits. 
Recent stalwart categories of insurgent activity, notably colour cosmetics and fragrances, have lost momentum as they struggle to adapt to long-term shifts in trends.
 A cooling-off of social media and particularly the Instagram aesthetic has been of severe detriment to colour cosmetics, as this formed the basis of many brands’ core strategies.
In 2018, the percentage of beauty and personal care products sold online reached double digits for the first time, accounting for 10% of all sales and registering growth in excess of 20% year-on-year.
On a global scale, this is significant, considering that it is the first time sales online overtook sales through direct sellers such as Avon and Natura. Online is considered the closest competitor to direct sellers in the direct-to-consumer environment.
Beauty specialist retailers continue to dominate the landscape, with sales of almost USD73 billion in 2018 and a 4% CAGR over 2013-2018. 
Online sales of beauty specialists, such as Ulta, Sephora and Space NK, and brand websites are buoyant, highlighting the importance of omnichannel retailing. 
Multi-brand retailers are experiencing a revival both online and offline, as the power of the brand declines and the brand-led format of department stores and mono-brand retailers weakens. 
Multi-brand retailers can instead merchandise on function, positioning or ethos rather than brand, and this is paying dividends.
The top five companies in the key regions of Western Europe, North America and Asia-Pacific are experiencing erosion of their combined share, notably in skincare and colour cosmetics, as these categories become more fragmented.
Whilst insurgents rise quickly to disrupt the status quo, continued innovation and renovation are required to stay ahead. 
In the past few years, brands in the fickle and trend-led make-up space, such as NYX, ELF and The Est?e Edit, have experienced rapid rises and falls. 
NYX, for example, was posting triple-digit growth rates at its peak, which slowed to single digits in 2018.
WHEN LUXURY BEAUTY BRANDS COME KNOCKING AT YOUR DOOR, you know you've done something very, very right. 
Julie Cusson's journey with Chanel began in 2010 when the brand invited her to be the new national makeup artist for Chanel Canada. 
In her role, Cusson brings the brand to life using its luxe products on celebs and models, and her work and expert advice have been featured in magazines around the world. 
Here, Cusson talks to Cosmetics about the Chanel products she can't live without and one that she would like to create.
WHEN LUXURY BEAUTY BRANDS COME KNOCKING AT YOUR DOOR, you know you've done something very, very right.
 Julie Cusson's journey with Chanel began in 2010 when the brand invited her to be the new national makeup artist for Chanel Canada. 
In her role, Cusson brings the brand to life using its luxe products on celebs and models, and her work and expert advice have been featured in magazines around the world. 
Here, Cusson talks to Cosmetics about the Chanel products she can't live without and one that she would like to create.
To me, Chanel is the house - it's everything! It's the technology, the creativity and the savoir faire when it comes to creating their products. 
They're always doing research to continue to advance the technology they use to create their products; it's something I really appreciate as a makeup artist.
If you could develop a product with the brand, what would it be?
I love to wet eyeshadows when I'm working, so I would be really interested in developing an eyeshadow that is already damp to achieve the same effect.
How do Chanel products differ from other luxe brands?
The importance they place on using new technology to develop innovative products, like the new Vitalumi?re loose powder foundation. 
I love the idea of using a loose powder to create a foundation with such a light texture. They also pay a lot of attention to making sure the colours are balanced.
 It's the commitment to always finding the right shade, and the right texture, which is really important for me as a makeup artist.
There are so many! How about my top three? My first one is Le Blanc de Chanel, a white, sheer illuminator. 
You can use it as a primer, it minimizes pores, it hides dark circles - I even put it on top of foundation to illuminate the skin.
 I also like to add a little to my foundation at the end of the summer for a subtle transition. 
The second one is the Eye Shadow Base in Beige. 
When used as a lid primer it conceals veins and imperfections and keeps the eyeshadow in place, but I also use it as a concealer for blemishes and to contour the lip line. 
Finally, I'm a big fan of mascara, and the Volume de Chanel mascara is my favourite. I love the shape of the brush and how it distributes product. -Lauren Kerbel
The global vegan cosmetics market size was estimated at USD 12.9 billion in 2017.
 Surging demand for personal care products, coupled with rising awareness regarding cruelty free beauty, is expected to be one of the key trends escalating market growth.
Consumers worldwide are becoming increasingly aware about effects of their purchasing preferences and patterns on society and the environment. 
Consumers are now more conscious about choosing products and they also take a note of raw materials and source origin.
The global vegan cosmetics market size was estimated at USD 12.9 billion in 2017. Surging demand for personal care products, coupled with rising awareness regarding cruelty free beauty, is expected to be one of the key trends escalating market growth.
Consumers worldwide are becoming increasingly aware about effects of their purchasing preferences and patterns on society and the environment. 
Consumers are now more conscious about choosing products and they also take a note of raw materials and source origin.
The cosmetics industry is highly competitive in nature and requires top performing items that reach the uppermost levels of innovation and effectiveness. 
Growing environmental awareness has impelled several companies to avoid usage of animal derived raw materials by developing natural resources that are identical and overtake performances of conventional resources.
 Increasing research and development activities in the field is anticipated to help the market gain tremendous traction.
In recent times, sharp rise in the number of beauty blogs and social media accounts that are committed to benefits of going chemical-free has worked in favor of the market by enhancing consumer’s information. 
In 2016, a Democratic senator from California, Dianne Feinstein, introduced the Personal Care Products Safety Act, a bill to reinforce regulations on ingredients in personal care products.
Changing perception of consumers towards animal free products, coupled with growing popularity of environment sustainable products, is fueling the demand for naturally derived products.
 Manufacturing of naturally-derived products aid in reducing pollution and reducing dependence on petroleum based products.
 Rising demand for chemical-free skin and hair products along with changing lifestyles of consumers is estimated to stoke the growth of the market.
Growing trend of veganism, especially among younger generation, is playing a pivotal role in the development of the market. 
With more millennials embracing vegan lifestyles, it is no longer considered unconventional. 
It is now trendy, with many A-list celebrities supporting the benefits of living a vegan lifestyle and increased options available online and on high street.
According to market demand, players are launching new outlets across various geographies. 
These outlets play an important role in analysing consumer purchasing behaviour, thereby making it easier for manufacturers to design new products. 
Online stores are emerging channel of distribution, which can be convenient medium for customers to buy these product.
Major cosmetic manufacturing companies across the globe have realized the importance of making products, which use ingredients that are mineral-based or plant-based, rather than manufacturing products that are infused with animal extracted ingredients. 
Animal free products have far superior characteristics and beneficial properties such as healing aliments and soothing skin.
The hair care cosmetics industry broadly comprises of shampoos & conditioners, which are easily available, and vegan hair mousse, gel, spray, hair mask and more, which are a little difficult to find, based on regional demographics.
 Several companies such as Zuzu Luxe and Bare Blossom invest heavily in research activities and explore the market to come up with organic (animal-free) ingredients such as babassu oil, which is procured from Brazil, and Western Australia’s sandalwood extracts, which are widely preferred by consumer base across the globe.
There are several advantages of using animal-free makeup products such as fewer ingredients usage, no harmful chemicals, bypassing animal byproducts, plenty of moisture from plant extracts, and favors sensitive skin. 
Rising awareness regarding advantages of these products is poised to spur the growth of the market.
Apart from regular cosmetics available for women across the world, several major multinationals are working in line with men’s grooming products. 
The most common cosmetics used by males include face wash gels, body wash, and deodorants among others. 
Companies such as Superdrug, Bulldog and The Body Shop have an exhaustive list of men’s cosmetics, which are used on a regular basis.
There are multiple user-friendly websites such as Vegan Cuts, Pangea the Vegan Store, and Vegan Essentials that provide seamless shopping experience to consumers globally.
 E-commerce is a prominent segment in the vegan cosmetics market, with women being the primary end users. 
Ease of shopping offered and availability of promotional offers and listed deals on variety of brands are contributing to the growth of the segment.
In several regions including Europe and the U.S., governments have provided flexibility in working hours of supermarkets and hypermarkets, which indicates that they can be open till late during night. 
This provides significant growth opportunities for companies to strategically place their products in such supermarkets rather than drugstores or specialty retailers.
Departmental stores are targeted by multiple companies engaged in sales and distribution of cruelty-free cosmetics, such as L’Or?al and Unilever, as they are the one stop market spaces for a variety of consumer needs. 
In China and other emerging economies, consumers prefer going to their nearby departmental stores for convenient smooth shopping experience, simultaneously saving time.
With increasing sales across the globe, marketers of large multinationals are focusing on effective sales and distribution channels to increase their product visibility and establish a strong network of supply. 
Primary reason for the growth of specialty stores is consumer’s ease of selecting colors and compare available brands.
Across the U.S. and Germany, stores such as Bloomingdale’ and Neiman Marcus are getting significant results by placing premium vegan cosmetics along with their range of luxury clothing and accessories. 
Apart from these departmental stores, several spas such as Bella Reina are extending their services to post spa make up and body massages by using cruelty-free cosmetics, which in turn is bolstering the growth of the market.
As per a survey conducted by The Physicians Committee across North America, more than 70% of the population claims to more likely use vegan cosmetics that are not animal tested and favors development of animal-tested alternatives.
The cosmetics market in the region is registering faster growth as compared to other regions. 
According to the CEO of L’Or?al, demand for cosmetics in North America is projected to be gradually rising, especially vegan products. 
Presently, rising demand for herbal cosmetics is supplementing the growth of the region. 
Therefore, large industry players in the region are launching new products for the population in the region.
Transdermal nature of cosmetics is the new concern for consumers across Asia Pacific., which is boosting the growth of animal-free cosmetics in the region. 
Color cosmetics are being increasingly used, followed by skin care, hair care, and face care products.
China and Japan are sights of high growth rates in Asia Pacific. 
Departmental stores and supermarkets are the most preferred retail platforms in the region. 
Spiraling demand for cruelty free products such as color cosmetics, skin, sun care, and hair care products is one of the primary factors propelling the regional market.
Vegan products are strictly manufactured without any animal products and are not tested on animals.
Focus on R&D activities is paving way for novel products and use of natural harmless chemicals, which is helping market players to consolidate their positions in the arena.
Cruelty free cosmetic manufacturers are responsible for designing, manufacturing, packaging, and storing end-to-end products, catering to all requirements. 
Some of the key manufacturers in the industry are Urban Decay, Ecco Bella, Bare Blossom, Billy Jealousy, MuLondon Organic, and Modern Minerals Makeup. 
These companies are widening their operational boundaries across different countries in the world.
By analysing consumer behaviour and preferences, companies innovate and launch new products to meet customer demands.
Moreover, comfort of consumers is also addressed while designing a product and its packaging layout. 
Key players use compostable packaging and agri-based inks for their packaging. 
Cosmetic manufacturers add a larger share of value to the final product with their efforts and innovations to manufacture a product.
Whiffs of fragrant roses, jasmine and bergamot are rising from the flask.
"You smell those flowery tones? These are the basis of all women's perfume," says Guy Delforge, spraying a bottle of his own creation.
From his workshop in the citadel perched above the Belgian city of Namur, he uses ingredients from around the world to craft his signature scents.
A perfumer for 34 years, Mr Delforge, 78, notes a shift in the industry with customers pushing for natural, sustainable fragrances.
"Perfumes have existed for 5,000 years and the scents haven't changed much," he says.
"But today... customers want to know the artisan making their perfume. It reminds me of when I started selling perfume from my garage in the 1980s."
Millennials are the driving force behind the trends reshaping the sector, according to beauty industry magazine Cosmetics Business. 
It says they want more transparency and more gender-neutral scents - often based on citrus smells.
Citrus is one of the seven families of smells; with floral, chypre (oak moss with fruity notes), and amber often regarded as female scents, while foug?re (lavender/woody) woody and leather are often grouped as male scents.
"A person is born liking one specific scent family and that preference rarely changes," says Mr Delforge, whose eponymous line carries 40 eau de parfums, with each 100ml bottle costing €51.
In the heart of Namur's old city, Romain Pantoustier, "le nez" or nose in French, provides customers with transparency about the ingredients used in his perfumes.
The glass bottles of his Nez Zen range are refillable, the perfumes are gender neutral and also vegan, avoiding the musks from deer and other animal-derived produce were used in the past. 
Deer musk is specifically a secretion produced from the scent gland of the male musk deer.
An international convention covers the trade in musk but most products in the perfume industry now contain synthetic versions of the previously used animal scents. 
This is the one area where millennials definitely prefer synthetic ingredients instead of natural ones in their perfumes.
In the past other animal-derived ingredients used in perfume included ambergris from sperm whales, produced by the mammal's digestive system, and castoreum - a secretion made by beavers.
Synthetic versions of lily of the valley - one of the world's most expensive flowers - are also available. 
Such use of synthetics can also make products more cost effective, but often make use of petroleum and its by-products.
Mr Pantoustier, 40, says nature is the basis of his inspiration. 
He quizzes each customer on their favourite colours, textures, feelings and hobbies before recommending a fragrance. 
He also designs tailor-made perfumes for individual clients for €1,500.
"When a person comes in I ask them what they like. 
I use words to create a mapping in my head to guide them through my fragrances," he says, while drinking water flavoured with an edible scent. "Who am I to say what gender a scent should have?"
The Frenchman founded the Belgium-based business in August 2016 with his wife Aur?lie, after working as a scent designer for some of Europe's biggest perfumers.
"I wanted to move away from a more industrial approach to perfuming, and get back to a much more artistic and emotional approach."
Across the Atlantic, Charna Ethier also used to work for large firms in the fragrance sector, before founding the Providence Perfume Company in 2009.
"I noticed there was a demand for natural botanical smells that was being neglected," she says. "There's lots of greenwashing in the sector."
Inspired by her childhood on a farm on the US east coast, she says many modern shoppers are so used to synthetic smells in perfumes they do not know what natural options smell like.
Ms Ethier, 44, distils fruits, flowers, wood and plants in a pure alcohol spirits base to create her natural perfume line. 
Providence Perfume's website says the final products contain no synthetics, petrochemicals, fragrance oils, dyes, parabens, phthalates or chemical fragrances.
All her perfumes are also gender neutral, something she says is particularly appreciated by her younger clientele.
"Millennials don't want to wear their mom's perfume. 
They want to smell different, like leather, crushed herbs and smoke," she explains. 
"I get a lot of women saying they don't want to smell like flowers."
Gender fluid fragrances have surged in popularity in recent years: 51% of all perfume launches in 2018 were gender neutral, up from 17% in 2010, according to industry figures.
At Coty, the world's largest fragrance firm, with 77 brands including Chlo?, Hugo Boss and Gucci, trends for more gender fluid, sustainable and exclusive products are closely monitored.
"The companies who are winning are the industry leaders that have refreshed their proposition, and new brands because millennials are brand agnostics," says Laurence Lienhard, Coty's vice president of consumer marketing insights.
Ms Lienhard says Coty has also worked hard over the past five years to use less packaging and more organic ingredients.
"It's time for bigger brands to take a stand - so we are working on it more and more.".
Ms Lienhard adds that demand for gender fluid, universal scents is particularly strong in English-speaking countries. 
It released its first unisex offering, Gucci M?moire d'une Odeur, earlier in 2019. The scent is marketed in both men's and women's sections of perfume stores.
Back in Belgium, Mr Delforge peruses through the French Perfumers Society's official perfumes guide, which he calls "the bible" of perfumery.
"This book describes all the different scents, but two perfumers could choose the same ingredients and create a different smell. 
It is like different chefs making different types of mayonnaise. It's still mayonnaise but it doesn't taste the same," he explains. "Perfume is the same."
The British Beauty Council is calling for an independent body to be set up to investigate claims of bullying and unfair dismissal in the industry, which does not have a trade union. 
It comes after the BBC's Victoria Derbyshire programme uncovered cases of bullying across all levels of the industry.
"I was seeing grown women, strong women, crying at their desks. It was so toxic and harsh that people were just desperate to leave," says Sarah (not her real name), who had a senior role working for an international beauty brand.
She says her boss was a bully who spoke behind her back and told suppliers she was sharing their confidential information.
"After that, the bosses only gave me junior roles within projects and I was taken off the project I had been working on very successfully for two years," she says.
"I was ignored by HR and the board of directors. I feel so much anger - but it's not even anger, it's heartbreak."
Sarah has since left the company.
The beauty industry contributed ?14.2bn to the UK economy last year and employs one in every 60 people.
The Victoria Derbyshire programme has spoken to more than 20 people, from a company director to make-up artists in department stores, who claim to be victims of bullying, abuse and bad practice.
Many said they had suffered from anxiety, depression and even suicidal thoughts as a result.
Nearly all said the industry was facing an institutional bullying crisis but feared if they complained they would never work in it again.
It has no union, so employees can find they have no-one to put their case to or seek advice from outside their company.
Many of those the Victoria Derbyshire programme spoke to had signed non-disclosure agreements (NDAs), which are usually part of a deal where the employees are offered thousands of pounds for their silence.
But both Sarah and another woman said that despite signing, they still wanted their stories to be heard but with their identities disguised.
"Nicole", who worked as an executive for a well-known beauty company, says she was pushed out after telling her bosses she was pregnant.
'I was left out of meetings, I wasn't given information, they stopped cc'ing me in emails," she says. "Then, within 10 weeks of me coming back from maternity leave, I was told I didn't have a future in the company and that I should just leave.
"I basically believed everything they told me, I believed I was a bad person. I was diagnosed with depression, with stress and burnout. I spent time in a facility. I'm really lucky I recovered… but so many people don't."
It is not just women who are affected. Zak, who is now a freelance make-up artist, says he has been treated badly in the past.
"There were a lot of times when they were like, 'Are you sure you want him to do your make-up? He's a guy, he doesn't know how to do make-up,'" he says. "People are greedy in that sense and they want everything to themselves and they don't care who they throw under the bus.
"I went through a bit of a depression phase, I felt everyone around me was fake."
Employment lawyer Karen Jackson says she has dealt with hundreds of discrimination cases, including bullying and harassment in the cosmetics industry.
"I've dealt with similar claims against the same companies who don't seem to learn from past mistakes and who tolerate unacceptable workplace conduct," she says. 
"I don't understand why they won't address it and weed it out making life better for everyone."
But there are people in the industry trying to make a change.
Celebrity make-up artist Lan Nguyen-Grealis says she was a victim of bullying and harassment earlier in her career and uses the experience to be even kinder.
"It's all about sisterhood - a lot of the girls have freedom to come and speak to me offside whenever they need to, or if they need to let off steam it's fine," she says. 
"It's important the girls know this industry is amazing, but if you're a really nice person, you will sustain a great career. "
The British Beauty Council represents the voices, opinions and needs of the industry.
After being shown the programme's findings, chief executive Millie Kendall said: "It's heartbreaking an industry that we are trying to pull together is so at each other's throats.
"It does fall on the government because this isn't just a beauty industry-related issue, this is a nationwide issue. 
I think there needs to be some sort of ombudsmen or an industry body set up to make sure there is a safe place for people to go."
The Department for Business said in a statement: "Through the Equality Act, employees are protected against harassment in the workplace on the grounds of gender, race, disability, religion or belief, sexual orientation or age, and workers have remedies against this behaviour in the employment tribunals."
Kylie Jenner will sell the majority of her cosmetics company for $600 million (?463 million).
The 22-year-old's brand, including Kylie Cosmetics and Kylie Skin, will be controlled by beauty giant Coty.
Kylie says she is building the brand into an "international beauty powerhouse".
Forbes reported that she made $360 million in sales in 2018, making her the youngest self-made billionaire ever.
The chairman of Coty's board called Kylie a "modern-day icon, with an incredible sense of the beauty consumer".
Her online influence is so powerful that she?reduced Snapchat's stock market value by $1.3bn?(?1bn) when she tweeted that she does not use the app anymore.
The reality TV star launched her brand in 2015 with a line of lipsticks, and has since then branched out into face make-up and skincare.
Although she's the youngest, Kylie is the highest earner in the Kardashian family.
She faced backlash after being named a "self-made" billionaire, but defended herself saying that none of her money has come from inheritance.
She has more than 151 million followers on her personal Instagram account, as well as 22 million on her Kylie cosmetics account.
Coty, which owns brands like Max Factor and Hugo Boss, will have a 51% stake in the company.
It said the deal will be completed in 2020.
From festive gift sets to the coziest candles, luxe styling tools and gorgeous makeup organizers,?there's no shortage of gift possibilities for the beauty lover in your life. 
But with so many options comes the challenge of sorting through what's worth the splurge and what's just hype, an especially tough task if you're not beauty obsessive.
That's why we've rounded up the best beauty gifts you can give this season, and grouped them by what we'll call beauty personality types.
And for everyone else on your list, we've got great?gift ideas for him,?gifts for her,?gifts for kids,?gifts for gamers?and?Oprah-approved gifts, too.
That's why we've rounded up the best beauty gifts you can give this season, and grouped them by what we'll call beauty personality types.
 So whether you're looking for skin care goodies, show-stopping lipsticks or savings on cult-favorite products, we've got you covered.
This bundle from Too Faced is all things holiday, dreamy and luxurious. 
Not only are you getting?incredible value (with?savings of almost $300), you're also getting enough makeup for an entire look. 
With three different palettes and deluxe sizes of Too Faced's signature mascara, liquid lipstick and eye primer, this set is the real deal.
Have a Disney beauty fanatic in your life?
 Truly wow her with Colourpop's PR bundle of the Disney Midnight Masquerade Collection. 
This bundle features the Midnight Masquerade Palette (which you can buy individually), in addition to eight princess bundles (Rapunzel, Tiana, Aurora and more!), which include one liquid lipstick and one blush or highlighter. 
Does anyone really need that many options? 
Probably not, but?the sheer amount, combined with the grandeur of the packaging, is something that will absolutely drop jaws. 
You also have the option of purchasing?the entire collection without the packaging bells and whistles, which drops the price to $183.
One of the most buzzed-about skin care brands,?Drunk Elephant is known for its devoted cult following, gorgeous minimalist product design and, of course, stellar products.
 One product the brand is best known for is its TLC Sukari Babyfacial mask, a once-a-week mask that revives and brightens your skin, among tons of other benefits. 
The full-size product is $80, which means for just an extra $8, you're getting four minis (perfect for a test run before you make a bigger purchase) and a gorgeous peach-colored travel bag.
If you aspire to recreate Rihanna's perfect pout, her Fenty Beauty line is well loved for having some of the juiciest and most conditioning lip glosses on the market.?
This mini gloss bomb set is the perfect opportunity to test five shades?(four new ones, in addition to the brand's best selling FU$$Y shade), and it all comes in a gorgeous keepsake tin.
This beautifully packaged set of three holiday-themed candles is luxury at its finest. 
With?wintery scents like Spiced Orange & Clove and Birchwood Pine, cozying up and relaxing with a good book (or nighttime skin care routine) has just been made that much more enjoyable.
Urban Decay has achieved legendary status in the makeup community, thanks to the brand's line of incredible eyeshadow palettes that combine quality, versatility and fun.
 And the honey-scented, warm tone Naked Honey Eyeshadow Palette is no exception.?
You're not only getting the palette, but four other honey-themed products, from Urban Decay's cult-favorite setting spray to a juicy lip plumper.
There's nothing like luxurious, moisturizing skin care when the weather gets colder and drier. 
And many would argue that?when it comes to nourishing body products, there are none better than those from Sol de Janeiro, a brand known for its incredible-smelling collection of creams and fragrances. 
Pistachio, salted caramel and vanilla are just perfect. In this bundle, you get to test the brand's bestsellers for $30 less than the cost of individual products. 
There's nothing like luxurious, moisturizing skin care when the weather gets colder and drier.
 And many would argue that?when it comes to nourishing body products, there are none better than those from Sol de Janeiro, a brand known for its incredible-smelling collection of creams and fragrances.
 Pistachio, salted caramel and vanilla are just perfect. In this bundle, you get to test the brand's bestsellers for $30 less than the cost of individual products. 
There's nothing like luxurious, moisturizing skin care when the weather gets colder and drier. 
And many would argue that?when it comes to nourishing body products, there are none better than those from Sol de Janeiro, a brand known for its incredible-smelling collection of creams and fragrances. 
Pistachio, salted caramel and vanilla are just perfect.
 In this bundle, you get to test the brand's bestsellers for $30 less than the cost of individual products.
If you're looking for a?gorgeous, affordable makeup set that's perfect for someone building a beauty arsenal, look no further. This set features a collection of must-have matte and stunning shimmery eye shadows, perfect for a cohesive everyday or glam look; two super-wearable blush shades; champagne and golden highlighters; cult-favorite mascara; and a supersoft double-ended brush. 
The best part is that it's all packaged together in a sleek travel case that has a huge mirror, perfect as an at-home vanity or traveling companion.
Facial sprays are great to use first thing in the morning, as an afternoon refresher and as a relaxing part of your nighttime skin care routine. 
Basically, you can never have enough of them.?
This trio from Mario Badescu is an ultra-affordable cult favorite?that's available in a charming holiday packaging perfect for gifting, or splitting up into multiple stocking stuffers.
If you know someone looking for simple, clean and reliable skin care for the drier winter months, this set from Kiehl's includes everything someone would need to get hooked. 
From goodies like the?Midnight Recovery Concentrate?and?Calendula Herbal-Extract Toner?(two of my can't-live-without winter skin care staples),?this collection is focused on hydration and radiance. Plus, it comes in an absolutely festive holiday gift box made from recycled materials.
When it comes to brightening, there's no better place to start than this gift set from Tatcha. 
Yes, the brand's products can be pricey, but if you or someone you love has sensitivity issues, Tatcha's products are some of the most gentle, yet effective, we've tried.
?They're great for all skin types and ages, and the packaging is just as showstopping as the products.?
And the savings on this bundle is incredible.
 With the original value of the three products being $251, you're essentially getting the Violet-C Radiance Mask for free.
Peter Thomas Roth is known for luxurious skin care, and the brand's masks are no exception.?
With this six-piece gift set, you'll be saving a ton: Originally valued at over $200, the five face masks (like the?24K Gold Mask Pure Luxury Lift & Firm Mask) and the?Water Drench Hyaluronic Cloud Cream Hydrating Moisturizer?are yours for just $75.
Glow Recipe's?Watermelon + AHA Glow Sleeping Mask?took the world by storm a couple years ago, but the phenomenon is still going strong. 
And?this gift set is both a perfect, economical option and a great excuse to treat yourself to a luxurious face mask. 
The mask is originally priced at $45, and in this set, you're getting a full-size mask, in addition to the Blueberry Bounce Gentle Cleanser and Watermelon Pink Juice Oil-Free Moisturizer, for just $48. Talk about gift set savings!
K-beauty (short for Korean beauty) has absolutely made its way into the American mainstream, and one of the best, affordable Korean skin care brands you can easily shop is Tonymoly.
?Not only are the brand's products absolutely adorable, they smell delicious (yet super clean) and are an inexpensive way to invest in a little pampering.?
This set includes rose- and peach-scented skin and body goodies like face masks, a foam cleanser, hand cream and more.
For the natural beauty lover, Origins is beloved for its use of organic skin care ingredients, 100% natural essential oils and dedication as a brand to more sustainable, eco-friendly packaging.
?This skin care essentials gift set comes with seven products (enough for a whole routine),?making this the perfect gift for someone who just moved or needs to revamp an entire routine.
 You'll get full-sized versions of the brand's most popular products, from the GinZing Gel Moisturizer to the GinZing Refreshing Eye Cream, for just $45, which is a huge bargain considering the original price of the products is $92.
Sunday Riley's?Good Genes Treatment?boasts a nearly 5-star review from over 500 reviewers on?Nordstrom.com, which is the proof of the pudding when it comes to the acclaim and popularity of this lactic acid treatment.
 With this set,?you?snag the?Good Genes Treatment, in addition to four travel-size versions of the brand's other best-selling skin correcting goodies?— from brightening serum to night oil — for a savings of over $50. 
Perfect for anyone who loves testing out high-end skin care, or is looking to make the leap to buy the full-size version.
Glamglow masks are among the best (and most fun) on the market, but they're definitely not cheap. 
Instead of splurging on one type of mask,?this set lets you try six treatment masks, one sheet mask and an eye mask.
 And if you have a couple of stockings to stuff, this is also a great item to split among people, since we highly doubt anyone's going to be disappointed receiving a Glamglow mask.
Fresh is a brand?beloved for its clean and gentle skin care that's ideal for sensitive skin types. 
This set has all you need for an effective, simple skin care regimen. The Soy Face Cleanser and Rose Collection smells and feels wonderful and works incredibly well.
 So whether you opt to give this to someone in need of a new routine (or to yourself, since you're saving $30 by purchasing the set), the products and cute packaging are sure to bring a smile to someone's face.
f you really want to wow someone, Pat McGrath is a high-end brand that creates what many would call the best eye shadows on the market. 
There's a reason that even at a steep price point, people still go crazy for the quality.
?This palette is essentially holiday perfection in 10 shades, and every detail, from the super luxurious case to the intricate design on the front, make this one of the most elegant beauty gifts someone could receive.
If you're shopping for someone who prefers a subtle glow, Glossier is your spot.
?The Makeup Set from Glossier includes three of its best-selling, fan-favorite products perfect for the ultimate everyday look. 
The Lash Slick Mascara is more lengthening than volumizing, but it absolutely stays on all day (and even during workouts). 
Cloud Paint is a subtle cheek tint with incredible staying power, and it comes in six adorable, flattering shades. 
And the Boy Brow is the ultimate product for someone who wants to keep brows in check, without too much additional shaping or color.
If you're gifting a single lipstick, make sure it's one that has it all, like this holiday-edition option from Yves Saint Laurent.?
It's luxurious, glamorous and also an incredible lipstick.?
While it's always recommended to pair a darker lip with a lip liner, these have impressive lasting power on their own — on top of being some of the most comfortable lipsticks we've ever tried.
 That's not to mention the stunning star-studded casing, which is sure to take center stage on anyone's vanity.
Laneige lip products are like no others —?incredibly moisturizing, delicious-smelling and available in the cutest (yet minimalistic) packaging.
 This set of three is perfect for having different options in your purse, at work and on your vanity, or a great opportunity to indulge in scents like peach and berry while you keep your lips moisturized.
The holiday season is the perfect time to sport a glittery, glamorous look?— and this highlighting palette from Charlotte Tilbury gives you all the tools to get there.
 Well, you'll need a brush, but other than that, these three gold shades are sure to catch the flash in holiday photos, garner tons of compliments and make you feel like a glowing goddess even if it's cold and dry outside.
A favorite of Kate Middleton, this luxurious candle might be a fairly simple gift option, but it is a classic that you can't go wrong with.?
Jo Malone candles have a long-lasting (but not overpowering) scent that quickly fills a room, and this one is sure to last you a while.
If the person you're gifting has enough neutral palettes (but isn't drawn to bright, neon colors),?this palette from Urban Decay features the perfect balance of stunning, yet wearable, colors and your necessary neutrals. 
The jewel tones are perfect for gorgeous holiday looks, and there's a great mix of matte, shimmer and metallic shades.
 The palette also includes a nice, large mirror — always a winner in our book.
Stila's liquid eye shadows are easy to use and absolutely show-stopping.?One of the most popular shades -— Kitten, a glittering pinky nude color — is available in this adorable set of minis, along with Starlight, a light champagne, and Vivid Smoky Quartz, a slightly darker brown espresso shade.
 Perfect for the person who enjoys all things sparkly, this set makes for a perfect gift or stocking stuffer.
If you know someone who's still building up her makeup toolkit, Real Techniques products are our mainstay.
?Nothing we've tried since has matched the brand's combination of great price and incredible quality when it comes to basic makeup brushes and sponges.?
Plus, the brushes are available in an orange-pink shade, which makes this a fun option.
The makeup world is filled with celebrities endorsing big brands in expensive advertisements. 
But Dollar General's humble, $5-and-under cosmetics line has gone viral, thanks to social media beauty bloggers.
In the spring,?Dollar General?(DG)?debuted Believe Beauty, an exclusive, private-label line of lipsticks, eye shadows, foundations, nail polishes and skin care accessories at the chain's more than?15,500 US stores. 
Dollar General partnered with a beauty manufacturer on the brand and is giving it prime real estate at stores: It's displaying the 150-product collection in dedicated sections at the end of store aisles, making it easy for customers to find.
The aspirational brand is "an important part of our strategy," chief executive Todd Vasos said in March.
Dollar General executives say they developed the brand to bolster the company's hold on existing customers and improve its?thin profit margins. 
Dollar General also hopes to draw Millennials with the brand. Millennials probably won't post online about snacks or a new mop they bought at Dollar General, but they have showing off their new makeup online.
The line has attracted attention from personalities with large social media followings reviewing the beauty products on YouTube and Instagram.
Beauty video bloggers, or vloggers, hold a lot of sway over makeup brands. 
Vloggers have long used YouTube and Instagram to review makeup and skincare products and to give tutorials to their followers, which for some can be up in the millions.
 Experts and beginners alike tune in to learn techniques, tips and to see if new products on the market are worth buying.
Dozens of?Believe?reviews on YouTube have already racked up hundreds of thousands of page views. 
One 16-minute YouTube review from a beauty vlogger has 125,000 views. Instagram is flooded with more than 3,000 posts using "#believebeauty."
"We're seeing really good results," Dollar General CFO John Garratt said at a presentation in June. 
He pointed to "a lot of buzz on social media" about the brand. Dollar General declined to provide the brand's sales figures for this article.
All that social media attention means free advertising for Dollar General.
 It boosts the company's image with younger shoppers and is helping lift the?dollar store empire.
"People like those kind of videos because it's something different," Taylor Horn, a blogger who reviewed Believe on her YouTube channel, told CNN Business.
 Her channel has more than 750,000 followers.
"It's cool when lines like Believe Beauty launch, where it's accessible," she said.
 "I think it's more achievable and the things that your everyday consumer can afford."
Believe may help Dollar General gain market share against drug and grocery stores, said Stephanie Wissink, who covers the beauty industry at Jefferies.
The majority of Dollar General's stores are in rural communities, so offering the brand might save small-town shoppers a longer drive out to Target or Walgreens, she said.
Dollar General added its own beauty brand to an industry that is surging.
 Sales of cosmetics, which includes makeup, lipsticks, nail polish and eye shadow, have grown 31% in the United States since 2013 to more than $17 billion last year, according to Euromonitor.
But the industry is changing rapidly. 
Social media and the rise of vloggers have democratized the market, opening it to new shoppers.
"If you're a beauty enthusiast in a small community, you can access the same content to inspire you as the metropolitan fashonista," said Wissink. 
"Historically, you would have had to subscribe to a magazine or wait until a trend reached you."
Social media has also allowed small, low-priced brands to gain loyal followings online. 
Brands such as ELF cosmetics, which sells for under $10, have appealed to bargain hunters and paved the way for mainstream retailers to offer their own affordable lines.
"People know that now they can get really good makeup for cheap," said Horn. 
"There's not as much of a stigma behind it."
Dollar General is following a similar strategy to Walgreens, Target, Zara, Forever 21 and even?7-Eleven. 
These companies have all added their own in-house cosmetics lines in recent years.
"The barriers to building a brand are not as stringent as they were in the past," said Wissink from Jefferies. 
"That's why you're seeing almost every retailer launching some level of beauty."
While skincare products, such as face masks and body scrubs, play a big role in self-care routines, they've traditionally been more workhorses than show ponies for the beauty industry.
Recently, that's started to change as skincare products have surged in popularity, thanks to interest in natural and organic ingredients, as well as a thriving community on social media.
Sales of skincare products in the US?grew by 13%?in 2018, hitting $5.6 billion, while makeup sales increased just 1%, according to data from The NPD Group, a market research company.
This is "the first time in a very long time" that the skincare industry is growing faster than the makeup industry, Stephan Kanlian, head of a think tank at New York's Fashion Institute of Technology that studies the future of the beauty industry, told CNN Business.
Conservative estimates valued?the global beauty industry at over $300 billion in 2018, according to a report from Trefis, a financial research and analysis firm.
 The global skincare market was valued at nearly $135 billion in 2018, increasing nearly 60% in the past 10 years.
 And Trefis projects it to reach $180 billion — an increase of over 30% — in the next five years.
A number of factors have contributed to the growth, according to Kanlian.
Greater scrutiny by customers of product ingredients has led to demand for natural and organic skincare brands.
New products and companies are launching at a dizzying pace,?including some formed around the concept of?subscription plans for facials.
Social media has opened up new avenues for purchasing and discussing skincare routines and trends.
Women, the primary consumers in the skincare industry, are adding anti-aging products to their skincare regimens as they get older.?
Men?are increasingly showing interest in skincare products, and in turn companies are launching new lines targeted at them.
The industry has particularly benefited from the visual nature of social platforms.
Kanlian pointed to the popularity of people posting pictures of themselves in face masks on Instagram. 
"Those are very visual," he said.
 "They're great for bloggers and vloggers to do online tutorials and consumers are taking photos of themselves in their favorite skincare masks."
Summer Fridays, a brand co-founded by Instagram influencers Marianna Hewitt and Lauren Gores Ireland, launched in 2018 with just one product: a face mask. 
They spent no money on marketing, relying solely on their social media presences. 
In less than two weeks, the mask became the best-selling skin-care product on Sephora.com with thousands of positive reviews.
 It also repeatedly sold out online.
Hewitt and Gores, with nearly one million combined followers, spent years testing products for other companies and answering questions on social media. 
Over about two years, the duo developed a product using their informal focus group on social media, ultimately coming up with a clean mask packaged in a TSA-compatible size, among other attributes suggested by their followers.
"Everything we do as far as packaging, and boxes, and messaging, and photography should really be thought of on social, first and foremost," Hewitt said.
Social media has also given consumers a platform to promote the products they love and helped them become more informed, Kanlian said.
"I don't think that we've ever seen consumers, certainly in the beauty industry, at such a high level of education and sophistication," Kanlian said. 
"People are looking anywhere that they can to find a definition for natural or organic."
That need for information is partly the result of a lack of regulation in the United States. 
Whereas Australia, Japan and many European countries have restricted thousands of ingredients, the US has only banned?11 chemicals?from being used in beauty products.
 The FDA's website says US law doesn't give it "the authority to require cosmetic manufacturers to submit their safety data."
While big legacy skincare brands such as Estee Lauder and L'Oreal have slowly started offering more natural alternatives, it took them a while to meet consumer demand, according to Kanlian.
That delay, along with the desire for more product information, created an opportunity for lots of smaller natural-based companies.
In 2018, natural skincare sales totaled $1.6 billion and accounted for more than a quarter of overall skincare sales — up 23% from the year before.
Nearly?50% of American women?who use skincare reported that they were looking for natural or organic products, according to NPD.
When Gregg Renfrew founded Beautycounter, a line of ingredient-conscious products, in 2013, she faced lots of skepticism.
"No one was talking about clean beauty," she told CNN Business.
But in recent years, the business has "grown explosively." 
Now Renfrew is committed to calling attention to the lack of regulation.
"We had to take things into our own hands," Renfrew told CNN Business. 
"We've restricted 1,600 ingredients from our formulations" based on independent research the company has done and guidelines set by other countries.
The direct-to-consumer company relies on independent consultants,?everyone from doctors to stay-at-home moms and students, to spread awareness about its products. 
The consultants direct shoppers to Beautycounter's website and can earn up to 35% in commission on sales.
In 2017, Beautycounter paid?over $80 million in commissions.
Beautycounter consultants share information about ingredients and also campaign for greater regulation in the industry. 
Beautycounter has even sent select top sellers?to Washington, DC to advocate for new laws.
"We knew that we had to build a movement of consumers who were really advocating for better beauty regulations," Renfrew said.
Kanlian sees this need for greater transparency as the future of the skincare industry.
 It's something smaller brands have realized and responded to quickly, and bigger brands are slowly starting to follow suit with new products of their own.
The FDA advised consumers not to use three Claire's brand cosmetic products after tests found they contained asbestos.
The agency issued a safety alert on Claire's Eye Shadows, Compact Powder and Contour Pallette, after they tested positive for tremolite asbestos. 
The FDA also detected asbestos in a Justice product, which had already been recalled in 2017,?according to the agency.
The three Claire's products are not believed to be for sale, but consumers who have them at home should stop using them, the FDA said.
Claire's, which sells makeup,?jewelry and accessories aimed at teens, tweens and kids, said that it has removed the three cosmetic items and any talc-based products from stores. 
The company said in a statement that its products are safe and that "customer safety is paramount."
The company took issue with the FDA's tests. 
"The recent test results the FDA have shared with us show significant errors. Specifically, the FDA test reports have mischaracterized fibers in the products as asbestos, in direct contradiction to established EPA and USP criterion for classifying asbestos fibers. 
Despite our efforts to discuss these issues with the FDA, they insisted on moving forward with their release."
Claire's said it was "disappointed that the FDA has taken this step, and we will continue to work with them to demonstrate the safety of our products."
The FDA said in a statement sent to CNN on Friday that it "is confident in the scientific validity of the testing results provided by two, separate third-party labs."
"In this case, it provided significant reassurances to the FDA when results from various tests conducted at the two, different labs aligned.
The bottom line is that because of the health risks posed by asbestos, which are well-documented by other government agencies, it was the FDA's responsibility to promptly share these findings with American consumers and warn them about their potential public health threat."
Claire's released another statement on Friday, saying it had switched to talc-free manufacturing of its cosmetics last year. 
In response to the FDA's concerns about the three products, the company said it had pulled any remaining talc-based items from its stores.
"We are taking these actions out of an abundance of caution and remain confident that any products purchased at Claire's are safe. 
We look forward to working with the Food and Drug Administration to ensure the highest safety standards for all cosmetics," the company said.
The retailer operates the Claire's and Icing brands, and?filed for bankruptcy in March last year, emerging from it in October.
In 2017, Claire's took nine makeup products off the shelves following a report by?CNN affiliate WJAR-TV?that tremolite asbestos was found in the makeup.
The FDA then tested some of the products and released the results Tuesday.
"We understand how concerning this finding is for any consumer and parents whose children may have used one of these products," according to the FDA.
 It encouraged health care professionals and consumers who may have medical issues related to the Claire's products to?report it to the FDA's adverse events reporting program.
There are no laws requiring companies to test their cosmetic products for safety, and the FDA has limited authority to ensure the safety of cosmetic products.
 It can't review products before they go to market like they do for drugs.
The agency said it's working with cosmetic manufacturers to better understand how they're making sure the talc is free of asbestos. Asbestos is often found near talc, which is mined and used in many make-up products.
Asbestos becomes dangerous when particles or fibers enter the lungs or stomach. 
If swallowed or inhaled,?tremolite asbestos?can lead to lung damage and cancer, including mesothelioma, an aggressive and deadly form of cancer.
The FDA announced that it's starting a voluntary registry for cosmetic companies to list their products and ingredients, including talc.
The?departing FDA Commissioner Scott Gottlieb, and Susan Mayne, the director of the Center for Food Safety and Applied Nutrition, called for a more modernized framework for the agency to "improve consumer safety" for cosmetics, including better ways to register products, ingredients, and access to consumer complaints.
Kylie Jenner?is selling a majority stake of her popular cosmetics line for $600 million, but she's staying on as its creative leader.
The celebrity businesswoman will sell 51% of her stake in Kylie Cosmetics to?Coty Inc.?(COTY), a New York-based cosmetics company that also owns a number of international consumer beauty brands, including CoverGirl.
 The partnership will help Kylie's brand expand globally and enter new beauty categories, the two companies said in a press release Monday.
"This partnership will allow me and my team to stay focused on the creation and development of each product while building the brand into an international beauty powerhouse," Jenner said.
Jenner's team will continue to handle her creative and communication efforts, which is notable because she's one of the most-followed people on social media. 
She has?150 million followers?on Instagram and 30 million?Twitter?(TWTR)?followers.
The cosmetics brand has generated roughly $177 million in revenue over the past year, according to Coty. 
The deal is expected to close in the third quarter of 2020.
Coty said the transaction is a "key milestone" for the company. It announced a $600 million turnaround plan in June that includes layoffs and streamlining its products and brands.
 The company also announced a $3 billion write down in value of?brands it acquired in 2015?from?Procter & Gamble?(PG), which included CoverGirl and Clairol.
Coty CEO Pierre Laubies said in the release that the deal is "an exciting next step in our transformation and will leverage our core strengths around fragrances, cosmetics and skincare, allowing Kylie's brands to reach their full potential."
Coty's stock has rallied 90% this year and jumped 4% in early Monday trading. 
The deal also helps the company future proof itself as younger shoppers are increasingly buying direct-to-consumer brands,?like Glossier.
Foundation?is a liquid or powder makeup applied to the face to create an even, uniform color to the?complexion, cover flaws and, sometimes, to change the natural?skin tone. Some foundations also function as a?moisturizer,?sunscreen,?astringent?or base layer for more complex?cosmetics. Foundation applied to the body is generally referred to as "body painting" or "body makeup."
﻿Rapid popularity of Internet of Things (IoT) and cloud computing permits neuroscientists to collect multilevel and multichannel brain data to better understand brain functions, diagnose diseases, and devise treatments.
To ensure secure and reliable data communication between end-to-end (E2E) devices supported by current IoT and cloud infrastructure, trust management is needed at the IoT and user ends.
This paper introduces a Neuro-Fuzzy based Brain-inspired trust management model (TMM) to secure IoT devices and relay nodes, and to ensure data reliability.
The proposed TMM utilizes node behavioral trust and data trust estimated using Adaptive Neuro-Fuzzy Inference System and weighted-additive methods respectively to assess the nodes trustworthiness.
In contrast to the existing fuzzy based TMMs, the NS2 simulation results confirm the robustness and accuracy of the proposed TMM in identifying malicious nodes in the communication network.
With the growing usage of cloud based IoT frameworks in Neuroscience research, integrating the proposed TMM into the existing infrastructure will assure secure and reliable data communication among the E2E devices.
In this paper, we address the problem of computing optimal paths through three consecutive points for the curvature-constrained forward moving Dubins vehicle.
Given initial and final configurations of the Dubins vehicle, and a midpoint with an unconstrained heading, the objective is to compute the midpoint heading that minimizes the total Dubins path length.
We provide a novel geometrical analysis of the optimal path, and establish new properties of the optimal Dubins' path through three points.
We then show how our method can be used to quickly refine Dubins TSP tours produced using state-of-the-art techniques.
We also provide extensive simulation results showing the improvement of the proposed approach in both runtime and solution quality over the conventional method of uniform discretization of the heading at the mid-point, followed by solving the minimum Dubins path for each discrete heading.
High quality upsampling of sparse 3D point clouds is critically useful for a wide range of geometric operations such as reconstruction, rendering, meshing, and analysis.
In this paper, we propose a data-driven algorithm that enables an upsampling of 3D point clouds without the need for hard-coded rules.
Our approach uses a deep network with Chamfer distance as the loss function, capable of learning the latent features in point clouds belonging to different object categories.
We evaluate our algorithm across different amplification factors, with upsampling learned and performed on objects belonging to the same category as well as different categories.
We also explore the desirable characteristics of input point clouds as a function of the distribution of the point samples.
Finally, we demonstrate the performance of our algorithm in single-category training versus multi-category training scenarios.
The final proposed model is compared against a baseline, optimization-based upsampling method.
Results indicate that our algorithm is capable of generating more uniform and accurate upsamplings.
Internet is the main source of information nowadays.
The search engines must have various alternative manners for the search results representation.
These representation methods will enable the end users especially the Visually Impaired VI web searchers to access the information on the web.
The aim of this paper is design, evaluate and improve the interface for the VI users to perform search and browse results.
This attempt provides a new accessibility tool for the VI web searchers.
The conceptual modelling technique proposed in this paper is based on the Formal Concept Analysis FCA that hides the detailed information for the collected data results.
This approach highlights the main discovered concepts to be focused on.
That is combined with context interactive navigation, in an interface called Interactive Search Engine (InteractSE), which minimize the time and effort required by the VI users.
There is no standardised set of guidelines or heuristics, which can be used for the evaluation of usability and accessibility aspects of such an interface.
Therefore, interactSE was evaluated with experts using Nielsen heuristics and Web Content Accessibility Guidelines WCAG 2.0 in terms of both usability and accessibility.
The analysis was carried out based on the number of usability problems identified and their average severity ratings.
The results show that the most frequently violated heuristics from the Nielsen set are consistency and documentation.
The average severity rating of all the problems found using Nielsen set is minor.
The results also show that the most frequently violated WCAG 2.0 guidelines are distinguishable, followed by navigable and affordance.
The average severity rating of all the problems found using WCAG 2.0 guidelines is also minor.
The results show that Nielsen heuristics and WCAG 2.0 guidelines both contributed in identifying a number of usability problems.
Automated Facial Expression Recognition (FER) has been a challenging task for decades.
Many of the existing works use hand-crafted features such as LBP, HOG, LPQ, and Histogram of Optical Flow (HOF) combined with classifiers such as Support Vector Machines for expression recognition.
These methods often require rigorous hyperparameter tuning to achieve good results.
Recently Deep Neural Networks (DNN) have shown to outperform traditional methods in visual object recognition.
In this paper, we propose a two-part network consisting of a DNN-based architecture followed by a Conditional Random Field (CRF) module for facial expression recognition in videos.
The first part captures the spatial relation within facial images using convolutional layers followed by three Inception-ResNet modules and two fully-connected layers.
To capture the temporal relation between the image frames, we use linear chain CRF in the second part of our network.
We evaluate our proposed network on three publicly available databases, viz.
Experiments are performed in subject-independent and cross-database manners.
Our experimental results show that cascading the deep network architecture with the CRF module considerably increases the recognition of facial expressions in videos and in particular it outperforms the state-of-the-art methods in the cross-database experiments and yields comparable results in the subject-independent experiments.
Existing deep multitask learning (MTL) approaches align layers shared between tasks in a parallel ordering.
Such an organization significantly constricts the types of shared structure that can be learned.
The necessity of parallel ordering for deep MTL is first tested by comparing it with permuted ordering of shared layers.
The results indicate that a flexible ordering can enable more effective sharing, thus motivating the development of a soft ordering approach, which learns how shared layers are applied in different ways for different tasks.
Deep MTL with soft ordering outperforms parallel ordering methods across a series of domains.
These results suggest that the power of deep MTL comes from learning highly general building blocks that can be assembled to meet the demands of each task.
In this paper we explore the use of electrical biosignals measured on scalp and corresponding to mental relaxation and concentration tasks in order to control an object in a video game.
To evaluate the requirements of such a system in terms of sensors and signal processing we compare two designs.
The first one uses only one scalp electroencephalographic (EEG) electrode and the power in the alpha frequency band.
The second one uses sixteen scalp EEG electrodes and machine learning methods.
The role of muscular activity is also evaluated using five electrodes positioned on the face and the neck.
Results show that the first design enabled 70% of the participants to successfully control the game, whereas 100% of the participants managed to do it with the second design based on machine learning.
Subjective questionnaires confirm these results: users globally felt to have control in both designs, with an increased feeling of control in the second one.
Offline analysis of face and neck muscle activity shows that this activity could also be used to distinguish between relaxation and concentration tasks.
Results suggest that the combination of muscular and brain activity could improve performance of this kind of system.
They also suggest that muscular activity has probably been recorded by EEG electrodes.
Low-density parity-check (LDPC) codes on symmetric memoryless channels have been analyzed using statistical physics by several authors.
In this paper, statistical mechanical analysis of LDPC codes is performed for asymmetric memoryless channels and general Markov channels.
It is shown that the saddle point equations of the replica symmetric solution for a Markov channel is equivalent to the density evolution of the belief propagation on the factor graph representing LDPC codes on the Markov channel.
The derivation uses the method of types for Markov chain.
Mobile agent networks, such as multi-UAV systems, are constrained by limited resources.
In particular, limited energy affects system performance directly, such as system lifetime.
It has been demonstrated in the wireless sensor network literature that the communication energy consumption dominates the computational and the sensing energy consumption.
Hence, the lifetime of the multi-UAV systems can be extended significantly by optimizing the amount of communication data, at the expense of increasing computational cost.
In this work, we aim at attaining an optimal trade-off between the communication and the computational energy.
Specifically, we propose a mixed-integer optimization formulation for a multi-hop hierarchical clustering-based self-organizing UAV network incorporating data aggregation, to obtain an energy-efficient information routing scheme.
The proposed framework is tested on two applications, namely target tracking and area mapping.
Based on simulation results, our method can significantly save energy compared to a baseline strategy, where there is no data aggregation and clustering scheme.
Inspired by speech recognition, recent state-of-the-art algorithms mostly consider scene text recognition as a sequence prediction problem.
Though achieving excellent performance, these methods usually neglect an important fact that text in images are actually distributed in two-dimensional space.
It is a nature quite different from that of speech, which is essentially a one-dimensional signal.
In principle, directly compressing features of text into a one-dimensional form may lose useful information and introduce extra noise.
In this paper, we approach scene text recognition from a two-dimensional perspective.
A simple yet effective model, called Character Attention Fully Convolutional Network (CA-FCN), is devised for recognizing the text of arbitrary shapes.
Scene text recognition is realized with a semantic segmentation network, where an attention mechanism for characters is adopted.
Combined with a word formation module, CA-FCN can simultaneously recognize the script and predict the position of each character.
Experiments demonstrate that the proposed algorithm outperforms previous methods on both regular and irregular text datasets.
Moreover, it is proven to be more robust to imprecise localizations in the text detection phase, which are very common in practice.
In this paper, we propose a design solution for the implementation of Virtualized Network Coding Functionality (VNCF) over a service coverage area.
Network Function Virtualization (NFV) and Network Coding (NC) architectural designs are integrated as a toolbox of NC design domains so that NC can be implemented over different underlying physical networks including satellite or hybrid networks.
The design includes identifying theoretical limits of NC over wireless networks in terms of achievable rate region and optimizing coding rates for nodes that implement VNCF.
The overall design target is to achieve a given multicast transmission target reliability at receiver sides.
In addition, the optimization problem uses databases with geo-tagged link statistics and geo-location information of network nodes in the deployment area for some computational complexity/energy constraints.
Numerical results provide validation of our design solution on how network conditions and system constraints impact the design and implementation of NC and how VNCF allows reliable communication over wireless networks with reliability and connectivity up to theoretical limits.
We propose a method that combines signals from many brain regions observed in functional Magnetic Resonance Imaging (fMRI) to predict the subject's behavior during a scanning session.
Such predictions suffer from the huge number of brain regions sampled on the voxel grid of standard fMRI data sets: the curse of dimensionality.
Dimensionality reduction is thus needed, but it is often performed using a univariate feature selection procedure, that handles neither the spatial structure of the images, nor the multivariate nature of the signal.
By introducing a hierarchical clustering of the brain volume that incorporates connectivity constraints, we reduce the span of the possible spatial configurations to a single tree of nested regions tailored to the signal.
We then prune the tree in a supervised setting, hence the name supervised clustering, in order to extract a parcellation (division of the volume) such that parcel-based signal averages best predict the target information.
Dimensionality reduction is thus achieved by feature agglomeration, and the constructed features now provide a multi-scale representation of the signal.
Comparisons with reference methods on both simulated and real data show that our approach yields higher prediction accuracy than standard voxel-based approaches.
Moreover, the method infers an explicit weighting of the regions involved in the regression or classification task.
As global political preeminence gradually shifted from the United Kingdom to the United States, so did the capacity to culturally influence the rest of the world.
In this work, we analyze how the world-wide varieties of written English are evolving.
We study both the spatial and temporal variations of vocabulary and spelling of English using a large corpus of geolocated tweets and the Google Books datasets corresponding to books published in the US and the UK.
The advantage of our approach is that we can address both standard written language (Google Books) and the more colloquial forms of microblogging messages (Twitter).
We find that American English is the dominant form of English outside the UK and that its influence is felt even within the UK borders.
Finally, we analyze how this trend has evolved over time and the impact that some cultural events have had in shaping it.
A battery swapping and charging station (BSCS) is an energy refueling station, where i) electric vehicles (EVs) with depleted batteries (DBs) can swap their DBs for fully-charged ones, and ii) the swapped DBs are then charged until they are fully-charged.
Successful deployment of a BSCS system necessitates a careful planning of swapping- and charging-related infrastructures, and thus a comprehensive performance evaluation of the BSCS is becoming crucial.
This paper studies such a performance evaluation problem with a novel mixed queueing network (MQN) model and validates this model with extensive numerical simulation.
We adopt the EVs' blocking probability as our quality-of-service measure and focus on studying the impact of the key parameters of the BSCS (e.g., the numbers of parking spaces, swapping islands, chargers, and batteries) on the blocking probability.
We prove a necessary and sufficient condition for showing the ergodicity of the MQN when the number of batteries approaches infinity, and further prove that the blocking probability has two different types of asymptotic behaviors.
Meanwhile, for each type of asymptotic behavior, we analytically derive the asymptotic lower bound of the blocking probability.
The success of graph embeddings or node representation learning in a variety of downstream tasks, such as node classification, link prediction, and recommendation systems, has led to their popularity in recent years.
Representation learning algorithms aim to preserve local and global network structure by identifying node neighborhood notions.
However, many existing algorithms generate embeddings that fail to properly preserve the network structure, or lead to unstable representations due to random processes (e.g., random walks to generate context) and, thus, cannot generate to multi-graph problems.
In this paper, we propose RECS, a novel, stable graph embedding algorithmic framework.
RECS learns graph representations using connection subgraphs by employing the analogy of graphs with electrical circuits.
It preserves both local and global connectivity patterns, and addresses the issue of high-degree nodes.
Further, it exploits the strength of weak ties and meta-data that have been neglected by baselines.
The experiments show that RECS outperforms state-of-the-art algorithms by up to 36.85% on multi-label classification problem.
Further, in contrast to baselines, RECS, being deterministic, is completely stable.
In this paper we propose right-angled Artin groups as a platform for secret sharing schemes based on the efficiency (linear time) of the word problem.
Inspired by previous work of Grigoriev-Shpilrain in the context of graphs, we define two new problems: Subgroup Isomorphism Problem and Group Homomorphism Problem.
Based on them, we also propose two new authentication schemes.
For right-angled Artin groups, the Group Homomorphism and Graph Homomorphism problems are equivalent, and the later is known to be NP-complete.
In the case of the Subgroup Isomorphism problem, we bring some results due to Bridson who shows there are right-angled Artin groups in which this problem is unsolvable.
Publishing articles in high-impact English journals is difficult for scholars around the world, especially for non-native English-speaking scholars (NNESs), most of whom struggle with proficiency in English.
In order to uncover the differences in English scientific writing between native English-speaking scholars (NESs) and NNESs, we collected a large-scale data set containing more than 150,000 full-text articles published in PLoS between 2006 and 2015.
We divided these articles into three groups according to the ethnic backgrounds of the first and corresponding authors, obtained by Ethnea, and examined the scientific writing styles in English from a two-fold perspective of linguistic complexity: (1) syntactic complexity, including measurements of sentence length and sentence complexity; and (2) lexical complexity, including measurements of lexical diversity, lexical density, and lexical sophistication.
The observations suggest marginal differences between groups in syntactical and lexical complexity.
Centrality is an important notion in complex networks; it could be used to characterize how influential a node or an edge is in the network.
It plays an important role in several other network analysis tools including community detection.
Even though there are a small number of axiomatic frameworks associated with this notion, the existing formalizations are not generic in nature.
In this paper we propose a generic axiomatic framework to capture all the intrinsic properties of a centrality measure (a.k.a. centrality index).
We analyze popular centrality measures along with other novel measures of centrality using this framework.
We observed that none of the centrality measures considered satisfies all the axioms.
Reconstruction of signals from compressively sensed measurements is an ill-posed problem.
In this paper, we leverage the recurrent generative model, RIDE, as an image prior for compressive image reconstruction.
Recurrent networks can model long-range dependencies in images and hence are suitable to handle global multiplexing in reconstruction from compressive imaging.
We perform MAP inference with RIDE using back-propagation to the inputs and projected gradient method.
We propose an entropy thresholding based approach for preserving texture in images well.
Our approach shows superior reconstructions compared to recent global reconstruction approaches like D-AMP and TVAL3 on both simulated and real data.
In the last decade, social media has evolved as one of the leading platform to create, share, or exchange information; it is commonly used as a way for individuals to maintain social connections.
In this online digital world, people use to post texts or pictures to express their views socially and create user-user engagement through discussions and conversations.
Thus, social media has established itself to bear signals relating to human behavior.
One can easily design user characteristic network by scraping through someone's social media profiles.
In this paper, we investigate the potential of social media in characterizing and understanding predominant drunk texters from the perspective of their social, psychological and linguistic behavior as evident from the content generated by them.
Our research aims to analyze the behavior of drunk texters on social media and to contrast this with non-drunk texters.
We use Twitter social media to obtain the set of drunk texters and non-drunk texters and show that we can classify users into these two respective sets using various psycholinguistic features with an overall average accuracy of 96.78% with very high precision and recall.
Note that such an automatic classification can have far-reaching impact - (i) on health research related to addiction prevention and control, and (ii) in eliminating abusive and vulgar contents from Twitter, borne by the tweets of drunk texters.
This paper explores the potential of extreme learning machine based supervised classification algorithm for land cover classification.
In comparison to a backpropagation neural network, which requires setting of several user-defined parameters and may produce local minima, extreme learning machine require setting of one parameter and produce a unique solution.
ETM+ multispectral data set (England) was used to judge the suitability of extreme learning machine for remote sensing classifications.
A back propagation neural network was used to compare its performance in term of classification accuracy and computational cost.
Results suggest that the extreme learning machine perform equally well to back propagation neural network in term of classification accuracy with this data set.
The computational cost using extreme learning machine is very small in comparison to back propagation neural network.
End-to-end models for goal-orientated dialogue are challenging to train, because linguistic and strategic aspects are entangled in latent state vectors.
We introduce an approach to learning representations of messages in dialogues by maximizing the likelihood of subsequent sentences and actions, which decouples the semantics of the dialogue utterance from its linguistic realization.
We then use these latent sentence representations for hierarchical language generation, planning and reinforcement learning.
Experiments show that our approach increases the end-task reward achieved by the model, improves the effectiveness of long-term planning using rollouts, and allows self-play reinforcement learning to improve decision making without diverging from human language.
Our hierarchical latent-variable model outperforms previous work both linguistically and strategically.
We design a new approach that allows robot learning of new activities from unlabeled human example videos.
Given videos of humans executing the same activity from a human's viewpoint (i.e., first-person videos), our objective is to make the robot learn the temporal structure of the activity as its future regression network, and learn to transfer such model for its own motor execution.
We present a new deep learning model: We extend the state-of-the-art convolutional object detection network for the representation/estimation of human hands in training videos, and newly introduce the concept of using a fully convolutional network to regress (i.e., predict) the intermediate scene representation corresponding to the future frame (e.g., 1-2 seconds later).
Combining these allows direct prediction of future locations of human hands and objects, which enables the robot to infer the motor control plan using our manipulation network.
We experimentally confirm that our approach makes learning of robot activities from unlabeled human interaction videos possible, and demonstrate that our robot is able to execute the learned collaborative activities in real-time directly based on its camera input.
In Future Internet it is possible to change elements of congestion control in order to eliminate jitter and batch loss caused by the current control mechanisms based on packet loss events.
We investigate the fundamental problem of adjusting sending rates to achieve optimal utilization of highly variable bandwidth of a network path using accurate packet rate information.
This is done by continuously controlling the sending rate with a function of the measured packet rate at the receiver.
We propose the relative loss of packet rate between the sender and the receiver (Relative Rate Reduction, RRR) as a new accurate and continuous measure of congestion of a network path, replacing the erratically fluctuating packet loss.
We demonstrate that with choosing various RRR based feedback functions the optimum is reached with adjustable congestion level.
The proposed method guarantees fair bandwidth sharing of competitive flows.
Finally, we present testbed experiments to demonstrate the performance of the algorithm.
Software quality in use comprises quality from the user's perspective.
It has gained its importance in e-government applications, mobile-based applications, embedded systems, and even business process development.
User's decisions on software acquisitions are often ad hoc or based on preference due to difficulty in quantitatively measuring software quality in use.
But, why is quality-in-use measurement difficult?
Although there are many software quality models, to the authors' knowledge no works survey the challenges related to software quality-in-use measurement.
This article has two main contributions: 1) it identifies and explains major issues and challenges in measuring software quality in use in the context of the ISO SQuaRE series and related software quality models and highlights open research areas; and 2) it sheds light on a research direction that can be used to predict software quality in use.
In short, the quality-in-use measurement issues are related to the complexity of the current standard models and the limitations and incompleteness of the customized software quality models.
A sentiment analysis of software reviews is proposed to deal with these issues.
Static type errors are a common stumbling block for newcomers to typed functional languages.
We present a dynamic approach to explaining type errors by generating counterexample witness inputs that illustrate how an ill-typed program goes wrong.
First, given an ill-typed function, we symbolically execute the body to synthesize witness values that make the program go wrong.
We prove that our procedure synthesizes general witnesses in that if a witness is found, then for all inhabited input types, there exist values that can make the function go wrong.
Second, we show how to extend this procedure to produce a reduction graph that can be used to interactively visualize and debug witness executions.
Third, we evaluate the coverage of our approach on two data sets comprising over 4,500 ill-typed student programs.
Our technique is able to generate witnesses for around 85% of the programs, our reduction graph yields small counterexamples for over 80% of the witnesses, and a simple heuristic allows us to use witnesses to locate the source of type errors with around 70% accuracy.
Finally, we evaluate whether our witnesses help students understand and fix type errors, and find that students presented with our witnesses show a greater understanding of type errors than those presented with a standard error message.
While large-scale knowledge graphs provide vast amounts of structured facts about entities, a short textual description can often be useful to succinctly characterize an entity and its type.
Unfortunately, many knowledge graph entities lack such textual descriptions.
In this paper, we introduce a dynamic memory-based network that generates a short open vocabulary description of an entity by jointly leveraging induced fact embeddings as well as the dynamic context of the generated sequence of words.
We demonstrate the ability of our architecture to discern relevant information for more accurate generation of type description by pitting the system against several strong baselines.
We consider the problem of extracting entropy by sparse transformations, namely functions with a small number of overall input-output dependencies.
In contrast to previous works, we seek extractors for essentially all the entropy without any assumption on the underlying distribution beyond a min-entropy requirement.
We give two simple constructions of sparse extractor families, which are collections of sparse functions such that for any distribution X on inputs of sufficiently high min-entropy, the output of most functions from the collection on a random input chosen from X is statistically close to uniform.
For strong extractor families (i.e., functions in the family do not take additional randomness) we give upper and lower bounds on the sparsity that are tight up to a constant factor for a wide range of min-entropies.
We then prove that for some min-entropies weak extractor families can achieve better sparsity.
We show how this construction can be used towards more efficient parallel transformation of (non-uniform) one-way functions into pseudorandom generators.
More generally, sparse extractor families can be used instead of pairwise independence in various randomized or nonuniform settings where preserving locality (i.e., parallelism) is of interest.
One of the most interesting features of Bayesian optimization for direct policy search is that it can leverage priors (e.g., from simulation or from previous tasks) to accelerate learning on a robot.
In this paper, we are interested in situations for which several priors exist but we do not know in advance which one fits best the current situation.
We tackle this problem by introducing a novel acquisition function, called Most Likely Expected Improvement (MLEI), that combines the likelihood of the priors and the expected improvement.
We evaluate this new acquisition function on a transfer learning task for a 5-DOF planar arm and on a possibly damaged, 6-legged robot that has to learn to walk on flat ground and on stairs, with priors corresponding to different stairs and different kinds of damages.
Our results show that MLEI effectively identifies and exploits the priors, even when there is no obvious match between the current situations and the priors.
This paper provides a general result on controlling local Rademacher complexities, which captures in an elegant form to relate the complexities with constraint on the expected norm to the corresponding ones with constraint on the empirical norm.
This result is convenient to apply in real applications and could yield refined local Rademacher complexity bounds for function classes satisfying general entropy conditions.
We demonstrate the power of our complexity bounds by applying them to derive effective generalization error bounds.
While machine learning approaches to image restoration offer great promise, current methods risk training models fixated on performing well only for image corruption of a particular level of difficulty---such as a certain level of noise or blur.
First, we examine the weakness of conventional "fixated" models and demonstrate that training general models to handle arbitrary levels of corruption is indeed non-trivial.
Then, we propose an on-demand learning algorithm for training image restoration models with deep convolutional neural networks.
The main idea is to exploit a feedback mechanism to self-generate training instances where they are needed most, thereby learning models that can generalize across difficulty levels.
On four restoration tasks---image inpainting, pixel interpolation, image deblurring, and image denoising---and three diverse datasets, our approach consistently outperforms both the status quo training procedure and curriculum learning alternatives.
One important factor determining the computational complexity of evaluating a probabilistic network is the cardinality of the state spaces of the nodes.
By varying the granularity of the state spaces, one can trade off accuracy in the result for computational efficiency.
We present an anytime procedure for approximate evaluation of probabilistic networks based on this idea.
On application to some simple networks, the procedure exhibits a smooth improvement in approximation quality as computation time increases.
This suggests that state-space abstraction is one more useful control parameter for designing real-time probabilistic reasoners.
In this paper we present the RuSentRel corpus including analytical texts in the sphere of international relations.
For each document we annotated sentiments from the author to mentioned named entities, and sentiments of relations between mentioned entities.
In the current experiments, we considered the problem of extracting sentiment relations between entities for the whole documents as a three-class machine learning task.
We experimented with conventional machine-learning methods (Naive Bayes, SVM, Random Forest).
This paper explores the idea that the universe is a virtual reality created by information processing, and relates this strange idea to the findings of modern physics about the physical world.
The virtual reality concept is familiar to us from online worlds, but our world as a virtual reality is usually a subject for science fiction rather than science.
Yet logically the world could be an information simulation running on a multi-dimensional space-time screen.
Indeed, if the essence of the universe is information, matter, charge, energy and movement could be aspects of information, and the many conservation laws could be a single law of information conservation.
If the universe were a virtual reality, its creation at the big bang would no longer be paradoxical, as every virtual system must be booted up.
It is suggested that whether the world is an objective reality or a virtual reality is a matter for science to resolve.
Modern information science can suggest how core physical properties like space, time, light, matter and movement could derive from information processing.
Such an approach could reconcile relativity and quantum theories, with the former being how information processing creates space-time, and the latter how it creates energy and matter.
A central problem to understanding intelligence is the concept of generalisation.
This allows previously learnt structure to be exploited to solve tasks in novel situations differing in their particularities.
We take inspiration from neuroscience, specifically the hippocampal-entorhinal system known to be important for generalisation.
We propose that to generalise structural knowledge, the representations of the structure of the world, i.e. how entities in the world relate to each other, need to be separated from representations of the entities themselves.
We show, under these principles, artificial neural networks embedded with hierarchy and fast Hebbian memory, can learn the statistics of memories and generalise structural knowledge.
Spatial neuronal representations mirroring those found in the brain emerge, suggesting spatial cognition is an instance of more general organising principles.
We further unify many entorhinal cell types as basis functions for constructing transition graphs, and show these representations effectively utilise memories.
We experimentally support model assumptions, showing a preserved relationship between entorhinal grid and hippocampal place cells across environments.
A mobile robot deployed for remote inspection, surveying or rescue missions can fail due to various possibilities and can be hardware or software related.
These failure scenarios necessitate manual recovery (self-rescue) of the robot from the environment.
It would bring unforeseen challenges to recover the mobile robot if the environment where it was deployed had hazardous or harmful conditions (e.g. ionizing radiations).
While it is not fully possible to predict all the failures in the robot, failures can be reduced by employing certain design/usage considerations.
Few example failure cases based on real experiences are presented in this short article along with generic suggestions on overcoming the illustrated failure situations.
This article presents the novel breakthrough general purpose algorithm for large scale optimization problems.
The novel algorithm is capable of achieving breakthrough speeds for very large-scale optimization on general purpose laptops and embedded systems.
Application of the algorithm to the Griewank function was possible in up to 1 billion decision variables in double precision took only 64485 seconds (~18 hours) to solve, while consuming 7,630 MB (7.6 GB) or RAM on a single threaded laptop CPU.
It shows that the algorithm is computationally and memory (space) linearly efficient, and can find the optimal or near-optimal solution in a fraction of the time and memory that many conventional algorithms require.
It is envisaged that this will open up new possibilities of real-time large-scale problems on personal laptops and embedded systems.
High level understanding of sequential visual input is important for safe and stable autonomy, especially in localization and object detection.
While traditional object classification and tracking approaches are specifically designed to handle variations in rotation and scale, current state-of-the-art approaches based on deep learning achieve better performance.
This paper focuses on developing a spatiotemporal model to handle videos containing moving objects with rotation and scale changes.
Built on models that combine Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) to classify sequential data, this work investigates the effectiveness of incorporating attention modules in the CNN stage for video classification.
The superiority of the proposed spatiotemporal model is demonstrated on the Moving MNIST dataset augmented with rotation and scaling.
We propose a mechanism that incorporates network coding into TCP with only minor changes to the protocol stack, thereby allowing incremental deployment.
In our scheme, the source transmits random linear combinations of packets currently in the congestion window.
At the heart of our scheme is a new interpretation of ACKs - the sink acknowledges every degree of freedom (i.e., a linear combination that reveals one unit of new information) even if it does not reveal an original packet immediately.
Such ACKs enable a TCP-like sliding-window approach to network coding.
Our scheme has the nice property that packet losses are essentially masked from the congestion control algorithm.
Our algorithm therefore reacts to packet drops in a smooth manner, resulting in a novel and effective approach for congestion control over networks involving lossy links such as wireless links.
Our experiments show that our algorithm achieves higher throughput compared to TCP in the presence of lossy wireless links.
We also establish the soundness and fairness properties of our algorithm.
We report the results of a project to control the use of end user computing tools for business critical applications in a banking environment.
Several workstreams were employed in order to bring about a cultural change within the bank towards the use of spreadsheets and other end-user tools, covering policy development, awareness and skills training, inventory monitoring, user licensing, key risk metrics and mitigation approaches.
The outcomes of these activities are discussed, and conclusions are drawn as to the need for appropriate organisational models to guide the use of these tools.
In this work we have proposed a geometric model that is employed to devise a scheme for identifying the hotspots and zones in a chip.
These spots or zone need to be guarded thermally to ensure performance and reliability of the chip.
The model namely continuous unit sphere model has been presented taking into account that the 3D region of the chip is uniform, thereby reflecting on the possible locations of heat sources and the target observation points.
The experimental results for the - continuous domain establish that a region which does not contain any heat sources may become hotter than the regions containing the thermal sources.
Thus a hotspot may appear away from the active sources, and placing heat sinks on the active thermal sources alone may not suffice to tackle thermal imbalance.
Power management techniques aid in obtaining a uniform power profile throughout the chip, but we propose an algorithm using minimum bipartite matching where we try to move the sources minimally (with minimum perturbation in the chip floor plan) near cooler points (blocks) to obtain a uniform power profile due to diffusion of heat from hotter point to cooler ones.
One of the key differences between the learning mechanism of humans and Artificial Neural Networks (ANNs) is the ability of humans to learn one task at a time.
ANNs, on the other hand, can only learn multiple tasks simultaneously.
Any attempts at learning new tasks incrementally cause them to completely forget about previous tasks.
This lack of ability to learn incrementally, called Catastrophic Forgetting, is considered a major hurdle in building a true AI system.
In this paper, our goal is to isolate the truly effective existing ideas for incremental learning from those that only work under certain conditions.
To this end, we first thoroughly analyze the current state of the art (iCaRL) method for incremental learning and demonstrate that the good performance of the system is not because of the reasons presented in the existing literature.
We conclude that the success of iCaRL is primarily due to knowledge distillation and recognize a key limitation of knowledge distillation, i.e, it often leads to bias in classifiers.
Finally, we propose a dynamic threshold moving algorithm that is able to successfully remove this bias.
We demonstrate the effectiveness of our algorithm on CIFAR100 and MNIST datasets showing near-optimal results.
Our implementation is available at https://github.com/Khurramjaved96/incremental-learning.
Policy gradient is an efficient technique for improving a policy in a reinforcement learning setting.
However, vanilla online variants are on-policy only and not able to take advantage of off-policy data.
In this paper we describe a new technique that combines policy gradient with off-policy Q-learning, drawing experience from a replay buffer.
This is motivated by making a connection between the fixed points of the regularized policy gradient algorithm and the Q-values.
This connection allows us to estimate the Q-values from the action preferences of the policy, to which we apply Q-learning updates.
We refer to the new technique as 'PGQL', for policy gradient and Q-learning.
We also establish an equivalency between action-value fitting techniques and actor-critic algorithms, showing that regularized policy gradient techniques can be interpreted as advantage function learning algorithms.
We conclude with some numerical examples that demonstrate improved data efficiency and stability of PGQL.
In particular, we tested PGQL on the full suite of Atari games and achieved performance exceeding that of both asynchronous advantage actor-critic (A3C) and Q-learning.
This work briefly surveys unconventional research in Russia from the end of the 19th until the beginning of the 21th centuries in areas related to generation and detection of a 'high-penetrating' emission of non-biological origin.
The overview is based on open scientific and journalistic materials.
The unique character of this research and its history, originating from governmental programs of the USSR, is shown.
Relations to modern studies on biological effects of weak electromagnetic emission, several areas of bioinformatics and theories of physical vacuum are discussed.
In wind farms, wake interaction leads to losses in power capture and accelerated structural degradation when compared to freestanding turbines.
One method to reduce wake losses is by misaligning the rotor with the incoming flow using its yaw actuator, thereby laterally deflecting the wake away from downstream turbines.
However, this demands an accurate and computationally tractable model of the wind farm dynamics.
This problem calls for a closed-loop solution.
This tutorial paper fills the scientific gap by demonstrating the full closed-loop controller synthesis cycle using a steady-state surrogate model.
Furthermore, a novel, computationally efficient and modular communication interface is presented that enables researchers to straight-forwardly test their control algorithms in large-eddy simulations.
High-fidelity simulations of a 9-turbine farm show a power production increase of up to 11% using the proposed closed-loop controller compared to traditional, greedy wind farm operation.
Behavior Trees (BTs) have become a popular framework for designing controllers of autonomous agents in the computer game and in the robotics industry.
One of the key advantages of BTs lies in their modularity, where independent modules can be composed to create more complex ones.
In the classical formulation of BTs, modules can be composed using one of the three operators: Sequence, Fallback, and Parallel.
The Parallel operator is rarely used despite its strong potential against other control architectures as Finite State Machines.
This is due to the fact that concurrent actions may lead to unexpected problems similar to the ones experienced in concurrent programming.
In this paper, we introduce Concurrent BTs (CBTs) as a generalization of BTs in which we introduce the notions of progress and resource usage.
We show how CBTs allow safe concurrent executions of actions and we analyze the approach from a mathematical standpoint.
To illustrate the use of CBTs, we provide a set of use cases in robotics scenarios.
This paper is concerned with the effect of overlay network topology on the performance of live streaming peer-to-peer systems.
The paper focuses on the evaluation of topologies which are aware of the delays experienced between different peers on the network.
Metrics are defined which assess the topologies in terms of delay, bandwidth usage and resilience to peer drop-out.
Several topology creation algorithms are tested and the metrics are measured in a simple simulation testbed.
This gives an assessment of the type of gains which might be expected from locality awareness in peer-to-peer networks.
We present the first parser for UCCA, a cross-linguistically applicable framework for semantic representation, which builds on extensive typological work and supports rapid annotation.
UCCA poses a challenge for existing parsing techniques, as it exhibits reentrancy (resulting in DAG structures), discontinuous structures and non-terminal nodes corresponding to complex semantic units.
To our knowledge, the conjunction of these formal properties is not supported by any existing parser.
Our transition-based parser, which uses a novel transition set and features based on bidirectional LSTMs, has value not just for UCCA parsing: its ability to handle more general graph structures can inform the development of parsers for other semantic DAG structures, and in languages that frequently use discontinuous structures.
Policy gradient methods are a widely used class of model-free reinforcement learning algorithms where a state-dependent baseline is used to reduce gradient estimator variance.
Several recent papers extend the baseline to depend on both the state and action and suggest that this significantly reduces variance and improves sample efficiency without introducing bias into the gradient estimates.
To better understand this development, we decompose the variance of the policy gradient estimator and numerically show that learned state-action-dependent baselines do not in fact reduce variance over a state-dependent baseline in commonly tested benchmark domains.
We confirm this unexpected result by reviewing the open-source code accompanying these prior papers, and show that subtle implementation decisions cause deviations from the methods presented in the papers and explain the source of the previously observed empirical gains.
Furthermore, the variance decomposition highlights areas for improvement, which we demonstrate by illustrating a simple change to the typical value function parameterization that can significantly improve performance.
Identifying the relations that exist between words (or entities) is important for various natural language processing tasks such as, relational search, noun-modifier classification and analogy detection.
A popular approach to represent the relations between a pair of words is to extract the patterns in which the words co-occur with from a corpus, and assign each word-pair a vector of pattern frequencies.
Despite the simplicity of this approach, it suffers from data sparseness, information scalability and linguistic creativity as the model is unable to handle previously unseen word pairs in a corpus.
In contrast, a compositional approach for representing relations between words overcomes these issues by using the attributes of each individual word to indirectly compose a representation for the common relations that hold between the two words.
This study aims to compare different operations for creating relation representations from word-level representations.
We investigate the performance of the compositional methods by measuring the relational similarities using several benchmark datasets for word analogy.
Moreover, we evaluate the different relation representations in a knowledge base completion task.
We present a language independent, unsupervised method for building word embeddings using morphological expansion of text.
Our model handles the problem of data sparsity and yields improved word embeddings by relying on training word embeddings on artificially generated sentences.
We evaluate our method using small sized training sets on eleven test sets for the word similarity task across seven languages.
Further, for English, we evaluated the impacts of our approach using a large training set on three standard test sets.
Our method improved results across all languages.
Efforts are underway at UT Austin to build autonomous robot systems that address the challenges of long-term deployments in office environments and of the more prescribed domestic service tasks of the RoboCup@Home competition.
We discuss the contrasts and synergies of these efforts, highlighting how our work to build a RoboCup@Home Domestic Standard Platform League entry led us to identify an integrated software architecture that could support both projects.
Further, naturalistic deployments of our office robot platform as part of the Building-Wide Intelligence project have led us to identify and research new problems in a traditional laboratory setting.
Dou Shou Qi is a game in which two players control a number of pieces, each of them aiming to move one of their pieces onto a given square.
We implemented an engine for analyzing the game.
Moreover, we created a series of endgame tablebases containing all configurations with up to four pieces.
These tablebases are the first steps towards theoretically solving the game.
Finally, we constructed decision trees based on the endgame tablebases.
In this note we report on some interesting patterns.
As deep neural networks (DNNs) have been integrated into critical systems, several methods to attack these systems have been developed.
These adversarial attacks make imperceptible modifications to an image that fool DNN classifiers.
We present an adaptive JPEG encoder which defends against many of these attacks.
Experimentally, we show that our method produces images with high visual quality while greatly reducing the potency of state-of-the-art attacks.
Our algorithm requires only a modest increase in encoding time, produces a compressed image which can be decompressed by an off-the-shelf JPEG decoder, and classified by an unmodified classifier
A conditional Generative Adversarial Network allows for generating samples conditioned on certain external information.
Being able to recover latent and conditional vectors from a condi- tional GAN can be potentially valuable in various applications, ranging from image manipulation for entertaining purposes to diagnosis of the neural networks for security purposes.
In this work, we show that it is possible to recover both latent and conditional vectors from generated images given the generator of a conditional generative adversarial network.
Such a recovery is not trivial due to the often multi-layered non-linearity of deep neural networks.
Furthermore, the effect of such recovery applied on real natural images are investigated.
We discovered that there exists a gap between the recovery performance on generated and real images, which we believe comes from the difference between generated data distribution and real data distribution.
Experiments are conducted to evaluate the recovered conditional vectors and the reconstructed images from these recovered vectors quantitatively and qualitatively, showing promising results.
Query-expansion via pseudo-relevance feedback is a popular method of overcoming the problem of vocabulary mismatch and of increasing average retrieval effectiveness.
In this paper, we develop a new method that estimates a query topic model from a set of pseudo-relevant documents using a new language modelling framework.
We assume that documents are generated via a mixture of multivariate Polya distributions, and we show that by identifying the topical terms in each document, we can appropriately select terms that are likely to belong to the query topic model.
The results of experiments on several TREC collections show that the new approach compares favourably to current state-of-the-art expansion methods.
Analyzing job hopping behavior is important for the understanding of job preference and career progression of working individuals.
When analyzed at the workforce population level, job hop analysis helps to gain insights of talent flow and organization competition.
Traditionally, surveys are conducted on job seekers and employers to study job behavior.
While surveys are good at getting direct user input to specially designed questions, they are often not scalable and timely enough to cope with fast-changing job landscape.
In this paper, we present a data science approach to analyze job hops performed by about 490,000 working professionals located in a city using their publicly shared profiles.
We develop several metrics to measure how much work experience is needed to take up a job and how recent/established the job is, and then examine how these metrics correlate with the propensity of hopping.
We also study how job hop behavior is related to job promotion/demotion.
Finally, we perform network analyses at the job and organization levels in order to derive insights on talent flow as well as job and organizational competitiveness.
This paper presents iterative Sequential Action Control (iSAC), a receding horizon approach for control of nonlinear systems.
The iSAC method has a closed-form open-loop solution, which is iteratively updated between time steps by introducing constant control values applied for short duration.
Application of a contractive constraint on the cost is shown to lead to closed-loop asymptotic stability under mild assumptions.
The effect of asymptotically decaying disturbances on system trajectories is also examined.
To demonstrate the applicability of iSAC to a variety of systems and conditions, we employ five different systems, including a 13-dimensional quaternion-based quadrotor.
Each system is tested in different scenarios, ranging from feasible and infeasible trajectory tracking, to setpoint stabilization, with or without the presence of external disturbances.
Finally, limitations of this work are discussed.
We consider a compressed sensing problem in which both the measurement and the sparsifying systems are assumed to be frames (not necessarily tight) of the underlying Hilbert space of signals, which may be finite or infinite dimensional.
The main result gives explicit bounds on the number of measurements in order to achieve stable recovery, which depends on the mutual coherence of the two systems.
As a simple corollary, we prove the efficiency of nonuniform sampling strategies in cases when the two systems are not incoherent, but only asymptotically incoherent, as with the recovery of wavelet coefficients from Fourier samples.
This general framework finds applications to inverse problems in partial differential equations, where the standard assumptions of compressed sensing are often not satisfied.
Several examples are discussed, with a special focus on electrical impedance tomography.
Social health and emotional wellness is a matter of concern in today's urban world.
Being the part of a metropolis has an effect on mental health through the influence of increased stressors and factors such as overcrowded and polluted environment, high levels of violence, and reduced social support.
It is important to realize that only healthy citizens can constitute together a smart city.
In this paper, we present a fuzzy-based approach for analyzing the well being of a person.
We track the general day to day activities of a person and analyze its performance.
To do so, we divide the factors affecting the wellness of a person into three components which are the physical, productive and social.
Using these parameters, we output a coefficient for the overall well being of a person.
The visual observation and tracking of cells and other micrometer-sized objects has many different biomedical applications.
The automation of those tasks based on computer methods helps in the evaluation of such measurements.
In this work, we present a general purpose algorithm that excels at evaluating deterministic behavior of micrometer-sized objects.
Our concrete application is the tracking of fast moving objects over large distances along deterministic trajectories in a microscopic video.
Thereby, we are able to determine characteristic properties of the objects.
For this purpose, we use a set of basic algorithms, including blob recognition, feature-based shape recognition and a graph algorithm, and combined them in a novel way.
An evaluation of the algorithms performance shows a high accuracy in the recognition of objects as well as of complete trajectories.
Moreover, a direct comparison to a similar algorithm shows superior recognition rates.
In iterative supervised learning algorithms it is common to reach a point in the search where no further induction seems to be possible with the available data.
If the search is continued beyond this point, the risk of overfitting increases significantly.
Following the recent developments in inductive semantic stochastic methods, this paper studies the feasibility of using information gathered from the semantic neighborhood to decide when to stop the search.
Two semantic stopping criteria are proposed and experimentally assessed in Geometric Semantic Genetic Programming (GSGP) and in the Semantic Learning Machine (SLM) algorithm (the equivalent algorithm for neural networks).
The experiments are performed on real-world high-dimensional regression datasets.
The results show that the proposed semantic stopping criteria are able to detect stopping points that result in a competitive generalization for both GSGP and SLM.
This approach also yields computationally efficient algorithms as it allows the evolution of neural networks in less than 3 seconds on average, and of GP trees in at most 10 seconds.
The usage of the proposed semantic stopping criteria in conjunction with the computation of optimal mutation/learning steps also results in small trees and neural networks.
Convolutional rectifier networks, i.e. convolutional neural networks with rectified linear activation and max or average pooling, are the cornerstone of modern deep learning.
However, despite their wide use and success, our theoretical understanding of the expressive properties that drive these networks is partial at best.
On the other hand, we have a much firmer grasp of these issues in the world of arithmetic circuits.
Specifically, it is known that convolutional arithmetic circuits possess the property of "complete depth efficiency", meaning that besides a negligible set, all functions that can be implemented by a deep network of polynomial size, require exponential size in order to be implemented (or even approximated) by a shallow network.
In this paper we describe a construction based on generalized tensor decompositions, that transforms convolutional arithmetic circuits into convolutional rectifier networks.
We then use mathematical tools available from the world of arithmetic circuits to prove new results.
First, we show that convolutional rectifier networks are universal with max pooling but not with average pooling.
Second, and more importantly, we show that depth efficiency is weaker with convolutional rectifier networks than it is with convolutional arithmetic circuits.
This leads us to believe that developing effective methods for training convolutional arithmetic circuits, thereby fulfilling their expressive potential, may give rise to a deep learning architecture that is provably superior to convolutional rectifier networks but has so far been overlooked by practitioners.
An adversarial example is an example that has been adjusted to produce the wrong label when presented to a system at test time.
If adversarial examples existed that could fool a detector, they could be used to (for example) wreak havoc on roads populated with smart vehicles.
Recently, we described our difficulties creating physical adversarial stop signs that fool a detector.
More recently, Evtimov et al. produced a physical adversarial stop sign that fools a proxy model of a detector.
In this paper, we show that these physical adversarial stop signs do not fool two standard detectors (YOLO and Faster RCNN) in standard configuration.
Evtimov et al.'s construction relies on a crop of the image to the stop sign; this crop is then resized and presented to a classifier.
We argue that the cropping and resizing procedure largely eliminates the effects of rescaling and of view angle.
Whether an adversarial attack is robust under rescaling and change of view direction remains moot.
We argue that attacking a classifier is very different from attacking a detector, and that the structure of detectors - which must search for their own bounding box, and which cannot estimate that box very accurately - likely makes it difficult to make adversarial patterns.
Finally, an adversarial pattern on a physical object that could fool a detector would have to be adversarial in the face of a wide family of parametric distortions (scale; view angle; box shift inside the detector; illumination; and so on).
Such a pattern would be of great theoretical and practical interest.
There is currently no evidence that such patterns exist.
The residual neural network (ResNet) is a popular deep network architecture which has the ability to obtain high-accuracy results on several image processing problems.
In order to analyze the behavior and structure of ResNet, recent work has been on establishing connections between ResNets and continuous-time optimal control problems.
In this work, we show that the post-activation ResNet is related to an optimal control problem with differential inclusions, and provide continuous-time stability results for the differential inclusion associated with ResNet.
Motivated by the stability conditions, we show that alterations of either the architecture or the optimization problem can generate variants of ResNet which improve the theoretical stability bounds.
In addition, we establish stability bounds for the full (discrete) network associated with two variants of ResNet, in particular, bounds on the growth of the features and a measure of the sensitivity of the features with respect to perturbations.
These results also help to show the relationship between the depth, regularization, and stability of the feature space.
Computational experiments on the proposed variants show that the accuracy of ResNet is preserved and that the accuracy seems to be monotone with respect to the depth and various corruptions.
Acute kidney injury (AKI) is a common and serious complication after a surgery which is associated with morbidity and mortality.
The majority of existing perioperative AKI risk score prediction models are limited in their generalizability and do not fully utilize the physiological intraoperative time-series data.
Thus, there is a need for intelligent, accurate, and robust systems, able to leverage information from large-scale data to predict patient's risk of developing postoperative AKI.
A retrospective single-center cohort of 2,911 adult patients who underwent surgery at the University of Florida Health has been used for this study.
We used machine learning and statistical analysis techniques to develop perioperative models to predict the risk of AKI (risk during the first 3 days, 7 days, and until the discharge day) before and after the surgery.
In particular, we examined the improvement in risk prediction by incorporating three intraoperative physiologic time series data, i.e., mean arterial blood pressure, minimum alveolar concentration, and heart rate.
For an individual patient, the preoperative model produces a probabilistic AKI risk score, which will be enriched by integrating intraoperative statistical features through a machine learning stacking approach inside a random forest classifier.
We compared the performance of our model based on the area under the receiver operating characteristics curve (AUROC), accuracy and net reclassification improvement (NRI).
The predictive performance of the proposed model is better than the preoperative data only model.
For AKI-7day outcome: The AUC was 0.86 (accuracy was 0.78) in the proposed model, while the preoperative AUC was 0.84 (accuracy 0.76).
Furthermore, with the integration of intraoperative features, we were able to classify patients who were misclassified in the preoperative model.
Smart cities are an actual trend being pursued by research that, fundamentally, tries to improve city's management on behalf of a better human quality of live.
This paper proposes a new autonomic complementary approach for smart cities management.
It is argued that smart city management systems with autonomic characteristics will improve and facilitate management functionalities in general.
A framework is also presented as use case considering specific application scenarios like smart-health, smart-grid, smart-environment and smart-streets.
Artificial intelligence and machine learning have been major research interests in computer science for the better part of the last few decades.
However, all too recently, both AI and ML have rapidly grown to be media frenzies, pressuring companies and researchers to claim they use these technologies.
As ML continues to percolate into daily life, we, as computer scientists and machine learning researchers, are responsible for ensuring we clearly convey the extent of our work and the humanity of our models.
Regularizing ML for mass adoption requires a rigorous standard for model interpretability, a deep consideration for human bias in data, and a transparent understanding of a model's societal effects.
Solar forecasting accuracy is affected by weather conditions, and weather awareness forecasting models are expected to improve the performance.
However, it may not be available and reliable to classify different forecasting tasks by using only meteorological weather categorization.
In this paper, an unsupervised clustering-based (UC-based) solar forecasting methodology is developed for short-term (1-hour-ahead) global horizontal irradiance (GHI) forecasting.
This methodology consists of three parts: GHI time series unsupervised clustering, pattern recognition, and UC-based forecasting.
The daily GHI time series is first clustered by an Optimized Cross-validated ClUsteRing (OCCUR) method, which determines the optimal number of clusters and best clustering results.
Then, support vector machine pattern recognition (SVM-PR) is adopted to recognize the category of a certain day using the first few hours' data in the forecasting stage.
GHI forecasts are generated by the most suitable models in different clusters, which are built by a two-layer Machine learning based Multi-Model (M3) forecasting framework.
The developed UC-based methodology is validated by using 1-year of data with six solar features.
Numerical results show that (i) UC-based models outperform non-UC (all-in-one) models with the same M3 architecture by approximately 20%; (ii) M3-based models also outperform the single-algorithm machine learning (SAML) models by approximately 20%.
We focus on adversarial patrolling games on arbitrary graphs, where the Defender can control a mobile resource, the targets are alarmed by an alarm system, and the Attacker can observe the actions of the mobile resource of the Defender and perform different attacks exploiting multiple resources.
This scenario can be modeled as a zero-sum extensive-form game in which each player can play multiple times.
The game tree is exponentially large both in the size of the graph and in the number of attacking resources.
We show that when the number of the Attacker's resources is free, the problem of computing the equilibrium path is NP-hard, while when the number of resources is fixed, the equilibrium path can be computed in poly-time.
We provide a dynamic-programming algorithm that, given the number of the Attacker's resources, computes the equilibrium path requiring poly-time in the size of the graph and exponential time in the number of the resources.
Furthermore, since in real-world scenarios it is implausible that the Defender knows the number of attacking resources, we study the robustness of the Defender's strategy when she makes a wrong guess about that number.
We show that even the error of just a single resource can lead to an arbitrary inefficiency, when the inefficiency is defined as the ratio of the Defender's utilities obtained with a wrong guess and a correct guess.
However, a more suitable definition of inefficiency is given by the difference of the Defender's utilities: this way, we observe that the higher the error in the estimation, the higher the loss for the Defender.
Then, we investigate the performance of online algorithms when no information about the Attacker's resources is available.
Finally, we resort to randomized online algorithms showing that we can obtain a competitive factor that is twice better than the one that can be achieved by any deterministic online algorithm.
Robotic systems are complex and critical: they are inherently hybrid, combining both hardware and software; they typically exhibit both cyber-physical attributes and autonomous capabilities; and are required to be at least safe and often ethical.
While for many engineered systems testing, either through real deployment or via simulation, is deemed sufficient the uniquely challenging elements of robotic systems, together with the crucial dependence on sophisticated software control and decision-making, requires a stronger form of verification.
The increasing deployment of robotic systems in safety-critical scenarios exacerbates this still further and leads us towards the use of formal methods to ensure the correctness of, and provide sufficient evidence for the certification of, robotic systems.
There have been many approaches that have used some variety of formal specification or formal verification in autonomous robotics, but there is no resource that collates this activity in to one place.
This paper systematically surveys the state-of-the art in specification formalisms and tools for verifying robotic systems.
Specifically, it describes the challenges arising from autonomy and software architectures, avoiding low-level hardware control and is subsequently identifies approaches for the specification and verification of robotic systems, while avoiding more general approaches.
This paper presents a multi-contact approach to generalized humanoid fall mitigation planning that unifies inertial shaping, protective stepping, and hand contact strategies.
The planner optimizes both the contact sequence and the robot state trajectories.
A high-level tree search is conducted to iteratively grow a contact transition tree.
At each edge of the tree, trajectory optimization is used to calculate robot stabilization trajectories that produce the desired contact transition while minimizing kinetic energy.
Also, at each node of the tree, the optimizer attempts to find a self-motion (inertial shaping movement) to eliminate kinetic energy.
This paper also presents an efficient and effective method to generate initial seeds to facilitate trajectory optimization.
Experiments demonstrate show that our proposed algorithm can generate complex stabilization strategies for a simulated robot under varying initial pushes and environment shapes.
Feedback mechanism based algorithms are frequently used to solve network optimization problems.
These schemes involve users and network exchanging information (e.g. requests for bandwidth allocation and pricing) to achieve convergence towards an optimal solution.
However, in the implementation, these algorithms do not guarantee that messages will be delivered to the destination when network congestion occurs.
This in turn often results in packet drops, which may cause information loss, and this condition may lead to algorithm failing to converge.
To prevent this failure, we propose least square (LS) estimation algorithm to recover the missing information when packets are dropped from the network.
The simulation results involving several scenarios demonstrate that LS estimation can provide the convergence for feedback mechanism based algorithm.
We propose a method to perform audio event detection under the common constraint that only limited training data are available.
In training a deep learning system to perform audio event detection, two practical problems arise.
Firstly, most datasets are "weakly labelled" having only a list of events present in each recording without any temporal information for training.
Secondly, deep neural networks need a very large amount of labelled training data to achieve good quality performance, yet in practice it is difficult to collect enough samples for most classes of interest.
In this paper, we propose a data-efficient training of a stacked convolutional and recurrent neural network.
This neural network is trained in a multi instance learning setting for which we introduce a new loss function that leads to improved training compared to the usual approaches for weakly supervised learning.
We successfully test our approach on two low-resource datasets that lack temporal labels.
Objects may appear at arbitrary scales in perspective images of a scene, posing a challenge for recognition systems that process images at a fixed resolution.
We propose a depth-aware gating module that adaptively selects the pooling field size in a convolutional network architecture according to the object scale (inversely proportional to the depth) so that small details are preserved for distant objects while larger receptive fields are used for those nearby.
The depth gating signal is provided by stereo disparity or estimated directly from monocular input.
We integrate this depth-aware gating into a recurrent convolutional neural network to perform semantic segmentation.
Our recurrent module iteratively refines the segmentation results, leveraging the depth and semantic predictions from the previous iterations.
Through extensive experiments on four popular large-scale RGB-D datasets, we demonstrate this approach achieves competitive semantic segmentation performance with a model which is substantially more compact.
We carry out extensive analysis of this architecture including variants that operate on monocular RGB but use depth as side-information during training, unsupervised gating as a generic attentional mechanism, and multi-resolution gating.
We find that gated pooling for joint semantic segmentation and depth yields state-of-the-art results for quantitative monocular depth estimation.
State-of-the-art branch and bound algorithms for mixed integer programming make use of special methods for making branching decisions.
Strategies that have gained prominence include modern variants of so-called strong branching (Applegate, et al.,1995) and reliability branching (Achterberg, Koch and Martin, 2005; Hendel, 2015), which select variables for branching by solving associated linear programs and exploit pseudo-costs (Benichou et al., 1971).
We suggest new branching criteria and propose alternative branching approaches called narrow gauge and analytical branching.
The perspective underlying our approaches is to focus on prioritization of child nodes to examine fewer candidate variables at the current node of the B&B tree, balanced with procedures to extrapolate the implications of choosing these candidates by generating a small-depth look-ahead tree.
Our procedures can also be used in rules to select among open tree nodes (those whose child nodes have not yet been generated).
We incorporate pre- and post-winnowing procedures to progressively isolate preferred branching candidates, and employ derivative (created) variables whose branches are able to explore the solution space more deeply.
We present a proof procedure for univariate real polynomial problems in Isabelle/HOL.
The core mathematics of our procedure is based on univariate cylindrical algebraic decomposition.
We follow the approach of untrusted certificates, separating solving from verifying: efficient external tools perform expensive real algebraic computations, producing evidence that is formally checked within Isabelle's logic.
This allows us to exploit highly-tuned computer algebra systems like Mathematica to guide our procedure without impacting the correctness of its results.
We present experiments demonstrating the efficacy of this approach, in many cases yielding orders of magnitude improvements over previous methods.
In this report, we present our findings from benchmarking experiments for information extraction on historical handwritten marriage records Esposalles from IEHHR - ICDAR 2017 robust reading competition.
The information extraction is modeled as semantic labeling of the sequence across 2 set of labels.
This can be achieved by sequentially or jointly applying handwritten text recognition (HTR) and named entity recognition (NER).
We deploy a pipeline approach where first we use state-of-the-art HTR and use its output as input for NER.
We show that given low resource setup and simple structure of the records, high performance of HTR ensures overall high performance.
We explore the various configurations of conditional random fields and neural networks to benchmark NER on given certain noisy input.
The best model on 10-fold cross-validation as well as blind test data uses n-gram features with bidirectional long short-term memory.
This paper structures a novel vision for OLAP by fundamentally redefining several of the pillars on which OLAP has been based for the last 20 years.
We redefine OLAP queries, in order to move to higher degrees of abstraction from roll-up's and drill-down's, and we propose a set of novel intentional OLAP operators, namely, describe, assess, explain, predict, and suggest, which express the user's need for results.
We fundamentally redefine what a query answer is, and escape from the constraint that the answer is a set of tuples; on the contrary, we complement the set of tuples with models (typically, but not exclusively, results of data mining algorithms over the involved data) that concisely represent the internal structure or correlations of the data.
Due to the diverse nature of the involved models, we come up (for the first time ever, to the best of our knowledge) with a unifying framework for them, that places its pillars on the extension of each data cell of a cube with information about the models that pertain to it -- practically converting the small parts that build up the models to data that annotate each cell.
We exploit this data-to-model mapping to provide highlights of the data, by isolating data and models that maximize the delivery of new information to the user.
We introduce a novel method for assessing the surprise that a new query result brings to the user, with respect to the information contained in previous results the user has seen via a new interestingness measure.
The individual parts of our proposal are integrated in a new data model for OLAP, which we call the Intentional Analytics Model.
We complement our contribution with a list of significant open problems for the community to address.
Recently, there have been increasing demands to construct compact deep architectures to remove unnecessary redundancy and to improve the inference speed.
While many recent works focus on reducing the redundancy by eliminating unneeded weight parameters, it is not possible to apply a single deep architecture for multiple devices with different resources.
When a new device or circumstantial condition requires a new deep architecture, it is necessary to construct and train a new network from scratch.
In this work, we propose a novel deep learning framework, called a nested sparse network, which exploits an n-in-1-type nested structure in a neural network.
A nested sparse network consists of multiple levels of networks with a different sparsity ratio associated with each level, and higher level networks share parameters with lower level networks to enable stable nested learning.
The proposed framework realizes a resource-aware versatile architecture as the same network can meet diverse resource requirements.
Moreover, the proposed nested network can learn different forms of knowledge in its internal networks at different levels, enabling multiple tasks using a single network, such as coarse-to-fine hierarchical classification.
In order to train the proposed nested sparse network, we propose efficient weight connection learning and channel and layer scheduling strategies.
We evaluate our network in multiple tasks, including adaptive deep compression, knowledge distillation, and learning class hierarchy, and demonstrate that nested sparse networks perform competitively, but more efficiently, compared to existing methods.
We present a quasi-experiment to investigate whether, and to what extent, sleep deprivation impacts the performance of novice software developers using the agile practice of test-first development (TFD).
We recruited 45 undergraduates and asked them to tackle a programming task.
Among the participants, 23 agreed to stay awake the night before carrying out the task, while 22 slept usually.
We analyzed the quality (i.e., the functional correctness) of the implementations delivered by the participants in both groups, their engagement in writing source code (i.e., the amount of activities performed in the IDE while tackling the programming task) and ability to apply TFD (i.e., the extent to which a participant can use this practice).
By comparing the two groups of participants, we found that a single night of sleep deprivation leads to a reduction of 50% in the quality of the implementations.
There is important evidence that the developers' engagement and their prowess to apply TFD are negatively impacted.
Our results also show that sleep-deprived developers make more fixes to syntactic mistakes in the source code.
We conclude that sleep deprivation has possibly disruptive effects on software development activities.
The results open opportunities for improving developers' performance by integrating the study of sleep with other psycho-physiological factors in which the software engineering research community has recently taken an interest in.
High-order parametric models that include terms for feature interactions are applied to various data mining tasks, where ground truth depends on interactions of features.
However, with sparse data, the high- dimensional parameters for feature interactions often face three issues: expensive computation, difficulty in parameter estimation and lack of structure.
Previous work has proposed approaches which can partially re- solve the three issues.
In particular, models with factorized parameters (e.g.Factorization Machines) and sparse learning algorithms (e.g.FTRL-Proximal) can tackle the first two issues but fail to address the third.
Regarding to unstructured parameters, constraints or complicated regularization terms are applied such that hierarchical structures can be imposed.
However, these methods make the optimization problem more challenging.
In this work, we propose Strongly Hierarchical Factorization Machines and ANOVA kernel regression where all the three issues can be addressed without making the optimization problem more difficult.
Experimental results show the proposed models significantly outperform the state-of-the-art in two data mining tasks: cold-start user response time prediction and stock volatility prediction.
Information Technology (IT) significantly impacts the environment throughout its life cycle.
Most enterprises have not paid enough attention to this until recently.
IT's environmental impact can be significantly reduced by behavioral changes, as well as technology changes.
Given the relative energy and materials inefficiency of most IT infrastructures today, many green IT initiatives can be easily tackled at no incremental cost.
The Green Grid - a non-profit trade organization of IT professionals is such an initiative, formed to initiate the issues of power and cooling in data centers, scattered world-wide.
The Green Grid seeks to define best practices for optimizing the efficient consumption of power at IT equipment and facility levels, as well as the manner in which cooling is delivered at these levels hence, providing promising attitude in bringing down the environmental hazards, as well as proceeding to the new era of green computing.
In this paper we review the various analytical aspects of The Green Grid upon the data centers and found green facts.
Sparse Subspace Clustering (SSC) has been used extensively for subspace identification tasks due to its theoretical guarantees and relative ease of implementation.
However SSC has quadratic computation and memory requirements with respect to the number of input data points.
This burden has prohibited SSCs use for all but the smallest datasets.
To overcome this we propose a new method, k-SSC, that screens out a large number of data points to both reduce SSC to linear memory and computational requirements.
We provide theoretical analysis for the bounds of success for k-SSC.
Our experiments show that k-SSC exceeds theoretical expectations and outperforms existing SSC approximations by maintaining the classification performance of SSC.
Furthermore in the spirit of reproducible research we have publicly released the source code for k-SSC
This paper describes a set of neural network architectures, called Prediction Neural Networks Set (PNNS), based on both fully-connected and convolutional neural networks, for intra image prediction.
The choice of neural network for predicting a given image block depends on the block size, hence does not need to be signalled to the decoder.
It is shown that, while fully-connected neural networks give good performance for small block sizes, convolutional neural networks provide better predictions in large blocks with complex textures.
Thanks to the use of masks of random sizes during training, the neural networks of PNNS well adapt to the available context that may vary, depending on the position of the image block to be predicted.
When integrating PNNS into a H.265 codec, PSNR-rate performance gains going from 1.46% to 5.20% are obtained.
These gains are on average 0.99% larger than those of prior neural network based methods.
Unlike the H.265 intra prediction modes, which are each specialized in predicting a specific texture, the proposed PNNS can model a large set of complex textures.
During the life span of large software projects, developers often apply the same code changes to different code locations in slight variations.
Since the application of these changes to all locations is time-consuming and error-prone, tools exist that learn change patterns from input examples, search for possible pattern applications, and generate corresponding recommendations.
In many cases, the generated recommendations are syntactically or semantically wrong due to code movements in the input examples.
Thus, they are of low accuracy and developers cannot directly copy them into their projects without adjustments.
We present the Accurate REcommendation System (ARES) that achieves a higher accuracy than other tools because its algorithms take care of code movements when creating patterns and recommendations.
On average, the recommendations by ARES have an accuracy of 96% with respect to code changes that developers have manually performed in commits of source code archives.
At the same time ARES achieves precision and recall values that are on par with other tools.
E-Learning is efficient, task relevant and just-in-time learning grown from the learning requirements of the new and dynamically changing world.
The term Semantic Web covers the steps to create a new WWW architecture that augments the content with formal semantics enabling better possibilities of navigation through the cyberspace and its contents.
In this paper, we present the Semantic Web-Based model for our e-learning system taking into account the learning environment at Saudi Arabian universities.
The proposed system is mainly based on ontology-based descriptions of content, context and structure of the learning materials.
It further provides flexible and personalized access to these learning materials.
The framework has been validated by an interview based qualitative method.
The assumption that training and testing samples are generated from the same distribution does not always hold for real-world machine-learning applications.
The procedure of tackling this discrepancy between the training (source) and testing (target) domains is known as domain adaptation.
We propose an unsupervised version of domain adaptation that considers the presence of only unlabelled data in the target domain.
Our approach centers on finding correspondences between samples of each domain.
The correspondences are obtained by treating the source and target samples as graphs and using a convex criterion to match them.
The criteria used are first-order and second-order similarities between the graphs as well as a class-based regularization.
We have also developed a computationally efficient routine for the convex optimization, thus allowing the proposed method to be used widely.
To verify the effectiveness of the proposed method, computer simulations were conducted on synthetic, image classification and sentiment classification datasets.
Results validated that the proposed local sample-to-sample matching method out-performs traditional moment-matching methods and is competitive with respect to current local domain-adaptation methods.
The novel "Volume-Enclosing Surface exTraction Algorithm" (VESTA) generates triangular isosurfaces from computed tomography volumetric images and/or three-dimensional (3D) simulation data.
Here, we present various benchmarks for GPU-based code implementations of both VESTA and the current state-of-the-art Marching Cubes Algorithm (MCA).
One major result of this study is that VESTA runs significantly faster than the MCA.
Geometric model fitting is a fundamental task in computer graphics and computer vision.
However, most geometric model fitting methods are unable to fit an arbitrary geometric model (e.g. a surface with holes) to incomplete data, due to that the similarity metrics used in these methods are unable to measure the rigid partial similarity between arbitrary models.
This paper hence proposes a novel rigid geometric similarity metric, which is able to measure both the full similarity and the partial similarity between arbitrary geometric models.
The proposed metric enables us to perform partial procedural geometric model fitting (PPGMF).
The task of PPGMF is to search a procedural geometric model space for the model rigidly similar to a query of non-complete point set.
Models in the procedural model space are generated according to a set of parametric modeling rules.
A typical query is a point cloud.
PPGMF is very useful as it can be used to fit arbitrary geometric models to non-complete (incomplete, over-complete or hybrid-complete) point cloud data.
For example, most laser scanning data is non-complete due to occlusion.
Our PPGMF method uses Markov chain Monte Carlo technique to optimize the proposed similarity metric over the model space.
To accelerate the optimization process, the method also employs a novel coarse-to-fine model dividing strategy to reject dissimilar models in advance.
Our method has been demonstrated on a variety of geometric models and non-complete data.
Experimental results show that the PPGMF method based on the proposed metric is able to fit non-complete data, while the method based on other metrics is unable.
It is also shown that our method can be accelerated by several times via early rejection.
Secure spontaneous authentication between devices worn at arbitrary location on the same body is a challenging, yet unsolved problem.
We propose BANDANA, the first-ever implicit secure device-to-device authentication scheme for devices worn on the same body.
Our approach leverages instantaneous variation in acceleration patterns from gait sequences to extract always-fresh secure secrets.
It enables secure spontaneous pairing of devices worn on the same body or interacted with.
The method is robust against noise in sensor readings and active attackers.
We demonstrate the robustness of BANDANA on two gait datasets and discuss the discriminability of intra- and inter-body cases, robustness to statistical bias, as well as possible attack scenarios.
Along with the emergence and popularity of social communications on the Internet, topic discovery from short texts becomes fundamental to many applications that require semantic understanding of textual content.
As a rising research field, short text topic modeling presents a new and complementary algorithmic methodology to supplement regular text topic modeling, especially targets to limited word co-occurrence information in short texts.
This paper presents the first comprehensive open-source package, called STTM, for use in Java that integrates the state-of-the-art models of short text topic modeling algorithms, benchmark datasets, and abundant functions for model inference and evaluation.
The package is designed to facilitate the expansion of new methods in this research field and make evaluations between the new approaches and existing ones accessible.
STTM is open-sourced at https://github.com/qiang2100/STTM.
Based on the in-depth analysis of the essence and features of vague phenomena, this paper focuses on establishing the axiomatical foundation of membership degree theory for vague phenomena, presents an axiomatic system to govern membership degrees and their interconnections.
On this basis, the concept of vague partition is introduced, further, the concept of fuzzy set introduced by Zadeh in 1965 is redefined based on vague partition from the perspective of axiomatization.
The thesis defended in this paper is that the relationship among vague attribute values should be the starting point to recognize and model vague phenomena from a quantitative view.
We consider a variation of Construction A of lattices from linear codes based on two classes of number fields, totally real and CM Galois number fields.
We propose a generic construction with explicit generator and Gram matrices, then focus on modular and unimodular lattices, obtained in the particular cases of totally real, respectively, imaginary, quadratic fields.
Our motivation comes from coding theory, thus some relevant properties of modular lattices, such as minimal norm, theta series, kissing number and secrecy gain are analyzed.
Interesting lattices are exhibited.
Current approaches in video forecasting attempt to generate videos directly in pixel space using Generative Adversarial Networks (GANs) or Variational Autoencoders (VAEs).
However, since these approaches try to model all the structure and scene dynamics at once, in unconstrained settings they often generate uninterpretable results.
Our insight is to model the forecasting problem at a higher level of abstraction.
Specifically, we exploit human pose detectors as a free source of supervision and break the video forecasting problem into two discrete steps.
First we explicitly model the high level structure of active objects in the scene---humans---and use a VAE to model the possible future movements of humans in the pose space.
We then use the future poses generated as conditional information to a GAN to predict the future frames of the video in pixel space.
By using the structured space of pose as an intermediate representation, we sidestep the problems that GANs have in generating video pixels directly.
We show through quantitative and qualitative evaluation that our method outperforms state-of-the-art methods for video prediction.
Extreme learning machine (ELM) is an extremely fast learning method and has a powerful performance for pattern recognition tasks proven by enormous researches and engineers.
However, its good generalization ability is built on large numbers of hidden neurons, which is not beneficial to real time response in the test process.
In this paper, we proposed new ways, named "constrained extreme learning machines" (CELMs), to randomly select hidden neurons based on sample distribution.
Compared to completely random selection of hidden nodes in ELM, the CELMs randomly select hidden nodes from the constrained vector space containing some basic combinations of original sample vectors.
The experimental results show that the CELMs have better generalization ability than traditional ELM, SVM and some other related methods.
Additionally, the CELMs have a similar fast learning speed as ELM.
Connection calculi allow for very compact implementations of goal-directed proof search.
We give an overview of our work related to connection tableaux calculi: First, we show optimised functional implementations of clausal and nonclausal proof search, including a consistent Skolemisation procedure for machine learning.
Then, we show two guidance methods based on machine learning, namely reordering of proof steps with Naive Bayesian probablities, and expansion of a proof search tree with Monte Carlo Tree Search.
Finally, we give a translation of connection proofs to LK, enabling proof certification and automatic proof search in interactive theorem provers.
The major aim of this survey is to identify the strengths and weaknesses of a representative set of Data-Mining and Integration (DMI) query languages.
We describe a set of properties of DMI-related languages that we use for a systematic evaluation of these languages.
In addition, we introduce a scoring system that we use to quantify our opinion on how well a DMI-related language supports a property.
The languages surveyed in this paper include: DMQL, MineSQL, MSQL, M2MQL, dmFSQL, OLEDB for DM, MINE RULE, and Oracle Data Mining.
This survey may help researchers to propose a DMI language that is beyond the state-of-the-art, or it may help practitioners to select an existing language that fits well a purpose.
Cooperative multi-agent planning (MAP) is a relatively recent research field that combines technologies, algorithms and techniques developed by the Artificial Intelligence Planning and Multi-Agent Systems communities.
While planning has been generally treated as a single-agent task, MAP generalizes this concept by considering multiple intelligent agents that work cooperatively to develop a course of action that satisfies the goals of the group.
This paper reviews the most relevant approaches to MAP, putting the focus on the solvers that took part in the 2015 Competition of Distributed and Multi-Agent Planning, and classifies them according to their key features and relative performance.
Computer graphics can not only generate synthetic images and ground truth but it also offers the possibility of constructing virtual worlds in which: (i) an agent can perceive, navigate, and take actions guided by AI algorithms, (ii) properties of the worlds can be modified (e.g., material and reflectance), (iii) physical simulations can be performed, and (iv) algorithms can be learnt and evaluated.
But creating realistic virtual worlds is not easy.
The game industry, however, has spent a lot of effort creating 3D worlds, which a player can interact with.
So researchers can build on these resources to create virtual worlds, provided we can access and modify the internal data structures of the games.
To enable this we created an open-source plugin UnrealCV (http://unrealcv.github.io) for a popular game engine Unreal Engine 4 (UE4).
We show two applications: (i) a proof of concept image dataset, and (ii) linking Caffe with the virtual world to test deep network algorithms.
Three-dimensional (3D) biomedical image sets are often acquired with in-plane pixel spacings that are far less than the out-of-plane spacings between images.
The resultant anisotropy, which can be detrimental in many applications, can be decreased using image interpolation.
Optical flow and/or other registration-based interpolators have proven useful in such interpolation roles in the past.
When acquired images are comprised of signals that describe the flow velocity of fluids, additional information is available to guide the interpolation process.
In this paper, we present an optical-flow based framework for image interpolation that also minimizes resultant divergence in the interpolated data.
In this paper we present a new and simple language-independent method for word-alignment based on the use of external sources of bilingual information such as machine translation systems.
We show that the few parameters of the aligner can be trained on a very small corpus, which leads to results comparable to those obtained by the state-of-the-art tool GIZA++ in terms of precision.
Regarding other metrics, such as alignment error rate or F-measure, the parametric aligner, when trained on a very small gold-standard (450 pairs of sentences), provides results comparable to those produced by GIZA++ when trained on an in-domain corpus of around 10,000 pairs of sentences.
Furthermore, the results obtained indicate that the training is domain-independent, which enables the use of the trained aligner 'on the fly' on any new pair of sentences.
Conventionally, Selective Harmonic Elimination (SHE) method in 2-level inverters, finds best switching angles to reach first voltage harmonic to reference level and eliminate other harmonics, simultaneously.
Considering Induction Motor (IM) as the inverter load, and wide DC bus voltage variations, the inverter must operate in both over-modulation and linear modulation region.
Main objective of the modified SHE is to reduce harmonic torques through finding the best switching angles.
In this paper, optimization is based on optimizing phasor equations in which harmonic torques are calculated.
The procedure of this method is that, first, the ratio of the same torque harmonics is estimated, secondly, by using that estimation, the ratio of voltage harmonics that generates homogeneous torques is calculated.
For the estimation and the calculation of the ratios motor parameter, mechanical speed of the rotor, the applied frequency, and the concept of slip are used.
The advantage of this approach is highlighted when mechanical load and DC bus voltage variations are taken into consideration.
Simulation results are presented under a wide range of working conditions in an induction motor to demonstrate the effectiveness of the proposed method.
Research on optical TEMPEST has moved forward since 2002 when the first pair of papers on the subject emerged independently and from widely separated locations in the world within a week of each other.
Since that time, vulnerabilities have evolved along with systems, and several new threat vectors have consequently appeared.
Although the supply chain ecosystem of Ethernet has reduced the vulnerability of billions of devices through use of standardised PHY solutions, other recent trends including the Internet of Things (IoT) in both industrial settings and the general population, High Frequency Trading (HFT) in the financial sector, the European General Data Protection Regulation (GDPR), and inexpensive drones have made it relevant again for consideration in the design of new products for privacy.
One of the general principles of security is that vulnerabilities, once fixed, sometimes do not stay that way.
Analyzing videos of human actions involves understanding the temporal relationships among video frames.
State-of-the-art action recognition approaches rely on traditional optical flow estimation methods to pre-compute motion information for CNNs.
Such a two-stage approach is computationally expensive, storage demanding, and not end-to-end trainable.
In this paper, we present a novel CNN architecture that implicitly captures motion information between adjacent frames.
We name our approach hidden two-stream CNNs because it only takes raw video frames as input and directly predicts action classes without explicitly computing optical flow.
Our end-to-end approach is 10x faster than its two-stage baseline.
Experimental results on four challenging action recognition datasets: UCF101, HMDB51, THUMOS14 and ActivityNet v1.2 show that our approach significantly outperforms the previous best real-time approaches.
Context: Pre-publication peer review of scientific articles is considered a key element of the research process in software engineering, yet it is often perceived as not to work fully well.
Objective: We aim at understanding the perceptions of and attitudes towards peer review of authors and reviewers at one of software engineering's most prestigious venues, the International Conference on Software Engineering (ICSE).
Method: We invited 932 ICSE 2014/15/16 authors and reviewers to participate in a survey with 10 closed and 9 open questions.
Results: We present a multitude of results, such as: Respondents perceive only one third of all reviews to be good, yet one third as useless or misleading; they propose double-blind or zero-blind reviewing regimes for improvement; they would like to see showable proofs of (good) reviewing work be introduced; attitude change trends are weak.
Conclusion: The perception of the current state of software engineering peer review is fairly negative.
Also, we found hardly any trend that suggests reviewing will improve by itself over time; the community will have to make explicit efforts.
Fortunately, our (mostly senior) respondents appear more open for trying different peer reviewing regimes than we had expected.
Inspired by previous work of Shoup, Lenstra-De Smit and Couveignes-Lercier, we give fast algorithms to compute in (the first levels of) the ell-adic closure of a finite field.
In many cases, our algorithms have quasi-linear complexity.
Convolutional neural networks are designed for dense data, but vision data is often sparse (stereo depth, point clouds, pen stroke, etc.).
We present a method to handle sparse depth data with optional dense RGB, and accomplish depth completion and semantic segmentation changing only the last layer.
Our proposal efficiently learns sparse features without the need of an additional validity mask.
We show how to ensure network robustness to varying input sparsities.
Our method even works with densities as low as 0.8% (8 layer lidar), and outperforms all published state-of-the-art on the Kitti depth completion benchmark.
We present Shrinking Horizon Model Predictive Control (SHMPC) for discrete-time linear systems with Signal Temporal Logic (STL) specification constraints under stochastic disturbances.
The control objective is to maximize an optimization function under the restriction that a given STL specification is satisfied with high probability against stochastic uncertainties.
We formulate a general solution, which does not require precise knowledge of the probability distributions of the (possibly dependent) stochastic disturbances; only the bounded support intervals of the density functions and moment intervals are used.
For the specific case of disturbances that are independent and normally distributed, we optimize the controllers further by utilizing knowledge of the disturbance probability distributions.
We show that in both cases, the control law can be obtained by solving optimization problems with linear constraints at each step.
We experimentally demonstrate effectiveness of this approach by synthesizing a controller for an HVAC system.
Bugs that surface in mobile applications can be difficult to reproduce and fix due to several confounding factors including the highly GUI-driven nature of mobile apps, varying contextual states, differing platform versions and device fragmentation.
It is clear that developers need support in the form of automated tools that allow for more precise reporting of application defects in order to facilitate more efficient and effective bug fixes.
In this paper, we present a tool aimed at supporting application testers and developers in the process of On-Device Bug Reporting.
Our tool, called ODBR, leverages the uiautomator framework and low-level event stream capture to offer support for recording and replaying a series of input gesture and sensor events that describe a bug in an Android application.
Evolution sculpts both the body plans and nervous systems of agents together over time.
In contrast, in AI and robotics, a robot's body plan is usually designed by hand, and control policies are then optimized for that fixed design.
The task of simultaneously co-optimizing the morphology and controller of an embodied robot has remained a challenge.
In psychology, the theory of embodied cognition posits that behavior arises from a close coupling between body plan and sensorimotor control, which suggests why co-optimizing these two subsystems is so difficult: most evolutionary changes to morphology tend to adversely impact sensorimotor control, leading to an overall decrease in behavioral performance.
Here, we further examine this hypothesis and demonstrate a technique for "morphological innovation protection", which temporarily reduces selection pressure on recently morphologically-changed individuals, thus enabling evolution some time to "readapt" to the new morphology with subsequent control policy mutations.
We show the potential for this method to avoid local optima and converge to similar highly fit morphologies across widely varying initial conditions, while sustaining fitness improvements further into optimization.
While this technique is admittedly only the first of many steps that must be taken to achieve scalable optimization of embodied machines, we hope that theoretical insight into the cause of evolutionary stagnation in current methods will help to enable the automation of robot design and behavioral training -- while simultaneously providing a testbed to investigate the theory of embodied cognition.
We propose a new design for a cellular neural network with spintronic neurons and CMOS-based synapses.
Harnessing the magnetoelectric and inverse Rashba-Edelstein effects allows natural emulation of the behavior of an ideal cellular network.
This combination of effects offers an increase in speed and efficiency over other spintronic neural networks.
A rigorous performance analysis via simulation is provided.
Integrating model-free and model-based approaches in reinforcement learning has the potential to achieve the high performance of model-free algorithms with low sample complexity.
However, this is difficult because an imperfect dynamics model can degrade the performance of the learning algorithm, and in sufficiently complex environments, the dynamics model will almost always be imperfect.
As a result, a key challenge is to combine model-based approaches with model-free learning in such a way that errors in the model do not degrade performance.
We propose stochastic ensemble value expansion (STEVE), a novel model-based technique that addresses this issue.
By dynamically interpolating between model rollouts of various horizon lengths for each individual example, STEVE ensures that the model is only utilized when doing so does not introduce significant errors.
Our approach outperforms model-free baselines on challenging continuous control benchmarks with an order-of-magnitude increase in sample efficiency, and in contrast to previous model-based approaches, performance does not degrade in complex environments.
The Internet of Things (IoT) being a promising technology of the future is expected to connect billions of devices.
The increased number of communication is expected to generate mountains of data and the security of data can be a threat.
The devices in the architecture are essentially smaller in size and low powered.
Conventional encryption algorithms are generally computationally expensive due to their complexity and requires many rounds to encrypt, essentially wasting the constrained energy of the gadgets.
Less complex algorithm, however, may compromise the desired integrity.
In this paper we propose a lightweight encryption algorithm named as Secure IoT (SIT).
It is a 64-bit block cipher and requires 64-bit key to encrypt the data.
The architecture of the algorithm is a mixture of feistel and a uniform substitution-permutation network.
Simulations result shows the algorithm provides substantial security in just five encryption rounds.
The hardware implementation of the algorithm is done on a low cost 8-bit micro-controller and the results of code size, memory utilization and encryption/decryption execution cycles are compared with benchmark encryption algorithms.
The MATLAB code for relevant simulations is available online at https://goo.gl/Uw7E0W.
Social media for news consumption is becoming increasingly popular due to its easy access, fast dissemination, and low cost.
However, social media also enable the wide propagation of "fake news", i.e., news with intentionally false information.
Fake news on social media poses significant negative societal effects, and also presents unique challenges.
To tackle the challenges, many existing works exploit various features, from a network perspective, to detect and mitigate fake news.
In essence, news dissemination ecosystem involves three dimensions on social media, i.e., a content dimension, a social dimension, and a temporal dimension.
In this chapter, we will review network properties for studying fake news, introduce popular network types and how these networks can be used to detect and mitigation fake news on social media.
Increasing numbers of software vulnerabilities are discovered every year whether they are reported publicly or discovered internally in proprietary code.
These vulnerabilities can pose serious risk of exploit and result in system compromise, information leaks, or denial of service.
We leveraged the wealth of C and C++ open-source code available to develop a large-scale function-level vulnerability detection system using machine learning.
To supplement existing labeled vulnerability datasets, we compiled a vast dataset of millions of open-source functions and labeled it with carefully-selected findings from three different static analyzers that indicate potential exploits.
The labeled dataset is available at: https://osf.io/d45bw/.
Using these datasets, we developed a fast and scalable vulnerability detection tool based on deep feature representation learning that directly interprets lexed source code.
We evaluated our tool on code from both real software packages and the NIST SATE IV benchmark dataset.
Our results demonstrate that deep feature representation learning on source code is a promising approach for automated software vulnerability detection.
Neural network based architectures used for sound recognition are usually adapted from other application domains, which may not harness sound related properties.
The ConditionaL Neural Network (CLNN) is designed to consider the relational properties across frames in a temporal signal, and its extension the Masked ConditionaL Neural Network (MCLNN) embeds a filterbank behavior within the network, which enforces the network to learn in frequency bands rather than bins.
Additionally, it automates the exploration of different feature combinations analogous to handcrafting the optimum combination of features for a recognition task.
We applied the MCLNN to the environmental sounds of the ESC-10 dataset.
The MCLNN achieved competitive accuracies compared to state-of-the-art convolutional neural networks and hand-crafted attempts.
Deep learning based speech enhancement and source separation systems have recently reached unprecedented levels of quality, to the point that performance is reaching a new ceiling.
Most systems rely on estimating the magnitude of a target source by estimating a real-valued mask to be applied to a time-frequency representation of the mixture signal.
A limiting factor in such approaches is a lack of phase estimation: the phase of the mixture is most often used when reconstructing the estimated time-domain signal.
Here, we propose `MagBook', `phasebook', and `Combook', three new types of layers based on discrete representations that can be used to estimate complex time-frequency masks.
MagBook layers extend classical sigmoidal units and a recently introduced convex softmax activation for mask-based magnitude estimation.
Phasebook layers use a similar structure to give an estimate of the phase mask without suffering from phase wrapping issues.
Combook layers are an alternative to the MagBook-Phasebook combination that directly estimate complex masks.
We present various training and inference regimes involving these representations, and explain in particular how to include them in an end-to-end learning framework.
We also present an oracle study to assess upper bounds on performance for various types of masks using discrete phase representations.
We evaluate the proposed methods on the wsj0-2mix dataset, a well-studied corpus for single-channel speaker-independent speaker separation, matching the performance of state-of-the-art mask-based approaches without requiring additional phase reconstruction steps.
Previous research has pointed that software applications should not depend on programmers to provide security for end-users as majority of programmers are not experts of computer security.
On the other hand, some studies have revealed that security experts believe programmers have a major role to play in ensuring the end-users' security.
However, there has been no investigation on what programmers perceive about their responsibility for the end-users' security of applications they develop.
In this work, by conducting a qualitative experimental study with 40 software developers, we attempted to understand the programmer's perception on who is responsible for ensuring end-users' security of the applications they develop.
Results revealed majority of programmers perceive that they are responsible for the end-users' security of applications they develop.
Furthermore, results showed that even though programmers aware of things they need to do to ensure end-users' security, they do not often follow them.
We believe these results would change the current view on the role that different stakeholders of the software development process (i.e. researchers, security experts, programmers and Application Programming Interface (API) developers) have to play in order to ensure the security of software applications.
DBSCAN is a classical density-based clustering procedure with tremendous practical relevance.
However, DBSCAN implicitly needs to compute the empirical density for each sample point, leading to a quadratic worst-case time complexity, which is too slow on large datasets.
We propose DBSCAN++, a simple modification of DBSCAN which only requires computing the densities for a chosen subset of points.
We show empirically that, compared to traditional DBSCAN, DBSCAN++ can provide not only competitive performance but also added robustness in the bandwidth hyperparameter while taking a fraction of the runtime.
We also present statistical consistency guarantees showing the trade-off between computational cost and estimation rates.
Surprisingly, up to a certain point, we can enjoy the same estimation rates while lowering computational cost, showing that DBSCAN++ is a sub-quadratic algorithm that attains minimax optimal rates for level-set estimation, a quality that may be of independent interest.
Generative models in vision have seen rapid progress due to algorithmic improvements and the availability of high-quality image datasets.
In this paper, we offer contributions in both these areas to enable similar progress in audio modeling.
First, we detail a powerful new WaveNet-style autoencoder model that conditions an autoregressive decoder on temporal codes learned from the raw audio waveform.
Second, we introduce NSynth, a large-scale and high-quality dataset of musical notes that is an order of magnitude larger than comparable public datasets.
Using NSynth, we demonstrate improved qualitative and quantitative performance of the WaveNet autoencoder over a well-tuned spectral autoencoder baseline.
Finally, we show that the model learns a manifold of embeddings that allows for morphing between instruments, meaningfully interpolating in timbre to create new types of sounds that are realistic and expressive.
In this paper, we present a set of simulation models to more realistically mimic the behaviour of users reading messages.
We propose a User Behaviour Model, where a simulated user reacts to a message by a flexible set of possible reactions (e.g. ignore, read, like, save, etc.) and a mobility-based reaction (visit a place, run away from danger, etc.).
We describe our models and their implementation in OMNeT++.
We strongly believe that these models will significantly contribute to the state of the art of simulating realistically opportunistic networks.
Recently, neural machine translation has achieved remarkable progress by introducing well-designed deep neural networks into its encoder-decoder framework.
From the optimization perspective, residual connections are adopted to improve learning performance for both encoder and decoder in most of these deep architectures, and advanced attention connections are applied as well.
Inspired by the success of the DenseNet model in computer vision problems, in this paper, we propose a densely connected NMT architecture (DenseNMT) that is able to train more efficiently for NMT.
The proposed DenseNMT not only allows dense connection in creating new features for both encoder and decoder, but also uses the dense attention structure to improve attention quality.
Our experiments on multiple datasets show that DenseNMT structure is more competitive and efficient.
Acoustic event detection for content analysis in most cases relies on lots of labeled data.
However, manually annotating data is a time-consuming task, which thus makes few annotated resources available so far.
Unlike audio event detection, automatic audio tagging, a multi-label acoustic event classification task, only relies on weakly labeled data.
This is highly desirable to some practical applications using audio analysis.
In this paper we propose to use a fully deep neural network (DNN) framework to handle the multi-label classification task in a regression way.
Considering that only chunk-level rather than frame-level labels are available, the whole or almost whole frames of the chunk were fed into the DNN to perform a multi-label regression for the expected tags.
The fully DNN, which is regarded as an encoding function, can well map the audio features sequence to a multi-tag vector.
A deep pyramid structure was also designed to extract more robust high-level features related to the target tags.
Further improved methods were adopted, such as the Dropout and background noise aware training, to enhance its generalization capability for new audio recordings in mismatched environments.
Compared with the conventional Gaussian Mixture Model (GMM) and support vector machine (SVM) methods, the proposed fully DNN-based method could well utilize the long-term temporal information with the whole chunk as the input.
The results show that our approach obtained a 15% relative improvement compared with the official GMM-based method of DCASE 2016 challenge.
In this paper a secret message/image transmission technique has been proposed through (2, 2) visual cryptographic share which is non-interpretable in general.
A binary image is taken as cover image and authenticating message/image has been fabricated into it through a hash function where two bits in each pixel within four bits from LSB of the pixel is embedded and as a result it converts the binary image to gray scale one.
(2,2) visual cryptographic shares are generated from this converted gray scale image.
During decoding shares are combined to regenerate the authenticated image from where the secret message/image is obtained through the same hash function along with reduction of noise.
Noise reduction is also done on regenerated authenticated image to regenerate original cover image at destination.
We illustrate how elementary information-theoretic ideas may be employed to provide proofs for well-known, nontrivial results in number theory.
Specifically, we give an elementary and fairly short proof of the following asymptotic result: The sum of (log p)/p, taken over all primes p not exceeding n, is asymptotic to log n as n tends to infinity.
We also give finite-n bounds refining the above limit.
This result, originally proved by Chebyshev in 1852, is closely related to the celebrated prime number theorem.
The Ring Learning-With-Errors (LWE) problem, whose security is based on hard ideal lattice problems, has proven to be a promising primitive with diverse applications in cryptography.
There are however recent discoveries of faster algorithms for the principal ideal SVP problem, and attempts to generalize the attack to non-principal ideals.
In this work, we study the LWE problem on group rings, and build cryptographic schemes based on this new primitive.
One can regard the LWE on cyclotomic integers as a special case when the underlying group is cyclic, while our proposal utilizes non-commutative groups, which eliminates the weakness associated with the principal ideal lattices.
In particular, we show how to build public key encryption schemes from dihedral group rings, which maintains the efficiency of the ring-LWE and improves its security.
This paper addresses the general problem of blind echo retrieval, i.e., given M sensors measuring in the discrete-time domain M mixtures of K delayed and attenuated copies of an unknown source signal, can the echo locations and weights be recovered?
This problem has broad applications in fields such as sonars, seismol-ogy, ultrasounds or room acoustics.
It belongs to the broader class of blind channel identification problems, which have been intensively studied in signal processing.
Existing methods in the literature proceed in two steps: (i) blind estimation of sparse discrete-time filters and (ii) echo information retrieval by peak-picking on filters.
The precision of these methods is fundamentally limited by the rate at which the signals are sampled: estimated echo locations are necessary on-grid, and since true locations never match the sampling grid, the weight estimation precision is impacted.
This is the so-called basis-mismatch problem in compressed sensing.
We propose a radically different approach to the problem, building on the framework of finite-rate-of-innovation sampling.
The approach operates directly in the parameter-space of echo locations and weights, and enables near-exact blind and off-grid echo retrieval from discrete-time measurements.
It is shown to outperform conventional methods by several orders of magnitude in precision.
When multiple radio-frequency sources are connected to multiple loads through a passive multiport matching network, perfect power transfer to the loads across all frequencies is generally impossible.
In this two-part paper, we provide analyses of bandwidth over which power transfer is possible.
Our principal tools include broadband multiport matching upper bounds, presented herein, on the integral over all frequency of the logarithm of a suitably defined power loss ratio.
In general, the larger the integral, the larger the bandwidth over which power transfer can be accomplished.
We apply these bounds in several ways: We show how the number of sources and loads, and the coupling between loads, affect achievable bandwidth.
We analyze the bandwidth of networks constrained to have certain architectures.
We characterize systems whose bandwidths scale as the ratio between the numbers of loads and sources.
The first part of the paper presents the bounds and uses them to analyze loads whose frequency responses can be represented by analytical circuit models.
The second part analyzes the bandwidth of realistic loads whose frequency responses are available numerically.
We provide applications to wireless transmitters where the loads are antennas being driven by amplifiers.
The derivations of the bounds are also included.
We design an active learning algorithm for cost-sensitive multiclass classification: problems where different errors have different costs.
Our algorithm, COAL, makes predictions by regressing to each label's cost and predicting the smallest.
On a new example, it uses a set of regressors that perform well on past data to estimate possible costs for each label.
It queries only the labels that could be the best, ignoring the sure losers.
We prove COAL can be efficiently implemented for any regression family that admits squared loss optimization; it also enjoys strong guarantees with respect to predictive performance and labeling effort.
We empirically compare COAL to passive learning and several active learning baselines, showing significant improvements in labeling effort and test cost on real-world datasets.
Peer-to-peer (P2P) locality has recently raised a lot of interest in the community.
Indeed, whereas P2P content distribution enables financial savings for the content providers, it dramatically increases the traffic on inter-ISP links.
To solve this issue, the idea to keep a fraction of the P2P traffic local to each ISP was introduced a few years ago.
Since then, P2P solutions exploiting locality have been introduced.
However, several fundamental issues on locality still need to be explored.
In particular, how far can we push locality, and what is, at the scale of the Internet, the reduction of traffic that can be achieved with locality?
In this paper, we perform extensive experiments on a controlled environment with up to 10,000 BitTorrent clients to evaluate the impact of high locality on inter-ISP links traffic and peers download completion time.
We introduce two simple mechanisms that make high locality possible in challenging scenarios and we show that we save up to several orders of magnitude inter-ISP traffic compared to traditional locality without adversely impacting peers download completion time.
In addition, we crawled 214,443 torrents representing 6,113,224 unique peers spread among 9,605 ASes.
We show that whereas the torrents we crawled generated 11.6 petabytes of inter-ISP traffic, our locality policy implemented for all torrents could have reduced the global inter-ISP traffic by up to 40%.
Recreation of flight trajectory is important among research areas.
The design of a flight trajectory recreation and playback system is presented in this paper.
Rather than transferring the flight data to diagram, graph and table, flight data is visualized on the 3D global of ossimPlanet. ossimPlanet is an open-source 3D global geo-spatial viewer and the system realization is based on analysis it.
Users are allowed to choose their interested flight of aerial mission.
The aerial photographs and corresponding configuration files in which flight data is included would be read in.
And the flight statuses would be stored.
The flight trajectory is then recreated.
Users can view the photographs and flight trajectory marks on the correct positions of 3D global.
The scene along flight trajectory is also simulated at the plane's eye point.
This paper provides a more intuitive way for recreation of flight trajectory.
The cost is decreased remarkably and security is ensured by secondary development on open-source platform.
We propose a novel neural method to extract drug-drug interactions (DDIs) from texts using external drug molecular structure information.
We encode textual drug pairs with convolutional neural networks and their molecular pairs with graph convolutional networks (GCNs), and then we concatenate the outputs of these two networks.
In the experiments, we show that GCNs can predict DDIs from the molecular structures of drugs in high accuracy and the molecular information can enhance text-based DDI extraction by 2.39 percent points in the F-score on the DDIExtraction 2013 shared task data set.
Modern advanced analytics applications make use of machine learning techniques and contain multiple steps of domain-specific and general-purpose processing with high resource requirements.
We present KeystoneML, a system that captures and optimizes the end-to-end large-scale machine learning applications for high-throughput training in a distributed environment with a high-level API.
This approach offers increased ease of use and higher performance over existing systems for large scale learning.
We demonstrate the effectiveness of KeystoneML in achieving high quality statistical accuracy and scalable training using real world datasets in several domains.
By optimizing execution KeystoneML achieves up to 15x training throughput over unoptimized execution on a real image classification application.
This paper presents an interconnected control-planning strategy for redundant manipulators, subject to system and environmental constraints.
The method incorporates low-level control characteristics and high-level planning components into a robust strategy for manipulators acting in complex environments, subject to joint limits.
This strategy is formulated using an adaptive control rule, the estimated dynamic model of the robotic system and the nullspace of the linearized constraints.
A path is generated that takes into account the capabilities of the platform.
The proposed method is computationally efficient, enabling its implementation on a real multi-body robotic system.
Through experimental results with a 7 DOF manipulator, we demonstrate the performance of the method in real-world scenarios.
We study the unsupervised learning of CNNs for optical flow estimation using proxy ground truth data.
Supervised CNNs, due to their immense learning capacity, have shown superior performance on a range of computer vision problems including optical flow prediction.
They however require the ground truth flow which is usually not accessible except on limited synthetic data.
Without the guidance of ground truth optical flow, unsupervised CNNs often perform worse as they are naturally ill-conditioned.
We therefore propose a novel framework in which proxy ground truth data generated from classical approaches is used to guide the CNN learning.
The models are further refined in an unsupervised fashion using an image reconstruction loss.
Our guided learning approach is competitive with or superior to state-of-the-art approaches on three standard benchmark datasets yet is completely unsupervised and can run in real time.
We propose an effective Hybrid Deep Learning (HDL) architecture for the task of determining the probability that a questioned handwritten word has been written by a known writer.
HDL is an amalgamation of Auto-Learned Features (ALF) and Human-Engineered Features (HEF).
To extract auto-learned features we use two methods: First, Two Channel Convolutional Neural Network (TC-CNN); Second, Two Channel Autoencoder (TC-AE).
Furthermore, human-engineered features are extracted by using two methods: First, Gradient Structural Concavity (GSC); Second, Scale Invariant Feature Transform (SIFT).
Experiments are performed by complementing one of the HEF methods with one ALF method on 150000 pairs of samples of the word "AND" cropped from handwritten notes written by 1500 writers.
Our results indicate that HDL architecture with AE-GSC achieves 99.7% accuracy on seen writer dataset and 92.16% accuracy on shuffled writer dataset which out performs CEDAR-FOX, as for unseen writer dataset, AE-SIFT performs comparable to this sophisticated handwriting comparison tool.
We show how faceted search using a combination of traditional classification systems and mixed-membership topic models can go beyond keyword search to inform resource discovery, hypothesis formulation, and argument extraction for interdisciplinary research.
Our test domain is the history and philosophy of scientific work on animal mind and cognition.
The methods can be generalized to other research areas and ultimately support a system for semi-automatic identification of argument structures.
We provide a case study for the application of the methods to the problem of identifying and extracting arguments about anthropomorphism during a critical period in the development of comparative psychology.
We show how a combination of classification systems and mixed-membership models trained over large digital libraries can inform resource discovery in this domain.
Through a novel approach of "drill-down" topic modeling---simultaneously reducing both the size of the corpus and the unit of analysis---we are able to reduce a large collection of fulltext volumes to a much smaller set of pages within six focal volumes containing arguments of interest to historians and philosophers of comparative psychology.
The volumes identified in this way did not appear among the first ten results of the keyword search in the HathiTrust digital library and the pages bear the kind of "close reading" needed to generate original interpretations that is the heart of scholarly work in the humanities.
Zooming back out, we provide a way to place the books onto a map of science originally constructed from very different data and for different purposes.
The multilevel approach advances understanding of the intellectual and societal contexts in which writings are interpreted.
There has been significant increase in penetration of renewable generation (RG) sources all over the world.
Localized concentration of many such generators could initiate a cascade tripping sequence that might threaten the stability of the entire system.
Understanding the impact of cascade tripping process would help the system planner identify trip sequences that must be blocked in order to increase stability.
In this work, we attempt to understand the consequences of cascade tripping mechanism through a Lyapunov approach.
A conservative definition for the stability region (SR) along with its estimation for a given cascading sequence using sum of squares (SOS) programming is proposed.
Finally, a simple probabilistic definition of the SR is used to visualize the risk of instability and understand the impact of blocking trip sequences.
A 3-machine system with significant RG penetration is used to demonstrate the idea.
Image processing and pixel-wise dense prediction have been advanced by harnessing the capabilities of deep learning.
One central issue of deep learning is the limited capacity to handle joint upsampling.
We present a deep learning building block for joint upsampling, namely guided filtering layer.
This layer aims at efficiently generating the high-resolution output given the corresponding low-resolution one and a high-resolution guidance map.
The proposed layer is composed of a guided filter, which is reformulated as a fully differentiable block.
To this end, we show that a guided filter can be expressed as a group of spatial varying linear transformation matrices.
This layer could be integrated with the convolutional neural networks (CNNs) and jointly optimized through end-to-end training.
To further take advantage of end-to-end training, we plug in a trainable transformation function that generates task-specific guidance maps.
By integrating the CNNs and the proposed layer, we form deep guided filtering networks.
The proposed networks are evaluated on five advanced image processing tasks.
Experiments on MIT-Adobe FiveK Dataset demonstrate that the proposed approach runs 10-100 times faster and achieves the state-of-the-art performance.
We also show that the proposed guided filtering layer helps to improve the performance of multiple pixel-wise dense prediction tasks.
The code is available at https://github.com/wuhuikai/DeepGuidedFilter.
This paper investigates and evaluates support vector machine active learning algorithms for use with imbalanced datasets, which commonly arise in many applications such as information extraction applications.
Algorithms based on closest-to-hyperplane selection and query-by-committee selection are combined with methods for addressing imbalance such as positive amplification based on prevalence statistics from initial random samples.
Three algorithms (ClosestPA, QBagPA, and QBoostPA) are presented and carefully evaluated on datasets for text classification and relation extraction.
The ClosestPA algorithm is shown to consistently outperform the other two in a variety of ways and insights are provided as to why this is the case.
While modern parallel computing systems offer high performance, utilizing these powerful computing resources to the highest possible extent demands advanced knowledge of various hardware architectures and parallel programming models.
Furthermore, optimized software execution on parallel computing systems demands consideration of many parameters at compile-time and run-time.
Determining the optimal set of parameters in a given execution context is a complex task, and therefore to address this issue researchers have proposed different approaches that use heuristic search or machine learning.
In this paper, we undertake a systematic literature review to aggregate, analyze and classify the existing software optimization methods for parallel computing systems.
We review approaches that use machine learning or meta-heuristics for software optimization at compile-time and run-time.
Additionally, we discuss challenges and future research directions.
The results of this study may help to better understand the state-of-the-art techniques that use machine learning and meta-heuristics to deal with the complexity of software optimization for parallel computing systems.
Furthermore, it may aid in understanding the limitations of existing approaches and identification of areas for improvement.
Device-free localization (DFL) methods use measured changes in the received signal strength (RSS) between many pairs of RF nodes to provide location estimates of a person inside the wireless network.
Fundamental challenges for RSS DFL methods include having a model of RSS measurements as a function of a person's location, and maintaining an accurate model as the environment changes over time.
Current methods rely on either labeled empty-area calibration or labeled fingerprints with a person at each location.
Both need to be frequently recalibrated or retrained to stay current with changing environments.
Other DFL methods only localize people in motion.
In this paper, we address these challenges by, first, introducing a new mixture model for link RSS as a function of a person's location, and second, providing the framework to update model parameters without ever being provided labeled data from either empty-area or known-location classes.
We develop two new Bayesian localization methods based on our mixture model and experimentally validate our system at three test sites with seven days of measurements.
We demonstrate that our methods localize a person with non-degrading performance in changing environments, and, in addition, reduce localization error by 11-51% compared to other DFL methods.
Network quantization is one of network compression techniques to reduce the redundancy of deep neural networks.
It reduces the number of distinct network parameter values by quantization in order to save the storage for them.
In this paper, we design network quantization schemes that minimize the performance loss due to quantization given a compression ratio constraint.
We analyze the quantitative relation of quantization errors to the neural network loss function and identify that the Hessian-weighted distortion measure is locally the right objective function for the optimization of network quantization.
As a result, Hessian-weighted k-means clustering is proposed for clustering network parameters to quantize.
When optimal variable-length binary codes, e.g., Huffman codes, are employed for further compression, we derive that the network quantization problem can be related to the entropy-constrained scalar quantization (ECSQ) problem in information theory and consequently propose two solutions of ECSQ for network quantization, i.e., uniform quantization and an iterative solution similar to Lloyd's algorithm.
Finally, using the simple uniform quantization followed by Huffman coding, we show from our experiments that the compression ratios of 51.25, 22.17 and 40.65 are achievable for LeNet, 32-layer ResNet and AlexNet, respectively.
In this paper, we analyze efficacy of the fast gradient sign method (FGSM) and the Carlini-Wagner's L2 (CW-L2) attack.
We prove that, within a certain regime, the untargeted FGSM can fool any convolutional neural nets (CNNs) with ReLU activation; the targeted FGSM can mislead any CNNs with ReLU activation to classify any given image into any prescribed class.
For a special two-layer neural network: a linear layer followed by the softmax output activation, we show that the CW-L2 attack increases the ratio of the classification probability between the target and ground truth classes.
Moreover, we provide numerical results to verify all our theoretical results.
R-CNN style methods are sorts of the state-of-the-art object detection methods, which consist of region proposal generation and deep CNN classification.
However, the proposal generation phase in this paradigm is usually time consuming, which would slow down the whole detection time in testing.
This paper suggests that the value discrepancies among features in deep convolutional feature maps contain plenty of useful spatial information, and proposes a simple approach to extract the information for fast region proposal generation in testing.
The proposed method, namely Relief R-CNN (R2-CNN), adopts a novel region proposal generator in a trained R-CNN style model.
The new generator directly generates proposals from convolutional features by some simple rules, thus resulting in a much faster proposal generation speed and a lower demand of computation resources.
Empirical studies show that R2-CNN could achieve the fastest detection speed with comparable accuracy among all the compared algorithms in testing.
Traditional authentication in radio-frequency (RF) systems enable secure data communication within a network through techniques such as digital signatures and hash-based message authentication codes (HMAC), which suffer from key recovery attacks.
State-of-the-art IoT networks such as Nest also use Open Authentication (OAuth 2.0) protocols that are vulnerable to cross-site-recovery forgery (CSRF), which shows that these techniques may not prevent an adversary from copying or modeling the secret IDs or encryption keys using invasive, side channel, learning or software attacks.
Physical unclonable functions (PUF), on the other hand, can exploit manufacturing process variations to uniquely identify silicon chips which makes a PUF-based system extremely robust and secure at low cost, as it is practically impossible to replicate the same silicon characteristics across dies.
Taking inspiration from human communication, which utilizes inherent variations in the voice signatures to identify a certain speaker, we present RF- PUF: a deep neural network-based framework that allows real-time authentication of wireless nodes, using the effects of inherent process variation on RF properties of the wireless transmitters (Tx), detected through in-situ machine learning at the receiver (Rx) end.
The proposed method utilizes the already-existing asymmetric RF communication framework and does not require any additional circuitry for PUF generation or feature extraction.
Simulation results involving the process variations in a standard 65 nm technology node, and features such as LO offset and I-Q imbalance detected with a neural network having 50 neurons in the hidden layer indicate that the framework can distinguish up to 4800 transmitters with an accuracy of 99.9% (~ 99% for 10,000 transmitters) under varying channel conditions, and without the need for traditional preambles.
Network coverage of wireless sensor network (WSN) means how well an area of interest is being monitored by the deployed network.
It depends mainly on sensing model of nodes.
In this paper, we present three types of sensing models viz.Boolean sensing model, shadow-fading sensing model and Elfes sensing model.
We investigate the impact of sensing models on network coverage.
We also investigate network coverage based on Poisson node distribution.
A comparative study between regular and random node placement has also been presented in this paper.
This study will be useful for coverage analysis of WSN.
Robustness of hybrid control systems to measurement noise, actuator disturbances, and more generally perturbations, is analyzed.
The relationship between the robustness of a hybrid control system and of its implementations is emphasized.
Firstly, a formal definition of implementation of a hybrid control system is provided, based on the uniqueness of the solutions.
Then, two examples are analyzed in detail, showing how the previously developed robustness property fails to guarantee that the implementations, necessarily used in control practice, are also robust.
A new concept of strong robustness is proposed, which guarantees that at least jumping-first and flowing-first implementations are robust when the hybrid control system is strongly robust.
In addition, we provide a sufficient condition for strong robustness based on the previously developed hybrid relaxation results.
Word reordering is one of the most difficult aspects of statistical machine translation (SMT), and an important factor of its quality and efficiency.
Despite the vast amount of research published to date, the interest of the community in this problem has not decreased, and no single method appears to be strongly dominant across language pairs.
Instead, the choice of the optimal approach for a new translation task still seems to be mostly driven by empirical trials.
To orientate the reader in this vast and complex research area, we present a comprehensive survey of word reordering viewed as a statistical modeling challenge and as a natural language phenomenon.
The survey describes in detail how word reordering is modeled within different string-based and tree-based SMT frameworks and as a stand-alone task, including systematic overviews of the literature in advanced reordering modeling.
We then question why some approaches are more successful than others in different language pairs.
We argue that, besides measuring the amount of reordering, it is important to understand which kinds of reordering occur in a given language pair.
To this end, we conduct a qualitative analysis of word reordering phenomena in a diverse sample of language pairs, based on a large collection of linguistic knowledge.
Empirical results in the SMT literature are shown to support the hypothesis that a few linguistic facts can be very useful to anticipate the reordering characteristics of a language pair and to select the SMT framework that best suits them.
Explaining the unreasonable effectiveness of deep learning has eluded researchers around the globe.
Various authors have described multiple metrics to evaluate the capacity of deep architectures.
In this paper, we allude to the radius margin bounds described for a support vector machine (SVM) with hinge loss, apply the same to the deep feed-forward architectures and derive the Vapnik-Chervonenkis (VC) bounds which are different from the earlier bounds proposed in terms of number of weights of the network.
In doing so, we also relate the effectiveness of techniques like Dropout and Dropconnect in bringing down the capacity of the network.
Finally, we describe the effect of maximizing the input as well as the output margin to achieve an input noise-robust deep architecture.
The dynamic character of most social networks requires to model evolution of networks in order to enable complex analysis of theirs dynamics.
The following paper focuses on the definition of differences between network snapshots by means of Graph Differential Tuple.
These differences enable to calculate the diverse distance measures as well as to investigate the speed of changes.
Four separate measures are suggested in the paper with experimental study on real social network data.
This paper presents a method based on linear programming for trajectory planning of automated vehicles, combining obstacle avoidance, time scheduling for the reaching of waypoints and time-optimal traversal of tube-like road segments.
System modeling is conducted entirely spatial-based.
Kinematic vehicle dynamics as well as time are expressed in a road-aligned coordinate frame with path along the road centerline serving as the dependent variable.
We elaborate on control rate constraints in the spatial domain.
A vehicle dimension constraint heuristic is proposed to constrain vehicle dimensions inside road boundaries.
It is outlined how friction constraints are accounted for.
The discussion is extended to dynamic vehicle models.
The benefits of the proposed method are illustrated by a comparison to a time-based method.
We consider a certain tiling problem of a planar region in which there are no long horizontal or vertical strips consisting of copies of the same tile.
Intuitively speaking, we would like to create a dappled pattern with two or more kinds of tiles.
We give an efficient algorithm to turn any tiling into one satisfying the condition, and discuss its applications in texturing.
Neural networks are very powerful learning systems, but they do not readily generalize from one task to the other.
This is partly due to the fact that they do not learn in a compositional way, that is, by discovering skills that are shared by different tasks, and recombining them to solve new problems.
In this paper, we explore the compositional generalization capabilities of recurrent neural networks (RNNs).
We first propose the lookup table composition domain as a simple setup to test compositional behaviour and show that it is theoretically possible for a standard RNN to learn to behave compositionally in this domain when trained with standard gradient descent and provided with additional supervision.
We then remove this additional supervision and perform a search over a large number of model initializations to investigate the proportion of RNNs that can still converge to a compositional solution.
We discover that a small but non-negligible proportion of RNNs do reach partial compositional solutions even without special architectural constraints.
This suggests that a combination of gradient descent and evolutionary strategies directly favouring the minority models that developed more compositional approaches might suffice to lead standard RNNs towards compositional solutions.
Autonomous path planning algorithms are significant to planetary exploration rovers, since relying on commands from Earth will heavily reduce their efficiency of executing exploration missions.
This paper proposes a novel learning-based algorithm to deal with global path planning problem for planetary exploration rovers.
Specifically, a novel deep convolutional neural network with double branches (DB-CNN) is designed and trained, which can plan path directly from orbital images of planetary surfaces without implementing environment mapping.
Moreover, the planning procedure requires no prior knowledge about planetary surface terrains.
Finally, experimental results demonstrate that DB-CNN achieves better performance on global path planning and faster convergence during training compared with the existing Value Iteration Network (VIN).
Since the proof of the four color theorem in 1976, computer-generated proofs have become a reality in mathematics and computer science.
During the last decade, we have seen formal proofs using verified proof assistants being used to verify the validity of such proofs.
In this paper, we describe a formalized theory of size-optimal sorting networks.
From this formalization we extract a certified checker that successfully verifies computer-generated proofs of optimality on up to 8 inputs.
The checker relies on an untrusted oracle to shortcut the search for witnesses on more than 1.6 million NP-complete subproblems.
In classifier (or regression) fusion the aim is to combine the outputs of several algorithms to boost overall performance.
Standard supervised fusion algorithms often require accurate and precise training labels.
However, accurate labels may be difficult to obtain in many remote sensing applications.
This paper proposes novel classification and regression fusion models that can be trained given ambiguosly and imprecisely labeled training data in which training labels are associated with sets of data points (i.e., "bags") instead of individual data points (i.e., "instances") following a multiple instance learning framework.
Experiments were conducted based on the proposed algorithms on both synthetic data and applications such as target detection and crop yield prediction given remote sensing data.
The proposed algorithms show effective classification and regression performance.
Generalized linear mixed-effects models in the context of genome-wide association studies (GWAS) represent a formidable computational challenge: the solution of millions of correlated generalized least-squares problems, and the processing of terabytes of data.
We present high performance in-core and out-of-core shared-memory algorithms for GWAS: By taking advantage of domain-specific knowledge, exploiting multi-core parallelism, and handling data efficiently, our algorithms attain unequalled performance.
When compared to GenABEL, one of the most widely used libraries for GWAS, on a 12-core processor we obtain 50-fold speedups.
As a consequence, our routines enable genome studies of unprecedented size.
The binary similarity problem consists in determining if two functions are similar by only considering their compiled form.
Advanced techniques for binary similarity recently gained momentum as they can be applied in several fields, such as copyright disputes, malware analysis, vulnerability detection, etc., and thus have an immediate practical impact.
Current solutions compare functions by first transforming their binary code in multi-dimensional vector representations (embeddings), and then comparing vectors through simple and efficient geometric operations.
However, embeddings are usually derived from binary code using manual feature extraction, that may fail in considering important function characteristics, or may consider features that are not important for the binary similarity problem.
In this paper we propose SAFE, a novel architecture for the embedding of functions based on a self-attentive neural network.
SAFE works directly on disassembled binary functions, does not require manual feature extraction, is computationally more efficient than existing solutions (i.e., it does not incur in the computational overhead of building or manipulating control flow graphs), and is more general as it works on stripped binaries and on multiple architectures.
We report the results from a quantitative and qualitative analysis that show how SAFE provides a noticeable performance improvement with respect to previous solutions.
Furthermore, we show how clusters of our embedding vectors are closely related to the semantic of the implemented algorithms, paving the way for further interesting applications (e.g. semantic-based binary function search).
The IETF recently standardized the Opus codec as RFC6716.
Opus targets a wide range of real-time Internet applications by combining a linear prediction coder with a transform coder.
We describe the transform coder, with particular attention to the psychoacoustic knowledge built into the format.
The result out-performs existing audio codecs that do not operate under real-time constraints.
Chemical multisensor devices need calibration algorithms to estimate gas concentrations.
Their possible adoption as indicative air quality measurements devices poses new challenges due to the need to operate in continuous monitoring modes in uncontrolled environments.
Several issues, including slow dynamics, continue to affect their real world performances.
At the same time, the need for estimating pollutant concentrations on board the devices, espe- cially for wearables and IoT deployments, is becoming highly desirable.
In this framework, several calibration approaches have been proposed and tested on a variety of proprietary devices and datasets; still, no thorough comparison is available to researchers.
This work attempts a benchmarking of the most promising calibration algorithms according to recent literature with a focus on machine learning approaches.
We test the techniques against absolute and dynamic performances, generalization capabilities and computational/storage needs using three different datasets sharing continuous monitoring operation methodology.
Our results can guide researchers and engineers in the choice of optimal strategy.
They show that non-linear multivariate techniques yield reproducible results, outperforming lin- ear approaches.
Specifically, the Support Vector Regression method consistently shows good performances in all the considered scenarios.
We highlight the enhanced suitability of shallow neural networks in a trade-off between performance and computational/storage needs.
We confirm, on a much wider basis, the advantages of dynamic approaches with respect to static ones that only rely on instantaneous sensor array response.
The latter have been shown to be best choice whenever prompt and precise response is needed.
The ultimate goal of this indoor mapping research is to automatically reconstruct a floorplan simply by walking through a house with a smartphone in a pocket.
This paper tackles this problem by proposing FloorNet, a novel deep neural architecture.
The challenge lies in the processing of RGBD streams spanning a large 3D space.
FloorNet effectively processes the data through three neural network branches: 1) PointNet with 3D points, exploiting the 3D information; 2) CNN with a 2D point density image in a top-down view, enhancing the local spatial reasoning; and 3) CNN with RGB images, utilizing the full image information.
FloorNet exchanges intermediate features across the branches to exploit the best of all the architectures.
We have created a benchmark for floorplan reconstruction by acquiring RGBD video streams for 155 residential houses or apartments with Google Tango phones and annotating complete floorplan information.
Our qualitative and quantitative evaluations demonstrate that the fusion of three branches effectively improves the reconstruction quality.
We hope that the paper together with the benchmark will be an important step towards solving a challenging vector-graphics reconstruction problem.
Code and data are available at https://github.com/art-programmer/FloorNet.
Business Process Management (BPM) is a central element of today organizations.
Despite over the years its main focus has been the support of processes in highly controlled domains, nowadays many domains of interest to the BPM community are characterized by ever-changing requirements, unpredictable environments and increasing amounts of data that influence the execution of process instances.
Under such dynamic conditions, BPM systems must increase their level of automation to provide the reactivity and flexibility necessary for process management.
On the other hand, the Artificial Intelligence (AI) community has concentrated its efforts on investigating dynamic domains that involve active control of computational entities and physical devices (e.g., robots, software agents, etc.).
In this context, Automated Planning, which is one of the oldest areas in AI, is conceived as a model-based approach to synthesize autonomous behaviours in automated way from a model.
In this paper, we discuss how automated planning techniques can be leveraged to enable new levels of automation and support for business processing, and we show some concrete examples of their successful application to the different stages of the BPM life cycle.
We consider the setting of a Master server, M, who possesses confidential data (e.g., personal, genomic or medical data) and wants to run intensive computations on it, as part of a machine learning algorithm for example.
The Master wants to distribute these computations to untrusted workers who have volunteered or are incentivized to help with this task.
However, the data must be kept private and not revealed to the individual workers.
Some of the workers may be stragglers, e.g., slow or busy, and will take a random time to finish the task assigned to them.
We are interested in reducing the delays experienced by the Master.
We focus on linear computations as an essential operation in many iterative algorithms such as principal component analysis, support vector machines and other gradient-descent based algorithms.
A classical solution is to use a linear secret sharing scheme, such as Shamir's scheme, to divide the data into secret shares on which the workers can perform linear computations.
However, classical codes can provide straggler mitigation assuming a worst-case scenario of a fixed number of stragglers.
We propose a solution based on new secure codes, called Staircase codes, introduced previously by two of the authors.
Staircase codes allow flexibility in the number of stragglers up to a given maximum, and universally achieve the information theoretic limit on the download cost by the Master, leading to latency reduction.
Under the shifted exponential model, we find upper and lower bounds on the Master's mean waiting time.
We derive the distribution of the Master's waiting time, and its mean, for systems with up to two stragglers.
For systems with any number of stragglers, we derive an expression that can give the exact distribution, and the mean, of the waiting time of the Master.
We show that Staircase codes always outperform classical secret sharing codes.
Learning graph representations via low-dimensional embeddings that preserve relevant network properties is an important class of problems in machine learning.
We here present a novel method to embed directed acyclic graphs.
Following prior work, we first advocate for using hyperbolic spaces which provably model tree-like structures better than Euclidean geometry.
Second, we view hierarchical relations as partial orders defined using a family of nested geodesically convex cones.
We prove that these entailment cones admit an optimal shape with a closed form expression both in the Euclidean and hyperbolic spaces, and they canonically define the embedding learning process.
Experiments show significant improvements of our method over strong recent baselines both in terms of representational capacity and generalization.
This paper presents a new algorithm, the Modified Moving Contracting Window Pattern Algorithm (CMCWPM), for the calculation of field similarity.
It strongly relies on previous work by Yang et al.
(2001), correcting previous work in which characters marked as inaccessible for further pattern matching were not treated as boundaries between subfields, occasionally leading to higher than expected scores of field similarity.
A reference Python implementation is provided.
Deep spiking neural networks (SNNs) hold great potential for improving the latency and energy efficiency of deep neural networks through event-based computation.
However, training such networks is difficult due to the non-differentiable nature of asynchronous spike events.
In this paper, we introduce a novel technique, which treats the membrane potentials of spiking neurons as differentiable signals, where discontinuities at spike times are only considered as noise.
This enables an error backpropagation mechanism for deep SNNs, which works directly on spike signals and membrane potentials.
Thus, compared with previous methods relying on indirect training and conversion, our technique has the potential to capture the statics of spikes more precisely.
Our novel framework outperforms all previously reported results for SNNs on the permutation invariant MNIST benchmark, as well as the N-MNIST benchmark recorded with event-based vision sensors.
The information available to robots in real tasks is widely distributed both in time and space, requiring the agent to search for relevant data.
In humans, that face the same problem when sounds, images and smells are presented to their sensors in a daily scene, a natural system is applied: Attention.
As vision plays an important role in our routine, most research regarding attention has involved this sensorial system and the same has been replicated to the robotics field.
However,most of the robotics tasks nowadays do not rely only in visual data, that are still costly.
To allow the use of attentive concepts with other robotics sensors that are usually used in tasks such as navigation, self-localization, searching and mapping, a generic attentional model has been previously proposed.
In this work, feature mapping functions were designed to build feature maps to this attentive model from data from range scanner and sonar sensors.
Experiments were performed in a high fidelity simulated robotics environment and results have demonstrated the capability of the model on dealing with both salient stimuli and goal-driven attention over multiple features extracted from multiple sensors.
In recent years, deep learning algorithms have become increasingly more prominent for their unparalleled ability to automatically learn discriminant features from large amounts of data.
However, within the field of electromyography-based gesture recognition, deep learning algorithms are seldom employed as they require an unreasonable amount of effort from a single person, to generate tens of thousands of examples.
This work's hypothesis is that general, informative features can be learned from the large amounts of data generated by aggregating the signals of multiple users, thus reducing the recording burden while enhancing gesture recognition.
Consequently, this paper proposes applying transfer learning on aggregated data from multiple users, while leveraging the capacity of deep learning algorithms to learn discriminant features from large datasets.
Two datasets comprised of 19 and 17 able-bodied participants respectively (the first one is employed for pre-training) were recorded for this work, using the Myo Armband.
A third Myo Armband dataset was taken from the NinaPro database and is comprised of 10 able-bodied participants.
Three different deep learning networks employing three different modalities as input (raw EMG, Spectrograms and Continuous Wavelet Transform (CWT)) are tested on the second and third dataset.
The proposed transfer learning scheme is shown to systematically and significantly enhance the performance for all three networks on the two datasets, achieving an offline accuracy of 98.31% for 7 gestures over 17 participants for the CWT-based ConvNet and 68.98% for 18 gestures over 10 participants for the raw EMG-based ConvNet.
Finally, a use-case study employing eight able-bodied participants suggests that real-time feedback allows users to adapt their muscle activation strategy which reduces the degradation in accuracy normally experienced over time.
Acoustic ranging based indoor positioning solutions have the advantage of higher ranging accuracy and better compatibility with commercial-off-the-self consumer devices.
However, similar to other time-domain based approaches using Time-of-Arrival and Time-Difference-of-Arrival, they suffer from performance degradation in presence of multi-path propagation and low received signal-to-noise ratio (SNR) in indoor environments.
In this paper, we improve upon our previous work on asynchronous acoustic indoor positioning and develop ARABIS, a robust and low-cost acoustic positioning system (IPS) for mobile devices.
We develop a low-cost acoustic board custom-designed to support large operational ranges and extensibility.
To mitigate the effects of low SNR and multi-path propagation, we devise a robust algorithm that iteratively removes possible outliers by taking advantage of redundant TDoA estimates.
Experiments have been carried in two testbeds of sizes 10.67m*7.76m and 15m*15m, one in an academic building and one in a convention center.
The proposed system achieves average and 95% quantile localization errors of 7.4cm and 16.0cm in the first testbed with 8 anchor nodes and average and 95% quantile localization errors of 20.4cm and 40.0cm in the second testbed with 4 anchor nodes only.
Clinical decision support systems (CDSS) are widely used to assist with medical decision making.
However, CDSS typically require manually curated rules and other data which are difficult to maintain and keep up-to-date.
Recent systems leverage advanced deep learning techniques and electronic health records (EHR) to provide more timely and precise results.
Many of these techniques have been developed with a common focus on predicting upcoming medical events.
However, while the prediction results from these approaches are promising, their value is limited by their lack of interpretability.
To address this challenge, we introduce CarePre, an intelligent clinical decision assistance system.
The system extends a state-of-the-art deep learning model to predict upcoming diagnosis events for a focal patient based on his/her historical medical records.
The system includes an interactive framework together with intuitive visualizations designed to support the diagnosis, treatment outcome analysis, and the interpretation of the analysis results.
We demonstrate the effectiveness and usefulness of CarePre system by reporting results from a quantities evaluation of the prediction algorithm and a case study and three interviews with senior physicians.
To ensure the maximum utilization of the limited bandwidth resources and improved quality of service (QoS) is the key issue for wireless communication networks.
Excessive call blocking is a constraint to attain the desired QoS.
In cellular network, as the traffic arrival rate increases, call blocking probability (CBP) increases considerably.
Paying profound concern, we proposed a scheme that reduces the call blocking probability with approximately steady call dropping probability (CDP).
Our proposed scheme also introduces the acceptance factor in specific guard channel where originating calls get access according to the acceptance factor.
The analytical performance proves better performance than the conventional new-call bounding scheme in case of higher and lower traffic arrival rate.
The different sets of regulations existing for differ-ent agencies within the government make the task of creating AI enabled solutions in government dif-ficult.
Regulatory restrictions inhibit sharing of da-ta across different agencies, which could be a significant impediment to training AI models.
We discuss the challenges that exist in environments where data cannot be freely shared and assess tech-nologies which can be used to work around these challenges.
We present results on building AI models using the concept of federated AI, which al-lows creation of models without moving the training data around.
Over the past three decades, considerable effort has been devoted to the study of software architecture.
A major portion of this effort has focused on the originally proposed view of four "C"s---components, connectors, configurations, and constraints---that are the building blocks of a system's architecture.
Despite being simple and appealing, this view has proven to be incomplete and has required further elaboration.
To that end, researchers have more recently tried to approach architectures from another important perspective---that of design decisions that yield a system's architecture.
These more recent efforts have lacked a precise understanding of several key questions, however: (1) What is an architectural design decision (definition)?
(2) How can architectural design decisions be found in existing systems (identification)?
(3) What system decisions are and are not architectural (classification)?
(4) How are architectural design decisions manifested in the code (reification)?
(5) How can important architectural decisions be preserved and/or changed as desired (evolution)?
This paper presents a technique targeted at answering these questions by analyzing information that is readily available about software systems.
We applied our technique on over 100 different versions of two widely adopted open- source systems, and found that it can accurately uncover the architectural design decisions embodied in the systems.
We are aiming at a semantics of logic programs with preferences defined on rules, which always selects a preferred answer set, if there is a non-empty set of (standard) answer sets of the given program.
It is shown in a seminal paper by Brewka and Eiter that the goal mentioned above is incompatible with their second principle and it is not satisfied in their semantics of prioritized logic programs.
Similarly, also according to other established semantics, based on a prescriptive approach, there are programs with standard answer sets, but without preferred answer sets.
According to the standard prescriptive approach no rule can be fired before a more preferred rule, unless the more preferred rule is blocked.
This is a rather imperative approach, in its spirit.
In our approach, rules can be blocked by more preferred rules, but the rules which are not blocked are handled in a more declarative style, their execution does not depend on the given preference relation on the rules.
An argumentation framework (different from the Dung's framework) is proposed in this paper.
Argu- mentation structures are derived from the rules of a given program.
An attack relation on argumentation structures is defined, which is derived from attacks of more preferred rules against the less preferred rules.
Preferred answer sets correspond to complete argumentation structures, which are not blocked by other complete argumentation structures.
Ranking algorithms are the information gatekeepers of the Internet era.
We develop a stylized model to study the effects of ranking algorithms on opinion dynamics.
We consider a search engine that uses an algorithm based on popularity and on personalization.
We find that popularity-based rankings generate an advantage of the fewer effect: fewer websites reporting a given signal attract relatively more traffic overall.
This highlights a novel, ranking-driven channel that explains the diffusion of misinformation, as websites reporting incorrect information may attract an amplified amount of traffic precisely because they are few.
Furthermore, when individuals provide sufficiently positive feedback to the ranking algorithm, popularity-based rankings tend to aggregate information while personalization acts in the opposite direction.
Citations are commonly held to represent scientific impact.
To date, however, there is no empirical evidence in support of this postulate that is central to research assessment exercises and Science of Science studies.
Here, we report on the first empirical verification of the degree to which citation numbers represent scientific impact as it is actually perceived by experts in their respective field.
We run a large-scale survey of about 2000 corresponding authors who performed a pairwise impact assessment task across more than 20000 scientific articles.
Results of the survey show that citation data and perceived impact do not align well, unless one properly accounts for strong psychological biases that affect the opinions of experts with respect to their own papers vs. those of others.
First, researchers tend to largely prefer their own publications to the most cited papers in their field of research.
Second, there is only a mild positive correlation between the number of citations of top-cited papers in given research areas and expert preference in pairwise comparisons.
This also applies to pairs of papers with several orders of magnitude differences in their total number of accumulated citations.
However, when researchers were asked to choose among pairs of their own papers, thus eliminating the bias favouring one's own papers over those of others, they did systematically prefer the most cited article.
We conclude that, when scientists have full information and are making unbiased choices, expert opinion on impact is congruent with citation numbers.
In this paper, we consider the notion of a direct type algorithm introduced by V.A.Bondarenko in 1983.
A direct type algorithm is a linear decision tree with some special properties.
Until recently, it was thought that the class of direct type algorithms is wide and includes many classical combinatorial algorithms, including the branch and bound algorithm for the traveling salesman problem, proposed by J.D.C.Little, K.G.Murty, D.W. Sweeney, C. Karel in 1963.
We show that this algorithm is not a direct type algorithm.
This work presents an algorithm for changing from latitudinal to longitudinal formation of autonomous aircraft squadrons.
The maneuvers are defined dynamically by using a predefined set of 3D basic maneuvers.
This formation changing is necessary when the squadron has to perform tasks which demand both formations, such as lift off, georeferencing, obstacle avoidance and landing.
Simulations show that the formation changing is made without collision.
The time complexity analysis of the transformation algorithm reveals that its efficiency is optimal, and the proof of correction ensures its longitudinal formation features.
We address the problem of super-resolution frequency recovery using prior knowledge of the structure of a spectrally sparse, undersampled signal.
In many applications of interest, some structure information about the signal spectrum is often known.
The prior information might be simply knowing precisely some signal frequencies or the likelihood of a particular frequency component in the signal.
We devise a general semidefinite program to recover these frequencies using theories of positive trigonometric polynomials.
Our theoretical analysis shows that, given sufficient prior information, perfect signal reconstruction is possible using signal samples no more than thrice the number of signal frequencies.
Numerical experiments demonstrate great performance enhancements using our method.
We show that the nominal resolution necessary for the grid-free results can be improved if prior information is suitably employed.
We present a Polyhedral Scene Generator system which creates a random scene based on a few user parameters, renders the scene from random view points and creates a dataset containing the renderings and corresponding annotation files.
We hope that this generator will enable research on how a program could parse a scene if it had multiple viewpoints to consider.
For ambiguous scenes, typically people move their head or change their position to see the scene from different angles as well as seeing how it changes while they move; this research field is called active perception.
The random scene generator presented is designed to support research in this field by generating images of scenes with known complexity characteristics and with verifiable properties with respect to the distribution of features across a population.
Thus, it is well-suited for research in active perception without the requirement of a live 3D environment and mobile sensing agent, including comparative performance evaluations.
The system is publicly available at https://polyhedral.eecs.yorku.ca.
While conventional depth estimation can infer the geometry of a scene from a single RGB image, it fails to estimate scene regions that are occluded by foreground objects.
This limits the use of depth prediction in augmented and virtual reality applications, that aim at scene exploration by synthesizing the scene from a different vantage point, or at diminished reality.
To address this issue, we shift the focus from conventional depth map prediction to the regression of a specific data representation called Layered Depth Image (LDI), which contains information about the occluded regions in the reference frame and can fill in occlusion gaps in case of small view changes.
We propose a novel approach based on Convolutional Neural Networks (CNNs) to jointly predict depth maps and foreground separation masks used to condition Generative Adversarial Networks (GANs) for hallucinating plausible color and depths in the initially occluded areas.
We demonstrate the effectiveness of our approach for novel scene view synthesis from a single image.
We present results of empirical studies on positive speech on Twitter.
By positive speech we understand speech that works for the betterment of a given situation, in this case relations between different communities in a conflict-prone country.
We worked with four Twitter data sets.
Through semi-manual opinion mining, we found that positive speech accounted for < 1% of the data .
In fully automated studies, we tested two approaches: unsupervised statistical analysis, and supervised text classification based on distributed word representation.
We discuss benefits and challenges of those approaches and report empirical evidence obtained in the study.
In this paper, we introduce "Power Linear Unit" (PoLU) which increases the nonlinearity capacity of a neural network and thus helps improving its performance.
PoLU adopts several advantages of previously proposed activation functions.
First, the output of PoLU for positive inputs is designed to be identity to avoid the gradient vanishing problem.
Second, PoLU has a non-zero output for negative inputs such that the output mean of the units is close to zero, hence reducing the bias shift effect.
Thirdly, there is a saturation on the negative part of PoLU, which makes it more noise-robust for negative inputs.
Furthermore, we prove that PoLU is able to map more portions of every layer's input to the same space by using the power function and thus increases the number of response regions of the neural network.
We use image classification for comparing our proposed activation function with others.
In the experiments, MNIST, CIFAR-10, CIFAR-100, Street View House Numbers (SVHN) and ImageNet are used as benchmark datasets.
The neural networks we implemented include widely-used ELU-Network, ResNet-50, and VGG16, plus a couple of shallow networks.
Experimental results show that our proposed activation function outperforms other state-of-the-art models with most networks.
The security of communication in everyday life becomes very important.
On the other hand, all existing encryption protocols require from user additional knowledge end resources.
In this paper we discuss the problem of public key distribution between interested parties.
We propose to use a popular social media as a channel to publish public keys.
This way of key distribution allows also easily connect owner of the key with real person institution (what is not always easy).
Recognizing that the mobile devices become the main tool of communication, we present description of mobile application that uses proposed security methods.
Mobile phone calling is one of the most widely used communication methods in modern society.
The records of calls among mobile phone users provide us a valuable proxy for the understanding of human communication patterns embedded in social networks.
Mobile phone users call each other forming a directed calling network.
If only reciprocal calls are considered, we obtain an undirected mutual calling network.
The preferential communication behavior between two connected users can be statistically tested and it results in two Bonferroni networks with statistically validated edges.
We perform a comparative analysis of the statistical properties of these four networks, which are constructed from the calling records of more than nine million individuals in Shanghai over a period of 110 days.
We find that these networks share many common structural properties and also exhibit idiosyncratic features when compared with previously studied large mobile calling networks.
The empirical findings provide us an intriguing picture of a representative large social network that might shed new lights on the modelling of large social networks.
In the k-Apex problem the task is to find at most k vertices whose deletion makes the given graph planar.
The graphs for which there exists a solution form a minor closed class of graphs, hence by the deep results of Robertson and Seymour, there is an O(n^3) time algorithm for every fixed value of k. However, the proof is extremely complicated and the constants hidden by the big-O notation are huge.
Here we give a much simpler algorithm for this problem with quadratic running time, by iteratively reducing the input graph and then applying techniques for graphs of bounded treewidth.
Massive sizes of real-world graphs, such as social networks and web graph, impose serious challenges to process and perform analytics on them.
These issues can be resolved by working on a small summary of the graph instead .
A summary is a compressed version of the graph that removes several details, yet preserves it's essential structure.
Generally, some predefined quality measure of the summary is optimized to bound the approximation error incurred by working on the summary instead of the whole graph.
All known summarization algorithms are computationally prohibitive and do not scale to large graphs.
In this paper we present an efficient randomized algorithm to compute graph summaries with the goal to minimize reconstruction error.
We propose a novel weighted sampling scheme to sample vertices for merging that will result in the least reconstruction error.
We provide analytical bounds on the running time of the algorithm and prove approximation guarantee for our score computation.
Efficiency of our algorithm makes it scalable to very large graphs on which known algorithms cannot be applied.
We test our algorithm on several real world graphs to empirically demonstrate the quality of summaries produced and compare to state of the art algorithms.
We use the summaries to answer several structural queries about original graph and report their accuracies.
Benefit from the quick development of deep learning techniques, salient object detection has achieved remarkable progresses recently.
However, there still exists following two major challenges that hinder its application in embedded devices, low resolution output and heavy model weight.
To this end, this paper presents an accurate yet compact deep network for efficient salient object detection.
More specifically, given a coarse saliency prediction in the deepest layer, we first employ residual learning to learn side-output residual features for saliency refinement, which can be achieved with very limited convolutional parameters while keep accuracy.
Secondly, we further propose reverse attention to guide such side-output residual learning in a top-down manner.
By erasing the current predicted salient regions from side-output features, the network can eventually explore the missing object parts and details which results in high resolution and accuracy.
Experiments on six benchmark datasets demonstrate that the proposed approach compares favorably against state-of-the-art methods, and with advantages in terms of simplicity, efficiency (45 FPS) and model size (81 MB).
Vajda and Buttyan (VB) proposed a set of five lightweight RFID authentication protocols.
Defend, Fu, and Juels (DFJ) did cryptanalysis on two of them - XOR and SUBSET.
To the XOR protocol, DFJ proposed repeated keys attack and nibble attack.
In this paper, we identify the vulnerability existed in the original VB's successive session key permutation algorithm.
We propose three enhancements to prevent DFJ's attacks and make XOR protocol stronger without introducing extra resource cost.
Fuzzy logic programming is a growing declarative paradigm aiming to integrate fuzzy logic into logic programming.
One of the most difficult tasks when specifying a fuzzy logic program is determining the right weights for each rule, as well as the most appropriate fuzzy connectives and operators.
In this paper, we introduce a symbolic extension of fuzzy logic programs in which some of these parameters can be left unknown, so that the user can easily see the impact of their possible values.
Furthermore, given a number of test cases, the most appropriate values for these parameters can be automatically computed.
To develop a knowledge-aware recommender system, a key data problem is how we can obtain rich and structured knowledge information for recommender system (RS) items.
Existing datasets or methods either use side information from original recommender systems (containing very few kinds of useful information) or utilize private knowledge base (KB).
In this paper, we present the first public linked KB dataset for recommender systems, named KB4Rec v1.0, which has linked three widely used RS datasets with the popular KB Freebase.
Based on our linked dataset, we first preform some interesting qualitative analysis experiments, in which we discuss the effect of two important factors (i.e. popularity and recency) on whether a RS item can be linked to a KB entity.
Finally, we present the comparison of several knowledge-aware recommendation algorithms on our linked dataset.
In a typical real-world application of re-id, a watch-list (gallery set) of a handful of target people (e.g.suspects) to track around a large volume of non-target people are demanded across camera views, and this is called the open-world person re-id.
Different from conventional (closed-world) person re-id, a large portion of probe samples are not from target people in the open-world setting.
And, it always happens that a non-target person would look similar to a target one and therefore would seriously challenge a re-id system.
In this work, we introduce a deep open-world group-based person re-id model based on adversarial learning to alleviate the attack problem caused by similar non-target people.
The main idea is learning to attack feature extractor on the target people by using GAN to generate very target-like images (imposters), and in the meantime the model will make the feature extractor learn to tolerate the attack by discriminative learning so as to realize group-based verification.
The framework we proposed is called the adversarial open-world person re-identification, and this is realized by our Adversarial PersonNet (APN) that jointly learns a generator, a person discriminator, a target discriminator and a feature extractor, where the feature extractor and target discriminator share the same weights so as to makes the feature extractor learn to tolerate the attack by imposters for better group-based verification.
While open-world person re-id is challenging, we show for the first time that the adversarial-based approach helps stabilize person re-id system under imposter attack more effectively.
Covariant-contravariant simulation and conformance simulation generalize plain simulation and try to capture the fact that it is not always the case that "the larger the number of behaviors, the better".
We have previously studied their logical characterizations and in this paper we present the axiomatizations of the preorders defined by the new simulation relations and their induced equivalences.
The interest of our results lies in the fact that the axiomatizations help us to know the new simulations better, understanding in particular the role of the contravariant characteristics and their interplay with the covariant ones; moreover, the axiomatizations provide us with a powerful tool to (algebraically) prove results of the corresponding semantics.
But we also consider our results interesting from a metatheoretical point of view: the fact that the covariant-contravariant simulation equivalence is indeed ground axiomatizable when there is no action that exhibits both a covariant and a contravariant behaviour, but becomes non-axiomatizable whenever we have together actions of that kind and either covariant or contravariant actions, offers us a new subtle example of the narrow border separating axiomatizable and non-axiomatizable semantics.
We expect that by studying these examples we will be able to develop a general theory separating axiomatizable and non-axiomatizable semantics.
In this work, we propose a robust Head-Related Transfer Function (HRTF)-based polynomial beamformer design which accounts for the influence of a humanoid robot's head on the sound field.
In addition, it allows for a flexible steering of our previously proposed robust HRTF-based beamformer design.
We evaluate the HRTF-based polynomial beamformer design and compare it to the original HRTF-based beamformer design by means of signal-independent measures as well as word error rates of an off-the-shelf speech recognition system.
Our results confirm the effectiveness of the polynomial beamformer design, which makes it a promising approach to robust beamforming for robot audition.
Mass segmentation is an important task in mammogram analysis, providing effective morphological features and regions of interest (ROI) for mass detection and classification.
Inspired by the success of using deep convolutional features for natural image analysis and conditional random fields (CRF) for structural learning, we propose an end-to-end network for mammographic mass segmentation.
The network employs a fully convolutional network (FCN) to model potential function, followed by a CRF to perform structural learning.
Because the mass distribution varies greatly with pixel position, the FCN is combined with position priori for the task.
Due to the small size of mammogram datasets, we use adversarial training to control over-fitting.
Four models with different convolutional kernels are further fused to improve the segmentation results.
Experimental results on two public datasets, INbreast and DDSM-BCRP, show that our end-to-end network combined with adversarial training achieves the-state-of-the-art results.
Inference models are a key component in scaling variational inference to deep latent variable models, most notably as encoder networks in variational auto-encoders (VAEs).
By replacing conventional optimization-based inference with a learned model, inference is amortized over data examples and therefore more computationally efficient.
However, standard inference models are restricted to direct mappings from data to approximate posterior estimates.
The failure of these models to reach fully optimized approximate posterior estimates results in an amortization gap.
We aim toward closing this gap by proposing iterative inference models, which learn to perform inference optimization through repeatedly encoding gradients.
Our approach generalizes standard inference models in VAEs and provides insight into several empirical findings, including top-down inference techniques.
We demonstrate the inference optimization capabilities of iterative inference models and show that they outperform standard inference models on several benchmark data sets of images and text.
Most of the crowd abnormal event detection methods rely on complex hand-crafted features to represent the crowd motion and appearance.
Convolutional Neural Networks (CNN) have shown to be a powerful tool with excellent representational capacities, which can leverage the need for hand-crafted features.
In this paper, we show that keeping track of the changes in the CNN feature across time can facilitate capturing the local abnormality.
We specifically propose a novel measure-based method which allows measuring the local abnormality in a video by combining semantic information (inherited from existing CNN models) with low-level Optical-Flow.
One of the advantage of this method is that it can be used without the fine-tuning costs.
The proposed method is validated on challenging abnormality detection datasets and the results show the superiority of our method compared to the state-of-the-art methods.
Traditional methods to achieve high localization accuracy with tactile sensors usually use a matrix of miniaturized individual sensors distributed on the area of interest.
This approach usually comes at a price of increased complexity in fabrication and circuitry, and can be hard to adapt for non planar geometries.
We propose to use low cost optic components mounted on the edges of the sensing area to measure how light traveling through an elastomer is affected by touch.
Multiple light emitters and receivers provide us with a rich signal set that contains the necessary information to pinpoint both the location and depth of an indentation with high accuracy.
We demonstrate sub-millimeter accuracy on location and depth on a 20mm by 20mm active sensing area.
Our sensor provides high depth sensitivity as a result of two different modalities in how light is guided through our elastomer.
This method results in a low cost, easy to manufacture sensor.
We believe this approach can be adapted to cover non-planar surfaces, simplifying future integration in robot skin applications.
The design and the implementation of a genetic algorithm are described.
The applicability domain is on structure-activity relationships expressed as multiple linear regressions and predictor variables are from families of structure-based molecular descriptors.
An experiment to compare different selection and survival strategies was designed and realized.
The genetic algorithm was run using the designed experiment on a set of 206 polychlorinated biphenyls searching on structure-activity relationships having known the measured octanol-water partition coefficients and a family of molecular descriptors.
The experiment shows that different selection and survival strategies create different partitions on the entire population of all possible genotypes.
This paper presents a novel statistical state-dependent timing model for voltage over scaled (VoS) logic circuits that accurately and rapidly finds the timing distribution of output bits.
Using this model erroneous VoS circuits can be represented as error-free circuits combined with an error-injector.
A case study of a two point DFT unit employing the proposed model is presented and compared to HSPICE circuit simulation.
Results show an accurate match, with significant speedup gains.
Convolutional neural networks (CNNs) show impressive performance for image classification and detection, extending heavily to the medical image domain.
Nevertheless, medical experts are sceptical in these predictions as the nonlinear multilayer structure resulting in a classification outcome is not directly graspable.
Recently, approaches have been shown which help the user to understand the discriminative regions within an image which are decisive for the CNN to conclude to a certain class.
Although these approaches could help to build trust in the CNNs predictions, they are only slightly shown to work with medical image data which often poses a challenge as the decision for a class relies on different lesion areas scattered around the entire image.
Using the DiaretDB1 dataset, we show that on retina images different lesion areas fundamental for diabetic retinopathy are detected on an image level with high accuracy, comparable or exceeding supervised methods.
On lesion level, we achieve few false positives with high sensitivity, though, the network is solely trained on image-level labels which do not include information about existing lesions.
Classifying between diseased and healthy images, we achieve an AUC of 0.954 on the DiaretDB1.
Abstract Dialectical Frameworks (ADFs) generalize Dung's argumentation frameworks allowing various relationships among arguments to be expressed in a systematic way.
We further generalize ADFs so as to accommodate arbitrary acceptance degrees for the arguments.
This makes ADFs applicable in domains where both the initial status of arguments and their relationship are only insufficiently specified by Boolean functions.
We define all standard ADF semantics for the weighted case, including grounded, preferred and stable semantics.
We illustrate our approach using acceptance degrees from the unit interval and show how other valuation structures can be integrated.
In each case it is sufficient to specify how the generalized acceptance conditions are represented by formulas, and to specify the information ordering underlying the characteristic ADF operator.
We also present complexity results for problems related to weighted ADFs.
Though convolutional neural networks have achieved state-of-the-art performance on various vision tasks, they are extremely vulnerable to adversarial examples, which are obtained by adding human-imperceptible perturbations to the original images.
Adversarial examples can thus be used as an useful tool to evaluate and select the most robust models in safety-critical applications.
However, most of the existing adversarial attacks only achieve relatively low success rates under the challenging black-box setting, where the attackers have no knowledge of the model structure and parameters.
To this end, we propose to improve the transferability of adversarial examples by creating diverse input patterns.
Instead of only using the original images to generate adversarial examples, our method applies random transformations to the input images at each iteration.
Extensive experiments on ImageNet show that the proposed attack method can generate adversarial examples that transfer much better to different networks than existing baselines.
To further improve the transferability, we (1) integrate the recently proposed momentum method into the attack process; and (2) attack an ensemble of networks simultaneously.
By evaluating our method against top defense submissions and official baselines from NIPS 2017 adversarial competition, this enhanced attack reaches an average success rate of 73.0%, which outperforms the top 1 attack submission in the NIPS competition by a large margin of 6.6%.
We hope that our proposed attack strategy can serve as a benchmark for evaluating the robustness of networks to adversaries and the effectiveness of different defense methods in future.
The code is public available at https://github.com/cihangxie/DI-2-FGSM.
We propose an approximation method for thresholding of singular values using Chebyshev polynomial approximation (CPA).
Many signal processing problems require iterative application of singular value decomposition (SVD) for minimizing the rank of a given data matrix with other cost functions and/or constraints, which is called matrix rank minimization.
In matrix rank minimization, singular values of a matrix are shrunk by hard-thresholding, soft-thresholding, or weighted soft-thresholding.
However, the computational cost of SVD is generally too expensive to handle high dimensional signals such as images; hence, in this case, matrix rank minimization requires enormous computation time.
In this paper, we leverage CPA to (approximately) manipulate singular values without computing singular values and vectors.
The thresholding of singular values is expressed by a multiplication of certain matrices, which is derived from a characteristic of CPA.
The multiplication is also efficiently computed using the sparsity of signals.
As a result, the computational cost is significantly reduced.
Experimental results suggest the effectiveness of our method through several image processing applications based on matrix rank minimization with nuclear norm relaxation in terms of computation time and approximation precision.
Rate control is widely adopted during video streaming to provide both high video qualities and low latency under various network conditions.
However, despite that many work have been proposed, they fail to tackle one major problem: previous methods determine a future transmission rate as a single for value which will be used in an entire time-slot, while real-world network conditions, unlike lab setup, often suffer from rapid and stochastic changes, resulting in the failures of predictions.
In this paper, we propose a delay-constrained rate control approach based on end-to-end deep learning.
The proposed model predicts future bit rate not as a single value, but as possible bit rate ranges using target delay gradient, with which the transmission delay is guaranteed.
We collect a large scale of real-world live streaming data to train our model, and as a result, it automatically learns the correlation between throughput and target delay gradient.
We build a testbed to evaluate our approach.
Compared with the state-of-the-art methods, our approach demonstrates a better performance in bandwidth utilization.
In all considered scenarios, a range based rate control approach outperforms the one without range by 19% to 35% in average QoE improvement.
Neural Machine Translation (NMT) is a new approach for Machine Translation (MT), and due to its success, it has absorbed the attention of many researchers in the field.
In this paper, we study NMT model on Persian-English language pairs, to analyze the model and investigate the appropriateness of the model for scarce-resourced scenarios, the situation that exists for Persian-centered translation systems.
We adjust the model for the Persian language and find the best parameters and hyper parameters for two tasks: translation and transliteration.
We also apply some preprocessing task on the Persian dataset which yields to increase for about one point in terms of BLEU score.
Also, we have modified the loss function to enhance the word alignment of the model.
This new loss function yields a total of 1.87 point improvements in terms of BLEU score in the translation quality.
Grid computing is a type of distributed computing which allows sharing of computer resources through Internet.
It not only allows us to share files but also most of the software and hardware resources.
An efficient resource discovery mechanism is the fundamental requirements for grid computing systems, as it supports resource management and scheduling of applications.
Among various discovery mechanisms,Peer-to-Peer (P2P) technology witnessed rapid development and the key component for this success is efficient lookup applications of P2P.
Chord is a P2P structural model widely used as a routing protocol to find resources in grid environment.
Plenty of ideas are implemented by researchers to improve the lookup performance of chord protocol in Grid environment.
In this paper, we discuss the recent researches made on Chord Structured P2P protocol and present our proposed methods in which we use the address of Recently Visited Node (RVN) and fuzzy technique to easily locate the grid resources by reducing message complexity and time complexity.
Binary code clone analysis is an important technique which has a wide range of applications in software engineering (e.g., plagiarism detection, bug detection).
The main challenge of the topic lies in the semantics-equivalent code transformation (e.g., optimization, obfuscation) which would alter representations of binary code tremendously.
Another chal- lenge is the trade-off between detection accuracy and coverage.
Unfortunately, existing techniques still rely on semantics-less code features which are susceptible to the code transformation.
Besides, they adopt merely either a static or a dynamic approach to detect binary code clones, which cannot achieve high accuracy and coverage simultaneously.
In this paper, we propose a semantics-based hybrid approach to detect binary clone functions.
We execute a template binary function with its test cases, and emulate the execution of every target function for clone comparison with the runtime information migrated from that template function.
The semantic signatures are extracted during the execution of the template function and emulation of the target function.
Lastly, a similarity score is calculated from their signatures to measure their likeness.
We implement the approach in a prototype system designated as BinMatch which analyzes IA-32 binary code on the Linux platform.
We evaluate BinMatch with eight real-world projects compiled with different compilation configurations and commonly-used obfuscation methods, totally performing over 100 million pairs of function comparison.
The experimental results show that BinMatch is robust to the semantics-equivalent code transformation.
Besides, it not only covers all target functions for clone analysis, but also improves the detection accuracy comparing to the state-of-the-art solutions.
In this paper, we propose the Fourier frequency vector (FFV), inherently, associated with multidimensional Fourier transform.
With the help of FFV, we are able to provide physical meaning of so called negative frequencies in multidimensional Fourier transform (MDFT), which in turn provide multidimensional spatial and space-time series analysis.
The complex exponential representation of sinusoidal function always yields two frequencies, negative frequency corresponding to positive frequency and vice versa, in the multidimensional Fourier spectrum.
Thus, using the MDFT, we propose multidimensional Hilbert transform (MDHT) and associated multidimensional analytic signal (MDAS) with following properties: (a) the extra and redundant positive, negative, or both frequencies, introduced due to complex exponential representation of multidimensional Fourier spectrum, are suppressed, (b) real part of MDAS is original signal, (c) real and imaginary part of MDAS are orthogonal, and (d) the magnitude envelope of a original signal is obtained as the magnitude of its associated MDAS, which is the instantaneous amplitude of the MDAS.
The proposed MDHT and associated DMAS are generalization of the 1D HT and AS, respectively.
We also provide the decomposition of an image into the AM-FM image model by the Fourier method and obtain explicit expression for the analytic image computation by 2DDFT.
In this paper, we will understand that the development of the Digital Video Broadcasting to a Handheld (DVB-H) standard makes it possible to deliver live broadcast television to a mobile handheld device.
Building upon the strengths of the Digital Video Broadcasting - Terrestrial (DVB-T) standard in use in millions of homes, DVB-H recognizes the trend towards the personal consumption of media.
We present here a new probabilistic inference algorithm that gives exact results in the domain of discrete probability distributions.
This algorithm, named the Statues algorithm, calculates the marginal probability distribution on probabilistic models defined as direct acyclic graphs.
These models are made up of well-defined primitives that allow to express, in particular, joint probability distributions, Bayesian networks, discrete Markov chains, conditioning and probabilistic arithmetic.
The Statues algorithm relies on a variable binding mechanism based on the generator construct, a special form of coroutine; being related to the enumeration algorithm, this new algorithm brings important improvements in terms of efficiency, which makes it valuable in regard to other exact marginalization algorithms.
After introduction of several definitions, primitives and compositional rules, we present in details the Statues algorithm.
Then, we briefly discuss the interest of this algorithm compared to others and we present possible extensions.
Finally, we introduce Lea and MicroLea, two Python libraries implementing the Statues algorithm, along with several use cases.
A proof of the correctness of the algorithm is provided in appendix.
The job of software effort estimation is a critical one in the early stages of the software development life cycle when the details of requirements are usually not clearly identified.
Various optimization techniques help in improving the accuracy of effort estimation.
The Support Vector Regression (SVR) is one of several different soft-computing techniques that help in getting optimal estimated values.
The idea of SVR is based upon the computation of a linear regression function in a high dimensional feature space where the input data are mapped via a nonlinear function.
Further, the SVR kernel methods can be applied in transforming the input data and then based on these transformations, an optimal boundary between the possible outputs can be obtained.
The main objective of the research work carried out in this paper is to estimate the software effort using use case point approach.
The use case point approach relies on the use case diagram to estimate the size and effort of software projects.
Then, an attempt has been made to optimize the results obtained from use case point analysis using various SVR kernel methods to achieve better prediction accuracy.
Recent advances in facial landmark detection achieve success by learning discriminative features from rich deformation of face shapes and poses.
Besides the variance of faces themselves, the intrinsic variance of image styles, e.g., grayscale vs. color images, light vs. dark, intense vs. dull, and so on, has constantly been overlooked.
This issue becomes inevitable as increasing web images are collected from various sources for training neural networks.
In this work, we propose a style-aggregated approach to deal with the large intrinsic variance of image styles for facial landmark detection.
Our method transforms original face images to style-aggregated images by a generative adversarial module.
The proposed scheme uses the style-aggregated image to maintain face images that are more robust to environmental changes.
Then the original face images accompanying with style-aggregated ones play a duet to train a landmark detector which is complementary to each other.
In this way, for each face, our method takes two images as input, i.e., one in its original style and the other in the aggregated style.
In experiments, we observe that the large variance of image styles would degenerate the performance of facial landmark detectors.
Moreover, we show the robustness of our method to the large variance of image styles by comparing to a variant of our approach, in which the generative adversarial module is removed, and no style-aggregated images are used.
Our approach is demonstrated to perform well when compared with state-of-the-art algorithms on benchmark datasets AFLW and 300-W. Code is publicly available on GitHub: https://github.com/D-X-Y/SAN
This work discusses how the MPContribs framework in the Materials Project (MP) allows user-contributed data to be shown and analyzed alongside the core MP database.
The Materials Project is a searchable database of electronic structure properties of over 65,000 bulk solid materials that is accessible through a web-based science-gateway.
We describe the motivation for enabling user contributions to the materials data and present the framework's features and challenges in the context of two real applications.
These use-cases illustrate how scientific collaborations can build applications with their own "user-contributed" data using MPContribs.
The Nanoporous Materials Explorer application provides a unique search interface to a novel dataset of hundreds of thousands of materials, each with tables of user-contributed values related to material adsorption and density at varying temperature and pressure.
The Unified Theoretical and Experimental x-ray Spectroscopy application discusses a full workflow for the association, dissemination and combined analyses of experimental data from the Advanced Light Source with MP's theoretical core data, using MPContribs tools for data formatting, management and exploration.
The capabilities being developed for these collaborations are serving as the model for how new materials data can be incorporated into the Materials Project website with minimal staff overhead while giving powerful tools for data search and display to the user community.
We carry out a theoretical analysis of the uplink (UL) of a massive MIMO system with per-user channel correlation and Rician fading, using two processing approaches.
Firstly, we examine the linear minimum-mean-square-error receiver under training-based imperfect channel estimates.
Secondly, we propose a statistical combining technique that is more suitable in environments with strong Line-of-Sight (LoS) components.
We derive closed-form asymptotic approximations of the UL spectral efficiency (SE) attained by each combining scheme in single and multi-cell settings, as a function of the system parameters.
These expressions are insightful in how different factors such as LoS propagation conditions and pilot contamination impact the overall system performance.
Furthermore, they are exploited to determine the optimal number of training symbols which is shown to be of significant interest at low Rician factors.
The study and numerical results substantiate that stronger LoS signals lead to better performances, and under such conditions, the statistical combining entails higher SE gains than the conventional receiver.
Discriminative Correlation Filters (DCF) are efficient in visual tracking but suffer from unwanted boundary effects.
Spatially Regularized DCF (SRDCF) has been suggested to resolve this issue by enforcing spatial penalty on DCF coefficients, which, inevitably, improves the tracking performance at the price of increasing complexity.
To tackle online updating, SRDCF formulates its model on multiple training images, further adding difficulties in improving efficiency.
In this work, by introducing temporal regularization to SRDCF with single sample, we present our spatial-temporal regularized correlation filters (STRCF).
Motivated by online Passive-Agressive (PA) algorithm, we introduce the temporal regularization to SRDCF with single sample, thus resulting in our spatial-temporal regularized correlation filters (STRCF).
The STRCF formulation can not only serve as a reasonable approximation to SRDCF with multiple training samples, but also provide a more robust appearance model than SRDCF in the case of large appearance variations.
Besides, it can be efficiently solved via the alternating direction method of multipliers (ADMM).
By incorporating both temporal and spatial regularization, our STRCF can handle boundary effects without much loss in efficiency and achieve superior performance over SRDCF in terms of accuracy and speed.
Experiments are conducted on three benchmark datasets: OTB-2015, Temple-Color, and VOT-2016.
Compared with SRDCF, STRCF with hand-crafted features provides a 5 times speedup and achieves a gain of 5.4% and 3.6% AUC score on OTB-2015 and Temple-Color, respectively.
Moreover, STRCF combined with CNN features also performs favorably against state-of-the-art CNN-based trackers and achieves an AUC score of 68.3% on OTB-2015.
We address the issue of incorporating a particular yet expressive form of integrity constraints (namely, denial constraints) into probabilistic databases.
To this aim, we move away from the common way of giving semantics to probabilistic databases, which relies on considering a unique interpretation of the data, and address two fundamental problems: consistency checking and query evaluation.
The former consists in verifying whether there is an interpretation which conforms to both the marginal probabilities of the tuples and the integrity constraints.
The latter is the problem of answering queries under a "cautious" paradigm, taking into account all interpretations of the data in accordance with the constraints.
In this setting, we investigate the complexity of the above-mentioned problems, and identify several tractable cases of practical relevance.
One way to interpret neural model predictions is to highlight the most important input features---for example, a heatmap visualization over the words in an input sentence.
In existing interpretation methods for NLP, a word's importance is determined by either input perturbation---measuring the decrease in model confidence when that word is removed---or by the gradient with respect to that word.
To understand the limitations of these methods, we use input reduction, which iteratively removes the least important word from the input.
This exposes pathological behaviors of neural models: the remaining words appear nonsensical to humans and are not the ones determined as important by interpretation methods.
As we confirm with human experiments, the reduced examples lack information to support the prediction of any label, but models still make the same predictions with high confidence.
To explain these counterintuitive results, we draw connections to adversarial examples and confidence calibration: pathological behaviors reveal difficulties in interpreting neural models trained with maximum likelihood.
To mitigate their deficiencies, we fine-tune the models by encouraging high entropy outputs on reduced examples.
Fine-tuned models become more interpretable under input reduction without accuracy loss on regular examples.
Personal connections between creators and evaluators of scientific works are ubiquitous, and the possibility of bias ever-present.
Although connections have been shown to bias prospective judgments of (uncertain) future performance, it is unknown whether such biases occur in the much more concrete task of assessing the scientific validity of already completed work, and if so, why.
This study presents evidence that personal connections between authors and reviewers of neuroscience manuscripts are associated with biased judgments and explores the mechanisms driving the effect.
Using reviews from 7,981 neuroscience manuscripts submitted to the journal PLOS ONE, which instructs reviewers to evaluate manuscripts only on scientific validity, we find that reviewers favored authors close in the co-authorship network by ~0.11 points on a 1.0 - 4.0 scale for each step of proximity.
PLOS ONE's validity-focused review and the substantial amount of favoritism shown by distant vs. very distant reviewers, both of whom should have little to gain from nepotism, point to the central role of substantive disagreements between scientists in different "schools of thought."
The results suggest that removing bias from peer review cannot be accomplished simply by recusing the closely-connected reviewers, and highlight the value of recruiting reviewers embedded in diverse professional networks.
Finding graph indices which are unbiased to network size and density is of high importance both within a given field and across fields for enhancing comparability of modern network science studies.
The degree variance is an important metric for characterising network heterogeneity.
Here, we provide an analytically valid normalisation of degree variance to replace previous normalisations which are either invalid or not applicable to all networks.
It is shown that this normalisation provides equal values for graphs and their complements; it is maximal in the star graph (and its complement); and its expected value is constant with respect to density for random networks of the same size.
We strengthen these results with model observations in weighted random networks, random geometric networks and resting-state brain networks, showing that the proposed normalisation is unbiased to both network size and density.
The closed form expression proposed also benefits from high computational efficiency and straightforward mathematical analysis.
In an application of a subnetwork comparability problem of nationwide and within state US airport networks, the nationwide US airport network is shown to be much more heterogeneous than most within-state networks, illustrating the importance of the increased reliability of this true normalisation.
Recently, deep residual networks have been successfully applied in many computer vision and natural language processing tasks, pushing the state-of-the-art performance with deeper and wider architectures.
In this work, we interpret deep residual networks as ordinary differential equations (ODEs), which have long been studied in mathematics and physics with rich theoretical and empirical success.
From this interpretation, we develop a theoretical framework on stability and reversibility of deep neural networks, and derive three reversible neural network architectures that can go arbitrarily deep in theory.
The reversibility property allows a memory-efficient implementation, which does not need to store the activations for most hidden layers.
Together with the stability of our architectures, this enables training deeper networks using only modest computational resources.
We provide both theoretical analyses and empirical results.
Experimental results demonstrate the efficacy of our architectures against several strong baselines on CIFAR-10, CIFAR-100 and STL-10 with superior or on-par state-of-the-art performance.
Furthermore, we show our architectures yield superior results when trained using fewer training data.
This is the preprint version of our paper on ICWL2015.
A virtual reality based enhanced technology for learning primary geography is proposed, which synthesizes several latest information technologies including virtual reality(VR), 3D geographical information system(GIS), 3D visualization and multimodal human-computer-interaction (HCI).
The main functions of the proposed system are introduced, i.e.Buffer analysis, Overlay analysis, Space convex hull calculation, Space convex decomposition, 3D topology analysis and 3D space intersection detection.
The multimodal technologies are employed in the system to enhance the immersive perception of the users.
In this work, we briefly outline the core 5G air interface improvements introduced by the latest New Radio (NR) specifications, as well as elaborate on the unique features of initial access in 5G NR with a particular emphasis on millimeter-wave (mmWave) frequency range.
The highly directional nature of 5G mmWave cellular systems poses a variety of fundamental differences and research problem formulations, and a holistic understanding of the key system design principles behind the 5G NR is essential.
Here, we condense the relevant information collected from a wide diversity of 5G NR standardization documents (based on 3GPP Release 15) to distill the essentials of directional access in 5G mmWave cellular, which becomes the foundation for any corresponding system-level analysis.
Distant supervision can effectively label data for relation extraction, but suffers from the noise labeling problem.
Recent works mainly perform soft bag-level noise reduction strategies to find the relatively better samples in a sentence bag, which is suboptimal compared with making a hard decision of false positive samples in sentence level.
In this paper, we introduce an adversarial learning framework, which we named DSGAN, to learn a sentence-level true-positive generator.
Inspired by Generative Adversarial Networks, we regard the positive samples generated by the generator as the negative samples to train the discriminator.
The optimal generator is obtained until the discrimination ability of the discriminator has the greatest decline.
We adopt the generator to filter distant supervision training dataset and redistribute the false positive instances into the negative set, in which way to provide a cleaned dataset for relation classification.
The experimental results show that the proposed strategy significantly improves the performance of distant supervision relation extraction comparing to state-of-the-art systems.
Optimal operation of a country's air transport infrastructure plays a major role in the economic development of nations.
Due to the increasing use of air transportation in today's world, flights' boarding times have become a concern for both airlines and airports, thus the importance of knowing beforehand how changes in flights demand parameters and physical airport layout will affect passengers flow and boarding times.
This paper presents a pedestrian modeling study in which a national airport passenger flow was analyzed.
The study was conducted at Vanguardia National Airport in Villavicencio, Meta, Colombia.
Different effects of structural changes are shown and provide judging elements for decision makers regarding passenger traffic in airport design.
A hot topic in data center design is to envision geo-distributed architectures spanning a few sites across wide area networks, allowing more proximity to the end users and higher survivability, defined as the capacity of a system to operate after failures.
As a shortcoming, this approach is subject to an increase of latency between servers, caused by their geographic distances.
In this paper, we address the trade-off between latency and survivability in geo-distributed data centers, through the formulation of an optimization problem.
Simulations considering realistic scenarios show that the latency increase is significant only in the case of very strong survivability requirements, whereas it is negligible for moderate survivability requirements.
For instance, the worst-case latency is less than 4~ms when guaranteeing that 80% of the servers are available after a failure, in a network where the latency could be up to 33 ms.
Dealing with structured data needs the use of expressive representation formalisms that, however, puts the problem to deal with the computational complexity of the machine learning process.
Furthermore, real world domains require tools able to manage their typical uncertainty.
Many statistical relational learning approaches try to deal with these problems by combining the construction of relevant relational features with a probabilistic tool.
When the combination is static (static propositionalization), the constructed features are considered as boolean features and used offline as input to a statistical learner; while, when the combination is dynamic (dynamic propositionalization), the feature construction and probabilistic tool are combined into a single process.
In this paper we propose a selective propositionalization method that search the optimal set of relational features to be used by a probabilistic learner in order to minimize a loss function.
The new propositionalization approach has been combined with the random subspace ensemble method.
Experiments on real-world datasets shows the validity of the proposed method.
We present in this paper, a modelling of an expertise in pragmatics.
We follow knowledge engineering techniques and observe the expert when he analyses a social discussion forum.
Then a number of models are defined.
These models emphasises the process followed by the expert and a number of criteria used in his analysis.
Results can be used as guides that help to understand and annotate discussion forum.
We aim at modelling other pragmatics analysis in order to complete the base of guides; criteria, process, etc. of discussion analysis
In this paper, we consider user location privacy in mobile edge clouds (MECs).
MECs are small clouds deployed at the network edge to offer cloud services close to mobile users, and many solutions have been proposed to maximize service locality by migrating services to follow their users.
Co-location of a user and his service, however, implies that a cyber eavesdropper observing service migrations between MECs can localize the user up to one MEC coverage area, which can be fairly small (e.g., a femtocell).
We consider using chaff services to defend against such an eavesdropper, with focus on strategies to control the chaffs.
Assuming the eavesdropper performs maximum likelihood (ML) detection, we consider both heuristic strategies that mimic the user's mobility and optimized strategies designed to minimize the detection or tracking accuracy.
We show that a single chaff controlled by the optimal strategy or its online variation can drive the eavesdropper's tracking accuracy to zero when the user's mobility is sufficiently random.
We further propose extended strategies that utilize randomization to defend against an advanced eavesdropper aware of the strategy.
The efficacy of our solutions is verified through both synthetic and trace-driven simulations.
The ARP-Path protocol has flourished as a promise for wired networks, creating shortest paths with the simplicity of pure bridging and competing directly with TRILL and SPB.
After analyzing different alternatives of ARP-Path and creating the All-Path family, the idea of migrating the protocol to wireless networks appeared to be a good alternative to protocols such as a AODV.
In this article, we check the implications of adapting ARP-Path to a wireless environment, and we prove that good ideas for wired networks might not be directly applicable to wireless networks, as not only the media differs, but also the characterization of these networks varies.
Foreground (FG) pixel labelling plays a vital role in video surveillance.
Recent engineering solutions have attempted to exploit the efficacy of deep learning (DL) models initially targeted for image classification to deal with FG pixel labelling.
One major drawback of such strategy is the lacking delineation of visual objects when training samples are limited.
To grapple with this issue, we introduce a multi-view receptive field fully convolutional neural network (MV-FCN) that harness recent seminal ideas, such as, fully convolutional structure, inception modules, and residual networking.
Therefrom, we implement a system in an encoder-decoder fashion that subsumes a core and two complementary feature flow paths.
The model exploits inception modules at early and late stages with three different sizes of receptive fields to capture invariance at various scales.
The features learned in the encoding phase are fused with appropriate feature maps in the decoding phase through residual connections for achieving enhanced spatial representation.
These multi-view receptive fields and residual feature connections are expected to yield highly generalized features for an accurate pixel-wise FG region identification.
It is, then, trained with database specific exemplary segmentations to predict desired FG objects.
The comparative experimental results on eleven benchmark datasets validate that the proposed model achieves very competitive performance with the prior- and state-of-the-art algorithms.
We also report that how well a transfer learning approach can be useful to enhance the performance of our proposed MV-FCN.
Numerous institutions and organizations need not only to preserve the material and publications they produce, but also have as their task (although it would be desirable it was an obligation) to publish, disseminate and make publicly available all the results of the research and any other scientific/academic material.
The Open Archives Initiative (OAI) and the introduction of Open Archives Initiative Protocol for Metadata Harvesting (OAI-PMH), make this task much easier.
The main objective of this work is to make a comparative and qualitative study of the data -metadata specifically- contained in the whole set of Argentine repositories listed in the ROAR portal, focusing on the functional perspective of the quality of this metadata.
Another objective is to offer an overview of the status of these repositories, in an attempt to detect common failures and errors institutions incur when storing the metadata of the resources contained in these repositories, and thus be able to suggest measures to be able to improve the load and further retrieval processes.
It was found that the eight most used Dublin Core fields are: identifier, type, title, date, subject, creator, language and description.
Not all repositories fill all the fields, and the lack of normalization, or the excessive use of fields like language, type, format and subject is somewhat striking, and in some cases even alarming
This work presents the application of the artificial neural networks, trained and structurally optimized by genetic algorithms, for modeling of crude distillation process at PKN ORLEN S.A. refinery.
Models for the main fractionator distillation column products were developed using historical data.
Quality of the fractions were predicted based on several chosen process variables.
The performance of the model was validated using test data.
Neural networks used in companion with genetic algorithms proved that they can accurately predict fractions quality shifts, reproducing the results of the standard laboratory analysis.
Simple knowledge extraction method from neural network model built was also performed.
Genetic algorithms can be successfully utilized in efficient training of large neural networks and finding their optimal structures.
Majority of the face recognition algorithms use query faces captured from uncontrolled, in the wild, environment.
Often caused by the cameras limited capabilities, it is common for these captured facial images to be blurred or low resolution.
Super resolution algorithms are therefore crucial in improving the resolution of such images especially when the image size is small requiring enlargement.
This paper aims to demonstrate the effect of one of the state-of-the-art algorithms in the field of image super resolution.
To demonstrate the functionality of the algorithm, various before and after 3D face alignment cases are provided using the images from the Labeled Faces in the Wild (lfw).
Resulting images are subject to testing on a closed set face recognition protocol using unsupervised algorithms with high dimension extracted features.
The inclusion of super resolution algorithm resulted in significant improved recognition rate over recently reported results obtained from unsupervised algorithms.
It is well established that humans decision making and instrumental control uses multiple systems, some which use habitual action selection and some which require deliberate planning.
Deliberate planning systems use predictions of action-outcomes using an internal model of the agent's environment, while habitual action selection systems learn to automate by repeating previously rewarded actions.
Habitual control is computationally efficient but may be inflexible in changing environments.
Conversely, deliberate planning may be computationally expensive, but flexible in dynamic environments.
This paper proposes a general architecture comprising both control paradigms by introducing an arbitrator that controls which subsystem is used at any time.
This system is implemented for a target-reaching task with a simulated two-joint robotic arm that comprises a supervised internal model and deep reinforcement learning.
Through permutation of target-reaching conditions, we demonstrate that the proposed is capable of rapidly learning kinematics of the system without a priori knowledge, and is robust to (A) changing environmental reward and kinematics, and (B) occluded vision.
The arbitrator model is compared to exclusive deliberate planning with the internal model and exclusive habitual control instances of the model.
The results show how such a model can harness the benefits of both systems, using fast decisions in reliable circumstances while optimizing performance in changing environments.
In addition, the proposed model learns very fast.
Finally, the system which includes internal models is able to reach the target under the visual occlusion, while the pure habitual system is unable to operate sufficiently under such conditions.
Motivated by many practical applications in logistics and mobility-as-a-service, we study the top-k optimal sequenced routes (KOSR) querying on large, general graphs where the edge weights may not satisfy the triangle inequality, e.g., road network graphs with travel times as edge weights.
The KOSR querying strives to find the top-k optimal routes (i.e., with the top-k minimal total costs) from a given source to a given destination, which must visit a number of vertices with specific vertex categories (e.g., gas stations, restaurants, and shopping malls) in a particular order (e.g., visiting gas stations before restaurants and then shopping malls).
To efficiently find the top-k optimal sequenced routes, we propose two algorithms PruningKOSR and StarKOSR.
In PruningKOSR, we define a dominance relationship between two partially-explored routes.
The partially-explored routes that can be dominated by other partially-explored routes are postponed being extended, which leads to a smaller searching space and thus improves efficiency.
In StarKOSR, we further improve the efficiency by extending routes in an A* manner.
With the help of a judiciously designed heuristic estimation that works for general graphs, the cost of partially explored routes to the destination can be estimated such that the qualified complete routes can be found early.
In addition, we demonstrate the high extensibility of the proposed algorithms by incorporating Hop Labeling, an effective label indexing technique for shortest path queries, to further improve efficiency.
Extensive experiments on multiple real-world graphs demonstrate that the proposed methods significantly outperform the baseline method.
Furthermore, when k=1, StarKOSR also outperforms the state-of-the-art method for the optimal sequenced route queries.
With the evolution of mobile devices, and smart-phones in particular, comes the ability to create new experiences that enhance the way we see, interact, and manipulate objects, within the world that surrounds us.
It is now possible to blend data from our senses and our devices in numerous ways that simply were not possible before using Augmented Reality technology.
In a near future, when all of the office devices as well as your personal electronic gadgets are on a common wireless network, operating them using a universal remote controller would be possible.
This paper presents an off-the-shelf, low-cost prototype that leverages the Augmented Reality technology to deliver a novel and interactive way of operating office network devices around using a mobile device.
We believe this type of system may provide benefits to controlling multiple integrated devices and visualizing interconnectivity or utilizing visual elements to pass information from one device to another, or may be especially beneficial to control devices when interacting with them physically may be difficult or pose danger or harm.
Estimating the engagement is critical for human - robot interaction.
Engagement measures typically rely on the dynamics of the social signals exchanged by the partners, especially speech and gaze.
However, the dynamics of these signals is likely to be influenced by individual and social factors, such as personality traits, as it is well documented that they critically influence how two humans interact with each other.
Here, we assess the influence of two factors, namely extroversion and negative attitude toward robots, on speech and gaze during a cooperative task, where a human must physically manipulate a robot to assemble an object.
We evaluate if the scores of extroversion and negative attitude towards robots co-variate with the duration and frequency of gaze and speech cues.
The experiments were carried out with the humanoid robot iCub and N=56 adult participants.
We found that the more people are extrovert, the more and longer they tend to talk with the robot; and the more people have a negative attitude towards robots, the less they will look at the robot face and the more they will look at the robot hands where the assembly and the contacts occur.
Our results confirm and provide evidence that the engagement models classically used in human-robot interaction should take into account attitudes and personality traits.
Recently, Visual Question Answering (VQA) has emerged as one of the most significant tasks in multimodal learning as it requires understanding both visual and textual modalities.
Existing methods mainly rely on extracting image and question features to learn their joint feature embedding via multimodal fusion or attention mechanism.
Some recent studies utilize external VQA-independent models to detect candidate entities or attributes in images, which serve as semantic knowledge complementary to the VQA task.
However, these candidate entities or attributes might be unrelated to the VQA task and have limited semantic capacities.
To better utilize semantic knowledge in images, we propose a novel framework to learn visual relation facts for VQA.
Specifically, we build up a Relation-VQA (R-VQA) dataset based on the Visual Genome dataset via a semantic similarity module, in which each data consists of an image, a corresponding question, a correct answer and a supporting relation fact.
A well-defined relation detector is then adopted to predict visual question-related relation facts.
We further propose a multi-step attention model composed of visual attention and semantic attention sequentially to extract related visual knowledge and semantic knowledge.
We conduct comprehensive experiments on the two benchmark datasets, demonstrating that our model achieves state-of-the-art performance and verifying the benefit of considering visual relation facts.
With the recent growth of conversational systems and intelligent assistants such as Apple Siri and Google Assistant, mobile devices are becoming even more pervasive in our lives.
As a consequence, users are getting engaged with the mobile apps and frequently search for an information need in their apps.
However, users cannot search within their apps through their intelligent assistants.
This requires a unified mobile search framework that identifies the target app(s) for the user's query, submits the query to the app(s), and presents the results to the user.
In this paper, we take the first step forward towards developing unified mobile search.
In more detail, we introduce and study the task of target apps selection, which has various potential real-world applications.
To this aim, we analyze attributes of search queries as well as user behaviors, while searching with different mobile apps.
The analyses are done based on thousands of queries that we collected through crowdsourcing.
We finally study the performance of state-of-the-art retrieval models for this task and propose two simple yet effective neural models that significantly outperform the baselines.
Our neural approaches are based on learning high-dimensional representations for mobile apps.
Our analyses and experiments suggest specific future directions in this research area.
Wireless sensor networks faces unbalanced energy consumption problem over time.
Clustering provides an energy efficient method to improve lifespan of the sensor network.
Cluster head collects data from other nodes and transmits it towards the sink node.
Cluster heads which are far-off from the sink, consumes more power in transmission of information towards the sink.
We propose Region Based Energy Balanced Inter-cluster communication protocol (RBEBP) to improve lifespan of the sensor network.
Monitored area has been divided into regions; cluster heads are selected from specific region based on the residual energy of nodes in that region.
If energy of nodes of the specific region is low, nodes from another region are selected as cluster heads.
Optimized selection of cluster heads helps in improving lifespan of the sensor network.
In our scheme, cluster heads which are far-off from the sink use another cluster heads as the relay nodes to transmit their data to the sink node.
So energy of cluster heads deplete in a uniform way and complete area remain covered by sensor nodes.
Simulation results demonstrate that RBEBP can effectively reduce total energy depletion and considerably extend lifespan of the network as compared to LEACH protocol.
RBEBP also minimize the problem of energy holes in monitored area and improve the throughput of the network
Effective data visualization is a key part of the discovery process in the era of big data.
It is the bridge between the quantitative content of the data and human intuition, and thus an essential component of the scientific path from data into knowledge and understanding.
Visualization is also essential in the data mining process, directing the choice of the applicable algorithms, and in helping to identify and remove bad data from the analysis.
However, a high complexity or a high dimensionality of modern data sets represents a critical obstacle.
How do we visualize interesting structures and patterns that may exist in hyper-dimensional data spaces?
A better understanding of how we can perceive and interact with multi dimensional information poses some deep questions in the field of cognition technology and human computer interaction.
To this effect, we are exploring the use of immersive virtual reality platforms for scientific data visualization, both as software and inexpensive commodity hardware.
These potentially powerful and innovative tools for multi dimensional data visualization can also provide an easy and natural path to a collaborative data visualization and exploration, where scientists can interact with their data and their colleagues in the same visual space.
Immersion provides benefits beyond the traditional desktop visualization tools: it leads to a demonstrably better perception of a datascape geometry, more intuitive data understanding, and a better retention of the perceived relationships in the data.
In modern heterogeneous MPSoCs, the management of shared memory resources is crucial in delivering end-to-end QoS.
Previous frameworks have either focused on singular QoS targets or the allocation of partitionable resources among CPU applications at relatively slow timescales.
However, heterogeneous MPSoCs typically require instant response from the memory system where most resources cannot be partitioned.
Moreover, the health of different cores in a heterogeneous MPSoC is often measured by diverse performance objectives.
In this work, we propose a Self-Aware Resource Allocation (SARA) framework for heterogeneous MPSoCs.
Priority-based adaptation allows cores to use different target performance and self-monitor their own intrinsic health.
In response, the system allocates non-partitionable resources based on priorities.
The proposed framework meets a diverse range of QoS demands from heterogeneous cores.
We consider a problem of dispersing points on disjoint intervals on a line.
Given n pairwise disjoint intervals sorted on a line, we want to find a point in each interval such that the minimum pairwise distance of these points is maximized.
Based on a greedy strategy, we present a linear time algorithm for the problem.
Further, we also solve in linear time the cycle version of the problem where the intervals are given on a cycle.
There is an increase in usage of smaller cells or femtocells to improve performance and coverage of next-generation heterogeneous wireless networks (HetNets).
However, the interference caused by femtocells to neighboring cells is a limiting performance factor in dense HetNets.
This interference is being managed via distributed resource allocation methods.
However, as the density of the network increases so does the complexity of such resource allocation methods.
Yet, unplanned deployment of femtocells requires an adaptable and self-organizing algorithm to make HetNets viable.
As such, we propose to use a machine learning approach based on Q-learning to solve the resource allocation problem in such complex networks.
By defining each base station as an agent, a cellular network is modelled as a multi-agent network.
Subsequently, cooperative Q-learning can be applied as an efficient approach to manage the resources of a multi-agent network.
Furthermore, the proposed approach considers the quality of service (QoS) for each user and fairness in the network.
In comparison with prior work, the proposed approach can bring more than a four-fold increase in the number of supported femtocells while using cooperative Q-learning to reduce resource allocation overhead.
Hashing has been widely used for large-scale search due to its low storage cost and fast query speed.
By using supervised information, supervised hashing can significantly outperform unsupervised hashing.
Recently, discrete supervised hashing and deep hashing are two representative progresses in supervised hashing.
On one hand, hashing is essentially a discrete optimization problem.
Hence, utilizing supervised information to directly guide discrete (binary) coding procedure can avoid sub-optimal solution and improve the accuracy.
On the other hand, deep hashing, which integrates deep feature learning and hash-code learning into an end-to-end architecture, can enhance the feedback between feature learning and hash-code learning.
The key in discrete supervised hashing is to adopt supervised information to directly guide the discrete coding procedure in hashing.
The key in deep hashing is to adopt the supervised information to directly guide the deep feature learning procedure.
However, there have not existed works which can use the supervised information to directly guide both discrete coding procedure and deep feature learning procedure in the same framework.
In this paper, we propose a novel deep hashing method, called deep discrete supervised hashing (DDSH), to address this problem.
DDSH is the first deep hashing method which can utilize supervised information to directly guide both discrete coding procedure and deep feature learning procedure, and thus enhance the feedback between these two important procedures.
Experiments on three real datasets show that DDSH can outperform other state-of-the-art baselines, including both discrete hashing and deep hashing baselines, for image retrieval.
Low Power Design has become a significant requirement when the CMOS technology entered the nanometer era.
Multiple-Supply Voltage (MSV) is a popular and effective method for both dynamic and static power reduction while maintaining performance.
Level shifters may cause area and Interconnect Length Overhead (ILO), and should be considered at both floorplanning and post-floorplanning stages.
In this paper, we propose a two phases algorithm framework, called VLSAF, to solve voltage and level shifter assignment problem.
At floorplanning phase, we use a convex cost network flow algorithm to assign voltage and a minimum cost flow algorithm to handle level-shifter assignment.
At post-floorplanning phase, a heuristic method is adopted to redistribute white spaces and calculate the positions and shapes of level shifters.
The experimental results show VLSAF is effective.
This paper presents a computational approach to modelling group creativity.
It presents an analysis of two studies of group creativity selected from different research cultures and identifies a common theme ("idea build-up") that is then used in the formalisation of an agent-based model used to support reasoning about the complex dynamics of building on the ideas of others.
We propose a new code design that aims to distribute an LDPC code over a relay channel.
It is based on a split-and-extend approach, which allows the relay to split the set of bits connected to some parity-check of the LDPC code into two or several subsets.
Subsequently, the sums of bits within each subset are used in a repeat-accumulate manner in order to generate extra bits sent from the relay toward the destination.
We show that the proposed design yields LDPC codes with enhanced correction capacity and can be advantageously applied to existing codes, which allows for addressing cooperation issues for evolving standards.
Finally, we derive density evolution equations for the proposed design, and we show that Split-Extended LDPC codes can approach very closely the capacity of the Gaussian relay channel.
For extracting meaningful topics from texts, their structures should be considered properly.
In this paper, we aim to analyze structured time-series documents such as a collection of news articles and a series of scientific papers, wherein topics evolve along time depending on multiple topics in the past and are also related to each other at each time.
To this end, we propose a dynamic and static topic model, which simultaneously considers the dynamic structures of the temporal topic evolution and the static structures of the topic hierarchy at each time.
We show the results of experiments on collections of scientific papers, in which the proposed method outperformed conventional models.
Moreover, we show an example of extracted topic structures, which we found helpful for analyzing research activities.
We propose an approach to decomposing a thematic information stream into principal components.
Each principal component is related to a narrow topic extracted from the information stream.
The essence of the approach arises from analogy with the Fourier transform.
We examine methods for analyzing the principal components and propose using multifractal analysis for identifying similar topics.
The decomposition technique is applied to the information stream dedicated to Brexit.
We provide a comparison between the principal components obtained by applying the decomposition to Brexit stream and the related topics extracted by Google Trends.
We present ABA+, a new approach to handling preferences in a well known structured argumentation formalism, Assumption-Based Argumentation (ABA).
In ABA+, preference information given over assumptions is incorporated directly into the attack relation, thus resulting in attack reversal.
ABA+ conservatively extends ABA and exhibits various desirable features regarding relationship among argumentation semantics as well as preference handling.
We also introduce Weak Contraposition, a principle concerning reasoning with rules and preferences that relaxes the standard principle of contraposition, while guaranteeing additional desirable features for ABA+.
The protection of confidential image data from unauthorized access is an important area of research in network communication.
This paper presents a high-level security encryption scheme for gray scale images.
The gray level image is first decomposed into binary images using bit scale decomposition.
Each binary image is then compressed by selecting a good scanning path that minimizes the total number of bits needed to encode the bit sequence along the scanning path using two dimensional run encoding.
The compressed bit string is then scrambled iteratively using a pseudo-random number generator and finally encrypted using a bit level permutation OMFLIP.
The performance is tested, illustrated and discussed.
This article presents the SIRIUS-LTG-UiO system for the SemEval 2018 Task 7 on Semantic Relation Extraction and Classification in Scientific Papers.
First we extract the shortest dependency path (sdp) between two entities, then we introduce a convolutional neural network (CNN) which takes the shortest dependency path embeddings as input and performs relation classification with differing objectives for each subtask of the shared task.
This approach achieved overall F1 scores of 76.7 and 83.2 for relation classification on clean and noisy data, respectively.
Furthermore, for combined relation extraction and classification on clean data, it obtained F1 scores of 37.4 and 33.6 for each phase.
Our system ranks 3rd in all three sub-tasks of the shared task.
Simplified Molecular Input Line Entry System (SMILES) is a single line text representation of a unique molecule.
One molecule can however have multiple SMILES strings, which is a reason that canonical SMILES have been defined, which ensures a one to one correspondence between SMILES string and molecule.
Here the fact that multiple SMILES represent the same molecule is explored as a technique for data augmentation of a molecular QSAR dataset modeled by a long short term memory (LSTM) cell based neural network.
The augmented dataset was 130 times bigger than the original.
The network trained with the augmented dataset shows better performance on a test set when compared to a model built with only one canonical SMILES string per molecule.
The correlation coefficient R2 on the test set was improved from 0.56 to 0.66 when using SMILES enumeration, and the root mean square error (RMS) likewise fell from 0.62 to 0.55.
The technique also works in the prediction phase.
By taking the average per molecule of the predictions for the enumerated SMILES a further improvement to a correlation coefficient of 0.68 and a RMS of 0.52 was found.
High-dimensional time series are common in many domains.
Since human cognition is not optimized to work well in high-dimensional spaces, these areas could benefit from interpretable low-dimensional representations.
However, most representation learning algorithms for time series data are difficult to interpret.
This is due to non-intuitive mappings from data features to salient properties of the representation and non-smoothness over time.
To address this problem, we propose a new representation learning framework building on ideas from interpretable discrete dimensionality reduction and deep generative modeling.
This framework allows us to learn discrete representations of time series, which give rise to smooth and interpretable embeddings with superior clustering performance.
We introduce a new way to overcome the non-differentiability in discrete representation learning and present a gradient-based version of the traditional self-organizing map algorithm that is more performant than the original.
Furthermore, to allow for a probabilistic interpretation of our method, we integrate a Markov model in the representation space.
This model uncovers the temporal transition structure, improves clustering performance even further and provides additional explanatory insights as well as a natural representation of uncertainty.
We evaluate our model in terms of clustering performance and interpretability on static (Fashion-)MNIST data, a time series of linearly interpolated (Fashion-)MNIST images, a chaotic Lorenz attractor system with two macro states, as well as on a challenging real world medical time series application on the eICU data set.
Our learned representations compare favorably with competitor methods and facilitate downstream tasks on the real world data.
Monocular depth estimation aims at estimating a pixelwise depth map for a single image, which has wide applications in scene understanding and autonomous driving.
Existing supervised and unsupervised methods face great challenges.
Supervised methods require large amounts of depth measurement data, which are generally difficult to obtain, while unsupervised methods are usually limited in estimation accuracy.
Synthetic data generated by graphics engines provide a possible solution for collecting large amounts of depth data.
However, the large domain gaps between synthetic and realistic data make directly training with them challenging.
In this paper, we propose to use the stereo matching network as a proxy to learn depth from synthetic data and use predicted stereo disparity maps for supervising the monocular depth estimation network.
Cross-domain synthetic data could be fully utilized in this novel framework.
Different strategies are proposed to ensure learned depth perception capability well transferred across different domains.
Our extensive experiments show state-of-the-art results of monocular depth estimation on KITTI dataset.
The objective of this paper is to introduce an artificial intelligence based optimization approach, which is inspired from Piagets theory on cognitive development.
The approach has been designed according to essential processes that an individual may experience while learning something new or improving his / her knowledge.
These processes are associated with the Piagets ideas on an individuals cognitive development.
The approach expressed in this paper is a simple algorithm employing swarm intelligence oriented tasks in order to overcome single-objective optimization problems.
For evaluating effectiveness of this early version of the algorithm, test operations have been done via some benchmark functions.
The obtained results show that the approach / algorithm can be an alternative to the literature in terms of single-objective optimization.
The authors have suggested the name: Cognitive Development Optimization Algorithm (CoDOA) for the related intelligent optimization approach.
Mobile phone 1800MHz band already allowed to be used in some airlines.
Many studies talked about lower the mobile phone output power to the lowest of 0dBm, but seldom talk about the Random Access Channel RACH, which will emit at the highest power of 30dBm at the instant of call making and is not controllable until the connection between the Base Station and mobile is made.Hence the impact of RACH and TDMA noise generated in the aircraft.
The motivation behind this paper is to dissecting the secure network for Business to Business (B2B) application by Implementing Access Control List (ACL) and Service Level Agreement (SLA).
This data provides the nature of attacks reported as external or internal attacks.
This paper presents the initial finding of attacks, types of attacks and their ratio within specific time.
It demonstrates the advance technique and methodology to reduce the attacks and vulnerabilities and minimize the ratio of attacks to the networks and application and keep the network secure and runs application smoothly regarding that.
It also identifies the location of attacks, the reason behind the attack and the technique used in attacking.
The whole field of system security is limitless and in an evolutionary stage.
To comprehend the exploration being performed today, foundation learning of the web and assaults, the security is vital and in this way they are investigated.
It provides the statistical analytics about various attacks and nature of attacks for acquiring the results through simulation to prove the hypothesis.
Identifying implicit discourse relations between text spans is a challenging task because it requires understanding the meaning of the text.
To tackle this task, recent studies have tried several deep learning methods but few of them exploited the syntactic information.
In this work, we explore the idea of incorporating syntactic parse tree into neural networks.
Specifically, we employ the Tree-LSTM model and Tree-GRU model, which are based on the tree structure, to encode the arguments in a relation.
Moreover, we further leverage the constituent tags to control the semantic composition process in these tree-structured neural networks.
Experimental results show that our method achieves state-of-the-art performance on PDTB corpus.
Recent years have seen major innovations in developing energy-efficient wireless technologies such as Bluetooth Low Energy (BLE) for Internet of Things (IoT).
Despite demonstrating significant benefits in providing low power transmission and massive connectivity, hardly any of these technologies have made it to directly connect to the Internet.
Recent advances demonstrate the viability of direct communication among heterogeneous IoT devices with incompatible physical (PHY) layers.
These techniques, however, require modifications in transmission power or time, which may affect the media access control (MAC) layer behaviors in legacy networks.
In this paper, we argue that the frequency domain can serve as a free side channel with minimal interruptions to legacy networks.
To this end, we propose DopplerFi, a communication framework that enables a two-way communication channel between BLE and Wi-Fi by injecting artificial Doppler shifts, which can be decoded by sensing the patterns in the Gaussian frequency shift keying (GFSK) demodulator and Channel State Information (CSI).
The artificial Doppler shifts can be compensated by the inherent frequency synchronization module and thus have a negligible impact on legacy communications.
Our evaluation using commercial off-the-shelf (COTS) BLE chips and 802.11-compliant testbeds have demonstrated that DopplerFi can achieve throughput up to 6.5~Kbps at the cost of merely less than 0.8% throughput loss.
Utilizing device-to-device (D2D) connections among mobile devices is promising to meet the increasing throughput demand over cellular links.
In particular, when mobile devices are in close proximity of each other and are interested in the same content, D2D connections such as Wi-Fi Direct can be opportunistically used to construct a cooperative (and jointly operating) cellular and D2D networking system.
However, it is crucial to understand, quantify, and exploit the potential of network coding for cooperating mobile devices in the joint cellular and D2D setup.
In this paper, we consider this problem, and (i) develop a network coding framework, namely NCMI, for cooperative mobile devices in the joint cellular and D2D setup, where cellular and D2D link capacities are the same, and (ii) characterize the performance of the proposed network coding framework, where we use packet completion time, which is the number of transmission slots to recover all packets, as a performance metric.
We demonstrate the benefits of our network coding framework through simulations.
An instance with a bad mask might make a composite image that uses it look fake.
This encourages us to learn segmentation by generating realistic composite images.
To achieve this, we propose a novel framework that exploits a new proposed prior called the independence prior based on Generative Adversarial Networks (GANs).
The generator produces an image with multiple category-specific instance providers, a layout module and a composition module.
Firstly, each provider independently outputs a category-specific instance image with a soft mask.
Then the provided instances' poses are corrected by the layout module.
Lastly, the composition module combines these instances into a final image.
Training with adversarial loss and penalty for mask area, each provider learns a mask that is as small as possible but enough to cover a complete category-specific instance.
Weakly supervised semantic segmentation methods widely use grouping cues modeling the association between image parts, which are either artificially designed or learned with costly segmentation labels or only modeled on local pairs.
Unlike them, our method automatically models the dependence between any parts and learns instance segmentation.
We apply our framework in two cases: (1) Foreground segmentation on category-specific images with box-level annotation.
(2) Unsupervised learning of instance appearances and masks with only one image of homogeneous object cluster (HOC).
We get appealing results in both tasks, which shows the independence prior is useful for instance segmentation and it is possible to unsupervisedly learn instance masks with only one image.
This article explores the coalitional stability of a new cooperative control policy for freeways and parallel queuing facilities with multiple servers.
Based on predicted future delays per queue or lane, a VOT-heterogeneous population of agents can agree to switch lanes or queues and transfer payments to each other in order to minimize the total cost of the incoming platoon.
The strategic interaction is captured by an n-level Stackelberg model with coalitions, while the cooperative structure is formulated as a partition function game (PFG).
The stability concept explored is the strong-core for PFGs which we found appropiate given the nature of the problem.
This concept ensures that the efficient allocation is individually rational and coalitionally stable.
We analyze this control mechanism for two settings: a static vertical queue and a dynamic horizontal queue.
For the former, we first characterize the properties of the underlying cooperative game.
Our simulation results suggest that the setting is always strong-core stable.
For the latter, we propose a new relaxation program for the strong-core concept.
Our simulation results on a freeway bottleneck with constant outflow using Newell's car-following model show the imputations to be generally strong-core stable and the coalitional instabilities to remain small with regard to users' costs.
Rigid structure-from-motion (RSfM) and non-rigid structure-from-motion (NRSfM) have long been treated in the literature as separate (different) problems.
Inspired by a previous work which solved directly for 3D scene structure by factoring the relative camera poses out, we revisit the principle of "maximizing rigidity" in structure-from-motion literature, and develop a unified theory which is applicable to both rigid and non-rigid structure reconstruction in a rigidity-agnostic way.
We formulate these problems as a convex semi-definite program, imposing constraints that seek to apply the principle of minimizing non-rigidity.
Our results demonstrate the efficacy of the approach, with state-of-the-art accuracy on various 3D reconstruction problems.
We explore the hypothesis that it is possible to obtain information about the dynamics of a blog network by analysing the temporal relationships between blogs at a semantic level, and that this type of analysis adds to the knowledge that can be extracted by studying the network only at the structural level of URL links.
We present an algorithm to automatically detect fine-grained discussion topics, characterized by n-grams and time intervals.
We then propose a probabilistic model to estimate the temporal relationships that blogs have with one another.
We define the precursor score of blog A in relation to blog B as the probability that A enters a new topic before B, discounting the effect created by asymmetric posting rates.
Network-level metrics of precursor and laggard behavior are derived from these dyadic precursor score estimations.
This model is used to analyze a network of French political blogs.
The scores are compared to traditional link degree metrics.
We obtain insights into the dynamics of topic participation on this network, as well as the relationship between precursor/laggard and linking behaviors.
We validate and analyze results with the help of an expert on the French blogosphere.
Finally, we propose possible applications to the improvement of search engine ranking algorithms.
The emergence and popularization of online social networks suddenly made available a large amount of data from social organization, interaction and human behavior.
All this information opens new perspectives and challenges to the study of social systems, being of interest to many fields.
Although most online social networks are recent (less than fifteen years old), a vast amount of scientific papers was already published on this topic, dealing with a broad range of analytical methods and applications.
This work describes how computational researches have approached this subject and the methods used to analyze such systems.
Founded on a wide though non-exaustive review of the literature, a taxonomy is proposed to classify and describe different categories of research.
Each research category is described and the main works, discoveries and perspectives are highlighted.
We consider a general small-scale market for agent-to-agent resource sharing, in which each agent could either be a server (seller) or a client (buyer) in each time period.
In every time period, a server has a certain amount of resources that any client could consume, and randomly gets matched with a client.
Our target is to maximize the resource utilization in such an agent-to-agent market, where the agents are strategic.
During each transaction, the server gets money and the client gets resources.
Hence, trade ratio maximization implies efficiency maximization of our system.
We model the proposed market system through a Mean Field Game approach and prove the existence of the Mean Field Equilibrium, which can achieve an almost 100% trade ratio.
Finally, we carry out a simulation study motivated by an agent-to-agent computing market, and a case study on a proposed photovoltaic market, and show the designed market benefits both individuals and the system as a whole.
In this contribution to the 3rd CHiME Speech Separation and Recognition Challenge (CHiME-3) we extend the acoustic front-end of the CHiME-3 baseline speech recognition system by a coherence-based Wiener filter which is applied to the output signal of the baseline beamformer.
To compute the time- and frequency-dependent postfilter gains the ratio between direct and diffuse signal components at the output of the baseline beamformer is estimated and used as approximation of the short-time signal-to-noise ratio.
The proposed spectral enhancement technique is evaluated with respect to word error rates of the CHiME-3 challenge baseline speech recognition system using real speech recorded in public environments.
Results confirm the effectiveness of the coherence-based postfilter when integrated into the front-end signal enhancement.
The prediction of the long-term impact of a scientific article is challenging task, addressed by the bibliometrician through resorting to a proxy whose reliability increases with the breadth of the citation window.
In the national research assessment exercises using metrics the citation window is necessarily short, but in some cases is sufficient to advise the use of simple citations.
For the Italian VQR 2011-2014, the choice was instead made to adopt a linear weighted combination of citations and journal metric percentiles, with weights differentiated by discipline and year.
Given the strategic importance of the exercise, whose results inform the allocation of a significant share of resources for the national academic system, we examined whether the predictive power of the proposed indicator is stronger than the simple citation count.
The results show the opposite, for all discipline in the sciences and a citation window above two years.
We propose a novel encoding/transmission scheme called continuous chain (CC) transmission that is able to improve the finite-length performance of a system using spatially-coupled low-density parity-check (SC-LDPC) codes.
In CC transmission, instead of transmitting a sequence of independent codewords from a terminated SC-LDPC code chain, we connect multiple chains in a layered format, where encoding, transmission, and decoding are now performed in a continuous fashion.
The connections between chains are created at specific points, chosen to improve the finite-length performance of the code structure under iterative decoding.
We describe the design of CC schemes for different SC-LDPC code ensembles constructed from protographs: a (J,K)-regular SC-LDPC code chain, a spatially-coupled repeat-accumulate (SC-RA) code, and a spatially-coupled accumulate-repeat-jagged-accumulate (SC- ARJA) code.
In all cases, significant performance improvements are reported and, in addition, it is shown that using CC transmission only requires a small increase in decoding complexity and decoding delay with respect to a system employing a single SC-LDPC code chain for transmission.
This paper presents a novel mechanism to endogenously determine the fair division of a state into electoral districts in a two-party setting.
No geometric constraints are imposed on voter distributions or district shapes; instead, it is assumed that any partition of the population into districts of equal population is feasible.
One party divides the map, then the other party chooses a minimum threshold level of support needed to win a district.
Districts in which neither party meets this threshold are awarded randomly.
Despite the inherent asymmetry, the equilibria of this mechanism always yield fair outcomes, up to integer rounding.
Parallel computing can offer an enormous advantage regarding the performance for very large applications in almost any field: scientific computing, computer vision, databases, data mining, and economics.
GPUs are high performance many-core processors that can obtain very high FLOP rates.
Since the first idea of using GPU for general purpose computing, things have evolved and now there are several approaches to GPU programming: CUDA from NVIDIA and Stream from AMD.
CUDA is now a popular programming model for general purpose computations on GPU for C/C++ programmers.
A great number of applications were ported to CUDA programming model and they obtain speedups of orders of magnitude comparing to optimized CPU implementations.
In this paper we present an implementation of a library for solving linear systems using the CCUDA framework.
We present the results of performance tests and show that using GPU one can obtain speedups of about of approximately 80 times comparing with a CPU implementation.
Cause-effect relations are an important part of human knowledge.
In real life, humans often reason about complex causes linked to complex effects.
By comparison, existing formalisms for representing knowledge about causal relations are quite limited in the kind of specifications of causes and effects they allow.
In this paper, we present the new language C-Log, which offers a significantly more expressive representation of effects, including such features as the creation of new objects.
We show how C-Log integrates with first-order logic, resulting in the language FO(C).
We also compare FO(C) with several related languages and paradigms, including inductive definitions, disjunctive logic programming, business rules and extensions of Datalog.
Vulnerability of dedicated hash functions to various attacks has made the task of designing hash function much more challenging.
This provides us a strong motivation to design a new cryptographic hash function viz.HF-hash.
This is a hash function, whose compression function is designed by using first 32 polynomials of HFE Challenge-1 with 64 variables by forcing remaining 16 variables as zero.
HF-hash gives 256 bits message digest and is as efficient as SHA-256.
It is secure against the differential attack proposed by Chabaud and Joux as well as by Wang et. al. applied to SHA-0 and SHA-1.
One major challenge in training Deep Neural Networks is preventing overfitting.
Many techniques such as data augmentation and novel regularizers such as Dropout have been proposed to prevent overfitting without requiring a massive amount of training data.
In this work, we propose a new regularizer called DeCov which leads to significantly reduced overfitting (as indicated by the difference between train and val performance), and better generalization.
Our regularizer encourages diverse or non-redundant representations in Deep Neural Networks by minimizing the cross-covariance of hidden activations.
This simple intuition has been explored in a number of past works but surprisingly has never been applied as a regularizer in supervised learning.
Experiments across a range of datasets and network architectures show that this loss always reduces overfitting while almost always maintaining or increasing generalization performance and often improving performance over Dropout.
Policy iteration (PI) is a recursive process of policy evaluation and improvement to solve an optimal decision-making, e.g., reinforcement learning (RL) or optimal control problem and has served as the fundamental to develop RL methods.
Motivated by integral PI (IPI) schemes in optimal control and RL methods in continuous time and space (CTS), this paper proposes on-policy IPI to solve the general RL problem in CTS, with its environment modeled by an ordinary differential equation (ODE).
In such continuous domain, we also propose four off-policy IPI methods---two are the ideal PI forms that use advantage and Q-functions, respectively, and the other two are natural extensions of the existing off-policy IPI schemes to our general RL framework.
Compared to the IPI methods in optimal control, the proposed IPI schemes can be applied to more general situations and do not require an initial stabilizing policy to run; they are also strongly relevant to the RL algorithms in CTS such as advantage updating, Q-learning, and value-gradient based (VGB) greedy policy improvement.
Our on-policy IPI is basically model-based but can be made partially model-free; each off-policy method is also either partially or completely model-free.
The mathematical properties of the IPI methods---admissibility, monotone improvement, and convergence towards the optimal solution---are all rigorously proven, together with the equivalence of on- and off-policy IPI.
Finally, the IPI methods are simulated with an inverted-pendulum model to support the theory and verify the performance.
Power and energy consumption is a fundamental issue in Body Sensor Networks (BSNs) since nodes must operate properly and autonomously for a certain period of time without battery replacement or change.
This is due to the fact that the sensors in BSNs are either implanted in the body or are in very near position to the body.
Thus, the duration of replacing the batteries should be of utmost importance.
Most of the existing researches suggested the development of a more improved battery cells or developing an energy aware routing protocol to tackle the energy consumption in WBSN.
But this is not the case as most energy consumption in WBSN occur as a result of mobility in routing and sensor node placement.
Therefore, improving the battery cells might not solve the energy consumption in WBSN.
The Graham-Diaconis inequality shows the equivalence between two well-known methods of measuring the similarity of two given ranked lists of items: Spearman's footrule and Kendall's tau.
The original inequality assumes unweighted items in input lists.
In this paper, we first define versions of these methods for weighted items.
We then prove a generalization of the inequality for the weighted versions.
We introduce Hair-GANs, an architecture of generative adversarial networks, to recover the 3D hair structure from a single image.
The goal of our networks is to build a parametric transformation from 2D hair maps to 3D hair structure.
The 3D hair structure is represented as a 3D volumetric field which encodes both the occupancy and the orientation information of the hair strands.
Given a single hair image, we first align it with a bust model and extract a set of 2D maps encoding the hair orientation information in 2D, along with the bust depth map to feed into our Hair-GANs.
With our generator network, we compute the 3D volumetric field as the structure guidance for the final hair synthesis.
The modeling results not only resemble the hair in the input image but also possesses many vivid details in other views.
The efficacy of our method is demonstrated by using a variety of hairstyles and comparing with the prior art.
In this work we combine two research threads from Vision/ Graphics and Natural Language Processing to formulate an image generation task conditioned on attributes in a multi-turn setting.
By multiturn, we mean the image is generated in a series of steps of user-specified conditioning information.
Our proposed approach is practically useful and offers insights into neural interpretability.
We introduce a framework that includes a novel training algorithm as well as model improvements built for the multi-turn setting.
We demonstrate that this framework generates a sequence of images that match the given conditioning information and that this task is useful for more detailed benchmarking and analysis of conditional image generation methods.
We study gaze estimation on tablets, our key design goal is uncalibrated gaze estimation using the front-facing camera during natural use of tablets, where the posture and method of holding the tablet is not constrained.
We collected the first large unconstrained gaze dataset of tablet users, labeled Rice TabletGaze dataset.
The dataset consists of 51 subjects, each with 4 different postures and 35 gaze locations.
Subjects vary in race, gender and in their need for prescription glasses, all of which might impact gaze estimation accuracy.
Driven by our observations on the collected data, we present a TabletGaze algorithm for automatic gaze estimation using multi-level HoG feature and Random Forests regressor.
The TabletGaze algorithm achieves a mean error of 3.17 cm.
We perform extensive evaluation on the impact of various factors such as dataset size, race, wearing glasses and user posture on the gaze estimation accuracy and make important observations about the impact of these factors.
Given the wide success of convolutional neural networks (CNNs) applied to natural images, researchers have begun to apply them to neuroimaging data.
To date, however, exploration of novel CNN architectures tailored to neuroimaging data has been limited.
Several recent works fail to leverage the 3D structure of the brain, instead treating the brain as a set of independent 2D slices.
Approaches that do utilize 3D convolutions rely on architectures developed for object recognition tasks in natural 2D images.
Such architectures make assumptions about the input that may not hold for neuroimaging.
For example, existing architectures assume that patterns in the brain exhibit translation invariance.
However, a pattern in the brain may have different meaning depending on where in the brain it is located.
There is a need to explore novel architectures that are tailored to brain images.
We present two simple modifications to existing CNN architectures based on brain image structure.
Applied to the task of brain age prediction, our network achieves a mean absolute error (MAE) of 1.4 years and trains 30% faster than a CNN baseline that achieves a MAE of 1.6 years.
Our results suggest that lessons learned from developing models on natural images may not directly transfer to neuroimaging tasks.
Instead, there remains a large space of unexplored questions regarding model development in this area, whose answers may differ from conventional wisdom.
Spoken dialogue systems allow humans to interact with machines using natural speech.
As such, they have many benefits.
By using speech as the primary communication medium, a computer interface can facilitate swift, human-like acquisition of information.
In recent years, speech interfaces have become ever more popular, as is evident from the rise of personal assistants such as Siri, Google Now, Cortana and Amazon Alexa.
Recently, data-driven machine learning methods have been applied to dialogue modelling and the results achieved for limited-domain applications are comparable to or outperform traditional approaches.
Methods based on Gaussian processes are particularly effective as they enable good models to be estimated from limited training data.
Furthermore, they provide an explicit estimate of the uncertainty which is particularly useful for reinforcement learning.
This article explores the additional steps that are necessary to extend these methods to model multiple dialogue domains.
We show that Gaussian process reinforcement learning is an elegant framework that naturally supports a range of methods, including prior knowledge, Bayesian committee machines and multi-agent learning, for facilitating extensible and adaptable dialogue systems.
The quality of the data in spreadsheets is less discussed than the structural integrity of the formulas.
Yet it is an area of great interest to the owners and users of the spreadsheet.
This paper provides an overview of Information Quality (IQ) and Data Quality (DQ) with specific reference to how data is sourced, structured, and presented in spreadsheets.
Users may strive to formulate an adequate textual query for their information need.
Search engines assist the users by presenting query suggestions.
To preserve the original search intent, suggestions should be context-aware and account for the previous queries issued by the user.
Achieving context awareness is challenging due to data sparsity.
We present a probabilistic suggestion model that is able to account for sequences of previous queries of arbitrary lengths.
Our novel hierarchical recurrent encoder-decoder architecture allows the model to be sensitive to the order of queries in the context while avoiding data sparsity.
Additionally, our model can suggest for rare, or long-tail, queries.
The produced suggestions are synthetic and are sampled one word at a time, using computationally cheap decoding techniques.
This is in contrast to current synthetic suggestion models relying upon machine learning pipelines and hand-engineered feature sets.
Results show that it outperforms existing context-aware approaches in a next query prediction setting.
In addition to query suggestion, our model is general enough to be used in a variety of other applications.
The paper introduces sufficient conditions for input-to-state stability (ISS) of a class of impulsive systems with jump maps that depend on time.
Such systems can naturally represent an interconnection of several impulsive systems with different impulse time sequences.
Using a concept of ISS-Lyapunov function for subsystems a small-gain type theorem equipped with a new dwell-time condition to verify ISS of an interconnection has been proven.
Odometry forms an important component of many manned and autonomous systems.
In the rail industry in particular, having precise and robust odometry is crucial for the correct operation of the Automatic Train Protection systems that ensure the safety of high-speed trains in operation around the world.
Two problems commonly encountered in such odometry systems are miscalibration of the wheel encoders and slippage of the wheels under acceleration and braking, resulting in incorrect velocity estimates.
This paper introduces an odometry system that addresses these problems.
It comprises of an Extended Kalman Filter that tracks the calibration of the wheel encoders as state variables, and a measurement pre-processing stage called Sensor Consensus Analysis (SCA) that scales the uncertainty of a measurement based on how consistent it is with the measurements of the other sensors.
SCA uses the statistical z-test to determine when an individual measurement is inconsistent with the other measurements, and scales the uncertainty until the z-test passes.
This system is demonstrated on data from German Intercity-Express high-speed trains and it is shown to successfully deal with errors due to miscalibration and wheel slip.
Bearing only cooperative localization has been used successfully on aerial and ground vehicles.
In this paper we present an extension of the approach to the underwater domain.
The focus is on adapting the technique to handle the challenging visibility conditions underwater.
Furthermore, data from inertial, magnetic, and depth sensors are utilized to improve the robustness of the estimation.
In addition to robotic applications, the presented technique can be used for cave mapping and for marine archeology surveying, both by human divers.
Experimental results from different environments, including a fresh water, low visibility, lake in South Carolina; a cavern in Florida; and coral reefs in Barbados during the day and during the night, validate the robustness and the accuracy of the proposed approach.
A key attribute that drives the unprecedented success of modern Recurrent Neural Networks (RNNs) on learning tasks which involve sequential data, is their ability to model intricate long-term temporal dependencies.
However, a well established measure of RNNs long-term memory capacity is lacking, and thus formal understanding of the effect of depth on their ability to correlate data throughout time is limited.
Specifically, existing depth efficiency results on convolutional networks do not suffice in order to account for the success of deep RNNs on data of varying lengths.
In order to address this, we introduce a measure of the network's ability to support information flow across time, referred to as the Start-End separation rank, which reflects the distance of the function realized by the recurrent network from modeling no dependency between the beginning and end of the input sequence.
We prove that deep recurrent networks support Start-End separation ranks which are combinatorially higher than those supported by their shallow counterparts.
Thus, we establish that depth brings forth an overwhelming advantage in the ability of recurrent networks to model long-term dependencies, and provide an exemplar of quantifying this key attribute which may be readily extended to other RNN architectures of interest, e.g. variants of LSTM networks.
We obtain our results by considering a class of recurrent networks referred to as Recurrent Arithmetic Circuits, which merge the hidden state with the input via the Multiplicative Integration operation, and empirically demonstrate the discussed phenomena on common RNNs.
Finally, we employ the tool of quantum Tensor Networks to gain additional graphic insight regarding the complexity brought forth by depth in recurrent networks.
Clustering is a useful data exploratory method with its wide applicability in multiple fields.
However, data clustering greatly relies on initialization of cluster centers that can result in large intra-cluster variance and dead centers, therefore leading to sub-optimal solutions.
This paper proposes a novel variance based version of the conventional Moving K-Means (MKM) algorithm called Variance Based Moving K-Means (VMKM) that can partition data into optimal homogeneous clusters, irrespective of cluster initialization.
The algorithm utilizes a novel distance metric and a unique data element selection criteria to transfer the selected elements between clusters to achieve low intra-cluster variance and subsequently avoid dead centers.
Quantitative and qualitative comparison with various clustering techniques is performed on four datasets selected from image processing, bioinformatics, remote sensing and the stock market respectively.
An extensive analysis highlights the superior performance of the proposed method over other techniques.
Image-to-image translation is considered a next frontier in the field of medical image analysis, with numerous potential applications.
However, recent advances in this field offer individualized solutions by utilizing specialized architectures which are task specific or by suffering from limited capacities and thus requiring refinement through non end-to-end training.
In this paper, we propose a novel general purpose framework for medical image-to-image translation, titled MedGAN, which operates in an end-to-end manner on the image level.
MedGAN builds upon recent advances in the field of generative adversarial networks(GANs) by combining the adversarial framework with a unique combination of non-adversarial losses which captures the high and low frequency components of the desired target modality.
Namely, we utilize a discriminator network as a trainable feature extractor which penalizes the discrepancy between the translated medical images and the desired modalities in the pixel and perceptual sense.
Moreover, style-transfer losses are utilized to match the textures and fine-structures of the desired target images to the outputs.
Additionally, we present a novel generator architecture, titled CasNet, which enhances the sharpness of the translated medical outputs through progressive refinement via encoder decoder pairs.
To demonstrate the effectiveness of our approach, we apply MedGAN on three novel and challenging applications: PET-CT translation, correction of MR motion artefacts and PET image denoising.
Qualitative and quantitative comparisons with state-of-the-art techniques have emphasized the superior performance of the proposed framework.
MedGAN can be directly applied as a general framework for future medical translation tasks.
In this research, we investigate the subject of path-finding.
A pruned version of visibility graph based on Candidate Vertices is formulated, followed by a new visibility check technique.
Such combination enables us to quickly identify the useful vertices and thus find the optimal path more efficiently.
The algorithm proposed is demonstrated on various path-finding cases.
The performance of the new technique on visibility graphs is compared to the traditional A* on Grids, Theta* and A* on Visibility Graphs in terms of path length, number of nodes evaluated, as well as computational time.
The key algorithmic contribution is that the new approach combines the merits of grid-based method and visibility graph-based method and thus yields better overall performance.
In the small target detection problem a pattern to be located is on the order of magnitude less numerous than other patterns present in the dataset.
This applies both to the case of supervised detection, where the known template is expected to match in just a few areas and unsupervised anomaly detection, as anomalies are rare by definition.
This problem is frequently related to the imaging applications, i.e. detection within the scene acquired by a camera.
To maximize available data about the scene, hyperspectral cameras are used; at each pixel, they record spectral data in hundreds of narrow bands.
The typical feature of hyperspectral imaging is that characteristic properties of target materials are visible in the small number of bands, where light of certain wavelength interacts with characteristic molecules.
A target-independent band selection method based on statistical principles is a versatile tool for solving this problem in different practical applications.
Combination of a regular background and a rare standing out anomaly will produce a distortion in the joint distribution of hyperspectral pixels.
Higher Order Cumulants Tensors are a natural `window' into this distribution, allowing to measure properties and suggest candidate bands for removal.
While there have been attempts at producing band selection algorithms based on the 3 rd cumulant's tensor i.e. the joint skewness, the literature lacks a systematic analysis of how the order of the cumulant tensor used affects effectiveness of band selection in detection applications.
In this paper we present an analysis of a general algorithm for band selection based on higher order cumulants.
We discuss its usability related to the observed breaking points in performance, depending both on method order and the desired number of bands.
Finally we perform experiments and evaluate these methods in a hyperspectral detection scenario.
Glaucoma is the second leading cause of blindness all over the world, with approximately 60 million cases reported worldwide in 2010.
If undiagnosed in time, glaucoma causes irreversible damage to the optic nerve leading to blindness.
The optic nerve head examination, which involves measurement of cup-to-disc ratio, is considered one of the most valuable methods of structural diagnosis of the disease.
Estimation of cup-to-disc ratio requires segmentation of optic disc and optic cup on eye fundus images and can be performed by modern computer vision algorithms.
This work presents universal approach for automatic optic disc and cup segmentation, which is based on deep learning, namely, modification of U-Net convolutional neural network.
Our experiments include comparison with the best known methods on publicly available databases DRIONS-DB, RIM-ONE v.3, DRISHTI-GS.
For both optic disc and cup segmentation, our method achieves quality comparable to current state-of-the-art methods, outperforming them in terms of the prediction time.
In the last two decades, DNA self-assembly has grown into a major area of research attracting people from diverse background.
It has numerous potential applications such as targeted drug delivery, artificial photosynthesis etc.
In the last decade, another area received wide attention known as DNA origami, where using M13 virus and carefully designed staple strands one can fold the DNA into desired 2-D and 3-D shapes.
In 2016, a group of researchers at MIT have developed an automated DNA nanostructures strategy and an open source software 'daedalus' based on MATLAB for developing the nanostructures.
In this work, we present a truly open source software '3dnaprinter' based on Java (without MATLAB) that can do the same work.
Speech emotion recognition is a challenging task for three main reasons: 1) human emotion is abstract, which means it is hard to distinguish; 2) in general, human emotion can only be detected in some specific moments during a long utterance; 3) speech data with emotional labeling is usually limited.
In this paper, we present a novel attention based fully convolutional network for speech emotion recognition.
We employ fully convolutional network as it is able to handle variable-length speech, free of the demand of segmentation to keep critical information not lost.
The proposed attention mechanism can make our model be aware of which time-frequency region of speech spectrogram is more emotion-relevant.
Considering limited data, the transfer learning is also adapted to improve the accuracy.
Especially, it's interesting to observe obvious improvement obtained with natural scene image based pre-trained model.
Validated on the publicly available IEMOCAP corpus, the proposed model outperformed the state-of-the-art methods with a weighted accuracy of 70.4% and an unweighted accuracy of 63.9% respectively.
Technological developments call for increasing perception and action capabilities of robots.
Among other skills, vision systems that can adapt to any possible change in the working conditions are needed.
Since these conditions are unpredictable, we need benchmarks which allow to assess the generalization and robustness capabilities of our visual recognition algorithms.
In this work we focus on robotic kitting in unconstrained scenarios.
As a first contribution, we present a new visual dataset for the kitting task.
Differently from standard object recognition datasets, we provide images of the same objects acquired under various conditions where camera, illumination and background are changed.
This novel dataset allows for testing the robustness of robot visual recognition algorithms to a series of different domain shifts both in isolation and unified.
Our second contribution is a novel online adaptation algorithm for deep models, based on batch-normalization layers, which allows to continuously adapt a model to the current working conditions.
Differently from standard domain adaptation algorithms, it does not require any image from the target domain at training time.
We benchmark the performance of the algorithm on the proposed dataset, showing its capability to fill the gap between the performances of a standard architecture and its counterpart adapted offline to the given target domain.
We study the problem of compressing recurrent neural networks (RNNs).
In particular, we focus on the compression of RNN acoustic models, which are motivated by the goal of building compact and accurate speech recognition systems which can be run efficiently on mobile devices.
In this work, we present a technique for general recurrent model compression that jointly compresses both recurrent and non-recurrent inter-layer weight matrices.
We find that the proposed technique allows us to reduce the size of our Long Short-Term Memory (LSTM) acoustic model to a third of its original size with negligible loss in accuracy.
This paper investigates the relations between three different properties, which are of importance in optimal control problems: dissipativity of the underlying dynamics with respect to a specific supply rate, optimal operation at steady state, and the turnpike property.
We show in a continuous-time setting that if along optimal trajectories a strict dissipation inequality is satisfied, then this implies optimal operation at this steady state and the existence of a turnpike at the same steady state.
Finally, we establish novel converse turnpike results, i.e., we show that the existence of a turnpike at a steady state implies optimal operation at this steady state and dissipativity with respect to this steady state.
We draw upon a numerical example to illustrate our findings.
Smart city projects address many of the current problems afflicting high populated areas and cities and, as such, are a target for government, institutions and private organizations that plan to explore its foreseen advantages.
In technical terms, smart city projects present a complex set of requirements including a large number users with highly different and heterogeneous requirements.
In this scenario, this paper proposes and analyses the impact and perspectives on adopting software-defined networking and artificial intelligence as innovative approaches for smart city project development and deployment.
Big data is also considered as an inherent element of most smart city project that must be tackled.
A framework layered view is proposed with a discussion about software-defined networking and machine learning impacts on innovation followed by a use case that demonstrates the potential benefits of cognitive learning for smart cities.
It is argued that the complexity of smart city projects do require new innovative approaches that potentially result in more efficient and intelligent systems.
The increasingly dense deployments of wireless CSMA networks arising from applications of Internet-of-things call for an improvement to mitigate the interference among simultaneous transmitting wireless devices.
For cost efficiency and backward compatibility with legacy transceiver hardware, a simple approach to address interference is by appropriately configuring the carrier sensing thresholds in wireless CSMA protocols, particularly in dense wireless networks.
Most prior studies of the configuration of carrier sensing thresholds are based on a simplified conflict graph model, whereas this paper considers a realistic signal-to-interference-and-noise ratio model.
We provide a comprehensive study for two effective wireless CSMA protocols: Cumulative-interference-Power Carrier Sensing and Incremental-interference-Power Carrier Sensing, in two aspects: (1) static approach that sets a universal carrier sensing threshold to ensure interference-safe transmissions regardless of network topology, and (2) adaptive approach that adjusts the carrier sensing thresholds dynamically based on the feedback of nearby transmissions.
We also provide simulation studies to evaluate the starvation ratio, fairness, and goodput of our approaches.
This study considers the control of parent-child systems where a parent system is acted on by a set of controllable child systems (i.e. a swarm).
Examples of such systems include a swarm of robots pushing an object over a surface, a swarm of aerial vehicles carrying a large load, or a set of end effectors manipulating an object.
In this paper, a general approach for decoupling the swarm from the parent system through a low-dimensional abstract state space is presented.
The requirements of this approach are given along with how constraints on both systems propagate through the abstract state and impact the requirements of the controllers for both systems.
To demonstrate, several controllers with hard state constraints are designed to track a given desired angle trajectory of a tilting plane with a swarm of robots driving on top.
Both homogeneous and heterogeneous swarms of varying sizes and properties are considered to test the robustness of this architecture.
The controllers are shown to be locally asymptotically stable and are demonstrated in simulation.
Label space expansion for multi-label classification (MLC) is a methodology that encodes the original label vectors to higher dimensional codes before training and decodes the predicted codes back to the label vectors during testing.
The methodology has been demonstrated to improve the performance of MLC algorithms when coupled with off-the-shelf error-correcting codes for encoding and decoding.
Nevertheless, such a coding scheme can be complicated to implement, and cannot easily satisfy a common application need of cost-sensitive MLC---adapting to different evaluation criteria of interest.
In this work, we show that a simpler coding scheme based on the concept of a reference pair of label vectors achieves cost-sensitivity more naturally.
In particular, our proposed cost-sensitive reference pair encoding (CSRPE) algorithm contains cluster-based encoding, weight-based training and voting-based decoding steps, all utilizing the cost information.
Furthermore, we leverage the cost information embedded in the code space of CSRPE to propose a novel active learning algorithm for cost-sensitive MLC.
Extensive experimental results verify that CSRPE performs better than state-of-the-art algorithms across different MLC criteria.
The results also demonstrate that the CSRPE-backed active learning algorithm is superior to existing algorithms for active MLC, and further justify the usefulness of CSRPE.
Label embedding (LE) is an important family of multi-label classification algorithms that digest the label information jointly for better performance.
Different real-world applications evaluate performance by different cost functions of interest.
Current LE algorithms often aim to optimize one specific cost function, but they can suffer from bad performance with respect to other cost functions.
In this paper, we resolve the performance issue by proposing a novel cost-sensitive LE algorithm that takes the cost function of interest into account.
The proposed algorithm, cost-sensitive label embedding with multidimensional scaling (CLEMS), approximates the cost information with the distances of the embedded vectors by using the classic multidimensional scaling approach for manifold learning.
CLEMS is able to deal with both symmetric and asymmetric cost functions, and effectively makes cost-sensitive decisions by nearest-neighbor decoding within the embedded vectors.
We derive theoretical results that justify how CLEMS achieves the desired cost-sensitivity.
Furthermore, extensive experimental results demonstrate that CLEMS is significantly better than a wide spectrum of existing LE algorithms and state-of-the-art cost-sensitive algorithms across different cost functions.
Grounding (i.e. localizing) arbitrary, free-form textual phrases in visual content is a challenging problem with many applications for human-computer interaction and image-text reference resolution.
Few datasets provide the ground truth spatial localization of phrases, thus it is desirable to learn from data with no or little grounding supervision.
We propose a novel approach which learns grounding by reconstructing a given phrase using an attention mechanism, which can be either latent or optimized directly.
During training our approach encodes the phrase using a recurrent network language model and then learns to attend to the relevant image region in order to reconstruct the input phrase.
At test time, the correct attention, i.e., the grounding, is evaluated.
If grounding supervision is available it can be directly applied via a loss over the attention mechanism.
We demonstrate the effectiveness of our approach on the Flickr 30k Entities and ReferItGame datasets with different levels of supervision, ranging from no supervision over partial supervision to full supervision.
Our supervised variant improves by a large margin over the state-of-the-art on both datasets.
The results of chest X-ray (CXR) analysis of 2D images to get the statistically reliable predictions (availability of tuberculosis) by computer-aided diagnosis (CADx) on the basis of deep learning are presented.
They demonstrate the efficiency of lung segmentation, lossless and lossy data augmentation for CADx of tuberculosis by deep convolutional neural network (CNN) applied to the small and not well-balanced dataset even.
CNN demonstrates ability to train (despite overfitting) on the pre-processed dataset obtained after lung segmentation in contrast to the original not-segmented dataset.
Lossless data augmentation of the segmented dataset leads to the lowest validation loss (without overfitting) and nearly the same accuracy (within the limits of standard deviation) in comparison to the original and other pre-processed datasets after lossy data augmentation.
The additional limited lossy data augmentation results in the lower validation loss, but with a decrease of the validation accuracy.
In conclusion, besides the more complex deep CNNs and bigger datasets, the better progress of CADx for the small and not well-balanced datasets even could be obtained by better segmentation, data augmentation, dataset stratification, and exclusion of non-evident outliers.
The study of eye gaze fixations on photographic images is an active research area.
In contrast, the image subcategory of freehand sketches has not received as much attention for such studies.
In this paper, we analyze the results of a free-viewing gaze fixation study conducted on 3904 freehand sketches distributed across 160 object categories.
Our analysis shows that fixation sequences exhibit marked consistency within a sketch, across sketches of a category and even across suitably grouped sets of categories.
This multi-level consistency is remarkable given the variability in depiction and extreme image content sparsity that characterizes hand-drawn object sketches.
In our paper, we show that the multi-level consistency in the fixation data can be exploited to (a) predict a test sketch's category given only its fixation sequence and (b) build a computational model which predicts part-labels underlying fixations on objects.
We hope that our findings motivate the community to deem sketch-like representations worthy of gaze-based studies vis-a-vis photographic images.
Social networks have been popular platforms for information propagation.
An important use case is viral marketing: given a promotion budget, an advertiser can choose some influential users as the seed set and provide them free or discounted sample products; in this way, the advertiser hopes to increase the popularity of the product in the users' friend circles by the world-of-mouth effect, and thus maximizes the number of users that information of the production can reach.
There has been a body of literature studying the influence maximization problem.
Nevertheless, the existing studies mostly investigate the problem on a one-off basis, assuming fixed known influence probabilities among users, or the knowledge of the exact social network topology.
In practice, the social network topology and the influence probabilities are typically unknown to the advertiser, which can be varying over time, i.e., in cases of newly established, strengthened or weakened social ties.
In this paper, we focus on a dynamic non-stationary social network and design a randomized algorithm, RSB, based on multi-armed bandit optimization, to maximize influence propagation over time.
The algorithm produces a sequence of online decisions and calibrates its explore-exploit strategy utilizing outcomes of previous decisions.
It is rigorously proven to achieve an upper-bounded regret in reward and applicable to large-scale social networks.
Practical effectiveness of the algorithm is evaluated using both synthetic and real-world datasets, which demonstrates that our algorithm outperforms previous stationary methods under non-stationary conditions.
We present two novel algorithms for learning formulas in Linear Temporal Logic (LTL) from examples.
The first learning algorithm reduces the learning task to a series of satisfiability problems in propositional Boolean logic and produces a smallest LTL formula (in terms of the number of subformulas) that is consistent with the given data.
Our second learning algorithm, on the other hand, combines the SAT-based learning algorithm with classical algorithms for learning decision trees.
The result is a learning algorithm that scales to real-world scenarios with hundreds of examples, but can no longer guarantee to produce minimal consistent LTL formulas.
We compare both learning algorithms and demonstrate their performance on a wide range of synthetic benchmarks.
Additionally, we illustrate their usefulness on the task of understanding executions of a leader election protocol.
Would you like to have your own cryptography method?
Experts say you should not do it.
If you think you can develop a better cryptography method anyway.
We present a brief discussion about some well known cryptography methods and how our model fails against the traditional attacks.
We do not want to discourage anybody, we just want to show that, despite of the importance of developing better cryptography models, it is a very hard task.
Compressed sensing (CS) is an innovative technique allowing to represent signals through a small number of their linear projections.
Hence, CS can be thought of as a natural candidate for acquisition of multidimensional signals, as the amount of data acquired and processed by conventional sensors could create problems in terms of computational complexity.
In this paper, we propose a framework for the acquisition and reconstruction of multidimensional correlated signals.
The approach is general and can be applied to D dimensional signals, even if the algorithms we propose to practically implement such architectures apply to 2-D and 3-D signals.
The proposed architectures employ iterative local signal reconstruction based on a hybrid transform/prediction correlation model, coupled with a proper initialization strategy.
The theory of distributed conceptual structures, as outlined in this paper, is concerned with the distribution and conception of knowledge.
It rests upon two related theories, Information Flow and Formal Concept Analysis, which it seeks to unify.
Information Flow (IF) is concerned with the distribution of knowledge.
The foundations of Information Flow is explicitly based upon a mathematical theory known as the Chu Construction in *-autonomous categories and implicitly based upon the mathematics of closed categories.
Formal Concept Analysis (FCA) is concerned with the conception and analysis of knowledge.
In this paper we connect these two studies by extending the basic theorem of Formal Concept Analysis to the distributed realm of Information Flow.
The main results are the categorical equivalence between classifications and concept lattices at the level of functions, and the categorical equivalence between bonds and complete adjoints at the level of relations.
With this we hope to accomplish a rapprochement between Information Flow and Formal Concept Analysis.
In this paper, we describe our algorithmic approach, which was used for submissions in the fifth Emotion Recognition in the Wild (EmotiW 2017) group-level emotion recognition sub-challenge.
We extracted feature vectors of detected faces using the Convolutional Neural Network trained for face identification task, rather than traditional pre-training on emotion recognition problems.
In the final pipeline an ensemble of Random Forest classifiers was learned to predict emotion score using available training set.
In case when the faces have not been detected, one member of our ensemble extracts features from the whole image.
During our experimental study, the proposed approach showed the lowest error rate when compared to other explored techniques.
In particular, we achieved 75.4% accuracy on the validation data, which is 20% higher than the handcrafted feature-based baseline.
The source code using Keras framework is publicly available.
Analysis of informative contents and sentiments of social users has been attempted quite intensively in the recent past.
Most of the systems are usable only for monolingual data and fails or gives poor results when used on data with code-mixing property.
To gather attention and encourage researchers to work on this crisis, we prepared gold standard Bengali-English code-mixed data with language and polarity tag for sentiment analysis purposes.
In this paper, we discuss the systems we prepared to collect and filter raw Twitter data.
In order to reduce manual work while annotation, hybrid systems combining rule based and supervised models were developed for both language and sentiment tagging.
The final corpus was annotated by a group of annotators following a few guidelines.
The gold standard corpus thus obtained has impressive inter-annotator agreement obtained in terms of Kappa values.
Various metrics like Code-Mixed Index (CMI), Code-Mixed Factor (CF) along with various aspects (language and emotion) also qualitatively polled the code-mixed and sentiment properties of the corpus.
Industrial Control Systems (ICS) are used worldwide in critical infrastructures.
An ICS system can be a single embedded system working stand-alone for controlling a simple process or ICS can also be a very complex Distributed Control System (DCS) connected to Supervisory Control And Data Acquisition (SCADA) system(s) in a nuclear power plant.
Although ICS are widely used to-day, there are very little research on the forensic acquisition and analyze ICS artefacts.
In this paper we present a case study of forensics in ICS where we de-scribe a method of safeguarding important volatile artefacts from an embedded industrial control system and several other sources
In this paper, we present a vision based collaborative localization framework for groups of micro aerial vehicles (MAV).
The vehicles are each assumed to be equipped with a forward-facing monocular camera, and to be capable of communicating with each other.
This collaborative localization approach is built upon a distributed algorithm where individual and relative pose estimation techniques are combined for the group to localize against surrounding environments.
The MAVs initially detect and match salient features between each other to create a sparse reconstruction of the observed environment, which acts as a global map.
Once a map is available, each MAV performs feature detection and tracking with a robust outlier rejection process to estimate its own six degree-of-freedom pose.
Occasionally, the MAVs can also fuse relative measurements with individual measurements through feature matching and multiple-view geometry based relative pose computation.
We present the implementation of this algorithm for MAVs and environments simulated within Microsoft AirSim, and discuss the results and the advantages of collaborative localization.
In standard graph clustering/community detection, one is interested in partitioning the graph into more densely connected subsets of nodes.
In contrast, the "search" problem of this paper aims to only find the nodes in a "single" such community, the target, out of the many communities that may exist.
To do so , we are given suitable side information about the target; for example, a very small number of nodes from the target are labeled as such.
We consider a general yet simple notion of side information: all nodes are assumed to have random weights, with nodes in the target having higher weights on average.
Given these weights and the graph, we develop a variant of the method of moments that identifies nodes in the target more reliably, and with lower computation, than generic community detection methods that do not use side information and partition the entire graph.
Our empirical results show significant gains in runtime, and also gains in accuracy over other graph clustering algorithms.
Knowledge Management (KM) is a relatively new phenomenon that appears in the field of Public Sector Organizations (PSO) bringing new paradigms of organizational management, challenges, risks and opportunities for its implementation, development and evaluation.
KM can be seen as a systematic and deliberate effort to coordinate people, technology, organizational structures and its environment through knowledge reuse and innovation.
This management approach has been established in parallel with the development and use of information and communications technologies (ICT).
Nowadays more PSO are embodying KM practices in their core processes for support them, and as an advanced management strategy to create a new culture based on technology and resources efficiency.
In this paper, we observed that KM can support organizational goals in PSO.
The aim of this paper is to understand KM factors and its associated components, and propose KM metrics for measure KM programs in PSO.
Through a critical literature review we analysed diverse studies related with KM performance indicators in PSO, then based on previous works we summarized the more convenient this purpose.
We found that, in academic literature, studies about KM measurement in PSO are uncommon and emerging.
As well, in the last section of this paper, we present a proposal of KM metrics for PSO, and some recommendations and practical implications for KM metrics development in PSO.
This academic endeavour seeks to contribute to theoretical debate about KM measure development for KM initiatives in PSO.
MPSoCs are gaining popularity because of its potential to solve computationally expensive applications.
A multi-core processor combines two or more independent cores (normally a CPU) into a single package composed of a single integrated circuit (Chip).
However, as the number of components on a single chip and their performance continue to increase, a shift from computation-based to communication-based design becomes mandatory.
As a result, the communication architecture plays a major role in the area, performance, and energy consumption of the overall system.
In this paper, multiple soft-cores (IPs) such as Micro Blaze in an FPGA is used to study the effect of different connection topologies on the performance of a parallel program.
Learning fine-grained details is a key issue in image aesthetic assessment.
Most of the previous methods extract the fine-grained details via random cropping strategy, which may undermine the integrity of semantic information.
Extensive studies show that humans perceive fine-grained details with a mixture of foveal vision and peripheral vision.
Fovea has the highest possible visual acuity and is responsible for seeing the details.
The peripheral vision is used for perceiving the broad spatial scene and selecting the attended regions for the fovea.
Inspired by these observations, we propose a Gated Peripheral-Foveal Convolutional Neural Network (GPF-CNN).
It is a dedicated double-subnet neural network, i.e. a peripheral subnet and a foveal subnet.
The former aims to mimic the functions of peripheral vision to encode the holistic information and provide the attended regions.
The latter aims to extract fine-grained features on these key regions.
Considering that the peripheral vision and foveal vision play different roles in processing different visual stimuli, we further employ a gated information fusion (GIF) network to weight their contributions.
The weights are determined through the fully connected layers followed by a sigmoid function.
We conduct comprehensive experiments on the standard AVA and Photo.net datasets for unified aesthetic prediction tasks: (i) aesthetic quality classification; (ii) aesthetic score regression; and (iii) aesthetic score distribution prediction.
The experimental results demonstrate the effectiveness of the proposed method.
We investigate a variety of problems of finding tours and cycle covers with minimum turn cost.
Questions of this type have been studied in the past, with complexity and approximation results as well as open problems dating back to work by Arkin et al.in 2001.
A wide spectrum of practical applications have renewed the interest in these questions, and spawned variants: for full coverage, every point has to be covered, for subset coverage, specific points have to be covered, and for penalty coverage, points may be left uncovered by incurring an individual penalty.
We make a number of contributions.
We first show that finding a minimum-turn (full) cycle cover is NP-hard even in 2-dimensional grid graphs, solving the long-standing open Problem 53 in The Open Problems Project edited by Demaine, Mitchell and O'Rourke.
We also prove NP-hardness of finding a subset cycle cover of minimum turn cost in thin grid graphs, for which Arkin et al.gave a polynomial-time algorithm for full coverage; this shows that their boundary techniques cannot be applied to compute exact solutions for subset and penalty variants.
On the positive side, we establish the first constant-factor approximation algorithms for all considered subset and penalty problem variants, making use of LP/IP techniques.
For full coverage in more general grid graphs (e.g., hexagonal grids), our approximation factors are better than the combinatorial ones of Arkin et al.
Our approach can also be extended to other geometric variants, such as scenarios with obstacles and linear combinations of turn and distance costs.
Monte Carlo Tree Search techniques have generally dominated General Video Game Playing, but recent research has started looking at Evolutionary Algorithms and their potential at matching Tree Search level of play or even outperforming these methods.
Online or Rolling Horizon Evolution is one of the options available to evolve sequences of actions for planning in General Video Game Playing, but no research has been done up to date that explores the capabilities of the vanilla version of this algorithm in multiple games.
This study aims to critically analyse the different configurations regarding population size and individual length in a set of 20 games from the General Video Game AI corpus.
Distinctions are made between deterministic and stochastic games, and the implications of using superior time budgets are studied.
Results show that there is scope for the use of these techniques, which in some configurations outperform Monte Carlo Tree Search, and also suggest that further research in these methods could boost their performance.
Although deep learning models have been successfully applied to a variety of tasks, due to the millions of parameters, they are becoming increasingly opaque and complex.
In order to establish trust for their widespread commercial use, it is important to formalize a principled framework to reason over these models.
In this work, we use ideas from causal inference to describe a general framework to reason over CNN models.
Specifically, we build a Structural Causal Model (SCM) as an abstraction over a specific aspect of the CNN.
We also formulate a method to quantitatively rank the filters of a convolution layer according to their counterfactual importance.
We illustrate our approach with popular CNN architectures such as LeNet5, VGG19, and ResNet32.
Numerous fake images spread on social media today and can severely jeopardize the credibility of online content to public.
In this paper, we employ deep networks to learn distinct fake image related features.
In contrast to authentic images, fake images tend to be eye-catching and visually striking.
Compared with traditional visual recognition tasks, it is extremely challenging to understand these psychologically triggered visual patterns in fake images.
Traditional general image classification datasets, such as ImageNet set, are designed for feature learning at the object level but are not suitable for learning the hyper-features that would be required by image credibility analysis.
In order to overcome the scarcity of training samples of fake images, we first construct a large-scale auxiliary dataset indirectly related to this task.
This auxiliary dataset contains 0.6 million weakly-labeled fake and real images collected automatically from social media.
Through an AdaBoost-like transfer learning algorithm, we train a CNN model with a few instances in the target training set and 0.6 million images in the collected auxiliary set.
This learning algorithm is able to leverage knowledge from the auxiliary set and gradually transfer it to the target task.
Experiments on a real-world testing set show that our proposed domain transferred CNN model outperforms several competing baselines.
It obtains superiror results over transfer learning methods based on the general ImageNet set.
Moreover, case studies show that our proposed method reveals some interesting patterns for distinguishing fake and authentic images.
Sentiment understanding has been a long-term goal of AI in the past decades.
This paper deals with sentence-level sentiment classification.
Though a variety of neural network models have been proposed very recently, however, previous models either depend on expensive phrase-level annotation, whose performance drops substantially when trained with only sentence-level annotation; or do not fully employ linguistic resources (e.g., sentiment lexicons, negation words, intensity words), thus not being able to produce linguistically coherent representations.
In this paper, we propose simple models trained with sentence-level annotation, but also attempt to generating linguistically coherent representations by employing regularizers that model the linguistic role of sentiment lexicons, negation words, and intensity words.
Results show that our models are effective to capture the sentiment shifting effect of sentiment, negation, and intensity words, while still obtain competitive results without sacrificing the models' simplicity.
The ability of deep convolutional neural networks (CNN) to learn discriminative spectro-temporal patterns makes them well suited to environmental sound classification.
However, the relative scarcity of labeled data has impeded the exploitation of this family of high-capacity models.
This study has two primary contributions: first, we propose a deep convolutional neural network architecture for environmental sound classification.
Second, we propose the use of audio data augmentation for overcoming the problem of data scarcity and explore the influence of different augmentations on the performance of the proposed CNN architecture.
Combined with data augmentation, the proposed model produces state-of-the-art results for environmental sound classification.
We show that the improved performance stems from the combination of a deep, high-capacity model and an augmented training set: this combination outperforms both the proposed CNN without augmentation and a "shallow" dictionary learning model with augmentation.
Finally, we examine the influence of each augmentation on the model's classification accuracy for each class, and observe that the accuracy for each class is influenced differently by each augmentation, suggesting that the performance of the model could be improved further by applying class-conditional data augmentation.
In this work, we propose using camera arrays coupled with coherent illumination as an effective method of improving spatial resolution in long distance images by a factor of ten and beyond.
Recent advances in ptychography have demonstrated that one can image beyond the diffraction limit of the objective lens in a microscope.
We demonstrate a similar imaging system to image beyond the diffraction limit in long range imaging.
We emulate a camera array with a single camera attached to an X-Y translation stage.
We show that an appropriate phase retrieval based reconstruction algorithm can be used to effectively recover the lost high resolution details from the multiple low resolution acquired images.
We analyze the effects of noise, required degree of image overlap, and the effect of increasing synthetic aperture size on the reconstructed image quality.
We show that coherent camera arrays have the potential to greatly improve imaging performance.
Our simulations show resolution gains of 10x and more are achievable.
Furthermore, experimental results from our proof-of-concept systems show resolution gains of 4x-7x for real scenes.
Finally, we introduce and analyze in simulation a new strategy to capture macroscopic Fourier Ptychography images in a single snapshot, albeit using a camera array.
The ability to semantically interpret hand-drawn line sketches, although very challenging, can pave way for novel applications in multimedia.
We propose SketchParse, the first deep-network architecture for fully automatic parsing of freehand object sketches.
SketchParse is configured as a two-level fully convolutional network.
The first level contains shared layers common to all object categories.
The second level contains a number of expert sub-networks.
Each expert specializes in parsing sketches from object categories which contain structurally similar parts.
Effectively, the two-level configuration enables our architecture to scale up efficiently as additional categories are added.
We introduce a router layer which (i) relays sketch features from shared layers to the correct expert (ii) eliminates the need to manually specify object category during inference.
To bypass laborious part-level annotation, we sketchify photos from semantic object-part image datasets and use them for training.
Our architecture also incorporates object pose prediction as a novel auxiliary task which boosts overall performance while providing supplementary information regarding the sketch.
We demonstrate SketchParse's abilities (i) on two challenging large-scale sketch datasets (ii) in parsing unseen, semantically related object categories (iii) in improving fine-grained sketch-based image retrieval.
As a novel application, we also outline how SketchParse's output can be used to generate caption-style descriptions for hand-drawn sketches.
Recently, compressed sensing (CS) computed tomography (CT) using sparse projection views has been extensively investigated to reduce the potential risk of radiation to patient.
However, due to the insufficient number of projection views, an analytic reconstruction approach results in severe streaking artifacts and CS-based iterative approach is computationally very expensive.
To address this issue, here we propose a novel deep residual learning approach for sparse view CT reconstruction.
Specifically, based on a novel persistent homology analysis showing that the manifold of streaking artifacts is topologically simpler than original ones, a deep residual learning architecture that estimates the streaking artifacts is developed.
Once a streaking artifact image is estimated, an artifact-free image can be obtained by subtracting the streaking artifacts from the input image.
Using extensive experiments with real patient data set, we confirm that the proposed residual learning provides significantly better image reconstruction performance with several orders of magnitude faster computational speed.
In structure learning, the output is generally a structure that is used as supervision information to achieve good performance.
Considering the interpretation of deep learning models has raised extended attention these years, it will be beneficial if we can learn an interpretable structure from deep learning models.
In this paper, we focus on Recurrent Neural Networks (RNNs) whose inner mechanism is still not clearly understood.
We find that Finite State Automaton (FSA) that processes sequential data has more interpretable inner mechanism and can be learned from RNNs as the interpretable structure.
We propose two methods to learn FSA from RNN based on two different clustering methods.
We first give the graphical illustration of FSA for human beings to follow, which shows the interpretability.
From the FSA's point of view, we then analyze how the performance of RNNs are affected by the number of gates, as well as the semantic meaning behind the transition of numerical hidden states.
Our results suggest that RNNs with simple gated structure such as Minimal Gated Unit (MGU) is more desirable and the transitions in FSA leading to specific classification result are associated with corresponding words which are understandable by human beings.
In this paper, we propose a novel waveform design which efficiently combines two air interface components: Frequency and Quadrature-Amplitude Modulation (FQAM) and Filter Bank Multicarrier (FBMC).
The proposed approach takes the unique characteristics of FQAM into consideration and exploits the design of prototype filters for FBMC to effectively avoid self-interference between adjacent subcarriers in the complex domain, thus providing improved performance compared with conventional solutions in terms of self-interference, spectrum confinement and complexity with negligible rate loss.
The moderation of content in many social media systems, such as Twitter and Facebook, motivated the emergence of a new social network system that promotes free speech, named Gab.
Soon after that, Gab has been removed from Google Play Store for violating the company's hate speech policy and it has been rejected by Apple for similar reasons.
In this paper we characterize Gab, aiming at understanding who are the users who joined it and what kind of content they share in this system.
Our findings show that Gab is a very politically oriented system that hosts banned users from other social networks, some of them due to possible cases of hate speech and association with extremism.
We provide the first measurement of news dissemination inside a right-leaning echo chamber, investigating a social media where readers are rarely exposed to content that cuts across ideological lines, but rather are fed with content that reinforces their current political or social views.
Sequential dynamics are a key feature of many modern recommender systems, which seek to capture the `context' of users' activities on the basis of actions they have performed recently.
To capture such patterns, two approaches have proliferated: Markov Chains (MCs) and Recurrent Neural Networks (RNNs).
Markov Chains assume that a user's next action can be predicted on the basis of just their last (or last few) actions, while RNNs in principle allow for longer-term semantics to be uncovered.
Generally speaking, MC-based methods perform best in extremely sparse datasets, where model parsimony is critical, while RNNs perform better in denser datasets where higher model complexity is affordable.
The goal of our work is to balance these two goals, by proposing a self-attention based sequential model (SASRec) that allows us to capture long-term semantics (like an RNN), but, using an attention mechanism, makes its predictions based on relatively few actions (like an MC).
At each time step, SASRec seeks to identify which items are `relevant' from a user's action history, and use them to predict the next item.
Extensive empirical studies show that our method outperforms various state-of-the-art sequential models (including MC/CNN/RNN-based approaches) on both sparse and dense datasets.
Moreover, the model is an order of magnitude more efficient than comparable CNN/RNN-based models.
Visualizations on attention weights also show how our model adaptively handles datasets with various density, and uncovers meaningful patterns in activity sequences.
In this paper we propose a segmentation-free query by string word spotting method.
Both the documents and query strings are encoded using a recently proposed word representa- tion that projects images and strings into a common atribute space based on a pyramidal histogram of characters(PHOC).
These attribute models are learned using linear SVMs over the Fisher Vector representation of the images along with the PHOC labels of the corresponding strings.
In order to search through the whole page, document regions are indexed per character bi- gram using a similar attribute representation.
On top of that, we propose an integral image representation of the document using a simplified version of the attribute model for efficient computation.
Finally we introduce a re-ranking step in order to boost retrieval performance.
We show state-of-the-art results for segmentation-free query by string word spotting in single-writer and multi-writer standard datasets
A basic setup of a two-tier network, where two mobile users exchange messages with a multi-antenna macrocell basestation, is studied from a rate perspective subject to beamforming and power constraints.
The communication is facilitated by two femtocell basestations which act as relays as there is no direct link between the macrocell basestation and the mobile users.
We propose a scheme based on physical-layer network coding and compute-and-forward combined with a novel approach that solves the problem of beamformer design and power allocation.
We also show that the optimal beamformers are always a convex combination of the channels between the macro- and femtocell basestations.
We then establish the cut-set bound of the setup to show that the presented scheme almost achieves the capacity of the setup numerically.
A large number of services for research data management strive to adhere to the FAIR guiding principles for scientific data management and stewardship.
To evaluate these services and to indicate possible improvements, use-case-centric metrics are needed as an addendum to existing metric frameworks.
The retrieval of spatially and temporally annotated images can exemplify such a use case.
The prototypical implementation indicates that currently no research data repository achieves the full score.
Suggestions on how to increase the score include automatic annotation based on the metadata inside the image file and support for content negotiation to retrieve the images.
These and other insights can lead to an improvement of data integration workflows, resulting in a better and more FAIR approach to manage research data.
Derivatives play a critical role in computational statistics, examples being Bayesian inference using Hamiltonian Monte Carlo sampling and the training of neural networks.
Automatic differentiation is a powerful tool to automate the calculation of derivatives and is preferable to more traditional methods, especially when differentiating complex algorithms and mathematical functions.
The implementation of automatic differentiation however requires some care to insure efficiency.
Modern differentiation packages deploy a broad range of computational techniques to improve applicability, run time, and memory management.
Among these techniques are operation overloading, region based memory, and expression templates.
There also exist several mathematical techniques which can yield high performance gains when applied to complex algorithms.
For example, semi-analytical derivatives can reduce by orders of magnitude the runtime required to numerically solve and differentiate an algebraic equation.
Open problems include the extension of current packages to provide more specialized routines, and efficient methods to perform higher-order differentiation.
Efficiently aggregating data from different sources is a challenging problem, particularly when samples from each source are distributed differently.
These differences can be inherent to the inference task or present for other reasons: sensors in a sensor network may be placed far apart, affecting their individual measurements.
Conversely, it is computationally advantageous to split Bayesian inference tasks across subsets of data, but data need not be identically distributed across subsets.
One principled way to fuse probability distributions is via the lens of optimal transport: the Wasserstein barycenter is a single distribution that summarizes a collection of input measures while respecting their geometry.
However, computing the barycenter scales poorly and requires discretization of all input distributions and the barycenter itself.
Improving on this situation, we present a scalable, communication-efficient, parallel algorithm for computing the Wasserstein barycenter of arbitrary distributions.
Our algorithm can operate directly on continuous input distributions and is optimized for streaming data.
Our method is even robust to nonstationary input distributions and produces a barycenter estimate that tracks the input measures over time.
The algorithm is semi-discrete, needing to discretize only the barycenter estimate.
To the best of our knowledge, we also provide the first bounds on the quality of the approximate barycenter as the discretization becomes finer.
Finally, we demonstrate the practical effectiveness of our method, both in tracking moving distributions on a sphere, as well as in a large-scale Bayesian inference task.
The identification of semantic relations between terms within texts is a fundamental task in Natural Language Processing which can support applications requiring a lightweight semantic interpretation model.
Currently, semantic relation classification concentrates on relations which are evaluated over open-domain data.
This work provides a critique on the set of abstract relations used for semantic relation classification with regard to their ability to express relationships between terms which are found in a domain-specific corpora.
Based on this analysis, this work proposes an alternative semantic relation model based on reusing and extending the set of abstract relations present in the DOLCE ontology.
The resulting set of relations is well grounded, allows to capture a wide range of relations and could thus be used as a foundation for automatic classification of semantic relations.
Ontologies provide features like a common vocabulary, reusability, machine-readable content, and also allows for semantic search, facilitate agent interaction and ordering & structuring of knowledge for the Semantic Web (Web 3.0) application.
However, the challenge in ontology engineering is automatic learning, i.e., the there is still a lack of fully automatic approach from a text corpus or dataset of various topics to form ontology using machine learning techniques.
In this paper, two topic modeling algorithms are explored, namely LSI & SVD and Mr.LDA for learning topic ontology.
The objective is to determine the statistical relationship between document and terms to build a topic ontology and ontology graph with minimum human intervention.
Experimental analysis on building a topic ontology and semantic retrieving corresponding topic ontology for the user's query demonstrating the effectiveness of the proposed approach.
Extended Kalman filter (EKF) does not guarantee consistent mean and covariance under linearization, even though it is the main framework for robotic localization.
While Lie group improves the modeling of the state space in localization, the EKF on Lie group still relies on the arbitrary Gaussian assumption in face of nonlinear models.
We instead use von Mises filter for orientation estimation together with the conventional Kalman filter for position estimation, and thus we are able to characterize the first two moments of the state estimates.
Since the proposed algorithm holds a solid probabilistic basis, it is fundamentally relieved from the inconsistency problem.
Furthermore, we extend the localization algorithm to fully circular representation even for position, which is similar to grid patterns found in mammalian brains and in recurrent neural networks.
The applicability of the proposed algorithms is substantiated not only by strong mathematical foundation but also by the comparison against other common localization methods.
The development of chemical reaction models aids understanding and prediction in areas ranging from biology to electrochemistry and combustion.
A systematic approach to building reaction network models uses observational data not only to estimate unknown parameters, but also to learn model structure.
Bayesian inference provides a natural approach to this data-driven construction of models.
Yet traditional Bayesian model inference methodologies that numerically evaluate the evidence for each model are often infeasible for nonlinear reaction network inference, as the number of plausible models can be combinatorially large.
Alternative approaches based on model-space sampling can enable large-scale network inference, but their realization presents many challenges.
In this paper, we present new computational methods that make large-scale nonlinear network inference tractable.
First, we exploit the topology of networks describing potential interactions among chemical species to design improved "between-model" proposals for reversible-jump Markov chain Monte Carlo.
Second, we introduce a sensitivity-based determination of move types which, when combined with network-aware proposals, yields significant additional gains in sampling performance.
These algorithms are demonstrated on inference problems drawn from systems biology, with nonlinear differential equation models of species interactions.
We study the problem of locating a particularly dangerous node, the so-called black hole in a synchronous anonymous ring network with mobile agents.
A black hole is a harmful stationary process residing in a node of the network and destroying destroys all mobile agents visiting that node without leaving any trace.
We consider the more challenging scenario when the agents are identical and initially scattered within the network.
Moreover, we solve the problem with agents that have constant-sized memory and carry a constant number of identical tokens, which can be placed at nodes of the network.
In contrast, the only known solutions for the case of scattered agents searching for a black hole, use stronger models where the agents have non-constant memory, can write messages in whiteboards located at nodes or are allowed to mark both the edges and nodes of the network with tokens.
This paper solves the problem for ring networks containing a single black hole.
We are interested in the minimum resources (number of agents and tokens) necessary for locating all links incident to the black hole.
We present deterministic algorithms for ring topologies and provide matching lower and upper bounds for the number of agents and the number of tokens required for deterministic solutions to the black hole search problem, in oriented or unoriented rings, using movable or unmovable tokens.
Our goal is to show that the standard model-theoretic concept of types can be applied in the study of order-invariant properties, i.e., properties definable in a logic in the presence of an auxiliary order relation, but not actually dependent on that order relation.
This is somewhat surprising since order-invariant properties are more of a combinatorial rather than a logical object.
We provide two applications of this notion.
One is a proof, from the basic principles, of a theorem by Courcelle stating that over trees, order-invariant MSO properties are expressible in MSO with counting quantifiers.
The other is an analog of the Feferman-Vaught theorem for order-invariant properties.
Machine learning (ML) has been widely applied to image classification.
Here, we extend this application to data generated by a camera comprised of only a standard CMOS image sensor with no lens.
We first created a database of lensless images of handwritten digits.
Then, we trained a ML algorithm on this dataset.
Finally, we demonstrated that the trained ML algorithm is able to classify the digits with accuracy as high as 99% for 2 digits.
Our approach clearly demonstrates the potential for non-human cameras in machine-based decision-making scenarios.
We design an algorithm to compute the Newton polytope of the resultant, known as resultant polytope, or its orthogonal projection along a given direction.
The resultant is fundamental in algebraic elimination, optimization, and geometric modeling.
Our algorithm exactly computes vertex- and halfspace-representations of the polytope using an oracle producing resultant vertices in a given direction, thus avoiding walking on the polytope whose dimension is alpha-n-1, where the input consists of alpha points in Z^n.
Our approach is output-sensitive as it makes one oracle call per vertex and facet.
It extends to any polytope whose oracle-based definition is advantageous, such as the secondary and discriminant polytopes.
Our publicly available implementation uses the experimental CGAL package triangulation.
Our method computes 5-, 6- and 7-dimensional polytopes with 35K, 23K and 500 vertices, respectively, within 2hrs, and the Newton polytopes of many important surface equations encountered in geometric modeling in <1sec, whereas the corresponding secondary polytopes are intractable.
It is faster than tropical geometry software up to dimension 5 or 6.
Hashing determinantal predicates accelerates execution up to 100 times.
One variant computes inner and outer approximations with, respectively, 90% and 105% of the true volume, up to 25 times faster.
Inspired by the trend on unifying theories of programming, this paper shows how the algebraic treatment of standard data dependency theory equips relational data with functional types and an associated type system which is useful for type checking database operations and for query optimization.
Such a typed approach to database programming is then shown to be of the same family as other programming logics such as eg.
Hoare logic or that of strongest invariant functions which has been used in the analysis of while statements.
The prospect of using automated deduction systems such as Prover9 for type-checking and query optimization on top of such an algebraic approach is considered.
Non-rigid structure-from-motion (NRSfM) has so far been mostly studied for recovering 3D structure of a single non-rigid/deforming object.
To handle the real world challenging multiple deforming objects scenarios, existing methods either pre-segment different objects in the scene or treat multiple non-rigid objects as a whole to obtain the 3D non-rigid reconstruction.
However, these methods fail to exploit the inherent structure in the problem as the solution of segmentation and the solution of reconstruction could not benefit each other.
In this paper, we propose a unified framework to jointly segment and reconstruct multiple non-rigid objects.
To compactly represent complex multi-body non-rigid scenes, we propose to exploit the structure of the scenes along both temporal direction and spatial direction, thus achieving a spatio-temporal representation.
Specifically, we represent the 3D non-rigid deformations as lying in a union of subspaces along the temporal direction and represent the 3D trajectories as lying in the union of subspaces along the spatial direction.
This spatio-temporal representation not only provides competitive 3D reconstruction but also outputs robust segmentation of multiple non-rigid objects.
The resultant optimization problem is solved efficiently using the Alternating Direction Method of Multipliers (ADMM).
Extensive experimental results on both synthetic and real multi-body NRSfM datasets demonstrate the superior performance of our proposed framework compared with the state-of-the-art methods.
Surface parameterizations have been widely used in computer graphics and geometry processing.
In particular, as simply-connected open surfaces are conformally equivalent to the unit disk, it is desirable to compute the disk conformal parameterizations of the surfaces.
In this paper, we propose a novel algorithm for the conformal parameterization of a simply-connected open surface onto the unit disk, which significantly speeds up the computation, enhances the conformality and stability, and guarantees the bijectivity.
The conformality distortions at the inner region and on the boundary are corrected by two steps, with the aid of an iterative scheme using quasi-conformal theories.
Experimental results demonstrate the effectiveness of our proposed method.
Correlation filter (CF) based trackers generally include two modules, i.e., feature representation and on-line model adaptation.
In existing off-line deep learning models for CF trackers, the model adaptation usually is either abandoned or has closed-form solution to make it feasible to learn deep representation in an end-to-end manner.
However, such solutions fail to exploit the advances in CF models, and cannot achieve competitive accuracy in comparison with the state-of-the-art CF trackers.
In this paper, we investigate the joint learning of deep representation and model adaptation, where an updater network is introduced for better tracking on future frame by taking current frame representation, tracking result, and last CF tracker as input.
By modeling the representor as convolutional neural network (CNN), we truncate the alternating direction method of multipliers (ADMM) and interpret it as a deep network of updater, resulting in our model for learning representation and truncated inference (RTINet).
Experiments demonstrate that our RTINet tracker achieves favorable tracking accuracy against the state-of-the-art trackers and its rapid version can run at a real-time speed of 24 fps.
The code and pre-trained models will be publicly available at https://github.com/tourmaline612/RTINet.
The discovery of frequent itemsets can serve valuable economic and research purposes.
Releasing discovered frequent itemsets, however, presents privacy challenges.
In this paper, we study the problem of how to perform frequent itemset mining on transaction databases while satisfying differential privacy.
We propose an approach, called PrivBasis, which leverages a novel notion called basis sets.
A theta-basis set has the property that any itemset with frequency higher than theta is a subset of some basis.
We introduce algorithms for privately constructing a basis set and then using it to find the most frequent itemsets.
Experiments show that our approach greatly outperforms the current state of the art.
Analysis and recognition of driving styles are profoundly important to intelligent transportation and vehicle calibration.
This paper presents a novel driving style analysis framework using the primitive driving patterns learned from naturalistic driving data.
In order to achieve this, first, a Bayesian nonparametric learning method based on a hidden semi-Markov model (HSMM) is introduced to extract primitive driving patterns from time series driving data without prior knowledge of the number of these patterns.
In the Bayesian nonparametric approach, we utilize a hierarchical Dirichlet process (HDP) instead of learning the unknown number of smooth dynamical modes of HSMM, thus generating the primitive driving patterns.
Each primitive pattern is clustered and then labeled using behavioral semantics according to drivers' physical and psychological perception thresholds.
For each driver, 75 primitive driving patterns in car-following scenarios are learned and semantically labeled.
In order to show the HDP-HSMM's utility to learn primitive driving patterns, other two Bayesian nonparametric approaches, HDP-HMM and sticky HDP-HMM, are compared.
The naturalistic driving data of 18 drivers were collected from the University of Michigan Safety Pilot Model Deployment (SPDM) database.
The individual driving styles are discussed according to distribution characteristics of the learned primitive driving patterns and also the difference in driving styles among drivers are evaluated using the Kullback-Leibler divergence.
The experiment results demonstrate that the proposed primitive pattern-based method can allow one to semantically understand driver behaviors and driving styles.
In this paper we examine the existence of correlation between movie similarity and low level features from respective movie content.
In particular, we demonstrate the extraction of multi-modal representation models of movies based on subtitles, audio and metadata mining.
We emphasize our research in topic modeling of movies based on their subtitles.
In order to demonstrate the proposed content representation approach, we have built a small dataset of 160 widely known movies.
We assert movie similarities, as propagated by the singular modalities and fusion models, in the form of recommendation rankings.
We showcase a novel topic model browser for movies that allows for exploration of the different aspects of similarities between movies and an information retrieval system for movie similarity based on multi-modal content.
A multilayer perceptron can behave as a generative classifier by applying bidirectional learning (BL).
It consists of training an undirected neural network to map input to output and vice-versa; therefore it can produce a classifier in one direction, and a generator in the opposite direction for the same data.
The learning process of BL tries to reproduce the neuroplasticity stated in Hebbian theory using only backward propagation of errors.
In this paper, two novel learning techniques are introduced which use BL for improving robustness to white noise static and adversarial examples.
The first method is bidirectional propagation of errors, which the error propagation occurs in backward and forward directions.
Motivated by the fact that its generative model receives as input a constant vector per class, we introduce as a second method the hybrid adversarial networks (HAN).
Its generative model receives a random vector as input and its training is based on generative adversarial networks (GAN).
To assess the performance of BL, we perform experiments using several architectures with fully and convolutional layers, with and without bias.
Experimental results show that both methods improve robustness to white noise static and adversarial examples, and even increase accuracy, but have different behavior depending on the architecture and task, being more beneficial to use the one or the other.
Nevertheless, HAN using a convolutional architecture with batch normalization presents outstanding robustness, reaching state-of-the-art accuracy on adversarial examples of hand-written digits.
In Android, communications between apps and system services are supported by a transaction-based Inter-Process Communication (IPC) mechanism.
Binder, as the cornerstone of this IPC mechanism, separates two communicating parties as client and server.
As with any client-server model, the server should not make any assumption on the validity (sanity) of client-side transaction.
To our surprise, we find this principle has frequently been overlooked in the implementation of Android system services.
In this paper, we demonstrate the prevalence and severity of this vulnerability surface and try to answer why developers keep making this seemingly simple mistake.
Specifically, we design and implement BinderCracker, an automatic testing framework that supports parameter-aware fuzzing and has identified more than 100 vulnerabilities in six major versions of Android, including the latest version Android 6.0, Marshmallow.
Some of the vulnerabilities have severe security implications, causing privileged code execution or permanent Denial-of-Service (DoS).
We analyzed the root causes of these vulnerabilities to find that most of them exist because system service developers only considered exploitations via public APIs.
We thus highlight the deficiency of testing only on client-side public APIs and argue for the necessity of testing and protection on the Binder interface - the actual security boundary.
Specifically, we discuss the effectiveness and practicality of potential countermeasures, such as precautionary testing and runtime diagnostic.
Artificial neural networks learn how to solve new problems through a computationally intense and time consuming process.
One way to reduce the amount of time required is to inject preexisting knowledge into the network.
To make use of past knowledge, we can take advantage of techniques that transfer the knowledge learned from one task, and reuse it on another (sometimes unrelated) task.
In this paper we propose a novel selective breeding technique that extends the transfer learning with behavioural genetics approach proposed by Kohli, Magoulas and Thomas (2013), and evaluate its performance on financial data.
Numerical evidence demonstrates the credibility of the new approach.
We provide insights on the operation of transfer learning and highlight the benefits of using behavioural principles and selective breeding when tackling a set of diverse financial applications problems.
We consider the communication scenario where K transmitters are each connected to a common receiver with an orthogonal noiseless link.
One of the transmitters has a message for the receiver, who is prohibited from learning anything in the information theoretic sense about which transmitter sends the message (transmitter anonymity is guaranteed).
The capacity of anonymous communications is the maximum number of bits of desired information that can be anonymously communicated per bit of total communication.
For this anonymous communication problem over a parallel channel with K transmitters and 1 receiver, we show that the capacity is 1/K, i.e., to communicate 1 bit anonymously, each transmitter must send a 1 bit signal.
Further, it is required that each transmitter has at least 1 bit correlated randomness (that is independent of the messages) per message bit and the size of correlated randomness at all K transmitters is at least K-1 bits per message bit.
Automatic recognition of spontaneous facial expressions is a major challenge in the field of affective computing.
Head rotation, face pose, illumination variation, occlusion etc. are the attributes that increase the complexity of recognition of spontaneous expressions in practical applications.
Effective recognition of expressions depends significantly on the quality of the database used.
Most well-known facial expression databases consist of posed expressions.
However, currently there is a huge demand for spontaneous expression databases for the pragmatic implementation of the facial expression recognition algorithms.
In this paper, we propose and establish a new facial expression database containing spontaneous expressions of both male and female participants of Indian origin.
The database consists of 428 segmented video clips of the spontaneous facial expressions of 50 participants.
In our experiment, emotions were induced among the participants by using emotional videos and simultaneously their self-ratings were collected for each experienced emotion.
Facial expression clips were annotated carefully by four trained decoders, which were further validated by the nature of stimuli used and self-report of emotions.
An extensive analysis was carried out on the database using several machine learning algorithms and the results are provided for future reference.
Such a spontaneous database will help in the development and validation of algorithms for recognition of spontaneous expressions.
The Apriori algorithm that mines frequent itemsets is one of the most popular and widely used data mining algorithms.
Now days many algorithms have been proposed on parallel and distributed platforms to enhance the performance of Apriori algorithm.
They differ from each other on the basis of load balancing technique, memory system, data decomposition technique and data layout used to implement them.
The problems with most of the distributed framework are overheads of managing distributed system and lack of high level parallel programming language.
Also with grid computing there is always potential chances of node failures which cause multiple re-executions of tasks.
These problems can be overcome by the MapReduce framework introduced by Google.
MapReduce is an efficient, scalable and simplified programming model for large scale distributed data processing on a large cluster of commodity computers and also used in cloud computing.
In this paper, we present the overview of parallel Apriori algorithm implemented on MapReduce framework.
They are categorized on the basis of Map and Reduce functions used to implement them e.g.1-phase vs. k-phase, I/O of Mapper, Combiner and Reducer, using functionality of Combiner inside Mapper etc.
This survey discusses and analyzes the various implementations of Apriori on MapReduce framework on the basis of their distinguishing characteristics.
Moreover, it also includes the advantages and limitations of MapReduce framework.
Most of the parameters in large vocabulary models are used in embedding layer to map categorical features to vectors and in softmax layer for classification weights.
This is a bottle-neck in memory constraint on-device training applications like federated learning and on-device inference applications like automatic speech recognition (ASR).
One way of compressing the embedding and softmax layers is to substitute larger units such as words with smaller sub-units such as characters.
However, often the sub-unit models perform poorly compared to the larger unit models.
We propose WEST, an algorithm for encoding categorical features and output classes with a sequence of random or domain dependent sub-units and demonstrate that this transduction can lead to significant compression without compromising performance.
WEST bridges the gap between larger unit and sub-unit models and can be interpreted as a MaxEnt model over sub-unit features, which can be of independent interest.
Books have the power to make us feel happiness, sadness, pain, surprise, or sorrow.
An author's dexterity in the use of these emotions captivates readers and makes it difficult for them to put the book down.
In this paper, we model the flow of emotions over a book using recurrent neural networks and quantify its usefulness in predicting success in books.
We obtained the best weighted F1-score of 69% for predicting books' success in a multitask setting (simultaneously predicting success and genre of books).
Model compression and knowledge distillation have been successfully applied for cross-architecture and cross-domain transfer learning.
However, a key requirement is that training examples are in correspondence across the domains.
We show that in many scenarios of practical importance such aligned data can be synthetically generated using computer graphics pipelines allowing domain adaptation through distillation.
We apply this technique to learn models for recognizing low-resolution images using labeled high-resolution images, non-localized objects using labeled localized objects, line-drawings using labeled color images, etc.
Experiments on various fine-grained recognition datasets demonstrate that the technique improves recognition performance on the low-quality data and beats strong baselines for domain adaptation.
Finally, we present insights into workings of the technique through visualizations and relating it to existing literature.
This document describes G2D, a software that enables capturing videos from Grand Theft Auto V (GTA V), a popular role playing game set in an expansive virtual city.
The target users of our software are computer vision researchers who wish to collect hyper-realistic computer-generated imagery of a city from the street level, under controlled 6DOF camera poses and varying environmental conditions (weather, season, time of day, traffic density, etc.).
G2D accesses/calls the native functions of the game; hence users can directly interact with G2D while playing the game.
Specifically, G2D enables users to manipulate conditions of the virtual environment on the fly, while the gameplay camera is set to automatically retrace a predetermined 6DOF camera pose trajectory within the game coordinate system.
Concurrently, automatic screen capture is executed while the virtual environment is being explored.
G2D and its source code are publicly available at https://goo.gl/SS7fS6   In addition, we demonstrate an application of G2D to generate a large-scale dataset with groundtruth camera poses for testing structure-from-motion (SfM) algorithms.
The dataset and generated 3D point clouds are also made available at https://goo.gl/DNzxHx
We study verifiable sufficient conditions and computable performance bounds for sparse recovery algorithms such as the Basis Pursuit, the Dantzig selector and the Lasso estimator, in terms of a newly defined family of quality measures for the measurement matrices.
With high probability, the developed measures for subgaussian random matrices are bounded away from zero as long as the number of measurements is reasonably large.
Comparing to the restricted isotropic constant based performance analysis, the arguments in this paper are much more concise and the obtained bounds are tighter.
Numerical experiments are presented to illustrate our theoretical results.
k-mers (nucleotide strings of length k) form the basis of several algorithms in computational genomics.
In particular, k-mer abundance information in sequence data is useful in read error correction, parameter estimation for genome assembly, digital normalization etc.
We give a streaming algorithm Kmerlight for computing the k-mer abundance histogram from sequence data.
Our algorithm is fast and uses very small memory footprint.
We provide analytical bounds on the error guarantees of our algorithm.
Kmerlight can efficiently process genome scale and metagenome scale data using standard desktop machines.
Few applications of abundance histograms computed by Kmerlight are also shown.
We use abundance histogram for de novo estimation of repetitiveness in the genome based on a simple probabilistic model that we propose.
We also show estimation of k-mer error rate in the sampling using abundance histogram.
Our algorithm can also be used for abundance estimation in a general streaming setting.
The Kmerlight tool is written in C++ and is available for download and use from https://github.com/nsivad/kmerlight.
The segmentation of transparent objects can be very useful in computer vision applications.
However, because they borrow texture from their background and have a similar appearance to their surroundings, transparent objects are not handled well by regular image segmentation methods.
We propose a method that overcomes these problems using the consistency and distortion properties of a light-field image.
Graph-cut optimization is applied for the pixel labeling problem.
The light-field linearity is used to estimate the likelihood of a pixel belonging to the transparent object or Lambertian background, and the occlusion detector is used to find the occlusion boundary.
We acquire a light field dataset for the transparent object, and use this dataset to evaluate our method.
The results demonstrate that the proposed method successfully segments transparent objects from the background.
We explore geometry of London's streets using computational mode of an excitable chemical system, Belousov-Zhabotinsky (BZ) medium.
We virtually fill in the streets with a BZ medium and study propagation of excitation waves for a range of excitability parameters, gradual transition from excitable to sub-excitable to non-excitable.
We demonstrate a pruning strategy adopted by the medium with decreasing excitability when wider and ballistically appropriate streets are selected.
We explain mechanics of streets selection and pruning.
The results of the paper will be used in future studies of studying dynamics of cities with living excitable substrates.
It is essential to find new ways of enabling experts in different disciplines to collaborate more efficient in the development of ever more complex systems, under increasing market pressures.
One possible solution for this challenge is to use a heterogeneous model-based approach where different teams can produce their conventional models and carry out their usual mono-disciplinary analysis, but in addition, the different models can be coupled for simulation (co-simulation), allowing the study of the global behavior of the system.
Due to its potential, co-simulation is being studied in many different disciplines but with limited sharing of findings.
Our aim with this work is to summarize, bridge, and enhance future research in this multidisciplinary area.
We provide an overview of co-simulation approaches, research challenges, and research opportunities, together with a detailed taxonomy with different aspects of the state of the art of co-simulation and classification for the past five years.
The main research needs identified are: finding generic approaches for modular, stable and accurate coupling of simulation units; and expressing the adaptations required to ensure that the coupling is correct.
We present a weakly supervised model that jointly performs both semantic- and instance-segmentation -- a particularly relevant problem given the substantial cost of obtaining pixel-perfect annotation for these tasks.
In contrast to many popular instance segmentation approaches based on object detectors, our method does not predict any overlapping instances.
Moreover, we are able to segment both "thing" and "stuff" classes, and thus explain all the pixels in the image.
"Thing" classes are weakly-supervised with bounding boxes, and "stuff" with image-level tags.
We obtain state-of-the-art results on Pascal VOC, for both full and weak supervision (which achieves about 95% of fully-supervised performance).
Furthermore, we present the first weakly-supervised results on Cityscapes for both semantic- and instance-segmentation.
Finally, we use our weakly supervised framework to analyse the relationship between annotation quality and predictive performance, which is of interest to dataset creators.
We consider the data shuffling problem in a distributed learning system, in which a master node is connected to a set of worker nodes, via a shared link, in order to communicate a set of files to the worker nodes.
The master node has access to a database of files.
In every shuffling iteration, each worker node processes a new subset of files, and has excess storage to partially cache the remaining files, assuming the cached files are uncoded.
The caches of the worker nodes are updated every iteration, and it should be designed to satisfy any possible unknown permutation of the files in subsequent iterations.
For this problem, we characterize the exact rate-memory trade-off for worst-case shuffling by deriving the minimum communication load for a given storage capacity per worker node.
As a byproduct, the exact rate-memory trade-off for any shuffling is characterized when the number of files is equal to the number of worker nodes.
We propose a novel deterministic coded shuffling scheme, which improves the state of the art, by exploiting the cache memories to create coded functions that can be decoded by several worker nodes.
Then, we prove the optimality of our proposed scheme by deriving a matching lower bound and showing that the placement phase of the proposed coded shuffling scheme is optimal over all shuffles.
The identification of nodes occupying important positions in a network structure is crucial for the understanding of the associated real-world system.
Usually, betweenness centrality is used to evaluate a node capacity to connect different graph regions.
However, we argue here that this measure is not adapted for that task, as it gives equal weight to "local" centers (i.e. nodes of high degree central to a single region) and to "global" bridges, which connect different communities.
This distinction is important as the roles of such nodes are different in terms of the local and global organisation of the network structure.
In this paper we propose a decomposition of betweenness centrality into two terms, one highlighting the local contributions and the other the global ones.
We call the latter bridgeness centrality and show that it is capable to specifically spot out global bridges.
In addition, we introduce an effective algorithmic implementation of this measure and demonstrate its capability to identify global bridges in air transportation and scientific collaboration networks.
Efficient processing of aggregated range queries on two-dimensional grids is a common requirement in information retrieval and data mining systems, for example in Geographic Information Systems and OLAP cubes.
We introduce a technique to represent grids supporting aggregated range queries that requires little space when the data points in the grid are clustered, which is common in practice.
We show how this general technique can be used to support two important types of aggregated queries, which are ranked range queries and counting range queries.
Our experimental evaluation shows that this technique can speed up aggregated queries up to more than an order of magnitude, with a small space overhead.
Background: Code review is a cognitively demanding and time-consuming process.
Previous qualitative studies hinted at how changesets divided according to a logical partitioning could be easier to review.
Aims: (1) Quantitatively measure the effects of change-decomposition on the outcome of code review (in terms of number of found defects, wrongly reported issues, suggested improvements, time, and understanding); (2) Qualitatively analyze how subjects approach the review and navigate the code building knowledge and addressing existing issues, in large vs. decomposed changes.
Method: Controlled experiment using the pull-based development model involving 28 software developers among professionals and graduate students.
Results: Change-decomposition leads to fewer wrongly reported issues, influences how subjects approach and conduct the review activity (by increasing context-seeking), yet impacts neither understanding the change rationale nor the number of found defects.
Conclusions: Change-decomposition reduces the noise for subsequent data analyses but also significantly support the tasks of the developers in charge of reviewing the changes.
As such, commits belonging to different concepts should be separated, adopting this as a best practice in software engineering.
Over the last few years, a rapidly increasing number of Internet-of-Things (IoT) systems that adopt voice as the primary user input have emerged.
These systems have been shown to be vulnerable to various types of voice spoofing attacks.
Existing defense techniques can usually only protect from a specific type of attack or require an additional authentication step that involves another device.
Such defense strategies are either not strong enough or lower the usability of the system.
Based on the fact that legitimate voice commands should only come from humans rather than a playback device, we propose a novel defense strategy that is able to detect the sound source of a voice command based on its acoustic features.
The proposed defense strategy does not require any information other than the voice command itself and can protect a system from multiple types of spoofing attacks.
Our proof-of-concept experiments verify the feasibility and effectiveness of this defense strategy.
Modeling human conversations is the essence for building satisfying chat-bots with multi-turn dialog ability.
Conversation modeling will notably benefit from domain knowledge since the relationships between sentences can be clarified due to semantic hints introduced by knowledge.
In this paper, a deep neural network is proposed to incorporate background knowledge for conversation modeling.
Through a specially designed Recall gate, domain knowledge can be transformed into the extra global memory of Long Short-Term Memory (LSTM), so as to enhance LSTM by cooperating with its local memory to capture the implicit semantic relevance between sentences within conversations.
In addition, this paper introduces the loose structured domain knowledge base, which can be built with slight amount of manual work and easily adopted by the Recall gate.
Our model is evaluated on the context-oriented response selecting task, and experimental results on both two datasets have shown that our approach is promising for modeling human conversations and building key components of automatic chatting systems.
FPGA becomes a popular technology for implementing Convolutional Neural Network (CNN) in recent years.
Most CNN applications on FPGA are domain-specific, e.g., detecting objects from specific categories, in which commonly-used CNN models pre-trained on general datasets may not be efficient enough.
This paper presents TuRF, an end-to-end CNN acceleration framework to efficiently deploy domain-specific applications on FPGA by transfer learning that adapts pre-trained models to specific domains, replacing standard convolution layers with efficient convolution blocks, and applying layer fusion to enhance hardware design performance.
We evaluate TuRF by deploying a pre-trained VGG-16 model for a domain-specific image recognition task onto a Stratix V FPGA.
Results show that designs generated by TuRF achieve better performance than prior methods for the original VGG-16 and ResNet-50 models, while for the optimised VGG-16 model TuRF designs are more accurate and easier to process.
Complex systems of systems (SoS) are characterized by multiple interconnected subsystems.
Typically, each subsystem is designed and analyzed using methodologies and formalisms that are specific to the particular subsystem model of computation considered --- Petri nets, continuous time ODEs, nondeterministic automata, to name a few.
When interconnecting subsystems, a designer needs to choose, based on the specific subsystems models, a common abstraction framework to analyze the composition.
In this paper we introduce a new framework for abstraction, composition and analysis of SoS that builds on results and methods developed in sheaf theory, category theory and topos theory.
In particular, we will be modeling behaviors of systems using sheaves, leverage category theoretic methods to define wiring diagrams and formalize composition and, by establishing a connection with topos theory, define a formal (intuitionistic/constructive) logic with a sound sheaf semantics
Recent work have shown that Reed-Muller (RM) codes achieve the erasure channel capacity.
However, this performance is obtained with maximum-likelihood decoding which can be costly for practical applications.
In this paper, we propose an encoding/decoding scheme for Reed-Muller codes on the packet erasure channel based on Plotkin construction.
We present several improvements over the generic decoding.
They allow, for a light cost, to compete with maximum-likelihood decoding performance, especially on high-rate codes, while significantly outperforming it in terms of speed.
Layer-wise Relevance Propagation (LRP) and saliency maps have been recently used to explain the predictions of Deep Learning models, specifically in the domain of text classification.
Given different attribution-based explanations to highlight relevant words for a predicted class label, experiments based on word deleting perturbation is a common evaluation method.
This word removal approach, however, disregards any linguistic dependencies that may exist between words or phrases in a sentence, which could semantically guide a classifier to a particular prediction.
In this paper, we present a feature-based evaluation framework for comparing the two attribution methods on customer reviews (public data sets) and Customer Due Diligence (CDD) extracted reports (corporate data set).
Instead of removing words based on the relevance score, we investigate perturbations based on embedded features removal from intermediate layers of Convolutional Neural Networks.
Our experimental study is carried out on embedded-word, embedded-document, and embedded-ngrams explanations.
Using the proposed framework, we provide a visualization tool to assist analysts in reasoning toward the model's final prediction.
Natural disasters affect hundreds of millions of people worldwide every year.
Early warning, humanitarian response and recovery mechanisms can be improved by using big data sources.
Measuring the different dimensions of the impact of natural disasters is critical for designing policies and building up resilience.
Detailed quantification of the movement and behaviours of affected populations requires the use of high granularity data that entails privacy risks.
Leveraging all this data is costly and has to be done ensuring privacy and security of large amounts of data.
Proxies based on social media and data aggregates would streamline this process by providing evidences and narrowing requirements.
We propose a framework that integrates environmental data, social media, remote sensing, digital topography and mobile phone data to understand different types of floods and how data can provide insights useful for managing humanitarian action and recovery plans.
Thus, data is dynamically requested upon data-based indicators forming a multi-granularity and multi-access data pipeline.
We present a composed study of three cases to show potential variability in the natures of floodings,as well as the impact and applicability of data sources.
Critical heterogeneity of the available data in the different cases has to be addressed in order to design systematic approaches based on data.
The proposed framework establishes the foundation to relate the physical and socio-economical impacts of floods.
Most brain-computer interfaces (BCIs) based on functional near-infrared spectroscopy (fNIRS) require that users perform mental tasks such as motor imagery, mental arithmetic, or music imagery to convey a message or to answer simple yes or no questions.
These cognitive tasks usually have no direct association with the communicative intent, which makes them difficult for users to perform.
In this paper, a 3-class intuitive BCI is presented which enables users to directly answer yes or no questions by covertly rehearsing the word 'yes' or 'no' for 15 s. The BCI also admits an equivalent duration of unconstrained rest which constitutes the third discernable task.
Twelve participants each completed one offline block and six online blocks over the course of 2 sessions.
The mean value of the change in oxygenated hemoglobin concentration during a trial was calculated for each channel and used to train a regularized linear discriminant analysis (RLDA) classifier.
By the final online block, 9 out of 12 participants were performing above chance (p<0.001), with a 3-class accuracy of 83.8+9.4%.
Even when considering all participants, the average online 3-class accuracy over the last 3 blocks was 64.1+20.6%, with only 3 participants scoring below chance (p<0.001).
For most participants, channels in the left temporal and temporoparietal cortex provided the most discriminative information.
To our knowledge, this is the first report of an online fNIRS 3-class imagined speech BCI.
Our findings suggest that imagined speech can be used as a reliable activation task for selected users for the development of more intuitive BCIs for communication.
We present a simple and effective approach to incorporating syntactic structure into neural attention-based encoder-decoder models for machine translation.
We rely on graph-convolutional networks (GCNs), a recent class of neural networks developed for modeling graph-structured data.
Our GCNs use predicted syntactic dependency trees of source sentences to produce representations of words (i.e. hidden states of the encoder) that are sensitive to their syntactic neighborhoods.
GCNs take word representations as input and produce word representations as output, so they can easily be incorporated as layers into standard encoders (e.g., on top of bidirectional RNNs or convolutional neural networks).
We evaluate their effectiveness with English-German and English-Czech translation experiments for different types of encoders and observe substantial improvements over their syntax-agnostic versions in all the considered setups.
Recent advances in optimization methods used for training convolutional neural networks (CNNs) with kernels, which are normalized according to particular constraints, have shown remarkable success.
This work introduces an approach for training CNNs using ensembles of joint spaces of kernels constructed using different constraints.
For this purpose, we address a problem of optimization on ensembles of products of submanifolds (PEMs) of convolution kernels.
To this end, we first propose three strategies to construct ensembles of PEMs in CNNs.
Next, we expound their geometric properties (metric and curvature properties) in CNNs.
We make use of our theoretical results by developing a geometry-aware SGD algorithm (G-SGD) for optimization on ensembles of PEMs to train CNNs.
Moreover, we analyze convergence properties of G-SGD considering geometric properties of PEMs.
In the experimental analyses, we employ G-SGD to train CNNs on Cifar-10, Cifar-100 and Imagenet datasets.
The results show that geometric adaptive step size computation methods of G-SGD can improve training loss and convergence properties of CNNs.
Moreover, we observe that classification performance of baseline CNNs can be boosted using G-SGD on ensembles of PEMs identified by multiple constraints.
The paper aims to show how an application can be developed that converts the English language into the Punjabi Language, and the same application can convert the Text to Speech(TTS) i.e. pronounce the text.
This application can be really beneficial for those with special needs.
In this paper, we consider noncoherent random linear coding networks (RLCNs) as a discrete memoryless channel (DMC) whose input and output alphabets consist of subspaces.
This contrasts with previous channel models in the literature which assume matrices as the channel input and output.
No particular assumptions are made on the network topology or the transfer matrix, except that the latter may be rank-deficient according to some rank deficiency probability distribution.
We introduce a random vector basis selection procedure which renders the DMC symmetric.
The capacity we derive can be seen as a lower bound on the capacity of noncoherent RLCNs, where subspace coding suffices to achieve this bound.
A significant weakness of most current deep Convolutional Neural Networks is the need to train them using vast amounts of manu- ally labelled data.
In this work we propose a unsupervised framework to learn a deep convolutional neural network for single view depth predic- tion, without requiring a pre-training stage or annotated ground truth depths.
We achieve this by training the network in a manner analogous to an autoencoder.
At training time we consider a pair of images, source and target, with small, known camera motion between the two such as a stereo pair.
We train the convolutional encoder for the task of predicting the depth map for the source image.
To do so, we explicitly generate an inverse warp of the target image using the predicted depth and known inter-view displacement, to reconstruct the source image; the photomet- ric error in the reconstruction is the reconstruction loss for the encoder.
The acquisition of this training data is considerably simpler than for equivalent systems, requiring no manual annotation, nor calibration of depth sensor to camera.
We show that our network trained on less than half of the KITTI dataset (without any further augmentation) gives com- parable performance to that of the state of art supervised methods for single view depth estimation.
Caricature generation is an interesting yet challenging task.
The primary goal is to generate plausible caricatures with reasonable exaggerations given face images.
Conventional caricature generation approaches mainly use low-level geometric transformations such as image warping to generate exaggerated images, which lack richness and diversity in terms of content and style.
The recent progress in generative adversarial networks (GANs) makes it possible to learn an image-to-image transformation from data, so that richer contents and styles can be generated.
However, directly applying the GAN-based models to this task leads to unsatisfactory results because there is a large variance in the caricature distribution.
Moreover, some models require strictly paired training data which largely limits their usage scenarios.
In this paper, we propose CariGAN overcome these problems.
Instead of training on paired data, CariGAN learns transformations only from weakly paired images.
Specifically, to enforce reasonable exaggeration and facial deformation, facial landmarks are adopted as an additional condition to constrain the generated image.
Furthermore, an attention mechanism is introduced to encourage our model to focus on the key facial parts so that more vivid details in these regions can be generated.
Finally, a Diversity Loss is proposed to encourage the model to produce diverse results to help alleviate the `mode collapse' problem of the conventional GAN-based models.
Extensive experiments on a new large-scale `WebCaricature' dataset show that the proposed CariGAN can generate more plausible caricatures with larger diversity compared with the state-of-the-art models.
This work deals with a classic problem: "Given a set of coins among which there is a counterfeit coin of a different weight, find this counterfeit coin using ordinary balance scales, with the minimum number of weighings possible, and indicate whether it weighs less or more than the rest".
The method proposed here not only calculates the minimum number of weighings necessary, but also indicates how to perform these weighings, it is easily mechanizeable and valid for any number of coins.
Instructions are also given as to how to generalize the procedure to include cases where there is more than one counterfeit coin.
Convolutional neural networks are ubiquitous in Machine Learning applications for solving a variety of problems.
They however can not be used in their native form when the domain of the data is commonly encountered manifolds such as the sphere, the special orthogonal group, the Grassmanian, the manifold of symmetric positive definite matrices and others.
Most recently, generalization of CNNs to data domains such as the 2-sphere has been reported by some research groups, which is referred to as the spherical CNNs (SCNNs).
The key property of SCNNs distinct from CNNs is that they exhibit the rotational equivariance property that allows for sharing learned weights within a layer.
In this paper, we theoretically generalize the CNNs to Riemannian homogeneous manifolds, that include but are not limited to the aforementioned example manifolds.
Our key contributions in this work are: (i) A theorem stating that linear group equivariance systems are fully characterized by correlation of functions on the domain manifold and vice-versa.
This is fundamental to the characterization of all linear group equivariant systems and parallels the widely used result in linear system theory for vector spaces.
(ii) As a corrolary, we prove the equivariance of the correlation operation to group actions admitted by the input domains which are Riemannian homogeneous manifolds.
(iii) We present the first end-to-end deep network architecture for classification of diffusion magnetic resonance image (dMRI) scans acquired from a cohort of 44 Parkinson Disease patients and 50 control/normal subjects.
(iv) A proof of concept experiment involving synthetic data generated on the manifold of symmetric positive definite matrices is presented to demonstrate the applicability of our network to other types of domains.
Augmented accuracy in prediction of diabetes will open up new frontiers in health prognostics.
Data overfitting is a performance-degrading issue in diabetes prognosis.
In this study, a prediction system for the disease of diabetes is pre-sented where the issue of overfitting is minimized by using the dropout method.
Deep learning neural network is used where both fully connected layers are fol-lowed by dropout layers.
The output performance of the proposed neural network is shown to have outperformed other state-of-art methods and it is recorded as by far the best performance for the Pima Indians Diabetes Data Set.
We propose a framework, named Aggregated Wasserstein, for computing a dissimilarity measure or distance between two Hidden Markov Models with state conditional distributions being Gaussian.
For such HMMs, the marginal distribution at any time spot follows a Gaussian mixture distribution, a fact exploited to softly match, aka register, the states in two HMMs.
We refer to such HMMs as Gaussian mixture model-HMM (GMM-HMM).
The registration of states is inspired by the intrinsic relationship of optimal transport and the Wasserstein metric between distributions.
Specifically, the components of the marginal GMMs are matched by solving an optimal transport problem where the cost between components is the Wasserstein metric for Gaussian distributions.
The solution of the optimization problem is a fast approximation to the Wasserstein metric between two GMMs.
The new Aggregated Wasserstein distance is a semi-metric and can be computed without generating Monte Carlo samples.
It is invariant to relabeling or permutation of the states.
This distance quantifies the dissimilarity of GMM-HMMs by measuring both the difference between the two marginal GMMs and the difference between the two transition matrices.
Our new distance is tested on the tasks of retrieval and classification of time series.
Experiments on both synthetic data and real data have demonstrated its advantages in terms of accuracy as well as efficiency in comparison with existing distances based on the Kullback-Leibler divergence.
A minimal solution using two affine correspondences is presented to estimate the common focal length and the fundamental matrix between two semi-calibrated cameras - known intrinsic parameters except a common focal length.
To the best of our knowledge, this problem is unsolved.
The proposed approach extends point correspondence-based techniques with linear constraints derived from local affine transformations.
The obtained multivariate polynomial system is efficiently solved by the hidden-variable technique.
Observing the geometry of local affinities, we introduce novel conditions eliminating invalid roots.
To select the best one out of the remaining candidates, a root selection technique is proposed outperforming the recent ones especially in case of high-level noise.
The proposed 2-point algorithm is validated on both synthetic data and 104 publicly available real image pairs.
A Matlab implementation of the proposed solution is included in the paper.
Unsupervised pre-trained word embeddings are used effectively for many tasks in natural language processing to leverage unlabeled textual data.
Often these embeddings are either used as initializations or as fixed word representations for task-specific classification models.
In this work, we extend our classification model's task loss with an unsupervised auxiliary loss on the word-embedding level of the model.
This is to ensure that the learned word representations contain both task-specific features, learned from the supervised loss component, and more general features learned from the unsupervised loss component.
We evaluate our approach on the task of temporal relation extraction, in particular, narrative containment relation extraction from clinical records, and show that continued training of the embeddings on the unsupervised objective together with the task objective gives better task-specific embeddings, and results in an improvement over the state of the art on the THYME dataset, using only a general-domain part-of-speech tagger as linguistic resource.
Lifelong machine learning methods acquire knowledge over a series of consecutive tasks, continually building upon their experience.
Current lifelong learning algorithms rely upon a single learning agent that has centralized access to all data.
In this paper, we extend the idea of lifelong learning from a single agent to a network of multiple agents that collectively learn a series of tasks.
Each agent faces some (potentially unique) set of tasks; the key idea is that knowledge learned from these tasks may benefit other agents trying to learn different (but related) tasks.
Our Collective Lifelong Learning Algorithm (CoLLA) provides an efficient way for a network of agents to share their learned knowledge in a distributed and decentralized manner, while preserving the privacy of the locally observed data.
Note that a decentralized scheme is a subclass of distributed algorithms where a central server does not exist and in addition to data, computations are also distributed among the agents.
We provide theoretical guarantees for robust performance of the algorithm and empirically demonstrate that CoLLA outperforms existing approaches for distributed multi-task learning on a variety of data sets.
Due to the proliferation of online social networks (OSNs), users find themselves participating in multiple OSNs.
These users leave their activity traces as they maintain friendships and interact with other users in these OSNs.
In this work, we analyze how users maintain friendship in multiple OSNs by studying users who have accounts in both Twitter and Instagram.
Specifically, we study the similarity of a user's friendship and the evenness of friendship distribution in multiple OSNs.
Our study shows that most users in Twitter and Instagram prefer to maintain different friendships in the two OSNs, keeping only a small clique of common friends in across the OSNs.
Based upon our empirical study, we conduct link prediction experiments to predict missing friendship links in multiple OSNs using the neighborhood features, neighborhood friendship maintenance features and cross-link features.
Our link prediction experiments shows that un- supervised methods can yield good accuracy in predicting links in one OSN using another OSN data and the link prediction accuracy can be further improved using supervised method with friendship maintenance and others measures as features.
A set S of n points is 2-color universal for a graph G on n vertices if for every proper 2-coloring of G and for every 2-coloring of S with the same sizes of color classes as G has, G is straight-line embeddable on S. We show that the so-called double chain is 2-color universal for paths if each of the two chains contains at least one fifth of all the points, but not if one of the chains is more than approximately 28 times longer than the other.
A 2-coloring of G is equitable if the sizes of the color classes differ by at most 1.
A bipartite graph is equitable if it admits an equitable proper coloring.
We study the case when S is the double-chain with chain sizes differing by at most 1 and G is an equitable bipartite graph.
We prove that this S is not 2-color universal if G is not a forest of caterpillars and that it is 2-color universal for equitable caterpillars with at most one half non-leaf vertices.
We also show that if this S is equitably 2-colored, then equitably properly 2-colored forests of stars can be embedded on it.
We introduce a new dataset of logical entailments for the purpose of measuring models' ability to capture and exploit the structure of logical expressions against an entailment prediction task.
We use this task to compare a series of architectures which are ubiquitous in the sequence-processing literature, in addition to a new model class---PossibleWorldNets---which computes entailment as a "convolution over possible worlds".
Results show that convolutional networks present the wrong inductive bias for this class of problems relative to LSTM RNNs, tree-structured neural networks outperform LSTM RNNs due to their enhanced ability to exploit the syntax of logic, and PossibleWorldNets outperform all benchmarks.
Requirements engineering provides several practices to analyze how a user wants to interact with a future software.
Mockups, prototypes, and scenarios are suitable to understand usability issues and user requirements early.
Nevertheless, users are often dissatisfied with the usability of a resulting software.
Apparently, previously explored information was lost or no longer accessible during the development phase.
Scenarios are one effective practice to describe behavior.
However, they are commonly notated in natural language which is often improper to capture and communicate interaction knowledge comprehensible to developers and users.
The dynamic aspect of interaction is lost if only static descriptions are used.
Digital prototyping enables the creation of interactive prototypes by adding responsive controls to hand- or digitally drawn mockups.
We propose to capture the events of these controls to obtain a representation of the interaction.
From this data, we generate videos, which demonstrate interaction sequences, as additional support for textual scenarios.
Variants of scenarios can be created by modifying the captured event sequences and mockups.
Any change is unproblematic since videos only need to be regenerated.
Thus, we achieve video as a by-product of digital prototyping.
This reduces the effort compared to video recording such as screencasts.
A first evaluation showed that such a generated video supports a faster understanding of a textual scenario compared to static mockups.
Face completion is a challenging generation task because it requires generating visually pleasing new pixels that are semantically consistent with the unmasked face region.
This paper proposes a geometry-aware Face Completion and Editing NETwork (FCENet) by systematically studying facial geometry from the unmasked region.
Firstly, a facial geometry estimator is learned to estimate facial landmark heatmaps and parsing maps from the unmasked face image.
Then, an encoder-decoder structure generator serves to complete a face image and disentangle its mask areas conditioned on both the masked face image and the estimated facial geometry images.
Besides, since low-rank property exists in manually labeled masks, a low-rank regularization term is imposed on the disentangled masks, enforcing our completion network to manage occlusion area with various shape and size.
Furthermore, our network can generate diverse results from the same masked input by modifying estimated facial geometry, which provides a flexible mean to edit the completed face appearance.
Extensive experimental results qualitatively and quantitatively demonstrate that our network is able to generate visually pleasing face completion results and edit face attributes as well.
This paper aims to compare between four different types of feature extraction approaches in terms of texture segmentation.
The feature extraction methods that were used for segmentation are Gabor filters (GF), Gaussian Markov random fields (GMRF), run-length matrix (RLM) and co-occurrence matrix (GLCM).
It was shown that the GF performed best in terms of quality of segmentation while the GLCM localises the texture boundaries better as compared to the other methods.
The finite element method (FEM) has several computational steps to numerically solve a particular problem, to which many efforts have been directed to accelerate the solution stage of the linear system of equations.
However, the finite element matrix construction, which is also time-consuming for unstructured meshes, has been less investigated.
The generation of the global finite element matrix is performed in two steps, computing the local matrices by numerical integration and assembling them into a global system, which has traditionally been done in serial computing.
This work presents a fast technique to construct the global finite element matrix that arises by solving the Poisson's equation in a three-dimensional domain.
The proposed methodology consists in computing the numerical integration, due to its intrinsic parallel opportunities, in the graphics processing unit (GPU) and computing the matrix assembly, due to its intrinsic serial operations, in the central processing unit (CPU).
In the numerical integration, only the lower triangular part of each local stiffness matrix is computed thanks to its symmetry, which saves GPU memory and computing time.
As a result of symmetry, the global sparse matrix also contains non-zero elements only in its lower triangular part, which reduces the assembly operations and memory usage.
This methodology allows generating the global sparse matrix from any unstructured finite element mesh size on GPUs with little memory capacity, only limited by the CPU memory.
Most of the mammal species hold polygynous mating systems.
The majority of the marriage systems of mankind were also polygynous over civilized history, however, socially imposed monogamy gradually prevails throughout the world.
This is difficult to understand because those mostly influential in society are themselves benefitted from polygyny.
Actually, the puzzle of monogamous marriage could be explained by a simple mechanism, which lies in the sexual selection dynamics of civilized human societies, driven by wealth redistribution.
The discussions in this paper are mainly based on the approach of social computing, with a combination of both experimental and analytical analysis.
We investigate the possibility of deriving metric trace semantics in a coalgebraic framework.
First, we generalize a technique for systematically lifting functors from the category Set of sets to the category PMet of pseudometric spaces, showing under which conditions also natural transformations, monads and distributive laws can be lifted.
By exploiting some recent work on an abstract determinization, these results enable the derivation of trace metrics starting from coalgebras in Set.
More precisely, for a coalgebra on Set we determinize it, thus obtaining a coalgebra in the Eilenberg-Moore category of a monad.
When the monad can be lifted to PMet, we can equip the final coalgebra with a behavioral distance.
The trace distance between two states of the original coalgebra is the distance between their images in the determinized coalgebra through the unit of the monad.
We show how our framework applies to nondeterministic automata and probabilistic automata.
Community detection of network flows conventionally assumes one-step dynamics on the links.
For sparse networks and interest in large-scale structures, longer timescales may be more appropriate.
Oppositely, for large networks and interest in small-scale structures, shorter timescales may be better.
However, current methods for analyzing networks at different timescales require expensive and often infeasible network reconstructions.
To overcome this problem, we introduce a method that takes advantage of the inner-workings of the map equation and evades the reconstruction step.
This makes it possible to efficiently analyze large networks at different Markov times with no extra overhead cost.
The method also evades the costly unipartite projection for identifying flow modules in bipartite networks.
Finding optimal data for inpainting is a key problem in the context of partial differential equation based image compression.
The data that yields the most accurate reconstruction is real-valued.
Thus, quantisation models are mandatory to allow an efficient encoding.
These can also be understood as challenging data clustering problems.
Although clustering approaches are well suited for this kind of compression codecs, very few works actually consider them.
Each pixel has a global impact on the reconstruction and optimal data locations are strongly correlated with their corresponding colour values.
These facts make it hard to predict which feature works best.
In this paper we discuss quantisation strategies based on popular methods such as k-means.
We are lead to the central question which kind of feature vectors are best suited for image compression.
To this end we consider choices such as the pixel values, the histogram or the colour map.
Our findings show that the number of colours can be reduced significantly without impacting the reconstruction quality.
Surprisingly, these benefits do not directly translate to a good image compression performance.
The gains in the compression ratio are lost due to increased storage costs.
This suggests that it is integral to evaluate the clustering on both, the reconstruction error and the final file size.
This is a study of the MOR cryptosystem using the special linear group over finite fields.
The automorphism group of the special linear group is analyzed for this purpose.
At our current state of knowledge, I show that the MOR cryptosystem has better security than the ElGamal cryptosystem over finite fields.
Recently, much progress has been made in learning general-purpose sentence representations that can be used across domains.
However, most of the existing models typically treat each word in a sentence equally.
In contrast, extensive studies have proven that human read sentences efficiently by making a sequence of fixation and saccades.
This motivates us to improve sentence representations by assigning different weights to the vectors of the component words, which can be treated as an attention mechanism on single sentences.
To that end, we propose two novel attention models, in which the attention weights are derived using significant predictors of human reading time, i.e., Surprisal, POS tags and CCG supertags.
The extensive experiments demonstrate that the proposed methods significantly improve upon the state-of-the-art sentence representation models.
Artificial Intelligence is a central topic in the computer science curriculum.
From the year 2011 a project-based learning methodology based on computer games has been designed and implemented into the intelligence artificial course at the University of the Bio-Bio.
The project aims to develop software-controlled agents (bots) which are programmed by using heuristic algorithms seen during the course.
This methodology allows us to obtain good learning results, however several challenges have been founded during its implementation.
In this paper we show how linguistic descriptions of data can help to provide students and teachers with technical and personalized feedback about the learned algorithms.
Algorithm behavior profile and a new Turing test for computer games bots based on linguistic modelling of complex phenomena are also proposed in order to deal with such challenges.
In order to show and explore the possibilities of this new technology, a web platform has been designed and implemented by one of authors and its incorporation in the process of assessment allows us to improve the teaching learning process.
Considering today's web scenario, there is a need of effective and meaningful search over the web which is provided by Semantic Web.
Existing search engines are keyword based.
They are vulnerable in answering intelligent queries from the user due to the dependence of their results on information available in web pages.
While semantic search engines provides efficient and relevant results as the semantic web is an extension of the current web in which information is given well defined meaning.
MetaCrawler is a search tool that uses several existing search engines and provides combined results by using their own page ranking algorithm.
This paper proposes development of a meta-semantic-search engine called SemanTelli which works within cloud.
SemanTelli fetches results from different semantic search engines such as Hakia, DuckDuckGo, SenseBot with the help of intelligent agents that eliminate the limitations of existing search engines.
We present a unified probabilistic framework for simultaneous trajectory estimation and planning (STEAP).
Estimation and planning problems are usually considered separately, however, within our framework we show that solving them simultaneously can be more accurate and efficient.
The key idea is to compute the full continuous-time trajectory from start to goal at each time-step.
While the robot traverses the trajectory, the history portion of the trajectory signifies the solution to the estimation problem, and the future portion of the trajectory signifies a solution to the planning problem.
Building on recent probabilistic inference approaches to continuous-time localization and mapping and continuous-time motion planning, we solve the joint problem by iteratively recomputing the maximum a posteriori trajectory conditioned on all available sensor data and cost information.
Our approach can contend with high-degree-of-freedom (DOF) trajectory spaces, uncertainty due to limited sensing capabilities, model inaccuracy, the stochastic effect of executing actions, and can find a solution in real-time.
We evaluate our framework empirically in both simulation and on a mobile manipulator.
Using blockchain technology, it is possible to create contracts that offer a reward in exchange for a trained machine learning model for a particular data set.
This would allow users to train machine learning models for a reward in a trustless manner.
The smart contract will use the blockchain to automatically validate the solution, so there would be no debate about whether the solution was correct or not.
Users who submit the solutions won't have counterparty risk that they won't get paid for their work.
Contracts can be created easily by anyone with a dataset, even programmatically by software agents.
This creates a market where parties who are good at solving machine learning problems can directly monetize their skillset, and where any organization or software agent that has a problem to solve with AI can solicit solutions from all over the world.
This will incentivize the creation of better machine learning models, and make AI more accessible to companies and software agents.
Advances in data collection and data storage technologies have given way to the establishment of transactional databases among companies and organizations, as they allow enormous amounts of data to be stored efficiently.
Useful knowledge can be mined from these data, which can be used in several ways depending on the nature of the data.
Quite often companies and organizations are willing to share data for the sake of mutual benefit.
However, the sharing of such data comes with risks, as problems with privacy may arise.
Sensitive data, along with sensitive knowledge inferred from this data, must be protected from unintentional exposure to unauthorized parties.
One form of the inferred knowledge is frequent patterns mined in the form of frequent itemsets from transactional databases.
The problem of protecting such patterns is known as the frequent itemset hiding problem.
In this paper we present a toolbox, which provides several implementations of frequent itemset hiding algorithms.
Firstly, we summarize the most important aspects of each algorithm.
We then introduce the architecture of the toolbox and its novel features.
Finally, we provide experimental results on real world datasets, demonstrating the efficiency of the toolbox and the convenience it offers in comparing different algorithms.
We investigate an experiential learning paradigm for acquiring an internal model of intuitive physics.
Our model is evaluated on a real-world robotic manipulation task that requires displacing objects to target locations by poking.
The robot gathered over 400 hours of experience by executing more than 100K pokes on different objects.
We propose a novel approach based on deep neural networks for modeling the dynamics of robot's interactions directly from images, by jointly estimating forward and inverse models of dynamics.
The inverse model objective provides supervision to construct informative visual features, which the forward model can then predict and in turn regularize the feature space for the inverse model.
The interplay between these two objectives creates useful, accurate models that can then be used for multi-step decision making.
This formulation has the additional benefit that it is possible to learn forward models in an abstract feature space and thus alleviate the need of predicting pixels.
Our experiments show that this joint modeling approach outperforms alternative methods.
Serious scientific games are games whose purpose is not only fun.
In the field of science, the serious goals include crucial activities for scientists: outreach, teaching and research.
The number of serious games is increasing rapidly, in particular citizen science games, games that allow people to produce and/or analyze scientific data.
Interestingly, it is possible to build a set of rules providing a guideline to create or improve serious games.
We present arguments gathered from our own experience ( Phylo , DocMolecules , HiRE-RNA contest and Pangu) as well as examples from the growing literature on scientific serious games.
Non-orthogonal multiple access (NOMA) is regarded as a candidate radio access technique for the next generation wireless networks because of its manifold spectral gains.
A two-phase cooperative relaying strategy (CRS) is proposed in this paper by exploiting the concept of both downlink and uplink NOMA (termed as DU-CNOMA).
In the proposed protocol, a transmitter considered as a source transmits a NOMA composite signal consisting of two symbols to the destination and relay during the first phase, following the principle of downlink NOMA.
In the second phase, the relay forwards the symbol decoded by successive interference cancellation to the destination, whereas the source transmits a new symbol to the destination in parallel with the relay, following the principle of uplink NOMA.
The ergodic sum capacity, outage probability, and outage sum capacity are investigated comprehensively along with analytical derivations, under both perfect and imperfect successive interference cancellation.
The performance improvement of the proposed DU-CNOMA over the conventional CRS using NOMA, is proved through analysis and computer simulation.
Furthermore, the correctness of the author's analysis is proved through a strong agreement between simulation and analytical results.
In this paper, the basic ideal of the Event Space Theory and Analyzing Events are expatiated on.
Then it is suggested that how to set up event base library in developing application software.
Based above the designing principle of facing methodology.
Finally, in order to explain how to apply the Event Space Theory in developing economic evaluation software, the software of "sewage treatment CAD" in a national "8th-Five Year Plan Research Project" of PRC is used as an example.
This software concerns economic effectiveness evaluation for construction projects.
Resource allocation with quality of service constraints is one of the most challenging problems in elastic optical networks which is normally formulated as an MINLP optimization program.
In this paper, we focus on novel properties of geometric optimization and provide a heuristic approach for resource allocation which is very faster than its MINLP counterpart.
Our heuristic consists of two main parts for routing/traffic ordering and power/spectrum assignment.
It aims at minimization of transmitted optical power and spectrum usage constrained to quality of service and physical requirements.
We consider three routing/traffic ordering procedures and compare them in terms of total transmitted optical power, total received noise power and total nonlinear interference including self- and cross-channel interferences.
We propose a posynomial expression for optical signal to noise ratio in which fiber nonlinearities and spontaneous emission noise have been addressed.
We also propose posynomial expressions that relate modulation spectral efficiency to its corresponding minimum required optical signal to noise ratio.
We then use the posynomial expressions to develop six geometric formulations for power/spectrum assignment part of the heuristic which are different in run time, complexity and accuracy.
Simulation results demonstrate that the proposed solution has a very good accuracy and much lower computational complexity in comparison with MINLP formulation.
As example for European Cost239 optical network with 46 transmit transponders, the geometric formulations can be more than 59 times faster than its MINLP counterpart.
Numerical results also reveal that in long-haul elastic optical networks, considering the product of the number of common fiber spans and the transmission bit rate is a better goal function for routing/traffic ordering sub-problem.
In this paper we shall introduce a simple, effective numerical method for finding differential operators for scalar and vector-valued functions on surfaces.
The key idea of our algorithm is to develop an intrinsic and unified way to compute directly the partial derivatives of functions defined on triangular meshes which are the discretization of regular surfaces under consideration.
Most importantly, the divergence theorem and conservation laws on triangular meshes are fulfilled.
Using deep learning for different machine learning tasks such as image classification and word embedding has recently gained many attentions.
Its appealing performance reported across specific Natural Language Processing (NLP) tasks in comparison with other approaches is the reason for its popularity.
Word embedding is the task of mapping words or phrases to a low dimensional numerical vector.
In this paper, we use deep learning to embed Wikipedia Concepts and Entities.
The English version of Wikipedia contains more than five million pages, which suggest its capability to cover many English Entities, Phrases, and Concepts.
Each Wikipedia page is considered as a concept.
Some concepts correspond to entities, such as a person's name, an organization or a place.
Contrary to word embedding, Wikipedia Concepts Embedding is not ambiguous, so there are different vectors for concepts with similar surface form but different mentions.
We proposed several approaches and evaluated their performance based on Concept Analogy and Concept Similarity tasks.
The results show that proposed approaches have the performance comparable and in some cases even higher than the state-of-the-art methods.
When conducting modern cybercrime investigations, evidence has often to be gathered from computer systems located at cloud-based data centres of hosting providers.
In cases where the investigation cannot rely on the cooperation of the hosting provider, or where documentation is not available, investigators can often find the identification of which distinct server among many is of interest difficult and extremely time consuming.
To address the problem of identifying these servers, in this paper a new approach to rapidly and reliably identify these cloud hosting computer systems is presented.
In the outlined approach, a handheld device composed of an embedded computer combined with a method of undetectable interception of Ethernet based communications is presented.
This device is tested and evaluated, and a discussion is provided on its usefulness in identifying of server of interest to an investigation.
VeriFast is a leading research prototype tool for the sound modular verification of safety and correctness properties of single-threaded and multithreaded C and Java programs.
It has been used as a vehicle for exploration and validation of novel program verification techniques and for industrial case studies; it has served well at a number of program verification competitions; and it has been used for teaching by multiple teachers independent of the authors.
However, until now, while VeriFast's operation has been described informally in a number of publications, and specific verification techniques have been formalized, a clear and precise exposition of how VeriFast works has not yet appeared.
In this article we present for the first time a formal definition and soundness proof of a core subset of the VeriFast program verification approach.
The exposition aims to be both accessible and rigorous: the text is based on lecture notes for a graduate course on program verification, and it is backed by an executable machine-readable definition and machine-checked soundness proof in Coq.
In this work, I present an optimization problem which consists of assigning entries of a stellar catalog to multiple entries of another stellar catalog such that the probability of such assignment is maximum.
I show a way of modeling it as a Maximum Weighted Stable Set Problem which is further used to solve a real astronomical instance and I partially characterize the forbidden subgraphs of the resulting family of graphs given by that reduction.
Finally, I prove that the problem is NP-Hard.
We present an efficient framework that can generate a coherent paragraph to describe a given video.
Previous works on video captioning usually focus on video clips.
They typically treat an entire video as a whole and generate the caption conditioned on a single embedding.
On the contrary, we consider videos with rich temporal structures and aim to generate paragraph descriptions that can preserve the story flow while being coherent and concise.
Towards this goal, we propose a new approach, which produces a descriptive paragraph by assembling temporally localized descriptions.
Given a video, it selects a sequence of distinctive clips and generates sentences thereon in a coherent manner.
Particularly, the selection of clips and the production of sentences are done jointly and progressively driven by a recurrent network -- what to describe next depends on what have been said before.
Here, the recurrent network is learned via self-critical sequence training with both sentence-level and paragraph-level rewards.
On the ActivityNet Captions dataset, our method demonstrated the capability of generating high-quality paragraph descriptions for videos.
Compared to those by other methods, the descriptions produced by our method are often more relevant, more coherent, and more concise.
Designing fast and scalable algorithm for mining frequent itemsets is always being a most eminent and promising problem of data mining.
Apriori is one of the most broadly used and popular algorithm of frequent itemset mining.
Designing efficient algorithms on MapReduce framework to process and analyze big datasets is contemporary research nowadays.
In this paper, we have focused on the performance of MapReduce based Apriori on homogeneous as well as on heterogeneous Hadoop cluster.
We have investigated a number of factors that significantly affects the execution time of MapReduce based Apriori running on homogeneous and heterogeneous Hadoop Cluster.
Factors are specific to both algorithmic and non-algorithmic improvements.
Considered factors specific to algorithmic improvements are filtered transactions and data structures.
Experimental results show that how an appropriate data structure and filtered transactions technique drastically reduce the execution time.
The non-algorithmic factors include speculative execution, nodes with poor performance, data locality & distribution of data blocks, and parallelism control with input split size.
We have applied strategies against these factors and fine tuned the relevant parameters in our particular application.
Experimental results show that if cluster specific parameters are taken care of then there is a significant reduction in execution time.
Also we have discussed the issues regarding MapReduce implementation of Apriori which may significantly influence the performance.
Ambient backscatter is an intriguing wireless communication paradigm that allows small devices to compute and communicate by using only the power they harvest from radio-frequency (RF) signals in the air.
Ambient backscattering devices reflect existing RF signals emitted by legacy communications systems, such as digital TV broadcasting, cellular or Wi-Fi ones, which would be otherwise treated as harmful sources of interference.
This paper deals with the ultimate performance limits of ambient backscatter systems in broadband fading environments, by considering different amounts of network state information at the receivers.
After introducing a detailed signal model of the relevant communication links, we study the influence of physical parameters on the capacity of both legacy and backscatter systems.
We find that, under reasonable operative conditions, a legacy system employing multicarrier modulation can turn the RF interference arising from the backscatter process into a form of multipath diversity that can be suitably exploited to noticeably increase its performance.
Moreover, we show that, even when employing simple single-carrier modulation techniques, the backscatter system can achieve significant data rates over relatively short distances, especially when the intended recipient of the backscatter signal is co-located with the legacy transmitter, i.e., they are on the same machine.
The paper presents an extension of Shannon fuzzy entropy for intuitionistic fuzzy one.
Firstly, we presented a new formula for calculating the distance and similarity of intuitionistic fuzzy information.
Then, we constructed measures for information features like score, certainty and uncertainty.
Also, a new concept was introduced, namely escort fuzzy information.
Then, using the escort fuzzy information, Shannon's formula for intuitionistic fuzzy information was obtained.
It should be underlined that Shannon's entropy for intuitionistic fuzzy information verifies the four defining conditions of intuitionistic fuzzy uncertainty.
The measures of its two components were also identified: fuzziness (ambiguity) and incompleteness (ignorance).
The distributionally robust Markov Decision Process (MDP) approach asks for a distributionally robust policy that achieves the maximal expected total reward under the most adversarial distribution of uncertain parameters.
In this paper, we study distributionally robust MDPs where ambiguity sets for the uncertain parameters are of a format that can easily incorporate in its description the uncertainty's generalized moment as well as statistical distance information.
In this way, we generalize existing works on distributionally robust MDP with generalized-moment-based and statistical-distance-based ambiguity sets to incorporate information from the former class such as moments and dispersions to the latter class that critically depends on empirical observations of the uncertain parameters.
We show that, under this format of ambiguity sets, the resulting distributionally robust MDP remains tractable under mild technical conditions.
To be more specific, a distributionally robust policy can be constructed by solving a sequence of one-stage convex optimization subproblems.
A scientist may publish tens or hundreds of papers over a career, but these contributions are not evenly spaced in time.
Sixty years of studies on career productivity patterns in a variety of fields suggest an intuitive and universal pattern: productivity tends to rise rapidly to an early peak and then gradually declines.
Here, we test the universality of this conventional narrative by analyzing the structures of individual faculty productivity time series, constructed from over 200,000 publications and matched with hiring data for 2453 tenure-track faculty in all 205 Ph.D-granting computer science departments in the U.S. and Canada.
Unlike prior studies, which considered only some faculty or some institutions, or lacked common career reference points, here we combine a large bibliographic dataset with comprehensive information on career transitions that covers an entire field of study.
We show that the conventional narrative confidently describes only one fifth of faculty, regardless of department prestige or researcher gender, and the remaining four fifths of faculty exhibit a rich diversity of productivity patterns.
To explain this diversity, we introduce a simple model of productivity trajectories, and explore correlations between its parameters and researcher covariates, showing that departmental prestige predicts overall individual productivity and the timing of the transition from first- to last-author publications.
These results demonstrate the unpredictability of productivity over time, and open the door for new efforts to understand how environmental and individual factors shape scientific productivity.
Current state-of-the-art deep learning systems for visual object recognition and detection use purely supervised training with regularization such as dropout to avoid overfitting.
The performance depends critically on the amount of labeled examples, and in current practice the labels are assumed to be unambiguous and accurate.
However, this assumption often does not hold; e.g. in recognition, class labels may be missing; in detection, objects in the image may not be localized; and in general, the labeling may be subjective.
In this work we propose a generic way to handle noisy and incomplete labeling by augmenting the prediction objective with a notion of consistency.
We consider a prediction consistent if the same prediction is made given similar percepts, where the notion of similarity is between deep network features computed from the input data.
In experiments we demonstrate that our approach yields substantial robustness to label noise on several datasets.
On MNIST handwritten digits, we show that our model is robust to label corruption.
On the Toronto Face Database, we show that our model handles well the case of subjective labels in emotion recognition, achieving state-of-the- art results, and can also benefit from unlabeled face images with no modification to our method.
On the ILSVRC2014 detection challenge data, we show that our approach extends to very deep networks, high resolution images and structured outputs, and results in improved scalable detection.
Developing an intelligent vehicle which can perform human-like actions requires the ability to learn basic driving skills from a large amount of naturalistic driving data.
The algorithms will become efficient if we could decompose the complex driving tasks into motion primitives which represent the elementary compositions of driving skills.
Therefore, the purpose of this paper is to segment unlabeled trajectory data into a library of motion primitives.
By applying a probabilistic inference based on an iterative Expectation-Maximization algorithm, our method segments the collected trajectories while learning a set of motion primitives represented by the dynamic movement primitives.
The proposed method utilizes the mutual dependencies between the segmentation and representation of motion primitives and the driving-specific based initial segmentation.
By utilizing this mutual dependency and the initial condition, this paper presents how we can enhance the performance of both the segmentation and the motion primitive library establishment.
We also evaluate the applicability of the primitive representation method to imitation learning and motion planning algorithms.
The model is trained and validated by using the driving data collected from the Beijing Institute of Technology intelligent vehicle platform.
The results show that the proposed approach can find the proper segmentation and establish the motion primitive library simultaneously.
Most of the recent successful methods in accurate object detection and localization used some variants of R-CNN style two stage Convolutional Neural Networks (CNN) where plausible regions were proposed in the first stage then followed by a second stage for decision refinement.
Despite the simplicity of training and the efficiency in deployment, the single stage detection methods have not been as competitive when evaluated in benchmarks consider mAP for high IoU thresholds.
In this paper, we proposed a novel single stage end-to-end trainable object detection network to overcome this limitation.
We achieved this by introducing Recurrent Rolling Convolution (RRC) architecture over multi-scale feature maps to construct object classifiers and bounding box regressors which are "deep in context".
We evaluated our method in the challenging KITTI dataset which measures methods under IoU threshold of 0.7.
We showed that with RRC, a single reduced VGG-16 based model already significantly outperformed all the previously published results.
At the time this paper was written our models ranked the first in KITTI car detection (the hard level), the first in cyclist detection and the second in pedestrian detection.
These results were not reached by the previous single stage methods.
The code is publicly available.
This paper presents an iterated local search for the fixed-charge uncapacitated network design problem with user-optimal flow (FCNDP-UOF), which concerns routing multiple commodities from its origin to its destination by signing a network through selecting arcs, with an objective of minimizing the sum of the fixed costs of the selected arcs plus the sum of variable costs associated to the flows on each arc.
Besides that, since the FCNDP-UOF is a bi-level problem, each commodity has to be transported through a shortest path, concerning the edges length, in the built network.
The proposed algorithm generate a initial solution using a variable fixing heuristic.
Then a local branching strategy is applied to improve the quality of the solution.
At last, an efficient perturbation strategy is presented to perform cycle-based moves to explore different parts of the solution space.
Computational experiments shows that the proposed solution method consistently produces high-quality solutions in reasonable computational times.
We open source fingerprint Match in Box, a complete end-to-end fingerprint recognition system embedded within a 4 inch cube.
Match in Box stands in contrast to a typical bulky and expensive proprietary fingerprint recognition system which requires sending a fingerprint image to an external host for processing and subsequent spoof detection and matching.
In particular, Match in Box is a first of a kind, portable, low-cost, and easy-to-assemble fingerprint reader with an enrollment database embedded within the reader's memory and open source fingerprint spoof detector, feature extractor, and matcher all running on the reader's internal vision processing unit (VPU).
An onboard touch screen and rechargeable battery pack make this device extremely portable and ideal for applying both fingerprint authentication (1:1 comparison) and fingerprint identification (1:N search) to applications (vaccination tracking, food and benefit distribution programs, human trafficking prevention) in rural communities, especially in developing countries.
We also show that Match in Box is suited for capturing neonate fingerprints due to its high resolution (1900 ppi) cameras.
We review some of the latest approaches to analysing cardiac electrophysiology data using machine learning and predictive modelling.
Cardiac arrhythmias, particularly atrial fibrillation, are a major global healthcare challenge.
Treatment is often through catheter ablation, which involves the targeted localized destruction of regions of the myocardium responsible for initiating or perpetuating the arrhythmia.
Ablation targets are either anatomically defined, or identified based on their functional properties as determined through the analysis of contact intracardiac electrograms acquired with increasing spatial density by modern electroanatomic mapping systems.
While numerous quantitative approaches have been investigated over the past decades for identifying these critical curative sites, few have provided a reliable and reproducible advance in success rates.
Machine learning techniques, including recent deep-learning approaches, offer a potential route to gaining new insight from this wealth of highly complex spatio-temporal information that existing methods struggle to analyse.
Coupled with predictive modelling, these techniques offer exciting opportunities to advance the field and produce more accurate diagnoses and robust personalised treatment.
We outline some of these methods and illustrate their use in making predictions from the contact electrogram and augmenting predictive modelling tools, both by more rapidly predicting future states of the system and by inferring the parameters of these models from experimental observations.
Recent improvements in computing allow for the processing and analysis of very large datasets in a variety of fields.
Often the analysis requires the creation of low-rank approximations to the datasets leading to efficient storage.
This article presents and analyzes a novel approach for creating nonnegative, structured dictionaries using NMF applied to reordered pixels of single, natural images.
We reorder the pixels based on patches and present our approach in general.
We investigate our approach when using the Singular Value Decomposition (SVD) and Nonnegative Matrix Factorizations (NMF) as low-rank approximations.
Peak Signal-to-Noise Ratio (PSNR) and Mean Structural Similarity Index (MSSIM) are used to evaluate the algorithm.
We report that while the SVD provides the best reconstructions, its dictionary of vectors lose both the sign structure of the original image and details of localized image content.
In contrast, the dictionaries produced using NMF preserves the sign structure of the original image matrix and offer a nonnegative, parts-based dictionary.
Mixture of Softmaxes (MoS) has been shown to be effective at addressing the expressiveness limitation of Softmax-based models.
Despite the known advantage, MoS is practically sealed by its large consumption of memory and computational time due to the need of computing multiple Softmaxes.
In this work, we set out to unleash the power of MoS in practical applications by investigating improved word coding schemes, which could effectively reduce the vocabulary size and hence relieve the memory and computation burden.
We show both BPE and our proposed Hybrid-LightRNN lead to improved encoding mechanisms that can halve the time and memory consumption of MoS without performance losses.
With MoS, we achieve an improvement of 1.5 BLEU scores on IWSLT 2014 German-to-English corpus and an improvement of 0.76 CIDEr score on image captioning.
Moreover, on the larger WMT 2014 machine translation dataset, our MoS-boosted Transformer yields 29.5 BLEU score for English-to-German and 42.1 BLEU score for English-to-French, outperforming the single-Softmax Transformer by 0.8 and 0.4 BLEU scores respectively and achieving the state-of-the-art result on WMT 2014 English-to-German task.
The Message Passing Interface (MPI) is the prevalent programming model used on today's supercomputers.
Therefore, MPI library developers are looking for the best possible performance (shortest run-time) of individual MPI functions across many different supercomputer architectures.
Several MPI benchmark suites have been developed to assess the performance of MPI implementations.
Unfortunately, the outcome of these benchmarks is often neither reproducible nor statistically sound.
To overcome these issues, we show which experimental factors have an impact on the run-time of blocking collective MPI operations and how to control them.
We address the problem of process and clock synchronization in MPI benchmarks.
Finally, we present a new experimental method that allows us to obtain reproducible and statistically sound MPI measurements.
The entropy of an ergodic source is the limit of properly rescaled 1-block entropies of sources obtained applying successive non-sequential recursive pairs substitutions (see P. Grassberger 2002 ArXiv:physics/0207023 and D. Benedetto, E. Caglioti and D. Gabrielli 2006 Jour.Stat.Mech.Theo.Exp.09 doi:10.1088/1742.-5468/2006/09/P09011).
In this paper we prove that the cross entropy and the Kullback-Leibler divergence can be obtained in a similar way.
Network super point is a kind of special host which plays an important role in network management and security.
For a core network, detecting super points in real time is a burden task because it requires plenty computing resources to keep up with the high speed of packets.
Previous works try to solve this problem by using expensive memory, such as static random access memory, and multi cores of CPU.
But the number of cores in CPU is small and each core of CPU has a high price.
In this work, we use a popular parallel computing platform, graphic processing unit GPU, to mining core network's super point.
We propose a double direction hash functions group which can map hosts randomly and restore them from a dense structure.
Because the high randomness and simple process of the double direction hash functions, our algorithm reduce the memory to smaller than one-fourth of other algorithms.
Because the small memory requirement of our algorithm, a low cost GPU, only worth 200 dollars, is fast enough to deal with a high speed network such as 750 Gb/s.
No other algorithm can cope with such a high bandwidth traffic as accuracy as our algorithm on such a cheap platform.
Experiments on the traffic collecting from a core network demonstrate the advantage of our efficient algorithm.
Future machine to machine (M2M) communications need to support a massive number of devices communicating with each other with little or no human intervention.
Random access techniques were originally proposed to enable M2M multiple access, but suffer from severe congestion and access delay in an M2M system with a large number of devices.
In this paper, we propose a novel multiple access scheme for M2M communications based on the capacity-approaching analog fountain code to efficiently minimize the access delay and satisfy the delay requirement for each device.
This is achieved by allowing M2M devices to transmit at the same time on the same channel in an optimal probabilistic manner based on their individual delay requirements.
Simulation results show that the proposed scheme achieves a near optimal rate performance and at the same time guarantees the delay requirements of the devices.
We further propose a simple random access strategy and characterized the required overhead.
Simulation results show the proposed approach significantly outperforms the existing random access schemes currently used in long term evolution advanced (LTE-A) standard in terms of the access delay.
We performed two online surveys of Stack Overflow answerers and visitors to assess their awareness to outdated code and software licenses in Stack Overflow answerers.
The answerer survey targeted 607 highly reputed Stack Overflow users and received a high response rate of 33%.
Our findings are as follows.
Although most of the code snippets in the answers are written from scratch, there are code snippets cloned from the corresponding questions, from personal or company projects, or from open source projects.
Stack Overflow answerers are aware that some of their snippets are outdated.
However, 19% of the participants report that they rarely or never fix their outdated code.
At least 98% of the answerers never include software licenses in their snippets and 69% never check for licensing conflicts with Stack Overflow's CC BY-SA 3.0 if they copy the code from other sources to Stack Overflow answers.
The visitor survey uses convenient sampling and received 89 responses.
We found that 66% of the participants experienced a problem from cloning and reusing Stack Overflow snippets.
Fifty-six percent of the visitors never reported the problems back to the Stack Overflow post.
Eighty-five percent of the participants are not aware that StackOverflow applies the CC BY-SA 3.0 license, and sixty-two percent never give attributions to Stack Overflow posts or answers they copied the code from.
Moreover, 66% of the participants do not check for licensing conflicts between the copied Stack Overflow code and their software.
With these findings, we suggest Stack Overflow raise awareness of their users, both answerers and visitors, to the problem of outdated and license-violating code snippets.
Optical flow, semantic segmentation, and surface normals represent different information modalities, yet together they bring better cues for scene understanding problems.
In this paper, we study the influence between the three modalities: how one impacts on the others and their efficiency in combination.
We employ a modular approach using a convolutional refinement network which is trained supervised but isolated from RGB images to enforce joint modality features.
To assist the training process, we create a large-scale synthetic outdoor dataset that supports dense annotation of semantic segmentation, optical flow, and surface normals.
The experimental results show positive influence among the three modalities, especially for objects' boundaries, region consistency, and scene structures.
In this paper, we propose a novel 3D human pose estimation algorithm from a single image based on neural networks.
We adopted the structure of the relational networks in order to capture the relations among different body parts.
In our method, each pair of different body parts generates features, and the average of the features from all the pairs are used for 3D pose estimation.
In addition, we propose a dropout method that can be used in relational modules, which inherently imposes robustness to the occlusions.
The proposed network achieves state-of-the-art performance for 3D pose estimation in Human 3.6M dataset, and it effectively produces plausible results even in the existence of missing joints.
Recent work has shown that it is possible to train deep neural networks that are verifiably robust to norm-bounded adversarial perturbations.
Most of these methods are based on minimizing an upper bound on the worst-case loss over all possible adversarial perturbations.
While these techniques show promise, they remain hard to scale to larger networks.
Through a comprehensive analysis, we show how a careful implementation of a simple bounding technique, interval bound propagation (IBP), can be exploited to train verifiably robust neural networks that beat the state-of-the-art in verified accuracy.
While the upper bound computed by IBP can be quite weak for general networks, we demonstrate that an appropriate loss and choice of hyper-parameters allows the network to adapt such that the IBP bound is tight.
This results in a fast and stable learning algorithm that outperforms more sophisticated methods and achieves state-of-the-art results on MNIST, CIFAR-10 and SVHN.
It also allows us to obtain the first verifiably robust model on a downscaled version of ImageNet.
A/B tests are randomized experiments frequently used by companies that offer services on the Web for assessing the impact of new features.
During an experiment, each user is randomly redirected to one of two versions of the website, called treatments.
Several response models were proposed to describe the behavior of a user in a social network website, where the treatment assigned to her neighbors must be taken into account.
However, there is no consensus as to which model should be applied to a given dataset.
In this work, we propose a new response model, derive theoretical limits for the estimation error of several models, and obtain empirical results for cases where the response model was misspecified.
Persistent Homology (PH) allows tracking homology features like loops, holes and their higher-dimensional analogs, along with a single-parameter family of nested spaces.
Currently, computing descriptors for complex data characterized by multiple functions is becoming an important task in several applications, including physics, chemistry, medicine, geography, etc.
Multiparameter Persistent Homology (MPH) generalizes persistent homology opening to the exploration and analysis of shapes endowed with multiple filtering functions.
Still, computational constraints prevent MPH to be feasible over real-sized data.
In this paper, we consider discrete Morse Theory as a tool to simplify the computation of MPH on a multiparameter dataset.
We propose a new algorithm, well suited for parallel and distributed implementations and we provide the first evaluation of the impact on MPH computations of a preprocessing approach.
Traffic speed is a key indicator for the efficiency of an urban transportation system.
Accurate modeling of the spatiotemporally varying traffic speed thus plays a crucial role in urban planning and development.
This paper addresses the problem of efficient fine-grained traffic speed prediction using big traffic data obtained from static sensors.
Gaussian processes (GPs) have been previously used to model various traffic phenomena, including flow and speed.
However, GPs do not scale with big traffic data due to their cubic time complexity.
In this work, we address their efficiency issues by proposing local GPs to learn from and make predictions for correlated subsets of data.
The main idea is to quickly group speed variables in both spatial and temporal dimensions into a finite number of clusters, so that future and unobserved traffic speed queries can be heuristically mapped to one of such clusters.
A local GP corresponding to that cluster can then be trained on the fly to make predictions in real-time.
We call this method localization.
We use non-negative matrix factorization for localization and propose simple heuristics for cluster mapping.
We additionally leverage on the expressiveness of GP kernel functions to model road network topology and incorporate side information.
Extensive experiments using real-world traffic data collected in the two U.S. cities of Pittsburgh and Washington, D.C., show that our proposed local GPs significantly improve both runtime performances and prediction accuracies compared to the baseline global and local GPs.
Exponential integrators are special time discretization methods where the traditional linear system solves used by implicit schemes are replaced with computing the action of matrix exponential-like functions on a vector.
A very general formulation of exponential integrators is offered by the Exponential Propagation Iterative methods of Runge-Kutta type (EPIRK) family of schemes.
The use of Jacobian approximations is an important strategy to drastically reduce the overall computational costs of implicit schemes while maintaining the quality of their solutions.
This paper extends the EPIRK class to allow the use of inexact Jacobians as arguments of the matrix exponential-like functions.
Specifically, we develop two new families of methods: EPIRK-W integrators that can accommodate any approximation of the Jacobian, and EPIRK-K integrators that rely on a specific Krylov-subspace projection of the exact Jacobian.
Classical order conditions theories are constructed for these families.
A practical EPIRK-W method of order three and an EPIRK-K method of order four are developed.
Numerical experiments indicate that the methods proposed herein are computationally favorable when compared to existing exponential integrators.
Pedestrian detection has achieved great improvements in recent years, while complex occlusion handling is still one of the most important problems.
To take advantage of the body parts and context information for pedestrian detection, we propose the part and context network (PCN) in this work.
PCN specially utilizes two branches which detect the pedestrians through body parts semantic and context information, respectively.
In the Part Branch, the semantic information of body parts can communicate with each other via recurrent neural networks.
In the Context Branch, we adopt a local competition mechanism for adaptive context scale selection.
By combining the outputs of all branches, we develop a strong complementary pedestrian detector with a lower miss rate and better localization accuracy, especially for occlusion pedestrian.
Comprehensive evaluations on two challenging pedestrian detection datasets (i.e.Caltech and INRIA) well demonstrated the effectiveness of the proposed PCN.
One of the challenges of using machine learning techniques with medical data is the frequent dearth of source image data on which to train.
A representative example is automated lung cancer diagnosis, where nodule images need to be classified as suspicious or benign.
In this work we propose an automatic synthetic lung nodule image generator.
Our 3D shape generator is designed to augment the variety of 3D images.
Our proposed system takes root in autoencoder techniques, and we provide extensive experimental characterization that demonstrates its ability to produce quality synthetic images.
Nowadays, a hot challenge for supermarket chains is to offer personalized services for their customers.
Next basket prediction, i.e., supplying the customer a shopping list for the next purchase according to her current needs, is one of these services.
Current approaches are not capable to capture at the same time the different factors influencing the customer's decision process: co-occurrency, sequentuality, periodicity and recurrency of the purchased items.
To this aim, we define a pattern Temporal Annotated Recurring Sequence (TARS) able to capture simultaneously and adaptively all these factors.
We define the method to extract TARS and develop a predictor for next basket named TBP (TARS Based Predictor) that, on top of TARS, is able to to understand the level of the customer's stocks and recommend the set of most necessary items.
By adopting the TBP the supermarket chains could crop tailored suggestions for each individual customer which in turn could effectively speed up their shopping sessions.
A deep experimentation shows that TARS are able to explain the customer purchase behavior, and that TBP outperforms the state-of-the-art competitors.
In the quest for knowledge about how to make good process models, recent research focus is shifting from studying the quality of process models to studying the process of process modeling (often abbreviated as PPM) itself.
This paper reports on our efforts to visualize this specific process in such a way that relevant characteristics of the modeling process can be observed graphically.
By recording each modeling operation in a modeling process, one can build an event log that can be used as input for the PPMChart Analysis plug-in we implemented in ProM.
The graphical representation this plug-in generates allows for the discovery of different patterns of the process of process modeling.
It also provides different views on the process of process modeling (by configuring and filtering the charts).
In research activities regarding Magnetic Resonance Imaging in medicine, simulation tools with a universal approach are rare.
Usually, simulators are developed and used which tend to be restricted to a particular, small range of applications.
This led to the design and implementation of a new simulator PARSPIN, the subject of this thesis.
In medical applications, the Bloch equation is a well-suited mathematical model of the underlying physics with a wide scope.
In this thesis, it is shown how analytical solutions of the Bloch equation can be found, which promise substantial execution time advantages over numerical solution methods.
From these analytical solutions of the Bloch equation, a new formalism for the description and the analysis of complex imaging experiments is derived, the K-t formalism.
It is shown that modern imaging methods can be better explained by the K-t formalism than by observing and analysing the magnetization of each spin of a spin ensemble.
Various approaches for a numerical simulation of Magnetic Resonance imaging are discussed.
It is shown that a simulation tool based on the K-t formalism promises a substantial gain in execution time.
Proper spatial discretization according to the sampling theorem, a topic rarely discussed in literature, is universally derived from the K-t formalism in this thesis.
A spin-based simulator is an application with high demands to computing facilities even on modern hardware.
In this thesis, two approaches for a parallelized software architecture are designed, analysed and evaluated with regard to a reduction of execution time.
A number of possible applications in research and education are demonstrated.
For a choice of imaging experiments, results produced both experimentally and by simulation are compared.
Deep generative models are tremendously successful in learning low-dimensional latent representations that well-describe the data.
These representations, however, tend to much distort relationships between points, i.e. pairwise distances tend to not reflect semantic similarities well.
This renders unsupervised tasks, such as clustering, difficult when working with the latent representations.
We demonstrate that taking the geometry of the generative model into account is sufficient to make simple clustering algorithms work well over latent representations.
Leaning on the recent finding that deep generative models constitute stochastically immersed Riemannian manifolds, we propose an efficient algorithm for computing geodesics (shortest paths) and computing distances in the latent space, while taking its distortion into account.
We further propose a new architecture for modeling uncertainty in variational autoencoders, which is essential for understanding the geometry of deep generative models.
Experiments show that the geodesic distance is very likely to reflect the internal structure of the data.
In this paper, we extend state of the art Model Predictive Control (MPC) approaches to generate safe bipedal walking on slippery surfaces.
In this setting, we formulate walking as a trade off between realizing a desired walking velocity and preserving robust foot-ground contact.
Exploiting this formulation inside MPC, we show that safe walking on various flat terrains can be achieved by compromising three main attributes, i. e. walking velocity tracking, the Zero Moment Point (ZMP) modulation, and the Required Coefficient of Friction (RCoF) regulation.
Simulation results show that increasing the walking velocity increases the possibility of slippage, while reducing the slippage possibility conflicts with reducing the tip-over possibility of the contact and vice versa.
Simulators are the most dominant and eminent tool for analyzing and investigating different type of networks.
The simulations can be executed with less cost as compared to large scale experiment as less computational resources are required and if the simulation model is carefully designed then it can be more practical than any well brought-up mathematical model.
Generally P2P research is based on the principle of simulate first and then experiment in the real world and there is no reason that simulation results cannot be reproducible.
A lack of standard documentation makes verification of results harder as well as due to such poor documentation implementation of well-known overlay algorithms was very difficult.
This Paper describes different types of existing P2P simulators as well as provides a survey and comparison of existing P2P simulators and extracting the best simulator among them.
Generative adversarial networks have been able to generate striking results in various domains.
This generation capability can be general while the networks gain deep understanding regarding the data distribution.
In many domains, this data distribution consists of anomalies and normal data, with the anomalies commonly occurring relatively less, creating datasets that are imbalanced.
The capabilities that generative adversarial networks offer can be leveraged to examine these anomalies and help alleviate the challenge that imbalanced datasets propose via creating synthetic anomalies.
This anomaly generation can be specifically beneficial in domains that have costly data creation processes as well as inherently imbalanced datasets.
One of the domains that fits this description is the host-based intrusion detection domain.
In this work, ADFA-LD dataset is chosen as the dataset of interest containing system calls of small foot-print next generation attacks.
The data is first converted into images, and then a Cycle-GAN is used to create images of anomalous data from images of normal data.
The generated data is combined with the original dataset and is used to train a model to detect anomalies.
By doing so, it is shown that the classification results are improved, with the AUC rising from 0.55 to 0.71, and the anomaly detection rate rising from 17.07% to 80.49%.
The results are also compared to SMOTE, showing the potential presented by generative adversarial networks in anomaly generation.
This chapter derives the properties of light from the properties of processing, including its ability to be both a wave and a particle, to respond to objects it doesn't physically touch, to take all paths to a destination, to choose a route after it arrives, and to spin both ways at once as it moves.
Here a photon is an entity program spreading as a processing wave of instances.
It becomes a "particle" if any part of it overloads the grid network that runs it, causing the photon program to reboot and restart at a new node.
The "collapse of the wave function" is how quantum processing creates what we call a physical photon.
This informational approach gives insights into issues like the law of least action, entanglement, superposition, counterfactuals, the holographic principle and the measurement problem.
The conceptual cost is that physical reality is a quantum processing output, i.e. virtual.
Dynamic topic modeling facilitates the identification of topical trends over time in temporal collections of unstructured documents.
We introduce a novel unsupervised neural dynamic topic model named as Recurrent Neural Network-Replicated Softmax Model (RNNRSM), where the discovered topics at each time influence the topic discovery in the subsequent time steps.
We account for the temporal ordering of documents by explicitly modeling a joint distribution of latent topical dependencies over time, using distributional estimators with temporal recurrent connections.
Applying RNN-RSM to 19 years of articles on NLP research, we demonstrate that compared to state-of-the art topic models, RNNRSM shows better generalization, topic interpretation, evolution and trends.
We also introduce a metric (named as SPAN) to quantify the capability of dynamic topic model to capture word evolution in topics over time.
Effective information analysis generally boils down to properly identifying the structure or geometry of the data, which is often represented by a graph.
In some applications, this structure may be partly determined by design constraints or pre-determined sensing arrangements, like in road transportation networks for example.
In general though, the data structure is not readily available and becomes pretty difficult to define.
In particular, the global smoothness assumptions, that most of the existing works adopt, are often too general and unable to properly capture localized properties of data.
In this paper, we go beyond this classical data model and rather propose to represent information as a sparse combination of localized functions that live on a data structure represented by a graph.
Based on this model, we focus on the problem of inferring the connectivity that best explains the data samples at different vertices of a graph that is a priori unknown.
We concentrate on the case where the observed data is actually the sum of heat diffusion processes, which is a quite common model for data on networks or other irregular structures.
We cast a new graph learning problem and solve it with an efficient nonconvex optimization algorithm.
Experiments on both synthetic and real world data finally illustrate the benefits of the proposed graph learning framework and confirm that the data structure can be efficiently learned from data observations only.
We believe that our algorithm will help solving key questions in diverse application domains such as social and biological network analysis where it is crucial to unveil proper geometry for data understanding and inference.
Computational models for sarcasm detection have often relied on the content of utterances in isolation.
However, speaker's sarcastic intent is not always obvious without additional context.
Focusing on social media discussions, we investigate two issues: (1) does modeling of conversation context help in sarcasm detection and (2) can we understand what part of conversation context triggered the sarcastic reply.
To address the first issue, we investigate several types of Long Short-Term Memory (LSTM) networks that can model both the conversation context and the sarcastic response.
We show that the conditional LSTM network (Rocktaschel et al., 2015) and LSTM networks with sentence level attention on context and response outperform the LSTM model that reads only the response.
To address the second issue, we present a qualitative analysis of attention weights produced by the LSTM models with attention and discuss the results compared with human performance on the task.
The distributed computing is done on many systems to solve a large scale problem.
The growing of high-speed broadband networks in developed and developing countries, the continual increase in computing power, and the rapid growth of the Internet have changed the way.
In it the society manages information and information services.
Historically, the state of computing has gone through a series of platform and environmental changes.
Distributed computing holds great assurance for using computer systems effectively.
As a result, supercomputer sites and data centers have changed from providing high performance floating point computing capabilities to concurrently servicing huge number of requests from billions of users.
The distributed computing system uses multiple computers to solve large-scale problems over the Internet.
It becomes data-intensive and network-centric.
The applications of distributed computing have become increasingly wide-spread.
In distributed computing, the main stress is on the large scale resource sharing and always goes for the best performance.
In this article, we have reviewed the work done in the area of distributed computing paradigms.
The main stress is on the evolving area of cloud computing.
Most researchers acknowledge an intrinsic hierarchy in the scholarly journals ('journal rank') that they submit their work to, and adjust not only their submission but also their reading strategies accordingly.
On the other hand, much has been written about the negative effects of institutionalizing journal rank as an impact measure.
So far, contributions to the debate concerning the limitations of journal rank as a scientific impact assessment tool have either lacked data, or relied on only a few studies.
In this review, we present the most recent and pertinent data on the consequences of our current scholarly communication system with respect to various measures of scientific quality (such as utility/citations, methodological soundness, expert ratings or retractions).
These data corroborate previous hypotheses: using journal rank as an assessment tool is bad scientific practice.
Moreover, the data lead us to argue that any journal rank (not only the currently-favored Impact Factor) would have this negative impact.
Therefore, we suggest that abandoning journals altogether, in favor of a library-based scholarly communication system, will ultimately be necessary.
This new system will use modern information technology to vastly improve the filter, sort and discovery functions of the current journal system.
The original problem of supervised classification considers the task of automatically assigning objects to their respective classes on the basis of numerical measurements derived from these objects.
Classifiers are the tools that implement the actual functional mapping from these measurements---also called features or inputs---to the so-called class label---or output.
The fields of pattern recognition and machine learning study ways of constructing such classifiers.
The main idea behind supervised methods is that of learning from examples: given a number of example input-output relations, to what extent can the general mapping be learned that takes any new and unseen feature vector to its correct class?
This chapter provides a basic introduction to the underlying ideas of how to come to a supervised classification problem.
In addition, it provides an overview of some specific classification techniques, delves into the issues of object representation and classifier evaluation, and (very) briefly covers some variations on the basic supervised classification task that may also be of interest to the practitioner.
Policy enforcers are sophisticated runtime components that can prevent failures by enforcing the correct behavior of the software.
While a single enforcer can be easily designed focusing only on the behavior of the application that must be monitored, the effect of multiple enforcers that enforce different policies might be hard to predict.
So far, mechanisms to resolve interferences between enforcers have been based on priority mechanisms and heuristics.
Although these methods provide a mechanism to take decisions when multiple enforcers try to affect the execution at a same time, they do not guarantee the lack of interference on the global behavior of the system.
In this paper we present a verification strategy that can be exploited to discover interferences between sets of enforcers and thus safely identify a-priori the enforcers that can co-exist at run-time.
In our evaluation, we experimented our verification method with several policy enforcers for Android and discovered some incompatibilities.
In the context of personalized medicine, text mining methods pose an interesting option for identifying disease-gene associations, as they can be used to generate novel links between diseases and genes which may complement knowledge from structured databases.
The most straightforward approach to extract such links from text is to rely on a simple assumption postulating an association between all genes and diseases that co-occur within the same document.
However, this approach (i) tends to yield a number of spurious associations, (ii) does not capture different relevant types of associations, and (iii) is incapable of aggregating knowledge that is spread across documents.
Thus, we propose an approach in which disease-gene co-occurrences and gene-gene interactions are represented in an RDF graph.
A machine learning-based classifier is trained that incorporates features extracted from the graph to separate disease-gene pairs into valid disease-gene associations and spurious ones.
On the manually curated Genetic Testing Registry, our approach yields a 30 points increase in F1 score over a plain co-occurrence baseline.
A distributed consensus algorithm for estimating the maximum value of the initial measurements in a sensor network with communication noise is proposed.
In the absence of communication noise, max estimation can be done by updating the state value with the largest received measurements in every iteration at each sensor.
In the presence of communication noise, however, the maximum estimate will incorrectly drift and the estimate at each sensor will diverge.
As a result, a soft-max approximation together with a non-linear consensus algorithm is introduced herein.
A design parameter controls the trade-off between the soft-max error and convergence speed.
An analysis of this trade-off gives a guideline towards how to choose the design parameter for the max estimate.
We also show that if some prior knowledge of the initial measurements is available, the consensus process can converge faster by using an optimal step size in the iterative algorithm.
A shifted non-linear bounded transmit function is also introduced for faster convergence when sensor nodes have some prior knowledge of the initial measurements.
Simulation results corroborating the theory are also provided.
In this paper we consider regular low-density parity-check codes over a binary-symmetric channel in the decoding regime.
We prove that up to a certain noise threshold the bit-error probability of the bit-sampling decoder converges in mean to zero over the code ensemble and the channel realizations.
To arrive at this result we show that the bit-error probability of the sampling decoder is equal to the derivative of a Bethe free entropy.
The method that we developed is new and is based on convexity of the free entropy and loop calculus.
Convexity is needed to exchange limit and derivative and the loop series enables us to express the difference between the bit-error probability and the Bethe free entropy.
We control the loop series using combinatorial techniques and a first moment method.
We stress that our method is versatile and we believe that it can be generalized for LDPC codes with general degree distributions and for asymmetric channels.
Myerson derived a simple and elegant solution to the single-parameter revenue-maximization problem in his seminal work on optimal auction design assuming the usual model of quasi-linear utilities.
In this paper, we consider a slight generalization of this usual model---from linear to convex "perceived" payments.
This more general problem does not appear to admit a solution as simple and elegant as Myerson's.
While some of Myerson's results extend to our setting, like his payment formula (suitably adjusted), others do not.
For example, we observe that the solutions to the Bayesian and the robust (i.e., non-Bayesian) optimal auction design problems in the convex perceived payment setting do not coincide like they do in the case of linear payments.
We therefore study the two problems in turn.
We derive an upper and a heuristic lower bound on expected revenue in our setting.
These bounds are easily computed pointwise, and yield monotonic allocation rules, so can be supported by Myerson payments (suitably adjusted).
In this way, our bounds yield heuristics that approximate the optimal robust auction, assuming convex perceived payments.
We close with experiments, the final set of which massages the output of one of the closed-form heuristics for the robust problem into an extremely fast, near-optimal heuristic solution to the Bayesian optimal auction design problem.
Different theories posit different sources for feelings of well-being and happiness.
Appraisal theory grounds our emotional responses in our goals and desires and their fulfillment, or lack of fulfillment.
Self Determination theory posits that the basis for well-being rests on our assessment of our competence, autonomy, and social connection.
And surveys that measure happiness empirically note that people require their basic needs to be met for food and shelter, but beyond that tend to be happiest when socializing, eating or having sex.
We analyze a corpus of private microblogs from a well-being application called ECHO, where users label each written post about daily events with a happiness score between 1 and 9.
Our goal is to ground the linguistic descriptions of events that users experience in theories of well-being and happiness, and then examine the extent to which different theoretical accounts can explain the variance in the happiness scores.
We show that recurrent event types, such as OBLIGATION and INCOMPETENCE, which affect people's feelings of well-being are not captured in current lexical or semantic resources.
We focus on robust and efficient iterative solvers for the pressure Poisson equation in incompressible Navier-Stokes problems.
Preconditioned Krylov subspace methods are popular for these problems, with BiCGStab and GMRES(m) most frequently used for nonsymmetric systems.
BiCGStab is popular because it has cheap iterations, but it may fail for stiff problems, especially early on as the initial guess is far from the solution.
Restarted GMRES is better, more robust, in this phase, but restarting may lead to very slow convergence.
Therefore, we evaluate the rGCROT method for these systems.
This method recycles a selected subspace of the search space (called recycle space) after a restart.
This generally improves the convergence drastically compared with GMRES(m).
Recycling subspaces is also advantageous for subsequent linear systems, if the matrix changes slowly or is constant.
However, rGCROT iterations are still expensive in memory and computation time compared with those of BiCGStab.
Hence, we propose a new, hybrid approach that combines the cheap iterations of BiCGStab with the robustness of rGCROT.
For the first few time steps the algorithm uses rGCROT and builds an effective recycle space, and then it recycles that space in the rBiCGStab solver.
We evaluate rGCROT on a turbulent channel flow problem, and we evaluate both rGCROT and the new, hybrid combination of rGCROT and rBiCGStab on a porous medium flow problem.
We see substantial performance gains on both problems.
This paper gives an introduction to the problem of mapping simple polygons with autonomous agents.
We focus on minimalistic agents that move from vertex to vertex along straight lines inside a polygon, using their sensors to gather local observations at each vertex.
Our attention revolves around the question whether a given configuration of sensors and movement capabilities of the agents allows them to capture enough data in order to draw conclusions regarding the global layout of the polygon.
In particular, we study the problem of reconstructing the visibility graph of a simple polygon by an agent moving either inside or on the boundary of the polygon.
Our aim is to provide insight about the algorithmic challenges faced by an agent trying to map a polygon.
We present an overview of techniques for solving this problem with agents that are equipped with simple sensorial capabilities.
We illustrate these techniques on examples with sensors that mea- sure angles between lines of sight or identify the previous location.
We give an overview over related problems in combinatorial geometry as well as graph exploration.
A model of a geometric algorithm is introduced and methodology of its operation is presented for the dynamic partitioning of data spaces.
In this paper, we consider the problem of machine reading task when the questions are in the form of keywords, rather than natural language.
In recent years, researchers have achieved significant success on machine reading comprehension tasks, such as SQuAD and TriviaQA.
These datasets provide a natural language question sentence and a pre-selected passage, and the goal is to answer the question according to the passage.
However, in the situation of interacting with machines by means of text, people are more likely to raise a query in form of several keywords rather than a complete sentence.
The keyword-based query comprehension is a new challenge, because small variations to a question may completely change its semantical information, thus yield different answers.
In this paper, we propose a novel neural network system that consists a Demand Optimization Model based on a passage-attention neural machine translation and a Reader Model that can find the answer given the optimized question.
The Demand Optimization Model optimizes the original query and output multiple reconstructed questions, then the Reader Model takes the new questions as input and locate the answers from the passage.
To make predictions robust, an evaluation mechanism will score the reconstructed questions so the final answer strike a good balance between the quality of both the Demand Optimization Model and the Reader Model.
Experimental results on several datasets show that our framework significantly improves multiple strong baselines on this challenging task.
The analysis of the use of social media for innovative entrepreneurship in the context has received little attention in the literature, especially in the context of Knowledge Intensive Business Services (KIBS).
Therefore, this paper focuses on bridging this gap by applying text mining and sentiment analysis techniques to identify the innovative entrepreneurship reflected by these companies in their social media.
Finally, we present and analyze the results of our quantitative analysis of 23.483 posts based on eleven Spanish and Italian consultancy KIBS Twitter Usernames and Keywords using data interpretation techniques such as clustering and topic modeling.
This paper suggests that there is a significant gap between the perceived potential of social media and the entrepreneurial behaviors at the social context in business-to-business (B2B) companies.
Scripts define knowledge about how everyday scenarios (such as going to a restaurant) are expected to unfold.
One of the challenges to learning scripts is the hierarchical nature of the knowledge.
For example, a suspect arrested might plead innocent or guilty, and a very different track of events is then expected to happen.
To capture this type of information, we propose an autoencoder model with a latent space defined by a hierarchy of categorical variables.
We utilize a recently proposed vector quantization based approach, which allows continuous embeddings to be associated with each latent variable value.
This permits the decoder to softly decide what portions of the latent hierarchy to condition on by attending over the value embeddings for a given setting.
Our model effectively encodes and generates scripts, outperforming a recent language modeling-based method on several standard tasks, and allowing the autoencoder model to achieve substantially lower perplexity scores compared to the previous language modeling-based method.
In this paper, automated user verification techniques for smartphones are investigated.
A unique non-commercial dataset, the University of Maryland Active Authentication Dataset 02 (UMDAA-02) for multi-modal user authentication research is introduced.
This paper focuses on three sensors - front camera, touch sensor and location service while providing a general description for other modalities.
Benchmark results for face detection, face verification, touch-based user identification and location-based next-place prediction are presented, which indicate that more robust methods fine-tuned to the mobile platform are needed to achieve satisfactory verification accuracy.
The dataset will be made available to the research community for promoting additional research.
Dense Multi-GPU systems have recently gained a lot of attention in the HPC arena.
Traditionally, MPI runtimes have been primarily designed for clusters with a large number of nodes.
However, with the advent of MPI+CUDA applications and CUDA-Aware MPI runtimes like MVAPICH2 and OpenMPI, it has become important to address efficient communication schemes for such dense Multi-GPU nodes.
This coupled with new application workloads brought forward by Deep Learning frameworks like Caffe and Microsoft CNTK pose additional design constraints due to very large message communication of GPU buffers during the training phase.
In this context, special-purpose libraries like NVIDIA NCCL have been proposed for GPU-based collective communication on dense GPU systems.
In this paper, we propose a pipelined chain (ring) design for the MPI_Bcast collective operation along with an enhanced collective tuning framework in MVAPICH2-GDR that enables efficient intra-/inter-node multi-GPU communication.
We present an in-depth performance landscape for the proposed MPI_Bcast schemes along with a comparative analysis of NVIDIA NCCL Broadcast and NCCL-based MPI_Bcast.
The proposed designs for MVAPICH2-GDR enable up to 14X and 16.6X improvement, compared to NCCL-based solutions, for intra- and inter-node broadcast latency, respectively.
In addition, the proposed designs provide up to 7% improvement over NCCL-based solutions for data parallel training of the VGG network on 128 GPUs using Microsoft CNTK.
It is well-known that the spacetime diagrams of some cellular automata have a fractal structure: for instance Pascal's triangle modulo 2 generates a Sierpinski triangle.
Explaining the fractal structure of the spacetime diagrams of cellular automata is a much explored topic, but virtually all of the results revolve around a special class of automata, whose typical features include irreversibility, an alphabet with a ring structure, a global evolution that is a ring homomorphism, and a property known as (weakly) p-Fermat.
The class of automata that we study in this article has none of these properties.
Their cell structure is weaker, as it does not come with a multiplication, and they are far from being p-Fermat, even weakly.
However, they do produce fractal spacetime diagrams, and we explain why and how.
Specialized classifiers, namely those dedicated to a subset of classes, are often adopted in real-world recognition systems.
However, integrating such classifiers is nontrivial.
Existing methods, e.g. weighted average, usually implicitly assume that all constituents of an ensemble cover the same set of classes.
Such methods can produce misleading predictions when used to combine specialized classifiers.
This work explores a novel approach.
Instead of combining predictions from individual classifiers directly, it first decomposes the predictions into sets of pairwise preferences, treating them as transition channels between classes, and thereon constructs a continuous-time Markov chain, and use the equilibrium distribution of this chain as the final prediction.
This way allows us to form a coherent picture over all specialized predictions.
On large public datasets, the proposed method obtains considerable improvement compared to mainstream ensemble methods, especially when the classifier coverage is highly unbalanced.
We consider two-way amplify and forward relaying, where multiple full-duplex user pairs exchange information via a shared full-duplex massive multiple-input multiple-output (MIMO) relay.
We derive closed-form lower bound for the spectral efficiency with zero-forcing processing at the relay, by using minimum mean squared error channel estimation.
The zero-forcing lower bound for the system model considered herein, which is valid for arbitrary number of antennas, is not yet derived in the massive MIMO relaying literature.
We numerically demonstrate the accuracy of the derived lower bound and the performance improvement achieved using zero-forcing processing.
We also numerically demonstrate the spectral gains achieved by a full-duplex system over a half-duplex one for various antenna regimes.
Halal is a notion that applies to both objects and actions, and means permissible according to Islamic law.
It may be most often associated with food and the rules of selecting, slaughtering, and cooking animals.
In the globalized world, halal can be found in street corners of New York and beauty shops of Manila.
In this study, we explore the cultural diversity of the concept, as revealed through social media, and specifically the way it is expressed by different populations around the world, and how it relates to their perception of (i) religious and (ii) governmental authority, and (iii) personal health.
Here, we analyze two Instagram datasets, using Halal in Arabic (325,665 posts) and in English (1,004,445 posts), which provide a global view of major Muslim populations around the world.
We find a great variety in the use of halal within Arabic, English, and Indonesian-speaking populations, with animal trade emphasized in first (making up 61% of the language's stream), food in second (80%), and cosmetics and supplements in third (70%).
The commercialization of the term halal is a powerful signal of its detraction from its traditional roots.
We find a complex social engagement around posts mentioning religious terms, such that when a food-related post is accompanied by a religious term, it on average gets more likes in English and Indonesian, but not in Arabic, indicating a potential shift out of its traditional moral framing.
Inference of space-time varying signals on graphs emerges naturally in a plethora of network science related applications.
A frequently encountered challenge pertains to reconstructing such dynamic processes, given their values over a subset of vertices and time instants.
The present paper develops a graph-aware kernel-based kriged Kalman filter that accounts for the spatio-temporal variations, and offers efficient online reconstruction, even for dynamically evolving network topologies.
The kernel-based learning framework bypasses the need for statistical information by capitalizing on the smoothness that graph signals exhibit with respect to the underlying graph.
To address the challenge of selecting the appropriate kernel, the proposed filter is combined with a multi-kernel selection module.
Such a data-driven method selects a kernel attuned to the signal dynamics on-the-fly within the linear span of a pre-selected dictionary.
The novel multi-kernel learning algorithm exploits the eigenstructure of Laplacian kernel matrices to reduce computational complexity.
Numerical tests with synthetic and real data demonstrate the superior reconstruction performance of the novel approach relative to state-of-the-art alternatives.
Many wireless protocols wait a small and random amount of time which is called jitter before sending a packet to avoid high contention and packet collision.
Jitter has been already proposed for many routing protocols including AODV and LOADng.
However, since they do not consider any link quality parameters or metrics (such as ETX) in routing, they fail to be efficient in metric-based routing protocols.
A metric-based jitter mechanism is proposed in this paper and a closed form expression is derived that enables us to obtain the probability of delay inversion for all jitter mechanisms available.
Simulation results are also presented to show the performance of different jitter mechanisms.
Context: Refactoring is recognized as an effective practice to maintain evolving software systems.
For software libraries, we study how library developers refactor their Application Programming Interfaces (APIs), especially when it impacts client users by breaking an API of the library.
Objective: Our work aims to understand how clients that use a library API are affected by refactoring activities.
We target popular libraries that potentially impact more library client users.
Method: We distinguish between library APIs based on their client-usage (refereed to as client-used APIs) in order to understand the extent to which API breakages relate to refactorings.
Our tool-based approach allows for a large-scale study across eight libraries (i.e., totaling 183 consecutive versions) with around 900 clients projects.
Results: We find that library maintainers are less likely to break client-used API classes.
Quantitatively, we find that refactoring activities break less than 37% of all client-used APIs.
In a more qualitative analysis, we show two documented cases of where non-refactoring API breaking changes are motivated other maintenance issues (i.e., bug fix and new features) and involve more complex refactoring operations.
Conclusion: Using our automated approach, we find that library developers are less likely to break APIs and tend to break client-used APIs when addressing these maintenance issues.
This paper proposes a method for direct torque control of Brushless DC (BLDC) motors.
Evaluating the trapezium of back-EMF is needed, and is done via a sliding mode observer employing just one measurement of stator current.
The effect of the proposed estimation algorithm is reducing the impact of switching noise and consequently eliminating the required filter.
Furthermore, to overcome the uncertainties related to BLDC motors, Recursive Least Square (RLS) is regarded as a real-time estimator of inertia and viscous damping coefficients of the BLDC motor.
By substituting the estimated load torque in mechanical dynamic equations, the rotor speed can be calculated.
Also, to increase the robustness and decrease the rise time of the system, Modified Model Reference Adaptive System (MMRAS) is applied in order to design a new speed controller.
Simulation results confirm the validity of this recommended method.
Deep Learning algorithms have recently become the de-facto paradigm for various prediction problems, which include many privacy-preserving applications like online medical image analysis.
Presumably, the privacy of data in a deep learning system is a serious concern.
There have been several efforts to analyze and exploit the information leakages from deep learning architectures to compromise data privacy.
In this paper, however, we attempt to provide an evaluation strategy for such information leakages through deep neural network architectures by considering a case study on Convolutional Neural Network (CNN) based image classifier.
The approach takes the aid of low-level hardware information, provided by Hardware Performance Counters (HPCs), during the execution of a CNN classifier and a simple hypothesis testing in order to produce an alarm if there exists any information leakage on the actual input.
Human vision possesses strong invariance in image recognition.
The cognitive capability of deep convolutional neural network (DCNN) is close to the human visual level because of hierarchical coding directly from raw image.
Owing to its superiority in feature representation, DCNN has exhibited remarkable performance in scene recognition of high-resolution remote sensing (HRRS) images and classification of hyper-spectral remote sensing images.
In-depth investigation is still essential for understanding why DCNN can accurately identify diverse ground objects via its effective feature representation.
Thus, we train the deep neural network called AlexNet on our large scale remote sensing image recognition benchmark.
At the neuron level in each convolution layer, we analyze the general properties of DCNN in HRRS image recognition by use of a framework of visual stimulation-characteristic response combined with feature coding-classification decoding.
Specifically, we use histogram statistics, representational dissimilarity matrix, and class activation mapping to observe the selective and invariance representations of DCNN in HRRS image recognition.
We argue that selective and invariance representations play important roles in remote sensing images tasks, such as classification, detection, and segment.
Also selective and invariance representations are significant to design new DCNN liked models for analyzing and understanding remote sensing images.
Once self-driving car becomes a reality and passengers are no longer worry about it, they will need to find new ways of entertainment.
However, retrieving entertainment contents at the Data Center (DC) can hinder content delivery service due to high delay of car-to-DC communication.
To address these challenges, we propose a deep learning based caching for self-driving car, by using Deep Learning approaches deployed on the Multi-access Edge Computing (MEC) structure.
First, at DC, Multi-Layer Perceptron (MLP) is used to predict the probabilities of contents to be requested in specific areas.
To reduce the car-DC delay, MLP outputs are logged into MEC servers attached to roadside units.
Second, in order to cache entertainment contents stylized for car passengers' features such as age and gender, Convolutional Neural Network (CNN) is used to predict age and gender of passengers.
Third, each car requests MLP output from MEC server and compares its CNN and MLP outputs by using k-means and binary classification.
Through this, the self-driving car can identify the contents need to be downloaded from the MEC server and cached.
Finally, we formulate deep learning based caching in the self-driving car that enhances entertainment services as an optimization problem whose goal is to minimize content downloading delay.
To solve the formulated problem, a Block Successive Majorization-Minimization (BS-MM) technique is applied.
The simulation results show that the accuracy of our prediction for the contents need to be cached in the areas of the self-driving car is achieved at 98.04% and our approach can minimize delay.
Graph based entropy, an index of the diversity of events in their distribution to parts of a co-occurrence graph, is proposed for detecting signs of structural changes in the data that are informative in explaining latent dynamics of consumers behavior.
For obtaining graph-based entropy, connected subgraphs are first obtained from the graph of co-occurrences of items in the data.
Then, the distribution of items occurring in events in the data to these sub-graphs is reflected on the value of graph-based entropy.
For the data on the position of sale, a change in this value is regarded as a sign of the appearance, the separation, the disappearance, or the uniting of consumers interests.
These phenomena are regarded as the signs of dynamic changes in consumers behavior that may be the effects of external events and information.
Experiments show that graph-based entropy outperforms baseline methods that can be used for change detection, in explaining substantial changes and their signs in consumers preference of items in supermarket stores.
In this paper, a new data-driven multiscale material modeling method, which we refer to as deep material network, is developed based on mechanistic homogenization theory of representative volume element (RVE) and advanced machine learning techniques.
We propose to use a collection of connected mechanistic building blocks with analytical homogenization solutions which avoids the loss of essential physics in generic neural networks, and this concept is demonstrated for 2-dimensional RVE problems and network depth up to 7.
Based on linear elastic RVE data from offline direct numerical simulations, the material network can be effectively trained using stochastic gradient descent with backpropagation algorithm, enhanced by model compression methods.
Importantly, the trained network is valid for any local material laws without the need for additional calibration or micromechanics assumption.
Its extrapolations to unknown material and loading spaces for a wide range of problems are validated through numerical experiments, including linear elasticity with high contrast of phase properties, nonlinear history-dependent plasticity and finite-strain hyperelasticity under large deformations.
By discovering a proper topological representation of RVE with fewer degrees of freedom, this intelligent material model is believed to open new possibilities of high-fidelity efficient concurrent simulations for a large-scale heterogeneous structure.
It also provides a mechanistic understanding of structure-property relations across material length scales and enables the development of parameterized microstructural database for material design and manufacturing.
"How common is interactive visualization on the web?"
"What is the most popular visualization design?"
"How prevalent are pie charts really?"
These questions intimate the role of interactive visualization in the real (online) world.
In this paper, we present our approach (and findings) to answering these questions.
First, we introduce Beagle, which mines the web for SVG-based visualizations and automatically classifies them by type (i.e., bar, pie, etc.).
With Beagle, we extract over 41,000 visualizations across five different tools and repositories, and classify them with 86% accuracy, across 24 visualization types.
Given this visualization collection, we study usage across tools.
We find that most visualizations fall under four types: bar charts, line charts, scatter charts, and geographic maps.
Though controversial, pie charts are relatively rare in practice.
Our findings also indicate that users may prefer tools that emphasize a succinct set of visualization types, and provide diverse expert visualization examples.
We address the problem of finding realistic geometric corrections to a foreground object such that it appears natural when composited into a background image.
To achieve this, we propose a novel Generative Adversarial Network (GAN) architecture that utilizes Spatial Transformer Networks (STNs) as the generator, which we call Spatial Transformer GANs (ST-GANs).
ST-GANs seek image realism by operating in the geometric warp parameter space.
In particular, we exploit an iterative STN warping scheme and propose a sequential training strategy that achieves better results compared to naive training of a single generator.
One of the key advantages of ST-GAN is its applicability to high-resolution images indirectly since the predicted warp parameters are transferable between reference frames.
We demonstrate our approach in two applications: (1) visualizing how indoor furniture (e.g. from product images) might be perceived in a room, (2) hallucinating how accessories like glasses would look when matched with real portraits.
Deep convolutional neural networks (DCNNs) have attracted much attention recently, and have shown to be able to recognize thousands of object categories in natural image databases.
Their architecture is somewhat similar to that of the human visual system: both use restricted receptive fields, and a hierarchy of layers which progressively extract more and more abstracted features.
Yet it is unknown whether DCNNs match human performance at the task of view-invariant object recognition, whether they make similar errors and use similar representations for this task, and whether the answers depend on the magnitude of the viewpoint variations.
To investigate these issues, we benchmarked eight state-of-the-art DCNNs, the HMAX model, and a baseline shallow model and compared their results to those of humans with backward masking.
Unlike in all previous DCNN studies, we carefully controlled the magnitude of the viewpoint variations to demonstrate that shallow nets can outperform deep nets and humans when variations are weak.
When facing larger variations, however, more layers were needed to match human performance and error distributions, and to have representations that are consistent with human behavior.
A very deep net with 18 layers even outperformed humans at the highest variation level, using the most human-like representations.
Cross-lingual word embeddings are becoming increasingly important in multilingual NLP.
Recently, it has been shown that these embeddings can be effectively learned by aligning two disjoint monolingual vector spaces through linear transformations, using no more than a small bilingual dictionary as supervision.
In this work, we propose to apply an additional transformation after the initial alignment step, which moves cross-lingual synonyms towards a middle point between them.
By applying this transformation our aim is to obtain a better cross-lingual integration of the vector spaces.
In addition, and perhaps surprisingly, the monolingual spaces also improve by this transformation.
This is in contrast to the original alignment, which is typically learned such that the structure of the monolingual spaces is preserved.
Our experiments confirm that the resulting cross-lingual embeddings outperform state-of-the-art models in both monolingual and cross-lingual evaluation tasks.
This study considers the 3D human pose estimation problem in a single RGB image by proposing a conditional random field (CRF) model over 2D poses, in which the 3D pose is obtained as a byproduct of the inference process.
The unary term of the proposed CRF model is defined based on a powerful heat-map regression network, which has been proposed for 2D human pose estimation.
This study also presents a regression network for lifting the 2D pose to 3D pose and proposes the prior term based on the consistency between the estimated 3D pose and the 2D pose.
To obtain the approximate solution of the proposed CRF model, the N-best strategy is adopted.
The proposed inference algorithm can be viewed as sequential processes of bottom-up generation of 2D and 3D pose proposals from the input 2D image based on deep networks and top-down verification of such proposals by checking their consistencies.
To evaluate the proposed method, we use two large-scale datasets: Human3.6M and HumanEva.
Experimental results show that the proposed method achieves the state-of-the-art 3D human pose estimation performance.
A computing environment is proposed, based on batch spreadsheet processing, which produces a spreadsheet display from plain text input files of commands, similar to the way documents are created using LaTeX.
In this environment, besides the usual spreadsheet rows and columns of cells, variables can be defined and are stored in a separate symbol table.
Cell and symbol formulas may contain cycles, and cycles which converge can be used to implement iterative algorithms.
Formulas are specified using the syntax of the C programming language, and all of C's numeric operators are supported, with operators such as ++, +=, etc. being implicitly cyclic.
User-defined functions can be written in C and are accessed using a dynamic link library.
The environment can be combined with a GUI front-end processor to enable easier interaction and graphics including plotting.
Human activity recognition based on wearable sensor data has been an attractive research topic due to its application in areas such as healthcare and smart environments.
In this context, many works have presented remarkable results using accelerometer, gyroscope and magnetometer data to represent the activities categories.
However, current studies do not consider important issues that lead to skewed results, making it hard to assess the quality of sensor-based human activity recognition and preventing a direct comparison of previous works.
These issues include the samples generation processes and the validation protocols used.
We emphasize that in other research areas, such as image classification and object detection, these issues are already well-defined, which brings more efforts towards the application.
Inspired by this, we conduct an extensive set of experiments that analyze different sample generation processes and validation protocols to indicate the vulnerable points in human activity recognition based on wearable sensor data.
For this purpose, we implement and evaluate several top-performance methods, ranging from handcrafted-based approaches to convolutional neural networks.
According to our study, most of the experimental evaluations that are currently employed are not adequate to perform the activity recognition in the context of wearable sensor data, in which the recognition accuracy drops considerably when compared to an appropriate evaluation approach.
To the best of our knowledge, this is the first study that tackles essential issues that compromise the understanding of the performance in human activity recognition based on wearable sensor data.
DNA sequences are fundamental for encoding genetic information.
The genetic information may not only be understood by symbolic sequences but also from the hidden signals inside the sequences.
The symbolic sequences need to be transformed into numerical sequences so the hidden signals can be revealed by signal processing techniques.
All current transformation methods encode DNA sequences into numerical values of the same length.
These representations have limitations in the applications of genomic signal compression, encryption, and steganography.
We propose an integer chaos game representation (iCGR) of DNA sequences and a lossless encoding method DNA sequences by the iCGR.
In the iCGR method, a DNA sequence is represented by the iterated function of the nucleotides and their positions in the sequence.
Then the DNA sequence can be uniquely encoded and recovered using three integers from iCGR.
One integer is the sequence length and the other two integers represent the accumulated distributions of nucleotides in the sequence.
The integer encoding scheme can compress a DNA sequence by 2 bits per nucleotide.
The integer representation of DNA sequences provides a prospective tool for sequence compression, encryption, and steganography.
The Python programs in this study are freely available to the public at https://github.com/cyinbox/iCGR
At the heart of deep learning we aim to use neural networks as function approximators - training them to produce outputs from inputs in emulation of a ground truth function or data creation process.
In many cases we only have access to input-output pairs from the ground truth, however it is becoming more common to have access to derivatives of the target output with respect to the input - for example when the ground truth function is itself a neural network such as in network compression or distillation.
Generally these target derivatives are not computed, or are ignored.
This paper introduces Sobolev Training for neural networks, which is a method for incorporating these target derivatives in addition the to target values while training.
By optimising neural networks to not only approximate the function's outputs but also the function's derivatives we encode additional information about the target function within the parameters of the neural network.
Thereby we can improve the quality of our predictors, as well as the data-efficiency and generalization capabilities of our learned function approximation.
We provide theoretical justifications for such an approach as well as examples of empirical evidence on three distinct domains: regression on classical optimisation datasets, distilling policies of an agent playing Atari, and on large-scale applications of synthetic gradients.
In all three domains the use of Sobolev Training, employing target derivatives in addition to target values, results in models with higher accuracy and stronger generalisation.
In this paper, we address the shape-from-shading problem by training deep networks with synthetic images.
Unlike conventional approaches that combine deep learning and synthetic imagery, we propose an approach that does not need any external shape dataset to render synthetic images.
Our approach consists of two synergistic processes: the evolution of complex shapes from simple primitives, and the training of a deep network for shape-from-shading.
The evolution generates better shapes guided by the network training, while the training improves by using the evolved shapes.
We show that our approach achieves state-of-the-art performance on a shape-from-shading benchmark.
We present an improved model and theory for time-causal and time-recursive spatio-temporal receptive fields, based on a combination of Gaussian receptive fields over the spatial domain and first-order integrators or equivalently truncated exponential filters coupled in cascade over the temporal domain.
Compared to previous spatio-temporal scale-space formulations in terms of non-enhancement of local extrema or scale invariance, these receptive fields are based on different scale-space axiomatics over time by ensuring non-creation of new local extrema or zero-crossings with increasing temporal scale.
Specifically, extensions are presented about (i) parameterizing the intermediate temporal scale levels, (ii) analysing the resulting temporal dynamics, (iii) transferring the theory to a discrete implementation, (iv) computing scale-normalized spatio-temporal derivative expressions for spatio-temporal feature detection and (v) computational modelling of receptive fields in the lateral geniculate nucleus (LGN) and the primary visual cortex (V1) in biological vision.
We show that by distributing the intermediate temporal scale levels according to a logarithmic distribution, we obtain much faster temporal response properties (shorter temporal delays) compared to a uniform distribution.
Specifically, these kernels converge very rapidly to a limit kernel possessing true self-similar scale-invariant properties over temporal scales, thereby allowing for true scale invariance over variations in the temporal scale, although the underlying temporal scale-space representation is based on a discretized temporal scale parameter.
We show how scale-normalized temporal derivatives can be defined for these time-causal scale-space kernels and how the composed theory can be used for computing basic types of scale-normalized spatio-temporal derivative expressions in a computationally efficient manner.
In this paper, we propose to infer music genre embeddings from audio datasets carrying semantic information about genres.
We show that such embeddings can be used for disambiguating genre tags (identification of different labels for the same genre, tag translation from a tag system to another, inference of hierarchical taxonomies on these genre tags).
These embeddings are built by training a deep convolutional neural network genre classifier with large audio datasets annotated with a flat tag system.
We show empirically that they makes it possible to retrieve the original taxonomy of a tag system, spot duplicates tags and translate tags from a tag system to another.
Incidental scene text detection, especially for multi-oriented text regions, is one of the most challenging tasks in many computer vision applications.
Different from the common object detection task, scene text often suffers from a large variance of aspect ratio, scale, and orientation.
To solve this problem, we propose a novel end-to-end scene text detector IncepText from an instance-aware segmentation perspective.
We design a novel Inception-Text module and introduce deformable PSROI pooling to deal with multi-oriented text detection.
Extensive experiments on ICDAR2015, RCTW-17, and MSRA-TD500 datasets demonstrate our method's superiority in terms of both effectiveness and efficiency.
Our proposed method achieves 1st place result on ICDAR2015 challenge and the state-of-the-art performance on other datasets.
Moreover, we have released our implementation as an OCR product which is available for public access.
Inspired by CapsNet's routing-by-agreement mechanism, with its ability to learn object properties, and by center-of-mass calculations from physics, we propose a CapsNet architecture with object coordinate atoms and an LSTM network for evaluation.
The first is based on CapsNet but uses a new routing algorithm to find the objects' approximate positions in the image coordinate system, and the second is a parameterized affine transformation network that can predict future positions from past positions by learning the translation transformation from 2D object coordinates generated from the first network.
We demonstrate the learned translation transformation is transferable to another dataset without the need to train the transformation network again.
Only the CapsNet needs training on the new dataset.
As a result, our work shows that object recognition and motion prediction can be separated, and that motion prediction can be transferred to another dataset with different object types.
We present a probabilistic approach to generate a small, query-able summary of a dataset for interactive data exploration.
Departing from traditional summarization techniques, we use the Principle of Maximum Entropy to generate a probabilistic representation of the data that can be used to give approximate query answers.
We develop the theoretical framework and formulation of our probabilistic representation and show how to use it to answer queries.
We then present solving techniques and give three critical optimizations to improve preprocessing time and query accuracy.
Lastly, we experimentally evaluate our work using a 5 GB dataset of flights within the United States and a 210 GB dataset from an astronomy particle simulation.
While our current work only supports linear queries, we show that our technique can successfully answer queries faster than sampling while introducing, on average, no more error than sampling and can better distinguish between rare and nonexistent values.
User data is the primary input of digital advertising, the fuel of free Internet as we know it.
As a result, web entities invest a lot in elaborate tracking mechanisms to acquire more and more user data that can sell to data markets and advertisers.
The primary identification mechanism of web is through cookies, where each entity assigns a userID on the user's side.
However, each tracker knows the same user with a different ID.
So how can the collected data be sold and merged with the associated user data of the buyer?
To address this, Cookie Synchronization (CSync) came to the rescue.
CSync facilitates an information sharing channel between third parties that may or may not have direct access to the website the user visits.
With CSync, they merge the user data they own in the background, but also reconstruct the browsing history of a user bypassing the same origin policy.
In this paper, we perform a first to our knowledge in-depth study of CSync in the wild, using a year-long dataset that includes web browsing activity from 850 real mobile users.
Through our study, we aim to understand the characteristics of the CSync protocol and the impact it has to the users privacy.
Our results show that 97% of the regular web users are exposed to CSync: most of them within the first week of their browsing.
In addition, the average user receives ~1 synchronization per 68 GET requests, and the median userID gets leaked, on average, to 3.5 different online entities.
In addition, we see that CSync increases the number of entities that track the user by a factor of 6.7.
Finally, we propose a novel, machine learning-based method for CSync detection, which can be effective when the synced IDs are obscured.
This paper proposes a novel algorithm to optimally size and place storage in low voltage (LV) networks based on a linearized multiperiod optimal power flow method which we call forward backward sweep optimal power flow (FBS-OPF).
We show that this method has good convergence properties, its solution deviates slightly from the optimum and makes the storage sizing and placement problem tractable for longer investment horizons.
We demonstrate the usefulness of our method by assessing the economic viability of distributed and centralized storage in LV grids with a high photovoltaic penetration (PV).
As a main result, we quantify that for the CIGRE LV test grid distributed storage configurations are preferable, since they allow for less PV curtailment due to grid constraints.
Recent terrorist attacks carried out on behalf of ISIS on American and European soil by lone wolf attackers or sleeper cells remind us of the importance of understanding the dynamics of radicalization mediated by social media communication channels.
In this paper, we shed light on the social media activity of a group of twenty-five thousand users whose association with ISIS online radical propaganda has been manually verified.
By using a computational tool known as dynamical activity-connectivity maps, based on network and temporal activity patterns, we investigate the dynamics of social influence within ISIS supporters.
We finally quantify the effectiveness of ISIS propaganda by determining the adoption of extremist content in the general population and draw a parallel between radical propaganda and epidemics spreading, highlighting that information broadcasters and influential ISIS supporters generate highly-infectious cascades of information contagion.
Our findings will help generate effective countermeasures to combat the group and other forms of online extremism.
Relational databases are valuable resources for learning novel and interesting relations and concepts.
Relational learning algorithms learn the Datalog definition of new relations in terms of the existing relations in the database.
In order to constraint the search through the large space of candidate definitions, users must tune the algorithm by specifying a language bias.
Unfortunately, specifying the language bias is done via trial and error and is guided by the expert's intuitions.
Hence, it normally takes a great deal of time and effort to effectively use these algorithms.
In particular, it is hard to find a user that knows computer science concepts, such as database schema, and has a reasonable intuition about the target relation in special domains, such as biology.
We propose AutoMode, a system that leverages information in the schema and content of the database to automatically induce the language bias used by popular relational learning systems.
We show that AutoMode delivers the same accuracy as using manually-written language bias by imposing only a slight overhead on the running time of the learning algorithm.
We investigate how well continuous-time fictitious play in two-player games performs in terms of average payoff, particularly compared to Nash equilibrium payoff.
We show that in many games, fictitious play outperforms Nash equilibrium on average or even at all times, and moreover that any game is linearly equivalent to one in which this is the case.
Conversely, we provide conditions under which Nash equilibrium payoff dominates fictitious play payoff.
A key step in our analysis is to show that fictitious play dynamics asymptotically converges the set of coarse correlated equilibria (a fact which is implicit in the literature).
Understanding and reasoning about cooking recipes is a fruitful research direction towards enabling machines to interpret procedural text.
In this work, we introduce RecipeQA, a dataset for multimodal comprehension of cooking recipes.
It comprises of approximately 20K instructional recipes with multiple modalities such as titles, descriptions and aligned set of images.
With over 36K automatically generated question-answer pairs, we design a set of comprehension and reasoning tasks that require joint understanding of images and text, capturing the temporal flow of events and making sense of procedural knowledge.
Our preliminary results indicate that RecipeQA will serve as a challenging test bed and an ideal benchmark for evaluating machine comprehension systems.
The data and leaderboard are available at http://hucvl.github.io/recipeqa.
In this paper, we propose a novel multi-task learning architecture, which incorporates recent advances in attention mechanisms.
Our approach, the Multi-Task Attention Network (MTAN), consists of a single shared network containing a global feature pool, together with task-specific soft-attention modules, which are trainable in an end-to-end manner.
These attention modules allow for learning of task-specific features from the global pool, whilst simultaneously allowing for features to be shared across different tasks.
The architecture can be built upon any feed-forward neural network, is simple to implement, and is parameter efficient.
Experiments on the CityScapes dataset show that our method outperforms several baselines in both single-task and multi-task learning, and is also more robust to the various weighting schemes in the multi-task loss function.
We further explore the effectiveness of our method through experiments over a range of task complexities, and show how our method scales well with task complexity compared to baselines.
We present an algorithm for computing a Smith form with multipliers of a regular matrix polynomial over a field.
This algorithm differs from previous ones in that it computes a local Smith form for each irreducible factor in the determinant separately and then combines them into a global Smith form, whereas other algorithms apply a sequence of unimodular row and column operations to the original matrix.
The performance of the algorithm in exact arithmetic is reported for several test cases.
The local descriptors have gained wide range of attention due to their enhanced discriminative abilities.
It has been proved that the consideration of multi-scale local neighborhood improves the performance of the descriptor, though at the cost of increased dimension.
This paper proposes a novel method to construct a local descriptor using multi-scale neighborhood by finding the local directional order among the intensity values at different scales in a particular direction.
Local directional order is the multi-radius relationship factor (i.e.) in a particular direction.
The proposed local directional order pattern (LDOP) for a particular pixel is computed by finding the relationship between the center pixel and local directional order indexes.
It is required to transform the center value in the range of neighboring orders.
Finally, the histogram of LDOP is computed over whole image to construct the descriptor.
In contrast to the state-of-the-art descriptors, the dimension of the proposed descriptor does not depend upon the number of neighbors involved to compute the order; it only depends upon the number of directions.
The introduced descriptor is evaluated over the image retrieval framework and compared with the state-of-the-art descriptors over challenging face databases such as PaSC, LFW, PubFig, FERET, AR, AT&T, and ExtendedYale.
The experimental results confirm the superiority and robustness of the LDOP descriptor.
We believe that there is no real data protection without our own tools.
Therefore, our permanent aim is to have more of our own codes.
In order to achieve that, it is necessary that a lot of young researchers become interested in cryptography.
We believe that the encoding of cryptographic algorithms is an important step in that direction, and it is the main reason why in this paper we present a software implementation of finding the inverse element, the operation which is essentially related to both ECC (Elliptic Curve Cryptography) and the RSA schemes of digital signature.
Big data analytics is gaining massive momentum in the last few years.
Applying machine learning models to big data has become an implicit requirement or an expectation for most analysis tasks, especially on high-stakes applications.Typical applications include sentiment analysis against reviews for analyzing on-line products, image classification in food logging applications for monitoring user's daily intake and stock movement prediction.
Extending traditional database systems to support the above analysis is intriguing but challenging.
First, it is almost impossible to implement all machine learning models in the database engines.
Second, expertise knowledge is required to optimize the training and inference procedures in terms of efficiency and effectiveness, which imposes heavy burden on the system users.
In this paper, we develop and present a system, called Rafiki, to provide the training and inference service of machine learning models, and facilitate complex analytics on top of cloud platforms.
Rafiki provides distributed hyper-parameter tuning for the training service, and online ensemble modeling for the inference service which trades off between latency and accuracy.
Experimental results confirm the efficiency, effectiveness, scalability and usability of Rafiki.
Automatic lane tracking involves estimating the underlying signal from a sequence of noisy signal observations.
Many models and methods have been proposed for lane tracking, and dynamic targets tracking in general.
The Kalman Filter is a widely used method that works well on linear Gaussian models.
But this paper shows that Kalman Filter is not suitable for lane tracking, because its Gaussian observation model cannot faithfully represent the procured observations.
We propose using a Particle Filter on top of a novel multiple mode observation model.
Experiments show that our method produces superior performance to a conventional Kalman Filter.
In imperfect-information games, the optimal strategy in a subgame may depend on the strategy in other, unreached subgames.
Thus a subgame cannot be solved in isolation and must instead consider the strategy for the entire game as a whole, unlike perfect-information games.
Nevertheless, it is possible to first approximate a solution for the whole game and then improve it by solving individual subgames.
This is referred to as subgame solving.
We introduce subgame-solving techniques that outperform prior methods both in theory and practice.
We also show how to adapt them, and past subgame-solving techniques, to respond to opponent actions that are outside the original action abstraction; this significantly outperforms the prior state-of-the-art approach, action translation.
Finally, we show that subgame solving can be repeated as the game progresses down the game tree, leading to far lower exploitability.
These techniques were a key component of Libratus, the first AI to defeat top humans in heads-up no-limit Texas hold'em poker.
In the last years, a large number of RDF data sets has become available on the Web.
However, due to the semi-structured nature of RDF data, missing values affect answer completeness of queries that are posed against this data.
To overcome this limitation, we propose RDF-Hunter, a novel hybrid query processing approach that brings together machine and human computation to execute queries against RDF data.
We develop a novel quality model and query engine in order to enable RDF-Hunter to on the fly decide which parts of a query should be executed through conventional technology or crowd computing.
To evaluate RDF-Hunter, we created a collection of 50 SPARQL queries against the DBpedia data set, executed them using our hybrid query engine, and analyzed the accuracy of the outcomes obtained from the crowd.
The experiments clearly show that the overall approach is feasible and produces query results that reliably and significantly enhance completeness of automatic query processing responses.
The objective of this paper is to design an efficient vehicle license plate recognition System and to implement it for automatic parking inventory system.
The system detects the vehicle first and then captures the image of the front view of the vehicle.
Vehicle license plate is localized and characters are segmented.
For finding the place of plate, a novel and real time method is expressed.
A new and robust technique based on directional chain code is used for character recognition.
The resulting vehicle number is then compared with the available database of all the vehicles so as to come up with information about the vehicle type and to charge entrance cost accordingly.
The system is then allowed to open parking barrier for the vehicle and generate entrance cost receipt.
The vehicle information (such as entrance time, date, and cost amount) is also stored in the database to maintain the record.
The hardware and software integrated system is implemented and a working prototype model is developed.
Under the available database, the average accuracy of locating vehicle license plate obtained 100%.
Using 70% samples of character for training, we tested our scheme on whole samples and obtained 100% correct recognition rate.
Further we tested our character recognition stage on Persian vehicle data set and we achieved 99% correct recognition.
Book covers communicate information to potential readers, but can that same information be learned by computers?
We propose using a deep Convolutional Neural Network (CNN) to predict the genre of a book based on the visual clues provided by its cover.
The purpose of this research is to investigate whether relationships between books and their covers can be learned.
However, determining the genre of a book is a difficult task because covers can be ambiguous and genres can be overarching.
Despite this, we show that a CNN can extract features and learn underlying design rules set by the designer to define a genre.
Using machine learning, we can bring the large amount of resources available to the book cover design process.
In addition, we present a new challenging dataset that can be used for many pattern recognition tasks.
The large spectrum available in the millimeter-Wave (mmWave) band has emerged as a promising solution for meeting the huge capacity requirements of the 5th generation (5G) wireless networks.
However, to fully harness the potential of mmWave communications, obstacles such as severe path loss, channel sparsity and hardware complexity should be overcome.
In this paper, we introduce a generalized reconfigurable antenna multiple-input multiple-output (MIMO) architecture that takes advantage of lens-based reconfigurable antennas.
The considered antennas can support multiple radiation patterns simultaneously by using a single RF chain.
The degrees of freedom provided by the reconfigurable antennas are used to, first, combat channel sparsity in MIMO mmWave systems.
Further, to suppress high path loss and shadowing at mmWave frequencies, we use a rate-one space-time block code.
Our analysis and simulations show that the proposed reconfigurable MIMO architecture achieves full-diversity gain by using linear receivers and without requiring channel state information at the transmitter.
Moreover, simulations show that the proposed architecture outperforms traditional MIMO transmission schemes in mmWave channel settings.
The remarkable technological advance in well-equipped wearable devices is pushing an increasing production of long first-person videos.
However, since most of these videos have long and tedious parts, they are forgotten or never seen.
Despite a large number of techniques proposed to fast-forward these videos by highlighting relevant moments, most of them are image based only.
Most of these techniques disregard other relevant sensors present in the current devices such as high-definition microphones.
In this work, we propose a new approach to fast-forward videos using psychoacoustic metrics extracted from the soundtrack.
These metrics can be used to estimate the annoyance of a segment allowing our method to emphasize moments of sound pleasantness.
The efficiency of our method is demonstrated through qualitative results and quantitative results as far as of speed-up and instability are concerned.
We explain how the prototype automatic chess problem composer, Chesthetica, successfully composed a rare and interesting chess problem using the new Digital Synaptic Neural Substrate (DSNS) computational creativity approach.
This problem represents a greater challenge from a creative standpoint because the checkmate is not always clear and the method of winning even less so.
Creating a decisive chess problem of this type without the aid of an omniscient 7-piece endgame tablebase (and one that also abides by several chess composition conventions) would therefore be a challenge for most human players and composers working on their own.
The fact that a small computer with relatively low processing power and memory was sufficient to compose such a problem using the DSNS approach in just 10 days is therefore noteworthy.
In this report we document the event and result in some detail.
It lends additional credence to the DSNS as a viable new approach in the field of computational creativity.
In particular, in areas where human-like creativity is required for targeted or specific problems with no clear path to the solution.
Length-matching is an important technique to bal- ance delays of bus signals in high-performance PCB routing.
Existing routers, however, may generate very dense meander segments.
Signals propagating along these meander segments exhibit a speedup effect due to crosstalk between the segments of the same wire, thus leading to mismatch of arrival times even under the same physical wire length.
In this paper, we present a post-processing method to enlarge the width and the distance of meander segments and hence distribute them more evenly on the board so that crosstalk can be reduced.
In the proposed framework, we model the sharing of available routing areas after removing dense meander segments from the initial routing, as well as the generation of relaxed meander segments and their groups for wire length compensation.
This model is transformed into an ILP problem and solved for a balanced distribution of wire patterns.
In addition, we adjust the locations of long wire segments according to wire priorities to swap free spaces toward critical wires that need much length compensation.
To reduce the problem space of the ILP model, we also introduce a progressive fixing technique so that wire patterns are grown gradually from the edge of the routing toward the center area.
Experimental results show that the proposed method can expand meander segments significantly even under very tight area constraints, so that the speedup effect can be alleviated effectively in high- performance PCB designs.
In this paper, a two-hop decode-and-forward cognitive radio system with deployed interference alignment is considered.
The relay node is energy-constrained and scavenges the energy from the interference signals.
In the literature, there are two main energy harvesting protocols, namely, time-switching relaying and power-splitting relaying.
We first demonstrate how to design the beamforming matrices for the considered primary and secondary networks.
Then, the system capacity under perfect and imperfect channel state information scenarios, considering different portions of time-switching and power-splitting protocols, is estimated.
We investigate the association between musical chords and lyrics by analyzing a large dataset of user-contributed guitar tablatures.
Motivated by the idea that the emotional content of chords is reflected in the words used in corresponding lyrics, we analyze associations between lyrics and chord categories.
We also examine the usage patterns of chords and lyrics in different musical genres, historical eras, and geographical regions.
Our overall results confirms a previously known association between Major chords and positive valence.
We also report a wide variation in this association across regions, genres, and eras.
Our results suggest possible existence of different emotional associations for other types of chords.
Non-intrusive load monitoring (NILM), also known as energy disaggregation, is a blind source separation problem where a household's aggregate electricity consumption is broken down into electricity usages of individual appliances.
In this way, the cost and trouble of installing many measurement devices over numerous household appliances can be avoided, and only one device needs to be installed.
The problem has been well-known since Hart's seminal paper in 1992, and recently significant performance improvements have been achieved by adopting deep networks.
In this work, we focus on the idea that appliances have on/off states, and develop a deep network for further performance improvements.
Specifically, we propose a subtask gated network that combines the main regression network with an on/off classification subtask network.
Unlike typical multitask learning algorithms where multiple tasks simply share the network parameters to take advantage of the relevance among tasks, the subtask gated network multiply the main network's regression output with the subtask's classification probability.
When standby-power is additionally learned, the proposed solution surpasses the state-of-the-art performance for most of the benchmark cases.
The subtask gated network can be very effective for any problem that inherently has on/off states.
A smart city provides its people with high standard of living through advanced technologies and transport is one of the major foci.
With the advent of autonomous vehicles (AVs), an AV-based public transportation system has been proposed recently, which is capable of providing new forms of transportation services with high efficiency, high flexibility, and low cost.
For the benefit of passengers, multitenancy can increase market competition leading to lower service charge and higher quality of service.
In this paper, we study the pricing issue of the multi-tenant AV public transportation system and three types of services are defined.
The pricing process for each service type is modeled as a combinatorial auction, in which the service providers, as bidders, compete for offering transportation services.
The winners of the auction are determined through an integer linear program.
To prevent the bidders from raising their bids for higher returns, we propose a strategy-proof Vickrey-Clarke-Groves-based charging mechanism, which can maximize the social welfare, to settle the final charges for the customers.
We perform extensive simulations to verify the analytical results and evaluate the performance of the charging mechanism.
The size of a website's active user base directly affects its value.
Thus, it is important to monitor and influence a user's likelihood to return to a site.
Essential to this is predicting when a user will return.
Current state of the art approaches to solve this problem come in two flavors: (1) Recurrent Neural Network (RNN) based solutions and (2) survival analysis methods.
We observe that both techniques are severely limited when applied to this problem.
Survival models can only incorporate aggregate representations of users instead of automatically learning a representation directly from a raw time series of user actions.
RNNs can automatically learn features, but can not be directly trained with examples of non-returning users who have no target value for their return time.
We develop a novel RNN survival model that removes the limitations of the state of the art methods.
We demonstrate that this model can successfully be applied to return time prediction on a large e-commerce dataset with a superior ability to discriminate between returning and non-returning users than either method applied in isolation.
An important challenge in neuroevolution is to evolve complex neural networks with multiple modes of behavior.
Indirect encodings can potentially answer this challenge.
Yet in practice, indirect encodings do not yield effective multimodal controllers.
Thus, this paper introduces novel multimodal extensions to HyperNEAT, a popular indirect encoding.
A previous multimodal HyperNEAT approach called situational policy geometry assumes that multiple brains benefit from being embedded within an explicit geometric space.
However, experiments here illustrate that this assumption unnecessarily constrains evolution, resulting in lower performance.
Specifically, this paper introduces HyperNEAT extensions for evolving many brains without assuming geometric relationships between them.
The resulting Multi-Brain HyperNEAT can exploit human-specified task divisions to decide when each brain controls the agent, or can automatically discover when brains should be used, by means of preference neurons.
A further extension called module mutation allows evolution to discover the number of brains, enabling multimodal behavior with even less expert knowledge.
Experiments in several multimodal domains highlight that multi-brain approaches are more effective than HyperNEAT without multimodal extensions, and show that brains without a geometric relation to each other outperform situational policy geometry.
The conclusion is that Multi-Brain HyperNEAT provides several promising techniques for evolving complex multimodal behavior.
The human activity recognition in the IoT environment plays the central role in the ambient assisted living, where the human activities can be represented as a concatenated event stream generated from various smart objects.
From the concatenated event stream, each activity should be distinguished separately for the human activity recognition to provide services that users may need.
In this regard, accurately segmenting the entire stream at the precise boundary of each activity is indispensable high priority task to realize the activity recognition.
Multiple human activities in an IoT environment generate varying event stream patterns, and the unpredictability of these patterns makes them include redundant or missing events.
In dealing with this complex segmentation problem, we figured out that the dynamic and confusing patterns cause major problems due to: inclusive event stream, redundant events, and shared events.
To address these problems, we exploited the contextual relationships associated with the activity status about either ongoing or terminated/started.
To discover the intrinsic relationships between the events in a stream, we utilized the LSTM model by rendering it for the activity segmentation.
Then, the inferred boundaries were revised by our validation algorithm for a bit shifted boundaries.
Our experiments show the surprising result of high accuracy above 95%, on our own testbed with various smart objects.
This is superior to the prior works that even do not assume the environment with multi-user activities, where their accuracies are slightly above 80% in their test environment.
It proves that our work is feasible enough to be applied in the IoT environment.
Cache-aided coded multicast leverages side information at wireless edge caches to efficiently serve multiple unicast demands via common multicast transmissions, leading to load reductions that are proportional to the aggregate cache size.
However, the increasingly dynamic, unpredictable, and personalized nature of the content that users consume challenges the efficiency of existing caching-based solutions in which only exact content reuse is explored.
This paper generalizes the cache-aided coded multicast problem to specifically account for the correlation among content files, such as, for example, the one between updated versions of dynamic data.
It is shown that (i) caching content pieces based on their correlation with the rest of the library, and (ii) jointly compressing requested files using cached information as references during delivery, can provide load reductions that go beyond those achieved with existing schemes.
This is accomplished via the design of a class of correlation-aware achievable schemes, shown to significantly outperform state-of-the-art correlation-unaware solutions.
Our results show that as we move towards real-time and/or personalized media dominated services, where exact cache hits are almost non-existent but updates can exhibit high levels of correlation, network cached information can still be useful as references for network compression.
Semantic segmentation and vision-based geolocalization in aerial images are challenging tasks in computer vision.
Due to the advent of deep convolutional nets and the availability of relatively low cost UAVs, they are currently generating a growing attention in the field.
We propose a novel multi-task multi-stage neural network that is able to handle the two problems at the same time, in a single forward pass.
The first stage of our network predicts pixelwise class labels, while the second stage provides a precise location using two branches.
One branch uses a regression network, while the other is used to predict a location map trained as a segmentation task.
From a structural point of view, our architecture uses encoder-decoder modules at each stage, having the same encoder structure re-used.
Furthermore, its size is limited to be tractable on an embedded GPU.
We achieve commercial GPS-level localization accuracy from satellite images with spatial resolution of 1 square meter per pixel in a city-wide area of interest.
On the task of semantic segmentation, we obtain state-of-the-art results on two challenging datasets, the Inria Aerial Image Labeling dataset and Massachusetts Buildings.
This article analyses the difference in timing between the online availability of articles and their corresponding print publication and how it affects two bibliometric indicators: Journal Impact Factor (JIF) and Immediacy Index.
This research examined 18,526 articles, the complete collection of articles and reviews published by a set of 61 journals on Urology and Nephrology in 2013 and 2014.
The findings suggest that Advance Online Publication (AOP) accelerates the citation of articles and affects the JIF and Immediacy Index values.
Regarding the JIF values, the comparison between journals with or without AOP showed statistically significant differences (P=0.001, Mann-Whitney U test).
The Spearman's correlation between the JIF and the median online-to-print publication delay was not statistically significant.
As to the Immediacy Index, a significant Spearman's correlation (rs=0.280, P=0.029) was found regarding the median online-to-print publication delays for journals published in 2014, although no statistically significant correlation was found for those published in 2013.
Most journals examined (n=52 out of 61) published their articles in AOP.
The analysis also showed different publisher practices: eight journals did not include the online posting dates in the full-text and nine journals published articles showing two different online posting dates--the date provided on the journal website and another provided by Elsevier's Science Direct.
These practices suggest the need for transparency and standardization of the AOP dates of scientific articles for calculating bibliometric indicators for journals.
Multi-agent predictive modeling is an essential step for understanding physical, social and team-play systems.
Recently, Interaction Networks (INs) were proposed for the task of modeling multi-agent physical systems, INs scale with the number of interactions in the system (typically quadratic or higher order in the number of agents).
In this paper we introduce VAIN, a novel attentional architecture for multi-agent predictive modeling that scales linearly with the number of agents.
We show that VAIN is effective for multi-agent predictive modeling.
Our method is evaluated on tasks from challenging multi-agent prediction domains: chess and soccer, and outperforms competing multi-agent approaches.
This paper proposes a new algorithm for Gaussian process classification based on posterior linearisation (PL).
In PL, a Gaussian approximation to the posterior density is obtained iteratively using the best possible linearisation of the conditional mean of the labels and accounting for the linearisation error.
Considering three widely-used likelihood functions, in general, PL provides lower classification errors in real data sets than expectation propagation and Laplace algorithms.
The success of blockchain as the underlying technology for cryptocurrencies has opened up possibilities for its use in other application domains as well.
The main advantages of blockchain for its potential use in other domains are its inherent security mechanisms and immunity to different attacks.
A blockchain relies on a consensus method for agreeing on any new data.
Most of the consensus methods which are currently used for the blockchain of different cryptocurrencies require high computational power and thus are not apt for resource constrained systems.
In this article, we discuss and survey the various blockchain based consensus methods that are applicable to resource constrained IoT devices and networks.
A typical IoT network consists of several devices which have limited computational and communications capabilities.
Most often, these devices cannot perform the intensive computations and are starved for bandwidth.
Therefore, we discuss the possible measures that can be taken to reduce the computational power and convergence time for the underlying consensus methods.
We also discuss some of the alternatives to the public blockchain like private blockchain and tangle, and their potential adoption for IoT networks.
Furthermore, we discuss the existing consensus methods and blockchain implementations and explore the possibility of utilizing them to realize a blockchain based IoT network.
Some of the open research challenges are also put forward.
Energy efficiency is a crucial performance metric in sensor networks, directly determining the network lifetime.
Consequently, a key factor in WSN is to improve overall energy efficiency to extend the network lifetime.
Although many algorithms have been presented to optimize the energy factor, energy efficiency is still one of the major problems of WSNs, especially when there is a need to sample an area with different types of loads.
Unlike other energy-efficient schemes for hierarchical sampling, our hypothesis is that it is achievable, in terms of prolonging the network lifetime, to adaptively re-modify CHs sensing rates (the processing and transmitting stages in particular) in some specific regions that are triggered significantly less than other regions.
In order to do so we introduce the Adaptive Distributed Hierarchical Sensing (ADHS) algorithm.
This algorithm employs a homogenous sensor network in a distributed fashion and changes the sampling rates of the CHs based on the variance of the sampled data without damaging significantly the accuracy of the sensed area.
Automated facial identification and facial expression recognition have been topics of active research over the past few decades.
Facial and expression recognition find applications in human-computer interfaces, subject tracking, real-time security surveillance systems and social networking.
Several holistic and geometric methods have been developed to identify faces and expressions using public and local facial image databases.
In this work we present the evolution in facial image data sets and the methodologies for facial identification and recognition of expressions such as anger, sadness, happiness, disgust, fear and surprise.
We observe that most of the earlier methods for facial and expression recognition aimed at improving the recognition rates for facial feature-based methods using static images.
However, the recent methodologies have shifted focus towards robust implementation of facial/expression recognition from large image databases that vary with space (gathered from the internet) and time (video recordings).
The evolution trends in databases and methodologies for facial and expression recognition can be useful for assessing the next-generation topics that may have applications in security systems or personal identification systems that involve "Quantitative face" assessments.
In this paper, we describe a synthesis algorithm for safety specifications described as circuits.
Our algorithm is based on fixpoint computations, abstraction and refinement, it uses binary decision diagrams as symbolic data structure.
We evaluate our tool on the benchmarks provided by the organizers of the synthesis competition organized within the SYNT'14 workshop.
Human mobility is known to be distributed across several orders of magnitude of physical distances , which makes it generally difficult to endogenously find or define typical and meaningful scales.
Relevant analyses, from movements to geographical partitions, seem to be relative to some ad-hoc scale, or no scale at all.
Relying on geotagged data collected from photo-sharing social media, we apply community detection to movement networks constrained by increasing percentiles of the distance distribution.
Using a simple parameter-free discontinuity detection algorithm, we discover clear phase transitions in the community partition space.
The detection of these phases constitutes the first objective method of characterising endogenous, natural scales of human movement.
Our study covers nine regions, ranging from cities to countries of various sizes and a transnational area.
For all regions, the number of natural scales is remarkably low (2 or 3).
Further, our results hint at scale-related behaviours rather than scale-related users.
The partitions of the natural scales allow us to draw discrete multi-scale geographical boundaries, potentially capable of providing key insights in fields such as epidemiology or cultural contagion where the introduction of spatial boundaries is pivotal.
Data similarity (or distance) computation is a fundamental research topic which underpins many high-level applications based on similarity measures in machine learning and data mining.
However, in large-scale real-world scenarios, the exact similarity computation has become daunting due to "3V" nature (volume, velocity and variety) of big data.
In such cases, the hashing techniques have been verified to efficiently conduct similarity estimation in terms of both theory and practice.
Currently, MinHash is a popular technique for efficiently estimating the Jaccard similarity of binary sets and furthermore, weighted MinHash is generalized to estimate the generalized Jaccard similarity of weighted sets.
This review focuses on categorizing and discussing the existing works of weighted MinHash algorithms.
In this review, we mainly categorize the Weighted MinHash algorithms into quantization-based approaches, "active index"-based ones and others, and show the evolution and inherent connection of the weighted MinHash algorithms, from the integer weighted MinHash algorithms to real-valued weighted MinHash ones (particularly the Consistent Weighted Sampling scheme).
Also, we have developed a python toolbox for the algorithms, and released it in our github.
Based on the toolbox, we experimentally conduct a comprehensive comparative study of the standard MinHash algorithm and the weighted MinHash ones.
Stream processing has reached the mainstream in the last years, as a new generation of open source distributed stream processing systems, designed for scaling horizontally on commodity hardware, has brought the capability for processing high volume and high velocity data streams to companies of all sizes.
In this work we propose a combination of temporal logic and property-based testing (PBT) for dealing with the challenges of testing programs that employ this programming model.
We formalize our approach in a discrete time temporal logic for finite words, with some additions to improve the expressiveness of properties, which includes timeouts for temporal operators and a binding operator for letters.
In particular we focus on testing Spark Streaming programs written with the Spark API for the functional language Scala, using the PBT library ScalaCheck.
For that we add temporal logic operators to a set of new ScalaCheck generators and properties, as part of our testing library sscheck.
Under consideration in Theory and Practice of Logic Programming (TPLP).
We present a fresh and broad yet simple approach towards information retrieval in general and diagnostics in particular by applying the theory of complex networks on multidimensional, dynamic images.
We demonstrate a successful use of our method with the time series generated from high content thermal imaging videos of patients suffering from the aqueous deficient dry eye (ADDE) disease.
Remarkably, network analyses of thermal imaging time series of contact lens users and patients upon whom Laser-Assisted in situ Keratomileusis (Lasik) surgery has been conducted, exhibit pronounced similarity with results obtained from ADDE patients.
We also propose a general framework for the transformation of multidimensional images to networks for futuristic biometry.
Our approach is general and scalable to other fluctuation-based devices where network parameters derived from fluctuations, act as effective discriminators and diagnostic markers.
A significant amount of research literature is dedicated to interference mitigation in Wireless Mesh Networks (WMNs), with a special emphasis on designing channel allocation (CA) schemes which alleviate the impact of interference on WMN performance.
But having countless CA schemes at one's disposal makes the task of choosing a suitable CA for a given WMN extremely tedious and time consuming.
In this work, we propose a new interference estimation and CA performance prediction algorithm called CALM, which is inspired by social theory.
We borrow the sociological idea of a "sui generis" social reality, and apply it to WMNs with significant success.
To achieve this, we devise a novel Sociological Idea Borrowing Mechanism that facilitates easy operationalization of sociological concepts in other domains.
Further, we formulate a heuristic Mixed Integer Programming (MIP) model called NETCAP which makes use of link quality estimates generated by CALM to offer a reliable framework for network capacity prediction.
We demonstrate the efficacy of CALM by evaluating its theoretical estimates against experimental data obtained through exhaustive simulations on ns-3 802.11g environment, for a comprehensive CA test-set of forty CA schemes.
We compare CALM with three existing interference estimation metrics, and demonstrate that it is consistently more reliable.
CALM boasts of accuracy of over 90% in performance testing, and in stress testing too it achieves an accuracy of 88%, while the accuracy of other metrics drops to under 75%.
It reduces errors in CA performance prediction by as much as 75% when compared to other metrics.
Finally, we validate the expected network capacity estimates generated by NETCAP, and show that they are quite accurate, deviating by as low as 6.4% on an average when compared to experimentally recorded results in performance testing.
Many of the creative and figurative elements that make language exciting are lost in translation in current natural language generation engines.
In this paper, we explore a method to harvest templates from positive and negative reviews in the restaurant domain, with the goal of vastly expanding the types of stylistic variation available to the natural language generator.
We learn hyperbolic adjective patterns that are representative of the strongly-valenced expressive language commonly used in either positive or negative reviews.
We then identify and delexicalize entities, and use heuristics to extract generation templates from review sentences.
We evaluate the learned templates against more traditional review templates, using subjective measures of "convincingness", "interestingness", and "naturalness".
Our results show that the learned templates score highly on these measures.
Finally, we analyze the linguistic categories that characterize the learned positive and negative templates.
We plan to use the learned templates to improve the conversational style of dialogue systems in the restaurant domain.
Blockchain has the potential to revolutionize the way we store, use, and process data.
Information on most blockchains can be viewed by every node hosting the blockchain, which means that most blockchains cannot handle private data.
Decentralized databases exist that guarantee privacy by encrypting user data with the user's private key, but this prevents easy data sharing.
However, in many real world applications, from student data to medical records, it is desirable that user data is anonymously searchable.
In this paper we present a novel system that gives users ownership over their data while at the same time enabling them to make their data searchable within previously agreed upon limits.
Our system implements a strong notion of ownership using a self-sovereign identity system and a weak notion of ownership using multiple centralized databases together with a blockchain and a tumbling process.
We discuss applications of our methods to university's student records and medical data.
Deep neural networks have been shown to achieve state-of-the-art performance in several machine learning tasks.
Stochastic Gradient Descent (SGD) is the preferred optimization algorithm for training these networks and asynchronous SGD (ASGD) has been widely adopted for accelerating the training of large-scale deep networks in a distributed computing environment.
However, in practice it is quite challenging to tune the training hyperparameters (such as learning rate) when using ASGD so as achieve convergence and linear speedup, since the stability of the optimization algorithm is strongly influenced by the asynchronous nature of parameter updates.
In this paper, we propose a variant of the ASGD algorithm in which the learning rate is modulated according to the gradient staleness and provide theoretical guarantees for convergence of this algorithm.
Experimental verification is performed on commonly-used image classification benchmarks: CIFAR10 and Imagenet to demonstrate the superior effectiveness of the proposed approach, compared to SSGD (Synchronous SGD) and the conventional ASGD algorithm.
Conventional image motion based structure from motion methods first compute optical flow, then solve for the 3D motion parameters based on the epipolar constraint, and finally recover the 3D geometry of the scene.
However, errors in optical flow due to regularization can lead to large errors in 3D motion and structure.
This paper investigates whether performance and consistency can be improved by avoiding optical flow estimation in the early stages of the structure from motion pipeline, and it proposes a new direct method based on image gradients (normal flow) only.
The main idea lies in a reformulation of the positive-depth constraint, which allows the use of well-known minimization techniques to solve for 3D motion.
The 3D motion estimate is then refined and structure estimated adding a regularization based on depth.
Experimental comparisons on standard synthetic datasets and the real-world driving benchmark dataset KITTI using three different optic flow algorithms show that the method achieves better accuracy in all but one case.
Furthermore, it outperforms existing normal flow based 3D motion estimation techniques.
Finally, the recovered 3D geometry is shown to be also very accurate.
Bilinear models provide rich representations compared with linear models.
They have been applied in various visual tasks, such as object recognition, segmentation, and visual question-answering, to get state-of-the-art performances taking advantage of the expanded representations.
However, bilinear representations tend to be high-dimensional, limiting the applicability to computationally complex tasks.
We propose low-rank bilinear pooling using Hadamard product for an efficient attention mechanism of multimodal learning.
We show that our model outperforms compact bilinear pooling in visual question-answering tasks with the state-of-the-art results on the VQA dataset, having a better parsimonious property.
Among existing privacy-preserving approaches, Differential Privacy (DP) is a powerful tool that can provide privacy-preserving noisy query answers over statistical databases and has been widely adopted in many practical fields.
In particular, as a privacy machine of DP, Randomized Aggregable Privacy-Preserving Ordinal Response (RAPPOR) enables strong privacy, efficient, and high-utility guarantees for each client string in data crowdsourcing.
However, as for Internet of Things(IoT), such as smart gird, data are often processed in batches.
Therefore, developing a new random response algorithm that can support batch-processing tend to make it more efficient and suitable for IoT applications than existing random response algorithms.
In this paper, we propose a new randomized response algorithm that can achieve differential-privacy and utility guar-antees for consumer's behaviors, and process a batch of data at each time.
Firstly, by applying sparse coding in this algorithm, a behavior signature dictionary is created from the aggregated energy consumption data in fog.
Then, we add noise into the behavior signature dictionary by classical randomized response techniques and achieve the differential privacy after data re-aggregation.
Through the security analysis with the principle of differential privacy and experimental results verification, we find that our Algorithm can preserve consumer's privacy with-out comprising utility.
In motion analysis and understanding it is important to be able to fit a suitable model or structure to the temporal series of observed data, in order to describe motion patterns in a compact way, and to discriminate between them.
In an unsupervised context, i.e., no prior model of the moving object(s) is available, such a structure has to be learned from the data in a bottom-up fashion.
In recent times, volumetric approaches in which the motion is captured from a number of cameras and a voxel-set representation of the body is built from the camera views, have gained ground due to attractive features such as inherent view-invariance and robustness to occlusions.
Automatic, unsupervised segmentation of moving bodies along entire sequences, in a temporally-coherent and robust way, has the potential to provide a means of constructing a bottom-up model of the moving body, and track motion cues that may be later exploited for motion classification.
Spectral methods such as locally linear embedding (LLE) can be useful in this context, as they preserve "protrusions", i.e., high-curvature regions of the 3D volume, of articulated shapes, while improving their separation in a lower dimensional space, making them in this way easier to cluster.
In this paper we therefore propose a spectral approach to unsupervised and temporally-coherent body-protrusion segmentation along time sequences.
Volumetric shapes are clustered in an embedding space, clusters are propagated in time to ensure coherence, and merged or split to accommodate changes in the body's topology.
Experiments on both synthetic and real sequences of dense voxel-set data are shown.
This supports the ability of the proposed method to cluster body-parts consistently over time in a totally unsupervised fashion, its robustness to sampling density and shape quality, and its potential for bottom-up model construction
In this paper, we discuss the formalized approach for generating and estimating symbols (and alphabets), which can be communicated by the wide range of non-verbal means based on specific user requirements (medium, priorities, type of information that needs to be conveyed).
The short characterization of basic terms and parameters of such symbols (and alphabets) with approaches to generate them are given.
Then the framework, experimental setup, and some machine learning methods to estimate usefulness and effectiveness of the nonverbal alphabets and systems are presented.
The previous results demonstrate that usage of multimodal data sources (like wearable accelerometer, heart monitor, muscle movements sensors, braincomputer interface) along with machine learning approaches can provide the deeper understanding of the usefulness and effectiveness of such alphabets and systems for nonverbal and situated communication.
The symbols (and alphabets) generated and estimated by such methods may be useful in various applications: from synthetic languages and constructed scripts to multimodal nonverbal and situated interaction between people and artificial intelligence systems through Human-Computer Interfaces, such as mouse gestures, touchpads, body gestures, eyetracking cameras, wearables, and brain-computing interfaces, especially in applications for elderly care and people with disabilities.
Optimization is becoming a crucial element in industrial applications involving sustainable alternative energy systems.
During the design of such systems, the engineer/decision maker would often encounter noise factors (e.g. solar insolation and ambient temperature fluctuations) when their system interacts with the environment.
In this chapter, the sizing and design optimization of the solar powered irrigation system was considered.
This problem is multivariate, noisy, nonlinear and multiobjective.
This design problem was tackled by first using the Fuzzy Type II approach to model the noise factors.
Consequently, the Bacterial Foraging Algorithm (BFA) (in the context of a weighted sum framework) was employed to solve this multiobjective fuzzy design problem.
This method was then used to construct the approximate Pareto frontier as well as to identify the best solution option in a fuzzy setting.
Comprehensive analyses and discussions were performed on the generated numerical results with respect to the implemented solution methods.
This methodology paper addresses high-performance high-productivity programming on spatial architectures.
Spatial architectures are efficient for executing dataflow algorithms, yet for high-performance programming, the productivity is low and verification is painful.
We show that coding and verification are the biggest obstacle to the wide adoption of spatial architectures.
We propose a new programming methodology, T2S (Temporal to Spatial), to remove this obstacle.
A programmer specifies a temporal definition and a spatial mapping.
The temporal definition defines the functionality to compute, while the spatial mapping defines how to decompose the functionality and map the decomposed pieces onto a spatial architecture.
The specification precisely controls a compiler to actually implement the loop and data transformations specified in the mapping.
The specification is loop-nest- and matrix-oriented, and thus lends itself to the compiler for automatic, static verification.
Many generic, strategic loop and data optimizations can be systematically expressed.
Consequently, high performance is expected with substantially higher productivity: compared with high-performance programming in today's high-level synthesis (HLS) languages or hardware description languages (HDLs), the engineering effort on coding and verification is expected to be reduced from months to hours, a reduction of 2 or 3 orders of magnitude.
Proof-of-Stake systems randomly choose, on each round, one of the participants as a consensus leader that extends the chain with the next block such that the selection probability is proportional to the owned stake.
However, distributed random number generation is notoriously difficult.
Systems that derive randomness from the previous blocks are completely insecure; solutions that provide secure random selection are inefficient due to their high communication complexity; and approaches that balance security and performance exhibit selection bias.
When block creation is rewarded with new stake, even a minor bias can have a severe cumulative effect.
In this paper, we propose Robust Round Robin, a new consensus scheme that addresses this selection problem.
We create reliable long-term identities by bootstrapping from an existing infrastructure, such as Intel's SGX processors, or by mining them starting from an initial fair distribution.
For leader selection we use a deterministic approach.
On each round, we select a set of the previously created identities as consensus leader candidates in round robin manner.
Because simple round-robin alone is vulnerable to attacks and offers poor liveness, we complement such deterministic selection policy with a lightweight endorsement mechanism that is an interactive protocol between the leader candidates and a small subset of other system participants.
Our solution has low good efficiency as it requires no expensive distributed randomness generation and it provides block creation fairness which is crucial in deployments that reward it with new stake.
Feature selection is a dimensionality reduction technique that selects a subset of representative features from high dimensional data by eliminating irrelevant and redundant features.
Recently, feature selection combined with sparse learning has attracted significant attention due to its outstanding performance compared with traditional feature selection methods that ignores correlation between features.
These works first map data onto a low-dimensional subspace and then select features by posing a sparsity constraint on the transformation matrix.
However, they are restricted by design to linear data transformation, a potential drawback given that the underlying correlation structures of data are often non-linear.
To leverage a more sophisticated embedding, we propose an autoencoder-based unsupervised feature selection approach that leverages a single-layer autoencoder for a joint framework of feature selection and manifold learning.
More specifically, we enforce column sparsity on the weight matrix connecting the input layer and the hidden layer, as in previous work.
Additionally, we include spectral graph analysis on the projected data into the learning process to achieve local data geometry preservation from the original data space to the low-dimensional feature space.
Extensive experiments are conducted on image, audio, text, and biological data.
The promising experimental results validate the superiority of the proposed method.
Although a number of solutions exist for the problems of coverage, search and target localization---commonly addressed separately---whether there exists a unified strategy that addresses these objectives in a coherent manner without being application-specific remains a largely open research question.
In this paper, we develop a receding-horizon ergodic control approach, based on hybrid systems theory, that has the potential to fill this gap.
The nonlinear model predictive control algorithm plans real-time motions that optimally improve ergodicity with respect to a distribution defined by the expected information density across the sensing domain.
We establish a theoretical framework for global stability guarantees with respect to a distribution.
Moreover, the approach is distributable across multiple agents, so that each agent can independently compute its own control while sharing statistics of its coverage across a communication network.
We demonstrate the method in both simulation and in experiment in the context of target localization, illustrating that the algorithm is independent of the number of targets being tracked and can be run in real-time on computationally limited hardware platforms.
For source sequences of length L symbols we proposed to use a more realistic value to the usual benchmark of number of code letters by source letters.
Our idea is based on a quantifier of information fluctuation of a source, F(U), which corresponds to the second central moment of the random variable that measures the information content of a source symbol.
An alternative interpretation of typical sequences is additionally provided through this approach.
Lossless Feedback Delay Networks (FDNs) are commonly used as a design prototype for artificial reverberation algorithms.
The lossless property is dependent on the feedback matrix, which connects the output of a set of delays to their inputs, and the lengths of the delays.
Both, unitary and triangular feedback matrices are known to constitute lossless FDNs, however, the most general class of lossless feedback matrices has not been identified.
In this contribution, it is shown that the FDN is lossless for any set of delays, if all irreducible components of the feedback matrix are diagonally similar to a unitary matrix.
The necessity of the generalized class of feedback matrices is demonstrated by examples of FDN designs proposed in literature.
This paper proposes a novel method to optimize bandwidth usage for object detection in critical communication scenarios.
We develop two operating models of active information seeking.
The first model identifies promising regions in low resolution imagery and progressively requests higher resolution regions on which to perform recognition of higher semantic quality.
The second model identifies promising regions in low resolution imagery while simultaneously predicting the approximate location of the object of higher semantic quality.
From this general framework, we develop a car recognition system via identification of its license plate and evaluate the performance of both models on a car dataset that we introduce.
Results are compared with traditional JPEG compression and demonstrate that our system saves up to one order of magnitude of bandwidth while sacrificing little in terms of recognition performance.
We study the commutative positive varieties of languages closed under various operations: shuffle, renaming and product over one-letter alphabets.
Imputing incomplete medical tests and predicting patient outcomes are crucial for guiding the decision making for therapy, such as after an Achilles Tendon Rupture (ATR).
We formulate the problem of data imputation and prediction for ATR relevant medical measurements into a recommender system framework.
By applying MatchBox, which is a collaborative filtering approach, on a real dataset collected from 374 ATR patients, we aim at offering personalized medical data imputation and prediction.
In this work, we show the feasibility of this approach and discuss potential research directions by conducting initial qualitative evaluations.
Spectral clustering is one of the most popular clustering approaches with the capability to handle some challenging clustering problems.
Most spectral clustering methods provide a nonlinear map from the data manifold to a subspace.
Only a little work focuses on the explicit linear map which can be viewed as the unsupervised distance metric learning.
In practice, the selection of the affinity matrix exhibits a tremendous impact on the unsupervised learning.
While much success of affinity learning has been achieved in recent years, some issues such as noise reduction remain to be addressed.
In this paper, we propose a novel method, dubbed Adaptive Affinity Matrix (AdaAM), to learn an adaptive affinity matrix and derive a distance metric from the affinity.
We assume the affinity matrix to be positive semidefinite with ability to quantify the pairwise dissimilarity.
Our method is based on posing the optimization of objective function as a spectral decomposition problem.
We yield the affinity from both the original data distribution and the widely-used heat kernel.
The provided matrix can be regarded as the optimal representation of pairwise relationship on the manifold.
Extensive experiments on a number of real-world data sets show the effectiveness and efficiency of AdaAM.
Multimedia streaming services over spoken dialog systems have become ubiquitous.
User-entity affinity modeling is critical for the system to understand and disambiguate user intents and personalize user experiences.
However, fully voice-based interaction demands quantification of novel behavioral cues to determine user affinities.
In this work, we propose using play duration cues to learn a matrix factorization based collaborative filtering model.
We first binarize play durations to obtain implicit positive and negative affinity labels.
The Bayesian Personalized Ranking objective and learning algorithm are employed in our low-rank matrix factorization approach.
To cope with uncertainties in the implicit affinity labels, we propose to apply a weighting function that emphasizes the importance of high confidence samples.
Based on a large-scale database of Alexa music service records, we evaluate the affinity models by computing Spearman correlation between play durations and predicted affinities.
Comparing different data utilizations and weighting functions, we find that employing both positive and negative affinity samples with a convex weighting function yields the best performance.
Further analysis demonstrates the model's effectiveness on individual entity level and provides insights on the temporal dynamics of observed affinities.
Reinforcement learning (RL) is an area of research that has blossomed tremendously in recent years and has shown remarkable potential for artificial intelligence based opponents in computer games.
This success is primarily due to the vast capabilities of convolutional neural networks, that can extract useful features from noisy and complex data.
Games are excellent tools to test and push the boundaries of novel RL algorithms because they give valuable insight into how well an algorithm can perform in isolated environments without the real-life consequences.
Real-time strategy games (RTS) is a genre that has tremendous complexity and challenges the player in short and long-term planning.
There is much research that focuses on applied RL in RTS games, and novel advances are therefore anticipated in the not too distant future.
However, there are to date few environments for testing RTS AIs.
Environments in the literature are often either overly simplistic, such as microRTS, or complex and without the possibility for accelerated learning on consumer hardware like StarCraft II.
This paper introduces the Deep RTS game environment for testing cutting-edge artificial intelligence algorithms for RTS games.
Deep RTS is a high-performance RTS game made specifically for artificial intelligence research.
It supports accelerated learning, meaning that it can learn at a magnitude of 50 000 times faster compared to existing RTS games.
Deep RTS has a flexible configuration, enabling research in several different RTS scenarios, including partially observable state-spaces and map complexity.
We show that Deep RTS lives up to our promises by comparing its performance with microRTS, ELF, and StarCraft II on high-end consumer hardware.
Using Deep RTS, we show that a Deep Q-Network agent beats random-play agents over 70% of the time.
Deep RTS is publicly available at https://github.com/cair/DeepRTS.
A brain computer interface (BCI) is a system which provides direct communication between the mind of a person and the outside world by using only brain activity (EEG).
The event-related potential (ERP)-based BCI problem consists of a binary pattern recognition.
Linear discriminant analysis (LDA) is widely used to solve this type of classification problems, but it fails when the number of features is large relative to the number of observations.
In this work we propose a penalized version of the sparse discriminant analysis (SDA), called Kullback-Leibler penalized sparse discriminant analysis (KLSDA).
This method inherits both the discriminative feature selection and classification properties of SDA and it also improves SDA performance through the addition of Kullback-Leibler class discrepancy information.
The KLSDA method is design to automatically select the optimal regularization parameters.
Numerical experiments with two real ERP-EEG datasets show that this new method outperforms standard SDA.
We describe a novel family of models of multi- layer feedforward neural networks in which the activation functions are encoded via penalties in the training problem.
Our approach is based on representing a non-decreasing activation function as the argmin of an appropriate convex optimiza- tion problem.
The new framework allows for algo- rithms such as block-coordinate descent methods to be applied, in which each step is composed of a simple (no hidden layer) supervised learning problem that is parallelizable across data points and/or layers.
Experiments indicate that the pro- posed models provide excellent initial guesses for weights for standard neural networks.
In addi- tion, the model provides avenues for interesting extensions, such as robustness against noisy in- puts and optimizing over parameters in activation functions.
Recently, researchers started to pay attention to the detection of temporal shifts in the meaning of words.
However, most (if not all) of these approaches restricted their efforts to uncovering change over time, thus neglecting other valuable dimensions such as social or political variability.
We propose an approach for detecting semantic shifts between different viewpoints--broadly defined as a set of texts that share a specific metadata feature, which can be a time-period, but also a social entity such as a political party.
For each viewpoint, we learn a semantic space in which each word is represented as a low dimensional neural embedded vector.
The challenge is to compare the meaning of a word in one space to its meaning in another space and measure the size of the semantic shifts.
We compare the effectiveness of a measure based on optimal transformations between the two spaces with a measure based on the similarity of the neighbors of the word in the respective spaces.
Our experiments demonstrate that the combination of these two performs best.
We show that the semantic shifts not only occur over time, but also along different viewpoints in a short period of time.
For evaluation, we demonstrate how this approach captures meaningful semantic shifts and can help improve other tasks such as the contrastive viewpoint summarization and ideology detection (measured as classification accuracy) in political texts.
We also show that the two laws of semantic change which were empirically shown to hold for temporal shifts also hold for shifts across viewpoints.
These laws state that frequent words are less likely to shift meaning while words with many senses are more likely to do so.
Author name ambiguity in a digital library may affect the findings of research that mines authorship data of the library.
This study evaluates author name disambiguation in DBLP, a widely used but insufficiently evaluated digital library for its disambiguation performance.
In doing so, this study takes a triangulation approach that author name disambiguation for a digital library can be better evaluated when its performance is assessed on multiple labeled datasets with comparison to baselines.
Tested on three types of labeled data containing 5,000 ~ 700K disambiguated names and 6M pairs of disambiguated names, DBLP is shown to assign author names quite accurately to distinct authors, resulting in pairwise precision, recall, and F1 measures around 0.90 or above overall.
DBLP's author name disambiguation performs well even on large ambiguous name blocks but deficiently on distinguishing authors with the same names.
When compared to other disambiguation algorithms, DBLP's disambiguation performance is quite competitive, possibly due to its hybrid disambiguation approach combining algorithmic disambiguation and manual error correction.
A discussion follows on strengths and weaknesses of labeled datasets used in this study for future efforts to evaluate author name disambiguation on a digital library scale.
Stories can have tremendous power -- not only useful for entertainment, they can activate our interests and mobilize our actions.
The degree to which a story resonates with its audience may be in part reflected in the emotional journey it takes the audience upon.
In this paper, we use machine learning methods to construct emotional arcs in movies, calculate families of arcs, and demonstrate the ability for certain arcs to predict audience engagement.
The system is applied to Hollywood films and high quality shorts found on the web.
We begin by using deep convolutional neural networks for audio and visual sentiment analysis.
These models are trained on both new and existing large-scale datasets, after which they can be used to compute separate audio and visual emotional arcs.
We then crowdsource annotations for 30-second video clips extracted from highs and lows in the arcs in order to assess the micro-level precision of the system, with precision measured in terms of agreement in polarity between the system's predictions and annotators' ratings.
These annotations are also used to combine the audio and visual predictions.
Next, we look at macro-level characterizations of movies by investigating whether there exist `universal shapes' of emotional arcs.
In particular, we develop a clustering approach to discover distinct classes of emotional arcs.
Finally, we show on a sample corpus of short web videos that certain emotional arcs are statistically significant predictors of the number of comments a video receives.
These results suggest that the emotional arcs learned by our approach successfully represent macroscopic aspects of a video story that drive audience engagement.
Such machine understanding could be used to predict audience reactions to video stories, ultimately improving our ability as storytellers to communicate with each other.
We propose a new differentially-private decision forest algorithm that minimizes both the number of queries required, and the sensitivity of those queries.
To do so, we build an ensemble of random decision trees that avoids querying the private data except to find the majority class label in the leaf nodes.
Rather than using a count query to return the class counts like the current state-of-the-art, we use the Exponential Mechanism to only output the class label itself.
This drastically reduces the sensitivity of the query -- often by several orders of magnitude -- which in turn reduces the amount of noise that must be added to preserve privacy.
Our improved sensitivity is achieved by using "smooth sensitivity", which takes into account the specific data used in the query rather than assuming the worst-case scenario.
We also extend work done on the optimal depth of random decision trees to handle continuous features, not just discrete features.
This, along with several other improvements, allows us to create a differentially private decision forest with substantially higher predictive power than the current state-of-the-art.
Magnetic induction (MI) based communication and power transfer systems have gained an increased attention in the recent years.
Typical applications for these systems lie in the area of wireless charging, near-field communication, and wireless sensor networks.
For an optimal system performance, the power efficiency needs to be maximized.
Typically, this optimization refers to the impedance matching and tracking of the split-frequencies.
However, an important role of magnitude and phase of the input signal has been mostly overlooked.
Especially for the wireless power transfer systems with multiple transmitter coils, the optimization of the transmit signals can dramatically improve the power efficiency.
In this work, we propose an iterative algorithm for the optimization of the transmit signals for a transmitter with three orthogonal coils and multiple single coil receivers.
The proposed scheme significantly outperforms the traditional baseline algorithms in terms of power efficiency.
Network function virtualization (NFV) based service function chaining (SFC) allows the provisioning of various security and traffic engineering applications in a cloud network.
Inefficient deployment of network functions can lead to security violations and performance overhead.
In an OpenFlow enabled cloud, the key problem with current mechanisms is that several packet field match and flow rule action sets associated with the network functions are non-overlapping and can be parallelized for performance enhancement.
We introduce Network Function Parallelism (NFP) SFC-NFP for OpenFlow network.
Our solution utilizes network function parallelism over the OpenFlow rules to improve SFC performance in the cloud network.
We have utilized the DPDK platform with an OpenFlow switch (OVS) for experimental analysis.
Our solution achieves a 1.40-1.90x reduction in latency for SFC in an OpenStack cloud network managed by the SDN framework.
We present a graph-based Tree Adjoining Grammar (TAG) parser that uses BiLSTMs, highway connections, and character-level CNNs.
Our best end-to-end parser, which jointly performs supertagging, POS tagging, and parsing, outperforms the previously reported best results by more than 2.2 LAS and UAS points.
The graph-based parsing architecture allows for global inference and rich feature representations for TAG parsing, alleviating the fundamental trade-off between transition-based and graph-based parsing systems.
We also demonstrate that the proposed parser achieves state-of-the-art performance in the downstream tasks of Parsing Evaluation using Textual Entailments (PETE) and Unbounded Dependency Recovery.
This provides further support for the claim that TAG is a viable formalism for problems that require rich structural analysis of sentences.
In this paper we propose a class of propagation models for multiple competing products over a social network.
We consider two propagation mechanisms: social conversion and self conversion, corresponding, respectively, to endogenous and exogenous factors.
A novel concept, the product-conversion graph, is proposed to characterize the interplay among competing products.
According to the chronological order of social and self conversions, we develop two Markov-chain models and, based on the independence approximation, we approximate them with two respective difference equations systems.
Theoretical analysis on these two approximation models reveals the dependency of the systems' asymptotic behavior on the structures of both the product-conversion graph and the social network, as well as the initial condition.
In addition to the theoretical work, accuracy of the independence approximation and the asymptotic behavior of the Markov-chain model are investigated via numerical analysis, for the case where social conversion occurs before self conversion.
Finally, we propose a class of multi-player and multi-stage competitive propagation games and discuss the seeding-quality trade-off, as well as the allocation of seeding resources among the individuals.
We investigate the unique Nash equilibrium at each stage and analyze the system's behavior when every player is adopting the policy at the Nash equilibrium.
We present novel graph kernels for graphs with node and edge labels that have ordered neighborhoods, i.e.
when neighbor nodes follow an order.
Graphs with ordered neighborhoods are a natural data representation for evolving graphs where edges are created over time, which induces an order.
Combining convolutional subgraph kernels and string kernels, we design new scalable algorithms for generation of explicit graph feature maps using sketching techniques.
We obtain precise bounds for the approximation accuracy and computational complexity of the proposed approaches and demonstrate their applicability on real datasets.
In particular, our experiments demonstrate that neighborhood ordering results in more informative features.
For the special case of general graphs, i.e.graphs without ordered neighborhoods, the new graph kernels yield efficient and simple algorithms for the comparison of label distributions between graphs.
We investigate the secret key generation in the multiterminal source model, where the users discuss under limited rate.
For the minimally connected hypergraphical sources, we give an explicit formula of the maximum achievable secret key rate, called the secrecy capacity, under any given total discussion rate.
Besides, we also partially characterize the region of achievable secret key rate and discussion rate tuple.
When specializes to the hypertree sources, our results give rise to a complete characterization of the region.
In order to obtain reliable accuracy estimates for automatic MOOC dropout predictors, it is important to train and test them in a manner consistent with how they will be used in practice.
Yet most prior research on MOOC dropout prediction has measured test accuracy on the same course used for training the classifier, which can lead to overly optimistic accuracy estimates.
In order to understand better how accuracy is affected by the training+testing regime, we compared the accuracy of a standard dropout prediction architecture (clickstream features + logistic regression) across 4 different training paradigms.
Results suggest that (1) training and testing on the same course ("post-hoc") can overestimate accuracy by several percentage points; (2) dropout classifiers trained on proxy labels based on students' persistence are surprisingly competitive with post-hoc training (87.33% versus 90.20% AUC averaged over 8 weeks of 40 HarvardX MOOCs); and (3) classifier performance does not vary significantly with the academic discipline.
Finally, we also research new dropout prediction architectures based on deep, fully-connected, feed-forward neural networks and find that (4) networks with as many as 5 hidden layers can statistically significantly increase test accuracy over that of logistic regression.
In this work we give a concise definition of information loss from a system-theoretic point of view.
Based on this definition, we analyze the information loss in static input-output systems subject to a continuous-valued input.
For a certain class of multiple-input, multiple-output systems the information loss is quantified.
An interpretation of this loss is accompanied by upper bounds which are simple to evaluate.
Finally, a class of systems is identified for which the information loss is necessarily infinite.
Quantizers and limiters are shown to belong to this class.
In this paper, we consider the secure transmission design for a multiple-input single-output Femtocell overlaid with a Macrocell in co-channel deployment.
The Femtocell base station sends confidential messages to information receiving Femtocell users (FUs) and energy signals to energy receiving (ER) FUs while limiting the interference to Macrocell users (MUs).
The ER FUs have the potential to wiretap the confidential messages.
By taking fairness into account, we propose a sum logarithmic secrecy rate maximization beamforming design problem under the interference constraints for MUs and energy harvesting (EH) constraints for ER FUs.
The formulated design problem is nontrivial to solve due to the nonconvexity which lies in the objective and the constraints.
To tackle the design problem, a semidefinite relaxation and successive convex approximation based algorithm is proposed.
Simulation results demonstrate the effectiveness of the proposed beamforming design.
This is a purely pedagogical paper with no new results.
The goal of the paper is to give a fairly self-contained introduction to Judea Pearl's do-calculus, including proofs of his 3 rules.
This paper exemplifies the implementation of an efficient Information Retrieval (IR) System to compute the similarity between a dataset and a query using Fuzzy Logic.
TREC dataset has been used for the same purpose.
The dataset is parsed to generate keywords index which is used for the similarity comparison with the user query.
Each query is assigned a score value based on its fuzzy similarity with the index keywords.
The relevant documents are retrieved based on the score value.
The performance and accuracy of the proposed fuzzy similarity model is compared with Cosine similarity model using Precision-Recall curves.
The results prove the dominance of Fuzzy Similarity based IR system.
MANY TECHNIQUES for synthesizing digital hardware from C-like languages have been proposed, but none have emerged as successful as Verilog or VHDL for register-transfer-level design.
This paper looks at two of the fundamental challenges: concurrency and timing control.
Traditionally, formal languages are defined as sets of words.
More recently, the alternative coalgebraic or coinductive representation as infinite tries, i.e., prefix trees branching over the alphabet, has been used to obtain compact and elegant proofs of classic results in language theory.
In this article, we study this representation in the Isabelle proof assistant.
We define regular operations on infinite tries and prove the axioms of Kleene algebra for those operations.
Thereby, we exercise corecursion and coinduction and confirm the coinductive view being profitable in formalizations, as it improves over the set-of-words view with respect to proof automation.
In this paper, we investigate few memristor-based analog circuits namely the phase shift oscillator, integrator, and differentiator which have been explored numerously using the traditional lumped components.
We use LTspice-IV platform for simulation of the above-said circuits.
The investigation resorts to the nonlinear dopant drift model of memristor and the window function portrayed in the literature for nonlinearity realization.
The results of our investigations depict good agreement with the conventional lumped component based phase shift oscillator, integrator, and differentiator circuits.
The results are evident to showcase the potential of the memristor as a promising candidate for the next generation analog circuits.
Reading comprehension has been widely studied.
One of the most representative reading comprehension tasks is Stanford Question Answering Dataset (SQuAD), on which machine is already comparable with human.
On the other hand, accessing large collections of multimedia or spoken content is much more difficult and time-consuming than plain text content for humans.
It's therefore highly attractive to develop machines which can automatically understand spoken content.
In this paper, we propose a new listening comprehension task - Spoken SQuAD.
On the new task, we found that speech recognition errors have catastrophic impact on machine comprehension, and several approaches are proposed to mitigate the impact.
One of the most important assets of any company is being able to easily access information on itself and on its business.
In this line, it has been observed that this important information is often stored in one of the millions of spreadsheets created every year, due to simplicity in using and manipulating such an artifact.
Unfortunately, in many cases it is quite difficult to retrieve the intended information from a spreadsheet: information is often stored in a huge unstructured matrix, with no care for readability or comprehensiveness.
In an attempt to aid users in the task of extracting information from a spreadsheet, researchers have been working on models, languages and tools to query.
In this paper we present an empirical study evaluating such proposals assessing their usage to query spreadsheets.
We investigate the use of the Google Query Function, textual model-driven querying, and visual model-driven querying.
To compare these different querying approaches we present an empirical study whose results show that the end-users' productivity increases when using model-driven queries, specially using its visual representation.
Convolutional networks (ConvNets) have achieved great successes in various challenging vision tasks.
However, the performance of ConvNets would degrade when encountering the domain shift.
The domain adaptation is more significant while challenging in the field of biomedical image analysis, where cross-modality data have largely different distributions.
Given that annotating the medical data is especially expensive, the supervised transfer learning approaches are not quite optimal.
In this paper, we propose an unsupervised domain adaptation framework with adversarial learning for cross-modality biomedical image segmentations.
Specifically, our model is based on a dilated fully convolutional network for pixel-wise prediction.
Moreover, we build a plug-and-play domain adaptation module (DAM) to map the target input to features which are aligned with source domain feature space.
A domain critic module (DCM) is set up for discriminating the feature space of both domains.
We optimize the DAM and DCM via an adversarial loss without using any target domain label.
Our proposed method is validated by adapting a ConvNet trained with MRI images to unpaired CT data for cardiac structures segmentations, and achieved very promising results.
We describe CITlab's recognition system for the ANWRESH-2014 competition attached to the 14. International Conference on Frontiers in Handwriting Recognition, ICFHR 2014.
The task comprises word recognition from segmented historical documents.
The core components of our system are based on multi-dimensional recurrent neural networks (MDRNN) and connectionist temporal classification (CTC).
The software modules behind that as well as the basic utility technologies are essentially powered by PLANET's ARGUS framework for intelligent text recognition and image processing.
In remote sensing, each sensor can provide complementary or reinforcing information.
It is valuable to fuse outputs from multiple sensors to boost overall performance.
Previous supervised fusion methods often require accurate labels for each pixel in the training data.
However, in many remote sensing applications, pixel-level labels are difficult or infeasible to obtain.
In addition, outputs from multiple sensors may have different levels of resolution or modalities (such as rasterized hyperspectral imagery versus LiDAR 3D point clouds).
This paper presents a Multiple Instance Multi-Resolution Fusion (MIMRF) framework that can fuse multi-resolution and multi-modal sensor outputs while learning from ambiguously and imprecisely labeled training data.
Experiments were conducted on the MUUFL Gulfport hyperspectral and LiDAR data set and a remotely-sensed soybean and weed data set.
Results show improved, consistent performance on scene understanding and agricultural applications when compared to traditional fusion methods.
Rate-Splitting Multiple Access (RSMA) is a general and powerful multiple access framework for downlink multi-antenna systems, and contains Space-Division Multiple Access (SDMA) and Non-Orthogonal Multiple Access (NOMA) as special cases.
RSMA relies on linearly precoded rate-splitting with Successive Interference Cancellation (SIC) to decode part of the interference and treat the remaining part of the interference as noise.
Recently, RSMA has been shown to outperform both SDMA and NOMA rate-wise in a wide range of network loads (underloaded and overloaded regimes) and user deployments (with a diversity of channel directions, channel strengths and qualities of Channel State Information at the Transmitter).
Moreover, RSMA was shown to provide spectral efficiency and QoS enhancements over NOMA at a lower computational complexity for the transmit scheduler and the receivers.
In this paper, we build upon those results and investigate the energy efficiency of RSMA compared to SDMA and NOMA.
Considering a multiple-input single-output broadcast channel, we show that RSMA is more energy-efficient than SDMA and NOMA in a wide range of user deployments (with a diversity of channel directions and channel strengths).
We conclude that RSMA is more spectrally and energy-efficient than SDMA and NOMA.
We discuss Bayesian inference (BI) for the probabilistic identification of material parameters.
This contribution aims to shed light on the use of BI for the identification of elastoplastic material parameters.
For this purpose a single spring is considered, for which the stress-strain curves are artificially created.
Besides offering a didactic introduction to BI, this paper proposes an approach to incorporate statistical errors both in the measured stresses, and in the measured strains.
It is assumed that the uncertainty is only due to measurement errors and the material is homogeneous.
Furthermore, a number of possible misconceptions on BI are highlighted based on the purely elastic case.
In this work, a recently proposed Head-Related Transfer Function (HRTF)-based Robust Least-Squares Frequency-Invariant (RLSFI) beamformer design is analyzed with respect to its robustness against localization errors, which lead to a mismatch between the HRTFs corresponding to the actual target source position and the HRTFs which have been used for the beamformer design.
The impact of this mismatch on the performance of the HRTF-based RLSFI beamformer is evaluated, including a comparison to the free-field-based beamformer design, using signal-based measures and word error rates for an off-the-shelf speech recognizer.
Music history, referring to the records of users' listening or downloading history in online music services, is the primary source for music service providers to analyze users' preferences on music and thus to provide personalized recommendations to users.
In order to engage users into the service and to improve user experience, it would be beneficial to provide visual analyses of one user's music history as well as visualized recommendations to that user.
In this paper, we take a user-centric approach to the design of such visual analyses.
We start by investigating user needs on such visual analyses and recommendations, then propose several different visualization schemes, and perform a pilot study to collect user feedback on the designed schemes.
We further conduct user studies to verify the utility of the proposed schemes, and the results not only demonstrate the effectiveness of our proposed visualization, but also provide important insights to guide the visualization design in the future.
In this paper, we investigated a C-arm tomographic technique as a new three dimensional (3D) kidney imaging method for nephrolithiasis and kidney stone detection over view angle less than 180o.
Our C-arm tomographic technique provides a series of two dimensional (2D) images with a single scan over 40o view angle.
Experimental studies were performed with a kidney phantom that was formed from a pig kidney with two embedded kidney stones.
Different reconstruction methods were developed for C-arm tomographic technique to generate 3D kidney information including: point by point back projection (BP), filtered back projection (FBP), simultaneous algebraic reconstruction technique (SART) and maximum likelihood expectation maximization (MLEM).
Computer simulation study was also done with simulated 3D spherical object to evaluate the reconstruction results.
Preliminary results demonstrated the capability of our C-arm tomographic technique to generate 3D kidney information for kidney stone detection with low exposure of radiation.
The kidney stones are visible on reconstructed planes with identifiable shapes and sizes.
This paper considers a multi-source multi-relay network, in which relay nodes employ a coding scheme based on random linear network coding on source packets and generate coded packets.
If a destination node collects enough coded packets, it can recover the packets of all source nodes.
The links between source-to-relay nodes and relay-to-destination nodes are modeled as packet erasure channels.
Improved bounds on the probability of decoding failure are presented, which are markedly close to simulation results and notably better than previous bounds.
Examples demonstrate the tightness and usefulness of the new bounds over the old bounds.
A robust and informative local shape descriptor plays an important role in mesh registration.
In this regard, spectral descriptors that are based on the spectrum of the Laplace-Beltrami operator have gained a spotlight among the researchers for the last decade due to their desirable properties, such as isometry invariance.
Despite such, however, spectral descriptors often fail to give a correct similarity measure for non-isometric cases where the metric distortion between the models is large.
Hence, they are in general not suitable for the registration problems, except for the special cases when the models are near-isometry.
In this paper, we investigate a way to develop shape descriptors for non-isometric registration tasks by embedding the spectral shape descriptors into a different metric space where the Euclidean distance between the elements directly indicates the geometric dissimilarity.
We design and train a Siamese deep neural network to find such an embedding, where the embedded descriptors are promoted to rearrange based on the geometric similarity.
We found our approach can significantly enhance the performance of the conventional spectral descriptors for the non-isometric registration tasks, and outperforms recent state-of-the-art method reported in literature.
Convolutional networks have marked their place over the last few years as the best performing model for various visual tasks.
They are, however, most suited for supervised learning from large amounts of labeled data.
Previous attempts have been made to use unlabeled data to improve model performance by applying unsupervised techniques.
These attempts require different architectures and training methods.
In this work we present a novel approach for unsupervised training of Convolutional networks that is based on contrasting between spatial regions within images.
This criterion can be employed within conventional neural networks and trained using standard techniques such as SGD and back-propagation, thus complementing supervised methods.
Many challenges in natural language processing require generating text, including language translation, dialogue generation, and speech recognition.
For all of these problems, text generation becomes more difficult as the text becomes longer.
Current language models often struggle to keep track of coherence for long pieces of text.
Here, we attempt to have the model construct and use an outline of the text it generates to keep it focused.
We find that the usage of an outline improves perplexity.
We do not find that using the outline improves human evaluation over a simpler baseline, revealing a discrepancy in perplexity and human perception.
Similarly, hierarchical generation is not found to improve human evaluation scores.
Case-Based Reasoning (CBR) has been widely used to generate good software effort estimates.
The predictive performance of CBR is a dataset dependent and subject to extremely large space of configuration possibilities.
Regardless of the type of adaptation technique, deciding on the optimal number of similar cases to be used before applying CBR is a key challenge.
In this paper we propose a new technique based on Bisecting k-medoids clustering algorithm to better understanding the structure of a dataset and discovering the the optimal cases for each individual project by excluding irrelevant cases.
Results obtained showed that understanding of the data characteristic prior prediction stage can help in automatically finding the best number of cases for each test project.
Performance figures of the proposed estimation method are better than those of other regular K-based CBR methods.
This paper presents an efficient automatic color image segmentation method using a seeded region growing and merging method based on square elemental regions.
Our segmentation method consists of the three steps: generating seed regions, merging the regions, and applying a pixel-wise boundary determination algorithm to the resultant polygonal regions.
The major features of our method are as follows: the use of square elemental regions instead of pixels as the processing unit, a seed generation method based on enhanced gradient values, a seed region growing method exploiting local gradient values, a region merging method using a similarity measure including a homogeneity distance based on Tsallis entropy, and a termination condition of region merging using an estimated desired number of regions.
Using square regions as the processing unit substantially reduces the time complexity of the algorithm and makes the performance stable.
The experimental results show that our method exhibits stable performance for a variety of natural images, including heavily textured areas, and produces good segmentation results using the same parameter values.
The results of our method are fairly comparable to, and in some respects better than, those of existing algorithms.
Research into the stylistic properties of translations is an issue which has received some attention in computational stylistics.
Previous work by Rybicki (2006) on the distinguishing of character idiolects in the work of Polish author Henryk Sienkiewicz and two corresponding English translations using Burrow's Delta method concluded that idiolectal differences could be observed in the source texts and this variation was preserved to a large degree in both translations.
This study also found that the two translations were also highly distinguishable from one another.
Burrows (2002) examined English translations of Juvenal also using the Delta method, results of this work suggest that some translators are more adept at concealing their own style when translating the works of another author whereas other authors tend to imprint their own style to a greater extent on the work they translate.
Our work examines the writing of a single author, Norwegian playwright Henrik Ibsen, and these writings translated into both German and English from Norwegian, in an attempt to investigate the preservation of characterization, defined here as the distinctiveness of textual contributions of characters.
Feature selection (FS) is a process which attempts to select more informative features.
In some cases, too many redundant or irrelevant features may overpower main features for classification.
Feature selection can remedy this problem and therefore improve the prediction accuracy and reduce the computational overhead of classification algorithms.
The main aim of feature selection is to determine a minimal feature subset from a problem domain while retaining a suitably high accuracy in representing the original features.
In this paper, Principal Component Analysis (PCA), Rough PCA, Unsupervised Quick Reduct (USQR) algorithm and Empirical Distribution Ranking (EDR) approaches are applied to discover discriminative features that will be the most adequate ones for classification.
Efficiency of the approaches is evaluated using standard classification metrics.
Unsupervised learning for visual perception of 3D geometry is of great interest to autonomous systems.
Recent works on unsupervised learning have made considerable progress on geometry perception; however, they perform poorly on dynamic objects and scenarios with dark and noisy environments.
In contrast, supervised learning algorithms, which are robust, require large labeled geometric data-set.
This paper introduces SIGNet, a novel framework that provides robust geometry perception without requiring geometrically informative labels.
Specifically, SIGNet integrates semantic information to make unsupervised robust geometric predictions for dynamic objects in low lighting and noisy environments.
SIGNet is shown to improve upon the state of art unsupervised learning for geometry perception by 30% (in squared relative error for depth prediction).
In particular, SIGNet improves the dynamic object class performance by 39% in depth prediction and 29% in flow prediction.
We propose a novel activation function that implements piece-wise orthogonal non-linear mappings based on permutations.
It is straightforward to implement, and very computationally efficient, also it has little memory requirements.
We tested it on two toy problems for feedforward and recurrent networks, it shows similar performance to tanh and ReLU.
OPLU activation function ensures norm preservance of the backpropagated gradients, therefore it is potentially good for the training of deep, extra deep, and recurrent neural networks.
User Interfaces (UIs) intensively rely on event-driven programming: widgets send UI events, which capture users' interactions, to dedicated objects called controllers.
Controllers use several UI listeners that handle these events to produce UI commands.
First, we reveal the presence of design smells in the code that describes and controls UIs.
Second, we demonstrate that specific code analyses are necessary to analyze and refactor UI code, because of its coupling with the rest of the code.
We conducted an empirical study on four large Java Swing and SWT open-source software systems.
We study to what extent the number of UI commands that a UI listener can produce has an impact on the change- and fault-proneness of the UI listener code.
We develop a static code analysis for detecting UI commands in the code.
We identify a new type of design smell, called Blob Listener that characterizes UI listeners that can produce more than two UI commands.
We propose a systematic static code analysis procedure that searches for Blob Listeners that we implement in InspectorGuidget.
We conducted experiments on the four software systems for which we manually identified 53 instances of Blob Listener.
InspectorGuidget successfully detected 52 Blob Listeners out of 53.
The results exhibit a precision of 81.25% and a recall of 98.11%.
We then developed a semi-automatically and behavior-preserving refactoring process to remove Blob Listeners.
49.06% of the 53 Blob Listeners were automatically refactored.
Patches for JabRef, and FreeCol have been accepted and merged.
Discussions with developers of the four software systems assess the relevance of the Blob Listener.
This work shows that UI code also suffers from design smells that have to be identified and characterized.
We argue that studies have to be conducted to find other UI design smells and tools that analyze UI code must be developed.
Entity-oriented search deals with a wide variety of information needs, from displaying direct answers to interacting with services.
In this work, we aim to understand what are prominent entity-oriented search intents and how they can be fulfilled.
We develop a scheme of entity intent categories, and use them to annotate a sample of queries.
Specifically, we annotate unique query refiners on the level of entity types.
We observe that, on average, over half of those refiners seek to interact with a service, while over a quarter of the refiners search for information that may be looked up in a knowledge base.
Segmentation is a fundamental task for extracting semantically meaningful regions from an image.
The goal of segmentation algorithms is to accurately assign object labels to each image location.
However, image-noise, shortcomings of algorithms, and image ambiguities cause uncertainty in label assignment.
Estimating the uncertainty in label assignment is important in multiple application domains, such as segmenting tumors from medical images for radiation treatment planning.
One way to estimate these uncertainties is through the computation of posteriors of Bayesian models, which is computationally prohibitive for many practical applications.
On the other hand, most computationally efficient methods fail to estimate label uncertainty.
We therefore propose in this paper the Active Mean Fields (AMF) approach, a technique based on Bayesian modeling that uses a mean-field approximation to efficiently compute a segmentation and its corresponding uncertainty.
Based on a variational formulation, the resulting convex model combines any label-likelihood measure with a prior on the length of the segmentation boundary.
A specific implementation of that model is the Chan-Vese segmentation model (CV), in which the binary segmentation task is defined by a Gaussian likelihood and a prior regularizing the length of the segmentation boundary.
Furthermore, the Euler-Lagrange equations derived from the AMF model are equivalent to those of the popular Rudin-Osher-Fatemi (ROF) model for image denoising.
Solutions to the AMF model can thus be implemented by directly utilizing highly-efficient ROF solvers on log-likelihood ratio fields.
We qualitatively assess the approach on synthetic data as well as on real natural and medical images.
For a quantitative evaluation, we apply our approach to the icgbench dataset.
Weighted automata are non-deterministic automata where the transitions are equipped with weights.
They can model quantitative aspects of systems like costs or energy consumption.
The value of a run can be computed, for example, as the maximum, limit average, or discounted sum of transition weights.
In multi-weighted automata, transitions carry several weights and can model, for example, the ratio between rewards and costs, or the efficiency of use of a primary resource under some upper bound constraint on a secondary resource.
Here, we introduce a general model for multi-weighted automata as well as a multiweighted MSO logic.
In our main results, we show that this multi-weighted MSO logic and multi-weighted automata are expressively equivalent both for finite and infinite words.
The translation process is effective, leading to decidability results for our multi-weighted MSO logic.
Scene modeling is very crucial for robots that need to perceive, reason about and manipulate the objects in their environments.
In this paper, we adapt and extend Boltzmann Machines (BMs) for contextualized scene modeling.
Although there are many models on the subject, ours is the first to bring together objects, relations, and affordances in a highly-capable generative model.
For this end, we introduce a hybrid version of BMs where relations and affordances are introduced with shared, tri-way connections into the model.
Moreover, we contribute a dataset for relation estimation and modeling studies.
We evaluate our method in comparison with several baselines on object estimation, out-of-context object detection, relation estimation, and affordance estimation tasks.
Moreover, to illustrate the generative capability of the model, we show several example scenes that the model is able to generate.
Consider a transmission scheme with a single transmitter and multiple receivers over a faulty broadcast channel.
For each receiver, the transmitter has a unique infinite stream of packets, and its goal is to deliver them at the highest throughput possible.
While such multiple-unicast models are unsolved in general, several network coding based schemes were suggested.
In such schemes, the transmitter can either send an uncoded packet, or a coded packet which is a function of a few packets.
The packets sent can be received by the designated receiver (with some probability) or heard and stored by other receivers.
Two functional modes are considered; the first presumes that the storage time is unlimited, while in the second it is limited by a given Time to Expire (TTE) parameter.
We model the transmission process as an infinite-horizon Markov Decision Process (MDP).
Since the large state space renders exact solutions computationally impractical, we introduce policy restricted and induced MDPs with significantly reduced state space, and prove that with proper reward function they have equal optimal value function (hence equal optimal throughput).
We then derive a reinforcement learning algorithm, which learns the optimal policy for the induced MDP.
This optimal strategy of the induced MDP, once applied to the policy restricted one, significantly improves over uncoded schemes.
Next, we enhance the algorithm by means of analysis of the structural properties of the resulting reward functional.
We demonstrate that our method scales well in the number of users, and automatically adapts to the packet loss rates, unknown in advance.
In addition, the performance is compared to the recent bound by Wang, which assumes much stronger coding (e.g., intra-session and buffering of coded packets), yet is shown to be comparable.
Given a web-scale graph that grows over time, how should its edges be stored and processed on multiple machines for rapid and accurate estimation of the count of triangles?
The count of triangles (i.e., cliques of size three) has proven useful in many applications, including anomaly detection, community detection, and link recommendation.
For triangle counting in large and dynamic graphs, recent work has focused largely on streaming algorithms and distributed algorithms.
To achieve the advantages of both approaches, we propose DiSLR, a distributed streaming algorithm that estimates the counts of global triangles and local triangles associated with each node.
Making one pass over the input stream, DiSLR carefully processes and stores the edges across multiple machines so that the redundant use of computational and storage resources is minimized.
Compared to its best competitors, DiSLR is (a) Accurate: giving up to 39X smaller estimation error, (b) Fast: up to 10.4X faster, scaling linearly with the number of edges in the input stream, and (c) Theoretically sound: yielding unbiased estimates with variances decreasing faster as the number of machines is scaled up.
Convolutional Neural Networks (CNNs) can be shifted across 2D images or 3D videos to segment them.
They have a fixed input size and typically perceive only small local contexts of the pixels to be classified as foreground or background.
In contrast, Multi-Dimensional Recurrent NNs (MD-RNNs) can perceive the entire spatio-temporal context of each pixel in a few sweeps through all pixels, especially when the RNN is a Long Short-Term Memory (LSTM).
Despite these theoretical advantages, however, unlike CNNs, previous MD-LSTM variants were hard to parallelize on GPUs.
Here we re-arrange the traditional cuboid order of computations in MD-LSTM in pyramidal fashion.
The resulting PyraMiD-LSTM is easy to parallelize, especially for 3D data such as stacks of brain slice images.
PyraMiD-LSTM achieved best known pixel-wise brain image segmentation results on MRBrainS13 (and competitive results on EM-ISBI12).
Optimizing floating-point arithmetic is vital because it is ubiquitous, costly, and used in compute-heavy workloads.
Implementing precise optimizations correctly, however, is difficult, since developers must account for all the esoteric properties of floating-point arithmetic to ensure that their transformations do not alter the output of a program.
Manual reasoning is error prone and stifles incorporation of new optimizations.
We present an approach to automate reasoning about floating-point optimizations using satisfiability modulo theories (SMT) solvers.
We implement the approach in LifeJacket, a system for automatically verifying precise floating-point optimizations for the LLVM assembly language.
We have used LifeJacket to verify 43 LLVM optimizations and to discover eight incorrect ones, including three previously unreported problems.
LifeJacket is an open source extension of the Alive system for optimization verification.
Formal modelling of Multi-Agent Systems (MAS) is a challenging task due to high complexity, interaction, parallelism and continuous change of roles and organisation between agents.
In this paper we record our research experience on formal modelling of MAS.
We review our research throughout the last decade, by describing the problems we have encountered and the decisions we have made towards resolving them and providing solutions.
Much of this work involved membrane computing and classes of P Systems, such as Tissue and Population P Systems, targeted to the modelling of MAS whose dynamic structure is a prominent characteristic.
More particularly, social insects (such as colonies of ants, bees, etc.), biology inspired swarms and systems with emergent behaviour are indicative examples for which we developed formal MAS models.
Here, we aim to review our work and disseminate our findings to fellow researchers who might face similar challenges and, furthermore, to discuss important issues for advancing research on the application of membrane computing in MAS modelling.
Long training times for high-accuracy deep neural networks (DNNs) impede research into new DNN architectures and slow the development of high-accuracy DNNs.
In this paper we present FireCaffe, which successfully scales deep neural network training across a cluster of GPUs.
We also present a number of best practices to aid in comparing advancements in methods for scaling and accelerating the training of deep neural networks.
The speed and scalability of distributed algorithms is almost always limited by the overhead of communicating between servers; DNN training is not an exception to this rule.
Therefore, the key consideration here is to reduce communication overhead wherever possible, while not degrading the accuracy of the DNN models that we train.
Our approach has three key pillars.
First, we select network hardware that achieves high bandwidth between GPU servers -- Infiniband or Cray interconnects are ideal for this.
Second, we consider a number of communication algorithms, and we find that reduction trees are more efficient and scalable than the traditional parameter server approach.
Third, we optionally increase the batch size to reduce the total quantity of communication during DNN training, and we identify hyperparameters that allow us to reproduce the small-batch accuracy while training with large batch sizes.
When training GoogLeNet and Network-in-Network on ImageNet, we achieve a 47x and 39x speedup, respectively, when training on a cluster of 128 GPUs.
Future wireless standards such as 5G envision dense wireless networks with large number of simultaneously connected devices.
In this context, interference management becomes critical in achieving high spectral efficiency.
Orthogonal signaling, which limits the number of users utilizing the resource simultaneously, gives a sum-rate that remains constant with increasing number of users.
An alternative approach called interference alignment promises a throughput that scales linearly with the number of users.
However, this approach requires very high SNR or long time duration for sufficient channel variation, and therefore may not be feasible in real wireless systems.
We explore ways to manage interference in large networks with delay and power constraints.
Specifically, we devise an interference phase alignment strategy that combines precoding and scheduling without using power control to exploit the diversity inherent in a system with large number of users.
We show that this scheme achieves a sum-rate that scales almost logarithmically with the number of users.
We also show that no scheme using single symbol phase alignment, which is asymmetric complex signaling restricted to a single complex symbol, can achieve better than logarithmic scaling of the sum-rate.
Face morphing attack is proved to be a serious threat to the existing face recognition systems.
Although a few face morphing detection methods have been put forward, the face morphing accomplice's facial restoration remains a challenging problem.
In this paper, a face-demorphing generative adversarial network (FD-GAN) is proposed to restore the accomplice's facial image.
It utilizes a symmetric dual network architecture and two levels of restoration losses to separate the identity feature of the morphing accomplice.
By exploiting the captured face image (containing the criminal's identity) from the face recognition system and the morphed image stored in the e-passport system (containing both criminal and accomplice's identities), the FD-GAN can effectively restore the accomplice's facial image.
Experimental results and analysis demonstrate the effectiveness of the proposed scheme.
It has great potential to be implemented for detecting the face morphing accomplice in a real identity verification scenario.
Many state-of-the-art algorithms for solving hard combinatorial problems in artificial intelligence (AI) include elements of stochasticity that lead to high variations in runtime, even for a fixed problem instance.
Knowledge about the resulting runtime distributions (RTDs) of algorithms on given problem instances can be exploited in various meta-algorithmic procedures, such as algorithm selection, portfolios, and randomized restarts.
Previous work has shown that machine learning can be used to individually predict mean, median and variance of RTDs.
To establish a new state-of-the-art in predicting RTDs, we demonstrate that the parameters of an RTD should be learned jointly and that neural networks can do this well by directly optimizing the likelihood of an RTD given runtime observations.
In an empirical study involving five algorithms for SAT solving and AI planning, we show that neural networks predict the true RTDs of unseen instances better than previous methods, and can even do so when only few runtime observations are available per training instance.
This paper presents the "Leipzig Corpus Miner", a technical infrastructure for supporting qualitative and quantitative content analysis.
The infrastructure aims at the integration of 'close reading' procedures on individual documents with procedures of 'distant reading', e.g. lexical characteristics of large document collections.
Therefore information retrieval systems, lexicometric statistics and machine learning procedures are combined in a coherent framework which enables qualitative data analysts to make use of state-of-the-art Natural Language Processing techniques on very large document collections.
Applicability of the framework ranges from social sciences to media studies and market research.
As an example we introduce the usage of the framework in a political science study on post-democracy and neoliberalism.
Today in fast technology development in wireless mobile adhoc network there is vast scope for research.
As it is known that wireless communication for mobile network has many application areas like routing services, security services etc.
The mobile adhoc network is the wireless network for communication in which the mobile nodes are organized without any centralized administrator.
There are many Manet routing protocols like reactive, proactive, hybrid etc.
In this paper the reactive Manet routing protocol like DSR is simulated for traffic analysis for 50 mobile nodes for IP traffic flows.
Also throughput is analyzed for DSR and ER-DSR protocol.
And finally the memory utilized during simulation of DSR and ER-DSR is evaluated in order to compare both.
The true distribution parameterizations of commonly used image datasets are inaccessible.
Rather than designing metrics for feature spaces with unknown characteristics, we propose to measure GAN performance by evaluating on explicitly parameterized, synthetic data distributions.
As a case study, we examine the performance of 16 GAN variants on six multivariate distributions of varying dimensionalities and training set sizes.
In this learning environment, we observe that: GANs exhibit similar performance trends across dimensionalities; learning depends on the underlying distribution and its complexity; the number of training samples can have a large impact on performance; evaluation and relative comparisons are metric-dependent; diverse sets of hyperparameters can produce a "best" result; and some GANs are more robust to hyperparameter changes than others.
These observations both corroborate findings of previous GAN evaluation studies and make novel contributions regarding the relationship between size, complexity, and GAN performance.
In this paper, we derive a simple method for separating topological noise from topological features using a novel measure for comparing persistence barcodes called persistent entropy.
We present a novel finite element integration method for low order elements on GPUs.
We achieve more than 100GF for element integration on first order discretizations of both the Laplacian and Elasticity operators.
We introduce a Noise-based prior Learning (NoL) approach for training neural networks that are intrinsically robust to adversarial attacks.
We find that the implicit generative modeling of random noise with the same loss function used during posterior maximization, improves a model's understanding of the data manifold furthering adversarial robustness.
We evaluate our approach's efficacy and provide a simplistic visualization tool for understanding adversarial data, using Principal Component Analysis.
Our analysis reveals that adversarial robustness, in general, manifests in models with higher variance along the high-ranked principal components.
We show that models learnt with our approach perform remarkably well against a wide-range of attacks.
Furthermore, combining NoL with state-of-the-art adversarial training extends the robustness of a model, even beyond what it is adversarially trained for, in both white-box and black-box attack scenarios.
We release to the community six large-scale sense-annotated datasets in multiple language to pave the way for supervised multilingual Word Sense Disambiguation.
Our datasets cover all the nouns in the English WordNet and their translations in other languages for a total of millions of sense-tagged sentences.
Experiments prove that these corpora can be effectively used as training sets for supervised WSD systems, surpassing the state of the art for low-resourced languages and providing competitive results for English, where manually annotated training sets are accessible.
The data is available at trainomatic.org.
The performance of value classes is highly dependent on how they are represented in the virtual machine.
Value class instances are immutable, have no identity, and can only refer to other value objects or primitive values and since they should be very lightweight and fast, it is important to optimize them carefully.
In this paper we present a technique to detect and compress common patterns of value class usage to improve memory usage and performance.
The technique identifies patterns of frequent value object references and introduces abbreviated forms for them.
This allows to store multiple inter-referenced value objects in an inlined memory representation, reducing the overhead stemming from meta-data and object references.
Applied to a small prototype and an implementation of the Racket language, we found improvements in memory usage and execution time for several micro-benchmarks.
Since its conception, smart app market has grown exponentially.
Success in the app market depends on many factors among which the quality of the app is a significant contributor, such as energy use.
Nevertheless, smartphones, as a subset of mobile computing devices. inherit the limited power resource constraint.
Therefore, there is a challenge of maintaining the resource while increasing the target app quality.
This paper introduces Learning Automata (LA) as an online learning method to learn and predict the app usage routines of the users.
Such prediction can leverage the app cache functionality of the operating system and thus (i) decreases app launch time and (ii) preserve battery.
Our algorithm, which is an online learning approach, temporally updates and improves the internal states of itself.
In particular, it learns the transition probabilities between app launching.
Each App launching instance updates the transition probabilities related to that App, and this will result in improving the prediction.
We benefit from a real-world lifelogging dataset and our experimental results show considerable success with respect to the two baseline methods that are used currently for smartphone app prediction approaches.
This work provides an in-depth analysis of the relation between the different types of collaboration and research productivity, showing how both are influenced by some personal and organizational variables.
By applying different cross-lagged panel models, we are able to analyze the relationship among research productivity, collaboration and their determinants.
In particular, we show that only collaboration at intramural and domestic level has a positive effect on research productivity.
Differently, all the forms of collaboration are positively affected by research productivity.
The results can favor the reexamination of the theories related to these issues, and inform policies that would be more suited to their management.
Stack Overflow (SO) has been a great source of natural language questions and their code solutions (i.e., question-code pairs), which are critical for many tasks including code retrieval and annotation.
In most existing research, question-code pairs were collected heuristically and tend to have low quality.
In this paper, we investigate a new problem of systematically mining question-code pairs from Stack Overflow (in contrast to heuristically collecting them).
It is formulated as predicting whether or not a code snippet is a standalone solution to a question.
We propose a novel Bi-View Hierarchical Neural Network which can capture both the programming content and the textual context of a code snippet (i.e., two views) to make a prediction.
On two manually annotated datasets in Python and SQL domain, our framework substantially outperforms heuristic methods with at least 15% higher F1 and accuracy.
Furthermore, we present StaQC (Stack Overflow Question-Code pairs), the largest dataset to date of ~148K Python and ~120K SQL question-code pairs, automatically mined from SO using our framework.
Under various case studies, we demonstrate that StaQC can greatly help develop data-hungry models for associating natural language with programming language.
This paper studies the problem of reconstructing a two-dimensional scalar field using a swarm of networked robots with local communication capabilities.
We consider the communication network of the robots to form either a chain or a grid topology.
We formulate the reconstruction problem as an optimization problem that is constrained by first-order linear dynamics on a large, interconnected system.
To solve this problem, we employ an optimization-based scheme that uses a gradient-based method with an analytical computation of the gradient.
In addition, we derive bounds on the trace of observability Gramian of the system, which helps us to quantify and compare the estimation capability of chain and grid networks.
A comparison based on a performance measure related to the H2 norm of the system is also used to study robustness of the network topologies.
Our resultsare validated using both simulated scalar fields and actual ocean salinity data.
We present two Secure Two Party Computation (STPC) protocols for piecewise function approximation on private data.
The protocols rely on a piecewise approximation of the to-be-computed function easing the implementation in a STPC setting.
The first protocol relies entirely on Garbled Circuit (GC) theory, while the second one exploits a hybrid construction where GC and Homomorphic Encryption (HE) are used together.
In addition to piecewise constant and linear approximation, polynomial interpolation is also considered.
From a communication complexity perspective, the full-GC implementation is preferable when the input and output variables can be represented with a small number of bits, while the hybrid solution is preferable otherwise.
With regard to computational complexity, the full-GC solution is generally more convenient.
Existing face recognition using deep neural networks is difficult to know what kind of features are used to discriminate the identities of face images clearly.
To investigate the effective features for face recognition, we propose a novel face recognition method, called a pairwise relational network (PRN), that obtains local appearance patches around landmark points on the feature map, and captures the pairwise relation between a pair of local appearance patches.
The PRN is trained to capture unique and discriminative pairwise relations among different identities.
Because the existence and meaning of pairwise relations should be identity dependent, we add a face identity state feature, which obtains from the long short-term memory (LSTM) units network with the sequential local appearance patches on the feature maps, to the PRN.
To further improve accuracy of face recognition, we combined the global appearance representation with the pairwise relational feature.
Experimental results on the LFW show that the PRN using only pairwise relations achieved 99.65% accuracy and the PRN using both pairwise relations and face identity state feature achieved 99.76% accuracy.
On the YTF, both the PRN using only pairwise relations and the PRN using pairwise relations and the face identity state feature achieved the state-of-the-art (95.7% and 96.3%).
The PRN also achieved comparable results to the state-of-the-art for both face verification and face identification tasks on the IJB-A, and the state-of-the-art on the IJB-B.
In a secondary spectrum market primaries set prices for their unused channels to the secondaries.
The payoff of a primary depends on the availability of unused channels of its competitors.
We consider a model were a primary can acquire its competitor's channel state information (C-CSI) at a cost.
We formulate a game between two primaries where each primary decides whether to acquire C-CSI or not and then selects its price based on that.
We first characterize the Nash Equilibrium (NE) of this game for a symmetric model where the C-CSI is perfect.
We show that the payoff of a primary is independent of the C-CSI acquisition cost.
We then generalize our analysis to allow for imperfect estimation and cases where the two primaries have different C-CSI costs or different channel availabilities.
Our results show interestingly that the payoff of a primary increases when there is estimation error.
We also show that surprisingly, the expected payoff of a primary may decrease when the C-CSI acquisition cost decreases when primaries have different availabilities.
Self-aligned double patterning (SADP) has become a promising technique to push pattern resolution limit to sub-22nm technology node.
Although SADP provides good overlay controllability, it encounters many challenges in physical design stages to obtain conflict-free layout decomposition.
In this paper, we study the impact on placement by different standard cell layout decomposition strategies.
We propose a SADP friendly standard cell configuration which provides pre-coloring results for standard cells.
These configurations are brought into the placement stage to help ensure layout decomposability and save the extra effort for solving conflicts in later stages.
Augmenting deep neural networks with skip connections, as introduced in the so called ResNet architecture, surprised the community by enabling the training of networks of more than 1000 layers with significant performance gains.
It has been shown that identity skip connections eliminate singularities and improve the optimization landscape of the network.
This paper deciphers ResNet by analyzing the of effect of skip connections in the backward path and sets forth new theoretical results on the advantages of identity skip connections in deep neural networks.
We prove that the skip connections in the residual blocks facilitate preserving the norm of the gradient and lead to well-behaved and stable back-propagation, which is a desirable feature from optimization perspective.
We also show that, perhaps surprisingly, as more residual blocks are stacked, the network becomes more norm-preserving.
Traditionally, norm-preservation is enforced on the network only at beginning of the training, by using initialization techniques.
However, we show that identity skip connection retain norm-preservation during the training procedure.
Our theoretical arguments are supported by extensive empirical evidence.
Can we push for more norm-preservation?
We answer this question by proposing zero-phase whitening of the fully-connected layer and adding norm-preserving transition layers.
Our numerical investigations demonstrate that the learning dynamics and the performance of ResNets can be improved by making it even more norm preserving through changing only a few blocks in very deep residual networks.
Our results and the introduced modification for ResNet, referred to as Procrustes ResNets, can be used as a guide for studying more complex architectures such as DenseNet, training deeper networks, and inspiring new architectures.
Recent excitement in the database community surrounding new applications?analytic, scientific, graph, geospatial, etc.
?has led to an explosion in research on database storage systems.
New storage systems are vital to the database community, as they are at the heart of making database systems perform well in new application domains.
Unfortunately, each such system also represents a substantial engineering effort including a great deal of duplication of mechanisms for features such as transactions and caching.
In this paper, we make the case for RodentStore, an adaptive and declarative storage system providing a high-level interface for describing the physical representation of data.
Specifically, RodentStore uses a declarative storage algebra whereby administrators (or database design tools) specify how a logical schema should be grouped into collections of rows, columns, and/or arrays, and the order in which those groups should be laid out on disk.
We describe the key operators and types of our algebra, outline the general architecture of RodentStore, which interprets algebraic expressions to generate a physical representation of the data, and describe the interface between RodentStore and other parts of a database system, such as the query optimizer and executor.
We provide a case study of the potential use of RodentStore in representing dense geospatial data collected from a mobile sensor network, showing the ease with which different storage layouts can be expressed using some of our algebraic constructs and the potential performance gains that a RodentStore-built storage system can offer.
Using Atomic Force Microscopes (AFM) to manipulate nano-objects is an actual challenge for surface scientists.
Basic haptic interfacesbetween the AFM and experimentalists have already been implemented.
Themulti-sensory renderings (seeing, hearing and feeling) studied from acognitive point of view increase the efficiency of the actual interfaces.
Toallow the experimentalist to feel and touch the nano-world, we add mixedrealities between an AFM and a force feedback device, enriching thus thedirect connection by a modeling engine.
We present in this paper the firstresults from a real-time remote-control handling of an AFM by our ForceFeedback Gestural Device through the example of the approach-retract curve.
In the modern era of radio frequency (RF) spectrum crunch, visible light communication (VLC) is a recent and promising alternative technology that operates at the visible light spectrum.
Thanks to its unlicensed and large bandwidth, VLC can deliver high throughput, better energy efficiency, and low cost data communications.
In this article, a hybrid RF/VLC architecture is considered that can simultaneously provide light- ing and communication coverage across a room.
Considered architecture involves a novel multi-element hemispherical bulb design, which can transmit multiple data streams over light emitting diode (LED) modules.
Simulations considering various VLC transmitter configurations and topologies show that good link quality and high spatial reuse can be maintained in typical indoor communication scenarios.
The regression test selection problem--selecting a subset of a test-suite given a change--has been studied widely over the past two decades.
However, the problem has seen little attention when constrained to high-criticality developments and where a "safe" selection of tests need to be chosen.
Further, no practical approaches have been presented for the programming language Ada.
In this paper, we introduce an approach to solving the selection problem given a combination of both static and dynamic data for a program and a change-set.
We present a change impact analysis for Ada that selects the safe set of tests that need to be re-executed to ensure no regressions.
We have implemented the approach in the commercial, unit-testing tool VectorCAST, and validated it on a number of open-source examples.
On an example of a fully-functioning Ada implementation of a DNS server (IRONSIDES), the experimental results show a 97% reduction in test-case execution.
Signal identification represents the task of a receiver to identify the signal type and its parameters, with applications to both military and commercial communications.
In this paper, we investigate the identification of spatial multiplexing (SM) and Alamouti (AL) space-time block code (STBC) with single carrier frequency division multiple access (SC-FDMA) signals, when the receiver is equipped with a single antenna.
We develop a discriminating feature based on a fourth-order statistic of the received signal, as well as a constant false alarm rate decision criterion which relies on the statistical properties of the feature estimate.
Furthermore, we present the theoretical performance analysis of the proposed identification algorithm.
The algorithm does not require channel or noise power estimation, modulation classification, and block synchronization.
Simulation results show the validity of the proposed algorithm, as well as a very good agreement with the theoretical analysis.
Samples from intimate (non-linear) mixtures are generally modeled as being drawn from a smooth manifold.
Scenarios where the data contains multiple intimate mixtures with some constituent materials in common can be thought of as manifolds which share a boundary.
Two important steps in the processing of such data are (i) to identify (cluster) the different mixture-manifolds present in the data and (ii) to eliminate the non-linearities present the data by mapping each mixture-manifold into some low-dimensional euclidean space (embedding).
Manifold clustering and embedding techniques appear to be an ideal tool for this task, but the present state-of-the-art algorithms perform poorly for hyperspectral data, particularly in the embedding task.
We propose a novel reconstruction-based algorithm for improved clustering and embedding of mixture-manifolds.
The algorithms attempts to reconstruct each target-point as an affine combination of its nearest neighbors with an additional rank penalty on the neighborhood to ensure that only neighbors on the same manifold as the target-point are used in the reconstruction.
The reconstruction matrix generated by using this technique is block-diagonal and can be used for clustering (using spectral clustering) and embedding.
The improved performance of the algorithms vis-a-vis its competitors is exhibited on a variety of simulated and real mixture datasets.
Recent developments in speech synthesis have produced systems capable of outcome intelligible speech, but now researchers strive to create models that more accurately mimic human voices.
One such development is the incorporation of multiple linguistic styles in various languages and accents.
HMM-based Speech Synthesis is of great interest to many researchers, due to its ability to produce sophisticated features with small footprint.
Despite such progress, its quality has not yet reached the level of the predominant unit-selection approaches that choose and concatenate recordings of real speech.
Recent efforts have been made in the direction of improving these systems.
In this paper we present the application of Long-Short Term Memory Deep Neural Networks as a Postfiltering step of HMM-based speech synthesis, in order to obtain closer spectral characteristics to those of natural speech.
The results show how HMM-voices could be improved using this approach.
New high-data-rate multimedia services and applications are evolving continuously and exponentially increasing the demand for wireless capacity of fifth-generation (5G) and beyond.
The existing radio frequency (RF) communication spectrum is insufficient to meet the demands of future high-datarate 5G services.
Optical wireless communication (OWC), which uses an ultra-wide range of unregulated spectrum, has emerged as a promising solution to overcome the RF spectrum crisis.
It has attracted growing research interest worldwide in the last decade for indoor and outdoor applications.
OWC offloads huge data traffic applications from RF networks.
A 100 Gb/s data rate has already been demonstrated through OWC.
It offers services indoors as well as outdoors, and communication distances range from several nm to more than 10000 km.
This paper provides a technology overview and a review on optical wireless technologies, such as visible light communication, light fidelity, optical camera communication, free space optical communication, and light detection and ranging.
We survey the key technologies for understanding OWC and present state-of-the-art criteria in aspects, such as classification, spectrum use, architecture, and applications.
The key contribution of this paper is to clarify the differences among different promising optical wireless technologies and between these technologies and their corresponding similar existing RF technologies
A group of transition probability functions form a Shannon's channel whereas a group of truth functions form a semantic channel.
By the third kind of Bayes' theorem, we can directly convert a Shannon's channel into an optimized semantic channel.
When a sample is not big enough, we can use a truth function with parameters to produce the likelihood function, then train the truth function by the conditional sampling distribution.
The third kind of Bayes' theorem is proved.
A semantic information theory is simply introduced.
The semantic information measure reflects Popper's hypothesis-testing thought.
The Semantic Information Method (SIM) adheres to maximum semantic information criterion which is compatible with maximum likelihood criterion and Regularized Least Squares criterion.
It supports Wittgenstein's view: the meaning of a word lies in its use.
Letting the two channels mutually match, we obtain the Channels' Matching (CM) algorithm for machine learning.
The CM algorithm is used to explain the evolution of the semantic meaning of natural language, such as "Old age".
The semantic channel for medical tests and the confirmation measures of test-positive and test-negative are discussed.
The applications of the CM algorithm to semi-supervised learning and non-supervised learning are simply introduced.
As a predictive model, the semantic channel fits variable sources and hence can overcome class-imbalance problem.
The SIM strictly distinguishes statistical probability and logical probability and uses both at the same time.
This method is compatible with the thoughts of Bayes, Fisher, Shannon, Zadeh, Tarski, Davidson, Wittgenstein, and Popper.It is a competitive alternative to Bayesian inference.
We study the related problems of subspace tracking in the presence of missing data (ST-miss) as well as robust subspace tracking with missing data (RST-miss).
Here "robust" refers to robustness to sparse outliers.
In recent work, we have studied the RST problem without missing data.
In this work, we show that simple modifications of our solution approach for RST also provably solve ST-miss and RST-miss under weaker and similar assumptions respectively.
To our knowledge, our result is the first complete guarantee for both ST-miss and RST-miss.
This means we are able to show that, under assumptions on only the algorithm inputs (input data and/or initialization), the output subspace estimates are close to the true data subspaces at all times.
Our guarantees hold under mild and easily interpretable assumptions and handle time-varying subspaces (unlike all previous work).
We also show that our algorithm and its extensions are fast and have competitive experimental performance when compared with existing methods.
The rapid advances in e-commerce and Web 2.0 technologies have greatly increased the impact of commercial advertisements on the general public.
As a key enabling technology, a multitude of recommender systems exists which analyzes user features and browsing patterns to recommend appealing advertisements to users.
In this work, we seek to study the characteristics or attributes that characterize an effective advertisement and recommend a useful set of features to aid the designing and production processes of commercial advertisements.
We analyze the temporal patterns from multimedia content of advertisement videos including auditory, visual and textual components, and study their individual roles and synergies in the success of an advertisement.
The objective of this work is then to measure the effectiveness of an advertisement, and to recommend a useful set of features to advertisement designers to make it more successful and approachable to users.
Our proposed framework employs the signal processing technique of cross modality feature learning where data streams from different components are employed to train separate neural network models and are then fused together to learn a shared representation.
Subsequently, a neural network model trained on this joint feature embedding representation is utilized as a classifier to predict advertisement effectiveness.
We validate our approach using subjective ratings from a dedicated user study, the sentiment strength of online viewer comments, and a viewer opinion metric of the ratio of the Likes and Views received by each advertisement from an online platform.
This is the preprint version of our paper on REHAB2015.
A balance measurement software based on Kinect2 sensor is evaluated by comparing to golden standard balance measure platform intuitively.
The software analysis the tracked body data from the user by Kinect2 sensor and get user's center of mass(CoM) as well as its motion route on a plane.
The software is evaluated by several comparison tests, the evaluation results preliminarily prove the reliability of the software.
In software-defined networking (SDN), as data plane scale expands, scalability and reliability of the control plane have become major concerns.
To mitigate such concerns, two kinds of solutions have been proposed separately.
One is multi- controller architecture, i.e., a logically centralized control plane with physically distributed controllers.
The other is control devolution, i.e., delegating control of some flows back to switches.
Most of existing solutions adopt either static switch-controller association or static devolution, which may not adapt well to the traffic variation, leading to high communication costs between switches and controller, and high computation costs of switches.
In this paper, we propose a novel scheme to jointly consider both solutions, i.e., we dynamically associate switches with controllers and dynamically devolve control of flows to switches.
Our scheme is an efficient online algorithm that does not need the statistics of traffic flows.
By adjusting a parameter, we can make a trade- off between costs and queue backlogs.
Theoretical analysis and extensive simulations show that our scheme yields much lower costs or latency compared to other schemes, as well as balanced loads among controllers.
A sufficient condition reported very recently for perfect recovery of a K-sparse vector via orthogonal matching pursuit (OMP) in K iterations is that the restricted isometry constant of the sensing matrix satisfies delta_K+1<1/(sqrt(delta_K+1)+1).
By exploiting an approximate orthogonality condition characterized via the achievable angles between two orthogonal sparse vectors upon compression, this paper shows that the upper bound on delta can be further relaxed to delta_K+1<(sqrt(1+4*delta_K+1)-1)/(2K).This result thus narrows the gap between the so far best known bound and the ultimate performance guarantee delta_K+1<1/(sqrt(delta_K+1)) that is conjectured by Dai and Milenkovic in 2009.
The proposed approximate orthogonality condition is also exploited to derive less restricted sufficient conditions for signal reconstruction in several compressive sensing problems, including signal recovery via OMP in a noisy environment, compressive domain interference cancellation, and support identification via the subspace pursuit algorithm.
Multiple query criteria active learning (MQCAL) methods have a higher potential performance than conventional active learning methods in which only one criterion is deployed for sample selection.
A central issue related to MQCAL methods concerns the development of an integration criteria strategy (ICS) that makes full use of all criteria.
The conventional ICS adopted in relevant research all facilitate the desired effects, but several limitations still must be addressed.
For instance, some of the strategies are not sufficiently scalable during the design process, and the number and type of criteria involved are dictated.
Thus, it is challenging for the user to integrate other criteria into the original process unless modifications are made to the algorithm.
Other strategies are too dependent on empirical parameters, which can only be acquired by experience or cross-validation and thus lack generality; additionally, these strategies are counter to the intention of active learning, as samples need to be labeled in the validation set before the active learning process can begin.
To address these limitations, we propose a novel MQCAL method for classification tasks that employs a third strategy via weighted rank aggregation.
The proposed method serves as a heuristic means to select high-value samples of high scalability and generality and is implemented through a three-step process: (1) the transformation of the sample selection to sample ranking and scoring, (2) the computation of the self-adaptive weights of each criterion, and (3) the weighted aggregation of each sample rank list.
Ultimately, the sample at the top of the aggregated ranking list is the most comprehensively valuable and must be labeled.
Several experiments generating 257 wins, 194 ties and 49 losses against other state-of-the-art MQCALs are conducted to verify that the proposed method can achieve superior results.
We consider the problem of cooperative output regulation for linear multi-agent systems.
A distributed dynamic output feedback design method is presented that solves the cooperative output regulation problem and also ensures that all agents track the desired reference signal without overshoot in their transient response.
Legged robots are becoming popular not only in research, but also in industry, where they can demonstrate their superiority over wheeled machines in a variety of applications.
Either when acting as mobile manipulators or just as all-terrain ground vehicles, these machines need to precisely track the desired base and end-effector trajectories, perform Simultaneous Localization and Mapping (SLAM), and move in challenging environments, all while keeping balance.
A crucial aspect for these tasks is that all onboard sensors must be properly calibrated and synchronized to provide consistent signals for all the software modules they feed.
In this paper, we focus on the problem of calibrating the relative pose between a set of cameras and the base link of a quadruped robot.
This pose is fundamental to successfully perform sensor fusion, state estimation, mapping, and any other task requiring visual feedback.
To solve this problem, we propose an approach based on factor graphs that jointly optimizes the mutual position of the cameras and the robot base using kinematics and fiducial markers.
We also quantitatively compare its performance with other state-of-the-art methods on the hydraulic quadruped robot HyQ.
The proposed approach is simple, modular, and independent from external devices other than the fiducial marker.
We propose a novel framework for the analysis of learning algorithms that allows us to say when such algorithms can and cannot generalize certain patterns from training data to test data.
In particular we focus on situations where the rule that must be learned concerns two components of a stimulus being identical.
We call such a basis for discrimination an identity-based rule.
Identity-based rules have proven to be difficult or impossible for certain types of learning algorithms to acquire from limited datasets.
This is in contrast to human behaviour on similar tasks.
Here we provide a framework for rigorously establishing which learning algorithms will fail at generalizing identity-based rules to novel stimuli.
We use this framework to show that such algorithms are unable to generalize identity-based rules to novel inputs unless trained on virtually all possible inputs.
We demonstrate these results computationally with a multilayer feedforward neural network.
The problem of communicating over the additive white Gaussian noise (AWGN) channel with lattice codes is addressed in this paper.
Theoretically, Voronoi constellations have proved to yield very powerful lattice codes when the fine/coding lattice is AWGN-good and the coarse/shaping lattice has an optimal shaping gain.
However, achieving Shannon capacity with these premises and practically implementable encoding algorithms is in general not an easy task.
In this work, a new way to encode and demap Construction-A Voronoi lattice codes is presented.
As a meaningful application of this scheme, the second part of the paper is focused on Leech constellations of low-density Construction-A (LDA) lattices: LDA Voronoi lattice codes are presented whose numerically measured waterfall region is situated at less than 0.8 dB from Shannon capacity.
These LDA lattice codes are based on dual-diagonal nonbinary low-density parity-check codes.
With this choice, encoding, iterative decoding, and demapping have all linear complexity in the blocklength.
The issue of representing attacks to attacks in argumentation is receiving an increasing attention as a useful conceptual modelling tool in several contexts.
In this paper we present AFRA, a formalism encompassing unlimited recursive attacks within argumentation frameworks.
AFRA satisfies the basic requirements of definition simplicity and rigorous compatibility with Dung's theory of argumentation.
This paper provides a complete development of the AFRA formalism complemented by illustrative examples and a detailed comparison with other recursive attack formalizations.
Recently, many healthcare organizations are adopting CRM as a strategy, which involves using technology to organize, automate, and coordinate business processes, in managing interactions with their patients.
CRM with the Web technology provides healthcare providers the ability to broaden their services beyond usual practices, and thus offers suitable environment using latest technology to achieve superb patient care.
This paper discusses and demonstrates how a new approach in CRM based on Web 2.0 will help the healthcare providers improving their customer support, avoiding conflict, and promoting better health to patient.
With this new approach patients will benefit from the customized personal service with full information access to perform self managed their own health.
It also helps healthcare providers retaining the right customer.
A conceptual framework of the new approach will be discussed.
Even though there are sophisticated AI planning algorithms, many integrated, large-scale projects do not use planning.
One reason seems to be the missing support by engineering tools such as syntax highlighting and visualization.
We propose myPDDL - a modular toolbox for efficiently creating PDDL domains and problems.
To evaluate myPDDL, we compare it to existing knowledge engineering tools for PDDL and experimentally assess its usefulness for novice PDDL users.
Dialog act recognition is an important step for dialog systems since it reveals the intention behind the uttered words.
Most approaches on the task use word-level tokenization.
In contrast, this paper explores the use of character-level tokenization.
This is relevant since there is information at the sub-word level that is related to the function of the words and, thus, their intention.
We also explore the use of different context windows around each token, which are able to capture important elements, such as affixes.
Furthermore, we assess the importance of punctuation and capitalization.
We performed experiments on both the Switchboard Dialog Act Corpus and the DIHANA Corpus.
In both cases, the experiments not only show that character-level tokenization leads to better performance than the typical word-level approaches, but also that both approaches are able to capture complementary information.
Thus, the best results are achieved by combining tokenization at both levels.
Correct inference of genetic regulations inside a cell is one of the greatest challenges in post genomic era for the biologist and researchers.
Several intelligent techniques and models were already proposed to identify the regulatory relations among genes from the biological database like time series microarray data.
Recurrent Neural Network (RNN) is one of the most popular and simple approach to model the dynamics as well as to infer correct dependencies among genes.
In this paper, Bat Algorithm (BA) is applied to optimize the model parameters of RNN model of Gene Regulatory Network (GRN).
Initially the proposed method is tested against small artificial network without any noise and the efficiency is observed in term of number of iteration, number of population and BA optimization parameters.
The model is also validated in presence of different level of random noise for the small artificial network and that proved its ability to infer the correct inferences in presence of noise like real world dataset.
In the next phase of this research, BA based RNN is applied to real world benchmark time series microarray dataset of E. coli.
The results prove that it can able to identify the maximum number of true positive regulation but also include some false positive regulations.
Therefore, BA is very suitable for identifying biological plausible GRN with the help RNN model.
We propose a method based on minimum-variance polynomial approximation to extract system poles from a data set of samples of the impulse response of a linear system.
The method is capable of handling the problem under general conditions of sampling and noise characteristics.
The superiority of the proposed method is demonstrated by statistical comparison of its performance with the performances of two exiting methods in the special case of uniform sampling.
This paper deals with the semantic interpretation of information resources (e.g., images, videos, 3D models).
We present a case study of an approach based on semantic and context dependent similarity applied to the industrial design.
Different application contexts are considered and modelled to browse a repository of 3D digital objects according to different perspectives.
The paper briefly summarises the basic concepts behind the semantic similarity approach and illustrates its application and results.
This paper presents a multi-platform, open-source application that aims to protect data stored and shared in existing cloud storage services.
The access to the cryptographic material used to protect data is implemented using the identification and authentication functionalities of national electronic identity (eID) tokens.
All peer to peer dialogs to exchange cryptographic material is implemented using the cloud storage facilities.
Furthermore, we have included a set of mechanisms to prevent files from being permanently lost or damaged due to concurrent modification, deletion and malicious tampering.
We have implemented a prototype in Java that is agnostic relatively to cloud storage providers; it only manages local folders, one of them being the local image of a cloud folder.
We have successfully tested our prototype in Windows, Mac OS X and Linux, with Dropbox, OneDrive, Google Drive and SugarSync.
We present a model for aggregation of product review snippets by joint aspect identification and sentiment analysis.
Our model simultaneously identifies an underlying set of ratable aspects presented in the reviews of a product (e.g., sushi and miso for a Japanese restaurant) and determines the corresponding sentiment of each aspect.
This approach directly enables discovery of highly-rated or inconsistent aspects of a product.
Our generative model admits an efficient variational mean-field inference algorithm.
It is also easily extensible, and we describe several modifications and their effects on model structure and inference.
We test our model on two tasks, joint aspect identification and sentiment analysis on a set of Yelp reviews and aspect identification alone on a set of medical summaries.
We evaluate the performance of the model on aspect identification, sentiment analysis, and per-word labeling accuracy.
We demonstrate that our model outperforms applicable baselines by a considerable margin, yielding up to 32% relative error reduction on aspect identification and up to 20% relative error reduction on sentiment analysis.
Ideas about how to increase the unconscious participation in interaction between 'a human' and 'a computer' are developed in this paper.
Evidence of impact of the unconscious functioning is presented.
The unconscious is characterised as being a responsive, contextual, and autonomous participant of human-computer interaction.
The unconscious participation occurs independently of one's cognitive and educational levels and, if ignored, leads to learning inefficiencies and compulsive behaviours, illustrations of which are provided.
Three practical approaches to a study of subjective user experience are outlined as follows: (a) tracing operant conditioning effects of software, (b) registering signs of brain activity psychological or information processing meaning of which is well-explored and (c) exploring submodality interfaces.
Implications for improvement of current usability study methods, such as eye-tracking, are generally considered.
Conclusions consider advantages and disadvantages of unconscious-embracing design and remind about a loss of human evolutionary choices if unconscious participation is ignored, complicated or blocked in interaction with computer interfaces and built environment.
Recently, great success has been achieved in offline handwritten Chinese character recognition by using deep learning methods.
Chinese characters are mainly logographic and consist of basic radicals, however, previous research mostly treated each Chinese character as a whole without explicitly considering its internal two-dimensional structure and radicals.
In this study, we propose a novel radical analysis network with densely connected architecture (DenseRAN) to analyze Chinese character radicals and its two-dimensional structures simultaneously.
DenseRAN first encodes input image to high-level visual features by employing DenseNet as an encoder.
Then a decoder based on recurrent neural networks is employed, aiming at generating captions of Chinese characters by detecting radicals and two-dimensional structures through attention mechanism.
The manner of treating a Chinese character as a composition of two-dimensional structures and radicals can reduce the size of vocabulary and enable DenseRAN to possess the capability of recognizing unseen Chinese character classes, only if the corresponding radicals have been seen in training set.
Evaluated on ICDAR-2013 competition database, the proposed approach significantly outperforms whole-character modeling approach with a relative character error rate (CER) reduction of 18.54%.
Meanwhile, for the case of recognizing 3277 unseen Chinese characters in CASIA-HWDB1.2 database, DenseRAN can achieve a character accuracy of about 41% while the traditional whole-character method has no capability to handle them.
Mobile apps can access a wide variety of secure information, such as contacts and location.
However, current mobile platforms include only coarse access control mechanisms to protect such data.
In this paper, we introduce interaction-based declassification policies, in which the user's interactions with the app constrain the release of sensitive information.
Our policies are defined extensionally, so as to be independent of the app's implementation, based on sequences of security-relevant events that occur in app runs.
Policies use LTL formulae to precisely specify which secret inputs, read at which times, may be released.
We formalize a semantic security condition, interaction-based noninterference, to define our policies precisely.
Finally, we describe a prototype tool that uses symbolic execution to check interaction-based declassification policies for Android, and we show that it enforces policies correctly on a set of apps.
Counting the frequency of small subgraphs is a fundamental technique in network analysis across various domains, most notably in bioinformatics and social networks.
The special case of triangle counting has received much attention.
Getting results for 4-vertex patterns is highly challenging, and there are few practical results known that can scale to massive sizes.
Indeed, even a highly tuned enumeration code takes more than a day on a graph with millions of edges.
Most previous work that runs for truly massive graphs employ clusters and massive parallelization.
We provide a sampling algorithm that provably and accurately approximates the frequencies of all 4-vertex pattern subgraphs.
Our algorithm is based on a novel technique of 3-path sampling and a special pruning scheme to decrease the variance in estimates.
We provide theoretical proofs for the accuracy of our algorithm, and give formal bounds for the error and confidence of our estimates.
We perform a detailed empirical study and show that our algorithm provides estimates within 1% relative error for all subpatterns (over a large class of test graphs), while being orders of magnitude faster than enumeration and other sampling based algorithms.
Our algorithm takes less than a minute (on a single commodity machine) to process an Orkut social network with 300 million edges.
Fractional order derivatives and integrals (differintegrals) are viewed from a frequency-domain perspective using the formalism of Riesz, providing a computational tool as well as a way to interpret the operations in the frequency domain.
Differintegrals provide a logical extension of current techniques, generalizing the notion of integral and differential operators and acting as kind of frequency-domain filtering that has many of the advantages of a nonlocal linear operator.
Several important properties of differintegrals are presented, and sample applications are given to one- and two-dimensional signals.
Computer code to carry out the computations is made available on the author's website.
Evolutionary clustering aims at capturing the temporal evolution of clusters.
This issue is particularly important in the context of social media data that are naturally temporally driven.
In this paper, we propose a new probabilistic model-based evolutionary clustering technique.
The Temporal Multinomial Mixture (TMM) is an extension of classical mixture model that optimizes feature co-occurrences in the trade-off with temporal smoothness.
Our model is evaluated for two recent case studies on opinion aggregation over time.
We compare four different probabilistic clustering models and we show the superiority of our proposal in the task of instance-oriented clustering.
Creating user defined functions (UDFs) is a powerful method to improve the quality of computer applications, in particular spreadsheets.
However, the only direct way to use UDFs in spreadsheets is to switch from the functional and declarative style of spreadsheet formulas to the imperative VBA, which creates a high entry barrier even for proficient spreadsheet users.
It has been proposed to extend Excel by UDFs declared by a spreadsheet: user defined spreadsheet functions (UDSFs).
In this paper we present a method to create a limited form of UDSFs in Excel without any use of VBA.
Calls to those UDSFs utilize what-if data tables to execute the same part of a worksheet several times, thus turning it into a reusable function definition.
Standard machine translation systems process sentences in isolation and hence ignore extra-sentential information, even though extended context can both prevent mistakes in ambiguous cases and improve translation coherence.
We introduce a context-aware neural machine translation model designed in such way that the flow of information from the extended context to the translation model can be controlled and analyzed.
We experiment with an English-Russian subtitles dataset, and observe that much of what is captured by our model deals with improving pronoun translation.
We measure correspondences between induced attention distributions and coreference relations and observe that the model implicitly captures anaphora.
It is consistent with gains for sentences where pronouns need to be gendered in translation.
Beside improvements in anaphoric cases, the model also improves in overall BLEU, both over its context-agnostic version (+0.7) and over simple concatenation of the context and source sentences (+0.6).
Chaotic compressive sensing is a nonlinear framework for compressive sensing.
Along the framework, this paper proposes a chaotic analog-to-information converter, chaotic modulation, to acquire and reconstruct band-limited sparse analog signals at sub-Nyquist rate.
In the chaotic modulation, the sparse signal is randomized through state modulation of continuous-time chaotic system and one state output is sampled as compressive measurements.
The reconstruction is achieved through the estimation of the sparse coefficients with principle of chaotic impulsive synchronization and Lp-norm regularized nonlinear least squares.
The concept of supreme local Lyapunov exponents (SLLE) is introduced to study the reconstructablity.
It is found that the sparse signals are reconstructable, if the largest SLLE of the error dynamical system is negative.
As examples, the Lorenz system and Liu system excited by the sparse multi-tone signals are taken to illustrate the principle and the performance.
Binary hypothesis testing under the Neyman-Pearson formalism is a statistical inference framework for distinguishing data generated by two different source distributions.
Privacy restrictions may require the curator of the data or the data respondents themselves to share data with the test only after applying a randomizing privacy mechanism.
Using mutual information as the privacy metric and the relative entropy between the two distributions of the output (postrandomization) source classes as the utility metric (motivated by the Chernoff-Stein Lemma), this work focuses on finding an optimal mechanism that maximizes the chosen utility function while ensuring that the mutual information based leakage for both source distributions is bounded.
Focusing on the high privacy regime, an Euclidean information-theoretic (E-IT) approximation to the tradeoff problem is presented.
It is shown that the solution to the E-IT approximation is independent of the alphabet size and clarifies that a mutual information based privacy metric preserves the privacy of the source symbols in inverse proportion to their likelihood.
In this paper, we present an approach that is able to handle with Z-numbers in the context of Multi-Criteria Decision Making (MCDM) problems.
Z-numbers are composed of two parts, the first one is a restriction on the values that can be assumed, and the second part is the reliability of the information.
As human beings we communicate with other people by means of natural language using sentences like: the journey time from home to university takes about half hour, very likely.
Firstly, Z-numbers are converted to fuzzy numbers using a standard procedure.
Next, the Z-TODIM and Z-TOPSIS are presented as a direct extension of the fuzzy TODIM and fuzzy TOPSIS, respectively.
The proposed methods are applied to two case studies and compared with the standard approach using crisp values.
Results obtained show the feasibility of the approach.
In addition, a graphical interface was built to handle with both methods Z- TODIM and Z-TOPSIS allowing ease of use for user in other areas of knowledge.
We provide an efficient algorithm for determining how a road network has evolved over time, given two snapshot instances from different dates.
To allow for such determinations across different databases and even against hand drawn maps, we take a strictly topological approach in this paper, so that we compare road networks based strictly on graph-theoretic properties.
Given two road networks of same region from two different dates, our approach allows one to match road network portions that remain intact and also point out added or removed portions.
We analyze our algorithm both theoretically, showing that it runs in polynomial time for non-degenerate road networks even though a related problem is NP-complete, and experimentally, using dated road networks from the TIGER/Line archive of the U.S. Census Bureau.
Self-powered, energy harvesting small cell base stations (SBS) are expected to be an integral part of next-generation wireless networks.
However, due to uncertainties in harvested energy, it is necessary to adopt energy efficient power control schemes to reduce an SBSs' energy consumption and thus ensure quality-of-service (QoS) for users.
Such energy-efficient design can also be done via the use of content caching which reduces the usage of the capacity-limited SBS backhaul. of popular content at SBS can also prove beneficial in this regard by reducing the backhaul usage.
In this paper, an online energy efficient power control scheme is developed for an energy harvesting SBS equipped with a wireless backhaul and local storage.
In our model, energy arrivals are assumed to be Poisson distributed and the popularity distribution of requested content is modeled using Zipf's law.
The power control problem is formulated as a (discounted) infinite horizon dynamic programming problem and solved numerically using the value iteration algorithm.
Using simulations, we provide valuable insights on the impact of energy harvesting and caching on the energy and sum-throughput performance of the SBS as the network size is varied.
Our results also show that the size of cache and energy harvesting equipment at the SBS can be traded off, while still meeting the desired system performance.
Querying graph structured data is a fundamental operation that enables important applications including knowledge graph search, social network analysis, and cyber-network security.
However, the growing size of real-world data graphs poses severe challenges for graph databases to meet the response-time requirements of the applications.
Planning the computational steps of query processing - Query Planning - is central to address these challenges.
In this paper, we study the problem of learning to speedup query planning in graph databases towards the goal of improving the computational-efficiency of query processing via training queries.We present a Learning to Plan (L2P) framework that is applicable to a large class of query reasoners that follow the Threshold Algorithm (TA) approach.
First, we define a generic search space over candidate query plans, and identify target search trajectories (query plans) corresponding to the training queries by performing an expensive search.
Subsequently, we learn greedy search control knowledge to imitate the search behavior of the target query plans.
We provide a concrete instantiation of our L2P framework for STAR, a state-of-the-art graph query reasoner.
Our experiments on benchmark knowledge graphs including DBpedia, YAGO, and Freebase show that using the query plans generated by the learned search control knowledge, we can significantly improve the speed of STAR with negligible loss in accuracy.
One of the key requirements for fifth-generation (5G) cellular networks is their ability to handle densely connected devices with different quality of service (QoS) requirements.
In this article, we present multi-service oriented multiple access (MOMA), an integrated access scheme for massive connections with diverse QoS profiles and/or traffic patterns originating from both handheld devices and machine-to-machine (M2M) transmissions.
MOMA is based on a) stablishing separate classes of users based on relevant criteria that go beyond the simple handheld/M2M split, b) class dependent hierarchical spreading of the data signal and c) a mix of multiuser and single-user detection schemes at the receiver.
Practical implementations of the MOMA principle are provided for base stations (BSs) that are equipped with a large number of antenna elements.
Finally, it is shown that such a massive-multiple-input-multiple-output (MIMO) scenario enables the achievement of all the benefits of MOMA even with a simple receiver structure that allows to concentrate the receiver complexity where effectively needed.
Two key factors dominate the development of effective production grade machine learning models.
First, it requires a local software implementation and iteration process.
Second, it requires distributed infrastructure to efficiently conduct training and hyperparameter optimization.
While modern machine learning frameworks are very effective at the former, practitioners are often left building ad hoc frameworks for the latter.
We present SigOpt Orchestrate, a library for such simultaneous training in a cloud environment.
We describe the motivating factors and resulting design of this library, feedback from initial testing, and future goals.
Multipath forwarding consists of using multiple paths simultaneously to transport data over the network.
While most such techniques require endpoint modifications, we investigate how multipath forwarding can be done inside the network, transparently to endpoint hosts.
With such a network-centric approach, packet reordering becomes a critical issue as it may cause critical performance degradation.
We present a Software Defined Network architecture which automatically sets up multipath forwarding, including solutions for reordering and performance improvement, both at the sending side through multipath scheduling algorithms, and the receiver side, by resequencing out-of-order packets in a dedicated in-network buffer.
We implemented a prototype with commonly available technology and evaluated it in both emulated and real networks.
Our results show consistent throughput improvements, thanks to the use of aggregated path capacity.
We give comparisons to Multipath TCP, where we show our approach can achieve a similar performance while offering the advantage of endpoint transparency.
This paper presents a new approach for detecting outliers by introducing the notion of object's proximity.
The main idea is that normal point has similar characteristics with several neighbors.
So the point in not an outlier if it has a high degree of proximity and its neighbors are several.
The performance of this approach is illustrated through real datasets
In this paper, we introduce a new channel model we term the q-ary multi-bit channel (QMBC).
This channel models a memory device, where q-ary symbols (q=2^s) are stored in the form of current/voltage levels.
The symbols are read in a measurement process, which provides a symbol bit in each measurement step, starting from the most significant bit.
An error event occurs when not all the symbol bits are known.
To deal with such error events, we use GF(q) low-density parity-check (LDPC) codes and analyze their decoding performance.
We start with iterative-decoding threshold analysis, and derive optimal edge-label distributions for maximizing the decoding threshold.
We later move to finite-length iterative-decoding analysis and propose an edge-labeling algorithm for improved decoding performance.
We then provide finite-length maximum-likelihood decoding analysis for both the standard non-binary random ensemble and LDPC ensembles.
Finally, we demonstrate by simulations that the proposed edge-labeling algorithm improves finite-length decoding performance by orders of magnitude.
In this paper, we model the document revision detection problem as a minimum cost branching problem that relies on computing document distances.
Furthermore, we propose two new document distance measures, word vector-based Dynamic Time Warping (wDTW) and word vector-based Tree Edit Distance (wTED).
Our revision detection system is designed for a large scale corpus and implemented in Apache Spark.
We demonstrate that our system can more precisely detect revisions than state-of-the-art methods by utilizing the Wikipedia revision dumps https://snap.stanford.edu/data/wiki-meta.html and simulated data sets.
This report describes my research activities in the Hasso Plattner Institute and summarizes my Ph.D. plan and several novels, end-to-end trainable approaches for analyzing medical images using deep learning algorithm.
In this report, as an example, we explore different novel methods based on deep learning for brain abnormality detection, recognition, and segmentation.
This report prepared for the doctoral consortium in the AIME-2017 conference.
In Wireless Sensor Networks, the sensor nodes are battery powered small devices designed for long battery life.
These devices also lack in terms of processing capability and memory.
In order to provide high confidentiality to these resource constrained network nodes, a suitable security algorithm is needed to be deployed that can establish a balance between security level and processing overhead.
The objective of this research work is to perform a security analysis and performance evaluation of recently proposed Secure Force algorithm.
This paper shows the comparison of Secure Force 64, 128, and 192 bit architecture on the basis of avalanche effect (key sensitivity), entropy change analysis, image histogram, and computational time.
Moreover, based on the evaluation results, the paper also suggests the possible solutions for the weaknesses of the SF algorithm.
This work proposes a unified heuristic algorithm for a large class of earliness-tardiness (E-T) scheduling problems.
We consider single/parallel machine E-T problems that may or may not consider some additional features such as idle time, setup times and release dates.
In addition, we also consider those problems whose objective is to minimize either the total (average) weighted completion time or the total (average) weighted flow time, which arise as particular cases when the due dates of all jobs are either set to zero or to their associated release dates, respectively.
The developed local search based metaheuristic framework is quite simple, but at the same time relies on sophisticated procedures for efficiently performing local search according to the characteristics of the problem.
We present efficient move evaluation approaches for some parallel machine problems that generalize the existing ones for single machine problems.
The algorithm was tested in hundreds of instances of several E-T problems and particular cases.
The results obtained show that our unified heuristic is capable of producing high quality solutions when compared to the best ones available in the literature that were obtained by specific methods.
Moreover, we provide an extensive annotated bibliography on the problems related to those considered in this work, where we not only indicate the approach(es) used in each publication, but we also point out the characteristics of the problem(s) considered.
Beyond that, we classify the existing methods in different categories so as to have a better idea of the popularity of each type of solution procedure.
Estimating the 6-DoF pose of a camera from a single image relative to a pre-computed 3D point-set is an important task for many computer vision applications.
Perspective-n-Point (PnP) solvers are routinely used for camera pose estimation, provided that a good quality set of 2D-3D feature correspondences are known beforehand.
However, finding optimal correspondences between 2D key-points and a 3D point-set is non-trivial, especially when only geometric (position) information is known.
Existing approaches to the simultaneous pose and correspondence problem use local optimisation, and are therefore unlikely to find the optimal solution without a good pose initialisation, or introduce restrictive assumptions.
Since a large proportion of outliers are common for this problem, we instead propose a globally-optimal inlier set cardinality maximisation approach which jointly estimates optimal camera pose and optimal correspondences.
Our approach employs branch-and-bound to search the 6D space of camera poses, guaranteeing global optimality without requiring a pose prior.
The geometry of SE(3) is used to find novel upper and lower bounds for the number of inliers and local optimisation is integrated to accelerate convergence.
The evaluation empirically supports the optimality proof and shows that the method performs much more robustly than existing approaches, including on a large-scale outdoor data-set.
We propose a novel approach to multi-fingered grasp planning leveraging learned deep neural network models.
We train a convolutional neural network to predict grasp success as a function of both visual information of an object and grasp configuration.
We can then formulate grasp planning as inferring the grasp configuration which maximizes the probability of grasp success.
We efficiently perform this inference using a gradient-ascent optimization inside the neural network using the backpropagation algorithm.
Our work is the first to directly plan high quality multifingered grasps in configuration space using a deep neural network without the need of an external planner.
We validate our inference method performing both multifinger and two-finger grasps on real robots.
Our experimental results show that our planning method outperforms existing planning methods for neural networks; while offering several other benefits including being data-efficient in learning and fast enough to be deployed in real robotic applications.
Sparse connectivity is an important factor behind the success of convolutional neural networks and recurrent neural networks.
In this paper, we consider the problem of learning sparse connectivity for feedforward neural networks (FNNs).
The key idea is that a unit should be connected to a small number of units at the next level below that are strongly correlated.
We use Chow-Liu's algorithm to learn a tree-structured probabilistic model for the units at the current level, use the tree to identify subsets of units that are strongly correlated, and introduce a new unit with receptive field over the subsets.
The procedure is repeated on the new units to build multiple layers of hidden units.
The resulting model is called a TRF-net.
Empirical results show that, when compared to dense FNNs, TRF-net achieves better or comparable classification performance with much fewer parameters and sparser structures.
They are also more interpretable.
In many common scenarios, programmers need to implement functionality that is already provided by some third party library.
This paper presents a tool called Hunter that facilitates code reuse by finding relevant methods in large code bases and automatically synthesizing any necessary wrapper code.
The key technical idea underlying our approach is to use types to both improve search results and guide synthesis.
Specifically, our method computes similarity metrics between types and uses this information to solve an integer linear programming (ILP) problem in which the objective is to minimize the cost of synthesis.
We have implemented Hunter as an Eclipse plug-in and evaluate it by (a) comparing it against S6, a state-of-the-art code reuse tool, and (b) performing a user study.
Our evaluation shows that Hunter compares favorably with S6 and significantly increases programmer productivity.
The application of psychophysiologicy in human-computer interaction is a growing field with significant potential for future smart personalised systems.
Working in this emerging field requires comprehension of an array of physiological signals and analysis techniques.
Eye tracking is a widely used method for tracking user attention with gaze location, but also provides information on the internal cognitive and contextual state, intention, and the locus of the user's visual attention in interactive settings through a number of eye and eyelid movement related parameters.
This paper presents a short review on the application of eye tracking in human-computer interaction.
This paper aims to serve as a primer for the novice, enabling rapid familiarisation with the latest core concepts.
We put special emphasis on everyday human-computer interface applications to distinguish from the more common clinical or sports uses of psychophysiology.
This paper is an extract from a comprehensive review of the entire field of ambulatory psychophysiology, including 12 similar chapters, plus application guidelines and systematic review.
Thus any citation should be made using the following reference:   B. Cowley, M. Filetti, K. Lukander, J. Torniainen, A. Henelius, L. Ahonen, O. Barral, I. Kosunen, T. Valtonen, M. Huotilainen, N. Ravaja, G. Jacucci.
The Psychophysiology Primer: a guide to methods and a broad review with a focus on human-computer interaction.
Foundations and Trends in Human-Computer Interaction, vol.9, no.3-4, pp.150--307, 2016.
Model based diagnosis finds a growing range of practical applications, and significant performance-wise improvements have been achieved in recent years.
Some of these improvements result from formulating the problem with maximum satisfiability (MaxSAT).
Whereas recent work focuses on analyzing failing observations separately, it is also the case that in practical settings there may exist many failing observations.
This paper first investigates the drawbacks of analyzing failing observations separately.
It then shows that existing solutions do not scale for large systems.
Finally, the paper proposes a novel approach for diagnosing systems with many failing observations.
The proposed approach is based on implicit hitting sets and so is tightly related with the original seminal work on model based diagnosis.
The experimental results demonstrate not only the importance of analyzing multiple observations simultaneously, but also the significance of the implicit hitting set approach.
A good measure of similarity between data points is crucial to many tasks in machine learning.
Similarity and metric learning methods learn such measures automatically from data, but they do not scale well respect to the dimensionality of the data.
In this paper, we propose a method that can learn efficiently similarity measure from high-dimensional sparse data.
The core idea is to parameterize the similarity measure as a convex combination of rank-one matrices with specific sparsity structures.
The parameters are then optimized with an approximate Frank-Wolfe procedure to maximally satisfy relative similarity constraints on the training data.
Our algorithm greedily incorporates one pair of features at a time into the similarity measure, providing an efficient way to control the number of active features and thus reduce overfitting.
It enjoys very appealing convergence guarantees and its time and memory complexity depends on the sparsity of the data instead of the dimension of the feature space.
Our experiments on real-world high-dimensional datasets demonstrate its potential for classification, dimensionality reduction and data exploration.
Reduction operations are extensively employed in many computational problems.
A reduction consists of, given a finite set of numeric elements, combining into a single value all elements in that set, using for this a combiner function.
A parallel reduction, in turn, is the reduction operation concurrently performed when multiple execution units are available.
The current work reports an investigation on this subject and depicts a GPU-based parallel approach for it.
Employing techniques like Loop Unrolling, Persistent Threads and Algebraic Expressions to avoid thread divergence, the presented approach was able to achieve a 2.8x speedup when compared to the work of Catanzaro, using a generic, simple and easily portable code.
Experiments conducted to evaluate the approach show that the strategy is able to perform efficiently in AMD and NVidia's hardware, as well as in OpenCL and CUDA.
We present a framework to learn privacy-preserving encodings of images that inhibit inference of chosen private attributes, while allowing recovery of other desirable information.
Rather than simply inhibiting a given fixed pre-trained estimator, our goal is that an estimator be unable to learn to accurately predict the private attributes even with knowledge of the encoding function.
We use a natural adversarial optimization-based formulation for this---training the encoding function against a classifier for the private attribute, with both modeled as deep neural networks.
The key contribution of our work is a stable and convergent optimization approach that is successful at learning an encoder with our desired properties---maintaining utility while inhibiting inference of private attributes, not just within the adversarial optimization, but also by classifiers that are trained after the encoder is fixed.
We adopt a rigorous experimental protocol for verification wherein classifiers are trained exhaustively till saturation on the fixed encoders.
We evaluate our approach on tasks of real-world complexity---learning high-dimensional encodings that inhibit detection of different scene categories---and find that it yields encoders that are resilient at maintaining privacy.
Relational representations in reinforcement learning allow for the use of structural information like the presence of objects and relationships between them in the description of value functions.
Through this paper, we show that such representations allow for the inclusion of background knowledge that qualitatively describes a state and can be used to design agents that demonstrate learning behavior in domains with large state and actions spaces such as computer games.
Restricted Boltzmann Machines (RBMs) and models derived from them have been successfully used as basic building blocks in deep artificial neural networks for automatic features extraction, unsupervised weights initialization, but also as density estimators.
Thus, their generative and discriminative capabilities, but also their computational time are instrumental to a wide range of applications.
Our main contribution is to look at RBMs from a topological perspective, bringing insights from network science.
Firstly, here we show that RBMs and Gaussian RBMs (GRBMs) are bipartite graphs which naturally have a small-world topology.
Secondly, we demonstrate both on synthetic and real-world datasets that by constraining RBMs and GRBMs to a scale-free topology (while still considering local neighborhoods and data distribution), we reduce the number of weights that need to be computed by a few orders of magnitude, at virtually no loss in generative performance.
Thirdly, we show that, for a fixed number of weights, our proposed sparse models (which by design have a higher number of hidden neurons) achieve better generative capabilities than standard fully connected RBMs and GRBMs (which by design have a smaller number of hidden neurons), at no additional computational costs.
Consider a device-to-device (D2D) fog-radio access network wherein a set of devices are required to store a set of files.
Each device is connected to a subset of the cloud data centers and thus possesses a subset of the data.
This paper investigates the problem of disseminating all files among the devices while reducing the total time of communication, i.e., the completion time, using instantly decodable network coding (IDNC).
While previous studies on the use of IDNC in D2D systems assume a fully connected communication network, this paper tackles the more realistic scenario of a partially connected network in which devices can only target devices in their transmission range.
The paper first formulates the optimal joint optimization of selecting the transmitting device(s) and the file combination(s) and exhibits its intractability.
The completion time is approximated using the celebrated decoding delay approach by deriving the relationship between the quantities in a partially connected network.
The paper introduces the cooperation graph and demonstrates that the relaxed problem is equivalent to a maximum weight clique problem over the newly designed graph wherein the weights are obtained by solving a similar problem on the local IDNC graphs.
Extensive simulations reveal that the proposed solution provides noticeable performance enhancement and outperforms previously proposed IDNC-based schemes.
A new algorithm to generate all Dyck words is presented, which is used in ranking and unranking Dyck words.
We emphasize the importance of using Dyck words in encoding objects related to Catalan numbers.
As a consequence of formulas used in the ranking algorithm we can obtain a recursive formula for the nth Catalan number.
Bots have been playing a crucial role in online platform ecosystems, as efficient and automatic tools to generate content and diffuse information to the social media human population.
In this chapter, we will discuss the role of social bots in content spreading dynamics in social media.
In particular, we will first investigate some differences between diffusion dynamics of content generated by bots, as opposed to humans, in the context of political communication, then study the characteristics of bots behind the diffusion dynamics of social media spam campaigns.
Binarization of degraded historical manuscript images is an important pre-processing step for many document processing tasks.
We formulate binarization as a pixel classification learning task and apply a novel Fully Convolutional Network (FCN) architecture that operates at multiple image scales, including full resolution.
The FCN is trained to optimize a continuous version of the Pseudo F-measure metric and an ensemble of FCNs outperform the competition winners on 4 of 7 DIBCO competitions.
This same binarization technique can also be applied to different domains such as Palm Leaf Manuscripts with good performance.
We analyze the performance of the proposed model w.r.t. the architectural hyperparameters, size and diversity of training data, and the input features chosen.
Diagrammatic models of feeding choices reveal fundamental robotic behaviors.
Successful choices are reinforced by positive feedback, while unsuccessful ones by negative feedback.
This paper will address robotic feeding by casually relating consequential behavior subtended by a strong dependence upon survival.
Unlike its deterministic counterpart, static and stochastic vehicle routing problems (SS-VRP) aim at modeling and solving real-life operational problems by considering uncertainty on data.
We consider the SS-VRPTW-CR introduced in Saint-Guillain et al.(2017).
Like the SS-VRP introduced by Bertsimas (1992), we search for optimal first stage routes for a fleet of vehicles to handle a set of stochastic customer demands, i.e., demands are uncertain and we only know their probabilities.
In addition to capacity constraints, customer demands are also constrained by time windows.
Unlike all SS-VRP variants, the SS-VRPTW-CR does not make any assumption on the time at which a stochastic demand is revealed, i.e., the reveal time is stochastic as well.
To handle this new problem, we introduce waiting locations: Each vehicle is assigned a sequence of waiting locations from which it may serve some associated demands, and the objective is to minimize the expected number of demands that cannot be satisfied in time.
In this paper, we propose two new recourse strategies for the SS-VRPTW-CR, together with their closed-form expressions for efficiently computing their expectations: The first one allows us to take vehicle capacities into account; The second one allows us to optimize routes by avoiding some useless trips.
We propose two algorithms for searching for routes with optimal expected costs: The first one is an extended branch-and-cut algorithm, based on a stochastic integer formulation, and the second one is a local search based heuristic method.
We also introduce a new public benchmark for the SS-VRPTW-CR, based on real-world data coming from the city of Lyon.
We evaluate our two algorithms on this benchmark and empirically demonstrate the expected superiority of the SS-VRPTW-CR anticipative actions over a basic "wait-and-serve" policy.
In this paper we present Foggy, an architectural framework and software platform based on Open Source technologies.
Foggy orchestrates application workload, negotiates resources and supports IoT operations for multi-tier, distributed, heterogeneous and decentralized Cloud Computing systems.
Foggy is tailored for emerging domains such as 5G Networks and IoT, which demand resources and services to be distributed and located close to data sources and users following the Fog Computing paradigm.
Foggy provides a platform for infrastructure owners and tenants (i.e., application providers) offering functionality of negotiation, scheduling and workload placement taking into account traditional requirements (e.g. based on RAM, CPU, disk) and non-traditional ones (e.g. based on networking) as well as diversified constraints on location and access rights.
Economics and pricing of resources can also be considered by the Foggy model in a near future.
The ability of Foggy to find a trade-off between infrastructure owners' and tenants' needs, in terms of efficient and optimized use of the infrastructure while satisfying the application requirements, is demonstrated through three use cases in the video surveillance and vehicle tracking contexts.
This paper describes a context free grammar (CFG) based grammatical relations for Myanmar sentences which combine corpus-based function tagging system.
Part of the challenge of statistical function tagging for Myanmar sentences comes from the fact that Myanmar has free-phrase-order and a complex morphological system.
Function tagging is a pre-processing step to show grammatical relations of Myanmar sentences.
In the task of function tagging, which tags the function of Myanmar sentences with correct segmentation, POS (part-of-speech) tagging and chunking information, we use Naive Bayesian theory to disambiguate the possible function tags of a word.
We apply context free grammar (CFG) to find out the grammatical relations of the function tags.
We also create a functional annotated tagged corpus for Myanmar and propose the grammar rules for Myanmar sentences.
Experiments show that our analysis achieves a good result with simple sentences and complex sentences.
Bimanual gestures are of the utmost importance for the study of motor coordination in humans and in everyday activities.
A reliable detection of bimanual gestures in unconstrained environments is fundamental for their clinical study and to assess common activities of daily living.
This paper investigates techniques for a reliable, unconstrained detection and classification of bimanual gestures.
It assumes the availability of inertial data originating from the two hands/arms, builds upon a previously developed technique for gesture modelling based on Gaussian Mixture Modelling (GMM) and Gaussian Mixture Regression (GMR), and compares different modelling and classification techniques, which are based on a number of assumptions inspired by literature about how bimanual gestures are represented and modelled in the brain.
Experiments show results related to 5 everyday bimanual activities, which have been selected on the basis of three main parameters: (not) constraining the two hands by a physical tool, (not) requiring a specific sequence of single-hand gestures, being recursive (or not).
In the best performing combination of modeling approach and classification technique, five out of five activities are recognized up to an accuracy of 97%, a precision of 82% and a level of recall of 100%.
This paper describes QCRI's machine translation systems for the IWSLT 2016 evaluation campaign.
We participated in the Arabic->English and English->Arabic tracks.
We built both Phrase-based and Neural machine translation models, in an effort to probe whether the newly emerged NMT framework surpasses the traditional phrase-based systems in Arabic-English language pairs.
We trained a very strong phrase-based system including, a big language model, the Operation Sequence Model, Neural Network Joint Model and Class-based models along with different domain adaptation techniques such as MML filtering, mixture modeling and using fine tuning over NNJM model.
However, a Neural MT system, trained by stacking data from different genres through fine-tuning, and applying ensemble over 8 models, beat our very strong phrase-based system by a significant 2 BLEU points margin in Arabic->English direction.
We did not obtain similar gains in the other direction but were still able to outperform the phrase-based system.
We also applied system combination on phrase-based and NMT outputs.
The splendid success of convolutional neural networks (CNNs) in computer vision is largely attributed to the availability of large annotated datasets, such as ImageNet and Places.
However, in biomedical imaging, it is very challenging to create such large annotated datasets, as annotating biomedical images is not only tedious, laborious, and time consuming, but also demanding of costly, specialty-oriented skills, which are not easily accessible.
To dramatically reduce annotation cost, this paper presents a novel method to naturally integrate active learning and transfer learning (fine-tuning) into a single framework, called AFT*, which starts directly with a pre-trained CNN to seek "worthy" samples for annotation and gradually enhance the (fine-tuned) CNN via continuous fine-tuning.
We have evaluated our method in three distinct biomedical imaging applications, demonstrating that it can cut the annotation cost by at least half, in comparison with the state-of-the-art method.
This performance is attributed to the several advantages derived from the advanced active, continuous learning capability of our method.
Although AFT* was initially conceived in the context of computer-aided diagnosis in biomedical imaging, it is generic and applicable to many tasks in computer vision and image analysis; we illustrate the key ideas behind AFT* with the Places database for scene interpretation in natural images.
In monocular vision systems, lack of knowledge about metric distances caused by the inherent scale ambiguity can be a strong limitation for some applications.
We offer a method for fusing inertial measurements with monocular odometry or tracking to estimate metric distances in inertial-monocular systems and to increase the rate of pose estimates.
As we performed the fusion in a loosely-coupled manner, each input block can be easily replaced with one's preference, which makes our method quite flexible.
We experimented our method using the ORB-SLAM algorithm for the monocular tracking input and Euler forward integration to process the inertial measurements.
We chose sets of data recorded on UAVs to design a suitable system for flying robots.
High Utility Itemset (HUI) mining problem is one of the important problems in the data mining literature.
The problem offers greater flexibility to a decision maker to incorporate her/his notion of utility into the pattern mining process.
The problem, however, requires the decision maker to choose a minimum utility threshold value for discovering interesting patterns.
This is quite challenging due to the disparate itemset characteristics and their utility distributions.
In order to address this issue, Top-K High Utility Itemset (THUI) mining problem was introduced in the literature.
THUI mining problem is primarily a variant of the HUI mining problem that allows a decision maker to specify the desired number of HUIs rather than the minimum utility threshold value.
Several algorithms have been introduced in the literature to efficiently mine top-k HUIs.
This paper systematically analyses the top-k HUI mining methods in the literature, describes the methods, and performs a comparative analysis.
The data structures, threshold raising strategies, and pruning strategies adopted for efficient top-k HUI mining are also presented and analysed.
Furthermore, the paper reviews several extensions of the top-k HUI mining problem such as data stream mining, sequential pattern mining and on-shelf utility mining.
The paper is likely to be useful for researchers to examine the key methods in top-k HUI mining, evaluate the gaps in literature, explore new research opportunities and enhance the state-of-the-art in high utility pattern mining.
Unmanned Aerial Vehicles (UAVs) have been recently considered as means to provide enhanced coverage or relaying services to mobile users (MUs) in wireless systems with limited or no infrastructure.
In this paper, a UAV-based mobile cloud computing system is studied in which a moving UAV is endowed with computing capabilities to offer computation offloading opportunities to MUs with limited local processing capabilities.
The system aims at minimizing the total mobile energy consumption while satisfying quality of service requirements of the offloaded mobile application.
Offloading is enabled by uplink and downlink communications between the mobile devices and the UAV that take place by means of frequency division duplex (FDD) via orthogonal or non-orthogonal multiple access (NOMA) schemes.
The problem of jointly optimizing the bit allocation for uplink and downlink communication as well as for computing at the UAV, along with the cloudlet's trajectory under latency and UAV's energy budget constraints is formulated and addressed by leveraging successive convex approximation (SCA) strategies.
Numerical results demonstrate the significant energy savings that can be accrued by means of the proposed joint optimization of bit allocation and cloudlet's trajectory as compared to local mobile execution as well as to partial optimization approaches that design only the bit allocation or the cloudlet's trajectory.
Semi-supervised node classification in attributed graphs, i.e., graphs with node features, involves learning to classify unlabeled nodes given a partially labeled graph.
Label predictions are made by jointly modeling the node and its' neighborhood features.
State-of-the-art models for node classification on such attributed graphs use differentiable recursive functions that enable aggregation and filtering of neighborhood information from multiple hops.
In this work, we analyze the representation capacity of these models to regulate information from multiple hops independently.
From our analysis, we conclude that these models despite being powerful, have limited representation capacity to capture multi-hop neighborhood information effectively.
Further, we also propose a mathematically motivated, yet simple extension to existing graph convolutional networks (GCNs) which has improved representation capacity.
We extensively evaluate the proposed model, F-GCN on eight popular datasets from different domains.
F-GCN outperforms the state-of-the-art models for semi-supervised learning on six datasets while being extremely competitive on the other two.
In this paper, we introduce a simple, yet powerful pipeline for medical image segmentation that combines Fully Convolutional Networks (FCNs) with Fully Convolutional Residual Networks (FC-ResNets).
We propose and examine a design that takes particular advantage of recent advances in the understanding of both Convolutional Neural Networks as well as ResNets.
Our approach focuses upon the importance of a trainable pre-processing when using FC-ResNets and we show that a low-capacity FCN model can serve as a pre-processor to normalize medical input data.
In our image segmentation pipeline, we use FCNs to obtain normalized images, which are then iteratively refined by means of a FC-ResNet to generate a segmentation prediction.
As in other fully convolutional approaches, our pipeline can be used off-the-shelf on different image modalities.
We show that using this pipeline, we exhibit state-of-the-art performance on the challenging Electron Microscopy benchmark, when compared to other 2D methods.
We improve segmentation results on CT images of liver lesions, when contrasting with standard FCN methods.
Moreover, when applying our 2D pipeline on a challenging 3D MRI prostate segmentation challenge we reach results that are competitive even when compared to 3D methods.
The obtained results illustrate the strong potential and versatility of the pipeline by achieving highly accurate results on multi-modality images from different anatomical regions and organs.
What properties about the internals of a program explain the possible differences in its overall running time for different inputs?
In this paper, we propose a formal framework for considering this question we dub trace-set discrimination.
We show that even though the algorithmic problem of computing maximum likelihood discriminants is NP-hard, approaches based on integer linear programming (ILP) and decision tree learning can be useful in zeroing-in on the program internals.
On a set of Java benchmarks, we find that compactly-represented decision trees scalably discriminate with high accuracy---more scalably than maximum likelihood discriminants and with comparable accuracy.
We demonstrate on three larger case studies how decision-tree discriminants produced by our tool are useful for debugging timing side-channel vulnerabilities (i.e., where a malicious observer infers secrets simply from passively watching execution times) and availability vulnerabilities.
We propose a Deep Texture Encoding Network (Deep-TEN) with a novel Encoding Layer integrated on top of convolutional layers, which ports the entire dictionary learning and encoding pipeline into a single model.
Current methods build from distinct components, using standard encoders with separate off-the-shelf features such as SIFT descriptors or pre-trained CNN features for material recognition.
Our new approach provides an end-to-end learning framework, where the inherent visual vocabularies are learned directly from the loss function.
The features, dictionaries and the encoding representation for the classifier are all learned simultaneously.
The representation is orderless and therefore is particularly useful for material and texture recognition.
The Encoding Layer generalizes robust residual encoders such as VLAD and Fisher Vectors, and has the property of discarding domain specific information which makes the learned convolutional features easier to transfer.
Additionally, joint training using multiple datasets of varied sizes and class labels is supported resulting in increased recognition performance.
The experimental results show superior performance as compared to state-of-the-art methods using gold-standard databases such as MINC-2500, Flickr Material Database, KTH-TIPS-2b, and two recent databases 4D-Light-Field-Material and GTOS.
The source code for the complete system are publicly available.
We present a formalized, fully decentralized runtime semantics for a core subset of ABS, a language and framework for modelling distributed object-oriented systems.
The semantics incorporates an abstract graph representation of a network infrastructure, with network endpoints represented as graph nodes, and links as arcs with buffers, corresponding to OSI layer 2 interconnects.
The key problem we wish to address is how to allocate computational tasks to nodes so that certain performance objectives are met.
To this end, we use the semantics as a foundation for performing network-adaptive task execution via object migration between nodes.
Adaptability is analyzed in terms of three Quality of Service objectives: node load, arc load and message latency.
We have implemented the key parts of our semantics in a simulator and evaluated how well objectives are achieved for some application-relevant choices of network topology, migration procedure and ABS program.
The evaluation suggests that it is feasible in a decentralized setting to continually meet both the objective of a node-balanced task allocation and make headway towards minimizing communication, and thus arc load and message latency.
Multiple automakers have in development or in production automated driving systems (ADS) that offer freeway-pilot functions.
This type of ADS is typically limited to restricted-access freeways only, that is, the transition from manual to automated modes takes place only after the ramp merging process is completed manually.
One major challenge to extend the automation to ramp merging is that the automated vehicle needs to incorporate and optimize long-term objectives (e.g. successful and smooth merge) when near-term actions must be safely executed.
Moreover, the merging process involves interactions with other vehicles whose behaviors are sometimes hard to predict but may influence the merging vehicle optimal actions.
To tackle such a complicated control problem, we propose to apply Deep Reinforcement Learning (DRL) techniques for finding an optimal driving policy by maximizing the long-term reward in an interactive environment.
Specifically, we apply a Long Short-Term Memory (LSTM) architecture to model the interactive environment, from which an internal state containing historical driving information is conveyed to a Deep Q-Network (DQN).
The DQN is used to approximate the Q-function, which takes the internal state as input and generates Q-values as output for action selection.
With this DRL architecture, the historical impact of interactive environment on the long-term reward can be captured and taken into account for deciding the optimal control policy.
The proposed architecture has the potential to be extended and applied to other autonomous driving scenarios such as driving through a complex intersection or changing lanes under varying traffic flow conditions.
The way in which electric power depends on the topology of circuits with mixed voltage and current sources is examined.
The power flowing in any steady-state DC circuit is shown to depend on a minimal set of key variables called fundamental node voltages and fundamental edge currents.
Every steady-state DC circuit can be decomposed into a voltage controlled subcircuit and a current controlled subcircuit.
In terms of such a decomposition, the I^2R losses of a mixed source circuit are always the sum of losses on the voltage controlled subcircuit and the current controlled subcircuit.
The paper concludes by showing that the total power flowing in a mixed source circuit can be found as critical points of the power expressed in terms of the key voltage and current variables mentioned above.
The possible relationship to topology control of electric grid operations is discussed.
We present an effect system for core Eff, a simplified variant of Eff, which is an ML-style programming language with first-class algebraic effects and handlers.
We define an expressive effect system and prove safety of operational semantics with respect to it.
Then we give a domain-theoretic denotational semantics of core Eff, using Pitts's theory of minimal invariant relations, and prove it adequate.
We use this fact to develop tools for finding useful contextual equivalences, including an induction principle.
To demonstrate their usefulness, we use these tools to derive the usual equations for mutable state, including a general commutativity law for computations using non-interfering references.
We have formalized the effect system, the operational semantics, and the safety theorem in Twelf.
The rank aggregation problem has received significant recent attention within the computer science community.
Its applications today range far beyond the original aim of building metasearch engines to problems in machine learning, recommendation systems and more.
Several algorithms have been proposed for these problems, and in many cases approximation guarantees have been proven for them.
However, it is also known that some Markov chain based algorithms (MC1, MC2, MC3, MC4) perform extremely well in practice, yet had no known performance guarantees.
We prove supra-constant lower bounds on approximation guarantees for all of them.
We also raise the lower bound for sorting by Copeland score from 3/2 to 2 and prove an upper bound of 11, before showing that in particular ways, MC4 can nevertheless be seen as a generalization of Copeland score.
Use case driven development methodologies put use cases at the center of the software development process.
However, in order to support automated development and analysis, use cases need to be appropriately formalized.
This will also help guarantee consistency between requirements specifications and the developed solutions.
Formal methods tend to suffer from take up issues, as they are usually hard to accept by industry.
In this context, it is relevant not only to produce languages and approaches to support formalization, but also to perform their validation.
In previous works we have developed an approach to formalize use cases resorting to ontologies.
In this paper we present the validation of one such approach.
Through a three stage study, we evaluate the acceptance of the language and supporting tool.
The first stage focusses on the acceptance of the process and language, the second on the support the tool provides to the process, and finally the third one on the tool's usability aspects.
Results show test subjects found the approach feasible and useful and the tool easy to use.
Contemporary electricity distribution systems are being challenged by the variability of renewable energy sources.
Slow response times and long energy management periods cannot efficiently integrate intermittent renewable generation and demand.
Yet stochasticity can be judiciously coupled with system flexibilities to enhance grid operation efficiency.
Voltage magnitudes for instance can transiently exceed regulation limits, while smart inverters can be overloaded over short time intervals.
To implement such a mode of operation, an ergodic energy management framework is developed here.
Considering a distribution grid with distributed energy sources and a feed-in tariff program, active power curtailment and reactive power compensation are formulated as a stochastic optimization problem.
Tighter operational constraints are enforced in an average sense, while looser margins are enforced to be satisfied at all times.
Stochastic dual subgradient solvers are developed based on exact and approximate grid models of varying complexity.
Numerical tests on a real-world 56-bus distribution grid and the IEEE 123-bus test feeder relying on both grid models corroborate the advantages of the novel schemes over their deterministic alternatives.
It is well known that the emptiness problem for binary probabilistic automata and so for quantum automata is undecidable.
We present the current status of the emptiness problems for unary probabilistic and quantum automata with connections with Skolem's and positivity problems.
We also introduce the concept of linear recurrence automata in order to show the connection naturally.
Then, we also give possible generalizations of linear recurrence relations and automata on vectors.
Advances in machine learning have led to broad deployment of systems with impressive performance on important problems.
Nonetheless, these systems can be induced to make errors on data that are surprisingly similar to examples the learned system handles correctly.
The existence of these errors raises a variety of questions about out-of-sample generalization and whether bad actors might use such examples to abuse deployed systems.
As a result of these security concerns, there has been a flurry of recent papers proposing algorithms to defend against such malicious perturbations of correctly handled examples.
It is unclear how such misclassifications represent a different kind of security problem than other errors, or even other attacker-produced examples that have no specific relationship to an uncorrupted input.
In this paper, we argue that adversarial example defense papers have, to date, mostly considered abstract, toy games that do not relate to any specific security concern.
Furthermore, defense papers have not yet precisely described all the abilities and limitations of attackers that would be relevant in practical security.
Towards this end, we establish a taxonomy of motivations, constraints, and abilities for more plausible adversaries.
Finally, we provide a series of recommendations outlining a path forward for future work to more clearly articulate the threat model and perform more meaningful evaluation.
There is a growing demand for accurate high-resolution land cover maps in many fields, e.g., in land-use planning and biodiversity conservation.
Developing such maps has been performed using Object-Based Image Analysis (OBIA) methods, which usually reach good accuracies, but require a high human supervision and the best configuration for one image can hardly be extrapolated to a different image.
Recently, the deep learning Convolutional Neural Networks (CNNs) have shown outstanding results in object recognition in the field of computer vision.
However, they have not been fully explored yet in land cover mapping for detecting species of high biodiversity conservation interest.
This paper analyzes the potential of CNNs-based methods for plant species detection using free high-resolution Google Earth T M images and provides an objective comparison with the state-of-the-art OBIA-methods.
We consider as case study the detection of Ziziphus lotus shrubs, which are protected as a priority habitat under the European Union Habitats Directive.
According to our results, compared to OBIA-based methods, the proposed CNN-based detection model, in combination with data-augmentation, transfer learning and pre-processing, achieves higher performance with less human intervention and the knowledge it acquires in the first image can be transferred to other images, which makes the detection process very fast.
The provided methodology can be systematically reproduced for other species detection.
We introduce a game-theoretic framework to study the hypothesis testing problem, in the presence of an adversary aiming at preventing a correct decision.
Specifically, the paper considers a scenario in which an analyst has to decide whether a test sequence has been drawn according to a probability mass function (pmf) P_X or not.
In turn, the goal of the adversary is to take a sequence generated according to a different pmf and modify it in such a way to induce a decision error.
P_X is known only through one or more training sequences.
We derive the asymptotic equilibrium of the game under the assumption that the analyst relies only on first order statistics of the test sequence, and compute the asymptotic payoff of the game when the length of the test sequence tends to infinity.
We introduce the concept of indistinguishability region, as the set of pmf's that can not be distinguished reliably from P_X in the presence of attacks.
Two different scenarios are considered: in the first one the analyst and the adversary share the same training sequence, in the second scenario, they rely on independent sequences.
The obtained results are compared to a version of the game in which the pmf P_X is perfectly known to the analyst and the adversary.
Motivated by online advertising auctions, we consider repeated Vickrey auctions where goods of unknown value are sold sequentially and bidders only learn (potentially noisy) information about a good's value once it is purchased.
We adopt an online learning approach with bandit feedback to model this problem and derive bidding strategies for two models: stochastic and adversarial.
In the stochastic model, the observed values of the goods are random variables centered around the true value of the good.
In this case, logarithmic regret is achievable when competing against well behaved adversaries.
In the adversarial model, the goods need not be identical and we simply compare our performance against that of the best fixed bid in hindsight.
We show that sublinear regret is also achievable in this case and prove matching minimax lower bounds.
To our knowledge, this is the first complete set of strategies for bidders participating in auctions of this type.
Testing is the most widely employed method to find vulnerabilities in real-world software programs.
Compositional analysis, based on symbolic execution, is an automated testing method to find vulnerabilities in medium- to large-scale programs consisting of many interacting components.
However, existing compositional analysis frameworks do not assess the severity of reported vulnerabilities.
In this paper, we present a framework to analyze vulnerabilities discovered by an existing compositional analysis tool and assign CVSS3 (Common Vulnerability Scoring System v3.0) scores to them, based on various heuristics such as interaction with related components, ease of reachability, complexity of design and likelihood of accepting unsanitized input.
By analyzing vulnerabilities reported with CVSS3 scores in the past, we train simple machine learning models.
By presenting our interactive framework to developers of popular open-source software and other security experts, we gather feedback on our trained models and further improve the features to increase the accuracy of our predictions.
By providing qualitative (based on community feedback) and quantitative (based on prediction accuracy) evidence from 21 open-source programs, we show that our severity prediction framework can effectively assist developers with assessing vulnerabilities.
To aid a variety of research studies, we propose TWIROLE, a hybrid model for role-related user classification on Twitter, which detects male-related, female-related, and brand-related (i.e., organization or institution) users.
TWIROLE leverages features from tweet contents, user profiles, and profile images, and then applies our hybrid model to identify a user's role.
To evaluate it, we used two existing large datasets about Twitter users, and conducted both intra- and inter-comparison experiments.
TWIROLE outperforms existing methods and obtains more balanced results over the several roles.
We also confirm that user names and profile images are good indicators for this task.
Our research extends prior work that does not consider brand-related users, and is an aid to future evaluation efforts relative to investigations that rely upon self-labeled datasets.
In this paper we present OSC, a scientific workflow specification language based on software architecture principles.
In contrast with other approaches, OSC employs connectors as first-class constructs.
In this way, we leverage reusability and compositionality in the workflow modeling process, specially in the configuration of mechanisms that manage non-functional attributes.
We present a new computational model for gaze prediction in egocentric videos by exploring patterns in temporal shift of gaze fixations (attention transition) that are dependent on egocentric manipulation tasks.
Our assumption is that the high-level context of how a task is completed in a certain way has a strong influence on attention transition and should be modeled for gaze prediction in natural dynamic scenes.
Specifically, we propose a hybrid model based on deep neural networks which integrates task-dependent attention transition with bottom-up saliency prediction.
In particular, the task-dependent attention transition is learned with a recurrent neural network to exploit the temporal context of gaze fixations, e.g.looking at a cup after moving gaze away from a grasped bottle.
Experiments on public egocentric activity datasets show that our model significantly outperforms state-of-the-art gaze prediction methods and is able to learn meaningful transition of human attention.
We present an online deliberation system using mutual evaluation in order to collaboratively develop solutions.
Participants submit their proposals and evaluate each other's proposals; some of them may then be invited by the system to rewrite 'problematic' proposals.
Two cases are discussed: a proposal supported by many, but not by a given person, who is then invited to rewrite it for making yet more acceptable; and a poorly presented but presumably interesting proposal.
The first of these cases has been successfully implemented.
Proposals are evaluated along two axes-understandability (or clarity, or, more generally, quality), and agreement.
The latter is used by the system to cluster proposals according to their ideas, while the former is used both to present the best proposals on top of their clusters, and to find poorly written proposals candidates for rewriting.
These functionalities may be considered as important components of a large scale online deliberation system.
In this paper, we propose multi-variable LSTM capable of accurate forecasting and variable importance interpretation for time series with exogenous variables.
Current attention mechanism in recurrent neural networks mostly focuses on the temporal aspect of data and falls short of characterizing variable importance.
To this end, the multi-variable LSTM equipped with tensorized hidden states is developed to learn hidden states for individual variables, which give rise to our mixture temporal and variable attention.
Based on such attention mechanism, we infer and quantify variable importance.
Extensive experiments using real datasets with Granger-causality test and the synthetic dataset with ground truth demonstrate the prediction performance and interpretability of multi-variable LSTM in comparison to a variety of baselines.
It exhibits the prospect of multi-variable LSTM as an end-to-end framework for both forecasting and knowledge discovery.
Computationally synthesized blood vessels can be used for training and evaluation of medical image analysis applications.
We propose a deep generative model to synthesize blood vessel geometries, with an application to coronary arteries in cardiac CT angiography (CCTA).
In the proposed method, a Wasserstein generative adversarial network (GAN) consisting of a generator and a discriminator network is trained.
While the generator tries to synthesize realistic blood vessel geometries, the discriminator tries to distinguish synthesized geometries from those of real blood vessels.
Both real and synthesized blood vessel geometries are parametrized as 1D signals based on the central vessel axis.
The generator can optionally be provided with an attribute vector to synthesize vessels with particular characteristics.
The GAN was optimized using a reference database with parametrizations of 4,412 real coronary artery geometries extracted from CCTA scans.
After training, plausible coronary artery geometries could be synthesized based on random vectors sampled from a latent space.
A qualitative analysis showed strong similarities between real and synthesized coronary arteries.
A detailed analysis of the latent space showed that the diversity present in coronary artery anatomy was accurately captured by the generator.
Results show that Wasserstein generative adversarial networks can be used to synthesize blood vessel geometries.
This paper presents an open platform, which collects multimodal environmental data related to air quality from several sources including official open sources, social media and citizens.
Collecting and fusing different sources of air quality data into a unified air quality indicator is a highly challenging problem, leveraging recent advances in image analysis, open hardware, machine learning and data fusion and is expected to result in increased geographical coverage and temporal granularity of air quality data.
Robust Stable Marriage (RSM) is a variant of the classical Stable Marriage problem, where the robustness of a given stable matching is measured by the number of modifications required for repairing it in case an unforeseen event occurs.
We focus on the complexity of finding an (a,b)-supermatch.
An (a,b)-supermatch is defined as a stable matching in which if any 'a' (non-fixed) men/women break up it is possible to find another stable matching by changing the partners of those 'a' men/women and also the partners of at most 'b' other couples.
In order to show deciding if there exists an (a,b)-supermatch is NP-Complete, we first introduce a SAT formulation that is NP-Complete by using Schaefer's Dichotomy Theorem.
Then, we show the equivalence between the SAT formulation and finding a (1,1)-supermatch on a specific family of instances.
We propose Nazr-CNN1, a deep learning pipeline for object detection and fine-grained classification in images acquired from Unmanned Aerial Vehicles (UAVs) for damage assessment and monitoring.
Nazr-CNN consists of two components.
The function of the first component is to localize objects (e.g. houses or infrastructure) in an image by carrying out a pixel-level classification.
In the second component, a hidden layer of a Convolutional Neural Network (CNN) is used to encode Fisher Vectors (FV) of the segments generated from the first component in order to help discriminate between different levels of damage.
To showcase our approach we use data from UAVs that were deployed to assess the level of damage in the aftermath of a devastating cyclone that hit the island of Vanuatu in 2015.
The collected images were labeled by a crowdsourcing effort and the labeling categories consisted of fine-grained levels of damage to built structures.
Since our data set is relatively small, a pre- trained network for pixel-level classification and FV encoding was used.
Nazr-CNN attains promising results both for object detection and damage assessment suggesting that the integrated pipeline is robust in the face of small data sets and labeling errors by annotators.
While the focus of Nazr-CNN is on assessment of UAV images in a post-disaster scenario, our solution is general and can be applied in many diverse settings.
We show one such case of transfer learning to assess the level of damage in aerial images collected after a typhoon in Philippines.
The most widely used activation functions in current deep feed-forward neural networks are rectified linear units (ReLU), and many alternatives have been successfully applied, as well.
However, none of the alternatives have managed to consistently outperform the rest and there is no unified theory connecting properties of the task and network with properties of activation functions for most efficient training.
A possible solution is to have the network learn its preferred activation functions.
In this work, we introduce Adaptive Blending Units (ABUs), a trainable linear combination of a set of activation functions.
Since ABUs learn the shape, as well as the overall scaling of the activation function, we also analyze the effects of adaptive scaling in common activation functions.
We experimentally demonstrate advantages of both adaptive scaling and ABUs over common activation functions across a set of systematically varied network specifications.
We further show that adaptive scaling works by mitigating covariate shifts during training, and that the observed advantages in performance of ABUs likewise rely largely on the activation function's ability to adapt over the course of training.
We explore learning-based approaches for feedback control of a dexterous five-finger hand performing non-prehensile manipulation.
First, we learn local controllers that are able to perform the task starting at a predefined initial state.
These controllers are constructed using trajectory optimization with respect to locally-linear time-varying models learned directly from sensor data.
In some cases, we initialize the optimizer with human demonstrations collected via teleoperation in a virtual environment.
We demonstrate that such controllers can perform the task robustly, both in simulation and on the physical platform, for a limited range of initial conditions around the trained starting state.
We then consider two interpolation methods for generalizing to a wider range of initial conditions: deep learning, and nearest neighbors.
We find that nearest neighbors achieve higher performance.
Nevertheless, the neural network has its advantages: it uses only tactile and proprioceptive feedback but no visual feedback about the object (i.e. it performs the task blind) and learns a time-invariant policy.
In contrast, the nearest neighbors method switches between time-varying local controllers based on the proximity of initial object states sensed via motion capture.
While both generalization methods leave room for improvement, our work shows that (i) local trajectory-based controllers for complex non-prehensile manipulation tasks can be constructed from surprisingly small amounts of training data, and (ii) collections of such controllers can be interpolated to form more global controllers.
Results are summarized in the supplementary video: https://youtu.be/E0wmO6deqjo
We introduce a deep residual recurrent neural network (DR-RNN) as an efficient model reduction technique for nonlinear dynamical systems.
The developed DR-RNN is inspired by the iterative steps of line search methods in finding the residual minimiser of numerically discretized differential equations.
We formulate this iterative scheme as stacked recurrent neural network (RNN) embedded with the dynamical structure of the emulated differential equations.
Numerical examples demonstrate that DR-RNN can effectively emulate the full order models of nonlinear physical systems with a significantly lower number of parameters in comparison to standard RNN architectures.
Further, we combined DR-RNN with Proper Orthogonal Decomposition (POD) for model reduction of time dependent partial differential equations.
The presented numerical results show the stability of proposed DR-RNN as an explicit reduced order technique.
We also show significant gains in accuracy by increasing the depth of proposed DR-RNN similar to other applications of deep learning.
Cybercrime forums enable modern criminal entrepreneurs to collaborate with other criminals into increasingly efficient and sophisticated criminal endeavors.
Understanding the connections between different products and services can often illuminate effective interventions.
However, generating this understanding of supply chains currently requires time-consuming manual effort.
In this paper, we propose a language-agnostic method to automatically extract supply chains from cybercrime forum posts and replies.
Our supply chain detection algorithm can identify 36% and 58% relevant chains within major English and Russian forums, respectively, showing improvements over the baselines of 13% and 36%, respectively.
Our analysis of the automatically generated supply chains demonstrates underlying connections between products and services within these forums.
For example, the extracted supply chain illuminated the connection between hack-for-hire services and the selling of rare and valuable `OG' accounts, which has only recently been reported.
The understanding of connections between products and services exposes potentially effective intervention points.
Millimeter-wave (mmWave) with large spectrum available is considered as the most promising frequency band for future wireless communications.
The IEEE 802.11ad and IEEE 802.11ay operating on 60 GHz mmWave are the two most expected wireless local area network (WLAN) technologies for ultra-high-speed communications.
For the IEEE 802.11ay standard still under development, there are plenty of proposals from companies and researchers who are involved with the IEEE 802.11ay task group.
In this survey, we conduct a comprehensive review on the medium access control layer (MAC) related issues for the IEEE 802.11ay, some cross-layer between physical layer (PHY) and MAC technologies are also included.
We start with MAC related technologies in the IEEE 802.11ad and discuss design challenges on mmWave communications, leading to some MAC related technologies for the IEEE 802.11ay.
We then elaborate on important design issues for IEEE 802.11ay.
Specifically, we review the channel bonding and aggregation for the IEEE 802.11ay, and point out the major differences between the two technologies.
Then, we describe channel access and channel allocation in the IEEE 802.11ay, including spatial sharing and interference mitigation technologies.
After that, we present an in-depth survey on beamforming training (BFT), beam tracking, single-user multiple-input-multiple-output (SU-MIMO) beamforming and multi-user multiple-input-multiple-output (MU-MIMO) beamforming.
Finally, we discuss some open design issues and future research directions for mmWave WLANs.
We hope that this paper provides a good introduction to this exciting research area for future wireless systems.
Feature representation of different modalities is the main focus of current cross-modal information retrieval research.
Existing models typically project texts and images into the same embedding space.
In this paper, we explore the multitudinous of textural relationships in text modeling.
Specifically, texts are represented by a graph generated using various textural relationships including semantic relations, statistical co-occurrence, and predefined knowledge base.
A joint neural model is proposed to learn feature representation individually in each modality.
We use Graph Convolutional Network (GCN) to capture relation-aware representations of texts and Convolutional Neural Network (CNN) to learn image representations.
Comprehensive experiments are conducted on two benchmark datasets.
The results show that our model outperforms the state-of-the-art models significantly by 6.3% on the CMPlaces data and 3.4% on English Wikipedia, respectively.
Malignant Pleural Mesothelioma (MPM) or malignant mesothelioma (MM) is an atypical, aggressive tumor that matures into cancer in the pleura, a stratum of tissue bordering the lungs.
Diagnosis of MPM is difficult and it accounts for about seventy-five percent of all mesothelioma diagnosed yearly in the United States of America.
Being a fatal disease, early identification of MPM is crucial for patient survival.
Our study implements logistic regression and develops association rules to identify early stage symptoms of MM.
We retrieved medical reports generated by Dicle University and implemented logistic regression to measure the model accuracy.
We conducted (a) logistic correlation, (b) Omnibus test and (c) Hosmer and Lemeshow test for model evaluation.
Moreover, we also developed association rules by confidence, rule support, lift, condition support and deployability.
Categorical logistic regression increases the training accuracy from 72.30% to 81.40% with a testing accuracy of 63.46%.
The study also shows the top 5 symptoms that is mostly likely indicates the presence in MM.
This study concludes that using predictive modeling can enhance primary presentation and diagnosis of MM.
Many people use Yelp to find a good restaurant.
Nonetheless, with only an overall rating for each restaurant, Yelp offers not enough information for independently judging its various aspects such as environment, service or flavor.
In this paper, we introduced a machine learning based method to characterize such aspects for particular types of restaurants.
The main approach used in this paper is to use a support vector machine (SVM) model to decipher the sentiment tendency of each review from word frequency.
Word scores generated from the SVM models are further processed into a polarity index indicating the significance of each word for special types of restaurant.
Customers overall tend to express more sentiment regarding service.
As for the distinction between different cuisines, results that match the common sense are obtained: Japanese cuisines are usually fresh, some French cuisines are overpriced while Italian Restaurants are often famous for their pizzas.
Identifying potential abuses of human rights through imagery is a novel and challenging task in the field of computer vision, that will enable to expose human rights violations over large-scale data that may otherwise be impossible.
While standard databases for object and scene categorisation contain hundreds of different classes, the largest available dataset of human rights violations contains only 4 classes.
Here, we introduce the `Human Rights Archive Database' (HRA), a verified-by-experts repository of 3050 human rights violations photographs, labelled with human rights semantic categories, comprising a list of the types of human rights abuses encountered at present.
With the HRA dataset and a two-phase transfer learning scheme, we fine-tuned the state-of-the-art deep convolutional neural networks (CNNs) to provide human rights violations classification CNNs (HRA-CNNs).
We also present extensive experiments refined to evaluate how well object-centric and scene-centric CNN features can be combined for the task of recognising human rights abuses.
With this, we show that HRA database poses a challenge at a higher level for the well studied representation learning methods, and provide a benchmark in the task of human rights violations recognition in visual context.
We expect this dataset can help to open up new horizons on creating systems able of recognising rich information about human rights violations.
Our dataset, codes and trained models are available online at https://github.com/GKalliatakis/Human-Rights-Archive-CNNs.
Human activity recognition based on video streams has received numerous attentions in recent years.
Due to lack of depth information, RGB video based activity recognition performs poorly compared to RGB-D video based solutions.
On the other hand, acquiring depth information, inertia etc. is costly and requires special equipment, whereas RGB video streams are available in ordinary cameras.
Hence, our goal is to investigate whether similar or even higher accuracy can be achieved with RGB-only modality.
In this regard, we propose a novel framework that couples skeleton data extracted from RGB video and deep Bidirectional Long Short Term Memory (BLSTM) model for activity recognition.
A big challenge of training such a deep network is the limited training data, and exploring RGB-only stream significantly exaggerates the difficulty.
We therefore propose a set of algorithmic techniques to train this model effectively, e.g., data augmentation, dynamic frame dropout and gradient injection.
The experiments demonstrate that our RGB-only solution surpasses the state-of-the-art approaches that all exploit RGB-D video streams by a notable margin.
This makes our solution widely deployable with ordinary cameras.
People with profound motor deficits could perform useful physical tasks for themselves by controlling robots that are comparable to the human body.
Whether this is possible without invasive interfaces has been unclear, due to the robot's complexity and the person's limitations.
We developed a novel, augmented reality interface and conducted two studies to evaluate the extent to which it enabled people with profound motor deficits to control robotic body surrogates.
15 novice users achieved meaningful improvements on a clinical manipulation assessment when controlling the robot in Atlanta from locations across the United States.
Also, one expert user performed 59 distinct tasks in his own home over seven days, including self-care tasks such as feeding.
Our results demonstrate that people with profound motor deficits can effectively control robotic body surrogates without invasive interfaces.
The advent of language implementation tools such as PyPy and Truffle/Graal have reinvigorated and broadened interest in topics related to automatic compiler generation and optimization.
Given this broader interest, we revisit the Futamura Projections using a novel diagram scheme.
Through these diagrams we emphasize the recurring patterns in the Futamura Projections while addressing their complexity and abstract nature.
We anticipate that this approach will improve the accessibility of the Futamura Projections and help foster analysis of those new tools through the lens of partial evaluation.
The share of videos in the internet traffic has been growing, therefore understanding how videos capture attention on a global scale is also of growing importance.
Most current research focus on modeling the number of views, but we argue that video engagement, or time spent watching is a more appropriate measure for resource allocation problems in attention, networking, and promotion activities.
In this paper, we present a first large-scale measurement of video-level aggregate engagement from publicly available data streams, on a collection of 5.3 million YouTube videos published over two months in 2016.
We study a set of metrics including time and the average percentage of a video watched.
We define a new metric, relative engagement, that is calibrated against video properties and strongly correlate with recognized notions of quality.
Moreover, we find that engagement measures of a video are stable over time, thus separating the concerns for modeling engagement and those for popularity -- the latter is known to be unstable over time and driven by external promotions.
We also find engagement metrics predictable from a cold-start setup, having most of its variance explained by video context, topics and channel information -- R2=0.77.
Our observations imply several prospective uses of engagement metrics -- choosing engaging topics for video production, or promoting engaging videos in recommender systems.
Android Inter-Component Communication (ICC) is complex, largely unconstrained, and hard for developers to understand.
As a consequence, ICC is a common source of security vulnerability in Android apps.
To promote secure programming practices, we have reviewed related research, and identified avoidable ICC vulnerabilities in Android-run devices and the security code smells that indicate their presence.
We explain the vulnerabilities and their corresponding smells, and we discuss how they can be eliminated or mitigated during development.
We present a lightweight static analysis tool on top of Android Lint that analyzes the code under development and provides just-in-time feedback within the IDE about the presence of such smells in the code.
Moreover, with the help of this tool we study the prevalence of security code smells in more than 700 open-source apps, and manually inspect around 15% of the apps to assess the extent to which identifying such smells uncovers ICC security vulnerabilities.
In this paper, we attempt to advance the research work done in human action recognition to a rather specialized application namely Indian Classical Dance (ICD) classification.
The variation in such dance forms in terms of hand and body postures, facial expressions or emotions and head orientation makes pose estimation an extremely challenging task.
To circumvent this problem, we construct a pose-oblivious shape signature which is fed to a sequence learning framework.
The pose signature representation is done in two-fold process.
First, we represent person-pose in first frame of a dance video using symmetric Spatial Transformer Networks (STN) to extract good person object proposals and CNN-based parallel single person pose estimator (SPPE).
Next, the pose basis are converted to pose flows by assigning a similarity score between successive poses followed by non-maximal suppression.
Instead of feeding a simple chain of joints in the sequence learner which generally hinders the network performance we constitute a feature vector of the normalized distance vectors, flow, angles between anchor joints which captures the adjacency configuration in the skeletal pattern.
Thus, the kinematic relationship amongst the body joints across the frames using pose estimation helps in better establishing the spatio-temporal dependencies.
We present an exhaustive empirical evaluation of state-of-the-art deep network based methods for dance classification on ICD dataset.
Replikativ is a replication middleware supporting a new kind of confluent replicated datatype resembling a distributed version control system.
It retains the order of write operations at the trade-off of reduced availability with after-the- fact conflict resolution.
The system allows to develop applications with distributed state in a similar fashion as native applications with exclusive local state, while transparently exposing the necessary compromises in terms of the CAP theorem.
In this paper, we give a specification of the replicated datatype and discuss its usage in the replikativ middleware.
Experiments with the implementation show the feasibility of the concept as a foundation for replication as a service (RaaS).
In this paper, we investigate the strength of six different SAT solvers in attacking various obfuscation schemes.
Our investigation revealed that Glucose and Lingeling SAT solvers are generally suited for attacking small-to-midsize obfuscated circuits, while the MapleGlucose, if the system is not memory bound, is best suited for attacking mid-to-difficult obfuscation methods.
Our experimental result indicates that when dealing with extremely large circuits and very difficult obfuscation problems, the SAT solver may be memory bound, and Lingeling, for having the most memory efficient implementation, is the best-suited solver for such problems.
Additionally, our investigation revealed that SAT solver execution times may vary widely across different SAT solvers.
Hence, when testing the hardness of an obfuscation method, although the increase in difficulty could be verified by one SAT solver, the pace of increase in difficulty is dependent on the choice of a SAT solver.
Many real-world problems involve massive amounts of data.
Under these circumstances learning algorithms often become prohibitively expensive, making scalability a pressing issue to be addressed.
A common approach is to perform sampling to reduce the size of the dataset and enable efficient learning.
Alternatively, one customizes learning algorithms to achieve scalability.
In either case, the key challenge is to obtain algorithmic efficiency without compromising the quality of the results.
In this paper we discuss a meta-learning algorithm (PSBML) which combines features of parallel algorithms with concepts from ensemble and boosting methodologies to achieve the desired scalability property.
We present both theoretical and empirical analyses which show that PSBML preserves a critical property of boosting, specifically, convergence to a distribution centered around the margin.
We then present additional empirical analyses showing that this meta-level algorithm provides a general and effective framework that can be used in combination with a variety of learning classifiers.
We perform extensive experiments to investigate the tradeoff achieved between scalability and accuracy, and robustness to noise, on both synthetic and real-world data.
These empirical results corroborate our theoretical analysis, and demonstrate the potential of PSBML in achieving scalability without sacrificing accuracy.
We present an iterative overlap estimation technique to augment existing point cloud registration algorithms that can achieve high performance in difficult real-world situations where large pose displacement and non-overlapping geometry would otherwise cause traditional methods to fail.
Our approach estimates overlapping regions through an iterative Expectation Maximization procedure that encodes the sensor field-of-view into the registration process.
The proposed technique, Expected Overlap Estimation (EOE), is derived from the observation that differences in field-of-view violate the iid assumption implicitly held by all maximum likelihood based registration techniques.
We demonstrate how our approach can augment many popular registration methods with minimal computational overhead.
Through experimentation on both synthetic and real-world datasets, we find that adding an explicit overlap estimation step can aid robust outlier handling and increase the accuracy of both ICP-based and GMM-based registration methods, especially in large unstructured domains and where the amount of overlap between point clouds is very small.
Researchers find weaknesses in current strategies for protecting privacy in large datasets.
Many anonymized datasets are reidentifiable, and norms for offering data subjects notice and consent over emphasize individual responsibility.
Based on fieldwork with data managers in the City of Seattle, I identify ways that these conventional approaches break down in practice.
Drawing on work from theorists in sociocultural anthropology, I propose that a Human Centered Data Science move beyond concepts like dataset identifiability and sensitivity toward a broader ontology of who is implicated by a dataset, and new ways of anticipating how data can be combined and used.
Statistical learning on biological data can be challenging due to confounding variables in sample collection and processing.
Confounders can cause models to generalize poorly and result in inaccurate prediction performance metrics if models are not validated thoroughly.
In this paper, we propose methods to control for confounding factors and further improve prediction performance.
We introduce OrthoNormal basis construction In cOnfounding factor Normalization (ONION) to remove confounding covariates and use the Domain-Adversarial Neural Network (DANN) to penalize models for encoding confounder information.
We apply the proposed methods to simulated and empirical patient data and show significant improvements in generalization.
Knowledge-based question answering relies on the availability of facts, the majority of which cannot be found in structured sources (e.g.Wikipedia info-boxes, Wikidata).
One of the major components of extracting facts from unstructured text is Relation Extraction (RE).
In this paper we propose a novel method for creating distant (weak) supervision labels for training a large-scale RE system.
We also provide new evidence about the effectiveness of neural network approaches by decoupling the model architecture from the feature design of a state-of-the-art neural network system.
Surprisingly, a much simpler classifier trained on similar features performs on par with the highly complex neural network system (at 75x reduction to the training time), suggesting that the features are a bigger contributor to the final performance.
A cellular automaton (CA) is a parallel synchronous computing model, which consists in a juxtaposition of finite automata (cells) whose state evolves according to that of their neighbors.
Its trace is the set of infinite words representing the sequence of states taken by some particular cell.
In this paper we study the ultimate trace of CA and partial CA (a CA restricted to a particular subshift).
The ultimate trace is the trace observed after a long time run of the CA.
We give sufficient conditions for a set of infinite words to be the trace of some CA and prove the undecidability of all properties over traces that are stable by ultimate coincidence.
We are experiencing an upcoming trend of using head mounted display systems in games and serious games, which is likely to become an established practice in the near future.
While these systems provide highly immersive experiences, many users have been reporting discomfort symptoms, such as nausea, sickness, and headaches, among others.
When using VR for health applications, this is more critical, since the discomfort may interfere a lot in treatments.
In this work we discuss possible causes of these issues, and present possible solutions as design guidelines that may mitigate them.
In this context, we go deeper within a dynamic focus solution to reduce discomfort in immersive virtual environments, when using first-person navigation.
This solution applies an heuristic model of visual attention that works in real time.
This work also discusses a case study (as a first-person spatial shooter demo) that applies this solution and the proposed design guidelines.
Much research has been devoted to optimizing algorithms of the Lempel-Ziv (LZ) 77 family, both in terms of speed and memory requirements.
Binary search trees and suffix trees (ST) are data structures that have been often used for this purpose, as they allow fast searches at the expense of memory usage.
In recent years, there has been interest on suffix arrays (SA), due to their simplicity and low memory requirements.
One key issue is that an SA can solve the sub-string problem almost as efficiently as an ST, using less memory.
This paper proposes two new SA-based algorithms for LZ encoding, which require no modifications on the decoder side.
Experimental results on standard benchmarks show that our algorithms, though not faster, use 3 to 5 times less memory than the ST counterparts.
Another important feature of our SA-based algorithms is that the amount of memory is independent of the text to search, thus the memory that has to be allocated can be defined a priori.
These features of low and predictable memory requirements are of the utmost importance in several scenarios, such as embedded systems, where memory is at a premium and speed is not critical.
Finally, we point out that the new algorithms are general, in the sense that they are adequate for applications other than LZ compression, such as text retrieval and forward/backward sub-string search.
The big breakthrough on the ImageNet challenge in 2012 was partially due to the `dropout' technique used to avoid overfitting.
Here, we introduce a new approach called `Spectral Dropout' to improve the generalization ability of deep neural networks.
We cast the proposed approach in the form of regular Convolutional Neural Network (CNN) weight layers using a decorrelation transform with fixed basis functions.
Our spectral dropout method prevents overfitting by eliminating weak and `noisy' Fourier domain coefficients of the neural network activations, leading to remarkably better results than the current regularization methods.
Furthermore, the proposed is very efficient due to the fixed basis functions used for spectral transformation.
In particular, compared to Dropout and Drop-Connect, our method significantly speeds up the network convergence rate during the training process (roughly x2), with considerably higher neuron pruning rates (an increase of ~ 30%).
We demonstrate that the spectral dropout can also be used in conjunction with other regularization approaches resulting in additional performance gains.
Objectives: This paper presents an up-to-date overview of research performed in the Virtual Reality (VR) environment ranging from definitions, its presence in the various fields, and existing market players and their projects in the VR technology.
Further an attempt is made to gain an insight on the psychological mechanism underlying experience in using VR device.
Methods: Our literature survey is based on the research articles, analysis of the projects of various companies and their findings for different areas of interest.
Findings: In our literature survey we observed that the recent advances in virtual reality enabling technologies have led to variety of virtual devices that facilitate people to interact with the digital world.
In fact in the past two decades researchers have tried to integrate reality and VR in the form of intuitive computer interface.
Improvements: This has led to variety of potential benefits of VR in many applications such as News, Healthcare, Entertainment, Tourism, Military and Defence etc.
However despite the extensive research efforts in creating virtual system environments it is yet to become apparent in normal daily life.
Vertex centrality measures are a multi-purpose analysis tool, commonly used in many application environments to retrieve information and unveil knowledge from the graphs and network structural properties.
However, the algorithms of such metrics are expensive in terms of computational resources when running real-time applications or massive real world networks.
Thus, approximation techniques have been developed and used to compute the measures in such scenarios.
In this paper, we demonstrate and analyze the use of neural network learning algorithms to tackle such task and compare their performance in terms of solution quality and computation time with other techniques from the literature.
Our work offers several contributions.
We highlight both the pros and cons of approximating centralities though neural learning.
By empirical means and statistics, we then show that the regression model generated with a feedforward neural networks trained by the Levenberg-Marquardt algorithm is not only the best option considering computational resources, but also achieves the best solution quality for relevant applications and large-scale networks.
Keywords: Vertex Centrality Measures, Neural Networks, Complex Network Models, Machine Learning, Regression Model
Clustering scientific publications in an important problem in bibliometric research.
We demonstrate how two software tools, CitNetExplorer and VOSviewer, can be used to cluster publications and to analyze the resulting clustering solutions.
CitNetExplorer is used to cluster a large set of publications in the field of astronomy and astrophysics.
The publications are clustered based on direct citation relations.
CitNetExplorer and VOSviewer are used together to analyze the resulting clustering solutions.
Both tools use visualizations to support the analysis of the clustering solutions, with CitNetExplorer focusing on the analysis at the level of individual publications and VOSviewer focusing on the analysis at an aggregate level.
The demonstration provided in this paper shows how a clustering of publications can be created and analyzed using freely available software tools.
Using the approach presented in this paper, bibliometricians are able to carry out sophisticated cluster analyses without the need to have a deep knowledge of clustering techniques and without requiring advanced computer skills.
Recent advances in saliency detection have utilized deep learning to obtain high level features to detect salient regions in a scene.
These advances have demonstrated superior results over previous works that utilize hand-crafted low level features for saliency detection.
In this paper, we demonstrate that hand-crafted features can provide complementary information to enhance performance of saliency detection that utilizes only high level features.
Our method utilizes both high level and low level features for saliency detection under a unified deep learning framework.
The high level features are extracted using the VGG-net, and the low level features are compared with other parts of an image to form a low level distance map.
The low level distance map is then encoded using a convolutional neural network(CNN) with multiple 1X1 convolutional and ReLU layers.
We concatenate the encoded low level distance map and the high level features, and connect them to a fully connected neural network classifier to evaluate the saliency of a query region.
Our experiments show that our method can further improve the performance of state-of-the-art deep learning-based saliency detection methods.
Outlier detection plays an essential role in many data-driven applications to identify isolated instances that are different from the majority.
While many statistical learning and data mining techniques have been used for developing more effective outlier detection algorithms, the interpretation of detected outliers does not receive much attention.
Interpretation is becoming increasingly important to help people trust and evaluate the developed models through providing intrinsic reasons why the certain outliers are chosen.
It is difficult, if not impossible, to simply apply feature selection for explaining outliers due to the distinct characteristics of various detection models, complicated structures of data in certain applications, and imbalanced distribution of outliers and normal instances.
In addition, the role of contrastive contexts where outliers locate, as well as the relation between outliers and contexts, are usually overlooked in interpretation.
To tackle the issues above, in this paper, we propose a novel Contextual Outlier INterpretation (COIN) method to explain the abnormality of existing outliers spotted by detectors.
The interpretability for an outlier is achieved from three aspects: outlierness score, attributes that contribute to the abnormality, and contextual description of its neighborhoods.
Experimental results on various types of datasets demonstrate the flexibility and effectiveness of the proposed framework compared with existing interpretation approaches.
Artificial neural networks are powerful models, which have been widely applied into many aspects of machine translation, such as language modeling and translation modeling.
Though notable improvements have been made in these areas, the reordering problem still remains a challenge in statistical machine translations.
In this paper, we present a novel neural reordering model that directly models word pairs and alignment.
By utilizing LSTM recurrent neural networks, much longer context could be learned for reordering prediction.
Experimental results on NIST OpenMT12 Arabic-English and Chinese-English 1000-best rescoring task show that our LSTM neural reordering feature is robust and achieves significant improvements over various baseline systems.
City-scale sensing holds the promise of enabling a deeper understanding of our urban environments.
However, a city-scale deployment requires physical installation, power management, and communications---all challenging tasks standing between a good idea and a realized one.
This indicates the need for a platform that enables easy deployment and experimentation for applications operating at city scale.
To address these challenges, we present Signpost, a modular, energy-harvesting platform for city-scale sensing.
Signpost simplifies deployment by eliminating the need for connection to wired infrastructure and instead harvesting energy from an integrated solar panel.
The platform furnishes the key resources necessary to support multiple, pluggable sensor modules while providing fair, safe, and reliable sharing in the face of dynamic energy constraints.
We deploy Signpost with several sensor modules, showing the viability of an energy-harvesting, multi-tenant, sensing system, and evaluate its ability to support sensing applications.
We believe Signpost reduces the difficulty inherent in city-scale deployments, enables new experimentation, and provides improved insights into urban health.
Programmers make rich use of natural language in the source code they write through identifiers and comments.
Source code identifiers are selected from a pool of tokens which are strongly related to the meaning, naming conventions, and context.
These tokens are often combined to produce more precise and obvious designations.
Such multi-part identifiers count for 97% of all naming tokens in the Public Git Archive - the largest dataset of Git repositories to date.
We introduce a bidirectional LSTM recurrent neural network to detect subtokens in source code identifiers.
We trained that network on 41.7 million distinct splittable identifiers collected from 182,014 open source projects in Public Git Archive, and show that it outperforms several other machine learning models.
The proposed network can be used to improve the upstream models which are based on source code identifiers, as well as improving developer experience allowing writing code without switching the keyboard case.
Growing consumer awareness as well as manufacturers' internal quality requirements lead to novel demands on supply chain traceability.
Existing centralized solutions suffer from isolated data storage and lacking trust when multiple parties are involved.
Decentralized blockchain-based approaches attempt to overcome these shortcomings by creating digital representations of physical goods to facilitate tracking across multiple entities.
However, they currently do not capture the transformation of goods in manufacturing processes.
Therefore, the relation between ingredients and product is lost, limiting the ability to trace a product's provenance.
We propose a blockchain-based supply chain traceability system using smart contracts.
In such contracts, manufacturers define the composition of products in the form of recipes.
Each ingredient of the recipe is a non-fungible token that corresponds to a batch of physical goods.
When the recipe is applied, its ingredients are consumed and a new token is produced.
This mechanism preserves the traceability of product transformations.
The system is implemented for the Ethereum Virtual Machine and is applicable to any blockchain configuration that supports it.
Our evaluation reveals that the gas costs scale linearly with the number of products considered in the system.
This leads to the conclusion that the solution can handle complex use cases.
Supporting programming on touchscreen devices requires effective text input and editing methods.
Unfortunately, the virtual keyboard can be inefficient and uses valuable screen space on already small devices.
Recent advances in stylus input make handwriting a potentially viable text input solution for programming on touchscreen devices.
The primary barrier, however, is that handwriting recognition systems are built to take advantage of the rules of natural language, not those of a programming language.
In this paper, we explore this particular problem of handwriting recognition for source code.
We collect and make publicly available a dataset of handwritten Python code samples from 15 participants and we characterize the typical recognition errors for this handwritten Python source code when using a state-of-the-art handwriting recognition tool.
We present an approach to improve the recognition accuracy by augmenting a handwriting recognizer with the programming language grammar rules.
Our experiment on the collected dataset shows an 8.6% word error rate and a 3.6% character error rate which outperforms standard handwriting recognition systems and compares favorably to typing source code on virtual keyboards.
We present a method for transferring neural representations from label-rich source domains to unlabeled target domains.
Recent adversarial methods proposed for this task learn to align features across domains by fooling a special domain critic network.
However, a drawback of this approach is that the critic simply labels the generated features as in-domain or not, without considering the boundaries between classes.
This can lead to ambiguous features being generated near class boundaries, reducing target classification accuracy.
We propose a novel approach, Adversarial Dropout Regularization (ADR), to encourage the generator to output more discriminative features for the target domain.
Our key idea is to replace the critic with one that detects non-discriminative features, using dropout on the classifier network.
The generator then learns to avoid these areas of the feature space and thus creates better features.
We apply our ADR approach to the problem of unsupervised domain adaptation for image classification and semantic segmentation tasks, and demonstrate significant improvement over the state of the art.
We also show that our approach can be used to train Generative Adversarial Networks for semi-supervised learning.
We propose robust methods for estimating camera egomotion in noisy, real-world monocular image sequences in the general case of unknown observer rotation and translation with two views and a small baseline.
This is a difficult problem because of the nonconvex cost function of the perspective camera motion equation and because of non-Gaussian noise arising from noisy optical flow estimates and scene non-rigidity.
To address this problem, we introduce the expected residual likelihood method (ERL), which estimates confidence weights for noisy optical flow data using likelihood distributions of the residuals of the flow field under a range of counterfactual model parameters.
We show that ERL is effective at identifying outliers and recovering appropriate confidence weights in many settings.
We compare ERL to a novel formulation of the perspective camera motion equation using a lifted kernel, a recently proposed optimization framework for joint parameter and confidence weight estimation with good empirical properties.
We incorporate these strategies into a motion estimation pipeline that avoids falling into local minima.
We find that ERL outperforms the lifted kernel method and baseline monocular egomotion estimation strategies on the challenging KITTI dataset, while adding almost no runtime cost over baseline egomotion methods.
Sampling efficiency in a highly constrained environment has long been a major challenge for sampling-based planners.
In this work, we propose Rapidly-exploring Random disjointed-Trees* (RRdT*), an incremental optimal multi-query planner.
RRdT* uses multiple disjointed-trees to exploit local-connectivity of spaces via Markov Chain random sampling, which utilises neighbourhood information derived from previous successful and failed samples.
To balance local exploitation, RRdT* actively explore unseen global spaces when local-connectivity exploitation is unsuccessful.
The active trade-off between local exploitation and global exploration is formulated as a multi-armed bandit problem.
We argue that the active balancing of global exploration and local exploitation is the key to improving sample efficient in sampling-based motion planners.
We provide rigorous proofs of completeness and optimal convergence for this novel approach.
Furthermore, we demonstrate experimentally the effectiveness of RRdT*'s locally exploring trees in granting improved visibility for planning.
Consequently, RRdT* outperforms existing state-of-the-art incremental planners, especially in highly constrained environments.
This paper describes a massively parallel code for a state-of-the art thermal lattice- Boltzmann method.
Our code has been carefully optimized for performance on one GPU and to have a good scaling behavior extending to a large number of GPUs.
Versions of this code have been already used for large-scale studies of convective turbulence.
GPUs are becoming increasingly popular in HPC applications, as they are able to deliver higher performance than traditional processors.
Writing efficient programs for large clusters is not an easy task as codes must adapt to increasingly parallel architectures, and the overheads of node-to-node communications must be properly handled.
We describe the structure of our code, discussing several key design choices that were guided by theoretical models of performance and experimental benchmarks.
We present an extensive set of performance measurements and identify the corresponding main bot- tlenecks; finally we compare the results of our GPU code with those measured on other currently available high performance processors.
Our results are a production-grade code able to deliver a sustained performance of several tens of Tflops as well as a design and op- timization methodology that can be used for the development of other high performance applications for computational physics.
Since the introduction of the Fortran programming language some 60 years ago, there has been little progress in making error messages more user-friendly.
A first step in this direction is to translate them into the natural language of the students.
In this paper we propose a simple script for Linux systems which gives word by word translations of error messages.
It works for most programming languages and for all natural languages.
Understanding the error messages generated by compilers is a major hurdle for students who are learning programming, particularly for non-native English speakers.
Not only may they never become "fluent" in programming but many give up programming altogether.
Whereas programming is a tool which can be useful in many human activities, e.g. history, genealogy, astronomy, entomology, in many countries the skill of programming remains confined to a narrow fringe of professional programmers.
In all societies, besides professional violinists there are also amateurs.
It should be the same for programming.
It is our hope that once translated and explained the error messages will be seen by the students as an aid rather than as an obstacle and that in this way more students will enjoy learning and practising programming.
They should see it as a funny game.
The e-commerce share in the global retail spend is showing a steady increase over the years indicating an evident shift of consumer attention from bricks and mortar to clicks in retail sector.
In recent years, online marketplaces have become one of the key contributors to this growth.
As the business model matures, the number and types of frauds getting reported in the area is also growing on a daily basis.
Fraudulent e-commerce buyers and their transactions are being studied in detail and multiple strategies to control and prevent them are discussed.
Another area of fraud happening in marketplaces are on the seller side and is called merchant fraud.
Goods/services offered and sold at cheap rates, but never shipped is a simple example of this type of fraud.
This paper attempts to suggest a framework to detect such fraudulent sellers with the help of machine learning techniques.
The model leverages the historic data from the marketplace and detect any possible fraudulent behaviours from sellers and alert to the marketplace.
Web applications are permanently being exposed to attacks that exploit their vulnerabilities.
In this work we investigate the application of machine learning techniques to leverage Web Application Firewall (WAF), a technology that is used to detect and prevent attacks.
We propose a combined approach of machine learning models, based on one-class classification and n-gram analysis, to enhance the detection and accuracy capabilities of MODSECURITY, an open source and widely used WAF.
The results are promising and outperform MODSECURITY when configured with the OWASP Core Rule Set, the baseline configuration setting of a widely deployed, rule-based WAF technology.
The proposed solution, combining both approaches, allow us to deploy a WAF when no training data for the application is available (using one-class classification), and an improved one using n-grams when training data is available.
We unify two prominent lines of work on multi-armed bandits: bandits with knapsacks (BwK) and combinatorial semi-bandits.
The former concerns limited "resources" consumed by the algorithm, e.g., limited supply in dynamic pricing.
The latter allows a huge number of actions but assumes combinatorial structure and additional feedback to make the problem tractable.
We define a common generalization, support it with several motivating examples, and design an algorithm for it.
Our regret bounds are comparable with those for BwK and combinatorial semi- bandits.
We present a real-time object-based SLAM system that leverages the largest object database to date.
Our approach comprises two main components: 1) a monocular SLAM algorithm that exploits object rigidity constraints to improve the map and find its real scale, and 2) a novel object recognition algorithm based on bags of binary words, which provides live detections with a database of 500 3D objects.
The two components work together and benefit each other: the SLAM algorithm accumulates information from the observations of the objects, anchors object features to especial map landmarks and sets constrains on the optimization.
At the same time, objects partially or fully located within the map are used as a prior to guide the recognition algorithm, achieving higher recall.
We evaluate our proposal on five real environments showing improvements on the accuracy of the map and efficiency with respect to other state-of-the-art techniques.
We introduce a novel approach for building language models based on a systematic, recursive exploration of skip n-gram models which are interpolated using modified Kneser-Ney smoothing.
Our approach generalizes language models as it contains the classical interpolation with lower order models as a special case.
In this paper we motivate, formalize and present our approach.
In an extensive empirical experiment over English text corpora we demonstrate that our generalized language models lead to a substantial reduction of perplexity between 3.1% and 12.7% in comparison to traditional language models using modified Kneser-Ney smoothing.
Furthermore, we investigate the behaviour over three other languages and a domain specific corpus where we observed consistent improvements.
Finally, we also show that the strength of our approach lies in its ability to cope in particular with sparse training data.
Using a very small training data set of only 736 KB text we yield improvements of even 25.7% reduction of perplexity.
Deep generative models such as Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs) have recently been applied to style and domain transfer for images, and in the case of VAEs, music.
GAN-based models employing several generators and some form of cycle consistency loss have been among the most successful for image domain transfer.
In this paper we apply such a model to symbolic music and show the feasibility of our approach for music genre transfer.
Evaluations using separate genre classifiers show that the style transfer works well.
In order to improve the fidelity of the transformed music, we add additional discriminators that cause the generators to keep the structure of the original music mostly intact, while still achieving strong genre transfer.
Visual and audible results further show the potential of our approach.
To the best of our knowledge, this paper represents the first application of GANs to symbolic music domain transfer.
We consider a multitask learning problem, in which several predictors are learned jointly.
Prior research has shown that learning the relations between tasks, and between the input features, together with the predictor, can lead to better generalization and interpretability, which proved to be useful for applications in many domains.
In this paper, we consider a formulation of multitask learning that learns the relationships both between tasks and between features, represented through a task covariance and a feature covariance matrix, respectively.
First, we demonstrate that existing methods proposed for this problem present an issue that may lead to ill-posed optimization.
We then propose an alternative formulation, as well as an efficient algorithm to optimize it.
Using ideas from optimization and graph theory, we propose an efficient coordinate-wise minimization algorithm that has a closed form solution for each block subproblem.
Our experiments show that the proposed optimization method is orders of magnitude faster than its competitors.
We also provide a nonlinear extension that is able to achieve better generalization than existing methods.
In this paper, we first address adverse effects of cyber-physical attacks on distributed synchronization of multi-agent systems, by providing necessary and sufficient conditions under which an attacker can destabilize the underlying network, as well as another set of necessary and sufficient conditions under which local neighborhood tracking errors of intact agents converge to zero.
Based on this analysis, we propose a Kullback-Liebler divergence based criterion in view of which each agent detects its neighbors' misbehavior and, consequently, forms a self-belief about the trustworthiness of the information it receives.
Agents continuously update their self-beliefs and communicate them with their neighbors to inform them of the significance of their outgoing information.
Moreover, if the self-belief of an agent is low, it forms trust on its neighbors.
Agents incorporate their neighbors' self-beliefs and their own trust values on their control protocols to slow down and mitigate attacks.
We show that using the proposed resilient approach, an agent discards the information it receives from a neighbor only if its neighbor is compromised, and not solely based on the discrepancy among neighbors' information, which might be caused by legitimate changes, and not attacks.
The proposed approach is guaranteed to work under mild connectivity assumptions.
This paper studies the geodesic diameter of polygonal domains having h holes and n corners.
For simple polygons (i.e., h = 0), the geodesic diameter is determined by a pair of corners of a given polygon and can be computed in linear time, as known by Hershberger and Suri.
For general polygonal domains with h >= 1, however, no algorithm for computing the geodesic diameter was known prior to this paper.
In this paper, we present the first algorithms that compute the geodesic diameter of a given polygonal domain in worst-case time O(n^7.73) or O(n^7 (log n + h)).
The main difficulty unlike the simple polygon case relies on the following observation revealed in this paper: two interior points can determine the geodesic diameter and in that case there exist at least five distinct shortest paths between the two.
This paper proposes a new optimal control synthesis algorithm for multi-robot systems under global temporal logic tasks.
Existing planning approaches under global temporal goals rely on graph search techniques applied to a product automaton constructed among the robots.
In this paper, we propose a new sampling-based algorithm that builds incrementally trees that approximate the state-space and transitions of the synchronous product automaton.
By approximating the product automaton by a tree rather than representing it explicitly, we require much fewer memory resources to store it and motion plans can be found by tracing sequences of parent nodes without the need for sophisticated graph search methods.
This significantly increases the scalability of our algorithm compared to existing optimal control synthesis methods.
We also show that the proposed algorithm is probabilistically complete and asymptotically optimal.
Finally, we present numerical experiments showing that our approach can synthesize optimal plans from product automata with billions of states, which is not possible using standard optimal control synthesis algorithms or off-the-shelf model checkers.
We consider a new Steiner tree problem, called vertex-cover-weighted Steiner tree problem.
This problem defines the weight of a Steiner tree as the minimum weight of vertex covers in the tree, and seeks a minimum-weight Steiner tree in a given vertex-weighted undirected graph.
Since it is included by the Steiner tree activation problem, the problem admits an O(log n)-approximation algorithm in general graphs with n vertices.
This approximation factor is tight up to a constant because it is NP-hard to achieve an o(log n)-approximation for the vertex-cover-weighted Steiner tree problem on general graphs even if the given vertex weights are uniform and a spanning tree is required instead of a Steiner tree.
In this paper, we present constant-factor approximation algorithms for the problem with unit disk graphs and with graphs excluding a fixed minor.
For the latter graph class, our algorithm can be also applied for the Steiner tree activation problem.
How to tell if a review is real or fake?
What does the underworld of fraudulent reviewing look like?
Detecting suspicious reviews has become a major issue for many online services.
We propose the use of a clique-finding approach to discover well-organized suspicious reviewers.
From a Yelp dataset with over one million reviews, we construct multiple Reviewer Similarity graphs to link users that have unusually similar behavior: two reviewers are connected in the graph if they have reviewed the same set of venues within a few days.
From these graphs, our algorithms extracted many large cliques and quasi-cliques, the largest one containing a striking 11 users who coordinated their review activities in identical ways.
Among the detected cliques, a large portion contain Yelp Scouts who are paid by Yelp to review venues in new areas.
Our work sheds light on their little-known operation.
Within a fairly short amount of time, the Islamic State of Iraq and Syria (ISIS) has managed to put large swaths of land in Syria and Iraq under their control.
To many observers, the sheer speed at which this "state" was established was dumbfounding.
To better understand the roots of this organization and its supporters we present a study using data from Twitter.
We start by collecting large amounts of Arabic tweets referring to ISIS and classify them into pro-ISIS and anti-ISIS.
This classification turns out to be easily done simply using the name variants used to refer to the organization: the full name and the description as "state" is associated with support, whereas abbreviations usually indicate opposition.
We then "go back in time" by analyzing the historic timelines of both users supporting and opposing and look at their pre-ISIS period to gain insights into the antecedents of support.
To achieve this, we build a classifier using pre-ISIS data to "predict", in retrospect, who will support or oppose the group.
The key story that emerges is one of frustration with failed Arab Spring revolutions.
ISIS supporters largely differ from ISIS opposition in that they refer a lot more to Arab Spring uprisings that failed.
We also find temporal patterns in the support and opposition which seems to be linked to major news, such as reported territorial gains, reports on gruesome acts of violence, and reports on airstrikes and foreign intervention.
Researchers are increasingly incorporating numeric high-order data, i.e., numeric tensors, within their practice.
Just like the matrix/vector (MV) paradigm, the development of multi-purpose, but high-performance, sparse data structures and algorithms for arithmetic calculations, e.g., those found in Einstein-like notation, is crucial for the continued adoption of tensors.
We use the example of high-order differential operators to illustrate this need.
As sparse tensor arithmetic is an emerging research topic, with challenges distinct from the MV paradigm, many aspects require further articulation.
We focus on three core facets.
First, aligning with prominent voices in the field, we emphasise the importance of data structures able to accommodate the operational complexity of tensor arithmetic.
However, we describe a linearised coordinate (LCO) data structure that provides faster and more memory-efficient sorting performance.
Second, flexible data structures, like the LCO, rely heavily on sorts and permutations.
We introduce an innovative permutation algorithm, based on radix sort, that is tailored to rearrange already-sorted sparse data, producing significant performance gains.
Third, we introduce a novel poly-algorithm for sparse tensor products, where hyper-sparsity is a possibility.
Different manifestations of hyper-sparsity demand their own approach, which our poly-algorithm is the first to provide.
These developments are incorporated within our LibNT and NTToolbox software libraries.
Benchmarks, frequently drawn from the high-order differential operators example, demonstrate the practical impact of our routines, with speed-ups of 40% or higher compared to alternative high-performance implementations.
Comparisons against the MATLAB Tensor Toolbox show over 10 times speed improvements.
Thus, these advancements produce significant practical improvements for sparse tensor arithmetic.
We study the problem of motion-planning for free-flying multi-link robots and develop a sampling-based algorithm that is specifically tailored for the task.
Our work is based on the simple observation that the set of configurations for which the robot is self-collision free is independent of the obstacles or of the exact placement of the robot.
This allows to eliminate the need to perform costly self-collision checks online when solving motion-planning problems, assuming some offline preprocessing.
In particular, given a specific robot type our algorithm precomputes a tiling roadmap, which efficiently and implicitly encodes the self-collision free (sub-)space over the entire configuration space, where the latter can be infinite for that matter.
To answer any query, in any given scenario, we traverse the tiling roadmap while only testing for collisions with obstacles.
Our algorithm suggests more flexibility than the prevailing paradigm in which a precomputed roadmap depends both on the robot and on the scenario at hand.
We show through various simulations the effectiveness of this approach on open and closed-chain multi-link robots, where in some settings our algorithm is more than fifty times faster than the state-of-the-art.
The k-nearest-neighbor method performs classification tasks for a query sample based on the information contained in its neighborhood.
Previous studies into the k-nearest-neighbor algorithm usually achieved the decision value for a class by combining the support of each sample in the neighborhood.
They have generally considered the nearest neighbors separately, and potentially integral neighborhood information important for classification was lost, e.g. the distribution information.
This article proposes a novel local learning method that organizes the information in the neighborhood through local distribution.
In the proposed method, additional distribution information in the neighborhood is estimated and then organized; the classification decision is made based on maximum posterior probability which is estimated from the local distribution in the neighborhood.
Additionally, based on the local distribution, we generate a generalized local classification form that can be effectively applied to various datasets through tuning the parameters.
We use both synthetic and real datasets to evaluate the classification performance of the proposed method; the experimental results demonstrate the dimensional scalability, efficiency, effectiveness and robustness of the proposed method compared to some other state-of-the-art classifiers.
The results indicate that the proposed method is effective and promising in a broad range of domains.
The computer vision community is currently focusing on solving action recognition problems in real videos, which contain thousands of samples with many challenges.
In this process, Deep Convolutional Neural Networks (D-CNNs) have played a significant role in advancing the state-of-the-art in various vision-based action recognition systems.
Recently, the introduction of residual connections in conjunction with a more traditional CNN model in a single architecture called Residual Network (ResNet) has shown impressive performance and great potential for image recognition tasks.
In this paper, we investigate and apply deep ResNets for human action recognition using skeletal data provided by depth sensors.
Firstly, the 3D coordinates of the human body joints carried in skeleton sequences are transformed into image-based representations and stored as RGB images.
These color images are able to capture the spatial-temporal evolutions of 3D motions from skeleton sequences and can be efficiently learned by D-CNNs.
We then propose a novel deep learning architecture based on ResNets to learn features from obtained color-based representations and classify them into action classes.
The proposed method is evaluated on three challenging benchmark datasets including MSR Action 3D, KARD, and NTU-RGB+D datasets.
Experimental results demonstrate that our method achieves state-of-the-art performance for all these benchmarks whilst requiring less computation resource.
In particular, the proposed method surpasses previous approaches by a significant margin of 3.4% on MSR Action 3D dataset, 0.67% on KARD dataset, and 2.5% on NTU-RGB+D dataset.
Aerial robots are becoming popular among general public, and with the development of artificial intelligence (AI), there is a trend to equip aerial robots with a natural user interface (NUI).
Hand/arm gestures are an intuitive way to communicate for humans, and various research works have focused on controlling an aerial robot with natural gestures.
However, the techniques in this area are still far from mature.
Many issues in this area have been poorly addressed, such as the principles of choosing gestures from the design point of view, hardware requirements from an economic point of view, considerations of data availability, and algorithm complexity from a practical perspective.
Our work focuses on building an economical monocular system particularly designed for gesture-based piloting of an aerial robot.
Natural arm gestures are mapped to rich target directions and convenient fine adjustment is achieved.
Practical piloting scenarios, hardware cost and algorithm applicability are jointly considered in our system design.
The entire system is successfully implemented in an aerial robot and various properties of the system are tested.
Human actions comprise of joint motion of articulated body parts or `gestures'.
Human skeleton is intuitively represented as a sparse graph with joints as nodes and natural connections between them as edges.
Graph convolutional networks have been used to recognize actions from skeletal videos.
We introduce a part-based graph convolutional network (PB-GCN) for this task, inspired by Deformable Part-based Models (DPMs).
We divide the skeleton graph into four subgraphs with joints shared across them and learn a recognition model using a part-based graph convolutional network.
We show that such a model improves performance of recognition, compared to a model using entire skeleton graph.
Instead of using 3D joint coordinates as node features, we show that using relative coordinates and temporal displacements boosts performance.
Our model achieves state-of-the-art performance on two challenging benchmark datasets NTURGB+D and HDM05, for skeletal action recognition.
Despite deep recurrent neural networks (RNNs) demonstrate strong performance in text classification, training RNN models are often expensive and requires an extensive collection of annotated data which may not be available.
To overcome the data limitation issue, existing approaches leverage either pre-trained word embedding or sentence representation to lift the burden of training RNNs from scratch.
In this paper, we show that jointly learning sentence representations from multiple text classification tasks and combining them with pre-trained word-level and sentence level encoders result in robust sentence representations that are useful for transfer learning.
Extensive experiments and analyses using a wide range of transfer and linguistic tasks endorse the effectiveness of our approach.
Predicting unseen weather phenomena is an important issue for disaster management.
In this paper, we suggest a model for a convolutional sequence-to-sequence autoencoder for predicting undiscovered weather situations from previous satellite images.
We also propose a symmetric skip connection between encoder and decoder modules to produce more comprehensive image predictions.
To examine our model performance, we conducted experiments for each suggested model to predict future satellite images from historical satellite images.
A specific combination of skip connection and sequence-to-sequence autoencoder was able to generate closest prediction from the ground truth image.
During the last years, Convolutional Neural Networks (CNNs) have achieved state-of-the-art performance in image classification.
Their architectures have largely drawn inspiration by models of the primate visual system.
However, while recent research results of neuroscience prove the existence of non-linear operations in the response of complex visual cells, little effort has been devoted to extend the convolution technique to non-linear forms.
Typical convolutional layers are linear systems, hence their expressiveness is limited.
To overcome this, various non-linearities have been used as activation functions inside CNNs, while also many pooling strategies have been applied.
We address the issue of developing a convolution method in the context of a computational model of the visual cortex, exploring quadratic forms through the Volterra kernels.
Such forms, constituting a more rich function space, are used as approximations of the response profile of visual cells.
Our proposed second-order convolution is tested on CIFAR-10 and CIFAR-100.
We show that a network which combines linear and non-linear filters in its convolutional layers, can outperform networks that use standard linear filters with the same architecture, yielding results competitive with the state-of-the-art on these datasets.
The impressive success of modern deep neural networks on computer vision tasks has been achieved through models of very large capacity compared to the number of available training examples.
This overparameterization is often said to be controlled with the help of different regularization techniques, mainly weight decay and dropout.
However, since these techniques reduce the effective capacity of the model, typically even deeper and wider architectures are required to compensate for the reduced capacity.
Therefore, there seems to be a waste of capacity in this practice.
In this paper we build upon recent research that suggests that explicit regularization may not be as important as widely believed and carry out an ablation study that concludes that weight decay and dropout may not be necessary for object recognition if enough data augmentation is introduced.
Real-time simultaneous tracking of hands manipulating and interacting with external objects has many potential applications in augmented reality, tangible computing, and wearable computing.
However, due to difficult occlusions, fast motions, and uniform hand appearance, jointly tracking hand and object pose is more challenging than tracking either of the two separately.
Many previous approaches resort to complex multi-camera setups to remedy the occlusion problem and often employ expensive segmentation and optimization steps which makes real-time tracking impossible.
In this paper, we propose a real-time solution that uses a single commodity RGB-D camera.
The core of our approach is a 3D articulated Gaussian mixture alignment strategy tailored to hand-object tracking that allows fast pose optimization.
The alignment energy uses novel regularizers to address occlusions and hand-object contacts.
For added robustness, we guide the optimization with discriminative part classification of the hand and segmentation of the object.
We conducted extensive experiments on several existing datasets and introduce a new annotated hand-object dataset.
Quantitative and qualitative results show the key advantages of our method: speed, accuracy, and robustness.
Multi-label classification (MLC) is an important learning problem that expects the learning algorithm to take the hidden correlation of the labels into account.
Extracting the hidden correlation is generally a challenging task.
In this work, we propose a novel deep learning framework to better extract the hidden correlation with the help of the memory structure within recurrent neural networks.
The memory stores the temporary guesses on the labels and effectively allows the framework to rethink about the goodness and correlation of the guesses before making the final prediction.
Furthermore, the rethinking process makes it easy to adapt to different evaluation criterion to match real-world application needs.
Experimental results across many real-world data sets justify that the rethinking process indeed improves MLC performance across different evaluation criteria and leads to superior performance over state-of-the-art MLC algorithms.
The ability to control a complex network towards a desired behavior relies on our understanding of the complex nature of these social and technological networks.
The existence of numerous control schemes in a network promotes us to wonder: what is the underlying relationship of all possible input nodes?
Here we introduce input graph, a simple geometry that reveals the complex relationship between all control schemes and input nodes.
We prove that the node adjacent to an input node in the input graph will appear in another control scheme, and the connected nodes in input graph have the same type in control, which they are either all possible input nodes or not.
Furthermore, we find that the giant components emerge in the input graphs of many real networks, which provides a clear topological explanation of bifurcation phenomenon emerging in dense networks and promotes us to design an efficient method to alter the node type in control.
The findings provide an insight into control principles of complex networks and offer a general mechanism to design a suitable control scheme for different purposes.
In this article we test the accuracy of three platforms used in computational modelling: MatLab, Octave and Scilab, running on i386 architecture and three operating systems (Windows, Ubuntu and Mac OS).
We submitted them to numerical tests using standard data sets and using the functions provided by each platform.
A Monte Carlo study was conducted in some of the datasets in order to verify the stability of the results with respect to small departures from the original input.
We propose a set of operations which include the computation of matrix determinants and eigenvalues, whose results are known.
We also used data provided by NIST (National Institute of Standards and Technology), a protocol which includes the computation of basic univariate statistics (mean, standard deviation and first-lag correlation), linear regression and extremes of probability distributions.
The assessment was made comparing the results computed by the platforms with certified values, that is, known results, computing the number of correct significant digits.
Recent innovations in the design of computer viruses have led to new trade-offs for the attacker.
Multiple variants of a malware may spread at different rates and have different levels of visibility to the network.
In this work we examine the optimal strategies for the attacker so as to trade off the extent of spread of the malware against the need for stealth.
We show that in the mean-field deterministic regime, this spread-stealth trade-off is optimized by computationally simple single-threshold policies.
Specifically, we show that only one variant of the malware is spread by the attacker at each time, as there exists a time up to which the attacker prioritizes maximizing the spread of the malware, and after which she prioritizes stealth.
Recurrent neural networks (RNNs) sequentially process data by updating their state with each new data point, and have long been the de facto choice for sequence modeling tasks.
However, their inherently sequential computation makes them slow to train.
Feed-forward and convolutional architectures have recently been shown to achieve superior results on some sequence modeling tasks such as machine translation, with the added advantage that they concurrently process all inputs in the sequence, leading to easy parallelization and faster training times.
Despite these successes, however, popular feed-forward sequence models like the Transformer fail to generalize in many simple tasks that recurrent models handle with ease, e.g.
copying strings or even simple logical inference when the string or formula lengths exceed those observed at training time.
We propose the Universal Transformer (UT), a parallel-in-time self-attentive recurrent sequence model which can be cast as a generalization of the Transformer model and which addresses these issues.
UTs combine the parallelizability and global receptive field of feed-forward sequence models like the Transformer with the recurrent inductive bias of RNNs.
We also add a dynamic per-position halting mechanism and find that it improves accuracy on several tasks.
In contrast to the standard Transformer, under certain assumptions, UTs can be shown to be Turing-complete.
Our experiments show that UTs outperform standard Transformers on a wide range of algorithmic and language understanding tasks, including the challenging LAMBADA language modeling task where UTs achieve a new state of the art, and machine translation where UTs achieve a 0.9 BLEU improvement over Transformers on the WMT14 En-De dataset.
In the framework of finite games in extensive form with perfect information and strict preferences, this paper introduces a new equilibrium concept: the Perfect Prediction Equilibrium (PPE).
In the Nash paradigm, rational players consider that the opponent's strategy is fixed while maximizing their payoff.
The PPE, on the other hand, models the behavior of agents with an alternate form of rationality that involves a Stackelberg competition with the past.
Agents with this form of rationality integrate in their reasoning that they have such accurate logical and predictive skills, that the world is fully transparent: all players share the same knowledge and know as much as an omniscient external observer.
In particular, there is common knowledge of the solution of the game including the reached outcome and the thought process leading to it.
The PPE is stable given each player's knowledge of its actual outcome and uses no assumptions at unreached nodes.
This paper gives the general definition and construction of the PPE as a fixpoint problem, proves its existence, uniqueness and Pareto optimality, and presents two algorithms to compute it.
Finally, the PPE is put in perspective with existing literature (Newcomb's Problem, Superrationality, Nash Equilibrium, Subgame Perfect Equilibrium, Backward Induction Paradox, Forward Induction).
Text mining can be applied to many fields.
One of the application is using text mining in digital newspaper to do politic sentiment analysis.
In this paper sentiment analysis is applied to get information from digital news articles about its positive or negative sentiment regarding particular politician.
This paper suggests a simple model to analyze digital newspaper sentiment polarity using naive Bayes classifier method.
The model uses a set of initial data to begin with which will be updated when new information appears.
The model showed promising result when tested and can be implemented to some other sentiment analysis problems.
One use of EEG-based brain-computer interfaces (BCIs) in rehabilitation is the detection of movement intention.
In this paper we investigate for the first time the instantaneous phase of movement related cortical potential (MRCP) and its application to the detection of gait intention.
We demonstrate the utility of MRCP phase in two independent datasets, in which 10 healthy subjects and 9 chronic stroke patients executed a self-initiated gait task in three sessions.
Phase features were compared to more conventional amplitude and power features.
The neurophysiology analysis showed that phase features have higher signal-to-noise ratio than the other features.
Also, BCI detectors of gait intention based on phase, amplitude, and their combination were evaluated under three conditions: session specific calibration, intersession transfer, and intersubject transfer.
Results show that the phase based detector is the most accurate for session specific calibration (movement intention was correctly detected in 66.5% of trials in healthy subjects, and in 63.3% in stroke patients).
However, in intersession and intersubject transfer, the detector that combines amplitude and phase features is the most accurate one and the only that retains its accuracy (62.5% in healthy subjects and 59% in stroke patients) w.r.t. session specific calibration.
Thus, MRCP phase features improve the detection of gait intention and could be used in practice to remove time-consuming BCI recalibration.
Nowadays, robots become a companion in everyday life.
To be well-accepted by humans, robots should efficiently understand meanings of their partners' motions and body language, and respond accordingly.
Learning concepts by imitation brings them this ability in a user-friendly way.
This paper presents a fast and robust model for Incremental Learning of Concepts by Imitation (ILoCI).
In ILoCI, observed multimodal spatio-temporal demonstrations are incrementally abstracted and generalized based on both their perceptual and functional similarities during the imitation.
In this method, perceptually similar demonstrations are abstracted by a dynamic model of mirror neuron system.
An incremental method is proposed to learn their functional similarities through a limited number of interactions with the teacher.
Learning all concepts together by the proposed memory rehearsal enables robot to utilize the common structural relations among concepts which not only expedites the learning process especially at the initial stages, but also improves the generalization ability and the robustness against discrepancies between observed demonstrations.
Performance of ILoCI is assessed using standard LASA handwriting benchmark data set.
The results show efficiency of ILoCI in concept acquisition, recognition and generation in addition to its robustness against variability in demonstrations.
The automated segmentation of cells in microscopic images is an open research problem that has important implications for studies of the developmental and cancer processes based on in vitro models.
In this paper, we present the approach for segmentation of the DIC images of cultured cells using G-neighbor smoothing followed by Kauwahara filtering and local standard deviation approach for boundary detection.
NIH FIJI/ImageJ tools are used to create the ground truth dataset.
The results of this work indicate that detection of cell boundaries using segmentation approach even in the case of realistic measurement conditions is a challenging problem.
Some notions in mathematics can be considered relative.
Relative is a term used to denote when the variation in the position of an observer implies variation in properties or measures on the observed object.
We know, from Skolem theorem, that there are first-order models where the set of real numbers is countable and some where it is not.
This fact depends on the position of the observer and on the instrument/language the obserevr uses as well, i.e., it depends on whether he/she is inside the model or not and in this particular case the use of first-order logic.
In this article, we assume that computation is based on finiteness rather than natural numbers and discuss Turing machines computable morphisms defined on top of the sole notion finiteness.
We explore the relativity of finiteness in models provided by toposes where the Axiom of Choice (AC) does not hold, since Tarski proved that if AC holds then all finiteness notions are equivalent.
Our toposes do not have natural numbers object (NNO) either, since in a topos with a NNO these finiteness notions are equivalent to Peano finiteness going back to computation on top of Natural Numbers.
The main contribution of this article is to show that although from inside every topos, with the properties previously stated, the computation model is standard, from outside some of these toposes, unexpected properties on the computation arise, e.g., infinitely long programs, finite computations containing infinitely long ones, infinitely branching computations.
We mainly consider Dedekind and Kuratowski notions of finiteness in this article.
This paper reports on ongoing research investigating more expressive approaches to spatial-temporal trajectory clustering.
Spatial-temporal data is increasingly becoming universal as a result of widespread use of GPS and mobile devices, which makes mining and predictive analyses based on trajectories a critical activity in many domains.
Trajectory analysis methods based on clustering techniques heavily often rely on a similarity definition to properly provide insights.
However, although trajectories are currently described in terms of its two dimensions (space and time), their representation is limited in that it is not expressive enough to capture, in a combined way, the structure of space and time as well as the contextual and semantic trajectory properties.
Moreover, the massive amounts of available trajectory data make trajectory mining and analyses very challenging.
In this paper, we briefly discuss (i) an improved trajectory representation that takes into consideration space-time structures, context and semantic properties of trajectories; (ii) new forms of relations between the dimensions of a pair of trajectories; and (iii) big data approaches that can be used to develop a novel spatial-temporal clustering framework.
Our goal is to answer questions about paragraphs describing processes (e.g., photosynthesis).
Texts of this genre are challenging because the effects of actions are often implicit (unstated), requiring background knowledge and inference to reason about the changing world states.
To supply this knowledge, we leverage VerbNet to build a rulebase (called the Semantic Lexicon) of the preconditions and effects of actions, and use it along with commonsense knowledge of persistence to answer questions about change.
Our evaluation shows that our system, ProComp, significantly outperforms two strong reading comprehension (RC) baselines.
Our contributions are two-fold: the Semantic Lexicon rulebase itself, and a demonstration of how a simulation-based approach to machine reading can outperform RC methods that rely on surface cues alone.
Since this work was performed, we have developed neural systems that outperform ProComp, described elsewhere (Dalvi et al., NAACL'18).
However, the Semantic Lexicon remains a novel and potentially useful resource, and its integration with neural systems remains a currently unexplored opportunity for further improvements in machine reading about processes.
This paper applies energy conservation principles to the Daala video codec using gain-shape vector quantization to encode a vector of AC coefficients as a length (gain) and direction (shape).
The technique originates from the CELT mode of the Opus audio codec, where it is used to conserve the spectral envelope of an audio signal.
Conserving energy in video has the potential to preserve textures rather than low-passing them.
Explicitly quantizing a gain allows a simple contrast masking model with no signaling cost.
Vector quantizing the shape keeps the number of degrees of freedom the same as scalar quantization, avoiding redundancy in the representation.
We demonstrate how to predict the vector by transforming the space it is encoded in, rather than subtracting off the predictor, which would make energy conservation impossible.
We also derive an encoding of the vector-quantized codewords that takes advantage of their non-uniform distribution.
We show that the resulting technique outperforms scalar quantization by an average of 0.90 dB on still images, equivalent to a 24.8% reduction in bitrate at equal quality, while for videos, the improvement averages 0.83 dB, equivalent to a 13.7% reduction in bitrate.
A recent independent study resulted in a ranking system which ranked Astronomy and Computing (ASCOM) much higher than most of the older journals highlighting the niche prominence of the particular journal.
We investigate the remarkable ascendancy in reputation of ASCOM by proposing a novel differential equation based modeling.
The Modeling is a consequence of knowledge discovery from big data-centric methods, namely L1-SVD.
The inadequacy of the ranking method in explaining the reason behind the growth in reputation of ASCOM is reasonable to understand given that the study was post-facto.
Thus, we propose a growth model by accounting for the behavior of parameters that contribute to the growth of a field.
It is worthwhile to spend some time in analysing the cause and control variables behind rapid rise in reputation of a journal in a niche area.
We intent to probe and bring out parameters responsible for its growing influence.
Delay differential equations are used to model the change of influence on a journal's status by exploiting the effects of historical data.
Iris recognition technology, used to identify individuals by photographing the iris of their eye, has become popular in security applications because of its ease of use, accuracy, and safety in controlling access to high-security areas.
Fusion of multiple algorithms for biometric verification performance improvement has received considerable attention.
The proposed method combines the zero-crossing 1 D wavelet Euler number, and genetic algorithm based for feature extraction.
The output from these three algorithms is normalized and their score are fused to decide whether the user is genuine or imposter.
This new strategies is discussed in this paper, in order to compute a multimodal combined score.
This paper addresses the problem of Human-Aware Navigation (HAN), using multi camera sensors to implement a vision-based person tracking system.
The main contributions of this paper are as follows: a novel and efficient Deep Learning person detection and a standardization of human-aware constraints.
In the first stage of the approach, we propose to cascade the Aggregate Channel Features (ACF) detector with a deep Convolutional Neural Network (CNN) to achieve fast and accurate Pedestrian Detection (PD).
Regarding the human awareness (that can be defined as constraints associated with the robot's motion), we use a mixture of asymmetric Gaussian functions, to define the cost functions associated to each constraint.
Both methods proposed herein are evaluated individually to measure the impact of each of the components.
The final solution (including both the proposed pedestrian detection and the human-aware constraints) is tested in a typical domestic indoor scenario, in four distinct experiments.
The results show that the robot is able to cope with human-aware constraints, defined after common proxemics and social rules.
We examine the non-Markovian nature of human mobility by exposing the inability of Markov models to capture criticality in human mobility.
In particular, the assumed Markovian nature of mobility was used to establish a theoretical upper bound on the predictability of human mobility (expressed as a minimum error probability limit), based on temporally correlated entropy.
Since its inception, this bound has been widely used and empirically validated using Markov chains.
We show that recurrent-neural architectures can achieve significantly higher predictability, surpassing this widely used upper bound.
In order to explain this anomaly, we shed light on several underlying assumptions in previous research works that has resulted in this bias.
By evaluating the mobility predictability on real-world datasets, we show that human mobility exhibits scale-invariant long-range correlations, bearing similarity to a power-law decay.
This is in contrast to the initial assumption that human mobility follows an exponential decay.
This assumption of exponential decay coupled with Lempel-Ziv compression in computing Fano's inequality has led to an inaccurate estimation of the predictability upper bound.
We show that this approach inflates the entropy, consequently lowering the upper bound on human mobility predictability.
We finally highlight that this approach tends to overlook long-range correlations in human mobility.
This explains why recurrent-neural architectures that are designed to handle long-range structural correlations surpass the previously computed upper bound on mobility predictability.
In the current Named Data Networking implementation, forwarding a data request requires finding an exact match between the prefix of the name carried in the request and a forwarding table entry.
However, consumers may not always know the exact naming, or an exact prefix, of their desired data.
The current approach to this problem-establishing naming conventions and performing name lookup-can be infeasible in highly ad hoc, heterogeneous, and dynamic environments: the same data can be named using different terms or even languages, naming conventions may be minimal if they exist at all, and name lookups can be costly.
In this paper, we present a fuzzy Interest forwarding approach that exploits semantic similarities between the names carried in Interest packets and the names of potentially matching data in CS and entries in FIB.
We describe the fuzzy Interest forwarding approach, outline the semantic understanding function that determines the name matching, and present our simulation study along with extended evaluation results.
Are human perception and decision biases grounded in a form of rationality?
You return to your camp after hunting or gathering.
You see the grass moving.
You do not know the probability that a snake is in the grass.
Should you cross the grass - at the risk of being bitten by a snake - or make a long, hence costly, detour?
Based on this storyline, we consider a rational decision maker maximizing expected discounted utility with learning.
We show that his optimal behavior displays three biases: status quo, salience, overestimation of small probabilities.
Biases can be the product of rational behavior.
The objective of deep learning methods based on encoder-decoder architectures for music source separation is to approximate either ideal time-frequency masks or spectral representations of the target music source(s).
The spectral representations are then used to derive time-frequency masks.
In this work we introduce a method to directly learn time-frequency masks from an observed mixture magnitude spectrum.
We employ recurrent neural networks and train them using prior knowledge only for the magnitude spectrum of the target source.
To assess the performance of the proposed method, we focus on the task of singing voice separation.
The results from an objective evaluation show that our proposed method provides comparable results to deep learning based methods which operate over complicated signal representations.
Compared to previous methods that approximate time-frequency masks, our method has increased performance of signal to distortion ratio by an average of 3.8 dB.
We introduce Question-Answer Meaning Representations (QAMRs), which represent the predicate-argument structure of a sentence as a set of question-answer pairs.
We also develop a crowdsourcing scheme to show that QAMRs can be labeled with very little training, and gather a dataset with over 5,000 sentences and 100,000 questions.
A detailed qualitative analysis demonstrates that the crowd-generated question-answer pairs cover the vast majority of predicate-argument relationships in existing datasets (including PropBank, NomBank, QA-SRL, and AMR) along with many previously under-resourced ones, including implicit arguments and relations.
The QAMR data and annotation code is made publicly available to enable future work on how best to model these complex phenomena.
Virtual reality 360-degree videos will become the first prosperous online VR application.
VR 360 videos are data-hungry and latency-sensitive that pose unique challenges to the networking infrastructure.
In this paper, we focus on the ultimate VR 360 that satisfies human eye fidelity.
The ultimate VR 360 requires downlink 1.5 Gbps for viewing and uplink 6.6 Gbps for live broadcasting, with round-trip time of less than 8.3 ms. On the other hand, wireless access to VR 360 services is preferred over wire-line transmission because of the better user experience and the safety concern (e.g., tripping hazard).
We explore in this paper whether the most advanced wireless technologies from both cellular communications and WiFi communications support the ultimate VR 360.
Specifically, we consider 5G in cellular communications, IEEE 802.11ac (operating in 5GHz) and IEEE 802.11ad (operating in 60GHz) in WiFi communications.
According to their performance specified in their standards and/or empirical measurements, we have the following findings: (1) Only 5G has the potential to support both the the ultimate VR 360 viewing and live broadcasting.
However, it is difficult for 5G to support multiple users of the ultimate VR live broadcasting at home; (2) IEEE 802.11ac supports the ultimate VR 360 viewing but fails to support the ultimate VR 360 live broadcasting because it does not meet the data rate requirement of the ultimate VR 360 live broadcasting; (3) IEEE 802.11ad fails to support the ultimate VR 360, because its current implementation incurs very high latency.
Our preliminary results indicate that more advanced wireless technologies are needed to fully support multiple ultimate VR 360 users at home.
When we have knowledge of the positions of nearby walls and buildings, estimating the source location becomes a very efficient way of characterizing and estimating a radio channel.
We consider localization performance with and without this knowledge.
We treat the multipath channel as a set of "virtual receivers" whose positions can be pre-stored in a channel database.
Using wall knowledge, we develop a generalized MUSIC algorithm that treats the wall reflection parameter as a nuisance variable.
We compare this to a classic MVDR direct positioning algorithm that lacks wall knowledge.
In a simple scenario, we find that lack of wall knowledge can increase location error by 7-100x, depending on the number of antennas, SNR, and true reflection parameter.
Interestingly, as the number of antennas increases, the value of wall knowledge decreases.
A key challenge in modern computing is to develop systems that address complex, dynamic problems in a scalable and efficient way, because the increasing complexity of software makes designing and maintaining efficient and flexible systems increasingly difficult.
Biological systems are thought to possess robust, scalable processing paradigms that can automatically manage complex, dynamic problem spaces, possessing several properties that may be useful in computer systems.
The biological properties of self-organisation, self-replication, self-management, and scalability are addressed in an interesting way by autopoiesis, a descriptive theory of the cell founded on the concept of a system's circular organisation to define its boundary with its environment.
In this paper, therefore, we review the main concepts of autopoiesis and then discuss how they could be related to fundamental concepts and theories of computation.
The paper is conceptual in nature and the emphasis is on the review of other people's work in this area as part of a longer-term strategy to develop a formal theory of autopoietic computing.
In this paper we discuss some reasons why temporal logic might not be suitable to model real life norms.
To show this, we present a novel deontic logic contrary-to-duty/derived permission paradox based on the interaction of obligations, permissions and contrary-to-duty obligations.
The paradox is inspired by real life norms.
Access to the cloud has the potential to provide scalable and cost effective enhancements of physical devices through the use of advanced computational processes run on apparently limitless cyber infrastructure.
On the other hand, cyber-physical systems and cloud-controlled devices are subject to numerous design challenges; among them is that of security.
In particular, recent advances in adversary technology pose Advanced Persistent Threats (APTs) which may stealthily and completely compromise a cyber system.
In this paper, we design a framework for the security of cloud-based systems that specifies when a device should trust commands from the cloud which may be compromised.
This interaction can be considered as a game between three players: a cloud defender/administrator, an attacker, and a device.
We use traditional signaling games to model the interaction between the cloud and the device, and we use the recently proposed FlipIt game to model the struggle between the defender and attacker for control of the cloud.
Because attacks upon the cloud can occur without knowledge of the defender, we assume that strategies in both games are picked according to prior commitment.
This framework requires a new equilibrium concept, which we call Gestalt Equilibrium, a fixed-point that expresses the interdependence of the signaling and FlipIt games.
We present the solution to this fixed-point problem under certain parameter cases, and illustrate an example application of cloud control of an unmanned vehicle.
Our results contribute to the growing understanding of cloud-controlled systems.
Scarcity of labeled data is one of the most frequent problems faced in machine learning.
This is particularly true in relation extraction in text mining, where large corpora of texts exists in many application domains, while labeling of text data requires an expert to invest much time to read the documents.
Overall, state-of-the art models, like the convolutional neural network used in this paper, achieve great results when trained on large enough amounts of labeled data.
However, from a practical point of view the question arises whether this is the most efficient approach when one takes the manual effort of the expert into account.
In this paper, we report on an alternative approach where we first construct a relation extraction model using distant supervision, and only later make use of a domain expert to refine the results.
Distant supervision provides a mean of labeling data given known relations in a knowledge base, but it suffers from noisy labeling.
We introduce an active learning based extension, that allows our neural network to incorporate expert feedback and report on first results on a complex data set.
Skeleton-based human action recognition has attracted a lot of research attention during the past few years.
Recent works attempted to utilize recurrent neural networks to model the temporal dependencies between the 3D positional configurations of human body joints for better analysis of human activities in the skeletal data.
The proposed work extends this idea to spatial domain as well as temporal domain to better analyze the hidden sources of action-related information within the human skeleton sequences in both of these domains simultaneously.
Based on the pictorial structure of Kinect's skeletal data, an effective tree-structure based traversal framework is also proposed.
In order to deal with the noise in the skeletal data, a new gating mechanism within LSTM module is introduced, with which the network can learn the reliability of the sequential data and accordingly adjust the effect of the input data on the updating procedure of the long-term context representation stored in the unit's memory cell.
Moreover, we introduce a novel multi-modal feature fusion strategy within the LSTM unit in this paper.
The comprehensive experimental results on seven challenging benchmark datasets for human action recognition demonstrate the effectiveness of the proposed method.
A key challenge for automatic hate-speech detection on social media is the separation of hate speech from other instances of offensive language.
Lexical detection methods tend to have low precision because they classify all messages containing particular terms as hate speech and previous work using supervised learning has failed to distinguish between the two categories.
We used a crowd-sourced hate speech lexicon to collect tweets containing hate speech keywords.
We use crowd-sourcing to label a sample of these tweets into three categories: those containing hate speech, only offensive language, and those with neither.
We train a multi-class classifier to distinguish between these different categories.
Close analysis of the predictions and the errors shows when we can reliably separate hate speech from other offensive language and when this differentiation is more difficult.
We find that racist and homophobic tweets are more likely to be classified as hate speech but that sexist tweets are generally classified as offensive.
Tweets without explicit hate keywords are also more difficult to classify.
This paper presents a new multi-view RGB-D dataset of nine kitchen scenes, each containing several objects in realistic cluttered environments including a subset of objects from the BigBird dataset.
The viewpoints of the scenes are densely sampled and objects in the scenes are annotated with bounding boxes and in the 3D point cloud.
Also, an approach for detection and recognition is presented, which is comprised of two parts: i) a new multi-view 3D proposal generation method and ii) the development of several recognition baselines using AlexNet to score our proposals, which is trained either on crops of the dataset or on synthetically composited training images.
Finally, we compare the performance of the object proposals and a detection baseline to the Washington RGB-D Scenes (WRGB-D) dataset and demonstrate that our Kitchen scenes dataset is more challenging for object detection and recognition.
The dataset is available at: http://cs.gmu.edu/~robot/gmu-kitchens.html.
We study the problem of finding and monitoring fixed-size subgraphs in a continually changing large-scale graph.
We present the first approach that (i) performs worst-case optimal computation and communication, (ii) maintains a total memory footprint linear in the number of input edges, and (iii) scales down per-worker computation, communication, and memory requirements linearly as the number of workers increases, even on adversarially skewed inputs.
Our approach is based on worst-case optimal join algorithms, recast as a data-parallel dataflow computation.
We describe the general algorithm and modifications that make it robust to skewed data, prove theoretical bounds on its resource requirements in the massively parallel computing model, and implement and evaluate it on graphs containing as many as 64 billion edges.
The underlying algorithm and ideas generalize from finding and monitoring subgraphs to the more general problem of computing and maintaining relational equi-joins over dynamic relations.
In this paper, we present our deep attention-based classification (DABC) network for robust single image depth prediction, in the context of the Robust Vision Challenge 2018 (ROB 2018).
Unlike conventional depth prediction, our goal is to design a model that can perform well in both indoor and outdoor scenes with a single parameter set.
However, robust depth prediction suffers from two challenging problems: a) How to extract more discriminative features for different scenes (compared to a single scene)?
b) How to handle the large differences of depth ranges between indoor and outdoor datasets?
To address these two problems, we first formulate depth prediction as a multi-class classification task and apply a softmax classifier to classify the depth label of each pixel.
We then introduce a global pooling layer and a channel-wise attention mechanism to adaptively select the discriminative channels of features and to update the original features by assigning important channels with higher weights.
Further, to reduce the influence of quantization errors, we employ a soft-weighted sum inference strategy for the final prediction.
Experimental results on both indoor and outdoor datasets demonstrate the effectiveness of our method.
It is worth mentioning that we won the 2-nd place in single image depth prediction entry of ROB 2018, in conjunction with IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2018.
In many applications, ultra-wide band (UWB) system experiences impulse noise due to surrounding physical noise sources.
Therefore, a conventional receiver (correlator or matched filter) designed for additive Gaussian noise system is not optimum for an impulse noise affected communication channel.
In this paper, we propose a new robust receiver design that utilizes the received UWB signal cluster sparsity to mitigate impulse noise.
Further, multipath channel diversity enhances the signal-to-noise ratio, as compared to the single path after impulse noise removal in the proposed receiver design.
The proposed receiver is analyzed in time hopping binary phase shift keying UWB system and is compared with popular blanking non-linearity based receiver in Bernoulli-Gaussian impulse noise over both single and multipath IEEE 802.15.4a channels.
Unlike existing designs, the proposed receiver does not require any training sequence.
The proposed receiver is observed to be robust with improved bit error rate performance as compared to a blanking receiver in the presence of impulse noise.
This work addresses our research on driving skill modeling using artificial neural networks for haptic assistance.
In this paper, we present a haptic driving training simulator with performance-based, error-corrective haptic feedback.
One key component of our simulator is the ability to learn an optimized driving skill model from the driving data of expert drivers.
To this end, we obtain a model utilizing artificial neural networks to extract a desired movement of a steering wheel and an accelerator pedal based on the experts' prediction.
Then, we can deliver haptic assistance based on a driver's performance error which is a difference between a current and the desired movement.
We validate the performance of our framework in two respective user experiments recruiting expert/novice drivers to show the feasibility and applicability of facilitating neural networks for performance-based haptic driving skill transfer.
Visual question answering (VQA) has witnessed great progress since May, 2015 as a classic problem unifying visual and textual data into a system.
Many enlightening VQA works explore deep into the image and question encodings and fusing methods, of which attention is the most effective and infusive mechanism.
Current attention based methods focus on adequate fusion of visual and textual features, but lack the attention to where people focus to ask questions about the image.
Traditional attention based methods attach a single value to the feature at each spatial location, which losses many useful information.
To remedy these problems, we propose a general method to perform saliency-like pre-selection on overlapped region features by the interrelation of bidirectional LSTM (BiLSTM), and use a novel element-wise multiplication based attention method to capture more competent correlation information between visual and textual features.
We conduct experiments on the large-scale COCO-VQA dataset and analyze the effectiveness of our model demonstrated by strong empirical results.
A recent trend in object oriented (OO) programming languages is the use of Access Permissions (APs) as an abstraction for controlling concurrent executions of programs.
The use of AP source code annotations defines a protocol specifying how object references can access the mutable state of objects.
Although the use of APs simplifies the task of writing concurrent code, an unsystematic use of them can lead to subtle problems.
This paper presents a declarative interpretation of APs as Linear Concurrent Constraint Programs (lcc).
We represent APs as constraints (i.e., formulas in logic) in an underlying constraint system whose entailment relation models the transformation rules of APs.
Moreover, we use processes in lcc to model the dependencies imposed by APs, thus allowing the faithful representation of their flow in the program.
We verify relevant properties about AP programs by taking advantage of the interpretation of lcc processes as formulas in Girard's intuitionistic linear logic (ILL).
Properties include deadlock detection, program correctness (whether programs adhere to their AP specifications or not), and the ability of methods to run concurrently.
By relying on a focusing discipline for ILL, we provide a complexity measure for proofs of the above mentioned properties.
The effectiveness of our verification techniques is demonstrated by implementing the Alcove tool that includes an animator and a verifier.
The former executes the lcc model, observing the flow of APs and quickly finding inconsistencies of the APs vis-a-vis the implementation.
The latter is an automatic theorem prover based on ILL.
This paper is under consideration for publication in Theory and Practice of Logic Programming (TPLP).
One of the most significant 5G technology enablers will be Device-to-Device (D2D) communications.
D2D communications constitute a promising way to improve spectral, energy and latency performance, exploiting the physical proximity of communicating devices and increasing resource utilization.
Furthermore, network infrastructure densification has been considered as one of the most substantial methods to increase system performance, taking advantage of base station proximity and spatial reuse of system resources.
However, could we improve system performance by leveraging both of these two 5G enabling technologies together in a multi-cell environment?
How does spectrum sharing affect performance enhancement?
This article investigates the implications of interference, densification and spectrum sharing in D2D performance gain.
The in-band D2D approach, where legacy users coexist with potential D2D pairs, is considered in a multi-cell system.
Overlay and underlay spectrum sharing approaches are employed in order for the potential D2D pairs to access the spectrum.
Given that two of the most critical problems in the D2D concept are mode selection and user scheduling, we jointly address them, aiming at maximizing the total system uplink throughput.
Thus, we present a radio resource management mechanism for intra-cell and cross-cell overlay/underlay D2D communications enabled in a multi-cell system.
System-level simulations are executed to evaluate the system performance and examine the trends of D2D communication gain for the different spectrum sharing approaches and various densification scenarios.
Finally, realworld SDR-based experiments are performed to test and assess D2D communications for overlay and underlay spectrum sharing.
Biclustering is found to be useful in areas like data mining and bioinformatics.
The term biclustering involves searching subsets of observations and features forming coherent structure.
This can be interpreted in different ways like spatial closeness, relation between features for selected observations etc.
This paper discusses different properties, objectives and approaches of biclustering algorithms.
We also present an algorithm which detects feature relation based biclusters using density based techniques.
Here we use relative density of regions to identify biclusters embedded in the data.
Properties of this algorithm are discussed and demonstrated using artificial datasets.
Proposed method is seen to give better results on these datasets using paired right tailed t test.
Usefulness of proposed method is also demonstrated using real life datasets.
We consider the design of wireless queueing network control policies with particular focus on combining stability with additional application-dependent requirements.
Thereby, we consequently pursue a cost function based approach that provides the flexibility to incorporate constraints and requirements of particular services or applications.
As typical examples of such requirements, we consider the reduction of buffer underflows in case of streaming traffic, and energy efficiency in networks of battery powered nodes.
Compared to the classical throughput optimal control problem, such requirements significantly complicate the control problem.
We provide easily verifyable theoretical conditions for stability, and, additionally, compare various candidate cost functions applied to wireless networks with streaming media traffic.
Moreover, we demonstrate how the framework can be applied to the problem of energy efficient routing, and we demonstrate the aplication of our framework in cross-layer control problems for wireless multihop networks, using an advanced power control scheme for interference mitigation, based on successive convex approximation.
In all scenarios, the performance of our control framework is evaluated using extensive numerical simulations.
Delay Tolerant Networks (DTNs) are sparse mobile networks, which experiences frequent disruptions in connectivity among nodes.
Usually, DTN follows store-carry-and forward mechanism for message forwarding, in which a node store and carry the message until it finds an appropriate relay node to forward further in the network.
So, The efficiency of DTN routing protocol relies on the intelligent selection of a relay node from a set of encountered nodes.
Although there are plenty of DTN routing schemes proposed in the literature based on different strategies of relay selection, there are not many mathematical models proposed to study the behavior of message forwarding in DTN.
In this paper, we have proposed a novel epidemic model, called as CISER model, for message propagation in DTN, based on Amoebiasis disease propagation in human population.
The proposed CISER model is an extension of SIR epidemic model with additional states to represent the resource constrained behavior of nodes in DTN.
Experimental results using both synthetic and real-world traces show that the proposed model improves the routing performance metrics, such as delivery ratio, overhead ratio and delivery delay compared to SIR model.
This paper introduces a generalization of Convolutional Neural Networks (CNNs) to graphs with irregular linkage structures, especially heterogeneous graphs with typed nodes and schemas.
We propose a novel spatial convolution operation to model the key properties of local connectivity and translation invariance, using high-order connection patterns or motifs.
We develop a novel deep architecture Motif-CNN that employs an attention model to combine the features extracted from multiple patterns, thus effectively capturing high-order structural and feature information.
Our experiments on semi-supervised node classification on real-world social networks and multiple representative heterogeneous graph datasets indicate significant gains of 6-21% over existing graph CNNs and other state-of-the-art techniques.
There has been a tremendous effort in improving wireless LAN for supporting the demanding multimedia application.
Many new protocols or ideas have been proposed and proved by using a mathematical model or running a simulation program.
That is satisfactory but these proposed designs might not work in the real world situation.
Testbed is an option to alleviate this gap and present the opportunity to see the real problem and ensure that the design works.
A framework architecture for building a testbed to test a new concept or design is presented in this paper.
The framework is designed in the modularity style in such a way that can be easily exchanged or modified.
A testbed based on the framework that implements the polling based mechanism has been created and the results have shown that the QoS of the real time traffic can be maintained in the presence of the high non-real time traffic.
The blooming availability of traces for social, biological, and communication networks opens up unprecedented opportunities in analyzing diffusion processes in networks.
However, the sheer sizes of the nowadays networks raise serious challenges in computational efficiency and scalability.
In this paper, we propose a new hyper-graph sketching framework for inflence dynamics in networks.
The central of our sketching framework, called SKIS, is an efficient importance sampling algorithm that returns only non-singular reverse cascades in the network.
Comparing to previously developed sketches like RIS and SKIM, our sketch significantly enhances estimation quality while substantially reducing processing time and memory-footprint.
Further, we present general strategies of using SKIS to enhance existing algorithms for influence estimation and influence maximization which are motivated by practical applications like viral marketing.
Using SKIS, we design high-quality influence oracle for seed sets with average estimation error up to 10x times smaller than those using RIS and 6x times smaller than SKIM.
In addition, our influence maximization using SKIS substantially improves the quality of solutions for greedy algorithms.
It achieves up to 10x times speed-up and 4x memory reduction for the fastest RIS-based DSSA algorithm, while maintaining the same theoretical guarantees.
In the encoder-decoder architecture for neural machine translation (NMT), the hidden states of the recurrent structures in the encoder and decoder carry the crucial information about the sentence.These vectors are generated by parameters which are updated by back-propagation of translation errors through time.
We argue that propagating errors through the end-to-end recurrent structures are not a direct way of control the hidden vectors.
In this paper, we propose to use word predictions as a mechanism for direct supervision.
More specifically, we require these vectors to be able to predict the vocabulary in target sentence.
Our simple mechanism ensures better representations in the encoder and decoder without using any extra data or annotation.
It is also helpful in reducing the target side vocabulary and improving the decoding efficiency.
Experiments on Chinese-English and German-English machine translation tasks show BLEU improvements by 4.53 and 1.3, respectively
Objective image quality assessment (IQA) is imperative in the current multimedia-intensive world, in order to assess the visual quality of an image at close to a human level of ability.
Many parameters such as color intensity, structure, sharpness, contrast, presence of an object, etc., draw human attention to an image.
Psychological vision research suggests that human vision is biased to the center area of an image and display screen.
As a result, if the center part contains any visually salient information, it draws human attention even more and any distortion in that part will be better perceived than other parts.
To the best of our knowledge, previous IQA methods have not considered this fact.
In this paper, we propose a full reference image quality assessment (FR-IQA) approach using visual saliency and contrast; however, we give extra attention to the center by increasing the sensitivity of the similarity maps in that region.
We evaluated our method on three large-scale popular benchmark databases used by most of the current IQA researchers (TID2008, CSIQ~and LIVE), having a total of 3345 distorted images with 28~different kinds of distortions.
Our method is compared with 13 state-of-the-art approaches.
This comparison reveals the stronger correlation of our method with human-evaluated values.
The prediction-of-quality score is consistent for distortion specific as well as distortion independent cases.
Moreover, faster processing makes it applicable to any real-time application.
The MATLAB code is publicly available to test the algorithm and can be found online at http://layek.khu.ac.kr/CEQI.
This paper concerns a deep learning approach to relevance ranking in information retrieval (IR).
Existing deep IR models such as DSSM and CDSSM directly apply neural networks to generate ranking scores, without explicit understandings of the relevance.
According to the human judgement process, a relevance label is generated by the following three steps: 1) relevant locations are detected, 2) local relevances are determined, 3) local relevances are aggregated to output the relevance label.
In this paper we propose a new deep learning architecture, namely DeepRank, to simulate the above human judgment process.
Firstly, a detection strategy is designed to extract the relevant contexts.
Then, a measure network is applied to determine the local relevances by utilizing a convolutional neural network (CNN) or two-dimensional gated recurrent units (2D-GRU).
Finally, an aggregation network with sequential integration and term gating mechanism is used to produce a global relevance score.
DeepRank well captures important IR characteristics, including exact/semantic matching signals, proximity heuristics, query term importance, and diverse relevance requirement.
Experiments on both benchmark LETOR dataset and a large scale clickthrough data show that DeepRank can significantly outperform learning to ranking methods, and existing deep learning methods.
Monoidal computer is a categorical model of intensional computation, where many different programs correspond to the same input-output behavior.
The upshot of yet another model of computation is that a categorical formalism should provide a much needed high level language for theory of computation, flexible enough to allow abstracting away the low level implementation details when they are irrelevant, or taking them into account when they are genuinely needed.
A salient feature of the approach through monoidal categories is the formal graphical language of string diagrams, which supports visual reasoning about programs and computations.
In the present paper, we provide a coalgebraic characterization of monoidal computer.
It turns out that the availability of interpreters and specializers, that make a monoidal category into a monoidal computer, is equivalent with the existence of a *universal state space*, that carries a weakly final state machine for any pair of input and output types.
Being able to program state machines in monoidal computers allows us to represent Turing machines, to capture their execution, count their steps, as well as, e.g., the memory cells that they use.
The coalgebraic view of monoidal computer thus provides a convenient diagrammatic language for studying computability and complexity.
The idea of video super resolution is to use different view points of a single scene to enhance the overall resolution and quality.
Classical energy minimization approaches first establish a correspondence of the current frame to all its neighbors in some radius and then use this temporal information for enhancement.
In this paper, we propose the first variational super resolution approach that computes several super resolved frames in one batch optimization procedure by incorporating motion information between the high-resolution image frames themselves.
As a consequence, the number of motion estimation problems grows linearly in the number of frames, opposed to a quadratic growth of classical methods and temporal consistency is enforced naturally.
We use infimal convolution regularization as well as an automatic parameter balancing scheme to automatically determine the reliability of the motion information and reweight the regularization locally.
We demonstrate that our approach yields state-of-the-art results and even is competitive with machine learning approaches.
The Internet facilitates large-scale collaborative projects and the emergence of Web 2.0 platforms, where producers and consumers of content unify, has drastically changed the information market.
On the one hand, the promise of the "wisdom of the crowd" has inspired successful projects such as Wikipedia, which has become the primary source of crowd-based information in many languages.
On the other hand, the decentralized and often un-monitored environment of such projects may make them susceptible to low quality content.
In this work, we focus on Urban Dictionary, a crowd-sourced online dictionary.
We combine computational methods with qualitative annotation and shed light on the overall features of Urban Dictionary in terms of growth, coverage and types of content.
We measure a high presence of opinion-focused entries, as opposed to the meaning-focused entries that we expect from traditional dictionaries.
Furthermore, Urban Dictionary covers many informal, unfamiliar words as well as proper nouns.
Urban Dictionary also contains offensive content, but highly offensive content tends to receive lower scores through the dictionary's voting system.
The low threshold to include new material in Urban Dictionary enables quick recording of new words and new meanings, but the resulting heterogeneous content can pose challenges in using Urban Dictionary as a source to study language innovation.
Proliferation of touch-based devices has made sketch-based image retrieval practical.
While many methods exist for sketch-based object detection/image retrieval on small datasets, relatively less work has been done on large (web)-scale image retrieval.
In this paper, we present an efficient approach for image retrieval from millions of images based on user-drawn sketches.
Unlike existing methods for this problem which are sensitive to even translation or scale variations, our method handles rotation, translation, scale (i.e. a similarity transformation) and small deformations.
The object boundaries are represented as chains of connected segments and the database images are pre-processed to obtain such chains that have a high chance of containing the object.
This is accomplished using two approaches in this work: a) extracting long chains in contour segment networks and b) extracting boundaries of segmented object proposals.
These chains are then represented by similarity-invariant variable length descriptors.
Descriptor similarities are computed by a fast Dynamic Programming-based partial matching algorithm.
This matching mechanism is used to generate a hierarchical k-medoids based indexing structure for the extracted chains of all database images in an offline process which is used to efficiently retrieve a small set of possible matched images for query chains.
Finally, a geometric verification step is employed to test geometric consistency of multiple chain matches to improve results.
Qualitative and quantitative results clearly demonstrate superiority of the approach over existing methods.
Generative neural models have recently achieved state-of-the-art results for constituency parsing.
However, without a feasible search procedure, their use has so far been limited to reranking the output of external parsers in which decoding is more tractable.
We describe an alternative to the conventional action-level beam search used for discriminative neural models that enables us to decode directly in these generative models.
We then show that by improving our basic candidate selection strategy and using a coarse pruning function, we can improve accuracy while exploring significantly less of the search space.
Applied to the model of Choe and Charniak (2016), our inference procedure obtains 92.56 F1 on section 23 of the Penn Treebank, surpassing prior state-of-the-art results for single-model systems.
Although the performance of commodity computers has improved drastically with the introduction of multicore processors and GPU computing, the standard R distribution is still based on single-threaded model of computation, using only a small fraction of the computational power available now for most desktops and laptops.
Modern statistical software packages rely on high performance implementations of the linear algebra routines there are at the core of several important leading edge statistical methods.
In this paper we present a GPU implementation of the GMRES iterative method for solving linear systems.
We compare the performance of this implementation with a pure single threaded version of the CPU.
We also investigate the performance of our implementation using different GPU packages available now for R such as gmatrix, gputools or gpuR which are based on CUDA or OpenCL frameworks.
In this research, a new indicator of disciplinarity-multidisciplinarity is developed, discussed and applied.
EBDI is based on the combination of the frequency distribution of subject categories of journals citing or cited by the analysis unit and the spread and diversity of the citations among subject categories measured with Shannon-Wiener entropy.
Its reproducibility, robustness and consistence are discussed.
Four of the combinations of its values when applied to the cited and citing dimensions lead to a suggested taxonomy of the role that the studied unit might have in terms of the transformation of knowledge from different disciplines in the scientific communication system and its position respect a hypothetical thematic core of the discipline in which it has been classified.
The indicator is applied to the journals belonging to the first quartile of JCR-SSCI 2011 Library and Information Science and an indicator-based taxonomy is applied and discussed, pointing to differential thematic roles of the journals analyzed.
Recently, a number of existing blockchain systems have witnessed major bugs and vulnerabilities within smart contracts.
Although the literature features a number of proposals for securing smart contracts, these proposals mostly focus on proving the correctness or absence of a certain type of vulnerability within a contract, but cannot protect deployed (legacy) contracts from being exploited.
In this paper, we address this problem in the context of re-entrancy exploits and propose a novel smart contract security technology, dubbed Sereum (Secure Ethereum), which protects existing, deployed contracts against re-entrancy attacks in a backwards compatible way based on run-time monitoring and validation.
Sereum does neither require any modification nor any semantic knowledge of existing contracts.
By means of implementation and evaluation using the Ethereum blockchain, we show that Sereum covers the actual execution flow of a smart contract to accurately detect and prevent attacks with a false positive rate as small as 0.06% and with negligible run-time overhead.
As a by-product, we develop three advanced re-entrancy attacks to demonstrate the limitations of existing offline vulnerability analysis tools.
Naive Bayes Nearest Neighbour (NBNN) is a simple and effective framework which addresses many of the pitfalls of K-Nearest Neighbour (KNN) classification.
It has yielded competitive results on several computer vision benchmarks.
Its central tenet is that during NN search, a query is not compared to every example in a database, ignoring class information.
Instead, NN searches are performed within each class, generating a score per class.
A key problem with NN techniques, including NBNN, is that they fail when the data representation does not capture perceptual (e.g.~class-based) similarity.
NBNN circumvents this by using independent engineered descriptors (e.g.~SIFT).
To extend its applicability outside of image-based domains, we propose to learn a metric which captures perceptual similarity.
Similar to how Neighbourhood Components Analysis optimizes a differentiable form of KNN classification, we propose "Class Conditional" metric learning (CCML), which optimizes a soft form of the NBNN selection rule.
Typical metric learning algorithms learn either a global or local metric.
However, our proposed method can be adjusted to a particular level of locality by tuning a single parameter.
An empirical evaluation on classification and retrieval tasks demonstrates that our proposed method clearly outperforms existing learned distance metrics across a variety of image and non-image datasets.
This paper investigates how secure information sharing with external vendors can be achieved in an Industrial Internet of Things (IIoT).
It also identifies necessary security requirements for secure information sharing based on identified security challenges stated by the industry.
The paper then proposes a roadmap for improving security in IIoT which investigates both short-term and long-term solutions for protecting IIoT devices.
The short-term solution is mainly based on integrating existing good practices.
The paper also outlines a long term solution for protecting IIoT devices with fine-grained access control for sharing data between external entities that would support cloud-based data storage.
We present a simple algorithm for computing the document array given the string collection and its suffix array as input.
Our algorithm runs in linear time using constant workspace for large collections of short strings.
We present a key recovery attack against Y. Wang's Random Linear Code Encryption (RLCE) scheme recently submitted to the NIST call for post-quantum cryptography.
This attack recovers the secret key for all the short key parameters proposed by the author.
Functioning is gaining recognition as an important indicator of global health, but remains under-studied in medical natural language processing research.
We present the first analysis of automatically extracting descriptions of patient mobility, using a recently-developed dataset of free text electronic health records.
We frame the task as a named entity recognition (NER) problem, and investigate the applicability of NER techniques to mobility extraction.
As text corpora focused on patient functioning are scarce, we explore domain adaptation of word embeddings for use in a recurrent neural network NER system.
We find that embeddings trained on a small in-domain corpus perform nearly as well as those learned from large out-of-domain corpora, and that domain adaptation techniques yield additional improvements in both precision and recall.
Our analysis identifies several significant challenges in extracting descriptions of patient mobility, including the length and complexity of annotated entities and high linguistic variability in mobility descriptions.
Automatically generating coherent and semantically meaningful text has many applications in machine translation, dialogue systems, image captioning, etc.
Recently, by combining with policy gradient, Generative Adversarial Nets (GAN) that use a discriminative model to guide the training of the generative model as a reinforcement learning policy has shown promising results in text generation.
However, the scalar guiding signal is only available after the entire text has been generated and lacks intermediate information about text structure during the generative process.
As such, it limits its success when the length of the generated text samples is long (more than 20 words).
In this paper, we propose a new framework, called LeakGAN, to address the problem for long text generation.
We allow the discriminative net to leak its own high-level extracted features to the generative net to further help the guidance.
The generator incorporates such informative signals into all generation steps through an additional Manager module, which takes the extracted features of current generated words and outputs a latent vector to guide the Worker module for next-word generation.
Our extensive experiments on synthetic data and various real-world tasks with Turing test demonstrate that LeakGAN is highly effective in long text generation and also improves the performance in short text generation scenarios.
More importantly, without any supervision, LeakGAN would be able to implicitly learn sentence structures only through the interaction between Manager and Worker.
A thorough comprehension of image content demands a complex grasp of the interactions that may occur in the natural world.
One of the key issues is to describe the visual relationships between objects.
When dealing with real world data, capturing these very diverse interactions is a difficult problem.
It can be alleviated by incorporating common sense in a network.
For this, we propose a framework that makes use of semantic knowledge and estimates the relevance of object pairs during both training and test phases.
Extracted from precomputed models and training annotations, this information is distilled into the neural network dedicated to this task.
Using this approach, we observe a significant improvement on all classes of Visual Genome, a challenging visual relationship dataset.
A 68.5% relative gain on the recall at 100 is directly related to the relevance estimate and a 32.7% gain to the knowledge distillation.
There have been numerous studies on the problem of flocking control for multiagent systems whose simplified models are presented in terms of point-mass elements.
Meanwhile, full dynamic models pose some challenging problems in addressing the flocking control problem of mobile robots due to their nonholonomic dynamic properties.
Taking practical constraints into consideration, we propose a novel approach to distributed flocking control of nonholonomic mobile robots by bounded feedback.
The flocking control objectives consist of velocity consensus, collision avoidance, and cohesion maintenance among mobile robots.
A flocking control protocol which is based on the information of neighbor mobile robots is constructed.
The theoretical analysis is conducted with the help of a Lyapunov-like function and graph theory.
Simulation results are shown to demonstrate the efficacy of the proposed distributed flocking control scheme.
This paper describes three programming problems that are simple enough to be used in the beginning of a CS undergraduate program but also interesting enough to be worth exploring with different approaches.
We are able to apply a mixture of programming practices, abstraction and algebraic approaches to the problems, so that these subjects may be presented as complementary and allowing for clear and elegant solutions.
This work aims to assess the state of the art of data parallel deep neural network training, trying to identify potential research tracks to be exploited for performance improvement.
Beside, it presents a design for a practical C++ library dedicated at implementing and unifying the current state of the art methodologies for parallel training in a performance-conscious framework, allowing the user to explore novel strategies without departing significantly from its usual work-flow.
Automatic License Plate Recognition (ALPR) has been a frequent topic of research due to many practical applications.
However, many of the current solutions are still not robust in real-world situations, commonly depending on many constraints.
This paper presents a robust and efficient ALPR system based on the state-of-the-art YOLO object detector.
The Convolutional Neural Networks (CNNs) are trained and fine-tuned for each ALPR stage so that they are robust under different conditions (e.g., variations in camera, lighting, and background).
Specially for character segmentation and recognition, we design a two-stage approach employing simple data augmentation tricks such as inverted License Plates (LPs) and flipped characters.
The resulting ALPR approach achieved impressive results in two datasets.
First, in the SSIG dataset, composed of 2,000 frames from 101 vehicle videos, our system achieved a recognition rate of 93.53% and 47 Frames Per Second (FPS), performing better than both Sighthound and OpenALPR commercial systems (89.80% and 93.03%, respectively) and considerably outperforming previous results (81.80%).
Second, targeting a more realistic scenario, we introduce a larger public dataset, called UFPR-ALPR dataset, designed to ALPR.
This dataset contains 150 videos and 4,500 frames captured when both camera and vehicles are moving and also contains different types of vehicles (cars, motorcycles, buses and trucks).
In our proposed dataset, the trial versions of commercial systems achieved recognition rates below 70%.
On the other hand, our system performed better, with recognition rate of 78.33% and 35 FPS.
Many computer vision applications, such as object recognition and segmentation, increasingly build on superpixels.
However, there have been so far few superpixel algorithms that systematically deal with noisy images.
We propose to first decompose the image into equal-sized rectangular patches, which also sets the maximum superpixel size.
Within each patch, a Potts model for simultaneous segmentation and denoising is applied, that guarantees connected and non-overlapping superpixels and also produces a denoised image.
The corresponding optimization problem is formulated as a mixed integer linear program (MILP), and solved by a commercial solver.
Extensive experiments on the BSDS500 dataset images with noises are compared with other state-of-the-art superpixel methods.
Our method achieves the best result in terms of a combined score (OP) composed of the under-segmentation error, boundary recall and compactness.
Capabilities of inference and prediction are significant components of visual systems.
In this paper, we address an important and challenging task of them: visual path prediction.
Its goal is to infer the future path for a visual object in a static scene.
This task is complicated as it needs high-level semantic understandings of both the scenes and motion patterns underlying video sequences.
In practice, cluttered situations have also raised higher demands on the effectiveness and robustness of the considered models.
Motivated by these observations, we propose a deep learning framework which simultaneously performs deep feature learning for visual representation in conjunction with spatio-temporal context modeling.
After that, we propose a unified path planning scheme to make accurate future path prediction based on the analytic results of the context models.
The highly effective visual representation and deep context models ensure that our framework makes a deep semantic understanding of the scene and motion pattern, consequently improving the performance of the visual path prediction task.
In order to comprehensively evaluate the model's performance on the visual path prediction task, we construct two large benchmark datasets from the adaptation of video tracking datasets.
The qualitative and quantitative experimental results show that our approach outperforms the existing approaches and owns a better generalization capability.
Being able to automatically and quickly understand the user context during a session is a main issue for recommender systems.
As a first step toward achieving that goal, we propose a model that observes in real time the diversity brought by each item relatively to a short sequence of consultations, corresponding to the recent user history.
Our model has a complexity in constant time, and is generic since it can apply to any type of items within an online service (e.g. profiles, products, music tracks) and any application domain (e-commerce, social network, music streaming), as long as we have partial item descriptions.
The observation of the diversity level over time allows us to detect implicit changes.
In the long term, we plan to characterize the context, i.e. to find common features among a contiguous sub-sequence of items between two changes of context determined by our model.
This will allow us to make context-aware and privacy-preserving recommendations, to explain them to users.
As this is an ongoing research, the first step consists here in studying the robustness of our model while detecting changes of context.
In order to do so, we use a music corpus of 100 users and more than 210,000 consultations (number of songs played in the global history).
We validate the relevancy of our detections by finding connections between changes of context and events, such as ends of session.
Of course, these events are a subset of the possible changes of context, since there might be several contexts within a session.
We altered the quality of our corpus in several manners, so as to test the performances of our model when confronted with sparsity and different types of items.
The results show that our model is robust and constitutes a promising approach.
Social graphs, representing online friendships among users, are one of the fundamental types of data for many applications, such as recommendation, virality prediction and marketing in social media.
However, this data may be unavailable due to the privacy concerns of users, or kept private by social network operators, which makes such applications difficult.
Inferring user interests and discovering user connections through their shared multimedia content has attracted more and more attention in recent years.
This paper proposes a Gaussian relational topic model for connection discovery using user shared images in social media.
The proposed model not only models user interests as latent variables through their shared images, but also considers the connections between users as a result of their shared images.
It explicitly relates user shared images to user connections in a hierarchical, systematic and supervisory way and provides an end-to-end solution for the problem.
This paper also derives efficient variational inference and learning algorithms for the posterior of the latent variables and model parameters.
It is demonstrated through experiments with over 200k images from Flickr that the proposed method significantly outperforms the methods in previous works.
Sharing data from various sources and of diverse kinds, and fusing them together for sophisticated analytics and mash-up applications are emerging trends, and are prerequisites for grand visions such as that of cyber-physical systems enabled smart cities.
Cloud infrastructure can enable such data sharing both because it can scale easily to an arbitrary volume of data and computation needs on demand, as well as because of natural collocation of diverse such data sets within the infrastructure.
However, in order to convince data owners that their data are well protected while being shared among cloud users, the cloud platform needs to provide flexible mechanisms for the users to express the constraints (access rules) subject to which the data should be shared, and likewise, enforce them effectively.
We study a comprehensive set of practical scenarios where data sharing needs to be enforced by methods such as aggregation, windowed frame, value constrains, etc., and observe that existing basic access control mechanisms do not provide adequate flexibility to enable effective data sharing in a secure and controlled manner.
In this paper, we thus propose a framework for cloud that extends popular XACML model significantly by integrating flexible access control decisions and data access in a seamless fashion.
We have prototyped the framework and deployed it on commercial cloud environment for experimental runs to test the efficacy of our approach and evaluate the performance of the implemented prototype.
In this paper we present a method for automatically planning optimal paths for a group of robots that satisfy a common high level mission specification.
Each robot's motion in the environment is modeled as a weighted transition system.
The mission is given as a Linear Temporal Logic formula.
In addition, an optimizing proposition must repeatedly be satisfied.
The goal is to minimize the maximum time between satisfying instances of the optimizing proposition.
Our method is guaranteed to compute an optimal set of robot paths.
We utilize a timed automaton representation in order to capture the relative position of the robots in the environment.
We then obtain a bisimulation of this timed automaton as a finite transition system that captures the joint behavior of the robots and apply our earlier algorithm for the single robot case to optimize the group motion.
We present a simulation of a persistent monitoring task in a road network environment.
This paper describes a new method of data encoding which may be used in various modern digital, computer and telecommunication systems and devices.
The method permits the compression of data for storage or transmission, allowing the exact original data to be reconstructed without any loss of content.
The method is characterized by the simplicity of implementation, as well as high speed and compression ratio.
The method is based on a unique scheme of binary-ternary prefix-free encoding of characters of the original data.
This scheme does not require the transmission of the code tables from encoder to decoder; allows for the linear presentation of the code lists; permits the usage of computable indexes of the prefix codes in a linear list for decoding; makes it possible to estimate the compression ratio prior to encoding; makes the usage of multiplication and division operations, as well as operations with the floating point unnecessary; proves to be effective for static as well as adaptive coding; applicable to character sets of any size; allows for repeated compression to improve the ratio.
Many deep models have been recently proposed for anomaly detection.
This paper presents comparison of selected generative deep models and classical anomaly detection methods on an extensive number of non--image benchmark datasets.
We provide statistical comparison of the selected models, in many configurations, architectures and hyperparamaters.
We arrive to conclusion that performance of the generative models is determined by the process of selection of their hyperparameters.
Specifically, performance of the deep generative models deteriorates with decreasing amount of anomalous samples used in hyperparameter selection.
In practical scenarios of anomaly detection, none of the deep generative models systematically outperforms the kNN.
The use of floating bipolar electrodes in electrowinning cells of copper constitutes a nonconventional technology that promises economic and operational impacts.
This paper presents a computational tool for the simulation and analysis of such electrochemical cells.
A new model is developed for floating electrodes and a method of finite difference is used to obtain the threedimensional distribution of the potential and the field of current density inside the cell.
The analysis of the results is based on a technique for the interactive visualization of three-dimensional vectorial fields as lines of flow.
This paper presents an overview of an assembler driven verification methodology (ADVM) that was created and implemented for a chip card project at Infineon Technologies AG.
The primary advantage of this methodology is that it enables rapid porting of directed tests to new targets and derivatives, with only a minimum amount of code refactoring.
As a consequence, considerable verification development time and effort was saved.
This paper addresses the problem of localizing an unknown number of targets, all having the same radar signature, by a distributed MIMO radar consisting of single antenna transmitters and receivers that cannot determine directions of departure and arrival.
Furthermore, we consider the presence of multipath propagation, and the possible (correlated) blocking of the direct paths (going from the transmitter and reflecting off a target to the receiver).
In its most general form, this problem can be cast as a Bayesian estimation problem where every multipath component is accounted for.
However, when the environment map is unknown, this problem is ill-posed and hence, a tractable approximation is derived where only direct paths are accounted for.
In particular, we take into account the correlated blocking by scatterers in the environment which appears as a prior term in the Bayesian estimation framework.
A sub-optimal polynomial-time algorithm to solve the Bayesian multi-target localization problem with correlated blocking is proposed and its performance is evaluated using simulations.
We found that when correlated blocking is severe, assuming the blocking events to be independent and having constant probability (as was done in previous papers) resulted in poor detection performance, with false alarms more likely to occur than detections.
Weakly supervised semantic segmentation and localiza- tion have a problem of focusing only on the most important parts of an image since they use only image-level annota- tions.
In this paper, we solve this problem fundamentally via two-phase learning.
Our networks are trained in two steps.
In the first step, a conventional fully convolutional network (FCN) is trained to find the most discriminative parts of an image.
In the second step, the activations on the most salient parts are suppressed by inference conditional feedback, and then the second learning is performed to find the area of the next most important parts.
By combining the activations of both phases, the entire portion of the tar- get object can be captured.
Our proposed training scheme is novel and can be utilized in well-designed techniques for weakly supervised semantic segmentation, salient region detection, and object location prediction.
Detailed experi- ments demonstrate the effectiveness of our two-phase learn- ing in each task.
When a large collection of objects (e.g., robots, sensors, etc.) has to be deployed in a given environment, it is often required to plan a coordinated motion of the objects from their initial position to a final configuration enjoying some global property.
In such a scenario, the problem of minimizing some function of the distance travelled, and therefore energy consumption, is of vital importance.
In this paper we study several motion planning problems that arise when the objects must be moved on a graph, in order to reach certain goals which are of interest for several network applications.
Among the others, these goals include broadcasting messages and forming connected or interference-free networks.
We study these problems with the aim of minimizing a number of natural measures such as the average/overall distance travelled, the maximum distance travelled, or the number of objects that need to be moved.
To this respect, we provide several approximability and inapproximability results, most of which are tight.
This paper targets at learning to score the figure skating sports videos.
To address this task, we propose a deep architecture that includes two complementary components, i.e., Self-Attentive LSTM and Multi-scale Convolutional Skip LSTM.
These two components can efficiently learn the local and global sequential information in each video.
Furthermore, we present a large-scale figure skating sports video dataset -- FisV dataset.
This dataset includes 500 figure skating videos with the average length of 2 minutes and 50 seconds.
Each video is annotated by two scores of nine different referees, i.e., Total Element Score(TES) and Total Program Component Score (PCS).
Our proposed model is validated on FisV and MIT-skate datasets.
The experimental results show the effectiveness of our models in learning to score the figure skating videos.
A deep learning network was used to predict future blood glucose levels, as this can permit diabetes patients to take action before imminent hyperglycaemia and hypoglycaemia.
A sequential model with one long-short-term memory (LSTM) layer, one bidirectional LSTM layer and several fully connected layers was used to predict blood glucose levels for different prediction horizons.
The method was trained and tested on 26 datasets from 20 real patients.
The proposed network outperforms the baseline methods in terms of all evaluation criteria.
We propose and study a novel continuous space-time model for wireless networks which takes into account the stochastic interactions in both space through interference and in time due to randomness in traffic.
Our model consists of an interacting particle birth-death dynamics incorporating information-theoretic spectrum sharing.
Roughly speaking, particles (or more generally wireless links) arrive according to a Poisson Point Process on space-time, and stay for a duration governed by the local configuration of points present and then exit the network after completion of a file transfer.
We analyze this particle dynamics to derive an explicit condition for time ergodicity (i.e. stability) which is tight.
We also prove that when the dynamics is ergodic, the steady-state point process of links (or particles) exhibits a form statistical clustering.
Based on the clustering, we propose a conjecture which we leverage to derive approximations, bounds and asymptotics on performance characteristics such as delay and mean number of links per unit-space in the stationary regime.
The mathematical analysis is combined with discrete event simulation to study the performance of this type of networks.
Gibbs sampling is a Markov Chain Monte Carlo sampling technique that iteratively samples variables from their conditional distributions.
There are two common scan orders for the variables: random scan and systematic scan.
Due to the benefits of locality in hardware, systematic scan is commonly used, even though most statistical guarantees are only for random scan.
While it has been conjectured that the mixing times of random scan and systematic scan do not differ by more than a logarithmic factor, we show by counterexample that this is not the case, and we prove that that the mixing times do not differ by more than a polynomial factor under mild conditions.
To prove these relative bounds, we introduce a method of augmenting the state space to study systematic scan using conductance.
We present a technique for estimating the similarity between objects such as movies or foods whose proper representation depends on human perception.
Our technique combines a modest number of human similarity assessments to infer a pairwise similarity function between the objects.
This similarity function captures some human notion of similarity which may be difficult or impossible to automatically extract, such as which movie from a collection would be a better substitute when the desired one is unavailable.
In contrast to prior techniques, our method does not assume that all similarity questions on the collection can be answered or that all users perceive similarity in the same way.
When combined with a user model, we find how each assessor's tastes vary, affecting their perception of similarity.
Variation graphs, which represent genetic variation within a population, are replacing sequences as reference genomes.
Path indexes are one of the most important tools for working with variation graphs.
They generalize text indexes to graphs, allowing one to find the paths matching the query string.
We propose using de Bruijn graphs as path indexes, compressing them by merging redundant subgraphs, and encoding them with the Burrows-Wheeler transform.
The resulting fast, space-efficient, and versatile index is used in the variation graph toolkit vg.
The study of representations invariant to common transformations of the data is important to learning.
Most techniques have focused on local approximate invariance implemented within expensive optimization frameworks lacking explicit theoretical guarantees.
In this paper, we study kernels that are invariant to the unitary group while having theoretical guarantees in addressing practical issues such as (1) unavailability of transformed versions of labelled data and (2) not observing all transformations.
We present a theoretically motivated alternate approach to the invariant kernel SVM.
Unlike previous approaches to the invariant SVM, the proposed formulation solves both issues mentioned.
We also present a kernel extension of a recent technique to extract linear unitary-group invariant features addressing both issues and extend some guarantees regarding invariance and stability.
We present experiments on the UCI ML datasets to illustrate and validate our methods.
We present a method that learns to answer visual questions by selecting image regions relevant to the text-based query.
Our method exhibits significant improvements in answering questions such as "what color," where it is necessary to evaluate a specific location, and "what room," where it selectively identifies informative image regions.
Our model is tested on the VQA dataset which is the largest human-annotated visual question answering dataset to our knowledge.
This paper presents the InScript corpus (Narrative Texts Instantiating Script structure).
InScript is a corpus of 1,000 stories centered around 10 different scenarios.
Verbs and noun phrases are annotated with event and participant types, respectively.
Additionally, the text is annotated with coreference information.
The corpus shows rich lexical variation and will serve as a unique resource for the study of the role of script knowledge in natural language processing.
This paper focuses on the temporal aspect for recognizing human activities in videos; an important visual cue that has long been either disregarded or ill-used.
We revisit the conventional definition of an activity and restrict it to "Complex Action": a set of one-actions with a weak temporal pattern that serves a specific purpose.
Related works use spatiotemporal 3D convolutions with fixed kernel size, too rigid to capture the varieties in temporal extents of complex actions, and too short for long-range temporal modeling.
In contrast, we use multi-scale temporal convolutions, and we reduce the complexity of 3D convolutions.
The outcome is Timeception convolution layers, which reasons about minute-long temporal patterns, a factor of 8 longer than best related works.
As a result, Timeception achieves impressive accuracy in recognizing human activities of Charades.
Further, we conduct analysis to demonstrate that Timeception learns long-range temporal dependencies and tolerate temporal extents of complex actions.
We introduce Mix&Match (M&M) - a training framework designed to facilitate rapid and effective learning in RL agents, especially those that would be too slow or too challenging to train otherwise.
The key innovation is a procedure that allows us to automatically form a curriculum over agents.
Through such a curriculum we can progressively train more complex agents by, effectively, bootstrapping from solutions found by simpler agents.
In contradistinction to typical curriculum learning approaches, we do not gradually modify the tasks or environments presented, but instead use a process to gradually alter how the policy is represented internally.
We show the broad applicability of our method by demonstrating significant performance gains in three different experimental setups: (1) We train an agent able to control more than 700 actions in a challenging 3D first-person task; using our method to progress through an action-space curriculum we achieve both faster training and better final performance than one obtains using traditional methods.
(2) We further show that M&M can be used successfully to progress through a curriculum of architectural variants defining an agents internal state.
(3) Finally, we illustrate how a variant of our method can be used to improve agent performance in a multitask setting.
Recurrent neural network models with an attention mechanism have proven to be extremely effective on a wide variety of sequence-to-sequence problems.
However, the fact that soft attention mechanisms perform a pass over the entire input sequence when producing each element in the output sequence precludes their use in online settings and results in a quadratic time complexity.
Based on the insight that the alignment between input and output sequence elements is monotonic in many problems of interest, we propose an end-to-end differentiable method for learning monotonic alignments which, at test time, enables computing attention online and in linear time.
We validate our approach on sentence summarization, machine translation, and online speech recognition problems and achieve results competitive with existing sequence-to-sequence models.
Washing machine is of great domestic necessity as it frees us from the burden of washing our clothes and saves ample of our time.
This paper will cover the aspect of designing and developing of Fuzzy Logic based, Smart Washing Machine.
The regular washing machine (timer based) makes use of multi-turned timer based start-stop mechanism which is mechanical as is prone to breakage.
In addition to its starting and stopping issues, the mechanical timers are not efficient with respect of maintenance and electricity usage.
Recent developments have shown that merger of digital electronics in optimal functionality of this machine is possible and nowadays in practice.
A number of international renowned companies have developed the machine with the introduction of smart artificial intelligence.
Such a machine makes use of sensors and smartly calculates the amount of run-time (washing time) for the main machine motor.
Realtime calculations and processes are also catered in optimizing the run-time of the machine.
The obvious result is smart time management, better economy of electricity and efficiency of work.
This paper deals with the indigenization of FLC (Fuzzy Logic Controller) based Washing Machine, which is capable of automating the inputs and getting the desired output (wash-time).
Generating graphs that are similar to real ones is an open problem, while the similarity notion is quite elusive and hard to formalize.
In this paper, we focus on sparse digraphs and propose SDG, an algorithm that aims at generating graphs similar to real ones.
Since real graphs are evolving and this evolution is important to study in order to understand the underlying dynamical system, we tackle the problem of generating series of graphs.
We propose SEDGE, an algorithm meant to generate series of graphs similar to a real series.
SEDGE is an extension of SDG.
We consider graphs that are representations of software programs and show experimentally that our approach outperforms other existing approaches.
Experiments show the performance of both algorithms.
In previous studies, much attention from multidisciplinary fields has been devoted to understand the mechanism of underlying scholarly networks including bibliographic networks, citation networks and co-citation networks.
Particularly focusing on networks constructed by means of either authors affinities or the mutual content.
Missing a valuable dimension of network, which is an audience scholarly paper.
We aim at this paper to assess the impact that social networks and media can have on scholarly papers.
We also examine the process of information flow in such networks.
We also mention some observa- tions of attractive incidents that our proposed network model revealed.
In wireless sensor networks (WSNs), security has a vital importance.
Recently, there was a huge interest to propose security solutions in WSNs because of their applications in both civilian and military domains.
Adversaries can launch different types of attacks, and cryptography is used to countering these attacks.
This paper presents challenges of security and a classification of the different possible attacks in WSNs.
The problems of security in each layer of the network's OSI model are discussed.
In this paper, we review multi-agent collective behavior algorithms in the literature and classify them according to their underlying mathematical structure.
For each mathematical technique, we identify the multi-agent coordination tasks it can be applied to, and we analyze its scalability, bandwidth use, and demonstrated maturity.
We highlight how versatile techniques such as artificial potential functions can be used for applications ranging from low-level position control to high-level coordination and task allocation, we discuss possible reasons for the slow adoption of complex distributed coordination algorithms in the field, and we highlight areas for further research and development.
In the last two decades, number of Higher Education Institutions (HEI) grows in leaps and bounds.
This causes a cut throat competition among these institutions while attracting the student get admission in these institutions.
To make reach up to the students institution makes effort of advertisement.
Similarly developing and developed both type of institution launch several services also to attract students.
Most of the institutions are opened in self finance mode.
So all time they feel short hand in expenditure.
Now a day a number of advertisement methods are available.
So it is difficult for an institution to make advertisement through all modes and launch all services at the same time due to different constraints.
In this paper we use support and confidence method to find out the best way of advertisement.
The position we advocate in this paper is that relational algebra can provide a unified language for both representing and computing with statistical-relational objects, much as linear algebra does for traditional single-table machine learning.
Relational algebra is implemented in the Structured Query Language (SQL), which is the basis of relational database management systems.
To support our position, we have developed the FACTORBASE system, which uses SQL as a high-level scripting language for statistical-relational learning of a graphical model structure.
The design philosophy of FACTORBASE is to manage statistical models as first-class citizens inside a database.
Our implementation shows how our SQL constructs in FACTORBASE facilitate fast, modular, and reliable program development.
Empirical evidence from six benchmark databases indicates that leveraging database system capabilities achieves scalable model structure learning.
Controller synthesis techniques for continuous systems with respect to temporal logic specifications typically use a finite-state symbolic abstraction of the system.
Constructing this abstraction for the entire system is computationally expensive, and does not exploit natural decompositions of many systems into interacting components.
We have recently introduced a new relation, called (approximate) disturbance bisimulation for compositional symbolic abstraction to help scale controller synthesis for temporal logic to larger systems.
In this paper, we extend the results to stochastic control systems modeled by stochastic differential equations.
Given any stochastic control system satisfying a stochastic version of the incremental input-to-state stability property and a positive error bound, we show how to construct a finite-state transition system (if there exists one) which is disturbance bisimilar to the given stochastic control system.
Given a network of stochastic control systems, we give conditions on the simultaneous existence of disturbance bisimilar abstractions to every component allowing for compositional abstraction of the network system.
Given a set of data, biclustering aims at finding simultaneous partitions in biclusters of its samples and of the features which are used for representing the samples.
Consistent biclusterings allow to obtain correct classifications of the samples from the known classification of the features, and vice versa, and they are very useful for performing supervised classifications.
The problem of finding consistent biclusterings can be seen as a feature selection problem, where the features that are not relevant for classification purposes are removed from the set of data, while the total number of features is maximized in order to preserve information.
This feature selection problem can be formulated as a linear fractional 0-1 optimization problem.
We propose a reformulation of this problem as a bilevel optimization problem, and we present a heuristic algorithm for an efficient solution of the reformulated problem.
Computational experiments show that the presented algorithm is able to find better solutions with respect to the ones obtained by employing previously presented heuristic algorithms.
Image description task has been invariably examined in a static manner with qualitative presumptions held to be universally applicable, regardless of the scope or target of the description.
In practice, however, different viewers may pay attention to different aspects of the image, and yield different descriptions or interpretations under various contexts.
Such diversity in perspectives is difficult to derive with conventional image description techniques.
In this paper, we propose a customized image narrative generation task, in which the users are interactively engaged in the generation process by providing answers to the questions.
We further attempt to learn the user's interest via repeating such interactive stages, and to automatically reflect the interest in descriptions for new images.
Experimental results demonstrate that our model can generate a variety of descriptions from single image that cover a wider range of topics than conventional models, while being customizable to the target user of interaction.
Multipath routing is a trivial way to exploit the path diversity to leverage the network throughput.
Technologies such as OSPF ECMP use all the available paths in the network to forward traffic, however, we argue that is not necessary to do so to load balance the network.
In this paper, we consider multipath routing with only a limited number of end-to-end paths for each source and destination, and found that this can still load balance the traffic.
We devised an algorithm to select a few paths for each source-destination pair so that when all traffic are forwarded over these paths, we can achieve a balanced load in the sense that the maximum link utilization is comparable to that of ECMP forwarding.
When the constraint of only shortest paths (i.e. equal paths) are relaxed, we can even outperform ECMP in certain cases.
As a result, we can use a few end-to-end tunnels between each source and destination nodes to achieve the load balancing of traffic.
In this paper, we consider the problem of leveraging existing fully labeled categories to improve the weakly supervised detection (WSD) of new object categories, which we refer to as mixed supervised detection (MSD).
Different from previous MSD methods that directly transfer the pre-trained object detectors from existing categories to new categories, we propose a more reasonable and robust objectness transfer approach for MSD.
In our framework, we first learn domain-invariant objectness knowledge from the existing fully labeled categories.
The knowledge is modeled based on invariant features that are robust to the distribution discrepancy between the existing categories and new categories; therefore the resulting knowledge would generalize well to new categories and could assist detection models to reject distractors (e.g., object parts) in weakly labeled images of new categories.
Under the guidance of learned objectness knowledge, we utilize multiple instance learning (MIL) to model the concepts of both objects and distractors and to further improve the ability of rejecting distractors in weakly labeled images.
Our robust objectness transfer approach outperforms the existing MSD methods, and achieves state-of-the-art results on the challenging ILSVRC2013 detection dataset and the PASCAL VOC datasets.
Data analytics and data science play a significant role in nowadays society.
In the context of Smart Grids (SG), the collection of vast amounts of data has seen the emergence of a plethora of data analysis approaches.
In this paper, we conduct a Systematic Mapping Study (SMS) aimed at getting insights about different facets of SG data analysis: application sub-domains (e.g., power load control), aspects covered (e.g., forecasting), used techniques (e.g., clustering), tool-support, research methods (e.g., experiments/simulations), replicability/reproducibility of research.
The final goal is to provide a view of the current status of research.
Overall, we found that each sub-domain has its peculiarities in terms of techniques, approaches and research methodologies applied.
Simulations and experiments play a crucial role in many areas.
The replicability of studies is limited concerning the provided implemented algorithms, and to a lower extent due to the usage of private datasets.
Future advancements in robot autonomy and sophistication of robotics tasks rest on robust, efficient, and task-dependent semantic understanding of the environment.
Semantic segmentation is the problem of simultaneous segmentation and categorization of a partition of sensory data.
The majority of current approaches tackle this using multi-class segmentation and labeling in a Conditional Random Field (CRF) framework or by generating multiple object hypotheses and combining them sequentially.
In practical settings, the subset of semantic labels that are needed depend on the task and particular scene and labelling every single pixel is not always necessary.
We pursue these observations in developing a more modular and flexible approach to multi-class parsing of RGBD data based on learning strategies for combining independent binary object-vs-background segmentations in place of the usual monolithic multi-label CRF approach.
Parameters for the independent binary segmentation models can be learned very efficiently, and the combination strategy---learned using reinforcement learning---can be set independently and can vary over different tasks and environments.
Accuracy is comparable to state-of-art methods on a subset of the NYU-V2 dataset of indoor scenes, while providing additional flexibility and modularity.
Understanding driving situations regardless the conditions of the traffic scene is a cornerstone on the path towards autonomous vehicles; however, despite common sensor setups already include complementary devices such as LiDAR or radar, most of the research on perception systems has traditionally focused on computer vision.
We present a LiDAR-based 3D object detection pipeline entailing three stages.
First, laser information is projected into a novel cell encoding for bird's eye view projection.
Later, both object location on the plane and its heading are estimated through a convolutional neural network originally designed for image processing.
Finally, 3D oriented detections are computed in a post-processing phase.
Experiments on KITTI dataset show that the proposed framework achieves state-of-the-art results among comparable methods.
Further tests with different LiDAR sensors in real scenarios assess the multi-device capabilities of the approach.
Digital image forensics is a young but maturing field, encompassing key areas such as camera identification, detection of forged images, and steganalysis.
However, large gaps exist between academic results and applications used by practicing forensic analysts.
To move academic discoveries closer to real-world implementations, it is important to use data that represent "in the wild" scenarios.
For detection of stego images created from steganography apps, images generated from those apps are ideal to use.
In this paper, we present our work to perform steg detection on images from mobile apps using two different approaches: "signature" detection, and machine learning methods.
A principal challenge of the ML task is to create a great many of stego images from different apps with certain embedding rates.
One of our main contributions is a procedure for generating a large image database by using Android emulators and reverse engineering techniques.
We develop algorithms and tools for signature detection on stego apps, and provide solutions to issues encountered when creating ML classifiers.
No two people are alike.
We usually ignore this diversity as we have the capability to adapt and, without noticing, become experts in interfaces that were probably misadjusted to begin with.
This adaptation is not always at the user's reach.
One neglected group is the blind.
Spatial ability, memory, and tactile sensitivity are some characteristics that diverge between users.
Regardless, all are presented with the same methods ignoring their capabilities and needs.
Interaction with mobile devices is highly visually demanding which widens the gap between blind people.
Our research goal is to identify the individual attributes that influence mobile interaction, considering the blind, and match them with mobile interaction modalities in a comprehensive and extensible design space.
We aim to provide knowledge both for device design, device prescription and interface adaptation.
State-of-the-art deep reading comprehension models are dominated by recurrent neural nets.
Their sequential nature is a natural fit for language, but it also precludes parallelization within an instances and often becomes the bottleneck for deploying such models to latency critical scenarios.
This is particularly problematic for longer texts.
Here we present a convolutional architecture as an alternative to these recurrent architectures.
Using simple dilated convolutional units in place of recurrent ones, we achieve results comparable to the state of the art on two question answering tasks, while at the same time achieving up to two orders of magnitude speedups for question answering.
Why deep neural networks (DNNs) capable of overfitting often generalize well in practice is a mystery in deep learning.
Existing works indicate that this observation holds for both complicated real datasets and simple datasets of one-dimensional (1-d) functions.
In this work, for fitting low-frequency dominant 1-d functions, memorizing natural images and classification problems, we empirically found that a DNN, i.e., full-connected DNN or convolutional neural networks with common settings first quickly captures the dominant low-frequency components, and then relatively slowly captures high-frequency ones.
We call this phenomenon Frequency Principle (F-Principle).
F-Principle can be observed over various DNN setups of different activation functions, layer structures and training algorithms in our experiments.
F-Principle can be used to understand (i) the behavior of DNN training in the information plane and (ii) why DNNs often generalize well albeit its ability of overfitting.
This F-Principle potentially can provide insights into understanding the general principle underlying DNN optimization and generalization for real datasets.
Named Entity Recognition (NER) is a key NLP task, which is all the more challenging on Web and user-generated content with their diverse and continuously changing language.
This paper aims to quantify how this diversity impacts state-of-the-art NER methods, by measuring named entity (NE) and context variability, feature sparsity, and their effects on precision and recall.
In particular, our findings indicate that NER approaches struggle to generalise in diverse genres with limited training data.
Unseen NEs, in particular, play an important role, which have a higher incidence in diverse genres such as social media than in more regular genres such as newswire.
Coupled with a higher incidence of unseen features more generally and the lack of large training corpora, this leads to significantly lower F1 scores for diverse genres as compared to more regular ones.
We also find that leading systems rely heavily on surface forms found in training data, having problems generalising beyond these, and offer explanations for this observation.
This paper presents a new connection between the generalized Marcum-Q function and the confluent hypergeometric function of two variables, phi3.
This result is then applied to the closed-form characterization of the bivariate Nakagami-m distribution and of the distribution of the minimum eigenvalue of correlated non-central Wishart matrices, both important in communication theory.
New expressions for the corresponding cumulative distributions are obtained and a number of communication-theoretic problems involving them are pointed out.
We propose a method to leapfrog pixel-wise, semantic segmentation of (aerial) images and predict objects in a vector representation directly.
PolyMapper predicts maps of cities from aerial images as collections of polygons with a learnable framework.
Instead of the usual multi-step procedure of semantic segmentation, shape improvement, conversion to polygons, and polygon refinement, our approach learns mappings with a single network architecture and directly outputs maps.
We demonstrate that our method is capable of drawing polygons of buildings and road networks that very closely approximate the structure of existing online maps such as OpenStreetMap, and it does so in a fully automated manner.
Validation on existing and novel large scale datasets of several cities show that our approach achieves good levels of performance.
The modeling of cascade processes in multi-agent systems in the form of complex networks has in recent years become an important topic of study due to its many applications: the adoption of commercial products, spread of disease, the diffusion of an idea, etc.
In this paper, we begin by identifying a desiderata of seven properties that a framework for modeling such processes should satisfy: the ability to represent attributes of both nodes and edges, an explicit representation of time, the ability to represent non-Markovian temporal relationships, representation of uncertain information, the ability to represent competing cascades, allowance of non-monotonic diffusion, and computational tractability.
We then present the MANCaLog language, a formalism based on logic programming that satisfies all these desiderata, and focus on algorithms for finding minimal models (from which the outcome of cascades can be obtained) as well as how this formalism can be applied in real world scenarios.
We are not aware of any other formalism in the literature that meets all of the above requirements.
Embarrassingly parallel problems can be split in parts that are characterized by a really low (or sometime absent) exchange of information during their computation in parallel.
As a consequence they can be effectively computed in parallel exploiting commodity hardware, hence without particularly sophisticated interconnection networks.
Basically, this means Clusters, Networks of Workstations and Desktops as well as Computational Clouds.
Despite the simplicity of this computational model, it can be exploited to compute a quite large range of problems.
This paper describes JJPF, a tool for developing task parallel applications based on Java and Jini that showed to be an effective and efficient solution in environment like Clusters and Networks of Workstations and Desktops.
Recent work has shown that fast, compact low-bitwidth neural networks can be surprisingly accurate.
These networks use homogeneous binarization: all parameters in each layer or (more commonly) the whole model have the same low bitwidth (e.g., 2 bits).
However, modern hardware allows efficient designs where each arithmetic instruction can have a custom bitwidth, motivating heterogeneous binarization, where every parameter in the network may have a different bitwidth.
In this paper, we show that it is feasible and useful to select bitwidths at the parameter granularity during training.
For instance a heterogeneously quantized version of modern networks such as AlexNet and MobileNet, with the right mix of 1-, 2- and 3-bit parameters that average to just 1.4 bits can equal the accuracy of homogeneous 2-bit versions of these networks.
Further, we provide analyses to show that the heterogeneously binarized systems yield FPGA- and ASIC-based implementations that are correspondingly more efficient in both circuit area and energy efficiency than their homogeneous counterparts.
Semi-autonomous vehicles are increasingly serving critical functions in various settings from mining to logistics to defence.
A key characteristic of such systems is the presence of the human (drivers) in the control loop.
To ensure safety, both the driver needs to be aware of the autonomous aspects of the vehicle and the automated features of the vehicle built to enable safer control.
In this paper we propose a framework to combine empirical models describing human behaviour with the environment and system models.
We then analyse, via model checking, interaction between the models for desired safety properties.
The aim is to analyse the design for safe vehicle-driver interaction.
We demonstrate the applicability of our approach using a case study involving semi-autonomous vehicles where the driver fatigue are factors critical to a safe journey.
We address the recognition of agent-in-place actions, which are associated with agents who perform them and places where they occur, in the context of outdoor home surveillance.
We introduce a representation of the geometry and topology of scene layouts so that a network can generalize from the layouts observed in the training set to unseen layouts in the test set.
This Layout-Induced Video Representation (LIVR) abstracts away low-level appearance variance and encodes geometric and topological relationships of places in a specific scene layout.
LIVR partitions the semantic features of a video clip into different places to force the network to learn place-based feature descriptions; to predict the confidence of each action, LIVR aggregates features from the place associated with an action and its adjacent places on the scene layout.
We introduce the Agent-in-Place Action dataset to show that our method allows neural network models to generalize significantly better to unseen scenes.
Molecular communication is an expanding body of research.
Recent advances in biology have encouraged using genetically engineered bacteria as the main component in the molecular communication.
This has stimulated a new line of research that attempts to study molecular communication among bacteria from an information-theoretic point of view.
Due to high randomness in the individual behavior of the bacterium, reliable communication between two bacteria is almost impossible.
Therefore, we recently proposed that a population of bacteria in a cluster is considered as a node capable of molecular transmission and reception.
This proposition enables us to form a reliable node out of many unreliable bacteria.
The bacteria inside a node sense the environment and respond accordingly.
In this paper, we study the communication between two nodes, one acting as the transmitter and the other as the receiver.
We consider the case in which the information is encoded in the concentration of molecules by the transmitter.
The molecules produced by the bacteria in the transmitter node propagate in the environment via the diffusion process.
Then, their concentration sensed by the bacteria in the receiver node would decode the information.
The randomness in the communication is caused by both the error in the molecular production at the transmitter and the reception of molecules at the receiver.
We study the theoretical limits of the information transfer rate in such a setup versus the number of bacteria per node.
Finally, we consider M-ary modulation schemes and study the achievable rates and their error probabilities.
Many projects have applied knowledge patterns (KPs) to the retrieval of specialized information.
Yet terminologists still rely on manual analysis of concordance lines to extract semantic information, since there are no user-friendly publicly available applications enabling them to find knowledge rich contexts (KRCs).
To fill this void, we have created the KP-based EcoLexicon Semantic SketchGrammar (ESSG) in the well-known corpus query system Sketch Engine.
For the first time, the ESSG is now publicly available inSketch Engine to query the EcoLexicon English Corpus.
Additionally, reusing the ESSG in any English corpus uploaded by the user enables Sketch Engine to extract KRCs codifying generic-specific, part-whole, location, cause and function relations, because most of the KPs are domain-independent.
The information is displayed in the form of summary lists (word sketches) containing the pairs of terms linked by a given semantic relation.
This paper describes the process of building a KP-based sketch grammar with special focus on the last stage, namely, the evaluation with refinement purposes.
We conducted an initial shallow precision and recall evaluation of the 64 English sketch grammar rules created so far for hyponymy, meronymy and causality.
Precision was measured based on a random sample of concordances extracted from each word sketch type.
Recall was assessed based on a random sample of concordances where known term pairs are found.
The results are necessary for the improvement and refinement of the ESSG.
The noise of false positives helped to further specify the rules, whereas the silence of false negatives allows us to find useful new patterns.
Automating the detection of anomalous events within long video sequences is challenging due to the ambiguity of how such events are defined.
We approach the problem by learning generative models that can identify anomalies in videos using limited supervision.
We propose end-to-end trainable composite Convolutional Long Short-Term Memory (Conv-LSTM) networks that are able to predict the evolution of a video sequence from a small number of input frames.
Regularity scores are derived from the reconstruction errors of a set of predictions with abnormal video sequences yielding lower regularity scores as they diverge further from the actual sequence over time.
The models utilize a composite structure and examine the effects of conditioning in learning more meaningful representations.
The best model is chosen based on the reconstruction and prediction accuracy.
The Conv-LSTM models are evaluated both qualitatively and quantitatively, demonstrating competitive results on anomaly detection datasets.
Conv-LSTM units are shown to be an effective tool for modeling and predicting video sequences.
Active learning identifies data points to label that are expected to be the most useful in improving a supervised model.
Opportunistic active learning incorporates active learning into interactive tasks that constrain possible queries during interactions.
Prior work has shown that opportunistic active learning can be used to improve grounding of natural language descriptions in an interactive object retrieval task.
In this work, we use reinforcement learning for such an object retrieval task, to learn a policy that effectively trades off task completion with model improvement that would benefit future tasks.
I present a method for lossy transform coding of digital audio that uses the Weyl symbol calculus for constructing the encoding and decoding transformation.
The method establishes a direct connection between a time-frequency representation of the signal dependent threshold of masked noise and the encode/decode pair.
The formalism also offers a time-frequency measure of perceptual entropy.
Despite the recent deep learning (DL) revolution, kernel machines still remain powerful methods for action recognition.
DL has brought the use of large datasets and this is typically a problem for kernel approaches, which are not scaling up efficiently due to kernel Gram matrices.
Nevertheless, kernel methods are still attractive and more generally applicable since they can equally manage different sizes of the datasets, also in cases where DL techniques show some limitations.
This work investigates these issues by proposing an explicit approximated representation that, together with a linear model, is an equivalent, yet scalable, implementation of a kernel machine.
Our approximation is directly inspired by the exact feature map that is induced by an RBF Gaussian kernel but, unlike the latter, it is finite dimensional and very compact.
We justify the soundness of our idea with a theoretical analysis which proves the unbiasedness of the approximation, and provides a vanishing bound for its variance, which is shown to decrease much rapidly than in alternative methods in the literature.
In a broad experimental validation, we assess the superiority of our approximation in terms of 1) ease and speed of training, 2) compactness of the model, and 3) improvements with respect to the state-of-the-art performance.
We introduce an exact reformulation of a broad class of neighborhood filters, among which the bilateral filters, in terms of two functional rearrangements: the decreasing and the relative rearrangements.
Independently of the image spatial dimension (one-dimensional signal, image, volume of images, etc.
), we reformulate these filters as integral operators defined in a one-dimensional space corresponding to the level sets measures.
We prove the equivalence between the usual pixel-based version and the rearranged version of the filter.
When restricted to the discrete setting, our reformulation of bilateral filters extends previous results for the so-called fast bilateral filtering.
We, in addition, prove that the solution of the discrete setting, understood as constant-wise interpolators, converges to the solution of the continuous setting.
Finally, we numerically illustrate computational aspects concerning quality approximation and execution time provided by the rearranged formulation.
Cellular Automata (CA) have been considered one of the most pronounced parallel computational tools in the recent era of nature and bio-inspired computing.
Taking advantage of their local connectivity, the simplicity of their design and their inherent parallelism, CA can be effectively applied to many image processing tasks.
In this paper, a CA approach for efficient salt-n-pepper noise filtering in grayscale images is presented.
Using a 2D Moore neighborhood, the classified "noisy" cells are corrected by averaging the non-noisy neighboring cells.
While keeping the computational burden really low, the proposed approach succeeds in removing high-noise levels from various images and yields promising qualitative and quantitative results, compared to state-of-the-art techniques.
Computation of the extended gcd of two quadratic integers.
The ring of integers considered is principal but could be euclidean or not euclidean ring.
This method rely on principal ideal ring and reduction of binary quadratic forms.
Making personalized and context-aware suggestions of venues to the users is very crucial in venue recommendation.
These suggestions are often based on matching the venues' features with the users' preferences, which can be collected from previously visited locations.
In this paper we present a novel user-modeling approach which relies on a set of scoring functions for making personalized suggestions of venues based on venues content and reviews as well as users context.
Our experiments, conducted on the dataset of the TREC Contextual Suggestion Track, prove that our methodology outperforms state-of-the-art approaches by a significant margin.
We propose a novel approach for deformation-aware neural networks that learn the weighting and synthesis of dense volumetric deformation fields.
Our method specifically targets the space-time representation of physical surfaces from liquid simulations.
Liquids exhibit highly complex, non-linear behavior under changing simulation conditions such as different initial conditions.
Our algorithm captures these complex phenomena in two stages: a first neural network computes a weighting function for a set of pre-computed deformations, while a second network directly generates a deformation field for refining the surface.
Key for successful training runs in this setting is a suitable loss function that encodes the effect of the deformations, and a robust calculation of the corresponding gradients.
To demonstrate the effectiveness of our approach, we showcase our method with several complex examples of flowing liquids with topology changes.
Our representation makes it possible to rapidly generate the desired implicit surfaces.
We have implemented a mobile application to demonstrate that real-time interactions with complex liquid effects are possible with our approach.
Effective emergency and natural disaster management depend on the efficient mission-critical voice and data communication between first responders and victims.
Land Mobile Radio System (LMRS) is a legacy narrowband technology used for critical voice communications with limited use for data applications.
Recently Long Term Evolution (LTE) emerged as a broadband communication technology that has a potential to transform the capabilities of public safety technologies by providing broadband, ubiquitous, and mission-critical voice and data support.
For example, in the United States, FirstNet is building a nationwide coast-to-coast public safety network based of LTE broadband technology.
This paper presents a comparative survey of legacy and the LTE-based public safety networks, and discusses the LMRS-LTE convergence as well as mission-critical push-to-talk over LTE.
A simulation study of LMRS and LTE band class 14 technologies is provided using the NS-3 open source tool.
An experimental study of APCO-25 and LTE band class 14 is also conducted using software-defined radio, to enhance the understanding of the public safety systems.
Finally, emerging technologies that may have strong potential for use in public safety networks are reviewed.
Recurrent Neural Networks (RNNs) and their variants, such as Long-Short Term Memory (LSTM) networks, and Gated Recurrent Unit (GRU) networks, have achieved promising performance in sequential data modeling.
The hidden layers in RNNs can be regarded as the memory units, which are helpful in storing information in sequential contexts.
However, when dealing with high dimensional input data, such as video and text, the input-to-hidden linear transformation in RNNs brings high memory usage and huge computational cost.
This makes the training of RNNs unscalable and difficult.
To address this challenge, we propose a novel compact LSTM model, named as TR-LSTM, by utilizing the low-rank tensor ring decomposition (TRD) to reformulate the input-to-hidden transformation.
Compared with other tensor decomposition methods, TR-LSTM is more stable.
In addition, TR-LSTM can complete an end-to-end training and also provide a fundamental building block for RNNs in handling large input data.
Experiments on real-world action recognition datasets have demonstrated the promising performance of the proposed TR-LSTM compared with the tensor train LSTM and other state-of-the-art competitors.
We introduce "Unspeech" embeddings, which are based on unsupervised learning of context feature representations for spoken language.
The embeddings were trained on up to 9500 hours of crawled English speech data without transcriptions or speaker information, by using a straightforward learning objective based on context and non-context discrimination with negative sampling.
We use a Siamese convolutional neural network architecture to train Unspeech embeddings and evaluate them on speaker comparison, utterance clustering and as a context feature in TDNN-HMM acoustic models trained on TED-LIUM, comparing it to i-vector baselines.
Particularly decoding out-of-domain speech data from the recently released Common Voice corpus shows consistent WER reductions.
We release our source code and pre-trained Unspeech models under a permissive open source license.
This paper presents a detailed study of the energy consumption of the different Java Collection Framework (JFC) implementations.
For each method of an implementation in this framework, we present its energy consumption when handling different amounts of data.
Knowing the greenest methods for each implementation, we present an energy optimization approach for Java programs: based on calls to JFC methods in the source code of a program, we select the greenest implementation.
Finally, we present preliminary results of optimizing a set of Java programs where we obtained 6.2% energy savings.
The attitude space has been parameterized in various ways for practical purposes.
Different representations gain preferences over others based on their intuitive understanding, ease of implementation, formulaic simplicity, and physical as well as mathematical complications involved in using them.
This technical note gives a brief overview and discusses the quaternions, which are fourth dimensional extended complex numbers and used to represent orientation.
Their relationship to other modes of attitude representation such as Euler angles and Axis-Angle representation is also explored and conversion from one representation to another is explained.
The conventions, intuitive understanding and formulas most frequently used and indispensable to any quaternion application are stated and wherever possible, derived.
We generalize previous studies on critical phenomena in communication networks by adding computational capabilities to the nodes to better describe real-world situations such as cloud computing.
A set of tasks with random origin and destination with a multi-tier computational structure is distributed on a network modeled as a graph.
The execution time (or latency) of each task is statically computed and the sum is used as the energy in a Montecarlo simulation in which the temperature parameter controls the resource allocation optimality.
We study the transition to congestion by varying temperature and system load.
A method to approximately recover the time-evolution of the system by interpolating the latency probability distributions is presented.
This allows us to study the standard transition to the congested phase by varying the task production rate.
We are able to reproduce the main known results on network congestion and to gain a deeper insight over the maximum theoretical performance of a system and its sensitivity to routing and load balancing errors.
In a previous paper, we have shown that any Boolean formula can be encoded as a linear programming problem in the framework of Bayesian probability theory.
When applied to NP-complete algorithms, this leads to the fundamental conclusion that P = NP.
Now, we implement this concept in elementary arithmetic and especially in multiplication.
This provides a polynomial time deterministic factoring algorithm, while no such algorithm is known to day.
This result clearly appeals for a revaluation of the current cryptosystems.
The Bayesian arithmetic environment can also be regarded as a toy model for quantum mechanics.
Unmanned aerial vehicles mounted base stations (UAV-BSs) are expected to become one of the significant components of the Next Generation Wireless Networks (NGWNs).
Rapid deployment, mobility, higher chances of unobstructed propagation path, and flexibility features of UAV-BSs have attracted significant attention.
Despite, potentially, high gains brought by UAV-BSs in NGWNs, many challenges are also introduced by them.
Optimal location assignment to UAV-BSs, arguably, is the most widely investigated problem in the literature on UAV-BSs in NGWNs.
This paper presents a comprehensive survey of the literature on the location optimization of UAV-BSs in NGWNs.
A generic optimization framework through a universal Mixed Integer Non-Linear Programming (MINLP) formulation is constructed and the specifications of its constituents are elaborated.
The generic problem is classified into a novel taxonomy.
Due to the highly challenging nature of the optimization problem a range of solutions are adopted in the literature which are also covered under the aforementioned classification.
Furthermore, future research directions on UAV-BS location optimization in 5G and beyond non-terrestrial aerial communication systems are discussed.
Given the great interest in creating keyframe summaries from video, it is surprising how little has been done to formalise their evaluation and comparison.
User studies are often carried out to demonstrate that a proposed method generates a more appealing summary than one or two rival methods.
But larger comparison studies cannot feasibly use such user surveys.
Here we propose a discrimination capacity measure as a formal way to quantify the improvement over the uniform baseline, assuming that one or more ground truth summaries are available.
Using the VSUMM video collection, we examine 10 video feature types, including CNN and SURF, and 6 methods for matching frames from two summaries.
Our results indicate that a simple frame representation through hue histograms suffices for the purposes of comparing keyframe summaries.
We subsequently propose a formal protocol for comparing summaries when ground truth is available.
The work takes another look at the number of runs that a string might contain and provides an alternative proof for the bound.
We also propose another stronger conjecture that states that, for a fixed order on the alphabet, within every factor of a word there are at most as many occurrences of Lyndon roots corresponding to runs in a word as the length of the factor (only first such occurrences for each run are considered).
In this research, it was used a segmentation and classification method to identify threat recognition in human scanner images of airport security.
The Department of Homeland Security's (DHS) in USA has a higher false alarm, produced from theirs algorithms using today's scanners at the airports.
To repair this problem they started a new competition at Kaggle site asking the science community to improve their detection with new algorithms.
The dataset used in this research comes from DHS at https://www.kaggle.com/c/passenger-screening-algorithm-challenge/data According to DHS: "This dataset contains a large number of body scans acquired by a new generation of millimeter wave scanner called the High Definition-Advanced Imaging Technology (HD-AIT) system.
They are comprised of volunteers wearing different clothing types (from light summer clothes to heavy winter clothes), different body mass indices, different genders, different numbers of threats, and different types of threats".
Using Python as a principal language, the preprocessed of the dataset images extracted features from 200 bodies using: intensity, intensity differences and local neighbourhood to detect, to produce segmentation regions and label those regions to be used as a truth in a training and test dataset.
The regions are subsequently give to a CNN deep learning classifier to predict 17 classes (that represents the body zones): zone1, zone2, ... zone17 and zones with threat in a total of 34 zones.
The analysis showed the results of the classifier an accuracy of 98.2863% and a loss of 0.091319, as well as an average of 100% for recall and precision.
Autonomous robot manipulation often involves both estimating the pose of the object to be manipulated and selecting a viable grasp point.
Methods using RGB-D data have shown great success in solving these problems.
However, there are situations where cost constraints or the working environment may limit the use of RGB-D sensors.
When limited to monocular camera data only, both the problem of object pose estimation and of grasp point selection are very challenging.
In the past, research has focused on solving these problems separately.
In this work, we introduce a novel method called SilhoNet that bridges the gap between these two tasks.
We use a Convolutional Neural Network (CNN) pipeline that takes in ROI proposals to simultaneously predict an intermediate silhouette representation for objects with an associated occlusion mask.
The 3D pose is then regressed from the predicted silhouettes.
Grasp points from a precomputed database are filtered by back-projecting them onto the occlusion mask to find which points are visible in the scene.
We show that our method achieves better overall performance than the state-of-the art PoseCNN network for 3D pose estimation on the YCB-video dataset.
This is the preprint version of our paper on ICONIP2015.
The proposed platform supports the integrated VRGIS functions including 3D spatial analysis functions, 3D visualization for spatial process and serves for 3D globe and digital city.
The 3D analysis and visualization of the concerned city massive information are conducted in the platform.
The amount of information that can be visualized with this platform is overwhelming, and the GIS based navigational scheme allows to have great flexibility to access the different available data sources.
Vlogs provide a rich public source of data in a novel setting.
This paper examined the continuous sentiment styles employed in 27,333 vlogs using a dynamic intra-textual approach to sentiment analysis.
Using unsupervised clustering, we identified seven distinct continuous sentiment trajectories characterized by fluctuations of sentiment throughout a vlog's narrative time.
We provide a taxonomy of these seven continuous sentiment styles and found that vlogs whose sentiment builds up towards a positive ending are the most prevalent in our sample.
Gender was associated with preferences for different continuous sentiment trajectories.
This paper discusses the findings with respect to previous work and concludes with an outlook towards possible uses of the corpus, method and findings of this paper for related areas of research.
A low carbon society aims at fighting global warming by stimulating synergic efforts from governments, industry and scientific communities.
Decision support systems should be adopted to provide policy makers with possible scenarios, options for prompt countermeasures in case of side effects on environment, economy and society due to low carbon society policies, and also options for information management.
A necessary precondition to fulfill this agenda is to face the complexity of this multi-disciplinary domain and to reach a common understanding on it as a formal specification.
Ontologies are widely accepted means to share knowledge.
Together with semantic rules, they enable advanced semantic services to manage knowledge in a smarter way.
Here we address the European Emissions Trading System (EU-ETS) and we present a knowledge base consisting of the EREON ontology and a catalogue of rules.
Then we describe two innovative semantic services to manage ETS data and information on ETS scenarios.
The economic model of the Internet of Things (IoT) consists of end users, advertisers and three different kinds of providers--IoT service provider (IoTSP), Wireless service provider (WSP) and cloud service provider (CSP).
We investigate three different kinds of interactions among the providers.
First, we consider that the IoTSP prices a bundled service to the end-users, and the WSP and CSP pay the IoTSP (push model).
Next, we consider the model where the end-users independently pay the each provider (pull model).
Finally, we consider a hybrid model of the above two where the IoTSP and WSP quote their prices to the end-users, but the CSP quotes its price to the IoTSP.
We characterize and quantify the impact of the advertisement revenue on the equilibrium pricing strategy and payoff of providers, and corresponding demands of end users in each of the above interaction models.
Our analysis reveals that the demand of end-users, and the payoffs of the providers are non decreasing functions of the advertisement revenue.
For sufficiently high advertisement revenue, the IoTSP will offer its service free of cost in each interaction model.
However, the payoffs of the providers, and the demand of end-users vary across different interaction models.
Our analysis shows that the demand of end-users, and the payoff of the WSP are the highest in the pull (push, resp.) model in the low (high, resp.) advertisement revenue regime.
The payoff of the IoTSP is always higher in the pull model irrespective of the advertisement revenue.
The payoff of the CSP is the highest in the hybrid model in the low advertisement revenue regime.
However, in the high advertisement revenue regime the payoff of the CSP in the hybrid model or in the push model can be higher depending on the equilibrium chosen in the push model.
In this paper we combine one method for hierarchical reinforcement learning - the options framework - with deep Q-networks (DQNs) through the use of different "option heads" on the policy network, and a supervisory network for choosing between the different options.
We utilise our setup to investigate the effects of architectural constraints in subtasks with positive and negative transfer, across a range of network capacities.
We empirically show that our augmented DQN has lower sample complexity when simultaneously learning subtasks with negative transfer, without degrading performance when learning subtasks with positive transfer.
A phaser is an expressive synchronization construct that unifies collective and point-to-point coordination with dynamic task parallelism.
Each task can participate in a phaser as a signaler, a waiter, or both.
The participants in a phaser may change over time as dynamic tasks are added and deleted.
In this poster, we present a highly concurrent and scalable design of phasers for a distributed memory environment that is suitable for use with asynchronous partitioned global address space programming models.
Our design for a distributed phaser employs a pair of skip lists augmented with the ability to collect and propagate synchronization signals.
To enable a high degree of concurrency, addition and deletion of participant tasks are performed in two phases: a "fast single-link-modify" step followed by multiple hand-overhand "lazy multi-link-modify" steps.
We show that the cost of synchronization and structural operations on a distributed phaser scales logarithmically, even in the presence of concurrent structural modifications.
To verify the correctness of our design for distributed phasers, we employ the SPIN model checker.
To address this issue of state space explosion, we describe how we decompose the state space to separately verify correct handling for different kinds of messages, which enables complete model checking of our phaser design.
ALPINE is to our knowledge the first anytime algorithm to mine frequent itemsets and closed frequent itemsets.
It guarantees that all itemsets with support exceeding the current checkpoint's support have been found before it proceeds further.
Thus, it is very attractive for extremely long mining tasks with very high dimensional data (for example in genetics) because it can offer intermediate meaningful and complete results.
This ANYTIME feature is the most important contribution of ALPINE, which is also fast but not necessarily the fastest algorithm around.
Another critical advantage of ALPINE is that it does not require the apriori decided minimum support value.
Increased accuracy in predictive models for handwritten character recognition will open up new frontiers for optical character recognition.
Major drawbacks of predictive machine learning models are headed by the elongated training time taken by some models, and the requirement that training and test data be in the same feature space and consist of the same distribution.
In this study, these obstacles are minimized by presenting a model for transferring knowledge from one task to another.
This model is presented for the recognition of handwritten numerals in Indic languages.
The model utilizes convolutional neural networks with backpropagation for error reduction and dropout for data overfitting.
The output performance of the proposed neural network is shown to have closely matched other state-of-the-art methods using only a fraction of time used by the state-of-the-arts.
Designing an optimal network topology while balancing multiple, possibly conflicting objectives like cost, performance, and resiliency to viruses is a challenging endeavor, let alone in the case of decentralized network formation.
We therefore propose a game-formation technique where each player aims to minimize its cost in installing links, the probability of being infected by a virus and the sum of hopcounts on its shortest paths to all other nodes.
In this article, we (1) determine the Nash Equilibria and the Price of Anarchy for our novel network formation game, (2) demonstrate that the Price of Anarchy (PoA) is usually low, which suggests that (near-)optimal topologies can be formed in a decentralized way, and (3) give suggestions for practitioners for those cases where the PoA is high and some centralized control/incentives are advisable.
This work considers the secure and reliable information transmission in two-hop relay wireless networks without the information of both eavesdropper channels and locations.
While the previous work on this problem mainly studied infinite networks and their asymptotic behavior and scaling law results, this papers focuses on a more practical network with finite number of system nodes and explores the corresponding exact results on the number of eavesdroppers the network can tolerant to ensure a desired secrecy and reliability.
For achieving secure and reliable information transmission in a finite network, two transmission protocols are considered in this paper, one adopts an optimal but complex relay selection process with less load balance capacity while the other adopts a random but simple relay selection process with good load balance capacity.
Theoretical analysis is further provided to determine the exact and maximum number of independent and also uniformly distributed eavesdroppers one network can tolerate to satisfy a specified requirement in terms of the maximum secrecy outage probability and maximum transmission outage probability allowed.
This article is an empirical contribution to the field of educational technology but also - and above all - a methodological contribution to the analysis of the activities enacted in this field.
It takes account of a pilot study conducted within the framework of doctoral research and consisted in describing, analysing and modelling the activity of a trainee teacher in a situation of autonomous use of a video-based digital learning environment (DLE).
We were particularly careful to describe the method in great detail.
Two types of data were collected and processed within the framework of "course-of-action": (i)activity observation data (dynamic screen capture) and (ii) data from resituating interviews supported by digital traces of that activity.
The findings (i) validate the method's relevance in relation to the object and issues of the research, (ii)show different levels of organization in the activity deployed in the situation of use, (iii) highlight four registers of concerns orienting use of the DLE.
We conclude from a perspective of educational technology, by discussing how, according to certain conditions and different time scales, the findings inform a process of continuous DLE design.
Joint privacy-cost optimization is studied for a smart grid consumer, whose electricity consumption is monitored in almost real time by the utility provider (UP).
It is assumed that an energy storage device, e.g., an electrical battery, is available to the consumer, which can be utilized both to achieve privacy and to reduce the energy cost by modifying the electricity consumption.
Privacy is measured via the mean squared distance between the smart meter readings and a target load profile, while time-of-use pricing is considered to compute the electricity cost.
The consumer also has the possibility to sell electricity back to the UP to further improve the privacy-cost trade-off.
Two privacy-preserving energy management policies (EMPs) are proposed, which differ in the way the target load profile is characterized.
Additionally, a simplified and more practical EMP, which optimizes the energy management less frequently, is considered.
Numerical results are presented to compare the performances of these EMPs in terms of the privacy-cost trade-off they achieve, considering a number of privacy indicators.
In blind motion deblurring, leading methods today tend towards highly non-convex approximations of the l0-norm, especially in the image regularization term.
In this paper, we propose a simple, effective and fast approach for the estimation of the motion blur-kernel, through a bi-l0-l2-norm regularization imposed on both the intermediate sharp image and the blur-kernel.
Compared with existing methods, the proposed regularization is shown to be more effective and robust, leading to a more accurate motion blur-kernel and a better final restored image.
A fast numerical scheme is deployed for alternatingly computing the sharp image and the blur-kernel, by coupling the operator splitting and augmented Lagrangian methods.
Experimental results on both a benchmark image dataset and real-world motion blurred images show that the proposed approach is highly competitive with state-of-the- art methods in both deblurring effectiveness and computational efficiency.
Game logic is a dynamic modal logic which models strategic two person games; it contains propositional dynamic logic (PDL) as a fragment.
We propose an interpretation of game logic based on stochastic effectivity functions.
A definition of these functions is proposed, and some algebraic properties of effectivity functions such as congruences are investigated.
The relationship to stochastic relations is characterized through a deduction system.
Logical and behavioral equivalence of game models is investigated.
Finally the completion of models receives some attention.
Processors may find some elementary operations to be faster than the others.
Although an operation may be conceptually as simple as some other operation, the processing speeds of the two can vary.
A clever programmer will always try to choose the faster instructions for the job.
This paper presents an algorithm to display squares of 1st N natural numbers without using multiplication (* operator).
Instead, the same work can be done using addition (+ operator).
The results can also be used to compute the sum of those squares.
If we compare the normal method of computing the squares of 1st N natural numbers with this method, we can conclude that the algorithm discussed in the paper is more optimized in terms of time complexity.
Traditional visual speech recognition systems consist of two stages, feature extraction and classification.
Recently, several deep learning approaches have been presented which automatically extract features from the mouth images and aim to replace the feature extraction stage.
However, research on joint learning of features and classification is very limited.
In this work, we present an end-to-end visual speech recognition system based on Long-Short Memory (LSTM) networks.
To the best of our knowledge, this is the first model which simultaneously learns to extract features directly from the pixels and perform classification and also achieves state-of-the-art performance in visual speech classification.
The model consists of two streams which extract features directly from the mouth and difference images, respectively.
The temporal dynamics in each stream are modelled by an LSTM and the fusion of the two streams takes place via a Bidirectional LSTM (BLSTM).
An absolute improvement of 9.7% over the base line is reported on the OuluVS2 database, and 1.5% on the CUAVE database when compared with other methods which use a similar visual front-end.
Image-based virtual try-on systems for fitting new in-shop clothes into a person image have attracted increasing research attention, yet is still challenging.
A desirable pipeline should not only transform the target clothes into the most fitting shape seamlessly but also preserve well the clothes identity in the generated image, that is, the key characteristics (e.g.texture, logo, embroidery) that depict the original clothes.
However, previous image-conditioned generation works fail to meet these critical requirements towards the plausible virtual try-on performance since they fail to handle large spatial misalignment between the input image and target clothes.
Prior work explicitly tackled spatial deformation using shape context matching, but failed to preserve clothing details due to its coarse-to-fine strategy.
In this work, we propose a new fully-learnable Characteristic-Preserving Virtual Try-On Network(CP-VTON) for addressing all real-world challenges in this task.
First, CP-VTON learns a thin-plate spline transformation for transforming the in-shop clothes into fitting the body shape of the target person via a new Geometric Matching Module (GMM) rather than computing correspondences of interest points as prior works did.
Second, to alleviate boundary artifacts of warped clothes and make the results more realistic, we employ a Try-On Module that learns a composition mask to integrate the warped clothes and the rendered image to ensure smoothness.
Extensive experiments on a fashion dataset demonstrate our CP-VTON achieves the state-of-the-art virtual try-on performance both qualitatively and quantitatively.
This paper describes our approach on Query Word Labeling as an attempt in the shared task on Mixed Script Information Retrieval at Forum for Information Retrieval Evaluation (FIRE) 2015.
The query is written in Roman script and the words were in English or transliterated from Indian regional languages.
A total of eight Indian languages were present in addition to English.
We also identified the Named Entities and special symbols as part of our task.
A CRF based machine learning framework was used for labeling the individual words with their corresponding language labels.
We used a dictionary based approach for language identification.
We also took into account the context of the word while identifying the language.
Our system demonstrated an overall accuracy of 75.5% for token level language identification.
The strict F-measure scores for the identification of token level language labels for Bengali, English and Hindi are 0.7486, 0.892 and 0.7972 respectively.
The overall weighted F-measure of our system was 0.7498.
Anomaly detectors are often used to produce a ranked list of statistical anomalies, which are examined by human analysts in order to extract the actual anomalies of interest.
Unfortunately, in realworld applications, this process can be exceedingly difficult for the analyst since a large fraction of high-ranking anomalies are false positives and not interesting from the application perspective.
In this paper, we aim to make the analyst's job easier by allowing for analyst feedback during the investigation process.
Ideally, the feedback influences the ranking of the anomaly detector in a way that reduces the number of false positives that must be examined before discovering the anomalies of interest.
In particular, we introduce a novel technique for incorporating simple binary feedback into tree-based anomaly detectors.
We focus on the Isolation Forest algorithm as a representative tree-based anomaly detector, and show that we can significantly improve its performance by incorporating feedback, when compared with the baseline algorithm that does not incorporate feedback.
Our technique is simple and scales well as the size of the data increases, which makes it suitable for interactive discovery of anomalies in large datasets.
Recurrent neural networks have proven to be very effective for natural language inference tasks.
We build on top of one such model, namely BiLSTM with max pooling, and show that adding a hierarchy of BiLSTM and max pooling layers yields state of the art results for the SNLI sentence encoding-based models and the SciTail dataset, as well as provides strong results for the MultiNLI dataset.
We also show that our sentence embeddings can be utilized in a wide variety of transfer learning tasks, outperforming InferSent on 7 out of 10 and SkipThought on 8 out of 9 SentEval sentence embedding evaluation tasks.
Furthermore, our model beats the InferSent model in 8 out of 10 recently published SentEval probing tasks designed to evaluate sentence embeddings' ability to capture some of the important linguistic properties of sentences.
Fine-grained object recognition that aims to identify the type of an object among a large number of subcategories is an emerging application with the increasing resolution that exposes new details in image data.
Traditional fully supervised algorithms fail to handle this problem where there is low between-class variance and high within-class variance for the classes of interest with small sample sizes.
We study an even more extreme scenario named zero-shot learning (ZSL) in which no training example exists for some of the classes.
ZSL aims to build a recognition model for new unseen categories by relating them to seen classes that were previously learned.
We establish this relation by learning a compatibility function between image features extracted via a convolutional neural network and auxiliary information that describes the semantics of the classes of interest by using training samples from the seen classes.
Then, we show how knowledge transfer can be performed for the unseen classes by maximizing this function during inference.
We introduce a new data set that contains 40 different types of street trees in 1-ft spatial resolution aerial data, and evaluate the performance of this model with manually annotated attributes, a natural language model, and a scientific taxonomy as auxiliary information.
The experiments show that the proposed model achieves 14.3% recognition accuracy for the classes with no training examples, which is significantly better than a random guess accuracy of 6.3% for 16 test classes, and three other ZSL algorithms.
Process mining has emerged as a way to analyze the behavior of an organization by extracting knowledge from event logs and by offering techniques to discover, monitor and enhance real processes.
In the discovery of process models, retrieving a complex one, i.e., a hardly readable process model, can hinder the extraction of information.
Even in well-structured process models, there is information that cannot be obtained with the current techniques.
In this paper, we present WoMine, an algorithm to retrieve frequent behavioural patterns from the model.
Our approach searches in process models extracting structures with sequences, selections, parallels and loops, which are frequently executed in the logs.
This proposal has been validated with a set of process models, including some from BPI Challenges, and compared with the state of the art techniques.
Experiments have validated that WoMine can find all types of patterns, extracting information that cannot be mined with the state of the art techniques.
A long-standing practical challenge in the optimization of higher-order languages is inlining functions with free variables.
Inlining code statically at a function call site is safe if the compiler can guarantee that the free variables have the same bindings at the inlining point as they do at the point where the function is bound as a closure (code and free variables).
There have been many attempts to create a heuristic to check this correctness condition, from Shivers' kCFA-based reflow analysis to Might's Delta-CFA and anodization, but all of those have performance unsuitable for practical compiler implementations.
In practice, modern language implementations rely on a series of tricks to capture some common cases (e.g., closures whose free variables are only top-level identifiers such as +) and rely on hand-inlining by the programmer for anything more complicated.
This work provides the first practical, general approach for inlining functions with free variables.
We also provide a proof of correctness, an evaluation of both the execution time and performance impact of this optimization, and some tips and tricks for implementing an efficient and precise control-flow analysis.
Convolutional neural networks with spatio-temporal 3D kernels (3D CNNs) have an ability to directly extract spatio-temporal features from videos for action recognition.
Although the 3D kernels tend to overfit because of a large number of their parameters, the 3D CNNs are greatly improved by using recent huge video databases.
However, the architecture of 3D CNNs is relatively shallow against to the success of very deep neural networks in 2D-based CNNs, such as residual networks (ResNets).
In this paper, we propose a 3D CNNs based on ResNets toward a better action representation.
We describe the training procedure of our 3D ResNets in details.
We experimentally evaluate the 3D ResNets on the ActivityNet and Kinetics datasets.
The 3D ResNets trained on the Kinetics did not suffer from overfitting despite the large number of parameters of the model, and achieved better performance than relatively shallow networks, such as C3D.
Our code and pretrained models (e.g.Kinetics and ActivityNet) are publicly available at https://github.com/kenshohara/3D-ResNets.
We extend the Multi-lane Spatial Logic MLSL, introduced in previous work for proving the safety (collision freedom) of traffic maneuvers on a multi-lane highway, by length measurement and dynamic modalities.
We investigate the proof theory of this extension, called EMLSL.
To this end, we prove the undecidability of EMLSL but nevertheless present a sound proof system which allows for reasoning about the safety of traffic situations.
We illustrate the latter by giving a formal proof for the reservation lemma we could only prove informally before.
Furthermore we prove a basic theorem showing that the length measurement is independent from the number of lanes on the highway.
A map merging component is crucial for the proper functionality of a multi-robot system performing exploration, since it provides the means to integrate and distribute the most important information carried by the agents: the explored-covered space and its exact (depending on the SLAM accuracy) morphology.
Map merging is a prerequisite for an intelligent multi-robot team aiming to deploy a smart exploration technique.
In the current work, a metric map merging approach based on environmental information is proposed, in conjunction with spatially scattered RFID tags localization.
This approach is divided into the following parts: the maps approximate rotation calculation via the obstacles poses and localized RFID tags, the translation employing the best localized common RFID tag and finally the transformation refinement using an ICP algorithm.
We develop a thermodynamic framework for modeling nonlinear ultrasonic damage sensing and prognosis in materials undergoing progressive damage.
The framework is based on the internal variable approach and relies on the construction of a pseudo-elastic strain energy function that captures the energetics associated with the damage progression.
The pseudo-elastic strain energy function is composed of two energy functions - one that describes how a material stores energy in an elastic fashion and the other describes how material dissipates energy or stores it in an inelastic fashion.
Experimental motivation for the choice of the above two functionals is discussed and some specific choices pertaining to damage progression during fatigue and creep are presented.
The thermodynamic framework is employed to model the nonlinear response of material undergoing stress relaxation and creep-like degradation.
For each of the above cases, evolution of the nonlinearity parameter with damage as well as with macroscopic measurables like accumulated plastic strain are obtained.
Visualizations have a potentially enormous influence on how data are used to make decisions across all areas of human endeavor.
However, it is not clear how this power connects to ethical duties: what obligations do we have when it comes to visualizations and visual analytics systems, beyond our duties as scientists and engineers?
Drawing on historical and contemporary examples, I address the moral components of the design and use of visualizations, identify some ongoing areas of visualization research with ethical dilemmas, and propose a set of additional moral obligations that we have as designers, builders, and researchers of visualizations.
Standard Time-to-Live (TTL) cache management prescribes the storage of entire files, or possibly fractions thereof, for a given amount of time after a request.
As a generalization of this approach, this work proposes the storage of a time-varying, diminishing, fraction of a requested file.
Accordingly, the cache progressively evicts parts of the file over an interval of time following a request.
The strategy, which is referred to as soft-TTL, is justified by the fact that traffic traces are often characterized by arrival processes that display a decreasing, but non-negligible, probability of observing a request as the time elapsed since the last request increases.
An optimization-based analysis of soft-TTL is presented, demonstrating the important role played by the hazard function of the inter-arrival request process, which measures the likelihood of observing a request as a function of the time since the most recent request.
The aim of this paper is to provide a general mathematical framework for group equivariance in the machine learning context.
The framework builds on a synergy between persistent homology and the theory of group actions.
We define group-equivariant non-expansive operators (GENEOs), which are maps between function spaces associated with groups of transformations.
We study the topological and metric properties of the space of GENEOs to evaluate their approximating power and set the basis for general strategies to initialise and compose operators.
We begin by defining suitable pseudo-metrics for the function spaces, the equivariance groups, and the set of non-expansive operators.
Basing on these pseudo-metrics, we prove that the space of GENEOs is compact and convex, under the assumption that the function spaces are compact and convex.
These results provide fundamental guarantees in a machine learning perspective.
We show examples on the MNIST and fashion-MNIST datasets.
By considering isometry-equivariant non-expansive operators, we describe a simple strategy to select and sample operators, and show how the selected and sampled operators can be used to perform both classical metric learning and an effective initialisation of the kernels of a convolutional neural network.
Most search engines sell slots to place advertisements on the search results page through keyword auctions.
Advertisers offer bids for how much they are willing to pay when someone enters a search query, sees the search results, and then clicks on one of their ads.
Search engines typically order the advertisements for a query by a combination of the bids and expected clickthrough rates for each advertisement.
In this paper, we extend a model of Yahoo's and Google's advertising auctions to include an effect where repeatedly showing less relevant ads has a persistent impact on all advertising on the search engine, an impact we designate as the pollution effect.
In Monte-Carlo simulations using distributions fitted to Yahoo data, we show that a modest pollution effect is sufficient to dramatically change the advertising rank order that yields the optimal advertising revenue for a search engine.
In addition, if a pollution effect exists, it is possible to maximize revenue while also increasing advertiser, and publisher utility.
Our results suggest that search engines could benefit from making relevant advertisements less expensive and irrelevant advertisements more costly for advertisers than is the current practice.
Due to their numerous advantages, communications over multicarrier schemes constitute an appealing approach for broadband wireless systems.
Especially, the strong penetration of orthogonal frequency division multiplexing (OFDM) into the communications standards has triggered heavy investigation on multicarrier systems, leading to re-consideration of different approaches as an alternative to OFDM.
The goal of the present survey is not only to provide a unified review of waveform design options for multicarrier schemes, but also to pave the way for the evolution of the multicarrier schemes from the current state of the art to future technologies.
In particular, a generalized framework on multicarrier schemes is presented, based on what to transmit, i.e., symbols, how to transmit, i.e., filters, and where/when to transmit, i.e., lattice.
Capitalizing on this framework, different variations of orthogonal, bi-orthogonal, and nonorthogonal multicarrier schemes are discussed.
In addition, filter design for various multicarrier systems is reviewed considering four different design perspectives: energy concentration, rapid decay, spectrum nulling, and channel/hardware characteristics.
Subsequently, evaluation tools which may be used to compare different filters in multicarrier schemes are studied.
Finally, multicarrier schemes are evaluated from the view of the practical implementation issues, such as lattice adaptation, equalization, synchronization, multiple antennas, and hardware impairments.
There has been a paradigm shift in the industrial wireless sensor domain caused by the Internet of Things (IoT).
IoT is a thriving technology leading the way in short range and fixed wireless sensing.
One of the issues in Industrial Wireless Sensor Network-IWSN is finding the optimal solution for minimizing the defect time in superframe scheduling.
This paper proposes a method using the evolutionary algorithms approach namely particle swarm optimization (PSO), Orthogonal Learning PSO, genetic algorithms (GA) and modified GA for optimizing the scheduling of superframe.
We have also evaluated a contemporary method, deadline monotonic scheduling on the ISA 100.11a.
By using this standard as a case study, the presented simulations are object-oriented based, with numerous variations in the number of timeslots and wireless sensor nodes.
The simulation results show that the use of GA and modified GA can provide better performance for idle and missed deadlines.
A comprehensive and detailed performance evaluation is given in the paper.
Will a new smartphone application diffuse deeply in the population or will it sink into oblivion soon?
To predict this, we argue that common models of spread of innovations based on cascade dynamics or epidemics may not be fully adequate.
Therefore we propose a novel stochastic network dynamics modeling the spread of a new technological asset, whose adoption is based on the word-of-mouth and the persuasion strength increases the more the product is diffused.
In this paper we carry on an analysis on large scale graphs to show off how the parameters of the model, the topology of the graph and, possibly, the initial diffusion of the asset, determine whether the spread of the asset is successful or not.
In particular, by means of stochastic dominations and deterministic approximations, we provide some general results for a large class of expansive graphs.
Finally we present numerical simulations trying to expand the analytical results we proved to even more general topologies.
In this paper, a point-to-point Orthogonal Frequency Division Multiplexing (OFDM) system with a decode-and-forward (DF) relay is considered.
The transmission consists of two hops.
The source transmits in the first hop, and the relay transmits in the second hop.
Each hop occupies one time slot.
The relay is half-duplex, and capable of decoding the message on a particular subcarrier in one time slot, and re-encoding and forwarding it on a different subcarrier in the next time slot.
Thus each message is transmitted on a pair of subcarriers in two hops.
It is assumed that the destination is capable of combining the signals from the source and the relay pertaining to the same message.
The goal is to maximize the weighted sum rate of the system by jointly optimizing subcarrier pairing and power allocation on each subcarrier in each hop.
The weighting of the rates is to take into account the fact that different subcarriers may carry signals for different services.
Both total and individual power constraints for the source and the relay are investigated.
For the situations where the relay does not transmit on some subcarriers because doing so does not improve the weighted sum rate, we further allow the source to transmit new messages on these idle subcarriers.
To the best of our knowledge, such a joint optimization inclusive of the destination combining has not been discussed in the literature.
The problem is first formulated as a mixed integer programming problem.
It is then transformed to a convex optimization problem by continuous relaxation, and solved in the dual domain.
Based on the optimization results, algorithms to achieve feasible solutions are also proposed.
Simulation results show that the proposed algorithms almost achieve the optimal weighted sum rate, and outperform the existing methods in various channel conditions.
Active learning (AL) is a learning paradigm where an active learner has to train a model (e.g., a classifier) which is in principal trained in a supervised way, but in AL it has to be done by means of a data set with initially unlabeled samples.
To get labels for these samples, the active learner has to ask an oracle (e.g., a human expert) for labels.
The goal is to maximize the performance of the model and to minimize the number of queries at the same time.
In this article, we first briefly discuss the state of the art and own, preliminary work in the field of AL.
Then, we propose the concept of collaborative active learning (CAL).
With CAL, we will overcome some of the harsh limitations of current AL.
In particular, we envision scenarios where an expert may be wrong for various reasons, there might be several or even many experts with different expertise, the experts may label not only samples but also knowledge at a higher level such as rules, and we consider that the labeling costs depend on many conditions.
Moreover, in a CAL process human experts will profit by improving their own knowledge, too.
Among many biometrics such as face, iris, fingerprint and others, periocular region has the advantages over other biometrics because it is non-intrusive and serves as a balance between iris or eye region (very stringent, small area) and the whole face region (very relaxed large area).
Research have shown that this is the region which does not get affected much because of various poses, aging, expression, facial changes and other artifacts, which otherwise would change to a large variation.
Active research has been carried out on this topic since past few years due to its obvious advantages over face and iris biometrics in unconstrained and uncooperative scenarios.
Many researchers have explored periocular biometrics involving both visible (VIS) and infra-red (IR) spectrum images.
For a system to work for 24/7 (such as in surveillance scenarios), the registration process may depend on the day time VIS periocular images (or any mug shot image) and the testing or recognition process may occur in the night time involving only IR periocular images.
This gives rise to a challenging research problem called the cross-spectral matching of images where VIS images are used for registration or as gallery images and IR images are used for testing or recognition process and vice versa.
After intensive research of more than two decades on face and iris biometrics in cross-spectral domain, a number of researchers have now focused their work on matching heterogeneous (cross-spectral) periocular images.
Though a number of surveys have been made on existing periocular biometric research, no study has been done on its cross-spectral aspect.
This paper analyses and reviews current state-of-the-art techniques in cross-spectral periocular recognition including various methodologies, databases, their protocols and current-state-of-the-art recognition performances.
Learning tasks on source code (i.e., formal languages) have been considered recently, but most work has tried to transfer natural language methods and does not capitalize on the unique opportunities offered by code's known syntax.
For example, long-range dependencies induced by using the same variable or function in distant locations are often not considered.
We propose to use graphs to represent both the syntactic and semantic structure of code and use graph-based deep learning methods to learn to reason over program structures.
In this work, we present how to construct graphs from source code and how to scale Gated Graph Neural Networks training to such large graphs.
We evaluate our method on two tasks: VarNaming, in which a network attempts to predict the name of a variable given its usage, and VarMisuse, in which the network learns to reason about selecting the correct variable that should be used at a given program location.
Our comparison to methods that use less structured program representations shows the advantages of modeling known structure, and suggests that our models learn to infer meaningful names and to solve the VarMisuse task in many cases.
Additionally, our testing showed that VarMisuse identifies a number of bugs in mature open-source projects.
We investigate the characteristics of factual and emotional argumentation styles observed in online debates.
Using an annotated set of "factual" and "feeling" debate forum posts, we extract patterns that are highly correlated with factual and emotional arguments, and then apply a bootstrapping methodology to find new patterns in a larger pool of unannotated forum posts.
This process automatically produces a large set of patterns representing linguistic expressions that are highly correlated with factual and emotional language.
Finally, we analyze the most discriminating patterns to better understand the defining characteristics of factual and emotional arguments.
This paper introduces a method to capture network traffic from medical IoT devices and automatically detect cleartext information that may reveal sensitive medical conditions and behaviors.
The research follows a three-step approach involving traffic collection, cleartext detection, and metadata analysis.
We analyze four popular consumer medical IoT devices, including one smart medical device that leaks sensitive health information in cleartext.
We also present a traffic capture and analysis system that seamlessly integrates with a home network and offers a user-friendly interface for consumers to monitor and visualize data transmissions of IoT devices in their homes.
Taking advantage of the rolling shutter effect of CMOS cameras in smartphones is a common practice to increase the transfered data rate with visible light communication (VLC) without employing external equipment such as photodiodes.
VLC can then be used as replacement of other marker based techniques for object identification for Augmented Reality and Ubiquitous computing applications.
However, the rolling shutter effect only allows to transmit data over a single dimension, which considerably limits the available bandwidth.
In this article we propose a new method exploiting spacial interference detection to enable parallel transmission and design a protocol that enables easy identification of interferences between two signals.
By introducing a second dimension, we are not only able to significantly increase the available bandwidth, but also identify and isolate light sources in close proximity.
Over the past few years, mobile operators are faced with enormous challenges.
Of such challenges, evolved user demands on personalized applications.
Telecommunications industry as well as research community have paid enormous attention to Next Generation Networks (NGN) to address this challenge.
NGN is perceived as a sophisticated platform where both application developers and mobile operators cooperate to develop user applications with enhanced quality of experience.
The objective of this paper is twofold: first we present an introduction to state-of-the-art NGN testbed to be developed at KAU, and second we provide initial analysis for deploying a mobile application on top of the testbed.
With the ubiquity of Internet technologies and growing demands for transparency and open data policies, the role of social networking and online deliberation tools for public engagement in decision-making has increased substantially in the last decades.
In this paper, we present the analysis of how social media are used by different public bodies to enhance public participation in deliberative democracy.
We collected and reviewed published information on the subject and carried out a field base assessment, involving structured interviews with different government representatives and urban policymakers.
In order to compare collected data, we used a framework for systematic analysis and comparison of e-participation platforms called the participatory cube.
The results we got were the following.
Participatory decision-making on matters of public concern justly consumes time and resources, therefore online tools should be applied with consideration of scale and efficiency, i.e. on burning issues for a majority of citizens or small-scale local platforms, and in combination with meetings in real time and space.
The budget and workforce allocated to managing online engagement tools should be proportionate to other political and administrative efforts to bring to execution proposed ideas and act on collected feedback in order to satisfy the needs expressed by the communities and not undermine their beliefs about their power to influence decisions.
RNN language models have achieved state-of-the-art perplexity results and have proven useful in a suite of NLP tasks, but it is as yet unclear what syntactic generalizations they learn.
Here we investigate whether state-of-the-art RNN language models represent long-distance filler-gap dependencies and constraints on them.
Examining RNN behavior on experimentally controlled sentences designed to expose filler-gap dependencies, we show that RNNs can represent the relationship in multiple syntactic positions and over large spans of text.
Furthermore, we show that RNNs learn a subset of the known restrictions on filler-gap dependencies, known as island constraints: RNNs show evidence for wh-islands, adjunct islands, and complex NP islands.
These studies demonstrates that state-of-the-art RNN models are able to learn and generalize about empty syntactic positions.
One of the biggest reasons for road accidents is curvy lanes and blind turns.
Even one of the biggest hurdles for new autonomous vehicles is to detect curvy lanes, multiple lanes and lanes with a lot of discontinuity and noise.
This paper presents very efficient and advanced algorithm for detecting curves having desired slopes (especially for detecting curvy lanes in real time) and detection of curves (lanes) with a lot of noise, discontinuity and disturbances.
Overall aim is to develop robust method for this task which is applicable even in adverse conditions.
Even in some of most famous and useful libraries like OpenCV and Matlab, there is no function available for detecting curves having desired slopes , shapes, discontinuities.
Only few predefined shapes like circle, ellipse, etc, can be detected using presently available functions.
Proposed algorithm can not only detect curves with discontinuity, noise, desired slope but also it can perform shadow and illumination correction and detect/ differentiate between different curves.
Breast Cancer is a major cause of death worldwide among women.
Hematoxylin and Eosin (H&E) stained breast tissue samples from biopsies are observed under microscopes for the primary diagnosis of breast cancer.
In this paper, we propose a deep learning-based method for classification of H&E stained breast tissue images released for BACH challenge 2018 by fine-tuning Inception-v3 convolutional neural network (CNN) proposed by Szegedy et al.
These images are to be classified into four classes namely, i) normal tissue, ii) benign tumor, iii) in-situ carcinoma and iv) invasive carcinoma.
Our strategy is to extract patches based on nuclei density instead of random or grid sampling, along with rejection of patches that are not rich in nuclei (non-epithelial) regions for training and testing.
Every patch (nuclei-dense region) in an image is classified in one of the four above mentioned categories.
The class of the entire image is determined using majority voting over the nuclear classes.
We obtained an average four class accuracy of 85% and an average two class (non-cancer vs. carcinoma) accuracy of 93%, which improves upon a previous benchmark by Araujo et al.
Periodic nonuniform sampling is a known method to sample spectrally sparse signals below the Nyquist rate.
This strategy relies on the implicit assumption that the individual samplers are exposed to the entire frequency range.
This assumption becomes impractical for wideband sparse signals.
The current paper proposes an alternative sampling stage that does not require a full-band front end.
Instead, signals are captured with an analog front end that consists of a bank of multipliers and lowpass filters whose cutoff is much lower than the Nyquist rate.
The problem of recovering the original signal from the low-rate samples can be studied within the framework of compressive sampling.
An appropriate parameter selection ensures that the samples uniquely determine the analog input.
Moreover, the analog input can be stably reconstructed with digital algorithms.
Numerical experiments support the theoretical analysis.
One of the prerequisites of any organization is an unvarying sustainability in the dynamic and competitive industrial environment.
Development of high quality software is therefore an inevitable constraint of any software industry.
Defect management being one of the highly influencing factors for the production of high quality software, it is obligatory for the software organizations to orient them towards effective defect management.
Since, the time of software evolution, testing is deemed a promising technique of defect management in all IT industries.
This paper provides an empirical investigation of several projects through a case study comprising of four software companies having various production capabilities.
The aim of this investigation is to analyze the efficiency of test team during software development process.
The study indicates very low-test efficiency at requirements analysis phase and even lesser test efficiency at design phase of software development.
Subsequently, the study calls for a strong need to improve testing approaches using techniques such as dynamic testing of design solutions in lieu of static testing of design document.
Dynamic testing techniques enhance the ability of detection and elimination of design flaws right at the inception phase and thereby reduce the cost and time of rework.
It further improves productivity, quality and sustainability of software industry.
Identity recognition from ear images is an active field of research within the biometric community.
The ability to capture ear images from a distance and in a covert manner makes ear recognition technology an appealing choice for surveillance and security applications as well as related application domains.
In contrast to other biometric modalities, where large datasets captured in uncontrolled settings are readily available, datasets of ear images are still limited in size and mostly of laboratory-like quality.
As a consequence, ear recognition technology has not benefited yet from advances in deep learning and convolutional neural networks (CNNs) and is still lacking behind other modalities that experienced significant performance gains owing to deep recognition technology.
In this paper we address this problem and aim at building a CNNbased ear recognition model.
We explore different strategies towards model training with limited amounts of training data and show that by selecting an appropriate model architecture, using aggressive data augmentation and selective learning on existing (pre-trained) models, we are able to learn an effective CNN-based model using a little more than 1300 training images.
The result of our work is the first CNN-based approach to ear recognition that is also made publicly available to the research community.
With our model we are able to improve on the rank one recognition rate of the previous state-of-the-art by more than 25% on a challenging dataset of ear images captured from the web (a.k.a. in the wild).
We study robust distributed learning that involves minimizing a non-convex loss function with saddle points.
We consider the Byzantine setting where some worker machines have abnormal or even arbitrary and adversarial behavior.
In this setting, the Byzantine machines may create fake local minima near a saddle point that is far away from any true local minimum, even when robust gradient estimators are used.
We develop ByzantinePGD, a robust first-order algorithm that can provably escape saddle points and fake local minima, and converge to an approximate true local minimizer with low iteration complexity.
As a by-product, we give a simpler algorithm and analysis for escaping saddle points in the usual non-Byzantine setting.
We further discuss three robust gradient estimators that can be used in ByzantinePGD, including median, trimmed mean, and iterative filtering.
We characterize their performance in concrete statistical settings, and argue for their near-optimality in low and high dimensional regimes.
Series elastic actuators (SEA) are playing an increasingly important role in the fields of physical human-robot interaction.
This paper focuses on the modeling and control of a cable-driven SEA.
First, the scheme of the cable-driven SEA has been proposed, and a velocity controlled DC motor has been used as its power source.
Based on this, the model of the cable-driven SEA has been built up.
Further, a two degrees of freedom (2-DOF) control approach has been employed to control the output torque.
Simulation results have shown that the 2-DOF method has achieved better robust performance than the PD method.
In this work, we propose an analysis of the presence of gender bias associated with professions in Portuguese word embeddings.
The objective of this work is to study gender implications related to stereotyped professions for women and men in the context of the Portuguese language.
Recently, deep learning (DL) methods have been introduced very successfully into human activity recognition (HAR) scenarios in ubiquitous and wearable computing.
Especially the prospect of overcoming the need for manual feature design combined with superior classification capabilities render deep neural networks very attractive for real-life HAR application.
Even though DL-based approaches now outperform the state-of-the-art in a number of recognitions tasks of the field, yet substantial challenges remain.
Most prominently, issues with real-life datasets, typically including imbalanced datasets and problematic data quality, still limit the effectiveness of activity recognition using wearables.
In this paper we tackle such challenges through Ensembles of deep Long Short Term Memory (LSTM) networks.
We have developed modified training procedures for LSTM networks and combine sets of diverse LSTM learners into classifier collectives.
We demonstrate, both formally and empirically, that Ensembles of deep LSTM learners outperform the individual LSTM networks.
Through an extensive experimental evaluation on three standard benchmarks (Opportunity, PAMAP2, Skoda) we demonstrate the excellent recognition capabilities of our approach and its potential for real-life applications of human activity recognition.
The formalism of active integrity constraints was introduced as a way to specify particular classes of integrity constraints over relational databases together with preferences on how to repair existing inconsistencies.
The rule-based syntax of such integrity constraints also provides algorithms for finding such repairs that achieve the best asymptotic complexity.
However, the different semantics that have been proposed for these integrity constraints all exhibit some counter-intuitive examples.
In this work, we look at active integrity constraints using ideas from algebraic fixpoint theory.
We show how database repairs can be modeled as fixpoints of particular operators on databases, and study how the notion of grounded fixpoint induces a corresponding notion of grounded database repair that captures several natural intuitions, and in particular avoids the problems of previous alternative semantics.
In order to study grounded repairs in their full generality, we need to generalize the notion of grounded fixpoint to non-deterministic operators.
We propose such a definition and illustrate its plausibility in the database context.
Fog Radio Access Network (F-RAN) architectures can leverage both cloud processing and edge caching for content delivery to the users.
To this end, F-RAN utilizes caches at the edge nodes (ENs) and fronthaul links connecting a cloud processor to ENs.
Assuming time-invariant content popularity, existing information-theoretic analyses of content delivery in F-RANs rely on offline caching with separate content placement and delivery phases.
In contrast, this work focuses on the scenario in which the set of popular content is time-varying, hence necessitating the online replenishment of the ENs' caches along with the delivery of the requested files.
The analysis is centered on the characterization of the long-term Normalized Delivery Time (NDT), which captures the temporal dependence of the coding latencies accrued across multiple time slots in the high signal-to-noise ratio regime.
Online edge caching and delivery schemes are investigated for both serial and pipelined transmission modes across fronthaul and edge segments.
Analytical results demonstrate that, in the presence of a time-varying content popularity, the rate of fronthaul links sets a fundamental limit to the long-term NDT of F- RAN system.
Analytical results are further verified by numerical simulation, yielding important design insights.
The Brazilian Ministry of Health has selected the openEHR model as a standard for electronic health record systems.
This paper presents a set of archetypes to represent the main data from the Brazilian Public Hospital Information System and the High Complexity Procedures Module of the Brazilian public Outpatient Health Information System.
The archetypes from the public openEHR Clinical Knowledge Manager (CKM), were examined in order to select archetypes that could be used to represent the data of the above mentioned systems.
For several concepts, it was necessary to specialize the CKM archetypes, or design new ones.
A total of 22 archetypes were used: 8 new, 5 specialized and 9 reused from CKM.
This set of archetypes can be used not only for information exchange, but also for generating a big anonymized dataset for testing openEHR-based systems.
In this paper, we consider the automated planning of optimal paths for a robotic team satisfying a high level mission specification.
Each robot in the team is modeled as a weighted transition system where the weights have associated deviation values that capture the non-determinism in the traveling times of the robot during its deployment.
The mission is given as a Linear Temporal Logic (LTL) formula over a set of propositions satisfied at the regions of the environment.
Additionally, we have an optimizing proposition capturing some particular task that must be repeatedly completed by the team.
The goal is to minimize the maximum time between successive satisfying instances of the optimizing proposition while guaranteeing that the mission is satisfied even under non-deterministic traveling times.
Our method relies on the communication capabilities of the robots to guarantee correctness and maintain performance during deployment.
After computing a set of optimal satisfying paths for the members of the team, we also compute a set of synchronization sequences for each robot to ensure that the LTL formula is never violated during deployment.
We implement and experimentally evaluate our method considering a persistent monitoring task in a road network environment.
Generative Adversarial Networks are a new family of generative models, frequently used for generating photorealistic images.
The theory promises for the GAN to eventually reach an equilibrium where generator produces pictures indistinguishable for the training set.
In practice, however, a range of problems frequently prevents the system from reaching this equilibrium, with training not progressing ahead due to instabilities or mode collapse.
This paper describes a series of experiments trying to identify patterns in regard to the effect of the training set on the dynamics and eventual outcome of the training.
The emerging Software Defined Networking (SDN) paradigm separates the data plane from the control plane and centralizes network control in an SDN controller.
Applications interact with controllers to implement network services, such as network transport with Quality of Service (QoS).
SDN facilitates the virtualization of network functions so that multiple virtual networks can operate over a given installed physical network infrastructure.
Due to the specific characteristics of optical (photonic) communication components and the high optical transmission capacities, SDN based optical networking poses particular challenges, but holds also great potential.
In this article, we comprehensively survey studies that examine the SDN paradigm in optical networks; in brief, we survey the area of Software Defined Optical Networks (SDONs).
We mainly organize the SDON studies into studies focused on the infrastructure layer, the control layer, and the application layer.
Moreover, we cover SDON studies focused on network virtualization, as well as SDON studies focused on the orchestration of multilayer and multidomain networking.
Based on the survey, we identify open challenges for SDONs and outline future directions.
Web developers routinely rely on third-party Java-Script libraries such as jQuery to enhance the functionality of their sites.
However, if not properly maintained, such dependencies can create attack vectors allowing a site to be compromised.
In this paper, we conduct the first comprehensive study of client-side JavaScript library usage and the resulting security implications across the Web.
Using data from over 133 k websites, we show that 37% of them include at least one library with a known vulnerability; the time lag behind the newest release of a library is measured in the order of years.
In order to better understand why websites use so many vulnerable or outdated libraries, we track causal inclusion relationships and quantify different scenarios.
We observe sites including libraries in ad hoc and often transitive ways, which can lead to different versions of the same library being loaded into the same document at the same time.
Furthermore, we find that libraries included transitively, or via ad and tracking code, are more likely to be vulnerable.
This demonstrates that not only website administrators, but also the dynamic architecture and developers of third-party services are to blame for the Web's poor state of library management.
The results of our work underline the need for more thorough approaches to dependency management, code maintenance and third-party code inclusion on the Web.
Using the matrix factorization technique in machine learning is very common mainly in areas like recommender systems.
Despite its high prediction accuracy and its ability to avoid over-fitting of the data, the Bayesian Probabilistic Matrix Factorization algorithm (BPMF) has not been widely used on large scale data because of the prohibitive cost.
In this paper, we propose a distributed high-performance parallel implementation of the BPMF using Gibbs sampling on shared and distributed architectures.
We show by using efficient load balancing using work stealing on a single node, and by using asynchronous communication in the distributed version we beat state of the art implementations.
Traditional automatic evaluation measures for natural language generation (NLG) use costly human-authored references to estimate the quality of a system output.
In this paper, we propose a referenceless quality estimation (QE) approach based on recurrent neural networks, which predicts a quality score for a NLG system output by comparing it to the source meaning representation only.
Our method outperforms traditional metrics and a constant baseline in most respects; we also show that synthetic data helps to increase correlation results by 21% compared to the base system.
Our results are comparable to results obtained in similar QE tasks despite the more challenging setting.
We propose a lightly-supervised approach for information extraction, in particular named entity classification, which combines the benefits of traditional bootstrapping, i.e., use of limited annotations and interpretability of extraction patterns, with the robust learning approaches proposed in representation learning.
Our algorithm iteratively learns custom embeddings for both the multi-word entities to be extracted and the patterns that match them from a few example entities per category.
We demonstrate that this representation-based approach outperforms three other state-of-the-art bootstrapping approaches on two datasets: CoNLL-2003 and OntoNotes.
Additionally, using these embeddings, our approach outputs a globally-interpretable model consisting of a decision list, by ranking patterns based on their proximity to the average entity embedding in a given class.
We show that this interpretable model performs close to our complete bootstrapping model, proving that representation learning can be used to produce interpretable models with small loss in performance.
Online Social Networks (OSNs) attract billions of users to share information and communicate where viral marketing has emerged as a new way to promote the sales of products.
An OSN provider is often hired by an advertiser to conduct viral marketing campaigns.
The OSN provider generates revenue from the commission paid by the advertiser which is determined by the spread of its product information.
Meanwhile, to propagate influence, the activities performed by users such as viewing video ads normally induce diffusion cost to the OSN provider.
In this paper, we aim to find a seed set to optimize a new profit metric that combines the benefit of influence spread with the cost of influence propagation for the OSN provider.
Under many diffusion models, our profit metric is the difference between two submodular functions which is challenging to optimize as it is neither submodular nor monotone.
We design a general two-phase framework to select seeds for profit maximization and develop several bounds to measure the quality of the seed set constructed.
Experimental results with real OSN datasets show that our approach can achieve high approximation guarantees and significantly outperform the baseline algorithms, including state-of-the-art influence maximization algorithms.
Observable operator models (OOMs) and related models are one of the most important and powerful tools for modeling and analyzing stochastic systems.
They exactly describe dynamics of finite-rank systems and can be efficiently and consistently estimated through spectral learning under the assumption of identically distributed data.
In this paper, we investigate the properties of spectral learning without this assumption due to the requirements of analyzing large-time scale systems, and show that the equilibrium dynamics of a system can be extracted from nonequilibrium observation data by imposing an equilibrium constraint.
In addition, we propose a binless extension of spectral learning for continuous data.
In comparison with the other continuous-valued spectral algorithms, the binless algorithm can achieve consistent estimation of equilibrium dynamics with only linear complexity.
We propose a method for demonstrating sub community structure in scientific networks of relatively small size from analyzing databases of publications.
Research relationships between the network members can be visualized as a graph with vertices corresponding to authors and with edges indicating joint authorship.
Using a fast clustering algorithm combined with a graph layout algorithm, we demonstrate how to display these clustering results in an attractive and informative way.
The small size of the graph allows us to develop tools that keep track of how these research sub communities evolve in time, as well as to present the research articles that create the links between the network members.
These tools are included in a web app, where the visitor can easily identify the various sub communities, providing also valuable information for administrational purposes.
Our method was developed for the GEAR mathematical network and it can be applied to other networks.
The project presented in this article aims to formalize criteria and procedures in order to extract semantic information from parsed dictionary glosses.
The actual purpose of the project is the generation of a semantic network (nearly an ontology) issued from a monolingual Italian dictionary, through unsupervised procedures.
Since the project involves rule-based Parsing, Semantic Tagging and Word Sense Disambiguation techniques, its outcomes may find an interest also beyond this immediate intent.
The cooperation of both syntactic and semantic features in meaning construction are investigated, and procedures which allows a translation of syntactic dependencies in semantic relations are discussed.
The procedures that rise from this project can be applied also to other text types than dictionary glosses, as they convert the output of a parsing process into a semantic representation.
In addition some mechanism are sketched that may lead to a kind of procedural semantics, through which multiple paraphrases of an given expression can be generated.
Which means that these techniques may find an application also in 'query expansion' strategies, interesting Information Retrieval, Search Engines and Question Answering Systems.
We introduce a novel method for robust and accurate 3D object pose estimation from a single color image under large occlusions.
Following recent approaches, we first predict the 2D projections of 3D points related to the target object and then compute the 3D pose from these correspondences using a geometric method.
Unfortunately, as the results of our experiments show, predicting these 2D projections using a regular CNN or a Convolutional Pose Machine is highly sensitive to partial occlusions, even when these methods are trained with partially occluded examples.
Our solution is to predict heatmaps from multiple small patches independently and to accumulate the results to obtain accurate and robust predictions.
Training subsequently becomes challenging because patches with similar appearances but different positions on the object correspond to different heatmaps.
However, we provide a simple yet effective solution to deal with such ambiguities.
We show that our approach outperforms existing methods on two challenging datasets: The Occluded LineMOD dataset and the YCB-Video dataset, both exhibiting cluttered scenes with highly occluded objects.
Project website: https://www.tugraz.at/institute/icg/research/team-lepetit/research-projects/robust-object-pose-estimation/
Cold-start is a very common and still open problem in the Recommender Systems literature.
Since cold start items do not have any interaction, collaborative algorithms are not applicable.
One of the main strategies is to use pure or hybrid content-based approaches, which usually yield to lower recommendation quality than collaborative ones.
Some techniques to optimize performance of this type of approaches have been studied in recent past.
One of them is called feature weighting, which assigns to every feature a real value, called weight, that estimates its importance.
Statistical techniques for feature weighting commonly used in Information Retrieval, like TF-IDF, have been adapted for Recommender Systems, but they often do not provide sufficient quality improvements.
More recent approaches, FBSM and LFW, estimate weights by leveraging collaborative information via machine learning, in order to learn the importance of a feature based on other users opinions.
This type of models have shown promising results compared to classic statistical analyzes cited previously.
We propose a novel graph, feature-based machine learning model to face the cold-start item scenario, learning the relevance of features from probabilities of item-based collaborative filtering algorithms.
In this paper, we apply the scattering transform (ST), a nonlinear map based off of a convolutional neural network (CNN), to classification of underwater objects using sonar signals.
The ST formalizes the observation that the filters learned by a CNN have wavelet like structure.
We achieve effective binary classification both on a real dataset of Unexploded Ordinance (UXOs), as well as synthetically generated examples.
We also explore the effects on the waveforms with respect to changes in the object domain (e.g., translation, rotation, and acoustic impedance, etc.), and examine the consequences coming from theoretical results for the scattering transform.
We show that the scattering transform is capable of excellent classification on both the synthetic and real problems, thanks to having more quasi-invariance properties that are well-suited to translation and rotation of the object.
Makespan minimization in tasks scheduling of infrastructure as a service (IaaS) cloud is an NP-hard problem.
A number of techniques had been used in the past to optimize the makespan time of scheduled tasks in IaaS cloud, which is propotional to the execution cost billed to customers.
In this paper, we proposed a League Championship Algorithm (LCA) based makespan time minimization scheduling technique in IaaS cloud.
The LCA is a sports-inspired population based algorithmic framework for global optimization over a continuous search space.
Three other existing algorithms that is, First Come First Served (FCFS), Last Job First (LJF) and Best Effort First (BEF) were used to evaluate the performance of the proposed algorithm.
All algorithms under consideration assumed to be non-preemptive.
The results obtained shows that, the LCA scheduling technique perform moderately better than the other algorithms in minimizing the makespan time of scheduled tasks in IaaS cloud.
Information transmission over channels with transceiver distortion is investigated via generalized mutual information (GMI) under Gaussian input distribution and nearest-neighbor decoding.
A canonical transceiver structure in which the channel output is processed by a minimum mean-squared error estimator before decoding is established to maximize the GMI, and the well-known Bussgang's decomposition is shown to be a heuristic that is consistent with the GMI under linear output processing.
The problem of unicity and reidentifiability of records in large-scale databases has been studied in different contexts and approaches, with focus on preserving privacy or matching records from different data sources.
With an increasing number of service providers nowadays routinely collecting location traces of their users on unprecedented scales, there is a pronounced interest in the possibility of matching records and datasets based on spatial trajectories.
Extending previous work on reidentifiability of spatial data and trajectory matching, we present the first large-scale analysis of user matchability in real mobility datasets on realistic scales, i.e. among two datasets that consist of several million people's mobility traces, coming from a mobile network operator and transportation smart card usage.
We extract the relevant statistical properties which influence the matching process and analyze their impact on the matchability of users.
We show that for individuals with typical activity in the transportation system (those making 3-4 trips per day on average), a matching algorithm based on the co-occurrence of their activities is expected to achieve a 16.8% success only after a one-week long observation of their mobility traces, and over 55% after four weeks.
We show that the main determinant of matchability is the expected number of co-occurring records in the two datasets.
Finally, we discuss different scenarios in terms of data collection frequency and give estimates of matchability over time.
We show that with higher frequency data collection becoming more common, we can expect much higher success rates in even shorter intervals.
Consumers with low demand, like households, are generally supplied single-phase power by connecting their service mains to one of the phases of a distribution transformer.
The distribution companies face the problem of keeping a record of consumer connectivity to a phase due to uninformed changes that happen.
The exact phase connectivity information is important for the efficient operation and control of distribution system.
We propose a new data driven approach to the problem based on Principal Component Analysis (PCA) and its Graph Theoretic interpretations, using energy measurements in equally timed short intervals, generated from smart meters.
We propose an algorithm for inferring phase connectivity from noisy measurements.
The algorithm is demonstrated using simulated data for phase connectivities in distribution networks.
Automated emotion recognition in the wild from facial images remains a challenging problem.
Although recent advances in Deep Learning have supposed a significant breakthrough in this topic, strong changes in pose, orientation and point of view severely harm current approaches.
In addition, the acquisition of labeled datasets is costly, and current state-of-the-art deep learning algorithms cannot model all the aforementioned difficulties.
In this paper, we propose to apply a multi-task learning loss function to share a common feature representation with other related tasks.
Particularly we show that emotion recognition benefits from jointly learning a model with a detector of facial Action Units (collective muscle movements).
The proposed loss function addresses the problem of learning multiple tasks with heterogeneously labeled data, improving previous multi-task approaches.
We validate the proposal using two datasets acquired in non controlled environments, and an application to predict compound facial emotion expressions.
Point pair features are a popular representation for free form 3D object detection and pose estimation.
In this paper, their performance in an industrial random bin picking context is investigated.
A new method to generate representative synthetic datasets is proposed.
This allows to investigate the influence of a high degree of clutter and the presence of self similar features, which are typical to our application.
We provide an overview of solutions proposed in literature and discuss their strengths and weaknesses.
A simple heuristic method to drastically reduce the computational complexity is introduced, which results in improved robustness, speed and accuracy compared to the naive approach.
The European Space Agency (ESA) defines an Earth Observation (EO) Level 2 product as a multispectral (MS) image corrected for geometric, atmospheric, adjacency and topographic effects, stacked with its scene classification map (SCM), whose legend includes quality layers such as cloud and cloud-shadow.
No ESA EO Level 2 product has ever been systematically generated at the ground segment.
To contribute toward filling an information gap from EO big data to the ESA EO Level 2 product, an original Stage 4 validation (Val) of the Satellite Image Automatic Mapper (SIAM) lightweight computer program was conducted by independent means on an annual Web-Enabled Landsat Data (WELD) image composite time-series of the conterminous U.S.
The core of SIAM is a one pass prior knowledge based decision tree for MS reflectance space hyperpolyhedralization into static color names presented in literature in recent years.
For the sake of readability this paper is split into two.
The present Part 1 Theory provides the multidisciplinary background of a priori color naming in cognitive science, from linguistics to computer vision.
To cope with dictionaries of MS color names and land cover class names that do not coincide and must be harmonized, an original hybrid guideline is proposed to identify a categorical variable pair relationship.
An original quantitative measure of categorical variable pair association is also proposed.
The subsequent Part 2 Validation discusses Stage 4 Val results collected by an original protocol for wall-to-wall thematic map quality assessment without sampling where the test and reference map legends can differ.
Conclusions are that the SIAM-WELD maps instantiate a Level 2 SCM product whose legend is the 4 class taxonomy of the FAO Land Cover Classification System at the Dichotomous Phase Level 1 vegetation/nonvegetation and Level 2 terrestrial/aquatic.
The ultimate goal of any software developer seeking a competitive edge is to meet stakeholders needs and expectations.
To achieve this, it is necessary to effectively and accurately manage stakeholders system requirements.
The paper proposes a systematic way of classifying stakeholders and then describes a novel method for calculating stakeholder priority taking into consideration the fact that different stakeholders will have different importance level and different requirement preference.
Finally the requirement preference calculation is done where stakeholders choose the best requirements based on two factors, value and urgency of the requirement.
The proposed method actively involves stakeholders in the requirement elicitation process.
Aboria is a powerful and flexible C++ library for the implementation of particle-based numerical methods.
The particles in such methods can represent actual particles (e.g.Molecular Dynamics) or abstract particles used to discretise a continuous function over a domain (e.g.Radial Basis Functions).
Aboria provides a particle container, compatible with the Standard Template Library, spatial search data structures, and a Domain Specific Language to specify non-linear operators on the particle set.
This paper gives an overview of Aboria's design, an example of use, and a performance benchmark.
In 'An asymptotic result on compressed sensing matrices', a new construction for compressed sensing matrices using combinatorial design theory was introduced.
In this paper, we use deterministic and probabilistic methods to analyse the performance of matrices obtained from this construction.
We provide new theoretical results and detailed simulations.
These simulations indicate that the construction is competitive with Gaussian random matrices, and that recovery is tolerant to noise.
A new recovery algorithm tailored to the construction is also given.
Evaluating the effectiveness and benefits of driver assistance systems is crucial for improving the system performance.
In this paper, we propose a novel framework for testing and evaluating lane departure correction systems at a low cost by using lane departure events reproduced from naturalistic driving data.
First, 529,096 lane departure events were extracted from the Safety Pilot Model Deployment (SPMD) database collected by the University of Michigan Transportation Research Institute.
Second, a stochastic lane departure model consisting of eight random key variables was developed to reduce the dimension of the data description and improve the computational efficiency.
As such, we used a bounded Gaussian mixture model (BGM) model to describe drivers' stochastic lane departure behaviors.
Then, a lane departure correction system with an aim point controller was designed, and a batch of lane departure events were reproduced from the learned stochastic driver model.
Finally, we assessed the developed evaluation approach by comparing lateral departure areas of vehicles between with and without correction controller.
The simulation results show that the proposed method can effectively evaluate lane departure correction systems.
ConcurORAM is a parallel, multi-client oblivious RAM (ORAM) that eliminates waiting for concurrent stateless clients and allows overall throughput to scale gracefully, without requiring trusted third party components (proxies) or direct inter-client coordination.
A key insight behind ConcurORAM is the fact that, during multi-client data access, only a subset of the concurrently-accessed server-hosted data structures require access privacy guarantees.
Everything else can be safely implemented as oblivious data structures that are later synced securely and efficiently during an ORAM "eviction".
Further, since a major contributor to latency is the eviction - in which client-resident data is reshuffled and reinserted back encrypted into the main server database - ConcurORAM also enables multiple concurrent clients to evict asynchronously, in parallel (without compromising consistency), and in the background without having to block ongoing queries.
As a result, throughput scales well with increasing number of concurrent clients and is not significantly impacted by evictions.
For example, about 65 queries per second can be executed in parallel by 30 concurrent clients, a 2x speedup over the state-of-the-art.
The query access time for individual clients increases by only 2x when compared to a single-client deployment.
Coexistence of Wi-Fi and LTE Unlicensed (LTE-U) in shared or unlicensed bands has drawn growing attention from both academia and industry.
An important consideration is fairness between Wi-Fi and duty cycled LTE-U, which is often defined in terms of channel access time, as adopted by the LTE-U Forum.
Despite many studies on duty cycle adaptation design for fair sharing, one crucial fact has often been neglected: LTE-U systems unilaterally control LTE-U duty cycles; hence, as self- interested users, they have incentives to misbehave, e.g., transmitting with a larger duty cycle that exceeds a given limit, so as to gain a greater share in channel access time and throughput.
In this paper, we propose a scheme that allows the spectrum manager managing the shared bands to estimate the duty cycle of a target LTE-U cell based on PHY layer observations from a nearby Wi-Fi AP, without interrupting normal Wi-Fi operations.
We further propose a thresholding scheme to detect duty cycling misbehavior (i.e., determining if the duty cycle exceeds the assigned limit), and analyze its performance in terms of detection and false alarm probabilities.
The proposed schemes are implemented in ns3 and evaluated with extensive simulations.
Our results show that the proposed scheme provides an estimate within +/- 1% of the true duty cycle, and detects misbehavior with a duty cycle 2.8% higher than the limit with a detection probability of at least 95%, while keeping the false alarm probability less than or equal to 1%.
We leverage stochastic geometry to characterize key performance metrics for neighboring Wi-Fi and LTE networks in unlicensed spectrum.
Our analysis focuses on a single unlicensed frequency band, where the locations for the Wi-Fi access points (APs) and LTE eNodeBs (eNBs) are modeled as two independent homogeneous Poisson point processes.
Three LTE coexistence mechanisms are investigated: (1) LTE with continuous transmission and no protocol modifications; (2) LTE with discontinuous transmission; and (3) LTE with listen-before-talk (LBT) and random back-off (BO).
For each scenario, we have derived the medium access probability (MAP), the signal-to-interference-plus-noise ratio (SINR) coverage probability, the density of successful transmissions (DST), and the rate coverage probability for both Wi-Fi and LTE.
Compared to the baseline scenario where one Wi-Fi network coexists with an additional Wi-Fi network, our results show that Wi-Fi performance is severely degraded when LTE transmits continuously.
However, LTE is able to improve the DST and rate coverage probability of Wi-Fi while maintaining acceptable data rate performance when it adopts one or more of the following coexistence features: a shorter transmission duty cycle, lower channel access priority, or more sensitive clear channel assessment (CCA) thresholds.
The problem of understanding people's participation in real-world events has been a subject of active research and can offer valuable insights for human behavior analysis and event-related recommendation/advertisement.
In this work, we study the latent factors for determining event popularity using large-scale datasets collected from the popular Meetup.com EBSN in three major cities around the world.
We have conducted modeling analysis of four contextual factors (spatial, group, temporal, and semantic), and also developed a group-based social influence propagation network to model group-specific influences on events.
By combining the Contextual features And Social Influence NetwOrk, our integrated prediction framework CASINO can capture the diverse influential factors of event participation and can be used by event organizers to predict/improve the popularity of their events.
Evaluations demonstrate that our CASINO framework achieves high prediction accuracy with contributions from all the latent features we capture.
In this paper, we present a novel deep learning based approach for addressing the problem of interaction recognition from a first person perspective.
The proposed approach uses a pair of convolutional neural networks, whose parameters are shared, for extracting frame level features from successive frames of the video.
The frame level features are then aggregated using a convolutional long short-term memory.
The hidden state of the convolutional long short-term memory, after all the input video frames are processed, is used for classification in to the respective categories.
The two branches of the convolutional neural network perform feature encoding on a short time interval whereas the convolutional long short term memory encodes the changes on a longer temporal duration.
In our network the spatio-temporal structure of the input is preserved till the very final processing stage.
Experimental results show that our method outperforms the state of the art on most recent first person interactions datasets that involve complex ego-motion.
In particular, on UTKinect-FirstPerson it competes with methods that use depth image and skeletal joints information along with RGB images, while it surpasses all previous methods that use only RGB images by more than 20% in recognition accuracy.
In this article we introduce the concept and the first implementation of a lightweight client-server-framework as middleware for distributed computing.
On the client side an installation without administrative rights or privileged ports can turn any computer into a worker node.
Only a Java runtime environment and the JAR files comprising the workflow client are needed.
To connect all clients to the engine one open server port is sufficient.
The engine submits data to the clients and orchestrates their work by workflow descriptions from a central database.
Clients request new task descriptions periodically, thus the system is robust against network failures.
In the basic set-up, data up- and downloads are handled via HTTP communication with the server.
The performance of the modular system could additionally be improved using dedicated file servers or distributed network file systems.
We demonstrate the design features of the proposed engine in real-world applications from mechanical engineering.
We have used this system on a compute cluster in design-of-experiment studies, parameter optimisations and robustness validations of finite element structures.
The paper makes a thermal predictive analysis of the electric power system security for a day ahead.
This predictive analysis is set as a thermal computation of the expected security.
This computation is obtained by cointegrating the daily electric power systen load and the weather, by finding the daily electric power system thermodynamics and by introducing tests for this thermodynamics.
The predictive analysis made shows the electricity consumers' wisdom.
Most of existing image denoising methods assume the corrupted noise to be additive white Gaussian noise (AWGN).
However, the realistic noise in real-world noisy images is much more complex than AWGN, and is hard to be modelled by simple analytical distributions.
As a result, many state-of-the-art denoising methods in literature become much less effective when applied to real-world noisy images captured by CCD or CMOS cameras.
In this paper, we develop a trilateral weighted sparse coding (TWSC) scheme for robust real-world image denoising.
Specifically, we introduce three weight matrices into the data and regularisation terms of the sparse coding framework to characterise the statistics of realistic noise and image priors.
TWSC can be reformulated as a linear equality-constrained problem and can be solved by the alternating direction method of multipliers.
The existence and uniqueness of the solution and convergence of the proposed algorithm are analysed.
Extensive experiments demonstrate that the proposed TWSC scheme outperforms state-of-the-art denoising methods on removing realistic noise.
A central goal of unsupervised learning is to acquire representations from unlabeled data or experience that can be used for more effective learning of downstream tasks from modest amounts of labeled data.
Many prior unsupervised learning works aim to do so by developing proxy objectives based on reconstruction, disentanglement, prediction, and other metrics.
Instead, we develop an unsupervised meta-learning method that explicitly optimizes for the ability to learn a variety of tasks from small amounts of data.
To do so, we construct tasks from unlabeled data in an automatic way and run meta-learning over the constructed tasks.
Surprisingly, we find that, when integrated with meta-learning, relatively simple task construction mechanisms, such as clustering embeddings, lead to good performance on a variety of downstream, human-specified tasks.
Our experiments across four image datasets indicate that our unsupervised meta-learning approach acquires a learning algorithm without any labeled data that is applicable to a wide range of downstream classification tasks, improving upon the embedding learned by four prior unsupervised learning methods.
In the past decade, Convolutional Neural Networks (CNNs) have been demonstrated successful for object detections.
However, the size of network input is limited by the amount of memory available on GPUs.
Moreover, performance degrades when detecting small objects.
To alleviate the memory usage and improve the performance of detecting small traffic signs, we proposed an approach for detecting small traffic signs from large images under real world conditions.
In particular, large images are broken into small patches as input to a Small-Object-Sensitive-CNN (SOS-CNN) modified from a Single Shot Multibox Detector (SSD) framework with a VGG-16 network as the base network to produce patch-level object detection results.
Scale invariance is achieved by applying the SOS-CNN on an image pyramid.
Then, image-level object detection is obtained by projecting all the patch-level detection results to the image at the original scale.
Experimental results on a real-world conditioned traffic sign dataset have demonstrated the effectiveness of the proposed method in terms of detection accuracy and recall, especially for those with small sizes.
The Web and its Semantic extension (i.e.
Linked Open Data) contain open global-scale knowledge and make it available to potentially intelligent machines that want to benefit from it.
Nevertheless, most of Linked Open Data lack ontological distinctions and have sparse axiomatisation.
For example, distinctions such as whether an entity is inherently a class or an individual, or whether it is a physical object or not, are hardly expressed in the data, although they have been largely studied and formalised by foundational ontologies (e.g.DOLCE, SUMO).
These distinctions belong to common sense too, which is relevant for many artificial intelligence tasks such as natural language understanding, scene recognition, and the like.
There is a gap between foundational ontologies, that often formalise or are inspired by pre-existing philosophical theories and are developed with a top-down approach, and Linked Open Data that mostly derive from existing databases or crowd-based effort (e.g.
DBpedia, Wikidata).
We investigate whether machines can learn foundational distinctions over Linked Open Data entities, and if they match common sense.
We want to answer questions such as "does the DBpedia entity for dog refer to a class or to an instance?".
We report on a set of experiments based on machine learning and crowdsourcing that show promising results.
Automated Program Repair (APR) is an emerging research field.
Many APR techniques, for different programming language and platforms, have been proposed and evaluated on several Benchmarks.
However, for our best knowledge, there not exists a well-defined benchmark based on mobile projects, consequently, there is a gap to leverage APR methods for mobile development.
Therefore, regarding the amount of Android Applications around the world, we present DroidBugs, an introductory benchmark based on the analyzes of 360 open projects for Android, each of them with more than 5,000 downloads.
From five applications, DroidBugs contains 13 single-bugs classified by the type of test that exposed them.
By using an APR tool, called Astor4Android, and two common Fault Localization strategy, it was observed how challenging is to find and fix mobile bugs.
The emergence of low-power wide area networks (LPWANs) as a new agent in the Internet of Things (IoT) will result in the incorporation into the digital world of low-automated processes from a wide variety of sectors.
The single-hop conception of typical LPWAN deployments, though simple and robust, overlooks the self-organization capabilities of network devices, suffers from lack of scalability in crowded scenarios, and pays little attention to energy consumption.
Aimed to take the most out of devices' capabilities, the HARE protocol stack is proposed in this paper as a new LPWAN technology flexible enough to adopt uplink multi-hop communications when proving energetically more efficient.
In this way, results from a real testbed show energy savings of up to 15% when using a multi-hop approach while keeping the same network reliability.
System's self-organizing capability and resilience have been also validated after performing numerous iterations of the association mechanism and deliberately switching off network devices.
Adapted from biological sequence alignment, trace alignment is a process mining technique used to visualize and analyze workflow data.
Any analysis done with this method, however, is affected by the alignment quality.
The best existing trace alignment techniques use progressive guide-trees to heuristically approximate the optimal alignment in O(N2L2) time.
These algorithms are heavily dependent on the selected guide-tree metric, often return sum-of-pairs-score-reducing errors that interfere with interpretation, and are computationally intensive for large datasets.
To alleviate these issues, we propose process-oriented iterative multiple alignment (PIMA), which contains specialized optimizations to better handle workflow data.
We demonstrate that PIMA is a flexible framework capable of achieving better sum-of-pairs score than existing trace alignment algorithms in only O(NL2) time.
We applied PIMA to analyzing medical workflow data, showing how iterative alignment can better represent the data and facilitate the extraction of insights from data visualization.
Optical coherence tomography (OCT) is used for non-invasive diagnosis of diabetic macular edema assessing the retinal layers.
In this paper, we propose a new fully convolutional deep architecture, termed ReLayNet, for end-to-end segmentation of retinal layers and fluid masses in eye OCT scans.
ReLayNet uses a contracting path of convolutional blocks (encoders) to learn a hierarchy of contextual features, followed by an expansive path of convolutional blocks (decoders) for semantic segmentation.
ReLayNet is trained to optimize a joint loss function comprising of weighted logistic regression and Dice overlap loss.
The framework is validated on a publicly available benchmark dataset with comparisons against five state-of-the-art segmentation methods including two deep learning based approaches to substantiate its effectiveness.
Higher-dimensional analogs of the predictable degree property and column reducedness are defined, and it is proved that the two properties are equivalent.
It is shown that every multidimensional convolutional code has, what is called, a minimal reduced polynomial resolution.
It is uniquely determined (up to isomorphism) and leads to a number of important integer invariants of the code generalizing classical Forney's indices.
A few decades of work in the AI field have focused efforts on developing a new generation of systems which can acquire knowledge via interaction with the world.
Yet, until very recently, most such attempts were underpinned by research which predominantly regarded linguistic phenomena as separated from the brain and body.
This could lead one into believing that to emulate linguistic behaviour, it suffices to develop 'software' operating on abstract representations that will work on any computational machine.
This picture is inaccurate for several reasons, which are elucidated in this paper and extend beyond sensorimotor and semantic resonance.
Beginning with a review of research, I list several heterogeneous arguments against disembodied language, in an attempt to draw conclusions for developing embodied multisensory agents which communicate verbally and non-verbally with their environment.
Without taking into account both the architecture of the human brain, and embodiment, it is unrealistic to replicate accurately the processes which take place during language acquisition, comprehension, production, or during non-linguistic actions.
While robots are far from isomorphic with humans, they could benefit from strengthened associative connections in the optimization of their processes and their reactivity and sensitivity to environmental stimuli, and in situated human-machine interaction.
The concept of multisensory integration should be extended to cover linguistic input and the complementary information combined from temporally coincident sensory impressions.
Until now mean-field-type game theory was not focused on cognitively-plausible models of choices in humans, animals, machines, robots, software-defined and mobile devices strategic interactions.
This work presents some effects of users' psychology in mean-field-type games.
In addition to the traditional "material" payoff modelling, psychological patterns are introduced in order to better capture and understand behaviors that are observed in engineering practice or in experimental settings.
The psychological payoff value depends upon choices, mean-field states, mean-field actions, empathy and beliefs.
It is shown that the affective empathy enforces mean-field equilibrium payoff equity and improves fairness between the players.
It establishes equilibrium systems for such interactive decision-making problems.
Basic empathy concepts are illustrated in several important problems in engineering including resource sharing, packet collision minimization, energy markets, and forwarding in Device-to-Device communications.
The work conducts also an experiment with 47 people who have to decide whether to cooperate or not.
The basic Interpersonal Reactivity Index of empathy metrics were used to measure the empathy distribution of each participant.
Android app called Empathizer is developed to analyze systematically the data obtained from the participants.
The experimental results reveal that the dominated strategies of the classical game theory are not dominated any more when users' psychology is involved, and a significant level of cooperation is observed among the users who are positively partially empathetic.
Learning by integrating multiple heterogeneous data sources is a common requirement in many tasks.
Collective Matrix Factorization (CMF) is a technique to learn shared latent representations from arbitrary collections of matrices.
It can be used to simultaneously complete one or more matrices, for predicting the unknown entries.
Classical CMF methods assume linearity in the interaction of latent factors which can be restrictive and fails to capture complex non-linear interactions.
In this paper, we develop the first deep-learning based method, called dCMF, for unsupervised learning of multiple shared representations, that can model such non-linear interactions, from an arbitrary collection of matrices.
We address optimization challenges that arise due to dependencies between shared representations through Multi-Task Bayesian Optimization and design an acquisition function adapted for collective learning of hyperparameters.
Our experiments show that dCMF significantly outperforms previous CMF algorithms in integrating heterogeneous data for predictive modeling.
Further, on two tasks - recommendation and prediction of gene-disease association - dCMF outperforms state-of-the-art matrix completion algorithms that can utilize auxiliary sources of information.
Learning about the social structure of hidden and hard-to-reach populations --- such as drug users and sex workers --- is a major goal of epidemiological and public health research on risk behaviors and disease prevention.
Respondent-driven sampling (RDS) is a peer-referral process widely used by many health organizations, where research subjects recruit other subjects from their social network.
In such surveys, researchers observe who recruited whom, along with the time of recruitment and the total number of acquaintances (network degree) of respondents.
However, due to privacy concerns, the identities of acquaintances are not disclosed.
In this work, we show how to reconstruct the underlying network structure through which the subjects are recruited.
We formulate the dynamics of RDS as a continuous-time diffusion process over the underlying graph and derive the likelihood for the recruitment time series under an arbitrary recruitment time distribution.
We develop an efficient stochastic optimization algorithm called RENDER (REspoNdent-Driven nEtwork Reconstruction) that finds the network that best explains the collected data.
We support our analytical results through an exhaustive set of experiments on both synthetic and real data.
Estimating the influence of a given feature to a model prediction is challenging.
We introduce ROAR, RemOve And Retrain, a benchmark to evaluate the accuracy of interpretability methods that estimate input feature importance in deep neural networks.
We remove a fraction of input features deemed to be most important according to each estimator and measure the change to the model accuracy upon retraining.
The most accurate estimator will identify inputs as important whose removal causes the most damage to model performance relative to all other estimators.
This evaluation produces thought-provoking results -- we find that several estimators are less accurate than a random assignment of feature importance.
However, averaging a set of squared noisy estimators (a variant of a technique proposed by Smilkov et al.(2017)), leads to significant gains in accuracy for each method considered and far outperforms such a random guess.
Efficient symbol detection algorithms carry critical importance for achieving the spatial multiplexing gains promised by multi-input multi-output (MIMO) systems.
In this paper, we consider a maximum a posteriori probability (MAP) based symbol detection algorithm, called M-BLAST, over uncoded quasi-static MIMO channels.
Relying on the successive interference cancellation (SIC) receiver, M-BLAST algorithm offers a superior error performance over its predecessor V-BLAST with a signal-to-noise ratio (SNR) gain of as large as 2 dB under various settings of recent interest.
Performance analysis of the M-BLAST algorithm is very complicated since the proposed detection order depends on the decision errors dynamically, which makes an already complex analysis of the conventional ordered SIC receivers even more difficult.
To this end, a rigorous analytical framework is proposed to analyze the outage behavior of the M-BLAST algorithm over binary complex alphabets and two transmitting antennas, which has a potential to be generalized to multiple transmitting antennas and multidimensional constellation sets.
The numerical results show a very good match between the analytical and simulation data under various SNR values and modulation alphabets.
We study how we can adapt a predictor to a non-stationary environment with advises from multiple experts.
We study the problem under complete feedback when the best expert changes over time from a decision theoretic point of view.
Proposed algorithm is based on popular exponential weighing method with exponential discounting.
We provide theoretical results bounding regret under the exponential discounting setting.
Upper bound on regret is derived for finite time horizon problem.
Numerical verification of different real life datasets are provided to show the utility of proposed algorithm.
An accurate knowledge of the per-unit length impedance of power cables is necessary to correctly predict electromagnetic transients in power systems.
In particular, skin, proximity, and ground return effects must be properly estimated.
In many applications, the medium that surrounds the cable is not uniform and can consist of multiple layers of different conductivity, such as dry and wet soil, water, or air.
We introduce a multilayer ground model for the recently-proposed MoM-SO method, suitable to accurately predict ground return effects in such scenarios.
The proposed technique precisely accounts for skin, proximity, ground and tunnel effects, and is applicable to a variety of cable configurations, including underground and submarine cables.
Numerical results show that the proposed method is more accurate than analytic formulas typically employed for transient analyses, and delivers an accuracy comparable to the finite element method (FEM).
With respect to FEM, however, MoM-SO is over 1000 times faster, and can calculate the impedance of a submarine cable inside a three-layer medium in 0.10~s per frequency point.
We investigate the scenario that a robot needs to reach a designated goal after taking a sequence of appropriate actions in a non-static environment that is partially structured.
One application example is to control a marine vehicle to move in the ocean.
The ocean environment is dynamic and oftentimes the ocean waves result in strong disturbances that can disturb the vehicle's motion.
Modeling such dynamic environment is non-trivial, and integrating such model in the robotic motion control is particularly difficult.
Fortunately, the ocean currents usually form some local patterns (e.g. vortex) and thus the environment is partially structured.
The historically observed data can be used to train the robot to learn to interact with the ocean tidal disturbances.
In this paper we propose a method that applies the deep reinforcement learning framework to learn such partially structured complex disturbances.
Our results show that, by training the robot under artificial and real ocean disturbances, the robot is able to successfully act in complex and spatiotemporal environments.
In this paper, we propose an efficient coding scheme for the two-link binary Chief Executive Officer (CEO) problem under logarithmic loss criterion.
The exact rate-distortion bound for a two-link binary CEO problem under the logarithmic loss has been obtained by Courtade and Weissman.
We propose an encoding scheme based on compound LDGM-LDPC codes to achieve the theoretical bounds.
In the proposed encoding, a binary quantizer using LDGM codes and a syndrome-coding employing LDPC codes are applied.
An iterative joint decoding is also designed as a fusion center.
The proposed CEO decoder is based on the sum-product algorithm and a soft estimator.
Recent breakthroughs in Neural Architectural Search (NAS) have achieved state-of-the-art performance in many tasks such as image classification and language understanding.
However, most existing works only optimize for model accuracy and largely ignore other important factors imposed by the underlying hardware and devices, such as latency and energy, when making inference.
In this paper, we first introduce the problem of NAS and provide a survey on recent works.
Then we deep dive into two recent advancements on extending NAS into multiple-objective frameworks: MONAS and DPP-Net.
Both MONAS and DPP-Net are capable of optimizing accuracy and other objectives imposed by devices, searching for neural architectures that can be best deployed on a wide spectrum of devices: from embedded systems and mobile devices to workstations.
Experimental results are poised to show that architectures found by MONAS and DPP-Net achieves Pareto optimality w.r.t the given objectives for various devices.
Generalized Fibonacci-like sequences appear in finite difference approximations of the Partial Differential Equations based upon replacing partial differential equations by finite difference equations.
This paper studies properties of the generalized Fibonacci-like sequence F_(n+2)=A+BF_(n+1)+CF_n.
It is shown that this sequence is periodic with the period T>2 if C=-1,|B|<2.
This chapter reviews the purpose and use of models from the field of complex systems and, in particular, the implications of trying to use models to understand or make decisions within complex situations, such as policy makers usually face.
A discussion of the different dimensions one can formalise situations, the different purposes for models and the different kinds of relationship they can have with the policy making process, is followed by an examination of the compromises forced by the complexity of the target issues.
Several modelling approaches from complexity science are briefly described, with notes as to their abilities and limitations.
These approaches include system dynamics, network theory, information theory, cellular automata, and agent-based modelling.
Some examples of policy models are presented and discussed in the context of the previous analysis.
Finally we conclude by outlining some of the major pitfalls facing those wishing to use such models for policy evaluation.
Object recognition in the video sequence or images is one of the sub-field of computer vision.
Moving object recognition from a video sequence is an appealing topic with applications in various areas such as airport safety, intrusion surveillance, video monitoring, intelligent highway, etc.
Moving object recognition is the most challenging task in intelligent video surveillance system.
In this regard, many techniques have been proposed based on different methods.
Despite of its importance, moving object recognition in complex environments is still far from being completely solved for low resolution videos, foggy videos, and also dim video sequences.
All in all, these make it necessary to develop exceedingly robust techniques.
This paper introduces multiple moving object recognition in the video sequence based on LoG Gabor-PCA approach and Angle based distance Similarity measures techniques used to recognize the object as a human, vehicle etc.
Number of experiments are conducted for indoor and outdoor video sequences of standard datasets and also our own collection of video sequences comprising of partial night vision video sequences.
Experimental results show that our proposed approach achieves an excellent recognition rate.
Results obtained are satisfactory and competent.
While deep learning models and techniques have achieved great empirical success, our understanding of the source of success in many aspects remains very limited.
In an attempt to bridge the gap, we investigate the decision boundary of a production deep learning architecture with weak assumptions on both the training data and the model.
We demonstrate, both theoretically and empirically, that the last weight layer of a neural network converges to a linear SVM trained on the output of the last hidden layer, for both the binary case and the multi-class case with the commonly used cross-entropy loss.
Furthermore, we show empirically that training a neural network as a whole, instead of only fine-tuning the last weight layer, may result in better bias constant for the last weight layer, which is important for generalization.
In addition to facilitating the understanding of deep learning, our result can be helpful for solving a broad range of practical problems of deep learning, such as catastrophic forgetting and adversarial attacking.
The experiment codes are available at https://github.com/lykaust15/NN_decision_boundary
Question answering (QA) systems are sensitive to the many different ways natural language expresses the same information need.
In this paper we turn to paraphrases as a means of capturing this knowledge and present a general framework which learns felicitous paraphrases for various QA tasks.
Our method is trained end-to-end using question-answer pairs as a supervision signal.
A question and its paraphrases serve as input to a neural scoring model which assigns higher weights to linguistic expressions most likely to yield correct answers.
We evaluate our approach on QA over Freebase and answer sentence selection.
Experimental results on three datasets show that our framework consistently improves performance, achieving competitive results despite the use of simple QA models.
The Arbitrary Pattern Formation problem asks to design a distributed algorithm that allows a set of autonomous mobile robots to form any specific but arbitrary geometric pattern given as input.
The problem has been extensively studied in literature in continuous domains.
This paper investigates a discrete version of the problem where the robots are operating on a two dimensional infinite grid.
The robots are assumed to be autonomous, identical, anonymous and oblivious.
They operate in Look-Compute-Move cycles under a fully asynchronous scheduler.
The robots do not agree on any common global coordinate system or chirality.
We have shown that a set of robots can form any arbitrary pattern, if their starting configuration is asymmetric.
The OpenAI Gym provides researchers and enthusiasts with simple to use environments for reinforcement learning.
Even the simplest environment have a level of complexity that can obfuscate the inner workings of RL approaches and make debugging difficult.
This whitepaper describes a Python framework that makes it very easy to create simple Markov-Decision-Process environments programmatically by specifying state transitions and rewards of deterministic and non-deterministic MDPs in a domain-specific language in Python.
It then presents results and visualizations created with this MDP framework.
Training deep recurrent neural network (RNN) architectures is complicated due to the increased network complexity.
This disrupts the learning of higher order abstracts using deep RNN.
In case of feed-forward networks training deep structures is simple and faster while learning long-term temporal information is not possible.
In this paper we propose a residual memory neural network (RMN) architecture to model short-time dependencies using deep feed-forward layers having residual and time delayed connections.
The residual connection paves way to construct deeper networks by enabling unhindered flow of gradients and the time delay units capture temporal information with shared weights.
The number of layers in RMN signifies both the hierarchical processing depth and temporal depth.
The computational complexity in training RMN is significantly less when compared to deep recurrent networks.
RMN is further extended as bi-directional RMN (BRMN) to capture both past and future information.
Experimental analysis is done on AMI corpus to substantiate the capability of RMN in learning long-term information and hierarchical information.
Recognition performance of RMN trained with 300 hours of Switchboard corpus is compared with various state-of-the-art LVCSR systems.
The results indicate that RMN and BRMN gains 6 % and 3.8 % relative improvement over LSTM and BLSTM networks.
Hierarchical Classification (HC) is a supervised learning problem where unlabeled instances are classified into a taxonomy of classes.
Several methods that utilize the hierarchical structure have been developed to improve the HC performance.
However, in most cases apriori defined hierarchical structure by domain experts is inconsistent; as a consequence performance improvement is not noticeable in comparison to flat classification methods.
We propose a scalable data-driven filter based rewiring approach to modify an expert-defined hierarchy.
Experimental comparisons of top-down HC with our modified hierarchy, on a wide range of datasets shows classification performance improvement over the baseline hierarchy (i:e:, defined by expert), clustered hierarchy and flattening based hierarchy modification approaches.
In comparison to existing rewiring approaches, our developed method (rewHier) is computationally efficient, enabling it to scale to datasets with large numbers of classes, instances and features.
We also show that our modified hierarchy leads to improved classification performance for classes with few training samples in comparison to flat and state-of-the-art HC approaches.
Logit-response dynamics (Alos-Ferrer and Netzer, Games and Economic Behavior 2010) are a rich and natural class of noisy best-response dynamics.
In this work we revise the price of anarchy and the price of stability by considering the quality of long-run equilibria in these dynamics.
Our results show that prior studies on simpler dynamics of this type can strongly depend on a synchronous schedule of the players' moves.
In particular, a small noise by itself is not enough to improve the quality of equilibria as soon as other very natural schedules are used.
The expansion of the electronic commerce, together with an increasing confidence of customers in electronic payments, makes of fraud detection a critical factor.
Detecting frauds in (nearly) real time setting demands the design and the implementation of scalable learning techniques able to ingest and analyse massive amounts of streaming data.
Recent advances in analytics and the availability of open source solutions for Big Data storage and processing open new perspectives to the fraud detection field.
In this paper we present a SCAlable Real-time Fraud Finder (SCARFF) which integrates Big Data tools (Kafka, Spark and Cassandra) with a machine learning approach which deals with imbalance, nonstationarity and feedback latency.
Experimental results on a massive dataset of real credit card transactions show that this framework is scalable, efficient and accurate over a big stream of transactions.
Inverse imaging problems are inherently under-determined, and hence it is important to employ appropriate image priors for regularization.
One recent popular prior---the graph Laplacian regularizer---assumes that the target pixel patch is smooth with respect to an appropriately chosen graph.
However, the mechanisms and implications of imposing the graph Laplacian regularizer on the original inverse problem are not well understood.
To address this problem, in this paper we interpret neighborhood graphs of pixel patches as discrete counterparts of Riemannian manifolds and perform analysis in the continuous domain, providing insights into several fundamental aspects of graph Laplacian regularization for image denoising.
Specifically, we first show the convergence of the graph Laplacian regularizer to a continuous-domain functional, integrating a norm measured in a locally adaptive metric space.
Focusing on image denoising, we derive an optimal metric space assuming non-local self-similarity of pixel patches, leading to an optimal graph Laplacian regularizer for denoising in the discrete domain.
We then interpret graph Laplacian regularization as an anisotropic diffusion scheme to explain its behavior during iterations, e.g., its tendency to promote piecewise smooth signals under certain settings.
To verify our analysis, an iterative image denoising algorithm is developed.
Experimental results show that our algorithm performs competitively with state-of-the-art denoising methods such as BM3D for natural images, and outperforms them significantly for piecewise smooth images.
Progressive efforts have been evolving continuously for the betterment of the services of the Information Technology for Educational Management(ITEM).
These services require data intensive and communication intensive applications.
Due to the massive growth of information, situation becomes difficult to manage these services.
Here the role of the Information and Communication Technology (ICT) infrastructure particularly data centre with communication components becomes important to facilitate these services.
The present paper discusses the related issues such as competent staff, appropriate ICT infrastructure, ICT acceptance level etc. required for ITEM competence building framework considering the earlier approach for core competences for ITEM.
It this connection, it is also necessary to consider the procurement of standard and appropriate ICT facilities.
This will help in the integration of these facilities for the future expansion.
This will also enable to create and foresee the impact of the pairing the management with information, technology, and education components individually and combined.
These efforts will establish a strong coupling between the ITEM activities and resource management for effective implementation of the framework.
The problem of computing the Betweenness Centrality (BC) is important in analyzing graphs in many practical applications like social networks, biological networks, transportation networks, electrical circuits, etc.
Since this problem is computation intensive, researchers have been developing algorithms using high performance computing resources like supercomputers, clusters, and Graphics Processing Units (GPUs).
Current GPU algorithms for computing BC employ Brandes' sequential algorithm with different trade-offs for thread scheduling, data structures, and atomic operations.
In this paper, we study three GPU algorithms for computing BC of unweighted, directed, scale-free networks.
We discuss and measure the trade-offs of their design choices about balanced thread scheduling, atomic operations, synchronizations and latency hiding.
Our program is written in NVIDIA CUDA C and was tested on an NVIDIA Tesla M2050 GPU.
We present a prototype of an integrated reasoning environment for educational purposes.
The presented tool is a fragment of a proof assistant and automated theorem prover.
We describe the existing and planned functionality of the theorem prover and especially the functionality of the educational fragment.
This currently supports working with terms of the untyped lambda calculus and addresses both undergraduate students and researchers.
We show how the tool can be used to support the students' understanding of functional programming and discuss general problems related to the process of building theorem proving software that aims at supporting both research and education.
Machine vision for plant phenotyping is an emerging research area for producing high throughput in agriculture and crop science applications.
Since 2D based approaches have their inherent limitations, 3D plant analysis is becoming state of the art for current phenotyping technologies.
We present an automated system for analyzing plant growth in indoor conditions.
A gantry robot system is used to perform scanning tasks in an automated manner throughout the lifetime of the plant.
A 3D laser scanner mounted as the robot's payload captures the surface point cloud data of the plant from multiple views.
The plant is monitored from the vegetative to reproductive stages in light/dark cycles inside a controllable growth chamber.
An efficient 3D reconstruction algorithm is used, by which multiple scans are aligned together to obtain a 3D mesh of the plant, followed by surface area and volume computations.
The whole system, including the programmable growth chamber, robot, scanner, data transfer and analysis is fully automated in such a way that a naive user can, in theory, start the system with a mouse click and get back the growth analysis results at the end of the lifetime of the plant with no intermediate intervention.
As evidence of its functionality, we show and analyze quantitative results of the rhythmic growth patterns of the dicot Arabidopsis thaliana(L.), and the monocot barley (Hordeum vulgare L.) plants under their diurnal light/dark cycles.
In this work we propose a novel approach to remove undesired objects from RGB-D sequences captured with freely moving cameras, which enables static 3D reconstruction.
Our method jointly uses existing information from multiple frames as well as generates new one via inpainting techniques.
We use balanced rules to select source frames; local homography based image warping method for alignment and Markov random field (MRF) based approach for combining existing information.
For the left holes, we employ exemplar based multi-view inpainting method to deal with the color image and coherently use it as guidance to complete the depth correspondence.
Experiments show that our approach is qualified for removing the undesired objects and inpainting the holes.
In this paper, we study how to fold a specified origami crease pattern in order to minimize the impact of paper thickness.
Specifically, origami designs are often expressed by a mountain-valley pattern (plane graph of creases with relative fold orientations), but in general this specification is consistent with exponentially many possible folded states.
We analyze the complexity of finding the best consistent folded state according to two metrics: minimizing the total number of layers in the folded state (so that a "flat folding" is indeed close to flat), and minimizing the total amount of paper required to execute the folding (where "thicker" creases consume more paper).
We prove both problems strongly NP-complete even for 1D folding.
On the other hand, we prove the first problem fixed-parameter tractable in 1D with respect to the number of layers.
We present a new model for singing synthesis based on a modified version of the WaveNet architecture.
Instead of modeling raw waveform, we model features produced by a parametric vocoder that separates the influence of pitch and timbre.
This allows conveniently modifying pitch to match any target melody, facilitates training on more modest dataset sizes, and significantly reduces training and generation times.
Our model makes frame-wise predictions using mixture density outputs rather than categorical outputs in order to reduce the required parameter count.
As we found overfitting to be an issue with the relatively small datasets used in our experiments, we propose a method to regularize the model and make the autoregressive generation process more robust to prediction errors.
Using a simple multi-stream architecture, harmonic, aperiodic and voiced/unvoiced components can all be predicted in a coherent manner.
We compare our method to existing parametric statistical and state-of-the-art concatenative methods using quantitative metrics and a listening test.
While naive implementations of the autoregressive generation algorithm tend to be inefficient, using a smart algorithm we can greatly speed up the process and obtain a system that's competitive in both speed and quality.
Convolutional neural network (CNN) based methods have recently achieved great success for image super-resolution (SR).
However, most deep CNN based SR models attempt to improve distortion measures (e.g.PSNR, SSIM, IFC, VIF) while resulting in poor quantified perceptual quality (e.g. human opinion score, no-reference quality measures such as NIQE).
Few works have attempted to improve the perceptual quality at the cost of performance reduction in distortion measures.
A very recent study has revealed that distortion and perceptual quality are at odds with each other and there is always a trade-off between the two.
Often the restoration algorithms that are superior in terms of perceptual quality, are inferior in terms of distortion measures.
Our work attempts to analyze the trade-off between distortion and perceptual quality for the problem of single image SR. To this end, we use the well-known SR architecture-enhanced deep super-resolution (EDSR) network and show that it can be adapted to achieve better perceptual quality for a specific range of the distortion measure.
While the original network of EDSR was trained to minimize the error defined based on per-pixel accuracy alone, we train our network using a generative adversarial network framework with EDSR as the generator module.
Our proposed network, called enhanced perceptual super-resolution network (EPSR), is trained with a combination of mean squared error loss, perceptual loss, and adversarial loss.
Our experiments reveal that EPSR achieves the state-of-the-art trade-off between distortion and perceptual quality while the existing methods perform well in either of these measures alone.
The problem of knowing who knows what is multi-faceted.
Knowledge and expertise lie on a spectrum and one's expertise in one topic area may have little bearing on one's knowledge in a disparate topic area.
In addition, we continue to learn new things over time.
Each of us see but a sliver of our acquaintances' and co-workers' areas of expertise.
By making explicit and visible many individual perceptions of cognitive authority, this work shows that a group can know what its members know about in a relatively efficient and inexpensive manner.
Structure from motion is an import theme in computer vision.
Although great progress has been made both in theory and applications, most of the algorithms only work for static scenes and rigid objects.
In recent years, structure and motion recovery of non-rigid objects and dynamic scenes have received a lot of attention.
In this paper, the state-of-the-art techniques for structure and motion factorization of non-rigid objects are reviewed and discussed.
First, an introduction of the structure from motion problem is presented, followed by a general formulation of non-rigid structure from motion.
Second, an augmented affined factorization framework, by using homogeneous representation, is presented to solve the registration issue in the presence of outlying and missing data.
Third, based on the observation that the reprojection residuals of outliers are significantly larger than those of inliers, a robust factorization strategy with outlier rejection is proposed by means of the reprojection residuals, followed by some comparative experimental evaluations.
Finally, some future research topics in non-rigid structure from motion are discussed.
Increasing distributed energy resources (DERs) may result in reactive power imbalance in a transmission power system (TPS).
An active distribution power system (DPS) having DERs reportedly can work as a reactive power prosumer to help balance the reactive power in the TPS.
The reactive power potential (RPP) of a DPS, which is the range between the maximal inductive and capacitive reactive power the DPS can reliably provide, should be accurately estimated.
However, an accurate estimation is difficult because of the network constraints, mixed discrete and continuous variables, and the nonnegligible uncertainty in the DPS.
To solve this problem, this paper proposes a robust RPP estimation method based on two-stage robust optimization, where the uncertainty in DERs and the boundary-bus voltage is considered.
In this two-stage robust model, the RPP is pre-estimated in the first stage and its robust feasibility for any possible instance of the uncertainty is checked via a tractable problem in the second stage.
The column-and-constraint generation algorithm is adopted, which solves this model in finite iterations.
Case studies show that this robust method excels in yielding a completely reliable RPP, and also that a DPS, even under the uncertainty, is still an effective reactive power prosumer for the TPS.
Handwritten mathematical expression recognition is a challenging problem due to the complicated two-dimensional structures, ambiguous handwriting input and variant scales of handwritten math symbols.
To settle this problem, we utilize the attention based encoder-decoder model that recognizes mathematical expression images from two-dimensional layouts to one-dimensional LaTeX strings.
We improve the encoder by employing densely connected convolutional networks as they can strengthen feature extraction and facilitate gradient propagation especially on a small training set.
We also present a novel multi-scale attention model which is employed to deal with the recognition of math symbols in different scales and save the fine-grained details that will be dropped by pooling operations.
Validated on the CROHME competition task, the proposed method significantly outperforms the state-of-the-art methods with an expression recognition accuracy of 52.8% on CROHME 2014 and 50.1% on CROHME 2016, by only using the official training dataset.
The ConditionaL Neural Network (CLNN) exploits the nature of the temporal sequencing of the sound signal represented in a spectrogram, and its variant the Masked ConditionaL Neural Network (MCLNN) induces the network to learn in frequency bands by embedding a filterbank-like sparseness over the network's links using a binary mask.
Additionally, the masking automates the exploration of different feature combinations concurrently analogous to handcrafting the optimum combination of features for a recognition task.
We have evaluated the MCLNN performance using the Urbansound8k dataset of environmental sounds.
Additionally, we present a collection of manually recorded sounds for rail and road traffic, YorNoise, to investigate the confusion rates among machine generated sounds possessing low-frequency components.
MCLNN has achieved competitive results without augmentation and using 12% of the trainable parameters utilized by an equivalent model based on state-of-the-art Convolutional Neural Networks on the Urbansound8k.
We extended the Urbansound8k dataset with YorNoise, where experiments have shown that common tonal properties affect the classification performance.
Enterprise databases usually contain large and complex schemas.
Authoring complete schema mapping queries in this case requires deep knowledge about the source and target schemas and is thereby very challenging to programmers.
Sample-driven schema mapping allows the user to describe the schema mapping using data records.
However, real data records are still harder to specify than other useful insights about the desired schema mapping the user might have.
In this project, we develop a schema mapping system, PRISM, that enables multiresolution schema mapping.
The end user is not limited to providing high-resolution constraints like exact data records but may also provide constraints of various resolutions, like incomplete data records, value ranges, and data types.
This new interaction paradigm gives the user more flexibility in describing the desired schema mapping.
This demonstration showcases how to use PRISM for schema mapping in a real database.
Testing in Continuous Integration (CI) involves test case prioritization, selection, and execution at each cycle.
Selecting the most promising test cases to detect bugs is hard if there are uncertainties on the impact of committed code changes or, if traceability links between code and tests are not available.
This paper introduces Retecs, a new method for automatically learning test case selection and prioritization in CI with the goal to minimize the round-trip time between code commits and developer feedback on failed test cases.
The Retecs method uses reinforcement learning to select and prioritize test cases according to their duration, previous last execution and failure history.
In a constantly changing environment, where new test cases are created and obsolete test cases are deleted, the Retecs method learns to prioritize error-prone test cases higher under guidance of a reward function and by observing previous CI cycles.
By applying Retecs on data extracted from three industrial case studies, we show for the first time that reinforcement learning enables fruitful automatic adaptive test case selection and prioritization in CI and regression testing.
We focus on the problem of language modeling for code-switched language, in the context of automatic speech recognition (ASR).
Language modeling for code-switched language is challenging for (at least) three reasons: (1) lack of available large-scale code-switched data for training; (2) lack of a replicable evaluation setup that is ASR directed yet isolates language modeling performance from the other intricacies of the ASR system; and (3) the reliance on generative modeling.
We tackle these three issues: we propose an ASR-motivated evaluation setup which is decoupled from an ASR system and the choice of vocabulary, and provide an evaluation dataset for English-Spanish code-switching.
This setup lends itself to a discriminative training approach, which we demonstrate to work better than generative language modeling.
Finally, we present an effective training protocol that integrates small amounts of code-switched data with large amounts of monolingual data, for both the generative and discriminative cases.
We present PEC, an Event Calculus (EC) style action language for reasoning about probabilistic causal and narrative information.
It has an action language style syntax similar to that of the EC variant Modular-E. Its semantics is given in terms of possible worlds which constitute possible evolutions of the domain, and builds on that of EFEC, an epistemic extension of EC.
We also describe an ASP implementation of PEC and show the sense in which this is sound and complete.
In this paper an analysis of the presence and possibilities of altmetrics for bibliometric and performance analysis is carried out.
Using the web based tool Impact Story, we have collected metrics for 20,000 random publications from the Web of Science.
We studied the presence and frequency of altmetrics in the set of publications, across fields, document types and also through the years.
The main result of the study is that less than 50% of the publications have some kind of altmetrics.
The source that provides most metrics is Mendeley, with metrics on readerships for around 37% of all the publications studied.
Other sources only provide marginal information.
Possibilities and limitations of these indicators are discussed and future research lines are outlined.
We also assessed the accuracy of the data retrieved through Impact Story by focusing on the analysis of the accuracy of data from Mendeley; in a follow up study, the accuracy and validity of other data sources not included here will be assessed.
Despite of the progress achieved by deep learning in face recognition (FR), more and more people find that racial bias explicitly degrades the performance in realistic FR systems.
Facing the fact that existing training and testing databases consist of almost Caucasian subjects, there are still no independent testing databases to evaluate racial bias and even no training databases and methods to reduce it.
To facilitate the research towards conquering those unfair issues, this paper contributes a new dataset called Racial Faces in-the-Wild (RFW) database with two important uses, 1) racial bias testing: four testing subsets, namely Caucasian, Asian, Indian and African, are constructed, and each contains about 3000 individuals with 6000 image pairs for face verification, 2) racial bias reducing: one labeled training subset with Caucasians and three unlabeled training subsets with Asians, Indians and Africans are offered to encourage FR algorithms to transfer recognition knowledge from Caucasians to other races.
For we all know, RFW is the first database for measuring racial bias in FR algorithms.
After proving the existence of domain gap among different races and the existence of racial bias in FR algorithms, we further propose a deep information maximization adaptation network (IMAN) to bridge the domain gap, and comprehensive experiments show that the racial bias could be narrowed-down by our algorithm.
The paper addresses the problem of vehicle rollover avoidance using reference governors applied to modify the driver steering input in vehicles with an active steering system.
Several reference governor designs are presented and tested with a detailed nonlinear simulation model.
The vehicle dynamics are highly nonlinear for large steering angles, including the conditions where the vehicle approaches a rollover onset, which necessitates reference governor design changes.
Simulation results show that reference governor designs are effective in avoiding rollover.
The results also demonstrate that the controllers are not overly conservative, adjusting the driver steering input only for very high steering angles.
Clustering is one of the most fundamental problems in data analysis and it has been studied extensively in the literature.
Though many clustering algorithms have been proposed, clustering theories that justify the use of these clustering algorithms are still unsatisfactory.
In particular, one of the fundamental challenges is to address the following question:   What is a cluster in a set of data points?
In this paper, we make an attempt to address such a question by considering a set of data points associated with a distance measure (metric).
We first propose a new cohesion measure in terms of the distance measure.
Using the cohesion measure, we define a cluster as a set of points that are cohesive to themselves.
For such a definition, we show there are various equivalent statements that have intuitive explanations.
We then consider the second question:   How do we find clusters and good partitions of clusters under such a definition?
For such a question, we propose a hierarchical agglomerative algorithm and a partitional algorithm.
Unlike standard hierarchical agglomerative algorithms, our hierarchical agglomerative algorithm has a specific stopping criterion and it stops with a partition of clusters.
Our partitional algorithm, called the K-sets algorithm in the paper, appears to be a new iterative algorithm.
Unlike the Lloyd iteration that needs two-step minimization, our K-sets algorithm only takes one-step minimization.
One of the most interesting findings of our paper is the duality result between a distance measure and a cohesion measure.
Such a duality result leads to a dual K-sets algorithm for clustering a set of data points with a cohesion measure.
The dual K-sets algorithm converges in the same way as a sequential version of the classical kernel K-means algorithm.
The key difference is that a cohesion measure does not need to be positive semi-definite.
Template-based code generation (TBCG) is a synthesis technique that produces code from high-level specifications, called templates.
TBCG is a popular technique in model-driven engineering (MDE) given that they both emphasize abstraction and automation.
Given the diversity of tools and approaches, it is necessary to classify existing TBCG techniques to better guide developers in their choices.
The goal of this article is to better understand the characteristics of TBCG techniques and associated tools, identify research trends, and assess the importance of the role of MDE in this code synthesis approach.
We conducted a systematic mapping study of the literature to paint an interesting picture about the trends and uses of TBCG.
Our study shows that the community has been diversely using TBCG over the past 15 years.
TBCG has greatly benefited from MDE.
It has favored a template style that is output-based and high level modeling languages as input.
TBCG is mainly used to generate source code and has been applied in a variety of domains.
Furthermore, both MDE and non-MDE tools are becoming effective development resources in industry.
Nearly all previous work on geo-locating latent states and activities from social media confounds general discussions about activities, self-reports of users participating in those activities at times in the past or future, and self-reports made at the immediate time and place the activity occurs.
Activities, such as alcohol consumption, may occur at different places and types of places, and it is important not only to detect the local regions where these activities occur, but also to analyze the degree of participation in them by local residents.
In this paper, we develop new machine learning based methods for fine-grained localization of activities and home locations from Twitter data.
We apply these methods to discover and compare alcohol consumption patterns in a large urban area, New York City, and a more suburban and rural area, Monroe County.
We find positive correlations between the rate of alcohol consumption reported among a community's Twitter users and the density of alcohol outlets, demonstrating that the degree of correlation varies significantly between urban and suburban areas.
While our experiments are focused on alcohol use, our methods for locating homes and distinguishing temporally-specific self-reports are applicable to a broad range of behaviors and latent states.
In this paper, we propose a novel neural approach for paraphrase generation.
Conventional para- phrase generation methods either leverage hand-written rules and thesauri-based alignments, or use statistical machine learning principles.
To the best of our knowledge, this work is the first to explore deep learning models for paraphrase generation.
Our primary contribution is a stacked residual LSTM network, where we add residual connections between LSTM layers.
This allows for efficient training of deep LSTMs.
We evaluate our model and other state-of-the-art deep learning models on three different datasets: PPDB, WikiAnswers and MSCOCO.
Evaluation results demonstrate that our model outperforms sequence to sequence, attention-based and bi- directional LSTM models on BLEU, METEOR, TER and an embedding-based sentence similarity metric.
This paper presents a new approach in understanding how deep neural networks (DNNs) work by applying homomorphic signal processing techniques.
Focusing on the task of multi-pitch estimation (MPE), this paper demonstrates the equivalence relation between a generalized cepstrum and a DNN in terms of their structures and functionality.
Such an equivalence relation, together with pitch perception theories and the recently established rectified-correlations-on-a-sphere (RECOS) filter analysis, provide an alternative way in explaining the role of the nonlinear activation function and the multi-layer structure, both of which exist in a cepstrum and a DNN.
To validate the efficacy of this new approach, a new feature designed in the same fashion is proposed for pitch salience function.
The new feature outperforms the one-layer spectrum in the MPE task and, as predicted, it addresses the issue of the missing fundamental effect and also achieves better robustness to noise.
We consider the task of evaluating a policy for a Markov decision process (MDP).
The standard unbiased technique for evaluating a policy is to deploy the policy and observe its performance.
We show that the data collected from deploying a different policy, commonly called the behavior policy, can be used to produce unbiased estimates with lower mean squared error than this standard technique.
We derive an analytic expression for the optimal behavior policy --- the behavior policy that minimizes the mean squared error of the resulting estimates.
Because this expression depends on terms that are unknown in practice, we propose a novel policy evaluation sub-problem, behavior policy search: searching for a behavior policy that reduces mean squared error.
We present a behavior policy search algorithm and empirically demonstrate its effectiveness in lowering the mean squared error of policy performance estimates.
The Universal Dependencies (UD) and Universal Morphology (UniMorph) projects each present schemata for annotating the morphosyntactic details of language.
Each project also provides corpora of annotated text in many languages - UD at the token level and UniMorph at the type level.
As each corpus is built by different annotators, language-specific decisions hinder the goal of universal schemata.
With compatibility of tags, each project's annotations could be used to validate the other's.
Additionally, the availability of both type- and token-level resources would be a boon to tasks such as parsing and homograph disambiguation.
To ease this interoperability, we present a deterministic mapping from Universal Dependencies v2 features into the UniMorph schema.
We validate our approach by lookup in the UniMorph corpora and find a macro-average of 64.13% recall.
We also note incompatibilities due to paucity of data on either side.
Finally, we present a critical evaluation of the foundations, strengths, and weaknesses of the two annotation projects.
Kernel audit logs are an invaluable source of information in the forensic investigation of a cyber-attack.
However, the coarse granularity of dependency information in audit logs leads to the construction of huge attack graphs which contain false or inaccurate dependencies.
To overcome this problem, we propose a system, called ProPatrol, which leverages the open compartmentalized design in families of enterprise applications used in security-sensitive contexts (e.g., browser, chat client, email client).
To achieve its goal, ProPatrol infers a model for an application's high-level tasks as input-processing compartments using purely the audit log events generated by that application.
The main benefit of this approach is that it does not rely on source code or binary instrumentation, but only on a preliminary and general knowledge of an application's architecture to bootstrap the analysis.
Our experiments with enterprise-level attacks demonstrate that ProPatrol significantly cuts down the forensic investigation effort and quickly pinpoints the root- cause of attacks.
ProPatrol incurs less than 2% runtime overhead on a commodity operating system.
Extracting text objects from the PDF images is a challenging problem.
The text data present in the PDF images contain certain useful information for automatic annotation, indexing etc.
However variations of the text due to differences in text style, font, size, orientation, alignment as well as complex structure make the problem of automatic text extraction extremely difficult and challenging job.
This paper presents two techniques under block-based classification.
After a brief introduction of the classification methods, two methods were enhanced and results were evaluated.
The performance metrics for segmentation and time consumption are tested for both the models.
Identifying the factors that influence academic performance is an essential part of educational research.
Previous studies have documented the importance of personality traits, class attendance, and social network structure.
Because most of these analyses were based on a single behavioral aspect and/or small sample sizes, there is currently no quantification of the interplay of these factors.
Here, we study the academic performance among a cohort of 538 undergraduate students forming a single, densely connected social network.
Our work is based on data collected using smartphones, which the students used as their primary phones for two years.
The availability of multi-channel data from a single population allows us to directly compare the explanatory power of individual and social characteristics.
We find that the most informative indicators of performance are based on social ties and that network indicators result in better model performance than individual characteristics (including both personality and class attendance).
We confirm earlier findings that class attendance is the most important predictor among individual characteristics.
Finally, our results suggest the presence of strong homophily and/or peer effects among university students.
Considering the level of competition prevailing in Business-to-Consumer (B2C) E-Commerce domain and the huge investments required to attract new customers, firms are now giving more focus to reduce their customer churn rate.
Churn rate is the ratio of customers who part away with the firm in a specific time period.
One of the best mechanism to retain current customers is to identify any potential churn and respond fast to prevent it.
Detecting early signs of a potential churn, recognizing what the customer is looking for by the movement and automating personalized win back campaigns are essential to sustain business in this era of competition.
E-Commerce firms normally possess large volume of data pertaining to their existing customers like transaction history, search history, periodicity of purchases, etc.
Data mining techniques can be applied to analyse customer behaviour and to predict the potential customer attrition so that special marketing strategies can be adopted to retain them.
This paper proposes an integrated model that can predict customer churn and also recommend personalized win back actions.
This paper introduces a novel anchor design to support anchor-based face detection for superior scale-invariant performance, especially on tiny faces.
To achieve this, we explicitly address the problem that anchor-based detectors drop performance drastically on faces with tiny sizes, e.g. less than 16x16 pixels.
In this paper, we investigate why this is the case.
We discover that current anchor design cannot guarantee high overlaps between tiny faces and anchor boxes, which increases the difficulty of training.
The new Expected Max Overlapping (EMO) score is proposed which can theoretically explain the low overlapping issue and inspire several effective strategies of new anchor design leading to higher face overlaps, including anchor stride reduction with new network architectures, extra shifted anchors, and stochastic face shifting.
Comprehensive experiments show that our proposed method significantly outperforms the baseline anchor-based detector, while consistently achieving state-of-the-art results on challenging face detection datasets with competitive runtime speed.
Image-to-image translation has recently received significant attention due to advances in deep learning.
Most works focus on learning either a one-to-one mapping in an unsupervised way or a many-to-many mapping in a supervised way.
However, a more practical setting is many-to-many mapping in an unsupervised way, which is harder due to the lack of supervision and the complex inner- and cross-domain variations.
To alleviate these issues, we propose the Exemplar Guided & Semantically Consistent Image-to-image Translation (EGSC-IT) network which conditions the translation process on an exemplar image in the target domain.
We assume that an image comprises of a content component which is shared across domains, and a style component specific to each domain.
Under the guidance of an exemplar from the target domain we apply Adaptive Instance Normalization to the shared content component, which allows us to transfer the style information of the target domain to the source domain.
To avoid semantic inconsistencies during translation that naturally appear due to the large inner- and cross-domain variations, we introduce the concept of feature masks that provide coarse semantic guidance without requiring the use of any semantic labels.
Experimental results on various datasets show that EGSC-IT does not only translate the source image to diverse instances in the target domain, but also preserves the semantic consistency during the process.
We present a hierarchical regression framework for estimating hand joint positions from single depth images based on local surface normals.
The hierarchical regression follows the tree structured topology of hand from wrist to finger tips.
We propose a conditional regression forest, i.e., the Frame Conditioned Regression Forest (FCRF) which uses a new normal difference feature.
At each stage of the regression, the frame of reference is established from either the local surface normal or previously estimated hand joints.
By making the regression with respect to the local frame, the pose estimation is more robust to rigid transformations.
We also introduce a new efficient approximation to estimate surface normals.
We verify the effectiveness of our method by conducting experiments on two challenging real-world datasets and show consistent improvements over previous discriminative pose estimation methods.
Existing visual tracking methods usually localize a target object with a bounding box, in which the performance of the foreground object trackers or detectors is often affected by the inclusion of background clutter.
To handle this problem, we learn a patch-based graph representation for visual tracking.
The tracked object is modeled by with a graph by taking a set of non-overlapping image patches as nodes, in which the weight of each node indicates how likely it belongs to the foreground and edges are weighted for indicating the appearance compatibility of two neighboring nodes.
This graph is dynamically learned and applied in object tracking and model updating.
During the tracking process, the proposed algorithm performs three main steps in each frame.
First, the graph is initialized by assigning binary weights of some image patches to indicate the object and background patches according to the predicted bounding box.
Second, the graph is optimized to refine the patch weights by using a novel alternating direction method of multipliers.
Third, the object feature representation is updated by imposing the weights of patches on the extracted image features.
The object location is predicted by maximizing the classification score in the structured support vector machine.
Extensive experiments show that the proposed tracking algorithm performs well against the state-of-the-art methods on large-scale benchmark datasets.
Social networks offer a ready channel for fake and misleading news to spread and exert influence.
This paper examines the performance of different reputation algorithms when applied to a large and statistically significant portion of the news that are spread via Twitter.
Our main result is that simple algorithms based on the identity of the users spreading the news, as well as the words appearing in the titles and descriptions of the linked articles, are able to identify a large portion of fake or misleading news, while incurring only very low (<1%) false positive rates for mainstream websites.
We believe that these algorithms can be used as the basis of practical, large-scale systems for indicating to consumers which news sites deserve careful scrutiny and skepticism.
This paper introduces an automated heuristic process able to achieve high accuracy when matching graphical user interface widgets across multiple versions of a target application.
The proposed implementation is flexible as it allows full customization of the process and easy integration with existing tools for long term graphical user interface test case maintenance, software visualization and analysis.
Social capital has been studied in economics, sociology and political science as one of the key elements that promote the development of modern societies.
It can be defined as the source of capital that facilitates cooperation through shared social norms.
In this work, we investigate whether and to what extent synchronization aspects of mobile communication patterns are associated with social capital metrics.
Interestingly, our results show that our synchronization-based approach well correlates with existing social capital metrics (i.e., Referendum turnout, Blood donations, and Association density), being also able to characterize the different role played by high synchronization within a close proximity-based community and high synchronization among different communities.
Hence, the proposed approach can provide timely, effective analysis at a limited cost over a large territory.
In this paper we describe the implementation of a convolutional neural network (CNN) used to assess online review helpfulness.
To our knowledge, this is the first use of this architecture to address this problem.
We explore the impact of two related factors impacting CNN performance: different word embedding initializations and different input review lengths.
We also propose an approach to combining rating star information with review text to further improve prediction accuracy.
We demonstrate that this can improve the overall accuracy by 2%.
Finally, we evaluate the method on a benchmark dataset and show an improvement in accuracy relative to published results for traditional methods of 2.5% for a model trained using only review text and 4.24% for a model trained on a combination of rating star information and review text.
The sharing of network traces is an important prerequisite for the development and evaluation of efficient anomaly detection mechanisms.
Unfortunately, privacy concerns and data protection laws prevent network operators from sharing these data.
Anonymization is a promising solution in this context; however, it is unclear if the sanitization of data preserves the traffic characteristics or introduces artifacts that may falsify traffic analysis results.
In this paper, we examine the utility of anonymized flow traces for anomaly detection.
We quantitatively evaluate the impact of IP address anonymization, namely variations of permutation and truncation, on the detectability of large-scale anomalies.
Specifically, we analyze three weeks of un-sampled and non-anonymized network traces from a medium-sized backbone network.
We find that all anonymization techniques, except prefix-preserving permutation, degrade the utility of data for anomaly detection.
We show that the degree of degradation depends to a large extent on the nature and mix of anomalies present in a trace.
Moreover, we present a case study that illustrates how traffic characteristics of individual hosts are distorted by anonymization.
Currently, software industries are using different SDLC (software development life cycle) models which are designed for specific purposes.
The use of technology is booming in every perspective of life and the software behind the technology plays an enormous role.
As the technical complexities are increasing, successful development of software solely depends on the proper management of development processes.
So, it is inevitable to introduce improved methodologies in the industry so that modern human centred software applications development can be managed and delivered to the user successfully.
So, in this paper, we have explored the facts of different SDLC models and perform their comparative analysis.
Deep neural networks require large amounts of resources which makes them hard to use on resource constrained devices such as Internet-of-things devices.
Offloading the computations to the cloud can circumvent these constraints but introduces a privacy risk since the operator of the cloud is not necessarily trustworthy.
We propose a technique that obfuscates the data before sending it to the remote computation node.
The obfuscated data is unintelligible for a human eavesdropper but can still be classified with a high accuracy by a neural network trained on unobfuscated images.
The reuse of code fragments by copying and pasting is widely practiced in software development and results in code clones.
Cloning is considered an anti-pattern as it negatively affects program correctness and increases maintenance efforts.
Programmable Logic Controller (PLC) software is no exception in the code clone discussion as reuse in development and maintenance is frequently achieved through copy, paste, and modification.
Even though the presence of code clones may not necessary be a problem per se, it is important to detect, track and manage clones as the software system evolves.
Unfortunately, tool support for clone detection and management is not commonly available for PLC software systems or limited to generic tools with a reduced set of features.
In this paper, we investigate code clones in a real-world PLC software system based on IEC 61131-3 Structured Text and C/C++.
We extended a widely used tool for clone detection with normalization support.
Furthermore, we evaluated the different types and natures of code clones in the studied system and their relevance for refactoring.
Results shed light on the applicability and usefulness of clone detection in the context of industrial automation systems and it demonstrates the benefit of adapting detection and management tools for IEC 611313-3 languages.
We investigate optimal geographical caching in heterogeneous cellular networks, where different types of base stations (BSs) have different cache capacities.
The content library contains files with different popularities.
The performance metric is the total hit probability.
The problem of optimally placing content in all BSs jointly is not convex in general.
However, we show that when BSs are deployed according to homogeneous Poisson point processes (PPP), independently for each type, we can formulate the problem as a convex problem.
We give the optimal solution to the joint problem for PPP deployment.
For the general case, we provide a distributed local optimization algorithm (LOA) that finds the optimal placement policies for different types of BSs.
We find the optimal placement policy of the small BSs (SBSs) depending on the placement policy of the macro BSs (MBSs).
We show that storing the most popular content in the MBSs is almost optimal if the SBSs are using an optimal placement policy.
Also, for the SBSs no such heuristic can be used; the optimal placement is significantly better than storing the most popular content.
Finally, we numerically verify that LOA gives the same hit probability as the joint optimal solution for the PPP model.
In the analysis of logic programs, abstract domains for detecting sharing and linearity information are widely used.
Devising abstract unification algorithms for such domains has proved to be rather hard.
At the moment, the available algorithms are correct but not optimal, i.e., they cannot fully exploit the information conveyed by the abstract domains.
In this paper, we define a new (infinite) domain ShLin-w which can be thought of as a general framework from which other domains can be easily derived by abstraction.
ShLin-w makes the interaction between sharing and linearity explicit.
We provide a constructive characterization of the optimal abstract unification operator on ShLin-w and we lift it to two well-known abstractions of ShLin-w. Namely, to the classical Sharing X Lin abstract domain and to the more precise ShLin-2 abstract domain by Andy King.
In the case of single binding substitutions, we obtain optimal abstract unification algorithms for such domains.
To appear in Theory and Practice of Logic Programming (TPLP).
Software-Defined Networks have seen an increasing in their deployment because they offer better network manageability compared to traditional networks.
Despite their immense success and popularity, various security issues in SDN remain open problems for research.
Particularly, the problem of securing the controllers in distributed environment is still short of any solutions.
This paper proposes a scheme to identify any rogue/malicious controller(s) in a distributed environment.
Our scheme is based on trust and reputation system which is centrally managed.
As such, our scheme identifies any controllers acting maliciously by comparing the state of installed flows/policies with policies that should be installed.
Controllers rate each other on this basis and report the results to a central entity, which reports it to the network administrator.
In this contribution, a direct comparison of the Offset-QAM-OFDM (OQAM-OFDM) and the Cyclic Prefix OFDM (CP-OFDM) scheme is given for an 802.11a based system.
Therefore, the chosen algorithms and choices of design are described and evaluated as a whole system in terms of bit and frame error rate (BER/FER) performance as well as spectral efficiency and complexity in the presence of multipath propagation for different modulation orders.
The results show that the OQAM-OFDM scheme exhibits similar BER and FER performance at a 24% higher spectral efficiency and achievable throughput at the cost of an up to five times increased computational complexity.
This paper focuses on numeric data, with emphasis on distinct characteristics like varying significance, unstructured format, mass volume and real-time processing.
We propose a novel, context-dependent valuation framework specifically devised to assess quality in numeric datasets.
Our framework uses eight relevant data quality dimensions, and provide a simple metric to evaluate dataset quality along each dimension.
We argue that the proposed set of dimensions and corresponding metrics adequately captures the unique quality antipatterns that are typically associated with numerical data.
The introduction of our framework is part of a wider research effort that aims at developing an articulated numerical data quality improvement approach for Oil and Gas exploration and production workflows that is based on artificial intelligence techniques.
The digitalization of the legal domain has been ongoing for a couple of years.
In that process, the application of different machine learning (ML) techniques is crucial.
Tasks such as the classification of legal documents or contract clauses as well as the translation of those are highly relevant.
On the other side, digitized documents are barely accessible in this field, particularly in Germany.
Today, deep learning (DL) is one of the hot topics with many publications and various applications.
Sometimes it provides results outperforming the human level.
Hence this technique may be feasible for the legal domain as well.
However, DL requires thousands of samples to provide decent results.
A potential solution to this problem is multi-task DL to enable transfer learning.
This approach may be able to overcome the data scarcity problem in the legal domain, specifically for the German language.
We applied the state of the art multi-task model on three tasks: translation, summarization, and multi-label classification.
The experiments were conducted on legal document corpora utilizing several task combinations as well as various model parameters.
The goal was to find the optimal configuration for the tasks at hand within the legal domain.
The multi-task DL approach outperformed the state of the art results in all three tasks.
This opens a new direction to integrate DL technology more efficiently in the legal domain.
Intelligent code completion has become an essential research task to accelerate modern software development.
To facilitate effective code completion for dynamically-typed programming languages, we apply neural language models by learning from large codebases, and develop a tailored attention mechanism for code completion.
However, standard neural language models even with attention mechanism cannot correctly predict the out-of-vocabulary (OoV) words that restrict the code completion performance.
In this paper, inspired by the prevalence of locally repeated terms in program source code, and the recently proposed pointer copy mechanism, we propose a pointer mixture network for better predicting OoV words in code completion.
Based on the context, the pointer mixture network learns to either generate a within-vocabulary word through an RNN component, or regenerate an OoV word from local context through a pointer component.
Experiments on two benchmarked datasets demonstrate the effectiveness of our attention mechanism and pointer mixture network on the code completion task.
I describe how real quantum annealers may be used to perform local (in state space) searches around specified states, rather than the global searches traditionally implemented in the quantum annealing algorithm.
The quantum annealing algorithm is an analogue of simulated annealing, a classical numerical technique which is now obsolete.
Hence, I explore strategies to use an annealer in a way which takes advantage of modern classical optimization algorithms, and additionally should be less sensitive to problem mis-specification then the traditional quantum annealing algorithm.
We constraint on computer the best linear unbiased generalized statistics of random field for the best linear unbiased generalized statistics of an unknown constant mean of random field and derive the numerical generalized least-squares estimator of an unknown constant mean of random field.
We derive the third constraint of spatial statistics and show that the classic generalized least-squares estimator of an unknown constant mean of the field is only an asymptotic disjunction of the numerical one.
Quaternion-valued wireless communication systems have been studied in the past.
Although progress has been made in this promising area, a crucial missing link is lack of effective and efficient quaternion-valued signal processing algorithms for channel equalisation and beamforming.
With most recent developments in quaternion-valued signal processing, in this work, we fill the gap to solve the problem and further derive the quaternion-valued Wiener solution for block-based calculation.
Graphs are fundamental data structures which concisely capture the relational structure in many important real-world domains, such as knowledge graphs, physical and social interactions, language, and chemistry.
Here we introduce a powerful new approach for learning generative models over graphs, which can capture both their structure and attributes.
Our approach uses graph neural networks to express probabilistic dependencies among a graph's nodes and edges, and can, in principle, learn distributions over any arbitrary graph.
In a series of experiments our results show that once trained, our models can generate good quality samples of both synthetic graphs as well as real molecular graphs, both unconditionally and conditioned on data.
Compared to baselines that do not use graph-structured representations, our models often perform far better.
We also explore key challenges of learning generative models of graphs, such as how to handle symmetries and ordering of elements during the graph generation process, and offer possible solutions.
Our work is the first and most general approach for learning generative models over arbitrary graphs, and opens new directions for moving away from restrictions of vector- and sequence-like knowledge representations, toward more expressive and flexible relational data structures.
Building self-adaptive and self-organizing (SASO) systems is a challenging problem, in part because SASO principles are not yet well understood and few platforms exist for exploring them.
Cellular automata (CA) are a well-studied approach to exploring the principles underlying self-organization.
A CA comprises a lattice of cells whose states change over time based on a discrete update function.
One challenge to developing CA is that the relationship of an update function, which describes the local behavior of each cell, to the global behavior of the entire CA is often unclear.
As a result, many researchers have used stochastic search techniques, such as evolutionary algorithms, to automatically discover update functions that produce a desired global behavior.
However, these update functions are typically defined in a way that does not provide for self-adaptation.
Here we describe an approach to discovering CA update functions that are both self-adaptive and self-organizing.
Specifically, we use a novel evolutionary algorithm-based approach to discover finite state machines (FSMs) that implement update functions for CA.
We show how this approach is able to evolve FSM-based update functions that perform well on the density classification task for 1-, 2-, and 3-dimensional CA.
Moreover, we show that these FSMs are self-adaptive, self-organizing, and highly scalable, often performing well on CA that are orders of magnitude larger than those used to evaluate performance during the evolutionary search.
These results demonstrate that CA are a viable platform for studying the integration of self-adaptation and self-organization, and strengthen the case for using evolutionary algorithms as a component of SASO systems.
Deep reinforcement learning, and especially the Asynchronous Advantage Actor-Critic algorithm, has been successfully used to achieve super-human performance in a variety of video games.
Starcraft II is a new challenge for the reinforcement learning community with the release of pysc2 learning environment proposed by Google Deepmind and Blizzard Entertainment.
Despite being a target for several AI developers, few have achieved human level performance.
In this project we explain the complexities of this environment and discuss the results from our experiments on the environment.
We have compared various architectures and have proved that transfer learning can be an effective paradigm in reinforcement learning research for complex scenarios requiring skill transfer.
Deep Neural Networks (DNNs) have advanced the state-of-the-art in a variety of machine learning tasks and are deployed in increasing numbers of products and services.
However, the computational requirements of training and evaluating large-scale DNNs are growing at a much faster pace than the capabilities of the underlying hardware platforms that they are executed upon.
In this work, we propose Dynamic Variable Effort Deep Neural Networks (DyVEDeep) to reduce the computational requirements of DNNs during inference.
Previous efforts propose specialized hardware implementations for DNNs, statically prune the network, or compress the weights.
Complementary to these approaches, DyVEDeep is a dynamic approach that exploits the heterogeneity in the inputs to DNNs to improve their compute efficiency with comparable classification accuracy.
DyVEDeep equips DNNs with dynamic effort mechanisms that, in the course of processing an input, identify how critical a group of computations are to classify the input.
DyVEDeep dynamically focuses its compute effort only on the critical computa- tions, while skipping or approximating the rest.
We propose 3 effort knobs that operate at different levels of granularity viz. neuron, feature and layer levels.
We build DyVEDeep versions for 5 popular image recognition benchmarks - one for CIFAR-10 and four for ImageNet (AlexNet, OverFeat and VGG-16, weight-compressed AlexNet).
Across all benchmarks, DyVEDeep achieves 2.1x-2.6x reduction in the number of scalar operations, which translates to 1.8x-2.3x performance improvement over a Caffe-based implementation, with < 0.5% loss in accuracy.
Application Programming Interfaces (APIs) often have usage constraints, such as restrictions on call order or call conditions.
API misuses, i.e., violations of these constraints, may lead to software crashes, bugs, and vulnerabilities.
Though researchers developed many API-misuse detectors over the last two decades, recent studies show that API misuses are still prevalent.
Therefore, we need to understand the capabilities and limitations of existing detectors in order to advance the state of the art.
In this paper, we present the first-ever qualitative and quantitative evaluation that compares static API-misuse detectors along the same dimensions, and with original author validation.
To accomplish this, we develop MUC, a classification of API misuses, and MUBenchPipe, an automated benchmark for detector comparison, on top of our misuse dataset, MUBench.
Our results show that the capabilities of existing detectors vary greatly and that existing detectors, though capable of detecting misuses, suffer from extremely low precision and recall.
A systematic root-cause analysis reveals that, most importantly, detectors need to go beyond the naive assumption that a deviation from the most-frequent usage corresponds to a misuse and need to obtain additional usage examples to train their models.
We present possible directions towards more-powerful API-misuse detectors.
Semiconstrained systems were recently suggested as a generalization of constrained systems, commonly used in communication and data-storage applications that require certain offending subsequences be avoided.
In an attempt to apply techniques from constrained systems, we study sequences of constrained systems that are contained in, or contain, a given semiconstrained system, while approaching its capacity.
In the case of contained systems we describe to such sequences resulting in constant-to-constant bit-rate block encoders and sliding-block encoders.
Surprisingly, in the case of containing systems we show that a "generic" semiconstrained system is never contained in a proper fully-constrained system.
Transfer learning using pre-trained Convolutional Neural Networks (CNNs) has been successfully applied to images for different classification tasks.
In this paper, we propose a new pipeline for pain expression recognition in neonates using transfer learning.
Specifically, we propose to exploit a pre-trained CNN that was originally trained on a relatively similar dataset for face recognition (VGG Face) as well as CNNs that were pre-trained on a relatively different dataset for image classification (iVGG F,M, and S) to extract deep features from neonates' faces.
In the final stage, several supervised machine learning classifiers are trained to classify neonates' facial expression into pain or no pain expression.
The proposed pipeline achieved, on a testing dataset, 0.841 AUC and 90.34 accuracy, which is approx.
7 higher than the accuracy of handcrafted traditional features.
We also propose to combine deep features with traditional features and hypothesize that the mixed features would improve pain classification performance.
Combining deep features with traditional features achieved 92.71 accuracy and 0.948 AUC.
These results show that transfer learning, which is a faster and more practical option than training CNN from the scratch, can be used to extract useful features for pain expression recognition in neonates.
It also shows that combining deep features with traditional handcrafted features is a good practice to improve the performance of pain expression recognition and possibly the performance of similar applications.
In this paper, we leverage the efficiency of Binarized Neural Networks (BNNs) to learn complex state transition models of planning domains with discretized factored state and action spaces.
In order to directly exploit this transition structure for planning, we present two novel compilations of the learned factored planning problem with BNNs based on reductions to Weighted Partial Maximum Boolean Satisfiability (FD-SAT-Plan+) as well as Binary Linear Programming (FD-BLP-Plan+).
Theoretically, we show that our SAT-based Bi-Directional Neuron Activation Encoding is asymptotically the most compact encoding in the literature and maintains the generalized arc-consistency property through unit propagation -- an important property that facilitates efficiency in SAT solvers.
Experimentally, we validate the computational efficiency of our Bi-Directional Neuron Activation Encoding in comparison to an existing neuron activation encoding and demonstrate the effectiveness of learning complex transition models with BNNs.
We test the runtime efficiency of both FD-SAT-Plan+ and FD-BLP-Plan+ on the learned factored planning problem showing that FD-SAT-Plan+ scales better with increasing BNN size and complexity.
Finally, we present a finite-time incremental constraint generation algorithm based on generalized landmark constraints to improve the planning accuracy of our encodings through simulated or real-world interaction.
The main aim of this paper is to discuss how the combination of Web 2.0, social media and geographic technologies can provide opportunities for learning and new forms of participation in an urban design studio.
This discussion is mainly based on our recent findings from two experimental urban design studio setups as well as former research and literature studies.
In brief, the web platform enabled us to extend the learning that took place in the design studio beyond the studio hours, to represent the design information in novel ways and allocate multiple communication forms.
We found that the student activity in the introduced web platform was related to their progress up to a certain extent.
Moreover, the students perceived the platform as a convenient medium and addressed it as a valuable resource for learning.
This study should be conceived as a continuation of a series of our Design Studio 2.0 experiments which involve the exploitation of opportunities provided by novel socio-geographic information and communication technologies for the improvement of the design learning processes.
We formalize the problem of multi-agent path finding with deadlines (MAPF-DL).
The objective is to maximize the number of agents that can reach their given goal vertices from their given start vertices within a given deadline, without colliding with each other.
We first show that the MAPF-DL problem is NP-hard to solve optimally.
We then present an optimal MAPF-DL algorithm based on a reduction of the MAPF-DL problem to a flow problem and a subsequent compact integer linear programming formulation of the resulting reduced abstracted multi-commodity flow network.
The considered problem is how to optimally allocate a set of jobs to technicians of different skills such that the number of technicians of each skill does not exceed the number of persons with that skill designation.
The key motivation is the quick sensitivity analysis in terms of the workforce size which is quite necessary in many industries in the presence of unexpected work orders.
A time-indexed mathematical model is proposed to minimize the total weighted completion time of the jobs.
The proposed model is decomposed into a number of single-skill sub-problems so that each one is a combination of a series of nested binary Knapsack problems.
A heuristic procedure is proposed to solve the problem.
Our experimental results, based on a real-world case study, reveal that the proposed method quickly produces a schedule statistically close to the optimal one while the classical optimal procedure is very time-consuming.
We introduce a new artificial intelligence (AI) approach called, the 'Digital Synaptic Neural Substrate' (DSNS).
It uses selected attributes from objects in various domains (e.g. chess problems, classical music, renowned artworks) and recombines them in such a way as to generate new attributes that can then, in principle, be used to create novel objects of creative value to humans relating to any one of the source domains.
This allows some of the burden of creative content generation to be passed from humans to machines.
The approach was tested in the domain of chess problem composition.
We used it to automatically compose numerous sets of chess problems based on attributes extracted and recombined from chess problems and tournament games by humans, renowned paintings, computer-evolved abstract art, photographs of people, and classical music tracks.
The quality of these generated chess problems was then assessed automatically using an existing and experimentally-validated computational chess aesthetics model.
They were also assessed by human experts in the domain.
The results suggest that attributes collected and recombined from chess and other domains using the DSNS approach can indeed be used to automatically generate chess problems of reasonably high aesthetic quality.
In particular, a low quality chess source (i.e. tournament game sequences between weak players) used in combination with actual photographs of people was able to produce three-move chess problems of comparable quality or better to those generated using a high quality chess source (i.e. published compositions by human experts), and more efficiently as well.
Why information from a foreign domain can be integrated and functional in this way remains an open question for now.
The DSNS approach is, in principle, scalable and applicable to any domain in which objects have attributes that can be represented using real numbers.
Content based Document Classification is one of the biggest challenges in the context of free text mining.
Current algorithms on document classifications mostly rely on cluster analysis based on bag-of-words approach.
However that method is still being applied to many modern scientific dilemmas.
It has established a strong presence in fields like economics and social science to merit serious attention from the researchers.
In this paper we would like to propose and explore an alternative grounded more securely on the dictionary classification and correlatedness of words and phrases.
It is expected that application of our existing knowledge about the underlying classification structure may lead to improvement of the classifier's performance.
Theories for visually guided action account for online control in the presence of reliable sources of visual information, and predictive control to compensate for visuomotor delay and temporary occlusion.
In this study, we characterize the temporal relationship between information integration window and prediction distance using computational models.
Subjects were immersed in a simulated environment and attempted to catch virtual balls that were transiently "blanked" during flight.
Recurrent neural networks were trained to reproduce subject's gaze and hand movements during blank.
The models successfully predict gaze behavior within 3 degrees, and hand movements within 8.5 cm as far as 500 ms in time, with integration window as short as 27 ms.
Furthermore, we quantified the contribution of each input source of information to motor output through an ablation study.
The model is a proof of concept for prediction as a discrete mapping between information integrated over time and a temporally distant motor output.
We outline a program in the area of formalization of mathematics to automate theorem proving in algebra and algebraic geometry.
We propose a construction of a dictionary between automated theorem provers and (La)TeX exploiting syntactic parsers.
We describe its application to a repository of human-written facts and definitions in algebraic geometry (The Stacks Project).
We use deep learning techniques.
We introduce a class of causal video understanding models that aims to improve efficiency of video processing by maximising throughput, minimising latency, and reducing the number of clock cycles.
Leveraging operation pipelining and multi-rate clocks, these models perform a minimal amount of computation (e.g. as few as four convolutional layers) for each frame per timestep to produce an output.
The models are still very deep, with dozens of such operations being performed but in a pipelined fashion that enables depth-parallel computation.
We illustrate the proposed principles by applying them to existing image architectures and analyse their behaviour on two video tasks: action recognition and human keypoint localisation.
The results show that a significant degree of parallelism, and implicitly speedup, can be achieved with little loss in performance.
In a voice-controlled smart-home, a controller must respond not only to user's requests but also according to the interaction context.
This paper describes Arcades, a system which uses deep reinforcement learning to extract context from a graphical representation of home automation system and to update continuously its behavior to the user's one.
This system is robust to changes in the environment (sensor breakdown or addition) through its graphical representation (scale well) and the reinforcement mechanism (adapt well).
The experiments on realistic data demonstrate that this method promises to reach long life context-aware control of smart-home.
The main thrust of the article is to provide interesting example, useful for students of using bitwise operations in the programming languages C ++ and Java.
As an example, we describe an algorithm for obtaining a Latin square of arbitrary order.
We will outline some techniques for the use of bitwise operations.
The interconnection network comprises a significant portion of the cost of large parallel computers, both in economic terms and power consumption.
Several previous proposals exploit large-radix routers to build scalable low-distance topologies with the aim of minimizing these costs.
However, they fail to consider potential unbalance in the network utilization, which in some cases results in suboptimal designs.
Based on an appropriate cost model, this paper advocates the use of networks based on incidence graphs of projective planes, broadly denoted as Projective Networks.
Projective Networks rely on highly symmetric generalized Moore graphs and encompass several proposed direct (PN and demi-PN) and indirect (OFT) topologies under a common mathematical framework.
Compared to other proposals with average distance between 2 and 3 hops, these networks provide very high scalability while preserving a balanced network utilization, resulting in low network costs.
Overall, Projective Networks constitute a competitive alternative for exascale-level interconnection network design.
The main goal of the paper is to provide Pepper with a near real-time object recognition system based on deep neural networks.
The proposed system is based on YOLO (You Only Look Once), a deep neural network that is able to detect and recognize objects robustly and at a high speed.
In addition, considering that YOLO cannot be run in the Pepper's internal computer in near real-time, we propose to use a Backpack for Pepper, which holds a Jetson TK1 card and a battery.
By using this card, Pepper is able to robustly detect and recognize objects in images of 320x320 pixels at about 5 frames per second.
We study two-receiver Poisson channels using tools derived from stochastic calculus.
We obtain a general formula for the mutual information over the Poisson channel that allows for conditioning and the use of auxiliary random variables.
We then use this formula to compute necessary and sufficient conditions under which one Poisson channel is less noisy and/or more capable than another, which turn out to be distinct from the conditions under which this ordering holds for the discretized versions of the channels.
We also use general formula to determine the capacity region of the more capable Poisson broadcast channel with independent message sets, the more capable Poisson wiretap channel, and the general two-decoder Poisson broadcast channel with degraded message sets.
Pre-operative Abdominal Aortic Aneurysm (AAA) 3D shape is critical for customized stent-graft design in Fenestrated Endovascular Aortic Repair (FEVAR).
Traditional segmentation approaches implement expert-designed feature extractors while recent deep neural networks extract features automatically with multiple non-linear modules.
Usually, a large training dataset is essential for applying deep learning on AAA segmentation.
In this paper, the AAA was segmented using U-net with a small number (two) of training subjects.
Firstly, Computed Tomography Angiography (CTA) slices were augmented with gray value variation and translation to avoid the overfitting caused by the small number of training subjects.
Then, U-net was trained to segment the AAA.
Dice Similarity Coefficients (DSCs) over 0.8 were achieved on the testing subjects.
The PLZ, DLZ and aortic branches are all reconstructed reasonably, which will facilitate stent graft customization and help shape instantiation for intra-operative surgery navigation in FEVAR.
Recent work on loglinear models in probabilistic constraint logic programming is applied to first-order probabilistic reasoning.
Probabilities are defined directly on the proofs of atomic formulae, and by marginalisation on the atomic formulae themselves.
We use Stochastic Logic Programs (SLPs) composed of labelled and unlabelled definite clauses to define the proof probabilities.
We have a conservative extension of first-order reasoning, so that, for example, there is a one-one mapping between logical and random variables.
We show how, in this framework, Inductive Logic Programming (ILP) can be used to induce the features of a loglinear model from data.
We also compare the presented framework with other approaches to first-order probabilistic reasoning.
Recommender systems in academia are not widely available.
This may be in part due to the difficulty and cost of developing and maintaining recommender systems.
Many operators of academic products such as digital libraries and reference managers avoid this effort, although a recommender system could provide significant benefits to their users.
In this paper, we introduce Mr. DLib's "Recommendations as-a-Service" (RaaS) API that allows operators of academic products to easily integrate a scientific recommender system into their products.
Mr. DLib generates recommendations for research articles but in the future, recommendations may include call for papers, grants, etc.
Operators of academic products can request recommendations from Mr. DLib and display these recommendations to their users.
Mr. DLib can be integrated in just a few hours or days; creating an equivalent recommender system from scratch would require several months for an academic operator.
Mr. DLib has been used by GESIS Sowiport and by the reference manager JabRef.
Mr. DLib is open source and its goal is to facilitate the application of, and research on, scientific recommender systems.
In this paper, we present the motivation for Mr. DLib, the architecture and details about the effectiveness.
Mr. DLib has delivered 94m recommendations over a span of two years with an average click-through rate of 0.12%.
Android is an open software platform for mobile devices with a large market share in the smartphone sector.
The openness of the system as well as its wide adoption lead to an increasing amount of malware developed for this platform.
ANANAS is an expandable and modular framework for analyzing Android applications.
It takes care of common needs for dynamic malware analysis and provides an interface for the development of plugins.
Adaptability and expandability have been main design goals during the development process.
An abstraction layer for simple user interaction and phone event simulation is also part of the framework.
It allows an analyst to script the required user simulation or phone events on demand or adjust the simulation to his needs.
Six plugins have been developed for ANANAS.
They represent well known techniques for malware analysis, such as system call hooking and network traffic analysis.
The focus clearly lies on dynamic analysis, as five of the six plugins are dynamic analysis methods.
In this paper, we bridge the gap between hyperparameter optimization and ensemble learning by performing Bayesian optimization of an ensemble with regards to its hyperparameters.
Our method consists in building a fixed-size ensemble, optimizing the configuration of one classifier of the ensemble at each iteration of the hyperparameter optimization algorithm, taking into consideration the interaction with the other models when evaluating potential performances.
We also consider the case where the ensemble is to be reconstructed at the end of the hyperparameter optimization phase, through a greedy selection over the pool of models generated during the optimization.
We study the performance of our proposed method on three different hyperparameter spaces, showing that our approach is better than both the best single model and a greedy ensemble construction over the models produced by a standard Bayesian optimization.
Reason and inference require process as well as memory skills by humans.
Neural networks are able to process tasks like image recognition (better than humans) but in memory aspects are still limited (by attention mechanism, size).
Recurrent Neural Network (RNN) and it's modified version LSTM are able to solve small memory contexts, but as context becomes larger than a threshold, it is difficult to use them.
The Solution is to use large external memory.
Still, it poses many challenges like, how to train neural networks for discrete memory representation, how to describe long term dependencies in sequential data etc.
Most prominent neural architectures for such tasks are Memory networks: inference components combined with long term memory and Neural Turing Machines: neural networks using external memory resources.
Also, additional techniques like attention mechanism, end to end gradient descent on discrete memory representation are needed to support these solutions.
Preliminary results of above neural architectures on simple algorithms (sorting, copying) and Question Answering (based on story, dialogs) application are comparable with the state of the art.
In this paper, I explain these architectures (in general), the additional techniques used and the results of their application.
In this project we propose a new approach for emotion recognition using web-based similarity (e.g. confidence, PMI and PMING).
We aim to extract basic emotions from short sentences with emotional content (e.g. news titles, tweets, captions), performing a web-based quantitative evaluation of semantic proximity between each word of the analyzed sentence and each emotion of a psychological model (e.g. Plutchik, Ekman, Lovheim).
The phases of the extraction include: text preprocessing (tokenization, stop words, filtering), search engine automated query, HTML parsing of results (i.e. scraping), estimation of semantic proximity, ranking of emotions according to proximity measures.
The main idea is that, since it is possible to generalize semantic similarity under the assumption that similar concepts co-occur in documents indexed in search engines, therefore also emotions can be generalized in the same way, through tags or terms that express them in a particular language, ranking emotions.
Training results are compared to human evaluation, then additional comparative tests on results are performed, both for the global ranking correlation (e.g. Kendall, Spearman, Pearson) both for the evaluation of the emotion linked to each single word.
Different from sentiment analysis, our approach works at a deeper level of abstraction, aiming at recognizing specific emotions and not only the positive/negative sentiment, in order to predict emotions as semantic data.
We compute the free energy of the planar monomer-dimer model.
Unlike the classical planar dimer model, an exact solution is not known in this case.
Even the computation of the low-density power series expansion requires heavy and nontrivial computations.
Despite of the exponential computational complexity, we compute almost three times more terms than were previously known.
Such an expansion provides both lower and upper bound for the free energy, and allows to obtain more accurate numerical values than previously possible.
We expect that our methods can be applied to other similar problems.
Cyber-physical systems of today are generating large volumes of time-series data.
As manual inspection of such data is not tractable, the need for learning methods to help discover logical structure in the data has increased.
We propose a logic-based framework that allows domain-specific knowledge to be embedded into formulas in a parametric logical specification over time-series data.
The key idea is to then map a time series to a surface in the parameter space of the formula.
Given this mapping, we identify the Hausdorff distance between boundaries as a natural distance metric between two time-series data under the lens of the parametric specification.
This enables embedding non-trivial domain-specific knowledge into the distance metric and then using off-the-shelf machine learning tools to label the data.
After labeling the data, we demonstrate how to extract a logical specification for each label.
Finally, we showcase our technique on real world traffic data to learn classifiers/monitors for slow-downs and traffic jams.
Flow is a new computational framework, built to support a key need triggered by the rapid growth of autonomy in ground traffic: controllers for autonomous vehicles in the presence of complex nonlinear dynamics in traffic.
Leveraging recent advances in deep Reinforcement Learning (RL), Flow enables the use of RL methods such as policy gradient for traffic control and enables benchmarking the performance of classical (including hand-designed) controllers with learned policies (control laws).
Flow integrates traffic microsimulator SUMO with deep reinforcement learning library rllab and enables the easy design of traffic tasks, including different networks configurations and vehicle dynamics.
We use Flow to develop reliable controllers for complex problems, such as controlling mixed-autonomy traffic (involving both autonomous and human-driven vehicles) in a ring road.
For this, we first show that state-of-the-art hand-designed controllers excel when in-distribution, but fail to generalize; then, we show that even simple neural network policies can solve the stabilization task across density settings and generalize to out-of-distribution settings.
Image caption generation systems are typically evaluated against reference outputs.
We show that it is possible to predict output quality without generating the captions, based on the probability assigned by the neural model to the reference captions.
Such pre-gen metrics are strongly correlated to standard evaluation metrics.
BRDF of most real world materials has two components, the surface BRDF due to the light reflecting at the surface of the material and the subsurface BRDF due to the light entering and going through many scattering events inside the material.
Each of these events modifies light's path, power, polarization state.
Computing polarized subsurface BRDF of a material requires simulating the light transport inside the material.
The transport of polarized light is modeled by the Vector Radiative Transfer Equation (VRTE), an integro-differential equation.
Computing solution to that equation is expensive.
The Discrete Ordinate Method (DOM) is a common approach to solving the VRTE.
Such solvers are very time consuming for complex uses such as BRDF computation, where one must solve VRTE for surface radiance distribution due to light incident from every direction of the hemisphere above the surface.
In this paper, we present a GPU based DOM solution of the VRTE to expedite the subsurface BRDF computation.
As in other DOM based solutions, our solution is based on Fourier expansions of the phase function and the radiance function.
This allows us to independently solve the VRTE for each order of expansion.
We take advantage of those repetitions and of the repetitions in each of the sub-steps of the solution process.
Our solver is implemented to run mainly on graphics hardware using the OpenCL library and runs up to seven times faster than its CPU equivalent, allowing the computation of subsurface BRDF in a matter of minutes.
We compute and present the subsurface BRDF lobes due to powders and paints of a few materials.
We also show the rendering of objects with the computed BRDF.
The solver is available for public use through the authors' web site.
Disjunctive Logic Programming (DLP) is a very expressive formalism: it allows for expressing every property of finite structures that is decidable in the complexity class SigmaP2 (= NP^NP).
Despite this high expressiveness, there are some simple properties, often arising in real-world applications, which cannot be encoded in a simple and natural manner.
Especially properties that require the use of arithmetic operators (like sum, times, or count) on a set or multiset of elements, which satisfy some conditions, cannot be naturally expressed in classic DLP.
To overcome this deficiency, we extend DLP by aggregate functions in a conservative way.
In particular, we avoid the introduction of constructs with disputed semantics, by requiring aggregates to be stratified.
We formally define the semantics of the extended language (called DLP^A), and illustrate how it can be profitably used for representing knowledge.
Furthermore, we analyze the computational complexity of DLP^A, showing that the addition of aggregates does not bring a higher cost in that respect.
Finally, we provide an implementation of DLP^A in DLV -- a state-of-the-art DLP system -- and report on experiments which confirm the usefulness of the proposed extension also for the efficiency of computation.
White-Fi refers to WiFi deployed in the TV white spaces.
Unlike its ISM band counterparts, White-Fi must obey requirements that protect TV reception.
As a result, optimization of citywide White-Fi networks faces the challenges of heterogeneous channel availability and link quality, over location.
The former is because, at any location, channels in use by TV networks are not available for use by White-Fi.
The latter is because the link quality achievable at a White-Fi receiver is determined by not only its link gain to its transmitter but also by its link gains to TV transmitters and its transmitter's link gains to TV receivers.
In this work, we model the medium access control (MAC) throughput of a White-Fi network.
We propose heuristic algorithms to optimize the throughput, given the described heterogeneity.
The algorithms assign power, access probability, and channels to nodes in the network, under the constraint that reception at TV receivers is not compromised.
We evaluate the efficacy of our approach over example city-wide White-Fi networks deployed over Denver and Columbus (respectively, low and high channel availability) in the USA, and compare with assignments cognizant of heterogeneity to a lesser degree, for example, akin to FCC regulations.
There has been an increasing interest in the millimeter wave (mmW) frequency regime in the design of next-generation wireless systems.
The focus of this work is on understanding mmW channel properties that have an important bearing on the feasibility of mmW systems in practice and have a significant impact on physical (PHY) layer design.
In this direction, simultaneous channel sounding measurements at 2.9, 29 and 61 GHz are performed at a number of transmit-receive location pairs in indoor office, shopping mall and outdoor environments.
Based on these measurements, this paper first studies large-scale properties such as path loss and delay spread across different carrier frequencies in these scenarios.
Towards the goal of understanding the feasibility of outdoor-to-indoor coverage, material measurements corresponding to mmW reflection and penetration are studied and significant notches in signal reception spread over a few GHz are reported.
Finally, implications of these measurements on system design are discussed and multiple solutions are proposed to overcome these impairments.
There has been significant interest in parallel graph processing recently due to the need to quickly analyze the large graphs available today.
Many graph codes have been designed for distributed memory or external memory.
However, today even the largest publicly-available real-world graph (the Hyperlink Web graph with over 3.5 billion vertices and 128 billion edges) can fit in the memory of a single commodity multicore server.
Nevertheless, most experimental work in the literature report results on much smaller graphs, and the ones that use the Hyperlink graph are done in distributed or external memory.
Therefore it is natural to ask whether we can efficiently solve a broad class of graph problems on this graph in memory.
This paper shows that theoretically-efficient parallel graph algorithms can scale to the largest publicly-available graphs using a single machine with a terabyte of RAM, processing them in minutes.
We give implementations of theoretically-efficient parallel algorithms for 13 important graph problems.
We also present the optimizations and techniques that we used in our implementations, which were crucial in enabling us to process these large graphs quickly.
We show that the running times of our implementations outperform existing state-of-the-art implementations on the largest real-world graphs.
For many of the problems that we consider, this is the first time they have been solved on graphs at this scale.
We have created a problem-based benchmark suite containing these problems that will be made publicly-available.
Difference sets and their generalisations to difference families arise from the study of designs and many other applications.
Here we give a brief survey of some of these applications, noting in particular the diverse definitions of difference families and the variations in priorities in constructions.
We propose a definition of disjoint difference families that encompasses these variations and allows a comparison of the similarities and disparities.
We then focus on two constructions of disjoint difference families arising from frequency hopping sequences and showed that they are in fact the same.
We conclude with a discussion of the notion of equivalence for frequency hopping sequences and for disjoint difference families.
Literature search is critical for any scientific research.
Different from Web or general domain search, a large portion of queries in scientific literature search are entity-set queries, that is, multiple entities of possibly different types.
Entity-set queries reflect user's need for finding documents that contain multiple entities and reveal inter-entity relationships and thus pose non-trivial challenges to existing search algorithms that model each entity separately.
However, entity-set queries are usually sparse (i.e., not so repetitive), which makes ineffective many supervised ranking models that rely heavily on associated click history.
To address these challenges, we introduce SetRank, an unsupervised ranking framework that models inter-entity relationships and captures entity type information.
Furthermore, we develop a novel unsupervised model selection algorithm, based on the technique of weighted rank aggregation, to automatically choose the parameter settings in SetRank without resorting to a labeled validation set.
We evaluate our proposed unsupervised approach using datasets from TREC Genomics Tracks and Semantic Scholar's query log.
The experiments demonstrate that SetRank significantly outperforms the baseline unsupervised models, especially on entity-set queries, and our model selection algorithm effectively chooses suitable parameter settings.
Social network platforms can use the data produced by their users to serve them better.
One of the services these platforms provide is recommendation service.
Recommendation systems can predict the future preferences of users using their past preferences.
In the recommendation systems literature there are various techniques, such as neighborhood based methods, machine-learning based methods and matrix-factorization based methods.
In this work, a set of well known methods from natural language processing domain, namely Word2Vec, is applied to recommendation systems domain.
Unlike previous works that use Word2Vec for recommendation, this work uses non-textual features, the check-ins, and it recommends venues to visit/check-in to the target users.
For the experiments, a Foursquare check-in dataset is used.
The results show that use of continuous vector space representations of items modeled by techniques of Word2Vec is promising for making recommendations.
This paper presents preliminary results of our work with a major financial company, where we try to use methods of plan recognition in order to investigate the interactions of a costumer with the company's online interface.
In this paper, we present the first steps of integrating a plan recognition algorithm in a real-world application for detecting and analyzing the interactions of a costumer.
It uses a novel approach for plan recognition from bare-bone UI data, which reasons about the plan library at the lowest recognition level in order to define the relevancy of actions in our domain, and then uses it to perform plan recognition.
We present preliminary results of inference on three different use-cases modeled by domain experts from the company, and show that this approach manages to decrease the overload of information required from an analyst to evaluate a costumer's session - whether this is a malicious or benign session, whether the intended tasks were completed, and if not - what actions are expected next.
The first cluster-based public computing for Monte Carlo simulation in Indonesia is introduced.
The system has been developed to enable public to perform Monte Carlo simulation on a parallel computer through an integrated and user friendly dynamic web interface.
The beta version, so called publicMC@BATAN, has been released and implemented for internal users at the National Nuclear Energy Agency (BATAN).
In this paper the concept and architecture of publicMC@BATAN are presented.
Training of neural networks for automated diagnosis of pigmented skin lesions is hampered by the small size and lack of diversity of available datasets of dermatoscopic images.
We tackle this problem by releasing the HAM10000 ("Human Against Machine with 10000 training images") dataset.
We collected dermatoscopic images from different populations acquired and stored by different modalities.
Given this diversity we had to apply different acquisition and cleaning methods and developed semi-automatic workflows utilizing specifically trained neural networks.
The final dataset consists of 10015 dermatoscopic images which are released as a training set for academic machine learning purposes and are publicly available through the ISIC archive.
This benchmark dataset can be used for machine learning and for comparisons with human experts.
Cases include a representative collection of all important diagnostic categories in the realm of pigmented lesions.
More than 50% of lesions have been confirmed by pathology, while the ground truth for the rest of the cases was either follow-up, expert consensus, or confirmation by in-vivo confocal microscopy.
Active learning algorithms propose which unlabeled objects should be queried for their labels to improve a predictive model the most.
We study active learners that minimize generalization bounds and uncover relationships between these bounds that lead to an improved approach to active learning.
In particular we show the relation between the bound of the state-of-the-art Maximum Mean Discrepancy (MMD) active learner, the bound of the Discrepancy, and a new and looser bound that we refer to as the Nuclear Discrepancy bound.
We motivate this bound by a probabilistic argument: we show it considers situations which are more likely to occur.
Our experiments indicate that active learning using the tightest Discrepancy bound performs the worst in terms of the squared loss.
Overall, our proposed loosest Nuclear Discrepancy generalization bound performs the best.
We confirm our probabilistic argument empirically: the other bounds focus on more pessimistic scenarios that are rarer in practice.
We conclude that tightness of bounds is not always of main importance and that active learning methods should concentrate on realistic scenarios in order to improve performance.
Split and rephrase is the task of breaking down a sentence into shorter ones that together convey the same meaning.
We extract a rich new dataset for this task by mining Wikipedia's edit history: WikiSplit contains one million naturally occurring sentence rewrites, providing sixty times more distinct split examples and a ninety times larger vocabulary than the WebSplit corpus introduced by Narayan et al.(2017) as a benchmark for this task.
Incorporating WikiSplit as training data produces a model with qualitatively better predictions that score 32 BLEU points above the prior best result on the WebSplit benchmark.
A large part of modern day communications are carried out through the medium of E-mails, especially corporate communications.
More and more people are using E-mail for personal uses too.
Companies also send notifications to their customers in E-mail.
In fact, in the Multinational business scenario E-mail is the most convenient and sought-after method of communication.
Important features of E-mail such as its speed, reliability, efficient storage options and a large number of added facilities make it highly popular among people from all sectors of business and society.
But being largely popular has its negative aspects too.
E-mails are the preferred medium for a large number of attacks over the internet.
Some of the most popular attacks over the internet include spams, and phishing mails.
Both spammers and phishers utilize E-mail services quite efficiently in spite of a large number of detection and prevention techniques already in place.
Very few methods are actually good in detection/prevention of spam/phishing related mails but they have higher false positives.
These techniques are implemented at the server and in addition to giving higher number of false positives, they add to the processing load on the server.
This paper outlines a novel approach to detect not only spam, but also scams, phishing and advertisement related mails.
In this method, we overcome the limitations of server-side detection techniques by utilizing some intelligence on the part of users.
Keywords parsing, token separation and knowledge bases are used in the background to detect almost all E-mail attacks.
The proposed methodology, if implemented, can help protect E-mail users from almost all kinds of unwanted mails with enhanced efficiency, reduced number of false positives while not increasing the load on E-mail servers.
Today's cloud networks are shared among many tenants.
Bandwidth guarantees and work conservation are two key properties to ensure predictable performance for tenant applications and high network utilization for providers.
Despite significant efforts, very little prior work can really achieve both properties simultaneously even some of them claimed so.
In this paper, we present QShare, an in-network based solution to achieve bandwidth guarantees and work conservation simultaneously.
QShare leverages weighted fair queuing on commodity switches to slice network bandwidth for tenants, and solves the challenge of queue scarcity through balanced tenant placement and dynamic tenant-queue binding.
QShare is readily implementable with existing switching chips.
We have implemented a QShare prototype and evaluated it via both testbed experiments and simulations.
Our results show that QShare ensures bandwidth guarantees while driving network utilization to over 91% even under unpredictable traffic demands.
The relationship between the complexity classes P and NP is an unsolved question in the field of theoretical computer science.
In this paper, we look at the link between the P - NP question and the "Deterministic" versus "Non Deterministic" nature of a problem, and more specifically at the temporal nature of the complexity within the NP class of problems.
Let us remind that the NP class is called the class of "Non Deterministic Polynomial" languages.
Using the meta argument that results in Mathematics should be "time independent" as they are reproducible, the paper shows that the P!=NP assertion is impossible to prove in the a-temporal framework of Mathematics.
In a previous version of the report, we use a similar argument based on randomness to show that the P = NP assertion was also impossible to prove, but this part of the paper was shown to be incorrect.
So, this version deletes it.
In fact, this paper highlights the time dependence of the complexity for any NP problem, linked to some pseudo-randomness in its heart.
Frame duplication is to duplicate a sequence of consecutive frames and insert or replace to conceal or imitate a specific event/content in the same source video.
To automatically detect the duplicated frames in a manipulated video, we propose a coarse-to-fine deep convolutional neural network framework to detect and localize the frame duplications.
We first run an I3D network to obtain the most candidate duplicated frame sequences and selected frame sequences, and then run a Siamese network with ResNet network to identify each pair of a duplicated frame and the corresponding selected frame.
We also propose a heuristic strategy to formulate the video-level score.
We then apply our inconsistency detector fine-tuned on the I3D network to distinguish duplicated frames from selected frames.
With the experimental evaluation conducted on two video datasets, we strongly demonstrate that our proposed method outperforms the current state-of-the-art methods.
Constraint Programming (CP) is a powerful declarative programming paradigm combining inference and search in order to find solutions to various type of constraint systems.
Dealing with highly disjunctive constraint systems is notoriously difficult in CP.
Apart from trying to solve each disjunct independently from each other, there is little hope and effort to succeed in constructing intermediate results combining the knowledge originating from several disjuncts.
In this paper, we propose If Then Else (ITE), a lightweight approach for implementing stratified constructive disjunction and negation on top of an existing CP solver, namely SICStus Prolog clp(FD).
Although constructive disjunction is known for more than three decades, it does not have straightforward implementations in most CP solvers.
ITE is a freely available library proposing stratified and constructive reasoning for various operators, including disjunction and negation, implication and conditional.
Our preliminary experimental results show that ITE is competitive with existing approaches that handle disjunctive constraint systems.
The analysis of algorithms mostly relies on counting classic elementary operations like additions, multiplications, comparisons, swaps etc.
This approach is often sufficient to quantify an algorithm's efficiency.
In some cases, however, features of modern processor architectures like pipelined execution and memory hierarchies have significant impact on running time and need to be taken into account to get a reliable picture.
One such example is Quicksort: It has been demonstrated experimentally that under certain conditions on the hardware the classically optimal balanced choice of the pivot as median of a sample gets harmful.
The reason lies in mispredicted branches whose rollback costs become dominating.
In this paper, we give the first precise analytical investigation of the influence of pipelining and the resulting branch mispredictions on the efficiency of (classic) Quicksort and Yaroslavskiy's dual-pivot Quicksort as implemented in Oracle's Java 7 library.
For the latter it is still not fully understood why experiments prove it 10% faster than a highly engineered implementation of a classic single-pivot version.
For different branch prediction strategies, we give precise asymptotics for the expected number of branch misses caused by the aforementioned Quicksort variants when their pivots are chosen from a sample of the input.
We conclude that the difference in branch misses is too small to explain the superiority of the dual-pivot algorithm.
Drones are driving numerous and evolving use cases, and creating transformative socio-economic benefits.
Drone operation needs wireless connectivity for communication between drones and ground control systems, among drones, and between drones and air traffic management systems.
Mobile networks are well positioned to identify, track, and control the growing fleet of drones.
The wide-area, quality, and secure connectivity provided by mobile networks can enhance the efficiency and effectiveness of drone operations beyond visual line-of-sight range.
In this article, we elaborate how the drone ecosystem can benefit from mobile technologies, summarize key capabilities required by drone applications, and analyze the service requirements on mobile networks.
We present field trial results collected in LTE-Advanced networks to gain insights into the capabilities of the current 4G+ networks for connected drones and share our vision on how 5G networks can further support diversified drone applications.
Recent works on single-image super-resolution are concentrated on improving performance through enhancing spatial encoding between convolutional layers.
In this paper, we focus on modeling the correlations between channels of convolutional features.
We present an effective deep residual network based on squeeze-and-excitation blocks (SEBlock) to reconstruct high-resolution (HR) image from low-resolution (LR) image.
SEBlock is used to adaptively recalibrate channel-wise feature mappings.
Further, short connections between each SEBlock are used to remedy information loss.
Extensive experiments show that our model can achieve the state-of-the-art performance and get finer texture details.
Indexing the Web is becoming a laborious task for search engines as the Web exponentially grows in size and distribution.
Presently, the most effective known approach to overcome this problem is the use of focused crawlers.
A focused crawler applies a proper algorithm in order to detect the pages on the Web that relate to its topic of interest.
For this purpose we proposed a custom method that uses specific HTML elements of a page to predict the topical focus of all the pages that have an unvisited link within the current page.
These recognized on-topic pages have to be sorted later based on their relevance to the main topic of the crawler for further actual downloads.
In the Treasure-Crawler, we use a hierarchical structure called the T-Graph which is an exemplary guide to assign appropriate priority score to each unvisited link.
These URLs will later be downloaded based on this priority.
This paper outlines the architectural design and embodies the implementation, test results and performance evaluation of the Treasure-Crawler system.
The Treasure-Crawler is evaluated in terms of information retrieval criteria such as recall and precision, both with values close to 0.5.
Gaining such outcome asserts the significance of the proposed approach.
Data stored in a data warehouse are inherently multidimensional, but most data-pruning techniques (such as iceberg and top-k queries) are unidimensional.
However, analysts need to issue multidimensional queries.
For example, an analyst may need to select not just the most profitable stores or--separately--the most profitable products, but simultaneous sets of stores and products fulfilling some profitability constraints.
To fill this need, we propose a new operator, the diamond dice.
Because of the interaction between dimensions, the computation of diamonds is challenging.
We present the first diamond-dicing experiments on large data sets.
Experiments show that we can compute diamond cubes over fact tables containing 100 million facts in less than 35 minutes using a standard PC.
The paper illustrates the research result of the application of semantic technology to ease the use and reuse of digital contents exposed as Linked Data on the web.
It focuses on the specific issue of explorative research for the resource selection: a context dependent semantic similarity assessment is proposed in order to compare datasets annotated through terminologies exposed as Linked Data (e.g. habitats, species).
Semantic similarity is shown as a building block technology to sift linked data resources.
From semantic similarity application, we derived a set of recommendations underlying open issues in scaling the similarity assessment up to the Web of Data.
This work proposed a novel learning objective to train a deep neural network to perform end-to-end image pixel clustering.
We applied the approach to instance segmentation, which is at the intersection of image semantic segmentation and object detection.
We utilize the most fundamental property of instance labeling -- the pairwise relationship between pixels -- as the supervision to formulate the learning objective, then apply it to train a fully convolutional network (FCN) for learning to perform pixel-wise clustering.
The resulting clusters can be used as the instance labeling directly.
To support labeling of an unlimited number of instance, we further formulate ideas from graph coloring theory into the proposed learning objective.
The evaluation on the Cityscapes dataset demonstrates strong performance and therefore proof of the concept.
Moreover, our approach won the second place in the lane detection competition of 2017 CVPR Autonomous Driving Challenge, and was the top performer without using external data.
The image biomarker standardisation initiative (IBSI) is an independent international collaboration which works towards standardising the extraction of image biomarkers from acquired imaging for the purpose of high-throughput quantitative image analysis (radiomics).
Lack of reproducibility and validation of high-throughput quantitative image analysis studies is considered to be a major challenge for the field.
Part of this challenge lies in the scantiness of consensus-based guidelines and definitions for the process of translating acquired imaging into high-throughput image biomarkers.
The IBSI therefore seeks to provide image biomarker nomenclature and definitions, benchmark data sets, and benchmark values to verify image processing and image biomarker calculations, as well as reporting guidelines, for high-throughput image analysis.
A public decision-making problem consists of a set of issues, each with multiple possible alternatives, and a set of competing agents, each with a preferred alternative for each issue.
We study adaptations of market economies to this setting, focusing on binary issues.
Issues have prices, and each agent is endowed with artificial currency that she can use to purchase probability for her preferred alternatives (we allow randomized outcomes).
We first show that when each issue has a single price that is common to all agents, market equilibria can be arbitrarily bad.
This negative result motivates a different approach.
We present a novel technique called "pairwise issue expansion", which transforms any public decision-making instance into an equivalent Fisher market, the simplest type of private goods market.
This is done by expanding each issue into many goods: one for each pair of agents who disagree on that issue.
We show that the equilibrium prices in the constructed Fisher market yield a "pairwise pricing equilibrium" in the original public decision-making problem which maximizes Nash welfare.
More broadly, pairwise issue expansion uncovers a powerful connection between the public decision-making and private goods settings; this immediately yields several interesting results about public decisions markets, and furthers the hope that we will be able to find a simple iterative voting protocol that leads to near-optimum decisions.
Growing interest in automatic speaker verification (ASV)systems has lead to significant quality improvement of spoofing attackson them.
Many research works confirm that despite the low equal er-ror rate (EER) ASV systems are still vulnerable to spoofing attacks.
Inthis work we overview different acoustic feature spaces and classifiersto determine reliable and robust countermeasures against spoofing at-tacks.
We compared several spoofing detection systems, presented so far,on the development and evaluation datasets of the Automatic SpeakerVerification Spoofing and Countermeasures (ASVspoof) Challenge 2015.Experimental results presented in this paper demonstrate that the useof magnitude and phase information combination provides a substantialinput into the efficiency of the spoofing detection systems.
Also wavelet-based features show impressive results in terms of equal error rate.
Inour overview we compare spoofing performance for systems based on dif-ferent classifiers.
Comparison results demonstrate that the linear SVMclassifier outperforms the conventional GMM approach.
However, manyresearchers inspired by the great success of deep neural networks (DNN)approaches in the automatic speech recognition, applied DNN in thespoofing detection task and obtained quite low EER for known and un-known type of spoofing attacks.
This paper reconstructs the Freebase data dumps to understand the underlying ontology behind Google's semantic search feature.
The Freebase knowledge base was a major Semantic Web and linked data technology that was acquired by Google in 2010 to support the Google Knowledge Graph, the backend for Google search results that include structured answers to queries instead of a series of links to external resources.
After its shutdown in 2016, Freebase is contained in a data dump of 1.9 billion Resource Description Format (RDF) triples.
A recomposition of the Freebase ontology will be analyzed in relation to concepts and insights from the literature on classification by Bowker and Star.
This paper will explore how the Freebase ontology is shaped by many of the forces that also shape classification systems through a deep dive into the ontology and a small correlational study.
These findings will provide a glimpse into the proprietary blackbox Knowledge Graph and what is meant by Google's mission to "organize the world's information and make it universally accessible and useful".
The distribution of impact factors has been modeled in the recent informetric literature using two-exponent law proposed by Mansilla et al.(2007).
This paper shows that two distributions widely-used in economics, namely the Dagum and Singh-Maddala models, possess several advantages over the two-exponent model.
Compared to the latter, the former give as good as or slightly better fit to data on impact factors in eight important scientific fields.
In contrast to the two-exponent model, both proposed distributions have closed-from probability density functions and cumulative distribution functions, which facilitates fitting these distributions to data and deriving their statistical properties.
We analyze the opportunistic relaying based on HARQ transmission over the block-fading channel with absence of channel state information (CSI) at the transmitter nodes.
We assume that both the source and the relay are allowed to vary their transmission rate between the HARQ transmission rounds.
We solve the problem of throughput maximization with respect to the transmission rates using double-recursive Dynamic Programming.
Simplifications are also proposed to diminish the complexity of the optimization.
The numerical results confirm that the variable-rate HARQ can increase the throughput significantly comparing to its fixed-rate counterpart.
The propagation of unreliable information is on the rise in many places around the world.
This expansion is facilitated by the rapid spread of information and anonymity granted by the Internet.
The spread of unreliable information is a wellstudied issue and it is associated with negative social impacts.
In a previous work, we have identified significant differences in the structure of news articles from reliable and unreliable sources in the US media.
Our goal in this work was to explore such differences in the Brazilian media.
We found significant features in two data sets: one with Brazilian news in Portuguese and another one with US news in English.
Our results show that features related to the writing style were prominent in both data sets and, despite the language difference, some features have a universal behavior, being significant to both US and Brazilian news articles.
Finally, we combined both data sets and used the universal features to build a machine learning classifier to predict the source type of a news article as reliable or unreliable.
We introduce autoregressive implicit quantile networks (AIQN), a fundamentally different approach to generative modeling than those commonly used, that implicitly captures the distribution using quantile regression.
AIQN is able to achieve superior perceptual quality and improvements in evaluation metrics, without incurring a loss of sample diversity.
The method can be applied to many existing models and architectures.
In this work we extend the PixelCNN model with AIQN and demonstrate results on CIFAR-10 and ImageNet using Inception score, FID, non-cherry-picked samples, and inpainting results.
We consistently observe that AIQN yields a highly stable algorithm that improves perceptual quality while maintaining a highly diverse distribution.
This is the preprint version of our paper on Advances in Engineering Software.
With several characteristics, such as large scale, diverse predictability and timeliness, the city traffic data falls in the range of definition of Big Data.
A Virtual Reality GIS based traffic analysis and visualization system is proposed as a promising and inspiring approach to manage and develop traffic big data.
In addition to the basic GIS interaction functions, the proposed system also includes some intelligent visual analysis and forecasting functions.
The passenger flow forecasting algorithm is introduced in detail.
Today dropshipping wins the Internet promptly and transformed to one of the basic tools of marketing in e-commerce.
Marketing features, mechanisms and value dropshipping in the conditions of network economy of the XXI century reveal in article.
The author carries out the comparative analysis of institutional development dropshipping in the USA, China and Russia.
We present two new large-scale datasets aimed at evaluating systems designed to comprehend a natural language query and extract its answer from a large corpus of text.
The Quasar-S dataset consists of 37000 cloze-style (fill-in-the-gap) queries constructed from definitions of software entity tags on the popular website Stack Overflow.
The posts and comments on the website serve as the background corpus for answering the cloze questions.
The Quasar-T dataset consists of 43000 open-domain trivia questions and their answers obtained from various internet sources.
ClueWeb09 serves as the background corpus for extracting these answers.
We pose these datasets as a challenge for two related subtasks of factoid Question Answering: (1) searching for relevant pieces of text that include the correct answer to a query, and (2) reading the retrieved text to answer the query.
We also describe a retrieval system for extracting relevant sentences and documents from the corpus given a query, and include these in the release for researchers wishing to only focus on (2).
We evaluate several baselines on both datasets, ranging from simple heuristics to powerful neural models, and show that these lag behind human performance by 16.4% and 32.1% for Quasar-S and -T respectively.
The datasets are available at https://github.com/bdhingra/quasar .
The objectives of cyber attacks are becoming sophisticated and the attackers are concealing their identity by disguising their characteristics to be others.
Cyber Threat Intelligence (CTI) analysis is gaining attention to generate meaningful knowledge for understanding the intention of an attacker and, eventually, to make predictions.
Developing the analysis technique requires a high volume and fine quality dataset.
However, the organizations which have useful data do not release it to the research community because they do not want to disclose threats toward them and the data assets they have.
Due to data inaccessibility, academic research tends to be biased towards the techniques for steps among each CTI process except for the analysis and production step.
In this paper, we propose the automated dataset generation system named CTIMiner.
The system collects threat data from publicly available security reports and malware repositories.
The data is stored in the structured format.
We release the source codes and the dataset to the public that includes about 628,000 records from 423 security reports published from 2008 to 2017.
Also, we present a statistical feature of the dataset and the techniques that can be developed using it.
Moreover, we demonstrate one application example of the dataset that analyzes the correlation and characteristics of incidents.
We believe our dataset promotes collaborative research of the threat information analysis to generate CTI.
Large-scale quantum computation is likely to require massive quantum error correction (QEC).
QEC codes and circuits are described via the stabilizer formalism, which represents stabilizer states by keeping track of the operators that preserve them.
Such states are obtained by stabilizer circuits (consisting of CNOT, Hadamard and Phase only) and can be represented compactly on conventional computers using Omega(n^2) bits, where n is the number of qubits.
Although techniques for the efficient simulation of stabilizer circuits have been studied extensively, techniques for efficient manipulation of stabilizer states are not currently available.
To this end, we design new algorithms for: (i) obtaining canonical generators for stabilizer states, (ii) obtaining canonical stabilizer circuits, and (iii) computing the inner product between stabilizer states.
Our inner-product algorithm takes O(n^3) time in general, but observes quadratic behavior for many practical instances relevant to QECC (e.g., GHZ states).
We prove that each n-qubit stabilizer state has exactly 4(2^n - 1) nearest-neighbor stabilizer states, and verify this claim experimentally using our algorithms.
We design techniques for representing arbitrary quantum states using stabilizer frames and generalize our algorithms to compute the inner product between two such frames.
The Internet of Things (IoTs) is an evolving new face of technology that provides state of the art services using ubiquitously connected smart objects.
These smart objects are capable of sensing, processing, collaborating, communicating the events and provide services.
The IoT is a collection of heterogeneous technologies like Sensor, RFID, Communication and nanotechnology.
These technologies enable smart objects to identify objects, collect information about their status,communicating the collected information for taking some desired actions.
Widespread adaptations of IoT based devices and services raised the ethical challenges for their users.
In this paper we highlight ethical challenges raised by IoT and discuss the solutions and methods for encouraging people to properly use these technologies according to Islamic teachings.
We consider the effects of decoding costs in energy harvesting communication systems.
In our setting, receivers, in addition to transmitters, rely solely on energy harvested from nature, and need to spend some energy in order to decode their intended packets.
We model the decoding energy as an increasing convex function of the rate of the incoming data.
In this setting, in addition to the traditional energy causality constraints at the transmitters, we have the decoding causality constraints at the receivers, where energy spent by the receiver for decoding cannot exceed its harvested energy.
We first consider the point-to-point single-user problem where the goal is to maximize the total throughput by a given deadline subject to both energy and decoding causality constraints.
We show that decoding costs at the receiver can be represented as generalized data arrivals at the transmitter, and thereby moving all system constraints to the transmitter side.
Then, we consider several multi-user settings.
We start with a two-hop network where the relay and the destination have decoding costs, and show that separable policies, where the transmitter's throughput is maximized irrespective of the relay's transmission energy profile, are optimal.
Next, we consider the multiple access channel (MAC) and the broadcast channel (BC) where the transmitters and the receivers harvest energy from nature, and characterize the maximum departure region.
In all multi-user settings considered, we decompose our problems into inner and outer problems.
We solve the inner problems by exploiting the structure of the particular model, and solve the outer problems by water-filling algorithms.
We propose a method to learn a distribution of shape trajectories from longitudinal data, i.e. the collection of individual objects repeatedly observed at multiple time-points.
The method allows to compute an average spatiotemporal trajectory of shape changes at the group level, and the individual variations of this trajectory both in terms of geometry and time dynamics.
First, we formulate a non-linear mixed-effects statistical model as the combination of a generic statistical model for manifold-valued longitudinal data, a deformation model defining shape trajectories via the action of a finite-dimensional set of diffeomorphisms with a manifold structure, and an efficient numerical scheme to compute parallel transport on this manifold.
Second, we introduce a MCMC-SAEM algorithm with a specific approach to shape sampling, an adaptive scheme for proposal variances, and a log-likelihood tempering strategy to estimate our model.
Third, we validate our algorithm on 2D simulated data, and then estimate a scenario of alteration of the shape of the hippocampus 3D brain structure during the course of Alzheimer's disease.
The method shows for instance that hippocampal atrophy progresses more quickly in female subjects, and occurs earlier in APOE4 mutation carriers.
We finally illustrate the potential of our method for classifying pathological trajectories versus normal ageing.
Convolutional neural networks (CNN) for medical imaging are constrained by the number of annotated data required in the training stage.
Usually, manual annotation is considered to be the "gold standard".
However, medical imaging datasets that include expert manual segmentation are scarce as this step is time-consuming, and therefore expensive.
Moreover, single-rater manual annotation is most often used in data-driven approaches making the network optimal with respect to only that single expert.
In this work, we propose a CNN for brain extraction in magnetic resonance (MR) imaging, that is fully trained with what we refer to as silver standard masks.
Our method consists of 1) developing a dataset with "silver standard" masks as input, and implementing both 2) a tri-planar method using parallel 2D U-Net-based CNNs (referred to as CONSNet) and 3) an auto-context implementation of CONSNet.
The term CONSNet refers to our integrated approach, i.e., training with silver standard masks and using a 2D U-Net-based architecture.
Our results showed that we outperformed (i.e., larger Dice coefficients) the current state-of-the-art SS methods.
Our use of silver standard masks reduced the cost of manual annotation, decreased inter-intra-rater variability, and avoided CNN segmentation super-specialization towards one specific manual annotation guideline that can occur when gold standard masks are used.
Moreover, the usage of silver standard masks greatly enlarges the volume of input annotated data because we can relatively easily generate labels for unlabeled data.
In addition, our method has the advantage that, once trained, it takes only a few seconds to process a typical brain image volume using modern hardware, such as a high-end graphics processing unit.
In contrast, many of the other competitive methods have processing times in the order of minutes.
A wide array of dynamic bandwidth allocation (DBA) mechanisms have recently been proposed for improving bandwidth utilization and reducing idle times and packets delays in passive optical networks (PONs).
The DBA evaluation studies commonly assumed that the report message for communicating the bandwidth demands of the distributed optical network units (ONUs) to the central optical line terminal (OLT) is scheduled for the end of an ONU's upstream transmission, after the ONU's payload data transmissions.
In this article, we conduct a detailed investigation of the impact of the report message scheduling (RMS), either at the beginning (i.e., before the pay load data) or the end of an ONU upstream transmission on PON performance.
We analytically characterize the reduction in channel idle time with reporting at the beginning of an upstream transmission compared to reporting at the end.
Our extensive simulation experiments consider both the Ethernet Passive Optical Networking (EPON) standard and the Gigabit PON (GPON) standard.
We find that for DBAs with offline sizing and scheduling of ONU upstream transmission grants at the end of a polling cycle, which processes requests from all ONUs, reporting at the beginning gives substantial reductions of mean packet delay at high loads.
For high-performing DBAs with online grant sizing and scheduling, which immediately processes individual ONU requests, or interleaving of ONUs groups, both reporting at the beginning or end give essentially the same average packet delays.
Real-world image recognition is often challenged by the variability of visual styles including object textures, lighting conditions, filter effects, etc.
Although these variations have been deemed to be implicitly handled by more training data and deeper networks, recent advances in image style transfer suggest that it is also possible to explicitly manipulate the style information.
Extending this idea to general visual recognition problems, we present Batch-Instance Normalization (BIN) to explicitly normalize unnecessary styles from images.
Considering certain style features play an essential role in discriminative tasks, BIN learns to selectively normalize only disturbing styles while preserving useful styles.
The proposed normalization module is easily incorporated into existing network architectures such as Residual Networks, and surprisingly improves the recognition performance in various scenarios.
Furthermore, experiments verify that BIN effectively adapts to completely different tasks like object classification and style transfer, by controlling the trade-off between preserving and removing style variations.
BIN can be implemented with only a few lines of code using popular deep learning frameworks.
Understanding and interacting with everyday physical scenes requires rich knowledge about the structure of the world, represented either implicitly in a value or policy function, or explicitly in a transition model.
Here we introduce a new class of learnable models--based on graph networks--which implement an inductive bias for object- and relation-centric representations of complex, dynamical systems.
Our results show that as a forward model, our approach supports accurate predictions from real and simulated data, and surprisingly strong and efficient generalization, across eight distinct physical systems which we varied parametrically and structurally.
We also found that our inference model can perform system identification.
Our models are also differentiable, and support online planning via gradient-based trajectory optimization, as well as offline policy optimization.
Our framework offers new opportunities for harnessing and exploiting rich knowledge about the world, and takes a key step toward building machines with more human-like representations of the world.
With the availability of vast amounts of user visitation history on location-based social networks (LBSN), the problem of Point-of-Interest (POI) prediction has been extensively studied.
However, much of the research has been conducted solely on voluntary checkin datasets collected from social apps such as Foursquare or Yelp.
While these data contain rich information about recreational activities (e.g., restaurants, nightlife, and entertainment), information about more prosaic aspects of people's lives is sparse.
This not only limits our understanding of users' daily routines, but more importantly the modeling assumptions developed based on characteristics of recreation-based data may not be suitable for richer check-in data.
In this work, we present an analysis of education "check-in" data using WiFi access logs collected at Purdue University.
We propose a heterogeneous graph-based method to encode the correlations between users, POIs, and activities, and then jointly learn embeddings for the vertices.
We evaluate our method compared to previous state-of-the-art POI prediction methods, and show that the assumptions made by previous methods significantly degrade performance on our data with dense(r) activity signals.
We also show how our learned embeddings could be used to identify similar students (e.g., for friend suggestions).
This work investigates into cost behaviors of binary classification measures in a background of class-imbalanced problems.
Twelve performance measures are studied, such as F measure, G-means in terms of accuracy rates, and of recall and precision, balance error rate (BER), Matthews correlation coefficient (MCC), Kappa coefficient, etc.
A new perspective is presented for those measures by revealing their cost functions with respect to the class imbalance ratio.
Basically, they are described by four types of cost functions.
The functions provides a theoretical understanding why some measures are suitable for dealing with class-imbalanced problems.
Based on their cost functions, we are able to conclude that G-means of accuracy rates and BER are suitable measures because they show "proper" cost behaviors in terms of "a misclassification from a small class will cause a greater cost than that from a large class".
On the contrary, F1 measure, G-means of recall and precision, MCC and Kappa coefficient measures do not produce such behaviors so that they are unsuitable to serve our goal in dealing with the problems properly.
We consider how fair treatment in society for people with disabilities might be impacted by the rise in the use of artificial intelligence, and especially machine learning methods.
We argue that fairness for people with disabilities is different to fairness for other protected attributes such as age, gender or race.
One major difference is the extreme diversity of ways disabilities manifest, and people adapt.
Secondly, disability information is highly sensitive and not always shared, precisely because of the potential for discrimination.
Given these differences, we explore definitions of fairness and how well they work in the disability space.
Finally, we suggest ways of approaching fairness for people with disabilities in AI applications.
Hybrid driving-stepping locomotion is an effective approach for navigating in a variety of environments.
Long, sufficiently even distances can be quickly covered by driving while obstacles can be overcome by stepping.
Our quadruped robot Momaro, with steerable pairs of wheels located at the end of each of its compliant legs, allows such locomotion.
Planning respective paths attracted only little attention so far.
We propose a navigation planning method which generates hybrid locomotion paths.
The planner chooses driving mode whenever possible and takes into account the detailed robot footprint.
If steps are required, the planner includes those.
To accelerate planning, steps are planned first as abstract manoeuvres and are expanded afterwards into detailed motion sequences.
Our method ensures at all times that the robot stays stable.
Experiments show that the proposed planner is capable of providing paths in feasible time, even for challenging terrain.
This summary of the doctoral thesis is created to emphasize the close connection of the proposed spectral analysis method with the Discrete Fourier Transform (DFT), the most extensively studied and frequently used approach in the history of signal processing.
It is shown that in a typical application case, where uniform data readings are transformed to the same number of uniformly spaced frequencies, the results of the classical DFT and proposed approach coincide.
The difference in performance appears when the length of the DFT is selected to be greater than the length of the data.
The DFT solves the unknown data problem by padding readings with zeros up to the length of the DFT, while the proposed Extended DFT (EDFT) deals with this situation in a different way, it uses the Fourier integral transform as a target and optimizes the transform basis in the extended frequency range without putting such restrictions on the time domain.
Consequently, the Inverse DFT (IDFT) applied to the result of EDFT returns not only known readings, but also the extrapolated data, where classical DFT is able to give back just zeros, and higher resolution are achieved at frequencies where the data has been successfully extended.
It has been demonstrated that EDFT able to process data with missing readings or gaps inside or even nonuniformly distributed data.
Thus, EDFT significantly extends the usability of the DFT-based methods, where previously these approaches have been considered as not applicable.
The EDFT founds the solution in an iterative way and requires repeated calculations to get the adaptive basis, and this makes it numerical complexity much higher compared to DFT.
This disadvantage was a serious problem in the 1990s, when the method has been proposed.
Fortunately, since then the power of computers has increased so much that nowadays EDFT application could be considered as a real alternative.
The problem of visual tracking evaluation is sporting a large variety of performance measures, and largely suffers from lack of consensus about which measures should be used in experiments.
This makes the cross-paper tracker comparison difficult.
Furthermore, as some measures may be less effective than others, the tracking results may be skewed or biased towards particular tracking aspects.
In this paper we revisit the popular performance measures and tracker performance visualizations and analyze them theoretically and experimentally.
We show that several measures are equivalent from the point of information they provide for tracker comparison and, crucially, that some are more brittle than the others.
Based on our analysis we narrow down the set of potential measures to only two complementary ones, describing accuracy and robustness, thus pushing towards homogenization of the tracker evaluation methodology.
These two measures can be intuitively interpreted and visualized and have been employed by the recent Visual Object Tracking (VOT) challenges as the foundation for the evaluation methodology.
Phase retrieval refers to recovering a signal from its Fourier magnitude.
This problem arises naturally in many scientific applications, such as ultra-short laser pulse characterization and diffraction imaging.
Unfortunately, phase retrieval is ill-posed for almost all one-dimensional signals.
In order to characterize a laser pulse and overcome the ill-posedness, it is common to use a technique called Frequency-Resolved Optical Gating (FROG).
In FROG, the measured data, referred to as FROG trace, is the Fourier magnitude of the product of the underlying signal with several translated versions of itself.
The FROG trace results in a system of phaseless quartic Fourier measurements.
In this paper, we prove that it suffices to consider only three translations of the signal to determine almost all bandlimited signals, up to trivial ambiguities.
In practice, one usually also has access to the signal's Fourier magnitude.
We show that in this case only two translations suffice.
Our results significantly improve upon earlier work.
In this paper, we present a simple and efficient method for training deep neural networks in a semi-supervised setting where only a small portion of training data is labeled.
We introduce self-ensembling, where we form a consensus prediction of the unknown labels using the outputs of the network-in-training on different epochs, and most importantly, under different regularization and input augmentation conditions.
This ensemble prediction can be expected to be a better predictor for the unknown labels than the output of the network at the most recent training epoch, and can thus be used as a target for training.
Using our method, we set new records for two standard semi-supervised learning benchmarks, reducing the (non-augmented) classification error rate from 18.44% to 7.05% in SVHN with 500 labels and from 18.63% to 16.55% in CIFAR-10 with 4000 labels, and further to 5.12% and 12.16% by enabling the standard augmentations.
We additionally obtain a clear improvement in CIFAR-100 classification accuracy by using random images from the Tiny Images dataset as unlabeled extra inputs during training.
Finally, we demonstrate good tolerance to incorrect labels.
Agile software development (ASD) methods were introduced as a reaction to traditional software development methods.
Principles of these methods are different from traditional methods and so there are some different processes and activities in agile methods comparing to traditional methods.
Thus ASD methods require different measurement practices comparing to traditional methods.
Agile teams often do their projects in the simplest and most effective way so, measurement practices in agile methods are more important than traditional methods, because lack of appropriate and effective measurement practices, will increase risk of project.
The aims of this paper are investigation on current measurement practices in ASD methods, collecting them together in one study and also reviewing agile version of Common Software Measurement International Consortium (COSMIC) publication.
Smoothed analysis is a new way of analyzing algorithms introduced by Spielman and Teng (J. ACM, 2004).
Classical methods like worst-case or average-case analysis have accompanying complexity classes, like P and AvgP, respectively.
While worst-case or average-case analysis give us a means to talk about the running time of a particular algorithm, complexity classes allows us to talk about the inherent difficulty of problems.
Smoothed analysis is a hybrid of worst-case and average-case analysis and compensates some of their drawbacks.
Despite its success for the analysis of single algorithms and problems, there is no embedding of smoothed analysis into computational complexity theory, which is necessary to classify problems according to their intrinsic difficulty.
We propose a framework for smoothed complexity theory, define the relevant classes, and prove some first hardness results (of bounded halting and tiling) and tractability results (binary optimization problems, graph coloring, satisfiability).
Furthermore, we discuss extensions and shortcomings of our model and relate it to semi-random models.
Making the right decision in traffic is a challenging task that is highly dependent on individual preferences as well as the surrounding environment.
Therefore it is hard to model solely based on expert knowledge.
In this work we use Deep Reinforcement Learning to learn maneuver decisions based on a compact semantic state representation.
This ensures a consistent model of the environment across scenarios as well as a behavior adaptation function, enabling on-line changes of desired behaviors without re-training.
The input for the neural network is a simulated object list similar to that of Radar or Lidar sensors, superimposed by a relational semantic scene description.
The state as well as the reward are extended by a behavior adaptation function and a parameterization respectively.
With little expert knowledge and a set of mid-level actions, it can be seen that the agent is capable to adhere to traffic rules and learns to drive safely in a variety of situations.
A growing issue in the modern cyberspace world is the direct identification of malicious activity over network connections.
The boom of the machine learning industry in the past few years has led to the increasing usage of machine learning technologies, which are especially prevalent in the network intrusion detection research community.
When utilizing these fairly contemporary techniques, the community has realized that datasets are pivotal for identifying malicious packets and connections, particularly ones associated with information concerning labeling in order to construct learning models.
However, there exists a shortage of publicly available, relevant datasets to researchers in the network intrusion detection community.
Thus, in this paper, we introduce a method to construct labeled flow data by combining the packet meta-information with IDS logs to infer labels for intrusion detection research.
Specifically, we designed a NetFlow-compatible format due to the capability of a a large body of network devices, such as routers and switches, to export NetFlow records from raw traffic.
In doing so, the introduced method at hand would aid researchers to access relevant network flow datasets along with label information.
Tensegrity mechanisms are composed of rigid and tensile parts that are in equilibrium.
They are interesting alternative designs for some applications, such as modelling musculo-skeleton systems.
Tensegrity mechanisms are more difficult to analyze than classical mechanisms as the static equilibrium conditions that must be satisfied generally result in complex equations.
A class of planar one-degree-of-freedom tensegrity mechanisms with three linear springs is analyzed in detail for the sake of systematic solution classifications.
The kinetostatic equations are derived and solved under several loading and geometric conditions.
It is shown that these mechanisms exhibit up to six equilibrium configurations, of which one or two are stable, depending on the geometric and loading conditions.
Discriminant varieties and cylindrical algebraic decomposition combined with Groebner base elimination are used to classify solutions as a function of the geometric, loading and actuator input parameters.
We present FoamGrid, a new implementation of the DUNE grid interface.
FoamGrid implements one- and two-dimensional grids in a physical space of arbitrary dimension, which allows for grids for curved domains.
Even more, the grids are not expected to have a manifold structure, i.e., more than two elements can share a common facet.
This makes FoamGrid the grid data structure of choice for simulating structures such as foams, discrete fracture networks, or network flow problems.
FoamGrid implements adaptive non-conforming refinement with element parametrizations.
As an additional feature it allows removal and addition of elements in an existing grid, which makes FoamGrid suitable for network growth problems.
We show how to use FoamGrid, with particular attention to the extensions of the grid interface needed to handle non-manifold topology and grid growth.
Three numerical examples demonstrate the possibilities offered by FoamGrid.
This article describes their biopolitical implications for design from psychological, cultural, legal, functional and aesthetic/perceptive ways, in the framework of Hyperconnectivity: the condition according to which person-to-person, person-to-machine and machine-to-machine communication progressively shift to networked and digital means.
A definition is given for the terms of "interface biopolitics" and "data biopolitics", as well as evidence supporting these definitions and a description of the technological, theoretical and practice-based innovations bringing them into meaningful existence.
Interfaces, algorithms, artificial intelligences of various types, the tendency in quantified self and the concept of "information bubbles" will be examined in terms of interface and data biopolitics, from the point of view of design, and for their implications in terms of freedoms, transparency, justice and accessibility to human rights.
A working hypothesis is described for technologically relevant design practices and education processes, in order to confront with these issues in critical, ethical and inclusive ways.
Finite volume methods (FVMs) constitute a popular class of methods for the numerical simulation of fluid flows.
Among the various components of these methods, the discretisation of the gradient operator has received less attention despite its fundamental importance with regards to the accuracy of the FVM.
The most popular gradient schemes are the divergence theorem (DT) (or Green-Gauss) scheme, and the least-squares (LS) scheme.
Both are widely believed to be second-order accurate, but the present study shows that in fact the common variant of the DT gradient is second-order accurate only on structured meshes whereas it is zeroth-order accurate on general unstructured meshes, and the LS gradient is second-order and first-order accurate, respectively.
This is explained through a theoretical analysis and is confirmed by numerical tests.
The schemes are then used within a FVM to solve a simple diffusion equation on unstructured grids generated by several methods; the results reveal that the zeroth-order accuracy of the DT gradient is inherited by the FVM as a whole, and the discretisation error does not decrease with grid refinement.
On the other hand, use of the LS gradient leads to second-order accurate results, as does the use of alternative, consistent, DT gradient schemes, including a new iterative scheme that makes the common DT gradient consistent at almost no extra cost.
The numerical tests are performed using both an in-house code and the popular public domain PDE solver OpenFOAM.
Recently, the hybrid convolutional neural network hidden Markov model (CNN-HMM) has been introduced for offline handwritten Chinese text recognition (HCTR) and has achieved state-of-the-art performance.
In a CNN-HMM system, a handwritten text line is modeled by a series of cascading HMMs, each representing one character, and the posterior distributions of HMM states are calculated by CNN.
However, modeling each of the large vocabulary of Chinese characters with a uniform and fixed number of hidden states requires high memory and computational costs and makes the tens of thousands of HMM state classes confusing.
Another key issue of CNN-HMM for HCTR is the diversified writing style, which leads to model strain and a significant performance decline for specific writers.
To address these issues, we propose a writer-aware CNN based on parsimonious HMM (WCNN-PHMM).
Validated on the ICDAR 2013 competition of CASIA-HWDB database, the more compact WCNN-PHMM of a 7360-class vocabulary can achieve a relative character error rate (CER) reduction of 16.6% over the conventional CNN-HMM without considering language modeling.
Moreover, the state-tying results of PHMM explicitly show the information sharing among similar characters and the confusion reduction of tied state classes.
Finally, we visualize the learned writer codes and demonstrate the strong relationship with the writing styles of different writers.
To the best of our knowledge, WCNN-PHMM yields the best results on the ICDAR 2013 competition set, demonstrating its power when enlarging the size of the character vocabulary.
A survey of dictionary models and formats is presented as well as a presentation of corresponding recent standardisation activities.
This Ontologies are widely used as a means for solving the information heterogeneity problems on the web because of their capability to provide explicit meaning to the information.
They become an efficient tool for knowledge representation in a structured manner.
There is always more than one ontology for the same domain.
Furthermore, there is no standard method for building ontologies, and there are many ontology building tools using different ontology languages.
Because of these reasons, interoperability between the ontologies is very low.
Current ontology tools mostly use functions to build, edit and inference the ontology.
Methods for merging heterogeneous domain ontologies are not included in most tools.
This paper presents ontology merging methodology for building a single global ontology from heterogeneous eXtensible Markup Language (XML) data sources to capture and maintain all the knowledge which XML data sources can contain
We introduce a novel, simple convolution neural network (CNN) architecture - multi-group norm constraint CNN (MGNC-CNN) that capitalizes on multiple sets of word embeddings for sentence classification.
MGNC-CNN extracts features from input embedding sets independently and then joins these at the penultimate layer in the network to form a final feature vector.
We then adopt a group regularization strategy that differentially penalizes weights associated with the subcomponents generated from the respective embedding sets.
This model is much simpler than comparable alternative architectures and requires substantially less training time.
Furthermore, it is flexible in that it does not require input word embeddings to be of the same dimensionality.
We show that MGNC-CNN consistently outperforms baseline models.
The rapid development of Internet of Things (IoT) technology, which is an inter connection of networks through an insecure public channel i.e.
Internet demands for authenticating the remote user trying to access the secure network resources.
In 2013, Ankita et al. proposed an improved three factor remote user authentication scheme.
In this poster we will show that Ankita et al scheme is vulnerable to known session specific temporary information attack, on successfully performing the attack, the adversary can perform all other major cryptographic attacks.
As a part of our contribution, we will propose an improved scheme which is resistance to all major cryptographic attacks and overcomes the defects in Ankita et al. scheme.
Human-swarm interaction (HSI) involves a number of human factors impacting human behaviour throughout the interaction.
As the technologies used within HSI advance, it is more tempting to increase the level of swarm autonomy within the interaction to reduce the workload on humans.
Yet, the prospective negative effects of high levels of autonomy on human situational awareness can hinder this process.
Flexible autonomy aims at trading-off these effects by changing the level of autonomy within the interaction when required; with mixed-initiatives combining human preferences and automation's recommendations to select an appropriate level of autonomy at a certain point of time.
However, the effective implementation of mixed-initiative systems raises fundamental questions on how to combine human preferences and automation recommendations, how to realise the selected level of autonomy, and what the future impacts on the cognitive states of a human are.
We explore open challenges that hamper the process of developing effective flexible autonomy.
We then highlight the potential benefits of using system modelling techniques in HSI by illustrating how they provide HSI designers with an opportunity to evaluate different strategies for assessing the state of the mission and for adapting the level of autonomy within the interaction to maximise mission success metrics.
We present Wasserstein introspective neural networks (WINN) that are both a generator and a discriminator within a single model.
WINN provides a significant improvement over the recent introspective neural networks (INN) method by enhancing INN's generative modeling capability.
WINN has three interesting properties: (1) A mathematical connection between the formulation of the INN algorithm and that of Wasserstein generative adversarial networks (WGAN) is made.
(2) The explicit adoption of the Wasserstein distance into INN results in a large enhancement to INN, achieving compelling results even with a single classifier --- e.g., providing nearly a 20 times reduction in model size over INN for unsupervised generative modeling.
(3) When applied to supervised classification, WINN also gives rise to improved robustness against adversarial examples in terms of the error reduction.
In the experiments, we report encouraging results on unsupervised learning problems including texture, face, and object modeling, as well as a supervised classification task against adversarial attacks.
We propose to consider ensembles of cycles (quadrics), which are interconnected through conformal-invariant geometric relations (e.g."to be orthogonal", "to be tangent", etc.), as new objects in an extended Moebius--Lie geometry.
It was recently demonstrated in several related papers, that such ensembles of cycles naturally parameterise many other conformally-invariant objects, e.g. loxodromes or continued fractions.
The paper describes a method, which reduces a collection of conformally invariant geometric relations to a system of linear equations, which may be accompanied by one fixed quadratic relation.
To show its usefulness, the method is implemented as a C++ library.
It operates with numeric and symbolic data of cycles in spaces of arbitrary dimensionality and metrics with any signatures.
Numeric calculations can be done in exact or approximate arithmetic.
In the two- and three-dimensional cases illustrations and animations can be produced.
An interactive Python wrapper of the library is provided as well.
With the rapid development of information technology and multimedia, the use of digital data is increasing day by day.
So it becomes very essential to protect multimedia information from piracy and also it is challenging.
A great deal of Copyright owners is worried about protecting any kind of illegal repetition of their information.
Hence, facing all these kinds of problems development of the techniques is very important.
Digital watermarking considered as a solution to prevent the multimedia data.
In this paper, an idea of watermarking is proposed and implemented.
In proposed watermarking method, the original image is rearranged using zigzag sequence and DWT is applied on rearranged image.
Then DCT and SVD are applied on all high bands LH, HL and HH.
Watermark is then embedded by modifying the singular values of these bands.
Extraction of watermark is performed by the inversion of watermark embedding process.
For choosing of these three bands it gives facility of mid-band and pure high band that ensures good imperceptibility and more robustness against different kinds of attacks.
Scenarios for the emergence or bootstrap of a lexicon involve the repeated interaction between at least two agents who must reach a consensus on how to name N objects using H words.
Here we consider minimal models of two types of learning algorithms: cross-situational learning, in which the individuals determine the meaning of a word by looking for something in common across all observed uses of that word, and supervised operant conditioning learning, in which there is strong feedback between individuals about the intended meaning of the words.
Despite the stark differences between these learning schemes, we show that they yield the same communication accuracy in the realistic limits of large N and H, which coincides with the result of the classical occupancy problem of randomly assigning N objects to H words.
The matching function for the problem of stereo reconstruction or optical flow has been traditionally designed as a function of the distance between the features describing matched pixels.
This approach works under assumption, that the appearance of pixels in two stereo cameras or in two consecutive video frames does not change dramatically.
However, this might not be the case, if we try to match pixels over a large interval of time.
In this paper we propose a method, which learns the matching function, that automatically finds the space of allowed changes in visual appearance, such as due to the motion blur, chromatic distortions, different colour calibration or seasonal changes.
Furthermore, it automatically learns the importance of matching scores of contextual features at different relative locations and scales.
Proposed classifier gives reliable estimations of pixel disparities already without any form of regularization.
We evaluated our method on two standard problems - stereo matching on KITTI outdoor dataset, optical flow on Sintel data set, and on newly introduced TimeLapse change detection dataset.
Our algorithm obtained very promising results comparable to the state-of-the-art.
This paper presents a novel decentralized control strategy for a multi-robot system that enables parallel multi-target exploration while ensuring a time-varying connected topology in cluttered 3D environments.
Flexible continuous connectivity is guaranteed by building upon a recent connectivity maintenance method, in which limited range, line-of-sight visibility, and collision avoidance are taken into account at the same time.
Completeness of the decentralized multi-target exploration algorithm is guaranteed by dynamically assigning the robots with different motion behaviors during the exploration task.
One major group is subject to a suitable downscaling of the main traveling force based on the traveling efficiency of the current leader and the direction alignment between traveling and connectivity force.
This supports the leader in always reaching its current target and, on a larger time horizon, that the whole team realizes the overall task in finite time.
Extensive Monte~Carlo simulations with a group of several quadrotor UAVs show the scalability and effectiveness of the proposed method and experiments validate its practicability.
Volume of text based documents have been increasing day by day.
Medical documents are located within this growing text documents.
In this study, the techniques used for text classification applied on medical documents and evaluated classification performance.
Used data sets are multi class and multi labelled.
Chi Square (CHI) technique was used for feature selection also SMO, NB, C4.5, RF and KNN algorithms was used for classification.
The aim of this study, success of various classifiers is evaluated on multi class and multi label data sets consisting of medical documents.
The first 400 features, while the most successful in the KNN classifier, feature number 400 and after the SMO has become the most successful classifier.
In this work, we consider a two-level hierarchical MIMO antenna array system, where each antenna of the upper level is made up of a subarray on the lower one.
The concept of spatial multiplexing is applied twice in this situation: Firstly, the spatial multiplexing of a Line-of-Sight (LoS) MIMO system is exploited.
It is based on appropriate (sub-)array distances and achieves multiplexing gain due to phase differences among the signals at the receive (sub-)arrays.
Secondly, one or more additional reflected paths of different angles (separated from the LoS path by different spatial beams at the subarrays) are used to exploit spatial multiplexing between paths.
By exploiting the above two multiplexing kinds simultaneously, a high dimensional system with maximum spatial multiplexing is proposed by jointly using 'phase differences' within paths and 'angular differences' between paths.
The system includes an advanced hybrid beamforming architecture with large subarray separation, which could occur in millimeter wave backhaul scenarios.
The possible gains of the system w.r.t. a pure LOS MIMO system are illustrated by evaluating the capacities with total transmit power constraints.
Mining social media messages for health and drug related information has received significant interest in pharmacovigilance research.
Social media sites (e.g., Twitter), have been used for monitoring drug abuse, adverse reactions of drug usage and analyzing expression of sentiments related to drugs.
Most of these studies are based on aggregated results from a large population rather than specific sets of individuals.
In order to conduct studies at an individual level or specific cohorts, identifying posts mentioning intake of medicine by the user is necessary.
Towards this objective, we train different deep neural network classification models on a publicly available annotated dataset and study their performances on identifying mentions of personal intake of medicine in tweets.
We also design and train a new architecture of a stacked ensemble of shallow convolutional neural network (CNN) ensembles.
We use random search for tuning the hyperparameters of the models and share the details of the values taken by the hyperparameters for the best learnt model in different deep neural network architectures.
Our system produces state-of-the-art results, with a micro- averaged F-score of 0.693.
We present a technique that uses images, videos and sensor data taken from first-person point-of-view devices to perform egocentric field-of-view (FOV) localization.
We define egocentric FOV localization as capturing the visual information from a person's field-of-view in a given environment and transferring this information onto a reference corpus of images and videos of the same space, hence determining what a person is attending to.
Our method matches images and video taken from the first-person perspective with the reference corpus and refines the results using the first-person's head orientation information obtained using the device sensors.
We demonstrate single and multi-user egocentric FOV localization in different indoor and outdoor environments with applications in augmented reality, event understanding and studying social interactions.
Message Passing Interface (MPI) is the most commonly used paradigm in writing parallel programs since it can be employed not only within a single processing node but also across several connected ones.
Data flow analysis concepts, techniques and tools are needed to understand and analyze MPI-based programs to detect bugs arise in these programs.
In this paper we propose two automated techniques to analyze and debug MPI-based programs source codes.
Kernel methods have produced state-of-the-art results for a number of NLP tasks such as relation extraction, but suffer from poor scalability due to the high cost of computing kernel similarities between natural language structures.
A recently proposed technique, kernelized locality-sensitive hashing (KLSH), can significantly reduce the computational cost, but is only applicable to classifiers operating on kNN graphs.
Here we propose to use random subspaces of KLSH codes for efficiently constructing an explicit representation of NLP structures suitable for general classification methods.
Further, we propose an approach for optimizing the KLSH model for classification problems by maximizing an approximation of mutual information between the KLSH codes (feature vectors) and the class labels.
We evaluate the proposed approach on biomedical relation extraction datasets, and observe significant and robust improvements in accuracy w.r.t. state-of-the-art classifiers, along with drastic (orders-of-magnitude) speedup compared to conventional kernel methods.
Recognising persons in everyday photos presents major challenges (occluded faces, different clothing, locations, etc.) for machine vision.
We propose a convnet based person recognition system on which we provide an in-depth analysis of informativeness of different body cues, impact of training data, and the common failure modes of the system.
In addition, we discuss the limitations of existing benchmarks and propose more challenging ones.
Our method is simple and is built on open source and open data, yet it improves the state of the art results on a large dataset of social media photos (PIPA).
In order to make a proper reaction to the collected information from internet of things (IoT) devices, location information of things should be available at the data center.
One challenge for the massive IoT networks is to identify the location map of whole sensor nodes from partially observed distance information.
In this paper, we propose a matrix completion based localization algorithm to reconstruct the location map of sensors using partially observed distance information.
From the numerical experiments, we show that the proposed method based on the modified conjugate gradient is effective in recovering the Euclidean distance matrix.
Dozens of new models on fixation prediction are published every year and compared on open benchmarks such as MIT300 and LSUN.
However, progress in the field can be difficult to judge because models are compared using a variety of inconsistent metrics.
Here we show that no single saliency map can perform well under all metrics.
Instead, we propose a principled approach to solve the benchmarking problem by separating the notions of saliency models, maps and metrics.
Inspired by Bayesian decision theory, we define a saliency model to be a probabilistic model of fixation density prediction and a saliency map to be a metric-specific prediction derived from the model density which maximizes the expected performance on that metric given the model density.
We derive these optimal saliency maps for the most commonly used saliency metrics (AUC, sAUC, NSS, CC, SIM, KL-Div) and show that they can be computed analytically or approximated with high precision.
We show that this leads to consistent rankings in all metrics and avoids the penalties of using one saliency map for all metrics.
Our method allows researchers to have their model compete on many different metrics with state-of-the-art in those metrics: "good" models will perform well in all metrics.
Resources such as labeled corpora are necessary to train automatic models within the natural language processing (NLP) field.
Historically, a large number of resources regarding a broad number of problems are available mostly in English.
One of such problems is known as Personality Identification where based on a psychological model (e.g.The Big Five Model), the goal is to find the traits of a subject's personality given, for instance, a text written by the same subject.
In this paper we introduce a new corpus in Spanish called Texts for Personality Identification (TxPI).
This corpus will help to develop models to automatically assign a personality trait to an author of a text document.
Our corpus, TxPI-u, contains information of 416 Mexican undergraduate students with some demographics information such as, age, gender, and the academic program they are enrolled.
Finally, as an additional contribution, we present a set of baselines to provide a comparison scheme for further research.
Since a tweet is limited to 140 characters, it is ambiguous and difficult for traditional Natural Language Processing (NLP) tools to analyse.
This research presents KeyXtract which enhances the machine learning based Stanford CoreNLP Part-of-Speech (POS) tagger with the Twitter model to extract essential keywords from a tweet.
The system was developed using rule-based parsers and two corpora.
The data for the research was obtained from a Twitter profile of a telecommunication company.
The system development consisted of two stages.
At the initial stage, a domain specific corpus was compiled after analysing the tweets.
The POS tagger extracted the Noun Phrases and Verb Phrases while the parsers removed noise and extracted any other keywords missed by the POS tagger.
The system was evaluated using the Turing Test.
After it was tested and compared against Stanford CoreNLP, the second stage of the system was developed addressing the shortcomings of the first stage.
It was enhanced using Named Entity Recognition and Lemmatization.
The second stage was also tested using the Turing test and its pass rate increased from 50.00% to 83.33%.
The performance of the final system output was measured using the F1 score.
Stanford CoreNLP with the Twitter model had an average F1 of 0.69 while the improved system had a F1 of 0.77.
The accuracy of the system could be improved by using a complete domain specific corpus.
Since the system used linguistic features of a sentence, it could be applied to other NLP tools.
Inspired by the great success of recurrent neural networks (RNNs) in sequential modeling, we introduce a novel RNN system to improve the performance of online signature verification.
The training objective is to directly minimize intra-class variations and to push the distances between skilled forgeries and genuine samples above a given threshold.
By back-propagating the training signals, our RNN network produced discriminative features with desired metrics.
Additionally, we propose a novel descriptor, called the length-normalized path signature (LNPS), and apply it to online signature verification.
LNPS has interesting properties, such as scale invariance and rotation invariance after linear combination, and shows promising results in online signature verification.
Experiments on the publicly available SVC-2004 dataset yielded state-of-the-art performance of 2.37% equal error rate (EER).
Single image super resolution is a very important computer vision task, with a wide range of applications.
In recent years, the depth of the super-resolution model has been constantly increasing, but with a small increase in performance, it has brought a huge amount of computation and memory consumption.
In this work, in order to make the super resolution models more effective, we proposed a novel single image super resolution method via recursive squeeze and excitation networks (SESR).
By introducing the squeeze and excitation module, our SESR can model the interdependencies and relationships between channels and that makes our model more efficiency.
In addition, the recursive structure and progressive reconstruction method in our model minimized the layers and parameters and enabled SESR to simultaneously train multi-scale super resolution in a single model.
After evaluating on four benchmark test sets, our model is proved to be above the state-of-the-art methods in terms of speed and accuracy.
In this paper, we address the symbol level precoding (SLP) design problem under max-min SINR criterion in the downlink of multiuser multiple-input single-output (MISO) channels.
First, we show that the distance preserving constructive interference regions (DPCIR) are always polyhedral angles (shifted pointed cones) for any given constellation point with unbounded decision region.
Then we prove that any signal in a given unbounded DPCIR has a norm larger than the norm of the corresponding vertex if and only if the convex hull of the constellation contains the origin.
Using these properties, we show that the power of the noiseless received signal lying on an unbounded DPCIR is an strictly increasing function of two parameters.
This allows us to reformulate the originally non-convex SLP max-min SINR as a convex optimization problem.
We discuss the loss due to our proposed convex reformulation and provide some simulation results.
In order to convey the most content in their limited space, advertisements embed references to outside knowledge via symbolism.
For example, a motorcycle stands for adventure (a positive property the ad wants associated with the product being sold), and a gun stands for danger (a negative property to dissuade viewers from undesirable behaviors).
We show how to use symbolic references to better understand the meaning of an ad.
We further show how anchoring ad understanding in general-purpose object recognition and image captioning improves results.
We formulate the ad understanding task as matching the ad image to human-generated statements that describe the action that the ad prompts, and the rationale it provides for taking this action.
Our proposed method outperforms the state of the art on this task, and on an alternative formulation of question-answering on ads.
We show additional applications of our learned representations for matching ads to slogans, and clustering ads according to their topic, without extra training.
Home automation platforms provide a new level of convenience by enabling consumers to automate various aspects of physical objects in their homes.
While the convenience is beneficial, security flaws in the platforms or integrated third-party products can have serious consequences for the integrity of a user's physical environment.
In this paper we perform a systematic security evaluation of two popular smart home platforms, Google's Nest platform and Philips Hue, that implement home automation "routines" (i.e., trigger-action programs involving apps and devices) via manipulation of state variables in a centralized data store.
Our semi-automated analysis examines, among other things, platform access control enforcement, the rigor of non-system enforcement procedures, and the potential for misuse of routines.
This analysis results in ten key findings with serious security implications.
For instance, we demonstrate the potential for the misuse of smart home routines in the Nest platform to perform a lateral privilege escalation, illustrate how Nest's product review system is ineffective at preventing multiple stages of this attack that it examines, and demonstrate how emerging platforms may fail to provide even bare-minimum security by allowing apps to arbitrarily add/remove other apps from the user's smart home.
Our findings draw attention to the unique security challenges of platforms that execute routines via centralized data stores and highlight the importance of enforcing security by design in emerging home automation platforms.
Several researchers have proposed solutions for secure data outsourcing on the public clouds based on encryption, secret-sharing, and trusted hardware.
Existing approaches, however, exhibit many limitations including high computational complexity, imperfect security, and information leakage.
This chapter describes an emerging trend in secure data processing that recognizes that an entire dataset may not be sensitive, and hence, non-sensitivity of data can be exploited to overcome some of the limitations of existing encryption-based approaches.
In particular, data and computation can be partitioned into sensitive or non-sensitive datasets - sensitive data can either be encrypted prior to outsourcing or stored/processed locally on trusted servers.
The non-sensitive dataset, on the other hand, can be outsourced and processed in the cleartext.
While partitioned computing can bring new efficiencies since it does not incur (expensive) encrypted data processing costs on non-sensitive data, it can lead to information leakage.
We study partitioned computing in two contexts - first, in the context of the hybrid cloud where local resources are integrated with public cloud resources to form an effective and secure storage and computational platform for enterprise data.
In the hybrid cloud, sensitive data is stored on the private cloud to prevent leakage and a computation is partitioned between private and public clouds.
Care must be taken that the public cloud cannot infer any information about sensitive data from inter-cloud data access during query processing.
We then consider partitioned computing in a public cloud only setting, where sensitive data is encrypted before outsourcing.
We formally define a partitioned security criterion that any approach to partitioned computing on public clouds must ensure in order to not introduce any new vulnerabilities to the existing secure solution.
We give a simple polynomial time approximation scheme for the weighted matroid matching problem on strongly base orderable matroids.
We also show that even the unweighted version of this problem is NP-complete and not in oracle-coNP.
Describing the color and textural information of a person image is one of the most crucial aspects of person re-identification (re-id).
In this paper, we present novel meta-descriptors based on a hierarchical distribution of pixel features.
Although hierarchical covariance descriptors have been successfully applied to image classification, the mean information of pixel features, which is absent from the covariance, tends to be the major discriminative information for person re-id.
To solve this problem, we describe a local region in an image via hierarchical Gaussian distribution in which both means and covariances are included in their parameters.
More specifically, the region is modeled as a set of multiple Gaussian distributions in which each Gaussian represents the appearance of a local patch.
The characteristics of the set of Gaussians are again described by another Gaussian distribution.
In both steps, we embed the parameters of the Gaussian into a point of Symmetric Positive Definite (SPD) matrix manifold.
By changing the way to handle mean information in this embedding, we develop two hierarchical Gaussian descriptors.
Additionally, we develop feature norm normalization methods with the ability to alleviate the biased trends that exist on the descriptors.
The experimental results conducted on five public datasets indicate that the proposed descriptors achieve remarkably high performance on person re-id.
We present Edina, the University of Edinburgh's social bot for the Amazon Alexa Prize competition.
Edina is a conversational agent whose responses utilize data harvested from Amazon Mechanical Turk (AMT) through an innovative new technique we call self-dialogues.
These are conversations in which a single AMT Worker plays both participants in a dialogue.
Such dialogues are surprisingly natural, efficient to collect and reflective of relevant and/or trending topics.
These self-dialogues provide training data for a generative neural network as well as a basis for soft rules used by a matching score component.
Each match of a soft rule against a user utterance is associated with a confidence score which we show is strongly indicative of reply quality, allowing this component to self-censor and be effectively integrated with other components.
Edina's full architecture features a rule-based system backing off to a matching score, backing off to a generative neural network.
Our hybrid data-driven methodology thus addresses both coverage limitations of a strictly rule-based approach and the lack of guarantees of a strictly machine-learning approach.
Datacenters running on-line, data-intensive applications (OLDIs) consume significant amounts of energy.
However, reducing their energy is challenging due to their tight response time requirements.
A key aspect of OLDIs is that each user query goes to all or many of the nodes in the cluster, so that the overall time budget is dictated by the tail of the replies' latency distribution; replies see latency variations both in the network and compute.
Previous work proposes to achieve load-proportional energy by slowing down the computation at lower datacenter loads based directly on response times (i.e., at lower loads, the proposal exploits the average slack in the time budget provisioned for the peak load).
In contrast, we propose TimeTrader to reduce energy by exploiting the latency slack in the sub- critical replies which arrive before the deadline (e.g., 80% of replies are 3-4x faster than the tail).
This slack is present at all loads and subsumes the previous work's load-related slack.
While the previous work shifts the leaves' response time distribution to consume the slack at lower loads, TimeTrader reshapes the distribution at all loads by slowing down individual sub-critical nodes without increasing missed deadlines.
TimeTrader exploits slack in both the network and compute budgets.
Further, TimeTrader leverages Earliest Deadline First scheduling to largely decouple critical requests from the queuing delays of sub- critical requests which can then be slowed down without hurting critical requests.
A combination of real-system measurements and at-scale simulations shows that without adding to missed deadlines, TimeTrader saves 15-19% and 41-49% energy at 90% and 30% loading, respectively, in a datacenter with 512 nodes, whereas previous work saves 0% and 31-37%.
Prediction of new drug-target interactions is extremely important as it can lead the researchers to find new uses for old drugs and to realize the therapeutic profiles or side effects thereof.
However, experimental prediction of drug-target interactions is expensive and time-consuming.
As a result, computational methods for prediction of new drug-target interactions have gained much interest in recent times.
We present iDTI-ESBoost, a prediction model for identification of drug-target interactions using evolutionary and structural features.
Our proposed method uses a novel balancing technique and a boosting technique for the binary classification problem of drug-target interaction.
On four benchmark datasets taken from a gold standard data, iDTI-ESBoost outperforms the state-of-the-art methods in terms of area under Receiver operating characteristic (auROC) curve. iDTI-ESBoost also outperforms the latest and the best-performing method in the literature to-date in terms of area under precision recall (auPR) curve.
This is significant as auPR curves are argued to be more appropriate as a metric for comparison for imbalanced datasets, like the one studied in this research.
In the sequel, our experiments establish the effectiveness of the classifier, balancing methods and the novel features incorporated in iDTI-ESBoost. iDTI-ESBoost is a novel prediction method that has for the first time exploited the structural features along with the evolutionary features to predict drug-protein interactions.
We believe the excellent performance of iDTI-ESBoost both in terms of auROC and auPR would motivate the researchers and practitioners to use it to predict drug-target interactions.
To facilitate that, iDTI-ESBoost is readily available for use at: http://farshidrayhan.pythonanywhere.com/iDTI-ESBoost/
We investigate the complexity of bounding the uncertainty of graphical games, and we provide new insight into the intrinsic difficulty of computing Nash equilibria.
In particular, we show that, if one adds very simple and natural additional requirements to a graphical game, the existence of Nash equilibria is no longer guaranteed, and computing an equilibrium is an intractable problem.
Moreover, if stronger equilibrium conditions are required for the game, we get hardness results for the second level of the polynomial hierarchy.
Our results offer a clear picture of the complexity of mixed Nash equilibria in graphical games, and answer some open research questions posed by Conitzer and Sandholm (2003).
This book consists of the chapters describing novel approaches to integrating fault tolerance into software development process.
They cover a wide range of topics focusing on fault tolerance during the different phases of the software development, software engineering techniques for verification and validation of fault tolerance means, and languages for supporting fault tolerance specification and implementation.
Accordingly, the book is structured into the following three parts: Part A: Fault tolerance engineering: from requirements to code; Part B: Verification and validation of fault tolerant systems; Part C: Languages and Tools for engineering fault tolerant systems.
One of the novelties brought by 5G is that wireless system design has increasingly turned its focus on guaranteeing reliability and latency.
This shifts the design objective of random access protocols from throughput optimization towards constraints based on reliability and latency.
For this purpose, we use frameless ALOHA, which relies on successive interference cancellation (SIC), and derive its exact finite-length analysis of the statistics of the unresolved users (reliability) as a function of the contention period length (latency).
The presented analysis can be used to derive the reliability-latency guarantees.
We also optimize the scheme parameters in order to maximize the reliability within a given latency.
Our approach represents an important step towards the general area of design and analysis of access protocols with reliability-latency guarantees.
Music, speech, and acoustic scene sound are often handled separately in the audio domain because of their different signal characteristics.
However, as the image domain grows rapidly by versatile image classification models, it is necessary to study extensible classification models in the audio domain as well.
In this study, we approach this problem using two types of sample-level deep convolutional neural networks that take raw waveforms as input and uses filters with small granularity.
One is a basic model that consists of convolution and pooling layers.
The other is an improved model that additionally has residual connections, squeeze-and-excitation modules and multi-level concatenation.
We show that the sample-level models reach state-of-the-art performance levels for the three different categories of sound.
Also, we visualize the filters along layers and compare the characteristics of learned filters.
The future of Internet of Things (IoT) is already upon us.
IoT applications have been widely used in many field of social production and social living such as healthcare, energy and industrial automation.
While enjoying the convenience and efficiency that IoT brings to us, new threats from IoT also have emerged.
There are increasing research works to ease these threats, but many problems remain open.
To better understand the essential reasons of new threats and the challenges in current research, this survey first proposes the concept of "IoT features".
Then, the security and privacy effects of eight IoT new features were discussed including the threats they cause, existing solutions and challenges yet to be solved.
To help researchers follow the up-to-date works in this field, this paper finally illustrates the developing trend of IoT security research and reveals how IoT features affect existing security research by investigating most existing research works related to IoT security from 2013 to 2017.
With the proliferation of web technologies it becomes more and more important to make the traditional negotiation pricing mechanism automated and intelligent.
The behaviour of software agents which negotiate on behalf of humans is determined by their tactics in the form of decision functions.
Prediction of partners behaviour in negotiation has been an active research direction in recent years as it will improve the utility gain for the adaptive negotiation agent and also achieve the agreement much quicker or look after much higher benefits.
In this paper we review the various negotiation methods and the existing architecture.
Although negotiation is practically very complex activity to automate without human intervention we have proposed architecture for predicting the opponents behaviour which will take into consideration various factors which affect the process of negotiation.
The basic concept is that the information about negotiators, their individual actions and dynamics can be used by software agents equipped with adaptive capabilities to learn from past negotiations and assist in selecting appropriate negotiation tactics.
Mel Frequency Cepstral Coefficients (MFCCs) are the most popularly used speech features in most speech and speaker recognition applications.
In this work, we propose a modified Mel filter bank to extract MFCCs from subsampled speech.
We also propose a stronger metric which effectively captures the correlation between MFCCs of original speech and MFCC of resampled speech.
It is found that the proposed method of filter bank construction performs distinguishably well and gives recognition performance on resampled speech close to recognition accuracies on original speech.
This paper performs the first investigation into depth for large-scale human action recognition in video where the depth cues are estimated from the videos themselves.
We develop a new framework called depth2action and experiment thoroughly into how best to incorporate the depth information.
We introduce spatio-temporal depth normalization (STDN) to enforce temporal consistency in our estimated depth sequences.
We also propose modified depth motion maps (MDMM) to capture the subtle temporal changes in depth.
These two components significantly improve the action recognition performance.
We evaluate our depth2action framework on three large-scale action recognition video benchmarks.
Our model achieves state-of-the-art performance when combined with appearance and motion information thus demonstrating that depth2action is indeed complementary to existing approaches.
This paper studies a spectrum estimation method for the case that the samples are obtained at a rate lower than the Nyquist rate.
The method is referred to as the correlogram for undersampled data.
The algorithm partitions the spectrum into a number of segments and estimates the average power within each spectral segment.
This method is able to estimate the power spectrum density of a signal from undersampled data without essentially requiring the signal to be sparse.
We derive the bias and the variance of the spectrum estimator, and show that there is a tradeoff between the accuracy of the estimation, the frequency resolution, and the complexity of the estimator.
A closed-form approximation of the estimation variance is also derived, which clearly shows how the variance is related to different parameters.
The asymptotic behavior of the estimator is also investigated, and it is proved that this spectrum estimator is consistent.
Moreover, the estimation made for different spectral segments becomes uncorrelated as the signal length tends to infinity.
Finally, numerical examples and simulation results are provided, which approve the theoretical conclusions.
Everyday place descriptions often contain place names of fine-grained features, such as buildings or businesses, that are more difficult to disambiguate than names referring to larger places, for example cities or natural geographic features.
Fine-grained places are often significantly more frequent and more similar to each other, and disambiguation heuristics developed for larger places, such as those based on population or containment relationships, are often not applicable in these cases.
In this research, we address the disambiguation of fine-grained place names from everyday place descriptions.
For this purpose, we evaluate the performance of different existing clustering-based approaches, since clustering approaches require no more knowledge other than the locations of ambiguous place names.
We consider not only approaches developed specifically for place name disambiguation, but also clustering algorithms developed for general data mining that could potentially be leveraged.
We compare these methods with a novel algorithm, and show that the novel algorithm outperforms the other algorithms in terms of disambiguation precision and distance error over several tested datasets.
In this work we introduce the application of black-box quantum control as an interesting rein- forcement learning problem to the machine learning community.
We analyze the structure of the reinforcement learning problems arising in quantum physics and argue that agents parameterized by long short-term memory (LSTM) networks trained via stochastic policy gradients yield a general method to solving them.
In this context we introduce a variant of the proximal policy optimization (PPO) algorithm called the memory proximal policy optimization (MPPO) which is based on this analysis.
We then show how it can be applied to specific learning tasks and present results of nu- merical experiments showing that our method achieves state-of-the-art results for several learning tasks in quantum control with discrete and continouous control parameters.
Storms and other severe weather events can result in fatalities, injuries, and property damage.
Therefore, preventing such outcomes to the extent possible is a key concern, and the scientific community faces an increasing demand for regularly updated appraisals of evolving climate conditions and extreme weather.
NOAA's Storm Events Database is undoubtedly an invaluable resource to the general public, to the professional, and to the researcher.
Due to such importance, the primary objective of this study was to explore this database and get clues about its reliability.
A complete investigation of the damage estimates, injuries or fatalities figures is unfeasible due to the extension of the database.
However, an exploratory data analysis with the resources of the R statistical data analysis language found that damage reports are missing in more than half of the records, that part of the damage values are incorrect, and that, despite all efforts of standardizations, non-standard event type names are still finding their way into the database.
These few results are enough to demonstrate that the database suffers from incompleteness and inconsistencies and should not be used without taking reservations and appropriate precautions before advancing any inferences from the data.
This paper presents a parallel memetic algorithm for solving the vehicle routing problem with time windows (VRPTW).
The VRPTW is a well-known NP-hard discrete optimization problem with two objectives.
The main objective is to minimize the number of vehicles serving customers scattered on the map, and the second one is to minimize the total distance traveled by the vehicles.
Here, the fleet size is minimized in the first phase of the proposed method using the parallel heuristic algorithm (PHA), and the traveled distance is minimized in the second phase by the parallel memetic algorithm (PMA).
In both parallel algorithms, the parallel components co-operate periodically in order to exchange the best solutions found so far.
An extensive experimental study performed on the Gehring and Homberger's benchmark proves the high convergence capabilities and robustness of both PHA and PMA.
Also, we present the speedup analysis of the PMA.
It is generally believed that the preference ranking method PROMETHEE has a quadratic time complexity.
In this paper, however, we present an exact algorithm that computes PROMETHEE's net flow scores in time O(qn log(n)), where q represents the number of criteria and n the number of alternatives.
The method is based on first sorting the alternatives after which the unicriterion flow scores of all alternatives can be computed in one scan over the sorted list of alternatives while maintaining a sliding window.
This method works with the linear and level criterion preference functions.
The algorithm we present is exact and, due to the sub-quadratic time complexity, vastly extends the applicability of the PROMETHEE method.
Experiments show that with the new algorithm, PROMETHEE can scale up to millions of tuples.
Lipschitz extensions were recently proposed as a tool for designing node differentially private algorithms.
However, efficiently computable Lipschitz extensions were known only for 1-dimensional functions (that is, functions that output a single real value).
In this paper, we study efficiently computable Lipschitz extensions for multi-dimensional (that is, vector-valued) functions on graphs.
We show that, unlike for 1-dimensional functions, Lipschitz extensions of higher-dimensional functions on graphs do not always exist, even with a non-unit stretch.
We design Lipschitz extensions with small stretch for the sorted degree list and for the degree distribution of a graph.
Crucially, our extensions are efficiently computable.
We also develop new tools for employing Lipschitz extensions in the design of differentially private algorithms.
Specifically, we generalize the exponential mechanism, a widely used tool in data privacy.
The exponential mechanism is given a collection of score functions that map datasets to real values.
It attempts to return the name of the function with nearly minimum value on the data set.
Our generalized exponential mechanism provides better accuracy when the sensitivity of an optimal score function is much smaller than the maximum sensitivity of score functions.
We use our Lipschitz extension and the generalized exponential mechanism to design a node-differentially private algorithm for releasing an approximation to the degree distribution of a graph.
Our algorithm is much more accurate than algorithms from previous work.
Machine translation has recently achieved impressive performance thanks to recent advances in deep learning and the availability of large-scale parallel corpora.
There have been numerous attempts to extend these successes to low-resource language pairs, yet requiring tens of thousands of parallel sentences.
In this work, we take this research direction to the extreme and investigate whether it is possible to learn to translate even without any parallel data.
We propose a model that takes sentences from monolingual corpora in two different languages and maps them into the same latent space.
By learning to reconstruct in both languages from this shared feature space, the model effectively learns to translate without using any labeled data.
We demonstrate our model on two widely used datasets and two language pairs, reporting BLEU scores of 32.8 and 15.1 on the Multi30k and WMT English-French datasets, without using even a single parallel sentence at training time.
In this work we present a novel approach for single depth map super-resolution.
Modern consumer depth sensors, especially Time-of-Flight sensors, produce dense depth measurements, but are affected by noise and have a low lateral resolution.
We propose a method that combines the benefits of recent advances in machine learning based single image super-resolution, i.e. deep convolutional networks, with a variational method to recover accurate high-resolution depth maps.
In particular, we integrate a variational method that models the piecewise affine structures apparent in depth data via an anisotropic total generalized variation regularization term on top of a deep network.
We call our method ATGV-Net and train it end-to-end by unrolling the optimization procedure of the variational method.
To train deep networks, a large corpus of training data with accurate ground-truth is required.
We demonstrate that it is feasible to train our method solely on synthetic data that we generate in large quantities for this task.
Our evaluations show that we achieve state-of-the-art results on three different benchmarks, as well as on a challenging Time-of-Flight dataset, all without utilizing an additional intensity image as guidance.
Relational cost analysis aims at formally establishing bounds on the difference in the evaluation costs of two programs.
As a particular case, one can also use relational cost analysis to establish bounds on the difference in the evaluation cost of the same program on two different inputs.
One way to perform relational cost analysis is to use a relational type-and-effect system that supports reasoning about relations between two executions of two programs.
Building on this basic idea, we present a type-and-effect system, called ARel, for reasoning about the relative cost of array-manipulating, higher-order functional-imperative programs.
The key ingredient of our approach is a new lightweight type refinement discipline that we use to track relations (differences) between two arrays.
This discipline combined with Hoare-style triples built into the types allows us to express and establish precise relative costs of several interesting programs which imperatively update their data.
By providing substantial amounts of data and standardized evaluation protocols, datasets in computer vision have helped fuel advances across all areas of visual recognition.
But even in light of breakthrough results on recent benchmarks, it is still fair to ask if our recognition algorithms are doing as well as we think they are.
The vision sciences at large make use of a very different evaluation regime known as Visual Psychophysics to study visual perception.
Psychophysics is the quantitative examination of the relationships between controlled stimuli and the behavioral responses they elicit in experimental test subjects.
Instead of using summary statistics to gauge performance, psychophysics directs us to construct item-response curves made up of individual stimulus responses to find perceptual thresholds, thus allowing one to identify the exact point at which a subject can no longer reliably recognize the stimulus class.
In this article, we introduce a comprehensive evaluation framework for visual recognition models that is underpinned by this methodology.
Over millions of procedurally rendered 3D scenes and 2D images, we compare the performance of well-known convolutional neural networks.
Our results bring into question recent claims of human-like performance, and provide a path forward for correcting newly surfaced algorithmic deficiencies.
The unrestricted block relocation problem is an important optimization problem encountered at terminals, where containers are stored in stacks.
It consists in determining the minimum number of container moves so as to empty the considered bay following a certain retrieval sequence.
A container move can be either the retrieval of a container or the relocation of a certain container on top of a stack to another stack.
The latter types of moves are necessary so as to provide access to containers which are currently not on top of a stack.
They might also be useful to prepare future removals.
In this paper, we propose the first local search type improvement heuristic for the block relocation problem.
It relies on a clever definition of the state space which is explored by means of a dynamic programming algorithm so as to identify the locally optimal sequence of moves of a given container.
Our results on large benchmark instance reveal unexpectedly high improvement potentials (up to 50%) compared to results obtained by state-of-the-art constructive heuristics.
For a graph formed by vertices and weighted edges, a generalized minimum dominating set (MDS) is a vertex set of smallest cardinality such that the summed weight of edges from each outside vertex to vertices in this set is equal to or larger than certain threshold value.
This generalized MDS problem reduces to the conventional MDS problem in the limiting case of all the edge weights being equal to the threshold value.
We treat the generalized MDS problem in the present paper by a replica-symmetric spin glass theory and derive a set of belief-propagation equations.
As a practical application we consider the problem of extracting a set of sentences that best summarize a given input text document.
We carry out a preliminary test of the statistical physics-inspired method to this automatic text summarization problem.
Feature engineering is a crucial step in the process of predictive modeling.
It involves the transformation of given feature space, typically using mathematical functions, with the objective of reducing the modeling error for a given target.
However, there is no well-defined basis for performing effective feature engineering.
It involves domain knowledge, intuition, and most of all, a lengthy process of trial and error.
The human attention involved in overseeing this process significantly influences the cost of model generation.
We present a new framework to automate feature engineering.
It is based on performance driven exploration of a transformation graph, which systematically and compactly enumerates the space of given options.
A highly efficient exploration strategy is derived through reinforcement learning on past examples.
Leadership games provide a powerful paradigm to model many real-world settings.
Most literature focuses on games with a single follower who acts optimistically, breaking ties in favour of the leader.
Unfortunately, for real-world applications, this is unlikely.
In this paper, we look for efficiently solvable games with multiple followers who play either optimistically or pessimistically, i.e., breaking ties in favour or against the leader.
We study the computational complexity of finding or approximating an optimistic or pessimistic leader-follower equilibrium in specific classes of succinct games---polymatrix like---which are equivalent to 2-player Bayesian games with uncertainty over the follower, with interdependent or independent types.
Furthermore, we provide an exact algorithm to find a pessimistic equilibrium for those game classes.
Finally, we show that in general polymatrix games the computation is harder even when players are forced to play pure strategies.
Recently, many methods to interpret and visualize deep neural network predictions have been proposed and significant progress has been made.
However, a more class-discriminative and visually pleasing explanation is required.
Thus, this paper proposes a region-based approach that estimates feature importance in terms of appropriately segmented regions.
By fusing the saliency maps generated from multi-scale segmentations, a more class-discriminative and visually pleasing map is obtained.
We incorporate this regional multi-scale concept into a prediction difference method that is model-agnostic.
An input image is segmented in several scales using the super-pixel method, and exclusion of a region is simulated by sampling a normal distribution constructed using the boundary prior.
The experimental results demonstrate that the regional multi-scale method produces much more class-discriminative and visually pleasing saliency maps.
We describe an adaptation and application of a search-based structured prediction algorithm "Searn" to unsupervised learning problems.
We show that it is possible to reduce unsupervised learning to supervised learning and demonstrate a high-quality unsupervised shift-reduce parsing model.
We additionally show a close connection between unsupervised Searn and expectation maximization.
Finally, we demonstrate the efficacy of a semi-supervised extension.
The key idea that enables this is an application of the predict-self idea for unsupervised learning.
DeepPrior is a simple approach based on Deep Learning that predicts the joint 3D locations of a hand given a depth map.
Since its publication early 2015, it has been outperformed by several impressive works.
Here we show that with simple improvements: adding ResNet layers, data augmentation, and better initial hand localization, we achieve better or similar performance than more sophisticated recent methods on the three main benchmarks (NYU, ICVL, MSRA) while keeping the simplicity of the original method.
Our new implementation is available at https://github.com/moberweger/deep-prior-pp .
Given a binary nonlinear code, we provide a deterministic algorithm to compute its weight and distance distribution, and in particular its minimum weight and its minimum distance, which takes advantage of fast Fourier techniques.
This algorithm's performance is similar to that of best-known algorithms for the average case, while it is especially efficient for codes with low information rate.
We provide complexity estimates for several cases of interest.
Given a point set P in 2D, the problem of finding the smallest set of unit disks that cover all of P is NP-hard.
We present a simple algorithm for this problem with an approximation factor of 25/6 in the Euclidean norm and 2 in the max norm, by restricting the disk centers to lie on parallel lines.
The run time and space of this algorithm is O(n log n) and O(n) respectively.
This algorithm extends to any Lp norm and is asymptotically faster than known alternative approximation algorithms for the same approximation factor.
For future mmWave mobile communication systems the use of analog/hybrid beamforming is envisioned be a key as- pect.
The synthesis of beams is a key technology of enable the best possible operation during beamsearch, data transmission and MU MIMO operation.
The developed method for synthesizing beams is based on previous work in radar technology considering only phase array antennas.
With this technique it is possible to generate a desired beam of any shape with the constraints of the desired target transceiver antenna frontend.
It is not constraint to a certain antenna array geometry, but can handle 1d, 2d and even 3d antenna array geometries like cylindric arrays.
The numerical examples show that the method can synthesize beams by considering a user defined tradeoff between gain, transition width and passband ripples.
This report describes an initial replication study of the PRECISE system and develops a clearer, more formal description of the approach.
Based on our evaluation, we conclude that the PRECISE results do not fully replicate.
However the formalization developed here suggests a road map to further enhance and extend the approach pioneered by PRECISE.
After a long, productive discussion with Ana-Maria Popescu (one of the authors of PRECISE) we got more clarity on the PRECISE approach and how the lexicon was authored for the GEO evaluation.
Based on this we built a more direct implementation over a repaired formalism.
Although our new evaluation is not yet complete, it is clear that the system is performing much better now.
We will continue developing our ideas and implementation and generate a future report/publication that more accurately evaluates PRECISE like approaches.
Distributed representations of words have been shown to capture lexical semantics, as demonstrated by their effectiveness in word similarity and analogical relation tasks.
But, these tasks only evaluate lexical semantics indirectly.
In this paper, we study whether it is possible to utilize distributed representations to generate dictionary definitions of words, as a more direct and transparent representation of the embeddings' semantics.
We introduce definition modeling, the task of generating a definition for a given word and its embedding.
We present several definition model architectures based on recurrent neural networks, and experiment with the models over multiple data sets.
Our results show that a model that controls dependencies between the word being defined and the definition words performs significantly better, and that a character-level convolution layer designed to leverage morphology can complement word-level embeddings.
Finally, an error analysis suggests that the errors made by a definition model may provide insight into the shortcomings of word embeddings.
One way to analyze Cyber-Physical Systems is by modeling them as hybrid automata.
Since reachability analysis for hybrid nonlinear automata is a very challenging and computationally expensive problem, in practice, engineers try to solve the requirements falsification problem.
In one method, the falsification problem is solved by minimizing a robustness metric induced by the requirements.
This optimization problem is usually a non-convex non-smooth problem that requires heuristic and analytical guidance to be solved.
In this paper, functional gradient descent for hybrid systems is utilized for locally decreasing the robustness metric.
The local descent method is combined with Simulated Annealing as a global optimization method to search for unsafe behaviors.
Semantic segmentation is challenging as it requires both object-level information and pixel-level accuracy.
Recently, FCN-based systems gained great improvement in this area.
Unlike classification networks, combining features of different layers plays an important role in these dense prediction models, as these features contains information of different levels.
A number of models have been proposed to show how to use these features.
However, what is the best architecture to make use of features of different layers is still a question.
In this paper, we propose a module, called mixed context network, and show that our presented system outperforms most existing semantic segmentation systems by making use of this module.
Deformable image registration is a fundamental task in medical image analysis, aiming to establish a dense and non-linear correspondence between a pair of images.
Previous deep-learning studies usually employ supervised neural networks to directly learn the spatial transformation from one image to another, requiring task-specific ground-truth registration for model training.
Due to the difficulty in collecting precise ground-truth registration, implementation of these supervised methods is practically challenging.
Although several unsupervised networks have been recently developed, these methods usually ignore the inherent inverse-consistent property (essential for diffeomorphic mapping) of transformations between a pair of images.
Also, existing approaches usually encourage the to-be-estimated transformation to be locally smooth via a smoothness constraint only, which could not completely avoid folding in the resulting transformation.
To this end, we propose an Inverse-Consistent deep Network (ICNet) for unsupervised deformable image registration.
Specifically, we develop an inverse-consistent constraint to encourage that a pair of images are symmetrically deformed toward one another, until both warped images are matched.
Besides using the conventional smoothness constraint, we also propose an anti-folding constraint to further avoid folding in the transformation.
The proposed method does not require any supervision information, while encouraging the diffeomoprhic property of the transformation via the proposed inverse-consistent and anti-folding constraints.
We evaluate our method on T1-weighted brain magnetic resonance imaging (MRI) scans for tissue segmentation and anatomical landmark detection, with results demonstrating the superior performance of our ICNet over several state-of-the-art approaches for deformable image registration.
Our code will be made publicly available.
The concepts of MIMO MC-CDMA are not new but the new technologies to improve their functioning are an emerging area of research.
In general, most mobile communication systems transmit bits of information in the radio space to the receiver.
The radio channels in mobile radio systems are usually multipath fading channels, which cause inter-symbol interference (ISI) in the received signal.
To remove ISI from the signal, there is a need of strong equalizer.
In this thesis we have focused on simulating the MIMO MC-CDMA systems in MATLAB and designed the channel estimation for them.
Given a pseudoword over suitable pseudovarieties, we associate to it a labeled linear order determined by the factorizations of the pseudoword.
We show that, in the case of the pseudovariety of aperiodic finite semigroups, the pseudoword can be recovered from the labeled linear order.
Recent work has shown that optical flow estimation can be formulated as a supervised learning task and can be successfully solved with convolutional networks.
Training of the so-called FlowNet was enabled by a large synthetically generated dataset.
The present paper extends the concept of optical flow estimation via convolutional networks to disparity and scene flow estimation.
To this end, we propose three synthetic stereo video datasets with sufficient realism, variation, and size to successfully train large networks.
Our datasets are the first large-scale datasets to enable training and evaluating scene flow methods.
Besides the datasets, we present a convolutional network for real-time disparity estimation that provides state-of-the-art results.
By combining a flow and disparity estimation network and training it jointly, we demonstrate the first scene flow estimation with a convolutional network.
We propose a straightforward method that simultaneously reconstructs the 3D facial structure and provides dense alignment.
To achieve this, we design a 2D representation called UV position map which records the 3D shape of a complete face in UV space, then train a simple Convolutional Neural Network to regress it from a single 2D image.
We also integrate a weight mask into the loss function during training to improve the performance of the network.
Our method does not rely on any prior face model, and can reconstruct full facial geometry along with semantic meaning.
Meanwhile, our network is very light-weighted and spends only 9.8ms to process an image, which is extremely faster than previous works.
Experiments on multiple challenging datasets show that our method surpasses other state-of-the-art methods on both reconstruction and alignment tasks by a large margin.
In this work, we present a simple and elegant approach to language modeling for bilingual code-switched text.
Since code-switching is a blend of two or more different languages, a standard bilingual language model can be improved upon by using structures of the monolingual language models.
We propose a novel technique called dual language models, which involves building two complementary monolingual language models and combining them using a probabilistic model for switching between the two.
We evaluate the efficacy of our approach using a conversational Mandarin-English speech corpus.
We prove the robustness of our model by showing significant improvements in perplexity measures over the standard bilingual language model without the use of any external information.
Similar consistent improvements are also reflected in automatic speech recognition error rates.
Understanding the fundamental end-to-end delay performance in mobile ad hoc networks (MANETs) is of great importance for supporting Quality of Service (QoS) guaranteed applications in such networks.
While upper bounds and approximations for end-to-end delay in MANETs have been developed in literature, which usually introduce significant errors in delay analysis, the modeling of exact end-to-end delay in MANETs remains a technical challenge.
This is partially due to the highly dynamical behaviors of MANETs, but also due to the lack of an efficient theoretical framework to capture such dynamics.
This paper demonstrates the potential application of the powerful Quasi-Birth-and-Death (QBD) theory in tackling the challenging issue of exact end-to-end delay modeling in MANETs.
We first apply the QBD theory to develop an efficient theoretical framework for capturing the complex dynamics in MANETs.
We then show that with the help of this framework, closed form models can be derived for the analysis of exact end-to-end delay and also per node throughput capacity in MANETs.
Simulation and numerical results are further provided to illustrate the efficiency of these QBD theory-based models as well as our theoretical findings.
There are many Local texture features each very in way they implement and each of the Algorithm trying improve the performance.
An attempt is made in this paper to represent a theoretically very simple and computationally effective approach for face recognition.
In our implementation the face image is divided into 3x3 sub-regions from which the features are extracted using the Local Binary Pattern (LBP) over a window, fuzzy membership function and at the central pixel.
The LBP features possess the texture discriminative property and their computational cost is very low.
By utilising the information from LBP, membership function, and central pixel, the limitations of traditional LBP is eliminated.
The bench mark database like ORL and Sheffield Databases are used for the evaluation of proposed features with SVM classifier.
For the proposed approach K-fold and ROC curves are obtained and results are compared.
Resource leak bugs in Android apps are pervasive and can cause serious performance degradation and system crashes.
In recent years, several resource leak detection techniques have been proposed to assist Android developers in correctly managing system resources.
Yet, there exist no common bug benchmarks for effectively and reliably comparing such techniques and quantitatively evaluating their strengths and weaknesses.
This paper describes our initial contribution towards constructing such a benchmark.
To locate real resource leak bugs, we mined 124,215 code revisions of 34 large-scale open-source Android apps.
We successfully found 298 fixed resource leaks, which cover a diverse set of resource classes, from 32 out of the 34 apps.
To understand the characteristics of these bugs, we conducted an empirical study, which revealed the root causes of frequent resource leaks in Android apps and common patterns of faults made by developers.
With our findings, we further implemented a static checker to detect a common pattern of resource leaks in Android apps.
Experiments showed that the checker can effectively locate real resource leaks in popular Android apps, confirming the usefulness of our work.
In a previous paper, it was discussed whether Bitcoin and/or its blockchain could be considered a complex system and, if so, whether a chaotic one, a positive response raising concerns about the likelihood of Bitcoin/blockchain entering a chaotic regime, with catastrophic consequences for financial systems based on it.
This paper intends to simplify and extend that analysis to other PoW, PoS, and hybrid protocol-based cryptocurrencies.
As before, this study was carried out with the help of Information Theory of Complex Systems, in general, and Crutchfield's Statistical Complexity measure, in particular.
This paper is a work-in-progress.
We intend to uncover some other measures that capture the qualitative notion of complexity of systems that can be applied to these cryptocurrencies to compare with the results here obtained.
Recently, the millimeter wave (mmWave) bands have been investigated as a means to support the foreseen extreme data rate demands of next-generation cellular networks (5G).
However, in order to overcome the severe isotropic path loss and the harsh propagation experienced at such high frequencies, a dense base station deployment is required, which may be infeasible because of the unavailability of fiber drops to provide wired backhauling.
To address this challenge, the 3GPP is investigating the concept of Integrated Access and Backhaul (IAB), i.e., the possibility of providing wireless backhaul to the mobile terminals.
In this paper, we (i) extend the capabilities of the existing mmWave module for ns-3 to support advanced IAB functionalities, and (ii) evaluate the end-to-end performance of the IAB architecture through system-level full-stack simulations in terms of experienced throughput and communication latency.
We finally provide guidelines on how to design optimal wireless backhaul solutions in the presence of resource-constrained and traffic-congested mmWave scenarios.
The multi-armed bandit problem has been extensively studied under the stationary assumption.
However in reality, this assumption often does not hold because the distributions of rewards themselves may change over time.
In this paper, we propose a change-detection (CD) based framework for multi-armed bandit problems under the piecewise-stationary setting, and study a class of change-detection based UCB (Upper Confidence Bound) policies, CD-UCB, that actively detects change points and restarts the UCB indices.
We then develop CUSUM-UCB and PHT-UCB, that belong to the CD-UCB class and use cumulative sum (CUSUM) and Page-Hinkley Test (PHT) to detect changes.
We show that CUSUM-UCB obtains the best known regret upper bound under mild assumptions.
We also demonstrate the regret reduction of the CD-UCB policies over arbitrary Bernoulli rewards and Yahoo! datasets of webpage click-through rates.
We propose an inexact method for the graph Fourier transform of a graph signal, as defined by the signal decomposition over the Jordan subspaces of the graph adjacency matrix.
This method projects the signal over the generalized eigenspaces of the adjacency matrix, which accelerates the transform computation over large, sparse, and directed adjacency matrices.
The trade-off between execution time and fidelity to the original graph structure is discussed.
In addition, properties such as a generalized Parseval's identity and total variation ordering of the generalized eigenspaces are discussed.
The method is applied to 2010-2013 NYC taxi trip data to identify traffic hotspots on the Manhattan grid.
Our results show that identical highly expressed geolocations can be identified with the inexact method and the method based on eigenvector projections, while reducing computation time by a factor of 26,000 and reducing energy dispersal among the spectral components corresponding to the multiple zero eigenvalue.
The clustering ensemble paradigm has emerged as an effective tool for community detection in multilayer networks, which allows for producing consensus solutions that are designed to be more robust to the algorithmic selection and configuration bias.
However, one limitation is related to the dependency on a co-association threshold that controls the degree of consensus in the community structure solution.
The goal of this work is to overcome this limitation with a new framework of ensemble-based multilayer community detection, which features parameter-free identification of consensus communities based on generative models of graph pruning that are able to filter out noisy co-associations.
We also present an enhanced version of the modularity-driven ensemble-based multilayer community detection method, in which community memberships of nodes are reconsidered to optimize the multilayer modularity of the consensus solution.
Experimental evidence on real-world networks confirms the beneficial effect of using model-based filtering methods and also shows the superiority of the proposed method on state-of-the-art multilayer community detection.
User engagement refers to the amount of interaction an instance (e.g., tweet, news, and forum post) achieves.
Ranking the items in social media websites based on the amount of user participation in them, can be used in different applications, such as recommender systems.
In this paper, we consider a tweet containing a rating for a movie as an instance and focus on ranking the instances of each user based on their engagement, i.e., the total number of retweets and favorites it will gain.
For this task, we define several features which can be extracted from the meta-data of each tweet.
The features are partitioned into three categories: user-based, movie-based, and tweet-based.
We show that in order to obtain good results, features from all categories should be considered.
We exploit regression and learning to rank methods to rank the tweets and propose to aggregate the results of regression and learning to rank methods to achieve better performance.
We have run our experiments on an extended version of MovieTweeting dataset provided by ACM RecSys Challenge 2014.
The results show that learning to rank approach outperforms most of the regression models and the combination can improve the performance significantly.
Automatic profiling of social media users is an important task for supporting a multitude of downstream applications.
While a number of studies have used social media content to extract and study collective social attributes, there is a lack of substantial research that addresses the detection of a user's industry.
We frame this task as classification using both feature engineering and ensemble learning.
Our industry-detection system uses both posted content and profile information to detect a user's industry with 64.3% accuracy, significantly outperforming the majority baseline in a taxonomy of fourteen industry classes.
Our qualitative analysis suggests that a person's industry not only affects the words used and their perceived meanings, but also the number and type of emotions being expressed.
The spread of online reviews, ratings and opinions and its growing influence on people's behavior and decisions boosted the interest to extract meaningful information from this data deluge.
Hence, crowdsourced ratings of products and services gained a critical role in business, governments, and others.
We propose a new reputation-based ranking system utilizing multipartite rating subnetworks, that clusters users by their similarities, using Kolmogorov complexity.
Our system is novel in that it reflects a diversity of opinions/preferences by assigning possibly distinct rankings, for the same item, for different groups of users.
We prove the convergence and efficiency of the system and show that it copes better with spamming/spurious users, and it is more robust to attacks than state-of-the-art approaches.
Future 5G and Internet of Things (IoT) applications will heavily rely on long-range communication technologies such as low-power wireless area networks (LPWANs).
In particular, LoRaWAN built on LoRa physical layer is gathering increasing interests, both from academia and industries, for enabling low-cost energy efficient IoT wireless sensor networks for, e.g., environmental monitoring over wide areas.
While its communication range may go up to 20 kilometers, the achievable bit rates in LoRaWAN are limited to a few kilobits per second.
In the event of collisions, the perceived rate is further reduced due to packet loss and retransmissions.
Firstly, to alleviate the harmful impacts of collisions, we propose a decoding algorithm that enables to resolve several superposed LoRa signals.
Our proposed method exploits the slight desynchronization of superposed signals and specific features of LoRa physical layer.
Secondly, we design a full MAC protocol enabling collision resolution.
The simulation results demonstrate that the proposed method outperforms conventional LoRaWAN jointly in terms of system throughput, energy efficiency as well as delay.
These results show that our scheme is well suited for 5G and IoT systems, as one of their major goals is to provide the best trade-off among these performance objectives.
We study nonlinear power systems consisting of generators, generator buses, and non-generator buses.
First, looking at a generator and its bus' variables jointly, we introduce a synchronization concept for a pair of such joint generators and buses.
We show that this concept is related to graph symmetry.
Next, we extend, in two ways, the synchronization from a pair to a partition of all generators in the networks and show that they are related to either graph symmetry or equitable partitions.
Finally, we show how an exact reduced model can be obtained by aggregating the generators and associated buses in the network when the original system is synchronized with respect to a partition, provided that the initial condition respects the partition.
Additionally, the aggregation-based reduced model is again a power system.
In this article we introduce the principles to detect leakage using a mathematical model based on machine learning and domestic water consumption monitoring in real time.
The model uses data which is measured from a water meter, analyzes the water consumption, and uses two criteria simultaneously: deviation from the average consumption, and comparison of steady water consumptions over a period of time.
Simulation of the model on a regular household consumer was implemented on Antileaks - device that we have built that designed to transfer consumption information from an analogue water meter to a digital form in real time.
In this paper, we present a communication-free algorithm for distributed coverage of an arbitrary network by a group of mobile agents with local sensing capabilities.
The network is represented as a graph, and the agents are arbitrarily deployed on some nodes of the graph.
Any node of the graph is covered if it is within the sensing range of at least one agent.
The agents are mobile devices that aim to explore the graph and to optimize their locations in a decentralized fashion by relying only on their sensory inputs.
We formulate this problem in a game theoretic setting and propose a communication-free learning algorithm for maximizing the coverage.
We discuss the suitability of spreadsheet processors as tools for programming streaming systems.
We argue that, while spreadsheets can function as powerful models for stream operators, their fundamental boundedness limits their scope of application.
We propose two extensions to the spreadsheet model and argue their utility in the context of programming streaming systems.
Finding the product of two polynomials is an essential and basic problem in computer algebra.
While most previous results have focused on the worst-case complexity, we instead employ the technique of adaptive analysis to give an improvement in many "easy" cases.
We present two adaptive measures and methods for polynomial multiplication, and also show how to effectively combine them to gain both advantages.
One useful feature of these algorithms is that they essentially provide a gradient between existing "sparse" and "dense" methods.
We prove that these approaches provide significant improvements in many cases but in the worst case are still comparable to the fastest existing algorithms.
Learning social media content is the basis of many real-world applications, including information retrieval and recommendation systems, among others.
In contrast with previous works that focus mainly on single modal or bi-modal learning, we propose to learn social media content by fusing jointly textual, acoustic, and visual information (JTAV).
Effective strategies are proposed to extract fine-grained features of each modality, that is, attBiGRU and DCRNN.
We also introduce cross-modal fusion and attentive pooling techniques to integrate multi-modal information comprehensively.
Extensive experimental evaluation conducted on real-world datasets demonstrates our proposed model outperforms the state-of-the-art approaches by a large margin.
Automatic Offline Handwritten Signature Verification has been researched over the last few decades from several perspectives, using insights from graphology, computer vision, signal processing, among others.
In spite of the advancements on the field, building classifiers that can separate between genuine signatures and skilled forgeries (forgeries made targeting a particular signature) is still hard.
We propose approaching the problem from a feature learning perspective.
Our hypothesis is that, in the absence of a good model of the data generation process, it is better to learn the features from data, instead of using hand-crafted features that have no resemblance to the signature generation process.
To this end, we use Deep Convolutional Neural Networks to learn features in a writer-independent format, and use this model to obtain a feature representation on another set of users, where we train writer-dependent classifiers.
We tested our method in two datasets: GPDS-960 and Brazilian PUC-PR.
Our experimental results show that the features learned in a subset of the users are discriminative for the other users, including across different datasets, reaching close to the state-of-the-art in the GPDS dataset, and improving the state-of-the-art in the Brazilian PUC-PR dataset.
Empirical evidence shows that co-authored publications achieve higher visibility and impact.
The aim of the current work is to test for the existence of a similar correlation for Italian publications.
We also verify if such correlation differs: i) by subject category and macro-area; ii) by document type; iii) over the course of time.
The results confirm world-level evidence, showing a consistent and significant linear growth in the citability of a publication with number of co-authors, in almost all subject categories.
The effects are more remarkable in the fields of Social Sciences and Art & Humanities than in the Sciences-a finding not so obvious scrutinizing previous studies.
Moreover, our results partly disavow the positive association between number of authors and prestige of the journal, as measured by its impact factor.
We consider the problem of constructing fast and small parallel prefix adders for non-uniform input arrival times.
This problem arises whenever the adder is embedded into a more complex circuit, e. g. a multiplier.
Most previous results are based on representing binary carry-propagate adders as so-called parallel prefix graphs, in which pairs of generate and propagate signals are combined using complex gates known as prefix gates.
Adders constructed in this model usually minimize the delay in terms of these prefix gates.
However, the delay in terms of logic gates can be worse by a factor of two.
In contrast, we aim to minimize the delay of the underlying logic circuit directly.
We prove a lower bound on the delay of a carry bit computation achievable by any prefix carry bit circuit and develop an algorithm that computes a prefix carry bit circuit with optimum delay up to a small additive constant.
Furthermore, we use this algorithm to construct a small parallel prefix adder.
Compared to existing algorithms we simultaneously improve the delay and size guarantee, as well as the running time for constructing prefix carry bit and adder circuits.
In this paper, we present a method for instance ranking and retrieval at fine-grained level based on the global features extracted from a multi-attribute recognition model which is not dependent on landmarks information or part-based annotations.
Further, we make this architecture suitable for mobile-device application by adopting the bilinear CNN to make the multi-attribute recognition model smaller (in terms of the number of parameters).
The experiments run on the Dress category of DeepFashion In-Shop Clothes Retrieval and CUB200 datasets show that the results of instance retrieval at fine-grained level are promising for these datasets, specially in terms of texture and color.
The nonstationary nature of signals and nonlinear systems require the time-frequency representation.
In time-domain signal, frequency information is derived from the phase of the Gabor's analytic signal which is practically obtained by the inverse Fourier transform.
This study presents time-frequency analysis by the Fourier transform which maps the time-domain signal into the frequency-domain.
In this study, we derive the time information from the phase of the frequency-domain signal and obtain the time-frequency representation.
In order to obtain the time information in Fourier domain, we define the concept of `frequentaneous time' which is frequency derivative of phase.
This is very similar to the group delay, which is also defined as frequency derivative of phase and it provide physical meaning only when it is positive.
The frequentaneous time is always positive or negative depending upon whether signal is defined for only positive or negative times, respectively.
If a signal is defined for both positive and negative times, then we divide the signal into two parts, signal for positive times and signal for negative times.
The proposed frequentaneous time and Fourier transform based time-frequency distribution contains only those frequencies which are present in the Fourier spectrum.
Simulations and numerical results, on many simulated as well as read data, demonstrate the efficacy of the proposed method for the time-frequency analysis of a signal.
Training complex machine learning models for prediction often requires a large amount of data that is not always readily available.
Leveraging these external datasets from related but different sources is therefore an important task if good predictive models are to be built for deployment in settings where data can be rare.
In this paper we propose a novel approach to the problem in which we use multiple GAN architectures to learn to translate from one dataset to another, thereby allowing us to effectively enlarge the target dataset, and therefore learn better predictive models than if we simply used the target dataset.
We show the utility of such an approach, demonstrating that our method improves the prediction performance on the target domain over using just the target dataset and also show that our framework outperforms several other benchmarks on a collection of real-world medical datasets.
We consider the problem of communication over a network containing a hidden and malicious adversary that can control a subset of network resources, and aims to disrupt communications.
We focus on omniscient node-based adversaries, i.e., the adversaries can control a subset of nodes, and know the message, network code and packets on all links.
Characterizing information-theoretically optimal communication rates as a function of network parameters and bounds on the adversarially controlled network is in general open, even for unicast (single source, single destination) problems.
In this work we characterize the information-theoretically optimal randomized capacity of such problems, i.e., under the assumption that the source node shares (an asymptotically negligible amount of) independent common randomness with each network node a priori (for instance, as part of network design).
We propose a novel computationally-efficient communication scheme whose rate matches a natural information-theoretically "erasure outer bound" on the optimal rate.
Our schemes require no prior knowledge of network topology, and can be implemented in a distributed manner as an overlay on top of classical distributed linear network coding.
This paper considers linear discrete-time systems with additive disturbances, and designs a Model Predictive Control (MPC) law to minimise a quadratic cost function subject to a chance constraint.
The chance constraint is defined as a discounted sum of violation probabilities on an infinite horizon.
By penalising violation probabilities close to the initial time and ignoring violation probabilities in the far future, this form of constraint enables the feasibility of the online optimisation to be guaranteed without an assumption of boundedness of the disturbance.
A computationally convenient MPC optimisation problem is formulated using Chebyshev's inequality and we introduce an online constraint-tightening technique to ensure recursive feasibility based on knowledge of a suboptimal solution.
The closed loop system is guaranteed to satisfy the chance constraint and a quadratic stability condition.
We study the task of Byzantine gathering in a network modeled as a graph.
Despite the presence of Byzantine agents, all the other (good) agents, starting from possibly different nodes and applying the same deterministic algorithm, have to meet at the same node in finite time and stop moving.
An adversary chooses the initial nodes of the agents and assigns a different label to each of them.
The agents move in synchronous rounds and communicate with each other only when located at the same node.
Within the team, f of the agents are Byzantine.
A Byzantine agent acts in an unpredictable way: in particular it may forge the label of another agent or create a completely new one.
Besides its label, which corresponds to a local knowledge, an agent is assigned some global knowledge GK that is common to all agents.
In literature, the Byzantine gathering problem has been analyzed in arbitrary n-node graphs by considering the scenario when GK=(n,f) and the scenario when GK=f.
In the first (resp. second) scenario, it has been shown that the minimum number of good agents guaranteeing deterministic gathering of all of them is f+1 (resp. f+2).
For both these scenarios, all the existing deterministic algorithms, whether or not they are optimal in terms of required number of good agents, have a time complexity that is exponential in n and L, where L is the largest label belonging to a good agent.
In this paper, we seek to design a deterministic solution for Byzantine gathering that makes a concession on the proportion of Byzantine agents within the team, but that offers a significantly lower complexity.
We also seek to use a global knowledge whose the length of the binary representation is small.
Assuming that the agents are in a strong team i.e., a team in which the number of good agents is at least some prescribed value that is quadratic in f, we give positive and negative results.
Recommender systems benefit us in tackling the problem of information overload by predicting our potential choices among diverse niche objects.
So far, a variety of personalized recommendation algorithms have been proposed and most of them are based on similarities, such as collaborative filtering and mass diffusion.
Here, we propose a novel vertex similarity index named CosRA, which combines advantages of both the cosine index and the resource-allocation (RA) index.
By applying the CosRA index to real recommender systems including MovieLens, Netflix and RYM, we show that the CosRA-based method has better performance in accuracy, diversity and novelty than some benchmark methods.
Moreover, the CosRA index is free of parameters, which is a significant advantage in real applications.
Further experiments show that the introduction of two turnable parameters cannot remarkably improve the overall performance of the CosRA index.
A lower bound for the interleaving distance on persistence vector spaces is given in terms of rank invariants.
This offers an alternative proof of the stability of rank invariants.
This paper proposes a hybrid self-adaptive evolutionary algorithm for graph coloring that is hybridized with the following novel elements: heuristic genotype-phenotype mapping, a swap local search heuristic, and a neutral survivor selection operator.
This algorithm was compared with the evolutionary algorithm with the SAW method of Eiben et al., the Tabucol algorithm of Hertz and de Werra, and the hybrid evolutionary algorithm of Galinier and Hao.
The performance of these algorithms were tested on a test suite consisting of randomly generated 3-colorable graphs of various structural features, such as graph size, type, edge density, and variability in sizes of color classes.
Furthermore, the test graphs were generated including the phase transition where the graphs are hard to color.
The purpose of the extensive experimental work was threefold: to investigate the behavior of the tested algorithms in the phase transition, to identify what impact hybridization with the DSatur traditional heuristic has on the evolutionary algorithm, and to show how graph structural features influence the performance of the graph-coloring algorithms.
The results indicate that the performance of the hybrid self-adaptive evolutionary algorithm is comparable with, or better than, the performance of the hybrid evolutionary algorithm which is one of the best graph-coloring algorithms today.
Moreover, the fact that all the considered algorithms performed poorly on flat graphs confirms that this type of graphs is really the hardest to color.
While both cost-sensitive learning and online learning have been studied extensively, the effort in simultaneously dealing with these two issues is limited.
Aiming at this challenge task, a novel learning framework is proposed in this paper.
The key idea is based on the fusion of online ensemble algorithms and the state of the art batch mode cost-sensitive bagging/boosting algorithms.
Within this framework, two separately developed research areas are bridged together, and a batch of theoretically sound online cost-sensitive bagging and online cost-sensitive boosting algorithms are first proposed.
Unlike other online cost-sensitive learning algorithms lacking theoretical analysis of asymptotic properties, the convergence of the proposed algorithms is guaranteed under certain conditions, and the experimental evidence with benchmark data sets also validates the effectiveness and efficiency of the proposed methods.
Object-oriented programming has long been regarded as too inefficient for SIMD high-performance computing, despite the fact that many important applications in HPC have an inherent object structure.
On SIMD accelerators including GPUs, this is mainly due to performance problems with memory allocation: There are a few libraries that support parallel memory allocation directly on accelerator devices, but all of them suffer from uncoalesed memory accesses.
In this work, we present DynaSOAr, a C++/CUDA data layout DSL for object-oriented programming, combined with a parallel dynamic object allocator.
DynaSOAr was designed for a class of object-oriented programs that we call Single-Method Multiple Objects (SMMO), in which parallelism is expressed over a set of objects.
DynaSOAr is the first GPU object allocator that provides a parallel do-all operation, which is the foundation of SMMO applications.
DynaSOAr improves the usage of allocated memory with a Structure of Arrays (SOA) data layout and achieves low memory fragmentation through efficient management of free and allocated memory blocks with lock-free, hierarchical bitmaps.
In our benchmarks, DynaSOAr achieves a significant speedup of application code of up to 3x over state-of-the-art allocators.
Moreover, DynaSOAr manages heap memory more efficiently than other allocators, allowing programmers to run up to 2x larger problem sizes with the same amount of memory.
Submodular extensions of an energy function can be used to efficiently compute approximate marginals via variational inference.
The accuracy of the marginals depends crucially on the quality of the submodular extension.
To identify the best possible extension, we show an equivalence between the submodular extensions of the energy and the objective functions of linear programming (LP) relaxations for the corresponding MAP estimation problem.
This allows us to (i) establish the worst-case optimality of the submodular extension for Potts model used in the literature; (ii) identify the worst-case optimal submodular extension for the more general class of metric labeling; and (iii) efficiently compute the marginals for the widely used dense CRF model with the help of a recently proposed Gaussian filtering method.
Using synthetic and real data, we show that our approach provides comparable upper bounds on the log-partition function to those obtained using tree-reweighted message passing (TRW) in cases where the latter is computationally feasible.
Importantly, unlike TRW, our approach provides the first practical algorithm to compute an upper bound on the dense CRF model.
Deep learning has significantly advanced the state of the art in artificial intelligence, gaining wide popularity from both industry and academia.
Special interest is around Convolutional Neural Networks (CNN), which take inspiration from the hierarchical structure of the visual cortex, to form deep layers of convolutional operations, along with fully connected classifiers.
Hardware implementations of these deep CNN architectures are challenged with memory bottlenecks that require many convolution and fully-connected layers demanding large amount of communication for parallel computation.
Multi-core CPU based solutions have demonstrated their inadequacy for this problem due to the memory wall and low parallelism.
Many-core GPU architectures show superior performance but they consume high power and also have memory constraints due to inconsistencies between cache and main memory.
FPGA design solutions are also actively being explored, which allow implementing the memory hierarchy using embedded BlockRAM.
This boosts the parallel use of shared memory elements between multiple processing units, avoiding data replicability and inconsistencies.
This makes FPGAs potentially powerful solutions for real-time classification of CNNs.
Both Altera and Xilinx have adopted OpenCL co-design framework from GPU for FPGA designs as a pseudo-automatic development solution.
In this paper, a comprehensive evaluation and comparison of Altera and Xilinx OpenCL frameworks for a 5-layer deep CNN is presented.
Hardware resources, temporal performance and the OpenCL architecture for CNNs are discussed.
Xilinx demonstrates faster synthesis, better FPGA resource utilization and more compact boards.
Altera provides multi-platforms tools, mature design community and better execution times.
Adaptive tracking-by-detection approaches are popular for tracking arbitrary objects.
They treat the tracking problem as a classification task and use online learning techniques to update the object model.
However, these approaches are heavily invested in the efficiency and effectiveness of their detectors.
Evaluating a massive number of samples for each frame (e.g., obtained by a sliding window) forces the detector to trade the accuracy in favor of speed.
Furthermore, misclassification of borderline samples in the detector introduce accumulating errors in tracking.
In this study, we propose a co-tracking based on the efficient cooperation of two detectors: a rapid adaptive exemplar-based detector and another more sophisticated but slower detector with a long-term memory.
The sampling labeling and co-learning of the detectors are conducted by an uncertainty sampling unit, which improves the speed and accuracy of the system.
We also introduce a budgeting mechanism which prevents the unbounded growth in the number of examples in the first detector to maintain its rapid response.
Experiments demonstrate the efficiency and effectiveness of the proposed tracker against its baselines and its superior performance against state-of-the-art trackers on various benchmark videos.
We study the problem of searching for and tracking a collection of moving targets using a robot with a limited Field-Of-View (FOV) sensor.
The actual number of targets present in the environment is not known a priori.
We propose a search and tracking framework based on the concept of Bayesian Random Finite Sets (RFSs).
Specifically, we generalize the Gaussian Mixture Probability Hypothesis Density (GM-PHD) filter which was previously applied for tracking problems to allow for simultaneous search and tracking with a limited FOV sensor.
The proposed framework can extract individual target tracks as well as estimate the number and the spatial density of targets.
We also show how to use the Gaussian Process (GP) regression to extract and predict non-linear target trajectories in this framework.
We demonstrate the efficacy of our techniques through representative simulations and a real data collected from an aerial robot.
Electronic medical records (EMR) contain longitudinal information about patients that can be used to analyze outcomes.
Typically, studies on EMR data have worked with established variables that have already been acknowledged to be associated with certain outcomes.
However, EMR data may also contain hitherto unrecognized factors for risk association and prediction of outcomes for a disease.
In this paper, we present a scalable data-driven framework to analyze EMR data corpus in a disease agnostic way that systematically uncovers important factors influencing outcomes in patients, as supported by data and without expert guidance.
We validate the importance of such factors by using the framework to predict for the relevant outcomes.
Specifically, we analyze EMR data covering approximately 47 million unique patients to characterize renal failure (RF) among type 2 diabetic (T2DM) patients.
We propose a specialized L1 regularized Cox Proportional Hazards (CoxPH) survival model to identify the important factors from those available from patient encounter history.
To validate the identified factors, we use a specialized generalized linear model (GLM) to predict the probability of renal failure for individual patients within a specified time window.
Our experiments indicate that the factors identified via our data-driven method overlap with the patient characteristics recognized by experts.
Our approach allows for scalable, repeatable and efficient utilization of data available in EMRs, confirms prior medical knowledge and can generate new hypothesis without expert supervision.
Cellular networks have special characteristics including highly variable channels, fast fluctuating capacities, deep per user buffers, self-inflicted queuing delays, radio uplink/downlink scheduling delays, etc.
These distinguishing properties make the problem of achieving low latency and high throughput in cellular networks more challenging than in wired networks.
That's why in this environment, TCP and its flavors, which are generally designed for wired networks, perform poorly.
To cope with these challenges, we present C2TCP, a flexible end-to-end solution targeting interactive applications requiring high throughput and low delay in cellular networks.
C2TCP stands on top of loss-based TCP and brings it delay sensitivity without requiring any network state profiling, channel prediction, or complicated rate adjustment mechanisms.
The key idea behind C2TCP is to absorb dynamics of unpredictable cellular channels by investigating local minimum delay of packets in a moving time window and react to the cellular network's capacity changes very fast.
Through extensive trace-based evaluations using traces from five commercial LTE and 3G networks, we have compared performance of C2TCP with various TCP variants, and state-of-the-art schemes including BBR, Verus, and Sprout.
Results show that on average, C2TCP outperforms these schemes and achieves lower average and 95th percentile delay for packets.
A robot operating in a real-world environment needs to perform reasoning over a variety of sensor modalities such as vision, language and motion trajectories.
However, it is extremely challenging to manually design features relating such disparate modalities.
In this work, we introduce an algorithm that learns to embed point-cloud, natural language, and manipulation trajectory data into a shared embedding space with a deep neural network.
To learn semantically meaningful spaces throughout our network, we use a loss-based margin to bring embeddings of relevant pairs closer together while driving less-relevant cases from different modalities further apart.
We use this both to pre-train its lower layers and fine-tune our final embedding space, leading to a more robust representation.
We test our algorithm on the task of manipulating novel objects and appliances based on prior experience with other objects.
On a large dataset, we achieve significant improvements in both accuracy and inference time over the previous state of the art.
We also perform end-to-end experiments on a PR2 robot utilizing our learned embedding space.
Optical coherence tomography (OCT) is a powerful and noninvasive method for retinal imaging.
In this paper, we introduce a fast segmentation method based on a new variant of spectral graph theory named diffusion maps.
The research is performed on spectral domain (SD) OCT images depicting macular and optic nerve head appearance.
The presented approach does not require edge-based image information and relies on regional image texture.
Consequently, the proposed method demonstrates robustness in situations of low image contrast or poor layer-to-layer image gradients.
Diffusion mapping is applied to 2D and 3D OCT datasets composed of two steps, one for partitioning the data into important and less important sections, and another one for localization of internal layers.In the first step, the pixels/voxels are grouped in rectangular/cubic sets to form a graph node.The weights of a graph are calculated based on geometric distances between pixels/voxels and differences of their mean intensity.The first diffusion map clusters the data into three parts, the second of which is the area of interest.
The other two sections are eliminated from the remaining calculations.
In the second step, the remaining area is subjected to another diffusion map assessment and the internal layers are localized based on their textural similarities.The proposed method was tested on 23 datasets from two patient groups (glaucoma and normals).
The mean unsigned border positioning errors(mean - SD) was 8.52 - 3.13 and 7.56 - 2.95 micrometer for the 2D and 3D methods, respectively.
Learning to act in unstructured environments, such as cluttered piles of objects, poses a substantial challenge for manipulation robots.
We present a novel neural network-based approach that separates unknown objects in clutter by selecting favourable push actions.
Our network is trained from data collected through autonomous interaction of a PR2 robot with randomly organized tabletop scenes.
The model is designed to propose meaningful push actions based on over-segmented RGB-D images.
We evaluate our approach by singulating up to 8 unknown objects in clutter.
We demonstrate that our method enables the robot to perform the task with a high success rate and a low number of required push actions.
Our results based on real-world experiments show that our network is able to generalize to novel objects of various sizes and shapes, as well as to arbitrary object configurations.
Videos of our experiments can be viewed at http://robotpush.cs.uni-freiburg.de
We present an approach for building an active agent that learns to segment its visual observations into individual objects by interacting with its environment in a completely self-supervised manner.
The agent uses its current segmentation model to infer pixels that constitute objects and refines the segmentation model by interacting with these pixels.
The model learned from over 50K interactions generalizes to novel objects and backgrounds.
To deal with noisy training signal for segmenting objects obtained by self-supervised interactions, we propose robust set loss.
A dataset of robot's interactions along-with a few human labeled examples is provided as a benchmark for future research.
We test the utility of the learned segmentation model by providing results on a downstream vision-based control task of rearranging multiple objects into target configurations from visual inputs alone.
Videos, code, and robotic interaction dataset are available at https://pathak22.github.io/seg-by-interaction/
In this paper, we present preliminary results of AFEL-REC, a recommender system for social learning environments.
AFEL-REC is build upon a scalable software architecture to provide recommendations of learning resources in near real-time.
Furthermore, AFEL-REC can cope with any kind of data that is present in social learning environments such as resource metadata, user interactions or social tags.
We provide a preliminary evaluation of three recommendation use cases implemented in AFEL-REC and we find that utilizing social data in form of tags is helpful for not only improving recommendation accuracy but also coverage.
This paper should be valuable for both researchers and practitioners interested in providing resource recommendations in social learning environments.
Deep learning has demonstrated tremendous break through in the area of image/video processing.
In this paper, a spatial-temporal residue network (STResNet) based in-loop filter is proposed to suppress visual artifacts such as blocking, ringing in video coding.
Specifically, the spatial and temporal information is jointly exploited by taking both current block and co-located block in reference frame into consideration during the processing of in-loop filter.
The architecture of STResNet only consists of four convolution layers which shows hospitality to memory and coding complexity.
Moreover, to fully adapt the input content and improve the performance of the proposed in-loop filter, coding tree unit (CTU) level control flag is applied in the sense of rate-distortion optimization.
Extensive experimental results show that our scheme provides up to 5.1% bit-rate reduction compared to the state-of-the-art video coding standard.
Researchers have extensively explored predictive control strategies for controlling heating, ventilation, and air conditioning (HVAC) units in commercial buildings.
Predictive control strategies, however, critically rely on weather and occupancy forecasts.
Existing state-of-the-art building simulators are incapable of analysing the influence of prediction errors (in weather and occupancy) on HVAC energy consumption and occupant comfort.
In this paper, we introduce ThermalSim, a building simulator that can quantify the effect of prediction errors on the HVAC operations.
ThermalSim has been implemented in C/C++ and MATLAB.
We describe its design, use, and input format.
We present a neural transducer model with visual attention that learns to generate LaTeX markup of a real-world math formula given its image.
Applying sequence modeling and transduction techniques that have been very successful across modalities such as natural language, image, handwriting, speech and audio; we construct an image-to-markup model that learns to produce syntactically and semantically correct LaTeX markup code over 150 words long and achieves a BLEU score of 89%; improving upon the previous state-of-art for the Im2Latex problem.
We also demonstrate with heat-map visualization how attention helps in interpreting the model and can pinpoint (detect and localize) symbols on the image accurately despite having been trained without any bounding box data.
Weakly Supervised Object Detection (WSOD), using only image-level annotations to train object detectors, is of growing importance in object recognition.
In this paper, we propose a novel deep network for WSOD.
Unlike previous networks that transfer the object detection problem to an image classification problem using Multiple Instance Learning (MIL), our strategy generates proposal clusters to learn refined instance classifiers by an iterative process.
The proposals in the same cluster are spatially adjacent and associated with the same object.
This prevents the network from concentrating too much on parts of objects instead of whole objects.
We first show that instances can be assigned object or background labels directly based on proposal clusters for instance classifier refinement, and then show that treating each cluster as a small new bag yields fewer ambiguities than the directly assigning label method.
The iterative instance classifier refinement is implemented online using multiple streams in convolutional neural networks, where the first is an MIL network and the others are for instance classifier refinement supervised by the preceding one.
Experiments are conducted on the PASCAL VOC, ImageNet detection, and MS-COCO benchmarks for WSOD.
Results show that our method outperforms the previous state of the art significantly.
Consider a movie studio aiming to produce a set of new movies for summer release: What types of movies it should produce?
Who would the movies appeal to?
How many movies should it make?
Similar issues are encountered by a variety of organizations, e.g., mobile-phone manufacturers and online magazines, who have to create new (non-existent) items to satisfy groups of users with different preferences.
In this paper, we present a joint problem formalization of these interrelated issues, and propose generative methods that address these questions simultaneously.
Specifically, we leverage the latent space obtained by training a deep generative model---the Variational Autoencoder (VAE)---via a loss function that incorporates both rating performance and item reconstruction terms.
We then apply a greedy search algorithm that utilizes this learned latent space to jointly obtain K plausible new items, and user groups that would find the items appealing.
An evaluation of our methods on a synthetic dataset indicates that our approach is able to generate novel items similar to highly-desirable unobserved items.
As case studies on real-world data, we applied our method on the MART abstract art and Movielens Tag Genome dataset, which resulted in promising results: small and diverse sets of novel items.
We introduce an improved unsupervised clustering protocol specially suited for large-scale structured data.
The protocol follows three steps: a dimensionality reduction of the data, a density estimation over the low dimensional representation of the data, and a final segmentation of the density landscape.
For the dimensionality reduction step we introduce a parallelized implementation of the well-known t-Stochastic Neighbouring Embedding (t-SNE) algorithm that significantly alleviates some inherent limitations, while improving its suitability for large datasets.
We also introduce a new adaptive Kernel Density Estimation particularly coupled with the t-SNE framework in order to get accurate density estimates out of the embedded data, and a variant of the rainfalling watershed algorithm to identify clusters within the density landscape.
The whole mapping protocol is wrapped in the bigMap R package, together with visualization and analysis tools to ease the qualitative and quantitative assessment of the clustering.
Volunteer computing is being used successfully for large scale scientific computations.
This research is in the context of Volpex, a programming framework that supports communicating parallel processes in a volunteer environment.
Redundancy and checkpointing are combined to ensure consistent forward progress with Volpex in this unique execution environment characterized by heterogeneous failure prone nodes and interdependent replicated processes.
An important parameter for optimizing performance with Volpex is the frequency of checkpointing.
The paper presents a mathematical model to minimize the completion time for inter-dependent parallel processes running in a volunteer environment by finding a suitable checkpoint interval.
Validation is performed with a sample real world application running on a pool of distributed volunteer nodes.
The results indicate that the performance with our predicted checkpoint interval is fairly close to the best performance obtained empirically by varying the checkpoint interval.
A definite Horn theory is a set of n-dimensional Boolean vectors whose characteristic function is expressible as a definite Horn formula, that is, as conjunction of definite Horn clauses.
The class of definite Horn theories is known to be learnable under different query learning settings, such as learning from membership and equivalence queries or learning from entailment.
We propose yet a different type of query: the closure query.
Closure queries are a natural extension of membership queries and also a variant, appropriate in the context of definite Horn formulas, of the so-called correction queries.
We present an algorithm that learns conjunctions of definite Horn clauses in polynomial time, using closure and equivalence queries, and show how it relates to the canonical Guigues-Duquenne basis for implicational systems.
We also show how the different query models mentioned relate to each other by either showing full-fledged reductions by means of query simulation (where possible), or by showing their connections in the context of particular algorithms that use them for learning definite Horn formulas.
This is the preprint version of our paper on REHAB2015.
A balance measurement software based on Kinect2 sensor is evaluated by comparing to Wii balance board in numerical analysis level, and further improved according to the consideration of BFP (Body fat percentage) values of the user.
Several person with different body types are involved into the test.
The algorithm is improved by comparing the body type of the user to the 'golden- standard' body type.
The evaluation results of the optimized algorithm preliminarily prove the reliability of the software.
Facebook News Feed personalization algorithm has a significant impact, on a daily basis, on the lifestyle, mood and opinion of millions of Internet users.
Nonetheless, the behavior of such algorithms usually lacks transparency, motivating measurements, modeling and analysis in order to understand and improve its properties.
In this paper, we propose a reproducible methodology encompassing measurements and an analytical model to capture the visibility of publishers over a News Feed.
First, measurements are used to parameterize and to validate the expressive power of the proposed model.
Then, we conduct a what-if analysis to assess the visibility bias incurred by the users against a baseline derived from the model.
Our results indicate that a significant bias exists and it is more prominent at the top position of the News Feed.
In addition, we found that the bias is non-negligible even for users that are deliberately set as neutral with respect to their political views.
We present Breakout, a group interaction platform for online courses that enables the creation and measurement of face-to-face peer learning groups in online settings.
Breakout is designed to help students easily engage in synchronous, video breakout session based peer learning in settings that otherwise force students to rely on asynchronous text-based communication.
The platform also offers data collection and intervention tools for studying the communication patterns inherent in online learning environments.
The goals of the system are twofold: to enhance student engagement in online learning settings and to create a platform for research into the relationship between distributed group interaction patterns and learning outcomes.
Multiagent systems where agents interact among themselves and with a stochastic environment can be formalized as stochastic games.
We study a subclass named Markov potential games (MPGs) that appear often in economic and engineering applications when the agents share a common resource.
We consider MPGs with continuous state-action variables, coupled constraints and nonconvex rewards.
Previous analysis followed a variational approach that is only valid for very simple cases (convex rewards, invertible dynamics, and no coupled constraints); or considered deterministic dynamics and provided open-loop (OL) analysis, studying strategies that consist in predefined action sequences, which are not optimal for stochastic environments.
We present a closed-loop (CL) analysis for MPGs and consider parametric policies that depend on the current state.
We provide easily verifiable, sufficient and necessary conditions for a stochastic game to be an MPG, even for complex parametric functions (e.g., deep neural networks); and show that a closed-loop Nash equilibrium (NE) can be found (or at least approximated) by solving a related optimal control problem (OCP).
This is useful since solving an OCP--which is a single-objective problem--is usually much simpler than solving the original set of coupled OCPs that form the game--which is a multiobjective control problem.
This is a considerable improvement over the previously standard approach for the CL analysis of MPGs, which gives no approximate solution if no NE belongs to the chosen parametric family, and which is practical only for simple parametric forms.
We illustrate the theoretical contributions with an example by applying our approach to a noncooperative communications engineering game.
We then solve the game with a deep reinforcement learning algorithm that learns policies that closely approximates an exact variational NE of the game.
Creating rankings might seem like a vain exercise in belly-button gazing, even more so for people so unlike that kind of things as programmers.
However, in this paper we will try to prove how creating city (or province) based rankings in Spain has led to all kind of interesting effects, including increased productivity and community building.
We describe the methodology we have used to search for programmers residing in a particular province focusing on those where most population is concentrated and apply different measures to show how these communities differ in structure, number and productivity.
Most Information Retrieval models compute the relevance score of a document for a given query by summing term weights specific to a document or a query.
Heuristic approaches, like TF-IDF, or probabilistic models, like BM25, are used to specify how a term weight is computed.
In this paper, we propose to leverage learning-to-rank principles to learn how to compute a term weight for a given document based on the term occurrence pattern.
A method is presented for solving the discrete-time finite-horizon Linear Quadratic Regulator (LQR) problem subject to auxiliary linear equality constraints, such as fixed end-point constraints.
The method explicitly determines an affine relationship between the control and state variables, as in standard Riccati recursion, giving rise to feedback control policies that account for constraints.
Since the linearly-constrained LQR problem arises commonly in robotic trajectory optimization, having a method that can efficiently compute these solutions is important.
We demonstrate some of the useful properties and interpretations of said control policies, and we compare the computation time of our method against existing methods.
Multi-kernel polar codes have recently been proposed to construct polar codes of lengths different from powers of two.
Decoder implementations for multi-kernel polar codes need to account for this feature, that becomes critical in memory management.
We propose an efficient, generalized memory management framework for implementation of successivecancellation decoding of multi-kernel polar codes.
It can be used on many types of hardware architectures and different flavors of SC decoding algorithms.
We illustrate the proposed solution for small kernel sizes, and give complexity estimates for various kernel combinations and code lengths.
Modern Code Review (MCR) plays a key role in software quality practices.
In MCR process, a new patch (i.e., a set of code changes) is encouraged to be examined by reviewers in order to identify weaknesses in source code prior to an integration into main software repositories.
To mitigate the risk of having future defects, prior work suggests that MCR should be performed with sufficient review participation.
Indeed, recent work shows that a low number of participated reviewers is associated with poor software quality.
However, there is a likely case that a new patch still suffers from poor review participation even though reviewers were invited.
Hence, in this paper, we set out to investigate the factors that are associated with the participation decision of an invited reviewer.
Through a case study of 230,090 patches spread across the Android, LibreOffice, OpenStack and Qt systems, we find that (1) 16%-66% of patches have at least one invited reviewer who did not respond to the review invitation; (2) human factors play an important role in predicting whether or not an invited reviewer will participate in a review; (3) a review participation rate of an invited reviewers and code authoring experience of an invited reviewer are highly associated with the participation decision of an invited reviewer.
These results can help practitioners better understand about how human factors associate with the participation decision of reviewers and serve as guidelines for inviting reviewers, leading to a better inviting decision and a better reviewer participation.
Today computers have become an integral part of life.
However, most people's interaction with computers in on end-user-level.
Computer engineers are needed while designing and developing a structure of computer systems, software and hardware systems and also they need when implementing and solving problems while using these systems.
Training of qualified computer engineers is vital to have a say in future technology.
Recently, big data analysis, cloud technologies, wearable technologies, mobile and online services become popular.
For that reason, computer engineering education should update itself regularly and keep up with the latest improvements.
In this study, it is touched on some topics which are suggested to extend computer engineering curricula such as big data analyses, wearable technologies internet of things, cloud technologies, identity management and cyber security which are expected to widening in the area and also demanded that computer engineering student should be qualified on.
Related topics will be described and usage areas will be explained, developments and future roles will be mentioned and also expected achievements will be described.
These achievement's relevance with learning outcomes of departments which are accredited by MUDEK will be defined.
We propose a Markov chain simulation method to generate simple connected random graphs with a specified degree sequence and level of clustering.
The networks generated by our algorithm are random in all other respects and can thus serve as generic models for studying the impacts of degree distributions and clustering on dynamical processes as well as null models for detecting other structural properties in empirical networks.
Deep metrics have been shown effective as similarity measures in multi-modal image registration; however, the metrics are currently constructed from aligned image pairs in the training data.
In this paper, we propose a strategy for learning such metrics from roughly aligned training data.
Symmetrizing the data corrects bias in the metric that results from misalignment in the data (at the expense of increased variance), while random perturbations to the data, i.e.dithering, ensures that the metric has a single mode, and is amenable to registration by optimization.
Evaluation is performed on the task of registration on separate unseen test image pairs.
The results demonstrate the feasibility of learning a useful deep metric from substantially misaligned training data, in some cases the results are significantly better than from Mutual Information.
Data augmentation via dithering is, therefore, an effective strategy for discharging the need for well-aligned training data; this brings deep metric registration from the realm of supervised to semi-supervised machine learning.
The effectiveness and scalability of MapReduce-based implementations of complex data-intensive tasks depend on an even redistribution of data between map and reduce tasks.
In the presence of skewed data, sophisticated redistribution approaches thus become necessary to achieve load balancing among all reduce tasks to be executed in parallel.
For the complex problem of entity resolution, we propose and evaluate two approaches for such skew handling and load balancing.
The approaches support blocking techniques to reduce the search space of entity resolution, utilize a preprocessing MapReduce job to analyze the data distribution, and distribute the entities of large blocks among multiple reduce tasks.
The evaluation on a real cloud infrastructure shows the value and effectiveness of the proposed load balancing approaches.
Automated tests play an important role in software evolution because they can rapidly detect faults introduced during changes.
In practice, code-coverage metrics are often used as criteria to evaluate the effectiveness of test suites with focus on regression faults.
However, code coverage only expresses which portion of a system has been executed by tests, but not how effective the tests actually are in detecting regression faults.
Our goal was to evaluate the validity of code coverage as a measure for test effectiveness.
To do so, we conducted an empirical study in which we applied an extreme mutation testing approach to analyze the tests of open-source projects written in Java.
We assessed the ratio of pseudo-tested methods (those tested in a way such that faults would not be detected) to all covered methods and judged their impact on the software project.
The results show that the ratio of pseudo-tested methods is acceptable for unit tests but not for system tests (that execute large portions of the whole system).
Therefore, we conclude that the coverage metric is only a valid effectiveness indicator for unit tests.
In this paper we propose a new parallel architecture based on Big Data technologies for real-time sentiment analysis on microblogging posts.
Polypus is a modular framework that provides the following functionalities: (1) massive text extraction from Twitter, (2) distributed non-relational storage optimized for time range queries, (3) memory-based intermodule buffering, (4) real-time sentiment classification, (5) near real-time keyword sentiment aggregation in time series, (6) a HTTP API to interact with the Polypus cluster and (7) a web interface to analyze results visually.
The whole architecture is self-deployable and based on Docker containers.
Due to the fact that Korean is a highly agglutinative, character-rich language, previous work on Korean morphological analysis typically employs the use of sub-character features known as graphemes or otherwise utilizes comprehensive prior linguistic knowledge (i.e., a dictionary of known morphological transformation forms, or actions).
These models have been created with the assumption that character-level, dictionary-less morphological analysis was intractable due to the number of actions required.
We present, in this study, a multi-stage action-based model that can perform morphological transformation and part-of-speech tagging using arbitrary units of input and apply it to the case of character-level Korean morphological analysis.
Among models that do not employ prior linguistic knowledge, we achieve state-of-the-art word and sentence-level tagging accuracy with the Sejong Korean corpus using our proposed data-driven Bi-LSTM model.
Fast development of sharing services becomes a crucial part of the process in constructing a cyber-enabled world, as sharing services reinvent how people exchange and obtain goods or services.
However, privacy leakage or disclosure is a key concern which may hinder the development of sharing services.
While significant efforts have been undertaken to address various privacy issues in recent years, there is a surprising lack of a review for privacy concerns in the cyber-enabled sharing world.
To bridge the gap, in this study, we survey and evaluate existing and emerging privacy issues relating to sharing services from various perspectives.
Differing from existing similar works on surveying sharing practices in various fields, our work comprehensively covers six directions of sharing services in the cyber-enabled world and selects solutions mostly from the recent five years.
Finally, we conclude the issues and solutions from three perspectives, namely, the user, platform and service provider perspectives.
We consider a wireless distributed computing system, in which multiple mobile users, connected wirelessly through an access point, collaborate to perform a computation task.
In particular, users communicate with each other via the access point to exchange their locally computed intermediate computation results, which is known as data shuffling.
We propose a scalable framework for this system, in which the required communication bandwidth for data shuffling does not increase with the number of users in the network.
The key idea is to utilize a particular repetitive pattern of placing the dataset (thus a particular repetitive pattern of intermediate computations), in order to provide coding opportunities at both the users and the access point, which reduce the required uplink communication bandwidth from users to access point and the downlink communication bandwidth from access point to users by factors that grow linearly with the number of users.
We also demonstrate that the proposed dataset placement and coded shuffling schemes are optimal (i.e., achieve the minimum required shuffling load) for both a centralized setting and a decentralized setting, by developing tight information-theoretic lower bounds.
Multigrid algorithms are among the fastest iterative methods known today for solving large linear and some non-linear systems of equations.
Greatly optimized for serial operation, they still have a great potential for parallelism not fully realized.
In this work, we present a novel multigrid algorithm designed to work entirely inside many-core architectures like the graphics processing units (GPUs), without memory transfers between the GPU and the central processing unit (CPU), avoiding low bandwitdth communications.
The algorithm is denoted as the high occupancy multigrid (HOMG) because it makes use of entire grid operations with interpolations and relaxations fused into one task, providing useful work for every thread in the grid.
For a given accuracy, its number of operations scale linearly with the total number of nodes in the grid.
Perfect scalability is observed for a large number of processors.
Current advances in the development of autonomous cars suggest that driverless cars may see wide-scale deployment in the near future.
Research by both industry and academia is driven by potential benefits of this new technology, including reductions in fatalities and improvements in traffic and fuel efficiency as well as greater mobility for people who will or cannot drive cars themselves.
A deciding factor for the adoption of self-driving cars besides safety will be the comfort of the passengers.
This report looks at cost functions currently used in motion planning methods for autonomous on-road driving.
Specifically, how the human perception of how comfortable a trajectory is can be formulated within cost functions.
Individuals have an intuitive perception of what makes a good coincidence.
Though the sensitivity to coincidences has often been presented as resulting from an erroneous assessment of probability, it appears to be a genuine competence, based on non-trivial computations.
The model presented here suggests that coincidences occur when subjects perceive complexity drops.
Co-occurring events are, together, simpler than if considered separately.
This model leads to a possible redefinition of subjective probability.
We first explain the notion of secret sharing and also threshold schemes, which can be implemented with the Shamir's secret sharing.
Subsequently, we review social secret sharing (NSG'10,NS'10) and its trust function.
In a secret sharing scheme, a secret is shared among a group of players who can later recover the secret.
We review the construction of a social secret sharing scheme and its application for resource management in cloud, as explained in NS'12.
To clarify the social secret sharing scheme, we first review its trust function according to NL'06.
In this scheme, a secret is maintained by assigning a trust value to each player based on his behavior, i.e., availability.
Much of the recent progress in Vision-to-Language (V2L) problems has been achieved through a combination of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs).
This approach does not explicitly represent high-level semantic concepts, but rather seeks to progress directly from image features to text.
We propose here a method of incorporating high-level concepts into the very successful CNN-RNN approach, and show that it achieves a significant improvement on the state-of-the-art performance in both image captioning and visual question answering.
We also show that the same mechanism can be used to introduce external semantic information and that doing so further improves performance.
In doing so we provide an analysis of the value of high level semantic information in V2L problems.
The functional programming language Erlang is well-suited for concurrent and distributed applications.
Numerical computing, however, is not seen as one of its strengths.
The recent introduction of Federated Learning, a concept according to which client devices are leveraged for decentralized machine learning tasks, while a central server updates and distributes a global model, provided the motivation for exploring how well Erlang is suited to that problem.
We present ffl-erl, a framework for Federated Learning, written in Erlang, and explore how well it performs in two scenarios: one in which the entire system has been written in Erlang, and another in which Erlang is relegated to coordinating client processes that rely on performing numerical computations in the programming language C. There is a concurrent as well as a distributed implementation of each case.
Erlang incurs a performance penalty, but for certain use cases this may not be detrimental, considering the trade-off between conciseness of the language and speed of development (Erlang) versus performance (C).
Thus, Erlang may be a viable alternative to C for some practical machine learning tasks.
Machine learning methods play increasingly important roles in pre-procedural planning for complex surgeries and interventions.
Very often, however, researchers find the historical records of emerging surgical techniques, such as the transcatheter aortic valve replacement (TAVR), are highly scarce in quantity.
In this paper, we address this challenge by proposing novel generative invertible networks (GIN) to select features and generate high-quality virtual patients that may potentially serve as an additional data source for machine learning.
Combining a convolutional neural network (CNN) and generative adversarial networks (GAN), GIN discovers the pathophysiologic meaning of the feature space.
Moreover, a test of predicting the surgical outcome directly using the selected features results in a high accuracy of 81.55%, which suggests little pathophysiologic information has been lost while conducting the feature selection.
This demonstrates GIN can generate virtual patients not only visually authentic but also pathophysiologically interpretable.
We present an analysis into the inner workings of Convolutional Neural Networks (CNNs) for processing text.
CNNs used for computer vision can be interpreted by projecting filters into image space, but for discrete sequence inputs CNNs remain a mystery.
We aim to understand the method by which the networks process and classify text.
We examine common hypotheses to this problem: that filters, accompanied by global max-pooling, serve as ngram detectors.
We show that filters may capture several different semantic classes of ngrams by using different activation patterns, and that global max-pooling induces behavior which separates important ngrams from the rest.
Finally, we show practical use cases derived from our findings in the form of model interpretability (explaining a trained model by deriving a concrete identity for each filter, bridging the gap between visualization tools in vision tasks and NLP) and prediction interpretability (explaining predictions).
As an important and challenging problem in computer vision, learning based optical flow estimation aims to discover the intrinsic correspondence structure between two adjacent video frames through statistical learning.
Therefore, a key issue to solve in this area is how to effectively model the multi-scale correspondence structure properties in an adaptive end-to-end learning fashion.
Motivated by this observation, we propose an end-to-end multi-scale correspondence structure learning (MSCSL) approach for optical flow estimation.
In principle, the proposed MSCSL approach is capable of effectively capturing the multi-scale inter-image-correlation correspondence structures within a multi-level feature space from deep learning.
Moreover, the proposed MSCSL approach builds a spatial Conv-GRU neural network model to adaptively model the intrinsic dependency relationships among these multi-scale correspondence structures.
Finally, the above procedures for correspondence structure learning and multi-scale dependency modeling are implemented in a unified end-to-end deep learning framework.
Experimental results on several benchmark datasets demonstrate the effectiveness of the proposed approach.
Modularisation, repetition, and symmetry are structural features shared by almost all biological neural networks.
These features are very unlikely to be found by the means of structural evolution of artificial neural networks.
This paper introduces NMODE, which is specifically designed to operate on neuro-modules.
NMODE addresses a second problem in the context of evolutionary robotics, which is incremental evolution of complex behaviours for complex machines, by offering a way to interface neuro-modules.
The scenario in mind is a complex walking machine, for which a locomotion module is evolved first, that is then extended by other modules in later stages.
We show that NMODE is able to evolve a locomotion behaviour for a standard six-legged walking machine in approximately 10 generations and show how it can be used for incremental evolution of a complex walking machine.
The entire source code used in this paper is publicly available through GitHub.
We present the Mixed Likelihood Gaussian process latent variable model (GP-LVM), capable of modeling data with attributes of different types.
The standard formulation of GP-LVM assumes that each observation is drawn from a Gaussian distribution, which makes the model unsuited for data with e.g. categorical or nominal attributes.
Our model, for which we use a sampling based variational inference, instead assumes a separate likelihood for each observed dimension.
This formulation results in more meaningful latent representations, and give better predictive performance for real world data with dimensions of different types.
Iterative Closest Point (ICP) is a widely used method for performing scan-matching and registration.
Being simple and robust method, it is still computationally expensive and may be challenging to use in real-time applications with limited resources on mobile platforms.
In this paper we propose novel effective method for acceleration of ICP which does not require substantial modifications to the existing code.
This method is based on an idea of Anderson acceleration which is an iterative procedure for finding a fixed point of contractive mapping.
The latter is often faster than a standard Picard iteration, usually used in ICP implementations.
We show that ICP, being a fixed point problem, can be significantly accelerated by this method enhanced by heuristics to improve overall robustness.
We implement proposed approach into Point Cloud Library (PCL) and make it available online.
Benchmarking on real-world data fully supports our claims.
In recent years two sets of planar (2D) shape attributes, provided with an intuitive physical meaning, were proposed to the remote sensing community by, respectively, Nagao & Matsuyama and Shackelford & Davis in their seminal works on the increasingly popular geographic object based image analysis (GEOBIA) paradigm.
These two published sets of intuitive geometric features were selected as initial conditions by the present R&D software project, whose multi-objective goal was to accomplish: (i) a minimally dependent and maximally informative design (knowledge/information representation) of a general purpose, user and application independent dictionary of 2D shape terms provided with a physical meaning intuitive to understand by human end users and (ii) an effective (accurate, scale invariant, easy to use) and efficient implementation of 2D shape descriptors.
To comply with the Quality Assurance Framework for Earth Observation guidelines, the proposed suite of geometric functions is validated by means of a novel quantitative quality assurance policy, centered on inter feature dependence (causality) assessment.
This innovative multivariate feature validation strategy is alternative to traditional feature selection procedures based on either inductive data learning classification accuracy estimation, which is inherently case specific, or cross correlation estimation, because statistical cross correlation does not imply causation.
The project deliverable is an original general purpose software suite of seven validated off the shelf 2D shape descriptors intuitive to use.
Alternative to existing commercial or open source software libraries of tens of planar shape functions whose informativeness remains unknown, it is eligible for use in (GE)OBIA systems in operating mode, expected to mimic human reasoning based on a convergence of evidence approach.
Knowledge Management is a global process in companies.
It includes all the processes that allow capitalization, sharing and evolution of the Knowledge Capital of the firm, generally recognized as a critical resource of the organization.
Several approaches have been defined to capitalize knowledge but few of them study how to learn from this knowledge.
We present in this paper an approach that helps to enhance learning from profession knowledge in an organisation.
We apply our approach on knitting industry.
One of the distinguishing aspects of human language is its compositionality, which allows us to describe complex environments with limited vocabulary.
Previously, it has been shown that neural network agents can learn to communicate in a highly structured, possibly compositional language based on disentangled input (e.g. hand- engineered features).
Humans, however, do not learn to communicate based on well-summarized features.
In this work, we train neural agents to simultaneously develop visual perception from raw image pixels, and learn to communicate with a sequence of discrete symbols.
The agents play an image description game where the image contains factors such as colors and shapes.
We train the agents using the obverter technique where an agent introspects to generate messages that maximize its own understanding.
Through qualitative analysis, visualization and a zero-shot test, we show that the agents can develop, out of raw image pixels, a language with compositional properties, given a proper pressure from the environment.
There is a plenty of research going on in field of object recognition, but object state recognition has not been addressed as much.
There are many important applications which can utilize object state recognition, such as, in robotics, to decide for how to grab an object.
A convolution neural network was designed to classify an image to one of its states.
The approach used for training is transfer learning with Inception v3 module of GoogLeNet used as the pre-trained model.
The model was trained on images of 18 cooking objects and tested on another set of cooking objects.
The model was able to classify those images with 76% accuracy.
In this letter, we develop a converse bound on the asymptotic load threshold of coded slotted ALOHA (CSA) schemes with K-multi packet reception capabilities at the receiver.
Density evolution is used to track the average probability of packet segment loss and an area matching condition is applied to obtain the converse.
For any given CSA rate, the converse normalized to K increases with K, which is in contrast with the results obtained so far for slotted ALOHA schemes based on successive interference cancellation.
We show how the derived bound can be approached using spatially-coupled CSA.
Traditional fact checking by experts and analysts cannot keep pace with the volume of newly created information.
It is important and necessary, therefore, to enhance our ability to computationally determine whether some statement of fact is true or false.
We view this problem as a link-prediction task in a knowledge graph, and present a discriminative path-based method for fact checking in knowledge graphs that incorporates connectivity, type information, and predicate interactions.
Given a statement S of the form (subject, predicate, object), for example, (Chicago, capitalOf, Illinois), our approach mines discriminative paths that alternatively define the generalized statement (U.S. city, predicate, U.S. state) and uses the mined rules to evaluate the veracity of statement S. We evaluate our approach by examining thousands of claims related to history, geography, biology, and politics using a public, million node knowledge graph extracted from Wikipedia and PubMedDB.
Not only does our approach significantly outperform related models, we also find that the discriminative predicate path model is easily interpretable and provides sensible reasons for the final determination.
This paper extends our previous work on regularization of neural networks using Eigenvalue Decay by employing a soft approximation of the dominant eigenvalue in order to enable the calculation of its derivatives in relation to the synaptic weights, and therefore the application of back-propagation, which is a primary demand for deep learning.
Moreover, we extend our previous theoretical analysis to deep neural networks and multiclass classification problems.
Our method is implemented as an additional regularizer in Keras, a modular neural networks library written in Python, and evaluated in the benchmark data sets Reuters Newswire Topics Classification, IMDB database for binary sentiment classification, MNIST database of handwritten digits and CIFAR-10 data set for image classification.
Fault based testing is a technique in which test cases are chosen to reveal certain classes of faults.
At present, testing professionals use their personal experience to select testing methods for fault classes considered the most likely to be present.
However, there is little empirical evidence available in the open literature to support these intuitions.
By examining the source code changes when faults were fixed in seven open source software artifacts, we have classified bug fix patterns into fault classes, and recorded the relative frequencies of the identified fault classes.
This paper reports our findings related to "if-conditional" fixes.
We have classified the "if-conditional" fixes into fourteen fault classes and calculated their frequencies.
We found the most common fault class related to changes within a single "atom".
The next most common fault was the omission of an "atom".
We analysed these results in the context of Boolean specification testing.
Skin cancer is one of the major types of cancers and its incidence has been increasing over the past decades.
Skin lesions can arise from various dermatologic disorders and can be classified to various types according to their texture, structure, color and other morphological features.
The accuracy of diagnosis of skin lesions, specifically the discrimination of benign and malignant lesions, is paramount to ensure appropriate patient treatment.
Machine learning-based classification approaches are among popular automatic methods for skin lesion classification.
While there are many existing methods, convolutional neural networks (CNN) have shown to be superior over other classical machine learning methods for object detection and classification tasks.
In this work, a fully automatic computerized method is proposed, which employs well established pre-trained convolutional neural networks and ensembles learning to classify skin lesions.
We trained the networks using 2000 skin lesion images available from the ISIC 2017 challenge, which has three main categories and includes 374 melanoma, 254 seborrheic keratosis and 1372 benign nevi images.
The trained classifier was then tested on 150 unlabeled images.
The results, evaluated by the challenge organizer and based on the area under the receiver operating characteristic curve (AUC), were 84.8% and 93.6% for Melanoma and seborrheic keratosis binary classification problem, respectively.
The proposed method achieved competitive results to experienced dermatologist.
Further improvement and optimization of the proposed method with a larger training dataset could lead to a more precise, reliable and robust method for skin lesion classification.
Permutation codes, in the form of rank modulation, have shown promise for applications such as flash memory.
One of the metrics recently suggested as appropriate for rank modulation is the Ulam metric, which measures the minimum translocation distance between permutations.
Multipermutation codes have also been proposed as a generalization of permutation codes that would improve code size (and consequently the code rate).
In this paper we analyze the Ulam metric in the context of multipermutations, noting some similarities and differences between the Ulam metric in the context of permutations.
We also consider sphere sizes for multipermutations under the Ulam metric and resulting bounds on code size.
Over the years the Artificial Intelligence (AI) community has produced several datasets which have given the machine learning algorithms the opportunity to learn various skills across various domains.
However, a subclass of these machine learning algorithms that aimed at learning logic programs, namely the Inductive Logic Programming algorithms, have often failed at the task due to the vastness of these datasets.
This has impacted the usability of knowledge representation and reasoning techniques in the development of AI systems.
In this research, we try to address this scalability issue for the algorithms that learn answer set programs.
We present a sound and complete algorithm which takes the input in a slightly different manner and performs an efficient and more user controlled search for a solution.
We show via experiments that our algorithm can learn from two popular datasets from machine learning community, namely bAbl (a question answering dataset) and MNIST (a dataset for handwritten digit recognition), which to the best of our knowledge was not previously possible.
The system is publicly available at https://goo.gl/KdWAcV.
This paper is under consideration for acceptance in TPLP.
Convolutional neural networks (CNNs) are inherently equivariant to translation.
Efforts to embed other forms of equivariance have concentrated solely on rotation.
We expand the notion of equivariance in CNNs through the Polar Transformer Network (PTN).
PTN combines ideas from the Spatial Transformer Network (STN) and canonical coordinate representations.
The result is a network invariant to translation and equivariant to both rotation and scale.
PTN is trained end-to-end and composed of three distinct stages: a polar origin predictor, the newly introduced polar transformer module and a classifier.
PTN achieves state-of-the-art on rotated MNIST and the newly introduced SIM2MNIST dataset, an MNIST variation obtained by adding clutter and perturbing digits with translation, rotation and scaling.
The ideas of PTN are extensible to 3D which we demonstrate through the Cylindrical Transformer Network.
We present a new family of one-coincidence sequence sets suitable for frequency hopping code division multiple access (FH-CDMA) systems with dispersed (low density) sequence elements.
These sets are derived from one-coincidence prime sequence sets, such that for each one-coincidence prime sequence set there is a new one-coincidence set comprised of sequences with dispersed sequence elements, required in some circumstances, for FH-CDMA systems.
Getting rid of crowdedness of sequence elements is achieved by doubling the size of the sequence element alphabet.
In addition, this doubling process eases control over the distance between adjacent sequence elements.
Properties of the new sets are discussed.
Recent years have seen proliferation in versatile mobile devices and an upsurge in the growth of data-consuming application services.
Orthogonal multiple access (OMA) technologies in today's mobile systems fall inefficient in the presence of such massive connectivity and traffic demands.
In this regards, non-orthogonal multiple access (NOMA) has been advocated by the research community to embrace unprecedented requirements.
Current NOMA designs have been demonstrated to largely improve conventional system performance in terms of throughput and latency, while their impact on the end users' perceived experience has not yet been comprehensively understood.
We envision that quality-of-experience (QoE) awareness is a key pillar for NOMA designs to fulfill versatile user demands in the 5th generation (5G) wireless communication systems.
This article systematically investigates QoE-aware NOMA designs that translate the physical-layer benefits of NOMA to the improvement of users' perceived experience in upper layers.
We shed light on design principles and key challenges in realizing QoE-aware NOMA designs.
With these principles and challenges in mind, we develop a general architecture with a dynamic network scheduling scheme.
We provide some implications for future QoE-aware NOMA designs by conducting a case study in video streaming applications.
Morphisms to finite semigroups can be used for recognizing omega-regular languages.
The so-called strongly recognizing morphisms can be seen as a deterministic computation model which provides minimal objects (known as the syntactic morphism) and a trivial complementation procedure.
We give a quadratic-time algorithm for computing the syntactic morphism from any given strongly recognizing morphism, thereby showing that minimization is easy as well.
In addition, we give algorithms for efficiently solving various decision problems for weakly recognizing morphisms.
Weakly recognizing morphism are often smaller than their strongly recognizing counterparts.
Finally, we describe the language operations needed for converting formulas in monadic second-order logic (MSO) into strongly recognizing morphisms, and we give some experimental results.
Software-Defined Networking (SDN) is an emerging paradigm that promises to change this state of affairs, by breaking vertical integration, separating the network's control logic from the underlying routers and switches, promoting (logical) centralization of network control, and introducing the ability to program the network.
The separation of concerns introduced between the definition of network policies, their implementation in switching hardware, and the forwarding of traffic, is key to the desired flexibility: by breaking the network control problem into tractable pieces, SDN makes it easier to create and introduce new abstractions in networking, simplifying network management and facilitating network evolution.
In this paper we present a comprehensive survey on SDN.
We start by introducing the motivation for SDN, explain its main concepts and how it differs from traditional networking, its roots, and the standardization activities regarding this novel paradigm.
Next, we present the key building blocks of an SDN infrastructure using a bottom-up, layered approach.
We provide an in-depth analysis of the hardware infrastructure, southbound and northbound APIs, network virtualization layers, network operating systems (SDN controllers), network programming languages, and network applications.
We also look at cross-layer problems such as debugging and troubleshooting.
In an effort to anticipate the future evolution of this new paradigm, we discuss the main ongoing research efforts and challenges of SDN.
In particular, we address the design of switches and control platforms -- with a focus on aspects such as resiliency, scalability, performance, security and dependability -- as well as new opportunities for carrier transport networks and cloud providers.
Last but not least, we analyze the position of SDN as a key enabler of a software-defined environment.
We present a sufficient condition for a non-injective function of a Markov chain to be a second-order Markov chain with the same entropy rate as the original chain.
This permits an information-preserving state space reduction by merging states or, equivalently, lossless compression of a Markov source on a sample-by-sample basis.
The cardinality of the reduced state space is bounded from below by the node degrees of the transition graph associated with the original Markov chain.
We also present an algorithm listing all possible information-preserving state space reductions, for a given transition graph.
We illustrate our results by applying the algorithm to a bi-gram letter model of an English text.
This paper introduces a general simulation framework that can allow the simulation of crashes and the evaluation of consequences on existing microsimulation packages.
A specific family of simple and reproducible conflict indicators is proposed and applied to many case studies.
In this approach driver failures are simulated by assuming that a driver stops reacting to an external stimulus and keeps driving at the current speed for a given time.
The trajectory of the distracted driver vehicle is thus evaluated and projected, for the given time steps, for the established distraction time, over the actual trajectories of other vehicles.
Every occurring crash is then evaluated in terms of energy involved in the crash, or with any other severity index (which can be easily calculated since the accident dynamics can be accurately simulated).
The simulation of a driver error allows not only the typology of crashes to be included, normally accounted for with surrogate safety measures, but also many other type of typical crashes that it is impossible to simulate with microsimulation and traditional methodologies being caused by vehicles who are driving on non-conflicting trajectories such as drivers speeding at a red light, drivers taking the wrong lane or side of the street or just driving off the road in isolated accidents against external obstacles or traffic barriers.
The total crash energy of all crashes is proposed as an indicator of risk and adopted in the case studies.
Moreover, the concepts introduced in this paper allow scientists to define other relevant variables that can be used as surrogate safety indicators that consider driving errors.
Preliminary results on different case studies have shown a great accordance of safety evaluations with statistical data and empirical expectations and also with other traditional safety indicators that are commonly used in microsimulation.
Photo lineups play a significant role in the eyewitness identification process.
This method is used to provide evidence in the prosecution and subsequent conviction of suspects.
Unfortunately, there are many cases where lineups have led to the conviction of an innocent suspect.
One of the key factors affecting the incorrect identification of a suspect is the lack of lineup fairness, i.e. that the suspect differs significantly from all other candidates.
Although the process of assembling fair lineup is both highly important and time-consuming, only a handful of tools are available to simplify the task.
In this paper, we describe our work towards using recommender systems for the photo lineup assembling task.
We propose and evaluate two complementary methods for item-based recommendation: one based on the visual descriptors of the deep neural network, the other based on the content-based attributes of persons.
The initial evaluation made by forensic technicians shows that although results favored visual descriptors over attribute-based similarity, both approaches are functional and highly diverse in terms of recommended objects.
Thus, future work should involve incorporating both approaches in a single prediction method, preference learning based on the feedback from forensic technicians and recommendation of assembled lineups instead of single candidates.
Deep neural networks have been investigated in learning latent representations of medical images, yet most of the studies limit their approach in a single supervised convolutional neural network (CNN), which usually rely heavily on a large scale annotated dataset for training.
To learn image representations with less supervision involved, we propose a deep Siamese CNN (SCNN) architecture that can be trained with only binary image pair information.
We evaluated the learned image representations on a task of content-based medical image retrieval using a publicly available multiclass diabetic retinopathy fundus image dataset.
The experimental results show that our proposed deep SCNN is comparable to the state-of-the-art single supervised CNN, and requires much less supervision for training.
Distributed learning platforms for processing large scale data-sets are becoming increasingly prevalent.
In typical distributed implementations, a centralized master node breaks the data-set into smaller batches for parallel processing across distributed workers to achieve speed-up and efficiency.
Several computational tasks are of sequential nature, and involve multiple passes over the data.
At each iteration over the data, it is common practice to randomly re-shuffle the data at the master node, assigning different batches for each worker to process.
This random re-shuffling operation comes at the cost of extra communication overhead, since at each shuffle, new data points need to be delivered to the distributed workers.
In this paper, we focus on characterizing the information theoretically optimal communication overhead for the distributed data shuffling problem.
We propose a novel coded data delivery scheme for the case of no excess storage, where every worker can only store the assigned data batches under processing.
Our scheme exploits a new type of coding opportunity and is applicable to any arbitrary shuffle, and for any number of workers.
We also present an information theoretic lower bound on the minimum communication overhead for data shuffling, and show that the proposed scheme matches this lower bound for the worst-case communication overhead.
Since 2010, we have built and maintained LensKit, an open-source toolkit for building, researching, and learning about recommender systems.
We have successfully used the software in a wide range of recommender systems experiments, to support education in traditional classroom and online settings, and as the algorithmic backend for user-facing recommendation services in movies and books.
This experience, along with community feedback, has surfaced a number of challenges with LensKit's design and environmental choices.
In response to these challenges, we are developing a new set of tools that leverage the PyData stack to enable the kinds of research experiments and educational experiences that we have been able to deliver with LensKit, along with new experimental structures that the existing code makes difficult.
The result is a set of research tools that should significantly increase research velocity and provide much smoother integration with other software such as Keras while maintaining the same level of reproducibility as a LensKit experiment.
In this paper, we reflect on the LensKit project, particularly on our experience using it for offline evaluation experiments, and describe the next-generation LKPY tools for enabling new offline evaluations and experiments with flexible, open-ended designs and well-tested evaluation primitives.
In this paper we propose a Deep Neural Network (DNN) based Speech Enhancement (SE) system that is designed to maximize an approximation of the Short-Time Objective Intelligibility (STOI) measure.
We formalize an approximate-STOI cost function and derive analytical expressions for the gradients required for DNN training and show that these gradients have desirable properties when used together with gradient based optimization techniques.
We show through simulation experiments that the proposed SE system achieves large improvements in estimated speech intelligibility, when tested on matched and unmatched natural noise types, at multiple signal-to-noise ratios.
Furthermore, we show that the SE system, when trained using an approximate-STOI cost function performs on par with a system trained with a mean square error cost applied to short-time temporal envelopes.
Finally, we show that the proposed SE system performs on par with a traditional DNN based Short-Time Spectral Amplitude (STSA) SE system in terms of estimated speech intelligibility.
These results are important because they suggest that traditional DNN based STSA SE systems might be optimal in terms of estimated speech intelligibility.
This paper presents an investigation of the approximation property of neural networks with unbounded activation functions, such as the rectified linear unit (ReLU), which is the new de-facto standard of deep learning.
The ReLU network can be analyzed by the ridgelet transform with respect to Lizorkin distributions.
By showing three reconstruction formulas by using the Fourier slice theorem, the Radon transform, and Parseval's relation, it is shown that a neural network with unbounded activation functions still satisfies the universal approximation property.
As an additional consequence, the ridgelet transform, or the backprojection filter in the Radon domain, is what the network learns after backpropagation.
Subject to a constructive admissibility condition, the trained network can be obtained by simply discretizing the ridgelet transform, without backpropagation.
Numerical examples not only support the consistency of the admissibility condition but also imply that some non-admissible cases result in low-pass filtering.
The paper explores the topic of Facial Action Unit (FAU) detection in the wild.
In particular, we are interested in answering the following questions: (1) how useful are residual connections across dense blocks for face analysis?
(2) how useful is the information from a network trained for categorical Facial Expression Recognition (FER) for the task of FAU detection?
The proposed network (ResiDen) exploits dense blocks along with residual connections and uses auxiliary information from a FER network.
The experiments are performed on the EmotionNet and DISFA datasets.
The experiments show the usefulness of facial expression information for AU detection.
The proposed network achieves state-of-art results on the two databases.
Analysis of the results for cross database protocol shows the effectiveness of the network.
CAPTCHAs or reverse Turing tests are real-time assessments used by programs (or computers) to tell humans and machines apart.
This is achieved by assigning and assessing hard AI problems that could only be solved easily by human but not by machines.
Applications of such assessments range from stopping spammers from automatically filling online forms to preventing hackers from performing dictionary attack.
Today, the race between makers and breakers of CAPTCHAs is at a juncture, where the CAPTCHAs proposed are not even answerable by humans.
We consider such CAPTCHAs as non user friendly.
In this paper, we propose a novel technique for reverse Turing test - we call it the Line CAPTCHAs - that mainly focuses on user friendliness while not compromising the security aspect that is expected to be provided by such a system.
Memories that exploit three-dimensional (3D)-stacking technology, which integrate memory and logic dies in a single stack, are becoming popular.
These memories, such as Hybrid Memory Cube (HMC), utilize a network-on-chip (NoC) design for connecting their internal structural organizations.
This novel usage of NoC, in addition to aiding processing-in-memory capabilities, enables numerous benefits such as high bandwidth and memory-level parallelism.
However, the implications of NoCs on the characteristics of 3D-stacked memories in terms of memory access latency and bandwidth have not been fully explored.
This paper addresses this knowledge gap by (i) characterizing an HMC prototype on the AC-510 accelerator board and revealing its access latency behaviors, and (ii) by investigating the implications of such behaviors on system and software designs.
Building a voice conversion (VC) system from non-parallel speech corpora is challenging but highly valuable in real application scenarios.
In most situations, the source and the target speakers do not repeat the same texts or they may even speak different languages.
In this case, one possible, although indirect, solution is to build a generative model for speech.
Generative models focus on explaining the observations with latent variables instead of learning a pairwise transformation function, thereby bypassing the requirement of speech frame alignment.
In this paper, we propose a non-parallel VC framework with a variational autoencoding Wasserstein generative adversarial network (VAW-GAN) that explicitly considers a VC objective when building the speech model.
Experimental results corroborate the capability of our framework for building a VC system from unaligned data, and demonstrate improved conversion quality.
Deep learning stands at the forefront in many computer vision tasks.
However, deep neural networks are usually data-hungry and require a huge amount of well-annotated training samples.
Collecting sufficient annotated data is very expensive in many applications, especially for pixel-level prediction tasks such as semantic segmentation.
To solve this fundamental issue, we consider a new challenging vision task, Internetly supervised semantic segmentation, which only uses Internet data with noisy image-level supervision of corresponding query keywords for segmentation model training.
We address this task by proposing the following solution.
A class-specific attention model unifying multiscale forward and backward convolutional features is proposed to provide initial segmentation "ground truth".
The model trained with such noisy annotations is then improved by an online fine-tuning procedure.
It achieves state-of-the-art performance under the weakly-supervised setting on PASCAL VOC2012 dataset.
The proposed framework also paves a new way towards learning from the Internet without human interaction and could serve as a strong baseline therein.
Code and data will be released upon the paper acceptance.
Multi robot systems have the potential to be utilized in a variety of applications.
In most of the previous works, the trajectory generation for multi robot systems is implemented in known environments.
To overcome that we present an online trajectory optimization algorithm that utilizes communication of robots' current states to account to the other robots while using local object based maps for identifying obstacles.
Based upon this data, we predict the trajectory expected to be traversed by the robots and utilize that to avoid collisions by formulating regions of free space that the robot can be without colliding with other robots and obstacles.
A trajectory is optimized constraining the robot to remain within this region.The proposed method is tested in simulations on Gazebo using ROS.
This paper presents a low-power ECG recording system-on-chip (SoC) with on-chip low-complexity lossless ECG compression for data reduction in wireless/ambulatory ECG sensor devices.
The chip uses a linear slope predictor for data compression, and incorporates a novel low-complexity dynamic coding-packaging scheme to frame the prediction error into fixed-length 16-bit format.
The proposed technique achieves an average compression ratio of 2.25x on MIT/BIH ECG database.
Implemented in a standard 0.35 um process, the compressor uses 0.565K gates/channel occupying 0.4 mm2 for four channels, and consumes 535 nW/channel at 2.4 V for ECG sampled at 512 Hz.
Small size and ultra-low power consumption makes the proposed technique suitable for wearable ECG sensor applications.
The most popular stability notion in games should be Nash equilibrium under the rationality of players who maximize their own payoff individually.
In contrast, in many scenarios, players can be (partly) irrational with some unpredictable factors.
Hence a strategy profile can be more robust if it is resilient against certain irrational behaviors.
In this paper, we propose a stability notion that is resilient against envy.
A strategy profile is said to be envy-proof if each player cannot gain a competitive edge with respect to the change in utility over the other players by deviation.
Together with Nash equilibrium and another stability notion called immunity, we show how these separate notions are related to each other, whether they exist in games, and whether and when a strategy profile satisfying these notions can be efficiently found.
We answer these questions by starting with the general two player game and extend the discussion for the approximate stability and for the corresponding fault-tolerance notions in multi-player games.
This extended abstract is about an effort to build a formal description of a triangulation algorithm starting with a naive description of the algorithm where triangles, edges, and triangulations are simply given as sets and the most complex notions are those of boundary and separating edges.
When performing proofs about this algorithm, questions of symmetry appear and this exposition attempts to give an account of how these symmetries can be handled.
All this work relies on formal developments made with Coq and the mathematical components library.
Mobile phone based potable water quality assessment device is developed to analyze and study water pollution level at Indus river.
Indus river is habitat of endangered Indus river dolphin and water pollution is one of major causes of survivability threats for this specie.
We tested device performance at the six locations of Lahore canal. pH of canal water deviates from the normal range of the irrigation water.
In future, we will study correlation between water pollution level and habitat usage of Indus river dolphin using water quality assessment device and hydrophone array based passive acoustic monitoring (PAM) system.
As a powerful representation paradigm for networked and multi-typed data, the heterogeneous information network (HIN) is ubiquitous.
Meanwhile, defining proper relevance measures has always been a fundamental problem and of great pragmatic importance for network mining tasks.
Inspired by our probabilistic interpretation of existing path-based relevance measures, we propose to study HIN relevance from a probabilistic perspective.
We also identify, from real-world data, and propose to model cross-meta-path synergy, which is a characteristic important for defining path-based HIN relevance and has not been modeled by existing methods.
A generative model is established to derive a novel path-based relevance measure, which is data-driven and tailored for each HIN.
We develop an inference algorithm to find the maximum a posteriori (MAP) estimate of the model parameters, which entails non-trivial tricks.
Experiments on two real-world datasets demonstrate the effectiveness of the proposed model and relevance measure.
Visual observation of Cumulus Oocyte Complexes provides only limited information about its functional competence, whereas the molecular evaluations methods are cumbersome or costly.
Image analysis of mammalian oocytes can provide attractive alternative to address this challenge.
However, it is complex, given the huge number of oocytes under inspection and the subjective nature of the features inspected for identification.
Supervised machine learning methods like random forest with annotations from expert biologists can make the analysis task standardized and reduces inter-subject variability.
We present a semi-automatic framework for predicting the class an oocyte belongs to, based on multi-object parametric segmentation on the acquired microscopic image followed by a feature based classification using random forests.
We present an open-source accessory for the NAO robot, which enables to test computationally demanding algorithms in an external platform while preserving robot's autonomy and mobility.
The platform has the form of a backpack, which can be 3D printed and replicated, and holds an ODROID XU4 board to process algorithms externally with ROS compatibility.
We provide also a software bridge between the B-Human's framework and ROS to have access to the robot's sensors close to real-time.
We tested the platform in several robotics applications such as data logging, visual SLAM, and robot vision with deep learning techniques.
The CAD model, hardware specifications and software are available online for the benefit of the community: https://github.com/uchile-robotics/nao-backpack
The Industry 4.0 paradigm emphasizes the crucial benefits that collaborative robots, i.e., robots able to work alongside and together with humans, could bring to the whole production process.
In this context, an enabling technology yet unreached is the design of flexible robots able to deal at all levels with humans' intrinsic variability, which is not only a necessary element for a comfortable working experience for the person but also a precious capability for efficiently dealing with unexpected events.
In this paper, a sensing, representation, planning and control architecture for flexible human-robot cooperation, referred to as FlexHRC, is proposed.
FlexHRC relies on wearable sensors for human action recognition, AND/OR graphs for the representation of and reasoning upon cooperation models, and a Task Priority framework to decouple action planning from robot motion planning and control.
XML data warehouses form an interesting basis for decision-support applications that exploit heterogeneous data from multiple sources.
However, XML-native database systems currently bear limited performances and it is necessary to research ways to optimize them.
In this paper, we propose a new index that is specifically adapted to the multidimensional architecture of XML warehouses and eliminates join operations, while preserving the information contained in the original warehouse.
A theoretical study and experimental results demonstrate the efficiency of our index, even when queries are complex.
Perceptual judgment of image similarity by humans relies on rich internal representations ranging from low-level features to high-level concepts, scene properties and even cultural associations.
However, existing methods and datasets attempting to explain perceived similarity use stimuli which arguably do not cover the full breadth of factors that affect human similarity judgments, even those geared toward this goal.
We introduce a new dataset dubbed Totally-Looks-Like (TLL) after a popular entertainment website, which contains images paired by humans as being visually similar.
The dataset contains 6016 image-pairs from the wild, shedding light upon a rich and diverse set of criteria employed by human beings.
We conduct experiments to try to reproduce the pairings via features extracted from state-of-the-art deep convolutional neural networks, as well as additional human experiments to verify the consistency of the collected data.
Though we create conditions to artificially make the matching task increasingly easier, we show that machine-extracted representations perform very poorly in terms of reproducing the matching selected by humans.
We discuss and analyze these results, suggesting future directions for improvement of learned image representations.
In this paper, we present a methodology for customized communication architecture synthesis that matches the communication requirements of the target application.
This is an important problem, particularly for network-based implementations of complex applications.
Our approach is based on using frequently encountered generic communication primitives as an alphabet capable of characterizing any given communication pattern.
The proposed algorithm searches through the entire design space for a solution that minimizes the system total energy consumption, while satisfying the other design constraints.
Compared to the standard mesh architecture, the customized architecture generated by the newly proposed approach shows about 36% throughput increase and 51% reduction in the energy required to encrypt 128 bits of data with a standard encryption algorithm.
In this paper, we give algorithms and methods of construction of self-dual codes over finite fields using orthogonal matrices.
Randomization in the orthogonal group, and code extension are the main tools.
Some optimal, almost MDS, and MDS self-dual codes over both small and large prime fields are constructed.
This study investigates how adequate coordination among the different cognitive processes of a humanoid robot can be developed through end-to-end learning of direct perception of visuomotor stream.
We propose a deep dynamic neural network model built on a dynamic vision network, a motor generation network, and a higher-level network.
The proposed model was designed to process and to integrate direct perception of dynamic visuomotor patterns in a hierarchical model characterized by different spatial and temporal constraints imposed on each level.
We conducted synthetic robotic experiments in which a robot learned to read human's intention through observing the gestures and then to generate the corresponding goal-directed actions.
Results verify that the proposed model is able to learn the tutored skills and to generalize them to novel situations.
The model showed synergic coordination of perception, action and decision making, and it integrated and coordinated a set of cognitive skills including visual perception, intention reading, attention switching, working memory, action preparation and execution in a seamless manner.
Analysis reveals that coherent internal representations emerged at each level of the hierarchy.
Higher-level representation reflecting actional intention developed by means of continuous integration of the lower-level visuo-proprioceptive stream.
Consider two horizontal lines in the plane.
A pair of a point on the top line and an interval on the bottom line defines a triangle between two lines.
The intersection graph of such triangles is called a simple-triangle graph.
This paper shows a vertex ordering characterization of simple-triangle graphs as follows: a graph is a simple-triangle graph if and only if there is a linear ordering of the vertices that contains both an alternating orientation of the graph and a transitive orientation of the complement of the graph.
We present data-driven techniques to augment Bag of Words (BoW) models, which allow for more robust modeling and recognition of complex long-term activities, especially when the structure and topology of the activities are not known a priori.
Our approach specifically addresses the limitations of standard BoW approaches, which fail to represent the underlying temporal and causal information that is inherent in activity streams.
In addition, we also propose the use of randomly sampled regular expressions to discover and encode patterns in activities.
We demonstrate the effectiveness of our approach in experimental evaluations where we successfully recognize activities and detect anomalies in four complex datasets.
Using WiFi signals for indoor localization is the main localization modality of the existing personal indoor localization systems operating on mobile devices.
WiFi fingerprinting is also used for mobile robots, as WiFi signals are usually available indoors and can provide rough initial position estimate or can be used together with other positioning systems.
Currently, the best solutions rely on filtering, manual data analysis, and time-consuming parameter tuning to achieve reliable and accurate localization.
In this work, we propose to use deep neural networks to significantly lower the work-force burden of the localization system design, while still achieving satisfactory results.
Assuming the state-of-the-art hierarchical approach, we employ the DNN system for building/floor classification.
We show that stacked autoencoders allow to efficiently reduce the feature space in order to achieve robust and precise classification.
The proposed architecture is verified on the publicly available UJIIndoorLoc dataset and the results are compared with other solutions.
Varying weather conditions, including rainfall and snowfall, are generally regarded as a challenge for computer vision algorithms.
One proposed solution to the challenges induced by rain and snowfall is to artificially remove the rain from images or video using rain removal algorithms.
It is the promise of these algorithms that the rain-removed image frames will improve the performance of subsequent segmentation and tracking algorithms.
However, rain removal algorithms are typically evaluated on their ability to remove synthetic rain on a small subset of images.
Currently, their behavior is unknown on real-world videos when integrated with a typical computer vision pipeline.
In this paper, we review the existing rain removal algorithms and propose a new dataset that consists of 22 traffic surveillance sequences under a broad variety of weather conditions that all include either rain or snowfall.
We propose a new evaluation protocol that evaluates the rain removal algorithms on their ability to improve the performance of subsequent segmentation, instance segmentation, and feature tracking algorithms under rain and snow.
If successful, the de-rained frames of a rain removal algorithm should improve segmentation performance and increase the number of accurately tracked features.
The results show that a recent single-frame-based rain removal algorithm increases the segmentation performance by 19.7% on our proposed dataset, but it eventually decreases the feature tracking performance and showed mixed results with recent instance segmentation methods.
However, the best video-based rain removal algorithm improves the feature tracking accuracy by 7.72%.
Coherent network error correction is the error-control problem in network coding with the knowledge of the network codes at the source and sink nodes.
With respect to a given set of local encoding kernels defining a linear network code, we obtain refined versions of the Hamming bound, the Singleton bound and the Gilbert-Varshamov bound for coherent network error correction.
Similar to its classical counterpart, this refined Singleton bound is tight for linear network codes.
The tightness of this refined bound is shown by two construction algorithms of linear network codes achieving this bound.
These two algorithms illustrate different design methods: one makes use of existing network coding algorithms for error-free transmission and the other makes use of classical error-correcting codes.
The implication of the tightness of the refined Singleton bound is that the sink nodes with higher maximum flow values can have higher error correction capabilities.
Probabilistic-driven classification techniques extend the role of traditional approaches that output labels (usually integer numbers) only.
Such techniques are more fruitful when dealing with problems where one is not interested in recognition/identification only, but also into monitoring the behavior of consumers and/or machines, for instance.
Therefore, by means of probability estimates, one can take decisions to work better in a number of scenarios.
In this paper, we propose a probabilistic-based Optimum Path Forest (OPF) classifier to handle with binary classification problems, and we show it can be more accurate than naive OPF in a number of datasets.
In addition to being just more accurate or not, probabilistic OPF turns to be another useful tool to the scientific community.
Nowadays, deep learning has been widely used.
In natural language learning, the analysis of complex semantics has been achieved because of its high degree of flexibility.
The deceptive opinions detection is an important application area in deep learning model, and related mechanisms have been given attention and researched.
On-line opinions are quite short, varied types and content.
In order to effectively identify deceptive opinions, we need to comprehensively study the characteristics of deceptive opinions, and explore novel characteristics besides the textual semantics and emotional polarity that have been widely used in text analysis.
The detection mechanism based on deep learning has better self-adaptability and can effectively identify all kinds of deceptive opinions.
In this paper, we optimize the convolution neural network model by embedding the word order characteristics in its convolution layer and pooling layer, which makes convolution neural network more suitable for various text classification and deceptive opinions detection.
The TensorFlow-based experiments demonstrate that the detection mechanism proposed in this paper achieve more accurate deceptive opinion detection results.
Simile is a figure of speech that compares two things through the use of connection words, but where comparison is not intended to be taken literally.
They are often used in everyday communication, but they are also a part of linguistic cultural heritage.
In this paper we present a methodology for semi-automated collection of similes from the World Wide Web using text mining and machine learning techniques.
We expanded an existing corpus by collecting 442 similes from the internet and adding them to the existing corpus collected by Vuk Stefanovic Karadzic that contained 333 similes.
We, also, introduce crowdsourcing to the collection of figures of speech, which helped us to build corpus containing 787 unique similes.
Microscopic histology image analysis is a cornerstone in early detection of breast cancer.
However these images are very large and manual analysis is error prone and very time consuming.
Thus automating this process is in high demand.
We proposed a hierarchical system of convolutional neural networks (CNN) that classifies automatically patches of these images into four pathologies: normal, benign, in situ carcinoma and invasive carcinoma.
We evaluated our system on the BACH challenge dataset of image-wise classification and a small dataset that we used to extend it.
Using a train/test split of 75%/25%, we achieved an accuracy rate of 0.99 on the test split for the BACH dataset and 0.96 on that of the extension.
On the test of the BACH challenge, we've reached an accuracy of 0.81 which rank us to the 8th out of 51 teams.
We present a constrained motion control framework for a redundant surgical system designed for minimally invasive treatment of pelvic osteolysis.
The framework comprises a kinematics model of a six Degrees-of-Freedom (DoF) robotic arm integrated with a one DoF continuum manipulator as well as a novel convex optimization redundancy resolution controller.
To resolve the redundancy resolution problem, formulated as a constrained l2-regularized quadratic minimization, we study and evaluate the potential use of an optimally tuned alternating direction method of multipliers (ADMM) algorithm.
To this end, we prove global convergence of the algorithm at linear rate and propose expressions for the involved parameters resulting in a fast convergence.
Simulations on the robotic system verified our analytical derivations and showed the capability and robustness of the ADMM algorithm in constrained motion control of our redundant surgical system.
An examination of object recognition challenge leaderboards (ILSVRC, PASCAL-VOC) reveals that the top-performing classifiers typically exhibit small differences amongst themselves in terms of error rate/mAP.
To better differentiate the top performers, additional criteria are required.
Moreover, the (test) images, on which the performance scores are based, predominantly contain fully visible objects.
Therefore, `harder' test images, mimicking the challenging conditions (e.g. occlusion) in which humans routinely recognize objects, need to be utilized for benchmarking.
To address the concerns mentioned above, we make two contributions.
First, we systematically vary the level of local object-part content, global detail and spatial context in images from PASCAL VOC 2010 to create a new benchmarking dataset dubbed PPSS-12.
Second, we propose an object-part based benchmarking procedure which quantifies classifiers' robustness to a range of visibility and contextual settings.
The benchmarking procedure relies on a semantic similarity measure that naturally addresses potential semantic granularity differences between the category labels in training and test datasets, thus eliminating manual mapping.
We use our procedure on the PPSS-12 dataset to benchmark top-performing classifiers trained on the ILSVRC-2012 dataset.
Our results show that the proposed benchmarking procedure enables additional differentiation among state-of-the-art object classifiers in terms of their ability to handle missing content and insufficient object detail.
Given this capability for additional differentiation, our approach can potentially supplement existing benchmarking procedures used in object recognition challenge leaderboards.
Loop closure detection, the task of identifying locations revisited by a robot in a sequence of odometry and perceptual observations, is typically formulated as a combination of two subtasks: (1) bag-of-words image retrieval and (2) post-verification using RANSAC geometric verification.
The main contribution of this study is the proposal of a novel post-verification framework that achieves good precision recall trade-off in loop closure detection.
This study is motivated by the fact that not all loop closure hypotheses are equally plausible (e.g., owing to mutual consistency between loop closure constraints) and that if we have evidence that one hypothesis is more plausible than the others, then it should be verified more frequently.
We demonstrate that the problem of loop closure detection can be viewed as an instance of a multi-model hypothesize-and-verify framework and build guided sampling strategies on the framework where loop closures proposed using image retrieval are verified in a planned order (rather than in a conventional uniform order) to operate in a constant time.
Experimental results using a stereo SLAM system confirm that the proposed strategy, the use of loop closure constraints and robot trajectory hypotheses as a guide, achieves promising results despite the fact that there exists a significant number of false positive constraints and hypotheses.
Wireless sensor networks become integral part of our life.
These networks can be used for monitoring the data in various domain due to their flexibility and functionality.
Query processing and optimization in the WSN is a very challenging task because of their energy and memory constraint.
In this paper, first our focus is to review the different approaches that have significant impacts on the development of query processing techniques for WSN.
Finally, we aim to illustrate the existing approach in popular query processing engines with future research challenges in query optimization.
This paper describes the HASYv2 dataset.
HASY is a publicly available, free of charge dataset of single symbols similar to MNIST.
It contains 168233 instances of 369 classes.
HASY contains two challenges: A classification challenge with 10 pre-defined folds for 10-fold cross-validation and a verification challenge.
In this paper we present a novel distributed coverage control framework for a network of mobile agents, in charge of covering a finite set of points of interest (PoI), such as people in danger, geographically dispersed equipment or environmental landmarks.
The proposed algorithm is inspired by C-Means, an unsupervised learning algorithm originally proposed for non-exclusive clustering and for identification of cluster centroids from a set of observations.
To cope with the agents' limited sensing range and avoid infeasible coverage solutions, traditional C-Means needs to be enhanced with proximity constraints, ensuring that each agent takes into account only neighboring PoIs.
The proposed coverage control framework provides useful information concerning the ranking or importance of the different PoIs to the agents, which can be exploited in further application-dependent data fusion processes, patrolling, or disaster relief applications.
Conversational agents are gaining popularity with the increasing ubiquity of smart devices.
However, training agents in a data driven manner is challenging due to a lack of suitable corpora.
This paper presents a novel method for gathering topical, unstructured conversational data in an efficient way: self-dialogues through crowd-sourcing.
Alongside this paper, we include a corpus of 3.6 million words across 23 topics.
We argue the utility of the corpus by comparing self-dialogues with standard two-party conversations as well as data from other corpora.
We propose to take a novel approach to robot system design where each building block of a larger system is represented as a differentiable program, i.e. a deep neural network.
This representation allows for integrating algorithmic planning and deep learning in a principled manner, and thus combine the benefits of model-free and model-based methods.
We apply the proposed approach to a challenging partially observable robot navigation task.
The robot must navigate to a goal in a previously unseen 3-D environment without knowing its initial location, and instead relying on a 2-D floor map and visual observations from an onboard camera.
We introduce the Navigation Networks (NavNets) that encode state estimation, planning and acting in a single, end-to-end trainable recurrent neural network.
In preliminary simulation experiments we successfully trained navigation networks to solve the challenging partially observable navigation task.
Developing methods of automated inference that are able to provide users with compelling human-readable justifications for why the answer to a question is correct is critical for domains such as science and medicine, where user trust and detecting costly errors are limiting factors to adoption.
One of the central barriers to training question answering models on explainable inference tasks is the lack of gold explanations to serve as training data.
In this paper we present a corpus of explanations for standardized science exams, a recent challenge task for question answering.
We manually construct a corpus of detailed explanations for nearly all publicly available standardized elementary science question (approximately 1,680 3rd through 5th grade questions) and represent these as "explanation graphs" -- sets of lexically overlapping sentences that describe how to arrive at the correct answer to a question through a combination of domain and world knowledge.
We also provide an explanation-centered tablestore, a collection of semi-structured tables that contain the knowledge to construct these elementary science explanations.
Together, these two knowledge resources map out a substantial portion of the knowledge required for answering and explaining elementary science exams, and provide both structured and free-text training data for the explainable inference task.
Symmetric positive definite (SPD) matrices are useful for capturing second-order statistics of visual data.
To compare two SPD matrices, several measures are available, such as the affine-invariant Riemannian metric, Jeffreys divergence, Jensen-Bregman logdet divergence, etc.
; however, their behaviors may be application dependent, raising the need of manual selection to achieve the best possible performance.
Further and as a result of their overwhelming complexity for large-scale problems, computing pairwise similarities by clever embedding of SPD matrices is often preferred to direct use of the aforementioned measures.
In this paper, we propose a discriminative metric learning framework, Information Divergence and Dictionary Learning (IDDL), that not only learns application specific measures on SPD matrices automatically, but also embeds them as vectors using a learned dictionary.
To learn the similarity measures (which could potentially be distinct for every dictionary atom), we use the recently introduced alpha-beta-logdet divergence, which is known to unify the measures listed above.
We propose a novel IDDL objective, that learns the parameters of the divergence and the dictionary atoms jointly in a discriminative setup and is solved efficiently using Riemannian optimization.
We showcase extensive experiments on eight computer vision datasets, demonstrating state-of-the-art performances.
We propose an improvement of an oceanographic three dimensional variational assimilation scheme (3D-VAR), named OceanVar, by introducing a recursive filter (RF) with the third order of accuracy (3rd-RF), instead of a RF with first order of accuracy (1st-RF), to approximate horizontal Gaussian covariances.
An advantage of the proposed scheme is that the CPU's time can be substantially reduced with benefits on the large scale applications.
Experiments estimating the impact of 3rd-RF are performed by assimilating oceanographic data in two realistic oceanographic applications.
The results evince benefits in terms of assimilation process computational time, accuracy of the Gaussian correlation modeling, and show that the 3rd-RF is a suitable tool for operational data assimilation.
Over the years, several meta-heuristic algorithms were proposed and are now emerging as common methods for constrained optimization problems.
Among them, genetic algorithms (GA's) shine as popular evolutionary algorithms (EA's) in engineering optimization.
Most engineering design problems are difficult to resolve with conventional optimization algorithms because they are highly nonlinear and contain constraints.
In order to handle these constraints, the most common technique is to apply penalty functions.
The major drawback is that they require tuning of parameters, which can be very challenging.
In this paper, we present a constraint-handling technique for GA's solely using the violation factor, called VCH (Violation Constraint-Handling) method.
Several benchmark problems from the literature are examined.
The VCH technique was able to provide a consistent performance and match results from other GA-based techniques.
With virtual reality, digital painting on 2D canvases is now being extended to 3D spaces.
Tilt Brush and Oculus Quill are widely accepted among artists as tools that pave the way to a new form of art - 3D emmersive painting.
Current 3D painting systems are only a start, emitting textured triangular geometries.
In this paper, we advance this new art of 3D painting to 3D volumetric painting that enables an artist to draw a huge scene with full control of spatial color fields.
Inspired by the fact that 2D paintings often use vast space to paint background and small but detailed space for foreground, we claim that supporting a large canvas in varying detail is essential for 3D painting.
In order to help artists focus and audiences to navigate the large canvas space, we provide small artist-defined areas, called rooms, that serve as beacons for artist-suggested scales, spaces, locations for intended appreciation view of the painting.
Artists and audiences can easily transport themselves between different rooms.
Technically, our canvas is represented as an array of deep octrees of depth 24 or higher, built on CPU for volume painting and on GPU for volume rendering using accurate ray casting.
In CPU side, we design an efficient iterative algorithm to refine or coarsen octree, as a result of volumetric painting strokes, at highly interactive rates, and update the corresponding GPU textures.
Then we use GPU-based ray casting algorithms to render the volumetric painting result.
We explore precision issues stemming from ray-casting the octree of high depth, and provide a new analysis and verification.
From our experimental results as well as the positive feedback from the participating artists, we strongly believe that our new 3D volume painting system can open up a new possibility for VR-driven digital art medium to professional artists as well as to novice users.
The concept of the augmented coaching ecosystem for non-obtrusive adaptive personalized elderly care is proposed on the basis of the integration of new and available ICT approaches.
They include the multimodal user interface (MMUI), augmented reality (AR), machine learning (ML), Internet of Things (IoT), and machine-to-machine (M2M) interactions.
The ecosystem is based on the Cloud-Fog-Dew computing paradigm services, providing a full symbiosis by integrating the whole range from low-level sensors up to high-level services using integration efficiency inherent in synergistic use of applied technologies.
Inside of this ecosystem, all of them are encapsulated in the following network layers: Dew, Fog, and Cloud computing layer.
Instead of the "spaghetti connections", "mosaic of buttons", "puzzles of output data", etc., the proposed ecosystem provides the strict division in the following dataflow channels: consumer interaction channel, machine interaction channel, and caregiver interaction channel.
This concept allows to decrease the physical, cognitive, and mental load on elderly care stakeholders by decreasing the secondary human-to-human (H2H), human-to-machine (H2M), and machine-to-human (M2H) interactions in favor of M2M interactions and distributed Dew Computing services environment.
It allows to apply this non-obtrusive augmented reality ecosystem for effective personalized elderly care to preserve their physical, cognitive, mental and social well-being.
Human and artificial organizations may be described as networks of interacting parts.
Those parts exchange data and control information and, as a result of these interactions, organizations produce emergent behaviors and purposes -- traits the characterize "the whole" as "greater than the sum of its parts".
In this chapter it is argued that, rather than a static and immutable property, emergence should be interpreted as the result of dynamic interactions between forces of opposite sign: centripetal (positive) forces strengthening emergence by consolidating the whole and centrifugal (negative) forces that weaken the social persona and as such are detrimental to emergence.
The result of this interaction is called in this chapter as "quality of emergence".
This problem is discussed in the context of a particular class of organizations: conventional hierarchies.
We highlight how traditional designs produce behaviors that may severely impact the quality of emergence.
Finally we discuss a particular class of organizations that do not suffer from the limitations typical of strict hierarchies and result in greater quality of emergence.
In some case, however, these enhancements are counterweighted by a reduced degree of controllability and verifiability.
Self Organizing Migrating Algorithm (SOMA) is a meta-heuristic algorithm based on the self-organizing behavior of individuals in a simulated social environment.
SOMA performs iterative computations on a population of potential solutions in the given search space to obtain an optimal solution.
In this paper, an Opportunistic Self Organizing Migrating Algorithm (OSOMA) has been proposed that introduces a novel strategy to generate perturbations effectively.
This strategy allows the individual to span across more possible solutions and thus, is able to produce better solutions.
A comprehensive analysis of OSOMA on multi-dimensional unconstrained benchmark test functions is performed.
OSOMA is then applied to solve real-time Dynamic Traveling Salesman Problem (DTSP).
The problem of real-time DTSP has been stipulated and simulated using real-time data from Google Maps with a varying cost-metric between any two cities.
Although DTSP is a very common and intuitive model in the real world, its presence in literature is still very limited.
OSOMA performs exceptionally well on the problems mentioned above.
To substantiate this claim, the performance of OSOMA is compared with SOMA, Differential Evolution and Particle Swarm Optimization.
Object detection typically assumes that training and test data are drawn from an identical distribution, which, however, does not always hold in practice.
Such a distribution mismatch will lead to a significant performance drop.
In this work, we aim to improve the cross-domain robustness of object detection.
We tackle the domain shift on two levels: 1) the image-level shift, such as image style, illumination, etc, and 2) the instance-level shift, such as object appearance, size, etc.
We build our approach based on the recent state-of-the-art Faster R-CNN model, and design two domain adaptation components, on image level and instance level, to reduce the domain discrepancy.
The two domain adaptation components are based on H-divergence theory, and are implemented by learning a domain classifier in adversarial training manner.
The domain classifiers on different levels are further reinforced with a consistency regularization to learn a domain-invariant region proposal network (RPN) in the Faster R-CNN model.
We evaluate our newly proposed approach using multiple datasets including Cityscapes, KITTI, SIM10K, etc.
The results demonstrate the effectiveness of our proposed approach for robust object detection in various domain shift scenarios.
In this paper we proposed reinforcement learning algorithms with the generalized reward function.
In our proposed method we use Q-learning and SARSA algorithms with generalised reward function to train the reinforcement learning agent.
We evaluated the performance of our proposed algorithms on two real-time strategy games called BattleCity and S3.
There are two main advantages of having such an approach as compared to other works in RTS.
(1) We can ignore the concept of a simulator which is often game specific and is usually hard coded in any type of RTS games (2) our system can learn from interaction with any opponents and quickly change the strategy according to the opponents and do not need any human traces as used in previous works.
Keywords : Reinforcement learning, Machine learning, Real time strategy, Artificial intelligence.
To overcome the tradeoff of the conventional normalized least mean square (NLMS) algorithm between fast convergence rate and low steady-state misalignment, this paper proposes a variable step size (VSS) NLMS algorithm by devising a new strategy to update the step size.
In this strategy, the input signal power and the cross-correlation between the input signal and the error signal are used to estimate the true tracking error power, reducing the effect of the system noise on the algorithm performance.
Moreover, the steady-state performances of the algorithm are provided for Gaussian white input signal and are verified by simulations.
Finally, simulation results in the context of the system identification and acoustic echo cancellation (AEC) have demonstrated that the proposed algorithm has lower steady-state misalignment than other VSS algorithms.
Despite the significant progress that has been made on estimating optical flow recently, most estimation methods, including classical and deep learning approaches, still have difficulty with multi-scale estimation, real-time computation, and/or occlusion reasoning.
In this paper, we introduce dilated convolution and occlusion reasoning into unsupervised optical flow estimation to address these issues.
The dilated convolution allows our network to avoid upsampling via deconvolution and the resulting gridding artifacts.
Dilated convolution also results in a smaller memory footprint which speeds up interference.
The occlusion reasoning prevents our network from learning incorrect deformations due to occluded image regions during training.
Our proposed method outperforms state-of-the-art unsupervised approaches on the KITTI benchmark.
We also demonstrate its generalization capability by applying it to action recognition in video.
Long Short-Term Memory networks trained with gradient descent and back-propagation have received great success in various applications.
However, point estimation of the weights of the networks is prone to over-fitting problems and lacks important uncertainty information associated with the estimation.
However, exact Bayesian neural network methods are intractable and non-applicable for real-world applications.
In this study, we propose an approximate estimation of the weights uncertainty using Ensemble Kalman Filter, which is easily scalable to a large number of weights.
Furthermore, we optimize the covariance of the noise distribution in the ensemble update step using maximum likelihood estimation.
To assess the proposed algorithm, we apply it to outlier detection in five real-world events retrieved from the Twitter platform.
The increasing deployment of sensor networks, ranging from home networks to industrial automation, leads to a similarly growing demand for storing and processing the collected sensor data.
To satisfy this demand, the most promising approach to date is the utilization of the dynamically scalable, on-demand resources made available via the cloud computing paradigm.
However, prevalent security and privacy concerns are a huge obstacle for the outsourcing of sensor data to the cloud.
Hence, sensor data needs to be secured properly before it can be outsourced to the cloud.
When securing the outsourcing of sensor data to the cloud, one important challenge lies in the representation of sensor data and the choice of security measures applied to it.
In this paper, we present the SensorCloud protocol, which enables the representation of sensor data and actuator commands using JSON as well as the encoding of the object security mechanisms applied to a given sensor data item.
Notably, we solely utilize mechanisms that have been or currently are in the process of being standardized at the IETF to aid the wide applicability of our approach.
In this paper, we consider an uplink heterogeneous cloud radio access network (H-CRAN), where a macro base station (BS) coexists with many remote radio heads (RRHs).
For cost-savings, only the BS is connected to the baseband unit (BBU) pool via fiber links.
The RRHs, however, are associated with the BBU pool through wireless fronthaul links, which share the spectrum resource with radio access networks.
Due to the limited capacity of fronthaul, the compress-and-forward scheme is employed, such as point-to-point compression or Wyner-Ziv coding.
Different decoding strategies are also considered.
This work aims to maximize the uplink ergodic sum-rate (SR) by jointly optimizing quantization noise matrix and bandwidth allocation between radio access networks and fronthaul links, which is a mixed time-scale issue.
To reduce computational complexity and communication overhead, we introduce an approximation problem of the joint optimization problem based on large-dimensional random matrix theory, which is a slow time-scale issue because it only depends on statistical channel information.
Finally, an algorithm based on Dinkelbach's algorithm is proposed to find the optimal solution to the approximate problem.
In summary, this work provides an economic solution to the challenge of constrained fronthaul capacity, and also provides a framework with less computational complexity to study how bandwidth allocation and fronthaul compression can affect the SR maximization problem.
Text classification is an important and classical problem in natural language processing.
There have been a number of studies that applied convolutional neural networks (convolution on regular grid, e.g., sequence) to classification.
However, only a limited number of studies have explored the more flexible graph convolutional neural networks (convolution on non-grid, e.g., arbitrary graph) for the task.
In this work, we propose to use graph convolutional networks for text classification.
We build a single text graph for a corpus based on word co-occurrence and document word relations, then learn a Text Graph Convolutional Network (Text GCN) for the corpus.
Our Text GCN is initialized with one-hot representation for word and document, it then jointly learns the embeddings for both words and documents, as supervised by the known class labels for documents.
Our experimental results on multiple benchmark datasets demonstrate that a vanilla Text GCN without any external word embeddings or knowledge outperforms state-of-the-art methods for text classification.
On the other hand, Text GCN also learns predictive word and document embeddings.
In addition, experimental results show that the improvement of Text GCN over state-of-the-art comparison methods become more prominent as we lower the percentage of training data, suggesting the robustness of Text GCN to less training data in text classification.
Owing to recorded incidents of Information technology inclined organisations failing to respond effectively to threat incidents, this project outlines the benefits of conducting a comprehensive risk assessment which would aid proficiency in responding to potential threats.
The ultimate goal is primarily to identify, quantify and control the key threats that are detrimental to achieving business objectives.
This project carries out a detailed risk assessment for a case study organisation.
It includes a comprehensive literature review analysing several professional views on pressing issues in Information security.
In the risk register, five prominent assets were identified in respect to their owners.
The work is followed by a qualitative analysis methodology to determine the magnitude of the potential threats and vulnerabilities.
Collating these parameters enabled the valuation of individual risk per asset, per threat and vulnerability.
Evaluating a risk appetite aided in prioritising and determining acceptable risks.
From the analysis, it was deduced that human being posed the greatest Information security risk through intentional/ unintentional human error.
In conclusion, effective control techniques based on defence in-depth were devised to mitigate the impact of the identified risks from risk register.
Sequence-to-sequence attention-based models on subword units allow simple open-vocabulary end-to-end speech recognition.
In this work, we show that such models can achieve competitive results on the Switchboard 300h and LibriSpeech 1000h tasks.
In particular, we report the state-of-the-art word error rates (WER) of 3.54% on the dev-clean and 3.82% on the test-clean evaluation subsets of LibriSpeech.
We introduce a new pretraining scheme by starting with a high time reduction factor and lowering it during training, which is crucial both for convergence and final performance.
In some experiments, we also use an auxiliary CTC loss function to help the convergence.
In addition, we train long short-term memory (LSTM) language models on subword units.
By shallow fusion, we report up to 27% relative improvements in WER over the attention baseline without a language model.
Deep learning tasks are often complicated and require a variety of components working together efficiently to perform well.
Due to the often large scale of these tasks, there is a necessity to iterate quickly in order to attempt a variety of methods and to find and fix bugs.
While participating in IARPA's Functional Map of the World challenge, we identified challenges along the entire deep learning pipeline and found various solutions to these challenges.
In this paper, we present the performance, engineering, and deep learning considerations with processing and modeling data, as well as underlying infrastructure considerations that support large-scale deep learning tasks.
We also discuss insights and observations with regard to satellite imagery and deep learning for image classification.
Concepts are the foundation of human deep learning, understanding, and knowledge integration and transfer.
We propose concept-oriented deep learning (CODL) which extends (machine) deep learning with concept representations and conceptual understanding capability.
CODL addresses some of the major limitations of deep learning: interpretability, transferability, contextual adaptation, and requirement for lots of labeled training data.
We discuss the major aspects of CODL including concept graph, concept representations, concept exemplars, and concept representation learning systems supporting incremental and continual learning.
The identification of reduced-order models from high-dimensional data is a challenging task, and even more so if the identified system should not only be suitable for a certain data set, but generally approximate the input-output behavior of the data source.
In this work, we consider the input-output dynamic mode decomposition method for system identification.
We compare excitation approaches for the data-driven identification process and describe an optimization-based stabilization strategy for the identified systems.
The syntactic nature and compositionality characteristic of stochastic process algebras make models to be easily understood by human beings, but not convenient for machines as well as people to directly carry out mathematical analysis and stochastic simulation.
This paper presents a numerical representation schema for the stochastic process algebra PEPA, which can provide a platform to directly and conveniently employ a variety of computational approaches to both qualitatively and quantitatively analyse the models.
Moreover, these approaches developed on the basis of the schema are demonstrated and discussed.
In particular, algorithms for automatically deriving the schema from a general PEPA model and simulating the model based on the derived schema to derive performance measures are presented.
It is important to detect anomalous inputs when deploying machine learning systems.
The use of larger and more complex inputs in deep learning magnifies the difficulty of distinguishing between anomalous and in-distribution examples.
At the same time, diverse image and text data are available in enormous quantities.
We propose leveraging these data to improve deep anomaly detection by training anomaly detectors against an auxiliary dataset of outliers, an approach we call Outlier Exposure (OE).
This enables anomaly detectors to generalize and detect unseen anomalies.
In extensive experiments on natural language processing and small- and large-scale vision tasks, we find that Outlier Exposure significantly improves detection performance.
We also observe that cutting-edge generative models trained on CIFAR-10 may assign higher likelihoods to SVHN images than to CIFAR-10 images; we use OE to mitigate this issue.
We also analyze the flexibility and robustness of Outlier Exposure, and identify characteristics of the auxiliary dataset that improve performance.
The cellular technology is mostly an urban technology that has been unable to serve rural areas well.
This is because the traditional cellular models are not economical for areas with low user density and lesser revenues.
In 5G cellular networks, the coverage dilemma is likely to remain the same, thus widening the rural-urban digital divide further.
It is about time to identify the root cause that has hindered the rural technology growth and analyse the possible options in 5G architecture to address this issue.
We advocate that it can only be accomplished in two phases by sequentially addressing economic viability followed by performance progression.
We deliberate how various works in literature focus on the later stage of this two-phase problem and are not feasible to implement in the first place.
We propose the concept of TV band white space (TVWS) dovetailed with 5G infrastructure for rural coverage and show that it can yield cost-effectiveness from a service provider perspective.
Color-depth cameras (RGB-D cameras) have become the primary sensors in most robotics systems, from service robotics to industrial robotics applications.
Typical consumer-grade RGB-D cameras are provided with a coarse intrinsic and extrinsic calibration that generally does not meet the accuracy requirements needed by many robotics applications (e.g., highly accurate 3D environment reconstruction and mapping, high precision object recognition and localization, ...).
In this paper, we propose a human-friendly, reliable and accurate calibration framework that enables to easily estimate both the intrinsic and extrinsic parameters of a general color-depth sensor couple.
Our approach is based on a novel two components error model.
This model unifies the error sources of RGB-D pairs based on different technologies, such as structured-light 3D cameras and time-of-flight cameras.
Our method provides some important advantages compared to other state-of-the-art systems: it is general (i.e., well suited for different types of sensors), based on an easy and stable calibration protocol, provides a greater calibration accuracy, and has been implemented within the ROS robotics framework.
We report detailed experimental validations and performance comparisons to support our statements.
The potential for agents, whether embodied or software, to learn by observing other agents performing procedures involving objects and actions is rich.
Current research on automatic procedure learning heavily relies on action labels or video subtitles, even during the evaluation phase, which makes them infeasible in real-world scenarios.
This leads to our question: can the human-consensus structure of a procedure be learned from a large set of long, unconstrained videos (e.g., instructional videos from YouTube) with only visual evidence?
To answer this question, we introduce the problem of procedure segmentation--to segment a video procedure into category-independent procedure segments.
Given that no large-scale dataset is available for this problem, we collect a large-scale procedure segmentation dataset with procedure segments temporally localized and described; we use cooking videos and name the dataset YouCook2.
We propose a segment-level recurrent network for generating procedure segments by modeling the dependencies across segments.
The generated segments can be used as pre-processing for other tasks, such as dense video captioning and event parsing.
We show in our experiments that the proposed model outperforms competitive baselines in procedure segmentation.
Efficient network management is one of the key challenges of the constantly growing and increasingly complex wide area networks (WAN).
The paradigm shift towards virtualized (NFV) and software defined networks (SDN) in the next generation of mobile networks (5G), as well as the latest scientific insights in the field of Artificial Intelligence (AI) enable the transition from manually managed networks nowadays to fully autonomic and dynamic self-organized networks (SON).
This helps to meet the KPIs and reduce at the same time operational costs (OPEX).
In this paper, an AI driven concept is presented for the malfunction detection in NFV applications with the help of semi-supervised learning.
For this purpose, a profile of the application under test is created.
This profile then is used as a reference to detect abnormal behaviour.
For example, if there is a bug in the updated version of the app, it is now possible to react autonomously and roll-back the NFV app to a previous version in order to avoid network outages.
Recent surveys have shown that an increasing portion of the US public believes the two major US parties adequately represent the US public opinion and think additional parties are needed.
However, there are high barriers for third parties in political elections.
In this paper, we aim to address two questions: "How well do the two major US parties represent the public's ideology?" and "Does a more-than-two-party system better represent the ideology of the public?".
To address these questions, we utilize the American National Election Studies Time series dataset.
We perform unsupervised clustering with Gaussian Mixture Model method on this dataset.
When clustered into two clusters, we find a large centrist cluster and a small right-wing cluster.
The Democratic Party's position (estimated using the mean position of the individuals self-identified with the parties) is similar to that of the centrist cluster, and the Republican Party's position is between the two clusters.
We investigate if more than two parties represent the population better by comparing the Akaike Information Criteria for clustering results of the various number of clusters.
We find that additional clusters give a better representation of the data, even after penalizing for the additional parameters.
This suggests a multiparty system represents of the ideology of the public better.
While an increasing interest in deep models for single-image depth estimation methods can be observed, established schemes for their evaluation are still limited.
We propose a set of novel quality criteria, allowing for a more detailed analysis by focusing on specific characteristics of depth maps.
In particular, we address the preservation of edges and planar regions, depth consistency, and absolute distance accuracy.
In order to employ these metrics to evaluate and compare state-of-the-art single-image depth estimation approaches, we provide a new high-quality RGB-D dataset.
We used a DSLR camera together with a laser scanner to acquire high-resolution images and highly accurate depth maps.
Experimental results show the validity of our proposed evaluation protocol.
General Purpose Graphic Processing Unit(GPGPU) is used widely for achieving high performance or high throughput in parallel programming.
This capability of GPGPUs is very famous in the new era and mostly used for scientific computing which requires more processing power than normal personal computers.
Therefore, most of the programmers, researchers and industry use this new concept for their work.
However, achieving high-performance or high-throughput using GPGPUs are not an easy task compared with conventional programming concepts in the CPU side.
In this research, the CPU's cache memory optimization techniques have been adopted to the GPGPU's cache memory to identify rare performance improvement techniques compared to GPGPU's best practices.
The cache optimization techniques of blocking, loop fusion, array merging and array transpose were tested on GPGPUs for finding suitability of these techniques.
Finally, we identified that some of the CPU cache optimization techniques go well with the cache memory system of the GPGPU and shows performance improvements while some others show the opposite effect on the GPGPUs compared with the CPUs.
This paper introduce a software system including widely-used Swarm Intelligence algorithms or approaches to be used for the related scientific research studies associated with the subject area.
The programmatic infrastructure of the system allows working on a fast, easy-to-use, interactive platform to perform Swarm Intelligence based studies in a more effective, efficient and accurate way.
In this sense, the system employs all of the necessary controls for the algorithms and it ensures an interactive platform on which computer users can perform studies on a wide spectrum of solution approaches associated with simple and also more advanced problems.
Papers on Agile Software Development methods are often focused on their applicability in commercial projects or organizations.
There are no current studies that we know about addressing the application of these methods in research projects.
The objective of this work is to describe the perception of researchers on the application of agile software development practices and principles for research projects.
A study was conducted by constructing and applying a questionnaire to Brazilian researchers of different affiliations, formation and research areas in order to obtain information about their knowledge and openness to follow agile software development principles and practices.
We study the computational complexity of an important property of simple, regular and weighted games, which is decisiveness.
We show that this concept can naturally be represented in the context of hypergraph theory, and that decisiveness can be decided for simple games in quasi-polynomial time, and for regular and weighted games in polynomial time.
The strongness condition poses the main difficulties, while properness reduces the complexity of the problem, especially if it is amplified by regularity.
On the other hand, regularity also allows to specify the problem instances much more economically, implying a reconsideration of the corresponding complexity measure that, as we prove, has important structural as well as algorithmic consequences.
We consider the following problem for a fixed graph H: given a graph G and two H-colorings of G, i.e. homomorphisms from G to H, can one be transformed (reconfigured) into the other by changing one color at a time, maintaining an H-coloring throughout.
This is the same as finding a path in the Hom(G,H) complex.
For H=K_k this is the problem of finding paths between k-colorings, which was shown to be in P for k<=3 and PSPACE-complete otherwise by Cereceda et al.2011
We generalize the positive side of this dichotomy by providing an algorithm that solves the problem in polynomial time for any H with no C_4 subgraph.
This gives a large class of constraints for which finding solutions to the Constraint Satisfaction Problem is NP-complete, but finding paths in the solution space is P.   The algorithm uses a characterization of possible reconfiguration sequences (paths in Hom(G,H)), whose main part is a purely topological condition described in algebraic terms of the fundamental groupoid of H seen as a topological space.
We illustrate the potential of massive MIMO for communication with unmanned aerial vehicles (UAVs).
We consider a scenario where multiple single-antenna UAVs simultaneously communicate with a ground station (GS) equipped with a large number of antennas.
Specifically, we discuss the achievable uplink (UAV to GS) capacity performance in the case of line-of-sight (LoS) conditions.
We develop a realistic geometric model which incorporates an arbitrary orientation of the GS and UAV antenna elements to characterize the polarization mismatch loss which occurs due to the movement and orientation of the UAVs.
A closed-form expression for a lower bound on the ergodic rate for a maximum-ratio combining receiver with estimated channel state information is derived.
The optimal antenna spacing that maximizes the ergodic rate achieved by an UAV is also determined for uniform linear and rectangular arrays.
It is shown that when the UAVs are spherically uniformly distributed around the GS, the ergodic rate per UAV is maximized for an antenna spacing equal to an integer multiple of one-half wavelength.
A large number of papers have introduced novel machine learning and feature extraction methods for automatic classification of AD.
However, they are difficult to reproduce because key components of the validation are often not readily available.
These components include selected participants and input data, image preprocessing and cross-validation procedures.
The performance of the different approaches is also difficult to compare objectively.
In particular, it is often difficult to assess which part of the method provides a real improvement, if any.
We propose a framework for reproducible and objective classification experiments in AD using three publicly available datasets (ADNI, AIBL and OASIS).
The framework comprises: i) automatic conversion of the three datasets into BIDS format, ii) a modular set of preprocessing pipelines, feature extraction and classification methods, together with an evaluation framework, that provide a baseline for benchmarking the different components.
We demonstrate the use of the framework for a large-scale evaluation on 1960 participants using T1 MRI and FDG PET data.
In this evaluation, we assess the influence of different modalities, preprocessing, feature types, classifiers, training set sizes and datasets.
Performances were in line with the state-of-the-art.
FDG PET outperformed T1 MRI for all classification tasks.
No difference in performance was found for the use of different atlases, image smoothing, partial volume correction of FDG PET images, or feature type.
Linear SVM and L2-logistic regression resulted in similar performance and both outperformed random forests.
The classification performance increased along with the number of subjects used for training.
Classifiers trained on ADNI generalized well to AIBL and OASIS.
All the code of the framework and the experiments is publicly available at: https://gitlab.icm-institute.org/aramislab/AD-ML.
Matrix games like Prisoner's Dilemma have guided research on social dilemmas for decades.
However, they necessarily treat the choice to cooperate or defect as an atomic action.
In real-world social dilemmas these choices are temporally extended.
Cooperativeness is a property that applies to policies, not elementary actions.
We introduce sequential social dilemmas that share the mixed incentive structure of matrix game social dilemmas but also require agents to learn policies that implement their strategic intentions.
We analyze the dynamics of policies learned by multiple self-interested independent learning agents, each using its own deep Q-network, on two Markov games we introduce here: 1. a fruit Gathering game and 2. a Wolfpack hunting game.
We characterize how learned behavior in each domain changes as a function of environmental factors including resource abundance.
Our experiments show how conflict can emerge from competition over shared resources and shed light on how the sequential nature of real world social dilemmas affects cooperation.
The document serves as a reference for researchers trying to capture a large portion of a mass event on video for several hours, while using a very limited budget.
Cryptocurrencies and their foundation technology, the Blockchain, are reshaping finance and economics, allowing a decentralized approach enabling trusted applications with no trusted counterpart.
More recently, the Blockchain and the programs running on it, called Smart Contracts, are also finding more and more applications in all fields requiring trust and sound certifications.
Some people have come to the point of saying that the "Blockchain revolution" can be compared to that of the Internet and the Web in their early days.
As a result, all the software development revolving around the Blockchain technology is growing at a staggering rate.
The feeling of many software engineers about such huge interest in Blockchain technologies is that of unruled and hurried software development, a sort of competition on a first-come-first-served basis which does not assure neither software quality, nor that the basic concepts of software engineering are taken into account.
This paper tries to cope with this issue, proposing a software development process to gather the requirement, analyze, design, develop, test and deploy Blockchain applications.
The process is based on several Agile practices, such as User Stories and iterative and incremental development based on them.
However, it makes also use of more formal notations, such as some UML diagrams describing the design of the system, with additions to represent specific concepts found in Blockchain development.
The method is described in good detail, and an example is given to show how it works.
In this paper we analyse the benefits of incorporating interval-valued fuzzy sets into the Bousi-Prolog system.
A syntax, declarative semantics and im- plementation for this extension is presented and formalised.
We show, by using potential applications, that fuzzy logic programming frameworks enhanced with them can correctly work together with lexical resources and ontologies in order to improve their capabilities for knowledge representation and reasoning.
In most computer vision applications, convolutional neural networks (CNNs) operate on dense image data generated by ordinary cameras.
Designing CNNs for sparse and irregularly spaced input data is still an open problem with numerous applications in autonomous driving, robotics, and surveillance.
To tackle this challenging problem, we introduce an algebraically-constrained convolution layer for CNNs with sparse input and demonstrate its capabilities for the scene depth completion task.
We propose novel strategies for determining the confidence from the convolution operation and propagating it to consecutive layers.
Furthermore, we propose an objective function that simultaneously minimizes the data error while maximizing the output confidence.
Comprehensive experiments are performed on the KITTI depth benchmark and the results clearly demonstrate that the proposed approach achieves superior performance while requiring three times fewer parameters than the state-of-the-art methods.
Moreover, our approach produces a continuous pixel-wise confidence map enabling information fusion, state inference, and decision support.
A variety of representation learning approaches have been investigated for reinforcement learning; much less attention, however, has been given to investigating the utility of sparse coding.
Outside of reinforcement learning, sparse coding representations have been widely used, with non-convex objectives that result in discriminative representations.
In this work, we develop a supervised sparse coding objective for policy evaluation.
Despite the non-convexity of this objective, we prove that all local minima are global minima, making the approach amenable to simple optimization strategies.
We empirically show that it is key to use a supervised objective, rather than the more straightforward unsupervised sparse coding approach.
We compare the learned representations to a canonical fixed sparse representation, called tile-coding, demonstrating that the sparse coding representation outperforms a wide variety of tilecoding representations.
CSMA (Carrier Sense Multiple Access) algorithms based on Gibbs sampling can achieve throughput optimality if certain parameters called the fugacities are appropriately chosen.
However, the problem of computing these fugacities is NP-hard.
In this work, we derive estimates of the fugacities by using a framework called the regional free energy approximations.
In particular, we derive explicit expressions for approximate fugacities corresponding to any feasible service rate vector.
We further prove that our approximate fugacities are exact for the class of chordal graphs.
A distinguishing feature of our work is that the regional approximations that we propose are tailored to conflict graphs with small cycles, which is a typical characteristic of wireless networks.
Numerical results indicate that the fugacities obtained by the proposed method are quite accurate and significantly outperform the existing Bethe approximation based techniques.
Despite the considerable interest in new dependent type theories, simple type theory (which dates from 1940) is sufficient to formalise serious topics in mathematics.
This point is seen by examining formal proofs of a theorem about stereographic projections.
A formalisation using the HOL Light proof assistant is contrasted with one using Isabelle/HOL.
Harrison's technique for formalising Euclidean spaces is contrasted with an approach using Isabelle/HOL's axiomatic type classes.
However, every formal system can be outgrown, and mathematics should be formalised with a view that it will eventually migrate to a new formalism.
Recent development of contraction theory based analysis of singularly perturbed system has opened the door for inspecting differential behavior of multi time-scale systems.
In this paper a contraction theory based framework is proposed for stabilization of singularly perturbed systems.
The primary objective is to design a feedback controller to achieve bounded tracking error for both standard and non-standard singularly perturbed systems.
This framework provides relaxation over traditional quadratic Lyapunov based method as there is no need to satisfy interconnection conditions during controller design algorithm.
Moreover, the stability bound does not depend on smallness of singularly perturbed parameter.
Combined with high gain scaling, the proposed technique is shown to assure contraction of approximate feedback linearizable systems.
These findings extend the class of nonlinear systems which can be made contracting.
Code optimization and high level synthesis can be posed as constraint satisfaction and optimization problems, such as graph coloring used in register allocation.
Graph coloring is also used to model more traditional CSPs relevant to AI, such as planning, time-tabling and scheduling.
Provably optimal solutions may be desirable for commercial and defense applications.
Additionally, for applications such as register allocation and code optimization, naturally-occurring instances of graph coloring are often small and can be solved optimally.
A recent wave of improvements in algorithms for Boolean satisfiability (SAT) and 0-1 Integer Linear Programming (ILP) suggests generic problem-reduction methods, rather than problem-specific heuristics, because (1) heuristics may be upset by new constraints, (2) heuristics tend to ignore structure, and (3) many relevant problems are provably inapproximable.
Problem reductions often lead to highly symmetric SAT instances, and symmetries are known to slow down SAT solvers.
In this work, we compare several avenues for symmetry breaking, in particular when certain kinds of symmetry are present in all generated instances.
Our focus on reducing CSPs to SAT allows us to leverage recent dramatic improvement in SAT solvers and automatically benefit from future progress.
We can use a variety of black-box SAT solvers without modifying their source code because our symmetry-breaking techniques are static, i.e., we detect symmetries and add symmetry breaking predicates (SBPs) during pre-processing.
An important result of our work is that among the types of instance-independent SBPs we studied and their combinations, the simplest and least complete constructions are the most effective.
Our experiments also clearly indicate that instance-independent symmetries should mostly be processed together with instance-specific symmetries rather than at the specification level, contrary to what has been suggested in the literature.
Mobility Management (MM) techniques have conventionally been centralized in nature, wherein a single network entity has been responsible for handling the mobility related tasks of the mobile nodes attached to the network.
However, an exponential growth in network traffic and the number of users has ushered in the concept of providing Mobility Management as a Service (MMaaS) to the wireless nodes attached to the 5G networks.
Allowing for on-demand mobility management solutions will not only provide the network with the flexibility that it needs to accommodate the many different use cases that are to be served by future networks, but it will also provide the network with the scalability that is needed alongside the flexibility to serve future networks.
And hence, in this paper, a detailed study of MMaaS has been provided, highlighting its benefits and challenges for 5G networks.
Additionally, the very important property of granularity of service which is deeply intertwined with the scalability and flexibility requirements of the future wireless networks, and a consequence of MMaaS, has also been discussed in detail.
With the technological advancements of aerial imagery and accurate 3d reconstruction of urban environments, more and more attention has been paid to the automated analyses of urban areas.
In our work, we examine two important aspects that allow live analysis of building structures in city models given oblique aerial imagery, namely automatic building extraction with convolutional neural networks (CNNs) and selective real-time depth estimation from aerial imagery.
We use transfer learning to train the Faster R-CNN method for real-time deep object detection, by combining a large ground-based dataset for urban scene understanding with a smaller number of images from an aerial dataset.
We achieve an average precision (AP) of about 80% for the task of building extraction on a selected evaluation dataset.
Our evaluation focuses on both dataset-specific learning and transfer learning.
Furthermore, we present an algorithm that allows for multi-view depth estimation from aerial imagery in real-time.
We adopt the semi-global matching (SGM) optimization strategy to preserve sharp edges at object boundaries.
In combination with the Faster R-CNN, it allows a selective reconstruction of buildings, identified with regions of interest (RoIs), from oblique aerial imagery.
Barring swarm robotics, a substantial share of current machine-human and machine-machine learning and interaction mechanisms are being developed and fed by results of agent-based computer simulations, game-theoretic models, or robotic experiments based on a dyadic communication pattern.
Yet, in real life, humans no less frequently communicate in groups, and gain knowledge and take decisions basing on information cumulatively gleaned from more than one single source.
These properties should be taken into consideration in the design of autonomous artificial cognitive systems construed to interact with learn from more than one contact or 'neighbour'.
To this end, significant practical import can be gleaned from research applying strict science methodology to human and social phenomena, e.g. to discovery of realistic creativity potential spans, or the 'exposure thresholds' after which new information could be accepted by a cognitive agent.
The results will be presented of a project analysing the social propagation of neologisms in a microblogging service.
From local, low-level interactions and information flows between agents inventing and imitating discrete lexemes we aim to describe the processes of the emergence of more global systemic order and dynamics, using the latest methods of complexity science.
Whether in order to mimic them, or to 'enhance' them, parameters gleaned from complexity science approaches to humans' social and humanistic behaviour should subsequently be incorporated as points of reference in the field of robotics and human-machine interaction.
Adams' extension of parsing expression grammars enables specifying indentation sensitivity using two non-standard grammar constructs --- indentation by a binary relation and alignment.
This paper proposes a step-by-step transformation of well-formed Adams' grammars for elimination of the alignment construct from the grammar.
The idea that alignment could be avoided was suggested by Adams but no process for achieving this aim has been described before.
Designing of touchless user interface is gaining popularity in various contexts.
Using such interfaces, users can interact with electronic devices even when the hands are dirty or non-conductive.
Also, user with partial physical disability can interact with electronic devices using such systems.
Research in this direction has got major boost because of the emergence of low-cost sensors such as Leap Motion, Kinect or RealSense devices.
In this paper, we propose a Leap Motion controller-based methodology to facilitate rendering of 2D and 3D shapes on display devices.
The proposed method tracks finger movements while users perform natural gestures within the field of view of the sensor.
In the next phase, trajectories are analyzed to extract extended Npen++ features in 3D.
These features represent finger movements during the gestures and they are fed to unidirectional left-to-right Hidden Markov Model (HMM) for training.
A one-to-one mapping between gestures and shapes is proposed.
Finally, shapes corresponding to these gestures are rendered over the display using MuPad interface.
We have created a dataset of 5400 samples recorded by 10 volunteers.
Our dataset contains 18 geometric and 18 non-geometric shapes such as "circle", "rectangle", "flower", "cone", "sphere" etc.
The proposed methodology achieves an accuracy of 92.87% when evaluated using 5-fold cross validation method.
Our experiments revel that the extended 3D features perform better than existing 3D features in the context of shape representation and classification.
The method can be used for developing useful HCI applications for smart display devices.
Given a finite set in a metric space, the topological analysis generalizes hierarchical clustering using a 1-parameter family of homology groups to quantify connectivity in all dimensions.
The connectivity is compactly described by the persistence diagram.
One limitation of the current framework is the reliance on metric distances, whereas in many practical applications objects are compared by non-metric dissimilarity measures.
Examples are the Kullback-Leibler divergence, which is commonly used for comparing text and images, and the Itakura-Saito divergence, popular for speech and sound.
These are two members of the broad family of dissimilarities called Bregman divergences.
We show that the framework of topological data analysis can be extended to general Bregman divergences, widening the scope of possible applications.
In particular, we prove that appropriately generalized Cech and Delaunay (alpha) complexes capture the correct homotopy type, namely that of the corresponding union of Bregman balls.
Consequently, their filtrations give the correct persistence diagram, namely the one generated by the uniformly growing Bregman balls.
Moreover, we show that unlike the metric setting, the filtration of Vietoris-Rips complexes may fail to approximate the persistence diagram.
We propose algorithms to compute the thus generalized Cech, Vietoris-Rips and Delaunay complexes and experimentally test their efficiency.
Lastly, we explain their surprisingly good performance by making a connection with discrete Morse theory.
Low-dose computed tomography (CT) has attracted a major attention in the medical imaging field, since CT-associated x-ray radiation carries health risks for patients.
The reduction of CT radiation dose, however, compromises the signal-to-noise ratio, and may compromise the image quality and the diagnostic performance.
Recently, deep-learning-based algorithms have achieved promising results in low-dose CT denoising, especially convolutional neural network (CNN) and generative adversarial network (GAN).
This article introduces a Contracting Path-based Convolutional Encoder-decoder (CPCE) network in 2D and 3D configurations within the GAN framework for low-dose CT denoising.
A novel feature of our approach is that an initial 3D CPCE denoising model can be directly obtained by extending a trained 2D CNN and then fine-tuned to incorporate 3D spatial information from adjacent slices.
Based on the transfer learning from 2D to 3D, the 3D network converges faster and achieves a better denoising performance than that trained from scratch.
By comparing the CPCE with recently published methods based on the simulated Mayo dataset and the real MGH dataset, we demonstrate that the 3D CPCE denoising model has a better performance, suppressing image noise and preserving subtle structures.
A simulation model based on parallel systems is established, aiming to explore the relation between the number of submissions and the overall quality of academic journals within a similar discipline under peer review.
The model can effectively simulate the submission, review and acceptance behaviors of academic journals, in a distributed manner.
According to the simulation experiments, it could possibly happen that the overall standard of academic journals may deteriorate due to excessive submissions.
We study probabilistic complexity classes and questions of derandomisation from a logical point of view.
For each logic L we introduce a new logic BPL, bounded error probabilistic L, which is defined from L in a similar way as the complexity class BPP, bounded error probabilistic polynomial time, is defined from PTIME.
Our main focus lies on questions of derandomisation, and we prove that there is a query which is definable in BPFO, the probabilistic version of first-order logic, but not in Cinf, finite variable infinitary logic with counting.
This implies that many of the standard logics of finite model theory, like transitive closure logic and fixed-point logic, both with and without counting, cannot be derandomised.
Similarly, we present a query on ordered structures which is definable in BPFO but not in monadic second-order logic, and a query on additive structures which is definable in BPFO but not in FO.
The latter of these queries shows that certain uniform variants of AC0 (bounded-depth polynomial sized circuits) cannot be derandomised.
These results are in contrast to the general belief that most standard complexity classes can be derandomised.
Finally, we note that BPIFP+C, the probabilistic version of fixed-point logic with counting, captures the complexity class BPP, even on unordered structures.
Based on the knowledge of dynamic systems, the shorter the transient response, or the faster a system reaches the steady-state after the introduction of the change, the smaller will be the output variability.
In lean manufacturing, the principle of reducing set-up times has the same purpose: reduce the transient time and improve production flow.
Analogously, the analysis of the transient response of project-driven systems may provide crucial information about how fast these systems react to a change and how that change affects their production output.
Although some studies have investigated flow variability in projects, few have looked at variability from the perspective that the transient state represents the changeovers on project-driven production systems and how the transient state affects the process' flow variability.
The purpose of this study is to investigate the effect of changes in project-driven production systems from a conceptual point of view, furthermore, measuring and correlating the transient response of five cases to their flow variability.
Results showed a proportional relationship between the percentile transient time and flow variability of a process.
That means that the quicker the production system reacts to change; the less the distress in the production output, consequently, lower levels of flow variability.
As practical implications, lean practices focusing on reducing set-up times (transient time) can have their effects measured on project-driven production flow.
The Time-Invariant Incremental Knapsack problem (IIK) is a generalization of Maximum Knapsack to a discrete multi-period setting.
At each time, capacity increases and items can be added, but not removed from the knapsack.
The goal is to maximize the sum of profits over all times.
IIK models various applications including specific financial markets and governmental decision processes.
IIK is strongly NP-hard and there has been work on giving approximation algorithms for some special cases.
In this paper, we settle the complexity of IIK by designing a PTAS based on rounding a disjuncive formulation, and provide several extensions of the technique.
Gaussian processes (GPs) are versatile tools that have been successfully employed to solve nonlinear estimation problems in machine learning, but that are rarely used in signal processing.
In this tutorial, we present GPs for regression as a natural nonlinear extension to optimal Wiener filtering.
After establishing their basic formulation, we discuss several important aspects and extensions, including recursive and adaptive algorithms for dealing with non-stationarity, low-complexity solutions, non-Gaussian noise models and classification scenarios.
Furthermore, we provide a selection of relevant applications to wireless digital communications.
Current grammar-based NeuroEvolution approaches have several shortcomings.
On the one hand, they do not allow the generation of Artificial Neural Networks (ANNs composed of more than one hidden-layer.
On the other, there is no way to evolve networks with more than one output neuron.
To properly evolve ANNs with more than one hidden-layer and multiple output nodes there is the need to know the number of neurons available in previous layers.
In this paper we introduce Dynamic Structured Grammatical Evolution (DSGE): a new genotypic representation that overcomes the aforementioned limitations.
By enabling the creation of dynamic rules that specify the connection possibilities of each neuron, the methodology enables the evolution of multi-layered ANNs with more than one output neuron.
Results in different classification problems show that DSGE evolves effective single and multi-layered ANNs, with a varying number of output neurons.
Due to their rapid growth and deployment, Internet of things (IoT) devices have become a central aspect of our daily lives.
However, they tend to have many vulnerabilities which can be exploited by an attacker.
Unsupervised techniques, such as anomaly detection, can help us secure the IoT devices.
However, an anomaly detection model must be trained for a long time in order to capture all benign behaviors.
This approach is vulnerable to adversarial attacks since all observations are assumed to be benign while training the anomaly detection model.
In this paper, we propose CIoTA, a lightweight framework that utilizes the blockchain concept to perform distributed and collaborative anomaly detection for devices with limited resources.
CIoTA uses blockchain to incrementally update a trusted anomaly detection model via self-attestation and consensus among IoT devices.
We evaluate CIoTA on our own distributed IoT simulation platform, which consists of 48 Raspberry Pis, to demonstrate CIoTA's ability to enhance the security of each device and the security of the network as a whole.
Mobile streaming video data accounts for a large and increasing percentage of wireless network traffic.
The available bandwidths of modern wireless networks are often unstable, leading to difficulties in delivering smooth, high-quality video.
Streaming service providers such as Netflix and YouTube attempt to adapt their systems to adjust in response to these bandwidth limitations by changing the video bitrate or, failing that, allowing playback interruptions (rebuffering).
Being able to predict end user' quality of experience (QoE) resulting from these adjustments could lead to perceptually-driven network resource allocation strategies that would deliver streaming content of higher quality to clients, while being cost effective for providers.
Existing objective QoE models only consider the effects on user QoE of video quality changes or playback interruptions.
For streaming applications, adaptive network strategies may involve a combination of dynamic bitrate allocation along with playback interruptions when the available bandwidth reaches a very low value.
Towards effectively predicting user QoE, we propose Video Assessment of TemporaL Artifacts and Stalls (Video ATLAS): a machine learning framework where we combine a number of QoE-related features, including objective quality features, rebuffering-aware features and memory-driven features to make QoE predictions.
We evaluated our learning-based QoE prediction model on the recently designed LIVE-Netflix Video QoE Database which consists of practical playout patterns, where the videos are afflicted by both quality changes and rebuffering events, and found that it provides improved performance over state-of-the-art video quality metrics while generalizing well on different datasets.
The proposed algorithm is made publicly available at http://live.ece.utexas.edu/research/Quality/VideoATLAS release_v2.rar.
In this paper, new context of Chinese Remainder Theorem (CRT) based analysis of combinatorial sequence generators has been presented.
CRT is exploited to establish fixed patterns in LFSR sequences and underlying cyclic structures of finite fields.
New methodology of direct computations of DFT spectral points in higher finite fields from known DFT spectra points of smaller constituent fields is also introduced.
Novel approach of CRT based structural analysis of LFSR based combinatorial sequence is given both in time and frequency domain.
The proposed approach is demonstrated on some examples of combiner generators and is scalable to general configuration of combiner generators.
A person is commonly described by attributes like height, build, cloth color, cloth type, and gender.
Such attributes are known as soft biometrics.
They bridge the semantic gap between human description and person retrieval in surveillance video.
The paper proposes a deep learning-based linear filtering approach for person retrieval using height, cloth color, and gender.
The proposed approach uses Mask R-CNN for pixel-wise person segmentation.
It removes background clutter and provides precise boundary around the person.
Color and gender models are fine-tuned using AlexNet and the algorithm is tested on SoftBioSearch dataset.
It achieves good accuracy for person retrieval using the semantic query in challenging conditions.
Massive open online courses pose a massive challenge for grading the answerscripts at a high accuracy.
Peer grading is often viewed as a scalable solution to this challenge, which largely depends on the altruism of the peer graders.
Some approaches in the literature treat peer grading as a 'best-effort service' of the graders, and statistically correct their inaccuracies before awarding the final scores, but ignore graders' strategic behavior.
Few other approaches incentivize non-manipulative actions of the peer graders but do not make use of certain additional information that is potentially available in a peer grading setting, e.g., the true grade can eventually be observed at an additional cost.
This cost can be thought of as an additional effort from the teaching staff if they had to finally take a look at the corrected papers post peer grading.
In this paper, we use such additional information and introduce a mechanism, TRUPEQA, that (a) uses a constant number of instructor-graded answerscripts to quantitatively measure the accuracies of the peer graders and corrects the scores accordingly, (b) ensures truthful revelation of their observed grades, (c) penalizes manipulation, but not inaccuracy, and (d) reduces the total cost of arriving at the true grades, i.e., the additional person-hours of the teaching staff.
We show that this mechanism outperforms several standard peer grading techniques used in practice, even at times when the graders are non-manipulative.
In high mobility applications of millimeter wave (mmWave) communications, e.g., vehicle-to-everything communication and next-generation cellular communication, frequent link configuration can be a source of significant overhead.
We use the sub-6 GHz channel covariance as an out-of-band side information for mmWave link configuration.
Assuming: (i) a fully digital architecture at sub-6 GHz; and (ii) a hybrid analog-digital architecture at mmWave, we propose an out-of-band covariance translation approach and an out-of-band aided compressed covariance estimation approach.
For covariance translation, we estimate the parameters of sub-6 GHz covariance and use them in theoretical expressions of covariance matrices to predict the mmWave covariance.
For out-of-band aided covariance estimation, we use weighted sparse signal recovery to incorporate out-of-band information in compressed covariance estimation.
The out-of-band covariance translation eliminates the in-band training completely, whereas out-of-band aided covariance estimation relies on in-band as well as out-of-band training.
We also analyze the loss in the signal-to-noise ratio due to an imperfect estimate of the covariance.
The simulation results show that the proposed covariance estimation strategies can reduce the training overhead compared to the in-band only covariance estimation.
The entropy region is constructed from vectors of random variables by collecting Shannon entropies of all subvectors.
Its shape is studied here by means of polymatroidal constructions, notably by convolution.
The closure of the region is decomposed into the direct sum of tight and modular parts, reducing the study to the tight part.
The relative interior of the reduction belongs to the entropy region.
Behavior of the decomposition under selfadhesivity is clarified.
Results are specialized to and completed for the region of four random variables.
This and computer experiments help to visualize approximations of a symmetrized part of the entropy region.
Four-atom conjecture on the minimization of Ingleton score is refuted.
English to Indian language machine translation poses the challenge of structural and morphological divergence.
This paper describes English to Indian language statistical machine translation using pre-ordering and suffix separation.
The pre-ordering uses rules to transfer the structure of the source sentences prior to training and translation.
This syntactic restructuring helps statistical machine translation to tackle the structural divergence and hence better translation quality.
The suffix separation is used to tackle the morphological divergence between English and highly agglutinative Indian languages.
We demonstrate that the use of pre-ordering and suffix separation helps in improving the quality of English to Indian Language machine translation.
The authors propose a parametric model called the arena model for prediction in paired competitions, i.e. paired comparisons with eliminations and bifurcations.
The arena model has a number of appealing advantages.
First, it predicts the results of competitions without rating many individuals.
Second, it takes full advantage of the structure of competitions.
Third, the model provides an easy method to quantify the uncertainty in competitions.
Fourth, some of our methods can be directly generalized for comparisons among three or more individuals.
Furthermore, the authors identify an invariant Bayes estimator with regard to the prior distribution and prove the consistency of the estimations of uncertainty.
Currently, the arena model is not effective in tracking the change of strengths of individuals, but its basic framework provides a solid foundation for future study of such cases.
Synchronizing sequences have been proposed in the late 60's to solve testing problems on systems modeled by finite state machines.
Such sequences lead a system, seen as a black box, from an unknown current state to a known final one.
This paper presents a first investigation of the computation of synchronizing sequences for systems modeled by bounded synchronized Petri nets.
In the first part of the paper, existing techniques for automata are adapted to this new setting.
Later on, new approaches, that exploit the net structure to efficiently compute synchronizing sequences without an exhaustive enumeration of the state space, are presented.
The Variational Autoencoder (VAE) is a powerful architecture capable of representation learning and generative modeling.
When it comes to learning interpretable (disentangled) representations, VAE and its variants show unparalleled performance.
However, the reasons for this are unclear, since a very particular alignment of the latent embedding is needed but the design of the VAE does not encourage it in any explicit way.
We address this matter and offer the following explanation: the diagonal approximation in the encoder together with the inherent stochasticity force local orthogonality of the decoder.
The local behavior of promoting both reconstruction and orthogonality matches closely how the PCA embedding is chosen.
Alongside providing an intuitive understanding, we justify the statement with full theoretical analysis as well as with experiments.
This paper investigates delay-distortion-power trade offs in transmission of quasi-stationary sources over block fading channels by studying encoder and decoder buffering techniques to smooth out the source and channel variations.
Four source and channel coding schemes that consider buffer and power constraints are presented to minimize the reconstructed source distortion.
The first one is a high performance scheme, which benefits from optimized source and channel rate adaptation.
In the second scheme, the channel coding rate is fixed and optimized along with transmission power with respect to channel and source variations; hence this scheme enjoys simplicity of implementation.
The two last schemes have fixed transmission power with optimized adaptive or fixed channel coding rate.
For all the proposed schemes, closed form solutions for mean distortion, optimized rate and power are provided and in the high SNR regime, the mean distortion exponent and the asymptotic mean power gains are derived.
The proposed schemes with buffering exploit the diversity due to source and channel variations.
Specifically, when the buffer size is limited, fixed channel rate adaptive power scheme outperforms an adaptive rate fixed power scheme.
Furthermore, analytical and numerical results demonstrate that with limited buffer size, the system performance in terms of reconstructed signal SNR saturates as transmission power is increased, suggesting that appropriate buffer size selection is important to achieve a desired reconstruction quality.
In this paper, we consider a MU-MISO system where users have highly accurate Channel State Information (CSI), while the Base Station (BS) has partial CSI consisting of an imperfect channel estimate and statistical knowledge of the CSI error.
With the objective of maximizing the Average Sum Rate (ASR) subject to a power constraint, a special transmission scheme is considered where the BS transmits a common symbol in a multicast fashion, in addition to the conventional private symbols.
This scheme is termed Joint Multicasting and Broadcasting (JMB).
The ASR problem is transformed into an augmented Average Weighted Sum Mean Square Error (AWSMSE) problem which is solved using Alternating Optimization (AO).
The enhanced rate performance accompanied with the incorporation of the multicast part is demonstrated through simulations.
Most saliency estimation methods aim to explicitly model low-level conspicuity cues such as edges or blobs and may additionally incorporate top-down cues using face or text detection.
Data-driven methods for training saliency models using eye-fixation data are increasingly popular, particularly with the introduction of large-scale datasets and deep architectures.
However, current methods in this latter paradigm use loss functions designed for classification or regression tasks whereas saliency estimation is evaluated on topographical maps.
In this work, we introduce a new saliency map model which formulates a map as a generalized Bernoulli distribution.
We then train a deep architecture to predict such maps using novel loss functions which pair the softmax activation function with measures designed to compute distances between probability distributions.
We show in extensive experiments the effectiveness of such loss functions over standard ones on four public benchmark datasets, and demonstrate improved performance over state-of-the-art saliency methods.
The IPv4 addresses exhaustion demands a protocol transition from IPv4 to IPv6.
The original transition technique, the dual stack, is not widely deployed yet and it demanded the creation of new transition techniques to extend the transition period.
This work makes an experimental comparison of techniques that use dual stack with a limited IPv4 address.
This limited address might be a RFC 1918 address with a NAT at the Internet Service Provider (ISP) gateway, also known as Carrier Grade NAT (CGN), or an Address Plus Port (A+P) shared IPv4 address.
The chosen techniques also consider an IPv6 only ISP network.
The transport of the IPv4 packets through the IPv6 only networks may use IPv4 packets encapsulated on IPv6 packets or a double translation, by making one IPv4 to IPv6 translation to enter the IPv6 only network and one IPv6 to IPv4 translation to return to the IPv4 network.
The chosen techniques were DS-Lite, 464XLAT, MAP-E and MAP-T.
The first part of the test is to check some of the most common usages of the Internet by a home user and the impacts of the transition techniques on the user experience.
The second part is a measured comparison considering bandwidth, jitter and latency introduced by the techniques and processor usage on the network equipment.
We present a method for finding correspondence between 3D models.
From an initial set of feature correspondences, our method uses a fast voting scheme to separate the inliers from the outliers.
The novelty of our method lies in the use of a combination of local and global constraints to determine if a vote should be cast.
On a local scale, we use simple, low-level geometric invariants.
On a global scale, we apply covariant constraints for finding compatible correspondences.
We guide the sampling for collecting voters by downward dependencies on previous voting stages.
All of this together results in an accurate matching procedure.
We evaluate our algorithm by controlled and comparative testing on different datasets, giving superior performance compared to state of the art methods.
In a final experiment, we apply our method for 3D object detection, showing potential use of our method within higher-level vision.
With the emergence of Non-Volatile Memories (NVMs) and their shortcomings such as limited endurance and high power consumption in write requests, several studies have suggested hybrid memory architecture employing both Dynamic Random Access Memory (DRAM) and NVM in a memory system.
By conducting a comprehensive experiments, we have observed that such studies lack to consider very important aspects of hybrid memories including the effect of: a) data migrations on performance, b) data migrations on power, and c) the granularity of data migration.
This paper presents an efficient data migration scheme at the Operating System level in a hybrid DRAMNVM memory architecture.
In the proposed scheme, two Least Recently Used (LRU) queues, one for DRAM section and one for NVM section, are used for the sake of data migration.
With careful characterization of the workloads obtained from PARSEC benchmark suite, the proposed scheme prevents unnecessary migrations and only allows migrations which benefits the system in terms of power and performance.
The experimental results show that the proposed scheme can reduce the power consumption up to 79% compared to DRAM-only memory and up to 48% compared to the state-of-the art techniques.
Corrective Transmission Switching can be used by the grid operator to relieve line overloading and voltage violations, improve system reliability, and reduce system losses.
Power grid optimization by means of line switching is typically formulated as a mixed integer programming problem (MIP).
Such problems are known to be computationally intractable, and accordingly, a number of heuristic approaches to grid topology reconfiguration have been proposed in the power systems literature.
By means of some low order examples (3-bus systems), it is shown that within a reasonably large class of greedy heuristics, none can be found that perform better than the others across all grid topologies.
Despite this cautionary tale, statistical evidence based on a large number of simulations using using IEEE 118- bus systems indicates that among three heuristics, a globally greedy heuristic is the most computationally intensive, but has the best chance of reducing generation costs while enforcing N-1 connectivity.
It is argued that, among all iterative methods, the locally optimal switches at each stage have a better chance in not only approximating a global optimal solution but also greatly limiting the number of lines that are switched.
One of the main difficulties in echo cancellation is the fact that the learning rate needs to vary according to conditions such as double-talk and echo path change.
In this paper we propose a new method of varying the learning rate of a frequency-domain echo canceller.
This method is based on the derivation of the optimal learning rate of the NLMS algorithm in the presence of noise.
The method is evaluated in conjunction with the multidelay block frequency domain (MDF) adaptive filter.
We demonstrate that it performs better than current double-talk detection techniques and is simple to implement.
Text Clustering is a text mining technique which divides the given set of text documents into significant clusters.
It is used for organizing a huge number of text documents into a well-organized form.
In the majority of the clustering algorithms, the number of clusters must be specified apriori, which is a drawback of these algorithms.
The aim of this paper is to show experimentally how to determine the number of clusters based on cluster quality.
Since partitional clustering algorithms are well-suited for clustering large document datasets, we have confined our analysis to a partitional clustering algorithm.
We consider the problem of reconstructing a 3-D scene from a moving camera with high frame rate using the affine projection model.
This problem is traditionally known as Affine Structure from Motion (Affine SfM), and can be solved using an elegant low-rank factorization formulation.
In this paper, we assume that an accelerometer and gyro are rigidly mounted with the camera, so that synchronized linear acceleration and angular velocity measurements are available together with the image measurements.
We extend the standard Affine SfM algorithm to integrate these measurements through the use of image derivatives.
Past research has shown the benefits of food journaling in promoting mindful eating and healthier food choices.
However, the links between journaling and healthy eating have not been thoroughly examined.
Beyond caloric restriction, do journalers consistently and sufficiently consume healthful diets?
How different are their eating habits compared to those of average consumers who tend to be less conscious about health?
In this study, we analyze the healthy eating behaviors of active food journalers using data from MyFitnessPal.
Surprisingly, our findings show that food journalers do not eat as healthily as they should despite their proclivity to health eating and their food choices resemble those of the general populace.
Furthermore, we find that the journaling duration is only a marginal determinant of healthy eating outcomes and sociodemographic factors, such as gender and regions of residence, are much more predictive of healthy food choices.
We construct a family of perfect polyphase sequences that has the Frank sequences, Chu sequences, and Milewski sequences as special cases.
This is not the most general construction of this type, but it has a particularly simple form.
We also include some remarks about the acyclic autocorrelations of our sequences.
We present a machine learning framework that leverages a mixture of metadata, network, and temporal features to detect extremist users, and predict content adopters and interaction reciprocity in social media.
We exploit a unique dataset containing millions of tweets generated by more than 25 thousand users who have been manually identified, reported, and suspended by Twitter due to their involvement with extremist campaigns.
We also leverage millions of tweets generated by a random sample of 25 thousand regular users who were exposed to, or consumed, extremist content.
We carry out three forecasting tasks, (i) to detect extremist users, (ii) to estimate whether regular users will adopt extremist content, and finally (iii) to predict whether users will reciprocate contacts initiated by extremists.
All forecasting tasks are set up in two scenarios: a post hoc (time independent) prediction task on aggregated data, and a simulated real-time prediction task.
The performance of our framework is extremely promising, yielding in the different forecasting scenarios up to 93% AUC for extremist user detection, up to 80% AUC for content adoption prediction, and finally up to 72% AUC for interaction reciprocity forecasting.
We conclude by providing a thorough feature analysis that helps determine which are the emerging signals that provide predictive power in different scenarios.
Wireless network applications, such as, searching, routing, self stabilization and query processing can be modeled as random walks on graphs.
Stateless Opportunistic routing technique is a robust distributed routing technique based on random walk approach, where nodes transfer the packets to one of their direct neighbors uniformly, until the packets reach their destinations.
Simplicity in execution, fault tolerance, low overhead and robustness to topology changes made it more suitable to wireless sensor networks scenarios.
But the main disadvantage of stateless opportunistic routing is estimating and studying the effect of network parameters on the packet latency.
In this work, we derived the analytical expressions for mean latency or average packet travel time for r-nearest neighbor cycle, r-nearest neighbor torus networks.
Further, we derived the generalized expression for mean latency for m-dimensional r- nearest neighbor torus networks and studied the effect of number of nodes, nearest neighbors and network dimension on average packet travel time.
The basic indicators of a researcher's productivity and impact are still the number of publications and their citation counts.
These metrics are clear, straightforward, and easy to obtain.
When a ranking of scholars is needed, for instance in grant, award, or promotion procedures, their use is the fastest and cheapest way of prioritizing some scientists over others.
However, due to their nature, there is a danger of oversimplifying scientific achievements.
Therefore, many other indicators have been proposed including the usage of the PageRank algorithm known for the ranking of webpages and its modifications suited to citation networks.
Nevertheless, this recursive method is computationally expensive and even if it has the advantage of favouring prestige over popularity, its application should be well justified, particularly when compared to the standard citation counts.
In this study, we analyze three large datasets of computer science papers in the categories of artificial intelligence, software engineering, and theory and methods and apply 12 different ranking methods to the citation networks of authors.
We compare the resulting rankings with self-compiled lists of outstanding researchers selected as frequent editorial board members of prestigious journals in the field and conclude that there is no evidence of PageRank-based methods outperforming simple citation counts.
System Gramian matrices are a well-known encoding for properties of input-output systems such as controllability, observability or minimality.
These so-called system Gramians were developed in linear system theory for applications such as model order reduction of control systems.
Empirical Gramian are an extension to the system Gramians for parametric and nonlinear systems as well as a data-driven method of computation.
The empirical Gramian framework - emgr - implements the empirical Gramians in a uniform and configurable manner, with applications such as Gramian-based (nonlinear) model reduction, decentralized control, sensitivity analysis, parameter identification and combined state and parameter reduction.
Deep Reinforcement Learning (DRL) has achieved impressive success in many applications.
A key component of many DRL models is a neural network representing a Q function, to estimate the expected cumulative reward following a state-action pair.
The Q function neural network contains a lot of implicit knowledge about the RL problems, but often remains unexamined and uninterpreted.
To our knowledge, this work develops the first mimic learning framework for Q functions in DRL.
We introduce Linear Model U-trees (LMUTs) to approximate neural network predictions.
An LMUT is learned using a novel on-line algorithm that is well-suited for an active play setting, where the mimic learner observes an ongoing interaction between the neural net and the environment.
Empirical evaluation shows that an LMUT mimics a Q function substantially better than five baseline methods.
The transparent tree structure of an LMUT facilitates understanding the network's learned knowledge by analyzing feature influence, extracting rules, and highlighting the super-pixels in image inputs.
The problem of robustly reconstructing a large number from its erroneous remainders with respect to several moduli, namely the robust remaindering problem, may occur in many applications including phase unwrapping, frequency detection from several undersampled waveforms, wireless sensor networks, etc.
Assuming that the dynamic range of the large number is the maximal possible one, i.e., the least common multiple (lcm) of all the moduli, a method called robust Chinese remainder theorem (CRT) for solving the robust remaindering problem has been recently proposed.
In this paper, by relaxing the assumption that the dynamic range is fixed to be the lcm of all the moduli, a trade-off between the dynamic range and the robustness bound for two-modular systems is studied.
It basically says that a decrease in the dynamic range may lead to an increase of the robustness bound.
We first obtain a general condition on the remainder errors and derive the exact dynamic range with a closed-form formula for the robustness to hold.
We then propose simple closed-form reconstruction algorithms.
Furthermore, the newly obtained two-modular results are applied to the robust reconstruction for multi-modular systems and generalized to real numbers.
Finally, some simulations are carried out to verify our proposed theoretical results.
In this paper we present a method for automatically planning robust optimal paths for a group of robots that satisfy a common high level mission specification.
Each robot's motion in the environment is modeled as a weighted transition system, and the mission is given as a Linear Temporal Logic (LTL) formula over a set of propositions satisfied by the regions of the environment.
In addition, an optimizing proposition must repeatedly be satisfied.
The goal is to minimize the maximum time between satisfying instances of the optimizing proposition while ensuring that the LTL formula is satisfied even with uncertainty in the robots' traveling times.
We characterize a class of LTL formulas that are robust to robot timing errors, for which we generate optimal paths if no timing errors are present, and we present bounds on the deviation from the optimal values in the presence of errors.
We implement and experimentally evaluate our method considering a persistent monitoring task in a road network environment.
The problem of finding dominators in a directed graph has many important applications, notably in global optimization of computer code.
Although linear and near-linear-time algorithms exist, they use sophisticated data structures.
We develop an algorithm for finding dominators that uses only a "static tree" disjoint set data structure in addition to simple lists and maps.
The algorithm runs in near-linear or linear time, depending on the implementation of the disjoint set data structure.
We give several versions of the algorithm, including one that computes loop nesting information (needed in many kinds of global code optimization) and that can be made self-certifying, so that the correctness of the computed dominators is very easy to verify.
In this letter, we address the symbol synchronization issue in molecular communication via diffusion (MCvD).
Symbol synchronization among chemical sensors and nanomachines is one of the critical challenges to manage complex tasks in the nanonetworks with molecular communication (MC).
As in diffusion-based MC, most of the molecules arrive at the receptor closer to the start of the symbol duration, the wrong estimation of the start of the symbol interval leads to high symbol detection error.
By utilizing two types of molecules with different diffusion coefficients we propose a synchronization technique for MCvD.
Moreover, we evaluate the symbol-error-rate performance under the proposed symbol synchronization scheme for equal and non-equal symbol duration MCvD systems.
In this lecture note, we describe high dynamic range (HDR) imaging systems; such systems are able to represent luminances of much larger brightness and, typically, also a larger range of colors than conventional standard dynamic range (SDR) imaging systems.
The larger luminance range greatly improve the overall quality of visual content, making it appears much more realistic and appealing to observers.
HDR is one of the key technologies of the future imaging pipeline, which will change the way the digital visual content is represented and manipulated today.
Bedside caregivers assess infants' pain at constant intervals by observing specific behavioral and physiological signs of pain.
This standard has two main limitations.
The first limitation is the intermittent assessment of pain, which might lead to missing pain when the infants are left unattended.
Second, it is inconsistent since it depends on the observer's subjective judgment and differs between observers.
The intermittent and inconsistent assessment can induce poor treatment and, therefore, cause serious long-term consequences.
To mitigate these limitations, the current standard can be augmented by an automated system that monitors infants continuously and provides quantitative and consistent assessment of pain.
Several automated methods have been introduced to assess infants' pain automatically based on analysis of behavioral or physiological pain indicators.
This paper comprehensively reviews the automated approaches (i.e., approaches to feature extraction) for analyzing infants' pain and the current efforts in automatic pain recognition.
In addition, it reviews the databases available to the research community and discusses the current limitations of the automated pain assessment.
A/B testing is a standard approach for evaluating the effect of online experiments; the goal is to estimate the `average treatment effect' of a new feature or condition by exposing a sample of the overall population to it.
A drawback with A/B testing is that it is poorly suited for experiments involving social interference, when the treatment of individuals spills over to neighboring individuals along an underlying social network.
In this work, we propose a novel methodology using graph clustering to analyze average treatment effects under social interference.
To begin, we characterize graph-theoretic conditions under which individuals can be considered to be `network exposed' to an experiment.
We then show how graph cluster randomization admits an efficient exact algorithm to compute the probabilities for each vertex being network exposed under several of these exposure conditions.
Using these probabilities as inverse weights, a Horvitz-Thompson estimator can then provide an effect estimate that is unbiased, provided that the exposure model has been properly specified.
Given an estimator that is unbiased, we focus on minimizing the variance.
First, we develop simple sufficient conditions for the variance of the estimator to be asymptotically small in n, the size of the graph.
However, for general randomization schemes, this variance can be lower bounded by an exponential function of the degrees of a graph.
In contrast, we show that if a graph satisfies a restricted-growth condition on the growth rate of neighborhoods, then there exists a natural clustering algorithm, based on vertex neighborhoods, for which the variance of the estimator can be upper bounded by a linear function of the degrees.
Thus we show that proper cluster randomization can lead to exponentially lower estimator variance when experimentally measuring average treatment effects under interference.
Deep CNNs have been pushing the frontier of visual recognition over past years.
Besides recognition accuracy, strong demands in understanding deep CNNs in the research community motivate developments of tools to dissect pre-trained models to visualize how they make predictions.
Recent works further push the interpretability in the network learning stage to learn more meaningful representations.
In this work, focusing on a specific area of visual recognition, we report our efforts towards interpretable face recognition.
We propose a spatial activation diversity loss to learn more structured face representations.
By leveraging the structure, we further design a feature activation diversity loss to push the interpretable representations to be discriminative and robust to occlusions.
We demonstrate on three face recognition benchmarks that our proposed method is able to improve face recognition accuracy with easily interpretable face representations.
In this paper, we address the question of information preservation in ill-posed, non-linear inverse problems, assuming that the measured data is close to a low-dimensional model set.
We provide necessary and sufficient conditions for the existence of a so-called instance optimal decoder, i.e., that is robust to noise and modelling error.
Inspired by existing results in compressive sensing, our analysis is based on a (Lower) Restricted Isometry Property (LRIP), formulated in a non-linear fashion.
We also provide sufficient conditions for non-uniform recovery with random measurement operators, with a new formulation of the LRIP.
We finish by describing typical strategies to prove the LRIP in both linear and non-linear cases, and illustrate our results by studying the invertibility of a one-layer neural net with random weights.
Business process models describe the way of working in an organization.
Typically, business process models distinguish between the normal flow of work and exceptions to that normal flow.
However, they often present an idealized view.
This means that unexpected exceptions - exceptions that are not modelled in the business process model - can also occur in practice.
This has an effect on the efficiency of the organization, because information systems are not developed to handle unexpected exceptions.
This paper studies the relation between the occurrence of exceptions and operational performance.
It does this by analyzing the execution logs of business processes from five organizations, classifying execution paths as normal or exceptional.
Subsequently, it analyzes the differences between normal and exceptional paths.
The results show that exceptions are related to worse operational performance in terms of a longer throughput time and that unexpected exceptions relate to a stronger increase in throughput time than expected exceptions.
In this paper, we introduce How2, a multimodal collection of instructional videos with English subtitles and crowdsourced Portuguese translations.
We also present integrated sequence-to-sequence baselines for machine translation, automatic speech recognition, spoken language translation, and multimodal summarization.
By making available data and code for several multimodal natural language tasks, we hope to stimulate more research on these and similar challenges, to obtain a deeper understanding of multimodality in language processing.
We replicate a variation of the image captioning architecture by Vinyals et al.(2015), then introduce dropout during inference mode to simulate the effects of neurodegenerative diseases like Alzheimer's disease (AD) and Wernicke's aphasia (WA).
We evaluate the effects of dropout on language production by measuring the KL-divergence of word frequency distributions and other linguistic metrics as dropout is added.
We find that the generated sentences most closely approximate the word frequency distribution of the training corpus when using a moderate dropout of 0.4 during inference.
If a robot is supposed to roam an environment and interact with objects, it is often necessary to know all possible objects in advance, so that a database with models of all objects can be generated for visual identification.
However, this constraint cannot always be fulfilled.
Due to that reason, a model based object recognition cannot be used to guide the robot's interactions.
Therefore, this paper proposes a system that analyzes features of encountered objects and then uses these features to compare unknown objects to already known ones.
From the resulting similarity appropriate actions can be derived.
Moreover, the system enables the robot to learn object categories by grouping similar objects or by splitting existing categories.
To represent the knowledge a hybrid form is used, consisting of both symbolic and subsymbolic representations.
This paper introduces a novel approach of clustering, which is based on group consensus of dynamic linear high-order multi-agent systems.
The graph topology is associated with a selected multi-agent system, with each agent corresponding to one vertex.
In order to reveal the cluster structure, the agents belonging to a similar cluster are expected to aggregate together.
As theoretical foundation, a necessary and sufficient condition is given to check the group consensus.
Two numerical instances are shown to illustrate the process of approach.
We reveal a complete set of constraints that need to be imposed on a set of 3-by-3 matrices to ensure that the matrices represent genuine homographies associated with multiple planes between two views.
We also show how to exploit the constraints to obtain more accurate estimates of homography matrices between two views.
Our study resolves a long-standing research question and provides a fresh perspective and a more in-depth understanding of the multiple homography estimation task.
As the number of charging Plug-in Electric Vehicles (PEVs) increase, due to the limited power capacity of the distribution feeders and the sensitivity of the mid-way distribution transformers to the excessive load, it is crucial to control the amount of power through each specific distribution feeder to avoid system overloads that may lead to breakdowns.
In this paper we develop, analyze and evaluate charging algorithms for PEVs with feeder overload constraints in the distribution grid.
The algorithms we propose jointly minimize the variance of the aggregate load and prevent overloading of the distribution feeders.
In this paper we present an online wide-area oscillation damping control (WAC) design for uncertain models of power systems using ideas from reinforcement learning.
We assume that the exact small-signal model of the power system at the onset of a contingency is not known to the operator and use the nominal model and online measurements of the generator states and control inputs to rapidly converge to a state-feedback controller that minimizes a given quadratic energy cost.
However, unlike conventional linear quadratic regulators (LQR), we intend our controller to be sparse, so its implementation reduces the communication costs.
We, therefore, employ the gradient support pursuit (GraSP) optimization algorithm to impose sparsity constraints on the control gain matrix during learning.
The sparse controller is thereafter implemented using distributed communication.
Using the IEEE 39-bus power system model with 1149 unknown parameters, it is demonstrated that the proposed learning method provides reliable LQR performance while the controller matched to the nominal model becomes unstable for severely uncertain systems.
Currently the area of VANET lacks in having some better designed algorithms to handle dynamic change and frequent disruption due to the high mobility of the vehicles.
There are many techniques to disseminate messages across the moving vehicles but they are all highly dependent on some conditions involving flow, density and speed.
The two techniques that are commonly used are AODV (Ad Hoc on Demand Distance Vector) and DSRC (Dedicated Short Range Communication).
This work presents a detailed analysis of AODV.
This study is focused on the use of AODV in Intelligent Transportation System.
The limitations in the working of AODV routing protocol has been identified and proved.
These limitations can be removed to some extent in order to increase the performance of vehicular networks and make the driving more safe and easy for a normal user as well as the implementation complications will be removed and an efficient system implementation will be possible.
We describe the LoopInvGen tool for generating loop invariants that can provably guarantee correctness of a program with respect to a given specification.
LoopInvGen is an efficient implementation of the inference technique originally proposed in our earlier work on PIE (https://doi.org/10.1145/2908080.2908099).
In contrast to existing techniques, LoopInvGen is not restricted to a fixed set of features -- atomic predicates that are composed together to build complex loop invariants.
Instead, we start with no initial features, and use program synthesis techniques to grow the set on demand.
This not only enables a less onerous and more expressive approach, but also appears to be significantly faster than the existing tools over the SyGuS-COMP 2017 benchmarks from the INV track.
We show how the spellings of known words can help us deal with unknown words in open-vocabulary NLP tasks.
The method we propose can be used to extend any closed-vocabulary generative model, but in this paper we specifically consider the case of neural language modeling.
Our Bayesian generative story combines a standard RNN language model (generating the word tokens in each sentence) with an RNN-based spelling model (generating the letters in each word type).
These two RNNs respectively capture sentence structure and word structure, and are kept separate as in linguistics.
By invoking the second RNN to generate spellings for novel words in context, we obtain an open-vocabulary language model.
For known words, embeddings are naturally inferred by combining evidence from type spelling and token context.
Comparing to baselines (including a novel strong baseline), we beat previous work and establish state-of-the-art results on multiple datasets.
The analysis of large collections of image data is still a challenging problem due to the difficulty of capturing the true concepts in visual data.
The similarity between images could be computed using different and possibly multimodal features such as color or edge information or even text labels.
This motivates the design of image analysis solutions that are able to effectively integrate the multi-view information provided by different feature sets.
We therefore propose a new image retrieval solution that is able to sort images through a random walk on a multi-layer graph, where each layer corresponds to a different type of information about the image data.
We study in depth the design of the image graph and propose in particular an effective method to select the edge weights for the multi-layer graph, such that the image ranking scores are optimised.
We then provide extensive experiments in different real-world photo collections, which confirm the high performance of our new image retrieval algorithm that generally surpasses state-of-the-art solutions due to a more meaningful image similarity computation.
New technological developments have made it possible to interact with computer systems and applications anywhere and anytime.
It is vital that these applications are able to adapt to the user, as a person, and to its current situation, whatever that is.
Therefore, the premises for evolution towards a learning society and a knowledge economy are present.
Hence, there is a stringent demand for new learner-centred frameworks that allow active participation of learners in knowledge creation within communities, organizations, territories and society, at large.
This paper presents the multi-agent architecture of our context-aware system and the learning scenarios within ubiquitous learning environments that the system provides support for.
This architecture is the outcome of our endeavour to develop ePH, a system for sharing public interest information and knowledge, which is accessible through always-on, context-aware services.
This paper presents an effective color normalization method for thin blood film images of peripheral blood specimens.
Thin blood film images can easily be separated to foreground (cell) and background (plasma) parts.
The color of the plasma region is used to estimate and reduce the differences arising from different illumination conditions.
A second stage normalization based on the database-gray world algorithm transforms the color of the foreground objects to match a reference color character.
The quantitative experiments demonstrate the effectiveness of the method and its advantages against two other general purpose color correction methods: simple gray world and Retinex.
How can a delivery robot navigate reliably to a destination in a new office building, with minimal prior information?
To tackle this challenge, this paper introduces a two-level hierarchical approach, which integrates model-free deep learning and model-based path planning.
At the low level, a neural-network motion controller, called the intention-net, is trained end-to-end to provide robust local navigation.
The intention-net maps images from a single monocular camera and "intentions" directly to robot controls.
At the high level, a path planner uses a crude map, e.g., a 2-D floor plan, to compute a path from the robot's current location to the goal.
The planned path provides intentions to the intention-net.
Preliminary experiments suggest that the learned motion controller is robust against perceptual uncertainty and by integrating with a path planner, it generalizes effectively to new environments and goals.
Fuzzing and symbolic execution are popular techniques for finding vulnerabilities and generating test-cases for programs.
Fuzzing, a blackbox method that mutates seed input values, is generally incapable of generating diverse inputs that exercise all paths in the program.
Due to the path-explosion problem and dependence on SMT solvers, symbolic execution may also not achieve high path coverage.
A hybrid technique involving fuzzing and symbolic execution may achieve better function coverage than fuzzing or symbolic execution alone.
In this paper, we present Munch, an open source framework implementing two hybrid techniques based on fuzzing and symbolic execution.
We empirically show using nine large open-source programs that overall, Munch achieves higher (in-depth) function coverage than symbolic execution or fuzzing alone.
Using metrics based on total analyses time and number of queries issued to the SMT solver, we also show that Munch is more efficient at achieving better function coverage.
This paper presents a generalized energy storage system model for voltage and angle stability analysis.
The proposed solution allows modeling most common energy storage technologies through a given set of linear differential algebraic equations (DAEs).
In particular, the paper considers, but is not limited to, compressed air, superconducting magnetic, electrochemical capacitor and battery energy storage devices.
While able to cope with a variety of different technologies, the proposed generalized model proves to be accurate for angle and voltage stability analysis, as it includes a balanced, fundamental-frequency model of the voltage source converter (VSC) and the dynamics of the dc link.
Regulators with inclusion of hard limits are also taken into account.
The transient behavior of the generalized model is compared with detailed fundamental-frequency balanced models as well as commonly-used simplified models of energy storage devices.
A comprehensive case study based on the WSCC 9-bus test system is presented and discussed.
The basic objective of data visualization is to provide an efficient graphical display for summarizing and reasoning about quantitative information.
During the last decades, political science has accumulated a large corpus of various kinds of data such as comprehensive factbooks and atlases, characterizing all or most of existing states by multiple and objectively assessed numerical indicators within certain time lapse.
As a consequence, there exists a continuous trend for political science to gradually become a more quantitative scientific field and to use quantitative information in the analysis and reasoning.
It is believed that any objective analysis in political science must be multidimensional and combine various sources of quantitative information; however, human capabilities for perception of large massifs of numerical information are limited.
Hence, methods and approaches for visualization of quantitative and qualitative data (and, especially multivariate data) is an extremely important topic.
Data visualization approaches can be classified into several groups, starting from creating informative charts and diagrams (statistical graphics and infographics) and ending with advanced statistical methods for visualizing multidimensional tables containing both quantitative and qualitative information.
In this article we provide a short review of existing methods of data visualization methods with applications in political and social science.
Chagas disease is a neglected disease, and information about its geographical spread is very scarse.
We analyze here mobility and calling patterns in order to identify potential risk zones for the disease, by using public health information and mobile phone records.
Geolocalized call records are rich in social and mobility information, which can be used to infer whether an individual has lived in an endemic area.
We present two case studies in Latin American countries.
Our objective is to generate risk maps which can be used by public health campaign managers to prioritize detection campaigns and target specific areas.
Finally, we analyze the value of mobile phone data to infer long-term migrations, which play a crucial role in the geographical spread of Chagas disease.
We revisit the Blind Deconvolution problem with a focus on understanding its robustness and convergence properties.
Provable robustness to noise and other perturbations is receiving recent interest in vision, from obtaining immunity to adversarial attacks to assessing and describing failure modes of algorithms in mission critical applications.
Further, many blind deconvolution methods based on deep architectures internally make use of or optimize the basic formulation, so a clearer understanding of how this sub-module behaves, when it can be solved, and what noise injection it can tolerate is a first order requirement.
We derive new insights into the theoretical underpinnings of blind deconvolution.
The algorithm that emerges has nice convergence guarantees and is provably robust in a sense we formalize in the paper.
Interestingly, these technical results play out very well in practice, where on standard datasets our algorithm yields results competitive with or superior to the state of the art.
Keywords: blind deconvolution, robust continuous optimization
Delayed feedback control is an easy realizable control method which generates control force by comparing the current and the delayed version of the system states.
In this paper, a new form of the delayed feedback structure is introduced.
Based on the proposed delayed feedback method, a new robust tracking system is designed.
This tracking system improves the features of the conventional state feedback with integral action and it is also able to reject higher order disturbances compared to the conventional method.
In addition, the proposed tracking system tracks the ramp-shape reference input signal as well, which this is not possible through the conventional state feedback.
Due to easy implementable feature of the proposed delayed feedback tracking system, it can be used in practical applications effectively.
Moreover, since the proposed method adds delays to the closed loop system dynamics, the ordinary differential equation of the system changes to a delay differential equation with an infinite number of characteristic roots.
Thus, conventional pole placement procedures cannot be used to design the delayed feedback controller parameters and place the unstable roots in the left half plane.
In this paper, the simulated annealing algorithm is used to determine the proposed control system parameters and move the unstable roots of the delay differential equation to the left half plane.
Finally, the efficiency of the proposed reference input tracker is demonstrated on a case study.
This paper considers the problem of single-server single-message private information retrieval with coded side information (PIR-CSI).
In this problem, there is a server storing a database, and a user which knows a linear combination of a subset of messages in the database as a side information.
The number of messages contributing to the side information is known to the server, but the indices and the coefficients of these messages are unknown to the server.
The user wishes to download a message from the server privately, i.e., without revealing which message it is requesting, while minimizing the download cost.
In this work, we consider two different settings for the PIR-CSI problem depending on the demanded message being or not being one of the messages contributing to the side information.
For each setting, we prove an upper bound on the maximum download rate as a function of the size of the database and the size of the side information, and propose a protocol that achieves the rate upper-bound.
Centrality is one of the most studied concepts in social network analysis.
There is a huge literature regarding centrality measures, as ways to identify the most relevant users in a social network.
The challenge is to find measures that can be computed efficiently, and that can be able to classify the users according to relevance criteria as close as possible to reality.
We address this problem in the context of the Twitter network, an online social networking service with millions of users and an impressive flow of messages that are published and spread daily by interactions between users.
Twitter has different types of users, but the greatest utility lies in finding the most influential ones.
The purpose of this article is to collect and classify the different Twitter influence measures that exist so far in literature.
These measures are very diverse.
Some are based on simple metrics provided by the Twitter API, while others are based on complex mathematical models.
Several measures are based on the PageRank algorithm, traditionally used to rank the websites on the Internet.
Some others consider the timeline of publication, others the content of the messages, some are focused on specific topics, and others try to make predictions.
We consider all these aspects, and some additional ones.
Furthermore, we include measures of activity and popularity, the traditional mechanisms to correlate measures, and some important aspects of computational complexity for this particular context.
Full duplex (FD) communications has the potential to double the capacity of a half duplex (HD) system at the link level.
However, in a cellular network, FD operation is not a straightforward extension of half duplex operations.
The increased interference due to a large number of simultaneous transmissions in FD operation and realtime traffic conditions limits the capacity improvement.
Realizing the potential of FD requires careful coordination of resource allocation among the cells as well as within the cell.
In this paper, we propose a distributed resource allocation, i.e., joint user selection and power allocation for a FD multi-cell system, assuming FD base stations (BSs) and HD user equipment (UEs).
Due to the complexity of finding the globally optimum solution, a sub-optimal solution for UE selection, and a novel geometric programming based solution for power allocation, are proposed.
The proposed distributed approach converges quickly and performs almost as well as a centralized solution, but with much lower signaling overhead.
It provides a hybrid scheduling policy which allows FD operations whenever it is advantageous, but otherwise defaults to HD operation.
We focus on small cell systems because they are more suitable for FD operation, given practical self-interference cancellation limits.With practical self-interference cancellation, it is shown that the proposed hybrid FD system achieves nearly two times throughput improvement for an indoor multi-cell scenario, and about 65% improvement for an outdoor multi-cell scenario compared to the HD system.
A family of reconfigurable parallel robots can change motion modes by passing through constraint singularities by locking and releasing some passive joints of the robot.
This paper is about the kinematics, the workspace and singularity analysis of a 3-PRPiR parallel robot involving lockable Pi and R (revolute) joints.
Here a Pi joint may act as a 1-DOF planar parallelogram if its lock-able P (prismatic) joint is locked or a 2-DOF RR serial chain if its lockable P joint is released.
The operation modes of the robot include a 3T operation modes to three 2T1R operation modes with two different directions of the rotation axis of the moving platform.
The inverse kinematics and forward kinematics of the robot in each operation modes are dealt with in detail.
The workspace analysis of the robot allow us to know the regions of the workspace that the robot can reach in each operation mode.
A prototype built at Heriot-Watt University is used to illustrate the results of this work.
Aspect-level sentiment classification aims to identify the sentiment expressed towards some aspects given context sentences.
In this paper, we introduce an attention-over-attention (AOA) neural network for aspect level sentiment classification.
Our approach models aspects and sentences in a joint way and explicitly captures the interaction between aspects and context sentences.
With the AOA module, our model jointly learns the representations for aspects and sentences, and automatically focuses on the important parts in sentences.
Our experiments on laptop and restaurant datasets demonstrate our approach outperforms previous LSTM-based architectures.
Fine-grained entity type classification (FETC) is the task of classifying an entity mention to a broad set of types.
Distant supervision paradigm is extensively used to generate training data for this task.
However, generated training data assigns same set of labels to every mention of an entity without considering its local context.
Existing FETC systems have two major drawbacks: assuming training data to be noise free and use of hand crafted features.
Our work overcomes both drawbacks.
We propose a neural network model that jointly learns entity mentions and their context representation to eliminate use of hand crafted features.
Our model treats training data as noisy and uses non-parametric variant of hinge loss function.
Experiments show that the proposed model outperforms previous state-of-the-art methods on two publicly available datasets, namely FIGER (GOLD) and BBN with an average relative improvement of 2.69% in micro-F1 score.
Knowledge learnt by our model on one dataset can be transferred to other datasets while using same model or other FETC systems.
These approaches of transferring knowledge further improve the performance of respective models.
We motivate a method for transparently identifying ineffectual computations in unmodified Deep Learning models and without affecting accuracy.
Specifically, we show that if we decompose multiplications down to the bit level the amount of work performed during inference for image classification models can be consistently reduced by two orders of magnitude.
In the best case studied of a sparse variant of AlexNet, this approach can ideally reduce computation work by more than 500x.
We present Laconic a hardware accelerator that implements this approach to improve execution time, and energy efficiency for inference with Deep Learning Networks.
Laconic judiciously gives up some of the work reduction potential to yield a low-cost, simple, and energy efficient design that outperforms other state-of-the-art accelerators.
For example, a Laconic configuration that uses a weight memory interface with just 128 wires outperforms a conventional accelerator with a 2K-wire weight memory interface by 2.3x on average while being 2.13x more energy efficient on average.
A Laconic configuration that uses a 1K-wire weight memory interface, outperforms the 2K-wire conventional accelerator by 15.4x and is 1.95x more energy efficient.
Laconic does not require but rewards advances in model design such as a reduction in precision, the use of alternate numeric representations that reduce the number of bits that are "1", or an increase in weight or activation sparsity.
In this letter, we present a unified Bayesian inference framework for generalized linear models (GLM) which iteratively reduces the GLM problem to a sequence of standard linear model (SLM) problems.
This framework provides new perspectives on some established GLM algorithms derived from SLM ones and also suggests novel extensions for some other SLM algorithms.
Specific instances elucidated under such framework are the GLM versions of approximate message passing (AMP), vector AMP (VAMP), and sparse Bayesian learning (SBL).
It is proved that the resultant GLM version of AMP is equivalent to the well-known generalized approximate message passing (GAMP).
Numerical results for 1-bit quantized compressed sensing (CS) demonstrate the effectiveness of this unified framework.
Recently, the end-to-end approach that learns hierarchical representations from raw data using deep convolutional neural networks has been successfully explored in the image, text and speech domains.
This approach was applied to musical signals as well but has been not fully explored yet.
To this end, we propose sample-level deep convolutional neural networks which learn representations from very small grains of waveforms (e.g.2 or 3 samples) beyond typical frame-level input representations.
Our experiments show how deep architectures with sample-level filters improve the accuracy in music auto-tagging and they provide results comparable to previous state-of-the-art performances for the Magnatagatune dataset and Million Song Dataset.
In addition, we visualize filters learned in a sample-level DCNN in each layer to identify hierarchically learned features and show that they are sensitive to log-scaled frequency along layer, such as mel-frequency spectrogram that is widely used in music classification systems.
This article examines the structure and spatial patterns of violent political organizations in the Sahel-Sahara, a region characterized by growing political instability over the last 20 years.
Drawing on a public collection of disaggregated data, the article uses network science to represent alliances and conflicts of 179 organizations that were involved in violent events between 1997 and 2014.
To this end, we combine two spectral embedding techniques that have previously been considered separately: one for directed graphs (relationships are asymmetric), and one for signed graphs (relationships are positive or negative).
Our result show that groups that are net attackers are indistinguishable at the level of their individual behavior, but clearly separate into pro- and anti-political violence based on the groups to which they are close.
The second part of the article maps a series of 389 events related to nine Trans-Saharan Islamist groups between 2004 and 2014.
Spatial analysis suggests that cross-border movement has intensified following the establishment of military bases by AQIM in Mali but reveals no evidence of a border sanctuary.
Owing to the transnational nature of conflict, the article shows that national management strategies and foreign military interventions have profoundly affected the movement of Islamist groups.
One viable solution for continuous reduction in energy-per-operation is to rethink functionality to cope with uncertainty by adopting computational approaches that are inherently robust to uncertainty.
It requires a novel look at data representations, associated operations, and circuits, and at materials and substrates that enable them.
3D integrated nanotechnologies combined with novel brain-inspired computational paradigms that support fast learning and fault tolerance could lead the way.
Recognizing the very size of the brain's circuits, hyperdimensional (HD) computing can model neural activity patterns with points in a HD space, that is, with hypervectors as large randomly generated patterns.
At its very core, HD computing is about manipulating and comparing these patterns inside memory.
Emerging nanotechnologies such as carbon nanotube field effect transistors (CNFETs) and resistive RAM (RRAM), and their monolithic 3D integration offer opportunities for hardware implementations of HD computing through tight integration of logic and memory, energy-efficient computation, and unique device characteristics.
We experimentally demonstrate and characterize an end-to-end HD computing nanosystem built using monolithic 3D integration of CNFETs and RRAM.
With our nanosystem, we experimentally demonstrate classification of 21 languages with measured accuracy of up to 98% on >20,000 sentences (6.4 million characters), training using one text sample (~100,000 characters) per language, and resilient operation (98% accuracy) despite 78% hardware errors in HD representation (outputs stuck at 0 or 1).
By exploiting the unique properties of the underlying nanotechnologies, we show that HD computing, when implemented with monolithic 3D integration, can be up to 420X more energy-efficient while using 25X less area compared to traditional silicon CMOS implementations.
Prevalent models based on artificial neural network (ANN) for sentence classification often classify sentences in isolation without considering the context in which sentences appear.
This hampers the traditional sentence classification approaches to the problem of sequential sentence classification, where structured prediction is needed for better overall classification performance.
In this work, we present a hierarchical sequential labeling network to make use of the contextual information within surrounding sentences to help classify the current sentence.
Our model outperforms the state-of-the-art results by 2%-3% on two benchmarking datasets for sequential sentence classification in medical scientific abstracts.
In this paper, we present TeachNet, a novel neural network architecture for intuitive and markerless vision-based teleoperation of dexterous robotic hands.
Robot joint angles are directly generated from depth images of the human hand that produce visually similar robot hand poses in an end-to-end fashion.
The special structure of TeachNet, combined with a consistency loss function, handles the differences in appearance and anatomy between human and robotic hands.
A synchronized human-robot training set is generated from an existing dataset of labeled depth images of the human hand and simulated depth images of a robotic hand.
The final training set includes 400K pairwise depth images and joint angles of a Shadow C6 robotic hand.
The network evaluation results verify the superiority of TeachNet, especially regarding the high-precision condition.
Imitation experiments and grasp tasks teleoperated by novice users demonstrate that TeachNet is more reliable and faster than the state-of-the-art vision-based teleoperation method.
Deep Neural Networks (DNNs) have demonstrated exceptional performance on most recognition tasks such as image classification and segmentation.
However, they have also been shown to be vulnerable to adversarial examples.
This phenomenon has recently attracted a lot of attention but it has not been extensively studied on multiple, large-scale datasets and structured prediction tasks such as semantic segmentation which often require more specialised networks with additional components such as CRFs, dilated convolutions, skip-connections and multiscale processing.
In this paper, we present what to our knowledge is the first rigorous evaluation of adversarial attacks on modern semantic segmentation models, using two large-scale datasets.
We analyse the effect of different network architectures, model capacity and multiscale processing, and show that many observations made on the task of classification do not always transfer to this more complex task.
Furthermore, we show how mean-field inference in deep structured models, multiscale processing (and more generally, input transformations) naturally implement recently proposed adversarial defenses.
Our observations will aid future efforts in understanding and defending against adversarial examples.
Moreover, in the shorter term, we show how to effectively benchmark robustness and show which segmentation models should currently be preferred in safety-critical applications due to their inherent robustness.
A key element in defending computer networks is to recognize the types of cyber attacks based on the observed malicious activities.
Obfuscation onto what could have been observed of an attack sequence may lead to mis-interpretation of its effect and intent, leading to ineffective defense or recovery deployments.
This work develops probabilistic graphical models to generalize a few obfuscation techniques and to enable analyses of the Expected Classification Accuracy (ECA) as a result of these different obfuscation on various attack models.
Determining the ECA is a NP-Hard problem due to the combinatorial number of possibilities.
This paper presents several polynomial-time algorithms to find the theoretically bounded approximation of ECA under different attack obfuscation models.
Comprehensive simulation shows the impact on ECA due to alteration, insertion and removal of attack action sequence, with increasing observation length, level of obfuscation and model complexity.
Clinical Decision Support Systems (CDSS) form an important area of research.
In spite of its importance, it is difficult for researchers to evaluate the domain primarily because of a considerable spread of relevant literature in interdisciplinary domains.
Previous surveys of CDSS have examined the domain from the perspective of individual disciplines.
However, to the best of our knowledge, no visual scientometric survey of CDSS has previously been conducted which provides a broader spectrum of the domain with a horizon covering multiple disciplines.
While traditional systematic literature surveys focus on analyzing literature using arbitrary results, visual surveys allow for the analysis of domains by using complex network-based analytical models.
In this paper, we present a detailed visual survey of CDSS literature using important papers selected from highly cited sources in the Thomson Reuters web of science.
We analyze the entire set of relevant literature indexed in the Web of Science database.
Our key results include the discovery of the articles which have served as key turning points in literature.
Additionally, we have identified highly cited authors and the key country of origin of top publications.
We also present the Universities with the strongest citation bursts.
Finally, our network analysis has also identified the key journals and subject categories both in terms of centrality and frequency.
It is our belief that this paper will thus serve as an important role for researchers as well as clinical practitioners interested in identifying key literature and resources in the domain of clinical decision support.
As open-ended human-chatbot interaction becomes commonplace, sensitive content detection gains importance.
In this work, we propose a two stage semi-supervised approach to bootstrap large-scale data for automatic sensitive language detection from publicly available web resources.
We explore various data selection methods including 1) using a blacklist to rank online discussion forums by the level of their sensitiveness followed by randomly sampling utterances and 2) training a weakly supervised model in conjunction with the blacklist for scoring sentences from online discussion forums to curate a dataset.
Our data collection strategy is flexible and allows the models to detect implicit sensitive content for which manual annotations may be difficult.
We train models using publicly available annotated datasets as well as using the proposed large-scale semi-supervised datasets.
We evaluate the performance of all the models on Twitter and Toxic Wikipedia comments testsets as well as on a manually annotated spoken language dataset collected during a large scale chatbot competition.
Results show that a model trained on this collected data outperforms the baseline models by a large margin on both in-domain and out-of-domain testsets, achieving an F1 score of 95.5% on an out-of-domain testset compared to a score of 75% for models trained on public datasets.
We also showcase that large scale two stage semi-supervision generalizes well across multiple classes of sensitivities such as hate speech, racism, sexual and pornographic content, etc. without even providing explicit labels for these classes, leading to an average recall of 95.5% versus the models trained using annotated public datasets which achieve an average recall of 73.2% across seven sensitive classes on out-of-domain testsets.
Autonomous planetary vehicles, also known as rovers, are small autonomous vehicles equipped with a variety of sensors used to perform exploration and experiments on a planet's surface.
Rovers work in a partially unknown environment, with narrow energy/time/movement constraints and, typically, small computational resources that limit the complexity of on-line planning and scheduling, thus they represent a great challenge in the field of autonomous vehicles.
Indeed, formal models for such vehicles usually involve hybrid systems with nonlinear dynamics, which are difficult to handle by most of the current planning algorithms and tools.
Therefore, when offline planning of the vehicle activities is required, for example for rovers that operate without a continuous Earth supervision, such planning is often performed on simplified models that are not completely realistic.
In this paper we show how the UPMurphi model checking based planning tool can be used to generate resource-optimal plans to control the engine of an autonomous planetary vehicle, working directly on its hybrid model and taking into account several safety constraints, thus achieving very accurate results.
Drawing inspiration from the theory of linear "decomposable systems", we provide a method, based on linear matrix inequalities (LMIs), which makes it possible to prove the convergence (or consensus) of a set of interacting agents with polynomial dynamic.
We also show that the use of a generalised version of the famous Kalman-Yakubovic-Popov lemma allows the development of an LMI test whose size does not depend on the number of agents.
The method is validated experimentally on two academic examples.
We consider high dimensional dynamic multi-product pricing with an evolving but low-dimensional linear demand model.
Assuming the temporal variation in cross-elasticities exhibits low-rank structure based on fixed (latent) features of the products, we show that the revenue maximization problem reduces to an online bandit convex optimization with side information given by the observed demands.
We design dynamic pricing algorithms whose revenue approaches that of the best fixed price vector in hindsight, at a rate that only depends on the intrinsic rank of the demand model and not the number of products.
Our approach applies a bandit convex optimization algorithm in a projected low-dimensional space spanned by the latent product features, while simultaneously learning this span via online singular value decomposition of a carefully-crafted matrix containing the observed demands.
Recent studies observe that app foreground is the most striking component that influences the access control decisions in mobile platform, as users tend to deny permission requests lacking visible evidence.
However, none of the existing permission models provides a systematic approach that can automatically answer the question: Is the resource access indicated by app foreground?
In this work, we present the design, implementation, and evaluation of COSMOS, a context-aware mediation system that bridges the semantic gap between foreground interaction and background access, in order to protect system integrity and user privacy.
Specifically, COSMOS learns from a large set of apps with similar functionalities and user interfaces to construct generic models that detect the outliers at runtime.
It can be further customized to satisfy specific user privacy preference by continuously evolving with user decisions.
Experiments show that COSMOS achieves both high precision and high recall in detecting malicious requests.
We also demonstrate the effectiveness of COSMOS in capturing specific user preferences using the decisions collected from 24 users and illustrate that COSMOS can be easily deployed on smartphones as a real-time guard with a very low performance overhead.
The numerical size of academic publications that are being published in recent years had grown rapidly.
Accessing and searching massive academic publications that are distributed over several locations need large amount of computing resources to increase the system performance.
Therefore, many grid-based search techniques were proposed to provide flexible methods for searching the distributed extensive data.
This paper proposes search technique that is capable of searching the extensive publications by utilizing grid computing technology.
The search technique is implemented as interconnected grid services to offer a mechanism to access different data locations.
The experimental result shows that the grid-based search technique has enhanced the performance of the search.
A tensor network is a diagram that specifies a way to "multiply" a collection of tensors together to produce another tensor (or matrix).
Many existing algorithms for tensor problems (such as tensor decomposition and tensor PCA), although they are not presented this way, can be viewed as spectral methods on matrices built from simple tensor networks.
In this work we leverage the full power of this abstraction to design new algorithms for certain continuous tensor decomposition problems.
An important and challenging family of tensor problems comes from orbit recovery, a class of inference problems involving group actions (inspired by applications such as cryo-electron microscopy).
Orbit recovery problems over finite groups can often be solved via standard tensor methods.
However, for infinite groups, no general algorithms are known.
We give a new spectral algorithm based on tensor networks for one such problem: continuous multi-reference alignment over the infinite group SO(2).
Our algorithm extends to the more general heterogeneous case.
An untested assumption behind the crowdsourced descriptions of the images in the Flickr30K dataset (Young et al., 2014) is that they "focus only on the information that can be obtained from the image alone" (Hodosh et al., 2013, p. 859).
This paper presents some evidence against this assumption, and provides a list of biases and unwarranted inferences that can be found in the Flickr30K dataset.
Finally, it considers methods to find examples of these, and discusses how we should deal with stereotype-driven descriptions in future applications.
Spatial information is often expressed using qualitative terms such as natural language expressions instead of coordinates; reasoning over such terms has several practical applications, such as bus routes planning.
Representing and reasoning on trajectories is a specific case of qualitative spatial reasoning that focuses on moving objects and their paths.
In this work, we propose two versions of a trajectory calculus based on the allowed properties over trajectories, where trajectories are defined as a sequence of non-overlapping regions of a partitioned map.
More specifically, if a given trajectory is allowed to start and finish at the same region, 6 base relations are defined (TC-6).
If a given trajectory should have different start and finish regions but cycles are allowed within, 10 base relations are defined (TC-10).
Both versions of the calculus are implemented as ASP programs; we propose several different encodings, including a generalised program capable of encoding any qualitative calculus in ASP.
All proposed encodings are experimentally evaluated using a real-world dataset.
Experiment results show that the best performing implementation can scale up to an input of 250 trajectories for TC-6 and 150 trajectories for TC-10 for the problem of discovering a consistent configuration, a significant improvement compared to previous ASP implementations for similar qualitative spatial and temporal calculi.
This manuscript is under consideration for acceptance in TPLP.
Information cascades, effectively facilitated by most social network platforms, are recognized as a major factor in almost every social success and disaster in these networks.
Can cascades be predicted?
While many believe that they are inherently unpredictable, recent work has shown that some key properties of information cascades, such as size, growth, and shape, can be predicted by a machine learning algorithm that combines many features.
These predictors all depend on a bag of hand-crafting features to represent the cascade network and the global network structure.
Such features, always carefully and sometimes mysteriously designed, are not easy to extend or to generalize to a different platform or domain.
Inspired by the recent successes of deep learning in multiple data mining tasks, we investigate whether an end-to-end deep learning approach could effectively predict the future size of cascades.
Such a method automatically learns the representation of individual cascade graphs in the context of the global network structure, without hand-crafted features and heuristics.
We find that node embeddings fall short of predictive power, and it is critical to learn the representation of a cascade graph as a whole.
We present algorithms that learn the representation of cascade graphs in an end-to-end manner, which significantly improve the performance of cascade prediction over strong baselines that include feature based methods, node embedding methods, and graph kernel methods.
Our results also provide interesting implications for cascade prediction in general.
In online learning the performance of an algorithm is typically compared to the performance of a fixed function from some class, with a quantity called regret.
Forster proposed a last-step min-max algorithm which was somewhat simpler than the algorithm of Vovk, yet with the same regret.
In fact the algorithm he analyzed assumed that the choices of the adversary are bounded, yielding artificially only the two extreme cases.
We fix this problem by weighing the examples in such a way that the min-max problem will be well defined, and provide analysis with logarithmic regret that may have better multiplicative factor than both bounds of Forster and Vovk.
We also derive a new bound that may be sub-logarithmic, as a recent bound of Orabona et.al, but may have better multiplicative factor.
Finally, we analyze the algorithm in a weak-type of non-stationary setting, and show a bound that is sub-linear if the non-stationarity is sub-linear as well.
Classical approaches for estimating optical flow have achieved rapid progress in the last decade.
However, most of them are too slow to be applied in real-time video analysis.
Due to the great success of deep learning, recent work has focused on using CNNs to solve such dense prediction problems.
In this paper, we investigate a new deep architecture, Densely Connected Convolutional Networks (DenseNet), to learn optical flow.
This specific architecture is ideal for the problem at hand as it provides shortcut connections throughout the network, which leads to implicit deep supervision.
We extend current DenseNet to a fully convolutional network to learn motion estimation in an unsupervised manner.
Evaluation results on three standard benchmarks demonstrate that DenseNet is a better fit than other widely adopted CNN architectures for optical flow estimation.
Live fish recognition is one of the most crucial elements of fisheries survey applications where vast amount of data are rapidly acquired.
Different from general scenarios, challenges to underwater image recognition are posted by poor image quality, uncontrolled objects and environment, as well as difficulty in acquiring representative samples.
Also, most existing feature extraction techniques are hindered from automation due to involving human supervision.
Toward this end, we propose an underwater fish recognition framework that consists of a fully unsupervised feature learning technique and an error-resilient classifier.
Object parts are initialized based on saliency and relaxation labeling to match object parts correctly.
A non-rigid part model is then learned based on fitness, separation and discrimination criteria.
For the classifier, an unsupervised clustering approach generates a binary class hierarchy, where each node is a classifier.
To exploit information from ambiguous images, the notion of partial classification is introduced to assign coarse labels by optimizing the "benefit" of indecision made by the classifier.
Experiments show that the proposed framework achieves high accuracy on both public and self-collected underwater fish images with high uncertainty and class imbalance.
We present an approach to learn a dense pixel-wise labeling from image-level tags.
Each image-level tag imposes constraints on the output labeling of a Convolutional Neural Network (CNN) classifier.
We propose Constrained CNN (CCNN), a method which uses a novel loss function to optimize for any set of linear constraints on the output space (i.e. predicted label distribution) of a CNN.
Our loss formulation is easy to optimize and can be incorporated directly into standard stochastic gradient descent optimization.
The key idea is to phrase the training objective as a biconvex optimization for linear models, which we then relax to nonlinear deep networks.
Extensive experiments demonstrate the generality of our new learning framework.
The constrained loss yields state-of-the-art results on weakly supervised semantic image segmentation.
We further demonstrate that adding slightly more supervision can greatly improve the performance of the learning algorithm.
News organizations are increasingly using social media to reach out to their audience aimed at raising their attention and engagement with news.
Given the continuous decrease in subscription rates and audience trust in news media, it is imperative for news organizations to understand factors contributing to their relationships with the audience.
Using Twitter data of 315 U.S. newspaper organizations and their audiences, this study uses multiple regression analysis to examine the influence of key news organization characteristics on audience engagement with news: (1) trustworthiness computed by the Trust Scores in Social Media (TSM) algorithm; (2) quantity of tweets; and (3) skillfulness of Twitter use.
The results show significant influence of a news organizations' trustworthiness and level of Twitter activity on its audiences' news engagement.
Methods to measure trustworthiness of news organizations and audience news engagement, as well as scalable algorithms to compute them from large-scale datasets, are also proposed.
A recent method employs 3D voxels to represent 3D shapes, but this limits the approach to low resolutions due to the computational cost caused by the cubic complexity of 3D voxels.
Hence the method suffers from a lack of detailed geometry.
To resolve this issue, we propose Y^2Seq2Seq, a view-based model, to learn cross-modal representations by joint reconstruction and prediction of view and word sequences.
Specifically, the network architecture of Y^2Seq2Seq bridges the semantic meaning embedded in the two modalities by two coupled `Y' like sequence-to-sequence (Seq2Seq) structures.
In addition, our novel hierarchical constraints further increase the discriminability of the cross-modal representations by employing more detailed discriminative information.
Experimental results on cross-modal retrieval and 3D shape captioning show that Y^2Seq2Seq outperforms the state-of-the-art methods.
We investigate the multi-step prediction of the drivable space, represented by Occupancy Grid Maps (OGMs), for autonomous vehicles.
Our motivation is that accurate multi-step prediction of the drivable space can efficiently improve path planning and navigation resulting in safe, comfortable and optimum paths in autonomous driving.
We train a variety of Recurrent Neural Network (RNN) based architectures on the OGM sequences from the KITTI dataset.
The results demonstrate significant improvement of the prediction accuracy using our proposed difference learning method, incorporating motion related features, over the state of the art.
We remove the egomotion from the OGM sequences by transforming them into a common frame.
Although in the transformed sequences the KITTI dataset is heavily biased toward static objects, by learning the difference between subsequent OGMs, our proposed method provides accurate prediction over both the static and moving objects.
In the past few years, consumer review sites have become the main target of deceptive opinion spam, where fictitious opinions or reviews are deliberately written to sound authentic.
Most of the existing work to detect the deceptive reviews focus on building supervised classifiers based on syntactic and lexical patterns of an opinion.
With the successful use of Neural Networks on various classification applications, in this paper, we propose FakeGAN a system that for the first time augments and adopts Generative Adversarial Networks (GANs) for a text classification task, in particular, detecting deceptive reviews.
Unlike standard GAN models which have a single Generator and Discriminator model, FakeGAN uses two discriminator models and one generative model.
The generator is modeled as a stochastic policy agent in reinforcement learning (RL), and the discriminators use Monte Carlo search algorithm to estimate and pass the intermediate action-value as the RL reward to the generator.
Providing the generator model with two discriminator models avoids the mod collapse issue by learning from both distributions of truthful and deceptive reviews.
Indeed, our experiments show that using two discriminators provides FakeGAN high stability, which is a known issue for GAN architectures.
While FakeGAN is built upon a semi-supervised classifier, known for less accuracy, our evaluation results on a dataset of TripAdvisor hotel reviews show the same performance in terms of accuracy as of the state-of-the-art approaches that apply supervised machine learning.
These results indicate that GANs can be effective for text classification tasks.
Specifically, FakeGAN is effective at detecting deceptive reviews.
Science is a social process with far-reaching impact on our modern society.
In the recent years, for the first time we are able to scientifically study the science itself.
This is enabled by massive amounts of data on scientific publications that is increasingly becoming available.
The data is contained in several databases such as Web of Science or PubMed, maintained by various public and private entities.
Unfortunately, these databases are not always consistent, which considerably hinders this study.
Relying on the powerful framework of complex networks, we conduct a systematic analysis of the consistency among six major scientific databases.
We found that identifying a single "best" database is far from easy.
Nevertheless, our results indicate appreciable differences in mutual consistency of different databases, which we interpret as recipes for future bibliometric studies.
We present a novel approach to estimate the delay observed between the occurrence and reporting of rape crimes.
We explore spatial, temporal and social effects in sparse aggregated (area-level) and high-dimensional disaggregated (event-level) data for New York and Los Angeles.
Focusing on inference, we apply Gradient Boosting and Random Forests to assess predictor importance, as well as Gaussian Processes to model spatial disparities in reporting times.
Our results highlight differences and similarities between the two cities.
We identify at-risk populations and communities which may be targeted with focused policies and interventions to support rape victims, apprehend perpetrators, and prevent future crimes.
Bilinear pooling has been recently proposed as a feature encoding layer, which can be used after the convolutional layers of a deep network, to improve performance in multiple vision tasks.
Different from conventional global average pooling or fully connected layer, bilinear pooling gathers 2nd order information in a translation invariant fashion.
However, a serious drawback of this family of pooling layers is their dimensionality explosion.
Approximate pooling methods with compact properties have been explored towards resolving this weakness.
Additionally, recent results have shown that significant performance gains can be achieved by adding 1st order information and applying matrix normalization to regularize unstable higher order information.
However, combining compact pooling with matrix normalization and other order information has not been explored until now.
In this paper, we unify bilinear pooling and the global Gaussian embedding layers through the empirical moment matrix.
In addition, we propose a novel sub-matrix square-root layer, which can be used to normalize the output of the convolution layer directly and mitigate the dimensionality problem with off-the-shelf compact pooling methods.
Our experiments on three widely used fine-grained classification datasets illustrate that our proposed architecture, MoNet, can achieve similar or better performance than with the state-of-art G2DeNet.
Furthermore, when combined with compact pooling technique, MoNet obtains comparable performance with encoded features with 96% less dimensions.
We introduce Eigen Evolution Pooling, an efficient method to aggregate a sequence of feature vectors.
Eigen evolution pooling is designed to produce compact feature representations for a sequence of feature vectors, while maximally preserving as much information about the sequence as possible, especially the temporal evolution of the features over time.
Eigen evolution pooling is a general pooling method that can be applied to any sequence of feature vectors, from low-level RGB values to high-level Convolutional Neural Network (CNN) feature vectors.
We show that eigen evolution pooling is more effective than average, max, and rank pooling for encoding the dynamics of human actions in video.
We demonstrate the power of eigen evolution pooling on UCF101 and Hollywood2 datasets, two human action recognition benchmarks, and achieve state-of-the-art performance.
A significant roadblock in multilingual neural language modeling is the lack of labeled non-English data.
One potential method for overcoming this issue is learning cross-lingual text representations that can be used to transfer the performance from training on English tasks to non-English tasks, despite little to no task-specific non-English data.
In this paper, we explore a natural setup for learning cross-lingual sentence representations: the dual-encoder.
We provide a comprehensive evaluation of our cross-lingual representations on a number of monolingual, cross-lingual, and zero-shot/few-shot learning tasks, and also give an analysis of different learned cross-lingual embedding spaces.
Recently, the so-called cell-free (CF) Massive MIMO architecture has been introduced, wherein a very large number of distributed access points (APs) simultaneously and jointly serve a much smaller number of mobile stations (MSs).
The paper extends the CF approach to the case in which both the APs and the MSs are equipped with multiple antennas, proposing a beamfoming scheme that, relying on the channel hardening effect, does not require channel estimation at the MSs.
We contrast the CF massive MIMO approach with a user-centric (UC) approach wherein each MS is served only by a limited number of APs.
Since far APs experience a bad SINR, it turns out that they are quite unhelpful in serving far users, and so, the UC approach, while requiring less backhaul overhead with respect to the CF approach, is shown here to achieve better performance results, in terms of achievable rate-per-user, for the vast majority of the MSs in the network.
Furthermore, in the paper we propose two power allocation strategy for the uplink and downlink, one aimed at maximizing the overall data-rate and another aimed at maximizing system fairness.
Sarcasm is a sophisticated speech act which commonly manifests on social communities such as Twitter and Reddit.
The prevalence of sarcasm on the social web is highly disruptive to opinion mining systems due to not only its tendency of polarity flipping but also usage of figurative language.
Sarcasm commonly manifests with a contrastive theme either between positive-negative sentiments or between literal-figurative scenarios.
In this paper, we revisit the notion of modeling contrast in order to reason with sarcasm.
More specifically, we propose an attention-based neural model that looks in-between instead of across, enabling it to explicitly model contrast and incongruity.
We conduct extensive experiments on six benchmark datasets from Twitter, Reddit and the Internet Argument Corpus.
Our proposed model not only achieves state-of-the-art performance on all datasets but also enjoys improved interpretability.
The fundamental role of hypernymy in NLP has motivated the development of many methods for the automatic identification of this relation, most of which rely on word distribution.
We investigate an extensive number of such unsupervised measures, using several distributional semantic models that differ by context type and feature weighting.
We analyze the performance of the different methods based on their linguistic motivation.
Comparison to the state-of-the-art supervised methods shows that while supervised methods generally outperform the unsupervised ones, the former are sensitive to the distribution of training instances, hurting their reliability.
Being based on general linguistic hypotheses and independent from training data, unsupervised measures are more robust, and therefore are still useful artillery for hypernymy detection.
With the increasing demand for image-based applications, the efficient and reliable evaluation of image quality has increased in importance.
Measuring the image quality is of fundamental importance for numerous image processing applications, where the goal of image quality assessment (IQA) methods is to automatically evaluate the quality of images in agreement with human quality judgments.
Numerous IQA methods have been proposed over the past years to fulfill this goal.
In this paper, a survey of the quality assessment methods for conventional image signals, as well as the newly emerged ones, which includes the high dynamic range (HDR) and 3-D images, is presented.
A comprehensive explanation of the subjective and objective IQA and their classification is provided.
Six widely used subjective quality datasets, and performance measures are reviewed.
Emphasis is given to the full-reference image quality assessment (FR-IQA) methods, and 9 often-used quality measures (including mean squared error (MSE), structural similarity index (SSIM), multi-scale structural similarity index (MS-SSIM), visual information fidelity (VIF), most apparent distortion (MAD), feature similarity measure (FSIM), feature similarity measure for color images (FSIMC), dynamic range independent measure (DRIM), and tone-mapped images quality index (TMQI)) are carefully described, and their performance and computation time on four subjective quality datasets are evaluated.
Furthermore, a brief introduction to 3-D IQA is provided and the issues related to this area of research are reviewed.
Traffic Matrix estimation has always caught attention from researchers for better network management and future planning.
With the advent of high traffic loads due to Cloud Computing platforms and Software Defined Networking based tunable routing and traffic management algorithms on the Internet, it is more necessary as ever to be able to predict current and future traffic volumes on the network.
For large networks such origin-destination traffic prediction problem takes the form of a large under-constrained and under-determined system of equations with a dynamic measurement matrix.
In this work, we present our Compressed Sensing with Dynamic Model Estimation (CS-DME) architecture suitable for modern software defined networks.
Our main contributions are: (1) we formulate an approach in which measurement matrix in the compressed sensing scheme can be accurately and dynamically estimated through a reformulation of the problem based on traffic demands.
(2) We show that the problem formulation using a dynamic measurement matrix based on instantaneous traffic demands may be used instead of a stationary binary routing matrix which is more suitable to modern Software Defined Networks that are constantly evolving in terms of routing by inspection of its Eigen Spectrum using two real world datasets.
(3) We also show that linking this compressed measurement matrix dynamically with the measured parameters can lead to acceptable estimation of Origin Destination (OD) Traffic flows with marginally poor results with other state-of-art schemes relying on fixed measurement matrices.
(4) Furthermore, using this compressed reformulated problem, a new strategy for selection of vantage points for most efficient traffic matrix estimation is also presented through a secondary compression technique based on subset of link measurements.
The paper describes the results of the first shared task on word sense induction (WSI) for the Russian language.
While similar shared tasks were conducted in the past for some Romance and Germanic languages, we explore the performance of sense induction and disambiguation methods for a Slavic language that shares many features with other Slavic languages, such as rich morphology and virtually free word order.
The participants were asked to group contexts of a given word in accordance with its senses that were not provided beforehand.
For instance, given a word "bank" and a set of contexts for this word, e.g."bank is a financial institution that accepts deposits" and "river bank is a slope beside a body of water", a participant was asked to cluster such contexts in the unknown in advance number of clusters corresponding to, in this case, the "company" and the "area" senses of the word "bank".
For the purpose of this evaluation campaign, we developed three new evaluation datasets based on sense inventories that have different sense granularity.
The contexts in these datasets were sampled from texts of Wikipedia, the academic corpus of Russian, and an explanatory dictionary of Russian.
Overall, 18 teams participated in the competition submitting 383 models.
Multiple teams managed to substantially outperform competitive state-of-the-art baselines from the previous years based on sense embeddings.
Among the patch-based image denoising processing methods, smooth ordering of local patches (patch ordering) has been shown to give state-of-art results.
For image denoising the patch ordering method forms two large TSPs (Traveling Salesman Problem) comprised of nodes in N-dimensional space.
Ten approximate solutions of the two large TSPs are then used in a filtering process to form the reconstructed image.
Use of large TSPs makes patch ordering a computationally intensive method.
A modified patch ordering method for image denoising is proposed.
In the proposed method, several smaller-sized TSPs are formed and the filtering process varied to work with solutions of these smaller TSPs.
In terms of PSNR, denoising results of the proposed method differed by 0.032 dB to 0.016 dB on average.
In original method, solving TSPs was observed to consume 85% of execution time.
In proposed method, the time for solving TSPs can be reduced to half of the time required in original method.
The proposed method can denoise images in 40% less time.
During the last decades, classical models in language theory have been extended by control mechanisms defined by monoids.
We study which monoids cause the extensions of context-free grammars, finite automata, or finite state transducers to exceed the capacity of the original model.
Furthermore, we investigate when, in the extended automata model, the nondeterministic variant differs from the deterministic one in capacity.
We show that all these conditions are in fact equivalent and present an algebraic characterization.
In particular, the open question of whether every language generated by a valence grammar over a finite monoid is context-free is provided with a positive answer.
High performance grid computing is a key enabler of large scale collaborative computational science.
With the promise of exascale computing, high performance grid systems are expected to incur electricity bills that grow super-linearly over time.
In order to achieve cost effectiveness in these systems, it is essential for the scheduling algorithms to exploit electricity price variations, both in space and time, that are prevalent in the dynamic electricity price markets.
In this paper, we present a metascheduling algorithm to optimize the placement of jobs in a compute grid which consumes electricity from the day-ahead wholesale market.
We formulate the scheduling problem as a Minimum Cost Maximum Flow problem and leverage queue waiting time and electricity price predictions to accurately estimate the cost of job execution at a system.
Using trace based simulation with real and synthetic workload traces, and real electricity price data sets, we demonstrate our approach on two currently operational grids, XSEDE and NorduGrid.
Our experimental setup collectively constitute more than 433K processors spread across 58 compute systems in 17 geographically distributed locations.
Experiments show that our approach simultaneously optimizes the total electricity cost and the average response time of the grid, without being unfair to users of the local batch systems.
The next generation of cellular networks will exploit mmWave frequencies to dramatically increase the network capacity.
The communication at such high frequencies, however, requires directionality to compensate the increase in propagation loss.
Users and base stations need to align their beams during both initial access and data transmissions, to ensure the maximum gain is reached.
The accuracy of the beam selection, and the delay in updating the beam pair or performing initial access, impact the end-to-end performance and the quality of service.
In this paper we will present the beam management procedures that 3GPP has included in the NR specifications, focusing on the different operations that can be performed in Standalone (SA) and in Non-Standalone (NSA) deployments.
We will also provide a performance comparison among different schemes, along with design insights on the most important parameters related to beam management frameworks.
Many real-world applications require the estimation of human body joints for higher-level tasks as, for example, human behaviour understanding.
In recent years, depth sensors have become a popular approach to obtain three-dimensional information.
The depth maps generated by these sensors provide information that can be employed to disambiguate the poses observed in two-dimensional images.
This work addresses the problem of 3D human pose estimation from depth maps employing a Deep Learning approach.
We propose a model, named Deep Depth Pose (DDP), which receives a depth map containing a person and a set of predefined 3D prototype poses and returns the 3D position of the body joints of the person.
In particular, DDP is defined as a ConvNet that computes the specific weights needed to linearly combine the prototypes for the given input.
We have thoroughly evaluated DDP on the challenging 'ITOP' and 'UBC3V' datasets, which respectively depict realistic and synthetic samples, defining a new state-of-the-art on them.
Universal language representation is the holy grail in machine translation (MT).
Thanks to the new neural MT approach, it seems that there are good perspectives towards this goal.
In this paper, we propose a new architecture based on combining variational autoencoders with encoder-decoders and introducing an interlingual loss as an additional training objective.
By adding and forcing this interlingual loss, we are able to train multiple encoders and decoders for each language, sharing a common universal representation.
Since the final objective of this universal representation is producing close results for similar input sentences (in any language), we propose to evaluate it by encoding the same sentence in two different languages, decoding both latent representations into the same language and comparing both outputs.
Preliminary results on the WMT 2017 Turkish/English task shows that the proposed architecture is capable of learning a universal language representation and simultaneously training both translation directions with state-of-the-art results.
In this world, globalization has become a basic and most popular human trend.
To globalize information, people are going to publish the documents in the internet.
As a result, information volume of internet has become huge.
To handle that huge volume of information, Web searcher uses search engines.
The Webpage indexing mechanism of a search engine plays a big role to retrieve Web search results in a faster way from the huge volume of Web resources.
Web researchers have introduced various types of Web-page indexing mechanism to retrieve Webpages from Webpage repository.
In this paper, we have illustrated a new approach of design and development of Webpage indexing.
The proposed Webpage indexing mechanism has applied on domain specific Webpages and we have identified the Webpage domain based on an Ontology.
In our approach, first we prioritize the Ontology terms that exist in the Webpage content then apply our own indexing mechanism to index that Webpage.
The main advantage of storing an index is to optimize the speed and performance while finding relevant documents from the domain specific search engine storage area for a user given search query.
This paper considers the transmission of confidential messages over noisy wireless ad hoc networks, where both background noise and interference from concurrent transmitters affect the received signals.
For the random networks where the legitimate nodes and the eavesdroppers are distributed as Poisson point processes, we study the secrecy transmission capacity (STC), as well as the connection outage probability and secrecy outage probability, based on the physical layer security.
We first consider the basic fixed transmission distance model, and establish a theoretical model of the STC.
We then extend the above results to a more realistic random distance transmission model, namely nearest receiver transmission.
Finally, extensive simulation and numerical results are provided to validate the efficiency of our theoretical results and illustrate how the STC is affected by noise, connection and secrecy outage probabilities, transmitter and eavesdropper densities, and other system parameters.
Remarkably, our results reveal that a proper amount of noise is helpful to the secrecy transmission capacity.
In this paper, we develop a distributed intermittent communication and task planning framework for mobile robot teams.
The goal of the robots is to accomplish complex tasks, captured by local Linear Temporal Logic formulas, and share the collected information with all other robots and possibly also with a user.
Specifically, we consider situations where the robot communication capabilities are not sufficient to form reliable and connected networks while the robots move to accomplish their tasks.
In this case, intermittent communication protocols are necessary that allow the robots to temporarily disconnect from the network in order to accomplish their tasks free of communication constraints.
We assume that the robots can only communicate with each other when they meet at common locations in space.
Our distributed control framework jointly determines local plans that allow all robots fulfill their assigned temporal tasks, sequences of communication events that guarantee information exchange infinitely often, and optimal communication locations that minimize a desired distance metric.
Simulation results verify the efficacy of the proposed controllers.
Urban scholars have studied street networks in various ways, but there are data availability and consistency limitations to the current urban planning/street network analysis literature.
To address these challenges, this article presents OSMnx, a new tool to make the collection of data and creation and analysis of street networks simple, consistent, automatable and sound from the perspectives of graph theory, transportation, and urban design.
OSMnx contributes five significant capabilities for researchers and practitioners: first, the automated downloading of political boundaries and building footprints; second, the tailored and automated downloading and constructing of street network data from OpenStreetMap; third, the algorithmic correction of network topology; fourth, the ability to save street networks to disk as shapefiles, GraphML, or SVG files; and fifth, the ability to analyze street networks, including calculating routes, projecting and visualizing networks, and calculating metric and topological measures.
These measures include those common in urban design and transportation studies, as well as advanced measures of the structure and topology of the network.
Finally, this article presents a simple case study using OSMnx to construct and analyze street networks in Portland, Oregon.
In many important machine learning applications, the training distribution used to learn a probabilistic classifier differs from the testing distribution on which the classifier will be used to make predictions.
Traditional methods correct the distribution shift by reweighting the training data with the ratio of the density between test and training data.
In many applications training takes place without prior knowledge of the testing distribution on which the algorithm will be applied in the future.
Recently, methods have been proposed to address the shift by learning causal structure, but those methods rely on the diversity of multiple training data to a good performance, and have complexity limitations in high dimensions.
In this paper, we propose a novel Deep Global Balancing Regression (DGBR) algorithm to jointly optimize a deep auto-encoder model for feature selection and a global balancing model for stable prediction across unknown environments.
The global balancing model constructs balancing weights that facilitate estimating of partial effects of features (holding fixed all other features), a problem that is challenging in high dimensions, and thus helps to identify stable, causal relationships between features and outcomes.
The deep auto-encoder model is designed to reduce the dimensionality of the feature space, thus making global balancing easier.
We show, both theoretically and with empirical experiments, that our algorithm can make stable predictions across unknown environments.
Our experiments on both synthetic and real world datasets demonstrate that our DGBR algorithm outperforms the state-of-the-art methods for stable prediction across unknown environments.
Full duplex (FD) communications, which increases spectral efficiency through simultaneous transmission and reception on the same frequency band, is a promising technology to meet the demand of next generation wireless networks.
In this paper, we consider the application of such FD communication to self-backhauled small cells.
We consider a FD capable small cell base station (BS) being wirelessly backhauled by a FD capable macro-cell BS.
FD communication enables simultaneous backhaul and access transmissions at small cell BSs, which reduces the need to orthogonalize allocated spectrum between access and backhaul.
However, in such simultaneous operations, all the links experience higher interference, which significantly suppresses the gains of FD operations.
We propose an interference-aware scheduling method to maximize the FD gain across multiple UEs in both uplink and downlink directions, while maintaining a level of fairness between all UEs.
It jointly schedules the appropriate links and traffic based on the back-pressure algorithm, and allocates appropriate transmission powers to the scheduled links using Geometric Programming.
Our simulation results show that the proposed scheduler nearly doubles the throughput of small cells compared to traditional half-duplex self-backhauling.
We will demonstrate a conversational products recommendation agent.
This system shows how we combine research in personalized recommendation systems with research in dialogue systems to build a virtual sales agent.
Based on new deep learning technologies we developed, the virtual agent is capable of learning how to interact with users, how to answer user questions, what is the next question to ask, and what to recommend when chatting with a human user.
Normally a descent conversational agent for a particular domain requires tens of thousands of hand labeled conversational data or hand written rules.
This is a major barrier when launching a conversation agent for a new domain.
We will explore and demonstrate the effectiveness of the learning solution even when there is no hand written rules or hand labeled training data.
Comtraces (combined traces) are extensions of Mazurkiewicz traces that can model the "not later than" relationship.
In this paper, we first introduce the novel notion of generalized comtraces, extensions of comtraces that can additionally model the "non-simultaneously" relationship.
Then we study some basic algebraic properties and canonical reprentations of comtraces and generalized comtraces.
Finally we analyze the relationship between generalized comtraces and generalized stratified order structures.
The major technical contribution of this paper is a proof showing that generalized comtraces can be represented by generalized stratified order structures.
The inversion of linear systems is a fundamental step in many inverse problems.
Computational challenges exist when trying to invert large linear systems, where limited computing resources mean that only part of the system can be kept in computer memory at any one time.
We are here motivated by tomographic inversion problems that often lead to linear inverse problems.
In state of the art x-ray systems, even a standard scan can produce 4 million individual measurements and the reconstruction of x-ray attenuation profiles typically requires the estimation of a million attenuation coefficients.
To deal with the large data sets encountered in real applications and to utilise modern graphics processing unit (GPU) based computing architectures, combinations of iterative reconstruction algorithms and parallel computing schemes are increasingly applied.
Although both row and column action methods have been proposed to utilise parallel computing architectures, individual computations in current methods need to know either the entire set of observations or the entire set of estimated x-ray absorptions, which can be prohibitive in many realistic big data applications.
We present a fully parallelizable computed tomography (CT) image reconstruction algorithm that works with arbitrary partial subsets of the data and the reconstructed volume.
We further develop a non-homogeneously randomised selection criteria which guarantees that sub-matrices of the system matrix are selected more frequently if they are dense, thus maximising information flow through the algorithm.
A grouped version of the algorithm is also proposed to further improve convergence speed and performance.
Algorithm performance is verified experimentally.
We describe a novel approach to interpret a polar code as a low-density parity-check (LDPC)-like code with an underlying sparse decoding graph.
This sparse graph is based on the encoding factor graph of polar codes and is suitable for conventional belief propagation (BP) decoding.
We discuss several pruning techniques based on the check node decoder (CND) and variable node decoder (VND) update equations, significantly reducing the size (i.e., decoding complexity) of the parity-check matrix.
As a result, iterative polar decoding can then be conducted on a sparse graph, akin to the traditional well-established LDPC decoding, e.g., using a fully parallel sum-product algorithm (SPA).
This facilitates the systematic analysis and design of polar codes using the well-established tools known from analyzing LDPC codes.
We show that the proposed iterative polar decoder has a negligible performance loss for short-to-intermediate codelengths compared to Arikan's original BP decoder.
Finally, the proposed decoder is shown to benefit from both reduced complexity and reduced memory requirements and, thus, is more suitable for hardware implementations.
The present work investigates whether different quantification mechanisms (set comparison, vague quantification, and proportional estimation) can be jointly learned from visual scenes by a multi-task computational model.
The motivation is that, in humans, these processes underlie the same cognitive, non-symbolic ability, which allows an automatic estimation and comparison of set magnitudes.
We show that when information about lower-complexity tasks is available, the higher-level proportional task becomes more accurate than when performed in isolation.
Moreover, the multi-task model is able to generalize to unseen combinations of target/non-target objects.
Consistently with behavioral evidence showing the interference of absolute number in the proportional task, the multi-task model no longer works when asked to provide the number of target objects in the scene.
Coverage and connectivity both are important in wireless sensor network (WSN).
Coverage means how well an area of interest is being monitored by the deployed network.
It depends on sensing model that has been used to design the network model.
Connectivity ensures the establishment of a wireless link between two nodes.
A link model studies the connectivity between two nodes.
The probability of establishing a wireless link between two nodes is a probabilistic phenomenon.
The connectivity between two nodes plays an important role in the determination of network connectivity.
In this paper, we investigate the impact of sensing model of nodes on the network coverage.
Also, we investigate the dependency of the connectivity and coverage on the shadow fading parameters.
It has been observed that shadowing effect reduces the network coverage while it enhances connectivity in a multi-hop wireless network.
The end-to-end throughput of multi-hop communication in wireless ad hoc networks is affected by the conflict between forwarding nodes.
It has been shown that sending more packets than maximum achievable end-to-end throughput not only fails to increase throughput, but also decreases throughput owing to high contention and collision.
Accordingly, it is of crucial importance for a source node to know the maximum end-to-end throughput.
The end-to-end throughput depends on multiple factors, such as physical layer limitations, MAC protocol properties, routing policy and nodes distribution.
There have been many studies on analytical modeling of end-to-end throughput but none of them has taken routing policy and nodes distribution as well as MAC layer altogether into account.
In this paper, the end-to-end throughput with perfect MAC layer is obtained based on routing policy and nodes distribution in one and two dimensional networks.
Then, imperfections of IEEE 802:11 protocol is added to the model to obtain precise value.
An exhaustive simulation is also made to validate the proposed models using NS2 simulator.
Results show that if the distribution to the next hop for a particular routing policy is known, our methodology can obtain the maximum end-to-end throughput precisely.
Skin cancer is a major public health problem, with over 5 million newly diagnosed cases in the United States each year.
Melanoma is the deadliest form of skin cancer, responsible for over 9,000 deaths each year.
In this paper, we propose an ensemble of deep convolutional neural networks to classify dermoscopy images into three classes.
To achieve the highest classification accuracy, we fuse the outputs of the softmax layers of four different neural architectures.
For aggregation, we consider the individual accuracies of the networks weighted by the confidence values provided by their final softmax layers.
This fusion-based approach outperformed all the individual neural networks regarding classification accuracy.
Android is the predominant mobile operating system for the past few years.
The prevalence of devices that can be powered by Android magnetized not merely application developers but also malware developers with criminal intention to design and spread malicious applications that can affect the normal work of Android phones and tablets, steal personal information and credential data, or even worse lock the phone and ask for ransom.
Researchers persistently devise countermeasures strategies to fight back malware.
One of these strategies applied in the past five years is the use of deep learning methods in Android malware detection.
This necessitates a review to inspect the accomplished work in order to know where the endeavors have been established, identify unresolved problems, and motivate future research directions.
In this work, an extensive survey of static analysis, dynamic analysis, and hybrid analysis that utilized deep learning methods are reviewed with an elaborated discussion on their key concepts, contributions, and limitations.
Minimizing job scheduling time is a fundamental issue in data center networks that has been extensively studied in recent years.
The incoming jobs require different CPU and memory units, and span different number of time slots.
The traditional solution is to design efficient heuristic algorithms with performance guarantee under certain assumptions.
In this paper, we improve a recently proposed job scheduling algorithm using deep reinforcement learning and extend it to multiple server clusters.
Our study reveals that deep reinforcement learning method has the potential to outperform traditional resource allocation algorithms in a variety of complicated environments.
We consider a computational model which is known as set automata.
The set automata are one-way finite automata with an additional storage---the set.
There are two kinds of set automata---the deterministic and the nondeterministic ones.
We denote them as DSA and NSA respectively.
The model was introduced by M. Kutrib, A. Malcher, M. Wendlandt in 2014.
It was shown that DSA-languages look similar to DCFL due to their closure properties and NSA-languages look similar to CFL due to their undecidability properties.
In this paper we show that this similarity is natural: we prove that languages recognizable by NSA form a rational cone, so as CFL.
The main topic of this paper is computational complexity: we prove that   - languages recognizable by DSA belong to P and there are P-complete languages among them;   - languages recognizable by NSA are in NP and there are NP-complete languages among them;   - the word membership problem is P-complete for DSA without epsilon-loops and PSPACE-complete for general DSA;   - the emptiness problem is in PSPACE for NSA and, moreover, it is PSPACE-complete for DSA.
Rapport, the close and harmonious relationship in which interaction partners are "in sync" with each other, was shown to result in smoother social interactions, improved collaboration, and improved interpersonal outcomes.
In this work, we are first to investigate automatic prediction of low rapport during natural interactions within small groups.
This task is challenging given that rapport only manifests in subtle non-verbal signals that are, in addition, subject to influences of group dynamics as well as inter-personal idiosyncrasies.
We record videos of unscripted discussions of three to four people using a multi-view camera system and microphones.
We analyse a rich set of non-verbal signals for rapport detection, namely facial expressions, hand motion, gaze, speaker turns, and speech prosody.
Using facial features, we can detect low rapport with an average precision of 0.7 (chance level at 0.25), while incorporating prior knowledge of participants' personalities can even achieve early prediction without a drop in performance.
We further provide a detailed analysis of different feature sets and the amount of information contained in different temporal segments of the interactions.
Heterogeneous information networks (HINs) are ubiquitous in real-world applications.
Due to the heterogeneity in HINs, the typed edges may not fully align with each other.
In order to capture the semantic subtlety, we propose the concept of aspects with each aspect being a unit representing one underlying semantic facet.
Meanwhile, network embedding has emerged as a powerful method for learning network representation, where the learned embedding can be used as features in various downstream applications.
Therefore, we are motivated to propose a novel embedding learning framework---AspEm---to preserve the semantic information in HINs based on multiple aspects.
Instead of preserving information of the network in one semantic space, AspEm encapsulates information regarding each aspect individually.
In order to select aspects for embedding purpose, we further devise a solution for AspEm based on dataset-wide statistics.
To corroborate the efficacy of AspEm, we conducted experiments on two real-words datasets with two types of applications---classification and link prediction.
Experiment results demonstrate that AspEm can outperform baseline network embedding learning methods by considering multiple aspects, where the aspects can be selected from the given HIN in an unsupervised manner.
Non-motorized transport is becoming increasingly important in urban development of cities in China.
How to evaluate the non-motorized transport popularity of urban roads is an interesting question to study.
The great amount of tracking data generated by smart mobile devices give us opportunities to solve this problem.
This study aims to provide a data driven method for evaluating the popularity (walkability and bikeability) of urban non-motorized transport system.
This paper defines a p-index to evaluate the popular degree of road segments which is based on the cycling, running, and walking GPS track data from outdoor activities logging applications.
According to the p-index definition, this paper evaluates the non-motorized transport popularity of urban area in Wuhan city within different temporal periods.
Systems for automatic extraction of semantic information about events from large textual resources are now available: these tools are capable to generate RDF datasets about text extracted events and this knowledge can be used to reason over the recognized events.
On the other hand, text based tasks for event recognition, as for example event coreference (i.e. recognizing whether two textual descriptions refer to the same event), do not take into account ontological information of the extracted events in their process.
In this paper, we propose a method to derive event coreference on text extracted event data using semantic based rule reasoning.
We demonstrate our method considering a limited (yet representative) set of event types: we introduce a formal analysis on their ontological properties and, on the base of this, we define a set of coreference criteria.
We then implement these criteria as RDF-based reasoning rules to be applied on text extracted event data.
We evaluate the effectiveness of our approach over a standard coreference benchmark dataset.
Whole genome prediction of complex phenotypic traits using high-density genotyping arrays has attracted a great deal of attention, as it is relevant to the fields of plant and animal breeding and genetic epidemiology.
As the number of genotypes is generally much bigger than the number of samples, predictive models suffer from the curse-of-dimensionality.
The curse-of-dimensionality problem not only affects the computational efficiency of a particular genomic selection method, but can also lead to poor performance, mainly due to correlation among markers.
In this work we proposed the first transductive feature selection method based on the MRMR (Max-Relevance and Min-Redundancy) criterion which we call MINT.
We applied MINT on genetic trait prediction problems and showed that in general MINT is a better feature selection method than the state-of-the-art inductive method mRMR.
Trends like digital transformation even intensify the already overwhelming mass of information knowledge workers face in their daily life.
To counter this, we have been investigating knowledge work and information management support measures inspired by human forgetting.
In this paper, we give an overview of solutions we have found during the last five years as well as challenges that still need to be tackled.
Additionally, we share experiences gained with the prototype of a first forgetful information system used 24/7 in our daily work for the last three years.
We also address the untapped potential of more explicated user context as well as features inspired by Memory Inhibition, which is our current focus of research.
Memorability is considered to be an important characteristic of visual content, whereas for advertisement and educational purposes it is the most important one.
Despite numerous studies on understanding and predicting image memorability, there are almost no achievements in memorability modification.
In this work, we study two possible approaches to image modification which likely may influence memorability.
The visual features which influence memorability directly stay unknown till now, hence it is impossible to control it manually.
As a solution, we let GAN learn it deeply using labeled data, and then use it for conditional generation of new images.
By analogy with algorithms which edit facial attributes, we consider memorability as yet another attribute and operate with it in the same way.
Obtained data is also interesting for analysis, simply because there are no real-world examples of successful change of image memorability while preserving its other attributes.
We believe this may give many new answers to the question "what makes an image memorable?"
Apart from that we also study the influence of conventional photo-editing tools (Photoshop, Instagram, etc.) used daily by a wide audience on memorability.
In this case, we start from real practical methods and study it using statistics and recent advances in memorability prediction.
Photographers, designers, and advertisers will benefit from the results of this study directly.
Sentence embedding is an important research topic in natural language processing.
It is essential to generate a good embedding vector that fully reflects the semantic meaning of a sentence in order to achieve an enhanced performance for various natural language processing tasks, such as machine translation and document classification.
Thus far, various sentence embedding models have been proposed, and their feasibility has been demonstrated through good performances on tasks following embedding, such as sentiment analysis and sentence classification.
However, because the performances of sentence classification and sentiment analysis can be enhanced by using a simple sentence representation method, it is not sufficient to claim that these models fully reflect the meanings of sentences based on good performances for such tasks.
In this paper, inspired by human language recognition, we propose the following concept of semantic coherence, which should be satisfied for a good sentence embedding method: similar sentences should be located close to each other in the embedding space.
Then, we propose the Paraphrase-Thought (P-thought) model to pursue semantic coherence as much as possible.
Experimental results on two paraphrase identification datasets (MS COCO and STS benchmark) show that the P-thought models outperform the benchmarked sentence embedding methods.
Technology of autonomous vehicles (AVs) is getting mature and many AVs will appear on the roads in the near future.
AVs become connected with the support of various vehicular communication technologies and they possess high degree of control to respond to instantaneous situations cooperatively with high efficiency and flexibility.
In this paper, we propose a new public transportation system based on AVs.
It manages a fleet of AVs to accommodate transportation requests, offering point-to-point services with ride sharing.
We focus on the two major problems of the system: scheduling and admission control.
The former is to configure the most economical schedules and routes for the AVs to satisfy the admissible requests while the latter is to determine the set of admissible requests among all requests to produce maximum profit.
The scheduling problem is formulated as a mixed-integer linear program and the admission control problem is cast as a bilevel optimization, which embeds the scheduling problem as the major constraint.
By utilizing the analytical properties of the problem, we develop an effective genetic-algorithm-based method to tackle the admission control problem.
We validate the performance of the algorithm with real-world transportation service data.
Conversational agents have become ubiquitous, ranging from goal-oriented systems for helping with reservations to chit-chat models found in modern virtual assistants.
In this survey paper, we explore this fascinating field.
We look at some of the pioneering work that defined the field and gradually move to the current state-of-the-art models.
We look at statistical, neural, generative adversarial network based and reinforcement learning based approaches and how they evolved.
Along the way we discuss various challenges that the field faces, lack of context in utterances, not having a good quantitative metric to compare models, lack of trust in agents because they do not have a consistent persona etc.
We structure this paper in a way that answers these pertinent questions and discusses competing approaches to solve them.
The use of interpolants in model checking is becoming an enabling technology to allow fast and robust verification of hardware and software.
The application of encodings based on the theory of arrays, however, is limited by the impossibility of deriving quantifier- free interpolants in general.
In this paper, we show that it is possible to obtain quantifier-free interpolants for a Skolemized version of the extensional theory of arrays.
We prove this in two ways: (1) non-constructively, by using the model theoretic notion of amalgamation, which is known to be equivalent to admit quantifier-free interpolation for universal theories; and (2) constructively, by designing an interpolating procedure, based on solving equations between array updates.
(Interestingly, rewriting techniques are used in the key steps of the solver and its proof of correctness.)
To the best of our knowledge, this is the first successful attempt of computing quantifier- free interpolants for a variant of the theory of arrays with extensionality.
A Software Engineering project depends significantly on team performance, as does any activity that involves human interaction.
In the last years, the traditional perspective on software development is changing and agile methods have received considerable attention.
Among other attributes, the ageists claim that fostering creativity is one of the keys to response to common problems and challenges of software development today.
The development of new software products requires the generation of novel and useful ideas.
It is a conceptual framework introduced in the Agile Manifesto in 2001.
This paper is written in support of agile practices in terms of significance of teamwork for the success of software projects.
Survey is used as a research method to know the significance of teamwork.
A co-evolutionary algorithm (CA) based chess player is presented.
Implementation details of the algorithms, namely coding, population, variation operators are described.
The alpha-beta or mini-max like behaviour of the player is achieved through two competitive or cooperative populations.
Special attention is given to the fitness function evaluation (the heart of the solution).
Test results on algorithms vs. algorithms or human player is provided.
In this paper we propose a method for applications oriented input design for linear systems under time-domain constraints on the amplitude of input and output signals.
The method guarantees a desired control performance for the estimated model in minimum time, by imposing some lower bound on the information matrix.
The problem is formulated as a time domain optimization problem, which is non-convex.
This is addressed through an alternating method, where we separate the problem into two steps and at each step we optimize the cost function with respect to one of two variables.
We alternate between these two steps until convergence.
A time recursive input design algorithm is performed, which enables us to use the algorithm with control.
Therefore, a receding horizon framework is used to solve each optimization problem.
Finally, we illustrate the method with two numerical examples which show the good ability of the proposed approach in generating an optimal input signal.
The popularity of digital currencies, especially cryptocurrencies, has been continuously growing since the appearance of Bitcoin.
Bitcoin's security lies in a proof-of-work scheme, which requires high computational resources at the miners.
Despite advances in mobile technology, existing cryptocurrencies cannot be maintained by mobile devices due to their low processing capabilities.
Mobile devices can only accommodate mobile applications (wallets) that allow users to exchange credits of cryptocurrencies.
In this work, we propose LocalCoin, an alternative cryptocurrency that requires minimal computational resources, produces low data traffic and works with off-the-shelf mobile devices.
LocalCoin replaces the computational hardness that is at the root of Bitcoin's security with the social hardness of ensuring that all witnesses to a transaction are colluders.
Localcoin features (i) a lightweight proof-of-work scheme and (ii) a distributed blockchain.
We analyze LocalCoin for double spending for passive and active attacks and prove that under the assumption of sufficient number of users and properly selected tuning parameters the probability of double spending is close to zero.
Extensive simulations on real mobility traces, realistic urban settings, and random geometric graphs show that the probability of success of one transaction converges to 1 and the probability of the success of a double spending attempt converges to 0.
XML data warehouses form an interesting basis for decision-support applications that exploit heterogeneous data from multiple sources.
However, XML-native database systems currently suffer from limited performances in terms of manageable data volume and response time for complex analytical queries.
Fragmenting and distributing XML data warehouses (e.g., on data grids) allow to address both these issues.
In this paper, we work on XML warehouse fragmentation.
In relational data warehouses, several studies recommend the use of derived horizontal fragmentation.
Hence, we propose to adapt it to the XML context.
We particularly focus on the initial horizontal fragmentation of dimensions' XML documents and exploit two alternative algorithms.
We experimentally validate our proposal and compare these alternatives with respect to a unified XML warehouse model we advocate for.
In this paper, we investigate the performance gains of adapting pilot spacing and power for Carrier Aggregation (CA)-OFDM systems in nonstationary wireless channels.
In current multi-band CA-OFDM wireless networks, all component carriers use the same pilot density, which is designed for poor channel environments.
This leads to unnecessary pilot overhead in good channel conditions and performance degradation in the worst channel conditions.
We propose adaptation of pilot spacing and power using a codebook-based approach, where the transmitter and receiver exchange information about the fading characteristics of the channel over a short period of time, which are stored as entries in a channel profile codebook.
We present a heuristic algorithm that maximizes the achievable rate by finding the optimal pilot spacing and power, from a set of candidate pilot configurations.
We also analyze the computational complexity of our proposed algorithm and the feedback overhead.
We describe methods to minimize the computation and feedback requirements for our algorithm in multi-band CA scenarios and present simulation results in typical terrestrial and air-to-ground/air-to-air nonstationary channels.
Our results show that significant performance gains can be achieved when adopting adaptive pilot spacing and power allocation in nonstationary channels.
We also discuss important practical considerations and provide guidelines to implement adaptive pilot spacing in CA-OFDM systems.
The constrained Cramer-Rao bound (CCRB) is a lower bound on the mean-squared-error (MSE) of estimators that satisfy some unbiasedness conditions.
Although the CCRB unbiasedness conditions are satisfied asymptotically by the constrained maximum likelihood (CML) estimator, in the non-asymptotic region these conditions are usually too strict and the commonly-used estimators, such as the CML estimator, do not satisfy them.
Therefore, the CCRB may not be a lower bound on the MSE matrix of such estimators.
In this paper, we propose a new definition for unbiasedness under constraints, denoted by C-unbiasedness, which is based on using Lehmann-unbiasedness with a weighted MSE (WMSE) risk and taking into account the parametric constraints.
In addition to C-unbiasedness, a Cramer-Rao-type bound on the WMSE of C-unbiased estimators, denoted as Lehmann-unbiased CCRB (LU-CCRB), is derived.
This bound is a scalar bound that depends on the chosen weighted combination of estimation errors.
It is shown that C-unbiasedness is less restrictive than the CCRB unbiasedness conditions.
Thus, the set of estimators that satisfy the CCRB unbiasedness conditions is a subset of the set of C-unbiased estimators and the proposed LU-CCRB may be an informative lower bound in cases where the corresponding CCRB is not.
In the simulations, we examine linear and nonlinear estimation problems under nonlinear constraints in which the CML estimator is shown to be C-unbiased and the LU-CCRB is an informative lower bound on the WMSE, while the corresponding CCRB on the WMSE is not a lower bound and is not informative in the non-asymptotic region.
Data retrieval systems such as online search engines and online social networks must comply with the privacy policies of personal and selectively shared data items, regulatory policies regarding data retention and censorship, and the provider's own policies regarding data use.
Enforcing these policies is difficult and error-prone.
Systematic techniques to enforce policies are either limited to type-based policies that apply uniformly to all data of the same type, or incur significant runtime overhead.
This paper presents Shai, the first system that systematically enforces data-specific policies with near-zero overhead in the common case.
Shai's key idea is to push as many policy checks as possible to an offline, ahead-of-time analysis phase, often relying on predicted values of runtime parameters such as the state of access control lists or connected users' attributes.
Runtime interception is used sparingly, only to verify these predictions and to make any remaining policy checks.
Our prototype implementation relies on efficient, modern OS primitives for sandboxing and isolation.
We present the design of Shai and quantify its overheads on an experimental data indexing and search pipeline based on the popular search engine Apache Lucene.
One of possible ways of obtaining continuous-space sentence representations is by training neural machine translation (NMT) systems.
The recent attention mechanism however removes the single point in the neural network from which the source sentence representation can be extracted.
We propose several variations of the attentive NMT architecture bringing this meeting point back.
Empirical evaluation suggests that the better the translation quality, the worse the learned sentence representations serve in a wide range of classification and similarity tasks.
Modern learning algorithms use gradient descent updates to train inferential models that best explain data.
Scaling these approaches to massive data sizes requires proper distributed gradient descent schemes where distributed worker nodes compute partial gradients based on their partial and local data sets, and send the results to a master node where all the computations are aggregated into a full gradient and the learning model is updated.
However, a major performance bottleneck that arises is that some of the worker nodes may run slow.
These nodes a.k.a. stragglers can significantly slow down computation as the slowest node may dictate the overall computational time.
We propose a distributed computing scheme, called Batched Coupon's Collector (BCC) to alleviate the effect of stragglers in gradient methods.
We prove that our BCC scheme is robust to a near optimal number of random stragglers.
We also empirically demonstrate that our proposed BCC scheme reduces the run-time by up to 85.4% over Amazon EC2 clusters when compared with other straggler mitigation strategies.
We also generalize the proposed BCC scheme to minimize the completion time when implementing gradient descent-based algorithms over heterogeneous worker nodes.
It's useful to automatically transform an image from its original form to some synthetic form (style, partial contents, etc.), while keeping the original structure or semantics.
We define this requirement as the "image-to-image translation" problem, and propose a general approach to achieve it, based on deep convolutional and conditional generative adversarial networks (GANs), which has gained a phenomenal success to learn mapping images from noise input since 2014.
In this work, we develop a two step (unsupervised) learning method to translate images between different domains by using unlabeled images without specifying any correspondence between them, so that to avoid the cost of acquiring labeled data.
Compared with prior works, we demonstrated the capacity of generality in our model, by which variance of translations can be conduct by a single type of model.
Such capability is desirable in applications like bidirectional translation
Facial expressions play a significant role in human communication and behavior.
Psychologists have long studied the relationship between facial expressions and emotions.
Paul Ekman et al., devised the Facial Action Coding System (FACS) to taxonomize human facial expressions and model their behavior.
The ability to recognize facial expressions automatically, enables novel applications in fields like human-computer interaction, social gaming, and psychological research.
There has been a tremendously active research in this field, with several recent papers utilizing convolutional neural networks (CNN) for feature extraction and inference.
In this paper, we employ CNN understanding methods to study the relation between the features these computational networks are using, the FACS and Action Units (AU).
We verify our findings on the Extended Cohn-Kanade (CK+), NovaEmotions and FER2013 datasets.
We apply these models to various tasks and tests using transfer learning, including cross-dataset validation and cross-task performance.
Finally, we exploit the nature of the FER based CNN models for the detection of micro-expressions and achieve state-of-the-art accuracy using a simple long-short-term-memory (LSTM) recurrent neural network (RNN).
The Cloud-Radio Access Network (C-RAN) cellular architecture relies on the transfer of complex baseband signals to and from a central unit (CU) over digital fronthaul links to enable the virtualization of the baseband processing functionalities of distributed radio units (RUs).
The standard design of digital fronthauling is based on either scalar quantization or on more sophisticated point to-point compression techniques operating on baseband signals.
Motivated by network-information theoretic results, techniques for fronthaul quantization and compression that improve over point-to-point solutions by allowing for joint processing across multiple fronthaul links at the CU have been recently proposed for both the uplink and the downlink.
For the downlink, a form of joint compression, known in network information theory as multivariate compression, was shown to be advantageous under a non-constructive asymptotic information-theoretic framework.
In this paper, instead, the design of a practical symbol-by-symbol fronthaul quantization algorithm that implements the idea of multivariate compression is investigated for the C-RAN downlink.
As compared to current standards, the proposed multivariate quantization (MQ) only requires changes in the CU processing while no modification is needed at the RUs.
The algorithm is extended to enable the joint optimization of downlink precoding and quantization, reduced-complexity MQ via successive block quantization, and variable-length compression.
Numerical results, which include performance evaluations over standard cellular models, demonstrate the advantages of MQ and the merits of a joint optimization with precoding.
Municipal solid waste management (MSWM) is a challenging issue of urban development in developing countries.
Each country having different socio-economic-environmental background, might not accept a particular disposal method as the optimal choice.
Selection of suitable disposal method in MSWM, under vague and imprecise information can be considered as multi criteria decision making problem (MCDM).
In the present paper, TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) methodology is extended based on credibility theory for evaluating the performances of MSW disposal methods under some criteria fixed by experts.
The proposed model helps decision makers to choose a preferable alternative for their municipal area.
A sensitivity analysis by our proposed model confirms this fact.
In contrast to the prevalent assumption of rich multipath in information theoretic analysis of wireless channels, physical channels exhibit sparse multipath, especially at large bandwidths.
We propose a model for sparse multipath fading channels and present results on the impact of sparsity on non-coherent capacity and reliability in the wideband regime.
A key implication of sparsity is that the statistically independent degrees of freedom in the channel, that represent the delay-Doppler diversity afforded by multipath, scale at a sub-linear rate with the signal space dimension (time-bandwidth product).
Our analysis is based on a training-based communication scheme that uses short-time Fourier (STF) signaling waveforms.
Sparsity in delay-Doppler manifests itself as time-frequency coherence in the STF domain.
From a capacity perspective, sparse channels are asymptotically coherent: the gap between coherent and non-coherent extremes vanishes in the limit of large signal space dimension without the need for peaky signaling.
From a reliability viewpoint, there is a fundamental tradeoff between channel diversity and learnability that can be optimized to maximize the error exponent at any rate by appropriately choosing the signaling duration as a function of bandwidth.
The ability to activate and manage effective collaborations is becoming an increasingly important criteria in policies on academic career advancement.
The rise of such policies leads to development of indicators that permit measurement of the propensity to collaborate for academics of different ranks, and to examine the role of several variables in collaboration, first among these being the researchers' disciplines.
In this work we apply an innovative bibliometric approach based on individual propensity for collaboration to measure the differences in propensity across academic ranks, by discipline and for choice of collaboration forms - intramural, extramural domestic and international.
The analysis is based on the scientific production of Italian academics for the period 2006 to 2010, totaling over 200,000 publications indexed in Web of Science.
It shows that assistant professors register a propensity for intramural collaboration that is clearly greater than for professors of higher ranks.
Vice versa, the higher ranks, but not quite so clearly, register greater propensity to collaborate at the international level.
In this work, we analyze the performance of the uplink (UL) of a massive MIMO network considering an asymptotically large number of antennas at base stations (BSs).
We model the locations of BSs as a homogeneous Poisson point process (PPP) and assume that their service regions are limited to their respective Poisson-Voronoi cells (PVCs).
Further, for each PVC, based on a threshold radius, we model the cell center (CC) region as the Johnson-Mehl (JM) cell of its BS while rest of the PVC is deemed as the cell edge (CE) region.
The CC and CE users are located uniformly at random independently of each other in the JM cell and CE region, respectively.
In addition, we consider a fractional pilot reuse (FPR) scheme where two different sets of pilot sequences are used for CC and CE users with the objective of reducing the interference due to pilot contamination for CE users.
Based on the above system model, we derive analytical expressions for the UL signal-to-interference-and-noise ratio (SINR) coverage probability and average spectral efficiency (SE) for randomly selected CC and CE users.
In addition, we present an approximate expression for the average cell SE.
One of the key intermediate results in our analysis is the approximate but accurate characterization of the distributions of the CC and CE areas of a typical cell.
Another key intermediate step is the accurate characterization of the pair correlation functions of the point processes formed by the interfering CC and CE users that subsequently enables the coverage probability analysis.
From our system analysis, we present a partitioning rule for the number of pilot sequences to be used for CC and CE users as a function of threshold radius that improves the average CE user SE while achieving similar CC user SE with respect to unity pilot reuse.
An Application Specific Instruction set Processor (ASIP) is an important component in designing embedded systems.
One of the problems in designing an instruction set for such processors is determining the number of registers is needed in the processor that will optimize the computational time and the cost.
The performance of a processor may fall short due to register spilling, which is caused by the lack of available registers in a processor.
In the design perspective, it will result in processors with great performance and low power consumption if we can avoid register spilling by deciding a value for the number of registers needed in an ASIP.
However, as of now, it has not clearly been recognized how the number of registers changes with different application domains.
In this paper, we evaluated whether different application domains have any significant effect on register spilling and therefore the performance of a processor so that we could use different number of registers when building ASIPs for different application domains rather than using a constant set of registers.
Such utilization of registers will result in processors with high performance, low cost and low power consumption.
OpenStreetMap offers a valuable source of worldwide geospatial data useful to urban researchers.
This study uses the OSMnx software to automatically download and analyze 27,000 US street networks from OpenStreetMap at metropolitan, municipal, and neighborhood scales - namely, every US city and town, census urbanized area, and Zillow-defined neighborhood.
It presents empirical findings on US urban form and street network characteristics, emphasizing measures relevant to graph theory, transportation, urban design, and morphology such as structure, connectedness, density, centrality, and resilience.
In the past, street network data acquisition and processing have been challenging and ad hoc.
This study illustrates the use of OSMnx and OpenStreetMap to consistently conduct street network analysis with extremely large sample sizes, with clearly defined network definitions and extents for reproducibility, and using nonplanar, directed graphs.
These street networks and measures data have been shared in a public repository for other researchers to use.
We design and implement the first private and anonymous decentralized crowdsourcing system ZebraLancer.
It realizes the fair exchange (i.e.security against malicious workers and dishonest requesters) without using any third-party arbiter.
More importantly, it overcomes two fundamental challenges of decentralization, i.e.
data leakage and identity breach.
First, our outsource-then-prove methodology resolves the critical tension between blockchain transparency and data confidentiality without sacrificing the fairness of exchange.
ZebraLancer ensures: a requester will not pay more than what data deserve, according to a policy announced when her task is published through the blockchain; each worker indeed gets a payment based on the policy, if submits data to the blockchain; the above properties are realized not only without a central arbiter, but also without leaking the data to blockchain network.
Furthermore, the blockchain transparency might allow one to infer private information of workers/requesters through their participation history.
ZebraLancer solves the problem by allowing anonymous participations without surrendering user accountability.
Specifically, workers cannot misuse anonymity to submit multiple times to reap rewards, and an anonymous requester cannot maliciously submit colluded answers to herself to repudiate payments.
The idea behind is a subtle linkability: if one authenticates twice in a task, everybody can tell, or else staying anonymous.
To realize such delicate linkability, we put forth a novel cryptographic notion, the common-prefix-linkable anonymous authentication.
Finally, we implement our protocol for a common image annotation task and deploy it in a test net of Ethereum.
The experiment results show the applicability of our protocol and highlight subtleties of tailoring the protocol to be compatible with the existing real-world open blockchain.
In this paper, we design an analytically and experimentally better online energy and job scheduling algorithm with the objective of maximizing net profit for a service provider in green data centers.
We first study the previously known algorithms and conclude that these online algorithms have provable poor performance against their worst-case scenarios.
To guarantee an online algorithm's performance in hindsight, we design a randomized algorithm to schedule energy and jobs in the data centers and prove the algorithm's expected competitive ratio in various settings.
Our algorithm is theoretical-sound and it outperforms the previously known algorithms in many settings using both real traces and simulated data.
An optimal offline algorithm is also implemented as an empirical benchmark.
A critical challenge problem of scene change detection is that noisy changes generated by varying illumination, shadows and camera viewpoint make variances of a scene difficult to define and measure since the noisy changes and semantic ones are entangled.
Following the intuitive idea of detecting changes by directly comparing dissimilarities between a pair of features, we propose a novel fully Convolutional siamese metric Network(CosimNet) to measure changes by customizing implicit metrics.
To learn more discriminative metrics, we utilize contrastive loss to reduce the distance between the unchanged feature pairs and to enlarge the distance between the changed feature pairs.
Specifically, to address the issue of large viewpoint differences, we propose Thresholded Contrastive Loss (TCL) with a more tolerant strategy to punish noisy changes.
We demonstrate the effectiveness of the proposed approach with experiments on three challenging datasets: CDnet, PCD2015, and VL-CMU-CD.
Our approach is robust to lots of challenging conditions, such as illumination changes, large viewpoint difference caused by camera motion and zooming.
In addition, we incorporate the distance metric into the segmentation framework and validate the effectiveness through visualization of change maps and feature distribution.
The source code is available at https://github.com/gmayday1997/ChangeDet.
In this paper, we proposed a novel Probabilistic Attribute Tree-CNN (PAT-CNN) to explicitly deal with the large intra-class variations caused by identity-related attributes, e.g., age, race, and gender.
Specifically, a novel PAT module with an associated PAT loss was proposed to learn features in a hierarchical tree structure organized according to attributes, where the final features are less affected by the attributes.
Then, expression-related features are extracted from leaf nodes.
Samples are probabilistically assigned to tree nodes at different levels such that expression-related features can be learned from all samples weighted by probabilities.
We further proposed a semi-supervised strategy to learn the PAT-CNN from limited attribute-annotated samples to make the best use of available data.
Experimental results on five facial expression datasets have demonstrated that the proposed PAT-CNN outperforms the baseline models by explicitly modeling attributes.
More impressively, the PAT-CNN using a single model achieves the best performance for faces in the wild on the SFEW dataset, compared with the state-of-the-art methods using an ensemble of hundreds of CNNs.
Most of the traditional work on intrinsic image decomposition rely on deriving priors about scene characteristics.
On the other hand, recent research use deep learning models as in-and-out black box and do not consider the well-established, traditional image formation process as the basis of their intrinsic learning process.
As a consequence, although current deep learning approaches show superior performance when considering quantitative benchmark results, traditional approaches are still dominant in achieving high qualitative results.
In this paper, the aim is to exploit the best of the two worlds.
A method is proposed that (1) is empowered by deep learning capabilities, (2) considers a physics-based reflection model to steer the learning process, and (3) exploits the traditional approach to obtain intrinsic images by exploiting reflectance and shading gradient information.
The proposed model is fast to compute and allows for the integration of all intrinsic components.
To train the new model, an object centered large-scale datasets with intrinsic ground-truth images are created.
The evaluation results demonstrate that the new model outperforms existing methods.
Visual inspection shows that the image formation loss function augments color reproduction and the use of gradient information produces sharper edges.
Datasets, models and higher resolution images are available at https://ivi.fnwi.uva.nl/cv/retinet.
Skin lesion segmentation is one of the first steps towards automatic Computer-Aided Diagnosis of skin cancer.
Vast variety in the appearance of the skin lesion makes this task very challenging.
The contribution of this paper is to apply a power foreground extraction technique called GrabCut for automatic skin lesion segmentation with minimal human interaction in HSV color space.
Preprocessing was performed for removing the outer black border.
Jaccard Index was measured to evaluate the performance of the segmentation method.
On average, 0.71 Jaccard Index was achieved on 1000 images from ISIC challenge 2017 Training Dataset.
This paper presents new alternatives to the well-known Bloom filter data structure.
The Bloom filter, a compact data structure supporting set insertion and membership queries, has found wide application in databases, storage systems, and networks.
Because the Bloom filter performs frequent random reads and writes, it is used almost exclusively in RAM, limiting the size of the sets it can represent.
This paper first describes the quotient filter, which supports the basic operations of the Bloom filter, achieving roughly comparable performance in terms of space and time, but with better data locality.
Operations on the quotient filter require only a small number of contiguous accesses.
The quotient filter has other advantages over the Bloom filter: it supports deletions, it can be dynamically resized, and two quotient filters can be efficiently merged.
The paper then gives two data structures, the buffered quotient filter and the cascade filter, which exploit the quotient filter advantages and thus serve as SSD-optimized alternatives to the Bloom filter.
The cascade filter has better asymptotic I/O performance than the buffered quotient filter, but the buffered quotient filter outperforms the cascade filter on small to medium data sets.
Both data structures significantly outperform recently-proposed SSD-optimized Bloom filter variants, such as the elevator Bloom filter, buffered Bloom filter, and forest-structured Bloom filter.
In experiments, the cascade filter and buffered quotient filter performed insertions 8.6-11 times faster than the fastest Bloom filter variant and performed lookups 0.94-2.56 times faster.
Autonomous sorting is a crucial task in industrial robotics which can be very challenging depending on the expected amount of automation.
Usually, to decide where to sort an object, the system needs to solve either an instance retrieval (known object) or a supervised classification (predefined set of classes) problem.
In this paper, we introduce a new decision making module, where the robotic system chooses how to sort the objects in an unsupervised way.
We call this problem Unsupervised Robotic Sorting (URS) and propose an implementation on an industrial robotic system, using deep CNN feature extraction and standard clustering algorithms.
We carry out extensive experiments on various standard datasets to demonstrate the efficiency of the proposed image clustering pipeline.
To evaluate the robustness of our URS implementation, we also introduce a complex real world dataset containing images of objects under various background and lighting conditions.
This dataset is used to fine tune the design choices (CNN and clustering algorithm) for URS.
Finally, we propose a method combining our pipeline with ensemble clustering to use multiple images of each object.
This redundancy of information about the objects is shown to increase the clustering results.
Data exchange is the problem of transforming data that is structured under a source schema into data structured under another schema, called the target schema, so that both the source and target data satisfy the relationship between the schemas.
Even though the formal framework of data exchange for relational database systems is well-established, it does not immediately carry over to the settings of temporal data, which necessitates reasoning over unbounded periods of time.
In this work, we study data exchange for temporal data.
We first motivate the need for two views of temporal data: the concrete view, which depicts how temporal data is compactly represented and on which the implementations are based, and the abstract view, which defines the semantics of temporal data as a sequence of snapshots.
We first extend the chase procedure for the abstract view to have a conceptual basis for the data exchange for temporal databases.
Considering non-temporal source-to-target tuple generating dependencies and equality generating dependencies, the chase algorithm can be applied on each snapshot independently.
Then we define a chase procedure (called c-chase) on concrete instances and show the result of c-chase on a concrete instance is semantically aligned with the result of chase on the corresponding abstract instance.
In order to interpret intervals as constants while checking if a dependency or a query is satisfied by a concrete database, we will normalize the instance with respect to the dependency or the query.
To obtain the semantic alignment, the nulls in the concrete view are annotated with temporal information.
Furthermore, we show that the result of the concrete chase provides a foundation for query answering.
We define naive evaluation on the result of the c-chase and show it produces certain answers.
Machine learning-based malware detection dominates current security defense approaches for Android apps.
However, due to the evolution of Android platforms and malware, existing such techniques are widely limited by their need for constant retraining that are costly, and reliance on new malware samples that may not be timely available.
As a result, new and emerging malware slips through, as seen from the continued surging of malware in the wild.
Thus, a more practical detector needs not only to be accurate but, more critically, to be able to sustain its capabilities over time without frequent retraining.
In this paper, we study how Android apps evolve as a population over time, in terms of their behaviors related to accesses to sensitive information and operations.
We first perform a longitudinal characterization of 6K benign and malicious apps developed across seven years, with focus on these sensitive accesses in app executions.
Our study reveals, during the long evolution, a consistent, clear differentiation between malware and benign apps regarding such accesses, measured by relative statistics of relevant method calls.
Following these findings, we developed DroidSpan, a novel classification system based on a new behavioral profile for Android apps.
Through an extensive evaluation, we showed that DroidSpan can not only effectively detect malware but sustain high detection accuracy (93% F1 measure) for four years (with 81% F1 for five years).
Through a dedicated study, we also showed its resiliency to sophisticated evasion schemes.
By comparing to a state-of-the-art malware detector, we demonstrated the largely superior sustainability of our approach at reasonable costs.
Deep generative architectures provide a way to model not only images, but also complex, 3-dimensional objects, such as point clouds.
In this work, we present a novel method to obtain meaningful representations of 3D shapes that can be used for clustering and reconstruction.
Contrary to existing methods for 3D point cloud generation that train separate decoupled models for representation learning and generation, our approach is the first end-to-end solution that allows to simultaneously learn a latent space of representation and generate 3D shape out of it.
To achieve this goal, we extend a deep Adversarial Autoencoder model (AAE) to accept 3D input and create 3D output.
Thanks to our end-to-end training regime, the resulting method called 3D Adversarial Autoencoder (3dAAE) obtains either binary or continuous latent space that covers much wider portion of training data distribution, hence allowing smooth interpolation between the shapes.
Finally, our extensive quantitative evaluation shows that 3dAAE provides state-of-the-art results on a set of benchmark tasks.
In this paper, we derive a temporal arbitrage policy for storage via reinforcement learning.
Real-time price arbitrage is an important source of revenue for storage units, but designing good strategies have proven to be difficult because of the highly uncertain nature of the prices.
Instead of current model predictive or dynamic programming approaches, we use reinforcement learning to design an optimal arbitrage policy.
This policy is learned through repeated charge and discharge actions performed by the storage unit through updating a value matrix.
We design a reward function that does not only reflect the instant profit of charge/discharge decisions but also incorporate the history information.
Simulation results demonstrate that our designed reward function leads to significant performance improvement compared with existing algorithms.
We introduce and analyze different strategies for the parallel-in-time integration method PFASST to recover from hard faults and subsequent data loss.
Since PFASST stores solutions at multiple time steps on different processors, information from adjacent steps can be used to recover after a processor has failed.
PFASST's multi-level hierarchy allows to use the coarse level for correcting the reconstructed solution, which can help to minimize overhead.
A theoretical model is devised linking overhead to the number of additional PFASST iterations required for convergence after a fault.
The potential efficiency of different strategies is assessed in terms of required additional iterations for examples of diffusive and advective type.
Large scale decentralized systems, such as P2P, sensor or IoT device networks are becoming increasingly common, and require robust protocols to address the challenges posed by the distribution of data and the large number of peers belonging to the network.
In this paper, we deal with the problem of mining frequent items in unstructured P2P networks.
This problem, of practical importance, has many useful applications.
We design P2PSS, a fully decentralized, gossip--based protocol for frequent items discovery, leveraging the Space-Saving algorithm.
We formally prove the correctness and theoretical error bound.
Extensive experimental results clearly show that P2PSS provides very good accuracy and scalability, also in the presence of highly dynamic P2P networks with churning.
To the best of our knowledge, this is the first gossip--based distributed algorithm providing strong theoretical guarantees for both the Approximate Frequent Items Problem in Unstructured P2P Networks and for the frequency estimation of discovered frequent items.
Mechanical learning is a computing system that is based on a set of simple and fixed rules, and can learn from incoming data.
A learning machine is a system that realizes mechanical learning.
Importantly, we emphasis that it is based on a set of simple and fixed rules, contrasting to often called machine learning that is sophisticated software based on very complicated mathematical theory, and often needs human intervene for software fine tune and manual adjustments.
Here, we discuss some basic facts and principles of such system, and try to lay down a framework for further study.
We propose 2 directions to approach mechanical learning, just like Church-Turing pair: one is trying to realize a learning machine, another is trying to well describe the mechanical learning.
In an increasing number of domains it has been demonstrated that deep learning models can be trained using relatively large batch sizes without sacrificing data efficiency.
However the limits of this massive data parallelism seem to differ from domain to domain, ranging from batches of tens of thousands in ImageNet to batches of millions in RL agents that play the game Dota 2.
To our knowledge there is limited conceptual understanding of why these limits to batch size differ or how we might choose the correct batch size in a new domain.
In this paper, we demonstrate that a simple and easy-to-measure statistic called the gradient noise scale predicts the largest useful batch size across many domains and applications, including a number of supervised learning datasets (MNIST, SVHN, CIFAR-10, ImageNet, Billion Word), reinforcement learning domains (Atari and Dota), and even generative model training (autoencoders on SVHN).
We find that the noise scale increases as the loss decreases over a training run and depends on the model size primarily through improved model performance.
Our empirically-motivated theory also describes the tradeoff between compute-efficiency and time-efficiency, and provides a rough model of the benefits of adaptive batch-size training.
Human face analysis is an important task in computer vision.
According to cognitive-psychological studies, facial dynamics could provide crucial cues for face analysis.
The motion of a facial local region in facial expression is related to the motion of other facial local regions.
In this paper, a novel deep learning approach, named facial dynamics interpreter network, has been proposed to interpret the important relations between local dynamics for estimating facial traits from expression sequence.
The facial dynamics interpreter network is designed to be able to encode a relational importance, which is used for interpreting the relation between facial local dynamics and estimating facial traits.
By comparative experiments, the effectiveness of the proposed method has been verified.
The important relations between facial local dynamics are investigated by the proposed facial dynamics interpreter network in gender classification and age estimation.
Moreover, experimental results show that the proposed method outperforms the state-of-the-art methods in gender classification and age estimation.
Most existing approaches to training object detectors rely on fully supervised learning, which requires the tedious manual annotation of object location in a training set.
Recently there has been an increasing interest in developing weakly supervised approach to detector training where the object location is not manually annotated but automatically determined based on binary (weak) labels indicating if a training image contains the object.
This is a challenging problem because each image can contain many candidate object locations which partially overlaps the object of interest.
Existing approaches focus on how to best utilise the binary labels for object location annotation.
In this paper we propose to solve this problem from a very different perspective by casting it as a transfer learning problem.
Specifically, we formulate a novel transfer learning based on learning to rank, which effectively transfers a model for automatic annotation of object location from an auxiliary dataset to a target dataset with completely unrelated object categories.
We show that our approach outperforms existing state-of-the-art weakly supervised approach to annotating objects in the challenging VOC dataset.
In this paper we study the facility leasing problem with penalties.
We present a primal-dual algorithm which is a 3-approximation, based on the algorithm by Nagarajan and Williamson for the facility leasing problem and on the algorithm by Charikar et al. for the facility location problem with penalties.
In this paper, we present a GPU implementation of a two-dimensional shallow water model.
Water simulations are useful for modeling floods, river/reservoir behavior, and dam break scenarios.
Our GPU implementation shows vast performance improvements over the original Fortran implementation.
By taking advantage of the GPU, researchers and engineers will be able to study water systems more efficiently and in greater detail.
This paper presents a practical approach towards implementing pathfinding algorithms on real-world and low-cost non- commercial hardware platforms.
While using robotics simulation platforms as a test-bed for our algorithms we easily overlook real- world exogenous problems that are developed by external factors.
Such problems involve robot wheel slips, asynchronous motors, abnormal sensory data or unstable power sources.
The real-world dynamics tend to be very painful even for executing simple algorithms like a Wavefront planner or A-star search.
This paper addresses designing techniques that tend to be robust as well as reusable for any hardware platforms; covering problems like controlling asynchronous drives, odometry offset issues and handling abnormal sensory feedback.
The algorithm implementation medium and hardware design tools have been kept general in order to present our work as a serving platform for future researchers and robotics enthusiast working in the field of path planning robotics.
In this paper, we study the problem of controlling a two-dimensional robotic swarm with the purpose of achieving high level and complex spatio-temporal patterns.
We use a rich spatio-temporal logic that is capable of describing a wide range of time varying and complex spatial configurations, and develop a method to encode such formal specifications as a set of mixed integer linear constraints, which are incorporated into a mixed integer linear programming problem.
We plan trajectories for each individual robot such that the whole swarm satisfies the spatio-temporal requirements, while optimizing total robot movement and/or a metric that shows how strongly the swarm trajectory resembles given spatio-temporal behaviors.
An illustrative case study is included.
We present the first formal algebraic specification of a hypertext reference model.
It is based on the well-known Dexter Hypertext Reference Model and includes modifications with respect to the development of hypertext since the WWW came up.
Our hypertext model was developed as a product model with the aim to automatically support the design process and is extended to a model of hypertext-systems in order to be able to describe the state transitions in this process.
While the specification should be easy to read for non-experts in algebraic specification, it guarantees a unique understanding and enables a close connection to logic-based development and verification.
We characterize the statistical bootstrap for the estimation of information-theoretic quantities from data, with particular reference to its use in the study of large-scale social phenomena.
Our methods allow one to preserve, approximately, the underlying axiomatic relationships of information theory---in particular, consistency under arbitrary coarse-graining---that motivate use of these quantities in the first place, while providing reliability comparable to the state of the art for Bayesian estimators.
We show how information-theoretic quantities allow for rigorous empirical study of the decision-making capacities of rational agents and the time-asymmetric flows of information in distributed systems.
We provide illustrative examples by reference to ongoing collaborative work on the semantic structure of the British Criminal Court system and the conflict dynamics of the contemporary Afghanistan insurgency.
Prices of NAND flash memories are falling drastically due to market growth and fabrication process mastering while research efforts from a technological point of view in terms of endurance and density are very active.
NAND flash memories are becoming the most important storage media in mobile computing and tend to be less confined to this area.
The major constraint of such a technology is the limited number of possible erase operations per block which tend to quickly provoke memory wear out.
To cope with this issue, state-of-the-art solutions implement wear leveling policies to level the wear out of the memory and so increase its lifetime.
These policies are integrated into the Flash Translation Layer (FTL) and greatly contribute in decreasing the write performance.
In this paper, we propose to reduce the flash memory wear out problem and improve its performance by absorbing the erase operations throughout a dual cache system replacing FTL wear leveling and garbage collection services.
We justify this idea by proposing a first performance evaluation of an exclusively cache based system for embedded flash memories.
Unlike wear leveling schemes, the proposed cache solution reduces the total number of erase operations reported on the media by absorbing them in the cache for workloads expressing a minimal global sequential rate.
Social media has played an important role in shaping political discourse over the last decade.
At the same time, it is often perceived to have increased political polarization, thanks to the scale of discussions and their public nature.
In this paper, we try to answer the question of whether political polarization in the US on Twitter has increased over the last eight years.
We analyze a large longitudinal Twitter dataset of 679,000 users and look at signs of polarization in their (i) network - how people follow political and media accounts, (ii) tweeting behavior - whether they retweet content from both sides, and (iii) content - how partisan the hashtags they use are.
Our analysis shows that online polarization has indeed increased over the past eight years and that, depending on the measure, the relative change is 10%-20%.
Our study is one of very few with such a long-term perspective, encompassing two US presidential elections and two mid-term elections, providing a rare longitudinal analysis.
The popularity of Tor as an anonymity system has made it a popular target for a variety of attacks.
We focus on traffic correlation attacks, which are no longer solely in the realm of academic research with recent revelations about the NSA and GCHQ actively working to implement them in practice.
Our first contribution is an empirical study that allows us to gain a high fidelity snapshot of the threat of traffic correlation attacks in the wild.
We find that up to 40% of all circuits created by Tor are vulnerable to attacks by traffic correlation from Autonomous System (AS)-level adversaries, 42% from colluding AS-level adversaries, and 85% from state-level adversaries.
In addition, we find that in some regions (notably, China and Iran) there exist many cases where over 95% of all possible circuits are vulnerable to correlation attacks, emphasizing the need for AS-aware relay-selection.
To mitigate the threat of such attacks, we build Astoria--an AS-aware Tor client.
Astoria leverages recent developments in network measurement to perform path-prediction and intelligent relay selection.
Astoria reduces the number of vulnerable circuits to 2% against AS-level adversaries, under 5% against colluding AS-level adversaries, and 25% against state-level adversaries.
In addition, Astoria load balances across the Tor network so as to not overload any set of relays.
Algorithms for many hypergraph problems, including partitioning, utilize multilevel frameworks to achieve a good trade-off between the performance and the quality of results.
In this paper we introduce two novel aggregative coarsening schemes and incorporate them within state-of-the-art hypergraph partitioner Zoltan.
Our coarsening schemes are inspired by the algebraic multigrid and stable matching approaches.
We demonstrate the effectiveness of the developed schemes as a part of multilevel hypergraph partitioning framework on a wide range of problems.
In many real-world applications, data are often collected in the form of stream, and thus the distribution usually changes in nature, which is referred as concept drift in literature.
We propose a novel and effective approach to handle concept drift via model reuse, leveraging previous knowledge by reusing models.
Each model is associated with a weight representing its reusability towards current data, and the weight is adaptively adjusted according to the model performance.
We provide generalization and regret analysis.
Experimental results also validate the superiority of our approach on both synthetic and real-world datasets.
Associative memories store content in such a way that the content can be later retrieved by presenting the memory with a small portion of the content, rather than presenting the memory with an address as in more traditional memories.
Associative memories are used as building blocks for algorithms within database engines, anomaly detection systems, compression algorithms, and face recognition systems.
A classical example of an associative memory is the Hopfield neural network.
Recently, Gripon and Berrou have introduced an alternative construction which builds on ideas from the theory of error correcting codes and which greatly outperforms the Hopfield network in capacity, diversity, and efficiency.
In this paper we implement a variation of the Gripon-Berrou associative memory on a general purpose graphical processing unit (GPU).
The work of Gripon and Berrou proposes two retrieval rules, sum-of-sum and sum-of-max.
The sum-of-sum rule uses only matrix-vector multiplication and is easily implemented on the GPU.
The sum-of-max rule is much less straightforward to implement because it involves non-linear operations.
However, the sum-of-max rule gives significantly better retrieval error rates.
We propose a hybrid rule tailored for implementation on a GPU which achieves a 880-fold speedup without sacrificing any accuracy.
We consider the problem of locating a black hole in synchronous anonymous networks using finite state agents.
A black hole is a harmful node in the network that destroys any agent visiting that node without leaving any trace.
The objective is to locate the black hole without destroying too many agents.
This is difficult to achieve when the agents are initially scattered in the network and are unaware of the location of each other.
Previous studies for black hole search used more powerful models where the agents had non-constant memory, were labelled with distinct identifiers and could either write messages on the nodes of the network or mark the edges of the network.
In contrast, we solve the problem using a small team of finite-state agents each carrying a constant number of identical tokens that could be placed on the nodes of the network.
Thus, all resources used in our algorithms are independent of the network size.
We restrict our attention to oriented torus networks and first show that no finite team of finite state agents can solve the problem in such networks, when the tokens are not movable.
In case the agents are equipped with movable tokens, we determine lower bounds on the number of agents and tokens required for solving the problem in torus networks of arbitrary size.
Further, we present a deterministic solution to the black hole search problem for oriented torus networks, using the minimum number of agents and tokens.
The ability of having a sparse representation for a certain class of signals has many applications in data analysis, image processing, and other research fields.
Among sparse representations, the cosparse analysis model has recently gained increasing interest.
Many signals exhibit a multidimensional structure, e.g. images or three-dimensional MRI scans.
Most data analysis and learning algorithms use vectorized signals and thereby do not account for this underlying structure.
The drawback of not taking the inherent structure into account is a dramatic increase in computational cost.
We propose an algorithm for learning a cosparse Analysis Operator that adheres to the preexisting structure of the data, and thus allows for a very efficient implementation.
This is achieved by enforcing a separable structure on the learned operator.
Our learning algorithm is able to deal with multidimensional data of arbitrary order.
We evaluate our method on volumetric data at the example of three-dimensional MRI scans.
The combination of aerial survey capabilities of Unmanned Aerial Vehicles with targeted intervention abilities of agricultural Unmanned Ground Vehicles can significantly improve the effectiveness of robotic systems applied to precision agriculture.
In this context, building and updating a common map of the field is an essential but challenging task.
The maps built using robots of different types show differences in size, resolution and scale, the associated geolocation data may be inaccurate and biased, while the repetitiveness of both visual appearance and geometric structures found within agricultural contexts render classical map merging techniques ineffective.
In this paper we propose AgriColMap, a novel map registration pipeline for that leverages a grid-based multi-modal environment representation which includes a vegetation index map and a Digital Surface Model.
We cast the data association problem between maps built from UAVs and UGVs as a multi-modal, large displacement dense optical flow estimation.
The dominant, coherent flows, selected using a voting scheme, are used as point-to-point correspondences to infer a preliminary non-rigid alignment between the maps.
A final refinement is then performed, by exploiting only meaningful parts of the registered maps.
We evaluate our system using real world data for 3 fields with different crop species.
The results show that our method outperforms several state of the art map registration and matching techniques by a large margin, and has a higher tolerance to large initial misalignments.
We release an implementation of the proposed approach along with the acquired datasets with this paper.
The use of future contextual information is typically shown to be helpful for acoustic modeling.
Recently, we proposed a RNN model called minimal gated recurrent unit with input projection (mGRUIP), in which a context module namely temporal convolution, is specifically designed to model the future context.
This model, mGRUIP with context module (mGRUIP-Ctx), has been shown to be able of utilizing the future context effectively, meanwhile with quite low model latency and computation cost.
In this paper, we continue to improve mGRUIP-Ctx with two revisions: applying BN methods and enlarging model context.
Experimental results on two Mandarin ASR tasks (8400 hours and 60K hours) show that, the revised mGRUIP-Ctx outperform LSTM with a large margin (11% to 38%).
It even performs slightly better than a superior BLSTM on the 8400h task, with 33M less parameters and just 290ms model latency.
We present a new parallel algorithm for solving triangular systems with multiple right hand sides (TRSM).
TRSM is used extensively in numerical linear algebra computations, both to solve triangular linear systems of equations as well as to compute factorizations with triangular matrices, such as Cholesky, LU, and QR.
Our algorithm achieves better theoretical scalability than known alternatives, while maintaining numerical stability, via selective use of triangular matrix inversion.
We leverage the fact that triangular inversion and matrix multiplication are more parallelizable than the standard TRSM algorithm.
By only inverting triangular blocks along the diagonal of the initial matrix, we generalize the usual way of TRSM computation and the full matrix inversion approach.
This flexibility leads to an efficient algorithm for any ratio of the number of right hand sides to the triangular matrix dimension.
We provide a detailed communication cost analysis for our algorithm as well as for the recursive triangular matrix inversion.
This cost analysis makes it possible to determine optimal block sizes and processor grids a priori.
Relative to the best known algorithms for TRSM, our approach can require asymptotically fewer messages, while performing optimal amounts of computation and communication in terms of words sent.
Detecting fake users (also called Sybils) in online social networks is a basic security research problem.
State-of-the-art approaches rely on a large amount of manually labeled users as a training set.
These approaches suffer from three key limitations: 1) it is time-consuming and costly to manually label a large training set, 2) they cannot detect new Sybils in a timely fashion, and 3) they are vulnerable to Sybil attacks that leverage information of the training set.
In this work, we propose SybilBlind, a structure-based Sybil detection framework that does not rely on a manually labeled training set.
SybilBlind works under the same threat model as state-of-the-art structure-based methods.
We demonstrate the effectiveness of SybilBlind using 1) a social network with synthetic Sybils and 2) two Twitter datasets with real Sybils.
For instance, SybilBlind achieves an AUC of 0.98 on a Twitter dataset.
Planning in partially observable Markov decision processes (POMDPs) remains a challenging topic in the artificial intelligence community, in spite of recent impressive progress in approximation techniques.
Previous research has indicated that online planning approaches are promising in handling large-scale POMDP domains efficiently as they make decisions "on demand" instead of proactively for the entire state space.
We present a Factored Hybrid Heuristic Online Planning (FHHOP) algorithm for large POMDPs.
FHHOP gets its power by combining a novel hybrid heuristic search strategy with a recently developed factored state representation.
On several benchmark problems, FHHOP substantially outperformed state-of-the-art online heuristic search approaches in terms of both scalability and quality.
In languages like C, buffer overflows are widespread.
A common mitigation technique is to use tools that detect them during execution and abort the program to prevent the leakage of data or the diversion of control flow.
However, for server applications, it would be desirable to prevent such errors while maintaining availability of the system.
To this end, we present an approach to handle buffer overflows without aborting the program.
This approach involves implementing a continuation logic in library functions based on an introspection function that allows querying the size of a buffer.
We demonstrate that introspection can be implemented in popular bug-finding and bug-mitigation tools such as LLVM's AddressSanitizer, SoftBound, and Intel-MPX-based bounds checking.
We evaluated our approach in a case study of real-world bugs and show that for tools that explicitly track bounds data, introspection results in a low performance overhead.
We describe an XML file format for storing data from computations in algebra and geometry.
We also present a formal specification based on a RELAX-NG schema.
Machine learning and data mining algorithms are becoming increasingly important in analyzing large volume, multi-relational and multi--modal datasets, which are often conveniently represented as multiway arrays or tensors.
It is therefore timely and valuable for the multidisciplinary research community to review tensor decompositions and tensor networks as emerging tools for large-scale data analysis and data mining.
We provide the mathematical and graphical representations and interpretation of tensor networks, with the main focus on the Tucker and Tensor Train (TT) decompositions and their extensions or generalizations.
Keywords: Tensor networks, Function-related tensors, CP decomposition, Tucker models, tensor train (TT) decompositions, matrix product states (MPS), matrix product operators (MPO), basic tensor operations, multiway component analysis, multilinear blind source separation, tensor completion, linear/multilinear dimensionality reduction, large-scale optimization problems, symmetric eigenvalue decomposition (EVD), PCA/SVD, huge systems of linear equations, pseudo-inverse of very large matrices, Lasso and Canonical Correlation Analysis (CCA) (This is Part 1)
The number of methods available for classification of multi-label data has increased rapidly over recent years, yet relatively few links have been made with the related task of classification of sequential data.
If labels indices are considered as time indices, the problems can often be seen as equivalent.
In this paper we detect and elaborate on connections between multi-label methods and Markovian models, and study the suitability of multi-label methods for prediction in sequential data.
From this study we draw upon the most suitable techniques from the area and develop two novel competitive approaches which can be applied to either kind of data.
We carry out an empirical evaluation investigating performance on real-world sequential-prediction tasks: electricity demand, and route prediction.
As well as showing that several popular multi-label algorithms are in fact easily applicable to sequencing tasks, our novel approaches, which benefit from a unified view of these areas, prove very competitive against established methods.
Deploying deep neural networks on mobile devices is a challenging task.
Current model compression methods such as matrix decomposition effectively reduce the deployed model size, but still cannot satisfy real-time processing requirement.
This paper first discovers that the major obstacle is the excessive execution time of non-tensor layers such as pooling and normalization without tensor-like trainable parameters.
This motivates us to design a novel acceleration framework: DeepRebirth through "slimming" existing consecutive and parallel non-tensor and tensor layers.
The layer slimming is executed at different substructures: (a) streamline slimming by merging the consecutive non-tensor and tensor layer vertically; (b) branch slimming by merging non-tensor and tensor branches horizontally.
The proposed optimization operations significantly accelerate the model execution and also greatly reduce the run-time memory cost since the slimmed model architecture contains less hidden layers.
To maximally avoid accuracy loss, the parameters in new generated layers are learned with layer-wise fine-tuning based on both theoretical analysis and empirical verification.
As observed in the experiment, DeepRebirth achieves more than 3x speed-up and 2.5x run-time memory saving on GoogLeNet with only 0.4% drop of top-5 accuracy on ImageNet.
Furthermore, by combining with other model compression techniques, DeepRebirth offers an average of 65ms inference time on the CPU of Samsung Galaxy S6 with 86.5% top-5 accuracy, 14% faster than SqueezeNet which only has a top-5 accuracy of 80.5%.
Vector-space word representations obtained from neural network models have been shown to enable semantic operations based on vector arithmetic.
In this paper, we explore the existence of similar information on vector representations of images.
For that purpose we define a methodology to obtain large, sparse vector representations of image classes, and generate vectors through the state-of-the-art deep learning architecture GoogLeNet for 20K images obtained from ImageNet.
We first evaluate the resultant vector-space semantics through its correlation with WordNet distances, and find vector distances to be strongly correlated with linguistic semantics.
We then explore the location of images within the vector space, finding elements close in WordNet to be clustered together, regardless of significant visual variances (e.g.118 dog types).
More surprisingly, we find that the space unsupervisedly separates complex classes without prior knowledge (e.g. living things).
Afterwards, we consider vector arithmetics.
Although we are unable to obtain meaningful results on this regard, we discuss the various problem we encountered, and how we consider to solve them.
Finally, we discuss the impact of our research for cognitive systems, focusing on the role of the architecture being used.
This paper presents the release of EmojiNet, the largest machine-readable emoji sense inventory that links Unicode emoji representations to their English meanings extracted from the Web.
EmojiNet is a dataset consisting of: (i) 12,904 sense labels over 2,389 emoji, which were extracted from the web and linked to machine-readable sense definitions seen in BabelNet, (ii) context words associated with each emoji sense, which are inferred through word embedding models trained over Google News corpus and a Twitter message corpus for each emoji sense definition, and (iii) recognizing discrepancies in the presentation of emoji on different platforms, specification of the most likely platform-based emoji sense for a selected set of emoji.
The dataset is hosted as an open service with a REST API and is available at http://emojinet.knoesis.org/.
The development of this dataset, evaluation of its quality, and its applications including emoji sense disambiguation and emoji sense similarity are discussed.
In this letter, we propose a control framework for human-in-the-loop systems, in which many human decision makers are involved in the feedback loop composed of a plant and a controller.
The novelty of the framework is that the decision makers are weakly controlled; in other words, they receive a set of admissible control actions from the controller and choose one of them in accordance with their private preferences.
For example, the decision makers can decide their actions to minimize their own costs or by simply relying on their experience and intuition.
A class of controllers which output set-valued signals is proposed, and it is shown that the overall control system is stable independently of the decisions made by the humans.
Finally, a learning algorithm is applied to the controller that updates the controller parameters to reduce the achievable minimal costs for the decision makers.
Effective use of the algorithm is demonstrated in a numerical experiment.
A Hamilton cycle is a cycle containing every vertex of a graph.
A graph is called Hamiltonian if it contains a Hamilton cycle.
The Hamilton cycle problem is to find the sufficient and necessary condition that a graph is Hamiltonian.
In this paper, we give out some new kind of definitions of the subgraphs and determine the Hamiltoncity of edges according to the existence of the subgraphs in a graph, and then obtain a new property of Hamilton graphs as being a necessary and sufficient condition characterized in the connectivity of the subgraph that induced from the cycle structure of a given graph.
Transfer in reinforcement learning refers to the notion that generalization should occur not only within a task but also across tasks.
We propose a transfer framework for the scenario where the reward function changes between tasks but the environment's dynamics remain the same.
Our approach rests on two key ideas: "successor features", a value function representation that decouples the dynamics of the environment from the rewards, and "generalized policy improvement", a generalization of dynamic programming's policy improvement operation that considers a set of policies rather than a single one.
Put together, the two ideas lead to an approach that integrates seamlessly within the reinforcement learning framework and allows the free exchange of information across tasks.
The proposed method also provides performance guarantees for the transferred policy even before any learning has taken place.
We derive two theorems that set our approach in firm theoretical ground and present experiments that show that it successfully promotes transfer in practice, significantly outperforming alternative methods in a sequence of navigation tasks and in the control of a simulated robotic arm.
Domain adaptation aims to learn models on a supervised source domain that perform well on an unsupervised target.
Prior work has examined domain adaptation in the context of stationary domain shifts, i.e. static data sets.
However, with large-scale or dynamic data sources, data from a defined domain is not usually available all at once.
For instance, in a streaming data scenario, dataset statistics effectively become a function of time.
We introduce a framework for adaptation over non-stationary distribution shifts applicable to large-scale and streaming data scenarios.
The model is adapted sequentially over incoming unsupervised streaming data batches.
This enables improvements over several batches without the need for any additionally annotated data.
To demonstrate the effectiveness of our proposed framework, we modify associative domain adaptation to work well on source and target data batches with unequal class distributions.
We apply our method to several adaptation benchmark datasets for classification and show improved classifier accuracy not only for the currently adapted batch, but also when applied on future stream batches.
Furthermore, we show the applicability of our associative learning modifications to semantic segmentation, where we achieve competitive results.
We present a novel efficient object detection and localization framework based on the probabilistic bisection algorithm.
A Convolutional Neural Network (CNN) is trained and used as a noisy oracle that provides answers to input query images.
The responses along with error probability estimates obtained from the CNN are used to update beliefs on the object location along each dimension.
We show that querying along each dimension achieves the same lower bound on localization error as the joint query design.
Finally, we compare our approach to the traditional sliding window technique on a real world face localization task and show speed improvements by at least an order of magnitude while maintaining accurate localization.
This paper considers the problem of removing costly features from a Bayesian network classifier.
We want the classifier to be robust to these changes, and maintain its classification behavior.
To this end, we propose a closeness metric between Bayesian classifiers, called the expected classification agreement (ECA).
Our corresponding trimming algorithm finds an optimal subset of features and a new classification threshold that maximize the expected agreement, subject to a budgetary constraint.
It utilizes new theoretical insights to perform branch-and-bound search in the space of feature sets, while computing bounds on the ECA.
Our experiments investigate both the runtime cost of trimming and its effect on the robustness and accuracy of the final classifier.
In the process of knowledge discovery and representation in large datasets using formal concept analysis, complexity plays a major role in identifying all the formal concepts and constructing the concept lattice(digraph of the concepts).
For identifying the formal concepts and constructing the digraph from the identified concepts in very large datasets, various distributed algorithms are available in the literature.
However, the existing distributed algorithms are not very well suitable for concept generation because it is an iterative process.
The existing algorithms are implemented using distributed frameworks like MapReduce and Open MP, these frameworks are not appropriate for iterative applications.
Hence, in this paper we proposed efficient distributed algorithms for both formal concept generation and concept lattice digraph construction in large formal contexts using Apache Spark.
Various performance metrics are considered for the evaluation of the proposed work, the results of the evaluation proves that the proposed algorithms are efficient for concept generation and lattice graph construction in comparison with the existing algorithms.
We present an algorithm to generate synthetic datasets of tunable difficulty on classification of Morse code symbols for supervised machine learning problems, in particular, neural networks.
The datasets are spatially one-dimensional and have a small number of input features, leading to high density of input information content.
This makes them particularly challenging when implementing network complexity reduction methods.
We explore how network performance is affected by deliberately adding various forms of noise and expanding the feature set and dataset size.
Finally, we establish several metrics to indicate the difficulty of a dataset, and evaluate their merits.
The algorithm and datasets are open-source.
We propose CornerNet, a new approach to object detection where we detect an object bounding box as a pair of keypoints, the top-left corner and the bottom-right corner, using a single convolution neural network.
By detecting objects as paired keypoints, we eliminate the need for designing a set of anchor boxes commonly used in prior single-stage detectors.
In addition to our novel formulation, we introduce corner pooling, a new type of pooling layer that helps the network better localize corners.
Experiments show that CornerNet achieves a 42.1% AP on MS COCO, outperforming all existing one-stage detectors.
Exiting deep-learning based dense stereo matching methods often rely on ground-truth disparity maps as the training signals, which are however not always available in many situations.
In this paper, we design a simple convolutional neural network architecture that is able to learn to compute dense disparity maps directly from the stereo inputs.
Training is performed in an end-to-end fashion without the need of ground-truth disparity maps.
The idea is to use image warping error (instead of disparity-map residuals) as the loss function to drive the learning process, aiming to find a depth-map that minimizes the warping error.
While this is a simple concept well-known in stereo matching, to make it work in a deep-learning framework, many non-trivial challenges must be overcome, and in this work we provide effective solutions.
Our network is self-adaptive to different unseen imageries as well as to different camera settings.
Experiments on KITTI and Middlebury stereo benchmark datasets show that our method outperforms many state-of-the-art stereo matching methods with a margin, and at the same time significantly faster.
Representation learning is an essential problem in a wide range of applications and it is important for performing downstream tasks successfully.
In this paper, we propose a new model that learns coupled representations of domains, intents, and slots by taking advantage of their hierarchical dependency in a Spoken Language Understanding system.
Our proposed model learns the vector representation of intents based on the slots tied to these intents by aggregating the representations of the slots.
Similarly, the vector representation of a domain is learned by aggregating the representations of the intents tied to a specific domain.
To the best of our knowledge, it is the first approach to jointly learning the representations of domains, intents, and slots using their hierarchical relationships.
The experimental results demonstrate the effectiveness of the representations learned by our model, as evidenced by improved performance on the contextual cross-domain reranking task.
In this paper, optimal filter design for generalized frequency-division multiplexing (GFDM) is considered under two design criteria: rate maximization and out-of-band (OOB) emission minimization.
First, the problem of GFDM filter optimization for rate maximization is formulated by expressing the transmission rate of GFDM as a function of GFDM filter coefficients.
It is shown that Dirichlet filters are rate-optimal in additive white Gaussian noise (AWGN) channels with no carrier frequency offset (CFO) under linear zero-forcing (ZF) or minimum mean-square error (MMSE) receivers, but in general channels perturbed by CFO a properly designed nontrivial GFDM filter can yield better performance than Dirichlet filters by adjusting the subcarrier waveform to cope with the channel-induced CFO.
Next, the problem of GFDM filter design for OOB emission minimization is formulated by expressing the power spectral density (PSD) of the GFDM transmit signal as a function of GFDM filter coefficients, and it is shown that the OOB emission can be reduced significantly by designing the GFDM filter properly.
Finally, joint design of GFDM filter and window for the two design criteria is considered.
For enhancing the privacy protections of databases, where the increasing amount of detailed personal data is stored and processed, multiple mechanisms have been developed, such as audit logging and alert triggers, which notify administrators about suspicious activities; however, the two main limitations in common are: 1) the volume of such alerts is often substantially greater than the capabilities of resource-constrained organizations, and 2) strategic attackers may disguise their actions or carefully choosing which records they touch, making incompetent the statistical detection models.
For solving them, we introduce a novel approach to database auditing that explicitly accounts for adversarial behavior by 1) prioritizing the order in which types of alerts are investigated and 2) providing an upper bound on how much resource to allocate for each type.
We model the interaction between a database auditor and potential attackers as a Stackelberg game in which the auditor chooses an auditing policy and attackers choose which records to target.
A corresponding approach combining linear programming, column generation, and heuristic search is proposed to derive an auditing policy.
For testing the policy-searching performance, a publicly available credit card application dataset are adopted, on which it shows that our methods produce high-quality mixed strategies as database audit policies, and our general approach significantly outperforms non-game-theoretic baselines.
We discuss the scheduling of a set of networked control systems implemented over a shared communication network.
Each control loop is described by a linear-time-invariant (LTI) system with an event-triggered implementation.
We assume the network can be used by at most one control loop at any time instant and after each controller update, a pre-defined channel occupancy time elapses before the network is available.
In our framework we offer the scheduler two options to avoid conflicts: using the event-triggering mechanism, where the scheduler can choose the triggering coefficient; or forcing controller updates at an earlier pre-defined time.
Our objective is avoiding communication conflict while guaranteeing stability of all control loops.
We formulate the original scheduling problem as a control synthesis problem over a network of timed game automata (NTGA) with a safety objective.
The NTGA is obtained by taking the parallel composition of the timed game automata (TGA) associated with the network and with all control loops.
The construction of TGA associated with control loops leverages recent results on the abstraction of timing models of event-triggered LTI systems.
In our problem, the safety objective is to avoid that update requests from a control loop happen while the network is in use by another task.
We showcase the results in some examples.
In this paper, we consider the task of learning control policies for text-based games.
In these games, all interactions in the virtual world are through text and the underlying state is not observed.
The resulting language barrier makes such environments challenging for automatic game players.
We employ a deep reinforcement learning framework to jointly learn state representations and action policies using game rewards as feedback.
This framework enables us to map text descriptions into vector representations that capture the semantics of the game states.
We evaluate our approach on two game worlds, comparing against baselines using bag-of-words and bag-of-bigrams for state representations.
Our algorithm outperforms the baselines on both worlds demonstrating the importance of learning expressive representations.
Traditional face editing methods often require a number of sophisticated and task specific algorithms to be applied one after the other --- a process that is tedious, fragile, and computationally intensive.
In this paper, we propose an end-to-end generative adversarial network that infers a face-specific disentangled representation of intrinsic face properties, including shape (i.e. normals), albedo, and lighting, and an alpha matte.
We show that this network can be trained on "in-the-wild" images by incorporating an in-network physically-based image formation module and appropriate loss functions.
Our disentangling latent representation allows for semantically relevant edits, where one aspect of facial appearance can be manipulated while keeping orthogonal properties fixed, and we demonstrate its use for a number of facial editing applications.
Stock market forecasting is very important in the planning of business activities.
Stock price prediction has attracted many researchers in multiple disciplines including computer science, statistics, economics, finance, and operations research.
Recent studies have shown that the vast amount of online information in the public domain such as Wikipedia usage pattern, news stories from the mainstream media, and social media discussions can have an observable effect on investors opinions towards financial markets.
The reliability of the computational models on stock market prediction is important as it is very sensitive to the economy and can directly lead to financial loss.
In this paper, we retrieved, extracted, and analyzed the effects of news sentiments on the stock market.
Our main contributions include the development of a sentiment analysis dictionary for the financial sector, the development of a dictionary-based sentiment analysis model, and the evaluation of the model for gauging the effects of news sentiments on stocks for the pharmaceutical market.
Using only news sentiments, we achieved a directional accuracy of 70.59% in predicting the trends in short-term stock price movement.
Deep convolutional neural networks have recently achieved state-of-the-art performance on a number of image recognition benchmarks, including the ImageNet Large-Scale Visual Recognition Challenge (ILSVRC-2012).
The winning model on the localization sub-task was a network that predicts a single bounding box and a confidence score for each object category in the image.
Such a model captures the whole-image context around the objects but cannot handle multiple instances of the same object in the image without naively replicating the number of outputs for each instance.
In this work, we propose a saliency-inspired neural network model for detection, which predicts a set of class-agnostic bounding boxes along with a single score for each box, corresponding to its likelihood of containing any object of interest.
The model naturally handles a variable number of instances for each class and allows for cross-class generalization at the highest levels of the network.
We are able to obtain competitive recognition performance on VOC2007 and ILSVRC2012, while using only the top few predicted locations in each image and a small number of neural network evaluations.
Motivated by a growing market that involves buying and selling data over the web, we study pricing schemes that assign value to queries issued over a database.
Previous work studied pricing mechanisms that compute the price of a query by extending a data seller's explicit prices on certain queries, or investigated the properties that a pricing function should exhibit without detailing a generic construction.
In this work, we present a formal framework for pricing queries over data that allows the construction of general families of pricing functions, with the main goal of avoiding arbitrage.
We consider two types of pricing schemes: instance-independent schemes, where the price depends only on the structure of the query, and answer-dependent schemes, where the price also depends on the query output.
Our main result is a complete characterization of the structure of pricing functions in both settings, by relating it to properties of a function over a lattice.
We use our characterization, together with information-theoretic methods, to construct a variety of arbitrage-free pricing functions.
Finally, we discuss various tradeoffs in the design space and present techniques for efficient computation of the proposed pricing functions.
While one of the first steps in many NLP systems is selecting what pre-trained word embeddings to use, we argue that such a step is better left for neural networks to figure out by themselves.
To that end, we introduce dynamic meta-embeddings, a simple yet effective method for the supervised learning of embedding ensembles, which leads to state-of-the-art performance within the same model class on a variety of tasks.
We subsequently show how the technique can be used to shed new light on the usage of word embeddings in NLP systems.
Event recognition systems rely on properly engineered knowledge bases of event definitions to infer occurrences of events in time.
The manual development of such knowledge is a tedious and error-prone task, thus event-based applications may benefit from automated knowledge construction techniques, such as Inductive Logic Programming (ILP), which combines machine learning with the declarative and formal semantics of First-Order Logic.
However, learning temporal logical formalisms, which are typically utilized by logic-based Event Recognition systems is a challenging task, which most ILP systems cannot fully undertake.
In addition, event-based data is usually massive and collected at different times and under various circumstances.
Ideally, systems that learn from temporal data should be able to operate in an incremental mode, that is, revise prior constructed knowledge in the face of new evidence.
Most ILP systems are batch learners, in the sense that in order to account for new evidence they have no alternative but to forget past knowledge and learn from scratch.
Given the increased inherent complexity of ILP and the volumes of real-life temporal data, this results to algorithms that scale poorly.
In this work we present an incremental method for learning and revising event-based knowledge, in the form of Event Calculus programs.
The proposed algorithm relies on abductive-inductive learning and comprises a scalable clause refinement methodology, based on a compressive summarization of clause coverage in a stream of examples.
We present an empirical evaluation of our approach on real and synthetic data from activity recognition and city transport applications.
The purported "black box"' nature of neural networks is a barrier to adoption in applications where interpretability is essential.
Here we present DeepLIFT (Deep Learning Important FeaTures), a method for decomposing the output prediction of a neural network on a specific input by backpropagating the contributions of all neurons in the network to every feature of the input.
DeepLIFT compares the activation of each neuron to its 'reference activation' and assigns contribution scores according to the difference.
By optionally giving separate consideration to positive and negative contributions, DeepLIFT can also reveal dependencies which are missed by other approaches.
Scores can be computed efficiently in a single backward pass.
We apply DeepLIFT to models trained on MNIST and simulated genomic data, and show significant advantages over gradient-based methods.
A detailed video tutorial on the method is at http://goo.gl/qKb7pL and code is at http://goo.gl/RM8jvH.
A small part of the Torah is arranged into a two dimensional array.
The characters are then permuted using a simple recursive deterministic algorithm.
The various permutations are then passed through three stochastic filters and one deterministic filter to identify the permutations which most closely approximate readable Biblical Hebrew.
Of the 15 Billion sequences available at the second level of recursion, 800 pass the a priori thresholds set for each filter.
The resulting "Biblical Hebrew" text is available for inspection and the generation of further material continues.
It is well known that modal satisfiability is PSPACE-complete (Ladner 1977).
However, the complexity may decrease if we restrict the set of propositional operators used.
Note that there exist an infinite number of propositional operators, since a propositional operator is simply a Boolean function.
We completely classify the complexity of modal satisfiability for every finite set of propositional operators, i.e., in contrast to previous work, we classify an infinite number of problems.
We show that, depending on the set of propositional operators, modal satisfiability is PSPACE-complete, coNP-complete, or in P. We obtain this trichotomy not only for modal formulas, but also for their more succinct representation using modal circuits.
We consider both the uni-modal and the multi-modal case, and study the dual problem of validity as well.
Generating images from word descriptions is a challenging task.
Generative adversarial networks(GANs) are shown to be able to generate realistic images of real-life objects.
In this paper, we propose a new neural network architecture of LSTM Conditional Generative Adversarial Networks to generate images of real-life objects.
Our proposed model is trained on the Oxford-102 Flowers and Caltech-UCSD Birds-200-2011 datasets.
We demonstrate that our proposed model produces the better results surpassing other state-of-art approaches.
Evaluating agent performance when outcomes are stochastic and agents use randomized strategies can be challenging when there is limited data available.
The variance of sampled outcomes may make the simple approach of Monte Carlo sampling inadequate.
This is the case for agents playing heads-up no-limit Texas hold'em poker, where man-machine competitions have involved multiple days of consistent play and still not resulted in statistically significant conclusions even when the winner's margin is substantial.
In this paper, we introduce AIVAT, a low variance, provably unbiased value assessment tool that uses an arbitrary heuristic estimate of state value, as well as the explicit strategy of a subset of the agents.
Unlike existing techniques which reduce the variance from chance events, or only consider game ending actions, AIVAT reduces the variance both from choices by nature and by players with a known strategy.
The resulting estimator in no-limit poker can reduce the number of hands needed to draw statistical conclusions by more than a factor of 10.
The demand for stream processing is increasing at an unprecedented rate.
Big data is no longer limited to processing of big volumes of data.
In most real-world scenarios, the need for processing stream data as it comes can only meet the business needs.
It is required for trading, fraud detection, system monitoring, product maintenance and of course social media data such as Twitter and YouTube videos.
In such cases, a "too late architecture" that focuses on batch processing cannot realize the use cases.
In this article, we present an end to end Big data platform called AlertMix for processing multi-source streaming data.
Its architecture and how various Big data technologies are utilized are explained in this work.
We present the performance of our platform on real live streaming data which is currently handled by the platform.
This paper presents a minimalist neural regression network as an aggregate of independent identical regression blocks that are trained simultaneously.
Moreover, it introduces a new multiplicative parameter, shared by all the neural units of a given layer, to maintain the quality of its gradients.
Furthermore, it increases its estimation accuracy via learning a weight factor whose quantity captures the redundancy between the estimated and actual values at each training iteration.
We choose the estimation of the direct weld parameters of different welding techniques to show a significant improvement in calculation of these parameters by our model in contrast to state-of-the-arts techniques in the literature.
Furthermore, we demonstrate the ability of our model to retain its performance when presented with combined data of different welding techniques.
This is a nontrivial result in attaining an scalable model whose quality of estimation is independent of adopted welding techniques.
The input to a neural sequence-to-sequence model is often determined by an up-stream system, e.g. a word segmenter, part of speech tagger, or speech recognizer.
These up-stream models are potentially error-prone.
Representing inputs through word lattices allows making this uncertainty explicit by capturing alternative sequences and their posterior probabilities in a compact form.
In this work, we extend the TreeLSTM (Tai et al., 2015) into a LatticeLSTM that is able to consume word lattices, and can be used as encoder in an attentional encoder-decoder model.
We integrate lattice posterior scores into this architecture by extending the TreeLSTM's child-sum and forget gates and introducing a bias term into the attention mechanism.
We experiment with speech translation lattices and report consistent improvements over baselines that translate either the 1-best hypothesis or the lattice without posterior scores.
We study probabilistic models of natural images and extend the autoregressive family of PixelCNN architectures by incorporating auxiliary variables.
Subsequently, we describe two new generative image models that exploit different image transformations as auxiliary variables: a quantized grayscale view of the image or a multi-resolution image pyramid.
The proposed models tackle two known shortcomings of existing PixelCNN models: 1) their tendency to focus on low-level image details, while largely ignoring high-level image information, such as object shapes, and 2) their computationally costly procedure for image sampling.
We experimentally demonstrate benefits of the proposed models, in particular showing that they produce much more realistically looking image samples than previous state-of-the-art probabilistic models.
This paper shows and evaluates a novel approach to integrate a non-invasive Brain-Computer Interface (BCI) with the Robot Operating System (ROS) to mentally drive a telepresence robot.
Controlling a mobile device by using human brain signals might improve the quality of life of people suffering from severe physical disabilities or elderly people who cannot move anymore.
Thus, the BCI user is able to actively interact with relatives and friends located in different rooms thanks to a video streaming connection to the robot.
To facilitate the control of the robot via BCI, we explore new ROS-based algorithms for navigation and obstacle avoidance, making the system safer and more reliable.
In this regard, the robot can exploit two maps of the environment, one for localization and one for navigation, and both can be used also by the BCI user to watch the position of the robot while it is moving.
As demonstrated by the experimental results, the user's cognitive workload is reduced, decreasing the number of commands necessary to complete the task and helping him/her to keep attention for longer periods of time.
The Unified Modeling Language (UML) community has started to define so-called profiles in order to better suit the needs of specific domains or settings.
Product lines1 represent a special breed of systems they are extensible semi-finished pieces of software.
Completing the semi-finished software leads to various software pieces, typically specific applications, which share the same core.
Though product lines have been developed for a wide range of domains, they apply common construction principles.
The intention of the UML-F profile (for framework architectures) is the definition of a UML subset, enriched with a few UML-compliant extensions, which allows the annotation of such artifacts.
This paper presents aspects of the profile with a focus on patterns and exemplifies the profile's usage.
With explosion of data size and limited storage space at a single location, data are often distributed at different locations.
We thus face the challenge of performing large-scale machine learning from these distributed data through communication networks.
In this paper, we study how the network communication constraints will impact the convergence speed of distributed machine learning optimization algorithms.
In particular, we give the convergence rate analysis of the distributed dual coordinate ascent in a general tree structured network.
Furthermore, by considering network communication delays, we optimize the network-constrained dual coordinate ascent algorithms to maximize its convergence speed.
Our results show that under different network communication delays, to achieve maximum convergence speed, one needs to adopt delay-dependent numbers of local and global iterations for distributed dual coordinate ascent.
A major challenge in obtaining large-scale evaluations, e.g., product or service reviews on online platforms, labeling images, grading in online courses, etc., is that of eliciting honest responses from agents in the absence of verifiability.
We propose a new reward mechanism with strong incentive properties applicable in a wide variety of such settings.
This mechanism has a simple and intuitive output agreement structure: an agent gets a reward only if her response for an evaluation matches that of her peer.
But instead of the reward being the same across different answers, it is inversely proportional to a popularity index of each answer.
This index is a second order population statistic that captures how frequently two agents performing the same evaluation agree on the particular answer.
Rare agreements thus earn a higher reward than agreements that are relatively more common.
In the regime where there are a large number of evaluation tasks, we show that truthful behavior is a strict Bayes-Nash equilibrium of the game induced by the mechanism.
Further, we show that the truthful equilibrium is approximately optimal in terms of expected payoffs to the agents across all symmetric equilibria, where the approximation error vanishes in the number of evaluation tasks.
Moreover, under a mild condition on strategy space, we show that any symmetric equilibrium that gives a higher expected payoff than the truthful equilibrium must be close to being fully informative if the number of evaluations is large.
These last two results are driven by a new notion of an agreement measure that is shown to be monotonic in information loss.
This notion and its properties are of independent interest.
We present an anytime algorithm that generates a collision-free configuration-space path that closely follows a desired path in task space, according to the discrete Frechet distance.
By leveraging tools from computational geometry, we approximate the search space using a cross-product graph.
We use a variant of Dijkstra's graph-search algorithm to efficiently search for and iteratively improve the solution.
We compare multiple proposed densification strategies and empirically show that our algorithm outperforms a set of state-of-the-art planners on a range of manipulation problems.
Finally, we offer a proof sketch of the asymptotic optimality of our algorithm.
The problem of finding the maximum number of vertex-disjoint uni-color paths in an edge-colored graph (called MaxCDP) has been recently introduced in literature, motivated by applications in social network analysis.
In this paper we investigate how the complexity of the problem depends on graph parameters (namely the number of vertices to remove to make the graph a collection of disjoint paths and the size of the vertex cover of the graph), which makes sense since graphs in social networks are not random and have structure.
The problem was known to be hard to approximate in polynomial time and not fixed-parameter tractable (FPT) for the natural parameter.
Here, we show that it is still hard to approximate, even in FPT-time.
Finally, we introduce a new variant of the problem, called MaxCDDP, whose goal is to find the maximum number of vertex-disjoint and color-disjoint uni-color paths.
We extend some of the results of MaxCDP to this new variant, and we prove that unlike MaxCDP, MaxCDDP is already hard on graphs at distance two from disjoint paths.
In this paper, we study the problem of question answering when reasoning over multiple facts is required.
We propose Query-Reduction Network (QRN), a variant of Recurrent Neural Network (RNN) that effectively handles both short-term (local) and long-term (global) sequential dependencies to reason over multiple facts.
QRN considers the context sentences as a sequence of state-changing triggers, and reduces the original query to a more informed query as it observes each trigger (context sentence) through time.
Our experiments show that QRN produces the state-of-the-art results in bAbI QA and dialog tasks, and in a real goal-oriented dialog dataset.
In addition, QRN formulation allows parallelization on RNN's time axis, saving an order of magnitude in time complexity for training and inference.
Regularization for matrix factorization (MF) and approximation problems has been carried out in many different ways.
Due to its popularity in deep learning, dropout has been applied also for this class of problems.
Despite its solid empirical performance, the theoretical properties of dropout as a regularizer remain quite elusive for this class of problems.
In this paper, we present a theoretical analysis of dropout for MF, where Bernoulli random variables are used to drop columns of the factors.
We demonstrate the equivalence between dropout and a fully deterministic model for MF in which the factors are regularized by the sum of the product of squared Euclidean norms of the columns.
Additionally, we inspect the case of a variable sized factorization and we prove that dropout achieves the global minimum of a convex approximation problem with (squared) nuclear norm regularization.
As a result, we conclude that dropout can be used as a low-rank regularizer with data dependent singular-value thresholding.
This paper deals with uncertain parabolic fluid flow problem where the uncertainty occurs due to the initial conditions and parameters involved in the system.
Uncertain values are considered as fuzzy and these are handled through a recently developed method.
Here the concepts of fuzzy numbers are combined with Finite Difference Method (FDM) and then Fuzzy Finite Difference Method (FFDM) has been proposed.
The proposed FFDM has been used to solve the fluid flow problem bounded by two parallel plates.
Finally sensitivity of the fuzzy parameters has also been analysed.
Context: Over the last decade, software researchers and engineers have developed a vast body of methodologies and technologies in requirements engineering for self-adaptive systems.
Although existing studies have explored various aspects of this field, no systematic study has been performed on summarizing modeling methods and corresponding requirements activities.
Objective: This study summarizes the state-of-the-art research trends, details the modeling methods and corresponding requirements activities, identifies relevant quality attributes and application domains and assesses the quality of each study.
Method: We perform a systematic literature review underpinned by a rigorously established and reviewed protocol.
To ensure the quality of the study, we choose 21 highly regarded publication venues and 8 popular digital libraries.
In addition, we apply text mining to derive search strings and use Kappa coefficient to mitigate disagreements of researchers.
Results: We selected 109 papers during the period of 2003-2013 and presented the research distributions over various kinds of factors.
We extracted 29 modeling methods which are classified into 8 categories and identified 14 requirements activities which are classified into 4 requirements timelines.
We captured 8 concerned software quality attributes based on the ISO 9126 standard and 12 application domains.
Conclusion: The frequency of application of modeling methods varies greatly.
Enterprise models were more widely used while behavior models were more rigorously evaluated.
Requirements-driven runtime adaptation was the most frequently studied requirements activity.
Activities at runtime were conveyed with more details.
Finally, we draw other conclusions by discussing how well modeling dimensions were considered in these modeling methods and how well assurance dimensions were conveyed in requirements activities.
Bayesian Neural Networks (BNNs) have been proposed to address the problem of model uncertainty in training and inference.
By introducing weights associated with conditioned probability distributions, BNNs are capable of resolving the overfitting issue commonly seen in conventional neural networks and allow for small-data training, through the variational inference process.
Frequent usage of Gaussian random variables in this process requires a properly optimized Gaussian Random Number Generator (GRNG).
The high hardware cost of conventional GRNG makes the hardware implementation of BNNs challenging.
In this paper, we propose VIBNN, an FPGA-based hardware accelerator design for variational inference on BNNs.
We explore the design space for massive amount of Gaussian variable sampling tasks in BNNs.
Specifically, we introduce two high performance Gaussian (pseudo) random number generators: the RAM-based Linear Feedback Gaussian Random Number Generator (RLF-GRNG), which is inspired by the properties of binomial distribution and linear feedback logics; and the Bayesian Neural Network-oriented Wallace Gaussian Random Number Generator.
To achieve high scalability and efficient memory access, we propose a deep pipelined accelerator architecture with fast execution and good hardware utilization.
Experimental results demonstrate that the proposed VIBNN implementations on an FPGA can achieve throughput of 321,543.4 Images/s and energy efficiency upto 52,694.8 Images/J while maintaining similar accuracy as its software counterpart.
We introduce Tempered Geodesic Markov Chain Monte Carlo (TG-MCMC) algorithm for initializing pose graph optimization problems, arising in various scenarios such as SFM (structure from motion) or SLAM (simultaneous localization and mapping).
TG-MCMC is first of its kind as it unites asymptotically global non-convex optimization on the spherical manifold of quaternions with posterior sampling, in order to provide both reliable initial poses and uncertainty estimates that are informative about the quality of individual solutions.
We devise rigorous theoretical convergence guarantees for our method and extensively evaluate it on synthetic and real benchmark datasets.
Besides its elegance in formulation and theory, we show that our method is robust to missing data, noise and the estimated uncertainties capture intuitive properties of the data.
In this paper, we present multi-threaded algorithms for graph coloring suitable to the shared memory programming model.
We modify an existing algorithm widely used in the literature and prove the correctness of the modified algorithm.
We also propose a new approach to solve the problem of coloring using locks.
Using datasets from real world graphs, we evaluate the performance of the algorithms on the Intel platform.
We compare the performance of the sequential approach v/s our proposed approach and analyze the speedup obtained against the existing algorithm from the literature.
The results show that the speedup obtained is consequential.
We also provide a direction for future work towards improving the performance further in terms of different metrics.
Policy gradient methods have enjoyed great success in deep reinforcement learning but suffer from high variance of gradient estimates.
The high variance problem is particularly exasperated in problems with long horizons or high-dimensional action spaces.
To mitigate this issue, we derive a bias-free action-dependent baseline for variance reduction which fully exploits the structural form of the stochastic policy itself and does not make any additional assumptions about the MDP.
We demonstrate and quantify the benefit of the action-dependent baseline through both theoretical analysis as well as numerical results, including an analysis of the suboptimality of the optimal state-dependent baseline.
The result is a computationally efficient policy gradient algorithm, which scales to high-dimensional control problems, as demonstrated by a synthetic 2000-dimensional target matching task.
Our experimental results indicate that action-dependent baselines allow for faster learning on standard reinforcement learning benchmarks and high-dimensional hand manipulation and synthetic tasks.
Finally, we show that the general idea of including additional information in baselines for improved variance reduction can be extended to partially observed and multi-agent tasks.
In timeline-based planning, domains are described as sets of independent, but interacting, components, whose behaviour over time (the set of timelines) is governed by a set of temporal constraints.
A distinguishing feature of timeline-based planning systems is the ability to integrate planning with execution by synthesising control strategies for flexible plans.
However, flexible plans can only represent temporal uncertainty, while more complex forms of nondeterminism are needed to deal with a wider range of realistic problems.
In this paper, we propose a novel game-theoretic approach to timeline-based planning problems, generalising the state of the art while uniformly handling temporal uncertainty and nondeterminism.
We define a general concept of timeline-based game and we show that the notion of winning strategy for these games is strictly more general than that of control strategy for dynamically controllable flexible plans.
Moreover, we show that the problem of establishing the existence of such winning strategies is decidable using a doubly exponential amount of space.
An LDPC coded modulation scheme with probabilistic shaping, optimized interleavers and noniterative demapping is proposed.
Full-field simulations show an increase in transmission distance by 8% compared to uniformly distributed input.
Data driven segmentation is the powerhouse behind the success of online advertising.
Various underlying challenges for successful segmentation have been studied by the academic community, with one notable exception - consumers incentives have been typically ignored.
This lacuna is troubling as consumers have much control over the data being collected.
Missing or manipulated data could lead to inferior segmentation.
The current work proposes a model of prior-free segmentation, inspired by models of facility location, and to the best of our knowledge provides the first segmentation mechanism that addresses incentive compatibility, efficient market segmentation and privacy in the absence of a common prior.
At the heart of the Bitcoin is a blockchain protocol, a protocol for achieving consensus on a public ledger that records bitcoin transactions.
To the extent that a blockchain protocol is used for applications such as contract signing and making certain transactions (such as house sales) public, we need to understand what guarantees the protocol gives us in terms of agents' knowledge.
Here, we provide a complete characterization of agent's knowledge when running a blockchain protocol using a variant of common knowledge that takes into account the fact that agents can enter and leave the system, it is not known which agents are in fact following the protocol (some agents may want to deviate if they can gain by doing so), and the fact that the guarantees provided by blockchain protocols are probabilistic.
We then consider some scenarios involving contracts and show that this level of knowledge suffices for some scenarios, but not others.
We present a novel hierarchical graphical model based context-aware hybrid brain-machine interface (hBMI) using probabilistic fusion of electroencephalographic (EEG) and electromyographic (EMG) activities.
Based on experimental data collected during stationary executions and subsequent imageries of five different hand gestures with both limbs, we demonstrate feasibility of the proposed hBMI system through within session and online across sessions classification analyses.
Furthermore, we investigate the context-aware extent of the model by a simulated probabilistic approach and highlight potential implications of our work in the field of neurophysiologically-driven robotic hand prosthetics.
It has been shown that most machine learning algorithms are susceptible to adversarial perturbations.
Slightly perturbing an image in a carefully chosen direction in the image space may cause a trained neural network model to misclassify it.
Recently, it was shown that physical adversarial examples exist: printing perturbed images then taking pictures of them would still result in misclassification.
This raises security and safety concerns.
However, these experiments ignore a crucial property of physical objects: the camera can view objects from different distances and at different angles.
In this paper, we show experiments that suggest that current constructions of physical adversarial examples do not disrupt object detection from a moving platform.
Instead, a trained neural network classifies most of the pictures taken from different distances and angles of a perturbed image correctly.
We believe this is because the adversarial property of the perturbation is sensitive to the scale at which the perturbed picture is viewed, so (for example) an autonomous car will misclassify a stop sign only from a small range of distances.
Our work raises an important question: can one construct examples that are adversarial for many or most viewing conditions?
If so, the construction should offer very significant insights into the internal representation of patterns by deep networks.
If not, there is a good prospect that adversarial examples can be reduced to a curiosity with little practical impact.
Verification activities are necessary to ensure that the requirements are specified in a correct way.
However, until now requirements verification research has focused on traditional up-front requirements.
Agile or just-in-time requirements are by definition incomplete, not specific and might be ambiguous when initially specified, indicating a different notion of 'correctness'.
We analyze how verification of agile requirements quality should be performed, based on literature of traditional and agile requirements.
This leads to an agile quality framework, instantiated for the specific requirement types of feature requests in open source projects and user stories in agile projects.
We have performed an initial qualitative validation of our framework for feature requests with eight practitioners from the Dutch agile community, receiving overall positive feedback.
The world is connected through the Internet.
As the abundance of Internet users connected into the Web and the popularity of cloud computing research, the need of Artificial Intelligence (AI) is demanding.
In this research, Genetic Algorithm (GA) as AI optimization method through natural selection and genetic evolution is utilized.
There are many applications of GA such as web mining, load balancing, routing, and scheduling or web service selection.
Hence, it is a challenging task to discover whether the code mainly server side and web based language technology affects the performance of GA. Travelling Salesperson Problem (TSP) as Non Polynomial-hard (NP-hard) problem is provided to be a problem domain to be solved by GA.
While many scientists prefer Python in GA implementation, another popular high-level interpreter programming language such as PHP (PHP Hypertext Preprocessor) and Ruby were benchmarked.
Line of codes, file sizes, and performances based on GA implementation and runtime were found varies among these programming languages.
Based on the result, the use of Ruby in GA implementation is recommended.
As Cook-Levin theorem showed, every NP problem can be reduced to SAT in polynomial time.
In this paper I show a simpler and more efficent method to reduce some factorization problems to the satisfability of a boolean formula.
Motivated by recent advance of machine learning using Deep Reinforcement Learning this paper proposes a modified architecture that produces more robust agents and speeds up the training process.
Our architecture is based on Asynchronous Advantage Actor-Critic (A3C) algorithm where the total input dimensionality is halved by dividing the input into two independent streams.
We use ViZDoom, 3D world software that is based on the classical first person shooter video game, Doom, as a test case.
The experiments show that in comparison to single input agents, the proposed architecture succeeds to have the same playing performance and shows more robust behavior, achieving significant reduction in the number of training parameters of almost 30%.
Sybil detection in social networks is a basic security research problem.
Structure-based methods have been shown to be promising at detecting Sybils.
Existing structure-based methods can be classified into Random Walk (RW)-based methods and Loop Belief Propagation (LBP)-based methods.
RW-based methods cannot leverage labeled Sybils and labeled benign users simultaneously, which limits their detection accuracy, and/or they are not robust to noisy labels.
LBP-based methods are not scalable and cannot guarantee convergence.
In this work, we propose SybilSCAR, a novel structure-based method to detect Sybils in social networks.
SybilSCAR is Scalable, Convergent, Accurate, and Robust to label noise.
We first propose a framework to unify RW-based and LBP-based methods.
Under our framework, these methods can be viewed as iteratively applying a (different) local rule to every user, which propagates label information among a social graph.
Second, we design a new local rule, which SybilSCAR iteratively applies to every user to detect Sybils.
We compare SybilSCAR with state-of-the-art RW-based and LBP-based methods theoretically and empirically.
Theoretically, we show that, with proper parameter settings, SybilSCAR has a tighter asymptotical bound on the number of Sybils that are falsely accepted into a social network than existing structure-based methods.
Empirically, we perform evaluation using both social networks with synthesized Sybils and a large-scale Twitter dataset (41.7M nodes and 1.2B edges) with real Sybils.
Our results show that 1) SybilSCAR is substantially more accurate and more robust to label noise than state-of-the-art RW-based methods; 2) SybilSCAR is more accurate and one order of magnitude more scalable than state-of-the-art LBP-based methods.
We introduce a space-filling curve for triangular and tetrahedral red-refinement that can be computed using bitwise interleaving operations similar to the well-known Z-order or Morton curve for cubical meshes.
To store sufficient information for random access, we define a low-memory encoding using 10 bytes per triangle and 14 bytes per tetrahedron.
We present algorithms that compute the parent, children, and face-neighbors of a mesh element in constant time, as well as the next and previous element in the space-filling curve and whether a given element is on the boundary of the root simplex or not.
Our presentation concludes with a scalability demonstration that creates and adapts selected meshes on a large distributed-memory system.
The well-known Smith-Waterman (SW) algorithm is the most commonly used method for local sequence alignments.
However, SW is very computationally demanding for large protein databases.
There exist several implementations that take advantage of computing parallelization on many-cores, FPGAs or GPUs, in order to increase the alignment throughtput.
In this paper, we have explored SW acceleration on Intel KNL processor.
The novelty of this architecture requires the revision of previous programming and optimization techniques on many-core architectures.
To the best of authors knowledge, this is the first KNL architecture assessment for SW algorithm.
Our evaluation, using the renowned Environmental NR database as benchmark, has shown that multi-threading and SIMD exploitation reports competitive performance (351 GCUPS) in comparison with other implementations.
This paper claims that a new field of empirical software engineering research and practice is emerging: data mining using/used-by optimizers for empirical studies, or DUO.
For example, data miners can generate the models that are explored by optimizers.Also, optimizers can advise how to best adjust the control parameters of a data miner.
This combined approach acts like an agent leaning over the shoulder of an analyst that advises "ask this question next" or "ignore that problem, it is not relevant to your goals".
Further, those agents can help us build "better" predictive models, where "better" can be either greater predictive accuracy, or faster modeling time (which, in turn, enables the exploration of a wider range of options).
We also caution that the era of papers that just use data miners is coming to an end.
Results obtained from an unoptimized data miner can be quickly refuted, just by applying an optimizer to produce a different (and better performing) model.
Our conclusion, hence, is that for software analytics it is possible, useful and necessary to combine data mining and optimization using DUO.
In programming by example, users "write" programs by generating a small number of input-output examples and asking the computer to synthesize consistent programs.
We consider a challenging problem in this domain: learning regular expressions (regexes) from positive and negative example strings.
This problem is challenging, as (1) user-generated examples may not be informative enough to sufficiently constrain the hypothesis space, and (2) even if user-generated examples are in principle informative, there is still a massive search space to examine.
We frame regex induction as the problem of inferring a probabilistic regular grammar and propose an efficient inference approach that uses a novel stochastic process recognition model.
This model incrementally "grows" a grammar using positive examples as a scaffold.
We show that this approach is competitive with human ability to learn regexes from examples.
Robotic grasping detection is one of the most important fields in robotics, in which great progress has been made recent years with the help of convolutional neural network (CNN).
However, including multiple objects in one scene can invalidate the existing CNN-based grasping detection algorithms, because manipulation relationships among objects are not considered, which are required to guide the robot to grasp things in the right order.
This paper presents a new CNN architecture called Visual Manipulation Relationship Network (VMRN) to help robot detect targets and predict the manipulation relationships in real time.
To implement end-to-end training and meet real-time requirements in robot tasks, we propose the Object Pairing Pooling Layer (OP2L) to help to predict all manipulation relationships in one forward process.
Moreover, in order to train VMRN, we collect a dataset named Visual Manipulation Relationship Dataset (VMRD) consisting of 5185 images with more than 17000 object instances and the manipulation relationships between all possible pairs of objects in every image, which is labeled by the manipulation relationship tree.
The experimental results show that the new network architecture can detect objects and predict manipulation relationships simultaneously and meet the real-time requirements in robot tasks.
We characterize the finite sets S of words such that that the iterated shuffle of S is co-finite and we give some bounds on the length of a longest word not in the iterated shuffle of S.
This paper presents an analytical taxonomy that can suitably describe, rather than simply classify, techniques for data presentation.
Unlike previous works, we do not consider particular aspects of visualization techniques, but their mechanisms and foundational vision perception.
Instead of just adjusting visualization research to a classification system, our aim is to better understand its process.
For doing so, we depart from elementary concepts to reach a model that can describe how visualization techniques work and how they convey meaning.
We introduce a novel Deep Network architecture that implements the full feature point handling pipeline, that is, detection, orientation estimation, and feature description.
While previous works have successfully tackled each one of these problems individually, we show how to learn to do all three in a unified manner while preserving end-to-end differentiability.
We then demonstrate that our Deep pipeline outperforms state-of-the-art methods on a number of benchmark datasets, without the need of retraining.
The problem of hand shape classification is challenging since a hand is characterized by a large number of degrees of freedom.
Numerous shape descriptors have been proposed and applied over the years to estimate and classify hand poses in reasonable time.
In this paper we discuss our parallel framework for real-time hand shape classification applicable in real-time applications.
We show how the number of gallery images influences the classification accuracy and execution time of the parallel algorithm.
We present the speedup and efficiency analyses that prove the efficacy of the parallel implementation.
Noteworthy, different methods can be used at each step of our parallel framework.
Here, we combine the shape contexts with the appearance-based techniques to enhance the robustness of the algorithm and to increase the classification score.
An extensive experimental study proves the superiority of the proposed approach over existing state-of-the-art methods.
This paper describes a method for the automatic inference of structural transfer rules to be used in a shallow-transfer machine translation (MT) system from small parallel corpora.
The structural transfer rules are based on alignment templates, like those used in statistical MT.
Alignment templates are extracted from sentence-aligned parallel corpora and extended with a set of restrictions which are derived from the bilingual dictionary of the MT system and control their application as transfer rules.
The experiments conducted using three different language pairs in the free/open-source MT platform Apertium show that translation quality is improved as compared to word-for-word translation (when no transfer rules are used), and that the resulting translation quality is close to that obtained using hand-coded transfer rules.
The method we present is entirely unsupervised and benefits from information in the rest of modules of the MT system in which the inferred rules are applied.
In this paper we study the complexity of the problems: given a loop, described by linear constraints over a finite set of variables, is there a linear or lexicographical-linear ranking function for this loop?
While existence of such functions implies termination, these problems are not equivalent to termination.
When the variables range over the rationals (or reals), it is known that both problems are PTIME decidable.
However, when they range over the integers, whether for single-path or multipath loops, the complexity has not yet been determined.
We show that both problems are coNP-complete.
However, we point out some special cases of importance of PTIME complexity.
We also present complete algorithms for synthesizing linear and lexicographical-linear ranking functions, both for the general case and the special PTIME cases.
Moreover, in the rational setting, our algorithm for synthesizing lexicographical-linear ranking functions extends existing ones, because our class of ranking functions is more general, yet it has polynomial time complexity.
An experiment to study the entropy method for an anomaly detection system has been performed.
The study has been conducted using real data generated from the distributed sensor networks at the Intel Berkeley Research Laboratory.
The experimental results were compared with the elliptical method and has been analyzed in two dimensional data sets acquired from temperature and humidity sensors across 52 micro controllers.
Using the binary classification to determine the upper and lower boundaries for each series of sensors, it has been shown that the entropy method are able to detect more number of out ranging sensor nodes than the elliptical methods.
It can be argued that the better result was mainly due to the lack of elliptical approach which is requiring certain correlation between two sensor series, while in the entropy approach each sensor series is treated independently.
This is very important in the current case where both sensor series are not correlated each other.
The Internet of things (IoT) comprises of wireless sensors and actuators connected via access points to the Internet.
Often, the sensing devices are remotely deployed with limited battery power and equipped with energy harvesting equipment such as solar panels.
These devices transmit real-time data to the base stations which is used in the detection of other applications.
Under sufficient power availability, wireless transmissions from sensors can be scheduled at regular time intervals to maintain real-time detection and information retrieval by the base station.
However, once the battery is significantly depleted, the devices enter into power saving mode and is required to be more selective in transmitting information to the base station (BS).
Transmitting a particular piece of sensed data will result in power consumption while discarding it might result in loss of utility at the BS.
The goal is to design an optimal dynamic policy which enables the device to decide whether to transmit or to discard a piece of sensing data particularly under the power saving mode.
This will enable the sensor to prolong its operation while causing minimum loss of utility of the application.
We develop a mathematical model to capture the utility of the IoT sensor transmissions and use tools from dynamic programming to derive an optimal real-time transmission policy that is based on the statistics of information arrival, the likelihood of harvested energy, and designed lifetime of the sensors.
Numerical results show that if the statistics of future data valuation can be accurately predicted, there is a significant increase in the utility obtained at the BS as well as the battery lifetime.
Contemporary social media networks can be viewed as a break to the early two-step flow model in which influential individuals act as intermediaries between the media and the public for information diffusion.
Today's social media platforms enable users to both generate and consume online contents.
Users continuously engage and disengage in discussions with varying degrees of interaction leading to formation of distinct online communities.
Such communities are often formed at high-level either based on metadata, such as hashtags on Twitter, or popular content triggered by few influential users.
These online communities often do not reflect true connectivity and lack the cohesiveness of traditional communities.
In this study, we investigate real-time formation of temporal communities on Twitter.
We aim at defining both high and low levels connections and to reveal the magnitude of clustering cohesion on temporal basis.
Inspired by a real-life event center sitting arrangement scenario, the proposed method aims to cluster users into distinct and cohesive online temporal communities.
Membership to a community relies on intrinsic tweet properties to define similarity as the basis for interaction networks.
The proposed method can be useful for local event monitoring and clique-based marketing among other applications.
Social Live Stream Services (SLSS) exploit a new level of social interaction.
One of the main challenges in these services is how to detect and prevent deviant behaviors that violate community guidelines.
In this work, we focus on adult content production and consumption in two widely used SLSS, namely Live.me and Loops Live, which have millions of users producing massive amounts of video content on a daily basis.
We use a pre-trained deep learning model to identify broadcasters of adult content.
Our results indicate that moderation systems in place are highly ineffective in suspending the accounts of such users.
We create two large datasets by crawling the social graphs of these platforms, which we analyze to identify characterizing traits of adult content producers and consumers, and discover interesting patterns of relationships among them, evident in both networks.
In this paper we investigate the problem of optimal MDS-encoded cache placement at the wireless edge to minimize the backhaul rate in heterogeneous networks.
We derive the backhaul rate performance of any caching scheme based on file splitting and MDS encoding and we formulate the optimal caching scheme as a convex optimization problem.
We then thoroughly investigate the performance of this optimal scheme for an important heterogeneous network scenario.
We compare it to several other caching strategies and we analyze the influence of the system parameters, such as the popularity and size of the library files and the capabilities of the small-cell base stations, on the overall performance of our optimal caching strategy.
Our results show that the careful placement of MDS-encoded content in caches at the wireless edge leads to a significant decrease of the load of the network backhaul and hence to a considerable performance enhancement of the network.
Triplet networks are widely used models that are characterized by good performance in classification and retrieval tasks.
In this work we propose to train a triplet network by putting it as the discriminator in Generative Adversarial Nets (GANs).
We make use of the good capability of representation learning of the discriminator to increase the predictive quality of the model.
We evaluated our approach on Cifar10 and MNIST datasets and observed significant improvement on the classification performance using the simple k-nn method.
This paper addresses the problem of manipulating images using natural language description.
Our task aims to semantically modify visual attributes of an object in an image according to the text describing the new visual appearance.
Although existing methods synthesize images having new attributes, they do not fully preserve text-irrelevant contents of the original image.
In this paper, we propose the text-adaptive generative adversarial network (TAGAN) to generate semantically manipulated images while preserving text-irrelevant contents.
The key to our method is the text-adaptive discriminator that creates word-level local discriminators according to input text to classify fine-grained attributes independently.
With this discriminator, the generator learns to generate images where only regions that correspond to the given text are modified.
Experimental results show that our method outperforms existing methods on CUB and Oxford-102 datasets, and our results were mostly preferred on a user study.
Extensive analysis shows that our method is able to effectively disentangle visual attributes and produce pleasing outputs.
Reeb graphs are structural descriptors that capture shape properties of a topological space from the perspective of a chosen function.
In this work we define a combinatorial metric for Reeb graphs of orientable surfaces in terms of the cost necessary to transform one graph into another by edit operations.
The main contributions of this paper are the stability property and the optimality of this edit distance.
More precisely, the stability result states that changes in the functions, measured by the maximum norm, imply not greater changes in the corresponding Reeb graphs, measured by the edit distance.
The optimality result states that our edit distance discriminates Reeb graphs better than any other metric for Reeb graphs of surfaces satisfying the stability property.
Many sequence learning tasks require the localization of certain events in sequences.
Because it can be expensive to obtain strong labeling that specifies the starting and ending times of the events, modern systems are often trained with weak labeling without explicit timing information.
Multiple instance learning (MIL) is a popular framework for learning from weak labeling.
In a common scenario of MIL, it is necessary to choose a pooling function to aggregate the predictions for the individual steps of the sequences.
In this paper, we compare the "max" and "noisy-or" pooling functions on a speech recognition task and a sound event detection task.
We find that max pooling is able to localize phonemes and sound events, while noisy-or pooling fails.
We provide a theoretical explanation of the different behavior of the two pooling functions on sequence learning tasks.
Boolean automata networks (BANs) are a well established model for biological regulation systems such as neural networks or genetic networks.
Studies on the dynamics of BANs, whether it is synchronous or asynchronous, have mainly focused on monotonic networks, where fundamental questions on the links relating their static and dynamical properties have been raised and addressed.
This paper explores analogous questions on asynchronous non-monotonic networks, xor-BANs, that are BANs where all the local transition functions are xor-functions.
Using algorithmic tools, we give a general characterisation of the asynchronous transition graphs for most of the cactus xor-BANs and strongly connected xor-BANs.
As an illustration of the results, we provide a complete description of the asynchronous dynamics of two particular classes of xor-BAN, namely xor-Flowers and xor-Cycle Chains.
This work also leads to new bisimulation equivalences specific to xor-BANs.
Focusing on only semantic instances that only salient in a scene gains more benefits for robot navigation and self-driving cars than looking at all objects in the whole scene.
This paper pushes the envelope on salient regions in a video to decompose them into semantically meaningful components, namely, semantic salient instances.
We provide the baseline for the new task of video semantic salient instance segmentation (VSSIS), that is, Semantic Instance - Salient Object (SISO) framework.
The SISO framework is simple yet efficient, leveraging advantages of two different segmentation tasks, i.e.semantic instance segmentation and salient object segmentation to eventually fuse them for the final result.
In SISO, we introduce a sequential fusion by looking at overlapping pixels between semantic instances and salient regions to have non-overlapping instances one by one.
We also introduce a recurrent instance propagation to refine the shapes and semantic meanings of instances, and an identity tracking to maintain both the identity and the semantic meaning of instances over the entire video.
Experimental results demonstrated the effectiveness of our SISO baseline, which can handle occlusions in videos.
In addition, to tackle the task of VSSIS, we augment the DAVIS-2017 benchmark dataset by assigning semantic ground-truth for salient instance labels, obtaining SEmantic Salient Instance Video (SESIV) dataset.
Our SESIV dataset consists of 84 high-quality video sequences with pixel-wisely per-frame ground-truth labels.
The optimal degree-of-freedom (DoF) region of the non-coherent multiple-access channels is still unknown in general.
In this paper, we make some progress by deriving the entire optimal DoF region in the case of the two-user single-input multiple-output (SIMO) generic block fading channels.
The achievability is based on a simple training-based scheme.
The novelty of our result lies in the converse using a genie-aided bound and the duality upper bound.
As a by-product, our result generalizes previous proofs for the single-user Rayleigh block fading channels.
Collections of biological specimens are fundamental to scientific understanding and characterization of natural diversity.
This paper presents a system for liberating useful information from physical collections by bringing specimens into the digital domain so they can be more readily shared, analyzed, annotated and compared.
It focuses on insects and is strongly motivated by the desire to accelerate and augment current practices in insect taxonomy which predominantly use text, 2D diagrams and images to describe and characterize species.
While these traditional kinds of descriptions are informative and useful, they cannot cover insect specimens "from all angles" and precious specimens are still exchanged between researchers and collections for this reason.
Furthermore, insects can be complex in structure and pose many challenges to computer vision systems.
We present a new prototype for a practical, cost-effective system of off-the-shelf components to acquire natural-colour 3D models of insects from around 3mm to 30mm in length.
Colour images are captured from different angles and focal depths using a digital single lens reflex (DSLR) camera rig and two-axis turntable.
These 2D images are processed into 3D reconstructions using software based on a visual hull algorithm.
The resulting models are compact (around 10 megabytes), afford excellent optical resolution, and can be readily embedded into documents and web pages, as well as viewed on mobile devices.
The system is portable, safe, relatively affordable, and complements the sort of volumetric data that can be acquired by computed tomography.
This system provides a new way to augment the description and documentation of insect species holotypes, reducing the need to handle or ship specimens.
It opens up new opportunities to collect data for research, education, art, entertainment, biodiversity assessment and biosecurity control.
Echocardiography is essential to modern cardiology.
However, human interpretation limits high throughput analysis, limiting echocardiography from reaching its full clinical and research potential for precision medicine.
Deep learning is a cutting-edge machine-learning technique that has been useful in analyzing medical images but has not yet been widely applied to echocardiography, partly due to the complexity of echocardiograms' multi view, multi modality format.
The essential first step toward comprehensive computer assisted echocardiographic interpretation is determining whether computers can learn to recognize standard views.
To this end, we anonymized 834,267 transthoracic echocardiogram (TTE) images from 267 patients (20 to 96 years, 51 percent female, 26 percent obese) seen between 2000 and 2017 and labeled them according to standard views.
Images covered a range of real world clinical variation.
We built a multilayer convolutional neural network and used supervised learning to simultaneously classify 15 standard views.
Eighty percent of data used was randomly chosen for training and 20 percent reserved for validation and testing on never seen echocardiograms.
Using multiple images from each clip, the model classified among 12 video views with 97.8 percent overall test accuracy without overfitting.
Even on single low resolution images, test accuracy among 15 views was 91.7 percent versus 70.2 to 83.5 percent for board-certified echocardiographers.
Confusional matrices, occlusion experiments, and saliency mapping showed that the model finds recognizable similarities among related views and classifies using clinically relevant image features.
In conclusion, deep neural networks can classify essential echocardiographic views simultaneously and with high accuracy.
Our results provide a foundation for more complex deep learning assisted echocardiographic interpretation.
This paper deals with area-based subpixel image registration under rotation-isometric scaling-translation transformation hypothesis.
Our approach is based on a parametrical modeling of geometrically transformed textural image fragments and maximum likelihood estimation of transformation vector between them.
Due to the parametrical approach based on the fractional Brownian motion modeling of the local fragments texture, the proposed estimator MLfBm (ML stands for "Maximum Likelihood" and fBm for "Fractal Brownian motion") has the ability to better adapt to real image texture content compared to other methods relying on universal similarity measures like mutual information or normalized correlation.
The main benefits are observed when assumptions underlying the fBm model are fully satisfied, e.g. for isotropic normally distributed textures with stationary increments.
Experiments on both simulated and real images and for high and weak correlation between registered images show that the MLfBm estimator offers significant improvement compared to other state-of-the-art methods.
It reduces translation vector, rotation angle and scaling factor estimation errors by a factor of about 1.75...2 and it decreases probability of false match by up to 5 times.
Besides, an accurate confidence interval for MLfBm estimates can be obtained from the Cramer-Rao lower bound on rotation-scaling-translation parameters estimation error.
This bound depends on texture roughness, noise level in reference and template images, correlation between these images and geometrical transformation parameters.
We present a Few-Shot Relation Classification Dataset (FewRel), consisting of 70, 000 sentences on 100 relations derived from Wikipedia and annotated by crowdworkers.
The relation of each sentence is first recognized by distant supervision methods, and then filtered by crowdworkers.
We adapt the most recent state-of-the-art few-shot learning methods for relation classification and conduct a thorough evaluation of these methods.
Empirical results show that even the most competitive few-shot learning models struggle on this task, especially as compared with humans.
We also show that a range of different reasoning skills are needed to solve our task.
These results indicate that few-shot relation classification remains an open problem and still requires further research.
Our detailed analysis points multiple directions for future research.
All details and resources about the dataset and baselines are released on http://zhuhao.me/fewrel.
Steering a car through traffic is a complex task that is difficult to cast into algorithms.
Therefore, researchers turn to training artificial neural networks from front-facing camera data stream along with the associated steering angles.
Nevertheless, most existing solutions consider only the visual camera frames as input, thus ignoring the temporal relationship between frames.
In this work, we propose a Convolutional Long Short-Term Memory Recurrent Neural Network (C-LSTM), that is end-to-end trainable, to learn both visual and dynamic temporal dependencies of driving.
Additionally, We introduce posing the steering angle regression problem as classification while imposing a spatial relationship between the output layer neurons.
Such method is based on learning a sinusoidal function that encodes steering angles.
To train and validate our proposed methods, we used the publicly available Comma.ai dataset.
Our solution improved steering root mean square error by 35% over recent methods, and led to a more stable steering by 87%.
In this work, we introduce a compositional framework for the construction of finite abstractions (a.k.a. symbolic models) of interconnected discrete-time control systems.
The compositional scheme is based on the joint dissipativity-type properties of discrete-time control subsystems and their finite abstractions.
In the first part of the paper, we use a notion of so-called storage function as a relation between each subsystem and its finite abstraction to construct compositionally a notion of so-called simulation function as a relation between interconnected finite abstractions and that of control systems.
The derived simulation function is used to quantify the error between the output behavior of the overall interconnected concrete system and that of its finite abstraction.
In the second part of the paper, we propose a technique to construct finite abstractions together with their corresponding storage functions for a class of discrete-time control systems under some incremental passivity property.
We show that if a discrete-time control system is so-called incrementally passivable, then one can construct its finite abstraction by a suitable quantization of the input and state sets together with the corresponding storage function.
Finally, the proposed results are illustrated by constructing a finite abstraction of a network of linear discrete-time control systems and its corresponding simulation function in a compositional way.
The compositional conditions in this example do not impose any restriction on the gains or the number of the subsystems which, in particular, elucidates the effectiveness of dissipativity-type compositional reasoning for networks of systems.
Online social media such as Twitter, Facebook, Wikis and Linkedin have made a great impact on the way we consume information in our day to day life.
Now it has become increasingly important that we come across appropriate content from the social media to avoid information explosion.
In case of Twitter, popular information can be tracked using hashtags.
Studying the characteristics of tweets containing hashtags becomes important for a number of tasks, such as breaking news detection, personalized message recommendation, friends recommendation, and sentiment analysis among others.
In this paper, we have analyzed Twitter data based on trending hashtags, which is widely used nowadays.
We have used event based hashtags to know users' thoughts on those events and to decide whether the rest of the users might find it interesting or not.
We have used topic modeling, which reveals the hidden thematic structure of the documents (tweets in this case) in addition to sentiment analysis in exploring and summarizing the content of the documents.
A technique to find the interestingness of event based twitter hashtag and the associated sentiment has been proposed.
The proposed technique helps twitter follower to read, relevant and interesting hashtag.
With the advancement of technology in the last few decades, leading to the widespread availability of miniaturized sensors and internet-connected things (IoT), security of electronic devices has become a top priority.
Side-channel attack (SCA) is one of the prominent methods to break the security of an encryption system by exploiting the information leaked from the physical devices.
Correlational power attack (CPA) is an efficient power side-channel attack technique, which analyses the correlation between the estimated and measured supply current traces to extract the secret key.
The existing countermeasures to the power attacks are mainly based on reducing the SNR of the leaked data, or introducing large overhead using techniques like power balancing.
This paper presents an attenuated signature AES (AS-AES), which resists SCA with minimal noise current overhead.
AS-AES uses a shunt low-drop-out (LDO) regulator to suppress the AES current signature by 400x in the supply current traces.
The shunt LDO has been fabricated and validated in 130 nm CMOS technology.
System-level implementation of the AS-AES along with noise injection, shows that the system remains secure even after 50K encryptions, with 10x reduction in power overhead compared to that of noise addition alone.
Learning features from massive unlabelled data is a vast prevalent topic for high-level tasks in many machine learning applications.
The recent great improvements on benchmark data sets achieved by increasingly complex unsupervised learning methods and deep learning models with lots of parameters usually requires many tedious tricks and much expertise to tune.
However, filters learned by these complex architectures are quite similar to standard hand-crafted features visually.
In this paper, unsupervised learning methods, such as PCA or auto-encoder, are employed as the building block to learn filter banks at each layer.
The lower layer responses are transferred to the last layer (trans-layer) to form a more complete representation retaining more information.
In addition, some beneficial methods such as local contrast normalization and whitening are added to the proposed deep trans-layer networks to further boost performance.
The trans-layer representations are followed by block histograms with binary encoder schema to learn translation and rotation invariant representations, which are utilized to do high-level tasks such as recognition and classification.
Compared to traditional deep learning methods, the implemented feature learning method has much less parameters and is validated in several typical experiments, such as digit recognition on MNIST and MNIST variations, object recognition on Caltech 101 dataset and face verification on LFW dataset.
The deep trans-layer unsupervised learning achieves 99.45% accuracy on MNIST dataset, 67.11% accuracy on 15 samples per class and 75.98% accuracy on 30 samples per class on Caltech 101 dataset, 87.10% on LFW dataset.
We generalize a result by Carlen and Cordero-Erausquin on the equivalence between the Brascamp-Lieb inequality and the subadditivity of relative entropy by allowing for random transformations (a broadcast channel).
This leads to a unified perspective on several functional inequalities that have been gaining popularity in the context of proving impossibility results.
We demonstrate that the information theoretic dual of the Brascamp-Lieb inequality is a convenient setting for proving properties such as data processing, tensorization, convexity and Gaussian optimality.
Consequences of the latter include an extension of the Brascamp-Lieb inequality allowing for Gaussian random transformations, the determination of the multivariate Wyner common information for Gaussian sources, and a multivariate version of Nelson's hypercontractivity theorem.
Finally we present an information theoretic characterization of a reverse Brascamp-Lieb inequality involving a random transformation (a multiple access channel).
Gradient descent training techniques are remarkably successful in training analog-valued artificial neural networks (ANNs).
Such training techniques, however, do not transfer easily to spiking networks due to the spike generation hard non-linearity and the discrete nature of spike communication.
We show that in a feedforward spiking network that uses a temporal coding scheme where information is encoded in spike times instead of spike rates, the network input-output relation is differentiable almost everywhere.
Moreover, this relation is piece-wise linear after a transformation of variables.
Methods for training ANNs thus carry directly to the training of such spiking networks as we show when training on the permutation invariant MNIST task.
In contrast to rate-based spiking networks that are often used to approximate the behavior of ANNs, the networks we present spike much more sparsely and their behavior can not be directly approximated by conventional ANNs.
Our results highlight a new approach for controlling the behavior of spiking networks with realistic temporal dynamics, opening up the potential for using these networks to process spike patterns with complex temporal information.
In the recent years it turned out that multidimensional recurrent neural networks (MDRNN) perform very well for offline handwriting recognition tasks like the OpenHaRT 2013 evaluation DIR.
With suitable writing preprocessing and dictionary lookup, our ARGUS software completed this task with an error rate of 26.27% in its primary setup.
Deep neural networks (DNNs) have been proven to have many redundancies.
Hence, many efforts have been made to compress DNNs.
However, the existing model compression methods treat all the input samples equally while ignoring the fact that the difficulties of various input samples being correctly classified are different.
To address this problem, DNNs with adaptive dropping mechanism are well explored in this work.
To inform the DNNs how difficult the input samples can be classified, a guideline that contains the information of input samples is introduced to improve the performance.
Based on the developed guideline and adaptive dropping mechanism, an innovative soft-guided adaptively-dropped (SGAD) neural network is proposed in this paper.
Compared with the 32 layers residual neural networks, the presented SGAD can reduce the FLOPs by 77% with less than 1% drop in accuracy on CIFAR-10.
The ability to consolidate information of different types is at the core of intelligence, and has tremendous practical value in allowing learning for one task to benefit from generalizations learned for others.
In this paper we tackle the challenging task of improving semantic parsing performance, taking UCCA parsing as a test case, and AMR, SDP and Universal Dependencies (UD) parsing as auxiliary tasks.
We experiment on three languages, using a uniform transition-based system and learning architecture for all parsing tasks.
Despite notable conceptual, formal and domain differences, we show that multitask learning significantly improves UCCA parsing in both in-domain and out-of-domain settings.
This paper studies the performance of a feedback control loop closed via an error-free digital communication channel with transmission delay.
The system comprises a discrete-time noisy linear time-invariant (LTI) plant whose single measurement output is mapped into its single control input by a causal, but otherwise arbitrary, coding and control scheme.
We consider a single-input multiple-output (SIMO) channel between the encoder-controller and the decoder-controller which is lossless and imposes random time delay.
We derive a lower bound on the minimum average feedback data rate that guarantees achieving a certain level of average quadratic performance over all possible realizations of the random delay.
For the special case of a constant channel delay, we obtain an upper bound by proposing linear source-coding schemes that attain desired performance levels with rates that are at most 1.254 bits per sample greater than the lower bound.
We give a numerical example demonstrating that bounds and operational rates are increasing functions of the constant delay.
In other words, to achieve a specific performance level, greater channel delay necessitates spending higher data rate.
We propose a new clustering method based on optimal transportation.
We solve optimal transportation with variational principles, and investigate the use of power diagrams as transportation plans for aggregating arbitrary domains into a fixed number of clusters.
We iteratively drive centroids through target domains while maintaining the minimum clustering energy by adjusting the power diagrams.
Thus, we simultaneously pursue clustering and the Wasserstein distances between the centroids and the target domains, resulting in a measure-preserving mapping.
We demonstrate the use of our method in domain adaptation, remeshing, and representation learning on synthetic and real data.
A Petri net is structurally cyclic if every configuration is reachable from itself in one or more steps.
We show that structural cyclicity is decidable in deterministic polynomial time.
For this, we adapt the Kosaraju's approach for the general reachability problem for Petri nets.
This paper presents text normalization which is an integral part of any text-to-speech synthesis system.
Text normalization is a set of methods with a task to write non-standard words, like numbers, dates, times, abbreviations, acronyms and the most common symbols, in their full expanded form are presented.
The whole taxonomy for classification of non-standard words in Croatian language together with rule-based normalization methods combined with a lookup dictionary are proposed.
Achieved token rate for normalization of Croatian texts is 95%, where 80% of expanded words are in correct morphological form.
Abstract Machine understanding of questions is tightly related to recognition of articulation in the context of the computational capabilities of an underlying processing algorithm.
In this paper a mathematical model to capture and distinguish the latent structure in the articulation of questions is presented.
We propose an objective-driven approach to represent this latent structure and show that such an approach is beneficial when examples of complementary objectives are not available.
We show that the latent structure can be represented as a system that maximizes a cost function related to the underlying objective.
Further, we show that the optimization formulation can be approximated to building a memory of patterns represented as a trained neural auto-encoder.
Experimental evaluation using many clusters of questions, each related to an objective, shows 80% recognition accuracy and negligible false positive across these clusters of questions.
We then extend the same memory to a related task where the goal is to iteratively refine a dataset of questions based on the latent articulation.
We also demonstrate a refinement scheme called K-fingerprints, that achieves nearly 100% recognition with negligible false positive across the different clusters of questions.
The paper exploits weak Manhattan constraints to parse the structure of indoor environments from RGB-D video sequences in an online setting.
We extend the previous approach for single view parsing of indoor scenes to video sequences and formulate the problem of recovering the floor plan of the environment as an optimal labeling problem solved using dynamic programming.
The temporal continuity is enforced in a recursive setting, where labeling from previous frames is used as a prior term in the objective function.
In addition to recovery of piecewise planar weak Manhattan structure of the extended environment, the orthogonality constraints are also exploited by visual odometry and pose graph optimization.
This yields reliable estimates in the presence of large motions and absence of distinctive features to track.
We evaluate our method on several challenging indoors sequences demonstrating accurate SLAM and dense mapping of low texture environments.
On existing TUM benchmark we achieve competitive results with the alternative approaches which fail in our environments.
We are motivated by the need for a generic object proposal generation algorithm which achieves good balance between object detection recall, proposal localization quality and computational efficiency.
We propose a novel object proposal algorithm, BING++, which inherits the virtue of good computational efficiency of BING but significantly improves its proposal localization quality.
At high level we formulate the problem of object proposal generation from a novel probabilistic perspective, based on which our BING++ manages to improve the localization quality by employing edges and segments to estimate object boundaries and update the proposals sequentially.
We propose learning the parameters efficiently by searching for approximate solutions in a quantized parameter space for complexity reduction.
We demonstrate the generalization of BING++ with the same fixed parameters across different object classes and datasets.
Empirically our BING++ can run at half speed of BING on CPU, but significantly improve the localization quality by 18.5% and 16.7% on both VOC2007 and Microhsoft COCO datasets, respectively.
Compared with other state-of-the-art approaches, BING++ can achieve comparable performance, but run significantly faster.
Identifying the occurrence of congestion in a Mobile Ad-hoc Network (MANET) is a major task.
The inbuilt congestion control techniques of existing Transmission Control Protocol (TCP) designed for wired networks do not handle the unique properties of shared wireless multi-hop link.
There are several approaches proposed for detecting and overcoming the congestion in the mobile ad-hoc network.
In this paper we present a Modified AD-hoc Transmission Control Protocol (M-ADTCP) method where the receiver detects the probable current network status and transmits this information to the sender as feedback.
The sender behavior is altered appropriately.
The proposed technique is also compatible with standard TCP.
We present an algorithm that incorporates a tabu search procedure into the framework of path relinking to tackle the job shop scheduling problem (JSP).
This tabu search/path relinking (TS/PR) algorithm comprises several distinguishing features, such as a specific relinking procedure and a reference solution determination method.
To test the performance of TS/PR, we apply it to tackle almost all of the benchmark JSP instances available in the literature.
The test results show that TS/PR obtains competitive results compared with state-of-the-art algorithms for JSP in the literature, demonstrating its efficacy in terms of both solution quality and computational efficiency.
In particular, TS/PR is able to improve the upper bounds for 49 out of the 205 tested instances and it solves a challenging instance that has remained unsolved for over 20 years.
This paper introduces a new computing model based on the cooperation among Turing machines called orchestrated machines.
Like universal Turing machines, orchestrated machines are also designed to simulate Turing machines but they can also modify the original operation of the included Turing machines to create a new layer of some kind of collective behavior.
Using this new model we can define some interested notions related to cooperation ability of Turing machines such as the intelligence quotient or the emotional intelligence quotient for Turing machines.
Social Networking Sites (SNSs) are powerful marketing and communication tools.
There are hundreds of SNSs that have entered and exited the market over time.
The coexistence of multiple SNSs is a rarely observed phenomenon.
Most coexisting SNSs either serve different purposes for its users or have cultural differences among them.
The introduction of a new SNS with a better set of features can lead to the demise of an existing SNS, as observed in the transition from Orkut to Facebook.
The paper proposes a model for analyzing the transition of users from one SNS to another, when a new SNS is introduced in the system.
The game theoretic model proposed considers two major factors in determining the success of a new SNS.
The first being time that an old SNS gets to stabilise.
We study whether the time that a SNS like Facebook received to monopolize its reach had a distinguishable effect.
The second factor is the set of features showcased by the new SNS.
The results of the model are also experimentally verified with data collected by means of a survey.
Many real-world applications are characterized by a number of conflicting performance measures.
As optimizing in a multi-objective setting leads to a set of non-dominated solutions, a preference function is required for selecting the solution with the appropriate trade-off between the objectives.
The question is: how good do estimations of these objectives have to be in order for the solution maximizing the preference function to remain unchanged?
In this paper, we introduce the concept of preference radius to characterize the robustness of the preference function and provide guidelines for controlling the quality of estimations in the multi-objective setting.
More specifically, we provide a general formulation of multi-objective optimization under the bandits setting.
We show how the preference radius relates to the optimal gap and we use this concept to provide a theoretical analysis of the Thompson sampling algorithm from multivariate normal priors.
We finally present experiments to support the theoretical results and highlight the fact that one cannot simply scalarize multi-objective problems into single-objective problems.
Energy consumption is a major limitation of low power and mobile devices.
Efficient transmission protocols are required to minimize an energy consumption of the mobile devices for ubiquitous connectivity in the next generation wireless networks.
Opportunistic schemes select a single relay using the criteria of the best channel and achieve a near-optimal diversity performance in a cooperative wireless system.
In this paper, we study the energy efficiency of the opportunistic schemes for device-to-device communication.
In the opportunistic approach, an energy consumed by devices is minimized by selecting a single neighboring device as a relay using the criteria of minimum consumed energy in each transmission in the uplink of a wireless network.
We derive analytical bounds and scaling laws on the expected energy consumption when the devices experience log-normal shadowing with respect to a base station considering both the transmission as well as circuit energy consumptions.
We show that the protocol improves the energy efficiency of the network comparing to the direct transmission even if only a few devices are considered for relaying.
We also demonstrate the effectiveness of the protocol by means of simulations in realistic scenarios of the wireless network.
It has been shown that an extension of the basic binary polar transformation also polarizes over finite fields.
With it the direct encoding of q-ary sources and channels is a process that can be implemented with simple and efficient algorithms.
However, direct polar decoding of q-ary sources and channels is more involved.
In this paper we obtain a recursive equation for the likelihood ratio expressed as a LR vector.
With it successive cancellation (SC) decoding is applied in a straightforward way.
The complexity is quadratic in the order of the field, but the use of the LR vector introduces factors that soften that complexity.
We also show that operations can be parallelized in the decoder.
The Bhattacharyya parameters are expressed as a function of the LR vectors, as in the binary case, simplifying the construction of the codes.
We have applied direct polar coding to several sources and channels and we have compared it with other multilevel strategies.
The direct q-ary polar coding is closer to the theoretical limit than other techniques when the alphabet size is large.
Our results suggest that direct q-ary polar coding could be used in real scenarios.
We introduce a corpus of 7,032 sentences rated by human annotators for formality, informativeness, and implicature on a 1-7 scale.
The corpus was annotated using Amazon Mechanical Turk.
Reliability in the obtained judgments was examined by comparing mean ratings across two MTurk experiments, and correlation with pilot annotations (on sentence formality) conducted in a more controlled setting.
Despite the subjectivity and inherent difficulty of the annotation task, correlations between mean ratings were quite encouraging, especially on formality and informativeness.
We further explored correlation between the three linguistic variables, genre-wise variation of ratings and correlations within genres, compatibility with automatic stylistic scoring, and sentential make-up of a document in terms of style.
To date, our corpus is the largest sentence-level annotated corpus released for formality, informativeness, and implicature.
Truck Factor (TF) is a metric proposed by the agile community as a tool to identify concentration of knowledge in software development environments.
It states the minimal number of developers that have to be hit by a truck (or quit) before a project is incapacitated.
In other words, TF helps to measure how prepared is a project to deal with developer turnover.
Despite its clear relevance, few studies explore this metric.
Altogether there is no consensus about how to calculate it, and no supporting evidence backing estimates for systems in the wild.
To mitigate both issues, we propose a novel (and automated) approach for estimating TF-values, which we execute against a corpus of 133 popular project in GitHub.
We later survey developers as a means to assess the reliability of our results.
Among others, we find that the majority of our target systems (65%) have TF <= 2.
Surveying developers from 67 target systems provides confidence towards our estimates; in 84% of the valid answers we collect, developers agree or partially agree that the TF's authors are the main authors of their systems; in 53% we receive a positive or partially positive answer regarding our estimated truck factors.
Different neural networks trained on the same dataset often learn similar input-output mappings with very different weights.
Is there some correspondence between these neural network solutions?
For linear networks, it has been shown that different instances of the same network architecture encode the same representational similarity matrix, and their neural activity patterns are connected by orthogonal transformations.
However, it is unclear if this holds for non-linear networks.
Using a shared response model, we show that different neural networks encode the same input examples as different orthogonal transformations of an underlying shared representation.
We test this claim using both standard convolutional neural networks and residual networks on CIFAR10 and CIFAR100.
Depth estimation from a single image in the wild remains a challenging problem.
One main obstacle is the lack of high-quality training data for images in the wild.
In this paper we propose a method to automatically generate such data through Structure-from-Motion (SfM) on Internet videos.
The core of this method is a Quality Assessment Network that identifies high-quality reconstructions obtained from SfM.
Using this method, we collect single-view depth training data from a large number of YouTube videos and construct a new dataset called YouTube3D.
Experiments show that YouTube3D is useful in training depth estimation networks and advances the state of the art of single-view depth estimation in the wild.
We present probabilistic neural programs, a framework for program induction that permits flexible specification of both a computational model and inference algorithm while simultaneously enabling the use of deep neural networks.
Probabilistic neural programs combine a computation graph for specifying a neural network with an operator for weighted nondeterministic choice.
Thus, a program describes both a collection of decisions as well as the neural network architecture used to make each one.
We evaluate our approach on a challenging diagram question answering task where probabilistic neural programs correctly execute nearly twice as many programs as a baseline model.
In this paper, we tackle the problem of constructing a differentially private synopsis for two-dimensional datasets such as geospatial datasets.
The current state-of-the-art methods work by performing recursive binary partitioning of the data domains, and constructing a hierarchy of partitions.
We show that the key challenge in partition-based synopsis methods lies in choosing the right partition granularity to balance the noise error and the non-uniformity error.
We study the uniform-grid approach, which applies an equi-width grid of a certain size over the data domain and then issues independent count queries on the grid cells.
This method has received no attention in the literature, probably due to the fact that no good method for choosing a grid size was known.
Based on an analysis of the two kinds of errors, we propose a method for choosing the grid size.
Experimental results validate our method, and show that this approach performs as well as, and often times better than, the state-of-the-art methods.
We further introduce a novel adaptive-grid method.
The adaptive grid method lays a coarse-grained grid over the dataset, and then further partitions each cell according to its noisy count.
Both levels of partitions are then used in answering queries over the dataset.
This method exploits the need to have finer granularity partitioning over dense regions and, at the same time, coarse partitioning over sparse regions.
Through extensive experiments on real-world datasets, we show that this approach consistently and significantly outperforms the uniform-grid method and other state-of-the-art methods.
Strategic suppression of grades, as well as early offers and contracts, are well-known phenomena in the matching process where graduating students apply to jobs or further education.
In this paper, we consider a game theoretic model of these phenomena introduced by Ostrovsky and Schwarz, and study the loss in social welfare resulting from strategic behavior of the schools, employers, and students.
We model grading of students as a game where schools suppress grades in order to improve their students' placements.
We also consider the quality loss due to unraveling of the matching market, the strategic behavior of students and employers in offering early contracts with the goal to improve the quality.
Our goal is to evaluate if strategic grading or unraveling of the market (or a combination of the two) can cause significant welfare loss compared to the optimal assignment of students to jobs.
To measure welfare of the assignment, we assume that welfare resulting from a job -- student pair is a separable and monotone function of student ability and the quality of the jobs.
Assuming uniform student quality distribution, we show that the quality loss from the above strategic manipulation is bounded by at most a factor of 2, and give improved bounds for some special cases of welfare functions.
Medical errors are leading causes of death in the US and as such, prevention of these errors is paramount to promoting health care.
Patient Safety Event reports are narratives describing potential adverse events to the patients and are important in identifying and preventing medical errors.
We present a neural network architecture for identifying the type of safety events which is the first step in understanding these narratives.
Our proposed model is based on a soft neural attention model to improve the effectiveness of encoding long sequences.
Empirical results on two large-scale real-world datasets of patient safety reports demonstrate the effectiveness of our method with significant improvements over existing methods.
The rapidly growing size of RDF graphs in recent years necessitates distributed storage and parallel processing strategies.
To obtain efficient query processing using computer clusters a wide variety of different approaches have been proposed.
Related to the approach presented in the current paper are systems built on top of Hadoop HDFS, for example using Apache Accumulo or using Apache Spark.
We present a new RDF store called PRoST (Partitioned RDF on Spark Tables) based on Apache Spark.
PRoST introduces an innovative strategy that combines the Vertical Partitioning approach with the Property Table, two preexisting models for storing RDF datasets.
We demonstrate that our proposal outperforms state-of-the-art systems w.r.t. the runtime for a wide range of query types and without any extensive precomputing phase.
Tracking moving objects from a video sequence requires segmentation of these objects from the background image.
However, getting the actual background image automatically without object detection and using only the video is difficult.
In this paper, we describe a novel algorithm that generates background from real world images without foreground detection.
The algorithm assumes that the background image is shown in the majority of the video.
Given this simple assumption, the method described in this paper is able to accurately generate, with high probability, the background image from a video using only a small number of binary operations.
Edge bundling is an important concept heavily used for graph visualization purposes.
To enable the comparison with other established near-planarity models in graph drawing, we formulate a new edge-bundling model which is inspired by the recently introduced fan-planar graphs.
In particular, we restrict the bundling to the end segments of the edges.
Similarly to 1-planarity, we call our model 1-fan-bundle-planarity, as we allow at most one crossing per bundle.
For the two variants where we allow either one or, more naturally, both end segments of each edge to be part of bundles, we present edge density results and consider various recognition questions, not only for general graphs, but also for the outer and 2-layer variants.
We conclude with a series of challenging questions.
The notion of a Persistent Phylogeny generalizes the well-known Perfect phylogeny model that has been thoroughly investigated and is used to explain a wide range of evolutionary phenomena.
More precisely, while the Perfect Phylogeny model allows each character to be acquired once in the entire evolutionary history while character losses are not allowed, the Persistent Phylogeny model allows each character to be both acquired and lost exactly once in the evolutionary history.
The Persistent Phylogeny Problem (PPP) is the problem of reconstructing a Persistent phylogeny tree, if it exists, from a binary matrix where the rows represent the species (or the individuals) studied and the columns represent the characters that each species can have.
While the Perfect Phylogeny has a linear-time algorithm, the computational complexity of PPP has been posed, albeit in an equivalent formulation, 20 years ago.
We settle the question by providing a polynomial time algorithm for the Persistent Phylogeny problem.
The scale of Android applications in the market is growing rapidly.
To efficiently detect the malicious behavior in these applications, an array of static analysis tools are proposed.
However, static analysis tools suffer from code hiding techniques like packing, dynamic loading, self modifying, and reflection.
In this paper, we thus present DexLego, a novel system that performs a reassembleable bytecode extraction for aiding static analysis tools to reveal the malicious behavior of Android applications.
DexLego leverages just-in-time collection to extract data and bytecode from an application at runtime, and reassembles them to a new Dalvik Executable (DEX) file offline.
The experiments on DroidBench and real-world applications show that DexLego correctly reconstructs the behavior of an application in the reassembled DEX file, and significantly improves analysis result of the existing static analysis systems.
We present a novel stereo vision algorithm that is capable of obstacle detection on a mobile-CPU processor at 120 frames per second.
Our system performs a subset of standard block-matching stereo processing, searching only for obstacles at a single depth.
By using an onboard IMU and state-estimator, we can recover the position of obstacles at all other depths, building and updating a full depth-map at framerate.
Here, we describe both the algorithm and our implementation on a high-speed, small UAV, flying at over 20 MPH (9 m/s) close to obstacles.
The system requires no external sensing or computation and is, to the best of our knowledge, the first high-framerate stereo detection system running onboard a small UAV.
Internet of Things is changing the world.
The manufacturing industry has already identified that the IoT brings great opportunities to retain its leading position in economy and society.
However, the adoption of this new technology changes the development process of the manufacturing system and raises many challenges.
In this paper the modern manufacturing system is considered as a composition of cyber-physical, cyber and human components and IoT is used as a glue for their integration as far as it regards their cyber interfaces.
The key idea is a UML profile for the IoT with an alternative to apply the approach also at the source code level specification of the component in case that a UML design specification is not available.
The proposed approach, namely UML4IoT, fully automates the generation process of the IoT-compliant layer that is required for the cyber-physical component to be integrated in the modern IoT manufacturing environment.
A prototype implementation of the myLiqueur laboratory system has been developed to demonstrate the applicability and effectiveness of the UML4IoT approach.
This paper proposes a novel approach for uncertainty quantification in dense Conditional Random Fields (CRFs).
The presented approach, called Perturb-and-MPM, enables efficient, approximate sampling from dense multi-label CRFs via random perturbations.
An analytic error analysis was performed which identified the main cause of approximation error as well as showed that the error is bounded.
Spatial uncertainty maps can be derived from the Perturb-and-MPM model, which can be used to visualize uncertainty in image segmentation results.
The method is validated on synthetic and clinical Magnetic Resonance Imaging data.
The effectiveness of the approach is demonstrated on the challenging problem of segmenting the tumor core in glioblastoma.
We found that areas of high uncertainty correspond well to wrongly segmented image regions.
Furthermore, we demonstrate the potential use of uncertainty maps to refine imaging biomarkers in the case of extent of resection and residual tumor volume in brain tumor patients.
Graphics Processing Units allow for running massively parallel applications offloading the CPU from computationally intensive resources, however GPUs have a limited amount of memory.
In this paper a trie compression algorithm for massively parallel pattern matching is presented demonstrating 85% less space requirements than the original highly efficient parallel failure-less aho-corasick, whilst demonstrating over 22 Gbps throughput.
The algorithm presented takes advantage of compressed row storage matrices as well as shared and texture memory on the GPU.
The h-index can be a useful metric for evaluating a person's output of Internet media.
Here we advocate and demonstrate adaption of the h-index and the g-index to the top video content creators on YouTube.
The h-index for Internet video media is based on videos and their view counts.
The index h is defined as the number of videos with >= h*10^5 views.
The index g is defined as the number of videos with >= g*10^5 views on average.
When compared to a video creator's total view count, the h-index and g-index better capture both productivity and impact in a single metric.
Surprisingly promising results have been achieved by deep learning (DL) systems in recent years.
Many of these achievements have been reached in academic settings, or by large technology companies with highly skilled research groups and advanced supporting infrastructure.
For companies without large research groups or advanced infrastructure, building high-quality production-ready systems with DL components has proven challenging.
There is a clear lack of well-functioning tools and best practices for building DL systems.
It is the goal of this research to identify what the main challenges are, by applying an interpretive research approach in close collaboration with companies of varying size and type.
A set of seven projects have been selected to describe the potential with this new technology and to identify associated main challenges.
A set of 12 main challenges has been identified and categorized into the three areas of development, production, and organizational challenges.
Furthermore, a mapping between the challenges and the projects is defined, together with selected motivating descriptions of how and why the challenges apply to specific projects.
Compared to other areas such as software engineering or database technologies, it is clear that DL is still rather immature and in need of further work to facilitate development of high-quality systems.
The challenges identified in this paper can be used to guide future research by the software engineering and DL communities.
Together, we could enable a large number of companies to start taking advantage of the high potential of the DL technology.
The sustainability of any Data Warehouse System (DWS) is closely correlated with user satisfaction.
Therefore, analysts, designers and developers focused more on achieving all its functionality, without considering others kinds of requirement such as dependability s aspects.
Moreover, these latter are often considered as properties of the system that will must be checked and corrected once the project is completed.
The practice of "fix it later" can cause the obsolescence of the entire Data Warehouse System.
Therefore, it requires the adoption of a methodology that will ensure the integration of aspects of dependability since the early stages of project DWS.
In this paper, we first define the concepts related to dependability of DWS.
Then we present our approach inspired from the MDA (Model Driven Architecture) approach to model dependability s aspects namely: availability, reliability, maintainability and security, taking into account their interaction.
Cloud computing changed the way of computing as utility services offered through public network.
Selecting multiple providers for various computational requirements improves performance and minimizes cost of cloud services than choosing a single cloud provider.
Federated cloud improves scalability, cost minimization, performance maximization, collaboration with other providers, multi-site deployment for fault tolerance and recovery, reliability and less energy consumption.
Both providers and consumers could benefit from federated cloud where providers serve the consumers by satisfying Service Level Agreement, minimizing overall management and infrastructure cost; consumers get best services with less deployment cost and high availability.
Efficient provisioning of resources to consumers in federated cloud is a challenging task.
In this paper, the benefits of utilizing services from federated cloud, architecture with various coupling levels, different optimized resource provisioning methods and challenges associated with it are discussed and a comparative study is carried out over these aspects.
To cluster sequences given only their read-set representations, one may try to reconstruct each one from the corresponding read set, and then employ conventional (dis)similarity measures such as the edit distance on the assembled sequences.
This approach is however problematic and we propose instead to estimate the similarities directly from the read sets.
Our approach is based on an adaptation of the Monge-Elkan similarity known from the field of databases.
It avoids the NP-hard problem of sequence assembly.
For low coverage data it results in a better approximation of the true sequence similarities and consequently in better clustering, in comparison to the first-assemble-then-cluster approach.
While off-policy temporal difference (TD) methods have widely been used in reinforcement learning due to their efficiency and simple implementation, their Bayesian counterparts have not been utilized as frequently.
One reason is that the non-linear max operation in the Bellman optimality equation makes it difficult to define conjugate distributions over the value functions.
In this paper, we introduce a novel Bayesian approach to off-policy TD methods, called as ADFQ, which updates beliefs on state-action values, Q, through an online Bayesian inference method known as Assumed Density Filtering.
In order to formulate a closed-form update, we approximately estimate analytic parameters of the posterior of the Q-beliefs.
Uncertainty measures in the beliefs not only are used in exploration but also provide a natural regularization for learning.
We show that ADFQ converges to Q-learning as the uncertainty measures of the Q-beliefs decrease.
ADFQ improves common drawbacks of other Bayesian RL algorithms such as computational complexity.
We also extend ADFQ with a neural network.
Our empirical results demonstrate that the proposed ADFQ algorithm outperforms comparable algorithms on various domains including continuous state domains and games from the Arcade Learning Environment.
Comparison between multidimensional persistent Betti numbers is often based on the multidimensional matching distance.
While this metric is rather simple to define and compute by considering a suitable family of filtering functions associated with lines having a positive slope, it has two main drawbacks.
First, it forgets the natural link between the homological properties of filtrations associated with lines that are close to each other.
As a consequence, part of the interesting homological information is lost.
Second, its intrinsically discontinuous definition makes it difficult to study its properties.
In this paper we introduce a new matching distance for 2D persistent Betti numbers, called coherent matching distance and based on matchings that change coherently with the filtrations we take into account.
Its definition is not trivial, as it must face the presence of monodromy in multidimensional persistence, i.e. the fact that different paths in the space parameterizing the above filtrations can induce different matchings between the associated persistent diagrams.
In our paper we prove that the coherent 2D matching distance is well-defined and stable.
In order to disseminate the exponential extent of knowledge being produced in the form of scientific publications, it would be best to design mechanisms that connect it with already existing rich repository of concepts -- the Wikipedia.
Not only does it make scientific reading simple and easy (by connecting the involved concepts used in the scientific articles to their Wikipedia explanations) but also improves the overall quality of the article.
In this paper, we present a novel metapath based method, WikiM, to efficiently wikify scientific abstracts -- a topic that has been rarely investigated in the literature.
One of the prime motivations for this work comes from the observation that, wikified abstracts of scientific documents help a reader to decide better, in comparison to the plain abstracts, whether (s)he would be interested to read the full article.
We perform mention extraction mostly through traditional tf-idf measures coupled with a set of smart filters.
The entity linking heavily leverages on the rich citation and author publication networks.
Our observation is that various metapaths defined over these networks can significantly enhance the overall performance of the system.
For mention extraction and entity linking, we outperform most of the competing state-of-the-art techniques by a large margin arriving at precision values of 72.42% and 73.8% respectively over a dataset from the ACL Anthology Network.
In order to establish the robustness of our scheme, we wikify three other datasets and get precision values of 63.41%-94.03% and 67.67%-73.29% respectively for the mention extraction and the entity linking phase.
In future traffic scenarios, vehicles and other traffic participants will be interconnected and equipped with various types of sensors, allowing for cooperation based on data or information exchange.
This article presents an approach to cooperative tracking of cyclists using smart devices and infrastructure-based sensors.
A smart device is carried by the cyclists and an intersection is equipped with a wide angle stereo camera system.
Two tracking models are presented and compared.
The first model is based on the stereo camera system detections only, whereas the second model cooperatively combines the camera based detections with velocity and yaw rate data provided by the smart device.
Our aim is to overcome limitations of tracking approaches based on single data sources.
We show in numerical evaluations on scenes where cyclists are starting or turning right that the cooperation leads to an improvement in both the ability to keep track of a cyclist and the accuracy of the track particularly when it comes to occlusions in the visual system.
We, therefore, contribute to the safety of vulnerable road users in future traffic.
Lifted Relational Neural Networks (LRNNs) describe relational domains using weighted first-order rules which act as templates for constructing feed-forward neural networks.
While previous work has shown that using LRNNs can lead to state-of-the-art results in various ILP tasks, these results depended on hand-crafted rules.
In this paper, we extend the framework of LRNNs with structure learning, thus enabling a fully automated learning process.
Similarly to many ILP methods, our structure learning algorithm proceeds in an iterative fashion by top-down searching through the hypothesis space of all possible Horn clauses, considering the predicates that occur in the training examples as well as invented soft concepts entailed by the best weighted rules found so far.
In the experiments, we demonstrate the ability to automatically induce useful hierarchical soft concepts leading to deep LRNNs with a competitive predictive power.
The aim of this study is to show the importance of two classification techniques, viz. decision tree and clustering, in prediction of learning disabilities (LD) of school-age children.
LDs affect about 10 percent of all children enrolled in schools.
The problems of children with specific learning disabilities have been a cause of concern to parents and teachers for some time.
Decision trees and clustering are powerful and popular tools used for classification and prediction in Data mining.
Different rules extracted from the decision tree are used for prediction of learning disabilities.
Clustering is the assignment of a set of observations into subsets, called clusters, which are useful in finding the different signs and symptoms (attributes) present in the LD affected child.
In this paper, J48 algorithm is used for constructing the decision tree and K-means algorithm is used for creating the clusters.
By applying these classification techniques, LD in any child can be identified.
Smartphone apps provide a vitally important opportunity for monitoring human mobility, human experience of ubiquitous information aids, and human activity in our increasingly well-instrumented spaces.
As wireless data capabilities move steadily up in performance, from 2&3G to 4G (today's LTE) and 5G, it has become more important to measure human activity in this connected world from the phones themselves.
The newer protocols serve larger areas than ever before and a wider range of data, not just voice calls, so only the phone can accurately measure its location.
Access to the application activity permits not only monitoring the performance and spatial coverage with which the users are served, but as a crowd-sourced, unbiased background source of input on all these subjects, becomes a uniquely valuable resource for input to social science and government as well as telecom providers
Membership inference attacks seek to infer membership of individual training instances of a model to which an adversary has black-box access through a machine learning-as-a-service API.
In providing an in-depth characterization of membership privacy risks against machine learning models, this paper presents a comprehensive study towards demystifying membership inference attacks from two complimentary perspectives.
First, we provide a generalized formulation of the development of a black-box membership inference attack model.
Second, we characterize the importance of model choice on model vulnerability through a systematic evaluation of a variety of machine learning models and model combinations using multiple datasets.
Through formal analysis and empirical evidence from extensive experimentation, we characterize under what conditions a model may be vulnerable to such black-box membership inference attacks.
We show that membership inference vulnerability is data-driven and corresponding attack models are largely transferable.
Though different model types display different vulnerabilities to membership inference, so do different datasets.
Our empirical results additionally show that (1) using the type of target model under attack within the attack model may not increase attack effectiveness and (2) collaborative learning exposes vulnerabilities to membership inference risks when the adversary is a participant.
We also discuss countermeasure and mitigation strategies.
Dropout is a simple yet effective algorithm for regularizing neural networks by randomly dropping out units through Bernoulli multiplicative noise, and for some restricted problem classes, such as linear or logistic regression, several theoretical studies have demonstrated the equivalence between dropout and a fully deterministic optimization problem with data-dependent Tikhonov regularization.
This work presents a theoretical analysis of dropout for matrix factorization, where Bernoulli random variables are used to drop a factor, thereby attempting to control the size of the factorization.
While recent work has demonstrated the empirical effectiveness of dropout for matrix factorization, a theoretical understanding of the regularization properties of dropout in this context remains elusive.
This work demonstrates the equivalence between dropout and a fully deterministic model for matrix factorization in which the factors are regularized by the sum of the product of the norms of the columns.
While the resulting regularizer is closely related to a variational form of the nuclear norm, suggesting that dropout may limit the size of the factorization, we show that it is possible to trivially lower the objective value by doubling the size of the factorization.
We show that this problem is caused by the use of a fixed dropout rate, which motivates the use of a rate that increases with the size of the factorization.
Synthetic experiments validate our theoretical findings.
Energy efficiency is a key requirement for the Internet of Things, as many sensors are expected to be completely stand-alone and able to run for years without battery replacement.
Data compression aims at saving some energy by reducing the volume of data sent over the network, but also affects the quality of the received information.
In this work, we formulate an optimization problem to jointly design the source coding and transmission strategies for time-varying channels and sources, with the twofold goal of extending the network lifetime and granting low distortion levels.
We propose a scalable offline optimal policy that allocates both energy and transmission parameters (i.e., times and powers) in a network with a dynamic Time Division Multiple Access (TDMA)-based access scheme.
After providing a brief historical overview on the synergies between artificial intelligence research, in the areas of evolutionary computations and machine learning, and the optimal design of interplanetary trajectories, we propose and study the use of deep artificial neural networks to represent, on-board, the optimal guidance profile of an interplanetary mission.
The results, limited to the chosen test case of an Earth-Mars orbital transfer, extend the findings made previously for landing scenarios and quadcopter dynamics, opening a new research area in interplanetary trajectory planning.
The University of Cambridge submission to the WMT18 news translation task focuses on the combination of diverse models of translation.
We compare recurrent, convolutional, and self-attention-based neural models on German-English, English-German, and Chinese-English.
Our final system combines all neural models together with a phrase-based SMT system in an MBR-based scheme.
We report small but consistent gains on top of strong Transformer ensembles.
An important goal common to domain adaptation and causal inference is to make accurate predictions when the distributions for the source (or training) domain(s) and target (or test) domain(s) differ.
In many cases, these different distributions can be modeled as different contexts of a single underlying system, in which each distribution corresponds to a different perturbation of the system, or in causal terms, an intervention.
We focus on a class of such causal domain adaptation problems, where data for one or more source domains are given, and the task is to predict the distribution of a certain target variable from measurements of other variables in one or more target domains.
We propose an approach for solving these problems that exploits causal inference and does not rely on prior knowledge of the causal graph, the type of interventions or the intervention targets.
We demonstrate our approach by evaluating a possible implementation on simulated and real world data.
The selection of the best classification algorithm for a given dataset is a very widespread problem, occuring each time one has to choose a classifier to solve a real-world problem.
It is also a complex task with many important methodological decisions to make.
Among those, one of the most crucial is the choice of an appropriate measure in order to properly assess the classification performance and rank the algorithms.
In this article, we focus on this specific task.
We present the most popular measures and compare their behavior through discrimination plots.
We then discuss their properties from a more theoretical perspective.
It turns out several of them are equivalent for classifiers comparison purposes.
Futhermore. they can also lead to interpretation problems.
Among the numerous measures proposed over the years, it appears that the classical overall success rate and marginal rates are the more suitable for classifier comparison task.
In this work we are interested in the problem of energy management in Mobile Ad-hoc Network (MANET).
The solving and optimization of MANET allow assisting the users to efficiently use their devices in order to minimize the batteries power consumption.
In this framework, we propose a modelling of the MANET in form of a Constraint Optimization Problem called COMANET.
Then, in the objective to minimize the consumption of batteries power, we present an approach based on an adaptation of the A star algorithm to the MANET problem called MANED.
Finally, we expose some experimental results showing utility of this approach.
Recent deep learning based denoisers often outperform state-of-the-art conventional denoisers such as BM3D.
They are typically trained to minimize the mean squared error (MSE) between the output of a deep neural network and the ground truth image.
In deep learning based denoisers, it is important to use high quality noiseless ground truth for high performance, but it is often challenging or even infeasible to obtain such a clean image in application areas such as hyperspectral remote sensing and medical imaging.
We propose a Stein's Unbiased Risk Estimator (SURE) based method for training deep neural network denoisers only with noisy images.
We demonstrated that our SURE based method without ground truth was able to train deep neural network denoisers to yield performance close to deep learning denoisers trained with ground truth and to outperform state-of-the-art BM3D.
Further improvements were achieved by including noisy test images for training denoiser networks using our proposed SURE based method.
As the interest in the representation of context dependent knowledge in the Semantic Web has been recognized, a number of logic based solutions have been proposed in this regard.
In our recent works, in response to this need, we presented the description logic-based Contextualized Knowledge Repository (CKR) framework.
CKR is not only a theoretical framework, but it has been effectively implemented over state-of-the-art tools for the management of Semantic Web data: inference inside and across contexts has been realized in the form of forward SPARQL-based rules over different RDF named graphs.
In this paper we present the first evaluation results for such CKR implementation.
In particular, in first experiment we study its scalability with respect to different reasoning regimes.
In a second experiment we analyze the effects of knowledge propagation on the computation of inferences.
Maximum-likelihood estimation (MLE) is widely used in sequence to sequence tasks for model training.
It uniformly treats the generation/prediction of each target token as multi-class classification, and yields non-smooth prediction probabilities: in a target sequence, some tokens are predicted with small probabilities while other tokens are with large probabilities.
According to our empirical study, we find that the non-smoothness of the probabilities results in low quality of generated sequences.
In this paper, we propose a sentence-wise regularization method which aims to output smooth prediction probabilities for all the tokens in the target sequence.
Our proposed method can automatically adjust the weights and gradients of each token in one sentence to ensure the predictions in a sequence uniformly well.
Experiments on three neural machine translation tasks and one text summarization task show that our method outperforms conventional MLE loss on all these tasks and achieves promising BLEU scores on WMT14 English-German and WMT17 Chinese-English translation task.
In this paper the problem of driving the state of a network of identical agents, modeled by boundary-controlled heat equations, towards a common steady-state profile is addressed.
Decentralized consensus protocols are proposed to address two distinct problems.
The first problem is that of steering the states of all agents towards the same constant steady-state profile which corresponds to the spatial average of the agents initial condition.
A linear local interaction rule addressing this requirement is given.
The second problem deals with the case where the controlled boundaries of the agents dynamics are corrupted by additive persistent disturbances.
To achieve synchronization between agents, while completely rejecting the effect of the boundary disturbances, a nonlinear sliding-mode based consensus protocol is proposed.
Performance of the proposed local interaction rules are analyzed by applying a Lyapunov-based approach.
Simulation results are presented to support the effectiveness of the proposed algorithms.
Over the last decade, the rise of the mobile internet and the usage of mobile devices has enabled ubiquitous traffic information.
With the increased adoption of specific smartphone applications, the number of users of routing applications has become large enough to disrupt traffic flow patterns in a significant manner.
Similarly, but at a slightly slower pace, novel services for freight transportation and city logistics improve the efficiency of goods transportation and change the use of road infrastructure.
The present article provides a general four-layer framework for modeling these new trends.
The main motivation behind the development is to provide a unifying formal system description that can at the same time encompass system physics (flow and motion of vehicles) as well as coordination strategies under various information and cooperation structures.
To showcase the framework, we apply it to the specific challenge of modeling and analyzing the integration of routing applications in today's transportation systems.
In this framework, at the lowest layer (flow dynamics) we distinguish app users from non-app users.
A distributed parameter model based on a non-local partial differential equation is introduced and analyzed.
The second layer incorporates connected services (e.g., routing) and other applications used to optimize the local performance of the system.
As inputs to those applications, we propose a third layer introducing the incentive design and global objectives, which are typically varying over the day depending on road and weather conditions, external events etc.
The high-level planning is handled on the fourth layer taking social long-term objectives into account.
We present a light formalism for proofs that encodes their inferential structure, along with a system that transforms these representations into flow-chart diagrams.
Such diagrams should improve the comprehensibility of proofs.
We discuss language syntax, diagram semantics, and our goal of building a repository of diagrammatic representations of proofs from canonical mathematical literature.
The repository will be available online in the form of a wiki at proofflow.org, where the flow chart drawing software will be deployable through the wiki editor.
We also consider the possibility of a semantic tagging of the assertions in a proof, to permit data mining.
We investigate a variant of variational autoencoders where there is a superstructure of discrete latent variables on top of the latent features.
In general, our superstructure is a tree structure of multiple super latent variables and it is automatically learned from data.
When there is only one latent variable in the superstructure, our model reduces to one that assumes the latent features to be generated from a Gaussian mixture model.
We call our model the latent tree variational autoencoder (LTVAE).
Whereas previous deep learning methods for clustering produce only one partition of data, LTVAE produces multiple partitions of data, each being given by one super latent variable.
This is desirable because high dimensional data usually have many different natural facets and can be meaningfully partitioned in multiple ways.
The two significant tasks of a focused Web crawler are finding relevant topic-specific documents on the Web and analytically prioritizing them for later effective and reliable download.
For the first task, we propose a sophisticated custom algorithm to fetch and analyze the most effective HTML structural elements of the page as well as the topical boundary and anchor text of each unvisited link, based on which the topical focus of an unvisited page can be predicted and elicited with a high accuracy.
Thus, our novel method uniquely combines both link-based and content-based approaches.
For the second task, we propose a scoring function of the relevant URLs through the use of T-Graph (Treasure Graph) to assist in prioritizing the unvisited links that will later be put into the fetching queue.
Our Web search system is called the Treasure-Crawler.
This research paper embodies the architectural design of the Treasure-Crawler system which satisfies the principle requirements of a focused Web crawler, and asserts the correctness of the system structure including all its modules through illustrations and by the test results.
The survey data sets are important sources of data and their successful exploitation is of key importance for informed policy-decision making.
We present how a survey analysis approach initially developed for customer satisfaction research in marketing can be adapted for the introduction of clinical pharmacy services into hospital.
We use two analytical approaches to extract relevant managerial consequences.
With OrdEval algorithm we first evaluate the importance of competences for the users of clinical pharmacy and extract their nature according to the users expectations.
Next, we build a model for predicting a successful introduction of clinical pharmacy to the clinical departments.
We the wards with the highest probability of successful cooperation with a clinical pharmacist.
We obtain useful managerially relevant information from a relatively small sample of highly relevant respondents.
We show how the OrdEval algorithm exploits the information hidden in the ordering of class and attribute values and their inherent correlation.
Its output can be effectively visualized and complemented with confidence intervals.
The recently developed variational autoencoders (VAEs) have proved to be an effective confluence of the rich representational power of neural networks with Bayesian methods.
However, most work on VAEs use a rather simple prior over the latent variables such as standard normal distribution, thereby restricting its applications to relatively simple phenomena.
In this work, we propose hierarchical nonparametric variational autoencoders, which combines tree-structured Bayesian nonparametric priors with VAEs, to enable infinite flexibility of the latent representation space.
Both the neural parameters and Bayesian priors are learned jointly using tailored variational inference.
The resulting model induces a hierarchical structure of latent semantic concepts underlying the data corpus, and infers accurate representations of data instances.
We apply our model in video representation learning.
Our method is able to discover highly interpretable activity hierarchies, and obtain improved clustering accuracy and generalization capacity based on the learned rich representations.
Finding minimum distortion of adversarial examples and thus certifying robustness in neural network classifiers for given data points is known to be a challenging problem.
Nevertheless, recently it has been shown to be possible to give a non-trivial certified lower bound of minimum adversarial distortion, and some recent progress has been made towards this direction by exploiting the piece-wise linear nature of ReLU activations.
However, a generic robustness certification for general activation functions still remains largely unexplored.
To address this issue, in this paper we introduce CROWN, a general framework to certify robustness of neural networks with general activation functions for given input data points.
The novelty in our algorithm consists of bounding a given activation function with linear and quadratic functions, hence allowing it to tackle general activation functions including but not limited to four popular choices: ReLU, tanh, sigmoid and arctan.
In addition, we facilitate the search for a tighter certified lower bound by adaptively selecting appropriate surrogates for each neuron activation.
Experimental results show that CROWN on ReLU networks can notably improve the certified lower bounds compared to the current state-of-the-art algorithm Fast-Lin, while having comparable computational efficiency.
Furthermore, CROWN also demonstrates its effectiveness and flexibility on networks with general activation functions, including tanh, sigmoid and arctan.
We propose to solve any algorithm on discrete variables by a technique of statistical estimation using deterministic convex analysis.
In this framework, the variables are represented by their probability and the distinction between the complexity classes vanishes.
The method is illustrated by solving the 3-SAT problem in polynomial time.
Information theory allows us to investigate information processing in neural systems in terms of information transfer, storage and modification.
Especially the measure of information transfer, transfer entropy, has seen a dramatic surge of interest in neuroscience.
Estimating transfer entropy from two processes requires the observation of multiple realizations of these processes to estimate associated probability density functions.
To obtain these observations, available estimators assume stationarity of processes to allow pooling of observations over time.
This assumption however, is a major obstacle to the application of these estimators in neuroscience as observed processes are often non-stationary.
As a solution, Gomez-Herrero and colleagues theoretically showed that the stationarity assumption may be avoided by estimating transfer entropy from an ensemble of realizations.
Such an ensemble is often readily available in neuroscience experiments in the form of experimental trials.
Thus, in this work we combine the ensemble method with a recently proposed transfer entropy estimator to make transfer entropy estimation applicable to non-stationary time series.
We present an efficient implementation of the approach that deals with the increased computational demand of the ensemble method's practical application.
In particular, we use a massively parallel implementation for a graphics processing unit to handle the computationally most heavy aspects of the ensemble method.
We test the performance and robustness of our implementation on data from simulated stochastic processes and demonstrate the method's applicability to magnetoencephalographic data.
While we mainly evaluate the proposed method for neuroscientific data, we expect it to be applicable in a variety of fields that are concerned with the analysis of information transfer in complex biological, social, and artificial systems.
The historical research line on the algebraic properties of structured CF languages initiated by McNaughton's Parenthesis Languages has recently attracted much renewed interest with the Balanced Languages, the Visibly Pushdown Automata languages (VPDA), the Synchronized Languages, and the Height-deterministic ones.
Such families preserve to a varying degree the basic algebraic properties of Regular languages: boolean closure, closure under reversal, under concatenation, and Kleene star.
We prove that the VPDA family is strictly contained within the Floyd Grammars (FG) family historically known as operator precedence.
Languages over the same precedence matrix are known to be closed under boolean operations, and are recognized by a machine whose pop or push operations on the stack are purely determined by terminal letters.
We characterize VPDA's as the subclass of FG having a peculiarly structured set of precedence relations, and balanced grammars as a further restricted case.
The non-counting invariance property of FG has a direct implication for VPDA too.
Graph representations have increasingly grown in popularity during the last years.
Existing representation learning approaches explicitly encode network structure.
Despite their good performance in downstream processes (e.g., node classification, link prediction), there is still room for improvement in different aspects, like efficacy, visualization, and interpretability.
In this paper, we propose, t-PINE, a method that addresses these limitations.
Contrary to baseline methods, which generally learn explicit graph representations by solely using an adjacency matrix, t-PINE avails a multi-view information graph, the adjacency matrix represents the first view, and a nearest neighbor adjacency, computed over the node features, is the second view, in order to learn explicit and implicit node representations, using the Canonical Polyadic (a.k.a.CP) decomposition.
We argue that the implicit and the explicit mapping from a higher-dimensional to a lower-dimensional vector space is the key to learn more useful, highly predictable, and gracefully interpretable representations.
Having good interpretable representations provides a good guidance to understand how each view contributes to the representation learning process.
In addition, it helps us to exclude unrelated dimensions.
Extensive experiments show that t-PINE drastically outperforms baseline methods by up to 158.6% with respect to Micro-F1, in several multi-label classification problems, while it has high visualization and interpretability utility.
Motivated by value function estimation in reinforcement learning, we study statistical linear inverse problems, i.e., problems where the coefficients of a linear system to be solved are observed in noise.
We consider penalized estimators, where performance is evaluated using a matrix-weighted two-norm of the defect of the estimator measured with respect to the true, unknown coefficients.
Two objective functions are considered depending whether the error of the defect measured with respect to the noisy coefficients is squared or unsquared.
We propose simple, yet novel and theoretically well-founded data-dependent choices for the regularization parameters for both cases that avoid data-splitting.
A distinguishing feature of our analysis is that we derive deterministic error bounds in terms of the error of the coefficients, thus allowing the complete separation of the analysis of the stochastic properties of these errors.
We show that our results lead to new insights and bounds for linear value function estimation in reinforcement learning.
We propose a novel semi-direct approach for monocular simultaneous localization and mapping (SLAM) that combines the complementary strengths of direct and feature-based methods.
The proposed pipeline loosely couples direct odometry and feature-based SLAM to perform three levels of parallel optimizations: (1) photometric bundle adjustment (BA) that jointly optimizes the local structure and motion, (2) geometric BA that refines keyframe poses and associated feature map points, and (3) pose graph optimization to achieve global map consistency in the presence of loop closures.
This is achieved in real-time by limiting the feature-based operations to marginalized keyframes from the direct odometry module.
Exhaustive evaluation on two benchmark datasets demonstrates that our system outperforms the state-of-the-art monocular odometry and SLAM systems in terms of overall accuracy and robustness.
Contexts play an important role in the saliency detection task.
However, given a context region, not all contextual information is helpful for the final task.
In this paper, we propose a novel pixel-wise contextual attention network, i.e., the PiCANet, to learn to selectively attend to informative context locations for each pixel.
Specifically, for each pixel, it can generate an attention map in which each attention weight corresponds to the contextual relevance at each context location.
An attended contextual feature can then be constructed by selectively aggregating the contextual information.
We formulate the proposed PiCANet in both global and local forms to attend to global and local contexts, respectively.
Both models are fully differentiable and can be embedded into CNNs for joint training.
We also incorporate the proposed models with the U-Net architecture to detect salient objects.
Extensive experiments show that the proposed PiCANets can consistently improve saliency detection performance.
The global and local PiCANets facilitate learning global contrast and homogeneousness, respectively.
As a result, our saliency model can detect salient objects more accurately and uniformly, thus performing favorably against the state-of-the-art methods.
We consider algorithms for "smoothed online convex optimization" problems, a variant of the class of online convex optimization problems that is strongly related to metrical task systems.
Prior literature on these problems has focused on two performance metrics: regret and the competitive ratio.
There exist known algorithms with sublinear regret and known algorithms with constant competitive ratios; however, no known algorithm achieves both simultaneously.
We show that this is due to a fundamental incompatibility between these two metrics - no algorithm (deterministic or randomized) can achieve sublinear regret and a constant competitive ratio, even in the case when the objective functions are linear.
However, we also exhibit an algorithm that, for the important special case of one-dimensional decision spaces, provides sublinear regret while maintaining a competitive ratio that grows arbitrarily slowly.
An article about the transformation of the theory and practice of marketing in terms of e-commerce and network economy.
The author considers Internet Marketing as an independent marketing communication in a virtual environment.
The main thesis of the article: virtual environment determines the transformation of marketing, changing methods, priorities and structure not only practice, but also the theory of marketing.
The handwriting of an individual may vary substantially with factors such as mood, time, space, writing speed, writing medium and tool, writing topic, etc.
It becomes challenging to perform automated writer verification/identification on a particular set of handwritten patterns (e.g., speedy handwriting) of a person, especially when the system is trained using a different set of writing patterns (e.g., normal speed) of that same person.
However, it would be interesting to experimentally analyze if there exists any implicit characteristic of individuality which is insensitive to high intra-variable handwriting.
In this paper, we study some handcrafted features and auto-derived features extracted from intra-variable writing.
Here, we work on writer identification/verification from offline Bengali handwriting of high intra-variability.
To this end, we use various models mainly based on handcrafted features with SVM (Support Vector Machine) and features auto-derived by the convolutional network.
For experimentation, we have generated two handwritten databases from two different sets of 100 writers and enlarged the dataset by a data-augmentation technique.
We have obtained some interesting results.
Network Functions Virtualization (NFV) and Network Coding (NC) have attracted much attention in recent years as key concepts for providing 5G networks with flexibility and differentiated reliability, respectively.
In this paper, we present the integration of NC architectural design and NFV.
In order to do so we first describe what we call a virtualization process upon our proposed architectural design of NC that should help to offer the reliability functionality to a network.
The process consists of identifying the required functional entities of NC and analyzing when the functionality should be activated towards complexity/energy efficiency.
The relevance of our proposed NC function virtualization is its applicability to any underlying physical network, satellite or hybrid thus enabling softwarization, and rapid innovative deployment.
Finally, we validate our framework to a study case of geo-control of network reliability that is based on device's geographical location-based signal/network information.
We present NAVREN-RL, an approach to NAVigate an unmanned aerial vehicle in an indoor Real ENvironment via end-to-end reinforcement learning RL.
A suitable reward function is designed keeping in mind the cost and weight constraints for micro drone with minimum number of sensing modalities.
Collection of small number of expert data and knowledge based data aggregation is integrated into the RL process to aid convergence.
Experimentation is carried out on a Parrot AR drone in different indoor arenas and the results are compared with other baseline technologies.
We demonstrate how the drone successfully avoids obstacles and navigates across different arenas.
Widespread use of memory unsafe programming languages (e.g., C and C++) leaves many systems vulnerable to memory corruption attacks.
A variety of defenses have been proposed to mitigate attacks that exploit memory errors to hijack the control flow of the code at run-time, e.g., (fine-grained) randomization or Control Flow Integrity.
However, recent work on data-oriented programming (DOP) demonstrated highly expressive (Turing-complete) attacks, even in the presence of these state-of-the-art defenses.
Although multiple real-world DOP attacks have been demonstrated, no efficient defenses are yet available.
We propose run-time scope enforcement (RSE), a novel approach designed to efficiently mitigate all currently known DOP attacks by enforcing compile-time memory safety constraints (e.g., variable visibility rules) at run-time.
We present HardScope, a proof-of-concept implementation of hardware-assisted RSE for the new RISC-V open instruction set architecture.
We discuss our systematic empirical evaluation of HardScope which demonstrates that it can mitigate all currently known DOP attacks, and has a real-world performance overhead of 3.2% in embedded benchmarks.
In "Reliable Communication in the Absence of a Common Clock" (Yeung et al., 2009), the authors introduce general run-length sets, which form a class of constrained systems that permit run-lengths from a countably infinite set.
For a particular definition of probabilistic capacity, they show that probabilistic capacity is equal to combinatorial capacity.
In the present work, it is shown that the same result also holds for Shannon's original definition of probabilistic capacity.
The derivation presented here is based on generating functions of constrained systems as developed in "On the Capacity of Constrained Systems" (Boecherer et al., 2010) and provides a unified information-theoretic treatment of general run-length sets.
Batch Normalization (BN) is a milestone technique in the development of deep learning, enabling various networks to train.
However, normalizing along the batch dimension introduces problems --- BN's error increases rapidly when the batch size becomes smaller, caused by inaccurate batch statistics estimation.
This limits BN's usage for training larger models and transferring features to computer vision tasks including detection, segmentation, and video, which require small batches constrained by memory consumption.
In this paper, we present Group Normalization (GN) as a simple alternative to BN.
GN divides the channels into groups and computes within each group the mean and variance for normalization.
GN's computation is independent of batch sizes, and its accuracy is stable in a wide range of batch sizes.
On ResNet-50 trained in ImageNet, GN has 10.6% lower error than its BN counterpart when using a batch size of 2; when using typical batch sizes, GN is comparably good with BN and outperforms other normalization variants.
Moreover, GN can be naturally transferred from pre-training to fine-tuning.
GN can outperform its BN-based counterparts for object detection and segmentation in COCO, and for video classification in Kinetics, showing that GN can effectively replace the powerful BN in a variety of tasks.
GN can be easily implemented by a few lines of code in modern libraries.
Collaborative Filtering (CF) is one of the most commonly used recommendation methods.
CF consists in predicting whether, or how much, a user will like (or dislike) an item by leveraging the knowledge of the user's preferences as well as that of other users.
In practice, users interact and express their opinion on only a small subset of items, which makes the corresponding user-item rating matrix very sparse.
Such data sparsity yields two main problems for recommender systems: (1) the lack of data to effectively model users' preferences, and (2) the lack of data to effectively model item characteristics.
However, there are often many other data sources that are available to a recommender system provider, which can describe user interests and item characteristics (e.g., users' social network, tags associated to items, etc.).
These valuable data sources may supply useful information to enhance a recommendation system in modeling users' preferences and item characteristics more accurately and thus, hopefully, to make recommenders more precise.
For various reasons, these data sources may be managed by clusters of different data centers, thus requiring the development of distributed solutions.
In this paper, we propose a new distributed collaborative filtering algorithm, which exploits and combines multiple and diverse data sources to improve recommendation quality.
Our experimental evaluation using real datasets shows the effectiveness of our algorithm compared to state-of-the-art recommendation algorithms.
Traditionally it had been a problem that researchers did not have access to enough spatial data to answer pressing research questions or build compelling visualizations.
Today, however, the problem is often that we have too much data.
Spatially redundant or approximately redundant points may refer to a single feature (plus noise) rather than many distinct spatial features.
We use a machine learning approach with density-based clustering to compress such spatial data into a set of representative features.
We present a novel framework for finding complex activities matching user-described queries in cluttered surveillance videos.
The wide diversity of queries coupled with unavailability of annotated activity data limits our ability to train activity models.
To bridge the semantic gap we propose to let users describe an activity as a semantic graph with object attributes and inter-object relationships associated with nodes and edges, respectively.
We learn node/edge-level visual predictors during training and, at test-time, propose to retrieve activity by identifying likely locations that match the semantic graph.
We formulate a novel CRF based probabilistic activity localization objective that accounts for mis-detections, mis-classifications and track-losses, and outputs a likelihood score for a candidate grounded location of the query in the video.
We seek groundings that maximize overall precision and recall.
To handle the combinatorial search over all high-probability groundings, we propose a highest precision subgraph matching algorithm.
Our method outperforms existing retrieval methods on benchmarked datasets.
The electricity market is threatened by supply scarcity, which may lead to very sharp price spikes in the spot market.
On the other hand, demand-side's activities could effectively mitigate the supply scarcity and absorb most of these shocks and therefore smooth out the price volatility.
In this paper, the positive effects of employing demand response programs on the spot market price are investigated.
A demand-price elasticity based model is used to simulate the customer reaction function in the presence of a real time pricing.
The demand achieve by DR program is used to adjust the spot market price by using a price regression model.
SAS software is used to run the multiple linear regression model and MATLAB is used to simulate the demand response model.
The approach is applied on one week data in summer 2014 of Connecticut in New England ISO.
It could be concluded from the results of this study that applying DR program smooths out most of the price spikes in the electricity spot market and considerably reduces the customers' electricity cost.
Object detection is considered as one of the most challenging problems in computer vision, since it requires correct prediction of both classes and locations of objects in images.
In this study, we define a more difficult scenario, namely zero-shot object detection (ZSD) where no visual training data is available for some of the target object classes.
We present a novel approach to tackle this ZSD problem, where a convex combination of embeddings are used in conjunction with a detection framework.
For evaluation of ZSD methods, we propose a simple dataset constructed from Fashion-MNIST images and also a custom zero-shot split for the Pascal VOC detection challenge.
The experimental results suggest that our method yields promising results for ZSD.
The rising trend of coauthored academic works obscures the credit assignment that is the basis for decisions of funding and career advancements.
In this paper, a simple model based on the assumption of an unvarying "author ability" is introduced.
With this assumption, the weight of author contributions to a body of coauthored work can be statistically estimated.
The method is tested on a set of some more than five-hundred authors in a coauthor network from the CiteSeerX database.
The ranking obtained agrees fairly well with that given by total fractional citation counts for an author, but noticeable differences exist.
To participate in the Outback Medical Express UAV Challenge 2016, a vehicle was designed and tested that can hover precisely, take-off and land vertically, fly fast forward efficiently and use computer vision to locate a person and a suitable landing location.
A rotor blade was designed that can deliver sufficient thrust in hover, while still being efficient in fast forward flight.
Energy measurements and windtunnel tests were performed.
A rotor-head and corresponding control algorithms were developed to allow transitioning flight with the non-conventional rotor dynamics.
Dedicated electronics were designed that meet vehicle needs and regulations to allow safe flight beyond visual line of sight.
Vision based search and guidance algorithms were developed and tested.
Flight tests and a competition participation illustrate the applicability of the DelftaCopter concept.
We make available to the community a new dataset to support action-recognition research.
This dataset is different from prior datasets in several key ways.
It is significantly larger.
It contains streaming video with long segments containing multiple action occurrences that often overlap in space and/or time.
All actions were filmed in the same collection of backgrounds so that background gives little clue as to action class.
We had five humans replicate the annotation of temporal extent of action occurrences labeled with their class and measured a surprisingly low level of intercoder agreement.
A baseline experiment shows that recent state-of-the-art methods perform poorly on this dataset.
This suggests that this will be a challenging dataset to foster advances in action-recognition research.
This manuscript serves to describe the novel content and characteristics of the LCA dataset, present the design decisions made when filming the dataset, and document the novel methods employed to annotate the dataset.
Future networks are expected to connect an enormous number of nodes wirelessly using wide-band transmission.
This brings great challenges.
To avoid collecting a large amount of data from the massive number of nodes, computation over multi-access channel (CoMAC) is proposed to compute a desired function over the air utilizing the signal-superposition property of MAC.
Due to frequency selective fading, wide-band CoMAC is more challenging and has never been studied before.
In this work, we propose the use of orthogonal frequency division multiplexing (OFDM) in wide-band CoMAC to transmit functions in a similar way to bit sequences through division, allocation and reconstruction of function.
An achievable rate without any adaptive resource allocation is derived.
To prevent a vanishing computation rate from the increase of the number of nodes, a novel sub-function allocation of sub-carriers is derived.
Furthermore, we formulate an optimization problem considering power allocation.
A sponge-squeezing algorithm adapted from the classical water-filling algorithm is proposed to solve the optimal power allocation problem.
The improved computation rate of the proposed framework and the corresponding allocation has been verified through both theoretical analysis and simulation.
The problem of recovering a signal from its phaseless Fourier transform measurements, called Fourier phase retrieval, arises in many applications in engineering and science.
Fourier phase retrieval poses fundamental theoretical and algorithmic challenges.
In general, there is no unique mapping between a one-dimensional signal and its Fourier magnitude and therefore the problem is ill-posed.
Additionally, while almost all multidimensional signals are uniquely mapped to their Fourier magnitude, the performance of existing algorithms is generally not well-understood.
In this chapter we survey methods to guarantee uniqueness in Fourier phase retrieval.
We then present different algorithmic approaches to retrieve the signal in practice.
We conclude by outlining some of the main open questions in this field.
We perform a Systematic Literature Review to discover how Humanoid robots are being applied in Socially Assistive Robotics experiments.
Our search returned 24 papers, from which 16 were included for closer analysis.
To do this analysis we used a conceptual framework inspired by Behavior-based Robotics.
The results of this study can be used for designing software frameworks targeting Humanoid Socially Assistive Robotics, especially in the context of Software Product Line Engineering projects.
One of the key research interests in the area of Constraint Satisfaction Problem (CSP) is to identify tractable classes of constraints and develop efficient solutions for them.
In this paper, we introduce generalized staircase (GS) constraints which is an important generalization of one such tractable class found in the literature, namely, staircase constraints.
GS constraints are of two kinds, down staircase (DS) and up staircase (US).
We first examine several properties of GS constraints, and then show that arc consistency is sufficient to determine a solution to a CSP over DS constraints.
Further, we propose an optimal O(cd) time and space algorithm to compute arc consistency for GS constraints where c is the number of constraints and d is the size of the largest domain.
Next, observing that arc consistency is not necessary for solving a DSCSP, we propose a more efficient algorithm for solving it.
With regard to US constraints, arc consistency is not known to be sufficient to determine a solution, and therefore, methods such as path consistency or variable elimination are required.
Since arc consistency acts as a subroutine for these existing methods, replacing it by our optimal O(cd) arc consistency algorithm produces a more efficient method for solving a USCSP.
Current State-of-the-Art High Throughput Satellite systems provide wide-area connectivity through multi-beam architectures.
Due to the tremendous system throughput requirements that next generation Satellite Communications (SatCom) expect to achieve, traditional 4-colour frequency reuse schemes are not sufficient anymore and more aggressive solutions as full frequency reuse are being considered for multi-beam SatCom.
These approaches require advanced interference management techniques to cope with the significantly increased inter-beam interference both at the transmitter, e.g., precoding, and at the receiver, e.g., Multi User Detection (MUD).
With respect to the former, several peculiar challenges arise when designed for SatCom systems.
In particular, multiple users are multiplexed in the same transmission radio frame, thus imposing to consider multiple channel matrices when computing the precoding coefficients.
In previous works, the main focus has been on the users' clustering and precoding design.
However, even though achieving significant throughput gains, no analysis has been performed on the impact of the system scheduling algorithm on multicast precoding, which is typically assumed random.
In this paper, we focus on this aspect by showing that, although the overall system performance is improved, a random scheduler does not properly tackle specific scenarios in which the precoding algorithm can poorly perform.
Based on these considerations, we design a Geographical Scheduling Algorithm (GSA) aimed at improving the precoding performance in these critical scenarios and, consequently, the performance at system level as well.
Through extensive numerical simulations, we show that the proposed GSA provides a significant performance improvement with respect to the legacy random scheduling.
A team of robots sharing a common goal can benefit from coordination of the activities of team members, helping the team to reach the goal more reliably or quickly.
We address the problem of coordinating the actions of a team of robots with periodic communication capability executing an information gathering task.
We cast the problem as a multi-agent optimal decision-making problem with an information theoretic objective function.
We show that appropriate techniques for solving decentralized partially observable Markov decision processes (Dec-POMDPs) are applicable in such information gathering problems.
We quantify the usefulness of coordinated information gathering through simulation studies, and demonstrate the feasibility of the method in a real-world target tracking domain.
Style transfer is an important problem in natural language processing (NLP).
However, the progress in language style transfer is lagged behind other domains, such as computer vision, mainly because of the lack of parallel data and principle evaluation metrics.
In this paper, we propose to learn style transfer with non-parallel data.
We explore two models to achieve this goal, and the key idea behind the proposed models is to learn separate content representations and style representations using adversarial networks.
We also propose novel evaluation metrics which measure two aspects of style transfer: transfer strength and content preservation.
We access our models and the evaluation metrics on two tasks: paper-news title transfer, and positive-negative review transfer.
Results show that the proposed content preservation metric is highly correlate to human judgments, and the proposed models are able to generate sentences with higher style transfer strength and similar content preservation score comparing to auto-encoder.
Modern cyber security operations collect an enormous amount of logging and alerting data.
While analysts have the ability to query and compute simple statistics and plots from their data, current analytical tools are too simple to admit deep understanding.
To detect advanced and novel attacks, analysts turn to manual investigations.
While commonplace, current investigations are time-consuming, intuition-based, and proving insufficient.
Our hypothesis is that arming the analyst with easy-to-use data science tools will increase their work efficiency, provide them with the ability to resolve hypotheses with scientific inquiry of their data, and support their decisions with evidence over intuition.
To this end, we present our work to build IDEAS (Interactive Data Exploration and Analysis System).
We present three real-world use-cases that drive the system design from the algorithmic capabilities to the user interface.
Finally, a modular and scalable software architecture is discussed along with plans for our pilot deployment with a security operation command.
In the framework of convolutional neural networks that lie at the heart of deep learning, downsampling is often performed with a max-pooling operation that only retains the element with maximum activation, while completely discarding the information contained in other elements in a pooling region.
To address this issue, a novel pooling scheme, Ordinal Pooling Network (OPN), is introduced in this work.
OPN rearranges all the elements of a pooling region in a sequence and assigns different weights to these elements based upon their orders in the sequence, where the weights are learned via the gradient-based optimisation.
The results of our small-scale experiments on image classification task demonstrate that this scheme leads to a consistent improvement in the accuracy over max-pooling operation.
This improvement is expected to increase in deeper networks, where several layers of pooling become necessary.
Language decoding studies have identified word representations which can be used to predict brain activity in response to novel words and sentences (Anderson et al., 2016; Pereira et al., 2018).
The unspoken assumption of these studies is that, during processing, linguistic information is transformed into some shared semantic space, and those semantic representations are then used for a variety of linguistic and non-linguistic tasks.
We claim that current studies vastly underdetermine the content of these representations, the algorithms which the brain deploys to produce and consume them, and the computational tasks which they are designed to solve.
We illustrate this indeterminacy with an extension of the sentence-decoding experiment of Pereira et al.
(2018), showing how standard evaluations fail to distinguish between language processing models which deploy different mechanisms and which are optimized to solve very different tasks.
We conclude by suggesting changes to the brain decoding paradigm which can support stronger claims of neural representation.
Vehicle color information is one of the important elements in ITS (Intelligent Traffic System).
In this paper, we present a vehicle color recognition method using convolutional neural network (CNN).
Naturally, CNN is designed to learn classification method based on shape information, but we proved that CNN can also learn classification based on color distribution.
In our method, we convert the input image to two different color spaces, HSV and CIE Lab, and run it to some CNN architecture.
The training process follow procedure introduce by Krizhevsky, that learning rate is decreasing by factor of 10 after some iterations.
To test our method, we use publicly vehicle color recognition dataset provided by Chen.
The results, our model outperform the original system provide by Chen with 2% higher overall accuracy.
Founsure is an open-source software library, distributed under LGPLv3 license and implements a multi-dimensional graph-based erasure coding entirely based on fast exclusive OR (XOR) logic.
Its implementation utilizes compiler optimizations and the multi-threaded implementation to generate the right assembly code for the given multi-core CPU architectures with vector processing capabilities.
Founsure (version 1.0) supports a variety of features that shall find interesting applications in modern data storage as well as communication and computer network systems which are becoming hungry in terms of network bandwidth, computational resources and average consumed power.
In particular, Founsure library provides a three dimensional design space that consists of computation complexity, coding overhead and data/node repair bandwidth to meet different requirements of modern distributed data storage and processing systems in which the data needs to be protected against device, hardware and node failures.
Unique features of Founsure include encoding, decoding, repairs/rebuilds and updates while the data and computation can be distributed across the network nodes.
The interaction between an artificial agent and its environment is bi-directional.
The agent extracts relevant information from the environment, and affects the environment by its actions in return to accumulate high expected reward.
Standard reinforcement learning (RL) deals with the expected reward maximization.
However, there are always information-theoretic limitations that restrict the expected reward, which are not properly considered by the standard RL.
In this work we consider RL objectives with information-theoretic limitations.
For the first time we derive a Bellman-type recursive equa- tion for the causal information between the environment and the agent, which is combined plausibly with the Bellman recursion for the value function.
The unified equitation serves to explore the typical behavior of artificial agents in an infinite time horizon.
Force-directed approach is one of the most widely used methods in graph drawing research.
There are two main problems with the traditional force-directed algorithms.
First, there is no mature theory to ensure the convergence of iteration sequence used in the algorithm and further, it is hard to estimate the rate of convergence even if the convergence is satisfied.
Second, the running time cost is increased intolerablely in drawing large- scale graphs, and therefore the advantages of the force-directed approach are limited in practice.
This paper is focused on these problems and presents a sufficient condition for ensuring the convergence of iterations.
We then develop a practical heuristic algorithm for speeding up the iteration in force-directed approach using a successive over-relaxation (SOR) strategy.
The results of computational tests on the several benchmark graph datasets used widely in graph drawing research show that our algorithm can dramatically improve the performance of force-directed approach by decreasing both the number of iterations and running time, and is 1.5 times faster than the latter on average.
The graph convolutional networks (GCN) recently proposed by Kipf and Welling are an effective graph model for semi-supervised learning.
This model, however, was originally designed to be learned with the presence of both training and test data.
Moreover, the recursive neighborhood expansion across layers poses time and memory challenges for training with large, dense graphs.
To relax the requirement of simultaneous availability of test data, we interpret graph convolutions as integral transforms of embedding functions under probability measures.
Such an interpretation allows for the use of Monte Carlo approaches to consistently estimate the integrals, which in turn leads to a batched training scheme as we propose in this work---FastGCN.
Enhanced with importance sampling, FastGCN not only is efficient for training but also generalizes well for inference.
We show a comprehensive set of experiments to demonstrate its effectiveness compared with GCN and related models.
In particular, training is orders of magnitude more efficient while predictions remain comparably accurate.
An open concept of rough evolution and an axiomatic approach to granules was also developed recently by the present author.
Subsequently the concepts were used in the formal framework of rough Y-systems (RYS) for developing on granular correspondences by her.
These have since been used for a new approach towards comparison of rough algebraic semantics across different semantic domains by way of correspondences that preserve rough evolution and try to avoid contamination.
In this research paper, new methods are proposed and a semantics for handling possibly contaminated operations and structured bigness is developed.
These would also be of natural interest for relative consistency of one collection of knowledge relative other.
This paper proposes several nonlinear control strategies for trajectory tracking of a quadcopter system based on the property of differential flatness.
Its originality is twofold.
Firstly, it provides a flat output for the quadcopter dynamics capable of creating full flat parametrization of the states and inputs.
Moreover, B-splines characterizations of the flat output and their properties allow for optimal trajectory generation subject to way-point constraints.
Secondly, several control strategies based on computed torque control and feedback linearization are presented and compared.
The advantages of flatness within each control strategy are analyzed and detailed through extensive simulation results.
Stack Overflow (SO) is the most popular question-and-answer website for software developers, providing a large amount of copyable code snippets.
Using those snippets raises maintenance and legal issues.
SO's license (CC BY-SA 3.0) requires attribution, i.e., referencing the original question or answer, and requires derived work to adopt a compatible license.
While there is a heated debate on SO's license model for code snippets and the required attribution, little is known about the extent to which snippets are copied from SO without proper attribution.
We present results of a large-scale empirical study analyzing the usage and attribution of non-trivial Java code snippets from SO answers in public GitHub (GH) projects.
We followed three different approaches to triangulate an estimate for the ratio of unattributed usages and conducted two online surveys with software developers to complement our results.
For the different sets of projects that we analyzed, the ratio of projects containing files with a reference to SO varied between 3.3% and 11.9%.
We found that at most 1.8% of all analyzed repositories containing code from SO used the code in a way compatible with CC BY-SA 3.0.
Moreover, we estimate that at most a quarter of the copied code snippets from SO are attributed as required.
Of the surveyed developers, almost one half admitted copying code from SO without attribution and about two thirds were not aware of the license of SO code snippets and its implications.
Human visual system relies on both binocular stereo cues and monocular focusness cues to gain effective 3D perception.
In computer vision, the two problems are traditionally solved in separate tracks.
In this paper, we present a unified learning-based technique that simultaneously uses both types of cues for depth inference.
Specifically, we use a pair of focal stacks as input to emulate human perception.
We first construct a comprehensive focal stack training dataset synthesized by depth-guided light field rendering.
We then construct three individual networks: a FocusNet to extract depth from a single focal stack, a EDoFNet to obtain the extended depth of field (EDoF) image from the focal stack, and a StereoNet to conduct stereo matching.
We then integrate them into a unified solution to obtain high quality depth maps.
Comprehensive experiments show that our approach outperforms the state-of-the-art in both accuracy and speed and effectively emulates human vision systems.
A fall is an abnormal activity that occurs rarely; however, missing to identify falls can have serious health and safety implications on an individual.
Due to the rarity of occurrence of falls, there may be insufficient or no training data available for them.
Therefore, standard supervised machine learning methods may not be directly applied to handle this problem.
In this paper, we present a taxonomy for the study of fall detection from the perspective of availability of fall data.
The proposed taxonomy is independent of the type of sensors used and specific feature extraction/selection methods.
The taxonomy identifies different categories of classification methods for the study of fall detection based on the availability of their data during training the classifiers.
Then, we present a comprehensive literature review within those categories and identify the approach of treating a fall as an abnormal activity to be a plausible research direction.
We conclude our paper by discussing several open research problems in the field and pointers for future research.
We present an active detection model for localizing objects in scenes.
The model is class-specific and allows an agent to focus attention on candidate regions for identifying the correct location of a target object.
This agent learns to deform a bounding box using simple transformation actions, with the goal of determining the most specific location of target objects following top-down reasoning.
The proposed localization agent is trained using deep reinforcement learning, and evaluated on the Pascal VOC 2007 dataset.
We show that agents guided by the proposed model are able to localize a single instance of an object after analyzing only between 11 and 25 regions in an image, and obtain the best detection results among systems that do not use object proposals for object localization.
With the recent advancements in Image Processing Techniques and development of new robust computer vision algorithms, new areas of research within Medical Diagnosis and Biomedical Engineering are picking up pace.
This paper provides a comprehensive in-depth case study of Image Processing, Feature Extraction and Analysis of Apical Periodontitis diagnostic cases in IOPA (Intra Oral Peri-Apical) Radiographs, a common case in oral diagnostic pipeline.
This paper provides a detailed analytical approach towards improving the diagnostic procedure with improved and faster results with higher accuracy targeting to eliminate True Negative and False Positive cases.
A common model for question answering (QA) is that a good answer is one that is closely related to the question, where relatedness is often determined using general-purpose lexical models such as word embeddings.
We argue that a better approach is to look for answers that are related to the question in a relevant way, according to the information need of the question, which may be determined through task-specific embeddings.
With causality as a use case, we implement this insight in three steps.
First, we generate causal embeddings cost-effectively by bootstrapping cause-effect pairs extracted from free text using a small set of seed patterns.
Second, we train dedicated embeddings over this data, by using task-specific contexts, i.e., the context of a cause is its effect.
Finally, we extend a state-of-the-art reranking approach for QA to incorporate these causal embeddings.
We evaluate the causal embedding models both directly with a casual implication task, and indirectly, in a downstream causal QA task using data from Yahoo!Answers.
We show that explicitly modeling causality improves performance in both tasks.
In the QA task our best model achieves 37.3% P@1, significantly outperforming a strong baseline by 7.7% (relative).
SimRank is a similarity measure between vertices in a graph, which has become a fundamental technique in graph analytics.
Recently, many algorithms have been proposed for efficient evaluation of SimRank similarities.
However, the existing SimRank computation algorithms either overlook uncertainty in graph structures or is based on an unreasonable assumption (Du et al).
In this paper, we study SimRank similarities on uncertain graphs based on the possible world model of uncertain graphs.
Following the random-walk-based formulation of SimRank on deterministic graphs and the possible worlds model of uncertain graphs, we define random walks on uncertain graphs for the first time and show that our definition of random walks satisfies Markov's property.
We formulate the SimRank measure based on random walks on uncertain graphs.
We discover a critical difference between random walks on uncertain graphs and random walks on deterministic graphs, which makes all existing SimRank computation algorithms on deterministic graphs inapplicable to uncertain graphs.
To efficiently compute SimRank similarities, we propose three algorithms, namely the baseline algorithm with high accuracy, the sampling algorithm with high efficiency, and the two-phase algorithm with comparable efficiency as the sampling algorithm and about an order of magnitude smaller relative error than the sampling algorithm.
The extensive experiments and case studies verify the effectiveness of our SimRank measure and the efficiency of our SimRank computation algorithms.
Many real-world problems are composed of several interacting components.
In order to facilitate research on such interactions, the Traveling Thief Problem (TTP) was created in 2013 as the combination of two well-understood combinatorial optimization problems.
With this article, we contribute in four ways.
First, we create a comprehensive dataset that comprises the performance data of 21 TTP algorithms on the full original set of 9720 TTP instances.
Second, we define 55 characteristics for all TPP instances that can be used to select the best algorithm on a per-instance basis.
Third, we use these algorithms and features to construct the first algorithm portfolios for TTP, clearly outperforming the single best algorithm.
Finally, we study which algorithms contribute most to this portfolio.
The widespread usage of surveillance cameras in smart cities has resulted in a gigantic volume of video data whose indexing, retrieval and management is a challenging issue.
Video summarization tends to detect important visual data from the surveillance stream and can help in efficient indexing and retrieval of required data from huge surveillance datasets.
In this research article, we propose an efficient convolutional neural network based summarization method for surveillance videos of resource-constrained devices.
Shot segmentation is considered as a backbone of video summarization methods and it affects the overall quality of the generated summary.
Thus, we propose an effective shot segmentation method using deep features.
Furthermore, our framework maintains the interestingness of the generated summary using image memorability and entropy.
Within each shot, the frame with highest memorability and entropy score is considered as a keyframe.
The proposed method is evaluated on two benchmark video datasets and the results are encouraging compared to state-of-the-art video summarization methods.
Learning to infer Bayesian posterior from a few-shot dataset is an important step towards robust meta-learning due to the model uncertainty inherent in the problem.
In this paper, we propose a novel Bayesian model-agnostic meta-learning method.
The proposed method combines scalable gradient-based meta-learning with nonparametric variational inference in a principled probabilistic framework.
During fast adaptation, the method is capable of learning complex uncertainty structure beyond a point estimate or a simple Gaussian approximation.
In addition, a robust Bayesian meta-update mechanism with a new meta-loss prevents overfitting during meta-update.
Remaining an efficient gradient-based meta-learner, the method is also model-agnostic and simple to implement.
Experiment results show the accuracy and robustness of the proposed method in various tasks: sinusoidal regression, image classification, active learning, and reinforcement learning.
This paper describes several results of Wimmics, a research lab which names stands for: web-instrumented man-machine interactions, communities, and semantics.
The approaches introduced here rely on graph-oriented knowledge representation, reasoning and operationalization to model and support actors, actions and interactions in web-based epistemic communities.
The re-search results are applied to support and foster interactions in online communities and manage their resources.
In the paper, the approximate sequence for entropy of some binary hidden Markov models has been found to have two bound sequences, the low bound sequence and the upper bound sequence.
The error bias of the approximate sequence is bound by a geometric sequence with a scale factor less than 1 which decreases quickly to zero.
It helps to understand the convergence of entropy rate of generic hidden Markov models, and it provides a theoretical base for estimating the entropy rate of some hidden Markov models at any accuracy.
The complexity of the healthcare ecosystem and the trans-disciplinary convergence which is essential for its function, makes it difficult to address healthcare as one domain.
Data curation and analysis of the information may boost our health related knowledge.
Increasing connectivity and improving infrastructure may help, among other things, to uncover facts and observations which may influence the future of global health.
Constrained model predictive control (MPC) is a widely used control strategy, which employs moving horizon-based on-line optimisation to compute the optimum path of the manipulated variables.
Nonlinear MPC can utilize detailed models but it is computationally expensive; on the other hand linear MPC may not be adequate.
Piecewise affine (PWA) models can describe the underlying nonlinear dynamics more accurately, therefore they can provide a viable trade-off through their use in multi-model linear MPC configurations, which avoid integer programming.
However, such schemes may introduce uncertainty affecting the closed loop stability.
In this work, we propose an input to output stability analysis for closed loop systems, consisting of PWA models, where an observer and multi-model linear MPC are applied together, under unstructured uncertainty.
Integral quadratic constraints (IQCs) are employed to assess the robustness of MPC under uncertainty.
We create a model pool, by performing linearisation on selected transient points.
All the possible uncertainties and nonlinearities (including the controller) can be introduced in the framework, assuming that they admit the appropriate IQCs, whilst the dissipation inequality can provide necessary conditions incorporating IQCs.
We demonstrate the existence of static multipliers, which can reduce the conservatism of the stability analysis significantly.
The proposed methodology is demonstrated through two engineering case studies.
Theoretical analysis of the error landscape of deep neural networks has garnered significant interest in recent years.
In this work, we theoretically study the importance of noise in the trajectories of gradient descent towards optimal solutions in multi-layer neural networks.
We show that adding noise (in different ways) to a neural network while training increases the rank of the product of weight matrices of a multi-layer linear neural network.
We thus study how adding noise can assist reaching a global optimum when the product matrix is full-rank (under certain conditions).
We establish theoretical foundations between the noise induced into the neural network - either to the gradient, to the architecture, or to the input/output to a neural network - and the rank of product of weight matrices.
We corroborate our theoretical findings with empirical results.
Privacy is a major good for users of personalized services such as recommender systems.
When applied to the field of health informatics, privacy concerns of users may be amplified, but the possible utility of such services is also high.
Despite availability of technologies such as k-anonymity, differential privacy, privacy-aware recommendation, and personalized privacy trade-offs, little research has been conducted on the users' willingness to share health data for usage in such systems.
In two conjoint-decision studies (sample size n=521), we investigate importance and utility of privacy-preserving techniques related to sharing of personal health data for k-anonymity and differential privacy.
Users were asked to pick a preferred sharing scenario depending on the recipient of the data, the benefit of sharing data, the type of data, and the parameterized privacy.
Users disagreed with sharing data for commercial purposes regarding mental illnesses and with high de-anonymization risks but showed little concern when data is used for scientific purposes and is related to physical illnesses.
Suggestions for health recommender system development are derived from the findings.
Visual object recognition plays an essential role in human daily life.
This ability is so efficient that we can recognize a face or an object seemingly without effort, though they may vary in position, scale, pose, and illumination.
In the field of computer vision, a large number of studies have been carried out to build a human-like object recognition system.
Recently, deep neural networks have shown impressive progress in object classification performance, and have been reported to surpass humans.
Yet there is still lack of thorough and fair comparison between humans and artificial recognition systems.
While some studies consider artificially degraded images, human recognition performance on dataset widely used for deep neural networks has not been fully evaluated.
The present paper carries out an extensive experiment to evaluate human classification accuracy on CIFAR10, a well-known dataset of natural images.
This then allows for a fair comparison with the state-of-the-art deep neural networks.
Our CIFAR10-based evaluations show very efficient object recognition of recent CNNs but, at the same time, prove that they are still far from human-level capability of generalization.
Moreover, a detailed investigation using multiple levels of difficulty reveals that easy images for humans may not be easy for deep neural networks.
Such images form a subset of CIFAR10 that can be employed to evaluate and improve future neural networks.
In this paper, we propose a mechanism for packet marking called Probabilistic Congestion Notification (PCN).
This scheme makes use of the 1-bit Explicit Congestion Notification (ECN) field in the Internet Protocol (IP) header.
It allows the source to estimate the exact level of congestion at each intermediate queue.
By knowing this, the source could take avoiding action either by adapting its sending rate or by using alternate routes.
The estimation mechanism makes use of time series analysis both to improve the quality of the congestion estimation and to predict, ahead of time, the congestion level which subsequent packets will encounter.
The proposed protocol is tested in ns-2 simulator using a background of real Internet traffic traces.
Results show that the methods can successfully calculate the congestion at any queue along the path with low error levels.
Digital platforms enable the observation of learning behaviors through fine-grained log traces, offering more detailed clues for analysis.
In addition to previous descriptive and predictive log analysis, this study aims to simultaneously model learner activities, event time spans, and interaction levels using the proposed Hidden Behavior Traits Model (HBTM).
We evaluated model performance and explored their capability of clustering learners on a public dataset, and tried to interpret the machine recognized latent behavior patterns.
Quantitative and qualitative results demonstrated the promising value of HBTM.
Results of this study can contribute to the literature of online learner modeling and learning service planning.
We present a framework and its implementation relying on Natural Language Processing methods, which aims at the identification of exercise item candidates from corpora.
The hybrid system combining heuristics and machine learning methods includes a number of relevant selection criteria.
We focus on two fundamental aspects: linguistic complexity and the dependence of the extracted sentences on their original context.
Previous work on exercise generation addressed these two criteria only to a limited extent, and a refined overall candidate sentence selection framework appears also to be lacking.
In addition to a detailed description of the system, we present the results of an empirical evaluation conducted with language teachers and learners which indicate the usefulness of the system for educational purposes.
We have integrated our system into a freely available online learning platform.
Citations are the cornerstone of knowledge propagation and the primary means of assessing the quality of research, as well as directing investments in science.
Science is increasingly becoming "data-intensive", where large volumes of data are collected and analyzed to discover complex patterns through simulations and experiments, and most scientific reference works have been replaced by online curated datasets.
Yet, given a dataset, there is no quantitative, consistent and established way of knowing how it has been used over time, who contributed to its curation, what results have been yielded or what value it has.
The development of a theory and practice of data citation is fundamental for considering data as first-class research objects with the same relevance and centrality of traditional scientific products.
Many works in recent years have discussed data citation from different viewpoints: illustrating why data citation is needed, defining the principles and outlining recommendations for data citation systems, and providing computational methods for addressing specific issues of data citation.
The current panorama is many-faceted and an overall view that brings together diverse aspects of this topic is still missing.
Therefore, this paper aims to describe the lay of the land for data citation, both from the theoretical (the why and what) and the practical (the how) angle.
We consider the problem of maximizing the harvested power in Multiple Input Multiple Output (MIMO) Simultaneous Wireless Information and Power Transfer (SWIPT) systems with power splitting reception.
Different from recently proposed designs, with our optimization problem formulation we target for the jointly optimal transmit precoding and receive uniform power splitting (UPS) ratio maximizing the harvested power, while ensuring that the quality-of-service requirement of the MIMO link is satisfied.
We assume practical Radio-Frequency (RF) energy harvesting (EH) receive operation that results in a non-convex optimization problem for the design parameters, which we first formulate in an equivalent generalized convex problem that we then solve optimally.
We also derive the globally optimal transmit precoding design for ideal reception.
Furthermore, we present analytical bounds for the key variables of both considered problems along with tight high signal-to-noise ratio approximations for their optimal solutions.
Two algorithms for the efficient computation of the globally optimal designs are outlined.
The first requires solving a small number of non-linear equations, while the second is based on a two-dimensional search having linear complexity.
Computer simulation results are presented validating the proposed analysis, providing key insights on various system parameters, and investigating the achievable EH gains over benchmark schemes.
We consider the framework of aggregative games, in which the cost function of each agent depends on his own strategy and on the average population strategy.
As first contribution, we investigate the relations between the concepts of Nash and Wardrop equilibrium.
By exploiting a characterization of the two equilibria as solutions of variational inequalities, we bound their distance with a decreasing function of the population size.
As second contribution, we propose two decentralized algorithms that converge to such equilibria and are capable of coping with constraints coupling the strategies of different agents.
Finally, we study the applications of charging of electric vehicles and of route choice on a road network.
Recent years have seen the increasing need of location awareness by mobile applications.
This paper presents a room-level indoor localization approach based on the measured room's echos in response to a two-millisecond single-tone inaudible chirp emitted by a smartphone's loudspeaker.
Different from other acoustics-based room recognition systems that record full-spectrum audio for up to ten seconds, our approach records audio in a narrow inaudible band for 0.1 seconds only to preserve the user's privacy.
However, the short-time and narrowband audio signal carries limited information about the room's characteristics, presenting challenges to accurate room recognition.
This paper applies deep learning to effectively capture the subtle fingerprints in the rooms' acoustic responses.
Our extensive experiments show that a two-layer convolutional neural network fed with the spectrogram of the inaudible echos achieve the best performance, compared with alternative designs using other raw data formats and deep models.
Based on this result, we design a RoomRecognize cloud service and its mobile client library that enable the mobile application developers to readily implement the room recognition functionality without resorting to any existing infrastructures and add-on hardware.
Extensive evaluation shows that RoomRecognize achieves 99.7%, 97.7%, 99%, and 89% accuracy in differentiating 22 and 50 residential/office rooms, 19 spots in a quiet museum, and 15 spots in a crowded museum, respectively.
Compared with the state-of-the-art approaches based on support vector machine, RoomRecognize significantly improves the Pareto frontier of recognition accuracy versus robustness against interfering sounds (e.g., ambient music).
In-memory computing is a promising approach to addressing the processor-memory data transfer bottleneck in computing systems.
We propose Spin-Transfer Torque Compute-in-Memory (STT-CiM), a design for in-memory computing with Spin-Transfer Torque Magnetic RAM (STT-MRAM).
The unique properties of spintronic memory allow multiple wordlines within an array to be simultaneously enabled, opening up the possibility of directly sensing functions of the values stored in multiple rows using a single access.
We propose modifications to STT-MRAM peripheral circuits that leverage this principle to perform logic, arithmetic, and complex vector operations.
We address the challenge of reliable in-memory computing under process variations by extending ECC schemes to detect and correct errors that occur during CiM operations.
We also address the question of how STT-CiM should be integrated within a general-purpose computing system.
To this end, we propose architectural enhancements to processor instruction sets and on-chip buses that enable STT-CiM to be utilized as a scratchpad memory.
Finally, we present data mapping techniques to increase the effectiveness of STT-CiM.
We evaluate STT-CiM using a device-to-architecture modeling framework, and integrate cycle-accurate models of STT-CiM with a commercial processor and on-chip bus (Nios II and Avalon from Intel).
Our system-level evaluation shows that STT-CiM provides system-level performance improvements of 3.93x on average (upto 10.4x), and concurrently reduces memory system energy by 3.83x on average (upto 12.4x).
Nowadays, editors tend to separate different subtopics of a long Wiki-pedia article into multiple sub-articles.
This separation seeks to improve human readability.
However, it also has a deleterious effect on many Wikipedia-based tasks that rely on the article-as-concept assumption, which requires each entity (or concept) to be described solely by one article.
This underlying assumption significantly simplifies knowledge representation and extraction, and it is vital to many existing technologies such as automated knowledge base construction, cross-lingual knowledge alignment, semantic search and data lineage of Wikipedia entities.
In this paper we provide an approach to match the scattered sub-articles back to their corresponding main-articles, with the intent of facilitating automated Wikipedia curation and processing.
The proposed model adopts a hierarchical learning structure that combines multiple variants of neural document pair encoders with a comprehensive set of explicit features.
A large crowdsourced dataset is created to support the evaluation and feature extraction for the task.
Based on the large dataset, the proposed model achieves promising results of cross-validation and significantly outperforms previous approaches.
Large-scale serving on the entire English Wikipedia also proves the practicability and scalability of the proposed model by effectively extracting a vast collection of newly paired main and sub-articles.
Context: Existing knowledge in agile software development suggests that individual competency (e.g. skills) is a critical success factor for agile projects.
While assuming that technical skills are important for every kind of software development project, many researchers suggest that non-technical individual skills are especially important in agile software development.
Objective: In this paper, we investigate whether non-technical individual skills can predict the use of agile practices.
Method: Through creating a set of multiple linear regression models using a total of 113 participants from agile teams in six software development organizations from The Netherlands and Brazil, we analyzed the predictive power of non-technical individual skills in relation to agile practices.
Results: The results show that there is surprisingly low power in using non-technical individual skills to predict (i.e. explain variance in) the mature use of agile practices in software development.
Conclusions: Therefore, we conclude that looking at non-technical individual skills is not the optimal level of analysis when trying to understand, and explain, the mature use of agile practices in the software development context.
We argue that it is more important to focus on the non-technical skills as a team-level capacity instead of assuring that all individuals possess such skills when understanding the use of the agile practices.
Scholarly document creation continues to face various obstacles.
Scholarly text production requires more complex word processors than other forms of texts because of the complex structures of citations, formulas and figures.
The need for peer review, often single-blind or double-blind, creates needs for document management that other texts do not require.
Additionally, the need for collaborative editing, security and strict document access rules means that many existing word processors are imperfect solutions for academics.
Nevertheless, most papers continue to be written using Microsoft Word (Sadeghi et al.
2017).
We here analyze some of the problems with existing academic solutions and then present an argument why we believe that running an open source academic writing solution for academic purposes, such as Fidus Writer, on a Network Attached Storage (NAS) server could be a viable alternative.
As the amount of textual data has been rapidly increasing over the past decade, efficient similarity search methods have become a crucial component of large-scale information retrieval systems.
A popular strategy is to represent original data samples by compact binary codes through hashing.
A spectrum of machine learning methods have been utilized, but they often lack expressiveness and flexibility in modeling to learn effective representations.
The recent advances of deep learning in a wide range of applications has demonstrated its capability to learn robust and powerful feature representations for complex data.
Especially, deep generative models naturally combine the expressiveness of probabilistic generative models with the high capacity of deep neural networks, which is very suitable for text modeling.
However, little work has leveraged the recent progress in deep learning for text hashing.
In this paper, we propose a series of novel deep document generative models for text hashing.
The first proposed model is unsupervised while the second one is supervised by utilizing document labels/tags for hashing.
The third model further considers document-specific factors that affect the generation of words.
The probabilistic generative formulation of the proposed models provides a principled framework for model extension, uncertainty estimation, simulation, and interpretability.
Based on variational inference and reparameterization, the proposed models can be interpreted as encoder-decoder deep neural networks and thus they are capable of learning complex nonlinear distributed representations of the original documents.
We conduct a comprehensive set of experiments on four public testbeds.
The experimental results have demonstrated the effectiveness of the proposed supervised learning models for text hashing.
Software ecosystems can be viewed as socio-technical networks consisting of technical components (software packages) and social components (communities of developers) that maintain the technical components.
Ecosystems evolve over time through socio-technical changes that may greatly impact the ecosystem's sustainability.
Social changes like developer turnover may lead to technical degradation.
This motivates the need to identify those factors leading to developer abandonment, in order to automate the process of identifying developers with high abandonment risk.
This paper compares such factors for two software package ecosystems, RubyGems and npm.
We analyse the evolution of their packages hosted on GitHub, considering development activity in terms of commits, and social interaction with other developers in terms of comments associated to commits, issues or pull requests.
We analyse this socio-technical activity for more than 30k and 60k developers for RubyGems and npm respectively.
We use survival analysis to identify which factors coincide with a lower survival probability.
Our results reveal that developers with a higher probability to abandon an ecosystem: do not engage in discussions with other developers; do not have strong social and technical activity intensity; communicate or commit less frequently; and do not participate to both technical and social activities for long periods of time.
Such observations could be used to automate the identification of developers with a high probability of abandoning the ecosystem and, as such, reduce the risks associated to knowledge loss.
Surrogate Text Representation (STR) is a profitable solution to efficient similarity search on metric space using conventional text search engines, such as Apache Lucene.
This technique is based on comparing the permutations of some reference objects in place of the original metric distance.
However, the Achilles heel of STR approach is the need to reorder the result set of the search according to the metric distance.
This forces to use a support database to store the original objects, which requires efficient random I/O on a fast secondary memory (such as flash-based storages).
In this paper, we propose to extend the Surrogate Text Representation to specifically address a class of visual metric objects known as Vector of Locally Aggregated Descriptors (VLAD).
This approach is based on representing the individual sub-vectors forming the VLAD vector with the STR, providing a finer representation of the vector and enabling us to get rid of the reordering phase.
The experiments on a publicly available dataset show that the extended STR outperforms the baseline STR achieving satisfactory performance near to the one obtained with the original VLAD vectors.
Three different algorithms used for eye pupil location were described and tested.
Algorithm efficiency comparison was based on human faces images taken from the BioID database.
Moreover all the eye localisation methods were implemented in a dedicated application supporting eye movement based computer control.
In this case human face images were acquired by a webcam and processed in a real-time.
To strengthen the anonymity of Bitcoin, several centralized coin-mixing providers (mixers) such as BitcoinFog.com, BitLaundry.com, and Blockchain.info assist users to mix Bitcoins through CoinJoin transactions with multiple inputs and multiple outputs to uncover the relationship between them.
However, these mixers know the output address of each user, such that they cannot provide true anonymity.
This paper proposes a centralized coin-mixing algorithm based on an elliptic curve blind signature scheme (denoted as Blind-Mixing) that obstructs mixers from linking an input address with an output address.
Comparisons among three blind signature based algorithms, Blind-Mixing, BlindCoin, and RSA Coin-Mixing, are conducted.
It is determined that BlindCoin may be deanonymized because of its use of a public log.
In RSA Coin-Mixing, a user's Bitcoins may be falsely claimed by another.
In addition, the blind signature scheme of Blind-Mixing executes 10.5 times faster than that of RSA Coin-Mixing.
In textual information extraction and other sequence labeling tasks it is now common to use recurrent neural networks (such as LSTM) to form rich embedded representations of long-term input co-occurrence patterns.
Representation of output co-occurrence patterns is typically limited to a hand-designed graphical model, such as a linear-chain CRF representing short-term Markov dependencies among successive labels.
This paper presents a method that learns embedded representations of latent output structure in sequence data.
Our model takes the form of a finite-state machine with a large number of latent states per label (a latent variable CRF), where the state-transition matrix is factorized---effectively forming an embedded representation of state-transitions capable of enforcing long-term label dependencies, while supporting exact Viterbi inference over output labels.
We demonstrate accuracy improvements and interpretable latent structure in a synthetic but complex task based on CoNLL named entity recognition.
In this work we seek for an approach to integrate safety in the learning process that relies on a partly known state-space model of the system and regards the unknown dynamics as an additive bounded disturbance.
We introduce a framework for safely learning a control strategy for a given system with an additive disturbance.
On the basis of the known part of the model, a safe set in which the system can learn safely, the algorithm can choose optimal actions for pursuing the target set as long as the safety-preserving condition is satisfied.
After some learning episodes, the disturbance can be updated based on real-world data.
To this end, Gaussian Process regression is conducted on the collected disturbance samples.
Since the unstable nature of the law of the real world, for example, change of friction or conductivity with the temperature, we expect to have the more robust solution of optimal control problem.
For evaluation of approach described above we choose an inverted pendulum as a benchmark model.
The proposed algorithm manages to learn a policy that does not violate the pre-specified safety constraints.
Observed performance is improved when it was incorporated exploration set up to make sure that an optimal policy is learned everywhere in the safe set.
Finally, we outline some promising directions for future research beyond the scope of this paper.
Widespread adoption of indoor positioning systems based on WiFi fingerprinting is at present hindered by the large efforts required for measurements collection during the offline phase.
Two approaches were recently proposed to address such issue: crowdsourcing and RSS radiomap prediction based on either interpolation or propagation channel model fitting from a small set of measurements.
RSS prediction promises better positioning accuracy when compared to crowdsourcing but no systematic analysis of the impact of system parameters on positioning accuracy is available.
This paper fills this gap by introducing ViFi, an indoor positioning system that relies on RSS prediction based on Multi-Wall Multi-Floor (MWMF) propagation model to generate a discrete RSS radiomap (virtual fingerprints).
The ViFi system is subject to an extensive experimental analysis in order to address the role of all relevant system parameters.
Experimental results obtained in two different testbeds show that the introduction of virtual fingerprints allows reduction by a factor of 10 of the number of measurements, without significant loss in positioning accuracy.
The use of two testbeds also allows to derive general guidelines for the design and the implementation of a virtual fingerprinting system.
Various studies have attempted to assess the amount of free full text available on the web and recent work have suggested that we are close to the 50% mark for freely available articles (Archambault et al.2013; Bjork et al.2010; Jamali and Nabavi 2015).
It is natural to wonder if this might reduce researchers' reliance on library subscriptions for access.
To do so, we need to determine not just what papers researchers are citing to that are free today, but to estimate if the papers they were citing were freely available at the time they were citing it.
We attempt to do so for a sample of citations made by researchers in the Singapore Management University in the field of Economics.
Recent studies have shown that sketches and diagrams play an important role in the daily work of software developers.
If these visual artifacts are archived, they are often detached from the source code they document, because there is no adequate tool support to assist developers in capturing, archiving, and retrieving sketches related to certain source code artifacts.
This paper presents SketchLink, a tool that aims at increasing the value of sketches and diagrams created during software development by supporting developers in these tasks.
Our prototype implementation provides a web application that employs the camera of smartphones and tablets to capture analog sketches, but can also be used on desktop computers to upload, for instance, computer-generated diagrams.
We also implemented a plugin for a Java IDE that embeds the links in Javadoc comments and visualizes them in situ in the source code editor as graphical icons.
Autism spectrum condition (ASC) or autism spectrum disorder (ASD) is primarily identified with the help of behavioral indications encompassing social, sensory and motor characteristics.
Although categorized, recurring motor actions are measured during diagnosis, quantifiable measures that ascertain kinematic physiognomies in the movement configurations of autistic persons are not adequately studied, hindering the advances in understanding the etiology of motor mutilation.
Subject aspects such as behavioral characters that influences ASD need further exploration.
Presently, limited autism datasets concomitant with screening ASD are available, and a majority of them are genetic.
Hence, in this study, we used a dataset related to autism screening enveloping ten behavioral and ten personal attributes that have been effective in diagnosing ASD cases from controls in behavior science.
ASD diagnosis is time exhaustive and uneconomical.
The burgeoning ASD cases worldwide mandate a need for the fast and economical screening tool.
Our study aimed to implement an artificial neural network with the Levenberg-Marquardt algorithm to detect ASD and examine its predictive accuracy.
Consecutively, develop a clinical decision support system for early ASD identification.
Information security is a critical issue in modern society and image watermarking can effectively prevent unauthorized information access.
Optical image watermarking techniques generally have advantages of parallel high-speed processing and multi-dimensional capabilities compared with digital approaches.
This paper provides a comprehensive review on the research works related to optical image hiding and watermarking techniques conducted in the past decade.
The past research works are focused on two major aspects, various optical systems for image hiding and the methods for embedding optical system output into a host image.
A summary of the state-of-the-art works is made from these two perspectives.
Data labeling is a necessary but often slow process that impedes the development of interactive systems for modern data analysis.
Despite rising demand for manual data labeling, there is a surprising lack of work addressing its high and unpredictable latency.
In this paper, we introduce CLAMShell, a system that speeds up crowds in order to achieve consistently low-latency data labeling.
We offer a taxonomy of the sources of labeling latency and study several large crowd-sourced labeling deployments to understand their empirical latency profiles.
Driven by these insights, we comprehensively tackle each source of latency, both by developing novel techniques such as straggler mitigation and pool maintenance and by optimizing existing methods such as crowd retainer pools and active learning.
We evaluate CLAMShell in simulation and on live workers on Amazon's Mechanical Turk, demonstrating that our techniques can provide an order of magnitude speedup and variance reduction over existing crowdsourced labeling strategies.
Over the years, software architecture has become a established discipline, both in academia and industry, and the interest on software architecture documentation has increased.
In this context, the improvement of methods, tools, and techniques around architecture documentation is of paramount importance.
We conducted a survey with 147 industrial participants (31 from Brazil), analyzing their current problems and future wishes.
We identified that Brazilian stakeholders need updated architecture documents with the right information.
Finally, the automation of some parts of the documentation will reduce the effort during the creation of the documents.
But first, is necessary to change the culture of the stakeholders.
They have to participate actively in the architecture documents creation.
Videos represent the primary source of information for surveillance applications and are available in large amounts but in most cases contain little or no annotation for supervised learning.
This article reviews the state-of-the-art deep learning based methods for video anomaly detection and categorizes them based on the type of model and criteria of detection.
We also perform simple studies to understand the different approaches and provide the criteria of evaluation for spatio-temporal anomaly detection.
Understanding causal explanations - reasons given for happenings in one's life - has been found to be an important psychological factor linked to physical and mental health.
Causal explanations are often studied through manual identification of phrases over limited samples of personal writing.
Automatic identification of causal explanations in social media, while challenging in relying on contextual and sequential cues, offers a larger-scale alternative to expensive manual ratings and opens the door for new applications (e.g. studying prevailing beliefs about causes, such as climate change).
Here, we explore automating causal explanation analysis, building on discourse parsing, and presenting two novel subtasks: causality detection (determining whether a causal explanation exists at all) and causal explanation identification (identifying the specific phrase that is the explanation).
We achieve strong accuracies for both tasks but find different approaches best: an SVM for causality prediction (F1 = 0.791) and a hierarchy of Bidirectional LSTMs for causal explanation identification (F1 = 0.853).
Finally, we explore applications of our complete pipeline (F1 = 0.868), showing demographic differences in mentions of causal explanation and that the association between a word and sentiment can change when it is used within a causal explanation.
Traditional approaches for color propagation in videos rely on some form of matching between consecutive video frames.
Using appearance descriptors, colors are then propagated both spatially and temporally.
These methods, however, are computationally expensive and do not take advantage of semantic information of the scene.
In this work we propose a deep learning framework for color propagation that combines a local strategy, to propagate colors frame-by-frame ensuring temporal stability, and a global strategy, using semantics for color propagation within a longer range.
Our evaluation shows the superiority of our strategy over existing video and image color propagation methods as well as neural photo-realistic style transfer approaches.
This paper presents an intelligent traffic monitoring system using wireless vision sensor network that captures and processes the real-time video image to obtain the traffic flow rate and vehicle speeds along different urban roadways.
This system will display the traffic states on the front roadways that can guide the drivers to select the right way and avoid potential traffic congestions.
On the other hand, it will also monitor the vehicle speeds and store the vehicle details, for those breaking the roadway speed limits, in its database.
The real-time traffic data is processed by the Personal Computer (PC) at the sub roadway station and the traffic flow rate data is transmitted to the main roadway station Arduino 3G via email, where the data is extracted and traffic flow rate displayed.
Emergency events involving fire are potentially harmful, demanding a fast and precise decision making.
The use of crowdsourcing image and videos on crisis management systems can aid in these situations by providing more information than verbal/textual descriptions.
Due to the usual high volume of data, automatic solutions need to discard non-relevant content without losing relevant information.
There are several methods for fire detection on video using color-based models.
However, they are not adequate for still image processing, because they can suffer on high false-positive results.
These methods also suffer from parameters with little physical meaning, which makes fine tuning a difficult task.
In this context, we propose a novel fire detection method for still images that uses classification based on color features combined with texture classification on superpixel regions.
Our method uses a reduced number of parameters if compared to previous works, easing the process of fine tuning the method.
Results show the effectiveness of our method of reducing false-positives while its precision remains compatible with the state-of-the-art methods.
Cooperative ITS is enabling vehicles to communicate with the infrastructure to provide improvements in traffic control.
A promising approach consists in anticipating the road profile and the upcoming dynamic events like traffic lights.
This topic has been addressed in the French public project Co-Drive through functions developed by Valeo named Green Light Optimal Speed Advisor (GLOSA).
The system advises the optimal speed to pass the next traffic light without stopping.
This paper presents results of its performance in different scenarios through simulations and real driving measurements.
A scaling is done in an urban area, with different penetration rates in vehicle and infrastructure equipment for vehicular communication.
Our simulation results indicate that GLOSA can reduce CO2 emissions, waiting time and travel time, both in experimental conditions and in real traffic conditions.
We introduce segmental recurrent neural networks (SRNNs) which define, given an input sequence, a joint probability distribution over segmentations of the input and labelings of the segments.
Representations of the input segments (i.e., contiguous subsequences of the input) are computed by encoding their constituent tokens using bidirectional recurrent neural nets, and these "segment embeddings" are used to define compatibility scores with output labels.
These local compatibility scores are integrated using a global semi-Markov conditional random field.
Both fully supervised training -- in which segment boundaries and labels are observed -- as well as partially supervised training -- in which segment boundaries are latent -- are straightforward.
Experiments on handwriting recognition and joint Chinese word segmentation/POS tagging show that, compared to models that do not explicitly represent segments such as BIO tagging schemes and connectionist temporal classification (CTC), SRNNs obtain substantially higher accuracies.
Deep neural networks have shown incredible performance for inference tasks in a variety of domains.
Unfortunately, most current deep networks are enormous cloud-based structures that require significant storage space, which limits scaling of deep learning as a service (DLaaS) and use for on-device augmented intelligence.
This paper is concerned with finding universal lossless compressed representations of deep feedforward networks with synaptic weights drawn from discrete sets, and directly performing inference without full decompression.
The basic insight that allows less rate than naive approaches is the recognition that the bipartite graph layers of feedforward networks have a kind of permutation invariance to the labeling of nodes, in terms of inferential operation.
We provide efficient algorithms to dissipate this irrelevant uncertainty and then use arithmetic coding to nearly achieve the entropy bound in a universal manner.
We also provide experimental results of our approach on the MNIST dataset.
Recent studies show the increasing popularity of distributed cloud applications, which are composed of multiple microservices.
Besides their known benefits, microservice architecture also enables to mix and match cloud applications and Network Function Virtualization (NFV) services (service chains), which are composed of Virtual Network Functions (VNFs).
Provisioning complex services containing VNFs and microservices in a combined NFV/cloud platform can enhance service quality and optimise cost.
Such a platform can be based on the multi-cloud concept.
However, current multi-cloud solutions do not support NFV requirements, making them inadequate to support complex services.
In this paper, we investigate these challenges and propose a solution for jointly managing and orchestrating microservices and virtual network functions.
In this paper, we address the issue of how to enhance the generalization performance of convolutional neural networks (CNN) in the early learning stage for image classification.
This is motivated by real-time applications that require the generalization performance of CNN to be satisfactory within limited training time.
In order to achieve this, a novel hierarchical transfer CNN framework is proposed.
It consists of a group of shallow CNNs and a cloud CNN, where the shallow CNNs are trained firstly and then the first layers of the trained shallow CNNs are used to initialize the first layer of the cloud CNN.
This method will boost the generalization performance of the cloud CNN significantly, especially during the early stage of training.
Experiments using CIFAR-10 and ImageNet datasets are performed to examine the proposed method.
Results demonstrate the improvement of testing accuracy is 12% on average and as much as 20% for the CIFAR-10 case while 5% testing accuracy improvement for the ImageNet case during the early stage of learning.
It is also shown that universal improvements of testing accuracy are obtained across different settings of dropout and number of shallow CNNs.
In this paper we study Z2Z4Z8-additive codes, which are the extension of recently introduced Z2Z4-additive codes.
We determine the standard forms of the generator and parity-check matrices of Z2Z4Z8-additive codes.
Moreover, we investigate Z2Z4Z8-cyclic codes giving their generator polynomials and spanning sets.
We also give some illustrative examples of both Z2Z4Z8-additive codes and Z2Z4Z8-cyclic codes.
We study two aspects of noisy computations during inference.
The first aspect is how to mitigate their side effects for naturally trained deep learning systems.
One of the motivations for looking into this problem is to reduce the high power cost of conventional computing of neural networks through the use of analog neuromorphic circuits.
Traditional GPU/CPU-centered deep learning architectures exhibit bottlenecks in power-restricted applications (e.g., embedded systems).
The use of specialized neuromorphic circuits, where analog signals passed through memory-cell arrays are sensed to accomplish matrix-vector multiplications, promises large power savings and speed gains but brings with it the problems of limited precision of computations and unavoidable analog noise.
We manage to improve inference accuracy from 21.1% to 99.5% for MNIST images, from 29.9% to 89.1% for CIFAR10, and from 15.5% to 89.6% for MNIST stroke sequences with the presence of strong noise (with signal-to-noise power ratio being 0 dB) by noise-injected training and a voting method.
This observation promises neural networks that are insensitive to inference noise, which reduces the quality requirements on neuromorphic circuits and is crucial for their practical usage.
The second aspect is how to utilize the noisy inference as a defensive architecture against black-box adversarial attacks.
During inference, by injecting proper noise to signals in the neural networks, the robustness of adversarially-trained neural networks against black-box attacks has been further enhanced by 0.5% and 1.13% for two adversarially trained models for MNIST and CIFAR10, respectively.
Network coding can significantly improve the transmission rate of communication networks with packet loss compared with routing.
However, using network coding usually incurs high computational and storage costs in the network devices and terminals.
For example, some network coding schemes require the computational and/or storage capacities of an intermediate network node to increase linearly with the number of packets for transmission, making such schemes difficult to be implemented in a router-like device that has only constant computational and storage capacities.
In this paper, we introduce BATched Sparse code (BATS code), which enables a digital fountain approach to resolve the above issue.
BATS code is a coding scheme that consists of an outer code and an inner code.
The outer code is a matrix generation of a fountain code.
It works with the inner code that comprises random linear coding at the intermediate network nodes.
BATS codes preserve such desirable properties of fountain codes as ratelessness and low encoding/decoding complexity.
The computational and storage capacities of the intermediate network nodes required for applying BATS codes are independent of the number of packets for transmission.
Almost capacity-achieving BATS code schemes are devised for unicast networks, two-way relay networks, tree networks, a class of three-layer networks, and the butterfly network.
For general networks, under different optimization criteria, guaranteed decoding rates for the receiving nodes can be obtained.
Case Law has a significant impact on the proceedings of legal cases.
Therefore, the information that can be obtained from previous court cases is valuable to lawyers and other legal officials when performing their duties.
This paper describes a methodology of applying discourse relations between sentences when processing text documents related to the legal domain.
In this study, we developed a mechanism to classify the relationships that can be observed among sentences in transcripts of United States court cases.
First, we defined relationship types that can be observed between sentences in court case transcripts.
Then we classified pairs of sentences according to the relationship type by combining a machine learning model and a rule-based approach.
The results obtained through our system were evaluated using human judges.
To the best of our knowledge, this is the first study where discourse relationships between sentences have been used to determine relationships among sentences in legal court case transcripts.
We analyze a large-scale mobile phone call dataset with the metadata of the mobile phone users, including age, gender, and billing locality, to uncover the nature of relationships between peers or individuals of similar ages.
We show that in addition to the age and gender of users, the information about the ranks of users to each other in their egocentric networks is crucial in characterizing intimate and casual relationships of peers.
The opposite-gender pairs in intimate relationships are found to show the highest levels of call frequency and daily regularity, consistent with small-scale studies on romantic partners.
This is followed by the same-gender pairs in intimate relationships, while the lowest call frequency and daily regularity are observed for the pairs in casual relationships.
We also find that older pairs tend to call less frequently and less regularly than younger pairs, while the average call durations exhibit a more complex dependence on age.
We expect that a more detailed analysis can help us better characterize the nature of peer relationships and distinguish various types of relations, such as siblings, friends, and romantic partners, more clearly.
Real-world applications could benefit from the ability to automatically generate a fine-grained ranking of photo aesthetics.
However, previous methods for image aesthetics analysis have primarily focused on the coarse, binary categorization of images into high- or low-aesthetic categories.
In this work, we propose to learn a deep convolutional neural network to rank photo aesthetics in which the relative ranking of photo aesthetics are directly modeled in the loss function.
Our model incorporates joint learning of meaningful photographic attributes and image content information which can help regularize the complicated photo aesthetics rating problem.
To train and analyze this model, we have assembled a new aesthetics and attributes database (AADB) which contains aesthetic scores and meaningful attributes assigned to each image by multiple human raters.
Anonymized rater identities are recorded across images allowing us to exploit intra-rater consistency using a novel sampling strategy when computing the ranking loss of training image pairs.
We show the proposed sampling strategy is very effective and robust in face of subjective judgement of image aesthetics by individuals with different aesthetic tastes.
Experiments demonstrate that our unified model can generate aesthetic rankings that are more consistent with human ratings.
To further validate our model, we show that by simply thresholding the estimated aesthetic scores, we are able to achieve state-or-the-art classification performance on the existing AVA dataset benchmark.
Smile and Learn is an Ed-Tech company that runs a smart library with more that 100 applications, games and interactive stories, aimed at children aged 2 to 10 and their families.
Given the complexity of navigating all the content, the library implements a recommender system.
The purpose of this paper is to evaluate two aspects of such system: the influence of the order of recommendations on user exploratory behavior, and the impact of the choice of the recommendation algorithm on engagement.
The assessment, based on data collected between 2018/10/15 and 2018/12/01, required the analysis of the number of clicks performed on the recommendations depending on their ordering, and an A/B/C testing where two recommender algorithms were compared with a random recommendation that served as baseline.
The results suggest a direct connection between the order of the recommendation and the interest raised, and the superiority of recommendations based on popularity against other alternatives.
In recent years, the persuasive interventions for inducing sustainable urban mobility behaviours has become a very active research field.
This review paper systematically analyses existing approaches and prototype systems and describes and classifies the persuasive strategies used for changing behaviour in the domain of transport.
It also studies the results and recommendations derived from pilot studies, and as a result of this analysis highlights the need for personalizing and tailoring persuasive technology to various user characteristics.
We also discuss the possible role of context-aware persuasive systems for increasing the number of sustainable choices.
Finally, recommendations for future investigations on scholarly persuasive systems are proposed.
We study pure-strategy Nash equilibria in multi-player concurrent deterministic games, for a variety of preference relations.
We provide a novel construction, called the suspect game, which transforms a multi-player concurrent game into a two-player turn-based game which turns Nash equilibria into winning strategies (for some objective that depends on the preference relations of the players in the original game).
We use that transformation to design algorithms for computing Nash equilibria in finite games, which in most cases have optimal worst-case complexity, for large classes of preference relations.
This includes the purely qualitative framework, where each player has a single omega-regular objective that she wants to satisfy, but also the larger class of semi-quantitative objectives, where each player has several omega-regular objectives equipped with a preorder (for instance, a player may want to satisfy all her objectives, or to maximise the number of objectives that she achieves.)
The overwhelming amount and rate of information update in online social media is making it increasingly difficult for users to allocate their attention to their topics of interest, thus there is a strong need for prioritizing news feeds.
The attractiveness of a post to a user depends on many complex contextual and temporal features of the post.
For instance, the contents of the post, the responsiveness of a third user, and the age of the post may all have impact.
So far, these static and dynamic features has not been incorporated in a unified framework to tackle the post prioritization problem.
In this paper, we propose a novel approach for prioritizing posts based on a feature modulated multi-dimensional point process.
Our model is able to simultaneously capture textual and sentiment features, and temporal features such as self-excitation, mutual-excitation and bursty nature of social interaction.
As an evaluation, we also curated a real-world conversational benchmark dataset crawled from Facebook.
In our experiments, we demonstrate that our algorithm is able to achieve the-state-of-the-art performance in terms of analyzing, predicting, and prioritizing events.
In terms of interpretability of our method, we observe that features indicating individual user profile and linguistic characteristics of the events work best for prediction and prioritization of new events.
NoSQL data storage systems have become very popular due to their scalability and ease of use.
This paper examines the maturity of security measures for NoSQL databases, addressing their new query and access mechanisms.
For example the emergence of new query formats makes the old SQL injection techniques irrelevant, but are NoSQL databases immune to injection in general?
The answer is NO.
Here we present a few techniques for attacking NoSQL databases such as injections and CSRF.
We analyze the source of these vulnerabilities and present methodologies to mitigate the attacks.
We show that this new vibrant technological area lacks the security measures and awareness which have developed over the years in traditional RDBMS SQL systems.
Time series forecasting gets much attention due to its impact on many practical applications.
Higher-order neural network with recurrent feedback is a powerful technique which used successfully for forecasting.
It maintains fast learning and the ability to learn the dynamics of the series over time.
For that, in this paper, we propose a novel model which is called Ridge Polynomial Neural Network with Error-Output Feedbacks (RPNN-EOFs) that combines the properties of higher order and error-output feedbacks.
The well-known Mackey-Glass time series is used to test the forecasting capability of RPNN-EOFS.
Simulation results showed that the proposed RPNN-EOFs provides better understanding for the Mackey-Glass time series with root mean square error equal to 0.00416.
This result is smaller than other models in the literature.
Therefore, we can conclude that the RPNN-EOFs can be applied successfully for time series forecasting.
We report results from a preliminary study exploring the memorability of spatial scientific visualizations, the goal of which is to understand the visual features that contribute to memorability.
The evaluation metrics include three objective measures (entropy, feature congestion, the number of edges), four subjective ratings (clutter, the number of distinct colors, familiarity, and realism), and two sentiment ratings (interestingness and happiness).
We curate 1142 scientific visualization (SciVis) images from the original 2231 images in published IEEE SciVis papers from 2008 to 2017 and compute memorability scores of 228 SciVis images from data collected on Amazon Mechanical Turk (MTurk).
Results showed that the memorability of SciVis images is mostly correlated with clutter and the number of distinct colors.
We further investigate the differences between scientific visualization and infographics as a means to understand memorability differences by data attributes.
Let G be a graph embedded on a surface of genus g with b boundary cycles.
We describe algorithms to compute multiple types of non-trivial cycles in G, using different techniques depending on whether or not G is an undirected graph.
If G is undirected, then we give an algorithm to compute a shortest non-separating cycle in 2^O(g) n log log n time.
Similar algorithms are given to compute a shortest non-contractible or non-null-homologous cycle in 2^O(g+b) n log log n time.
Our algorithms for undirected G combine an algorithm of Kutz with known techniques for efficiently enumerating homotopy classes of curves that may be shortest non-trivial cycles.
Our main technical contributions in this work arise from assuming G is a directed graph with possibly asymmetric edge weights.
For this case, we give an algorithm to compute a shortest non-contractible cycle in G in O((g^3 + g b)n log n) time.
In order to achieve this time bound, we use a restriction of the infinite cyclic cover that may be useful in other contexts.
We also describe an algorithm to compute a shortest non-null-homologous cycle in G in O((g^2 + g b)n log n) time, extending a known algorithm of Erickson to compute a shortest non-separating cycle.
In both the undirected and directed cases, our algorithms improve the best time bounds known for many values of g and b.
Impervious surface area is a direct consequence of the urbanization, which also plays an important role in urban planning and environmental management.
With the rapidly technical development of remote sensing, monitoring urban impervious surface via high spatial resolution (HSR) images has attracted unprecedented attention recently.
Traditional multi-classes models are inefficient for impervious surface extraction because it requires labeling all needed and unneeded classes that occur in the image exhaustively.
Therefore, we need to find a reliable one-class model to classify one specific land cover type without labeling other classes.
In this study, we investigate several one-class classifiers, such as Presence and Background Learning (PBL), Positive Unlabeled Learning (PUL), OCSVM, BSVM and MAXENT, to extract urban impervious surface area using high spatial resolution imagery of GF-1, China's new generation of high spatial remote sensing satellite, and evaluate the classification accuracy based on artificial interpretation results.
Compared to traditional multi-classes classifiers (ANN and SVM), the experimental results indicate that PBL and PUL provide higher classification accuracy, which is similar to the accuracy provided by ANN model.
Meanwhile, PBL and PUL outperforms OCSVM, BSVM, MAXENT and SVM models.
Hence, the one-class classifiers only need a small set of specific samples to train models without losing predictive accuracy, which is supposed to gain more attention on urban impervious surface extraction or other one specific land cover type.
Relays in cellular systems are interference limited.
The highest end-to-end sum rates are achieved when the relays are jointly optimized with the transmit strategy.
Unfortunately, interference couples the links together making joint optimization challenging.
Further, the end-to-end multi-hop performance is sensitive to rate mismatch, when some links have a dominant first link while others have a dominant second link.
This paper proposes an algorithm for designing the linear transmit precoders at the transmitters and relays of the relay interference broadcast channel, a generic model for relay-based cellular systems, to maximize the end-to-end sum-rates.
First, the relays are designed to maximize the second-hop sum-rates.
Next, approximate end-to-end rates that depend on the time-sharing fraction and the second-hop rates are used to formulate a sum-utility maximization problem for designing the transmitters.
This problem is solved by iteratively minimizing the weighted sum of mean square errors.
Finally, the norms of the transmit precoders at the transmitters are adjusted to eliminate rate mismatch.
The proposed algorithm allows for distributed implementation and has fast convergence.
Numerical results show that the proposed algorithm outperforms a reasonable application of single-hop interference management strategies separately on two hops.
Current analysis of tumor proliferation, the most salient prognostic biomarker for invasive breast cancer, is limited to subjective mitosis counting by pathologists in localized regions of tissue images.
This study presents the first data-driven integrative approach to characterize the severity of tumor growth and spread on a categorical and molecular level, utilizing multiple biologically salient deep learning classifiers to develop a comprehensive prognostic model.
Our approach achieves pathologist-level performance on three-class categorical tumor severity prediction.
It additionally pioneers prediction of molecular expression data from a tissue image, obtaining a Spearman's rank correlation coefficient of 0.60 with ex vivo mean calculated RNA expression.
Furthermore, our framework is applied to identify over two hundred unprecedented biomarkers critical to the accurate assessment of tumor proliferation, validating our proposed integrative pipeline as the first to holistically and objectively analyze histopathological images.
Deep reinforcement learning has led to several recent breakthroughs, though the learned policies are often based on black-box neural networks.
This makes them difficult to interpret and to impose desired specification constraints during learning.
We present an iterative framework, MORL, for improving the learned policies using program synthesis.
Concretely, we propose to use synthesis techniques to obtain a symbolic representation of the learned policy, which can then be debugged manually or automatically using program repair.
After the repair step, we use behavior cloning to obtain the policy corresponding to the repaired program, which is then further improved using gradient descent.
This process continues until the learned policy satisfies desired constraints.
We instantiate MORL for the simple CartPole problem and show that the programmatic representation allows for high-level modifications that in turn lead to improved learning of the policies.
This paper presents a study of the Internet infrastructure in India from the point of view of censorship.
First, we show that the current state of affairs---where each ISP implements its own content filters (nominally as per a governmental blacklist)---results in dramatic differences in the censorship experienced by customers.
In practice, a well-informed Indian citizen can escape censorship through a judicious choice of service provider.
We then consider the question of whether India might potentially follow the Chinese model and institute a single, government-controlled filter.
This would not be difficult, as the Indian Internet is quite centralized already.
A few "key" ASes (approx 1% of Indian ASes) collectively intercept approx 95% of paths to the censored sites we sample in our study, and also to all publicly-visible DNS servers.
5,000 routers spanning these key ASes would suffice to carry out IP or DNS filtering for the entire country; approx 70% of these routers belong to only two private ISPs.
If the government is willing to employ more powerful measures, such as an IP Prefix Hijacking attack, any one of several key ASes can censor traffic for nearly all Indian users.
Finally, we demonstrate that such federated censorship by India would cause substantial collateral damage to non-Indian ASes whose traffic passes through Indian cyberspace (which do not legally come under Indian jurisdiction at all).
The main goal of group testing with inhibitors (GTI) is to efficiently identify a small number of defective items and inhibitor items in a large set of items.
A test on a subset of items is positive if the subset satisfies some specific properties.
Inhibitor items cancel the effects of defective items, which often make the outcome of a test containing defective items negative.
Different GTI models can be formulated by considering how specific properties have different cancellation effects.
This work introduces generalized GTI (GGTI) in which a new type of items is added, i.e., hybrid items.
A hybrid item plays the roles of both defectives items and inhibitor items.
Since the number of instances of GGTI is large (more than 7 million), we introduce a framework for classifying all types of items non-adaptively, i.e., all tests are designed in advance.
We then explain how GGTI can be used to classify neurons in neuroscience.
Finally, we show how to realize our proposed scheme in practice.
The key limiting factor in graphical model inference and learning is the complexity of the partition function.
We thus ask the question: what are general conditions under which the partition function is tractable?
The answer leads to a new kind of deep architecture, which we call sum-product networks (SPNs).
SPNs are directed acyclic graphs with variables as leaves, sums and products as internal nodes, and weighted edges.
We show that if an SPN is complete and consistent it represents the partition function and all marginals of some graphical model, and give semantics to its nodes.
Essentially all tractable graphical models can be cast as SPNs, but SPNs are also strictly more general.
We then propose learning algorithms for SPNs, based on backpropagation and EM.
Experiments show that inference and learning with SPNs can be both faster and more accurate than with standard deep networks.
For example, SPNs perform image completion better than state-of-the-art deep networks for this task.
SPNs also have intriguing potential connections to the architecture of the cortex.
Disentangling factors of variation has become a very challenging problem on representation learning.
Existing algorithms suffer from many limitations, such as unpredictable disentangling factors, poor quality of generated images from encodings, lack of identity information, etc.
In this paper, we propose a supervised learning model called DNA-GAN which tries to disentangle different factors or attributes of images.
The latent representations of images are DNA-like, in which each individual piece (of the encoding) represents an independent factor of the variation.
By annihilating the recessive piece and swapping a certain piece of one latent representation with that of the other one, we obtain two different representations which could be decoded into two kinds of images with the existence of the corresponding attribute being changed.
In order to obtain realistic images and also disentangled representations, we further introduce the discriminator for adversarial training.
Experiments on Multi-PIE and CelebA datasets finally demonstrate that our proposed method is effective for factors disentangling and even overcome certain limitations of the existing methods.
The paper describes a novel social network-based open educational resource for learning foreign languages in real time from native speakers, based on the predefined teaching materials.
This virtual learning platform, named i2istudy, eliminates misunderstanding by providing prepared and predefined scenarios, enabling the participants to understand each other and, as a consequence, to communicate freely.
The system allows communication through the real time video and audio feed.
In addition to establishing the communication, it tracks the student progress and allows rating the instructor, based on the learner's experience.
The system went live in April 2014, and had over six thousand active daily users, with over 40,000 total registered users.
Currently monetization is being added to the system, and time will show how popular the system will become in the future.
A blocking quadruple (BQ) is a quadruple of vertices of a graph such that any two vertices of the quadruple either miss (have no neighbours on) some path connecting the remaining two vertices of the quadruple, or are connected by some path missed by the remaining two vertices.
This is akin to the notion of asteroidal triple used in the classical characterization of interval graphs by Lekkerkerker and Boland.
We show that a circular-arc graph cannot have a blocking quadruple.
We also observe that the absence of blocking quadruples is not in general sufficient to guarantee that a graph is a circular-arc graph.
Nonetheless, it can be shown to be sufficient for some special classes of graphs, such as those investigated by Bonomo et al.
In this note, we focus on chordal graphs, and study the relationship between the structure of chordal graphs and the presence/absence of blocking quadruples.
Our contribution is two-fold.
Firstly, we provide a forbidden induced subgraph characterization of chordal graphs without blocking quadruples.
In particular, we observe that all the forbidden subgraphs are variants of the subgraphs forbidden for interval graphs.
Secondly, we show that the absence of blocking quadruples is sufficient to guarantee that a chordal graph with no independent set of size five is a circular-arc graph.
In our proof we use a novel geometric approach, constructing a circular-arc representation by traversing around a carefully chosen clique tree.
The application of mobile computing is currently altering patterns of our behavior to a greater degree than perhaps any other invention.
In combination with the introduction of power efficient wireless communication technologies, such as Bluetooth Low Energy (BLE), designers are today increasingly empowered to shape the way we interact with our physical surroundings and thus build entirely new experiences.
However, our evaluations of BLE and its abilities to facilitate mobile location-based experiences in public environments revealed a number of potential problems.
Most notably, the position and orientation of the user in combination with various environmental factors, such as crowds of people traversing the space, were found to cause major fluctuations of the received BLE signal strength.
These issues are rendering a seamless functioning of any location-based application practically impossible.
Instead of achieving seamlessness by eliminating these technical issues, we thus choose to advocate the use of a seamful approach, i.e. to reveal and exploit these problems and turn them into a part of the actual experience.
In order to demonstrate the viability of this approach, we designed, implemented and evaluated the Ghost Detector - an educational location-based museum game for children.
By presenting a qualitative evaluation of this game and by motivating our design decisions, this paper provides insight into some of the challenges and possible solutions connected to the process of developing location-based BLE-enabled experiences for public cultural spaces.
Robots with flexible spines based on tensegrity structures have potential advantages over traditional designs with rigid torsos.
However, these robots can be difficult to control due to their high-dimensional nonlinear dynamics.
To overcome these issues, this work presents two controllers for tensegrity spine robots, using model-predictive control (MPC), and demonstrates the first closed-loop control of such structures.
The first of the two controllers is formulated using only state tracking with smoothing constraints.
The second controller, newly introduced in this work, tracks both state and input reference trajectories without smoothing.
The reference input trajectory is calculated using a rigid-body reformulation of the inverse kinematics of tensegrity structures, and introduces the first feasible solutions to the problem for certain tensegrity topologies.
This second controller significantly reduces the number of parameters involved in designing the control system, making the task much easier.
The controllers are simulated with 2D and 3D models of a particular tensegrity spine, designed for use as the backbone of a quadruped robot.
These simulations illustrate the different benefits of the higher performance of the smoothing controller versus the lower tuning complexity of the more general input-tracking formulation.
Both controllers show noise insensitivity and low tracking error, and can be used for different control goals.
The reference input tracking controller is also simulated against an additional model of a similar robot, thereby demonstrating its generality.
In the present day, AES is one the most widely used and most secure Encryption Systems prevailing.
So, naturally lots of research work is going on to mount a significant attack on AES.
Many different forms of Linear and differential cryptanalysis have been performed on AES.
Of late, an active area of research has been Algebraic Cryptanalysis of AES, where although fast progress is being made, there are still numerous scopes for research and improvement.
One of the major reasons behind this being that algebraic cryptanalysis mainly depends on I/O relations of the AES S- Box (a major component of the AES).
As, already known, that the key recovery algorithm of AES can be broken down as an MQ problem which is itself considered hard.
Solving these equations depends on our ability reduce them into linear forms which are easily solvable under our current computational prowess.
The lower the degree of these equations, the easier it is for us to linearlize hence the attack complexity reduces.
The aim of this paper is to analyze the various relations involving small number of monomials of the AES S- Box and to answer the question whether it is actually possible to have such monomial equations for the S- Box if we restrict the degree of the monomials.
In other words this paper aims to study such equations and see if they can be applicable for AES.
We consider stochastic transition matrices from large social and information networks.
For these matrices, we describe and evaluate three fast methods to estimate one column of the matrix exponential.
The methods are designed to exploit the properties inherent in social networks, such as a power-law degree distribution.
Using only this property, we prove that one of our algorithms has a sublinear runtime.
We present further experimental evidence showing that all of them run quickly on social networks with billions of edges and accurately identify the largest elements of the column.
The area of online machine learning in big data streams covers algorithms that are (1) distributed and (2) work from data streams with only a limited possibility to store past data.
The first requirement mostly concerns software architectures and efficient algorithms.
The second one also imposes nontrivial theoretical restrictions on the modeling methods: In the data stream model, older data is no longer available to revise earlier suboptimal modeling decisions as the fresh data arrives.
In this article, we provide an overview of distributed software architectures and libraries as well as machine learning models for online learning.
We highlight the most important ideas for classification, regression, recommendation, and unsupervised modeling from streaming data, and we show how they are implemented in various distributed data stream processing systems.
This article is a reference material and not a survey.
We do not attempt to be comprehensive in describing all existing methods and solutions; rather, we give pointers to the most important resources in the field.
All related sub-fields, online algorithms, online learning, and distributed data processing are hugely dominant in current research and development with conceptually new research results and software components emerging at the time of writing.
In this article, we refer to several survey results, both for distributed data processing and for online machine learning.
Compared to past surveys, our article is different because we discuss recommender systems in extended detail.
In this work, we propose a step towards a more accurate prediction of the environment light given a single picture of a known object.
To achieve this, we developed a deep learning method that is able to encode the latent space of indoor lighting using few parameters and that is trained on a database of environment maps.
This latent space is then used to generate predictions of the light that are both more realistic and accurate than previous methods.
To achieve this, our first contribution is a deep autoencoder which is capable of learning the feature space that compactly models lighting.
Our second contribution is a convolutional neural network that predicts the light from a single image of a known object.
To train these networks, our third contribution is a novel dataset that contains 21,000 HDR indoor environment maps.
The results indicate that the predictor can generate plausible lighting estimations even from diffuse objects.
With rapid growth in the amount of unstructured data produced by memory-intensive applications, large scale data analytics has recently attracted increasing interest.
Processing, managing and analyzing this huge amount of data poses several challenges in cloud and data center computing domain.
Especially, conventional frameworks for distributed data analytics are based on the assumption of homogeneity and non-stochastic distribution of different data-processing nodes.
The paper argues the fundamental limiting factors for scaling big data computation.
It is shown that as the number of series and parallel computing servers increase, the tail (mean and variance) of the job execution time increase.
We will first propose a model to predict the response time of highly distributed processing tasks and then propose a new practical computational algorithm to optimize the response time.
Time-varying delays adversely affect the performance of networked control sys-tems (NCS) and in the worst-case can destabilize the entire system.
Therefore, modelling network delays is important for designing NCS.
However, modelling time-varying delays is challenging because of their dependence on multiple pa-rameters such as length, contention, connected devices, protocol employed, and channel loading.
Further, these multiple parameters are inherently random and de-lays vary in a non-linear fashion with respect to time.
This makes estimating ran-dom delays challenging.
This investigation presents a methodology to model de-lays in NCS using experiments and general regression neural network (GRNN) due to their ability to capture non-linear relationship.
To compute the optimal smoothing parameter that computes the best estimates, genetic algorithm is used.
The objective of the genetic algorithm is to compute the optimal smoothing pa-rameter that minimizes the mean absolute percentage error (MAPE).
Our results illustrate that the resulting GRNN is able to predict the delays with less than 3% error.
The proposed delay model gives a framework to design compensation schemes for NCS subjected to time-varying delays.
The aggregate behaviors of users can collectively encode deep semantic information about the objects with which they interact.
In this paper, we demonstrate novel ways in which the synthesis of these data can illuminate the terrain of users' environment and support them in their decision making and wayfinding.
A novel application of Recurrent Neural Networks and skip-gram models, approaches popularized by their application to modeling language, are brought to bear on student university enrollment sequences to create vector representations of courses and map out traversals across them.
We present demonstrations of how scrutability from these neural networks can be gained and how the combination of these techniques can be seen as an evolution of content tagging and a means for a recommender to balance user preferences inferred from data with those explicitly specified.
From validation of the models to the development of a UI, we discuss additional requisite functionality informed by the results of a usability study leading to the ultimate deployment of the system at a university.
In this study, both Bayesian classifiers and mutual information classifiers are examined for binary classifications with or without a reject option.
The general decision rules in terms of distinctions on error types and reject types are derived for Bayesian classifiers.
A formal analysis is conducted to reveal the parameter redundancy of cost terms when abstaining classifications are enforced.
The redundancy implies an intrinsic problem of "non-consistency" for interpreting cost terms.
If no data is given to the cost terms, we demonstrate the weakness of Bayesian classifiers in class-imbalanced classifications.
On the contrary, mutual-information classifiers are able to provide an objective solution from the given data, which shows a reasonable balance among error types and reject types.
Numerical examples of using two types of classifiers are given for confirming the theoretical differences, including the extremely-class-imbalanced cases.
Finally, we briefly summarize the Bayesian classifiers and mutual-information classifiers in terms of their application advantages, respectively.
Effective monitoring and management of environment pollution is key to the development of modern metropolitan cities.
To sustain and to cope with the exponential growth of the cities with high industrialization, expert decision making is very essential in this process.
A good governance system must be supported by an actively participating population.
In participatory sensing, individuals and groups engages in the data collection actively and the helps the city governance to make proper decisions.
In this paper, we propose a participatory sensing based three-tier framework to fight environment pollution in urban areas of Bangladesh.
The framework includes an android application named `My City, My Environment', a server for storage and computation and also a web server for the authority to monitor and maintain environmental issues through expert decision making.
We have already developed a prototype system and deployed it to a small scale and demonstrated the effectiveness of this framework.
The transparency nature of Open Data is beneficial for citizens to evaluate government work performance.
In Indonesia, each government bodies or ministry have their own standard operating procedure on data treatment resulting in incoherent information between agent and likely to miss valuable insight.
Therefore, our motivation is to show the advantage of Open Data movement to support unified government decision making.
We use the dataset from data.go.id which publish official data from each government bodies.
The idea is by using those official but limited data, we can find important pattern.
The case study is on Human Development Index value prediction and its clustered nature.
We explore the data pattern using two important data analytics methods classification and clustering procedure.
Data analytics is the collection of activities to reveal unknown data pattern.
Specifically, we use Artificial Neural Network classification and K-means clustering.
The classification objective is to categorize different level of Human Development Index of cities or region in Indonesia based on Gross Domestic Product, Number of Population in Poverty, Number of Internet User, Number of Labors and Number of Population indicators data.
We determined which city belongs to four categories of Human Development stated by UNDP standard.
The clustering objective is to find the group characteristics between Human Development Index and Gross Domestic Product.
We study networks of human decision-makers who independently decide how to protect themselves against Susceptible-Infected-Susceptible (SIS) epidemics.
Motivated by studies in behavioral economics showing that humans perceive probabilities in a nonlinear fashion, we examine the impacts of such misperceptions on the equilibrium protection strategies.
In our setting, nodes choose their curing rates to minimize the infection probability under the degree-based mean-field approximation of the SIS epidemic plus the cost of their selected curing rate.
We establish the existence of a degree based equilibrium under both true and nonlinear perceptions of infection probabilities (under suitable assumptions).
When the per-unit cost of curing rate is sufficiently high, we show that true expectation minimizers choose the curing rate to be zero at the equilibrium, while curing rate is nonzero under nonlinear probability weighting.
We consider linear precoder design for a multiple-input multiple-output (MIMO) Gaussian wiretap channel, which comprises two legitimate nodes, i.e., Alice and Bob, operating in Full-Duplex (FD) mode and exchanging confidential messages in the presence of a passive eavesdropper.
Using the sum secrecy degrees of freedoms (sum S.D.o.F.) as reliability measure, we formulate an optimization problem with respect to the precoding matrices.
In order to solve this problem, we first propose a cooperative secrecy transmission scheme, and prove that its feasible set is sufficient to achieve the maximum sum S.D.o.F.. Based on that feasible set, we then determine the maximum achievable sum S.D.o.F. in closed form, and provide a method for constructing the precoding matrix pair which achieves the maximum sum S.D.o.F..
Results show that, the FD based network provides an attractive secrecy transmission rate performance.
Differential privacy is a promising framework for addressing the privacy concerns in sharing sensitive datasets for others to analyze.
However differential privacy is a highly technical area and current deployments often require experts to write code, tune parameters, and optimize the trade-off between the privacy and accuracy of statistical releases.
For differential privacy to achieve its potential for wide impact, it is important to design usable systems that enable differential privacy to be used by ordinary data owners and analysts.
PSI is a tool that was designed for this purpose, allowing researchers to release useful differentially private statistical information about their datasets without being experts in computer science, statistics, or privacy.
We conducted a thorough usability study of PSI to test whether it accomplishes its goal of usability by non-experts.
The usability test illuminated which features of PSI are most user-friendly and prompted us to improve aspects of the tool that caused confusion.
The test also highlighted some general principles and lessons for designing usable systems for differential privacy, which we discuss in depth.
Correctly identifying crosswalks is an essential task for the driving activity and mobility autonomy.
Many crosswalk classification, detection and localization systems have been proposed in the literature over the years.
These systems use different perspectives to tackle the crosswalk classification problem: satellite imagery, cockpit view (from the top of a car or behind the windshield), and pedestrian perspective.
Most of the works in the literature are designed and evaluated using small and local datasets, i.e. datasets that present low diversity.
Scaling to large datasets imposes a challenge for the annotation procedure.
Moreover, there is still need for cross-database experiments in the literature because it is usually hard to collect the data in the same place and conditions of the final application.
In this paper, we present a crosswalk classification system based on deep learning.
For that, crowdsourcing platforms, such as OpenStreetMap and Google Street View, are exploited to enable automatic training via automatic acquisition and annotation of a large-scale database.
Additionally, this work proposes a comparison study of models trained using fully-automatic data acquisition and annotation against models that were partially annotated.
Cross-database experiments were also included in the experimentation to show that the proposed methods enable use with real world applications.
Our results show that the model trained on the fully-automatic database achieved high overall accuracy (94.12%), and that a statistically significant improvement (to 96.30%) can be achieved by manually annotating a specific part of the database.
Finally, the results of the cross-database experiments show that both models are robust to the many variations of image and scenarios, presenting a consistent behavior.
Recently, increasing attention has been directed to the study of the speech emotion recognition, in which global acoustic features of an utterance are mostly used to eliminate the content differences.
However, the expression of speech emotion is a dynamic process, which is reflected through dynamic durations, energies, and some other prosodic information when one speaks.
In this paper, a novel local dynamic pitch probability distribution feature, which is obtained by drawing the histogram, is proposed to improve the accuracy of speech emotion recognition.
Compared with most of the previous works using global features, the proposed method takes advantage of the local dynamic information conveyed by the emotional speech.
Several experiments on Berlin Database of Emotional Speech are conducted to verify the effectiveness of the proposed method.
The experimental results demonstrate that the local dynamic information obtained with the proposed method is more effective for speech emotion recognition than the traditional global features.
Combining deep model-free reinforcement learning with on-line planning is a promising approach to building on the successes of deep RL.
On-line planning with look-ahead trees has proven successful in environments where transition models are known a priori.
However, in complex environments where transition models need to be learned from data, the deficiencies of learned models have limited their utility for planning.
To address these challenges, we propose TreeQN, a differentiable, recursive, tree-structured model that serves as a drop-in replacement for any value function network in deep RL with discrete actions.
TreeQN dynamically constructs a tree by recursively applying a transition model in a learned abstract state space and then aggregating predicted rewards and state-values using a tree backup to estimate Q-values.
We also propose ATreeC, an actor-critic variant that augments TreeQN with a softmax layer to form a stochastic policy network.
Both approaches are trained end-to-end, such that the learned model is optimised for its actual use in the tree.
We show that TreeQN and ATreeC outperform n-step DQN and A2C on a box-pushing task, as well as n-step DQN and value prediction networks (Oh et al.2017) on multiple Atari games.
Furthermore, we present ablation studies that demonstrate the effect of different auxiliary losses on learning transition models.
Mining financial text documents and understanding the sentiments of individual investors, institutions and markets is an important and challenging problem in the literature.
Current approaches to mine sentiments from financial texts largely rely on domain specific dictionaries.
However, dictionary based methods often fail to accurately predict the polarity of financial texts.
This paper aims to improve the state-of-the-art and introduces a novel sentiment analysis approach that employs the concept of financial and non-financial performance indicators.
It presents an association rule mining based hierarchical sentiment classifier model to predict the polarity of financial texts as positive, neutral or negative.
The performance of the proposed model is evaluated on a benchmark financial dataset.
The model is also compared against other state-of-the-art dictionary and machine learning based approaches and the results are found to be quite promising.
The novel use of performance indicators for financial sentiment analysis offers interesting and useful insights.
We consider the numerical modeling of the Farley-Buneman instability development in the earth's ionosphere plasma.
The ion behavior is governed by the kinetic Landau equation in the four-dimensional phase space, and since the finite difference discretization on a tensor product grid is used, this equation becomes the most computationally challenging part of the scheme.
To relax the complexity and memory consumption, an adaptive model reduction using the low-rank separation of variables, namely the Tensor Train format, is employed.
The approach was verified via the prototype MATLAB implementation.
Numerical experiments demonstrate the possibility of efficient separation of space and velocity variables, resulting in the solution storage reduction by a factor of order tens.
Optimizing a deep neural network is a fundamental task in computer vision, yet direct training methods often suffer from over-fitting.
Teacher-student optimization aims at providing complementary cues from a model trained previously, but these approaches are often considerably slow due to the pipeline of training a few generations in sequence, i.e., time complexity is increased by several times.
This paper presents snapshot distillation (SD), the first framework which enables teacher-student optimization in one generation.
The idea of SD is very simple: instead of borrowing supervision signals from previous generations, we extract such information from earlier epochs in the same generation, meanwhile make sure that the difference between teacher and student is sufficiently large so as to prevent under-fitting.
To achieve this goal, we implement SD in a cyclic learning rate policy, in which the last snapshot of each cycle is used as the teacher for all iterations in the next cycle, and the teacher signal is smoothed to provide richer information.
In standard image classification benchmarks such as CIFAR100 and ILSVRC2012, SD achieves consistent accuracy gain without heavy computational overheads.
We also verify that models pre-trained with SD transfers well to object detection and semantic segmentation in the PascalVOC dataset.
MirBot is a collaborative application for smartphones that allows users to perform object recognition.
This app can be used to take a photograph of an object, select the region of interest and obtain the most likely class (dog, chair, etc.) by means of similarity search using features extracted from a convolutional neural network (CNN).
The answers provided by the system can be validated by the user so as to improve the results for future queries.
All the images are stored together with a series of metadata, thus enabling a multimodal incremental dataset labeled with synset identifiers from the WordNet ontology.
This dataset grows continuously thanks to the users' feedback, and is publicly available for research.
This work details the MirBot object recognition system, analyzes the statistics gathered after more than four years of usage, describes the image classification methodology, and performs an exhaustive evaluation using handcrafted features, convolutional neural codes and different transfer learning techniques.
After comparing various models and transformation methods, the results show that the CNN features maintain the accuracy of MirBot constant over time, despite the increasing number of new classes.
The app is freely available at the Apple and Google Play stores.
The goal of few-shot learning is to learn a classifier that generalizes well even when trained with a limited number of training instances per class.
The recently introduced meta-learning approaches tackle this problem by learning a generic classifier across a large number of multiclass classification tasks and generalizing the model to a new task.
Yet, even with such meta-learning, the low-data problem in the novel classification task still remains.
In this paper, we propose Transductive Propagation Network (TPN), a novel meta-learning framework for transductive inference that classifies the entire test set at once to alleviate the low-data problem.
Specifically, we propose to learn to propagate labels from labeled instances to unlabeled test instances, by learning a graph construction module that exploits the manifold structure in the data.
TPN jointly learns both the parameters of feature embedding and the graph construction in an end-to-end manner.
We validate TPN on multiple benchmark datasets, on which it largely outperforms existing few-shot learning approaches and achieves the state-of-the-art results.
Automated writing evaluation (AWE) has been shown to be an effective mechanism for quickly providing feedback to students.
It has already seen wide adoption in enterprise-scale applications and is starting to be adopted in large-scale contexts.
Training an AWE model has historically required a single batch of several hundred writing examples and human scores for each of them.
This requirement limits large-scale adoption of AWE since human-scoring essays is costly.
Here we evaluate algorithms for ensuring that AWE models are consistently trained using the most informative essays.
Our results show how to minimize training set sizes while maximizing predictive performance, thereby reducing cost without unduly sacrificing accuracy.
We conclude with a discussion of how to integrate this approach into large-scale AWE systems.
It is necessary for a mobile robot to be able to efficiently plan a path from its starting, or current, location to a desired goal location.
This is a trivial task when the environment is static.
However, the operational environment of the robot is rarely static, and it often has many moving obstacles.
The robot may encounter one, or many, of these unknown and unpredictable moving obstacles.
The robot will need to decide how to proceed when one of these obstacles is obstructing it's path.
A method of dynamic replanning using RRT* is presented.
The robot will modify it's current plan when an unknown random moving obstacle obstructs the path.
Various experimental results show the effectiveness of the proposed method.
Cloud infrastructures enable the efficient parallel execution of data-intensive tasks such as entity resolution on large datasets.
We investigate challenges and possible solutions of using the MapReduce programming model for parallel entity resolution.
In particular, we propose and evaluate two MapReduce-based implementations for Sorted Neighborhood blocking that either use multiple MapReduce jobs or apply a tailored data replication.
Time-aware encoding of frame sequences in a video is a fundamental problem in video understanding.
While many attempted to model time in videos, an explicit study on quantifying video time is missing.
To fill this lacuna, we aim to evaluate video time explicitly.
We describe three properties of video time, namely a) temporal asymmetry, b)temporal continuity and c) temporal causality.
Based on each we formulate a task able to quantify the associated property.
This allows assessing the effectiveness of modern video encoders, like C3D and LSTM, in their ability to model time.
Our analysis provides insights about existing encoders while also leading us to propose a new video time encoder, which is better suited for the video time recognition tasks than C3D and LSTM.
We believe the proposed meta-analysis can provide a reasonable baseline to assess video time encoders on equal grounds on a set of temporal-aware tasks.
Event Related Potentials (ERPs) are very feeble alterations in the ongoing Electroencephalogram (EEG) and their detection is a challenging problem.
Based on the unique time-based parameters derived from wavelet coefficients and the asymmetry property of wavelets a novel algorithm to separate ERP components in single-trial EEG data is described.
Though illustrated as a specific application to N170 ERP detection, the algorithm is a generalized approach that can be easily adapted to isolate different kinds of ERP components.
The algorithm detected the N170 ERP component with a high level of accuracy.
We demonstrate that the asymmetry method is more accurate than the matching wavelet algorithm and t-CWT method by 48.67 and 8.03 percent respectively.
This paper provides an off-line demonstration of the algorithm and considers issues related to the extension of the algorithm to real-time applications.
In a multi-agent system, transitioning from a centralized to a distributed decision-making strategy can introduce vulnerability to adversarial manipulation.
We study the potential for adversarial manipulation in a class of graphical coordination games where the adversary can pose as a friendly agent in the game, thereby influencing the decision-making rules of a subset of agents.
The adversary's influence can cascade throughout the system, indirectly influencing other agents' behavior and significantly impacting the emergent collective behavior.
The main results in this paper focus on characterizing conditions under which the adversary's local influence can dramatically impact the emergent global behavior, e.g., destabilize efficient Nash equilibria.
Neural machine translation (NMT), a new approach to machine translation, has been proved to outperform conventional statistical machine translation (SMT) across a variety of language pairs.
Translation is an open-vocabulary problem, but most existing NMT systems operate with a fixed vocabulary, which causes the incapability of translating rare words.
This problem can be alleviated by using different translation granularities, such as character, subword and hybrid word-character.
Translation involving Chinese is one of the most difficult tasks in machine translation, however, to the best of our knowledge, there has not been any other work exploring which translation granularity is most suitable for Chinese in NMT.
In this paper, we conduct an extensive comparison using Chinese-English NMT as a case study.
Furthermore, we discuss the advantages and disadvantages of various translation granularities in detail.
Our experiments show that subword model performs best for Chinese-to-English translation with the vocabulary which is not so big while hybrid word-character model is most suitable for English-to-Chinese translation.
Moreover, experiments of different granularities show that Hybrid_BPE method can achieve best result on Chinese-to-English translation task.
In order to achieve high efficiency of classification in intrusion detection, a compressed model is proposed in this paper which combines horizontal compression with vertical compression.
OneR is utilized as horizontal com-pression for attribute reduction, and affinity propagation is employed as vertical compression to select small representative exemplars from large training data.
As to be able to computationally compress the larger volume of training data with scalability, MapReduce based parallelization approach is then implemented and evaluated for each step of the model compression process abovementioned, on which common but efficient classification methods can be directly used.
Experimental application study on two publicly available datasets of intrusion detection, KDD99 and CMDC2012, demonstrates that the classification using the compressed model proposed can effectively speed up the detection procedure at up to 184 times, most importantly at the cost of a minimal accuracy difference with less than 1% on average.
In this paper we present reclaimID: An architecture that allows users to reclaim their digital identities by securely sharing identity attributes without the need for a centralised service provider.
We propose a design where user attributes are stored in and shared over a name system under user-owned namespaces.
Attributes are encrypted using attribute-based encryption (ABE), allowing the user to selectively authorize and revoke access of requesting parties to subsets of his attributes.
We present an implementation based on the decentralised GNU Name System (GNS) in combination with ciphertext-policy ABE using type-1 pairings.
To show the practicality of our implementation, we carried out experimental evaluations of selected implementation aspects including attribute resolution performance.
Finally, we show that our design can be used as a standard OpenID Connect Identity Provider allowing our implementation to be integrated into standard-compliant services.
In modern OCaml, single-argument datatype declarations (variants with a single constructor, records with a single field) can sometimes be `unboxed'.
This means that their memory representation is the same as their single argument (omitting the variant or record constructor and an indirection), thus achieving better time and memory efficiency.
However, in the case of generalized/guarded algebraic datatypes (GADTs), unboxing is not always possible due to a subtle assumption about the runtime representation of OCaml values.
The current correctness check is incomplete, rejecting many valid definitions, in particular those involving mutually-recursive datatype declarations.
In this paper, we explain the notion of separability as a semantic for the unboxing criterion, and propose a set of inference rules to check separability.
From these inference rules, we derive a new implementation of the unboxing check that properly supports mutually-recursive definitions.
Frequency agile radar (FAR) is known to have excellent electronic counter-countermeasures (ECCM) performance and the potential to realize spectrum sharing in dense electromagnetic environments.
Many compressed sensing (CS) based algorithms have been developed for joint range and Doppler estimation in FAR.
This paper considers theoretical analysis of FAR via CS algorithms.
In particular, we analyze the properties of the sensing matrix, which is a highly structured random matrix.
We then derive bounds on the number of recoverable targets.
Numerical simulations and field experiments validate the theoretical findings and demonstrate the effectiveness of CS approaches to FAR.
Understanding the semantic relationships between terms is a fundamental task in natural language processing applications.
While structured resources that can express those relationships in a formal way, such as ontologies, are still scarce, a large number of linguistic resources gathering dictionary definitions is becoming available, but understanding the semantic structure of natural language definitions is fundamental to make them useful in semantic interpretation tasks.
Based on an analysis of a subset of WordNet's glosses, we propose a set of semantic roles that compose the semantic structure of a dictionary definition, and show how they are related to the definition's syntactic configuration, identifying patterns that can be used in the development of information extraction frameworks and semantic models.
We present GHTraffic, a dataset of significant size comprising HTTP transactions extracted from GitHub data and augmented with synthetic transaction data.
The dataset facilitates reproducible research on many aspects of service-oriented computing.
This paper discusses use cases for such a dataset and extracts a set of requirements from these use cases.
We then discuss the design of GHTraffic, and the methods and tool used to construct it.
We conclude our contribution with some selective metrics that characterise GHTraffic.
Hindustani classical music is entirely based on the Raga structures.
In Hindustani music, a Gharana or school refers to the adherence of a group of musicians to a particular musical style of performing a certain raga.
The objective of this work was to find out if any characteristic acoustic cues exist which discriminates a particular gharana from the other.
Another intriguing fact is if the artists of the same gharana keep their singing style unchanged over generations or evolution of music takes place like everything else in nature.
In this work, we chose to study the similarities and differences in singing style of some artists from at least four consecutive generations representing four different gharanas using robust non-linear methods.
For this, alap parts of a particular raga sung by all the artists were analyzed with the help of non linear multifractal analysis (MFDFA) technique.
The spectral width obtained from the MFDFA method gives an estimate of the complexity of the signal.
The observations give a cue in the direction to the scientific recognition of guru-shisya parampara (teacher-student tradition) a hitherto much-heard philosophical term.
Moreover the variation in the complexity patterns among various gharanas will give a hint of the characteristic feature of that particular gharana as well as the effect of globalization in the field of classical music happening through past few decades.
Telematics data is becoming increasingly available due to the ubiquity of devices that collect data during drives, for different purposes, such as usage based insurance (UBI), fleet management, navigation of connected vehicles, etc.
Consequently, a variety of data-analytic applications have become feasible that extract valuable insights from the data.
In this paper, we address the especially challenging problem of discovering behavior-based driving patterns from only externally observable phenomena (e.g. vehicle's speed).
We present a trajectory segmentation approach capable of discovering driving patterns as separate segments, based on the behavior of drivers.
This segmentation approach includes a novel transformation of trajectories along with a dynamic programming approach for segmentation.
We apply the segmentation approach on a real-word, rich dataset of personal car trajectories provided by a major insurance company based in Columbus, Ohio.
Analysis and preliminary results show the applicability of approach for finding significant driving patterns.
A remote-sensing system that can determine the position of hidden objects has applications in many critical real-life scenarios, such as search and rescue missions and safe autonomous driving.
Previous work has shown the ability to range and image objects hidden from the direct line of sight, employing advanced optical imaging technologies aimed at small objects at short range.
In this work we demonstrate a long-range tracking system based on single laser illumination and single-pixel single-photon detection.
This enables us to track one or more people hidden from view at a stand-off distance of over 50m.
These results pave the way towards next generation LiDAR systems that will reconstruct not only the direct-view scene but also the main elements hidden behind walls or corners.
Glaucoma is a disease in which the optic nerve is chronically damaged by the elevation of the intra-ocular pressure, resulting in visual field defect.
Therefore, it is important to monitor and treat suspected patients before they are confirmed with glaucoma.
In this paper, we propose a 2-stage ranking-CNN that classifies fundus images as normal, suspicious, and glaucoma.
Furthermore, we propose a method of using the class activation map as a mask filter and combining it with the original fundus image as an intermediate input.
Our results have improved the average accuracy by about 10% over the existing 3-class CNN and ranking-CNN, and especially improved the sensitivity of suspicious class by more than 20% over 3-class CNN.
In addition, the extracted ROI was also found to overlap with the diagnostic criteria of the physician.
The method we propose is expected to be efficiently applied to any medical data where there is a suspicious condition between normal and disease.
Biometrics emerged as a robust solution for security systems.
However, given the dissemination of biometric applications, criminals are developing techniques to circumvent them by simulating physical or behavioral traits of legal users (spoofing attacks).
Despite face being a promising characteristic due to its universality, acceptability and presence of cameras almost everywhere, face recognition systems are extremely vulnerable to such frauds since they can be easily fooled with common printed facial photographs.
State-of-the-art approaches, based on Convolutional Neural Networks (CNNs), present good results in face spoofing detection.
However, these methods do not consider the importance of learning deep local features from each facial region, even though it is known from face recognition that each facial region presents different visual aspects, which can also be exploited for face spoofing detection.
In this work we propose a novel CNN architecture trained in two steps for such task.
Initially, each part of the neural network learns features from a given facial region.
Afterwards, the whole model is fine-tuned on the whole facial images.
Results show that such pre-training step allows the CNN to learn different local spoofing cues, improving the performance and the convergence speed of the final model, outperforming the state-of-the-art approaches.
The Robinson-Goforth topology of swaps in adjoining payoffs elegantly arranges 2x2 ordinal games in accordance with important properties including symmetry, number of dominant strategies and Nash Equilibria, and alignment of interests.
Adding payoff families based on Nash Equilibria illustrates an additional aspect of this order and aids visualization of the topology.
Making ties through half-swaps not only creates simpler games within the topology, but, in reverse, breaking ties shows the evolution of preferences, yielding a natural ordering for the topology of 2x2 games with ties.
An ordinal game not only represents an equivalence class of games with real values, but also a discrete equivalent of the normalized version of those games.
The topology provides coordinates which could be used to identify related games in a semantic web ontology and facilitate comparative analysis of agent-based simulations and other research in game theory, as well as charting relationships and potential moves between games as a tool for institutional analysis and design.
Due to the ubiquity of batch data processing in cloud computing, the related problem of scheduling malleable batch tasks and its extensions have received significant attention recently.
In this paper, we consider a fundamental model where a set of n tasks is to be processed on C identical machines and each task is specified by a value, a workload, a deadline and a parallelism bound.
Within the parallelism bound, the number of machines assigned to a task can vary over time without affecting its workload.
For this model, we obtain two core results: a sufficient and necessary condition such that a set of tasks can be finished by their deadlines on C machines, and an algorithm to produce such a schedule.
These core results provide a conceptual tool and an optimal scheduling algorithm that enable proposing new algorithmic analysis and design and improving existing algorithms under various objectives.
We introduce a novel model for spatially varying variational data fusion, driven by point-wise confidence values.
The proposed model allows for the joint estimation of the data and the confidence values based on the spatial coherence of the data.
We discuss the main properties of the introduced model as well as suitable algorithms for estimating the solution of the corresponding biconvex minimization problem and their convergence.
The performance of the proposed model is evaluated considering the problem of depth image fusion by using both synthetic and real data from publicly available datasets.
We present a convolutional network capable of inferring a 3D representation of a previously unseen object given a single image of this object.
Concretely, the network can predict an RGB image and a depth map of the object as seen from an arbitrary view.
Several of these depth maps fused together give a full point cloud of the object.
The point cloud can in turn be transformed into a surface mesh.
The network is trained on renderings of synthetic 3D models of cars and chairs.
It successfully deals with objects on cluttered background and generates reasonable predictions for real images of cars.
This paper addresses the problem of automated vehicle tracking and recognition from aerial image sequences.
Motivated by its successes in the existing literature focus on the use of linear appearance subspaces to describe multi-view object appearance and highlight the challenges involved in their application as a part of a practical system.
A working solution which includes steps for data extraction and normalization is described.
In experiments on real-world data the proposed methodology achieved promising results with a high correct recognition rate and few, meaningful errors (type II errors whereby genuinely similar targets are sometimes being confused with one another).
Directions for future research and possible improvements of the proposed method are discussed.
Recent efforts in practical symbolic execution have successfully mitigated the path-explosion problem to some extent with search-based heuristics and compositional approaches.
Similarly, due to an increase in the performance of cheap multi-core commodity computers, fuzzing as a viable method of random mutation-based testing has also seen promise.
However, the possibility of combining symbolic execution and fuzzing, thereby providing an opportunity to mitigate drawbacks in each other, has not been sufficiently explored.
Fuzzing could, for example, expedite path-exploration in symbolic execution, and symbolic execution could make seed input generation in fuzzing more efficient.
There have only been, in our view, very few hybrid solution proposals with symbolic execution and fuzzing at their centre.
By analyzing 77 relevant and systematically selected papers, we (1) present an overview of hybrid solution proposals of symbolic execution and fuzzing, (2) perform a gap analysis in research of hybrid techniques to improve both, plain symbolic execution and fuzzing, (3) propose new ideas for hybrid test-case generation techniques.
We introduce MilkQA, a question answering dataset from the dairy domain dedicated to the study of consumer questions.
The dataset contains 2,657 pairs of questions and answers, written in the Portuguese language and originally collected by the Brazilian Agricultural Research Corporation (Embrapa).
All questions were motivated by real situations and written by thousands of authors with very different backgrounds and levels of literacy, while answers were elaborated by specialists from Embrapa's customer service.
Our dataset was filtered and anonymized by three human annotators.
Consumer questions are a challenging kind of question that is usually employed as a form of seeking information.
Although several question answering datasets are available, most of such resources are not suitable for research on answer selection models for consumer questions.
We aim to fill this gap by making MilkQA publicly available.
We study the behavior of four answer selection models on MilkQA: two baseline models and two convolutional neural network archictetures.
Our results show that MilkQA poses real challenges to computational models, particularly due to linguistic characteristics of its questions and to their unusually longer lengths.
Only one of the experimented models gives reasonable results, at the cost of high computational requirements.
Generating secure random numbers is vital to the security and privacy infrastructures we rely on today.
Having a computer system generate a secure random number is not a trivial problem due to the deterministic nature of computer systems.
Servers commonly deal with this problem through hardware-based random number generators, which can come in the form of expansion cards, dongles, or integrated into the CPU itself.
With the explosion of network- and internet-connected devices, however, the problem of cryptography is no longer a server-centric problem; even small devices need a reliable source of randomness for cryptographic operations - for example, network devices and appliances like routers, switches and access points, as well as various Internet-of-Things (IoT) devices for security and remote management.
This paper proposes a software solution based on side-channel measurements as a source of high-quality entropy (nicknamed "SideRand"), that can theoretically be applied to most platforms (large servers, appliances, even maker boards like RaspberryPi or Arduino), and generates a seed for a regular CSPRNG to enable proper cryptographic operations for security and privacy.
This paper also proposes two criteria - openness and auditability - as essential requirements for confidence in any random generator for cryptographic use, and discusses how SideRand meets the two criteria (and how most hardware devices do not).
Compared to other behavioural biometrics, mouse dynamics is a less explored area.
General purpose data sets containing unrestricted mouse usage data are usually not available.
The Balabit data set was released in 2016 for a data science competition, which against the few subjects, can be considered the first adequate publicly available one.
This paper presents a performance evaluation study on this data set for impostor detection.
The existence of very short test sessions makes this data set challenging.
Raw data were segmented into mouse move, point and click and drag and drop types of mouse actions, then several features were extracted.
In contrast to keystroke dynamics, mouse data is not sensitive, therefore it is possible to collect negative mouse dynamics data and to use two-class classifiers for impostor detection.
Both action- and set of actions-based evaluations were performed.
Set of actions-based evaluation achieves 0.92 AUC on the test part of the data set.
However, the same type of evaluation conducted on the training part of the data set resulted in maximal AUC (1) using only 13 actions.
Drag and drop mouse actions proved to be the best actions for impostor detection.
Relational reasoning is a central component of generally intelligent behavior, but has proven difficult for neural networks to learn.
In this paper we describe how to use Relation Networks (RNs) as a simple plug-and-play module to solve problems that fundamentally hinge on relational reasoning.
We tested RN-augmented networks on three tasks: visual question answering using a challenging dataset called CLEVR, on which we achieve state-of-the-art, super-human performance; text-based question answering using the bAbI suite of tasks; and complex reasoning about dynamic physical systems.
Then, using a curated dataset called Sort-of-CLEVR we show that powerful convolutional networks do not have a general capacity to solve relational questions, but can gain this capacity when augmented with RNs.
Our work shows how a deep learning architecture equipped with an RN module can implicitly discover and learn to reason about entities and their relations.
A crucial and time-sensitive task when any disaster occurs is to rescue victims and distribute resources to the right groups and locations.
This task is challenging in populated urban areas, due to the huge burst of help requests generated in a very short period.
To improve the efficiency of the emergency response in the immediate aftermath of a disaster, we propose a heuristic multi-agent reinforcement learning scheduling algorithm, named as ResQ, which can effectively schedule the rapid deployment of volunteers to rescue victims in dynamic settings.
The core concept is to quickly identify victims and volunteers from social network data and then schedule rescue parties with an adaptive learning algorithm.
This framework performs two key functions: 1) identify trapped victims and rescue volunteers, and 2) optimize the volunteers' rescue strategy in a complex time-sensitive environment.
The proposed ResQ algorithm can speed up the training processes through a heuristic function which reduces the state-action space by identifying the set of particular actions over others.
Experimental results showed that the proposed heuristic multi-agent reinforcement learning based scheduling outperforms several state-of-art methods, in terms of both reward rate and response times.
The reliable fraction of information is an attractive score for quantifying (functional) dependencies in high-dimensional data.
In this paper, we systematically explore the algorithmic implications of using this measure for optimization.
We show that the problem is NP-hard, which justifies the usage of worst-case exponential-time as well as heuristic search methods.
We then substantially improve the practical performance for both optimization styles by deriving a novel admissible bounding function that has an unbounded potential for additional pruning over the previously proposed one.
Finally, we empirically investigate the approximation ratio of the greedy algorithm and show that it produces highly competitive results in a fraction of time needed for complete branch-and-bound style search.
N-continuous orthogonal frequency division multiplexing (NC-OFDM) was demonstrated to provide significant sidelobe suppression for baseband OFDM signals.
However, it will introduce severe interference to the transmit signals.
Hence in this letter, we specifically design a class of low-interference NC-OFDM schemes for alleviating the introduced interference.
Meanwhile, we also obtain an asymptotic spectrum analysis by a closed-form expression.
It is shown that the proposed scheme is capable of reducing the interference to a negligible level, and hence to save the high complexity of signal recovery at the receiver, while maintaining similar sidelobe suppression performance compared to traditional NC-OFDM.
Using (a,b)-trees as an example, we show how to perform a parallel split with logarithmic latency and parallel join, bulk updates, intersection, union (or merge), and (symmetric) set difference with logarithmic latency and with information theoretically optimal work.
We present both asymptotically optimal solutions and simplified versions that perform well in practice - they are several times faster than previous implementations.
In this article we derive a Pontryagin maximum principle (PMP) for discrete-time optimal control problems on matrix Lie groups.
The PMP provides first order necessary conditions for optimality; these necessary conditions typically yield two point boundary value problems, and these boundary value problems can then solved to extract optimal control trajectories.
Constrained optimal control problems for mechanical systems, in general, can only be solved numerically, and this motivates the need to derive discrete-time models that are accurate and preserve the non-flat manifold structures of the underlying continuous-time controlled systems.
The PMPs for discrete-time systems evolving on Euclidean spaces are not readily applicable to discrete-time models evolving on non-flat manifolds.
In this article we bridge this lacuna and establish a discrete-time PMP on matrix Lie groups.
Our discrete-time models are derived via discrete mechanics, (a structure preserving discretization scheme,) leading to the preservation of the underlying manifold over time, thereby resulting in greater numerical accuracy of our technique.
This PMP caters to a class of constrained optimal control problems that includes point-wise state and control action constraints, and encompasses a large class of control problems that arise in various field of engineering and the applied sciences.
As the senior population rapidly increases, it is challenging yet crucial to provide effective long-term care for seniors who live at home or in senior care facilities.
Smart senior homes, which have gained widespread interest in the healthcare community, have been proposed to improve the well-being of seniors living independently.
In particular, non-intrusive, cost-effective sensors placed in these senior homes enable gait characterization, which can provide clinically relevant information including mobility level and early neurodegenerative disease risk.
In this paper, we present a method to perform gait analysis from a single camera placed within the home.
We show that we can accurately calculate various gait parameters, demonstrating the potential for our system to monitor the long-term gait of seniors and thus aid clinicians in understanding a patient's medical profile.
Several exact recovery criteria (ERC) ensuring that orthogonal matching pursuit (OMP) identifies the correct support of sparse signals have been developed in the last few years.
These ERC rely on the restricted isometry property (RIP), the associated restricted isometry constant (RIC) and sometimes the restricted orthogonality constant (ROC).
In this paper, three of the most recent ERC for OMP are examined.
The contribution is to show that these ERC remain valid for a generalization of OMP, entitled simultaneous orthogonal matching pursuit (SOMP), that is capable to process several measurement vectors simultaneously and return a common support estimate for the underlying sparse vectors.
The sharpness of the bounds is also briefly discussed in light of previous works focusing on OMP.
The words of a language reflect the structure of the human mind, allowing us to transmit thoughts between individuals.
However, language can represent only a subset of our rich and detailed cognitive architecture.
Here, we ask what kinds of common knowledge (semantic memory) are captured by word meanings (lexical semantics).
We examine a prominent computational model that represents words as vectors in a multidimensional space, such that proximity between word-vectors approximates semantic relatedness.
Because related words appear in similar contexts, such spaces - called "word embeddings" - can be learned from patterns of lexical co-occurrences in natural language.
Despite their popularity, a fundamental concern about word embeddings is that they appear to be semantically "rigid": inter-word proximity captures only overall similarity, yet human judgments about object similarities are highly context-dependent and involve multiple, distinct semantic features.
For example, dolphins and alligators appear similar in size, but differ in intelligence and aggressiveness.
Could such context-dependent relationships be recovered from word embeddings?
To address this issue, we introduce a powerful, domain-general solution: "semantic projection" of word-vectors onto lines that represent various object features, like size (the line extending from the word "small" to "big"), intelligence (from "dumb" to "smart"), or danger (from "safe" to "dangerous").
This method, which is intuitively analogous to placing objects "on a mental scale" between two extremes, recovers human judgments across a range of object categories and properties.
We thus show that word embeddings inherit a wealth of common knowledge from word co-occurrence statistics and can be flexibly manipulated to express context-dependent meanings.
This document provides the results of the tests of acoustic parameter estimation algorithms on the Acoustic Characterization of Environments (ACE) Challenge Evaluation dataset which were subsequently submitted and written up into papers for the Proceedings of the ACE Challenge.
This document is supporting material for a forthcoming journal paper on the ACE Challenge which will provide further analysis of the results.
In this paper, we develop a system for the low-cost indoor localization and tracking problem using radio signal strength indicator, Inertial Measurement Unit (IMU), and magnetometer sensors.
We develop a novel and simplified probabilistic IMU motion model as the proposal distribution of the sequential Monte-Carlo technique to track the robot trajectory.
Our algorithm can globally localize and track a robot with a priori unknown location, given an informative prior map of the Bluetooth Low Energy (BLE) beacons.
Also, we formulate the problem as an optimization problem that serves as the Back-end of the algorithm mentioned above (Front-end).
Thus, by simultaneously solving for the robot trajectory and the map of BLE beacons, we recover a continuous and smooth trajectory of the robot, corrected locations of the BLE beacons, and the time-varying IMU bias.
The evaluations achieved using hardware show that through the proposed closed-loop system the localization performance can be improved; furthermore, the system becomes robust to the error in the map of beacons by feeding back the optimized map to the Front-end.
Current deep learning based text classification methods are limited by their ability to achieve fast learning and generalization when the data is scarce.
We address this problem by integrating a meta-learning procedure that uses the knowledge learned across many tasks as an inductive bias towards better natural language understanding.
Based on the Model-Agnostic Meta-Learning framework (MAML), we introduce the Attentive Task-Agnostic Meta-Learning (ATAML) algorithm for text classification.
The essential difference between MAML and ATAML is in the separation of task-agnostic representation learning and task-specific attentive adaptation.
The proposed ATAML is designed to encourage task-agnostic representation learning by way of task-agnostic parameterization and facilitate task-specific adaptation via attention mechanisms.
We provide evidence to show that the attention mechanism in ATAML has a synergistic effect on learning performance.
In comparisons with models trained from random initialization, pretrained models and meta trained MAML, our proposed ATAML method generalizes better on single-label and multi-label classification tasks in miniRCV1 and miniReuters-21578 datasets.
In the recent years we have witnessed a rapid development of new algorithmic techniques for parameterized algorithms for graph separation problems.
We present experimental evaluation of two cornerstone theoretical results in this area: linear-time branching algorithms guided by half-integral relaxations and kernelization (preprocessing) routines based on representative sets in matroids.
A side contribution is a new set of benchmark instances of (unweighted, vertex-deletion) Multiway Cut.
Caches in Content-Centric Networks (CCN) are increasingly adopting flash memory based storage.
The current flash cache technology stores all files with the largest possible expiry date, i.e. the files are written in the memory so that they are retained for as long as possible.
This, however, does not leverage the CCN data characteristics where content is typically short-lived and has a distinct popularity profile.
Writing files in a cache using the longest retention time damages the memory device thus reducing its lifetime.
However, writing using a small retention time can increase the content retrieval delay, since, at the time a file is requested, the file may already have been expired from the memory.
This motivates us to consider a joint optimization wherein we obtain optimal policies for jointly minimizing the content retrieval delay (which is a network-centric objective) and the flash damage (which is a device-centric objective).
Caching decisions now not only involve what to cache but also for how long to cache each file.
We design provably optimal policies and numerically compare them against prior policies.
In this paper, we propose an interpretable LSTM recurrent neural network, i.e., multi-variable LSTM for time series with exogenous variables.
Currently, widely used attention mechanism in recurrent neural networks mostly focuses on the temporal aspect of data and falls short of characterizing variable importance.
To this end, our multi-variable LSTM equipped with tensorized hidden states is developed to learn variable specific representations, which give rise to both temporal and variable level attention.
Preliminary experiments demonstrate comparable prediction performance of multi-variable LSTM w.r.t. encoder-decoder based baselines.
More interestingly, variable importance in real datasets characterized by the variable attention is highly in line with that determined by statistical Granger causality test, which exhibits the prospect of multi-variable LSTM as a simple and uniform end-to-end framework for both forecasting and knowledge discovery.
Autism Spectrum Disorder (ASD) is neurodevelopmental condition characterized by social interaction and communication difficulties, along with narrow and repetitive interests.
Being an spectrum disorder, ASD affects individuals with a large range of combinations of challenges along dimensions such intelligence, social skills, or sensory processing.
Hence, any interactive technology for ASD ought to be customizable to fit the particular profile of each individual that uses it.
The goal of this paper is to characterize the support of customization in this area.
To do so, we performed a focused study that identifies the dimensions of ASD where customization has been considered on wearable and natural surfaces technologies, two of the most promising technologies for ASD, and assess the empirical evaluation that supports them.
Our study revealed that, even though its critical importance, customization has fundamentally not been addressed in this domain and it opened avenues for research at the intersection of human-computer interaction and software engineering.
Current reconfiguration techniques are based on starting the system in a consistent configuration, in which all participating entities are in their initial state.
Starting from that state, the system must preserve consistency as long as a predefined churn rate of processors joins and leaves is not violated, and unbounded storage is available.
Many working systems cannot control this churn rate and do not have access to unbounded storage.
System designers that neglect the outcome of violating the above assumptions may doom the system to exhibit illegal behaviors.
We present the first automatically recovering reconfiguration scheme that recovers from transient faults, such as temporal violations of the above assumptions.
Our self-stabilizing solutions regain safety automatically by assuming temporal access to reliable failure detectors.
Once safety is re-established, the failure detector reliability is no longer needed.
Still, liveness is conditioned by the failure detector's unreliable signals.
We show that our self-stabilizing reconfiguration techniques can serve as the basis for the implementation of several dynamic services over message passing systems.
Examples include self-stabilizing reconfigurable virtual synchrony, which, in turn, can be used for implementing a self-stabilizing reconfigurable state-machine replication and self-stabilizing reconfigurable emulation of shared memory.
Context information around words helps in determining their actual meaning, for example "networks" used in contexts of artificial neural networks or biological neuron networks.
Generative topic models infer topic-word distributions, taking no or only little context into account.
Here, we extend a neural autoregressive topic model to exploit the full context information around words in a document in a language modeling fashion.
This results in an improved performance in terms of generalization, interpretability and applicability.
We apply our modeling approach to seven data sets from various domains and demonstrate that our approach consistently outperforms stateof-the-art generative topic models.
With the learned representations, we show on an average a gain of 9.6% (0.57 Vs 0.52) in precision at retrieval fraction 0.02 and 7.2% (0.582 Vs 0.543) in F1 for text categorization.
We examine connections between combinatorial notions that arise in machine learning and topological notions in cubical/simplicial geometry.
These connections enable to export results from geometry to machine learning.
Our first main result is based on a geometric construction by Tracy Hall (2004) of a partial shelling of the cross-polytope which can not be extended.
We use it to derive a maximum class of VC dimension 3 that has no corners.
This refutes several previous works in machine learning from the past 11 years.
In particular, it implies that all previous constructions of optimal unlabeled sample compression schemes for maximum classes are erroneous.
On the positive side we present a new construction of an unlabeled sample compression scheme for maximum classes.
We leave as open whether our unlabeled sample compression scheme extends to ample (a.k.a. lopsided or extremal) classes, which represent a natural and far-reaching generalization of maximum classes.
Towards resolving this question, we provide a geometric characterization in terms of unique sink orientations of the 1-skeletons of associated cubical complexes.
We explore the role of interaction for the problem of reliable computation over two-way multicast networks.
Specifically we consider a four-node network in which two nodes wish to compute a modulo-sum of two independent Bernoulli sources generated from the other two, and a similar task is done in the other direction.
The main contribution of this work lies in the characterization of the computation capacity region for a deterministic model of the network via a novel transmission scheme.
One consequence of this result is that, not only we can get an interaction gain over the one-way non-feedback computation capacities, but also we can sometimes get all the way to perfect-feedback computation capacities simultaneously in both directions.
This result draws a parallel with the recent result developed in the context of two-way interference channels.
Metaheuristic particle swarm optimization (PSO) algorithm has emerged as one of the most promising optimization techniques in solving highly constrained non-linear and non-convex optimization problems in different areas of electrical engineering.
Economic operation of the power system is one of the most important areas of electrical engineering where PSO has been used efficiently in solving various issues of practical systems.
In this paper, a comprehensive survey of research works in solving various aspects of economic load dispatch (ELD) problems of power system engineering using different types of PSO algorithms is presented.
Five important areas of ELD problems have been identified, and the papers published in the general area of ELD using PSO have been classified into these five sections.
These five areas are (i) single objective economic load dispatch, (ii) dynamic economic load dispatch, (iii) economic load dispatch with non-conventional sources, (iv) multi-objective environmental/economic dispatch, and (v) economic load dispatch of microgrids.
At the end of each category, a table is provided which describes the main features of the papers in brief.
The promising future works are given at the conclusion of the review.
The use of modern technology in Education is the key to an increased drive for learning which shape learners critical and analytic competencies with respect to disciplinary knowledge.
Distance education (DE) is a system of learning driven by computer linked to internet.
The flexible nature of DE avail students who are unable to attend full time education due to age, social or religious barriers.
However, in Nigeria, the University of Lagos distance learning Institute has its shortfalls traced to poor student support system, which affects the service delivery to students.
The study examined Influence of Information Support on ICT use by distance learners.
Transformation of Machine Learning (ML) from a boutique science to a generally accepted technology has increased importance of reproduction and transportability of ML studies.
In the current work, we investigate how corpus characteristics of textual data sets correspond to text classification results.
We work with two data sets gathered from sub-forums of an online health-related forum.
Our empirical results are obtained for a multi-class sentiment analysis application.
A visible light communication broadcast channel is considered, in which a transmitter luminaire communicates with two legitimate receivers in the presence of an external eavesdropper.
A number of trusted cooperative half-duplex relay luminaires are deployed to aid with securing the transmitted data.
Transmitters are equipped with single light fixtures, containing multiple light emitting diodes, and receiving nodes are equipped with single photo-detectors, rendering the considered setting as a single-input single-output system.
Transmission is amplitude-constrained to maintain operation within the light emitting diodes' dynamic range.
Achievable secrecy rate regions are derived under such amplitude constraints for this multi-receiver wiretap channel, first for direct transmission without the relays, and then for multiple relaying schemes: cooperative jamming, decode-and-forward, and amplify-and-forward.
Superposition coding with uniform signaling is used at the transmitter and the relays.
Further, for each relaying scheme, secure beamforming vectors are carefully designed at the relay nodes in order to hurt the eavesdropper and/or benefit the legitimate receivers.
Superiority of the proposed relaying schemes, with secure beamforming, is shown over direct transmission.
It is also shown that the best relaying scheme depends on how far the eavesdropper is located from the transmitter and the relays, the number of relays, and their geometric layout.
The quality of high-level AI of non-player characters (NPCs) in commercial open-world games (OWGs) has been increasing during the past years.
However, due to constraints specific to the game industry, this increase has been slow and it has been driven by larger budgets rather than adoption of new complex AI techniques.
Most of the contemporary AI is still expressed as hard-coded scripts.
The complexity and manageability of the script codebase is one of the key limiting factors for further AI improvements.
In this paper we address this issue.
We present behavior objects - a general approach to development of NPC behaviors for large OWGs.
Behavior objects are inspired by object-oriented programming and extend the concept of smart objects.
Our approach promotes encapsulation of data and code for multiple related behaviors in one place, hiding internal details and embedding intelligence in the environment.
Behavior objects are a natural abstraction of five different techniques that we have implemented to manage AI complexity in an upcoming AAA OWG.
We report the details of the implementations in the context of behavior trees and the lessons learned during development.
Our work should serve as inspiration for AI architecture designers from both the academia and the industry.
The ozone level prediction is an important task of air quality agencies of modern cities.
In this paper, we design an ozone level alarm system (OLP) for Isfahan city and test it through the real word data from 1-1-2000 to 7-6-2011.
We propose a computer based system with three inputs and single output.
The inputs include three sensors of solar ultraviolet (UV), total solar radiation (TSR) and total ozone (O3).
And the output of the system is the predicted O3 of the next day and the alarm massages.
A developed artificial intelligence (AI) algorithm is applied to determine the output, based on the inputs variables.
For this issue, AI models, including supervised brain emotional learning (BEL), adaptive neuro-fuzzy inference system (ANFIS) and artificial neural networks (ANNs), are compared in order to find the best model.
The simulation of the proposed system shows that it can be used successfully in prediction of major cities ozone level.
With the popularity of deep learning (DL), artificial intelligence (AI) has been applied in many areas of human life.
Neural network or artificial neural network (NN), the main technique behind DL, has been extensively studied to facilitate computer vision and natural language recognition.
However, the more we rely on information technology, the more vulnerable we are.
That is, malicious NNs could bring huge threat in the so-called coming AI era.
In this paper, for the first time in the literature, we propose a novel approach to design and insert powerful neural-level trojans or PoTrojan in pre-trained NN models.
Most of the time, PoTrojans remain inactive, not affecting the normal functions of their host NN models.
PoTrojans could only be triggered in very rare conditions.
Once activated, however, the PoTrojans could cause the host NN models to malfunction, either falsely predicting or classifying, which is a significant threat to human society of the AI era.
We would explain the principles of PoTrojans and the easiness of designing and inserting them in pre-trained deep learning models.
PoTrojans doesn't modify the existing architecture or parameters of the pre-trained models, without re-training.
Hence, the proposed method is very efficient.
Interpreting black box classifiers, such as deep networks, allows an analyst to validate a classifier before it is deployed in a high-stakes setting.
A natural idea is to visualize the deep network's representations, so as to "see what the network sees".
In this paper, we demonstrate that standard dimension reduction methods in this setting can yield uninformative or even misleading visualizations.
Instead, we present DarkSight, which visually summarizes the predictions of a classifier in a way inspired by notion of dark knowledge.
DarkSight embeds the data points into a low-dimensional space such that it is easy to compress the deep classifier into a simpler one, essentially combining model compression and dimension reduction.
We compare DarkSight against t-SNE both qualitatively and quantitatively, demonstrating that DarkSight visualizations are more informative.
Our method additionally yields a new confidence measure based on dark knowledge by quantifying how unusual a given vector of predictions is.
We describe an approach to understand the peculiar and counterintuitive generalization properties of deep neural networks.
The approach involves going beyond worst-case theoretical capacity control frameworks that have been popular in machine learning in recent years to revisit old ideas in the statistical mechanics of neural networks.
Within this approach, we present a prototypical Very Simple Deep Learning (VSDL) model, whose behavior is controlled by two control parameters, one describing an effective amount of data, or load, on the network (that decreases when noise is added to the input), and one with an effective temperature interpretation (that increases when algorithms are early stopped).
Using this model, we describe how a very simple application of ideas from the statistical mechanics theory of generalization provides a strong qualitative description of recently-observed empirical results regarding the inability of deep neural networks not to overfit training data, discontinuous learning and sharp transitions in the generalization properties of learning algorithms, etc.
Accurate noise modelling is important for training of deep learning reconstruction algorithms.
While noise models are well known for traditional imaging techniques, the noise distribution of a novel sensor may be difficult to determine a priori.
Therefore, we propose learning arbitrary noise distributions.
To do so, this paper proposes a fully connected neural network model to map samples from a uniform distribution to samples of any explicitly known probability density function.
During the training, the Jensen-Shannon divergence between the distribution of the model's output and the target distribution is minimized.
We experimentally demonstrate that our model converges towards the desired state.
It provides an alternative to existing sampling methods such as inversion sampling, rejection sampling, Gaussian mixture models and Markov-Chain-Monte-Carlo.
Our model has high sampling efficiency and is easily applied to any probability distribution, without the need of further analytical or numerical calculations.
With joint learning of sampling and recovery, the deep learning-based compressive sensing (DCS) has shown significant improvement in performance and running time reduction.
Its reconstructed image, however, losses high-frequency content especially at low subrates.
This happens similarly in the multi-scale sampling scheme which also samples more low-frequency components.
In this paper, we propose a multi-scale DCS convolutional neural network (MS-DCSNet) in which we convert image signal using multiple scale-based wavelet transform, then capture it through convolution block by block across scales.
The initial reconstructed image is directly recovered from multi-scale measurements.
Multi-scale wavelet convolution is utilized to enhance the final reconstruction quality.
The network is able to learn both multi-scale sampling and multi-scale reconstruction, thus results in better reconstruction quality.
Sybil attacks are becoming increasingly widespread and pose a significant threat to online social systems; a single adversary can inject multiple colluding identities in the system to compromise security and privacy.
Recent works have leveraged social network-based trust relationships to defend against Sybil attacks.
However, existing defenses are based on oversimplified assumptions about network structure, which do not necessarily hold in real-world social networks.
Recognizing these limitations, we propose SybilFuse, a defense-in-depth framework for Sybil detection when the oversimplified assumptions are relaxed.
SybilFuse adopts a collective classification approach by first training local classifiers to compute local trust scores for nodes and edges, and then propagating the local scores through the global network structure via weighted random walk and loopy belief propagation mechanisms.
We evaluate our framework on both synthetic and real-world network topologies, including a large-scale, labeled Twitter network comprising 20M nodes and 265M edges, and demonstrate that SybilFuse outperforms state-of-the-art approaches significantly.
In particular, SybilFuse achieves 98% of Sybil coverage among top-ranked nodes.
Dedicated Short Range Communication (DSRC) was designed to provide reliable wireless communication for intelligent transportation system applications.
Sharing information among cars and between cars and the infrastructure, pedestrians, or "the cloud" has great potential to improve safety, mobility and fuel economy.
DSRC is being considered by the US Department of Transportation to be required for ground vehicles.
In the past, their performance has been assessed thoroughly in the labs and limited field testing, but not on a large fleet.
In this paper, we present the analysis of DSRC performance using data from the world's largest connected vehicle test program - Safety Pilot Model Deployment lead by the University of Michigan.
We first investigate their maximum and effective range, and then study the effect of environmental factors, such as trees/foliage, weather, buildings, vehicle travel direction, and road elevation.
The results can be used to guide future DSRC equipment placement and installation, and can be used to develop DSRC communication models for numerical simulations.
Interactive visualizations are crucial in ad hoc data exploration and analysis.
However, with the growing number of massive datasets, generating visualizations in interactive timescales is increasingly challenging.
One approach for improving the speed of the visualization tool is via data reduction in order to reduce the computational overhead, but at a potential cost in visualization accuracy.
Common data reduction techniques, such as uniform and stratified sampling, do not exploit the fact that the sampled tuples will be transformed into a visualization for human consumption.
We propose a visualization-aware sampling (VAS) that guarantees high quality visualizations with a small subset of the entire dataset.
We validate our method when applied to scatter and map plots for three common visualization goals: regression, density estimation, and clustering.
The key to our sampling method's success is in choosing tuples which minimize a visualization-inspired loss function.
Our user study confirms that optimizing this loss function correlates strongly with user success in using the resulting visualizations.
We also show the NP-hardness of our optimization problem and propose an efficient approximation algorithm.
Our experiments show that, compared to previous methods, (i) using the same sample size, VAS improves user's success by up to 35% in various visualization tasks, and (ii) VAS can achieve a required visualization quality up to 400 times faster.
Existing corpora for intrinsic evaluation are not targeted towards tasks in informal domains such as Twitter or news comment forums.
We want to test whether a representation of informal words fulfills the promise of eliding explicit text normalization as a preprocessing step.
One possible evaluation metric for such domains is the proximity of spelling variants.
We propose how such a metric might be computed and how a spelling variant dataset can be collected using UrbanDictionary.
Segmentation of retinal vessels from retinal fundus images is the key step in the automatic retinal image analysis.
In this paper, we propose a new unsupervised automatic method to segment the retinal vessels from retinal fundus images.
Contrast enhancement and illumination correction are carried out through a series of image processing steps followed by adaptive histogram equalization and anisotropic diffusion filtering.
This image is then converted to a gray scale using weighted scaling.
The vessel edges are enhanced by boosting the detail curvelet coefficients.
Optic disk pixels are removed before applying fuzzy C-mean classification to avoid the misclassification.
Morphological operations and connected component analysis are applied to obtain the segmented retinal vessels.
The performance of the proposed method is evaluated using DRIVE database to be able to compare with other state-of-art supervised and unsupervised methods.
The overall segmentation accuracy of the proposed method is 95.18% which outperforms the other algorithms.
Affective computing has become a very important research area in human-machine interaction.
However, affects are subjective, subtle, and uncertain.
So, it is very difficult to obtain a large number of labeled training samples, compared with the number of possible features we could extract.
Thus, dimensionality reduction is critical in affective computing.
This paper presents our preliminary study on dimensionality reduction for affect classification.
Five popular dimensionality reduction approaches are introduced and compared.
Experiments on the DEAP dataset showed that no approach can universally outperform others, and performing classification using the raw features directly may not always be a bad choice.
The problem of distributed dynamic state estimation in wireless sensor networks is studied.
Two important properties of local estimates, namely, the consistency and confidence, are emphasized.
On one hand, the consistency, which means that the approximated error covariance is lower bounded by the true unknown one, has to be guaranteed so that the estimate is not over-confident.
On the other hand, since the confidence indicates the accuracy of the estimate, the estimate should be as confident as possible.
We first analyze two different information fusion strategies used in the case of information sources with, respectively, uncorrelated errors and unknown but correlated errors.
Then a distributed hybrid information fusion algorithm is proposed, where each agent uses the information obtained not only by itself, but also from its neighbors through communication.
The proposed algorithm not only guarantees the consistency of the estimates, but also utilizes the available information sources in a more efficient manner and hence improves the confidence.
Besides, the proposed algorithm is fully distributed and guarantees convergence with the sufficient condition formulated.
The comparisons with existing algorithms are shown.
In this paper, we propose a novel deep neural network architecture, Sequence-to-Sequence Audio2Vec, for unsupervised learning of fixed-length vector representations of audio segments excised from a speech corpus, where the vectors contain semantic information pertaining to the segments, and are close to other vectors in the embedding space if their corresponding segments are semantically similar.
The design of the proposed model is based on the RNN Encoder-Decoder framework, and borrows the methodology of continuous skip-grams for training.
The learned vector representations are evaluated on 13 widely used word similarity benchmarks, and achieved competitive results to that of GloVe.
The biggest advantage of the proposed model is its capability of extracting semantic information of audio segments taken directly from raw speech, without relying on any other modalities such as text or images, which are challenging and expensive to collect and annotate.
In three dimensional integrated circuits (3D-ICs), through silicon via (TSV) is a critical technique in providing vertical connections.
However, the yield and reliability is one of the key obstacles to adopt the TSV based 3D-ICs technology in industry.
Various fault-tolerance structures using spare TSVs to repair faulty functional TSVs have been proposed in literature for yield and reliability enhancement, but a valid structure cannot always be found due to the lack of effective generation methods for fault-tolerance structures.
In this paper, we focus on the problem of adaptive fault-tolerance structure generation.
Given the relations between functional TSVs and spare TSVs, we first calculate the maximum number of tolerant faults in each TSV group.
Then we propose an integer linear programming (ILP) based model to construct adaptive fault-tolerance struc- ture with minimal multiplexer delay overhead and hardware cost.
We further develop a speed-up technique through efficient min-cost-max-flow (MCMF) model.
All the proposed method- ologies are embedded in a top-down TSV planning framework to form functional TSV groups and generate adaptive fault- tolerance structures.
Experimental results show that, compared with state-of-the-art, the number of spare TSVs used for fault tolerance can be effectively reduced.
This article considers application of genetic algorithms for finite machine synthesis.
The resulting genetic finite state machines synthesis algorithm allows for creation of machines with less number of states and within shorter time.
This makes it possible to use hardware-oriented genetic finite machines synthesis algorithm in autonomous systems on reconfigurable platforms.
Network functions (e.g., firewalls, load balancers, etc.) have been traditionally provided through proprietary hardware appliances.
Often, hardware appliances need to be hardwired back to back to form a service chain providing chained network functions.
Hardware appliances cannot be provisioned on demand since they are statically embedded in the network topology, making creation, insertion, modification, upgrade, and removal of service chains complex, and also slowing down service innovation.
Hence, network operators are starting to deploy Virtual Network Functions (VNFs), which are virtualized over commodity hardware.
VNFs can be deployed in Data Centers (DCs) or in Network Function Virtualization (NFV) capable network elements (nodes) such as routers and switches.
NFV capable nodes and DCs together form a Network enabled Cloud (NeC) that helps to facilitate the dynamic service chaining required to support evolving network traffic and its service demands.
In this study, we focus on the VNF service chain placement and traffic routing problem, and build a model for placing a VNF service chain while minimizing network resource consumption.
Our results indicate that a NeC having a DC and NFV capable nodes can significantly reduce network-resource consumption.
When scripts in untyped languages grow into large programs, maintaining them becomes difficult.
A lack of explicit type annotations in typical scripting languages forces programmers to must (re)discover critical pieces of design information every time they wish to change a program.
This analysis step both slows down the maintenance process and may even introduce mistakes due to the violation of undiscovered invariants.
This paper presents Typed Scheme, an explicitly typed extension of PLT Scheme, an untyped scripting language.
Its type system is based on the novel notion of occurrence typing, which we formalize and mechanically prove sound.
The implementation of Typed Scheme additionally borrows elements from a range of approaches, including recursive types, true unions and subtyping, plus polymorphism combined with a modicum of local inference.
The formulation of occurrence typing naturally leads to a simple and expressive version of predicates to describe refinement types.
A Typed Scheme program can use these refinement types to keep track of arbitrary classes of values via the type system.
Further, we show how the Typed Scheme type system, in conjunction with simple recursive types, is able to encode refinements of existing datatypes, thus expressing both proposed variations of refinement types.
Ensembling multiple predictions is a widely-used technique to improve the accuracy of various machine learning tasks.
One obvious drawback of the ensembling is its higher execution cost during inference.
In this paper, we first describe our insights on relationship between the probability of the prediction and the effect of ensembling with current deep neural networks; ensembling does not help mispredictions for inputs predicted with a high probability even when there is a non-negligible number of mispredicted inputs.
This finding motivates us to develop a new technique called adaptive ensemble prediction, which achieves the benefits of ensembling with much smaller additional execution costs.
If the prediction for an input reaches a high enough probability on the basis of the confidence level, we stop ensembling for this input to avoid wasting computation power.
We evaluated the adaptive ensembling by using various datasets and showed that it reduces the computation cost significantly while achieving similar accuracy to the naive ensembling.
We also showed that our statistically rigorous confidence-level-based termination condition reduces the burden of the task-dependent parameter tuning compared to the naive termination based on the pre-defined threshold in addition to yielding a better accuracy with the same cost.
Radio pollution and power consumption problems lead to innovative development of green heterogeneous networks (HetNet).
Time reversal (TR) technique which has been validated from wide- to narrow-band transmissions is evaluated as one of most prominent linear precoders with superior capability of harvesting signal energy.
In this paper, we consider a new HetNet model, in which TR-employed femtocell is proposed to attain saving power benefits whereas macrocell utilizes the beam-forming algorithm based on zero-forcing principle, over frequency selective channels.
In the considered HetNet, the practical case of limited signaling information exchanged via backhaul connections is also taken under advisement.
We hence organize a distributed power loading strategy, in which macrocell users are treated with a superior priority compared to femtocell users.
By Monte-Carlo simulation, the obtained results show that TR is preferred to zero-forcing in the perspective of beamforming technique for femtocell environments due to very high achievable gain in saving energy, and the validity of power loading strategy is verified over multipath channels.
Learning representations for knowledge base entities and concepts is becoming increasingly important for NLP applications.
However, recent entity embedding methods have relied on structured resources that are expensive to create for new domains and corpora.
We present a distantly-supervised method for jointly learning embeddings of entities and text from an unnanotated corpus, using only a list of mappings between entities and surface forms.
We learn embeddings from open-domain and biomedical corpora, and compare against prior methods that rely on human-annotated text or large knowledge graph structure.
Our embeddings capture entity similarity and relatedness better than prior work, both in existing biomedical datasets and a new Wikipedia-based dataset that we release to the community.
Results on analogy completion and entity sense disambiguation indicate that entities and words capture complementary information that can be effectively combined for downstream use.
The densification and expansion of wireless network pose new challenges on interference management and reducing energy consumption.
This paper studies energy-efficient resource management in heterogeneous networks by jointly optimizing cell activation, user association and multicell multiuser channel assignment, according to the long-term average traffic and channel conditions.
The proposed framework is built on characterizing the interference coupling by pre-defined interference patterns, and performing resource allocation among these patterns.
In this way, the interference fluctuation caused by (de)activating cells is explicitly taken into account when calculating the user achievable rates.
A tailored algorithm is developed to solve the formulated problem in the dual domain by exploiting the problem structure, which gives a significant complexity saving.
Numerical results show a huge improvement in energy saving achieved by the proposed scheme.
The user association derived from the proposed joint resource optimization is mapped to standard-compliant cell selection biasing.
This mapping reveals that the cell-specific biasing for energy saving is quite different from that for load balancing investigated in the literature.
In this paper we introduce a new, high-quality, dataset of images containing fruits.
We also present the results of some numerical experiment for training a neural network to detect fruits.
We discuss the reason why we chose to use fruits in this project by proposing a few applications that could use this kind of neural network.
Network testing plays an important role in the iterative process of developing new communication protocols and algorithms.
However, test environments have to keep up with the evolution of technology and require continuous update and redesign.
In this paper, we propose COINS, a framework that can be used by wireless technology developers to enable continuous integration (CI) practices in their testbed infrastructure.
As a proof-of-concept, we provide a reference architecture and implementation of COINS for controlled testing of multi-technology 5G Machine Type Communication (MTC) networks.
The implementation upgrades an existing wireless experimentation testbed with new software and hardware functionalities.
It blends web service technology and operating system virtualization technologies with emerging Internet of Things technologies enabling CI for wireless networks.
Moreover, we also extend an existing qualitative methodology for comparing similar frameworks and identify and discuss open challenges for wider use of CI practices in wireless technology development.
We propose the first deep learning solution to video frame inpainting, a challenging instance of the general video inpainting problem with applications in video editing, manipulation, and forensics.
Our task is less ambiguous than frame interpolation and video prediction because we have access to both the temporal context and a partial glimpse of the future, allowing us to better evaluate the quality of a model's predictions objectively.
We devise a pipeline composed of two modules: a bidirectional video prediction module, and a temporally-aware frame interpolation module.
The prediction module makes two intermediate predictions of the missing frames, one conditioned on the preceding frames and the other conditioned on the following frames, using a shared convolutional LSTM-based encoder-decoder.
The interpolation module blends the intermediate predictions to form the final result.
Specifically, it utilizes time information and hidden activations from the video prediction module to resolve disagreements between the predictions.
Our experiments demonstrate that our approach produces more accurate and qualitatively satisfying results than a state-of-the-art video prediction method and many strong frame inpainting baselines.
Interpretability and small labelled datasets are key issues in the practical application of deep learning, particularly in areas such as medicine.
In this paper, we present a semi-supervised technique that addresses both these issues by leveraging large unlabelled datasets to encode and decode images into a dense latent representation.
Using chest radiography as an example, we apply this encoder to other labelled datasets and apply simple models to the latent vectors to learn algorithms to identify heart failure.
For each prediction, we generate visual rationales by optimizing a latent representation to minimize the prediction of disease while constrained by a similarity measure in image space.
Decoding the resultant latent representation produces an image without apparent disease.
The difference between the original decoding and the altered image forms an interpretable visual rationale for the algorithm's prediction on that image.
We also apply our method to the MNIST dataset and compare the generated rationales to other techniques described in the literature.
Finding interesting association rules is an important and active research field in data mining.
The algorithms of the Apriori family are based on two rule extraction measures, support and confidence.
Although these two measures have the virtue of being algorithmically fast, they generate a prohibitive number of rules most of which are redundant and irrelevant.
It is therefore necessary to use further measures which filter uninteresting rules.
Many synthesis studies were then realized on the interestingness measures according to several points of view.
Different reported studies have been carried out to identify "good" properties of rule extraction measures and these properties have been assessed on 61 measures.
The purpose of this paper is twofold.
First to extend the number of the measures and properties to be studied, in addition to the formalization of the properties proposed in the literature.
Second, in the light of this formal study, to categorize the studied measures.
This paper leads then to identify categories of measures in order to help the users to efficiently select an appropriate measure by choosing one or more measure(s) during the knowledge extraction process.
The properties evaluation on the 61 measures has enabled us to identify 7 classes of measures, classes that we obtained using two different clustering techniques.
In recent years, fuzz testing has proven itself to be one of the most effective techniques for finding correctness bugs and security vulnerabilities in practice.
One particular fuzz testing tool, American Fuzzy Lop or AFL, has become popular thanks to its ease-of-use and bug-finding power.
However, AFL remains limited in the depth of program coverage it achieves, in particular because it does not consider which parts of program inputs should not be mutated in order to maintain deep program coverage.
We propose an approach, FairFuzz, that helps alleviate this limitation in two key steps.
First, FairFuzz automatically prioritizes inputs exercising rare parts of the program under test.
Second, it automatically adjusts the mutation of inputs so that the mutated inputs are more likely to exercise these same rare parts of the program.
We conduct evaluation on real-world programs against state-of-the-art versions of AFL, thoroughly repeating experiments to get good measures of variability.
We find that on certain benchmarks FairFuzz shows significant coverage increases after 24 hours compared to state-of-the-art versions of AFL, while on others it achieves high program coverage at a significantly faster rate.
We propose a sign-based online learning (SOL) algorithm for a neuromorphic hardware framework called Trainable Analogue Block (TAB).
The TAB framework utilises the principles of neural population coding, implying that it encodes the input stimulus using a large pool of nonlinear neurons.
The SOL algorithm is a simple weight update rule that employs the sign of the hidden layer activation and the sign of the output error, which is the difference between the target output and the predicted output.
The SOL algorithm is easily implementable in hardware, and can be used in any artificial neural network framework that learns weights by minimising a convex cost function.
We show that the TAB framework can be trained for various regression tasks using the SOL algorithm.
We give optimal sorting algorithms in the evolving data framework, where an algorithm's input data is changing while the algorithm is executing.
In this framework, instead of producing a final output, an algorithm attempts to maintain an output close to the correct output for the current state of the data, repeatedly updating its best estimate of a correct output over time.
We show that a simple repeated insertion-sort algorithm can maintain an O(n) Kendall tau distance, with high probability, between a maintained list and an underlying total order of n items in an evolving data model where each comparison is followed by a swap between a random consecutive pair of items in the underlying total order.
This result is asymptotically optpimal, since there is an Omega(n) lower bound for Kendall tau distance for this problem.
Our result closes the gap between this lower bound and the previous best algorithm for this problem, which maintains a Kendall tau distance of O(n log log n) with high probability.
It also confirms previous experimental results that suggested that insertion sort tends to perform better than quicksort in practice.
Computational devices combining two or more different parts, one controlling the operation of the other, for example, derive their power from the interaction, in addition to the capabilities of the parts.
Non-classical computation has tended to consider only single computational models: neural, analog, quantum, chemical, biological, neglecting to account for the contribution from the experimental controls.
In this position paper, we propose a framework suitable for analysing combined computational models, from abstract theory to practical programming tools.
Focusing on the simplest example of one system controlled by another through a sequence of operations in which only one system is active at a time, the output from one system becomes the input to the other for the next step, and vice versa.
We outline the categorical machinery required for handling diverse computational systems in such combinations, with their interactions explicitly accounted for.
Drawing on prior work in refinement and retrenchment, we suggest an appropriate framework for developing programming tools from the categorical framework.
We place this work in the context of two contrasting concepts of "efficiency": theoretical comparisons to determine the relative computational power do not always reflect the practical comparison of real resources for a finite-sized computational task, especially when the inputs include (approximations of) real numbers.
Finally we outline the limitations of our simple model, and identify some of the extensions that will be required to treat more complex interacting computational systems.
Currently, most speech processing techniques use magnitude spectrograms as front-end and are therefore by default discarding part of the signal: the phase.
In order to overcome this limitation, we propose an end-to-end learning method for speech denoising based on Wavenet.
The proposed model adaptation retains Wavenet's powerful acoustic modeling capabilities, while significantly reducing its time-complexity by eliminating its autoregressive nature.
Specifically, the model makes use of non-causal, dilated convolutions and predicts target fields instead of a single target sample.
The discriminative adaptation of the model we propose, learns in a supervised fashion via minimizing a regression loss.
These modifications make the model highly parallelizable during both training and inference.
Both computational and perceptual evaluations indicate that the proposed method is preferred to Wiener filtering, a common method based on processing the magnitude spectrogram.
We consider a network of event-based systems that use a shared wireless medium to communicate with their respective controllers.
These systems use a contention resolution mechanism to arbitrate access to the shared network.
We identify sufficient conditions for Lyapunov mean square stability of each control system in the network, and design event-based policies that guarantee it.
Our stability analysis is based on a Markov model that removes the network-induced correlation between the states of the control systems in the network.
Analyzing the stability of this Markov model remains a challenge, as the event-triggering policy renders the estimation error non-Gaussian.
Hence, we identify an auxiliary system that furnishes an upper bound for the variance of the system states.
Using the stability analysis, we design policies, such as the constant-probability policy, for adapting the event-triggering thresholds to the delay in accessing the network.
Realistic wireless networked control examples illustrate the applicability of the presented approach.
Artist recognition is a task of modeling the artist's musical style.
This problem is challenging because there is no clear standard.
We propose a hybrid method of the generative model i-vector and the discriminative model deep convolutional neural network.
We show that this approach achieves state-of-the-art performance by complementing each other.
In addition, we briefly explain the advantages and disadvantages of each approach.
Surrogate models provide a low computational cost alternative to evaluating expensive functions.
The construction of accurate surrogate models with large numbers of independent variables is currently prohibitive because it requires a large number of function evaluations.
Gradient-enhanced kriging has the potential to reduce the number of function evaluations for the desired accuracy when efficient gradient computation, such as an adjoint method, is available.
However, current gradient-enhanced kriging methods do not scale well with the number of sampling points due to the rapid growth in the size of the correlation matrix where new information is added for each sampling point in each direction of the design space.
They do not scale well with the number of independent variables either due to the increase in the number of hyperparameters that needs to be estimated.
To address this issue, we develop a new gradient-enhanced surrogate model approach that drastically reduced the number of hyperparameters through the use of the partial-least squares method that maintains accuracy.
In addition, this method is able to control the size of the correlation matrix by adding only relevant points defined through the information provided by the partial-least squares method.
To validate our method, we compare the global accuracy of the proposed method with conventional kriging surrogate models on two analytic functions with up to 100 dimensions, as well as engineering problems of varied complexity with up to 15 dimensions.
We show that the proposed method requires fewer sampling points than conventional methods to obtain the desired accuracy, or provides more accuracy for a fixed budget of sampling points.
In some cases, we get over 3 times more accurate models than a bench of surrogate models from the literature, and also over 3200 times faster than standard gradient-enhanced kriging models.
To deploy a spoken language understanding (SLU) model to a new language, language transferring is desired to avoid the trouble of acquiring and labeling a new big SLU corpus.
Translating the original SLU corpus into the target language is an attractive strategy.
However, SLU corpora consist of plenty of semantic labels (slots), which general-purpose translators cannot handle well, not to mention additional culture differences.
This paper focuses on the language transferring task given a tiny in-domain parallel SLU corpus.
The in-domain parallel corpus can be used as the first adaptation on the general translator.
But more importantly, we show how to use reinforcement learning (RL) to further finetune the adapted translator, where translated sentences with more proper slot tags receive higher rewards.
We evaluate our approach on Chinese to English language transferring for SLU systems.
The experimental results show that the generated English SLU corpus via adaptation and reinforcement learning gives us over 97% in the slot F1 score and over 84% accuracy in domain classification.
It demonstrates the effectiveness of the proposed language transferring method.
Compared with naive translation, our proposed method improves domain classification accuracy by relatively 22%, and the slot filling F1 score by relatively more than 71%.
Vector Quantization, VQ is a popular image compression technique with a simple decoding architecture and high compression ratio.
Codebook designing is the most essential part in Vector Quantization.
LindeBuzoGray, LBG is a traditional method of generation of VQ Codebook which results in lower PSNR value.
A Codebook affects the quality of image compression, so the choice of an appropriate codebook is a must.
Several optimization techniques have been proposed for global codebook generation to enhance the quality of image compression.
In this paper, a novel algorithm called IDE-LBG is proposed which uses Improved Differential Evolution Algorithm coupled with LBG for generating optimum VQ Codebooks.
The proposed IDE works better than the traditional DE with modifications in the scaling factor and the boundary control mechanism.
The IDE generates better solutions by efficient exploration and exploitation of the search space.
Then the best optimal solution obtained by the IDE is provided as the initial Codebook for the LBG.
This approach produces an efficient Codebook with less computational time and the consequences include excellent PSNR values and superior quality reconstructed images.
It is observed that the proposed IDE-LBG find better VQ Codebooks as compared to IPSO-LBG, BA-LBG and FA-LBG.
In this paper we propose a signature scheme based on two intractable problems, namely the integer factorization problem and the discrete logarithm problem for elliptic curves.
It is suitable for applications requiring long-term security and provides a more efficient solution than the existing ones.
This paper studies the fundamental limits of content delivery in a cache-aided broadcast network for correlated content generated by a discrete memoryless source with arbitrary joint distribution.
Each receiver is equipped with a cache of equal capacity, and the requested files are delivered over a shared error-free broadcast link.
A class of achievable correlation-aware schemes based on a two-step source coding approach is proposed.
Library files are first compressed, and then cached and delivered using a combination of correlation-unaware multiple-request cache-aided coded multicast schemes.
The first step uses Gray-Wyner source coding to represent the library via private descriptions and descriptions that are common to more than one file.
The second step then becomes a multiple-request caching problem, where the demand structure is dictated by the configuration of the compressed library, and it is interesting in its own right.
The performance of the proposed two-step scheme is evaluated by comparing its achievable rate with a lower bound on the optimal peak and average rate-memory tradeoffs in a two-file multiple-receiver network, and in a three-file two-receiver network.
Specifically, in a network with two files and two receivers, the achievable rate matches the lower bound for a significant memory regime and it is within half of the conditional entropy of files for all other memory values.
In the three-file two-receiver network, the two-step strategy achieves the lower bound for large cache capacities, and it is within half of the joint entropy of two of the sources conditioned on the third one for all other cache sizes.
One of the biggest challenges in the research of generative adversarial networks (GANs) is assessing the quality of generated samples and detecting various levels of mode collapse.
In this work, we construct a novel measure of performance of a GAN by comparing geometrical properties of the underlying data manifold and the generated one, which provides both qualitative and quantitative means for evaluation.
Our algorithm can be applied to datasets of an arbitrary nature and is not limited to visual data.
We test the obtained metric on various real-life models and datasets and demonstrate that our method provides new insights into properties of GANs.
In this paper, we present an improved feedforward sequential memory networks (FSMN) architecture, namely Deep-FSMN (DFSMN), by introducing skip connections between memory blocks in adjacent layers.
These skip connections enable the information flow across different layers and thus alleviate the gradient vanishing problem when building very deep structure.
As a result, DFSMN significantly benefits from these skip connections and deep structure.
We have compared the performance of DFSMN to BLSTM both with and without lower frame rate (LFR) on several large speech recognition tasks, including English and Mandarin.
Experimental results shown that DFSMN can consistently outperform BLSTM with dramatic gain, especially trained with LFR using CD-Phone as modeling units.
In the 2000 hours Fisher (FSH) task, the proposed DFSMN can achieve a word error rate of 9.4% by purely using the cross-entropy criterion and decoding with a 3-gram language model, which achieves a 1.5% absolute improvement compared to the BLSTM.
In a 20000 hours Mandarin recognition task, the LFR trained DFSMN can achieve more than 20% relative improvement compared to the LFR trained BLSTM.
Moreover, we can easily design the lookahead filter order of the memory blocks in DFSMN to control the latency for real-time applications.
This paper presents two new approaches to decomposing and solving large Markov decision problems (MDPs), a partial decoupling method and a complete decoupling method.
In these approaches, a large, stochastic decision problem is divided into smaller pieces.
The first approach builds a cache of policies for each part of the problem independently, and then combines the pieces in a separate, light-weight step.
A second approach also divides the problem into smaller pieces, but information is communicated between the different problem pieces, allowing intelligent decisions to be made about which piece requires the most attention.
Both approaches can be used to find optimal policies or approximately optimal policies with provable bounds.
These algorithms also provide a framework for the efficient transfer of knowledge across problems that share similar structure.
The collaborative development methods pioneered by the open source software community offer a way to create lessons that are open, accessible, and sustainable.
This paper presents ten simple rules for doing this drawn from our experience with several successful projects.
This article consists of a brief introduction to the Shannon information theory.
Two topics, entropy and channel capacity, are mainly covered.
All these concepts are developed in a totally combinatorial favor.
Some issues usually not addressed in the literature are discussed here as well.
Protograph-based Raptor-like low-density parity-check codes (PBRL codes) are a recently proposed family of easily encodable and decodable rate-compatible LDPC (RC-LDPC) codes.
These codes have an excellent iterative decoding threshold and performance across all design rates.
PBRL codes designed thus far, for both long and short block-lengths, have been based on optimizing the iterative decoding threshold of the protograph of the RC code family at various design rates.
In this work, we propose a design method to obtain better quasi-cyclic (QC) RC-LDPC codes with PBRL structure for short block-lengths (of a few hundred bits).
We achieve this by maximizing an upper bound on the minimum distance of any QC-LDPC code that can be obtained from the protograph of a PBRL ensemble.
The obtained codes outperform the original PBRL codes at short block-lengths by significantly improving the error floor behavior at all design rates.
Furthermore, we identify a reduction in complexity of the design procedure, facilitated by the general structure of a PBRL ensemble.
We give a description of the weighted Reed-Muller codes over a prime field in a modular algebra.
A description of the homogeneous Reed-Muller codes in the same ambient space is presented for the binary case.
A decoding procedure using the Landrock-Manz method is developed.
Algorithms that use hardware transactional memory (HTM) must provide a software-only fallback path to guarantee progress.
The design of the fallback path can have a profound impact on performance.
If the fallback path is allowed to run concurrently with hardware transactions, then hardware transactions must be instrumented, adding significant overhead.
Otherwise, hardware transactions must wait for any processes on the fallback path, causing concurrency bottlenecks, or move to the fallback path.
We introduce an approach that combines the best of both worlds.
The key idea is to use three execution paths: an HTM fast path, an HTM middle path, and a software fallback path, such that the middle path can run concurrently with each of the other two.
The fast path and fallback path do not run concurrently, so the fast path incurs no instrumentation overhead.
Furthermore, fast path transactions can move to the middle path instead of waiting or moving to the software path.
We demonstrate our approach by producing an accelerated version of the tree update template of Brown et al., which can be used to implement fast lock-free data structures based on down-trees.
We used the accelerated template to implement two lock-free trees: a binary search tree (BST), and an (a,b)-tree (a generalization of a B-tree).
Experiments show that, with 72 concurrent processes, our accelerated (a,b)-tree performs between 4.0x and 4.2x as many operations per second as an implementation obtained using the original tree update template.
This paper proposes a new objective metric of exceptional motion in VR video contents for VR sickness assessment.
In VR environment, VR sickness can be caused by several factors which are mismatched motion, field of view, motion parallax, viewing angle, etc.
Similar to motion sickness, VR sickness can induce a lot of physical symptoms such as general discomfort, headache, stomach awareness, nausea, vomiting, fatigue, and disorientation.
To address the viewing safety issues in virtual environment, it is of great importance to develop an objective VR sickness assessment method that predicts and analyses the degree of VR sickness induced by the VR content.
The proposed method takes into account motion information that is one of the most important factors in determining the overall degree of VR sickness.
In this paper, we detect the exceptional motion that is likely to induce VR sickness.
Spatio-temporal features of the exceptional motion in the VR video content are encoded using a convolutional autoencoder.
For objectively assessing the VR sickness, the level of exceptional motion in VR video content is measured by using the convolutional autoencoder as well.
The effectiveness of the proposed method has been successfully evaluated by subjective assessment experiment using simulator sickness questionnaires (SSQ) in VR environment.
Stochastic gradient descent (SGD) is widely believed to perform implicit regularization when used to train deep neural networks, but the precise manner in which this occurs has thus far been elusive.
We prove that SGD minimizes an average potential over the posterior distribution of weights along with an entropic regularization term.
This potential is however not the original loss function in general.
So SGD does perform variational inference, but for a different loss than the one used to compute the gradients.
Even more surprisingly, SGD does not even converge in the classical sense: we show that the most likely trajectories of SGD for deep networks do not behave like Brownian motion around critical points.
Instead, they resemble closed loops with deterministic components.
We prove that such "out-of-equilibrium" behavior is a consequence of highly non-isotropic gradient noise in SGD; the covariance matrix of mini-batch gradients for deep networks has a rank as small as 1% of its dimension.
We provide extensive empirical validation of these claims, proven in the appendix.
We introduce a method for automated temporal segmentation of human motion data into distinct actions and compositing motion primitives based on self-similar structures in the motion sequence.
We use neighbourhood graphs for the partitioning and the similarity information in the graph is further exploited to cluster the motion primitives into larger entities of semantic significance.
The method requires no assumptions about the motion sequences at hand and no user interaction is required for the segmentation or clustering.
In addition, we introduce a feature bundling preprocessing technique to make the segmentation more robust to noise, as well as a notion of motion symmetry for more refined primitive detection.
We test our method on several sensor modalities, including markered and markerless motion capture as well as on electromyograph and accelerometer recordings.
The results highlight our system's capabilities for both segmentation and for analysis of the finer structures of motion data, all in a completely unsupervised manner.
Most companies' new business practices are based on customer data.
These practices have raised privacy concerns because of the associated risks.
Privacy laws require companies to gain customer consent before using their information, which stands as the biggest roadblock to monetise this asset.
Privacy literature suggests that reducing privacy concerns and building trust may increase individuals' intention to authorise the use of personal information.
Fair information practices (FIPs) are potential means to achieve this goal.
However, there is lack of empirical evidence on the mechanisms through which the FIPs affect privacy concerns and trust.
This research argues that FIPs load individuals with control, which has been found to influence privacy concerns and trust level.
We will use an experimental design methodology to conduct the study.
The results are expected to have both theoretical and managerial implications.
Italy adopted a performance-based system for funding universities that is centered on the results of a national research assessment exercise, realized by a governmental agency (ANVUR).
ANVUR evaluated papers by using 'a dual system of evaluation', that is by informed peer review or by bibliometrics.
In view of validating that system, ANVUR performed an experiment for estimating the agreement between informed review and bibliometrics.
Ancaiani et al.(2015) presents the main results of the experiment.
Baccini and De Nicolao (2017) documented in a letter, among other critical issues, that the statistical analysis was not realized on a random sample of articles.
A reply to the letter has been published by Research Evaluation (Benedetto et al.2017).
This note highlights that in the reply there are (1) errors in data, (2) problems with 'representativeness' of the sample, (3) unverifiable claims about weights used for calculating kappas, (4) undisclosed averaging procedures; (5) a statement about 'same protocol in all areas' contradicted by official reports.
Last but not least: the data used by the authors continue to be undisclosed.
A general warning concludes: many recently published papers use data originating from Italian research assessment exercise.
These data are not accessible to the scientific community and consequently these papers are not reproducible.
They can be hardly considered as containing sound evidence at least until authors or ANVUR disclose the data necessary for replication.
Human computer conversation is regarded as one of the most difficult problems in artificial intelligence.
In this paper, we address one of its key sub-problems, referred to as short text conversation, in which given a message from human, the computer returns a reasonable response to the message.
We leverage the vast amount of short conversation data available on social media to study the issue.
We propose formalizing short text conversation as a search problem at the first step, and employing state-of-the-art information retrieval (IR) techniques to carry out the task.
We investigate the significance as well as the limitation of the IR approach.
Our experiments demonstrate that the retrieval-based model can make the system behave rather "intelligently", when combined with a huge repository of conversation data from social media.
The effect of transport-related pollution on human health is fast becoming recognised as a major issue in cities worldwide.
Cyclists, in particular, face great risks, as they typically are most exposed to tail-pipe emissions.
Three avenues are being explored worldwide in the fight against urban pollution: (i) outright bans on polluting vehicles and embracing zero tailpipe emission vehicles; (ii) measuring air-quality as a means to better informing citizens of zones of higher pollution; and (iii) developing smart mobility devices that seek to minimize the effect of polluting devices on citizens as they transport goods and individuals in our cities.
Following this latter direction, in this paper we present a new way to protect cyclists from the effect of urban pollution.
Namely, by exploiting the actuation possibilities afforded by pedelecs or e-bikes (electric bikes), we design a cyber-physical system that mitigates the effect of urban pollution by indirectly controlling the breathing rate of cyclists in polluted areas.
Results from a real device are presented to illustrate the efficacy of our system.
Driving is a social activity: drivers often indicate their intent to change lanes via motion cues.
We consider mixed-autonomy traffic where a Human-driven Vehicle (HV) and an Autonomous Vehicle (AV) drive together.
We propose a planning framework where the degree to which the AV considers the other agent's reward is controlled by a selfishness factor.
We test our approach on a simulated two-lane highway where the AV and HV merge into each other's lanes.
In a user study with 21 subjects and 6 different selfishness factors, we found that our planning approach was sound and that both agents had less merging times when a factor that balances the rewards for the two agents was chosen.
Our results on double lane merging suggest it to be a non-zero-sum game and encourage further investigation on collaborative decision making algorithms for mixed-autonomy traffic.
This paper proposes a deep cerebellar model articulation controller (DCMAC) for adaptive noise cancellation (ANC).
We expand upon the conventional CMAC by stacking sin-gle-layer CMAC models into multiple layers to form a DCMAC model and derive a modified backpropagation training algorithm to learn the DCMAC parameters.
Com-pared with conventional CMAC, the DCMAC can characterize nonlinear transformations more effectively because of its deep structure.
Experimental results confirm that the pro-posed DCMAC model outperforms the CMAC in terms of residual noise in an ANC task, showing that DCMAC provides enhanced modeling capability based on channel characteristics.
Late Gadolinium Enhanced Cardiac MRI (LGE-CMRI) for detecting atrial scars in atrial fibrillation (AF) patients has recently emerged as a promising technique to stratify patients, guide ablation therapy and predict treatment success.
Visualisation and quantification of scar tissues require a segmentation of both the left atrium (LA) and the high intensity scar regions from LGE-CMRI images.
These two segmentation tasks are challenging due to the cancelling of healthy tissue signal, low signal-to-noise ratio and often limited image quality in these patients.
Most approaches require manual supervision and/or a second bright-blood MRI acquisition for anatomical segmentation.
Segmenting both the LA anatomy and the scar tissues automatically from a single LGE-CMRI acquisition is highly in demand.
In this study, we proposed a novel fully automated multiview two-task (MVTT) recursive attention model working directly on LGE-CMRI images that combines a sequential learning and a dilated residual learning to segment the LA (including attached pulmonary veins) and delineate the atrial scars simultaneously via an innovative attention model.
Compared to other state-of-the-art methods, the proposed MVTT achieves compelling improvement, enabling to generate a patient-specific anatomical and atrial scar assessment model.
Emotion estimation in music listening is confronting challenges to capture the emotion variation of listeners.
Recent years have witnessed attempts to exploit multimodality fusing information from musical contents and physiological signals captured from listeners to improve the performance of emotion recognition.
In this paper, we present a study of fusion of signals of electroencephalogram (EEG), a tool to capture brainwaves at a high-temporal resolution, and musical features at decision level in recognizing the time-varying binary classes of arousal and valence.
Our empirical results showed that the fusion could outperform the performance of emotion recognition using only EEG modality that was suffered from inter-subject variability, and this suggested the promise of multimodal fusion in improving the accuracy of music-emotion recognition.
When training neural networks, the use of Synthetic Gradients (SG) allows layers or modules to be trained without update locking - without waiting for a true error gradient to be backpropagated - resulting in Decoupled Neural Interfaces (DNIs).
This unlocked ability of being able to update parts of a neural network asynchronously and with only local information was demonstrated to work empirically in Jaderberg et al (2016).
However, there has been very little demonstration of what changes DNIs and SGs impose from a functional, representational, and learning dynamics point of view.
In this paper, we study DNIs through the use of synthetic gradients on feed-forward networks to better understand their behaviour and elucidate their effect on optimisation.
We show that the incorporation of SGs does not affect the representational strength of the learning system for a neural network, and prove the convergence of the learning system for linear and deep linear models.
On practical problems we investigate the mechanism by which synthetic gradient estimators approximate the true loss, and, surprisingly, how that leads to drastically different layer-wise representations.
Finally, we also expose the relationship of using synthetic gradients to other error approximation techniques and find a unifying language for discussion and comparison.
Cluster analysis plays an important role in decision making process for many knowledge-based systems.
There exist a wide variety of different approaches for clustering applications including the heuristic techniques, probabilistic models, and traditional hierarchical algorithms.
In this paper, a novel heuristic approach based on big bang-big crunch algorithm is proposed for clustering problems.
The proposed method not only takes advantage of heuristic nature to alleviate typical clustering algorithms such as k-means, but it also benefits from the memory based scheme as compared to its similar heuristic techniques.
Furthermore, the performance of the proposed algorithm is investigated based on several benchmark test functions as well as on the well-known datasets.
The experimental results show the significant superiority of the proposed method over the similar algorithms.
In aspect-based sentiment analysis, most existing methods either focus on aspect/opinion terms extraction or aspect terms categorization.
However, each task by itself only provides partial information to end users.
To generate more detailed and structured opinion analysis, we propose a finer-grained problem, which we call category-specific aspect and opinion terms extraction.
This problem involves the identification of aspect and opinion terms within each sentence, as well as the categorization of the identified terms.
To this end, we propose an end-to-end multi-task attention model, where each task corresponds to aspect/opinion terms extraction for a specific category.
Our model benefits from exploring the commonalities and relationships among different tasks to address the data sparsity issue.
We demonstrate its state-of-the-art performance on three benchmark datasets.
Detecting controversy in general web pages is a daunting task, but increasingly essential to efficiently moderate discussions and effectively filter problematic content.
Unfortunately, controversies occur across many topics and domains, with great changes over time.
This paper investigates neural classifiers as a more robust methodology for controversy detection in general web pages.
Current models have often cast controversy detection on general web pages as Wikipedia linking, or exact lexical matching tasks.
The diverse and changing nature of controversies suggest that semantic approaches are better able to detect controversy.
We train neural networks that can capture semantic information from texts using weak signal data.
By leveraging the semantic properties of word embeddings we robustly improve on existing controversy detection methods.
To evaluate model stability over time and to unseen topics, we asses model performance under varying training conditions to test cross-temporal, cross-topic, cross-domain performance and annotator congruence.
In doing so, we demonstrate that weak-signal based neural approaches are closer to human estimates of controversy and are more robust to the inherent variability of controversies.
Recent trends in targeted cyber-attacks has increased the interest of research in the field of cyber security.
Such attacks have massive disruptive effects on rganizations, enterprises and governments.
Cyber kill chain is a model to describe cyber-attacks so as to develop incident response and analysis capabilities.
Cyber kill chain in simple terms is an attack chain, the path that an intruder takes to penetrate information systems over time to execute an attack on the target.
This paper broadly categories the methodologies, techniques and tools involved in cyber-attacks.
This paper intends to help a cyber security researcher to realize the options available to an attacker at every stage of a cyber-attack.
Patent data represent a significant source of information on innovation and the evolution of technology through networks of citations, co-invention and co-assignment of new patents.
A major obstacle to extracting useful information from this data is the problem of name disambiguation: linking alternate spellings of individuals or institutions to a single identifier to uniquely determine the parties involved in the creation of a technology.
In this paper, we describe a new algorithm that uses high-resolution geolocation to disambiguate both inventor and assignees on more than 3.6 million patents found in the European Patent Office (EPO), under the Patent Cooperation treaty (PCT), and in the US Patent and Trademark Office (USPTO).
We show that our algorithm has both high precision and recall in comparison to a manual disambiguation of EPO assignee names in Boston and Paris, and show it performs well for a benchmark of USPTO inventor names that can be linked to a high-resolution address (but poorly for inventors that never provided a high quality address).
The most significant benefit of this work is the high quality assignee disambiguation with worldwide coverage coupled with an inventor disambiguation that is competitive with other state of the art approaches.
To our knowledge this is the broadest and most accurate simultaneous disambiguation and cross-linking of the inventor and assignee names for a significant fraction of patents in these three major patent collections.
We propose a Fourier domain asymmetric cryptosystem for multimodal biometric security.
One modality of biometrics (such as face) is used as the plaintext, which is encrypted by another modality of biometrics (such as fingerprint).
A private key is synthesized from the encrypted biometric signature by complex spatial Fourier processing.
The encrypted biometric signature is further encrypted by other biometric modalities, and the corresponding private keys are synthesized.
The resulting biometric signature is privacy protected since the encryption keys are provided by the human, and hence those are private keys.
Moreover, the decryption keys are synthesized using those private encryption keys.
The encrypted signatures are decrypted using the synthesized private keys and inverse complex spatial Fourier processing.
Computer simulations demonstrate the feasibility of the technique proposed.
HTTP/2 supersedes HTTP/1.1 to tackle the performance challenges of the modern Web.
A highly anticipated feature is Server Push, enabling servers to send data without explicit client requests, thus potentially saving time.
Although guidelines on how to use Server Push emerged, measurements have shown that it can easily be used in a suboptimal way and hurt instead of improving performance.
We thus tackle the question if the current Web can make better use of Server Push.
First, we enable real-world websites to be replayed in a testbed to study the effects of different Server Push strategies.
Using this, we next revisit proposed guidelines to grasp their performance impact.
Finally, based on our results, we propose a novel strategy using an alternative server scheduler that enables to interleave resources.
This improves the visual progress for some websites, with minor modifications to the deployment.
Still, our results highlight the limits of Server Push: a deep understanding of web engineering is required to make optimal use of it, and not every site will benefit.
Low-density parity-check (LPDC) decoders assume the channel estate information (CSI) is known and they have the true a posteriori probability (APP) for each transmitted bit.
But in most cases of interest, the CSI needs to be estimated with the help of a short training sequence and the LDPC decoder has to decode the received word using faulty APP estimates.
In this paper, we study the uncertainty in the CSI estimate and how it affects the bit error rate (BER) output by the LDPC decoder.
To improve these APP estimates, we propose a Bayesian equalizer that takes into consideration not only the uncertainty due to the noise in the channel, but also the uncertainty in the CSI estimate, reducing the BER after the LDPC decoder.
Dense subgraph discovery is a key primitive in many graph mining applications, such as detecting communities in social networks and mining gene correlation from biological data.
Most studies on dense subgraph mining only deal with one graph.
However, in many applications, we have more than one graph describing relations among a same group of entities.
In this paper, given two graphs sharing the same set of vertices, we investigate the problem of detecting subgraphs that contrast the most with respect to density.
We call such subgraphs Density Contrast Subgraphs, or DCS in short.
Two widely used graph density measures, average degree and graph affinity, are considered.
For both density measures, mining DCS is equivalent to mining the densest subgraph from a "difference" graph, which may have both positive and negative edge weights.
Due to the existence of negative edge weights, existing dense subgraph detection algorithms cannot identify the subgraph we need.
We prove the computational hardness of mining DCS under the two graph density measures and develop efficient algorithms to find DCS.
We also conduct extensive experiments on several real-world datasets to evaluate our algorithms.
The experimental results show that our algorithms are both effective and efficient.
Training a Deep Neural Network (DNN) from scratch requires a large amount of labeled data.
For a classification task where only small amount of training data is available, a common solution is to perform fine-tuning on a DNN which is pre-trained with related source data.
This consecutive training process is time consuming and does not consider explicitly the relatedness between different source and target tasks.
In this paper, we propose a novel method to jointly fine-tune a Deep Neural Network with source data and target data.
By adding an Optimal Transport loss (OT loss) between source and target classifier predictions as a constraint on the source classifier, the proposed Joint Transfer Learning Network (JTLN) can effectively learn useful knowledge for target classification from source data.
Furthermore, by using different kind of metric as cost matrix for the OT loss, JTLN can incorporate different prior knowledge about the relatedness between target categories and source categories.
We carried out experiments with JTLN based on Alexnet on image classification datasets and the results verify the effectiveness of the proposed JTLN in comparison with standard consecutive fine-tuning.
This Joint Transfer Learning with OT loss is general and can also be applied to other kind of Neural Networks.
People's interests and people's social relationships are intuitively connected, but understanding their interplay and whether they can help predict each other has remained an open question.
We examine the interface of two decisive structures forming the backbone of online social media: the graph structure of social networks - who connects with whom - and the set structure of topical affiliations - who is interested in what.
In studying this interface, we identify key relationships whereby each of these structures can be understood in terms of the other.
The context for our analysis is Twitter, a complex social network of both follower relationships and communication relationships.
On Twitter, "hashtags" are used to label conversation topics, and we examine hashtag usage alongside these social structures.
We find that the hashtags that users adopt can predict their social relationships, and also that the social relationships between the initial adopters of a hashtag can predict the future popularity of that hashtag.
By studying weighted social relationships, we observe that while strong reciprocated ties are the easiest to predict from hashtag structure, they are also much less useful than weak directed ties for predicting hashtag popularity.
Importantly, we show that computationally simple structural determinants can provide remarkable performance in both tasks.
While our analyses focus on Twitter, we view our findings as broadly applicable to topical affiliations and social relationships in a host of diverse contexts, including the movies people watch, the brands people like, or the locations people frequent.
This paper concerns the maximum coding rate at which data can be transmitted over a noncoherent, single-antenna, Rayleigh block-fading channel using an error-correcting code of a given blocklength with a block-error probability not exceeding a given value.
A high-SNR normal approximation of the maximum coding rate is presented that becomes accurate as the signal-to-noise ratio (SNR) and the number of coherence intervals L over which we code tend to infinity.
Numerical analyses suggest that the approximation is accurate already at SNR values of 15 dB and when the number of coherence intervals is 10 or more.
Firewalls have long been in use to protect local networks from threats of the larger Internet.
Although firewalls are effective in preventing attacks initiated from outside, they are vulnerable to insider threats, e.g., malicious insiders may access and alter firewall configurations, and disable firewall services.
In this paper, we develop an innovative distributed architecture to obliviously manage and evaluate firewalls to prevent both insider and external attacks oriented to the firewalls.
Our proposed structure alleviates these issues by obfuscating the firewall rules or policies themselves, then distributing the function of evaluating these rules across multiple servers.
Thus, both accessing and altering the rules are considerably more difficult thereby providing better protection to the local network as well as greater security for the firewall itself.
We achieve this by integrating multiple areas of research such as secret sharing schemes and multi-party computation, as well as Bloom filters and Byzantine agreement protocols.
Our resulting solution is an efficient and secure means by which a firewall may be distributed, and obfuscated while maintaining the ability for multiple servers to obliviously evaluate its functionality.
In this work we present a deep learning framework for video compressive sensing.
The proposed formulation enables recovery of video frames in a few seconds at significantly improved reconstruction quality compared to previous approaches.
Our investigation starts by learning a linear mapping between video sequences and corresponding measured frames which turns out to provide promising results.
We then extend the linear formulation to deep fully-connected networks and explore the performance gains using deeper architectures.
Our analysis is always driven by the applicability of the proposed framework on existing compressive video architectures.
Extensive simulations on several video sequences document the superiority of our approach both quantitatively and qualitatively.
Finally, our analysis offers insights into understanding how dataset sizes and number of layers affect reconstruction performance while raising a few points for future investigation.
Code is available at Github: https://github.com/miliadis/DeepVideoCS
As examples such as the Monty Hall puzzle show, applying conditioning to update a probability distribution on a ``naive space', which does not take into account the protocol used, can often lead to counterintuitive results.
Here we examine why.
A criterion known as CAR (coarsening at random) in the statistical literature characterizes when ``naive' conditioning in a naive space works.
We show that the CAR condition holds rather infrequently.
We then consider more generalized notions of update such as Jeffrey conditioning and minimizing relative entropy (MRE).
We give a generalization of the CAR condition that characterizes when Jeffrey conditioning leads to appropriate answers, but show that there are no such conditions for MRE.
This generalizes and interconnects previous results obtained in the literature on CAR and MRE.
Existing region-based object detectors are limited to regions with fixed box geometry to represent objects, even if those are highly non-rectangular.
In this paper we introduce DP-FCN, a deep model for object detection which explicitly adapts to shapes of objects with deformable parts.
Without additional annotations, it learns to focus on discriminative elements and to align them, and simultaneously brings more invariance for classification and geometric information to refine localization.
DP-FCN is composed of three main modules: a Fully Convolutional Network to efficiently maintain spatial resolution, a deformable part-based RoI pooling layer to optimize positions of parts and build invariance, and a deformation-aware localization module explicitly exploiting displacements of parts to improve accuracy of bounding box regression.
We experimentally validate our model and show significant gains.
DP-FCN achieves state-of-the-art performances of 83.1% and 80.9% on PASCAL VOC 2007 and 2012 with VOC data only.
Although outdoor navigation systems are mostly dependent on GPS, indoor systems have to rely upon different techniques for localizing the user, due to unavailability of GPS signals in indoor environments.
Over the past decade various indoor navigation systems have been developed.
In this paper an overview of some existing indoor navigation systems for visually impaired people are presented and they are compared from different perspectives.
The evaluated techniques are ultrasonic systems, RFID-based solutions, computer vision aided navigation systems, ans smartphone-based applications.
We present a novel method for high detail-preserving human avatar creation from monocular video.
A parameterized body model is refined and optimized to maximally resemble subjects from a video showing them from all sides.
Our avatars feature a natural face, hairstyle, clothes with garment wrinkles, and high-resolution texture.
Our paper contributes facial landmark and shading-based human body shape refinement, a semantic texture prior, and a novel texture stitching strategy, resulting in the most sophisticated-looking human avatars obtained from a single video to date.
Numerous results show the robustness and versatility of our method.
A user study illustrates its superiority over the state-of-the-art in terms of identity preservation, level of detail, realism, and overall user preference.
Most tabular data visualization techniques focus on overviews, yet many practical analysis tasks are concerned with investigating individual items of interest.
At the same time, relating an item to the rest of a potentially large table is important.
In this work we present Taggle, a tabular visualization technique for exploring and presenting large and complex tables.
Taggle takes an item-centric,spreadsheet-like approach, visualizing each row in the source data individually using visual encodings for the cells.
At the same time, Taggle introduces data-driven aggregation of data subsets.
The aggregation strategy is complemented by interaction methods tailored to answer specific analysis questions, such as sorting based on multiple columns and rich data selection and filtering capabilities.
We evaluate Taggle using a qualitative user study and a case study conducted by a domain expert on complex genomics data analysis for the purpose of drug discovery.
A compiler approach for generating low-level computer code from high-level input for discontinuous Galerkin finite element forms is presented.
The input language mirrors conventional mathematical notation, and the compiler generates efficient code in a standard programming language.
This facilitates the rapid generation of efficient code for general equations in varying spatial dimensions.
Key concepts underlying the compiler approach and the automated generation of computer code are elaborated.
The approach is demonstrated for a range of common problems, including the Poisson, biharmonic, advection--diffusion and Stokes equations.
In this paper, we propose in Dezert-Smarandache Theory (DSmT) framework, a new probabilistic transformation, called DSmP, in order to build a subjective probability measure from any basic belief assignment defined on any model of the frame of discernment.
Several examples are given to show how the DSmP transformation works and we compare it to main existing transformations proposed in the literature so far.
We show the advantages of DSmP over classical transformations in term of Probabilistic Information Content (PIC).
The direct extension of this transformation for dealing with qualitative belief assignments is also presented.
When modeling geo-spatial data, it is critical to capture spatial correlations for achieving high accuracy.
Spatial Auto-Regression (SAR) is a common tool used to model such data, where the spatial contiguity matrix (W) encodes the spatial correlations.
However, the efficacy of SAR is limited by two factors.
First, it depends on the choice of contiguity matrix, which is typically not learnt from data, but instead, is assumed to be known apriori.
Second, it assumes that the observations can be explained by linear models.
In this paper, we propose a Convolutional Neural Network (CNN) framework to model geo-spatial data (specifi- cally housing prices), to learn the spatial correlations automatically.
We show that neighborhood information embedded in satellite imagery can be leveraged to achieve the desired spatial smoothing.
An additional upside of our framework is the relaxation of linear assumption on the data.
Specific challenges we tackle while implementing our framework include, (i) how much of the neighborhood is relevant while estimating housing prices?
(ii) what is the right approach to capture multiple resolutions of satellite imagery? and (iii) what other data-sources can help improve the estimation of spatial correlations?
We demonstrate a marked improvement of 57% on top of the SAR baseline through the use of features from deep neural networks for the cities of London, Birmingham and Liverpool.
Empirical software engineering has received much attention in recent years and coined the shift from a more design-science-driven engineering discipline to an insight-oriented, and theory-centric one.
Yet, we still face many challenges, among which some increase the need for interdisciplinary research.
This is especially true for the investigation of human-centric aspects of software engineering.
Although we can already observe an increased recognition of the need for more interdisciplinary research in (empirical) software engineering, such research configurations come with challenges barely discussed from a scientific point of view.
In this position paper, we critically reflect upon the epistemological setting of empirical software engineering and elaborate its configuration as an Interdiscipline.
In particular, we (1) elaborate a pragmatic view on empirical research for software engineering reflecting a cyclic process for knowledge creation, (2) motivate a path towards symmetrical interdisciplinary research, and (3) adopt five rules of thumb from other interdisciplinary collaborations in our field before concluding with new emerging challenges.
This shall support stopping to treating empirical software engineering as a developing discipline moving towards a paradigmatic stage of normal science, but as a configuration of symmetric interdisciplinary teams and research methods.
How does the collaboration network of researchers coalesce around a scientific topic?
What sort of social restructuring occurs as a new field develops?
Previous empirical explorations of these questions have examined the evolution of co-authorship networks associated with several fields of science, each noting a characteristic shift in network structure as fields develop.
Historically, however, such studies have tended to rely on manually annotated datasets and therefore only consider a handful of disciplines, calling into question the universality of the observed structural signature.To overcome this limitation and test the robustness of this phenomenon, we use a comprehensive dataset of over 189,000 scientific articles and develop a framework for partitioning articles and their authors into coherent, semantically-related groups representing scientific fields of varying size and specificity.
We then use the resulting population of fields to study the structure of evolving co-authorship networks.
Consistent with earlier findings, we observe a global topological transition as the co-authorship networks coalesce from a disjointed aggregate into a dense giant connected component that dominates the network.
We validate these results using a separate, complimentary corpus of scientific articles, and, overall, we find that the previously reported characteristic structural evolution of a scientific field's associated co-authorship network is robust across a large number of scientific fields of varying size, scope, and specificity.
Additionally, the framework developed in this study may be used in other scientometric contexts in order to extend studies to compare across a larger range of scientific disciplines.
Processing of multi-word expressions (MWEs) is a known problem for any natural language processing task.
Even neural machine translation (NMT) struggles to overcome it.
This paper presents results of experiments on investigating NMT attention allocation to the MWEs and improving automated translation of sentences that contain MWEs in English->Latvian and English->Czech NMT systems.
Two improvement strategies were explored -(1) bilingual pairs of automatically extracted MWE candidates were added to the parallel corpus used to train the NMT system, and (2) full sentences containing the automatically extracted MWE candidates were added to the parallel corpus.
Both approaches allowed to increase automated evaluation results.
The best result - 0.99 BLEU point increase - has been reached with the first approach, while with the second approach minimal improvements achieved.
We also provide open-source software and tools used for MWE extraction and alignment inspection.
Tax manipulation comes in a variety of forms with different motivations and of varying complexities.
In this paper, we deal with a specific technique used by tax-evaders known as circular trading.
In particular, we define algorithms for the detection and analysis of circular trade.
To achieve this, we have modelled the whole system as a directed graph with the actors being vertices and the transactions among them as directed edges.
We illustrate the results obtained after running the proposed algorithm on the commercial tax dataset of the government of Telangana, India, which contains the transaction details of a set of participants involved in a known circular trade.
In this paper, we present Arap-Tweet, which is a large-scale and multi-dialectal corpus of Tweets from 11 regions and 16 countries in the Arab world representing the major Arabic dialectal varieties.
To build this corpus, we collected data from Twitter and we provided a team of experienced annotators with annotation guidelines that they used to annotate the corpus for age categories, gender, and dialectal variety.
During the data collection effort, we based our search on distinctive keywords that are specific to the different Arabic dialects and we also validated the location using Twitter API.
In this paper, we report on the corpus data collection and annotation efforts.
We also present some issues that we encountered during these phases.
Then, we present the results of the evaluation performed to ensure the consistency of the annotation.
The provided corpus will enrich the limited set of available language resources for Arabic and will be an invaluable enabler for developing author profiling tools and NLP tools for Arabic.
Deep Neural Networks have been shown to succeed at a range of natural language tasks such as machine translation and text summarization.
While tasks on source code (ie, formal languages) have been considered recently, most work in this area does not attempt to capitalize on the unique opportunities offered by its known syntax and structure.
In this work, we introduce SmartPaste, a first task that requires to use such information.
The task is a variant of the program repair problem that requires to adapt a given (pasted) snippet of code to surrounding, existing source code.
As first solutions, we design a set of deep neural models that learn to represent the context of each variable location and variable usage in a data flow-sensitive way.
Our evaluation suggests that our models can learn to solve the SmartPaste task in many cases, achieving 58.6% accuracy, while learning meaningful representation of variable usages.
Despite the performance advantages of modern sampling-based motion planners, solving high dimensional planning problems in near real-time remains a challenge.
Applications include hyper-redundant manipulators, snake-like and humanoid robots.
Based on the intuition that many of these problem instances do not require the robots to exercise every degree of freedom independently, we introduce an enhancement to popular sampling-based planning algorithms aimed at circumventing the exponential dependence on dimensionality.
We propose beginning the search in a lower dimensional subspace of the configuration space in the hopes that a simple solution will be found quickly.
After a certain number of samples are generated, if no solution is found, we increase the dimension of the search subspace by one and continue sampling in the higher dimensional subspace.
In the worst case, the search subspace expands to include the full configuration space - making the completeness properties identical to the underlying sampling-based planer.
Our experiments comparing the enhanced and traditional version of RRT, RRT-Connect, and BidirectionalT-RRT on both a planar hyper-redundant manipulator and the Baxter humanoid robot indicate that a solution is typically found much faster using this approach and the run time appears to be less sensitive to the dimension of the full configuration space.
We explore important implementation issues in the sampling process and discuss its limitations.
We present a complexity reduction algorithm for a family of parameter-dependent linear systems when the system parameters belong to a compact semi-algebraic set.
This algorithm potentially describes the underlying dynamical system with fewer parameters or state variables.
To do so, it minimizes the distance (i.e., H-infinity-norm of the difference) between the original system and its reduced version.
We present a sub-optimal solution to this problem using sum-of-squares optimization methods.
We present the results for both continuous-time and discrete-time systems.
Lastly, we illustrate the applicability of our proposed algorithm on numerical examples.
Over the past years, literature has shown that attacks exploiting the microarchitecture of modern processors pose a serious threat to the privacy of mobile phone users.
This is because applications leave distinct footprints in the processor, which can be used by malware to infer user activities.
In this work, we show that these inference attacks are considerably more practical when combined with advanced AI techniques.
In particular, we focus on profiling the activity in the last-level cache (LLC) of ARM processors.
We employ a simple Prime+Probe based monitoring technique to obtain cache traces, which we classify with Deep Learning methods including Convolutional Neural Networks.
We demonstrate our approach on an off-the-shelf Android phone by launching a successful attack from an unprivileged, zeropermission App in well under a minute.
The App thereby detects running applications with an accuracy of 98% and reveals opened websites and streaming videos by monitoring the LLC for at most 6 seconds.
This is possible, since Deep Learning compensates measurement disturbances stemming from the inherently noisy LLC monitoring and unfavorable cache characteristics such as random line replacement policies.
In summary, our results show that thanks to advanced AI techniques, inference attacks are becoming alarmingly easy to implement and execute in practice.
This once more calls for countermeasures that confine microarchitectural leakage and protect mobile phone applications, especially those valuing the privacy of their users.
XML access control policies involving updates may contain security flaws, here called inconsistencies, in which a forbidden operation may be simulated by performing a sequence of allowed operations.
This paper investigates the problem of deciding whether a policy is consistent, and if not, how its inconsistencies can be repaired.
We consider policies expressed in terms of annotated DTDs defining which operations are allowed or denied for the XML trees that are instances of the DTD.
We show that consistency is decidable in PTIME for such policies and that consistent partial policies can be extended to unique "least-privilege" consistent total policies.
We also consider repair problems based on deleting privileges to restore consistency, show that finding minimal repairs is NP-complete, and give heuristics for finding repairs.
Housing costs have a significant impact on individuals, families, businesses, and governments.
Recently, online companies such as Zillow have developed proprietary systems that provide automated estimates of housing prices without the immediate need of professional appraisers.
Yet, our understanding of what drives the value of houses is very limited.
In this paper, we use multiple sources of data to entangle the economic contribution of the neighborhood's characteristics such as walkability and security perception.
We also develop and release a framework able to now-cast housing prices from Open data, without the need for historical transactions.
Experiments involving 70,000 houses in 8 Italian cities highlight that the neighborhood's vitality and walkability seem to drive more than 20% of the housing value.
Moreover, the use of this information improves the nowcast by 60%.
Hence, the use of property's surroundings' characteristics can be an invaluable resource to appraise the economic and social value of houses after neighborhood changes and, potentially, anticipate gentrification.
Instance segmentation has attracted recent attention in computer vision and existing methods in this domain mostly have an object detection stage.
In this paper, we study the intrinsic challenge of the instance segmentation problem, the presence of a quotient space (swapping the labels of different instances leads to the same result), and propose new methods that are object proposal- and object detection- free.
We propose three alternative methods, namely pixel-based affinity mapping, superpixel-based affinity learning, and boundary-based component segmentation, all focusing on performing labeling transformations to cope with the quotient space problem.
By adopting fully convolutional neural networks (FCN) like models, our framework attains competitive results on both the PASCAL dataset (object-centric) and the Gland dataset (texture-centric), which the existing methods are not able to do.
Our work also has the advantages in its transparency, simplicity, and being all segmentation based.
A unified method for extracting geometric shape features from binary image data using a steady state partial differential equation (PDE) system as a boundary value problem is presented in this paper.
The PDE and functions are formulated to extract the thickness, orientation, and skeleton simultaneously.
The main advantages of the proposed method is that the orientation is defined without derivatives and thickness computation is not imposed a topological constraint on the target shape.
A one-dimensional analytical solution is provided to validate the proposed method.
In addition, two and three-dimensional numerical examples are presented to confirm the usefulness of the proposed method.
Estimating scene flow in RGB-D videos is attracting much interest of the computer vision researchers, due to its potential applications in robotics.
The state-of-the-art techniques for scene flow estimation, typically rely on the knowledge of scene structure of the frame and the correspondence between frames.
However, with the increasing amount of RGB-D data captured from sophisticated sensors like Microsoft Kinect, and the recent advances in the area of sophisticated deep learning techniques, introduction of an efficient deep learning technique for scene flow estimation, is becoming important.
This paper introduces a first effort to apply a deep learning method for direct estimation of scene flow by presenting a fully convolutional neural network with an encoder-decoder (ED) architecture.
The proposed network SceneEDNet involves estimation of three dimensional motion vectors of all the scene points from sequence of stereo images.
The training for direct estimation of scene flow is done using consecutive pairs of stereo images and corresponding scene flow ground truth.
The proposed architecture is applied on a huge dataset and provides meaningful results.
Deep Learning is increasingly being adopted by industry for computer vision applications running on embedded devices.
While Convolutional Neural Networks' accuracy has achieved a mature and remarkable state, inference latency and throughput are a major concern especially when targeting low-cost and low-power embedded platforms.
CNNs' inference latency may become a bottleneck for Deep Learning adoption by industry, as it is a crucial specification for many real-time processes.
Furthermore, deployment of CNNs across heterogeneous platforms presents major compatibility issues due to vendor-specific technology and acceleration libraries.
In this work, we present QS-DNN, a fully automatic search based on Reinforcement Learning which, combined with an inference engine optimizer, efficiently explores through the design space and empirically finds the optimal combinations of libraries and primitives to speed up the inference of CNNs on heterogeneous embedded devices.
We show that, an optimized combination can achieve 45x speedup in inference latency on CPU compared to a dependency-free baseline and 2x on average on GPGPU compared to the best vendor library.
Further, we demonstrate that, the quality of results and time "to-solution" is much better than with Random Search and achieves up to 15x better results for a short-time search.
In this paper we consider the task of recognizing human actions in realistic video where human actions are dominated by irrelevant factors.
We first study the benefits of removing non-action video segments, which are the ones that do not portray any human action.
We then learn a non-action classifier and use it to down-weight irrelevant video segments.
The non-action classifier is trained using ActionThread, a dataset with shot-level annotation for the occurrence or absence of a human action.
The non-action classifier can be used to identify non-action shots with high precision and subsequently used to improve the performance of action recognition systems.
Research on generative models is a central project in the emerging field of network science, and it studies how statistical patterns found in real networks could be generated by formal rules.
Output from these generative models is then the basis for designing and evaluating computational methods on networks, and for verification and simulation studies.
During the last two decades, a variety of models has been proposed with an ultimate goal of achieving comprehensive realism for the generated networks.
In this study, we (a) introduce a new generator, termed ReCoN; (b) explore how ReCoN and some existing models can be fitted to an original network to produce a structurally similar replica, (c) use ReCoN to produce networks much larger than the original exemplar, and finally (d) discuss open problems and promising research directions.
In a comparative experimental study, we find that ReCoN is often superior to many other state-of-the-art network generation methods.
We argue that ReCoN is a scalable and effective tool for modeling a given network while preserving important properties at both micro- and macroscopic scales, and for scaling the exemplar data by orders of magnitude in size.
Molecular communication via diffusion (MCvD) is a molecular communication method that utilizes the free diffusion of carrier molecules to transfer information at the nano-scale.
Due to the random propagation of carrier molecules, inter-symbol interference (ISI) is a major issue in an MCvD system.
Alongside ISI, inter-link interference (ILI) is also an issue that increases the total interference for MCvD-based multiple-input-multiple-output (MIMO) approaches.
Inspired by the antenna index modulation (IM) concept in traditional communication systems, this paper introduces novel IM-based transmission schemes for MCvD systems.
In the paper, molecular space shift keying (MSSK) is proposed as a novel modulation for molecular MIMO systems, and it is found that this method combats ISI and ILI considerably better than existing MIMO approaches.
For nano-machines that have access to two different molecules, the direct extension of MSSK, quadrature molecular space shift keying (QMSSK) is also proposed.
QMSSK is found to combat ISI considerably well whilst not performing well against ILI-caused errors.
In order to combat ILI more effectively, another dual-molecule-based novel modulation scheme called the molecular spatial modulation (MSM) is proposed.
Combined with the Gray mapping imposed on the antenna indices, MSM is observed to yield reliable error rates for molecular MIMO systems.
Motion planning is a key tool that allows robots to navigate through an environment without collisions.
The problem of robot motion planning has been studied in great detail over the last several decades, with researchers initially focusing on systems such as planar mobile robots and low degree-of-freedom (DOF) robotic arms.
The increased use of high DOF robots that must perform tasks in real time in complex dynamic environments spurs the need for fast motion planning algorithms.
In this overview, we discuss several types of strategies for motion planning in high dimensional spaces and dissect some of them, namely grid search based, sampling based and trajectory optimization based approaches.
We compare them and outline their advantages and disadvantages, and finally, provide an insight into future research opportunities.
Low-rank learning has attracted much attention recently due to its efficacy in a rich variety of real-world tasks, e.g., subspace segmentation and image categorization.
Most low-rank methods are incapable of capturing low-dimensional subspace for supervised learning tasks, e.g., classification and regression.
This paper aims to learn both the discriminant low-rank representation (LRR) and the robust projecting subspace in a supervised manner.
To achieve this goal, we cast the problem into a constrained rank minimization framework by adopting the least squares regularization.
Naturally, the data label structure tends to resemble that of the corresponding low-dimensional representation, which is derived from the robust subspace projection of clean data by low-rank learning.
Moreover, the low-dimensional representation of original data can be paired with some informative structure by imposing an appropriate constraint, e.g., Laplacian regularizer.
Therefore, we propose a novel constrained LRR method.
The objective function is formulated as a constrained nuclear norm minimization problem, which can be solved by the inexact augmented Lagrange multiplier algorithm.
Extensive experiments on image classification, human pose estimation, and robust face recovery have confirmed the superiority of our method.
Inspired by the principles of speed reading, we introduce Skim-RNN, a recurrent neural network (RNN) that dynamically decides to update only a small fraction of the hidden state for relatively unimportant input tokens.
Skim-RNN gives computational advantage over an RNN that always updates the entire hidden state.
Skim-RNN uses the same input and output interfaces as a standard RNN and can be easily used instead of RNNs in existing models.
In our experiments, we show that Skim-RNN can achieve significantly reduced computational cost without losing accuracy compared to standard RNNs across five different natural language tasks.
In addition, we demonstrate that the trade-off between accuracy and speed of Skim-RNN can be dynamically controlled during inference time in a stable manner.
Our analysis also shows that Skim-RNN running on a single CPU offers lower latency compared to standard RNNs on GPUs.
Wireless on-chip communication is a promising candidate to address the performance and efficiency issues that arise when scaling current Network-on-Chip (NoC) techniques to manycore processors.
A Wireless Network-on-Chip (WNoC) can serve global and broadcast traffic with ultra-low latency even in thousand-core chips, thus acting as a natural complement of conventional and throughput-oriented wireline NoCs.
However, the development of Medium Access Control (MAC) strategies needed to efficiently share the wireless medium among the increasing number of cores remains as a considerable challenge given the singularities of the environment and the novelty of the research area.
In this position paper, we present a context analysis describing the physical constraints, performance objectives, and traffic characteristics of the on-chip communication paradigm.
We summarize the main differences with respect to traditional wireless scenarios, to then discuss their implications on the design of MAC protocols for manycore WNoCs, with the ultimate goal of kickstarting this arguably unexplored research area.
Neural approaches to sequence labeling often use a Conditional Random Field (CRF) to model their output dependencies, while Recurrent Neural Networks (RNN) are used for the same purpose in other tasks.
We set out to establish RNNs as an attractive alternative to CRFs for sequence labeling.
To do so, we address one of the RNN's most prominent shortcomings, the fact that it is not exposed to its own errors with the maximum-likelihood training.
We frame the prediction of the output sequence as a sequential decision-making process, where we train the network with an adjusted actor-critic algorithm (AC-RNN).
We comprehensively compare this strategy with maximum-likelihood training for both RNNs and CRFs on three structured-output tasks.
The proposed AC-RNN efficiently matches the performance of the CRF on NER and CCG tagging, and outperforms it on Machine Transliteration.
We also show that our training strategy is significantly better than other techniques for addressing RNN's exposure bias, such as Scheduled Sampling, and Self-Critical policy training.
In this paper, we compare the individual rate of MIMO-NOMA and MIMO-OMA when users are paired into clusters.
A power allocation (PA) strategy is proposed, which ensures that MIMO-NOMA achieves a higher individual rate for each user than MIMO-OMA with arbitrary PA and optimal degrees of freedom split.
In addition, a special case with equal degrees of freedom and arbitrary PA for OMA is considered, for which the individual rate superiority of NOMA still holds.
Moreover, it is shown that NOMA can attain better fairness through appropriate PA.
Finally, simulations are carried out, which validate the developed analytical results.
Exploring contextual information in the local region is important for shape understanding and analysis.
Existing studies often employ hand-crafted or explicit ways to encode contextual information of local regions.
However, it is hard to capture fine-grained contextual information in hand-crafted or explicit manners, such as the correlation between different areas in a local region, which limits the discriminative ability of learned features.
To resolve this issue, we propose a novel deep learning model for 3D point clouds, named Point2Sequence, to learn 3D shape features by capturing fine-grained contextual information in a novel implicit way.
Point2Sequence employs a novel sequence learning model for point clouds to capture the correlations by aggregating multi-scale areas of each local region with attention.
Specifically, Point2Sequence first learns the feature of each area scale in a local region.
Then, it captures the correlation between area scales in the process of aggregating all area scales using a recurrent neural network (RNN) based encoder-decoder structure, where an attention mechanism is proposed to highlight the importance of different area scales.
Experimental results show that Point2Sequence achieves state-of-the-art performance in shape classification and segmentation tasks.
The relationship between reading and writing (RRW) is one of the major themes in learning science.
One of its obstacles is that it is difficult to define or measure the latent background knowledge of the individual.
However, in an academic research setting, scholars are required to explicitly list their background knowledge in the citation sections of their manuscripts.
This unique opportunity was taken advantage of to observe RRW, especially in the published academic commentary scenario.
RRW was visualized under a proposed topic process model by using a state of the art version of latent Dirichlet allocation (LDA).
The empirical study showed that the academic commentary is modulated both by its target paper and the author's background knowledge.
Although this conclusion was obtained in a unique environment, we suggest its implications can also shed light on other similar interesting areas, such as dialog and conversation, group discussion, and social media.
We present a new approach for building source-to-source transformations that can run on multiple programming languages, based on a new way of representing programs called incremental parametric syntax.
We implement this approach in Haskell in our Cubix system, and construct incremental parametric syntaxes for C, Java, JavaScript, Lua, and Python.
We demonstrate a whole-program refactoring tool that runs on all of them, along with three smaller transformations that each run on several.
Our evaluation shows that (1) once a transformation is written, little work is required to configure it for a new language (2) transformations built this way output readable code which preserve the structure of the original, according to participants in our human study, and (3) our transformations can still handle language corner-cases, as validated on compiler test suites.
We introduce a new entity typing task: given a sentence with an entity mention, the goal is to predict a set of free-form phrases (e.g. skyscraper, songwriter, or criminal) that describe appropriate types for the target entity.
This formulation allows us to use a new type of distant supervision at large scale: head words, which indicate the type of the noun phrases they appear in.
We show that these ultra-fine types can be crowd-sourced, and introduce new evaluation sets that are much more diverse and fine-grained than existing benchmarks.
We present a model that can predict open types, and is trained using a multitask objective that pools our new head-word supervision with prior supervision from entity linking.
Experimental results demonstrate that our model is effective in predicting entity types at varying granularity; it achieves state of the art performance on an existing fine-grained entity typing benchmark, and sets baselines for our newly-introduced datasets.
Our data and model can be downloaded from: http://nlp.cs.washington.edu/entity_type
Automatically determining the optimal size of a neural network for a given task without prior information currently requires an expensive global search and training many networks from scratch.
In this paper, we address the problem of automatically finding a good network size during a single training cycle.
We introduce *nonparametric neural networks*, a non-probabilistic framework for conducting optimization over all possible network sizes and prove its soundness when network growth is limited via an L_p penalty.
We train networks under this framework by continuously adding new units while eliminating redundant units via an L_2 penalty.
We employ a novel optimization algorithm, which we term *adaptive radial-angular gradient descent* or *AdaRad*, and obtain promising results.
This letter describes a network that is able to capture spatiotemporal correlations over arbitrary timestamps.
The proposed scheme operates as a complementary, extended network over spatiotemporal regions.
Recently, multimodal fusion has been extensively researched in deep learning.
For action recognition, the spatial and temporal streams are vital components of deep Convolutional Neural Network (CNNs), but reducing the occurrence of overfitting and fusing these two streams remain open problems.
The existing fusion approach is to average the two streams.
To this end, we propose a correlation network with a Shannon fusion to learn a CNN that has already been trained.
Long-range video may consist of spatiotemporal correlation over arbitrary times.
This correlation can be captured using simple fully connected layers to form the correlation network.
This is found to be complementary to the existing network fusion methods.
We evaluate our approach on the UCF-101 and HMDB-51 datasets, and the resulting improvement in accuracy demonstrates the importance of multimodal correlation.
Representing the semantics of words is a long-standing problem for the natural language processing community.
Most methods compute word semantics given their textual context in large corpora.
More recently, researchers attempted to integrate perceptual and visual features.
Most of these works consider the visual appearance of objects to enhance word representations but they ignore the visual environment and context in which objects appear.
We propose to unify text-based techniques with vision-based techniques by simultaneously leveraging textual and visual context to learn multimodal word embeddings.
We explore various choices for what can serve as a visual context and present an end-to-end method to integrate visual context elements in a multimodal skip-gram model.
We provide experiments and extensive analysis of the obtained results.
In this paper we discuss the stability properties of convolutional neural networks.
Convolutional neural networks are widely used in machine learning.
In classification they are mainly used as feature extractors.
Ideally, we expect similar features when the inputs are from the same class.
That is, we hope to see a small change in the feature vector with respect to a deformation on the input signal.
This can be established mathematically, and the key step is to derive the Lipschitz properties.
Further, we establish that the stability results can be extended for more general networks.
We give a formula for computing the Lipschitz bound, and compare it with other methods to show it is closer to the optimal value.
In this work, we study the effects of finite buffers on the throughput and delay of line networks with erasure links.
We identify the calculation of performance parameters such as throughput and delay to be equivalent to determining the stationary distribution of an irreducible Markov chain.
We note that the number of states in the Markov chain grows exponentially in the size of the buffers with the exponent scaling linearly with the number of hops in a line network.
We then propose a simplified iterative scheme to approximately identify the steady-state distribution of the chain by decoupling the chain to smaller chains.
The approximate solution is then used to understand the effect of buffer sizes on throughput and distribution of packet delay.
Further, we classify nodes based on congestion that yields an intelligent scheme for memory allocation using the proposed framework.
Finally, by simulations we confirm that our framework yields an accurate prediction of the variation of the throughput and delay distribution.
When dealing with process calculi and automata which express both nondeterministic and probabilistic behavior, it is customary to introduce the notion of scheduler to solve the nondeterminism.
It has been observed that for certain applications, notably those in security, the scheduler needs to be restricted so not to reveal the outcome of the protocol's random choices, or otherwise the model of adversary would be too strong even for ``obviously correct'' protocols.
We propose a process-algebraic framework in which the control on the scheduler can be specified in syntactic terms, and we show how to apply it to solve the problem mentioned above.
We also consider the definition of (probabilistic) may and must preorders, and we show that they are precongruences with respect to the restricted schedulers.
Furthermore, we show that all the operators of the language, except replication, distribute over probabilistic summation, which is a useful property for verification.
This paper presents an adaptive and intelligent sparse model for digital image sampling and recovery.
In the proposed sampler, we adaptively determine the number of required samples for retrieving image based on space-frequency-gradient information content of image patches.
By leveraging texture in space, sparsity locations in DCT domain, and directional decomposition of gradients, the sampler structure consists of a combination of uniform, random, and nonuniform sampling strategies.
For reconstruction, we model the recovery problem as a two-state cellular automaton to iteratively restore image with scalable windows from generation to generation.
We demonstrate the recovery algorithm quickly converges after a few generations for an image with arbitrary degree of texture.
For a given number of measurements, extensive experiments on standard image-sets, infra-red, and mega-pixel range imaging devices show that the proposed measurement matrix considerably increases the overall recovery performance, or equivalently decreases the number of sampled pixels for a specific recovery quality compared to random sampling matrix and Gaussian linear combinations employed by the state-of-the-art compressive sensing methods.
In practice, the proposed measurement-adaptive sampling/recovery framework includes various applications from intelligent compressive imaging-based acquisition devices to computer vision and graphics, and image processing technology.
Simulation codes are available online for reproduction purposes.
Most existing knowledge graphs (KGs) in academic domains suffer from problems of insufficient multi-relational information, name ambiguity and improper data format for large-scale machine processing.
In this paper, we present AceKG, a new large-scale KG in academic domain.
AceKG not only provides clean academic information, but also offers a large-scale benchmark dataset for researchers to conduct challenging data mining projects including link prediction, community detection and scholar classification.
Specifically, AceKG describes 3.13 billion triples of academic facts based on a consistent ontology, including necessary properties of papers, authors, fields of study, venues and institutes, as well as the relations among them.
To enrich the proposed knowledge graph, we also perform entity alignment with existing databases and rule-based inference.
Based on AceKG, we conduct experiments of three typical academic data mining tasks and evaluate several state-of- the-art knowledge embedding and network representation learning approaches on the benchmark datasets built from AceKG.
Finally, we discuss several promising research directions that benefit from AceKG.
In this paper, a novel multiple criteria decision making (MCDM) methodology is presented for assessing and prioritizing medical tourism destinations in uncertain environment.
A systematic evaluation and assessment method is proposed by integrating rough number based AHP (Analytic Hierarchy Process) and rough number based MABAC (Multi-Attributive Border Approximation area Comparison).
Rough number is used to aggregate individual judgments and preferences to deal with vagueness in decision making due to limited data.
Rough AHP analyzes the relative importance of criteria based on their preferences given by experts.
Rough MABAC evaluates the alternative sites based on the criteria weights.
The proposed methodology is explained through a case study considering different cities for healthcare service in India.
The validity of the obtained ranking for the given decision making problem is established by testing criteria proposed by Wang and Triantaphyllou (2008) along with further analysis and discussion.
In real world everything is an object which represents particular classes.
Every object can be fully described by its attributes.
Any real world dataset contains large number of attributes and objects.
Classifiers give poor performance when these huge datasets are given as input to it for proper classification.
So from these huge dataset most useful attributes need to be extracted that contribute the maximum to the decision.
In the paper, attribute set is reduced by generating reducts using the indiscernibility relation of Rough Set Theory (RST).
The method measures similarity among the attributes using relative indiscernibility relation and computes attribute similarity set.
Then the set is minimized and an attribute similarity table is constructed from which attribute similar to maximum number of attributes is selected so that the resultant minimum set of selected attributes (called reduct) cover all attributes of the attribute similarity table.
The method has been applied on glass dataset collected from the UCI repository and the classification accuracy is calculated by various classifiers.
The result shows the efficiency of the proposed method.
Many important real-world applications-such as social networks or distributed data bases-can be modeled as hypergraphs.
In such a model, vertices represent entities-such as users or data records-whereas hyperedges model a group membership of the vertices-such as the authorship in a specific topic or the membership of a data record in a specific replicated shard.
To optimize such applications, we need an efficient and effective solution to the NP-hard balanced k-way hypergraph partitioning problem.
However, existing hypergraph partitioners that scale to very large graphs do not effectively exploit the hypergraph structure when performing the partitioning decisions.
We propose HYPE, a hypergraph partitionier that exploits the neighborhood relations between vertices in the hypergraph using an efficient implementation of neighborhood expansion.
HYPE improves partitioning quality by up to 95% and reduces runtime by up to 39% compared to streaming partitioning.
With crimes on the rise all around the world, video surveillance is becoming more important day by day.
Due to the lack of human resources to monitor this increasing number of cameras manually new computer vision algorithms to perform lower and higher level tasks are being developed.
We have developed a new method incorporating the most acclaimed Histograms of Oriented Gradients the theory of Visual Saliency and the saliency prediction model Deep Multi Level Network to detect human beings in video sequences.
Furthermore we implemented the k Means algorithm to cluster the HOG feature vectors of the positively detected windows and determined the path followed by a person in the video.
We achieved a detection precision of 83.11% and a recall of 41.27%.
We obtained these results 76.866 times faster than classification on normal images.
This paper details the application of a genetic programming framework for classification of decision tree of Soil data to classify soil texture.
The database contains measurements of soil profile data.
We have applied GATree for generating classification decision tree.
GATree is a decision tree builder that is based on Genetic Algorithms (GAs).
The idea behind it is rather simple but powerful.
Instead of using statistic metrics that are biased towards specific trees we use a more flexible, global metric of tree quality that try to optimize accuracy and size.
GATree offers some unique features not to be found in any other tree inducers while at the same time it can produce better results for many difficult problems.
Experimental results are presented which illustrate the performance of generating best decision tree for classifying soil texture for soil data set.
Current research environments are witnessing high enormities of presentations occurring in different sessions at academic conferences.
This situation makes it difficult for researchers (especially juniors) to attend the right presentation session(s) for effective collaboration.
In this paper, we propose an innovative venue recommendation algorithm to enhance smart conference participation.
Our proposed algorithm, Social Aware Recommendation of Venues and Environments (SARVE), computes the Pearson Correlation and social characteristic information of conference participants.
SARVE further incorporates the current context of both the smart conference community and participants in order to model a recommendation process using distributed community detection.
Through the integration of the above computations and techniques, we are able to recommend presentation sessions of active participant presenters that may be of high interest to a particular participant.
We evaluate SARVE using a real world dataset.
Our experimental results demonstrate that SARVE outperforms other state-of-the-art methods.
Generative adversarial networks (GANs) are powerful tools for learning generative models.
In practice, the training may suffer from lack of convergence.
GANs are commonly viewed as a two-player zero-sum game between two neural networks.
Here, we leverage this game theoretic view to study the convergence behavior of the training process.
Inspired by the fictitious play learning process, a novel training method, referred to as Fictitious GAN, is introduced.
Fictitious GAN trains the deep neural networks using a mixture of historical models.
Specifically, the discriminator (resp. generator) is updated according to the best-response to the mixture outputs from a sequence of previously trained generators (resp. discriminators).
It is shown that Fictitious GAN can effectively resolve some convergence issues that cannot be resolved by the standard training approach.
It is proved that asymptotically the average of the generator outputs has the same distribution as the data samples.
Unsupervised learning permits the development of algorithms that are able to adapt to a variety of different data sets using the same underlying rules thanks to the autonomous discovery of discriminating features during training.
Recently, a new class of Hebbian-like and local unsupervised learning rules for neural networks have been developed that minimise a similarity matching cost-function.
These have been shown to perform sparse representation learning.
This study tests the effectiveness of one such learning rule for learning features from images.
The rule implemented is derived from a nonnegative classical multidimensional scaling cost-function, and is applied to both single and multi-layer architectures.
The features learned by the algorithm are then used as input to an SVM to test their effectiveness in classification on the established CIFAR-10 image dataset.
The algorithm performs well in comparison to other unsupervised learning algorithms and multi-layer networks, thus suggesting its validity in the design of a new class of compact, online learning networks.
Modern biological science produces vast amounts of genomic sequence data.
This is fuelling the need for efficient algorithms for sequence compression and analysis.
Data compression and the associated techniques coming from information theory are often perceived as being of interest for data communication and storage.
In recent years, a substantial effort has been made for the application of textual data compression techniques to various computational biology tasks, ranging from storage and indexing of large datasets to comparison of genomic databases.
This paper presents a differential compression algorithm that is based on production of difference sequences according to op-code table in order to optimize the compression of homologous sequences in dataset.
Therefore, the stored data are composed of reference sequence, the set of differences, and differences locations, instead of storing each sequence individually.
This algorithm does not require a priori knowledge about the statistics of the sequence set.
The algorithm was applied to three different datasets of genomic sequences, it achieved up to 195-fold compression rate corresponding to 99.4% space saving.
The problem of quickest detection of an anomalous process among M processes is considered.
At each time, a subset of the processes can be observed, and the observations from each chosen process follow two different distributions, depending on whether the process is normal or abnormal.
The objective is a sequential search strategy that minimizes the expected detection time subject to an error probability constraint.
This problem can be considered as a special case of active hypothesis testing first considered by Chernoff in 1959 where a randomized strategy, referred to as the Chernoff test, was proposed and shown to be asymptotically (as the error probability approaches zero) optimal.
For the special case considered in this paper, we show that a simple deterministic test achieves asymptotic optimality and offers better performance in the finite regime.
We further extend the problem to the case where multiple anomalous processes are present.
In particular, we examine the case where only an upper bound on the number of anomalous processes is known.
Sequence data is challenging for machine learning approaches, because the lengths of the sequences may vary between samples.
In this paper, we present an unsupervised learning model for sequence data, called the Integrated Sequence Autoencoder (ISA), to learn a fixed-length vectorial representation by minimizing the reconstruction error.
Specifically, we propose to integrate two classical mechanisms for sequence reconstruction which takes into account both the global silhouette information and the local temporal dependencies.
Furthermore, we propose a stop feature that serves as a temporal stamp to guide the reconstruction process, which results in a higher-quality representation.
The learned representation is able to effectively summarize not only the apparent features, but also the underlying and high-level style information.
Take for example a speech sequence sample: our ISA model can not only recognize the spoken text (apparent feature), but can also discriminate the speaker who utters the audio (more high-level style).
One promising application of the ISA model is that it can be readily used in the semi-supervised learning scenario, in which a large amount of unlabeled data is leveraged to extract high-quality sequence representations and thus to improve the performance of the subsequent supervised learning tasks on limited labeled data.
It is desirable for detection and classification algorithms to generalize to unfamiliar environments, but suitable benchmarks for quantitatively studying this phenomenon are not yet available.
We present a dataset designed to measure recognition generalization to novel environments.
The images in our dataset are harvested from twenty camera traps deployed to monitor animal populations.
Camera traps are fixed at one location, hence the background changes little across images; capture is triggered automatically, hence there is no human bias.
The challenge is learning recognition in a handful of locations, and generalizing animal detection and classification to new locations where no training data is available.
In our experiments state-of-the-art algorithms show excellent performance when tested at the same location where they were trained.
However, we find that generalization to new locations is poor, especially for classification systems.
Using time series of US patents per million inhabitants, knowledge-generating cycles can be distinguished.
These cycles partly coincide with Kondratieff long waves.
The changes in the slopes between them indicate discontinuities in the knowledge-generating paradigms.
The knowledge-generating paradigms can be modeled in terms of interacting dimensions (for example, in university-industry-government relations) that set limits to the maximal efficiency of innovation systems.
The maximum values of the parameters in the model are of the same order as the regression coefficients of the empirical waves.
The mechanism of the increase in the dimensionality is specified as self-organization which leads to the breaking of existing relations into the more diversified structure of a fractal-like network.
This breaking can be modeled in analogy to 2D and 3D (Koch) snowflakes.
The boost of knowledge generation leads to newly emerging technologies that can be expected to be more diversified and show shorter life cycles than before.
Time spans of the knowledge-generating cycles can also be analyzed in terms of Fibonacci numbers.
This perspective allows for forecasting expected dates of future possible paradigm changes.
In terms of policy implications, this suggests a shift in focus from the manufacturing technologies to developing new organizational technologies and formats of human interactions
Large-scale collection of human behavioral data by companies raises serious privacy concerns.
We show that behavior captured in the form of application usage data collected from smartphones is highly unique even in very large datasets encompassing millions of individuals.
This makes behavior-based re-identification of users across datasets possible.
We study 12 months of data from 3.5 million users and show that four apps are enough to uniquely re-identify 91.2% of users using a simple strategy based on public information.
Furthermore, we show that there is seasonal variability in uniqueness and that application usage fingerprints drift over time at an average constant rate.
The paper presents some theoretical and practical considerations regarding the TV information distribution in local (small and medium) networks, using different technologies and architectures.
The SMATV concept is chosen to be presented extensively.
The most important design formulae are presented with a software package supporting the network planner to design and optimize the network.
A case study is realized, using standard components in SMATV, for a 5 floor building.
The study proved that it is possible to design and optimize the entire network, without realizing first a costly experimental setup.
It is also possible to run different architectures, optimizing also the costs of the final solution of network.
We trained Binarized Neural Networks (BNNs) on the high resolution ImageNet ILSVRC-2102 dataset classification task and achieved a good performance.
With a moderate size network of 13 layers, we obtained top-5 classification accuracy rate of 84.1 % on validation set through network distillation, much better than previous published results of 73.2% on XNOR network and 69.1% on binarized GoogleNET.
We expect networks of better performance can be obtained by following our current strategies.
We provide a detailed discussion and preliminary analysis on strategies used in the network training.
We are proposing an extension of the recursive neural network that makes use of a variant of the long short-term memory architecture.
The extension allows information low in parse trees to be stored in a memory register (the `memory cell') and used much later higher up in the parse tree.
This provides a solution to the vanishing gradient problem and allows the network to capture long range dependencies.
Experimental results show that our composition outperformed the traditional neural-network composition on the Stanford Sentiment Treebank.
Open forms of global constraints allow the addition of new variables to an argument during the execution of a constraint program.
Such forms are needed for difficult constraint programming problems where problem construction and problem solving are interleaved, and fit naturally within constraint logic programming.
However, in general, filtering that is sound for a global constraint can be unsound when the constraint is open.
This paper provides a simple characterization, called contractibility, of the constraints where filtering remains sound when the constraint is open.
With this characterization we can easily determine whether a constraint has this property or not.
In the latter case, we can use it to derive a contractible approximation to the constraint.
We demonstrate this work on both hard and soft constraints.
In the process, we formulate two general classes of soft constraints.
Sections are the building blocks of Wikipedia articles.
They enhance readability and can be used as a structured entry point for creating and expanding articles.
Structuring a new or already existing Wikipedia article with sections is a hard task for humans, especially for newcomers or less experienced editors, as it requires significant knowledge about how a well-written article looks for each possible topic.
Inspired by this need, the present paper defines the problem of section recommendation for Wikipedia articles and proposes several approaches for tackling it.
Our systems can help editors by recommending what sections to add to already existing or newly created Wikipedia articles.
Our basic paradigm is to generate recommendations by sourcing sections from articles that are similar to the input article.
We explore several ways of defining similarity for this purpose (based on topic modeling, collaborative filtering, and Wikipedia's category system).
We use both automatic and human evaluation approaches for assessing the performance of our recommendation system, concluding that the category-based approach works best, achieving precision@10 of about 80% in the human evaluation.
We consider the problem of jointly optimizing channel pairing, channel-user assignment, and power allocation, to maximize the weighted sum-rate, in a single-relay cooperative system with multiple channels and multiple users.
Common relaying strategies are considered, and transmission power constraints are imposed on both individual transmitters and the aggregate over all transmitters.
The joint optimization problem naturally leads to a mixed-integer program.
Despite the general expectation that such problems are intractable, we construct an efficient algorithm to find an optimal solution, which incurs computational complexity that is polynomial in the number of channels and the number of users.
We further demonstrate through numerical experiments that the jointly optimal solution can significantly improve system performance over its suboptimal alternatives.
Current state-of-the-art semantic role labeling (SRL) uses a deep neural network with no explicit linguistic features.
However, prior work has shown that gold syntax trees can dramatically improve SRL decoding, suggesting the possibility of increased accuracy from explicit modeling of syntax.
In this work, we present linguistically-informed self-attention (LISA): a neural network model that combines multi-head self-attention with multi-task learning across dependency parsing, part-of-speech tagging, predicate detection and SRL.
Unlike previous models which require significant pre-processing to prepare linguistic features, LISA can incorporate syntax using merely raw tokens as input, encoding the sequence only once to simultaneously perform parsing, predicate detection and role labeling for all predicates.
Syntax is incorporated by training one attention head to attend to syntactic parents for each token.
Moreover, if a high-quality syntactic parse is already available, it can be beneficially injected at test time without re-training our SRL model.
In experiments on CoNLL-2005 SRL, LISA achieves new state-of-the-art performance for a model using predicted predicates and standard word embeddings, attaining 2.5 F1 absolute higher than the previous state-of-the-art on newswire and more than 3.5 F1 on out-of-domain data, nearly 10% reduction in error.
On ConLL-2012 English SRL we also show an improvement of more than 2.5 F1.
LISA also out-performs the state-of-the-art with contextually-encoded (ELMo) word representations, by nearly 1.0 F1 on news and more than 2.0 F1 on out-of-domain text.
Packet parsing is a key step in SDN-aware devices.
Packet parsers in SDN networks need to be both reconfigurable and fast, to support the evolving network protocols and the increasing multi-gigabit data rates.
The combination of packet processing languages with FPGAs seems to be the perfect match for these requirements.
In this work, we develop an open-source FPGA-based configurable architecture for arbitrary packet parsing to be used in SDN networks.
We generate low latency and high-speed streaming packet parsers directly from a packet processing program.
Our architecture is pipelined and entirely modeled using templated C++ classes.
The pipeline layout is derived from a parser graph that corresponds a P4 code after a series of graph transformation rounds.
The RTL code is generated from the C++ description using Xilinx Vivado HLS and synthesized with Xilinx Vivado.
Our architecture achieves 100 Gb/s data rate in a Xilinx Virtex-7 FPGA while reducing the latency by 45% and the LUT usage by 40% compared to the state-of-the-art.
In order to better manage the premiums and encourage safe driving, many commercial insurance companies (e.g., Geico, Progressive) are providing options for their customers to install sensors on their vehicles which collect individual vehicle's traveling data.
The driver's insurance is linked to his/her driving behavior.
At the other end, through analyzing the historical traveling data from a large number of vehicles, the insurance company could build a classifier to predict a new driver's driving style: aggressive or defensive.
However, collection of such vehicle traveling data explicitly breaches the drivers' personal privacy.
To tackle such privacy concerns, this paper presents a privacy-preserving driving style recognition technique to securely predict aggressive and defensive drivers for the insurance company without compromising the privacy of all the participating parties.
The insurance company cannot learn any private information from the vehicles, and vice-versa.
Finally, the effectiveness and efficiency of the privacy-preserving driving style recognition technique are validated with experimental results.
This letter introduces a 3D space-time-space block code for future digital TV systems.
The code is based on a double layer structure for inter-cell and intra-cell transmission mode in single frequency networks.
Without increasing the complexity of the receiver, the proposed code is very efficient for different transmission scenarios.
Midpoint subdivision generalizes the Lane-Riesenfeld algorithm for uniform tensor product splines and can also be applied to non regular meshes.
For example, midpoint subdivision of degree 2 is a specific Doo-Sabin algorithm and midpoint subdivision of degree 3 is a specific Catmull-Clark algorithm.
In 2001, Zorin and Schroeder were able to prove C1-continuity for midpoint subdivision surfaces analytically up to degree 9.
Here, we develop general analysis tools to show that the limiting surfaces under midpoint subdivision of any degree >= 2 are C1-continuous at their extraordinary points.
In this work we present simple grapheme-based system for low-resource speech recognition using Babel data for Turkish spontaneous speech (80 hours).
We have investigated different neural network architectures performance, including fully-convolutional, recurrent and ResNet with GRU.
Different features and normalization techniques are compared as well.
We also proposed CTC-loss modification using segmentation during training, which leads to improvement while decoding with small beam size.
Our best model achieved word error rate of 45.8%, which is the best reported result for end-to-end systems using in-domain data for this task, according to our knowledge.
In this article, a theoretical justification of one type of skew-symmetric optimal translational motion (moving in the minimal acceptable time) of a flexible object carried by a robot from its initial to its final position of absolute quiescence with the exception of the oscillations at the end of the motion is presented.
The Hamilton-Ostrogradsky principle is used as a criterion for searching an optimal control.
The data of experimental verification of the control are presented using the Orthoglide robot for translational motions and several masses were attached to a flexible beam.
The standard reasoning problem, concept satisfiability, in the basic description logic ALC is PSPACE-complete, and it is EXPTIME-complete in the presence of unrestricted axioms.
Several fragments of ALC, notably logics in the FL, EL, and DL-Lite families, have an easier satisfiability problem; sometimes it is even tractable.
We classify the complexity of the standard satisfiability problems for all possible Boolean and quantifier fragments of ALC in the presence of general axioms.
In zero-shot learning (ZSL), a classifier is trained to recognize visual classes without any image samples.
Instead, it is given semantic information about the class, like a textual description or a set of attributes.
Learning from attributes could benefit from explicitly modeling structure of the attribute space.
Unfortunately, learning of general structure from empirical samples is hard with typical dataset sizes.
Here we describe LAGO, a probabilistic model designed to capture natural soft and-or relations across groups of attributes.
We show how this model can be learned end-to-end with a deep attribute-detection model.
The soft group structure can be learned from data jointly as part of the model, and can also readily incorporate prior knowledge about groups if available.
The soft and-or structure succeeds to capture meaningful and predictive structures, improving the accuracy of zero-shot learning on two of three benchmarks.
Finally, LAGO reveals a unified formulation over two ZSL approaches: DAP (Lampert et al., 2009) and ESZSL (Romera-Paredes & Torr, 2015).
Interestingly, taking only one singleton group for each attribute, introduces a new soft-relaxation of DAP, that outperforms DAP by 40.
Predicting issue lifetime can help software developers, managers, and stakeholders effectively prioritize work, allocate development resources, and better understand project timelines.
Progress had been made on this prediction problem, but prior work has reported low precision and high false alarms.
The latest results also use complex models such as random forests that detract from their readability.
We solve both issues by using small, readable decision trees (under 20 lines long) and correlation feature selection to predict issue lifetime, achieving high precision and low false alarms (medians of 71% and 13% respectively).
We also address the problem of high class imbalance within issue datasets - when local data fails to train a good model, we show that cross-project data can be used in place of the local data.
In fact, cross-project data works so well that we argue it should be the default approach for learning predictors for issue lifetime.
The detection of weapons concealed underneath a person cloths is very much important to the improvement of the security of the public as well as the safety of public assets like airports, buildings and railway stations etc.
With the advent of drones, aerial video analysis becomes increasingly important; yet, it has received scant attention in the literature.
This paper addresses a new problem of parsing low-resolution aerial videos of large spatial areas, in terms of 1) grouping, 2) recognizing events and 3) assigning roles to people engaged in events.
We propose a novel framework aimed at conducting joint inference of the above tasks, as reasoning about each in isolation typically fails in our setting.
Given noisy tracklets of people and detections of large objects and scene surfaces (e.g., building, grass), we use a spatiotemporal AND-OR graph to drive our joint inference, using Markov Chain Monte Carlo and dynamic programming.
We also introduce a new formalism of spatiotemporal templates characterizing latent sub-events.
For evaluation, we have collected and released a new aerial videos dataset using a hex-rotor flying over picnic areas rich with group events.
Our results demonstrate that we successfully address above inference tasks under challenging conditions.
Many social media researchers and data scientists collected geo-tagged tweets to conduct spatial analysis or identify spatiotemporal patterns of filtered messages for specific topics or events.
This paper provides a systematic view to illustrate the characteristics (data noises, user biases, and system errors) of geo-tagged tweets from the Twitter Streaming API.
First, we found that a small percentage (1%) of active Twitter users can create a large portion (16%) of geo-tagged tweets.
Second, there is a significant amount (57.3%) of geo-tagged tweets located outside the Twitter Streaming API's bounding box in San Diego.
Third, we can detect spam, bot, cyborg tweets (data noises) by examining the "source" metadata field.
The portion of data noises in geo-tagged tweets is significant (29.42% in San Diego, CA and 53.47% in Columbus, OH) in our case study.
Finally, the majority of geo-tagged tweets are not created by the generic Twitter apps in Android or iPhone devices, but by other platforms, such as Instagram and Foursquare.
We recommend a multi-step procedure to remove these noises for the future research projects utilizing geo-tagged tweets.
The increasing demand for higher data rates, better quality of service, fully mobile and connected wireless networks lead the researchers to seek new solutions beyond 4G wireless systems.
It is anticipated that 5G wireless networks, which are expected to be introduced around 2020, will achieve ten times higher spectral and energy efficiency than current 4G wireless networks and will support data rates up to 10 Gbps for low mobility users.
The ambitious goals set for 5G wireless networks require dramatic changes in the design of different layers for next generation communications systems.
Massive multiple-input multiple-output (MIMO) systems, filter bank multi-carrier (FBMC) modulation, relaying technologies, and millimeter-wave communications have been considered as some of the strong candidates for the physical layer design of 5G networks.
In this article, we shed light on the potential and implementation of index modulation (IM) techniques for MIMO and multi-carrier communications systems which are expected to be two of the key technologies for 5G systems.
Specifically, we focus on two promising applications of IM: spatial modulation (SM) and orthogonal frequency division multiplexing with IM (OFDM-IM), and we discuss the recent advances and future research directions in IM technologies towards spectral and energy-efficient 5G wireless networks.
The Internet of Things (IoT) is a crucial component of Industry 4.0.
Due to growing demands of customers, the current IoT architecture will not be reliable and responsive for next generation IoT applications and upcoming services.
In this paper, the next generation IoT architecture based on new technologies is proposed in which the requirements of future applications, services, and generated data are addressed.
Particularly, this architecture consists of Nano-chip, millimeter Wave (mmWave), Heterogeneous Networks (HetNet), device-todevice (D2D) communication, 5G-IoT, Machine-Type Communication (MTC), Wireless Network Function virtualization (WNFV), Wireless Software Defined Networks (WSDN), Advanced Spectrum Sharing and Interference Management (Advanced SSIM), Mobile Edge Computing (MEC), Mobile Cloud Computing (MCC), Data Analytics and Big Data.
This combination of technologies is able to satisfy requirements of new applications.
The proposed novel architecture is modular, efficient, agile, scalable, simple, and it is able to satisfy the high amount of data and application demands.
In sentence classification tasks, additional contexts, such as the neighboring sentences, may improve the accuracy of the classifier.
However, such contexts are domain-dependent and thus cannot be used for another classification task with an inappropriate domain.
In contrast, we propose the use of translated sentences as context that is always available regardless of the domain.
We find that naive feature expansion of translations gains only marginal improvements and may decrease the performance of the classifier, due to possible inaccurate translations thus producing noisy sentence vectors.
To this end, we present multiple context fixing attachment (MCFA), a series of modules attached to multiple sentence vectors to fix the noise in the vectors using the other sentence vectors as context.
We show that our method performs competitively compared to previous models, achieving best classification performance on multiple data sets.
We are the first to use translations as domain-free contexts for sentence classification.
An approach to the formal description of service contracts is presented in terms of automata.
We focus on the basic property of guaranteeing that in the multi-party composition of principals each of them gets his requests satisfied, so that the overall composition reaches its goal.
Depending on whether requests are satisfied synchronously or asynchronously, we construct an orchestrator that at static time either yields composed services enjoying the required properties or detects the principals responsible for possible violations.
To do that in the asynchronous case we resort to Linear Programming techniques.
We also relate our automata with two logically based methods for specifying contracts.
We present a novel distributed Gauss-Newton method for the non-linear state estimation (SE) model based on a probabilistic inference method called belief propagation (BP).
The main novelty of our work comes from applying BP sequentially over a sequence of linear approximations of the SE model, akin to what is done by the Gauss-Newton method.
The resulting iterative Gauss-Newton belief propagation (GN-BP) algorithm can be interpreted as a distributed Gauss-Newton method with the same accuracy as the centralized SE, however, introducing a number of advantages of the BP framework.
The paper provides extensive numerical study of the GN-BP algorithm, provides details on its convergence behavior, and gives a number of useful insights for its implementation.
Traditional event detection methods heavily rely on manually engineered rich features.
Recent deep learning approaches alleviate this problem by automatic feature engineering.
But such efforts, like tradition methods, have so far only focused on single-token event mentions, whereas in practice events can also be a phrase.
We instead use forward-backward recurrent neural networks (FBRNNs) to detect events that can be either words or phrases.
To the best our knowledge, this is one of the first efforts to handle multi-word events and also the first attempt to use RNNs for event detection.
Experimental results demonstrate that FBRNN is competitive with the state-of-the-art methods on the ACE 2005 and the Rich ERE 2015 event detection tasks.
In this paper, we propose a novelmethod to search for precise locations of paired note onset and offset in a singing voice signal.
In comparison with the existing onset detection algorithms,our approach differs in two key respects.
First, we employ Correntropy, a generalized correlation function inspired from Reyni's entropy, as a detection function to capture the instantaneous flux while preserving insensitiveness to outliers.
Next, a novel peak picking algorithm is specially designed for this detection function.
By calculating the fitness of a pre-defined inverse hyperbolic kernel to a detection function, it is possible to find an onset and its corresponding offset simultaneously.
Experimental results show that the proposed method achieves performance significantly better than or comparable to other state-of-the-art techniques for onset detection in singing voice.
Principle Component Analysis PCA is a classical feature extraction and data representation technique widely used in pattern recognition.
It is one of the most successful techniques in face recognition.
But it has drawback of high computational especially for big size database.
This paper conducts a study to optimize the time complexity of PCA (eigenfaces) that does not affects the recognition performance.
The authors minimize the participated eigenvectors which consequently decreases the computational time.
A comparison is done to compare the differences between the recognition time in the original algorithm and in the enhanced algorithm.
The performance of the original and the enhanced proposed algorithm is tested on face94 face database.
Experimental results show that the recognition time is reduced by 35% by applying our proposed enhanced algorithm.
DET Curves are used to illustrate the experimental results.
Domain adversarial learning aligns the feature distributions across the source and target domains in a two-player minimax game.
Existing domain adversarial networks generally assume identical label space across different domains.
In the presence of big data, there is strong motivation of transferring deep models from existing big domains to unknown small domains.
This paper introduces partial domain adaptation as a new domain adaptation scenario, which relaxes the fully shared label space assumption to that the source label space subsumes the target label space.
Previous methods typically match the whole source domain to the target domain, which are vulnerable to negative transfer for the partial domain adaptation problem due to the large mismatch between label spaces.
We present Partial Adversarial Domain Adaptation (PADA), which simultaneously alleviates negative transfer by down-weighing the data of outlier source classes for training both source classifier and domain adversary, and promotes positive transfer by matching the feature distributions in the shared label space.
Experiments show that PADA exceeds state-of-the-art results for partial domain adaptation tasks on several datasets.
The 2016 U.S. presidential election has witnessed the major role of Twitter in the year's most important political event.
Candidates used this social media platform extensively for online campaigns.
Meanwhile, social media has been filled with rumors, which might have had huge impacts on voters' decisions.
In this paper, we present a thorough analysis of rumor tweets from the followers of two presidential candidates: Hillary Clinton and Donald Trump.
To overcome the difficulty of labeling a large amount of tweets as training data, we detect rumor tweets by matching them with verified rumor articles.
We analyze over 8 million tweets collected from the followers of the two candidates.
Our results provide answers to several primary concerns about rumors in this election, including: which side of the followers posted the most rumors, who posted these rumors, what rumors they posted, and when they posted these rumors.
The insights of this paper can help us understand the online rumor behaviors in American politics.
The image-to-GPS verification problem asks whether a given image is taken at a claimed GPS location.
In this paper, we treat it as an image verification problem -- whether a query image is taken at the same place as a reference image retrieved at the claimed GPS location.
We make three major contributions: 1) we propose a novel custom bottom-up pattern matching (BUPM) deep neural network solution; 2) we demonstrate that the verification can be directly done by cross-checking a perspective-looking query image and a panorama reference image, and 3) we collect and clean a dataset of 30K pairs query and reference.
Our experimental results show that the proposed BUPM solution outperforms the state-of-the-art solutions in terms of both verification and localization.
Intrinsic image decomposition is a severely under-constrained problem.
User interactions can help to reduce the ambiguity of the decomposition considerably.
The traditional way of user interaction is to draw scribbles that indicate regions with constant reflectance or shading.
However the effect scopes of the scribbles are quite limited, so dozens of scribbles are often needed to rectify the whole decomposition, which is time consuming.
In this paper we propose an efficient way of user interaction that users need only to annotate the color composition of the image.
Color composition reveals the global distribution of reflectance, so it can help to adapt the whole decomposition directly.
We build a generative model of the process that the albedo of the material produces both the reflectance through imaging and the color labels by color naming.
Our model fuses effectively the physical properties of image formation and the top-down information from human color perception.
Experimental results show that color naming can improve the performance of intrinsic image decomposition, especially in cleaning the shadows left in reflectance and solving the color constancy problem.
Recent years have seen growing interest in exploiting dual- and multi-energy measurements in computed tomography (CT) in order to characterize material properties as well as object shape.
Material characterization is performed by decomposing the scene into constitutive basis functions, such as Compton scatter and photoelectric absorption functions.
While well motivated physically, the joint recovery of the spatial distribution of photoelectric and Compton properties is severely complicated by the fact that the data are several orders of magnitude more sensitive to Compton scatter coefficients than to photoelectric absorption, so small errors in Compton estimates can create large artifacts in the photoelectric estimate.
To address these issues, we propose a model-based iterative approach which uses patch-based regularization terms to stabilize inversion of photoelectric coefficients, and solve the resulting problem though use of computationally attractive Alternating Direction Method of Multipliers (ADMM) solution techniques.
Using simulations and experimental data acquired on a commercial scanner, we demonstrate that the proposed processing can lead to more stable material property estimates which should aid materials characterization in future dual- and multi-energy CT systems.
Recently ensemble selection for consensus clustering has emerged as a research problem in Machine Intelligence.
Normally consensus clustering algorithms take into account the entire ensemble of clustering, where there is a tendency of generating a very large size ensemble before computing its consensus.
One can avoid considering the entire ensemble and can judiciously select few partitions in the ensemble without compromising on the quality of the consensus.
This may result in an efficient consensus computation technique and may save unnecessary computational overheads.
The ensemble selection problem addresses this issue of consensus clustering.
In this paper, we propose an efficient method of ensemble selection for a large ensemble.
We prioritize the partitions in the ensemble based on diversity and frequency.
Our method selects top K of the partitions in order of priority, where K is decided by the user.
We observe that considering jointly the diversity and frequency helps in identifying few representative partitions whose consensus is qualitatively better than the consensus of the entire ensemble.
Experimental analysis on a large number of datasets shows our method gives better results than earlier ensemble selection methods.
Mining the silent members of an online community, also called lurkers, has been recognized as an important problem that accompanies the extensive use of online social networks (OSNs).
Existing solutions to the ranking of lurkers can aid understanding the lurking behaviors in an OSN.
However, they are limited to use only structural properties of the static network graph, thus ignoring any relevant information concerning the time dimension.
Our goal in this work is to push forward research in lurker mining in a twofold manner: (i) to provide an in-depth analysis of temporal aspects that aims to unveil the behavior of lurkers and their relations with other users, and (ii) to enhance existing methods for ranking lurkers by integrating different time-aware properties concerning information-production and information-consumption actions.
Network analysis and ranking evaluation performed on Flickr, FriendFeed and Instagram networks allowed us to draw interesting remarks on both the understanding of lurking dynamics and on transient and cumulative scenarios of time-aware ranking.
Humans and most animals can learn new tasks without forgetting old ones.
However, training artificial neural networks (ANNs) on new tasks typically cause it to forget previously learned tasks.
This phenomenon is the result of "catastrophic forgetting", in which training an ANN disrupts connection weights that were important for solving previous tasks, degrading task performance.
Several recent studies have proposed methods to stabilize connection weights of ANNs that are deemed most important for solving a task, which helps alleviate catastrophic forgetting.
Here, drawing inspiration from algorithms that are believed to be implemented in vivo, we propose a complementary method: adding a context-dependent gating signal, such that only sparse, mostly non-overlapping patterns of units are active for any one task.
This method is easy to implement, requires little computational overhead, and allows ANNs to maintain high performance across large numbers of sequentially presented tasks when combined with weight stabilization.
This work provides another example of how neuroscience-inspired algorithms can benefit ANN design and capability.
Autonomous vehicles (AVs) will revolutionarize ground transport and take a substantial role in the future transportation system.
Most AVs are likely to be electric vehicles (EVs) and they can participate in the vehicle-to-grid (V2G) system to support various V2G services.
Although it is generally infeasible for EVs to dictate their routes, we can design AV travel plans to fulfill certain system-wide objectives.
In this paper, we focus on the AVs looking for parking and study how they can be led to appropriate parking facilities to support V2G services.
We formulate the Coordinated Parking Problem (CPP), which can be solved by a standard integer linear program solver but requires long computational time.
To make it more practical, we develop a distributed algorithm to address CPP based on dual decomposition.
We carry out a series of simulations to evaluate the proposed solution methods.
Our results show that the distributed algorithm can produce nearly optimal solutions with substantially less computational time.
A coarser time scale can improve computational time but degrade the solution quality resulting in possible infeasible solution.
Even with communication loss, the distributed algorithm can still perform well and converge with only little degradation in speed.
What tweet features are associated with higher effectiveness in tweets?
Through the mining of 122 million engagements of 2.5 million original tweets, we present a systematic review of tweet time, entities, composition, and user account features.
We show that the relationship between various features and tweeting effectiveness is non-linear; for example, tweets that use a few hashtags have higher effectiveness than using no or too many hashtags.
This research closely relates to various industrial applications that are based on tweet features, including the analysis of advertising campaigns, the prediction of user engagement, the extraction of signals for automated trading, etc.
Computer science provides an in-depth understanding of technical aspects of programming concepts, but if we want to understand how programming concepts evolve, how programmers think and talk about them and how they are used in practice, we need to consider a broader perspective that includes historical, philosophical and cognitive aspects.
In this paper, we develop such broader understanding of monads, a programming concept that has an infamous formal definition, syntactic support in several programming languages and a reputation for being elegant and powerful, but also intimidating and difficult to grasp.
This paper is not a monad tutorial.
It will not tell you what a monad is.
Instead, it helps you understand how computer scientists and programmers talk about monads and why they do so.
To answer these questions, we review the history of monads in the context of programming and study the development through the perspectives of philosophy of science, philosophy of mathematics and cognitive sciences.
More generally, we present a framework for understanding programming concepts that considers them at three levels: formal, metaphorical and implementation.
We base such observations on established results about the scientific method and mathematical entities -- cognitive sciences suggest that the metaphors used when thinking about monads are more important than widely accepted, while philosophy of science explains how the research paradigm from which monads originate influences and restricts their use.
Finally, we provide evidence for why a broader philosophical, sociological look at programming concepts should be of interest for programmers.
It lets us understand programming concepts better and, fundamentally, choose more appropriate abstractions as illustrated in number of case studies that conclude the paper.
Mobile edge cloud is emerging as a promising technology to the internet of things and cyber-physical system applications such as smart home and intelligent video surveillance.
In a smart home, various sensors are deployed to monitor the home environment and physiological health of individuals.
The data collected by sensors are sent to an application, where numerous algorithms for emotion and sentiment detection, activity recognition and situation management are applied to provide healthcare- and emergency-related services and to manage resources at the home.
The executions of these algorithms require a vast amount of computing and storage resources.
To address the issue, the conventional approach is to send the collected data to an application on an internet cloud.
This approach has several problems such as high communication latency, communication energy consumption and unnecessary data traffic to the core network.
To overcome the drawbacks of the conventional cloud-based approach, a new system called mobile edge cloud is proposed.
In mobile edge cloud, multiple mobiles and stationary devices interconnected through wireless local area networks are combined to create a small cloud infrastructure at a local physical area such as a home.
Compared to traditional mobile distributed computing systems, mobile edge cloud introduces several complex challenges due to the heterogeneous computing environment, heterogeneous and dynamic network environment, node mobility, and limited battery power.
The real-time requirements associated with the internet of things and cyber-physical system applications make the problem even more challenging.
In this paper, we describe the applications and challenges associated with the design and development of mobile edge cloud system and propose an architecture based on a cross layer design approach for effective decision making.
Adaptive Computation Time for Recurrent Neural Networks (ACT) is one of the most promising architectures for variable computation.
ACT adapts to the input sequence by being able to look at each sample more than once, and learn how many times it should do it.
In this paper, we compare ACT to Repeat-RNN, a novel architecture based on repeating each sample a fixed number of times.
We found surprising results, where Repeat-RNN performs as good as ACT in the selected tasks.
Source code in TensorFlow and PyTorch is publicly available at https://imatge-upc.github.io/danifojo-2018-repeatrnn/
Almost all known secret sharing schemes work on numbers.
Such methods will have difficulty in sharing graphs since the number of graphs increases exponentially with the number of nodes.
We propose a secret sharing scheme for graphs where we use graph intersection for reconstructing the secret which is hidden as a sub graph in the shares.
Our method does not rely on heavy computational operations such as modular arithmetic or polynomial interpolation but makes use of very basic operations like assignment and checking for equality, and graph intersection can also be performed visually.
In certain cases, the secret could be reconstructed using just pencil and paper by authorised parties but cannot be broken by an adversary even with unbounded computational power.
The method achieves perfect secrecy for (2, n) scheme and requires far fewer operations compared to Shamir's algorithm.
The proposed method could be used to share objects such as matrices, sets, plain text and even a heterogeneous collection of these.
Since we do not require a previously agreed upon encoding scheme, the method is very suitable for sharing heterogeneous collection of objects in a dynamic fashion.
In multi-source sequence-to-sequence tasks, the attention mechanism can be modeled in several ways.
This topic has been thoroughly studied on recurrent architectures.
In this paper, we extend the previous work to the encoder-decoder attention in the Transformer architecture.
We propose four different input combination strategies for the encoder-decoder attention: serial, parallel, flat, and hierarchical.
We evaluate our methods on tasks of multimodal translation and translation with multiple source languages.
The experiments show that the models are able to use multiple sources and improve over single source baselines.
Machine learning has celebrated a lot of achievements on computer vision tasks such as object detection, but the traditionally used models work with relatively low resolution images.
The resolution of recording devices is gradually increasing and there is a rising need for new methods of processing high resolution data.
We propose an attention pipeline method which uses two staged evaluation of each image or video frame under rough and refined resolution to limit the total number of necessary evaluations.
For both stages, we make use of the fast object detection model YOLO v2.
We have implemented our model in code, which distributes the work across GPUs.
We maintain high accuracy while reaching the average performance of 3-6 fps on 4K video and 2 fps on 8K video.
Conversational agents are exploding in popularity.
However, much work remains in the area of social conversation as well as free-form conversation over a broad range of domains and topics.
To advance the state of the art in conversational AI, Amazon launched the Alexa Prize, a 2.5-million-dollar university competition where sixteen selected university teams were challenged to build conversational agents, known as socialbots, to converse coherently and engagingly with humans on popular topics such as Sports, Politics, Entertainment, Fashion and Technology for 20 minutes.
The Alexa Prize offers the academic community a unique opportunity to perform research with a live system used by millions of users.
The competition provided university teams with real user conversational data at scale, along with the user-provided ratings and feedback augmented with annotations by the Alexa team.
This enabled teams to effectively iterate and make improvements throughout the competition while being evaluated in real-time through live user interactions.
To build their socialbots, university teams combined state-of-the-art techniques with novel strategies in the areas of Natural Language Understanding, Context Modeling, Dialog Management, Response Generation, and Knowledge Acquisition.
To support the efforts of participating teams, the Alexa Prize team made significant scientific and engineering investments to build and improve Conversational Speech Recognition, Topic Tracking, Dialog Evaluation, Voice User Experience, and tools for traffic management and scalability.
This paper outlines the advances created by the university teams as well as the Alexa Prize team to achieve the common goal of solving the problem of Conversational AI.
This is a reflection on the author's experience in teaching logic at the graduate level in a computer science department.
The main lesson is that model building and the process of modelling must be placed at the centre stage of logic teaching.
Furthermore, effective use must be supported with adequate tools.
Finally, logic is the methodology underlying many applications, it is hence paramount to pass on its principles, methods and concepts to computer science audiences.
We present the first method to capture the 3D total motion of a target person from a monocular view input.
Given an image or a monocular video, our method reconstructs the motion from body, face, and fingers represented by a 3D deformable mesh model.
We use an efficient representation called 3D Part Orientation Fields (POFs), to encode the 3D orientations of all body parts in the common 2D image space.
POFs are predicted by a Fully Convolutional Network (FCN), along with the joint confidence maps.
To train our network, we collect a new 3D human motion dataset capturing diverse total body motion of 40 subjects in a multiview system.
We leverage a 3D deformable human model to reconstruct total body pose from the CNN outputs by exploiting the pose and shape prior in the model.
We also present a texture-based tracking method to obtain temporally coherent motion capture output.
We perform thorough quantitative evaluations including comparison with the existing body-specific and hand-specific methods, and performance analysis on camera viewpoint and human pose changes.
Finally, we demonstrate the results of our total body motion capture on various challenging in-the-wild videos.
Our code and newly collected human motion dataset will be publicly shared.
Nauticle is a general-purpose simulation tool for the flexible and highly configurable application of particle-based methods of either discrete or continuum phenomena.
It is presented that Nauticle has three distinct layers for users and developers, then the top two layers are discussed in detail.
The paper introduces the Symbolic Form Language (SFL) of Nauticle, which facilitates the formulation of user-defined numerical models at the top level in text-based configuration files and provides simple application examples of use.
On the other hand, at the intermediate level, it is shown that the SFL can be intuitively extended with new particle methods without tedious recoding or even the knowledge of the bottom level.
Finally, the efficiency of the code is also tested through a performance benchmark.
This paper proposes a novel approach for efficiently evaluating regular path queries over provenance graphs of workflows that may include recursion.
The approach assumes that an execution g of a workflow G is labeled with query-agnostic reachability labels using an existing technique.
At query time, given g, G and a regular path query R, the approach decomposes R into a set of subqueries R1, ..., Rk that are safe for G. For each safe subquery Ri, G is rewritten so that, using the reachability labels of nodes in g, whether or not there is a path which matches Ri between two nodes can be decided in constant time.
The results of each safe subquery are then composed, possibly with some small unsafe remainder, to produce an answer to R. The approach results in an algorithm that significantly reduces the number of subqueries k over existing techniques by increasing their size and complexity, and that evaluates each subquery in time bounded by its input and output size.
Experimental results demonstrate the benefit of this approach.
We propose a new algorithm to the problem of polygonal curve approximation based on a multiresolution approach.
This algorithm is suboptimal but still maintains some optimality between successive levels of resolution using dynamic programming.
We show theoretically and experimentally that this algorithm has a linear complexity in time and space.
We experimentally compare the outcomes of our algorithm to the optimal "full search" dynamic programming solution and finally to classical merge and split approaches.
The experimental evaluations confirm the theoretical derivations and show that the proposed approach evaluated on 2D coastal maps either show a lower time complexity or provide polygonal approximations closer to the input discrete curves.
Mild traumatic brain injury is a growing public health problem with an estimated incidence of over 1.7 million people annually in US.
Diagnosis is based on clinical history and symptoms, and accurate, concrete measures of injury are lacking.
This work aims to directly use diffusion MR images obtained within one month of trauma to detect injury, by incorporating deep learning techniques.
To overcome the challenge due to limited training data, we describe each brain region using the bag of word representation, which specifies the distribution of representative patch patterns.
We apply a convolutional auto-encoder to learn the patch-level features, from overlapping image patches extracted from the MR images, to learn features from diffusion MR images of brain using an unsupervised approach.
Our experimental results show that the bag of word representation using patch level features learnt by the auto encoder provides similar performance as that using the raw patch patterns, both significantly outperform earlier work relying on the mean values of MR metrics in selected brain regions.
Surrogate models are a well established approach to reduce the number of expensive function evaluations in continuous optimization.
In the context of genetic programming, surrogate modeling still poses a challenge, due to the complex genotype-phenotype relationships.
We investigate how different genotypic and phenotypic distance measures can be used to learn Kriging models as surrogates.
We compare the measures and suggest to use their linear combination in a kernel.
We test the resulting model in an optimization framework, using symbolic regression problem instances as a benchmark.
Our experiments show that the model provides valuable information.
Firstly, the model enables an improved optimization performance compared to a model-free algorithm.
Furthermore, the model provides information on the contribution of different distance measures.
The data indicates that a phenotypic distance measure is important during the early stages of an optimization run when less data is available.
In contrast, genotypic measures, such as the tree edit distance, contribute more during the later stages.
Understanding the financial burden of chronic diseases in developing regions still remains an important economical factor which influences the successful implementation of sensor based applications for continuous monitoring of chronic conditions.
Our research focused on a comparison of literature-based data with real costs of the management and treatment of chronic diseases in a developing country, and we are using Kosovo as an example here.
The results reveal that the actual living costs exceed the minimum expenses that chronic diseases impose.
Following the potential of a positive economic impact of sensor based platforms for monitoring chronic conditions, we further examined the users perception of digital technology.
The purpose of this paper is to present the varying cost levels of treating chronic diseases, identify the users concerns and requirements towards digital technology and discuss issues and challenges that the application of sensor based platforms imply in low and middle income countries.
Visual Object tracking research has undergone significant improvement in the past few years.
The emergence of tracking by detection approach in tracking paradigm has been quite successful in many ways.
Recently, deep convolutional neural networks have been extensively used in most successful trackers.
Yet, the standard approach has been based on correlation or feature selection with minimal consideration given to motion consistency.
Thus, there is still a need to capture various physical constraints through motion consistency which will improve accuracy, robustness and more importantly rotation adaptiveness.
Therefore, one of the major aspects of this paper is to investigate the outcome of rotation adaptiveness in visual object tracking.
Among other key contributions, the paper also includes various consistencies that turn out to be extremely effective in numerous challenging sequences than the current state-of-the-art.
Authcoin is an alternative approach to the commonly used public key infrastructures such as central authorities and the PGP web of trust.
It combines a challenge response-based validation and authentication process for domains, certificates, email accounts and public keys with the advantages of a block chain-based storage system.
As a result, Authcoin does not suffer from the downsides of existing solutions and is much more resilient to sybil attacks.
The regular K-10 curriculums often do not get the necessary of affordable technology involving interactive ways of teaching the prescribed curriculum with effective analytical skill building.
In this paper, we present "PlutoAR", a paper-based augmented reality interpreter which is scalable, affordable, portable and can be used as a platform for skill building for the kids.
PlutoAR manages to overcome the conventional albeit non-interactive ways of teaching by incorporating augmented reality (AR) through an interactive toolkit to provide students with the best of both worlds.
Students cut out paper "tiles" and place these tiles one by one on a larger paper surface called "Launchpad" and use the PlutoAR mobile application which runs on any Android device with a camera and uses augmented reality to output each step of the program like an interpreter.
PlutoAR has inbuilt AR experiences like stories, maze solving using conditional loops, simple elementary mathematics and the intuition of gravity.
Network visualization allows a quick glance at how nodes (or actors) are connected by edges (or ties).
A conventional network diagram of "contact tree" maps out a root and branches that represent the structure of nodes and edges, often without further specifying leaves or fruits that would have grown from small branches.
By furnishing such a network structure with leaves and fruits, we reveal details about "contacts" in our ContactTrees that underline ties and relationships.
Our elegant design employs a bottom-up approach that resembles a recent attempt to understand subjective well-being by means of a series of emotions.
Such a bottom-up approach to social-network studies decomposes each tie into a series of interactions or contacts, which help deepen our understanding of the complexity embedded in a network structure.
Unlike previous network visualizations, ContactTrees can highlight how relationships form and change based upon interactions among actors, and how relationships and networks vary by contact attributes.
Based on a botanical tree metaphor, the design is easy to construct and the resulting tree-like visualization can display many properties at both tie and contact levels, a key ingredient missing from conventional techniques of network visualization.
We first demonstrate ContactTrees using a dataset consisting of three waves of 3-month contact diaries over the 2004-2012 period, then compare ContactTrees with alternative tools and discuss how this tool can be applied to other types of datasets.
In social choice settings with linear preferences, random dictatorship is known to be the only social decision scheme satisfying strategyproofness and ex post efficiency.
When also allowing indifferences, random serial dictatorship (RSD) is a well-known generalization of random dictatorship that retains both properties.
RSD has been particularly successful in the special domain of random assignment where indifferences are unavoidable.
While executing RSD is obviously feasible, we show that computing the resulting probabilities is #P-complete and thus intractable, both in the context of voting and assignment.
Toxic online content has become a major issue in today's world due to an exponential increase in the use of internet by people of different cultures and educational background.
Differentiating hate speech and offensive language is a key challenge in automatic detection of toxic text content.
In this paper, we propose an approach to automatically classify tweets on Twitter into three classes: hateful, offensive and clean.
Using Twitter dataset, we perform experiments considering n-grams as features and passing their term frequency-inverse document frequency (TFIDF) values to multiple machine learning models.
We perform comparative analysis of the models considering several values of n in n-grams and TFIDF normalization methods.
After tuning the model giving the best results, we achieve 95.6% accuracy upon evaluating it on test data.
We also create a module which serves as an intermediate between user and Twitter.
Automatic annotation of images with descriptive words is a challenging problem with vast applications in the areas of image search and retrieval.
This problem can be viewed as a label-assignment problem by a classifier dealing with a very large set of labels, i.e., the vocabulary set.
We propose a novel annotation method that employs two layers of sparse coding and performs coarse-to-fine labeling.
Themes extracted from the training data are treated as coarse labels.
Each theme is a set of training images that share a common subject in their visual and textual contents.
Our system extracts coarse labels for training and test images without requiring any prior knowledge.
Vocabulary words are the fine labels to be associated with images.
Most of the annotation methods achieve low recall due to the large number of available fine labels, i.e., vocabulary words.
These systems also tend to achieve high precision for highly frequent words only while relatively rare words are more important for search and retrieval purposes.
Our system not only outperforms various previously proposed annotation systems, but also achieves symmetric response in terms of precision and recall.
Our system scores and maintains high precision for words with a wide range of frequencies.
Such behavior is achieved by intelligently reducing the number of available fine labels or words for each image based on coarse labels assigned to it.
This paper reports a new reading for wavelets, which is based on the classical 'De Broglie' principle.
The wave-particle duality principle is adapted to wavelets.
Every continuous basic wavelet is associated with a proper probability density, allowing defining the Shannon entropy of a wavelet.
Further entropy definitions are considered, such as Jumarie or Renyi entropy of wavelets.
We proved that any wavelet of the same family has the same Shannon entropy of its mother wavelet.
Finally, the Shannon entropy for a few standard wavelet families is determined.
There is a plethora of datasets in various formats which are usually stored in files, hosted in catalogs, or accessed through SPARQL endpoints.
In most cases, these datasets cannot be straightforwardly explored by end users, for satisfying recall-oriented information needs.
To fill this gap, in this paper we present the design and implementation of Facetize, an editor that allows users to transform (in an interactive manner) datasets, either static (i.e. stored in files), or dynamic (i.e. being the results of SPARQL queries), to datasets that can be directly explored effectively by themselves or other users.
The latter (exploration) is achieved through the familiar interaction paradigm of Faceted Search (and Preference-enriched Faceted Search).
Specifically in this paper we describe the requirements, we introduce the required set of transformations, and then we detail the functionality and the implementation of the editor Facetize that realizes these transformations.
The supported operations cover a wide range of tasks (selection, visibility, deletions, edits, definition of hierarchies, intervals, derived attributes, and others) and Facetize enables the user to carry them out in a user-friendly and guided manner, without presupposing any technical background (regarding data representation or query languages).
Finally we present the results of an evaluation with users.
To the best of your knowledge, this is the first editor for this kind of tasks.
Using Low Cost Portable Eye Tracking for Biometric Identification Or Verification: Eye tracking technologies have in recent years become available outside of specialised labs, and are starting to become integrated in tablets and virtual reality headsets.
This offers new opportunities for use in common office- and home environments, such as for biometric recognition (identification or verification), alone or in combination with other technologies.
This paper exposes two fundamentally different approaches that have been suggested, based on spatial and temporal signatures respectively.
While deploying different stimulation paradigms for recording, it also proposes an alternative way to analyze spatial domain signatures using Fourier transformation.
Empirical data recorded from two subjects over two weeks, three months apart, are found to support previous results.
Further, variations and stability of some of the proposed signatures are analyzed over the extended timeframe and under slightly varying conditions.
Convex optimization problems arise frequently in diverse machine learning (ML) applications.
First-order methods, i.e., those that solely rely on the gradient information, are most commonly used to solve these problems.
This choice is motivated by their simplicity and low per-iteration cost.
Second-order methods that rely on curvature information through the dense Hessian matrix have, thus far, proven to be prohibitively expensive at scale, both in terms of computational and memory requirements.
We present a novel multi-GPU distributed formulation of a second order (Newton-type) solver for convex finite sum minimization problems for multi-class classification.
Our distributed formulation relies on the Alternating Direction of Multipliers Method (ADMM), which requires only one round of communication per-iteration -- significantly reducing communication overheads, while incurring minimal convergence overhead.
By leveraging the computational capabilities of GPUs, we demonstrate that per-iteration costs of Newton-type methods can be significantly reduced to be on-par with, if not better than, state-of-the-art first-order alternatives.
Given their significantly faster convergence rates, we demonstrate that our methods can process large data-sets in much shorter time (orders of magnitude in many cases) compared to existing first and second order methods, while yielding similar test-accuracy results.
Our results demonstrated that a previously reported protein name co-occurrence method (5-mention PubGene) which was not based on a hypothesis testing framework, it is generally statistically more significant than the 99th percentile of Poisson distribution-based method of calculating co-occurrence.
It agrees with previous methods using natural language processing to extract protein-protein interaction from text as more than 96% of the interactions found by natural language processing methods to overlap with the results from 5-mention PubGene method.
However, less than 2% of the gene co-expressions analyzed by microarray were found from direct co-occurrence or interaction information extraction from the literature.
At the same time, combining microarray and literature analyses, we derive a novel set of 7 potential functional protein-protein interactions that had not been previously described in the literature.
Articulated and flexible objects constitute a challenge for robot manipulation tasks but are present in different real-world settings, including home and industrial environments.
Current approaches to the manipulation of articulated and flexible objects employ ad hoc strategies to sequence and perform actions on them depending on a number of physical or geometrical characteristics related to those objects, as well as on an a priori classification of target object configurations.
In this paper, we propose an action planning and execution framework, which (i) considers abstract representations of articulated or flexible objects, (ii) integrates action planning to reason upon such configurations and to sequence an appropriate set of actions with the aim of obtaining a target configuration provided as a goal, and (iii) is able to cooperate with humans to collaboratively carry out the plan.
On the one hand, we show that a trade-off exists between the way articulated or flexible objects are perceived and how the system represents them.
Such a trade-off greatly impacts on the complexity of the planning process.
On the other hand, we demonstrate the system's capabilities in allowing humans to interrupt robot action execution, and - in general - to contribute to the whole manipulation process.
Results related to planning performance are discussed, and examples of a Baxter dual-arm manipulator performing actions collaboratively with humans are shown.
Information extraction traditionally focuses on extracting relations between identifiable entities, such as <Monterey, locatedIn, California>.
Yet, texts often also contain Counting information, stating that a subject is in a specific relation with a number of objects, without mentioning the objects themselves, for example, "California is divided into 58 counties".
Such counting quantifiers can help in a variety of tasks such as query answering or knowledge base curation, but are neglected by prior work.
This paper develops the first full-fledged system for extracting counting information from text, called CINEX.
We employ distant supervision using fact counts from a knowledge base as training seeds, and develop novel techniques for dealing with several challenges: (i) non-maximal training seeds due to the incompleteness of knowledge bases, (ii) sparse and skewed observations in text sources, and (iii) high diversity of linguistic patterns.
Experiments with five human-evaluated relations show that CINEX can achieve 60% average precision for extracting counting information.
In a large-scale experiment, we demonstrate the potential for knowledge base enrichment by applying CINEX to 2,474 frequent relations in Wikidata.
CINEX can assert the existence of 2.5M facts for 110 distinct relations, which is 28% more than the existing Wikidata facts for these relations.
We propose an online framework to detect cyber attacks on Automatic Generation Control (AGC).
A cyber at- tack detection algorithm is designed based on the approach of Dynamic Watermarking.
The detection algorithm provides a theoretical guarantee of detection of cyber attacks launched by sophisticated attackers possessing extensive knowledge of the physical and statistical models of targeted power systems.
The proposed framework is practically implementable, as it needs no hardware update on generation units.
The efficacy of the proposed framework is validated in both four-area system and 140-bus system.
Cities are engines of the knowledge-based economy, because they are the primary sites of knowledge production activities that subsequently shape the rate and direction of technological change and economic growth.
Patents provide a wealth of information to analyse the knowledge specialization at specific places, such as technological details and information on inventors and entities involved, including address information.
The technology codes on each patent document indicate the specialization and scope of the underlying technological knowledge of a given invention.
In this paper we introduce tools for portfolio analysis in terms of patents that provide insights into the technological specialization of cities.
The mapping and analysis of patent portfolios of cities using data of the Unites States Patent and Trademark Office (USPTO) website (at http://www.uspto.gov) and dedicated tools (at http://www.leydesdorff.net/portfolio) can be used to analyse the specialisation patterns of inventive activities among cities.
The results allow policy makers and other stakeholders to identify promising areas of further knowledge development and 'smart specialisation' strategies.
The task of unsupervised domain adaptation is proposed to transfer the knowledge of a label-rich domain (source domain) to a label-scarce domain (target domain).
Matching feature distributions between different domains is a widely applied method for the aforementioned task.
However, the method does not perform well when classes in the two domains are not identical.
Specifically, when the classes of the target correspond to a subset of those of the source, target samples can be incorrectly aligned with the classes that exist only in the source.
This problem setting is termed as partial domain adaptation (PDA).
In this study, we propose a novel method called Two Weighted Inconsistency-reduced Networks (TWINs) for PDA.
We utilize two classification networks to estimate the ratio of the target samples in each class with which a classification loss is weighted to adapt the classes present in the target domain.
Furthermore, to extract discriminative features for the target, we propose to minimize the divergence between domains measured by the classifiers' inconsistency on target samples.
We empirically demonstrate that reducing the inconsistency between two networks is effective for PDA and that our method outperforms other existing methods with a large margin in several datasets.
Predicting how Congressional legislators will vote is important for understanding their past and future behavior.
However, previous work on roll-call prediction has been limited to single session settings, thus did not consider generalization across sessions.
In this paper, we show that metadata is crucial for modeling voting outcomes in new contexts, as changes between sessions lead to changes in the underlying data generation process.
We show how augmenting bill text with the sponsors' ideologies in a neural network model can achieve an average of a 4% boost in accuracy over the previous state-of-the-art.
Language provides simple ways of communicating generalizable knowledge to each other (e.g., "Birds fly", "John hikes", "Fire makes smoke").
Though found in every language and emerging early in development, the language of generalization is philosophically puzzling and has resisted precise formalization.
Here, we propose the first formal account of generalizations conveyed with language that makes quantitative predictions about human understanding.
We test our model in three diverse domains: generalizations about categories (generic language), events (habitual language), and causes (causal language).
The model explains the gradience in human endorsement through the interplay between a simple truth-conditional semantic theory and diverse beliefs about properties, formalized in a probabilistic model of language understanding.
This work opens the door to understanding precisely how abstract knowledge is learned from language.
Heart failure (HF) is one of the leading causes of hospital admissions in the US.
Readmission within 30 days after a HF hospitalization is both a recognized indicator for disease progression and a source of considerable financial burden to the healthcare system.
Consequently, the identification of patients at risk for readmission is a key step in improving disease management and patient outcome.
In this work, we used a large administrative claims dataset to (1)explore the systematic application of neural network-based models versus logistic regression for predicting 30 days all-cause readmission after discharge from a HF admission, and (2)to examine the additive value of patients' hospitalization timelines on prediction performance.
Based on data from 272,778 (49% female) patients with a mean (SD) age of 73 years (14) and 343,328 HF admissions (67% of total admissions), we trained and tested our predictive readmission models following a stratified 5-fold cross-validation scheme.
Among the deep learning approaches, a recurrent neural network (RNN) combined with conditional random fields (CRF) model (RNNCRF) achieved the best performance in readmission prediction with 0.642 AUC (95% CI, 0.640-0.645).
Other models, such as those based on RNN, convolutional neural networks and CRF alone had lower performance, with a non-timeline based model (MLP) performing worst.
A competitive model based on logistic regression with LASSO achieved a performance of 0.643 AUC (95%CI, 0.640-0.646).
We conclude that data from patient timelines improve 30 day readmission prediction for neural network-based models, that a logistic regression with LASSO has equal performance to the best neural network model and that the use of administrative data result in competitive performance compared to published approaches based on richer clinical datasets.
In this paper we compare several Python tools for automatic differentiation.
In order to assess the difference in performance and precision, the problem of finding the optimal geometrical structure of the cluster with identical atoms is used as follows.
First, we compare performance of calculating gradients for the objective function.
We showed that the PyADOL-C and PyCppAD tools have much better performance for big clusters than the other ones.
Second, we assess precision of these two tools by calculating the difference between the obtained at the optimal configuration gradient norms.
We conclude that PyCppAD has the best performance among others, while having almost the same precision as the second- best performing tool - PyADOL-C.
Conventional seq2seq chatbot models only try to find the sentences with the highest probabilities conditioned on the input sequences, without considering the sentiment of the output sentences.
Some research works trying to modify the sentiment of the output sequences were reported.
In this paper, we propose five models to scale or adjust the sentiment of the chatbot response: persona-based model, reinforcement learning, plug and play model, sentiment transformation network and cycleGAN, all based on the conventional seq2seq model.
We also develop two evaluation metrics to estimate if the responses are reasonable given the input.
These metrics together with other two popularly used metrics were used to analyze the performance of the five proposed models on different aspects, and reinforcement learning and cycleGAN were shown to be very attractive.
The evaluation metrics were also found to be well correlated with human evaluation.
The present paper proposes a novel transmission strategy, referred to as cocktail BPSK, whereat two independent BPSKs are superposed with the non-orthogonal basis in a parallel transmission.
In contrast to the conventional signal superpositions, the proposed scheme avoids the interference between the two symbols, allows the symbol-energy-reuse of each other and gains the extra energy.
Based on the formulation of the mutual informations, the theoretical analysis shows that the cocktail BPSK scheme can achieve high data rate beyond the channel capacity at very low SNR, and the numerical results confirm this approach eventually.
End-to-end approaches have drawn much attention recently for significantly simplifying the construction of an automatic speech recognition (ASR) system.
RNN transducer (RNN-T) is one of the popular end-to-end methods.
Previous studies have shown that RNN-T is difficult to train and a very complex training process is needed for a reasonable performance.
In this paper, we explore RNN-T for a Chinese large vocabulary continuous speech recognition (LVCSR) task and aim to simplify the training process while maintaining performance.
First, a new strategy of learning rate decay is proposed to accelerate the model convergence.
Second, we find that adding convolutional layers at the beginning of the network and using ordered data can discard the pre-training process of the encoder without loss of performance.
Besides, we design experiments to find a balance among the usage of GPU memory, training circle and model performance.
Finally, we achieve 16.9% character error rate (CER) on our test set which is 2% absolute improvement from a strong BLSTM CE system with language model trained on the same text corpus.
Verifying the identity of a person using handwritten signatures is challenging in the presence of skilled forgeries, where a forger has access to a person's signature and deliberately attempt to imitate it.
In offline (static) signature verification, the dynamic information of the signature writing process is lost, and it is difficult to design good feature extractors that can distinguish genuine signatures and skilled forgeries.
This reflects in a relatively poor performance, with verification errors around 7% in the best systems in the literature.
To address both the difficulty of obtaining good features, as well as improve system performance, we propose learning the representations from signature images, in a Writer-Independent format, using Convolutional Neural Networks.
In particular, we propose a novel formulation of the problem that includes knowledge of skilled forgeries from a subset of users in the feature learning process, that aims to capture visual cues that distinguish genuine signatures and forgeries regardless of the user.
Extensive experiments were conducted on four datasets: GPDS, MCYT, CEDAR and Brazilian PUC-PR datasets.
On GPDS-160, we obtained a large improvement in state-of-the-art performance, achieving 1.72% Equal Error Rate, compared to 6.97% in the literature.
We also verified that the features generalize beyond the GPDS dataset, surpassing the state-of-the-art performance in the other datasets, without requiring the representation to be fine-tuned to each particular dataset.
qPCF is a paradigmatic quantum programming language that ex- tends PCF with quantum circuits and a quantum co-processor.
Quantum circuits are treated as classical data that can be duplicated and manipulated in flexible ways by means of a dependent type system.
The co-processor is essentially a standard QRAM device, albeit we avoid to store permanently quantum states in between two co-processor's calls.
Despite its quantum features, qPCF retains the classic programming approach of PCF.
We introduce qPCF syntax, typing rules, and its operational semantics.
We prove fundamental properties of the system, such as Preservation and Progress Theorems.
Moreover, we provide some higher-order examples of circuit encoding.
The Resource Description Framework (RDF) is a W3C standard for representing graph-structured data, and SPARQL is the standard query language for RDF.
Recent advances in Information Extraction, Linked Data Management and the Semantic Web have led to a rapid increase in both the volume and the variety of RDF data that are publicly available.
As businesses start to capitalize on RDF data, RDF data management systems are being exposed to workloads that are far more diverse and dynamic than what they were designed to handle.
Consequently, there is a growing need for developing workload-adaptive and self-tuning RDF data management systems.
To realize this vision, we introduce a fast and efficient method for dynamically clustering records in an RDF data management system.
Specifically, we assume nothing about the workload upfront, but as SPARQL queries are executed, we keep track of records that are co-accessed by the queries in the workload and physically cluster them.
To decide dynamically (hence, in constant-time) where a record needs to be placed in the storage system, we develop a new locality-sensitive hashing (LSH) scheme, Tunable-LSH.
Using Tunable-LSH, records that are co-accessed across similar sets of queries can be hashed to the same or nearby physical pages in the storage system.
What sets Tunable-LSH apart from existing LSH schemes is that it can auto-tune to achieve the aforementioned clustering objective with high accuracy even when the workloads change.
Experimental evaluation of Tunable-LSH in our prototype RDF data management system, chameleon-db, as well as in a standalone hashtable shows significant end-to-end improvements over existing solutions.
The reconfigurability, energy-efficiency, and massive parallelism on FPGAs make them one of the best choices for implementing efficient deep learning accelerators.
However, state-of-art implementations seldom consider the balance between high throughput of computation power and the ability of the memory subsystem to support it.
In this paper, we implement an accelerator on FPGA by combining the sparse Winograd convolution, clusters of small-scale systolic arrays, and a tailored memory layout design.
We also provide an analytical model analysis for the general Winograd convolution algorithm as a design reference.
Experimental results on VGG16 show that it achieves very high computational resource utilization, 20x ~ 30x energy efficiency, and more than 5x speedup compared with the dense implementation.
Recurrent neural networks (RNNs) have shown excellent performance in processing sequence data.
However, they are both complex and memory intensive due to their recursive nature.
These limitations make RNNs difficult to embed on mobile devices requiring real-time processes with limited hardware resources.
To address the above issues, we introduce a method that can learn binary and ternary weights during the training phase to facilitate hardware implementations of RNNs.
As a result, using this approach replaces all multiply-accumulate operations by simple accumulations, bringing significant benefits to custom hardware in terms of silicon area and power consumption.
On the software side, we evaluate the performance (in terms of accuracy) of our method using long short-term memories (LSTMs) on various sequential models including sequence classification and language modeling.
We demonstrate that our method achieves competitive results on the aforementioned tasks while using binary/ternary weights during the runtime.
On the hardware side, we present custom hardware for accelerating the recurrent computations of LSTMs with binary/ternary weights.
Ultimately, we show that LSTMs with binary/ternary weights can achieve up to 12x memory saving and 10x inference speedup compared to the full-precision implementation on an ASIC platform.
Embedding data into vector spaces is a very popular strategy of pattern recognition methods.
When distances between embeddings are quantized, performance metrics become ambiguous.
In this paper, we present an analysis of the ambiguity quantized distances introduce and provide bounds on the effect.
We demonstrate that it can have a measurable effect in empirical data in state-of-the-art systems.
We also approach the phenomenon from a computer security perspective and demonstrate how someone being evaluated by a third party can exploit this ambiguity and greatly outperform a random predictor without even access to the input data.
We also suggest a simple solution making the performance metrics, which rely on ranking, totally deterministic and impervious to such exploits.
The increasing nature of World Wide Web has imposed great challenges for researchers in improving the search efficiency over the internet.
Now days web document clustering has become an important research topic to provide most relevant documents in huge volumes of results returned in response to a simple query.
In this paper, first we proposed a novel approach, to precisely define clusters based on maximal frequent item set (MFI) by Apriori algorithm.
Afterwards utilizing the same maximal frequent item set (MFI) based similarity measure for Hierarchical document clustering.
By considering maximal frequent item sets, the dimensionality of document set is decreased.
Secondly, providing privacy preserving of open web documents is to avoiding duplicate documents.
There by we can protect the privacy of individual copy rights of documents.
This can be achieved using equivalence relation.
Human beings cannot be happy with any kind of tiredness based work, so they focused on machines to work on behalf of humans.
The Internet-based latest technology provides the platforms for human beings to relax and unburden feeling.
The Internet of Things (IoT) field efficiently helps human beings with smart decisions through Machine-to-Machine (M2M) communication all over the world.
It has been difficult to ignore the importance of the IoT field with the new development of applications such as a smartphone in the present era.
The IoT field sensor plays a vital role in sensing the intelligent object/things and making an intelligent decision after sensing the objects.
The rapid development of new applications using smartphones in the world caused all users of the IoT community to be faced with one major challenge of security in the form of side channel attacks against highly intensive 3D printing systems.
The smartphone formulated Intellectual property (IP) of side channel attacks investigate against 3D printer in the physical domain through reconstructed G-code file through primitive operations.
The smartphone (Nexus 5) solved the main problems such as orientation fixing, model accuracy of frame size and validate the feasibility and effectiveness in real case studies against the 3D printer.
The 3D printing estimated value reached 20.2 billion of dollars in 2021.
The thermal camera is used for exploring the side channel attacks after reconstructing the objects against 3D printers.
The researcher analyzed IoT security relevant issues which were avoided in future by enhanced strong security mechanism strategy, encryption, and machine learning-based algorithms, latest technologies, schemes and protocols utilized in an efficient way.
Keywords: - Internet of Things (IoT), Machine-to-Machine (M2M), Security, 3D printer, smartphone
We describe a neural network-based system for text-to-speech (TTS) synthesis that is able to generate speech audio in the voice of many different speakers, including those unseen during training.
Our system consists of three independently trained components: (1) a speaker encoder network, trained on a speaker verification task using an independent dataset of noisy speech from thousands of speakers without transcripts, to generate a fixed-dimensional embedding vector from seconds of reference speech from a target speaker; (2) a sequence-to-sequence synthesis network based on Tacotron 2, which generates a mel spectrogram from text, conditioned on the speaker embedding; (3) an auto-regressive WaveNet-based vocoder that converts the mel spectrogram into a sequence of time domain waveform samples.
We demonstrate that the proposed model is able to transfer the knowledge of speaker variability learned by the discriminatively-trained speaker encoder to the new task, and is able to synthesize natural speech from speakers that were not seen during training.
We quantify the importance of training the speaker encoder on a large and diverse speaker set in order to obtain the best generalization performance.
Finally, we show that randomly sampled speaker embeddings can be used to synthesize speech in the voice of novel speakers dissimilar from those used in training, indicating that the model has learned a high quality speaker representation.
This paper introduces the ongoing integration of Contiki's uIP stack into the OMNeT++ port of the Network Simulation Cradle (NSC).
The NSC utilizes code from real world stack implementations and allows for an accurate simulation and comparison of different TCP/IP stacks. uIP(v6) provides resource-constrained devices with an RFC-compliant TCP/IP stack and promotes the use of IPv6 in the vastly growing field of Internet of Things scenarios.
This work-in-progress report discusses our motivation to integrate uIP into the NSC, our chosen approach and possible use cases for the simulation of uIP in OMNeT++.
This paper describes an architecture for robots that combines the complementary strengths of probabilistic graphical models and declarative programming to represent and reason with logic-based and probabilistic descriptions of uncertainty and domain knowledge.
An action language is extended to support non-boolean fluents and non-deterministic causal laws.
This action language is used to describe tightly-coupled transition diagrams at two levels of granularity, with a fine-resolution transition diagram defined as a refinement of a coarse-resolution transition diagram of the domain.
The coarse-resolution system description, and a history that includes (prioritized) defaults, are translated into an Answer Set Prolog (ASP) program.
For any given goal, inference in the ASP program provides a plan of abstract actions.
To implement each such abstract action, the robot automatically zooms to the part of the fine-resolution transition diagram relevant to this action.
A probabilistic representation of the uncertainty in sensing and actuation is then included in this zoomed fine-resolution system description, and used to construct a partially observable Markov decision process (POMDP).
The policy obtained by solving the POMDP is invoked repeatedly to implement the abstract action as a sequence of concrete actions, with the corresponding observations being recorded in the coarse-resolution history and used for subsequent reasoning.
The architecture is evaluated in simulation and on a mobile robot moving objects in an indoor domain, to show that it supports reasoning with violation of defaults, noisy observations and unreliable actions, in complex domains.
Temporal difference (TD) learning is an important approach in reinforcement learning, as it combines ideas from dynamic programming and Monte Carlo methods in a way that allows for online and incremental model-free learning.
A key idea of TD learning is that it is learning predictive knowledge about the environment in the form of value functions, from which it can derive its behavior to address long-term sequential decision making problems.
The agent's horizon of interest, that is, how immediate or long-term a TD learning agent predicts into the future, is adjusted through a discount rate parameter.
In this paper, we introduce an alternative view on the discount rate, with insight from digital signal processing, to include complex-valued discounting.
Our results show that setting the discount rate to appropriately chosen complex numbers allows for online and incremental estimation of the Discrete Fourier Transform (DFT) of a signal of interest with TD learning.
We thereby extend the types of knowledge representable by value functions, which we show are particularly useful for identifying periodic effects in the reward sequence.
This paper describes a technique to compare large text sources using word vector representations (word2vec) and dimensionality reduction (t-SNE) and how it can be implemented using Python.
The technique provides a bird's-eye view of text sources, e.g. text summaries and their source material, and enables users to explore text sources like a geographical map.
Word vector representations capture many linguistic properties such as gender, tense, plurality and even semantic concepts like "capital city of".
Using dimensionality reduction, a 2D map can be computed where semantically similar words are close to each other.
The technique uses the word2vec model from the gensim Python library and t-SNE from scikit-learn.
We overview dataflow matrix machines as a Turing complete generalization of recurrent neural networks and as a programming platform.
We describe vector space of finite prefix trees with numerical leaves which allows us to combine expressive power of dataflow matrix machines with simplicity of traditional recurrent neural networks.
In this paper, linear systems with a crisp real coefficient matrix and with a vector of fuzzy triangular numbers on the right-hand side are studied.
A new method, which is based on the geometric representations of linear transformations, is proposed to find solutions.
The method uses the fact that a vector of fuzzy triangular numbers forms a rectangular prism in n-dimensional space and that the image of a parallelepiped is also a parallelepiped under a linear transformation.
The suggested method clarifies why in general case different approaches do not generate solutions as fuzzy numbers.
It is geometrically proved that if the coefficient matrix is a generalized permutation matrix, then the solution of a fuzzy linear system (FLS) is a vector of fuzzy numbers irrespective of the vector on the right-hand side.
The most important difference between this and previous papers on FLS is that the solution is sought as a fuzzy set of vectors (with real components) rather than a vector of fuzzy numbers.
Each vector in the solution set solves the given FLS with a certain possibility.
The suggested method can also be applied in the case when the right-hand side is a vector of fuzzy numbers in parametric form.
However, in this case, -cuts of the solution can not be determined by geometric similarity and additional computations are needed.
In the paper, we analyze the distribution of complexities in the Vai script, an indigenous syllabic writing system from Liberia.
It is found that the uniformity hypothesis for complexities fails for this script.
The models using Poisson distribution for the number of components and hyper-Poisson distribution for connections provide good fits in the case of the Vai script.
Outsourcing jobs to a public cloud is a cost-effective way to address the problem of satisfying the peak resource demand when the local cloud has insufficient resources.
In this paper, we study on managing deadline-constrained bag-of-tasks jobs on hybrid clouds.
We present a binary nonlinear programming (BNP) problem to model the hybrid cloud management where the utilization of physical machines (PMs) in the local cloud/cluster is maximized when the local resources are enough to satisfy the deadline constraints of jobs, while when not, the rent cost from the public cloud is minimized.
To solve this BNP problem in polynomial time, we proposed a heuristic algorithm.
Its main idea is assigning the task closest to its deadline to current core until the core cannot finish any task within its deadline.
When there is no available core, the algorithm adds an available PM with most capacity or rents a new VM with highest cost-performance ratio.
Extensive experimental results show that our heuristic algorithm saves 16.2%-76% rent cost and improves 47.3%-182.8% resource utilizations satisfying deadline constraints, compared with first fit decreasing algorithm.
In this paper, we introduce iBoW-LCD, a novel appearance-based loop closure detection method.
The presented approach makes use of an incremental Bag-of-Words (BoW) scheme based on binary descriptors to retrieve previously seen similar images, avoiding any vocabulary training stage usually required by classic BoW models.
In addition, to detect loop closures, iBoW-LCD builds on the concept of dynamic islands, a simple but effective mechanism to group similar images close in time, which reduces the computational times typically associated to Bayesian frameworks.
Our approach is validated using several indoor and outdoor public datasets, taken under different environmental conditions, achieving a high accuracy and outperforming other state-of-the-art solutions.
This review considers methods of nonlinear dynamics to apply for analysis of time series corresponding to information streams on the Internet.
In the main, these methods are based on correlation, fractal, multifractal, wavelet, and Fourier analysis.
The article is dedicated to a detailed description of these approaches and interconnections among them.
The methods and corresponding algorithms presented can be used for detecting key points in the dynamic of information processes; identifying periodicity, anomaly, self-similarity, and correlations; forecasting various information processes.
The methods discussed can form the basis for detecting information attacks, campaigns, operations, and wars.
Datasets are important for researchers to build models and test how well their machine learning algorithms perform.
This paper presents the Rainforest Automation Energy (RAE) dataset to help smart grid researchers test their algorithms which make use of smart meter data.
This initial release of RAE contains 1Hz data (mains and sub-meters) from two a residential house.
In addition to power data, environmental and sensor data from the house's thermostat is included.
Sub-meter data from one of the houses includes heat pump and rental suite captures which is of interest to power utilities.
We also show and energy breakdown of each house and show (by example) how RAE can be used to test non-intrusive load monitoring (NILM) algorithms.
Several variants of the Constraint Satisfaction Problem have been proposed and investigated in the literature for modelling those scenarios where solutions are associated with some given costs.
Within these frameworks computing an optimal solution is an NP-hard problem in general; yet, when restricted over classes of instances whose constraint interactions can be modelled via (nearly-)acyclic graphs, this problem is known to be solvable in polynomial time.
In this paper, larger classes of tractable instances are singled out, by discussing solution approaches based on exploiting hypergraph acyclicity and, more generally, structural decomposition methods, such as (hyper)tree decompositions.
This paper studies the problem of predicting the coding effort for a subsequent year of development by analysing metrics extracted from project repositories, with an emphasis on projects containing XML code.
The study considers thirteen open source projects and applies machine learning algorithms to generate models to predict one-year coding effort, measured in terms of lines of code added, modified and deleted.
Both organisational and code metrics associated to revisions are taken into account.
The results show that coding effort is highly determined by the expertise of developers while source code metrics have little effect on improving the accuracy of estimations of coding effort.
The study also shows that models trained on one project are unreliable at estimating effort in other projects.
The Statistical Learning Theory (SLT) provides the theoretical guarantees for supervised machine learning based on the Empirical Risk Minimization Principle (ERMP).
Such principle defines an upper bound to ensure the uniform convergence of the empirical risk Remp(f), i.e., the error measured on a given data sample, to the expected value of risk R(f) (a.k.a. actual risk), which depends on the Joint Probability Distribution P(X x Y) mapping input examples x in X to class labels y in Y.
The uniform convergence is only ensured when the Shattering coefficient N(F,2n) has a polynomial growing behavior.
This paper proves the Shattering coefficient for any Hilbert space H containing the input space X and discusses its effects in terms of learning guarantees for supervised machine algorithms.
The metrics play increasingly fundamental role in the design, development, deployment and operation of telecommunication systems.
Despite their importance, the studies of metrics are usually limited to a narrow area or a well-defined objective.
Our study aims to more broadly survey the metrics that are commonly used for analyzing, developing and managing telecommunication networks in order to facilitate understanding of the current metrics landscape.
The metrics are simple abstractions of systems, and they directly influence how the systems are perceived by different stakeholders.
However, defining and using metrics for telecommunication systems with ever increasing complexity is a complicated matter which has not been so far systematically and comprehensively considered in the literature.
The common metrics sources are identified, and how the metrics are used and selected is discussed.
The most commonly used metrics for telecommunication systems are categorized and presented as energy and power metrics, quality-of-service metrics, quality-of-experience metrics, security metrics, and reliability and resilience metrics.
Finally, the research directions and recommendations how the metrics can evolve, and be defined and used more effectively are outlined.
The proliferation of online biometric authentication has necessitated security requirements of biometric templates.
The existing secure biometric authentication schemes feature a server-centric model, where a service provider maintains a biometric database and is fully responsible for the security of the templates.
The end-users have to fully trust the server in storing, processing and managing their private templates.
As a result, the end-users' templates could be compromised by outside attackers or even the service provider itself.
In this paper, we propose a user-centric biometric authentication scheme (PassBio) that enables end-users to encrypt their own templates with our proposed light-weighted encryption scheme.
During authentication, all the templates remain encrypted such that the server will never see them directly.
However, the server is able to determine whether the distance of two encrypted templates is within a pre-defined threshold.
Our security analysis shows that no critical information of the templates can be revealed under both passive and active attacks.
PassBio follows a "compute-then-compare" computational model over encrypted data.
More specifically, our proposed Threshold Predicate Encryption (TPE) scheme can encrypt two vectors x and y in such a manner that the inner product of x and y can be evaluated and compared to a pre-defined threshold.
TPE guarantees that only the comparison result is revealed and no key information about x and y can be learned.
Furthermore, we show that TPE can be utilized as a flexible building block to evaluate different distance metrics such as Hamming distance and Euclidean distance over encrypted data.
Such a compute-then-compare computational model, enabled by TPE, can be widely applied in many interesting applications such as searching over encrypted data while ensuring data security and privacy.
Supervised feature learning using convolutional neural networks (CNNs) can provide concise and disease relevant representations of medical images.
However, training CNNs requires annotated image data.
Annotating medical images can be a time-consuming task and even expert annotations are subject to substantial inter- and intra-rater variability.
Assessing visual similarity of images instead of indicating specific pathologies or estimating disease severity could allow non-experts to participate, help uncover new patterns, and possibly reduce rater variability.
We consider the task of assessing emphysema extent in chest CT scans.
We derive visual similarity triplets from visually assessed emphysema extent and learn a low dimensional embedding using CNNs.
We evaluate the networks on 973 images, and show that the CNNs can learn disease relevant feature representations from derived similarity triplets.
To our knowledge this is the first medical image application where similarity triplets has been used to learn a feature representation that can be used for embedding unseen test images
An important property of programming language semantics is that they should be compositional.
However, unstructured low-level code contains goto-like commands making it hard to define a semantics that is compositional.
In this paper, we follow the ideas of Saabas and Uustalu to structure low-level code.
This gives us the possibility to define a compositional denotational semantics based on least fixed points to allow for the use of inductive verification methods.
We capture the semantics of communication using finite traces similar to the denotations of CSP.
In addition, we examine properties of this semantics and give an example that demonstrates reasoning about communication and jumps.
With this semantics, we lay the foundations for a proof calculus that captures both, the semantics of unstructured low-level code and communication.
The amount of Android malware has increased greatly during the last few years.
Static analysis is widely used in detecting such malware by analyzing the code without execution.
The effectiveness of current tools relies on the app model as well as the malware detection algorithm which analyzes the app model.
If the model and/or the algorithm is inadequate, then sophisticated attacks that are triggered by specific sequences of events will not be detected.
This paper presents a static analysis framework called Dexteroid, which uses reverse-engineered life cycle models to accurately capture the behaviors of Android components.
Dexteroid systematically derives event sequences from the models, and uses them to detect attacks launched by specific ordering of events.
A prototype implementation of Dexteroid detects two types of attacks: (1) leakage of private information, and (2) sending SMS to premium-rate numbers.
A series of experiments are conducted on 1526 Google Play apps, 1259 Genome Malware apps, and a suite of benchmark apps called DroidBench and the results are compared with a state-of-the-art static analysis tool called FlowDroid.
The evaluation results show that the proposed framework is effective and efficient in terms of precision, recall, and execution time.
Visual reasoning is a special visual question answering problem that is multi-step and compositional by nature, and also requires intensive text-vision interactions.
We propose CMM: Cascaded Mutual Modulation as a novel end-to-end visual reasoning model.
CMM includes a multi-step comprehension process for both question and image.
In each step, we use a Feature-wise Linear Modulation (FiLM) technique to enable textual/visual pipeline to mutually control each other.
Experiments show that CMM significantly outperforms most related models, and reach state-of-the-arts on two visual reasoning benchmarks: CLEVR and NLVR, collected from both synthetic and natural languages.
Ablation studies confirm that both our multistep framework and our visual-guided language modulation are critical to the task.
Our code is available at https://github.com/FlamingHorizon/CMM-VR.
Predicting business process behaviour is an important aspect of business process management.
Motivated by research in natural language processing, this paper describes an application of deep learning with recurrent neural networks to the problem of predicting the next event in a business process.
This is both a novel method in process prediction, which has largely relied on explicit process models, and also a novel application of deep learning methods.
The approach is evaluated on two real datasets and our results surpass the state-of-the-art in prediction precision.
Distant supervision for relation extraction is an efficient method to reduce labor costs and has been widely used to seek novel relational facts in large corpora, which can be identified as a multi-instance multi-label problem.
However, existing distant supervision methods suffer from selecting important words in the sentence and extracting valid sentences in the bag.
Towards this end, we propose a novel approach to address these problems in this paper.
Firstly, we propose a linear attenuation simulation to reflect the importance of words in the sentence with respect to the distances between entities and words.
Secondly, we propose a non-independent and identically distributed (non-IID) relevance embedding to capture the relevance of sentences in the bag.
Our method can not only capture complex information of words about hidden relations, but also express the mutual information of instances in the bag.
Extensive experiments on a benchmark dataset have well-validated the effectiveness of the proposed method.
We investigate the 'Digital Synaptic Neural Substrate' (DSNS) computational creativity approach further with respect to the size and quality of images that can be used to seed the process.
In previous work we demonstrated how combining photographs of people and sequences taken from chess games between weak players can be used to generate chess problems or puzzles of higher aesthetic quality, on average, compared to alternative approaches.
In this work we show experimentally that using larger images as opposed to smaller ones improves the output quality even further.
The same is also true for using clearer or less corrupted images.
The reasons why these things influence the DSNS process is presently not well-understood and debatable but the findings are nevertheless immediately applicable for obtaining better results.
In this paper, we present the step by step knowledge acquisition process by choosing a structured method through using a questionnaire as a knowledge acquisition tool.
Here we want to depict the problem domain as, how to evaluate teachers performance in higher education through the use of expert system technology.
The problem is how to acquire the specific knowledge for a selected problem efficiently and effectively from human experts and encode it in the suitable computer format.
Acquiring knowledge from human experts in the process of expert systems development is one of the most common problems cited till yet.
This questionnaire was sent to 87 domain experts within all public and private universities in Pakistani.
Among them 25 domain experts sent their valuable opinions.
Most of the domain experts were highly qualified, well experienced and highly responsible persons.
The whole questionnaire was divided into 15 main groups of factors, which were further divided into 99 individual questions.
These facts were analyzed further to give a final shape to the questionnaire.
This knowledge acquisition technique may be used as a learning tool for further research work.
Many computer vision problems are formulated as the optimization of a cost function.
This approach faces two main challenges: (i) designing a cost function with a local optimum at an acceptable solution, and (ii) developing an efficient numerical method to search for one (or multiple) of these local optima.
While designing such functions is feasible in the noiseless case, the stability and location of local optima are mostly unknown under noise, occlusion, or missing data.
In practice, this can result in undesirable local optima or not having a local optimum in the expected place.
On the other hand, numerical optimization algorithms in high-dimensional spaces are typically local and often rely on expensive first or second order information to guide the search.
To overcome these limitations, this paper proposes Discriminative Optimization (DO), a method that learns search directions from data without the need of a cost function.
Specifically, DO explicitly learns a sequence of updates in the search space that leads to stationary points that correspond to desired solutions.
We provide a formal analysis of DO and illustrate its benefits in the problem of 3D point cloud registration, camera pose estimation, and image denoising.
We show that DO performed comparably or outperformed state-of-the-art algorithms in terms of accuracy, robustness to perturbations, and computational efficiency.
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Particular attention has been paid to the frequent exchange of Cooperative Awareness Messages (CAMs) on which many road safety appli
A marginal problem asks whether a given family of marginal distributions for some set of random variables arises from some joint distribution of these variables.
Here we point out that the existence of such a joint distribution imposes non-trivial conditions already on the level of Shannon entropies of the given marginals.
These entropic inequalities are necessary (but not sufficient) criteria for the existence of a joint distribution.
For every marginal problem, a list of such Shannon-type entropic inequalities can be calculated by Fourier-Motzkin elimination, and we offer a software interface to a Fourier-Motzkin solver for doing so.
For the case that the hypergraph of given marginals is a cycle graph, we provide a complete analytic solution to the problem of classifying all relevant entropic inequalities, and use this result to bound the decay of correlations in stochastic processes.
Furthermore, we show that Shannon-type inequalities for differential entropies are not relevant for continuous-variable marginal problems; non-Shannon-type inequalities are, both in the discrete and in the continuous case.
In contrast to other approaches, our general framework easily adapts to situations where one has additional (conditional) independence requirements on the joint distribution, as in the case of graphical models.
We end with a list of open problems.
A complementary article discusses applications to quantum nonlocality and contextuality.
Semantic annotation is fundamental to deal with large-scale lexical information, mapping the information to an enumerable set of categories over which rules and algorithms can be applied, and foundational ontology classes can be used as a formal set of categories for such tasks.
A previous alignment between WordNet noun synsets and DOLCE provided a starting point for ontology-based annotation, but in NLP tasks verbs are also of substantial importance.
This work presents an extension to the WordNet-DOLCE noun mapping, aligning verbs according to their links to nouns denoting perdurants, transferring to the verb the DOLCE class assigned to the noun that best represents that verb's occurrence.
To evaluate the usefulness of this resource, we implemented a foundational ontology-based semantic annotation framework, that assigns a high-level foundational category to each word or phrase in a text, and compared it to a similar annotation tool, obtaining an increase of 9.05% in accuracy.
Clinical trial registries can be used to monitor the production of trial evidence and signal when systematic reviews become out of date.
However, this use has been limited to date due to the extensive manual review required to search for and screen relevant trial registrations.
Our aim was to evaluate a new method that could partially automate the identification of trial registrations that may be relevant for systematic review updates.
We identified 179 systematic reviews of drug interventions for type 2 diabetes, which included 537 clinical trials that had registrations in ClinicalTrials.gov.
We tested a matrix factorisation approach that uses a shared latent space to learn how to rank relevant trial registrations for each systematic review, comparing the performance to document similarity to rank relevant trial registrations.
The two approaches were tested on a holdout set of the newest trials from the set of type 2 diabetes systematic reviews and an unseen set of 141 clinical trial registrations from 17 updated systematic reviews published in the Cochrane Database of Systematic Reviews.
The matrix factorisation approach outperformed the document similarity approach with a median rank of 59 and recall@100 of 60.9%, compared to a median rank of 138 and recall@100 of 42.8% in the document similarity baseline.
In the second set of systematic reviews and their updates, the highest performing approach used document similarity and gave a median rank of 67 (recall@100 of 62.9%).
The proposed method was useful for ranking trial registrations to reduce the manual workload associated with finding relevant trials for systematic review updates.
The results suggest that the approach could be used as part of a semi-automated pipeline for monitoring potentially new evidence for inclusion in a review update.
We propose a new iterative segmentation model which can be accurately learned from a small dataset.
A common approach is to train a model to directly segment an image, requiring a large collection of manually annotated images to capture the anatomical variability in a cohort.
In contrast, we develop a segmentation model that recursively evolves a segmentation in several steps, and implement it as a recurrent neural network.
We learn model parameters by optimizing the interme- diate steps of the evolution in addition to the final segmentation.
To this end, we train our segmentation propagation model by presenting incom- plete and/or inaccurate input segmentations paired with a recommended next step.
Our work aims to alleviate challenges in segmenting heart structures from cardiac MRI for patients with congenital heart disease (CHD), which encompasses a range of morphological deformations and topological changes.
We demonstrate the advantages of this approach on a dataset of 20 images from CHD patients, learning a model that accurately segments individual heart chambers and great vessels.
Com- pared to direct segmentation, the iterative method yields more accurate segmentation for patients with the most severe CHD malformations.
This paper studies different signaling techniques on the continuous spectrum (CS) of nonlinear optical fiber defined by nonlinear Fourier transform.
Three different signaling techniques are proposed and analyzed based on the statistics of the noise added to CS after propagation along the nonlinear optical fiber.
The proposed methods are compared in terms of error performance, distance reach, and complexity.
Furthermore, the effect of chromatic dispersion on the data rate and noise in nonlinear spectral domain is investigated.
It is demonstrated that, for a given sequence of CS symbols, an optimal bandwidth (or symbol rate) can be determined so that the temporal duration of the propagated signal at the end of the fiber is minimized.
In effect, the required guard interval between the subsequently transmitted data packets in time is minimized and the effective data rate is significantly enhanced.
Moreover, by selecting the proper signaling method and design criteria a reach distance of 7100 km is reported by only singling on the CS at a rate of 9.6 Gbps.
The new model of quantum computation is proposed, for which an effective algorithm of solving any task in NP is described.
The work is based and inspired be the Grover's algorithm for solving NP-tasks with quadratic speedup compared to the classical computation model.
The provided model and algorithm exhibit the exponential speedup over that described by Grover.
This contribution reports an application of MultiFractal Detrended Fluctuation Analysis, MFDFA based novel feature extraction technique for automated detection of epilepsy.
In fractal geometry, Multifractal Detrended Fluctuation Analysis MFDFA is a popular technique to examine the self-similarity of a nonlinear, chaotic and noisy time series.
In the present research work, EEG signals representing healthy, interictal (seizure free) and ictal activities (seizure) are acquired from an existing available database.
The acquired EEG signals of different states are at first analyzed using MFDFA.
To requisite the time series singularity quantification at local and global scales, a novel set of fourteen different features.
Suitable feature ranking employing students t-test has been done to select the most statistically significant features which are henceforth being used as inputs to a support vector machines (SVM) classifier for the classification of different EEG signals.
Eight different classification problems have been presented in this paper and it has been observed that the overall classification accuracy using MFDFA based features are reasonably satisfactory for all classification problems.
The performance of the proposed method are also found to be quite commensurable and in some cases even better when compared with the results published in existing literature studied on the similar data set.
Message Passing Interface (MPI) is widely used to implement parallel programs.
Although Windowsbased architectures provide the facilities of parallel execution and multi-threading, little attention has been focused on using MPI on these platforms.
In this paper we use the dual core Window-based platform to study the effect of parallel processes number and also the number of cores on the performance of three MPI parallel implementations for some sorting algorithms.
Timbre and pitch are the two main perceptual properties of musical sounds.
Depending on the target applications, we sometimes prefer to focus on one of them, while reducing the effect of the other.
Researchers have managed to hand-craft such timbre-invariant or pitch-invariant features using domain knowledge and signal processing techniques, but it remains difficult to disentangle them in the resulting feature representations.
Drawing upon state-of-the-art techniques in representation learning, we propose in this paper two deep convolutional neural network models for learning disentangled representation of musical timbre and pitch.
Both models use encoders/decoders and adversarial training to learn music representations, but the second model additionally uses skip connections to deal with the pitch information.
As music is an art of time, the two models are supervised by frame-level instrument and pitch labels using a new dataset collected from MuseScore.
We compare the result of the two disentangling models with a new evaluation protocol called "timbre crossover", which leads to interesting applications in audio-domain music editing.
Via various objective evaluations, we show that the second model can better change the instrumentation of a multi-instrument music piece without much affecting the pitch structure.
By disentangling timbre and pitch, we envision that the model can contribute to generating more realistic music audio as well.
Cloud Computing emerges from the global economic crisis as an option to use computing resources from a more rational point of view.
In other words, a cheaper way to have IT resources.
However, issues as security and privacy, SLA (Service Layer Agreement), resource sharing, and billing has left open questions about the real gains of that model.
This study aims to investigate state-of-the-art in Cloud Computing, identify gaps, challenges, synthesize available evidences both its use and development, and provides relevant information, clarifying open questions and common discussed issues about that model through literature.
The good practices of systematic map- ping study methodology were adopted in order to reach those objectives.
Al- though Cloud Computing is based on a business model with over 50 years of existence, evidences found in this study indicate that Cloud Computing still presents limitations that prevent the full use of the proposal on-demand.
One of the open challenges in designing robots that operate successfully in the unpredictable human environment is how to make them able to predict what actions they can perform on objects, and what their effects will be, i.e., the ability to perceive object affordances.
Since modeling all the possible world interactions is unfeasible, learning from experience is required, posing the challenge of collecting a large amount of experiences (i.e., training data).
Typically, a manipulative robot operates on external objects by using its own hands (or similar end-effectors), but in some cases the use of tools may be desirable, nevertheless, it is reasonable to assume that while a robot can collect many sensorimotor experiences using its own hands, this cannot happen for all possible human-made tools.
Therefore, in this paper we investigate the developmental transition from hand to tool affordances: what sensorimotor skills that a robot has acquired with its bare hands can be employed for tool use?
By employing a visual and motor imagination mechanism to represent different hand postures compactly, we propose a probabilistic model to learn hand affordances, and we show how this model can generalize to estimate the affordances of previously unseen tools, ultimately supporting planning, decision-making and tool selection tasks in humanoid robots.
We present experimental results with the iCub humanoid robot, and we publicly release the collected sensorimotor data in the form of a hand posture affordances dataset.
On-device intelligence is gaining significant attention recently as it offers local data processing and low power consumption.
In this research, an on-device training circuitry for threshold-current memristors integrated in a crossbar structure is proposed.
Furthermore, alternate approaches of mapping the synaptic weights into fully-trained and semi-trained crossbars are investigated.
In a semi-trained crossbar a confined subset of memristors are tuned and the remaining subset of memristors are not programmed.
This translates to optimal resource utilization and power consumption, compared to a fully programmed crossbar.
The semi-trained crossbar architecture is applicable to a broad class of neural networks.
System level verification is performed with an extreme learning machine for binomial and multinomial classification.
The total power for a single 4x4 layer network, when implemented in IBM 65nm node, is estimated to be ~ 42.16uW and the area is estimated to be 26.48um x 22.35um.
Attention distributions of the generated translations are a useful bi-product of attention-based recurrent neural network translation models and can be treated as soft alignments between the input and output tokens.
In this work, we use attention distributions as a confidence metric for output translations.
We present two strategies of using the attention distributions: filtering out bad translations from a large back-translated corpus, and selecting the best translation in a hybrid setup of two different translation systems.
While manual evaluation indicated only a weak correlation between our confidence score and human judgments, the use-cases showed improvements of up to 2.22 BLEU points for filtering and 0.99 points for hybrid translation, tested on English<->German and English<->Latvian translation.
The Grey Wolf Optimizer (GWO) is a swarm intelligence meta-heuristic algorithm inspired by the hunting behaviour and social hierarchy of grey wolves in nature.
This paper analyses the use of chaos theory in this algorithm to improve its ability to escape local optima by replacing the key parameters by chaotic variables.
The optimal choice of chaotic maps is then used to apply the Chaotic Grey Wolf Optimizer (CGWO) to the problem of factoring a large semi prime into its prime factors.
Assuming the number of digits of the factors to be equal, this is a computationally difficult task upon which the RSA-cryptosystem relies.
This work proposes the use of a new objective function to solve the problem and uses the CGWO to optimize it and compute the factors.
It is shown that this function performs better than its predecessor for large semi primes and CGWO is an efficient algorithm to optimize it.
The Intel Core i7 processor code named Nehalem provides a feature named Turbo Boost which opportunistically varies the frequencies of the processor's cores.
The frequency of a core is determined by core temperature, the number of active cores, the estimated power consumption, the estimated current consumption, and operating system frequency scaling requests.
For a chip multi-processor(CMP) that has a small number of physical cores and a small set of performance states, deciding the Turbo Boost frequency to use on a given core might not be difficult.
However, we do not know the complexity of this decision making process in the context of a large number of cores, scaling to the 100s, as predicted by researchers in the field.
Superpixels provide an efficient low/mid-level representation of image data, which greatly reduces the number of image primitives for subsequent vision tasks.
Existing superpixel algorithms are not differentiable, making them difficult to integrate into otherwise end-to-end trainable deep neural networks.
We develop a new differentiable model for superpixel sampling that leverages deep networks for learning superpixel segmentation.
The resulting "Superpixel Sampling Network" (SSN) is end-to-end trainable, which allows learning task-specific superpixels with flexible loss functions and has fast runtime.
Extensive experimental analysis indicates that SSNs not only outperform existing superpixel algorithms on traditional segmentation benchmarks, but can also learn superpixels for other tasks.
In addition, SSNs can be easily integrated into downstream deep networks resulting in performance improvements.
Despite being the appearance-based classifier of choice in recent years, relatively few works have examined how much convolutional neural networks (CNNs) can improve performance on accepted expression recognition benchmarks and, more importantly, examine what it is they actually learn.
In this work, not only do we show that CNNs can achieve strong performance, but we also introduce an approach to decipher which portions of the face influence the CNN's predictions.
First, we train a zero-bias CNN on facial expression data and achieve, to our knowledge, state-of-the-art performance on two expression recognition benchmarks: the extended Cohn-Kanade (CK+) dataset and the Toronto Face Dataset (TFD).
We then qualitatively analyze the network by visualizing the spatial patterns that maximally excite different neurons in the convolutional layers and show how they resemble Facial Action Units (FAUs).
Finally, we use the FAU labels provided in the CK+ dataset to verify that the FAUs observed in our filter visualizations indeed align with the subject's facial movements.
In this paper, probabilistic shaping is numerically and experimentally investigated for increasing the transmission reach of wavelength division multiplexed (WDM) optical communication system employing quadrature amplitude modulation (QAM).
An optimized probability mass function (PMF) of the QAM symbols is first found from a modified Blahut-Arimoto algorithm for the optical channel.
A turbo coded bit interleaved coded modulation system is then applied, which relies on many-to-one labeling to achieve the desired PMF, thereby achieving shaping gain.
Pilot symbols at rate at most 2% are used for synchronization and equalization, making it possible to receive input constellations as large as 1024QAM.
The system is evaluated experimentally on a 10 GBaud, 5 channels WDM setup.
The maximum system reach is increased w.r.t. standard 1024QAM by 20% at input data rate of 4.65 bits/symbol and up to 75% at 5.46 bits/symbol.
It is shown that rate adaptation does not require changing of the modulation format.
The performance of the proposed 1024QAM shaped system is validated on all 5 channels of the WDM signal for selected distances and rates.
Finally, it was shown via EXIT charts and BER analysis that iterative demapping, while generally beneficial to the system, is not a requirement for achieving the shaping gain.
This paper presents a bionic reflex control strategy for a kinematically constrained robotic finger.
Here, the bionic reflex is achieved through a force tracking impedance control strategy.
The dynamic model of the finger is reduced subject to kinematic constraints.
Thereafter, an impedance control strategy that allows exact tracking of forces is discussed.
Simulation results for a single finger holding a rectangular object against a flat surface are presented.
Bionic reflex response time is of the order of milliseconds.
The common feature of nearly all logic and memory devices is that they make use of stable units to represent 0's and 1's.
A completely different paradigm is based on three-terminal stochastic units which could be called "p-bits", where the output is a random telegraphic signal continuously fluctuating between 0 and 1 with a tunable mean. p-bits can be interconnected to receive weighted contributions from others in a network, and these weighted contributions can be chosen to not only solve problems of optimization and inference but also to implement precise Boolean functions in an inverted mode.
This inverted operation of Boolean gates is particularly striking: They provide inputs consistent to a given output along with unique outputs to a given set of inputs.
The existing demonstrations of accurate invertible logic are intriguing, but will these striking properties observed in computer simulations carry over to hardware implementations?
This paper uses individual micro controllers to emulate p-bits, and we present results for a 4-bit ripple carry adder with 48 p-bits and a 4-bit multiplier with 46 p-bits working in inverted mode as a factorizer.
Our results constitute a first step towards implementing p-bits with nano devices, like stochastic Magnetic Tunnel Junctions.
Integer factorization is one of the vital algorithms discussed as a part of analysis of any black-box cipher suites where the cipher algorithm is based on number theory.
The origin of the problem is from Discrete Logarithmic Problem which appears under the analysis of the crypto-graphic algorithms as seen by a crypt-analyst.
The integer factorization algorithm poses a potential in computational science too, obtaining the factors of a very large number is challenging with a limited computing infrastructure.
This paper analyses the Pollards Rho heuristic with a varying input size to evaluate the performance under a multi-core environment and also to estimate the threshold for each computing infrastructure.
The volume of convolutional neural network (CNN) models proposed for face recognition has been continuously growing larger to better fit large amount of training data.
When training data are obtained from internet, the labels are likely to be ambiguous and inaccurate.
This paper presents a Light CNN framework to learn a compact embedding on the large-scale face data with massive noisy labels.
First, we introduce a variation of maxout activation, called Max-Feature-Map (MFM), into each convolutional layer of CNN.
Different from maxout activation that uses many feature maps to linearly approximate an arbitrary convex activation function, MFM does so via a competitive relationship.
MFM can not only separate noisy and informative signals but also play the role of feature selection between two feature maps.
Second, three networks are carefully designed to obtain better performance meanwhile reducing the number of parameters and computational costs.
Lastly, a semantic bootstrapping method is proposed to make the prediction of the networks more consistent with noisy labels.
Experimental results show that the proposed framework can utilize large-scale noisy data to learn a Light model that is efficient in computational costs and storage spaces.
The learned single network with a 256-D representation achieves state-of-the-art results on various face benchmarks without fine-tuning.
The code is released on https://github.com/AlfredXiangWu/LightCNN.
Online social networks (OSNs) have become the main medium for connecting people, sharing knowledge and information, and for communication.
The social connections between people using these OSNs are formed as virtual links (e.g., friendship and following connections) that connect people.
These links are the heart of today's OSNs as they facilitate all of the activities that the members of a social network can do.
However, many of these networks suffer from noisy links, i.e., links that do not reflect a real relationship or links that have a low intensity, that change the structure of the network and prevent accurate analysis of these networks.
Hence, a process for assessing and ranking the links in a social network is crucial in order to sustain a healthy and real network.
Here, we define link assessment as the process of identifying noisy and non-noisy links in a network.
In this paper, we address the problem of link assessment and link ranking in social networks using external interaction networks.
In addition to a friendship social network, additional exogenous interaction networks are utilized to make the assessment process more meaningful.
We employed machine learning classifiers for assessing and ranking the links in the social network of interest using the data from exogenous interaction networks.
The method was tested with two different datasets, each containing the social network of interest, with the ground truth, along with the exogenous interaction networks.
The results show that it is possible to effectively assess the links of a social network using only the structure of a single network of the exogenous interaction networks, and also using the structure of the whole set of exogenous interaction networks.
The experiments showed that some classifiers do better than others regarding both link classification and link ranking.
We present and evaluate a compiler from Prolog (and extensions) to JavaScript which makes it possible to use (constraint) logic programming to develop the client side of web applications while being compliant with current industry standards.
Targeting JavaScript makes (C)LP programs executable in virtually every modern computing device with no additional software requirements from the point of view of the user.
In turn, the use of a very high-level language facilitates the development of high-quality, complex software.
The compiler is a back end of the Ciao system and supports most of its features, including its module system and its rich language extension mechanism based on packages.
We present an overview of the compilation process and a detailed description of the run-time system, including the support for modular compilation into separate JavaScript code.
We demonstrate the maturity of the compiler by testing it with complex code such as a CLP(FD) library written in Prolog with attributed variables.
Finally, we validate our proposal by measuring the performance of some LP and CLP(FD) benchmarks running on top of major JavaScript engines.
We propose a novel training algorithm for reinforcement learning which combines the strength of deep Q-learning with a constrained optimization approach to tighten optimality and encourage faster reward propagation.
Our novel technique makes deep reinforcement learning more practical by drastically reducing the training time.
We evaluate the performance of our approach on the 49 games of the challenging Arcade Learning Environment, and report significant improvements in both training time and accuracy.
We present a straightforward procedure to evaluate the scientific contribution of territories and institutions that combines the size-dependent geometric mean, Q, of the number of research documents (N) and citations (C), and a scale-free measure of quality, q=C/N.
We introduce a Global Research Output (GRO-index) as the geometric mean of Q and q.
We show that the GRO-index correlates with the h-index, but appears to be more strongly correlated with other well known, widely used bibliometric indicators.
We also compute relative GRO-indexes (GROr) associated with the scientific production within research fields.
We note that although total sums of GROr values are larger than the GRO-index, due to the non-linearity in the computation of the geometric means, both counts are nevertheless highly correlated.
That enables us to make useful comparative analyses among territories and institutions.
Furthermore, to identify strengths and weaknesses of a given country or institution, we compute a Relative Research Output count (RROr-index) to tackle variations of the C/N ratio across research fields.
Moreover, by using a wealth-index also based on quantitative and qualitative variables, we show that the GRO and RRO indexes are highly correlated with the wealth of the countries and the states of the USA.
Given the simplicity of the procedures introduced in this paper and the fact that their results are easily understandable by non-specialists, we believe they could become as useful for the assessment of the research output of countries and institutions as the impact factor is for journals or the h-index for individuals.
Hybrid beamforming (HB) has been widely studied for reducing the number of costly radio frequency (RF) chains in massive multiple-input multiple-output (MIMO) systems.
However, previous works on HB are limited to a single user equipment (UE) or a single group of UEs, employing the frequency-flat first-level analog beamforming (AB) that cannot be applied to multiple groups of UEs served in different frequency resources in an orthogonal frequency-division multiplexing (OFDM) system.
In this paper, a novel HB algorithm with unified AB based on the spatial covariance matrix (SCM) knowledge of all UEs is proposed for a massive MIMO-OFDM system in order to support multiple groups of UEs.
The proposed HB method with a much smaller number of RF chains can achieve more than 95% performance of full digital beamforming.
In addition, a novel practical subspace construction (SC) algorithm based on partial channel state information is proposed to estimate the required SCM.
The proposed SC method can offer more than 97% performance of the perfect SCM case.
With the proposed methods, significant cost and power savings can be achieved without large loss in performance.
Furthermore, the proposed methods can be applied to massive MIMO-OFDM systems in both time-division duplex and frequency-division duplex.
Recent developments in the field of Networking have provided opportunities for networks to efficiently cater application specific needs of a user.
In this context, a routing path is not only dependent upon the network states but also is calculated in the best interest of an application using the network.
These advanced routing algorithms can exploit application state data to enhance advanced network services such as anycast, edge cloud computing and cyber physical systems (CPS).
In this work, we aim to design such a routing algorithm where the router decisions are based upon convex optimization techniques.
This paper presents a learning-based approach for impromptu trajectory tracking for non-minimum phase systems, i.e., systems with unstable inverse dynamics.
Inversion-based feedforward approaches are commonly used for improving tracking performance; however, these approaches are not directly applicable to non-minimum phase systems due to their inherent instability.
In order to resolve the instability issue, existing methods have assumed that the system model is known and used pre-actuation or inverse approximation techniques.
In this work, we propose an approach for learning a stable, approximate inverse of a non-minimum phase baseline system directly from its input-output data.
Through theoretical discussions, simulations, and experiments on two different platforms, we show the stability of our proposed approach and its effectiveness for high-accuracy, impromptu tracking.
Our approach also shows that including more information in the training, as is commonly assumed to be useful, does not lead to better performance but may trigger instability and impact the effectiveness of the overall approach.
Centroid-based methods including k-means and fuzzy c-means are known as effective and easy-to-implement approaches to clustering purposes in many applications.
However, these algorithms cannot be directly applied to supervised tasks.
This paper thus presents a generative model extending the centroid-based clustering approach to be applicable to classification and regression tasks.
Given an arbitrary loss function, the proposed approach, termed Supervised Fuzzy Partitioning (SFP), incorporates labels information into its objective function through a surrogate term penalizing the empirical risk.
Entropy-based regularization is also employed to fuzzify the partition and to weight features, enabling the method to capture more complex patterns, identify significant features, and yield better performance facing high-dimensional data.
An iterative algorithm based on block coordinate descent scheme is formulated to efficiently find a local optimum.
Extensive classification experiments on synthetic, real-world, and high-dimensional datasets demonstrate that the predictive performance of SFP is competitive with state-of-the-art algorithms such as random forest and SVM.
The SFP has a major advantage over such methods, in that it not only leads to a flexible, nonlinear model but also can exploit any convex loss function in the training phase without compromising computational efficiency.
Recently, a technique called Layer-wise Relevance Propagation (LRP) was shown to deliver insightful explanations in the form of input space relevances for understanding feed-forward neural network classification decisions.
In the present work, we extend the usage of LRP to recurrent neural networks.
We propose a specific propagation rule applicable to multiplicative connections as they arise in recurrent network architectures such as LSTMs and GRUs.
We apply our technique to a word-based bi-directional LSTM model on a five-class sentiment prediction task, and evaluate the resulting LRP relevances both qualitatively and quantitatively, obtaining better results than a gradient-based related method which was used in previous work.
We present a dockerized version of a real-time strategy game StarCraft: Brood War, commonly used as a domain for AI research, with a pre-installed collection of AI developement tools supporting all the major types of StarCraft bots.
This provides a convenient way to deploy StarCraft AIs on numerous hosts at once and across multiple platforms despite limited OS support of StarCraft.
In this technical report, we describe the design of our Docker images and present a few use cases.
Compressive sensing is a method to recover the original image from undersampled measurements.
In order to overcome the ill-posedness of this inverse problem, image priors are used such as sparsity in the wavelet domain, minimum total-variation, or self-similarity.
Recently, deep learning based compressive image recovery methods have been proposed and have yielded state-of-the-art performances.
They used deep learning based data-driven approaches instead of hand-crafted image priors to solve the ill-posed inverse problem with undersampled data.
Ironically, training deep neural networks for them requires "clean" ground truth images, but obtaining the best quality images from undersampled data requires well-trained deep neural networks.
To resolve this dilemma, we propose novel methods based on two well-grounded theories: denoiser-approximate message passing and Stein's unbiased risk estimator.
Our proposed methods were able to train deep learning based image denoisers from undersampled measurements without ground truth images and without image priors, and to recover images with state-of-the-art qualities from undersampled data.
We evaluated our methods for various compressive sensing recovery problems with Gaussian random, coded diffraction pattern, and compressive sensing MRI measurement matrices.
Our methods yielded state-of-the-art performances for all cases without ground truth images and without image priors.
They also yielded comparable performances to the methods with ground truth data.
We prove that an auxiliary two-point boundary value problem presented in V. L. Kharitonov, Lyapunov matrices for a class of time delay systems, Systems & Control Letters 55 (2006) 610-617 has linearly dependent boundary conditions, and consequently a unique solution does not exist.
Therefore, the two-point boundary value problem presented therein fails to be a basis for constructing Lyapunov matrices for the class of time delay systems investigated.
Linear programming is now included in algorithm undergraduate and postgraduate courses for computer science majors.
We give a self-contained treatment of an interior-point method which is particularly tailored to the typical mathematical background of CS students.
In particular, only limited knowledge of linear algebra and calculus is assumed.
We present a versatile and fast MATLAB program (UmUTracker) that automatically detects and tracks particles by analyzing video sequences acquired by either light microscopy or digital in-line holographic microscopy.
Our program detects the 2D lateral positions of particles with an algorithm based on the isosceles triangle transform, and reconstructs their 3D axial positions by a fast implementation of the Rayleigh-Sommerfeld model using a radial intensity profile.
To validate the accuracy and performance of our program, we first track the 2D position of polystyrene particles using bright field and digital holographic microscopy.
Second, we determine the 3D particle position by analyzing synthetic and experimentally acquired holograms.
Finally, to highlight the full program features, we profile the microfluidic flow in a 100 micrometer high flow chamber.
This result agrees with computational fluid dynamic simulations.
On a regular desktop computer UmUTracker can detect, analyze, and track multiple particles at 5 frames per second for a template size of 201 x 201 in a 1024 x 1024 image.
To enhance usability and to make it easy to implement new functions we used object-oriented programming.
UmUTracker is suitable for studies related to: particle dynamics, cell localization, colloids and microfluidic flow measurement.
We present Web-STAR, an online platform for story understanding built on top of the STAR reasoning engine for STory comprehension through ARgumentation.
The platform includes a web-based IDE, integration with the STAR system, and a web service infrastructure to support integration with other systems that rely on story understanding functionality to complete their tasks.
The platform also delivers a number of "social" features, including a community repository for public story sharing with a built-in commenting system, and tools for collaborative story editing that can be used for team development projects and for educational purposes.
In this work, we consider the problem of estimating a behaviour policy for use in Off-Policy Policy Evaluation (OPE) when the true behaviour policy is unknown.
Via a series of empirical studies, we demonstrate how accurate OPE is strongly dependent on the calibration of estimated behaviour policy models: how precisely the behaviour policy is estimated from data.
We show how powerful parametric models such as neural networks can result in highly uncalibrated behaviour policy models on a real-world medical dataset, and illustrate how a simple, non-parametric, k-nearest neighbours model produces better calibrated behaviour policy estimates and can be used to obtain superior importance sampling-based OPE estimates.
We present a framework for online inference in the presence of a nonexhaustively defined set of classes that incorporates supervised classification with class discovery and modeling.
A Dirichlet process prior (DPP) model defined over class distributions ensures that both known and unknown class distributions originate according to a common base distribution.
In an attempt to automatically discover potentially interesting class formations, the prior model is coupled with a suitably chosen data model, and sequential Monte Carlo sampling is used to perform online inference.
Our research is driven by a biodetection application, where a new class of pathogen may suddenly appear, and the rapid increase in the number of samples originating from this class indicates the onset of an outbreak.
It is difficult to estimate the midsagittal plane of human subjects with craniomaxillofacial (CMF) deformities.
We have developed a LAndmark GEometric Routine (LAGER), which automatically estimates a midsagittal plane for such subjects.
The LAGER algorithm was based on the assumption that the optimal midsagittal plane of a patient with a deformity is the premorbid midsagittal plane of the patient (i.e.hypothetically normal without deformity).
The LAGER algorithm consists of three steps.
The first step quantifies the asymmetry of the landmarks using a Euclidean distance matrix analysis and ranks the landmarks according to their degree of asymmetry.
The second step uses a recursive algorithm to drop outlier landmarks.
The third step inputs the remaining landmarks into an optimization algorithm to determine an optimal midsaggital plane.
We validate LAGER on 20 synthetic models mimicking the skulls of real patients with CMF deformities.
The results indicated that all the LAGER algorithm-generated midsagittal planes met clinical criteria.
Thus it can be used clinically to determine the midsagittal plane for patients with CMF deformities.
Distantly-supervised Relation Extraction (RE) methods train an extractor by automatically aligning relation instances in a Knowledge Base (KB) with unstructured text.
In addition to relation instances, KBs often contain other relevant side information, such as aliases of relations (e.g., founded and co-founded are aliases for the relation founderOfCompany).
RE models usually ignore such readily available side information.
In this paper, we propose RESIDE, a distantly-supervised neural relation extraction method which utilizes additional side information from KBs for improved relation extraction.
It uses entity type and relation alias information for imposing soft constraints while predicting relations.
RESIDE employs Graph Convolution Networks (GCN) to encode syntactic information from text and improves performance even when limited side information is available.
Through extensive experiments on benchmark datasets, we demonstrate RESIDE's effectiveness.
We have made RESIDE's source code available to encourage reproducible research.
Optimal use of computing resources requires extensive coding, tuning and benchmarking.
To boost developer productivity in these time consuming tasks, we introduce the Experimental Linear Algebra Performance Studies framework (ELAPS), a multi-platform open source environment for fast yet powerful performance experimentation with dense linear algebra kernels, algorithms, and libraries.
ELAPS allows users to construct experiments to investigate how performance and efficiency vary depending on factors such as caching, algorithmic parameters, problem size, and parallelism.
Experiments are designed either through Python scripts or a specialized GUI, and run on the whole spectrum of architectures, ranging from laptops to clusters, accelerators, and supercomputers.
The resulting experiment reports provide various metrics and statistics that can be analyzed both numerically and visually.
We demonstrate the use of ELAPS in four concrete application scenarios and in as many computing environments, illustrating its practical value in supporting critical performance decisions.
Keeping students engaged with the course content outside the classroom is a challenging task.
Since learning during undergraduate years occurs not only as student engagement in class, but also during out of class activities, we need to redesign and reinvent such activities for this and future generation of students.
Although active learning has been used widely to improve in class student learning and engagement, its usage outside the classroom is not widespread and researched.
Active learning is often not utilized for out of class activities and traditional unsupervised activities are used mostly to keep students engaged in the content after they leave the classroom.
Although there has been tremendous research performed to improve student learning and engagement in the classroom, there are a few pieces of researches on improving out of class learning and student engagement.
This poster will present an approach to redesign the traditional out of class activities with the help of mobile apps, which are interactive and adaptive, and will provide personalization to satisfy student's needs outside the classroom so that optimal learning experience can be achieved.
The conventional high-speed Wi-Fi has recently become a contender for low-power Internet-of-Things (IoT) communications.
OFDM continues its adoption in the new IoT Wi-Fi standard due to its spectrum efficiency that can support the demand of massive IoT connectivity.
While the IoT Wi-Fi standard offers many new features to improve power and spectrum efficiency, the basic physical layer (PHY) structure of transceiver design still conforms to its conventional design rationale where access points (AP) and clients employ the same OFDM PHY.
In this paper, we argue that current Wi-Fi PHY design does not take full advantage of the inherent asymmetry between AP and IoT.
To fill the gap, we propose an asymmetric design where IoT devices transmit uplink packets using the lowest power while pushing all the decoding burdens to the AP side.
Such a design utilizes the sufficient power and computational resources at AP to trade for the transmission (TX) power of IoT devices.
The core technique enabling this asymmetric design is that the AP takes full power of its high clock rate to boost the decoding ability.
We provide an implementation of our design and show that it can reduce the IoT's TX power by boosting the decoding capability at the receivers.
The recent trend toward increasingly deep convolutional neural networks (CNNs) leads to a higher demand of computational power and memory storage.
Consequently, the deployment of CNNs in hardware has become more challenging.
In this paper, we propose an Intra-Kernel Regular (IKR) pruning scheme to reduce the size and computational complexity of the CNNs by removing redundant weights at a fine-grained level.
Unlike other pruning methods such as Fine-Grained pruning, IKR pruning maintains regular kernel structures that are exploitable in a hardware accelerator.
Experimental results demonstrate up to 10x parameter reduction and 7x computational reduction at a cost of less than 1% degradation in accuracy versus the un-pruned case.
We provide theoretical investigation of curriculum learning in the context of stochastic gradient descent when optimizing the convex linear regression loss.
We prove that the rate of convergence of an ideal curriculum learning method is monotonically increasing with the difficulty of the examples.
Moreover, among all equally difficult points, convergence is faster when using points which incur higher loss with respect to the current hypothesis.
We then analyze curriculum learning in the context of training a CNN.
We describe a method which infers the curriculum by way of transfer learning from another network, pre-trained on a different task.
While this approach can only approximate the ideal curriculum, we observe empirically similar behavior to the one predicted by the theory, namely, a significant boost in convergence speed at the beginning of training.
When the task is made more difficult, improvement in generalization performance is also observed.
Finally, curriculum learning exhibits robustness against unfavorable conditions such as excessive regularization.
A graph G=(V,E) is a pairwise compatibility graph (PCG) if there exists an edge-weighted tree T and two non-negative real numbers `d' and `D' such that each leaf `u' of T is a node of V and the edge `(u,v) belongs to E' iff `d <= d_T(u, v) <= D' where d_T(u, v) is the sum of weights of the edges on the unique path from `u' to `v' in T. The main issue on these graphs consists in characterizing them.
In this note we prove the inclusion in the PCG class of threshold tolerance graphs and the non-inclusion of a number of intersection graphs, such as disk and grid intersection graphs, circular arc and tolerance graphs.
The non-inclusion of some superclasses (trapezoid, permutation and rectangle intersection graphs) follows.
Lurking is a complex user-behavioral phenomenon that occurs in all large-scale online communities and social networks.
It generally refers to the behavior characterizing users that benefit from the information produced by others in the community without actively contributing back to the production of social content.
The amount and evolution of lurkers may strongly affect an online social environment, therefore understanding the lurking dynamics and identifying strategies to curb this trend are relevant problems.
In this regard, we introduce the Lurker Game, i.e., a model for analyzing the transitions from a lurking to a non-lurking (i.e., active) user role, and vice versa, in terms of evolutionary game theory.
We evaluate the proposed Lurker Game by arranging agents on complex networks and analyzing the system evolution, seeking relations between the network topology and the final equilibrium of the game.
Results suggest that the Lurker Game is suitable to model the lurking dynamics, showing how the adoption of rewarding mechanisms combined with the modeling of hypothetical heterogeneity of users' interests may lead users in an online community towards a cooperative behavior.
The paper discusses various applications of permutation group theory in the synthesis of reversible logic circuits consisting of Toffoli gates with negative control lines.
An asymptotically optimal synthesis algorithm for circuits consisting of gates from the NCT library is described.
An algorithm for gate complexity reduction, based on equivalent replacements of gates compositions, is introduced.
A new approach for combining a group-theory-based synthesis algorithm with a Reed-Muller-spectra-based synthesis algorithm is described.
Experimental results are presented to show that the proposed synthesis techniques allow a reduction in input lines count, gate complexity or quantum cost of reversible circuits for various benchmark functions.
The instance segmentation can be considered an extension of the object detection problem where bounding boxes are replaced by object contours.
Strictly speaking the problem requires to identify each pixel instance and class independently of the artifice used for this mean.
The advantage of instance segmentation over the usual object detection lies in the precise delineation of objects improving object localization.
Additionally, object contours allow the evaluation of partial occlusion with basic image processing algorithms.
This work approaches the instance segmentation problem as an annotation problem and presents a novel technique to encode and decode ground truth annotations.
We propose a mathematical representation of instances that any deep semantic segmentation model can learn and generalize.
Each individual instance is represented by a center of mass and a field of vectors pointing to it.
This encoding technique has been denominated Distance to Center of Mass Encoding (DCME).
In this article we consider the basic ideas, approaches and results of developing of mathematical knowledge management technologies based on ontologies.
These solutions form the basis of a specialized digital ecosystem OntoMath which consists of the ontology of the logical structure of mathematical documents Mocassin and ontology of mathematical knowledge OntoMathPRO, tools of text analysis, recommender system and other applications to manage mathematical knowledge.
The studies are in according to the ideas of creating a distributed system of interconnected repositories of digitized versions of mathematical documents and project to create a World Digital Mathematical Library.
Automated melodic phrase detection and segmentation is a classical task in content-based music information retrieval and also the key towards automated music structure analysis.
However, traditional methods still cannot satisfy practical requirements.
In this paper, we explore and adapt various neural network architectures to see if they can be generalized to work with the symbolic representation of music and produce satisfactory melodic phrase segmentation.
The main issue of applying deep-learning methods to phrase detection is the sparse labeling problem of training sets.
We proposed two tailored label engineering with corresponding training techniques for different neural networks in order to make decisions at a sequential level.
Experiment results show that the CNN-CRF architecture performs the best, being able to offer finer segmentation and faster to train, while CNN, Bi-LSTM-CNN and Bi-LSTM-CRF are acceptable alternatives.
We present in this paper a new family of implicit function for synthesizing a wide variety of 3D surfaces.
The basis of this family consists of the usual functions that are: the function rectangular pulses, the function saw-tooth pulses, the function of triangular pulses, the staircase function and the power function.
By combining these common functions, named constituent functions, in one implicit function and by varying some parameters of this function we can synthesize a wide variety of 3D surfaces with the possibility to set their deformations.
The lack of reliable data in developing countries is a major obstacle to sustainable development, food security, and disaster relief.
Poverty data, for example, is typically scarce, sparse in coverage, and labor-intensive to obtain.
Remote sensing data such as high-resolution satellite imagery, on the other hand, is becoming increasingly available and inexpensive.
Unfortunately, such data is highly unstructured and currently no techniques exist to automatically extract useful insights to inform policy decisions and help direct humanitarian efforts.
We propose a novel machine learning approach to extract large-scale socioeconomic indicators from high-resolution satellite imagery.
The main challenge is that training data is very scarce, making it difficult to apply modern techniques such as Convolutional Neural Networks (CNN).
We therefore propose a transfer learning approach where nighttime light intensities are used as a data-rich proxy.
We train a fully convolutional CNN model to predict nighttime lights from daytime imagery, simultaneously learning features that are useful for poverty prediction.
The model learns filters identifying different terrains and man-made structures, including roads, buildings, and farmlands, without any supervision beyond nighttime lights.
We demonstrate that these learned features are highly informative for poverty mapping, even approaching the predictive performance of survey data collected in the field.
Many recent works that study the performance of multi-input multi-output (MIMO) systems in practice assume a Kronecker model where the variances of the channel entries, upon decomposition on to the transmit and the receive eigen-bases, admit a separable form.
Measurement campaigns, however, show that the Kronecker model results in poor estimates for capacity.
Motivated by these observations, a channel model that does not impose a separable structure has been recently proposed and shown to fit the capacity of measured channels better.
In this work, we show that this recently proposed modeling framework can be viewed as a natural consequence of channel decomposition on to its canonical coordinates, the transmit and/or the receive eigen-bases.
Using tools from random matrix theory, we then establish the theoretical basis behind the Kronecker mismatch at the low- and the high-SNR extremes: 1) Sparsity of the dominant statistical degrees of freedom (DoF) in the true channel at the low-SNR extreme, and 2) Non-regularity of the sparsity structure (disparities in the distribution of the DoF across the rows and the columns) at the high-SNR extreme.
The Model / View / Controller design pattern divides an application environment into three components to handle the user-interactions, computations and output respectively.
This separation greatly favors architectural reusability.
The pattern works well in the case of single-address space and not proven to be efficient for web applications involving multiple address spaces.
Web applications force the designers to decide which of the components of the pattern are to be partitioned between the server and client(s) before the design phase commences.
For any rapidly growing web application, it is very difficult to incorporate future changes in policies related to partitioning.
One solution to this problem is to duplicate the Model and controller components at both server and client(s).
However, this may add further problems like delayed data fetch, security and scalability issues.
In order to overcome this, a new architecture SPIM has been proposed that deals with the partitioning problem in an alternative way.
SPIM shows tremendous improvements in performance when compared with a similar architecture.
With the considerable development of customer-to-customer (C2C) e-commerce in the recent years, there is a big demand for an effective recommendation system that suggests suitable websites for users to sell their items with some specified needs.
Nonetheless, e-commerce recommendation systems are mostly designed for business-to-customer (B2C) websites, where the systems offer the consumers the products that they might like to buy.
Almost none of the related research works focus on choosing selling sites for target items.
In this paper, we introduce an approach that recommends the selling websites based upon the item's description, category, and desired selling price.
This approach employs NoSQL data-based machine learning techniques for building and training topic models and classification models.
The trained models can then be used to rank the websites dynamically with respect to the user needs.
The experimental results with real-world datasets from Vietnam C2C websites will demonstrate the effectiveness of our proposed method.
Snapshot compressive imaging (SCI) refers to compressive imaging systems where multiple frames are mapped into a single measurement, with video compressive imaging and hyperspectral compressive imaging as two representative applications.
Though exciting results of high-speed videos and hyperspectral images have been demonstrated, the poor reconstruction quality precludes SCI from wide applications.This paper aims to boost the reconstruction quality of SCI via exploiting the high-dimensional structure in the desired signal.
We build a joint model to integrate the nonlocal self-similarity of video/hyperspectral frames and the rank minimization approach with the SCI sensing process.
Following this, an alternating minimization algorithm is developed to solve this non-convex problem.
We further investigate the special structure of the sampling process in SCI to tackle the computational workload and memory issues in SCI reconstruction.
Both simulation and real data (captured by four different SCI cameras) results demonstrate that our proposed algorithm leads to significant improvements compared with current state-of-the-art algorithms.
We hope our results will encourage the researchers and engineers to pursue further in compressive imaging for real applications.
How do you learn to navigate an Unmanned Aerial Vehicle (UAV) and avoid obstacles?
One approach is to use a small dataset collected by human experts: however, high capacity learning algorithms tend to overfit when trained with little data.
An alternative is to use simulation.
But the gap between simulation and real world remains large especially for perception problems.
The reason most research avoids using large-scale real data is the fear of crashes!
In this paper, we propose to bite the bullet and collect a dataset of crashes itself!
We build a drone whose sole purpose is to crash into objects: it samples naive trajectories and crashes into random objects.
We crash our drone 11,500 times to create one of the biggest UAV crash dataset.
This dataset captures the different ways in which a UAV can crash.
We use all this negative flying data in conjunction with positive data sampled from the same trajectories to learn a simple yet powerful policy for UAV navigation.
We show that this simple self-supervised model is quite effective in navigating the UAV even in extremely cluttered environments with dynamic obstacles including humans.
For supplementary video see: https://youtu.be/u151hJaGKUo
Learning language of protein sequences, which captures non-local interactions between amino acids close in the spatial structure, is a long-standing bioinformatics challenge, which requires at least context-free grammars.
However, complex character of protein interactions impedes unsupervised learning of context-free grammars.
Using structural information to constrain the syntactic trees proved effective in learning probabilistic natural and RNA languages.
In this work, we establish a framework for learning probabilistic context-free grammars for protein sequences from syntactic trees partially constrained using amino acid contacts obtained from wet experiments or computational predictions, whose reliability has substantially increased recently.
Within the framework, we implement the maximum-likelihood and contrastive estimators of parameters for simple yet practical grammars.
Tested on samples of protein motifs, grammars developed within the framework showed improved precision in recognition and higher fidelity to protein structures.
The framework is applicable to other biomolecular languages and beyond wherever knowledge of non-local dependencies is available.
In this work we propose, implement, and evaluate novel models called Third-Order Hidden Markov Models (HMM3s) to enhance low performance of text-independent speaker identification in shouted talking environments.
The proposed models have been tested on our collected speech database using Mel-Frequency Cepstral Coefficients (MFCCs).
Our results demonstrate that HMM3s significantly improve speaker identification performance in such talking environments by 11.3% and 166.7% compared to second-order hidden Markov models (HMM2s) and first-order hidden Markov models (HMM1s), respectively.
The achieved results based on the proposed models are close to those obtained in subjective assessment by human listeners.
In mobile crowdsensing, finding the best match between tasks and users is crucial to ensure both the quality and effectiveness of a crowdsensing system.
Existing works usually assume a centralized task assignment by the crowdsensing platform, without addressing the need of fine-grained personalized task matching.
In this paper, we argue that it is essential to match tasks to users based on a careful characterization of both the users' preference and reliability.
To that end, we propose a personalized task recommender system for mobile crowdsensing, which recommends tasks to users based on a recommendation score that jointly takes each user's preference and reliability into consideration.
We first present a hybrid preference metric to characterize users' preference by exploiting their implicit feedback.
Then, to profile users' reliability levels, we formalize the problem as a semi-supervised learning model, and propose an efficient block coordinate descent algorithm to solve the problem.
For some tasks that lack users' historical information, we further propose a matrix factorization method to infer the users' reliability levels on those tasks.
We conduct extensive experiments to evaluate the performance of our system, and the evaluation results demonstrate that our system can achieve superior performance to the benchmarks in both user profiling and personalized task recommendation.
This study proposes a logic architecture for the high-speed and power efficiently training of a gradient boosting decision tree model of binary classification.
We implemented the proposed logic architecture on an FPGA and compared training time and power efficiency with three general GBDT software libraries using CPU and GPU.
The training speed of the logic architecture on the FPGA was 26-259 times faster than the software libraries.
The power efficiency of the logic architecture was 90-1,104 times higher than the software libraries.
The results show that the logic architecture suits for high-performance and edge computing.
This paper proposes a general framework for structure-preserving model reduction of a secondorder network system based on graph clustering.
In this approach, vertex dynamics are captured by the transfer functions from inputs to individual states, and the dissimilarities of vertices are quantified by the H2-norms of the transfer function discrepancies.
A greedy hierarchical clustering algorithm is proposed to place those vertices with similar dynamics into same clusters.
Then, the reduced-order model is generated by the Petrov-Galerkin method, where the projection is formed by the characteristic matrix of the resulting network clustering.
It is shown that the simplified system preserves an interconnection structure, i.e., it can be again interpreted as a second-order system evolving over a reduced graph.
Furthermore, this paper generalizes the definition of network controllability Gramian to second-order network systems.
Based on it, we develop an efficient method to compute H2-norms and derive the approximation error between the full-order and reduced-order models.
Finally, the approach is illustrated by the example of a small-world network.
Solving tasks in Reinforcement Learning is no easy feat.
As the goal of the agent is to maximize the accumulated reward, it often learns to exploit loopholes and misspecifications in the reward signal resulting in unwanted behavior.
While constraints may solve this issue, there is no closed form solution for general constraints.
In this work we present a novel multi-timescale approach for constrained policy optimization, called `Reward Constrained Policy Optimization' (RCPO), which uses an alternative penalty signal to guide the policy towards a constraint satisfying one.
We prove the convergence of our approach and provide empirical evidence of its ability to train constraint satisfying policies.
It has been well demonstrated that adversarial examples, i.e., natural images with visually imperceptible perturbations added, generally exist for deep networks to fail on image classification.
In this paper, we extend adversarial examples to semantic segmentation and object detection which are much more difficult.
Our observation is that both segmentation and detection are based on classifying multiple targets on an image (e.g., the basic target is a pixel or a receptive field in segmentation, and an object proposal in detection), which inspires us to optimize a loss function over a set of pixels/proposals for generating adversarial perturbations.
Based on this idea, we propose a novel algorithm named Dense Adversary Generation (DAG), which generates a large family of adversarial examples, and applies to a wide range of state-of-the-art deep networks for segmentation and detection.
We also find that the adversarial perturbations can be transferred across networks with different training data, based on different architectures, and even for different recognition tasks.
In particular, the transferability across networks with the same architecture is more significant than in other cases.
Besides, summing up heterogeneous perturbations often leads to better transfer performance, which provides an effective method of black-box adversarial attack.
Gait is an important biometric trait for surveillance and forensic applications, which can be used to identify individuals at a large distance through CCTV cameras.
However, it is very difficult to develop robust automated gait recognition systems, since gait may be affected by many covariate factors such as clothing, walking surface, walking speed, camera view angle, etc.
Out of them, large view angle was deemed as the most challenging factor since it may alter the overall gait appearance substantially.
Recently, some deep learning approaches (such as CNNs) have been employed to extract view-invariant features, and achieved encouraging results on small datasets.
However, they do not scale well to large dataset, and the performance decreases significantly w.r.t. number of subjects, which is impractical to large-scale surveillance applications.
To address this issue, in this work we propose a Discriminant Gait Generative Adversarial Network (DiGGAN) framework, which not only can learn view-invariant gait features for cross-view gait recognition tasks, but also can be used to reconstruct the gait templates in all views --- serving as important evidences for forensic applications.
We evaluated our DiGGAN framework on the world's largest multi-view OU-MVLP dataset (which includes more than 10000 subjects), and our method outperforms state-of-the-art algorithms significantly on various cross-view gait identification scenarios (e.g., cooperative/uncooperative mode).
Our DiGGAN framework also has the best results on the popular CASIA-B dataset, and it shows great generalisation capability across different datasets.
Neural networks are capable of learning rich, nonlinear feature representations shown to be beneficial in many predictive tasks.
In this work, we use these models to explore the use of geographical features in predicting colorectal cancer survival curves for patients in the state of Iowa, spanning the years 1989 to 2012.
Specifically, we compare model performance using a newly defined metric -- area between the curves (ABC) -- to assess (a) whether survival curves can be reasonably predicted for colorectal cancer patients in the state of Iowa, (b) whether geographical features improve predictive performance, and (c) whether a simple binary representation or richer, spectral clustering-based representation perform better.
Our findings suggest that survival curves can be reasonably estimated on average, with predictive performance deviating at the five-year survival mark.
We also find that geographical features improve predictive performance, and that the best performance is obtained using richer, spectral analysis-elicited features.
With the advent of the 5th generation of wireless standards and an increasing demand for higher throughput, methods to improve the spectral efficiency of wireless systems have become very important.
In the context of cognitive radio, a substantial increase in throughput is possible if the secondary user can make smart decisions regarding which channel to sense and when or how often to sense.
Here, we propose an algorithm to not only select a channel for data transmission but also to predict how long the channel will remain unoccupied so that the time spent on channel sensing can be minimized.
Our algorithm learns in two stages - a reinforcement learning approach for channel selection and a Bayesian approach to determine the optimal duration for which sensing can be skipped.
Comparisons with other learning methods are provided through extensive simulations.
We show that the number of sensing is minimized with negligible increase in primary interference; this implies that lesser energy is spent by the secondary user in sensing and also higher throughput is achieved by saving on sensing.
Due to its simplicity and versatility, k-means remains popular since it was proposed three decades ago.
The performance of k-means has been enhanced from different perspectives over the years.
Unfortunately, a good trade-off between quality and efficiency is hardly reached.
In this paper, a novel k-means variant is presented.
Different from most of k-means variants, the clustering procedure is driven by an explicit objective function, which is feasible for the whole l2-space.
The classic egg-chicken loop in k-means has been simplified to a pure stochastic optimization procedure.
The procedure of k-means becomes simpler and converges to a considerably better local optima.
The effectiveness of this new variant has been studied extensively in different contexts, such as document clustering, nearest neighbor search and image clustering.
Superior performance is observed across different scenarios.
Electric Vehicle (EV) is playing a significant role in the distribution energy management systems since the power consumption level of the EVs is much higher than the other regular home appliances.
The randomness of the EV driver behaviors make the optimal charging or discharging scheduling even more difficult due to the uncertain charging session parameters.
To minimize the impact of behavioral uncertainties, it is critical to develop effective methods to predict EV load for smart EV energy management.
Using the EV smart charging infrastructures on UCLA campus and city of Santa Monica as testbeds, we have collected real-world datasets of EV charging behaviors, based on which we proposed an EV user modeling technique which combines statistical analysis and machine learning approaches.
Specifically, unsupervised clustering algorithm, and multilayer perceptron are applied to historical charging record to make the day-ahead EV parking and load prediction.
Experimental results with cross-validation show that our model can achieve good performance for charging control scheduling and online EV load forecasting.
The same-origin policy is a fundamental part of the Web.
Despite the restrictions imposed by the policy, embedding of third-party JavaScript code is allowed and commonly used.
Nothing is guaranteed about the integrity of such code.
To tackle this deficiency, solutions such as the subresource integrity standard have been recently introduced.
Given this background, this paper presents the first empirical study on the temporal integrity of cross-origin JavaScript code.
According to the empirical results based on a ten day polling period of over 35 thousand scripts collected from popular websites, (i) temporal integrity changes are relatively common; (ii) the adoption of the subresource integrity standard is still in its infancy; and (iii) it is possible to statistically predict whether a temporal integrity change is likely to occur.
With these results and the accompanying discussion, the paper contributes to the ongoing attempts to better understand security and privacy in the current Web.
We present a general-purpose tagger based on convolutional neural networks (CNN), used for both composing word vectors and encoding context information.
The CNN tagger is robust across different tagging tasks: without task-specific tuning of hyper-parameters, it achieves state-of-the-art results in part-of-speech tagging, morphological tagging and supertagging.
The CNN tagger is also robust against the out-of-vocabulary problem, it performs well on artificially unnormalized texts.
Recommendation system is a type of information filtering systems that recommend various objects from a vast variety and quantity of items which are of the user interest.
This results in guiding an individual in personalized way to interesting or useful objects in a large space of possible options.
Such systems also help many businesses to achieve more profits to sustain in their filed against their rivals.
But looking at the amount of information which a business holds it becomes difficult to identify the items of user interest.
Therefore personalization or user profiling is one of the challenging tasks that give access to user relevant information which can be used in solving the difficult task of classification and ranking items according to an individuals interest.
Profiling can be done in various ways such assupervised or unsupervised, individual or group profiling, distributive or and non distributive profiling.
Our focus in this paper will be on the dataset which we will use, we identify some interesting facts by using Weka Tool that can be used for recommending the items from dataset.
Our aim is to present a novel technique to achieve user profiling in recommendation system.
Impedance control is a well-established technique to control interaction forces in robotics.
However, real implementations of impedance control with an inner loop may suffer from several limitations.
Although common practice in designing nested control systems is to maximize the bandwidth of the inner loop to improve tracking performance, it may not be the most suitable approach when a certain range of impedance parameters has to be rendered.
In particular, it turns out that the viable range of stable stiffness and damping values can be strongly affected by the bandwidth of the inner control loops (e.g. a torque loop) as well as by the filtering and sampling frequency.
This paper provides an extensive analysis on how these aspects influence the stability region of impedance parameters as well as the passivity of the system.
This will be supported by both simulations and experimental data.
Moreover, a methodology for designing joint impedance controllers based on an inner torque loop and a positive velocity feedback loop will be presented.
The goal of the velocity feedback is to increase (given the constraints to preserve stability) the bandwidth of the torque loop without the need of a complex controller.
This paper proposes an Agile Aggregating Multi-Level feaTure framework (Agile Amulet) for salient object detection.
The Agile Amulet builds on previous works to predict saliency maps using multi-level convolutional features.
Compared to previous works, Agile Amulet employs some key innovations to improve training and testing speed while also increase prediction accuracy.
More specifically, we first introduce a contextual attention module that can rapidly highlight most salient objects or regions with contextual pyramids.
Thus, it effectively guides the learning of low-layer convolutional features and tells the backbone network where to look.
The contextual attention module is a fully convolutional mechanism that simultaneously learns complementary features and predicts saliency scores at each pixel.
In addition, we propose a novel method to aggregate multi-level deep convolutional features.
As a result, we are able to use the integrated side-output features of pre-trained convolutional networks alone, which significantly reduces the model parameters leading to a model size of 67 MB, about half of Amulet.
Compared to other deep learning based saliency methods, Agile Amulet is of much lighter-weight, runs faster (30 fps in real-time) and achieves higher performance on seven public benchmarks in terms of both quantitative and qualitative evaluation.
The technology related to networking moves wired connection to wireless connection.The basic problem concern in the wireless domain, random packet loss for the end to end connection.
In this paper we show the performance and the impact of the packet loss and delay, by the bit error rate throughput etc with respect to the real world scenario vehicular ad hoc network in 3-dimension space (VANET in 3D).
Over the years software development has responded to the increasing growth of wireless connectivity in developing network enabled software.
In this paper we consider the real world physical problem in three dimensional wireless domain and map the problem to analytical problem .
In this paper we simulate that analytic problem with respect to real world scenario by using enhanced antenna position system (EAPS) mounted over the mobile node in 3D space.
In this paper we convert the real world problem into lab oriented problem by using the EAPS -system and shown the performance in wireless domain in 3 dimensional space.
Excluding irrelevant features in a pattern recognition task plays an important role in maintaining a simpler machine learning model and optimizing the computational efficiency.
Nowadays with the rise of large scale datasets, feature selection is in great demand as it becomes a central issue when facing high-dimensional datasets.
The present study provides a new measure of saliency for features by employing a Sensitivity Analysis (SA) technique called the extended Fourier amplitude sensitivity test, and a well-trained Feedforward Neural Network (FNN) model, which ultimately leads to the selection of a promising optimal feature subset.
Ideas of the paper are mainly demonstrated based on adopting FNN model for feature selection in classification problems.
But in the end, a generalization framework is discussed in order to give insights into the usage in regression problems as well as expressing how other function approximate models can be deployed.
Effectiveness of the proposed method is verified by result analysis and data visualization for a series of experiments over several well-known datasets drawn from UCI machine learning repository.
The hyperlink prediction task, that of proposing new links between webpages, can be used to improve search engines, expand the visibility of web pages, and increase the connectivity and navigability of the web.
Hyperlink prediction is typically performed on webgraphs composed by thousands or millions of vertices, where on average each webpage contains less than fifty links.
Algorithms processing graphs so large and sparse require to be both scalable and precise, a challenging combination.
Similarity-based algorithms are among the most scalable solutions within the link prediction field, due to their parallel nature and computational simplicity.
These algorithms independently explore the nearby topological features of every missing link from the graph in order to determine its likelihood.
Unfortunately, the precision of similarity-based algorithms is limited, which has prevented their broad application so far.
In this work we explore the performance of similarity-based algorithms for the particular problem of hyperlink prediction on large webgraphs, and propose a novel method which assumes the existence of hierarchical properties.
We evaluate this new approach on several webgraphs and compare its performance with that of the current best similarity-based algorithms.
Its remarkable performance leads us to argue on the applicability of the proposal, identifying several use cases of hyperlink prediction.
We also describes the approach we took for the computation of large-scale graphs from the perspective of high-performance computing, providing details on the implementation and parallelization of code.
Easy access and vast amount of data, especially from long period of time, allows to divide social network into timeframes and create temporal social network.
Such network enables to analyse its dynamics.
One aspect of the dynamics is analysis of social communities evolution, i.e., how particular group changes over time.
To do so, the complete group evolution history is needed.
That is why in this paper the new method for group evolution extraction called GED is presented.
Current remote sensing image classification problems have to deal with an unprecedented amount of heterogeneous and complex data sources.
Upcoming missions will soon provide large data streams that will make land cover/use classification difficult.
Machine learning classifiers can help at this, and many methods are currently available.
A popular kernel classifier is the Gaussian process classifier (GPC), since it approaches the classification problem with a solid probabilistic treatment, thus yielding confidence intervals for the predictions as well as very competitive results to state-of-the-art neural networks and support vector machines.
However, its computational cost is prohibitive for large scale applications, and constitutes the main obstacle precluding wide adoption.
This paper tackles this problem by introducing two novel efficient methodologies for Gaussian Process (GP) classification.
We first include the standard random Fourier features approximation into GPC, which largely decreases its computational cost and permits large scale remote sensing image classification.
In addition, we propose a model which avoids randomly sampling a number of Fourier frequencies, and alternatively learns the optimal ones within a variational Bayes approach.
The performance of the proposed methods is illustrated in complex problems of cloud detection from multispectral imagery and infrared sounding data.
Excellent empirical results support the proposal in both computational cost and accuracy.
We combine conditional variational autoencoders (VAE) with adversarial censoring in order to learn invariant representations that are disentangled from nuisance/sensitive variations.
In this method, an adversarial network attempts to recover the nuisance variable from the representation, which the VAE is trained to prevent.
Conditioning the decoder on the nuisance variable enables clean separation of the representation, since they are recombined for model learning and data reconstruction.
We show this natural approach is theoretically well-founded with information-theoretic arguments.
Experiments demonstrate that this method achieves invariance while preserving model learning performance, and results in visually improved performance for style transfer and generative sampling tasks.
Many people take photos and videos with smartphones and more recently with 360-degree cameras at popular places and events, and share them in social media.
Such visual content is produced in large volumes in urban areas, and it is a source of information that online users could exploit to learn what has got the interest of the general public on the streets of the cities where they live or plan to visit.
A key step to providing users with that information is to identify the most popular k spots in specified areas.
In this paper, we propose a clustering and incremental sampling (C&IS) approach that trades off accuracy of top-k results for detection speed.
It uses clustering to determine areas with high density of visual content, and incremental sampling, controlled by stopping criteria, to limit the amount of computational work.
It leverages spatial metadata, which represent the scenes in the visual content, to rapidly detect the hotspots, and uses a recently proposed Gaussian probability model to describe the capture intention distribution in the query area.
We evaluate the approach with metadata, derived from a non-synthetic, user-generated dataset, for regular mobile and 360-degree visual content.
Our results show that the C&IS approach offers 2.8x-19x reductions in processing time over an optimized baseline, while in most cases correctly identifying 4 out of 5 top locations.
Cloud gaming enables playing high end games, originally designed for PC or game console setups, on low end devices, such as net-books and smartphones, by offloading graphics rendering to GPU powered cloud servers.
However, transmitting the high end graphics requires a large amount of available network bandwidth, even though it is a compressed video stream.
Foveated video encoding (FVE) reduces the bandwidth requirement by taking advantage of the non-uniform acuity of human visual system and by knowing where the user is looking.
We have designed and implemented a system for cloud gaming with foveated graphics using a consumer grade real-time eye tracker and an open source cloud gaming platform.
In this article, we describe the system and its evaluation through measurements with representative games from different genres to understand the effect of parameterization of the FVE scheme on bandwidth requirements and to understand its feasibility from the latency perspective.
We also present results from a user study.
The results suggest that it is possible to find a "sweet spot" for the encoding parameters so that the users hardly notice the presence of foveated encoding but at the same time the scheme yields most of the bandwidth savings achievable.
In this paper we introduce Smooth Particle Networks (SPNets), a framework for integrating fluid dynamics with deep networks.
SPNets adds two new layers to the neural network toolbox: ConvSP and ConvSDF, which enable computing physical interactions with unordered particle sets.
We use these lay- ers in combination with standard neural network layers to directly implement fluid dynamics inside a deep network, where the parameters of the network are the fluid parameters themselves (e.g., viscosity, cohesion, etc.).
Because SPNets are imple- mented as a neural network, the resulting fluid dynamics are fully differentiable.
We then show how this can be successfully used to learn fluid parameters from data, perform liquid control tasks, and learn policies to manipulate liquids.
This paper develops a novel methodology for using symbolic knowledge in deep learning.
From first principles, we derive a semantic loss function that bridges between neural output vectors and logical constraints.
This loss function captures how close the neural network is to satisfying the constraints on its output.
An experimental evaluation shows that it effectively guides the learner to achieve (near-)state-of-the-art results on semi-supervised multi-class classification.
Moreover, it significantly increases the ability of the neural network to predict structured objects, such as rankings and paths.
These discrete concepts are tremendously difficult to learn, and benefit from a tight integration of deep learning and symbolic reasoning methods.
In this paper we present the ADAPT system built for the Basque to English Low Resource MT Evaluation Campaign.
Basque is a low-resourced, morphologically-rich language.
This poses a challenge for Neural Machine Translation models which usually achieve better performance when trained with large sets of data.
Accordingly, we used synthetic data to improve the translation quality produced by a model built using only authentic data.
Our proposal uses back-translated data to: (a) create new sentences, so the system can be trained with more data; and (b) translate sentences that are close to the test set, so the model can be fine-tuned to the document to be translated.
The universal scalability law (USL) is an analytic model used to quantify application scaling.
It is universal because it subsumes Amdahl's law and Gustafson linearized scaling as special cases.
Using simulation, we show: (i) that the USL is equivalent to synchronous queueing in a load-dependent machine repairman model and (ii) how USL, Amdahl's law, and Gustafson scaling can be regarded as boundaries defining three scalability zones.
Typical throughput measurements lie across all three zones.
Simulation scenarios provide deeper insight into queueing effects and thus provide a clearer indication of which application features should be tuned to get into the optimal performance zone.
A social network grows over a period of time with the formation of new connections and relations.
In recent years we have witnessed a massive growth of online social networks like Facebook, Twitter etc.
So it has become a problem of extreme importance to know the destiny of these networks.
Thus predicting the evolution of a social network is a question of extreme importance.
A good model for evolution of a social network can help in understanding the properties responsible for the changes occurring in a network structure.
In this paper we propose such a model for evolution of social networks.
We model the social network as an undirected graph where nodes represent people and edges represent the friendship between them.
We define the evolution process as a set of rules which resembles very closely to how a social network grows in real life.
We simulate the evolution process and show, how starting from an initial network, a network evolves using this model.
We also discuss how our model can be used to model various complex social networks other than online social networks like political networks, various organizations etc..
We consider distributed optimization over orthogonal collision channels in spatial random access networks.
Users are spatially distributed and each user is in the interference range of a few other users.
Each user is allowed to transmit over a subset of the shared channels with a certain attempt probability.
We study both the non-cooperative and cooperative settings.
In the former, the goal of each user is to maximize its own rate irrespective of the utilities of other users.
In the latter, the goal is to achieve proportionally fair rates among users.
Simple distributed learning algorithms are developed to solve these problems.
The efficiencies of the proposed algorithms are demonstrated via both theoretical analysis and simulation results.
In the era of big data and cloud computing, large amounts of data are generated from user applications and need to be processed in the datacenter.
Data-parallel computing frameworks, such as Apache Spark, are widely used to perform such data processing at scale.
Specifically, Spark leverages distributed memory to cache the intermediate results, represented as Resilient Distributed Datasets (RDDs).
This gives Spark an advantage over other parallel frameworks for implementations of iterative machine learning and data mining algorithms, by avoiding repeated computation or hard disk accesses to retrieve RDDs.
By default, caching decisions are left at the programmer's discretion, and the LRU policy is used for evicting RDDs when the cache is full.
However, when the objective is to minimize total work, LRU is woefully inadequate, leading to arbitrarily suboptimal caching decisions.
In this paper, we design an algorithm for multi-stage big data processing platforms to adaptively determine and cache the most valuable intermediate datasets that can be reused in the future.
Our solution automates the decision of which RDDs to cache: this amounts to identifying nodes in a direct acyclic graph (DAG) representing computations whose outputs should persist in the memory.
Our experiment results show that our proposed cache optimization solution can improve the performance of machine learning applications on Spark decreasing the total work to recompute RDDs by 12%.
Automatic sleep staging has been often treated as a simple classification problem that aims at determining the label of individual target polysomnography (PSG) epochs one at a time.
In this work, we tackle the task as a sequence-to-sequence classification problem that receives a sequence of multiple epochs as input and classifies all of their labels at once.
For this purpose, we propose a hierarchical recurrent neural network named SeqSleepNet.
At the epoch processing level, the network consists of a filterbank layer tailored to learn frequency-domain filters for preprocessing and an attention-based recurrent layer designed for short-term sequential modelling.
At the sequence processing level, a recurrent layer placed on top of the learned epoch-wise features for long-term modelling of sequential epochs.
The classification is then carried out on the output vectors at every time step of the top recurrent layer to produce the sequence of output labels.
Despite being hierarchical, we present a strategy to train the network in an end-to-end fashion.
We show that the proposed network outperforms state-of-the-art approaches, achieving an overall accuracy, macro F1-score, and Cohen's kappa of 87.1%, 83.3%, and 0.815 on a publicly available dataset with 200 subjects.
There is a resurging interest in developing a neural-network-based solution to the supervised machine learning problem.
The convolutional neural network (CNN) will be studied in this note.
To begin with, we introduce a RECOS transform as a basic building block of CNNs.
The "RECOS" is an acronym for "REctified-COrrelations on a Sphere".
It consists of two main concepts: 1) data clustering on a sphere and 2) rectification.
Afterwards, we interpret a CNN as a network that implements the guided multi-layer RECOS transform with three highlights.
First, we compare the traditional single-layer and modern multi-layer signal analysis approaches, point out key ingredients that enable the multi-layer approach, and provide a full explanation to the operating principle of CNNs.
Second, we discuss how guidance is provided by labels through backpropagation (BP) in the training.
Third, we show that a trained network can be greatly simplified in the testing stage demanding only one-bit representation for both filter weights and inputs.
The beer game is a widely used in-class game that is played in supply chain management classes to demonstrate the bullwhip effect.
The game is a decentralized, multi-agent, cooperative problem that can be modeled as a serial supply chain network in which agents cooperatively attempt to minimize the total cost of the network even though each agent can only observe its own local information.
Each agent chooses order quantities to replenish its stock.
Under some conditions, a base-stock replenishment policy is known to be optimal.
However, in a decentralized supply chain in which some agents (stages) may act irrationally (as they do in the beer game), there is no known optimal policy for an agent wishing to act optimally.
We propose a machine learning algorithm, based on deep Q-networks, to optimize the replenishment decisions at a given stage.
When playing alongside agents who follow a base-stock policy, our algorithm obtains near-optimal order quantities.
It performs much better than a base-stock policy when the other agents use a more realistic model of human ordering behavior.
Unlike most other algorithms in the literature, our algorithm does not have any limits on the beer game parameter values.
Like any deep learning algorithm, training the algorithm can be computationally intensive, but this can be performed ahead of time; the algorithm executes in real time when the game is played.
Moreover, we propose a transfer learning approach so that the training performed for one agent and one set of cost coefficients can be adapted quickly for other agents and costs.
Our algorithm can be extended to other decentralized multi-agent cooperative games with partially observed information, which is a common type of situation in real-world supply chain problems.
Lane detection is to detect lanes on the road and provide the accurate location and shape of each lane.
It severs as one of the key techniques to enable modern assisted and autonomous driving systems.
However, several unique properties of lanes challenge the detection methods.
The lack of distinctive features makes lane detection algorithms tend to be confused by other objects with similar local appearance.
Moreover, the inconsistent number of lanes on a road as well as diverse lane line patterns, e.g. solid, broken, single, double, merging, and splitting lines further hamper the performance.
In this paper, we propose a deep neural network based method, named LaneNet, to break down the lane detection into two stages: lane edge proposal and lane line localization.
Stage one uses a lane edge proposal network for pixel-wise lane edge classification, and the lane line localization network in stage two then detects lane lines based on lane edge proposals.
Please note that the goal of our LaneNet is built to detect lane line only, which introduces more difficulties on suppressing the false detections on the similar lane marks on the road like arrows and characters.
Despite all the difficulties, our lane detection is shown to be robust to both highway and urban road scenarios method without relying on any assumptions on the lane number or the lane line patterns.
The high running speed and low computational cost endow our LaneNet the capability of being deployed on vehicle-based systems.
Experiments validate that our LaneNet consistently delivers outstanding performances on real world traffic scenarios.
Estimation of Distribution Algorithms (EDAs) require flexible probability models that can be efficiently learned and sampled.
Autoencoders (AE) are generative stochastic networks with these desired properties.
We integrate a special type of AE, the Denoising Autoencoder (DAE), into an EDA and evaluate the performance of DAE-EDA on several combinatorial optimization problems with a single objective.
We asses the number of fitness evaluations as well as the required CPU times.
We compare the results to the performance to the Bayesian Optimization Algorithm (BOA) and RBM-EDA, another EDA which is based on a generative neural network which has proven competitive with BOA.
For the considered problem instances, DAE-EDA is considerably faster than BOA and RBM-EDA, sometimes by orders of magnitude.
The number of fitness evaluations is higher than for BOA, but competitive with RBM-EDA.
These results show that DAEs can be useful tools for problems with low but non-negligible fitness evaluation costs.
Identification of causal effects is one of the most fundamental tasks of causal inference.
We consider an identifiability problem where some experimental and observational data are available but neither data alone is sufficient for the identification of the causal effect of interest.
Instead of the outcome of interest, surrogate outcomes are measured in the experiments.
This problem is a generalization of identifiability using surrogate experiments and we label it as surrogate outcome identifiability.
We show that the concept of transportability provides a sufficient criteria for determining surrogate outcome identifiability for a large class of queries.
The notion of a spiral unfolding of a convex polyhedron, resulting by flattening a special type of Hamiltonian cut-path, is explored.
The Platonic and Archimedian solids all have nonoverlapping spiral unfoldings, although among generic polyhedra, overlap is more the rule than the exception.
The structure of spiral unfoldings is investigated, primarily by analyzing one particular class, the polyhedra of revolution.
This paper presents data analysis from a course on Software Engineering in an effort to identify metrics and techniques that would allow instructor to act proactively and identify patterns of low engagement and inefficient peer collaboration.
Over the last two terms, 106 students in their second year of studies formed 20 groups and worked collaboratively to develop video games.
Throughout the lab, students have to use a variety of tools for managing and developing their projects, such as software version control, static analysis tools, wikis, mailing lists, etc.
The students are also supported by weekly meetings with teaching assistants and instructors regarding group progress, code quality, and management issues.
Through these meetings and their interactions with the software tools, students leave a detailed trace of data related to their individual engagement and their collaboration behavior in their groups.
The paper provides discussion on the different source of data that can be monitored, and present preliminary results on how these data can be used to analyze students' activity.
We propose a new method to estimate the 6-dof trajectory of a flying object such as a quadrotor UAV within a 3D airspace monitored using multiple fixed ground cameras.
It is based on a new structure from motion formulation for the 3D reconstruction of a single moving point with known motion dynamics.
Our main contribution is a new bundle adjustment procedure which in addition to optimizing the camera poses, regularizes the point trajectory using a prior based on motion dynamics (or specifically flight dynamics).
Furthermore, we can infer the underlying control input sent to the UAV's autopilot that determined its flight trajectory.
Our method requires neither perfect single-view tracking nor appearance matching across views.
For robustness, we allow the tracker to generate multiple detections per frame in each video.
The true detections and the data association across videos is estimated using robust multi-view triangulation and subsequently refined during our bundle adjustment procedure.
Quantitative evaluation on simulated data and experiments on real videos from indoor and outdoor scenes demonstrates the effectiveness of our method.
Existing scrubbing techniques for SEU mitigation on FPGAs do not guarantee an error-free operation after SEU recovering if the affected configuration bits do belong to feedback loops of the implemented circuits.
In this paper, we a) provide a netlist-based circuit analysis technique to distinguish so-called critical configuration bits from essential bits in order to identify configuration bits which will need also state-restoring actions after a recovered SEU and which not.
Furthermore, b) an alternative classification approach using fault injection is developed in order to compare both classification techniques.
Moreover, c) we will propose a floorplanning approach for reducing the effective number of scrubbed frames and d), experimental results will give evidence that our optimization methodology not only allows to detect errors earlier but also to minimize the Mean-Time-To-Repair (MTTR) of a circuit considerably.
In particular, we show that by using our approach, the MTTR for datapath-intensive circuits can be reduced by up to 48.5% in comparison to standard approaches.
In this paper, we introduce the syndrome loss, an alternative loss function for neural error-correcting decoders based on a relaxation of the syndrome.
The syndrome loss penalizes the decoder for producing outputs that do not correspond to valid codewords.
We show that training with the syndrome loss yields decoders with consistently lower frame error rate for a number of short block codes, at little additional cost during training and no additional cost during inference.
The proposed method does not depend on knowledge of the transmitted codeword, making it a promising tool for online adaptation to changing channel conditions.
In this paper, we propose Emo2Vec which encodes emotional semantics into vectors.
We train Emo2Vec by multi-task learning six different emotion-related tasks, including emotion/sentiment analysis, sarcasm classification, stress detection, abusive language classification, insult detection, and personality recognition.
Our evaluation of Emo2Vec shows that it outperforms existing affect-related representations, such as Sentiment-Specific Word Embedding and DeepMoji embeddings with much smaller training corpora.
When concatenated with GloVe, Emo2Vec achieves competitive performances to state-of-the-art results on several tasks using a simple logistic regression classifier.
The Landau collision integral is an accurate model for the small-angle dominated Coulomb collisions in fusion plasmas.
We investigate a high order accurate, fully conservative, finite element discretization of the nonlinear multi-species Landau integral with adaptive mesh refinement using the PETSc library (www.mcs.anl.gov/petsc).
We develop algorithms and techniques to efficiently utilize emerging architectures with an approach that minimizes memory usage and movement and is suitable for vector processing.
The Landau collision integral is vectorized with Intel AVX-512 intrinsics and the solver sustains as much as 22% of the theoretical peak flop rate of the Second Generation Intel Xeon Phi, Knights Landing, processor.
In this work, a study on Variable Neighborhood Search algorithms for multi-depot dial-a-ride problems is presented.
In dial-a-ride problems patients need to be transported from pre-specified pickup locations to pre-specified delivery locations, under different considerations.
The addressed problem presents several constraints and features, such as heterogeneous vehicles, distributed in different depots, and heterogeneous patients.
The aim is of minimizing the total routing cost, while respecting time-window, ride-time, capacity and route duration constraints.
The objective of the study is of determining the best algorithm configuration in terms of initial solution, neighborhood and local search procedures.
At this aim, two different procedures for the computation of an initial solution, six different type of neighborhoods and five local search procedures, where only intra-route changes are made, have been considered and compared.
We have also evaluated an "adjusting procedure" that aims to produce feasible solutions from infeasible solutions with small constraints violations.
The different VNS algorithms have been tested on instances from literature as well as on random instances arising from a real-world healthcare application.
This paper studies the problem of self-organizing heterogeneous LTE systems.
We propose a model that jointly considers several important characteristics of heterogeneous LTE system, including the usage of orthogonal frequency division multiple access (OFDMA), the frequency-selective fading for each link, the interference among different links, and the different transmission capabilities of different types of base stations.
We also consider the cost of energy by taking into account the power consumption, including that for wireless transmission and that for operation, of base stations and the price of energy.
Based on this model, we aim to propose a distributed protocol that improves the spectrum efficiency of the system, which is measured in terms of the weighted proportional fairness among the throughputs of clients, and reduces the cost of energy.
We identify that there are several important components involved in this problem.
We propose distributed strategies for each of these components.
Each of the proposed strategies requires small computational and communicational overheads.
Moreover, the interactions between components are also considered in the proposed strategies.
Hence, these strategies result in a solution that jointly considers all factors of heterogeneous LTE systems.
Simulation results also show that our proposed strategies achieve much better performance than existing ones.
This paper explores the spatial and temporal diffusion of political violence in North and West Africa.
It does so by endeavoring to represent the mental landscape that lives in the back of a group leader's mind as he contemplates strategic targeting.
We assume that this representation is a combination of the physical geography of the target environment, and the mental and physical cost of following a seemingly random pattern of attacks.
Focusing on the distance and time between attacks and taking into consideration the transaction costs that state boundaries impose, we wish to understand what constrains a group leader to attack at a location other than the one that would seem to yield the greatest overt payoff.
By its very nature, the research problem defies the collection of a full set of structural data.
Instead, we leverage functional data from the Armed Conflict Location and Event Data project (ACLED) dataset that, inter alia, meticulously catalogues violent extremist incidents in North and West Africa since 1997, to generate a network whose nodes are administrative regions.
These nodes are connected by edges of qualitatively different types: undirected edges representing geographic distance, undirected edges representing borders, and directed edges representing consecutive attacks by the same group at the two endpoints.
We analyze the resulting network using novel spectral embedding techniques that are able to account fully for the different types of edges.
The result is a map of North and West Africa that depicts the permeability to violence.
A better understanding of how location, time, and borders condition attacks enables planning, prepositioning, and response.
Gender inequality starts before birth.
Parents tend to prefer boys over girls, which is manifested in reproductive behavior, marital life, and parents' pastimes and investments in their children.
While social media and sharing information about children (so-called "sharenting") have become an integral part of parenthood, it is not well-known if and how gender preference shapes online behavior of users.
In this paper, we investigate public mentions of daughters and sons on social media.
We use data from a popular social networking site on public posts from 635,665 users.
We find that both men and women mention sons more often than daughters in their posts.
We also find that posts featuring sons get more "likes" on average.
Our results indicate that girls are underrepresented in parents' digital narratives about their children.
This gender imbalance may send a message that girls are less important than boys, or that they deserve less attention, thus reinforcing gender inequality.
This paper presents a novel design of a crawler robot which is capable of transforming its chassis from an Omni crawler mode to a large-sized wheel mode using a novel mechanism.
The transformation occurs without any additional actuators.
Interestingly the robot can transform into a large diameter and small width wheel which enhances its maneuverability like small turning radius and fast/efficient locomotion.
This paper contributes on improving the locomotion mode of previously developed hybrid compliant omnicrawler robot CObRaSO.
In addition to legged and tracked mechanism, CObRaSO can now display large wheel mode which contributes to its locomotion capabilities.
Mechanical design of the robot has been explained in a detailed manner in this paper and also the transforming experiment and torque analysis has been shown clearly
This paper describes an approach to automatically extracting floor plans from the kinds of incomplete measurements that could be acquired by an autonomous mobile robot.
The approach proceeds by reasoning about extended structural layout surfaces which are automatically extracted from the available data.
The scheme can be run in an online manner to build water tight representations of the environment.
The system effectively speculates about room boundaries and free space regions which provides useful guidance to subsequent motion planning systems.
Experimental results are presented on multiple data sets.
Integration Adapters are a fundamental part of an integration system, since they provide (business) applications access to its messaging channel.
However, their modeling and configuration remain under-represented.
In previous work, the integration control and data flow syntax and semantics have been expressed in the Business Process Model and Notation (BPMN) as a semantic model for message-based integration, while adapter and the related quality of service modeling were left for further studies.
In this work we specify common adapter capabilities and derive general modeling patterns, for which we define a compliant representation in BPMN.
The patterns extend previous work by the adapter flow, evaluated syntactically and semantically for common adapter characteristics.
This paper presents a formal specification of the Ad hoc On-Demand Distance Vector (AODV) routing protocol using AWN (Algebra for Wireless Networks), a recent process algebra which has been tailored for the modelling of Mobile Ad Hoc Networks and Wireless Mesh Network protocols.
Our formalisation models the exact details of the core functionality of AODV, such as route discovery, route maintenance and error handling.
We demonstrate how AWN can be used to reason about critical protocol properties by providing detailed proofs of loop freedom and route correctness.
Applications of perceptual image quality assessment (IQA) in image and video processing, such as image acquisition, image compression, image restoration and multimedia communication, have led to the development of many IQA metrics.
In this paper, a reliable full reference IQA model is proposed that utilize gradient similarity (GS), chromaticity similarity (CS), and deviation pooling (DP).
By considering the shortcomings of the commonly used GS to model human visual system (HVS), a new GS is proposed through a fusion technique that is more likely to follow HVS.
We propose an efficient and effective formulation to calculate the joint similarity map of two chromatic channels for the purpose of measuring color changes.
In comparison with a commonly used formulation in the literature, the proposed CS map is shown to be more efficient and provide comparable or better quality predictions.
Motivated by a recent work that utilizes the standard deviation pooling, a general formulation of the DP is presented in this paper and used to compute a final score from the proposed GS and CS maps.
This proposed formulation of DP benefits from the Minkowski pooling and a proposed power pooling as well.
The experimental results on six datasets of natural images, a synthetic dataset, and a digitally retouched dataset show that the proposed index provides comparable or better quality predictions than the most recent and competing state-of-the-art IQA metrics in the literature, it is reliable and has low complexity.
The MATLAB source code of the proposed metric is available at https://www.mathworks.com/matlabcentral/fileexchange/59809.
We compare the visibility of Latin American and Caribbean (LAC) publications in the Core Collection indexes of the Web of Science (WoS)--Science Citation Index Expanded, Social Sciences Citation Index, and Arts & Humanities Citation Index--and the SciELO Citation Index (SciELO CI) which was integrated into the larger WoS platform in 2014.
The purpose of this comparison is to contribute to our understanding of the communication of scientific knowledge produced in Latin America and the Caribbean, and to provide some reflections on the potential benefits of the articulation of regional indexing exercises into WoS for a better understanding of geographic and disciplinary contributions.
How is the regional level of SciELO CI related to the global range of WoS?
In WoS, LAC authors are integrated at the global level in international networks, while SciELO has provided a platform for interactions among LAC researchers.
The articulation of SciELO into WoS may improve the international visibility of the regional journals, but at the cost of independent journal inclusion criteria.
We address the problem of localisation of objects as bounding boxes in images with weak labels.
This weakly supervised object localisation problem has been tackled in the past using discriminative models where each object class is localised independently from other classes.
We propose a novel framework based on Bayesian joint topic modelling.
Our framework has three distinctive advantages over previous works: (1) All object classes and image backgrounds are modelled jointly together in a single generative model so that "explaining away" inference can resolve ambiguity and lead to better learning and localisation.
(2) The Bayesian formulation of the model enables easy integration of prior knowledge about object appearance to compensate for limited supervision.
(3) Our model can be learned with a mixture of weakly labelled and unlabelled data, allowing the large volume of unlabelled images on the Internet to be exploited for learning.
Extensive experiments on the challenging VOC dataset demonstrate that our approach outperforms the state-of-the-art competitors.
False information can be created and spread easily through the web and social media platforms, resulting in widespread real-world impact.
Characterizing how false information proliferates on social platforms and why it succeeds in deceiving readers are critical to develop efficient detection algorithms and tools for early detection.
A recent surge of research in this area has aimed to address the key issues using methods based on feature engineering, graph mining, and information modeling.
Majority of the research has primarily focused on two broad categories of false information: opinion-based (e.g., fake reviews), and fact-based (e.g., false news and hoaxes).
Therefore, in this work, we present a comprehensive survey spanning diverse aspects of false information, namely (i) the actors involved in spreading false information, (ii) rationale behind successfully deceiving readers, (iii) quantifying the impact of false information, (iv) measuring its characteristics across different dimensions, and finally, (iv) algorithms developed to detect false information.
In doing so, we create a unified framework to describe these recent methods and highlight a number of important directions for future research.
Despite the importance of predicting evacuation mobility dynamics after large scale disasters for effective first response and disaster relief, our general understanding of evacuation behavior remains limited because of the lack of empirical evidence on the evacuation movement of individuals across multiple disaster instances.
Here we investigate the GPS trajectories of a total of more than 1 million anonymized mobile phone users whose positions are tracked for a period of 2 months before and after four of the major earthquakes that occurred in Japan.
Through a cross comparative analysis between the four disaster instances, we find that in contrast with the assumed complexity of evacuation decision making mechanisms in crisis situations, the individuals' evacuation probability is strongly dependent on the seismic intensity that they experience.
In fact, we show that the evacuation probabilities in all earthquakes collapse into a similar pattern, with a critical threshold at around seismic intensity 5.5.
This indicates that despite the diversity in the earthquakes profiles and urban characteristics, evacuation behavior is similarly dependent on seismic intensity.
Moreover, we found that probability density functions of the distances that individuals evacuate are not dependent on seismic intensities that individuals experience.
These insights from empirical analysis on evacuation from multiple earthquake instances using large scale mobility data contributes to a deeper understanding of how people react to earthquakes, and can potentially assist decision makers to simulate and predict the number of evacuees in urban areas with little computational time and cost, by using population density information and seismic intensity which can be observed instantaneously after the shock.
In this paper, we construct MDS Euclidean self-dual codes which are extended cyclic duadic codes.
And we obtain many new MDS Euclidean self-dual codes.
We also construct MDS Hermitian self-dual codes from generalized Reed-Solomon codes and constacyclic codes.
And we give some results on Hermitian self-dual codes, which are the extended cyclic duadic codes.
In the dawn of computer science and the eve of neuroscience we participate in rebirth of neuroscience due to new technology that allows us to deeply and precisely explore whole new world that dwells in our brains.
Increasingly, Software Engineering (SE) researchers use search-based optimization techniques to solve SE problems with multiple conflicting objectives.
These techniques often apply CPU-intensive evolutionary algorithms to explore generations of mutations to a population of candidate solutions.
An alternative approach, proposed in this paper, is to start with a very large population and sample down to just the better solutions.
We call this method "SWAY", short for "the sampling way".
Sway is very simple to implement and, in studies with various software engineering models, this sampling approach was found to be competitive with corresponding state-of-the-art evolutionary algorithms while requiring far less computation cost.
Considering the simplicity and effectiveness of Sway, we, therefore, propose this approach as a baseline method for search-based software engineering models, especially for models that are very slow to execute.
Numerous propagation models describing social influence in social networks can be found in the literature.
This makes the choice of an appropriate model in a given situation difficult.
Selecting the most relevant model requires the ability to objectively compare them.
This comparison can only be made at the cost of describing models based on a common formalism and yet independent from them.
We propose to use graph rewriting to formally describe propagation mechanisms as local transformation rules applied according to a strategy.
This approach makes sense when it is supported by a visual analytics framework dedicated to graph rewriting.
The paper first presents our methodology to describe some propagation models as a graph rewriting problem.
Then, we illustrate how our visual analytics framework allows to interactively manipulate models, and underline their differences based on measures computed on simulation traces.
Bitcoin blockchain faces the bitcoin scalability problem, for which bitcoin's blocks contain the transactions on the bitcoin network.
The on-chain transaction processing capacity of the bitcoin network is limited by the average block creation time of 10 minutes and the block size limit.
These jointly constrain the network's throughput.
The transaction processing capacity maximum is estimated between 3.3 and 7 transactions per second (TPS).
A Layer2 Network, named Lightning Network, is proposed and activated solutions to address this problem.
LN operates on top of the bitcoin network as a cache to allow payments to be affected that are not immediately put on the blockchain.
However, it also brings some drawbacks.
In this paper, we observe a specific payment issue among current LN, which requires additional claims to blockchain and is time-consuming.
We call the issue as shares issue.
Therefore, we propose Rapido to explicitly address the shares issue.
Furthermore, a new smart contract, D-HTLC, is equipped with Rapido as the payment protocol.
We finally provide a proof of concept implementation and simulation for both Rapido and LN, in which Rapdio not only mitigates the shares issue but also mitigates the skewness issue thus is proved to be more applicable for various transactions than LN.
In this paper we establish a connection between non-convex optimization methods for training deep neural networks and nonlinear partial differential equations (PDEs).
Relaxation techniques arising in statistical physics which have already been used successfully in this context are reinterpreted as solutions of a viscous Hamilton-Jacobi PDE.
Using a stochastic control interpretation allows we prove that the modified algorithm performs better in expectation that stochastic gradient descent.
Well-known PDE regularity results allow us to analyze the geometry of the relaxed energy landscape, confirming empirical evidence.
The PDE is derived from a stochastic homogenization problem, which arises in the implementation of the algorithm.
The algorithms scale well in practice and can effectively tackle the high dimensionality of modern neural networks.
Few ideas have enjoyed as large an impact on deep learning as convolution.
For any problem involving pixels or spatial representations, common intuition holds that convolutional neural networks may be appropriate.
In this paper we show a striking counterexample to this intuition via the seemingly trivial coordinate transform problem, which simply requires learning a mapping between coordinates in (x,y) Cartesian space and one-hot pixel space.
Although convolutional networks would seem appropriate for this task, we show that they fail spectacularly.
We demonstrate and carefully analyze the failure first on a toy problem, at which point a simple fix becomes obvious.
We call this solution CoordConv, which works by giving convolution access to its own input coordinates through the use of extra coordinate channels.
Without sacrificing the computational and parametric efficiency of ordinary convolution, CoordConv allows networks to learn either complete translation invariance or varying degrees of translation dependence, as required by the end task.
CoordConv solves the coordinate transform problem with perfect generalization and 150 times faster with 10--100 times fewer parameters than convolution.
This stark contrast raises the question: to what extent has this inability of convolution persisted insidiously inside other tasks, subtly hampering performance from within?
A complete answer to this question will require further investigation, but we show preliminary evidence that swapping convolution for CoordConv can improve models on a diverse set of tasks.
Using CoordConv in a GAN produced less mode collapse as the transform between high-level spatial latents and pixels becomes easier to learn.
A Faster R-CNN detection model trained on MNIST showed 24% better IOU when using CoordConv, and in the RL domain agents playing Atari games benefit significantly from the use of CoordConv layers.
Management of data in education sector particularly management of data for big universities with several employees, departments and students is a very challenging task.
There are also problems such as lack of proper funds and manpower for management of such data in universities.
Education sector can easily and effectively take advantage of cloud computing skills for management of data.
It can enhance the learning experience as a whole and can add entirely new dimensions to the way in which education is imbibed.
Several benefits of Cloud computing such as monetary benefits, environmental benefits and remote data access for management of data such as university database can be used in education sector.
Therefore, in this paper we have proposed an effective framework for managing university data using a cloud based environment.
We have also proposed cloud data management simulator: a new simulation framework which demonstrates the applicability of cloud in the current education sector.
The framework consists of a cloud developed for processing a universities database which consists of staff and students.
It has the following features (i) support for modeling cloud computing infrastructure, which includes data centers containing university database; (ii) a user friendly interface; (iii) flexibility to switch between the different types of users; and (iv) virtualized access to cloud data.
Organizing data into semantically more meaningful is one of the fundamental modes of understanding and learning.
Cluster analysis is a formal study of methods for understanding and algorithm for learning.
K-mean clustering algorithm is one of the most fundamental and simple clustering algorithms.
When there is no prior knowledge about the distribution of data sets, K-mean is the first choice for clustering with an initial number of clusters.
In this paper a novel distance metric called Design Specification (DS) distance measure function is integrated with K-mean clustering algorithm to improve cluster accuracy.
The K-means algorithm with proposed distance measure maximizes the cluster accuracy to 99.98% at P = 1.525, which is determined through the iterative procedure.
The performance of Design Specification (DS) distance measure function with K - mean algorithm is compared with the performances of other standard distance functions such as Euclidian, squared Euclidean, City Block, and Chebshew similarity measures deployed with K-mean algorithm.The proposed method is evaluated on the engineering materials database.
The experiments on cluster analysis and the outlier profiling show that these is an excellent improvement in the performance of the proposed method.
SDN controllers must be periodically modified to add features, improve performance, and fix bugs, but current techniques for implementing dynamic updates are inadequate.
Simply halting old controllers and bringing up new ones can cause state to be lost, which often leads to incorrect behavior-e.g., if the state represents hosts blacklisted by a firewall, then traffic that should be blocked may be allowed to pass through.
Techniques based on record and replay can reconstruct state automatically, but they are expensive to deploy and can lead to incorrect behavior.
Problematic scenarios are especially likely to arise in distributed controllers and with semantics-altering updates.
This paper presents a new approach to implementing dynamic controller updates based on explicit state transfer.
Instead of attempting to infer state changes automatically-an approach that is expensive and fundamentally incomplete-our framework gives programmers effective tools for implementing correct updates that avoid major disruptions.
We develop primitives that enable programmers to directly (and easily, in most cases) initialize the new controller's state as a function of old state and we design protocols that ensure consistent behavior during the transition.
We also present a prototype implementation called Morpheus, and evaluate its effectiveness on representative case studies.
Convolutional auto-encoders have shown their remarkable performance in stacking to deep convolutional neural networks for classifying image data during past several years.
However, they are unable to construct the state-of-the-art convolutional neural networks due to their intrinsic architectures.
In this regard, we propose a flexible convolutional auto-encoder by eliminating the constraints on the numbers of convolutional layers and pooling layers from the traditional convolutional auto-encoder.
We also design an architecture discovery method by using particle swarm optimization, which is capable of automatically searching for the optimal architectures of the proposed flexible convolutional auto-encoder with much less computational resource and without any manual intervention.
We use the designed architecture optimization algorithm to test the proposed flexible convolutional auto-encoder through utilizing one graphic processing unit card on four extensively used image classification datasets.
Experimental results show that our work in this paper significantly outperform the peer competitors including the state-of-the-art algorithm.
This paper is about detecting functional objects and inferring human intentions in surveillance videos of public spaces.
People in the videos are expected to intentionally take shortest paths toward functional objects subject to obstacles, where people can satisfy certain needs (e.g., a vending machine can quench thirst), by following one of three possible intent behaviors: reach a single functional object and stop, or sequentially visit several functional objects, or initially start moving toward one goal but then change the intent to move toward another.
Since detecting functional objects in low-resolution surveillance videos is typically unreliable, we call them "dark matter" characterized by the functionality to attract people.
We formulate the Agent-based Lagrangian Mechanics wherein human trajectories are probabilistically modeled as motions of agents in many layers of "dark-energy" fields, where each agent can select a particular force field to affect its motions, and thus define the minimum-energy Dijkstra path toward the corresponding source "dark matter".
For evaluation, we compiled and annotated a new dataset.
The results demonstrate our effectiveness in predicting human intent behaviors and trajectories, and localizing functional objects, as well as discovering distinct functional classes of objects by clustering human motion behavior in the vicinity of functional objects.
High-Level Synthesis (HLS) is emerging as a mainstream design methodology, allowing software designers to enjoy the benefits of a hardware implementation.
Significant work has led to effective compilers that produce high-quality hardware designs from software specifications.
However, in order to fully benefit from the promise of HLS, a complete ecosystem that provides the ability to analyze, debug, and optimize designs is essential.
This ecosystem has to be accessible to software designers.
This is challenging, since software developers view their designs very differently than how they are physically implemented on-chip.
Rather than individual sequential lines of code, the implementation consists of gates operating in parallel across multiple clock cycles.
In this paper, we report on our efforts to create an ecosystem that allows software designers to debug HLS-generated circuits in a familiar manner.
We have implemented our ideas in a debug framework that will be included in the next release of the popular LegUp high-level synthesis tool.
Reinforcement learning has enjoyed multiple successes in recent years.
However, these successes typically require very large amounts of data before an agent achieves acceptable performance.
This paper introduces a novel way of combating such requirements by leveraging existing (human or agent) knowledge.
In particular, this paper uses demonstrations from agents and humans, allowing an untrained agent to quickly achieve high performance.
We empirically compare with, and highlight the weakness of, HAT and CHAT, methods of transferring knowledge from a source agent/human to a target agent.
This paper introduces an effective transfer approach, DRoP, combining the offline knowledge (demonstrations recorded before learning) with online confidence-based performance analysis.
DRoP dynamically involves the demonstrator's knowledge, integrating it into the reinforcement learning agent's online learning loop to achieve efficient and robust learning.
With the rise of machine learning, there is a great deal of interest in treating programs as data to be fed to learning algorithms.
However, programs do not start off in a form that is immediately amenable to most off-the-shelf learning techniques.
Instead, it is necessary to transform the program to a suitable representation before a learning technique can be applied.
In this paper, we use abstractions of traces obtained from symbolic execution of a program as a representation for learning word embeddings.
We trained a variety of word embeddings under hundreds of parameterizations, and evaluated each learned embedding on a suite of different tasks.
In our evaluation, we obtain 93% top-1 accuracy on a benchmark consisting of over 19,000 API-usage analogies extracted from the Linux kernel.
In addition, we show that embeddings learned from (mainly) semantic abstractions provide nearly triple the accuracy of those learned from (mainly) syntactic abstractions.
Owing to high device density, scalability and non-volatility, Magnetic Tunnel Junction-based crossbars have garnered significant interest for implementing the weights of an artificial neural network.
The existence of only two stable states in MTJs implies a high overhead of obtaining optimal binary weights in software.
We illustrate that the inherent parallelism in the crossbar structure makes it highly appropriate for in-situ training, wherein the network is taught directly on the hardware.
It leads to significantly smaller training overhead as the training time is independent of the size of the network, while also circumventing the effects of alternate current paths in the crossbar and accounting for manufacturing variations in the device.
We show how the stochastic switching characteristics of MTJs can be leveraged to perform probabilistic weight updates using the gradient descent algorithm.
We describe how the update operations can be performed on crossbars both with and without access transistors and perform simulations on them to demonstrate the effectiveness of our techniques.
The results reveal that stochastically trained MTJ-crossbar NNs achieve a classification accuracy nearly same as that of real-valued-weight networks trained in software and exhibit immunity to device variations.
Tumor segmentation from magnetic resonance imaging (MRI) data is an important but time consuming manual task performed by medical experts.
Automating this process is a challenging task because of the high diversity in the appearance of tumor tissues among different patients and in many cases similarity with the normal tissues.
MRI is an advanced medical imaging technique providing rich information about the human soft-tissue anatomy.
There are different brain tumor detection and segmentation methods to detect and segment a brain tumor from MRI images.
These detection and segmentation approaches are reviewed with an importance placed on enlightening the advantages and drawbacks of these methods for brain tumor detection and segmentation.
The use of MRI image detection and segmentation in different procedures are also described.
Here a brief review of different segmentation for detection of brain tumor from MRI of brain has been discussed.
Learning structured representations has emerged as an important problem in many domains, including document and Web data mining, bioinformatics, and image analysis.
One approach to learning complex structures is to integrate many smaller, incomplete and noisy structure fragments.
In this work, we present an unsupervised probabilistic approach that extends affinity propagation to combine the small ontological fragments into a collection of integrated, consistent, and larger folksonomies.
This is a challenging task because the method must aggregate similar structures while avoiding structural inconsistencies and handling noise.
We validate the approach on a real-world social media dataset, comprised of shallow personal hierarchies specified by many individual users, collected from the photosharing website Flickr.
Our empirical results show that our proposed approach is able to construct deeper and denser structures, compared to an approach using only the standard affinity propagation algorithm.
Additionally, the approach yields better overall integration quality than a state-of-the-art approach based on incremental relational clustering.
We propose a multi-label multi-task framework based on a convolutional recurrent neural network to unify detection of isolated and overlapping audio events.
The framework leverages the power of convolutional recurrent neural network architectures; convolutional layers learn effective features over which higher recurrent layers perform sequential modelling.
Furthermore, the output layer is designed to handle arbitrary degrees of event overlap.
At each time step in the recurrent output sequence, an output triple is dedicated to each event category of interest to jointly model event occurrence and temporal boundaries.
That is, the network jointly determines whether an event of this category occurs, and when it occurs, by estimating onset and offset positions at each recurrent time step.
We then introduce three sequential losses for network training: multi-label classification loss, distance estimation loss, and confidence loss.
We demonstrate good generalization on two datasets: ITC-Irst for isolated audio event detection, and TUT-SED-Synthetic-2016 for overlapping audio event detection.
By the Gibbard--Satterthwaite theorem, every reasonable voting rule for three or more alternatives is susceptible to manipulation: there exist elections where one or more voters can change the election outcome in their favour by unilaterally modifying their vote.
When a given election admits several such voters, strategic voting becomes a game among potential manipulators: a manipulative vote that leads to a better outcome when other voters are truthful may lead to disastrous results when other voters choose to manipulate as well.
We consider this situation from the perspective of a boundedly rational voter, and use the cognitive hierarchy framework to identify good strategies.
We then investigate the associated algorithmic questions under the k-approval voting rule.
We obtain positive algorithmic results for k=1 and 2, and NP- and coNP-hardness results for k>3.
Grid and cloud computing systems have been extensively used to solve large and complex problems in science and engineering areas.
These systems include powerful computing resources connected through high-speed networks.
Due to recent advances in mobile computing and networking technologies, it has become feasible to integrate various mobile devices such as robots, aerial vehicles, sensors, and smartphones with grid and cloud computing systems.
This integration enables design and development of next generation of applications through sharing of resources in mobile environments and also introduces several challenges due to dynamic and unpredictable network.
This paper discusses applications, research challenges involved in design and development of mobile grid and cloud computing systems, and recent advances in the field.
In today's typical industrial environments, the computation of the data distribution schedules is highly centralised.
Typically, a central entity configures the data forwarding paths so as to guarantee low delivery delays between data producers and consumers.
However, these requirements might become impossible to meet later on, due to link or node failures, or excessive degradation of their performance.
In this paper, we focus on maintaining the network functionality required by the applications after such events.
We avoid continuously recomputing the configuration centrally, by designing an energy efficient local and distributed path reconfiguration method.
Specifically, given the operational parameters required by the applications, we provide several algorithmic functions which locally reconfigure the data distribution paths, when a communication link or a network node fails.
We compare our method through simulations to other state of the art methods and we demonstrate performance gains in terms of energy consumption and data delivery success rate as well as some emerging key insights which can lead to further performance gains.
Many popular form factors of digital assistant---such as Amazon Echo, Apple Homepod or Google Home---enable the user to hold a conversation with the assistant based only on the speech modality.
The lack of a screen from which the user can read text or watch supporting images or video presents unique challenges.
In order to satisfy the information need of a user, we believe that the presentation of the answer needs to be optimized for such voice-only interactions.
In this paper we propose a task of evaluating usefulness of prosody modifications for the purpose of voice-only question answering.
We describe a crowd-sourcing setup where we evaluate the quality of these modifications along multiple dimensions corresponding to the informativeness, naturalness, and ability of the user to identify the key part of the answer.
In addition, we propose a set of simple prosodic modifications that highlight important parts of the answer using various acoustic cues.
Topological aspects, like community structure, and temporal activity patterns, like burstiness, have been shown to severly influence the speed of spreading in temporal networks.
We study the influence of the topology on the susceptible-infected (SI) spreading on time stamped communication networks, as obtained from a dataset of mobile phone records.
We consider city level networks with intra- and inter-city connections.
The networks using only intra-city links are usually sparse, where the spreading depends mainly on the average degree.
The inter-city links serve as bridges in spreading, speeding up considerably the process.
We demonstrate the effect also on model simulations.
Autonomous unmanned aerial vehicles (UAVs) that can execute aggressive (i.e., high-speed and high-acceleration) maneuvers have attracted significant attention in the past few years.
In this paper, we propose a novel control law for accurate tracking of aggressive quadcopter trajectories.
The proposed method tracks position and yaw angle with their derivatives of up to fourth order, specifically, the position, velocity, acceleration, jerk, and snap along with the yaw angle, yaw rate and yaw acceleration.
Two key aspects of the proposed method are the following.
First, the controller exploits the differential flatness of the quadcopter dynamics to generate feedforward inputs for attitude rate and attitude acceleration in order to track the jerk and snap references.
The tracking is enabled by direct control of body torque using closed-loop control of all four propeller speeds based on optical encoders attached to the motors.
Second, the controller utilizes the incremental nonlinear dynamic inversion (INDI) method for accurate tracking of linear and angular accelerations despite external disturbances.
Hence, no prior modeling of aerodynamic effects is required.
We rigorously analyze the proposed controller through response analysis, and we demonstrate it in experiments.
The proposed control law enables a 1-kg quadcopter UAV to track complex 3D trajectories, reaching speeds up to 8.2 m/s and accelerations up to 2g, while keeping the root-mean-square tracking error down to 4 cm, in a flight volume that is roughly 6.5 m long, 6.5 m wide, and 1.5 m tall.
We also demonstrate the robustness of the controller by attaching a drag plate to the UAV in flight tests and by pulling on the UAV with a rope during hover.
Heterogeneous face recognition (HFR) refers to matching face images acquired from different sources (i.e., different sensors or different wavelengths) for identification.
HFR plays an important role in both biometrics research and industry.
In spite of promising progresses achieved in recent years, HFR is still a challenging problem due to the difficulty to represent two heterogeneous images in a homogeneous manner.
Existing HFR methods either represent an image ignoring the spatial information, or rely on a transformation procedure which complicates the recognition task.
Considering these problems, we propose a novel graphical representation based HFR method (G-HFR) in this paper.
Markov networks are employed to represent heterogeneous image patches separately, which takes the spatial compatibility between neighboring image patches into consideration.
A coupled representation similarity metric (CRSM) is designed to measure the similarity between obtained graphical representations.
Extensive experiments conducted on multiple HFR scenarios (viewed sketch, forensic sketch, near infrared image, and thermal infrared image) show that the proposed method outperforms state-of-the-art methods.
Breast tissue segmentation into dense and fat tissue is important for determining the breast density in mammograms.
Knowing the breast density is important both in diagnostic and computer-aided detection applications.
There are many different ways to express the density of a breast and good quality segmentation should provide the possibility to perform accurate classification no matter which classification rule is being used.
Knowing the right breast density and having the knowledge of changes in the breast density could give a hint of a process which started to happen within a patient.
Mammograms generally suffer from a problem of different tissue overlapping which results in the possibility of inaccurate detection of tissue types.
Fibroglandular tissue presents rather high attenuation of X-rays and is visible as brighter in the resulting image but overlapping fibrous tissue and blood vessels could easily be replaced with fibroglandular tissue in automatic segmentation algorithms.
Small blood vessels and microcalcifications are also shown as bright objects with similar intensities as dense tissue but do have some properties which makes possible to suppress them from the final results.
In this paper we try to divide dense and fat tissue by suppressing the scattered structures which do not represent glandular or dense tissue in order to divide mammograms more accurately in the two major tissue types.
For suppressing blood vessels and microcalcifications we have used Gabor filters of different size and orientation and a combination of morphological operations on filtered image with enhanced contrast.
Fixing a software error requires understanding its root cause.
In this paper, we introduce ''causality traces'', crafted execution traces augmented with the information needed to reconstruct the causal chain from the root cause of a bug to an execution error.
We propose an approach and a tool, called Casper, for dynamically constructing causality traces for null dereference errors.
The core idea of Casper is to inject special values, called ''ghosts'', into the execution stream to construct the causality trace at runtime.
We evaluate our contribution by providing and assessing the causality traces of 14 real null dereference bugs collected over six large, popular open-source projects.
Over this data set, Casper builds a causality trace in less than 5 seconds.
Continuous Integration (CI) implies that a whole developer team works together on the mainline of a software project.
CI systems automate the builds of a software.
Sometimes a developer checks in code, which breaks the build.
A broken build might not be a problem by itself, but it has the potential to disrupt co-workers, hence it affects the performance of the team.
In this study, we investigate the interplay between nonfunctional requirements (NFRs) and builds statuses from 1,283 software projects.
We found significant differences among NFRs related-builds statuses.
Thus, tools can be proposed to improve CI with focus on new ways to prevent failures into CI, specially for efficiency and usability related builds.
Also, the time required to put a broken build back on track indicates a bimodal distribution along all NFRs, with higher peaks within a day and lower peaks in six weeks.
Our results suggest that more planned schedule for maintainability for Ruby, and for functionality and reliability for Java would decrease delays related to broken builds.
Working adults spend nearly one third of their daily time at their jobs.
In this paper, we study job-related social media discourse from a community of users.
We use both crowdsourcing and local expertise to train a classifier to detect job-related messages on Twitter.
Additionally, we analyze the linguistic differences in a job-related corpus of tweets between individual users vs. commercial accounts.
The volumes of job-related tweets from individual users indicate that people use Twitter with distinct monthly, daily, and hourly patterns.
We further show that the moods associated with jobs, positive and negative, have unique diurnal rhythms.
One of the big restrictions in brain computer interface field is the very limited training samples, it is difficult to build a reliable and usable system with such limited data.
Inspired by generative adversarial networks, we propose a conditional Deep Convolutional Generative Adversarial (cDCGAN) Networks method to generate more artificial EEG signal automatically for data augmentation to improve the performance of convolutional neural networks in brain computer interface field and overcome the small training dataset problems.
We evaluate the proposed cDCGAN method on BCI competition dataset of motor imagery.
The results show that the generated artificial EEG data from Gaussian noise can learn the features from raw EEG data and has no less than the classification accuracy of raw EEG data in the testing dataset.
Also by using generated artificial data can effectively improve classification accuracy at the same model with limited training data.
We present a simple yet effective approach for linking entities in queries.
The key idea is to search sentences similar to a query from Wikipedia articles and directly use the human-annotated entities in the similar sentences as candidate entities for the query.
Then, we employ a rich set of features, such as link-probability, context-matching, word embeddings, and relatedness among candidate entities as well as their related entities, to rank the candidates under a regression based framework.
The advantages of our approach lie in two aspects, which contribute to the ranking process and final linking result.
First, it can greatly reduce the number of candidate entities by filtering out irrelevant entities with the words in the query.
Second, we can obtain the query sensitive prior probability in addition to the static link-probability derived from all Wikipedia articles.
We conduct experiments on two benchmark datasets on entity linking for queries, namely the ERD14 dataset and the GERDAQ dataset.
Experimental results show that our method outperforms state-of-the-art systems and yields 75.0% in F1 on the ERD14 dataset and 56.9% on the GERDAQ dataset.
While objects from different categories can be reliably decoded from fMRI brain response patterns, it has proved more difficult to distinguish visually similar inputs, such as different instances of the same category.
Here, we apply a recently developed deep learning system to the reconstruction of face images from human fMRI patterns.
We trained a variational auto-encoder (VAE) neural network using a GAN (Generative Adversarial Network) unsupervised training procedure over a large dataset of celebrity faces.
The auto-encoder latent space provides a meaningful, topologically organized 1024-dimensional description of each image.
We then presented several thousand face images to human subjects, and learned a simple linear mapping between the multi-voxel fMRI activation patterns and the 1024 latent dimensions.
Finally, we applied this mapping to novel test images, turning the obtained fMRI patterns into VAE latent codes, and ultimately the codes into face reconstructions.
Qualitative and quantitative evaluation of the reconstructions revealed robust pairwise decoding (>95% correct), and a strong improvement relative to a baseline model (PCA decomposition).
Furthermore, this brain decoding model can readily be recycled to probe human face perception along many dimensions of interest; for example, the technique allowed for accurate gender classification, and even to decode which face was imagined, rather than seen by the subject.
We hypothesize that the latent space of modern deep learning generative models could serve as a valid approximation for human brain representations.
In this paper, the problem of secure transmission of sensitive contents over the public network Internet is addressed by proposing a novel data hiding method in encrypted images with dual-level security.
The secret information is divided into three blocks using a specific pattern, followed by an encryption mechanism based on the three-level encryption algorithm (TLEA).
The input image is scrambled using a secret key, and the encrypted sub-message blocks are then embedded in the scrambled image by cyclic18 least significant bit (LSB) substitution method, utilizing LSBs and intermediate LSB planes.
Furthermore, the cover image and its planes are rotated at different angles using a secret key prior to embedding, deceiving the attacker during data extraction.
The usage of message blocks division, TLEA, image scrambling, and the cyclic18 LSB method results in an advanced security system, maintaining the visual transparency of resultant images and increasing the security of embedded data.
In addition, employing various secret keys for image scrambling, data encryption, and data hiding using the cyclic18 LSB method makes the data recovery comparatively more challenging for attackers.
Experimental results not only validate the effectiveness of the proposed framework in terms of visual quality and security compared to other state-of-the-art methods, but also suggest its feasibility for secure transmission of diagnostically important keyframes to healthcare centers and gastroenterologists during wireless capsule endoscopy.
Today, online privacy is the domain of regulatory measures and privacy-enhancing technologies.
Transparency in the form of external and public assessments has been proposed for improving privacy and security because it exposes otherwise hidden deficiencies.
Previous work has studied privacy attitudes and behavior of consumers.
However, little is known on how organizations react to measures that employ public "naming and shaming" as an incentive for improvement.
We performed the first study on this aspect by conducting a qualitative survey with 152 German health insurers.
We scanned their websites with PrivacyScore.org to generate a public ranking and confronted the insurers with the results.
We obtained a response rate of 27%.
Responses ranged from positive feedback to legal threats.
Only 12% of the sites - mostly non-responders - improved during our study.
Our results show that insurers struggle due to unawareness, reluctance, and incapability, and demonstrate the general difficulties of transparency-based approaches.
In digital painting software, layers organize paintings.
However, layers are not explicitly represented, transmitted, or published with the final digital painting.
We propose a technique to decompose a digital painting into layers.
In our decomposition, each layer represents a coat of paint of a single paint color applied with varying opacity throughout the image.
Our decomposition is based on the painting's RGB-space geometry.
In RGB-space, a geometric structure is revealed due to the linear nature of the standard Porter-Duff "over" pixel compositing operation.
The vertices of the convex hull of pixels in RGB-space suggest paint colors.
Users choose the degree of simplification to perform on the convex hull, as well as a layer order for the colors.
We solve a constrained optimization problem to find maximally translucent, spatially coherent opacity for each layer, such that the composition of the layers reproduces the original image.
We demonstrate the utility of the resulting decompositions for re-editing.
This paper proposes a novel method for understanding daily hand-object manipulation by developing computer vision-based techniques.
Specifically, we focus on recognizing hand grasp types, object attributes and manipulation actions within an unified framework by exploring their contextual relationships.
Our hypothesis is that it is necessary to jointly model hands, objects and actions in order to accurately recognize multiple tasks that are correlated to each other in hand-object manipulation.
In the proposed model, we explore various semantic relationships between actions, grasp types and object attributes, and show how the context can be used to boost the recognition of each component.
We also explore the spatial relationship between the hand and object in order to detect the manipulated object from hand in cluttered environment.
Experiment results on all three recognition tasks show that our proposed method outperforms traditional appearance-based methods which are not designed to take into account contextual relationships involved in hand-object manipulation.
The visualization and generalizability study of the learned context further supports our hypothesis.
The problem of finding conflict-free trajectories for multiple agents of identical circular shape, operating in shared 2D workspace, is addressed in the paper and decoupled, e.g., prioritized, approach is used to solve this problem.
Agents' workspace is tessellated into the square grid on which any-angle moves are allowed, e.g. each agent can move into an arbitrary direction as long as this move follows the straight line segment whose endpoints are tied to the distinct grid elements.
A novel any-angle planner based on Safe Interval Path Planning (SIPP) algorithm is proposed to find trajectories for an agent moving amidst dynamic obstacles (other agents) on a grid.
This algorithm is then used as part of a prioritized multi-agent planner AA-SIPP(m).
On the theoretical, side we show that AA-SIPP(m) is complete under well-defined conditions.
On the experimental side, in simulation tests with up to 200 agents involved, we show that our planner finds much better solutions in terms of cost (up to 20%) compared to the planners relying on cardinal moves only.
Graph databases in many applications---semantic web, transport or biological networks among others---are not only large, but also frequently modified.
Evaluating graph queries in this dynamic context is a challenging task, as those queries often combine first-order and navigational features.
Motivated by recent results on maintaining dynamic reachability, we study the dynamic evaluation of traditional query languages for graphs in the descriptive complexity framework.
Our focus is on maintaining regular path queries, and extensions thereof, by first-order formulas.
In particular we are interested in path queries defined by non-regular languages and in extended conjunctive regular path queries (which allow to compare labels of paths based on word relations).
Further we study the closely related problems of maintaining distances in graphs and reachability in product graphs.
In this preliminary study we obtain upper bounds for those problems in restricted settings, such as undirected and acyclic graphs, or under insertions only, and negative results regarding quantifier-free update formulas.
In addition we point out interesting directions for further research.
This article presents the consensus of a saturated second order multi-agent system with non-switching dynamics that can be represented by a directed graph.
The system is affected by data processing (input delay) and communication time-delays that are assumed to be asynchronous.
The agents have saturation nonlinearities, each of them is approximated into separate linear and nonlinear elements.
Nonlinear elements are represented by describing functions.
Describing functions and stability of linear elements are used to estimate the existence of limit cycles in the system with multiple control laws.
Stability analysis of the linear element is performed using Lyapunov-Krasovskii functions and frequency domain analysis.
A comparison of pros and cons of both the analyses with respect to time-delay ranges, applicability and computation complexity is presented.
Simulation and corresponding hardware implementation results are demonstrated to support theoretical results.
We consider the flow network model to solve the multiprocessor real-time task scheduling problems.
Using the flow network model or its generic form, linear programming (LP) formulation, for the problems is not new.
However, the previous works have limitations, for example, that they are classified as offline scheduling techniques since they establish a flow network model or an LP problem considering a very long time interval.
In this study, we propose how to construct the flow network model for online scheduling periodic real-time tasks on multiprocessors.
Our key idea is to construct the flow network only for the active instances of tasks at the current scheduling time, while guaranteeing the existence of an optimal schedule for the future instances of the tasks.
The optimal scheduling is here defined to ensure that all real-time tasks meet their deadlines when the total utilization demand of the given tasks does not exceed the total processing capacity.
We then propose the flow network model-based polynomial-time scheduling algorithms.
Advantageously, the flow network model allows the task workload to be collected unfairly within a certain time interval without losing the optimality.
It thus leads us to designing three unfair-but-optimal scheduling algorithms on both continuous and discrete-time models.
Especially, our unfair-but-optimal scheduling algorithm on a discrete-time model is, to the best of our knowledge, the first in the problem domain.
We experimentally demonstrate that it significantly alleviates the scheduling overheads, i.e., the reduced number of preemptions with the comparable number of task migrations across processors.
Generation and load balance is required in the economic scheduling of generating units in the smart grid.
Variable energy generations, particularly from wind and solar energy resources, are witnessing a rapid boost, and, it is anticipated that with a certain level of their penetration, they can become noteworthy sources of uncertainty.
As in the case of load demand, energy forecasting can also be used to mitigate some of the challenges that arise from the uncertainty in the resource.
While wind energy forecasting research is considered mature, solar energy forecasting is witnessing a steadily growing attention from the research community.
This paper presents a support vector regression model to produce solar power forecasts on a rolling basis for 24 hours ahead over an entire year, to mimic the practical business of energy forecasting.
Twelve weather variables are considered from a high-quality benchmark dataset and new variables are extracted.
The added value of the heat index and wind speed as additional variables to the model is studied across different seasons.
The support vector regression model performance is compared with artificial neural networks and multiple linear regression models for energy forecasting.
The Internet of Things (IoT) propagates the paradigm of interconnecting billions of heterogeneous devices by various manufacturers.
To enable IoT applications, the communication between IoT devices follows specifications defined by standard developing organizations.
In this paper, we present a case study that investigates disclosed insecurities of the popular IoT standard ZigBee, and derive general lessons about security economics in IoT standardization efforts.
We discuss the motivation of IoT standardization efforts that are primarily driven from an economic perspective, in which large investments in security are not considered necessary since the consumers do not reward them.
Success at the market is achieved by being quick-to-market, providing functional features and offering easy integration for complementors.
Nevertheless, manufacturers should not only consider economic reasons but also see their responsibility to protect humans and technological infrastructures from being threatened by insecure IoT products.
In this context, we propose a number of recommendations to strengthen the security design in future IoT standardization efforts, ranging from the definition of a precise security model to the enforcement of an update policy.
The success of various applications including robotics, digital content creation, and visualization demand a structured and abstract representation of the 3D world from limited sensor data.
Inspired by the nature of human perception of 3D shapes as a collection of simple parts, we explore such an abstract shape representation based on primitives.
Given a single depth image of an object, we present 3D-PRNN, a generative recurrent neural network that synthesizes multiple plausible shapes composed of a set of primitives.
Our generative model encodes symmetry characteristics of common man-made objects, preserves long-range structural coherence, and describes objects of varying complexity with a compact representation.
We also propose a method based on Gaussian Fields to generate a large scale dataset of primitive-based shape representations to train our network.
We evaluate our approach on a wide range of examples and show that it outperforms nearest-neighbor based shape retrieval methods and is on-par with voxel-based generative models while using a significantly reduced parameter space.
Recently, discriminatively learned correlation filters (DCF) has drawn much attention in visual object tracking community.
The success of DCF is potentially attributed to the fact that a large amount of samples are utilized to train the ridge regression model and predict the location of object.
To solve the regression problem in an efficient way, these samples are all generated by circularly shifting from a search patch.
However, these synthetic samples also induce some negative effects which weaken the robustness of DCF based trackers.
In this paper, we propose a Convolutional Regression framework for visual tracking (CRT).
Instead of learning the linear regression model in a closed form, we try to solve the regression problem by optimizing a one-channel-output convolution layer with Gradient Descent (GD).
In particular, the receptive field size of the convolution layer is set to the size of object.
Contrary to DCF, it is possible to incorporate all "real" samples clipped from the whole image.
A critical issue of the GD approach is that most of the convolutional samples are negative and the contribution of positive samples will be suppressed.
To address this problem, we propose a novel Automatic Hard Negative Mining method to eliminate easy negatives and enhance positives.
Extensive experiments are conducted on a widely-used benchmark with 100 sequences.
The results show that the proposed algorithm achieves outstanding performance and outperforms almost all the existing DCF based algorithms.
In this paper, we propose a novel deep learning architecture for multi-label zero-shot learning (ML-ZSL), which is able to predict multiple unseen class labels for each input instance.
Inspired by the way humans utilize semantic knowledge between objects of interests, we propose a framework that incorporates knowledge graphs for describing the relationships between multiple labels.
Our model learns an information propagation mechanism from the semantic label space, which can be applied to model the interdependencies between seen and unseen class labels.
With such investigation of structured knowledge graphs for visual reasoning, we show that our model can be applied for solving multi-label classification and ML-ZSL tasks.
Compared to state-of-the-art approaches, comparable or improved performances can be achieved by our method.
Multilingual knowledge graph (KG) embeddings provide latent semantic representations of entities and structured knowledge with cross-lingual inferences, which benefit various knowledge-driven cross-lingual NLP tasks.
However, precisely learning such cross-lingual inferences is usually hindered by the low coverage of entity alignment in many KGs.
Since many multilingual KGs also provide literal descriptions of entities, in this paper, we introduce an embedding-based approach which leverages a weakly aligned multilingual KG for semi-supervised cross-lingual learning using entity descriptions.
Our approach performs co-training of two embedding models, i.e. a multilingual KG embedding model and a multilingual literal description embedding model.
The models are trained on a large Wikipedia-based trilingual dataset where most entity alignment is unknown to training.
Experimental results show that the performance of the proposed approach on the entity alignment task improves at each iteration of co-training, and eventually reaches a stage at which it significantly surpasses previous approaches.
We also show that our approach has promising abilities for zero-shot entity alignment, and cross-lingual KG completion.
Context: Visual aesthetics is increasingly seen as an essential factor in perceived usability, interaction, and overall appraisal of user interfaces especially with respect to mobile applications.
Yet, a question that remains is how to assess and to which extend users agree on visual aesthetics.
Objective: This paper analyzes the inter-rater agreement on visual aesthetics of user interfaces of Android apps as a basis for guidelines and evaluation models.
Method: We systematically collected ratings on the visual aesthetics of 100 user interfaces of Android apps from 10 participants and analyzed the frequency distribution, reliability and influencing design aspects.
Results: In general, user interfaces of Android apps are perceived more ugly than beautiful.
Yet, raters only moderately agree on the visual aesthetics.
Disagreements seem to be related to subtle differences with respect to layout, shapes, colors, typography, and background images.
Conclusion: Visual aesthetics is a key factor for the success of apps.
However, the considerable disagreement of raters on the perceived visual aesthetics indicates the need for a better understanding of this software quality with respect to mobile apps.
Despite advances in deep learning, neural networks can only learn multiple tasks when trained on them jointly.
When tasks arrive sequentially, they lose performance on previously learnt tasks.
This phenomenon called catastrophic forgetting is a fundamental challenge to overcome before neural networks can learn continually from incoming data.
In this work, we derive inspiration from human memory to develop an architecture capable of learning continuously from sequentially incoming tasks, while averting catastrophic forgetting.
Specifically, our contributions are: (i) a dual memory architecture emulating the complementary learning systems (hippocampus and the neocortex) in the human brain, (ii) memory consolidation via generative replay of past experiences, (iii) demonstrating advantages of generative replay and dual memories via experiments, and (iv) improved performance retention on challenging tasks even for low capacity models.
Our architecture displays many characteristics of the mammalian memory and provides insights on the connection between sleep and learning.
Logic programs are now used as a representation of object-oriented source code in academic prototypes for about a decade.
This representation allows a clear and concise implementation of analyses of the object-oriented source code.
The full potential of this approach is far from being explored.
In this paper, we report about an application of the well-established theory of update propagation within logic programs.
Given the representation of the object-oriented code as facts in a logic program, a change to the code corresponds to an update of these facts.
We demonstrate how update propagation provides a generic way to generate incremental versions of such analyses.
The natural way to use Answer Set Programming (ASP) to represent knowledge in Artificial Intelligence or to solve a combinatorial problem is to elaborate a first order logic program with default negation.
In a preliminary step this program with variables is translated in an equivalent propositional one by a first tool: the grounder.
Then, the propositional program is given to a second tool: the solver.
This last one computes (if they exist) one or many answer sets (stable models) of the program, each answer set encoding one solution of the initial problem.
Until today, almost all ASP systems apply this two steps computation.
In this article, the project ASPeRiX is presented as a first order forward chaining approach for Answer Set Computing.
This project was amongst the first to introduce an approach of answer set computing that escapes the preliminary phase of rule instantiation by integrating it in the search process.
The methodology applies a forward chaining of first order rules that are grounded on the fly by means of previously produced atoms.
Theoretical foundations of the approach are presented, the main algorithms of the ASP solver ASPeRiX are detailed and some experiments and comparisons with existing systems are provided.
Event management in sensor networks is a multidisciplinary field involving several steps across the processing chain.
In this paper, we discuss the major steps that should be performed in real- or near real-time event handling including event detection, correlation, prediction and filtering.
First, we discuss existing univariate and multivariate change detection schemes for the online event detection over sensor data.
Next, we propose an online event correlation scheme that intends to unveil the internal dynamics that govern the operation of a system and are responsible for the generation of various types of events.
We show that representation of event dependencies can be accommodated within a probabilistic temporal knowledge representation framework that allows the formulation of rules.
We also address the important issue of identifying outdated dependencies among events by setting up a time-dependent framework for filtering the extracted rules over time.
The proposed theory is applied on the maritime domain and is validated through extensive experimentation with real sensor streams originating from large-scale sensor networks deployed in ships.
Monte Carlo Tree Search (MCTS) methods have proven powerful in planning for sequential decision-making problems such as Go and video games, but their performance can be poor when the planning depth and sampling trajectories are limited or when the rewards are sparse.
We present an adaptation of PGRD (policy-gradient for reward-design) for learning a reward-bonus function to improve UCT (a MCTS algorithm).
Unlike previous applications of PGRD in which the space of reward-bonus functions was limited to linear functions of hand-coded state-action-features, we use PGRD with a multi-layer convolutional neural network to automatically learn features from raw perception as well as to adapt the non-linear reward-bonus function parameters.
We also adopt a variance-reducing gradient method to improve PGRD's performance.
The new method improves UCT's performance on multiple ATARI games compared to UCT without the reward bonus.
Combining PGRD and Deep Learning in this way should make adapting rewards for MCTS algorithms far more widely and practically applicable than before.
We present a temporal 6-DOF tracking method which leverages deep learning to achieve state-of-the-art performance on challenging datasets of real world capture.
Our method is both more accurate and more robust to occlusions than the existing best performing approaches while maintaining real-time performance.
To assess its efficacy, we evaluate our approach on several challenging RGBD sequences of real objects in a variety of conditions.
Notably, we systematically evaluate robustness to occlusions through a series of sequences where the object to be tracked is increasingly occluded.
Finally, our approach is purely data-driven and does not require any hand-designed features: robust tracking is automatically learned from data.
Robust and lane-level positioning is essential for autonomous vehicles.
As an irreplaceable sensor, LiDAR can provide continuous and high-frequency pose estimation by means of mapping, on condition that enough environment features are available.
The error of mapping can accumulate over time.
Therefore, LiDAR is usually integrated with other sensors.
In diverse urban scenarios, the environment feature availability relies heavily on the traffic (moving and static objects) and the degree of urbanization.
Common LiDAR-based SLAM demonstrations tend to be studied in light traffic and less urbanized area.
However, its performance can be severely challenged in deep urbanized cities, such as Hong Kong, Tokyo, and New York with dense traffic and tall buildings.
This paper proposes to analyze the performance of standalone NDT-based graph SLAM and its reliability estimation in diverse urban scenarios to further evaluate the relationship between the performance of LiDAR-based SLAM and scenario conditions.
The normal distribution transform (NDT) is employed to calculate the transformation between frames of point clouds.
Then, the LiDAR odometry is performed based on the calculated continuous transformation.
The state-of-the-art graph-based optimization is used to integrate the LiDAR odometry measurements to implement optimization.
The 3D building models are generated and the definition of the degree of urbanization based on Skyplot is proposed.
Experiments are implemented in different scenarios with different degrees of urbanization and traffic conditions.
The results show that the performance of the LiDAR-based SLAM using NDT is strongly related to the traffic condition and degree of urbanization.
Link prediction appears as a central problem of network science, as it calls for unfolding the mechanisms that govern the micro-dynamics of the network.
In this work, we are interested in ego-networks, that is the mere information of interactions of a node to its neighbors, in the context of social relationships.
As the structural information is very poor, we rely on another source of information to predict links among egos' neighbors: the timing of interactions.
We define several features to capture different kinds of temporal information and apply machine learning methods to combine these various features and improve the quality of the prediction.
We demonstrate the efficiency of this temporal approach on a cellphone interaction dataset, pointing out features which prove themselves to perform well in this context, in particular the temporal profile of interactions and elapsed time between contacts.
We present MBIS (Multivariate Bayesian Image Segmentation tool), a clustering tool based on the mixture of multivariate normal distributions model.
MBIS supports multi-channel bias field correction based on a B-spline model.
A second methodological novelty is the inclusion of graph-cuts optimization for the stationary anisotropic hidden Markov random field model.
Along with MBIS, we release an evaluation framework that contains three different experiments on multi-site data.
We first validate the accuracy of segmentation and the estimated bias field for each channel.
MBIS outperforms a widely used segmentation tool in a cross-comparison evaluation.
The second experiment demonstrates the robustness of results on atlas-free segmentation of two image sets from scan-rescan protocols on 21 healthy subjects.
Multivariate segmentation is more replicable than the monospectral counterpart on T1-weighted images.
Finally, we provide a third experiment to illustrate how MBIS can be used in a large-scale study of tissue volume change with increasing age in 584 healthy subjects.
This last result is meaningful as multivariate segmentation performs robustly without the need for prior knowledge
Channel estimation is useful in millimeter wave (mmWave) MIMO communication systems.
Channel state information allows optimized designs of precoders and combiners under different metrics such as mutual information or signal-to-interference-noise (SINR) ratio.
At mmWave, MIMO precoders and combiners are usually hybrid, since this architecture provides a means to trade-off power consumption and achievable rate.
Channel estimation is challenging when using these architectures, however, since there is no direct access to the outputs of the different antenna elements in the array.
The MIMO channel can only be observed through the analog combining network, which acts as a compression stage of the received signal.
Most of prior work on channel estimation for hybrid architectures assumes a frequency-flat mmWave channel model.
In this paper, we consider a frequency-selective mmWave channel and propose compressed-sensing-based strategies to estimate the channel in the frequency domain.
We evaluate different algorithms and compute their complexity to expose trade-offs in complexity-overhead-performance as compared to those of previous approaches.
Organisations store huge amounts of data from multiple heterogeneous sources in the form of Knowledge Graphs (KGs).
One of the ways to query these KGs is to use SPARQL queries over a database engine.
Since SPARQL follows exact match semantics, the queries may return too few or no results.
Recent works have proposed query relaxation where the query engine judiciously replaces a query predicate with similar predicates using weighted relaxation rules mined from the KG.
The space of possible relaxations is potentially too large to fully explore and users are typically interested in only top-k results, so such query engines use top-k algorithms for query processing.
However, they may still process all the relaxations, many of whose answers do not contribute towards top-k answers.
This leads to computation overheads and delayed response times.
We propose Spec-QP, a query planning framework that speculatively determines which relaxations will have their results in the top-k answers.
Only these relaxations are processed using the top-k operators.
We, therefore, reduce the computation overheads and achieve faster response times without adversely affecting the quality of results.
We tested Spec-QP over two datasets - XKG and Twitter, to demonstrate the efficiency of our planning framework at reducing runtimes with reasonable accuracy for query engines supporting relaxations.
AISHELL-1 is by far the largest open-source speech corpus available for Mandarin speech recognition research.
It was released with a baseline system containing solid training and testing pipelines for Mandarin ASR.
In AISHELL-2, 1000 hours of clean read-speech data from iOS is published, which is free for academic usage.
On top of AISHELL-2 corpus, an improved recipe is developed and released, containing key components for industrial applications, such as Chinese word segmentation, flexible vocabulary expension and phone set transformation etc.
Pipelines support various state-of-the-art techniques, such as time-delayed neural networks and Lattic-Free MMI objective funciton.
In addition, we also release dev and test data from other channels(Android and Mic).
For research community, we hope that AISHELL-2 corpus can be a solid resource for topics like transfer learning and robust ASR.
For industry, we hope AISHELL-2 recipe can be a helpful reference for building meaningful industrial systems and products.
We consider a gossip approach for finding a Nash equilibrium in a distributed multi-player network game.
We extend previous results on Nash equilibrium seeking to the case when the players' cost functions may be affected by the actions of any subset of players.
An interference graph is employed to illustrate the partially-coupled cost functions and the asymmetric information requirements.
For a given interference graph, we design a generalized communication graph so that players with possibly partially-coupled cost functions exchange only their required information and make decisions based on them.
Using a set of standard assumptions on the cost functions, interference and communication graphs, we prove almost sure convergence to a Nash equilibrium for diminishing step sizes.
We then quantify the effect of the second largest eigenvalue of the expected communication matrix on the convergence rate, and illustrate the trade-off between the parameters associated with the communication and the interference graphs.
Finally, the efficacy of the proposed algorithm on a large-scale networked game is demonstrated via simulation.
In the cloud computing environment, cloud virtual machine (VM) will be more and more the number of virtual machine security and management faced giant Challenge.
In order to address security issues cloud computing virtualization environment, this paper presents a virtual machine based on efficient and dynamic deployment VM security management model state migration and scheduling, study of which virtual machine security architecture, based on AHP (Analytic Hierarchy Process) virtual machine deployment and scheduling method, based on CUSUM (Cumulative Sum) DDoS attack detection algorithm, and the above-described method for functional testing and validation.
A current trend in networking and cloud computing is to provide compute resources over widely dispersed places exemplified by initiatives like Network Function Virtualisation.
This paves the way for a widespread service deployment and can improve service quality; a nearby server can reduce the user-perceived response times.
But always using the nearest server is a bad decision if that server is already highly utilized.
This paper investigates the optimal assignment of users to widespread resources -- a convex capacitated facility location problem with integrated queuing systems.
We determine the response times depending on the number of used resources.
This enables service providers to balance between resource costs and the corresponding service quality.
We also present a linear problem reformulation showing small optimality gaps and faster solving times; this speed-up enables a swift reaction to demand changes.
Finally, we compare solutions by either considering or ignoring queuing systems and discuss the response time reduction by using the more complex model.
Our investigations are backed by large-scale numerical evaluations.
ERP systems contain huge amounts of data related to the actual execution of business processes.
These systems have a particular way of recording activities which results in an unclear display of business processes in event logs.
Several works have been conducted on ERP systems, most of them focusing on the development of new algorithms for the automatic discovery of business processes.
We focused on addressing issues like, how can organizations with ERP systems apply process mining for analyzing their business processes in order to improve them.
The data handling aspect of ERP systems contrasts with those of BPMS or workflow based systems, whose systematical storage of events facilitates the application of process mining techniques.
CRISP-DM has emerged as the de facto standard for developing data mining and knowledge discovery projects.
Successful data mining requires three families of analytical capabilities namely reporting, classification and forecasting.
A data miner uses more than one analytical method to get the best results.
The objective of this paper is to improve the usability and understandability of process mining techniques, by implementing CRISP-DM methodology for their application in ERP contexts, detailed in terms of specific implementation tools and step by step coordination.
Our study confirms that data discovery from ERP system improves strategic and operational decision making.
Analog/digital hybrid precoder and combiner have been widely used in millimeter wave (mmWave) multiple-input multiple-output (MIMO) systems due to its energy-efficient and economic superiorities.
Infinite resolution of phase shifters (PSs) for the analog beamformer can achieve very close performance compared to the full-digital scheme but will result in high complexity and intensive power consumption.
Thus, more cost effective and energy efficient low resolution PSs are typically used in practical mmWave MIMO systems.
In this paper, we consider the joint hybrid precoder and combiner design with one-bit quantized PSs in mmWave MIMO systems.
We propose to firstly design the analog precoder and combiner pair for each data stream successively, aiming at conditionally maximizing the spectral efficiency.
We present a novel binary analog precoder and combiner optimization algorithm under a Rank-1 approximation of the interference-included equivalent channel with lower than quadratic complexity.
Then the digital precoder and combiner are computed based on the obtained baseband effective channel to further enhance the spectral efficiency.
Simulation results demonstrate that the proposed algorithm outperforms the existing one-bit PSs based hybrid beamforming scheme.
Sensor networks aim at monitoring their surroundings for event detection and object tracking.
But, due to failure, or death of sensors, false signal can be transmitted.
In this paper, we consider the problems of distributed fault detection in wireless sensor network (WSN).
In particular, we consider how to take decision regarding fault detection in a noisy environment as a result of false detection or false response of event by some sensors, where the sensors are placed at the center of regular hexagons and the event can occur at only one hexagon.
We propose fault detection schemes that explicitly introduce the error probabilities into the optimal event detection process.
We introduce two types of detection probabilities, one for the center node, where the event occurs and the other one for the adjacent nodes.
This second type of detection probability is new in sensor network literature.
We develop schemes under the model selection procedure, multiple model selection procedure and use the concept of Bayesian model averaging to identify a set of likely fault sensors and obtain an average predictive error.
Agriculture is vital for human survival and remains a major driver of several economies around the world; more so in underdeveloped and developing economies.
With increasing demand for food and cash crops, due to a growing global population and the challenges posed by climate change, there is a pressing need to increase farm outputs while incurring minimal costs.
Previous machine vision technologies developed for selective weeding have faced the challenge of reliable and accurate weed detection.
We present approaches for plant seedlings classification with a dataset that contains 4,275 images of approximately 960 unique plants belonging to 12 species at several growth stages.
We compare the performances of two traditional algorithms and a Convolutional Neural Network (CNN), a deep learning technique widely applied to image recognition, for this task.
Our findings show that CNN-driven seedling classification applications when used in farming automation has the potential to optimize crop yield and improve productivity and efficiency when designed appropriately.
Thinning is the removal of contour pixels/points of connected components in an image to produce their skeleton with retained connectivity and structural properties.
The output requirements of a thinning procedure often vary with application.
This paper proposes a sequential algorithm that is very easy to understand and modify based on application to perform the thinning of multi-dimensional binary patterns.
The algorithm was tested on 2D and 3D patterns and showed very good results.
Moreover, comparisons were also made with two of the state-of-the-art methods used for 2D patterns.
The results obtained prove the validity of the procedure.
Fads, product adoption, mobs, rumors, memes, and emergent norms are diverse social contagions that have been modeled as network cascades.
Empirical study of these cascades is vulnerable to what we describe as the "opacity problem": the inability to observe the critical level of peer influence required to trigger an individual's behavioral change.
Even with maximal information, network cascades reveal intervals that bound critical levels of peer exposure, rather than critical values themselves.
Existing practice uses interval maxima, which systematically over-estimates the social influence required for behavioral change.
Simulations reveal that the over-estimation is likely common and large in magnitude.
This is confirmed by an empirical study of hashtag cascades among 3.2 million Twitter users: one in five hashtag adoptions suffers critical value uncertainty due to the opacity problem.
Different assumptions about these intervals lead to qualitatively different conclusions about the role of peer reinforcement in diffusion.
We introduce a solution that combines identifying tightly bounded intervals with predicting uncertain critical values using node-level information.
Ontologies are one of the core foundations of the Semantic Web.
To participate in Semantic Web projects, domain experts need to be able to understand the ontologies involved.
Visual notations can provide an overview of the ontology and help users to understand the connections among entities.
However, the users first need to learn the visual notation before they can interpret it correctly.
Controlled natural language representation would be readable right away and might be preferred in case of complex axioms, however, the structure of the ontology would remain less apparent.
We propose to combine ontology visualizations with contextual ontology verbalizations of selected ontology (diagram) elements, displaying controlled natural language (CNL) explanations of OWL axioms corresponding to the selected visual notation elements.
Thus, the domain experts will benefit from both the high-level overview provided by the graphical notation and the detailed textual explanations of particular elements in the diagram.
Modal logics are widely used in computer science.
The complexity of their satisfiability problems has been an active field of research since the 1970s.
We prove that even very "simple" modal logics can be undecidable: We show that there is an undecidable modal logic that can be obtained by restricting the allowed models with a first-order formula in which only universal quantifiers appear.
Cops and robbers is a vertex-pursuit game played on graphs.
In the classical cops-and-robbers game, a set of cops and a robber occupy the vertices of the graph and move alternately along the graph's edges with perfect information about each other's positions.
If a cop eventually occupies the same vertex as the robber, then the cops win; the robber wins if she can indefinitely evade capture.
Aigner and Frommer established that in every connected planar graph, three cops are sufficient to capture a single robber.
In this paper, we consider a recently studied variant of the cops-and-robbers game, alternately called the one-active-cop game, one-cop-moves game or the lazy-cops-and-robbers game, where at most one cop can move during any round.
We show that Aigner and Frommer's result does not generalise to this game variant by constructing a connected planar graph on which a robber can indefinitely evade three cops in the one-cop-moves game.
This answers a question recently raised by Sullivan, Townsend and Werzanski.
Distributed Denial-of-Service (DDoS) is a menace for service provider and prominent issue in network security.
Defeating or defending the DDoS is a prime challenge.
DDoS make a service unavailable for a certain time.
This phenomenon harms the service providers, and hence, loss of business revenue.
Therefore, DDoS is a grand challenge to defeat.
There are numerous mechanism to defend DDoS, however, this paper surveys the deployment of Bloom Filter in defending a DDoS attack.
The Bloom Filter is a probabilistic data structure for membership query that returns either true or false.
Bloom Filter uses tiny memory to store information of large data.
Therefore, packet information is stored in Bloom Filter to defend and defeat DDoS.
This paper presents a survey on DDoS defending technique using Bloom Filter.
Deep neural networks have shown excellent performance for stereo matching.
Many efforts focus on the feature extraction and similarity measurement of the matching cost computation step while less attention is paid on cost aggregation which is crucial for stereo matching.
In this paper, we present a learning-based cost aggregation method for stereo matching by a novel sub-architecture in the end-to-end trainable pipeline.
We reformulate the cost aggregation as a learning process of the generation and selection of cost aggregation proposals which indicate the possible cost aggregation results.
The cost aggregation sub-architecture is realized by a two-stream network: one for the generation of cost aggregation proposals, the other for the selection of the proposals.
The criterion for the selection is determined by the low-level structure information obtained from a light convolutional network.
The two-stream network offers a global view guidance for the cost aggregation to rectify the mismatching value stemming from the limited view of the matching cost computation.
The comprehensive experiments on challenge datasets such as KITTI and Scene Flow show that our method outperforms the state-of-the-art methods.
Owing to the expeditious growth in the information and communication technologies, smart cities have raised the expectations in terms of efficient functioning and management.
One key aspect of residents' daily comfort is assured through affording reliable traffic management and route planning.
Comprehensively, the majority of the present trip planning applications and service providers are enabling their trip planning recommendations relying on shortest paths and/or fastest routes.
However, such suggestions may discount drivers' preferences with respect to safe and less disturbing trips.
Road anomalies such as cracks, potholes, and manholes induce risky driving scenarios and can lead to vehicles damages and costly repairs.
Accordingly, in this paper, we propose a crowdsensing based dynamic route planning system.
Leveraging both the vehicle motion sensors and the inertial sensors within the smart devices, road surface types and anomalies have been detected and categorized.
In addition, the monitored events are geo-referenced utilizing GPS receivers on both vehicles and smart devices.
Consequently, road segments assessments are conducted using fuzzy system models based on aspects such as the number of anomalies and their severity levels in each road segment.
Afterward, another fuzzy model is adopted to recommend the best trip routes based on the road segments quality in each potential route.
Extensive road experiments are held to build and show the potential of the proposed system.
We report on an extended robot control application of a contact-less and airborne ultrasonic tactile display (AUTD) stimulus-based brain-computer interface (BCI) paradigm, which received last year The Annual BCI Research Award 2014.
In the award winning human communication augmentation paradigm the six palm positions are used to evoke somatosensory brain responses, in order to define a novel contactless tactile BCI.
An example application of a small robot management is also presented in which the users control a small robot online.
We discuss deep reinforcement learning in an overview style.
We draw a big picture, filled with details.
We discuss six core elements, six important mechanisms, and twelve applications, focusing on contemporary work, and in historical contexts.
We start with background of artificial intelligence, machine learning, deep learning, and reinforcement learning (RL), with resources.
Next we discuss RL core elements, including value function, policy, reward, model, exploration vs. exploitation, and representation.
Then we discuss important mechanisms for RL, including attention and memory, unsupervised learning, hierarchical RL, multi-agent RL, relational RL, and learning to learn.
After that, we discuss RL applications, including games, robotics, natural language processing (NLP), computer vision, finance, business management, healthcare, education, energy, transportation, computer systems, and, science, engineering, and art.
Finally we summarize briefly, discuss challenges and opportunities, and close with an epilogue.
This paper presents the probability hypothesis density filter (PHD) and the cardinality PHD (CPHD) filter for sets of trajectories, which are referred to as the trajectory PHD (TPHD) and trajectory CPHD (TCPHD) filters.
Contrary to the PHD/CPHD filters, the TPHD/TCPHD filters are able to produce trajectory estimates from first principles.
The TPHD filter is derived by recursively obtaining the best Poisson multitrajectory density approximation to the posterior density over the alive trajectories by minimising the Kullback-Leibler divergence.
The TCPHD is derived in the same way but propagating an independent identically distributed (IID) cluster multitrajectory density approximation.
We also propose the Gaussian mixture implementations of the TPHD and TCPHD recursions, the Gaussian mixture TPHD (GMTPHD) and the Gaussian mixture TCPHD (GMTCPHD), and the L-scan computationally efficient implementations, which only update the density of the trajectory states of the last L time steps.
Secure email is increasingly being touted as usable by novice users, with a push for adoption based on recent concerns about government surveillance.
To determine whether secure email is for grassroots adoption, we employ a laboratory user study that recruits pairs of novice to install and use several of the latest systems to exchange secure messages.
We present quantitative and qualitative results from 25 pairs of novice users as they use Pwm, Tutanota, and Virtru.
Participants report being more at ease with this type of study and better able to cope with mistakes since both participants are "on the same page".
We find that users prefer integrated solutions over depot-based solutions, and that tutorials are important in helping first-time users.
Hiding the details of how a secure email system provides security can lead to a lack of trust in the system.
Participants expressed a desire to use secure email, but few wanted to use it regularly and most were unsure of when they might use it.
Nowadays, the major challenge in machine learning is the Big Data challenge.
The big data problems due to large number of data points or large number of features in each data point, or both, the training of models have become very slow.
The training time has two major components: Time to access the data and time to process (learn from) the data.
So far, the research has focused only on the second part, i.e., learning from the data.
In this paper, we have proposed one possible solution to handle the big data problems in machine learning.
The idea is to reduce the training time through reducing data access time by proposing systematic sampling and cyclic/sequential sampling to select mini-batches from the dataset.
To prove the effectiveness of proposed sampling techniques, we have used Empirical Risk Minimization, which is commonly used machine learning problem, for strongly convex and smooth case.
The problem has been solved using SAG, SAGA, SVRG, SAAG-II and MBSGD (Mini-batched SGD), each using two step determination techniques, namely, constant step size and backtracking line search method.
Theoretical results prove the same convergence for systematic sampling, cyclic sampling and the widely used random sampling technique, in expectation.
Experimental results with bench marked datasets prove the efficacy of the proposed sampling techniques and show up to six times faster training.
In this paper, a complete preprocessing methodology for discovering patterns in web usage mining process to improve the quality of data by reducing the quantity of data has been proposed.
A dynamic ART1 neural network clustering algorithm to group users according to their Web access patterns with its neat architecture is also proposed.
Several experiments are conducted and the results show the proposed methodology reduces the size of Web log files down to 73-82% of the initial size and the proposed ART1 algorithm is dynamic and learns relatively stable quality clusters.
This paper studies dynamic spectrum leasing in a cognitive radio network.
There are two spectrum sellers, who are two primary networks, each with an amount of licensed spectrum bandwidth.
When a seller has some unused spectrum, it would like to lease the unused spectrum to secondary users.
A coordinator helps to perform the spectrum leasing stage-by-stage.
As the two sellers may have different leasing period, there are three epochs, in which seller 1 has spectrum to lease in Epochs II and III, while seller 2 has spectrum to lease in Epochs I and II.
Each seller needs to decide how much spectrum it should lease to secondary users in each stage of its leasing period, with a target at revenue maximization.
It is shown that, when the two sellers both have spectrum to lease (i.e., in Epoch II), the spectrum leasing can be formulated as a non-cooperative game.
Nash equilibria of the game are found in closed form.
Solutions of the two users in the three epochs are derived.
Nowadays stochastic approximation methods are one of the major research direction to deal with the large-scale machine learning problems.
From stochastic first order methods, now the focus is shifting to stochastic second order methods due to their faster convergence.
In this paper, we have proposed a novel Stochastic Trust RegiOn inexact Newton method, called as STRON, which uses conjugate gradient (CG) to solve trust region subproblem.
The method uses progressive subsampling in the calculation of gradient and Hessian values to take the advantage of both stochastic approximation and full batch regimes.
We have extended STRON using existing variance reduction techniques to deal with the noisy gradients, and using preconditioned conjugate gradient (PCG) as subproblem solver.
We further extend STRON to solve SVM.
Finally, the theoretical results prove superlinear convergence for STRON and the empirical results prove the efficacy of the proposed method against existing methods with bench marked datasets.
Neural networks are becoming central in several areas of computer vision and image processing and different architectures have been proposed to solve specific problems.
The impact of the loss layer of neural networks, however, has not received much attention in the context of image processing: the default and virtually only choice is L2.
In this paper, we bring attention to alternative choices for image restoration.
In particular, we show the importance of perceptually-motivated losses when the resulting image is to be evaluated by a human observer.
We compare the performance of several losses, and propose a novel, differentiable error function.
We show that the quality of the results improves significantly with better loss functions, even when the network architecture is left unchanged.
Semantic segmentation, like other fields of computer vision, has seen a remarkable performance advance by the use of deep convolution neural networks.
However, considering that neighboring pixels are heavily dependent on each other, both learning and testing of these methods have a lot of redundant operations.
To resolve this problem, the proposed network is trained and tested with only 0.37% of total pixels by superpixel-based sampling and largely reduced the complexity of upsampling calculation.
The hypercolumn feature maps are constructed by pyramid module in combination with the convolution layers of the base network.
Since the proposed method uses a very small number of sampled pixels, the end-to-end learning of the entire network is difficult with a common learning rate for all the layers.
In order to resolve this problem, the learning rate after sampling is controlled by statistical process control (SPC) of gradients in each layer.
The proposed method performs better than or equal to the conventional methods that use much more samples on Pascal Context, SUN-RGBD dataset.
Deep learning is an effective approach to solving image recognition problems.
People draw intuitive conclusions from trading charts; this study uses the characteristics of deep learning to train computers in imitating this kind of intuition in the context of trading charts.
The three steps involved are as follows: 1. Before training, we pre-process the input data from quantitative data to images.
2. We use a convolutional neural network (CNN), a type of deep learning, to train our trading model.
3. We evaluate the model's performance in terms of the accuracy of classification.
A trading model is obtained with this approach to help devise trading strategies.
The main application is designed to help clients automatically obtain personalized trading strategies.
Model pruning has become a useful technique that improves the computational efficiency of deep learning, making it possible to deploy solutions in resource-limited scenarios.
A widely-used practice in relevant work assumes that a smaller-norm parameter or feature plays a less informative role at the inference time.
In this paper, we propose a channel pruning technique for accelerating the computations of deep convolutional neural networks (CNNs) that does not critically rely on this assumption.
Instead, it focuses on direct simplification of the channel-to-channel computation graph of a CNN without the need of performing a computationally difficult and not-always-useful task of making high-dimensional tensors of CNN structured sparse.
Our approach takes two stages: first to adopt an end-to- end stochastic training method that eventually forces the outputs of some channels to be constant, and then to prune those constant channels from the original neural network by adjusting the biases of their impacting layers such that the resulting compact model can be quickly fine-tuned.
Our approach is mathematically appealing from an optimization perspective and easy to reproduce.
We experimented our approach through several image learning benchmarks and demonstrate its interesting aspects and competitive performance.
Advancements in technology and culture lead to changes in our language.
These changes create a gap between the language known by users and the language stored in digital archives.
It affects user's possibility to firstly find content and secondly interpret that content.
In previous work we introduced our approach for Named Entity Evolution Recognition~(NEER) in newspaper collections.
Lately, increasing efforts in Web preservation lead to increased availability of Web archives covering longer time spans.
However, language on the Web is more dynamic than in traditional media and many of the basic assumptions from the newspaper domain do not hold for Web data.
In this paper we discuss the limitations of existing methodology for NEER.
We approach these by adapting an existing NEER method to work on noisy data like the Web and the Blogosphere in particular.
We develop novel filters that reduce the noise and make use of Semantic Web resources to obtain more information about terms.
Our evaluation shows the potentials of the proposed approach.
We propose an algebraic setup for end-to-end physical-layer network coding based on submodule transmission.
We introduce a distance function between modules, describe how it relates to information loss and errors, and show how to compute it.
Then we propose a definition of submodule error-correcting code, and investigate bounds and constructions for such codes.
The main challenge of online multi-object tracking is to reliably associate object trajectories with detections in each video frame based on their tracking history.
In this work, we propose the Recurrent Autoregressive Network (RAN), a temporal generative modeling framework to characterize the appearance and motion dynamics of multiple objects over time.
The RAN couples an external memory and an internal memory.
The external memory explicitly stores previous inputs of each trajectory in a time window, while the internal memory learns to summarize long-term tracking history and associate detections by processing the external memory.
We conduct experiments on the MOT 2015 and 2016 datasets to demonstrate the robustness of our tracking method in highly crowded and occluded scenes.
Our method achieves top-ranked results on the two benchmarks.
A new wave of decision-support systems are being built today using AI services that draw insights from data (like text and video) and incorporate them in human-in-the-loop assistance.
However, just as we expect humans to be ethical, the same expectation needs to be met by automated systems that increasingly get delegated to act on their behalf.
A very important aspect of an ethical behavior is to avoid (intended, perceived, or accidental) bias.
Bias occurs when the data distribution is not representative enough of the natural phenomenon one wants to model and reason about.
The possibly biased behavior of a service is hard to detect and handle if the AI service is merely being used and not developed from scratch, since the training data set is not available.
In this situation, we envisage a 3rd party rating agency that is independent of the API producer or consumer and has its own set of biased and unbiased data, with customizable distributions.
We propose a 2-step rating approach that generates bias ratings signifying whether the AI service is unbiased compensating, data-sensitive biased, or biased.
The approach also works on composite services.
We implement it in the context of text translation and report interesting results.
We develop a novel method, based on the statistical concept of the Vapnik-Chervonenkis dimension, to evaluate the selectivity (output cardinality) of SQL queries - a crucial step in optimizing the execution of large scale database and data-mining operations.
The major theoretical contribution of this work, which is of independent interest, is an explicit bound to the VC-dimension of a range space defined by all possible outcomes of a collection (class) of queries.
We prove that the VC-dimension is a function of the maximum number of Boolean operations in the selection predicate and of the maximum number of select and join operations in any individual query in the collection, but it is neither a function of the number of queries in the collection nor of the size (number of tuples) of the database.
We leverage on this result and develop a method that, given a class of queries, builds a concise random sample of a database, such that with high probability the execution of any query in the class on the sample provides an accurate estimate for the selectivity of the query on the original large database.
The error probability holds simultaneously for the selectivity estimates of all queries in the collection, thus the same sample can be used to evaluate the selectivity of multiple queries, and the sample needs to be refreshed only following major changes in the database.
The sample representation computed by our method is typically sufficiently small to be stored in main memory.
We present extensive experimental results, validating our theoretical analysis and demonstrating the advantage of our technique when compared to complex selectivity estimation techniques used in PostgreSQL and the Microsoft SQL Server.
Remote sensing image classification is a fundamental task in remote sensing image processing.
Remote sensing field still lacks of such a large-scale benchmark compared to ImageNet, Place2.
We propose a remote sensing image classification benchmark (RSI-CB) based on crowd-source data which is massive, scalable, and diversity.
Using crowdsource data, we can efficiently annotate ground objects in remotes sensing image by point of interests, vectors data from OSM or other crowd-source data.
Based on this method, we construct a worldwide large-scale benchmark for remote sensing image classification.
In this benchmark, there are two sub datasets with 256 * 256 and 128 * 128 size respectively since different convolution neural networks requirement different image size.
The former sub dataset contains 6 categories with 35 subclasses with total of more than 24,000 images; the later one contains 6 categories with 45 subclasses with total of more than 36,000 images.
The six categories are agricultural land, construction land and facilities, transportation and facilities, water and water conservancy facilities, woodland and other land, and each category has several subclasses.
This classification system is defined according to the national standard of land use classification in China, and is inspired by the hierarchy mechanism of ImageNet.
Finally, we have done a large number of experiments to compare RSI-CB with SAT-4, UC-Merced datasets on handcrafted features, such as such as SIFT, and classical CNN models, such as AlexNet, VGG, GoogleNet, and ResNet.
We also show CNN models trained by RSI-CB have good performance when transfer to other dataset, i.e.UC-Merced, and good generalization ability.
The experiments show that RSI-CB is more suitable as a benchmark for remote sensing image classification task than other ones in big data era, and can be potentially used in practical applications.
Understanding the loss surface of neural networks is essential for the design of models with predictable performance and their success in applications.
Experimental results suggest that sufficiently deep and wide neural networks are not negatively impacted by suboptimal local minima.
Despite recent progress, the reason for this outcome is not fully understood.
Could deep networks have very few, if at all, suboptimal local optima? or could all of them be equally good?
We provide a construction to show that suboptimal local minima (i.e. non-global ones), even though degenerate, exist for fully connected neural networks with sigmoid activation functions.
The local minima obtained by our proposed construction belong to a connected set of local solutions that can be escaped from via a non-increasing path on the loss curve.
For extremely wide neural networks with two hidden layers, we prove that every suboptimal local minimum belongs to such a connected set.
This provides a partial explanation for the successful application of deep neural networks.
In addition, we also characterize under what conditions the same construction leads to saddle points instead of local minima for deep neural networks.
The most data-efficient algorithms for reinforcement learning (RL) in robotics are based on uncertain dynamical models: after each episode, they first learn a dynamical model of the robot, then they use an optimization algorithm to find a policy that maximizes the expected return given the model and its uncertainties.
It is often believed that this optimization can be tractable only if analytical, gradient-based algorithms are used; however, these algorithms require using specific families of reward functions and policies, which greatly limits the flexibility of the overall approach.
In this paper, we introduce a novel model-based RL algorithm, called Black-DROPS (Black-box Data-efficient RObot Policy Search) that: (1) does not impose any constraint on the reward function or the policy (they are treated as black-boxes), (2) is as data-efficient as the state-of-the-art algorithm for data-efficient RL in robotics, and (3) is as fast (or faster) than analytical approaches when several cores are available.
The key idea is to replace the gradient-based optimization algorithm with a parallel, black-box algorithm that takes into account the model uncertainties.
We demonstrate the performance of our new algorithm on two standard control benchmark problems (in simulation) and a low-cost robotic manipulator (with a real robot).
Advances in machine learning have produced systems that attain human-level performance on certain visual tasks, e.g., object identification.
Nonetheless, other tasks requiring visual expertise are unlikely to be entrusted to machines for some time, e.g., satellite and medical imagery analysis.
We describe a human-machine cooperative approach to visual search, the aim of which is to outperform either human or machine acting alone.
The traditional route to augmenting human performance with automatic classifiers is to draw boxes around regions of an image deemed likely to contain a target.
Human experts typically reject this type of hard highlighting.
We propose instead a soft highlighting technique in which the saliency of regions of the visual field is modulated in a graded fashion based on classifier confidence level.
We report on experiments with both synthetic and natural images showing that soft highlighting achieves a performance synergy surpassing that attained by hard highlighting.
In this paper the effect of posibilistic or mixed background risk on the level of optimal prevention is studied.
In the framework of five purely possibilistic or mixed models, necessary and sufficient conditions are found such that the level of optimal saving decreases or increases as a result of the actions of various types of background risk.
This way our results complete those obtained by Courbage and Rey for some prevention models with probabilistic background risk.
Through a combination of experimental and simulation results, we illustrate that passive recommendations encoded in typical computer user-interfaces (UIs) can subdue users' natural proclivity to access diverse information sources.
Inspired by traditional demonstrations of a part-set cueing effect in the cognitive science literature, we performed an online experiment manipulating the operation of the 'New Tab' page for consenting volunteers over a two month period.
Examination of their browsing behavior reveals that typical frequency and recency-based methods for displaying websites in these displays subdues users' propensity to access infrequently visited pages compared to a situation wherein no web page icons are displayed on the new tab page.
Using a carefully designed simulation study, representing user behavior as a random walk on a graph, we inferred quantitative predictions about the extent to which discovery of new sources of information may be hampered by personalized 'New Tab' recommendations in typical computer UIs.
We show that our results are significant at the individual level and explain the potential consequences of the observed suppression in web-exploration.
Although the latent factor model achieves good accuracy in rating prediction, it suffers from many problems including cold-start, non-transparency, and suboptimal results for individual user-item pairs.
In this paper, we exploit textual reviews and item images together with ratings to tackle these limitations.
Specifically, we first apply a proposed multi-modal aspect-aware topic model (MATM) on text reviews and item images to model users' preferences and items' features from different aspects, and also estimate the aspect importance of a user towards an item.
Then the aspect importance is integrated into a novel aspect-aware latent factor model (ALFM), which learns user's and item's latent factors based on ratings.
In particular, ALFM introduces a weight matrix to associate those latent factors with the same set of aspects in MATM, such that the latent factors could be used to estimate aspect ratings.
Finally, the overall rating is computed via a linear combination of the aspect ratings, which are weighted by the corresponding aspect importance.
To this end, our model could alleviate the data sparsity problem and gain good interpretability for recommendation.
Besides, every aspect rating is weighted by its aspect importance, which is dependent on the targeted user's preferences and the targeted item's features.
Therefore, it is expected that the proposed method can model a user's preferences on an item more accurately for each user-item pair.
Comprehensive experimental studies have been conducted on the Yelp 2017 Challenge dataset and Amazon product datasets to demonstrate the effectiveness of our method.
In spite of recent advances in field delineation methods, bibliometricians still don't know the extent to which their topic detection algorithms reconstruct `ground truths', i.e. thematic structures in the scientific literature.
In this paper, we demonstrate a new approach to the delineation of thematic structures that attempts to match the algorithm to theoretically derived and empirically observed properties all thematic structures have in common.
We cluster citation links rather than publication nodes, use predominantly local information and search for communities of links starting from seed subgraphs in order to allow for pervasive overlaps of topics.
We evaluate sets of links with a new cost function and assume that local minima in the cost landscape correspond to link communities.
Because this cost landscape has many local minima we define a valid community as the community with the lowest minimum within a certain range.
Since finding all valid communities is impossible for large networks, we designed a memetic algorithm that combines probabilistic evolutionary strategies with deterministic local searches.
We apply our approach to a network of about 15,000 Astronomy & Astrophysics papers published 2010 and their cited sources, and to a network of about 100,000 Astronomy & Astrophysics papers (published 2003--2010) which are linked through direct citations.
The construction of business process models has become an important requisite in the analysis and optimization of processes.
The success of the analysis and optimization efforts heavily depends on the quality of the models.
Therefore, a research domain emerged that studies the process of process modeling.
This paper contributes to this research by presenting a way of visualizing the different steps a modeler undertakes to construct a process model, in a so-called process of process modeling Chart.
The graphical representation lowers the cognitive efforts to discover properties of the modeling process, which facilitates the research and the development of theory, training and tool support for improving model quality.
The paper contains an extensive overview of applications of the tool that demonstrate its usefulness for research and practice and discusses the observations from the visualization in relation to other work.
The visualization was evaluated through a qualitative study that confirmed its usefulness and added value compared to the Dotted Chart on which the visualization was inspired.
Current paper reports the advantages of the application of GitHub and LaTeX for the MSc thesis writing.
The existing code-based program implemented in GitHub portal provides a great tool for scientists and students for data sharing and notification of the co-workers, tutors and supervisors involved in research about actual updates.
It enables to connect collaborators to share around current results, release datasets and updates and more.
Using standard command-line interface GitHub allows registered users to push repositories on the website.
The availability of both public and private repositories enables to share current data updates with target audience: e.g. unpublished research work only for co-authors or supervisors, or vice versa.
Therefore, there is a need in academic centres and universities to strongly popularize and increase the use of GitHub for student works.
The case study is given on the graduate study: an MSc work written and maintained using open source GitHub service at the University of Twente, Faculty of Geo-Information Science and Earth Observation (Netherlands).
It reports my successful experience of writing MSc thesis based on the effective combination of LaTeX and GitHub.
The safety of infinite state systems can be checked by a backward reachability procedure.
For certain classes of systems, it is possible to prove the termination of the procedure and hence conclude the decidability of the safety problem.
Although backward reachability is property-directed, it can unnecessarily explore (large) portions of the state space of a system which are not required to verify the safety property under consideration.
To avoid this, invariants can be used to dramatically prune the search space.
Indeed, the problem is to guess such appropriate invariants.
In this paper, we present a fully declarative and symbolic approach to the mechanization of backward reachability of infinite state systems manipulating arrays by Satisfiability Modulo Theories solving.
Theories are used to specify the topology and the data manipulated by the system.
We identify sufficient conditions on the theories to ensure the termination of backward reachability and we show the completeness of a method for invariant synthesis (obtained as the dual of backward reachability), again, under suitable hypotheses on the theories.
We also present a pragmatic approach to interleave invariant synthesis and backward reachability so that a fix-point for the set of backward reachable states is more easily obtained.
Finally, we discuss heuristics that allow us to derive an implementation of the techniques in the model checker MCMT, showing remarkable speed-ups on a significant set of safety problems extracted from a variety of sources.
This paper presents an integrated multi-agents architecture for indexing and retrieving video information.The focus of our work is to elaborate an extensible approach that gathers a priori almost of the mandatory tools which palliate to the major intertwining problems raised in the whole process of the video lifecycle (classification, indexing and retrieval).
In fact, effective and optimal retrieval video information needs a collaborative approach based on multimodal aspects.
Clearly, it must to take into account the distributed aspect of the data sources, the adaptation of the contents, semantic annotation, personalized request and active feedback which constitute the backbone of a vigorous system which improve its performances in a smart way
The problem of improving the efficiency of the teaching department through the development of teaching department work area is described.
Development of an automated workplace of a teaching department who allows to realize monitoring of progress of students, monitoring of mastering of disciplines by students, is synchronized with an automated workplace of the teacher of the higher school and autocompletes the report of movement of the contingent.
Besides, the designed system allows to increase efficiency and efficiency of activities of employees of a teaching department.
In a modern recommender system, it is important to understand how products relate to each other.
For example, while a user is looking for mobile phones, it might make sense to recommend other phones, but once they buy a phone, we might instead want to recommend batteries, cases, or chargers.
These two types of recommendations are referred to as substitutes and complements: substitutes are products that can be purchased instead of each other, while complements are products that can be purchased in addition to each other.
Here we develop a method to infer networks of substitutable and complementary products.
We formulate this as a supervised link prediction task, where we learn the semantics of substitutes and complements from data associated with products.
The primary source of data we use is the text of product reviews, though our method also makes use of features such as ratings, specifications, prices, and brands.
Methodologically, we build topic models that are trained to automatically discover topics from text that are successful at predicting and explaining such relationships.
Experimentally, we evaluate our system on the Amazon product catalog, a large dataset consisting of 9 million products, 237 million links, and 144 million reviews.
This short paper reports the algorithms we used and the evaluation performances for ISIC Challenge 2018.
Our team participates in all the tasks in this challenge.
In lesion segmentation task, the pyramid scene parsing network (PSPNet) is modified to segment the lesions.
In lesion attribute detection task, the modified PSPNet is also adopted in a multi-label way.
In disease classification task, the DenseNet-169 is adopted for multi-class classification.
Requirements about the quality of clinical guidelines can be represented by schemata borrowed from the theory of abductive diagnosis, using temporal logic to model the time-oriented aspects expressed in a guideline.
Previously, we have shown that these requirements can be verified using interactive theorem proving techniques.
In this paper, we investigate how this approach can be mapped to the facilities of a resolution-based theorem prover, Otter, and a complementary program that searches for finite models of first-order statements, Mace.
It is shown that the reasoning required for checking the quality of a guideline can be mapped to such fully automated theorem-proving facilities.
The medical quality of an actual guideline concerning diabetes mellitus 2 is investigated in this way.
Cloud Service Providers (CSPs) offer a wide variety of scalable, flexible, and cost-efficient services to cloud users on demand and pay-per-utilization basis.
However, vast diversity in available cloud service providers leads to numerous challenges for users to determine and select the best suitable service.
Also, sometimes users need to hire the required services from multiple CSPs which introduce difficulties in managing interfaces, accounts, security, supports, and Service Level Agreements (SLAs).
To circumvent such problems having a Cloud Service Broker (CSB) be aware of service offerings and users Quality of Service (QoS) requirements will benefit both the CSPs as well as users.
In this work, we proposed a Fuzzy Rough Set based Cloud Service Brokerage Architecture, which is responsible for ranking and selecting services based on users QoS requirements, and finally monitor the service execution.
We have used the fuzzy rough set technique for dimension reduction.
Used weighted Euclidean distance to rank the CSPs.
To prioritize user QoS request, we intended to use user assign weights, also incorporated system assigned weights to give the relative importance to QoS attributes.
We compared the proposed ranking technique with an existing method based on the system response time.
The case study experiment results show that the proposed approach is scalable, resilience, and produce better results with less searching time.
This paper evaluates eight parallel graph processing systems: Hadoop, HaLoop, Vertica, Giraph, GraphLab (PowerGraph), Blogel, Flink Gelly, and GraphX (SPARK) over four very large datasets (Twitter, World Road Network, UK 200705, and ClueWeb) using four workloads (PageRank, WCC, SSSP and K-hop).
The main objective is to perform an independent scale-out study by experimentally analyzing the performance, usability, and scalability (using up to 128 machines) of these systems.
In addition to performance results, we discuss our experiences in using these systems and suggest some system tuning heuristics that lead to better performance.
In this paper we propose an ensemble of local and deep features for object classification.
We also compare and contrast effectiveness of feature representation capability of various layers of convolutional neural network.
We demonstrate with extensive experiments for object classification that the representation capability of features from deep networks can be complemented with information captured from local features.
We also find out that features from various deep convolutional networks encode distinctive characteristic information.
We establish that, as opposed to conventional practice, intermediate layers of deep networks can augment the classification capabilities of features obtained from fully connected layers.
The Hospitals / Residents problem with Couples (HRC) models the allocation of intending junior doctors to hospitals where couples are allowed to submit joint preference lists over pairs of (typically geographically close) hospitals.
It is known that a stable matching need not exist, so we consider MIN BP HRC, the problem of finding a matching that admits the minimum number of blocking pairs (i.e., is "as stable as possible").
We show that this problem is NP-hard and difficult to approximate even in the highly restricted case that each couple finds only one hospital pair acceptable.
However if we further assume that the preference list of each single resident and hospital is of length at most 2, we give a polynomial-time algorithm for this case.
We then present the first Integer Programming (IP) and Constraint Programming (CP) models for MIN BP HRC.
Finally, we discuss an empirical evaluation of these models applied to randomly-generated instances of MIN BP HRC.
We find that on average, the CP model is about 1.15 times faster than the IP model, and when presolving is applied to the CP model, it is on average 8.14 times faster.
We further observe that the number of blocking pairs admitted by a solution is very small, i.e., usually at most 1, and never more than 2, for the (28,000) instances considered.
Disasters lead to devastating structural damage not only to buildings and transport infrastructure, but also to other critical infrastructure, such as the power grid and communication backbones.
Following such an event, the availability of minimal communication services is however crucial to allow efficient and coordinated disaster response, to enable timely public information, or to provide individuals in need with a default mechanism to post emergency messages.
The Internet of Things consists in the massive deployment of heterogeneous devices, most of which battery-powered, and interconnected via wireless network interfaces.
Typical IoT communication architectures enables such IoT devices to not only connect to the communication backbone (i.e. the Internet) using an infrastructure-based wireless network paradigm, but also to communicate with one another autonomously, without the help of any infrastructure, using a spontaneous wireless network paradigm.
In this paper, we argue that the vast deployment of IoT-enabled devices could bring benefits in terms of data network resilience in face of disaster.
Leveraging their spontaneous wireless networking capabilities, IoT devices could enable minimal communication services (e.g. emergency micro-message delivery) while the conventional communication infrastructure is out of service.
We identify the main challenges that must be addressed in order to realize this potential in practice.
These challenges concern various technical aspects, including physical connectivity requirements, network protocol stack enhancements, data traffic prioritization schemes, as well as social and political aspects.
Though deep neural network has hit a huge success in recent studies and applica- tions, it still remains vulnerable to adversarial perturbations which are imperceptible to humans.
To address this problem, we propose a novel network called ReabsNet to achieve high classification accuracy in the face of various attacks.
The approach is to augment an existing classification network with a guardian network to detect if a sample is natural or has been adversarially perturbed.
Critically, instead of simply rejecting adversarial examples, we revise them to get their true labels.
We exploit the observation that a sample containing adversarial perturbations has a possibility of returning to its true class after revision.
We demonstrate that our ReabsNet outperforms the state-of-the-art defense method under various adversarial attacks.
This paper presents a study of improvement in stability in a single machine connected to infinite bus (SMIB) power system by using static compensator (STATCOM).
The gains of Proportional-Integral-Derivative (PID) controller in STATCOM are being optimized by heuristic technique based on Particle swarm optimization (PSO).
Further, Bacterial Foraging Optimization (BFO) as an alternative heuristic method is also applied to select optimal gains of PID controller.
The performance of STATCOM with the above soft-computing techniques are studied and compared with the conventional PID controller under various scenarios.
The simulation results are accompanied with performance indices based quantitative analysis.
The analysis clearly signifies the robustness of the new scheme in terms of stability and voltage regulation when compared with conventional PID.
Compressive sensing (CS) is a new approach for the acquisition and recovery of sparse signals and images that enables sampling rates significantly below the classical Nyquist rate.
Despite significant progress in the theory and methods of CS, little headway has been made in compressive video acquisition and recovery.
Video CS is complicated by the ephemeral nature of dynamic events, which makes direct extensions of standard CS imaging architectures and signal models difficult.
In this paper, we develop a new framework for video CS for dynamic textured scenes that models the evolution of the scene as a linear dynamical system (LDS).
This reduces the video recovery problem to first estimating the model parameters of the LDS from compressive measurements, and then reconstructing the image frames.
We exploit the low-dimensional dynamic parameters (the state sequence) and high-dimensional static parameters (the observation matrix) of the LDS to devise a novel compressive measurement strategy that measures only the dynamic part of the scene at each instant and accumulates measurements over time to estimate the static parameters.
This enables us to lower the compressive measurement rate considerably.
We validate our approach with a range of experiments involving both video recovery, sensing hyper-spectral data, and classification of dynamic scenes from compressive data.
Together, these applications demonstrate the effectiveness of the approach.
We consider distributed elections, where there is a center and k sites.
In such distributed elections, each voter has preferences over some set of candidates, and each voter is assigned to exactly one site such that each site is aware only of the voters assigned to it.
The center is able to directly communicate with all sites.
We are interested in designing communication-efficient protocols, allowing the center to maintain a candidate which, with arbitrary high probability, is guaranteed to be a winner, or at least close to being a winner.
We consider various single-winner voting rules, such as variants of Approval voting and scoring rules, tournament-based voting rules, and several round-based voting rules.
For the voting rules we consider, we show that, using communication which is logarithmic in the number of voters, it is possible for the center to maintain such approximate winners; that is, upon a query at any time the center can immediately return a candidate which is guaranteed to be an approximate winner with high probability.
We complement our protocols with lower bounds.
Our results have implications in various scenarios, such as aggregating customer preferences in online shopping websites or supermarket chains and collecting votes from different polling stations of political elections.
A recurrent neural network model of phonological pattern learning is proposed.
The model is a relatively simple neural network with one recurrent layer, and displays biases in learning that mimic observed biases in human learning.
Single-feature patterns are learned faster than two-feature patterns, and vowel or consonant-only patterns are learned faster than patterns involving vowels and consonants, mimicking the results of laboratory learning experiments.
In non-recurrent models, capturing these biases requires the use of alpha features or some other representation of repeated features, but with a recurrent neural network, these elaborations are not necessary.
E-voting systems (EVS)are having potential advantages over many existing voting schemes.Security, transparency, accuracy and reliability are the major concern in these systems.EVS continues to grow as the technology advances.It is inexpensive and efficient as the resources become reusable.Fast and accurate computation of results with voter privacy is the added advantage.In the proposed system we make use of secret sharing technique and secure multi party computation(SMC) to achieve security and reliability.Secret sharing is an important technique used for SMC.
Multi-party computation is typically accomplished using secret sharing by making shares of the input and manipulating the shares to compute a typical function of the input.The proposed system make use of bitwise representation of votes and only the shares are used for transmission and computation of result.Secure sum evaluation can be done with shares distributed using Shamir's secret sharing scheme.The scheme is hence secure and reliable and does not make any number theoretic assumptions for security.We also propose a unique method which calculates the candidates individual votes keeping the anonymity.
We present a cheap, lightweight, and fast fruit counting pipeline that uses a single monocular camera.
Our pipeline that relies only on a monocular camera, achieves counting performance comparable to state-of-the-art fruit counting system that utilizes an expensive sensor suite including LiDAR and GPS/INS on a mango dataset.
Our monocular camera pipeline begins with a fruit detection component that uses a deep neural network.
It then uses semantic structure from motion (SFM) to convert these detections into fruit counts by estimating landmark locations of the fruit in 3D, and using these landmarks to identify double counting scenarios.
There are many benefits of developing a low cost and lightweight fruit counting system, including applicability to agriculture in developing countries, where monetary constraints or unstructured environments necessitate cheaper hardware solutions.
Computer algorithms are written with the intent that when run they perform a useful function.
Typically any information obtained is unknown until the algorithm is run.
However, if the behavior of an algorithm can be fully described by precomputing just once how this algorithm will respond when executed on any input, this precomputed result provides a complete specification for all solutions in the problem domain.
We apply this idea to a previous anomaly detection algorithm, and in doing so transform it from one that merely detects individual anomalies when asked to discover potentially anomalous values, into an algorithm also capable of generating a complete specification for those values it would deem to be anomalous.
This specification is derived by examining no more than a small training data, can be obtained in very small constant time, and is inherently far more useful than results obtained by repeated execution of this tool.
For example, armed with such a specification one can ask how close an anomaly is to being deemed normal, and can validate this answer not by exhaustively testing the algorithm but by examining if the specification so generated is indeed correct.
This powerful idea can be applied to any algorithm whose runtime behavior can be recovered from its construction and so has wide applicability.
As grids are in essence heterogeneous, dynamic, shared and distributed environments, managing these kinds of platforms efficiently is extremely complex.
A promising scalable approach to deal with these intricacies is the design of self-managing of autonomic applications.
Autonomic applications adapt their execution accordingly by considering knowledge about their own behaviour and environmental conditions.QoS based User Driven scheduling for grid that provides the self-optimizing ability in autonomic applications.
Computational grids to provide a user to solve large scale problem by spreading a single large computation across multiple machines of physical location.
QoS based User Driven scheduler for grid also provides reliability of the grid systems and increase the performance of the grid to reducing the execution time of job by applying scheduling policies defined by the user.
The main aim of this paper is to distribute the computational load among the available grid nodes and to developed a QoS based scheduling algorithm for grid and making grid more reliable.Grid computing system is different from conventional distributed computing systems by its focus on large scale resource sharing, where processors and communication have significant inuence on Grid computing reliability.
Reliability capabilities initiated by end users from within applications they submit to the grid for execution.
Reliability of infrastructure and management services that perform essential functions necessary for grid systems to operate, such as resource allocation and scheduling.
Cover song detection is a very relevant task in Music Information Retrieval (MIR) studies and has been mainly addressed using audio-based systems.
Despite its potential impact in industrial contexts, low performances and lack of scalability have prevented such systems from being adopted in practice for large applications.
In this work, we investigate whether textual music information (such as metadata and lyrics) can be used along with audio for large-scale cover identification problem in a wide digital music library.
We benchmark this problem using standard text and state of the art audio similarity measures.
Our studies shows that these methods can significantly increase the accuracy and scalability of cover detection systems on Million Song Dataset (MSD) and Second Hand Song (SHS) datasets.
By only leveraging standard tf-idf based text similarity measures on song titles and lyrics, we achieved 35.5% of absolute increase in mean average precision compared to the current scalable audio content-based state of the art methods on MSD.
These experimental results suggests that new methodologies can be encouraged among researchers to leverage and identify more sophisticated NLP-based techniques to improve current cover song identification systems in digital music libraries with metadata.
The key contribution of this work is to develop transmitter and receiver algorithms in discrete-time for turbo- coded offset QPSK signals.
The proposed synchronization and detection techniques perform effectively at an SNR per bit close to 1.5 dB, in the presence of a frequency offset as large as 30 % of the symbol-rate and a clock offset of 25 ppm (parts per million).
Due to the use of up-sampling and matched filtering and a feedforward approach, the acquisition time for clock recovery is just equal to the length of the preamble.
The carrier recovery algorithm does not exhibit any phase ambiguity, alleviating the need for differentially encoding the data at the transmitter.
The proposed techniques are well suited for discrete-time implementation.
Full-Duplex (FD) wireless and Device-to-Device (D2D) communication are two promising technologies that aspire to enhance the spectrum and energy efficiency of wireless networks, thus fulfilling key requirements of the 5th generation (5G) of mobile networks.
Both technologies, however, generate excessive interference, which, if not managed effectively, threatens to compromise system performance.
To this direction, we propose two transmission policies that enhance the communication of two interfering FD-enabled D2D pairs, derived from game theory and optimization theory.
The game-theoretic policy allows the pairs to choose their transmission modes independently and the optimal policy to maximize their throughput, achieving significant gains when the pairs interfere strongly with each other.
Kudekar et al. proved that the belief-propagation (BP) threshold for low-density parity-check codes can be boosted up to the maximum-a-posteriori (MAP) threshold by spatial coupling.
In this paper, spatial coupling is applied to randomly-spread code-division multiple-access (CDMA) systems in order to improve the performance of BP-based multiuser detection (MUD).
Spatially-coupled CDMA systems can be regarded as multi-code CDMA systems with two transmission phases.
The large-system analysis shows that spatial coupling can improve the BP performance, while there is a gap between the BP performance and the individually-optimal (IO) performance.
Guetzli is a new JPEG encoder that aims to produce visually indistinguishable images at a lower bit-rate than other common JPEG encoders.
It optimizes both the JPEG global quantization tables and the DCT coefficient values in each JPEG block using a closed-loop optimizer.
Guetzli uses Butteraugli, our perceptual distance metric, as the source of feedback in its optimization process.
We reach a 29-45% reduction in data size for a given perceptual distance, according to Butteraugli, in comparison to other compressors we tried.
Guetzli's computation is currently extremely slow, which limits its applicability to compressing static content and serving as a proof- of-concept that we can achieve significant reductions in size by combining advanced psychovisual models with lossy compression techniques.
In this paper, we make use of channel symmetry properties to determine the capacity region of three types of two-way networks: (a) two-user memoryless two-way channels (TWCs), (b) two-user TWCs with memory, and (c) three-user multiaccess/degraded broadcast (MA/DB) TWCs.
For each network, symmetry conditions under which Shannon's random coding inner bound is tight are given.
For two-user memoryless TWCs, prior results are substantially generalized by viewing a TWC as two interacting state-dependent one-way channels.
The capacity of symmetric TWCs with memory, whose outputs are functions of the inputs and independent stationary and ergodic noise processes, is also obtained.
Moreover, various channel symmetry properties under which Shannon's inner bound is tight are identified for three-user MA/DB TWCs.
The results not only enlarge the class of symmetric TWCs whose capacity region can be exactly determined but also imply that adaptive coding, not improving capacity, is unnecessary for such channels.
Convolutional neural networks belong to the most successul image classifiers, but the adaptation of their network architecture to a particular problem is computationally expensive.
We show that an evolutionary algorithm saves training time during the network architecture optimization, if learned network weights are inherited over generations by Lamarckian evolution.
Experiments on typical image datasets show similar or significantly better test accuracies and improved convergence speeds compared to two different baselines without weight inheritance.
On CIFAR-10 and CIFAR-100 a 75 % improvement in data efficiency is observed.
This work investigates a central problem in steganography, that is: How much data can safely be hidden without being detected?
To answer this question, a formal definition of steganographic capacity is presented.
Once this has been defined, a general formula for the capacity is developed.
The formula is applicable to a very broad spectrum of channels due to the use of an information-spectrum approach.
This approach allows for the analysis of arbitrary steganalyzers as well as non-stationary, non-ergodic encoder and attack channels.
After the general formula is presented, various simplifications are applied to gain insight into example hiding and detection methodologies.
Finally, the context and applications of the work are summarized in a general discussion.
Mobile manipulation tasks are one of the key challenges in the field of search and rescue (SAR) robotics requiring robots with flexible locomotion and manipulation abilities.
Since the tasks are mostly unknown in advance, the robot has to adapt to a wide variety of terrains and workspaces during a mission.
The centaur-like robot Centauro has a hybrid legged-wheeled base and an anthropomorphic upper body to carry out complex tasks in environments too dangerous for humans.
Due to its high number of degrees of freedom, controlling the robot with direct teleoperation approaches is challenging and exhausting.
Supervised autonomy approaches are promising to increase quality and speed of control while keeping the flexibility to solve unknown tasks.
We developed a set of operator assistance functionalities with different levels of autonomy to control the robot for challenging locomotion and manipulation tasks.
The integrated system was evaluated in disaster response scenarios and showed promising performance.
This article presents a new methodology called deep ToC that estimates the solutions of partial differential equations (PDEs) by combining neural networks with the Theory of Connections (ToC).
ToC is used to transform PDEs with boundary conditions into unconstrained optimization problems by embedding the boundary conditions into a "constrained expression" that contains a neural network.
The loss function for the unconstrained optimization problem is taken to be the square of the residual of the PDE.
Then, the neural network is trained in an unsupervised manner to solve the unconstrained optimization problem.
This methodology has two major advantages over other popular methods used to estimate the solutions of PDEs.
First, this methodology does not need to discretize the domain into a grid, which becomes prohibitive as the dimensionality of the PDE increases.
Instead, this methodology randomly samples points from the domain during the training phase.
Second, after training, this methodology represents a closed form, analytical, differentiable approximation of the solution throughout the entire training domain.
In contrast, other popular methods require interpolation if the estimated solution is desired at points that do not lie on the discretized grid.
The deep ToC method for estimating the solution of PDEs is demonstrated on four problems with a variety of boundary conditions.
Music recommender systems have become a key technology supporting the access to increasingly larger music catalogs in on-line music streaming services, on-line music shops, and private collections.
The interaction of users with large music catalogs is a complex phenomenon researched from different disciplines.
We survey our works investigating the machine learning and data mining aspects of hybrid music recommender systems (i.e., systems that integrate different recommendation techniques).
We proposed hybrid music recommender systems based solely on data and robust to the so-called "cold-start problem" for new music items, favoring the discovery of relevant but non-popular music.
We thoroughly studied the specific task of music playlist continuation, by analyzing fundamental playlist characteristics, song feature representations, and the relationship between playlists and the songs therein.
A subspace projection to improve channel estimation in massive multi-antenna systems is proposed and analyzed.
Together with power-controlled hand-off, it can mitigate the pilot contamination problem without the need for coordination among cells.
The proposed method is blind in the sense that it does not require pilot data to find the appropriate subspace.
It is based on the theory of large random matrices that predicts that the eigenvalue spectra of large sample covariance matrices can asymptotically decompose into disjoint bulks as the matrix size grows large.
Random matrix and free probability theory are utilized to predict under which system parameters such a bulk decomposition takes place.
Simulation results are provided to confirm that the proposed method outperforms conventional linear channel estimation if bulk separation occurs.
Recurrent neural networks (RNNs) are widely used to model sequential data but their non-linear dependencies between sequence elements prevent parallelizing training over sequence length.
We show the training of RNNs with only linear sequential dependencies can be parallelized over the sequence length using the parallel scan algorithm, leading to rapid training on long sequences even with small minibatch size.
We develop a parallel linear recurrence CUDA kernel and show that it can be applied to immediately speed up training and inference of several state of the art RNN architectures by up to 9x.
We abstract recent work on linear RNNs into a new framework of linear surrogate RNNs and develop a linear surrogate model for the long short-term memory unit, the GILR-LSTM, that utilizes parallel linear recurrence.
We extend sequence learning to new extremely long sequence regimes that were previously out of reach by successfully training a GILR-LSTM on a synthetic sequence classification task with a one million timestep dependency.
DNA sequencing is the physical or biochemical process of identifying the location of the four bases (Adenine, Guanine, Cytosine, Thymine) in a DNA strand.
As semiconductor technology revolutionized computing, DNA sequencing technology (termed Next Generation Sequencing, NGS) revolutionized genomic research.
Modern NGS platforms can sequence hundreds of millions of short DNA fragments in parallel.
The output short DNA fragments from NGS platforms are termed reads.
Mapping each output read to a reference genome of the same species (i.e., read mapping) is a common critical first step in a rich and diverse set of emerging bioinformatics applications.
The importance of read mapping motivated various sequence alignment and mapping algorithms, which start to fall short of tackling the growing scale of the problem.
Mapping represents a search-heavy memory-intensive operation and barely requires complex floating point arithmetic, therefore, can greatly benefit from in- or near-memory processing, where non-volatile memory can accommodate the large memory footprint in an area and energy efficient manner.
This paper introduces a scalable, energy-efficient high-throughput near (non-volatile) memory read mapping accelerator: BioMAP.
Instead of optimizing an algorithm developed for general-purpose computers or GPUs, BioMAP rethinks the algorithm and accelerator design together from the ground up.
Thereby BioMAP can improve the throughput of read mapping by 4.0 times while reducing the energy consumption by 26.2 times when compared to a highly-optimized algorithm for modern GPUs.
Today's wireless networks are characterized by fixed spectrum assignment policy.
The limited available spectrum and the inefficiency in the spectrum usage necessitate a new communication paradigm to exploit the existing wireless spectrum opportunistically.
Cognitive radio is a paradigm for wireless communication in which either a network or a wireless node changes its transmission or reception parameters to communicate efficiently avoiding interference with licensed or unlicensed users.
In this work, a fuzzy logic based system for spectrum management is proposed where the radio can share unused spectrum depending on some parameters like distance, signal strength, node velocity and availability of unused spectrum.
The system is simulated and is found to give satisfactory results.
Cars can nowadays record several thousands of signals through the CAN bus technology and potentially provide real-time information on the car, the driver and the surrounding environment.
This paper proposes a new method for the analysis and classification of driver behavior using a selected subset of CAN bus signals, specifically gas pedal position, brake pedal pressure, steering wheel angle, steering wheel momentum, velocity, RPM, frontal and lateral acceleration.
Data has been collected in a completely uncontrolled experiment, where 64 people drove 10 cars for or a total of over 2000 driving trips without any type of pre-determined driving instruction on a wide variety of road scenarios.
We propose an unsupervised learning technique that clusters drivers in different groups, and offers a validation method to test the robustness of clustering in a wide range of experimental settings.
The minimal amount of data needed to preserve robust driver clustering is also computed.
The presented study provides a new methodology for near-real-time classification of driver behavior in uncontrolled environments.
Driven by applications like Micro Aerial Vehicles (MAVs), driver-less cars, etc, localization solution has become an active research topic in the past decade.
In recent years, Ultra Wideband (UWB) emerged as a promising technology because of its impressive performance in both indoor and outdoor positioning.
But algorithms relying only on UWB sensor usually result in high latency and low bandwidth, which is undesirable in some situations such as controlling a MAV.
To alleviate this problem, an Extended Kalman Filter (EKF) based algorithm is proposed to fuse the Inertial Measurement Unit (IMU) and UWB, which achieved 80Hz 3D localization with significantly improved accuracy and almost no delay.
To verify the effectiveness and reliability of the proposed approach, a swarm of 6 MAVs is set up to perform a light show in an indoor exhibition hall.
Video and source codes are available at https://github.com/lijx10/uwb-localization
The course description provided by instructors is an important piece of information as it defines what is expected from the instructor and what he/she is going to deliver during a particular course.
One of the key components of a course description is the Learning Outcomes section.
The contents of this section are used by program managers who are tasked to compare and match two different courses during the development of Transfer Agreements between different institutions.
This research introduces the development of visual tools for understanding the two different courses and making comparisons.
We designed methods to extract the text from a course description document, developed an algorithm to perform semantic analysis, and displayed the results in a web interface.
We are able to achieve the intermediate results of the research which includes extracting, analyzing and visualizing the data.
Machine learning based solutions have been successfully employed for automatic detection of malware in Android applications.
However, machine learning models are known to lack robustness against inputs crafted by an adversary.
So far, the adversarial examples can only deceive Android malware detectors that rely on syntactic features, and the perturbations can only be implemented by simply modifying Android manifest.
While recent Android malware detectors rely more on semantic features from Dalvik bytecode rather than manifest, existing attacking/defending methods are no longer effective.
In this paper, we introduce a new highly-effective attack that generates adversarial examples of Android malware and evades being detected by the current models.
To this end, we propose a method of applying optimal perturbations onto Android APK using a substitute model.
Based on the transferability concept, the perturbations that successfully deceive the substitute model are likely to deceive the original models as well.
We develop an automated tool to generate the adversarial examples without human intervention to apply the attacks.
In contrast to existing works, the adversarial examples crafted by our method can also deceive recent machine learning based detectors that rely on semantic features such as control-flow-graph.
The perturbations can also be implemented directly onto APK's Dalvik bytecode rather than Android manifest to evade from recent detectors.
We evaluated the proposed manipulation methods for adversarial examples by using the same datasets that Drebin and MaMadroid (5879 malware samples) used.
Our results show that, the malware detection rates decreased from 96% to 1% in MaMaDroid, and from 97% to 1% in Drebin, with just a small distortion generated by our adversarial examples manipulation method.
We consider the provision of public goods on networks of strategic agents.
We study different effort outcomes of these network games, namely, the Nash equilibria, Pareto efficient effort profiles, and semi-cooperative equilibria (effort profiles resulting from interactions among coalitions of agents).
We identify necessary and sufficient conditions on the structure of the network for the uniqueness of the Nash equilibrium.
We show that our finding unifies (and strengthens) existing results in the literature.
We also identify conditions for the existence of Nash equilibria for the subclasses of games at the two extremes of our model, namely games of strategic complements and games of strategic substitutes.
We provide a graph-theoretical interpretation of agents' efforts at the Nash equilibrium, as well as the Pareto efficient outcomes and semi-cooperative equilibria, by linking an agent's decision to her centrality in the interaction network.
Using this connection, we separate the effects of incoming and outgoing edges on agents' efforts and uncover an alternating effect over walks of different length in the network.
In this paper, we study a nonconvex continuous relaxation of MAP inference in discrete Markov random fields (MRFs).
We show that for arbitrary MRFs, this relaxation is tight, and a discrete stationary point of it can be easily reached by a simple block coordinate descent algorithm.
In addition, we study the resolution of this relaxation using popular gradient methods, and further propose a more effective solution using a multilinear decomposition framework based on the alternating direction method of multipliers (ADMM).
Experiments on many real-world problems demonstrate that the proposed ADMM significantly outperforms other nonconvex relaxation based methods, and compares favorably with state of the art MRF optimization algorithms in different settings.
Given a set V of n elements on m attributes, we want to find a partition of V on the minimum number of clusters such that the associated R-squared ratio is at least a given threshold.
We denote this problem as Goal Clustering (GC).
This problem represents a new perspective, characterizing a different methodology within unsupervised non-hierarchical clustering.
In effect, while in the k-means we set the number of clusters in advance and then test the associated R-squared ratio; in the GC we set an R-squared threshold lower limit in advance and minimize k. We present two Variable Neighborhood Search (VNS) based heuristics for the GC problem.
The two heuristics use different methodologies to start the VNS algorithms.
One is based on the Ward's construction and the other one resorts to the k-means method.
Computational tests are conducted over a set of large sized instances in order to show the performance of the two proposed heuristics.
Assisted text input techniques can save time and effort and improve text quality.
In this paper, we investigate how grounded and conditional extensions to standard neural language models can bring improvements in the tasks of word prediction and completion.
These extensions incorporate a structured knowledge base and numerical values from the text into the context used to predict the next word.
Our automated evaluation on a clinical dataset shows extended models significantly outperform standard models.
Our best system uses both conditioning and grounding, because of their orthogonal benefits.
For word prediction with a list of 5 suggestions, it improves recall from 25.03% to 71.28% and for word completion it improves keystroke savings from 34.35% to 44.81%, where theoretical bound for this dataset is 58.78%.
We also perform a qualitative investigation of how models with lower perplexity occasionally fare better at the tasks.
We found that at test time numbers have more influence on the document level than on individual word probabilities.
Temporal object detection has attracted significant attention, but most popular detection methods can not leverage the rich temporal information in videos.
Very recently, many different algorithms have been developed for video detection task, but real-time online approaches are frequently deficient.
In this paper, based on attention mechanism and convolutional long short-term memory (ConvLSTM), we propose a temporal signal-shot detector (TSSD) for real-world detection.
Distinct from previous methods, we take aim at temporally integrating pyramidal feature hierarchy using ConvLSTM, and design a novel structure including a low-level temporal unit as well as a high-level one (HL-TU) for multi-scale feature maps.
Moreover, we develop a creative temporal analysis unit, namely, attentional ConvLSTM (AC-LSTM), in which a temporal attention module is specially tailored for background suppression and scale suppression while a ConvLSTM integrates attention-aware features through time.
An association loss is designed for temporal coherence.
Besides, online tubelet analysis (OTA) is exploited for identification.
Finally, our method is evaluated on ImageNet VID dataset and 2DMOT15 dataset.
Extensive comparisons on the detection and tracking capability validate the superiority of the proposed approach.
Consequently, the developed TSSD-OTA is fairly faster and achieves an overall competitive performance in terms of detection and tracking.
The source code will be made available.
An energy-harvesting sensor node that is sending status updates to a destination is considered.
The sensor is equipped with a battery of finite size to save its incoming energy, and consumes one unit of energy per status update transmission, which is delivered to the destination instantly over an error-free channel.
The setting is online in which the harvested energy is revealed to the sensor causally over time, and the goal is to design status update transmission policy such that the long term average age of information (AoI) is minimized.
AoI is defined as the time elapsed since the latest update has reached at the destination.
Two energy arrival models are considered: a random battery recharge (RBR) model, and an incremental battery recharge (IBR) model.
In both models, energy arrives according to a Poisson process with unit rate, with values that completely fill up the battery in the RBR model, and with values that fill up the battery incrementally, unit-by-unit, in the IBR model.
The key approach to characterizing the optimal status update policy for both models is showing the optimality of renewal policies, in which the inter-update times follow a specific renewal process that depends on the energy arrival model and the battery size.
It is then shown that the optimal renewal policy has an energy-dependent threshold structure, in which the sensor sends a status update only if the AoI grows above a certain threshold that depends on the energy available.
For both the RBR and the IBR models, the optimal energy-dependent thresholds are characterized explicitly, i.e., in closed-form, in terms of the optimal long term average AoI.
It is also shown that the optimal thresholds are monotonically decreasing in the energy available in the battery, and that the smallest threshold, which comes in effect when the battery is full, is equal to the optimal long term average AoI.
In this note, we generalize the results of arXiv:0901.2703v1 We show that all one-way quantum finite automaton (QFA) models that are at least as general as Kondacs-Watrous QFA's are equivalent in power to classical probabilistic finite automata in this setting.
Unlike their probabilistic counterparts, allowing the tape head to stay put for some steps during its traversal of the input does enlarge the class of languages recognized by such QFA's with unbounded error.
(Note that, the proof of Theorem 1 in the abstract was presented in the previous version (arXiv:0901.2703v1).)
Information hiding is an active area of research where secret information is embedded in innocent-looking carriers such as images and videos for hiding its existence while maintaining their visual quality.
Researchers have presented various image steganographic techniques since the last decade, focusing on payload and image quality.
However, there is a trade-off between these two metrics and keeping a better balance between them is still a challenging issue.
In addition, the existing methods fail to achieve better security due to direct embedding of secret data inside images without encryption consideration, making data extraction relatively easy for adversaries.
Therefore, in this work, we propose a secure image steganographic framework based on stego key-directed adaptive least significant bit (SKA-LSB) substitution method and multi-level cryptography.
In the proposed scheme, stego key is encrypted using a two-level encryption algorithm (TLEA); secret data is encrypted using a multi-level encryption algorithm (MLEA), and the encrypted information is then embedded in the host image using an adaptive LSB substitution method, depending on secret key, red channel, MLEA, and sensitive contents.
The quantitative and qualitative experimental results indicate that the proposed framework maintains a better balance between image quality and security, achieving a reasonable payload with relatively less computational complexity, which confirms its effectiveness compared to other state-of-the-art techniques.
Program transformations are widely used in synthesis, optimization, and maintenance of software.
Correctness of program transformations depends on preservation of some important properties of the input program.
By regarding programs as Kripke structures, many interesting properties of programs can be expressed in temporal logics.
In temporal logic, a formula is interpreted on a single program.
However, to prove correctness of transformations, we encounter formulae which contain some subformulae interpreted on the input program and some on the transformed program.
An example where such a situation arises is verification of optimizing program transformations applied by compilers.
In this paper, we present a logic called Temporal Transformation Logic (TTL) to reason about such formulae.
We consider different types of primitive transformations and present TTL inference rules for them.
Our definitions of program transformations and temporal logic operators are novel in their use of the boolean matrix algebra.
This results in specifications that are succinct and constructive.
Further, we use the boolean matrix algebra in a uniform manner to prove soundness of the TTL inference rules.
Multilingual spoken dialogue systems have gained prominence in the recent past necessitating the requirement for a front-end Language Identification (LID) system.
Most of the existing LID systems rely on modeling the language discriminative information from low-level acoustic features.
Due to the variabilities of speech (speaker and emotional variabilities, etc.), large-scale LID systems developed using low-level acoustic features suffer from a degradation in the performance.
In this approach, we have attempted to model the higher level language discriminative phonotactic information for developing an LID system.
In this paper, the input speech signal is tokenized to phone sequences by using a language independent phone recognizer.
The language discriminative phonotactic information in the obtained phone sequences are modeled using statistical and recurrent neural network based language modeling approaches.
As this approach, relies on higher level phonotactical information it is more robust to variabilities of speech.
Proposed approach is computationally light weight, highly scalable and it can be used in complement with the existing LID systems.
The idea of style transfer has largely only been explored in image-based tasks, which we attribute in part to the specific nature of loss functions used for style transfer.
We propose a general formulation of style transfer as an extension of generative adversarial networks, by using a discriminator to regularize a generator with an otherwise separate loss function.
We apply our approach to the task of learning to play chess in the style of a specific player, and present empirical evidence for the viability of our approach.
In this paper, we present UNet++, a new, more powerful architecture for medical image segmentation.
Our architecture is essentially a deeply-supervised encoder-decoder network where the encoder and decoder sub-networks are connected through a series of nested, dense skip pathways.
The re-designed skip pathways aim at reducing the semantic gap between the feature maps of the encoder and decoder sub-networks.
We argue that the optimizer would deal with an easier learning task when the feature maps from the decoder and encoder networks are semantically similar.
We have evaluated UNet++ in comparison with U-Net and wide U-Net architectures across multiple medical image segmentation tasks: nodule segmentation in the low-dose CT scans of chest, nuclei segmentation in the microscopy images, liver segmentation in abdominal CT scans, and polyp segmentation in colonoscopy videos.
Our experiments demonstrate that UNet++ with deep supervision achieves an average IoU gain of 3.9 and 3.4 points over U-Net and wide U-Net, respectively.
Neural network accelerators with low latency and low energy consumption are desirable for edge computing.
To create such accelerators, we propose a design flow for accelerating the extremely low bit-width neural network (ELB-NN) in embedded FPGAs with hybrid quantization schemes.
This flow covers both network training and FPGA-based network deployment, which facilitates the design space exploration and simplifies the tradeoff between network accuracy and computation efficiency.
Using this flow helps hardware designers to deliver a network accelerator in edge devices under strict resource and power constraints.
We present the proposed flow by supporting hybrid ELB settings within a neural network.
Results show that our design can deliver very high performance peaking at 10.3 TOPS and classify up to 325.3 image/s/watt while running large-scale neural networks for less than 5W using embedded FPGA.
To the best of our knowledge, it is the most energy efficient solution in comparison to GPU or other FPGA implementations reported so far in the literature.
A temporal graph is a data structure, consisting of nodes and edges in which the edges are associated with time labels.
To analyze the temporal graph, the first step is to find a proper graph dataset/benchmark.
While many temporal graph datasets exist online, none could be found that used the interval labels in which each edge is associated with a starting and ending time.
Therefore we create a temporal graph data based on Wikipedia reference graph for temporal analysis.
This report aims to provide more details of this graph benchmark to those who are interested in using it.
In order to boost the performance of data-intensive computing on HPC systems, in-memory computing frameworks, such as Apache Spark and Flink, use local DRAM for data storage.
Optimizing the memory allocation to data storage is critical to delivering performance to traditional HPC compute jobs and throughput to data-intensive applications sharing the HPC resources.
Current practices that statically configure in-memory storage may leave inadequate space for compute jobs or lose the opportunity to utilize more available space for data-intensive applications.
In this paper, we explore techniques to dynamically adjust in-memory storage and make the right amount of space for compute jobs.
We have developed a dynamic memory controller, DynIMS, which infers memory demands of compute tasks online and employs a feedback-based control model to adapt the capacity of in-memory storage.
We test DynIMS using mixed HPCC and Spark workloads on a HPC cluster.
Experimental results show that DynIMS can achieve up to 5X performance improvement compared to systems with static memory allocations.
This paper discusses sample allocation problem (SAP) in frequency-domain Compressive Sampling (CS) of time-domain signals.
An analysis that is relied on two fundamental CS principles; the Uniform Random Sampling (URS) and the Uncertainty Principle (UP), is presented.
We show that CS on a single- and multi-band signals performs better if the URS is done only within the band and suppress the out-band parts, compared to ordinary URS that ignore the band limits.
It means that sampling should only be done at the signal support, while the non-support should be masked and suppressed in the reconstruction process.
We also show that for an N-length discrete time signal with K-number of frequency components (Fourier coefficients), given the knowledge of the spectrum, URS leads to exact sampling on the location of the K-spectral peaks.
These results are used to formulate a sampling scheme when the boundaries of the bands are not sharply distinguishable, such as in a triangular- or a stacked-band- spectral signals.
When analyzing these cases, CS will face a paradox; in which narrowing the band leads to a more number of required samples, whereas widening it leads to lessen the number.
Accordingly; instead of signal analysis by dividing the signal's spectrum vertically into bands of frequencies, slicing horizontally magnitude-wise yields less number of required sample and better reconstruction results.
Moreover, it enables sample reuse that reduces the sample number even further.
The horizontal slicing and sample reuse methods imply non-uniform random sampling, where larger-magnitude part of the spectrum should be allocated more sample than the lower ones.
An IC-plane graph is a topological graph where every edge is crossed at most once and no two crossed edges share a vertex.
We show that every IC-plane graph has a visibility drawing where every vertex is an L-shape, and every edge is either a horizontal or vertical segment.
As a byproduct of our drawing technique, we prove that an IC-plane graph has a RAC drawing in quadratic area with at most two bends per edge.
Here a method is presented for detecting precursors of earthquakes from time series data on earthquakes in a target region.
Regional Entropy of Seismic Information, a quantity representing the average influence of an earthquake in the target region to the diversity of clusters to which earthquakes distribute, is introduced.
Based on a rough qualitative model of the dynamics of land crust, it is hypothesized that the saturation after the increase in the Regional Entropy of Seismic Information precedes the activation of earthquakes.
On the open earthquake catalog, this hypothesis is validated.
This temporal change turned out to correlate more with the activation of earthquakes in Japanese regions, by one to two years precedence, than the compared baseline methods.
An important challenge in the process of tracking and detecting the dissemination of misinformation is to understand the political gap between people that engage with the so called "fake news".
A possible factor responsible for this gap is opinion polarization, which may prompt the general public to classify content that they disagree or want to discredit as fake.
In this work, we study the relationship between political polarization and content reported by Twitter users as related to "fake news".
We investigate how polarization may create distinct narratives on what misinformation actually is.
We perform our study based on two datasets collected from Twitter.
The first dataset contains tweets about US politics in general, from which we compute the degree of polarization of each user towards the Republican and Democratic Party.
In the second dataset, we collect tweets and URLs that co-occurred with "fake news" related keywords and hashtags, such as #FakeNews and #AlternativeFact, as well as reactions towards such tweets and URLs.
We then analyze the relationship between polarization and what is perceived as misinformation, and whether users are designating information that they disagree as fake.
Our results show an increase in the polarization of users and URLs associated with fake-news keywords and hashtags, when compared to information not labeled as "fake news".
We discuss the impact of our findings on the challenges of tracking "fake news" in the ongoing battle against misinformation.
In recent decade, many state-of-the-art algorithms on image classification as well as audio classification have achieved noticeable successes with the development of deep convolutional neural network (CNN).
However, most of the works only exploit single type of training data.
In this paper, we present a study on classifying bird species by exploiting the combination of both visual (images) and audio (sounds) data using CNN, which has been sparsely treated so far.
Specifically, we propose CNN-based multimodal learning models in three types of fusion strategies (early, middle, late) to settle the issues of combining training data cross domains.
The advantage of our proposed method lies on the fact that We can utilize CNN not only to extract features from image and audio data (spectrogram) but also to combine the features across modalities.
In the experiment, we train and evaluate the network structure on a comprehensive CUB-200-2011 standard data set combing our originally collected audio data set with respect to the data species.
We observe that a model which utilizes the combination of both data outperforms models trained with only an either type of data.
We also show that transfer learning can significantly increase the classification performance.
Poor road conditions are a public nuisance, causing passenger discomfort, damage to vehicles, and accidents.
In the U.S., road-related conditions are a factor in 22,000 of the 42,000 traffic fatalities each year.
Although we often complain about bad roads, we have no way to detect or report them at scale.
To address this issue, we developed a system to detect potholes and assess road conditions in real-time.
Our solution is a mobile application that captures data on a car's movement from gyroscope and accelerometer sensors in the phone.
To assess roads using this sensor data, we trained SVM models to classify road conditions with 93% accuracy and potholes with 92% accuracy, beating the base rate for both problems.
As the user drives, the models use the sensor data to classify whether the road is good or bad, and whether it contains potholes.
Then, the classification results are used to create data-rich maps that illustrate road conditions across the city.
Our system will empower civic officials to identify and repair damaged roads which inconvenience passengers and cause accidents.
This paper details our data science process for collecting training data on real roads, transforming noisy sensor data into useful signals, training and evaluating machine learning models, and deploying those models to production through a real-time classification app.
It also highlights how cities can use our system to crowdsource data and deliver road repair resources to areas in need.
In this paper, we propose a novel approach for verification of on-line signatures based on user dependent feature selection and symbolic representation.
Unlike other signature verification methods, which work with same features for all users, the proposed approach introduces the concept of user dependent features.
It exploits the typicality of each and every user to select different features for different users.
Initially all possible features are extracted for all users and a method of feature selection is employed for selecting user dependent features.
The selected features are clustered using Fuzzy C means algorithm.
In order to preserve the intra-class variation within each user, we recommend to represent each cluster in the form of an interval valued symbolic feature vector.
A method of signature verification based on the proposed cluster based symbolic representation is also presented.
Extensive experimentations are conducted on MCYT-100 User (DB1) and MCYT-330 User (DB2) online signature data sets to demonstrate the effectiveness of the proposed novel approach.
We consider the problem of stealth communication over a multipath network in the presence of an active adversary.
The multipath network consists of multiple parallel noiseless links, and the adversary is able to eavesdrop and jam a subset of links.
We consider two types of jamming --- erasure jamming and overwrite jamming.
We require the communication to be both stealthy and reliable, i.e., the adversary should be unable to detect whether or not meaningful communication is taking place, while the legitimate receiver should reconstruct any potential messages from the transmitter with high probability simultaneously.
We provide inner bounds on the robust stealth capacities under both adversarial erasure and adversarial overwrite jamming.
Nature has always been an inspiration to researchers with its diversity and robustness of its systems, and Artificial Immune Systems are one of them.
Many algorithms were inspired by ongoing discoveries of biological immune systems techniques and approaches.
One of the basic and most common approach is the Negative Selection Approach, which is simple and easy to implement.
It was applied in many fields, but mostly in anomaly detection for the similarity of its basic idea.
In this paper, a review is given on the application of negative selection approach in network security, specifically the intrusion detection system.
As the work in this field is limited, we need to understand what the challenges of this approach are.
Recommendations are given by the end of the paper for future work.
We introduce an algorithm for detection of bugs in sequential circuits.
This algorithm is incomplete i.e. its failure to find a bug breaking a property P does not imply that P holds.
The appeal of incomplete algorithms is that they scale better than their complete counterparts.
However, to make an incomplete algorithm effective one needs to guarantee that the probability of finding a bug is reasonably high.
We try to achieve such effectiveness by employing the Test-As-Proofs (TAP) paradigm.
In our TAP based approach, a counterexample is built as a sequence of states extracted from proofs that some local variations of property P hold.
This increases the probability that a) a representative set of states is examined and that b) the considered states are relevant to property P.   We describe an algorithm of test generation based on the TAP paradigm and give preliminary experimental results.
Calculi of string diagrams are increasingly used to present the syntax and algebraic structure of various families of circuits, including signal flow graphs, electrical circuits and quantum processes.
In many such approaches, the semantic interpretation for diagrams is given in terms of relations or corelations (generalised equivalence relations) of some kind.
In this paper we show how semantic categories of both relations and corelations can be characterised as colimits of simpler categories.
This modular perspective is important as it simplifies the task of giving a complete axiomatisation for semantic equivalence of string diagrams.
Moreover, our general result unifies various theorems that are independently found in literature and are relevant for program semantics, quantum computation and control theory.
This article presents a measure of semantic similarity in an IS-A taxonomy based on the notion of shared information content.
Experimental evaluation against a benchmark set of human similarity judgments demonstrates that the measure performs better than the traditional edge-counting approach.
The article presents algorithms that take advantage of taxonomic similarity in resolving syntactic and semantic ambiguity, along with experimental results demonstrating their effectiveness.
The basic idea behind information algebras is that information comes in pieces, each referring to a certain question, that these pieces can be combined or aggregated and that the part relating to a given question can be extracted.
This algebraic structure can be given different forms.
Questions were originally represented by subsets of variables.
Pieces of information were then represented by valuations associated with the domains of variables.
This leads to an algebraic structure called valuation algebras.
The basic axiomatics of this algebraic structure was in essence proposed by Shenoy and Shafer.
Here a much more general view of systems of questions is proposed and pieces of information are related to the elements of this system of questions.
This leads to a new and extended system of axioms for information algebras.
Classical valuation algebras are essentially a special case of this new system.
A full discussion of the algebraic theory of this new information algebras is given, including local computation, duality between labeled and domain-free versions of the algebras, order of information, finiteness of information and approximation, compact and continuous information algebras.
Finally a rather complete discussion of uncertain information, based on random maps into information algebras is presented.
This is shown to represent a generalisation of classical Dempster-Shafer theory.
Recent networking research has identified that data-driven congestion control (CC) can be more efficient than traditional CC in TCP.
Deep reinforcement learning (RL), in particular, has the potential to learn optimal network policies.
However, RL suffers from instability and over-fitting, deficiencies which so far render it unacceptable for use in datacenter networks.
In this paper, we analyze the requirements for RL to succeed in the datacenter context.
We present a new emulator, Iroko, which we developed to support different network topologies, congestion control algorithms, and deployment scenarios.
Iroko interfaces with the OpenAI gym toolkit, which allows for fast and fair evaluation of different RL and traditional CC algorithms under the same conditions.
We present initial benchmarks on three deep RL algorithms compared to TCP New Vegas and DCTCP.
Our results show that these algorithms are able to learn a CC policy which exceeds the performance of TCP New Vegas on a dumbbell and fat-tree topology.
We make our emulator open-source and publicly available: https://github.com/dcgym/iroko
We consider the problem of minimizing a linear function over an affine section of the cone of positive semidefinite matrices, with the additional constraint that the feasible matrix has prescribed rank.
When the rank constraint is active, this is a non-convex optimization problem, otherwise it is a semidefinite program.
Both find numerous applications especially in systems control theory and combinatorial optimization, but even in more general contexts such as polynomial optimization or real algebra.
While numerical algorithms exist for solving this problem, such as interior-point or Newton-like algorithms, in this paper we propose an approach based on symbolic computation.
We design an exact algorithm for solving rank-constrained semidefinite programs, whose complexity is essentially quadratic on natural degree bounds associated to the given optimization problem: for subfamilies of the problem where the size of the feasible matrix is fixed, the complexity is polynomial in the number of variables.
The algorithm works under assumptions on the input data: we prove that these assumptions are generically satisfied.
We also implement it in Maple and discuss practical experiments.
Weakly supervised temporal action detection is a Herculean task in understanding untrimmed videos, since no supervisory signal except the video-level category label is available on training data.
Under the supervision of category labels, weakly supervised detectors are usually built upon classifiers.
However, there is an inherent contradiction between classifier and detector; i.e., a classifier in pursuit of high classification performance prefers top-level discriminative video clips that are extremely fragmentary, whereas a detector is obliged to discover the whole action instance without missing any relevant snippet.
To reconcile this contradiction, we train a detector by driving a series of classifiers to find new actionness clips progressively, via step-by-step erasion from a complete video.
During the test phase, all we need to do is to collect detection results from the one-by-one trained classifiers at various erasing steps.
To assist in the collection process, a fully connected conditional random field is established to refine the temporal localization outputs.
We evaluate our approach on two prevailing datasets, THUMOS'14 and ActivityNet.
The experiments show that our detector advances state-of-the-art weakly supervised temporal action detection results, and even compares with quite a few strongly supervised methods.
With the increase in interchange of data, there is a growing necessity of security.
Considering the volumes of digital data that is transmitted, they are in need to be secure.
Among the many forms of tampering possible, one widespread technique is Copy Move Forgery CMF.
This forgery occurs when parts of the image are copied and duplicated elsewhere in the same image.
There exist a number of algorithms to detect such a forgery in which the primary step involved is feature extraction.
The feature extraction techniques employed must have lesser time and space complexity involved for an efficient and faster processing of media.
Also, majority of the existing state of art techniques often tend to falsely match similar genuine objects as copy move forged during the detection process.
To tackle these problems, the paper proposes a novel algorithm that recognizes a unique approach of using Hus Invariant Moments and Log polar Transformations to reduce feature vector dimension to one feature per block simultaneously detecting CMF among genuine similar objects in an image.
The qualitative and quantitative results obtained demonstrate the effectiveness of this algorithm.
Neural machine translation (NMT) becomes a new approach to machine translation and generates much more fluent results compared to statistical machine translation (SMT).
However, SMT is usually better than NMT in translation adequacy.
It is therefore a promising direction to combine the advantages of both NMT and SMT.
In this paper, we propose a neural system combination framework leveraging multi-source NMT, which takes as input the outputs of NMT and SMT systems and produces the final translation.
Extensive experiments on the Chinese-to-English translation task show that our model archives significant improvement by 5.3 BLEU points over the best single system output and 3.4 BLEU points over the state-of-the-art traditional system combination methods.
Deep Neural Networks (DNNs) often struggle with one-shot learning where we have only one or a few labeled training examples per category.
In this paper, we argue that by using side information, we may compensate the missing information across classes.
We introduce two statistical approaches for fusing side information into data representation learning to improve one-shot learning.
First, we propose to enforce the statistical dependency between data representations and multiple types of side information.
Second, we introduce an attention mechanism to efficiently treat examples belonging to the 'lots-of-examples' classes as quasi-samples (additional training samples) for 'one-example' classes.
We empirically show that our learning architecture improves over traditional softmax regression networks as well as state-of-the-art attentional regression networks on one-shot recognition tasks.
In recent years, the importance of deep learning has significantly increased in pattern recognition, computer vision, and artificial intelligence research, as well as in industry.
However, despite the existence of multiple deep learning frameworks, there is a lack of comprehensible and easy-to-use high-level tools for the design, training, and testing of deep neural networks (DNNs).
In this paper, we introduce Barista, an open-source graphical high-level interface for the Caffe deep learning framework.
While Caffe is one of the most popular frameworks for training DNNs, editing prototext files in order to specify the net architecture and hyper parameters can become a cumbersome and error-prone task.
Instead, Barista offers a fully graphical user interface with a graph-based net topology editor and provides an end-to-end training facility for DNNs, which allows researchers to focus on solving their problems without having to write code, edit text files, or manually parse logged data.
Privacy definitions provide ways for trading-off the privacy of individuals in a statistical database for the utility of downstream analysis of the data.
In this paper, we present Blowfish, a class of privacy definitions inspired by the Pufferfish framework, that provides a rich interface for this trade-off.
In particular, we allow data publishers to extend differential privacy using a policy, which specifies (a) secrets, or information that must be kept secret, and (b) constraints that may be known about the data.
While the secret specification allows increased utility by lessening protection for certain individual properties, the constraint specification provides added protection against an adversary who knows correlations in the data (arising from constraints).
We formalize policies and present novel algorithms that can handle general specifications of sensitive information and certain count constraints.
We show that there are reasonable policies under which our privacy mechanisms for k-means clustering, histograms and range queries introduce significantly lesser noise than their differentially private counterparts.
We quantify the privacy-utility trade-offs for various policies analytically and empirically on real datasets.
This paper shows a vulnerability of the pay-per-click accounting of Google Ads and proposes a statistical tradeoff-based approach to manage this vulnerability.
The result of this paper is a model to calculate the overhead cost per click necessary to protect the subscribers and a simple algorithm to implement this protection.
Simulations validate the correctness of the model and the economical applicability.
We propose a simple solution to the uncertain delay problem in USRP (Universal Software Radio Peripheral)-based SDR (Software-Defined Radio)-radar systems.
Instead of time-synchronization as employed in (pseudo-) passive radar configurations, which require at least two synchronized receivers, we use direct reception signal in a single receiver system as a reference to the exact location of the target echoes.
After finding the reference position, reordering of the echoes is conducted by circular shift so that the reference moved to the origin.
We demonstrate the effectiveness of the proposed method by simulating the problem on Matlab and implementing a 128 length random code radar on a USRP.
The random code is constructed from zero padded Barker sequence product.
Experiments on measuring multiple echoes of the targets at precise range bins confirm the applicability of the proposed method.
In this paper, we propose a novel medical image segmentation using iterative deep learning framework.
We have combined an iterative learning approach and an encoder-decoder network to improve segmentation results, which enables to precisely localize the regions of interest (ROIs) including complex shapes or detailed textures of medical images in an iterative manner.
The proposed iterative deep convolutional encoder-decoder network consists of two main paths: convolutional encoder path and convolutional decoder path with iterative learning.
Experimental results show that the proposed iterative deep learning framework is able to yield excellent medical image segmentation performances for various medical images.
The effectiveness of the proposed method has been proved by comparing with other state-of-the-art medical image segmentation methods.
Autonomous dual-arm manipulation is an essential skill to deploy robots in unstructured scenarios.
However, this is a challenging undertaking, particularly in terms of perception and planning.
Unstructured scenarios are full of objects with different shapes and appearances that have to be grasped in a very specific manner so they can be functionally used.
In this paper we present an integrated approach to perform dual-arm pick tasks autonomously.
Our method consists of semantic segmentation, object pose estimation, deformable model registration, grasp planning and arm trajectory optimization.
The entire pipeline can be executed on-board and is suitable for on-line grasping scenarios.
For this, our approach makes use of accumulated knowledge expressed as convolutional neural network models and low-dimensional latent shape spaces.
For manipulating objects, we propose a stochastic trajectory optimization that includes a kinematic chain closure constraint.
Evaluation in simulation and on the real robot corroborates the feasibility and applicability of the proposed methods on a task of picking up unknown watering cans and drills using both arms.
Learning from a real-world data stream and continuously updating the model without explicit supervision is a new challenge for NLP applications with machine learning components.
In this work, we have developed an adaptive learning system for text simplification, which improves the underlying learning-to-rank model from usage data, i.e.how users have employed the system for the task of simplification.
Our experimental result shows that, over a period of time, the performance of the embedded paraphrase ranking model increases steadily improving from a score of 62.88% up to 75.70% based on the NDCG@10 evaluation metrics.
To our knowledge, this is the first study where an NLP component is adaptively improved through usage.
Minimisation of discrete energies defined over factors is an important problem in computer vision, and a vast number of MAP inference algorithms have been proposed.
Different inference algorithms perform better on factor graph models (GMs) from different underlying problem classes, and in general it is difficult to know which algorithm will yield the lowest energy for a given GM.
To mitigate this difficulty, survey papers advise the practitioner on what algorithms perform well on what classes of models.
We take the next step forward, and present a technique to automatically select the best inference algorithm for an input GM.
We validate our method experimentally on an extended version of the OpenGM2 benchmark, containing a diverse set of vision problems.
On average, our method selects an inference algorithm yielding labellings with 96% of variables the same as the best available algorithm.
Domain knowledge can often be encoded in the structure of a network, such as convolutional layers for vision, which has been shown to increase generalization and decrease sample complexity, or the number of samples required for successful learning.
In this study, we ask whether sample complexity can be reduced for systems where the structure of the domain is unknown beforehand, and the structure and parameters must both be learned from the data.
We show that sample complexity reduction through learning structure is possible for at least two simple cases.
In studying these cases, we also gain insight into how this might be done for more complex domains.
In this paper, we present "FabSearch", a prototype search engine for sourcing manufacturer service providers, by making use of the product manufacturing information contained within a 3D digital file of a product.
FabSearch is designed to take in a query 3D model, such as the .STEP file of a part model which then produces a ranked list of job shop service providers who are best suited to fabricate the part.
Service providers may have potentially built hundreds to thousands of parts with associated part 3D models over time.
FabSearch assumes that these service providers have shared shape signatures of the part models built previously to enable the algorithm to most effectively rank the service providers who have the most experience to build the query part model.
FabSearch has two important features that helps it produce relevant results.
First, it makes use of the shape characteristics of the 3D part by calculating the Spherical Harmonics signature of the part to calculate the most similar shapes built previously be job shop service providers.
Second, FabSearch utilizes meta-data about each part, such as material specification, tolerance requirements to help improve the search results based on the specific query model requirements.
The algorithm is tested against a repository containing more than 2000 models distributed across various job shop service providers.
For the first time, we show the potential for utilizing the rich information contained within a 3D part model to automate the sourcing and eventual selection of manufacturing service providers.
We slightly improve the known lower bound on the asymptotic competitive ratio for online bin packing of rectangles.
We present a complete proof for the new lower bound, whose value is above 1.91.
Provided significant future progress in artificial intelligence and computing, it may ultimately be possible to create multiple Artificial General Intelligences (AGIs), and possibly entire societies living within simulated environments.
In that case, it should be possible to improve the problem solving capabilities of the system by increasing the speed of the simulation.
If a minimal simulation with sufficient capabilities is created, it might manage to increase its own speed by accelerating progress in science and technology, in a way similar to the Technological Singularity.
This may ultimately lead to large simulated civilizations unfolding at extreme temporal speedups, achieving what from the outside would look like a Temporal Singularity.
Here we discuss the feasibility of the minimal simulation and the potential advantages, dangers, and connection to the Fermi paradox of the Temporal Singularity.
The medium-term importance of the topic derives from the amount of computational power required to start the process, which could be available within the next decades, making the Temporal Singularity theoretically possible before the end of the century.
Modal analysis is the process of estimating a system's modal parameters such as its natural frequencies and mode shapes.
One application of modal analysis is in structural health monitoring (SHM), where a network of sensors may be used to collect vibration data from a physical structure such as a building or bridge.
There is a growing interest in developing automated techniques for SHM based on data collected in a wireless sensor network.
In order to conserve power and extend battery life, however, it is desirable to minimize the amount of data that must be collected and transmitted in such a sensor network.
In this paper, we highlight the fact that modal analysis can be formulated as an atomic norm minimization (ANM) problem, which can be solved efficiently and in some cases recover perfectly a structure's mode shapes and frequencies.
We survey a broad class of sampling and compression strategies that one might consider in a physical sensor network, and we provide bounds on the sample complexity of these compressive schemes in order to recover a structure's mode shapes and frequencies via ANM.
A main contribution of our paper is to establish a bound on the sample complexity of modal analysis with random temporal compression, and in this scenario we prove that the samples per sensor can actually decrease as the number of sensors increases.
We also extend an atomic norm denoising problem to the multiple measurement vector (MMV) setting in the case of uniform sampling.
In order to handle the complexity and heterogeneity of mod- ern instruction set architectures, analysis platforms share a common design, the adoption of hardware-independent intermediate representa- tions.
The usage of these platforms to verify systems down to binary-level is appealing due to the high degree of automation they provide.
How- ever, it introduces the need for trusting the correctness of the translation from binary code to intermediate language.
Achieving a high degree of trust is challenging since this transpilation must handle (i) all the side effects of the instructions, (ii) multiple instruction encoding (e.g.ARM Thumb), and (iii) variable instruction length (e.g.Intel).
We overcome these problems by formally modeling one of such intermediate languages in the interactive theorem prover HOL4 and by implementing a proof- producing transpiler.
This tool translates ARMv8 programs to the in- termediate language and generates a HOL4 proof that demonstrates the correctness of the translation in the form of a simulation theorem.
We also show how the transpiler theorems can be used to transfer properties verified on the intermediate language to the binary code.
Systems for symbolic event recognition infer occurrences of events in time using a set of event definitions in the form of first-order rules.
The Event Calculus is a temporal logic that has been used as a basis in event recognition applications, providing among others, direct connections to machine learning, via Inductive Logic Programming (ILP).
We present an ILP system for online learning of Event Calculus theories.
To allow for a single-pass learning strategy, we use the Hoeffding bound for evaluating clauses on a subset of the input stream.
We employ a decoupling scheme of the Event Calculus axioms during the learning process, that allows to learn each clause in isolation.
Moreover, we use abductive-inductive logic programming techniques to handle unobserved target predicates.
We evaluate our approach on an activity recognition application and compare it to a number of batch learning techniques.
We obtain results of comparable predicative accuracy with significant speed-ups in training time.
We also outperform hand-crafted rules and match the performance of a sound incremental learner that can only operate on noise-free datasets.
This paper is under consideration for acceptance in TPLP.
The measurement of the biological tissue's electrical impedance is an active research field that has attracted a lot of attention during the last decades.
Bio-impedances are closely related to a large variety of physiological conditions; therefore, they are useful for diagnosis and monitoring in many medical applications.
Measuring living tissues, however, is a challenging task that poses countless technical and practical problems, in particular if the tissues need to be measured under the skin.
This paper presents a bio-impedance sensor ASIC targeting a battery-free, miniature size, implantable device, which performs accurate 4-point complex impedance extraction in the frequency range from 2 kHz to 2 MHz.
The ASIC is fabricated in 150 nm CMOS, has a size of 1.22 mm x 1.22 mm and consumes 165 uA from a 1.8 V power supply.
The ASIC is embedded in a prototype which communicates with, and is powered by an external reader device through inductive coupling.
The prototype is validated by measuring the impedances of different combinations of discrete components, measuring the electrochemical impedance of physiological solution, and performing ex vivo measurements on animal organs.
The proposed ASIC is able to extract complex impedances with around 1 Ohm resolution; therefore enabling accurate wireless tissue measurements.
The paper approaches the problem of image-to-text with attention-based encoder-decoder networks that are trained to handle sequences of characters rather than words.
We experiment on lines of text from a popular handwriting database with different attention mechanisms for the decoder.
The model trained with softmax attention achieves the lowest test error, outperforming several other RNN-based models.
Our results show that softmax attention is able to learn a linear alignment whereas the alignment generated by sigmoid attention is linear but much less precise.
Strongly multiplicative linear secret sharing schemes (LSSS) have been a powerful tool for constructing secure multiparty computation protocols.
However, it remains open whether or not there exist efficient constructions of strongly multiplicative LSSS from general LSSS.
In this paper, we propose the new concept of a 3-multiplicative LSSS, and establish its relationship with strongly multiplicative LSSS.
More precisely, we show that any 3-multiplicative LSSS is a strongly multiplicative LSSS, but the converse is not true; and that any strongly multiplicative LSSS can be efficiently converted into a 3-multiplicative LSSS.
Furthermore, we apply 3-multiplicative LSSS to the computation of unbounded fan-in multiplication, which reduces its round complexity to four (from five of the previous protocol based on strongly multiplicative LSSS).
We also give two constructions of 3-multiplicative LSSS from Reed-Muller codes and algebraic geometric codes.
We believe that the construction and verification of 3-multiplicative LSSS are easier than those of strongly multiplicative LSSS.
This presents a step forward in settling the open problem of efficient constructions of strongly multiplicative LSSS from general LSSS.
Training models for the automatic correction of machine-translated text usually relies on data consisting of (source, MT, human post- edit) triplets providing, for each source sentence, examples of translation errors with the corresponding corrections made by a human post-editor.
Ideally, a large amount of data of this kind should allow the model to learn reliable correction patterns and effectively apply them at test stage on unseen (source, MT) pairs.
In practice, however, their limited availability calls for solutions that also integrate in the training process other sources of knowledge.
Along this direction, state-of-the-art results have been recently achieved by systems that, in addition to a limited amount of available training data, exploit artificial corpora that approximate elements of the "gold" training instances with automatic translations.
Following this idea, we present eSCAPE, the largest freely-available Synthetic Corpus for Automatic Post-Editing released so far. eSCAPE consists of millions of entries in which the MT element of the training triplets has been obtained by translating the source side of publicly-available parallel corpora, and using the target side as an artificial human post-edit.
Translations are obtained both with phrase-based and neural models.
For each MT paradigm, eSCAPE contains 7.2 million triplets for English-German and 3.3 millions for English-Italian, resulting in a total of 14,4 and 6,6 million instances respectively.
The usefulness of eSCAPE is proved through experiments in a general-domain scenario, the most challenging one for automatic post-editing.
For both language directions, the models trained on our artificial data always improve MT quality with statistically significant gains.
The current version of eSCAPE can be freely downloaded from: http://hltshare.fbk.eu/QT21/eSCAPE.html.
Visual reasoning with compositional natural language instructions, e.g., based on the newly-released Cornell Natural Language Visual Reasoning (NLVR) dataset, is a challenging task, where the model needs to have the ability to create an accurate mapping between the diverse phrases and the several objects placed in complex arrangements in the image.
Further, this mapping needs to be processed to answer the question in the statement given the ordering and relationship of the objects across three similar images.
In this paper, we propose a novel end-to-end neural model for the NLVR task, where we first use joint bidirectional attention to build a two-way conditioning between the visual information and the language phrases.
Next, we use an RL-based pointer network to sort and process the varying number of unordered objects (so as to match the order of the statement phrases) in each of the three images and then pool over the three decisions.
Our model achieves strong improvements (of 4-6% absolute) over the state-of-the-art on both the structured representation and raw image versions of the dataset.
In this paper, two robust model predictive control (MPC) schemes are proposed for tracking control of nonholonomic systems with bounded disturbances: tube-MPC and nominal robust MPC (NRMPC).
In tube-MPC, the control signal consists of a control action and a nonlinear feedback law based on the deviation of the actual states from the states of a nominal system.
It renders the actual trajectory within a tube centered along the optimal trajectory of the nominal system.
Recursive feasibility and input-to-state stability are established and the constraints are ensured by tightening the input domain and the terminal region.
While in NRMPC, an optimal control sequence is obtained by solving an optimization problem based on the current state, and the first portion of this sequence is applied to the real system in an open-loop manner during each sampling period.
The state of nominal system model is updated by the actual state at each step, which provides additional a feedback.
By introducing a robust state constraint and tightening the terminal region, recursive feasibility and input-to-state stability are guaranteed.
Simulation results demonstrate the effectiveness of both strategies proposed.
The unprecedented growth of Internet users in recent years has resulted in an abundance of unstructured information in the form of social media text.
A large percentage of this population is actively engaged in health social networks to share health-related information.
In this paper, we address an important and timely topic by analyzing the users' sentiments and emotions w.r.t their medical conditions.
Towards this, we examine users on popular medical forums (Patient.info,dailystrength.org), where they post on important topics such as asthma, allergy, depression, and anxiety.
First, we provide a benchmark setup for the task by crawling the data, and further define the sentiment specific fine-grained medical conditions (Recovered, Exist, Deteriorate, and Other).
We propose an effective architecture that uses a Convolutional Neural Network (CNN) as a data-driven feature extractor and a Support Vector Machine (SVM) as a classifier.
We further develop a sentiment feature which is sensitive to the medical context.
Here, we show that the use of medical sentiment feature along with extracted features from CNN improves the model performance.
In addition to our dataset, we also evaluate our approach on the benchmark "CLEF eHealth 2014" corpora and show that our model outperforms the state-of-the-art techniques.
Unlike many complex networks studied in the literature, social networks rarely exhibit unanimous behavior, or consensus.
This requires a development of mathematical models that are sufficiently simple to be examined and capture, at the same time, the complex behavior of real social groups, where opinions and actions related to them may form clusters of different size.
One such model, proposed by Friedkin and Johnsen, extends the idea of conventional consensus algorithm (also referred to as the iterative opinion pooling) to take into account the actors' prejudices, caused by some exogenous factors and leading to disagreement in the final opinions.
In this paper, we offer a novel multidimensional extension, describing the evolution of the agents' opinions on several topics.
Unlike the existing models, these topics are interdependent, and hence the opinions being formed on these topics are also mutually dependent.
We rigorous examine stability properties of the proposed model, in particular, convergence of the agents' opinions.
Although our model assumes synchronous communication among the agents, we show that the same final opinions may be reached "on average" via asynchronous gossip-based protocols.
Directional or Circular statistics are pertaining to the analysis and interpretation of directions or rotations.
In this work, a novel probability distribution is proposed to model multidimensional sparse directional data.
The Generalised Directional Laplacian Distribution (DLD) is a hybrid between the Laplacian distribution and the von Mises-Fisher distribution.
The distribution's parameters are estimated using Maximum-Likelihood Estimation over a set of training data points.
Mixtures of Directional Laplacian Distributions (MDLD) are also introduced in order to model multiple concentrations of sparse directional data.
The author explores the application of the derived DLD mixture model to cluster sound sources that exist in an underdetermined instantaneous sound mixture.
The proposed model can solve the general K x L (K<L) underdetermined instantaneous source separation problem, offering a fast and stable solution.
Encoder-decoder models typically only employ words that are frequently used in the training corpus to reduce the computational costs and exclude noise.
However, this vocabulary set may still include words that interfere with learning in encoder-decoder models.
This paper proposes a method for selecting more suitable words for learning encoders by utilizing not only frequency, but also co-occurrence information, which we capture using the HITS algorithm.
We apply our proposed method to two tasks: machine translation and grammatical error correction.
For Japanese-to-English translation, this method achieves a BLEU score that is 0.56 points more than that of a baseline.
It also outperforms the baseline method for English grammatical error correction, with an F0.5-measure that is 1.48 points higher.
In this paper we are going to introduce a new nearest neighbours based approach to clustering, and compare it with previous solutions; the resulting algorithm, which takes inspiration from both DBscan and minimum spanning tree approaches, is deterministic but proves simpler, faster and doesnt require to set in advance a value for k, the number of clusters.
We review the work on data-driven grasp synthesis and the methodologies for sampling and ranking candidate grasps.
We divide the approaches into three groups based on whether they synthesize grasps for known, familiar or unknown objects.
This structure allows us to identify common object representations and perceptual processes that facilitate the employed data-driven grasp synthesis technique.
In the case of known objects, we concentrate on the approaches that are based on object recognition and pose estimation.
In the case of familiar objects, the techniques use some form of a similarity matching to a set of previously encountered objects.
Finally for the approaches dealing with unknown objects, the core part is the extraction of specific features that are indicative of good grasps.
Our survey provides an overview of the different methodologies and discusses open problems in the area of robot grasping.
We also draw a parallel to the classical approaches that rely on analytic formulations.
This paper proposes a new approach to a novel value network architecture for the game Go, called a multi-labelled (ML) value network.
In the ML value network, different values (win rates) are trained simultaneously for different settings of komi, a compensation given to balance the initiative of playing first.
The ML value network has three advantages, (a) it outputs values for different komi, (b) it supports dynamic komi, and (c) it lowers the mean squared error (MSE).
This paper also proposes a new dynamic komi method to improve game-playing strength.
This paper also performs experiments to demonstrate the merits of the architecture.
First, the MSE of the ML value network is generally lower than the value network alone.
Second, the program based on the ML value network wins by a rate of 67.6% against the program based on the value network alone.
Third, the program with the proposed dynamic komi method significantly improves the playing strength over the baseline that does not use dynamic komi, especially for handicap games.
To our knowledge, up to date, no handicap games have been played openly by programs using value networks.
This paper provides these programs with a useful approach to playing handicap games.
Public scientists (scientists only from now onwards), understood as a member of the teaching and/or research staff of a public university or a public research organization (including humanities and social sciences), benefit the academic community, industry and other social collectives through teaching and research.
Active involvement of scientists in culture is part of the richness of developed societies.
Some voices in current debates on the evaluation of societal impact and the role of universities towards social development are claiming a refocus from a socioeconomic perspective to also including sociocultural benefits from academiaIn this paper we will focus in one facet of cultural engagement; writing literary fiction.
We will narrow our general objective to local activities, due to the interest in the engagement of scientist on this geographic dimension.
Do local publishers include the literary work of scientists?
Are works written by scientists more likely to be local than works not written by scientists?
Despite the recent popularity of word embedding methods, there is only a small body of work exploring the limitations of these representations.
In this paper, we consider one aspect of embedding spaces, namely their stability.
We show that even relatively high frequency words (100-200 occurrences) are often unstable.
We provide empirical evidence for how various factors contribute to the stability of word embeddings, and we analyze the effects of stability on downstream tasks.
Graphics Processing Units (GPUs) maintain a large register file to increase the thread level parallelism (TLP).
To increase the TLP further, recent GPUs have increased the number of on-chip registers in every generation.
However, with the increase in the register file size, the leakage power increases.
Also, with the technology advances, the leakage power component has increased and has become an important consideration for the manufacturing process.
The leakage power of a register file can be reduced by turning infrequently used registers into low power (drowsy or off) state after accessing them.
A major challenge in doing so is the lack of runtime register access information.
This paper proposes GREENER (GPU REgister file ENErgy Reducer): a system to minimize leakage energy of the register file of GPUs.
GREENER employs a compile-time analysis to estimate the run-time register access information.
The result of the analysis is used to determine the power state of the registers (ON, SLEEP, or OFF) after each instruction.
We propose a power optimized assembly instruction set that allows GREENER to encode the power state of the registers in the executable itself.
The modified assembly, along with a run-time optimization to update the power state of a register during execution, results in significant power reduction.
We implemented GREENER in GPGPU-Sim simulator, and used GPUWattch framework to measure the register file's leakage power.
Evaluation of GREENER on 21 kernels from CUDASDK, GPGPU-SIM, Parboil, and Rodinia benchmarks suites shows an average reduction of register leakage energy by 69.04% and maximum reduction of 87.95% with a negligible number of simulation cycles overhead (0.53% on average).
3D pose estimation is a key component of many important computer vision tasks such as autonomous navigation and 3D scene understanding.
Most state-of-the-art approaches to 3D pose estimation solve this problem as a pose-classification problem in which the pose space is discretized into bins and a CNN classifier is used to predict a pose bin.
We argue that the 3D pose space is continuous and propose to solve the pose estimation problem in a CNN regression framework with a suitable representation, data augmentation and loss function that captures the geometry of the pose space.
Experiments on PASCAL3D+ show that the proposed 3D pose regression approach achieves competitive performance compared to the state-of-the-art.
The NIPS 2018 Adversarial Vision Challenge is a competition to facilitate measurable progress towards robust machine vision models and more generally applicable adversarial attacks.
This document is an updated version of our competition proposal that was accepted in the competition track of 32nd Conference on Neural Information Processing Systems (NIPS 2018).
Estimation of surface curvature from range data is important for a range of tasks in computer vision and robotics, object segmentation, object recognition and robotic grasping estimation.
This work presents a fast method of robustly computing accurate metric principal curvature values from noisy point clouds which was implemented on GPU.
In contrast to existing readily available solutions which first differentiate the surface to estimate surface normals and then differentiate these to obtain curvature, amplifying noise, our method iteratively fits parabolic quadric surface patches to the data.
Additionally previous methods with a similar formulation use less robust techniques less applicable to a high noise sensor.
We demonstrate that our method is fast and provides better curvature estimates than existing techniques.
In particular we compare our method to several alternatives to demonstrate the improvement.
Many machine learning image classifiers are vulnerable to adversarial attacks, inputs with perturbations designed to intentionally trigger misclassification.
Current adversarial methods directly alter pixel colors and evaluate against pixel norm-balls: pixel perturbations smaller than a specified magnitude, according to a measurement norm.
This evaluation, however, has limited practical utility since perturbations in the pixel space do not correspond to underlying real-world phenomena of image formation that lead to them and has no security motivation attached.
Pixels in natural images are measurements of light that has interacted with the geometry of a physical scene.
As such, we propose the direct perturbation of physical parameters that underly image formation: lighting and geometry.
As such, we propose a novel evaluation measure, parametric norm-balls, by directly perturbing physical parameters that underly image formation.
One enabling contribution we present is a physically-based differentiable renderer that allows us to propagate pixel gradients to the parametric space of lighting and geometry.
Our approach enables physically-based adversarial attacks, and our differentiable renderer leverages models from the interactive rendering literature to balance the performance and accuracy trade-offs necessary for a memory-efficient and scalable adversarial data augmentation workflow.
This work focuses on the construction of optimized binary signaling schemes for two-sender uncoded transmission of correlated sources over non-orthogonal Gaussian multiple access channels.
Specifically, signal constellations with binary pulse-amplitude-modulation are designed for two senders to optimize the overall system performance.
Although the two senders transmit their own messages independently, it is observed that the correlation between message sources can be exploited to mitigate the interference present in the non-orthogonal multiple access channel.
Based on a performance analysis under joint maximum-a-posteriori decoding, optimized constellations for various basic waveform correlations between the senders are derived.
Numerical results further confirm the effectiveness of the proposed design.
This paper introduces CLEO, a novel preference elicitation algorithm capable of recommending complex objects in hybrid domains, characterized by both discrete and continuous attributes and constraints defined over them.
The algorithm assumes minimal initial information, i.e., a set of catalog attributes, and defines decisional features as logic formulae combining Boolean and algebraic constraints over the attributes.
The (unknown) utility of the decision maker (DM) is modelled as a weighted combination of features.
CLEO iteratively alternates a preference elicitation step, where pairs of candidate solutions are selected based on the current utility model, and a refinement step where the utility is refined by incorporating the feedback received.
The elicitation step leverages a Max-SMT solver to return optimal hybrid solutions according to the current utility model.
The refinement step is implemented as learning to rank, and a sparsifying norm is used to favour the selection of few informative features in the combinatorial space of candidate decisional features.
CLEO is the first preference elicitation algorithm capable of dealing with hybrid domains, thanks to the use of Max-SMT technology, while retaining uncertainty in the DM utility and noisy feedback.
Experimental results on complex recommendation tasks show the ability of CLEO to quickly focus towards optimal solutions, as well as its capacity to recover from suboptimal initial choices.
While no competitors exist in the hybrid setting, CLEO outperforms a state-of-the-art Bayesian preference elicitation algorithm when applied to a purely discrete task.
For reliable transmission across a noisy communication channel, classical results from information theory show that it is asymptotically optimal to separate out the source and channel coding processes.
However, this decomposition can fall short in the finite bit-length regime, as it requires non-trivial tuning of hand-crafted codes and assumes infinite computational power for decoding.
In this work, we propose to jointly learn the encoding and decoding processes using a new discrete variational autoencoder model.
By adding noise into the latent codes to simulate the channel during training, we learn to both compress and error-correct given a fixed bit-length and computational budget.
We obtain codes that are not only competitive against several separation schemes, but also learn useful robust representations of the data for downstream tasks such as classification.
Finally, inference amortization yields an extremely fast neural decoder, almost an order of magnitude faster compared to standard decoding methods based on iterative belief propagation.
We present a mathematical model to predict pedestrian motion over a finite horizon, intended for use in collision avoidance algorithms for autonomous driving.
The model is based on a road map structure, and assumes a rational pedestrian behavior.
We compare our model with the state-of-the art and discuss its accuracy, and limitations, both in simulations and in comparison to real data.
Markerless tracking of hands and fingers is a promising enabler for human-computer interaction.
However, adoption has been limited because of tracking inaccuracies, incomplete coverage of motions, low framerate, complex camera setups, and high computational requirements.
In this paper, we present a fast method for accurately tracking rapid and complex articulations of the hand using a single depth camera.
Our algorithm uses a novel detection-guided optimization strategy that increases the robustness and speed of pose estimation.
In the detection step, a randomized decision forest classifies pixels into parts of the hand.
In the optimization step, a novel objective function combines the detected part labels and a Gaussian mixture representation of the depth to estimate a pose that best fits the depth.
Our approach needs comparably less computational resources which makes it extremely fast (50 fps without GPU support).
The approach also supports varying static, or moving, camera-to-scene arrangements.
We show the benefits of our method by evaluating on public datasets and comparing against previous work.
In general, professionals still ignore scientific evidence in place of expert opinions in most of their decision-making.
For this reason, it is still common to see the adoption of new software technologies in the field without any scientific basis or well-grounded criteria, but on the opinions of experts.
Experimental Software Engineering is of paramount importance to provide the foundations to understand the limits and applicability of software technologies.
The need to better observe and understand the practice of Software Engineering leads us to look for alternative experimental approaches to support our studies.
Different research strategies can be used to explore different Software Engineering practices.
Action Research can be seen as one alternative to intensify the conducting of important experimental studies with results of great value while investigating the Software Engineering practices in depth.
In this paper, a discussion on the use of Action Research in Software Engineering is presented.
Aiming at better explaining the application of Action Research, an experimental study (in vivo) on the investigation of the subjective decisions of software developers, concerned with the refactoring of source code to improve source code quality in a distributed software development context is depicted.
In addition, some guidance on how to accomplish an Action Research study in Software Engineering supplement the discussions.
In human face-based biometrics, gender classification and age estimation are two typical learning tasks.
Although a variety of approaches have been proposed to handle them, just a few of them are solved jointly, even so, these joint methods do not yet specifically concern the semantic difference between human gender and age, which is intuitively helpful for joint learning, consequently leaving us a room of further improving the performance.
To this end, in this work we firstly propose a general learning framework for jointly estimating human gender and age by specially attempting to formulate such semantic relationships as a form of near-orthogonality regularization and then incorporate it into the objective of the joint learning framework.
In order to evaluate the effectiveness of the proposed framework, we exemplify it by respectively taking the widely used binary-class SVM for gender classification, and two threshold-based ordinal regression methods (i.e., the discriminant learning for ordinal regression and support vector ordinal regression) for age estimation, and crucially coupling both through the proposed semantic formulation.
Moreover, we develop its kernelized nonlinear counterpart by deriving a representer theorem for the joint learning strategy.
Finally, through extensive experiments on three aging datasets FG-NET, Morph Album I and Morph Album II, we demonstrate the effectiveness and superiority of the proposed joint learning strategy.
Phase retrieval refers to the problem of recovering real- or complex-valued vectors from magnitude measurements.
The best-known algorithms for this problem are iterative in nature and rely on so-called spectral initializers that provide accurate initialization vectors.
We propose a novel class of estimators suitable for general nonlinear measurement systems, called linear spectral estimators (LSPEs), which can be used to compute accurate initialization vectors for phase retrieval problems.
The proposed LSPEs not only provide accurate initialization vectors for noisy phase retrieval systems with structured or random measurement matrices, but also enable the derivation of sharp and nonasymptotic mean-squared error bounds.
We demonstrate the efficacy of LSPEs on synthetic and real-world phase retrieval problems, and show that our estimators significantly outperform existing methods for structured measurement systems that arise in practice.
Manipulations of return addresses on the stack are the basis for a variety of attacks on programs written in memory unsafe languages.
Dual stack schemes for protecting return addresses promise an efficient and effective defense against such attacks.
By introducing a second, safe stack to separate return addresses from potentially unsafe stack objects, they prevent attacks that, for example, maliciously modify a return address by overflowing a buffer.
However, the security of dual stacks is based on the concealment of the safe stack in memory.
Unfortunately, all current dual stack schemes are vulnerable to information disclosure attacks that are able to reveal the safe stack location, and therefore effectively break their promised security properties.
In this paper, we present a new, leak-resilient dual stack scheme capable of withstanding sophisticated information disclosure attacks.
We carefully study previous dual stack schemes and systematically develop a novel design for stack separation that eliminates flaws leading to the disclosure of safe stacks.
We show the feasibility and practicality of our approach by presenting a full integration into the LLVM compiler framework with support for the x86-64 and ARM64 architectures.
With an average of 2.7% on x86-64 and 0.0% on ARM64, the performance overhead of our implementation is negligible.
We live in a world where our personal data are both valuable and vulnerable to misappropriation through exploitation of security vulnerabilities in online services.
For instance, Dropbox, a popular cloud storage tool, has certain security flaws that can be exploited to compromise a user's data, one of which being that a user's access pattern is unprotected.
We have thus created an implementation of Path Oblivious RAM (Path ORAM) for Dropbox users to obfuscate path access information to patch this vulnerability.
This implementation differs significantly from the standard usage of Path ORAM, in that we introduce several innovations, including a dynamically growing and shrinking tree architecture, multi-block fetching, block packing and the possibility for multi-client use.
Our optimizations together produce about a 77% throughput increase and a 60% reduction in necessary tree size; these numbers vary with file size distribution.
Word embeddings have been found to provide meaningful representations for words in an efficient way; therefore, they have become common in Natural Language Processing sys- tems.
In this paper, we evaluated different word embedding models trained on a large Portuguese corpus, including both Brazilian and European variants.
We trained 31 word embedding models using FastText, GloVe, Wang2Vec and Word2Vec.
We evaluated them intrinsically on syntactic and semantic analogies and extrinsically on POS tagging and sentence semantic similarity tasks.
The obtained results suggest that word analogies are not appropriate for word embedding evaluation; task-specific evaluations appear to be a better option.
This paper introduces a new IEEE 802.15.4 simulation model for OMNeT++ / INET.
802.15.4 is an important underlying standard for wireless sensor networks and Internet of Things scenarios.
The presented implementation is designed to be compatible with OMNeT++ 4.x and INET 2.x and laid-out to be expandable for newer revisions of the 802.15.4 standard.
The source code is available online https://github.com/michaelkirsche/IEEE802154INET-Standalone
Community detection is one of the most active fields in complex networks analysis, due to its potential value in practical applications.
Many works inspired by different paradigms are devoted to the development of algorithmic solutions allowing to reveal the network structure in such cohesive subgroups.
Comparative studies reported in the literature usually rely on a performance measure considering the community structure as a partition (Rand Index, Normalized Mutual information, etc.).
However, this type of comparison neglects the topological properties of the communities.
In this article, we present a comprehensive comparative study of a representative set of community detection methods, in which we adopt both types of evaluation.
Community-oriented topological measures are used to qualify the communities and evaluate their deviation from the reference structure.
In order to mimic real-world systems, we use artificially generated realistic networks.
It turns out there is no equivalence between both approaches: a high performance does not necessarily correspond to correct topological properties, and vice-versa.
They can therefore be considered as complementary, and we recommend applying both of them in order to perform a complete and accurate assessment.
This article presents the prediction difference analysis method for visualizing the response of a deep neural network to a specific input.
When classifying images, the method highlights areas in a given input image that provide evidence for or against a certain class.
It overcomes several shortcoming of previous methods and provides great additional insight into the decision making process of classifiers.
Making neural network decisions interpretable through visualization is important both to improve models and to accelerate the adoption of black-box classifiers in application areas such as medicine.
We illustrate the method in experiments on natural images (ImageNet data), as well as medical images (MRI brain scans).
Computer vision has made remarkable progress in recent years.
Deep neural network (DNN) models optimized to identify objects in images exhibit unprecedented task-trained accuracy and, remarkably, some generalization ability: new visual problems can now be solved more easily based on previous learning.
Biological vision (learned in life and through evolution) is also accurate and general-purpose.
Is it possible that these different learning regimes converge to similar problem-dependent optimal computations?
We therefore asked whether the human system-level computation of visual perception has DNN correlates and considered several anecdotal test cases.
We found that perceptual sensitivity to image changes has DNN mid-computation correlates, while sensitivity to segmentation, crowding and shape has DNN end-computation correlates.
Our results quantify the applicability of using DNN computation to estimate perceptual loss, and are consistent with the fascinating theoretical view that properties of human perception are a consequence of architecture-independent visual learning.
In this paper, we propose a novel deep convolutional neural network (CNN)-based algorithm for solving ill-posed inverse problems.
Regularized iterative algorithms have emerged as the standard approach to ill-posed inverse problems in the past few decades.
These methods produce excellent results, but can be challenging to deploy in practice due to factors including the high computational cost of the forward and adjoint operators and the difficulty of hyper parameter selection.
The starting point of our work is the observation that unrolled iterative methods have the form of a CNN (filtering followed by point-wise non-linearity) when the normal operator (H*H, the adjoint of H times H) of the forward model is a convolution.
Based on this observation, we propose using direct inversion followed by a CNN to solve normal-convolutional inverse problems.
The direct inversion encapsulates the physical model of the system, but leads to artifacts when the problem is ill-posed; the CNN combines multiresolution decomposition and residual learning in order to learn to remove these artifacts while preserving image structure.
We demonstrate the performance of the proposed network in sparse-view reconstruction (down to 50 views) on parallel beam X-ray computed tomography in synthetic phantoms as well as in real experimental sinograms.
The proposed network outperforms total variation-regularized iterative reconstruction for the more realistic phantoms and requires less than a second to reconstruct a 512 x 512 image on GPU.
The heterogeneous cloud radio access network (H-CRAN) is a promising paradigm which incorporates the cloud computing into heterogeneous networks (HetNets), thereby taking full advantage of cloud radio access networks (C-RANs) and HetNets.
Characterizing the cooperative beamforming with fronthaul capacity and queue stability constraints is critical for multimedia applications to improving energy efficiency (EE) in H-CRANs.
An energy-efficient optimization objective function with individual fronthaul capacity and inter-tier interference constraints is presented in this paper for queue-aware multimedia H-CRANs.
To solve this non-convex objective function, a stochastic optimization problem is reformulated by introducing the general Lyapunov optimization framework.
Under the Lyapunov framework, this optimization problem is equivalent to an optimal network-wide cooperative beamformer design algorithm with instantaneous power, average power and inter-tier interference constraints, which can be regarded as the weighted sum EE maximization problem and solved by a generalized weighted minimum mean square error approach.
The mathematical analysis and simulation results demonstrate that a tradeoff between EE and queuing delay can be achieved, and this tradeoff strictly depends on the fronthaul constraint.
Transferring the knowledge of pretrained networks to new domains by means of finetuning is a widely used practice for applications based on discriminative models.
To the best of our knowledge this practice has not been studied within the context of generative deep networks.
Therefore, we study domain adaptation applied to image generation with generative adversarial networks.
We evaluate several aspects of domain adaptation, including the impact of target domain size, the relative distance between source and target domain, and the initialization of conditional GANs.
Our results show that using knowledge from pretrained networks can shorten the convergence time and can significantly improve the quality of the generated images, especially when the target data is limited.
We show that these conclusions can also be drawn for conditional GANs even when the pretrained model was trained without conditioning.
Our results also suggest that density may be more important than diversity and a dataset with one or few densely sampled classes may be a better source model than more diverse datasets such as ImageNet or Places.
Many tasks in music information retrieval, such as recommendation, and playlist generation for online radio, fall naturally into the query-by-example setting, wherein a user queries the system by providing a song, and the system responds with a list of relevant or similar song recommendations.
Such applications ultimately depend on the notion of similarity between items to produce high-quality results.
Current state-of-the-art systems employ collaborative filter methods to represent musical items, effectively comparing items in terms of their constituent users.
While collaborative filter techniques perform well when historical data is available for each item, their reliance on historical data impedes performance on novel or unpopular items.
To combat this problem, practitioners rely on content-based similarity, which naturally extends to novel items, but is typically out-performed by collaborative filter methods.
In this article, we propose a method for optimizing contentbased similarity by learning from a sample of collaborative filter data.
The optimized content-based similarity metric can then be applied to answer queries on novel and unpopular items, while still maintaining high recommendation accuracy.
The proposed system yields accurate and efficient representations of audio content, and experimental results show significant improvements in accuracy over competing content-based recommendation techniques.
The potential for machine learning (ML) systems to amplify social inequities and unfairness is receiving increasing popular and academic attention.
A surge of recent work has focused on the development of algorithmic tools to assess and mitigate such unfairness.
If these tools are to have a positive impact on industry practice, however, it is crucial that their design be informed by an understanding of real-world needs.
Through 35 semi-structured interviews and an anonymous survey of 267 ML practitioners, we conduct the first systematic investigation of commercial product teams' challenges and needs for support in developing fairer ML systems.
We identify areas of alignment and disconnect between the challenges faced by industry practitioners and solutions proposed in the fair ML research literature.
Based on these findings, we highlight directions for future ML and HCI research that will better address industry practitioners' needs.
Algorithmic and data bias are gaining attention as a pressing issue in popular press - and rightly so.
However, beyond these calls to action, standard processes and tools for practitioners do not readily exist to assess and address unfair algorithmic and data biases.
The literature is relatively scattered and the needed interdisciplinary approach means that very different communities are working on the topic.
We here provide a number of challenges encountered in assessing and addressing algorithmic and data bias in practice.
We describe an early approach that attempts to translate the literature into processes for (production) teams wanting to assess both intended data and algorithm characteristics and unintended, unfair biases.
In this study, a shell-and-tube heat exchanger (STHX) design based on seven continuous independent design variables is proposed.
Delayed Rejection Adaptive Metropolis hasting (DRAM) was utilized as a powerful tool in the Markov chain Monte Carlo (MCMC) sampling method.
This Reverse Sampling (RS) method was used to find the probability distribution of design variables of the shell and tube heat exchanger.
Thanks to this probability distribution, an uncertainty analysis was also performed to find the quality of these variables.
In addition, a decision-making strategy based on confidence intervals of design variables and on the Total Annual Cost (TAC) provides the final selection of design variables.
Results indicated high accuracies for the estimation of design variables which leads to marginally improved performance compared to commonly used optimization methods.
In order to verify the capability of the proposed method, a case of study is also presented, it shows that a significant cost reduction is feasible with respect to multi-objective and single-objective optimization methods.
Furthermore, the selected variables have good quality (in terms of probability distribution) and a lower TAC was also achieved.
Results show that the costs of the proposed design are lower than those obtained from optimization method reported in previous studies.
The algorithm was also used to determine the impact of using probability values for the design variables rather than single values to obtain the best heat transfer area and pumping power.
In particular, a reduction of the TAC up to 3.5% was achieved in the case considered.
Convolutional Neural Networks (CNNs) have gained a remarkable success on many real-world problems in recent years.
However, the performance of CNNs is highly relied on their architectures.
For some state-of-the-art CNNs, their architectures are hand-crafted with expertise in both CNNs and the investigated problems.
To this end, it is difficult for researchers, who have no extended expertise in CNNs, to explore CNNs for their own problems of interest.
In this paper, we propose an automatic architecture design method for CNNs by using genetic algorithms, which is capable of discovering a promising architecture of a CNN on handling image classification tasks.
The proposed algorithm does not need any pre-processing before it works, nor any post-processing on the discovered CNN, which means it is completely automatic.
The proposed algorithm is validated on widely used benchmark datasets, by comparing to the state-of-the-art peer competitors covering eight manually designed CNNs, four semi-automatically designed CNNs and additional four automatically designed CNNs.
The experimental results indicate that the proposed algorithm achieves the best classification accuracy consistently among manually and automatically designed CNNs.
Furthermore, the proposed algorithm also shows the competitive classification accuracy to the semi-automatic peer competitors, while reducing 10 times of the parameters.
In addition, on the average the proposed algorithm takes only one percentage of computational resource compared to that of all the other architecture discovering algorithms.
SEMAT/OMG Essence provides a powerful Language and a Kernel for describing software development processes.
How can it be tweaked to apply it to systems engineering methods description?
We must harmonize Essence and various systems engineering standards in order to provide a more formal system approach to obtaining a Systems Engineering Essence.
In this paper, an approach of using Essence for systems engineering is presented.
In this approach we partly modified a Kernel only within engineering solution area of concerns and completely preserved Language as an excellent situational method engineering foundation.
Cyberbullying is a disturbing online misbehaviour with troubling consequences.
It appears in different forms, and in most of the social networks, it is in textual format.
Automatic detection of such incidents requires intelligent systems.
Most of the existing studies have approached this problem with conventional machine learning models and the majority of the developed models in these studies are adaptable to a single social network at a time.
In recent studies, deep learning based models have found their way in the detection of cyberbullying incidents, claiming that they can overcome the limitations of the conventional models, and improve the detection performance.
In this paper, we investigate the findings of a recent literature in this regard.
We successfully reproduced the findings of this literature and validated their findings using the same datasets, namely Wikipedia, Twitter, and Formspring, used by the authors.
Then we expanded our work by applying the developed methods on a new YouTube dataset (~54k posts by ~4k users) and investigated the performance of the models in new social media platforms.
We also transferred and evaluated the performance of the models trained on one platform to another platform.
Our findings show that the deep learning based models outperform the machine learning models previously applied to the same YouTube dataset.
We believe that the deep learning based models can also benefit from integrating other sources of information and looking into the impact of profile information of the users in social networks.
To improve the accuracy of existing dust concentration measurements, a dust concentration measurement based on Moment of inertia in Gray level-Rank Co-occurrence Matrix (GRCM), which is from the dust image sample measured by a machine vision system is proposed in this paper.
Firstly, a Polynomial computational model between dust Concentration and Moment of inertia (PCM) is established by experimental methods and fitting methods.
Then computing methods for GRCM and its Moment of inertia are constructed by theoretical and mathematical analysis methods.
And then developing an on-line dust concentration vision measurement experimental system, the cement dust concentration measurement in a cement production workshop is taken as a practice example with the system and the PCM measurement.
The results show that measurement error is within 9%, and the measurement range is 0.5-1000 mg/m3.
Finally, comparing with the filter membrane weighing measurement, light scattering measurement and laser measurement, the proposed PCM measurement has advantages on error and cost, which can be provided a valuable reference for the dust concentration vision measurements.
In this paper we consider the problem of robot navigation in simple maze-like environments where the robot has to rely on its onboard sensors to perform the navigation task.
In particular, we are interested in solutions to this problem that do not require localization, mapping or planning.
Additionally, we require that our solution can quickly adapt to new situations (e.g., changing navigation goals and environments).
To meet these criteria we frame this problem as a sequence of related reinforcement learning tasks.
We propose a successor feature based deep reinforcement learning algorithm that can learn to transfer knowledge from previously mastered navigation tasks to new problem instances.
Our algorithm substantially decreases the required learning time after the first task instance has been solved, which makes it easily adaptable to changing environments.
We validate our method in both simulated and real robot experiments with a Robotino and compare it to a set of baseline methods including classical planning-based navigation.
This is an intuitive introduction to classic sliding mode control that shows how the associated assumptions and condition for its use arise in the context of a derivation of the method.
It derives a controller that obviates the need for the assumption of any sign for the control input vector, answers why it is said that it deals only with matched disturbances and why a system that it may apply to 'must' be linear in the control signal.
Additionally, it may be viewed as an example of how a control design method might be developed, adding to its pedagogical usefulness.
We present new algorithms for inference in credal networks --- directed acyclic graphs associated with sets of probabilities.
Credal networks are here interpreted as encoding strong independence relations among variables.
We first present a theory of credal networks based on separately specified sets of probabilities.
We also show that inference with polytrees is NP-hard in this setting.
We then introduce new techniques that reduce the computational effort demanded by inference, particularly in polytrees, by exploring separability of credal sets.
Neural encoder-decoder models of machine translation have achieved impressive results, rivalling traditional translation models.
However their modelling formulation is overly simplistic, and omits several key inductive biases built into traditional models.
In this paper we extend the attentional neural translation model to include structural biases from word based alignment models, including positional bias, Markov conditioning, fertility and agreement over translation directions.
We show improvements over a baseline attentional model and standard phrase-based model over several language pairs, evaluating on difficult languages in a low resource setting.
The prevention of domestic violence (DV) have aroused serious concerns in Taiwan because of the disparity between the increasing amount of reported DV cases that doubled over the past decade and the scarcity of social workers.
Additionally, a large amount of data was collected when social workers use the predominant case management approach to document case reports information.
However, these data were not properly stored or organized.
To improve the efficiency of DV prevention and risk management, we worked with Taipei City Government and utilized the 2015 data from its DV database to perform a spatial pattern analysis of the reports of DV cases to build a DV risk map.
However, during our map building process, the issue of confounding bias arose because we were not able to verify if reported cases truly reflected real violence occurrence or were simply false reports from potential victim's neighbors.
Therefore, we used the random forest method to build a repeat victimization risk prediction model.
The accuracy and F1-measure of our model were 96.3% and 62.8%.
This model helped social workers differentiate the risk level of new cases, which further reduced their major workload significantly.
To our knowledge, this is the first project that utilized machine learning in DV prevention.
The research approach and results of this project not only can improve DV prevention process, but also be applied to other social work or criminal prevention areas.
Publishing fast changing dynamic data as open data on the web in a scalable manner is not trivial.
So far the only approaches describe publishing as much data as possible, which then leads to problems, like server capacity overload, network latency or unwanted knowledge disclosure.
With this paper we show ways how to publish dynamic data in a scalable, meaningful manner by applying context-dependent publication heuristics.
The outcome shows that the application of the right publication heuristics in the right domain can improve the publication performance significantly.
Good knowledge about the domain help choosing the right publication heuristic and hence lead to very good publication results.
Human Activity Recognition (HAR) based on motion sensors has drawn a lot of attention over the last few years, since perceiving the human status enables context-aware applications to adapt their services on users' needs.
However, motion sensor fusion and feature extraction have not reached their full potentials, remaining still an open issue.
In this paper, we introduce PerceptionNet, a deep Convolutional Neural Network (CNN) that applies a late 2D convolution to multimodal time-series sensor data, in order to extract automatically efficient features for HAR.
We evaluate our approach on two public available HAR datasets to demonstrate that the proposed model fuses effectively multimodal sensors and improves the performance of HAR.
In particular, PerceptionNet surpasses the performance of state-of-the-art HAR methods based on: (i) features extracted from humans, (ii) deep CNNs exploiting early fusion approaches, and (iii) Long Short-Term Memory (LSTM), by an average accuracy of more than 3%.
Toponym Resolution, the task of assigning a location mention in a document to a geographic referent (i.e., latitude/longitude), plays a pivotal role in analyzing location-aware content.
However, the ambiguities of natural language and a huge number of possible interpretations for toponyms constitute insurmountable hurdles for this task.
In this paper, we study the problem of toponym resolution with no additional information other than a gazetteer and no training data.
We demonstrate that a dearth of large enough annotated data makes supervised methods less capable of generalizing.
Our proposed method estimates the geographic scope of documents and leverages the connections between nearby place names as evidence to resolve toponyms.
We explore the interactions between multiple interpretations of mentions and the relationships between different toponyms in a document to build a model that finds the most coherent resolution.
Our model is evaluated on three news corpora, two from the literature and one collected and annotated by us; then, we compare our methods to the state-of-the-art unsupervised and supervised techniques.
We also examine three commercial products including Reuters OpenCalais, Yahoo!
YQL Placemaker, and Google Cloud Natural Language API.
The evaluation shows that our method outperforms the unsupervised technique as well as Reuters OpenCalais and Google Cloud Natural Language API on all three corpora; also, our method shows a performance close to that of the state-of-the-art supervised method and outperforms it when the test data has 40% or more toponyms that are not seen in the training data.
This work examines the mean-square error performance of diffusion stochastic algorithms under a generalized coordinate-descent scheme.
In this setting, the adaptation step by each agent is limited to a random subset of the coordinates of its stochastic gradient vector.
The selection of coordinates varies randomly from iteration to iteration and from agent to agent across the network.
Such schemes are useful in reducing computational complexity at each iteration in power-intensive large data applications.
They are also useful in modeling situations where some partial gradient information may be missing at random.
Interestingly, the results show that the steady-state performance of the learning strategy is not always degraded, while the convergence rate suffers some degradation.
The results provide yet another indication of the resilience and robustness of adaptive distributed strategies.
Motivation: Recognizing human actions in a video is a challenging task which has applications in various fields.
Previous works in this area have either used images from a 2D or 3D camera.
Few have used the idea that human actions can be easily identified by the movement of the joints in the 3D space and instead used a Recurrent Neural Network (RNN) for modeling.
Convolutional neural networks (CNN) have the ability to recognise even the complex patterns in data which makes it suitable for detecting human actions.
Thus, we modeled a CNN which can predict the human activity using the joint data.
Furthermore, using the joint data representation has the benefit of lower dimensionality than image or video representations.
This makes our model simpler and faster than the RNN models.
In this study, we have developed a six layer convolutional network, which reduces each input feature vector of the form 15x1961x4 to an one dimensional binary vector which gives us the predicted activity.
Results: Our model is able to recognise an activity correctly upto 87% accuracy.
Joint data is taken from the Cornell Activity Datasets which have day to day activities like talking, relaxing, eating, cooking etc.
This letter is about a principal weakness of the published article by Li et al. in 2014.
It seems that the mentioned work has a terrible conceptual mistake while presenting its theoretical approach.
In fact, the work has tried to design a new attack and its effective solution for a basic watermarking algorithm by Zhu et al. published in 2013, however in practice, we show the Li et al.'s approach is not correct to obtain the aim.
For disproof of the incorrect approach, we only apply a numerical example as the counterexample of the Li et al.'s approach.
In this paper, we propose a novel CS approach in which the acquisition of non-visible information is also avoided.
Engineering software systems is a multidisciplinary activity, whereby a number of artifacts must be created - and maintained - synchronously.
In this paper we investigate whether production code and the accompanying tests co-evolve by exploring a project's versioning system, code coverage reports and size-metrics.
Three open source case studies teach us that testing activities usually start later on during the lifetime and are more "phased", although we did not observe increasing testing activity before releases.
Furthermore, we note large differences in the levels of test coverage given the proportion of test code.
In neural machine translation (NMT), the most common practice is to stack a number of recurrent or feed-forward layers in the encoder and the decoder.
As a result, the addition of each new layer improves the translation quality significantly.
However, this also leads to a significant increase in the number of parameters.
In this paper, we propose to share parameters across all the layers thereby leading to a recurrently stacked NMT model.
We empirically show that the translation quality of a model that recurrently stacks a single layer 6 times is comparable to the translation quality of a model that stacks 6 separate layers.
We also show that using pseudo-parallel corpora by back-translation leads to further significant improvements in translation quality.
The deployment of deep convolutional neural networks (CNNs) in many real world applications is largely hindered by their high computational cost.
In this paper, we propose a novel learning scheme for CNNs to simultaneously 1) reduce the model size; 2) decrease the run-time memory footprint; and 3) lower the number of computing operations, without compromising accuracy.
This is achieved by enforcing channel-level sparsity in the network in a simple but effective way.
Different from many existing approaches, the proposed method directly applies to modern CNN architectures, introduces minimum overhead to the training process, and requires no special software/hardware accelerators for the resulting models.
We call our approach network slimming, which takes wide and large networks as input models, but during training insignificant channels are automatically identified and pruned afterwards, yielding thin and compact models with comparable accuracy.
We empirically demonstrate the effectiveness of our approach with several state-of-the-art CNN models, including VGGNet, ResNet and DenseNet, on various image classification datasets.
For VGGNet, a multi-pass version of network slimming gives a 20x reduction in model size and a 5x reduction in computing operations.
Wireless device-to-device (D2D) communication underlaying cellular network is a promising concept to improve user experience and resource utilization.
Unlike traditional D2D communication where two mobile devices in the proximity establish a direct local link bypassing the base station, in this work we focus on relay-aided D2D communication.
Relay-aided transmission could enhance the performance of D2D communication when D2D user equipments (UEs) are far apart from each other and/or the quality of D2D link is not good enough for direct communication.
Considering the uncertainties in wireless links, we model and analyze the performance of a relay-aided D2D communication network, where the relay nodes serve both the cellular and D2D users.
In particular, we formulate the radio resource allocation problem in a two-hop network to guarantee the data rate of the UEs while protecting other receiving nodes from interference.
Utilizing time sharing strategy, we provide a centralized solution under bounded channel uncertainty.
With a view to reducing the computational burden at relay nodes, we propose a distributed solution approach using stable matching to allocate radio resources in an efficient and computationally inexpensive way.
Numerical results show that the performance of the proposed method is close to the centralized optimal solution and there is a distance margin beyond which relaying of D2D traffic improves network performance.
We present a computational analysis of three language varieties: native, advanced non-native, and translation.
Our goal is to investigate the similarities and differences between non-native language productions and translations, contrasting both with native language.
Using a collection of computational methods we establish three main results: (1) the three types of texts are easily distinguishable; (2) non-native language and translations are closer to each other than each of them is to native language; and (3) some of these characteristics depend on the source or native language, while others do not, reflecting, perhaps, unified principles that similarly affect translations and non-native language.
Community structures are critical towards understanding not only the network topology but also how the network functions.
However, how to evaluate the quality of detected community structures is still challenging and remains unsolved.
The most widely used metric, normalized mutual information (NMI), was proved to have finite size effect, and its improved form relative normalized mutual information (rNMI) has reverse finite size effect.
Corrected normalized mutual information (cNMI) was thus proposed and has neither finite size effect nor reverse finite size effect.
However, in this paper we show that cNMI violates the so-called proportionality assumption.
In addition, NMI-type metrics have the problem of ignoring importance of small communities.
Finally, they cannot be used to evaluate a single community of interest.
In this paper, we map the computed community labels to the ground-truth ones through integer linear programming, then use kappa index and F-score to evaluate the detected community structures.
Experimental results demonstrate the advantages of our method.
This paper is to create a practical steganographic implementation for 4-bit images.The proposed technique converts 4 bit image into 4 shaded Gray Scale image.
This image will be act as reference image to hide the text.
Using this grey scale reference image any text can be hidden.
Single character of a text can be represented by 8-bit.
The 8-bit character can be split into 4X2 bit information.
If the reference image and the data file are transmitted through network separately, we can achieve the effect of Steganography.
Here the image is not at all distorted because said image is only used for referencing.
Any huge mount of text material can be hidden using a very small image.
Decipher the text is not possible intercepting the image or data file separately.
So, it is more secure.
Triangular meshes have gained much interest in image representation and have been widely used in image processing.
This paper introduces a framework of anisotropic mesh adaptation (AMA) methods to image representation and proposes a GPRAMA method that is based on AMA and greedy-point removal (GPR) scheme.
Different than many other methods that triangulate sample points to form the mesh, the AMA methods start directly with a triangular mesh and then adapt the mesh based on a user-defined metric tensor to represent the image.
The AMA methods have clear mathematical framework and provides flexibility for both image representation and image reconstruction.
A mesh patching technique is developed for the implementation of the GPRAMA method, which leads to an improved version of the popular GPRFS-ED method.
The GPRAMA method can achieve better quality than the GPRFS-ED method but with lower computational cost.
Word embeddings, which represent a word as a point in a vector space, have become ubiquitous to several NLP tasks.
A recent line of work uses bilingual (two languages) corpora to learn a different vector for each sense of a word, by exploiting crosslingual signals to aid sense identification.
We present a multi-view Bayesian non-parametric algorithm which improves multi-sense word embeddings by (a) using multilingual (i.e., more than two languages) corpora to significantly improve sense embeddings beyond what one achieves with bilingual information, and (b) uses a principled approach to learn a variable number of senses per word, in a data-driven manner.
Ours is the first approach with the ability to leverage multilingual corpora efficiently for multi-sense representation learning.
Experiments show that multilingual training significantly improves performance over monolingual and bilingual training, by allowing us to combine different parallel corpora to leverage multilingual context.
Multilingual training yields comparable performance to a state of the art mono-lingual model trained on five times more training data.
Twitter is one of the most popular social media.
Due to the ease of availability of data, Twitter is used significantly for research purposes.
Twitter is known to evolve in many aspects from what it was at its birth; nevertheless, how it evolved its own linguistic style is still relatively unknown.
In this paper, we study the evolution of various sociolinguistic aspects of Twitter over large time scales.
To the best of our knowledge, this is the first comprehensive study on the evolution of such aspects of this OSN.
We performed quantitative analysis both on the word level as well as on the hashtags since it is perhaps one of the most important linguistic units of this social media.
We studied the (in)formality aspects of the linguistic styles in Twitter and find that it is neither fully formal nor completely informal; while on one hand, we observe that Out-Of-Vocabulary words are decreasing over time (pointing to a formal style), on the other hand it is quite evident that whitespace usage is getting reduced with a huge prevalence of running texts (pointing to an informal style).
We also analyze and propose quantitative reasons for repetition and coalescing of hashtags in Twitter.
We believe that such phenomena may be strongly tied to different evolutionary aspects of human languages.
MmWave communications, one of the cornerstones of future 5G mobile networks, are characterized at the same time by a potential multi-gigabit capacity and by a very dynamic channel, sensitive to blockage, wide fluctuations in the received signal quality, and possibly also sudden link disruption.
While the performance of physical and MAC layer schemes that address these issues has been thoroughly investigated in the literature, the complex interactions between mmWave links and transport layer protocols such as TCP are still relatively unexplored.
This paper uses the ns-3 mmWave module, with its channel model based on real measurements in New York City, to analyze the performance of the Linux TCP/IP stack (i) with and without link-layer retransmissions, showing that they are fundamental to reach a high TCP throughput on mmWave links and (ii) with Multipath TCP (MP-TCP) over multiple LTE and mmWave links, illustrating which are the throughput-optimal combinations of secondary paths and congestion control algorithms in different conditions.
Image-based generative methods, such as generative adversarial networks (GANs) have already been able to generate realistic images with much context control, specially when they are conditioned.
However, most successful frameworks share a common procedure which performs an image-to-image translation with pose of figures in the image untouched.
When the objective is reposing a figure in an image while preserving the rest of the image, the state-of-the-art mainly assumes a single rigid body with simple background and limited pose shift, which can hardly be extended to the images under normal settings.
In this paper, we introduce an image "inner space" preserving model that assigns an interpretable low-dimensional pose descriptor (LDPD) to an articulated figure in the image.
Figure reposing is then generated by passing the LDPD and the original image through multi-stage augmented hourglass networks in a conditional GAN structure, called inner space preserving generative pose machine (ISP-GPM).
We evaluated ISP-GPM on reposing human figures, which are highly articulated with versatile variations.
Test of a state-of-the-art pose estimator on our reposed dataset gave an accuracy over 80% on PCK0.5 metric.
The results also elucidated that our ISP-GPM is able to preserve the background with high accuracy while reasonably recovering the area blocked by the figure to be reposed.
Inspired by the recent success of methods that employ shape priors to achieve robust 3D reconstructions, we propose a novel recurrent neural network architecture that we call the 3D Recurrent Reconstruction Neural Network (3D-R2N2).
The network learns a mapping from images of objects to their underlying 3D shapes from a large collection of synthetic data.
Our network takes in one or more images of an object instance from arbitrary viewpoints and outputs a reconstruction of the object in the form of a 3D occupancy grid.
Unlike most of the previous works, our network does not require any image annotations or object class labels for training or testing.
Our extensive experimental analysis shows that our reconstruction framework i) outperforms the state-of-the-art methods for single view reconstruction, and ii) enables the 3D reconstruction of objects in situations when traditional SFM/SLAM methods fail (because of lack of texture and/or wide baseline).
This paper reviews the requirements for the security mechanisms that are currently being developed in the framework of the European research project INDECT.
An overview of features for integrated technologies such as Virtual Private Networks (VPNs), Cryptographic Algorithms, Quantum Cryptography, Federated ID Management and Secure Mobile Ad-hoc networking are described together with their expected use in INDECT.
We propose an efficient method to generate white-box adversarial examples to trick a character-level neural classifier.
We find that only a few manipulations are needed to greatly decrease the accuracy.
Our method relies on an atomic flip operation, which swaps one token for another, based on the gradients of the one-hot input vectors.
Due to efficiency of our method, we can perform adversarial training which makes the model more robust to attacks at test time.
With the use of a few semantics-preserving constraints, we demonstrate that HotFlip can be adapted to attack a word-level classifier as well.
This paper proposes a variant of the normalized cut algorithm for spectral clustering.
Although the normalized cut algorithm applies the K-means algorithm to the eigenvectors of a normalized graph Laplacian for finding clusters, our algorithm instead uses a minimum volume enclosing ellipsoid for them.
We show that the algorithm shares similarity with the ellipsoidal rounding algorithm for separable nonnegative matrix factorization.
Our theoretical insight implies that the algorithm can serve as a bridge between spectral clustering and separable NMF.
The K-means algorithm has the issues in that the choice of initial points affects the construction of clusters and certain choices result in poor clustering performance.
The normalized cut algorithm inherits these issues since K-means is incorporated in it, whereas the algorithm proposed here does not.
An empirical study is presented to examine the performance of the algorithm.
Automatic License Plate Recognition (ALPR) has been the focus of many researches in the past years.
In general, ALPR is divided into the following problems: detection of on-track vehicles, license plates detection, segmention of license plate characters and optical character recognition (OCR).
Even though commercial solutions are available for controlled acquisition conditions, e.g., the entrance of a parking lot, ALPR is still an open problem when dealing with data acquired from uncontrolled environments, such as roads and highways when relying only on imaging sensors.
Due to the multiple orientations and scales of the license plates captured by the camera, a very challenging task of the ALPR is the License Plate Character Segmentation (LPCS) step, which effectiveness is required to be (near) optimal to achieve a high recognition rate by the OCR.
To tackle the LPCS problem, this work proposes a novel benchmark composed of a dataset designed to focus specifically on the character segmentation step of the ALPR within an evaluation protocol.
Furthermore, we propose the Jaccard-Centroid coefficient, a new evaluation measure more suitable than the Jaccard coefficient regarding the location of the bounding box within the ground-truth annotation.
The dataset is composed of 2,000 Brazilian license plates consisting of 14,000 alphanumeric symbols and their corresponding bounding box annotations.
We also present a new straightforward approach to perform LPCS efficiently.
Finally, we provide an experimental evaluation for the dataset based on four LPCS approaches and demonstrate the importance of character segmentation for achieving an accurate OCR.
Multi-agent cooperation is an important feature of the natural world.
Many tasks involve individual incentives that are misaligned with the common good, yet a wide range of organisms from bacteria to insects and humans are able to overcome their differences and collaborate.
Therefore, the emergence of cooperative behavior amongst self-interested individuals is an important question for the fields of multi-agent reinforcement learning (MARL) and evolutionary theory.
Here, we study a particular class of multi-agent problems called intertemporal social dilemmas (ISDs), where the conflict between the individual and the group is particularly sharp.
By combining MARL with appropriately structured natural selection, we demonstrate that individual inductive biases for cooperation can be learned in a model-free way.
To achieve this, we introduce an innovative modular architecture for deep reinforcement learning agents which supports multi-level selection.
We present results in two challenging environments, and interpret these in the context of cultural and ecological evolution.
Classifying single image patches is important in many different applications, such as road detection or scene understanding.
In this paper, we present convolutional patch networks, which are convolutional networks learned to distinguish different image patches and which can be used for pixel-wise labeling.
We also show how to incorporate spatial information of the patch as an input to the network, which allows for learning spatial priors for certain categories jointly with an appearance model.
In particular, we focus on road detection and urban scene understanding, two application areas where we are able to achieve state-of-the-art results on the KITTI as well as on the LabelMeFacade dataset.
Furthermore, our paper offers a guideline for people working in the area and desperately wandering through all the painstaking details that render training CNs on image patches extremely difficult.
In order to ensure high availability of Web services, recently, a new approach was proposed based on the use of communities.
In composition, this approach consists in replacing the failed Web service by another web service joining a community offering the same functionality of the service failed.
However, this substitution may cause inconsistency in the semantic composition and alter its mediation initially taken to resolve the semantic heterogeneities between Web services.
This paper presents a context oriented solution to this problem by forcing the community to adopt the semantic of the failed web service before the substitution in which all inputs and outputs to/from the latter must be converted according to this adopted semantic, avoiding any alteration of a semantic mediation in web service composition.
The recently introduced Deep Q-Networks (DQN) algorithm has gained attention as one of the first successful combinations of deep neural networks and reinforcement learning.
Its promise was demonstrated in the Arcade Learning Environment (ALE), a challenging framework composed of dozens of Atari 2600 games used to evaluate general competency in AI.
It achieved dramatically better results than earlier approaches, showing that its ability to learn good representations is quite robust and general.
This paper attempts to understand the principles that underlie DQN's impressive performance and to better contextualize its success.
We systematically evaluate the importance of key representational biases encoded by DQN's network by proposing simple linear representations that make use of these concepts.
Incorporating these characteristics, we obtain a computationally practical feature set that achieves competitive performance to DQN in the ALE.
Besides offering insight into the strengths and weaknesses of DQN, we provide a generic representation for the ALE, significantly reducing the burden of learning a representation for each game.
Moreover, we also provide a simple, reproducible benchmark for the sake of comparison to future work in the ALE.
Understanding software design practice is critical to understanding modern information systems development.
New developments in empirical software engineering, information systems design science and the interdisciplinary design literature combined with recent advances in process theory and testability have created a situation ripe for innovation.
Consequently, this paper utilizes these breakthroughs to formulate a process theory of software design practice: Sensemaking-Coevolution-Implementation Theory explains how complex software systems are created by collocated software development teams in organizations.
It posits that an independent agent (design team) creates a software system by alternating between three activities: organizing their perceptions about the context, mutually refining their understandings of the context and design space, and manifesting their understanding of the design space in a technological artifact.
This theory development paper defines and illustrates Sensemaking-Coevolution-Implementation Theory, grounds its concepts and relationships in existing literature, conceptually evaluates the theory and situates it in the broader context of information systems development.
In this paper, we claim that Vector Cosine, which is generally considered one of the most efficient unsupervised measures for identifying word similarity in Vector Space Models, can be outperformed by a completely unsupervised measure that evaluates the extent of the intersection among the most associated contexts of two target words, weighting such intersection according to the rank of the shared contexts in the dependency ranked lists.
This claim comes from the hypothesis that similar words do not simply occur in similar contexts, but they share a larger portion of their most relevant contexts compared to other related words.
To prove it, we describe and evaluate APSyn, a variant of Average Precision that, independently of the adopted parameters, outperforms the Vector Cosine and the co-occurrence on the ESL and TOEFL test sets.
In the best setting, APSyn reaches 0.73 accuracy on the ESL dataset and 0.70 accuracy in the TOEFL dataset, beating therefore the non-English US college applicants (whose average, as reported in the literature, is 64.50%) and several state-of-the-art approaches.
Millimeter wave (mmWave) systems are emerging as an essential technology to enable extremely high data rate wireless communications.
The main limiting factors of mmWave systems are blockage (high penetration loss) and deafness (misalignment between the beams of the transmitter and receiver).
To alleviate these problems, it is imperative to incorporate efficient association and relaying between terminals and access points.
Unfortunately, the existing association techniques are designed for the traditional interference-limited networks, and thus are highly suboptimal for mmWave communications due to narrow-beam operations and the resulting non-negligible interference-free behavior.
This paper introduces a distributed approach that solves the joint association and relaying problem in mmWave networks considering the load balancing at access points.
The problem is posed as a novel stochastic optimization problem, which is solved by distributed auction algorithms where the clients and relays act asynchronously to achieve optimal client-relay-access point association.
It is shown that the algorithms provably converge to a solution that maximizes the aggregate logarithmic utility within a desired bound.
Numerical results allow to quantify the performance enhancements introduced by the relays, and the substantial improvements of the network throughput and fairness among the clients by the proposed association method as compared to standard approaches.
It is concluded that mmWave communications with proper association and relaying mechanisms can support extremely high data rates, connection reliability, and fairness among the clients.
Deep reinforcement learning (deep RL) has achieved superior performance in complex sequential tasks by using deep neural networks as function approximators to learn directly from raw input images.
However, learning directly from raw images is data inefficient.
The agent must learn feature representation of complex states in addition to learning a policy.
As a result, deep RL typically suffers from slow learning speeds and often requires a prohibitively large amount of training time and data to reach reasonable performance, making it inapplicable to real-world settings where data is expensive.
In this work, we improve data efficiency in deep RL by addressing one of the two learning goals, feature learning.
We leverage supervised learning to pre-train on a small set of non-expert human demonstrations and empirically evaluate our approach using the asynchronous advantage actor-critic algorithms (A3C) in the Atari domain.
Our results show significant improvements in learning speed, even when the provided demonstration is noisy and of low quality.
This article presents two area/latency optimized gate level asynchronous full adder designs which correspond to early output logic.
The proposed full adders are constructed using the delay-insensitive dual-rail code and adhere to the four-phase return-to-zero handshaking.
For an asynchronous ripple carry adder (RCA) constructed using the proposed early output full adders, the relative-timing assumption becomes necessary and the inherent advantages of the relative-timed RCA are: (1) computation with valid inputs, i.e., forward latency is data-dependent, and (2) computation with spacer inputs involves a bare minimum constant reverse latency of just one full adder delay, thus resulting in the optimal cycle time.
With respect to different 32-bit RCA implementations, and in comparison with the optimized strong-indication, weak-indication, and early output full adder designs, one of the proposed early output full adders achieves respective reductions in latency by 67.8, 12.3 and 6.1 %, while the other proposed early output full adder achieves corresponding reductions in area by 32.6, 24.6 and 6.9 %, with practically no power penalty.
Further, the proposed early output full adders based asynchronous RCAs enable minimum reductions in cycle time by 83.4, 15, and 8.8 % when considering carry-propagation over the entire RCA width of 32-bits, and maximum reductions in cycle time by 97.5, 27.4, and 22.4 % for the consideration of a typical carry chain length of 4 full adder stages, when compared to the least of the cycle time estimates of various strong-indication, weak-indication, and early output asynchronous RCAs of similar size.
All the asynchronous full adders and RCAs were realized using standard cells in a semi-custom design fashion based on a 32/28 nm CMOS process technology.
In recent years, we have seen an emergence of data-driven approaches in robotics.
However, most existing efforts and datasets are either in simulation or focus on a single task in isolation such as grasping, pushing or poking.
In order to make progress and capture the space of manipulation, we would need to collect a large-scale dataset of diverse tasks such as pouring, opening bottles, stacking objects etc.
But how does one collect such a dataset?
In this paper, we present the largest available robotic-demonstration dataset (MIME) that contains 8260 human-robot demonstrations over 20 different robotic tasks (https://sites.google.com/view/mimedataset).
These tasks range from the simple task of pushing objects to the difficult task of stacking household objects.
Our dataset consists of videos of human demonstrations and kinesthetic trajectories of robot demonstrations.
We also propose to use this dataset for the task of mapping 3rd person video features to robot trajectories.
Furthermore, we present two different approaches using this dataset and evaluate the predicted robot trajectories against ground-truth trajectories.
We hope our dataset inspires research in multiple areas including visual imitation, trajectory prediction, and multi-task robotic learning.
Background: The speed and precision with which objects are moved by hand or hand-tool interaction under image guidance depend on a specific type of visual and spatial sensorimotor learning.
Novices have to learn to optimally control what their hands are doing in a real-world environment while looking at an image representation of the scene on a video monitor.
Previous research has shown slower task execution times and lower performance scores under image-guidance compared with situations of direct action viewing.
The cognitive processes for overcoming this drawback by training are not yet understood.
Methods: We investigated the effects of training on the time and precision of direct view versus image guided object positioning on targets of a Real-world Action Field (RAF).
Two men and two women had to learn to perform the task as swiftly and as precisely as possible with their dominant hand, using a tool or not and wearing a glove or not.
Individuals were trained in sessions of mixed trial blocks with no feed-back.
Results: As predicted, image-guidance produced significantly slower times and lesser precision in all trainees and sessionscompared with direct viewing.
With training, all trainees get faster in all conditions, but only one of them gets reliably more precise in the image-guided conditions.
Speed-accuracy trade-offs in the individual performance data show that the highest precision scores and steepest learning curve, for time and precision, were produced by the slowest starter.Conclusions: Performance evolution towards optimal precision is compromised when novices start by going as fast as they can.
The findings have direct implications for individual skill monitoring in training programmes for image-guided technology applications with human operators.
We propose the neural programmer-interpreter (NPI): a recurrent and compositional neural network that learns to represent and execute programs.
NPI has three learnable components: a task-agnostic recurrent core, a persistent key-value program memory, and domain-specific encoders that enable a single NPI to operate in multiple perceptually diverse environments with distinct affordances.
By learning to compose lower-level programs to express higher-level programs, NPI reduces sample complexity and increases generalization ability compared to sequence-to-sequence LSTMs.
The program memory allows efficient learning of additional tasks by building on existing programs.
NPI can also harness the environment (e.g. a scratch pad with read-write pointers) to cache intermediate results of computation, lessening the long-term memory burden on recurrent hidden units.
In this work we train the NPI with fully-supervised execution traces; each program has example sequences of calls to the immediate subprograms conditioned on the input.
Rather than training on a huge number of relatively weak labels, NPI learns from a small number of rich examples.
We demonstrate the capability of our model to learn several types of compositional programs: addition, sorting, and canonicalizing 3D models.
Furthermore, a single NPI learns to execute these programs and all 21 associated subprograms.
We introduce a fast and efficient convolutional neural network, ESPNet, for semantic segmentation of high resolution images under resource constraints.
ESPNet is based on a new convolutional module, efficient spatial pyramid (ESP), which is efficient in terms of computation, memory, and power.
ESPNet is 22 times faster (on a standard GPU) and 180 times smaller than the state-of-the-art semantic segmentation network PSPNet, while its category-wise accuracy is only 8% less.
We evaluated ESPNet on a variety of semantic segmentation datasets including Cityscapes, PASCAL VOC, and a breast biopsy whole slide image dataset.
Under the same constraints on memory and computation, ESPNet outperforms all the current efficient CNN networks such as MobileNet, ShuffleNet, and ENet on both standard metrics and our newly introduced performance metrics that measure efficiency on edge devices.
Our network can process high resolution images at a rate of 112 and 9 frames per second on a standard GPU and edge device, respectively.
Search engines are nowadays one of the most important entry points for Internet users and a central tool to solve most of their information needs.
Still, there exist a substantial amount of users' searches which obtain unsatisfactory results.
Needless to say, several lines of research aim to increase the relevancy of the results users retrieve.
In this paper the authors frame this problem within the much broader (and older) one of information overload.
They argue that users' dissatisfaction with search engines is a currently common manifestation of such a problem, and propose a different angle from which to tackle with it.
As it will be discussed, their approach shares goals with a current hot research topic (namely, learning to rank for information retrieval) but, unlike the techniques commonly applied in that field, their technique cannot be exactly considered machine learning and, additionally, it can be used to change the search engine's response in real-time, driven by the users behavior.
Their proposal adapts concepts from Swarm Intelligence (in particular, Ant Algorithms) from an Information Foraging point of view.
It will be shown that the technique is not only feasible, but also an elegant solution to the stated problem; what's more, it achieves promising results, both increasing the performance of a major search engine for informational queries, and substantially reducing the time users require to answer complex information needs.
Globalization and the world wide web has resulted in academia and science being an international and multicultural community forged by researchers and scientists with different ethnicities.
How ethnicity shapes the evolution of membership, status and interactions of the scientific community, however, is not well understood.
This is due to the difficulty of ethnicity identification at the large scale.
We use name ethnicity classification as an indicator of ethnicity.
Based on automatic name ethnicity classification of 1.7+ million authors gathered from Web, the name ethnicity of computer science scholars is investigated by population size, publication contribution and collaboration strength.
By showing the evolution of name ethnicity from 1936 to 2010, we discover that ethnicity diversity has increased significantly over time and that different research communities in certain publication venues have different ethnicity compositions.
We notice a clear rise in the number of Asian name ethnicities in papers.
Their fraction of publication contribution increases from approximately 10% to near 50% from 1970 to 2010.
We also find that name ethnicity acts as a homophily factor on coauthor networks, shaping the formation of coauthorship as well as evolution of research communities.
Next generation cellular networks will have to leverage large cell densifications to accomplish the ambitious goals for aggregate multi-user sum rates, for which CRAN architecture is a favored network design.
This shifts the attention back to applicable resource allocation (RA), which need to be applicable for very short radio frames, large and dense sets of radio heads, and large user populations in the coordination area.
So far, mainly CSI-based RA schemes have been proposed for this task.
However, they have considerable complexity and also incur a significant CSI acquisition overhead on the system.
In this paper, we study an alternative approach which promises lower complexity with also a lower overhead.
We propose to base the RA in multi-antenna CRAN systems on the position information of user terminals only.
We use Random Forests as supervised machine learning approach to determine the multi-user RAs.
This likely leads to lower overhead costs, as the acquisition of position information requires less radio resources in comparison to the acquisition of instantaneous CSI.
The results show the following findings: I) In general, learning-based RA schemes can achieve comparable spectral efficiency to CSI-based scheme; II) If taking the system overhead into account, learning-based RA scheme utilizing position information outperform legacy CSI-based scheme by up to 100%; III) Despite their dependency on the training data, Random Forests based RA scheme is robust against position inaccuracies and changes in the propagation scenario; IV) The most important factor influencing the performance of learning-based RA scheme is the antenna orientation, for which we present three approaches that restore most of the original performance results.
To the best of our knowledge, these insights are new and indicate a novel as well as promising approach to master the complexity in future cellular networks.
The growing complexity of heterogeneous cellular networks (HetNets) has necessitated the need to consider variety of user and base station (BS) configurations for realistic performance evaluation and system design.
This is directly reflected in the HetNet simulation models considered by standardization bodies, such as the third generation partnership project (3GPP).
Complementary to these simulation models, stochastic geometry based approach modeling the user and BS locations as independent and homogeneous Poisson point processes (PPPs) has gained prominence in the past few years.
Despite its success in revealing useful insights, this PPP-based model is not rich enough to capture all the spatial configurations that appear in real world HetNet deployments (on which 3GPP simulation models are based).
In this paper, we bridge the gap between the 3GPP simulation models and the popular PPP-based analytical model by developing a new unified HetNet model in which a fraction of users and some BS tiers are modeled as Poisson cluster processes (PCPs).
This model captures both non-uniformity and coupling in the BS and user locations.
For this setup, we derive exact expression for downlink coverage probability under maximum signal-to-interference ratio (SIR) cell association model.
As intermediate results, we define and evaluate sum-product functionals for PPP and PCP.
Special instances of the proposed model are shown to closely resemble different configurations considered in 3GPP HetNet models.
Our results concretely demonstrate that the performance trends are highly sensitive to the assumptions made on the user and SBS configurations.
We introduce a novel framework for image captioning that can produce natural language explicitly grounded in entities that object detectors find in the image.
Our approach reconciles classical slot filling approaches (that are generally better grounded in images) with modern neural captioning approaches (that are generally more natural sounding and accurate).
Our approach first generates a sentence `template' with slot locations explicitly tied to specific image regions.
These slots are then filled in by visual concepts identified in the regions by object detectors.
The entire architecture (sentence template generation and slot filling with object detectors) is end-to-end differentiable.
We verify the effectiveness of our proposed model on different image captioning tasks.
On standard image captioning and novel object captioning, our model reaches state-of-the-art on both COCO and Flickr30k datasets.
We also demonstrate that our model has unique advantages when the train and test distributions of scene compositions -- and hence language priors of associated captions -- are different.
Code has been made available at: https://github.com/jiasenlu/NeuralBabyTalk
Facilitating the coexistence of radar systems with communication systems has been a major area of research in radar engineering.
The current work presents a new way to sense the environment using the channel equalization block of existing communication systems.
We have named this system CommSense.
In the current paper we demonstrate the feasibility of the system using Global System for Mobile Communications (GSM) signals.
The implementation has been done using open-source Software Defined Radio (SDR) environment.
In the preliminary results obtained in our work we show that it is possible to distinguish environmental changes using the proposed system.
The major advantage of the system is that it is inexpensive as channel estimation is an inherent block in any communication system and hence the added cost to make it work as an environment sensor is minimal.
The major challenge, on which we are continuing our work, is how to characterize the features in the environmental changes.
This is an acute challenge given the fact that the bandwidth available is narrow and the system is inherently a forward looking radar.
However the initial results, as shown in this paper, are encouraging and we intend to use an application specific instrumentation (ASIN) scheme to distinguish the environmental changes.
Many software development organizations still lack support for obtaining intellectual control over their software development processes and for determining the performance of their processes and the quality of the produced products.
Systematic support for detecting and reacting to critical project states in order to achieve planned goals is usually missing.
One means to institutionalize measurement on the basis of explicit models is the development and establishment of a so-called Software Project Control Center (SPCC) for systematic quality assurance and management support.
An SPCC is comparable to a control room, which is a well known term in the mechanical production domain.
Its tasks include collecting, in- terpreting, and visualizing measurement data in order to provide context-, purpose-, and role-oriented information for all stakeholders (e.g., project managers, quality assurance manager, developers) during the execution of a software development project.
The article will present an overview of SPCC concepts, a concrete instantiation that supports goal-oriented data visualization (G-SPCC approach), and experiences from practical applications.
Money laundering is a crime that makes it possible to finance other crimes, for this reason, it is important for criminal organizations and their combat is prioritized by nations around the world.
The anti-money laundering process has not evolved as expected because it has prioritized only the signaling of suspicious transactions.
The constant increasing in the volume of transactions has overloaded the indispensable human work of final evaluation of the suspicions.
This article presents a multiagent system that aims to go beyond the capture of suspicious transactions, seeking to assist the human expert in the analysis of suspicions.
The agents created use data mining techniques to create transactional behavioral profiles; apply rules generated in learning process in conjunction with specific rules based on legal aspects and profiles created to capture suspicious transactions; and analyze these suspicious transactions indicating to the human expert those that require more detailed analysis.
This article analyzes Twitter as a potential alternative source of external links for use in webometric analysis because of its capacity to embed hyperlinks in different tweets.
Given the limitations on searching Twitter's public API, we decided to use the Topsy search engine as a source for compiling tweets.
To this end, we took a global sample of 200 universities and compiled all the tweets with hyperlinks to any of these institutions.
Further link data was obtained from alternative sources (MajesticSEO and OpenSiteExplorer) in order to compare the results.
Thereafter, various statistical tests were performed to determine the correlation between the indicators and the ability to predict external links from the collected tweets.
The results indicate a high volume of tweets, although they are skewed by the presence and performance of specific universities and countries.
The data provided by Topsy correlated significantly with all link indicators, particularly with OpenSiteExplorer (r=0.769).
Finally, prediction models do not provide optimum results because of high error rates, which fall slightly in nonlinear models applied to specific environments.
We conclude that the use of Twitter (via Topsy) as a source of hyperlinks to universities produces promising results due to its high correlation with link indicators, though limited by policies and culture regarding use and presence in social networks.
This paper addresses the important problem of discerning hateful content in social media.
We propose a detection scheme that is an ensemble of Recurrent Neural Network (RNN) classifiers, and it incorporates various features associated with user-related information, such as the users' tendency towards racism or sexism.
These data are fed as input to the above classifiers along with the word frequency vectors derived from the textual content.
Our approach has been evaluated on a publicly available corpus of 16k tweets, and the results demonstrate its effectiveness in comparison to existing state of the art solutions.
More specifically, our scheme can successfully distinguish racism and sexism messages from normal text, and achieve higher classification quality than current state-of-the-art algorithms.
We give an algorithm to compute a morph between any two convex drawings of the same plane graph.
The morph preserves the convexity of the drawing at any time instant and moves each vertex along a piecewise linear curve with linear complexity.
The linear bound is asymptotically optimal in the worst case.
We present a hybrid control framework for solving a motion planning problem among a collection of heterogenous agents.
The proposed approach utilizes a finite set of low-level motion primitives, each based on a piecewise affine feedback control, to generate complex motions in a gridded workspace.
The constraints on allowable sequences of successive motion primitives are formalized through a maneuver automaton.
At the higher level, a control policy generated by a shortest path non-deterministic algorithm determines which motion primitive is executed in each box of the gridded workspace.
The overall framework yields a highly robust control design on both the low and high levels.
We experimentally demonstrate the efficacy and robustness of this framework for multiple quadrocopters maneuvering in a 2D or 3D workspace.
The standard classification of emotions involves categorizing the expression of emotions.
In this paper, parameters underlying some emotions are identified and a new classification based on these parameters is suggested.
Group centrality is an extension of the classical notion of centrality for individuals, to make it applicable to sets of them.
We perform a SWOT (strengths, weaknesses, opportunities and threats) analysis of the use of group centrality in semantic networks, for different centrality notions: degree, closeness, betweenness, giving prominence to random walks.
Among our main results stand out the relevance and NP-hardness of the problem of finding the most central set in a semantic network for an specific centrality measure.
Sparse coding (SC) is attracting more and more attention due to its comprehensive theoretical studies and its excellent performance in many signal processing applications.
However, most existing sparse coding algorithms are nonconvex and are thus prone to becoming stuck into bad local minima, especially when there are outliers and noisy data.
To enhance the learning robustness, in this paper, we propose a unified framework named Self-Paced Sparse Coding (SPSC), which gradually include matrix elements into SC learning from easy to complex.
We also generalize the self-paced learning schema into different levels of dynamic selection on samples, features and elements respectively.
Experimental results on real-world data demonstrate the efficacy of the proposed algorithms.
Spiking neural networks (SNNs) could play a key role in unsupervised machine learning applications, by virtue of strengths related to learning from the fine temporal structure of event-based signals.
However, some spike-timing-related strengths of SNNs are hindered by the sensitivity of spike-timing-dependent plasticity (STDP) rules to input spike rates, as fine temporal correlations may be obstructed by coarser correlations between firing rates.
In this article, we propose a spike-timing-dependent learning rule that allows a neuron to learn from the temporally-coded information despite the presence of rate codes.
Our long-term plasticity rule makes use of short-term synaptic fatigue dynamics.
We show analytically that, in contrast to conventional STDP rules, our fatiguing STDP (FSTDP) helps learn the temporal code, and we derive the necessary conditions to optimize the learning process.
We showcase the effectiveness of FSTDP in learning spike-timing correlations among processes of different rates in synthetic data.
Finally, we use FSTDP to detect correlations in real-world weather data from the United States in an experimental realization of the algorithm that uses a neuromorphic hardware platform comprising phase-change memristive devices.
Taken together, our analyses and demonstrations suggest that FSTDP paves the way for the exploitation of the spike-based strengths of SNNs in real-world applications.
Though quite challenging, leveraging large-scale unlabeled or partially labeled data in learning systems (e.g., model/classifier training) has attracted increasing attentions due to its fundamental importance.
To address this problem, many active learning (AL) methods have been proposed that employ up-to-date detectors to retrieve representative minority samples according to predefined confidence or uncertainty thresholds.
However, these AL methods cause the detectors to ignore the remaining majority samples (i.e., those with low uncertainty or high prediction confidence).
In this work, by developing a principled active sample mining (ASM) framework, we demonstrate that cost-effectively mining samples from these unlabeled majority data is key to training more powerful object detectors while minimizing user effort.
Specifically, our ASM framework involves a switchable sample selection mechanism for determining whether an unlabeled sample should be manually annotated via AL or automatically pseudo-labeled via a novel self-learning process.
The proposed process can be compatible with mini-batch based training (i.e., using a batch of unlabeled or partially labeled data as a one-time input) for object detection.
In addition, a few samples with low-confidence predictions are selected and annotated via AL.
Notably, our method is suitable for object categories that are not seen in the unlabeled data during the learning process.
Extensive experiments clearly demonstrate that our ASM framework can achieve performance comparable to that of alternative methods but with significantly fewer annotations.
Evaluating human-computer interaction is essential as a broadening population uses machines, sometimes in sensitive contexts.
However, traditional evaluation methods may fail to combine real-time measures, an "objective" approach and data contextualization.
In this review we look at how adding neuroimaging techniques can respond to such needs.
We focus on electroencephalography (EEG), as it could be handled effectively during a dedicated evaluation phase.
We identify workload, attention, vigilance, fatigue, error recognition, emotions, engagement, flow and immersion as being recognizable by EEG.
We find that workload, attention and emotions assessments would benefit the most from EEG.
Moreover, we advocate to study further error recognition through neuroimaging to enhance usability and increase user experience.
Conventional Open Information Extraction (Open IE) systems are usually built on hand-crafted patterns from other NLP tools such as syntactic parsing, yet they face problems of error propagation.
In this paper, we propose a neural Open IE approach with an encoder-decoder framework.
Distinct from existing methods, the neural Open IE approach learns highly confident arguments and relation tuples bootstrapped from a state-of-the-art Open IE system.
An empirical study on a large benchmark dataset shows that the neural Open IE system significantly outperforms several baselines, while maintaining comparable computational efficiency.
Under normality and homoscedasticity assumptions, Linear Discriminant Analysis (LDA) is known to be optimal in terms of minimising the Bayes error for binary classification.
In the heteroscedastic case, LDA is not guaranteed to minimise this error.
Assuming heteroscedasticity, we derive a linear classifier, the Gaussian Linear Discriminant (GLD), that directly minimises the Bayes error for binary classification.
In addition, we also propose a local neighbourhood search (LNS) algorithm to obtain a more robust classifier if the data is known to have a non-normal distribution.
We evaluate the proposed classifiers on two artificial and ten real-world datasets that cut across a wide range of application areas including handwriting recognition, medical diagnosis and remote sensing, and then compare our algorithm against existing LDA approaches and other linear classifiers.
The GLD is shown to outperform the original LDA procedure in terms of the classification accuracy under heteroscedasticity.
While it compares favourably with other existing heteroscedastic LDA approaches, the GLD requires as much as 60 times lower training time on some datasets.
Our comparison with the support vector machine (SVM) also shows that, the GLD, together with the LNS, requires as much as 150 times lower training time to achieve an equivalent classification accuracy on some of the datasets.
Thus, our algorithms can provide a cheap and reliable option for classification in a lot of expert systems.
Traditionally, the performance of ocr algorithms and systems is based on the recognition of isolated characters.
When a system classifies an individual character, its output is typically a character label or a reject marker that corresponds to an unrecognized character.
By comparing output labels with the correct labels, the number of correct recognition, substitution errors misrecognized characters, and rejects unrecognized characters are determined.
Nowadays, although recognition of printed isolated characters is performed with high accuracy, recognition of handwritten characters still remains an open problem in the research arena.
The ability to identify machine printed characters in an automated or a semi automated manner has obvious applications in numerous fields.
Since creating an algorithm with a one hundred percent correct recognition rate is quite probably impossible in our world of noise and different font styles, it is important to design character recognition algorithms with these failures in mind so that when mistakes are inevitably made, they will at least be understandable and predictable to the person working with the
Recently deep neural networks based on tanh activation function have shown their impressive power in image denoising.
In this letter, we try to use rectifier function instead of tanh and propose a dual-pathway rectifier neural network by combining two rectifier neurons with reversed input and output weights in the same hidden layer.
We drive the equivalent activation function and compare it to some typical activation functions for image denoising under the same network architecture.
The experimental results show that our model achieves superior performances faster especially when the noise is small.
Tokenization is the task of chopping it up into pieces, called tokens, perhaps at the same time throwing away certain characters, such as punctuation.
A token is an instance of token a sequence of characters in some particular document that are grouped together as a useful semantic unit for processing.
New software tool and algorithm to support the IRS at tokenization process are presented.
Our proposed tool will filter out the three computer character Sequences: IP-Addresses, Web URLs, Date, and Email Addresses.
Our tool will use the pattern matching algorithms and filtration methods.
After this process, the IRS can start a new tokenization process on the new retrieved text which will be free of these sequences.
Most of open-source software systems become available on the internet today.
Thus, we need automatic methods to label software code.
Software code can be labeled with a set of keywords.
These keywords in this paper referred as software labels.
The goal of this paper is to provide a quick view of the software code vocabulary.
This paper proposes an automatic approach to document the object-oriented software by labeling its code.
The approach exploits all software identifiers to label software code.
The paper presents the results of study conducted on the ArgoUML and drawing shapes case studies.
Results showed that all code labels were correctly identified.
This work discusses an important issue in the area of human resource management by proposing a novel model for creation and evaluation of software teams.
The model consists of several assessments, including a technical test, a quality of life test and a psychological-sociological test.
Since the technical test requires particular organizational specifications and cannot be examined without reference to a specific company, only the sociological test and the quality of life tests are extensively discussed in this work.
Two strategies are discussed for assigning roles in a project.
Initially, six software projects were selected, and after extensive analysis of the projects, two projects were chosen and correctives actions were applied.
An empirical evaluation was also conducted to assess the model effectiveness.
The experimental results demonstrate that the application of the model improved the productivity of project teams.
In this paper we introduce Epiphany as a high-performance energy-efficient manycore architecture suitable for real-time embedded systems.
This scalable architecture supports floating point operations in hardware and achieves 50 GFLOPS/W in 28 nm technology, making it suitable for high performance streaming applications like radio base stations and radar signal processing.
Through an efficient 2D mesh Network-on-Chip and a distributed shared memory model, the architecture is scalable to thousands of cores on a single chip.
An Epiphany-based open source computer named Parallella was launched in 2012 through Kickstarter crowd funding and has now shipped to thousands of customers around the world.
The increasing number of applications requiring the solution of large scale singular value problems have rekindled interest in iterative methods for the SVD.
Some promising recent ad- vances in large scale iterative methods are still plagued by slow convergence and accuracy limitations for computing smallest singular triplets.
Furthermore, their current implementations in MATLAB cannot address the required large problems.
Recently, we presented a preconditioned, two-stage method to effectively and accurately compute a small number of extreme singular triplets.
In this research, we present a high-performance software, PRIMME SVDS, that implements our hybrid method based on the state-of-the-art eigensolver package PRIMME for both largest and smallest singular values.
PRIMME SVDS fills a gap in production level software for computing the partial SVD, especially with preconditioning.
The numerical experiments demonstrate its superior performance compared to other state-of-the-art software and its good parallel performance under strong and weak scaling.
Support vector machines represent a promising development in machine learning research that is not widely used within the remote sensing community.
This paper reports the results of Multispectral(Landsat-7 ETM+) and Hyperspectral DAIS)data in which multi-class SVMs are compared with maximum likelihood and artificial neural network methods in terms of classification accuracy.
Our results show that the SVM achieves a higher level of classification accuracy than either the maximum likelihood or the neural classifier, and that the support vector machine can be used with small training datasets and high-dimensional data.
This paper presents an approach that exploits Java annotations to provide meta information needed to automatically transform plain Java programs into parallel code that can be run on multicore workstation.
Programmers just need to decorate the methods that will eventually be executed in parallel with standard Java annotations.
Annotations are automatically processed at launch-time and parallel byte code is derived.
Once in execution the program automatically retrieves the information about the executing platform and evaluates the information specified inside the annotations to transform the byte-code into a semantically equivalent multithreaded version, depending on the target architecture features.
The results returned by the annotated methods, when invoked, are futures with a wait-by-necessity semantics.
Bug localization in object oriented program ha s always been an important issue in softeware engineering.
In this paper, I propose a source level bug localization technique for object oriented embedded programs.
My proposed technique, presents the idea of debugging an object oriented program in class level, incorporating the object state information into the Class Dependence Graph (ClDG).
Given a program (having buggy statement) and an input that fails and others pass, my approach uses concrete as well as symbolic execution to synthesize the passing inputs that marginally from the failing input in their control flow behavior.
A comparison of the execution traces of the failing input and the passing input provides necessary clues to the root-cause of the failure.
A state trace difference, regarding the respective nodes of the ClDG is obtained, which leads to detect the bug in the program.
We propose a robust classifier to predict buying intentions based on user behaviour within a large e-commerce website.
In this work we compare traditional machine learning techniques with the most advanced deep learning approaches.
We show that both Deep Belief Networks and Stacked Denoising auto-Encoders achieved a substantial improvement by extracting features from high dimensional data during the pre-train phase.
They prove also to be more convenient to deal with severe class imbalance.
The problem of MIMO channel estimation at millimeter wave (mmWave) frequencies, both in a single-user and in a multi-user setting, is tackled in this paper.
Using a subspace approach, we develop a protocol enabling the estimation of the right (resp. left) singular vectors at the transmitter (resp. receiver) side; then, we adapt the projection approximation subspace tracking with deflation (PASTd) and the orthogonal Oja (OOJA) algorithms to our framework and obtain two channel estimation algorithms.
We also present an alternative algorithm based on the least squares (LS) approach.
The hybrid analog/digital nature of the beamformer is also explicitly taken into account at the algorithm design stage.
Our results clearly show that the proposed algorithms are very effective in estimating the principal directions of the MIMO channel matrix, and that they compare favorably, in terms of the performance-complexity trade-off, with respect to several competing alternatives.
Sequential neural networks models are powerful tools in a variety of Natural Language Processing (NLP) tasks.
The sequential nature of these models raises the questions: to what extent can these models implicitly learn hierarchical structures typical to human language, and what kind of grammatical phenomena can they acquire?
We focus on the task of agreement prediction in Basque, as a case study for a task that requires implicit understanding of sentence structure and the acquisition of a complex but consistent morphological system.
Analyzing experimental results from two syntactic prediction tasks -- verb number prediction and suffix recovery -- we find that sequential models perform worse on agreement prediction in Basque than one might expect on the basis of a previous agreement prediction work in English.
Tentative findings based on diagnostic classifiers suggest the network makes use of local heuristics as a proxy for the hierarchical structure of the sentence.
We propose the Basque agreement prediction task as challenging benchmark for models that attempt to learn regularities in human language.
In multi-cycle assignment problems with rotational diversity, a set of tasks has to be repeatedly assigned to a set of agents.
Over multiple cycles, the goal is to achieve a high diversity of assignments from tasks to agents.
At the same time, the assignments' profit has to be maximized in each cycle.
Due to changing availability of tasks and agents, planning ahead is infeasible and each cycle is an independent assignment problem but influenced by previous choices.
We approach the multi-cycle assignment problem as a two-part problem: Profit maximization and rotation are combined into one objective value, and then solved as a General Assignment Problem.
Rotational diversity is maintained with a single execution of the costly assignment model.
Our simple, yet effective method is applicable to different domains and applications.
Experiments show the applicability on a multi-cycle variant of the multiple knapsack problem and a real-world case study on the test case selection and assignment problem, an example from the software engineering domain, where test cases have to be distributed over compatible test machines.
Lane mark detection is an important element in the road scene analysis for Advanced Driver Assistant System (ADAS).
Limited by the onboard computing power, it is still a challenge to reduce system complexity and maintain high accuracy at the same time.
In this paper, we propose a Lane Marking Detector (LMD) using a deep convolutional neural network to extract robust lane marking features.
To improve its performance with a target of lower complexity, the dilated convolution is adopted.
A shallower and thinner structure is designed to decrease the computational cost.
Moreover, we also design post-processing algorithms to construct 3rd-order polynomial models to fit into the curved lanes.
Our system shows promising results on the captured road scenes.
We propose a type system for a calculus of contracting processes.
Processes can establish sessions by stipulating contracts, and then can interact either by keeping the promises made, or not.
Type safety guarantees that a typeable process is honest - that is, it abides by the contracts it has stipulated in all possible contexts, even in presence of dishonest adversaries.
Type inference is decidable, and it allows to safely approximate the honesty of processes using either synchronous or asynchronous communication.
The Daala project is a royalty-free video codec that attempts to compete with the best patent-encumbered codecs.
Part of our strategy is to replace core tools of traditional video codecs with alternative approaches, many of them designed to take perceptual aspects into account, rather than optimizing for simple metrics like PSNR.
This paper documents some of our experiences with these tools, which ones worked and which did not, and what we've learned from them.
The result is a codec which compares favorably with HEVC on still images, and is on a path to do so for video as well.
Majority of Artificial Neural Network (ANN) implementations in autonomous systems use a fixed/user-prescribed network topology, leading to sub-optimal performance and low portability.
The existing neuro-evolution of augmenting topology or NEAT paradigm offers a powerful alternative by allowing the network topology and the connection weights to be simultaneously optimized through an evolutionary process.
However, most NEAT implementations allow the consideration of only a single objective.
There also persists the question of how to tractably introduce topological diversification that mitigates overfitting to training scenarios.
To address these gaps, this paper develops a multi-objective neuro-evolution algorithm.
While adopting the basic elements of NEAT, important modifications are made to the selection, speciation, and mutation processes.
With the backdrop of small-robot path-planning applications, an experience-gain criterion is derived to encapsulate the amount of diverse local environment encountered by the system.
This criterion facilitates the evolution of genes that support exploration, thereby seeking to generalize from a smaller set of mission scenarios than possible with performance maximization alone.
The effectiveness of the single-objective (optimizing performance) and the multi-objective (optimizing performance and experience-gain) neuro-evolution approaches are evaluated on two different small-robot cases, with ANNs obtained by the multi-objective optimization observed to provide superior performance in unseen scenarios.
Stream computation is one of the approaches suitable for FPGA-based custom computing due to its high throughput capability brought by pipelining with regular memory access.
To increase performance of iterative stream computation, we can exploit both temporal and spatial parallelism by deepening and duplicating pipelines, respectively.
However, the performance is constrained by several factors including available hardware resources on FPGA, an external memory bandwidth, and utilization of pipeline stages, and therefore we need to find the best mix of the different parallelism to achieve the highest performance per power.
In this paper, we present a domain-specific language (DSL) based design space exploration for temporally and/or spatially parallel stream computation with FPGA.
We define a DSL where we can easily design a hierarchical structure of parallel stream computation with abstract description of computation.
For iterative stream computation of fluid dynamics simulation, we design hardware structures with a different mix of the temporal and spatial parallelism.
By measuring the performance and the power consumption, we find the best among them.
While motivation is of great interest to computing educators, relatively little work has been done on understanding faculty attitudes toward student motivation.
Two previous qualitative studies of instructor attitudes found results identical to those from other disciplines, but neither study considered whether instructors perceive student motivation to be more important in certain computing classes.
In this work we present quantitative results about the perceived importance of student motivation in computing courses on the part of computing educators.
Our survey results show that while a majority of respondents believe student motivation is necessary in all computing courses, the structure and audience in certain computing classes elevate the importance of student motivation.
We determine necessary conditions on the structure of symbol error rate (SER) optimal quantizers for limited feedback beamforming in wireless networks with one transmitter-receiver pair and R parallel amplify-and-forward relays.
We call a quantizer codebook "small" if its cardinality is less than R, and "large" otherwise.
A "d-codebook" depends on the power constraints and can be optimized accordingly, while an "i-codebook" remains fixed.
It was previously shown that any i-codebook that contains the single-relay selection (SRS) codebook achieves the full-diversity order, R. We prove the following:   Every full-diversity i-codebook contains the SRS codebook, and thus is necessarily large.
In general, as the power constraints grow to infinity, the limit of an optimal large d-codebook contains an SRS codebook, provided that it exists.
For small codebooks, the maximal diversity is equal to the codebook cardinality.
Every diversity-optimal small i-codebook is an orthogonal multiple-relay selection (OMRS) codebook.
Moreover, the limit of an optimal small d-codebook is an OMRS codebook.
We observe that SRS is nothing but a special case of OMRS for codebooks with cardinality equal to R. As a result, we call OMRS as "the universal necessary condition" for codebook optimality.
Finally, we confirm our analytical findings through simulations.
We equip dynamic geometry software (DGS) with a user-friendly method that enables massively parallel calculations on the graphics processing unit (GPU).
This interplay of DGS and GPU opens up various applications in education and mathematical research.
The GPU-aided discovery of mathematical properties, interactive visualizations of algebraic surfaces (raycasting), the mathematical deformation of images and footage in real-time, and computationally demanding numerical simulations of PDEs are examples from the long and versatile list of new domains that our approach makes accessible within a DGS.
We ease the development of complex (mathematical) visualizations and provide a rapid-prototyping scheme for general-purpose computations (GPGPU).
The possibility to program both CPU and GPU with the use of only one high-level (scripting) programming language is a crucial aspect of our concept.
We embed shader programming seamlessly within a high-level (scripting) programming environment.
The aforementioned requires the symbolic process of the transcompilation of a high-level programming language into shader programming language for GPU and, in this article, we address the challenge of the automatic translation of a high-level programming language to a shader language of the GPU.
To maintain platform independence and the possibility to use our technology on modern devices, we focus on a realization through WebGL.
In context of efforts of composing category-theoretic and logical methods in the area of knowledge representation we propose the notion of conceptory.
We consider intersection/union and other constructions in conceptories as expressive alternative to category-theoretic (co)limits and show they have features similar to (pro-, in-)jections.
Then we briefly discuss approaches to development of formal systems built on the base of conceptories and describe possible application of such system to the specific ontology.
A challenge in multiagent control systems is to ensure that they are appropriately resilient to communication failures between the various agents.
In many common game-theoretic formulations of these types of systems, it is implicitly assumed that all agents have access to as much information about other agents' actions as needed.
This paper endeavors to augment these game-theoretic methods with policies that would allow agents to react on-the-fly to losses of this information.
Unfortunately, we show that even if a single agent loses communication with one other weakly-coupled agent, this can cause arbitrarily-bad system states to emerge as various solution concepts of an associated game, regardless of how the agent accounts for the communication failure and regardless of how weakly coupled the agents are.
Nonetheless, we show that the harm that communication failures can cause is limited by the structure of the problem; when agents' action spaces are richer, problems are more susceptible to these types of pathologies.
Finally, we undertake an initial study into how a system designer might prevent these pathologies, and explore a few limited settings in which communication failures cannot cause harm.
CANDECOMP/PARAFAC (CPD) approximates multiway data by sum of rank-1 tensors.
Our recent study has presented a method to rank-1 tensor deflation, i.e. sequential extraction of the rank-1 components.
In this paper, we extend the method to block deflation problem.
When at least two factor matrices have full column rank, one can extract two rank-1 tensors simultaneously, and rank of the data tensor is reduced by 2.
For decomposition of order-3 tensors of size R x R x R and rank-R, the block deflation has a complexity of O(R^3) per iteration which is lower than the cost O(R^4) of the ALS algorithm for the overall CPD.
As an ubiquitous method in natural language processing, word embeddings are extensively employed to map semantic properties of words into a dense vector representation.
They capture semantic and syntactic relations among words but the vector corresponding to the words are only meaningful relative to each other.
Neither the vector nor its dimensions have any absolute, interpretable meaning.
We introduce an additive modification to the objective function of the embedding learning algorithm that encourages the embedding vectors of words that are semantically related to a predefined concept to take larger values along a specified dimension, while leaving the original semantic learning mechanism mostly unaffected.
In other words, we align words that are already determined to be related, along predefined concepts.
Therefore, we impart interpretability to the word embedding by assigning meaning to its vector dimensions.
The predefined concepts are derived from an external lexical resource, which in this paper is chosen as Roget's Thesaurus.
We observe that alignment along the chosen concepts is not limited to words in the Thesaurus and extends to other related words as well.
We quantify the extent of interpretability and assignment of meaning from our experimental results.
We also demonstrate the preservation of semantic coherence of the resulting vector space by using word-analogy and word-similarity tests.
These tests show that the interpretability-imparted word embeddings that are obtained by the proposed framework do not sacrifice performances in common benchmark tests.
Ever since the feasibility of in-band full-duplex (FD) at the Physical (PHY) layer has been established, several studies have emerged investigating protocol aspects of enabling FD operation in various legacy wireless technologies.
Recently, the adoption of a simultaneous transmit and receive (STR) mode for next generation wireless local area networks (WLANs) has received significant attention.
Enabling STR mode (FD communication mode) in 802.11 WLANs creates bi-directional FD (BFD) and uni-directional FD (UFD) links.
STR mode in 802.11 WLANs must be enabled with minimal protocol modifications while accounting for the co-existence and compatibility with legacy nodes and protocols.
This paper provides a novel solution, that can leverage carrier sense multiple access with enhanced collision avoidance (CSMA/ECA) and adaptive sensitivity control mechanisms, for enabling STR operation.
The key aspects of the proposed solution include co-existence with legacy nodes, identification of eligible nodes for UFD, optimization of secondary BFD and UFD transmissions, and creation of UFD opportunities.
Performance evaluation demonstrates that the proposed solution is effective in achieving the gains provided by STR operation.
Starting from a 3D electrothermal field problem discretized by the Finite Integration Technique, the equivalence to a circuit description is shown by exploiting the analogy to the Modified Nodal Analysis approach.
Using this analogy, an algorithm for the automatic generation of a monolithic SPICE netlist is presented.
Joule losses from the electrical circuit are included as heat sources in the thermal circuit.
The thermal simulation yields nodal temperatures that influence the electrical conductivity.
Apart from the used field discretization, this approach applies no further simplifications.
An example 3D chip package is used to validate the algorithm.
Deep learning techniques have been paramount in the last years, mainly due to their outstanding results in a number of applications, that range from speech recognition to face-based user identification.
Despite other techniques employed for such purposes, Deep Boltzmann Machines are among the most used ones, which are composed of layers of Restricted Boltzmann Machines (RBMs) stacked on top of each other.
In this work, we evaluate the concept of temperature in DBMs, which play a key role in Boltzmann-related distributions, but it has never been considered in this context up to date.
Therefore, the main contribution of this paper is to take into account this information and to evaluate its influence in DBMs considering the task of binary image reconstruction.
We expect this work can foster future research considering the usage of different temperatures during learning in DBMs.
Information quality in social media is an increasingly important issue, but web-scale data hinders experts' ability to assess and correct much of the inaccurate content, or `fake news,' present in these platforms.
This paper develops a method for automating fake news detection on Twitter by learning to predict accuracy assessments in two credibility-focused Twitter datasets: CREDBANK, a crowdsourced dataset of accuracy assessments for events in Twitter, and PHEME, a dataset of potential rumors in Twitter and journalistic assessments of their accuracies.
We apply this method to Twitter content sourced from BuzzFeed's fake news dataset and show models trained against crowdsourced workers outperform models based on journalists' assessment and models trained on a pooled dataset of both crowdsourced workers and journalists.
All three datasets, aligned into a uniform format, are also publicly available.
A feature analysis then identifies features that are most predictive for crowdsourced and journalistic accuracy assessments, results of which are consistent with prior work.
We close with a discussion contrasting accuracy and credibility and why models of non-experts outperform models of journalists for fake news detection in Twitter.
In this paper, we focus on online representation learning in non-stationary environments which may require continuous adaptation of model architecture.
We propose a novel online dictionary-learning (sparse-coding) framework which incorporates the addition and deletion of hidden units (dictionary elements), and is inspired by the adult neurogenesis phenomenon in the dentate gyrus of the hippocampus, known to be associated with improved cognitive function and adaptation to new environments.
In the online learning setting, where new input instances arrive sequentially in batches, the neuronal-birth is implemented by adding new units with random initial weights (random dictionary elements); the number of new units is determined by the current performance (representation error) of the dictionary, higher error causing an increase in the birth rate.
Neuronal-death is implemented by imposing l1/l2-regularization (group sparsity) on the dictionary within the block-coordinate descent optimization at each iteration of our online alternating minimization scheme, which iterates between the code and dictionary updates.
Finally, hidden unit connectivity adaptation is facilitated by introducing sparsity in dictionary elements.
Our empirical evaluation on several real-life datasets (images and language) as well as on synthetic data demonstrates that the proposed approach can considerably outperform the state-of-art fixed-size (nonadaptive) online sparse coding of Mairal et al.
(2009) in the presence of nonstationary data.
Moreover, we identify certain properties of the data (e.g., sparse inputs with nearly non-overlapping supports) and of the model (e.g., dictionary sparsity) associated with such improvements.
Decreasing costs of vision sensors and advances in embedded hardware boosted lane related research detection, estimation, and tracking in the past two decades.
The interest in this topic has increased even more with the demand for advanced driver assistance systems (ADAS) and self-driving cars.
Although extensively studied independently, there is still need for studies that propose a combined solution for the multiple problems related to the ego-lane, such as lane departure warning (LDW), lane change detection, lane marking type (LMT) classification, road markings detection and classification, and detection of adjacent lanes (i.e., immediate left and right lanes) presence.
In this paper, we propose a real-time Ego-Lane Analysis System (ELAS) capable of estimating ego-lane position, classifying LMTs and road markings, performing LDW and detecting lane change events.
The proposed vision-based system works on a temporal sequence of images.
Lane marking features are extracted in perspective and Inverse Perspective Mapping (IPM) images that are combined to increase robustness.
The final estimated lane is modeled as a spline using a combination of methods (Hough lines with Kalman filter and spline with particle filter).
Based on the estimated lane, all other events are detected.
To validate ELAS and cover the lack of lane datasets in the literature, a new dataset with more than 20 different scenes (in more than 15,000 frames) and considering a variety of scenarios (urban road, highways, traffic, shadows, etc.) was created.
The dataset was manually annotated and made publicly available to enable evaluation of several events that are of interest for the research community (i.e., lane estimation, change, and centering; road markings; intersections; LMTs; crosswalks and adjacent lanes).
ELAS achieved high detection rates in all real-world events and proved to be ready for real-time applications.
Optical Character Recognition (OCR) has been a topic of interest for many years.
It is defined as the process of digitizing a document image into its constituent characters.
Despite decades of intense research, developing OCR with capabilities comparable to that of human still remains an open challenge.
Due to this challenging nature, researchers from industry and academic circles have directed their attentions towards Optical Character Recognition.
Over the last few years, the number of academic laboratories and companies involved in research on Character Recognition has increased dramatically.
This research aims at summarizing the research so far done in the field of OCR.
It provides an overview of different aspects of OCR and discusses corresponding proposals aimed at resolving issues of OCR.
Yes, it can.
Data augmentation is perhaps the oldest preprocessing step in computer vision literature.
Almost every computer vision model trained on imaging data uses some form of augmentation.
In this paper, we use the inter-vertebral disk segmentation task alongside a deep residual U-Net as the learning model, to explore the effectiveness of augmentation.
In the extreme, we observed that a model trained on patches extracted from just one scan, with each patch augmented 50 times; achieved a Dice score of 0.73 in a validation set of 40 cases.
Qualitative evaluation indicated a clinically usable segmentation algorithm, which appropriately segments regions of interest, alongside limited false positive specks.
When the initial patches are extracted from nine scans the average Dice coefficient jumps to 0.86 and most of the false positives disappear.
While this still falls short of state-of-the-art deep learning based segmentation of discs reported in literature, qualitative examination reveals that it does yield segmentation, which can be amended by expert clinicians with minimal effort to generate additional data for training improved deep models.
Extreme augmentation of training data, should thus be construed as a strategy for training deep learning based algorithms, when very little manually annotated data is available to work with.
Models trained with extreme augmentation can then be used to accelerate the generation of manually labelled data.
Hence, we show that extreme augmentation can be a valuable tool in addressing scaling up small imaging data sets to address medical image segmentation tasks.
The successes of previous and current Mars rovers have encouraged space agencies worldwide to pursue additional planetary exploration missions with more ambitious navigation goals.
For example, NASA's planned Mars Sample Return mission will be a multi-year undertaking that will require a solar-powered rover to drive over 150 metres per sol for approximately three months.
This paper reviews the mobility planning framework used by current rovers and surveys the major challenges involved in continuous long-distance navigation on the Red Planet.
It also discusses recent work related to environment-aware and energy-aware navigation, and provides a perspective on how such work may eventually allow a solar-powered rover to achieve autonomous long-distance navigation on Mars.
GENESIS3 is the new version of the GENESIS software environment for musical creation by means of mass-interaction physics network modeling.
It was designed, and developed from scratch, in hindsight of more than 10 years working on and using the previous version.
We take the opportunity of this birth to provide in this article (1) an analysis of the peculiarities in GENESIS, aiming at highlighting its core ?software paradigm?
; and (2) an update on the features of the new version as compared to the last.
We investigate conditions under which a co-computably enumerable set in a computable metric space is computable.
Using higher-dimensional chains and spherical chains we prove that in each computable metric space which is locally computable each co-computably enumerable sphere is computable and each co-c.e. cell with co-c.e. boundary sphere is computable.
We present a method for discovering never-seen-before objects in 3D point clouds obtained from sensors like Microsoft Kinect.
We generate supervoxels directly from the point cloud data and use them with a Siamese network, built on a recently proposed 3D convolutional neural network architecture.
We use known objects to train a non-linear embedding of supervoxels, by optimizing the criteria that supervoxels which fall on the same object should be closer than those which fall on different objects, in the embedding space.
We test on unknown objects, which were not seen during training, and perform clustering in the learned embedding space of supervoxels to effectively perform novel object discovery.
We validate the method with extensive experiments, quantitatively showing that it can discover numerous unseen objects while being trained on only a few dense 3D models.
We also show very good qualitative results of object discovery in point cloud data when the test objects, either specific instances or even categories, were never seen during training.
Convolutional Neural Networks (CNNs) have demonstrated great results for the single-image super-resolution (SISR) problem.
Currently, most CNN algorithms promote deep and computationally expensive models to solve SISR.
However, we propose a novel SISR method that uses relatively less number of computations.
On training, we get group convolutions that have unused connections removed.
We have refined this system specifically for the task at hand by removing unnecessary modules from original CondenseNet.
Further, a reconstruction network consisting of deconvolutional layers has been used in order to upscale to high resolution.
All these steps significantly reduce the number of computations required at testing time.
Along with this, bicubic upsampled input is added to the network output for easier learning.
Our model is named SRCondenseNet.
We evaluate the method using various benchmark datasets and show that it performs favourably against the state-of-the-art methods in terms of both accuracy and number of computations required.
We consider the two-sided stable matching setting in which there may be uncertainty about the agents' preferences due to limited information or communication.
We consider three models of uncertainty: (1) lottery model --- in which for each agent, there is a probability distribution over linear preferences, (2) compact indifference model --- for each agent, a weak preference order is specified and each linear order compatible with the weak order is equally likely and (3) joint probability model --- there is a lottery over preference profiles.
For each of the models, we study the computational complexity of computing the stability probability of a given matching as well as finding a matching with the highest probability of being stable.
We also examine more restricted problems such as deciding whether a certainly stable matching exists.
We find a rich complexity landscape for these problems, indicating that the form uncertainty takes is significant.
Current language models have a significant limitation in the ability to encode and decode factual knowledge.
This is mainly because they acquire such knowledge from statistical co-occurrences although most of the knowledge words are rarely observed.
In this paper, we propose a Neural Knowledge Language Model (NKLM) which combines symbolic knowledge provided by the knowledge graph with the RNN language model.
By predicting whether the word to generate has an underlying fact or not, the model can generate such knowledge-related words by copying from the description of the predicted fact.
In experiments, we show that the NKLM significantly improves the performance while generating a much smaller number of unknown words.
Lensless imaging is an important and challenging problem.
One notable solution to lensless imaging is a single pixel camera which benefits from ideas central to compressive sampling.
However, traditional single pixel cameras require many illumination patterns which result in a long acquisition process.
Here we present a method for lensless imaging based on compressive ultrafast sensing.
Each sensor acquisition is encoded with a different illumination pattern and produces a time series where time is a function of the photon's origin in the scene.
Currently available hardware with picosecond time resolution enables time tagging photons as they arrive to an omnidirectional sensor.
This allows lensless imaging with significantly fewer patterns compared to regular single pixel imaging.
To that end, we develop a framework for designing lensless imaging systems that use ultrafast detectors.
We provide an algorithm for ideal sensor placement and an algorithm for optimized active illumination patterns.
We show that efficient lensless imaging is possible with ultrafast measurement and compressive sensing.
This paves the way for novel imaging architectures and remote sensing in extreme situations where imaging with a lens is not possible.
Middleboxes have become a vital part of modern networks by providing service functions such as content filtering, load balancing and optimization of network traffic.
An ordered sequence of middleboxes composing a logical service is called service chain.
Service Function Chaining (SFC) enables us to define these service chains.
Recent optimization models of SFCs assume that the functionality of a middlebox is provided by a single software appliance, commonly known as Virtual Network Function (VNF).
This assumption limits SFCs to the throughput of an individual VNF and resources of a physical machine hosting the VNF instance.
Moreover, typical service providers offer VNFs with heterogeneous throughput and resource configurations.
Thus, deploying a service chain with custom throughput can become a tedious process of stitching heterogeneous VNF instances.
In this paper, we describe how we can overcome these limitations without worrying about underlying VNF configurations and resource constraints.
This prospect is achieved by distributed deploying multiple VNF instances providing the functionality of a middlebox and modeling the optimal deployment of a service chain as a mixed integer programming problem.
The proposed model optimizes host and bandwidth resources allocation, and determines the optimal placement of VNF instances, while balancing workload and routing traffic among these VNF instances.
We show that this problem is NP-Hard and propose a heuristic solution called Kariz.
Kariz utilizes a tuning parameter to control the trade-off between speed and accuracy of the solution.
Finally, our solution is evaluated using simulations in data-center networks.
Antitubercular activity of Sulfathiazole Derivitives series were subjected to Quantitative Structure Activity Relationship (QSAR) Analysis with an attempt to derive and understand a correlation between the Biologically Activity as dependent variable and various descriptors as independent variables.
QSAR models generated using 28 compounds.
Several statistical regression expressions were obtained using Partial Least Squares (PLS) Regression, Multiple Linear Regression (MLR) and Principal Component Regression (PCR) methods.
The among these methods, Partial Least Square Regression (PLS) method has shown very promising result as compare to other two methods.
A QSAR model was generated by a training set of 18 molecules with correlation coefficient r (r square) of 0.9191, significant cross validated correlation coefficient (q square) of 0.8300, F test of 53.5783, r square for external test set pred_r square -3.6132, coefficient of correlation of predicted data set pred_r_se square 1.4859 and degree of freedom 14 by Partial Least Squares Regression Method.
When studying networks using random graph models, one is sometimes faced with situations where the notion of adjacency between nodes reflects multiple constraints.
Traditional random graph models are insufficient to handle such situations.
A simple idea to account for multiple constraints consists in taking the intersection of random graphs.
In this paper we initiate the study of random graphs so obtained through a simple example.
We examine the intersection of an Erdos-Renyi graph and of one-dimensional geometric random graphs.
We investigate the zero-one laws for the property that there are no isolated nodes.
When the geometric component is defined on the unit circle, a full zero-one law is established and we determine its critical scaling.
When the geometric component lies in the unit interval, there is a gap in that the obtained zero and one laws are found to express deviations from different critical scalings.
In particular, the first moment method requires a larger critical scaling than in the unit circle case in order to obtain the one law.
This discrepancy is somewhat surprising given that the zero-one laws for the absence of isolated nodes are identical in the geometric random graphs on both the unit interval and unit circle.
The angle between two compressed sparse vectors subject to the norm/distance constraints imposed by the restricted isometry property (RIP) of the sensing matrix plays a crucial role in the studies of many compressive sensing (CS) problems.
Assuming that (i) u and v are two sparse vectors separated by an angle thetha, and (ii) the sensing matrix Phi satisfies RIP, this paper is aimed at analytically characterizing the achievable angles between Phi*u and Phi*v. Motivated by geometric interpretations of RIP and with the aid of the well-known law of cosines, we propose a plane geometry based formulation for the study of the considered problem.
It is shown that all the RIP-induced norm/distance constraints on Phi*u and Phi*v can be jointly depicted via a simple geometric diagram in the two-dimensional plane.
This allows for a joint analysis of all the considered algebraic constraints from a geometric perspective.
By conducting plane geometry analyses based on the constructed diagram, closed-form formulae for the maximal and minimal achievable angles are derived.
Computer simulations confirm that the proposed solution is tighter than an existing algebraic-based estimate derived using the polarization identity.
The obtained results are used to derive a tighter restricted isometry constant of structured sensing matrices of a certain kind, to wit, those in the form of a product of an orthogonal projection matrix and a random sensing matrix.
Follow-up applications to three CS problems, namely, compressed-domain interference cancellation, RIP-based analysis of the orthogonal matching pursuit algorithm, and the study of democratic nature of random sensing matrices are investigated.
Many clustering problems in computer vision and other contexts are also classification problems, where each cluster shares a meaningful label.
Subspace clustering algorithms in particular are often applied to problems that fit this description, for example with face images or handwritten digits.
While it is straightforward to request human input on these datasets, our goal is to reduce this input as much as possible.
We present a pairwise-constrained clustering algorithm that actively selects queries based on the union-of-subspaces model.
The central step of the algorithm is in querying points of minimum margin between estimated subspaces; analogous to classifier margin, these lie near the decision boundary.
We prove that points lying near the intersection of subspaces are points with low margin.
Our procedure can be used after any subspace clustering algorithm that outputs an affinity matrix.
We demonstrate on several datasets that our algorithm drives the clustering error down considerably faster than the state-of-the-art active query algorithms on datasets with subspace structure and is competitive on other datasets.
Robotic systems, working together as a team, are becoming valuable players in different real-world applications, from disaster response to warehouse fulfillment services.
Centralized solutions for coordinating multi-robot teams often suffer from poor scalability and vulnerability to communication disruptions.
This paper develops a decentralized multi-agent task allocation (Dec-MATA) algorithm for multi-robot applications.
The task planning problem is posed as a maximum-weighted matching of a bipartite graph, the solution of which using the blossom algorithm allows each robot to autonomously identify the optimal sequence of tasks it should undertake.
The graph weights are determined based on a soft clustering process, which also plays a problem decomposition role seeking to reduce the complexity of the individual-agents' task assignment problems.
To evaluate the new Dec-MATA algorithm, a series of case studies (of varying complexity) are performed, with tasks being distributed randomly over an observable 2D environment.
A centralized approach, based on a state-of-the-art MILP formulation of the multi-Traveling Salesman problem is used for comparative analysis.
While getting within 7-28% of the optimal cost obtained by the centralized algorithm, the Dec-MATA algorithm is found to be 1-3 orders of magnitude faster and minimally sensitive to task-to-robot ratios, unlike the centralized algorithm.
Hand motion capture is a popular research field, recently gaining more attention due to the ubiquity of RGB-D sensors.
However, even most recent approaches focus on the case of a single isolated hand.
In this work, we focus on hands that interact with other hands or objects and present a framework that successfully captures motion in such interaction scenarios for both rigid and articulated objects.
Our framework combines a generative model with discriminatively trained salient points to achieve a low tracking error and with collision detection and physics simulation to achieve physically plausible estimates even in case of occlusions and missing visual data.
Since all components are unified in a single objective function which is almost everywhere differentiable, it can be optimized with standard optimization techniques.
Our approach works for monocular RGB-D sequences as well as setups with multiple synchronized RGB cameras.
For a qualitative and quantitative evaluation, we captured 29 sequences with a large variety of interactions and up to 150 degrees of freedom.
Local descriptors based on the image noise residual have proven extremely effective for a number of forensic applications, like forgery detection and localization.
Nonetheless, motivated by promising results in computer vision, the focus of the research community is now shifting on deep learning.
In this paper we show that a class of residual-based descriptors can be actually regarded as a simple constrained convolutional neural network (CNN).
Then, by relaxing the constraints, and fine-tuning the net on a relatively small training set, we obtain a significant performance improvement with respect to the conventional detector.
After the concept of industry cluster was tangibly applied in many countries, SMEs trended to link to each other to maintain their competitiveness in the market.
The major key success factors of the cluster are knowledge sharing and collaboration between partners.
This knowledge is collected in form of tacit and explicit knowledge from experts and institutions within the cluster.
The objective of this study is about enhancing the industry cluster with knowledge management by using knowledge engineering which is one of the most important method for managing knowledge.
This work analyzed three well known knowledge engineering methods, i.e.MOKA, SPEDE and CommonKADS, and compares the capability to be implemented in the cluster context.
Then, we selected one method and proposed the adapted methodology.
At the end of this paper, we validated and demonstrated the proposed methodology with some primary result by using case study of handicraft cluster in Thailand.
This paper describes the realization of the Ontology Web Search Engine.
The Ontology Web Search Engine is realizable as independent project and as a part of other projects.
The main purpose of this paper is to present the Ontology Web Search Engine realization details as the part of the Semantic Web Expert System and to present the results of the Ontology Web Search Engine functioning.
It is expected that the Semantic Web Expert System will be able to process ontologies from the Web, generate rules from these ontologies and develop its knowledge base.
Modelling, simulation and optimization form an integrated part of modern design practice in engineering and industry.
Tremendous progress has been observed for all three components over the last few decades.
However, many challenging issues remain unresolved, and the current trends tend to use nature-inspired algorithms and surrogate-based techniques for modelling and optimization.
This 4th workshop on Computational Optimization, Modelling and Simulation (COMS 2013) at ICCS 2013 will further summarize the latest developments of optimization and modelling and their applications in science, engineering and industry.
In this review paper, we will analyse the recent trends in modelling and optimization, and their associated challenges.
We will discuss important topics for further research, including parameter-tuning, large-scale problems, and the gaps between theory and applications.
This paper deals with segmentation of organs at risk (OAR) in head and neck area in CT images which is a crucial step for reliable intensity modulated radiotherapy treatment.
We introduce a convolution neural network with encoder-decoder architecture and a new loss function, the batch soft Dice loss function, used to train the network.
The resulting model produces segmentations of every OAR in the public MICCAI 2015 Head And Neck Auto-Segmentation Challenge dataset.
Despite the heavy class imbalance in the data, we improve accuracy of current state-of-the-art methods by 0.33 mm in terms of average surface distance and by 0.11 in terms of Dice overlap coefficient on average.
Measures of complex network analysis, such as vertex centrality, have the potential to unveil existing network patterns and behaviors.
They contribute to the understanding of networks and their components by analyzing their structural properties, which makes them useful in several computer science domains and applications.
Unfortunately, there is a large number of distinct centrality measures and little is known about their common characteristics in practice.
By means of an empirical analysis, we aim at a clear understanding of the main centrality measures available, unveiling their similarities and differences in a large number of distinct social networks.
Our experiments show that the vertex centrality measures known as information, eigenvector, subgraph, walk betweenness and betweenness can distinguish vertices in all kinds of networks with a granularity performance at 95%, while other metrics achieved a considerably lower result.
In addition, we demonstrate that several pairs of metrics evaluate the vertices in a very similar way, i.e. their correlation coefficient values are above 0.7.
This was unexpected, considering that each metric presents a quite distinct theoretical and algorithmic foundation.
Our work thus contributes towards the development of a methodology for principled network analysis and evaluation.
The network transport of 3D video, which contains two views of a video scene, poses significant challenges due to the increased video data compared to conventional single-view video.
Addressing these challenges requires a thorough understanding of the traffic and multiplexing characteristics of the different representation formats of 3D video.
We examine the average bitrate-distortion (RD) and bitrate variability-distortion (VD) characteristics of three main representation formats.
Specifically, we compare multiview video (MV) representation and encoding, frame sequential (FS) representation, and side-by-side (SBS) representation, whereby conventional single-view encoding is employed for the FS and SBS representations.
Our results for long 3D videos in full HD format indicate that the MV representation and encoding achieves the highest RD efficiency, while exhibiting the highest bitrate variabilities.
We examine the impact of these bitrate variabilities on network transport through extensive statistical multiplexing simulations.
We find that when multiplexing a small number of streams, the MV and FS representations require the same bandwidth.
However, when multiplexing a large number of streams or smoothing traffic, the MV representation and encoding reduces the bandwidth requirement relative to the FS representation.
Kernel methods give powerful, flexible, and theoretically grounded approaches to solving many problems in machine learning.
The standard approach, however, requires pairwise evaluations of a kernel function, which can lead to scalability issues for very large datasets.
Rahimi and Recht (2007) suggested a popular approach to handling this problem, known as random Fourier features.
The quality of this approximation, however, is not well understood.
We improve the uniform error bound of that paper, as well as giving novel understandings of the embedding's variance, approximation error, and use in some machine learning methods.
We also point out that surprisingly, of the two main variants of those features, the more widely used is strictly higher-variance for the Gaussian kernel and has worse bounds.
The development of computer technology has been rapid.
Not so long ago, the first computer was developed which was large and bulky.
Now, the latest generation of smartphones has a calculation power, which would have been considered those of supercomputers in 1990.
For a smart environment, the person recognition and re-recognition is an important topic.
The distribution of new technologies like wearable computing is a new approach to the field of person recognition and re-recognition.
This article lays out the idea of identifying and re-identifying wearable computing devices by listening to their wireless communication connectivity like Wi-Fi and Bluetooth and building a classification of interaction scenarios for the combination of human-wearable-environment.
Control systems behavior can be analyzed taking into account a large number of parameters: performances, reliability, availability, security.
Each control system presents various security vulnerabilities that affect in lower or higher measure its functioning.
In this paper the authors present a method to assess the impact of security issues on the systems availability.
A fuzzy model for estimating the availability of the system based on the security level and achieved availability coefficient (depending on MTBF and MTR) is developed and described.
The results of the fuzzy inference system (FIS) are presented in the last section of the paper.
An object detector performs suboptimally when applied to image data taken from a viewpoint different from the one with which it was trained.
In this paper, we present a viewpoint adaptation algorithm that allows a trained single-view object detector to be adapted to a new, distinct viewpoint.
We first illustrate how a feature space transformation can be inferred from a known homography between the source and target viewpoints.
Second, we show that a variety of trained classifiers can be modified to behave as if that transformation were applied to each testing instance.
The proposed algorithm is evaluated on a person detection task using images from the PETS 2007 and CAVIAR datasets, as well as from a new synthetic multi-view person detection dataset.
It yields substantial performance improvements when adapting single-view person detectors to new viewpoints, and simultaneously reduces computational complexity.
This work has the potential to improve detection performance for cameras viewing objects from arbitrary viewpoints, while simplifying data collection and feature extraction.
This paper addresses the problem of finding multiple near-optimal, spatially-dissimilar paths that can be considered as alternatives in the decision making process, for finding optimal corridors in which to construct a new road.
We further consider combinations of techniques for reducing the costs associated with the computation and increasing the accuracy of the cost formulation.
Numerical results for five algorithms to solve the dissimilar multipath problem show that a "bidirectional approach" yields the fastest running times and the most robust algorithm.
Further modifications of the algorithms to reduce the running time were tested and it is shown that running time can be reduced by an average of 56 percent without compromising the quality of the results.
Cloud Computing is an emerging area for accessing computing resources.
In general, Cloud service providers offer services that can be clustered into three categories: SaaS, PaaS and IaaS.
This paper discusses the Cloud workload analysis.
The efficient Cloud workload resource mapping technique is proposed.
This paper aims to provide a means of understanding and investigating IaaS Cloud workloads and the resources.
In this paper, regression analysis is used to analyze the Cloud workloads and identifies the relationship between Cloud workloads and available resources.
The effective organization of dynamic nature resources can be done with the help of Cloud workloads.
Till Cloud workload is considered a vital talent, the Cloud resources cannot be consumed in an effective style.
The proposed technique has been validated by Z Formal specification language.
This approach is effective in minimizing the cost and submission burst time of Cloud workloads.
This paper presents HEALER, a software agent that recommends sequential intervention plans for use by homeless shelters, who organize these interventions to raise awareness about HIV among homeless youth.
HEALER's sequential plans (built using knowledge of social networks of homeless youth) choose intervention participants strategically to maximize influence spread, while reasoning about uncertainties in the network.
While previous work presents influence maximizing techniques to choose intervention participants, they do not address three real-world issues: (i) they completely fail to scale up to real-world sizes; (ii) they do not handle deviations in execution of intervention plans; (iii) constructing real-world social networks is an expensive process.
HEALER handles these issues via four major contributions: (i) HEALER casts this influence maximization problem as a POMDP and solves it using a novel planner which scales up to previously unsolvable real-world sizes; (ii) HEALER allows shelter officials to modify its recommendations, and updates its future plans in a deviation-tolerant manner; (iii) HEALER constructs social networks of homeless youth at low cost, using a Facebook application.
Finally, (iv) we show hardness results for the problem that HEALER solves.
HEALER will be deployed in the real world in early Spring 2016 and is currently undergoing testing at a homeless shelter.
In a vertex-colored graph, an edge is happy if its endpoints have the same color.
Similarly, a vertex is happy if all its incident edges are happy.
Motivated by the computation of homophily in social networks, we consider the algorithmic aspects of the following Maximum Happy Edges (k-MHE) problem: given a partially k-colored graph G, find an extended full k-coloring of G maximizing the number of happy edges.
When we want to maximize the number of happy vertices, the problem is known as Maximum Happy Vertices (k-MHV).
We further study the complexity of the problems and their weighted variants.
For instance, we prove that for every k >= 3, both problems are NP-complete for bipartite graphs and k-MHV remains hard for split graphs.
In terms of exact algorithms, we show both problems can be solved in time O*(2^n), and give an even faster O*(1.89^n)-time algorithm when k = 3.
From a parameterized perspective, we give a linear vertex kernel for Weighted k-MHE, where edges are weighted and the goal is to obtain happy edges of at least a specified total weight.
Finally, we prove both problems are solvable in polynomial-time when the graph has bounded treewidth or bounded neighborhood diversity.
Interactive reinforcement learning (IRL) extends traditional reinforcement learning (RL) by allowing an agent to interact with parent-like trainers during a task.
In this paper, we present an IRL approach using dynamic audio-visual input in terms of vocal commands and hand gestures as feedback.
Our architecture integrates multi-modal information to provide robust commands from multiple sensory cues along with a confidence value indicating the trustworthiness of the feedback.
The integration process also considers the case in which the two modalities convey incongruent information.
Additionally, we modulate the influence of sensory-driven feedback in the IRL task using goal-oriented knowledge in terms of contextual affordances.
We implement a neural network architecture to predict the effect of performed actions with different objects to avoid failed-states, i.e., states from which it is not possible to accomplish the task.
In our experimental setup, we explore the interplay of multimodal feedback and task-specific affordances in a robot cleaning scenario.
We compare the learning performance of the agent under four different conditions: traditional RL, multi-modal IRL, and each of these two setups with the use of contextual affordances.
Our experiments show that the best performance is obtained by using audio-visual feedback with affordancemodulated IRL.
The obtained results demonstrate the importance of multi-modal sensory processing integrated with goal-oriented knowledge in IRL tasks.
Semantic image inpainting is a challenging task where large missing regions have to be filled based on the available visual data.
Existing methods which extract information from only a single image generally produce unsatisfactory results due to the lack of high level context.
In this paper, we propose a novel method for semantic image inpainting, which generates the missing content by conditioning on the available data.
Given a trained generative model, we search for the closest encoding of the corrupted image in the latent image manifold using our context and prior losses.
This encoding is then passed through the generative model to infer the missing content.
In our method, inference is possible irrespective of how the missing content is structured, while the state-of-the-art learning based method requires specific information about the holes in the training phase.
Experiments on three datasets show that our method successfully predicts information in large missing regions and achieves pixel-level photorealism, significantly outperforming the state-of-the-art methods.
Scheduling in Grid computing has been active area of research since its beginning.
However, beginners find very difficult to understand related concepts due to a large learning curve of Grid computing.
Thus, there is a need of concise understanding of scheduling in Grid computing area.
This paper strives to present concise understanding of scheduling and related understanding of Grid computing system.
The paper describes overall picture of Grid computing and discusses important sub-systems that enable Grid computing possible.
Moreover, the paper also discusses concepts of resource scheduling and application scheduling and also presents classification of scheduling algorithms.
Furthermore, the paper also presents methodology used for evaluating scheduling algorithms including both real system and simulation based approaches.
The presented work on scheduling in Grid containing concise understandings of scheduling system, scheduling algorithm, and scheduling methodology would be very useful to users and researchers
Genomics are rapidly transforming medical practice and basic biomedical research, providing insights into disease mechanisms and improving therapeutic strategies, particularly in cancer.
The ability to predict the future course of a patient's disease from high-dimensional genomic profiling will be essential in realizing the promise of genomic medicine, but presents significant challenges for state-of-the-art survival analysis methods.
In this abstract we present an investigation in learning genomic representations with neural networks to predict patient survival in cancer.
We demonstrate the advantages of this approach over existing survival analysis methods using brain tumor data.
Social media are becoming an increasingly important source of information about the public mood regarding issues such as elections, Brexit, stock market, etc.
In this paper we focus on sentiment classification of Twitter data.
Construction of sentiment classifiers is a standard text mining task, but here we address the question of how to properly evaluate them as there is no settled way to do so.
Sentiment classes are ordered and unbalanced, and Twitter produces a stream of time-ordered data.
The problem we address concerns the procedures used to obtain reliable estimates of performance measures, and whether the temporal ordering of the training and test data matters.
We collected a large set of 1.5 million tweets in 13 European languages.
We created 138 sentiment models and out-of-sample datasets, which are used as a gold standard for evaluations.
The corresponding 138 in-sample datasets are used to empirically compare six different estimation procedures: three variants of cross-validation, and three variants of sequential validation (where test set always follows the training set).
We find no significant difference between the best cross-validation and sequential validation.
However, we observe that all cross-validation variants tend to overestimate the performance, while the sequential methods tend to underestimate it.
Standard cross-validation with random selection of examples is significantly worse than the blocked cross-validation, and should not be used to evaluate classifiers in time-ordered data scenarios.
In this paper, we propose a new sparse signal recovery algorithm, referred to as sparse Kalman tree search (sKTS), that provides a robust reconstruction of the sparse vector when the sequence of correlated observation vectors are available.
The proposed sKTS algorithm builds on expectation-maximization (EM) algorithm and consists of two main operations: 1) Kalman smoothing to obtain the a posteriori statistics of the source signal vectors and 2) greedy tree search to estimate the support of the signal vectors.
Through numerical experiments, we demonstrate that the proposed sKTS algorithm is effective in recovering the sparse signals and performs close to the Oracle (genie-based) Kalman estimator.
For Nonlinear-Frequency Division-Multiplexed (NFDM) systems, the statistics of the received nonlinear spectrum in the presence of additive white Gaussian noise (AWGN) is an open problem.
We present a novel method, based on the Fourier collocation algorithm, to compute these statistics.
We consider the problem of computing a binary linear transformation using unreliable components when all circuit components are unreliable.
Two noise models of unreliable components are considered: probabilistic errors and permanent errors.
We introduce the "ENCODED" technique that ensures that the error probability of the computation of the linear transformation is kept bounded below a small constant independent of the size of the linear transformation even when all logic gates in the computation are noisy.
Further, we show that the scheme requires fewer operations (in order sense) than its "uncoded" counterpart.
By deriving a lower bound, we show that in some cases, the scheme is order-optimal.
Using these results, we examine the gain in energy-efficiency from use of "voltage-scaling" scheme where gate-energy is reduced by lowering the supply voltage.
We use a gate energy-reliability model to show that tuning gate-energy appropriately at different stages of the computation ("dynamic" voltage scaling), in conjunction with ENCODED, can lead to order-sense energy-savings over the classical "uncoded" approach.
Finally, we also examine the problem of computing a linear transformation when noiseless decoders can be used, providing upper and lower bounds to the problem.
There is no known way of giving a domain-theoretic semantics to higher-order probabilistic languages, in such a way that the involved domains are continuous or quasi-continuous - the latter is required to do any serious mathematics.
We argue that the problem naturally disappears for languages with two kinds of types, where one kind is interpreted in a Cartesian-closed category of continuous dcpos, and the other is interpreted in a category that is closed under the probabilistic powerdomain functor.
Such a setting is provided by Paul B.Levy's call-by-push-value paradigm.
Following this insight, we define a call-by-push-value language, with probabilistic choice sitting inside the value types, and where conversion from a value type to a computation type involves demonic non-determinism.
We give both a domain-theoretic semantics and an operational semantics for the resulting language, and we show that they are sound and adequate.
With the addition of statistical termination testers and parallel if, we show that the language is even fully abstract - and those two primitives are required for that.
This paper investigates sparse signal recovery based on expectation propagation (EP) from unitarily invariant measurements.
A rigorous analysis is presented for the state evolution (SE) of an EP-based message-passing algorithm in the large system limit, where both input and output dimensions tend to infinity at an identical speed.
The main result is the justification of an SE formula conjectured by Ma and Ping.
A transformation network describes how one set of resources can be transformed into another via technological processes.
Transformation networks in economics are useful because they can highlight areas for future innovations, both in terms of new products, new production techniques, or better efficiency.
They also make it easy to detect areas where an economy might be fragile.
In this paper, we use computational simulations to investigate how the density of a transformation network affects the economic performance, as measured by the gross domestic product (GDP), of an artificial economy.
Our results show that on average, the GDP of our economy increases as the density of the transformation network increases.
We also find that while the average performance increases, the maximum possible performance decreases and the minimum possible performance increases.
Attributing the culprit of a cyber-attack is widely considered one of the major technical and policy challenges of cyber-security.
The lack of ground truth for an individual responsible for a given attack has limited previous studies.
Here, we overcome this limitation by leveraging DEFCON capture-the-flag (CTF) exercise data where the actual ground-truth is known.
In this work, we use various classification techniques to identify the culprit in a cyberattack and find that deceptive activities account for the majority of misclassified samples.
We also explore several heuristics to alleviate some of the misclassification caused by deception.
In this paper, we describe a tool for debugging the output and attention weights of neural machine translation (NMT) systems and for improved estimations of confidence about the output based on the attention.
The purpose of the tool is to help researchers and developers find weak and faulty example translations that their NMT systems produce without the need for reference translations.
Our tool also includes an option to directly compare translation outputs from two different NMT engines or experiments.
In addition, we present a demo website of our tool with examples of good and bad translations: http://attention.lielakeda.lv
Effective data analysis ideally requires the analyst to have high expertise as well as high knowledge of the data.
Even with such familiarity, manually pursuing all potential hypotheses and exploring all possible views is impractical.
We present DataSite, a proactive visual analytics system where the burden of selecting and executing appropriate computations is shared by an automatic server-side computation engine.
Salient features identified by these automatic background processes are surfaced as notifications in a feed timeline.
DataSite effectively turns data analysis into a conversation between analyst and computer, thereby reducing the cognitive load and domain knowledge requirements.
We validate the system with a user study comparing it to a recent visualization recommendation system, yielding significant improvement, particularly for complex analyses that existing analytics systems do not support well.
We propose a novel two-layered attention network based on Bidirectional Long Short-Term Memory for sentiment analysis.
The novel two-layered attention network takes advantage of the external knowledge bases to improve the sentiment prediction.
It uses the Knowledge Graph Embedding generated using the WordNet.
We build our model by combining the two-layered attention network with the supervised model based on Support Vector Regression using a Multilayer Perceptron network for sentiment analysis.
We evaluate our model on the benchmark dataset of SemEval 2017 Task 5.
Experimental results show that the proposed model surpasses the top system of SemEval 2017 Task 5.
The model performs significantly better by improving the state-of-the-art system at SemEval 2017 Task 5 by 1.7 and 3.7 points for sub-tracks 1 and 2 respectively.
IP networks became the most dominant type of information networks nowadays.
It provides a number of services and makes it easy for users to be connected.
IP networks provide an efficient way with a large number of services compared to other ways of voice communication.
This leads to the migration to make voice calls via IP networks.
Despite the wide range of IP networks services, availability, and its capabilities, there still a large number of security threats that affect IP networks and for sure affecting other services based on it and voice is one of them.
This paper discusses reasons of migration from making voice calls via IP networks and leaving legacy networks, requirements to be available in IP networks to support voice transport, and concentrating on SPIT attack and its detection methods.
Experiments took place to compare the different approaches used to detect spam over VoIP networks.
Secure communication is a promising technology for wireless networks because it ensures secure transmission of information.
In this paper, we investigate the joint subcarrier (SC) assignment and power allocation problem for non-orthogonal multiple access (NOMA) amplify-and-forward two-way relay wireless networks, in the presence of eavesdroppers.
By exploiting cooperative jamming (CJ) to enhance the security of the communication link, we aim to maximize the achievable secrecy energy efficiency by jointly designing the SC assignment, user pair scheduling and power allocation.
Assuming the perfect knowledge of the channel state information (CSI) at the relay station, we propose a low-complexity subcarrier assignment scheme (SCAS-1), which is equivalent to many-to-many matching games, and then SCAS-2 is formulated as a secrecy energy efficiency maximization problem.
The secure power allocation problem is modeled as a convex geometric programming problem, and then solved by interior point methods.
Simulation results demonstrate that the effectiveness of the proposed SSPA algorithms under scenarios of using and not using CJ, respectively.
3D image processing constitutes nowadays a challenging topic in many scientific fields such as medicine, computational physics and informatics.
Therefore, development of suitable tools that guaranty a best treatment is a necessity.
Spherical shapes are a big class of 3D images whom processing necessitates adoptable tools.
This encourages researchers to develop spherical wavelets and spherical harmonics as special mathematical bases able for 3D spherical shapes.
The present work lies in the whole topic of 3D image processing with the special spherical harmonics bases.
A spherical harmonics based approach is proposed for the reconstruction of images provided with spherical harmonics Shannon-type entropy to evaluate the order/disorder of the reconstructed image.
Efficiency and accuracy of the approach is demonstrated by a simulation study on several spherical models.
Bug fixing is generally a manually-intensive task.
However, recent work has proposed the idea of automated program repair, which aims to repair (at least a subset of) bugs in different ways such as code mutation, etc.
Following in the same line of work as automated bug repair, in this paper we aim to leverage past fixes to propose fixes of current/future bugs.
Specifically, we propose Ratchet, a corrective patch generation system using neural machine translation.
By learning corresponding pre-correction and post-correction code in past fixes with a neural sequence-to-sequence model, Ratchet is able to generate a fix code for a given bug-prone code query.
We perform an empirical study with five open source projects, namely Ambari, Camel, Hadoop, Jetty and Wicket, to evaluate the effectiveness of Ratchet.
Our findings show that Ratchet can generate syntactically valid statements 98.7% of the time, and achieve an F1-measure between 0.41-0.83 with respect to the actual fixes adopted in the code base.
In addition, we perform a qualitative validation using 20 participants to see whether the generated statements can be helpful in correcting bugs.
Our survey showed that Ratchet's output was considered to be helpful in fixing the bugs on many occasions, even if fix was not 100% correct.
We perform a statistical analysis of scientific-publication data with a goal to provide quantitative analysis of scientific process.
Such an investigation belongs to the newly established field of scientometrics: a branch of the general science of science that covers all quantitative methods to analyze science and research process.
As a case study we consider download and citation statistics of the journal `Europhysics Letters' (EPL), as Europe's flagship letters journal of broad interest to the physics community.
While citations are usually considered as an indicator of academic impact, downloads reflect rather the level of attractiveness or popularity of a publication.
We discuss peculiarities of both processes and correlations between them.
Cloud computing services provide a scalable solution for the storage and processing of images and multimedia files.
However, concerns about privacy risks prevent users from sharing their personal images with third-party services.
In this paper, we describe the design and implementation of CryptoImg, a library of modular privacy preserving image processing operations over encrypted images.
By using homomorphic encryption, CryptoImg allows the users to delegate their image processing operations to remote servers without any privacy concerns.
Currently, CryptoImg supports a subset of the most frequently used image processing operations such as image adjustment, spatial filtering, edge sharpening, histogram equalization and others.
We implemented our library as an extension to the popular computer vision library OpenCV.
CryptoImg can be used from either mobile or desktop clients.
Our experimental results demonstrate that CryptoImg is efficient while performing operations over encrypted images with negligible error and reasonable time overheads on the supported platforms
The paper tailors the so-called wave-based control popular in the field of flexible mechanical structures to the field of distributed control of vehicular platoons.
The proposed solution augments the symmetric bidirectional control algorithm with a wave-absorbing controller implemented on the leader, and/or on the rear-end vehicle.
The wave-absorbing controller actively absorbs an incoming wave of positional changes in the platoon and thus prevents oscillations of inter-vehicle distances.
The proposed controller significantly improves the performance of platoon manoeuvrers such as acceleration/deceleration or changing the distances between vehicles without making the platoon string unstable.
Numerical simulations show that the wave-absorbing controller performs efficiently even for platoons with a large number of vehicles, for which other platooning algorithms are inefficient or require wireless communication between vehicles.
The availability of corpora is a major factor in building natural language processing applications.
However, the costs of acquiring corpora can prevent some researchers from going further in their endeavours.
The ease of access to freely available corpora is urgent needed in the NLP research community especially for language such as Arabic.
Currently, there is not easy was to access to a comprehensive and updated list of freely available Arabic corpora.
We present in this paper, the results of a recent survey conducted to identify the list of the freely available Arabic corpora and language resources.
Our preliminary results showed an initial list of 66 sources.
We presents our findings in the various categories studied and we provided the direct links to get the data when possible.
A code of the natural numbers is a uniquely-decodable binary code of the natural numbers with non-decreasing codeword lengths, which satisfies Kraft's inequality tightly.
We define a natural partial order on the set of codes, and show how to construct effectively a code better than a given sequence of codes, in a certain precise sense.
As an application, we prove that the existence of a scale of codes (a well-ordered set of codes which contains a code better than any given code) is independent of ZFC.
With the rapid advancements in digital imaging systems and networking, low-cost hand-held image capture devices equipped with network connectivity are becoming ubiquitous.
This ease of digital image capture and sharing is also accompanied by widespread usage of user-friendly image editing software.
Thus, we are in an era where digital images can be very easily used for the massive spread of false information and their integrity need to be seriously questioned.
Application of multiple lossy compressions on images is an essential part of any image editing pipeline involving lossy compressed images.
This paper aims to address the problem of classifying images based on the number of JPEG compressions they have undergone, by utilizing deep convolutional neural networks in DCT domain.
The proposed system incorporates a well designed pre-processing step before feeding the image data to CNN to capture essential characteristics of compression artifacts and make the system image content independent.
Detailed experiments are performed to optimize different aspects of the system, such as depth of CNN, number of DCT frequencies, and execution time.
Results on the standard UCID dataset demonstrate that the proposed system outperforms existing systems for multiple JPEG compression detection and is capable of classifying more number of re-compression cycles then existing systems.
The total variation (TV) model and its related variants have already been proposed for image processing in previous literature.
In this paper a novel total variation model based on kernel functions is proposed.
In this novel model, we first map each pixel value of an image into a Hilbert space by using a nonlinear map, and then define a coupled image of an original image in order to construct a kernel function.
Finally, the proposed model is solved in a kernel function space instead of in the projecting space from a nonlinear map.
For the proposed model, we theoretically show under what conditions the mapping image is in the space of bounded variation when the original image is in the space of bounded variation.
It is also found that the proposed model further extends the generalized TV model and the information from three different channels of color images can be fused by adopting various kernel functions.
A series of experiments on some gray and color images are carried out to demonstrate the effectiveness of the proposed model.
For nanotechnology, the semiconductor device is scaled down dramatically with additional strain engineering for device enhancement, the overall device characteristic is no longer dominated by the device size but also circuit layout.
The higher order layout effects, such as well proximity effect (WPE), oxide spacing effect (OSE) and poly spacing effect (PSE), play an important role for the device performance, it is critical to understand Design for Manufacturability (DFM) impacts with various layout topology toward the overall circuit performance.
Currently, the layout effects (WPE, OSE and PSE) are validated through digital standard cell and analog differential pair test structure.
However, two analog layout structures: the guard ring and dummy fill impact are not well studied yet, then, this paper describes the current mirror test circuit to examine the guard ring and dummy fills DFM impacts using TSMC 28nm HPM process.
KAF consists of a process and some templates to guide the planning and execution of audits of knowledge resources, with emphasis on sharing.
KAF is based on methodological blueprint provided by the Data Audit Framework (DAF)conceived by the JISC-funded DAFD project.KAF enables organisations to find out what knowledge resources are associated with the project, and how they are shared.KAF is available in two versionsKAF-g (generic, domain independent) KAF-se (targets systems enegineering knowledge)
We present a technique for static enforcement of high-level, declarative information flow policies.
Given a program that manipulates sensitive data and a set of declarative policies on the data, our technique automatically inserts policy-enforcing code throughout the program to make it provably secure with respect to the policies.
We achieve this through a new approach we call type-targeted program synthesis, which enables the application of traditional synthesis techniques in the context of global policy enforcement.
The key insight is that, given an appropriate encoding of policy compliance in a type system, we can use type inference to decompose a global policy enforcement problem into a series of small, local program synthesis problems that can be solved independently.
We implement this approach in Lifty, a core DSL for data-centric applications.
Our experience using the DSL to implement three case studies shows that (1) Lifty's centralized, declarative policy definitions make it easier to write secure data-centric applications, and (2) the Lifty compiler is able to efficiently synthesize all necessary policy-enforcing code, including the code required to prevent several reported real-world information leaks.
Humans learn to solve tasks of increasing complexity by building on top of previously acquired knowledge.
Typically, there exists a natural progression in the tasks that we learn - most do not require completely independent solutions, but can be broken down into simpler subtasks.
We propose to represent a solver for each task as a neural module that calls existing modules (solvers for simpler tasks) in a functional program-like manner.
Lower modules are a black box to the calling module, and communicate only via a query and an output.
Thus, a module for a new task learns to query existing modules and composes their outputs in order to produce its own output.
Our model effectively combines previous skill-sets, does not suffer from forgetting, and is fully differentiable.
We test our model in learning a set of visual reasoning tasks, and demonstrate improved performances in all tasks by learning progressively.
By evaluating the reasoning process using human judges, we show that our model is more interpretable than an attention-based baseline.
The ConditionaL Neural Networks (CLNN) and the Masked ConditionaL Neural Networks (MCLNN) exploit the nature of multi-dimensional temporal signals.
The CLNN captures the conditional temporal influence between the frames in a window and the mask in the MCLNN enforces a systematic sparseness that follows a filterbank-like pattern over the network links.
The mask induces the network to learn about time-frequency representations in bands, allowing the network to sustain frequency shifts.
Additionally, the mask in the MCLNN automates the exploration of a range of feature combinations, usually done through an exhaustive manual search.
We have evaluated the MCLNN performance using the Ballroom and Homburg datasets of music genres.
MCLNN has achieved accuracies that are competitive to state-of-the-art handcrafted attempts in addition to models based on Convolutional Neural Networks.
In this work, we propose a multi-modal Convolutional Neural Network (CNN) approach for brain tumor segmentation.
We investigate how to combine different modalities efficiently in the CNN framework.We adapt various fusion methods, which are previously employed on video recognition problem, to the brain tumor segmentation problem,and we investigate their efficiency in terms of memory and performance.Our experiments, which are performed on BRATS dataset, lead us to the conclusion that learning separate representations for each modality and combining them for brain tumor segmentation could increase the performance of CNN systems.
The aim of this article is to present an overview of the major families of state-of-the-art data-base benchmarks, namely: relational benchmarks, object and object-relational benchmarks, XML benchmarks, and decision-support benchmarks, and to discuss the issues, tradeoffs and future trends in database benchmarking.
We particularly focus on XML and decision-support benchmarks, which are currently the most innovative tools that are developed in this area.
Face anti-spoofing is the crucial step to prevent face recognition systems from a security breach.
Previous deep learning approaches formulate face anti-spoofing as a binary classification problem.
Many of them struggle to grasp adequate spoofing cues and generalize poorly.
In this paper, we argue the importance of auxiliary supervision to guide the learning toward discriminative and generalizable cues.
A CNN-RNN model is learned to estimate the face depth with pixel-wise supervision, and to estimate rPPG signals with sequence-wise supervision.
Then we fuse the estimated depth and rPPG to distinguish live vs. spoof faces.
In addition, we introduce a new face anti-spoofing database that covers a large range of illumination, subject, and pose variations.
Experimental results show that our model achieves the state-of-the-art performance on both intra-database and cross-database testing.
In this work, we propose a novel sampling method for Design of Experiments.
This method allows to sample such input values of the parameters of a computational model for which the constructed surrogate model will have the least possible approximation error.
High efficiency of the proposed method is demonstrated by its comparison with other sampling techniques (LHS, Sobol' sequence sampling, and Maxvol sampling) on the problem of least-squares polynomial approximation.
Also, numerical experiments for the Lebesgue constant growth for the points sampled by the proposed method are carried out.
Furui first demonstrated that the identity of both consonant and vowel can be perceived from the C-V transition; later, Stevens proposed that acoustic landmarks are the primary cues for speech perception, and that steady-state regions are secondary or supplemental.
Acoustic landmarks are perceptually salient, even in a language one doesn't speak, and it has been demonstrated that non-speakers of the language can identify features such as the primary articulator of the landmark.
These factors suggest a strategy for developing language-independent automatic speech recognition: landmarks can potentially be learned once from a suitably labeled corpus and rapidly applied to many other languages.
This paper proposes enhancing the cross-lingual portability of a neural network by using landmarks as the secondary task in multi-task learning (MTL).
The network is trained in a well-resourced source language with both phone and landmark labels (English), then adapted to an under-resourced target language with only word labels (Iban).
Landmark-tasked MTL reduces source-language phone error rate by 2.9% relative, and reduces target-language word error rate by 1.9%-5.9% depending on the amount of target-language training data.
These results suggest that landmark-tasked MTL causes the DNN to learn hidden-node features that are useful for cross-lingual adaptation.
The present work provides a new approach to evolve ligand structures which represent possible drug to be docked to the active site of the target protein.
The structure is represented as a tree where each non-empty node represents a functional group.
It is assumed that the active site configuration of the target protein is known with position of the essential residues.
In this paper the interaction energy of the ligands with the protein target is minimized.
Moreover, the size of the tree is difficult to obtain and it will be different for different active sites.
To overcome the difficulty, a variable tree size configuration is used for designing ligands.
The optimization is done using a quantum discrete PSO.
The result using fixed length and variable length configuration are compared.
Efficient exploration is an unsolved problem in Reinforcement Learning which is usually addressed by reactively rewarding the agent for fortuitously encountering novel situations.
This paper introduces an efficient active exploration algorithm, Model-Based Active eXploration (MAX), which uses an ensemble of forward models to plan to observe novel events.
This is carried out by optimizing agent behaviour with respect to a measure of novelty derived from the Bayesian perspective of exploration, which is estimated using the disagreement between the futures predicted by the ensemble members.
We show empirically that in semi-random discrete environments where directed exploration is critical to make progress, MAX is at least an order of magnitude more efficient than strong baselines.
MAX scales to high-dimensional continuous environments where it builds task-agnostic models that can be used for any downstream task.
Network Functions Virtualization (NFV) and Software-Defined Networking (SDN) are new paradigms in the move towards open software and network hardware.
While NFV aims to virtualize network functions and deploy them into general purpose hardware, SDN makes networks programmable by separating the control and data planes.
NFV and SDN are complementary technologies capable of providing one network solution.
SDN can provide connectivity between Virtual Network Functions (VNFs) in a flexible and automated way, whereas NFV can use SDN as part of a service function chain.
There are many studies designing NFV/SDN architectures in different environments.
Researchers have been trying to address reliability, performance, and scalability problems using different architectural designs.
This Systematic Literature Review (SLR) focuses on integrated NFV/SDN architectures, with the following goals: i) to investigate and provide an in-depth review of the state-of-the-art of NFV/SDN architectures, ii) to synthesize their architectural designs, and iii) to identify areas for further improvements.
Broadly, this SLR will encourage researchers to advance the current stage of development (i.e., the state-of-the-practice) of integrated NFV/SDN architectures, and shed some light on future research efforts and the challenges faced.
Facial analysis technologies have recently measured up to the capabilities of expert clinicians in syndrome identification.
To date, these technologies could only identify phenotypes of a few diseases, limiting their role in clinical settings where hundreds of diagnoses must be considered.
We developed a facial analysis framework, DeepGestalt, using computer vision and deep learning algorithms, that quantifies similarities to hundreds of genetic syndromes based on unconstrained 2D images.
DeepGestalt is currently trained with over 26,000 patient cases from a rapidly growing phenotype-genotype database, consisting of tens of thousands of validated clinical cases, curated through a community-driven platform.
DeepGestalt currently achieves 91% top-10-accuracy in identifying over 215 different genetic syndromes and has outperformed clinical experts in three separate experiments.
We suggest that this form of artificial intelligence is ready to support medical genetics in clinical and laboratory practices and will play a key role in the future of precision medicine.
We investigate the problem of automatically determining what type of shoe left an impression found at a crime scene.
This recognition problem is made difficult by the variability in types of crime scene evidence (ranging from traces of dust or oil on hard surfaces to impressions made in soil) and the lack of comprehensive databases of shoe outsole tread patterns.
We find that mid-level features extracted by pre-trained convolutional neural nets are surprisingly effective descriptors for this specialized domains.
However, the choice of similarity measure for matching exemplars to a query image is essential to good performance.
For matching multi-channel deep features, we propose the use of multi-channel normalized cross-correlation and analyze its effectiveness.
Our proposed metric significantly improves performance in matching crime scene shoeprints to laboratory test impressions.
We also show its effectiveness in other cross-domain image retrieval problems: matching facade images to segmentation labels and aerial photos to map images.
Finally, we introduce a discriminatively trained variant and fine-tune our system through our proposed metric, obtaining state-of-the-art performance.
In ultra-wideband (UWB) communication systems with impulse radio (IR) modulation, the bandwidth is usually 1GHz or more.
To process the received signal digitally, high sampling rate analog-digital-converters (ADC) are required.
Due to the high complexity and large power consumption, monobit ADC is appropriate.
The optimal monobit receiver has been derived.
But it is not efficient to combat intersymbol interference (ISI).
Decision feedback equalization (DFE) is an effect way dealing with ISI.
In this paper, we proposed a algorithm that combines Viterbi decoding and DFE together for monobit receivers.
In this way, we suppress the impact of ISI effectively, thus improving the bit error rate (BER) performance.
By state expansion, we achieve better performance.
The simulation results show that the algorithm has about 1dB SNR gain compared to separate demodulation and decoding method and 1dB loss compared to the BER performance in the channel without ISI.
Compare to the full resolution detection in fading channel without ISI, it has 3dB SNR loss after state expansion.
This work tackles the face recognition task on images captured using thermal camera sensors which can operate in the non-light environment.
While it can greatly increase the scope and benefits of the current security surveillance systems, performing such a task using thermal images is a challenging problem compared to face recognition task in the Visible Light Domain (VLD).
This is partly due to the much smaller amount number of thermal imagery data collected compared to the VLD data.
Unfortunately, direct application of the existing very strong face recognition models trained using VLD data into the thermal imagery data will not produce a satisfactory performance.
This is due to the existence of the domain gap between the thermal and VLD images.
To this end, we propose a Thermal-to-Visible Generative Adversarial Network (TV-GAN) that is able to transform thermal face images into their corresponding VLD images whilst maintaining identity information which is sufficient enough for the existing VLD face recognition models to perform recognition.
Some examples are presented in Figure 1.
Unlike the previous methods, our proposed TV-GAN uses an explicit closed-set face recognition loss to regularize the discriminator network training.
This information will then be conveyed into the generator network in the forms of gradient loss.
In the experiment, we show that by using this additional explicit regularization for the discriminator network, the TV-GAN is able to preserve more identity information when translating a thermal image of a person which is not seen before by the TV-GAN.
We analyse a generalisation of the Quicksort algorithm, where k uniformly at random chosen pivots are used for partitioning an array of n distinct keys.
Specifically, the expected cost of this scheme is obtained, under the assumption of linearity of the cost needed for the partition process.
The integration constants of the expected cost are computed using Vandermonde matrices.
We develop a novel framework that aims to create bridges between the computational social choice and the database management communities.
This framework enriches the tasks currently supported in computational social choice with relational database context, thus making it possible to formulate sophisticated queries about voting rules, candidates, voters, issues, and positions.
At the conceptual level, we give rigorous semantics to queries in this framework by introducing the notions of necessary answers and possible answers to queries.
At the technical level, we embark on an investigation of the computational complexity of the necessary answers.
We establish a number of results about the complexity of the necessary answers of conjunctive queries involving positional scoring rules that contrast sharply with earlier results about the complexity of the necessary winners.
The importance of hierarchically structured representations for tractable planning has long been acknowledged.
However, the questions of how people discover such abstractions and how to define a set of optimal abstractions remain open.
This problem has been explored in cognitive science in the problem solving literature and in computer science in hierarchical reinforcement learning.
Here, we emphasize an algorithmic perspective on learning hierarchical representations in which the objective is to efficiently encode the structure of the problem, or, equivalently, to learn an algorithm with minimal length.
We introduce a novel problem-solving paradigm that links problem solving and program induction under the Markov Decision Process (MDP) framework.
Using this task, we target the question of whether humans discover hierarchical solutions by maximizing efficiency in number of actions they generate or by minimizing the complexity of the resulting representation and find evidence for the primacy of representational efficiency.
In this paper we perform a comparative analysis of three models for feature representation of text documents in the context of document classification.
In particular, we consider the most often used family of models bag-of-words, recently proposed continuous space models word2vec and doc2vec, and the model based on the representation of text documents as language networks.
While the bag-of-word models have been extensively used for the document classification task, the performance of the other two models for the same task have not been well understood.
This is especially true for the network-based model that have been rarely considered for representation of text documents for classification.
In this study, we measure the performance of the document classifiers trained using the method of random forests for features generated the three models and their variants.
The results of the empirical comparison show that the commonly used bag-of-words model has performance comparable to the one obtained by the emerging continuous-space model of doc2vec.
In particular, the low-dimensional variants of doc2vec generating up to 75 features are among the top-performing document representation models.
The results finally point out that doc2vec shows a superior performance in the tasks of classifying large documents.
A prognostic watch of the electric power system (EPS)is framed up, which detects the threat to EPS for a day ahead according to the characteristic times for a day ahead and according to the droop for a day ahead.
Therefore, a prognostic analysis of the EPS development for a day ahead is carried out.
Also the power grid, the electricity marker state, the grid state and the level of threat for a power grid are found for a day ahead.
The accuracy of the built up prognostic watch is evaluated.
While neural networks demonstrate stronger capabilities in pattern recognition nowadays, they are also becoming larger and deeper.
As a result, the effort needed to train a network also increases dramatically.
In many cases, it is more practical to use a neural network intellectual property (IP) that an IP vendor has already trained.
As we do not know about the training process, there can be security threats in the neural IP: the IP vendor (attacker) may embed hidden malicious functionality, i.e. neural Trojans, into the neural IP.
We show that this is an effective attack and provide three mitigation techniques: input anomaly detection, re-training, and input preprocessing.
All the techniques are proven effective.
The input anomaly detection approach is able to detect 99.8% of Trojan triggers although with 12.2% false positive.
The re-training approach is able to prevent 94.1% of Trojan triggers from triggering the Trojan although it requires that the neural IP be reconfigurable.
In the input preprocessing approach, 90.2% of Trojan triggers are rendered ineffective and no assumption about the neural IP is needed.
In simulations, probabilistic algorithms and statistical tests, we often generate random integers in an interval (e.g., [0,s)).
For example, random integers in an interval are essential to the Fisher-Yates random shuffle.
Consequently, popular languages like Java, Python, C++, Swift and Go include ranged random integer generation functions as part of their runtime libraries.
Pseudo-random values are usually generated in words of a fixed number of bits (e.g., 32 bits, 64 bits) using algorithms such as a linear congruential generator.
We need functions to convert such random words to random integers in an interval ([0,s)) without introducing statistical biases.
The standard functions in programming languages such as Java involve integer divisions.
Unfortunately, division instructions are relatively expensive.
We review an unbiased function to generate ranged integers from a source of random words that avoids integer divisions with high probability.
To establish the practical usefulness of the approach, we show that this algorithm can multiply the speed of unbiased random shuffling on x64 processors.
Our proposed approach has been adopted by the Go language for its implementation of the shuffle function.
In this paper, we present a detailed design of dynamic video segmentation network (DVSNet) for fast and efficient semantic video segmentation.
DVSNet consists of two convolutional neural networks: a segmentation network and a flow network.
The former generates highly accurate semantic segmentations, but is deeper and slower.
The latter is much faster than the former, but its output requires further processing to generate less accurate semantic segmentations.
We explore the use of a decision network to adaptively assign different frame regions to different networks based on a metric called expected confidence score.
Frame regions with a higher expected confidence score traverse the flow network.
Frame regions with a lower expected confidence score have to pass through the segmentation network.
We have extensively performed experiments on various configurations of DVSNet, and investigated a number of variants for the proposed decision network.
The experimental results show that our DVSNet is able to achieve up to 70.4% mIoU at 19.8 fps on the Cityscape dataset.
A high speed version of DVSNet is able to deliver an fps of 30.4 with 63.2% mIoU on the same dataset.
DVSNet is also able to reduce up to 95% of the computational workloads.
Face recognition performance has improved remarkably in the last decade.
Much of this success can be attributed to the development of deep learning techniques such as convolutional neural networks (CNNs).
While CNNs have pushed the state-of-the-art forward, their training process requires a large amount of clean and correctly labelled training data.
If a CNN is intended to tolerate facial pose, then we face an important question: should this training data be diverse in its pose distribution, or should face images be normalized to a single pose in a pre-processing step?
To address this question, we evaluate a number of popular facial landmarking and pose correction algorithms to understand their effect on facial recognition performance.
Additionally, we introduce a new, automatic, single-image frontalization scheme that exceeds the performance of current algorithms.
CNNs trained using sets of different pre-processing methods are used to extract features from the Point and Shoot Challenge (PaSC) and CMU Multi-PIE datasets.
We assert that the subsequent verification and recognition performance serves to quantify the effectiveness of each pose correction scheme.
Researchers have proposed various methods to extract 3D keypoints from the surface of 3D mesh models over the last decades, but most of them are based on geometric methods, which lack enough flexibility to meet the requirements for various applications.
In this paper, we propose a new method on the basis of deep learning by formulating the 3D keypoint detection as a regression problem using deep neural network (DNN) with sparse autoencoder (SAE) as our regression model.
Both local information and global information of a 3D mesh model in multi-scale space are fully utilized to detect whether a vertex is a keypoint or not.
SAE can effectively extract the internal structure of these two kinds of information and formulate high-level features for them, which is beneficial to the regression model.
Three SAEs are used to formulate the hidden layers of the DNN and then a logistic regression layer is trained to process the high-level features produced in the third SAE.
Numerical experiments show that the proposed DNN based 3D keypoint detection algorithm outperforms current five state-of-the-art methods for various 3D mesh models.
We extend the idea of end-to-end learning of communications systems through deep neural network (NN)-based autoencoders to orthogonal frequency division multiplexing (OFDM) with cyclic prefix (CP).
Our implementation has the same benefits as a conventional OFDM system, namely singletap equalization and robustness against sampling synchronization errors, which turned out to be one of the major challenges in previous single-carrier implementations.
This enables reliable communication over multipath channels and makes the communication scheme suitable for commodity hardware with imprecise oscillators.
We show that the proposed scheme can be realized with state-of-the-art deep learning software libraries as transmitter and receiver solely consist of differentiable layers required for gradient-based training.
We compare the performance of the autoencoder-based system against that of a state-of-the-art OFDM baseline over frequency-selective fading channels.
Finally, the impact of a non-linear amplifier is investigated and we show that the autoencoder inherently learns how to deal with such hardware impairments.
This paper presents a distributed painting algorithm for painting a priori known rectangular region by swarm of autonomous mobile robots.
We assume that the region is obstacle free and of rectangular in shape.
The basic approach is to divide the region into some cells, and to let each robot to paint one of these cells.
Assignment of different cells to the robots is done by ranking the robots according to their relative positions.
In this algorithm, the robots follow the basic Wait-Observe-Compute-Move model together with the synchronous timing model.
This paper also presents a simulation of the proposed algorithm.
The simulation is performed using the Player/Stage Robotic Simulator on Ubuntu 10.04 (Lucid Lynx) platform.
Parameter sweeping is a widely used algorithmic technique in computational science.
It is specially suited for high-throughput computing since the jobs evaluating the parameter space are loosely coupled or independent.
A tool that integrates the modeling of a parameter study with the control of jobs in a distributed architecture is presented.
The main task is to facilitate the creation and deletion of job templates, which are the elements describing the jobs to be run.
Extra functionality relies upon the GridWay Metascheduler, acting as the middleware layer for job submission and control.
It supports interesting features like multi-dimensional sweeping space, wildcarding of parameters, functional evaluation of ranges, value-skipping and job template automatic indexation.
The use of this tool increases the reliability of the parameter sweep study thanks to the systematic bookkeping of job templates and respective job statuses.
Furthermore, it simplifies the porting of the target application to the grid reducing the required amount of time and effort.
We consider the problem of learning optimal reserve price in repeated auctions against non-myopic bidders, who may bid strategically in order to gain in future rounds even if the single-round auctions are truthful.
Previous algorithms, e.g., empirical pricing, do not provide non-trivial regret rounds in this setting in general.
We introduce algorithms that obtain small regret against non-myopic bidders either when the market is large, i.e., no bidder appears in a constant fraction of the rounds, or when the bidders are impatient, i.e., they discount future utility by some factor mildly bounded away from one.
Our approach carefully controls what information is revealed to each bidder, and builds on techniques from differentially private online learning as well as the recent line of works on jointly differentially private algorithms.
This paper presents and analyses the implementation of a novel active queue management (AQM) named FavorQueue that aims to improve delay transfer of short lived TCP flows over best-effort networks.
The idea is to dequeue packets that do not belong to a flow previously enqueued first.
The rationale is to mitigate the delay induced by long-lived TCP flows over the pace of short TCP data requests and to prevent dropped packets at the beginning of a connection and during recovery period.
Although the main target of this AQM is to accelerate short TCP traffic, we show that FavorQueue does not only improve the performance of short TCP traffic but also improves the performance of all TCP traffic in terms of drop ratio and latency whatever the flow size.
In particular, we demonstrate that FavorQueue reduces the loss of a retransmitted packet, decreases the number of dropped packets recovered by RTO and improves the latency up to 30% compared to DropTail.
Finally, we show that this scheme remains compliant with recent TCP updates such as the increase of the initial slow-start value.
This paper proposes a novel channel estimation method and a cluster-based opportunistic scheduling policy, for a wireless energy transfer (WET) system consisting of multiple low-complex energy receivers (ERs) with limited processing capabilities.
Firstly, in the training stage, the energy transmitter (ET) obtains a set of Received Signal Strength Indicator (RSSI) feedback values from all ERs, and these values are used to estimate the channels between the ET and all ERs.
Next, based on the channel estimates, the ERs are grouped into clusters, and the cluster that has its members closest to its centroid in phase is selected for dedicated WET.
The beamformer that maximizes the minimum harvested energy among all ERs in the selected cluster is found by solving a convex optimization problem.
All ERs have the same chance of being selected regardless of their distances from the ET, and hence, this scheduling policy can be considered to be opportunistic as well as fair.
It is shown that the proposed method achieves significant performance gains over benchmark schemes.
Hybrid multiple-antenna transceivers, which combine large-dimensional analog pre/postprocessing with lower-dimensional digital processing, are the most promising approach for reducing the hardware cost and training overhead in massive MIMO systems.
This paper provides a comprehensive survey of the various incarnations of such structures that have been proposed in the literature.
We provide a taxonomy in terms of the required channel state information (CSI), namely whether the processing adapts to the instantaneous or the average (second-order) CSI; while the former provides somewhat better signal-to-noise and interference ratio (SNIR), the latter has much lower overhead for CSI acquisition.
We furthermore distinguish hardware structures of different complexities.
Finally, we point out the special design aspects for operation at millimeter-wave frequencies.
We propose a new 2D shape decomposition method based on the short-cut rule.
The short-cut rule originates from cognition research, and states that the human visual system prefers to partition an object into parts using the shortest possible cuts.
We propose and implement a computational model for the short-cut rule and apply it to the problem of shape decomposition.
The model we proposed generates a set of cut hypotheses passing through the points on the silhouette which represent the negative minima of curvature.
We then show that most part-cut hypotheses can be eliminated by analysis of local properties of each.
Finally, the remaining hypotheses are evaluated in ascending length order, which guarantees that of any pair of conflicting cuts only the shortest will be accepted.
We demonstrate that, compared with state-of-the-art shape decomposition methods, the proposed approach achieves decomposition results which better correspond to human intuition as revealed in psychological experiments.
The recently advancement in Wireless Sensor Network (WSN) technology has brought new distributed sensing applications such as water quality monitoring.
With sensing capabilities and using parameters like pH, conductivity and temperature, the quality of water can be known.
This paper proposes a novel design based on IEEE 802.15.4 (Zig-Bee protocol) and solar energy called Autonomous Water Quality Monitoring Prototype (AWQMP).
The prototype is designed to use ECHERP routing protocol and Adruino Mega 2560, an open-source electronic prototyping platform for data acquisition.
AWQMP is expected to give real time data acquirement and to reduce the cost of manual water quality monitoring due to its autonomous characteristic.
Moreover, the proposed prototype will help to study the behavior of aquatic animals in deployed water bodies.
Recent accounts from researchers, journalists, as well as federal investigators, reached a unanimous conclusion: social media are systematically exploited to manipulate and alter public opinion.
Some disinformation campaigns have been coordinated by means of bots, social media accounts controlled by computer scripts that try to disguise themselves as legitimate human users.
In this study, we describe one such operation occurred in the run up to the 2017 French presidential election.
We collected a massive Twitter dataset of nearly 17 million posts occurred between April 27 and May 7, 2017 (Election Day).
We then set to study the MacronLeaks disinformation campaign: By leveraging a mix of machine learning and cognitive behavioral modeling techniques, we separated humans from bots, and then studied the activities of the two groups taken independently, as well as their interplay.
We provide a characterization of both the bots and the users who engaged with them and oppose it to those users who didn't.
Prior interests of disinformation adopters pinpoint to the reasons of the scarce success of this campaign: the users who engaged with MacronLeaks are mostly foreigners with a preexisting interest in alt-right topics and alternative news media, rather than French users with diverse political views.
Concluding, anomalous account usage patterns suggest the possible existence of a black-market for reusable political disinformation bots.
This paper shows experimental results on learning based randomized bin-picking combined with iterative visual recognition.
We use the random forest to predict whether or not a robot will successfully pick an object for given depth images of the pile taking the collision between a finger and a neighboring object into account.
For the discriminator to be accurate, we consider estimating objects' poses by merging multiple depth images of the pile captured from different points of view by using a depth sensor attached at the wrist.
We show that, even if a robot is predicted to fail in picking an object with a single depth image due to its large occluded area, it is finally predicted as success after merging multiple depth images.
In addition, we show that the random forest can be trained with the small number of training data.
The paper introduces concentric Echo State Network, an approach to design reservoir topologies that tries to bridge the gap between deterministically constructed simple cycle models and deep reservoir computing approaches.
We show how to modularize the reservoir into simple unidirectional and concentric cycles with pairwise bidirectional jump connections between adjacent loops.
We provide a preliminary experimental assessment showing how concentric reservoirs yield to superior predictive accuracy and memory capacity with respect to single cycle reservoirs and deep reservoir models.
As online shopping becomes ever more prevalent, customers rely increasingly on product rating websites for making purchase decisions.
The reliability of online ratings, however, is potentially compromised by the so-called herding effect: when rating a product, customers may be biased to follow other customers' previous ratings of the same product.
This is problematic because it skews long-term customer perception through haphazard early ratings.
The study of herding poses methodological challenges.
In particular, observational studies are impeded by the lack of counterfactuals: simply correlating early with subsequent ratings is insufficient because we cannot know what the subsequent ratings would have looked like had the first ratings been different.
The methodology introduced here exploits a setting that comes close to an experiment, although it is purely observational---a natural experiment.
Our key methodological device consists in studying the same product on two separate rating sites, focusing on products that received a high first rating on one site, and a low first rating on the other.
This largely controls for confounds such as a product's inherent quality, advertising, and producer identity, and lets us isolate the effect of the first rating on subsequent ratings.
In a case study, we focus on beers as products and jointly study two beer rating sites, but our method applies to any pair of sites across which products can be matched.
We find clear evidence of herding in beer ratings.
For instance, if a beer receives a very high first rating, its second rating is on average half a standard deviation higher, compared to a situation where the identical beer receives a very low first rating.
Moreover, herding effects tend to last a long time and are noticeable even after 20 or more ratings.
Our results have important implications for the design of better rating systems.
HDBSCAN*, a state-of-the-art density-based hierarchical clustering method, produces a hierarchical organization of clusters in a dataset w.r.t. a parameter mpts.
While the performance of HDBSCAN* is robust w.r.t. mpts in the sense that a small change in mpts typically leads to only a small or no change in the clustering structure, choosing a "good" mpts value can be challenging: depending on the data distribution, a high or low value for mpts may be more appropriate, and certain data clusters may reveal themselves at different values of mpts.
To explore results for a range of mpts values, however, one has to run HDBSCAN* for each value in the range independently, which is computationally inefficient.
In this paper, we propose an efficient approach to compute all HDBSCAN* hierarchies for a range of mpts values by replacing the graph used by HDBSCAN* with a much smaller graph that is guaranteed to contain the required information.
An extensive experimental evaluation shows that with our approach one can obtain over one hundred hierarchies for the computational cost equivalent to running HDBSCAN* about 2 times.
Silent speech interfaces have been recently proposed as a way to enable communication when the acoustic signal is not available.
This introduces the need to build visual speech recognition systems for silent and whispered speech.
However, almost all the recently proposed systems have been trained on vocalised data only.
This is in contrast with evidence in the literature which suggests that lip movements change depending on the speech mode.
In this work, we introduce a new audiovisual database which is publicly available and contains normal, whispered and silent speech.
To the best of our knowledge, this is the first study which investigates the differences between the three speech modes using the visual modality only.
We show that an absolute decrease in classification rate of up to 3.7% is observed when training and testing on normal and whispered, respectively, and vice versa.
An even higher decrease of up to 8.5% is reported when the models are tested on silent speech.
This reveals that there are indeed visual differences between the 3 speech modes and the common assumption that vocalized training data can be used directly to train a silent speech recognition system may not be true.
In this work, we present an extension of Gaussian process (GP) models with sophisticated parallelization and GPU acceleration.
The parallelization scheme arises naturally from the modular computational structure w.r.t. datapoints in the sparse Gaussian process formulation.
Additionally, the computational bottleneck is implemented with GPU acceleration for further speed up.
Combining both techniques allows applying Gaussian process models to millions of datapoints.
The efficiency of our algorithm is demonstrated with a synthetic dataset.
Its source code has been integrated into our popular software library GPy.
The complex interconnections between heterogeneous critical infrastructure sectors make the system of systems (SoS) vulnerable to natural or human-made disasters and lead to cascading failures both within and across sectors.
Hence, the robustness and resilience of the interdependent critical infrastructures (ICIs) against extreme events are essential for delivering reliable and efficient services to our society.
To this end, we first establish a holistic probabilistic network model to model the interdependencies between infrastructure components.
To capture the underlying failure and recovery dynamics of ICIs, we further propose a Markov decision processes (MDP) model in which the repair policy determines a long-term performance of the ICIs.
To address the challenges that arise from the curse of dimensionality of the MDP, we reformulate the problem as an approximate linear program and then simplify it using factored graphs.
We further obtain the distributed optimal control for ICIs under mild assumptions.
Finally, we use a case study of the interdependent power and subway systems to corroborate the results and show that the optimal resilience resource planning and allocation can reduce the failure probability and mitigate the impact of failures caused by natural or artificial disasters.
Virtual reality (VR) training simulators of liver needle insertion in the hepatic area of breathing virtual patients currently need 4D data acquisitions as a prerequisite.
Here, first a population-based breathing virtual patient 4D atlas can be built and second the requirement of a dose-relevant or expensive acquisition of a 4D data set for a new static 3D patient can be mitigated by warping the mean atlas motion.
The breakthrough contribution of this work is the construction and reuse of population-based learned 4D motion models.
In this paper, we propose a distributed control strategy for the design of an energy market.
The method relies on a hierarchical structure of aggregators for the coordination of prosumers (agents which can produce and consume energy).
The hierarchy reflects the voltage level separations of the electrical grid and allows aggregating prosumers in pools, while taking into account the grid operational constraints.
To reach optimal coordination, the prosumers communicate their forecasted power profile to the upper level of the hierarchy.
Each time the information crosses upwards a level of the hierarchy, it is first aggregated, both to strongly reduce the data flow and to preserve the privacy.
In the first part of the paper, the decomposition algorithm, which is based on the alternating direction method of multipliers (ADMM), is presented.
In the second part, we explore how the proposed algorithm scales with increasing number of prosumers and hierarchical levels, through extensive simulations based on randomly generated scenarios.
In many economic, social and political situations individuals carry out activities in groups (coalitions) rather than alone and on their own.
Examples range from households and sport clubs to research networks, political parties and trade unions.
The underlying game theoretic framework is known as coalition formation.
This survey discusses the notion of core stability in hedonic coalition formation (where each player's happiness only depends on the other members of his coalition but not on how the remaining players outside his coalition are grouped).
We present the central concepts and algorithmic approaches in the area, provide many examples, and pose a number of open problems.
Most face super-resolution methods assume that low-resolution and high-resolution manifolds have similar local geometrical structure, hence learn local models on the lowresolution manifolds (e.g. sparse or locally linear embedding models), which are then applied on the high-resolution manifold.
However, the low-resolution manifold is distorted by the oneto-many relationship between low- and high- resolution patches.
This paper presents a method which learns linear models based on the local geometrical structure on the high-resolution manifold rather than on the low-resolution manifold.
For this, in a first step, the low-resolution patch is used to derive a globally optimal estimate of the high-resolution patch.
The approximated solution is shown to be close in Euclidean space to the ground-truth but is generally smooth and lacks the texture details needed by state-ofthe-art face recognizers.
This first estimate allows us to find the support of the high-resolution manifold using sparse coding (SC), which are then used as support for learning a local projection (or upscaling) model between the low-resolution and the highresolution manifolds using Multivariate Ridge Regression (MRR).
Experimental results show that the proposed method outperforms six face super-resolution methods in terms of both recognition and quality.
These results also reveal that the recognition and quality are significantly affected by the method used for stitching all super-resolved patches together, where quilting was found to better preserve the texture details which helps to achieve higher recognition rates.
A domain analysis & description calculus is introduced.
It is shown to alleviate the issue of implicit semantics.
The claim is made that domain descriptions, whether informal, or as also here, formal, amount to an explicit semantics for what is otherwise implicit if not described.
This paper introduces an analytical framework to investigate optimal design choices for the placement of virtual controllers along the cloud-to-things continuum.
The main application scenarios include low-latency cyber-physical systems in which real-time control actions are required in response to the changes in states of an IoT node.
In such cases, deploying controller software on a cloud server is often not tolerable due to delay from the network edge to the cloud.
Hence, it is desirable to trade reliability with latency by moving controller logic closer to the network edge.
Modeling the IoT node as a dynamical system that evolves linearly in time with quadratic penalty for state deviations, recursive expressions for the optimum control policy and the resulting minimum cost value are obtained by taking virtual fog controller reliability and response time latency into account.
Our results indicate that latency is more critical than reliability in provisoning virtualized control services over fog endpoints, as it determines the swiftness of the fog control system as well as the timeliness of state measurements.
Based on a realistic drone trajectory tracking model, an extensive simulation study is also performed to illustrate the influence of reliability and latency on the control of autonomous vehicles over fog.
Quality diversity is a recent family of evolutionary search algorithms which focus on finding several well-performing (quality) yet different (diversity) solutions with the aim to maintain an appropriate balance between divergence and convergence during search.
While quality diversity has already delivered promising results in complex problems, the capacity of divergent search variants for quality diversity remains largely unexplored.
Inspired by the notion of surprise as an effective driver of divergent search and its orthogonal nature to novelty this paper investigates the impact of the former to quality diversity performance.
For that purpose we introduce three new quality diversity algorithms which employ surprise as a diversity measure, either on its own or combined with novelty, and compare their performance against novelty search with local competition, the state of the art quality diversity algorithm.
The algorithms are tested in a robot navigation task across 60 highly deceptive mazes.
Our findings suggest that allowing surprise and novelty to operate synergistically for divergence and in combination with local competition leads to quality diversity algorithms of significantly higher efficiency, speed and robustness.
It is a well-known fact that feedback does not increase the capacity of point-to-point memoryless channels, however, its effect in secure communications is not fully understood yet.
In this work, an achievable scheme for the wiretap channel with generalized feedback is presented.
This scheme, which uses the feedback signal to generate a shared secret key between the legitimate users, encrypts the message to be sent at the bit level.
New capacity results for a class of channels are provided, as well as some new insights into the secret key agreement problem.
Moreover, this scheme recovers previously reported rate regions from the literature, and thus it can be seen as a generalization that unifies several results in the field.
Only a few studies have been reported regarding human ear recognition in long wave infrared band.
Thus, we have created ear database based on long wave infrared band.
We have called that the database is long wave infrared band MIDAS consisting of 2430 records of 81 subjects.
Thermal band provides seamless operation both night and day, robust against spoofing with understanding live ear and invariant to illumination conditions for human ear recognition.
We have proposed to use different algorithms to reveal the distinctive features.
Then, we have reduced the number of dimensions using subspace methods.
Finally, the dimension of data is reduced in accordance with the classifier methods.
After this, the decision is determined by the best sores or combining some of the best scores with matching fusion.
The results have showed that the fusion technique was successful.
We have reached 97.71% for rank-1 with 567 test probes.
Furthermore, we have defined the perfect rank which is rank number when recognition rate reaches 100% in cumulative matching curve.
This evaluation is important for especially forensics, for example corpse identification, criminal investigation etc.
Collaborative filtering algorithms find useful patterns in rating and consumption data and exploit these patterns to guide users to good items.
Many of the patterns in rating datasets reflect important real-world differences between the various users and items in the data; other patterns may be irrelevant or possibly undesirable for social or ethical reasons, particularly if they reflect undesired discrimination, such as discrimination in publishing or purchasing against authors who are women or ethnic minorities.
In this work, we examine the response of collaborative filtering recommender algorithms to the distribution of their input data with respect to a dimension of social concern, namely content creator gender.
Using publicly-available book ratings data, we measure the distribution of the genders of the authors of books in user rating profiles and recommendation lists produced from this data.
We find that common collaborative filtering algorithms differ in the gender distribution of their recommendation lists, and in the relationship of that output distribution to user profile distribution.
This paper addresses the task of community detection and proposes a local approach based on a distributed list building, where each vertex broadcasts basic information that only depends on its degree and that of its neighbours.
A decentralised external process then unveils the community structure.
The relevance of the proposed method is experimentally shown on both artificial and real data.
The quantity of event logs available is increasing rapidly, be they produced by industrial processes, computing systems, or life tracking, for instance.
It is thus important to design effective ways to uncover the information they contain.
Because event logs often record repetitive phenomena, mining periodic patterns is especially relevant when considering such data.
Indeed, capturing such regularities is instrumental in providing condensed representations of the event sequences.
We present an approach for mining periodic patterns from event logs while relying on a Minimum Description Length (MDL) criterion to evaluate candidate patterns.
Our goal is to extract a set of patterns that suitably characterises the periodic structure present in the data.
We evaluate the interest of our approach on several real-world event log datasets.
A geometrical pattern is a set of points with all pairwise distances (or, more generally, relative distances) specified.
Finding matches to such patterns has applications to spatial data in seismic, astronomical, and transportation contexts.
For example, a particularly interesting geometric pattern in astronomy is the Einstein cross, which is an astronomical phenomenon in which a single quasar is observed as four distinct sky objects (due to gravitational lensing) when captured by earth telescopes.
Finding such crosses, as well as other geometric patterns, is a challenging problem as the potential number of sets of elements that compose shapes is exponentially large in the size of the dataset and the pattern.
In this paper, we denote geometric patterns as constellation queries and propose algorithms to find them in large data applications.
Our methods combine quadtrees, matrix multiplication, and unindexed join processing to discover sets of points that match a geometric pattern within some additive factor on the pairwise distances.
Our distributed experiments show that the choice of composition algorithm (matrix multiplication or nested loops) depends on the freedom introduced in the query geometry through the distance additive factor.
Three clearly identified blocks of threshold values guide the choice of the best composition algorithm.
Finally, solving the problem for relative distances requires a novel continuous-to-discrete transformation.
To the best of our knowledge this paper is the first to investigate constellation queries at scale.
In a world of global trading, maritime safety, security and efficiency are crucial issues.
We propose a multi-task deep learning framework for vessel monitoring using Automatic Identification System (AIS) data streams.
We combine recurrent neural networks with latent variable modeling and an embedding of AIS messages to a new representation space to jointly address key issues to be dealt with when considering AIS data streams: massive amount of streaming data, noisy data and irregular timesampling.
We demonstrate the relevance of the proposed deep learning framework on real AIS datasets for a three-task setting, namely trajectory reconstruction, anomaly detection and vessel type identification.
Many geometric estimation problems take the form of synchronization over the special Euclidean group: estimate the values of a set of poses given noisy measurements of a subset of their pairwise relative transforms.
This problem is typically formulated as a maximum-likelihood estimation that requires solving a nonconvex nonlinear program, which is computationally intractable in general.
Nevertheless, in this paper we present an algorithm that is able to efficiently recover certifiably globally optimal solutions of this estimation problem in a non-adversarial noise regime.
The crux of our approach is the development of a semidefinite relaxation of the maximum-likelihood estimation whose minimizer provides the exact MLE so long as the magnitude of the noise corrupting the available measurements falls below a certain critical threshold; furthermore, whenever exactness obtains, it is possible to verify this fact a posteriori, thereby certifying the optimality of the recovered estimate.
We develop a specialized optimization scheme for solving large-scale instances of this semidefinite relaxation by exploiting its low-rank, geometric, and graph-theoretic structure to reduce it to an equivalent optimization problem on a low-dimensional Riemannian manifold, and then design a Riemannian truncated-Newton trust-region method to solve this reduction efficiently.
We combine this fast optimization approach with a simple rounding procedure to produce our algorithm, SE-Sync.
Experimental evaluation on a variety of simulated and real-world pose-graph SLAM datasets shows that SE-Sync is capable of recovering globally optimal solutions when the available measurements are corrupted by noise up to an order of magnitude greater than that typically encountered in robotics applications, and does so at a computational cost that scales comparably with that of direct Newton-type local search techniques.
As humans, we regularly interpret images based on the relations between image regions.
For example, a person riding object X, or a plank bridging two objects.
Current methods provide limited support to search for images based on such relations.
We present RAID, a relation-augmented image descriptor that supports queries based on inter-region relations.
The key idea of our descriptor is to capture the spatial distribution of simple point-to-region relationships to describe more complex relationships between two image regions.
We evaluate the proposed descriptor by querying into a large subset of the Microsoft COCO database and successfully extract nontrivial images demonstrating complex inter-region relations, which are easily missed or erroneously classified by existing methods.
In this paper, we propose a neural-based coding scheme in which an artificial neural network is exploited to automatically compress and decompress speech signals by a trainable approach.
Having a two-stage training phase, the system can be fully specified to each speech frame and have robust performance across different speakers and wide range of spoken utterances.
Indeed, Frame-based nonlinear predictive coding (FNPC) would code a frame in the procedure of training to predict the frame samples.
The motivating objective is to analyze the system behavior in regenerating not only the envelope of spectra, but also the spectra phase.
This scheme has been evaluated in time and discrete cosine transform (DCT) domains and the output of predicted phonemes show the potentiality of the FNPC to reconstruct complicated signals.
The experiments were conducted on three voiced plosive phonemes, b/d/g/ in time and DCT domains versus the number of neurons in the hidden layer.
Experiments approve the FNPC capability as an automatic coding system by which /b/d/g/ phonemes have been reproduced with a good accuracy.
Evaluations revealed that the performance of FNPC system, trained to predict DCT coefficients is more desirable, particularly for frames with the wider distribution of energy, compared to time samples.
This paper proposes an extension to the Generative Adversarial Networks (GANs), namely as ARTGAN to synthetically generate more challenging and complex images such as artwork that have abstract characteristics.
This is in contrast to most of the current solutions that focused on generating natural images such as room interiors, birds, flowers and faces.
The key innovation of our work is to allow back-propagation of the loss function w.r.t. the labels (randomly assigned to each generated images) to the generator from the discriminator.
With the feedback from the label information, the generator is able to learn faster and achieve better generated image quality.
Empirically, we show that the proposed ARTGAN is capable to create realistic artwork, as well as generate compelling real world images that globally look natural with clear shape on CIFAR-10.
We have been developing a system for recognising human activity given a symbolic representation of video content.
The input of our system is a set of time-stamped short-term activities detected on video frames.
The output of our system is a set of recognised long-term activities, which are pre-defined temporal combinations of short-term activities.
The constraints on the short-term activities that, if satisfied, lead to the recognition of a long-term activity, are expressed using a dialect of the Event Calculus.
We illustrate the expressiveness of the dialect by showing the representation of several typical complex activities.
Furthermore, we present a detailed evaluation of the system through experimentation on a benchmark dataset of surveillance videos.
The emergence of distributed ledger technologies in the vehicular applications' arena is decisively contributing to their improvement and shaping of the public opinion about their future.
The Tangle is a technology at its infancy, but showing enormous potential to become a key solution by addressing several of the blockchain's limitations.
This paper focuses the use of the Tangle to improve the security of both in-vehicle and off-vehicle functions in vehicular applications.
To this end, key operational performance parameters are identified, evaluated and discussed with emphasis on their limitations and potential impact in future vehicular applications.
A fruitful approach for solving signal deconvolution problems consists of resorting to a frame-based convex variational formulation.
In this context, parallel proximal algorithms and related alternating direction methods of multipliers have become popular optimization techniques to approximate iteratively the desired solution.
Until now, in most of these methods, either Lipschitz differentiability properties or tight frame representations were assumed.
In this paper, it is shown that it is possible to relax these assumptions by considering a class of non necessarily tight frame representations, thus offering the possibility of addressing a broader class of signal restoration problems.
In particular, it is possible to use non necessarily maximally decimated filter banks with perfect reconstruction, which are common tools in digital signal processing.
The proposed approach allows us to solve both frame analysis and frame synthesis problems for various noise distributions.
In our simulations, it is applied to the deconvolution of data corrupted with Poisson noise or Laplacian noise by using (non-tight) discrete dual-tree wavelet representations and filter bank structures.
It is often the case that the best performing language model is an ensemble of a neural language model with n-grams.
In this work, we propose a method to improve how these two models are combined.
By using a small network which predicts the mixture weight between the two models, we adapt their relative importance at each time step.
Because the gating network is small, it trains quickly on small amounts of held out data, and does not add overhead at scoring time.
Our experiments carried out on the One Billion Word benchmark show a significant improvement over the state of the art ensemble without retraining of the basic modules.
Automatic Word problem solving has always posed a great challenge for the NLP community.
Usually a word problem is a narrative comprising of a few sentences and a question is asked about a quantity referred in the sentences.
Solving word problem involves reasoning across sentences, identification of operations, their order, relevant quantities and discarding irrelevant quantities.
In this paper, we present a novel approach for automatic arithmetic word problem solving.
Our approach starts with frame identification.
Each frame can either be classified as a state or an action frame.
The frame identification is dependent on the verb in a sentence.
Every frame is unique and is identified by its slots.
The slots are filled using dependency parsed output of a sentence.
The slots are entity holder, entity, quantity of the entity, recipient, additional information like place, time.
The slots and frames helps to identify the type of question asked and the entity referred.
Action frames act on state frame(s) which causes a change in quantities of the state frames.
The frames are then used to build a graph where any change in quantities can be propagated to the neighboring nodes.
Most of the current solvers can only answer questions related to the quantity, while our system can answer different kinds of questions like `who', `what' other than the quantity related questions `how many'.
There are three major contributions of this paper.1Frame Annotated Corpus (with a frame annotation tool) 2.Frame Identification Module 3.A new easily understandable Framework for word problem solving
Model transformations are the cornerstone of Model-Driven Engineering, and provide the essential mechanisms for manipulating and transforming models.
Checking whether the output of a model transformation is correct is a manual and error-prone task, this is referred to as the oracle problem in the software testing literature.
The correctness of the model transformation program is crucial for the proper generation of its output, so it should be tested.
Metamorphic testing is a testing technique to alleviate the oracle problem consisting on exploiting the relations between different inputs and outputs of the program under test, so-called metamorphic relations.
In this paper we give an insight into our approach to generically define metamorphic relations for model transformations, which can be automatically instantiated given any specific model transformation.
We provide accurate upper bounds on the Boolean circuit complexity of the standard and the Karatsuba methods of integer multiplication
Detecting PE malware files is now commonly approached using statistical and machine learning models.
While these models commonly use features extracted from the structure of PE files, we propose that icons from these files can also help better predict malware.
We propose an innovative machine learning approach to extract information from icons.
Our proposed approach consists of two steps: 1) extracting icon features using summary statics, histogram of gradients (HOG), and a convolutional autoencoder, 2) clustering icons based on the extracted icon features.
Using publicly available data and by using machine learning experiments, we show our proposed icon clusters significantly boost the efficacy of malware prediction models.
In particular, our experiments show an average accuracy increase of 10% when icon clusters are used in the prediction model.
Agent-Based Computing is a diverse research domain concerned with the building of intelligent software based on the concept of "agents".
In this paper, we use Scientometric analysis to analyze all sub-domains of agent-based computing.
Our data consists of 1,064 journal articles indexed in the ISI web of knowledge published during a twenty year period: 1990-2010.
These were retrieved using a topic search with various keywords commonly used in sub-domains of agent-based computing.
In our proposed approach, we have employed a combination of two applications for analysis, namely Network Workbench and CiteSpace - wherein Network Workbench allowed for the analysis of complex network aspects of the domain, detailed visualization-based analysis of the bibliographic data was performed using CiteSpace.
Our results include the identification of the largest cluster based on keywords, the timeline of publication of index terms, the core journals and key subject categories.
We also identify the core authors, top countries of origin of the manuscripts along with core research institutes.
Finally, our results have interestingly revealed the strong presence of agent-based computing in a number of non-computing related scientific domains including Life Sciences, Ecological Sciences and Social Sciences.
The recent progress of computing, machine learning, and especially deep learning, for image recognition brings a meaningful effect for automatic detection of various diseases from chest X-ray images (CXRs).
Here efficiency of lung segmentation and bone shadow exclusion techniques is demonstrated for analysis of 2D CXRs by deep learning approach to help radiologists identify suspicious lesions and nodules in lung cancer patients.
Training and validation was performed on the original JSRT dataset (dataset #01), BSE-JSRT dataset, i.e. the same JSRT dataset, but without clavicle and rib shadows (dataset #02), original JSRT dataset after segmentation (dataset #03), and BSE-JSRT dataset after segmentation (dataset #04).
The results demonstrate the high efficiency and usefulness of the considered pre-processing techniques in the simplified configuration even.
The pre-processed dataset without bones (dataset #02) demonstrates the much better accuracy and loss results in comparison to the other pre-processed datasets after lung segmentation (datasets #02 and #03).
In this paper, we aim to improve the state-of-the-art video generative adversarial networks (GANs) with a view towards multi-functional applications.
Our improved video GAN model does not separate foreground from background nor dynamic from static patterns, but learns to generate the entire video clip conjointly.
Our model can thus be trained to generate - and learn from - a broad set of videos with no restriction.
This is achieved by designing a robust one-stream video generation architecture with an extension of the state-of-the-art Wasserstein GAN framework that allows for better convergence.
The experimental results show that our improved video GAN model outperforms state-of-theart video generative models on multiple challenging datasets.
Furthermore, we demonstrate the superiority of our model by successfully extending it to three challenging problems: video colorization, video inpainting, and future prediction.
To the best of our knowledge, this is the first work using GANs to colorize and inpaint video clips.
User modeling is a very important task for making relevant suggestions of venues to the users.
These suggestions are often based on matching the venues' features with the users' preferences, which can be collected from previously visited locations.
In this paper, we present a set of relevance scores for making personalized suggestions of points of interest.
These scores model each user by focusing on the different types of information extracted from venues that they have previously visited.
In particular, we focus on scores extracted from social information available on location-based social networks.
Our experiments, conducted on the dataset of the TREC Contextual Suggestion Track, show that social scores are more effective than scores based venues' content.
In the panoply of pattern classification techniques, few enjoy the intuitive appeal and simplicity of the nearest neighbor rule: given a set of samples in some metric domain space whose value under some function is known, we estimate the function anywhere in the domain by giving the value of the nearest sample per the metric.
More generally, one may use the modal value of the m nearest samples, where m is a fixed positive integer (although m=1 is known to be admissible in the sense that no larger value is asymptotically superior in terms of prediction error).
The nearest neighbor rule is nonparametric and extremely general, requiring in principle only that the domain be a metric space.
The classic paper on the technique, proving convergence under independent, identically-distributed (iid) sampling, is due to Cover and Hart (1967).
Because taking samples is costly, there has been much research in recent years on selective sampling, in which each sample is selected from a pool of candidates ranked by a heuristic; the heuristic tries to guess which candidate would be the most "informative" sample.
Lindenbaum et al.(2004) apply selective sampling to the nearest neighbor rule, but their approach sacrifices the austere generality of Cover and Hart; furthermore, their heuristic algorithm is complex and computationally expensive.
Here we report recent results that enable selective sampling in the original Cover-Hart setting.
Our results pose three selection heuristics and prove that their nearest neighbor rule predictions converge to the true pattern.
Two of the algorithms are computationally cheap, with complexity growing linearly in the number of samples.
We believe that these results constitute an important advance in the art.
We propose a framework for localization and classification of masses in breast ultrasound (BUS) images.
We have experimentally found that training convolutional neural network based mass detectors with large, weakly annotated datasets presents a non-trivial problem, while overfitting may occur with those trained with small, strongly annotated datasets.
To overcome these problems, we use a weakly annotated dataset together with a smaller strongly annotated dataset in a hybrid manner.
We propose a systematic weakly and semi-supervised training scenario with appropriate training loss selection.
Experimental results show that the proposed method can successfully localize and classify masses with less annotation effort.
The results trained with only 10 strongly annotated images along with weakly annotated images were comparable to results trained from 800 strongly annotated images, with the 95% confidence interval of difference -3.00%--5.00%, in terms of the correct localization (CorLoc) measure, which is the ratio of images with intersection over union with ground truth higher than 0.5.
With the same number of strongly annotated images, additional weakly annotated images can be incorporated to give a 4.5% point increase in CorLoc, from 80.00% to 84.50% (with 95% confidence intervals 76.00%--83.75% and 81.00%--88.00%).
The effects of different algorithmic details and varied amount of data are presented through ablative analysis.
Time-varying renewable energy generation can result in serious under-/over-voltage conditions in future distribution grids.
Augmenting conventional utility-owned voltage regulating equipment with the reactive power capabilities of distributed generation units is a viable solution.
Local control options attaining global voltage regulation optimality at fast convergence rates is the goal here.
In this context, novel reactive power control rules are analyzed under a unifying linearized grid model.
For single-phase grids, our proximal gradient scheme has computational complexity comparable to that of the rule suggested by the IEEE 1547.8 standard, but it enjoys well-characterized convergence guarantees.
Adding memory to the scheme results in accelerated convergence.
For three-phase grids, it is shown that reactive injections have a counter-intuitive effect on bus voltage magnitudes across phases.
Nevertheless, when our control scheme is applied to unbalanced conditions, it is shown to reach an equilibrium point.
Yet this point may not correspond to the minimizer of a voltage regulation problem.
Numerical tests using the IEEE 13-bus, the IEEE 123-bus, and a Southern California Edison 47-bus feeder with increased renewable penetration verify the convergence properties of the schemes and their resiliency to grid topology reconfigurations.
This paper begins with a discussion of integration over probability types (p-types).
After doing that, the paper re-visits 3 mainstay problems of classical (non-quantum) Shannon Information Theory (SIT): source coding without distortion, channel coding, and source coding with distortion.
The paper proves well-known, conventional results for each of these 3 problems.
However, the proofs given for these results are not conventional.
They are based on complex integration techniques (approximations obtained by applying the method of steepest descent to p-type integrals) instead of the usual delta & epsilon and typical sequences arguments.
Another unconventional feature of this paper is that we make ample use of classical Bayesian networks (CB nets).
This paper showcases some of the benefits of using CB nets to do classical SIT.
A family of graphs optimized as the topologies for supercomputer interconnection networks is proposed.
The special needs of such network topologies, minimal diameter and mean path length, are met by special constructions of the weight vectors in a representation of the symplectic algebra.
Such theoretical design of topologies can conveniently reconstruct the mesh and hypercubic graphs, widely used as today's network topologies.
Our symplectic algebraic approach helps generate many classes of graphs suitable for network topologies.
The ability to map descriptions of scenes to 3D geometric representations has many applications in areas such as art, education, and robotics.
However, prior work on the text to 3D scene generation task has used manually specified object categories and language that identifies them.
We introduce a dataset of 3D scenes annotated with natural language descriptions and learn from this data how to ground textual descriptions to physical objects.
Our method successfully grounds a variety of lexical terms to concrete referents, and we show quantitatively that our method improves 3D scene generation over previous work using purely rule-based methods.
We evaluate the fidelity and plausibility of 3D scenes generated with our grounding approach through human judgments.
To ease evaluation on this task, we also introduce an automated metric that strongly correlates with human judgments.
Hypernymy, textual entailment, and image captioning can be seen as special cases of a single visual-semantic hierarchy over words, sentences, and images.
In this paper we advocate for explicitly modeling the partial order structure of this hierarchy.
Towards this goal, we introduce a general method for learning ordered representations, and show how it can be applied to a variety of tasks involving images and language.
We show that the resulting representations improve performance over current approaches for hypernym prediction and image-caption retrieval.
A system is described for exchanging encrypted short messages between computers which remain permanently isolated from any network accessible to the attacker.
The main advantage is effective protection of these computers from malware which could circumvent the encryption.
For transmission, the ciphertext is passed between isolated and connected computers in the form of a QR code, which is displayed on and scanned from a screen.
The security of qrypt0 therefore rests on the cryptography and the computer's physical isolation rather than on the computer security of the encrypting device.
Fault Tree Analysis (FTA) is a dependability analysis technique that has been widely used to predict reliability, availability and safety of many complex engineering systems.
Traditionally, these FTA-based analyses are done using paper-and-pencil proof methods or computer simulations, which cannot ascertain absolute correctness due to their inherent limitations.
As a complementary approach, we propose to use the higher-order-logic theorem prover HOL4 to conduct the FTA-based analysis of safety-critical systems where accuracy of failure analysis is a dire need.
In particular, the paper presents a higher-order-logic formalization of generic Fault Tree gates, i.e., AND, OR, NAND, NOR, XOR and NOT and the formal verification of their failure probability expressions.
Moreover, we have formally verified the generic probabilistic inclusion-exclusion principle, which is one of the foremost requirements for conducting the FTA-based failure analysis of any given system.
For illustration purposes, we conduct the FTA-based failure analysis of a solar array that is used as the main source of power for the Dong Fang Hong-3 (DFH-3) satellite.
We propose a new learning-based method for estimating 2D human pose from a single image, using Dual-Source Deep Convolutional Neural Networks (DS-CNN).
Recently, many methods have been developed to estimate human pose by using pose priors that are estimated from physiologically inspired graphical models or learned from a holistic perspective.
In this paper, we propose to integrate both the local (body) part appearance and the holistic view of each local part for more accurate human pose estimation.
Specifically, the proposed DS-CNN takes a set of image patches (category-independent object proposals for training and multi-scale sliding windows for testing) as the input and then learns the appearance of each local part by considering their holistic views in the full body.
Using DS-CNN, we achieve both joint detection, which determines whether an image patch contains a body joint, and joint localization, which finds the exact location of the joint in the image patch.
Finally, we develop an algorithm to combine these joint detection/localization results from all the image patches for estimating the human pose.
The experimental results show the effectiveness of the proposed method by comparing to the state-of-the-art human-pose estimation methods based on pose priors that are estimated from physiologically inspired graphical models or learned from a holistic perspective.
We have recently seen many successful applications of recurrent neural networks (RNNs) on electronic medical records (EMRs), which contain histories of patients' diagnoses, medications, and other various events, in order to predict the current and future states of patients.
Despite the strong performance of RNNs, it is often challenging for users to understand why the model makes a particular prediction.
Such black-box nature of RNNs can impede its wide adoption in clinical practice.
Furthermore, we have no established methods to interactively leverage users' domain expertise and prior knowledge as inputs for steering the model.
Therefore, our design study aims to provide a visual analytics solution to increase interpretability and interactivity of RNNs via a joint effort of medical experts, artificial intelligence scientists, and visual analytics researchers.
Following the iterative design process between the experts, we design, implement, and evaluate a visual analytics tool called RetainVis, which couples a newly improved, interpretable and interactive RNN-based model called RetainEX and visualizations for users' exploration of EMR data in the context of prediction tasks.
Our study shows the effective use of RetainVis for gaining insights into how individual medical codes contribute to making risk predictions, using EMRs of patients with heart failure and cataract symptoms.
Our study also demonstrates how we made substantial changes to the state-of-the-art RNN model called RETAIN in order to make use of temporal information and increase interactivity.
This study will provide a useful guideline for researchers that aim to design an interpretable and interactive visual analytics tool for RNNs.
An optimal data partitioning in parallel & distributed implementation of clustering algorithms is a necessary computation as it ensures independent task completion, fair distribution, less number of affected points and better & faster merging.
Though partitioning using Kd Tree is being conventionally used in academia, it suffers from performance drenches and bias (non equal distribution) as dimensionality of data increases and hence is not suitable for practical use in industry where dimensionality can be of order of 100s to 1000s.
To address these issues we propose two new partitioning techniques using existing mathematical models & study their feasibility, performance (bias and partitioning speed) & possible variants in choosing initial seeds.
First method uses an n dimensional hashed grid based approach which is based on mapping the points in space to a set of cubes which hashes the points.
Second method uses a tree of voronoi planes where each plane corresponds to a partition.
We found that grid based approach was computationally impractical, while using a tree of voronoi planes (using scalable K-Means++ initial seeds) drastically outperformed the Kd-tree tree method as dimensionality increased.
Context: This paper presents the concept of open programming language interpreters and the implementation of a framework-level metaobject protocol (MOP) to support them.
Inquiry: We address the problem of dynamic interpreter adaptation to tailor the interpreter's behavior on the task to be solved and to introduce new features to fulfill unforeseen requirements.
Many languages provide a MOP that to some degree supports reflection.
However, MOPs are typically language-specific, their reflective functionality is often restricted, and the adaptation and application logic are often mixed which hardens the understanding and maintenance of the source code.
Our system overcomes these limitations.
Approach: We designed and implemented a system to support open programming language interpreters.
The prototype implementation is integrated in the Neverlang framework.
The system exposes the structure, behavior and the runtime state of any Neverlang-based interpreter with the ability to modify it.
Knowledge: Our system provides a complete control over interpreter's structure, behavior and its runtime state.
The approach is applicable to every Neverlang-based interpreter.
Adaptation code can potentially be reused across different language implementations.
Grounding: Having a prototype implementation we focused on feasibility evaluation.
The paper shows that our approach well addresses problems commonly found in the research literature.
We have a demonstrative video and examples that illustrate our approach on dynamic software adaptation, aspect-oriented programming, debugging and context-aware interpreters.
Importance: To our knowledge, our paper presents the first reflective approach targeting a general framework for language development.
Our system provides full reflective support for free to any Neverlang-based interpreter.
We are not aware of any prior application of open implementations to programming language interpreters in the sense defined in this paper.
Rather than substituting other approaches, we believe our system can be used as a complementary technique in situations where other approaches present serious limitations.
Unpaired image-to-image translation is the problem of mapping an image in the source domain to one in the target domain, without requiring corresponding image pairs.
To ensure the translated images are realistically plausible, recent works, such as Cycle-GAN, demands this mapping to be invertible.
While, this requirement demonstrates promising results when the domains are unimodal, its performance is unpredictable in a multi-modal scenario such as in an image segmentation task.
This is because, invertibility does not necessarily enforce semantic correctness.
To this end, we present a semantically-consistent GAN framework, dubbed Sem-GAN, in which the semantics are defined by the class identities of image segments in the source domain as produced by a semantic segmentation algorithm.
Our proposed framework includes consistency constraints on the translation task that, together with the GAN loss and the cycle-constraints, enforces that the images when translated will inherit the appearances of the target domain, while (approximately) maintaining their identities from the source domain.
We present experiments on several image-to-image translation tasks and demonstrate that Sem-GAN improves the quality of the translated images significantly, sometimes by more than 20% on the FCN score.
Further, we show that semantic segmentation models, trained with synthetic images translated via Sem-GAN, leads to significantly better segmentation results than other variants.
As the popularity of electric vehicles increases, the demand for more power can increase more rapidly than our ability to install additional generating capacity.
In the long term we expect that the supply and demand will become balanced.
However, in the interim the rate at which electric vehicles can be deployed will depend on our ability to charge these vehicles without inconveniencing their owners.
In this paper, we investigate using fairness mechanisms to distribute power to electric vehicles on a smart grid.
We assume that during peak demand there is insufficient power to charge all the vehicles simultaneously.
In each five minute interval of time we select a subset of the vehicles to charge, based upon information about the vehicles.
We evaluate the selection mechanisms using published data on the current demand for electric power as a function of time of day, current driving habits for commuting, and the current rates at which electric vehicles can be charged on home outlets.
We found that conventional selection strategies, such as first-come-first-served or round robin, may delay a significant fraction of the vehicles by more than two hours, even when the total available power over the course of a day is two or three times the power required by the vehicles.
However, a selection mechanism that minimizes the maximum delay can reduce the delays to a few minutes, even when the capacity available for charging electric vehicles exceeds their requirements by as little as 5%.
A new certification authority authorization (CAA) resource record for the domain name system (DNS) was standardized in 2013.
Motivated by the later 2017 decision to enforce mandatory CAA checking for most certificate authorities, this paper surveys the early adoption of CAA by using an empirical sample collected from the Alexa's top-million domains.
According to the results, (i) the adoption of CAA is still at a modest level; only a little below two percent of the popular domains sampled have adopted CAA.
Among the domains that have adopted CAA, (ii) authorizations dealing with wildcard certificates are rare compared to conventional certificates.
Interestingly, (iii) the results only partially reflect the market structure of the global certificate business.
With these timely results, the paper contributes to the ongoing large-scale empirical research on the use of encryption technologies.
This paper proposes a parallel optimization algorithm for cooperative automation of large-scale connected vehicles.
The task of cooperative automation is formulated as a centralized optimization problem taking the whole decision space of all vehicles into account.
Considering the uncertainty of the environment, the problem is solved in a receding horizon fashion.
Then, we employ the alternating direction method of multipliers (ADMM) to solve the centralized optimization in a parallel way, which scales more favorably to large-scale instances.
Also, Taylor series is used to linearize nonconvex constraints caused by coupling collision avoidance constraints among interactive vehicles.
Simulations with two typical traffic scenes for multiple vehicles demonstrate the effectiveness and efficiency of our method.
This paper considers the design of the beamformers for a multiple-input single-output (MISO) downlink system that seeks to mitigate the impact of the imperfections in the channel state information (CSI) that is available at the base station (BS).
The goal of the design is to minimize the outage probability of specified signal-to-interference-and-noise ratio (SINR) targets, while satisfying per-antenna power constraints (PAPCs), and to do so at a low computational cost.
Based on insights from the offset maximization technique for robust beamforming, and observations regarding the structure of the optimality conditions, low-complexity iterative algorithms that involve the evaluation of closed-form expressions are developed.
To further reduce the computational cost, algorithms are developed for per-antenna power-constrained variants of the zero-forcing (ZF) and maximum ratio transmission (MRT) beamforming directions.
In the MRT case, our low-complexity version for systems with a large number of antennas may be of independent interest.
The proposed algorithms are extended to systems with both PAPCs and a total power constraint.
Simulation results show that the proposed robust designs can provide substantial gains in the outage probability while satisfying the PAPCs.
Flow fields are often represented by a set of static arrows to illustrate scientific vulgarization, documentary film, meteorology, etc.
This simple schematic representation lets an observer intuitively interpret the main properties of a flow: its orientation and velocity magnitude.
We propose to generate dynamic versions of such representations for 2D unsteady flow fields.
Our algorithm smoothly animates arrows along the flow while controlling their density in the domain over time.
Several strategies have been combined to lower the unavoidable popping artifacts arising when arrows appear and disappear and to achieve visually pleasing animations.
Disturbing arrow rotations in low velocity regions are also handled by continuously morphing arrow glyphs to semi-transparent discs.
To substantiate our method, we provide results for synthetic and real velocity field datasets.
Modern big data frameworks (such as Hadoop and Spark) allow multiple users to do large-scale analysis simultaneously.
Typically, users deploy Data-Intensive Workflows (DIWs) for their analytical tasks.
These DIWs of different users share many common parts (i.e, 50-80%), which can be materialized to reuse them in future executions.
The materialization improves the overall processing time of DIWs and also saves computational resources.
Current solutions for materialization store data on Distributed File Systems (DFS) by using a fixed data format.
However, a fixed choice might not be the optimal one for every situation.
For example, it is well-known that different data fragmentation strategies (i.e., horizontal, vertical or hybrid) behave better or worse according to the access patterns of the subsequent operations.
In this paper, we present a cost-based approach which helps deciding the most appropriate storage format in every situation.
A generic cost-based storage format selector framework considering the three fragmentation strategies is presented.
Then, we use our framework to instantiate cost models for specific Hadoop data formats (namely SequenceFile, Avro and Parquet), and test it with realistic use cases.
Our solution gives on average 33% speedup over SequenceFile, 11% speedup over Avro, 32% speedup over Parquet, and overall, it provides upto 25% performance gain.
In recent years, online communities have formed around suicide and self-harm prevention.
While these communities offer support in moment of crisis, they can also normalize harmful behavior, discourage professional treatment, and instigate suicidal ideation.
In this work, we focus on how interaction with others in such a community affects the mental state of users who are seeking support.
We first build a dataset of conversation threads between users in a distressed state and community members offering support.
We then show how to construct a classifier to predict whether distressed users are helped or harmed by the interactions in the thread, and we achieve a macro-F1 score of up to 0.69.
Mobile gaming has emerged as a promising market with billion-dollar revenues.
A variety of mobile game platforms and services have been developed around the world.
One critical challenge for these platforms and services is to understand user churn behavior in mobile games.
Accurate churn prediction will benefit many stakeholders such as game developers, advertisers, and platform operators.
In this paper, we present the first large-scale churn prediction solution for mobile games.
In view of the common limitations of the state-of-the-art methods built upon traditional machine learning models, we devise a novel semi-supervised and inductive embedding model that jointly learns the prediction function and the embedding function for user-app relationships.
We model these two functions by deep neural networks with a unique edge embedding technique that is able to capture both contextual information and relationship dynamics.
We also design a novel attributed random walk technique that takes into consideration both topological adjacency and attribute similarities.
To evaluate the performance of our solution, we collect real-world data from the Samsung Game Launcher platform that includes tens of thousands of games and hundreds of millions of user-app interactions.
The experimental results with this data demonstrate the superiority of our proposed model against existing state-of-the-art methods.
We study how the behavior of deep policy gradient algorithms reflects the conceptual framework motivating their development.
We propose a fine-grained analysis of state-of-the-art methods based on key aspects of this framework: gradient estimation, value prediction, optimization landscapes, and trust region enforcement.
We find that from this perspective, the behavior of deep policy gradient algorithms often deviates from what their motivating framework would predict.
Our analysis suggests first steps towards solidifying the foundations of these algorithms, and in particular indicates that we may need to move beyond the current benchmark-centric evaluation methodology.
In this work, we define a parabolic equation on digital spaces and study its properties.
The equation can be used in investigation of mechanical, aerodynamic, structural and technological properties of a Moebius strip, which is used as a basic element of a new configuration of an airplane wing.
Condition for existence of exact solutions by a matrix method and a method of separation of variables are studied and determined.
As examples, numerical solutions on Moebius strip and projective plane are presented.
The key idea of variational auto-encoders (VAEs) resembles that of traditional auto-encoder models in which spatial information is supposed to be explicitly encoded in the latent space.
However, the latent variables in VAEs are vectors, which can be interpreted as multiple feature maps of size 1x1.
Such representations can only convey spatial information implicitly when coupled with powerful decoders.
In this work, we propose spatial VAEs that use feature maps of larger size as latent variables to explicitly capture spatial information.
This is achieved by allowing the latent variables to be sampled from matrix-variate normal (MVN) distributions whose parameters are computed from the encoder network.
To increase dependencies among locations on latent feature maps and reduce the number of parameters, we further propose spatial VAEs via low-rank MVN distributions.
Experimental results show that the proposed spatial VAEs outperform original VAEs in capturing rich structural and spatial information.
Background: Clinical decision support systems (CDSS) are a category of health information technologies that can assist clinicians to choose optimal treatments.
These support systems are based on clinical trials and expert knowledge; however, the amount of data available to these systems is limited.
For this reason, CDSSs could be significantly improved by using the knowledge obtained by treating patients.
This knowledge is mainly contained in patient records, whose usage is restricted due to privacy and confidentiality constraints.
Methods: A treatment effectiveness measure, containing valuable information for treatment prescription, was defined and a method to extract this measure from patient records was developed.
This method uses an advanced cryptographic technology, known as secure Multiparty Computation (henceforth referred to as MPC), to preserve the privacy of the patient records and the confidentiality of the clinicians' decisions.
Results: Our solution enables to compute the effectiveness measure of a treatment based on patient records, while preserving privacy.
Moreover, clinicians are not burdened with the computational and communication costs introduced by the privacy-preserving techniques that are used.
Our system is able to compute the effectiveness of 100 treatments for a specific patient in less than 24 minutes, querying a database containing 20,000 patient records.
Conclusion: This paper presents a novel and efficient clinical decision support system, that harnesses the potential and insights acquired from treatment data, while preserving the privacy of patient records and the confidentiality of clinician decisions.
Computational models for sarcasm detection have often relied on the content of utterances in isolation.
However, the speaker's sarcastic intent is not always apparent without additional context.
Focusing on social media discussions, we investigate three issues: (1) does modeling conversation context help in sarcasm detection; (2) can we identify what part of conversation context triggered the sarcastic reply; and (3) given a sarcastic post that contains multiple sentences, can we identify the specific sentence that is sarcastic.
To address the first issue, we investigate several types of Long Short-Term Memory (LSTM) networks that can model both the conversation context and the current turn.
We show that LSTM networks with sentence-level attention on context and current turn, as well as the conditional LSTM network (Rocktaschel et al.2016), outperform the LSTM model that reads only the current turn.
As conversation context, we consider the prior turn, the succeeding turn or both.
Our computational models are tested on two types of social media platforms: Twitter and discussion forums.
We discuss several differences between these datasets ranging from their size to the nature of the gold-label annotations.
To address the last two issues, we present a qualitative analysis of attention weights produced by the LSTM models (with attention) and discuss the results compared with human performance on the two tasks.
We propose a measure and a metric on the sets of infinite traces generated by a set of atomic propositions.
To compute these quantities, we first map properties to subsets of the real numbers and then take the Lebesgue measure of the resulting sets.
We analyze how this measure is computed for Linear Temporal Logic (LTL) formulas.
An implementation for computing the measure of bounded LTL properties is provided and explained.
This implementation leverages SAT model counting and effects independence checks on subexpressions to compute the measure and metric compositionally.
We present sum-set inequalities specialized to the generalized degrees of freedom (GDoF) framework.
These are information theoretic lower bounds on the entropy of bounded density linear combinations of discrete, power-limited dependent random variables in terms of the joint entropies of arbitrary linear combinations of new random variables that are obtained by power level partitioning of the original random variables.
These bounds generalize the aligned image sets approach, and are useful instruments to obtain GDoF characterizations for wireless networks, especially with multiple antenna nodes, subject to arbitrary channel strength and channel uncertainty levels.
To demonstrate the utility of these bounds, we consider a non-trivial instance of wireless networks - a two user interference channel with different number of antennas at each node, and different levels of partial channel knowledge available to the transmitters.
We obtain tight GDoF characterization for specific instance of this channel with the aid of sum-set inequalities.
Loss of thrust emergencies-e.g., induced by bird/drone strikes or fuel exhaustion-create the need for dynamic data-driven flight trajectory planning to advise pilots or control UAVs.
While total loss of thrust trajectories to nearby airports can be pre-computed for all initial points in a 3D flight plan, dynamic aspects such as partial power and airplane surface damage must be considered for accuracy.
In this paper, we propose a new Dynamic Data-Driven Avionics Software (DDDAS) approach which during flight updates a damaged aircraft performance model, used in turn to generate plausible flight trajectories to a safe landing site.
Our damaged aircraft model is parameterized on a baseline glide ratio for a clean aircraft configuration assuming best gliding airspeed on straight flight.
The model predicts purely geometric criteria for flight trajectory generation, namely, glide ratio and turn radius for different bank angles and drag configurations.
Given actual aircraft performance data, we dynamically infer the baseline glide ratio to update the damaged aircraft model.
Our new flight trajectory generation algorithm thus can significantly improve upon prior Dubins based trajectory generation work by considering these data-driven geometric criteria.
We further introduce a trajectory utility function to rank trajectories for safety.
As a use case, we consider the Hudson River ditching of US Airways 1549 in January 2009 using a flight simulator to evaluate our trajectories and to get sensor data.
In this case, a baseline glide ratio of 17.25:1 enabled us to generate trajectories up to 28 seconds after the birds strike, whereas, a 19:1 baseline glide ratio enabled us to generate trajectories up to 36 seconds after the birds strike.
DDDAS can significantly improve the accuracy of generated flight trajectories thereby enabling better decision support systems for pilots in emergency conditions.
This paper proposes a deep leaning method to address the challenging facial attractiveness prediction problem.
The method constructs a convolutional neural network of facial beauty prediction using a new deep cascaded fine-turning scheme with various face inputting channels, such as the original RGB face image, the detail layer image, and the lighting layer image.
With a carefully designed CNN model of deep structure, large input size and small convolutional kernels, we have achieved a high prediction correlation of 0.88.
This result convinces us that the problem of facial attractiveness prediction can be solved by deep learning approach, and it also shows the important roles of the facial smoothness, lightness, and color information that were involved in facial beauty perception, which is consistent with the result of recent psychology studies.
Furthermore, we analyze the high-level features learnt by CNN through visualization of its hidden layers, and some interesting phenomena were observed.
It is found that the contours and appearance of facial features, especially eyes and moth, are the most significant facial attributes for facial attractiveness prediction, which is also consistent with the visual perception intuition of human.
The Kaczmarz algorithm is popular for iteratively solving an overdetermined system of linear equations.
The traditional Kaczmarz algorithm can approximate the solution in few sweeps through the equations but a randomized version of the Kaczmarz algorithm was shown to converge exponentially and independent of number of equations.
Recently an algorithm for finding sparse solution to a linear system of equations has been proposed based on weighted randomized Kaczmarz algorithm.
These algorithms solves single measurement vector problem; however there are applications were multiple-measurements are available.
In this work, the objective is to solve a multiple measurement vector problem with common sparse support by modifying the randomized Kaczmarz algorithm.
We have also modeled the problem of face recognition from video as the multiple measurement vector problem and solved using our proposed technique.
We have compared the proposed algorithm with state-of-art spectral projected gradient algorithm for multiple measurement vectors on both real and synthetic datasets.
The Monte Carlo simulations confirms that our proposed algorithm have better recovery and convergence rate than the MMV version of spectral projected gradient algorithm under fairness constraints.
We investigate sparse representations for control in reinforcement learning.
While these representations are widely used in computer vision, their prevalence in reinforcement learning is limited to sparse coding where extracting representations for new data can be computationally intensive.
Here, we begin by demonstrating that learning a control policy incrementally with a representation from a standard neural network fails in classic control domains, whereas learning with a representation obtained from a neural network that has sparsity properties enforced is effective.
We provide evidence that the reason for this is that the sparse representation provides locality, and so avoids catastrophic interference, and particularly keeps consistent, stable values for bootstrapping.
We then discuss how to learn such sparse representations.
We explore the idea of Distributional Regularizers, where the activation of hidden nodes is encouraged to match a particular distribution that results in sparse activation across time.
We identify a simple but effective way to obtain sparse representations, not afforded by previously proposed strategies, making it more practical for further investigation into sparse representations for reinforcement learning.
Data pre-processing is one of the most time consuming and relevant steps in a data analysis process (e.g., classification task).
A given data pre-processing operator (e.g., transformation) can have positive, negative or zero impact on the final result of the analysis.
Expert users have the required knowledge to find the right pre-processing operators.
However, when it comes to non-experts, they are overwhelmed by the amount of pre-processing operators and it is challenging for them to find operators that would positively impact their analysis (e.g., increase the predictive accuracy of a classifier).
Existing solutions either assume that users have expert knowledge, or they recommend pre-processing operators that are only "syntactically" applicable to a dataset, without taking into account their impact on the final analysis.
In this work, we aim at providing assistance to non-expert users by recommending data pre-processing operators that are ranked according to their impact on the final analysis.
We developed a tool PRESISTANT, that uses Random Forests to learn the impact of pre-processing operators on the performance (e.g., predictive accuracy) of 5 different classification algorithms, such as J48, Naive Bayes, PART, Logistic Regression, and Nearest Neighbor.
Extensive evaluations on the recommendations provided by our tool, show that PRESISTANT can effectively help non-experts in order to achieve improved results in their analytical tasks.
It is well known that matched filtering and sampling (MFS) demodulation together with minimum Euclidean distance (MD) detection constitute the optimal receiver for the additive white Gaussian noise channel.
However, for a general nonlinear transmission medium, MFS does not provide sufficient statistics, and therefore is suboptimal.
Nonetheless, this receiver is widely used in optical systems, where the Kerr nonlinearity is the dominant impairment at high powers.
In this paper, we consider a suite of receivers for a two-user channel subject to a type of nonlinear interference that occurs in wavelength-division-multiplexed channels.
The asymptotes of the symbol error rate (SER) of the considered receivers at high powers are derived or bounded analytically.
Moreover, Monte-Carlo simulations are conducted to evaluate the SER for all the receivers.
Our results show that receivers that are based on MFS cannot achieve arbitrary low SERs, whereas the SER goes to zero as the power grows for the optimal receiver.
Furthermore, we devise a heuristic demodulator, which together with the MD detector yields a receiver that is simpler than the optimal one and can achieve arbitrary low SERs.
The SER performance of the proposed receivers is also evaluated for some single-span fiber-optical channels via split-step Fourier simulations.
In this paper, we propose a new autonomous braking system based on deep reinforcement learning.
The proposed autonomous braking system automatically decides whether to apply the brake at each time step when confronting the risk of collision using the information on the obstacle obtained by the sensors.
The problem of designing brake control is formulated as searching for the optimal policy in Markov decision process (MDP) model where the state is given by the relative position of the obstacle and the vehicle's speed, and the action space is defined as whether brake is stepped or not.
The policy used for brake control is learned through computer simulations using the deep reinforcement learning method called deep Q-network (DQN).
In order to derive desirable braking policy, we propose the reward function which balances the damage imposed to the obstacle in case of accident and the reward achieved when the vehicle runs out of risk as soon as possible.
DQN is trained for the scenario where a vehicle is encountered with a pedestrian crossing the urban road.
Experiments show that the control agent exhibits desirable control behavior and avoids collision without any mistake in various uncertain environments.
We introduce a multi-task setup of identifying and classifying entities, relations, and coreference clusters in scientific articles.
We create SciERC, a dataset that includes annotations for all three tasks and develop a unified framework called Scientific Information Extractor (SciIE) for with shared span representations.
The multi-task setup reduces cascading errors between tasks and leverages cross-sentence relations through coreference links.
Experiments show that our multi-task model outperforms previous models in scientific information extraction without using any domain-specific features.
We further show that the framework supports construction of a scientific knowledge graph, which we use to analyze information in scientific literature.
Let G denote a graph and let K be a subset of vertices that are a set of target vertices of G. The K-terminal reliability of G is defined as the probability that all target vertices in K are connected, considering the possible failures of non-target vertices of G. The problem of computing K-terminal reliability is known to be #P-complete for polygon-circle graphs, and can be solved in polynomial-time for t-polygon graphs, which are a subclass of polygon-circle graphs.
The class of circle graphs is a subclass of polygon-circle graphs and a superclass of t-polygon graphs.
Therefore, the problem of computing K-terminal reliability for circle graphs is of particular interest.
This paper proves that the problem remains #P-complete even for circle graphs.
Additionally, this paper proposes a linear-time algorithm for solving the problem for proper circular-arc graphs, which are a subclass of circle graphs and a superclass of proper interval graphs.
The labyrinth game is a simple yet challenging platform, not only for humans but also for control algorithms and systems.
The game is easy to understand but still very hard to master.
From a system point of view, the ball behaviour is in general easy to model but close to the obstacles there are severe non-linearities.
Additionally, the far from flat surface on which the ball rolls provides for changing dynamics depending on the ball position.
The general dynamics of the system can easliy be handled by traditional automatic control methods.
Taking the obstacles and uneaven surface into accout would require very detailed models of the system.
A simple deterministic control algorithm is combined with a learning control method.
The simple control method provides initial training data.
As the learning method is trained, the system can learn from the results of its own actions and the performance improves well beyond the performance of the initial controller.
A vision system and image analysis is used to estimate the ball position while a combination of a PID controller and a learning controller based on LWPR is used to learn to navigate the ball through the maze.
Flow correlation is the core technique used in a multitude of deanonymization attacks on Tor.
Despite the importance of flow correlation attacks on Tor, existing flow correlation techniques are considered to be ineffective and unreliable in linking Tor flows when applied at a large scale, i.e., they impose high rates of false positive error rates or require impractically long flow observations to be able to make reliable correlations.
In this paper, we show that, unfortunately, flow correlation attacks can be conducted on Tor traffic with drastically higher accuracies than before by leveraging emerging learning mechanisms.
We particularly design a system, called DeepCorr, that outperforms the state-of-the-art by significant margins in correlating Tor connections.
DeepCorr leverages an advanced deep learning architecture to learn a flow correlation function tailored to Tor's complex network this is in contrast to previous works' use of generic statistical correlation metrics to correlated Tor flows.
We show that with moderate learning, DeepCorr can correlate Tor connections (and therefore break its anonymity) with accuracies significantly higher than existing algorithms, and using substantially shorter lengths of flow observations.
For instance, by collecting only about 900 packets of each target Tor flow (roughly 900KB of Tor data), DeepCorr provides a flow correlation accuracy of 96% compared to 4% by the state-of-the-art system of RAPTOR using the same exact setting.
We hope that our work demonstrates the escalating threat of flow correlation attacks on Tor given recent advances in learning algorithms, calling for the timely deployment of effective countermeasures by the Tor community.
Sequence-to-sequence models have shown promising improvements on the temporal task of video captioning, but they optimize word-level cross-entropy loss during training.
First, using policy gradient and mixed-loss methods for reinforcement learning, we directly optimize sentence-level task-based metrics (as rewards), achieving significant improvements over the baseline, based on both automatic metrics and human evaluation on multiple datasets.
Next, we propose a novel entailment-enhanced reward (CIDEnt) that corrects phrase-matching based metrics (such as CIDEr) to only allow for logically-implied partial matches and avoid contradictions, achieving further significant improvements over the CIDEr-reward model.
Overall, our CIDEnt-reward model achieves the new state-of-the-art on the MSR-VTT dataset.
In this work, we are interested in generalizing convolutional neural networks (CNNs) from low-dimensional regular grids, where image, video and speech are represented, to high-dimensional irregular domains, such as social networks, brain connectomes or words' embedding, represented by graphs.
We present a formulation of CNNs in the context of spectral graph theory, which provides the necessary mathematical background and efficient numerical schemes to design fast localized convolutional filters on graphs.
Importantly, the proposed technique offers the same linear computational complexity and constant learning complexity as classical CNNs, while being universal to any graph structure.
Experiments on MNIST and 20NEWS demonstrate the ability of this novel deep learning system to learn local, stationary, and compositional features on graphs.
In this paper, we present a novel way to summarize the structure of large graphs, based on non-parametric estimation of edge density in directed multigraphs.
Following coclustering approach, we use a clustering of the vertices, with a piecewise constant estimation of the density of the edges across the clusters, and address the problem of automatically and reliably inferring the number of clusters, which is the granularity of the coclustering.
We use a model selection technique with data-dependent prior and obtain an exact evaluation criterion for the posterior probability of edge density estimation models.
We demonstrate, both theoretically and empirically, that our data-dependent modeling technique is consistent, resilient to noise, valid non asymptotically and asymptotically behaves as an universal approximator of the true edge density in directed multigraphs.
We evaluate our method using artificial graphs and present its practical interest on real world graphs.
The method is both robust and scalable.
It is able to extract insightful patterns in the unsupervised learning setting and to provide state of the art accuracy when used as a preparation step for supervised learning.
Accurately determining dependency structure is critical to discovering a system's causal organization.
We recently showed that the transfer entropy fails in a key aspect of this---measuring information flow---due to its conflation of dyadic and polyadic relationships.
We extend this observation to demonstrate that this is true of all such Shannon information measures when used to analyze multivariate dependencies.
This has broad implications, particularly when employing information to express the organization and mechanisms embedded in complex systems, including the burgeoning efforts to combine complex network theory with information theory.
Here, we do not suggest that any aspect of information theory is wrong.
Rather, the vast majority of its informational measures are simply inadequate for determining the meaningful dependency structure within joint probability distributions.
Therefore, such information measures are inadequate for discovering intrinsic causal relations.
We close by demonstrating that such distributions exist across an arbitrary set of variables.
We investigate the connection between measure, capacity and algorithmic randomness for the space of closed sets.
For any computable measure m, a computable capacity T may be defined by letting T(Q) be the measure of the family of closed sets K which have nonempty intersection with Q.
We prove an effective version of Choquet's capacity theorem by showing that every computable capacity may be obtained from a computable measure in this way.
We establish conditions on the measure m that characterize when the capacity of an m-random closed set equals zero.
This includes new results in classical probability theory as well as results for algorithmic randomness.
For certain computable measures, we construct effectively closed sets with positive capacity and with Lebesgue measure zero.
We show that for computable measures, a real q is upper semi-computable if and only if there is an effectively closed set with capacity q.
How can we design a product or movie that will attract, for example, the interest of Pennsylvania adolescents or liberal newspaper critics?
What should be the genre of that movie and who should be in the cast?
In this work, we seek to identify how we can design new movies with features tailored to a specific user population.
We formulate the movie design as an optimization problem over the inference of user-feature scores and selection of the features that maximize the number of attracted users.
Our approach, PNP, is based on a heterogeneous, tripartite graph of users, movies and features (e.g., actors, directors, genres), where users rate movies and features contribute to movies.
We learn the preferences by leveraging user similarities defined through different types of relations, and show that our method outperforms state-of-the-art approaches, including matrix factorization and other heterogeneous graph-based analysis.
We evaluate PNP on publicly available real-world data and show that it is highly scalable and effectively provides movie designs oriented towards different groups of users, including men, women, and adolescents.
Generating novel, yet realistic, images of persons is a challenging task due to the complex interplay between the different image factors, such as the foreground, background and pose information.
In this work, we aim at generating such images based on a novel, two-stage reconstruction pipeline that learns a disentangled representation of the aforementioned image factors and generates novel person images at the same time.
First, a multi-branched reconstruction network is proposed to disentangle and encode the three factors into embedding features, which are then combined to re-compose the input image itself.
Second, three corresponding mapping functions are learned in an adversarial manner in order to map Gaussian noise to the learned embedding feature space, for each factor respectively.
Using the proposed framework, we can manipulate the foreground, background and pose of the input image, and also sample new embedding features to generate such targeted manipulations, that provide more control over the generation process.
Experiments on Market-1501 and Deepfashion datasets show that our model does not only generate realistic person images with new foregrounds, backgrounds and poses, but also manipulates the generated factors and interpolates the in-between states.
Another set of experiments on Market-1501 shows that our model can also be beneficial for the person re-identification task.
Impact analysis is concerned with the identification of consequences of changes and is therefore an important activity for software evolution.
In modelbased software development, models are core artifacts, which are often used to generate essential parts of a software system.
Changes to a model can thus substantially affect different artifacts of a software system.
In this paper, we propose a modelbased approach to impact analysis, in which explicit impact rules can be specified in a domain specific language (DSL).
These impact rules define consequences of designated UML class diagram changes on software artifacts and the need of dependent activities such as data evolution.
The UML class diagram changes are identified automatically using model differencing.
The advantage of using explicit impact rules is that they enable the formalization of knowledge about a product.
By explicitly defining this knowledge, it is possible to create a checklist with hints about development steps that are (potentially) necessary to manage the evolution.
To validate the feasibility of our approach, we provide results of a case study.
In this paper, we present the system we have used for the Implicit WASSA 2018 Implicit Emotion Shared Task.
The task is to predict the emotion of a tweet of which the explicit mentions of emotion terms have been removed.
The idea is to come up with a model which has the ability to implicitly identify the emotion expressed given the context words.
We have used a Gated Recurrent Neural Network (GRU) and a Capsule Network based model for the task.
Pre-trained word embeddings have been utilized to incorporate contextual knowledge about words into the model.
GRU layer learns latent representations using the input word embeddings.
Subsequent Capsule Network layer learns high-level features from that hidden representation.
The proposed model managed to achieve a macro-F1 score of 0.692.
A developmental disorder that severely damages communicative and social functions, the Autism Spectrum Disorder (ASD) also presents aspects related to mental rigidity, repetitive behavior, and difficulty in abstract reasoning.
More, imbalances between excitatory and inhibitory brain states, in addition to cortical connectivity disruptions, are at the source of the autistic behavior.
Our main goal consists in unveiling the way by which these local excitatory imbalances and/or long brain connections disruptions are linked to the above mentioned cognitive features.
We developed a theoretical model based on Self-Organizing Maps (SOM), where a three-level artificial neural network qualitatively incorporates these kinds of alterations observed in brains of patients with ASD.
Computational simulations of our model indicate that high excitatory states or long distance under-connectivity are at the origins of cognitive alterations, as difficulty in categorization and mental rigidity.
More specifically, the enlargement of excitatory synaptic reach areas in a cortical map development conducts to low categorization (over-selectivity) and poor concepts formation.
And, both the over-strengthening of local excitatory synapses and the long distance under-connectivity, although through distinct mechanisms, contribute to impaired categorization (under-selectivity) and mental rigidity.
Our results indicate how, together, both local and global brain connectivity alterations give rise to spoiled cortical structures in distinct ways and in distinct cortical areas.
These alterations would disrupt the codification of sensory stimuli, the representation of concepts and, thus, the process of categorization - by this way imposing serious limits to the mental flexibility and to the capacity of generalization in the autistic reasoning.
In a world of pervasive cameras, public spaces are often captured from multiple perspectives by cameras of different types, both fixed and mobile.
An important problem is to organize these heterogeneous collections of videos by finding connections between them, such as identifying correspondences between the people appearing in the videos and the people holding or wearing the cameras.
In this paper, we wish to solve two specific problems: (1) given two or more synchronized third-person videos of a scene, produce a pixel-level segmentation of each visible person and identify corresponding people across different views (i.e., determine who in camera A corresponds with whom in camera B), and (2) given one or more synchronized third-person videos as well as a first-person video taken by a mobile or wearable camera, segment and identify the camera wearer in the third-person videos.
Unlike previous work which requires ground truth bounding boxes to estimate the correspondences, we perform person segmentation and identification jointly.
We find that solving these two problems simultaneously is mutually beneficial, because better fine-grained segmentation allows us to better perform matching across views, and information from multiple views helps us perform more accurate segmentation.
We evaluate our approach on two challenging datasets of interacting people captured from multiple wearable cameras, and show that our proposed method performs significantly better than the state-of-the-art on both person segmentation and identification.
This is the preprint version of our paper on 2015 9th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth2015).
An assistive training tool software for rehabilitation of dysphonic patients is evaluated according to the practical clinical feedback from the treatments.
One stroke sufferer and one parkinson sufferer have provided earnest suggestions for the improvement of our tool software.
The assistive tool employs a serious game as the attractive logic part, and running on the tablet with normal microphone as input device.
Seven pitch estimation algorithms have been evaluated and compared with selected patients voice database.
A series of benchmarks have been generated during the evaluation process for technology selection.
This paper explores the problem of breast tissue classification of microscopy images.
Based on the predominant cancer type the goal is to classify images into four categories of normal, benign, in situ carcinoma, and invasive carcinoma.
Given a suitable training dataset, we utilize deep learning techniques to address the classification problem.
Due to the large size of each image in the training dataset, we propose a patch-based technique which consists of two consecutive convolutional neural networks.
The first "patch-wise" network acts as an auto-encoder that extracts the most salient features of image patches while the second "image-wise" network performs classification of the whole image.
The first network is pre-trained and aimed at extracting local information while the second network obtains global information of an input image.
We trained the networks using the ICIAR 2018 grand challenge on BreAst Cancer Histology (BACH) dataset.
The proposed method yields 95 % accuracy on the validation set compared to previously reported 77 % accuracy rates in the literature.
Our code is publicly available at https://github.com/ImagingLab/ICIAR2018
This paper focuses on a class of important two-hop relay mobile ad hoc networks (MANETs) with limited-buffer constraint and any mobility model that leads to the uniform distribution of the locations of nodes in steady state, and develops a general theoretical framework for the end-to-end (E2E) delay modeling there.
We first combine the theories of Fixed-Point, Quasi-Birth-and-Death process and embedded Markov chain to model the limiting distribution of the occupancy states of a relay buffer, and then apply the absorbing Markov chain theory to characterize the packet delivery process, such that a complete theoretical framework is developed for the E2E delay analysis.
With the help of this framework, we derive a general and exact expression for the E2E delay based on the modeling of both packet queuing delay and delivery delay.
To demonstrate the application of our framework, case studies are further provided under two network scenarios with different MAC protocols to show how the E2E delay can be analytically determined for a given network scenario.
Finally, we present extensive simulation and numerical results to illustrate the efficiency of our delay analysis as well as the impacts of network parameters on delay performance.
This paper focuses on improved edge model based on Curvelet coefficients analysis.
Curvelet transform is a powerful tool for multiresolution representation of object with anisotropic edge.
Curvelet coefficients contributions have been analyzed using Scale Invariant Feature Transform (SIFT), commonly used to study local structure in images.
The permutation of Curvelet coefficients from original image and edges image obtained from gradient operator is used to improve original edges.
Experimental results show that this method brings out details on edges when the decomposition scale increases.
In this paper, we present the results of an online study with the aim to shed light on the impact that semantic context cues have on the user acceptance of tag recommendations.
Therefore, we conducted a work-integrated social bookmarking scenario with 17 university employees in order to compare the user acceptance of a context-aware tag recommendation algorithm called 3Layers with the user acceptance of a simple popularity-based baseline.
In this scenario, we validated and verified the hypothesis that semantic context cues have a higher impact on the user acceptance of tag recommendations in a collaborative tagging setting than in an individual tagging setting.
With this paper, we contribute to the sparse line of research presenting online recommendation studies.
Multi-person articulated pose tracking in unconstrained videos is an important while challenging problem.
In this paper, going along the road of top-down approaches, we propose a decent and efficient pose tracker based on pose flows.
First, we design an online optimization framework to build the association of cross-frame poses and form pose flows (PF-Builder).
Second, a novel pose flow non-maximum suppression (PF-NMS) is designed to robustly reduce redundant pose flows and re-link temporal disjoint ones.
Extensive experiments show that our method significantly outperforms best-reported results on two standard Pose Tracking datasets by 13 mAP 25 MOTA and 6 mAP 3 MOTA respectively.
Moreover, in the case of working on detected poses in individual frames, the extra computation of pose tracker is very minor, guaranteeing online 10FPS tracking.
Our source codes are made publicly available(https://github.com/YuliangXiu/PoseFlow).
Generative Adversarial Networks (GANs) can successfully approximate a probability distribution and produce realistic samples.
However, open questions such as sufficient convergence conditions and mode collapse still persist.
In this paper, we build on existing work in the area by proposing a novel framework for training the generator against an ensemble of discriminator networks, which can be seen as a one-student/multiple-teachers setting.
We formalize this problem within the full-information adversarial bandit framework, where we evaluate the capability of an algorithm to select mixtures of discriminators for providing the generator with feedback during learning.
To this end, we propose a reward function which reflects the progress made by the generator and dynamically update the mixture weights allocated to each discriminator.
We also draw connections between our algorithm and stochastic optimization methods and then show that existing approaches using multiple discriminators in literature can be recovered from our framework.
We argue that less expressive discriminators are smoother and have a general coarse grained view of the modes map, which enforces the generator to cover a wide portion of the data distribution support.
On the other hand, highly expressive discriminators ensure samples quality.
Finally, experimental results show that our approach improves samples quality and diversity over existing baselines by effectively learning a curriculum.
These results also support the claim that weaker discriminators have higher entropy improving modes coverage.
High resolution magnetic resonance (MR) imaging is desirable in many clinical applications due to its contribution to more accurate subsequent analyses and early clinical diagnoses.
Single image super resolution (SISR) is an effective and cost efficient alternative technique to improve the spatial resolution of MR images.
In the past few years, SISR methods based on deep learning techniques, especially convolutional neural networks (CNNs), have achieved state-of-the-art performance on natural images.
However, the information is gradually weakened and training becomes increasingly difficult as the network deepens.
The problem is more serious for medical images because lacking high quality and effective training samples makes deep models prone to underfitting or overfitting.
Nevertheless, many current models treat the hierarchical features on different channels equivalently, which is not helpful for the models to deal with the hierarchical features discriminatively and targetedly.
To this end, we present a novel channel splitting network (CSN) to ease the representational burden of deep models.
The proposed CSN model divides the hierarchical features into two branches, i.e., residual branch and dense branch, with different information transmissions.
The residual branch is able to promote feature reuse, while the dense branch is beneficial to the exploration of new features.
Besides, we also adopt the merge-and-run mapping to facilitate information integration between different branches.
Extensive experiments on various MR images, including proton density (PD), T1 and T2 images, show that the proposed CSN model achieves superior performance over other state-of-the-art SISR methods.
Key substitution vulnerable signature schemes are signature schemes that permit an intruder, given a public verification key and a signed message, to compute a pair of signature and verification keys such that the message appears to be signed with the new signature key.
A digital signature scheme is said to be vulnerable to destructive exclusive ownership property (DEO) If it is computationaly feasible for an intruder, given a public verification key and a pair of message and its valid signature relatively to the given public key, to compute a pair of signature and verification keys and a new message such that the given signature appears to be valid for the new message relatively to the new verification key.
In this paper, we prove decidability of the insecurity problem of cryptographic protocols where the signature schemes employed in the concrete realisation have this two properties.
In this paper we address the problems of modeling the acoustic space generated by a full-spectrum sound source and of using the learned model for the localization and separation of multiple sources that simultaneously emit sparse-spectrum sounds.
We lay theoretical and methodological grounds in order to introduce the binaural manifold paradigm.
We perform an in-depth study of the latent low-dimensional structure of the high-dimensional interaural spectral data, based on a corpus recorded with a human-like audiomotor robot head.
A non-linear dimensionality reduction technique is used to show that these data lie on a two-dimensional (2D) smooth manifold parameterized by the motor states of the listener, or equivalently, the sound source directions.
We propose a probabilistic piecewise affine mapping model (PPAM) specifically designed to deal with high-dimensional data exhibiting an intrinsic piecewise linear structure.
We derive a closed-form expectation-maximization (EM) procedure for estimating the model parameters, followed by Bayes inversion for obtaining the full posterior density function of a sound source direction.
We extend this solution to deal with missing data and redundancy in real world spectrograms, and hence for 2D localization of natural sound sources such as speech.
We further generalize the model to the challenging case of multiple sound sources and we propose a variational EM framework.
The associated algorithm, referred to as variational EM for source separation and localization (VESSL) yields a Bayesian estimation of the 2D locations and time-frequency masks of all the sources.
Comparisons of the proposed approach with several existing methods reveal that the combination of acoustic-space learning with Bayesian inference enables our method to outperform state-of-the-art methods.
We propose a strikingly novel, simple, and effective approach to model online user behavior: we extract and analyze digital DNA sequences from user online actions and we use Twitter as a benchmark to test our proposal.
We obtain an incisive and compact DNA-inspired characterization of user actions.
Then, we apply standard DNA analysis techniques to discriminate between genuine and spambot accounts on Twitter.
An experimental campaign supports our proposal, showing its effectiveness and viability.
To the best of our knowledge, we are the first ones to identify and adapt DNA-inspired techniques to online user behavioral modeling.
While Twitter spambot detection is a specific use case on a specific social media, our proposed methodology is platform and technology agnostic, hence paving the way for diverse behavioral characterization tasks.
This thesis describes the development of fast algorithms for the computation of PERcentage CLOSure of eyes (PERCLOS) and Saccadic Ratio (SR).
PERCLOS and SR are two ocular parameters reported to be measures of alertness levels in human beings.
PERCLOS is the percentage of time in which at least 80% of the eyelid remains closed over the pupil.
Saccades are fast and simultaneous movement of both the eyes in the same direction.
SR is the ratio of peak saccadic velocity to the saccadic duration.
This thesis addresses the issues of image based estimation of PERCLOS and SR, prevailing in the literature such as illumination variation, poor illumination conditions, head rotations etc.
In this work, algorithms for real-time PERCLOS computation has been developed and implemented on an embedded platform.
The platform has been used as a case study for assessment of loss of attention in automotive drivers.
The SR estimation has been carried out offline as real-time implementation requires high frame rates of processing which is difficult to achieve due to hardware limitations.
The accuracy in estimation of the loss of attention using PERCLOS and SR has been validated using brain signals, which are reported to be an authentic cue for estimating the state of alertness in human beings.
The major contributions of this thesis include database creation, design and implementation of fast algorithms for estimating PERCLOS and SR on embedded computing platforms.
Recently, many approaches have been introduced by several researchers to identify plants.
Now, applications of texture, shape, color and vein features are common practices.
However, there are many possibilities of methods can be developed to improve the performance of such identification systems.
Therefore, several experiments had been conducted in this research.
As a result, a new novel approach by using combination of Gray-Level Co-occurrence Matrix, lacunarity and Shen features and a Bayesian classifier gives a better result compared to other plant identification systems.
For comparison, this research used two kinds of several datasets that were usually used for testing the performance of each plant identification system.
The results show that the system gives an accuracy rate of 97.19% when using the Flavia dataset and 95.00% when using the Foliage dataset and outperforms other approaches.
In this paper, we introduce a shape-based, time-scale invariant feature descriptor for 1-D sensor signals.
The time-scale invariance of the feature allows us to use feature from one training event to describe events of the same semantic class which may take place over varying time scales such as walking slow and walking fast.
Therefore it requires less training set.
The descriptor takes advantage of the invariant location detection in the scale space theory and employs a high level shape encoding scheme to capture invariant local features of events.
Based on this descriptor, a scale-invariant classifier with "R" metric (SIC-R) is designed to recognize multi-scale events of human activities.
The R metric combines the number of matches of keypoint in scale space with the Dynamic Time Warping score.
SICR is tested on various types of 1-D sensors data from passive infrared, accelerometer and seismic sensors with more than 90% classification accuracy.
Sequence to sequence (SEQ2SEQ) models often lack diversity in their generated translations.
This can be attributed to the limitation of SEQ2SEQ models in capturing lexical and syntactic variations in a parallel corpus resulting from different styles, genres, topics, or ambiguity of the translation process.
In this paper, we develop a novel sequence to sequence mixture (S2SMIX) model that improves both translation diversity and quality by adopting a committee of specialized translation models rather than a single translation model.
Each mixture component selects its own training dataset via optimization of the marginal loglikelihood, which leads to a soft clustering of the parallel corpus.
Experiments on four language pairs demonstrate the superiority of our mixture model compared to a SEQ2SEQ baseline with standard or diversity-boosted beam search.
Our mixture model uses negligible additional parameters and incurs no extra computation cost during decoding.
We present monaa, a monitoring tool over a real-time property specified by either a timed automaton or a timed regular expression.
It implements a timed pattern matching algorithm that combines 1) features suited for online monitoring, and 2) acceleration by automata-based skipping.
Our experiments demonstrate monaa's performance advantage, especially in online usage.
In 2002 Jurdzinski and Lorys settled a long-standing conjecture that palindromes are not a Church-Rosser language.
Their proof required a sophisticated theory about computation graphs of 2-stack automata.
We present their proof in terms of 1-tape Turing machines.We also provide an alternative proof of Buntrock and Otto's result that the set of non-square bitstrings, which is context-free, is not Church-Rosser.
We solve the problem of output feedback stabilization of a class of nonlinear systems, which may have unstable zero dynamics.
We allow for any globally stabilizing full state feedback control scheme to be used as long as it satisfies a particular ISS condition.
We show semi-global stability of the origin of the closed-loop system and also the recovery of the performance of an auxiliary system using a full-order observer.
This observer is based on the use of an extended high-gain observer to provide estimates of the output and its derivatives plus a signal used by an extended Kalman filter to provide estimates of the remaining states.
Finally, we provide a simulation example that illustrates the design procedure.
In this paper, we generalize a secured direct communication process between N users with partial and full cooperation of quantum server.
The security analysis of authentication and communication processes against many types of attacks proved that the attacker cannot gain any information during intercepting either authentication or communication processes.
Hence, the security of transmitted message among N users is ensured as the attacker introduces an error probability irrespective of the sequence of measurement.
In this work, we consider diffusion-based molecular communication with and without drift between two static nano-machines.
We employ type-based information encoding, releasing a single molecule per information bit.
At the receiver, we consider an asynchronous detection algorithm which exploits the arrival order of the molecules.
In such systems, transposition errors fundamentally undermine reliability and capacity.
Thus, in this work we study the impact of transpositions on the system performance.
Towards this, we present an analytical expression for the exact bit error probability (BEP) caused by transpositions and derive computationally tractable approximations of the BEP for diffusion-based channels with and without drift.
Based on these results, we analyze the BEP when background is not negligible and derive the optimal bit interval that minimizes the BEP.
Simulation results confirm the theoretical results and show the error and goodput performance for different parameters such as block size or noise generation rate.
While for the evaluation of robustness of eye tracking algorithms the use of real-world data is essential, there are many applications where simulated, synthetic eye images are of advantage.
They can generate labelled ground-truth data for appearance based gaze estimation algorithms or enable the development of model based gaze estimation techniques by showing the influence on gaze estimation error of different model factors that can then be simplified or extended.
We extend the generation of synthetic eye images by a simulation of refraction and reflection for eyeglasses.
On the one hand this allows for the testing of pupil and glint detection algorithms under different illumination and reflection conditions, on the other hand the error of gaze estimation routines can be estimated in conjunction with different eyeglasses.
We show how a polynomial function fitting calibration performs equally well with and without eyeglasses, and how a geometrical eye model behaves when exposed to glasses.
The quantum error correction theory is as a rule formulated in a rather convoluted way, in comparison to classical algebraic theory.
This work revisits the error correction in a noisy quantum channel so as to make it intelligible to engineers.
An illustrative example is presented of a naive perfect quantum code (Hamming-like code) with five-qubits for transmitting a single qubit of information.
Also the (9,1)-Shor codes is addressed.
This article describes our experiments in neural machine translation using the recent Tensor2Tensor framework and the Transformer sequence-to-sequence model (Vaswani et al., 2017).
We examine some of the critical parameters that affect the final translation quality, memory usage, training stability and training time, concluding each experiment with a set of recommendations for fellow researchers.
In addition to confirming the general mantra "more data and larger models", we address scaling to multiple GPUs and provide practical tips for improved training regarding batch size, learning rate, warmup steps, maximum sentence length and checkpoint averaging.
We hope that our observations will allow others to get better results given their particular hardware and data constraints.
Future cellular systems based on the use of above-6 GHz frequencies, the so-called millimeter wave (mmWave) bandwidths, will heavily rely on the use of antenna arrays both at the transmitter and at the receiver, possibly with a large number of elements.
For complexity reasons, fully digital precoding and postcoding structures may turn out to be unfeasible, and thus suboptimal structures, making use of simplified hardware and a limited number of RF chains, have been investigated.
This paper considers and makes a comparative assessment, both from a spectral efficiency and energy efficiency point of view, of several suboptimal precoding and postcoding beamforming structures for the downlink of a cellular multiuser MIMO (MU-MIMO) system.
Based on the most recently available data for the energy consumption of phase shifters and switches, we show that there are cases where fully-digital beamformers may achieve a larger energy efficiency than lower-complexity solutions, as well as that structures based on the exclusive use of switches achieve quite unsatisfactory performance in realistic scenarios.
Medical images with specific pathologies are scarce, but a large amount of data is usually required for a deep convolutional neural network (DCNN) to achieve good accuracy.
We consider the problem of segmenting the left ventricular (LV) myocardium on late gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) scans of which only some of the scans have scar tissue.
We propose ScarGAN to simulate scar tissue on healthy myocardium using chained generative adversarial networks (GAN).
Our novel approach factorizes the simulation process into 3 steps: 1) a mask generator to simulate the shape of the scar tissue; 2) a domain-specific heuristic to produce the initial simulated scar tissue from the simulated shape; 3) a refining generator to add details to the simulated scar tissue.
Unlike other approaches that generate samples from scratch, we simulate scar tissue on normal scans resulting in highly realistic samples.
We show that experienced radiologists are unable to distinguish between real and simulated scar tissue.
Training a U-Net with additional scans with scar tissue simulated by ScarGAN increases the percentage of scar pixels correctly included in LV myocardium prediction from 75.9% to 80.5%.
Optic disk segmentation is a prerequisite step in automatic retinal screening systems.
In this paper, we propose an algorithm for optic disk segmentation based on a local adaptive thresholding method.
Location of the optic disk is validated by intensity and average vessel width of retinal images.
Then an adaptive thresholding is applied on the temporal and nasal part of the optic disc separately.
Adaptive thresholding, makes our algorithm robust to illumination variations and various image acquisition conditions.
Moreover, experimental results on the DRIVE and KHATAM databases show promising results compared to the recent literature.
In the DRIVE database, the optic disk in all images is correctly located and the mean overlap reached to 43.21%.
The optic disk is correctly detected in 98% of the images with the mean overlap of 36.32% in the KHATAM database.
Applications such as web search and social networking have been moving from centralized to decentralized cloud architectures to improve their scalability.
MapReduce, a programming framework for processing large amounts of data using thousands of machines in a single cloud, also needs to be scaled out to multiple clouds to adapt to this evolution.
The challenge of building a multi-cloud distributed architecture is substantial.
Notwithstanding, the ability to deal with the new types of faults introduced by such setting, such as the outage of a whole datacenter or an arbitrary fault caused by a malicious cloud insider, increases the endeavor considerably.
In this paper we propose Medusa, a platform that allows MapReduce computations to scale out to multiple clouds and tolerate several types of faults.
Our solution fulfills four objectives.
First, it is transparent to the user, who writes her typical MapReduce application without modification.
Second, it does not require any modification to the widely used Hadoop framework.
Third, the proposed system goes well beyond the fault-tolerance offered by MapReduce to tolerate arbitrary faults, cloud outages, and even malicious faults caused by corrupt cloud insiders.
Fourth, it achieves this increased level of fault tolerance at reasonable cost.
We performed an extensive experimental evaluation in the ExoGENI testbed, demonstrating that our solution significantly reduces execution time when compared to traditional methods that achieve the same level of resilience.
Given the cost of HPC clusters, making best use of them is crucial to improve infrastructure ROI.
Likewise, reducing failed HPC jobs and related waste in terms of user wait times is crucial to improve HPC user productivity (aka human ROI).
While most efforts (e.g.,debugging HPC programs) explore technical aspects to improve ROI of HPC clusters, we hypothesize non-technical (human) aspects are worth exploring to make non-trivial ROI gains; specifically, understanding non-technical aspects and how they contribute to the failure of HPC jobs.
In this regard, we conducted a case study in the context of Beocat cluster at Kansas State University.
The purpose of the study was to learn the reasons why users terminate jobs and to quantify wasted computations in such jobs in terms of system utilization and user wait time.
The data from the case study helped identify interesting and actionable reasons why users terminate HPC jobs.
It also helped confirm that user terminated jobs may be associated with non-trivial amount of wasted computation, which if reduced can help improve the ROI of HPC clusters.
When performing a national research assessment, some countries rely on citation metrics whereas others, such as the UK, primarily use peer review.
In the influential Metric Tide report, a low agreement between metrics and peer review in the UK Research Excellence Framework (REF) was found.
However, earlier studies observed much higher agreement between metrics and peer review in the REF and argued in favour of using metrics.
This shows that there is considerable ambiguity in the discussion on agreement between metrics and peer review.
We provide clarity in this discussion by considering four important points: (1) the level of aggregation of the analysis; (2) the use of either a size-dependent or a size-independent perspective; (3) the suitability of different measures of agreement; and (4) the uncertainty in peer review.
In the context of the REF, we argue that agreement between metrics and peer review should be assessed at the institutional level rather than at the publication level.
Both a size-dependent and a size-independent perspective are relevant in the REF.
The interpretation of correlations may be problematic and as an alternative we therefore use measures of agreement that are based on the absolute or relative differences between metrics and peer review.
To get an idea of the uncertainty in peer review, we rely on a model to bootstrap peer review outcomes.
We conclude that particularly in Physics, Clinical Medicine, and Public Health, metrics agree quite well with peer review and may offer an alternative to peer review.
In this paper we develop a new framework that captures the common landscape underlying the common non-convex low-rank matrix problems including matrix sensing, matrix completion and robust PCA.
In particular, we show for all above problems (including asymmetric cases): 1) all local minima are also globally optimal; 2) no high-order saddle points exists.
These results explain why simple algorithms such as stochastic gradient descent have global converge, and efficiently optimize these non-convex objective functions in practice.
Our framework connects and simplifies the existing analyses on optimization landscapes for matrix sensing and symmetric matrix completion.
The framework naturally leads to new results for asymmetric matrix completion and robust PCA.
Vertex colouring is a well-known problem in combinatorial optimisation, whose alternative integer programming formulations have recently attracted considerable attention.
This paper briefly surveys seven known formulations of vertex colouring and introduces a formulation of vertex colouring using a suitable clique partition of the graph.
This formulation is applicable in timetabling applications, where such a clique partition of the conflict graph is given implicitly.
In contrast with some alternatives, the presented formulation can also be easily extended to accommodate complex performance indicators (``soft constraints'') imposed in a number of real-life course timetabling applications.
Its performance depends on the quality of the clique partition, but encouraging empirical results for the Udine Course Timetabling problem are reported.
This paper presents a simple, robust and (almost) unsupervised dictionary-based method, qwn-ppv (Q-WordNet as Personalized PageRanking Vector) to automatically generate polarity lexicons.
We show that qwn-ppv outperforms other automatically generated lexicons for the four extrinsic evaluations presented here.
It also shows very competitive and robust results with respect to manually annotated ones.
Results suggest that no single lexicon is best for every task and dataset and that the intrinsic evaluation of polarity lexicons is not a good performance indicator on a Sentiment Analysis task.
The qwn-ppv method allows to easily create quality polarity lexicons whenever no domain-based annotated corpora are available for a given language.
The interpretation of propositional dynamic logic (PDL) through Kripke models requires the relations constituting the interpreting Kripke model to closely observe the syntax of the modal operators.
This poses a significant challenge for an interpretation of PDL through stochastic Kripke models, because the programs' operations do not always have a natural counterpart in the set of stochastic relations.
We use rewrite rules for building up an interpretation of PDL.
It is shown that each program corresponds to an essentially unique irreducible tree, which in turn is assigned a predicate lifting, serving as the program's interpretation.
The paper establishes and studies this interpretation.
It discusses the expressivity of probabilistic models for PDL and relates properties like logical and behavioral equivalence or bisimilarity to the corresponding properties of a Kripke model for a closely related non-dynamic logic of the Hennessy-Milner type.
We present a C-language implementation of the lambda-pi calculus by extending the (call-by-need) stack machine of Ariola, Chang and Felleisen to hold types, using a typeless- tagless- final interpreter strategy.
It has the advantage of expressing all operations as folds over terms, including by-need evaluation, recovery of the initial syntax-tree encoding for any term, and eliminating most garbage-collection tasks.
These are made possible by a disciplined approach to handling the spine of each term, along with a robust stack-based API.
Type inference is not covered in this work, but also derives several advantages from the present stack transformation.
Timing and maximum stack space usage results for executing benchmark problems are presented.
We discuss how the design choices for this interpreter allow the language to be used as a high-level scripting language for automatic distributed parallel execution of common scientific computing workflows.
With the rapid increasing of software project size and maintenance cost, adherence to coding standards especially by managing identifier naming, is attracting a pressing concern from both computer science educators and software managers.
Software developers mainly use identifier names to represent the knowledge recorded in source code.
However, the popularity and adoption consistency of identifier naming conventions have not been revealed yet in this field.
Taking forty-eight popular open source projects written in three top-ranking programming languages Java, C and C++ as examples, an identifier extraction tool based on regular expression matching is developed.
In the subsequent investigation, some interesting findings are obtained.
For the identifier naming popularity, it is found that Camel and Pascal naming conventions are leading the road while Hungarian notation is vanishing.
For the identifier naming consistency, we have found that the projects written in Java have a much better performance than those written in C and C++.
Finally, academia and software industry are urged to adopt the most popular naming conventions consistently in their practices so as to lead the identifier naming to a standard, unified and high-quality road.
Bilateral filters have wide spread use due to their edge-preserving properties.
The common use case is to manually choose a parametric filter type, usually a Gaussian filter.
In this paper, we will generalize the parametrization and in particular derive a gradient descent algorithm so the filter parameters can be learned from data.
This derivation allows to learn high dimensional linear filters that operate in sparsely populated feature spaces.
We build on the permutohedral lattice construction for efficient filtering.
The ability to learn more general forms of high-dimensional filters can be used in several diverse applications.
First, we demonstrate the use in applications where single filter applications are desired for runtime reasons.
Further, we show how this algorithm can be used to learn the pairwise potentials in densely connected conditional random fields and apply these to different image segmentation tasks.
Finally, we introduce layers of bilateral filters in CNNs and propose bilateral neural networks for the use of high-dimensional sparse data.
This view provides new ways to encode model structure into network architectures.
A diverse set of experiments empirically validates the usage of general forms of filters.
This paper presents a novel hierarchical spatiotemporal orientation representation for spacetime image analysis.
It is designed to combine the benefits of the multilayer architecture of ConvNets and a more controlled approach to spacetime analysis.
A distinguishing aspect of the approach is that unlike most contemporary convolutional networks no learning is involved; rather, all design decisions are specified analytically with theoretical motivations.
This approach makes it possible to understand what information is being extracted at each stage and layer of processing as well as to minimize heuristic choices in design.
Another key aspect of the network is its recurrent nature, whereby the output of each layer of processing feeds back to the input.
To keep the network size manageable across layers, a novel cross-channel feature pooling is proposed.
The multilayer architecture that results systematically reveals hierarchical image structure in terms of multiscale, multiorientation properties of visual spacetime.
To illustrate its utility, the network has been applied to the task of dynamic texture recognition.
Empirical evaluation on multiple standard datasets shows that it sets a new state-of-the-art.
We propose to use deep convolutional neural networks to address the problem of cross-view image geolocalization, in which the geolocation of a ground-level query image is estimated by matching to georeferenced aerial images.
We use state-of-the-art feature representations for ground-level images and introduce a cross-view training approach for learning a joint semantic feature representation for aerial images.
We also propose a network architecture that fuses features extracted from aerial images at multiple spatial scales.
To support training these networks, we introduce a massive database that contains pairs of aerial and ground-level images from across the United States.
Our methods significantly out-perform the state of the art on two benchmark datasets.
We also show, qualitatively, that the proposed feature representations are discriminative at both local and continental spatial scales.
Faster R-CNN is one of the most representative and successful methods for object detection, and has been becoming increasingly popular in various objection detection applications.
In this report, we propose a robust deep face detection approach based on Faster R-CNN.
In our approach, we exploit several new techniques including new multi-task loss function design, online hard example mining, and multi-scale training strategy to improve Faster R-CNN in multiple aspects.
The proposed approach is well suited for face detection, so we call it Face R-CNN.
Extensive experiments are conducted on two most popular and challenging face detection benchmarks, FDDB and WIDER FACE, to demonstrate the superiority of the proposed approach over state-of-the-arts.
Here we explore a new algorithmic framework for multi-agent reinforcement learning, called Malthusian reinforcement learning, which extends self-play to include fitness-linked population size dynamics that drive ongoing innovation.
In Malthusian RL, increases in a subpopulation's average return drive subsequent increases in its size, just as Thomas Malthus argued in 1798 was the relationship between preindustrial income levels and population growth.
Malthusian reinforcement learning harnesses the competitive pressures arising from growing and shrinking population size to drive agents to explore regions of state and policy spaces that they could not otherwise reach.
Furthermore, in environments where there are potential gains from specialization and division of labor, we show that Malthusian reinforcement learning is better positioned to take advantage of such synergies than algorithms based on self-play.
The basic idea behind an active queue management (AQM) is to sense the congestion level within the network and inform the packet sources about, so that they reduce their sending rate.
In literature a lot off mechanisms of AQM are studied.
But there are not used in the context of the DiffServ architecture where different types of packet with different requirements of QoS share the same link.
In this paper, we study an access control mechanism for RT and NRT packets arriving in a buffer implemented at an end user in HSDPA.
The mechanism uses thresholds to mange access in the buffer and gives access priority to RT packets.
In order to control the arrival rate of the NRT packets in the buffer an active queue management is used.
We study the effect of the feedback function on the QoS parameters for both kinds of packets .Mathematical description and analytical results are given, and numerical results show that the proposed function achieves higher QoS for the NRT packets in the system.
A longstanding question in computer vision concerns the representation of 3D shapes for recognition: should 3D shapes be represented with descriptors operating on their native 3D formats, such as voxel grid or polygon mesh, or can they be effectively represented with view-based descriptors?
We address this question in the context of learning to recognize 3D shapes from a collection of their rendered views on 2D images.
We first present a standard CNN architecture trained to recognize the shapes' rendered views independently of each other, and show that a 3D shape can be recognized even from a single view at an accuracy far higher than using state-of-the-art 3D shape descriptors.
Recognition rates further increase when multiple views of the shapes are provided.
In addition, we present a novel CNN architecture that combines information from multiple views of a 3D shape into a single and compact shape descriptor offering even better recognition performance.
The same architecture can be applied to accurately recognize human hand-drawn sketches of shapes.
We conclude that a collection of 2D views can be highly informative for 3D shape recognition and is amenable to emerging CNN architectures and their derivatives.
Political identity is often manifested in language variation, but the relationship between the two is still relatively unexplored from a quantitative perspective.
This study examines the use of Catalan, a language local to the semi-autonomous region of Catalonia in Spain, on Twitter in discourse related to the 2017 independence referendum.
We corroborate prior findings that pro-independence tweets are more likely to include the local language than anti-independence tweets.
We also find that Catalan is used more often in referendum-related discourse than in other contexts, contrary to prior findings on language variation.
This suggests a strong role for the Catalan language in the expression of Catalonian political identity.
Regarding some papers and notes submitted to, or presented at, the second congress of the International Torah Codes Society in Jerusalem, Israel, June 2000.
Perception is often described as a predictive process based on an optimal inference with respect to a generative model.
We study here the principled construction of a generative model specifically crafted to probe motion perception.
In that context, we first provide an axiomatic, biologically-driven derivation of the model.
This model synthesizes random dynamic textures which are defined by stationary Gaussian distributions obtained by the random aggregation of warped patterns.
Importantly, we show that this model can equivalently be described as a stochastic partial differential equation.
Using this characterization of motion in images, it allows us to recast motion-energy models into a principled Bayesian inference framework.
Finally, we apply these textures in order to psychophysically probe speed perception in humans.
In this framework, while the likelihood is derived from the generative model, the prior is estimated from the observed results and accounts for the perceptual bias in a principled fashion.
We provide an up-to-date view on the knowledge management system ScienceWISE (SW) and address issues related to the automatic assignment of articles to research topics.
So far, SW has been proven to be an effective platform for managing large volumes of technical articles by means of ontological concept-based browsing.
However, as the publication of research articles accelerates, the expressivity and the richness of the SW ontology turns into a double-edged sword: a more fine-grained characterization of articles is possible, but at the cost of introducing more spurious relations among them.
In this context, the challenge of continuously recommending relevant articles to users lies in tackling a network partitioning problem, where nodes represent articles and co-occurring concepts create edges between them.
In this paper, we discuss the three research directions we have taken for solving this issue: i) the identification of generic concepts to reinforce inter-article similarities; ii) the adoption of a bipartite network representation to improve scalability; iii) the design of a clustering algorithm to identify concepts for cross-disciplinary articles and obtain fine-grained topics for all articles.
Most of the semi-supervised classification methods developed so far use unlabeled data for regularization purposes under particular distributional assumptions such as the cluster assumption.
In contrast, recently developed methods of classification from positive and unlabeled data (PU classification) use unlabeled data for risk evaluation, i.e., label information is directly extracted from unlabeled data.
In this paper, we extend PU classification to also incorporate negative data and propose a novel semi-supervised classification approach.
We establish generalization error bounds for our novel methods and show that the bounds decrease with respect to the number of unlabeled data without the distributional assumptions that are required in existing semi-supervised classification methods.
Through experiments, we demonstrate the usefulness of the proposed methods.
Unfortunately, the article "A Comparative Study to Benchmark Cross-project Defect Prediction Approaches" has a problem in the statistical analysis which was pointed out almost immediately after the pre-print of the article appeared online.
While the problem does not negate the contribution of the the article and all key findings remain the same, it does alter some rankings of approaches used in the study.
Within this correction, we will explain the problem, how we resolved it, and present the updated results.
Networks created from real-world data contain some inaccuracies or noise, manifested as small changes in the network structure.
An important question is whether these small changes can significantly affect the analysis results.
In this paper, we study the effect of noise in changing ranks of the high centrality vertices.
We compare, using the Jaccard Index (JI), how many of the top-k high centrality nodes from the original network are also part of the top-k ranked nodes from the noisy network.
We deem a network as stable if the JI value is high.
We observe two features that affect the stability.
First, the stability is dependent on the number of top-ranked vertices considered.
When the vertices are ordered according to their centrality values, they group into clusters.
Perturbations to the network can change the relative ranking within the cluster, but vertices rarely move from one cluster to another.
Second, the stability is dependent on the local connections of the high ranking vertices.
The network is highly stable if the high ranking vertices are connected to each other.
Our findings show that the stability of a network is affected by the local properties of high centrality vertices, rather than the global properties of the entire network.
Based on these local properties we can identify the stability of a network, without explicitly applying a noise model.
The upcoming technology support for semantic web promises fresh directions for Software Engineering community.
Also semantic web has its roots in knowledge engineering that provoke software engineers to look for application of ontology applications throughout the Software Engineering life cycle.
The internal components of a semantic web are light weight and may be of less quality standards than the externally visible modules.
In fact the internal components are generated from external (ontological) component.
That is the reason agile development approaches such as feature driven development are suitable for applications internal component development.
As yet there is no particular procedure that describes the role of ontology in the processes.
Therefore we propose an ontology based feature driven development for semantic web application that can be used form application model development to feature design and implementation.
Features are precisely defined in the OWL-based domain model.
Transition from OWL based domain model to feature list is directly defined in transformation rules.
On the other hand the ontology based overall model can be easily validated through automated tools.
Advantages of ontology-based feature Driven development are also discussed.
In principle, a network can transfer data at nearly the speed of light.
Today's Internet, however, is much slower: our measurements show that latencies are typically more than one, and often more than two orders of magnitude larger than the lower bound implied by the speed of light.
Closing this gap would not only add value to today's Internet applications, but might also open the door to exciting new applications.
Thus, we propose a grand challenge for the networking research community: building a speed-of-light Internet.
Towards addressing this goal, we begin by investigating the causes of latency inflation in the Internet across the network stack.
Our analysis reveals that while protocol overheads, which have dominated the community's attention, are indeed important, infrastructural inefficiencies are a significant and under-explored problem.
Thus, we propose a radical, yet surprisingly low-cost approach to mitigating latency inflation at the lowest layers and building a nearly speed-of-light Internet infrastructure.
Reinforcement learning in multi-agent systems has been studied in the fields of economic game theory, artificial intelligence and statistical physics by developing an analytical understanding of the learning dynamics (often in relation to the replicator dynamics of evolutionary game theory).
However, the majority of these analytical studies focuses on repeated normal form games, which only have a single environmental state.
Environmental dynamics, i.e. changes in the state of an environment affecting the agents' payoffs has received less attention, lacking a universal method to obtain deterministic equations from established multi-state reinforcement learning algorithms.
In this work we present a novel methodology to derive the deterministic limit resulting from an interaction-adaptation time scales separation of a general class of reinforcement learning algorithms, called temporal difference learning.
This form of learning is equipped to function in more realistic multi-state environments by using the estimated value of future environmental states to adapt the agent's behavior.
We demonstrate the potential of our method with the three well established learning algorithms Q learning, SARSA learning and Actor-Critic learning.
Illustrations of their dynamics on two multi-agent, multi-state environments reveal a wide range of different dynamical regimes, such as convergence to fixed points, limit cycles and even deterministic chaos.
We introduce a novel notion of invariance feedback entropy to quantify the state information that is required by any controller that enforces a given subset of the state space to be invariant.
We establish a number of elementary properties, e.g. we provide conditions that ensure that the invariance feedback entropy is finite and show for the deterministic case that we recover the well-known notion of entropy for deterministic control systems.
We prove the data rate theorem, which shows that the invariance entropy is a tight lower bound of the data rate of any coder-controller that achieves invariance in the closed loop.
We analyze uncertain linear control systems and derive a universal lower bound of the invariance feedback entropy.
The lower bound depends on the absolute value of the determinant of the system matrix and a ratio involving the volume of the invariant set and the set of uncertainties.
Furthermore, we derive a lower bound of the data rate of any static, memoryless coder-controller.
Both lower bounds are intimately related and for certain cases it is possible to bound the performance loss due to the restriction to static coder-controllers by 1 bit/time unit.
We provide various examples throughout the paper to illustrate and discuss different definitions and results.
This paper investigates how retailers at different stages of e-commerce maturity evaluate their entry to e-commerce activities.
The study was conducted using qualitative approach interviewing 16 retailers in Saudi Arabia.
It comes up with 22 factors that are believed the most influencing factors for retailers in Saudi Arabia.
Interestingly, there seem to be differences between retailers in companies at different maturity stages in terms of having different attitudes regarding the issues of using e-commerce.
The businesses that have reached a high stage of e-commerce maturity provide practical evidence of positive and optimistic attitudes and practices regarding use of e-commerce, whereas the businesses that have not reached higher levels of maturity provide practical evidence of more negative and pessimistic attitudes and practices.
The study, therefore, should contribute to efforts leading to greater e-commerce development in Saudi Arabia and other countries with similar context.
We present a novel approach for the reconstruction of dynamic geometric shapes using a single hand-held consumer-grade RGB-D sensor at real-time rates.
Our method does not require a pre-defined shape template to start with and builds up the scene model from scratch during the scanning process.
Geometry and motion are parameterized in a unified manner by a volumetric representation that encodes a distance field of the surface geometry as well as the non-rigid space deformation.
Motion tracking is based on a set of extracted sparse color features in combination with a dense depth-based constraint formulation.
This enables accurate tracking and drastically reduces drift inherent to standard model-to-depth alignment.
We cast finding the optimal deformation of space as a non-linear regularized variational optimization problem by enforcing local smoothness and proximity to the input constraints.
The problem is tackled in real-time at the camera's capture rate using a data-parallel flip-flop optimization strategy.
Our results demonstrate robust tracking even for fast motion and scenes that lack geometric features.
Image captioning is an important but challenging task, applicable to virtual assistants, editing tools, image indexing, and support of the disabled.
Its challenges are due to the variability and ambiguity of possible image descriptions.
In recent years significant progress has been made in image captioning, using Recurrent Neural Networks powered by long-short-term-memory (LSTM) units.
Despite mitigating the vanishing gradient problem, and despite their compelling ability to memorize dependencies, LSTM units are complex and inherently sequential across time.
To address this issue, recent work has shown benefits of convolutional networks for machine translation and conditional image generation.
Inspired by their success, in this paper, we develop a convolutional image captioning technique.
We demonstrate its efficacy on the challenging MSCOCO dataset and demonstrate performance on par with the baseline, while having a faster training time per number of parameters.
We also perform a detailed analysis, providing compelling reasons in favor of convolutional language generation approaches.
Smart contracts are computer programs that can be consistently executed by a network of mutually distrusting nodes, without the arbitration of a trusted authority.
Because of their resilience to tampering, smart contracts are appealing in many scenarios, especially in those which require transfers of money to respect certain agreed rules (like in financial services and in games).
Over the last few years many platforms for smart contracts have been proposed, and some of them have been actually implemented and used.
We study how the notion of smart contract is interpreted in some of these platforms.
Focussing on the two most widespread ones, Bitcoin and Ethereum, we quantify the usage of smart contracts in relation to their application domain.
We also analyse the most common programming patterns in Ethereum, where the source code of smart contracts is available.
We study a general class of dynamic games with asymmetric information where agents' beliefs are strategy dependent, i.e. signaling occurs.
We show that the notion of sufficient information, introduced in the companion paper team, can be used to effectively compress the agents' information in a mutually consistent manner that is sufficient for decision-making purposes.
We present instances of dynamic games with asymmetric information where we can characterize a time-invariant information state for each agent.
Based on the notion of sufficient information, we define a class of equilibria for dynamic games called Sufficient Information Based Perfect Bayesian Equilibrium (SIB-PBE).
Utilizing the notion of SIB-PBE, we provide a sequential decomposition of dynamic games with asymmetric information over time; this decomposition leads to a dynamic program that determines SIB-PBE of dynamic games.
Furthermore, we provide conditions under which we can guarantee the existence of SIB-PBE.
Recurrent Neural Networks (RNNs) are an important class of neural networks designed to retain and incorporate context into current decisions.
RNNs are particularly well suited for machine learning problems in which context is important, such as speech recognition or language translation.
This work presents RNNFast, a hardware accelerator for RNNs that leverages an emerging class of non-volatile memory called domain-wall memory (DWM).
We show that DWM is very well suited for RNN acceleration due to its very high density and low read/write energy.
At the same time, the sequential nature of input/weight processing of RNNs mitigates one of the downsides of DWM, which is the linear (rather than constant) data access time.
RNNFast is very efficient and highly scalable, with flexible mapping of logical neurons to RNN hardware blocks.
The basic hardware primitive, the RNN processing element (PE) includes custom DWM-based multiplication, sigmoid and tanh units for high density and low-energy.
The accelerator is designed to minimize data movement by closely interleaving DWM storage and computation.
We compare our design with a state-of-the-art GPGPU and find 21.8x better performance with 70x lower energy.
Photovoltaic (PV) power production increased drastically in Europe throughout the last years.
About the 6% of electricity in Italy comes from PV and for an efficient management of the power grid an accurate and reliable forecasting of production would be needed.
Starting from a dataset of electricity production of 65 Italian solar plants for the years 2011-2012 we investigate the possibility to forecast daily production from one to ten days of lead time without using on site measurements.
Our study is divided in two parts: an assessment of the predictability of meteorological variables using weather forecasts and an analysis on the application of data-driven modelling in predicting solar power production.
We calibrate a SVM model using available observations and then we force the same model with the predicted variables from weather forecasts with a lead time from one to ten days.
As expected, solar power production is strongly influenced by cloudiness and clear sky, in fact we observe that while during summer we obtain a general error under the 10% (slightly lower in south Italy), during winter the error is abundantly above the 20%.
This paper describes a Naive-Bayesian predictive model for 2016 U.S. Presidential Election based on Twitter data.
We use 33,708 tweets gathered since December 16, 2015 until February 29, 2016.
We introduce a simpler data preprocessing method to label the data and train the model.
The model achieves 95.8% accuracy on 10-fold cross validation and predicts Ted Cruz and Bernie Sanders as Republican and Democratic nominee respectively.
It achieves a comparable result to those in its competitor methods.
In contrary to traditional media streaming services where a unique media content is delivered to different users, interactive multiview navigation applications enable users to choose their own viewpoints and freely navigate in a 3-D scene.
The interactivity brings new challenges in addition to the classical rate-distortion trade-off, which considers only the compression performance and viewing quality.
On the one hand, interactivity necessitates sufficient viewpoints for richer navigation; on the other hand, it requires to provide low bandwidth and delay costs for smooth navigation during view transitions.
In this paper, we formally describe the novel trade-offs posed by the navigation interactivity and classical rate-distortion criterion.
Based on an original formulation, we look for the optimal design of the data representation by introducing novel rate and distortion models and practical solving algorithms.
Experiments show that the proposed data representation method outperforms the baseline solution by providing lower resource consumptions and higher visual quality in all navigation configurations, which certainly confirms the potential of the proposed data representation in practical interactive navigation systems.
This paper considers the problem of efficiently answering reachability queries over views of provenance graphs, derived from executions of workflows that may include recursion.
Such views include composite modules and model fine-grained dependencies between module inputs and outputs.
A novel view-adaptive dynamic labeling scheme is developed for efficient query evaluation, in which view specifications are labeled statically (i.e. as they are created) and data items are labeled dynamically as they are produced during a workflow execution.
Although the combination of fine-grained dependencies and recursive workflows entail, in general, long (linear-size) data labels, we show that for a large natural class of workflows and views, labels are compact (logarithmic-size) and reachability queries can be evaluated in constant time.
Experimental results demonstrate the benefit of this approach over the state-of-the-art technique when applied for labeling multiple views.
We present the state of the art in representing and reasoning with fuzzy knowledge in Semantic Web Languages such as triple languages RDF/RDFS, conceptual languages of the OWL 2 family and rule languages.
We further show how one may generalise them to so-called annotation domains, that cover also e.g. temporal and provenance extensions.
The use of educational games for pedagogical practice can provide new conceptions of teaching-learning in an interactive environment stimulating the acquisition of new knowledge.
The so-called serious games are focused on the goal of transmitting educational content or training to the user.
In the context of entrepreneurship, serious games appear to have greater importance due to the multidisciplinary of the knowledge needed.
Therefore, we propose the adoption of the Entrexplorer game in the context of a university classroom.
The game is a cloud-based serious game about the theme of entrepreneurship where users can access learning contents that will assist them in the acquisition of entrepreneurial skills.
The organization of the game in eight levels with six additional floors let students learn the different dimensions of an entrepreneurship project while progressing during the gameplay.
This paper formulates a novel problem on graphs: find the minimal subset of edges in a fully connected graph, such that the resulting graph contains all spanning trees for a set of specifed sub-graphs.
This formulation is motivated by an un-supervised grammar induction problem from computational linguistics.
We present a reduction to some known problems and algorithms from graph theory, provide computational complexity results, and describe an approximation algorithm.
Nonnegative matrix factorization (NMF) is one of the most frequently-used matrix factorization models in data analysis.
A significant reason to the popularity of NMF is its interpretability and the `parts of whole' interpretation of its components.
Recently, max-times, or subtropical, matrix factorization (SMF) has been introduced as an alternative model with equally interpretable `winner takes it all' interpretation.
In this paper we propose a new mixed linear--tropical model, and a new algorithm, called Latitude, that combines NMF and SMF, being able to smoothly alternate between the two.
In our model, the data is modeled using the latent factors and latent parameters that control whether the factors are interpreted as NMF or SMF features, or their mixtures.
We present an algorithm for our novel matrix factorization.
Our experiments show that our algorithm improves over both baselines, and can yield interpretable results that reveal more of the latent structure than either NMF or SMF alone.
Autonomous vehicles require knowledge of the surrounding road layout, which can be predicted by state-of-the-art CNNs.
This work addresses the current lack of data for determining lane instances, which are needed for various driving manoeuvres.
The main issue is the time-consuming manual labelling process, typically applied per image.
We notice that driving the car is itself a form of annotation.
Therefore, we propose a semi-automated method that allows for efficient labelling of image sequences by utilising an estimated road plane in 3D based on where the car has driven and projecting labels from this plane into all images of the sequence.
The average labelling time per image is reduced to 5 seconds and only an inexpensive dash-cam is required for data capture.
We are releasing a dataset of 24,000 images and additionally show experimental semantic segmentation and instance segmentation results.
Online fashion sales present a challenging use case for personalized recommendation: Stores offer a huge variety of items in multiple sizes.
Small stocks, high return rates, seasonality, and changing trends cause continuous turnover of articles for sale on all time scales.
Customers tend to shop rarely, but often buy multiple items at once.
We report on backtest experiments with sales data of 100k frequent shoppers at Zalando, Europe's leading online fashion platform.
To model changing customer and store environments, our recommendation method employs a pair of neural networks: To overcome the cold start problem, a feedforward network generates article embeddings in "fashion space," which serve as input to a recurrent neural network that predicts a style vector in this space for each client, based on their past purchase sequence.
We compare our results with a static collaborative filtering approach, and a popularity ranking baseline.
Satellite Communication systems are a promising solution to extend and complement terrestrial networks in unserved or under-served areas.
This aspect is reflected by recent commercial and standardisation endeavours.
In particular, 3GPP recently initiated a Study Item for New Radio-based, i.e., 5G, Non-Terrestrial Networks aimed at deploying satellite systems either as a stand-alone solution or as an integration to terrestrial networks in mobile broadband and machine-type communication scenarios.
However, typical satellite channel impairments, as large path losses, delays, and Doppler shifts, pose severe challenges to the realisation of a satellite-based NR network.
In this paper, based on the architecture options currently being discussed in the standardisation fora, we discuss and assess the impact of the satellite channel characteristics on the physical and Medium Access Control layers, both in terms of transmitted waveforms and procedures for enhanced Mobile BroadBand (eMBB) and NarrowBand-Internet of Things (NB-IoT) applications.
The proposed analysis shows that the main technical challenges are related to the PHY/MAC procedures, in particular Random Access (RA), Timing Advance (TA), and Hybrid Automatic Repeat reQuest (HARQ) and, depending on the considered service and architecture, different solutions are proposed.
Target-oriented sentiment classification aims at classifying sentiment polarities over individual opinion targets in a sentence.
RNN with attention seems a good fit for the characteristics of this task, and indeed it achieves the state-of-the-art performance.
After re-examining the drawbacks of attention mechanism and the obstacles that block CNN to perform well in this classification task, we propose a new model to overcome these issues.
Instead of attention, our model employs a CNN layer to extract salient features from the transformed word representations originated from a bi-directional RNN layer.
Between the two layers, we propose a component to generate target-specific representations of words in the sentence, meanwhile incorporate a mechanism for preserving the original contextual information from the RNN layer.
Experiments show that our model achieves a new state-of-the-art performance on a few benchmarks.
Workflows specify collections of tasks that must be executed under the responsibility or supervision of human users.
Workflow management systems and workflow-driven applications need to enforce security policies in the form of access control, specifying which users can execute which tasks, and authorization constraints, such as Separation of Duty, further restricting the execution of tasks at run-time.
Enforcing these policies is crucial to avoid frauds and malicious use, but it may lead to situations where a workflow instance cannot be completed without the violation of the policy.
The Workflow Satisfiability Problem (WSP) asks whether there exists an assignment of users to tasks in a workflow such that every task is executed and the policy is not violated.
The WSP is inherently hard, but solutions to this problem have a practical application in reconciling business compliance and business continuity.
Solutions to related problems, such as workflow resiliency (i.e., whether a workflow instance is still satisfiable even in the absence of users), are important to help in policy design.
Several variations of the WSP and similar problems have been defined in the literature and there are many solution methods available.
In this paper, we survey the work done on these problems in the past 20 years.
In this paper we present a queueing network approach to the problem of routing and rebalancing a fleet of self-driving vehicles providing on-demand mobility within a capacitated road network.
We refer to such systems as autonomous mobility-on-demand systems, or AMoD.
We first cast an AMoD system into a closed, multi-class BCMP queueing network model.
Second, we present analysis tools that allow the characterization of performance metrics for a given routing policy, in terms, e.g., of vehicle availabilities, and first and second order moments of vehicle throughput.
Third, we propose a scalable method for the synthesis of routing policies, with performance guarantees in the limit of large fleet sizes.
Finally, we validate our theoretical results on a case study of New York City.
Collectively, this paper provides a unifying framework for the analysis and control of AMoD systems, which subsumes earlier Jackson and network flow models, provides a quite large set of modeling options (e.g., the inclusion of road capacities and general travel time distributions), and allows the analysis of second and higher-order moments for the performance metrics.
Deep learning has been successfully applied to various tasks, but its underlying mechanism remains unclear.
Neural networks associate similar inputs in the visible layer to the same state of hidden variables in deep layers.
The fraction of inputs that are associated to the same state is a natural measure of similarity and is simply related to the cost in bits required to represent these inputs.
The degeneracy of states with the same information cost provides instead a natural measure of noise and is simply related the entropy of the frequency of states, that we call relevance.
Representations with minimal noise, at a given level of similarity (resolution), are those that maximise the relevance.
A signature of such efficient representations is that frequency distributions follow power laws.
We show, in extensive numerical experiments, that deep neural networks extract a hierarchy of efficient representations from data, because they i) achieve low levels of noise (i.e. high relevance) and ii) exhibit power law distributions.
We also find that the layer that is most efficient to reliably generate patterns of training data is the one for which relevance and resolution are traded at the same price, which implies that frequency distribution follows Zipf's law.
The unrelenting increase in the population of mobile users and their traffic demands drive cellular network operators to densify their network infrastructure.
Network densification shrinks the footprint of base stations (BSs) and reduces the number of users associated with each BS, leading to an improved spatial frequency reuse and spectral efficiency, and thus, higher network capacity.
However, the densification gain come at the expense of higher handover rates and network control overhead.
Hence, users mobility can diminish or even nullifies the foreseen densification gain.
In this context, splitting the control plane (C-plane) and user plane (U-plane) is proposed as a potential solution to harvest densification gain with reduced cost in terms of handover rate and network control overhead.
In this article, we use stochastic geometry to develop a tractable mobility-aware model for a two-tier downlink cellular network with ultra-dense small cells and C-plane/U-plane split architecture.
The developed model is then used to quantify the effect of mobility on the foreseen densification gain with and without C-plane/U-plane split.
To this end, we shed light on the handover problem in dense cellular environments, show scenarios where the network fails to support certain mobility profiles, and obtain network design insights.
The strategy of sustainable development in the governance of information and communication technology (ICT) is a sector of advanced research that leads to rising challenges posed by social and environmental requirements in the implementation and establishment of the governance strategy.
This paper offers new generation governance model that we call "ICT Green Governance".
The proposed framework provides an original model based on the Corporate Social Responsibility (CSR) concept and Green IT strategy.
Facing increasing pressure from stakeholders, the model offers a new vision of ICT governance to ensure effective and efficient use of ICT in enabling an enterprise to achieve its goals.
We present here the relevance of our model, on the basis of a literature review, and provide guidelines and principles for effective ICT governance in the way of sustainable development, in order to improve the economic, social and environmental performance of companies.
This work presents a supervised learning based approach to the computer vision problem of frame interpolation.
The presented technique could also be used in the cartoon animations since drawing each individual frame consumes a noticeable amount of time.
The most existing solutions to this problem use unsupervised methods and focus only on real life videos with already high frame rate.
However, the experiments show that such methods do not work as well when the frame rate becomes low and object displacements between frames becomes large.
This is due to the fact that interpolation of the large displacement motion requires knowledge of the motion structure thus the simple techniques such as frame averaging start to fail.
In this work the deep convolutional neural network is used to solve the frame interpolation problem.
In addition, it is shown that incorporating the prior information such as optical flow improves the interpolation quality significantly.
In this paper, cyber attack detection and isolation is studied on a network of UAVs in a formation flying setup.
As the UAVs communicate to reach consensus on their states while making the formation, the communication network among the UAVs makes them vulnerable to a potential attack from malicious adversaries.
Two types of attacks pertinent to a network of UAVs have been considered: a node attack on the UAVs and a deception attack on the communication between the UAVs.
UAVs formation control presented using a consensus algorithm to reach a pre-specified formation.
A node and a communication path deception cyber attacks on the UAV's network are considered with their respective models in the formation setup.
For these cyber attacks detection, a bank of Unknown Input Observer (UIO) based distributed fault detection scheme proposed to detect and identify the compromised UAV in the formation.
A rule based on the residuals generated using the bank of UIOs are used to detect attacks and identify the compromised UAV in the formation.
Further, an algorithm developed to remove the faulty UAV from the network once an attack detected and the compromised UAV isolated while maintaining the formation flight with a missing UAV node.
Semantic labeling for numerical values is a task of assigning semantic labels to unknown numerical attributes.
The semantic labels could be numerical properties in ontologies, instances in knowledge bases, or labeled data that are manually annotated by domain experts.
In this paper, we refer to semantic labeling as a retrieval setting where the label of an unknown attribute is assigned by the label of the most relevant attribute in labeled data.
One of the greatest challenges is that an unknown attribute rarely has the same set of values with the similar one in the labeled data.
To overcome the issue, statistical interpretation of value distribution is taken into account.
However, the existing studies assume a specific form of distribution.
It is not appropriate in particular to apply open data where there is no knowledge of data in advance.
To address these problems, we propose a neural numerical embedding model (EmbNum) to learn useful representation vectors for numerical attributes without prior assumptions on the distribution of data.
Then, the "semantic similarities" between the attributes are measured on these representation vectors by the Euclidean distance.
Our empirical experiments on City Data and Open Data show that EmbNum significantly outperforms state-of-the-art methods for the task of numerical attribute semantic labeling regarding effectiveness and efficiency.
The field of satellite communications is enjoying a renewed interest in the global telecom market, and very high throughput satellites (V/HTS), with their multiple spot-beams, are key for delivering the future rate demands.
In this article, the state-of-the-art and open research challenges of signal processing techniques for V/HTS systems are presented for the first time, with focus on novel approaches for efficient interference mitigation.
The main signal processing topics for the ground, satellite, and user segment are addressed.
Also, the critical components for the integration of satellite and terrestrial networks are studied, such as cognitive satellite systems and satellite-terrestrial backhaul for caching.
All the reviewed techniques are essential in empowering satellite systems to support the increasing demands of the upcoming generation of communication networks.
The connected autonomous vehicle has been often touted as a technology that will become pervasive in society in the near future.
Rather than being stand alone, we examine the need for autonomous vehicles to cooperate and interact within their socio-cyber-physical environments, including the problems cooperation will solve, but also the issues and challenges.
Lung cancer is the deadliest type of cancer for both men and women.
Feature selection plays a vital role in cancer classification.
This paper investigates the feature selection process in Computed Tomographic (CT) lung cancer images using soft set theory.
We propose a new soft set based unsupervised feature selection algorithm.
Nineteen features are extracted from the segmented lung images using gray level co-occurence matrix (GLCM) and gray level different matrix (GLDM).
In this paper, an efficient Unsupervised Soft Set based Quick Reduct (SSUSQR) algorithm is presented.
This method is used to select features from the data set and compared with existing rough set based unsupervised feature selection methods.
Then K-Means and Self Organizing Map (SOM) clustering algorithms are used to cluster the data.
The performance of the feature selection algorithms is evaluated based on performance of clustering techniques.
The results show that the proposed method effectively removes redundant features.
We propose a network for Congested Scene Recognition called CSRNet to provide a data-driven and deep learning method that can understand highly congested scenes and perform accurate count estimation as well as present high-quality density maps.
The proposed CSRNet is composed of two major components: a convolutional neural network (CNN) as the front-end for 2D feature extraction and a dilated CNN for the back-end, which uses dilated kernels to deliver larger reception fields and to replace pooling operations.
CSRNet is an easy-trained model because of its pure convolutional structure.
We demonstrate CSRNet on four datasets (ShanghaiTech dataset, the UCF_CC_50 dataset, the WorldEXPO'10 dataset, and the UCSD dataset) and we deliver the state-of-the-art performance.
In the ShanghaiTech Part_B dataset, CSRNet achieves 47.3% lower Mean Absolute Error (MAE) than the previous state-of-the-art method.
We extend the targeted applications for counting other objects, such as the vehicle in TRANCOS dataset.
Results show that CSRNet significantly improves the output quality with 15.4% lower MAE than the previous state-of-the-art approach.
Deep Neural Networks (DNNs) are typically trained by backpropagation in a batch learning setting, which requires the entire training data to be made available prior to the learning task.
This is not scalable for many real-world scenarios where new data arrives sequentially in a stream form.
We aim to address an open challenge of "Online Deep Learning" (ODL) for learning DNNs on the fly in an online setting.
Unlike traditional online learning that often optimizes some convex objective function with respect to a shallow model (e.g., a linear/kernel-based hypothesis), ODL is significantly more challenging since the optimization of the DNN objective function is non-convex, and regular backpropagation does not work well in practice, especially for online learning settings.
In this paper, we present a new online deep learning framework that attempts to tackle the challenges by learning DNN models of adaptive depth from a sequence of training data in an online learning setting.
In particular, we propose a novel Hedge Backpropagation (HBP) method for online updating the parameters of DNN effectively, and validate the efficacy of our method on large-scale data sets, including both stationary and concept drifting scenarios.
Recently, Yuan et al.(2016) have shown the effectiveness of using Long Short-Term Memory (LSTM) for performing Word Sense Disambiguation (WSD).
Their proposed technique outperformed the previous state-of-the-art with several benchmarks, but neither the training data nor the source code was released.
This paper presents the results of a reproduction study of this technique using only openly available datasets (GigaWord, SemCore, OMSTI) and software (TensorFlow).
From them, it emerged that state-of-the-art results can be obtained with much less data than hinted by Yuan et al.
All code and trained models are made freely available.
Beatmania is a rhythm action game where players take on the role of a DJ who performs music by pressing specific controller buttons to mix "Keysounds" (audio samples) at the correct time.
Unlike other rhythm action games such as Dance Dance Revolution, players must play certain notes from up to eight different instruments.
Creating game stages, called "charts," is considered a difficult and time-consuming task, and in this paper we explore approaches in computer generation for these maps.
We present a deep neural network based process for automatically generating Beatmania charts for arbitrary pieces of music.
Given a raw audio track of a song, we identify notes with its corresponding instrument, and use a neural network to classify each note as playable or nonplayable.
The final chart is produced by mapping playable notes to controls.
We achieve an F1-score on the core task of Sample Selection that significantly beats LSTM baselines.
Maurice Gross (1934-2001) was both a great linguist and a pioneer in natural language processing.
This article is written in homage to his memory
The acknowledged model for networks of collaborations is the hypergraph model.
Nonetheless when it comes to be visualized hypergraphs are transformed into simple graphs.
Very often, the transformation is made by clique expansion of the hyperedges resulting in a loss of information for the user and in artificially more complex graphs due to the high number of edges represented.
The extra-node representation gives substantial improvement in the visualisation of hypergraphs and in the retrieval of information.
This paper aims at showing qualitatively and quantitatively how the extra-node representation can improve the visualisation of hypergraphs without loss of information.
The goal of the DSLDI workshop is to bring together researchers and practitioners interested in sharing ideas on how DSLs should be designed, implemented, supported by tools, and applied in realistic application contexts.
We are both interested in discovering how already known domains such as graph processing or machine learning can be best supported by DSLs, but also in exploring new domains that could be targeted by DSLs.
More generally, we are interested in building a community that can drive forward the development of modern DSLs.
These informal post-proceedings contain the submitted talk abstracts to the 3rd DSLDI workshop (DSLDI'15), and a summary of the panel discussion on Language Composition.
As wireless devices boom, and bandwidth-hungry applications (e.g., video and cloud uploading) get popular, today's Wireless Local Area Networks (WLANs) become not only crowded but also stressed at throughput.
Multi-user Multiple-Input and Multiple-Output (MU-MIMO), an advanced form of MIMO, has gained attention due to its huge potential in improving the performance of WLANs.
This paper surveys random access based MAC protocols for MU-MIMO enabled WLANs.
It first provides background information about the evolution and the fundamental MAC schemes of IEEE 802.11 Standards and Amendments, and then identifies the key requirements of designing MU-MIMO MAC protocols for WLANs.
After that, the most representative MU-MIMO MAC proposals in the literature are overviewed by benchmarking their MAC procedures and examining the key components, such as the channel state information acquisition, de/pre-coding and scheduling schemes.
Classifications and discussions on important findings of the surveyed MAC protocols are provided, based on which, the research challenges for designing effective MU-MIMO MAC protocols, as well as the envisaged MAC's role in the future heterogeneous networks, are highlighted.
Several statistical and machine learning methods are proposed to estimate the type and intensity of physical load and accumulated fatigue .
They are based on the statistical analysis of accumulated and moving window data subsets with construction of a kurtosis-skewness diagram.
This approach was applied to the data gathered by the wearable heart monitor for various types and levels of physical activities, and for people with various physical conditions.
The different levels of physical activities, loads, and fitness can be distinguished from the kurtosis-skewness diagram, and their evolution can be monitored.
Several metrics for estimation of the instant effect and accumulated effect (physical fatigue) of physical loads were proposed.
The data and results presented allow to extend application of these methods for modeling and characterization of complex human activity patterns, for example, to estimate the actual and accumulated physical load and fatigue, model the potential dangerous development, and give cautions and advice in real time.
The evaluation of machine learning algorithms in biomedical fields for applications involving sequential data lacks standardization.
Common quantitative scalar evaluation metrics such as sensitivity and specificity can often be misleading depending on the requirements of the application.
Evaluation metrics must ultimately reflect the needs of users yet be sufficiently sensitive to guide algorithm development.
Feedback from critical care clinicians who use automated event detection software in clinical applications has been overwhelmingly emphatic that a low false alarm rate, typically measured in units of the number of errors per 24 hours, is the single most important criterion for user acceptance.
Though using a single metric is not often as insightful as examining performance over a range of operating conditions, there is a need for a single scalar figure of merit.
In this paper, we discuss the deficiencies of existing metrics for a seizure detection task and propose several new metrics that offer a more balanced view of performance.
We demonstrate these metrics on a seizure detection task based on the TUH EEG Corpus.
We show that two promising metrics are a measure based on a concept borrowed from the spoken term detection literature, Actual Term-Weighted Value, and a new metric, Time-Aligned Event Scoring (TAES), that accounts for the temporal alignment of the hypothesis to the reference annotation.
We also demonstrate that state of the art technology based on deep learning, though impressive in its performance, still needs significant improvement before it will meet very strict user acceptance guidelines.
Automatic speech recognition (ASR) has been widely researched with supervised approaches, while many low-resourced languages lack audio-text aligned data, and supervised methods cannot be applied on them.
In this work, we propose a framework to achieve unsupervised ASR on a read English speech dataset, where audio and text are unaligned.
In the first stage, each word-level audio segment in the utterances is represented by a vector representation extracted by a sequence-of-sequence autoencoder, in which phonetic information and speaker information are disentangled.
Secondly, semantic embeddings of audio segments are trained from the vector representations using a skip-gram model.
Last but not the least, an unsupervised method is utilized to transform semantic embeddings of audio segments to text embedding space, and finally the transformed embeddings are mapped to words.
With the above framework, we are towards unsupervised ASR trained by unaligned text and speech only.
Recent technology advancements in the areas of compute, storage and networking, along with the increased demand for organizations to cut costs while remaining responsive to increasing service demands have led to the growth in the adoption of cloud computing services.
Cloud services provide the promise of improved agility, resiliency, scalability and a lowered Total Cost of Ownership (TCO).
This research introduces a framework for minimizing cost and maximizing resource utilization by using an Integer Linear Programming (ILP) approach to optimize the assignment of workloads to servers on Amazon Web Services (AWS) cloud infrastructure.
The model is based on the classical minimum-cost flow model, known as the assignment model.
The success of online auctions has given buyers access to greater product diversity with potentially lower prices.
It has provided sellers with access to large numbers of potential buyers and reduced transaction costs by enabling auctions to take place without regard to time or place.
However it is difficult to spend more time period with system and closely monitor the auction until auction participant wins the bid or closing of the auction.
Determining which items to bid on or what may be the recommended bid and when to bid it are difficult questions to answer for online auction participants.
The multi agent auction advisor system JADE and TRACE, which is connected with decision support system, gives the recommended bid to buyers for online auctions.
The auction advisor system relies on intelligent agents both for the retrieval of relevant auction data and for the processing of that data to enable meaningful recommendations, statistical reports and market prediction report to be made to auction participants.
Semi-supervised learning is attracting increasing attention due to the fact that datasets of many domains lack enough labeled data.
Variational Auto-Encoder (VAE), in particular, has demonstrated the benefits of semi-supervised learning.
The majority of existing semi-supervised VAEs utilize a classifier to exploit label information, where the parameters of the classifier are introduced to the VAE.
Given the limited labeled data, learning the parameters for the classifiers may not be an optimal solution for exploiting label information.
Therefore, in this paper, we develop a novel approach for semi-supervised VAE without classifier.
Specifically, we propose a new model called Semi-supervised Disentangled VAE (SDVAE), which encodes the input data into disentangled representation and non-interpretable representation, then the category information is directly utilized to regularize the disentangled representation via the equality constraint.
To further enhance the feature learning ability of the proposed VAE, we incorporate reinforcement learning to relieve the lack of data.
The dynamic framework is capable of dealing with both image and text data with its corresponding encoder and decoder networks.
Extensive experiments on image and text datasets demonstrate the effectiveness of the proposed framework.
We generalize the class of split graphs to the directed case and show that these split digraphs can be identified from their degree sequences.
The first degree sequence characterization is an extension of the concept of splittance to directed graphs, while the second characterization says a digraph is split if and only if its degree sequence satisfies one of the Fulkerson inequalities (which determine when an integer-pair sequence is digraphic) with equality.
A salient dynamic property of social media is bursting behavior.
In this paper, we study bursting behavior in terms of the temporal relation between a preceding baseline fluctuation and the successive burst response using a frequency time series of 3,000 keywords on Twitter.
We found that there is a fluctuation threshold up to which the burst size increases as the fluctuation increases and that above the threshold, there appears a variety of burst sizes.
We call this threshold the critical threshold.
Investigating this threshold in relation to endogenous bursts and exogenous bursts based on peak ratio and burst size reveals that the bursts below this threshold are endogenously caused and above this threshold, exogenous bursts emerge.
Analysis of the 3,000 keywords shows that all the nouns have both endogenous and exogenous origins of bursts and that each keyword has a critical threshold in the baseline fluctuation value to distinguish between the two.
Having a threshold for an input value for activating the system implies that Twitter is an excitable medium.
These findings are useful for characterizing how excitable a keyword is on Twitter and could be used, for example, to predict the response to particular information on social media.
This work presents a novel approach for the early recognition of the type of a laparoscopic surgery from its video.
Early recognition algorithms can be beneficial to the development of 'smart' OR systems that can provide automatic context-aware assistance, and also enable quick database indexing.
The task is however ridden with challenges specific to videos belonging to the domain of laparoscopy, such as high visual similarity across surgeries and large variations in video durations.
To capture the spatio-temporal dependencies in these videos, we choose as our model a combination of a Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) network.
We then propose two complementary approaches for improving early recognition performance.
The first approach is a CNN fine-tuning method that encourages surgeries to be distinguished based on the initial frames of laparoscopic videos.
The second approach, referred to as 'Future-State Predicting LSTM', trains an LSTM to predict information related to future frames, which helps in distinguishing between the different types of surgeries.
We evaluate our approaches on a large dataset of 425 laparoscopic videos containing 9 types of surgeries (Laparo425), and achieve on average an accuracy of 75% having observed only the first 10 minutes of a surgery.
These results are quite promising from a practical standpoint and also encouraging for other types of image-guided surgeries.
As wireless ad hoc and mobile networks are emerging and the transferred data become more sensitive, information security measures should make use of all the available contextual resources to secure information flows.
The physical layer security framework provides models, algorithms, and proofs of concept for generating pairwise symmetric keys over single links between two nodes within communication range.
In this study, we focus on cooperative group key generation over multiple Impulse Radio - Ultra Wideband (IR-UWB) channels according to the source model.
The main idea, proposed in previous work, consists in generating receiver-specific signals, also called s-signals, so that only the intended receiver has access to the non-observable channels corresponding to its non-adjacent links.
Herein, we complete the analysis of the proposed protocol and investigate several signal processing algorithms to generate the s-signal expressed as a solution to a deconvolution problem in the case of IR-UWB.
Our findings indicate that it is compulsory to add a parameterizable constraint to the searched s-signal and that the Expectation-Maximization algorithm can provide a stable self-parameterizable solution.
Compared to physical layer key distribution methods, the proposed key generation protocol requires less traffic overhead for small cooperative groups while being robust at medium and high signal-to-noise ratios.
New ideas in distributed systems (algorithms or protocols) are commonly tested by simulation, because experimenting with a prototype deployed on a realistic platform is cumbersome.
However, a prototype not only measures performance but also verifies assumptions about the underlying system.
We developed dfuntest - a testing framework for distributed applications that defines abstractions and test structure, and automates experiments on distributed platforms.
Dfuntest aims to be jUnit's analogue for distributed applications; a framework that enables the programmer to write robust and flexible scenarios of experiments.
Dfuntest requires minimal bindings that specify how to deploy and interact with the application.
Dfuntest's abstractions allow execution of a scenario on a single machine, a cluster, a cloud, or any other distributed infrastructure, e.g. on PlanetLab.
A scenario is a procedure; thus, our framework can be used both for functional tests and for performance measurements.
We show how to use dfuntest to deploy our DHT prototype on 60 PlanetLab nodes and verify whether the prototype maintains a correct topology.
Collaborative object transportation using multiple Micro Aerial Vehicles (MAVs) with limited communication is a challenging problem.
In this paper we address the problem of multiple MAVs mechanically coupled to a bulky object for transportation purposes without explicit communication between agents.
The apparent physical properties of each agent are reshaped to achieve robustly stable transportation.
Parametric uncertainties and unmodeled dynamics of each agent are quantified and techniques from robust control theory are employed to choose the physical parameters of each agent to guarantee stability.
Extensive simulation analysis and experimental results show that the proposed method guarantees stability in worst case scenarios.
Location-Based Services (LBSs) build upon geographic information to provide users with location-dependent functionalities.
In such a context, it is particularly important that geographic locations claimed by users are the actual ones.
Centralized verification approaches proposed in the last few years are not satisfactory, as they entail a high risk to the privacy of users.
In this paper, we present and evaluate a novel decentralized, infrastructure-independent proof-of-location scheme based on the blockchain technology.
Our scheme guarantees both location trustworthiness and user privacy preservation.
Composition and parameterization of multicomponent predictive systems (MCPSs) consisting of chains of data transformation steps are a challenging task.
Auto-WEKA is a tool to automate the combined algorithm selection and hyperparameter (CASH) optimization problem.
In this paper, we extend the CASH problem and Auto-WEKA to support the MCPS, including preprocessing steps for both classification and regression tasks.
We define the optimization problem in which the search space consists of suitably parameterized Petri nets forming the sought MCPS solutions.
In the experimental analysis, we focus on examining the impact of considerably extending the search space (from approximately 22,000 to 812 billion possible combinations of methods and categorical hyperparameters).
In a range of extensive experiments, three different optimization strategies are used to automatically compose MCPSs for 21 publicly available data sets.
The diversity of the composed MCPSs found is an indication that fully and automatically exploiting different combinations of data cleaning and preprocessing techniques is possible and highly beneficial for different predictive models.
We also present the results on seven data sets from real chemical production processes.
Our findings can have a major impact on the development of high-quality predictive models as well as their maintenance and scalability aspects needed in modern applications and deployment scenarios.
Linear Discriminant Analysis (LDA) is a widely-used supervised dimensionality reduction method in computer vision and pattern recognition.
In null space based LDA (NLDA), a well-known LDA extension, between-class distance is maximized in the null space of the within-class scatter matrix.
However, there are some limitations in NLDA.
Firstly, for many data sets, null space of within-class scatter matrix does not exist, thus NLDA is not applicable to those datasets.
Secondly, NLDA uses arithmetic mean of between-class distances and gives equal consideration to all between-class distances, which makes larger between-class distances can dominate the result and thus limits the performance of NLDA.
In this paper, we propose a harmonic mean based Linear Discriminant Analysis, Multi-Class Discriminant Analysis (MCDA), for image classification, which minimizes the reciprocal of weighted harmonic mean of pairwise between-class distance.
More importantly, MCDA gives higher priority to maximize small between-class distances.
MCDA can be extended to multi-label dimension reduction.
Results on 7 single-label data sets and 4 multi-label data sets show that MCDA has consistently better performance than 10 other single-label approaches and 4 other multi-label approaches in terms of classification accuracy, macro and micro average F1 score.
Human ability of both versatile grasping of given objects and grasping of novel (as of yet unseen) objects is truly remarkable.
This probably arises from the experience infants gather by actively playing around with diverse objects.
Moreover, knowledge acquired during this process is reused during learning of how to grasp novel objects.
We conjecture that this combined process of active and transfer learning boils down to a random search around an object, suitably biased by prior experience, to identify promising grasps.
In this paper we present an active learning method for learning of grasps for given objects, and a transfer learning method for learning of grasps for novel objects.
Our learning methods apply a kernel adaptive Metropolis-Hastings sampler that learns an approximation of the grasps' probability density of an object while drawing grasp proposals from it.
The sampler employs simulated annealing to search for globally-optimal grasps.
Our empirical results show promising applicability of our proposed learning schemes.
We present a set of experiments to demonstrate that deep recurrent neural networks (RNNs) learn internal representations that capture soft hierarchical notions of syntax from highly varied supervision.
We consider four syntax tasks at different depths of the parse tree; for each word, we predict its part of speech as well as the first (parent), second (grandparent) and third level (great-grandparent) constituent labels that appear above it.
These predictions are made from representations produced at different depths in networks that are pretrained with one of four objectives: dependency parsing, semantic role labeling, machine translation, or language modeling.
In every case, we find a correspondence between network depth and syntactic depth, suggesting that a soft syntactic hierarchy emerges.
This effect is robust across all conditions, indicating that the models encode significant amounts of syntax even in the absence of an explicit syntactic training supervision.
With the emergence of graph databases, the task of frequent subgraph discovery has been extensively addressed.
Although the proposed approaches in the literature have made this task feasible, the number of discovered frequent subgraphs is still very high to be efficiently used in any further exploration.
Feature selection for graph data is a way to reduce the high number of frequent subgraphs based on exact or approximate structural similarity.
However, current structural similarity strategies are not efficient enough in many real-world applications, besides, the combinatorial nature of graphs makes it computationally very costly.
In order to select a smaller yet structurally irredundant set of subgraphs, we propose a novel approach that mines the top-k topological representative subgraphs among the frequent ones.
Our approach allows detecting hidden structural similarities that existing approaches are unable to detect such as the density or the diameter of the subgraph.
In addition, it can be easily extended using any user defined structural or topological attributes depending on the sought properties.
Empirical studies on real and synthetic graph datasets show that our approach is fast and scalable.
QR decomposition is used prevalently in wireless communication.
In this paper, we express the Givens-rotation-based QR decomposition algorithm on a spatial architecture using T2S (Temporal To Spatial), a high-productivity spatial programming methodology for expressing high-performance spatial designs.
There are interesting challenges: the loop iteration space is not rectangular, and it is not obvious how the imperative algorithm can be expressed in a functional notation, the starting point of T2S.
Using QR decomposition as an example, this paper elucidates some general principle, and de-mystifies high-performance spatial programming.
The paper also serves as a tutorial of spatial programming for programmers who are not mathematicians, not expert programmers, and not experts on spatial architectures, but still hope to intuitively identify a high-performance design and map to spatial architectures efficiently.
Although automated reasoning with diagrams has been possible for some years, tools for diagrammatic reasoning are generally much less sophisticated than their sentential cousins.
The tasks of exploring levels of automation and abstraction in the construction of proofs and of providing explanations of solutions expressed in the proofs remain to be addressed.
In this paper we take an interactive proof assistant for Euler diagrams, Speedith, and add tactics to its reasoning engine, providing a level of automation in the construction of proofs.
By adding tactics to Speedith's repertoire of inferences, we ease the interaction between the user and the system and capture a higher level explanation of the essence of the proof.
We analysed the design options for tactics by using metrics which relate to human readability, such as the number of inferences and the amount of clutter present in diagrams.
Thus, in contrast to the normal case with sentential tactics, our tactics are designed to not only prove the theorem, but also to support explanation.
In this paper, we analytically study the bit error rate (BER) performance of underwater visible light communication (UVLC) systems with binary pulse position modulation (BPPM).
We simulate the channel fading-free impulse response (FFIR) based on Monte Carlo numerical method to take into account the absorption and scattering effects.
Additionally, to characterize turbulence effects, we multiply the aforementioned FFIR by a fading coefficient which for weak oceanic turbulence can be modeled as a lognormal random variable (RV).
Moreover, to mitigate turbulence effects, we employ multiple transmitters and/or receivers, i.e., spatial diversity technique over UVLC links.
Closed-form expressions for the system BER are provided, when equal gain combiner (EGC) is employed at the receiver side, thanks to Gauss-Hermite quadrature formula and approximation to the sum of lognormal RVs.
We further apply saddle-point approximation, an accurate photon-counting-based method, to evaluate the system BER in the presence of shot noise.
Both laser-based collimated and light emitting diode (LED)-based diffusive links are investigated.
Since multiple-scattering effect of UVLC channels on the propagating photons causes considerable inter-symbol interference (ISI), especially for diffusive channels, we also obtain the optimum multiple-symbol detection (MSD) algorithm to significantly alleviate ISI effects and improve the system performance.
Our numerical analysis indicates good matches between the analytical and photon-counting results implying the negligibility of signal-dependent shot noise, and also between analytical results and numerical simulations confirming the accuracy of our derived closed-form expressions for the system BER.
Besides, our results show that spatial diversity significantly mitigates fading impairments while MSD considerably alleviates ISI deteriorations.
Publications in the life sciences are characterized by a large technical vocabulary, with many lexical and semantic variations for expressing the same concept.
Towards addressing the problem of relevance in biomedical literature search, we introduce a deep learning model for the relevance of a document's text to a keyword style query.
Limited by a relatively small amount of training data, the model uses pre-trained word embeddings.
With these, the model first computes a variable-length Delta matrix between the query and document, representing a difference between the two texts, which is then passed through a deep convolution stage followed by a deep feed-forward network to compute a relevance score.
This results in a fast model suitable for use in an online search engine.
The model is robust and outperforms comparable state-of-the-art deep learning approaches.
We consider the problem of learning underlying tree structure from noisy, mixed data obtained from a linear model.
To achieve this, we use the expectation maximization algorithm combined with Chow-Liu minimum spanning tree algorithm.
This algorithm is sub-optimal, but has low complexity and is applicable to model selection problems through any linear model.
A key limitation of current multi-robot systems is a lack of relative localization, particularly in environments without GPS or motion capture systems.
This article presents a centralized method for relatively localizing a 2D swarm using sensors and beacons on the robots themselves.
The UKF-based algorithm as well as the requisite novel and cost-effective sensing hardware are discussed.
Comparisons with a motion capture system show that the method is capable of localization with errors on the order of the size of the robots.
How does one verify that the output of a complicated program is correct?
One can formally prove that the program is correct, but this may be beyond the power of existing methods.
Alternatively one can check that the output produced for a particular input satisfies the desired input-output relation, by running a checker on the input-output pair.
Then one only needs to prove the correctness of the checker.
But for some problems even such a checker may be too complicated to formally verify.
There is a third alternative: augment the original program to produce not only an output but also a correctness certificate, with the property that a very simple program (whose correctness is easy to prove) can use the certificate to verify that the input-output pair satisfies the desired input-output relation.
We consider the following important instance of this general question: How does one verify that the dominator tree of a flow graph is correct?
Existing fast algorithms for finding dominators are complicated, and even verifying the correctness of a dominator tree in the absence of additional information seems complicated.
We define a correctness certificate for a dominator tree, show how to use it to easily verify the correctness of the tree, and show how to augment fast dominator-finding algorithms so that they produce a correctness certificate.
We also relate the dominator certificate problem to the problem of finding independent spanning trees in a flow graph, and we develop algorithms to find such trees.
All our algorithms run in linear time.
Previous algorithms apply just to the special case of only trivial dominators, and they take at least quadratic time.
In this paper, we propose a novel 3D-RecGAN approach, which reconstructs the complete 3D structure of a given object from a single arbitrary depth view using generative adversarial networks.
Unlike the existing work which typically requires multiple views of the same object or class labels to recover the full 3D geometry, the proposed 3D-RecGAN only takes the voxel grid representation of a depth view of the object as input, and is able to generate the complete 3D occupancy grid by filling in the occluded/missing regions.
The key idea is to combine the generative capabilities of autoencoders and the conditional Generative Adversarial Networks (GAN) framework, to infer accurate and fine-grained 3D structures of objects in high-dimensional voxel space.
Extensive experiments on large synthetic datasets show that the proposed 3D-RecGAN significantly outperforms the state of the art in single view 3D object reconstruction, and is able to reconstruct unseen types of objects.
Our code and data are available at: https://github.com/Yang7879/3D-RecGAN.
Segmentation of histological images is one of the most crucial tasks for many biomedical analyses including quantification of certain tissue type.
However, challenges are posed by high variability and complexity of structural features in such images, in addition to imaging artifacts.
Further, the conventional approach of manual thresholding is labor-intensive, and highly sensitive to inter- and intra-image intensity variations.
An accurate and robust automated segmentation method is of high interest.
We propose and evaluate an elegant convolutional neural network (CNN) designed for segmentation of histological images, particularly those with Masson's trichrome stain.
The network comprises of 11 successive convolutional - rectified linear unit - batch normalization layers, and outperformed state-of-the-art CNNs on a dataset of cardiac histological images (labeling fibrosis, myocytes, and background) with a Dice similarity coefficient of 0.947.
With 100 times fewer (only 300 thousand) trainable parameters, our CNN is less susceptible to overfitting, and is efficient.
Additionally, it retains image resolution from input to output, captures fine-grained details, and can be trained end-to-end smoothly.
To the best of our knowledge, this is the first deep CNN tailored for the problem of concern, and may be extended to solve similar segmentation tasks to facilitate investigations into pathology and clinical treatment.
In this paper, we theoretically address three fundamental problems involving deep convolutional networks regarding invariance, depth and hierarchy.
We introduce the paradigm of Transformation Networks (TN) which are a direct generalization of Convolutional Networks (ConvNets).
Theoretically, we show that TNs (and thereby ConvNets) are can be invariant to non-linear transformations of the input despite pooling over mere local translations.
Our analysis provides clear insights into the increase in invariance with depth in these networks.
Deeper networks are able to model much richer classes of transformations.
We also find that a hierarchical architecture allows the network to generate invariance much more efficiently than a non-hierarchical network.
Our results provide useful insight into these three fundamental problems in deep learning using ConvNets.
High-throughput data acquisition in synthetic biology leads to an abundance of data that need to be processed and aggregated into useful biological models.
Building dynamical models based on this wealth of data is of paramount importance to understand and optimize designs of synthetic biology constructs.
However, building models manually for each data set is inconvenient and might become infeasible for highly complex synthetic systems.
In this paper, we present state-of-the-art system identification techniques and combine them with chemical reaction network theory (CRNT) to generate dynamic models automatically.
On the system identification side, Sparse Bayesian Learning offers methods to learn from data the sparsest set of dictionary functions necessary to capture the dynamics of the system into ODE models; on the CRNT side, building on such sparse ODE models, all possible network structures within a given parameter uncertainty region can be computed.
Additionally, the system identification process can be complemented with constraints on the parameters to, for example, enforce stability or non-negativity---thus offering relevant physical constraints over the possible network structures.
In this way, the wealth of data can be translated into biologically relevant network structures, which then steers the data acquisition, thereby providing a vital step for closed-loop system identification.
Bankruptcy prediction is very important for all the organization since it affects the economy and rise many social problems with high costs.
There are large number of techniques have been developed to predict the bankruptcy, which helps the decision makers such as investors and financial analysts.
One of the bankruptcy prediction models is the hybrid model using Fuzzy C-means clustering and MARS, which uses static ratios taken from the bank financial statements for prediction, which has its own theoretical advantages.
The performance of existing bankruptcy model can be improved by selecting the best features dynamically depend on the nature of the firm.
This dynamic selection can be accomplished by Genetic Algorithm and it improves the performance of prediction model.
The discrete logarithm problem is one of the backbones in public key cryptography.
In this paper we study the discrete logarithm problem in the group of circulant matrices over a finite field.
This gives rise to secure and fast public key cryptosystems.
Despite its remarkable empirical success as a highly competitive branch of artificial intelligence, deep learning is often blamed for its widely known low interpretation and lack of firm and rigorous mathematical foundation.
However, most theoretical endeavor is devoted in discriminative deep learning case, whose complementary part is generative deep learning.
To the best of our knowledge, we firstly highlight landscape of empirical error in generative case to complete the full picture through exquisite design of image super resolution under norm based capacity control.
Our theoretical advance in interpretation of the training dynamic is achieved from both mathematical and biological sides.
Image registration between histology and magnetic resonance imaging (MRI) is a challenging task due to differences in structural content and contrast.
Too thick and wide specimens cannot be processed all at once and must be cut into smaller pieces.
This dramatically increases the complexity of the problem, since each piece should be individually and manually pre-aligned.
To the best of our knowledge, no automatic method can reliably locate such piece of tissue within its respective whole in the MRI slice, and align it without any prior information.
We propose here a novel automatic approach to the joint problem of multimodal registration between histology and MRI, when only a fraction of tissue is available from histology.
The approach relies on the representation of images using their level lines so as to reach contrast invariance.
Shape elements obtained via the extraction of bitangents are encoded in a projective-invariant manner, which permits the identification of common pieces of curves between two images.
We evaluated the approach on human brain histology and compared resulting alignments against manually annotated ground truths.
Considering the complexity of the brain folding patterns, preliminary results are promising and suggest the use of characteristic and meaningful shape elements for improved robustness and efficiency.
The exponential growth in data generation and large-scale data analysis creates an unprecedented need for inexpensive, low-latency, and high-density information storage.
This need has motivated significant research into multi-level memory systems that can store multiple bits of information per device.
Although both the memory state of these devices and much of the data they store are intrinsically analog-valued, both are quantized for use with digital systems and discrete error correcting codes.
Using phase change memory as a prototypical multi-level storage technology, we herein demonstrate that analog-valued devices can achieve higher capacities when paired with analog codes.
Further, we find that storing analog signals directly through joint-coding can achieve low distortion with reduced coding complexity.
By jointly optimizing for signal statistics, device statistics, and a distortion metric, finite-length analog encodings can perform comparable to digital systems with asymptotically infinite large encodings.
These results show that end-to-end analog memory systems have not only the potential to reach higher storage capacities than discrete systems, but also to significantly lower coding complexity, leading to faster and more energy efficient storage.
In this work we propose a methodology for an automatic food classification system which recognizes the contents of the meal from the images of the food.
We developed a multi-layered deep convolutional neural network (CNN) architecture that takes advantages of the features from other deep networks and improves the efficiency.
Numerous classical handcrafted features and approaches are explored, among which CNNs are chosen as the best performing features.
Networks are trained and fine-tuned using preprocessed images and the filter outputs are fused to achieve higher accuracy.
Experimental results on the largest real-world food recognition database ETH Food-101 and newly contributed Indian food image database demonstrate the effectiveness of the proposed methodology as compared to many other benchmark deep learned CNN frameworks.
A weak asynchronous system is a trace monoid with a partial action on a set.
A polygonal morphism between weak asynchronous systems commutes with the actions and preserves the independence of events.
We prove that the category of weak asynchronous systems and polygonal morphisms has all limits and colimits.
The complexity of deep neural network algorithms for hardware implementation can be much lowered by optimizing the word-length of weights and signals.
Direct quantization of floating-point weights, however, does not show good performance when the number of bits assigned is small.
Retraining of quantized networks has been developed to relieve this problem.
In this work, the effects of retraining are analyzed for a feedforward deep neural network (FFDNN) and a convolutional neural network (CNN).
The network complexity is controlled to know their effects on the resiliency of quantized networks by retraining.
The complexity of the FFDNN is controlled by varying the unit size in each hidden layer and the number of layers, while that of the CNN is done by modifying the feature map configuration.
We find that the performance gap between the floating-point and the retrain-based ternary (+1, 0, -1) weight neural networks exists with a fair amount in 'complexity limited' networks, but the discrepancy almost vanishes in fully complex networks whose capability is limited by the training data, rather than by the number of connections.
This research shows that highly complex DNNs have the capability of absorbing the effects of severe weight quantization through retraining, but connection limited networks are less resilient.
This paper also presents the effective compression ratio to guide the trade-off between the network size and the precision when the hardware resource is limited.
Mobile ad-hoc network (MANET) is a dynamic collection of mobile computers without the need for any existing infrastructure.
Nodes in a MANET act as hosts and routers.
Designing of robust routing algorithms for MANETs is a challenging task.
Disjoint multipath routing protocols address this problem and increase the reliability, security and lifetime of network.
However, selecting an optimal multipath is an NP-complete problem.
In this paper, Hopfield neural network (HNN) which its parameters are optimized by particle swarm optimization (PSO) algorithm is proposed as multipath routing algorithm.
Link expiration time (LET) between each two nodes is used as the link reliability estimation metric.
This approach can find either node-disjoint or link-disjoint paths in single phase route discovery.
Simulation results confirm that PSO-HNN routing algorithm has better performance as compared to backup path set selection algorithm (BPSA) in terms of the path set reliability and number of paths in the set.
Consumer trust is one of the key obstacles to online vendors seeking to extend their consumers across cultures.
This research identifies culture at the individual consumer level.
Based on the Stimulus-Organism-Response (SOR) model, this study focuses on the moderating role of uncertainty avoidance culture value on privacy and security as cognition influences, joy and fear as emotional influences (Stimuli), and individualism-collectivism on social networking services as social influence and subsequently on interpersonal trust (cognitive and affect-based trust) (Organism) towards purchase intention (Response).
Data were collected in Australia and the Partial least squares (PLS) approach was used to test the research model.
The findings confirmed the moderating role of individual level culture on consumer's cognitive and affect-based trust in B2Ce-commerce websites with diverse degrees of uncertainty avoidance and individualism.
Caching algorithms are usually described by the eviction method and analyzed using a metric of hit probability.
Since contents have different importance (e.g. popularity), the utility of a high hit probability, and the cost of transmission can vary across contents.
In this paper, we consider timer-based (TTL) policies across a cache network, where contents have differentiated timers over which we optimize.
Each content is associated with a utility measured in terms of the corresponding hit probability.
We start our analysis from a linear cache network: we propose a utility maximization problem where the objective is to maximize the sum of utilities and a cost minimization problem where the objective is to minimize the content transmission cost across the network.
These frameworks enable us to design online algorithms for cache management, for which we prove achieving optimal performance.
Informed by the results of our analysis, we formulate a non-convex optimization problem for a general cache network.
We show that the duality gap is zero, hence we can develop a distributed iterative primal-dual algorithm for content management in the network.
Numerical evaluations show that our algorithm significant outperforms path replication with traditional caching algorithms over some network topologies.
Finally, we consider a direct application of our cache network model to content distribution.
This letter investigates joint power control and user clustering for downlink non-orthogonal multiple access systems.
Our aim is to minimize the total power consumption by taking into account not only the conventional transmission power but also the decoding power of the users.
To solve this optimization problem, it is firstly transformed into an equivalent problem with tractable constraints.
Then, an efficient algorithm is proposed to tackle the equivalent problem by using the techniques of reweighted 1-norm minimization and majorization-minimization.
Numerical results validate the superiority of the proposed algorithm over the conventional algorithms including the popular matching-based algorithm.
This research tested the following well known strategies to deal with binary imbalanced data on 82 different real life data sets (sampled to imbalance rates of 5%, 3%, 1%, and 0.1%): class weight, SMOTE, Underbagging, and a baseline (just the base classifier).
As base classifiers we used SVM with RBF kernel, random forests, and gradient boosting machines and we measured the quality of the resulting classifier using 6 different metrics (Area under the curve, Accuracy, F-measure, G-mean, Matthew's correlation coefficient and Balanced accuracy).
The best strategy strongly depends on the metric used to measure the quality of the classifier.
For AUC and accuracy class weight and the baseline perform better; for F-measure and MCC, SMOTE performs better; and for G-mean and balanced accuracy, underbagging.
Full-duplex systems are expected to double the spectral efficiency compared to conventional half-duplex systems if the self-interference signal can be significantly mitigated.
Digital cancellation is one of the lowest complexity self-interference cancellation techniques in full-duplex systems.
However, its mitigation capability is very limited, mainly due to transmitter and receiver circuit's impairments.
In this paper, we propose a novel digital self-interference cancellation technique for full-duplex systems.
The proposed technique is shown to significantly mitigate the self-interference signal as well as the associated transmitter and receiver impairments.
In the proposed technique, an auxiliary receiver chain is used to obtain a digital-domain copy of the transmitted Radio Frequency (RF) self-interference signal.
The self-interference copy is then used in the digital-domain to cancel out both the self-interference signal and the associated impairments.
Furthermore, to alleviate the receiver phase noise effect, a common oscillator is shared between the auxiliary and ordinary receiver chains.
A thorough analytical and numerical analysis for the effect of the transmitter and receiver impairments on the cancellation capability of the proposed technique is presented.
Finally, the overall performance is numerically investigated showing that using the proposed technique, the self-interference signal could be mitigated to ~3dB higher than the receiver noise floor, which results in up to 76% rate improvement compared to conventional half-duplex systems at 20dBm transmit power values.
When supervising an object detector with weakly labeled data, most existing approaches are prone to trapping in the discriminative object parts, e.g., finding the face of a cat instead of the full body, due to lacking the supervision on the extent of full objects.
To address this challenge, we incorporate object segmentation into the detector training, which guides the model to correctly localize the full objects.
We propose the multiple instance curriculum learning (MICL) method, which injects curriculum learning (CL) into the multiple instance learning (MIL) framework.
The MICL method starts by automatically picking the easy training examples, where the extent of the segmentation masks agree with detection bounding boxes.
The training set is gradually expanded to include harder examples to train strong detectors that handle complex images.
The proposed MICL method with segmentation in the loop outperforms the state-of-the-art weakly supervised object detectors by a substantial margin on the PASCAL VOC datasets.
Kernel alignment measures the degree of similarity between two kernels.
In this paper, inspired from kernel alignment, we propose a new Linear Discriminant Analysis (LDA) formulation, kernel alignment LDA (kaLDA).
We first define two kernels, data kernel and class indicator kernel.
The problem is to find a subspace to maximize the alignment between subspace-transformed data kernel and class indicator kernel.
Surprisingly, the kernel alignment induced kaLDA objective function is very similar to classical LDA and can be expressed using between-class and total scatter matrices.
This can be extended to multi-label data.
We use a Stiefel-manifold gradient descent algorithm to solve this problem.
We perform experiments on 8 single-label and 6 multi-label data sets.
Results show that kaLDA has very good performance on many single-label and multi-label problems.
In this note we shall introduce a simple, effective numerical method for solving partial differential equations for scalar and vector-valued data defined on surfaces.
Even though we shall follow the traditional way to approximate the regular surfaces under consideration by triangular meshes, the key idea of our algorithm is to develop an intrinsic and unified way to compute directly the partial derivatives of functions defined on triangular meshes.
We shall present examples in computer graphics and image processing applications.
Traditional pattern mining algorithms generally suffer from a lack of flexibility.
In this paper, we propose a SAT formulation of the problem to successfully mine frequent flexible sequences occurring in transactional datasets.
Our SAT-based approach can easily be extended with extra constraints to address a broad range of pattern mining applications.
To demonstrate this claim, we formulate and add several constraints, such as gap and span constraints, to our model in order to extract more specific patterns.
We also use interactive solving to perform important derived tasks, such as closed pattern mining or maximal pattern mining.
Finally, we prove the practical feasibility of our SAT model by running experiments on two real datasets.
While the smart surveillance system enhanced by the Internet of Things (IoT) technology becomes an essential part of Smart Cities, it also brings new concerns in security of the data.
Compared to the traditional surveillance systems that is built following a monolithic architecture to carry out lower level operations, such as monitoring and recording, the modern surveillance systems are expected to support more scalable and decentralized solutions for advanced video stream analysis at the large volumes of distributed edge devices.
In addition, the centralized architecture of the conventional surveillance systems is vulnerable to single point of failure and privacy breach owning to the lack of protection to the surveillance feed.
This position paper introduces a novel secure smart surveillance system based on microservices architecture and blockchain technology.
Encapsulating the video analysis algorithms as various independent microservices not only isolates the video feed from different sectors, but also improve the system availability and robustness by decentralizing the operations.
The blockchain technology securely synchronizes the video analysis databases among microservices across surveillance domains, and provides tamper proof of data in the trustless network environment.
Smart contract enabled access authorization strategy prevents any unauthorized user from accessing the microservices and offers a scalable, decentralized and fine-grained access control solution for smart surveillance systems.
We propose a semantics for permutation equivalence in higher-order rewriting.
This semantics takes place in cartesian closed 2-categories, and is proved sound and complete.
In recent years, neural network approaches have been widely adopted for machine learning tasks, with applications in computer vision.
More recently, unsupervised generative models based on neural networks have been successfully applied to model data distributions via low-dimensional latent spaces.
In this paper, we use Generative Adversarial Networks (GANs) to impose structure in compressed sensing problems, replacing the usual sparsity constraint.
We propose to train the GANs in a task-aware fashion, specifically for reconstruction tasks.
We also show that it is possible to train our model without using any (or much) non-compressed data.
Finally, we show that the latent space of the GAN carries discriminative information and can further be regularized to generate input features for general inference tasks.
We demonstrate the effectiveness of our method on a variety of reconstruction and classification problems.
Outlier detection is the identification of points in a dataset that do not conform to the norm.
Outlier detection is highly sensitive to the choice of the detection algorithm and the feature subspace used by the algorithm.
Extracting domain-relevant insights from outliers needs systematic exploration of these choices since diverse outlier sets could lead to complementary insights.
This challenge is especially acute in an interactive setting, where the choices must be explored in a time-constrained manner.
In this work, we present REMIX, the first system to address the problem of outlier detection in an interactive setting.
REMIX uses a novel mixed integer programming (MIP) formulation for automatically selecting and executing a diverse set of outlier detectors within a time limit.
This formulation incorporates multiple aspects such as (i) an upper limit on the total execution time of detectors (ii) diversity in the space of algorithms and features, and (iii) meta-learning for evaluating the cost and utility of detectors.
REMIX provides two distinct ways for the analyst to consume its results: (i) a partitioning of the detectors explored by REMIX into perspectives through low-rank non-negative matrix factorization; each perspective can be easily visualized as an intuitive heatmap of experiments versus outliers, and (ii) an ensembled set of outliers which combines outlier scores from all detectors.
We demonstrate the benefits of REMIX through extensive empirical validation on real-world data.
Phishing is a common online weapon, used against users, by Phishers for acquiring a confidential information through deception.
Since the inception of internet, nearly everything, ranging from money transaction to sharing information, is done online in most parts of the world.
This has also given rise to malicious activities such as Phishing.
Detecting Phishing is an intricate process due to complexity, ambiguity and copious amount of possibilities of factors responsible for phishing .
Rough sets can be a powerful tool, when working on such kind of Applications containing vague or imprecise data.
This paper proposes an approach towards Phishing Detection Using Rough Set Theory.
The Thirteen basic factors, directly responsible towards Phishing, are grouped into four Strata.
Reliability Factor is determined on the basis of the outcome of these strata, using Rough Set Theory .
Reliability Factor determines the possibility of a suspected site to be Valid or Fake.
Using Rough set Theory most and the least influential factors towards Phishing are also determined.
Real-time algorithms for automatically recognizing surgical phases are needed to develop systems that can provide assistance to surgeons, enable better management of operating room (OR) resources and consequently improve safety within the OR.
State-of-the-art surgical phase recognition algorithms using laparoscopic videos are based on fully supervised training.
This limits their potential for widespread application, since creation of manual annotations is an expensive process considering the numerous types of existing surgeries and the vast amount of laparoscopic videos available.
In this work, we propose a new self-supervised pre-training approach based on the prediction of remaining surgery duration (RSD) from laparoscopic videos.
The RSD prediction task is used to pre-train a convolutional neural network (CNN) and long short-term memory (LSTM) network in an end-to-end manner.
Our proposed approach utilizes all available data and reduces the reliance on annotated data, thereby facilitating the scaling up of surgical phase recognition algorithms to different kinds of surgeries.
Additionally, we present EndoN2N, an end-to-end trained CNN-LSTM model for surgical phase recognition and evaluate the performance of our approach on a dataset of 120 Cholecystectomy laparoscopic videos (Cholec120).
This work also presents the first systematic study of self-supervised pre-training approaches to understand the amount of annotations required for surgical phase recognition.
Interestingly, the proposed RSD pre-training approach leads to performance improvement even when all the training data is manually annotated and outperforms the single pre-training approach for surgical phase recognition presently published in the literature.
It is also observed that end-to-end training of CNN-LSTM networks boosts surgical phase recognition performance.
This paper presents a planning system for autonomous driving among many pedestrians.
A key ingredient of our approach is PORCA, a pedestrian motion prediction model that accounts for both a pedestrian's global navigation intention and local interactions with the vehicle and other pedestrians.
Unfortunately, the autonomous vehicle does not know the pedestrian's intention a priori and requires a planning algorithm that hedges against the uncertainty in pedestrian intentions.
Our planning system combines a POMDP algorithm with the pedestrian motion model and runs in near real time.
Experiments show that it enables a robot vehicle to drive safely, efficiently, and smoothly among a crowd with a density of nearly one person per square meter.
The semantic localization problem in robotics consists in determining the place where a robot is located by means of semantic categories.
The problem is usually addressed as a supervised classification process, where input data correspond to robot perceptions while classes to semantic categories, like kitchen or corridor.
In this paper we propose a framework, implemented in the PCL library, which provides a set of valuable tools to easily develop and evaluate semantic localization systems.
The implementation includes the generation of 3D global descriptors following a Bag-of-Words approach.
This allows the generation of dimensionality-fixed descriptors from any type of keypoint detector and feature extractor combinations.
The framework has been designed, structured and implemented in order to be easily extended with different keypoint detectors, feature extractors as well as classification models.
The proposed framework has also been used to evaluate the performance of a set of already implemented descriptors, when used as input for a specific semantic localization system.
The results obtained are discussed paying special attention to the internal parameters of the BoW descriptor generation process.
Moreover, we also review the combination of some keypoint detectors with different 3D descriptor generation techniques.
This paper presents a neural network-based end-to-end clustering framework.
We design a novel strategy to utilize the contrastive criteria for pushing data-forming clusters directly from raw data, in addition to learning a feature embedding suitable for such clustering.
The network is trained with weak labels, specifically partial pairwise relationships between data instances.
The cluster assignments and their probabilities are then obtained at the output layer by feed-forwarding the data.
The framework has the interesting characteristic that no cluster centers need to be explicitly specified, thus the resulting cluster distribution is purely data-driven and no distance metrics need to be predefined.
The experiments show that the proposed approach beats the conventional two-stage method (feature embedding with k-means) by a significant margin.
It also compares favorably to the performance of the standard cross entropy loss for classification.
Robustness analysis also shows that the method is largely insensitive to the number of clusters.
Specifically, we show that the number of dominant clusters is close to the true number of clusters even when a large k is used for clustering.
Proportionate-type normalized suband adaptive filter (PNSAF-type) algorithms are very attractive choices for echo cancellation.
To further obtain both fast convergence rate and low steady-state error, in this paper, a variable step size (VSS) version of the presented improved PNSAF (IPNSAF) algorithm is proposed by minimizing the square of the noise-free a posterior subband error signals.
A noniterative shrinkage method is used to recover the noise-free a priori subband error signals from the noisy subband error signals.
Significantly, the proposed VSS strategy can be applied to any other PNSAF-type algorithm, since it is independent of the proportionate principles.
Simulation results in the context of acoustic echo cancellation have demonstrated the effectiveness of the proposed method.
In recent times Massive Multiplayer Online Game has appeared as a computer game that enables hundreds of players from all parts of the world to interact in a game world (common platform) at the same time instance.
Current architecture used for MMOGs based on the classic tightly coupled distributed system.
While, MMOGs are getting more interactive same time number of interacting users is increasing, classic implementation architecture may raise scalability and interdependence issues.
This requires a loosely coupled service oriented architecture to support evolution in MMOG application.
Data flow architecture, Event driven architecture and client server architecture are basic date orchestration approaches used by any service oriented architecture.
Real time service is hottest issue for service oriented architecture.
The basic requirement of any real time service oriented architecture is to ensure the quality of service.
In this paper we have proposed a service oriented architecture for massive multiplayer online game and a specific middleware (based on open source DDS) in MMOGs for fulfilling real time constraints.
A foundation for closing the gap between biometrics in the narrower and the broader perspective is presented trough a conceptualization of biometric systems in both perspectives.
A clear distinction between verification, identification and classification systems is made as well as shown that there are additional classes of biometric systems.
In the end a Unified Modeling Language model is developed showing the connections between the two perspectives.
In this paper we present our winning entry at the 2018 ECCV PoseTrack Challenge on 3D human pose estimation.
Using a fully-convolutional backbone architecture, we obtain volumetric heatmaps per body joint, which we convert to coordinates using soft-argmax.
Absolute person center depth is estimated by a 1D heatmap prediction head.
The coordinates are back-projected to 3D camera space, where we minimize the L1 loss.
Key to our good results is the training data augmentation with randomly placed occluders from the Pascal VOC dataset.
In addition to reaching first place in the Challenge, our method also surpasses the state-of-the-art on the full Human3.6M benchmark among methods that use no additional pose datasets in training.
Code for applying synthetic occlusions is availabe at https://github.com/isarandi/synthetic-occlusion.
An alternative pathway for the human brain to communicate with the outside world is by means of a brain computer interface (BCI).
A BCI can decode electroencephalogram (EEG) signals of brain activities, and then send a command or an intent to an external interactive device, such as a wheelchair.
The effectiveness of the BCI depends on the performance in decoding the EEG.
Usually, the EEG is contaminated by different kinds of artefacts (e.g., electromyogram (EMG), background activity), which leads to a low decoding performance.
A number of filtering methods can be utilized to remove or weaken the effects of artefacts, but they generally fail when the EEG contains extreme artefacts.
In such cases, the most common approach is to discard the whole data segment containing extreme artefacts.
This causes the fatal drawback that the BCI cannot output decoding results during that time.
In order to solve this problem, we employ the Lomb-Scargle periodogram to estimate the spectral power from incomplete EEG (after removing only parts contaminated by artefacts), and Denoising Autoencoder (DAE) for learning.
The proposed method is evaluated with motor imagery EEG data.
The results show that our method can successfully decode incomplete EEG to good effect.
This article is an attempt to combine different ways of working with sets of objects and their classes for designing and development of artificial intelligent systems (AIS) of analysis information, using object-oriented programming (OOP).
This paper contains analysis of basic concepts of OOP and their relation with set theory and artificial intelligence (AI).
Process of sets and multisets creation from different sides, in particular mathematical set theory, OOP and AI is considered.
Definition of object and its properties, homogeneous and inhomogeneous classes of objects, set of objects, multiset of objects and constructive methods of their creation and classification are proposed.
In addition, necessity of some extension of existing OOP tools for the purpose of practical implementation AIS of analysis information, using proposed approach, is shown.
A recommender system's basic task is to estimate how users will respond to unseen items.
This is typically modeled in terms of how a user might rate a product, but here we aim to extend such approaches to model how a user would write about the product.
To do so, we design a character-level Recurrent Neural Network (RNN) that generates personalized product reviews.
The network convincingly learns styles and opinions of nearly 1000 distinct authors, using a large corpus of reviews from BeerAdvocate.com.
It also tailors reviews to describe specific items, categories, and star ratings.
Using a simple input replication strategy, the Generative Concatenative Network (GCN) preserves the signal of static auxiliary inputs across wide sequence intervals.
Without any additional training, the generative model can classify reviews, identifying the author of the review, the product category, and the sentiment (rating), with remarkable accuracy.
Our evaluation shows the GCN captures complex dynamics in text, such as the effect of negation, misspellings, slang, and large vocabularies gracefully absent any machinery explicitly dedicated to the purpose.
The performance of prediction models is often based on "abstract metrics" that estimate the model's ability to limit residual errors between the observed and predicted values.
However, meaningful evaluation and selection of prediction models for end-user domains requires holistic and application-sensitive performance measures.
Inspired by energy consumption prediction models used in the emerging "big data" domain of Smart Power Grids, we propose a suite of performance measures to rationally compare models along the dimensions of scale independence, reliability, volatility and cost.
We include both application independent and dependent measures, the latter parameterized to allow customization by domain experts to fit their scenario.
While our measures are generalizable to other domains, we offer an empirical analysis using real energy use data for three Smart Grid applications: planning, customer education and demand response, which are relevant for energy sustainability.
Our results underscore the value of the proposed measures to offer a deeper insight into models' behavior and their impact on real applications, which benefit both data mining researchers and practitioners.
This paper studies the effects on user welfare of imposing network neutrality, using a game-theoretic model of provider interactions based on a two-sided market framework: we assume that the platform--the last-mile access providers (ISPs)--are monopolists, and consider content providers (CPs) entry decisions.
All decisions affect the choices made by users, who are sensitive both to CP and ISP investments (in content creation and quality-of-service, respectively).
In a non-neutral regime, CPs and ISPs can charge each other, while such charges are prohibited in the neutral regime.
We assume those charges (if any) are chosen by CPs, a direction rarely considered in the literature, where they are assumed fixed by ISPs.
Our analysis suggests that, unexpectedly, more CPs enter the market in a non-neutral regime where they pay ISPs, than without such payments.
Additionally, in this case ISPs tend to invest more than in the neutral regime.
From our results, the best regime in terms of user welfare is parameter dependent, calling for caution in designing neutrality regulations.
The accuracy of indoor wireless localization systems can be substantially enhanced by map-awareness, i.e., by the knowledge of the map of the environment in which localization signals are acquired.
In fact, this knowledge can be exploited to cancel out, at least to some extent, the signal degradation due to propagation through physical obstructions, i.e., to the so called non-line-of-sight bias.
This result can be achieved by developing novel localization techniques that rely on proper map-aware statistical modelling of the measurements they process.
In this manuscript a unified statistical model for the measurements acquired in map-aware localization systems based on time-of-arrival and received signal strength techniques is developed and its experimental validation is illustrated.
Finally, the accuracy of the proposed map-aware model is assessed and compared with that offered by its map-unaware counterparts.
Our numerical results show that, when the quality of acquired measurements is poor, map-aware modelling can enhance localization accuracy by up to 110% in certain scenarios.
Rate adaptation and transmission power control in 802.11 WLANs have received a lot of attention from the research community, with most of the proposals aiming at maximising throughput based on network conditions.
Considering energy consumption, an implicit assumption is that optimality in throughput implies optimality in energy efficiency, but this assumption has been recently put into question.
In this paper, we address via analysis, simulation and experimentation the relation between throughput performance and energy efficiency in multi-rate 802.11 scenarios.
We demonstrate the trade-off between these performance figures, confirming that they may not be simultaneously optimised, and analyse their sensitivity towards the energy consumption parameters of the device.
We analyse this trade-off in existing rate adaptation with transmission power control algorithms, and discuss how to design novel schemes taking energy consumption into account.
Spurred by the growth of transportation network companies and increasing data capabilities, vehicle routing and ride-matching algorithms can improve the efficiency of private transportation services.
However, existing routing solutions do not address where drivers should travel after dropping off a passenger and before receiving the next passenger ride request, i.e., during the between-ride period.
We address this problem by developing an efficient algorithm to find the optimal policy for drivers between rides in order to maximize driver profits.
We model the road network as a graph, and we show that the between-ride routing problem is equivalent to a stochastic shortest path problem, an infinite dynamic program with no discounting.
We prove under reasonable assumptions that an optimal routing policy exists that avoids cycles; policies of this type can be efficiently found.
We present an iterative approach to find an optimal routing policy.
Our approach can account for various factors, including the frequency of passenger ride requests at different locations, traffic conditions, and surge pricing.
We demonstrate the effectiveness of the approach by implementing it on road network data from Boston and New York City.
Large-scale distributed training of deep neural networks suffer from the generalization gap caused by the increase in the effective mini-batch size.
Previous approaches try to solve this problem by varying the learning rate and batch size over epochs and layers, or some ad hoc modification of the batch normalization.
We propose an alternative approach using a second-order optimization method that shows similar generalization capability to first-order methods, but converges faster and can handle larger mini-batches.
To test our method on a benchmark where highly optimized first-order methods are available as references, we train ResNet-50 on ImageNet.
We converged to 75% Top-1 validation accuracy in 35 epochs for mini-batch sizes under 16,384, and achieved 75% even with a mini-batch size of 131,072, which took 100 epochs.
This article presents a two-stage topological algorithm for recovering an estimate of a quasiperiodic function from a set of noisy measurements.
The first stage of the algorithm is a topological phase estimator, which detects the quasiperiodic structure of the function without placing additional restrictions on the function.
By respecting this phase estimate, the algorithm avoids creating distortion even when it uses a large number of samples for the estimate of the function.
WPaxos is a multileader Paxos protocol that provides low-latency and high-throughput consensus across wide-area network (WAN) deployments.
Unlike statically partitioned multiple Paxos deployments, WPaxos perpetually adapts to the changing access locality through object stealing.
Multiple concurrent leaders coinciding in different zones steal ownership of objects from each other using phase-1 of Paxos, and then use phase-2 to commit update-requests on these objects locally until they are stolen by other leaders.
To achieve fast phase-2 commits, WPaxos adopts the flexible quorums idea in a novel manner, and appoints phase-2 acceptors to be close to their respective leaders.
We implemented WPaxos and evaluated it on WAN deployments across 5 AWS regions.
The dynamic partitioning of the object-space and emphasis on zone-local commits allow WPaxos to significantly outperform both partitioned Paxos deployments and leaderless Paxos approaches, while providing the same consistency guarantees.
The increasing use of social networks generates enormous amounts of data that can be used for many types of analysis.
Some of these data have temporal and geographical information, which can be used for comprehensive examination.
In this paper, we propose a new method to analyze the massive volume of messages available in Twitter to identify places in the world where topics such as TV shows, climate change, disasters, and sports are emerging.
The proposed method is based on a neural network that is used to detect outliers from a time series, which is built upon statistical data from tweets located on different political divisions (i.e., countries, cities).
The outliers are used to identify topics within an abnormal behavior in Twitter.
The effectiveness of our method is evaluated in an online environment indicating new findings on modeling local people's behavior from different places.
Functional neuroimaging can measure the brain?s response to an external stimulus.
It is used to perform brain mapping: identifying from these observations the brain regions involved.
This problem can be cast into a linear supervised learning task where the neuroimaging data are used as predictors for the stimulus.
Brain mapping is then seen as a support recovery problem.
On functional MRI (fMRI) data, this problem is particularly challenging as i) the number of samples is small due to limited acquisition time and ii) the variables are strongly correlated.
We propose to overcome these difficulties using sparse regression models over new variables obtained by clustering of the original variables.
The use of randomization techniques, e.g. bootstrap samples, and clustering of the variables improves the recovery properties of sparse methods.
We demonstrate the benefit of our approach on an extensive simulation study as well as two fMRI datasets.
Evidence-based health care (EBHC) is an important practice of medicine which attempts to provide systematic scientific evidence to answer clinical questions.
In this context, Epistemonikos (www.epistemonikos.org) is one of the first and most important online systems in the field, providing an interface that supports users on searching and filtering scientific articles for practicing EBHC.
The system nowadays requires a large amount of expert human effort, where close to 500 physicians manually curate articles to be utilized in the platform.
In order to scale up the large and continuous amount of data to keep the system updated, we introduce EpistAid, an interactive intelligent interface which supports clinicians in the process of curating documents for Epistemonikos within lists of papers called evidence matrices.
We introduce the characteristics, design and algorithms of our solution, as well as a prototype implementation and a case study to show how our solution addresses the information overload problem in this area.
Input validation is the first line of defense against malformed or malicious inputs.
It is therefore critical that the validator (which is often part of the parser) is free of bugs.
To build dependable input validators, we propose using parser generators for context-free languages.
In the context of network protocols, various works have pointed at context-free languages as falling short to specify precisely or concisely common idioms found in protocols.
We review those assessments and perform a rigorous, language-theoretic analysis of several common protocol idioms.
We then demonstrate the practical value of our findings by developing a modular, robust, and efficient input validator for HTTP relying on context-free grammars and regular expressions.
In the framework of computational complexity and in an effort to define a more natural reduction for problems of equivalence, we investigate the recently introduced kernel reduction, a reduction that operates on each element of a pair independently.
This paper details the limitations and uses of kernel reductions.
We show that kernel reductions are weaker than many-one reductions and provide conditions under which complete problems exist.
Ultimately, the number and size of equivalence classes can dictate the existence of a kernel reduction.
We leave unsolved the unconditional existence of a complete problem under polynomial-time kernel reductions for the standard complexity classes.
Improving patient care safety is an ultimate objective for medical cyber-physical systems.
A recent study shows that the patients' death rate can be significantly reduced by computerizing medical best practice guidelines.
To facilitate the development of computerized medical best practice guidelines, statecharts are often used as a modeling tool because of their high resemblances to disease and treatment models and their capabilities to provide rapid prototyping and simulation for clinical validations.
However, some implementations of statecharts, such as Yakindu statecharts, are priority-based and have synchronous execution semantics which makes it difficult to model certain functionalities that are essential in modeling medical guidelines, such as two-way communications and configurable execution orders.
Rather than introducing new statechart elements or changing the statechart implementation's underline semantics, we use existing basic statechart elements to design model patterns for the commonly occurring issues.
In particular, we show the design of model patterns for two-way communications and configurable execution orders and formally prove the correctness of these model patterns.
We further use a simplified airway laser surgery scenario as a case study to demonstrate how the developed model patterns address the two-way communication and configurable execution order issues and their impact on validation and verification of medical safety properties.
Recommender systems take inputs from user history, use an internal ranking algorithm to generate results and possibly optimize this ranking based on feedback.
However, often the recommender system is unaware of the actual intent of the user and simply provides recommendations dynamically without properly understanding the thought process of the user.
An intelligent recommender system is not only useful for the user but also for businesses which want to learn the tendencies of their users.
Finding out tendencies or intents of a user is a difficult problem to solve.
Keeping this in mind, we sought out to create an intelligent system which will keep track of the user's activity on a web-application as well as determine the intent of the user in each session.
We devised a way to encode the user's activity through the sessions.
Then, we have represented the information seen by the user in a high dimensional format which is reduced to lower dimensions using tensor factorization techniques.
The aspect of intent awareness (or scoring) is dealt with at this stage.
Finally, combining the user activity data with the contextual information gives the recommendation score.
The final recommendations are then ranked using filtering and collaborative recommendation techniques to show the top-k recommendations to the user.
A provision for feedback is also envisioned in the current system which informs the model to update the various weights in the recommender system.
Our overall model aims to combine both frequency-based and context-based recommendation systems and quantify the intent of a user to provide better recommendations.
We ran experiments on real-world timestamped user activity data, in the setting of recommending reports to the users of a business analytics tool and the results are better than the baselines.
We also tuned certain aspects of our model to arrive at optimized results.
Applications in many domains require processing moving object trajectories.
In this work, we focus on a trajectory similarity search that finds all trajectories within a given distance of a query trajectory over a time interval, which we call the distance threshold similarity search.
We develop three indexing strategies with spatial, temporal and spatiotemporal selectivity for the GPU that differ significantly from indexes suitable for the CPU, and show the conditions under which each index achieves good performance.
Furthermore, we show that the GPU implementations outperform multithreaded CPU implementations in a range of experimental scenarios, making the GPU an attractive technology for processing moving object trajectories.
We test our implementations on two synthetic and one real-world dataset of a galaxy merger.
This paper describes a resolution based Description Logic reasoning system called DLog.
DLog transforms Description Logic axioms into a Prolog program and uses the standard Prolog execution for efficiently answering instance retrieval queries.
From the Description Logic point of view, DLog is an ABox reasoning engine for the full SHIQ language.
The DLog approach makes it possible to store the individuals in a database instead of memory, which results in better scalability and helps using description logic ontologies directly on top of existing information sources.
To appear in Theory and Practice of Logic Programming (TPLP).
Any non-trivial concurrent system warrants synchronisation, regardless of the concurrency model.
Actor-based concurrency serialises all computations in an actor through asynchronous message passing.
In contrast, lock-based concurrency serialises some computations by following a lock--unlock protocol for accessing certain data.
Both systems require sound reasoning about pointers and aliasing to exclude data-races.
If actor isolation is broken, so is the single-thread-of-control abstraction.
Similarly for locks, if a datum is accessible outside of the scope of the lock, the datum is not governed by the lock.
In this paper we discuss how to balance aliasing and synchronisation.
In previous work, we defined a type system that guarantees data-race freedom of actor-based concurrency and lock-based concurrency.
This paper extends this work by the introduction of two programming constructs; one for decoupling isolation and synchronisation and one for constructing higher-level atomicity guarantees from lower-level synchronisation.
We focus predominantly on actors, and in particular the Encore programming language, but our ultimate goal is to define our constructs in such a way that they can be used both with locks and actors, given that combinations of both models occur frequently in actual systems.
We discuss the design space, provide several formalisations of different semantics and discuss their properties, and connect them to case studies showing how our proposed constructs can be useful.
We also report on an on-going implementation of our proposed constructs in Encore.
Multi-tenant cloud networks have various security and monitoring service functions (SFs) that constitute a service function chain (SFC) between two endpoints.
SF rule ordering overlaps and policy conflicts can cause increased latency, service disruption and security breaches in cloud networks.
Software Defined Network (SDN) based Network Function Virtualization (NFV) has emerged as a solution that allows dynamic SFC composition and traffic steering in a cloud network.
We propose an SDN enabled Universal Policy Checking (SUPC) framework, to provide 1) Flow Composition and Ordering by translating various SF rules into the OpenFlow format.
This ensures elimination of redundant rules and policy compliance in SFC.
2) Flow conflict analysis to identify conflicts in header space and actions between various SF rules.
Our results show a significant reduction in SF rules on composition.
Additionally, our conflict checking mechanism was able to identify several rule conflicts that pose security, efficiency, and service availability issues in the cloud network.
Sparse tensors appear in many large-scale applications with multidimensional and sparse data.
While multidimensional sparse data often need to be processed on manycore processors, attempts to develop highly-optimized GPU-based implementations of sparse tensor operations are rare.
The irregular computation patterns and sparsity structures as well as the large memory footprints of sparse tensor operations make such implementations challenging.
We leverage the fact that sparse tensor operations share similar computation patterns to propose a unified tensor representation called F-COO.
Combined with GPU-specific optimizations, F-COO provides highly-optimized implementations of sparse tensor computations on GPUs.
The performance of the proposed unified approach is demonstrated for tensor-based kernels such as the Sparse Matricized Tensor- Times-Khatri-Rao Product (SpMTTKRP) and the Sparse Tensor- Times-Matrix Multiply (SpTTM) and is used in tensor decomposition algorithms.
Compared to state-of-the-art work we improve the performance of SpTTM and SpMTTKRP up to 3.7 and 30.6 times respectively on NVIDIA Titan-X GPUs.
We implement a CANDECOMP/PARAFAC (CP) decomposition and achieve up to 14.9 times speedup using the unified method over state-of-the-art libraries on NVIDIA Titan-X GPUs.
We study two mixed robust/average-case submodular partitioning problems that we collectively call Submodular Partitioning.
These problems generalize both purely robust instances of the problem (namely max-min submodular fair allocation (SFA) and min-max submodular load balancing (SLB) and also generalize average-case instances (that is the submodular welfare problem (SWP) and submodular multiway partition (SMP).
While the robust versions have been studied in the theory community, existing work has focused on tight approximation guarantees, and the resultant algorithms are not, in general, scalable to very large real-world applications.
This is in contrast to the average case, where most of the algorithms are scalable.
In the present paper, we bridge this gap, by proposing several new algorithms (including those based on greedy, majorization-minimization, minorization-maximization, and relaxation algorithms) that not only scale to large sizes but that also achieve theoretical approximation guarantees close to the state-of-the-art, and in some cases achieve new tight bounds.
We also provide new scalable algorithms that apply to additive combinations of the robust and average-case extreme objectives.
We show that these problems have many applications in machine learning (ML).
This includes: 1) data partitioning and load balancing for distributed machine algorithms on parallel machines; 2) data clustering; and 3) multi-label image segmentation with (only) Boolean submodular functions via pixel partitioning.
We empirically demonstrate the efficacy of our algorithms on real-world problems involving data partitioning for distributed optimization of standard machine learning objectives (including both convex and deep neural network objectives), and also on purely unsupervised (i.e., no supervised or semi-supervised learning, and no interactive segmentation) image segmentation.
We consider two classes of computations which admit taking linear combinations of execution runs: probabilistic sampling and generalized animation.
We argue that the task of program learning should be more tractable for these architectures than for conventional deterministic programs.
We look at the recent advances in the "sampling the samplers" paradigm in higher-order probabilistic programming.
We also discuss connections between partial inconsistency, non-monotonic inference, and vector semantics.
Mobile phone usage provides a wealth of information, which can be used to better understand the demographic structure of a population.
In this paper we focus on the population of Mexican mobile phone users.
Our first contribution is an observational study of mobile phone usage according to gender and age groups.
We were able to detect significant differences in phone usage among different subgroups of the population.
Our second contribution is to provide a novel methodology to predict demographic features (namely age and gender) of unlabeled users by leveraging individual calling patterns, as well as the structure of the communication graph.
We provide details of the methodology and show experimental results on a real world dataset that involves millions of users.
In recent work, we formalized the theory of optimal-size sorting networks with the goal of extracting a verified checker for the large-scale computer-generated proof that 25 comparisons are optimal when sorting 9 inputs, which required more than a decade of CPU time and produced 27 GB of proof witnesses.
The checker uses an untrusted oracle based on these witnesses and is able to verify the smaller case of 8 inputs within a couple of days, but it did not scale to the full proof for 9 inputs.
In this paper, we describe several non-trivial optimizations of the algorithm in the checker, obtained by appropriately changing the formalization and capitalizing on the symbiosis with an adequate implementation of the oracle.
We provide experimental evidence of orders of magnitude improvements to both runtime and memory footprint for 8 inputs, and actually manage to check the full proof for 9 inputs.
In this paper we present an adaptable fast matrix multiplication (AFMM) algorithm, for two nxn dense matrices which computes the product matrix with average complexity Tavg(n) = d1d2n3 with the acknowledgement that the average count is obtained for addition as the basic operation rather than multiplication which is probably the unquestionable choice for basic operation in existing matrix multiplication algorithms.
Communication systems with low-resolution analog-to-digital-converters (ADCs) can exploit channel state information at the transmitter (CSIT) and receiver.
This paper presents initial results on codebook design and performance analysis for limited feedback systems with one-bit ADCs.
Different from the high-resolution case, the absolute phase at the receiver is important to align the phase of the received signals when the received signal is sliced by one-bit ADCs.
A new codebook design for the beamforming case is proposed that separately quantizes the channel direction and the residual phase.
Identification of minimum number of local regions of a handwritten character image, containing well-defined discriminating features which are sufficient for a minimal but complete description of the character is a challenging task.
A new region selection technique based on the idea of an enhanced Harmony Search methodology has been proposed here.
The powerful framework of Harmony Search has been utilized to search the region space and detect only the most informative regions for correctly recognizing the handwritten character.
The proposed method has been tested on handwritten samples of Bangla Basic, Compound and mixed (Basic and Compound characters) characters separately with SVM based classifier using a longest run based feature-set obtained from the image subregions formed by a CG based quad-tree partitioning approach.
Applying this methodology on the above mentioned three types of datasets, respectively 43.75%, 12.5% and 37.5% gains have been achieved in terms of region reduction and 2.3%, 0.6% and 1.2% gains have been achieved in terms of recognition accuracy.
The results show a sizeable reduction in the minimal number of descriptive regions as well a significant increase in recognition accuracy for all the datasets using the proposed technique.
Thus the time and cost related to feature extraction is decreased without dampening the corresponding recognition accuracy.
Several researchers have argued that a machine learning system's interpretability should be defined in relation to a specific agent or task: we should not ask if the system is interpretable, but to whom is it interpretable.
We describe a model intended to help answer this question, by identifying different roles that agents can fulfill in relation to the machine learning system.
We illustrate the use of our model in a variety of scenarios, exploring how an agent's role influences its goals, and the implications for defining interpretability.
Finally, we make suggestions for how our model could be useful to interpretability researchers, system developers, and regulatory bodies auditing machine learning systems.
It is generally accepted as common wisdom that receiving social feedback is helpful to (i) keep an individual engaged with a community and to (ii) facilitate an individual's positive behavior change.
However, quantitative data on the effect of social feedback on continued engagement in an online health community is scarce.
In this work we apply Mahalanobis Distance Matching (MDM) to demonstrate the importance of receiving feedback in the "loseit" weight loss community on Reddit.
Concretely we show that (i) even when correcting for differences in word choice, users receiving more positive feedback on their initial post are more likely to return in the future, and that (ii) there are diminishing returns and social feedback on later posts is less important than for the first post.
We also give a description of the type of initial posts that are more likely to attract this valuable social feedback.
Though we cannot yet argue about ultimate weight loss success or failure, we believe that understanding the social dynamics underlying online health communities is an important step to devise more effective interventions.
Tor is the most widely used anonymity network, currently serving millions of users each day.
However, there is no access control in place for all these users, leaving the network vulnerable to botnet abuse and attacks.
For example, criminals frequently use exit relays as stepping stones for attacks, causing service providers to serve CAPTCHAs to exit relay IP addresses or blacklisting them altogether, which leads to severe usability issues for legitimate Tor users.
To address this problem, we propose TorPolice, the first privacy-preserving access control framework for Tor.
TorPolice enables abuse-plagued service providers such as Yelp to enforce access rules to police and throttle malicious requests coming from Tor while still providing service to legitimate Tor users.
Further, TorPolice equips Tor with global access control for relays, enhancing Tor's resilience to botnet abuse.
We show that TorPolice preserves the privacy of Tor users, implement a prototype of TorPolice, and perform extensive evaluations to validate our design goals.
Emergency communications requires reliability and flexibility for disaster recovery and relief operation.
Based upon existing commercial portable devices (e.g., smartphones, tablets, laptops), we propose a network architecture that uses cellular networks and WiFi connections to deliver large files in emergency scenarios under the impairments of wireless channel such as packet losses and intermittent connection issues.
Network coding (NC) is exploited to improve the delivery probability.
We first review the state-of-the-art of NC for emergency communications.
Then, we present the proposed network architecture which utilizes multiple radio interfaces of portable devices to support data delivery.
A random linear NC scheme is exploited at source to enhance the reliability for content delivery against packet losses.
Besides, an analytical model for the successful decoding probability in linear NC is derived.
Finally, we evaluate the effectiveness of the proposed architecture with NC in terms of the delivery ratio of content for intermittent connectivity scenarios.
Photoacoustic spectral analysis is a novel tool for studying various parameters affecting signals in Photoacoustic microscopy.
But only observing frequency components of photoacoustic signals doesn't make enough data for a desirable analysis.
Thus a hybrid time-domain and frequency-domain analysis scheme has been proposed to investigate effects of various parameters like depth of microscopy, laser focal spot size and contrast agent concentration on Photoacoustic signals.
Liquids are an important part of many common manipulation tasks in human environments.
If we wish to have robots that can accomplish these types of tasks, they must be able to interact with liquids in an intelligent manner.
In this paper, we investigate ways for robots to perceive and reason about liquids.
That is, a robot asks the questions What in the visual data stream is liquid? and How can I use that to infer all the potential places where liquid might be?
We collected two datasets to evaluate these questions, one using a realistic liquid simulator and another on our robot.
We used fully convolutional neural networks to learn to detect and track liquids across pouring sequences.
Our results show that these networks are able to perceive and reason about liquids, and that integrating temporal information is important to performing such tasks well.
Recent incidents of data breaches call for organizations to proactively identify cyber attacks on their systems.
Darkweb/Deepweb (D2web) forums and marketplaces provide environments where hackers anonymously discuss existing vulnerabilities and commercialize malicious software to exploit those vulnerabilities.
These platforms offer security practitioners a threat intelligence environment that allows to mine for patterns related to organization-targeted cyber attacks.
In this paper, we describe a system (called DARKMENTION) that learns association rules correlating indicators of attacks from D2web to real-world cyber incidents.
Using the learned rules, DARKMENTION generates and submits warnings to a Security Operations Center (SOC) prior to attacks.
Our goal was to design a system that automatically generates enterprise-targeted warnings that are timely, actionable, accurate, and transparent.
We show that DARKMENTION meets our goal.
In particular, we show that it outperforms baseline systems that attempt to generate warnings of cyber attacks related to two enterprises with an average increase in F1 score of about 45% and 57%.
Additionally, DARKMENTION was deployed as part of a larger system that is built under a contract with the IARPA Cyber-attack Automated Unconventional Sensor Environment (CAUSE) program.
It is actively producing warnings that precede attacks by an average of 3 days.
How many links can be cut before a network is bisected?
What is the maximal bandwidth that can be pushed between two nodes of a network?
These questions are closely related to network resilience, path choice for multipath routing or bisection bandwidth estimations in data centers.
The answer is quantified using metrics such as the number of edge-disjoint paths between two network nodes and the cumulative bandwidth that can flow over these paths.
In practice though, such calculations are far from simple due to the restrictive effect of network policies on path selection.
Policies are set by network administrators to conform to service level agreements, protect valuable resources or optimize network performance.
In this work, we introduce a general methodology for estimating lower and upper bounds for the policy-compliant path diversity and bisection bandwidth between two nodes of a network, effectively quantifying the effect of policies on these metrics.
Exact values can be obtained if certain conditions hold.
The approach is based on regular languages and can be applied in a variety of use cases.
Shannon entropy was defined for probability distributions and then its using was expanded to measure the uncertainty of knowledge for systems with complete information.
In this article, it is proposed to extend the using of Shannon entropy to under-defined or over-defined information systems.
To be able to use Shannon entropy, the information is normalized by an affine transformation.
The construction of affine transformation is done in two stages: one for homothety and another for translation.
Moreover, the case of information with a certain degree of imprecision was included in this approach.
Besides, the article shows the using of Shannon entropy for some particular cases such as: neutrosophic information both in the trivalent and bivalent case, bifuzzy information, intuitionistic fuzzy information, imprecise fuzzy information, and fuzzy partitions.
The increasing use of online channels for service delivery raises new challenges in service failure prevention.
This work-in-progress paper reports on the first phase of an action-design research project to develop a service failure prevention methodology.
In this paper we review the literature on online services, failure prevention and failure recovery and develop a theoretical framework for online service failure prevention.
This provides the theoretical grounding for the artefact (the methodology) to be developed.
We use this framework to develop an initial draft of our methodology.
We then outline the remaining phases of the research, and offer some initial conclusions gained from the project to date.
Recent work has shown that deep reinforcement-learning agents can learn to follow language-like instructions from infrequent environment rewards.
However, this places on environment designers the onus of designing language-conditional reward functions which may not be easily or tractably implemented as the complexity of the environment and the language scales.
To overcome this limitation, we present a framework within which instruction-conditional RL agents are trained using rewards obtained not from the environment, but from reward models which are jointly trained from expert examples.
As reward models improve, they learn to accurately reward agents for completing tasks for environment configurations---and for instructions---not present amongst the expert data.
This framework effectively separates the representation of what instructions require from how they can be executed.
In a simple grid world, it enables an agent to learn a range of commands requiring interaction with blocks and understanding of spatial relations and underspecified abstract arrangements.
We further show the method allows our agent to adapt to changes in the environment without requiring new expert examples.
We analyzed the relation between surgical service providers' network structure and surgical team size with patient outcome during the operation.
We did correlation analysis to evaluate the associations among the network structure measures in the intra-operative networks of surgical service providers.
We focused on intra-operative networks of surgical service providers, in a quaternary-care academic medical center, using retrospective Electronic Medical Record (EMR) data.
We used de-identified intra-operative data for adult patients who received nonambulatory/nonobstetric surgery in a main operating room at Shands at the University of Florida between June 1, 2011 and November 1, 2014.
The intra-operative dataset contained 30,211 unique surgical cases.
To perform the analysis, we created the networks of surgical service providers and calculated several network structure measures at both team and individual levels.
We considered number of patients' complications as the target variable and assessed its interrelations with the calculated network measures along with other influencing factors (e.g. surgical team size, type of surgery).
Our results confirm the significant role of interactions among surgical providers on patient outcome.
In addition, we observed that highly central providers at the global network level are more likely to be associated with a lower number of surgical complications, while locally important providers might be associated with higher number of complications.
We also found a positive relation between age of patients and number of complications.
In many practical cases, the engineer has access to prior knowledge like rough values of the DC-gain or the main time constant of the system.
In order to improve the accuracy of subspace-based identification techniques using the model Markov parameters, we derive in this short paper the direct links between these impulse response coefficients and this prior information.
The next step will consist in introducing this prior knowledge explicitly in Kung's algorithm thank to dedicated equality and equality constraints.
Virtual machine is built on group of real servers which are scattered globally and connect together through the telecommunications systems, it has an increasingly important role in the operation, providing the ability to exploit virtual resources.
The latest technique helps to use computing resources more effectively and has many benefits, such as cost reduction of power, cooling and, hence, contributes to the Green Computing.
To ensure the supply of these resources to demand processes correctly and promptly, avoiding any duplication or conflict, especially remote resources, it is necessary to study and propose a reliable solution appropriate to be the foundation for internal control systems in the cloud.
In the scope of this paper, we find a way to produce efficient distributed resources which emphasizes solutions preventing deadlock and proposing methods to avoid resource shortage issue.
With this approach, the outcome result is the checklist of re-sources state which has the possibility of deadlock and lack of resources, by sending messages to the servers, the server would know the situation and have corresponding reaction.
A novel semantic approach to data selection and compression is presented for the dynamic adaptation of IoT data processing and transmission within "wireless islands", where a set of sensing devices (sensors) are interconnected through one-hop wireless links to a computational resource via a local access point.
The core of the proposed technique is a cooperative framework where local classifiers at the mobile nodes are dynamically crafted and updated based on the current state of the observed system, the global processing objective and the characteristics of the sensors and data streams.
The edge processor plays a key role by establishing a link between content and operations within the distributed system.
The local classifiers are designed to filter the data streams and provide only the needed information to the global classifier at the edge processor, thus minimizing bandwidth usage.
However, the better the accuracy of these local classifiers, the larger the energy necessary to run them at the individual sensors.
A formulation of the optimization problem for the dynamic construction of the classifiers under bandwidth and energy constraints is proposed and demonstrated on a synthetic example.
The training complexity of deep learning-based channel decoders scales exponentially with the codebook size and therefore with the number of information bits.
Thus, neural network decoding (NND) is currently only feasible for very short block lengths.
In this work, we show that the conventional iterative decoding algorithm for polar codes can be enhanced when sub-blocks of the decoder are replaced by neural network (NN) based components.
Thus, we partition the encoding graph into smaller sub-blocks and train them individually, closely approaching maximum a posteriori (MAP) performance per sub-block.
These blocks are then connected via the remaining conventional belief propagation decoding stage(s).
The resulting decoding algorithm is non-iterative and inherently enables a high-level of parallelization, while showing a competitive bit error rate (BER) performance.
We examine the degradation through partitioning and compare the resulting decoder to state-of-the-art polar decoders such as successive cancellation list and belief propagation decoding.
Handwriting of Chinese has long been an important skill in East Asia.
However, automatic generation of handwritten Chinese characters poses a great challenge due to the large number of characters.
Various machine learning techniques have been used to recognize Chinese characters, but few works have studied the handwritten Chinese character generation problem, especially with unpaired training data.
In this work, we formulate the Chinese handwritten character generation as a problem that learns a mapping from an existing printed font to a personalized handwritten style.
We further propose DenseNet CycleGAN to generate Chinese handwritten characters.
Our method is applied not only to commonly used Chinese characters but also to calligraphy work with aesthetic values.
Furthermore, we propose content accuracy and style discrepancy as the evaluation metrics to assess the quality of the handwritten characters generated.
We then use our proposed metrics to evaluate the generated characters from CASIA dataset as well as our newly introduced Lanting calligraphy dataset.
Single document summarization is the task of producing a shorter version of a document while preserving its principal information content.
In this paper we conceptualize extractive summarization as a sentence ranking task and propose a novel training algorithm which globally optimizes the ROUGE evaluation metric through a reinforcement learning objective.
We use our algorithm to train a neural summarization model on the CNN and DailyMail datasets and demonstrate experimentally that it outperforms state-of-the-art extractive and abstractive systems when evaluated automatically and by humans.
Computer aided diagnostic (CAD) system is crucial for modern med-ical imaging.
But almost all CAD systems operate on reconstructed images, which were optimized for radiologists.
Computer vision can capture features that is subtle to human observers, so it is desirable to design a CAD system op-erating on the raw data.
In this paper, we proposed a deep-neural-network-based detection system for lung nodule detection in computed tomography (CT).
A primal-dual-type deep reconstruction network was applied first to convert the raw data to the image space, followed by a 3-dimensional convolutional neural network (3D-CNN) for the nodule detection.
For efficient network training, the deep reconstruction network and the CNN detector was trained sequentially first, then followed by one epoch of end-to-end fine tuning.
The method was evaluated on the Lung Image Database Consortium image collection (LIDC-IDRI) with simulated forward projections.
With 144 multi-slice fanbeam pro-jections, the proposed end-to-end detector could achieve comparable sensitivity with the reference detector, which was trained and applied on the fully-sampled image data.
It also demonstrated superior detection performance compared to detectors trained on the reconstructed images.
The proposed method is general and could be expanded to most detection tasks in medical imaging.
Deep learning has seen tremendous success over the past decade in computer vision, machine translation, and gameplay.
This success rests in crucial ways on gradient-descent optimization and the ability to learn parameters of a neural network by backpropagating observed errors.
However, neural network architectures are growing increasingly sophisticated and diverse, which motivates an emerging quest for even more general forms of differentiable programming, where arbitrary parameterized computations can be trained by gradient descent.
In this paper, we take a fresh look at automatic differentiation (AD) techniques, and especially aim to demystify the reverse-mode form of AD that generalizes backpropagation in neural networks.
We uncover a tight connection between reverse-mode AD and delimited continuations, which permits implementing reverse-mode AD purely via operator overloading and without any auxiliary data structures.
We further show how this formulation of AD can be fruitfully combined with multi-stage programming (staging), leading to a highly efficient implementation that combines the performance benefits of deep learning frameworks based on explicit reified computation graphs (e.g., TensorFlow) with the expressiveness of pure library approaches (e.g., PyTorch).
We briefly report on a successful linear program reconstruction attack performed on a production statistical queries system and using a real dataset.
The attack was deployed in test environment in the course of the Aircloak Challenge bug bounty program and is based on the reconstruction algorithm of Dwork, McSherry, and Talwar.
We empirically evaluate the effectiveness of the algorithm and a related algorithm by Dinur and Nissim with various dataset sizes, error rates, and numbers of queries in a Gaussian noise setting.
This paper deals with the issue of the perceptual quality evaluation of user-generated videos shared online, which is an important step toward designing video-sharing services that maximize users' satisfaction in terms of quality.
We first analyze viewers' quality perception patterns by applying graph analysis techniques to subjective rating data.
We then examine the performance of existing state-of-the-art objective metrics for the quality estimation of user-generated videos.
In addition, we investigate the feasibility of metadata accompanied with videos in online video-sharing services for quality estimation.
Finally, various issues in the quality assessment of online user-generated videos are discussed, including difficulties and opportunities.
Disentangled distributed representations of data are desirable for machine learning, since they are more expressive and can generalize from fewer examples.
However, for complex data, the distributed representations of multiple objects present in the same input can interfere and lead to ambiguities, which is commonly referred to as the binding problem.
We argue for the importance of the binding problem to the field of representation learning, and develop a probabilistic framework that explicitly models inputs as a composition of multiple objects.
We propose an unsupervised algorithm that uses denoising autoencoders to dynamically bind features together in multi-object inputs through an Expectation-Maximization-like clustering process.
The effectiveness of this method is demonstrated on artificially generated datasets of binary images, showing that it can even generalize to bind together new objects never seen by the autoencoder during training.
Information processing has reached the era of big data.
Big data challenges are difficult to address with traditional Von Neumann or Turing approach.
Hence implementation of new computational techniques is highly essential.
Nanophotonics with its remarkable speed and multiplexing capability is a promising candidate for such implementations.
This paper proposes a novel photonic computing system made-up of Mach-Zehnder interferometer and an optical fiber spool to emulate a powerful machine learning technique called reservoir computing.
The proposed system is also integrated with a time-division-multiplexing circuit to facilitate parallel computation of multiple tasks which is first of its kind.
The proposed design performs large-scale tasks like spoken digit recognition, channel equalization, and time-series prediction.
Experimental results with standard photonic simulator demonstrate significant performance in terms of speed and accuracy compared to state of the art digital and software implementations.
Actor-critic methods can achieve incredible performance on difficult reinforcement learning problems, but they are also prone to instability.
This is partly due to the interaction between the actor and critic during learning, e.g., an inaccurate step taken by one of them might adversely affect the other and destabilize the learning.
To avoid such issues, we propose to regularize the learning objective of the actor by penalizing the temporal difference (TD) error of the critic.
This improves stability by avoiding large steps in the actor update whenever the critic is highly inaccurate.
The resulting method, which we call the TD-regularized actor-critic method, is a simple plug-and-play approach to improve stability and overall performance of the actor-critic methods.
Evaluations on standard benchmarks confirm this.
Ezhil is a Tamil language based interpreted procedural programming language.
Tamil keywords and grammar are chosen to make the native Tamil speaker write programs in the Ezhil system.
Ezhil allows easy representation of computer program closer to the Tamil language logical constructs equivalent to the conditional, branch and loop statements in modern English based programming languages.
Ezhil is a compact programming language aimed towards Tamil speaking novice computer users.
Grammar for Ezhil and a few example programs are reported here, from the initial proof-of-concept implementation using the Python programming language1.
To the best of our knowledge, Ezhil language is the first freely available Tamil programming language.
Recent hardware developments have made unprecedented amounts of data parallelism available for accelerating neural network training.
Among the simplest ways to harness next-generation accelerators is to increase the batch size in standard mini-batch neural network training algorithms.
In this work, we aim to experimentally characterize the effects of increasing the batch size on training time, as measured in the number of steps necessary to reach a goal out-of-sample error.
Eventually, increasing the batch size will no longer reduce the number of training steps required, but the exact relationship between the batch size and how many training steps are necessary is of critical importance to practitioners, researchers, and hardware designers alike.
We study how this relationship varies with the training algorithm, model, and data set and find extremely large variation between workloads.
Along the way, we reconcile disagreements in the literature on whether batch size affects model quality.
Finally, we discuss the implications of our results for efforts to train neural networks much faster in the future.
In this article we show how power transformations can be used as a common framework for the derivation of local term weights.
We found that under some parametric conditions, BM25 and inverse regression produce equivalent results.
As a special case of inverse regression, we show that the largest increment in term weight occurs when a term is mentioned for the second time.
A model based on inverse regression (BM25IR) is presented.
Simulations suggest that BM25IR works fairly well for different BM25 parametric conditions and document lengths.
The identity of a user is permanently lost if biometric data gets compromised since the biometric information is irreplaceable and irrevocable.
To revoke and reissue a new template in place of the compromised biometric template, the idea of cancelable biometrics has been introduced.
The concept behind cancelable biometric is to irreversibly transform the original biometric template and perform the comparison in the protected domain.
In this paper, a coprime transformation scheme has been proposed to derive a protected fingerprint template.
The method divides the fingerprint region into a number of sectors with respect to each minutiae point and identifies the nearest-neighbor minutiae in each sector.
Then, ridge features for all neighboring minutiae points are computed and mapped onto co-prime positions of a random matrix to generate the cancelable template.
The proposed approach achieves an EER of 1.82, 1.39, 4.02 and 5.77 on DB1, DB2, DB3 and DB4 datasets of the FVC2002 and an EER of 8.70, 7.95, 5.23 and 4.87 on DB1, DB2, DB3 and DB4 datasets of FVC2004 databases, respectively.
Experimental evaluations indicate that the method outperforms in comparison to the current state-of-the-art.
Moreover, it has been confirmed from the security analysis that the proposed method fulfills the desired characteristics of diversity, revocability, and non-invertibility with a minor performance degradation caused by the transformation.
Cloze-style reading comprehension has been a popular task for measuring the progress of natural language understanding in recent years.
In this paper, we design a novel multi-perspective framework, which can be seen as the joint training of heterogeneous experts and aggregate context information from different perspectives.
Each perspective is modeled by a simple aggregation module.
The outputs of multiple aggregation modules are fed into a one-timestep pointer network to get the final answer.
At the same time, to tackle the problem of insufficient labeled data, we propose an efficient sampling mechanism to automatically generate more training examples by matching the distribution of candidates between labeled and unlabeled data.
We conduct our experiments on a recently released cloze-test dataset CLOTH (Xie et al., 2017), which consists of nearly 100k questions designed by professional teachers.
Results show that our method achieves new state-of-the-art performance over previous strong baselines.
In this paper, a contrastive evaluation of massively parallel implementations of suffix tree and suffix array to accelerate genome sequence matching are proposed based on Intel Core i7 3770K quad-core and NVIDIA GeForce GTX680 GPU.
Besides suffix array only held approximately 20%~30% of the space relative to suffix tree, the coalesced binary search and tile optimization make suffix array clearly outperform suffix tree using GPU.
Consequently, the experimental results show that multiple genome sequence matching based on suffix array is more than 99 times speedup than that of CPU serial implementation.
There is no doubt that massively parallel matching algorithm based on suffix array is an efficient approach to high-performance bioinformatics applications.
Facility location queries identify the best locations to set up new facilities for providing service to its users.
Majority of the existing works in this space assume that the user locations are static.
Such limitations are too restrictive for planning many modern real-life services such as fuel stations, ATMs, convenience stores, cellphone base-stations, etc. that are widely accessed by mobile users.
The placement of such services should, therefore, factor in the mobility patterns or trajectories of the users rather than simply their static locations.
In this work, we introduce the TOPS (Trajectory-Aware Optimal Placement of Services) query that locates the best k sites on a road network.
The aim is to optimize a wide class of objective functions defined over the user trajectories.
We show that the problem is NP-hard and even the greedy heuristic with an approximation bound of (1-1/e) fails to scale on urban-scale datasets.
To overcome this challenge, we develop a multi-resolution clustering based indexing framework called NetClus.
Empirical studies on real road network trajectory datasets show that NetClus offers solutions that are comparable in terms of quality with those of the greedy heuristic, while having practical response times and low memory footprints.
Additionally, the NetClus framework can absorb dynamic updates in mobility patterns, handle constraints such as site-costs and capacity, and existing services, thereby providing an effective solution for modern urban-scale scenarios.
Content-Centric Networking (CCN) is an internetworking paradigm that offers an alternative to today's IP-based Internet Architecture.
Instead of focusing on hosts and their locations, CCN emphasizes addressable named content.
By decoupling content from its location, CCN allows opportunistic in-network content caching, thus enabling better network utilization, at least for scalable content distribution.
However, in order to be considered seriously, CCN must support basic security services, including content authenticity, integrity, confidentiality, authorization and access control.
Current approaches rely on content producers to perform authorization and access control.
This general approach has several disadvantages.
First, consumer privacy vis-a-vis producers is not preserved.
Second, identity management and access control impose high computational overhead on producers.
Also, unnecessary repeated authentication and access control decisions must be made for each content request.
These issues motivate our design of KRB-CCN - a complete authorization and access control system for private CCNs.
Inspired by Kerberos in IP-based networks, KRB-CCN involves distinct authentication and authorization authorities.
By doing so, KRB-CCN obviates the need for producers to make consumer authentication and access control decisions.
KRB-CCN preserves consumer privacy since producers are unaware of consumer identities.
Producers are also not required to keep any hard state and only need to perform two symmetric key operations to guarantee that sensitive content is confidentially delivered only to authenticated and authorized consumers.
Most importantly, unlike prior designs, KRB-CCN leaves the network (i.e., CCN routers) out of any authorization, access control or confidentiality issues.
We describe KRB-CCN design and implementation, analyze its security, and report on its performance.
In order to extract the best possible performance from asynchronous stochastic gradient descent one must increase the mini-batch size and scale the learning rate accordingly.
In order to achieve further speedup we introduce a technique that delays gradient updates effectively increasing the mini-batch size.
Unfortunately with the increase of mini-batch size we worsen the stale gradient problem in asynchronous stochastic gradient descent (SGD) which makes the model convergence poor.
We introduce local optimizers which mitigate the stale gradient problem and together with fine tuning our momentum we are able to train a shallow machine translation system 27% faster than an optimized baseline with negligible penalty in BLEU.
Entity Linking aims to link entity mentions in texts to knowledge bases, and neural models have achieved recent success in this task.
However, most existing methods rely on local contexts to resolve entities independently, which may usually fail due to the data sparsity of local information.
To address this issue, we propose a novel neural model for collective entity linking, named as NCEL.
NCEL applies Graph Convolutional Network to integrate both local contextual features and global coherence information for entity linking.
To improve the computation efficiency, we approximately perform graph convolution on a subgraph of adjacent entity mentions instead of those in the entire text.
We further introduce an attention scheme to improve the robustness of NCEL to data noise and train the model on Wikipedia hyperlinks to avoid overfitting and domain bias.
In experiments, we evaluate NCEL on five publicly available datasets to verify the linking performance as well as generalization ability.
We also conduct an extensive analysis of time complexity, the impact of key modules, and qualitative results, which demonstrate the effectiveness and efficiency of our proposed method.
In this paper, we propose a fully convolutional network for 3D human pose estimation from monocular images.
We use limb orientations as a new way to represent 3D poses and bind the orientation together with the bounding box of each limb region to better associate images and predictions.
The 3D orientations are modeled jointly with 2D keypoint detections.
Without additional constraints, this simple method can achieve good results on several large-scale benchmarks.
Further experiments show that our method can generalize well to novel scenes and is robust to inaccurate bounding boxes.
This paper first describes an `obfuscating' compiler technology developed for encrypted computing, then examines if the trivial case without encryption produces much-sought indistinguishability obfuscation.
Learning to rank has recently emerged as an attractive technique to train deep convolutional neural networks for various computer vision tasks.
Pairwise ranking, in particular, has been successful in multi-label image classification, achieving state-of-the-art results on various benchmarks.
However, most existing approaches use the hinge loss to train their models, which is non-smooth and thus is difficult to optimize especially with deep networks.
Furthermore, they employ simple heuristics, such as top-k or thresholding, to determine which labels to include in the output from a ranked list of labels, which limits their use in the real-world setting.
In this work, we propose two techniques to improve pairwise ranking based multi-label image classification: (1) we propose a novel loss function for pairwise ranking, which is smooth everywhere and thus is easier to optimize; and (2) we incorporate a label decision module into the model, estimating the optimal confidence thresholds for each visual concept.
We provide theoretical analyses of our loss function in the Bayes consistency and risk minimization framework, and show its benefit over existing pairwise ranking formulations.
We demonstrate the effectiveness of our approach on three large-scale datasets, VOC2007, NUS-WIDE and MS-COCO, achieving the best reported results in the literature.
This paper deals with the problem of enforcing modular diagnosability for discrete-event systems that don't satisfy this property by their natural modularity.
We introduce an approach to achieve this property combining existing modules into new virtual modules.
An underlining mathematical problem is to find a partition of a set, such that the partition satisfies the required property.
The time complexity of such problem is very high.
To overcome it, the paper introduces a structural analysis of the system's modules.
In the analysis we focus on the case when the modules participate in diagnosis with their observations, rather then the case when indistinguishable observations are blocked due to concurrency.
We report findings related to a two dimensional viscous fingering problem solved with a timespace method and anisotropic elements.
Timespace methods have attracted interest for solution of time dependent partial differential equations due to the implications of parallelism in the temporal dimension, but there are also attractive features in the context of anisotropic mesh adaptation; not only are heuristics and interpolation errors avoided, but slanted elements in timespace also correspond to long and accurate timesteps, i.e. the anisotropy in timespace can be exploited.
We show that our timespace method is restricted by a minimum timestep size, which is due to the growth of numerical perturbations.
The lower bound on the timestep is, however, quite high, which is indicative that the number of timesteps can be reduced with several orders of magnitude for practical applications.
Android, the #1 mobile app framework, enforces the single-GUI-thread model, in which a single UI thread manages GUI rendering and event dispatching.
Due to this model, it is vital to avoid blocking the UI thread for responsiveness.
One common practice is to offload long-running tasks into async threads.
To achieve this, Android provides various async programming constructs, and leaves developers themselves to obey the rules implied by the model.
However, as our study reveals, more than 25% apps violate these rules and introduce hard-to-detect, fail-stop errors, which we term as aysnc programming errors (APEs).
To this end, this paper introduces APEChecker, a technique to automatically and efficiently manifest APEs.
The key idea is to characterize APEs as specific fault patterns, and synergistically combine static analysis and dynamic UI exploration to detect and verify such errors.
Among the 40 real-world Android apps, APEChecker unveils and processes 61 APEs, of which 51 are confirmed (83.6% hit rate).
Specifically, APEChecker detects 3X more APEs than the state-of-art testing tools (Monkey, Sapienz and Stoat), and reduces testing time from half an hour to a few minutes.
On a specific type of APEs, APEChecker confirms 5X more errors than the data race detection tool, EventRacer, with very few false alarms.
The selection of the best classification algorithm for a given dataset is a very widespread problem.
It is also a complex one, in the sense it requires to make several important methodological choices.
Among them, in this work we focus on the measure used to assess the classification performance and rank the algorithms.
We present the most popular measures and discuss their properties.
Despite the numerous measures proposed over the years, many of them turn out to be equivalent in this specific case, to have interpretation problems, or to be unsuitable for our purpose.
Consequently, classic overall success rate or marginal rates should be preferred for this specific task.
Federated Learning is a machine learning setting where the goal is to train a high-quality centralized model while training data remains distributed over a large number of clients each with unreliable and relatively slow network connections.
We consider learning algorithms for this setting where on each round, each client independently computes an update to the current model based on its local data, and communicates this update to a central server, where the client-side updates are aggregated to compute a new global model.
The typical clients in this setting are mobile phones, and communication efficiency is of the utmost importance.
In this paper, we propose two ways to reduce the uplink communication costs: structured updates, where we directly learn an update from a restricted space parametrized using a smaller number of variables, e.g. either low-rank or a random mask; and sketched updates, where we learn a full model update and then compress it using a combination of quantization, random rotations, and subsampling before sending it to the server.
Experiments on both convolutional and recurrent networks show that the proposed methods can reduce the communication cost by two orders of magnitude.
Graphs are an essential data structure that can represent the structure of social networks.
Many online companies, in order to provide intelligent and personalized services for their users, aim to comprehensively analyze a significant amount of graph data with different features.
One example is k-core decomposition which captures the degree of connectedness in social graphs.
The main purpose of this report is to explore a distributed algorithm for k-core decomposition on Apache Giraph.
Namely, we would like to determine whether a cluster-based, Giraph implementation of k-core decomposition that we provide is more efficient than a single-machine, disk-based implementation on GraphChi for large networks.
In this report, we describe (a) the programming model of Giraph and GraphChi, (b) the specific implementation of k-core decomposition with Giraph, and (c) the result comparison between Giraph and GraphChi.
By analyzing the results, we conclude that Giraph is faster than GraphChi when dealing with large data.
However, since worker nodes need time to communicate with each other, Giraph is not very efficient for small data.
In an uncoordinated network, the link performance between the devices might degrade significantly due to the interference from other links in the network sharing the same spectrum.
As a solution, in this study, the concept of partially overlapping tones (POT) is introduced.
The interference energy observed at the victim receiver is mitigated by partially overlapping the individual subcarriers via an intentional carrier frequency offset between the links.
Also, it is shown that while orthogonal transformations at the receiver cannot mitigate the other-user interference without losing spectral efficiency, non-orthogonal transformations are able to mitigate the other-user interference without any spectral efficiency loss at the expense of self-interference.
Using spatial Poisson point process, a tractable bit error rate analysis is provided to demonstrate potential benefits emerging from POT.
We study the problem of conditional generative modeling based on designated semantics or structures.
Existing models that build conditional generators either require massive labeled instances as supervision or are unable to accurately control the semantics of generated samples.
We propose structured generative adversarial networks (SGANs) for semi-supervised conditional generative modeling.
SGAN assumes the data x is generated conditioned on two independent latent variables: y that encodes the designated semantics, and z that contains other factors of variation.
To ensure disentangled semantics in y and z, SGAN builds two collaborative games in the hidden space to minimize the reconstruction error of y and z, respectively.
Training SGAN also involves solving two adversarial games that have their equilibrium concentrating at the true joint data distributions p(x, z) and p(x, y), avoiding distributing the probability mass diffusely over data space that MLE-based methods may suffer.
We assess SGAN by evaluating its trained networks, and its performance on downstream tasks.
We show that SGAN delivers a highly controllable generator, and disentangled representations; it also establishes start-of-the-art results across multiple datasets when applied for semi-supervised image classification (1.27%, 5.73%, 17.26% error rates on MNIST, SVHN and CIFAR-10 using 50, 1000 and 4000 labels, respectively).
Benefiting from the separate modeling of y and z, SGAN can generate images with high visual quality and strictly following the designated semantic, and can be extended to a wide spectrum of applications, such as style transfer.
The growing popularity of location-based systems, allowing unknown/untrusted servers to easily collect huge amounts of information regarding users' location, has recently started raising serious privacy concerns.
In this paper we study geo-indistinguishability, a formal notion of privacy for location-based systems that protects the user's exact location, while allowing approximate information - typically needed to obtain a certain desired service - to be released.
Our privacy definition formalizes the intuitive notion of protecting the user's location within a radius r with a level of privacy that depends on r, and corresponds to a generalized version of the well-known concept of differential privacy.
Furthermore, we present a perturbation technique for achieving geo-indistinguishability by adding controlled random noise to the user's location.
We demonstrate the applicability of our technique on a LBS application.
Finally, we compare our mechanism with other ones in the literature.
It turns our that our mechanism offers the best privacy guarantees, for the same utility, among all those which do not depend on the prior.
Adversarial examples are known to have a negative effect on the performance of classifiers which have otherwise good performance on undisturbed images.
These examples are generated by adding non-random noise to the testing samples in order to make classifier misclassify the given data.
Adversarial attacks use these intentionally generated examples and they pose a security risk to the machine learning based systems.
To be immune to such attacks, it is desirable to have a pre-processing mechanism which removes these effects causing misclassification while keeping the content of the image.
JPEG and JPEG2000 are well-known image compression techniques which suppress the high-frequency content taking the human visual system into account.
JPEG has been also shown to be an effective method for reducing adversarial noise.
In this paper, we propose applying JPEG2000 compression as an alternative and systematically compare the classification performance of adversarial images compressed using JPEG and JPEG2000 at different target PSNR values and maximum compression levels.
Our experiments show that JPEG2000 is more effective in reducing adversarial noise as it allows higher compression rates with less distortion and it does not introduce blocking artifacts.
Clifford algebras have broad applications in science and engineering.
The use of Clifford algebras can be further promoted in these fields by availability of computational tools that automate tedious routine calculations.
We offer an extensive demonstration of the applications of Clifford algebras in electromagnetism using the geometric algebra G3 = Cl(3,0) as a computational model in the Maxima computer algebra system.
We compare the geometric algebra-based approach with conventional symbolic tensor calculations supported by Maxima, based on the itensor package.
The Clifford algebra functionality of Maxima is distributed as two new packages called clifford - for basic simplification of Clifford products, outer products, scalar products and inverses; and cliffordan - for applications of geometric calculus.
Photography usually requires optics in conjunction with a recording device (an image sensor).
Eliminating the optics could lead to new form factors for cameras.
Here, we report a simple demonstration of imaging using a bare CMOS sensor that utilizes computation.
The technique relies on the space variant point-spread functions resulting from the interaction of a point source in the field of view with the image sensor.
These space-variant point-spread functions are combined with a reconstruction algorithm in order to image simple objects displayed on a discrete LED array as well as on an LCD screen.
We extended the approach to video imaging at the native frame rate of the sensor.
Finally, we performed experiments to analyze the parametric impact of the object distance.
Improving the sensor designs and reconstruction algorithms can lead to useful cameras without optics.
In many computer vision applications, obtaining images of high resolution in both the spatial and spectral domains are equally important.
However, due to hardware limitations, one can only expect to acquire images of high resolution in either the spatial or spectral domains.
This paper focuses on hyperspectral image super-resolution (HSI-SR), where a hyperspectral image (HSI) with low spatial resolution (LR) but high spectral resolution is fused with a multispectral image (MSI) with high spatial resolution (HR) but low spectral resolution to obtain HR HSI.
Existing deep learning-based solutions are all supervised that would need a large training set and the availability of HR HSI, which is unrealistic.
Here, we make the first attempt to solving the HSI-SR problem using an unsupervised encoder-decoder architecture that carries the following uniquenesses.
First, it is composed of two encoder-decoder networks, coupled through a shared decoder, in order to preserve the rich spectral information from the HSI network.
Second, the network encourages the representations from both modalities to follow a sparse Dirichlet distribution which naturally incorporates the two physical constraints of HSI and MSI.
Third, the angular difference between representations are minimized in order to reduce the spectral distortion.
We refer to the proposed architecture as unsupervised Sparse Dirichlet-Net, or uSDN.
Extensive experimental results demonstrate the superior performance of uSDN as compared to the state-of-the-art.
When simulating trajectories by integrating time-continuous car-following models, standard integration schemes such as the forth-order Runge-Kutta method (RK4) are rarely used while the simple Euler's method is popular among researchers.
We compare four explicit methods: Euler's method, ballistic update, Heun's method (trapezoidal rule), and the standard forth-order RK4.
As performance metrics, we plot the global discretization error as a function of the numerical complexity.
We tested the methods on several time-continuous car-following models in several multi-vehicle simulation scenarios with and without discontinuities such as stops or a discontinuous behavior of an external leader.
We find that the theoretical advantage of RK4 (consistency order~4) only plays a role if both the acceleration function of the model and the external data of the simulation scenario are sufficiently often differentiable.
Otherwise, we obtain lower (and often fractional) consistency orders.
Although, to our knowledge, Heun's method has never been used for integrating car-following models, it turns out to be the best scheme for many practical situations.
The ballistic update always prevails Euler's method although both are of first order.
The agent program, called Samu, is an experiment to build a disembodied DevRob (Developmental Robotics) chatter bot that can talk in a natural language like humans do.
One of the main design feature is that Samu can be interacted with using only a character terminal.
This is important not only for practical aspects of Turing test or Loebner prize, but also for the study of basic principles of Developmental Robotics.
Our purpose is to create a rapid prototype of Q-learning with neural network approximators for Samu.
We sketch out the early stages of the development process of this prototype, where Samu's task is to predict the next sentence of tales or conversations.
The basic objective of this paper is to reach the same results using reinforcement learning with general function approximators that can be achieved by using the classical Q lookup table on small input samples.
The paper is closed by an experiment that shows a significant improvement in Samu's learning when using LZW tree to narrow the number of possible Q-actions.
Nowadays, the need for system interoperability in or across enterprises has become more and more ubiquitous.
Lots of research works have been carried out in the information exchange, transformation, discovery and reuse.
One of the main challenges in these researches is to overcome the semantic heterogeneity between enterprise applications along the lifecycle of a product.
As a possible solution to assist the semantic interoperability, semantic annotation has gained more and more attentions and is widely used in different domains.
In this paper, based on the investigation of the context and the related works, we identify some existing drawbacks and propose a formal semantic annotation approach to support the semantics enrichment of models in a PLM environment.
With an increasing number of web services, providing an end-to-end Quality of Service (QoS) guarantee in responding to user queries is becoming an important concern.
Multiple QoS parameters (e.g., response time, latency, throughput, reliability, availability, success rate) are associated with a service, thereby, service composition with a large number of candidate services is a challenging multi-objective optimization problem.
In this paper, we study the multi-constrained multi-objective QoS aware web service composition problem and propose three different approaches to solve the same, one optimal, based on Pareto front construction and two other based on heuristically traversing the solution space.
We compare the performance of the heuristics against the optimal, and show the effectiveness of our proposals over other classical approaches for the same problem setting, with experiments on WSC-2009 and ICEBE-2005 datasets.
We present a novel proof-of-concept attack named Trojan of Things (ToT), which aims to attack NFC- enabled mobile devices such as smartphones.
The key idea of ToT attacks is to covertly embed maliciously programmed NFC tags into common objects routinely encountered in daily life such as banknotes, clothing, or furniture, which are not considered as NFC touchpoints.
To fully explore the threat of ToT, we develop two striking techniques named ToT device and Phantom touch generator.
These techniques enable an attacker to carry out various severe and sophisticated attacks unbeknownst to the device owner who unintentionally puts the device close to a ToT.
We discuss the feasibility of the attack as well as the possible countermeasures against the threats of ToT attacks.
In this work, we present a Multi-Channel deep convolutional Pyramid Person Matching Network (MC-PPMN) based on the combination of the semantic-components and the color-texture distributions to address the problem of person re-identification.
In particular, we learn separate deep representations for semantic-components and color-texture distributions from two person images and then employ pyramid person matching network (PPMN) to obtain correspondence representations.
These correspondence representations are fused to perform the re-identification task.
Further, the proposed framework is optimized via a unified end-to-end deep learning scheme.
Extensive experiments on several benchmark datasets demonstrate the effectiveness of our approach against the state-of-the-art literature, especially on the rank-1 recognition rate.
Steganography is the technique of hiding confidential information within any media.
Steganography is often confused with cryptography because the two are similar in the way that they both are used to protect confidential information.
The difference between the two is in the appearance in the processed output; the output of steganography operation is not apparently visible but in cryptography the output is scrambled so that it can draw attention.
Steganlysis is process to detect of presence of steganography.
In this article we have tried to elucidate the different approaches towards implementation of steganography using 'multimedia' file (text, static image, audio and video) and Network IP datagram as cover.
Also some methods of steganalysis will be discussed.
This paper describes an alignment-based model for interpreting natural language instructions in context.
We approach instruction following as a search over plans, scoring sequences of actions conditioned on structured observations of text and the environment.
By explicitly modeling both the low-level compositional structure of individual actions and the high-level structure of full plans, we are able to learn both grounded representations of sentence meaning and pragmatic constraints on interpretation.
To demonstrate the model's flexibility, we apply it to a diverse set of benchmark tasks.
On every task, we outperform strong task-specific baselines, and achieve several new state-of-the-art results.
In stream-based programming, data sources are abstracted as a stream of values that can be manipulated via callback functions.
Stream-based programming is exploding in popularity, as it provides a powerful and expressive paradigm for handling asynchronous data sources in interactive software.
However, high-level stream abstractions can also make it difficult for developers to reason about control- and data-flow relationships in their programs.
This is particularly impactful when asynchronous stream-based code interacts with thread-limited features such as UI frameworks that restrict UI access to a single thread, since the threading behavior of streaming constructs is often non-intuitive and insufficiently documented.
In this paper, we present a type-based approach that can statically prove the thread-safety of UI accesses in stream-based software.
Our key insight is that the fluent APIs of stream-processing frameworks enable the tracking of threads via type-refinement, making it possible to reason automatically about what thread a piece of code runs on -- a difficult problem in general.
We implement the system as an annotation-based Java typechecker for Android programs built upon the popular ReactiveX framework and evaluate its efficacy by annotating and analyzing 8 open-source apps, where we find 33 instances of unsafe UI access while incurring an annotation burden of only one annotation per 186 source lines of code.
We also report on our experience applying the typechecker to two much larger apps from the Uber Technologies Inc. codebase, where it currently runs on every code change and blocks changes that introduce potential threading bugs.
In the online packet buffering problem (also known as the unweighted FIFO variant of buffer management), we focus on a single network packet switching device with several input ports and one output port.
This device forwards unit-size, unit-value packets from input ports to the output port.
Buffers attached to input ports may accumulate incoming packets for later transmission; if they cannot accommodate all incoming packets, their excess is lost.
A packet buffering algorithm has to choose from which buffers to transmit packets in order to minimize the number of lost packets and thus maximize the throughput.
We present a tight lower bound of e/(e-1) ~ 1.582 on the competitive ratio of the throughput maximization, which holds even for fractional or randomized algorithms.
This improves the previously best known lower bound of 1.4659 and matches the performance of the algorithm Random Schedule.
Our result contradicts the claimed performance of the algorithm Random Permutation; we point out a flaw in its original analysis.
The increasing accuracy of automatic chord estimation systems, the availability of vast amounts of heterogeneous reference annotations, and insights from annotator subjectivity research make chord label personalization increasingly important.
Nevertheless, automatic chord estimation systems are historically exclusively trained and evaluated on a single reference annotation.
We introduce a first approach to automatic chord label personalization by modeling subjectivity through deep learning of a harmonic interval-based chord label representation.
After integrating these representations from multiple annotators, we can accurately personalize chord labels for individual annotators from a single model and the annotators' chord label vocabulary.
Furthermore, we show that chord personalization using multiple reference annotations outperforms using a single reference annotation.
Heterogeneous cellular networks (HCNs) usually exhibit spatial separation amongst base stations (BSs) of different types (termed tiers in this paper).
For instance, operators will usually not deploy a picocell in close proximity to a macrocell, thus inducing separation amongst the locations of pico and macrocells.
This separation has recently been captured by modeling the small cell locations by a Poisson Hole Process (PHP) with the hole centers being the locations of the macrocells.
Due to the presence of exclusion zones, the analysis of the resulting model is significantly more complex compared to the more popular Poisson Point Process (PPP) based models.
In this paper, we derive a tight bound on the distribution of the distance of a typical user to the closest point of a PHP.
Since the exact distribution of this distance is not known, it is often approximated in the literature.
For this model, we then provide tight characterization of the downlink coverage probability for a typical user in a two-tier closed-access HCN under two cases: (i) typical user is served by the closest macrocell, and (ii) typical user is served by its closest small cell.
The proposed approach can be extended to analyze other relevant cases of interest, e.g., coverage in a PHP-based open access HCN.
How can we analyze enormous networks including the Web and social networks which have hundreds of billions of nodes and edges?
Network analyses have been conducted by various graph mining methods including shortest path computation, PageRank, connected component computation, random walk with restart, etc.
These graph mining methods can be expressed as generalized matrix-vector multiplication which consists of few operations inspired by typical matrix-vector multiplication.
Recently, several graph processing systems based on matrix-vector multiplication or their own primitives have been proposed to deal with large graphs; however, they all have failed on Web-scale graphs due to insufficient memory space or the lack of consideration for I/O costs.
In this paper, we propose PMV (Pre-partitioned generalized Matrix-Vector multiplication), a scalable distributed graph mining method based on generalized matrix-vector multiplication on distributed systems.
PMV significantly decreases the communication cost, which is the main bottleneck of distributed systems, by partitioning the input graph in advance and judiciously applying execution strategies based on the density of the pre-partitioned sub-matrices.
Experiments show that PMV succeeds in processing up to 16x larger graphs than existing distributed memory-based graph mining methods, and requires 9x less time than previous disk-based graph mining methods by reducing I/O costs significantly.
Given a collection of strings, each with an associated probability of occurrence, the guesswork of each of them is their position in a list ordered from most likely to least likely, breaking ties arbitrarily.
Guesswork is central to several applications in information theory: Average guesswork provides a lower bound on the expected computational cost of a sequential decoder to decode successfully the transmitted message; the complementary cumulative distribution function of guesswork gives the error probability in list decoding; the logarithm of guesswork is the number of bits needed in optimal lossless one-to-one source coding; and guesswork is the number of trials required of an adversary to breach a password protected system in a brute-force attack.
In this paper, we consider memoryless string-sources that generate strings consisting of i.i.d. characters drawn from a finite alphabet, and characterize their corresponding guesswork.
Our main tool is the tilt operation.
We show that the tilt operation on a memoryless string-source parametrizes an exponential family of memoryless string-sources, which we refer to as the tilted family.
We provide an operational meaning to the tilted families by proving that two memoryless string-sources result in the same guesswork on all strings of all lengths if and only if their respective categorical distributions belong to the same tilted family.
Establishing some general properties of the tilt operation, we generalize the notions of weakly typical set and asymptotic equipartition property to tilted weakly typical sets of different orders.
We use this new definition to characterize the large deviations for all atypical strings and characterize the volume of weakly typical sets of different orders.
We subsequently build on this characterization to prove large deviation bounds on guesswork and provide an accurate approximation of its PMF.
For security and privacy management and enforcement purposes, various policy languages have been presented.
We give an overview on 27 security and privacy policy languages and present a categorization framework for policy languages.
We show how the current policy languages are represented in the framework and summarize our interpretation.
We show up identified gaps and motivate for the adoption of policy languages for the specification of privacy-utility trade-off policies.
Previous machine comprehension (MC) datasets are either too small to train end-to-end deep learning models, or not difficult enough to evaluate the ability of current MC techniques.
The newly released SQuAD dataset alleviates these limitations, and gives us a chance to develop more realistic MC models.
Based on this dataset, we propose a Multi-Perspective Context Matching (MPCM) model, which is an end-to-end system that directly predicts the answer beginning and ending points in a passage.
Our model first adjusts each word-embedding vector in the passage by multiplying a relevancy weight computed against the question.
Then, we encode the question and weighted passage by using bi-directional LSTMs.
For each point in the passage, our model matches the context of this point against the encoded question from multiple perspectives and produces a matching vector.
Given those matched vectors, we employ another bi-directional LSTM to aggregate all the information and predict the beginning and ending points.
Experimental result on the test set of SQuAD shows that our model achieves a competitive result on the leaderboard.
Energy-efficiency, high data rates and secure communications are essential requirements of the future wireless networks.
In this paper, optimizing the secrecy energy efficiency is considered.
The optimal beamformer is designed for a MISO system with and without considering the minimum required secrecy rate.
Further, the optimal power control in a SISO system is carried out using an efficient iterative method, and this is followed by analyzing the trade-off between the secrecy energy efficiency and the secrecy rate for both MISO and SISO systems.
In this paper, we propose a framework for generating 3D point cloud of an object from a single-view RGB image.
Most previous work predict the 3D point coordinates from single RGB images directly.
We decompose this problem into depth estimation from single images and point completion from partial point clouds.
Our method sequentially predicts the depth maps and then infers the complete 3D object point clouds based on the predicted partial point clouds.
We explicitly impose the camera model geometrical constraint in our pipeline and enforce the alignment of the generated point clouds and estimated depth maps.
Experimental results for the single image 3D object reconstruction task show that the proposed method outperforms state-of-the-art methods.
Both the qualitative and quantitative results demonstrate the generality and suitability of our method.
Electroluminescence (EL) imaging is a useful modality for the inspection of photovoltaic (PV) modules.
EL images provide high spatial resolution, which makes it possible to detect even finest defects on the surface of PV modules.
However, the analysis of EL images is typically a manual process that is expensive, time-consuming, and requires expert knowledge of many different types of defects.
In this work, we investigate two approaches for automatic detection of such defects in a single image of a PV cell.
The approaches differ in their hardware requirements, which are dictated by their respective application scenarios.
The more hardware-efficient approach is based on hand-crafted features that are classified in a Support Vector Machine (SVM).
To obtain a strong performance, we investigate and compare various processing variants.
The more hardware-demanding approach uses an end-to-end deep Convolutional Neural Network (CNN) that runs on a Graphics Processing Unit (GPU).
Both approaches are trained on 1,968 cells extracted from high resolution EL intensity images of mono- and polycrystalline PV modules.
The CNN is more accurate, and reaches an average accuracy of 88.42%.
The SVM achieves a slightly lower average accuracy of 82.44%, but can run on arbitrary hardware.
Both automated approaches make continuous, highly accurate monitoring of PV cells feasible.
The school timetabling problem can be described as scheduling a set of lessons (combination of classes, teachers, subjects and rooms) in a weekly timetable.
This paper presents a novel way to generate timetables for high schools.
The algorithm has three phases.
Pre-scheduling, initial phase and optimization through tabu search.
In the first phase, a graph based algorithm used to create groups of lessons to be scheduled simultaneously; then an initial solution is built by a sequential greedy heuristic.
Finally, the solution is optimized using tabu search algorithm based on frequency based diversification.
The algorithm has been tested on a set of real problems gathered from Iranian high schools.
Experiments show that the proposed algorithm can effectively build acceptable timetables.
We show how to extend traditional intrinsic image decompositions to incorporate further layers above albedo and shading.
It is hard to obtain data to learn a multi-layer decomposition.
Instead, we can learn to decompose an image into layers that are "like this" by authoring generative models for each layer using proxy examples that capture the Platonic ideal (Mondrian images for albedo; rendered 3D primitives for shading; material swatches for shading detail).
Our method then generates image layers, one from each model, that explain the image.
Our approach rests on innovation in generative models for images.
We introduce a Convolutional Variational Auto Encoder (conv-VAE), a novel VAE architecture that can reconstruct high fidelity images.
The approach is general, and does not require that layers admit a physical interpretation.
This study covers an analytical approach to calculate positively invariant sets of dynamical systems.
Using Lyapunov techniques and quantifier elimination methods, an automatic procedure for determining bounds in the state space as an enclosure of attractors is proposed.
The available software tools permit an algorithmizable process, which normally requires a good insight into the systems dynamics and experience.
As a result we get an estimation of the attractor, whose conservatism only results from the initial choice of the Lyapunov candidate function.
The proposed approach is illustrated on the well-known Lorenz system.
This short paper presents the video browsing tool of VIREO team which has been used in the Video Browser Showdown 2018.
All added functions in the final version are introduced and experiences gained from the benchmark are also shared.
Online class imbalance learning constitutes a new problem and an emerging research topic that focusses on the challenges of online learning under class imbalance and concept drift.
Class imbalance deals with data streams that have very skewed distributions while concept drift deals with changes in the class imbalance status.
Little work exists that addresses these challenges and in this paper we introduce queue-based resampling, a novel algorithm that successfully addresses the co-existence of class imbalance and concept drift.
The central idea of the proposed resampling algorithm is to selectively include in the training set a subset of the examples that appeared in the past.
Results on two popular benchmark datasets demonstrate the effectiveness of queue-based resampling over state-of-the-art methods in terms of learning speed and quality.
Legal probabilism (LP) claims the degrees of conviction in juridical fact-finding are to be modeled exactly the way degrees of beliefs are modeled in standard bayesian epistemology.
Classical legal probabilism (CLP) adds that the conviction is justified if the credence in guilt given the evidence is above an appropriate guilt probability threshold.
The views are challenged on various counts, especially by the proponents of the so-called narrative approach, on which the fact-finders' decision is the result of a dynamic interplay between competing narratives of what happened.
I develop a way a bayesian epistemologist can make sense of the narrative approach.
I do so by formulating a probabilistic framework for evaluating competing narrations in terms of formal explications of the informal evaluation criteria used in the narrative approach.
As the Industrial Internet of Things (IIoT) grows, systems are increasingly being monitored by arrays of sensors returning time-series data at ever-increasing 'volume, velocity and variety' (i.e.Industrial Big Data).
An obvious use for these data is real-time systems condition monitoring and prognostic time to failure analysis (remaining useful life, RUL). (e.g.See white papers by Senseye.io, and output of the NASA Prognostics Center of Excellence (PCoE).)
However, as noted by Agrawal and Choudhary 'Our ability to collect "big data" has greatly surpassed our capability to analyze it, underscoring the emergence of the fourth paradigm of science, which is data-driven discovery.'
In order to fully utilize the potential of Industrial Big Data we need data-driven techniques that operate at scales that process models cannot.
Here we present a prototype technique for data-driven anomaly detection to operate at industrial scale.
The method generalizes to application with almost any multivariate dataset based on independent ordinations of repeated (bootstrapped) partitions of the dataset and inspection of the joint distribution of ordinal distances.
Using machine learning algorithms, including deep learning, we studied the prediction of personal attributes from the text of tweets, such as gender, occupation, and age groups.
We applied word2vec to construct word vectors, which were then used to vectorize tweet blocks.
The resulting tweet vectors were used as inputs for training models, and the prediction accuracy of those models was examined as a function of the dimension of the tweet vectors and the size of the tweet blacks.
The results showed that the machine learning algorithms could predict the three personal attributes of interest with 60-70% accuracy.
Background subtraction is the primary task of the majority of video inspection systems.
The most important part of the background subtraction which is common among different algorithms is background modeling.
In this regard, our paper addresses the problem of background modeling in a computationally efficient way, which is important for current eruption of "big data" processing coming from high resolution multi-channel videos.
Our model is based on the assumption that background in natural images lies on a low-dimensional subspace.
We formulated and solved this problem in a low-rank matrix completion framework.
In modeling the background, we benefited from the in-face extended Frank-Wolfe algorithm for solving a defined convex optimization problem.
We evaluated our fast robust matrix completion (fRMC) method on both background models challenge (BMC) and Stuttgart artificial background subtraction (SABS) datasets.
The results were compared with the robust principle component analysis (RPCA) and low-rank robust matrix completion (RMC) methods, both solved by inexact augmented Lagrangian multiplier (IALM).
The results showed faster computation, at least twice as when IALM solver is used, while having a comparable accuracy even better in some challenges, in subtracting the backgrounds in order to detect moving objects in the scene.
In order for autonomous robots to be able to support people's well-being in homes and everyday environments, new interactive capabilities will be required, as exemplified by the soft design used for Disney's recent robot character Baymax in popular fiction.
Home robots will be required to be easy to interact with and intelligent--adaptive, fun, unobtrusive and involving little effort to power and maintain--and capable of carrying out useful tasks both on an everyday level and during emergencies.
The current article adopts an exploratory medium fidelity prototyping approach for testing some new robotic capabilities in regard to recognizing people's activities and intentions and behaving in a way which is transparent to people.
Results are discussed with the aim of informing next designs.
For future traffic scenarios, we envision interconnected traffic participants, who exchange information about their current state, e.g., position, their predicted intentions, allowing to act in a cooperative manner.
Vulnerable road users (VRUs), e.g., pedestrians and cyclists, will be equipped with smart device that can be used to detect their intentions and transmit these detected intention to approaching cars such that their drivers can be warned.
In this article, we focus on detecting the initial movement of cyclist using smart devices.
Smart devices provide the necessary sensors, namely accelerometer and gyroscope, and therefore pose an excellent instrument to detect movement transitions (e.g., waiting to moving) fast.
Convolutional Neural Networks prove to be the state-of-the-art solution for many problems with an ever increasing range of applications.
Therefore, we model the initial movement detection as a classification problem.
In terms of Organic Computing (OC) it be seen as a step towards self-awareness and self-adaptation.
We apply residual network architectures to the task of detecting the initial starting movement of cyclists.
We develop a multiexposure image fusion method based on texture features, which exploits the edge preserving and intraregion smoothing property of nonlinear diffusion filters based on partial differential equations (PDE).
With the captured multiexposure image series, we first decompose images into base layers and detail layers to extract sharp details and fine details, respectively.
The magnitude of the gradient of the image intensity is utilized to encourage smoothness at homogeneous regions in preference to inhomogeneous regions.
Then, we have considered texture features of the base layer to generate a mask (i.e., decision mask) that guides the fusion of base layers in multiresolution fashion.
Finally, well-exposed fused image is obtained that combines fused base layer and the detail layers at each scale across all the input exposures.
Proposed algorithm skipping complex High Dynamic Range Image (HDRI) generation and tone mapping steps to produce detail preserving image for display on standard dynamic range display devices.
Moreover, our technique is effective for blending flash/no-flash image pair and multifocus images, that is, images focused on different targets.
Recently, Image-to-Image Translation (IIT) has achieved great progress in image style transfer and semantic context manipulation for images.
However, existing approaches require exhaustively labelling training data, which is labor demanding, difficult to scale up, and hard to adapt to a new domain.
To overcome such a key limitation, we propose Sparsely Grouped Generative Adversarial Networks (SG-GAN) as a novel approach that can translate images in sparsely grouped datasets where only a few train samples are labelled.
Using a one-input multi-output architecture, SG-GAN is well-suited for tackling multi-task learning and sparsely grouped learning tasks.
The new model is able to translate images among multiple groups using only a single trained model.
To experimentally validate the advantages of the new model, we apply the proposed method to tackle a series of attribute manipulation tasks for facial images as a case study.
Experimental results show that SG-GAN can achieve comparable results with state-of-the-art methods on adequately labelled datasets while attaining a superior image translation quality on sparsely grouped datasets.
This study investigates the role of both cultural and technological factors in determining audience formation on a global scale.
It integrates theories of media choice with theories of global cultural consumption and tests them by analyzing shared audience traffic between the world's 1000 most popular Websites.
We find that language and geographic similarities are more powerful predictors of audience overlap than hyperlinks and genre similarity, highlighting the role of cultural structures in shaping global media use.
Accurate state estimation is a fundamental module for various intelligent applications, such as robot navigation, autonomous driving, virtual and augmented reality.
Visual and inertial fusion is a popular technology for 6-DOF state estimation in recent years.
Time instants at which different sensors' measurements are recorded are of crucial importance to the system's robustness and accuracy.
In practice, timestamps of each sensor typically suffer from triggering and transmission delays, leading to temporal misalignment (time offsets) among different sensors.
Such temporal offset dramatically influences the performance of sensor fusion.
To this end, we propose an online approach for calibrating temporal offset between visual and inertial measurements.
Our approach achieves temporal offset calibration by jointly optimizing time offset, camera and IMU states, as well as feature locations in a SLAM system.
Furthermore, the approach is a general model, which can be easily employed in several feature-based optimization frameworks.
Simulation and experimental results demonstrate the high accuracy of our calibration approach even compared with other state-of-art offline tools.
The VIO comparison against other methods proves that the online temporal calibration significantly benefits visual-inertial systems.
The source code of temporal calibration is integrated into our public project, VINS-Mono.
We provide a detailed overview of the various approaches that were proposed to date to solve the task of Open Information Extraction.
We present the major challenges that such systems face, show the evolution of the suggested approaches over time and depict the specific issues they address.
In addition, we provide a critique of the commonly applied evaluation procedures for assessing the performance of Open IE systems and highlight some directions for future work.
One-class support vector machine (OC-SVM) for a long time has been one of the most effective anomaly detection methods and extensively adopted in both research as well as industrial applications.
The biggest issue for OC-SVM is yet the capability to operate with large and high-dimensional datasets due to optimization complexity.
Those problems might be mitigated via dimensionality reduction techniques such as manifold learning or autoencoder.
However, previous work often treats representation learning and anomaly prediction separately.
In this paper, we propose autoencoder based one-class support vector machine (AE-1SVM) that brings OC-SVM, with the aid of random Fourier features to approximate the radial basis kernel, into deep learning context by combining it with a representation learning architecture and jointly exploit stochastic gradient descent to obtain end-to-end training.
Interestingly, this also opens up the possible use of gradient-based attribution methods to explain the decision making for anomaly detection, which has ever been challenging as a result of the implicit mappings between the input space and the kernel space.
To the best of our knowledge, this is the first work to study the interpretability of deep learning in anomaly detection.
We evaluate our method on a wide range of unsupervised anomaly detection tasks in which our end-to-end training architecture achieves a performance significantly better than the previous work using separate training.
We consider the complexity of the firefighter problem where b>=1 firefighters are available at each time step.
This problem is proved NP-complete even on trees of degree at most three and budget one (Finbow et al.,2007) and on trees of bounded degree b+3 for any fixed budget b>=2 (Bazgan et al.,2012).
In this paper, we provide further insight into the complexity landscape of the problem by showing that the pathwidth and the maximum degree of the input graph govern its complexity.
More precisely, we first prove that the problem is NP-complete even on trees of pathwidth at most three for any fixed budget b>=1.
We then show that the problem turns out to be fixed parameter-tractable with respect to the combined parameter "pathwidth" and "maximum degree" of the input graph.
Scientists, journalists, and photographers have used advanced camera technology to capture extremely high-resolution timelapse and developed information visualization tools for data exploration and analysis.
However, it takes a great deal of effort for professionals to form and tell stories after exploring data, since these tools usually provide little aids in creating visual elements.
We present a web-based timelapse editor to support the creation of guided video tours and interactive slideshows from a collection of large-scale spatial and temporal images.
Professionals can embed these two visual elements into web pages in conjunction with various forms of digital media to tell multimodal and interactive stories.
Methods from computational topology are becoming more and more popular in computer vision and have shown to improve the state-of-the-art in several tasks.
In this paper, we investigate the applicability of topological descriptors in the context of 3D surface analysis for the classification of different surface textures.
We present a comprehensive study on topological descriptors, investigate their robustness and expressiveness and compare them with state-of-the-art methods including Convolutional Neural Networks (CNNs).
Results show that class-specific information is reflected well in topological descriptors.
The investigated descriptors can directly compete with non-topological descriptors and capture complementary information.
As a consequence they improve the state-of-the-art when combined with non-topological descriptors.
The goal of precipitation nowcasting is to predict the future rainfall intensity in a local region over a relatively short period of time.
Very few previous studies have examined this crucial and challenging weather forecasting problem from the machine learning perspective.
In this paper, we formulate precipitation nowcasting as a spatiotemporal sequence forecasting problem in which both the input and the prediction target are spatiotemporal sequences.
By extending the fully connected LSTM (FC-LSTM) to have convolutional structures in both the input-to-state and state-to-state transitions, we propose the convolutional LSTM (ConvLSTM) and use it to build an end-to-end trainable model for the precipitation nowcasting problem.
Experiments show that our ConvLSTM network captures spatiotemporal correlations better and consistently outperforms FC-LSTM and the state-of-the-art operational ROVER algorithm for precipitation nowcasting.
State-of-the-art Natural Language Processing algorithms rely heavily on efficient word segmentation.
Urdu is amongst languages for which word segmentation is a complex task as it exhibits space omission as well as space insertion issues.
This is partly due to the Arabic script which although cursive in nature, consists of characters that have inherent joining and non-joining attributes regardless of word boundary.
This paper presents a word segmentation system for Urdu which uses a Conditional Random Field sequence modeler with orthographic, linguistic and morphological features.
Our proposed model automatically learns to predict white space as word boundary as well as Zero Width Non-Joiner (ZWNJ) as sub-word boundary.
Using a manually annotated corpus, our model achieves F1 score of 0.97 for word boundary identification and 0.85 for sub-word boundary identification tasks.
We have made our code and corpus publicly available to make our results reproducible.
With the ubiquity of large-scale graph data in a variety of application domains, querying them effectively is a challenge.
In particular, reachability queries are becoming increasingly important, especially for containment, subsumption, and connectivity checks.
Whereas many methods have been proposed for static graph reachability, many real-world graphs are constantly evolving, which calls for dynamic indexing.
In this paper, we present a fully dynamic reachability index over dynamic graphs.
Our method, called DAGGER, is a light-weight index based on interval labeling, that scales to million node graphs and beyond.
Our extensive experimental evaluation on real-world and synthetic graphs confirms its effectiveness over baseline methods.
Synthesizing programs using example input/outputs is a classic problem in artificial intelligence.
We present a method for solving Programming By Example (PBE) problems by using a neural model to guide the search of a constraint logic programming system called miniKanren.
Crucially, the neural model uses miniKanren's internal representation as input; miniKanren represents a PBE problem as recursive constraints imposed by the provided examples.
We explore Recurrent Neural Network and Graph Neural Network models.
We contribute a modified miniKanren, drivable by an external agent, available at https://github.com/xuexue/neuralkanren.
We show that our neural-guided approach using constraints can synthesize programs faster in many cases, and importantly, can generalize to larger problems.
While foreground extraction is fundamental to virtual reality systems and has been studied for decades, majority of the professional softwares today still rely substantially on human interventions, e.g., providing trimaps or labeling key frames.
This is not only time consuming, but is also sensitive to human error.
In this paper, we present a fully automatic foreground extraction algorithm which does not require any trimap or scribble.
Our solution is based on a newly developed concept called the Multi-Agent Consensus Equilibrium (MACE), a framework which allows us to integrate multiple sources of expertise to produce an overall superior result.
The MACE framework consists of three agents: (1) A new dual layer closed-form matting agent to estimate the foreground mask using the color image and a background image; (2) A background probability estimator using color difference and object segmentation; (3) A total variation minimization agent to control the smoothness of the foreground masks.
We show how these agents are constructed, and how their interactions lead to better performance.
We evaluate the performance of the proposed algorithm by comparing to several state-of-the-art methods.
On the real datasets we tested, our results show less error compared to the other methods.
We present a transition-based dependency parser that uses a convolutional neural network to compose word representations from characters.
The character composition model shows great improvement over the word-lookup model, especially for parsing agglutinative languages.
These improvements are even better than using pre-trained word embeddings from extra data.
On the SPMRL data sets, our system outperforms the previous best greedy parser (Ballesteros et al., 2015) by a margin of 3% on average.
We propose Range and Roots which are two common patterns useful for specifying a wide range of counting and occurrence constraints.
We design specialised propagation algorithms for these two patterns.
Counting and occurrence constraints specified using these patterns thus directly inherit a propagation algorithm.
To illustrate the capabilities of the Range and Roots constraints, we specify a number of global constraints taken from the literature.
Preliminary experiments demonstrate that propagating counting and occurrence constraints using these two patterns leads to a small loss in performance when compared to specialised global constraints and is competitive with alternative decompositions using elementary constraints.
Community structure is an important area of research.
It has received a considerable attention from the scientific community.
Despite its importance, one of the key problems in locating information about community detection is the diverse spread of related articles across various disciplines.
To the best of our knowledge, there is no current comprehensive review of recent literature which uses a scientometric analysis using complex networks analysis covering all relevant articles from the Web of Science (WoS).
Here we present a visual survey of key literature using CiteSpace.
The idea is to identify emerging trends besides using network techniques to examine the evolution of the domain.
Towards that end, we identify the most influential, central, as well as active nodes using scientometric analyses.
We examine authors, key articles, cited references, core subject categories, key journals, institutions, as well as countries.
The exploration of the scientometric literature of the domain reveals that Yong Wang is a pivot node with the highest centrality.
Additionally, we have observed that Mark Newman is the most highly cited author in the network.
We have also identified that the journal, "Reviews of Modern Physics" has the strongest citation burst.
In terms of cited documents, an article by Andrea Lancichinetti has the highest centrality score.
We have also discovered that the origin of the key publications in this domain is from the United States.
Whereas Scotland has the strongest and longest citation burst.
Additionally, we have found that the categories of "Computer Science" and "Engineering" lead other categories based on frequency and centrality respectively.
A container is a group of processes isolated from other groups via distinct kernel namespaces and resource allocation quota.
Attacks against containers often leverage kernel exploits through system call interface.
In this paper, we present an approach that mines sandboxes for containers.
We first explore the behaviors of a container by leveraging automatic testing, and extract the set of system calls accessed during testing.
The set of system calls then results as a sandbox of the container.
The mined sandbox restricts the container's access to system calls which are not seen during testing and thus reduces the attack surface.
In the experiment, our approach requires less than eleven minutes to mine sandbox for each of the containers.
The enforcement of mined sandboxes does not impact the regular functionality of a container and incurs low performance overhead.
Layout hotpot detection is one of the main steps in modern VLSI design.
A typical hotspot detection flow is extremely time consuming due to the computationally expensive mask optimization and lithographic simulation.
Recent researches try to facilitate the procedure with a reduced flow including feature extraction, training set generation and hotspot detection, where feature extraction methods and hotspot detection engines are deeply studied.
However, the performance of hotspot detectors relies highly on the quality of reference layout libraries which are costly to obtain and usually predetermined or randomly sampled in previous works.
In this paper, we propose an active learning-based layout pattern sampling and hotspot detection flow, which simultaneously optimizes the machine learning model and the training set that aims to achieve similar or better hotspot detection performance with much smaller number of training instances.
Experimental results show that our proposed method can significantly reduce lithography simulation overhead while attaining satisfactory detection accuracy on designs under both DUV and EUV lithography technologies.
The advancements in wireless mesh networks (WMN), and the surge in multi-radio multi-channel (MRMC) WMN deployments have spawned a multitude of network performance issues.
These issues are intricately linked to the adverse impact of endemic interference.
Thus, interference mitigation is a primary design objective in WMNs.
Interference alleviation is often effected through efficient channel allocation (CA) schemes which fully utilize the potential of MRMC environment and also restrain the detrimental impact of interference.
However, numerous CA schemes have been proposed in research literature and there is a lack of CA performance prediction techniques which could assist in choosing a suitable CA for a given WMN.
In this work, we propose a reliable interference estimation and CA performance prediction approach.
We demonstrate its efficacy by substantiating the CA performance predictions for a given WMN with experimental data obtained through rigorous simulations on an ns-3 802.11g environment.
Target encoding plays a central role when learning Convolutional Neural Networks.
In this realm, One-hot encoding is the most prevalent strategy due to its simplicity.
However, this so widespread encoding schema assumes a flat label space, thus ignoring rich relationships existing among labels that can be exploited during training.
In large-scale datasets, data does not span the full label space, but instead lies in a low-dimensional output manifold.
Following this observation, we embed the targets into a low-dimensional space, drastically improving convergence speed while preserving accuracy.
Our contribution is two fold: (i) We show that random projections of the label space are a valid tool to find such lower dimensional embeddings, boosting dramatically convergence rates at zero computational cost; and (ii) we propose a normalized eigenrepresentation of the class manifold that encodes the targets with minimal information loss, improving the accuracy of random projections encoding while enjoying the same convergence rates.
Experiments on CIFAR-100, CUB200-2011, Imagenet, and MIT Places demonstrate that the proposed approach drastically improves convergence speed while reaching very competitive accuracy rates.
Text segmentation (TS) aims at dividing long text into coherent segments which reflect the subtopic structure of the text.
It is beneficial to many natural language processing tasks, such as Information Retrieval (IR) and document summarisation.
Current approaches to text segmentation are similar in that they all use word-frequency metrics to measure the similarity between two regions of text, so that a document is segmented based on the lexical cohesion between its words.
Various NLP tasks are now moving towards the semantic web and ontologies, such as ontology-based IR systems, to capture the conceptualizations associated with user needs and contents.
Text segmentation based on lexical cohesion between words is hence not sufficient anymore for such tasks.
This paper proposes OntoSeg, a novel approach to text segmentation based on the ontological similarity between text blocks.
The proposed method uses ontological similarity to explore conceptual relations between text segments and a Hierarchical Agglomerative Clustering (HAC) algorithm to represent the text as a tree-like hierarchy that is conceptually structured.
The rich structure of the created tree further allows the segmentation of text in a linear fashion at various levels of granularity.
The proposed method was evaluated on a wellknown dataset, and the results show that using ontological similarity in text segmentation is very promising.
Also we enhance the proposed method by combining ontological similarity with lexical similarity and the results show an enhancement of the segmentation quality.
Medical image analysis and computer-assisted intervention problems are increasingly being addressed with deep-learning-based solutions.
Established deep-learning platforms are flexible but do not provide specific functionality for medical image analysis and adapting them for this application requires substantial implementation effort.
Thus, there has been substantial duplication of effort and incompatible infrastructure developed across many research groups.
This work presents the open-source NiftyNet platform for deep learning in medical imaging.
The ambition of NiftyNet is to accelerate and simplify the development of these solutions, and to provide a common mechanism for disseminating research outputs for the community to use, adapt and build upon.
NiftyNet provides a modular deep-learning pipeline for a range of medical imaging applications including segmentation, regression, image generation and representation learning applications.
Components of the NiftyNet pipeline including data loading, data augmentation, network architectures, loss functions and evaluation metrics are tailored to, and take advantage of, the idiosyncracies of medical image analysis and computer-assisted intervention.
NiftyNet is built on TensorFlow and supports TensorBoard visualization of 2D and 3D images and computational graphs by default.
We present 3 illustrative medical image analysis applications built using NiftyNet: (1) segmentation of multiple abdominal organs from computed tomography; (2) image regression to predict computed tomography attenuation maps from brain magnetic resonance images; and (3) generation of simulated ultrasound images for specified anatomical poses.
NiftyNet enables researchers to rapidly develop and distribute deep learning solutions for segmentation, regression, image generation and representation learning applications, or extend the platform to new applications.
Event-based cameras offer much potential to the fields of robotics and computer vision, in part due to their large dynamic range and extremely high "frame rates".
These attributes make them, at least in theory, particularly suitable for enabling tasks like navigation and mapping on high speed robotic platforms under challenging lighting conditions, a task which has been particularly challenging for traditional algorithms and camera sensors.
Before these tasks become feasible however, progress must be made towards adapting and innovating current RGB-camera-based algorithms to work with event-based cameras.
In this paper we present ongoing research investigating two distinct approaches to incorporating event-based cameras for robotic navigation: the investigation of suitable place recognition / loop closure techniques, and the development of efficient neural implementations of place recognition techniques that enable the possibility of place recognition using event-based cameras at very high frame rates using neuromorphic computing hardware.
This work considers multiple-input multiple-output (MIMO) communication systems using hierarchical modulation.
A disadvantage of the maximum-likelihood (ML) MIMO detector is that computational complexity increases exponentially with the number of transmit antennas.
To reduce complexity, we propose a hierarchical modulation scheme to be used in MIMO trans- mission where base and enhancement layers are incorporated.
In the proposed receiver, the base layer is detected first with a minimum mean square error (MMSE) detector which is followed by ML detection of the enhancement layer.
Our results indicate that the proposed low complexity scheme does not compromise performance when design parameters such as code rates and constellation ratio are chosen carefully.
Recent work has shown that convolutional neural networks (CNNs) can be applied successfully in disparity estimation, but these methods still suffer from errors in regions of low-texture, occlusions and reflections.
Concurrently, deep learning for semantic segmentation has shown great progress in recent years.
In this paper, we design a CNN architecture that combines these two tasks to improve the quality and accuracy of disparity estimation with the help of semantic segmentation.
Specifically, we propose a network structure in which these two tasks are highly coupled.
One key novelty of this approach is the two-stage refinement process.
Initial disparity estimates are refined with an embedding learned from the semantic segmentation branch of the network.
The proposed model is trained using an unsupervised approach, in which images from one half of the stereo pair are warped and compared against images from the other camera.
Another key advantage of the proposed approach is that a single network is capable of outputting disparity estimates and semantic labels.
These outputs are of great use in autonomous vehicle operation; with real-time constraints being key, such performance improvements increase the viability of driving applications.
Experiments on KITTI and Cityscapes datasets show that our model can achieve state-of-the-art results and that leveraging embedding learned from semantic segmentation improves the performance of disparity estimation.
We propose a context-dependent model to map utterances within an interaction to executable formal queries.
To incorporate interaction history, the model maintains an interaction-level encoder that updates after each turn, and can copy sub-sequences of previously predicted queries during generation.
Our approach combines implicit and explicit modeling of references between utterances.
We evaluate our model on the ATIS flight planning interactions, and demonstrate the benefits of modeling context and explicit references.
The practical realization of beam steering mechanisms in millimeter wave communications has a large impact on performance.
The key challenge is to find a pragmatic trade-off between throughput performance and the overhead of periodic beam sweeping required to improve link quality in case of transient link blockage.
This is particularly critical in commercial off-the-shelf devices, which require simple yet efficient solutions.
First, we analyze the operation of such a commercial device to understand the impact of link blockage in practice.
To this end, we measure TCP throughput for different traffic loads while blocking the link at regular intervals.
Second, we derive a Markov model based on our practical insights to compute throughput for the case of transient blockage.
We use this model to evaluate the trade-off between throughput and periodic beam sweeping.
Finally, we validate our results using throughput traces collected using the aforementioned commercial device.
Both our model and our practical measurements show that transient blockage causes significant signal fluctuation due to suboptimal beam realignment.
In particular, fluctuations increase with traffic load, limiting the achievable throughput.
We show that choosing lower traffic loads allows us to reduce fluctuations by 41% while achieving the same net throughput than with higher traffic loads.
The human action classification task is a widely researched topic and is still an open problem.
Many state-of-the-arts approaches involve the usage of bag-of-video-words with spatio-temporal local features to construct characterizations for human actions.
In order to improve beyond this standard approach, we investigate the usage of co-occurrences between local features.
We propose the usage of co-occurrences information to characterize human actions.
A trade-off factor is used to define an optimal trade-off between vocabulary size and classification rate.
Next, a spatio-temporal co-occurrence technique is applied to extract co-occurrence information between labeled local features.
Novel characterizations for human actions are then constructed.
These include a vector quantized correlogram-elements vector, a highly discriminative PCA (Principal Components Analysis) co-occurrence vector and a Haralick texture vector.
Multi-channel kernel SVM (support vector machine) is utilized for classification.
For evaluation, the well known KTH as well as the challenging UCF-Sports action datasets are used.
We obtained state-of-the-arts classification performance.
We also demonstrated that we are able to fully utilize co-occurrence information, and improve the standard bag-of-video-words approach.
Lowpass envelope approximation of smooth continuous-variable signals are introduced in this work.
Envelope approximations are necessary when a given signal has to be approximated always to a larger value (such as in TV white space protection regions).
In this work, a near-optimal approximate algorithm for finding a signal's envelope, while minimizing a mean-squared cost function, is detailed.
The sparse (lowpass) signal approximation is obtained in the linear Fourier series basis.
This approximate algorithm works by discretizing the envelope property from an infinite number of points to a large (but finite) number of points.
It is shown that this approximate algorithm is near-optimal and can be solved by using efficient convex optimization programs available in the literature.
Simulation results are provided towards the end to gain more insights into the analytical results presented.
On-demand video accounts for the majority of wireless data traffic.
Video distribution schemes based on caching combined with device-to-device (D2D) communications promise order-of-magnitude greater spectral efficiency for video delivery, but hinge on the principle of "concentrated demand distributions."
This letter presents, for the first time, evaluations of the spectral efficiency of such schemes based on measured cellular demand distributions.
In particular, we use a database with more than 100 million requests (689,461 for cellular users) from the BBC iPlayer, a popular video streaming service in the U.K., to evaluate the throughput-outage tradeoff of a random caching D2D based scheme, and find that also for this realistic case, order-of-magnitude improvements can be achieved.
The gains depend on the size of the local cache in the devices; e.g., with a cache size of 32 GB, a throughput increase of two orders of magnitude at an outage probability between 0.01 and 0.1 can be obtained.
Prevailing computational tools available to and used by architecture and engineering professionals purport to gather and present thorough and accurate perspectives of the environmental impacts associated with their contributions to the built environment.
The presented research of building modeling and analysis software used by the Architecture, Engineering, Construction, and Operations (AECO) industry reveals that many of the most heavily relied-upon industry tools are isolated in functionality, utilize incomplete models and data, and are disruptive to normative design and building optimization workflows.
This paper describes the current models and tools, their primary functions and limitations, and presents our concurrent research to develop more advanced models to assess lifetime building energy consumption alongside operating energy use.
A series of case studies describes the current state-of-the-art in tools and building energy analysis followed by the research models and novel design and analysis Tool that the Green Scale Research Group has developed in response.
A fundamental goal of this effort is to increase the use and efficacy of building impact studies conducted by architects, engineers, and building owners and operators during the building design process.
We present an approach to automatically classify clinical text at a sentence level.
We are using deep convolutional neural networks to represent complex features.
We train the network on a dataset providing a broad categorization of health information.
Through a detailed evaluation, we demonstrate that our method outperforms several approaches widely used in natural language processing tasks by about 15%.
This paper proposes a secure surveillance framework for Internet of things (IoT) systems by intelligent integration of video summarization and image encryption.
First, an efficient video summarization method is used to extract the informative frames using the processing capabilities of visual sensors.
When an event is detected from keyframes, an alert is sent to the concerned authority autonomously.
As the final decision about an event mainly depends on the extracted keyframes, their modification during transmission by attackers can result in severe losses.
To tackle this issue, we propose a fast probabilistic and lightweight algorithm for the encryption of keyframes prior to transmission, considering the memory and processing requirements of constrained devices that increase its suitability for IoT systems.
Our experimental results verify the effectiveness of the proposed method in terms of robustness, execution time, and security compared to other image encryption algorithms.
Furthermore, our framework can reduce the bandwidth, storage, transmission cost, and the time required for analysts to browse large volumes of surveillance data and make decisions about abnormal events, such as suspicious activity detection and fire detection in surveillance applications.
Using the machine learning approach known as reservoir computing, it is possible to train one dynamical system to emulate another.
We show that such trained reservoir computers reproduce the properties of the attractor of the chaotic system sufficiently well to exhibit chaos synchronisation.
That is, the trained reservoir computer, weakly driven by the chaotic system, will synchronise with the chaotic system.
Conversely, the chaotic system, weakly driven by a trained reservoir computer, will synchronise with the reservoir computer.
We illustrate this behaviour on the Mackey-Glass and Lorenz systems.
We then show that trained reservoir computers can be used to crack chaos based cryptography and illustrate this on a chaos cryptosystem based on the Mackey-Glass system.
We conclude by discussing why reservoir computers are so good at emulating chaotic systems.
A single-letter lower bound on the sum rate of multiple description coding with tree-structured distortion constraints is established by generalizing Ozarow's celebrated converse argument through the introduction of auxiliary random variables that form a Markov tree.
For the quadratic vector Gaussian case, this lower bound is shown to be achievable by an extended version of the El Gamal-Cover scheme, yielding a complete sum-rate characterization.
Synchronized Random Access Channel (RACH) attempts by Internet of Things (IoT) devices could result in Radio Access Network (RAN) overload in LTE-A.
3GPP adopted Barring Bitmap Enabled-Extended Access Barring (EAB-BB) mechanism that announces the EAB information (i.e., a list of barred Access Classes) through a barring bitmap as the baseline solution to mitigate the RAN overload.
EAB-BB was analyzed for its optimal performance in a recent work.
However, there has been no work that analyzes Barring Factor Enabled-Extended Access Barring (EAB-BF), an alternative mechanism that was considered during the standardization process.
Due to the modeling complexity involved, not only has it been difficult to analyze EAB-BF, but also, a much more far-reaching issue, like the effect of these schemes on key network performance parameter, like eNodeB energy consumption, has been overlooked.
In this regard, for the first time, we develop a novel analytical model for EAB-BF to obtain its performance metrics.
Results obtained from our analysis and simulation are seen to match very well.
Furthermore, we also build an eNodeB energy consumption model to serve the IoT RACH requests.
We then show that our analytical and energy consumption models can be combined to obtain EAB-BF settings that can minimize eNodeB energy consumption, while simultaneously providing optimal Quality of Service (QoS) performance.
Results obtained reveal that the optimal performance of EAB-BF is better than that of EAB-BB.
Furthermore, we also show that not only all the three 3GPP-proposed EAB-BF settings considered during standardization provide sub-optimal QoS to devices, but also result in excessive eNodeB energy consumption, thereby acutely penalizing the network.
Finally, we provide corrections to these 3GPP-settings that can lead to significant gains in EAB-BF performance.
Storage networking technology has enjoyed strong growth in recent years, but security concerns and threats facing networked data have grown equally fast.
Today, there are many potential threats that are targeted at storage networks, including data modification, destruction and theft, DoS attacks, malware, hardware theft and unauthorized access, among others.
In order for a Storage Area Network (SAN) to be secure, each of these threats must be individually addressed.
In this paper, we present a comparative study by implementing different security methods in IP Storage network.
LSTMs and other RNN variants have shown strong performance on character-level language modeling.
These models are typically trained using truncated backpropagation through time, and it is common to assume that their success stems from their ability to remember long-term contexts.
In this paper, we show that a deep (64-layer) transformer model with fixed context outperforms RNN variants by a large margin, achieving state of the art on two popular benchmarks: 1.13 bits per character on text8 and 1.06 on enwik8.
To get good results at this depth, we show that it is important to add auxiliary losses, both at intermediate network layers and intermediate sequence positions.
Computational Social Choice is an interdisciplinary research area involving Economics, Political Science, and Social Science on the one side, and Mathematics and Computer Science (including Artificial Intelligence and Multiagent Systems) on the other side.
Typical computational problems studied in this field include the vulnerability of voting procedures against attacks, or preference aggregation in multi-agent systems.
Parameterized Algorithmics is a subfield of Theoretical Computer Science seeking to exploit meaningful problem-specific parameters in order to identify tractable special cases of in general computationally hard problems.
In this paper, we propose nine of our favorite research challenges concerning the parameterized complexity of problems appearing in this context.
We present and evaluate an approach for human-in-the-loop specification of shape reconstruction with annotations for basic robot-object interactions.
Our method is based on the idea of model annotation: the addition of simple cues to an underlying object model to specify shape and delineate a simple task.
The goal is to explore reducing the complexity of CAD-like interfaces so that novice users can quickly recover an object's shape and describe a manipulation task that is then carried out by a robot.
The object modeling and interaction annotation capabilities are tested with a user study and compared against results obtained using existing approaches.
The approach has been analyzed using a variety of shape comparison, grasping, and manipulation metrics, and tested with the PR2 robot platform, where it was shown to be successful.
In the last decade, deep learning algorithms have become very popular thanks to the achieved performance in many machine learning and computer vision tasks.
However, most of the deep learning architectures are vulnerable to so called adversarial examples.
This questions the security of deep neural networks (DNN) for many security- and trust-sensitive domains.
The majority of the proposed existing adversarial attacks are based on the differentiability of the DNN cost function.Defence strategies are mostly based on machine learning and signal processing principles that either try to detect-reject or filter out the adversarial perturbations and completely neglect the classical cryptographic component in the defence.
In this work, we propose a new defence mechanism based on the second Kerckhoffs's cryptographic principle which states that the defence and classification algorithm are supposed to be known, but not the key.
To be compliant with the assumption that the attacker does not have access to the secret key, we will primarily focus on a gray-box scenario and do not address a white-box one.
More particularly, we assume that the attacker does not have direct access to the secret block, but (a) he completely knows the system architecture, (b) he has access to the data used for training and testing and (c) he can observe the output of the classifier for each given input.
We show empirically that our system is efficient against most famous state-of-the-art attacks in black-box and gray-box scenarios.
Current approaches to cross-lingual sentiment analysis try to leverage the wealth of labeled English data using bilingual lexicons, bilingual vector space embeddings, or machine translation systems.
Here we show that it is possible to use a single linear transformation, with as few as 2000 word pairs, to capture fine-grained sentiment relationships between words in a cross-lingual setting.
We apply these cross-lingual sentiment models to a diverse set of tasks to demonstrate their functionality in a non-English context.
By effectively leveraging English sentiment knowledge without the need for accurate translation, we can analyze and extract features from other languages with scarce data at a very low cost, thus making sentiment and related analyses for many languages inexpensive.
We study an uplink multi secondary user (SU) cognitive radio system having average delay constraints as well as an instantaneous interference constraint to the primary user (PU).
If the interference channels from the SUs to the PU have independent but not identically distributed fading coefficients, then the SUs will experience heterogeneous delay performances.
This is because SUs causing low interference to the PU will be scheduled more frequently, and/or allocated more transmission power than those causing high interference.
We propose a dynamic scheduling-and-power-control algorithm that can provide the required average delay guarantees to all SUs as well as protecting the PU from interference.
Using the Lyapunov technique, we show that our algorithm is asymptotically delay optimal while satisfying the delay and interference constraints.
We support our findings by extensive system simulations and show the robustness of the proposed algorithm against channel estimation errors.
We propose a new MDS paradigm called reader-aware multi-document summarization (RA-MDS).
Specifically, a set of reader comments associated with the news reports are also collected.
The generated summaries from the reports for the event should be salient according to not only the reports but also the reader comments.
To tackle this RA-MDS problem, we propose a sparse-coding-based method that is able to calculate the salience of the text units by jointly considering news reports and reader comments.
Another reader-aware characteristic of our framework is to improve linguistic quality via entity rewriting.
The rewriting consideration is jointly assessed together with other summarization requirements under a unified optimization model.
To support the generation of compressive summaries via optimization, we explore a finer syntactic unit, namely, noun/verb phrase.
In this work, we also generate a data set for conducting RA-MDS.
Extensive experiments on this data set and some classical data sets demonstrate the effectiveness of our proposed approach.
Industrial Control Systems are under increased scrutiny.
Their security is historically sub-par, and although measures are being taken by the manufacturers to remedy this, the large installed base of legacy systems cannot easily be updated with state-of-the-art security measures.
We propose a system that uses electromagnetic side-channel measurements to detect behavioural changes of the software running on industrial control systems.
To demonstrate the feasibility of this method, we show it is possible to profile and distinguish between even small changes in programs on Siemens S7-317 PLCs, using methods from cryptographic side-channel analysis.
Objective: Radiomics-driven Computer Aided Diagnosis (CAD) has shown considerable promise in recent years as a potential tool for improving clinical decision support in medical oncology, particularly those based around the concept of Discovery Radiomics, where radiomic sequencers are discovered through the analysis of medical imaging data.
One of the main limitations with current CAD approaches is that it is very difficult to gain insight or rationale as to how decisions are made, thus limiting their utility to clinicians.
Methods: In this study, we propose CLEAR-DR, a novel interpretable CAD system based on the notion of CLass-Enhanced Attentive Response Discovery Radiomics for the purpose of clinical decision support for diabetic retinopathy.
Results: In addition to disease grading via the discovered deep radiomic sequencer, the CLEAR-DR system also produces a visual interpretation of the decision-making process to provide better insight and understanding into the decision-making process of the system.
Conclusion: We demonstrate the effectiveness and utility of the proposed CLEAR-DR system of enhancing the interpretability of diagnostic grading results for the application of diabetic retinopathy grading.
Significance: CLEAR-DR can act as a potential powerful tool to address the uninterpretability issue of current CAD systems, thus improving their utility to clinicians.
We propose a new approach for solving a class of discrete decision making problems under uncertainty with positive cost.
This issue concerns multiple and diverse fields such as engineering, economics, artificial intelligence, cognitive science and many others.
Basically, an agent has to choose a single or series of actions from a set of options, without knowing for sure their consequences.
Schematically, two main approaches have been followed: either the agent learns which option is the correct one to choose in a given situation by trial and error, or the agent already has some knowledge on the possible consequences of his decisions; this knowledge being generally expressed as a conditional probability distribution.
In the latter case, several optimal or suboptimal methods have been proposed to exploit this uncertain knowledge in various contexts.
In this work, we propose following a different approach, based on the geometric intuition of distance.
More precisely, we define a goal independent quasimetric structure on the state space, taking into account both cost function and transition probability.
We then compare precision and computation time with classical approaches.
Modular optical switch architectures combining wavelength routing based on arrayed waveguide grating (AWG) devices and multicasting based on star couplers hold promise for flexibly addressing the exponentially growing traffic demands in a cost- and power-efficient fashion.
In a default switching scenario, an input port of the AWG is connected to an output port via a single wavelength.
This can severely limit the capacity between broadcast domains, resulting in interdomain traffic switching bottlenecks.
In this paper, we examine the possibility of resolving capacity bottlenecks by exploiting multiple AWG free spectral ranges (FSRs), i.e., setting up multiple parallel connections between each pair of broadcast domains.
To this end, we introduce a multi-FSR scheduling algorithm for interconnecting broadcast domains by fairly distributing the wavelength resources among them.
We develop a general-purpose analytical framework to study the blocking probabilities in a multistage switching scenario and compare our results with Monte Carlo simulations.
Our study points to significant improvements with a moderate increase in the number of FSRs.
We show that an FSR count beyond four results in diminishing returns.
Furthermore, to investigate the trade-offs between the network- and physical-layer effects, we conduct a cross-layer analysis, taking into account pulse amplitude modulation (PAM) and rate-adaptive forward error correction (FEC).
We illustrate how the effective bit rate per port increases with an increase in the number of FSRs.
%We also look at the advantages of an impairment-aware scheduling strategy in a multi-FSR switching scenario.
This paper presents an adaptive fault-tolerant control (FTC) scheme for a class of nonlinear uncertain multi-agent systems.
A local FTC scheme is designed for each agent using local measurements and suitable information exchanged between neighboring agents.
Each local FTC scheme consists of a fault diagnosis module and a reconfigurable controller module comprised of a baseline controller and two adaptive fault-tolerant controllers activated after fault detection and after fault isolation, respectively.
Under certain assumptions, the closed-loop system's stability and leader-follower consensus properties are rigorously established under different modes of the FTC system, including the time-period before possible fault detection, between fault detection and possible isolation, and after fault isolation.
Ubiquitous computing helps make data and services available to users anytime and anywhere.
This makes the cooperation of devices a crucial need.
In return, such cooperation causes an overload of the devices and/or networks, resulting in network malfunction and suspension of its activities.
Our goal in this paper is to propose an approach of devices reconfiguration in order to help to reduce the energy consumption in ubiquitous environments.
The idea is that when high-energy consumption is detected, we proceed to a change in component distribution on the devices to reduce and/or balance the energy consumption.
We also investigate the possibility to detect high-energy consumption of devices/network based on devices abilities.
As a result, our idea realizes a reconfiguration of devices aimed at reducing the consumption of energy and/or load balancing in ubiquitous environments.
To synthesize Maxwell optics systems, the mathematical apparatus of tensor and vector analysis is generally employed.
This mathematical apparatus implies executing a great number of simple stereotyped operations, which are adequately supported by computer algebra systems.
In this paper, we distinguish between two stages of working with a mathematical model: model development and model usage.
Each of these stages implies its own computer algebra system.
As a model problem, we consider the problem of geometrization of Maxwell's equations.
Two computer algebra systems---Cadabra and FORM---are selected for use at different stages of investigation.
This paper is motivated by the automation of neuropsychological tests involving discourse analysis in the retellings of narratives by patients with potential cognitive impairment.
In this scenario the task of sentence boundary detection in speech transcripts is important as discourse analysis involves the application of Natural Language Processing tools, such as taggers and parsers, which depend on the sentence as a processing unit.
Our aim in this paper is to verify which embedding induction method works best for the sentence boundary detection task, specifically whether it be those which were proposed to capture semantic, syntactic or morphological similarities.
Musical counterpoint, a musical technique in which two or more independent melodies are played simultaneously with the goal of creating harmony, has been around since the baroque era.
However, to our knowledge computational generation of aesthetically pleasing linear counterpoint based on subjective fitness assessment has not been explored by the evolutionary computation community (although generation using objective fitness has been attempted in quite a few cases).
The independence of contrapuntal melodies and the subjective nature of musical aesthetics provide an excellent platform for the application of genetic algorithms.
In this paper, a genetic algorithm approach to generating contrapuntal melodies is explained, with a description of the various musical heuristics used and of how variable-length chromosome strings are used to avoid generating "jerky" rhythms and melodic phrases, as well as how subjectivity is incorporated into the algorithm's fitness measures.
Next, results from empirical testing of the algorithm are presented, with a focus on how a user's musical sophistication influences their experience.
Lastly, further musical and compositional applications of the algorithm are discussed along with planned future work on the algorithm.
Distributed word representations (word embeddings) have recently contributed to competitive performance in language modeling and several NLP tasks.
In this work, we train word embeddings for more than 100 languages using their corresponding Wikipedias.
We quantitatively demonstrate the utility of our word embeddings by using them as the sole features for training a part of speech tagger for a subset of these languages.
We find their performance to be competitive with near state-of-art methods in English, Danish and Swedish.
Moreover, we investigate the semantic features captured by these embeddings through the proximity of word groupings.
We will release these embeddings publicly to help researchers in the development and enhancement of multilingual applications.
The distribution semantics is one of the most prominent approaches for the combination of logic programming and probability theory.
Many languages follow this semantics, such as Independent Choice Logic, PRISM, pD, Logic Programs with Annotated Disjunctions (LPADs) and ProbLog.
When a program contains functions symbols, the distribution semantics is well-defined only if the set of explanations for a query is finite and so is each explanation.
Well-definedness is usually either explicitly imposed or is achieved by severely limiting the class of allowed programs.
In this paper we identify a larger class of programs for which the semantics is well-defined together with an efficient procedure for computing the probability of queries.
Since LPADs offer the most general syntax, we present our results for them, but our results are applicable to all languages under the distribution semantics.
We present the algorithm "Probabilistic Inference with Tabling and Answer subsumption" (PITA) that computes the probability of queries by transforming a probabilistic program into a normal program and then applying SLG resolution with answer subsumption.
PITA has been implemented in XSB and tested on six domains: two with function symbols and four without.
The execution times are compared with those of ProbLog, cplint and CVE, PITA was almost always able to solve larger problems in a shorter time, on domains with and without function symbols.
Dropout Variational Inference, or Dropout Sampling, has been recently proposed as an approximation technique for Bayesian Deep Learning and evaluated for image classification and regression tasks.
This paper investigates the utility of Dropout Sampling for object detection for the first time.
We demonstrate how label uncertainty can be extracted from a state-of-the-art object detection system via Dropout Sampling.
We evaluate this approach on a large synthetic dataset of 30,000 images, and a real-world dataset captured by a mobile robot in a versatile campus environment.
We show that this uncertainty can be utilized to increase object detection performance under the open-set conditions that are typically encountered in robotic vision.
A Dropout Sampling network is shown to achieve a 12.3% increase in recall (for the same precision score as a standard network) and a 15.1% increase in precision (for the same recall score as the standard network).
We establish an equivalence between two seemingly different theories: one is the traditional axiomatisation of incomplete preferences on horse lotteries based on the mixture independence axiom; the other is the theory of desirable gambles developed in the context of imprecise probability.
The equivalence allows us to revisit incomplete preferences from the viewpoint of desirability and through the derived notion of coherent lower previsions.
Perhaps most importantly, we argue throughout that desirability is a powerful and natural setting to model, and work with, incomplete preferences, even in case of non-Archimedean problems.
This leads us to suggest that desirability, rather than preference, should be the primitive notion at the basis of decision-theoretic axiomatisations.
For the task of subdecimeter aerial imagery segmentation, fine-grained semantic segmentation results are usually difficult to obtain because of complex remote sensing content and optical conditions.
Recently, convolutional neural networks (CNNs) have shown outstanding performance on this task.
Although many deep neural network structures and techniques have been applied to improve the accuracy, few have paid attention to better differentiating the easily confused classes.
In this paper, we propose TreeSegNet which adopts an adaptive network to increase the classification rate at the pixelwise level.
Specifically, based on the infrastructure of DeepUNet, a Tree-CNN block in which each node represents a ResNeXt unit is constructed adaptively according to the confusion matrix and the proposed TreeCutting algorithm.
By transporting feature maps through concatenating connections, the Tree-CNN block fuses multiscale features and learns best weights for the model.
In experiments on the ISPRS 2D semantic labeling Potsdam dataset, the results obtained by TreeSegNet are better than those of other published state-of-the-art methods.
Detailed comparison and analysis show that the improvement brought by the adaptive Tree-CNN block is significant.
We give a new simple and short ("one-line") analysis for the runtime of the well-known Euclidean Algorithm.
While very short simple, the obtained upper bound in near-optimal.
Driving support systems, such as car navigation systems are becoming common and they support driver in several aspects.
Non-intrusive method of detecting Fatigue and drowsiness based on eye-blink count and eye directed instruction controlhelps the driver to prevent from collision caused by drowsy driving.
Eye detection and tracking under various conditions such as illumination, background, face alignment and facial expression makes the problem complex.Neural Network based algorithm is proposed in this paper to detect the eyes efficiently.
In the proposed algorithm, first the neural Network is trained to reject the non-eye regionbased on images with features of eyes and the images with features of non-eye using Gabor filter and Support Vector Machines to reduce the dimension and classify efficiently.
In the algorithm, first the face is segmented using L*a*btransform color space, then eyes are detected using HSV and Neural Network approach.
The algorithm is tested on nearly 100 images of different persons under different conditions and the results are satisfactory with success rate of 98%.The Neural Network is trained with 50 non-eye images and 50 eye images with different angles using Gabor filter.
This paper is a part of research work on "Development of Non-Intrusive system for real-time Monitoring and Prediction of Driver Fatigue and drowsiness" project sponsored by Department of Science & Technology, Govt. of India, New Delhi at Vignan Institute of Technology and Sciences, Vignan Hills, Hyderabad.
Recent developments in image quality, data storage, and computational capacity have heightened the need for texture analysis in image process.
To date various methods have been developed and introduced for assessing textures in images.
One of the most popular texture analysis methods is the Texture Energy Measure (TEM) and it has been used for detecting edges, levels, waves, spots and ripples by employing predefined TEM masks to images.
Despite several success- ful studies, TEM has a number of serious weaknesses in use.
The major drawback is; the masks are predefined therefore they cannot be adapted to image.
A new method, Adaptive Texture Energy Measure Method (aTEM), was offered to over- come this disadvantage of TEM by using adaptive masks by adjusting the contrast, sharpening and orientation angle of the mask.
To assess the applicability of aTEM, it is compared with TEM.
The accuracy of the classification of butterfly, flower seed and Brodatz datasets are 0.08, 0.3292 and 0.3343, respectively by TEM and 0.0053, 0.2417 and 0.3153, respectively by aTEM.
The results of this study indicate that aTEM is a successful method for texture analysis.
We introduce a hierarchy of fast-growing complexity classes and show its suitability for completeness statements of many non elementary problems.
This hierarchy allows the classification of many decision problems with a non-elementary complexity, which occur naturally in logic, combinatorics, formal languages, verification, etc., with complexities ranging from simple towers of exponentials to Ackermannian and beyond.
Automatic voice-controlled systems have changed the way humans interact with a computer.
Voice or speech recognition systems allow a user to make a hands-free request to the computer, which in turn processes the request and serves the user with appropriate responses.
After years of research and developments in machine learning and artificial intelligence, today voice-controlled technologies have become more efficient and are widely applied in many domains to enable and improve human-to-human and human-to-computer interactions.
The state-of-the-art e-commerce applications with the help of web technologies offer interactive and user-friendly interfaces.
However, there are some instances where people, especially with visual disabilities, are not able to fully experience the serviceability of such applications.
A voice-controlled system embedded in a web application can enhance user experience and can provide voice as a means to control the functionality of e-commerce websites.
In this paper, we propose a taxonomy of speech recognition systems (SRS) and present a voice-controlled commodity purchase e-commerce application using IBM Watson speech-to-text to demonstrate its usability.
The prototype can be extended to other application scenarios such as government service kiosks and enable analytics of the converted text data for scenarios such as medical diagnosis at the clinics.
Network analysis defines a number of centrality measures to identify the most central nodes in a network.
Fast computation of those measures is a major challenge in algorithmic network analysis.
Aside from closeness and betweenness, Katz centrality is one of the established centrality measures.
In this paper, we consider the problem of computing rankings for Katz centrality.
In particular, we propose upper and lower bounds on the Katz score of a given node.
While previous approaches relied on numerical approximation or heuristics to compute Katz centrality rankings, we construct an algorithm that iteratively improves those upper and lower bounds until a correct Katz ranking is obtained.
We extend our algorithm to dynamic graphs while maintaining its correctness guarantees.
Experiments demonstrate that our static graph algorithm outperforms both numerical approaches and heuristics with speedups between 1.5x and 3.5x, depending on the desired quality guarantees.
Our dynamic graph algorithm improves upon the static algorithm for update batches of less than 10000 edges.
We provide efficient parallel CPU and GPU implementations of our algorithms that enable near real-time Katz centrality computation for graphs with hundreds of millions of nodes in fractions of seconds.
Surrogate-based optimization and nature-inspired metaheuristics have become the state-of-the-art in solving real-world optimization problems.
Still, it is difficult for beginners and even experts to get an overview that explains their advantages in comparison to the large number of available methods in the scope of continuous optimization.
Available taxonomies lack the integration of surrogate-based approaches and thus their embedding in the larger context of this broad field.
This article presents a taxonomy of the field, which further matches the idea of nature-inspired algorithms, as it is based on the human behavior in path finding.
Intuitive analogies make it easy to conceive the most basic principles of the search algorithms, even for beginners and non-experts in this area of research.
However, this scheme does not oversimplify the high complexity of the different algorithms, as the class identifier only defines a descriptive meta-level of the algorithm search strategies.
The taxonomy was established by exploring and matching algorithm schemes, extracting similarities and differences, and creating a set of classification indicators to distinguish between five distinct classes.
In practice, this taxonomy allows recommendations for the applicability of the corresponding algorithms and helps developers trying to create or improve their own algorithms.
Distributed stateful stream processing enables the deployment and execution of large scale continuous computations in the cloud, targeting both low latency and high throughput.
One of the most fundamental challenges of this paradigm is providing processing guarantees under potential failures.
Existing approaches rely on periodic global state snapshots that can be used for failure recovery.
Those approaches suffer from two main drawbacks.
First, they often stall the overall computation which impacts ingestion.
Second, they eagerly persist all records in transit along with the operation states which results in larger snapshots than required.
In this work we propose Asynchronous Barrier Snapshotting (ABS), a lightweight algorithm suited for modern dataflow execution engines that minimises space requirements.
ABS persists only operator states on acyclic execution topologies while keeping a minimal record log on cyclic dataflows.
We implemented ABS on Apache Flink, a distributed analytics engine that supports stateful stream processing.
Our evaluation shows that our algorithm does not have a heavy impact on the execution, maintaining linear scalability and performing well with frequent snapshots.
While the incipient internet was largely text-based, the modern digital world is becoming increasingly multi-modal.
Here, we examine multi-modal classification where one modality is discrete, e.g. text, and the other is continuous, e.g. visual representations transferred from a convolutional neural network.
In particular, we focus on scenarios where we have to be able to classify large quantities of data quickly.
We investigate various methods for performing multi-modal fusion and analyze their trade-offs in terms of classification accuracy and computational efficiency.
Our findings indicate that the inclusion of continuous information improves performance over text-only on a range of multi-modal classification tasks, even with simple fusion methods.
In addition, we experiment with discretizing the continuous features in order to speed up and simplify the fusion process even further.
Our results show that fusion with discretized features outperforms text-only classification, at a fraction of the computational cost of full multi-modal fusion, with the additional benefit of improved interpretability.
Given a social network with diffusion probabilities as edge weights and an integer k, which k nodes should be chosen for initial injection of information to maximize influence in the network?
This problem is known as Target Set Selection in a social network (TSS Problem) and more popularly, Social Influence Maximization Problem (SIM Problem).
This is an active area of research in computational social network analysis domain since one and half decades or so.
Due to its practical importance in various domains, such as viral marketing, target advertisement, personalized recommendation, the problem has been studied in different variants, and different solution methodologies have been proposed over the years.
Hence, there is a need for an organized and comprehensive review on this topic.
This paper presents a survey on the progress in and around TSS Problem.
At last, it discusses current research trends and future research directions as well.
Many applications in different domains produce large amount of time series data.
Making accurate forecasting is critical for many decision makers.
Various time series forecasting methods exist which use linear and nonlinear models separately or combination of both.
Studies show that combining of linear and nonlinear models can be effective to improve forecasting performance.
However, some assumptions that those existing methods make, might restrict their performance in certain situations.
We provide a new Autoregressive Integrated Moving Average (ARIMA)-Artificial Neural Network(ANN) hybrid method that work in a more general framework.
Experimental results show that strategies for decomposing the original data and for combining linear and nonlinear models throughout the hybridization process are key factors in the forecasting performance of the methods.
By using appropriate strategies, our hybrid method can be an effective way to improve forecasting accuracy obtained by traditional hybrid methods and also either of the individual methods used separately.
Dynamically typed programming languages such as JavaScript and Python defer type checking to run time.
In order to maximize performance, dynamic language VM implementations must attempt to eliminate redundant dynamic type checks.
However, type inference analyses are often costly and involve tradeoffs between compilation time and resulting precision.
This has lead to the creation of increasingly complex multi-tiered VM architectures.
This paper introduces lazy basic block versioning, a simple JIT compilation technique which effectively removes redundant type checks from critical code paths.
This novel approach lazily generates type-specialized versions of basic blocks on-the-fly while propagating context-dependent type information.
This does not require the use of costly program analyses, is not restricted by the precision limitations of traditional type analyses and avoids the implementation complexity of speculative optimization techniques.
We have implemented intraprocedural lazy basic block versioning in a JavaScript JIT compiler.
This approach is compared with a classical flow-based type analysis.
Lazy basic block versioning performs as well or better on all benchmarks.
On average, 71% of type tests are eliminated, yielding speedups of up to 50%.
We also show that our implementation generates more efficient machine code than TraceMonkey, a tracing JIT compiler for JavaScript, on several benchmarks.
The combination of implementation simplicity, low algorithmic complexity and good run time performance makes basic block versioning attractive for baseline JIT compilers.
We give an example of a three-person deterministic graphical game that has no Nash equilibrium in pure stationary strategies.
The game has seven positions, four outcomes (a unique cycle and three terminal positions), and its normal form is of size 2 x 2 x 4 only.
Thus, our example strengthens significantly the one obtained in 2014 by Gurvich and Oudalov; the latter has four players, five terminals, and a 2 x 4 x 6 x 8 normal form.
Furthermore, our example is minimal with respect to the number of players.
Both examples are tight but not Nash-solvable.
Such examples were known since 1975, but they were not related to deterministic graphical games.
Moreover, due to the small size of our example, we can strengthen it further by showing that it has no Nash equilibrium not only in pure but also in independently mixed strategies, for both Markovian and a priori evaluations.
Principal component analysis (PCA) has well-documented merits for data extraction and dimensionality reduction.
PCA deals with a single dataset at a time, and it is challenged when it comes to analyzing multiple datasets.
Yet in certain setups, one wishes to extract the most significant information of one dataset relative to other datasets.
Specifically, the interest may be on identifying, namely extracting features that are specific to a single target dataset but not the others.
This paper develops a novel approach for such so-termed discriminative data analysis, and establishes its optimality in the least-squares (LS) sense under suitable data modeling assumptions.
The criterion reveals linear combinations of variables by maximizing the ratio of the variance of the target data to that of the remainders.
The novel approach solves a generalized eigenvalue problem by performing SVD just once.
Numerical tests using synthetic and real datasets showcase the merits of the proposed approach relative to its competing alternatives.
We analyze the time evolution of citations acquired by articles from journals of the American Physical Society (PRA, PRB, PRC, PRD, PRE and PRL).
The observed change over time in the number of papers published in each journal is considered an exogenously caused variation in citability that is accounted for by a normalization.
The appropriately inflation-adjusted citation rates are found to be separable into a preferential-attachment-type growth kernel and a purely obsolescence-related (i.e., monotonously decreasing as a function of time since publication) aging function.
Variations in the empirically extracted parameters of the growth kernels and aging functions associated with different journals point to research-field-specific characteristics of citation intensity and knowledge flow.
Comparison with analogous results for the citation dynamics of technology-disaggregated cohorts of patents provides deeper insight into the basic principles of information propagation as indicated by citing behavior.
This study concerns with the diagnosis of aerospace structure defects by applying a HPC parallel implementation of a novel learning algorithm, named U-BRAIN.
The Soft Computing approach allows advanced multi-parameter data processing in composite materials testing.
The HPC parallel implementation overcomes the limits due to the great amount of data and the complexity of data processing.
Our experimental results illustrate the effectiveness of the U-BRAIN parallel implementation as defect classifier in aerospace structures.
The resulting system is implemented on a Linux-based cluster with multi-core architecture.
The Wang tiling is a classical problem in combinatorics.
A major theoretical question is to find a (small) set of tiles which tiles the plane only aperiodically.
In this case, resulting tilings are rather restrictive.
On the other hand, Wang tiles are used as a tool to generate textures and patterns in computer graphics.
In these applications, a set of tiles is normally chosen so that it tiles the plane or its sub-regions easily in many different ways.
With computer graphics applications in mind, we introduce a class of such tileset, which we call sequentially permissive tilesets, and consider tiling problems with constrained boundary.
We apply our methodology to a special set of Wang tiles, called Brick Wang tiles, introduced by Derouet-Jourdan et al. in 2015 to model wall patterns.
We generalise their result by providing a linear algorithm to decide and solve the tiling problem for arbitrary planar regions with holes.
Many real world applications can be framed as multi-objective optimization problems, where we wish to simultaneously optimize for multiple criteria.
Bayesian optimization techniques for the multi-objective setting are pertinent when the evaluation of the functions in question are expensive.
Traditional methods for multi-objective optimization, both Bayesian and otherwise, are aimed at recovering the Pareto front of these objectives.
However, in certain cases a practitioner might desire to identify Pareto optimal points only in a particular region of the Pareto front due to external considerations.
In this work, we propose a strategy based on random scalarizations of the objectives that addresses this problem.
While being computationally similar or cheaper than other approaches, our approach is flexible enough to sample from specified subsets of the Pareto front or the whole of it.
We also introduce a novel notion of regret in the multi-objective setting and show that our strategy achieves sublinear regret.
We experiment with both synthetic and real-life problems, and demonstrate superior performance of our proposed algorithm in terms of flexibility, scalability and regret.
Early diagnosis of pulmonary nodules (PNs) can improve the survival rate of patients and yet is a challenging task for radiologists due to the image noise and artifacts in computed tomography (CT) images.
In this paper, we propose a novel and effective abnormality detector implementing the attention mechanism and group convolution on 3D single-shot detector (SSD) called group-attention SSD (GA-SSD).
We find that group convolution is effective in extracting rich context information between continuous slices, and attention network can learn the target features automatically.
We collected a large-scale dataset that contained 4146 CT scans with annotations of varying types and sizes of PNs (even PNs smaller than 3mm were annotated).
To the best of our knowledge, this dataset is the largest cohort with relatively complete annotations for PNs detection.
Our experimental results show that the proposed group-attention SSD outperforms the classic SSD framework as well as the state-of-the-art 3DCNN, especially on some challenging lesion types.
To understand a node's centrality in a multiplex network, its centrality values in all the layers of the network can be aggregated.
This requires a normalization of the values, to allow their meaningful comparison and aggregation over networks with different sizes and orders.
The concrete choices of such preprocessing steps like normalization and aggregation are almost never discussed in network analytic papers.
In this paper, we show that even sticking to the most simple centrality index (the degree) but using different, classic choices of normalization and aggregation strategies, can turn a node from being among the most central to being among the least central.
We present our results by using an aggregation operator which scales between different, classic aggregation strategies based on three multiplex networks.
We also introduce a new visualization and characterization of a node's sensitivity to the choice of a normalization and aggregation strategy in multiplex networks.
The observed high sensitivity of single nodes to the specific choice of aggregation and normalization strategies is of strong importance, especially for all kinds of intelligence-analytic software as it questions the interpretations of the findings.
Semantic segmentation and object detection research have recently achieved rapid progress.
However, the former task has no notion of different instances of the same object, and the latter operates at a coarse, bounding-box level.
We propose an Instance Segmentation system that produces a segmentation map where each pixel is assigned an object class and instance identity label.
Most approaches adapt object detectors to produce segments instead of boxes.
In contrast, our method is based on an initial semantic segmentation module, which feeds into an instance subnetwork.
This subnetwork uses the initial category-level segmentation, along with cues from the output of an object detector, within an end-to-end CRF to predict instances.
This part of our model is dynamically instantiated to produce a variable number of instances per image.
Our end-to-end approach requires no post-processing and considers the image holistically, instead of processing independent proposals.
Therefore, unlike some related work, a pixel cannot belong to multiple instances.
Furthermore, far more precise segmentations are achieved, as shown by our state-of-the-art results (particularly at high IoU thresholds) on the Pascal VOC and Cityscapes datasets.
This paper deals with the problem of control of partially known nonlinear systems, which have an open-loop stable equilibrium, but we would like to add a PI controller to regulate its behavior around another operating point.
Our main contribution is the identification of a class of systems for which a globally stable PI can be designed knowing only the systems input matrix and measuring only the actuated coordinates.
The construction of the PI is done invoking passivity theory.
The difficulties encountered in the design of adaptive PI controllers with the existing theoretical tools are also discussed.
As an illustration of the theory, we consider port--Hamiltonian systems and a class of thermal processes.
Kernel-based nonlinear mixing models have been applied to unmix spectral information of hyperspectral images when the type of mixing occurring in the scene is too complex or unknown.
Such methods, however, usually require the inversion of matrices of sizes equal to the number of spectral bands.
Reducing the computational load of these methods remains a challenge in large scale applications.
This paper proposes a centralized method for band selection (BS) in the reproducing kernel Hilbert space (RKHS).
It is based upon the coherence criterion, which sets the largest value allowed for correlations between the basis kernel functions characterizing the unmixing model.
We show that the proposed BS approach is equivalent to solving a maximum clique problem (MCP), that is, searching for the biggest complete subgraph in a graph.
Furthermore, we devise a strategy for selecting the coherence threshold and the Gaussian kernel bandwidth using coherence bounds for linearly independent bases.
Simulation results illustrate the efficiency of the proposed method.
Learning with recurrent neural networks (RNNs) on long sequences is a notoriously difficult task.
There are three major challenges: 1) complex dependencies, 2) vanishing and exploding gradients, and 3) efficient parallelization.
In this paper, we introduce a simple yet effective RNN connection structure, the DilatedRNN, which simultaneously tackles all of these challenges.
The proposed architecture is characterized by multi-resolution dilated recurrent skip connections and can be combined flexibly with diverse RNN cells.
Moreover, the DilatedRNN reduces the number of parameters needed and enhances training efficiency significantly, while matching state-of-the-art performance (even with standard RNN cells) in tasks involving very long-term dependencies.
To provide a theory-based quantification of the architecture's advantages, we introduce a memory capacity measure, the mean recurrent length, which is more suitable for RNNs with long skip connections than existing measures.
We rigorously prove the advantages of the DilatedRNN over other recurrent neural architectures.
The code for our method is publicly available at https://github.com/code-terminator/DilatedRNN
We propose CAVIA, a meta-learning method for fast adaptation that is scalable, flexible, and easy to implement.
CAVIA partitions the model parameters into two parts: context parameters that serve as additional input to the model and are adapted on individual tasks, and shared parameters that are meta-trained and shared across tasks.
At test time, the context parameters are updated with one or several gradient steps on a task-specific loss that is backpropagated through the shared part of the network.
Compared to approaches that adjust all parameters on a new task (e.g., MAML), CAVIA can be scaled up to larger networks without overfitting on a single task, is easier to implement, and is more robust to the inner-loop learning rate.
We show empirically that CAVIA outperforms MAML on regression, classification, and reinforcement learning problems.
We introduce a neural reading comprehension model that integrates external commonsense knowledge, encoded as a key-value memory, in a cloze-style setting.
Instead of relying only on document-to-question interaction or discrete features as in prior work, our model attends to relevant external knowledge and combines this knowledge with the context representation before inferring the answer.
This allows the model to attract and imply knowledge from an external knowledge source that is not explicitly stated in the text, but that is relevant for inferring the answer.
Our model improves results over a very strong baseline on a hard Common Nouns dataset, making it a strong competitor of much more complex models.
By including knowledge explicitly, our model can also provide evidence about the background knowledge used in the RC process.
The web graph is a commonly-used network representation of the hyperlink structure of a website.
A network of similar structure to the web graph, which we call the session graph has properties that reflect the browsing habits of the agents in the web server logs.
In this paper, we apply session graphs to compare the activity of humans against web robots or crawlers.
Understanding these properties will enable us to improve models of HTTP traffic, which can be used to predict and generate realistic traffic for testing and improving web server efficiency, as well as devising new caching algorithms.
We apply large-scale network properties, such as the connectivity and degree distribution of human and Web robot session graphs in order to identify characteristics of the traffic which would be useful for modeling web traffic and improving cache performance.
We find that the empirical degree distributions of session graphs for human and robot requests on one Web server are best fit by different theoretical distributions, indicating at a difference in the processes which generate the traffic.
This paper investigates and bounds the expected solution quality of combinatorial optimization problems when feasible solutions are chosen at random.
Loose general bounds are discovered, as well as families of combinatorial optimization problems for which random feasible solutions are expected to be a constant factor of optimal.
One implication of this result is that, for graphical problems, if the average edge weight in a feasible solution is sufficiently small, then any randomly chosen feasible solution to the problem will be a constant factor of optimal.
For example, under certain well-defined circumstances, the expected constant of approximation of a randomly chosen feasible solution to the Steiner network problem is bounded above by 3.
Empirical analysis supports these bounds and actually suggest that they might be tightened.
The investment on the stock market is prone to be affected by the Internet.
For the purpose of improving the prediction accuracy, we propose a multi-task stock prediction model that not only considers the stock correlations but also supports multi-source data fusion.
Our proposed model first utilizes tensor to integrate the multi-sourced data, including financial Web news, investors' sentiments extracted from the social network and some quantitative data on stocks.
In this way, the intrinsic relationships among different information sources can be captured, and meanwhile, multi-sourced information can be complemented to solve the data sparsity problem.
Secondly, we propose an improved sub-mode coordinate algorithm (SMC).
SMC is based on the stock similarity, aiming to reduce the variance of their subspace in each dimension produced by the tensor decomposition.
The algorithm is able to improve the quality of the input features, and thus improves the prediction accuracy.
And the paper utilizes the Long Short-Term Memory (LSTM) neural network model to predict the stock fluctuation trends.
Finally, the experiments on 78 A-share stocks in CSI 100 and thirteen popular HK stocks in the year 2015 and 2016 are conducted.
The results demonstrate the improvement on the prediction accuracy and the effectiveness of the proposed model.
Although Neural Machine Translation (NMT) has achieved remarkable progress in the past several years, most NMT systems still suffer from a fundamental shortcoming as in other sequence generation tasks: errors made early in generation process are fed as inputs to the model and can be quickly amplified, harming subsequent sequence generation.
To address this issue, we propose a novel model regularization method for NMT training, which aims to improve the agreement between translations generated by left-to-right (L2R) and right-to-left (R2L) NMT decoders.
This goal is achieved by introducing two Kullback-Leibler divergence regularization terms into the NMT training objective to reduce the mismatch between output probabilities of L2R and R2L models.
In addition, we also employ a joint training strategy to allow L2R and R2L models to improve each other in an interactive update process.
Experimental results show that our proposed method significantly outperforms state-of-the-art baselines on Chinese-English and English-German translation tasks.
The modeling of speech can be used for speech synthesis and speech recognition.
We present a speech analysis method based on pole-zero modeling of speech with mixed block sparse and Gaussian excitation.
By using a pole-zero model, instead of the all-pole model, a better spectral fitting can be expected.
Moreover, motivated by the block sparse glottal flow excitation during voiced speech and the white noise excitation for unvoiced speech, we model the excitation sequence as a combination of block sparse signals and white noise.
A variational EM (VEM) method is proposed for estimating the posterior PDFs of the block sparse residuals and point estimates of mod- elling parameters within a sparse Bayesian learning framework.
Compared to conventional pole-zero and all-pole based methods, experimental results show that the proposed method has lower spectral distortion and good performance in reconstructing of the block sparse excitation.
Cloud computing is recognized as one of the most promising solutions to information technology, e.g., for storing and sharing data in the web service which is sustained by a company or third party instead of storing data in a hard drive or other devices.
It is essentially a physical storage system which provides large storage of data and faster computing to users over the Internet.
In this cloud system, the third party allows to preserve data of clients or users only for business purpose and also for a limited period of time.
The users are used to share data confidentially among themselves and to store data virtually to save the cost of physical devices as well as the time.
In this paper, we propose a discrete dynamical system for cloud computing and data management of the storage service between a third party and users.
A framework, comprised of different techniques and procedures for distribution of storage and their implementation with users and the third party is given.
For illustration purpose, the model is considered for two users and a third party, and its dynamical properties are briefly analyzed and discussed.
It is shown that the discrete system exhibits periodic, quasiperiodic and chaotic states.
The latter discerns that the cloud computing system with distribution of data and storage between users and the third party may be secured.
Some issues of data security are discussed and a random replication scheme is proposed to ensure that the data loss can be highly reduced compared to the existing schemes in the literature.
Leveraging human grasping skills to teach a robot to perform a manipulation task is appealing, but there are several limitations to this approach: time-inefficient data capture procedures, limited generalization of the data to other grasps and objects, and inability to use that data to learn more about how humans perform and evaluate grasps.
This paper presents a data capture protocol that partially addresses these deficiencies by asking participants to specify ranges over which a grasp is valid.
The protocol is verified both qualitatively through online survey questions (where 95.38% of within-range grasps are identified correctly with the nearest extreme grasp) and quantitatively by showing that there is small variation in grasps ranges from different participants as measured by joint angles, contact points, and position.
We demonstrate that these grasp ranges are valid through testing on a physical robot (93.75% of grasps interpolated from grasp ranges are successful).
This paper studies convolutional neural networks (CNN) to learn unsupervised feature representations for 44 different plant species, collected at the Royal Botanic Gardens, Kew, England.
To gain intuition on the chosen features from the CNN model (opposed to a 'black box' solution), a visualisation technique based on the deconvolutional networks (DN) is utilized.
It is found that venations of different order have been chosen to uniquely represent each of the plant species.
Experimental results using these CNN features with different classifiers show consistency and superiority compared to the state-of-the art solutions which rely on hand-crafted features.
Today there are many universal compression algorithms, but in most cases is for specific data better using specific algorithm - JPEG for images, MPEG for movies, etc.
For textual documents there are special methods based on PPM algorithm or methods with non-character access, e.g. word-based compression.
In the past, several papers describing variants of word-based compression using Huffman encoding or LZW method were published.
The subject of this paper is the description of a word-based compression variant based on the LZ77 algorithm.
The LZ77 algorithm and its modifications are described in this paper.
Moreover, various ways of sliding window implementation and various possibilities of output encoding are described, as well.
This paper also includes the implementation of an experimental application, testing of its efficiency and finding the best combination of all parts of the LZ77 coder.
This is done to achieve the best compression ratio.
In conclusion there is comparison of this implemented application with other word-based compression programs and with other commonly used compression programs.
Selecting the most appropriate data examples to present a deep neural network (DNN) at different stages of training is an unsolved challenge.
Though practitioners typically ignore this problem, a non-trivial data scheduling method may result in a significant improvement in both convergence and generalization performance.
In this paper, we introduce Self-Paced Learning with Adaptive Deep Visual Embeddings (SPL-ADVisE), a novel end-to-end training protocol that unites self-paced learning (SPL) and deep metric learning (DML).
We leverage the Magnet Loss to train an embedding convolutional neural network (CNN) to learn a salient representation space.
The student CNN classifier dynamically selects similar instance-level training examples to form a mini-batch, where the easiness from the cross-entropy loss and the true diverseness of examples from the learned metric space serve as sample importance priors.
To demonstrate the effectiveness of SPL-ADVisE, we use deep CNN architectures for the task of supervised image classification on several coarse- and fine-grained visual recognition datasets.
Results show that, across all datasets, the proposed method converges faster and reaches a higher final accuracy than other SPL variants, particularly on fine-grained classes.
In this paper, we reviewed the notes on using Web map image provided by Web map service, from the viewpoint of copyright act.
The copyright act aims to contribute to creation of culture by protecting the rights of authors and others, and promoting fair exploitation of cultural products.
Therefore, everyone can use copyrighted materials to the extent of the copyright limitation based on copyright act.
The Web map image, including maps, aerial photo and satellite image, are one of copyrighted materials, so it can be used within the limits of copyright.
However, the available range of Web map image under the copyright act is not wide.
In addition, it is pointed out that the copyright act has not been able to follow the progress of digitalization of copyrighted materials.
It is expected to revise the copyright act corresponding to digitalization of copyrighted work.
We investigate the problem of learning discrete, undirected graphical models in a differentially private way.
We show that the approach of releasing noisy sufficient statistics using the Laplace mechanism achieves a good trade-off between privacy, utility, and practicality.
A naive learning algorithm that uses the noisy sufficient statistics "as is" outperforms general-purpose differentially private learning algorithms.
However, it has three limitations: it ignores knowledge about the data generating process, rests on uncertain theoretical foundations, and exhibits certain pathologies.
We develop a more principled approach that applies the formalism of collective graphical models to perform inference over the true sufficient statistics within an expectation-maximization framework.
We show that this learns better models than competing approaches on both synthetic data and on real human mobility data used as a case study.
Industry-grade database systems are expected to produce the same result if the same query is repeatedly run on the same input.
However, the numerous sources of non-determinism in modern systems make reproducible results difficult to achieve.
This is particularly true if floating-point numbers are involved, where the order of the operations affects the final result.
As part of a larger effort to extend database engines with data representations more suitable for machine learning and scientific applications, in this paper we explore the problem of making relational GroupBy over floating-point formats bit-reproducible, i.e., ensuring any execution of the operator produces the same result up to every single bit.
To that aim, we first propose a numeric data type that can be used as drop-in replacement for other number formats and is---unlike standard floating-point formats---associative.
We use this data type to make state-of-the-art GroupBy operators reproducible, but this approach incurs a slowdown between 4x and 12x compared to the same operator using conventional database number formats.
We thus explore how to modify existing GroupBy algorithms to make them bit-reproducible and efficient.
By using vectorized summation on batches and carefully balancing batch size, cache footprint, and preprocessing costs, we are able to reduce the slowdown due to reproducibility to a factor between 1.9x and 2.4x of aggregation in isolation and to a mere 2.7% of end-to-end query performance even on aggregation-intensive queries in MonetDB.
We thereby provide a solid basis for supporting more reproducible operations directly in relational engines.
This document is an extended version of an article currently in print for the proceedings of ICDE'18 with the same title and by the same authors.
The main additions are more implementation details and experiments.
In this paper we study the long-standing open question regarding the computational complexity of one of the core problems in supply chains management, the periodic joint replenishment problem.
This problem has received a lot of attention over the years and many heuristic and approximation algorithms were suggested.
However, in spite of the vast effort, the complexity of the problem remained unresolved.
In this paper, we provide a proof that the problem is indeed strongly NP-hard.
Gaze behavior is an important non-verbal cue in social signal processing and human-computer interaction.
In this paper, we tackle the problem of person- and head pose-independent 3D gaze estimation from remote cameras, using a multi-modal recurrent convolutional neural network (CNN).
We propose to combine face, eyes region, and face landmarks as individual streams in a CNN to estimate gaze in still images.
Then, we exploit the dynamic nature of gaze by feeding the learned features of all the frames in a sequence to a many-to-one recurrent module that predicts the 3D gaze vector of the last frame.
Our multi-modal static solution is evaluated on a wide range of head poses and gaze directions, achieving a significant improvement of 14.6% over the state of the art on EYEDIAP dataset, further improved by 4% when the temporal modality is included.
A canonical scenario in Machine-Type Communications (MTC) is the one featuring a large number of devices, each of them with sporadic traffic.
Hence, the number of served devices in a single LTE cell is not determined by the available aggregate rate, but rather by the limitations of the LTE access reservation protocol.
Specifically, the limited number of contention preambles and the limited amount of uplink grants per random access response are crucial to consider when dimensioning LTE networks for MTC.
We propose a low-complexity model of LTE's access reservation protocol that encompasses these two limitations and allows us to evaluate the outage probability at click-speed.
The model is based chiefly on closed-form expressions, except for the part with the feedback impact of retransmissions, which is determined by solving a fixed point equation.
Our model overcomes the incompleteness of the existing models that are focusing solely on the preamble collisions.
A comparison with the simulated LTE access reservation procedure that follows the 3GPP specifications, confirms that our model provides an accurate estimation of the system outage event and the number of supported MTC devices.
We present a new dataset and models for comprehending paragraphs about processes (e.g., photosynthesis), an important genre of text describing a dynamic world.
The new dataset, ProPara, is the first to contain natural (rather than machine-generated) text about a changing world along with a full annotation of entity states (location and existence) during those changes (81k datapoints).
The end-task, tracking the location and existence of entities through the text, is challenging because the causal effects of actions are often implicit and need to be inferred.
We find that previous models that have worked well on synthetic data achieve only mediocre performance on ProPara, and introduce two new neural models that exploit alternative mechanisms for state prediction, in particular using LSTM input encoding and span prediction.
The new models improve accuracy by up to 19%.
The dataset and models are available to the community at http://data.allenai.org/propara.
Satirical news is considered to be entertainment, but it is potentially deceptive and harmful.
Despite the embedded genre in the article, not everyone can recognize the satirical cues and therefore believe the news as true news.
We observe that satirical cues are often reflected in certain paragraphs rather than the whole document.
Existing works only consider document-level features to detect the satire, which could be limited.
We consider paragraph-level linguistic features to unveil the satire by incorporating neural network and attention mechanism.
We investigate the difference between paragraph-level features and document-level features, and analyze them on a large satirical news dataset.
The evaluation shows that the proposed model detects satirical news effectively and reveals what features are important at which level.
Image similarity involves fetching similar looking images given a reference image.
Our solution called SimNet, is a deep siamese network which is trained on pairs of positive and negative images using a novel online pair mining strategy inspired by Curriculum learning.
We also created a multi-scale CNN, where the final image embedding is a joint representation of top as well as lower layer embedding's.
We go on to show that this multi-scale siamese network is better at capturing fine grained image similarities than traditional CNN's.
The Internet Threat Monitoring (ITM),is a globally scoped Internet monitoring system whose goal is to measure, detect, characterize, and track threats such as distribute denial of service(DDoS) attacks and worms.
To block the monitoring system in the internet the attackers are targeted the ITM system.
In this paper we address flooding attack against ITM system in which the attacker attempt to exhaust the network and ITM's resources, such as network bandwidth, computing power, or operating system data structures by sending the malicious traffic.
We propose an information-theoretic frame work that models the flooding attacks using Botnet on ITM.
Based on this model we generalize the flooding attacks and propose an effective attack detection using Honeypots.
This paper introduces TakeFive, a new semantic role labeling method that transforms a text into a frame-oriented knowledge graph.
It performs dependency parsing, identifies the words that evoke lexical frames, locates the roles and fillers for each frame, runs coercion techniques, and formalises the results as a knowledge graph.
This formal representation complies with the frame semantics used in Framester, a factual-linguistic linked data resource.
The obtained precision, recall and F1 values indicate that TakeFive is competitive with other existing methods such as SEMAFOR, Pikes, PathLSTM and FRED.
We finally discuss how to combine TakeFive and FRED, obtaining higher values of precision, recall and F1.
The ability of intelligent agents to play games in human-like fashion is popularly considered a benchmark of progress in Artificial Intelligence.
Similarly, performance on multi-disciplinary tasks such as Visual Question Answering (VQA) is considered a marker for gauging progress in Computer Vision.
In our work, we bring games and VQA together.
Specifically, we introduce the first computational model aimed at Pictionary, the popular word-guessing social game.
We first introduce Sketch-QA, an elementary version of Visual Question Answering task.
Styled after Pictionary, Sketch-QA uses incrementally accumulated sketch stroke sequences as visual data.
Notably, Sketch-QA involves asking a fixed question ("What object is being drawn?") and gathering open-ended guess-words from human guessers.
We analyze the resulting dataset and present many interesting findings therein.
To mimic Pictionary-style guessing, we subsequently propose a deep neural model which generates guess-words in response to temporally evolving human-drawn sketches.
Our model even makes human-like mistakes while guessing, thus amplifying the human mimicry factor.
We evaluate our model on the large-scale guess-word dataset generated via Sketch-QA task and compare with various baselines.
We also conduct a Visual Turing Test to obtain human impressions of the guess-words generated by humans and our model.
Experimental results demonstrate the promise of our approach for Pictionary and similarly themed games.
Deep reinforcement learning (DRL) has shown incredible performance in learning various tasks to the human level.
However, unlike human perception, current DRL models connect the entire low-level sensory input to the state-action values rather than exploiting the relationship between and among entities that constitute the sensory input.
Because of this difference, DRL needs vast amount of experience samples to learn.
In this paper, we propose a Multi-focus Attention Network (MANet) which mimics human ability to spatially abstract the low-level sensory input into multiple entities and attend to them simultaneously.
The proposed method first divides the low-level input into several segments which we refer to as partial states.
After this segmentation, parallel attention layers attend to the partial states relevant to solving the task.
Our model estimates state-action values using these attended partial states.
In our experiments, MANet attains highest scores with significantly less experience samples.
Additionally, the model shows higher performance compared to the Deep Q-network and the single attention model as benchmarks.
Furthermore, we extend our model to attentive communication model for performing multi-agent cooperative tasks.
In multi-agent cooperative task experiments, our model shows 20% faster learning than existing state-of-the-art model.
Understanding when and how computational complexity can be used to protect elections against different manipulative actions has been a highly active research area over the past two decades.
A recent body of work, however, has shown that many of the NP-hardness shields, previously obtained, vanish when the electorate has single-peaked or nearly single-peaked preferences.
In light of these results, we investigate whether it is possible to reimpose NP-hardness shields for such electorates by allowing the voters to specify partial preferences instead of insisting they cast complete ballots.
In particular, we show that in single-peaked and nearly single-peaked electorates, if voters are allowed to submit top-truncated ballots, then the complexity of manipulation and bribery for many voting rules increases from being in P to being NP-complete.
In this paper a Metaheuristic approach for solving the N-Queens Problem is introduced to find the best possible solution in a reasonable amount of time.
Genetic Algorithm is used with a novel fitness function as the Metaheuristic.
The aim of N-Queens Problem is to place N queens on an N x N chessboard, in a way so that no queen is in conflict with the others.
Chromosome representation and genetic operations like Mutation and Crossover are described in detail.
Results show that this approach yields promising and satisfactory results in less time compared to that obtained from the previous approaches for several large values of N.
Word sense induction (WSI), which addresses polysemy by unsupervised discovery of multiple word senses, resolves ambiguities for downstream NLP tasks and also makes word representations more interpretable.
This paper proposes an accurate and efficient graph-based method for WSI that builds a global non-negative vector embedding basis (which are interpretable like topics) and clusters the basis indexes in the ego network of each polysemous word.
By adopting distributional inclusion vector embeddings as our basis formation model, we avoid the expensive step of nearest neighbor search that plagues other graph-based methods without sacrificing the quality of sense clusters.
Experiments on three datasets show that our proposed method produces similar or better sense clusters and embeddings compared with previous state-of-the-art methods while being significantly more efficient.
Statistical shape models (SSMs) represent a class of shapes as a normal distribution of point variations, whose parameters are estimated from example shapes.
Principal component analysis (PCA) is applied to obtain a low-dimensional representation of the shape variation in terms of the leading principal components.
In this paper, we propose a generalization of SSMs, called Gaussian Process Morphable Models (GPMMs).
We model the shape variations with a Gaussian process, which we represent using the leading components of its Karhunen-Loeve expansion.
To compute the expansion, we make use of an approximation scheme based on the Nystrom method.
The resulting model can be seen as a continuous analogon of an SSM.
However, while for SSMs the shape variation is restricted to the span of the example data, with GPMMs we can define the shape variation using any Gaussian process.
For example, we can build shape models that correspond to classical spline models, and thus do not require any example data.
Furthermore, Gaussian processes make it possible to combine different models.
For example, an SSM can be extended with a spline model, to obtain a model that incorporates learned shape characteristics, but is flexible enough to explain shapes that cannot be represented by the SSM.
We introduce a simple algorithm for fitting a GPMM to a surface or image.
This results in a non-rigid registration approach, whose regularization properties are defined by a GPMM.
We show how we can obtain different registration schemes,including methods for multi-scale, spatially-varying or hybrid registration, by constructing an appropriate GPMM.
As our approach strictly separates modelling from the fitting process, this is all achieved without changes to the fitting algorithm.
We show the applicability and versatility of GPMMs on a clinical use case, where the goal is the model-based segmentation of 3D forearm images.
Construction frequently appears at the bottom of productivity charts with decreasing indexes of productivity over the years.
Lack of innovation and delayed adoption, informal processes or insufficient rigor and consistency in process execution, insufficient knowledge transfer from project to project, weak project monitoring, little cross- functional cooperation, little collaboration with suppliers, conservative company culture, and a shortage of young talent and people development are usual issues.
Whereas work has been carried out on information technology and automation in construction their application is isolated without an interconnected information flow.
This paper suggests a framework to address production issues on construction by implementing an integrated automatic supervisory control and data acquisition for management and operations.
The system is divided into planning, monitoring, controlling, and executing groups clustering technologies to track both the project product and production.
This research stands on the four pillars of manufacturing knowledge and lean production (production processes, production management, equipment/tool design, and automated systems and control).
The framework offers benefits such as increased information flow, detection and prevention of overburdening equipment or labor (Muri) and production unevenness (Mura), reduction of waste (Muda), evidential and continuous process standardization and improvement, reuse and abstraction of project information across endeavors.
In the C-V2X sidelink Mode 4 communication, the sensing-based semi-persistent scheduling (SPS) implements a message collision avoidance algorithm to cope with the undesirable effects of wireless channel congestion.
Still, the current standard mechanism produces high number of packet collisions, which may hinder the high-reliability communications required in future C-V2X applications such as autonomous driving.
In this paper, we show that by drastically reducing the uncertainties in the choice of the resource to use for SPS, we can significantly reduce the message collisions in the C-V2X sidelink Mode 4.
Specifically, we propose the use of the "lookahead," which contains the next starting resource location in the time-frequency plane.
By exchanging the lookahead information piggybacked on the periodic safety message, vehicular user equipments (UEs) can eliminate most message collisions arising from the ignorance of other UEs' internal decisions.
Although the proposed scheme would require the inclusion of the lookahead in the control part of the packet, the benefit may outweigh the bandwidth cost, considering the stringent reliability requirement in future C-V2X applications.
Overlapping of cervical cells and poor contrast of cell cytoplasm are the major issues in accurate detection and segmentation of cervical cells.
An unsupervised cell segmentation approach is presented here.
Cell clump segmentation was carried out using the extended depth of field (EDF) image created from the images of different focal planes.
A modified Otsu method with prior class weights is proposed for accurate segmentation of nuclei from the cell clumps.
The cell cytoplasm was further segmented from cell clump depending upon the number of nucleus detected in that cell clump.
Level set model was used for cytoplasm segmentation.
A cellular automaton is a parallel synchronous computing model, which consists in a juxtaposition of finite automata whose state evolves according to that of their neighbors.
It induces a dynamical system on the set of configurations, i.e. the infinite sequences of cell states.
The limit set of the cellular automaton is the set of configurations which can be reached arbitrarily late in the evolution.
In this paper, we prove that all properties of limit sets of cellular automata with binary-state cells are undecidable, except surjectivity.
This is a refinement of the classical "Rice Theorem" that Kari proved on cellular automata with arbitrary state sets.
In ontology-based data access (OBDA), users are provided with a conceptual view of a (relational) data source that abstracts away details about data storage.
This conceptual view is realized through an ontology that is connected to the data source through declarative mappings, and query answering is carried out by translating the user queries over the conceptual view into SQL queries over the data source.
Standard translation techniques in OBDA try to transform the user query into a union of conjunctive queries (UCQ), following the heuristic argument that UCQs can be efficiently evaluated by modern relational database engines.
In this work, we show that translating to UCQs is not always the best choice, and that, under certain conditions on the interplay between the ontology, the map- pings, and the statistics of the data, alternative translations can be evaluated much more efficiently.
To find the best translation, we devise a cost model together with a novel cardinality estimation that takes into account all such OBDA components.
Our experiments confirm that (i) alternatives to the UCQ translation might produce queries that are orders of magnitude more efficient, and (ii) the cost model we propose is faithful to the actual query evaluation cost, and hence is well suited to select the best translation.
While state-of-the-art kernels for graphs with discrete labels scale well to graphs with thousands of nodes, the few existing kernels for graphs with continuous attributes, unfortunately, do not scale well.
To overcome this limitation, we present hash graph kernels, a general framework to derive kernels for graphs with continuous attributes from discrete ones.
The idea is to iteratively turn continuous attributes into discrete labels using randomized hash functions.
We illustrate hash graph kernels for the Weisfeiler-Lehman subtree kernel and for the shortest-path kernel.
The resulting novel graph kernels are shown to be, both, able to handle graphs with continuous attributes and scalable to large graphs and data sets.
This is supported by our theoretical analysis and demonstrated by an extensive experimental evaluation.
Moving Object Segmentation is a challenging task for jittery/wobbly videos.
For jittery videos, the non-smooth camera motion makes discrimination between foreground objects and background layers hard to solve.
While most recent works for moving video object segmentation fail in this scenario, our method generates an accurate segmentation of a single moving object.
The proposed method performs a sparse segmentation, where frame-wise labels are assigned only to trajectory coordinates, followed by the pixel-wise labeling of frames.
The sparse segmentation involving stabilization and clustering of trajectories in a 3-stage iterative process.
At the 1st stage, the trajectories are clustered using pairwise Procrustes distance as a cue for creating an affinity matrix.
The 2nd stage performs a block-wise Procrustes analysis of the trajectories and estimates Frechet means (in Kendall's shape space) of the clusters.
The Frechet means represent the average trajectories of the motion clusters.
An optimization function has been formulated to stabilize the Frechet means, yielding stabilized trajectories at the 3rd stage.
The accuracy of the motion clusters are iteratively refined, producing distinct groups of stabilized trajectories.
Next, the labels obtained from the sparse segmentation are propagated for pixel-wise labeling of the frames, using a GraphCut based energy formulation.
Use of Procrustes analysis and energy minimization in Kendall's shape space for moving object segmentation in jittery videos, is the novelty of this work.
Second contribution comes from experiments performed on a dataset formed of 20 real-world natural jittery videos, with manually annotated ground truth.
Experiments are done with controlled levels of artificial jitter on videos of SegTrack2 dataset.
Qualitative and quantitative results indicate the superiority of the proposed method.
This paper introduces a new method to solve the cross-domain recognition problem.
Different from the traditional domain adaption methods which rely on a global domain shift for all classes between source and target domain, the proposed method is more flexible to capture individual class variations across domains.
By adopting a natural and widely used assumption -- "the data samples from the same class should lay on a low-dimensional subspace, even if they come from different domains", the proposed method circumvents the limitation of the global domain shift, and solves the cross-domain recognition by finding the compact joint subspaces of source and target domain.
Specifically, given labeled samples in source domain, we construct subspaces for each of the classes.
Then we construct subspaces in the target domain, called anchor subspaces, by collecting unlabeled samples that are close to each other and highly likely all fall into the same class.
The corresponding class label is then assigned by minimizing a cost function which reflects the overlap and topological structure consistency between subspaces across source and target domains, and within anchor subspaces, respectively.We further combine the anchor subspaces to corresponding source subspaces to construct the compact joint subspaces.
Subsequently, one-vs-rest SVM classifiers are trained in the compact joint subspaces and applied to unlabeled data in the target domain.
We evaluate the proposed method on two widely used datasets: object recognition dataset for computer vision tasks, and sentiment classification dataset for natural language processing tasks.
Comparison results demonstrate that the proposed method outperforms the comparison methods on both datasets.
Energy management of plug-in Hybrid Electric Vehicles (HEVs) has different challenges from non-plug-in HEVs, due to bigger batteries and grid recharging.
Instead of tackling it to pursue energetic efficiency, an approach minimizing the driving cost incurred by the user - the combined costs of fuel, grid energy and battery degradation - is here proposed.
A real-time approximation of the resulting optimal policy is then provided, as well as some analytic insight into its dependence on the system parameters.
The advantages of the proposed formulation and the effectiveness of the real-time strategy are shown by means of a thorough simulation campaign.
Theory of Mind is the ability to attribute mental states (beliefs, intents, knowledge, perspectives, etc.) to others and recognize that these mental states may differ from one's own.
Theory of Mind is critical to effective communication and to teams demonstrating higher collective performance.
To effectively leverage the progress in Artificial Intelligence (AI) to make our lives more productive, it is important for humans and AI to work well together in a team.
Traditionally, there has been much emphasis on research to make AI more accurate, and (to a lesser extent) on having it better understand human intentions, tendencies, beliefs, and contexts.
The latter involves making AI more human-like and having it develop a theory of our minds.
In this work, we argue that for human-AI teams to be effective, humans must also develop a theory of AI's mind (ToAIM) - get to know its strengths, weaknesses, beliefs, and quirks.
We instantiate these ideas within the domain of Visual Question Answering (VQA).
We find that using just a few examples (50), lay people can be trained to better predict responses and oncoming failures of a complex VQA model.
We further evaluate the role existing explanation (or interpretability) modalities play in helping humans build ToAIM.
Explainable AI has received considerable scientific and popular attention in recent times.
Surprisingly, we find that having access to the model's internal states - its confidence in its top-k predictions, explicit or implicit attention maps which highlight regions in the image (and words in the question) the model is looking at (and listening to) while answering a question about an image - do not help people better predict its behavior.
Clinical measurements that can be represented as time series constitute an important fraction of the electronic health records and are often both uncertain and incomplete.
Recurrent neural networks are a special class of neural networks that are particularly suitable to process time series data but, in their original formulation, cannot explicitly deal with missing data.
In this paper, we explore imputation strategies for handling missing values in classifiers based on recurrent neural network (RNN) and apply a recently proposed recurrent architecture, the Gated Recurrent Unit with Decay, specifically designed to handle missing data.
We focus on the problem of detecting surgical site infection in patients by analyzing time series of their blood sample measurements and we compare the results obtained with different RNN-based classifiers.
Clinical Named Entity Recognition (CNER) aims to identify and classify clinical terms such as diseases, symptoms, treatments, exams, and body parts in electronic health records, which is a fundamental and crucial task for clinical and translational research.
In recent years, deep neural networks have achieved significant success in named entity recognition and many other Natural Language Processing (NLP) tasks.
Most of these algorithms are trained end to end, and can automatically learn features from large scale labeled datasets.
However, these data-driven methods typically lack the capability of processing rare or unseen entities.
Previous statistical methods and feature engineering practice have demonstrated that human knowledge can provide valuable information for handling rare and unseen cases.
In this paper, we address the problem by incorporating dictionaries into deep neural networks for the Chinese CNER task.
Two different architectures that extend the Bi-directional Long Short-Term Memory (Bi-LSTM) neural network and five different feature representation schemes are proposed to handle the task.
Computational results on the CCKS-2017 Task 2 benchmark dataset show that the proposed method achieves the highly competitive performance compared with the state-of-the-art deep learning methods.
Increasingly stringent performance requirements for motion control necessitate the use of increasingly detailed models of the system behavior.
Motion systems inherently move, therefore, spatio-temporal models of the flexible dynamics are essential.
In this paper, a two-step approach for the identification of the spatio-temporal behavior of mechanical systems is developed and applied to a prototype industrial wafer stage with a lightweight design for fast and highly accurate positioning.
The proposed approach exploits a modal modeling framework and combines recently developed powerful linear time invariant (LTI) identification tools with a spline-based mode-shape interpolation approach to estimate the spatial system behavior.
The experimental results for the wafer stage application confirm the suitability of the proposed approach for the identification of complex position-dependent mechanical systems, and show the pivotal role of the obtained models for improved motion control performance.
As more and more personal photos are shared and tagged in social media, avoiding privacy risks such as unintended recognition becomes increasingly challenging.
We propose a new hybrid approach to obfuscate identities in photos by head replacement.
Our approach combines state of the art parametric face synthesis with latest advances in Generative Adversarial Networks (GAN) for data-driven image synthesis.
On the one hand, the parametric part of our method gives us control over the facial parameters and allows for explicit manipulation of the identity.
On the other hand, the data-driven aspects allow for adding fine details and overall realism as well as seamless blending into the scene context.
In our experiments, we show highly realistic output of our system that improves over the previous state of the art in obfuscation rate while preserving a higher similarity to the original image content.
With rapidly increasing data, clustering algorithms are important tools for data analytics in modern research.
They have been successfully applied to a wide range of domains; for instance, bioinformatics, speech recognition, and financial analysis.
Formally speaking, given a set of data instances, a clustering algorithm is expected to divide the set of data instances into the subsets which maximize the intra-subset similarity and inter-subset dissimilarity, where a similarity measure is defined beforehand.
In this work, the state-of-the-arts clustering algorithms are reviewed from design concept to methodology; Different clustering paradigms are discussed.
Advanced clustering algorithms are also discussed.
After that, the existing clustering evaluation metrics are reviewed.
A summary with future insights is provided at the end.
Amidst growing concern over media manipulation, NLP attention has focused on overt strategies like censorship and "fake news'".
Here, we draw on two concepts from the political science literature to explore subtler strategies for government media manipulation: agenda-setting (selecting what topics to cover) and framing (deciding how topics are covered).
We analyze 13 years (100K articles) of the Russian newspaper Izvestia and identify a strategy of distraction: articles mention the U.S. more frequently in the month directly following an economic downturn in Russia.
We introduce embedding-based methods for cross-lingually projecting English frames to Russian, and discover that these articles emphasize U.S. moral failings and threats to the U.S. Our work offers new ways to identify subtle media manipulation strategies at the intersection of agenda-setting and framing.
Ventricular Fibrillation (VF), one of the most dangerous arrhythmias, is responsible for sudden cardiac arrests.
Thus, various algorithms have been developed to predict VF from Electrocardiogram (ECG), which is a binary classification problem.
In the literature, we find a number of algorithms based on signal processing, where, after some robust mathematical operations the decision is given based on a predefined threshold over a single value.
On the other hand, some machine learning based algorithms are also reported in the literature; however, these algorithms merely combine some parameters and make a prediction using those as features.
Both the approaches have their perks and pitfalls; thus our motivation was to coalesce them to get the best out of the both worlds.
Hence we have developed, VFPred that, in addition to employing a signal processing pipeline, namely, Empirical Mode Decomposition and Discrete Time Fourier Transform for useful feature extraction, uses a Support Vector Machine for efficient classification.
VFPred turns out to be a robust algorithm as it is able to successfully segregate the two classes with equal confidence (Sensitivity = 99.99%, Specificity = 98.40%) even from a short signal of 5 seconds long, whereas existing works though requires longer signals, flourishes in one but fails in the other.
A mobile ad hoc network (MANET) is a non-centralised, multihop, wireless network that lacks a common infrastructure and hence it needs self-organisation.
The biggest challenge in MANETs is to find a path between communicating nodes, which is the MANET routing problem.
Biology-inspired techniques such as ant colony optimisation (ACO) which have proven to be very adaptable in other problem domains, have been applied to the MANET routing problem as it forms a good fit to the problem.
The general characteristics of these biological systems, which include their capability for self-organisation, self-healing and local decision making, make them suitable for routing in MANETs.
In this paper, we discuss a few ACO based protocols, namely AntNet, hybrid ACO (AntHocNet), ACO based routing algorithm (ARA), imProved ant colony optimisation routing algorithm for mobile ad hoc NETworks (PACONET), ACO based on demand distance vector (Ant-AODV) and ACO based dynamic source routing (Ant-DSR), and determine their performance in terms of quality of service (QoS) parameters, such as end-to-end delay and packet delivery ratio, using Network Simulator 2 (NS2).
We also compare them with well known protocols, ad hoc on demand distance vector (AODV) and dynamic source routing (DSR), based on the random waypoint mobility model.
The simulation results show how this biology-inspired approach helps in improving QoS parameters.
Big Data concern large-volume, growing data sets that are complex and have multiple autonomous sources.
Earlier technologies were not able to handle storage and processing of huge data thus Big Data concept comes into existence.
This is a tedious job for users unstructured data.
So, there should be some mechanism which classify unstructured data into organized form which helps user to easily access required data.
Classification techniques over big transactional database provide required data to the users from large datasets more simple way.
There are two main classification techniques, supervised and unsupervised.
In this paper we focused on to study of different supervised classification techniques.
Further this paper shows a advantages and limitations.
In this work, we investigate the structure and evolution of a peer-to-peer (P2P) payment application.
A unique aspect of the network under consideration is that the edges among nodes represent financial transactions among individuals who shared an offline social interaction.
Our dataset comes from Venmo, the most popular P2P mobile payment service.
We present a series of static and dynamic measurements that summarize the key aspects of any social network, namely the degree distribution, density and connectivity.
We find that the degree distributions do not follow a power-law distribution, confirming previous studies that real-world social networks are rarely scale-free.
The giant component of Venmo is eventually composed of 99.9% of all nodes, and its clustering coefficient reaches 0.2.
Last, we examine the "topological" version of the small-world hypothesis and find that Venmo users are separated by a mean of 5.9 steps and a median of 6 steps.
Reading comprehension has embraced a booming in recent NLP research.
Several institutes have released the Cloze-style reading comprehension data, and these have greatly accelerated the research of machine comprehension.
In this work, we firstly present Chinese reading comprehension datasets, which consist of People Daily news dataset and Children's Fairy Tale (CFT) dataset.
Also, we propose a consensus attention-based neural network architecture to tackle the Cloze-style reading comprehension problem, which aims to induce a consensus attention over every words in the query.
Experimental results show that the proposed neural network significantly outperforms the state-of-the-art baselines in several public datasets.
Furthermore, we setup a baseline for Chinese reading comprehension task, and hopefully this would speed up the process for future research.
Identifying the stance of a news article body with respect to a certain headline is the first step to automated fake news detection.
In this paper, we introduce a 2-stage ensemble model to solve the stance detection task.
By using only hand-crafted features as input to a gradient boosting classifier, we are able to achieve a score of 9161.5 out of 11651.25 (78.63%) on the official Fake News Challenge (Stage 1) dataset.
We identify the most useful features for detecting fake news and discuss how sampling techniques can be used to improve recall accuracy on a highly imbalanced dataset.
The challenge of describing model drift is an open question in unsupervised learning.
It can be difficult to evaluate at what point an unsupervised model has deviated beyond what would be expected from a different sample from the same population.
This is particularly true for models without a probabilistic interpretation.
One such family of techniques, Topological Data Analysis, and the Mapper algorithm in particular, has found use in a variety of fields, but describing model drift for Mapper graphs is an understudied area as even existing techniques for measuring distances between related constructs like graphs or simplicial complexes fail to account for the fact that Mapper graphs represent a combination of topological, metric, and density information.
In this paper, we develop an optimal transport based metric which we call the Network Augmented Wasserstein Distance for evaluating distances between Mapper graphs and demonstrate the value of the metric for model drift analysis by using the metric to transform the model drift problem into an anomaly detection problem over dynamic graphs.
This paper proposes three measures to quantify the characteristics of online signature templates in terms of distinctiveness, complexity and repeatability.
A distinctiveness measure of a signature template is computed from a set of enrolled signature samples and a statistical assumption about random signatures.
Secondly, a complexity measure of the template is derived from a set of enrolled signature samples.
Finally, given a signature template, a measure to quantify the repeatability of the online signature is derived from a validation set of samples.
These three measures can then be used as an indicator for the performance of the system in rejecting random forgery samples and skilled forgery samples and the performance of users in providing accepted genuine samples, respectively.
The effectiveness of these three measures and their applications are demonstrated through experiments performed on three online signature datasets and one keystroke dynamics dataset using different verification algorithms.
This paper reports on work performed in the context of the COMPASS SESAR-JU WP-E project, on developing an approach for identifying and filtering inaccurate trajectories (ghost flights) in historical data originating from the EUROCONTROL-operated Demand Data Repository (DDR).
Neural Machine Translation (NMT), like many other deep learning domains, typically suffers from over-parameterization, resulting in large storage sizes.
This paper examines three simple magnitude-based pruning schemes to compress NMT models, namely class-blind, class-uniform, and class-distribution, which differ in terms of how pruning thresholds are computed for the different classes of weights in the NMT architecture.
We demonstrate the efficacy of weight pruning as a compression technique for a state-of-the-art NMT system.
We show that an NMT model with over 200 million parameters can be pruned by 40% with very little performance loss as measured on the WMT'14 English-German translation task.
This sheds light on the distribution of redundancy in the NMT architecture.
Our main result is that with retraining, we can recover and even surpass the original performance with an 80%-pruned model.
Many malware families utilize domain generation algorithms (DGAs) to establish command and control (C&C) connections.
While there are many methods to pseudorandomly generate domains, we focus in this paper on detecting (and generating) domains on a per-domain basis which provides a simple and flexible means to detect known DGA families.
Recent machine learning approaches to DGA detection have been successful on fairly simplistic DGAs, many of which produce names of fixed length.
However, models trained on limited datasets are somewhat blind to new DGA variants.
In this paper, we leverage the concept of generative adversarial networks to construct a deep learning based DGA that is designed to intentionally bypass a deep learning based detector.
In a series of adversarial rounds, the generator learns to generate domain names that are increasingly more difficult to detect.
In turn, a detector model updates its parameters to compensate for the adversarially generated domains.
We test the hypothesis of whether adversarially generated domains may be used to augment training sets in order to harden other machine learning models against yet-to-be-observed DGAs.
We detail solutions to several challenges in training this character-based generative adversarial network (GAN).
In particular, our deep learning architecture begins as a domain name auto-encoder (encoder + decoder) trained on domains in the Alexa one million.
Then the encoder and decoder are reassembled competitively in a generative adversarial network (detector + generator), with novel neural architectures and training strategies to improve convergence.
Upcoming many core processors are expected to employ a distributed memory architecture similar to currently available supercomputers, but parallel pattern mining algorithms amenable to the architecture are not comprehensively studied.
We present a novel closed pattern mining algorithm with a well-engineered communication protocol, and generalize it to find statistically significant patterns from personal genome data.
For distributing communication evenly, it employs global load balancing with multiple stacks distributed on a set of cores organized as a hypercube with random edges.
Our algorithm achieved up to 1175-fold speedup by using 1200 cores for solving a problem with 11,914 items and 697 transactions, while the naive approach of separating the search space failed completely.
Genetic algorithms are considered as an original way to solve problems, probably because of their generality and of their "blind" nature.
But GAs are also unusual since the features of many implementations (among all that could be thought of) are principally led by the biological metaphor, while efficiency measurements intervene only afterwards.
We propose here to examine the relevance of these biomimetic aspects, by pointing out some fundamental similarities and divergences between GAs and the genome of living beings shaped by natural selection.
One of the main differences comes from the fact that GAs rely principally on the so-called implicit parallelism, while giving to the mutation/selection mechanism the second role.
Such differences could suggest new ways of employing GAs on complex problems, using complex codings and starting from nearly homogeneous populations.
To overcome the travelling difficulty for the visually impaired group, this paper presents a novel ETA (Electronic Travel Aids)-smart guiding device in the shape of a pair of eyeglasses for giving these people guidance efficiently and safely.
Different from existing works, a novel multi sensor fusion based obstacle avoiding algorithm is proposed, which utilizes both the depth sensor and ultrasonic sensor to solve the problems of detecting small obstacles, and transparent obstacles, e.g. the French door.
For totally blind people, three kinds of auditory cues were developed to inform the direction where they can go ahead.
Whereas for weak sighted people, visual enhancement which leverages the AR (Augment Reality) technique and integrates the traversable direction is adopted.
The prototype consisting of a pair of display glasses and several low cost sensors is developed, and its efficiency and accuracy were tested by a number of users.
The experimental results show that the smart guiding glasses can effectively improve the user's travelling experience in complicated indoor environment.
Thus it serves as a consumer device for helping the visually impaired people to travel safely.
The value 1 problem is a decision problem for probabilistic automata over finite words: given a probabilistic automaton, are there words accepted with probability arbitrarily close to 1?
This problem was proved undecidable recently; to overcome this, several classes of probabilistic automata of different nature were proposed, for which the value 1 problem has been shown decidable.
In this paper, we introduce yet another class of probabilistic automata, called leaktight automata, which strictly subsumes all classes of probabilistic automata whose value 1 problem is known to be decidable.
We prove that for leaktight automata, the value 1 problem is decidable (in fact, PSPACE-complete) by constructing a saturation algorithm based on the computation of a monoid abstracting the behaviours of the automaton.
We rely on algebraic techniques developed by Simon to prove that this abstraction is complete.
Furthermore, we adapt this saturation algorithm to decide whether an automaton is leaktight.
Finally, we show a reduction allowing to extend our decidability results from finite words to infinite ones, implying that the value 1 problem for probabilistic leaktight parity automata is decidable.
We show that the matching problem that underlies optical flow requires multiple strategies, depending on the amount of image motion and other factors.
We then study the implications of this observation on training a deep neural network for representing image patches in the context of descriptor based optical flow.
We propose a metric learning method, which selects suitable negative samples based on the nature of the true match.
This type of training produces a network that displays multiple strategies depending on the input and leads to state of the art results on the KITTI 2012 and KITTI 2015 optical flow benchmarks.
In the present paper we describe the technology for translating algorithmic descriptions of discrete functions to SAT.
The proposed methods and algorithms of translation are aimed at application to the problems of SAT-based cryptanalysis.
In the theoretical part of the paper we justify the main principles of general reduction to SAT for discrete functions from a class containing the majority of functions employed in cryptography.
Based on these principles we describe the Transalg software system, developed with SAT-based cryptanalysis specifics in mind.
We show the results of applications of Transalg to construction of a number of attacks on various cryptographic functions.
Some of the corresponding attacks are state of the art.
In the paper we also present the vast experimental data, obtained using the SAT-solvers that took first places at the SAT-competitions in the recent several years.
The main goal in many fields in empirical sciences is to discover causal relationships among a set of variables from observational data.
PC algorithm is one of the promising solutions to learn the underlying causal structure by performing a number of conditional independence tests.
In this paper, we propose a novel GPU-based parallel algorithm, called cuPC, to accelerate an order-independent version of PC.
The cuPC algorithm has two variants, cuPC-E and cuPC-S, which parallelize conditional independence tests over the pairs of variables under the tests, and over the conditional sets, respectively.
In particular, cuPC-E offers two degrees of parallelization by performing tests of multiple pairs of variables and also the tests of each pair in parallel.
In the other hand, cuPC-S reuses the results of computations of a test for a given conditional set in other tests on the same conditional set.
Experiment results on GTX 1080 GPU show two to three orders of magnitude speedup.
For instance, in one of the most challenging benchmarks, cuPC-S reduces the runtime from about 73 hours to about one minute and achieves a significant speedup factor of about 4000 X.
In previous work, we developed a closed-loop speech chain model based on deep learning, in which the architecture enabled the automatic speech recognition (ASR) and text-to-speech synthesis (TTS) components to mutually improve their performance.
This was accomplished by the two parts teaching each other using both labeled and unlabeled data.
This approach could significantly improve model performance within a single-speaker speech dataset, but only a slight increase could be gained in multi-speaker tasks.
Furthermore, the model is still unable to handle unseen speakers.
In this paper, we present a new speech chain mechanism by integrating a speaker recognition model inside the loop.
We also propose extending the capability of TTS to handle unseen speakers by implementing one-shot speaker adaptation.
This enables TTS to mimic voice characteristics from one speaker to another with only a one-shot speaker sample, even from a text without any speaker information.
In the speech chain loop mechanism, ASR also benefits from the ability to further learn an arbitrary speaker's characteristics from the generated speech waveform, resulting in a significant improvement in the recognition rate.
We represent planning as a set of loosely coupled network flow problems, where each network corresponds to one of the state variables in the planning domain.
The network nodes correspond to the state variable values and the network arcs correspond to the value transitions.
The planning problem is to find a path (a sequence of actions) in each network such that, when merged, they constitute a feasible plan.
In this paper we present a number of integer programming formulations that model these loosely coupled networks with varying degrees of flexibility.
Since merging may introduce exponentially many ordering constraints we implement a so-called branch-and-cut algorithm, in which these constraints are dynamically generated and added to the formulation when needed.
Our results are very promising, they improve upon previous planning as integer programming approaches and lay the foundation for integer programming approaches for cost optimal planning.
This study deals with the missing link prediction problem: the problem of predicting the existence of missing connections between entities of interest.
We address link prediction using coupled analysis of relational datasets represented as heterogeneous data, i.e., datasets in the form of matrices and higher-order tensors.
We propose to use an approach based on probabilistic interpretation of tensor factorisation models, i.e., Generalised Coupled Tensor Factorisation, which can simultaneously fit a large class of tensor models to higher-order tensors/matrices with com- mon latent factors using different loss functions.
Numerical experiments demonstrate that joint analysis of data from multiple sources via coupled factorisation improves the link prediction performance and the selection of right loss function and tensor model is crucial for accurately predicting missing links.
Boolean network models have gained popularity in computational systems biology over the last dozen years.
Many of these networks use canalizing Boolean functions, which has led to increased interest in the study of these functions.
The canalizing depth of a function describes how many canalizing variables can be recursively picked off, until a non-canalizing function remains.
In this paper, we show how every Boolean function has a unique algebraic form involving extended monomial layers and a well-defined core polynomial.
This generalizes recent work on the algebraic structure of nested canalizing functions, and it yields a stratification of all Boolean functions by their canalizing depth.
As a result, we obtain closed formulas for the number of n-variable Boolean functions with depth k, which simultaneously generalizes enumeration formulas for canalizing, and nested canalizing functions.
The optimization of algorithm (hyper-)parameters is crucial for achieving peak performance across a wide range of domains, ranging from deep neural networks to solvers for hard combinatorial problems.
The resulting algorithm configuration (AC) problem has attracted much attention from the machine learning community.
However, the proper evaluation of new AC procedures is hindered by two key hurdles.
First, AC benchmarks are hard to set up.
Second and even more significantly, they are computationally expensive: a single run of an AC procedure involves many costly runs of the target algorithm whose performance is to be optimized in a given AC benchmark scenario.
One common workaround is to optimize cheap-to-evaluate artificial benchmark functions (e.g., Branin) instead of actual algorithms; however, these have different properties than realistic AC problems.
Here, we propose an alternative benchmarking approach that is similarly cheap to evaluate but much closer to the original AC problem: replacing expensive benchmarks by surrogate benchmarks constructed from AC benchmarks.
These surrogate benchmarks approximate the response surface corresponding to true target algorithm performance using a regression model, and the original and surrogate benchmark share the same (hyper-)parameter space.
In our experiments, we construct and evaluate surrogate benchmarks for hyperparameter optimization as well as for AC problems that involve performance optimization of solvers for hard combinatorial problems, drawing training data from the runs of existing AC procedures.
We show that our surrogate benchmarks capture overall important characteristics of the AC scenarios, such as high- and low-performing regions, from which they were derived, while being much easier to use and orders of magnitude cheaper to evaluate.
Information extraction (IE) from text has largely focused on relations between individual entities, such as who has won which award.
However, some facts are never fully mentioned, and no IE method has perfect recall.
Thus, it is beneficial to also tap contents about the cardinalities of these relations, for example, how many awards someone has won.
We introduce this novel problem of extracting cardinalities and discusses the specific challenges that set it apart from standard IE.
We present a distant supervision method using conditional random fields.
A preliminary evaluation results in precision between 3% and 55%, depending on the difficulty of relations.
We address the problem of learning hierarchical deep neural network policies for reinforcement learning.
In contrast to methods that explicitly restrict or cripple lower layers of a hierarchy to force them to use higher-level modulating signals, each layer in our framework is trained to directly solve the task, but acquires a range of diverse strategies via a maximum entropy reinforcement learning objective.
Each layer is also augmented with latent random variables, which are sampled from a prior distribution during the training of that layer.
The maximum entropy objective causes these latent variables to be incorporated into the layer's policy, and the higher level layer can directly control the behavior of the lower layer through this latent space.
Furthermore, by constraining the mapping from latent variables to actions to be invertible, higher layers retain full expressivity: neither the higher layers nor the lower layers are constrained in their behavior.
Our experimental evaluation demonstrates that we can improve on the performance of single-layer policies on standard benchmark tasks simply by adding additional layers, and that our method can solve more complex sparse-reward tasks by learning higher-level policies on top of high-entropy skills optimized for simple low-level objectives.
Super point is a kind of special host in the network which contacts with huge of other hosts.
Estimating its cardinality, the number of other hosts contacting with it, plays important roles in network management.
But all of existing works focus on discrete time window super point cardinality estimation which has great latency and ignores many measuring periods.
Sliding time window measures super point cardinality in a finer granularity than that of discrete time window but also more complex.
This paper firstly introduces an algorithm to estimate super point cardinality under sliding time window from distributed edge routers.
This algorithm's ability of sliding super point cardinality estimating comes from a novel method proposed in this paper which can record the time that a host appears.
Based on this method, two sliding cardinality estimators, sliding rough estimator and sliding linear estimator, are devised for super points detection and their cardinalities estimation separately.
When using these two estimators together, the algorithm consumes the smallest memory with the highest accuracy.
This sliding super point cardinality algorithm can be deployed in distributed environment and acquire the global super points' cardinality by merging estimators of distributed nodes.
Both of these estimators could process packets parallel which makes it becom possible to deal with high speed network in real time by GPU.
Experiments on a real world traffic show that this algorithm have the highest accuracy and the smallest memory comparing with others when running under discrete time window.
Under sliding time window, this algorithm also has the same performance as under discrete time window.
Analogy-based effort estimation (ABE) is one of the efficient methods for software effort estimation because of its outstanding performance and capability of handling noisy datasets.
Conventional ABE models usually use the same number of analogies for all projects in the datasets in order to make good estimates.
The authors' claim is that using same number of analogies may produce overall best performance for the whole dataset but not necessarily best performance for each individual project.
Therefore there is a need to better understand the dataset characteristics in order to discover the optimum set of analogies for each project rather than using a static k nearest projects.
Method: We propose a new technique based on Bisecting k-medoids clustering algorithm to come up with the best set of analogies for each individual project before making the prediction.
Results & Conclusions: With Bisecting k-medoids it is possible to better understand the dataset characteristic, and automatically find best set of analogies for each test project.
Performance figures of the proposed estimation method are promising and better than those of other regular ABE models
In the context of resource allocation in cloud-radio access networks, recent studies assume either signal-level or scheduling-level coordination.
This paper, instead, considers a hybrid level of coordination for the scheduling problem in the downlink of a multi-cloud radio-access network, as a means to benefit from both scheduling policies.
Consider a multi-cloud radio access network, where each cloud is connected to several base-stations (BSs) via high capacity links, and therefore allows joint signal processing between them.
Across the multiple clouds, however, only scheduling-level coordination is permitted, as it requires a lower level of backhaul communication.
The frame structure of every BS is composed of various time/frequency blocks, called power-zones (PZs), and kept at fixed power level.
The paper addresses the problem of maximizing a network-wide utility by associating users to clouds and scheduling them to the PZs, under the practical constraints that each user is scheduled, at most, to a single cloud, but possibly to many BSs within the cloud, and can be served by one or more distinct PZs within the BSs' frame.
The paper solves the problem using graph theory techniques by constructing the conflict graph.
The scheduling problem is, then, shown to be equivalent to a maximum-weight independent set problem in the constructed graph, in which each vertex symbolizes an association of cloud, user, BS and PZ, with a weight representing the utility of that association.
Simulation results suggest that the proposed hybrid scheduling strategy provides appreciable gain as compared to the scheduling-level coordinated networks, with a negligible degradation to signal-level coordination.
Early detection of breast cancer can increase treatment efficiency.
Architectural Distortion (AD) is a very subtle contraction of the breast tissue and may represent the earliest sign of cancer.
Since it is very likely to be unnoticed by radiologists, several approaches have been proposed over the years but none using deep learning techniques.
To train a Convolutional Neural Network (CNN), which is a deep neural architecture, is necessary a huge amount of data.
To overcome this problem, this paper proposes a data augmentation approach applied to clinical image dataset to properly train a CNN.
Results using receiver operating characteristic analysis showed that with a very limited dataset we could train a CNN to detect AD in digital mammography with area under the curve (AUC = 0.74).
Deep reinforcement learning (deep RL) research has grown significantly in recent years.
A number of software offerings now exist that provide stable, comprehensive implementations for benchmarking.
At the same time, recent deep RL research has become more diverse in its goals.
In this paper we introduce Dopamine, a new research framework for deep RL that aims to support some of that diversity.
Dopamine is open-source, TensorFlow-based, and provides compact and reliable implementations of some state-of-the-art deep RL agents.
We complement this offering with a taxonomy of the different research objectives in deep RL research.
While by no means exhaustive, our analysis highlights the heterogeneity of research in the field, and the value of frameworks such as ours.
Irregular low-density parity check (LDPC) codes are particularly well-suited for transmission schemes that require unequal error protection (UEP) of the transmitted data due to the different connection degrees of its variable nodes.
However, this UEP capability is strongly dependent on the connection profile among the protection classes.
This paper applies a multi-edge type analysis of LDPC codes for optimizing such connection profile according to the performance requirements of each protection class.
This allows the construction of UEP-LDPC codes where the difference between the performance of the protection classes can be adjusted and with an UEP capability that does not vanish as the number of decoding iterations grows.
This paper presents a formal approach to specify and verify object-oriented programs written in the `programming to interfaces' paradigm.
Besides the methods to be invoked by its clients, an interface also declares a set of abstract function/predicate symbols, together with a set of constraints on these symbols.
For each method declared in this interface, a specification template is given using these abstract symbols.
A class implementing this interface can give its own definitions to the abstract symbols, as long as all the constraints are satisfied.
This class implements all the methods declared in the interface such that the method specification templates declared in the interface are satisfied w.r.t. the definitions of the abstract function symbols in this class.
Based on the constraints on the abstract symbols, the client code using interfaces can be specified and verified precisely without knowing what classes implement these interfaces.
Given more information about the implementing classes, the specifications of the client code can be specialized into more precise ones without re-verifying the client code.
Several commonly used interfaces and their implementations (including Iterator, Observer, Comparable, and Comparator) are used to demonstrate that the approach in this paper is both precise and flexible.
The number of word forms in agglutinative languages is theoretically infinite and this variety in word forms introduces sparsity in many natural language processing tasks.
Part-of-speech tagging (PoS tagging) is one of these tasks that often suffers from sparsity.
In this paper, we present an unsupervised Bayesian model using Hidden Markov Models (HMMs) for joint PoS tagging and stemming for agglutinative languages.
We use stemming to reduce sparsity in PoS tagging.
Two tasks are jointly performed to provide a mutual benefit in both tasks.
Our results show that joint POS tagging and stemming improves PoS tagging scores.
We present results for Turkish and Finnish as agglutinative languages and English as a morphologically poor language.
MADNESS (multiresolution adaptive numerical environment for scientific simulation) is a high-level software environment for solving integral and differential equations in many dimensions that uses adaptive and fast harmonic analysis methods with guaranteed precision based on multiresolution analysis and separated representations.
Underpinning the numerical capabilities is a powerful petascale parallel programming environment that aims to increase both programmer productivity and code scalability.
This paper describes the features and capabilities of MADNESS and briefly discusses some current applications in chemistry and several areas of physics.
Catering to the incentives of people with limited rationality is a challenging research direction that requires novel paradigms to design mechanisms and approximation algorithms.
Obviously strategyproof (OSP) mechanisms have recently emerged as the concept of interest to this research agenda.
However, the majority of the literature in the area has either highlighted the shortcomings of OSP or focused on the "right" definition rather than on the construction of these mechanisms.
We here give the first set of tight results on the approximation guarantee of OSP mechanisms for scheduling related machines and a characterization of optimal OSP mechanisms for set system problems.
By extending the well-known cycle monotonicity technique, we are able to concentrate on the algorithmic component of OSP mechanisms and provide some novel paradigms for their design.
One major goal of vision is to infer physical models of objects, surfaces, and their layout from sensors.
In this paper, we aim to interpret indoor scenes from one RGBD image.
Our representation encodes the layout of orthogonal walls and the extent of objects, modeled with CAD-like 3D shapes.
We parse both the visible and occluded portions of the scene and all observable objects, producing a complete 3D parse.
Such a scene interpretation is useful for robotics and visual reasoning, but difficult to produce due to the well-known challenge of segmentation, the high degree of occlusion, and the diversity of objects in indoor scenes.
We take a data-driven approach, generating sets of potential object regions, matching to regions in training images, and transferring and aligning associated 3D models while encouraging fit to observations and spatial consistency.
We use support inference to aid interpretation and propose a retrieval scheme that uses convolutional neural networks (CNNs) to classify regions and retrieve objects with similar shapes.
We demonstrate the performance of our method on our newly annotated NYUd v2 dataset with detailed 3D shapes.
As robots become increasingly prevalent in human environments, there will inevitably be times when a robot needs to interrupt a human to initiate an interaction.
Our work introduces the first interruptibility-aware mobile robot system, and evaluates the effects of interruptibility-awareness on human task performance, robot task performance, and on human interpretation of the robot's social aptitude.
Our results show that our robot is effective at predicting interruptibility at high accuracy, allowing it to interrupt at more appropriate times.
Results of a large-scale user study show that while participants are able to maintain task performance even in the presence of interruptions, interruptibility-awareness improves the robot's task performance and improves participant social perception of the robot.
Commercial detection in news broadcast videos involves judicious selection of meaningful audio-visual feature combinations and efficient classifiers.
And, this problem becomes much simpler if these combinations can be learned from the data.
To this end, we propose an Multiple Kernel Learning based method for boosting successful kernel functions while ignoring the irrelevant ones.
We adopt a intermediate fusion approach where, a SVM is trained with a weighted linear combination of different kernel functions instead of single kernel function.
Each kernel function is characterized by a feature set and kernel type.
We identify the feature sub-space locations of the prediction success of a particular classifier trained only with particular kernel function.
We propose to estimate a weighing function using support vector regression (with RBF kernel) for each kernel function which has high values (near 1.0) where the classifier learned on kernel function succeeded and lower values (nearly 0.0) otherwise.
Second contribution of this work is TV News Commercials Dataset of 150 Hours of News videos.
Classifier trained with our proposed scheme has outperformed the baseline methods on 6 of 8 benchmark dataset and our own TV commercials dataset.
We formulate two estimation problems for pipeline systems in which measurements of compressible gas flow through a network of pipes is affected by time-varying injections, withdrawals, and compression.
We consider a state estimation problem that is then extended to a joint state and parameter estimation problem that can be used for data assimilation.
In both formulations, the flow dynamics are described on each pipe by space- and time-dependent density and mass flux that evolve according to a system of coupled partial differential equations, in which momentum dissipation is modelled using the Darcy-Wiesbach friction approximation.
These dynamics are first spatially discretized to obtain a system of nonlinear ordinary differential equations on which state and parameter estimation formulations are given as nonlinear least squares problems.
A rapid, scalable computational method for performing a nonlinear least squares estimation is developed.
Extensive simulations and computational experiments on multiple pipeline test networks demonstrate the effectiveness of the formulations in obtaining state and parameter estimates in the presence of measurement and process noise.
Supporting IPv6/UDP/CoAP protocols over Low Power Wide Area Networks (LPWANs) can bring open networking, interconnection, and cooperation to this new type of Internet of Things networks.
However, accommodating these protocols over these very low bandwidth networks requires efficient header compression schemes to meet the limited frame size of these networks, where only one or two octets are available to transmit all headers.
Recently, the Internet Engineering Task Force (IETF) LPWAN working group drafted the Static Context Header Compression (SCHC), a new header compression scheme for LPWANs, which can provide a good compression factor without complex synchronization.
In this paper, we present an implementation and evaluation of SCHC.
We compare SCHC with IPHC, which also targets constrained networks.
Additionally, we propose an enhancement of SCHC, Layered SCHC (LSCHC).
LSCHC is a layered context that reduces memory consumption and processing complexity, and adds flexibility when compressing packets.
Finally, we perform calculations to show the impact of SCHC/LSCHC on an example LPWAN technology, e.g.
LoRaWAN, from the point of view of transmission time and reliability.
In this paper, we propose a novel continuous authentication system for smartphone users.
The proposed system entirely relies on unlabeled phone movement patterns collected through smartphone accelerometer.
The data was collected in a completely unconstrained environment over five to twelve days.
The contexts of phone usage were identified using k-means clustering.
Multiple profiles, one for each context, were created for every user.
Five machine learning algorithms were employed for classification of genuine and impostors.
The performance of the system was evaluated over a diverse population of 57 users.
The mean equal error rates achieved by Logistic Regression, Neural Network, kNN, SVM, and Random Forest were 13.7%, 13.5%, 12.1%, 10.7%, and 5.6% respectively.
A series of statistical tests were conducted to compare the performance of the classifiers.
The suitability of the proposed system for different types of users was also investigated using the failure to enroll policy.
Energy storage has great potential in grid congestion relief.
By making large-scale energy storage portable through trucking, its capability to address grid congestion can be greatly enhanced.
This paper explores a business model of large-scale portable energy storage for spatiotemporal arbitrage over nodes with congestion.
We propose a spatiotemporal arbitrage model to determine the optimal operation and transportation schedules of portable storage.
To validate the business model, we simulate the schedules of a Tesla Semi full of Tesla Powerpack doing arbitrage over two nodes in California with local transmission congestion.
The results indicate that the contributions of portable storage to congestion relief are much greater than that of stationary storage, and that trucking storage can bring net profit in energy arbitrage applications.
This work explores fundamental modeling and algorithmic issues arising in the well-established MapReduce framework.
First, we formally specify a computational model for MapReduce which captures the functional flavor of the paradigm by allowing for a flexible use of parallelism.
Indeed, the model diverges from a traditional processor-centric view by featuring parameters which embody only global and local memory constraints, thus favoring a more data-centric view.
Second, we apply the model to the fundamental computation task of matrix multiplication presenting upper and lower bounds for both dense and sparse matrix multiplication, which highlight interesting tradeoffs between space and round complexity.
Finally, building on the matrix multiplication results, we derive further space-round tradeoffs on matrix inversion and matching.
Morphological declension, which aims to inflect nouns to indicate number, case and gender, is an important task in natural language processing (NLP).
This research proposal seeks to address the degree to which Recurrent Neural Networks (RNNs) are efficient in learning to decline noun cases.
Given the challenge of data sparsity in processing morphologically rich languages and also, the flexibility of sentence structures in such languages, we believe that modeling morphological dependencies can improve the performance of neural network models.
It is suggested to carry out various experiments to understand the interpretable features that may lead to a better generalization of the learned models on cross-lingual tasks.
A typical problem in MOOCs is the missing opportunity for course conductors to individually support students in overcoming their problems and misconceptions.
This paper presents the results of automatically intervening on struggling students during programming exercises and offering peer feedback and tailored bonus exercises.
To improve learning success, we do not want to abolish instructionally desired trial and error but reduce extensive struggle and demotivation.
Therefore, we developed adaptive automatic just-in-time interventions to encourage students to ask for help if they require considerably more than average working time to solve an exercise.
Additionally, we offered students bonus exercises tailored for their individual weaknesses.
The approach was evaluated within a live course with over 5,000 active students via a survey and metrics gathered alongside.
Results show that we can increase the call outs for help by up to 66% and lower the dwelling time until issuing action.
Learnings from the experiments can further be used to pinpoint course material to be improved and tailor content to be audience specific.
In this paper, we propose an efficient coding scheme for the binary Chief Executive Officer (CEO) problem under logarithmic loss criterion.
Courtade and Weissman obtained the exact rate-distortion bound for a two-link binary CEO problem under this criterion.
We find the optimal test-channel model and its parameters for the encoder of each link by using the given bound.
Furthermore, an efficient encoding scheme based on compound LDGM-LDPC codes is presented to achieve the theoretical rates.
In the proposed encoding scheme, a binary quantizer using LDGM codes and a syndrome-decoding employing LDPC codes are applied.
An iterative decoding is also presented as a fusion center to reconstruct the observation bits.
The proposed decoder consists of a sum-product algorithm with a side information from other decoder and a soft estimator.
The output of the CEO decoder is the probability of source bits conditional to the received sequences of both links.
This method outperforms the majority-based estimation of the source bits utilized in the prior studies of the binary CEO problem.
Our numerical examples verify a close performance of the proposed coding scheme to the theoretical bound in several cases.
A database of objects discovered in houses in the Roman city of Pompeii provides a unique view of ordinary life in an ancient city.
Experts have used this collection to study the structure of Roman households, exploring the distribution and variability of tasks in architectural spaces, but such approaches are necessarily affected by modern cultural assumptions.
In this study we present a data-driven approach to household archeology, treating it as an unsupervised labeling problem.
This approach scales to large data sets and provides a more objective complement to human interpretation.
There is more to images than their objective physical content: for example, advertisements are created to persuade a viewer to take a certain action.
We propose the novel problem of automatic advertisement understanding.
To enable research on this problem, we create two datasets: an image dataset of 64,832 image ads, and a video dataset of 3,477 ads.
Our data contains rich annotations encompassing the topic and sentiment of the ads, questions and answers describing what actions the viewer is prompted to take and the reasoning that the ad presents to persuade the viewer ("What should I do according to this ad, and why should I do it?"), and symbolic references ads make (e.g. a dove symbolizes peace).
We also analyze the most common persuasive strategies ads use, and the capabilities that computer vision systems should have to understand these strategies.
We present baseline classification results for several prediction tasks, including automatically answering questions about the messages of the ads.
In this paper, we present a resistive switching memristor cell for implementing universal logic gates.
The cell has a weighted control input whose resistance is set based on a control signal that generalizes the operational regime from NAND to NOR functionality.
We further show how threshold logic in the voltage-controlled resistive cell can be used to implement a XOR logic.
Building on the same principle we implement a half adder and a 4-bit CLA (Carry Look-ahead Adder) and show that in comparison with CMOS-only logic, the proposed system shows significant improvements in terms of device area, power dissipation and leakage power.
We present a novel parallelisation scheme that simplifies the adaptation of learning algorithms to growing amounts of data as well as growing needs for accurate and confident predictions in critical applications.
In contrast to other parallelisation techniques, it can be applied to a broad class of learning algorithms without further mathematical derivations and without writing dedicated code, while at the same time maintaining theoretical performance guarantees.
Moreover, our parallelisation scheme is able to reduce the runtime of many learning algorithms to polylogarithmic time on quasi-polynomially many processing units.
This is a significant step towards a general answer to an open question on the efficient parallelisation of machine learning algorithms in the sense of Nick's Class (NC).
The cost of this parallelisation is in the form of a larger sample complexity.
Our empirical study confirms the potential of our parallelisation scheme with fixed numbers of processors and instances in realistic application scenarios.
Communication tools make the world like a small village and as a consequence people can contact with others who are from different societies or who speak different languages.
This communication cannot happen effectively without Machine Translation because they can be found anytime and everywhere.
There are a number of studies that have developed Machine Translation for the English language with so many other languages except the Arabic it has not been considered yet.
Therefore we aim to highlight a roadmap for our proposed translation machine to provide an enhanced Arabic English translation based on Semantic.
This article provides a quantitative analysis of privacy-compromising mechanisms on 1 million popular websites.
Findings indicate that nearly 9 in 10 websites leak user data to parties of which the user is likely unaware; more than 6 in 10 websites spawn third- party cookies; and more than 8 in 10 websites load Javascript code from external parties onto users' computers.
Sites that leak user data contact an average of nine external domains, indicating that users may be tracked by multiple entities in tandem.
By tracing the unintended disclosure of personal browsing histories on the Web, it is revealed that a handful of U.S. companies receive the vast bulk of user data.
Finally, roughly 1 in 5 websites are potentially vulnerable to known National Security Agency spying techniques at the time of analysis.
Thinking of todays web search scenario which is mainly keyword based, leads to the need of effective and meaningful search provided by Semantic Web.
Existing search engines are vulnerable to provide relevant answers to users query due to their dependency on simple data available in web pages.
On other hand, semantic search engines provide efficient and relevant results as the semantic web manages information with well defined meaning using ontology.
A Meta-Search engine is a search tool that forwards users query to several existing search engines and provides combined results by using their own page ranking algorithm.
SemanTelli is a meta semantic search engine that fetches results from different semantic search engines such as Hakia, DuckDuckGo, SenseBot through intelligent agents.
This paper proposes enhancement of SemanTelli with improved snippet analysis based page ranking algorithm and support for image and news search.
CASP is an extension of ASP that allows for numerical constraints to be added in the rules.
PDDL+ is an extension of the PDDL standard language of automated planning for modeling mixed discrete-continuous dynamics.
In this paper, we present CASP solutions for dealing with PDDL+ problems, i.e., encoding from PDDL+ to CASP, and extensions to the algorithm of the EZCSP CASP solver in order to solve CASP programs arising from PDDL+ domains.
An experimental analysis, performed on well-known linear and non-linear variants of PDDL+ domains, involving various configurations of the EZCSP solver, other CASP solvers, and PDDL+ planners, shows the viability of our solution.
Understanding procedural language requires anticipating the causal effects of actions, even when they are not explicitly stated.
In this work, we introduce Neural Process Networks to understand procedural text through (neural) simulation of action dynamics.
Our model complements existing memory architectures with dynamic entity tracking by explicitly modeling actions as state transformers.
The model updates the states of the entities by executing learned action operators.
Empirical results demonstrate that our proposed model can reason about the unstated causal effects of actions, allowing it to provide more accurate contextual information for understanding and generating procedural text, all while offering more interpretable internal representations than existing alternatives.
With the prevalence of video sharing, there are increasing demands for automatic video digestion such as highlight detection.
Recently, platforms with crowdsourced time-sync video comments have emerged worldwide, providing a good opportunity for highlight detection.
However, this task is non-trivial: (1) time-sync comments often lag behind their corresponding shot; (2) time-sync comments are semantically sparse and noisy; (3) to determine which shots are highlights is highly subjective.
The present paper aims to tackle these challenges by proposing a framework that (1) uses concept-mapped lexical-chains for lag calibration; (2) models video highlights based on comment intensity and combination of emotion and concept concentration of each shot; (3) summarize each detected highlight using improved SumBasic with emotion and concept mapping.
Experiments on large real-world datasets show that our highlight detection method and summarization method both outperform other benchmarks with considerable margins.
In this paper, we present a new approach of distributed clustering for spatial datasets, based on an innovative and efficient aggregation technique.
This distributed approach consists of two phases: 1) local clustering phase, where each node performs a clustering on its local data, 2) aggregation phase, where the local clusters are aggregated to produce global clusters.
This approach is characterised by the fact that the local clusters are represented in a simple and efficient way.
And The aggregation phase is designed in such a way that the final clusters are compact and accurate while the overall process is efficient in both response time and memory allocation.
We evaluated the approach with different datasets and compared it to well-known clustering techniques.
The experimental results show that our approach is very promising and outperforms all those algorithms
General treebank analyses are graph structured, but parsers are typically restricted to tree structures for efficiency and modeling reasons.
We propose a new representation and algorithm for a class of graph structures that is flexible enough to cover almost all treebank structures, while still admitting efficient learning and inference.
In particular, we consider directed, acyclic, one-endpoint-crossing graph structures, which cover most long-distance dislocation, shared argumentation, and similar tree-violating linguistic phenomena.
We describe how to convert phrase structure parses, including traces, to our new representation in a reversible manner.
Our dynamic program uniquely decomposes structures, is sound and complete, and covers 97.3% of the Penn English Treebank.
We also implement a proof-of-concept parser that recovers a range of null elements and trace types.
Machine Learning models are vulnerable to adversarial attacks that rely on perturbing the input data.
This work proposes a novel strategy using Autoencoder Deep Neural Networks to defend a machine learning model against two gradient-based attacks: The Fast Gradient Sign attack and Fast Gradient attack.
First we use an autoencoder to denoise the test data, which is trained with both clean and corrupted data.
Then, we reduce the dimension of the denoised data using the hidden layer representation of another autoencoder.
We perform this experiment for multiple values of the bound of adversarial perturbations, and consider different numbers of reduced dimensions.
When the test data is preprocessed using this cascaded pipeline, the tested deep neural network classifier yields a much higher accuracy, thus mitigating the effect of the adversarial perturbation.
Ultra-reliable and low-latency communications (URLLC) is expected to be supported without compromising the resource usage efficiency.
In this paper, we study how to maximize energy efficiency (EE) for URLLC under the stringent quality of service (QoS) requirement imposed on the end-to-end (E2E) delay and overall packet loss, where the E2E delay includes queueing delay and transmission delay, and the overall packet loss consists of queueing delay violation, transmission error with finite blocklength channel codes, and proactive packet dropping in deep fading.
Transmit power, bandwidth and number of active antennas are jointly optimized to maximize the system EE under the QoS constraints.
Since the achievable rate with finite blocklength channel codes is not convex in radio resources, it is challenging to optimize resource allocation.
By analyzing the properties of the optimization problem, the global optimal solution is obtained.
Simulation and numerical results validate the analysis and show that the proposed policy can improve EE significantly compared with existing policy.
In this paper, we define a distance for the HSL colour system.
Next, the proposed distance is used for a fuzzy colour clustering algorithm construction.
The presented algorithm is related to the well-known fuzzy c-means algorithm.
Finally, the clustering algorithm is used as colour reduction method.
The obtained experimental results are presented to demonstrate the effectiveness of our approach.
Hierarchical temporal memory (HTM) is a biomimetic sequence memory algorithm that holds promise for invariant representations of spatial and spatiotemporal inputs.
This paper presents a comprehensive neuromemristive crossbar architecture for the spatial pooler (SP) and the sparse distributed representation classifier, which are fundamental to the algorithm.
There are several unique features in the proposed architecture that tightly link with the HTM algorithm.
A memristor that is suitable for emulating the HTM synapses is identified and a new Z-window function is proposed.
The architecture exploits the concept of synthetic synapses to enable potential synapses in the HTM.
The crossbar for the SP avoids dark spots caused by unutilized crossbar regions and supports rapid on-chip training within 2 clock cycles.
This research also leverages plasticity mechanisms such as neurogenesis and homeostatic intrinsic plasticity to strengthen the robustness and performance of the SP.
The proposed design is benchmarked for image recognition tasks using MNIST and Yale faces datasets, and is evaluated using different metrics including entropy, sparseness, and noise robustness.
Detailed power analysis at different stages of the SP operations is performed to demonstrate the suitability for mobile platforms.
Results of image stitching can be perceptually divided into single-perspective and multiple-perspective.
Compared to the multiple-perspective result, the single-perspective result excels in perspective consistency but suffers from projective distortion.
In this paper, we propose two single-perspective warps for natural image stitching.
The first one is a parametric warp, which is a combination of the as-projective-as-possible warp and the quasi-homography warp via dual-feature.
The second one is a mesh-based warp, which is determined by optimizing a total energy function that simultaneously emphasizes different characteristics of the single-perspective warp, including alignment, naturalness, distortion and saliency.
A comprehensive evaluation demonstrates that the proposed warp outperforms some state-of-the-art warps, including homography, APAP, AutoStitch, SPHP and GSP.
The monocular visual-inertial system (VINS), which consists one camera and one low-cost inertial measurement unit (IMU), is a popular approach to achieve accurate 6-DOF state estimation.
However, such locally accurate visual-inertial odometry is prone to drift and cannot provide absolute pose estimation.
Leveraging history information to relocalize and correct drift has become a hot topic.
In this paper, we propose a monocular visual-inertial SLAM system, which can relocalize camera and get the absolute pose in a previous-built map.
Then 4-DOF pose graph optimization is performed to correct drifts and achieve global consistent.
The 4-DOF contains x, y, z, and yaw angle, which is the actual drifted direction in the visual-inertial system.
Furthermore, the proposed system can reuse a map by saving and loading it in an efficient way.
Current map and previous map can be merged together by the global pose graph optimization.
We validate the accuracy of our system on public datasets and compare against other state-of-the-art algorithms.
We also evaluate the map merging ability of our system in the large-scale outdoor environment.
The source code of map reuse is integrated into our public code, VINS-Mono.
The major sources of abundant data are constantly expanding with the available data collection methodologies in various applications - medical, insurance, scientific, bio-informatics and business.
These data sets may be distributed geographically, rich in size and as well as dimensions also.
To analyze these data sets to find out the hidden patterns, it is required to down- load the data to a centralized site which is a challenging task in terms of the limited bandwidth available and computationally also expensive.
The covariance matrix is one of the methods to estimate the relation between any two dimensions.
In this paper, we propose a communication efficient algorithm to estimate the covariance matrix in a distributed manner.
The global covariance matrix is computed by merging the local covariance matrices using a distributed approach.
The results show that it is exactly same as centralized method with good speed-up in terms of computation.
The reason for speed-up is because of the parallel construction of local covariances and distributing the cross-covariances among the nodes so that the load is balanced.
The results are analyzed by considering Mfeat data set on the various partitions which address the scalability also.
Surveys can be viewed as programs, complete with logic, control flow, and bugs.
Word choice or the order in which questions are asked can unintentionally bias responses.
Vague, confusing, or intrusive questions can cause respondents to abandon a survey.
Surveys can also have runtime errors: inattentive respondents can taint results.
This effect is especially problematic when deploying surveys in uncontrolled settings, such as on the web or via crowdsourcing platforms.
Because the results of surveys drive business decisions and inform scientific conclusions, it is crucial to make sure they are correct.
We present SurveyMan, a system for designing, deploying, and automatically debugging surveys.
Survey authors write their surveys in a lightweight domain-specific language aimed at end users.
SurveyMan statically analyzes the survey to provide feedback to survey authors before deployment.
It then compiles the survey into JavaScript and deploys it either to the web or a crowdsourcing platform.
SurveyMan's dynamic analyses automatically find survey bugs, and control for the quality of responses.
We evaluate SurveyMan's algorithms analytically and empirically, demonstrating its effectiveness with case studies of social science surveys conducted via Amazon's Mechanical Turk.
In this paper we consider two social organizations -- service-oriented communities and fractal organizations -- and discuss how their main characteristics provide an answer to several shortcomings of traditional organizations.
In particular, we highlight their ability to tap into the vast basins of "social energy" of our societies.
This is done through the establishing of mutualistic relationships among the organizational components.
The paper also introduces a mathematical model of said mutualistic processes as well as its translation in terms of semantic service description and matching.
Preliminary investigations of the resilience of fractal social organizations are reported.
Simulations show that fractal organizations outperform non-fractal organizations and are able to quickly recover from disruptions and changes characterizing dynamic environments.
A growing number of people are changing the way they consume news, replacing the traditional physical newspapers and magazines by their virtual online versions or/and weblogs.
The interactivity and immediacy present in online news are changing the way news are being produced and exposed by media corporations.
News websites have to create effective strategies to catch people's attention and attract their clicks.
In this paper we investigate possible strategies used by online news corporations in the design of their news headlines.
We analyze the content of 69,907 headlines produced by four major global media corporations during a minimum of eight consecutive months in 2014.
In order to discover strategies that could be used to attract clicks, we extracted features from the text of the news headlines related to the sentiment polarity of the headline.
We discovered that the sentiment of the headline is strongly related to the popularity of the news and also with the dynamics of the posted comments on that particular news.
This paper proposes a new method to provide personalized tour recommendation for museum visits.
It combines an optimization of preference criteria of visitors with an automatic extraction of artwork importance from museum information based on Natural Language Processing using textual energy.
This project includes researchers from computer and social sciences.
Some results are obtained with numerical experiments.
They show that our model clearly improves the satisfaction of the visitor who follows the proposed tour.
This work foreshadows some interesting outcomes and applications about on-demand personalized visit of museums in a very near future.
Interference is emerging as a fundamental bottleneck in many important wireless communication scenarios, including dense cellular networks and cognitive networks with spectrum sharing by multiple service providers.
Although multipleantenna (MIMO) signal processing is known to offer useful degrees of freedom to cancel interference, extreme-value theoretic analysis recently showed that, even in the absence of MIMO processing, the scaling law of the capacity in the number of users for a multi-cell network with and without inter-cell interference was asymptotically identical provided a simple signal to noise and interference ratio (SINR) maximizing scheduler is exploited.
This suggests that scheduling can help reduce inter-cell interference substantially, thus possibly limiting the need for multiple-antenna processing.
However, the convergence limits of interference after scheduling in a multi-cell setting are not yet identified.
In this paper1 we analyze such limits theoretically.
We consider channel statistics under Rayleigh fading with equal path loss for all users or with unequal path loss.
We uncover two surprisingly different behaviors for such systems.
For the equal path loss case, we show that scheduling alone can cause the residual interference to converge to zero for large number of users.
With unequal path loss however, the interference are shown to converge in average to a nonzero constant.
Simulations back our findings.
One of the purposes of Big Data systems is to support analysis of data gathered from heterogeneous data sources.
Since data warehouses have been used for several decades to achieve the same goal, they could be leveraged also to provide analysis of data stored in Big Data systems.
The problem of adapting data warehouse data and schemata to changes in these requirements as well as data sources has been studied by many researchers worldwide.
However, innovative methods must be developed also to support evolution of data warehouses that are used to analyze data stored in Big Data systems.
In this paper, we propose a data warehouse architecture that allows to perform different kinds of analytical tasks, including OLAP-like analysis, on big data loaded from multiple heterogeneous data sources with different latency and is capable of processing changes in data sources as well as evolving analysis requirements.
The operation of the architecture is highly based on the metadata that are outlined in the paper.
Data security, which is concerned with the prevention of unauthorized access to computers, databases, and websites, helps protect digital privacy and ensure data integrity.
It is extremely difficult, however, to make security watertight, and security breaches are not uncommon.
The consequences of stolen credentials go well beyond the leakage of other types of information because they can further compromise other systems.
This paper criticizes the practice of using clear-text identity attributes, such as Social Security or driver's license numbers -- which are in principle not even secret -- as acceptable authentication tokens or assertions of ownership, and proposes a simple protocol that straightforwardly applies public-key cryptography to make identity claims verifiable, even when they are issued remotely via the Internet.
This protocol has the potential of elevating the business practices of credit providers, rental agencies, and other service companies that have hitherto exposed consumers to the risk of identity theft, to where identity theft becomes virtually impossible.
We investigate nearest neighbor and generative models for transferring pose between persons.
We take in a video of one person performing a sequence of actions and attempt to generate a video of another person performing the same actions.
Our generative model (pix2pix) outperforms k-NN at both generating corresponding frames and generalizing outside the demonstrated action set.
Our most salient contribution is determining a pipeline (pose detection, face detection, k-NN based pairing) that is effective at perform-ing the desired task.
We also detail several iterative improvements and failure modes.
In this paper, we describe a new Las Vegas algorithm to solve the elliptic curve discrete logarithm problem.
The algorithm depends on a property of the group of rational points of an elliptic curve and is thus not a generic algorithm.
The algorithm that we describe has some similarities with the most powerful index-calculus algorithm for the discrete logarithm problem over a finite field.
Virtual heart models have been proposed to enhance the safety of implantable cardiac devices through closed loop validation.
To communicate with a virtual heart, devices have been driven by cardiac signals at specific sites.
As a result, only the action potentials of these sites are sensed.
However, the real device implanted in the heart will sense a complex combination of near and far-field extracellular potential signals.
Therefore many device functions, such as blanking periods and refractory periods, are designed to handle these unexpected signals.
To represent these signals, we develop an intracardiac electrogram (IEGM) model as an interface between the virtual heart and the device.
The model can capture not only the local excitation but also far-field signals and pacing afterpotentials.
Moreover, the sensing controller can specify unipolar or bipolar electrogram (EGM) sensing configurations and introduce various oversensing and undersensing modes.
The simulation results show that the model is able to reproduce clinically observed sensing problems, which significantly extends the capabilities of the virtual heart model in the context of device validation.
N-continuous orthogonal frequency division multiplexing (NC-OFDM) is a promising technique to obtain significant sidelobe suppression for baseband OFDM signals, in future 5G wireless communications.
However, the precoder of NC-OFDM usually causes severe interference and high complexity.
To reduce the interference and complexity, this paper proposes an improved time-domain N-continuous OFDM (TD-NC-OFDM) by shortening the smooth signal, which is linearly combined by rectangularly pulsed OFDM basis signals truncated by a smooth window.
Furthermore, we obtain an asymptotic spectrum analysis of the TD-NC-OFDM signals by a closed-form expression, calculate its low complexity in OFDM transceiver, and derive a closed-form expression of the received signal-to-interference-plus-noise ratio (SINR).
Simulation results show that the proposed low-interference TD-NC-OFDM can achieve similar suppression performance but introduce negligible bit error rate (BER) degradation and much lower computational complexity, compared to conventional NC-OFDM.
Software defect prediction is an important aspect of preventive maintenance of a software.
Many techniques have been employed to improve software quality through defect prediction.
This paper introduces an approach of defect prediction through a machine learning algorithm, support vector machines (SVM), by using the code smells as the factor.
Smell prediction model based on support vector machines was used to predict defects in the subsequent releases of the eclipse software.
The results signify the role of smells in predicting the defects of a software.
The results can further be used as a baseline to investigate further the role of smells in predicting defects.
Network coding permits to deploy distributed packet delivery algorithms that locally adapt to the network availability in media streaming applications.
However, it may also increase delay and computational complexity if it is not implemented efficiently.
We address here the effective placement of nodes that implement randomized network coding in overlay networks, so that the goodput is kept high while the delay for decoding stays small in streaming applications.
We first estimate the decoding delay at each client, which depends on the innovative rate in the network.
This estimation permits to identify the nodes that have to perform coding for a reduced decoding delay.
We then propose two iterative algorithms for selecting the nodes that should perform network coding.
The first algorithm relies on the knowledge of the full network statistics.
The second algorithm uses only local network statistics at each node.
Simulation results show that large performance gains can be achieved with the selection of only a few network coding nodes.
Moreover, the second algorithm performs very closely to the central estimation strategy, which demonstrates that the network coding nodes can be selected efficiently in a distributed manner.
Our scheme shows large gains in terms of achieved throughput, delay and video quality in realistic overlay networks when compared to methods that employ traditional streaming strategies as well as random network nodes selection algorithms.
We model the coexistence of DSRC and WiFi networks as a strategic form game with the networks as the players.
Nodes in a DSRC network must support messaging of status updates that are time sensitive.
Such nodes would like to achieve a small age of information of status updates.
In contrast, nodes in a WiFi network would like to achieve large throughputs.
Each network chooses a medium access probability to be used by all its nodes.
We investigate Nash and Stackelberg equilibrium strategies.
In the first chapter of Shannon's "A Mathematical Theory of Communication," it is shown that the maximum entropy rate of an input process of a constrained system is limited by the combinatorial capacity of the system.
Shannon considers systems where the constraints define regular languages and uses results from matrix theory in his derivations.
In this work, the regularity constraint is dropped.
Using generating functions, it is shown that the maximum entropy rate of an input process is upper-bounded by the combinatorial capacity in general.
The presented results also allow for a new approach to systems with regular constraints.
As an example, the results are applied to binary sequences that fulfill the (j,k) run-length constraint and by using the proposed framework, a simple formula for the combinatorial capacity is given and a maxentropic input process is defined.
T-Reqs is a text-based requirements management solution based on the git version control system.
It combines useful conventions, templates and helper scripts with powerful existing solutions from the git ecosystem and provides a working solution to address some known requirements engineering challenges in large-scale agile system development.
Specifically, it allows agile cross-functional teams to be aware of requirements at system level and enables them to efficiently propose updates to those requirements.
Based on our experience with T-Reqs, we i) relate known requirements challenges of large-scale agile system development to tool support; ii) list key requirements for tooling in such a context; and iii) propose concrete solutions for challenges.
The list segment predicate ls used in separation logic for verifying programs with pointers is well-suited to express properties on singly-linked lists.
We study the effects of adding ls to the full propositional separation logic with the separating conjunction and implication, which is motivated by the recent design of new fragments in which all these ingredients are used indifferently and verification tools start to handle the magic wand connective.
This is a very natural extension that has not been studied so far.
We show that the restriction without the separating implication can be solved in polynomial space by using an appropriate abstraction for memory states whereas the full extension is shown undecidable by reduction from first-order separation logic.
Many variants of the logic and fragments are also investigated from the computational point of view when ls is added, providing numerous results about adding reachability predicates to propositional separation logic.
In this paper, we propose a novel approach (SAPEO) to support the survival selection process in multi-objective evolutionary algorithms with surrogate models - it dynamically chooses individuals to evaluate exactly based on the model uncertainty and the distinctness of the population.
We introduce variants that differ in terms of the risk they allow when doing survival selection.
Here, the anytime performance of different SAPEO variants is evaluated in conjunction with an SMS-EMOA using the BBOB bi-objective benchmark.
We compare the obtained results with the performance of the regular SMS-EMOA, as well as another surrogate-assisted approach.
The results open up general questions about the applicability and required conditions for surrogate-assisted multi-objective evolutionary algorithms to be tackled in the future.
Motivated by applications in social network community analysis, we introduce a new clustering paradigm termed motif clustering.
Unlike classical clustering, motif clustering aims to minimize the number of clustering errors associated with both edges and certain higher order graph structures (motifs) that represent "atomic units" of social organizations.
Our contributions are two-fold: We first introduce motif correlation clustering, in which the goal is to agnostically partition the vertices of a weighted complete graph so that certain predetermined "important" social subgraphs mostly lie within the same cluster, while "less relevant" social subgraphs are allowed to lie across clusters.
We then proceed to introduce the notion of motif covers, in which the goal is to cover the vertices of motifs via the smallest number of (near) cliques in the graph.
Motif cover algorithms provide a natural solution for overlapping clustering and they also play an important role in latent feature inference of networks.
For both motif correlation clustering and its extension introduced via the covering problem, we provide hardness results, algorithmic solutions and community detection results for two well-studied social networks.
In this paper, Suprasegmental Hidden Markov Models (SPHMMs) have been used to enhance the recognition performance of text-dependent speaker identification in the shouted environment.
Our speech database consists of two databases: our collected database and the Speech Under Simulated and Actual Stress (SUSAS) database.
Our results show that SPHMMs significantly enhance speaker identification performance compared to Second-Order Circular Hidden Markov Models (CHMM2s) in the shouted environment.
Using our collected database, speaker identification performance in this environment is 68% and 75% based on CHMM2s and SPHMMs respectively.
Using the SUSAS database, speaker identification performance in the same environment is 71% and 79% based on CHMM2s and SPHMMs respectively.
When engineering complex and distributed software and hardware systems (increasingly used in many sectors, such as manufacturing, aerospace, transportation, communication, energy, and health-care), quality has become a big issue, since failures can have economics consequences and can also endanger human life.
Model-based specifications of a component-based system permit to explicitly model the structure and behaviour of components and their integration.
In particular Software Architectures (SA) has been advocated as an effective means to produce quality systems.
In this chapter by combining different technologies and tools for analysis and development, we propose an architecture-centric model-driven approach to validate required properties and to generate the system code.
Functional requirements are elicited and used for identifying expected properties the architecture shall express.
The architectural compliance to the properties is formally demonstrated, and the produced architectural model is used to automatically generate the Java code.
Suitable transformations assure that the code is conforming to both structural and behavioural SA constraints.
This chapter describes the process and discusses how some existing tools and languages can be exploited to support the approach.
Blind people can now use maps located at Mapy.cz, thanks to the long-standing joint efforts of the ELSA Center at the Czech Technical University in Prague, the Teiresias Center at Masaryk University, and the company Seznam.cz.
Conventional map underlays are automatically adjusted so that they could be read through touch after being printed on microcapsule paper, which opens a whole new perspective in the use of tactile maps.
Users may select an area of their choice in the Czech Republic (only within its boundaries, for the time being) and also the production of tactile maps, including the preparation of the map underlays, takes no more than several minutes.
Multimodal medical image fusion helps to increase efficiency in medical diagnosis.
This paper presents multimodal medical image fusion by selecting relevant features using Principle Component Analysis (PCA) and Particle Swarm Optimization techniques (PSO).
DTCWT is used for decomposition of the images into low and high frequency coefficients.
Fusion rules such as combination of minimum, maximum and simple averaging are applied to approximate and detailed coefficients.
The fused image is reconstructed by inverse DTCWT.
Performance metrics are evaluated and it shows that DTCWT-PCA performs better than DTCWT-PSO in terms of Structural Similarity Index Measure (SSIM) and Cross Correlation (CC).
Computation time and feature vector size is reduced in DTCWT-PCA compared to DTCWT-PSO for feature selection which proves robustness and storage capacity.
We study the relationship between the sentiment levels of Twitter users and the evolving network structure that the users created by @-mentioning each other.
We use a large dataset of tweets to which we apply three sentiment scoring algorithms, including the open source SentiStrength program.
Specifically we make three contributions.
Firstly we find that people who have potentially the largest communication reach (according to a dynamic centrality measure) use sentiment differently than the average user: for example they use positive sentiment more often and negative sentiment less often.
Secondly we find that when we follow structurally stable Twitter communities over a period of months, their sentiment levels are also stable, and sudden changes in community sentiment from one day to the next can in most cases be traced to external events affecting the community.
Thirdly, based on our findings, we create and calibrate a simple agent-based model that is capable of reproducing measures of emotive response comparable to those obtained from our empirical dataset.
In this project, we combine AlphaGo algorithm with Curriculum Learning to crack the game of Gomoku.
Modifications like Double Networks Mechanism and Winning Value Decay are implemented to solve the intrinsic asymmetry and short-sight of Gomoku.
Our final AI AlphaGomoku, through two days' training on a single GPU, has reached humans' playing level.
This paper presents a solution based on dual quaternion algebra to the general problem of pose (i.e., position and orientation) consensus for systems composed of multiple rigid-bodies.
The dual quaternion algebra is used to model the agents' poses and also in the distributed control laws, making the proposed technique easily applicable to formation control of general robotic systems.
The proposed pose consensus protocol has guaranteed convergence when the interaction among the agents is represented by directed graphs with directed spanning trees, which is a more general result when compared to the literature on formation control.
In order to illustrate the proposed pose consensus protocol and its extension to the problem of formation control, we present a numerical simulation with a large number of free-flying agents and also an application of cooperative manipulation by using real mobile manipulators.
We present a new technique for learning visual-semantic embeddings for cross-modal retrieval.
Inspired by hard negative mining, the use of hard negatives in structured prediction, and ranking loss functions, we introduce a simple change to common loss functions used for multi-modal embeddings.
That, combined with fine-tuning and use of augmented data, yields significant gains in retrieval performance.
We showcase our approach, VSE++, on MS-COCO and Flickr30K datasets, using ablation studies and comparisons with existing methods.
On MS-COCO our approach outperforms state-of-the-art methods by 8.8% in caption retrieval and 11.3% in image retrieval (at R@1).
This paper investigates stochastic nondeterminism on continuous state spaces by relating nondeterministic kernels and stochastic effectivity functions to each other.
Nondeterministic kernels are functions assigning each state a set o subprobability measures, and effectivity functions assign to each state an upper-closed set of subsets of measures.
Both concepts are generalizations of Markov kernels used for defining two different models: Nondeterministic labelled Markov processes and stochastic game models, respectively.
We show that an effectivity function that maps into principal filters is given by an image-countable nondeterministic kernel, and that image-finite kernels give rise to effectivity functions.
We define state bisimilarity for the latter, considering its connection to morphisms.
We provide a logical characterization of bisimilarity in the finitary case.
A generalization of congruences (event bisimulations) to effectivity functions and its relation to the categorical presentation of bisimulation are also studied.
This paper proposes a novel multimodal fusion approach, aiming to produce best possible decisions by integrating information coming from multiple media.
While most of the past multimodal approaches either work by projecting the features of different modalities into the same space, or by coordinating the representations of each modality through the use of constraints, our approach borrows from both visions.
More specifically, assuming each modality can be processed by a separated deep convolutional network, allowing to take decisions independently from each modality, we introduce a central network linking the modality specific networks.
This central network not only provides a common feature embedding but also regularizes the modality specific networks through the use of multi-task learning.
The proposed approach is validated on 4 different computer vision tasks on which it consistently improves the accuracy of existing multimodal fusion approaches.
Algebraic effects are computational effects that can be represented by an equational theory whose operations produce the effects at hand.
The free model of this theory induces the expected computational monad for the corresponding effect.
Algebraic effects include exceptions, state, nondeterminism, interactive input/output, and time, and their combinations.
Exception handling, however, has so far received no algebraic treatment.
We present such a treatment, in which each handler yields a model of the theory for exceptions, and each handling construct yields the homomorphism induced by the universal property of the free model.
We further generalise exception handlers to arbitrary algebraic effects.
The resulting programming construct includes many previously unrelated examples from both theory and practice, including relabelling and restriction in Milner's CCS, timeout, rollback, and stream redirection.
This paper proposes a novel framework to reconstruct the dynamic magnetic resonance images (DMRI) with motion compensation (MC).
Due to the inherent motion effects during DMRI acquisition, reconstruction of DMRI using motion estimation/compensation (ME/MC) has been studied under a compressed sensing (CS) scheme.
In this paper, by embedding the intensity-based optical flow (OF) constraint into the traditional CS scheme, we are able to couple the DMRI reconstruction with motion field estimation.
The formulated optimization problem is solved by a primal-dual algorithm with linesearch due to its efficiency when dealing with non-differentiable problems.
With the estimated motion field, the DMRI reconstruction is refined through MC.
By employing the multi-scale coarse-to-fine strategy, we are able to update the variables(temporal image sequences and motion vectors) and to refine the image reconstruction alternately.
Moreover, the proposed framework is capable of handling a wide class of prior information (regularizations) for DMRI reconstruction, such as sparsity, low rank and total variation.
Experiments on various DMRI data, ranging from in vivo lung to cardiac dataset, validate the reconstruction quality improvement using the proposed scheme in comparison to several state-of-the-art algorithms.
In recent years, there have been many works that use website fingerprinting techniques to enable a local adversary to determine which website a Tor user is visiting.
However, most of these works rely on manually extracted features, and thus are fragile: a small change in the protocol or a simple defense often renders these attacks useless.
In this work, we leverage deep learning techniques to create a more robust attack that does not require any manually extracted features.
Specifically, we propose Var-CNN, an attack that uses model variations on convolutional neural networks with both the packet sequence and packet timing data.
In open-world settings, Var-CNN attains higher true positive rate and lower false positive rate than any prior work at 90.9% and 0.3%, respectively.
Moreover, these improvements are observed even with low amounts of training data, where deep learning techniques often suffer.
Given the severity of our attacks, we also introduce a new countermeasure, DynaFlow, based on dynamically adjusting flows to protect against website fingerprinting attacks.
DynaFlow provides a similar level of security as current state-of-the-art and defeats all attacks, including our own, while being over 40% more efficient than existing defenses.
Moreover, unlike many prior defenses, DynaFlow can protect dynamically generated websites as well.
The discovery of influential entities in all kinds of networks (e.g. social, digital, or computer) has always been an important field of study.
In recent years, Online Social Networks (OSNs) have been established as a basic means of communication and often influencers and opinion makers promote politics, events, brands or products through viral content.
In this work, we present a systematic review across i) online social influence metrics, properties, and applications and ii) the role of semantic in modeling OSNs information.
We end up with the conclusion that both areas can jointly provide useful insights towards the qualitative assessment of viral user-generated content, as well as for modeling the dynamic properties of influential content and its flow dynamics.
Proper management of requirements is crucial to successful development software within limited time and cost.
Nonfunctional requirements (NFR) are one of the key criteria to derive a comparison among various software systems.
In most of software development NFR have be specified as an additional requirement of software.
NFRs such as performance, reliability, maintainability, security, accuracy etc. have to be considered at the early stage of software development as functional requirement (FR).
However, identifying NFR is not an easy task.
Although there are well developed techniques for eliciting functional requirement, there is a lack of elicitation mechanism for NFR and there is no proper consensus regarding NFR elicitation techniques.
Eliciting NFRs are considered to be one of the challenging jobs in requirement analysis.
This paper proposes a UML use case based questionary approach to identifying and classifying NFR of a system.
The proposed approach is illustrated by using a Point of Sale (POS) case study
In this study, a novel illuminant color estimation framework is proposed for color constancy, which incorporates the high representational capacity of deep-learning-based models and the great interpretability of assumption-based models.
The well-designed building block, feature map reweight unit (ReWU), helps to achieve comparative accuracy on benchmark datasets with respect to prior state-of-the-art models while requiring only 1%-5% model size and 8%-20% computational cost.
In addition to local color estimation, a confidence estimation branch is also included such that the model is able to produce point estimate and its uncertainty estimate simultaneously, which provides useful clues for local estimates aggregation and multiple illumination estimation.
The source code and the dataset are available at https://github.com/QiuJueqin/Reweight-CC.
Many modern Artificial Intelligence (AI) systems make use of data embeddings, particularly in the domain of Natural Language Processing (NLP).
These embeddings are learnt from data that has been gathered "from the wild" and have been found to contain unwanted biases.
In this paper we make three contributions towards measuring, understanding and removing this problem.
We present a rigorous way to measure some of these biases, based on the use of word lists created for social psychology applications; we observe how gender bias in occupations reflects actual gender bias in the same occupations in the real world; and finally we demonstrate how a simple projection can significantly reduce the effects of embedding bias.
All this is part of an ongoing effort to understand how trust can be built into AI systems.
In a full-duplex (FD) multi-user network, the system performance is not only limited by the self-interference but also by the co-channel interference due to the simultaneous uplink and downlink transmissions.
Joint design of the uplink/downlink transmission direction of users and the power allocation is crucial for achieving high system performance in the FD multi-user network.
In this paper, we investigate the joint uplink/downlink transmission direction assignment (TDA), user paring (UP) and power allocation problem for maximizing the system max-min fairness (MMF) rate in a FD multi-user orthogonal frequency division multiple access (OFDMA) system.
The problem is formulated with a two-time-scale structure where the TDA and the UP variables are for optimizing a long-term MMF rate while the power allocation is for optimizing an instantaneous MMF rate during each channel coherence interval.
We show that the studied joint MMF rate maximization problem is NP-hard in general.
To obtain high-quality suboptimal solutions, we propose efficient methods based on simple relaxation and greedy rounding techniques.
Simulation results are presented to show that the proposed algorithms are effective and achieve higher MMF rates than the existing heuristic methods.
A social network consists of a set of actors and a set of relationships between them which describe certain patterns of communication.
Most current networks are huge and difficult to analyze and visualize.
One of the methods frequently used is to extract the most important features, namely to create a certain abstraction, that is the transformation of a large network to a much smaller one, so the latter is a useful summary of the original one, still keeping the most important characteristics.
In the case of a social network it can be achieved in two ways.
One is to find groups of actors and present only them and relationships between them.
The other is to find actors who play similar roles and to construct a smaller network in which the connection between the actors would be replaced with connections between the roles.
Classifying actors by the roles they are playing in the network can help to understand 'who is who' in a social network.
This classification can be very useful, because it gives us a comprehensive view of the network and helps to understand how the network is organized, and to predict how it could behave in the case of certain events (internal or external).
Communicating and sharing intelligence among agents is an important facet of achieving Artificial General Intelligence.
As a first step towards this challenge, we introduce a novel framework for image generation: Message Passing Multi-Agent Generative Adversarial Networks (MPM GANs).
While GANs have recently been shown to be very effective for image generation and other tasks, these networks have been limited to mostly single generator-discriminator networks.
We show that we can obtain multi-agent GANs that communicate through message passing to achieve better image generation.
The objectives of the individual agents in this framework are two fold: a co-operation objective and a competing objective.
The co-operation objective ensures that the message sharing mechanism guides the other generator to generate better than itself while the competing objective encourages each generator to generate better than its counterpart.
We analyze and visualize the messages that these GANs share among themselves in various scenarios.
We quantitatively show that the message sharing formulation serves as a regularizer for the adversarial training.
Qualitatively, we show that the different generators capture different traits of the underlying data distribution.
Energy harvesting is a technology for enabling green, sustainable, and autonomous wireless networks.
In this paper, a large-scale wireless network with energy harvesting transmitters is considered, where a group of transmitters forms a cluster to cooperatively serve a desired receiver amid interference and noise.
To characterize the link-level performance, closed-form expressions are derived for the transmission success probability at a receiver in terms of key parameters such as node densities, energy harvesting parameters, channel parameters, and cluster size, for a given cluster geometry.
The analysis is further extended to characterize a network-level performance metric, capturing the tradeoff between link quality and the fraction of receivers served.
Numerical simulations validate the accuracy of the analytical model.
Several useful insights are provided.
For example, while more cooperation helps improve the link-level performance, the network-level performance might degrade with the cluster size.
Numerical results show that a small cluster size (typically 3 or smaller) optimizes the network-level performance.
Furthermore, substantial performance can be extracted with a relatively small energy buffer.
Moreover, the utility of having a large energy buffer increases with the energy harvesting rate as well as with the cluster size in sufficiently dense networks.
The majority of deep neural network (DNN) based speech enhancement algorithms rely on the mean-square error (MSE) criterion of short-time spectral amplitudes (STSA), which has no apparent link to human perception, e.g. speech intelligibility.
Short-Time Objective Intelligibility (STOI), a popular state-of-the-art speech intelligibility estimator, on the other hand, relies on linear correlation of speech temporal envelopes.
This raises the question if a DNN training criterion based on envelope linear correlation (ELC) can lead to improved speech intelligibility performance of DNN based speech enhancement algorithms compared to algorithms based on the STSA-MSE criterion.
In this paper we derive that, under certain general conditions, the STSA-MSE and ELC criteria are practically equivalent, and we provide empirical data to support our theoretical results.
Furthermore, our experimental findings suggest that the standard STSA minimum-MSE estimator is near optimal, if the objective is to enhance noisy speech in a manner which is optimal with respect to the STOI speech intelligibility estimator.
The availability of large-scale annotated image datasets coupled with recent advances in supervised deep learning methods are enabling the derivation of representative image features that can potentially impact different image analysis problems.
However, such supervised approaches are not feasible in the medical domain where it is challenging to obtain a large volume of labelled data due to the complexity of manual annotation and inter- and intra-observer variability in label assignment.
Algorithms designed to work on small annotated datasets are useful but have limited applications.
In an effort to address the lack of annotated data in the medical image analysis domain, we propose an algorithm for hierarchical unsupervised feature learning.
Our algorithm introduces three new contributions: (i) we use kernel learning to identify and represent invariant characteristics across image sub-patches in an unsupervised manner; (ii) we leverage the sparsity inherent to medical image data and propose a new sparse convolutional kernel network (S-CKN) that can be pre-trained in a layer-wise fashion, thereby providing initial discriminative features for medical data; and (iii) we propose a spatial pyramid pooling framework to capture subtle geometric differences in medical image data.
Our experiments evaluate our algorithm in two common application areas of medical image retrieval and classification using two public datasets.
Our results demonstrate that the medical image feature representations extracted with our algorithm enable a higher accuracy in both application areas compared to features extracted from other conventional unsupervised methods.
Furthermore, our approach achieves an accuracy that is competitive with state-of-the-art supervised CNNs.
External or internal domain-specific languages (DSLs) or (fluent) APIs?
Whoever you are -- a developer or a user of a DSL -- you usually have to choose your side; you should not!
What about metamorphic DSLs that change their shape according to your needs?
We report on our 4-years journey of providing the "right" support (in the domain of feature modeling), leading us to develop an external DSL, different shapes of an internal API, and maintain all these languages.
A key insight is that there is no one-size-fits-all solution or no clear superiority of a solution compared to another.
On the contrary, we found that it does make sense to continue the maintenance of an external and internal DSL.
The vision that we foresee for the future of software languages is their ability to be self-adaptable to the most appropriate shape (including the corresponding integrated development environment) according to a particular usage or task.
We call metamorphic DSL such a language, able to change from one shape to another shape.
Enterprise software systems make complex interactions with other services in their environment.
Developing and testing for production-like conditions is therefore a challenging task.
Prior approaches include emulations of the dependency services using either explicit modelling or record-and-replay approaches.
Models require deep knowledge of the target services while record-and-replay is limited in accuracy.
We present a new technique that improves the accuracy of record-and-replay approaches, without requiring prior knowledge of the services.
The approach uses multiple sequence alignment to derive message prototypes from recorded system interactions and a scheme to match incoming request messages against message prototypes to generate response messages.
We introduce a modified Needleman-Wunsch algorithm for distance calculation during message matching, wildcards in message prototypes for high variability sections, and entropy-based weightings in distance calculations for increased accuracy.
Combined, our new approach has shown greater than 99% accuracy for four evaluated enterprise system messaging protocols.
Deep Learning is one of the newest trends in Machine Learning and Artificial Intelligence research.
It is also one of the most popular scientific research trends now-a-days.
Deep learning methods have brought revolutionary advances in computer vision and machine learning.
Every now and then, new and new deep learning techniques are being born, outperforming state-of-the-art machine learning and even existing deep learning techniques.
In recent years, the world has seen many major breakthroughs in this field.
Since deep learning is evolving at a huge speed, its kind of hard to keep track of the regular advances especially for new researchers.
In this paper, we are going to briefly discuss about recent advances in Deep Learning for past few years.
Structured prediction is ubiquitous in applications of machine learning such as knowledge extraction and natural language processing.
Structure often can be formulated in terms of logical constraints.
We consider the question of how to perform efficient active learning in the presence of logical constraints among variables inferred by different classifiers.
We propose several methods and provide theoretical results that demonstrate the inappropriateness of employing uncertainty guided sampling, a commonly used active learning method.
Furthermore, experiments on ten different datasets demonstrate that the methods significantly outperform alternatives in practice.
The results are of practical significance in situations where labeled data is scarce.
Accurate Traffic Sign Detection (TSD) can help intelligent systems make better decisions according to the traffic regulations.
TSD, regarded as a typical small object detection problem in some way, is fundamental in Advanced Driver Assistance Systems (ADAS) and self-driving.
However, although deep neural networks have achieved human even superhuman performance on several tasks, due to their own limitations, small object detection is still an open question.
In this paper, we proposed a brain-inspired network, named as KB-RANN, to handle this problem.
Attention mechanism is an essential function of our brain, we used a novel recurrent attentive neural network to improve the detection accuracy in a fine-grained manner.
Further, we combined domain specific knowledge and intuitive knowledge to improve the efficiency.
Experimental result shows that our methods achieved better performance than several popular methods widely used in object detection.
More significantly, we transplanted our method on our designed embedded system and deployed on our self-driving car successfully.
Convolutional Neural Networks (CNNs) have recently emerged as the dominant model in computer vision.
If provided with enough training data, they predict almost any visual quantity.
In a discrete setting, such as classification, CNNs are not only able to predict a label but often predict a confidence in the form of a probability distribution over the output space.
In continuous regression tasks, such a probability estimate is often lacking.
We present a regression framework which models the output distribution of neural networks.
This output distribution allows us to infer the most likely labeling following a set of physical or modeling constraints.
These constraints capture the intricate interplay between different input and output variables, and complement the output of a CNN.
However, they may not hold everywhere.
Our setup further allows to learn a confidence with which a constraint holds, in the form of a distribution of the constrain satisfaction.
We evaluate our approach on the problem of intrinsic image decomposition, and show that constrained structured regression significantly increases the state-of-the-art.
Several studies have been conducted on understanding third-party user tracking on the web.
However, web trackers can only track users on sites where they are embedded by the publisher, thus obtaining a fragmented view of a user's online footprint.
In this work, we investigate a different form of user tracking, where browser extensions are repurposed to capture the complete online activities of a user and communicate the collected sensitive information to a third-party domain.
We conduct an empirical study of spying browser extensions on the Chrome Web Store.
First, we present an in-depth analysis of the spying behavior of these extensions.
We observe that these extensions steal a variety of sensitive user information, such as the complete browsing history (e.g., the sequence of web traversals), online social network (OSN) access tokens, IP address, and user geolocation.
Second, we investigate the potential for automatically detecting spying extensions by applying machine learning schemes.
We show that using a Recurrent Neural Network (RNN), the sequences of browser API calls can be a robust feature, outperforming hand-crafted features (used in prior work on malicious extensions) to detect spying extensions.
Our RNN based detection scheme achieves a high precision (90.02%) and recall (93.31%) in detecting spying extensions.
The new era of computing called Cloud Computing allows the user to access the cloud services dynamically over the Internet wherever and whenever needed.
Cloud consists of data and resources; and the cloud services include the delivery of software, infrastructure, applications, and storage over the Internet based on user demand through Internet.
In short, cloud computing is a business and economic model allowing the users to utilize high-end computing and storage virtually with minimal infrastructure on their end.
Cloud has three service models namely, Cloud Software-as-a-Service (SaaS), Cloud Platform-as-a-Service (PaaS), and Cloud Infrastructure-as-a-Service (IaaS).
This paper talks in depth of cloud infrastructure service management.
The capability to operate cloud-native applications can generate enormous business growth and value.
But enterprise architects should be aware that cloud-native applications are vulnerable to vendor lock-in.
We investigated cloud-native application design principles, public cloud service providers, and industrial cloud standards.
All results indicate that most cloud service categories seem to foster vendor lock-in situations which might be especially problematic for enterprise architectures.
This might sound disillusioning at first.
However, we present a reference model for cloud-native applications that relies only on a small subset of well standardized IaaS services.
The reference model can be used for codifying cloud technologies.
It can guide technology identification, classification, adoption, research and development processes for cloud-native application and for vendor lock-in aware enterprise architecture engineering methodologies.
Variational auto-encoders (VAEs) provide an attractive solution to image generation problem.
However, they tend to produce blurred and over-smoothed images due to their dependence on pixel-wise reconstruction loss.
This paper introduces a new approach to alleviate this problem in the VAE based generative models.
Our model simultaneously learns to match the data, reconstruction loss and the latent distributions of real and fake images to improve the quality of generated samples.
To compute the loss distributions, we introduce an auto-encoder based discriminator model which allows an adversarial learning procedure.
The discriminator in our model also provides perceptual guidance to the VAE by matching the learned similarity metric of the real and fake samples in the latent space.
To stabilize the overall training process, our model uses an error feedback approach to maintain the equilibrium between competing networks in the model.
Our experiments show that the generated samples from our proposed model exhibit a diverse set of attributes and facial expressions and scale up to high-resolution images very well.
This paper applies the multibond graph approach for rigid multibody systems to model the dynamics of general spatial mechanisms.
The commonly used quick return mechanism which comprises of revolute as well as prismatic joints has been chosen as a representative example to demonstrate the application of this technique and its resulting advantages.
In this work, the links of the quick return mechanism are modeled as rigid bodies.
The rigid links are then coupled at the joints based on the nature of constraint.
This alternative method of formulation of system dynamics, using Bond Graphs, offers a rich set of features that include pictorial representation of the dynamics of translation and rotation for each link of the mechanism in the inertial frame, representation and handling of constraints at the joints, depiction of causality, obtaining dynamic reaction forces and moments at various locations in the mechanism and so on.
Yet another advantage of this approach is that the coding for simulation can be carried out directly from the Bond Graph in an algorithmic manner, without deriving system equations.
In this work, the program code for simulation is written in MATLAB.
The vector and tensor operations are conveniently represented in MATLAB, resulting in a compact and optimized code.
The simulation results are plotted and discussed in detail.
In the field of robust geometric computation it is often necessary to make exact decisions based on inexact floating-point arithmetic.
One common approach is to store the computation history in an arithmetic expression dag and to re-evaluate the expression with increasing precision until an exact decision can be made.
We show that exact-decisions number types based on expression dags can be evaluated faster in practice through parallelization on multiple cores.
We compare the impact of several restructuring methods for the expression dag on its running time in a parallel environment.
This document describes a library for similarity searching.
Even though the library contains a variety of metric-space access methods, our main focus is on search methods for non-metric spaces.
Because there are fewer exact solutions for non-metric spaces, many of our methods give only approximate answers.
Thus, the methods are evaluated in terms of efficiency-effectiveness trade-offs rather than merely in terms of their efficiency.
Our goal is, therefore, to provide not only state-of-the-art approximate search methods for both non-metric and metric spaces, but also the tools to measure search quality.
We concentrate on technical details, i.e., how to compile the code, run the benchmarks, evaluate results, and use our code in other applications.
Additionally, we explain how to extend the code by adding new search methods and spaces.
One source of disturbance in a pulsed T-ray signal is attributed to ambient water vapor.
Water molecules in the gas phase selectively absorb T-rays at discrete frequencies corresponding to their molecular rotational transitions.
This results in prominent resonances spread over the T-ray spectrum, and in the time domain the T-ray signal is observed as fluctuations after the main pulse.
These effects are generally undesired, since they may mask critical spectroscopic data.
So, ambient water vapor is commonly removed from the T-ray path by using a closed chamber during the measurement.
Yet, in some applications a closed chamber is not applicable.
This situation, therefore, motivates the need for another method to reduce these unwanted artifacts.
This paper presents a study on a computational means to address the problem.
Initially, a complex frequency response of water vapor is modeled from a spectroscopic catalog.
Using a deconvolution technique, together with fine tuning of the strength of each resonance, parts of the water-vapor response are removed from a measured T-ray signal, with minimal signal distortion.
Deep-neural-network (DNN) based noise suppression systems yield significant improvements over conventional approaches such as spectral subtraction and non-negative matrix factorization, but do not generalize well to noise conditions they were not trained for.
In comparison to DNNs, humans show remarkable noise suppression capabilities that yield successful speech intelligibility under various adverse listening conditions and negative signal-to-noise ratios (SNRs).
Motivated by the excellent human performance, this paper explores whether numerical models that simulate human cochlear signal processing can be combined with DNNs to improve the robustness of DNN based noise suppression systems.
Five cochlear models were coupled to fully-connected and recurrent NN-based noise suppression systems and were trained and evaluated for a variety of noise conditions using objective metrics: perceptual speech quality (PESQ), segmental SNR and cepstral distance.
The simulations show that biophysically-inspired cochlear models improve the generalizability of DNN-based noise suppression systems for unseen noise and negative SNRs.
This approach thus leads to robust noise suppression systems that are less sensitive to the noise type and noise level.
Because cochlear models capture the intrinsic nonlinearities and dynamics of peripheral auditory processing, it is shown here that accounting for their deterministic signal processing improves machine hearing and avoids overtraining of multi-layer DNNs.
We hence conclude that machines hear better when realistic cochlear models are used at the input of DNNs.
Computing layer similarities is an important way of characterizing multiplex networks because various static properties and dynamic processes depend on the relationships between layers.
We provide a taxonomy and experimental evaluation of approaches to compare layers in multiplex networks.
Our taxonomy includes, systematizes and extends existing approaches, and is complemented by a set of practical guidelines on how to apply them.
Recent state-of-the-art scene text recognition methods have primarily focused on horizontal text in images.
However, in several Asian countries, including China, large amounts of text in signs, books, and TV commercials are vertically directed.
Because the horizontal and vertical texts exhibit different characteristics, developing an algorithm that can simultaneously recognize both types of text in real environments is necessary.
To address this problem, we adopted the direction encoding mask (DEM) and selective attention network (SAN) methods based on supervised learning.
DEM contains directional information to compensate in cases that lack text direction; therefore, our network is trained using this information to handle the vertical text.
The SAN method is designed to work individually for both types of text.
To train the network to recognize both types of text and to evaluate the effectiveness of the designed model, we prepared a new synthetic vertical text dataset and collected an actual vertical text dataset (VTD142) from the Web.
Using these datasets, we proved that our proposed model can accurately recognize both vertical and horizontal text and can achieve state-of-the-art results in experiments using benchmark datasets, including the street view test (SVT), IIIT-5k, and ICDAR.
Although our model is relatively simple as compared to its predecessors, it maintains the accuracy and is trained in an end-to-end manner.
The area of Handwritten Signature Verification has been broadly researched in the last decades, but remains an open research problem.
The objective of signature verification systems is to discriminate if a given signature is genuine (produced by the claimed individual), or a forgery (produced by an impostor).
This has demonstrated to be a challenging task, in particular in the offline (static) scenario, that uses images of scanned signatures, where the dynamic information about the signing process is not available.
Many advancements have been proposed in the literature in the last 5-10 years, most notably the application of Deep Learning methods to learn feature representations from signature images.
In this paper, we present how the problem has been handled in the past few decades, analyze the recent advancements in the field, and the potential directions for future research.
One of the main aims of the so-called Web of Data is to be able to handle heterogeneous resources where data can be expressed in either XML or RDF.
The design of programming languages able to handle both XML and RDF data is a key target in this context.
In this paper we present a framework called XQOWL that makes possible to handle XML and RDF/OWL data with XQuery.
XQOWL can be considered as an extension of the XQuery language that connects XQuery with SPARQL and OWL reasoners.
XQOWL embeds SPARQL queries (via Jena SPARQL engine) in XQuery and enables to make calls to OWL reasoners (HermiT, Pellet and FaCT++) from XQuery.
It permits to combine queries against XML and RDF/OWL resources as well as to reason with RDF/OWL data.
Therefore input data can be either XML or RDF/OWL and output data can be formatted in XML (also using RDF/OWL XML serialization).
A major issue of locally repairable codes is their robustness.
If a local repair group is not able to perform the repair process, this will result in increasing the repair cost.
Therefore, it is critical for a locally repairable code to have multiple repair groups.
In this paper we consider robust locally repairable coding schemes which guarantee that there exist multiple alternative local repair groups for any single failure such that the failed node can still be repaired locally even if some of the repair groups are not available.
We use linear programming techniques to establish upper bounds on the code size of these codes.
Furthermore, we address the update efficiency problem of the distributed data storage networks.
Any modification on the stored data will result in updating the content of the storage nodes.
Therefore, it is essential to minimise the number of nodes which need to be updated by any change in the stored data.
We characterise the update-efficient storage code properties and establish the necessary conditions that the weight enumerator of these codes need to satisfy.
In this paper we present the performance of parallel text processing with Map Reduce on a cloud platform.
Scientific papers in Turkish language are processed using Zemberek NLP library.
Experiments were run on a Hadoop cluster and compared with the single machines performance.
We present a transition-based AMR parser that directly generates AMR parses from plain text.
We use Stack-LSTMs to represent our parser state and make decisions greedily.
In our experiments, we show that our parser achieves very competitive scores on English using only AMR training data.
Adding additional information, such as POS tags and dependency trees, improves the results further.
Vision algorithms capable of interpreting scenes from a real-time video stream are necessary for computer-assisted surgery systems to achieve context-aware behavior.
In laparoscopic procedures one particular algorithm needed for such systems is the identification of surgical phases, for which the current state of the art is a model based on a CNN-LSTM.
A number of previous works using models of this kind have trained them in a fully supervised manner, requiring a fully annotated dataset.
Instead, our work confronts the problem of learning surgical phase recognition in scenarios presenting scarce amounts of annotated data (under 25% of all available video recordings).
We propose a teacher/student type of approach, where a strong predictor called the teacher, trained beforehand on a small dataset of ground truth-annotated videos, generates synthetic annotations for a larger dataset, which another model - the student - learns from.
In our case, the teacher features a novel CNN-biLSTM-CRF architecture, designed for offline inference only.
The student, on the other hand, is a CNN-LSTM capable of making real-time predictions.
Results for various amounts of manually annotated videos demonstrate the superiority of the new CNN-biLSTM-CRF predictor as well as improved performance from the CNN-LSTM trained using synthetic labels generated for unannotated videos.
For both offline and online surgical phase recognition with very few annotated recordings available, this new teacher/student strategy provides a valuable performance improvement by efficiently leveraging the unannotated data.
Information extraction identifies useful and relevant text in a document and converts unstructured text into a form that can be loaded into a database table.
Named entity extraction is a main task in the process of information extraction and is a classification problem in which words are assigned to one or more semantic classes or to a default non-entity class.
A word which can belong to one or more classes and which has a level of uncertainty in it can be best handled by a self learning Fuzzy Logic Technique.
This paper proposes a method for detecting the presence of spatial uncertainty in the text and dealing with spatial ambiguity using named entity extraction techniques coupled with self learning fuzzy logic techniques
Confluence denotes the property of a state transition system that states can be rewritten in more than one way yielding the same result.
Although it is a desirable property, confluence is often too strict in practical applications because it also considers states that can never be reached in practice.
Additionally, sometimes states that have the same semantics in the practical context are considered as different states due to different syntactic representations.
By introducing suitable invariants and equivalence relations on the states, programs may have the property to be confluent modulo the equivalence relation w.r.t. the invariant which often is desirable in practice.
In this paper, a sufficient and necessary criterion for confluence modulo equivalence w.r.t. an invariant for Constraint Handling Rules (CHR) is presented.
It is the first approach that covers invariant-based confluence modulo equivalence for the de facto standard semantics of CHR.
There is a trade-off between practical applicability and the simplicity of proving a confluence property.
Therefore, a better manageable subset of equivalence relations has been identified that allows for the proposed confluence criterion and and simplifies the confluence proofs by using well established CHR analysis methods.
It is widely acknowledged that function symbols are an important feature in answer set programming, as they make modeling easier, increase the expressive power, and allow us to deal with infinite domains.
The main issue with their introduction is that the evaluation of a program might not terminate and checking whether it terminates or not is undecidable.
To cope with this problem, several classes of logic programs have been proposed where the use of function symbols is restricted but the program evaluation termination is guaranteed.
Despite the significant body of work in this area, current approaches do not include many simple practical programs whose evaluation terminates.
In this paper, we present the novel classes of rule-bounded and cycle-bounded programs, which overcome different limitations of current approaches by performing a more global analysis of how terms are propagated from the body to the head of rules.
Results on the correctness, the complexity, and the expressivity of the proposed approach are provided.
We propose two robust methods for anomaly detection in dynamic networks in which the properties of normal traffic are time-varying.
We formulate the robust anomaly detection problem as a binary composite hypothesis testing problem and propose two methods: a model-free and a model-based one, leveraging techniques from the theory of large deviations.
Both methods require a family of Probability Laws (PLs) that represent normal properties of traffic.
We devise a two-step procedure to estimate this family of PLs.
We compare the performance of our robust methods and their vanilla counterparts, which assume that normal traffic is stationary, on a network with a diurnal normal pattern and a common anomaly related to data exfiltration.
Simulation results show that our robust methods perform better than their vanilla counterparts in dynamic networks.
This paper addresses the problem of distributed event localization using noisy range measurements with respect to sensors with known positions.
Event localization is fundamental in many wireless sensor network applications such as homeland security, law enforcement, and environmental studies.
However, most existing distributed algorithms require the target event to be within the convex hull of the deployed sensors.
Based on the alternating direction method of multipliers (ADMM), we propose two scalable distributed algorithms named GS-ADMM and J-ADMM which do not require the target event to be within the convex hull of the deployed sensors.
More specifically, the two algorithms can be implemented in a scenario in which the entire sensor network is divided into several clusters with cluster heads collecting measurements within each cluster and exchanging intermediate computation information to achieve localization consistency (consensus) across all clusters.
This scenario is important in many applications such as homeland security and law enforcement.
Simulation results confirm effectiveness of the proposed algorithms.
In recent years, an increasing amount of data is collected in different and often, not cooperative, databases.
The problem of privacy-preserving, distributed calculations over separated databases and, a relative to it, issue of private data release were intensively investigated.
However, despite a considerable progress, computational complexity, due to an increasing size of data, remains a limiting factor in real-world deployments, especially in case of privacy-preserving computations.
In this paper, we present a general method for trade off between performance and accuracy of distributed calculations by performing data sampling.
Sampling was a topic of extensive research that recently received a boost of interest.
We provide a sampling method targeted at separate, non-collaborating, vertically partitioned datasets.
The method is exemplified and tested on approximation of intersection set both without and with privacy-preserving mechanism.
An analysis of the bound on error as a function of the sample size is discussed and heuristic algorithm is suggested to further improve the performance.
The algorithms were implemented and experimental results confirm the validity of the approach.
The set covering problem (SCP) is one of the representative combinatorial optimization problems, having many practical applications.
This paper investigates the development of an algorithm to solve SCP by employing chemical reaction optimization (CRO), a general-purpose metaheuristic.
It is tested on a wide range of benchmark instances of SCP.
The simulation results indicate that this algorithm gives outstanding performance compared with other heuristics and metaheuristics in solving SCP.
This study investigates the mean capacity of multiple-input multiple-output (MIMO) systems for spatially semi-correlated flat fading channels.
In reality, the capacity degrades dramatic due to the channel covariance (CC) when correlations exist at the transmitter or receiver or on both sides.
Most existing works have so far considered the traditional channel covariance matrices that have not been entirely constructed.
Thus, we propose an iterative channel covariance (ICC) matrix using a matrix splitting (MS) technique with a guaranteed zero correlations coefficient in the case of the downlink correlated MIMO channel, to maximize the mean capacity.
Our numerical results show that the proposed ICC method achieves the maximum channel gains with high signal-to-noise ratio (SNR) scenarios.
Today, the largest Lustre file systems store billions of entries.
On such systems, classic tools based on namespace scanning become unusable.
Operations such as managing file lifetime, scheduling data copies, and generating overall filesystem statistics become painful as they require collecting, sorting and aggregating information for billions of records.
Robinhood Policy Engine is an open source software developed to address these challenges.
It makes it possible to schedule automatic actions on huge numbers of filesystem entries.
It also gives a synthetic understanding of file systems contents by providing overall statistics about data ownership, age and size profiles.
Even if it can be used with any POSIX filesystem, Robinhood supports Lustre specific features like OSTs, pools, HSM, ChangeLogs, and DNE.
It implements specific support for these features, and takes advantage of them to manage Lustre file systems efficiently.
The importance of graph search algorithm choice to the directed relation graph with error propagation (DRGEP) method is studied by comparing basic and modified depth-first search, basic and R-value-based breadth-first search (RBFS), and Dijkstra's algorithm.
By using each algorithm with DRGEP to produce skeletal mechanisms from a detailed mechanism for n-heptane with randomly-shuffled species order, it is demonstrated that only Dijkstra's algorithm and RBFS produce results independent of species order.
In addition, each algorithm is used with DRGEP to generate skeletal mechanisms for n-heptane covering a comprehensive range of autoignition conditions for pressure, temperature, and equivalence ratio.
Dijkstra's algorithm combined with a coefficient scaling approach is demonstrated to produce the most compact skeletal mechanism with a similar performance compared to larger skeletal mechanisms resulting from the other algorithms.
The computational efficiency of each algorithm is also compared by applying the DRGEP method with each search algorithm on the large detailed mechanism for n-alkanes covering n-octane to n-hexadecane with 2115 species and 8157 reactions.
Dijkstra's algorithm implemented with a binary heap priority queue is demonstrated as the most efficient method, with a CPU cost two orders of magnitude less than the other search algorithms.
Since the advent of deep learning, it has been used to solve various problems using many different architectures.
The application of such deep architectures to auditory data is also not uncommon.
However, these architectures do not always adequately consider the temporal dependencies in data.
We thus propose a new generic architecture called the Deep Belief Network - Bidirectional Long Short-Term Memory (DBN-BLSTM) network that models sequences by keeping track of the temporal information while enabling deep representations in the data.
We demonstrate this new architecture by applying it to the task of music generation and obtain state-of-the-art results.
This paper explores the problem of page migration in ring networks.
A ring network is a connected graph, in which each node is connected with exactly two other nodes.
In this problem, one of the nodes in a given network holds a page of size D. This node is called the server and the page is a non-duplicable data in the network.
Requests are issued by nodes to access the page one after another.
Every time a new request is issued, the server must serve the request and may migrate to another node before the next request arrives.
A service costs the distance between the server and the requesting node, and the migration costs the distance of the migration multiplied by D. The problem is to minimize the total costs of services and migrations.
We study this problem in uniform model, for which the page has a unit size, i.e.D=1.
A 3.326-competitive algorithm improving the current best upper bound is designed.
We show that this ratio is tight for our algorithm.
Selecting a representative vector for a set of vectors is a very common requirement in many algorithmic tasks.
Traditionally, the mean or median vector is selected.
Ontology classes are sets of homogeneous instance objects that can be converted to a vector space by word vector embeddings.
This study proposes a methodology to derive a representative vector for ontology classes whose instances were converted to the vector space.
We start by deriving five candidate vectors which are then used to train a machine learning model that would calculate a representative vector for the class.
We show that our methodology out-performs the traditional mean and median vector representations.
The present study proposes LitStoryTeller, an interactive system for visually exploring the semantic structure of a scientific article.
We demonstrate how LitStoryTeller could be used to answer some of the most fundamental research questions, such as how a new method was built on top of existing methods, based on what theoretical proof and experimental evidences.
More importantly, LitStoryTeller can assist users to understand the full and interesting story a scientific paper, with a concise outline and important details.
The proposed system borrows a metaphor from screen play, and visualizes the storyline of a scientific paper by arranging its characters (scientific concepts or terminologies) and scenes (paragraphs/sentences) into a progressive and interactive storyline.
Such storylines help to preserve the semantic structure and logical thinking process of a scientific paper.
Semantic structures, such as scientific concepts and comparative sentences, are extracted using existing named entity recognition APIs and supervised classifiers, from a scientific paper automatically.
Two supplementary views, ranked entity frequency view and entity co-occurrence network view, are provided to help users identify the "main plot" of such scientific storylines.
When collective documents are ready, LitStoryTeller also provides a temporal entity evolution view and entity community view for collection digestion.
Compatibility between items, such as clothes and shoes, is a major factor among customer's purchasing decisions.
However, learning "compatibility" is challenging due to (1) broader notions of compatibility than those of similarity, (2) the asymmetric nature of compatibility, and (3) only a small set of compatible and incompatible items are observed.
We propose an end-to-end trainable system to embed each item into a latent vector and project a query item into K compatible prototypes in the same space.
These prototypes reflect the broad notions of compatibility.
We refer to both the embedding and prototypes as "Compatibility Family".
In our learned space, we introduce a novel Projected Compatibility Distance (PCD) function which is differentiable and ensures diversity by aiming for at least one prototype to be close to a compatible item, whereas none of the prototypes are close to an incompatible item.
We evaluate our system on a toy dataset, two Amazon product datasets, and Polyvore outfit dataset.
Our method consistently achieves state-of-the-art performance.
Finally, we show that we can visualize the candidate compatible prototypes using a Metric-regularized Conditional Generative Adversarial Network (MrCGAN), where the input is a projected prototype and the output is a generated image of a compatible item.
We ask human evaluators to judge the relative compatibility between our generated images and images generated by CGANs conditioned directly on query items.
Our generated images are significantly preferred, with roughly twice the number of votes as others.
In generalized zero shot learning (GZSL), the set of classes are split into seen and unseen classes, where training relies on the semantic features of the seen and unseen classes and the visual representations of only the seen classes, while testing uses the visual representations of the seen and unseen classes.
Current methods address GZSL by learning a transformation from the visual to the semantic space, exploring the assumption that the distribution of classes in the semantic and visual spaces is relatively similar.
Such methods tend to transform unseen testing visual representations into one of the seen classes' semantic features instead of the semantic features of the correct unseen class, resulting in low accuracy GZSL classification.
Recently, generative adversarial networks (GAN) have been explored to synthesize visual representations of the unseen classes from their semantic features - the synthesized representations of the seen and unseen classes are then used to train the GZSL classifier.
This approach has been shown to boost GZSL classification accuracy, however, there is no guarantee that synthetic visual representations can generate back their semantic feature in a multi-modal cycle-consistent manner.
This constraint can result in synthetic visual representations that do not represent well their semantic features.
In this paper, we propose the use of such constraint based on a new regularization for the GAN training that forces the generated visual features to reconstruct their original semantic features.
Once our model is trained with this multi-modal cycle-consistent semantic compatibility, we can then synthesize more representative visual representations for the seen and, more importantly, for the unseen classes.
Our proposed approach shows the best GZSL classification results in the field in several publicly available datasets.
A graph-based classification method is proposed for semi-supervised learning in the case of Euclidean data and for classification in the case of graph data.
Our manifold learning technique is based on a convex optimization problem involving a convex quadratic regularization term and a concave quadratic loss function with a trade-off parameter carefully chosen so that the objective function remains convex.
As shown empirically, the advantage of considering a concave loss function is that the learning problem becomes more robust in the presence of noisy labels.
Furthermore, the loss function considered here is then more similar to a classification loss while several other methods treat graph-based classification problems as regression problems.
This paper studies the relation between activity on Twitter and sales.
While research exists into the relation between Tweets and movie and book sales, this paper shows that the same relations do not hold for products that receive less attention on social media.
For such products, classification of Tweets is far more important to determine a relation.
Also, for such products advanced statistical relations, in addition to correlation, are required to relate Twitter activity and sales.
In a case study that involves Tweets and sales from a company in four countries, the paper shows how, by classifying Tweets, such relations can be identified.
In particular, the paper shows evidence that positive Tweets by persons (as opposed to companies) can be used to forecast sales and that peaks in positive Tweets by persons are strongly related to an increase in sales.
These results can be used to improve sales forecasts and to increase sales in marketing campaigns.
Over the last years, scientific workflows have become mature enough to be used in a production style.
However, despite the increasing maturity, there is still a shortage of tools for searching, adapting, and reusing workflows that hinders a more generalized adoption by the scientific communities.
Indeed, due to the limited availability of machine-readable scientific metadata and the heterogeneity of workflow specification formats and representations, new ways to leverage alternative sources of information that complement existing approaches are needed.
In this paper we address such limitations by applying statistically enriched generalized trie structures to exploit workflow execution provenance information in order to assist the analysis, indexing and search of scientific workflows.
Our method bridges the gap between the description of what a workflow is supposed to do according to its specification and related metadata and what it actually does as recorded in its provenance execution trace.
In doing so, we also prove that the proposed method outperforms SPARQL 1.1 Property Paths for querying provenance graphs.
Most recent MaxSAT algorithms rely on a succession of calls to a SAT solver in order to find an optimal solution.
In particular, several algorithms take advantage of the ability of SAT solvers to identify unsatisfiable subformulas.
Usually, these MaxSAT algorithms perform better when small unsatisfiable subformulas are found early.
However, this is not the case in many problem instances, since the whole formula is given to the SAT solver in each call.
In this paper, we propose to partition the MaxSAT formula using a resolution-based graph representation.
Partitions are then iteratively joined by using a proximity measure extracted from the graph representation of the formula.
The algorithm ends when only one partition remains and the optimal solution is found.
Experimental results show that this new approach further enhances a state of the art MaxSAT solver to optimally solve a larger set of industrial problem instances.
Machine learning (ML) is becoming a commodity.
Numerous ML frameworks and services are available to data holders who are not ML experts but want to train predictive models on their data.
It is important that ML models trained on sensitive inputs (e.g., personal images or documents) not leak too much information about the training data.
We consider a malicious ML provider who supplies model-training code to the data holder, does not observe the training, but then obtains white- or black-box access to the resulting model.
In this setting, we design and implement practical algorithms, some of them very similar to standard ML techniques such as regularization and data augmentation, that "memorize" information about the training dataset in the model yet the model is as accurate and predictive as a conventionally trained model.
We then explain how the adversary can extract memorized information from the model.
We evaluate our techniques on standard ML tasks for image classification (CIFAR10), face recognition (LFW and FaceScrub), and text analysis (20 Newsgroups and IMDB).
In all cases, we show how our algorithms create models that have high predictive power yet allow accurate extraction of subsets of their training data.
Data reconciliation (DR) and Principal Component Analysis (PCA) are two popular data analysis techniques in process industries.
Data reconciliation is used to obtain accurate and consistent estimates of variables and parameters from erroneous measurements.
PCA is primarily used as a method for reducing the dimensionality of high dimensional data and as a preprocessing technique for denoising measurements.
These techniques have been developed and deployed independently of each other.
The primary purpose of this article is to elucidate the close relationship between these two seemingly disparate techniques.
This leads to a unified framework for applying PCA and DR. Further, we show how the two techniques can be deployed together in a collaborative and consistent manner to process data.
The framework has been extended to deal with partially measured systems and to incorporate partial knowledge available about the process model.
For optimal placement and orchestration of network services, it is crucial that their structure and semantics are specified clearly and comprehensively and are available to an orchestrator.
Existing specification approaches are either ambiguous or miss important aspects regarding the behavior of virtual network functions (VNFs) forming a service.
We propose to formally and unambiguously specify the behavior of these functions and services using Queuing Petri Nets (QPNs).
QPNs are an established method that allows to express queuing, synchronization, stochastically distributed processing delays, and changing traffic volume and characteristics at each VNF.
With QPNs, multiple VNFs can be connected to complete network services in any structure, even specifying bidirectional network services containing loops.
We propose a tool-based workflow that supports the specification of network services and the automatic generation of corresponding simulation code to enable an in-depth analysis of their behavior and performance.
In a case study, we show how developers can benefit from analysis insights, e.g., to anticipate the impact of different service configurations.
We also discuss how management and orchestration systems can benefit from our clear and comprehensive specification approach and its extensive analysis possibilities, leading to better placement of VNFs and improved Quality of Service.
Although deep learning can provide promising results in medical image analysis, the lack of very large annotated datasets confines its full potential.
Furthermore, limited positive samples also create unbalanced datasets which limit the true positive rates of trained models.
As unbalanced datasets are mostly unavoidable, it is greatly beneficial if we can extract useful knowledge from negative samples to improve classification accuracy on limited positive samples.
To this end, we propose a new strategy for building medical image analysis pipelines that target disease detection.
We train a discriminative segmentation model only on normal images to provide a source of knowledge to be transferred to a disease detection classifier.
We show that using the feature maps of a trained segmentation network, deviations from normal anatomy can be learned by a two-class classification network on an extremely unbalanced training dataset with as little as one positive for 17 negative samples.
We demonstrate that even though the segmentation network is only trained on normal cardiac computed tomography images, the resulting feature maps can be used to detect pericardial effusion and cardiac septal defects with two-class convolutional classification networks.
Appearance based person re-identification in a real-world video surveillance system with non-overlapping camera views is a challenging problem for many reasons.
Current state-of-the-art methods often address the problem by relying on supervised learning of similarity metrics or ranking functions to implicitly model appearance transformation between cameras for each camera pair, or group, in the system.
This requires considerable human effort to annotate data.
Furthermore, the learned models are camera specific and not transferable from one set of cameras to another.
Therefore, the annotation process is required after every network expansion or camera replacement, which strongly limits their applicability.
Alternatively, we propose a novel modeling approach to harness complementary appearance information without supervised learning that significantly outperforms current state-of-the-art unsupervised methods on multiple benchmark datasets.
Machine comprehension(MC) style question answering is a representative problem in natural language processing.
Previous methods rarely spend time on the improvement of encoding layer, especially the embedding of syntactic information and name entity of the words, which are very crucial to the quality of encoding.
Moreover, existing attention methods represent each query word as a vector or use a single vector to represent the whole query sentence, neither of them can handle the proper weight of the key words in query sentence.
In this paper, we introduce a novel neural network architecture called Multi-layer Embedding with Memory Network(MEMEN) for machine reading task.
In the encoding layer, we employ classic skip-gram model to the syntactic and semantic information of the words to train a new kind of embedding layer.
We also propose a memory network of full-orientation matching of the query and passage to catch more pivotal information.
Experiments show that our model has competitive results both from the perspectives of precision and efficiency in Stanford Question Answering Dataset(SQuAD) among all published results and achieves the state-of-the-art results on TriviaQA dataset.
Energy consumption is a growing issue in data centers, impacting their economic viability and their public image.
In this work we empirically characterize the power and energy consumed by different types of servers.
In particular, in order to understand the behavior of their energy and power consumption, we perform measurements in different servers.
In each of them, we exhaustively measure the power consumed by the CPU, the disk, and the network interface under different configurations, identifying the optimal operational levels.
One interesting conclusion of our study is that the curve that defines the minimal CPU power as a function of the load is neither linear nor purely convex as has been previously assumed.
Moreover, we find that the efficiency of the various server components can be maximized by tuning the CPU frequency and the number of active cores as a function of the system and network load, while the block size of I/O operations should be always maximized by applications.
We also show how to estimate the energy consumed by an application as a function of some simple parameters, like the CPU load, and the disk and network activity.
We validate the proposed approach by accurately estimating the energy of a map-reduce computation in a Hadoop platform.
Accuracy-driven computation is a strategy widely used in exact-decisions number types for robust geometric algorithms.
This work provides an overview on the usage of error bounds in accuracy-driven computation, compares different approaches on the representation and computation of these error bounds and points out some caveats.
The stated claims are supported by experiments.
The field of property testing of probability distributions, or distribution testing, aims to provide fast and (most likely) correct answers to questions pertaining to specific aspects of very large datasets.
In this work, we consider a property of particular interest, monotonicity of distributions.
We focus on the complexity of monotonicity testing across different models of access to the distributions; and obtain results in these new settings that differ significantly from the known bounds in the standard sampling model.
In this paper we present a theoretical analysis of graph-based service composition in terms of its dependency with service discovery.
Driven by this analysis we define a composition framework by means of integration with fine-grained I/O service discovery that enables the generation of a graph-based composition which contains the set of services that are semantically relevant for an input-output request.
The proposed framework also includes an optimal composition search algorithm to extract the best composition from the graph minimising the length and the number of services, and different graph optimisations to improve the scalability of the system.
A practical implementation used for the empirical analysis is also provided.
This analysis proves the scalability and flexibility of our proposal and provides insights on how integrated composition systems can be designed in order to achieve good performance in real scenarios for the Web.
Structured prediction energy networks (SPENs; Belanger & McCallum 2016) use neural network architectures to define energy functions that can capture arbitrary dependencies among parts of structured outputs.
Prior work used gradient descent for inference, relaxing the structured output to a set of continuous variables and then optimizing the energy with respect to them.
We replace this use of gradient descent with a neural network trained to approximate structured argmax inference.
This "inference network" outputs continuous values that we treat as the output structure.
We develop large-margin training criteria for joint training of the structured energy function and inference network.
On multi-label classification we report speed-ups of 10-60x compared to (Belanger et al, 2017) while also improving accuracy.
For sequence labeling with simple structured energies, our approach performs comparably to exact inference while being much faster at test time.
We then demonstrate improved accuracy by augmenting the energy with a "label language model" that scores entire output label sequences, showing it can improve handling of long-distance dependencies in part-of-speech tagging.
Finally, we show how inference networks can replace dynamic programming for test-time inference in conditional random fields, suggestive for their general use for fast inference in structured settings.
It has been shown recently that deep convolutional generative adversarial networks (GANs) can learn to generate music in the form of piano-rolls, which represent music by binary-valued time-pitch matrices.
However, existing models can only generate real-valued piano-rolls and require further post-processing, such as hard thresholding (HT) or Bernoulli sampling (BS), to obtain the final binary-valued results.
In this paper, we study whether we can have a convolutional GAN model that directly creates binary-valued piano-rolls by using binary neurons.
Specifically, we propose to append to the generator an additional refiner network, which uses binary neurons at the output layer.
The whole network is trained in two stages.
Firstly, the generator and the discriminator are pretrained.
Then, the refiner network is trained along with the discriminator to learn to binarize the real-valued piano-rolls the pretrained generator creates.
Experimental results show that using binary neurons instead of HT or BS indeed leads to better results in a number of objective measures.
Moreover, deterministic binary neurons perform better than stochastic ones in both objective measures and a subjective test.
The source code, training data and audio examples of the generated results can be found at https://salu133445.github.io/bmusegan/ .
Neural Machine Translation (NMT) has obtained state-of-the art performance for several language pairs, while only using parallel data for training.
Target-side monolingual data plays an important role in boosting fluency for phrase-based statistical machine translation, and we investigate the use of monolingual data for NMT.
In contrast to previous work, which combines NMT models with separately trained language models, we note that encoder-decoder NMT architectures already have the capacity to learn the same information as a language model, and we explore strategies to train with monolingual data without changing the neural network architecture.
By pairing monolingual training data with an automatic back-translation, we can treat it as additional parallel training data, and we obtain substantial improvements on the WMT 15 task English<->German (+2.8-3.7 BLEU), and for the low-resourced IWSLT 14 task Turkish->English (+2.1-3.4 BLEU), obtaining new state-of-the-art results.
We also show that fine-tuning on in-domain monolingual and parallel data gives substantial improvements for the IWSLT 15 task English->German.
In this paper, we present a new method for detecting road users in an urban environment which leads to an improvement in multiple object tracking.
Our method takes as an input a foreground image and improves the object detection and segmentation.
This new image can be used as an input to trackers that use foreground blobs from background subtraction.
The first step is to create foreground images for all the frames in an urban video.
Then, starting from the original blobs of the foreground image, we merge the blobs that are close to one another and that have similar optical flow.
The next step is extracting the edges of the different objects to detect multiple objects that might be very close (and be merged in the same blob) and to adjust the size of the original blobs.
At the same time, we use the optical flow to detect occlusion of objects that are moving in opposite directions.
Finally, we make a decision on which information we keep in order to construct a new foreground image with blobs that can be used for tracking.
The system is validated on four videos of an urban traffic dataset.
Our method improves the recall and precision metrics for the object detection task compared to the vanilla background subtraction method and improves the CLEAR MOT metrics in the tracking tasks for most videos.
With the advent of semantic web, various tools and techniques have been introduced for presenting and organizing knowledge.
Concept hierarchies are one such technique which gained significant attention due to its usefulness in creating domain ontologies that are considered as an integral part of semantic web.
Automated concept hierarchy learning algorithms focus on extracting relevant concepts from unstructured text corpus and connect them together by identifying some potential relations exist between them.
In this paper, we propose a novel approach for identifying relevant concepts from plain text and then learns hierarchy of concepts by exploiting subsumption relation between them.
To start with, we model topics using a probabilistic topic model and then make use of some lightweight linguistic process to extract semantically rich concepts.
Then we connect concepts by identifying an "is-a" relationship between pair of concepts.
The proposed method is completely unsupervised and there is no need for a domain specific training corpus for concept extraction and learning.
Experiments on large and real-world text corpora such as BBC News dataset and Reuters News corpus shows that the proposed method outperforms some of the existing methods for concept extraction and efficient concept hierarchy learning is possible if the overall task is guided by a probabilistic topic modeling algorithm.
We present a dual subspace ascent algorithm for support vector machine training that respects a budget constraint limiting the number of support vectors.
Budget methods are effective for reducing the training time of kernel SVM while retaining high accuracy.
To date, budget training is available only for primal (SGD-based) solvers.
Dual subspace ascent methods like sequential minimal optimization are attractive for their good adaptation to the problem structure, their fast convergence rate, and their practical speed.
By incorporating a budget constraint into a dual algorithm, our method enjoys the best of both worlds.
We demonstrate considerable speed-ups over primal budget training methods.
Multi-task learning (MTL) aims to improve generalization performance by learning multiple related tasks simultaneously.
While sometimes the underlying task relationship structure is known, often the structure needs to be estimated from data at hand.
In this paper, we present a novel family of models for MTL, applicable to regression and classification problems, capable of learning the structure of task relationships.
In particular, we consider a joint estimation problem of the task relationship structure and the individual task parameters, which is solved using alternating minimization.
The task relationship structure learning component builds on recent advances in structure learning of Gaussian graphical models based on sparse estimators of the precision (inverse covariance) matrix.
We illustrate the effectiveness of the proposed model on a variety of synthetic and benchmark datasets for regression and classification.
We also consider the problem of combining climate model outputs for better projections of future climate, with focus on temperature in South America, and show that the proposed model outperforms several existing methods for the problem.
When setup/hold times of bistable elements are violated, they may become metastable, i.e., enter a transient state that is neither digital 0 nor 1.
In general, metastability cannot be avoided, a problem that manifests whenever taking discrete measurements of analog values.
Metastability of the output then reflects uncertainty as to whether a measurement should be rounded up or down to the next possible measurement outcome.
Surprisingly, Lenzen and Medina (ASYNC 2016) showed that metastability can be contained, i.e., measurement values can be correctly sorted without resolving metastability first.
However, both their work and the state of the art by Bund et al.
(DATE 2017) leave open whether such a solution can be as small and fast as standard sorting networks.
We show that this is indeed possible, by providing a circuit that sorts Gray code inputs (possibly containing a metastable bit) and has asymptotically optimal depth and size.
Concretely, for 10-channel sorting networks and 16-bit wide inputs, we improve by 48.46% in delay and by 71.58% in area over Bund et al.
Our simulations indicate that straightforward transistor-level optimization is likely to result in performance on par with standard (non-containing) solutions.
Many problems in NLP require aggregating information from multiple mentions of the same entity which may be far apart in the text.
Existing Recurrent Neural Network (RNN) layers are biased towards short-term dependencies and hence not suited to such tasks.
We present a recurrent layer which is instead biased towards coreferent dependencies.
The layer uses coreference annotations extracted from an external system to connect entity mentions belonging to the same cluster.
Incorporating this layer into a state-of-the-art reading comprehension model improves performance on three datasets -- Wikihop, LAMBADA and the bAbi AI tasks -- with large gains when training data is scarce.
Cloud for Gaming refers to the use of cloud computing technologies to build large-scale gaming infrastructures, with the goal of improving scalability and responsiveness, improve the user's experience and enable new business models.
Hyperspectral image (HSI) classification is a hot topic in the remote sensing community.
This paper proposes a new framework of spectral-spatial feature extraction for HSI classification, in which for the first time the concept of deep learning is introduced.
Specifically, the model of autoencoder is exploited in our framework to extract various kinds of features.
First we verify the eligibility of autoencoder by following classical spectral information based classification and use autoencoders with different depth to classify hyperspectral image.
Further in the proposed framework, we combine PCA on spectral dimension and autoencoder on the other two spatial dimensions to extract spectral-spatial information for classification.
The experimental results show that this framework achieves the highest classification accuracy among all methods, and outperforms classical classifiers such as SVM and PCA-based SVM.
Software testing is an important and valuable part of the software development life cycle.
Due to time, cost and other circumstances, exhaustive testing is not feasible that's why there is a need to automate the software testing process.
Testing effectiveness can be achieved by the State Transition Testing (STT) which is commonly used in real time, embedded and web-based type of software systems.
Aim of the current paper is to present an algorithm by applying an ant colony optimization technique, for generation of optimal and minimal test sequences for behavior specification of software.
Present paper approach generates test sequence in order to obtain the complete software coverage.
This paper also discusses the comparison between two metaheuristic techniques (Genetic Algorithm and Ant Colony optimization) for transition based testing
Being able to automatically repair programs is an extremely challenging task.
In this paper, we present MintHint, a novel technique for program repair that is a departure from most of today's approaches.
Instead of trying to fully automate program repair, which is often an unachievable goal, MintHint performs statistical correlation analysis to identify expressions that are likely to occur in the repaired code and generates, using pattern-matching based synthesis, repair hints from these expressions.
Intuitively, these hints suggest how to rectify a faulty statement and help developers find a complete, actual repair.
MintHint can address a variety of common faults, including incorrect, spurious, and missing expressions.
We present a user study that shows that developers' productivity can improve manyfold with the use of repair hints generated by MintHint -- compared to having only traditional fault localization information.
We also apply MintHint to several faults of a widely used Unix utility program to further assess the effectiveness of the approach.
Our results show that MintHint performs well even in situations where (1) the repair space searched does not contain the exact repair, and (2) the operational specification obtained from the test cases for repair is incomplete or even imprecise.
HistCite TM is a large-scale computer tool for mapping science.
Its power of visualization combines the production of historiographs on the basis of the analysis of co-citations of documents, with the use of bibliometrics specific indicators.
The objective of this article is, to present the advantages of the new bibliometrics configuration of HistCite TM (2004) when identifying articles.
The analysis of the histograms that produces HistCite TM , in terms of cumulative advantage and aging of the citations.
And the comparative study of the results of HistCite TM , in its indicators of amplitude and recognition.
Also is examined its treatment of the sampling problems, by formalizing the Kendall method of estimating the robust standard deviation.
The most successful parallel SAT and MaxSAT solvers follow a portfolio approach, where each thread applies a different algorithm (or the same algorithm configured differently) to solve a given problem instance.
The main goal of building a portfolio is to diversify the search process being carried out by each thread.
As soon as one thread finishes, the instance can be deemed solved.
In this paper we present a new open source distributed solver for MaxSAT solving that addresses two issues commonly found in multicore parallel solvers, namely memory contention and scalability.
Preliminary results show that our non-portfolio distributed MaxSAT solver outperforms its sequential version and is able to solve more instances as the number of processes increases.
Seam-cutting and seam-driven techniques have been proven effective for handling imperfect image series in image stitching.
Generally, seam-driven is to utilize seam-cutting to find a best seam from one or finite alignment hypotheses based on a predefined seam quality metric.
However, the quality metrics in most methods are defined to measure the average performance of the pixels on the seam without considering the relevance and variance among them.
This may cause that the seam with the minimal measure is not optimal (perception-inconsistent) in human perception.
In this paper, we propose a novel coarse-to-fine seam estimation method which applies the evaluation in a different way.
For pixels on the seam, we develop a patch-point evaluation algorithm concentrating more on the correlation and variation of them.
The evaluations are then used to recalculate the difference map of the overlapping region and reestimate a stitching seam.
This evaluation-reestimation procedure iterates until the current seam changes negligibly comparing with the previous seams.
Experiments show that our proposed method can finally find a nearly perception-consistent seam after several iterations, which outperforms the conventional seam-cutting and other seam-driven methods.
The aim of this work is studying the use of copulas and vines in the optimization with Estimation of Distribution Algorithms (EDAs).
Two EDAs are built around the multivariate product and normal copulas, and other two are based on pair-copula decomposition of vine models.
Empirically we study the effect of both marginal distributions and dependence structure separately, and show that both aspects play a crucial role in the success of the optimization.
The results show that the use of copulas and vines opens new opportunities to a more appropriate modeling of search distributions in EDAs.
Recent research on deep neural networks has focused primarily on improving accuracy.
For a given accuracy level, it is typically possible to identify multiple DNN architectures that achieve that accuracy level.
With equivalent accuracy, smaller DNN architectures offer at least three advantages: (1) Smaller DNNs require less communication across servers during distributed training.
(2) Smaller DNNs require less bandwidth to export a new model from the cloud to an autonomous car.
(3) Smaller DNNs are more feasible to deploy on FPGAs and other hardware with limited memory.
To provide all of these advantages, we propose a small DNN architecture called SqueezeNet.
SqueezeNet achieves AlexNet-level accuracy on ImageNet with 50x fewer parameters.
Additionally, with model compression techniques we are able to compress SqueezeNet to less than 0.5MB (510x smaller than AlexNet).
The SqueezeNet architecture is available for download here: https://github.com/DeepScale/SqueezeNet
Most existing GANs architectures that generate images use transposed convolution or resize-convolution as their upsampling algorithm from lower to higher resolution feature maps in the generator.
We argue that this kind of fixed operation is problematic for GANs to model objects that have very different visual appearances.
We propose a novel adaptive convolution method that learns the upsampling algorithm based on the local context at each location to address this problem.
We modify a baseline GANs architecture by replacing normal convolutions with adaptive convolutions in the generator.
Experiments on CIFAR-10 dataset show that our modified models improve the baseline model by a large margin.
Furthermore, our models achieve state-of-the-art performance on CIFAR-10 and STL-10 datasets in the unsupervised setting.
We introduce MeSys, a meaning-based approach, for solving English math word problems (MWPs) via understanding and reasoning in this paper.
It first analyzes the text, transforms both body and question parts into their corresponding logic forms, and then performs inference on them.
The associated context of each quantity is represented with proposed role-tags (e.g., nsubj, verb, etc.), which provides the flexibility for annotating an extracted math quantity with its associated context information (i.e., the physical meaning of this quantity).
Statistical models are proposed to select the operator and operands.
A noisy dataset is designed to assess if a solver solves MWPs mainly via understanding or mechanical pattern matching.
Experimental results show that our approach outperforms existing systems on both benchmark datasets and the noisy dataset, which demonstrates that the proposed approach understands the meaning of each quantity in the text more.
This paper addresses the problem of IDE interface complexity by introducing single-window graphical user interface.
This approach lies in removing additional child windows from IDE, thus allowing a user to keep only text editor window open.
We describe an abstract model of IDE GUI that is based on most popular modern integrated environments and has generalized user interface parts.
Then this abstract model is reorganized into single windowed interface model: access to common IDE functions is provided from the code editing window while utility windows are removed without loss of IDE functionality.
After that the implementation of single-window GUI on KDevelop 4 is described.
And finally tool views and usability of several well- known IDEs are surveyed.
Recently, the dense binary pixel Gigavision camera had been introduced, emulating a digital version of the photographic film.
While seems to be a promising solution for HDR imaging, its output is not directly usable and requires an image reconstruction process.
In this work, we formulate this problem as the minimization of a convex objective combining a maximum-likelihood term with a sparse synthesis prior.
We present MLNet - a novel feed-forward neural network, producing acceptable output quality at a fixed complexity and is two orders of magnitude faster than iterative algorithms.
We present state of the art results in the abstract.
The objective of Dem@Care is the development of a complete system providing personal health services to people with dementia, as well as medical professionals and caregivers, by using a multitude of sensors, for context-aware, multi-parametric monitoring of lifestyle, ambient environment, and health parameters.
Multi-sensor data analysis, combined with intelligent decision making mechanisms, will allow an accurate representation of the person's current status and will provide the appropriate feedback, both to the person and the associated caregivers, enhancing the standard clinical workflow.
Within the project framework, several data collection activities have taken place to assist technical development and evaluation tasks.
In all these activities, particular attention has been paid to adhere to ethical guidelines and preserve the participants' privacy.
This technical report describes shorty the (a) the main objectives of the project, (b) the main ethical principles and (c) the datasets that have been already created.
Containers are an emerging technology that hold promise for improving productivity and code portability in scientific computing.
We examine Linux container technology for the distribution of a non-trivial scientific computing software stack and its execution on a spectrum of platforms from laptop computers through to high performance computing (HPC) systems.
We show on a workstation and a leadership-class HPC system that when deployed appropriately there are no performance penalties running scientific programs inside containers.
For Python code run on large parallel computers, the run time is reduced inside a container due to faster library imports.
The software distribution approach and data that we present will help developers and users decide on whether container technology is appropriate for them.
We also provide guidance for the vendors of HPC systems that rely on proprietary libraries for performance on what they can do to make containers work seamlessly and without performance penalty.
Context: Software code reviews are an important part of the development process, leading to better software quality and reduced overall costs.
However, finding appropriate code reviewers is a complex and time-consuming task.
Goals: In this paper, we propose a large-scale study to compare performance of two main source code reviewer recommendation algorithms (RevFinder and a Naive Bayes-based approach) in identifying the best code reviewers for opened pull requests.
Method: We mined data from Github and Gerrit repositories, building a large dataset of 51 projects, with more than 293K pull requests analyzed, 180K owners and 157K reviewers.
Results: Based on the large analysis, we can state that i) no model can be generalized as best for all projects, ii) the usage of a different repository (Gerrit, GitHub) can have impact on the the recommendation results, iii) exploiting sub-projects information available in Gerrit can improve the recommendation results.
We present a selective bibliography about efficient SAT solving, focused on optimizations for the CDCL-based algorithms.
Rate adaptation in 802.11 WLANs has received a lot of attention from the research community, with most of the proposals aiming at maximising throughput based on network conditions.
Considering energy consumption, an implicit assumption is that optimality in throughput implies optimality in energy efficiency, but this assumption has been recently put into question.
In this paper, we address via analysis and experimentation the relation between throughput performance and energy efficiency in multi-rate 802.11 scenarios.
We demonstrate the trade-off between these performance figures, confirming that they may not be simultaneously optimised, and analyse their sensitivity towards the energy consumption parameters of the device.
Our results provide the means to design novel rate adaptation schemes that takes energy consumption into account.
Glaucoma is a chronic eye disease that leads to irreversible vision loss.
Most of the existing automatic screening methods firstly segment the main structure, and subsequently calculate the clinical measurement for detection and screening of glaucoma.
However, these measurement-based methods rely heavily on the segmentation accuracy, and ignore various visual features.
In this paper, we introduce a deep learning technique to gain additional image-relevant information, and screen glaucoma from the fundus image directly.
Specifically, a novel Disc-aware Ensemble Network (DENet) for automatic glaucoma screening is proposed, which integrates the deep hierarchical context of the global fundus image and the local optic disc region.
Four deep streams on different levels and modules are respectively considered as global image stream, segmentation-guided network, local disc region stream, and disc polar transformation stream.
Finally, the output probabilities of different streams are fused as the final screening result.
The experiments on two glaucoma datasets (SCES and new SINDI datasets) show our method outperforms other state-of-the-art algorithms.
The purpose of the current study is to systematically review the crowdsourcing literature, extract the activities which have been cited, and synthesise these activities into a general process model.
For this to happen, we reviewed the related literature on crowdsourcing methods as well as relevant case studies and extracted the activities which they referred to as part of crowdsourcing projects.
The systematic review of the related literature and an in-depth analysis of the steps in those papers were followed by a synthesis of the extracted activities resulting in an eleven-phase process model.
This process model covers all of the activities suggested by the literature.
This paper then briefly discusses activities in each phase and concludes with a number of implications for both academics and practitioners.
Many computer vision algorithms depend on a variety of parameter choices and settings that are typically hand-tuned in the course of evaluating the algorithm.
While such parameter tuning is often presented as being incidental to the algorithm, correctly setting these parameter choices is frequently critical to evaluating a method's full potential.
Compounding matters, these parameters often must be re-tuned when the algorithm is applied to a new problem domain, and the tuning process itself often depends on personal experience and intuition in ways that are hard to describe.
Since the performance of a given technique depends on both the fundamental quality of the algorithm and the details of its tuning, it can be difficult to determine whether a given technique is genuinely better, or simply better tuned.
In this work, we propose a meta-modeling approach to support automated hyper parameter optimization, with the goal of providing practical tools to replace hand-tuning with a reproducible and unbiased optimization process.
Our approach is to expose the underlying expression graph of how a performance metric (e.g. classification accuracy on validation examples) is computed from parameters that govern not only how individual processing steps are applied, but even which processing steps are included.
A hyper parameter optimization algorithm transforms this graph into a program for optimizing that performance metric.
Our approach yields state of the art results on three disparate computer vision problems: a face-matching verification task (LFW), a face identification task (PubFig83) and an object recognition task (CIFAR-10), using a single algorithm.
More broadly, we argue that the formalization of a meta-model supports more objective, reproducible, and quantitative evaluation of computer vision algorithms, and that it can serve as a valuable tool for guiding algorithm development.
Modern cryptocurrencies exploit decentralised blockchains to record a public and unalterable history of transactions.
Besides transactions, further information is stored for different, and often undisclosed, purposes, making the blockchains a rich and increasingly growing source of valuable information, in part of difficult interpretation.
Many data analytics have been developed, mostly based on specifically designed and ad-hoc engineered approaches.
We propose a general-purpose framework, seamlessly supporting data analytics on both Bitcoin and Ethereum - currently the two most prominent cryptocurrencies.
Such a framework allows us to integrate relevant blockchain data with data from other sources, and to organise them in a database, either SQL or NoSQL.
Our framework is released as an open-source Scala library.
We illustrate the distinguishing features of our approach on a set of significant use cases, which allow us to empirically compare ours to other competing proposals, and evaluate the impact of the database choice on scalability.
In this paper, the idea of a new artificial intelligence based optimization algorithm, which is inspired from the nature of vortex, has been provided briefly.
As also a bio-inspired computation algorithm, the idea is generally focused on a typical vortex flow / behavior in nature and inspires from some dynamics that are occurred in the sense of vortex nature.
Briefly, the algorithm is also a swarm-oriented evolutional problem solution approach; because it includes many methods related to elimination of weak swarm members and trying to improve the solution process by supporting the solution space via new swarm members.
In order have better idea about success of the algorithm; it has been tested via some benchmark functions.
At this point, the obtained results show that the algorithm can be an alternative to the literature in terms of single-objective optimization solution ways.
Vortex Optimization Algorithm (VOA) is the name suggestion by the authors; for this new idea of intelligent optimization approach.
The use of open-source software (OSS) is ever-increasing, and so is the number of open-source vulnerabilities being discovered and publicly disclosed.
The gains obtained from the reuse of community-developed libraries may be offset by the cost of detecting, assessing, and mitigating their vulnerabilities in a timely fashion.
In this paper we present a novel method to detect, assess and mitigate OSS vulnerabilities that improves on state-of-the-art approaches, which commonly depend on metadata to identify vulnerable OSS dependencies.
Our solution instead is code-centric and combines static and dynamic analysis to determine the reachability of the vulnerable portion of libraries used (directly or transitively) by an application.
Taking this usage into account, our approach then supports developers in choosing among the existing non-vulnerable library versions.
VULAS, the tool implementing our code-centric and usage-based approach, is officially recommended by SAP to scan its Java software, and has been successfully used to perform more than 250000 scans of about 500 applications since December 2016.
We report on our experience and on the lessons we learned when maturing the tool from a research prototype to an industrial-grade solution.
Recently deep reinforcement learning (DRL) has achieved outstanding success on solving many difficult and large-scale RL problems.
However the high sample cost required for effective learning often makes DRL unaffordable in resource-limited applications.
With the aim of improving sample efficiency and learning performance, we will develop a new DRL algorithm in this paper that seamless integrates entropy-induced and bootstrap-induced techniques for efficient and deep exploration of the learning environment.
Specifically, a general form of Tsallis entropy regularizer will be utilized to drive entropy-induced exploration based on efficient approximation of optimal action-selection policies.
Different from many existing works that rely on action dithering strategies for exploration, our algorithm is efficient in exploring actions with clear exploration value.
Meanwhile, by employing an ensemble of Q-networks under varied Tsallis entropy regularization, the diversity of the ensemble can be further enhanced to enable effective bootstrap-induced exploration.
Experiments on Atari game playing tasks clearly demonstrate that our new algorithm can achieve more efficient and effective exploration for DRL, in comparison to recently proposed exploration methods including Bootstrapped Deep Q-Network and UCB Q-Ensemble.
Android apps should be designed to cope with stop-start events, which are the events that require stopping and restoring the execution of an app while leaving its state unaltered.
These events can be caused by run-time configuration changes, such as a screen rotation, and by context-switches, such as a switch from one app to another.
When a stop-start event occurs, Android saves the state of the app, handles the event, and finally restores the saved state.
To let Android save and restore the state correctly, apps must provide the appropriate support.
Unfortunately, Android developers often implement this support incorrectly, or do not implement it at all.
This bad practice makes apps to incorrectly react to stop-start events, thus generating what we defined data loss problems, that is Android apps that lose user data, behave unexpectedly, and crash due to program variables that lost their values.
Data loss problems are difficult to detect because they might be observed only when apps are in specific states and with specific inputs.
Covering all the possible cases with testing may require a large number of test cases whose execution must be checked manually to discover whether the app under test has been correctly restored after each stop-start event.
It is thus important to complement traditional in-house testing activities with mechanisms that can protect apps as soon as a data loss problem occurs in the field.
In this paper we present DataLossHealer, a technique for automatically identifying and healing data loss problems in the field as soon as they occur.
DataLossHealer is a technique that checks at run-time whether states are recovered correctly, and heals the app when needed.
DataLossHealer can learn from experience, incrementally reducing the overhead that is introduced avoiding to monitor interactions that have been managed correctly by the app in the past.
With the rapid growth of online fashion market, demand for effective fashion recommendation systems has never been greater.
In fashion recommendation, the ability to find items that goes well with a few other items based on style is more important than picking a single item based on the user's entire purchase history.
Since the same user may have purchased dress suits in one month and casual denims in another, it is impossible to learn the latent style features of those items using only the user ratings.
If we were able to represent the style features of fashion items in a reasonable way, we will be able to recommend new items that conform to some small subset of pre-purchased items that make up a coherent style set.
We propose Style2Vec, a vector representation model for fashion items.
Based on the intuition of distributional semantics used in word embeddings, Style2Vec learns the representation of a fashion item using other items in matching outfits as context.
Two different convolutional neural networks are trained to maximize the probability of item co-occurrences.
For evaluation, a fashion analogy test is conducted to show that the resulting representation connotes diverse fashion related semantics like shapes, colors, patterns and even latent styles.
We also perform style classification using Style2Vec features and show that our method outperforms other baselines.
Traditional data mining algorithms are exceptional at seeing patterns in data that humans cannot, but are often confused by details that are obvious to the organic eye.
Algorithms that include humans "in-the-loop" have proved beneficial for accuracy by allowing a user to provide direction in these situations, but the slowness of human interactions causes execution times to increase exponentially.
Thus, we seek to formalize frameworks that include humans "over-the-loop", giving the user an option to intervene when they deem it necessary while not having user feedback be an execution requirement.
With this strategy, we hope to increase the accuracy of solutions with minimal losses in execution time.
This paper describes our vision of this strategy and associated problems.
Recent works have shown promise in using microarchitectural execution patterns to detect malware programs.
These detectors belong to a class of detectors known as signature-based detectors as they catch malware by comparing a program's execution pattern (signature) to execution patterns of known malware programs.
In this work, we propose a new class of detectors - anomaly-based hardware malware detectors - that do not require signatures for malware detection, and thus can catch a wider range of malware including potentially novel ones.
We use unsupervised machine learning to build profiles of normal program execution based on data from performance counters, and use these profiles to detect significant deviations in program behavior that occur as a result of malware exploitation.
We show that real-world exploitation of popular programs such as IE and Adobe PDF Reader on a Windows/x86 platform can be detected with nearly perfect certainty.
We also examine the limits and challenges in implementing this approach in face of a sophisticated adversary attempting to evade anomaly-based detection.
The proposed detector is complementary to previously proposed signature-based detectors and can be used together to improve security.
Advances in deep learning have led to substantial increases in prediction accuracy but have been accompanied by increases in the cost of rendering predictions.
We conjecture that fora majority of real-world inputs, the recent advances in deep learning have created models that effectively "overthink" on simple inputs.
In this paper, we revisit the classic question of building model cascades that primarily leverage class asymmetry to reduce cost.
We introduce the "I Don't Know"(IDK) prediction cascades framework, a general framework to systematically compose a set of pre-trained models to accelerate inference without a loss in prediction accuracy.
We propose two search based methods for constructing cascades as well as a new cost-aware objective within this framework.
The proposed IDK cascade framework can be easily adopted in the existing model serving systems without additional model re-training.
We evaluate the proposed techniques on a range of benchmarks to demonstrate the effectiveness of the proposed framework.
Research on influence maximization has often to cope with marketing needs relating to the propagation of information towards specific users.
However, little attention has been paid to the fact that the success of an information diffusion campaign might depend not only on the number of the initial influencers to be detected but also on their diversity w.r.t. the target of the campaign.
Our main hypothesis is that if we learn seeds that are not only capable of influencing but also are linked to more diverse (groups of) users, then the influence triggers will be diversified as well, and hence the target users will get higher chance of being engaged.
Upon this intuition, we define a novel problem, named Diversity-sensitive Targeted Influence Maximization (DTIM), which assumes to model user diversity by exploiting only topological information within a social graph.
To the best of our knowledge, we are the first to bring the concept of topology-driven diversity into targeted IM problems, for which we define two alternative definitions.
Accordingly, we propose approximate solutions of DTIM, which detect a size-k set of users that maximizes the diversity-sensitive capital objective function, for a given selection of target users.
We evaluate our DTIM methods on a special case of user engagement in online social networks, which concerns users who are not actively involved in the community life.
Experimental evaluation on real networks has demonstrated the meaningfulness of our approach, also highlighting the opportunity of further development of solutions for DTIM applications.
Mobility and network traffic have been traditionally studied separately.
Their interaction is vital for generations of future mobile services and effective caching, but has not been studied in depth with real-world big data.
In this paper, we characterize mobility encounters and study the correlation between encounters and web traffic profiles using large-scale datasets (30TB in size) of WiFi and NetFlow traces.
The analysis quantifies these correlations for the first time, across spatio-temporal dimensions, for device types grouped into on-the-go Flutes and sit-to-use Cellos.
The results consistently show a clear relation between mobility encounters and traffic across different buildings over multiple days, with encountered pairs showing higher traffic similarity than non-encountered pairs, and long encounters being associated with the highest similarity.
We also investigate the feasibility of learning encounters through web traffic profiles, with implications for dissemination protocols, and contact tracing.
This provides a compelling case to integrate both mobility and web traffic dimensions in future models, not only at an individual level, but also at pairwise and collective levels.
We have released samples of code and data used in this study on GitHub, to support reproducibility and encourage further research (https://github.com/BabakAp/encounter-traffic).
This paper proposes an evolutionary Particle Filter with a memory guided proposal step size update and an improved, fully-connected Quantum-behaved Particle Swarm Optimization (QPSO) resampling scheme for visual tracking applications.
The proposal update step uses importance weights proportional to velocities encountered in recent memory to limit the swarm movement within probable regions of interest.
The QPSO resampling scheme uses a fitness weighted mean best update to bias the swarm towards the fittest section of particles while also employing a simulated annealing operator to avoid subpar fine tune during latter course of iterations.
By moving particles closer to high likelihood landscapes of the posterior distribution using such constructs, the sample impoverishment problem that plagues the Particle Filter is mitigated to a great extent.
Experimental results using benchmark sequences imply that the proposed method outperforms competitive candidate trackers such as the Particle Filter and the traditional Particle Swarm Optimization based Particle Filter on a suite of tracker performance indices.
In many sequential decision making tasks, it is challenging to design reward functions that help an RL agent efficiently learn behavior that is considered good by the agent designer.
A number of different formulations of the reward-design problem, or close variants thereof, have been proposed in the literature.
In this paper we build on the Optimal Rewards Framework of Singh et.al. that defines the optimal intrinsic reward function as one that when used by an RL agent achieves behavior that optimizes the task-specifying or extrinsic reward function.
Previous work in this framework has shown how good intrinsic reward functions can be learned for lookahead search based planning agents.
Whether it is possible to learn intrinsic reward functions for learning agents remains an open problem.
In this paper we derive a novel algorithm for learning intrinsic rewards for policy-gradient based learning agents.
We compare the performance of an augmented agent that uses our algorithm to provide additive intrinsic rewards to an A2C-based policy learner (for Atari games) and a PPO-based policy learner (for Mujoco domains) with a baseline agent that uses the same policy learners but with only extrinsic rewards.
Our results show improved performance on most but not all of the domains.
Event-based collections are often started with a web search, but the search results you find on Day 1 may not be the same as those you find on Day 7.
In this paper, we consider collections that originate from extracting URIs (Uniform Resource Identifiers) from Search Engine Result Pages (SERPs).
Specifically, we seek to provide insight about the retrievability of URIs of news stories found on Google, and to answer two main questions: first, can one "refind" the same URI of a news story (for the same query) from Google after a given time?
Second, what is the probability of finding a story on Google over a given period of time?
To answer these questions, we issued seven queries to Google every day for over seven months (2017-05-25 to 2018-01-12) and collected links from the first five SERPs to generate seven collections for each query.
The queries represent public interest stories: "healthcare bill," "manchester bombing," "london terrorism," "trump russia," "travel ban," "hurricane harvey," and "hurricane irma."
We tracked each URI in all collections over time to estimate the discoverability of URIs from the first five SERPs.
Our results showed that the daily average rate at which stories were replaced on the default Google SERP ranged from 0.21 -0.54, and a weekly rate of 0.39 - 0.79, suggesting the fast replacement of older stories by newer stories.
The probability of finding the same URI of a news story after one day from the initial appearance on the SERP ranged from 0.34 - 0.44.
After a week, the probability of finding the same news stories diminishes rapidly to 0.01 - 0.11.
Our findings suggest that due to the difficulty in retrieving the URIs of news stories from Google, collection building that originates from search engines should begin as soon as possible in order to capture the first stages of events, and should persist in order to capture the evolution of the events...
Human Skin detection deals with the recognition of skin-colored pixels and regions in a given image.
Skin color is often used in human skin detection because it is invariant to orientation and size and is fast to process.
A new human skin detection algorithm is proposed in this paper.
The three main parameters for recognizing a skin pixel are RGB (Red, Green, Blue), HSV (Hue, Saturation, Value) and YCbCr (Luminance, Chrominance) color models.
The objective of proposed algorithm is to improve the recognition of skin pixels in given images.
The algorithm not only considers individual ranges of the three color parameters but also takes into ac- count combinational ranges which provide greater accuracy in recognizing the skin area in a given image.
Anomaly detection plays an important role in modern data-driven security applications, such as detecting suspicious access to a socket from a process.
In many cases, such events can be described as a collection of categorical values that are considered as entities of different types, which we call heterogeneous categorical events.
Due to the lack of intrinsic distance measures among entities, and the exponentially large event space, most existing work relies heavily on heuristics to calculate abnormal scores for events.
Different from previous work, we propose a principled and unified probabilistic model APE (Anomaly detection via Probabilistic pairwise interaction and Entity embedding) that directly models the likelihood of events.
In this model, we embed entities into a common latent space using their observed co-occurrence in different events.
More specifically, we first model the compatibility of each pair of entities according to their embeddings.
Then we utilize the weighted pairwise interactions of different entity types to define the event probability.
Using Noise-Contrastive Estimation with "context-dependent" noise distribution, our model can be learned efficiently regardless of the large event space.
Experimental results on real enterprise surveillance data show that our methods can accurately detect abnormal events compared to other state-of-the-art abnormal detection techniques.
This paper presents a genetic stereo matching algorithm with fuzzy evaluation function.
The proposed algorithm presents a new encoding scheme in which a chromosome is represented by a disparity matrix.
Evolution is controlled by a fuzzy fitness function able to deal with noise and uncertain camera measurements, and uses classical evolutionary operators.
The result of the algorithm is accurate dense disparity maps obtained in a reasonable computational time suitable for real-time applications as shown in experimental results.
The last two decades have seen the emergence and steady development of tangible user interfaces.
While most of these interfaces are applied for input - with output still on traditional computer screens - the goal of programmable matter and actuated shape-changing materials is to directly use the physical objects for visual or tangible feedback.
Advances in material sciences and flexible display technologies are investigated to enable such reconfigurable physical objects.
While existing solutions aim for making physical objects more controllable via the digital world, we propose an approach where holograms (virtual objects) in a mixed reality environment are augmented with physical variables such as shape, texture or temperature.
As such, the support for mobility forms an important contribution of the proposed solution since it enables users to freely move within and across environments.
Furthermore, our augmented virtual objects can co-exist in a single environment with programmable matter and other actuated shape-changing solutions.
The future potential of the proposed approach is illustrated in two usage scenarios and we hope that the presentation of our work in progress on a novel way to realise tangible holograms will foster some lively discussions in the CHI community.
Analyzing signals arising from dynamical systems typically requires many modeling assumptions and parameter estimation.
In high dimensions, this modeling is particularly difficult due to the "curse of dimensionality".
In this paper, we propose a method for building an intrinsic representation of such signals in a purely data-driven manner.
First, we apply a manifold learning technique, diffusion maps, to learn the intrinsic model of the latent variables of the dynamical system, solely from the measurements.
Second, we use concepts and tools from control theory and build a linear contracting observer to estimate the latent variables in a sequential manner from new incoming measurements.
The effectiveness of the presented framework is demonstrated by applying it to a toy problem and to a music analysis application.
In these examples we show that our method reveals the intrinsic variables of the analyzed dynamical systems.
Artificial perception is traditionally handled by hand-designing task specific algorithms.
However, a truly autonomous robot should develop perceptive abilities on its own, by interacting with its environment, and adapting to new situations.
The sensorimotor contingencies theory proposes to ground the development of those perceptive abilities in the way the agent can actively transform its sensory inputs.
We propose a sensorimotor approach, inspired by this theory, in which the agent explores the world and discovers its properties by capturing the sensorimotor regularities they induce.
This work presents an application of this approach to the discovery of a so-called visual field as the set of regularities that a visual sensor imposes on a naive agent's experience.
A formalism is proposed to describe how those regularities can be captured in a sensorimotor predictive model.
Finally, the approach is evaluated on a simulated system coarsely inspired from the human retina.
This paper describes a dataset containing small images of text from everyday scenes.
The purpose of the dataset is to support the development of new automated systems that can detect and analyze text.
Although much research has been devoted to text detection and recognition in scanned documents, relatively little attention has been given to text detection in other types of images, such as photographs that are posted on social-media sites.
This new dataset, known as COCO-Text-Patch, contains approximately 354,000 small images that are each labeled as "text" or "non-text".
This dataset particularly addresses the problem of text verification, which is an essential stage in the end-to-end text detection and recognition pipeline.
In order to evaluate the utility of this dataset, it has been used to train two deep convolution neural networks to distinguish text from non-text.
One network is inspired by the GoogLeNet architecture, and the second one is based on CaffeNet.
Accuracy levels of 90.2% and 90.9% were obtained using the two networks, respectively.
All of the images, source code, and deep-learning trained models described in this paper will be publicly available
Stochastic configuration networks (SCNs) as a class of randomized learner model have been successfully employed in data analytics due to its universal approximation capability and fast modelling property.
The technical essence lies in stochastically configuring hidden nodes (or basis functions) based on a supervisory mechanism rather than data-independent randomization as usually adopted for building randomized neural networks.
Given image data modelling tasks, the use of one-dimensional SCNs potentially demolishes the spatial information of images, and may result in undesirable performance.
This paper extends the original SCNs to two-dimensional version, termed 2DSCNs, for fast building randomized learners with matrix-inputs.
Some theoretical analyses on the goodness of 2DSCNs against SCNs, including the complexity of the random parameter space, and the superiority of generalization, are presented.
Empirical results over one regression, four benchmark handwritten digits classification, and two human face recognition datasets demonstrate that the proposed 2DSCNs perform favourably and show good potential for image data analytics.
Higher-level cognition depends on the ability to learn models of the world.
We can characterize this at the computational level as a structure-learning problem with the goal of best identifying the prevailing causal relationships among a set of relata.
However, the computational cost of performing exact Bayesian inference over causal models grows rapidly as the number of relata increases.
This implies that the cognitive processes underlying causal learning must be substantially approximate.
A powerful class of approximations that focuses on the sequential absorption of successive inputs is captured by the Neurath's ship metaphor in philosophy of science, where theory change is cast as a stochastic and gradual process shaped as much by people's limited willingness to abandon their current theory when considering alternatives as by the ground truth they hope to approach.
Inspired by this metaphor and by algorithms for approximating Bayesian inference in machine learning, we propose an algorithmic-level model of causal structure learning under which learners represent only a single global hypothesis that they update locally as they gather evidence.
We propose a related scheme for understanding how, under these limitations, learners choose informative interventions that manipulate the causal system to help elucidate its workings.
We find support for our approach in the analysis of four experiments.
The use of explicit object detectors as an intermediate step to image captioning - which used to constitute an essential stage in early work - is often bypassed in the currently dominant end-to-end approaches, where the language model is conditioned directly on a mid-level image embedding.
We argue that explicit detections provide rich semantic information, and can thus be used as an interpretable representation to better understand why end-to-end image captioning systems work well.
We provide an in-depth analysis of end-to-end image captioning by exploring a variety of cues that can be derived from such object detections.
Our study reveals that end-to-end image captioning systems rely on matching image representations to generate captions, and that encoding the frequency, size and position of objects are complementary and all play a role in forming a good image representation.
It also reveals that different object categories contribute in different ways towards image captioning.
We aim to review available literature related to the telemonitoring of maternal health care for a comprehensive understanding of the roles of Medical Cyber-Physical-Systems (MCPS) as cutting edge technology in maternal risk factor management, and for understanding the possible research gap in the domain.
In this regard, we search literature through google scholar and PubMed databases for published studies that focus on maternal telemonitoring systems using sensors, Cyber-Physical-System (CPS) and information decision systems for addressing risk factors We extract 1340 articles relevant to maternal health care that addresses different risk factors as their managerial issues.
Of a large number of relevant articles, we included 26 prospective studies relating to sensors or Medical Cyber-Physical-Systems (MCPS) based maternal telemonitoring.
Of the 1340 primary articles, we have short-listed 26 articles (12 articles for risk factor analysis, 9 for synthesis matrices and 5 papers for finding essential elements.
We have extracted 17 vital symptoms as maternal risk factors during pregnancy.
Moreover, we have identified a number of cyber-frameworks as the basis of information decision support system to cope with the different maternal complexities.
We have found the Medical Cyber-Physical System (MCPS) as a promising technology to manage the vital risk factors quickly and efficiently by the care provider from a distant place to reduce the fatal risks.
Despite communication issues, MCPS is a key-enabling technology to cope with the advancement of telemonitoring paradigm in the maternal health care system.
XML query can be modeled by twig pattern query (TPQ) specifying predicates on XML nodes and XPath relationships satisfied between them.
A lot of TPQ types have been proposed; this paper takes into account a TPQ model extended by a specification of output and non-output query nodes since it complies with the XQuery semantics and, in many cases, it leads to a more efficient query processing.
In general, there are two approaches to process the TPQ: holistic joins and binary joins.
Whereas the binary join approach builds a query plan as a tree of interconnected binary operators, the holistic join approach evaluates a whole query using one operator (i.e., using one complex algorithm).
Surprisingly, a thorough analytical and experimental comparison is still missing despite an enormous research effort in this area.
In this paper, we try to fill this gap; we analytically and experimentally show that the binary joins used in a fully-pipelined plan (i.e., the plan where each join operation does not wait for the complete result of the previous operation and no explicit sorting is used) can often outperform the holistic joins, especially for TPQs with a higher ratio of non-output query nodes.
The main contributions of this paper can be summarized as follows: (i) we introduce several improvements of existing binary join approaches allowing to build a fully-pipelined plan for a TPQ considering non-output query nodes, (ii) we prove that for a certain class of TPQs such a plan has the linear time complexity with respect to the size of the input and output as well as the linear space complexity with respect to the XML document depth (i.e., the same complexity as the holistic join approaches), (iii) we show that our improved binary join approach outperforms the holistic join approaches in many situations, and (iv) we propose a simple combined approach that uses advantages of both types of approaches.
Sparse principal component analysis (sparse PCA) aims at finding a sparse basis to improve the interpretability over the dense basis of PCA, meanwhile the sparse basis should cover the data subspace as much as possible.
In contrast to most of existing work which deal with the problem by adding some sparsity penalties on various objectives of PCA, in this paper, we propose a new method SPCArt, whose motivation is to find a rotation matrix and a sparse basis such that the sparse basis approximates the basis of PCA after the rotation.
The algorithm of SPCArt consists of three alternating steps: rotate PCA basis, truncate small entries, and update the rotation matrix.
Its performance bounds are also given.
SPCArt is efficient, with each iteration scaling linearly with the data dimension.
It is easy to choose parameters in SPCArt, due to its explicit physical explanations.
Besides, we give a unified view to several existing sparse PCA methods and discuss the connection with SPCArt.
Some ideas in SPCArt are extended to GPower, a popular sparse PCA algorithm, to overcome its drawback.
Experimental results demonstrate that SPCArt achieves the state-of-the-art performance.
It also achieves a good tradeoff among various criteria, including sparsity, explained variance, orthogonality, balance of sparsity among loadings, and computational speed.
Interest has been revived in the creation of a "bill of rights" for Internet users.
This paper analyzes users' rights into ten broad principles, as a basis for assessing what users regard as important and for comparing different multi-issue Internet policy proposals.
Stability of the principles is demonstrated in an experimental survey, which also shows that freedoms of users to participate in the design and coding of platforms appear to be viewed as inessential relative to other rights.
An analysis of users' rights frameworks that have emerged over the past twenty years similarly shows that such proposals tend to leave out freedoms related to software platforms, as opposed to user data or public networks.
Evaluating policy frameworks in a comparative analysis based on prior principles may help people to see what is missing and what is important as the future of the Internet continues to be debated.
A finite length analysis is introduced for irregular repetition slotted ALOHA (IRSA) that enables to accurately estimate its performance in the moderate-to-high packet loss probability regime, i.e., in the so-called waterfall region.
The analysis is tailored to the collision channel model, which enables mapping the description of the successive interference cancellation process onto the iterative erasure decoding of low-density parity-check codes.
The analysis provides accurate estimates of the packet loss probability of IRSA in the waterfall region as demonstrated by Monte Carlo simulations.
Deep convolutional neural networks (CNN) are widely used in modern artificial intelligence (AI) and smart vision systems but also limited by computation latency, throughput, and energy efficiency on a resource-limited scenario, such as mobile devices, internet of things (IoT), unmanned aerial vehicles (UAV), and so on.
A hardware streaming architecture is proposed to accelerate convolution and pooling computations for state-of-the-art deep CNNs.
It is optimized for energy efficiency by maximizing local data reuse to reduce off-chip DRAM data access.
In addition, image and feature decomposition techniques are introduced to optimize memory access pattern for an arbitrary size of image and number of features within limited on-chip SRAM capacity.
A prototype accelerator was implemented in TSMC 65 nm CMOS technology with 2.3 mm x 0.8 mm core area, which achieves 144 GOPS peak throughput and 0.8 TOPS/W peak energy efficiency.
Over the period of 6 years and three phases, the SEE-GRID programme has established a strong regional human network in the area of distributed scientific computing and has set up a powerful regional Grid infrastructure.
It attracted a number of user communities and applications from diverse fields from countries throughout the South-Eastern Europe.
From the infrastructure point view, the first project phase has established a pilot Grid infrastructure with more than 20 resource centers in 11 countries.
During the subsequent two phases of the project, the infrastructure has grown to currently 55 resource centers with more than 6600 CPUs and 750 TBs of disk storage, distributed in 16 participating countries.
Inclusion of new resource centers to the existing infrastructure, as well as a support to new user communities, has demanded setup of regionally distributed core services, development of new monitoring and operational tools, and close collaboration of all partner institution in managing such a complex infrastructure.
In this paper we give an overview of the development and current status of SEE-GRID regional infrastructure and describe its transition to the NGI-based Grid model in EGI, with the strong SEE regional collaboration.
A trusted electronic election system requires that all the involved information must go public, that is, it focuses not only on transparency but also privacy issues.
In other words, each ballot should be counted anonymously, correctly, and efficiently.
In this work, a lightweight E-voting system is proposed for voters to minimize their trust in the authority or government.
We ensure the transparency of election by putting all message on the Ethereum blockchain, in the meantime, the privacy of individual voter is protected via an efficient and effective ring signature mechanism.
Besides, the attractive self-tallying feature is also built in our system, which guarantees that everyone who can access the blockchain network is able to tally the result on his own, no third party is required after voting phase.
More importantly, we ensure the correctness of voting results and keep the Ethereum gas cost of individual participant as low as possible, at the same time.
Clearly, the pre-described characteristics make our system more suitable for large-scale election.
Text analytics based on supervised machine learning classifiers has shown great promise in a multitude of domains, but has yet to be applied to Seismology.
We test various standard models (Naive Bayes, k-Nearest Neighbors, Support Vector Machines, and Random Forests) on a seismological corpus of 100 articles related to the topic of precursory accelerating seismicity, spanning from 1988 to 2010.
This corpus was labelled in Mignan (2011) with the precursor whether explained by critical processes (i.e., cascade triggering) or by other processes (such as signature of main fault loading).
We investigate rather the classification process can be automatized to help analyze larger corpora in order to better understand trends in earthquake predictability research.
We find that the Naive Bayes model performs best, in agreement with the machine learning literature for the case of small datasets, with cross-validation accuracies of 86% for binary classification.
For a refined multiclass classification ('non-critical process' < 'agnostic' < 'critical process assumed' < 'critical process demonstrated'), we obtain up to 78% accuracy.
Prediction on a dozen of articles published since 2011 shows however a weak generalization with a F1-score of 60%, only slightly better than a random classifier, which can be explained by a change of authorship and use of different terminologies.
Yet, the model shows F1-scores greater than 80% for the two multiclass extremes ('non-critical process' versus 'critical process demonstrated') while it falls to random classifier results (around 25%) for papers labelled 'agnostic' or 'critical process assumed'.
Those results are encouraging in view of the small size of the corpus and of the high degree of abstraction of the labelling.
Domain knowledge engineering remains essential but can be made transparent by an investigation of Naive Bayes keyword posterior probabilities.
To improve system performance, modern operating systems (OSes) often undertake activities that require modification of virtual-to-physical page translation mappings.
For example, the OS may migrate data between physical frames to defragment memory and enable superpages.
The OS may migrate pages of data between heterogeneous memory devices.
We refer to all such activities as page remappings.
Unfortunately, page remappings are expensive.
We show that translation coherence is a major culprit and that systems employing virtualization are especially badly affected by their overheads.
In response, we propose hardware translation invalidation and coherence or HATRIC, a readily implementable hardware mechanism to piggyback translation coherence atop existing cache coherence protocols.
We perform detailed studies using KVM-based virtualization, showing that HATRIC achieves up to 30% performance and 10% energy benefits, for per-CPU area overheads of 2%.
We also quantify HATRIC's benefits on systems running Xen and find up to 33% performance improvements.
The Biham-Middleton-Levine (BML) traffic model is a simple two-dimensional, discrete Cellular Automaton (CA) that has been used to study self-organization and phase transitions arising in traffic flows.
From the computational point of view, the BML model exhibits the usual features of discrete CA, where the state of the automaton are updated according to simple rules that depend on the state of each cell and its neighbors.
In this paper we study the impact of various optimizations for speeding up CA computations by using the BML model as a case study.
In particular, we describe and analyze the impact of several parallel implementations that rely on CPU features, such as multiple cores or SIMD instructions, and on GPUs.
Experimental evaluation provides quantitative measures of the payoff of each technique in terms of speedup with respect to a plain serial implementation.
Our findings show that the performance gap between CPU and GPU implementations of the BML traffic model can be reduced by clever exploitation of all CPU features.
Learning with limited data is a key challenge for visual recognition.
Few-shot learning methods address this challenge by learning an instance embedding function from seen classes and apply the function to instances from unseen classes with limited labels.
This style of transfer learning is task-agnostic: the embedding function is not learned optimally discriminative with respect to the unseen classes, where discerning among them is the target task.
In this paper, we propose a novel approach to adapt the embedding model to the target classification task, yielding embeddings that are task-specific and are discriminative.
To this end, we employ a type of self-attention mechanism called Transformer to transform the embeddings from task-agnostic to task-specific by focusing on relating instances from the test instances to the training instances in both seen and unseen classes.
Our approach also extends to both transductive and generalized few-shot classification, two important settings that have essential use cases.
We verify the effectiveness of our model on two standard benchmark few-shot classification datasets --- MiniImageNet and CUB, where our approach demonstrates state-of-the-art empirical performance.
We revisit a technique of S. Lehr on automata and use it to prove old and new results in a simple way.
We give a very simple proof of the 1986 theorem of Honkala that it is decidable whether a given k-automatic sequence is ultimately periodic.
We prove that it is decidable whether a given k-automatic sequence is overlap-free (or squareefree, or cubefree, etc.)
We prove that the lexicographically least sequence in the orbit closure of a k-automatic sequence is k-automatic, and use this last result to show that several related quantities, such as the critical exponent, irrationality measure, and recurrence quotient for Sturmian words with slope alpha, have automatic continued fraction expansions if alpha does.
This paper introduces a new constraint domain for reasoning about data with uncertainty.
It extends convex modeling with the notion of p-box to gain additional quantifiable information on the data whereabouts.
Unlike existing approaches, the p-box envelops an unknown probability instead of approximating its representation.
The p-box bounds are uniform cumulative distribution functions (cdf) in order to employ linear computations in the probabilistic domain.
The reasoning by means of p-box cdf-intervals is an interval computation which is exerted on the real domain then it is projected onto the cdf domain.
This operation conveys additional knowledge represented by the obtained probabilistic bounds.
The empirical evaluation of our implementation shows that, with minimal overhead, the output solution set realizes a full enclosure of the data along with tighter bounds on its probabilistic distributions.
In many real-world machine learning applications, unlabeled data are abundant whereas class labels are expensive and scarce.
An active learner aims to obtain a model of high accuracy with as few labeled instances as possible by effectively selecting useful examples for labeling.
We propose a new selection criterion that is based on statistical leverage scores and present two novel active learning methods based on this criterion: ALEVS for querying single example at each iteration and DBALEVS for querying a batch of examples.
To assess the representativeness of the examples in the pool, ALEVS and DBALEVS use the statistical leverage scores of the kernel matrices computed on the examples of each class.
Additionally, DBALEVS selects a diverse a set of examples that are highly representative but are dissimilar to already labeled examples through maximizing a submodular set function defined with the statistical leverage scores and the kernel matrix computed on the pool of the examples.
The submodularity property of the set scoring function let us identify batches with a constant factor approximate to the optimal batch in an efficient manner.
Our experiments on diverse datasets show that querying based on leverage scores is a powerful strategy for active learning.
Many projects relies on cognitives sciences, neurosciences, computer sciences and robotics.
They concerned today the building of autonomous artificial beings able to think.
This paper shows a model to compare the human thinking with an hypothetic numerical way of thinking based on four hierarchies : the information system classification, the cognitive pyramid, the linguistic pyramid and the digital information hierarchy.
After a state of art on the nature of human thinking, feasibility of autonomous multi-agent systems provided with artificial consciousness which are able to think is discussed.
The ethical aspects and consequences for humanity of such systems is evaluated.
These systems lead the scientific community to react.
In recent years, heatmap regression based models have shown their effectiveness in face alignment and pose estimation.
However, Conventional Heatmap Regression (CHR) is not accurate nor stable when dealing with high-resolution facial videos, since it finds the maximum activated location in heatmaps which are generated from rounding coordinates, and thus leads to quantization errors when scaling back to the original high-resolution space.
In this paper, we propose a Fractional Heatmap Regression (FHR) for high-resolution video-based face alignment.
The proposed FHR can accurately estimate the fractional part according to the 2D Gaussian function by sampling three points in heatmaps.
To further stabilize the landmarks among continuous video frames while maintaining the precise at the same time, we propose a novel stabilization loss that contains two terms to address time delay and non-smooth issues, respectively.
Experiments on 300W, 300-VW and Talking Face datasets clearly demonstrate that the proposed method is more accurate and stable than the state-of-the-art models.
Recently, many graph matching methods that incorporate pairwise constraint and that can be formulated as a quadratic assignment problem (QAP) have been proposed.
Although these methods demonstrate promising results for the graph matching problem, they have high complexity in space or time.
In this paper, we introduce an adaptively transforming graph matching (ATGM) method from the perspective of functional representation.
More precisely, under a transformation formulation, we aim to match two graphs by minimizing the discrepancy between the original graph and the transformed graph.
With a linear representation map of the transformation, the pairwise edge attributes of graphs are explicitly represented by unary node attributes, which enables us to reduce the space and time complexity significantly.
Due to an efficient Frank-Wolfe method-based optimization strategy, we can handle graphs with hundreds and thousands of nodes within an acceptable amount of time.
Meanwhile, because transformation map can preserve graph structures, a domain adaptation-based strategy is proposed to remove the outliers.
The experimental results demonstrate that our proposed method outperforms the state-of-the-art graph matching algorithms.
Spear phishing is a complex targeted attack in which, an attacker harvests information about the victim prior to the attack.
This information is then used to create sophisticated, genuine-looking attack vectors, drawing the victim to compromise confidential information.
What makes spear phishing different, and more powerful than normal phishing, is this contextual information about the victim.
Online social media services can be one such source for gathering vital information about an individual.
In this paper, we characterize and examine a true positive dataset of spear phishing, spam, and normal phishing emails from Symantec's enterprise email scanning service.
We then present a model to detect spear phishing emails sent to employees of 14 international organizations, by using social features extracted from LinkedIn.
Our dataset consists of 4,742 targeted attack emails sent to 2,434 victims, and 9,353 non targeted attack emails sent to 5,912 non victims; and publicly available information from their LinkedIn profiles.
We applied various machine learning algorithms to this labeled data, and achieved an overall maximum accuracy of 97.76% in identifying spear phishing emails.
We used a combination of social features from LinkedIn profiles, and stylometric features extracted from email subjects, bodies, and attachments.
However, we achieved a slightly better accuracy of 98.28% without the social features.
Our analysis revealed that social features extracted from LinkedIn do not help in identifying spear phishing emails.
To the best of our knowledge, this is one of the first attempts to make use of a combination of stylometric features extracted from emails, and social features extracted from an online social network to detect targeted spear phishing emails.
This paper summarizes the work done by the authors for the Zero Resource Speech Challenge organized in the technical program of Interspeech 2015.
The goal of the challenge is to discover linguistic units directly from unlabeled speech data.
The Multi-layered Acoustic Tokenizer (MAT) proposed in this work automatically discovers multiple sets of acoustic tokens from the given corpus.
Each acoustic token set is specified by a set of hyperparameters that describe the model configuration.
These sets of acoustic tokens carry different characteristics of the given corpus and the language behind thus can be mutually reinforced.
The multiple sets of token labels are then used as the targets of a Multi-target DNN (MDNN) trained on low-level acoustic features.
Bottleneck features extracted from the MDNN are used as feedback for the MAT and the MDNN itself.
We call this iterative system the Multi-layered Acoustic Tokenizing Deep Neural Network (MAT-DNN) which generates high quality features for track 1 of the challenge and acoustic tokens for track 2 of the challenge.
As the realization of vehicular communication such as vehicle-to-vehicle (V2V) or vehicle-to-infrastructure (V2I) is imperative for the autonomous driving cars, the understanding of realistic vehicle-to-everything (V2X) models is needed.
While previous research has mostly targeted vehicular models in which vehicles are randomly distributed and the variable of carrier frequency was not considered, a more realistic analysis of the V2X model is proposed in this paper.
We use a one-dimensional (1D) Poisson cluster process (PCP) to model a realistic scenario of vehicle distribution in a perpendicular cross line road urban area and compare the coverage results with the previous research that distributed vehicles randomly by Poisson Point Process (PPP).
Moreover, we incorporate the effect of different carrier frequencies, mmWave and sub-6 GHz, to our analysis by altering the antenna radiation pattern accordingly.
Results indicated that while the effect of clustering led to lower outage, using mmWave had even more significance in leading to lower outage.
Moreover, line-of-sight (LoS) interference links are shown to be more dominant in lowering the outage than the non-line-of-sight (NLoS) links even though they are less in number.
The analytical results give insight into designing and analyzing the urban V2X channels, and are verified by actual urban area three-dimensional (3D) ray-tracing simulation.
In this paper we propose and carefully evaluate a sequence labeling framework which solely utilizes sparse indicator features derived from dense distributed word representations.
The proposed model obtains (near) state-of-the art performance for both part-of-speech tagging and named entity recognition for a variety of languages.
Our model relies only on a few thousand sparse coding-derived features, without applying any modification of the word representations employed for the different tasks.
The proposed model has favorable generalization properties as it retains over 89.8% of its average POS tagging accuracy when trained at 1.2% of the total available training data, i.e.~150 sentences per language.
Wireless systems are getting deployed in many new environments with different antenna heights, frequency bands and multipath conditions.
This has led to an increasing demand for more channel measurements to understand wireless propagation in specific environments and assist deployment engineering.
We design and implement a rapid wireless channel sounding system, using the Universal Software Radio Peripheral (USRP) and GNU Radio software, to address these demands.
Our design measures channel propagation characteristics simultaneously from multiple transmitter locations.
The system consists of multiple battery-powered transmitters and receivers.
Therefore, we can set-up the channel sounder rapidly at a field location and measure expeditiously by analyzing different transmitters signals during a single walk or drive through the environment.
Our design can be used for both indoor and outdoor channel measurements in the frequency range of 1 MHz to 6 GHz.
We expect that the proposed approach, with a few further refinements, can transform the task of propagation measurement as a routine part of day-to-day wireless network engineering.
Past decade has seen the development of many shared-memory graph processing frameworks intended to reduce the effort of developing high performance parallel applications.
However, many of these frameworks, based on Vertex-centric or Edge-centric paradigms suffer from several issues such as poor cache utilization, irregular memory accesses, heavy use of synchronization primitives or theoretical inefficiency, that deteriorate overall performance and scalability.
In this paper, we generalize a recent partition-centric paradigm for PageRank computation to a novel Graph Processing Over Partitions (GPOP) framework that exploits the locality of partitioning to dramatically improve the cache performance of a variety of graph algorithms.
It achieves high scalability by enabling completely lock and atomic free computation.
Its built-in analytical performance model enables it to use a hybrid of source and partition centric communication modes in a way that ensures work-efficiency each iteration while simultaneously boosting high bandwidth sequential memory accesses.
Finally, the GPOP framework is designed with programmability in mind.
It completely abstracts away underlying programming model details from the user and provides an easy to program set of APIs with the ability to selectively continue the active vertex set across iterations.
We extensively evaluate the performance of GPOP for a variety of graph algorithms, using several large datasets.
We observe that GPOP incurs upto 8.6x and 5.2x less L2 cache misses compared to Ligra and GraphMat, respectively.
In terms of execution time, GPOP is upto 19x and 6.1x faster than Ligra and GraphMat, respectively.
In 2016, 2017, and 2018 at the IEEE Conference on Computational Intelligence in Games, the authors of this paper ran a competition for agents that can play classic text-based adventure games.
This competition fills a gap in existing game AI competitions that have typically focussed on traditional card/board games or modern video games with graphical interfaces.
By providing a platform for evaluating agents in text-based adventures, the competition provides a novel benchmark for game AI with unique challenges for natural language understanding and generation.
This paper summarises the three competitions ran in 2016, 2017, and 2018 (including details of open source implementations of both the competition framework and our competitors) and presents the results of an improved evaluation of these competitors across 20 games.
We address the problem of attack detection and isolation for a class of discrete-time nonlinear systems under (potentially unbounded) sensor attacks and measurement noise.
We consider the case when a subset of sensors is subject to additive false data injection attacks.
Using a bank of observers, each observer leading to an Input-to-State Stable (ISS) estimation error, we propose two algorithms for detecting and isolating sensor attacks.
These algorithms make use of the ISS property of the observers to check whether the trajectories of observers are `consistent' with the attack-free trajectories of the system.
Simulations results are presented to illustrate the performance of the proposed algorithms.
Most state-of-the-art systems today produce morphological analysis based only on orthographic patterns.
In contrast, we propose a model for unsupervised morphological analysis that integrates orthographic and semantic views of words.
We model word formation in terms of morphological chains, from base words to the observed words, breaking the chains into parent-child relations.
We use log-linear models with morpheme and word-level features to predict possible parents, including their modifications, for each word.
The limited set of candidate parents for each word render contrastive estimation feasible.
Our model consistently matches or outperforms five state-of-the-art systems on Arabic, English and Turkish.
The objective of this research was to design a 2.4 GHz class AB Power Amplifier, with 0.18 um SMIC CMOS technology by using Cadence software, for health care applications.
The ultimate goal for such application is to minimize the trade-offs between performance and cost, and between performance and low power consumption design.
The performance of the power amplifier meets the specification requirements of the desired.
Recent advances in AI and robotics have claimed many incredible results with deep learning, yet no work to date has applied deep learning to the problem of liquid perception and reasoning.
In this paper, we apply fully-convolutional deep neural networks to the tasks of detecting and tracking liquids.
We evaluate three models: a single-frame network, multi-frame network, and a LSTM recurrent network.
Our results show that the best liquid detection results are achieved when aggregating data over multiple frames, in contrast to standard image segmentation.
They also show that the LSTM network outperforms the other two in both tasks.
This suggests that LSTM-based neural networks have the potential to be a key component for enabling robots to handle liquids using robust, closed-loop controllers.
In this paper, we consider the patient similarity matching problem over a cancer cohort of more than 220,000 patients.
Our approach first leverages on Word2Vec framework to embed ICD codes into vector-valued representation.
We then propose a sequential algorithm for case-control matching on this representation space of diagnosis codes.
The novel practice of applying the sequential matching on the vector representation lifted the matching accuracy measured through multiple clinical outcomes.
We reported the results on a large-scale dataset to demonstrate the effectiveness of our method.
For such a large dataset where most clinical information has been codified, the new method is particularly relevant.
Stigmergy has proved its great superiority in terms of distributed control, robustness and adaptability, thus being regarded as an ideal solution for large-scale swarm control problems.
Based on new discoveries on astrocytes in regulating synaptic transmission in the brain, this paper has mapped stigmergy mechanism into the interaction between synapses and investigated its characteristics and advantages.
Particularly, we have divided the interaction between synapses which are not directly connected into three phases and proposed a stigmergic learning model.
In this model, the state change of a stigmergy agent will expand its influence to affect the states of others.
The strength of the interaction is determined by the level of neural activity as well as the distance between stigmergy agents.
Inspired by the morphological and functional changes in astrocytes during environmental enrichment, it is likely that the regulation of distance between stigmergy agents plays a critical role in the stigmergy learning process.
Simulation results have verified its importance and indicated that the well-regulated distance between stigmergy agents can help to obtain stigmergy learning gain.
Automatic note-level transcription is considered one of the most challenging tasks in music information retrieval.
The specific case of flamenco singing transcription poses a particular challenge due to its complex melodic progressions, intonation inaccuracies, the use of a high degree of ornamentation and the presence of guitar accompaniment.
In this study, we explore the limitations of existing state of the art transcription systems for the case of flamenco singing and propose a specific solution for this genre: We first extract the predominant melody and apply a novel contour filtering process to eliminate segments of the pitch contour which originate from the guitar accompaniment.
We formulate a set of onset detection functions based on volume and pitch characteristics to segment the resulting vocal pitch contour into discrete note events.
A quantised pitch label is assigned to each note event by combining global pitch class probabilities with local pitch contour statistics.
The proposed system outperforms state of the art singing transcription systems with respect to voicing accuracy, onset detection and overall performance when evaluated on flamenco singing datasets.
This paper reports on the 2018 PIRM challenge on perceptual super-resolution (SR), held in conjunction with the Perceptual Image Restoration and Manipulation (PIRM) workshop at ECCV 2018.
In contrast to previous SR challenges, our evaluation methodology jointly quantifies accuracy and perceptual quality, therefore enabling perceptual-driven methods to compete alongside algorithms that target PSNR maximization.
Twenty-one participating teams introduced algorithms which well-improved upon the existing state-of-the-art methods in perceptual SR, as confirmed by a human opinion study.
We also analyze popular image quality measures and draw conclusions regarding which of them correlates best with human opinion scores.
We conclude with an analysis of the current trends in perceptual SR, as reflected from the leading submissions.
Independent component analysis (ICA) is a statistical method for transforming an observable multidimensional random vector into components that are as statistically independent as possible from each other.Usually the ICA framework assumes a model according to which the observations are generated (such as a linear transformation with additive noise).
ICA over finite fields is a special case of ICA in which both the observations and the independent components are over a finite alphabet.
In this work we consider a generalization of this framework in which an observation vector is decomposed to its independent components (as much as possible) with no prior assumption on the way it was generated.
This generalization is also known as Barlow's minimal redundancy representation problem and is considered an open problem.
We propose several theorems and show that this NP hard problem can be accurately solved with a branch and bound search tree algorithm, or tightly approximated with a series of linear problems.
Our contribution provides the first efficient and constructive set of solutions to Barlow's problem.The minimal redundancy representation (also known as factorial code) has many applications, mainly in the fields of Neural Networks and Deep Learning.
The Binary ICA (BICA) is also shown to have applications in several domains including medical diagnosis, multi-cluster assignment, network tomography and internet resource management.
In this work we show this formulation further applies to multiple disciplines in source coding such as predictive coding, distributed source coding and coding of large alphabet sources.
This paper proposes the first user-independent inter-keystroke timing attacks on PINs.
Our attack method is based on an inter-keystroke timing dictionary built from a human cognitive model whose parameters can be determined by a small amount of training data on any users (not necessarily the target victims).
Our attacks can thus be potentially launched on a large scale in real-world settings.
We investigate inter-keystroke timing attacks in different online attack settings and evaluate their performance on PINs at different strength levels.
Our experimental results show that the proposed attack performs significantly better than random guessing attacks.
We further demonstrate that our attacks pose a serious threat to real-world applications and propose various ways to mitigate the threat.
Pedestrian attribute inference is a demanding problem in visual surveillance that can facilitate person retrieval, search and indexing.
To exploit semantic relations between attributes, recent research treats it as a multi-label image classification task.
The visual cues hinting at attributes can be strongly localized and inference of person attributes such as hair, backpack, shorts, etc., are highly dependent on the acquired view of the pedestrian.
In this paper we assert this dependence in an end-to-end learning framework and show that a view-sensitive attribute inference is able to learn better attribute predictions.
Our proposed model jointly predicts the coarse pose (view) of the pedestrian and learns specialized view-specific multi-label attribute predictions.
We show in an extensive evaluation on three challenging datasets (PETA, RAP and WIDER) that our proposed end-to-end view-aware attribute prediction model provides competitive performance and improves on the published state-of-the-art on these datasets.
Non-uniform and multi-illuminant color constancy are important tasks, the solution of which will allow to discard information about lighting conditions in the image.
Non-uniform illumination and shadows distort colors of real-world objects and mostly do not contain valuable information.
Thus, many computer vision and image processing techniques would benefit from automatic discarding of this information at the pre-processing step.
In this work we propose novel view on this classical problem via generative end-to-end algorithm, namely image conditioned Generative Adversarial Network.
We also demonstrate the potential of the given approach for joint shadow detection and removal.
Forced by the lack of training data, we render the largest existing shadow removal dataset and make it publicly available.
It consists of approximately 6,000 pairs of wide field of view synthetic images with and without shadows.
Image style transfer models based on convolutional neural networks usually suffer from high temporal inconsistency when applied to videos.
Some video style transfer models have been proposed to improve temporal consistency, yet they fail to guarantee fast processing speed, nice perceptual style quality and high temporal consistency at the same time.
In this paper, we propose a novel real-time video style transfer model, ReCoNet, which can generate temporally coherent style transfer videos while maintaining favorable perceptual styles.
A novel luminance warping constraint is added to the temporal loss at the output level to capture luminance changes between consecutive frames and increase stylization stability under illumination effects.
We also propose a novel feature-map-level temporal loss to further enhance temporal consistency on traceable objects.
Experimental results indicate that our model exhibits outstanding performance both qualitatively and quantitatively.
Light clients, also known as Simple Payment Verification (SPV) clients, are nodes which only download a small portion of the data in a blockchain, and use indirect means to verify that a given chain is valid.
Typically, instead of validating block data, they assume that the chain favoured by the blockchain's consensus algorithm only contains valid blocks, and that the majority of block producers are honest.
By allowing such clients to receive fraud proofs generated by fully validating nodes that show that a block violates the protocol rules, and combining this with probabilistic sampling techniques to verify that all of the data in a block actually is available to be downloaded, we can eliminate the honest-majority assumption, and instead make much weaker assumptions about a minimum number of honest nodes that rebroadcast data.
Fraud and data availability proofs are key to enabling on-chain scaling of blockchains (e.g. via sharding or bigger blocks) while maintaining a strong assurance that on-chain data is available and valid.
We present, implement, and evaluate a novel fraud and data availability proof system.
Nonlinear electromagnetic (EM) inverse scattering is a quantitative and super-resolution imaging technique, in which more realistic interactions between the internal structure of scene and EM wavefield are taken into account in the imaging procedure, in contrast to conventional tomography.
However, it poses important challenges arising from its intrinsic strong nonlinearity, ill-posedness, and expensive computation costs.
To tackle these difficulties, we, for the first time to our best knowledge, exploit a connection between the deep neural network (DNN) architecture and the iterative method of nonlinear EM inverse scattering.
This enables the development of a novel DNN-based methodology for nonlinear EM inverse problems (termed here DeepNIS).
The proposed DeepNIS consists of a cascade of multi-layer complexvalued residual convolutional neural network (CNN) modules.
We numerically and experimentally demonstrate that the DeepNIS outperforms remarkably conventional nonlinear inverse scattering methods in terms of both the image quality and computational time.
We show that DeepNIS can learn a general model approximating the underlying EM inverse scattering system.
It is expected that the DeepNIS will serve as powerful tool in treating highly nonlinear EM inverse scattering problems over different frequency bands, involving large-scale and high-contrast objects, which are extremely hard and impractical to solve using conventional inverse scattering methods.
Learning a high-dimensional dense representation for vocabulary terms, also known as a word embedding, has recently attracted much attention in natural language processing and information retrieval tasks.
The embedding vectors are typically learned based on term proximity in a large corpus.
This means that the objective in well-known word embedding algorithms, e.g., word2vec, is to accurately predict adjacent word(s) for a given word or context.
However, this objective is not necessarily equivalent to the goal of many information retrieval (IR) tasks.
The primary objective in various IR tasks is to capture relevance instead of term proximity, syntactic, or even semantic similarity.
This is the motivation for developing unsupervised relevance-based word embedding models that learn word representations based on query-document relevance information.
In this paper, we propose two learning models with different objective functions; one learns a relevance distribution over the vocabulary set for each query, and the other classifies each term as belonging to the relevant or non-relevant class for each query.
To train our models, we used over six million unique queries and the top ranked documents retrieved in response to each query, which are assumed to be relevant to the query.
We extrinsically evaluate our learned word representation models using two IR tasks: query expansion and query classification.
Both query expansion experiments on four TREC collections and query classification experiments on the KDD Cup 2005 dataset suggest that the relevance-based word embedding models significantly outperform state-of-the-art proximity-based embedding models, such as word2vec and GloVe.
The use of key-dependent shiftRows can be considered as one of the applied methods for altering the quality of a cryptographic algorithm.
This article describes one approach for changing the ShiftRows transformation employed in the algorithm AES.
The approach employs methods inspired from DNA processes and structure which depended on the key while the parameters of the created new ShiftRows have characteristics identical to those of the original algorithm AES in addition to increase its resistance against attacks.
The proposed new ShiftRows were tested for coefficient correlation for dynamic and static independence between the input and output.
The NIST Test Suite tests were used to test the randomness for the block cipher that used the new transformation.
This paper presents an infrastructure to test the functionality of the specific architectures output by a high-level compiler targeting dynamically reconfigurable hardware.
It results in a suitable scheme to verify the architectures generated by the compiler, each time new optimization techniques are included or changes in the compiler are performed.
We believe this kind of infrastructure is important to verify, by functional simulation, further research techniques, as far as compilation to Field-Programmable Gate Array (FPGA) platforms is concerned.
This paper presents a methodology for temporal logic verification of discrete-time stochastic systems.
Our goal is to find a lower bound on the probability that a complex temporal property is satisfied by finite traces of the system.
Desired temporal properties of the system are expressed using a fragment of linear temporal logic, called safe LTL over finite traces.
We propose to use barrier certificates for computations of such lower bounds, which is computationally much more efficient than the existing discretization-based approaches.
The new approach is discretization-free and does not suffer from the curse of dimensionality caused by discretizing state sets.
The proposed approach relies on decomposing the negation of the specification into a union of sequential reachabilities and then using barrier certificates to compute upper bounds for these reachability probabilities.
We demonstrate the effectiveness of the proposed approach on case studies with linear and polynomial dynamics.
In today's dynamic ICT environments, the ability to control users' access to resources becomes ever important.
On the one hand, it should adapt to the users' changing needs; on the other hand, it should not be compromised.
Therefore, it is essential to have a flexible access control model, incorporating dynamically changing context information.
Towards this end, this paper introduces a policy framework for context-aware access control (CAAC) applications that extends the role-based access control model with both dynamic associations of user-role and role-permission capabilities.
We first present a formal model of CAAC policies for our framework.
Using this model, we then introduce an ontology-based approach and a software prototype for modelling and enforcing CAAC policies.
In addition, we evaluate our policy ontology model and framework by considering (i) the completeness of the ontology concepts, specifying different context-aware user-role and role-permission assignment policies from the healthcare scenarios; (ii) the correctness and consistency of the ontology semantics, assessing the core and domain-specific ontologies through the healthcare case study; and (iii) the performance of the framework by means of response time.
The evaluation results demonstrate the feasibility of our framework and quantify the performance overhead of achieving context-aware access control to information resources.
This work addresses challenges arising from extracting entities from textual data, including the high cost of data annotation, model accuracy, selecting appropriate evaluation criteria, and the overall quality of annotation.
We present a framework that integrates Entity Set Expansion (ESE) and Active Learning (AL) to reduce the annotation cost of sparse data and provide an online evaluation method as feedback.
This incremental and interactive learning framework allows for rapid annotation and subsequent extraction of sparse data while maintaining high accuracy.
We evaluate our framework on three publicly available datasets and show that it drastically reduces the cost of sparse entity annotation by an average of 85% and 45% to reach 0.9 and 1.0 F-Scores respectively.
Moreover, the method exhibited robust performance across all datasets.
Linear dynamical relations that may exist in continuous-time, or at some natural sampling rate, are not directly discernable at reduced observational sampling rates.
Indeed, at reduced rates, matricial spectral densities of vectorial time series have maximal rank and thereby cannot be used to ascertain potential dynamic relations between their entries.
This hitherto undeclared source of inaccuracies appears to plague off-the-shelf identification techniques seeking remedy in hypothetical observational noise.
In this paper we explain the exact relation between stochastic models at different sampling rates and show how to construct stochastic models at the finest time scale that data allows.
We then point out that the correct number of dynamical dependences can only be ascertained by considering stochastic models at this finest time scale, which in general is faster than the observational sampling rate.
The main goal of this work is to establish a bijection between Dyck words and a family of Eulerian digraphs.
We do so by providing two algorithms implementing such bijection in both directions.
The connection between Dyck words and Eulerian digraphs exploits a novel combinatorial structure: a binary matrix, we call Dyck matrix, representing the cycles of an Eulerian digraph.
We present Solrex,an automated solver for the game of Reverse Hex.Reverse Hex, also known as Rex, or Misere Hex, is the variant of the game of Hex in which the player who joins her two sides loses the game.
Solrex performs a mini-max search of the state space using Scalable Parallel Depth First Proof Number Search, enhanced by the pruning of inferior moves and the early detection of certain winning strategies.
Solrex is implemented on the same code base as the Hex program Solver, and can solve arbitrary positions on board sizes up to 6x6, with the hardest position taking less than four hours on four threads.
We show how a simple convolutional neural network (CNN) can be trained to accurately and robustly regress 6 degrees of freedom (6DoF) 3D head pose, directly from image intensities.
We further explain how this FacePoseNet (FPN) can be used to align faces in 2D and 3D as an alternative to explicit facial landmark detection for these tasks.
We claim that in many cases the standard means of measuring landmark detector accuracy can be misleading when comparing different face alignments.
Instead, we compare our FPN with existing methods by evaluating how they affect face recognition accuracy on the IJB-A and IJB-B benchmarks: using the same recognition pipeline, but varying the face alignment method.
Our results show that (a) better landmark detection accuracy measured on the 300W benchmark does not necessarily imply better face recognition accuracy.
(b) Our FPN provides superior 2D and 3D face alignment on both benchmarks.
Finally, (c), FPN aligns faces at a small fraction of the computational cost of comparably accurate landmark detectors.
For many purposes, FPN is thus a far faster and far more accurate face alignment method than using facial landmark detectors.
High transmission rate and secure communication have been identified as the key targets that need to be effectively addressed by fifth generation (5G) wireless systems.
In this context, the concept of physical-layer security becomes attractive, as it can establish perfect security using only the characteristics of wireless medium.
Nonetheless, to further increase the spectral efficiency, an emerging concept, termed physical-layer service integration (PHY-SI), has been recognized as an effective means.
Its basic idea is to combine multiple coexisting services, i.e., multicast/broadcast service and confidential service, into one integral service for one-time transmission at the transmitter side.
This article first provides a tutorial on typical PHY-SI models.
Furthermore, we propose some state-of-the-art solutions to improve the overall performance of PHY-SI in certain important communication scenarios.
In particular, we highlight the extension of several concepts borrowed from conventional single-service communications, such as artificial noise (AN), eigenmode transmission etc., to the scenario of PHY-SI.
These techniques are shown to be effective in the design of reliable and robust PHY-SI schemes.
Finally, several potential research directions are identified for future work.
Efficient and accurate path-sensitive analyses pose the challenges of: (a) analyzing an exponentially-increasing number of paths in a control-flow graph (CFG), and (b) checking feasibility of paths in a CFG.
We address these challenges by introducing an equivalence relation on the CFG paths to partition them into equivalence classes.
It is then sufficient to perform analysis on these equivalence classes rather than on the individual paths in a CFG.
This technique has two major advantages: (a) although the number of paths in a CFG can be exponentially large, the essential information to be analyzed is captured by a small number of equivalence classes, and (b) checking path feasibility becomes simpler.
The key challenge is how to efficiently compute equivalence classes of paths in a CFG without examining each path in the CFG?
In this paper, we present a linear-time algorithm to form equivalence classes without the need for examination of each path in a CFG.
The key to this algorithm is construction of an event-flow graph (EFG), a compact derivative of the CFG, in which each path represents an equivalence class of paths in the corresponding CFG.
EFGs are defined with respect to the set of events that are in turn defined by the analyzed property.
The equivalence classes are thus guaranteed to preserve all the event traces in the original CFG.
We present an empirical evaluation of the Linux kernel (v3.12).
The EFGs in our evaluation are defined with respect to events of the spin safe-synchronization property.
Evaluation results show that there are many fewer EFG-based equivalence classes compared to the corresponding number of paths in a CFG.
This reduction is close to 99% for CFGs with a large number of paths.
Moreover, our controlled experiment results show that EFGs are human comprehensible and compact compared to their corresponding CFGs.
Age estimation from a single face image has been an essential task in the field of human-computer interaction and computer vision which has a wide range of practical application value.
Concerning the problem that accuracy of age estimation of face images in the wild are relatively low for existing methods, where they take into account only the whole features of face image while neglecting the fine-grained features of age-sensitive area, we propose a method based on Attention LSTM network for Fine-Grained age estimation in the wild based on the idea of Fine-Grained categories and visual attention mechanism.
This method combines ResNets or RoR models with LSTM unit to construct AL-ResNets or AL-RoR networks to extract age-sensitive local regions, which effectively improves age estimation accuracy.
Firstly, ResNets or RoR model pre-trained on ImageNet dataset is selected as the basic model, which is then fine-tuned on the IMDB-WIKI-101 dataset for age estimation.
Then, we fine-tune ResNets or RoR on the target age datasets to extract the global features of face images.
To extract the local characteristics of age-sensitive areas, the LSTM unit is then presented to obtain the coordinates of the age-sensitive region automatically.
Finally, the age group classification experiment is conducted directly on the Adience dataset, and age-regression experiments are performed by the Deep EXpectation algorithm (DEX) on MORPH Album 2, FG-NET and LAP datasets.
By combining the global and local features, we got our final prediction results.
Our experiments illustrate the effectiveness of AL-ResNets or AL-RoR for age estimation in the wild, where it achieves new state-of-the-art performance than all other CNN methods on the Adience, MORPH Album 2, FG-NET and LAP datasets.
Deep neural network architectures designed for application domains other than sound, especially image recognition, may not optimally harness the time-frequency representation when adapted to the sound recognition problem.
In this work, we explore the ConditionaL Neural Network (CLNN) and the Masked ConditionaL Neural Network (MCLNN) for multi-dimensional temporal signal recognition.
The CLNN considers the inter-frame relationship, and the MCLNN enforces a systematic sparseness over the network's links to enable learning in frequency bands rather than bins allowing the network to be frequency shift invariant mimicking a filterbank.
The mask also allows considering several combinations of features concurrently, which is usually handcrafted through exhaustive manual search.
We applied the MCLNN to the environmental sound recognition problem using the ESC-10 and ESC-50 datasets.
MCLNN achieved competitive performance, using 12% of the parameters and without augmentation, compared to state-of-the-art Convolutional Neural Networks.
We study the stable marriage problem in the partial information setting where the agents, although they have an underlying true strict linear order, are allowed to specify partial orders.
Specifically, we focus on the case where the agents are allowed to submit strict weak orders and we try to address the following questions from the perspective of a market-designer: i) How can a designer generate matchings that are robust? ii) What is the trade-off between the amount of missing information and the "quality" of solution one can get?
With the goal of resolving these questions through a simple and prior-free approach, we suggest looking at matchings that minimize the maximum number of blocking pairs with respect to all the possible underlying true orders as a measure of "quality", and subsequently provide results on finding such matchings.
In particular, we first restrict our attention to matchings that have to be stable with respect to at least one of the completions and show that in this case arbitrarily filling-in the missing information and computing the resulting stable matching can give a non-trivial approximation factor for our problem in certain cases.
We complement this result by showing that, even under severe restrictions on the preferences of the agents, the factor obtained is asymptotically tight in many cases.
We then investigate a special case, where only agents on one side provide strict weak orders and all the missing information is at the bottom of their preference orders, and show that here the negative result mentioned above can be circumvented in order to get a much better approximation factor; this result, too, is tight in many cases.
Finally, we move away from the restriction mentioned above and show a general hardness of approximation result and also discuss one possible approach that can lead us to a near-tight approximation bound.
Swarming peer-to-peer systems play an increasingly instrumental role in Internet content distribution.
It is therefore important to better understand how these systems behave in practice.
Recent research efforts have looked at various protocol parameters and have measured how they affect system performance and robustness.
However, the importance of the strategy based on which peers establish connections has been largely overlooked.
This work utilizes extensive simulations to examine the default overlay construction strategy in BitTorrent systems.
Based on the results, we identify a critical parameter, the maximum allowable number of outgoing connections at each peer, and evaluate its impact on the robustness of the generated overlay.
We find that there is no single optimal value for this parameter using the default strategy.
We then propose an alternative strategy that allows certain new peer connection requests to replace existing connections.
Further experiments with the new strategy demonstrate that it outperforms the default one for all considered metrics by creating an overlay more robust to churn.
Additionally, our proposed strategy exhibits optimal behavior for a well-defined value of the maximum number of outgoing connections, thereby removing the need to set this parameter in an ad-hoc manner.
OpenCL is an open standard for parallel programming of heterogeneous compute devices, such as GPUs, CPUs, DSPs or FPGAs.
However, the verbosity of its C host API can hinder application development.
In this paper we present cf4ocl, a software library for rapid development of OpenCL programs in pure C. It aims to reduce the verbosity of the OpenCL API, offering straightforward memory management, integrated profiling of events (e.g., kernel execution and data transfers), simple but extensible device selection mechanism and user-friendly error management.
We compare two versions of a conceptual application example, one based on cf4ocl, the other developed directly with the OpenCL host API.
Results show that the former is simpler to implement and offers more features, at the cost of an effectively negligible computational overhead.
Additionally, the tools provided with cf4ocl allowed for a quick analysis on how to optimize the application.
Variance reduction techniques have been shown by others in the past to be a useful tool to reduce variance in Simulation studies.
However, their application and success in the past has been mainly domain specific, with relatively little guidelines as to their general applicability, in particular for novices in this area.
To facilitate their use, this study aims to investigate the robustness of individual techniques across a set of scenarios from different domains.
Experimental results show that Control Variates is the only technique which achieves a reduction in variance across all domains.
Furthermore, applied individually, Antithetic Variates and Control Variates perform particularly well in the Cross-docking scenarios, which was previously unknown.
This paper explores the problem of ranking short social media posts with respect to user queries using neural networks.
Instead of starting with a complex architecture, we proceed from the bottom up and examine the effectiveness of a simple, word-level Siamese architecture augmented with attention-based mechanisms for capturing semantic soft matches between query and post terms.
Extensive experiments on datasets from the TREC Microblog Tracks show that our simple models not only demonstrate better effectiveness than existing approaches that are far more complex or exploit a more diverse set of relevance signals, but also achieve 4 times speedup in model training and inference.
Network Function Virtualization (NFV) aims to simplify deployment of network services by running Virtual Network Functions (VNFs) on commercial off-the-shelf servers.
Service deployment involves placement of VNFs and in-sequence routing of traffic flows through VNFs comprising a Service Chain (SC).
The joint VNF placement and traffic routing is usually referred as SC mapping.
In a Wide Area Network (WAN), a situation may arise where several traffic flows, generated by many distributed node pairs, require the same SC, one single instance (or occurrence) of that SC might not be enough.
SC mapping with multiple SC instances for the same SC turns out to be a very complex problem, since the sequential traversal of VNFs has to be maintained while accounting for traffic flows in various directions.
Our study is the first to deal with SC mapping with multiple SC instances to minimize network resource consumption.
Exact mathematical modeling of this problem results in a quadratic formulation.
We propose a two-phase column-generation-based model and solution in order to get results over large network topologies within reasonable computational times.
Using such an approach, we observe that an appropriate choice of only a small set of SC instances can lead to solution very close to the minimum bandwidth consumption.
This paper presents an approach to dynamic component composition that facilitates creating new composed components using existing ones at runtime and without any code generation.
The dynamic abilities are supported by extended type notion and implementation based on additional superstructure provided with its Java API and corresponding JavaBeans components.
The new component composition is performed by building the composed prototype object that can be dynamically transformed into the new instantiable type (component).
That approach demonstrates interrelations between prototype-based and class-based component-oriented programming.
The component model proposed can be used when implementing user-defined types in declarative languages for event-driven applications programming.
Graphics Processing Units (GPUs) are becoming popular accelerators in modern High-Performance Computing (HPC) clusters.
Installing GPUs on each node of the cluster is not efficient resulting in high costs and power consumption as well as underutilisation of the accelerator.
The research reported in this paper is motivated towards the use of few physical GPUs by providing cluster nodes access to remote GPUs on-demand for a financial risk application.
We hypothesise that sharing GPUs between several nodes, referred to as multi-tenancy, reduces the execution time and energy consumed by an application.
Two data transfer modes between the CPU and the GPUs, namely concurrent and sequential, are explored.
The key result from the experiments is that multi-tenancy with few physical GPUs using sequential data transfers lowers the execution time and the energy consumed, thereby improving the overall performance of the application.
No-regret learning has emerged as a powerful tool for solving extensive-form games.
This was facilitated by the counterfactual-regret minimization (CFR) framework, which relies on the instantiation of regret minimizers for simplexes at each information set of the game.
We use an instantiation of the CFR framework to develop algorithms for solving behaviorally-constrained (and, as a special case, perturbed in the Selten sense) extensive-form games, which allows us to compute approximate Nash equilibrium refinements.
Nash equilibrium refinements are motivated by a major deficiency in Nash equilibrium: it provides virtually no guarantees on how it will play in parts of the game tree that are reached with zero probability.
Refinements can mend this issue, but have not been adopted in practice, mostly due to a lack of scalable algorithms.
We show that, compared to standard algorithms, our method finds solutions that have substantially better refinement properties, while enjoying a convergence rate that is comparable to that of state-of-the-art algorithms for Nash equilibrium computation both in theory and practice.
Recognition of Off-line Chinese characters is still a challenging problem, especially in historical documents, not only in the number of classes extremely large in comparison to contemporary image retrieval methods, but also new unseen classes can be expected under open learning conditions (even for CNN).
Chinese character recognition with zero or a few training samples is a difficult problem and has not been studied yet.
In this paper, we propose a new Chinese character recognition method by multi-type attributes, which are based on pronunciation, structure and radicals of Chinese characters, applied to character recognition in historical books.
This intermediate attribute code has a strong advantage over the common `one-hot' class representation because it allows for understanding complex and unseen patterns symbolically using attributes.
First, each character is represented by four groups of attribute types to cover a wide range of character possibilities: Pinyin label, layout structure, number of strokes, three different input methods such as Cangjie, Zhengma and Wubi, as well as a four-corner encoding method.
A convolutional neural network (CNN) is trained to learn these attributes.
Subsequently, characters can be easily recognized by these attributes using a distance metric and a complete lexicon that is encoded in attribute space.
We evaluate the proposed method on two open data sets: printed Chinese character recognition for zero-shot learning, historical characters for few-shot learning and a closed set: handwritten Chinese characters.
Experimental results show a good general classification of seen classes but also a very promising generalization ability to unseen characters.
In the medical domain, identifying and expanding abbreviations in clinical texts is a vital task for both better human and machine understanding.
It is a challenging task because many abbreviations are ambiguous especially for intensive care medicine texts, in which phrase abbreviations are frequently used.
Besides the fact that there is no universal dictionary of clinical abbreviations and no universal rules for abbreviation writing, such texts are difficult to acquire, expensive to annotate and even sometimes, confusing to domain experts.
This paper proposes a novel and effective approach - exploiting task-oriented resources to learn word embeddings for expanding abbreviations in clinical notes.
We achieved 82.27% accuracy, close to expert human performance.
It is well known that the reserves/redundancies built into the transmission grid in order to address a variety of contingencies over a long planning horizon may, in the short run, cause economic dispatch inefficiency.
Accordingly, power grid optimization by means of short term line switching has been proposed and is typically formulated as a mixed integer programming problem by treating the state of the transmission lines as a binary decision variable, i.e. in-service or out-of-service, in the optimal power flow problem.
To handle the combinatorial explosion, a number of heuristic approaches to grid topology reconfiguration have been proposed in the literature.
This paper extends our recent results on the iterative heuristics and proposes a fast grid decomposition algorithm based on vertex cut sets with the purpose of further reducing the computational cost.
The paper concludes with a discussion of the possible relationship between vertex cut sets in transmission networks and power trading.
In this paper, the average successful throughput, i.e., goodput, of a coded 3-node cooperative network is studied in a Rayleigh fading environment.
It is assumed that a simple automatic repeat request (ARQ) technique is employed in the network so that erroneously received codeword is retransmitted until successful delivery.
The relay is assumed to operate in either amplify-and-forward (AF) or decode-and-forward (DF) mode.
Under these assumptions, retransmission mechanisms and protocols are described, and the average time required to send information successfully is determined.
Subsequently, the goodput for both AF and DF relaying is formulated.
The tradeoffs and interactions between the goodput, transmission rates, and relay location are investigated and optimal strategies are identified.
Two genres of heuristics that are frequently reported to perform much better on "real-world" instances than in the worst case are greedy algorithms and local search algorithms.
In this paper, we systematically study these two types of algorithms for the problem of maximizing a monotone submodular set function subject to downward-closed feasibility constraints.
We consider perturbation-stable instances, in the sense of Bilu and Linial, and precisely identify the stability threshold beyond which these algorithms are guaranteed to recover the optimal solution.
Byproducts of our work include the first definition of perturbation-stability for non-additive objective functions, and a resolution of the worst-case approximation guarantee of local search in p-extendible systems.
Audio Event Detection (AED) aims to recognize sounds within audio and video recordings.
AED employs machine learning algorithms commonly trained and tested on annotated datasets.
However, available datasets are limited in number of samples and hence it is difficult to model acoustic diversity.
Therefore, we propose combining labeled audio from a dataset and unlabeled audio from the web to improve the sound models.
The audio event detectors are trained on the labeled audio and ran on the unlabeled audio downloaded from YouTube.
Whenever the detectors recognized any of the known sounds with high confidence, the unlabeled audio was use to re-train the detectors.
The performance of the re-trained detectors is compared to the one from the original detectors using the annotated test set.
Results showed an improvement of the AED, and uncovered challenges of using web audio from videos.
Automated negotiation is a rising topic in Artificial Intelligence research.
Monte Carlo methods have got increasing interest, in particular since they have been used with success on games with high branching factor such as go.In this paper, we describe an Monte Carlo Negotiating Agent (MoCaNA) whose bidding strategy relies on Monte Carlo Tree Search.
We provide our agent with opponent modeling tehcniques for bidding strtaegy and utility.
MoCaNA can negotiate on continuous negotiating domains and in a context where no bound has been specified.
We confront MoCaNA and the finalists of ANAC 2014 and a RandomWalker on different negotiation domains.
Our agent ouperforms the RandomWalker in a domain without bound and the majority of the ANAC finalists in a domain with a bound.
Independent Component Analysis (ICA) is a popular model for blind signal separation.
The ICA model assumes that a number of independent source signals are linearly mixed to form the observed signals.
We propose a new algorithm, PEGI (for pseudo-Euclidean Gradient Iteration), for provable model recovery for ICA with Gaussian noise.
The main technical innovation of the algorithm is to use a fixed point iteration in a pseudo-Euclidean (indefinite "inner product") space.
The use of this indefinite "inner product" resolves technical issues common to several existing algorithms for noisy ICA.
This leads to an algorithm which is conceptually simple, efficient and accurate in testing.
Our second contribution is combining PEGI with the analysis of objectives for optimal recovery in the noisy ICA model.
It has been observed that the direct approach of demixing with the inverse of the mixing matrix is suboptimal for signal recovery in terms of the natural Signal to Interference plus Noise Ratio (SINR) criterion.
There have been several partial solutions proposed in the ICA literature.
It turns out that any solution to the mixing matrix reconstruction problem can be used to construct an SINR-optimal ICA demixing, despite the fact that SINR itself cannot be computed from data.
That allows us to obtain a practical and provably SINR-optimal recovery method for ICA with arbitrary Gaussian noise.
Currently, Markov-Gibbs random field (MGRF) image models which include high-order interactions are almost always built by modelling responses of a stack of local linear filters.
Actual interaction structure is specified implicitly by the filter coefficients.
In contrast, we learn an explicit high-order MGRF structure by considering the learning process in terms of general exponential family distributions nested over base models, so that potentials added later can build on previous ones.
We relatively rapidly add new features by skipping over the costly optimisation of parameters.
We introduce the use of local binary patterns as features in MGRF texture models, and generalise them by learning offsets to the surrounding pixels.
These prove effective as high-order features, and are fast to compute.
Several schemes for selecting high-order features by composition or search of a small subclass are compared.
Additionally we present a simple modification of the maximum likelihood as a texture modelling-specific objective function which aims to improve generalisation by local windowing of statistics.
The proposed method was experimentally evaluated by learning high-order MGRF models for a broad selection of complex textures and then performing texture synthesis, and succeeded on much of the continuum from stochastic through irregularly structured to near-regular textures.
Learning interaction structure is very beneficial for textures with large-scale structure, although those with complex irregular structure still provide difficulties.
The texture models were also quantitatively evaluated on two tasks and found to be competitive with other works: grading of synthesised textures by a panel of observers; and comparison against several recent MGRF models by evaluation on a constrained inpainting task.
Nowadays, the usefulness of a formal language for ensuring the consistency of requirements is well established.
The work presented here is part of the definition of a formally-grounded, model-based requirements engineering method for critical and complex systems.
Requirements are captured through the SysML/KAOS method and the targeted formal specification is written using the Event-B method.
Firstly, an Event-B skeleton is produced from the goal hierarchy provided by the SysML/KAOS goal model.
This skeleton is then completed in a second step by the Event-B specification obtained from system application domain properties that gives rise to the system structure.
Considering that the domain is represented using ontologies through the SysML/KAOS Domain Model method, is it possible to automatically produce the structural part of system Event-B models ?
This paper proposes a set of generic rules that translate SysML/KAOS domain ontologies into an Event-B specification.
The rules have been expressed, verified and validated through the Rodin tool using the Event-B method.
They are illustrated through a case study dealing with a landing gear system.
Our proposition makes it possible to automatically obtain, from a representation of the system application domain in the form of ontologies, the structural part of the Event-B specification which will be used to formally validate the consistency of system requirements.
In this paper, we propose the 3DFeat-Net which learns both 3D feature detector and descriptor for point cloud matching using weak supervision.
Unlike many existing works, we do not require manual annotation of matching point clusters.
Instead, we leverage on alignment and attention mechanisms to learn feature correspondences from GPS/INS tagged 3D point clouds without explicitly specifying them.
We create training and benchmark outdoor Lidar datasets, and experiments show that 3DFeat-Net obtains state-of-the-art performance on these gravity-aligned datasets.
In this paper we define the overflow problem of a network coding storage system in which the encoding parameter and the storage parameter are mismatched.
Through analyses and experiments, we first show the impacts of the overflow problem in a network coding scheme, which not only waste storage spaces, but also degrade coding efficiency.
To avoid the overflow problem, we then develop the network coding based secure storage (NCSS) scheme.
Thanks to considering both security and storage requirements in encoding procedures and distributed architectures, the NCSS can improve the performance of a cloud storage system from both the aspects of storage cost and coding processing time.
We analyze the maximum allowable stored encoded data under the perfect secrecy criterion, and provide the design guidelines for the secure cloud storage system to enhance coding efficiency and achieve the minimal storage cost.
The useful life of electrochemical energy storage (EES) is a critical factor to EES planning, operation, and economic assessment.
Today, systems commonly assume a physical end-of-life criterion, retiring EES when the remaining capacity reaches a threshold below which the EES is of little use because of functionality degradation.
Here, we propose an economic end of life criterion, where EES is retired when it cannot earn positive net economic benefit in its intended application.
This criterion depends on the use case and degradation characteristics of the EES, but is independent of initial capital cost.
Using an intertemporal operational framework to consider functionality and profitability degradation, our case study shows that the economic end of life could occur significantly faster than the physical end of life.
We argue that both criteria should be applied in EES system planning and assessment.
We also analyze how R&D efforts should consider cycling capability and calendar degradation rate when considering the economic end-of-life of EES.
While the Internet of things (IoT) promises to improve areas such as energy efficiency, health care, and transportation, it is highly vulnerable to cyberattacks.
In particular, distributed denial-of-service (DDoS) attacks overload the bandwidth of a server.
But many IoT devices form part of cyber-physical systems (CPS).
Therefore, they can be used to launch "physical" denial-of-service attacks (PDoS) in which IoT devices overflow the "physical bandwidth" of a CPS.
In this paper, we quantify the population-based risk to a group of IoT devices targeted by malware for a PDoS attack.
In order to model the recruitment of bots, we develop a "Poisson signaling game," a signaling game with an unknown number of receivers, which have varying abilities to detect deception.
Then we use a version of this game to analyze two mechanisms (legal and economic) to deter botnet recruitment.
Equilibrium results indicate that 1) defenders can bound botnet activity, and 2) legislating a minimum level of security has only a limited effect, while incentivizing active defense can decrease botnet activity arbitrarily.
This work provides a quantitative foundation for proactive PDoS defense.
Future wireless systems are expected to provide a wide range of services to more and more users.
Advanced scheduling strategies thus arise not only to perform efficient radio resource management, but also to provide fairness among the users.
On the other hand, the users' perceived quality, i.e., Quality of Experience (QoE), is becoming one of the main drivers within the schedulers design.
In this context, this paper starts by providing a comprehension of what is QoE and an overview of the evolution of wireless scheduling techniques.
Afterwards, a survey on the most recent QoE-based scheduling strategies for wireless systems is presented, highlighting the application/service of the different approaches reported in the literature, as well as the parameters that were taken into account for QoE optimization.
Therefore, this paper aims at helping readers interested in learning the basic concepts of QoE-oriented wireless resources scheduling, as well as getting in touch with the present time research frontier.
Supporting programmable states in the data plane of a forwarding element, e.g., a switch or a NIC, has recently attracted the interest of the research community, which is now looking for the right abstraction to enable the programming of stateful network functions in hardware at line rate.
We challenge the conservative assumptions of state-of-the-art abstractions in this field, e.g. always assuming minimum size packets arriving back-to-back.
Using trace-based simulations we show that by making more realistic assumptions on the traffic characteristics, e.g. larger average packet size, we can relax the design constraints that currently limit the set of functions that can be implemented at line rate, allowing for more complex functions, with no harm for performance.
Personalized driver models play a key role in the development of advanced driver assistance systems and automated driving systems.
Traditionally, physical-based driver models with fixed structures usually lack the flexibility to describe the uncertainties and high non-linearity of driver behaviors.
In this paper, two kinds of learning-based car-following personalized driver models were developed using naturalistic driving data collected from the University of Michigan Safety Pilot Model Deployment program.
One model is developed by combining the Gaussian Mixture Model (GMM) and the Hidden Markov Model (HMM), and the other one is developed by combining the Gaussian Mixture Model (GMM) and Probability Density Functions (PDF).
Fitting results between the two approaches were analyzed with different model inputs and numbers of GMM components.
Statistical analyses show that both models provide good performance of fitting while the GMM--PDF approach shows a higher potential to increase the model accuracy given a higher dimension of training data.
The traction force of a kite can be used to drive a cyclic motion for extracting wind energy from the atmosphere.
This paper presents a novel quasi-steady modelling framework for predicting the power generated over a full pumping cycle.
The cycle is divided into traction, retraction and transition phases, each described by an individual set of analytic equations.
The effect of gravity on the airborne system components is included in the framework.
A trade-off is made between modelling accuracy and computation speed such that the model is specifically useful for system optimisation and scaling in economic feasibility studies.
Simulation results are compared to experimental measurements of a 20 kW kite power system operated up to a tether length of 720 m. Simulation and experiment agree reasonably well, both for moderate and for strong wind conditions, indicating that the effect of gravity has to be taken into account for a predictive performance simulation.
Recursive query processing has experienced a recent resurgence, as a result of its use in many modern application domains, including data integration, graph analytics, security, program analysis, networking and decision making.
Due to the large volumes of data being processed, several research efforts, across multiple communities, have explored how to scale up recursive queries, typically expressed in Datalog.
Our experience with these tools indicated that their performance does not translate across domains (e.g., a tool design for large-scale graph analytics does not exhibit the same performance on program-analysis tasks, and vice versa).
As a result, we designed and implemented a general-purpose Datalog engine, called RecStep, on top of a parallel single-node relational system.
In this paper, we outline the different techniques we use in RecStep, and the contribution of each technique to overall performance.
We also present results from a detailed set of experiments comparing RecStep with a number of other Datalog systems using both graph analytics and program-analysis tasks, summarizing pros and cons of existing techniques based on the analysis of our observations.
We show that RecStep generally outperforms the state-of-the-art parallel Datalog engines on complex and large-scale Datalog program evaluation, by a 4-6X margin.
An additional insight from our work is that we show that it is possible to build a high-performance Datalog system on top of a relational engine, an idea that has been dismissed in past work in this area.
With the resurgence of chat-based dialog systems in consumer and enterprise applications, there has been much success in developing data-driven and rule-based natural language models to understand human intent.
Since these models require large amounts of data and in-domain knowledge, expanding an equivalent service into new markets is disrupted by language barriers that inhibit dialog automation.
This paper presents a user study to evaluate the utility of out-of-the-box machine translation technology to (1) rapidly bootstrap multilingual spoken dialog systems and (2) enable existing human analysts to understand foreign language utterances.
We additionally evaluate the utility of machine translation in human assisted environments, where a portion of the traffic is processed by analysts.
In English->Spanish experiments, we observe a high potential for dialog automation, as well as the potential for human analysts to process foreign language utterances with high accuracy.
With the advancement of software engineering in recent years, the model checking techniques are widely applied in various areas to do the verification for the system model.
However, it is difficult to apply the model checking to verify requirements due to lacking the details of the design.
Unlike other model checking tools, LTSA provides the structure diagram, which can bridge the gap between the requirements and the design.
In this paper, we demonstrate the abilities of LTSA shipped with the classic case study of the steam boiler system.
The structure diagram of LTSA can specify the interactions between the controller and the steam boiler, which can be derived from UML requirements model such as system sequence diagram of the steam boiler system.
The start-up design model of LTSA can be generated from the structure diagram.
Furthermore, we provide a variation law of the steam rate to avoid the issue of state space explosion and show how explicitly and implicitly model the time that reflects the difference between system modeling and the physical world.
Finally, the derived model is verified against the required properties.
Our work demonstrates the potential power of integrating UML with model checking tools in requirement elicitation, system design, and verification.
This paper addresses the problem of reassembling images from disjointed fragments.
More specifically, given an unordered set of fragments, we aim at reassembling one or several possibly incomplete images.
The main contributions of this work are: 1) several deep neural architectures to predict the relative position of image fragments that outperform the previous state of the art; 2) casting the reassembly problem into the shortest path in a graph problem for which we provide several construction algorithms depending on available information; 3) a new dataset of images taken from the Metropolitan Museum of Art (MET) dedicated to image reassembly for which we provide a clear setup and a strong baseline.
This paper studies the subspace clustering problem.
Given some data points approximately drawn from a union of subspaces, the goal is to group these data points into their underlying subspaces.
Many subspace clustering methods have been proposed and among which sparse subspace clustering and low-rank representation are two representative ones.
Despite the different motivations, we observe that many existing methods own the common block diagonal property, which possibly leads to correct clustering, yet with their proofs given case by case.
In this work, we consider a general formulation and provide a unified theoretical guarantee of the block diagonal property.
The block diagonal property of many existing methods falls into our special case.
Second, we observe that many existing methods approximate the block diagonal representation matrix by using different structure priors, e.g., sparsity and low-rankness, which are indirect.
We propose the first block diagonal matrix induced regularizer for directly pursuing the block diagonal matrix.
With this regularizer, we solve the subspace clustering problem by Block Diagonal Representation (BDR), which uses the block diagonal structure prior.
The BDR model is nonconvex and we propose an alternating minimization solver and prove its convergence.
Experiments on real datasets demonstrate the effectiveness of BDR.
This paper is a reply to the comments on 'Integer SEC-DED codes for low power communications'.
In settings where only unlabelled speech data is available, speech technology needs to be developed without transcriptions, pronunciation dictionaries, or language modelling text.
A similar problem is faced when modelling infant language acquisition.
In these cases, categorical linguistic structure needs to be discovered directly from speech audio.
We present a novel unsupervised Bayesian model that segments unlabelled speech and clusters the segments into hypothesized word groupings.
The result is a complete unsupervised tokenization of the input speech in terms of discovered word types.
In our approach, a potential word segment (of arbitrary length) is embedded in a fixed-dimensional acoustic vector space.
The model, implemented as a Gibbs sampler, then builds a whole-word acoustic model in this space while jointly performing segmentation.
We report word error rates in a small-vocabulary connected digit recognition task by mapping the unsupervised decoded output to ground truth transcriptions.
The model achieves around 20% error rate, outperforming a previous HMM-based system by about 10% absolute.
Moreover, in contrast to the baseline, our model does not require a pre-specified vocabulary size.
This note provides a description of a procedure that is designed to efficiently optimize expensive black-box functions.
It uses the response surface methodology by incorporating radial basis functions as the response model.
A simple method based on a Latin hypercube is used for initial sampling.
A modified version of CORS algorithm with space rescaling is used for the subsequent sampling.
The procedure is able to scale on multicore processors by performing multiple function evaluations in parallel.
The source code of the procedure is written in Python.
The software development process for embedded systems is getting faster and faster, which generally incurs an increase in the associated complexity.
As a consequence, consumer electronics companies usually invest a lot of resources in fast and automatic verification processes, in order to create robust systems and reduce product recall rates.
Because of that, the present paper proposes a simplified version of the Qt framework, which is integrated into the Efficient SMT-Based Bounded Model Checking tool to verify actual applications that use the mentioned framework.
The method proposed in this paper presents a success rate of 94.45%, for the developed test suite.
Analysing and explaining relationships between entities in a graph is a fundamental problem associated with many practical applications.
For example, a graph of biological pathways can be used for discovering a previously unknown relationship between two proteins.
Domain experts, however, may be reluctant to trust such a discovery without a detailed explanation as to why exactly the two proteins are deemed related in the graph.
This paper provides an overview of the types of solutions, their associated methods and strategies, that have been proposed for finding entity relatedness explanations in graphs.
The first type of solution relies on information inherent to the paths connecting the entities.
This type of solution provides entity relatedness explanations in the form of a list of ranked paths.
The rank of a path is measured in terms of importance, uniqueness, novelty and informativeness.
The second type of solution relies on measures of node relevance.
In this case, the relevance of nodes is measured w.r.t. the entities of interest, and relatedness explanations are provided in the form of a subgraph that maximises node relevance scores.
This paper uses this classification of approaches to discuss and contrast some of the key concepts that guide different solutions to the problem of entity relatedness explanation in graphs.
Defects4J is a large, peer-reviewed, structured dataset of real-world Java bugs.
Each bug in Defects4J comes with a test suite and at least one failing test case that triggers the bug.
In this paper, we report on an experiment to explore the effectiveness of automatic test-suite based repair on Defects4J.
The result of our experiment shows that the considered state-of-the-art repair methods can generate patches for 47 out of 224 bugs.
However, those patches are only test-suite adequate, which means that they pass the test suite and may potentially be incorrect beyond the test-suite satisfaction correctness criterion.
We have manually analyzed 84 different patches to assess their real correctness.
In total, 9 real Java bugs can be correctly repaired with test-suite based repair.
This analysis shows that test-suite based repair suffers from under-specified bugs, for which trivial or incorrect patches still pass the test suite.
With respect to practical applicability, it takes on average 14.8 minutes to find a patch.
The experiment was done on a scientific grid, totaling 17.6 days of computation time.
All the repair systems and experimental results are publicly available on Github in order to facilitate future research on automatic repair.
Most machine learning tools work with a single table where each row is an instance and each column is an attribute.
Each cell of the table contains an attribute value for an instance.
This representation prevents one important form of learning, which is, classification based on groups of correlated records, such as multiple exams of a single patient, internet customer preferences, weather forecast or prediction of sea conditions for a given day.
To some extent, relational learning methods, such as inductive logic programming, can capture this correlation through the use of intensional predicates added to the background knowledge.
In this work, we propose SPPAM, an algorithm that aggregates past observations in one single record.
We show that applying SPPAM to the original correlated data, before the learning task, can produce classifiers that are better than the ones trained using all records.
We consider a general regularised interpolation problem for learning a parameter vector from data.
The well known representer theorem says that under certain conditions on the regulariser there exists a solution in the linear span of the data points.
This is the core of kernel methods in machine learning as it makes the problem computationally tractable.
Necessary and sufficient conditions for differentiable regularisers on Hilbert spaces to admit a representer theorem have been proved.
We extend those results to nondifferentiable regularisers on uniformly convex and uniformly smooth Banach spaces.
This gives a (more) complete answer to the question when there is a representer theorem.
We then note that for regularised interpolation in fact the solution is determined by the function space alone and independent of the regulariser, making the extension to Banach spaces even more valuable.
Kron reduction is used to simplify the analysis of multi-machine power systems under certain steady state assumptions that underly the usage of phasors.
In this paper we show how to perform Kron reduction for a class of electrical networks without steady state assumptions.
The reduced models can thus be used to analyze the transient as well as the steady state behavior of these electrical networks.
L1 adaptive controller has been recognized for having a structure that allows decoupling between robustness and adaption owing to the introduction of a low pass filter with adjustable gain in the feedback loop.
The trade-off between performance, fast adaptation and robustness, is the main criteria when selecting the structure or the coefficients of the filter.
Several off-line methods with varying levels of complexity exist to help finding bounds or initial values for these coefficients.
Such values may require further refinement using trial-and-error procedures upon implementation.
Subsequently, these approaches suggest that once implemented these values are kept fixed leading to sub-optimal performance in both speed of adaptation and robustness.
In this paper, a new practical approach based on fuzzy rules for online continuous tuning of these coefficients is proposed.
The fuzzy controller is optimally tuned using Particle Swarm Optimization (PSO) taking into accounts both the tracking error and the controller output signal range.
The simulation of several examples of systems with moderate to severe nonlinearities demonstrate that the proposed approach offers improved control performance.
Keywords: Fuzzy logic control, single-objective, multi-objective particle swarm optimization, L1 Adaptive control, fuzzy L1 adaptive controller, L1 fuzzy adaptive control, L1 fuzzy adaptive controller, fuzzy L1 adaptive control, Filter tuning, Fuzzy membership function tuning, optimal, optimal tuning, Fuzzy membership function optimization, Robustness, Adaptation, multi-input multi-output, single-input single-output, estimate, PSO, FLC, nonlinear, adaptive, online, off-line, Fuzzy adaptive controller, Fuzzy adaptive control, single input single output, multi input multi output, SISO, MIMO, robust, uncertain, uncertain nonlinear system, disturbance, unknown, Adaptive Fuzzy Control Design, stable.
Gradually typed languages allow statically typed and dynamically typed code to interact while maintaining benefits of both styles.
The key to reasoning about these mixed programs is Siek-Vitousek-Cimini-Boyland's (dynamic) gradual guarantee, which says that giving components of a program more precise types only adds runtime type checking, and does not otherwise change behavior.
In this paper, we give a semantic reformulation of the gradual guarantee called graduality.
We change the name to promote the analogy that graduality is to gradual typing what parametricity is to polymorphism.
Each gives a local-to-global, syntactic-to-semantic reasoning principle that is formulated in terms of a kind of observational approximation.
Utilizing the analogy, we develop a novel logical relation for proving graduality.
We show that embedding-projection pairs (ep pairs) are to graduality what relations are to parametricity.
We argue that casts between two types where one is "more dynamic" (less precise) than the other necessarily form an ep pair, and we use this to cleanly prove the graduality cases for casts from the ep-pair property.
To construct ep pairs, we give an analysis of the type dynamism relation (also known as type precision or naive subtyping) that interprets the rules for type dynamism as compositional constructions on ep pairs, analogous to the coercion interpretation of subtyping.
The recently proposed Minimal Complexity Machine (MCM) finds a hyperplane classifier by minimizing an exact bound on the Vapnik-Chervonenkis (VC) dimension.
The VC dimension measures the capacity of a learning machine, and a smaller VC dimension leads to improved generalization.
On many benchmark datasets, the MCM generalizes better than SVMs and uses far fewer support vectors than the number used by SVMs.
In this paper, we describe a neural network based on a linear dynamical system, that converges to the MCM solution.
The proposed MCM dynamical system is conducive to an analogue circuit implementation on a chip or simulation using Ordinary Differential Equation (ODE) solvers.
Numerical experiments on benchmark datasets from the UCI repository show that the proposed approach is scalable and accurate, as we obtain improved accuracies and fewer number of support vectors (upto 74.3% reduction) with the MCM dynamical system.
We determine lower and upper bounds on the capacity of bandlimited optical intensity channels (BLOIC) with white Gaussian noise.
Three types of input power constraints are considered: 1) only an average power constraint, 2) only a peak power constraint, and 3) an average and a peak power constraint.
Capacity lower bounds are derived by a two-step process including 1) for each type of constraint, designing admissible pulse amplitude modulated input waveform ensembles, and 2) lower bounding the maximum achievable information rates of the designed input ensembles.
Capacity upper bounds are derived by exercising constraint relaxations and utilizing known results on discrete-time optical intensity channels.
We obtain degrees-of-freedom-optimal (DOF-optimal) lower bounds which have the same pre-log factor as the upper bounds, thereby characterizing the high SNR capacity of BLOIC to within a finite gap.
We further derive intersymbol-interference-free (ISI-free) signaling based lower bounds, which perform well for all practical SNR values.
In particular, the ISI-free signaling based lower bounds outperform the DOF-optimal lower bound when the SNR is below 10 dB.
We introduce new diversification methods for zero-one optimization that significantly extend strategies previously introduced in the setting of metaheuristic search.
Our methods incorporate easily implemented strategies for partitioning assignments of values to variables, accompanied by processes called augmentation and shifting which create greater flexibility and generality.
We then show how the resulting collection of diversified solutions can be further diversified by means of permutation mappings, which equally can be used to generate diversified collections of permutations for applications such as scheduling and routing.
These methods can be applied to non-binary vectors by the use of binarization procedures and by Diversification-Based Learning (DBL) procedures which also provide connections to applications in clustering and machine learning.
Detailed pseudocode and numerical illustrations are provided to show the operation of our methods and the collections of solutions they create.
This paper presents a method to improve the localization accuracy of robots operating in a range-based localization network.
The method is favorable especially when the robots operate in harsh environments where the access to a robust and reliable localization system is limited.
A state estimator is used for a six degree of freedom object using inertial sensors as well as an Ultra-wideband (UWB) range measurement sensor.
The estimator is incorporated into an adaptive algorithm, improving the localization quality of an agent by using a mobile UWB ranging sensor, where the mobile anchor moves to improve localization quality.
The algorithm reconstructs localization network in real-time to minimize the determinant of the covariance matrix in the sense of least square error.
Finally, the proposed algorithm is experimentally validated in a network consisting of one mobile and four fixed anchors.
Staying abroad during their studies is increasingly popular for students.
However, there are various challenges for both students and universities.
One important question for students is whether or not achievements performed at different universities can be taken into account for either enrolling at a foreign university or for completing the studies at their home university.
In addition to university achievements, an increasing proportion of the 195 million students worldwide increasingly receive certificates from MOOCs or other social media services.
The integration of such services into university teaching is still in the initial stages and presents some challenges.
In this paper we describe the idea to manage all these study achievements worldwide in a blockchain, which might solve the national and international challenges regarding the recognition of student achievements.
The aim of this paper is to encourage discussion in the global community instead of presenting a finished concept.
Some of the open research questions are: How to ensure student data protection, how to deal with fraud and how to deal with the possibility that students can analytically calculate the easiest way through their studies?
The paper is devoted to a mathematical model of concurrency the special case of which is asynchronous system.
Distributed asynchronous automata are introduced here.
It is proved that the Petri nets and transition systems with independence can be considered like the distributed asynchronous automata.
Time distributed asynchronous automata are defined in standard way by the map which assigns time intervals to events.
It is proved that the time distributed asynchronous automata are generalized the time Petri nets and asynchronous systems.
Pagination - the process of determining where to break an article across pages in a multi-article layout is a common layout challenge for most commercially printed newspapers and magazines.
To date, no one has created an algorithm that determines a minimal pagination break point based on the content of the article.
Existing approaches for automatic multi-article layout focus exclusively on maximizing content (number of articles) and optimizing aesthetic presentation (e.g., spacing between articles).
However, disregarding the semantic information within the article can lead to overly aggressive cutting, thereby eliminating key content and potentially confusing the reader, or setting too generous of a break point, thereby leaving in superfluous content and making automatic layout more difficult.
This is one of the remaining challenges on the path from manual layouts to fully automated processes that still ensure article content quality.
In this work, we present a new approach to calculating a document minimal break point for the task of pagination.
Our approach uses a statistical language model to predict minimal break points based on the semantic content of an article.
We then compare 4 novel candidate approaches, and 4 baselines (currently in use by layout algorithms).
Results from this experiment show that one of our approaches strongly outperforms the baselines and alternatives.
Results from a second study suggest that humans are not able to agree on a single "best" break point.
Therefore, this work shows that a semantic-based lower bound break point prediction is necessary for ideal automated document synthesis within a real-world context.
This paper presents a new type of evolutionary algorithm (EA) based on the concept of "meme", where the individuals forming the population are represented by semantic networks and the fitness measure is defined as a function of the represented knowledge.
Our work can be classified as a novel memetic algorithm (MA), given that (1) it is the units of culture, or information, that are undergoing variation, transmission, and selection, very close to the original sense of memetics as it was introduced by Dawkins; and (2) this is different from existing MA, where the idea of memetics has been utilized as a means of local refinement by individual learning after classical global sampling of EA.
The individual pieces of information are represented as simple semantic networks that are directed graphs of concepts and binary relations, going through variation by memetic versions of operators such as crossover and mutation, which utilize knowledge from commonsense knowledge bases.
In evaluating this introductory work, as an interesting fitness measure, we focus on using the structure mapping theory of analogical reasoning from psychology to evolve pieces of information that are analogous to a given base information.
Considering other possible fitness measures, the proposed representation and algorithm can serve as a computational tool for modeling memetic theories of knowledge, such as evolutionary epistemology and cultural selection theory.
Prepositions are among the most frequent words in English and play complex roles in the syntax and semantics of sentences.
Not surprisingly, they pose well-known difficulties in automatic processing of sentences (prepositional attachment ambiguities and idiosyncratic uses in phrases).
Existing methods on preposition representation treat prepositions no different from content words (e.g., word2vec and GloVe).
In addition, recent studies aiming at solving prepositional attachment and preposition selection problems depend heavily on external linguistic resources and use dataset-specific word representations.
In this paper we use word-triple counts (one of the triples being a preposition) to capture a preposition's interaction with its attachment and complement.
We then derive preposition embeddings via tensor decomposition on a large unlabeled corpus.
We reveal a new geometry involving Hadamard products and empirically demonstrate its utility in paraphrasing phrasal verbs.
Furthermore, our preposition embeddings are used as simple features in two challenging downstream tasks: preposition selection and prepositional attachment disambiguation.
We achieve results comparable to or better than the state-of-the-art on multiple standardized datasets.
Estimation of facial shapes plays a central role for face transfer and animation.
Accurate 3D face reconstruction, however, often deploys iterative and costly methods preventing real-time applications.
In this work we design a compact and fast CNN model enabling real-time face reconstruction on mobile devices.
For this purpose, we first study more traditional but slow morphable face models and use them to automatically annotate a large set of images for CNN training.
We then investigate a class of efficient MobileNet CNNs and adapt such models for the task of shape regression.
Our evaluation on three datasets demonstrates significant improvements in the speed and the size of our model while maintaining state-of-the-art reconstruction accuracy.
The application of deep learning techniques using convolutional neural networks to the classification of particle collisions in High Energy Physics is explored.
An intuitive approach to transform physical variables, like momenta of particles and jets, into a single image that captures the relevant information, is proposed.
The idea is tested using a well known deep learning framework on a simulation dataset, including leptonic ttbar events and the corresponding background at 7 TeV from the CMS experiment at LHC, available as Open Data.
This initial test shows competitive results when compared to more classical approaches, like those using feedforward neural networks.
We present an attention based visual analysis framework to compute grasp-relevant information in order to guide grasp planning using a multi-fingered robotic hand.
Our approach uses a computational visual attention model to locate regions of interest in a scene, and uses a deep convolutional neural network to detect grasp type and point for a sub-region of the object presented in a region of interest.
We demonstrate the proposed framework in object grasping tasks, in which the information generated from the proposed framework is used as prior information to guide the grasp planning.
Results show that the proposed framework can not only speed up grasp planning with more stable configurations, but also is able to handle unknown objects.
Furthermore, our framework can handle cluttered scenarios.
A new Grasp Type Dataset (GTD) that considers 6 commonly used grasp types and covers 12 household objects is also presented.
Media publisher platforms often face an effectiveness-nuisance tradeoff: more annoying ads can be more effective for some advertisers because of their ability to attract attention, but after attracting viewers' attention, their nuisance to viewers can decrease engagement with the platform over time.
With the rise of mobile technology and ad blockers, many platforms are becoming increasingly concerned about how to improve monetization through digital ads while improving viewer experience.
We study an online ad auction mechanism that incorporates a charge for ad impact on user experience as a criterion for ad selection and pricing.
Like a Pigovian tax, the charge causes advertisers to internalize the hidden cost of foregone future platform revenue due to ad impact on user experience.
Over time, the mechanism provides an incentive for advertisers to develop ads that are effective while offering viewers a more pleasant experience.
We show that adopting the mechanism can simultaneously benefit the publisher, advertisers, and viewers, even in the short term.
Incorporating a charge for ad impact can increase expected advertiser profits if enough advertisers compete.
A stronger effectiveness-nuisance tradeoff, meaning that ad effectiveness is more strongly associated with negative impact on user experience, increases the amount of competition required for the mechanism to benefit advertisers.
The findings suggest that the mechanism can benefit the marketplace for ad slots that consistently attract many advertisers.
This paper proposes a novel framework to regularize the highly ill-posed and non-linear Fourier ptychography problem using generative models.
We demonstrate experimentally that our proposed algorithm, Deep Ptych, outperforms the existing Fourier ptychography techniques, in terms of quality of reconstruction and robustness against noise, using far fewer samples.
We further modify the proposed approach to allow the generative model to explore solutions outside the range, leading to improved performance.
This paper considers an energy-efficient packet scheduling problem over quasi-static block fading channels.
The goal is to minimize the total energy for transmitting a sequence of data packets under the first-in-first-out rule and strict delay constraints.
Conventionally, such design problem is studied under the assumption that the packet transmission rate can be characterized by the classical Shannon capacity formula, which, however, may provide inaccurate energy consumption estimation, especially when the code blocklength is finite.
In this paper, we formulate a new energy-efficient packet scheduling problem by adopting a recently developed channel capacity formula for finite blocklength codes.
The newly formulated problem is fundamentally more challenging to solve than the traditional one because the transmission energy function under the new channel capacity formula neither can be expressed in closed form nor possesses desirable monotonicity and convexity in general.
We analyze conditions on the code blocklength for which the transmission energy function is monotonic and convex.
Based on these properties, we develop efficient offline packet scheduling algorithms as well as a rolling-window based online algorithm for real-time packet scheduling.
Simulation results demonstrate not only the efficacy of the proposed algorithms but also the fact that the traditional design using the Shannon capacity formula can considerably underestimate the transmission energy for reliable communications.
Computational synthesis planning approaches have achieved recent success in organic chemistry, where tabulated synthesis procedures are readily available for supervised learning.
The syntheses of inorganic materials, however, exist primarily as natural language narratives contained within scientific journal articles.
This synthesis information must first be extracted from the text in order to enable analogous synthesis planning methods for inorganic materials.
In this work, we present a system for automatically extracting structured representations of synthesis procedures from the texts of materials science journal articles that describe explicit, experimental syntheses of inorganic compounds.
We define the structured representation as a set of linked events made up of extracted scientific entities and evaluate two unsupervised approaches for extracting these structures on expert-annotated articles: a strong heuristic baseline and a generative model of procedural text.
We also evaluate a variety of supervised models for extracting scientific entities.
Our results provide insight into the nature of the data and directions for further work in this exciting new area of research.
We consider the problem of fusing an arbitrary number of multiband, i.e., panchromatic, multispectral, or hyperspectral, images belonging to the same scene.
We use the well-known forward observation and linear mixture models with Gaussian perturbations to formulate the maximum-likelihood estimator of the endmember abundance matrix of the fused image.
We calculate the Fisher information matrix for this estimator and examine the conditions for the uniqueness of the estimator.
We use a vector total-variation penalty term together with nonnegativity and sum-to-one constraints on the endmember abundances to regularize the derived maximum-likelihood estimation problem.
The regularization facilitates exploiting the prior knowledge that natural images are mostly composed of piecewise smooth regions with limited abrupt changes, i.e., edges, as well as coping with potential ill-posedness of the fusion problem.
We solve the resultant convex optimization problem using the alternating direction method of multipliers.
We utilize the circular convolution theorem in conjunction with the fast Fourier transform to alleviate the computational complexity of the proposed algorithm.
Experiments with multiband images constructed from real hyperspectral datasets reveal the superior performance of the proposed algorithm in comparison with the state-of-the-art algorithms, which need to be used in tandem to fuse more than two multiband images.
An output-polynomial algorithm for the listing of minimal dominating sets in graphs is a challenging open problem and is known to be equivalent to the well-known Transversal problem which asks for an output-polynomial algorithm for listing the set of minimal hitting sets in hypergraphs.
We give a polynomial delay algorithm to list the set of minimal dominating sets in chordal graphs, an important and well-studied graph class where such an algorithm was open for a while.
In two-view geometry, the essential matrix describes the relative position and orientation of two calibrated images.
In three views, a similar role is assigned to the calibrated trifocal tensor.
It is a particular case of the (uncalibrated) trifocal tensor and thus it inherits all its properties but, due to the smaller degrees of freedom, satisfies a number of additional algebraic constraints.
Some of them are described in this paper.
More specifically, we define a new notion --- the trifocal essential matrix.
On the one hand, it is a generalization of the ordinary (bifocal) essential matrix, and, on the other hand, it is closely related to the calibrated trifocal tensor.
We prove the two necessary and sufficient conditions that characterize the set of trifocal essential matrices.
Based on these characterizations, we propose three necessary conditions on a calibrated trifocal tensor.
They have a form of 15 quartic and 99 quintic polynomial equations.
We show that in the practically significant real case the 15 quartic constraints are also sufficient.
Wireless networking allows users to access information and services regardless of location and physical infrastructure.
It is a fast growing technology due to its availability of wireless devices, flexibility, ease of installation and configuration.
With this rapid expansion of information and Communication Technology (ICT), the consumption of energy is also increasing.
In the early age of wireless technology, computing infrastructure focused on everywhere access, capacity and speed of technology.
But now computing infrastructure should be energy efficient because, in wireless networking, devices are mostly powered by a battery that is a limited source of energy and is a challenge for the researchers.
In computing infrastructure energy saving and environmental protection has become a global demand.
This paper proposed a computing infrastructure based on green computing for energy efficient wireless networking.
Further, some challenges and techniques like power consumption in network architecture, algorithm efficiency, virtualization, and dynamic power saving will be discussed to make energy efficient computing infrastructure.
Exogenous state variables and rewards can slow down reinforcement learning by injecting uncontrolled variation into the reward signal.
We formalize exogenous state variables and rewards and identify conditions under which an MDP with exogenous state can be decomposed into an exogenous Markov Reward Process involving only the exogenous state+reward and an endogenous Markov Decision Process defined with respect to only the endogenous rewards.
We also derive a variance-covariance condition under which Monte Carlo policy evaluation on the endogenous MDP is accelerated compared to using the full MDP.
Similar speedups are likely to carry over to all RL algorithms.
We develop two algorithms for discovering the exogenous variables and test them on several MDPs.
Results show that the algorithms are practical and can significantly speed up reinforcement learning.
This notebook paper presents our system in the ActivityNet Dense Captioning in Video task (task 3).
Temporal proposal generation and caption generation are both important to the dense captioning task.
Therefore, we propose a proposal ranking model to employ a set of effective feature representations for proposal generation, and ensemble a series of caption models enhanced with context information to generate captions robustly on predicted proposals.
Our approach achieves the state-of-the-art performance on the dense video captioning task with 8.529 METEOR score on the challenge testing set.
We present a Bayesian object observation model for complete probabilistic semantic SLAM.
Recent studies on object detection and feature extraction have become important for scene understanding and 3D mapping.
However, 3D shape of the object is too complex to formulate the probabilistic observation model; therefore, performing the Bayesian inference of the object-oriented features as well as their pose is less considered.
Besides, when the robot equipped with an RGB mono camera only observes the projected single view of an object, a significant amount of the 3D shape information is abandoned.
Due to these limitations, semantic SLAM and viewpoint-independent loop closure using volumetric 3D object shape is challenging.
In order to enable the complete formulation of probabilistic semantic SLAM, we approximate the observation model of a 3D object with a tractable distribution.
We also estimate the variational likelihood from the 2D image of the object to exploit its observed single view.
In order to evaluate the proposed method, we perform pose and feature estimation, and demonstrate that the automatic loop closure works seamlessly without additional loop detector in various environments.
We define and study error detection and correction tasks that are useful for 3D reconstruction of neurons from electron microscopic imagery, and for image segmentation more generally.
Both tasks take as input the raw image and a binary mask representing a candidate object.
For the error detection task, the desired output is a map of split and merge errors in the object.
For the error correction task, the desired output is the true object.
We call this object mask pruning, because the candidate object mask is assumed to be a superset of the true object.
We train multiscale 3D convolutional networks to perform both tasks.
We find that the error-detecting net can achieve high accuracy.
The accuracy of the error-correcting net is enhanced if its input object mask is "advice" (union of erroneous objects) from the error-detecting net.
Machine learning is used to compute achievable information rates (AIRs) for a simplified fiber channel.
The approach jointly optimizes the input distribution (constellation shaping) and the auxiliary channel distribution to compute AIRs without explicit channel knowledge in an end-to-end fashion.
Answer Set Programming (ASP) is a well-established declarative problem solving paradigm which became widely used in AI and recognized as a powerful tool for knowledge representation and reasoning (KRR), especially for its high expressiveness and the ability to deal also with incomplete knowledge.
Recently, thanks to the availability of a number of robust and efficient implementations, ASP has been increasingly employed in a number of different domains, and used for the development of industrial-level and enterprise applications.
This made clear the need for proper development tools and interoperability mechanisms for easing interaction and integration with external systems in the widest range of real-world scenarios, including mobile applications and educational contexts.
In this work we present a framework for integrating the KRR capabilities of ASP into generic applications.
We show the use of the framework by illustrating proper specializations for some relevant ASP systems over different platforms, including the mobile setting; furthermore, the potential of the framework for educational purposes is illustrated by means of the development of several ASP-based applications.
Today, with the continued growth in using information and communication technologies (ICT) for business purposes, business organizations become increasingly dependent on their information systems.
Thus, they need to protect them from the different attacks exploiting their vulnerabilities.
To do so, the organization has to use security technologies, which may be proactive or reactive ones.
Each security technology has a relative cost and addresses specific vulnerabilities.
Therefore, the organization has to put in place the appropriate security technologies set that minimizes the information system s vulnerabilities with a minimal cost.
This bi objective problem will be considered as a resources allocation problem (RAP) where security technologies represent the resources to be allocated.
However, the set of vulnerabilities may change, periodically, with the continual appearance of new ones.
Therefore, the security technologies set should be flexible to face these changes, in real time, and the problem becomes a dynamic one.
In this paper, we propose a harmony search based algorithm to solve the bi objective dynamic resource allocation decision model.
This approach was compared to a genetic algorithm and provided good results.
Online recommender systems often deal with continuous, potentially fast and unbounded flows of data.
Ensemble methods for recommender systems have been used in the past in batch algorithms, however they have never been studied with incremental algorithms that learn from data streams.
We evaluate online bagging with an incremental matrix factorization algorithm for top-N recommendation with positive-only -- binary -- ratings.
Our results show that online bagging is able to improve accuracy up to 35% over the baseline, with small computational overhead.
This paper proposes an end-to-end approach for single-channel speaker-independent multi-speaker speech separation, where time-frequency (T-F) masking, the short-time Fourier transform (STFT), and its inverse are represented as layers within a deep network.
Previous approaches, rather than computing a loss on the reconstructed signal, used a surrogate loss based on the target STFT magnitudes.
This ignores reconstruction error introduced by phase inconsistency.
In our approach, the loss function is directly defined on the reconstructed signals, which are optimized for best separation.
In addition, we train through unfolded iterations of a phase reconstruction algorithm, represented as a series of STFT and inverse STFT layers.
While mask values are typically limited to lie between zero and one for approaches using the mixture phase for reconstruction, this limitation is less relevant if the estimated magnitudes are to be used together with phase reconstruction.
We thus propose several novel activation functions for the output layer of the T-F masking, to allow mask values beyond one.
On the publicly-available wsj0-2mix dataset, our approach achieves state-of-the-art 12.6 dB scale-invariant signal-to-distortion ratio (SI-SDR) and 13.1 dB SDR, revealing new possibilities for deep learning based phase reconstruction and representing a fundamental progress towards solving the notoriously-hard cocktail party problem.
One of the most challenging fields in vehicular communications has been the experimental assessment of protocols and novel technologies.
Researchers usually tend to simulate vehicular scenarios and/or partially validate new contributions in the area by using constrained testbeds and carrying out minor tests.
In this line, the present work reviews the issues that pioneers in the area of vehicular communications and, in general, in telematics, have to deal with if they want to perform a good evaluation campaign by real testing.
The key needs for a good experimental evaluation is the use of proper software tools for gathering testing data, post-processing and generating relevant figures of merit and, finally, properly showing the most important results.
For this reason, a key contribution of this paper is the presentation of an evaluation environment called AnaVANET, which covers the previous needs.
By using this tool and presenting a reference case of study, a generic testing methodology is described and applied.
This way, the usage of the IPv6 protocol over a vehicle-to-vehicle routing protocol, and supporting IETF-based network mobility, is tested at the same time the main features of the AnaVANET system are presented.
This work contributes in laying the foundations for a proper experimental evaluation of vehicular networks and will be useful for many researchers in the area.
Diversification-Based Learning (DBL) derives from a collection of principles and methods introduced in the field of metaheuristics that have broad applications in computing and optimization.
We show that the DBL framework goes significantly beyond that of the more recent Opposition-based learning (OBL) framework introduced in Tizhoosh (2005), which has become the focus of numerous research initiatives in machine learning and metaheuristic optimization.
We unify and extend earlier proposals in metaheuristic search (Glover, 1997, Glover and Laguna, 1997) to give a collection of approaches that are more flexible and comprehensive than OBL for creating intensification and diversification strategies in metaheuristic search.
We also describe potential applications of DBL to various subfields of machine learning and optimization.
In this paper we present our work on a case study on Statistical Machine Translation (SMT) and Rule based machine translation (RBMT) for translation from English to Malayalam and Malayalam to English.
One of the motivations of our study is to make a three way performance comparison, such as, a) SMT and RBMT b) English to Malayalam SMT and Malayalam to English SMT c) English to Malayalam RBMT and Malayalam to English RBMT.
We describe the development of English to Malayalam and Malayalam to English baseline phrase based SMT system and the evaluation of its performance compared against the RBMT system.
Based on our study the observations are: a) SMT systems outperform RBMT systems, b) In the case of SMT, English - Malayalam systems perform better than that of Malayalam - English systems, c) In the case RBMT, Malayalam to English systems are performing better than English to Malayalam systems.
Based on our evaluations and detailed error analysis, we describe the requirements of incorporating morphological processing into the SMT to improve the accuracy of translation.
Reinforcement learning is widely used for dialogue policy optimization where the reward function often consists of more than one component, e.g., the dialogue success and the dialogue length.
In this work, we propose a structured method for finding a good balance between these components by searching for the optimal reward component weighting.
To render this search feasible, we use multi-objective reinforcement learning to significantly reduce the number of training dialogues required.
We apply our proposed method to find optimized component weights for six domains and compare them to a default baseline.
This article considers the task of automatically inducing role-semantic annotations in the FrameNet paradigm for new languages.
We propose a general framework that is based on annotation projection, phrased as a graph optimization problem.
It is relatively inexpensive and has the potential to reduce the human effort involved in creating role-semantic resources.
Within this framework, we present projection models that exploit lexical and syntactic information.
We provide an experimental evaluation on an English-German parallel corpus which demonstrates the feasibility of inducing high-precision German semantic role annotation both for manually and automatically annotated English data.
Online forums enable users to discuss together around various topics.
One of the serious problems of these environments is high volume of discussions and thus information overload problem.
Unfortunately without considering the users interests, traditional Information Retrieval (IR) techniques are not able to solve the problem.
Therefore, employment of a Recommender System (RS) that could suggest favorite's topics of users according to their tastes could increases the dynamism of forum and prevent the users from duplicate posts.
In addition, consideration of semantics can be useful for increasing the performance of IR based RS.
Our goal is study of impact of ontology and data mining techniques on improving of content-based RS.
For this purpose, at first, three type of ontologies will be constructed from the domain corpus with utilization of text mining, Natural Language Processing (NLP) and Wordnet and then they will be used as an input in two kind of RS: one, fully ontology-based and one with enriching the user profile vector with ontology in vector space model (VSM) (proposed method).
Afterward the results will be compared with the simple VSM based RS.
Given results show that the proposed RS presents the highest performance.
In this paper we present the state of advancement of the French ANR WebStand project.
The objective of this project is to construct a customizable XML based warehouse platform to acquire, transform, analyze, store, query and export data from the web, in particular mailing lists, with the final intension of using this data to perform sociological studies focused on social groups of World Wide Web, with a specific emphasis on the temporal aspects of this data.
We are currently using this system to analyze the standardization process of the W3C, through its social network of standard setters.
Researchers spend a great deal of time reading research papers.
Keshav (2012) provides a three-pass method to researchers to improve their reading skills.
This article extends Keshav's method for reading a research compendium.
Research compendia are an increasingly used form of publication, which packages not only the research paper's text and figures, but also all data and software for better reproducibility.
We introduce the existing conventions for research compendia and suggest how to utilise their shared properties in a structured reading process.
Unlike the original, this article is not build upon a long history but intends to provide guidance at the outset of an emerging practice.
Fault tolerance is essential for building reliable services; however, it comes at the price of redundancy, mainly the "replication factor" and "diversity".
With the increasing reliance on Internet-based services, more machines (mainly servers) are needed to scale out, multiplied with the extra expense of replication.
This paper revisits the very fundamentals of fault tolerance and presents "artificial redundancy": a formal generalization of "exact copy" redundancy in which new sources of redundancy are exploited to build fault tolerant systems.
On this concept, we show how to build "artificial replication" and design "artificial fault tolerance" (AFT).
We discuss the properties of these new techniques showing that AFT extends current fault tolerant approaches to use other forms of redundancy aiming at reduced cost and high diversity.
In this paper, we propose to use a set of simple, uniform in architecture LSTM-based models to recover different kinds of temporal relations from text.
Using the shortest dependency path between entities as input, the same architecture is used to extract intra-sentence, cross-sentence, and document creation time relations.
A "double-checking" technique reverses entity pairs in classification, boosting the recall of positive cases and reducing misclassifications between opposite classes.
An efficient pruning algorithm resolves conflicts globally.
Evaluated on QA-TempEval (SemEval2015 Task 5), our proposed technique outperforms state-of-the-art methods by a large margin.
This paper has dual aims.
First is to develop practical universal coding methods for unlabeled graphs.
Second is to use these for graph anomaly detection.
The paper develops two coding methods for unlabeled graphs: one based on the degree distribution, the second based on the triangle distribution.
It is shown that these are efficient for different types of random graphs, and on real-world graphs.
These coding methods is then used for detecting anomalous graphs, based on structure alone.
It is shown that anomalous graphs can be detected with high probability.
The present study proposes a new structure selection approach for non-linear system identification based on Two-Dimensional particle swarms (2D-UPSO).
The 2D learning framework essentially extends the learning dimension of the conventional particle swarms and explicitly incorporates the information about the cardinality, i.e., number of terms, into the search process.
This property of the 2D-UPSO has been exploited to determine the correct structure of the non-linear systems.
The efficacy of the proposed approach is demonstrated by considering several simulated benchmark nonlinear systems in discrete and in continuous domain.
In addition, the proposed approach is applied to identify a parsimonious structure from practical non-linear wave-force data.
The results of the comparative investigation with Genetic Algorithm (GA), Binary Particle Swarm Optimization (BPSO) and the classical Orthogonal Forward Regression (OFR) methods illustrate that the proposed 2D-UPSO could successfully detect the correct structure of the non-linear systems.
Substitution Box or S-Box had been generated using 4-bit Boolean Functions (BFs) for Encryption and Decryption Algorithm of Lucifer and Data Encryption Standard (DES) in late sixties and late seventies respectively.
The S-box of Advance Encryption Standard have also been generated using Irreducible Polynomials over Galois field GF(2^8) adding an additive constant in early twenty first century.
In this paper Substitution Boxes have been generated from Irreducible or Reducible Polynomials over Galois field GF(p^q).
Binary Galois fields have been used to generate Substitution Boxes.
Since the Galois Field Number or the Number generated from coefficients of a polynomial over a particular Binary Galois field (2q) is similar to log 2 q+1 bit BFs.
So generation of log 2 q+1 bit S-boxes is Possible.
Now if p = prime or non-prime number then generation of S-Boxes is possible using Galois field GF (p^q). where, q = p-1.
Upper limb Prosthetic can be viewed as an independent cognitive system in order to develop a conceptual space.
In this paper, we provide a detailed analogical reasoning of prosthetic arm to build the conceptual spaces with the help of the theory called geometric framework of conceptual spaces proposed by Gardenfors.
Terminologies of conceptual spaces such as concepts, similarities, properties, quality dimensions and prototype are applied for a specific prosthetic system and conceptual space is built for prosthetic arm.
Concept lattice traversals are used on the lattice represented conceptual spaces.
Cognitive functionalities such as generalization (Similarities) and specialization (Differences) are achieved in the lattice represented conceptual space.
This might well prove to design intelligent prosthetics to assist challenged humans.
Geometric framework of conceptual spaces holds similar concepts closer in geometric structures in a way similar to concept lattices.
Hence, we also propose to use concept lattice to represent concepts of geometric framework of conceptual spaces.
Also, we extend our discussion with our insights on conceptual spaces of bidirectional hand prosthetics.
In this paper, we analyze the throughput performance of two co-existing downlink multiuser underlay secondary networks that use fixed-rate transmissions.
We assume that the interference temperature limit (ITL) is apportioned to accommodate two concurrent transmissions using an interference temperature apportioning parameter so as to ensure that the overall interference to the primary receiver does not exceed the ITL.
Using the derived analytical expressions for throughput, when there is only one secondary user in each network, or when the secondary networks do not employ opportunistic user selection (use round robin scheduling for example), there exists a critical fixed-rate below which sum throughput with co-existing secondary networks is higher than the throughput with a single secondary network.
We derive an expression for this critical fixed-rate.
Below this critical rate, we show that careful apportioning of the ITL is critical to maximizing sum throughput of the co-existing networks.
We derive an expression for this apportioning parameter.
Throughput is seen to increase with increase in number of users in each of the secondary networks.
Computer simulations demonstrate accuracy of the derived expressions.
The visual representation of concepts or ideas through the use of simple shapes has always been explored in the history of Humanity, and it is believed to be the origin of writing.
We focus on computational generation of visual symbols to represent concepts.
We aim to develop a system that uses background knowledge about the world to find connections among concepts, with the goal of generating symbols for a given concept.
We are also interested in exploring the system as an approach to visual dissociation and visual conceptual blending.
This has a great potential in the area of Graphic Design as a tool to both stimulate creativity and aid in brainstorming in projects such as logo, pictogram or signage design.
Existing approaches to protect the privacy of Electronic Health Records are either insufficient for existing medical laws or they are too restrictive in their usage.
For example, smart card-based encryption systems require the patient to be always present to authorize access to medical records.
Questionnaires were administered by 50 medical practitioners to identify and categorize different Electronic Health Records attributes.
The system was implemented using multi biometrics of patients to access patient record in pre-hospital care.The software development tools employed were JAVA and MySQL database.
The system provides applicable security when patients records are shared either with other practitioners, employers, organizations or research institutes.
The result of the system evaluation shows that the average response time of 6 seconds and 11.1 seconds for fingerprint and iris respectively after ten different simulations.
The system protects privacy and confidentiality by limiting the amount of data exposed to users.The system also enables emergency medical technicians to gain easy and reliable access to necessary attributes of patients Electronic Health Records while still maintaining the privacy and confidentiality of the data using the patients fingerprint and iris.
Content Delivery Networks (CDNs) deliver a majority of the user-requested content on the Internet, including web pages, videos, and software downloads.
A CDN server caches and serves the content requested by users.
Designing caching algorithms that automatically adapt to the heterogeneity, burstiness, and non-stationary nature of real-world content requests is a major challenge and is the focus of our work.
While there is much work on caching algorithms for stationary request traffic, the work on non-stationary request traffic is very limited.
Consequently, most prior models are inaccurate for production CDN traffic that is non-stationary.
We propose two TTL-based caching algorithms and provide provable guarantees for content request traffic that is bursty and non-stationary.
The first algorithm called d-TTL dynamically adapts a TTL parameter using a stochastic approximation approach.
Given a feasible target hit rate, we show that the hit rate of d-TTL converges to its target value for a general class of bursty traffic that allows Markov dependence over time and non-stationary arrivals.
The second algorithm called f-TTL uses two caches, each with its own TTL.
The first-level cache adaptively filters out non-stationary traffic, while the second-level cache stores frequently-accessed stationary traffic.
Given feasible targets for both the hit rate and the expected cache size, f-TTL asymptotically achieves both targets.
We implement d-TTL and f-TTL and evaluate both algorithms using an extensive nine-day trace consisting of 500 million requests from a production CDN server.
We show that both d-TTL and f-TTL converge to their hit rate targets with an error of about 1.3%.
But, f-TTL requires a significantly smaller cache size than d-TTL to achieve the same hit rate, since it effectively filters out the non-stationary traffic for rarely-accessed objects.
This paper considers the task of thorax disease classification on chest X-ray images.
Existing methods generally use the global image as input for network learning.
Such a strategy is limited in two aspects.
1) A thorax disease usually happens in (small) localized areas which are disease specific.
Training CNNs using global image may be affected by the (excessive) irrelevant noisy areas.
2) Due to the poor alignment of some CXR images, the existence of irregular borders hinders the network performance.
In this paper, we address the above problems by proposing a three-branch attention guided convolution neural network (AG-CNN).
AG-CNN 1) learns from disease-specific regions to avoid noise and improve alignment, 2) also integrates a global branch to compensate the lost discriminative cues by local branch.
Specifically, we first learn a global CNN branch using global images.
Then, guided by the attention heat map generated from the global branch, we inference a mask to crop a discriminative region from the global image.
The local region is used for training a local CNN branch.
Lastly, we concatenate the last pooling layers of both the global and local branches for fine-tuning the fusion branch.
The Comprehensive experiment is conducted on the ChestX-ray14 dataset.
We first report a strong global baseline producing an average AUC of 0.841 with ResNet-50 as backbone.
After combining the local cues with the global information, AG-CNN improves the average AUC to 0.868.
While DenseNet-121 is used, the average AUC achieves 0.871, which is a new state of the art in the community.
Data diversity is critical to success when training deep learning models.
Medical imaging data sets are often imbalanced as pathologic findings are generally rare, which introduces significant challenges when training deep learning models.
In this work, we propose a method to generate synthetic abnormal MRI images with brain tumors by training a generative adversarial network using two publicly available data sets of brain MRI.
We demonstrate two unique benefits that the synthetic images provide.
First, we illustrate improved performance on tumor segmentation by leveraging the synthetic images as a form of data augmentation.
Second, we demonstrate the value of generative models as an anonymization tool, achieving comparable tumor segmentation results when trained on the synthetic data versus when trained on real subject data.
Together, these results offer a potential solution to two of the largest challenges facing machine learning in medical imaging, namely the small incidence of pathological findings, and the restrictions around sharing of patient data.
Recent work in data mining and related areas has highlighted the importance of the statistical assessment of data mining results.
Crucial to this endeavour is the choice of a non-trivial null model for the data, to which the found patterns can be contrasted.
The most influential null models proposed so far are defined in terms of invariants of the null distribution.
Such null models can be used by computation intensive randomization approaches in estimating the statistical significance of data mining results.
Here, we introduce a methodology to construct non-trivial probabilistic models based on the maximum entropy (MaxEnt) principle.
We show how MaxEnt models allow for the natural incorporation of prior information.
Furthermore, they satisfy a number of desirable properties of previously introduced randomization approaches.
Lastly, they also have the benefit that they can be represented explicitly.
We argue that our approach can be used for a variety of data types.
However, for concreteness, we have chosen to demonstrate it in particular for databases and networks.
We present a new environment for computations in particle physics phenomenology employing recent developments in cloud computing.
On this environment users can create and manage "virtual" machines on which the phenomenology codes/tools can be deployed easily in an automated way.
We analyze the performance of this environment based on "virtual" machines versus the utilization of "real" physical hardware.
In this way we provide a qualitative result for the influence of the host operating system on the performance of a representative set of applications for phenomenology calculations.
A resource-bounded version of the statement "no algorithm recognizes all non-halting Turing machines" is equivalent to an infinitely often (i.o.) superpolynomial speedup for the time required to accept any coNP-complete language and also equivalent to a superpolynomial speedup in proof length in propositional proof systems for tautologies, each of which implies P!=NP.
This suggests a correspondence between the properties 'has no algorithm at all' and 'has no best algorithm' which seems relevant to open problems in computational and proof complexity.
The paper presents to address this challenge, we have proposed the use of Adaptive Window Positioning technique which focuses on not just the meaning of the handwritten signature but also on the individuality of the writer.
This innovative technique divides the handwritten signature into 13 small windows of size nxn(13x13).This size should be large enough to contain ample information about the style of the author and small enough to ensure a good identification performance.The process was tested with a GPDS data set containing 4870 signature samples from 90 different writers by comparing the robust features of the test signature with that of the user signature using an appropriate classifier.
Experimental results reveal that adaptive window positioning technique proved to be the efficient and reliable method for accurate signature feature extraction for the identification of offline handwritten signatures.The contribution of this technique can be used to detect signatures signed under emotional duress.
Everything in the world is being connected, and things are becoming interactive.
The future of the interactive world depends on the future Internet of Things (IoT).
Software-defined networking (SDN) technology, a new paradigm in the networking area, can be useful in creating an IoT because it can handle interactivity by controlling physical devices, transmission of data among them, and data acquisition.
However, digital signage can be one of the promising technologies in this era of technology that is progressing toward the interactive world, connecting users to the IoT network through device-to-device communication technology.
This article illustrates a novel prototype that is mainly focused on a smart digital signage system comprised of software-defined IoT (SD-IoT) and invisible image sensor communication technology.
We have proposed an SDN scheme with a view to initiating its flexibility and compatibility for an IoT network-based smart digital signage system.
The idea of invisible communication can make the users of the technology trendier to it, and the usage of unused resources such as images and videos can be ensured.
In addition, this communication has paved the way for interactivity between the user and digital signage, where the digital signage and the camera of a smartphone can be operated as a transmitter and a receiver, respectively.
The proposed scheme might be applicable to real-world applications because SDN has the flexibility to adapt with the alteration of network status without any hardware modifications while displays and smartphones are available everywhere.
A performance analysis of this system showed the advantages of an SD-IoT network over an Internet protocol-based IoT network considering a queuing analysis for a dynamic link allocation process in the case of user access to the IoT network.
Scientific computation is a discipline that combines numerical analysis, physical understanding, algorithm development, and structured programming.
Several yottacycles per year on the world's largest computers are spent simulating problems as diverse as weather prediction, the properties of material composites, the behavior of biomolecules in solution, and the quantum nature of chemical compounds.
This article is intended to review specfic languages features and their use in computational science.
We will review the strengths and weaknesses of different programming styles, with examples taken from widely used scientific codes.
The lack of realistic and open benchmarking datasets for pedestrian visual-inertial odometry has made it hard to pinpoint differences in published methods.
Existing datasets either lack a full six degree-of-freedom ground-truth or are limited to small spaces with optical tracking systems.
We take advantage of advances in pure inertial navigation, and develop a set of versatile and challenging real-world computer vision benchmark sets for visual-inertial odometry.
For this purpose, we have built a test rig equipped with an iPhone, a Google Pixel Android phone, and a Google Tango device.
We provide a wide range of raw sensor data that is accessible on almost any modern-day smartphone together with a high-quality ground-truth track.
We also compare resulting visual-inertial tracks from Google Tango, ARCore, and Apple ARKit with two recent methods published in academic forums.
The data sets cover both indoor and outdoor cases, with stairs, escalators, elevators, office environments, a shopping mall, and metro station.
Recent studies have shown that adaptively regulating the sampling rate results in significant reduction in computational resources in embedded software based control.
Selecting a uniform sampling rate for a control loop is robust, but overtly pessimistic for sharing processors among multiple control loops.
Fine grained regulation of periodicity achieves better resource utilization, but is hard to implement online in a robust way.
In this paper we propose multi-mode sampling period selection, derived from an offline control theoretic analysis of the system.
We report significant gains in computational efficiency without trading off control performance.
Recently, a chaotic image encryption algorithm based on perceptron model was proposed.
The present paper analyzes security of the algorithm and finds that the equivalent secret key can be reconstructed with only one pair of known-plaintext/ciphertext, which is supported by both mathematical proof and experiment results.
In addition, some other security defects are also reported.
Several state-of-the-art video deblurring methods are based on a strong assumption that the captured scenes are static.
These methods fail to deblur blurry videos in dynamic scenes.
We propose a video deblurring method to deal with general blurs inherent in dynamic scenes, contrary to other methods.
To handle locally varying and general blurs caused by various sources, such as camera shake, moving objects, and depth variation in a scene, we approximate pixel-wise kernel with bidirectional optical flows.
Therefore, we propose a single energy model that simultaneously estimates optical flows and latent frames to solve our deblurring problem.
We also provide a framework and efficient solvers to optimize the energy model.
By minimizing the proposed energy function, we achieve significant improvements in removing blurs and estimating accurate optical flows in blurry frames.
Extensive experimental results demonstrate the superiority of the proposed method in real and challenging videos that state-of-the-art methods fail in either deblurring or optical flow estimation.
Authors propose a conceptual model of participation in viral diffusion process composed of four stages: awareness, infection, engagement and action.
To verify the model it has been applied and studied in the virtual social chat environment settings.
The study investigates the behavioral paths of actions that reflect the stages of participation in the diffusion and presents shortcuts, that lead to the final action, i.e. the attendance in a virtual event.
The results show that the participation in each stage of the process increases the probability of reaching the final action.
Nevertheless, the majority of users involved in the virtual event did not go through each stage of the process but followed the shortcuts.
That suggests that the viral diffusion process is not necessarily a linear sequence of human actions but rather a dynamic system.
What is here called controlled natural language (CNL) has traditionally been given many different names.
Especially during the last four decades, a wide variety of such languages have been designed.
They are applied to improve communication among humans, to improve translation, or to provide natural and intuitive representations for formal notations.
Despite the apparent differences, it seems sensible to put all these languages under the same umbrella.
To bring order to the variety of languages, a general classification scheme is presented here.
A comprehensive survey of existing English-based CNLs is given, listing and describing 100 languages from 1930 until today.
Classification of these languages reveals that they form a single scattered cloud filling the conceptual space between natural languages such as English on the one end and formal languages such as propositional logic on the other.
The goal of this article is to provide a common terminology and a common model for CNL, to contribute to the understanding of their general nature, to provide a starting point for researchers interested in the area, and to help developers to make design decisions.
This paper concerns model reduction of dynamical systems using the nuclear norm of the Hankel matrix to make a trade-off between model fit and model complexity.
This results in a convex optimization problem where this trade-off is determined by one crucial design parameter.
The main contribution is a methodology to approximately calculate all solutions up to a certain tolerance to the model reduction problem as a function of the design parameter.
This is called the regularization path in sparse estimation and is a very important tool in order to find the appropriate balance between fit and complexity.
We extend this to the more complicated nuclear norm case.
The key idea is to determine when to exactly calculate the optimal solution using an upper bound based on the so-called duality gap.
Hence, by solving a fixed number of optimization problems the whole regularization path up to a given tolerance can be efficiently computed.
We illustrate this approach on some numerical examples.
Creative telescoping algorithms compute linear differential equations satisfied by multiple integrals with parameters.
We describe a precise and elementary algorithmic version of the Griffiths-Dwork method for the creative telescoping of rational functions.
This leads to bounds on the order and degree of the coefficients of the differential equation, and to the first complexity result which is simply exponential in the number of variables.
One of the important features of the algorithm is that it does not need to compute certificates.
The approach is vindicated by a prototype implementation.
How would you search for a unique, fashionable shoe that a friend wore and you want to buy, but you didn't take a picture?
Existing approaches propose interactive image search as a promising venue.
However, they either entrust the user with taking the initiative to provide informative feedback, or give all control to the system which determines informative questions to ask.
Instead, we propose a mixed-initiative framework where both the user and system can be active participants, depending on whose initiative will be more beneficial for obtaining high-quality search results.
We develop a reinforcement learning approach which dynamically decides which of three interaction opportunities to give to the user: drawing a sketch, providing free-form attribute feedback, or answering attribute-based questions.
By allowing these three options, our system optimizes both the informativeness and exploration capabilities allowing faster image retrieval.
We outperform three baselines on three datasets and extensive experimental settings.
A novel control design approach for general nonlinear systems is presented in this paper.
The approach is based on the identification of a polynomial model of the system to control and on the on-line inversion of this model.
An efficient technique is developed to perform the inversion, which allows an effective control implementation on real-time processors.
This large-scale study, consisting of 24.5 million hand hygiene opportunities spanning 19 distinct facilities in 10 different states, uses linear predictive models to expose factors that may affect hand hygiene compliance.
We examine the use of features such as temperature, relative humidity, influenza severity, day/night shift, federal holidays and the presence of new residents in predicting daily hand hygiene compliance.
The results suggest that colder temperatures and federal holidays have an adverse effect on hand hygiene compliance rates, and that individual cultures and attitudes regarding hand hygiene seem to exist among facilities.
Scissor lifts, a staple of mechanical design, especially in competitive robotics, are a type of linkage that can be used to raise a load to some height, when acted upon by some force, usually exerted by an actuator.
The position of this actuator, however, can affect the mechanical advantage and velocity ratio of the system.
Hence, there needs to be a concrete way to analytically compare different actuator positions.
However, all current research into the analysis of scissor lifts either focusses only on the screw jack configuration, or derives separate force expressions for different actuator positions.
This, once again, leaves the decision between different actuator positions to trial and error, since the expression to test the potency of the position can only be derived once the position is chosen.
This paper proposes a derivation for a general force expression, in terms of a few carefully chosen position variables, which can be used to generate the force expression for any actuator position.
Hence, this expression illustrates exactly how each of the position variables (called a, b and i in this paper, as defined later) affect the force output, and hence can be used to pick an appropriate actuator position, by choosing values for the position variables that give the desired result.
Authoring documents in MKM formats like OMDoc is a very tedious task.
After years of working on a semantically annotated corpus of sTeX documents (GenCS), we identified a set of common, time-consuming subtasks, which can be supported in an integrated authoring environment.
We have adapted the modular Eclipse IDE into sTeXIDE, an authoring solution for enhancing productivity in contributing to sTeX based corpora. sTeXIDE supports context-aware command completion, module management, semantic macro retrieval, and theory graph navigation.
Group communication implies a many-to-many communication and it goes beyond both one-to-one communication (i.e., unicast) and one-to-many communication (i.e., multicast).
Unlike most user authentication protocols that authenticate a single user each time, we propose a new type of authentication, called group authentication, that authenticates all users in a group at once.
The group authentication protocol is specially designed to support group communications.
There is a group manager who is responsible to manage the group communication.
During registration, each user of a group obtains an unique token from the group manager.
Users present their tokens to determine whether they all belong to the same group or not.
The group authentication protocol allows users to reuse their tokens without compromising the security of tokens.
In addition, the group authentication can protect the identity of each user.
The number of bandwidth-hungry applications and services is constantly growing.
HTTP adaptive streaming of audio-visual content accounts for the majority of today's internet traffic.
Although the internet bandwidth increases also constantly, audio-visual compression technology is inevitable and we are currently facing the challenge to be confronted with multiple video codecs.
This paper proposes a multi-codec DASH dataset comprising AVC, HEVC, VP9, and AV1 in order to enable interoperability testing and streaming experiments for the efficient usage of these codecs under various conditions.
We adopt state of the art encoding and packaging options and also provide basic quality metrics along with the DASH segments.
Additionally, we briefly introduce a multi-codec DASH scheme and possible usage scenarios.
Finally, we provide a preliminary evaluation of the encoding efficiency in the context of HTTP adaptive streaming services and applications.
We propose a novel GAN-based framework for detecting shadows in images, in which a shadow detection network (D-Net) is trained together with a shadow attenuation network (A-Net) that generates adversarial training examples.
The A-Net modifies the original training images constrained by a simplified physical shadow model and is focused on fooling the D-Net's shadow predictions.
Hence, it is effectively augmenting the training data for D-Net with hard-to-predict cases.
The D-Net is trained to predict shadows in both original images and generated images from the A-Net.
Our experimental results show that the additional training data from A-Net significantly improves the shadow detection accuracy of D-Net.
Our method outperforms the state-of-the-art methods on the most challenging shadow detection benchmark (SBU) and also obtains state-of-the-art results on a cross-dataset task, testing on UCF.
Furthermore, the proposed method achieves accurate real-time shadow detection at 45 frames per second.
Individual neurons in convolutional neural networks supervised for image-level classification tasks have been shown to implicitly learn semantically meaningful concepts ranging from simple textures and shapes to whole or partial objects - forming a "dictionary" of concepts acquired through the learning process.
In this work we introduce a simple, efficient zero-shot learning approach based on this observation.
Our approach, which we call Neuron Importance-AwareWeight Transfer (NIWT), learns to map domain knowledge about novel "unseen" classes onto this dictionary of learned concepts and then optimizes for network parameters that can effectively combine these concepts - essentially learning classifiers by discovering and composing learned semantic concepts in deep networks.
Our approach shows improvements over previous approaches on the CUBirds and AWA2 generalized zero-shot learning benchmarks.
We demonstrate our approach on a diverse set of semantic inputs as external domain knowledge including attributes and natural language captions.
Moreover by learning inverse mappings, NIWT can provide visual and textual explanations for the predictions made by the newly learned classifiers and provide neuron names.
Our code is available at https://github.com/ramprs/neuron-importance-zsl.
The Universal Turing Machine (TM) is a model for VonNeumann computers --- general-purpose computers.
A human brain can inside-skull-automatically learn a universal TM so that he acts as a general-purpose computer and writes a computer program for any practical purposes.
It is unknown whether a machine can accomplish the same.
This theoretical work shows how the Developmental Network (DN) can accomplish this.
Unlike a traditional TM, the TM learned by DN is a super TM --- Grounded, Emergent, Natural, Incremental, Skulled, Attentive, Motivated, and Abstractive (GENISAMA).
A DN is free of any central controller (e.g., Master Map, convolution, or error back-propagation).
Its learning from a teacher TM is one transition observation at a time, immediate, and error-free until all its neurons have been initialized by early observed teacher transitions.
From that point on, the DN is no longer error-free but is always optimal at every time instance in the sense of maximal likelihood, conditioned on its limited computational resources and the learning experience.
This letter also extends the Church-Turing thesis to automatic programming for general purposes and sketchily proved it.
This paper presents a method for imaging of moving targets using multi-static SAR by treating the problem as one of spatial reflectivity signal inversion over an overcomplete dictionary of target velocities.
Since SAR sensor returns can be related to the spatial frequency domain projections of the scattering field, we exploit insights from compressed sensing theory to show that moving targets can be effectively imaged with transmitters and receivers randomly dispersed in a multi-static geometry within a narrow forward cone around the scene of interest.
Existing approaches to dealing with moving targets in SAR solve a coupled non-linear problem of target scattering and motion estimation typically through matched filtering.
In contrast, by using an overcomplete dictionary approach we effectively linearize the forward model and solve the moving target problem as a larger, unified regularized inversion problem subject to sparsity constraints.
An energy management scheme is presented for a grid-connected hybrid power system comprising of a photovoltaic generator as the primary power source and fuel-cell stacks as backup generation.
Power production is managed between the two sources such that a flexible operation is achieved, allowing the hybrid power system to supply a desired power demand by the grid operator.
In addition, the energy management algorithm and the control system are designed such that the hybrid power system supports the grid in case of both symmetrical and asymmetrical voltage sags, thus, adding low voltage ride-through capability, a requirement imposed by a number of modern grid codes on distributed generation.
During asymmetrical voltage sags, the injected active power is kept constant and grid currents are maintained sinusoidal with low harmonic content without requiring a phase locked loop or positive-negative sequence extraction, hence, lowering the computational complexity and design requirements of the control system.
Several test case scenarios are simulated using detailed component models using the SimPowerSystems toolbox of MATLAB/Simulink computing environment to demonstrate effectiveness of the proposed energy management control system under normal operating conditions and voltage sags.
Deep Neural Networks have been shown to be beneficial for a variety of tasks, in particular allowing for end-to-end learning and reducing the requirement for manual design decisions.
However, still many parameters have to be chosen in advance, also raising the need to optimize them.
One important, but often ignored system parameter is the selection of a proper activation function.
Thus, in this paper we target to demonstrate the importance of activation functions in general and show that for different tasks different activation functions might be meaningful.
To avoid the manual design or selection of activation functions, we build on the idea of genetic algorithms to learn the best activation function for a given task.
In addition, we introduce two new activation functions, ELiSH and HardELiSH, which can easily be incorporated in our framework.
In this way, we demonstrate for three different image classification benchmarks that different activation functions are learned, also showing improved results compared to typically used baselines.
Policy evaluation is a crucial step in many reinforcement-learning procedures, which estimates a value function that predicts states' long-term value under a given policy.
In this paper, we focus on policy evaluation with linear function approximation over a fixed dataset.
We first transform the empirical policy evaluation problem into a (quadratic) convex-concave saddle point problem, and then present a primal-dual batch gradient method, as well as two stochastic variance reduction methods for solving the problem.
These algorithms scale linearly in both sample size and feature dimension.
Moreover, they achieve linear convergence even when the saddle-point problem has only strong concavity in the dual variables but no strong convexity in the primal variables.
Numerical experiments on benchmark problems demonstrate the effectiveness of our methods.
The development of cyber-physical system (CPS) is a big challenge because of its complexity and its complex requirements.
Especially in Requirements Engineering (RE), there exist many redundant and conflict requirements.
Eliminating conflict requirements and merged redundant/common requirements lead a challenging task at the elicitation phase in the requirements engineering process for CPS.
Collecting and optimizing requirements through appropriate process reduce both development time and cost as every functional requirement gets refined and optimized at very first stage (requirements elicitation phase) of the whole development process.
Existing researches have focused on requirements those have already been collected.
However, none of the researches have worked on how the requirements are collected and refined.
This paper provides a requirements model for CPS that gives a direction about the requirements be gathered, refined and cluster in order to developing the CPS independently.
The paper also shows a case study about the application of the proposed model to transport system.
Bibliometric methods are used in multiple fields for a variety of purposes, namely for research evaluation.
Most bibliometric analyses have in common their data sources: Thomson Reuters' Web of Science (WoS) and Elsevier's Scopus.
This research compares the journal coverage of both databases in terms of fields, countries and languages, using Ulrich's extensive periodical directory as a base for comparison.
Results indicate that the use of either WoS or Scopus for research evaluation may introduces biases that favor Natural Sciences and Engineering as well as Biomedical Research to the detriment of Social Sciences and Arts and Humanities.
Similarly, English-language journals are overrepresented to the detriment of other languages.
While both databases share these biases, their coverage differs substantially.
As a consequence, the results of bibliometric analyses may vary depending on the database used.
For data integration in information ecosystems, semantic heterogeneity is a known difficulty.
In this paper, we propose Shadow Theory as the philosophical foundation to address this issue.
It is based on the notion of shadows in Plato's Allegory of the Cave.
What we can observe are just shadows, and meanings of shadows are mental entities that only exist in viewers' cognitive structures.
With enterprise customer data integration example, we proposed six design principles and algebra to support required operations.
To enhance the performance of affective models and reduce the cost of acquiring physiological signals for real-world applications, we adopt multimodal deep learning approach to construct affective models from multiple physiological signals.
For unimodal enhancement task, we indicate that the best recognition accuracy of 82.11% on SEED dataset is achieved with shared representations generated by Deep AutoEncoder (DAE) model.
For multimodal facilitation tasks, we demonstrate that the Bimodal Deep AutoEncoder (BDAE) achieves the mean accuracies of 91.01% and 83.25% on SEED and DEAP datasets, respectively, which are much superior to the state-of-the-art approaches.
For cross-modal learning task, our experimental results demonstrate that the mean accuracy of 66.34% is achieved on SEED dataset through shared representations generated by EEG-based DAE as training samples and shared representations generated by eye-based DAE as testing sample, and vice versa.
Privacy problems are lethal and getting more attention than any other issue with the notion of the Internet of Things (IoT).
Since IoT has many application areas including smart home, smart grids, smart healthcare system, smart and intelligent transportation and many more.
Most of these applications are fueled by the resource-constrained sensor network, such as Smart healthcare system is powered by Wireless Body Area Network (WBAN) and Smart home and weather monitoring systems are fueled by Wireless Sensor Networks (WSN).
In the mentioned application areas sensor node life is a very important aspect of these technologies as it explicitly effects the network life and performance.
Data aggregation techniques are used to increase sensor node life by decreasing communication overhead.
However, when the data is aggregated at intermediate nodes to reduce communication overhead, data privacy problems becomes more vulnerable.
Different Privacy-Preserving Data Aggregation (PPDA) techniques have been proposed to ensure data privacy during data aggregation in resource-constrained sensor nodes.
We provide a review and comparative analysis of the state of the art PPDA techniques in this paper.
The comparative analysis is based on Computation Cost, Communication overhead, Privacy Level, resistance against malicious aggregator, sensor node life and energy consumption by the sensor node.
We have studied the most recent techniques and provide in-depth analysis of the minute steps involved in these techniques.
To the best of our knowledge, this survey is the most recent and comprehensive study of PPDA techniques.
This paper presents an approach for transforming data granularity in hierarchical databases for binary decision problems by applying regression to categorical attributes at the lower grain levels.
Attributes from a lower hierarchy entity in the relational database have their information content optimized through regression on the categories histogram trained on a small exclusive labelled sample, instead of the usual mode category of the distribution.
The paper validates the approach on a binary decision task for assessing the quality of secondary schools focusing on how logistic regression transforms the students and teachers attributes into school attributes.
Experiments were carried out on Brazilian schools public datasets via 10-fold cross-validation comparison of the ranking score produced also by logistic regression.
The proposed approach achieved higher performance than the usual distribution mode transformation and equal to the expert weighing approach measured by the maximum Kolmogorov-Smirnov distance and the area under the ROC curve at 0.01 significance level.
Phonemic segmentation of speech is a critical step of speech recognition systems.
We propose a novel unsupervised algorithm based on sequence prediction models such as Markov chains and recurrent neural network.
Our approach consists in analyzing the error profile of a model trained to predict speech features frame-by-frame.
Specifically, we try to learn the dynamics of speech in the MFCC space and hypothesize boundaries from local maxima in the prediction error.
We evaluate our system on the TIMIT dataset, with improvements over similar methods.
As a collection of 3D points sampled from surfaces of objects, a 3D point cloud is widely used in robotics, autonomous driving and augmented reality.
Due to the physical limitations of 3D sensing devices, 3D point clouds are usually noisy, which influences subsequent computations, such as surface reconstruction, recognition and many others.
To denoise a 3D point cloud, we present a novel algorithm, called weighted multi-projection.
Compared to many previous works on denoising, instead of directly smoothing the coordinates of 3D points, we use a two-fold smoothing: We first estimate a local tangent plane at each 3D point and then reconstruct each 3D point by weighted averaging of its projections on multiple tangent planes.
We also provide the theoretical analysis for the surface normal estimation and achieve a tighter bound than in a previous work.
We validate the empirical performance on the dataset of ShapeNetCore and show that weighted multi-projection outperforms its competitors in all nine classes.
In a multi-user millimeter (mm) wave communication system, we consider the problem of estimating the channel response between the central node (base station) and each of the user equipments (UE).
We propose three different strategies: 1) Each UE estimates its channel separately, 2) Base station estimates all the UEs channels jointly, and 3) Two stage process with estimation done at both UE and base station.
Exploiting the low rank nature of the mm wave channels, we propose a generalized block orthogonal matching pursuit (G.BOMP) framework for channel estimation in all the three strategies.
Our simulation results show that, the average beamforming gain of the G.BOMP algorithm is higher than that of the conventional OMP algorithm and other existing works on the multi-user mm wave system.
In this paper, we present an open data set extracted from the transaction log of the social sciences academic search engine sowiport.
The data set includes a filtered set of 484,449 retrieval sessions which have been carried out by sowiport users in the period from April 2014 to April 2015.
We propose a description of interactions performed by the academic search engine users that can be used in different applications such as result ranking improvement, user modeling, query reformulation analysis, search pattern recognition.
Each year, the treatment decisions for more than 230,000 breast cancer patients in the U.S. hinge on whether the cancer has metastasized away from the breast.
Metastasis detection is currently performed by pathologists reviewing large expanses of biological tissues.
This process is labor intensive and error-prone.
We present a framework to automatically detect and localize tumors as small as 100 x 100 pixels in gigapixel microscopy images sized 100,000 x 100,000 pixels.
Our method leverages a convolutional neural network (CNN) architecture and obtains state-of-the-art results on the Camelyon16 dataset in the challenging lesion-level tumor detection task.
At 8 false positives per image, we detect 92.4% of the tumors, relative to 82.7% by the previous best automated approach.
For comparison, a human pathologist attempting exhaustive search achieved 73.2% sensitivity.
We achieve image-level AUC scores above 97% on both the Camelyon16 test set and an independent set of 110 slides.
In addition, we discover that two slides in the Camelyon16 training set were erroneously labeled normal.
Our approach could considerably reduce false negative rates in metastasis detection.
Increasing interest in securing Android ecosystem has spawned numerous efforts to assist app developers in building secure apps.
These efforts have resulted in tools and techniques capable of detecting vulnerabilities (and malicious behaviors) in apps.
However, there has been no evaluation of the effectiveness of these tools and techniques in detecting known vulnerabilities.
Absence of such evaluations puts app developers at a disadvantage when choosing security analysis tools to secure their apps.
In this regard, we evaluated the effectiveness of vulnerability detection tools for Android apps.
We considered 64 tools and empirically evaluated 14 vulnerability detection tools (incidentally along with 5 malicious behavior detection tools) against 42 known unique vulnerabilities captured by Ghera benchmarks, which are composed of both vulnerable and secure apps.
Of the 24 observations from the evaluation, the key observation is existing vulnerability detection tools for Android apps are very limited in their ability to detect known vulnerabilities --- all of the evaluated tools together could only detect 30 of the 42 known unique vulnerabilities.
More effort is required if security analysis tools are to help developers build secure apps.
We hope the observations from this evaluation will help app developers choose appropriate security analysis tools and persuade tool developers and researchers to identify and address limitations in their tools and techniques.
We also hope this evaluation will catalyze or spark a conversation in the software engineering and security communities to require more rigorous and explicit evaluation of security analysis tools and techniques.
With the rapidly changing technological realm, there is an urgent need to provide and protect the confidentiality of confidential images when stored in a cloud environment.
To overcome the security risks associated with single cloud, multiple clouds offered by unrelated cloud providers have to be used.
This paper outlines an integrated encryption scheme for the secure storage of confidential images on multiple clouds based on DNA sequences.
The current work proposes an application of DEA methodology for measurement of technical and allocative efficiency of university research activity.
The analysis is based on bibliometric data from the Italian university system for the five year period 2004-2008.
Technical and allocative efficiency is measured with input being considered as a university's research staff, classified according to academic rank, and with output considered as the field-standardized impact of the research product realized by these staff.
The analysis is applied to all scientific disciplines of the so-called hard sciences, and conducted at subfield level, thus at a greater level of detail than ever before achieved in national-scale research assessments.
To design trustworthy robots, we need to understand the impact factors of trust: people's attitudes, experience, and characteristics; the robot's physical design, reliability, and performance; a task's specification and the circumstances under which it is to be performed, e.g. at leisure or under time pressure.
As robots are used for a wide variety of tasks and applications, robot designers ought to be provided with evidence and guidance, to inform their decisions to achieve safe, trustworthy and efficient human-robot interactions.
In this work, the impact factors of trust in a collaborative manufacturing scenario are studied by conducting an experiment with a real robot and participants where a physical object was assembled and then disassembled.
Objective and subjective measures were employed to evaluate the development of trust, under faulty and non-faulty robot conditions, and the effect of previous experience with robots, and personality traits.
Our findings highlight differences when compared to other, more social, scenarios with robotic assistants (such as a home care assistant), in that the condition (faulty or not) does not have a significant impact on the human's perception of the robot in terms of human-likeliness, likeability, trustworthiness, and even competence.
However, personality and previous experience do have an effect on how the robot is perceived by participants, even though that is relatively small.
Various studies have empirically shown that the majority of Java and Android apps misuse cryptographic libraries, causing devastating breaches of data security.
Therefore, it is crucial to detect such misuses early in the development process.
The fact that insecure usages are not the exception but the norm precludes approaches based on property inference and anomaly detection.
In this paper, we present CrySL, a definition language that enables cryptography experts to specify the secure usage of the cryptographic libraries that they provide.
CrySL combines the generic concepts of method-call sequences and data-flow constraints with domain-specific constraints related to cryptographic algorithms and their parameters.
We have implemented a compiler that translates a CrySL ruleset into a context- and flow-sensitive demand-driven static analysis.
The analysis automatically checks a given Java or Android app for violations of the CrySL-encoded rules.
We empirically evaluated our ruleset through analyzing 10,001 Android apps.
Our results show that misuse of cryptographic APIs is still widespread, with 96% of apps containing at least one misuse.
However, we observed fewer of the misuses that were reported in previous work.
Mobile ad-hoc networks (MANETs) are a set of self organized wireless mobile nodes that works without any predefined infrastructure.
For routing data in MANETs, the routing protocols relay on mobile wireless nodes.
In general, any routing protocol performance suffers i) with resource constraints and ii) due to the mobility of the nodes.
Due to existing routing challenges in MANETs clustering based protocols suffers frequently with cluster head failure problem, which degrades the cluster stability.
This paper proposes, Enhanced CBRP, a schema to improve the cluster stability and in-turn improves the performance of traditional cluster based routing protocol (CBRP), by electing better cluster head using weighted clustering algorithm and considering some crucial routing challenges.
Moreover, proposed protocol suggests a secondary cluster head for each cluster, to increase the stability of the cluster and implicitly the network infrastructure in case of sudden failure of cluster head.
Computer Vision, either alone or combined with other technologies such as radar or Lidar, is one of the key technologies used in Advanced Driver Assistance Systems (ADAS).
Its role understanding and analysing the driving scene is of great importance as it can be noted by the number of ADAS applications that use this technology.
However, porting a vision algorithm to an embedded automotive system is still very challenging, as there must be a trade-off between several design requisites.
Furthermore, there is not a standard implementation platform, so different alternatives have been proposed by both the scientific community and the industry.
This paper aims to review the requisites and the different embedded implementation platforms that can be used for Computer Vision-based ADAS, with a critical analysis and an outlook to future trends.
Regret theory is a theory that describes human decision-making under risk.
The key of obtaining a quantitative model of regret theory is to measure the preference in humans' mind when they choose among a set of options.
Unlike physical quantities, measuring psychological preference is not procedure invariant, i.e. the readings alter when the methods change.
In this work, we alleviate this influence by choosing the procedure compatible with the way that an individual makes a choice.
We believe the resulting model is closer to the nature of human decision-making.
The preference elicitation process is decomposed into a series of short surveys to reduce cognitive workload and increase response accuracy.
To make the questions natural and familiar to the subjects, we follow the insight that humans generate, quantify and communicate preference in natural language.
The fuzzy-set theory is hence utilized to model responses from subjects.
Based on these ideas, a graphical human-computer interface (HCI) is designed to articulate the information as well as to efficiently collect human responses.
The design also accounts for human heuristics and biases, e.g. range effect and anchoring effect, to enhance its reliability.
The overall performance of the survey is satisfactory because the measured model shows prediction accuracy equivalent to the revisit-performance of the subjects.
With the fast-growing economy in the past ten years, cities in China have experience great changes, meanwhile, huge volume of urban grid management data has been recorded.
Studies on urban grid management are not common so far.
This kind of study is important, however, because the urban grid data describes the individual behaviors and detailed problems in community, and reveals the dynamics of changing policies and social relations.
In this article, we did a preliminary study on the urban grid management data of Shanghai, and investigated the key characteristics of the interactions between local government and citizen in such a fast-growing metropolitan.
Our investigation illustrates the dynamics of coevolution between economy and living environments.
We also developed mathematical model to quantitatively discover the spatial and temporal intra-relations among events found in data, providing insights to local government to fine tune the policy of resource allocation and give proper incentives to drive the coevolution to the optimal state, thereby achieving the good governance.
The problem of Learning from Demonstration is targeted at learning to perform tasks based on observed examples.
One approach to Learning from Demonstration is Inverse Reinforcement Learning, in which actions are observed to infer rewards.
This work combines a feature based state evaluation approach to Inverse Reinforcement Learning with neuroevolution, a paradigm for modifying neural networks based on their performance on a given task.
Neural networks are used to learn from a demonstrated expert policy and are evolved to generate a policy similar to the demonstration.
The algorithm is discussed and evaluated against competitive feature-based Inverse Reinforcement Learning approaches.
At the cost of execution time, neural networks allow for non-linear combinations of features in state evaluations.
These valuations may correspond to state value or state reward.
This results in better correspondence to observed examples as opposed to using linear combinations.
This work also extends existing work on Bayesian Non-Parametric Feature Construction for Inverse Reinforcement Learning by using non-linear combinations of intermediate data to improve performance.
The algorithm is observed to be specifically suitable for a linearly solvable non-deterministic Markov Decision Processes in which multiple rewards are sparsely scattered in state space.
A conclusive performance hierarchy between evaluated algorithms is presented.
Botnets continue to be an active threat against firms or companies and individuals worldwide.
Previous research regarding botnets has unveiled information on how the system and their stakeholders operate, but an insight on the economic structure that supports these stakeholders is lacking.
The objective of this research is to analyse the business model and determine the revenue stream of a botnet owner.
We also study the botnet life-cycle and determine the costs associated with it on the basis of four case studies.
We conclude that building a full scale cyber army from scratch is very expensive where as acquiring a previously developed botnet requires a little cost.
We find that initial setup and monthly costs were minimal compared to total revenue.
While many image colorization algorithms have recently shown the capability of producing plausible color versions from gray-scale photographs, they still suffer from the problems of context confusion and edge color bleeding.
To address context confusion, we propose to incorporate the pixel-level object semantics to guide the image colorization.
The rationale is that human beings perceive and distinguish colors based on the object's semantic categories.
We propose a hierarchical neural network with two branches.
One branch learns what the object is while the other branch learns the object's colors.
The network jointly optimizes a semantic segmentation loss and a colorization loss.
To attack edge color bleeding we generate more continuous color maps with sharp edges by adopting a joint bilateral upsamping layer at inference.
Our network is trained on PASCAL VOC2012 and COCO-stuff with semantic segmentation labels and it produces more realistic and finer results compared to the colorization state-of-the-art.
This article investigates emergence and complexity in complex systems that can share information on a network.
To this end, we use a theoretical approach from information theory, computability theory, and complex networks.
One key studied question is how much emergent complexity (or information) arises when a population of computable systems is networked compared with when this population is isolated.
First, we define a general model for networked theoretical machines, which we call algorithmic networks.
Then, we narrow our scope to investigate algorithmic networks that optimize the average fitnesses of nodes in a scenario in which each node imitates the fittest neighbor and the randomly generated population is networked by a time-varying graph.
We show that there are graph-topological conditions that cause these algorithmic networks to have the property of expected emergent open-endedness for large enough populations.
In other words, the expected emergent algorithmic complexity of a node tends to infinity as the population size tends to infinity.
Given a dynamic network, we show that these conditions imply the existence of a central time to trigger expected emergent open-endedness.
Moreover, we show that networks with small diameter compared to the network size meet these conditions.
We also discuss future research based on how our results are related to some problems in network science, information theory, computability theory, distributed computing, game theory, evolutionary biology, and synergy in complex systems.
Learning a deep neural network requires solving a challenging optimization problem: it is a high-dimensional, non-convex and non-smooth minimization problem with a large number of terms.
The current practice in neural network optimization is to rely on the stochastic gradient descent (SGD) algorithm or its adaptive variants.
However, SGD requires a hand-designed schedule for the learning rate.
In addition, its adaptive variants tend to produce solutions that generalize less well on unseen data than SGD with a hand-designed schedule.
We present an optimization method that offers empirically the best of both worlds: our algorithm yields good generalization performance while requiring only one hyper-parameter.
Our approach is based on a composite proximal framework, which exploits the compositional nature of deep neural networks and can leverage powerful convex optimization algorithms by design.
Specifically, we employ the Frank-Wolfe (FW) algorithm for SVM, which computes an optimal step-size in closed-form at each time-step.
We further show that the descent direction is given by a simple backward pass in the network, yielding the same computational cost per iteration as SGD.
We present experiments on the CIFAR and SNLI data sets, where we demonstrate the significant superiority of our method over Adam, Adagrad, as well as the recently proposed BPGrad and AMSGrad.
Furthermore, we compare our algorithm to SGD with a hand-designed learning rate schedule, and show that it provides similar generalization while converging faster.
The code is publicly available at https://github.com/oval-group/dfw.
Virtual network services that span multiple data centers are important to support emerging data-intensive applications in fields such as bioinformatics and retail analytics.
Successful virtual network service composition and maintenance requires flexible and scalable 'constrained shortest path management' both in the management plane for virtual network embedding (VNE) or network function virtualization service chaining (NFV-SC), as well as in the data plane for traffic engineering (TE).
In this paper, we show analytically and empirically that leveraging constrained shortest paths within recent VNE, NFV-SC and TE algorithms can lead to network utilization gains (of up to 50%) and higher energy efficiency.
The management of complex VNE, NFV-SC and TE algorithms can be, however, intractable for large scale substrate networks due to the NP-hardness of the constrained shortest path problem.
To address such scalability challenges, we propose a novel, exact constrained shortest path algorithm viz., 'Neighborhoods Method' (NM).
Our NM uses novel search space reduction techniques and has a theoretical quadratic speed-up making it practically faster (by an order of magnitude) than recent branch-and-bound exhaustive search solutions.
Finally, we detail our NM-based SDN controller implementation in a real-world testbed to further validate practical NM benefits for virtual network services.
Regular languages (RL) are the simplest family in Chomsky's hierarchy.
Thanks to their simplicity they enjoy various nice algebraic and logic properties that have been successfully exploited in many application fields.
Practically all of their related problems are decidable, so that they support automatic verification algorithms.
Also, they can be recognized in real-time.
Context-free languages (CFL) are another major family well-suited to formalize programming, natural, and many other classes of languages; their increased generative power w.r.t.
RL, however, causes the loss of several closure properties and of the decidability of important problems; furthermore they need complex parsing algorithms.
Thus, various subclasses thereof have been defined with different goals, spanning from efficient, deterministic parsing to closure properties, logic characterization and automatic verification techniques.
Among CFL subclasses, so-called structured ones, i.e., those where the typical tree-structure is visible in the sentences, exhibit many of the algebraic and logic properties of RL, whereas deterministic CFL have been thoroughly exploited in compiler construction and other application fields.
After surveying and comparing the main properties of those various language families, we go back to operator precedence languages (OPL), an old family through which R. Floyd pioneered deterministic parsing, and we show that they offer unexpected properties in two fields so far investigated in totally independent ways: they enable parsing parallelization in a more effective way than traditional sequential parsers, and exhibit the same algebraic and logic properties so far obtained only for less expressive language families.
Restoring face images from distortions is important in face recognition applications and is challenged by multiple scale issues, which is still not well-solved in research area.
In this paper, we present a Sequential Gating Ensemble Network (SGEN) for multi-scale face restoration issue.
We first employ the principle of ensemble learning into SGEN architecture design to reinforce predictive performance of the network.
The SGEN aggregates multi-level base-encoders and base-decoders into the network, which enables the network to contain multiple scales of receptive field.
Instead of combining these base-en/decoders directly with non-sequential operations, the SGEN takes base-en/decoders from different levels as sequential data.
Specifically, the SGEN learns to sequentially extract high level information from base-encoders in bottom-up manner and restore low level information from base-decoders in top-down manner.
Besides, we propose to realize bottom-up and top-down information combination and selection with Sequential Gating Unit (SGU).
The SGU sequentially takes two inputs from different levels and decides the output based on one active input.
Experiment results demonstrate that our SGEN is more effective at multi-scale human face restoration with more image details and less noise than state-of-the-art image restoration models.
By using adversarial training, SGEN also produces more visually preferred results than other models through subjective evaluation.
Typically an ontology matching technique is a combination of much different type of matchers operating at various abstraction levels such as structure, semantic, syntax, instance etc.
An ontology matching technique which employs matchers at all possible abstraction levels is expected to give, in general, best results in terms of precision, recall and F-measure due to improvement in matching opportunities and if we discount efficiency issues which may improve with better computing resources such as parallel processing.
A gold standard ontology matching model is derived from a model classification of ontology matching techniques.
A suitable metric is also defined based on gold standard ontology matching model.
A review of various ontology matching techniques specified in recent research papers in the area was undertaken to categorize an ontology matching technique as per newly proposed gold standard model and a metric value for the whole group was computed.
The results of the above study support proposed gold standard ontology matching model.
One of the defining features of a cryptocurrency is that its ledger, containing all transactions that have ever taken place, is globally visible.
As one consequence of this degree of transparency, a long line of recent research has demonstrated that - even in cryptocurrencies that are specifically designed to improve anonymity - it is often possible to track flows of money as it changes hands, and in some cases to de-anonymize users entirely.
With the recent proliferation of alternative cryptocurrencies, however, it becomes relevant to ask not only whether or not money can be traced as it moves within the ledger of a single cryptocurrency, but if it can in fact be traced as it moves across ledgers.
This is especially pertinent given the rise in popularity of automated trading platforms such as ShapeShift, which make it effortless to carry out such cross-currency trades.
In this paper, we use data scraped from ShapeShift over a six-month period and the data from eight different blockchains in order to explore this question.
Beyond developing new heuristics and demonstrating the ability to create new types of links across cryptocurrency ledgers, we also identify various patterns of cross-currency trades and of the general usage of these platforms, with the ultimate goal of understanding whether they serve either a criminal or a profit-driven agenda.
Advanced Encryption Standard (AES) is a symmetric key encryption algorithm which is extensively used in secure electronic data transmission.
When introduced, although it was tested and declared as secure, in 2005, a researcher named Bernstein claimed that it is vulnerable to side channel attacks.
The cache-based timing attack is the type of side channel attack demonstrated by Bernstein, which uses the timing variation in cache hits and misses.
This kind of attacks can be prevented by masking the actual timing information from the attacker.
Such masking can be performed by altering the original AES software implementation while preserving its semantics.
This paper presents possible software implementation level countermeasures against Bernstein's cache timing attack.
Two simple software based countermeasures based on the concept of "constant-encryption-time" were demonstrated against the remote cache timing attack with positive outcomes, in which we establish a secured environment for the AES encryption.
In this work, we explore the outage probability (OP) analysis of selective decode and forward (SDF) cooperation protocol employing multiple-input multipleoutput (MIMO) orthogonal space-time block-code (OSTBC) over time varying Rayleigh fading channel conditions with imperfect channel state information (CSI) and mobile nodes.
The closed-form expressions of the per-block average OP, probability distribution function (PDF) of sum of independent and identically distributed (i.i.d.)
Gamma random variables (RVs), and cumulative distribution function (CDF) are derived and used to investigate the performance of the relaying network.
A mathematical framework is developed to derive the optimal source-relay power allocation factors.
It is shown that source node mobility affects the per-block average OP performance more significantly than the destination node mobility.
Nevertheless, in other node mobility situations, cooperative systems are constrained by an error floor with a higher signal to noise ratio (SNR) regimes.
Simulation results show that the equal power allocation is the only possible optimal solution when source to relay link is stronger than the relay to destination link.
Also, we allocate almost all the power to the source node when source to relay link is weaker than the relay to destination link.
Simulation results also show that OP simulated plots are in close agreement with the OP analytic plots at high SNR regimes.
Digital predistortion (DPD) is a widely adopted baseband processing technique in current radio transmitters.
While DPD can effectively suppress unwanted spurious spectrum emissions stemming from imperfections of analog RF and baseband electronics, it also introduces extra processing complexity and poses challenges on efficient and flexible implementations, especially for mobile cellular transmitters, considering their limited computing power compared to basestations.
In this paper, we present high data rate implementations of broadband DPD on modern embedded processors, such as mobile GPU and multicore CPU, by taking advantage of emerging parallel computing techniques for exploiting their computing resources.
We further verify the suppression effect of DPD experimentally on real radio hardware platforms.
Performance evaluation results of our DPD design demonstrate the high efficacy of modern general purpose mobile processors on accelerating DPD processing for a mobile transmitter.
Multiplayer Online Battle Arena (MOBA) is currently one of the most popular genres of digital games around the world.
The domain of knowledge contained in these complicated games is large.
It is hard for humans and algorithms to evaluate the real-time game situation or predict the game result.
In this paper, we introduce MOBA-Slice, a time slice based evaluation framework of relative advantage between teams in MOBA games.
MOBA-Slice is a quantitative evaluation method based on learning, similar to the value network of AlphaGo.
It establishes a foundation for further MOBA related research including AI development.
In MOBA-Slice, with an analysis of the deciding factors of MOBA game results, we design a neural network model to fit our discounted evaluation function.
Then we apply MOBA-Slice to Defense of the Ancients 2 (DotA2), a typical and popular MOBA game.
Experiments on a large number of match replays show that our model works well on arbitrary matches.
MOBA-Slice not only has an accuracy 3.7% higher than DotA Plus Assistant at result prediction, but also supports the prediction of the remaining time of the game, and then realizes the evaluation of relative advantage between teams.
We study the problem of stochastic optimization for deep learning in the parallel computing environment under communication constraints.
A new algorithm is proposed in this setting where the communication and coordination of work among concurrent processes (local workers), is based on an elastic force which links the parameters they compute with a center variable stored by the parameter server (master).
The algorithm enables the local workers to perform more exploration, i.e. the algorithm allows the local variables to fluctuate further from the center variable by reducing the amount of communication between local workers and the master.
We empirically demonstrate that in the deep learning setting, due to the existence of many local optima, allowing more exploration can lead to the improved performance.
We propose synchronous and asynchronous variants of the new algorithm.
We provide the stability analysis of the asynchronous variant in the round-robin scheme and compare it with the more common parallelized method ADMM.
We show that the stability of EASGD is guaranteed when a simple stability condition is satisfied, which is not the case for ADMM.
We additionally propose the momentum-based version of our algorithm that can be applied in both synchronous and asynchronous settings.
Asynchronous variant of the algorithm is applied to train convolutional neural networks for image classification on the CIFAR and ImageNet datasets.
Experiments demonstrate that the new algorithm accelerates the training of deep architectures compared to DOWNPOUR and other common baseline approaches and furthermore is very communication efficient.
This article presents a novel intruder model for automated reasoning about anonymity (vote-privacy) and secrecy properties of voting systems.
We adapt the lazy spy for this purpose, as it avoids the eagerness of pre-computation of unnecessary deductions, reducing the required state space for the analysis.
This powerful intruder behaves as a Dolev-Yao intruder, which not only observes a protocol run but also interacts with the protocol participants, overhears communication channels, intercepts and spoofs any messages that he has learned or generated from any prior knowledge.
We make several important modifications in relation to existing channel types and the deductive system.
For the former, we define various channel types for different threat models.
For the latter, we construct a large deductive system over the space of messages transmitted in the voting system model.
The model represents the first formal treatment of the vVote system, which was used in November 2014, in state elections in Victoria, Australia.
This paper presents the kinematic analysis of the 3-PPPS parallel robot with an equilateral mobile platform and a U-shape base.
The proposed design and appropriate selection of parameters allow to formulate simpler direct and inverse kinematics for the manipulator under study.
The parallel singularities associated with the manipulator depend only on the orientation of the end-effector, and thus depend only on the orientation of the end effector.
The quaternion parameters are used to represent the aspects, i.e. the singularity free regions of the workspace.
A cylindrical algebraic decomposition is used to characterize the workspace and joint space with a low number of cells.
The dis-criminant variety is obtained to describe the boundaries of each cell.
With these simplifications, the 3-PPPS parallel robot with proposed design can be claimed as the simplest 6 DOF robot, which further makes it useful for the industrial applications.
A large semantic gap between the high-level synthesis (HLS) design and the low-level (on-board or RTL) simulation environment often creates a barrier for those who are not FPGA experts.
Moreover, such low-level simulation takes a long time to complete.
Software-based HLS simulators can help bridge this gap and accelerate the simulation process; however, we found that the current FPGA HLS commercial software simulators sometimes produce incorrect results.
In order to solve this correctness issue while maintaining the high speed of a software-based simulator, this paper proposes a new HLS simulation flow named FLASH.
The main idea behind the proposed flow is to extract the scheduling information from the HLS tool and automatically construct an equivalent cycle-accurate simulation model while preserving C semantics.
Experimental results show that FLASH runs three orders of magnitude faster than the RTL simulation.
Video description is the automatic generation of natural language sentences that describe the contents of a given video.
It is useful for helping the visually impaired, video subtitling and robotics.
The past few years have seen a surge of research in this area due to the unprecedented success of deep learning in computer vision and natural language processing.
Numerous methods, datasets and evaluation metrics have been proposed in the literature, calling the need for a comprehensive survey to focus research efforts in this flourishing new direction.
This paper fills the gap by surveying the state of the art approaches with a focus on deep learning models; comparing benchmark datasets in terms of their domain, number of classes, and repository size; and identifying the pros and cons of various evaluation metrics like SPICE, CIDEr, ROUGE, BLEU, METEOR, and WMD.
Classical approaches combined subject, object and verb detection with template based language models to generate sentences.
However, the release of large datasets revealed that these methods can not cope with the diversity in open domain videos.
Classical approaches were followed by a very short era of statistical methods which were soon replaced with deep learning, the current state of the art in video description.
Our survey shows that despite the fast-paced developments, video description research is still in its infancy due to the following reasons.
Firstly, existing datasets neither contain adequate visual diversity nor complexity of linguistic structures.
Secondly, current evaluation metrics fall short of measuring the agreement between machine generated descriptions with that of humans.
From an algorithmic point of view, diagnosis of new models is challenging because it is difficult to ascertain the contributions of the visual features and the adopted language model to the final description.
We conclude...
Actor of its presentation and actor of its online representation, the diarist draws his diegetic existence by setting up a strategy of automediation.
The Self-representation is a personal creation determined by the interface and the functionalities of the software.
A pragmatic approach of the Self-representation in the Livejournal Blog and the Touchgraph Livejournal browser provides a way to observe the play between intimacy and intersubjectivity.
The software leads the user from the lonely space of writing to the community space of publication.
We propose an approach to address two issues that commonly occur during training of unsupervised GANs.
First, since GANs use only a continuous latent distribution to embed multiple classes or clusters of data, they often do not correctly handle the structural discontinuity between disparate classes in a latent space.
Second, discriminators of GANs easily forget about past generated samples by generators, incurring instability during adversarial training.
We argue that these two infamous problems of unsupervised GAN training can be largely alleviated by a learnable memory network to which both generators and discriminators can access.
Generators can effectively learn representation of training samples to understand underlying cluster distributions of data, which ease the structure discontinuity problem.
At the same time, discriminators can better memorize clusters of previously generated samples, which mitigate the forgetting problem.
We propose a novel end-to-end GAN model named memoryGAN, which involves a memory network that is unsupervisedly trainable and integrable to many existing GAN models.
With evaluations on multiple datasets such as Fashion-MNIST, CelebA, CIFAR10, and Chairs, we show that our model is probabilistically interpretable, and generates realistic image samples of high visual fidelity.
The memoryGAN also achieves the state-of-the-art inception scores over unsupervised GAN models on the CIFAR10 dataset, without any optimization tricks and weaker divergences.
We introduce a new generative model for human planning under the Bayesian Inverse Reinforcement Learning (BIRL) framework which takes into account the fact that humans often plan using hierarchical strategies.
We describe the Bayesian Inverse Hierarchical RL (BIHRL) algorithm for inferring the values of hierarchical planners, and use an illustrative toy model to show that BIHRL retains accuracy where standard BIRL fails.
Furthermore, BIHRL is able to accurately predict the goals of `Wikispeedia' game players, with inclusion of hierarchical structure in the model resulting in a large boost in accuracy.
We show that BIHRL is able to significantly outperform BIRL even when we only have a weak prior on the hierarchical structure of the plans available to the agent, and discuss the significant challenges that remain for scaling up this framework to more realistic settings.
Automated detection of abnormalities in data has been studied in research area in recent years because of its diverse applications in practice including video surveillance, industrial damage detection and network intrusion detection.
However, building an effective anomaly detection system is a non-trivial task since it requires to tackle challenging issues of the shortage of annotated data, inability of defining anomaly objects explicitly and the expensive cost of feature engineering procedure.
Unlike existing appoaches which only partially solve these problems, we develop a unique framework to cope the problems above simultaneously.
Instead of hanlding with ambiguous definition of anomaly objects, we propose to work with regular patterns whose unlabeled data is abundant and usually easy to collect in practice.
This allows our system to be trained completely in an unsupervised procedure and liberate us from the need for costly data annotation.
By learning generative model that capture the normality distribution in data, we can isolate abnormal data points that result in low normality scores (high abnormality scores).
Moreover, by leverage on the power of generative networks, i.e. energy-based models, we are also able to learn the feature representation automatically rather than replying on hand-crafted features that have been dominating anomaly detection research over many decades.
We demonstrate our proposal on the specific application of video anomaly detection and the experimental results indicate that our method performs better than baselines and are comparable with state-of-the-art methods in many benchmark video anomaly detection datasets.
Multiview representation learning is very popular for latent factor analysis.
It naturally arises in many data analysis, machine learning, and information retrieval applications to model dependent structures among multiple data sources.
For computational convenience, existing approaches usually formulate the multiview representation learning as convex optimization problems, where global optima can be obtained by certain algorithms in polynomial time.
However, many pieces of evidence have corroborated that heuristic nonconvex approaches also have good empirical computational performance and convergence to the global optima, although there is a lack of theoretical justification.
Such a gap between theory and practice motivates us to study a nonconvex formulation for multiview representation learning, which can be efficiently solved by a simple stochastic gradient descent (SGD) algorithm.
We first illustrate the geometry of the nonconvex formulation; Then, we establish asymptotic global rates of convergence to the global optima by diffusion approximations.
Numerical experiments are provided to support our theory.
We propose an efficient solution to peer-to-peer localization in a wireless sensor network which works in two stages.
At the first stage the optimization problem is relaxed into a convex problem, given in the form recently proposed by Soares, Xavier, and Gomes.
The convex problem is efficiently solved in a distributed way by an ADMM approach, which provides a significant improvement in speed with respect to the original solution.
In the second stage, a soft transition to the original, non-convex, non relaxed formulation is applied in such a way to force the solution towards a local minimum.
The algorithm is built in such a way to be fully distributed, and it is tested in meaningful situations, showing its effectiveness in localization accuracy and speed of convergence, as well as its inner robustness.
Broadcasting systems have to deal with channel variability in order to offer the best rate to the users.
Hierarchical modulation is a practical solution to provide different rates to the receivers in function of the channel quality.
Unfortunately, the performance evaluation of such modulations requires time consuming simulations.
We propose in this paper a novel approach based on the channel capacity to avoid these simulations.
The method allows to study the performance of hierarchical and also classical modulations combined with error correcting codes.
We will also compare hierarchical modulation with time sharing strategy in terms of achievable rates and indisponibility.
Our work will be applied to the DVB-SH and DVB-S2 standards, which both consider hierarchical modulation as an optional feature.
We describe here a library aimed at automating the solution of partial differential equations using the finite element method.
By employing novel techniques for automated code generation, the library combines a high level of expressiveness with efficient computation.
Finite element variational forms may be expressed in near mathematical notation, from which low-level code is automatically generated, compiled and seamlessly integrated with efficient implementations of computational meshes and high-performance linear algebra.
Easy-to-use object-oriented interfaces to the library are provided in the form of a C++ library and a Python module.
This paper discusses the mathematical abstractions and methods used in the design of the library and its implementation.
A number of examples are presented to demonstrate the use of the library in application code.
The aim of this paper is to propose an application of mutual information-based ensemble methods to the analysis and classification of heart beats associated with different types of Arrhythmia.
Models of multilayer perceptrons, support vector machines, and radial basis function neural networks were trained and tested using the MIT-BIH arrhythmia database.
This research brings a focus to an ensemble method that, to our knowledge, is a novel application in the area of ECG Arrhythmia detection.
The proposed classifier ensemble method showed improved performance, relative to either majority voting classifier integration or to individual classifier performance.
The overall ensemble accuracy was 98.25%.
This paper presents our contribution to the ChaLearn Challenge 2015 on Cultural Event Classification.
The challenge in this task is to automatically classify images from 50 different cultural events.
Our solution is based on the combination of visual features extracted from convolutional neural networks with temporal information using a hierarchical classifier scheme.
We extract visual features from the last three fully connected layers of both CaffeNet (pretrained with ImageNet) and our fine tuned version for the ChaLearn challenge.
We propose a late fusion strategy that trains a separate low-level SVM on each of the extracted neural codes.
The class predictions of the low-level SVMs form the input to a higher level SVM, which gives the final event scores.
We achieve our best result by adding a temporal refinement step into our classification scheme, which is applied directly to the output of each low-level SVM.
Our approach penalizes high classification scores based on visual features when their time stamp does not match well an event-specific temporal distribution learned from the training and validation data.
Our system achieved the second best result in the ChaLearn Challenge 2015 on Cultural Event Classification with a mean average precision of 0.767 on the test set.
The Cloud Computing paradigm is providing system architects with a new powerful tool for building scalable applications.
Clouds allow allocation of resources on a "pay-as-you-go" model, so that additional resources can be requested during peak loads and released after that.
However, this flexibility asks for appropriate dynamic reconfiguration strategies.
In this paper we describe SAVER (qoS-Aware workflows oVER the Cloud), a QoS-aware algorithm for executing workflows involving Web Services hosted in a Cloud environment.
SAVER allows execution of arbitrary workflows subject to response time constraints.
SAVER uses a passive monitor to identify workload fluctuations based on the observed system response time.
The information collected by the monitor is used by a planner component to identify the minimum number of instances of each Web Service which should be allocated in order to satisfy the response time constraint.
SAVER uses a simple Queueing Network (QN) model to identify the optimal resource allocation.
Specifically, the QN model is used to identify bottlenecks, and predict the system performance as Cloud resources are allocated or released.
The parameters used to evaluate the model are those collected by the monitor, which means that SAVER does not require any particular knowledge of the Web Services and workflows being executed.
Our approach has been validated through numerical simulations, whose results are reported in this paper.
Ethics in the emerging world of data science are often discussed through cautionary tales about the dire consequences of missteps taken by high profile companies or organizations.
We take a different approach by foregrounding the ways that ethics are implicated in the day-to-day work of data science, focusing on instances in which data scientists recognize, grapple with, and conscientiously respond to ethical challenges.
This paper presents a case study of ethical dilemmas that arose in a "data science for social good" (DSSG) project focused on improving navigation for people with limited mobility.
We describe how this particular DSSG team responded to those dilemmas, and how those responses gave rise to still more dilemmas.
While the details of the case discussed here are unique, the ethical dilemmas they illuminate can commonly be found across many DSSG projects.
These include: the risk of exacerbating disparities; the thorniness of algorithmic accountability; the evolving opportunities for mischief presented by new technologies; the subjective and value- laden interpretations at the heart of any data-intensive project; the potential for data to amplify or mute particular voices; the possibility of privacy violations; and the folly of technological solutionism.
Based on our tracing of the team's responses to these dilemmas, we distill lessons for an ethical data science practice that can be more generally applied across DSSG projects.
Specifically, this case experience highlights the importance of: 1) Setting the scene early on for ethical thinking 2) Recognizing ethical decision-making as an emergent phenomenon intertwined with the quotidian work of data science for social good 3) Approaching ethical thinking as a thoughtful and intentional balancing of priorities rather than a binary differentiation between right and wrong.
In todays world there is a wide availability of huge amount of data and thus there is a need for turning this data into useful information which is referred to as knowledge.
This demand for knowledge discovery process has led to the development of many algorithms used to determine the association rules.
One of the major problems faced by these algorithms is generation of candidate sets.
The FP Tree algorithm is one of the most preferred algorithms for association rule mining because it gives association rules without generating candidate sets.
But in the process of doing so, it generates many CP trees which decreases its efficiency.
In this research paper, an improvised FP tree algorithm with a modified header table, along with a spare table and the MFI algorithm for association rule mining is proposed.
This algorithm generates frequent item sets without using candidate sets and CP trees.
The nonnegative matrix factorization (NMF) is widely used in signal and image processing, including bio-informatics, blind source separation and hyperspectral image analysis in remote sensing.
A great challenge arises when dealing with a nonlinear formulation of the NMF.
Within the framework of kernel machines, the models suggested in the literature do not allow the representation of the factorization matrices, which is a fallout of the curse of the pre-image.
In this paper, we propose a novel kernel-based model for the NMF that does not suffer from the pre-image problem, by investigating the estimation of the factorization matrices directly in the input space.
For different kernel functions, we describe two schemes for iterative algorithms: an additive update rule based on a gradient descent scheme and a multiplicative update rule in the same spirit as in the Lee and Seung algorithm.
Within the proposed framework, we develop several extensions to incorporate constraints, including sparseness, smoothness, and spatial regularization with a total-variation-like penalty.
The effectiveness of the proposed method is demonstrated with the problem of unmixing hyperspectral images, using well-known real images and results with state-of-the-art techniques.
The synchronization problem is investigated for the class of locally strongly transitive automata introduced in a previous work of the authors.
Some extensions of this problem related to the notions of stable set and word of minimal rank of an automaton are studied.
An application to synchronizing colorings of aperiodic graphs with a Hamiltonian path is also considered.
Using Deep Reinforcement Learning (DRL) can be a promising approach to handle various tasks in the field of (simulated) autonomous driving.
However, recent publications mainly consider learning in unusual driving environments.
This paper presents Driving School for Autonomous Agents (DSA^2), a software for validating DRL algorithms in more usual driving environments based on artificial and realistic road networks.
We also present the results of applying DSA^2 for handling the task of driving on a straight road while regulating the velocity of one vehicle according to different speed limits.
Arabic word segmentation is essential for a variety of NLP applications such as machine translation and information retrieval.
Segmentation entails breaking words into their constituent stems, affixes and clitics.
In this paper, we compare two approaches for segmenting four major Arabic dialects using only several thousand training examples for each dialect.
The two approaches involve posing the problem as a ranking problem, where an SVM ranker picks the best segmentation, and as a sequence labeling problem, where a bi-LSTM RNN coupled with CRF determines where best to segment words.
We are able to achieve solid segmentation results for all dialects using rather limited training data.
We also show that employing Modern Standard Arabic data for domain adaptation and assuming context independence improve overall results.
Genome-to-genome comparisons require designating anchor points, which are given by Maximum Exact Matches (MEMs) between their sequences.
For large genomes this is a challenging problem and the performance of existing solutions, even in parallel regimes, is not quite satisfactory.
We present a new algorithm, copMEM, that allows to sparsely sample both input genomes, with sampling steps being coprime.
Despite being a single-threaded implementation, copMEM computes all MEMs of minimum length 100 between the human and mouse genomes in less than 2 minutes, using less than 10 GB of RAM memory.
Moldable tasks allow schedulers to determine the number of processors assigned to a task, enabling efficient use of large-scale parallel processing systems.
A generic assumption is that every task is monotonic, i.e., its workload increases but its execution time decreases as the number of assigned processors increases.
In this paper, we study the problem of scheduling moldable tasks on processors.
Motivated by many benchmark studies, we introduce a new speedup model: it is linear when the number of assigned processors is small, up to some threshold; then, it possibly declines and even become negative as the number increases.
Given any threshold value achievable, we propose a generic approximation algorithm to minimize the makespan, which is simpler and achieves a better performance guarantee than the existing ones under the monotonic assumption.
As a by-product, we also propose an approximation algorithm to maximize the sum of values of tasks completed by a deadline; this scheduling objective is considered for moldable tasks for the first time while similar works have been done for other types of parallel tasks.
Most of the existing work on automatic facial expression analysis focuses on discrete emotion recognition, or facial action unit detection.
However, facial expressions do not always fall neatly into pre-defined semantic categories.
Also, the similarity between expressions measured in the action unit space need not correspond to how humans perceive expression similarity.
Different from previous work, our goal is to describe facial expressions in a continuous fashion using a compact embedding space that mimics human visual preferences.
To achieve this goal, we collect a large-scale faces-in-the-wild dataset with human annotations in the form: Expressions A and B are visually more similar when compared to expression C, and use this dataset to train a neural network that produces a compact (16-dimensional) expression embedding.
We experimentally demonstrate that the learned embedding can be successfully used for various applications such as expression retrieval, photo album summarization, and emotion recognition.
We also show that the embedding learned using the proposed dataset performs better than several other embeddings learned using existing emotion or action unit datasets.
We consider Markov models of large-scale networks where nodes are characterized by their local behavior and by a mobility model over a two-dimensional lattice.
By assuming random walk, we prove convergence to a system of partial differential equations (PDEs) whose size depends neither on the lattice size nor on the population of nodes.
This provides a macroscopic view of the model which approximates discrete stochastic movements with continuous deterministic diffusions.
We illustrate the practical applicability of this result by modeling a network of mobile nodes with on/off behavior performing file transfers with connectivity to 802.11 access points.
By means of an empirical validation against discrete-event simulation we show high quality of the PDE approximation even for low populations and coarse lattices.
In addition, we confirm the computational advantage in using the PDE limit over a traditional ordinary differential equation limit where the lattice is modeled discretely, yielding speed-ups of up to two orders of magnitude.
Analyzing multivariate time series data is important for many applications such as automated control, fault diagnosis and anomaly detection.
One of the key challenges is to learn latent features automatically from dynamically changing multivariate input.
In visual recognition tasks, convolutional neural networks (CNNs) have been successful to learn generalized feature extractors with shared parameters over the spatial domain.
However, when high-dimensional multivariate time series is given, designing an appropriate CNN model structure becomes challenging because the kernels may need to be extended through the full dimension of the input volume.
To address this issue, we present two structure learning algorithms for deep CNN models.
Our algorithms exploit the covariance structure over multiple time series to partition input volume into groups.
The first algorithm learns the group CNN structures explicitly by clustering individual input sequences.
The second algorithm learns the group CNN structures implicitly from the error backpropagation.
In experiments with two real-world datasets, we demonstrate that our group CNNs outperform existing CNN based regression methods.
In this article, we propose a new implementation of John von Neumann's middle square random number generator (RNG).
A Weyl sequence is utilized to keep the generator running through a long period.
Many machine intelligence techniques are developed in E-commerce and one of the most essential components is the representation of IDs, including user ID, item ID, product ID, store ID, brand ID, category ID etc.
The classical encoding based methods (like one-hot encoding) are inefficient in that it suffers sparsity problems due to its high dimension, and it cannot reflect the relationships among IDs, either homogeneous or heterogeneous ones.
In this paper, we propose an embedding based framework to learn and transfer the representation of IDs.
As the implicit feedbacks of users, a tremendous amount of item ID sequences can be easily collected from the interactive sessions.
By jointly using these informative sequences and the structural connections among IDs, all types of IDs can be embedded into one low-dimensional semantic space.
Subsequently, the learned representations are utilized and transferred in four scenarios: (i) measuring the similarity between items, (ii) transferring from seen items to unseen items, (iii) transferring across different domains, (iv) transferring across different tasks.
We deploy and evaluate the proposed approach in Hema App and the results validate its effectiveness.
We create and release the first publicly available commercial customer service corpus with annotated relational segments.
Human-computer data from three live customer service Intelligent Virtual Agents (IVAs) in the domains of travel and telecommunications were collected, and reviewers marked all text that was deemed unnecessary to the determination of user intention.
After merging the selections of multiple reviewers to create highlighted texts, a second round of annotation was done to determine the classes of language present in the highlighted sections such as the presence of Greetings, Backstory, Justification, Gratitude, Rants, or Emotions.
This resulting corpus is a valuable resource for improving the quality and relational abilities of IVAs.
As well as discussing the corpus itself, we compare the usage of such language in human-human interactions on TripAdvisor forums.
We show that removal of this language from task-based inputs has a positive effect on IVA understanding by both an increase in confidence and improvement in responses, demonstrating the need for automated methods of its discovery.
We derive an upper bound on the number of models for exact satisfiability (XSAT) of arbitrary CNF formulas F. The bound can be calculated solely from the distribution of positive and negated literals in the formula.
For certain subsets of CNF instances the new bound can be computed in sub-exponential time, namely in at most O(exp(sqrt(n))) , where n is the number of variables of F. A wider class of SAT problems beyond XSAT is defined to which the method can be extended.
Prior social contagion models consider the spread of either one contagion at a time on interdependent networks or multiple contagions on single layer networks or under assumptions of competition.
We propose a new threshold model for the diffusion of multiple contagions.
Individuals are placed on a multiplex network with a periodic lattice and a random-regular-graph layer.
On these population structures, we study the interface between two key aspects of the diffusion process: the level of synergy between two contagions, and the rate at which individuals become dormant after adoption.
Dormancy is defined as a looser form of immunity that models the ability to spread without resistance.
Monte Carlo simulations reveal lower synergy makes contagions more susceptible to percolation, especially those that diffuse on lattices.
Faster diffusion of one contagion with dormancy probabilistically blocks the diffusion of the other, in a way similar to ring vaccination.
We show that within a band of synergy, contagions on the lattices undergo bimodal or trimodal branching if they are the slower diffusing contagion.
Item-item collaborative filtering (CF) models are a well known and studied family of recommender systems, however current literature does not provide any theoretical explanation of the conditions under which item-based recommendations will succeed or fail.
We investigate the existence of an ideal item-based CF method able to make perfect recommendations.
This CF model is formalized as an eigenvalue problem, where estimated ratings are equivalent to the true (unknown) ratings multiplied by a user-specific eigenvalue of the similarity matrix.
Preliminary experiments show that the magnitude of the eigenvalue is proportional to the accuracy of recommendations for that user and therefore it can provide reliable measure of confidence.
The use of millimeter wave (mmWave) frequencies for communication will be one of the innovations of the next generation of cellular mobile networks (5G).
It will provide unprecedented data rates, but is highly susceptible to rapid channel variations and suffers from severe isotropic pathloss.
Highly directional antennas at the transmitter and the receiver will be used to compensate for these shortcomings and achieve sufficient link budget in wide area networks.
However, directionality demands precise alignment of the transmitter and the receiver beams, an operation which has important implications for control plane procedures, such as initial access, and may increase the delay of the data transmission.
This paper provides a comparison of measurement frameworks for initial access in mmWave cellular networks in terms of detection accuracy, reactiveness and overhead, using parameters recently standardized by the 3GPP and a channel model based on real-world measurements.
We show that the best strategy depends on the specific environment in which the nodes are deployed, and provide guidelines to characterize the optimal choice as a function of the system parameters.
For survival, a living agent must have the ability to assess risk (1) by temporally anticipating accidents before they occur, and (2) by spatially localizing risky regions in the environment to move away from threats.
In this paper, we take an agent-centric approach to study the accident anticipation and risky region localization tasks.
We propose a novel soft-attention Recurrent Neural Network (RNN) which explicitly models both spatial and appearance-wise non-linear interaction between the agent triggering the event and another agent or static-region involved.
In order to test our proposed method, we introduce the Epic Fail (EF) dataset consisting of 3000 viral videos capturing various accidents.
In the experiments, we evaluate the risk assessment accuracy both in the temporal domain (accident anticipation) and spatial domain (risky region localization) on our EF dataset and the Street Accident (SA) dataset.
Our method consistently outperforms other baselines on both datasets.
One of the fundamental elements impacting the performance of a wireless system is interference, which has been a long-term issue in wireless networks.
In the case of cognitive radio (CR) networks, the problem of interference is tremendously crucial.
In other words, CR keeps the important promise of not producing any harmful interference to the primary user (PU) system.
Thus, it is essential to investigate the impact of interference caused to the PUs so that its detrimental effect on the performance of the PU system performance is reduced.
Study of cognitive interference generally includes developing a model to statistically demonstrate the power of cognitive interference at the PUs, which then can be utilized to examine different performance measures.
Having inspected the different models for channel interference present in the literature, it can be obviously seen that interference models have been gradually evolved in terms of complication and sophistication.
Although numerous papers can be found in the literature that have investigated different models for interference, to the best of our knowledge, very few publications are available that provide a review of all models and their comparisons.
This paper is a collection of state-of-the-art in interference modeling which overviews and compares different models in the literature to provide the valuable insights for researchers when modeling the interference in a specific scenario.
The estimation of inertial parameters of a robotic system is crucial for better trajectory tracking performance, specially when model-based controllers are used for carrying out precise tasks.
In this paper, we consider the scenario of grasping an object of unknown properties by a free-flyer space robot with limited actuation.
The problem is to find the inertial parameters of the complete system after grasping has been performed.
Excitation is provided in inertial space, and the excitation trajectories are found by optimization.
Truncated Fourier series are used to represent the reference as well as tracked trajectory.
An approach based on the energy balance between the actuation work and the rate of change of kinetic energy is introduced to calculate the number of harmonics in the Fourier series used to represent the executed trajectory, while trying to find a balance between accounting for saturation effects and keeping out noise.
The effect of input saturation on parameter estimation is also studied.
Simulation results using the Space CoBot free-flyer robot are presented to show the feasibility of the approach.
Air traffic control increasingly depends on information and communication technology (ICT) to manage traffic flow through highly congested and increasingly interdependent airspace regions.
While these systems are critical to ensuring the efficiency and safety of our airspace, they are also increasingly vulnerable to cyber threats that could potentially lead to reduction in capacity and/or reorganization of traffic flows.
In this paper, we model various cyber threats to air traffic control systems, and analyze how these attacks could impact the flow of aircraft through the airspace.
To perform this analysis, we consider a model for wide-area air traffic based on a dynamic queuing network model.
Then we introduce three different attacks (Route Denial of Service, Route Selection Tampering, and Sector Denial of Service) to the air traffic control system, and explore how these attacks manipulate the sector flows by evaluating the queue backlogs for each sector's outflows.
Furthermore, we then explore graph-level vulnerability metrics to identify the sectors that are most vulnerable to various flow manipulations, and compare them to case-study simulations of the various attacks.
The results suggest that Route Denial of Service attacks have a significant impact on the target sector and lead to the largest degradation to the overall air traffic flows.
Furthermore, the impact of Sector Denial of Service attack impacts are primarily confined to the target sector, while the Route Selection Tampering impacts are mostly confined to certain aircraft.
Generative adversarial nets (GANs) are widely used to learn the data sampling process and their performance may heavily depend on the loss functions, given a limited computational budget.
This study revisits MMD-GAN that uses the maximum mean discrepancy (MMD) as the loss function for GAN and makes two contributions.
First, we argue that the existing MMD loss function may discourage the learning of fine details in data as it attempts to contract the discriminator outputs of real data.
To address this issue, we propose a repulsive loss function to actively learn the difference among the real data by simply rearranging the terms in MMD.
Second, inspired by the hinge loss, we propose a bounded Gaussian kernel to stabilize the training of MMD-GAN with the repulsive loss function.
The proposed methods are applied to the unsupervised image generation tasks on CIFAR-10, STL-10, CelebA, and LSUN bedroom datasets.
Results show that the repulsive loss function significantly improves over the MMD loss at no additional computational cost and outperforms other representative loss functions.
The proposed methods achieve an FID score of 16.21 on the CIFAR-10 dataset using a single DCGAN network and spectral normalization.
It is known that Boosting can be interpreted as a gradient descent technique to minimize an underlying loss function.
Specifically, the underlying loss being minimized by the traditional AdaBoost is the exponential loss, which is proved to be very sensitive to random noise/outliers.
Therefore, several Boosting algorithms, e.g., LogitBoost and SavageBoost, have been proposed to improve the robustness of AdaBoost by replacing the exponential loss with some designed robust loss functions.
In this work, we present a new way to robustify AdaBoost, i.e., incorporating the robust learning idea of Self-paced Learning (SPL) into Boosting framework.
Specifically, we design a new robust Boosting algorithm based on SPL regime, i.e., SPLBoost, which can be easily implemented by slightly modifying off-the-shelf Boosting packages.
Extensive experiments and a theoretical characterization are also carried out to illustrate the merits of the proposed SPLBoost.
The present paper introduces the initial implementation of a software exploration tool targeting graphical user interface (GUI) driven applications.
GUITracer facilitates the comprehension of GUI-driven applications by starting from their most conspicuous artefact - the user interface itself.
The current implementation of the tool can be used with any Java-based target application that employs one of the AWT, Swing or SWT toolkits.
The tool transparently instruments the target application and provides real time information about the GUI events fired.
For each event, call relations within the application are displayed at method, class or package level, together with detailed coverage information.
The tool facilitates feature location, program comprehension as well as GUI test creation by revealing the link between the application's GUI and its underlying code.
As such, GUITracer is intended for software practitioners developing or maintaining GUI-driven applications.
We believe our tool to be especially useful for entry-level practitioners as well as students seeking to understand complex GUI-driven software systems.
The present paper details the rationale as well as the technical implementation of the tool.
As a proof-of-concept implementation, we also discuss further development that can lead to our tool's integration into a software development workflow.
Opacity is a property that characterizes the system's capability to keep its "secret" from being inferred by an intruder that partially observes the system's behavior.
In this paper, we are concerned with enhancing the opacity using insertion functions, while at the same time, enforcing the task specification in a parametric stochastic discrete event system.
We first obtain the parametric Markov decision process that encodes all the possible insertions.
Based on which, we convert this parameter and insertion function co-synthesis problem into a nonlinear program.
We prove that if the output of this program satisfies all the constraints, it will be a valid solution to our problem.
Therefore, the security and the capability of enforcing the task specification can be simultaneously guaranteed.
In today's WLANs, scheduling of packet transmissions solely relies on the collision and success a station may experience.
To better support traffic differentiation in dense WLANs, in this paper, we propose a distributed reservation mechanism for the Carrier Sense Multiple Access Extended Collision Avoidance (CSMA/ECA) MAC protocol, termed CSMA/ECA-DR, based on which stations can collaboratively achieve higher network performance.
In addition, proper Contention Window (CW) will be chosen based on the instantaneously estimated number of active contenders in the network.
Simulation results from dense scenarios with traffic differentiation demonstrate that CSMA/ECA-DR can greatly improve the efficiency of WLANs for traffic differentiation even with large numbers of contenders.
The new frontier in cellular networks is harnessing the enormous spectrum available at millimeter wave (mmWave) frequencies above 28 GHz.
The challenging radio propagation characteristics at these frequencies, and the use of highly directional beamforming, lead to intermittent links between the base station (BS) and the user equipment (UE).
In this paper, we revisit the problem of cell selection to maintain an acceptable level of service, despite the underlying intermittent link connectivity typical of mmWave links.
We propose a Markov Decision Process (MDP) framework to study the properties and performance of our proposed cell selection strategy, which jointly considers several factors such as dynamic channel load and link quality.
We use the Value Iteration Algorithm (VIA) to solve the MDP, and obtain the optimal set of associations.
We address the multi user problem through a distributed iterative approach, in which each UE characterizes the evolution of the system based on stationary channel distribution and cell selection statistics of other UEs.
Through simulation results, we show that our proposed technique makes judicious handoff choices, thereby providing a significant improvement in the overall network capacity.
Further, our technique reduces the total number of handoffs, thus lowering the signaling overhead, while providing a higher quality of service to the UEs.
Deep convolutional networks have demonstrated the state-of-the-art performance on various medical image computing tasks.
Leveraging images from different modalities for the same analysis task holds clinical benefits.
However, the generalization capability of deep models on test data with different distributions remain as a major challenge.
In this paper, we propose the PnPAdaNet (plug-and-play adversarial domain adaptation network) for adapting segmentation networks between different modalities of medical images, e.g., MRI and CT. We propose to tackle the significant domain shift by aligning the feature spaces of source and target domains in an unsupervised manner.
Specifically, a domain adaptation module flexibly replaces the early encoder layers of the source network, and the higher layers are shared between domains.
With adversarial learning, we build two discriminators whose inputs are respectively multi-level features and predicted segmentation masks.
We have validated our domain adaptation method on cardiac structure segmentation in unpaired MRI and CT.
The experimental results with comprehensive ablation studies demonstrate the excellent efficacy of our proposed PnP-AdaNet.
Moreover, we introduce a novel benchmark on the cardiac dataset for the task of unsupervised cross-modality domain adaptation.
We will make our code and database publicly available, aiming to promote future studies on this challenging yet important research topic in medical imaging.
Offline signature verification is one of the most challenging tasks in biometrics and document forensics.
Unlike other verification problems, it needs to model minute but critical details between genuine and forged signatures, because a skilled falsification might often resembles the real signature with small deformation.
This verification task is even harder in writer independent scenarios which is undeniably fiscal for realistic cases.
In this paper, we model an offline writer independent signature verification task with a convolutional Siamese network.
Siamese networks are twin networks with shared weights, which can be trained to learn a feature space where similar observations are placed in proximity.
This is achieved by exposing the network to a pair of similar and dissimilar observations and minimizing the Euclidean distance between similar pairs while simultaneously maximizing it between dissimilar pairs.
Experiments conducted on cross-domain datasets emphasize the capability of our network to model forgery in different languages (scripts) and handwriting styles.
Moreover, our designed Siamese network, named SigNet, exceeds the state-of-the-art results on most of the benchmark signature datasets, which paves the way for further research in this direction.
This paper addresses the problem of designing LDPC decoders robust to transient errors introduced by a faulty hardware.
We assume that the faulty hardware introduces errors during the message passing updates and we propose a general framework for the definition of the message update faulty functions.
Within this framework, we define symmetry conditions for the faulty functions, and derive two simple error models used in the analysis.
With this analysis, we propose a new interpretation of the functional Density Evolution threshold previously introduced, and show its limitations in case of highly unreliable hardware.
However, we show that under restricted decoder noise conditions, the functional threshold can be used to predict the convergence behavior of FAIDs under faulty hardware.
In particular, we reveal the existence of robust and non-robust FAIDs and propose a framework for the design of robust decoders.
We finally illustrate robust and non-robust decoders behaviors of finite length codes using Monte Carlo simulations.
Earlier formulations of the DNA assembly problem were all in the context of perfect assembly; i.e., given a set of reads from a long genome sequence, is it possible to perfectly reconstruct the original sequence?
In practice, however, it is very often the case that the read data is not sufficiently rich to permit unambiguous reconstruction of the original sequence.
While a natural generalization of the perfect assembly formulation to these cases would be to consider a rate-distortion framework, partial assemblies are usually represented in terms of an assembly graph, making the definition of a distortion measure challenging.
In this work, we introduce a distortion function for assembly graphs that can be understood as the logarithm of the number of Eulerian cycles in the assembly graph, each of which correspond to a candidate assembly that could have generated the observed reads.
We also introduce an algorithm for the construction of an assembly graph and analyze its performance on real genomes.
Much research has been conducted on both face identification and face verification, with greater focus on the latter.
Research on face identification has mostly focused on using closed-set protocols, which assume that all probe images used in evaluation contain identities of subjects that are enrolled in the gallery.
Real systems, however, where only a fraction of probe sample identities are enrolled in the gallery, cannot make this closed-set assumption.
Instead, they must assume an open set of probe samples and be able to reject/ignore those that correspond to unknown identities.
In this paper, we address the widespread misconception that thresholding verification-like scores is a good way to solve the open-set face identification problem, by formulating an open-set face identification protocol and evaluating different strategies for assessing similarity.
Our open-set identification protocol is based on the canonical labeled faces in the wild (LFW) dataset.
Additionally to the known identities, we introduce the concepts of known unknowns (known, but uninteresting persons) and unknown unknowns (people never seen before) to the biometric community.
We compare three algorithms for assessing similarity in a deep feature space under an open-set protocol: thresholded verification-like scores, linear discriminant analysis (LDA) scores, and an extreme value machine (EVM) probabilities.
Our findings suggest that thresholding EVM probabilities, which are open-set by design, outperforms thresholding verification-like scores.
Equating users' true needs and desires with behavioural measures of 'engagement' is problematic.
However, good metrics of 'true preferences' are difficult to define, as cognitive biases make people's preferences change with context and exhibit inconsistencies over time.
Yet, HCI research often glosses over the philosophical and theoretical depth of what it means to infer what users really want.
In this paper, we present an alternative yet very real discussion of this issue, via a fictive dialogue between senior executives in a tech company aimed at helping people live the life they `really' want to live.
How will the designers settle on a metric for their product to optimise?
Economies are instances of complex socio-technical systems that are shaped by the interactions of large numbers of individuals.
The individual behavior and decision-making of consumer agents is determined by complex psychological dynamics that include their own assessment of present and future economic conditions as well as those of others, potentially leading to feedback loops that affect the macroscopic state of the economic system.
We propose that the large-scale interactions of a nation's citizens with its online resources can reveal the complex dynamics of their collective psychology, including their assessment of future system states.
Here we introduce a behavioral index of Chinese Consumer Confidence (C3I) that computationally relates large-scale online search behavior recorded by Google Trends data to the macroscopic variable of consumer confidence.
Our results indicate that such computational indices may reveal the components and complex dynamics of consumer psychology as a collective socio-economic phenomenon, potentially leading to improved and more refined economic forecasting.
Information technologies today can inform each of us about the best alternatives for shortest paths from origins to destinations, but they do not contain incentives or alternatives that manage the information efficiently to get collective benefits.
To obtain such benefits, we need to have not only good estimates of how the traffic is formed but also to have target strategies to reduce enough vehicles from the best possible roads in a feasible way.
The opportunity is that during large events the traffic inconveniences in large cities are unusually high, yet temporary, and the entire population may be more willing to adopt collective recommendations for social good.
In this paper, we integrate for the first time big data resources to quantify the impact of events and propose target strategies for collective good at urban scale.
In the context of the Olympic Games in Rio de Janeiro, we first predict the expected increase in traffic.
To that end, we integrate data from: mobile phones, Airbnb, Waze, and transit information, with game schedules and information of venues.
Next, we evaluate the impact of the Olympic Games to the travel of commuters, and propose different route choice scenarios during the peak hours.
Moreover, we gather information on the trips that contribute the most to the global congestion and that could be redirected from vehicles to transit.
Interestingly, we show that (i) following new route alternatives during the event with individual shortest path can save more collective travel time than keeping the routine routes, uncovering the positive value of information technologies during events; (ii) with only a small proportion of people selected from specific areas switching from driving to public transport, the collective travel time can be reduced to a great extent.
Results are presented on-line for the evaluation of the public and policy makers.
Image Segmentation is a technique of partitioning the original image into some distinct classes.
Many possible solutions may be available for segmenting an image into a certain number of classes, each one having different quality of segmentation.
In our proposed method, multilevel thresholding technique has been used for image segmentation.
A new approach of Cuckoo Search (CS) is used for selection of optimal threshold value.
In other words, the algorithm is used to achieve the best solution from the initial random threshold values or solutions and to evaluate the quality of a solution correlation function is used.
Finally, MSE and PSNR are measured to understand the segmentation quality.
Stochastic behaviors of resistive random access memory (RRAM) play an important role in the design of cross-point memory arrays.
A Monte Carlo compact model of oxide RRAM is developed and calibrated with experiments on various device stack configurations.
With Monte Carlo SPICE simulations, we show that an increase in array size and interconnect wire resistance will statistically deteriorate write functionality.
Write failure probability (WFP) has an exponential dependency on device uniformity and supply voltage (VDD), and the array bias scheme is a key knob.
Lowering array VDD leads to higher effective energy consumption (EEC) due to the increase in WFP when the variation statistics are included in the analysis.
Random-access simulations indicate that data sparsity statistically benefits write functionality and energy consumption.
Finally, we show that a pseudo-sub-array topology with uniformly distributed pre-forming cells in the pristine high resistance state is able to reduce both WFP and EEC, enabling higher net capacity for memory circuits due to improved variation tolerance.
Path planning is typically considered in Artificial Intelligence as a graph searching problem and R* is state-of-the-art algorithm tailored to solve it.
The algorithm decomposes given path finding task into the series of subtasks each of which can be easily (in computational sense) solved by well-known methods (such as A*).
Parameterized random choice is used to perform the decomposition and as a result R* performance largely depends on the choice of its input parameters.
In our work we formulate a range of assumptions concerning possible upper and lower bounds of R* parameters, their interdependency and their influence on R* performance.
Then we evaluate these assumptions by running a large number of experiments.
As a result we formulate a set of heuristic rules which can be used to initialize the values of R* parameters in a way that leads to algorithm's best performance.
Standard algorithms for finding the shortest path in a graph require that the cost of a path be additive in edge costs, and typically assume that costs are deterministic.
We consider the problem of uncertain edge costs, with potential probabilistic dependencies among the costs.
Although these dependencies violate the standard dynamic-programming decomposition, we identify a weaker stochastic consistency condition that justifies a generalized dynamic-programming approach based on stochastic dominance.
We present a revised path-planning algorithm and prove that it produces optimal paths under time-dependent uncertain costs.
We test the algorithm by applying it to a model of stochastic bus networks, and present empirical performance results comparing it to some alternatives.
Finally, we consider extensions of these concepts to a more general class of problems of heuristic search under uncertainty.
Linear rules have played an increasing role in structural proof theory in recent years.
It has been observed that the set of all sound linear inference rules in Boolean logic is already coNP-complete, i.e. that every Boolean tautology can be written as a (left- and right-)linear rewrite rule.
In this paper we study properties of systems consisting only of linear inferences.
Our main result is that the length of any 'nontrivial' derivation in such a system is bound by a polynomial.
As a consequence there is no polynomial-time decidable sound and complete system of linear inferences, unless coNP=NP.
We draw tools and concepts from term rewriting, Boolean function theory and graph theory in order to access some required intermediate results.
At the same time we make several connections between these areas that, to our knowledge, have not yet been presented and constitute a rich theoretical framework for reasoning about linear TRSs for Boolean logic.
Reinforcement learning has significant applications for multi-agent systems, especially in unknown dynamic environments.
However, most multi-agent reinforcement learning (MARL) algorithms suffer from such problems as exponential computation complexity in the joint state-action space, which makes it difficult to scale up to realistic multi-agent problems.
In this paper, a novel algorithm named negotiation-based MARL with sparse interactions (NegoSI) is presented.
In contrast to traditional sparse-interaction based MARL algorithms, NegoSI adopts the equilibrium concept and makes it possible for agents to select the non-strict Equilibrium Dominating Strategy Profile (non-strict EDSP) or Meta equilibrium for their joint actions.
The presented NegoSI algorithm consists of four parts: the equilibrium-based framework for sparse interactions, the negotiation for the equilibrium set, the minimum variance method for selecting one joint action and the knowledge transfer of local Q-values.
In this integrated algorithm, three techniques, i.e., unshared value functions, equilibrium solutions and sparse interactions are adopted to achieve privacy protection, better coordination and lower computational complexity, respectively.
To evaluate the performance of the presented NegoSI algorithm, two groups of experiments are carried out regarding three criteria: steps of each episode (SEE), rewards of each episode (REE) and average runtime (AR).
The first group of experiments is conducted using six grid world games and shows fast convergence and high scalability of the presented algorithm.
Then in the second group of experiments NegoSI is applied to an intelligent warehouse problem and simulated results demonstrate the effectiveness of the presented NegoSI algorithm compared with other state-of-the-art MARL algorithms.
Logic programming provides a very high-level view of programming, which comes at the cost of some execution efficiency.
Improving performance of logic programs is thus one of the holy grails of Prolog system implementations and a wide range of approaches have historically been taken towards this goal.
Designing computational models that both exploit the available parallelism in a given application and that try hard to reduce the explored search space has been an ongoing line of research for many years.
These goals in particular have motivated the design of several computational models, one of which is the Extended Andorra Model (EAM).
In this paper, we present a preliminary specification and implementation of the EAM with Implicit Control, the WAM2EAM, which supplies regular WAM instructions with an EAM-centered interpretation.
We present some of the experiments we have performed to best test our design for a library for MathScheme, the mechanized mathematics software system we are building.
We wish for our library design to use and reflect, as much as possible, the mathematical structure present in the objects which populate the library.
This manual describes the competition software for the Simulated Car Racing Championship, an international competition held at major conferences in the field of Evolutionary Computation and in the field of Computational Intelligence and Games.
It provides an overview of the architecture, the instructions to install the software and to run the simple drivers provided in the package, the description of the sensors and the actuators.
In this paper, we introduce a rule-based approach to annotate Locative and Directional Expressions in Arabic natural language text.
The annotation is based on a constructed semantic map of the spatiality domain.
Challenges are twofold: first, we need to study how locative and directional expressions are expressed linguistically in these texts; and second, we need to automatically annotate the relevant textual segments accordingly.
The research method we will use in this article is analytic-descriptive.
We will validate this approach on specific novel rich with these expressions and show that it has very promising results.
We will be using NOOJ as a software tool to implement finite-state transducers to annotate linguistic elements according to Locative and Directional Expressions.
In conclusion, NOOJ allowed us to write linguistic rules for the automatic annotation in Arabic text of Locative and Directional Expressions.
Orthogonal frequency division multiplexing (OFDM) and single-carrier frequency domain equalization (SC-FDE) are two commonly adopted modulation schemes for frequency-selective channels.
Compared to SC-FDE, OFDM generally achieves higher data rate, but at the cost of higher transmit signal peak-to-average power ratio (PAPR) that leads to lower power amplifier efficiency.
This paper proposes a new modulation scheme, called flexible multi-group single-carrier (FMG-SC), which encapsulates both OFDM and SC-FDE as special cases, thus achieving more flexible rate-PAPR trade-offs between them.
Specifically, a set of frequency subcarriers are flexibly divided into orthogonal groups based on their channel gains, and SC-FDE is applied over each of the groups to send different data streams in parallel.
We aim to maximize the achievable sum-rate of all groups by optimizing the subcarrier-group mapping.
We propose two low-complexity subcarrier grouping methods and show via simulation that they perform very close to the optimal grouping by exhaustive search.
Simulation results also show the effectiveness of the proposed FMG-SC modulation scheme with optimized subcarrier grouping in improving the rate-PAPR trade-off over conventional OFDM and SC-FDE.
In 2013, Tsai et al. cryptanalyzed Yeh et al. scheme and shown that Yeh et al., scheme is vulnerable to various cryptographic attacks and proposed an improved scheme.
In this poster we will show that Tsai et al., scheme is also vulnerable to undetectable online password guessing attack, on success of the attack, the adversary can perform all major cryptographic attacks.
As apart of our contribution, we have proposed an improved scheme which overcomes the defects in Tsai et al. and Yeh et al. schemes.
Breast cancer is the second most common malignancy among women and has become a major public health problem in current society.
Traditional breast cancer identification requires experienced pathologists to carefully read the breast slice, which is laborious and suffers from inter-observer variations.
Consequently, an automatic classification framework for breast cancer identification is worthwhile to develop.
Recent years witnessed the development of deep learning technique.
Increasing number of medical applications start to use deep learning to improve diagnosis accuracy.
In this paper, we proposed a novel training strategy, namely reversed active learning (RAL), to train network to automatically classify breast cancer images.
Our RAL is applied to the training set of a simple convolutional neural network (CNN) to remove mislabeled images.
We evaluate the CNN trained with RAL on publicly available ICIAR 2018 Breast Cancer Dataset (IBCD).
The experimental results show that our RAL increases the slice-based accuracy of CNN from 93.75% to 96.25%.
We present the first sample compression algorithm for nearest neighbors with non-trivial performance guarantees.
We complement these guarantees by demonstrating almost matching hardness lower bounds, which show that our bound is nearly optimal.
Our result yields new insight into margin-based nearest neighbor classification in metric spaces and allows us to significantly sharpen and simplify existing bounds.
Some encouraging empirical results are also presented.
There are several distinct failure modes for overoptimization of systems on the basis of metrics.
This occurs when a metric which can be used to improve a system is used to an extent that further optimization is ineffective or harmful, and is sometimes termed Goodhart's Law.
This class of failure is often poorly understood, partly because terminology for discussing them is ambiguous, and partly because discussion using this ambiguous terminology ignores distinctions between different failure modes of this general type.
This paper expands on an earlier discussion by Garrabrant, which notes there are "(at least) four different mechanisms" that relate to Goodhart's Law.
This paper is intended to explore these mechanisms further, and specify more clearly how they occur.
This discussion should be helpful in better understanding these types of failures in economic regulation, in public policy, in machine learning, and in Artificial Intelligence alignment.
The importance of Goodhart effects depends on the amount of power directed towards optimizing the proxy, and so the increased optimization power offered by artificial intelligence makes it especially critical for that field.
Literary works reference a variety of globally shared themes including well-known people, events, and time periods.
It is particularly interesting to locate patterns that are either invariant across time or exhibit a characteristic change across time, as they could imply something important about society that those works record.
This paper suggests the use of Google n-gram viewer as a fast prototyping method for examining time-based properties over a rich sample of literary prose.
Using this method, we find that some repeating periods of time, like Sunday, are referenced disproportionally, allowing us to pose questions such as why a day like Thursday is so unpopular.
Furthermore, by treating software as a work of prose, we can apply a similar analysis to open-source software repositories and explore time-based relations in commit logs.
Doing a simple statistical analysis on a few temporal keywords in the log records, we reinforce and weaken a few beliefs on how college students approach open source software.
Finally, we help readers working on their own temporal analysis by comparing the fundamental differences between literary works and code repositories, and suggest blog or wiki as recently-emerging works.
This paper presents a new way to study registration based trackers by decomposing them into three constituent sub modules: appearance model, state space model and search method.
It is often the case that when a new tracker is introduced in literature, it only contributes to one or two of these sub modules while using existing methods for the rest.
Since these are often selected arbitrarily by the authors, they may not be optimal for the new method.
In such cases, our breakdown can help to experimentally find the best combination of methods for these sub modules while also providing a framework within which the contributions of the new tracker can be clearly demarcated and thus studied better.
We show how existing trackers can be broken down using the suggested methodology and compare the performance of the default configuration chosen by the authors against other possible combinations to demonstrate the new insights that can be gained by such an approach.
We also present an open source system that provides a convenient interface to plug in a new method for any sub module and test it against all possible combinations of methods for the other two sub modules while also serving as a fast and efficient solution for practical tracking requirements.
The inability to interpret the model prediction in semantically and visually meaningful ways is a well-known shortcoming of most existing computer-aided diagnosis methods.
In this paper, we propose MDNet to establish a direct multimodal mapping between medical images and diagnostic reports that can read images, generate diagnostic reports, retrieve images by symptom descriptions, and visualize attention, to provide justifications of the network diagnosis process.
MDNet includes an image model and a language model.
The image model is proposed to enhance multi-scale feature ensembles and utilization efficiency.
The language model, integrated with our improved attention mechanism, aims to read and explore discriminative image feature descriptions from reports to learn a direct mapping from sentence words to image pixels.
The overall network is trained end-to-end by using our developed optimization strategy.
Based on a pathology bladder cancer images and its diagnostic reports (BCIDR) dataset, we conduct sufficient experiments to demonstrate that MDNet outperforms comparative baselines.
The proposed image model obtains state-of-the-art performance on two CIFAR datasets as well.
In state-of-the-art Neural Machine Translation (NMT), an attention mechanism is used during decoding to enhance the translation.
At every step, the decoder uses this mechanism to focus on different parts of the source sentence to gather the most useful information before outputting its target word.
Recently, the effectiveness of the attention mechanism has also been explored for multimodal tasks, where it becomes possible to focus both on sentence parts and image regions that they describe.
In this paper, we compare several attention mechanism on the multimodal translation task (English, image to German) and evaluate the ability of the model to make use of images to improve translation.
We surpass state-of-the-art scores on the Multi30k data set, we nevertheless identify and report different misbehavior of the machine while translating.
This paper develops new theory and algorithms to recover signals that are approximately sparse in some general dictionary (i.e., a basis, frame, or over-/incomplete matrix) but corrupted by a combination of interference having a sparse representation in a second general dictionary and measurement noise.
The algorithms and analytical recovery conditions consider varying degrees of signal and interference support-set knowledge.
Particular applications covered by the proposed framework include the restoration of signals impaired by impulse noise, narrowband interference, or saturation/clipping, as well as image in-painting, super-resolution, and signal separation.
Two application examples for audio and image restoration demonstrate the efficacy of the approach.
Sliding super point is a special host defined under sliding time window with which there are huge other hosts contact.
It plays important roles in network security and management.
But how to detect them in real time from nowadays high-speed network which contains several distributed routers is a hard task.
Distributed sliding super point detection requires an algorithm that can estimate the number of contacting hosts incrementally, scan packets faster than their flowing speed and reconstruct sliding super point at the end of a time period.
But no existing algorithm satisfies these three requirements simultaneously.
To solve this problem, this paper firstly proposed a distributed sliding super point detection algorithm running on GPU.
The advantage of this algorithm comes from a novel sliding estimator, which can estimate contacting host number incrementally under a sliding window, and a set of reversible hash functions, by which sliding super points could be regained without storing additional data such as IP list.
There are two main procedures in this algorithm: packets scanning and sliding super points reconstruction.
Both could run parallel without any data reading conflict.
When deployed on a low cost GPU, this algorithm could deal with traffic with bandwidth as high as 680 Gb/s.
A real world core network traffic is used to evaluate the performance of this sliding super point detection algorithm on a cheap GPU, Nvidia GTX950 with 4 GB graphic memory.
Experiments comparing with other algorithms under discrete time window show that this algorithm has the highest accuracy.
Under sliding time widow, this algorithm has the same performance as in discrete time window, where no other algorithms can work.
We study a two-level uncapacitated lot-sizing problem with inventory bounds that occurs in a supply chain composed of a supplier and a retailer.
The first level with the demands is the retailer level and the second one is the supplier level.
The aim is to minimize the cost of the supply chain so as to satisfy the demands when the quantity of item that can be held in inventory at each period is limited.
The inventory bounds can be imposed at the retailer level, at the supplier level or at both levels.
We propose a polynomial dynamic programming algorithm to solve this problem when the inventory bounds are set on the retailer level.
When the inventory bounds are set on the supplier level, we show that the problem is NP-hard.
We give a pseudo-polynomial algorithm which solves this problem when there are inventory bounds on both levels.
In the case where demand lot-splitting is not allowed, i.e. each demand has to be satisfied by a single order, we prove that the uncapacitated lot-sizing problem with inventory bounds is strongly NP-hard.
This implies that the two-level lot-sizing problems with inventory bounds are also strongly NP-hard when demand lot-splitting is considered.
Methods for teaching machines to answer visual questions have made significant progress in the last few years, but although demonstrating impressive results on particular datasets, these methods lack some important human capabilities, including integrating new visual classes and concepts in a modular manner, providing explanations for the answer and handling new domains without new examples.
In this paper we present a system that achieves state-of-the-art results on the CLEVR dataset without any questions-answers training, utilizes real visual estimators and explains the answer.
The system includes a question representation stage followed by an answering procedure, which invokes an extendable set of visual estimators.
It can explain the answer, including its failures, and provide alternatives to negative answers.
The scheme builds upon a framework proposed recently, with extensions allowing the system to deal with novel domains without relying on training examples.
Technical Universities (TUs) exhibit a distinct ranking performance in comparison with other universities.
In this paper we identify 137 TUs included in the THE Ranking (2017 edition) and analyse their scores statistically.
The results highlight the existence of clusters of TUs showing a general high performance in the Industry Income category and, in many cases, a low performance on Research and Teaching.
Finally, the global score weights were simulated, creating several scenarios that confirmed that the majority of TUs (except those with a world-class status) would increase their final scores if industrial income was accounted for at the levels parametrised.
Communication systems for multicasting information and energy simultaneously to more than one user are investigated.
In the system under study, a transmitter sends the same message and signal to multiple receivers over distinct and independent channels.
In this setting, results for compound channels are applied to relate the operational compound capacity to the informational measurements.
The fundamental limit under a received energy constraint, called the multicast capacity-energy function, is studied and a single-letter expression is derived.
The ideas are illustrated via a numerical example with two receivers.
The problem of receiver segmentation, in which the receivers are divided into several groups, is also considered.
Over-segmentation, or super-pixel generation, is a common preliminary stage for many computer vision applications.
New acquisition technologies enable the capturing of 3D point clouds that contain color and geometrical information.
This 3D information introduces a new conceptual change that can be utilized to improve the results of over-segmentation, which uses mainly color information, and to generate clusters of points we call super-points.
We consider a variety of possible 3D extensions of the Local Variation (LV) graph based over-segmentation algorithms, and compare them thoroughly.
We consider different alternatives for constructing the connectivity graph, for assigning the edge weights, and for defining the merge criterion, which must now account for the geometric information and not only color.
Following this evaluation, we derive a new generic algorithm for over-segmentation of 3D point clouds.
We call this new algorithm Point Cloud Local Variation (PCLV).
The advantages of the new over-segmentation algorithm are demonstrated on both outdoor and cluttered indoor scenes.
Performance analysis of the proposed approach compared to state-of-the-art 2D and 3D over-segmentation algorithms shows significant improvement according to the common performance measures.
In the article, an experiment is aimed at clarifying the transfer efficiency of the database in the cloud infrastructure.
The system was added to the control unit, which has guided the database search in the local part or in the cloud.
It is shown that the time data acquisition remains unchanged as a result of modification.
Suggestions have been made about the use of the theory of dynamic systems to hybrid cloud database.
The present work is aimed at attracting the attention of spe-cialists in the field of cloud database to the apparatus control theory.
The experiment presented in this article allows the use of the description of the known methods for solving important practical problems.
Modern networks are large, highly complex and dynamic.
Add to that the mobility of the agents comprising many of these networks.
It is difficult or even impossible for such systems to be managed centrally in an efficient manner.
It is imperative for such systems to attain a degree of self-management.
Self-healing i.e. the capability of a system in a good state to recover to another good state in face of an attack, is desirable for such systems.
In this paper, we discuss the self-healing model for dynamic reconfigurable systems.
In this model, an omniscient adversary inserts or deletes nodes from a network and the algorithm responds by adding a limited number of edges in order to maintain invariants of the network.
We look at some of the results in this model and argue for their applicability and further extensions of the results and the model.
We also look at some of the techniques we have used in our earlier work, in particular, we look at the idea of maintaining virtual graphs mapped over the existing network and assert that this may be a useful technique to use in many problem domains.
Query expansion is a method for alleviating the vocabulary mismatch problem present in information retrieval tasks.
Previous works have shown that terms selected for query expansion by traditional methods such as pseudo-relevance feedback are not always helpful to the retrieval process.
In this paper, we show that this is also true for more recently proposed embedding-based query expansion methods.
We then introduce an artificial neural network classifier to predict the usefulness of query expansion terms.
This classifier uses term word embeddings as inputs.
We perform experiments on four TREC newswire and web collections show that using terms selected by the classifier for expansion significantly improves retrieval performance when compared to competitive baselines.
The results are also shown to be more robust than the baselines.
This paper presents a novel approach for learning self-awareness models for autonomous vehicles.
The proposed technique is based on the availability of synchronized multi-sensor dynamic data related to different maneuvering tasks performed by a human operator.
It is shown that different machine learning approaches can be used to first learn single modality models using coupled Dynamic Bayesian Networks; such models are then correlated at event level to discover contextual multi-modal concepts.
In the presented case, visual perception and localization are used as modalities.
Cross-correlations among modalities in time is discovered from data and are described as probabilistic links connecting shared and private multi-modal DBNs at the event (discrete) level.
Results are presented on experiments performed on an autonomous vehicle, highlighting potentiality of the proposed approach to allow anomaly detection and autonomous decision making based on learned self-awareness models.
The relation between Science (what we can explain) and Art (what we can't) has long been acknowledged and while every science contains an artistic part, every art form also needs a bit of science.
Among all scientific disciplines, programming holds a special place for two reasons.
First, the artistic part is not only undeniable but also essential.
Second, and much like in a purely artistic discipline, the act of programming is driven partly by the notion of aesthetics: the pleasure we have in creating beautiful things.
Even though the importance of aesthetics in the act of programming is now unquestioned, more could still be written on the subject.
The field called "psychology of programming" focuses on the cognitive aspects of the activity, with the goal of improving the productivity of programmers.
While many scientists have emphasized their concern for aesthetics and the impact it has on their activity, few computer scientists have actually written about their thought process while programming.
What makes us like or dislike such and such language or paradigm?
Why do we shape our programs the way we do?
By answering these questions from the angle of aesthetics, we may be able to shed some new light on the art of programming.
Starting from the assumption that aesthetics is an inherently transversal dimension, it should be possible for every programmer to find the same aesthetic driving force in every creative activity they undertake, not just programming, and in doing so, get deeper insight on why and how they do things the way they do.
On the other hand, because our aesthetic sensitivities are so personal, all we can really do is relate our own experiences and share it with others, in the hope that it will inspire them to do the same.
My personal life has been revolving around three major creative activities, of equal importance: programming in Lisp, playing Jazz music, and practicing Aikido.
But why so many of them, why so different ones, and why these specifically?
By introspecting my personal aesthetic sensitivities, I eventually realized that my tastes in the scientific, artistic, and physical domains are all motivated by the same driving forces, hence unifying Lisp, Jazz, and Aikido as three expressions of a single essence, not so different after all.
Lisp, Jazz, and Aikido are governed by a limited set of rules which remain simple and unobtrusive.
Conforming to them is a pleasure.
Because Lisp, Jazz, and Aikido are inherently introspective disciplines, they also invite you to transgress the rules in order to find your own.
Breaking the rules is fun.
Finally, if Lisp, Jazz, and Aikido unify so many paradigms, styles, or techniques, it is not by mere accumulation but because they live at the meta-level and let you reinvent them.
Working at the meta-level is an enlightening experience.
Understand your aesthetic sensitivities and you may gain considerable insight on your own psychology of programming.
Mine is perhaps common to most lispers.
Perhaps also common to other programming communities, but that, is for the reader to decide...
We propose a method to improve traditional character-based PPM text compression algorithms.
Consider a text file as a sequence of alternating words and non-words, the basic idea of our algorithm is to encode non-words and prefixes of words using character-based context models and encode suffixes of words using dictionary models.
By using dictionary models, the algorithm can encode multiple characters as a whole, and thus enhance the compression efficiency.
The advantages of the proposed algorithm are: 1) it does not require any text preprocessing; 2) it does not need any explicit codeword to identify switch between context and dictionary models; 3) it can be applied to any character-based PPM algorithms without incurring much additional computational cost.
Test results show that significant improvements can be obtained over character-based PPM, especially in low order cases.
Dodis et al. proposed an improved version of the fuzzy vault scheme, one of the most popular primitives used in biometric cryptosystems, requiring less storage and leaking less information.
Recently, Blanton and Aliasgari have shown that the relation of two improved fuzzy vault records of the same individual may be determined by solving a system of non-linear equations.
However, they conjectured that this is feasible for small parameters only.
In this paper, we present a new attack against the improved fuzzy vault scheme based on the extended Euclidean algorithm that determines if two records are related and recovers the elements by which the protected features, e.g., the biometric templates, differ.
Our theoretical and empirical analysis demonstrates that the attack is very effective and efficient for practical parameters.
Furthermore, we show how this attack can be extended to fully recover both feature sets from related vault records much more efficiently than possible by attacking each record individually.
We complement this work by deriving lower bounds for record multiplicity attacks and use these to show that our attack is asymptotically optimal in an information theoretic sense.
Finally, we propose remedies to harden the scheme against record multiplicity attacks.
In a reversible language, any forward computation can be undone by a finite sequence of backward steps.
Reversible computing has been studied in the context of different programming languages and formalisms, where it has been used for testing and verification, among others.
In this paper, we consider a subset of Erlang, a functional and concurrent programming language based on the actor model.
We present a formal semantics for reversible computation in this language and prove its main properties, including its causal consistency.
We also build on top of it a rollback operator that can be used to undo the actions of a process up to a given checkpoint.
Network slicing to enable resource sharing among multiple tenants --network operators and/or services-- is considered a key functionality for next generation mobile networks.
This paper provides an analysis of a well-known model for resource sharing, the 'share-constrained proportional allocation' mechanism, to realize network slicing.
This mechanism enables tenants to reap the performance benefits of sharing, while retaining the ability to customize their own users' allocation.
This results in a network slicing game in which each tenant reacts to the user allocations of the other tenants so as to maximize its own utility.
We show that, under appropriate conditions, the game associated with such strategic behavior converges to a Nash equilibrium.
At the Nash equilibrium, a tenant always achieves the same, or better, performance than under a static partitioning of resources, hence providing the same level of protection as such static partitioning.
We further analyze the efficiency and fairness of the resulting allocations, providing tight bounds for the price of anarchy and envy-freeness.
Our analysis and extensive simulation results confirm that the mechanism provides a comprehensive practical solution to realize network slicing.
Our theoretical results also fill a gap in the literature regarding the analysis of this resource allocation model under strategic players.
Issues regarding explainable AI involve four components: users, laws & regulations, explanations and algorithms.
Together these components provide a context in which explanation methods can be evaluated regarding their adequacy.
The goal of this chapter is to bridge the gap between expert users and lay users.
Different kinds of users are identified and their concerns revealed, relevant statements from the General Data Protection Regulation are analyzed in the context of Deep Neural Networks (DNNs), a taxonomy for the classification of existing explanation methods is introduced, and finally, the various classes of explanation methods are analyzed to verify if user concerns are justified.
Overall, it is clear that (visual) explanations can be given about various aspects of the influence of the input on the output.
However, it is noted that explanation methods or interfaces for lay users are missing and we speculate which criteria these methods / interfaces should satisfy.
Finally it is noted that two important concerns are difficult to address with explanation methods: the concern about bias in datasets that leads to biased DNNs, as well as the suspicion about unfair outcomes.
Air quality forecasting has been regarded as the key problem of air pollution early warning and control management.
In this paper, we propose a novel deep learning model for air quality (mainly PM2.5) forecasting, which learns the spatial-temporal correlation features and interdependence of multivariate air quality related time series data by hybrid deep learning architecture.
Due to the nonlinear and dynamic characteristics of multivariate air quality time series data, the base modules of our model include one-dimensional Convolutional Neural Networks (CNN) and Bi-directional Long Short-term Memory networks (Bi-LSTM).
The former is to extract the local trend features and the latter is to learn long temporal dependencies.
Then we design a jointly hybrid deep learning framework which based on one-dimensional CNN and Bi-LSTM for shared representation features learning of multivariate air quality related time series data.
The experiment results show that our model is capable of dealing with PM2.5 air pollution forecasting with satisfied accuracy.
Near-miss experiences are one of the main sources of intense emotions.
Despite people's consistency when judging near-miss situations and when communicating about them, there is no integrated theoretical account of the phenomenon.
In particular, individuals' reaction to near-miss situations is not correctly predicted by rationality-based or probability-based optimization.
The present study suggests that emotional intensity in the case of near-miss is in part predicted by Simplicity Theory.
To improve the efficiency of surgical trajectory segmentation for robot learning in robot-assisted minimally invasive surgery, this paper presents a fast unsupervised method using video and kinematic data, followed by a promoting procedure to address the over-segmentation issue.
Unsupervised deep learning network, stacking convolutional auto-encoder, is employed to extract more discriminative features from videos in an effective way.
To further improve the accuracy of segmentation, on one hand, wavelet transform is used to filter out the noises existed in the features from video and kinematic data.
On the other hand, the segmentation result is promoted by identifying the adjacent segments with no state transition based on the predefined similarity measurements.
Extensive experiments on a public dataset JIGSAWS show that our method achieves much higher accuracy of segmentation than state-of-the-art methods in the shorter time.
Current state-of-the-art approaches to skeleton-based action recognition are mostly based on recurrent neural networks (RNN).
In this paper, we propose a novel convolutional neural networks (CNN) based framework for both action classification and detection.
Raw skeleton coordinates as well as skeleton motion are fed directly into CNN for label prediction.
A novel skeleton transformer module is designed to rearrange and select important skeleton joints automatically.
With a simple 7-layer network, we obtain 89.3% accuracy on validation set of the NTU RGB+D dataset.
For action detection in untrimmed videos, we develop a window proposal network to extract temporal segment proposals, which are further classified within the same network.
On the recent PKU-MMD dataset, we achieve 93.7% mAP, surpassing the baseline by a large margin.
To better detect pedestrians of various scales, deep multi-scale methods usually detect pedestrians of different scales by different in-network layers.
However, the semantic levels of features from different layers are usually inconsistent.
In this paper, we propose a multi-branch and high-level semantic network by gradually splitting a base network into multiple different branches.
As a result, the different branches have the same depth and the output features of different branches have similarly high-level semantics.
Due to the difference of receptive fields, the different branches are suitable to detect pedestrians of different scales.
Meanwhile, the multi-branch network does not introduce additional parameters by sharing convolutional weights of different branches.
To further improve detection performance, skip-layer connections among different branches are used to add context to the branch of relatively small receptive filed, and dilated convolution is incorporated into part branches to enlarge the resolutions of output feature maps.
When they are embedded into Faster RCNN architecture, the weighted scores of proposal generation network and proposal classification network are further proposed.
Experiments on KITTI dataset, Caltech pedestrian dataset, and Citypersons dataset demonstrate the effectiveness of proposed method.
On these pedestrian datasets, the proposed method achieves state-of-the-art detection performance.
Moreover, experiments on COCO benchmark show the proposed method is also suitable for general object detection.
This paper presents an iterative smoothing technique for polygonal approximation of digital image boundary.
The technique starts with finest initial segmentation points of a curve.
The contribution of initially segmented points towards preserving the original shape of the image boundary is determined by computing the significant measure of every initial segmentation points which is sensitive to sharp turns, which may be missed easily when conventional significant measures are used for detecting dominant points.
The proposed method differentiates between the situations when a point on the curve between two points on a curve projects directly upon the line segment or beyond this line segment.
It not only identifies these situations, but also computes its significant contribution for these situations differently.
This situation-specific treatment allows preservation of points with high curvature even as revised set of dominant points are derived.
The experimental results show that the proposed technique competes well with the state of the art techniques.
We consider the learning of algorithmic tasks by mere observation of input-output pairs.
Rather than studying this as a black-box discrete regression problem with no assumption whatsoever on the input-output mapping, we concentrate on tasks that are amenable to the principle of divide and conquer, and study what are its implications in terms of learning.
This principle creates a powerful inductive bias that we leverage with neural architectures that are defined recursively and dynamically, by learning two scale-invariant atomic operations: how to split a given input into smaller sets, and how to merge two partially solved tasks into a larger partial solution.
Our model can be trained in weakly supervised environments, namely by just observing input-output pairs, and in even weaker environments, using a non-differentiable reward signal.
Moreover, thanks to the dynamic aspect of our architecture, we can incorporate the computational complexity as a regularization term that can be optimized by backpropagation.
We demonstrate the flexibility and efficiency of the Divide-and-Conquer Network on several combinatorial and geometric tasks: convex hull, clustering, knapsack and euclidean TSP.
Thanks to the dynamic programming nature of our model, we show significant improvements in terms of generalization error and computational complexity.
Incremental learning from non-stationary data poses special challenges to the field of machine learning.
Although new algorithms have been developed for this, assessment of results and comparison of behaviors are still open problems, mainly because evaluation metrics, adapted from more traditional tasks, can be ineffective in this context.
Overall, there is a lack of common testing practices.
This paper thus presents a testbed for incremental non-stationary learning algorithms, based on specially designed synthetic datasets.
Also, test results are reported for some well-known algorithms to show that the proposed methodology is effective at characterizing their strengths and weaknesses.
It is expected that this methodology will provide a common basis for evaluating future contributions in the field.
An event-based state estimation approach for reducing communication in a networked control system is proposed.
Multiple distributed sensor-actuator-agents observe a dynamic process and sporadically exchange their measurements and inputs over a bus network.
Based on these data, each agent estimates the full state of the dynamic system, which may exhibit arbitrary inter-agent couplings.
Local event-based protocols ensure that data is transmitted only when necessary to meet a desired estimation accuracy.
This event-based scheme is shown to mimic a centralized Luenberger observer design up to guaranteed bounds, and stability is proven in the sense of bounded estimation errors for bounded disturbances.
The stability result extends to the distributed control system that results when the local state estimates are used for distributed feedback control.
Simulation results highlight the benefit of the event-based approach over classical periodic ones in reducing communication requirements.
We propose the use of incomplete dot products (IDP) to dynamically adjust the number of input channels used in each layer of a convolutional neural network during feedforward inference.
IDP adds monotonically non-increasing coefficients, referred to as a "profile", to the channels during training.
The profile orders the contribution of each channel in non-increasing order.
At inference time, the number of channels used can be dynamically adjusted to trade off accuracy for lowered power consumption and reduced latency by selecting only a beginning subset of channels.
This approach allows for a single network to dynamically scale over a computation range, as opposed to training and deploying multiple networks to support different levels of computation scaling.
Additionally, we extend the notion to multiple profiles, each optimized for some specific range of computation scaling.
We present experiments on the computation and accuracy trade-offs of IDP for popular image classification models and datasets.
We demonstrate that, for MNIST and CIFAR-10, IDP reduces computation significantly, e.g., by 75%, without significantly compromising accuracy.
We argue that IDP provides a convenient and effective means for devices to lower computation costs dynamically to reflect the current computation budget of the system.
For example, VGG-16 with 50% IDP (using only the first 50% of channels) achieves 70% in accuracy on the CIFAR-10 dataset compared to the standard network which achieves only 35% accuracy when using the reduced channel set.
We present an approach that exploits hierarchical Recurrent Neural Networks (RNNs) to tackle the video captioning problem, i.e., generating one or multiple sentences to describe a realistic video.
Our hierarchical framework contains a sentence generator and a paragraph generator.
The sentence generator produces one simple short sentence that describes a specific short video interval.
It exploits both temporal- and spatial-attention mechanisms to selectively focus on visual elements during generation.
The paragraph generator captures the inter-sentence dependency by taking as input the sentential embedding produced by the sentence generator, combining it with the paragraph history, and outputting the new initial state for the sentence generator.
We evaluate our approach on two large-scale benchmark datasets: YouTubeClips and TACoS-MultiLevel.
The experiments demonstrate that our approach significantly outperforms the current state-of-the-art methods with BLEU@4 scores 0.499 and 0.305 respectively.
We consider the spatial stochastic model of single-tier downlink cellular networks, where the wireless base stations are deployed according to a general stationary point process on the Euclidean plane with general i.i.d. propagation effects.
Recently, Ganti & Haenggi (2016) consider the same general cellular network model and, as one of many significant results, derive the tail asymptotics of the signal-to-interference ratio (SIR) distribution.
However, they do not mention any conditions under which the result holds.
In this paper, we compensate their result for the lack of the condition and expose a sufficient condition for the asymptotic result to be valid.
We further illustrate some examples satisfying such a sufficient condition and indicate the corresponding asymptotic results for the example models.
We give also a simple counterexample violating the sufficient condition.
Network intrusion detection is the process of identifying malicious behaviors that target a network and its resources.
Current systems implementing intrusion detection processes observe traffic at several data collecting points in the network but analysis is often centralized or partly centralized.
These systems are not scalable and suffer from the single point of failure, i.e. attackers only need to target the central node to compromise the whole system.
This paper proposes an anomaly-based fully distributed network intrusion detection system where analysis is run at each data collecting point using a naive Bayes classifier.
Probability values computed by each classifier are shared among nodes using an iterative average consensus protocol.
The final analysis is performed redundantly and in parallel at the level of each data collecting point, thus avoiding the single point of failure issue.
We run simulations focusing on DDoS attacks with several network configurations, comparing the accuracy of our fully distributed system with a hierarchical one.
We also analyze communication costs and convergence speed during consensus phases.
Standard artificial neural networks suffer from the well-known issue of catastrophic forgetting, making continual or lifelong learning problematic.
Recently, numerous methods have been proposed for continual learning, but due to differences in evaluation protocols it is difficult to directly compare their performance.
To enable more meaningful comparisons, we identified three distinct continual learning scenarios based on whether task identity is known and, if it is not, whether it needs to be inferred.
Performing the split and permuted MNIST task protocols according to each of these scenarios, we found that regularization-based approaches (e.g., elastic weight consolidation) failed when task identity needed to be inferred.
In contrast, generative replay combined with distillation (i.e., using class probabilities as "soft targets") achieved superior performance in all three scenarios.
In addition, we reduced the computational cost of generative replay by integrating the generative model into the main model by equipping it with generative feedback connections.
This Replay-through-Feedback approach substantially shortened training time with no or negligible loss in performance.
We believe this to be an important first step towards making the powerful technique of generative replay scalable to real-world continual learning applications.
Given a network of nodes, minimizing the spread of a contagion using a limited budget is a well-studied problem with applications in network security, viral marketing, social networks, and public health.
In real graphs, virus may infect a node which in turn infects its neighbor nodes and this may trigger an epidemic in the whole graph.
The goal thus is to select the best k nodes (budget constraint) that are immunized (vaccinated, screened, filtered) so as the remaining graph is less prone to the epidemic.
It is known that the problem is, in all practical models, computationally intractable even for moderate sized graphs.
In this paper we employ ideas from spectral graph theory to define relevance and importance of nodes.
Using novel graph theoretic techniques, we then design an efficient approximation algorithm to immunize the graph.
Theoretical guarantees on the running time of our algorithm show that it is more efficient than any other known solution in the literature.
We test the performance of our algorithm on several real world graphs.
Experiments show that our algorithm scales well for large graphs and outperforms state of the art algorithms both in quality (containment of epidemic) and efficiency (runtime and space complexity).
Connectivity of wireless sensor networks (WSNs) is a fundamental global property expected to be maintained even though some sensor nodes are at fault.
In this paper, we investigate the connectivity of random geometric graphs (RGGs) in the node fault model as an abstract model of ad hoc WSNs with unreliable nodes.
In the model, each node is assumed to be stochastically at fault, i.e., removed from a graph.
As a measure of reliability, the network breakdown probability is then defined as the average probability that a resulting survival graph is disconnected over RGGs.
We examine RGGs with general connection functions as an extension of a conventional RGG model and provide two mathematical analyses: the asymptotic analysis for infinite RGGs that reveals the phase transition thresholds of connectivity, and the non-asymptotic analysis for finite RGGs that provides a useful approximation formula.
Those analyses are supported by numerical simulations in the Rayleigh SISO model reflecting a practical wireless channel.
Diffusion Tensor Imaging (DTI) is an effective tool for the analysis of structural brain connectivity in normal development and in a broad range of brain disorders.
However efforts to derive inherent characteristics of structural brain networks have been hampered by the very high dimensionality of the data, relatively small sample sizes, and the lack of widely acceptable connectivity-based regions of interests (ROIs).
Typical approaches have focused either on regions defined by standard anatomical atlases that do not incorporate anatomical connectivity, or have been based on voxel-wise analysis, which results in loss of statistical power relative to structure-wise connectivity analysis.
In this work, we propose a novel, computationally efficient iterative clustering method to generate connectivity-based whole-brain parcellations that converge to a stable parcellation in a few iterations.
Our algorithm is based on a sparse representation of the whole brain connectivity matrix, which reduces the number of edges from around a half billion to a few million while incorporating the necessary spatial constraints.
We show that the resulting regions in a sense capture the inherent connectivity information present in the data, and are stable with respect to initialization and the randomization scheme within the algorithm.
These parcellations provide consistent structural regions across the subjects of population samples that are homogeneous with respect to anatomic connectivity.
Our method also derives connectivity structures that can be used to distinguish between population samples with known different structural connectivity.
In particular, new results in structural differences for different population samples such as Females vs Males, Normal Controls vs Schizophrenia, and different age groups in Normal Controls are also shown.
Our goal is to train a policy for autonomous driving via imitation learning that is robust enough to drive a real vehicle.
We find that standard behavior cloning is insufficient for handling complex driving scenarios, even when we leverage a perception system for preprocessing the input and a controller for executing the output on the car: 30 million examples are still not enough.
We propose exposing the learner to synthesized data in the form of perturbations to the expert's driving, which creates interesting situations such as collisions and/or going off the road.
Rather than purely imitating all data, we augment the imitation loss with additional losses that penalize undesirable events and encourage progress -- the perturbations then provide an important signal for these losses and lead to robustness of the learned model.
We show that the ChauffeurNet model can handle complex situations in simulation, and present ablation experiments that emphasize the importance of each of our proposed changes and show that the model is responding to the appropriate causal factors.
Finally, we demonstrate the model driving a car in the real world.
The unsupervised Pretraining method has been widely used in aiding human action recognition.
However, existing methods focus on reconstructing the already present frames rather than generating frames which happen in future.In this paper, We propose an improved Variantial Autoencoder model to extract the features with a high connection to the coming scenarios, also known as Predictive Learning.
Our framework lists as following: two steam 3D-convolution neural networks are used to extract both spatial and temporal information as latent variables.
Then a resample method is introduced to create new normal distribution probabilistic latent variables and finally, the deconvolution neural network will use these latent variables generate next frames.
Through this possess, we train the model to focus more on how to generate the future and thus it will extract the future high connected features.
In the experiment stage, A large number of experiments on UT and UCF101 datasets reveal that future generation aids Prediction does improve the performance.
Moreover, the Future Representation Learning Network reach a higher score than other methods when in half observation.
This means that Future Representation Learning is better than the traditional Representation Learning and other state- of-the-art methods in solving the human action prediction problems to some extends.
HTTP-based video streaming technologies allow for flexible rate selection strategies that account for time-varying network conditions.
Such rate changes may adversely affect the user's Quality of Experience; hence online prediction of the time varying subjective quality can lead to perceptually optimised bitrate allocation policies.
Recent studies have proposed to use dynamic network approaches for continuous-time prediction; yet they do not consider multiple video quality models as inputs nor consider forecasting ensembles.
Here we address the problem of predicting continuous-time subjective quality using multiple inputs fed to a non-linear autoregressive network.
By considering multiple network configurations and by applying simple averaging forecasting techniques, we are able to considerably improve prediction performance and decrease forecasting errors.
The notes which play the most important and second most important roles in expressing a raga are called Vadi and Samvadi swars respectively in (North) Indian Classical music.
Like Bageshree, Bhairavi, Shankara, Hamir and Kalingra, Rageshree is another controversial raga so far as the choice of Vadi-Samvadi selection is concerned where there are two different opinions.
In the present work, a two minute vocal recording of raga Rageshree is subjected to a careful statistical analysis.
Our analysis is broken into three phases: first half, middle half and last half.
Under a multinomial model set up holding appreciably in the first two phases, only one opinion is found acceptable.
In the last phase the distribution seems to be quasi multinomial, characterized by an unstable nature of relative occurrence of pitch of all the notes and although the note whose relative occurrence of pitch suddenly shoots is the Vadi swar selected from our analysis of the first two phases, we take it as an outlier demanding a separate treatment like any other in statistics.
Selection of Vadi-Samvadi notes in a quasi-multinomial set up is still an open research problem.
An interesting musical cocktail is proposed, however, embedding several ideas like melodic property of notes, note combinations and pitch movements between notes, using some weighted combination of psychological and statistical stability of notes along with watching carefully the sudden shoot of one or more notes whenever there is enough evidence that multinomial model has broken down.
In the measurement process, there are many parameters affecting the measurement results: the influence of the probe system, material stiffness of measured workpiece, the calibration of the probe with a reference sphere, the thermal effects.
We want to obtain the limits of a measurement methodology to be able to validate a result.
The study is applied to a simple part.
We observe the dispersion of the position of different drilled holes (XYZ values in a coordinate system) when we change the quality of the part and the method of calculation.
We use the Design of Experiment (Taguchi method) to realize our study.
We study the influence of the part quality on a measurement results.
We consider two parameters to define the part quality (flatness and perpendicularity).
We will also study the influence of different methods of calculation to determine the coordinate system.
We can use two options in Metrolog XG software (tangent plane with or without orientation constraint).
The originality of this paper is that we present a method for the design of experiment that uses CATIA (CAD system) to generate the measured parts.
In this way we can realize a design of experiment with a largest number of experimental results.
This is a positive point for a statistical analysis.
We are also free to define the parts we want to study without manufacturing difficulties.
Neural networks with random hidden nodes have gained increasing interest from researchers and practical applications.
This is due to their unique features such as very fast training and universal approximation property.
In these networks the weights and biases of hidden nodes determining the nonlinear feature mapping are set randomly and are not learned.
Appropriate selection of the intervals from which weights and biases are selected is extremely important.
This topic has not yet been sufficiently explored in the literature.
In this work a method of generating random weights and biases is proposed.
This method generates the parameters of the hidden nodes in such a way that nonlinear fragments of the activation functions are located in the input space regions with data and can be used to construct the surface approximating a nonlinear target function.
The weights and biases are dependent on the input data range and activation function type.
The proposed methods allows us to control the generalization degree of the model.
These all lead to improvement in approximation performance of the network.
Several experiments show very promising results.
The main objective of this project is to segment different breast ultrasound images to find out lesion area by discarding the low contrast regions as well as the inherent speckle noise.
The proposed method consists of three stages (removing noise, segmentation, classification) in order to extract the correct lesion.
We used normalized cuts approach to segment ultrasound images into regions of interest where we can possibly finds the lesion, and then K-means classifier is applied to decide finally the location of the lesion.
For every original image, an annotated ground-truth image is given to perform comparison with the obtained experimental results, providing accurate evaluation measures.
This research paper designates the importance and usage of the case study approach effectively to educating and training software designers and software engineers both in academic and industry.
Subsequently an account of the use of case studies based on software engineering in the education of professionals, there is a conversation of issues in training software designers and how a case teaching method can be used to state these issues.
The paper describes a software project titled Online Tower Plotting System (OTPS) to develop a complete and comprehensive case study, along with supporting educational material.
The case study is aimed to demonstrate a variety of software areas, modules and courses: from bachelor through masters, doctorates and even for ongoing professional development.
Recommender systems recommend items more accurately by analyzing users' potential interest on different brands' items.
In conjunction with users' rating similarity, the presence of users' implicit feedbacks like clicking items, viewing items specifications, watching videos etc. have been proved to be helpful for learning users' embedding, that helps better rating prediction of users.
Most existing recommender systems focus on modeling of ratings and implicit feedbacks ignoring users' explicit feedbacks.
Explicit feedbacks can be used to validate the reliability of the particular users and can be used to learn about the users' characteristic.
Users' characteristic mean what type of reviewers they are.
In this paper, we explore three different models for recommendation with more accuracy focusing on users' explicit feedbacks and implicit feedbacks.
First one is RHC-PMF that predicts users' rating more accurately based on user's three explicit feedbacks (rating, helpfulness score and centrality) and second one is RV-PMF, where user's implicit feedback (view relationship) is considered.
Last one is RHCV-PMF, where both type of feedbacks are considered.
In this model users' explicit feedbacks' similarity indicate the similarity of their reliability and characteristic and implicit feedback's similarity indicates their preference similarity.
Extensive experiments on real world dataset, i.e.Amazon.com online review dataset shows that our models perform better compare to base-line models in term of users' rating prediction.
RHCV-PMF model also performs better rating prediction compare to baseline models for cold start users and cold start items.
This study improves the performance of neural named entity recognition by a margin of up to 11% in F-score on the example of a low-resource language like German, thereby outperforming existing baselines and establishing a new state-of-the-art on each single open-source dataset.
Rather than designing deeper and wider hybrid neural architectures, we gather all available resources and perform a detailed optimization and grammar-dependent morphological processing consisting of lemmatization and part-of-speech tagging prior to exposing the raw data to any training process.
We test our approach in a threefold monolingual experimental setup of a) single, b) joint, and c) optimized training and shed light on the dependency of downstream-tasks on the size of corpora used to compute word embeddings.
Modern cities and metropolitan areas all over the world face new management challenges in the 21st century primarily due to increasing demands on living standards by the urban population.
These challenges range from climate change, pollution, transportation, and citizen engagement, to urban planning, and security threats.
The primary goal of a Smart City is to counteract these problems and mitigate their effects by means of modern ICT to improve urban administration and infrastructure.
Key ideas are to utilise network communication to inter-connect public authorities; but also to deploy and integrate numerous sensors and actuators throughout the city infrastructure - which is also widely known as the Internet of Things (IoT).
Thus, IoT technologies will be an integral part and key enabler to achieve many objectives of the Smart City vision.
The contributions of this paper are as follows.
We first examine a number of IoT platforms, technologies and network standards that can help to foster a Smart City environment.
Second, we introduce the EU project MONICA which aims for demonstration of large-scale IoT deployments at public, inner-city events and give an overview on its IoT platform architecture.
And third, we provide a case-study report on SmartCity activities by the City of Hamburg and provide insights on recent (on-going) field tests of a vertically integrated, end-to-end IoT sensor application.
Trajectory Prediction of dynamic objects is a widely studied topic in the field of artificial intelligence.
Thanks to a large number of applications like predicting abnormal events, navigation system for the blind, etc. there have been many approaches to attempt learning patterns of motion directly from data using a wide variety of techniques ranging from hand-crafted features to sophisticated deep learning models for unsupervised feature learning.
All these approaches have been limited by problems like inefficient features in the case of hand crafted features, large error propagation across the predicted trajectory and no information of static artefacts around the dynamic moving objects.
We propose an end to end deep learning model to learn the motion patterns of humans using different navigational modes directly from data using the much popular sequence to sequence model coupled with a soft attention mechanism.
We also propose a novel approach to model the static artefacts in a scene and using these to predict the dynamic trajectories.
The proposed method, tested on trajectories of pedestrians, consistently outperforms previously proposed state of the art approaches on a variety of large scale data sets.
We also show how our architecture can be naturally extended to handle multiple modes of movement (say pedestrians, skaters, bikers and buses) simultaneously.
Greater penetration of Distributed Energy Resources (DERs) in power networks requires coordination strategies that allow for self-adjustment of contributions in a network of DERs, owing to variability in generation and demand.
In this article, a distributed scheme is proposed that enables a DER in a network to arrive at viable power reference commands that satisfies the DERs local constraints on its generation and loads it has to service, while, the aggregated behavior of multiple DERs in the network and their respective loads meet the ancillary services demanded by the grid.
The Net-load Management system for a single unit is referred to as the Local Inverter System (LIS) in this article .
A distinguishing feature of the proposed consensus based solution is the distributed finite time termination of the algorithm that allows each LIS unit in the network to determine power reference commands in the presence of communication delays in a distributed manner.
The proposed scheme allows prioritization of Renewable Energy Sources (RES) in the network and also enables auto-adjustment of contributions from LIS units with lower priority resources (non-RES).
The methods are validated using hardware-in-the-loop simulations with Raspberry PI devices as distributed control units, implementing the proposed distributed algorithm and responsible for determining and dispatching realtime power reference commands to simulated power electronics interface emulating LIS units for demand response.
In computer vision, the estimation of the fundamental matrix is a basic problem that has been extensively studied.
The accuracy of the estimation imposes a significant influence on subsequent tasks such as the camera trajectory determination and 3D reconstruction.
In this paper we propose a new method for fundamental matrix estimation that makes use of clustering a group of 4D vectors.
The key insight is the observation that among the 4D vectors constructed from matching pairs of points obtained from the SIFT algorithm, well-defined cluster points tend to be reliable inliers suitable for fundamental matrix estimation.
Based on this, we utilizes a recently proposed efficient clustering method through density peaks seeking and propose a new clustering assisted method.
Experimental results show that the proposed algorithm is faster and more accurate than currently commonly used methods.
In this paper, we investigate two decomposition methods for their convergence rate which are used to solve security constrained economic dispatch (SCED): 1) Lagrangian Relaxation (LR), and 2) Augmented Lagrangian Relaxation (ALR).
First, the centralized SCED problem is posed for a 6-bus test network and then it is decomposed into subproblems using both of the methods.
In order to model the tie-line between decomposed areas of the test network, a novel method is proposed.
The advantages and drawbacks of each method are discussed in terms of accuracy and information privacy.
We show that there is a tradeoff between the information privacy and the convergence rate.
It has been found that ALR converges faster compared to LR, due to the large amount of shared data.
Currency trading (Forex) is the largest world market in terms of volume.
We analyze trading and tweeting about the EUR-USD currency pair over a period of three years.
First, a large number of tweets were manually labeled, and a Twitter stance classification model is constructed.
The model then classifies all the tweets by the trading stance signal: buy, hold, or sell (EUR vs. USD).
The Twitter stance is compared to the actual currency rates by applying the event study methodology, well-known in financial economics.
It turns out that there are large differences in Twitter stance distribution and potential trading returns between the four groups of Twitter users: trading robots, spammers, trading companies, and individual traders.
Additionally, we observe attempts of reputation manipulation by post festum removal of tweets with poor predictions, and deleting/reposting of identical tweets to increase the visibility without tainting one's Twitter timeline.
The past several years have witnessed the rapid progress of end-to-end Neural Machine Translation (NMT).
However, there exists discrepancy between training and inference in NMT when decoding, which may lead to serious problems since the model might be in a part of the state space it has never seen during training.
To address the issue, Scheduled Sampling has been proposed.
However, there are certain limitations in Scheduled Sampling and we propose two dynamic oracle-based methods to improve it.
We manage to mitigate the discrepancy by changing the training process towards a less guided scheme and meanwhile aggregating the oracle's demonstrations.
Experimental results show that the proposed approaches improve translation quality over standard NMT system.
Deep convolutional neural networks have led to breakthrough results in practical feature extraction applications.
The mathematical analysis of these networks was pioneered by Mallat, 2012.
Specifically, Mallat considered so-called scattering networks based on identical semi-discrete wavelet frames in each network layer, and proved translation-invariance as well as deformation stability of the resulting feature extractor.
The purpose of this paper is to develop Mallat's theory further by allowing for different and, most importantly, general semi-discrete frames (such as, e.g., Gabor frames, wavelets, curvelets, shearlets, ridgelets) in distinct network layers.
This allows to extract wider classes of features than point singularities resolved by the wavelet transform.
Our generalized feature extractor is proven to be translation-invariant, and we develop deformation stability results for a larger class of deformations than those considered by Mallat.
For Mallat's wavelet-based feature extractor, we get rid of a number of technical conditions.
The mathematical engine behind our results is continuous frame theory, which allows us to completely detach the invariance and deformation stability proofs from the particular algebraic structure of the underlying frames.
In the context of natural language processing, representation learning has emerged as a newly active research subject because of its excellent performance in many applications.
Learning representations of words is a pioneering study in this school of research.
However, paragraph (or sentence and document) embedding learning is more suitable/reasonable for some tasks, such as sentiment classification and document summarization.
Nevertheless, as far as we are aware, there is relatively less work focusing on the development of unsupervised paragraph embedding methods.
Classic paragraph embedding methods infer the representation of a given paragraph by considering all of the words occurring in the paragraph.
Consequently, those stop or function words that occur frequently may mislead the embedding learning process to produce a misty paragraph representation.
Motivated by these observations, our major contributions in this paper are twofold.
First, we propose a novel unsupervised paragraph embedding method, named the essence vector (EV) model, which aims at not only distilling the most representative information from a paragraph but also excluding the general background information to produce a more informative low-dimensional vector representation for the paragraph.
Second, in view of the increasing importance of spoken content processing, an extension of the EV model, named the denoising essence vector (D-EV) model, is proposed.
The D-EV model not only inherits the advantages of the EV model but also can infer a more robust representation for a given spoken paragraph against imperfect speech recognition.
Planarity Testing is the problem of determining whether a given graph is planar while planar embedding is the corresponding construction problem.
The bounded space complexity of these problems has been determined to be exactly Logspace by Allender and Mahajan with the aid of Reingold's result.
Unfortunately, the algorithm is quite daunting and generalizing it to say, the bounded genus case seems a tall order.
In this work, we present a simple planar embedding algorithm running in logspace.
We hope this algorithm will be more amenable to generalization.
The algorithm is based on the fact that 3-connected planar graphs have a unique embedding, a variant of Tutte's criterion on conflict graphs of cycles and an explicit change of cycle basis.% for planar graphs.
We also present a logspace algorithm to find obstacles to planarity, viz. a Kuratowski minor, if the graph is non-planar.
To the best of our knowledge this is the first logspace algorithm for this problem.
We describe an innovative framework for prescription of personalised health apps by integrating Personal Health Records (PHR) with disease-specific mobile applications for managing medical conditions and the communication with clinical professionals.
The prescribed apps record multiple variables including medical history enriched with innovative features such as integration with medical monitoring devices and wellbeing trackers to provide patients and clinicians with a personalised support on disease management.
Our framework is based on an existing PHR ecosystem called TreC, uniquely positioned between healthcare provider and the patients, which is being used by over 70.000 patients in Trentino region in Northern Italy.
We also describe three important aspects of health app prescription and how medical information is automatically encoded through the TreC framework and is prescribed as a personalised app, ready to be installed in the patients' smartphone.
On a daily investment decision in a security market, the price earnings (PE) ratio is one of the most widely applied methods being used as a firm valuation tool by investment experts.
Unfortunately, recent academic developments in financial econometrics and machine learning rarely look at this tool.
In practice, fundamental PE ratios are often estimated only by subjective expert opinions.
The purpose of this research is to formalize a process of fundamental PE estimation by employing advanced dynamic Bayesian network (DBN) methodology.
The estimated PE ratio from our model can be used either as a information support for an expert to make investment decisions, or as an automatic trading system illustrated in experiments.
Forward-backward inference and EM parameter estimation algorithms are derived with respect to the proposed DBN structure.
Unlike existing works in literatures, the economic interpretation of our DBN model is well-justified by behavioral finance evidences of volatility.
A simple but practical trading strategy is invented based on the result of Bayesian inference.
Extensive experiments show that our trading strategy equipped with the inferenced PE ratios consistently outperforms standard investment benchmarks.
Two approaches are proposed for cross-pose face recognition, one is based on the 3D reconstruction of facial components and the other is based on the deep Convolutional Neural Network (CNN).
Unlike most 3D approaches that consider holistic faces, the proposed approach considers 3D facial components.
It segments a 2D gallery face into components, reconstructs the 3D surface for each component, and recognizes a probe face by component features.
The segmentation is based on the landmarks located by a hierarchical algorithm that combines the Faster R-CNN for face detection and the Reduced Tree Structured Model for landmark localization.
The core part of the CNN-based approach is a revised VGG network.
We study the performances with different settings on the training set, including the synthesized data from 3D reconstruction, the real-life data from an in-the-wild database, and both types of data combined.
We investigate the performances of the network when it is employed as a classifier or designed as a feature extractor.
The two recognition approaches and the fast landmark localization are evaluated in extensive experiments, and compared to stateof-the-art methods to demonstrate their efficacy.
In this paper, we implement an information-theoretic approach to travel behaviour analysis by introducing a generative modelling framework to identify informative latent characteristics in travel decision making.
It involves developing a joint tri-partite Bayesian graphical network model using a Restricted Boltzmann Machine (RBM) generative modelling framework.
We apply this framework on a mode choice survey data to identify abstract latent variables and compare the performance with a traditional latent variable model with specific latent preferences -- safety, comfort, and environmental.
Data collected from a joint stated and revealed preference mode choice survey in Quebec, Canada were used to calibrate the RBM model.
Results show that a signficant impact on model likelihood statistics and suggests that machine learning tools are highly suitable for modelling complex networks of conditional independent behaviour interactions.
Autonomous vehicles (AVs) require accurate metric and topological location estimates for safe, effective navigation and decision-making.
Although many high-definition (HD) roadmaps exist, they are not always accurate since public roads are dynamic, shaped unpredictably by both human activity and nature.
Thus, AVs must be able to handle situations in which the topology specified by the map does not agree with reality.
We present the Variable Structure Multiple Hidden Markov Model (VSM-HMM) as a framework for localizing in the presence of topological uncertainty, and demonstrate its effectiveness on an AV where lane membership is modeled as a topological localization process.
VSM-HMMs use a dynamic set of HMMs to simultaneously reason about location within a set of most likely current topologies and therefore may also be applied to topological structure estimation as well as AV lane estimation.
In addition, we present an extension to the Earth Mover's Distance which allows uncertainty to be taken into account when computing the distance between belief distributions on simplices of arbitrary relative sizes.
Reservoir Computing is a bio-inspired computing paradigm for processing time dependent signals.
The performance of its analogue implementation are comparable to other state of the art algorithms for tasks such as speech recognition or chaotic time series prediction, but these are often constrained by the offline training methods commonly employed.
Here we investigated the online learning approach by training an opto-electronic reservoir computer using a simple gradient descent algorithm, programmed on an FPGA chip.
Our system was applied to wireless communications, a quickly growing domain with an increasing demand for fast analogue devices to equalise the nonlinear distorted channels.
We report error rates up to two orders of magnitude lower than previous implementations on this task.
We show that our system is particularly well-suited for realistic channel equalisation by testing it on a drifting and a switching channels and obtaining good performances
Various machine learning methods for writer independent recognition of Malayalam handwritten district names are discussed in this paper.
Data collected from 56 different writers are used for the experiments.
The proposed work can be used for the recognition of district in the address written in Malayalam.
Different methods for Dimensionality reduction are discussed.
Features consider for the recognition are Histogram of Oriented Gradient descriptor, Number of Black Pixels in the upper half and lower half, length of image.
Classifiers used in this work are Neural Network, SVM and RandomForest.
We describe an embarrassingly parallel, anytime Monte Carlo method for likelihood-free models.
The algorithm starts with the view that the stochasticity of the pseudo-samples generated by the simulator can be controlled externally by a vector of random numbers u, in such a way that the outcome, knowing u, is deterministic.
For each instantiation of u we run an optimization procedure to minimize the distance between summary statistics of the simulator and the data.
After reweighing these samples using the prior and the Jacobian (accounting for the change of volume in transforming from the space of summary statistics to the space of parameters) we show that this weighted ensemble represents a Monte Carlo estimate of the posterior distribution.
The procedure can be run embarrassingly parallel (each node handling one sample) and anytime (by allocating resources to the worst performing sample).
The procedure is validated on six experiments.
In this paper, we show how unsupervised sense representations can be used to improve hypernymy extraction.
We present a method for extracting disambiguated hypernymy relationships that propagates hypernyms to sets of synonyms (synsets), constructs embeddings for these sets, and establishes sense-aware relationships between matching synsets.
Evaluation on two gold standard datasets for English and Russian shows that the method successfully recognizes hypernymy relationships that cannot be found with standard Hearst patterns and Wiktionary datasets for the respective languages.
Mobile health applications that track activities, such as exercise, sleep, and diet, are becoming widely used.
While these activity tracking applications have the potential to improve our health, user engagement and retention are critical factors for their success.
However, long-term user engagement patterns in real-world activity tracking applications are not yet well understood.
Here we study user engagement patterns within a mobile physical activity tracking application consisting of 115 million logged activities taken by over a million users over 31 months.
Specifically, we show that over 75% of users return and re-engage with the application after prolonged periods of inactivity, no matter the duration of the inactivity.
We find a surprising result that the re-engagement usage patterns resemble those of the start of the initial engagement period, rather than being a simple continuation of the end of the initial engagement period.
This evidence points to a conceptual model of multiple lives of user engagement, extending the prevalent single life view of user activity.
We demonstrate that these multiple lives occur because the users have a variety of different primary intents or goals for using the app.
We find evidence for users being more likely to stop using the app once they achieved their primary intent or goal (e.g., weight loss).
However, these users might return once their original intent resurfaces (e.g., wanting to lose newly gained weight).
Based on insights developed in this work, including a marker of improved primary intent performance, our prediction models achieve 71% ROC AUC.
Overall, our research has implications for modeling user re-engagement in health activity tracking applications and has consequences for how notifications, recommendations as well as gamification can be used to increase engagement.
Linear prediction (LP) technique estimates an optimum all-pole filter of a given order for a frame of speech signal.
The coefficients of the all-pole filter, 1/A(z) are referred to as LP coefficients (LPCs).
The gain of the inverse of the all-pole filter, A(z) at z = 1, i.e, at frequency = 0, A(1) corresponds to the sum of LPCs, which has the property of being lower (higher) than a threshold for the sonorants (fricatives).
When the inverse-tan of A(1), denoted as T(1), is used a feature and tested on the sonorant and fricative frames of the entire TIMIT database, an accuracy of 99.07% is obtained.
Hence, we refer to T(1) as sonorant-fricative discrimination index (SFDI).
This property has also been tested for its robustness for additive white noise and on the telephone quality speech of the NTIMIT database.
These results are comparable to, or in some respects, better than the state-of-the-art methods proposed for a similar task.
Such a property may be used for segmenting a speech signal or for non-uniform frame-rate analysis.
Current image captioning approaches generate descriptions which lack specific information, such as named entities that are involved in the images.
In this paper we propose a new task which aims to generate informative image captions, given images and hashtags as input.
We propose a simple but effective approach to tackle this problem.
We first train a convolutional neural networks - long short term memory networks (CNN-LSTM) model to generate a template caption based on the input image.
Then we use a knowledge graph based collective inference algorithm to fill in the template with specific named entities retrieved via the hashtags.
Experiments on a new benchmark dataset collected from Flickr show that our model generates news-style image descriptions with much richer information.
Our model outperforms unimodal baselines significantly with various evaluation metrics.
The rising popularity of social media has radically changed the way news content is propagated, including interactive attempts with new dimensions.
To date, traditional news media such as newspapers, television and radio have already adapted their activities to the online news media by utilizing social media, blogs, websites etc.
This paper provides some insight into the social media presence of worldwide popular news media outlets.
Despite the fact that these large news media propagate content via social media environments to a large extent and very little is known about the news item producers, providers and consumers in the news media community in social media.To better understand these interactions, this work aims to analyze news items in two large social media, Twitter and Facebook.
Towards that end, we collected all published posts on Twitter and Facebook from 48 news media to perform descriptive and predictive analyses using the dataset of 152K tweets and 80K Facebook posts.
We explored a set of news media that originate content by themselves in social media, those who distribute their news items to other news media and those who consume news content from other news media and/or share replicas.
We propose a predictive model to increase news media popularity among readers based on the number of posts, number of followers and number of interactions performed within the news media community.
The results manifested that, news media should disperse their own content and they should publish first in social media in order to become a popular news media and receive more attractions to their news items from news readers.
Scholars have often relied on name initials to resolve name ambiguities in large-scale coauthorship network research.
This approach bears the risk of incorrectly merging or splitting author identities.
The use of initial-based disambiguation has been justified by the assumption that such errors would not affect research findings too much.
This paper tests this assumption by analyzing coauthorship networks from five academic fields - biology, computer science, nanoscience, neuroscience, and physics - and an interdisciplinary journal, PNAS.
Name instances in datasets of this study were disambiguated based on heuristics gained from previous algorithmic disambiguation solutions.
We use disambiguated data as a proxy of ground-truth to test the performance of three types of initial-based disambiguation.
Our results show that initial-based disambiguation can misrepresent statistical properties of coauthorship networks: it deflates the number of unique authors, number of component, average shortest paths, clustering coefficient, and assortativity, while it inflates average productivity, density, average coauthor number per author, and largest component size.
Also, on average, more than half of top 10 productive or collaborative authors drop off the lists.
Asian names were found to account for the majority of misidentification by initial-based disambiguation due to their common surname and given name initials.
Enlarged perivascular spaces (EPVS) in the brain are an emerging imaging marker for cerebral small vessel disease, and have been shown to be related to increased risk of various neurological diseases, including stroke and dementia.
Automatic quantification of EPVS would greatly help to advance research into its etiology and its potential as a risk indicator of disease.
We propose a convolutional network regression method to quantify the extent of EPVS in the basal ganglia from 3D brain MRI.
We first segment the basal ganglia and subsequently apply a 3D convolutional regression network designed for small object detection within this region of interest.
The network takes an image as input, and outputs a quantification score of EPVS.
The network has significantly more convolution operations than pooling ones and no final activation, allowing it to span the space of real numbers.
We validated our approach using a dataset of 2000 brain MRI scans scored visually.
Experiments with varying sizes of training and test sets showed that a good performance can be achieved with a training set of only 200 scans.
With a training set of 1000 scans, the intraclass correlation coefficient (ICC) between our scoring method and the expert's visual score was 0.74.
Our method outperforms by a large margin - more than 0.10 - four more conventional automated approaches based on intensities, scale-invariant feature transform, and random forest.
We show that the network learns the structures of interest and investigate the influence of hyper-parameters on the performance.
We also evaluate the reproducibility of our network using a set of 60 subjects scanned twice (scan-rescan reproducibility).
On this set our network achieves an ICC of 0.93, while the intrarater agreement reaches 0.80.
Furthermore, the automatic EPVS scoring correlates similarly to age as visual scoring.
Machine-learning models have been recently used for detecting malicious Android applications, reporting impressive performances on benchmark datasets, even when trained only on features statically extracted from the application, such as system calls and permissions.
However, recent findings have highlighted the fragility of such in-vitro evaluations with benchmark datasets, showing that very few changes to the content of Android malware may suffice to evade detection.
How can we thus trust that a malware detector performing well on benchmark data will continue to do so when deployed in an operating environment?
To mitigate this issue, the most popular Android malware detectors use linear, explainable machine-learning models to easily identify the most influential features contributing to each decision.
In this work, we generalize this approach to any black-box machine- learning model, by leveraging a gradient-based approach to identify the most influential local features.
This enables using nonlinear models to potentially increase accuracy without sacrificing interpretability of decisions.
Our approach also highlights the global characteristics learned by the model to discriminate between benign and malware applications.
Finally, as shown by our empirical analysis on a popular Android malware detection task, it also helps identifying potential vulnerabilities of linear and nonlinear models against adversarial manipulations.
We present a statistical-modelling method for piano reduction, i.e.converting an ensemble score into piano scores, that can control performance difficulty.
While previous studies have focused on describing the condition for playable piano scores, it depends on player's skill and can change continuously with the tempo.
We thus computationally quantify performance difficulty as well as musical fidelity to the original score, and formulate the problem as optimization of musical fidelity under constraints on difficulty values.
First, performance difficulty measures are developed by means of probabilistic generative models for piano scores and the relation to the rate of performance errors is studied.
Second, to describe musical fidelity, we construct a probabilistic model integrating a prior piano-score model and a model representing how ensemble scores are likely to be edited.
An iterative optimization algorithm for piano reduction is developed based on statistical inference of the model.
We confirm the effect of the iterative procedure; we find that subjective difficulty and musical fidelity monotonically increase with controlled difficulty values; and we show that incorporating sequential dependence of pitches and fingering motion in the piano-score model improves the quality of reduction scores in high-difficulty cases.
Deep learning and convolutional neural networks (CNN) have been intensively used in many image processing topics during last years.
As far as steganalysis is concerned, the use of CNN allows reaching the state-of-the-art results.
The performances of such networks often rely on the size of their learning database.
An obvious preliminary assumption could be considering that "the bigger a database is, the better the results are".
However, it appears that cautions have to be taken when increasing the database size if one desire to improve the classification accuracy i.e. enhance the steganalysis efficiency.
To our knowledge, no study has been performed on the enrichment impact of a learning database on the steganalysis performance.
What kind of images can be added to the initial learning set?
What are the sensitive criteria: the camera models used for acquiring the images, the treatments applied to the images, the cameras proportions in the database, etc?
This article continues the work carried out in a previous paper, and explores the ways to improve the performances of CNN.
It aims at studying the effects of "base augmentation" on the performance of steganalysis using a CNN.
We present the results of this study using various experimental protocols and various databases to define the good practices in base augmentation for steganalysis.
Object queries are essential in information seeking and decision making in vast areas of applications.
However, a query may involve complex conditions on objects and sets, which can be arbitrarily nested and aliased.
The objects and sets involved as well as the demand---the given parameter values of interest---can change arbitrarily.
How to implement object queries efficiently under all possible updates, and furthermore to provide complexity guarantees?
This paper describes an automatic method.
The method allows powerful queries to be written completely declaratively.
It transforms demand as well as all objects and sets into relations.
Most importantly, it defines invariants for not only the query results, but also all auxiliary values about the objects and sets involved, including those for propagating demand, and incrementally maintains all of them.
Implementation and experiments with problems from a variety of application areas, including distributed algorithms and probabilistic queries, confirm the analyzed complexities, trade-offs, and significant improvements over prior work.
This study concerned the active use of Wikipedia as a teaching tool in the classroom in higher education, trying to identify different usage profiles and their characterization.
A questionnaire survey was administrated to all full-time and part-time teachers at the Universitat Oberta de Catalunya and the Universitat Pompeu Fabra, both in Barcelona, Spain.
The questionnaire was designed using the Technology Acceptance Model as a reference, including items about teachers web 2.0 profile, Wikipedia usage, expertise, perceived usefulness, easiness of use, visibility and quality, as well as Wikipedia status among colleagues and incentives to use it more actively.
Clustering and statistical analysis were carried out using the k-medoids algorithm and differences between clusters were assessed by means of contingency tables and generalized linear models (logit).
The respondents were classified in four clusters, from less to more likely to adopt and use Wikipedia in the classroom, namely averse (25.4%), reluctant (17.9%), open (29.5%) and proactive (27.2%).
Proactive faculty are mostly men teaching part-time in STEM fields, mainly engineering, while averse faculty are mostly women teaching full-time in non-STEM fields.
Nevertheless, questionnaire items related to visibility, quality, image, usefulness and expertise determine the main differences between clusters, rather than age, gender or domain.
Clusters involving a positive view of Wikipedia and at least some frequency of use clearly outnumber those with a strictly negative stance.
This goes against the common view that faculty members are mostly sceptical about Wikipedia.
Environmental factors such as academic culture and colleagues opinion are more important than faculty personal characteristics, especially with respect to what they think about Wikipedia quality.
Regularizing the gradient norm of the output of a neural network with respect to its inputs is a powerful technique, rediscovered several times.
This paper presents evidence that gradient regularization can consistently improve classification accuracy on vision tasks, using modern deep neural networks, especially when the amount of training data is small.
We introduce our regularizers as members of a broader class of Jacobian-based regularizers.
We demonstrate empirically on real and synthetic data that the learning process leads to gradients controlled beyond the training points, and results in solutions that generalize well.
Modern multi-core systems have a large number of design parameters, most of which are discrete-valued, and this number is likely to keep increasing as chip complexity rises.
Further, the accurate evaluation of a potential design choice is computationally expensive because it requires detailed cycle-accurate system simulation.
If the discrete parameter space can be embedded into a larger continuous parameter space, then continuous space techniques can, in principle, be applied to the system optimization problem.
Such continuous space techniques often scale well with the number of parameters.
We propose a novel technique for embedding the discrete parameter space into an extended continuous space so that continuous space techniques can be applied to the embedded problem using cycle accurate simulation for evaluating the objective function.
This embedding is implemented using simulation-based ergodic interpolation, which, unlike spatial interpolation, produces the interpolated value within a single simulation run irrespective of the number of parameters.
We have implemented this interpolation scheme in a cycle-based system simulator.
In a characterization study, we observe that the interpolated performance curves are continuous, piece-wise smooth, and have low statistical error.
We use the ergodic interpolation-based approach to solve a large multi-core design optimization problem with 31 design parameters.
Our results indicate that continuous space optimization using ergodic interpolation-based embedding can be a viable approach for large multi-core design optimization problems.
Plasmas with varying collisionalities occur in many applications, such as tokamak edge regions, where the flows are characterized by significant variations in density and temperature.
While a kinetic model is necessary for weakly-collisional high-temperature plasmas, high collisionality in colder regions render the equations numerically stiff due to disparate time scales.
In this paper, we propose an implicit-explicit algorithm for such cases, where the collisional term is integrated implicitly in time, while the advective term is integrated explicitly in time, thus allowing time step sizes that are comparable to the advective time scales.
This partitioning results in a more efficient algorithm than those using explicit time integrators, where the time step sizes are constrained by the stiff collisional time scales.
We implement semi-implicit additive Runge-Kutta methods in COGENT, a finite-volume gyrokinetic code for mapped, multiblock grids and test the accuracy, convergence, and computational cost of these semi-implicit methods for test cases with highly-collisional plasmas.
Magnetic skyrmions are promising candidates for next-generation information carriers, owing to their small size, topological stability, and ultralow depinning current density.
A wide variety of skyrmionic device concepts and prototypes have been proposed, highlighting their potential applications.
Here, we report on a bioinspired skyrmionic device with synaptic plasticity.
The synaptic weight of the proposed device can be strengthened/weakened by positive/negative stimuli, mimicking the potentiation/depression process of a biological synapse.
Both short-term plasticity(STP) and long-term potentiation(LTP) functionalities have been demonstrated for a spiking time-dependent plasticity(STDP) scheme.
This proposal suggests new possibilities for synaptic devices for use in spiking neuromorphic computing applications.
Word segmentation is the task of inserting or deleting word boundary characters in order to separate character sequences that correspond to words in some language.
In this article we propose an approach based on a beam search algorithm and a language model working at the byte/character level, the latter component implemented either as an n-gram model or a recurrent neural network.
The resulting system analyzes the text input with no word boundaries one token at a time, which can be a character or a byte, and uses the information gathered by the language model to determine if a boundary must be placed in the current position or not.
Our aim is to use this system in a preprocessing step for a microtext normalization system.
This means that it needs to effectively cope with the data sparsity present on this kind of texts.
We also strove to surpass the performance of two readily available word segmentation systems: The well-known and accessible Word Breaker by Microsoft, and the Python module WordSegment by Grant Jenks.
The results show that we have met our objectives, and we hope to continue to improve both the precision and the efficiency of our system in the future.
We present a structural clustering algorithm for large-scale datasets of small labeled graphs, utilizing a frequent subgraph sampling strategy.
A set of representatives provides an intuitive description of each cluster, supports the clustering process, and helps to interpret the clustering results.
The projection-based nature of the clustering approach allows us to bypass dimensionality and feature extraction problems that arise in the context of graph datasets reduced to pairwise distances or feature vectors.
While achieving high quality and (human) interpretable clusterings, the runtime of the algorithm only grows linearly with the number of graphs.
Furthermore, the approach is easy to parallelize and therefore suitable for very large datasets.
Our extensive experimental evaluation on synthetic and real world datasets demonstrates the superiority of our approach over existing structural and subspace clustering algorithms, both, from a runtime and quality point of view.
We consider the problem of property testing for differential privacy: with black-box access to a purportedly private algorithm, can we verify its privacy guarantees?
In particular, we show that any privacy guarantee that can be efficiently verified is also efficiently breakable in the sense that there exist two databases between which we can efficiently distinguish.
We give lower bounds on the query complexity of verifying pure differential privacy, approximate differential privacy, random pure differential privacy, and random approximate differential privacy.
We also give algorithmic upper bounds.
The lower bounds obtained in the work are infeasible for the scale of parameters that are typically considered reasonable in the differential privacy literature, even when we suppose that the verifier has access to an (untrusted) description of the algorithm.
A central message of this work is that verifying privacy requires compromise by either the verifier or the algorithm owner.
Either the verifier has to be satisfied with a weak privacy guarantee, or the algorithm owner has to compromise on side information or access to the algorithm.
This paper describes the stages faced during the development of an Android program which obtains and decodes live images from DJI Phantom 3 Professional Drone and implements certain features of the TensorFlow Android Camera Demo application.
Test runs were made and outputs of the application were noted.
A lake was classified as seashore, breakwater and pier with the proximities of 24.44%, 21.16% and 12.96% respectfully.
The joystick of the UAV controller and laptop keyboard was classified with the proximities of 19.10% and 13.96% respectfully.
The laptop monitor was classified as screen, monitor and television with the proximities of 18.77%, 14.76% and 14.00% respectfully.
The computer used during the development of this study was classified as notebook and laptop with the proximities of 20.04% and 11.68% respectfully.
A tractor parked at a parking lot was classified with the proximity of 12.88%.
A group of cars in the same parking lot were classified as sports car, racer and convertible with the proximities of 31.75%, 18.64% and 13.45% respectfully at an inference time of 851ms.
We propose a novel unsupervised image segmentation algorithm, which aims to segment an image into several coherent parts.
It requires no user input, no supervised learning phase and assumes an unknown number of segments.
It achieves this by first over-segmenting the image into several hundred superpixels.
These are iteratively joined on the basis of a discriminative classifier trained on color and texture information obtained from each superpixel.
The output of the classifier is regularized by a Markov random field that lends more influence to neighbouring superpixels that are more similar.
In each iteration, similar superpixels fall under the same label, until only a few coherent regions remain in the image.
The algorithm was tested on a standard evaluation data set, where it performs on par with state-of-the-art algorithms in term of precision and greatly outperforms the state of the art by reducing the oversegmentation of the object of interest.
Dropout is a very effective way of regularizing neural networks.
Stochastically "dropping out" units with a certain probability discourages over-specific co-adaptations of feature detectors, preventing overfitting and improving network generalization.
Besides, Dropout can be interpreted as an approximate model aggregation technique, where an exponential number of smaller networks are averaged in order to get a more powerful ensemble.
In this paper, we show that using a fixed dropout probability during training is a suboptimal choice.
We thus propose a time scheduling for the probability of retaining neurons in the network.
This induces an adaptive regularization scheme that smoothly increases the difficulty of the optimization problem.
This idea of "starting easy" and adaptively increasing the difficulty of the learning problem has its roots in curriculum learning and allows one to train better models.
Indeed, we prove that our optimization strategy implements a very general curriculum scheme, by gradually adding noise to both the input and intermediate feature representations within the network architecture.
Experiments on seven image classification datasets and different network architectures show that our method, named Curriculum Dropout, frequently yields to better generalization and, at worst, performs just as well as the standard Dropout method.
An important issue with oversampled FIR analysis filter banks (FBs) is to determine inverse synthesis FBs, when they exist.
Given any complex oversampled FIR analysis FB, we first provide an algorithm to determine whether there exists an inverse FIR synthesis system.
We also provide a method to ensure the Hermitian symmetry property on the synthesis side, which is serviceable to processing real-valued signals.
As an invertible analysis scheme corresponds to a redundant decomposition, there is no unique inverse FB.
Given a particular solution, we parameterize the whole family of inverses through a null space projection.
The resulting reduced parameter set simplifies design procedures, since the perfect reconstruction constrained optimization problem is recast as an unconstrained optimization problem.
The design of optimized synthesis FBs based on time or frequency localization criteria is then investigated, using a simple yet efficient gradient algorithm.
Face recognition technology has demonstrated tremendous progress over the past few years, primarily due to advances in representation learning.
As we witness the widespread adoption of these systems, it is imperative to consider the security of face representations.
In this paper, we explore the practicality of using a fully homomorphic encryption based framework to secure a database of face templates.
This framework is designed to preserve the privacy of users and prevent information leakage from the templates, while maintaining their utility through template matching directly in the encrypted domain.
Additionally, we also explore a batching and dimensionality reduction scheme to trade-off face matching accuracy and computational complexity.
Experiments on benchmark face datasets (LFW, IJB-A, IJB-B, CASIA) indicate that secure face matching can be practically feasible (16 KB template size and 0.01 sec per match pair for 512-dimensional features from SphereFace) while exhibiting minimal loss in matching performance.
Modern information systems are changing the idea of "data processing" to the idea of "concept processing", meaning that instead of processing words, such systems process semantic concepts which carry meaning and share contexts with other concepts.
Ontology is commonly used as a structure that captures the knowledge about a certain area via providing concepts and relations between them.
Traditionally, concept hierarchies have been built manually by knowledge engineers or domain experts.
However, the manual construction of a concept hierarchy suffers from several limitations such as its coverage and the enormous costs of its extension and maintenance.
Ontology learning, usually referred to the (semi-)automatic support in ontology development, is usually divided into steps, going from concepts identification, passing through hierarchy and non-hierarchy relations detection and, seldom, axiom extraction.
It is reasonable to say that among these steps the current frontier is in the establishment of concept hierarchies, since this is the backbone of ontologies and, therefore, a good concept hierarchy is already a valuable resource for many ontology applications.
The automatic construction of concept hierarchies from texts is a complex task and much work have been proposing approaches to better extract relations between concepts.
These different proposals have never been contrasted against each other on the same set of data and across different languages.
Such comparison is important to see whether they are complementary or incremental.
Also, we can see whether they present different tendencies towards recall and precision.
This paper evaluates these different methods on the basis of hierarchy metrics such as density and depth, and evaluation metrics such as Recall and Precision.
Results shed light over the comprehensive set of methods according to the literature in the area.
It is well known that closed-form analytical solutions for AC power flow equations do not exist in general.
This paper proposes a multi-dimensional holomorphic embedding method (MDHEM) to obtain an explicit approximate analytical AC power-flow solution by finding a physical germ solution and arbitrarily embedding each power, each load or groups of loads with respective scales.
Based on the MDHEM, the complete approximate analytical solutions to the power flow equations in the high-dimensional space become achievable, since the voltage vector of each bus can be explicitly expressed by a convergent multivariate power series of all the loads.
Unlike the traditional iterative methods for power flow calculation and inaccurate sensitivity analysis method for voltage control, the algebraic variables of a power system in all operating conditions can be prepared offline and evaluated online by only plugging in the values of any operating conditions into the scales of the non-linear multivariate power series.
Case studies implemented on the 4-bus test system and the IEEE 14-bus standard system confirm the effectiveness of the proposed method.
It is well-known that degree two finite field extensions can be equipped with a Hermitian-like structure similar to the extension of the complex field over the reals.
In this contribution, using this structure, we develop a modular character theory and the appropriate Fourier transform for some particular kind of finite Abelian groups.
Moreover we introduce the notion of bent functions for finite field valued functions rather than usual complex-valued functions, and we study several of their properties.
In particular we prove that this bentness notion is a consequence of that of Logachev, Salnikov and Yashchenko, introduced in "Bent functions on a finite Abelian group" (1997).
In addition this new bentness notion is also generalized to a vectorial setting.
The basic features of some of the most versatile and popular open source frameworks for machine learning (TensorFlow, Deep Learning4j, and H2O) are considered and compared.
Their comparative analysis was performed and conclusions were made as to the advantages and disadvantages of these platforms.
The performance tests for the de facto standard MNIST data set were carried out on H2O framework for deep learning algorithms designed for CPU and GPU platforms for single-threaded and multithreaded modes of operation.
We take up the challenge of designing realistic computational models of large interacting cell populations.
The goal is essentially to bring Gillespie's celebrated stochastic methodology to the level of an interacting population of cells.
Specifically, we are interested in how the gold standard of single cell computational modeling, here taken to be spatial stochastic reaction-diffusion models, may be efficiently coupled with a similar approach at the cell population level.
Concretely, we target a recently proposed set of pathways for pattern formation involving Notch-Delta signaling mechanisms.
These involve cell-to-cell communication as mediated both via direct membrane contact sites as well as via cellular protrusions.
We explain how to simulate the process in growing tissue using a multilevel approach and we discuss implications for future development of the associated computational methods.
We report on a data-driven investigation aimed at understanding the dynamics of message spreading in a real-world dynamical network of human proximity.
We use data collected by means of a proximity-sensing network of wearable sensors that we deployed at three different social gatherings, simultaneously involving several hundred individuals.
We simulate a message spreading process over the recorded proximity network, focusing on both the topological and the temporal properties.
We show that by using an appropriate technique to deal with the temporal heterogeneity of proximity events, a universal statistical pattern emerges for the delivery times of messages, robust across all the data sets.
Our results are useful to set constraints for generic processes of data dissemination, as well as to validate established models of human mobility and proximity that are frequently used to simulate realistic behaviors.
This paper proposes a novel deep architecture to address multi-label image recognition, a fundamental and practical task towards general visual understanding.
Current solutions for this task usually rely on an extra step of extracting hypothesis regions (i.e., region proposals), resulting in redundant computation and sub-optimal performance.
In this work, we achieve the interpretable and contextualized multi-label image classification by developing a recurrent memorized-attention module.
This module consists of two alternately performed components: i) a spatial transformer layer to locate attentional regions from the convolutional feature maps in a region-proposal-free way and ii) an LSTM (Long-Short Term Memory) sub-network to sequentially predict semantic labeling scores on the located regions while capturing the global dependencies of these regions.
The LSTM also output the parameters for computing the spatial transformer.
On large-scale benchmarks of multi-label image classification (e.g., MS-COCO and PASCAL VOC 07), our approach demonstrates superior performances over other existing state-of-the-arts in both accuracy and efficiency.
Millimeter wave (mmWave) communication is one feasible solution for high data-rate applications like vehicular-to-everything communication and next generation cellular communication.
Configuring mmWave links, which can be done through channel estimation or beam-selection, however, is a source of significant overhead.
In this paper, we propose to use spatial information extracted at sub-6 GHz to help establish the mmWave link.
First, we review the prior work on frequency dependent channel behavior and outline a simulation strategy to generate multi-band frequency dependent channels.
Second, assuming: (i) narrowband channels and a fully digital architecture at sub-6 GHz; and (ii) wideband frequency selective channels, OFDM signaling, and an analog architecture at mmWave, we outline strategies to incorporate sub-6 GHz spatial information in mmWave compressed beam selection.
We formulate compressed beam-selection as a weighted sparse signal recovery problem, and obtain the weighting information from sub-6 GHz channels.
In addition, we outline a structured precoder/combiner design to tailor the training to out-of-band information.
We also extend the proposed out-of-band aided compressed beam-selection approach to leverage information from all active OFDM subcarriers.
The simulation results for achievable rate show that out-of-band aided beam-selection can reduce the training overhead of in-band only beam-selection by 4x.
This paper describes a distributed MapReduce implementation of the minimum Redundancy Maximum Relevance algorithm, a popular feature selection method in bioinformatics and network inference problems.
The proposed approach handles both tall/narrow and wide/short datasets.
We further provide an open source implementation based on Hadoop/Spark, and illustrate its scalability on datasets involving millions of observations or features.
With the prevalence of accessible depth sensors, dynamic human body skeletons have attracted much attention as a robust modality for action recognition.
Previous methods model skeletons based on RNN or CNN, which has limited expressive power for irregular joints.
In this paper, we represent skeletons naturally on graphs and propose a generalized graph convolutional neural networks (GGCN) for skeleton-based action recognition, aiming to capture space-time variation via spectral graph theory.
In particular, we construct a generalized graph over consecutive frames, where each joint is not only connected to its neighboring joints in the same frame strongly or weakly, but also linked with relevant joints in the previous and subsequent frames.
The generalized graphs are then fed into GGCN along with the coordinate matrix of the skeleton sequence for feature learning, where we deploy high-order and fast Chebyshev approximation of spectral graph convolution in the network.
Experiments show that we achieve the state-of-the-art performance on the widely used NTU RGB+D, UT-Kinect and SYSU 3D datasets.
Human action recognition in 3D skeleton sequences has attracted a lot of research attention.
Recently, Long Short-Term Memory (LSTM) networks have shown promising performance in this task due to their strengths in modeling the dependencies and dynamics in sequential data.
As not all skeletal joints are informative for action recognition, and the irrelevant joints often bring noise which can degrade the performance, we need to pay more attention to the informative ones.
However, the original LSTM network does not have explicit attention ability.
In this paper, we propose a new class of LSTM network, Global Context-Aware Attention LSTM (GCA-LSTM), for skeleton based action recognition.
This network is capable of selectively focusing on the informative joints in each frame of each skeleton sequence by using a global context memory cell.
To further improve the attention capability of our network, we also introduce a recurrent attention mechanism, with which the attention performance of the network can be enhanced progressively.
Moreover, we propose a stepwise training scheme in order to train our network effectively.
Our approach achieves state-of-the-art performance on five challenging benchmark datasets for skeleton based action recognition.
This article defines a complement of a function and conditions for existence of such a complement function and presents few algorithms to construct a complement.
We consider the task of learning to estimate human pose in still images.
In order to avoid the high cost of full supervision, we propose to use a diverse data set, which consists of two types of annotations: (i) a small number of images are labeled using the expensive ground-truth pose; and (ii) other images are labeled using the inexpensive action label.
As action information helps narrow down the pose of a human, we argue that this approach can help reduce the cost of training without significantly affecting the accuracy.
To demonstrate this we design a probabilistic framework that employs two distributions: (i) a conditional distribution to model the uncertainty over the human pose given the image and the action; and (ii) a prediction distribution, which provides the pose of an image without using any action information.
We jointly estimate the parameters of the two aforementioned distributions by minimizing their dissimilarity coefficient, as measured by a task-specific loss function.
During both training and testing, we only require an efficient sampling strategy for both the aforementioned distributions.
This allows us to use deep probabilistic networks that are capable of providing accurate pose estimates for previously unseen images.
Using the MPII data set, we show that our approach outperforms baseline methods that either do not use the diverse annotations or rely on pointwise estimates of the pose.
In this work, we consider the problem of influence maximization on a hypergraph.
We first extend the Independent Cascade (IC) model to hypergraphs, and prove that the traditional influence maximization problem remains submodular.
We then present a variant of the influence maximization problem (HEMI) where one seeks to maximize the number of hyperedges, a majority of whose nodes are influenced.
We prove that HEMI is non-submodular under the diffusion model proposed.
Open-domain human-computer conversation has been attracting increasing attention over the past few years.
However, there does not exist a standard automatic evaluation metric for open-domain dialog systems; researchers usually resort to human annotation for model evaluation, which is time- and labor-intensive.
In this paper, we propose RUBER, a Referenced metric and Unreferenced metric Blended Evaluation Routine, which evaluates a reply by taking into consideration both a groundtruth reply and a query (previous user-issued utterance).
Our metric is learnable, but its training does not require labels of human satisfaction.
Hence, RUBER is flexible and extensible to different datasets and languages.
Experiments on both retrieval and generative dialog systems show that RUBER has a high correlation with human annotation.
Although the recent progress is substantial, deep learning methods can be vulnerable to the maliciously generated adversarial examples.
In this paper, we present a novel training procedure and a thresholding test strategy, towards robust detection of adversarial examples.
In training, we propose to minimize the reverse cross-entropy (RCE), which encourages a deep network to learn latent representations that better distinguish adversarial examples from normal ones.
In testing, we propose to use a thresholding strategy as the detector to filter out adversarial examples for reliable predictions.
Our method is simple to implement using standard algorithms, with little extra training cost compared to the common cross-entropy minimization.
We apply our method to defend various attacking methods on the widely used MNIST and CIFAR-10 datasets, and achieve significant improvements on robust predictions under all the threat models in the adversarial setting.
Recently, a RGB image encryption algorithm based on DNA encoding and chaos map has been proposed.
It was reported that the encryption algorithm can be broken with four pairs of chosen plain-images and the corresponding cipher-images.
This paper re-evaluates the security of the encryption algorithm, and finds that the encryption algorithm can be broken efficiently with only one known plain-image.
The effectiveness of the proposed known-plaintext attack is supported by both rigorous theoretical analysis and experimental results.
In addition, two other security defects are also reported.
We revisit the problem of asymmetric binary hypothesis testing against a composite alternative hypothesis.
We introduce a general framework to treat such problems when the alternative hypothesis adheres to certain axioms.
In this case we find the threshold rate, the optimal error and strong converse exponents (at large deviations from the threshold) and the second order asymptotics (at small deviations from the threshold).
We apply our results to find operational interpretations of various Renyi information measures.
In case the alternative hypothesis is comprised of bipartite product distributions, we find that the optimal error and strong converse exponents are determined by variations of Renyi mutual information.
In case the alternative hypothesis consists of tripartite distributions satisfying the Markov property, we find that the optimal exponents are determined by variations of Renyi conditional mutual information.
In either case the relevant notion of Renyi mutual information depends on the precise choice of the alternative hypothesis.
As such, our work also strengthens the view that different definitions of Renyi mutual information, conditional entropy and conditional mutual information are adequate depending on the context in which the measures are used.
We consider perfect secret key generation for a ``pairwise independent network'' model in which every pair of terminals share a random binary string, with the strings shared by distinct terminal pairs being mutually independent.
The terminals are then allowed to communicate interactively over a public noiseless channel of unlimited capacity.
All the terminals as well as an eavesdropper observe this communication.
The objective is to generate a perfect secret key shared by a given set of terminals at the largest rate possible, and concealed from the eavesdropper.
First, we show how the notion of perfect omniscience plays a central role in characterizing perfect secret key capacity.
Second, a multigraph representation of the underlying secrecy model leads us to an efficient algorithm for perfect secret key generation based on maximal Steiner tree packing.
This algorithm attains capacity when all the terminals seek to share a key, and, in general, attains at least half the capacity.
Third, when a single ``helper'' terminal assists the remaining ``user'' terminals in generating a perfect secret key, we give necessary and sufficient conditions for the optimality of the algorithm; also, a ``weak'' helper is shown to be sufficient for optimality.
The goal of this paper is to identify individuals by analyzing their gait.
Instead of using binary silhouettes as input data (as done in many previous works) we propose and evaluate the use of motion descriptors based on densely sampled short-term trajectories.
We take advantage of state-of-the-art people detectors to define custom spatial configurations of the descriptors around the target person, obtaining a rich representation of the gait motion.
The local motion features (described by the Divergence-Curl-Shear descriptor) extracted on the different spatial areas of the person are combined into a single high-level gait descriptor by using the Fisher Vector encoding.
The proposed approach, coined Pyramidal Fisher Motion, is experimentally validated on `CASIA' dataset (parts B and C), `TUM GAID' dataset, `CMU MoBo' dataset and the recent `AVA Multiview Gait' dataset.
The results show that this new approach achieves state-of-the-art results in the problem of gait recognition, allowing to recognize walking people from diverse viewpoints on single and multiple camera setups, wearing different clothes, carrying bags, walking at diverse speeds and not limited to straight walking paths.
Unmanned Aerial Vehicles (UAVs) have recently rapidly grown to facilitate a wide range of innovative applications that can fundamentally change the way cyber-physical systems (CPSs) are designed.
CPSs are a modern generation of systems with synergic cooperation between computational and physical potentials that can interact with humans through several new mechanisms.
The main advantages of using UAVs in CPS application is their exceptional features, including their mobility, dynamism, effortless deployment, adaptive altitude, agility, adjustability, and effective appraisal of real-world functions anytime and anywhere.
Furthermore, from the technology perspective, UAVs are predicted to be a vital element of the development of advanced CPSs.
Therefore, in this survey, we aim to pinpoint the most fundamental and important design challenges of multi-UAV systems for CPS applications.
We highlight key and versatile aspects that span the coverage and tracking of targets and infrastructure objects, energy-efficient navigation, and image analysis using machine learning for fine-grained CPS applications.
Key prototypes and testbeds are also investigated to show how these practical technologies can facilitate CPS applications.
We present and propose state-of-the-art algorithms to address design challenges with both quantitative and qualitative methods and map these challenges with important CPS applications to draw insightful conclusions on the challenges of each application.
Finally, we summarize potential new directions and ideas that could shape future research in these areas.
This paper considers a downlink cloud radio access network (C-RAN) in which all the base-stations (BSs) are connected to a central computing cloud via digital backhaul links with finite capacities.
Each user is associated with a user-centric cluster of BSs; the central processor shares the user's data with the BSs in the cluster, which then cooperatively serve the user through joint beamforming.
Under this setup, this paper investigates the user scheduling, BS clustering and beamforming design problem from a network utility maximization perspective.
Differing from previous works, this paper explicitly considers the per-BS backhaul capacity constraints.
We formulate the network utility maximization problem for the downlink C-RAN under two different models depending on whether the BS clustering for each user is dynamic or static over different user scheduling time slots.
In the former case, the user-centric BS cluster is dynamically optimized for each scheduled user along with the beamforming vector in each time-frequency slot, while in the latter case the user-centric BS cluster is fixed for each user and we jointly optimize the user scheduling and the beamforming vector to account for the backhaul constraints.
In both cases, the nonconvex per-BS backhaul constraints are approximated using the reweighted l1-norm technique.
This approximation allows us to reformulate the per-BS backhaul constraints into weighted per-BS power constraints and solve the weighted sum rate maximization problem through a generalized weighted minimum mean square error approach.
This paper shows that the proposed dynamic clustering algorithm can achieve significant performance gain over existing naive clustering schemes.
This paper also proposes two heuristic static clustering schemes that can already achieve a substantial portion of the gain.
One Monad to Prove Them All is a modern fairy tale about curiosity and perseverance, two important properties of a successful PhD student.
We follow the PhD student Mona on her adventure of proving properties about Haskell programs in the proof assistant Coq.
On the one hand, as a PhD student in computer science Mona observes an increasing demand for correct software products.
In particular, because of the large amount of existing software, verifying existing software products becomes more important.
Verifying programs in the functional programming language Haskell is no exception.
On the other hand, Mona is delighted to see that communities in the area of theorem proving are becoming popular.
Thus, Mona sets out to learn more about the interactive theorem prover Coq and verifying Haskell programs in Coq.
To prove properties about a Haskell function in Coq, Mona has to translate the function into Coq code.
As Coq programs have to be total and Haskell programs are often not, Mona has to model partiality explicitly in Coq.
In her quest for a solution Mona finds an ancient manuscript that explains how properties about Haskell functions can be proven in the proof assistant Agda by translating Haskell programs into monadic Agda programs.
By instantiating the monadic program with a concrete monad instance the proof can be performed in either a total or a partial setting.
Mona discovers that the proposed transformation does not work in Coq due to a restriction in the termination checker.
In fact the transformation does not work in Agda anymore as well, as the termination checker in Agda has been improved.
We follow Mona on an educational journey through the land of functional programming where she learns about concepts like free monads and containers as well as basics and restrictions of proof assistants like Coq.
These concepts are well-known individually, but their interplay gives rise to a solution for Mona's problem based on the originally proposed monadic tranformation that has not been presented before.
When Mona starts to test her approach by proving a statement about simple Haskell functions, she realizes that her approach has an additional advantage over the original idea in Agda.
Mona's final solution not only works for a specific monad instance but even allows her to prove monad-generic properties.
Instead of proving properties over and over again for specific monad instances she is able to prove properties that hold for all monads representable by a container-based instance of the free monad.
In order to strengthen her confidence in the practicability of her approach, Mona evaluates her approach in a case study that compares two implementations for queues.
In order to share the results with other functional programmers the fairy tale is available as a literate Coq file.
If you are a citizen of the land of functional programming or are at least familiar with its customs, had a journey that involved reasoning about functional programs of your own, or are just a curious soul looking for the next story about monads and proofs, then this tale is for you.
Fully Homomorphic Encryption (FHE) refers to a set of encryption schemes that allow computations to be applied directly on encrypted data without requiring a secret key.
This enables novel application scenarios where a client can safely offload storage and computation to a third-party cloud provider without having to trust the software and the hardware vendors with the decryption keys.
Recent advances in both FHE schemes and implementations have moved such applications from theoretical possibilities into the realm of practicalities.
This paper proposes a compact and well-reasoned interface called the Homomorphic Instruction Set Architecture (HISA) for developing FHE applications.
Just as the hardware ISA interface enabled hardware advances to proceed independent of software advances in the compiler and language runtimes, HISA decouples compiler optimizations and runtimes for supporting FHE applications from advancements in the underlying FHE schemes.
This paper demonstrates the capabilities of HISA by building an end-to-end software stack for evaluating neural network models on encrypted data.
Our stack includes an end-to-end compiler, runtime, and a set of optimizations.
Our approach shows generated code, on a set of popular neural network architectures, is faster than hand-optimized implementations.
This research considers the task of evolving the physical structure of a robot to enhance its performance in various environments, which is a significant problem in the field of Evolutionary Robotics.
Inspired by the fields of evolutionary art and sculpture, we evolve only targeted parts of a robot, which simplifies the optimisation problem compared to traditional approaches that must simultaneously evolve both (actuated) body and brain.
Exploration fidelity is emphasised in areas of the robot most likely to benefit from shape optimisation, whilst exploiting existing robot structure and control.
Our approach uses a Genetic Algorithm to optimise collections of Bezier splines that together define the shape of a legged robot's tibia, and leg performance is evaluated in parallel in a high-fidelity simulator.
The leg is represented in the simulator as 3D-printable file, and as such can be readily instantiated in reality.
Provisional experiments in three distinct environments show the evolution of environment-specific leg structures that are both high-performing and notably different to those evolved in the other environments.
This proof-of-concept represents an important step towards the environment-dependent optimisation of performance-critical components for a range of ubiquitous, standard, and already-capable robots that can carry out a wide variety of tasks.
Suppose there is a large file which should be transmitted (or stored) and there are several (say, m) admissible data-compressors.
It seems natural to try all the compressors and then choose the best, i.e. the one that gives the shortest compressed file.
Then transfer (or store) the index number of the best compressor (it requires log m bits) and the compressed file.The only problem is the time, which essentially increases due to the need to compress the file m times (in order to find the best compressor).
We propose a method that encodes the file with the optimal compressor, but uses a relatively small additional time: the ratio of this extra time and the total time of calculation can be limited by an arbitrary positive constant.
Generally speaking, in many situations it may be necessary find the best data compressor out of a given set, which is often done by comparing them empirically.
One of the goals of this work is to turn such a selection process into a part of the data compression method, automating and optimizing it.
We propose a novel reflection color model consisting of body essence and (mixed) neuter, and present an effective method for separating dichromatic reflection components using a single image.
Body essence is an entity invariant to interface reflection, and has two degrees of freedom unlike hue and maximum chromaticity.
As a result, the proposed method is insensitive to noise and proper for colors around CMY (cyan, magenta, and yellow) as well as RGB (red, green, and blue), contrary to the maximum chromaticity-based methods.
Interface reflection is separated by using a Gaussian function, which removes a critical thresholding problem.
Furthermore, the method does not require any region segmentation.
Experimental results show the efficacy of the proposed model and method.
In order to improve the performances of recently-presented improved normalized subband adaptive filter (INSAF) and proportionate INSAF algorithms for highly noisy system, this paper proposes their set-membership versions by exploiting the theory of set-membership filtering.
Apart from obtaining smaller steady-state error, the proposed algorithms significantly reduce the overall computational complexity.
In addition, to further improve the steady-state performance for the algorithms, their smooth variants are developed by using the smoothed absolute subband output errors to update the step sizes.
Simulation results in the context of acoustic echo cancellation have demonstrated the superiority of the proposed algorithms.
In response to failures of central planning, the Chinese government has experimented not only with free-market trade zones, but with allowing non-profit foundations to operate in a decentralized fashion.
A network study shows how these foundations have connected together by sharing board members, in a structural parallel to what is seen in corporations in the United States and Europe.
This board interlocking leads to the emergence of an elite group with privileged network positions.
While the presence of government officials on non-profit boards is widespread, government officials are much less common in a subgroup of foundations that control just over half of all revenue in the network.
This subgroup, associated with business elites, not only enjoys higher levels of within-elite links, but even preferentially excludes government officials from the NGOs with higher degree.
The emergence of this structurally autonomous sphere is associated with major political and social events in the state-society relationship.
Cluster analysis reveals multiple internal components within this sphere that share similar levels of network influence.
Rather than a core-periphery structure centered around government officials, the Chinese non-profit world appears to be a multipolar one of distinct elite groups, many of which achieve high levels of independence from direct government control.
The traditional methods of the biology, based on illustrative descriptions and linear logic explanations, are discussed.
This work aims to improve this approach by introducing alternative tools to describe and represent complex biological systems.
Two models were developed, one mathematical and another computational, both were made in order to study the biological process between free radicals and antioxidants.
Each model was used to study the same process but in different scenarios.
The mathematical model was used to study the biological process in an epithelial cells culture; this model was validated with the experimental data of Anne Hanneken's research group from the Department of Molecular and Experimental Medicine, published by the journal Investigative Ophthalmology and Visual Science in July 2006.
The computational model was used to study the same process in an individual.
The model was made using C++ programming language, supported by the network theory of aging.
We summarise the results of RoboCup 2D Soccer Simulation League in 2016 (Leipzig), including the main competition and the evaluation round.
The evaluation round held in Leipzig confirmed the strength of RoboCup-2015 champion (WrightEagle, i.e.WE2015) in the League, with only eventual finalists of 2016 competition capable of defeating WE2015.
An extended, post-Leipzig, round-robin tournament which included the top 8 teams of 2016, as well as WE2015, with over 1000 games played for each pair, placed WE2015 third behind the champion team (Gliders2016) and the runner-up (HELIOS2016).
This establishes WE2015 as a stable benchmark for the 2D Simulation League.
We then contrast two ranking methods and suggest two options for future evaluation challenges.
The first one, "The Champions Simulation League", is proposed to include 6 previous champions, directly competing against each other in a round-robin tournament, with the view to systematically trace the advancements in the League.
The second proposal, "The Global Challenge", is aimed to increase the realism of the environmental conditions during the simulated games, by simulating specific features of different participating countries.
Data analysis and monitoring of road networks in terms of reliability and performance are valuable but hard to achieve, especially when the analytical information has to be available to decision makers on time.
The gathering and analysis of the observable facts can be used to infer knowledge about traffic congestion over time and gain insights into the roads safety.
However, the continuous monitoring of live traffic information produces a vast amount of data that makes it difficult for business intelligence (BI) tools to generate metrics and key performance indicators (KPI) in nearly real-time.
In order to overcome these limitations, we propose the application of a big-data based and process-centric approach that integrates with operational traffic information systems to give insights into the road network's efficiency.
This paper demonstrates how the adoption of an existent process-oriented DSS solution with big-data support can be leveraged to monitor and analyse live traffic data on an acceptable response time basis.
This work studies the representational mapping across multimodal data such that given a piece of the raw data in one modality the corresponding semantic description in terms of the raw data in another modality is immediately obtained.
Such a representational mapping can be found in a wide spectrum of real-world applications including image/video retrieval, object recognition, action/behavior recognition, and event understanding and prediction.
To that end, we introduce a simplified training objective for learning multimodal embeddings using the skip-gram architecture by introducing convolutional "pseudowords:" embeddings composed of the additive combination of distributed word representations and image features from convolutional neural networks projected into the multimodal space.
We present extensive results of the representational properties of these embeddings on various word similarity benchmarks to show the promise of this approach.
Rotation estimation of known rigid objects is important for robotic applications such as dexterous manipulation.
Most existing methods for rotation estimation use intermediate representations such as templates, global or local feature descriptors, or object coordinates, which require multiple steps in order to infer the object pose.
We propose to directly regress a pose vector from raw point cloud segments using a convolutional neural network.
Experimental results show that our method can potentially achieve competitive performance compared to a state-of-the-art method, while also showing more robustness against occlusion.
Our method does not require any post processing such as refinement with the iterative closest point algorithm.
The high computational and parameter complexity of neural networks makes their training very slow and difficult to deploy on energy and storage-constrained computing systems.
Many network complexity reduction techniques have been proposed including fixed-point implementation.
However, a systematic approach for designing full fixed-point training and inference of deep neural networks remains elusive.
We describe a precision assignment methodology for neural network training in which all network parameters, i.e., activations and weights in the feedforward path, gradients and weight accumulators in the feedback path, are assigned close to minimal precision.
The precision assignment is derived analytically and enables tracking the convergence behavior of the full precision training, known to converge a priori.
Thus, our work leads to a systematic methodology of determining suitable precision for fixed-point training.
The near optimality (minimality) of the resulting precision assignment is validated empirically for four networks on the CIFAR-10, CIFAR-100, and SVHN datasets.
The complexity reduction arising from our approach is compared with other fixed-point neural network designs.
A description and annotation guidelines for the Yahoo Webscope release of Query Treebank, Version 1.0, May 2016.
We present a novel deformable groupwise registration method, applied to large 3D image groups.
Our approach extracts 3D SURF keypoints from images, computes matched pairs of keypoints and registers the group by minimizing pair distances in a hubless way i.e. without computing any central mean image.
Using keypoints significantly reduces the problem complexity compared to voxel-based approaches, and enables us to provide an in-core global optimization, similar to the Bundle Adjustment for 3D reconstruction.
As we aim at registering images of different patients, the matching step yields many outliers.
Then we propose a new EM-weighting algorithm which efficiently discards outliers.
Global optimization is carried out with a fast gradient descent algorithm.
This allows our approach to robustly register large datasets.
The result is a set of half transforms which link the volumes together and can be subsequently exploited for computational anatomy, landmark detection or image segmentation.
We show experimental results on whole-body CT scans, with groups of up to 103 volumes.
On a benchmark based on anatomical landmarks, our algorithm compares favorably with the star-groupwise voxel-based ANTs and NiftyReg approaches while being much faster.
We also discuss the limitations of our approach for lower resolution images such as brain MRI.
Logical systems with classical negation and means for sentential or propositional self-reference involve, in some way, paradoxical statements such as the liar.
However, the paradox disappears if one replaces classical by an appropriate non-classical negation such as a paraconsistent one (no paradox arises if the liar is both true and false).
We consider a non-Fregean logic which is a revised and extended version (Lewitzka 2012) of Epsilon-T-Logic originally introduced by (Straeter 1992) as a logic with a total truth predicate and propositional quantifiers.
Self-reference is achieved by means of equations between formulas which are interpreted over a model-theoretic universe of propositions.
Paradoxical statements, such as the liar, can be asserted only by unsatisfiable equations and do not correlate with propositions.
In this paper, we generalize Epsilon-T-Logic to a four-valued logic related to Dunn/Belnap logic B_4.
We also define three-valued versions related to Kleene's logic K_3 and Priest's Logic of Paradox P_3, respectively.
In this many-valued setting, models may contain liars and other "paradoxical" propositions which are ruled out by the more restrictive classical semantics.
We introduce these many-valued non-Fregean logics as extensions of abstract parameter logics such that parameter logic and extension are of the same logical type.
For this purpose, we define and study abstract logics of type B_4, K_3 and P_3.
Using semantic methods we show compactness of the consequence relation of abstract logics of type B_4, give a representation as minimally generated logics and establish a connection to the approach of (Font 1997).
Finally, we present a complete sequent calculus for the Epsilon-T-style extension of classical abstract logics simplifying constructions originally developed by (Straeter 1992, Zeitz 2000, Lewitzka 1998).
In this paper, we examine the problem of robotic manipulation of granular media.
We evaluate multiple predictive models used to infer the dynamics of scooping and dumping actions.
These models are evaluated on a task that involves manipulating the media in order to deform it into a desired shape.
Our best performing model is based on a highly-tailored convolutional network architecture with domain-specific optimizations, which we show accurately models the physical interaction of the robotic scoop with the underlying media.
We empirically demonstrate that explicitly predicting physical mechanics results in a policy that out-performs both a hand-crafted dynamics baseline, and a "value-network", which must otherwise implicitly predict the same mechanics in order to produce accurate value estimates.
How much is the h-index of an editor of a well ranked journal improved due to citations which occur after his or her appointment?
Scientific recognition within academia is widely measured nowadays by the number of citations or h-index.
Our dataset is based on a sample of four editors from a well ranked journal (impact factor - IF - greater than 2).
The target group consists of two editors who seem to benefit by their position through an increased citation number (and subsequently h-index) within journal.
The total amount of citations for the target group is bigger than 600.
The control group is formed by another set of two editors from the same journal whose relations between their positions and their citation records remain neutral.
The total amount of citations for the control group is more than 1200.
The timespan for which pattern of citations has been studied is 1975-2015.
Previous coercive citations for a journal benefit (increase its IF) has been signaled.
To the best of our knowledge, this is a pioneering work on coercive citations for personal (or editors) benefit.
Editorial teams should be aware about this type of potentially unethical behavior and act accordingly.
The JCT-VC standardized Screen Content Coding (SCC) extension in the HEVC HM RExt + SCM reference codec offers an impressive coding efficiency performance when compared with HM RExt alone; however, it is not significantly perceptually optimized.
For instance, it does not include advanced HVS-based perceptual coding methods, such as JND-based spatiotemporal masking schemes.
In this paper, we propose a novel JND-based perceptual video coding technique for HM RExt + SCM.
The proposed method is designed to further improve the compression performance of HM RExt + SCM when applied to YCbCr 4:4:4 SC video data.
In the proposed technique, luminance masking and chrominance masking are exploited to perceptually adjust the Quantization Step Size (QStep) at the Coding Block (CB) level.
Compared with HM RExt 16.10 + SCM 8.0, the proposed method considerably reduces bitrates (Kbps), with a maximum reduction of 48.3%.
In addition to this, the subjective evaluations reveal that SC-PAQ achieves visually lossless coding at very low bitrates.
State-of-the-art in network science of teams offers effective recommendation methods to answer questions like who is the best replacement, what is the best team expansion strategy, but lacks intuitive ways to explain why the optimization algorithm gives the specific recommendation for a given team optimization scenario.
To tackle this problem, we develop an interactive prototype system, EXTRA, as the first step towards addressing such a sense-making challenge, through the lens of the underlying network where teams embed, to explain the team recommendation results.
The main advantages are (1) Algorithm efficacy: we propose an effective and fast algorithm to explain random walk graph kernel, the central technique for networked team recommendation; (2) Intuitive visual explanation: we present intuitive visual analysis of the recommendation results, which can help users better understand the rationality of the underlying team recommendation algorithm.
Face sketches are able to capture the spatial topology of a face while lacking some facial attributes such as race, skin, or hair color.
Existing sketch-photo recognition approaches have mostly ignored the importance of facial attributes.
In this paper, we propose a new loss function, called attribute-centered loss, to train a Deep Coupled Convolutional Neural Network (DCCNN) for the facial attribute guided sketch to photo matching.
Specifically, an attribute-centered loss is proposed which learns several distinct centers, in a shared embedding space, for photos and sketches with different combinations of attributes.
The DCCNN simultaneously is trained to map photos and pairs of testified attributes and corresponding forensic sketches around their associated centers, while preserving the spatial topology information.
Importantly, the centers learn to keep a relative distance from each other, related to their number of contradictory attributes.
Extensive experiments are performed on composite (E-PRIP) and semi-forensic (IIIT-D Semi-forensic) databases.
The proposed method significantly outperforms the state-of-the-art.
Due to the increasing dependency of critical infrastructure on synchronized clocks, network time synchronization protocols have become an attractive target for attackers.
We identify data origin authentication as the key security objective and suggest to employ recently proposed high-performance digital signature schemes (Ed25519 and MQQ-SIG)) as foundation of a novel set of security measures to secure multicast time synchronization.
We conduct experiments to verify the computational and communication efficiency for using these signatures in the standard time synchronization protocols NTP and PTP.
We propose additional security measures to prevent replay attacks and to mitigate delay attacks.
Our proposed solutions cover 1-step mode for NTP and PTP and we extend our security measures specifically to 2-step mode (PTP) and show that they have no impact on time synchronization's precision.
Approaches to decision-making under uncertainty in the belief function framework are reviewed.
Most methods are shown to blend criteria for decision under ignorance with the maximum expected utility principle of Bayesian decision theory.
A distinction is made between methods that construct a complete preference relation among acts, and those that allow incomparability of some acts due to lack of information.
Methods developed in the imprecise probability framework are applicable in the Dempster-Shafer context and are also reviewed.
Shafer's constructive decision theory, which substitutes the notion of goal for that of utility, is described and contrasted with other approaches.
The paper ends by pointing out the need to carry out deeper investigation of fundamental issues related to decision-making with belief functions and to assess the descriptive, normative and prescriptive values of the different approaches.
This articles surveys the existing literature on the methods currently used by web services to track the user online as well as their purposes, implications, and possible user's defenses.
A significant majority of reviewed articles and web resources are from years 2012-2014.
Privacy seems to be the Achilles' heel of today's web.
Web services make continuous efforts to obtain as much information as they can about the things we search, the sites we visit, the people with who we contact, and the products we buy.
Tracking is usually performed for commercial purposes.
We present 5 main groups of methods used for user tracking, which are based on sessions, client storage, client cache, fingerprinting, or yet other approaches.
A special focus is placed on mechanisms that use web caches, operational caches, and fingerprinting, as they are usually very rich in terms of using various creative methodologies.
We also show how the users can be identified on the web and associated with their real names, e-mail addresses, phone numbers, or even street addresses.
We show why tracking is being used and its possible implications for the users (price discrimination, assessing financial credibility, determining insurance coverage, government surveillance, and identity theft).
For each of the tracking methods, we present possible defenses.
Apart from describing the methods and tools used for keeping the personal data away from being tracked, we also present several tools that were used for research purposes - their main goal is to discover how and by which entity the users are being tracked on their desktop computers or smartphones, provide this information to the users, and visualize it in an accessible and easy to follow way.
Finally, we present the currently proposed future approaches to track the user and show that they can potentially pose significant threats to the users' privacy.
We study motion feasibility conditions of decentralized multi-agent control systems on Lie groups with collision avoidance constraints, modeled by an undirected graph.
We first consider agents modeled by a kinematic left invariant control systems (single integrator) and next as dynamical control systems (double integrator) determined by a left-trivialized Lagrangian function.
In the kinematic approach, we study the problem of determining whether there are nontrivial trajectories of all agent kinematics that maintain the collision avoidance constraints.
Solutions of the problem give rise to linear combinations of the control inputs in a linear subspace annihilating the constraints.
In the dynamical problem, first order necessary conditions for the existence of feasible motions are obtained using techniques from variational calculus on manifolds and by introducing collision avoidance constraints among agents into an augmented action functional by using the Lagrange multipliers theorem.
Deep learning based on artificial neural networks is a powerful machine learning method that, in the last few years, has been successfully used to realize tasks, e.g., image classification, speech recognition, translation of languages, etc., that are usually simple to execute by human beings but extremely difficult to perform by machines.
This is one of the reasons why deep learning is considered to be one of the main enablers to realize the notion of artificial intelligence.
In order to identify the best architecture of an artificial neural network that allows one to fit input-output data pairs, the current methodology in deep learning methods consists of employing a data-driven approach.
Once the artificial neural network is trained, it is capable of responding to never-observed inputs by providing the optimum output based on past acquired knowledge.
In this context, a recent trend in the deep learning community is to complement pure data-driven approaches with prior information based on expert knowledge.
In this work, we describe two methods that implement this strategy, which aim at optimizing wireless communication networks.
In addition, we illustrate numerical results in order to assess the performance of the proposed approaches compared with pure data-driven implementations.
We introduce XtraPuLP, a new distributed-memory graph partitioner designed to process trillion-edge graphs.
XtraPuLP is based on the scalable label propagation community detection technique, which has been demonstrated as a viable means to produce high quality partitions with minimal computation time.
On a collection of large sparse graphs, we show that XtraPuLP partitioning quality is comparable to state-of-the-art partitioning methods.
We also demonstrate that XtraPuLP can produce partitions of real-world graphs with billion+ vertices in minutes.
Further, we show that using XtraPuLP partitions for distributed-memory graph analytics leads to significant end-to-end execution time reduction.
For the first time a mathematical object is presented - a reversible cellular Automaton - with many paradoxical qualities, the main ones among them are: a frequent quickly return to its original state, the presence of a large number of conservation laws and paradoxical "fuzzy" symmetries, which connects the current position of the automaton with its signature Main Integral.
We propose a method for annotating the location of objects in ImageNet.
Traditionally, this is cast as an image window classification problem, where each window is considered independently and scored based on its appearance alone.
Instead, we propose a method which scores each candidate window in the context of all other windows in the image, taking into account their similarity in appearance space as well as their spatial relations in the image plane.
We devise a fast and exact procedure to optimize our scoring function over all candidate windows in an image, and we learn its parameters using structured output regression.
We demonstrate on 92000 images from ImageNet that this significantly improves localization over recent techniques that score windows in isolation.
Recent advances in video super-resolution have shown that convolutional neural networks combined with motion compensation are able to merge information from multiple low-resolution (LR) frames to generate high-quality images.
Current state-of-the-art methods process a batch of LR frames to generate a single high-resolution (HR) frame and run this scheme in a sliding window fashion over the entire video, effectively treating the problem as a large number of separate multi-frame super-resolution tasks.
This approach has two main weaknesses: 1) Each input frame is processed and warped multiple times, increasing the computational cost, and 2) each output frame is estimated independently conditioned on the input frames, limiting the system's ability to produce temporally consistent results.
In this work, we propose an end-to-end trainable frame-recurrent video super-resolution framework that uses the previously inferred HR estimate to super-resolve the subsequent frame.
This naturally encourages temporally consistent results and reduces the computational cost by warping only one image in each step.
Furthermore, due to its recurrent nature, the proposed method has the ability to assimilate a large number of previous frames without increased computational demands.
Extensive evaluations and comparisons with previous methods validate the strengths of our approach and demonstrate that the proposed framework is able to significantly outperform the current state of the art.
Similarity searching of molecular structure has been an important application in the Chemoinformatics, especially in drug discovery.
Similarity searching is a common method used for identification of molecular structure.
It involve three main principal component of similarity searching: structure representation; weighting scheme; and similarity coefficient.
In this paper, we introduces Weighted Tanimoto Coefficient based on weighted Euclidean distance in order to investigate the effect of weight function on the result for similarity searching.
The Tanimoto coefficient is one of the popular similarity coefficients used to measure the similarity between pairs of the molecule.
The most of research area found that the similarity searching is based on binary or fingerprint data.
Meanwhile, we used non-binary data and was set amphetamine structure as a reference or targeted structure and the rest of the dataset becomes a database structure.
Throughout this study, it showed that there is definitely gives a different result between a similarity searching with and without weight.
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It explores possible origins of Physical Human-Human communication, more precisely, the hypothes
Image steganography is a growing research field, where sensitive contents are embedded in images, keeping their visual quality intact.
Researchers have used correlated color space such as RGB, where modification to one channel affects the overall quality of stego-images, hence decreasing its suitability for steganographic algorithms.
Therefore, in this paper, we propose an adaptive LSB substitution method using uncorrelated color space, increasing the property of imperceptibility while minimizing the chances of detection by the human vision system.
In the proposed scheme, the input image is passed through an image scrambler, resulting in an encrypted image, preserving the privacy of image contents, and then converted to HSV color space for further processing.
The secret contents are encrypted using an iterative magic matrix encryption algorithm (IMMEA) for better security, producing the cipher contents.
An adaptive LSB substitution method is then used to embed the encrypted data inside the V-plane of HSV color model based on secret key-directed block magic LSB mechanism.
The idea of utilizing HSV color space for data hiding is inspired from its properties including de-correlation, cost-effectiveness in processing, better stego image quality, and suitability for steganography as verified by our experiments, compared to other color spaces such as RGB, YCbCr, HSI, and Lab.
The quantitative and qualitative experimental results of the proposed framework and its application for addressing the security and privacy of visual contents in online social networks (OSNs), confirm its effectiveness in contrast to state-of-the-art methods.
We propose novel model transfer-learning methods that refine a decision forest model M learned within a "source" domain using a training set sampled from a "target" domain, assumed to be a variation of the source.
We present two random forest transfer algorithms.
The first algorithm searches greedily for locally optimal modifications of each tree structure by trying to locally expand or reduce the tree around individual nodes.
The second algorithm does not modify structure, but only the parameter (thresholds) associated with decision nodes.
We also propose to combine both methods by considering an ensemble that contains the union of the two forests.
The proposed methods exhibit impressive experimental results over a range of problems.
Previous work on surgical skill assessment using intraoperative tool motion in the operating room (OR) has focused on highly-structured surgical tasks such as cholecystectomy.
Further, these methods only considered generic motion metrics such as time and number of movements, which are of limited instructive value.
In this paper, we developed and evaluated an automated approach to the surgical skill assessment of nasal septoplasty in the OR.
The obstructed field of view and highly unstructured nature of septoplasty precludes trainees from efficiently learning the procedure.
We propose a descriptive structure of septoplasty consisting of two types of activity: (1) brushing activity directed away from the septum plane characterizing the consistency of the surgeon's wrist motion and (2) activity along the septal plane characterizing the surgeon's coverage pattern.
We derived features related to these two activity types that classify a surgeon's level of training with an average accuracy of about 72%.
The features we developed provide surgeons with personalized, actionable feedback regarding their tool motion.
Studying materials informatics from a data mining perspective can be beneficial for manufacturing and other industrial engineering applications.
Predictive data mining technique and machine learning algorithm are combined to design a knowledge discovery system for the selection of engineering materials that meet the design specifications.
Predictive method-Naive Bayesian classifier and Machine learning Algorithm - Pearson correlation coefficient method were implemented respectively for materials classification and selection.
The knowledge extracted from the engineering materials data sets is proposed for effective decision making in advanced engineering materials design applications.
Interactive Music Systems (IMS) have introduced a new world of music-making modalities.
But can we really say that they create music, as in true autonomous creation?
Here we discuss Video Interactive VST Orchestra (VIVO), an IMS that considers extra-musical information by adopting a simple salience based model of user-system interaction when simulating intentionality in automatic music generation.
Key features of the theoretical framework, a brief overview of pilot research, and a case study providing validation of the model are presented.
This research demonstrates that a meaningful user/system interplay is established in what we define as reflexive multidominance.
The QUIC protocol combines features that were initially found inside the TCP, TLS and HTTP/2 protocols.
The IETF is currently finalising a complete specification of this protocol.
More than a dozen of independent implementations have been developed in parallel with these standardisation activities.
We propose and implement a QUIC test suite that interacts with public QUIC servers to verify their conformance with key features of the IETF specification.
Our measurements, gathered over a semester, provide a unique viewpoint on the evolution of a protocol and of its implementations.
They highlight the arrival of new features and some regressions among the different implementations.
In this study, we describe the behavior of LTE over the sea and investigate the problem of radio resource block allocation in such SINR limited maritime channels.
For simulations of such sea environment, we considered a network scenario of Bosphorus Strait in Istanbul, Turkey with different number of ships ferrying between two ports at a given time.
After exploiting the network characteristics, we formulated and solved the radio resource allocation problem by max-min integer linear programming method.
The radio resource allocation fairness in terms of Jain's fairness index was computed and it was compared with round robin and opportunistic methods.
Results show that the max-min optimization method performs better than the opportunistic and round robin methods.
This result in turn reflects that the max-min optimization method gives us the high minimum best throughput as compared to other two methods considering different ship density scenarios in the sea.
Also, it was observed that as the number of ships begin to increase in the sea, the max-min method performs significantly better with good fairness as compared to the other two methods.
In the never-ending quest for tools that enable an ISP to smooth troubleshooting and improve awareness of network behavior, very much effort has been devoted in the collection of data by active and passive measurement at the data plane and at the control plane level.
Exploitation of collected data has been mostly focused on anomaly detection and on root-cause analysis.
Our objective is somewhat in the middle.
We consider traceroutes collected by a network of probes and aim at introducing a practically applicable methodology to quickly spot measurements that are related to high-impact events happened in the network.
Such filtering process eases further in- depth human-based analysis, for example with visual tools which are effective only when handling a limited amount of data.
We introduce the empathy relation between traceroutes as the cornerstone of our formal characterization of the traceroutes related to a network event.
Based on this model, we describe an algorithm that finds traceroutes related to high-impact events in an arbitrary set of measurements.
Evidence of the effectiveness of our approach is given by experimental results produced on real-world data.
In this paper, the problem of finding a Nash equilibrium of a multi-player game is considered.
The players are only aware of their own cost functions as well as the action space of all players.
We develop a relatively fast algorithm within the framework of inexact-ADMM.
It requires a communication graph for the information exchange between the players as well as a few mild assumptions on cost functions.
The convergence proof of the algorithm to a Nash equilibrium of the game is then provided.
Moreover, the convergence rate is investigated via simulations.
This Note investigates the bias of the sampling importance resampling (SIR) filter in estimation of the state transition noise in the state space model.
The SIR filter may suffer from sample impoverishment that is caused by the resampling and therefore will benefit from a sampling proposal that has a heavier tail, e.g. the state transition noise simulated for particle preparation is bigger than the true noise involved with the state dynamics.
This is because a comparably big transition noise used for particle propagation can spread overlapped particles to counteract impoverishment, giving better approximation of the posterior.
As such, the SIR filter tends to yield a biased (bigger-than-the-truth) estimate of the transition noise if it is unknown and needs to be estimated, at least, in the forward-only filtering estimation.
The bias is elaborated via the direct roughening approach by means of both qualitative logical deduction and quantitative numerical simulation.
In order to avoid the "Midas Touch" problem, gaze-based interfaces for selection often introduce a dwell time: a fixed amount of time the user must fixate upon an object before it is selected.
Past interfaces have used a uniform dwell time across all objects.
Here, we propose an algorithm for adjusting the dwell times of different objects based on the inferred probability that the user intends to select them.
In particular, we introduce a probabilistic model of natural gaze behavior while surfing the web to infer the probability that each hyperlink is the intended hyperlink.
We assign shorter dwell times to more likely hyperlinks and longer dwell times to less likely hyperlinks, resulting a variable dwell time gaze-based browser.
We have evaluated this method objectively both in simulation and experimentally, and subjectively through questionnaires.
Our results demonstrate that the proposed algorithm achieves a better tradeoff between accuracy and speed.
Domain adaptation (DA) aims to generalize a learning model across training and testing data despite the mismatch of their data distributions.
In light of a theoretical estimation of upper error bound, we argue in this paper that an effective DA method should 1) search a shared feature subspace where source and target data are not only aligned in terms of distributions as most state of the art DA methods do, but also discriminative in that instances of different classes are well separated; 2) account for the geometric structure of the underlying data manifold when inferring data labels on the target domain.
In comparison with a baseline DA method which only cares about data distribution alignment between source and target, we derive three different DA models, namely CDDA, GA-DA, and DGA-DA, to highlight the contribution of Close yet Discriminative DA(CDDA) based on 1), Geometry Aware DA (GA-DA) based on 2), and finally Discriminative and Geometry Aware DA (DGA-DA) implementing jointly 1) and 2).
Using both synthetic and real data, we show the effectiveness of the proposed approach which consistently outperforms state of the art DA methods over 36 image classification DA tasks through 6 popular benchmarks.
We further carry out in-depth analysis of the proposed DA method in quantifying the contribution of each term of our DA model and provide insights into the proposed DA methods in visualizing both real and synthetic data.
We define and construct efficient depth-universal and almost-size-universal quantum circuits.
Such circuits can be viewed as general-purpose simulators for central classes of quantum circuits and can be used to capture the computational power of the circuit class being simulated.
For depth we construct universal circuits whose depth is the same order as the circuits being simulated.
For size, there is a log factor blow-up in the universal circuits constructed here.
We prove that this construction is nearly optimal.
Long Short-Term Memory (LSTM) is the primary recurrent neural networks architecture for acoustic modeling in automatic speech recognition systems.
Residual learning is an efficient method to help neural networks converge easier and faster.
In this paper, we propose several types of residual LSTM methods for our acoustic modeling.
Our experiments indicate that, compared with classic LSTM, our architecture shows more than 8% relative reduction in Phone Error Rate (PER) on TIMIT tasks.
At the same time, our residual fast LSTM approach shows 4% relative reduction in PER on the same task.
Besides, we find that all this architecture could have good results on THCHS-30, Librispeech and Switchboard corpora.
We present a number of powerful local mechanisms for maintaining a dynamic swarm of robots with limited capabilities and information, in the presence of external forces and permanent node failures.
We propose a set of local continuous algorithms that together produce a generalization of a Euclidean Steiner tree.
At any stage, the resulting overall shape achieves a good compromise between local thickness, global connectivity, and flexibility to further continuous motion of the terminals.
The resulting swarm behavior scales well, is robust against node failures, and performs close to the best known approximation bound for a corresponding centralized static optimization problem.
Machine translation is a natural candidate problem for reinforcement learning from human feedback: users provide quick, dirty ratings on candidate translations to guide a system to improve.
Yet, current neural machine translation training focuses on expensive human-generated reference translations.
We describe a reinforcement learning algorithm that improves neural machine translation systems from simulated human feedback.
Our algorithm combines the advantage actor-critic algorithm (Mnih et al., 2016) with the attention-based neural encoder-decoder architecture (Luong et al., 2015).
This algorithm (a) is well-designed for problems with a large action space and delayed rewards, (b) effectively optimizes traditional corpus-level machine translation metrics, and (c) is robust to skewed, high-variance, granular feedback modeled after actual human behaviors.
Inverse dynamics is used extensively in robotics and biomechanics applications.
In manipulator and legged robots, it can form the basis of an effective nonlinear control strategy by providing a robot with both accurate positional tracking and active compliance.
In biomechanics applications, inverse dynamics control can approximately determine the net torques applied at anatomical joints that correspond to an observed motion.
In the context of robot control, using inverse dynamics requires knowledge of all contact forces acting on the robot; accurately perceiving external forces applied to the robot requires filtering and thus significant time delay.
An alternative approach has been suggested in recent literature: predicting contact and actuator forces simultaneously under the assumptions of rigid body dynamics, rigid contact, and friction.
Existing such inverse dynamics approaches have used approximations to the contact models, which permits use of fast numerical linear algebra algorithms.
In contrast, we describe inverse dynamics algorithms that are derived only from first principles and use established phenomenological models like Coulomb friction.
We assess these inverse dynamics algorithms in a control context using two virtual robots: a locomoting quadrupedal robot and a fixed-based manipulator gripping a box while using perfectly accurate sensor data from simulation.
The data collected from these experiments gives an upper bound on the performance of such controllers in situ.
For points of comparison, we assess performance on the same tasks with both error feedback control and inverse dynamics control with virtual contact force sensing.
We introduce Delay Pruning, a simple yet powerful technique to regularize dynamic Boltzmann machines (DyBM).
The recently introduced DyBM provides a particularly structured Boltzmann machine, as a generative model of a multi-dimensional time-series.
This Boltzmann machine can have infinitely many layers of units but allows exact inference and learning based on its biologically motivated structure.
DyBM uses the idea of conduction delays in the form of fixed length first-in first-out (FIFO) queues, with a neuron connected to another via this FIFO queue, and spikes from a pre-synaptic neuron travel along the queue to the post-synaptic neuron with a constant period of delay.
Here, we present Delay Pruning as a mechanism to prune the lengths of the FIFO queues (making them zero) by setting some delay lengths to one with a fixed probability, and finally selecting the best performing model with fixed delays.
The uniqueness of structure and a non-sampling based learning rule in DyBM, make the application of previously proposed regularization techniques like Dropout or DropConnect difficult, leading to poor generalization.
First, we evaluate the performance of Delay Pruning to let DyBM learn a multidimensional temporal sequence generated by a Markov chain.
Finally, we show the effectiveness of delay pruning in learning high dimensional sequences using the moving MNIST dataset, and compare it with Dropout and DropConnect methods.
Separating an image into reflectance and shading layers poses a challenge for learning approaches because no large corpus of precise and realistic ground truth decompositions exists.
The Intrinsic Images in the Wild~(IIW) dataset provides a sparse set of relative human reflectance judgments, which serves as a standard benchmark for intrinsic images.
A number of methods use IIW to learn statistical dependencies between the images and their reflectance layer.
Although learning plays an important role for high performance, we show that a standard signal processing technique achieves performance on par with current state-of-the-art.
We propose a loss function for CNN learning of dense reflectance predictions.
Our results show a simple pixel-wise decision, without any context or prior knowledge, is sufficient to provide a strong baseline on IIW.
This sets a competitive baseline which only two other approaches surpass.
We then develop a joint bilateral filtering method that implements strong prior knowledge about reflectance constancy.
This filtering operation can be applied to any intrinsic image algorithm and we improve several previous results achieving a new state-of-the-art on IIW.
Our findings suggest that the effect of learning-based approaches may have been over-estimated so far.
Explicit prior knowledge is still at least as important to obtain high performance in intrinsic image decompositions.
Breast cancer is becoming pervasive with each passing day.
Hence, its early detection is a big step in saving the life of any patient.
Mammography is a common tool in breast cancer diagnosis.
The most important step here is classification of mammogram patches as normal-abnormal and benign-malignant.
Texture of a breast in a mammogram patch plays a significant role in these classifications.
We propose a variation of Histogram of Gradients (HOG) and Gabor filter combination called Histogram of Oriented Texture (HOT) that exploits this fact.
We also revisit the Pass Band - Discrete Cosine Transform (PB-DCT) descriptor that captures texture information well.
All features of a mammogram patch may not be useful.
Hence, we apply a feature selection technique called Discrimination Potentiality (DP).
Our resulting descriptors, DP-HOT and DP-PB-DCT, are compared with the standard descriptors.
Density of a mammogram patch is important for classification, and has not been studied exhaustively.
The Image Retrieval in Medical Application (IRMA) database from RWTH Aachen, Germany is a standard database that provides mammogram patches, and most researchers have tested their frameworks only on a subset of patches from this database.
We apply our two new descriptors on all images of the IRMA database for density wise classification, and compare with the standard descriptors.
We achieve higher accuracy than all of the existing standard descriptors (more than 92%).
In this paper we present a working model of an automatic pill reminder and dispenser setup that can alleviate irregularities in taking prescribed dosage of medicines at the right time dictated by the medical practitioner and switch from approaches predominantly dependent on human memory to automation with negligible supervision, thus relieving persons from error-prone tasks of giving wrong medicine at the wrong time in the wrong amount.
Visual illusions teach us that what we see is not always what it is represented in the physical world.
Its special nature make them a fascinating tool to test and validate any new vision model proposed.
In general, current vision models are based on the concatenation of linear convolutions and non-linear operations.
In this paper we get inspiration from the similarity of this structure with the operations present in Convolutional Neural Networks (CNNs).
This motivated us to study if CNNs trained for low-level visual tasks are deceived by visual illusions.
In particular, we show that CNNs trained for image denoising, image deblurring, and computational color constancy are able to replicate the human response to visual illusions, and that the extent of this replication varies with respect to variation in architecture and spatial pattern size.
We believe that this CNNs behaviour appears as a by-product of the training for the low level vision tasks of denoising, color constancy or deblurring.
Our work opens a new bridge between human perception and CNNs: in order to obtain CNNs that better replicate human behaviour, we may need to start aiming for them to better replicate visual illusions.
This paper presents Dokei, an effective supervised domain adaptation method to transform a pre-trained CNN model to one involving efficient grouped convolution.
The basis of this approach is formalised as a novel optimisation problem constrained by group sparsity pattern (GSP), and a practical solution based on structured regularisation and maximal bipartite matching is provided.
We show that it is vital to keep the connections specified by GSP when mapping pre-trained weights to grouped convolution.
We evaluate Dokei on various domains and hardware platforms to demonstrate its effectiveness.
The models resulting from Dokei are shown to be more accurate and slimmer than prior work targeting grouped convolution, and more regular and easier to deploy than other pruning techniques.
Underwater images suffer from color distortion and low contrast, because light is attenuated while it propagates through water.
Attenuation under water varies with wavelength, unlike terrestrial images where attenuation is assumed to be spectrally uniform.
The attenuation depends both on the water body and the 3D structure of the scene, making color restoration difficult.
Unlike existing single underwater image enhancement techniques, our method takes into account multiple spectral profiles of different water types.
By estimating just two additional global parameters: the attenuation ratios of the blue-red and blue-green color channels, the problem is reduced to single image dehazing, where all color channels have the same attenuation coefficients.
Since the water type is unknown, we evaluate different parameters out of an existing library of water types.
Each type leads to a different restored image and the best result is automatically chosen based on color distribution.
We collected a dataset of images taken in different locations with varying water properties, showing color charts in the scenes.
Moreover, to obtain ground truth, the 3D structure of the scene was calculated based on stereo imaging.
This dataset enables a quantitative evaluation of restoration algorithms on natural images and shows the advantage of our method.
Ecological Momentary Assessment (EMA) data is organized in multiple levels (per-subject, per-day, etc.) and this particular structure should be taken into account in machine learning algorithms used in EMA like decision trees and its variants.
We propose a new algorithm called BBT (standing for Bagged Boosted Trees) that is enhanced by a over/under sampling method and can provide better estimates for the conditional class probability function.
Experimental results on a real-world dataset show that BBT can benefit EMA data classification and performance.
Lambda calculus is the basis of functional programming and higher order proof assistants.
However, little is known about combinatorial properties of lambda terms, in particular, about their asymptotic distribution and random generation.
This paper tries to answer questions like: How many terms of a given size are there?
What is a "typical" structure of a simply typable term?
Despite their ostensible simplicity, these questions still remain unanswered, whereas solutions to such problems are essential for testing compilers and optimizing programs whose expected efficiency depends on the size of terms.
Our approach toward the afore-mentioned problems may be later extended to any language with bound variables, i.e., with scopes and declarations.
This paper presents two complementary approaches: one, theoretical, uses complex analysis and generating functions, the other, experimental, is based on a generator of lambda-terms.
Thanks to de Bruijn indices, we provide three families of formulas for the number of closed lambda terms of a given size and we give four relations between these numbers which have interesting combinatorial interpretations.
As a by-product of the counting formulas, we design an algorithm for generating lambda terms.
Performed tests provide us with experimental data, like the average depth of bound variables and the average number of head lambdas.
We also create random generators for various sorts of terms.
Thereafter, we conduct experiments that answer questions like: What is the ratio of simply typable terms among all terms?
(Very small!)
How are simply typable lambda terms distributed among all lambda terms?
(A typable term almost always starts with an abstraction.)
In this paper, abstractions and applications have size 1 and variables have size 0.
Twitter introduced lists in late 2009 as a means of curating tweets into meaningful themes.
Lists were quickly adopted by media companies as a means of organising content around news stories.
Thus the curation of these lists is important, they should contain the key information gatekeepers and present a balanced perspective on the story.
Identifying members to add to a list on an emerging topic is a delicate process.
From a network analysis perspective there are a number of views on the Twitter network that can be explored, e.g. followers, retweets mentions etc.
We present a process for integrating these views in order to recommend authoritative commentators to include on a list.
This process is evaluated on manually curated lists about unrest in Bahrain and the Iowa caucuses for the 2012 US election.
This paper describes a novel approach to analyze and control systems with multi-mode oscillation problems.
Traditional single dominant mode analysis fails to provide effective control actions when several modes have similar low damping ratios.
This work addresses this problem by considering all modes in the formulation of the system kinetic oscillation energy.
The integral of energy over time defines the total action as a measure of dynamic performance, and its sensitivity allows comparing the performance of different actuators/locations in the system to select the most effective one to damp the oscillation energy.
Time domain simulations in the IEEE 9-bus system and IEEE 39-bus system verify the findings obtained by the oscillation energy based analysis.
Applications of the proposed method in control and system planning are discussed.
We present a novel semi-supervised approach for sequence transduction and apply it to semantic parsing.
The unsupervised component is based on a generative model in which latent sentences generate the unpaired logical forms.
We apply this method to a number of semantic parsing tasks focusing on domains with limited access to labelled training data and extend those datasets with synthetically generated logical forms.
The abilities to perceive, learn, and use generalities, similarities, classes, i.e., semantic memory (SM), is central to cognition.
Machine learning (ML), neural network, and AI research has been primarily driven by tasks requiring such abilities.
However, another central facet of cognition, single-trial formation of permanent memories of experiences, i.e., episodic memory (EM), has had relatively little focus.
Only recently has EM-like functionality been added to Deep Learning (DL) models, e.g., Neural Turing Machine, Memory Networks.
However, in these cases: a) EM is implemented as a separate module, which entails substantial data movement (and so, time and power) between the DL net itself and EM; and b) individual items are stored localistically within the EM, precluding realizing the exponential representational efficiency of distributed over localist coding.
We describe Sparsey, an unsupervised, hierarchical, spatial/spatiotemporal associative memory model differing fundamentally from mainstream ML models, most crucially, in its use of sparse distributed representations (SDRs), or, cell assemblies, which admits an extremely efficient, single-trial learning algorithm that maps input similarity into code space similarity (measured as intersection).
SDRs of individual inputs are stored in superposition and because similarity is preserved, the patterns of intersections over the assigned codes reflect the similarity, i.e., statistical, structure, of all orders, not simply pairwise, over the inputs.
Thus, SM, i.e., a generative model, is built as a computationally free side effect of the act of storing episodic memory traces of individual inputs, either spatial patterns or sequences.
We report initial results on MNIST and on the Weizmann video event recognition benchmarks.
While we have not yet attained SOTA class accuracy, learning takes only minutes on a single CPU.
Recent advances in self-interference cancellation enable radios to transmit and receive on the same frequency at the same time.
Such a full duplex radio is being considered as a potential candidate for the next generation of wireless networks due to its ability to increase the spectral efficiency of wireless systems.
In this paper, the performance of full duplex radio in small cellular systems is analyzed by assuming full duplex capable base stations and half duplex user equipment.
However, using only full duplex base stations increases interference leading to outage.
We therefore propose a mixed multi-cell system, composed of full duplex and half duplex cells.
A stochastic geometry based model of the proposed mixed system is provided, which allows us to derive the outage and area spectral efficiency of such a system.
The effect of full duplex cells on the performance of the mixed system is presented under different network parameter settings.
We show that the fraction of cells that have full duplex base stations can be used as a design parameter by the network operator to target an optimal tradeoff between area spectral efficiency and outage in a mixed system.
The recent development of multi-agent simulations brings about a need for population synthesis.
It is a task of reconstructing the entire population from a sampling survey of limited size (1% or so), supplying the initial conditions from which simulations begin.
This paper presents a new kernel density estimator for this task.
Our method is an analogue of the classical Breiman-Meisel-Purcell estimator, but employs novel techniques that harness the huge degree of freedom which is required to model high-dimensional nonlinearly correlated datasets: the crossover kernel, the k-nearest neighbor restriction of the kernel construction set and the bagging of kernels.
The performance as a statistical estimator is examined through real and synthetic datasets.
We provide an "optimization-free" parameter selection rule for our method, a theory of how our method works and a computational cost analysis.
To demonstrate the usefulness as a population synthesizer, our method is applied to a household synthesis task for an urban micro-simulator.
Currently there is an active Post-Quantum Cryptography (PQC) solutions search, which attempts to find cryptographic protocols resistant to attacks by means of for instance Shor polynomial time algorithm for numerical field problems like integer factorization (IFP) or the discrete logarithm (DLP).
The use of non-commutative or non-associative structures are, among others, valid choices for these kinds of protocols.
In our case, we focus on a permutation subgroup of high order and belonging to the symmetric group S381.
Using adequate one-way functions (OWF), we derived a Diffie-Hellman key exchange and an ElGamal ciphering procedure that only relies on combinatorial operations.
Both OWF pose hard search problems which are assumed as not belonging to BQP time-complexity class.
Obvious advantages of present protocols are their conceptual simplicity, fast throughput implementations, high cryptanalytic security and no need for arithmetic operations and therefore extended precision libraries.
Such features make them suitable for low performance and low power consumption platforms like smart cards, USB-keys and cellphones.
In practical mobile communication engineering applications, surfaces of antenna array deployment regions are usually uneven.
Therefore, massive multi-input-multi-output (MIMO) communication systems usually transmit wireless signals by irregular antenna arrays.
To evaluate the performance of irregular antenna arrays, the matrix correlation coefficient and ergodic received gain are defined for massive MIMO communication systems with mutual coupling effects.
Furthermore, the lower bound of the ergodic achievable rate, symbol error rate (SER) and average outage probability are firstly derived for multi-user massive MIMO communication systems using irregular antenna arrays.
Asymptotic results are also derived when the number of antennas approaches infinity.
Numerical results indicate that there exists a maximum achievable rate when the number of antennas keeps increasing in massive MIMO communication systems using irregular antenna arrays.
Moreover, the irregular antenna array outperforms the regular antenna array in the achievable rate of massive MIMO communication systems when the number of antennas is larger than or equal to a given threshold.
Community detection in a complex network is an important problem of much interest in recent years.
In general, a community detection algorithm chooses an objective function and captures the communities of the network by optimizing the objective function, and then, one uses various heuristics to solve the optimization problem to extract the interesting communities for the user.
In this article, we demonstrate the procedure to transform a graph into points of a metric space and develop the methods of community detection with the help of a metric defined for a pair of points.
We have also studied and analyzed the community structure of the network therein.
The results obtained with our approach are very competitive with most of the well-known algorithms in the literature, and this is justified over the large collection of datasets.
On the other hand, it can be observed that time taken by our algorithm is quite less compared to other methods and justifies the theoretical findings.
This paper presents a wp-style calculus for obtaining bounds on the expected run-time of probabilistic programs.
Its application includes determining the (possibly infinite) expected termination time of a probabilistic program and proving positive almost-sure termination - does a program terminate with probability one in finite expected time?
We provide several proof rules for bounding the run-time of loops, and prove the soundness of the approach with respect to a simple operational model.
We show that our approach is a conservative extension of Nielson's approach for reasoning about the run-time of deterministic programs.
We analyze the expected run-time of some example programs including a one-dimensional random walk and the coupon collector problem.
Spurred by the development of cloud computing, there has been considerable recent interest in the Database-as-a-Service (DaaS) paradigm.
Users lacking in expertise or computational resources can outsource their data and database management needs to a third-party service provider.
Outsourcing, however, raises an important issue of result integrity: how can the client verify with lightweight overhead that the query results returned by the service provider are correct (i.e., the same as the results of query execution locally)?
This survey focuses on categorizing and reviewing the progress on the current approaches for result integrity of SQL query evaluation in the DaaS model.
The survey also includes some potential future research directions for result integrity verification of the outsourced computations.
We propose a cost-effective framework for preference elicitation and aggregation under the Plackett-Luce model with features.
Given a budget, our framework iteratively computes the most cost-effective elicitation questions in order to help the agents make a better group decision.
We illustrate the viability of the framework with experiments on Amazon Mechanical Turk, which we use to estimate the cost of answering different types of elicitation questions.
We compare the prediction accuracy of our framework when adopting various information criteria that evaluate the expected information gain from a question.
Our experiments show carefully designed information criteria are much more efficient, i.e., they arrive at the correct answer using fewer queries, than randomly asking questions given the budget constraint.
We study complex time series (spike trains) of online user communication while spreading messages about the discovery of the Higgs boson in Twitter.
We focus on online social interactions among users such as retweet, mention, and reply, and construct different types of active (performing an action) and passive (receiving an action) spike trains for each user.
The spike trains are analyzed by means of local variation, to quantify the temporal behavior of active and passive users, as a function of their activity and popularity.
We show that the active spike trains are bursty, independently of their activation frequency.
For passive spike trains, in contrast, the local variation of popular users presents uncorrelated (Poisson random) dynamics.
We further characterize the correlations of the local variation in different interactions.
We obtain high values of correlation, and thus consistent temporal behavior, between retweets and mentions, but only for popular users, indicating that creating online attention suggests an alignment in the dynamics of the two interactions.
This paper contains description of such knowledge representation model as Object-Oriented Dynamic Network (OODN), which gives us an opportunity to represent knowledge, which can be modified in time, to build new relations between objects and classes of objects and to represent results of their modifications.
The model is based on representation of objects via their properties and methods.
It gives us a possibility to classify the objects and, in a sense, to build hierarchy of their types.
Furthermore, it enables to represent relation of modification between concepts, to build new classes of objects based on existing classes and to create sets and multisets of concepts.
OODN can be represented as a connected and directed graph, where nodes are concepts and edges are relations between them.
Using such model of knowledge representation, we can consider modifications of knowledge and movement through the graph of network as a process of logical reasoning or finding the right solutions or creativity, etc.
The proposed approach gives us an opportunity to model some aspects of human knowledge system and main mechanisms of human thought, in particular getting a new experience and knowledge.
Dynamic oracles provide strong supervision for training constituency parsers with exploration, but must be custom defined for a given parser's transition system.
We explore using a policy gradient method as a parser-agnostic alternative.
In addition to directly optimizing for a tree-level metric such as F1, policy gradient has the potential to reduce exposure bias by allowing exploration during training; moreover, it does not require a dynamic oracle for supervision.
On four constituency parsers in three languages, the method substantially outperforms static oracle likelihood training in almost all settings.
For parsers where a dynamic oracle is available (including a novel oracle which we define for the transition system of Dyer et al.2016), policy gradient typically recaptures a substantial fraction of the performance gain afforded by the dynamic oracle.
Given the recent advances in depth prediction from Convolutional Neural Networks (CNNs), this paper investigates how predicted depth maps from a deep neural network can be deployed for accurate and dense monocular reconstruction.
We propose a method where CNN-predicted dense depth maps are naturally fused together with depth measurements obtained from direct monocular SLAM.
Our fusion scheme privileges depth prediction in image locations where monocular SLAM approaches tend to fail, e.g. along low-textured regions, and vice-versa.
We demonstrate the use of depth prediction for estimating the absolute scale of the reconstruction, hence overcoming one of the major limitations of monocular SLAM.
Finally, we propose a framework to efficiently fuse semantic labels, obtained from a single frame, with dense SLAM, yielding semantically coherent scene reconstruction from a single view.
Evaluation results on two benchmark datasets show the robustness and accuracy of our approach.
A distributed discrete-time algorithm is proposed for multi-agent networks to achieve a common least squares solution of a group of linear equations, in which each agent only knows some of the equations and is only able to receive information from its nearby neighbors.
For fixed, connected, and undirected networks, the proposed discrete-time algorithm results in each agents solution estimate to converging exponentially fast to the same least squares solution.
Moreover, the convergence does not require careful choices of time-varying small step sizes.
With the rapid growth of medical imaging research, there is a great interest in the automated detection of skin lesions with computer algorithms.
The state-of-the-art datasets for skin lesions are often accompanied with very limited amount of ground truth labeling as it is laborious and expensive.
The region of interest (ROI) detection is vital to locate the lesion accurately and robust to subtle features of different skin lesion types.
In this work, we propose the use of two object localization meta-architectures for end-to-end ROI skin lesion detection in dermoscopic images.
We trained the Faster-RCNN-InceptionV2 and SSD-InceptionV2 on ISBI-2017 training dataset and evaluate the performances on ISBI-2017 testing set, PH2 and HAM10000 datasets.
Since there was no earlier work in ROI detection for skin lesion with CNNs, we compare the performance of skin localization methods with the state-of-the-art segmentation method.
The localization methods proved superiority over the segmentation method in ROI detection on skin lesion datasets.
In addition, based on the detected ROI, an automated natural data-augmentation method is proposed.
To demonstrate the potential of our work, we developed a real-time mobile application for automated skin lesions detection.
The codes and mobile application will be made available for further research purposes.
Humans develop a common sense of style compatibility between items based on their attributes.
We seek to automatically answer questions like "Does this shirt go well with that pair of jeans?"
In order to answer these kinds of questions, we attempt to model human sense of style compatibility in this paper.
The basic assumption of our approach is that most of the important attributes for a product in an online store are included in its title description.
Therefore it is feasible to learn style compatibility from these descriptions.
We design a Siamese Convolutional Neural Network architecture and feed it with title pairs of items, which are either compatible or incompatible.
Those pairs will be mapped from the original space of symbolic words into some embedded style space.
Our approach takes only words as the input with few preprocessing and there is no laborious and expensive feature engineering.
Time-Series Classification (TSC) has attracted a lot of attention in pattern recognition, because wide range of applications from different domains such as finance and health informatics deal with time-series signals.
Bag of Features (BoF) model has achieved a great success in TSC task by summarizing signals according to the frequencies of "feature words" of a data-learned dictionary.
This paper proposes embedding the Recurrence Plots (RP), a visualization technique for analysis of dynamic systems, in the BoF model for TSC.
While the traditional BoF approach extracts features from 1D signal segments, this paper uses the RP to transform time-series into 2D texture images and then applies the BoF on them.
Image representation of time-series enables us to explore different visual descriptors that are not available for 1D signals and to treats TSC task as a texture recognition problem.
Experimental results on the UCI time-series classification archive demonstrates a significant accuracy boost by the proposed Bag of Recurrence patterns (BoR), compared not only to the existing BoF models, but also to the state-of-the art algorithms.
Network based on distributed caching of content is a new architecture to alleviate the ongoing explosive demands for rate of multi-media traffic.
In caching networks, coded caching is a recently proposed technique that achieves significant performance gains compared to uncoded caching schemes.
In this paper, we derive a lower bound on the average rate with a memory constraint for a family of caching allocation placement and a family of XOR cooperative delivery.
The lower bound inspires us how placement and delivery affect the rate memory tradeoff.
Based on the clues, we design a new placement and two new delivery algorithms.
On one hand, the new placement scheme can allocate the cache more flexibly compared to grouping scheme.
On the other hand, the new delivery can exploit more cooperative opportunities compared to the known schemes.
The simulations validate our idea.
Recently, convolutional neural network (CNN) has attracted much attention in different areas of computer vision, due to its powerful abstract feature representation.
Visual object tracking is one of the interesting and important areas in computer vision that achieves remarkable improvements in recent years.
In this work, we aim to improve both the motion and observation models in visual object tracking by leveraging representation power of CNNs.
To this end, a motion estimation network (named MEN) is utilized to seek the most likely locations of the target and prepare a further clue in addition to the previous target position.
Hence the motion estimation would be enhanced by generating a small number of candidates near two plausible positions.
The generated candidates are then fed into a trained Siamese network to detect the most probable candidate.
Each candidate is compared to an adaptable buffer, which is updated under a predefined condition.
To take into account the target appearance changes, a weighting CNN (called WCNN) adaptively assigns weights to the final similarity scores of the Siamese network using sequence-specific information.
Evaluation results on well-known benchmark datasets (OTB100, OTB50 and OTB2013) prove that the proposed tracker outperforms the state-of-the-art competitors.
We present a new network model accounting for multidimensional assortativity.
Each node is characterized by a number of features and the probability of a link between two nodes depends on common features.
We do not fix a priori the total number of possible features.
The bipartite network of the nodes and the features evolves according to a stochastic dynamics that depends on three parameters that respectively regulate the preferential attachment in the transmission of the features to the nodes, the number of new features per node, and the power-law behavior of the total number of observed features.
Our model also takes into account a mechanism of triadic closure.
We provide theoretical results and statistical estimators for the parameters of the model.
We validate our approach by means of simulations and an empirical analysis of a network of scientific collaborations.
This paper studies the joint support recovery of similar sparse vectors on the basis of a limited number of noisy linear measurements, i.e., in a multiple measurement vector (MMV) model.
The additive noise signals on each measurement vector are assumed to be Gaussian and to exhibit different variances.
The simultaneous orthogonal matching pursuit (SOMP) algorithm is generalized to weight the impact of each measurement vector on the choice of the atoms to be picked according to their noise levels.
The new algorithm is referred to as SOMP-NS where NS stands for noise stabilization.
To begin with, a theoretical framework to analyze the performance of the proposed algorithm is developed.
This framework is then used to build conservative lower bounds on the probability of partial or full joint support recovery.
Numerical simulations show that the proposed algorithm outperforms SOMP and that the theoretical lower bound provides a great insight into how SOMP-NS behaves when the weighting strategy is modified.
Opportunistic detection rules (ODRs) are variants of fixed-sample-size detection rules in which the statistician is allowed to make an early decision on the alternative hypothesis opportunistically based on the sequentially observed samples.
From a sequential decision perspective, ODRs are also mixtures of one-sided and truncated sequential detection rules.
Several results regarding ODRs are established in this paper.
In the finite regime, the maximum sample size is modeled either as a fixed finite number, or a geometric random variable with a fixed finite mean.
For both cases, the corresponding Bayesian formulations are investigated.
The former case is a slight variation of the well-known finite-length sequential hypothesis testing procedure in the literature, whereas the latter case is new, for which the Bayesian optimal ODR is shown to be a sequence of likelihood ratio threshold tests with two different thresholds: a running threshold, which is determined by solving a stationary state equation, is used when future samples are still available, and a terminal threshold (simply the ratio between the priors scaled by costs) is used when the statistician reaches the final sample and thus has to make a decision immediately.
In the asymptotic regime, the tradeoff among the exponents of the (false alarm and miss) error probabilities and the normalized expected stopping time under the alternative hypothesis is completely characterized and proved to be tight, via an information-theoretic argument.
Within the tradeoff region, one noteworthy fact is that the performance of the Stein-Chernoff Lemma is attainable by ODRs.
Modeling should play a central role in K-12 STEM education, where it could make classes much more engaging.
A model underlies every scientific theory, and models are central to all the STEM disciplines (Science, Technology, Engineering, Math).
This paper describes executable concept modeling of STEM concepts using immutable objects and pure functions in Python.
I present examples in math, physics, chemistry, and engineering, built using a proof-of-concept tool called PySTEMM .
The approach applies to all STEM areas and supports learning with pictures, narrative, animation, and graph plots.
Models can extend each other, simplifying getting started.
The functional-programming style reduces incidental complexity and code debugging.
In this paper, naive Bayesian and C4.5 Decision Tree Classifiers(DTC) are successively applied on materials informatics to classify the engineering materials into different classes for the selection of materials that suit the input design specifications.
Here, the classifiers are analyzed individually and their performance evaluation is analyzed with confusion matrix predictive parameters and standard measures, the classification results are analyzed on different class of materials.
Comparison of classifiers has found that naive Bayesian classifier is more accurate and better than the C4.5 DTC.
The knowledge discovered by the naive bayesian classifier can be employed for decision making in materials selection in manufacturing industries.
This paper presents a proposal (story) of how statically detecting unreachable objects (in Java) could be used to improve a particular runtime verification approach (for Java), namely parametric trace slicing.
Monitoring algorithms for parametric trace slicing depend on garbage collection to (i) cleanup data-structures storing monitored objects, ensuring they do not become unmanageably large, and (ii) anticipate the violation of (non-safety) properties that cannot be satisfied as a monitored object can no longer appear later in the trace.
The proposal is that both usages can be improved by making the unreachability of monitored objects explicit in the parametric property and statically introducing additional instrumentation points generating related events.
The ideas presented in this paper are still exploratory and the intention is to integrate the described techniques into the MarQ monitoring tool for quantified event automata.
In neural abstractive summarization, the conventional sequence-to-sequence (seq2seq) model often suffers from repetition and semantic irrelevance.
To tackle the problem, we propose a global encoding framework, which controls the information flow from the encoder to the decoder based on the global information of the source context.
It consists of a convolutional gated unit to perform global encoding to improve the representations of the source-side information.
Evaluations on the LCSTS and the English Gigaword both demonstrate that our model outperforms the baseline models, and the analysis shows that our model is capable of reducing repetition.
Mammogram classification is directly related to computer-aided diagnosis of breast cancer.
Traditional methods requires great effort to annotate the training data by costly manual labeling and specialized computational models to detect these annotations during test.
Inspired by the success of using deep convolutional features for natural image analysis and multi-instance learning for labeling a set of instances/patches, we propose end-to-end trained deep multi-instance networks for mass classification based on whole mammogram without the aforementioned costly need to annotate the training data.
We explore three different schemes to construct deep multi-instance networks for whole mammogram classification.
Experimental results on the INbreast dataset demonstrate the robustness of proposed deep networks compared to previous work using segmentation and detection annotations in the training.
This paper proposes a new cubical space model for the representation of continuous objects and surfaces in the n-dimensional Euclidean space by discrete sets of points.
The cubical space model concerns the process of converting a continuous object in its digital counterpart, which is a graph, enabling us to apply notions and operations used in digital imaging to cubical spaces.
We formulate a definition of a simple n-cube and prove that deleting or attaching a simple n-cube does not change the homotopy type of a cubical space.
Relying on these results, we design a procedure, which preserves basic topological properties of an n-dimensional object, for constructing compressed cubical and digital models.
With the ever increasing size of web, relevant information extraction on the Internet with a query formed by a few keywords has become a big challenge.
To overcome this, query expansion (QE) plays a crucial role in improving the Internet searches, where the user's initial query is reformulated to a new query by adding new meaningful terms with similar significance.
QE -- as part of information retrieval (IR) -- has long attracted researchers' attention.
It has also become very influential in the field of personalized social document, Question Answering over Linked Data (QALD), and, Text Retrieval Conference (TREC) and REAL sets.
This paper surveys QE techniques in IR from 1960 to 2017 with respect to core techniques, data sources used, weighting and ranking methodologies, user participation and applications (of QE techniques) -- bringing out similarities and differences.
The efficient sparse coding and reconstruction of signal vectors via linear observations has received a tremendous amount of attention over the last decade.
In this context, the automated learning of a suitable basis or overcomplete dictionary from training data sets of certain signal classes for use in sparse representations has turned out to be of particular importance regarding practical signal processing applications.
Most popular dictionary learning algorithms involve NP-hard sparse recovery problems in each iteration, which may give some indication about the complexity of dictionary learning but does not constitute an actual proof of computational intractability.
In this technical note, we show that learning a dictionary with which a given set of training signals can be represented as sparsely as possible is indeed NP-hard.
Moreover, we also establish hardness of approximating the solution to within large factors of the optimal sparsity level.
Furthermore, we give NP-hardness and non-approximability results for a recent dictionary learning variation called the sensor permutation problem.
Along the way, we also obtain a new non-approximability result for the classical sparse recovery problem from compressed sensing.
The purpose of this study is to determine whether current video datasets have sufficient data for training very deep convolutional neural networks (CNNs) with spatio-temporal three-dimensional (3D) kernels.
Recently, the performance levels of 3D CNNs in the field of action recognition have improved significantly.
However, to date, conventional research has only explored relatively shallow 3D architectures.
We examine the architectures of various 3D CNNs from relatively shallow to very deep ones on current video datasets.
Based on the results of those experiments, the following conclusions could be obtained: (i) ResNet-18 training resulted in significant overfitting for UCF-101, HMDB-51, and ActivityNet but not for Kinetics.
(ii) The Kinetics dataset has sufficient data for training of deep 3D CNNs, and enables training of up to 152 ResNets layers, interestingly similar to 2D ResNets on ImageNet.
ResNeXt-101 achieved 78.4% average accuracy on the Kinetics test set.
(iii) Kinetics pretrained simple 3D architectures outperforms complex 2D architectures, and the pretrained ResNeXt-101 achieved 94.5% and 70.2% on UCF-101 and HMDB-51, respectively.
The use of 2D CNNs trained on ImageNet has produced significant progress in various tasks in image.
We believe that using deep 3D CNNs together with Kinetics will retrace the successful history of 2D CNNs and ImageNet, and stimulate advances in computer vision for videos.
The codes and pretrained models used in this study are publicly available. https://github.com/kenshohara/3D-ResNets-PyTorch
Active contour models based on local region fitting energy can segment images with intensity inhomogeneity effectively, but their segmentation results are easy to error if the initial contour is inappropriate.
In this paper, we present a simple and universal method of improving the robustness of initial contour for these local fitting-based models.
The core idea of proposed method is exchanging the fitting values on the two sides of contour, so that the fitting values inside the contour are always larger (or smaller) than the values outside the contour in the process of curve evolution.
In this way, the whole curve will evolve along the inner (or outer) boundaries of object, and less likely to be stuck in the object or background.
Experimental results have proved that using the proposed method can enhance the robustness of initial contour and meanwhile keep the original advantages in the local fitting-based models.
Consider a set of agents that wish to estimate a vector of parameters of their mutual interest.
For this estimation goal, agents can sense and communicate.
When sensing, an agent measures (in additive gaussian noise) linear combinations of the unknown vector of parameters.
When communicating, an agent can broadcast information to a few other agents, by using the channels that happen to be randomly at its disposal at the time.
To coordinate the agents towards their estimation goal, we propose a novel algorithm called FADE (Fast and Asymptotically efficient Distributed Estimator), in which agents collaborate at discrete time-steps; at each time-step, agents sense and communicate just once, while also updating their own estimate of the unknown vector of parameters.
FADE enjoys five attractive features: first, it is an intuitive estimator, simple to derive; second, it withstands dynamic networks, that is, networks whose communication channels change randomly over time; third, it is strongly consistent in that, as time-steps play out, each agent's local estimate converges (almost surely) to the true vector of parameters; fourth, it is both asymptotically unbiased and efficient, which means that, across time, each agent's estimate becomes unbiased and the mean-square error (MSE) of each agent's estimate vanishes to zero at the same rate of the MSE of the optimal estimator at an almighty central node; fifth, and most importantly, when compared with a state-of-art consensus+innovation (CI) algorithm, it yields estimates with outstandingly lower mean-square errors, for the same number of communications -- for example, in a sparsely connected network model with 50 agents, we find through numerical simulations that the reduction can be dramatic, reaching several orders of magnitude.
Temporary earth retaining structures (TERS) help prevent collapse during construction excavation.
To ensure that these structures are operating within design specifications, load forces on supports must be monitored.
Current monitoring approaches are expensive, sparse, off-line, and thus difficult to integrate into predictive models.
This work aims to show that wirelessly connected battery powered sensors are feasible, practical, and have similar accuracy to existing sensor systems.
We present the design and validation of ReStructure, an end-to-end prototype wireless sensor network for collection, communication, and aggregation of strain data.
ReStructure was validated through a six months deployment on a real-life excavation site with all but one node producing valid and accurate strain measurements at higher frequency than existing ones.
These results and the lessons learnt provide the basis for future widespread wireless TERS monitoring that increase measurement density and integrate closely with predictive models to provide timely alerts of damage or potential failure.
In this letter, a very simple no-reference image quality assessment (NR-IQA) model for JPEG compressed images is proposed.
The proposed metric called median of unique gradients (MUG) is based on the very simple facts of unique gradient magnitudes of JPEG compressed images.
MUG is a parameterless metric and does not need training.
Unlike other NR-IQAs, MUG is independent to block size and cropping.
A more stable index called MUG+ is also introduced.
The experimental results on six benchmark datasets of natural images and a benchmark dataset of synthetic images show that MUG is comparable to the state-of-the-art indices in literature.
In addition, its performance remains unchanged for the case of the cropped images in which block boundaries are not known.
The MATLAB source code of the proposed metrics is available at https://dl.dropboxusercontent.com/u/74505502/MUG.m and https://dl.dropboxusercontent.com/u/74505502/MUGplus.m.
Several BPMN graphical tools support, at least partly, the OMG's BPMN specification.
The BPMN standard is an essential guide for tools' makers when implementing the rules regarding depiction of BPMN diagrammatic constructs.
Process modelers should also know how to rigorously use BPMN constructs when depicting business processes either for business or IT purposes.
Several already published OMG's standards include the formal specification of well-formedness rules concern-ing the metamodels they address.
However, the BPMN standard does not.
Instead, the rules regarding BPMN elements are only informally specified in natural language throughout the overall BPMN documentation.
Without strict rules concerning the correct usage of BPMN elements, no wonder that plenty of available BPMN tools fail to enforce BPMN process models' correctness.
To mitigate this problem, and therefore contribute for achieving BPMN models' correctness, we propose to supplement the BPMN metamodel with well-formedness rules expressed by OCL invariants.
So, this document contributes to bring together a set of requirements that tools' makers must comply with, in order to claim a broader BPMN 2 compliance.
For the regular process modeler, this report provides an extensive and pragmatic catalog of BPMN elements' usage, to be followed in order to attain correct BPMN process models.
Existing neural conversational models process natural language primarily on a lexico-syntactic level, thereby ignoring one of the most crucial components of human-to-human dialogue: its affective content.
We take a step in this direction by proposing three novel ways to incorporate affective/emotional aspects into long short term memory (LSTM) encoder-decoder neural conversation models: (1) affective word embeddings, which are cognitively engineered, (2) affect-based objective functions that augment the standard cross-entropy loss, and (3) affectively diverse beam search for decoding.
Experiments show that these techniques improve the open-domain conversational prowess of encoder-decoder networks by enabling them to produce emotionally rich responses that are more interesting and natural.
Trace norm regularization is a widely used approach for learning low rank matrices.
A standard optimization strategy is based on formulating the problem as one of low rank matrix factorization which, however, leads to a non-convex problem.
In practice this approach works well, and it is often computationally faster than standard convex solvers such as proximal gradient methods.
Nevertheless, it is not guaranteed to converge to a global optimum, and the optimization can be trapped at poor stationary points.
In this paper we show that it is possible to characterize all critical points of the non-convex problem.
This allows us to provide an efficient criterion to determine whether a critical point is also a global minimizer.
Our analysis suggests an iterative meta-algorithm that dynamically expands the parameter space and allows the optimization to escape any non-global critical point, thereby converging to a global minimizer.
The algorithm can be applied to problems such as matrix completion or multitask learning, and our analysis holds for any random initialization of the factor matrices.
Finally, we confirm the good performance of the algorithm on synthetic and real datasets.
This paper presents a statistically sound method for measuring the accuracy with which a probabilistic model reflects the growth of a network, and a method for optimising parameters in such a model.
The technique is data-driven, and can be used for the modeling and simulation of any kind of evolving network.
The overall framework, a Framework for Evolving Topology Analysis (FETA), is tested on data sets collected from the Internet AS-level topology, social networking websites and a co-authorship network.
Statistical models of the growth of these networks are produced and tested using a likelihood-based method.
The models are then used to generate artificial topologies with the same statistical properties as the originals.
This work can be used to predict future growth patterns for a known network, or to generate artificial models of graph topology evolution for simulation purposes.
Particular application examples include strategic network planning, user profiling in social networks or infrastructure deployment in managed overlay-based services.
Recent studies on face attribute transfer have achieved great success.
A lot of models are able to transfer face attributes with an input image.
However, they suffer from three limitations: (1) incapability of generating image by exemplars; (2) being unable to transfer multiple face attributes simultaneously; (3) low quality of generated images, such as low-resolution or artifacts.
To address these limitations, we propose a novel model which receives two images of opposite attributes as inputs.
Our model can transfer exactly the same type of attributes from one image to another by exchanging certain part of their encodings.
All the attributes are encoded in a disentangled manner in the latent space, which enables us to manipulate several attributes simultaneously.
Besides, our model learns the residual images so as to facilitate training on higher resolution images.
With the help of multi-scale discriminators for adversarial training, it can even generate high-quality images with finer details and less artifacts.
We demonstrate the effectiveness of our model on overcoming the above three limitations by comparing with other methods on the CelebA face database.
A pytorch implementation is available at https://github.com/Prinsphield/ELEGANT.
In this paper, we study the complexity of execution in higher-order programming languages.
Our study has two facets: on the one hand we give an upper bound to the length of interactions between bounded P-visible strategies in Hyland-Ong game semantics.
This result covers models of programming languages with access to computational effects like non-determinism, state or control operators, but its semantic formulation causes a loose connection to syntax.
On the other hand we give a syntactic counterpart of our semantic study: a non-elementary upper bound to the length of the linear head reduction sequence (a low-level notion of reduction, close to the actual implementation of the reduction of higher-order programs by abstract machines) of simply-typed lambda-terms.
In both cases our upper bounds are proved optimal by giving matching lower bounds.
These two results, although different in scope, are proved using the same method: we introduce a simple reduction on finite trees of natural numbers, hereby called interaction skeletons.
We study this reduction and give upper bounds to its complexity.
We then apply this study by giving two simulation results: a semantic one measuring progress in game-theoretic interaction via interaction skeletons, and a syntactic one establishing a correspondence between linear head reduction of terms satisfying a locality condition called local scope and the reduction of interaction skeletons.
This result is then generalized to arbitrary terms by a local scopization transformation.
Resistive crossbars have emerged as promising building blocks for realizing DNNs due to their ability to compactly and efficiently realize the dominant DNN computational kernel, viz., vector-matrix multiplication.
However, a key challenge with resistive crossbars is that they suffer from a range of device and circuit level non-idealities such as interconnect parasitics, peripheral circuits, sneak paths, and process variations.
These non-idealities can lead to errors in vector-matrix multiplication that eventually degrade the DNN's accuracy.
There has been no study of the impact of non-idealities on the accuracy of large-scale DNNs, in part because existing device and circuit models are infeasible to use in application-level evaluation.
In this work, we present a fast and accurate simulation framework to enable evaluation and re-training of large-scale DNNs on resistive crossbar based hardware fabrics.
We first characterize the impact of crossbar non-idealities on errors incurred in the realized vector-matrix multiplications and observe that the errors have significant data and hardware-instance dependence that should be considered.
We propose a Fast Crossbar Model (FCM) to accurately capture the errors arising due to crossbar non-idealities while being four-to-five orders of magnitude faster than circuit simulation.
Finally, we develop RxNN, a software framework to evaluate and re-train DNNs on resistive crossbar systems.
RxNN is based on the popular Caffe machine learning framework, and we use it to evaluate a suite of large-scale DNNs developed for the ImageNet Challenge (ILSVRC).
Our experiments reveal that resistive crossbar non-idealities can lead to significant accuracy degradations (9.6%-32%) for these large-scale DNNs.
To the best of our knowledge, this work is the first quantitative evaluation of the accuracy of large-scale DNNs on resistive crossbar based hardware.
Although agreement between annotators has been studied in the past from a statistical viewpoint, little work has attempted to quantify the extent to which this phenomenon affects the evaluation of computer vision (CV) object detection algorithms.
Many researchers utilise ground truth (GT) in experiments and more often than not this GT is derived from one annotator's opinion.
How does the difference in opinion affect an algorithm's evaluation?
Four examples of typical CV problems are chosen, and a methodology is applied to each to quantify the inter-annotator variance and to offer insight into the mechanisms behind agreement and the use of GT.
It is found that when detecting linear objects annotator agreement is very low.
The agreement in object position, linear or otherwise, can be partially explained through basic image properties.
Automatic object detectors are compared to annotator agreement and it is found that a clear relationship exists.
Several methods for calculating GTs from a number of annotations are applied and the resulting differences in the performance of the object detectors are quantified.
It is found that the rank of a detector is highly dependent upon the method used to form the GT.
It is also found that although the STAPLE and LSML GT estimation methods appear to represent the mean of the performance measured using the individual annotations, when there are few annotations, or there is a large variance in them, these estimates tend to degrade.
Furthermore, one of the most commonly adopted annotation combination methods--consensus voting--accentuates more obvious features, which results in an overestimation of the algorithm's performance.
Finally, it is concluded that in some datasets it may not be possible to state with any confidence that one algorithm outperforms another when evaluating upon one GT and a method for calculating confidence bounds is discussed.
Reviews spams are prevalent in e-commerce to manipulate product ranking and customers decisions maliciously.
While spams generated based on simple spamming strategy can be detected effectively, hardened spammers can evade regular detectors via more advanced spamming strategies.
Previous work gave more attention to evasion against text and graph-based detectors, but evasions against behavior-based detectors are largely ignored, leading to vulnerabilities in spam detection systems.
Since real evasion data are scarce, we first propose EMERAL (Evasion via Maximum Entropy and Rating sAmpLing) to generate evasive spams to certain existing detectors.
EMERAL can simulate spammers with different goals and levels of knowledge about the detectors, targeting at different stages of the life cycle of target products.
We show that in the evasion-defense dynamic, only a few evasion types are meaningful to the spammers, and any spammer will not be able to evade too many detection signals at the same time.
We reveal that some evasions are quite insidious and can fail all detection signals.
We then propose DETER (Defense via Evasion generaTion using EmeRal), based on model re-training on diverse evasive samples generated by EMERAL.
Experiments confirm that DETER is more accurate in detecting both suspicious time window and individual spamming reviews.
In terms of security, DETER is versatile enough to be vaccinated against diverse and unexpected evasions, is agnostic about evasion strategy and can be released without privacy concern.
The expressive nature of the voice provides a powerful medium for communicating sonic ideas, motivating recent research on methods for query by vocalisation.
Meanwhile, deep learning methods have demonstrated state-of-the-art results for matching vocal imitations to imitated sounds, yet little is known about how well learned features represent the perceptual similarity between vocalisations and queried sounds.
In this paper, we address this question using similarity ratings between vocal imitations and imitated drum sounds.
We use a linear mixed effect regression model to show how features learned by convolutional auto-encoders (CAEs) perform as predictors for perceptual similarity between sounds.
Our experiments show that CAEs outperform three baseline feature sets (spectrogram-based representations, MFCCs, and temporal features) at predicting the subjective similarity ratings.
We also investigate how the size and shape of the encoded layer effects the predictive power of the learned features.
The results show that preservation of temporal information is more important than spectral resolution for this application.
Non-maximum suppression (NMS) is essential for state-of-the-art object detectors to localize object from a set of candidate locations.
However, accurate candidate location sometimes is not associated with a high classification score, which leads to object localization failure during NMS.
In this paper, we introduce a novel bounding box regression loss for learning bounding box transformation and localization variance together.
The resulting localization variance exhibits a strong connection to localization accuracy, which is then utilized in our new non-maximum suppression method to improve localization accuracy for object detection.
On MS-COCO, we boost the AP of VGG-16 faster R-CNN from 23.6% to 29.1% with a single model and nearly no additional computational overhead.
More importantly, our method is able to improve the AP of ResNet-50 FPN fast R-CNN from 36.8% to 37.8%, which achieves state-of-the-art bounding box refinement result.
This study provides a conceptual overview of the literature dealing with the process of citing documents (focusing on the literature from the recent decade).
It presents theories, which have been proposed for explaining the citation process, and studies having empirically analyzed this process.
The overview is referred to as conceptual, because it is structured based on core elements in the citation process: the context of the cited document, processes from selection to citation of documents, and the context of the citing document.
The core elements are presented in a schematic representation.
The overview can be used to find answers on basic questions about the practice of citing documents.
Besides understanding of the process of citing, it delivers basic information for the proper application of citations in research evaluation.
Developing information technology to democratize scientific knowledge and support citizen empowerment is a challenging task.
In our case, a local community suffered from air pollution caused by industrial activity.
The residents lacked the technological fluency to gather and curate diverse scientific data to advocate for regulatory change.
We collaborated with the community in developing an air quality monitoring system which integrated heterogeneous data over a large spatial and temporal scale.
The system afforded strong scientific evidence by using animated smoke images, air quality data, crowdsourced smell reports, and wind data.
In our evaluation, we report patterns of sharing smoke images among stakeholders.
Our survey study shows that the scientific knowledge provided by the system encourages agonistic discussions with regulators, empowers the community to support policy making, and rebalances the power relationship between stakeholders.
The paper describes the verifying methods of medical specialty from user profile of online community for health-related advices.
To avoid critical situations with the proliferation of unverified and inaccurate information in medical online community, it is necessary to develop a comprehensive software solution for verifying the user medical specialty of online community for health-related advices.
The algorithm for forming the information profile of a medical online community user is designed.
The scheme systems of formation of indicators of user specialization in the profession based on a training sample is presented.
The method of forming the user information profile of online community for healthrelated advices by computer-linguistic analysis of the information content is suggested.
The system of indicators based on a training sample of users in medical online communities is formed.
The matrix of medical specialties indicators and method of determining weight coefficients these indicators is investigated.
The proposed method of verifying the medical specialty from user profile is tested in online medical community.
With large student enrollment, MOOC instructors face the unique challenge in deciding when to intervene in forum discussions with their limited bandwidth.
We study this problem of instructor intervention.
Using a large sample of forum data culled from 61 courses, we design a binary classifier to predict whether an instructor should intervene in a discussion thread or not.
By incorporating novel information about a forum's type into the classification process, we improve significantly over the previous state-of-the-art.
We show how difficult this decision problem is in the real world by validating against indicative human judgment, and empirically show the problem's sensitivity to instructors' intervention preferences.
We conclude this paper with our take on the future research issues in intervention.
This paper discusses how distribution matching losses, such as those used in CycleGAN, when used to synthesize medical images can lead to mis-diagnosis of medical conditions.
It seems appealing to use these new image synthesis methods for translating images from a source to a target domain because they can produce high quality images and some even do not require paired data.
However, the basis of how these image translation models work is through matching the translation output to the distribution of the target domain.
This can cause an issue when the data provided in the target domain has an over or under representation of some classes (e.g.healthy or sick).
When the output of an algorithm is a transformed image there are uncertainties whether all known and unknown class labels have been preserved or changed.
Therefore, we recommend that these translated images should not be used for direct interpretation (e.g.by doctors) because they may lead to misdiagnosis of patients based on hallucinated image features by an algorithm that matches a distribution.
However there are many recent papers that seem as though this is the goal.
Redundancy is abundant in Fog networks (i.e., many computing and storage points) and grows linearly with network size.
We demonstrate the transformational role of coding in Fog computing for leveraging such redundancy to substantially reduce the bandwidth consumption and latency of computing.
In particular, we discuss two recently proposed coding concepts, namely Minimum Bandwidth Codes and Minimum Latency Codes, and illustrate their impacts in Fog computing.
We also review a unified coding framework that includes the above two coding techniques as special cases, and enables a tradeoff between computation latency and communication load to optimize system performance.
At the end, we will discuss several open problems and future research directions.
In many real-world scenarios, rewards extrinsic to the agent are extremely sparse, or absent altogether.
In such cases, curiosity can serve as an intrinsic reward signal to enable the agent to explore its environment and learn skills that might be useful later in its life.
We formulate curiosity as the error in an agent's ability to predict the consequence of its own actions in a visual feature space learned by a self-supervised inverse dynamics model.
Our formulation scales to high-dimensional continuous state spaces like images, bypasses the difficulties of directly predicting pixels, and, critically, ignores the aspects of the environment that cannot affect the agent.
The proposed approach is evaluated in two environments: VizDoom and Super Mario Bros. Three broad settings are investigated: 1) sparse extrinsic reward, where curiosity allows for far fewer interactions with the environment to reach the goal; 2) exploration with no extrinsic reward, where curiosity pushes the agent to explore more efficiently; and 3) generalization to unseen scenarios (e.g. new levels of the same game) where the knowledge gained from earlier experience helps the agent explore new places much faster than starting from scratch.
Demo video and code available at https://pathak22.github.io/noreward-rl/
In this paper we aim at increasing the descriptive power of the covariance matrix, limited in capturing linear mutual dependencies between variables only.
We present a rigorous and principled mathematical pipeline to recover the kernel trick for computing the covariance matrix, enhancing it to model more complex, non-linear relationships conveyed by the raw data.
To this end, we propose Kernelized-COV, which generalizes the original covariance representation without compromising the efficiency of the computation.
In the experiments, we validate the proposed framework against many previous approaches in the literature, scoring on par or superior with respect to the state of the art on benchmark datasets for 3D action recognition.
The high probability of hardware failures prevents many advanced robots (e.g., legged robots) from being confidently deployed in real-world situations (e.g., post-disaster rescue).
Instead of attempting to diagnose the failures, robots could adapt by trial-and-error in order to be able to complete their tasks.
In this situation, damage recovery can be seen as a Reinforcement Learning (RL) problem.
However, the best RL algorithms for robotics require the robot and the environment to be reset to an initial state after each episode, that is, the robot is not learning autonomously.
In addition, most of the RL methods for robotics do not scale well with complex robots (e.g., walking robots) and either cannot be used at all or take too long to converge to a solution (e.g., hours of learning).
In this paper, we introduce a novel learning algorithm called "Reset-free Trial-and-Error" (RTE) that (1) breaks the complexity by pre-generating hundreds of possible behaviors with a dynamics simulator of the intact robot, and (2) allows complex robots to quickly recover from damage while completing their tasks and taking the environment into account.
We evaluate our algorithm on a simulated wheeled robot, a simulated six-legged robot, and a real six-legged walking robot that are damaged in several ways (e.g., a missing leg, a shortened leg, faulty motor, etc.) and whose objective is to reach a sequence of targets in an arena.
Our experiments show that the robots can recover most of their locomotion abilities in an environment with obstacles, and without any human intervention.
Unlike its image based counterpart, point cloud based retrieval for place recognition has remained as an unexplored and unsolved problem.
This is largely due to the difficulty in extracting local feature descriptors from a point cloud that can subsequently be encoded into a global descriptor for the retrieval task.
In this paper, we propose the PointNetVLAD where we leverage on the recent success of deep networks to solve point cloud based retrieval for place recognition.
Specifically, our PointNetVLAD is a combination/modification of the existing PointNet and NetVLAD, which allows end-to-end training and inference to extract the global descriptor from a given 3D point cloud.
Furthermore, we propose the "lazy triplet and quadruplet" loss functions that can achieve more discriminative and generalizable global descriptors to tackle the retrieval task.
We create benchmark datasets for point cloud based retrieval for place recognition, and the experimental results on these datasets show the feasibility of our PointNetVLAD.
Our code and the link for the benchmark dataset downloads are available in our project website. http://github.com/mikacuy/pointnetvlad/
Cyber-physical systems involve a network of discrete controllers that control physical processes.
Examples range from autonomous cars to implantable medical devices, which are highly safety critical.
Hybrid Automata (HA) based formal approach is gaining momentum for the specification and validation of CPS.
HA combines the model of the plant along with its discrete controller resulting in a piece-wise continuous system with discontinuities.
Accurate detection of these discontinuities, using appropriate level crossing detectors, is a key challenge to simulation of CPS based on HA.
Existing techniques employ time discrete numerical integration with bracketing for level crossing detection.
These techniques involve back-tracking and are highly non-deterministic and hence error prone.
As level crossings happen based on the values of continuous variables, Quantized State System (QSS)- integration may be more suitable.
Existing QSS integrators, based on fixed quanta, are also unsuitable for simulating HAs.
This is since the quantum selected is not dependent on the HA guard conditions, which are the main cause of discontinuities.
Considering this, we propose a new dynamic quanta based formal model called Quantized State Hybrid Automata (QSHA).
The developed formal model and the associated simulation framework guarantees that (1) all level crossings are accurately detected and (2) the time of the level crossing is also accurate within floating point error bounds.
Interestingly, benchmarking results reveal that the proposed simulation technique takes 720, 1.33 and 4.41 times fewer simulation steps compared to standard Quantized State System (QSS)-1, Runge-Kutta (RK)-45, and Differential Algebraic System Solver (DASSL) integration based techniques respectively.
RFID systems are among the major infrastructures of the Internet of Things, which follow ISO and EPC standards.
In addition, ISO standard constitutes the main layers of supply chain, and many RFID systems benefit from ISO standard for different purposes.
In this paper, we tried to introduce addressing systems based on ISO standards, through which the range of things connected to the Internet of Things will grow.
Our proposed methods are addressing methods which can be applied to both ISO and EPC standards.
The proposed methods are simple, hierarchical, and low cost implementation.
In addition, the presented methods enhance interoperability among RFIDs, and also enjoys a high scalability, since it well covers all of EPC schemes and ISO supply chain standards.
Further, by benefiting from a new algorithm for long EPCs known as selection algorithm, they can significantly facilitate and accelerate the operation of address mapping.
A self-organizing map (SOM) is a type of competitive artificial neural network, which projects the high-dimensional input space of the training samples into a low-dimensional space with the topology relations preserved.
This makes SOMs supportive of organizing and visualizing complex data sets and have been pervasively used among numerous disciplines with different applications.
Notwithstanding its wide applications, the self-organizing map is perplexed by its inherent randomness, which produces dissimilar SOM patterns even when being trained on identical training samples with the same parameters every time, and thus causes usability concerns for other domain practitioners and precludes more potential users from exploring SOM based applications in a broader spectrum.
Motivated by this practical concern, we propose a deterministic approach as a supplement to the standard self-organizing map.
In accordance with the theoretical design, the experimental results with satellite cloud data demonstrate the effective and efficient organization as well as simplification capabilities of the proposed approach.
This paper presents a hierarchical framework based on deep reinforcement learning that learns a diversity of policies for humanoid balance control.
Conventional zero moment point based controllers perform limited actions during under-actuation, whereas the proposed framework can perform human-like balancing behaviors such as active push-off of ankles.
The learning is done through the design of an explainable reward based on physical constraints.
The simulated results are presented and analyzed.
The successful emergence of human-like behaviors through deep reinforcement learning proves the feasibility of using an AI-based approach for learning humanoid balancing control in a unified framework.
Tabling is a powerful resolution mechanism for logic programs that captures their least fixed point semantics more faithfully than plain Prolog.
In many tabling applications, we are not interested in the set of all answers to a goal, but only require an aggregation of those answers.
Several works have studied efficient techniques, such as lattice-based answer subsumption and mode-directed tabling, to do so for various forms of aggregation.
While much attention has been paid to expressivity and efficient implementation of the different approaches, soundness has not been considered.
This paper shows that the different implementations indeed fail to produce least fixed points for some programs.
As a remedy, we provide a formal framework that generalises the existing approaches and we establish a soundness criterion that explains for which programs the approach is sound.
This article is under consideration for acceptance in TPLP.
An Artificial Neural Network-based error compensation method is proposed for improving the accuracy of resolver-based 16-bit encoders by compensating for their respective systematic error profiles.
The error compensation procedure, for a particular encoder, involves obtaining its error profile by calibrating it on a precision rotary table, training the neural network by using a part of this data and then determining the corrected encoder angle by subtracting the ANN-predicted error from the measured value of the encoder angle.
Since it is not guaranteed that all the resolvers will have exactly similar error profiles because of the inherent differences in their construction on a micro scale, the ANN has been trained on one error profile at a time and the corresponding weight file is then used only for compensating the systematic error of this particular encoder.
The systematic nature of the error profile for each of the encoders has also been validated by repeated calibration of the encoders over a period of time and it was found that the error profiles of a particular encoder recorded at different epochs show near reproducible behavior.
The ANN-based error compensation procedure has been implemented for 4 encoders by training the ANN with their respective error profiles and the results indicate that the accuracy of encoders can be improved by nearly an order of magnitude from quoted values of ~6 arc-min to ~0.65 arc-min when their corresponding ANN-generated weight files are used for determining the corrected encoder angle.
A new phenomenon emerging within virtual communities is a blurring between the social and commercial activities and motivations of participants.
This paper explores motivations for participating in social commerce at a micro-business level between members of a virtual community of Malay lifestyle bloggers.
The selected community was observed in order to understand the community and 21 participants were interviewed.
We used laddering techniques to explore community attributes, the perceived consequences, and their links to the values of participants.
We found that virtual community relationship was the main influential factor, and virtual community relationship contributed to the sense of social support as well as customers' trust in social commerce.
We investigate the physical layer security of uplink single-carrier frequency-division multiple-access (SC-FDMA) systems.
Multiple users, Alices, send confidential messages to a common legitimate base-station, Bob, in the presence of an eavesdropper, Eve.
To secure the legitimate transmissions, each user superimposes an artificial noise (AN) signal on the time-domain SC-FDMA data block.
We reduce the computational and storage requirements at Bob's receiver by assuming simple per-subchannel detectors.
We assume that Eve has global channel knowledge of all links in addition to high computational capabilities, where she adopts high-complexity detectors such as single-user maximum likelihood (ML), multiuser minimum-mean-square-error (MMSE), and multiuser ML.
We analyze the correlation properties of the time-domain AN signal and illustrate how Eve can exploit them to reduce the AN effects.
We prove that the number of useful AN streams that can degrade Eve's signal-to-noise ratio (SNR) is dependent on the channel memories of Alices-Bob and Alices-Eve links.
Furthermore, we enhance the system security for the case of partial Alices-Bob channel knowledge at Eve, where Eve only knows the precoding matrices of the data and AN signals instead of knowing the entire Alices-Bob channel matrices, and propose a hybrid scheme that integrates temporal AN with channel-based secret-key extraction.
In a competitive marketing, there are a large number of players which produce the same product.
Each firm aims to diffuse its product information widely so that it's product will become popular among potential buyers.
The more popular is a product of a firm, the higher is the revenue for the firm.
A model is developed in which two players compete to spread information in the large network.
Players choose their initial seed nodes simultaneously and the information is diffused according to Independent Cascade model (ICM).
The main aim of the player is to choose the seed nodes such that they will spread its information to as many nodes as possible in a social network.
The rate of spreading of information also plays a very important role in information diffusion process.
Any node in a social network will get influenced by none or one or more than one information.
We also analyzed how much fraction of nodes in different compartment changes by changing the rate of spreading of information.
Finally, a game theory model is developed to obtain the Nash equilibrium based on best response function of the players.
This model is based on Hotelling's model of electoral competition.
State-of-the-art methods of people counting in crowded scenes rely on deep networks to estimate people density in the image plane.
Perspective distortion effects are handled implicitly by either learning scale-invariant features or estimating density in patches of different sizes, neither of which accounts for the fact that scale changes must be consistent over the whole scene.
In this paper, we show that feeding an explicit model of the scale changes to the network considerably increases performance.
An added benefit is that it lets us reason in terms of number of people per square meter on the ground, allowing us to enforce physically-inspired temporal consistency constraints that do not have to be learned.
This yields an algorithm that outperforms state-of-the-art methods on crowded scenes, especially when perspective effects are strong.
Mobile automated video surveillance system involves application of real-time image and video processing algorithms which require a vast quantity of computing and storage resources.
To support the execution of mobile automated video surveillance system, a mobile ad hoc cloud computing and networking infrastructure is proposed in which multiple mobile devices interconnected through a mobile ad hoc network are combined to create a virtual supercomputing node.
An energy efficient resource allocation scheme has also been proposed for allocation of realtime automated video surveillance tasks.
To enable communication between mobile devices, a Wi-Fi Direct based mobile ad hoc cloud networking infrastructure has been developed.
More specifically, a routing layer has been developed to support communication between Wi-Fi Direct devices in a group and multi-hop communication between devices across the group.
The proposed system has been implemented on a group of Wi-Fi Direct-enabled Samsung mobile devices.
In big data era, machine learning is one of fundamental techniques in intrusion detection systems (IDSs).
However, practical IDSs generally update their decision module by feeding new data then retraining learning models in a periodical way.
Hence, some attacks that comprise the data for training or testing classifiers significantly challenge the detecting capability of machine learning-based IDSs.
Poisoning attack, which is one of the most recognized security threats towards machine learning-based IDSs, injects some adversarial samples into the training phase, inducing data drifting of training data and a significant performance decrease of target IDSs over testing data.
In this paper, we adopt the Edge Pattern Detection (EPD) algorithm to design a novel poisoning method that attack against several machine learning algorithms used in IDSs.
Specifically, we propose a boundary pattern detection algorithm to efficiently generate the points that are near to abnormal data but considered to be normal ones by current classifiers.
Then, we introduce a Batch-EPD Boundary Pattern (BEBP) detection algorithm to overcome the limitation of the number of edge pattern points generated by EPD and to obtain more useful adversarial samples.
Based on BEBP, we further present a moderate but effective poisoning method called chronic poisoning attack.
Extensive experiments on synthetic and three real network data sets demonstrate the performance of the proposed poisoning method against several well-known machine learning algorithms and a practical intrusion detection method named FMIFS-LSSVM-IDS.
Training deep neural networks requires many training samples, but in practice training labels are expensive to obtain and may be of varying quality, as some may be from trusted expert labelers while others might be from heuristics or other sources of weak supervision such as crowd-sourcing.
This creates a fundamental quality versus-quantity trade-off in the learning process.
Do we learn from the small amount of high-quality data or the potentially large amount of weakly-labeled data?
We argue that if the learner could somehow know and take the label-quality into account when learning the data representation, we could get the best of both worlds.
To this end, we propose "fidelity-weighted learning" (FWL), a semi-supervised student-teacher approach for training deep neural networks using weakly-labeled data.
FWL modulates the parameter updates to a student network (trained on the task we care about) on a per-sample basis according to the posterior confidence of its label-quality estimated by a teacher (who has access to the high-quality labels).
Both student and teacher are learned from the data.
We evaluate FWL on two tasks in information retrieval and natural language processing where we outperform state-of-the-art alternative semi-supervised methods, indicating that our approach makes better use of strong and weak labels, and leads to better task-dependent data representations.
In this paper we demonstrate the use of intelligent optimization methodologies on the visualization optimization of virtual / simulated environments.
The problem of automatic selection of an optimized set of views, which better describes an on-going simulation over a virtual environment is addressed in the context of the RoboCup Rescue Simulation domain.
A generic architecture for optimization is proposed and described.
We outline the possible extensions of this architecture and argue on how several problems within the fields of Interactive Rendering and Visualization can benefit from it.
We show that the skip-gram formulation of word2vec trained with negative sampling is equivalent to a weighted logistic PCA.
This connection allows us to better understand the objective, compare it to other word embedding methods, and extend it to higher dimensional models.
This paper introduces an end-to-end fine-tuning method to improve hand-eye coordination in modular deep visuo-motor policies (modular networks) where each module is trained independently.
Benefiting from weighted losses, the fine-tuning method significantly improves the performance of the policies for a robotic planar reaching task.
Consider a network of k parties, each holding a long sequence of n entries (a database), with minimum vertex-cut greater than t. We show that any empirical statistic across the network of databases can be computed by each party with perfect privacy, against any set of t < k/2 passively colluding parties, such that the worst-case distortion and communication cost (in bits per database entry) both go to zero as n, the number of entries in the databases, goes to infinity.
This is based on combining a striking dimensionality reduction result for random sampling with unconditionally secure multi-party computation protocols.
It has been a long time, since data mining technologies have made their ways to the field of data management.
Classification is one of the most important data mining tasks for label prediction, categorization of objects into groups, advertisement and data management.
In this paper, we focus on the standard classification problem which is predicting unknown labels in Euclidean space.
Most efforts in Machine Learning communities are devoted to methods that use probabilistic algorithms which are heavy on Calculus and Linear Algebra.
Most of these techniques have scalability issues for big data, and are hardly parallelizable if they are to maintain their high accuracies in their standard form.
Sampling is a new direction for improving scalability, using many small parallel classifiers.
In this paper, rather than conventional sampling methods, we focus on a discrete classification algorithm with O(n) expected running time.
Our approach performs a similar task as sampling methods.
However, we use column-wise sampling of data, rather than the row-wise sampling used in the literature.
In either case, our algorithm is completely deterministic.
Our algorithm, proposes a way of combining 2D convex hulls in order to achieve high classification accuracy as well as scalability in the same time.
First, we thoroughly describe and prove our O(n) algorithm for finding the convex hull of a point set in 2D.
Then, we show with experiments our classifier model built based on this idea is very competitive compared with existing sophisticated classification algorithms included in commercial statistical applications such as MATLAB.
Handwritten Numeral recognition plays a vital role in postal automation services especially in countries like India where multiple languages and scripts are used Discrete Hidden Markov Model (HMM) and hybrid of Neural Network (NN) and HMM are popular methods in handwritten word recognition system.
The hybrid system gives better recognition result due to better discrimination capability of the NN.
A major problem in handwriting recognition is the huge variability and distortions of patterns.
Elastic models based on local observations and dynamic programming such HMM are not efficient to absorb this variability.
But their vision is local.
But they cannot face to length variability and they are very sensitive to distortions.
Then the SVM is used to estimate global correlations and classify the pattern.
Support Vector Machine (SVM) is an alternative to NN.
In Handwritten recognition, SVM gives a better recognition result.
The aim of this paper is to develop an approach which improve the efficiency of handwritten recognition using artificial neural network
An orthogonal approach to the fuzzification of both multisets and hybrid sets is presented.
In particular, we introduce L-multi-fuzzy and L-fuzzy hybrid sets, which are general enough and in spirit with the basic concepts of fuzzy set theory.
In addition, we study the properties of these structures.
Also, the usefulness of these structures is examined in the framework of mechanical multiset processing.
More specifically, we introduce a variant of fuzzy P systems and, since simple fuzzy membrane systems have been introduced elsewhere, we simply extend previously stated results and ideas.
Modeling distributions of citations to scientific papers is crucial for understanding how science develops.
However, there is a considerable empirical controversy on which statistical model fits the citation distributions best.
This paper is concerned with rigorous empirical detection of power-law behaviour in the distribution of citations received by the most highly cited scientific papers.
We have used a large, novel data set on citations to scientific papers published between 1998 and 2002 drawn from Scopus.
The power-law model is compared with a number of alternative models using a likelihood ratio test.
We have found that the power-law hypothesis is rejected for around half of the Scopus fields of science.
For these fields of science, the Yule, power-law with exponential cut-off and log-normal distributions seem to fit the data better than the pure power-law model.
On the other hand, when the power-law hypothesis is not rejected, it is usually empirically indistinguishable from most of the alternative models.
The pure power-law model seems to be the best model only for the most highly cited papers in "Physics and Astronomy".
Overall, our results seem to support theories implying that the most highly cited scientific papers follow the Yule, power-law with exponential cut-off or log-normal distribution.
Our findings suggest also that power laws in citation distributions, when present, account only for a very small fraction of the published papers (less than 1% for most of science fields) and that the power-law scaling parameter (exponent) is substantially higher (from around 3.2 to around 4.7) than found in the older literature.
We provide initial seedings to the Quick Shift clustering algorithm, which approximate the locally high-density regions of the data.
Such seedings act as more stable and expressive cluster-cores than the singleton modes found by Quick Shift.
We establish statistical consistency guarantees for this modification.
We then show strong clustering performance on real datasets as well as promising applications to image segmentation.
Many reinforcement-learning researchers treat the reward function as a part of the environment, meaning that the agent can only know the reward of a state if it encounters that state in a trial run.
However, we argue that this is an unnecessary limitation and instead, the reward function should be provided to the learning algorithm.
The advantage is that the algorithm can then use the reward function to check the reward for states that the agent hasn't even encountered yet.
In addition, the algorithm can simultaneously learn policies for multiple reward functions.
For each state, the algorithm would calculate the reward using each of the reward functions and add the rewards to its experience replay dataset.
The Hindsight Experience Replay algorithm developed by Andrychowicz et al.(2017) does just this, and learns to generalize across a distribution of sparse, goal-based rewards.
We extend this algorithm to linearly-weighted, multi-objective rewards and learn a single policy that can generalize across all linear combinations of the multi-objective reward.
Whereas other multi-objective algorithms teach the Q-function to generalize across the reward weights, our algorithm enables the policy to generalize, and can thus be used with continuous actions.
In this paper we present a novel unsupervised representation learning approach for 3D shapes, which is an important research challenge as it avoids the manual effort required for collecting supervised data.
Our method trains an RNN-based neural network architecture to solve multiple view inter-prediction tasks for each shape.
Given several nearby views of a shape, we define view inter-prediction as the task of predicting the center view between the input views, and reconstructing the input views in a low-level feature space.
The key idea of our approach is to implement the shape representation as a shape-specific global memory that is shared between all local view inter-predictions for each shape.
Intuitively, this memory enables the system to aggregate information that is useful to better solve the view inter-prediction tasks for each shape, and to leverage the memory as a view-independent shape representation.
Our approach obtains the best results using a combination of L_2 and adversarial losses for the view inter-prediction task.
We show that VIP-GAN outperforms state-of-the-art methods in unsupervised 3D feature learning on three large scale 3D shape benchmarks.
In this paper, we design and evaluate a convolutional autoencoder that perturbs an input face image to impart privacy to a subject.
Specifically, the proposed autoencoder transforms an input face image such that the transformed image can be successfully used for face recognition but not for gender classification.
In order to train this autoencoder, we propose a novel training scheme, referred to as semi-adversarial training in this work.
The training is facilitated by attaching a semi-adversarial module consisting of a pseudo gender classifier and a pseudo face matcher to the autoencoder.
The objective function utilized for training this network has three terms: one to ensure that the perturbed image is a realistic face image; another to ensure that the gender attributes of the face are confounded; and a third to ensure that biometric recognition performance due to the perturbed image is not impacted.
Extensive experiments confirm the efficacy of the proposed architecture in extending gender privacy to face images.
Rapid categorization paradigms have a long history in experimental psychology: Characterized by short presentation times and speedy behavioral responses, these tasks highlight the efficiency with which our visual system processes natural object categories.
Previous studies have shown that feed-forward hierarchical models of the visual cortex provide a good fit to human visual decisions.
At the same time, recent work in computer vision has demonstrated significant gains in object recognition accuracy with increasingly deep hierarchical architectures.
But it is unclear how well these models account for human visual decisions and what they may reveal about the underlying brain processes.
We have conducted a large-scale psychophysics study to assess the correlation between computational models and human participants on a rapid animal vs. non-animal categorization task.
We considered visual representations of varying complexity by analyzing the output of different stages of processing in three state-of-the-art deep networks.
We found that recognition accuracy increases with higher stages of visual processing (higher level stages indeed outperforming human participants on the same task) but that human decisions agree best with predictions from intermediate stages.
Overall, these results suggest that human participants may rely on visual features of intermediate complexity and that the complexity of visual representations afforded by modern deep network models may exceed those used by human participants during rapid categorization.
Background: It is widely recognized that software effort estimation is a regression problem.
Model Tree (MT) is one of the Machine Learning based regression techniques that is useful for software effort estimation, but as other machine learning algorithms, the MT has a large space of configuration and requires to carefully setting its parameters.
The choice of such parameters is a dataset dependent so no general guideline can govern this process which forms the motivation of this work.
Aims: This study investigates the effect of using the most recent optimization algorithm called Bees algorithm to specify the optimal choice of MT parameters that fit a dataset and therefore improve prediction accuracy.
Method: We used MT with optimal parameters identified by the Bees algorithm to construct software effort estimation model.
The model has been validated over eight datasets come from two main sources: PROMISE and ISBSG.
Also we used 3-Fold cross validation to empirically assess the prediction accuracies of different estimation models.
As benchmark, results are also compared to those obtained with Stepwise Regression Case-Based Reasoning and Multi-Layer Perceptron.
Results: The results obtained from combination of MT and Bees algorithm are encouraging and outperforms other well-known estimation methods applied on employed datasets.
They are also interesting enough to suggest the effectiveness of MT among the techniques that are suitable for effort estimation.
Conclusions: The use of the Bees algorithm enabled us to automatically find optimal MT parameters required to construct effort estimation models that fit each individual dataset.
Also it provided a significant improvement on prediction accuracy.
Extracting valuable facts or informative summaries from multi-dimensional tables, i.e. insight mining, is an important task in data analysis and business intelligence.
However, ranking the importance of insights remains a challenging and unexplored task.
The main challenge is that explicitly scoring an insight or giving it a rank requires a thorough understanding of the tables and costs a lot of manual efforts, which leads to the lack of available training data for the insight ranking problem.
In this paper, we propose an insight ranking model that consists of two parts: A neural ranking model explores the data characteristics, such as the header semantics and the data statistical features, and a memory network model introduces table structure and context information into the ranking process.
We also build a dataset with text assistance.
Experimental results show that our approach largely improves the ranking precision as reported in multi evaluation metrics.
Design and architecture of cloud storage system plays a vital role in cloud computing infrastructure in order to improve the storage capacity as well as cost effectiveness.
Usually cloud storage system provides users to efficient storage space with elasticity feature.
One of the challenges of cloud storage system is difficult to balance the providing huge elastic capacity of storage and investment of expensive cost for it.
In order to solve this issue in the cloud storage infrastructure, low cost PC cluster based storage server is configured to be activated for large amount of data to provide cloud users.
Moreover, one of the contributions of this system is proposed an analytical model using M/M/1 queuing network model, which is modeled on intended architecture to provide better response time, utilization of storage as well as pending time when the system is running.
According to the analytical result on experimental testing, the storage can be utilized more than 90% of storage space.
In this paper, two parts have been described such as (i) design and architecture of PC cluster based cloud storage system.
On this system, related to cloud applications, services configurations are explained in detailed.
(ii) Analytical model has been enhanced to be increased the storage utilization on the target architecture.
It is possible to associate a highly constrained subset of relative 6 DoF poses between two 3D shapes, as long as the local surface orientation, the normal vector, is available at every surface point.
Local shape features can be used to find putative point correspondences between the models due to their ability to handle noisy and incomplete data.
However, this correspondence set is usually contaminated by outliers in practical scenarios, which has led to many past contributions based on robust detectors such as the Hough transform or RANSAC.
The key insight of our work is that a single correspondence between oriented points on the two models is constrained to cast votes in a 1 DoF rotational subgroup of the full group of poses, SE(3).
Kernel density estimation allows combining the set of votes efficiently to determine a full 6 DoF candidate pose between the models.
This modal pose with the highest density is stable under challenging conditions, such as noise, clutter, and occlusions, and provides the output estimate of our method.
We first analyze the robustness of our method in relation to noise and show that it handles high outlier rates much better than RANSAC for the task of 6 DoF pose estimation.
We then apply our method to four state of the art data sets for 3D object recognition that contain occluded and cluttered scenes.
Our method achieves perfect recall on two LIDAR data sets and outperforms competing methods on two RGB-D data sets, thus setting a new standard for general 3D object recognition using point cloud data.
An observer increases in relative entropy as it receives information from what it is observing.
In a system of only an observer and the observed, an increase in the relative entropy of the observer is a decrease in the relative entropy of the observed.
Linking together these directional entropy disequilibriums we show that NAND and NOR functionality arise in such networks at very low levels of complexity.
In this paper we present two different variants of method for symmetric matrix inversion, based on modified Gaussian elimination.
Both methods avoid computation of square roots and have a reduced machine time's spending.
Further, both of them can be used efficiently not only for positive (semi-) definite, but for any non-singular symmetric matrix inversion.
We use simulation to verify results, which represented in this paper.
In order to improve usability and safety, modern unmanned aerial vehicles (UAVs) are equipped with sensors to monitor the environment, such as laser-scanners and cameras.
One important aspect in this monitoring process is to detect obstacles in the flight path in order to avoid collisions.
Since a large number of consumer UAVs suffer from tight weight and power constraints, our work focuses on obstacle avoidance based on a lightweight stereo camera setup.
We use disparity maps, which are computed from the camera images, to locate obstacles and to automatically steer the UAV around them.
For disparity map computation we optimize the well-known semi-global matching (SGM) approach for the deployment on an embedded FPGA.
The disparity maps are then converted into simpler representations, the so called U-/V-Maps, which are used for obstacle detection.
Obstacle avoidance is based on a reactive approach which finds the shortest path around the obstacles as soon as they have a critical distance to the UAV.
One of the fundamental goals of our work was the reduction of development costs by closing the gap between application development and hardware optimization.
Hence, we aimed at using high-level synthesis (HLS) for porting our algorithms, which are written in C/C++, to the embedded FPGA.
We evaluated our implementation of the disparity estimation on the KITTI Stereo 2015 benchmark.
The integrity of the overall realtime reactive obstacle avoidance algorithm has been evaluated by using Hardware-in-the-Loop testing in conjunction with two flight simulators.
The holy Quran is the holy book of the Muslims.
It contains information about many domains.
Often people search for particular concepts of holy Quran based on the relations among concepts.
An ontological modeling of holy Quran can be useful in such a scenario.
In this paper, we have modeled nature related concepts of holy Quran using OWL (Web Ontology Language) / RDF (Resource Description Framework).
Our methodology involves identifying nature related concepts mentioned in holy Quran and identifying relations among those concepts.
These concepts and relations are represented as classes/instances and properties of an OWL ontology.
Later, in the result section it is shown that, using the Ontological model, SPARQL queries can retrieve verses and concepts of interest.
Thus, this modeling helps semantic search and query on the holy Quran.
In this work, we have used English translation of the holy Quran by Sahih International, Protege OWL Editor and for querying we have used SPARQL.
Deep hashing methods have received much attention recently, which achieve promising results by taking advantage of the strong representation power of deep networks.
However, most existing deep hashing methods learn a whole set of hashing functions independently, while ignore the correlations between different hashing functions that can promote the retrieval accuracy greatly.
Inspired by the sequential decision ability of deep reinforcement learning, we propose a new Deep Reinforcement Learning approach for Image Hashing (DRLIH).
Our proposed DRLIH approach models the hashing learning problem as a sequential decision process, which learns each hashing function by correcting the errors imposed by previous ones and promotes retrieval accuracy.
To the best of our knowledge, this is the first work to address hashing problem from deep reinforcement learning perspective.
The main contributions of our proposed DRLIH approach can be summarized as follows: (1) We propose a deep reinforcement learning hashing network.
In the proposed network, we utilize recurrent neural network (RNN) as agents to model the hashing functions, which take actions of projecting images into binary codes sequentially, so that the current hashing function learning can take previous hashing functions' error into account.
(2) We propose a sequential learning strategy based on proposed DRLIH.
We define the state as a tuple of internal features of RNN's hidden layers and image features, which can reflect history decisions made by the agents.
We also propose an action group method to enhance the correlation of hash functions in the same group.
Experiments on three widely-used datasets demonstrate the effectiveness of our proposed DRLIH approach.
Accurate information of inertial parameters is critical to motion planning and control of space robots.
Before the launch, only a rudimentary estimate of the inertial parameters is available from experiments and computer-aided design (CAD) models.
After the launch, on-orbit operations substantially alter the value of inertial parameters.
In this work, we propose a new momentum model-based method for identifying the minimal parameters of a space robot while on orbit.
Minimal parameters are combinations of the inertial parameters of the links and uniquely define the momentum and dynamic models.
Consequently, they are sufficient for motion planning and control of both the satellite and robotic arms mounted on it.
The key to the proposed framework is the unique formulation of momentum model in the linear form of minimal parameters.
Further, to estimate the minimal parameters, we propose a novel joint trajectory planning and optimization technique based on direction combinations of joints' velocity.
The efficacy of the identification framework is demonstrated on a 12 degrees-of-freedom, spatial, dual-arm space robot.
The methodology is developed for tree-type space robots, requires just the pose and twist data, and scalable with increasing number of joints.
Power grids are one of the most important components of infrastructure in today's world.
Every nation is dependent on the security and stability of its own power grid to provide electricity to the households and industries.
A malfunction of even a small part of a power grid can cause loss of productivity, revenue and in some cases even life.
Thus, it is imperative to design a system which can detect the health of the power grid and take protective measures accordingly even before a serious anomaly takes place.
To achieve this objective, we have set out to create an artificially intelligent system which can analyze the grid information at any given time and determine the health of the grid through the usage of sophisticated formal models and novel machine learning techniques like recurrent neural networks.
Our system simulates grid conditions including stimuli like faults, generator output fluctuations, load fluctuations using Siemens PSS/E software and this data is trained using various classifiers like SVM, LSTM and subsequently tested.
The results are excellent with our methods giving very high accuracy for the data.
This model can easily be scaled to handle larger and more complex grid architectures.
An undesirable side effect of reversible color space transformation, which consists of lifting steps, is that while removing correlation it contaminates transformed components with noise from other components.
To remove correlation without increasing noise, we integrate denoising into the lifting steps and obtain a reversible image component transformation.
For JPEG-LS, JPEG 2000, and JPEG XR algorithms in lossless mode, we find that the proposed method applied to the RDgDb color space transformation with a simple denoising filter is especially effective for images in the native optical resolutions of acquisition devices, but may lead to increased bitrates for typical images.
We also present an efficient estimator of image component transformation effects.
Improving the quality of end-of-life care for hospitalized patients is a priority for healthcare organizations.
Studies have shown that physicians tend to over-estimate prognoses, which in combination with treatment inertia results in a mismatch between patients wishes and actual care at the end of life.
We describe a method to address this problem using Deep Learning and Electronic Health Record (EHR) data, which is currently being piloted, with Institutional Review Board approval, at an academic medical center.
The EHR data of admitted patients are automatically evaluated by an algorithm, which brings patients who are likely to benefit from palliative care services to the attention of the Palliative Care team.
The algorithm is a Deep Neural Network trained on the EHR data from previous years, to predict all-cause 3-12 month mortality of patients as a proxy for patients that could benefit from palliative care.
Our predictions enable the Palliative Care team to take a proactive approach in reaching out to such patients, rather than relying on referrals from treating physicians, or conduct time consuming chart reviews of all patients.
We also present a novel interpretation technique which we use to provide explanations of the model's predictions.
Convolutional sparse representations are a form of sparse representation with a structured, translation invariant dictionary.
Most convolutional dictionary learning algorithms to date operate in batch mode, requiring simultaneous access to all training images during the learning process, which results in very high memory usage and severely limits the training data that can be used.
Very recently, however, a number of authors have considered the design of online convolutional dictionary learning algorithms that offer far better scaling of memory and computational cost with training set size than batch methods.
This paper extends our prior work, improving a number of aspects of our previous algorithm; proposing an entirely new one, with better performance, and that supports the inclusion of a spatial mask for learning from incomplete data; and providing a rigorous theoretical analysis of these methods.
We consider the problem of object recognition in 3D using an ensemble of attribute-based classifiers.
We propose two new concepts to improve classification in practical situations, and show their implementation in an approach implemented for recognition from point-cloud data.
First, the viewing conditions can have a strong influence on classification performance.
We study the impact of the distance between the camera and the object and propose an approach to fuse multiple attribute classifiers, which incorporates distance into the decision making.
Second, lack of representative training samples often makes it difficult to learn the optimal threshold value for best positive and negative detection rate.
We address this issue, by setting in our attribute classifiers instead of just one threshold value, two threshold values to distinguish a positive, a negative and an uncertainty class, and we prove the theoretical correctness of this approach.
Empirical studies demonstrate the effectiveness and feasibility of the proposed concepts.
To face future reliability challenges, it is necessary to quantify the risk of error in any part of a computing system.
To this goal, the Architectural Vulnerability Factor (AVF) has long been used for chips.
However, this metric is used for offline characterisation, which is inappropriate for memory.
We survey the literature and formalise one of the metrics used, the Memory Vulnerability Factor, and extend it to take into account false errors.
These are reported errors which would have no impact on the program if they were ignored.
We measure the False Error Aware MVF (FEA) and related metrics precisely in a cycle-accurate simulator, and compare them with the effects of injecting faults in a program's data, in native parallel runs.
Our findings show that MVF and FEA are the only two metrics that are safe to use at runtime, as they both consistently give an upper bound on the probability of incorrect program outcome.
FEA gives a tighter bound than MVF, and is the metric that correlates best with the incorrect outcome probability of all considered metrics.
Universities and research centers in Spain are subject to a national open access (OA) mandate and to their own OA institutional policies, if any, but compliance with these requirements has not been fully monitored yet.
We studied the degree of OA archiving of publications of 28 universities within the period 2012-2014.
Of these, 12 have an institutional OA mandate, 9 do not require but request or encourage OA of scholarly outputs, and 7 do not have a formal OA statement but are well known for their support of the OA movement.
The potential OA rate was calculated according to the publisher open access policies indicated in Sherpa/Romeo directory.
The universities showed an asymmetric distribution of 1% to 63% of articles archived in repositories that matched those indexed by the Web of Science in the same period, of which 1% to 35% were OA and the rest were closed access.
For articles on work carried out with public funding and subject to the Spanish Science law, the percentage was similar or slightly higher.
However, the analysis of potential OA showed that the figure could have reached 80% in some cases.
This means that the real proportion of articles in OA is far below what it could potentially be.
For most deep learning algorithms training is notoriously time consuming.
Since most of the computation in training neural networks is typically spent on floating point multiplications, we investigate an approach to training that eliminates the need for most of these.
Our method consists of two parts: First we stochastically binarize weights to convert multiplications involved in computing hidden states to sign changes.
Second, while back-propagating error derivatives, in addition to binarizing the weights, we quantize the representations at each layer to convert the remaining multiplications into binary shifts.
Experimental results across 3 popular datasets (MNIST, CIFAR10, SVHN) show that this approach not only does not hurt classification performance but can result in even better performance than standard stochastic gradient descent training, paving the way to fast, hardware-friendly training of neural networks.
Network Functions Virtualization (NFV) aims to support service providers to deploy various services in a more agile and cost-effective way.
However, the softwarization and cloudification of network functions can result in severe congestion and low network performance.
In this paper, we propose a solution to address this issue.
We analyze and solve the online load balancing problem using multipath routing in NFV to optimize network performance in response to the dynamic changes of user demands.
In particular, we first formulate the optimization problem of load balancing as a mixed integer linear program for achieving the optimal solution.
We then develop the ORBIT algorithm that solves the online load balancing problem.
The performance guarantee of ORBIT is analytically proved in comparison with the optimal offline solution.
The experiment results on real-world datasets show that ORBIT performs very well for distributing traffic of each service demand across multipaths without knowledge of future demands, especially under high-load conditions.
Training state-of-the-art offline handwriting recognition (HWR) models requires large labeled datasets, but unfortunately such datasets are not available in all languages and domains due to the high cost of manual labeling.
We address this problem by showing how high resource languages can be leveraged to help train models for low resource languages.
We propose a transfer learning methodology where we adapt HWR models trained on a source language to a target language that uses the same writing script.
This methodology only requires labeled data in the source language, unlabeled data in the target language, and a language model of the target language.
The language model is used in a bootstrapping fashion to refine predictions in the target language for use as ground truth in training the model.
Using this approach we demonstrate improved transferability among French, English, and Spanish languages using both historical and modern handwriting datasets.
In the best case, transferring with the proposed methodology results in character error rates nearly as good as full supervised training.
While Wikipedia exists in 287 languages, its content is unevenly distributed among them.
In this work, we investigate the generation of open domain Wikipedia summaries in underserved languages using structured data from Wikidata.
To this end, we propose a neural network architecture equipped with copy actions that learns to generate single-sentence and comprehensible textual summaries from Wikidata triples.
We demonstrate the effectiveness of the proposed approach by evaluating it against a set of baselines on two languages of different natures: Arabic, a morphological rich language with a larger vocabulary than English, and Esperanto, a constructed language known for its easy acquisition.
We guess humans start acquiring grasping skills as early as at the infant stage by virtue of two key processes.
First, infants attempt to learn grasps for known objects by imitating humans.
Secondly, knowledge acquired during this process is reused in learning to grasp novel objects.
We argue that these processes of active and transfer learning boil down to a random search of grasps on an object, suitably biased by prior experience.
In this paper we introduce active learning of grasps for known objects as well as transfer learning of grasps for novel objects grounded on kernel adaptive, mode-hopping Markov Chain Monte Carlo.
Our experiments show promising applicability of our proposed learning methods.
Person-to-person evaluations are prevalent in all kinds of discourse and important for establishing reputations, building social bonds, and shaping public opinion.
Such evaluations can be analyzed separately using signed social networks and textual sentiment analysis, but this misses the rich interactions between language and social context.
To capture such interactions, we develop a model that predicts individual A's opinion of individual B by synthesizing information from the signed social network in which A and B are embedded with sentiment analysis of the evaluative texts relating A to B.
We prove that this problem is NP-hard but can be relaxed to an efficiently solvable hinge-loss Markov random field, and we show that this implementation outperforms text-only and network-only versions in two very different datasets involving community-level decision-making: the Wikipedia Requests for Adminship corpus and the Convote U.S. Congressional speech corpus.
While a number of touch-based visualization systems have appeared in recent years, relatively little work has been done to evaluate these systems.
The prevailing methods compare these systems to desktop-class applications or utilize traditional training-based usability studies.
We argue that existing studies, while useful, fail to address a key aspect of mobile application usage - initial impression and discoverability-driven usability.
Over the past few years, we have developed a tablet-based visualization system, Tangere, for analyzing tabular data in a multiple coordinated view configuration.
This article describes a discoverability-based user study of Tangere in which the system is compared to a commercially available visualization system for tablets - Tableau's Vizable.
The study highlights aspects of each system's design that resonate with study participants, and we reflect upon those findings to identify design principles for future tablet-based data visualization systems.
The popularity of ASR (automatic speech recognition) systems, like Google Voice, Cortana, brings in security concerns, as demonstrated by recent attacks.
The impacts of such threats, however, are less clear, since they are either less stealthy (producing noise-like voice commands) or requiring the physical presence of an attack device (using ultrasound).
In this paper, we demonstrate that not only are more practical and surreptitious attacks feasible but they can even be automatically constructed.
Specifically, we find that the voice commands can be stealthily embedded into songs, which, when played, can effectively control the target system through ASR without being noticed.
For this purpose, we developed novel techniques that address a key technical challenge: integrating the commands into a song in a way that can be effectively recognized by ASR through the air, in the presence of background noise, while not being detected by a human listener.
Our research shows that this can be done automatically against real world ASR applications.
We also demonstrate that such CommanderSongs can be spread through Internet (e.g., YouTube) and radio, potentially affecting millions of ASR users.
We further present a new mitigation technique that controls this threat.
Stewards of social science data face a fundamental tension.
On one hand, they want to make their data accessible to as many researchers as possible to facilitate new discoveries.
At the same time, they want to restrict access to their data as much as possible in order to protect the people represented in the data.
In this paper, we provide a case study addressing this common tension in an uncommon setting: the Fragile Families Challenge, a scientific mass collaboration designed to yield insights that could improve the lives of disadvantaged children in the United States.
We describe our process of threat modeling, threat mitigation, and third-party guidance.
We also describe the ethical principles that formed the basis of our process.
We are open about our process and the trade-offs that we made in the hopes that others can improve on what we have done.
Community detection is one of the most studied problems on complex networks.
Although hundreds of methods have been proposed so far, there is still no universally accepted formal definition of what is a good community.
As a consequence, the problem of the evaluation and the comparison of the quality of the solutions produced by these algorithms is still an open question, despite constant progress on the topic.
In this article, we investigate how using a multi-criteria evaluation can solve some of the existing problems of community evaluation, in particular the question of multiple equally-relevant solutions of different granularity.
After exploring several approaches, we introduce a new quality function, called MDensity, and propose a method that can be related both to a widely used community detection metric, the Modularity, and to the Precision/Recall approach, ubiquitous in information retrieval.
This paper presents generalized probabilistic models for high-order projective dependency parsing and an algorithmic framework for learning these statistical models involving dependency trees.
Partition functions and marginals for high-order dependency trees can be computed efficiently, by adapting our algorithms which extend the inside-outside algorithm to higher-order cases.
To show the effectiveness of our algorithms, we perform experiments on three languages---English, Chinese and Czech, using maximum conditional likelihood estimation for model training and L-BFGS for parameter estimation.
Our methods achieve competitive performance for English, and outperform all previously reported dependency parsers for Chinese and Czech.
There is a large variety of objects and appliances in human environments, such as stoves, coffee dispensers, juice extractors, and so on.
It is challenging for a roboticist to program a robot for each of these object types and for each of their instantiations.
In this work, we present a novel approach to manipulation planning based on the idea that many household objects share similarly-operated object parts.
We formulate the manipulation planning as a structured prediction problem and design a deep learning model that can handle large noise in the manipulation demonstrations and learns features from three different modalities: point-clouds, language and trajectory.
In order to collect a large number of manipulation demonstrations for different objects, we developed a new crowd-sourcing platform called Robobarista.
We test our model on our dataset consisting of 116 objects with 249 parts along with 250 language instructions, for which there are 1225 crowd-sourced manipulation demonstrations.
We further show that our robot can even manipulate objects it has never seen before.
This work presents CascadeCNN, an automated toolflow that pushes the quantisation limits of any given CNN model, aiming to perform high-throughput inference.
A two-stage architecture tailored for any given CNN-FPGA pair is generated, consisting of a low- and high-precision unit in a cascade.
A confidence evaluation unit is employed to identify misclassified cases from the excessively low-precision unit and forward them to the high-precision unit for re-processing.
Experiments demonstrate that the proposed toolflow can achieve a performance boost up to 55% for VGG-16 and 48% for AlexNet over the baseline design for the same resource budget and accuracy, without the need of retraining the model or accessing the training data.
Classification systems typically act in isolation, meaning they are required to implicitly memorize the characteristics of all candidate classes in order to classify.
The cost of this is increased memory usage and poor sample efficiency.
We propose a model which instead verifies using reference images during the classification process, reducing the burden of memorization.
The model uses iterative nondifferentiable queries in order to classify an image.
We demonstrate that such a model is feasible to train and can match baseline accuracy while being more parameter efficient.
However, we show that finding the correct balance between image recognition and verification is essential to pushing the model towards desired behavior, suggesting that a pipeline of recognition followed by verification is a more promising approach.
Compressed sensing is a technique for finding sparse solutions to underdetermined linear systems.
This technique relies on properties of the sensing matrix such as the restricted isometry property.
Sensing matrices that satisfy this property with optimal parameters are mainly obtained via probabilistic arguments.
Deciding whether a given matrix satisfies the restricted isometry property is a non-trivial computational problem.
Indeed, we show in this paper that restricted isometry parameters cannot be approximated in polynomial time within any constant factor under the assumption that the hidden clique problem is hard.
Moreover, on the positive side we propose an improvement on the brute-force enumeration algorithm for checking the restricted isometry property.
Hyperparameters are critical in machine learning, as different hyperparameters often result in models with significantly different performance.
Hyperparameters may be deemed confidential because of their commercial value and the confidentiality of the proprietary algorithms that the learner uses to learn them.
In this work, we propose attacks on stealing the hyperparameters that are learned by a learner.
We call our attacks hyperparameter stealing attacks.
Our attacks are applicable to a variety of popular machine learning algorithms such as ridge regression, logistic regression, support vector machine, and neural network.
We evaluate the effectiveness of our attacks both theoretically and empirically.
For instance, we evaluate our attacks on Amazon Machine Learning.
Our results demonstrate that our attacks can accurately steal hyperparameters.
We also study countermeasures.
Our results highlight the need for new defenses against our hyperparameter stealing attacks for certain machine learning algorithms.
Despite being one of the most basic tasks in software development, debugging is still performed in a mostly manual way, leading to high cost and low performance.
To address this problem, researchers have studied promising approaches, such as Spectrum-based Fault Localization (SFL) techniques, which pinpoint program elements more likely to contain faults.
This survey discusses the state-of-the-art of SFL, including the different techniques that have been proposed, the type and number of faults they address, the types of spectra they use, the programs they utilize in their validation, the testing data that support them, and their use at industrial settings.
Notwithstanding the advances, there are still challenges for the industry to adopt these techniques, which we analyze in this paper.
SFL techniques should propose new ways to generate reduced sets of suspicious entities, combine different spectra to fine-tune the fault localization ability, use strategies to collect fine-grained coverage levels from suspicious coarser levels for balancing execution costs and output precision, and propose new techniques to cope with multiple-fault programs.
Moreover, additional user studies are needed to understand better how SFL techniques can be used in practice.
We conclude by presenting a concept map about topics and challenges for future research in SFL.
In light of the 40th jubilee of Requirements Engineering (RE), roughly 40 experts met in Switzerland to discuss where our discipline stands today.
As of today, the common view is, indisputably, that RE as a discipline is stable and respected, as pointed out by Sarah Gregory when covering the seminar in her column to which articles like this one are invited to present ongoing research.
However, it is also evident that after 40 years of promising research, conducting research that industry needs is still an ongoing challenge.
Research that industry needs means research that solves industrial problems practitioners face; but do we really understand those problems?
Here, I want to recapitulate on this research challenge and outline an initiative, the Naming the Pain in Requirements Engineering Initiative, that aims at tackling this problem.
In this article, we quantitatively analyze how the term "fake news" is being shaped in news media in recent years.
We study the perception and the conceptualization of this term in the traditional media using eight years of data collected from news outlets based in 20 countries.
Our results not only corroborate previous indications of a high increase in the usage of the expression "fake news", but also show contextual changes around this expression after the United States presidential election of 2016.
Among other results, we found changes in the related vocabulary, in the mentioned entities, in the surrounding topics and in the contextual polarity around the term "fake news", suggesting that this expression underwent a change in perception and conceptualization after 2016.
These outcomes expand the understandings on the usage of the term "fake news", helping to comprehend and more accurately characterize this relevant social phenomenon linked to misinformation and manipulation.
Machine Reading Comprehension (MRC) has become enormously popular recently and has attracted a lot of attention.
However, existing reading comprehension datasets are mostly in English.
To add diversity in reading comprehension datasets, in this paper we propose a new Chinese reading comprehension dataset for accelerating related research in the community.
The proposed dataset contains two different types: cloze-style reading comprehension and user query reading comprehension, associated with large-scale training data as well as human-annotated validation and hidden test set.
Along with this dataset, we also hosted the first Evaluation on Chinese Machine Reading Comprehension (CMRC-2017) and successfully attracted tens of participants, which suggest the potential impact of this dataset.
When analyzing the genome, researchers have discovered that proteins bind to DNA based on certain patterns of the DNA sequence known as "motifs".
However, it is difficult to manually construct motifs due to their complexity.
Recently, externally learned memory models have proven to be effective methods for reasoning over inputs and supporting sets.
In this work, we present memory matching networks (MMN) for classifying DNA sequences as protein binding sites.
Our model learns a memory bank of encoded motifs, which are dynamic memory modules, and then matches a new test sequence to each of the motifs to classify the sequence as a binding or nonbinding site.
Recently, image-to-image translation has been made much progress owing to the success of conditional Generative Adversarial Networks (cGANs).
And some unpaired methods based on cycle consistency loss such as DualGAN, CycleGAN and DiscoGAN are really popular.
However, it's still very challenging for translation tasks with the requirement of high-level visual information conversion, such as photo-to-caricature translation that requires satire, exaggeration, lifelikeness and artistry.
We present an approach for learning to translate faces in the wild from the source photo domain to the target caricature domain with different styles, which can also be used for other high-level image-to-image translation tasks.
In order to capture global structure with local statistics while translation, we design a dual pathway model with one coarse discriminator and one fine discriminator.
For generator, we provide one extra perceptual loss in association with adversarial loss and cycle consistency loss to achieve representation learning for two different domains.
Also the style can be learned by the auxiliary noise input.
Experiments on photo-to-caricature translation of faces in the wild show considerable performance gain of our proposed method over state-of-the-art translation methods as well as its potential real applications.
After defining a pure-action profile in a nonatomic aggregative game, where players have specific compact convex pure-action sets and nonsmooth convex cost functions, as a square-integrable function, we characterize a Wardrop equilibrium as a solution to an infinite-dimensional generalized variational inequality.
We show the existence of Wardrop equilibrium and variational Wardrop equilibrium, a concept of equilibrium adapted to the presence of coupling constraints, in monotone nonatomic aggregative games.
The uniqueness of (variational) Wardrop equilibrium is proved for strictly or aggregatively strictly monotone nonatomic aggregative games.
We then show that, for a sequence of finite-player aggregative games with aggregative constraints, if the players' pure-action sets converge to those of a strongly (resp.aggregatively strongly) monotone nonatomic aggregative game, and the aggregative constraints in the finite-player games converge to the aggregative constraint of the nonatomic game, then a sequence of so-called variational Nash equilibria in these finite-player games converge to the variational Wardrop equilibrium in pure-action profile (resp.aggregate-action profile).
In particular, it allows the construction of an auxiliary sequence of games with finite-dimensional equilibria to approximate the infinite-dimensional equilibrium in such a nonatomic game.
Finally, we show how to construct auxiliary finite-player games for two general classes of nonatomic games.
Several studies assert that the random access procedure of the Long Term Evolution (LTE) cellular standard may not be effective whenever a massive number of simultaneous connection attempts are performed by terminals, as may happen in a typical Internet of Things or Smart City scenario.
Nevertheless, simulation studies in real deployment scenarios are missing because many system-level simulators do not implement the LTE random access procedure in detail.
In this paper, we propose a patch for the LTE module of ns-3, one of the most prominent open-source network simulators, to improve the accuracy of the routine that simulates the LTE Random Access Channel (RACH).
The patched version of the random access procedure is compared with the default one and the issues arising from massive simultaneous access from mobile terminals in LTE are assessed via a simulation campaign.
For the efficient execution of deep convolutional neural networks (CNN) on edge devices, various approaches have been presented which reduce the bit width of the network parameters down to 1 bit.
Binarization of the first layer was always excluded, as it leads to a significant error increase.
Here, we present the novel concept of binary input layer (BIL), which allows the usage of binary input data by learning bit specific binary weights.
The concept is evaluated on three datasets (PAMAP2, SVHN, CIFAR-10).
Our results show that this approach is in particular beneficial for multimodal datasets (PAMAP2) where it outperforms networks using full precision weights in the first layer by 1:92 percentage points (pp) while consuming only 2 % of the chip area.
We present Neural Wavetable, a proof-of-concept wavetable synthesizer that uses neural networks to generate playable wavetables.
The system can produce new, distinct waveforms through the interpolation of traditional wavetables in an autoencoder's latent space.
It is available as a VST/AU plugin for use in a Digital Audio Workstation.
The iterative decoding threshold of low-density parity-check (LDPC) codes over the binary erasure channel (BEC) fulfills an upper bound depending only on the variable and check nodes with minimum distance 2.
This bound is a consequence of the stability condition, and is here referred to as stability bound.
In this paper, a stability bound over the BEC is developed for doubly-generalized LDPC codes, where the variable and the check nodes can be generic linear block codes, assuming maximum a posteriori erasure correction at each node.
It is proved that in this generalized context as well the bound depends only on the variable and check component codes with minimum distance 2.
A condition is also developed, namely the derivative matching condition, under which the bound is achieved with equality.
We analyze the path arrival rate for an inroom radio channel with directive antennas.
The impulse response of this channel exhibits a transition from early separate components followed by a diffuse reverberation tail.
Under the assumption that the transmitter's (or receiver's) position and orientation are picked uniformly at random we derive an exact expression of the mean arrival rate for a rectangular room predicted by the mirror source theory.
The rate is quadratic in delay, inversely proportional to the room volume, and proportional to the product of beam coverage fractions of the transmitter and receiver antennas.
Making use of the exact formula, we characterize the onset of the diffuse tail by defining a "mixing time" as the point in time where the arrival rate exceeds one component per transmit pulse duration.
We also give an approximation for the power-delay spectrum.
It turns out that the power-delay spectrum is unaffected by the antenna directivity.
However, Monte Carlo simulations show that antenna directivity does indeed play an important role for the distribution of instantaneous mean delay and rms delay spread
This paper provides a technical introduction to the PATSTAT Register database, which contains bibliographical, procedural and legal status data on patent applications handled by the European Patent Office.
It presents eight MySQL queries that cover some of the most relevant aspects of the database for research purposes.
It targets academic researchers and practitioners who are familiar with the PATSTAT database and the MySQL language.
Novel scientific knowledge is constantly produced by the scientific community.
Understanding the level of novelty characterized by scientific literature is key for modeling scientific dynamics and analyzing the growth mechanisms of scientific knowledge.
Metrics derived from bibliometrics and citation analysis were effectively used to characterize the novelty in scientific development.
However, time is required before we can observe links between documents such as citation links or patterns derived from the links, which makes these techniques more effective for retrospective analysis than predictive analysis.
In this study, we present a new approach to measuring the novelty of a research topic in a scientific community over a specific period by tracking semantic changes of the terms and characterizing the research topic in their usage context.
The semantic changes are derived from the text data of scientific literature by temporal embedding learning techniques.
We validated the effects of the proposed novelty metric on predicting the future growth of scientific publications and investigated the relations between novelty and growth by panel data analysis applied in a large-scale publication dataset (MEDLINE/PubMed).
Key findings based on the statistical investigation indicate that the novelty metric has significant predictive effects on the growth of scientific literature and the predictive effects may last for more than ten years.
We demonstrated the effectiveness and practical implications of the novelty metric in three case studies.
Enabling technologies for energy sustainable Internet of Things (IoT) are of paramount importance since the proliferation of high data communication demands of low power network devices.
In this paper, we consider a Multiple Input Single Output (MISO) multicasting IoT system comprising of a multiantenna Transmitter (TX) simultaneously transferring information and power to low power and data hungry IoT Receivers (RXs).
Each IoT device is assumed to be equipped with Power Splitting (PS) hardware that enables Energy Harvesting (EH) and imposes an individual Quality of Service (QoS) constraint to the downlink communication.
We study the joint design of TX precoding and IoT PS ratios for the considered MISO Simultaneous Wireless Information and Power Transfer (SWIPT) multicasting IoT system with the objective of maximizing the minimum harvested energy among IoT, while satisfying their individual QoS requirements.
In our novel EH fairness maximization formulation, we adopt a generic Radio Frequency (RF) EH model capturing practical rectification operation, and resulting in a nonconvex optimization problem.
For this problem, we first present an equivalent semi-definite relaxation formulation and then prove it possesses unique global optimality.
We also derive tight upper and lower bounds on the globally optimal solution that are exploited in obtaining low complexity algorithmic implementations for the targeted joint design.
Analytical expressions for the optimal TX beamforming directions, power allocation, and IoT PS ratios are also presented.
Our representative numerical results including comparisons with benchmark designs corroborate the usefulness of proposed framework and provide useful insights on the interplay of critical system parameters.
In network tomography, one goal is to identify a small set of failed links in a network, by sending a few packets through the network and seeing which reach their destination.
This problem can be seen as a variant of combinatorial group testing, which has been studied before under the moniker "graph-constrained group testing."
The main contribution of this work is to show that for most graphs, the "constraints" imposed by the underlying network topology are no constraint at all.
That is, the number of tests required to identify the failed links in "graph-constrained" group testing is near-optimal even for the corresponding group testing problem with no graph constraints.
Our approach is based on a simple randomized construction of tests, to analyze our construction, we prove new results about the size of giant components in randomly sparsified graphs.
Finally, we provide empirical results which suggest that our connected-subgraph tests perform better not just in theory but also in practice, and in particular perform better on a real-world network topology.
A general framework of spatio-spectral segmentation for multi-spectral images is introduced in this paper.
The method is based on classification-driven stochastic watershed (WS) by Monte Carlo simulations, and it gives more regular and reliable contours than standard WS.
The present approach is decomposed into several sequential steps.
First, a dimensionality-reduction stage is performed using the factor-correspondence analysis method.
In this context, a new way to select the factor axes (eigenvectors) according to their spatial information is introduced.
Then, a spectral classification produces a spectral pre-segmentation of the image.
Subsequently, a probability density function (pdf) of contours containing spatial and spectral information is estimated by simulation using a stochastic WS approach driven by the spectral classification.
The pdf of the contours is finally segmented by a WS controlled by markers from a regularization of the initial classification.
A number of algorithms for computing the simulation preorder are available.
Let Sigma denote the state space, -> the transition relation and Psim the partition of Sigma induced by simulation equivalence.
The algorithms by Henzinger, Henzinger, Kopke and by Bloom and Paige run in O(|Sigma||->|)-time and, as far as time-complexity is concerned, they are the best available algorithms.
However, these algorithms have the drawback of a space complexity that is more than quadratic in the size of the state space.
The algorithm by Gentilini, Piazza, Policriti--subsequently corrected by van Glabbeek and Ploeger--appears to provide the best compromise between time and space complexity.
Gentilini et al.'s algorithm runs in O(|Psim|^2|->|)-time while the space complexity is in O(|Psim|^2 + |Sigma|log|Psim|).
We present here a new efficient simulation algorithm that is obtained as a modification of Henzinger et al.'s algorithm and whose correctness is based on some techniques used in applications of abstract interpretation to model checking.
Our algorithm runs in O(|Psim||->|)-time and O(|Psim||Sigma|log|Sigma|)-space.
Thus, this algorithm improves the best known time bound while retaining an acceptable space complexity that is in general less than quadratic in the size of the state space.
An experimental evaluation showed good comparative results with respect to Henzinger, Henzinger and Kopke's algorithm.
Recently Trajectory-pooled Deep-learning Descriptors were shown to achieve state-of-the-art human action recognition results on a number of datasets.
This paper improves their performance by applying rank pooling to each trajectory, encoding the temporal evolution of deep learning features computed along the trajectory.
This leads to Evolution-Preserving Trajectory (EPT) descriptors, a novel type of video descriptor that significantly outperforms Trajectory-pooled Deep-learning Descriptors.
EPT descriptors are defined based on dense trajectories, and they provide complimentary benefits to video descriptors that are not based on trajectories.
In particular, we show that the combination of EPT descriptors and VideoDarwin leads to state-of-the-art performance on Hollywood2 and UCF101 datasets.
With the growing economy, e-learning consequently gained increasing attention as it conveys knowledge globally with improved interactivity, assistance, and reduced costs.
For the past few years, accidental challenges have become the severe problem with railway units due to irresponsibility, lack of knowledge and improper guidance of station controllers (learners).
While focusing on e-learning technologies railway units failed to admit learner's need, cultural diversity and background skills by creating ethnically impartial e-learning environments, which resulted in inadequate training and degraded performance.
The purpose of this study is to understand the vision of a global diverse group of station traffic controllers about e-learning courses developed by their individual railway units.
The opinions of these officials have been verified by questionnaires on the basis of course organization, course accuracy, course effectiveness, course relevance, course productivity and course interactivity.
The results obtained show that the developed e-learning course was highly helpful, interactive, creative, and user-friendly for learners.
This lead to making e-learning conquered among independent learners.
The task of person re-identification has recently received rising attention due to the high performance achieved by new methods based on deep learning.
In particular, in the context of video-based re-identification, many state-of-the-art works have explored the use of Recurrent Neural Networks (RNNs) to process input sequences.
In this work, we revisit this tool by deriving an approximation which reveals the small effect of recurrent connections, leading to a much simpler feed-forward architecture.
Using the same parameters as the recurrent version, our proposed feed-forward architecture obtains very similar accuracy.
More importantly, our model can be combined with a new training process to significantly improve re-identification performance.
Our experiments demonstrate that the proposed models converge substantially faster than recurrent ones, with accuracy improvements by up to 5% on two datasets.
The performance achieved is better or on par with other RNN-based person re-identification techniques.
SQL declaratively specifies what the desired output of a query is.
This work shows that a non-standard interpretation of the SQL semantics can, instead, disclose where a piece of the output originated in the input and why that piece found its way into the result.
We derive such data provenance for very rich SQL dialects (including recursion, windowed aggregates, and user-defined functions) at the fine-grained level of individual table cells.
The approach is non-invasive and implemented as a compositional source-level SQL rewrite: an input SQL query is transformed into its own interpreter that wields data dependencies instead of regular values.
We deliberately design this transformation to preserve the shape of both data and query, which allows provenance derivation to scale to complex queries without overwhelming the underlying database system.
A solution to the problem of asymptotically optimum perfect universal steganography of finite memoryless sources with a passive warden is provided, which is then extended to contemplate a distortion constraint.
The solution rests on the fact that Slepian's Variant I permutation coding implements first-order perfect universal steganography of finite host signals with optimum embedding rate.
The duality between perfect universal steganography with asymptotically optimum embedding rate and lossless universal source coding with asymptotically optimum compression rate is evinced in practice by showing that permutation coding can be implemented by means of adaptive arithmetic coding.
Next, a distortion constraint between the host signal and the information-carrying signal is considered.
Such a constraint is essential whenever real-world host signals with memory (e.g., images, audio, or video) are decorrelated to conform to the memoryless assumption.
The constrained version of the problem requires trading off embedding rate and distortion.
Partitioned permutation coding is shown to be a practical way to implement this trade-off, performing close to an unattainable upper bound on the rate-distortion function of the problem.
High-utility Itemset Mining (HUIM) finds itemsets from a transaction database with utility no less than a user-defined threshold where the utility of an itemset is defined as the sum of the utilities of its items.
In this paper, we introduce the notion of generalized utility functions that need not be the sum of individual utilities.
In particular, we study subadditive monotone (SM) utility functions and prove that it generalizes the HUIM problem mentioned above.
Moving on to HUIM algorithms, the existing algorithms use upper-bounds like `Transaction Weighted Utility' and `Exact-Utility, Remaining Utility' for efficient search-space exploration.
We derive analogous and tighter upper-bounds for SM utility functions and explain how existing HUIM algorithms of different classes can be adapted using our upper bound.
We experimentally compared adaptations of some of the latest algorithms and point out some caveats that should be kept in mind while handling general utility functions.
Acute kidney injury (AKI) in critically ill patients is associated with significant morbidity and mortality.
Development of novel methods to identify patients with AKI earlier will allow for testing of novel strategies to prevent or reduce the complications of AKI.
We developed data-driven prediction models to estimate the risk of new AKI onset.
We generated models from clinical notes within the first 24 hours following intensive care unit (ICU) admission extracted from Medical Information Mart for Intensive Care III (MIMIC-III).
From the clinical notes, we generated clinically meaningful word and concept representations and embeddings, respectively.
Five supervised learning classifiers and knowledge-guided deep learning architecture were used to construct prediction models.
The best configuration yielded a competitive AUC of 0.779.
Our work suggests that natural language processing of clinical notes can be applied to assist clinicians in identifying the risk of incident AKI onset in critically ill patients upon admission to the ICU.
The complexity of the graph isomorphism problem for trapezoid graphs has been open over a decade.
This paper shows that the problem is GI-complete.
More precisely, we show that the graph isomorphism problem is GI-complete for comparability graphs of partially ordered sets with interval dimension 2 and height 3.
In contrast, the problem is known to be solvable in polynomial time for comparability graphs of partially ordered sets with interval dimension at most 2 and height at most 2.
The log-rank conjecture is one of the fundamental open problems in communication complexity.
It speculates that the deterministic communication complexity of any two-party function is equal to the log of the rank of its associated matrix, up to polynomial factors.
Despite much research, we still know very little about this conjecture.
Recently, there has been renewed interest in this conjecture and its relations to other fundamental problems in complexity theory.
This survey describes some of the recent progress, and hints at potential directions for future research.
Despite the advancements in search engine features, ranking methods, technologies, and the availability of programmable APIs, current-day open-access digital libraries still rely on crawl-based approaches for acquiring their underlying document collections.
In this paper, we propose a novel search-driven framework for acquiring documents for scientific portals.
Within our framework, publicly-available research paper titles and author names are used as queries to a Web search engine.
Next, research papers and sources of research papers are identified from the search results using accurate classification modules.
Our experiments highlight not only the performance of our individual classifiers but also the effectiveness of our overall Search/Crawl framework.
Indeed, we were able to obtain approximately 0.665 million research documents through our fully-automated framework using about 0.076 million queries.
These prolific results position Web search as an effective alternative to crawl methods for acquiring both the actual documents and seed URLs for future crawls.
We introduce EigenRec; a versatile and efficient Latent-Factor framework for Top-N Recommendations that includes the well-known PureSVD algorithm as a special case.
EigenRec builds a low dimensional model of an inter-item proximity matrix that combines a similarity component, with a scaling operator, designed to control the influence of the prior item popularity on the final model.
Seeing PureSVD within our framework provides intuition about its inner workings, exposes its inherent limitations, and also, paves the path towards painlessly improving its recommendation performance.
A comprehensive set of experiments on the MovieLens and the Yahoo datasets based on widely applied performance metrics, indicate that EigenRec outperforms several state-of-the-art algorithms, in terms of Standard and Long-Tail recommendation accuracy, exhibiting low susceptibility to sparsity, even in its most extreme manifestations -- the Cold-Start problems.
At the same time EigenRec has an attractive computational profile and it can apply readily in large-scale recommendation settings.
We propose a bridge between functional and object-oriented programming in the first-year curriculum.
Traditionally, curricula that begin with functional programming transition to a professional, usually object-oriented, language in the second course.
This transition poses obstacles for students, and often results in confusing the details of development environments, syntax, and libraries with the fundamentals of OO programming that the course should focus on.
Instead, we propose to begin the second course with a sequence of custom teaching languages which minimize the transition from the first course, and allow students to focus on core ideas.
After working through the sequence of pedagogical languages, we then transition to Java, at which point students have a strong command of the basic principles.
We have 3 years of experience with this course, with notable success.
Word2vec (Mikolov et al., 2013) has proven to be successful in natural language processing by capturing the semantic relationships between different words.
Built on top of single-word embeddings, paragraph vectors (Le and Mikolov, 2014) find fixed-length representations for pieces of text with arbitrary lengths, such as documents, paragraphs, and sentences.
In this work, we propose a novel interpretation for neural-network-based paragraph vectors by developing an unsupervised generative model whose maximum likelihood solution corresponds to traditional paragraph vectors.
This probabilistic formulation allows us to go beyond point estimates of parameters and to perform Bayesian posterior inference.
We find that the entropy of paragraph vectors decreases with the length of documents, and that information about posterior uncertainty improves performance in supervised learning tasks such as sentiment analysis and paraphrase detection.
The spread of ideas in the scientific community is often viewed as a competition, in which good ideas spread further because of greater intrinsic fitness, and publication venue and citation counts correlate with importance and impact.
However, relatively little is known about how structural factors influence the spread of ideas, and specifically how where an idea originates might influence how it spreads.
Here, we investigate the role of faculty hiring networks, which embody the set of researcher transitions from doctoral to faculty institutions, in shaping the spread of ideas in computer science, and the importance of where in the network an idea originates.
We consider comprehensive data on the hiring events of 5032 faculty at all 205 Ph.D.-granting departments of computer science in the U.S. and Canada, and on the timing and titles of 200,476 associated publications.
Analyzing five popular research topics, we show empirically that faculty hiring can and does facilitate the spread of ideas in science.
Having established such a mechanism, we then analyze its potential consequences using epidemic models to simulate the generic spread of research ideas and quantify the impact of where an idea originates on its longterm diffusion across the network.
We find that research from prestigious institutions spreads more quickly and completely than work of similar quality originating from less prestigious institutions.
Our analyses establish the theoretical trade-offs between university prestige and the quality of ideas necessary for efficient circulation.
Our results establish faculty hiring as an underlying mechanism that drives the persistent epistemic advantage observed for elite institutions, and provide a theoretical lower bound for the impact of structural inequality in shaping the spread of ideas in science.
Purpose.
To obtain the interference immunity of the data exchange by spread spectrum signals with variable entropy of the telemetric information data exchange with autonomous mobile robots.
Methodology.
The results have been obtained by the theoretical investigations and have been confirmed by the modeling experiments.
Findings.
The interference immunity in form of dependence of bit error probability on normalized signal/noise ratio of the data exchange by spread spectrum signals with variable entropy has been obtained.It has been proved that the interference immunity factor (needed normalized signal/noise ratio) is at least 2 dB better under condition of equal time complexity as compared with correlation processing methods of orthogonal signals.
Originality.
For the first time the interference immunity in form of dependence of bit error probability on normalized signal/noise ratio of the data exchange by spread spectrum signals with variable entropy has been obtained.
Practical value.
The obtained results prove the feasibility of using variable entropy spread spectrum signals data exchange method in the distributed telemetric information processing systems in specific circumstances.
We study two-player games played on the infinite graph of sentential forms induced by a context-free grammar (that comes with an ownership partitioning of the non-terminals).
The winning condition is inclusion of the derived terminal word in the language of a finite automaton.
Our contribution is a new algorithm to decide the winning player and to compute her strategy.
It is based on a novel representation of all plays starting in a non-terminal.
The representation uses the domain of Boolean formulas over the transition monoid of the target automaton.
The elements of the monoid are essentially procedure summaries, and our approach can be seen as the first summary-based algorithm for the synthesis of recursive programs.
We show that our algorithm has optimal (doubly exponential) time complexity, that it is compatible with recent antichain optimizations, and that it admits a lazy evaluation strategy.
Our preliminary experiments indeed show encouraging results, indicating a speed up of three orders of magnitude over a competitor.
Whereas it is believed that techniques such as Adam, batch normalization and, more recently, SeLU nonlinearities "solve" the exploding gradient problem, we show that this is not the case in general and that in a range of popular MLP architectures, exploding gradients exist and that they limit the depth to which networks can be effectively trained, both in theory and in practice.
We explain why exploding gradients occur and highlight the *collapsing domain problem*, which can arise in architectures that avoid exploding gradients.
ResNets have significantly lower gradients and thus can circumvent the exploding gradient problem, enabling the effective training of much deeper networks.
We show this is a direct consequence of the Pythagorean equation.
By noticing that *any neural network is a residual network*, we devise the *residual trick*, which reveals that introducing skip connections simplifies the network mathematically, and that this simplicity may be the major cause for their success.
Solar energy generation requires efficient monitoring and management in moving towards technologies for net-zero energy buildings.
This paper presents a dependable control system based on the Internet of Things (IoT) to control and manage the energy flow of renewable energy collected by solar panels within a microgrid.
Data for optimal control include not only measurements from local sensors but also meteorological information retrieved in real-time from online sources.
For system fault tolerance across the whole distributed control system featuring multiple controllers, dependable controllers are developed to control and optimise the tracking performance of photovoltaic arrays to maximally capture solar radiation and maintain system resilience and reliability in real time despite failures of one or more redundant controllers due to a problem with communication, hardware or cybersecurity.
Experimental results have been obtained to evaluate the validity of the proposed approach.
cryptographic hash function is a deterministic procedure that compresses an arbitrary block of numerical data and returns a fixed-size bit string.
There exist many hash functions: MD5, HAVAL, SHA, ...
It was reported that these hash functions are not longer secure.
Our work is focused in the construction of a new hash function based on composition of functions.
The construction used the NP-completeness of Three-dimensional contingency tables and the relaxation of the constraint that a hash function should also be a compression function.
Detection of Alzheimer's Disease (AD) from neuroimaging data such as MRI through machine learning have been a subject of intense research in recent years.
Recent success of deep learning in computer vision have progressed such research further.
However, common limitations with such algorithms are reliance on a large number of training images, and requirement of careful optimization of the architecture of deep networks.
In this paper, we attempt solving these issues with transfer learning, where state-of-the-art architectures such as VGG and Inception are initialized with pre-trained weights from large benchmark datasets consisting of natural images, and the fully-connected layer is re-trained with only a small number of MRI images.
We employ image entropy to select the most informative slices for training.
Through experimentation on the OASIS MRI dataset, we show that with training size almost 10 times smaller than the state-of-the-art, we reach comparable or even better performance than current deep-learning based methods.
Cloud computing is a cost-effective way for start-up life sciences laboratories to store and manage their data.
However, in many instances the data stored over the cloud could be redundant which makes cloud-based data management inefficient and costly because one has to pay for every byte of data stored over the cloud.
Here, we tested efficient management of data generated by an electron cryo microscopy (cryoEM) lab on a cloud-based environment.
The test data was obtained from cryoEM repository EMPIAR.
All the images were subjected to an in-house parallelized version of principal component analysis.
An efficient cloud-based MapReduce modality was used for parallelization.
We showed that large data in order of terabytes could be efficiently reduced to its minimal essential self in a cost-effective scalable manner.
Furthermore, on-spot instance on Amazon EC2 was shown to reduce costs by a margin of about 27 percent.
This approach could be scaled to data of any large volume and type.
Sentence simplification aims to simplify the content and structure of complex sentences, and thus make them easier to interpret for human readers, and easier to process for downstream NLP applications.
Recent advances in neural machine translation have paved the way for novel approaches to the task.
In this paper, we adapt an architecture with augmented memory capacities called Neural Semantic Encoders (Munkhdalai and Yu, 2017) for sentence simplification.
Our experiments demonstrate the effectiveness of our approach on different simplification datasets, both in terms of automatic evaluation measures and human judgments.
Change detection is one of the most challenging issues when analyzing remotely sensed images.
Comparing several multi-date images acquired through the same kind of sensor is the most common scenario.
Conversely, designing robust, flexible and scalable algorithms for change detection becomes even more challenging when the images have been acquired by two different kinds of sensors.
This situation arises in case of emergency under critical constraints.
This paper presents, to the best of authors' knowledge, the first strategy to deal with optical images characterized by dissimilar spatial and spectral resolutions.
Typical considered scenarios include change detection between panchromatic or multispectral and hyperspectral images.
The proposed strategy consists of a 3-step procedure: i) inferring a high spatial and spectral resolution image by fusion of the two observed images characterized one by a low spatial resolution and the other by a low spectral resolution, ii) predicting two images with respectively the same spatial and spectral resolutions as the observed images by degradation of the fused one and iii) implementing a decision rule to each pair of observed and predicted images characterized by the same spatial and spectral resolutions to identify changes.
The performance of the proposed framework is evaluated on real images with simulated realistic changes.
In this paper we address the challenge of assessing the quality of Wikipedia pages using scores derived from edit contribution and contributor authoritativeness measures.
The hypothesis is that pages with significant contributions from authoritative contributors are likely to be high-quality pages.
Contributions are quantified using edit longevity measures and contributor authoritativeness is scored using centrality metrics in either the Wikipedia talk or co-author networks.
The results suggest that it is useful to take into account the contributor authoritativeness when assessing the information quality of Wikipedia content.
The percentile visualization of the quality scores provides some insights about the anomalous articles, and can be used to help Wikipedia editors to identify Start and Stub articles that are of relatively good quality.
We propose Hilbert transform (HT) and analytic signal (AS) construction for signals over graphs.
This is motivated by the popularity of HT, AS, and modulation analysis in conventional signal processing, and the observation that complementary insight is often obtained by viewing conventional signals in the graph setting.
Our definitions of HT and AS use a conjugate-symmetry-like property exhibited by the graph Fourier transform (GFT).
We show that a real graph signal (GS) can be represented using smaller number of GFT coefficients than the signal length.
We show that the graph HT (GHT) and graph AS (GAS) operations are linear and shift-invariant over graphs.
Using the GAS, we define the amplitude, phase, and frequency modulations for a graph signal (GS).
Further, we use convex optimization to develop an alternative definition of envelope for a GS.
We illustrate the proposed concepts by showing applications to synthesized and real-world signals.
For example, we show that the GHT is suitable for anomaly detection/analysis over networks and that GAS reveals complementary information in speech signals.
For any company, multiple channels are available for reaching a population in order to market its products.
Some of the most well-known channels are (a) mass media advertisement, (b) recommendations using social advertisement, and (c) viral marketing using social networks.
The company would want to maximize its reach while also accounting for simultaneous marketing of competing products, where the product marketings may not be independent.
In this direction, we propose and analyze a multi-featured generalization of the classical linear threshold model.
We hence develop a framework for integrating the considered marketing channels into the social network, and an approach for allocating budget among these channels.
Website can be easily design but to efficient user navigation is not a easy task since user behavior is keep changing and developer view is quite different from what user wants, so to improve navigation one way is reorganization of website structure.
For reorganization here proposed strategy is farthest first traversal clustering algorithm perform clustering on two numeric parameters and for finding frequent traversal path of user Apriori algorithm is used.
Our aim is to perform reorganization with fewer changes in website structure.
Latent periodic elements in genomes play important roles in genomic functions.
Many complex periodic elements in genomes are difficult to be detected by commonly used digital signal processing (DSP).
We present a novel method to compute the periodic power spectrum of a DNA sequence based on the nucleotide distributions on periodic positions of the sequence.
The method directly calculates full periodic spectrum of a DNA sequence rather than frequency spectrum by Fourier transform.
The magnitude of the periodic power spectrum reflects the strength of the periodicity signals, thus, the algorithm can capture all the latent periodicities in DNA sequences.
We apply this method on detection of latent periodicities in different genome elements, including exons and microsatellite DNA sequences.
The results show that the method minimizes the impact of spectral leakage, captures a much broader latent periodicities in genomes, and outperforms the conventional Fourier transform.
Spatiotemporal forecasting has various applications in neuroscience, climate and transportation domain.
Traffic forecasting is one canonical example of such learning task.
The task is challenging due to (1) complex spatial dependency on road networks, (2) non-linear temporal dynamics with changing road conditions and (3) inherent difficulty of long-term forecasting.
To address these challenges, we propose to model the traffic flow as a diffusion process on a directed graph and introduce Diffusion Convolutional Recurrent Neural Network (DCRNN), a deep learning framework for traffic forecasting that incorporates both spatial and temporal dependency in the traffic flow.
Specifically, DCRNN captures the spatial dependency using bidirectional random walks on the graph, and the temporal dependency using the encoder-decoder architecture with scheduled sampling.
We evaluate the framework on two real-world large scale road network traffic datasets and observe consistent improvement of 12% - 15% over state-of-the-art baselines.
Surface parameterizations have been widely applied to computer graphics and digital geometry processing.
In this paper, we propose a novel stretch energy minimization (SEM) algorithm for the computation of equiareal parameterizations of simply connected open surfaces with a very small area distortion and a highly improved computational efficiency.
In addition, the existence of nontrivial limit points of the SEM algorithm is guaranteed under some mild assumptions of the mesh quality.
Numerical experiments indicate that the efficiency, accuracy, and robustness of the proposed SEM algorithm outperform other state-of-the-art algorithms.
Applications of the SEM on surface remeshing and surface registration for simply connected open surfaces are demonstrated thereafter.
Thanks to the SEM algorithm, the computations for these applications can be carried out efficiently and robustly.
Over the past two decades, High-Performance Computing (HPC) communities have developed many models for delivering education aiming to help students understand and harness the power of parallel and distributed computing.
Most of these courses either lack a hands-on component or heavily focus on theoretical characterization behind complex algorithms.
To bridge the gap between application and scientific theory, NVIDIA Deep Learning Institute (DLI) (www.nvidia.com/dli) has designed an on-line education and training platform that helps students, developers, and engineers solve real-world problems in a wide range of domains using deep learning and accelerated computing.
DLI's accelerated computing course content starts with the fundamentals of accelerating applications with CUDA and OpenACC in addition to other courses in training and deploying neural networks for deep learning.
Advanced and domain-specific courses in deep learning are also available.
The online platform enables students to use the latest AI frameworks, SDKs, and GPU-accelerated technologies on fully-configured GPU servers in the cloud so the focus is more on learning and less on environment setup.
Students are offered project-based assessment and certification at the end of some courses.
To support academics and university researchers teaching accelerated computing and deep learning, the DLI University Ambassador Program enables educators to teach free DLI courses to university students, faculty, and researchers.
Fairness in algorithmic decision-making processes is attracting increasing concern.
When an algorithm is applied to human-related decision-making an estimator solely optimizing its predictive power can learn biases on the existing data, which motivates us the notion of fairness in machine learning. while several different notions are studied in the literature, little studies are done on how these notions affect the individuals.
We demonstrate such a comparison between several policies induced by well-known fairness criteria, including the color-blind (CB), the demographic parity (DP), and the equalized odds (EO).
We show that the EO is the only criterion among them that removes group-level disparity.
Empirical studies on the social welfare and disparity of these policies are conducted.
Most previous works on opinion modeling lack the simultaneous study of individual mental activity and group behavior.
This paper is motivated to propose an agent-based online opinion formation model based on attitude change theory, group behavior theory and evolutionary game theory in the perspective of sociology and psychology.
In this model, there are three factors influencing the persuasion process, including credibility of the leaders, characteristic of the recipient, and group environment.
The proposed model is applied to Twitter to analyze the influence of topic type, parameter changing, and opinion leaders on opinion formation.
Experimental results show that the opinion evolution of controversial topic shows greater uncertainty and sustainability.
The ratio of benefit to cost has a significant impact on opinion formation and a moderate ratio will result in the longest relaxation time or most unified global opinions.
Furthermore, celebrities with a large number of followers are more capable of influencing public opinion than experts.
This paper enriches the researches on opinion formation modeling, and the results provide managerial insights for business on public relations and market prediction.
GPUs are dedicated processors used for complex calculations and simulations and they can be effectively used for tropical algebra computations.
Tropical algebra is based on max-plus algebra and min-plus algebra.
In this paper we proposed and designed a library based on Tropical Algebra which is used to provide standard vector and matrix operations namely Basic Tropical Algebra Subroutines (BTAS).
The testing of BTAS library is conducted by implementing the sequential version of Floyd Warshall Algorithm on CPU and furthermore parallel version on GPU.
The developed library for tropical algebra delivered extensively better results on a less expensive GPU as compared to the same on CPU.
The potential number of drug like small molecules is estimated to be between 10^23 and 10^60 while current databases of known compounds are orders of magnitude smaller with approximately 10^8 compounds.
This discrepancy has led to an interest in generating virtual libraries using hand crafted chemical rules and fragment based methods to cover a larger area of chemical space and generate chemical libraries for use in in silico drug discovery endeavors.
Here it is explored to what extent a recurrent neural network with long short term memory cells can figure out sensible chemical rules and generate synthesizable molecules by being trained on existing compounds encoded as SMILES.
The networks can to a high extent generate novel, but chemically sensible molecules.
The properties of the molecules are tuned by training on two different datasets consisting of fragment like molecules and drug like molecules.
The produced molecules and the training databases have very similar distributions of molar weight, predicted logP, number of hydrogen bond acceptors and donors, number of rotatable bonds and topological polar surface area when compared to their respective training sets.
The compounds are for the most cases synthesizable as assessed with SA score and Wiley ChemPlanner.
Machine vision applications are low cost and high precision measurement systems which are frequently used in production lines.
With these systems that provide contactless control and measurement, production facilities are able to reach high production numbers without errors.
Machine vision operations such as product counting, error control, dimension measurement can be performed through a camera.
In this paper, a machine vision application is proposed, which can perform object-independent product counting.
The proposed approach is based on Otsu thresholding and Hough transformation and performs automatic counting independently of product type and color.
Basically one camera is used in the system.
Through this camera, an image of the products passing through a conveyor is taken and various image processing algorithms are applied to these images.
In this approach using images obtained from a real experimental setup, a real-time machine vision application was installed.
As a result of the experimental studies performed, it has been determined that the proposed approach gives fast, accurate and reliable results.
Recently, the rapid development of word embedding and neural networks has brought new inspiration to various NLP and IR tasks.
In this paper, we describe a staged hybrid model combining Recurrent Convolutional Neural Networks (RCNN) with highway layers.
The highway network module is incorporated in the middle takes the output of the bi-directional Recurrent Neural Network (Bi-RNN) module in the first stage and provides the Convolutional Neural Network (CNN) module in the last stage with the input.
The experiment shows that our model outperforms common neural network models (CNN, RNN, Bi-RNN) on a sentiment analysis task.
Besides, the analysis of how sequence length influences the RCNN with highway layers shows that our model could learn good representation for the long text.
We develop a static complexity analysis for a higher-order functional language with structural list recursion.
The complexity of an expression is a pair consisting of a cost and a potential.
The former is defined to be the size of the expression's evaluation derivation in a standard big-step operational semantics.
The latter is a measure of the "future" cost of using the value of that expression.
A translation function tr maps target expressions to complexities.
Our main result is the following Soundness Theorem: If t is a term in the target language, then the cost component of tr(t) is an upper bound on the cost of evaluating t. The proof of the Soundness Theorem is formalized in Coq, providing certified upper bounds on the cost of any expression in the target language.
Existing works for extracting navigation objects from webpages focus on navigation menus, so as to reveal the information architecture of the site.
However, web 2.0 sites such as social networks, e-commerce portals etc. are making the understanding of the content structure in a web site increasingly difficult.
Dynamic and personalized elements such as top stories, recommended list in a webpage are vital to the understanding of the dynamic nature of web 2.0 sites.
To better understand the content structure in web 2.0 sites, in this paper we propose a new extraction method for navigation objects in a webpage.
Our method will extract not only the static navigation menus, but also the dynamic and personalized page-specific navigation lists.
Since the navigation objects in a webpage naturally come in blocks, we first cluster hyperlinks into different blocks by exploiting spatial locations of hyperlinks, the hierarchical structure of the DOM-tree and the hyperlink density.
Then we identify navigation objects from those blocks using the SVM classifier with novel features such as anchor text lengths etc.
Experiments on real-world data sets with webpages from various domains and styles verified the effectiveness of our method.
Normalization methods are a central building block in the deep learning toolbox.
They accelerate and stabilize training, while decreasing the dependence on manually tuned learning rate schedules.
When learning from multi-modal distributions, the effectiveness of batch normalization (BN), arguably the most prominent normalization method, is reduced.
As a remedy, we propose a more flexible approach: by extending the normalization to more than a single mean and variance, we detect modes of data on-the-fly, jointly normalizing samples that share common features.
We demonstrate that our method outperforms BN and other widely used normalization techniques in several experiments, including single and multi-task datasets.
Cloud computing provides engineers or scientists a place to run complex computing tasks.
Finding a workflow's deployment configuration in a cloud environment is not easy.
Traditional workflow scheduling algorithms were based on some heuristics, e.g. reliability greedy, cost greedy, cost-time balancing, etc., or more recently, the meta-heuristic methods, such as genetic algorithms.
These methods are very slow and not suitable for rescheduling in the dynamic cloud environment.
This paper introduces RIOT (Randomized Instance Order Types), a stochastic based method for workflow scheduling.
RIOT groups the tasks in the workflow into virtual machines via a probability model and then uses an effective surrogate-based method to assess a large amount of potential scheduling.
Experiments in dozens of study cases showed that RIOT executes tens of times faster than traditional methods while generating comparable results to other methods.
Given a large population, it is an intensive task to gather individual preferences over a set of alternatives and arrive at an aggregate or collective preference of the population.
We show that social network underlying the population can be harnessed to accomplish this task effectively, by sampling preferences of a small subset of representative nodes.
We first develop a Facebook app to create a dataset consisting of preferences of nodes and the underlying social network, using which, we develop models that capture how preferences are distributed among nodes in a typical social network.
We hence propose an appropriate objective function for the problem of selecting best representative nodes.
We devise two algorithms, namely, Greedy-min which provides a performance guarantee for a wide class of popular voting rules, and Greedy-sum which exhibits excellent performance in practice.
We compare the performance of these proposed algorithms against random-polling and popular centrality measures, and provide a detailed analysis of the obtained results.
Our analysis suggests that selecting representatives using social network information is advantageous for aggregating preferences related to personal topics (e.g., lifestyle), while random polling with a reasonable sample size is good enough for aggregating preferences related to social topics (e.g., government policies).
On a constant quest for inspiration, designers can become more effective with tools that facilitate their creative process and let them overcome design fixation.
This paper explores the practicality of applying neural style transfer as an emerging design tool for generating creative digital content.
To this aim, the present work explores a well-documented neural style transfer algorithm (Johnson 2016) in four experiments on four relevant visual parameters: number of iterations, learning rate, total variation, content vs. style weight.
The results allow a pragmatic recommendation of parameter configuration (number of iterations: 200 to 300, learning rate: 2e-1 to 4e-1, total variation: 1e-4 to 1e-8, content weights vs. style weights: 50:100 to 200:100) that saves extensive experimentation time and lowers the technical entry barrier.
With this rule-of-thumb insight, visual designers can effectively apply deep learning to create artistic visual variations of digital content.
This could enable designers to leverage AI for creating design works as state-of-the-art.
In this correspondence, we illustrate among other things the use of the stationarity property of the set of capacity-achieving inputs in capacity calculations.
In particular, as a case study, we consider a bit-patterned media recording channel model and formulate new lower and upper bounds on its capacity that yield improvements over existing results.
Inspired by the observation that the new bounds are tight at low noise levels, we also characterize the capacity of this model as a series expansion in the low-noise regime.
The key to these results is the realization of stationarity in the supremizing input set in the capacity formula.
While the property is prevalent in capacity formulations in the ergodic-theoretic literature, we show that this realization is possible in the Shannon-theoretic framework where a channel is defined as a sequence of finite-dimensional conditional probabilities, by defining a new class of consistent stationary and ergodic channels.
A self-learning optimal control algorithm for episodic fixed-horizon manufacturing processes with time-discrete control actions is proposed and evaluated on a simulated deep drawing process.
The control model is built during consecutive process executions under optimal control via reinforcement learning, using the measured product quality as reward after each process execution.
Prior model formulation, which is required by state-of-the-art algorithms from model predictive control and approximate dynamic programming, is therefore obsolete.
This avoids several difficulties namely in system identification, accurate modelling, and runtime complexity, that arise when dealing with processes subject to nonlinear dynamics and stochastic influences.
Instead of using pre-created process and observation models, value function-based reinforcement learning algorithms build functions of expected future reward, which are used to derive optimal process control decisions.
The expectation functions are learned online, by interacting with the process.
The proposed algorithm takes stochastic variations of the process conditions into account and is able to cope with partial observability.
A Q-learning-based method for adaptive optimal control of partially observable episodic fixed-horizon manufacturing processes is developed and studied.
The resulting algorithm is instantiated and evaluated by applying it to a simulated stochastic optimal control problem in metal sheet deep drawing.
We present a general framework and method for simultaneous detection and segmentation of an object in a video that moves (or comes into view of the camera) at some unknown time in the video.
The method is an online approach based on motion segmentation, and it operates under dynamic backgrounds caused by a moving camera or moving nuisances.
The goal of the method is to detect and segment the object as soon as it moves.
Due to stochastic variability in the video and unreliability of the motion signal, several frames are needed to reliably detect the object.
The method is designed to detect and segment with minimum delay subject to a constraint on the false alarm rate.
The method is derived as a problem of Quickest Change Detection.
Experiments on a dataset show the effectiveness of our method in minimizing detection delay subject to false alarm constraints.
Coronary heart disease is one of the top rank leading cause of mortality in the world which can be because of plaque burden inside the arteries.
Intravascular Ultrasound (IVUS) has been recognized as power- ful imaging technology which captures the real time and high resolution images of the coronary arteries and can be used for the analysis of these plaques.
The IVUS segmentation involves the extraction of two arterial walls components namely, lumen and media.
In this paper, we investi- gate the effectiveness of Convolutional Neural Networks including U-Net to segment ultrasound scans of arteries.
In particular, the proposed seg- mentation network was built based on the the U-Net with the VGG16 encoder.
Experiments were done for evaluating the proposed segmen- tation architecture which show promising quantitative and qualitative results.
Imitation learning has traditionally been applied to learn a single task from demonstrations thereof.
The requirement of structured and isolated demonstrations limits the scalability of imitation learning approaches as they are difficult to apply to real-world scenarios, where robots have to be able to execute a multitude of tasks.
In this paper, we propose a multi-modal imitation learning framework that is able to segment and imitate skills from unlabelled and unstructured demonstrations by learning skill segmentation and imitation learning jointly.
The extensive simulation results indicate that our method can efficiently separate the demonstrations into individual skills and learn to imitate them using a single multi-modal policy.
The video of our experiments is available at http://sites.google.com/view/nips17intentiongan
In this paper we analyze the Friedkin-Johnsen model of opinions when the coefficients weighting the agent susceptibilities to interpersonal influence approach 1.
We will show that in this case, under suitable assumptions, the model converges to a quasi-consensus condition among the agents.
In general the achieved consensus value will be different to the one obtained by the corresponding DeGroot model
The steady increase in the volume of indicators of compromise (IoC) as well as their volatile nature makes their processing challenging.
Once compromised infrastructures are cleaned up, threat actors are moving to on to other target infrastructures or simply changing attack strategies.
To ease the evaluation of IoCs as well as to harness the combined analysis capabilities, threat intelligence sharing platforms were introduced in order to foster collaboration on a community level.
In this paper, the open-source threat intelligence platform MISP is used to implement and showcase a generic scoring model for decaying IoCs shared within MISP communities matching their heterogeneous objectives.
The model takes into account existing meta-information shared along with indicators of compromise,facilitating the decision making process for machines in regards to the validity of the shared indicator of compromise.
The model is applied on common use-cases that are normally encountered during incident response.
Motivated by a project to create a system for people who are deaf or hard-of-hearing that would use automatic speech recognition (ASR) to produce real-time text captions of spoken English during in-person meetings with hearing individuals, we have augmented a transcript of the Switchboard conversational dialogue corpus with an overlay of word-importance annotations, with a numeric score for each word, to indicate its importance to the meaning of each dialogue turn.
Further, we demonstrate the utility of this corpus by training an automatic word importance labeling model; our best performing model has an F-score of 0.60 in an ordinal 6-class word-importance classification task with an agreement (concordance correlation coefficient) of 0.839 with the human annotators (agreement score between annotators is 0.89).
Finally, we discuss our intended future applications of this resource, particularly for the task of evaluating ASR performance, i.e.
creating metrics that predict ASR-output caption text usability for DHH users better thanWord Error Rate (WER).
Boolean satisfiability (SAT) has an extensive application domain in computer science, especially in electronic design automation applications.
Circuit synthesis, optimization, and verification problems can be solved by transforming original problems to SAT problems.
However, the SAT problem is known as NP-complete, which means there is no efficient method to solve it.
Therefore, an efficient SAT solver to enhance the performance is always desired.
We propose a hardware acceleration method for SAT problems.
By surveying the properties of SAT problems and the decoding of low-density parity-check (LDPC) codes, a special class of error-correcting codes, we discover that both of them are constraint satisfaction problems.
The belief propagation algorithm has been successfully applied to the decoding of LDPC, and the corresponding decoder hardware designs are extensively studied.
Therefore, we proposed a belief propagation based algorithm to solve SAT problems.
With this algorithm, the SAT solver can be accelerated by hardware.
A software simulator is implemented to verify the proposed algorithm and the performance improvement is estimated.
Our experiment results show that time complexity does not increase with the size of SAT problems and the proposed method can achieve at least 30x speedup compared to MiniSat.
In this paper we give a compact presentation of the theory of abstract spaces for convolutional codes and convolutional encoders, and show a connection between them that seems to be missing in the literature.
We use it for a short proof of two facts: the size of a convolutional encoder of a polynomial matrix is at least its inner degree, and the minimal encoder has the size of the external degree if the matrix is reduced.
Conference publications in computer science (CS) have attracted scholarly attention due to their unique status as a main research outlet unlike other science fields where journals are dominantly used for communicating research findings.
One frequent research question has been how different conference and journal publications are, considering a paper as a unit of analysis.
This study takes an author-based approach to analyze publishing patterns of 517,763 scholars who have ever published both in CS conferences and journals for the last 57 years, as recorded in DBLP.
The analysis shows that the majority of CS scholars tend to make their scholarly debut, publish more papers, and collaborate with more coauthors in conferences than in journals.
Importantly, conference papers seem to serve as a distinct channel of scholarly communication, not a mere preceding step to journal publications: coauthors and title words of authors across conferences and journals tend not to overlap much.
This study corroborates findings of previous studies on this topic from a distinctive perspective and suggests that conference authorship in CS calls for more special attention from scholars and administrators outside CS who have focused on journal publications to mine authorship data and evaluate scholarly performance.
Despite the growing attention of researcher, healthcare managers and policy makers, data gathering and information management practices are largely untheorized areas.
In this work are presented and discussed some early-stage conceptualizations: Patient-Generated Health Data (PGHD), Observations of Daily Living (ODLs) and Personal Health Information Management (PHIM).
As I shall try to demonstrate, these labels are not neutral rather they underpin quite different perspectives with respect to health, patient-doctor relationship, and the status of data.
Modeling fashion compatibility is challenging due to its complexity and subjectivity.
Existing work focuses on predicting compatibility between product images (e.g. an image containing a t-shirt and an image containing a pair of jeans).
However, these approaches ignore real-world 'scene' images (e.g. selfies); such images are hard to deal with due to their complexity, clutter, variations in lighting and pose (etc.) but on the other hand could potentially provide key context (e.g. the user's body type, or the season) for making more accurate recommendations.
In this work, we propose a new task called 'Complete the Look', which seeks to recommend visually compatible products based on scene images.
We design an approach to extract training data for this task, and propose a novel way to learn the scene-product compatibility from fashion or interior design images.
Our approach measures compatibility both globally and locally via CNNs and attention mechanisms.
Extensive experiments show that our method achieves significant performance gains over alternative systems.
Human evaluation and qualitative analysis are also conducted to further understand model behavior.
We hope this work could lead to useful applications which link large corpora of real-world scenes with shoppable products.
A new characteristic of paired nodes in a directed weight complex network is considered.
A method (named as K-method) of the characteristics calculation for complex networks is proposed.
The method is based on transforming the initial network with the subsequent application of the Kirchhoff rules.
The scope of the method for sparse complex networks is proposed.
The nodes of these complex networks are concepts of the real world, and the connections have a cause-effect character of the so-called "cognitive maps".
Two new characteristics of concept nodes having a semantic interpretation are proposed, namely "pressure" and "influence" taking into account the influence of all nodes on each other.
In this paper, we study the implications of the commonplace assumption that most social media studies make with respect to the nature of message shares (such as retweets) as a predominantly positive interaction.
By analyzing two large longitudinal Brazilian Twitter datasets containing 5 years of conversations on two polarizing topics - Politics and Sports - we empirically demonstrate that groups holding antagonistic views can actually retweet each other more often than they retweet other groups.
We show that assuming retweets as endorsement interactions can lead to misleading conclusions with respect to the level of antagonism among social communities, and that this apparent paradox is explained in part by the use of retweets to quote the original content creator out of the message's original temporal context, for humor and criticism purposes.
As a consequence, messages diffused on online media can have their polarity reversed over time, what poses challenges for social and computer scientists aiming to classify and track opinion groups on online media.
On the other hand, we found that the time users take to retweet a message after it has been originally posted can be a useful signal to infer antagonism in social platforms, and that surges of out-of-context retweets correlate with sentiment drifts triggered by real-world events.
We also discuss how such evidences can be embedded in sentiment analysis models.
Network technologies are traditionally centered on wireline solutions.
Wireless broadband technologies nowadays provide unlimited broadband usage to users that have been previously offered simply to wireline users.
In this paper, we discuss some of the upcoming standards of one of the emerging wireless broadband technology i.e.IEEE 802.11.
The newest and the emerging standards fix technology issues or add functionality that will be expected to overcome many of the current standing problems with IEEE 802.11.
Spreadsheets that are informally created are harder to test than they should be.
Simple cross-foot checks or being easily readable are modest but attainable goals for every spreadsheet developer.
This paper lists some tips on building self-checking into a spreadsheet in order to provide more confidence to the reader that a spreadsheet is robust.
We study a semi-supervised learning method based on the similarity graph and RegularizedLaplacian.
We give convenient optimization formulation of the Regularized Laplacian method and establishits various properties.
In particular, we show that the kernel of the methodcan be interpreted in terms of discrete and continuous time random walks and possesses several importantproperties of proximity measures.
Both optimization and linear algebra methods can be used for efficientcomputation of the classification functions.
We demonstrate on numerical examples that theRegularized Laplacian method is competitive with respect to the other state of the art semi-supervisedlearning methods.
Historically studies of behaviour on networks have focused on the behaviour of individuals (node-based) or on the aggregate behaviour of the entire network.
We propose a new method to decompose a temporal network into macroscale components and to analyse the behaviour of these components, or collectives of nodes, across time.
This method utilises all available information in the temporal network (i.e. no temporal aggregation), combining both topological and temporal structure using temporal motifs and inter-event times.
This allows us create an embedding of a temporal network in order to describe behaviour over time and at different timescales.
We illustrate this method using an example of digital communication data collected from an online social network.
With the growing demand of real-time traffic monitoring nowadays, software-based image processing can hardly meet the real-time data processing requirement due to the serial data processing nature.
In this paper, the implementation of a hardware-based feature detection and networking system prototype for real-time traffic monitoring as well as data transmission is presented.
The hardware architecture of the proposed system is mainly composed of three parts: data collection, feature detection, and data transmission.
Overall, the presented prototype can tolerate a high data rate of about 60 frames per second.
By integrating the feature detection and data transmission functions, the presented system can be further developed for various VANET application scenarios to improve road safety and traffic efficiency.
For example, detection of vehicles that violate traffic rules, parking enforcement, etc.
Video semantic segmentation has been one of the research focus in computer vision recently.
It serves as a perception foundation for many fields such as robotics and autonomous driving.
The fast development of semantic segmentation attributes enormously to the large scale datasets, especially for the deep learning related methods.
Currently, there already exist several semantic segmentation datasets for complex urban scenes, such as the Cityscapes and CamVid datasets.
They have been the standard datasets for comparison among semantic segmentation methods.
In this paper, we introduce a new high resolution UAV video semantic segmentation dataset as complement, UAVid.
Our UAV dataset consists of 30 video sequences capturing high resolution images.
In total, 300 images have been densely labelled with 8 classes for urban scene understanding task.
Our dataset brings out new challenges.
We provide several deep learning baseline methods, among which the proposed novel Multi-Scale-Dilation net performs the best via multi-scale feature extraction.
We have also explored the usability of sequence data by leveraging on CRF model in both spatial and temporal domain.
In this paper, we propose a single-agent modal logic framework for reasoning about goal-direct "knowing how" based on ideas from linguistics, philosophy, modal logic and automated planning.
We first define a modal language to express "I know how to guarantee phi given psi" with a semantics not based on standard epistemic models but labelled transition systems that represent the agent's knowledge of his own abilities.
A sound and complete proof system is given to capture the valid reasoning patterns about "knowing how" where the most important axiom suggests its compositional nature.
Neural machine translation (NMT) models are usually trained with the word-level loss using the teacher forcing algorithm, which not only evaluates the translation improperly but also suffers from exposure bias.
Sequence-level training under the reinforcement framework can mitigate the problems of the word-level loss, but its performance is unstable due to the high variance of the gradient estimation.
On these grounds, we present a method with a differentiable sequence-level training objective based on probabilistic n-gram matching which can avoid the reinforcement framework.
In addition, this method performs greedy search in the training which uses the predicted words as context just as at inference to alleviate the problem of exposure bias.
Experiment results on the NIST Chinese-to-English translation tasks show that our method significantly outperforms the reinforcement-based algorithms and achieves an improvement of 1.5 BLEU points on average over a strong baseline system.
In this work, we investigated the contribution of the glottal waveform in human vocal emotion expressing.
Seven emotional states including moderate and intense versions of three emotional families as anger, joy, and sadness, plus a neutral state are considered, with speech samples in Mandarin Chinese.
The glottal waveform extracted from speech samples of different emotion states are first analyzed in both time domain and frequency domain to discover their differences.
Comparative emotion classifications are then taken out based on features extracted from original whole speech signal and only glottal wave signal.
In experiments of generation of a performance-driven hierarchical classifier architecture, and pairwise classification on individual emotional states, the low difference between accuracies obtained from speech signal and glottal signal proved that a majority of emotional cues in speech could be conveyed through glottal waveform.
The best distinguishable emotional pair by glottal waveform is intense anger against moderate sadness, with the accuracy of 92.45%.
It is also concluded in this work that glottal waveform represent better valence cues than arousal cues of emotion.
Despite the tremendous empirical success of neural models in natural language processing, many of them lack the strong intuitions that accompany classical machine learning approaches.
Recently, connections have been shown between convolutional neural networks (CNNs) and weighted finite state automata (WFSAs), leading to new interpretations and insights.
In this work, we show that some recurrent neural networks also share this connection to WFSAs.
We characterize this connection formally, defining rational recurrences to be recurrent hidden state update functions that can be written as the Forward calculation of a finite set of WFSAs.
We show that several recent neural models use rational recurrences.
Our analysis provides a fresh view of these models and facilitates devising new neural architectures that draw inspiration from WFSAs.
We present one such model, which performs better than two recent baselines on language modeling and text classification.
Our results demonstrate that transferring intuitions from classical models like WFSAs can be an effective approach to designing and understanding neural models.
Rapid miniaturization and cost reduction of computing, along with the availability of wearable and implantable physiological sensors have led to the growth of human Body Area Network (BAN) formed by a network of such sensors and computing devices.
One promising application of such a network is wearable health monitoring where the collected data from the sensors would be transmitted and analyzed to assess the health of a person.
Typically, the devices in a BAN are connected through wireless (WBAN), which suffers from energy inefficiency due to the high-energy consumption of wireless transmission.
Human Body Communication (HBC) uses the relatively low loss human body as the communication medium to connect these devices, promising order(s) of magnitude better energy-efficiency and built-in security compared to WBAN.
In this paper, we demonstrate a health monitoring device and system built using Commercial-Off-The- Shelf (COTS) sensors and components, that can collect data from physiological sensors and transmit it through a) intra-body HBC to another device (hub) worn on the body or b) upload health data through HBC-based human-machine interaction to an HBC capable machine.
The system design constraints and signal transfer characteristics for the implemented HBC-based wearable health monitoring system are measured and analyzed, showing reliable connectivity with >8x power savings compared to Bluetooth lowenergy (BTLE).
In the paper, the control problem with limitations on the magnitude and rate of the control action in aircraft control systems, is studied.
Existence of hidden oscillations in the case of actuator position and rate limitations is demonstrated by the examples of piloted aircraft pilot involved oscillations (PIO) phenomenon and the airfoil flutter suppression system.
In the real world, a learning system could receive an input that looks nothing like anything it has seen during training, and this can lead to unpredictable behaviour.
We thus need to know whether any given input belongs to the population distribution of the training data to prevent unpredictable behaviour in deployed systems.
A recent surge of interest on this problem has led to the development of sophisticated techniques in the deep learning literature.
However, due to the absence of a standardized problem formulation or an exhaustive evaluation, it is not evident if we can rely on these methods in practice.
What makes this problem different from a typical supervised learning setting is that we cannot model the diversity of out-of-distribution samples in practice.
The distribution of outliers used in training may not be the same as the distribution of outliers encountered in the application.
Therefore, classical approaches that learn inliers vs. outliers with only two datasets can yield optimistic results.
We introduce OD-test, a three-dataset evaluation scheme as a practical and more reliable strategy to assess progress on this problem.
The OD-test benchmark provides a straightforward means of comparison for methods that address the out-of-distribution sample detection problem.
We present an exhaustive evaluation of a broad set of methods from related areas on image classification tasks.
Furthermore, we show that for realistic applications of high-dimensional images, the existing methods have low accuracy.
Our analysis reveals areas of strength and weakness of each method.
Abstract Meaning Representation (AMR) annotation efforts have mostly focused on English.
In order to train parsers on other languages, we propose a method based on annotation projection, which involves exploiting annotations in a source language and a parallel corpus of the source language and a target language.
Using English as the source language, we show promising results for Italian, Spanish, German and Chinese as target languages.
Besides evaluating the target parsers on non-gold datasets, we further propose an evaluation method that exploits the English gold annotations and does not require access to gold annotations for the target languages.
This is achieved by inverting the projection process: a new English parser is learned from the target language parser and evaluated on the existing English gold standard.
In this paper, a new offline actor-critic learning algorithm is introduced: Sampled Policy Gradient (SPG).
SPG samples in the action space to calculate an approximated policy gradient by using the critic to evaluate the samples.
This sampling allows SPG to search the action-Q-value space more globally than deterministic policy gradient (DPG), enabling it to theoretically avoid more local optima.
SPG is compared to Q-learning and the actor-critic algorithms CACLA and DPG in a pellet collection task and a self play environment in the game Agar.io.
The online game Agar.io has become massively popular on the internet due to intuitive game design and the ability to instantly compete against players around the world.
From the point of view of artificial intelligence this game is also very intriguing: The game has a continuous input and action space and allows to have diverse agents with complex strategies compete against each other.
The experimental results show that Q-Learning and CACLA outperform a pre-programmed greedy bot in the pellet collection task, but all algorithms fail to outperform this bot in a fighting scenario.
The SPG algorithm is analyzed to have great extendability through offline exploration and it matches DPG in performance even in its basic form without extensive sampling.
We introduce a universe of regular datatypes with variable binding information, for which we define generic formation and elimination (i.e. induction /recursion) operators.
We then define a generic alpha-equivalence relation over the types of the universe based on name-swapping, and derive iteration and induction principles which work modulo alpha-conversion capturing Barendregt's Variable Convention.
We instantiate the resulting framework so as to obtain the Lambda Calculus and System F, for which we derive substitution operations and substitution lemmas for alpha-conversion and substitution composition.
The whole work is carried out in Constructive Type Theory and machine-checked by the system Agda.
While RANSAC-based methods are robust to incorrect image correspondences (outliers), their hypothesis generators are not robust to correct image correspondences (inliers) with positional error (noise).
This slows down their convergence because hypotheses drawn from a minimal set of noisy inliers can deviate significantly from the optimal model.
This work addresses this problem by introducing ANSAC, a RANSAC-based estimator that accounts for noise by adaptively using more than the minimal number of correspondences required to generate a hypothesis.
ANSAC estimates the inlier ratio (the fraction of correct correspondences) of several ranked subsets of candidate correspondences and generates hypotheses from them.
Its hypothesis-generation mechanism prioritizes the use of subsets with high inlier ratio to generate high-quality hypotheses.
ANSAC uses an early termination criterion that keeps track of the inlier ratio history and terminates when it has not changed significantly for a period of time.
The experiments show that ANSAC finds good homography and fundamental matrix estimates in a few iterations, consistently outperforming state-of-the-art methods.
Automated program repair techniques, which target to generating correct patches for real world defects automatically, have gained a lot of attention in the last decade.
Many different techniques and tools have been proposed and developed.
However, even the most sophisticated program repair techniques can only repair a small portion of defects while producing a lot of incorrect patches.
A possible reason for this low performance is that the test suites of real world programs are usually too weak to guarantee the behavior of the program.
To understand to what extent defects can be fixed with weak test suites, we analyzed 50 real world defects from Defects4J, in which we found that up to 84% of them could be correctly fixed.
This result suggests that there is plenty of space for current automated program repair techniques to improve.
Furthermore, we summarized seven fault localization strategies and seven patch generation strategies that were useful in localizing and fixing these defects, and compared those strategies with current repair techniques.
The results indicate potential directions to improve automatic program repair in the future research.
One of the activities of the Pacific Rim Applications and Grid Middleware Assembly (PRAGMA) is fostering Virtual Biodiversity Expeditions (VBEs) by bringing domain scientists and cyber infrastructure specialists together as a team.
Over the past few years PRAGMA members have been collaborating on virtualizing the Lifemapper software.
Virtualization and cloud computing have introduced great flexibility and efficiency into IT projects.
Virtualization provides application scalability, maximizes resources utilization, and creates a more efficient, agile, and automated infrastructure.
However, there are downsides to the complexity inherent in these environments, including the need for special techniques to deploy cluster hosts, dependence on virtual environments, and challenging application installation, management, and configuration.
In this paper, we report on progress of the Lifemapper virtualization framework focused on a reproducible and highly configurable infrastructure capable of fast deployment.
A key contribution of this work is describing the practical experience in taking a complex, clustered, domain-specific, data analysis and simulation system and making it available to operate on a variety of system configurations.
Uses of this portability range from whole cluster replication to teaching and experimentation on a single laptop.
System virtualization is used to practically define and make portable the full application stack, including all of its complex set of supporting software.
The need for customizable properties in autonomous robotic platforms, such as in-home nursing care for the elderly and parallel implementations of human-to-machine control interfaces creates an opportunity to introduce methods deploying commonly available mobile devices running robotic command applications in managed code.
This paper will discuss a human-to-machine interface and demonstrate a prototype consisting of a mobile device running a configurable application communicating with a mobile robot using a managed, type-safe language, C#.NET, over Bluetooth.
Programs that transform other programs often require access to the internal structure of the program to be transformed.
This is at odds with the usual extensional view of functional programming, as embodied by the lambda calculus and SK combinator calculus.
The recently-developed SF combinator calculus offers an alternative, intensional model of computation that may serve as a foundation for developing principled languages in which to express intensional computation, including program transformation.
Until now there have been no static analyses for reasoning about or verifying programs written in SF-calculus.
We take the first step towards remedying this by developing a formulation of the popular control flow analysis 0CFA for SK-calculus and extending it to support SF-calculus.
We prove its correctness and demonstrate that the analysis is invariant under the usual translation from SK-calculus into SF-calculus.
Wearable robotic hand rehabilitation devices can allow greater freedom and flexibility than their workstation-like counterparts.
However, the field is generally lacking effective methods by which the user can operate the device: such controls must be effective, intuitive, and robust to the wide range of possible impairment patterns.
Even when focusing on a specific condition, such as stroke, the variety of encountered upper limb impairment patterns means that a single sensing modality, such as electromyography (EMG), might not be sufficient to enable controls for a broad range of users.
To address this significant gap, we introduce a multimodal sensing and interaction paradigm for an active hand orthosis.
In our proof-of-concept implementation, EMG is complemented by other sensing modalities, such as finger bend and contact pressure sensors.
We propose multimodal interaction methods that utilize this sensory data as input, and show they can enable tasks for stroke survivors who exhibit different impairment patterns.
We believe that robotic hand orthoses developed as multimodal sensory platforms with help address some of the key challenges in physical interaction with the user.
In recent years, sharing of security information among organizations, particularly information on both successful and failed security breaches, has been proposed as a method for improving the state of cybersecurity.
However, there is a conflict between individual and social goals in these agreements: despite the benefits of making such information available, the associated disclosure costs (e.g., drop in market value and loss of reputation) act as a disincentive for firms' full disclosure.
In this work, we take a game theoretic approach to understanding firms' incentives for disclosing their security information given such costs.
We propose a repeated game formulation of these interactions, allowing for the design of inter-temporal incentives (i.e., conditioning future cooperation on the history of past interactions).
Specifically, we show that a rating/assessment system can play a key role in enabling the design of appropriate incentives for supporting cooperation among firms.
We further show that in the absence of a monitor, similar incentives can be designed if participating firms are provided with a communication platform, through which they can share their beliefs about others' adherence to the agreement.
Recently there has been significant interest in training machine-learning models at low precision: by reducing precision, one can reduce computation and communication by one order of magnitude.
We examine training at reduced precision, both from a theoretical and practical perspective, and ask: is it possible to train models at end-to-end low precision with provable guarantees?
Can this lead to consistent order-of-magnitude speedups?
We present a framework called ZipML to answer these questions.
For linear models, the answer is yes.
We develop a simple framework based on one simple but novel strategy called double sampling.
Our framework is able to execute training at low precision with no bias, guaranteeing convergence, whereas naive quantization would introduce significant bias.
We validate our framework across a range of applications, and show that it enables an FPGA prototype that is up to 6.5x faster than an implementation using full 32-bit precision.
We further develop a variance-optimal stochastic quantization strategy and show that it can make a significant difference in a variety of settings.
When applied to linear models together with double sampling, we save up to another 1.7x in data movement compared with uniform quantization.
When training deep networks with quantized models, we achieve higher accuracy than the state-of-the-art XNOR-Net.
Finally, we extend our framework through approximation to non-linear models, such as SVM.
We show that, although using low-precision data induces bias, we can appropriately bound and control the bias.
We find in practice 8-bit precision is often sufficient to converge to the correct solution.
Interestingly, however, in practice we notice that our framework does not always outperform the naive rounding approach.
We discuss this negative result in detail.
This paper proposes an image-processing-based method for personalization of calorie consumption assessment during exercising.
An experiment is carried out where several actions are required in an exercise called broadcast gymnastics, especially popular in Japan and China.
We use Kinect, which captures body actions by separating the body into joints and segments that contain them, to monitor body movements to test the velocity of each body joint and capture the subject's image for calculating the mass of each body joint that differs for each subject.
By a kinetic energy formula, we obtain the kinetic energy of each body joint, and calories consumed during exercise are calculated in this process.
We evaluate the performance of our method by benchmarking it to Fitbit, a smart watch well-known for health monitoring during exercise.
The experimental results in this paper show that our method outperforms a state-of-the-art calorie assessment method, which we base on and improve, in terms of the error rate from Fitbit's ground-truth values.
This paper proposes a novel entropy encoding technique for lossless data compression.
Representing a message string by its lexicographic index in the permutations of its symbols results in a compressed version matching Shannon entropy of the message.
Commercial data compression standards make use of Huffman or arithmetic coding at some stage of the compression process.
In the proposed method, like arithmetic coding entire string is mapped to an integer but is not based on fractional numbers.
Unlike both arithmetic and Huffman coding no prior entropy model of the source is required.
Simple intuitive algorithm based on multinomial coefficients is developed for entropy encoding that adoptively uses low number of bits for more frequent symbols.
Correctness of the algorithm is demonstrated by an example.
Microfluidic devices are utilized to control and direct flow behavior in a wide variety of applications, particularly in medical diagnostics.
A particularly popular form of microfluidics -- called inertial microfluidic flow sculpting -- involves placing a sequence of pillars to controllably deform an initial flow field into a desired one.
Inertial flow sculpting can be formally defined as an inverse problem, where one identifies a sequence of pillars (chosen, with replacement, from a finite set of pillars, each of which produce a specific transformation) whose composite transformation results in a user-defined desired transformation.
Endemic to most such problems in engineering, inverse problems are usually quite computationally intractable, with most traditional approaches based on search and optimization strategies.
In this paper, we pose this inverse problem as a Reinforcement Learning (RL) problem.
We train a DoubleDQN agent to learn from this environment.
The results suggest that learning is possible using a DoubleDQN model with the success frequency reaching 90% in 200,000 episodes and the rewards converging.
While most of the results are obtained by fixing a particular target flow shape to simplify the learning problem, we later demonstrate how to transfer the learning of an agent based on one target shape to another, i.e. from one design to another and thus be useful for a generic design of a flow shape.
The emergence of smartwatches poses new challenges to information security.
Although there are mature touch-based authentication methods for smartphones, the effectiveness of using these methods on smartwatches is still unclear.
We conducted a user study (n=16) to evaluate how authentication methods (PIN and Pattern), UIs (Square and Circular), and display sizes (38mm and 42mm) affect authentication accuracy, speed, and security.
Circular UIs are tailored to smartwatches with fewer UI elements.
Results show that 1) PIN is more accurate and secure than Pattern; 2) Pattern is much faster than PIN; 3) Square UIs are more secure but less accurate than Circular UIs; 4) display size does not affect accuracy or speed, but security; 5) Square PIN is the most secure method of all.
The study also reveals a security concern that participants' favorite method is not the best in any of the measures.
We finally discuss implications for future touch-based smartwatch authentication design.
Point-Of-Interest (POI) recommendation aims to mine a user's visiting history and find her/his potentially preferred places.
Although location recommendation methods have been studied and improved pervasively, the challenges w.r.t employing various influences including temporal aspect still remain.
Inspired by the fact that time includes numerous granular slots (e.g. minute, hour, day, week and etc.
), in this paper, we define a new problem to perform recommendation through exploiting all diversified temporal factors.
In particular, we argue that most existing methods only focus on a limited number of time-related features and neglect others.
Furthermore, considering a specific granularity (e.g. time of a day) in recommendation cannot always apply to each user or each dataset.
To address the challenges, we propose a probabilistic generative model, named after Multi-aspect Time-related Influence (MATI) to promote POI recommendation.
We also develop a novel optimization algorithm based on Expectation Maximization (EM).
Our MATI model firstly detects a user's temporal multivariate orientation using her check-in log in Location-based Social Networks(LBSNs).
It then performs recommendation using temporal correlations between the user and proposed locations.
Our method is adaptable to various types of recommendation systems and can work efficiently in multiple time-scales.
Extensive experimental results on two large-scale LBSN datasets verify the effectiveness of our method over other competitors.
The emergence of academic search engines (mainly Google Scholar and Microsoft Academic Search) that aspire to index the entirety of current academic knowledge has revived and increased interest in the size of the academic web.
The main objective of this paper is to propose various methods to estimate the current size (number of indexed documents) of Google Scholar (May 2014) and to determine its validity, precision and reliability.
To do this, we present, apply and discuss three empirical methods: an external estimate based on empirical studies of Google Scholar coverage, and two internal estimate methods based on direct, empty and absurd queries, respectively.
The results, despite providing disparate values, place the estimated size of Google Scholar at around 160 to 165 million documents.
However, all the methods show considerable limitations and uncertainties due to inconsistencies in the Google Scholar search functionalities.
Monitoring the number of insect pests is a crucial component in pheromone-based pest management systems.
In this paper, we propose an automatic detection pipeline based on deep learning for identifying and counting pests in images taken inside field traps.
Applied to a commercial codling moth dataset, our method shows promising performance both qualitatively and quantitatively.
Compared to previous attempts at pest detection, our approach uses no pest-specific engineering which enables it to adapt to other species and environments with minimal human effort.
It is amenable to implementation on parallel hardware and therefore capable of deployment in settings where real-time performance is required.
The IoT area has grown significantly in the last few years and is expected to reach a gigantic amount of 50 billion devices by 2020.
The appearance of serverless architectures, specifically highlighting FaaS, raises the question of the of using such in IoT environments.
Combining IoT with a serverless architectural design can be effective when trying to make use of the local processing power that exists in a local network of IoT devices and creating a fog layer that leverages computational capabilities that are closer to the end-user.
In this approach, which is placed between the device and the serverless function, when a device requests for the execution of a serverless function will decide based on previous metrics of execution if the serverless function should be executed locally, in the fog layer of a local network of IoT devices, or if it should be executed remotely, in one of the available cloud servers.
Therefore, this approach allows to dynamically allocating functions to the most suitable layer.
Binary classification is one of the most common problem in machine learning.
It consists in predicting whether a given element belongs to a particular class.
In this paper, a new algorithm for binary classification is proposed using a hypergraph representation.
Each element to be classified is partitioned according to its interactions with the training set.
For each class, a seminorm over the training set partition is learnt to represent the distribution of evidence supporting this class.
The method is agnostic to data representations, can work with multiple data sources or in non-metric spaces, and accommodates with missing values.
As a result, it drastically reduces the need for data preprocessing or feature engineering.
Empirical validation demonstrates its high potential on a wide range of well-known datasets and the results are compared to the state-of-the-art.
The time complexity is given and empirically validated.
Its capacity to provide good performances without hyperparameter tuning compared to standard classification methods is studied.
Finally, the limitation of the model space is discussed, and some potential solutions proposed.
The notion of o-polynomial comes from finite projective geometry.
In 2011 and later, it has been shown that those objects play an important role in symmetric cryptography and coding theory to design bent Boolean functions, bent vectorial Boolean functions, semi-bent functions and to construct good linear codes.
In this note, we characterize o-polynomials by the Walsh transform of the associated vectorial functions.
The ubiquity of online fashion shopping demands effective recommendation services for customers.
In this paper, we study two types of fashion recommendation: (i) suggesting an item that matches existing components in a set to form a stylish outfit (a collection of fashion items), and (ii) generating an outfit with multimodal (images/text) specifications from a user.
To this end, we propose to jointly learn a visual-semantic embedding and the compatibility relationships among fashion items in an end-to-end fashion.
More specifically, we consider a fashion outfit to be a sequence (usually from top to bottom and then accessories) and each item in the outfit as a time step.
Given the fashion items in an outfit, we train a bidirectional LSTM (Bi-LSTM) model to sequentially predict the next item conditioned on previous ones to learn their compatibility relationships.
Further, we learn a visual-semantic space by regressing image features to their semantic representations aiming to inject attribute and category information as a regularization for training the LSTM.
The trained network can not only perform the aforementioned recommendations effectively but also predict the compatibility of a given outfit.
We conduct extensive experiments on our newly collected Polyvore dataset, and the results provide strong qualitative and quantitative evidence that our framework outperforms alternative methods.
Revision control is a vital component in the collaborative development of artifacts such as software code and multimedia.
While revision control has been widely deployed for text files, very few attempts to control the versioning of binary files can be found in the literature.
This can be inconvenient for graphics applications that use a significant amount of binary data, such as images, videos, meshes, and animations.
Existing strategies such as storing whole files for individual revisions or simple binary deltas, respectively consume significant storage and obscure semantic information.
To overcome these limitations, in this paper we present a revision control system for digital images that stores revisions in form of graphs.
Besides, being integrated with Git, our revision control system also facilitates artistic creation processes in common image editing and digital painting workflows.
A preliminary user study demonstrates the usability of the proposed system.
The paper investigates epistemic properties of information flow under communication protocols with a given topological structure of the communication network.
The main result is a sound and complete logical system that describes all such properties.
The system consists of a variation of the multi-agent epistemic logic S5 extended by a new network-specific Gateway axiom.
With the advancement of research in word sense disambiguation and deep learning, large sense-annotated datasets are increasingly important for training supervised systems.
However, gathering high-quality sense-annotated data for as many instances as possible is an arduous task.
This has led to the proliferation of automatic and semi-automatic methods for overcoming the so-called knowledge-acquisition bottleneck.
In this paper we present an overview of currently available sense-annotated corpora, both manually and automatically constructed, for various languages and resources (i.e.WordNet, Wikipedia, BabelNet).
General statistics and specific features of each sense-annotated dataset are also provided.
Non-extractive fish abundance estimation with the aid of visual analysis has drawn increasing attention.
Unstable illumination, ubiquitous noise and low frame rate video capturing in the underwater environment, however, make conventional tracking methods unreliable.
In this paper, we present a multiple fish tracking system for low-contrast and low-frame-rate stereo videos with the use of a trawl-based underwater camera system.
An automatic fish segmentation algorithm overcomes the low-contrast issues by adopting a histogram backprojection approach on double local-thresholded images to ensure an accurate segmentation on the fish shape boundaries.
Built upon a reliable feature-based object matching method, a multiple-target tracking algorithm via a modified Viterbi data association is proposed to overcome the poor motion continuity and frequent entrance/exit of fish targets under low-frame-rate scenarios.
In addition, a computationally efficient block-matching approach performs successful stereo matching, which enables an automatic fish-body tail compensation to greatly reduce segmentation error and allows for an accurate fish length measurement.
Experimental results show that an effective and reliable tracking performance for multiple live fish with underwater stereo cameras is achieved.
Shadow detection and shadow removal are fundamental and challenging tasks, requiring an understanding of the global image semantics.
This paper presents a novel deep neural network design for shadow detection and removal by analyzing the image context in a direction-aware manner.
To achieve this, we first formulate the direction-aware attention mechanism in a spatial recurrent neural network (RNN) by introducing attention weights when aggregating spatial context features in the RNN.
By learning these weights through training, we can recover direction-aware spatial context (DSC) for detecting and removing shadows.
This design is developed into the DSC module and embedded in a convolutional neural network (CNN) to learn the DSC features in different levels.
Moreover, we design a weighted cross entropy loss to make effective the training for shadow detection and further adopt the network for shadow removal by using a Euclidean loss function and formulating a color transfer function to address the color and luminosity inconsistency in the training pairs.
We employ two shadow detection benchmark datasets and two shadow removal benchmark datasets, and perform various experiments to evaluate our method.
Experimental results show that our method clearly outperforms state-of-the-art methods for both shadow detection and shadow removal.
In an increasingly polarized world, demagogues who reduce complexity down to simple arguments based on emotion are gaining in popularity.
Are opinions and online discussions falling into demagoguery?
In this work, we aim to provide computational tools to investigate this question and, by doing so, explore the nature and complexity of online discussions and their space of opinions, uncovering where each participant lies.
More specifically, we present a modeling framework to construct latent representations of opinions in online discussions which are consistent with human judgements, as measured by online voting.
If two opinions are close in the resulting latent space of opinions, it is because humans think they are similar.
Our modeling framework is theoretically grounded and establishes a surprising connection between opinions and voting models and the sign-rank of a matrix.
Moreover, it also provides a set of practical algorithms to both estimate the dimension of the latent space of opinions and infer where opinions expressed by the participants of an online discussion lie in this space.
Experiments on a large dataset from Yahoo!
News, Yahoo!
Finance, Yahoo!
Sports, and the Newsroom app suggest that unidimensional opinion models may often be unable to accurately represent online discussions, provide insights into human judgements and opinions, and show that our framework is able to circumvent language nuances such as sarcasm or humor by relying on human judgements instead of textual analysis.
The Dynamic Scalability of resources, a problem in Infrastructure as a Service (IaaS) has been the hotspot for research and industry communities.
The heterogeneous and dynamic nature of the Cloud workloads depends on the Quality of Service (QoS) allocation of appropriate workloads to appropriate resources.
A workload is an abstraction of work that instance or set of instances that are going to perform.
Running a web service or being a Hadoop data node is valid workloads.
The efficient management of dynamic nature resources can be done with the help of workloads.
Until workload is considered a fundamental capability, the Cloud resources cannot be utilized in an efficient manner.
In this paper, different workloads have been identified and categorized along with their characteristics and constraints.
The metrics based on Quality of Service (QoS) requirements have been identified for each workload and have been analyzed for creating better application design.
Today it is crucial for organizations to pay even greater attention on quality management as the importance of this function in achieving ultimate business objectives is increasingly becoming clearer.
Importance of the Quality Management Function in achieving basic need by ensuring compliance with Capability Maturity Model Integrated or International Organization for Standardization is a basic demand from business nowadays.
However, Quality Management Function and its processes need to be made much more mature to prevent delivery outages and to achieve business excellence through their review and auditing capability.
Many organizations now face challenges in determining the maturity of the Quality Management group along with the service offered by them and the right way to elevate the maturity of the same.
The objective of this whitepaper is to propose a new model, the Audit Maturity Model which will provide organizations with a measure of their maturity in quality management in the perspective of auditing, along with recommendations for preventing delivery outage, and identifying risk to achieve business excellence.
This will enable organizations to assess Quality Management maturity higher than basic hygiene and will also help them to identify gaps and to take corrective actions for achieving higher maturity levels.
Hence the objective is to envisage a new auditing model as a part of organisation quality management function which can be a guide for them to achieve higher level of maturity and ultimately help to achieve delivery and business excellence.
Distant pointing is still not efficient, accurate or flexible enough for many applications, although many researchers have focused on it.
To improve upon distant pointing, we propose MPP3D, which is especially suitable for high-resolution displays.
MPP3D uses two dimensions of hand positioning to move a pointer, and it also uses the third dimension to adjust the precision of the movement.
Based on the idea of MPP3D, we propose four techniques which combine two ways of mapping and two techniques for precision adjustment.
We further provide three types of mapping scheme and visual feedback for each technique.
The potential of the proposed techniques was investigated through experimentation.
The results show that these techniques were competent for usual computer operations with a cursor, and the adjustment for pointing precision was beneficial for both pointing efficiency and accuracy.
We present a framework for learning efficient holistic representation for handwritten word images.
The proposed method uses a deep convolutional neural network with traditional classification loss.
The major strengths of our work lie in: (i) the efficient usage of synthetic data to pre-train a deep network, (ii) an adapted version of ResNet-34 architecture with region of interest pooling (referred as HWNet v2) which learns discriminative features with variable sized word images, and (iii) realistic augmentation of training data with multiple scales and elastic distortion which mimics the natural process of handwriting.
We further investigate the process of fine-tuning at various layers to reduce the domain gap between synthetic and real domain and also analyze the in-variances learned at different layers using recent visualization techniques proposed in literature.
Our representation leads to state of the art word spotting performance on standard handwritten datasets and historical manuscripts in different languages with minimal representation size.
On the challenging IAM dataset, our method is first to report an mAP above 0.90 for word spotting with a representation size of just 32 dimensions.
Further more, we also present results on printed document datasets in English and Indic scripts which validates the generic nature of the proposed framework for learning word image representation.
We investigate the recovery of signals exhibiting a sparse representation in a general (i.e., possibly redundant or incomplete) dictionary that are corrupted by additive noise admitting a sparse representation in another general dictionary.
This setup covers a wide range of applications, such as image inpainting, super-resolution, signal separation, and recovery of signals that are impaired by, e.g., clipping, impulse noise, or narrowband interference.
We present deterministic recovery guarantees based on a novel uncertainty relation for pairs of general dictionaries and we provide corresponding practicable recovery algorithms.
The recovery guarantees we find depend on the signal and noise sparsity levels, on the coherence parameters of the involved dictionaries, and on the amount of prior knowledge about the signal and noise support sets.
Point clouds obtained from 3D scans are typically sparse, irregular, and noisy, and required to be consolidated.
In this paper, we present the first deep learning based edge-aware technique to facilitate the consolidation of point clouds.
We design our network to process points grouped in local patches, and train it to learn and help consolidate points, deliberately for edges.
To achieve this, we formulate a regression component to simultaneously recover 3D point coordinates and point-to-edge distances from upsampled features, and an edge-aware joint loss function to directly minimize distances from output points to 3D meshes and to edges.
Compared with previous neural network based works, our consolidation is edge-aware.
During the synthesis, our network can attend to the detected sharp edges and enable more accurate 3D reconstructions.
Also, we trained our network on virtual scanned point clouds, demonstrated the performance of our method on both synthetic and real point clouds, presented various surface reconstruction results, and showed how our method outperforms the state-of-the-arts.
In Graph Theory a number of results were devoted to studying the computational complexity of the number modulo 2 of a graph's edge set decompositions of various kinds, first of all including its Hamiltonian decompositions, as well as the number modulo 2 of, say, Hamiltonian cycles/paths etc.
While the problems of finding a Hamiltonian decomposition and Hamiltonian cycle are NP-complete, counting these objects modulo 2 in polynomial time is yet possible for certain types of regular undirected graphs.
Some of the most known examples are the theorems about the existence of an even number of Hamiltonian decompositions in a 4-regular graph and an even number of such decompositions where two given edges e and g belong to different cycles (Thomason, 1978), as well as an even number of Hamiltonian cycles passing through any given edge in a regular odd-degreed graph (Smith's theorem).
The present article introduces a new algebraic technique which generalizes the notion of counting modulo 2 via applying fields of Characteristic 2 and determinants and, for instance, allows to receive a polynomial-time formula for the number modulo 2 of a 4-regular bipartite graph's Hamiltonian decompositions such that a given edge and a given path of length 2 belong to different Hamiltonian cycles - hence refining/extending (in a computational sense) Thomason's result for bipartite graphs.
This technique also provides a polynomial-time calculation of the number modulo 2 of a graph's edge set decompositions into simple cycles each containing at least one element of a given set of its edges what is a similar kind of extension of Thomason's theorem as well.
As cloud computing is increasingly transforming the information technology landscape, organizations and businesses are exhibiting strong interest in Software-as-a-Service (SaaS) offerings that can help them increase business agility and reduce their operational costs.
They increasingly demand services that can meet their functional and non-functional requirements.
Given the plethora and the variety of SaaS offerings, we propose, in this paper, a framework for SaaS provisioning, which relies on brokered Service Level agreements (SLAs), between service consumers and SaaS providers.
The Cloud Service Broker (CSB) helps service consumers find the right SaaS providers that can fulfil their functional and non-functional requirements.
The proposed selection algorithm ranks potential SaaS providers by matching their offerings against the requirements of the service consumer using an aggregate utility function.
Furthermore, the CSB is in charge of conducting SLA negotiation with selected SaaS providers, on behalf of service consumers, and performing SLA compliance monitoring.
We consider the well-studied partial sums problem in succint space where one is to maintain an array of n k-bit integers subject to updates such that partial sums queries can be efficiently answered.
We present two succint versions of the Fenwick Tree - which is known for its simplicity and practicality.
Our results hold in the encoding model where one is allowed to reuse the space from the input data.
Our main result is the first that only requires nk + o(n) bits of space while still supporting sum/update in O(log_b n) / O(b log_b n) time where 2 <= b <= log^O(1) n. The second result shows how optimal time for sum/update can be achieved while only slightly increasing the space usage to nk + o(nk) bits.
Beyond Fenwick Trees, the results are primarily based on bit-packing and sampling - making them very practical - and they also allow for simple optimal parallelization.
Building structures can allow a robot to surmount large obstacles, expanding the set of areas it can reach.
This paper presents a planning algorithm to automatically determine what structures a construction-capable robot must build in order to traverse its entire environment.
Given an environment, a set of building blocks, and a robot capable of building structures, we seek a optimal set of structures (using a minimum number of building blocks) that could be built to make the entire environment traversable with respect to the robot's movement capabilities.
We show that this problem is NP-Hard, and present a complete, optimal algorithm that solves it using a branch-and-bound strategy.
The algorithm runs in exponential time in the worst case, but solves typical problems with practical speed.
In hardware experiments, we show that the algorithm solves 3D maps of real indoor environments in about one minute, and that the structures selected by the algorithm allow a robot to traverse the entire environment.
An accompanying video is available online at https://youtu.be/B9WM557NP44.
The investigation of spatio-temporal dynamics of bacterial cells and their molecular components requires automated image analysis tools to track cell shape properties and molecular component locations inside the cells.
In the study of bacteria aging, the molecular components of interest are protein aggregates accumulated near bacteria boundaries.
This particular location makes very ambiguous the correspondence between aggregates and cells, since computing accurately bacteria boundaries in phase-contrast time-lapse imaging is a challenging task.
This paper proposes an active skeleton formulation for bacteria modeling which provides several advantages: an easy computation of shape properties (perimeter, length, thickness, orientation), an improved boundary accuracy in noisy images, and a natural bacteria-centered coordinate system that permits the intrinsic location of molecular components inside the cell.
Starting from an initial skeleton estimate, the medial axis of the bacterium is obtained by minimizing an energy function which incorporates bacteria shape constraints.
Experimental results on biological images and comparative evaluation of the performances validate the proposed approach for modeling cigar-shaped bacteria like Escherichia coli.
The Image-J plugin of the proposed method can be found online at http://fluobactracker.inrialpes.fr.
In this paper, the problem of joint caching and resource allocation is investigated for a network of cache-enabled unmanned aerial vehicles (UAVs) that service wireless ground users over the LTE licensed and unlicensed (LTE-U) bands.
The considered model focuses on users that can access both licensed and unlicensed bands while receiving contents from either the cache units at the UAVs directly or via content server-UAV-user links.
This problem is formulated as an optimization problem which jointly incorporates user association, spectrum allocation, and content caching.
To solve this problem, a distributed algorithm based on the machine learning framework of liquid state machine (LSM) is proposed.
Using the proposed LSM algorithm, the cloud can predict the users' content request distribution while having only limited information on the network's and users' states.
The proposed algorithm also enables the UAVs to autonomously choose the optimal resource allocation strategies that maximize the number of users with stable queues depending on the network states.
Based on the users' association and content request distributions, the optimal contents that need to be cached at UAVs as well as the optimal resource allocation are derived.
Simulation results using real datasets show that the proposed approach yields up to 33.3% and 50.3% gains, respectively, in terms of the number of users that have stable queues compared to two baseline algorithms: Q-learning with cache and Q-learning without cache.
The results also show that LSM significantly improves the convergence time of up to 33.3% compared to conventional learning algorithms such as Q-learning.
This paper presents a word-entity duet framework for utilizing knowledge bases in ad-hoc retrieval.
In this work, the query and documents are modeled by word-based representations and entity-based representations.
Ranking features are generated by the interactions between the two representations, incorporating information from the word space, the entity space, and the cross-space connections through the knowledge graph.
To handle the uncertainties from the automatically constructed entity representations, an attention-based ranking model AttR-Duet is developed.
With back-propagation from ranking labels, the model learns simultaneously how to demote noisy entities and how to rank documents with the word-entity duet.
Evaluation results on TREC Web Track ad-hoc task demonstrate that all of the four-way interactions in the duet are useful, the attention mechanism successfully steers the model away from noisy entities, and together they significantly outperform both word-based and entity-based learning to rank systems.
Computerization of research activities led to the creation of large specialized information resources, platforms, services and software to support scientific research.
However, their shortcomings do not allow to fully realizing the comprehensive support of scientific activity, and the absence of a single entry point to divide the scientific community fragmented groups interests.
The article based on analysing the existing solutions and approaches to the tools of information and communication technologies of various types of scientific activity, and taking into account the research lifecycle proposed and formulated the basic principles of designing and implementing an integrated information system to support scientific research.
Clustering is crucial for many computer vision applications such as robust tracking, object detection and segmentation.
This work presents a real-time clustering technique that takes advantage of the unique properties of event-based vision sensors.
Since event-based sensors trigger events only when the intensity changes, the data is sparse, with low redundancy.
Thus, our approach redefines the well-known mean-shift clustering method using asynchronous events instead of conventional frames.
The potential of our approach is demonstrated in a multi-target tracking application using Kalman filters to smooth the trajectories.
We evaluated our method on an existing dataset with patterns of different shapes and speeds, and a new dataset that we collected.
The sensor was attached to the Baxter robot in an eye-in-hand setup monitoring real-world objects in an action manipulation task.
Clustering accuracy achieved an F-measure of 0.95, reducing the computational cost by 88% compared to the frame-based method.
The average error for tracking was 2.5 pixels and the clustering achieved a consistent number of clusters along time.
Sparse representation of structured signals requires modelling strategies that maintain specific signal properties, in addition to preserving original information content and achieving simpler signal representation.
Therefore, the major design challenge is to introduce adequate problem formulations and offer solutions that will efficiently lead to desired representations.
In this context, sparse representation of covariance and precision matrices, which appear as feature descriptors or mixture model parameters, respectively, will be in the main focus of this paper.
Mesh labeling is the key problem of classifying the facets of a 3D mesh with a label among a set of possible ones.
State-of-the-art methods model mesh labeling as a Markov Random Field over the facets.
These algorithms map image segmentations to the mesh by minimizing an energy function that comprises a data term, a smoothness terms, and class-specific priors.
The latter favor a labeling with respect to another depending on the orientation of the facet normals.
In this paper we propose a novel energy term that acts as a prior, but does not require any prior knowledge about the scene nor scene-specific relationship among classes.
It bootstraps from a coarse mapping of the 2D segmentations on the mesh, and it favors the facets to be labeled according to the statistics of the mesh normals in their neighborhood.
We tested our approach against five different datasets and, even if we do not inject prior knowledge, our method adapts to the data and overcomes the state-of-the-art.
Fingerprint-based indoor localization methods are promising due to the high availability of deployed access points and compatibility with commercial-off-the-shelf user devices.
However, to train regression models for localization, an extensive site survey is required to collect fingerprint data from the target areas.
In this paper, we consider the problem of informative path planning (IPP) to find the optimal walk for site survey subject to a budget constraint.
IPP for location fingerprint collection is related to the well-known orienteering problem (OP) but is more challenging due to edge-based non-additive rewards and revisits.
Given the NP-hardness of IPP, we propose two heuristic approaches: a Greedy algorithm and a genetic algorithm.
We show through experimental data collected from two indoor environments with different characteristics that the two algorithms have low computation complexity, can generally achieve higher utility and lower localization errors compared to the extension of two state-of-the-art approaches to OP.
Assessing the magnitude of cause-and-effect relations is one of the central challenges found throughout the empirical sciences.
The problem of identification of causal effects is concerned with determining whether a causal effect can be computed from a combination of observational data and substantive knowledge about the domain under investigation, which is formally expressed in the form of a causal graph.
In many practical settings, however, the knowledge available for the researcher is not strong enough so as to specify a unique causal graph.
Another line of investigation attempts to use observational data to learn a qualitative description of the domain called a Markov equivalence class, which is the collection of causal graphs that share the same set of observed features.
In this paper, we marry both approaches and study the problem of causal identification from an equivalence class, represented by a partial ancestral graph (PAG).
We start by deriving a set of graphical properties of PAGs that are carried over to its induced subgraphs.
We then develop an algorithm to compute the effect of an arbitrary set of variables on an arbitrary outcome set.
We show that the algorithm is strictly more powerful than the current state of the art found in the literature.
A bag-of-words based probabilistic classifier is trained using regularized logistic regression to detect vandalism in the English Wikipedia.
Isotonic regression is used to calibrate the class membership probabilities.
Learning curve, reliability, ROC, and cost analysis are performed.
Current neural network-based classifiers are susceptible to adversarial examples even in the black-box setting, where the attacker only has query access to the model.
In practice, the threat model for real-world systems is often more restrictive than the typical black-box model where the adversary can observe the full output of the network on arbitrarily many chosen inputs.
We define three realistic threat models that more accurately characterize many real-world classifiers: the query-limited setting, the partial-information setting, and the label-only setting.
We develop new attacks that fool classifiers under these more restrictive threat models, where previous methods would be impractical or ineffective.
We demonstrate that our methods are effective against an ImageNet classifier under our proposed threat models.
We also demonstrate a targeted black-box attack against a commercial classifier, overcoming the challenges of limited query access, partial information, and other practical issues to break the Google Cloud Vision API.
Context is an essential capability for robots that are to be as adaptive as possible in challenging environments.
Although there are many context modeling efforts, they assume a fixed structure and number of contexts.
In this paper, we propose an incremental deep model that extends Restricted Boltzmann Machines.
Our model gets one scene at a time, and gradually extends the contextual model when necessary, either by adding a new context or a new context layer to form a hierarchy.
We show on a scene classification benchmark that our method converges to a good estimate of the contexts of the scenes, and performs better or on-par on several tasks compared to other incremental models or non-incremental models.
Answering visual questions need acquire daily common knowledge and model the semantic connection among different parts in images, which is too difficult for VQA systems to learn from images with the only supervision from answers.
Meanwhile, image captioning systems with beam search strategy tend to generate similar captions and fail to diversely describe images.
To address the aforementioned issues, we present a system to have these two tasks compensate with each other, which is capable of jointly producing image captions and answering visual questions.
In particular, we utilize question and image features to generate question-related captions and use the generated captions as additional features to provide new knowledge to the VQA system.
For image captioning, our system attains more informative results in term of the relative improvements on VQA tasks as well as competitive results using automated metrics.
Applying our system to the VQA tasks, our results on VQA v2 dataset achieve 65.8% using generated captions and 69.1% using annotated captions in validation set and 68.4% in the test-standard set.
Further, an ensemble of 10 models results in 69.7% in the test-standard split.
In complex inferential tasks like question answering, machine learning models must confront two challenges: the need to implement a compositional reasoning process, and, in many applications, the need for this reasoning process to be interpretable to assist users in both development and prediction.
Existing models designed to produce interpretable traces of their decision-making process typically require these traces to be supervised at training time.
In this paper, we present a novel neural modular approach that performs compositional reasoning by automatically inducing a desired sub-task decomposition without relying on strong supervision.
Our model allows linking different reasoning tasks though shared modules that handle common routines across tasks.
Experiments show that the model is more interpretable to human evaluators compared to other state-of-the-art models: users can better understand the model's underlying reasoning procedure and predict when it will succeed or fail based on observing its intermediate outputs.
We consider the problem of blind identification and equalization of single-input multiple-output (SIMO) nonlinear channels.
Specifically, the nonlinear model consists of multiple single-channel Wiener systems that are excited by a common input signal.
The proposed approach is based on a well-known blind identification technique for linear SIMO systems.
By transforming the output signals into a reproducing kernel Hilbert space (RKHS), a linear identification problem is obtained, which we propose to solve through an iterative procedure that alternates between canonical correlation analysis (CCA) to estimate the linear parts, and kernel canonical correlation (KCCA) to estimate the memoryless nonlinearities.
The proposed algorithm is able to operate on systems with as few as two output channels, on relatively small data sets and on colored signals.
Simulations are included to demonstrate the effectiveness of the proposed technique.
We consider the problem of maximizing the harvested power in Multiple Input Multiple Output (MIMO) Simultaneous Wireless Information and Power Transfer (SWIPT) systems with power splitting reception.
Different from recently proposed designs, we target with our novel problem formulation at the jointly optimal transmit precoding and receive uniform power splitting (UPS) ratio maximizing the harvested power, while ensuring that the Quality-of-Service (QoS) requirement of the MIMO link is satisfied.
We assume generic practical Radio Frequency (RF) Energy Harvesting (EH) receive operation that results in a non-convex optimization problem for the design parameters, which we then solve optimally after formulating it in an equivalent generalized convex form.
Our representative results including comparisons of achievable EH gains with benchmark schemes provide key insights on various system parameters.
Meningioma brain tumour discrimination is challenging as many histological patterns are mixed between the different subtypes.
In clinical practice, dominant patterns are investigated for signs of specific meningioma pathology; however the simple observation could result in inter- and intra-observer variation due to the complexity of the histopathological patterns.
Also employing a computerised feature extraction approach applied at a single resolution scale might not suffice in accurately delineating the mixture of histopathological patterns.
In this work we propose a novel multiresolution feature extraction approach for characterising the textural properties of the different pathological patterns (i.e. mainly cell nuclei shape, orientation and spatial arrangement within the cytoplasm).
The pattern textural properties are characterised at various scales and orientations for an improved separability between the different extracted features.
The Gabor filter energy output of each magnitude response was combined with four other fixed-resolution texture signatures (2 model-based and 2 statistical-based) with and without cell nuclei segmentation.
The highest classification accuracy of 95% was reported when combining the Gabor filters energy and the meningioma subimage fractal signature as a feature vector without performing any prior cell nuceli segmentation.
This indicates that characterising the cell-nuclei self-similarity properties via Gabor filters can assists in achieving an improved meningioma subtype classification, which can assist in overcoming variations in reported diagnosis.
We design monitor optimisations for detectEr, a runtime-verification tool synthesising systems of concurrent monitors from correctness properties for Erlang programs.
We implement these optimisations as part of the existing tool and show that they yield considerably lower runtime overheads when compared to the unoptimised monitor synthesis.
This paper discusses two existing approaches to the correlation analysis between automatic evaluation metrics and human scores in the area of natural language generation.
Our experiments show that depending on the usage of a system- or sentence-level correlation analysis, correlation results between automatic scores and human judgments are inconsistent.
Data communication in cloud-based distributed stream data analytics often involves a collection of parallel and pipelined TCP flows.
As the standard TCP congestion control mechanism is designed for achieving "fairness" among competing flows and is agnostic to the application layer contexts, the bandwidth allocation among a set of TCP flows traversing bottleneck links often leads to sub-optimal application-layer performance measures, e.g., stream processing throughput or average tuple complete latency.
Motivated by this and enabled by the rapid development of the Software-Defined Networking (SDN) techniques, in this paper, we re-investigate the design space of the bandwidth allocation problem and propose a cross-layer framework which utilizes the additional information obtained from the application layer and provides on-the-fly and dynamic bandwidth adjustment algorithms for helping the stream analytics applications achieving better performance during the runtime.
We implement a prototype cross-layer bandwidth allocation framework based on a popular open-source distributed stream processing platform, Apache Storm, together with the OpenDaylight controller, and carry out extensive experiments with real-world analytical workloads on top of a local cluster consisting of 10 workstations interconnected by a SDN-enabled switch.
The experiment results clearly validate the effectiveness and efficiency of our proposed framework and algorithms.
We focus on generative autoencoders, such as variational or adversarial autoencoders, which jointly learn a generative model alongside an inference model.
Generative autoencoders are those which are trained to softly enforce a prior on the latent distribution learned by the inference model.
We call the distribution to which the inference model maps observed samples, the learned latent distribution, which may not be consistent with the prior.
We formulate a Markov chain Monte Carlo (MCMC) sampling process, equivalent to iteratively decoding and encoding, which allows us to sample from the learned latent distribution.
Since, the generative model learns to map from the learned latent distribution, rather than the prior, we may use MCMC to improve the quality of samples drawn from the generative model, especially when the learned latent distribution is far from the prior.
Using MCMC sampling, we are able to reveal previously unseen differences between generative autoencoders trained either with or without a denoising criterion.
Nowadays, many vegetables are grown insidegreenhouses in which environment is controlled and nutrition can be supplied through water supply using electrical pump, namely fertigation.
Dosage of nutrition in water for many vegetable plants are also known so that by controllingwater supply all the needsfor the plants to grow are available.
Furthermore, water supply can be controlled using electrical pump which is activated according to theplants conditionin relation with water supply.
In order to supply water and nutrition in the right amount and time, plants condition can be observed using a CCD camera attached to image processing facilitiesto develop a speaking plant approach.
In this study, plants development during their growing periodare observedusing image processing.
Three populationsof tomato plants, with less, enough, and exceeded nutrition in water,are captured using a CCD camera every three days, and the images were analyzed using a developed computer program for the heightof plants.
The results showed that the development of the plants can be monitored using this method.
After that, the responseof plant growth in the same condition was monitored, and the responsewas used as input for the fertigation system to turn electrical pump automatically on and off, so the fertigation system could maintain the growth of the plants.
In this work, we investigate an efficient numerical approach for solving higher order statistical methods for blind and semi-blind signal recovery from non-ideal channels.
We develop numerical algorithms based on convex optimization relaxation for minimization of higher order statistical cost functions.
The new formulation through convex relaxation overcomes the local convergence problem of existing gradient descent based algorithms and applies to several well-known cost functions for effective blind signal recovery including blind equalization and blind source separation in both single-input-single-output (SISO) and multi-input-multi-output (MIMO) systems.
We also propose a fourth order pilot based cost function that benefits from this approach.
The simulation results demonstrate that our approach is suitable for short-length packet data transmission using only a few pilot symbols.
We propose a data-dependent denoising procedure to restore noisy images.
Different from existing denoising algorithms which search for patches from either the noisy image or a generic database, the new algorithm finds patches from a database that contains only relevant patches.
We formulate the denoising problem as an optimal filter design problem and make two contributions.
First, we determine the basis function of the denoising filter by solving a group sparsity minimization problem.
The optimization formulation generalizes existing denoising algorithms and offers systematic analysis of the performance.
Improvement methods are proposed to enhance the patch search process.
Second, we determine the spectral coefficients of the denoising filter by considering a localized Bayesian prior.
The localized prior leverages the similarity of the targeted database, alleviates the intensive Bayesian computation, and links the new method to the classical linear minimum mean squared error estimation.
We demonstrate applications of the proposed method in a variety of scenarios, including text images, multiview images and face images.
Experimental results show the superiority of the new algorithm over existing methods.
For popular websites most important concern is to handle incoming load dynamically among web servers, so that they can respond to their client without any wait or failure.
Different websites use different strategies to distribute load among web servers but most of the schemes concentrate on only one factor that is number of requests, but none of the schemes consider the point that different type of requests will require different level of processing efforts to answer, status record of all the web servers that are associated with one domain name and mechanism to handle a situation when one of the servers is not working.
Therefore, there is a fundamental need to develop strategy for dynamic load allocation on web side.
In this paper, an effort has been made to introduce a cluster based frame work to solve load distribution problem.
This framework aims to distribute load among clusters on the basis of their operational capabilities.
Moreover, the experimental results are shown with the help of example, algorithm and analysis of the algorithm.
Lack of moderation in online communities enables participants to incur in personal aggression, harassment or cyberbullying, issues that have been accentuated by extremist radicalisation in the contemporary post-truth politics scenario.
This kind of hostility is usually expressed by means of toxic language, profanity or abusive statements.
Recently Google has developed a machine-learning-based toxicity model in an attempt to assess the hostility of a comment; unfortunately, it has been suggested that said model can be deceived by adversarial attacks that manipulate the text sequence of the comment.
In this paper we firstly characterise such adversarial attacks as using obfuscation and polarity transformations.
The former deceives by corrupting toxic trigger content with typographic edits, whereas the latter deceives by grammatical negation of the toxic content.
Then, we propose a two--stage approach to counter--attack these anomalies, bulding upon a recently proposed text deobfuscation method and the toxicity scoring model.
Lastly, we conducted an experiment with approximately 24000 distorted comments, showing how in this way it is feasible to restore toxicity of the adversarial variants, while incurring roughly on a twofold increase in processing time.
Even though novel adversary challenges would keep coming up derived from the versatile nature of written language, we anticipate that techniques combining machine learning and text pattern recognition methods, each one targeting different layers of linguistic features, would be needed to achieve robust detection of toxic language, thus fostering aggression--free digital interaction.
Urban and Bierman introduced a calculus of proof terms for the sequent calculus LK with a strongly normalizing reduction relation.
We extend this calculus to simply-typed higher-order logic with inferences for induction and equality, albeit without strong normalization.
We implement thiscalculus in GAPT, our library for proof transformations.
Evaluating the normalization on both artificial and real-world benchmarks, we show that this algorithm is typically several orders of magnitude faster than the existing Gentzen-like cut-reduction, and an order of magnitude faster than any other cut-elimination procedure implemented in GAPT.
In this paper we describe an end to end Neural Model for Named Entity Recognition NER) which is based on Bi-Directional RNN-LSTM.
Almost all NER systems for Hindi use Language Specific features and handcrafted rules with gazetteers.
Our model is language independent and uses no domain specific features or any handcrafted rules.
Our models rely on semantic information in the form of word vectors which are learnt by an unsupervised learning algorithm on an unannotated corpus.
Our model attained state of the art performance in both English and Hindi without the use of any morphological analysis or without using gazetteers of any sort.
Plastic surgery and disguise variations are two of the most challenging co-variates of face recognition.
The state-of-art deep learning models are not sufficiently successful due to the availability of limited training samples.
In this paper, a novel framework is proposed which transfers fundamental visual features learnt from a generic image dataset to supplement a supervised face recognition model.
The proposed algorithm combines off-the-shelf supervised classifier and a generic, task independent network which encodes information related to basic visual cues such as color, shape, and texture.
Experiments are performed on IIITD plastic surgery face dataset and Disguised Faces in the Wild (DFW) dataset.
Results showcase that the proposed algorithm achieves state of the art results on both the datasets.
Specifically on the DFW database, the proposed algorithm yields over 87% verification accuracy at 1% false accept rate which is 53.8% better than baseline results computed using VGGFace.
The detection of a volumetric attack involves collecting statistics on the network traffic, and identifying suspicious activities.
We assume that available statistical information includes the number of packets and the number of bytes passed per flow.
We apply methods of machine learning to detect malicious traffic.
A prototype project is implemented as a module for the Floodlight controller.
The prototype was tested on the Mininet simulation platform.
The simulated topology includes a number of edge switches, a connected graph of core switches, and a number of server and user hosts.
The server hosts run simple web servers.
The user hosts simulate web clients.
The controller employs Dijkstra's algorithm to find the best flow in the graph.
The controller periodically polls the edge switches and provides current and historical statistics on each active flow.
The streaming analytics evaluates the traffic volume and detects volumetric attacks.
Citizen Broadband Radio Service band (3550 - 3700 GHz) is seen as one of the key frequency bands to enable improvements in performance of wireless broadband and cellular systems.
A careful study of interference caused by a secondary cellular communication system coexisting with an incumbent naval radar is required to establish a pragmatic protection distance, which not only protects the incumbent from harmful interference but also increases the spectrum access opportunity for the secondary system.
In this context, this paper investigates the co-channel and adjacent channel coexistence of a ship-borne naval radar and a wide-area cellular communication system and presents the analysis of interference caused by downlink transmission in the cellular system on the naval radar for different values of radar protection distance.
The results of such analysis suggest that maintaining a protection distance of 30 km from the radar will ensure the required INR protection criterion of -6 dB at the radar receiver with > 0.9 probability, even when the secondary network operates in the same channel as the radar.
Novel power control algorithms to assign operating powers to the coexisting cellular devices are also proposed to further reduce the protection distance from radar while still meeting the radar INR protection requirement.
Task-motion planning (TMP) addresses the problem of efficiently generating executable and low-cost task plans in a discrete space such that the (initially unknown) action costs are determined by motion plans in a corresponding continuous space.
However, a task-motion plan can be sensitive to unexpected domain uncertainty and changes, leading to suboptimal behaviors or execution failures.
In this paper, we propose a novel framework, TMP-RL, which is an integration of TMP and reinforcement learning (RL) from the execution experience, to solve the problem of robust task-motion planning in dynamic and uncertain domains.
TMP-RL features two nested planning-learning loops.
In the inner TMP loop, the robot generates a low-cost, feasible task-motion plan by iteratively planning in the discrete space and updating relevant action costs evaluated by the motion planner in continuous space.
In the outer loop, the plan is executed, and the robot learns from the execution experience via model-free RL, to further improve its task-motion plans.
RL in the outer loop is more accurate to the current domain but also more expensive, and using less costly task and motion planning leads to a jump-start for learning in the real world.
Our approach is evaluated on a mobile service robot conducting navigation tasks in an office area.
Results show that TMP-RL approach significantly improves adaptability and robustness (in comparison to TMP methods) and leads to rapid convergence (in comparison to task planning (TP)-RL methods).
We also show that TMP-RL can reuse learned values to smoothly adapt to new scenarios during long-term deployments.
In January 2015 we distributed an online survey about failures in robotics and intelligent systems across robotics researchers.
The aim of this survey was to find out which types of failures currently exist, what their origins are, and how systems are monitored and debugged - with a special focus on performance bugs.
This report summarizes the findings of the survey.
As of today, abuse is a pressing issue to participants and administrators of Online Social Networks (OSN).
Abuse in Twitter can spawn from arguments generated for influencing outcomes of a political election, the use of bots to automatically spread misinformation, and generally speaking, activities that deny, disrupt, degrade or deceive other participants and, or the network.
Given the difficulty in finding and accessing a large enough sample of abuse ground truth from the Twitter platform, we built and deployed a custom crawler that we use to judiciously collect a new dataset from the Twitter platform with the aim of characterizing the nature of abusive users, a.k.a abusive birds, in the wild.
We provide a comprehensive set of features based on users' attributes, as well as social-graph metadata.
The former includes metadata about the account itself, while the latter is computed from the social graph among the sender and the receiver of each message.
Attribute-based features are useful to characterize user's accounts in OSN, while graph-based features can reveal the dynamics of information dissemination across the network.
In particular, we derive the Jaccard index as a key feature to reveal the benign or malicious nature of directed messages in Twitter.
To the best of our knowledge, we are the first to propose such a similarity metric to characterize abuse in Twitter.
We develop new polynomial methods for studying systems of word equations.
We use them to improve some earlier results and to analyze how sizes of systems of word equations satisfying certain independence properties depend on the lengths of the equations.
These methods give the first nontrivial upper bounds for the sizes of the systems.
Quadrature sampling has been widely applied in coherent radar systems to extract in-phase and quadrature (I and Q) components in the received radar signal.
However, the sampling is inefficient because the received signal contains only a small number of significant target signals.
This paper incorporates the compressive sampling (CS) theory into the design of the quadrature sampling system, and develops a quadrature compressive sampling (QuadCS) system to acquire the I and Q components with low sampling rate.
The QuadCS system first randomly projects the received signal into a compressive bandpass signal and then utilizes the quadrature sampling to output compressive I and Q components.
The compressive outputs are used to reconstruct the I and Q components.
To understand the system performance, we establish the frequency domain representation of the QuadCS system.
With the waveform-matched dictionary, we prove that the QuadCS system satisfies the restricted isometry property with overwhelming probability.
For K target signals in the observation interval T, simulations show that the QuadCS requires just O(Klog(BT/K)) samples to stably reconstruct the signal, where B is the signal bandwidth.
The reconstructed signal-to-noise ratio decreases by 3dB for every octave increase in the target number K and increases by 3dB for every octave increase in the compressive bandwidth.
Theoretical analyses and simulations verify that the proposed QuadCS is a valid system to acquire the I and Q components in the received radar signals.
Rule-based modelling allows to represent molecular interactions in a compact and natural way.
The underlying molecular dynamics, by the laws of stochastic chemical kinetics, behaves as a continuous-time Markov chain.
However, this Markov chain enumerates all possible reaction mixtures, rendering the analysis of the chain computationally demanding and often prohibitive in practice.
We here describe how it is possible to efficiently find a smaller, aggregate chain, which preserves certain properties of the original one.
Formal methods and lumpability notions are used to define algorithms for automated and efficient construction of such smaller chains (without ever constructing the original ones).
We here illustrate the method on an example and we discuss the applicability of the method in the context of modelling large signalling pathways.
Hand gesture recognition possesses extensive applications in virtual reality, sign language recognition, and computer games.
The direct interface of hand gestures provides us a new way for communicating with the virtual environment.
In this paper a novel and real-time approach for hand gesture recognition system is presented.
In the suggested method, first, the hand gesture is extracted from the main image by the image segmentation and morphological operation and then is sent to feature extraction stage.
In feature extraction stage the Cross-correlation coefficient is applied on the gesture to recognize it.
In the result part, the proposed approach is applied on American Sign Language (ASL) database and the accuracy rate obtained 98.34%.
Through a series of examples, we illustrate some important drawbacks that the action logic framework suffers from in its ability to represent the dynamics of information updates.
We argue that these problems stem from the fact that the action model, a central construct designed to encode agents' uncertainty about actions, is itself effectively common knowledge amongst the agents.
In response to these difficulties, we motivate and propose an alternative semantics that avoids them by (roughly speaking) endogenizing the action model.
We discuss the relationship to action logic, and provide a sound and complete axiomatization.
Building multi-turn information-seeking conversation systems is an important and challenging research topic.
Although several advanced neural text matching models have been proposed for this task, they are generally not efficient for industrial applications.
Furthermore, they rely on a large amount of labeled data, which may not be available in real-world applications.
To alleviate these problems, we study transfer learning for multi-turn information seeking conversations in this paper.
We first propose an efficient and effective multi-turn conversation model based on convolutional neural networks.
After that, we extend our model to adapt the knowledge learned from a resource-rich domain to enhance the performance.
Finally, we deployed our model in an industrial chatbot called AliMe Assist (https://consumerservice.taobao.com/online-help) and observed a significant improvement over the existing online model.
We present a method to determine Fashion DNA, coordinate vectors locating fashion items in an abstract space.
Our approach is based on a deep neural network architecture that ingests curated article information such as tags and images, and is trained to predict sales for a large set of frequent customers.
In the process, a dual space of customer style preferences naturally arises.
Interpretation of the metric of these spaces is straightforward: The product of Fashion DNA and customer style vectors yields the forecast purchase likelihood for the customer-item pair, while the angle between Fashion DNA vectors is a measure of item similarity.
Importantly, our models are able to generate unbiased purchase probabilities for fashion items based solely on article information, even in absence of sales data, thus circumventing the "cold-start problem" of collaborative recommendation approaches.
Likewise, it generalizes easily and reliably to customers outside the training set.
We experiment with Fashion DNA models based on visual and/or tag item data, evaluate their recommendation power, and discuss the resulting article similarities.
To analyze the failure risk of asynchronous digital circuits the time-parameter is introduced into the Boolean algebra replacing the arithmetic operations by logical operations.
There considered an example of construction of signals passing through the logical elements, using the described below mathematical apparatus.
In this paper, we propose a novel edge preserving and multi-scale contextual neural network for salient object detection.
The proposed framework is aiming to address two limits of the existing CNN based methods.
First, region-based CNN methods lack sufficient context to accurately locate salient object since they deal with each region independently.
Second, pixel-based CNN methods suffer from blurry boundaries due to the presence of convolutional and pooling layers.
Motivated by these, we first propose an end-to-end edge-preserved neural network based on Fast R-CNN framework (named RegionNet) to efficiently generate saliency map with sharp object boundaries.
Later, to further improve it, multi-scale spatial context is attached to RegionNet to consider the relationship between regions and the global scenes.
Furthermore, our method can be generally applied to RGB-D saliency detection by depth refinement.
The proposed framework achieves both clear detection boundary and multi-scale contextual robustness simultaneously for the first time, and thus achieves an optimized performance.
Experiments on six RGB and two RGB-D benchmark datasets demonstrate that the proposed method achieves state-of-the-art performance.
The surprising results of Karp, Vazirani and Vazirani and (respectively) Buchbinder et al are examples where rather simple randomizations provide provably better approximations than the corresponding deterministic counterparts for online bipartite matching and (respectively) unconstrained non-monotone submodular maximization.
We show that seemingly strong extensions of the deterministic online computation model can at best match the performance of naive randomization.
More specifically, for bipartite matching, we show that in the priority model (allowing very general ways to order the input stream), we cannot improve upon the trivial 1/2-approximation achieved by any greedy maximal matching algorithm and likewise cannot improve upon this approximation by any log n/log log n number of online algorithms running in parallel.
The latter result yields an improved log log n - log log log n lower bound for the number of advice bits needed.
For max-sat, we adapt the recent de-randomization approach of Buchbinder and Feldman applied to the Buchbinbder et al algorithm for max-sat to obtain a deterministic 3/4-approximation algorithm using width 2n parallelism.
In order to improve upon this approximation, we show that exponential width parallelism of online algorithms is necessary (in a model that is more general than what is needed for the width 2n algorithm).
This paper focuses on a novel and challenging vision task, dense video captioning, which aims to automatically describe a video clip with multiple informative and diverse caption sentences.
The proposed method is trained without explicit annotation of fine-grained sentence to video region-sequence correspondence, but is only based on weak video-level sentence annotations.
It differs from existing video captioning systems in three technical aspects.
First, we propose lexical fully convolutional neural networks (Lexical-FCN) with weakly supervised multi-instance multi-label learning to weakly link video regions with lexical labels.
Second, we introduce a novel submodular maximization scheme to generate multiple informative and diverse region-sequences based on the Lexical-FCN outputs.
A winner-takes-all scheme is adopted to weakly associate sentences to region-sequences in the training phase.
Third, a sequence-to-sequence learning based language model is trained with the weakly supervised information obtained through the association process.
We show that the proposed method can not only produce informative and diverse dense captions, but also outperform state-of-the-art single video captioning methods by a large margin.
Real-world optimisation problems are often dynamic.
Previously good solutions must be updated or replaced due to changes in objectives and constraints.
It is often claimed that evolutionary algorithms are particularly suitable for dynamic optimisation because a large population can contain different solutions that may be useful in the future.
However, rigorous theoretical demonstrations for how populations in dynamic optimisation can be essential are sparse and restricted to special cases.
This paper provides theoretical explanations of how populations can be essential in evolutionary dynamic optimisation in a general and natural setting.
We describe a natural class of dynamic optimisation problems where a sufficiently large population is necessary to keep track of moving optima reliably.
We establish a relationship between the population-size and the probability that the algorithm loses track of the optimum.
Due to various green initiatives, renewable energy will be massively incorporated into the future smart grid.
However, the intermittency of the renewables may result in power imbalance, thus adversely affecting the stability of a power system.
Frequency regulation may be used to maintain the power balance at all times.
As electric vehicles (EVs) become popular, they may be connected to the grid to form a vehicle-to-grid (V2G) system.
An aggregation of EVs can be coordinated to provide frequency regulation services.
However, V2G is a dynamic system where the participating EVs come and go independently.
Thus it is not easy to estimate the regulation capacities for V2G.
In a preliminary study, we modeled an aggregation of EVs with a queueing network, whose structure allows us to estimate the capacities for regulation-up and regulation-down, separately.
The estimated capacities from the V2G system can be used for establishing a regulation contract between an aggregator and the grid operator, and facilitating a new business model for V2G.
In this paper, we extend our previous development by designing a smart charging mechanism which can adapt to given characteristics of the EVs and make the performance of the actual system follow the analytical model.
In this paper, we focus on image inpainting task, aiming at recovering the missing area of an incomplete image given the context information.
Recent development in deep generative models enables an efficient end-to-end framework for image synthesis and inpainting tasks, but existing methods based on generative models don't exploit the segmentation information to constrain the object shapes, which usually lead to blurry results on the boundary.
To tackle this problem, we propose to introduce the semantic segmentation information, which disentangles the inter-class difference and intra-class variation for image inpainting.
This leads to much clearer recovered boundary between semantically different regions and better texture within semantically consistent segments.
Our model factorizes the image inpainting process into segmentation prediction (SP-Net) and segmentation guidance (SG-Net) as two steps, which predict the segmentation labels in the missing area first, and then generate segmentation guided inpainting results.
Experiments on multiple public datasets show that our approach outperforms existing methods in optimizing the image inpainting quality, and the interactive segmentation guidance provides possibilities for multi-modal predictions of image inpainting.
We introduce a seemingly impossible task: given only an audio clip of someone speaking, decide which of two face images is the speaker.
In this paper we study this, and a number of related cross-modal tasks, aimed at answering the question: how much can we infer from the voice about the face and vice versa?
We study this task "in the wild", employing the datasets that are now publicly available for face recognition from static images (VGGFace) and speaker identification from audio (VoxCeleb).
These provide training and testing scenarios for both static and dynamic testing of cross-modal matching.
We make the following contributions: (i) we introduce CNN architectures for both binary and multi-way cross-modal face and audio matching, (ii) we compare dynamic testing (where video information is available, but the audio is not from the same video) with static testing (where only a single still image is available), and (iii) we use human testing as a baseline to calibrate the difficulty of the task.
We show that a CNN can indeed be trained to solve this task in both the static and dynamic scenarios, and is even well above chance on 10-way classification of the face given the voice.
The CNN matches human performance on easy examples (e.g. different gender across faces) but exceeds human performance on more challenging examples (e.g. faces with the same gender, age and nationality).
The rapid uptake of mobile devices and the rising popularity of mobile applications and services pose unprecedented demands on mobile and wireless networking infrastructure.
Upcoming 5G systems are evolving to support exploding mobile traffic volumes, agile management of network resource to maximize user experience, and extraction of fine-grained real-time analytics.
Fulfilling these tasks is challenging, as mobile environments are increasingly complex, heterogeneous, and evolving.
One potential solution is to resort to advanced machine learning techniques to help managing the rise in data volumes and algorithm-driven applications.
The recent success of deep learning underpins new and powerful tools that tackle problems in this space.
In this paper we bridge the gap between deep learning and mobile and wireless networking research, by presenting a comprehensive survey of the crossovers between the two areas.
We first briefly introduce essential background and state-of-the-art in deep learning techniques with potential applications to networking.
We then discuss several techniques and platforms that facilitate the efficient deployment of deep learning onto mobile systems.
Subsequently, we provide an encyclopedic review of mobile and wireless networking research based on deep learning, which we categorize by different domains.
Drawing from our experience, we discuss how to tailor deep learning to mobile environments.
We complete this survey by pinpointing current challenges and open future directions for research.
It has been shown that increasing model depth improves the quality of neural machine translation.
However, different architectural variants to increase model depth have been proposed, and so far, there has been no thorough comparative study.
In this work, we describe and evaluate several existing approaches to introduce depth in neural machine translation.
Additionally, we explore novel architectural variants, including deep transition RNNs, and we vary how attention is used in the deep decoder.
We introduce a novel "BiDeep" RNN architecture that combines deep transition RNNs and stacked RNNs.
Our evaluation is carried out on the English to German WMT news translation dataset, using a single-GPU machine for both training and inference.
We find that several of our proposed architectures improve upon existing approaches in terms of speed and translation quality.
We obtain best improvements with a BiDeep RNN of combined depth 8, obtaining an average improvement of 1.5 BLEU over a strong shallow baseline.
We release our code for ease of adoption.
We prove that every 1-planar graph G has a z-parallel visibility representation, i.e., a 3D visibility representation in which the vertices are isothetic disjoint rectangles parallel to the xy-plane, and the edges are unobstructed z-parallel visibilities between pairs of rectangles.
In addition, the constructed representation is such that there is a plane that intersects all the rectangles, and this intersection defines a bar 1-visibility representation of G.
Gaussian mixture alignment is a family of approaches that are frequently used for robustly solving the point-set registration problem.
However, since they use local optimisation, they are susceptible to local minima and can only guarantee local optimality.
Consequently, their accuracy is strongly dependent on the quality of the initialisation.
This paper presents the first globally-optimal solution to the 3D rigid Gaussian mixture alignment problem under the L2 distance between mixtures.
The algorithm, named GOGMA, employs a branch-and-bound approach to search the space of 3D rigid motions SE(3), guaranteeing global optimality regardless of the initialisation.
The geometry of SE(3) was used to find novel upper and lower bounds for the objective function and local optimisation was integrated into the scheme to accelerate convergence without voiding the optimality guarantee.
The evaluation empirically supported the optimality proof and showed that the method performed much more robustly on two challenging datasets than an existing globally-optimal registration solution.
Accurate prediction of students knowledge is a fundamental building block of personalized learning systems.
Here, we propose a novel ensemble model to predict student knowledge gaps.
Applying our approach to student trace data from the online educational platform Duolingo we achieved highest score on both evaluation metrics for all three datasets in the 2018 Shared Task on Second Language Acquisition Modeling.
We describe our model and discuss relevance of the task compared to how it would be setup in a production environment for personalized education.
Content marketing is todays one of the most remarkable approaches in the context of marketing processes of companies.
Value of this kind of marketing has improved in time, thanks to the latest developments regarding to computer and communication technologies.
Nowadays, especially social media based platforms have a great importance on enabling companies to design multimedia oriented, interactive content.
But on the other hand, there is still something more to do for improved content marketing approaches.
In this context, objective of this study is to focus on intelligent content marketing, which can be done by using artificial intelligence.
Artificial Intelligence is todays one of the most remarkable research fields and it can be used easily as multidisciplinary.
So, this study has aimed to discuss about its potential on improving content marketing.
In detail, the study has enabled readers to improve their awareness about the intersection point of content marketing and artificial intelligence.
Furthermore, the authors have introduced some example models of intelligent content marketing, which can be achieved by using current Web technologies and artificial intelligence techniques.
Image quality assessment (IQA) is traditionally classified into full-reference (FR) IQA and no-reference (NR) IQA according to whether the original image is required.
Although NR-IQA is widely used in practical applications, room for improvement still remains because of the lack of the reference image.
Inspired by the fact that in many applications, such as parameter selection, a series of distorted images are available, the authors propose a novel comparison-based image quality assessment (C-IQA) method.
The new comparison-based framework parallels FR-IQA by requiring two input images, and resembles NR-IQA by not using the original image.
As a result, the new comparison-based approach has more application scenarios than FR-IQA does, and takes greater advantage of the accessible information than the traditional single-input NR-IQA does.
Further, C-IQA is compared with other state-of-the-art NR-IQA methods on two widely used IQA databases.
Experimental results show that C-IQA outperforms the other NR-IQA methods for parameter selection, and the parameter trimming framework combined with C-IQA saves the computation of iterative image reconstruction up to 80%.
We investigate the maximum coding rate for a given average blocklength and error probability over a K-user discrete memoryless broadcast channel for the scenario where a common message is transmitted using variable-length stop-feedback codes.
For the point-to-point case, Polyanskiy et al.(2011) demonstrated that variable-length coding combined with stop-feedback significantly increases the speed of convergence of the maximum coding rate to capacity.
This speed-up manifests itself in the absence of a square-root penalty in the asymptotic expansion of the maximum coding rate for large blocklengths, i.e., zero dispersion.
In this paper, we present nonasymptotic achievability and converse bounds on the maximum coding rate of the common-message K-user discrete memoryless broadcast channel, which strengthen and generalize the ones reported in Trillingsgaard et al.(2015) for the two-user case.
An asymptotic analysis of these bounds reveals that zero dispersion cannot be achieved for certain common-message broadcast channels (e.g., the binary symmetric broadcast channel).
Furthermore, we identify conditions under which our converse and achievability bounds are tight up to the second order.
Through numerical evaluations, we illustrate that our second-order expansions approximate accurately the maximum coding rate and that the speed of convergence to capacity is indeed slower than for the point-to-point case.
With the popularity of mobile devices and the development of geo-positioning technology, location-based services (LBS) attract much attention and top-k spatial keyword queries become increasingly complex.
It is common to see that clients issue a query to find a restaurant serving pizza and steak, low in price and noise level particularly.
However, most of prior works focused only on the spatial keyword while ignoring these independent numerical attributes.
In this paper we demonstrate, for the first time, the Attributes-Aware Spatial Keyword Query (ASKQ), and devise a two-layer hybrid index structure called Quad-cluster Dual-filtering R-Tree (QDR-Tree).
In the keyword cluster layer, a Quad-Cluster Tree (QC-Tree) is built based on the hierarchical clustering algorithm using kernel k-means to classify keywords.
In the spatial layer, for each leaf node of the QC-Tree, we attach a Dual-Filtering R-Tree (DR-Tree) with two filtering algorithms, namely, keyword bitmap-based and attributes skyline-based filtering.
Accordingly, efficient query processing algorithms are proposed.
Through theoretical analysis, we have verified the optimization both in processing time and space consumption.
Finally, massive experiments with real-data demonstrate the efficiency and effectiveness of QDR-Tree.
A During last two decades, there has been a prolific growth in the chaos based image encryption algorithms.
Up to an extent these algorithms have been able to provide an alternative to exchange large media files (images and videos) over the networks in a secure way.
However, there have been some issues with the implementation of chaos based image ciphers in practice.
One of them is reduced/small key space due to the fact that chaotic behavior is only observed for certain range of system parameters/initial conditions of the chaotic system used in such algorithms.
To overcome this difficulty, we propose a simple, efficient and robust image encryption algorithm based on combined applications of quasigroups and chaotic standard map.
The proposed image cipher is based on the popular substitution-diffusion architecture (Shanon) where a quasigroup of order 256 and chaotic standard map have been used for the substitution and permutation of image pixels respectively.
Due to the introduction of quasigroup as part of the secret key along with the parameter and initial conditions of the chaotic standard map, the key space has been increased significantly.
The proposed image cipher is very fast due to the fact that the substitution based on the quasigroup operations is very simple and can be executed easily through the lookup table operations on Latin squares (which are Cayley operation tables of quasigroups) and the permutation is performed row-by-row as well as column-by-column using the pseudo random number sequences gener-ated through the chaotic standard map.
The security and performance have been analyzed through the histograms, correlation coefficients, information entropy, key sensitivity analysis, differential analysis, key space analysis etc. and the results prove the efficiency and robustness of the proposed image cipher against the possible security threats.
In cloud computing systems slow processing nodes, often referred to as "stragglers", can significantly extend the computation time.
Recent results have shown that error correction coding can be used to reduce the effect of stragglers.
In this work we introduce a scheme that, in addition to using error correction to distribute mixed jobs across nodes, is also able to exploit the work completed by all nodes, including stragglers.
We first consider vector-matrix multiplication and apply maximum distance separable (MDS) codes to small blocks of sub-matrices.
The worker nodes process blocks sequentially, working block-by-block, transmitting partial per-block results to the master as they are completed.
Sub-blocking allows a more continuous completion process, which thereby allows us to exploit the work of a much broader spectrum of processors and reduces computation time.
We then apply this technique to matrix-matrix multiplication using product code.
In this case, we show that the order of computing sub-tasks is a new degree of design freedom that can be exploited to reduce computation time further.
We propose a novel approach to analyze the finishing time, which is different from typical order statistics.
Simulation results show that the expected computation time decreases by a factor of at least two in compared to previous methods.
This paper is devoted to the factorization of multivariate polynomials into products of linear forms, a problem which has applications to differential algebra, to the resolution of systems of polynomial equations and to Waring decomposition (i.e., decomposition in sums of d-th powers of linear forms; this problem is also known as symmetric tensor decomposition).
We provide three black box algorithms for this problem.
Our main contribution is an algorithm motivated by the application to Waring decomposition.
This algorithm reduces the corresponding factorization problem to simultaenous matrix diagonalization, a standard task in linear algebra.
The algorithm relies on ideas from invariant theory, and more specifically on Lie algebras.
Our second algorithm reconstructs a factorization from several bi-variate projections.
Our third algorithm reconstructs it from the determination of the zero set of the input polynomial, which is a union of hyperplanes.
The goal of this work is to improve images of traffic scenes that are degraded by natural causes such as fog, rain and limited visibility during the night.
For these applications, it is next to impossible to get pixel perfect pairs of the same scene, with and without the degrading conditions.
This makes it unsuitable for conventional supervised learning approaches, however, it is easy to collect unpaired images of the scenes in a perfect and in a degraded condition.
To enhance the images taken in a poor visibility condition, domain transfer models can be trained to transform an image from the degraded to the clear domain.
A well-known concept for unsupervised domain transfer are cycle-consistent generative adversarial models.
Unfortunately, the resulting generators often change the structure of the scene.
This causes an undesirable change in the semantics.
We propose three ways to cope with this problem depending on the type of degradation.
A multiple-input multiple-output (MIMO) version of the dirty paper channel is studied, where the channel input and the dirt experience the same fading process and the fading channel state is known at the receiver (CSIR).
This represents settings where signal and interference sources are co-located, such as in the broadcast channel.
First, a variant of Costa's dirty paper coding (DPC) is presented, whose achievable rates are within a constant gap to capacity for all signal and dirt powers.
Additionally, a lattice coding and decoding scheme is proposed, whose decision regions are independent of the channel realizations.
Under Rayleigh fading, the gap to capacity of the lattice coding scheme vanishes with the number of receive antennas, even at finite Signal-to-Noise Ratio (SNR).
Thus, although the capacity of the fading dirty paper channel remains unknown, this work shows it is not far from its dirt-free counterpart.
The insights from the dirty paper channel directly lead to transmission strategies for the two-user MIMO broadcast channel (BC), where the transmitter emits a superposition of desired and undesired (dirt) signals with respect to each receiver.
The performance of the lattice coding scheme is analyzed under different fading dynamics for the two users, showing that high-dimensional lattices achieve rates close to capacity.
SRAM-based FPGAs are increasingly popular in the aerospace industry due to their field programmability and low cost.
However, they suffer from cosmic radiation induced Single Event Upsets (SEUs).
In safety-critical applications, the dependability of the design is a prime concern since failures may have catastrophic consequences.
An early analysis of the relationship between dependability metrics, performability-area trade-off, and different mitigation techniques for such applications can reduce the design effort while increasing the design confidence.
This paper introduces a novel methodology based on probabilistic model checking, for the analysis of the reliability, availability, safety and performance-area tradeoffs of safety-critical systems for early design decisions.
Starting from the high-level description of a system, a Markov reward model is constructed from the Control Data Flow Graph (CDFG) and a component characterization library targeting FPGAs.
The proposed model and exhaustive analysis capture all the failure states (based on the fault detection coverage) and repairs possible in the system.
We present quantitative results based on an FIR filter circuit to illustrate the applicability of the proposed approach and to demonstrate that a wide range of useful dependability and performability properties can be analyzed using the proposed methodology.
The modeling results show the relationship between different mitigation techniques and fault detection coverage, exposing their direct impact on the design for early decisions.
This paper considers a scenario in which a source-destination pair needs to establish a confidential connection against an external eavesdropper, aided by the interference generated by another source-destination pair that exchanges public messages.
The goal is to compute the maximum achievable secrecy degrees of freedom (S.D.o.F) region of a MIMO two-user wiretap network.
First, a cooperative secrecy transmission scheme is proposed, whose feasible set is shown to achieve all S.D.o.F. pairs on the S.D.o.F. region boundary.
In this way, the determination of the S.D.o.F. region is reduced to a problem of maximizing the S.D.o.F. pair over the proposed transmission scheme.
The maximum achievable S.D.o.F. region boundary points are obtained in closed form, and the construction of the precoding matrices achieving the maximum S.D.o.F. region boundary is provided.
The obtained analytical expressions clearly show the relation between the maximum achievable S.D.o.F. region and the number of antennas at each terminal.
The synchronizing word of deterministic automaton is a word in the alphabet of colors (considered as letters) of its edges that maps the automaton to a single state.
A coloring of edges of a directed graph is synchronizing if the coloring turns the graph into deterministic finite automaton possessing a synchronizing word.
The road coloring problem is a problem of synchronizing coloring of directed finite strongly connected graph with constant outdegree of all its vertices if the greatest common divisor of lengths of all its cycles is one.
The problem was posed by Adler, Goodwyn and Weiss over 30 years ago and evoked a noticeable interest among the specialists in theory of graphs, deterministic automata and symbolic dynamics.
The problem is described even in "Wikipedia" - the popular Internet Encyclopedia.
The positive solution of the road coloring problem is presented.
A paraphrase is a restatement of the meaning of a text in other words.
Paraphrases have been studied to enhance the performance of many natural language processing tasks.
In this paper, we propose a novel task iParaphrasing to extract visually grounded paraphrases (VGPs), which are different phrasal expressions describing the same visual concept in an image.
These extracted VGPs have the potential to improve language and image multimodal tasks such as visual question answering and image captioning.
How to model the similarity between VGPs is the key of iParaphrasing.
We apply various existing methods as well as propose a novel neural network-based method with image attention, and report the results of the first attempt toward iParaphrasing.
In horizontal collaborations, carriers form coalitions in order to perform parts of their logistics operations jointly.
By exchanging transportation requests among each other, they can operate more efficiently and in a more sustainable way.
Collaborative vehicle routing has been extensively discussed in the literature.
We identify three major streams of research: (i) centralized collaborative planning, (ii) decentralized planning without auctions, and (ii) auction-based decentralized planning.
For each of them we give a structured overview on the state of knowledge and discuss future research directions.
Estimation of social influence in networks can be substantially biased in observational studies due to homophily and network correlation in exposure to exogenous events.
Randomized experiments, in which the researcher intervenes in the social system and uses randomization to determine how to do so, provide a methodology for credibly estimating of causal effects of social behaviors.
In addition to addressing questions central to the social sciences, these estimates can form the basis for effective marketing and public policy.
In this review, we discuss the design space of experiments to measure social influence through combinations of interventions and randomizations.
We define an experiment as combination of (1) a target population of individuals connected by an observed interaction network, (2) a set of treatments whereby the researcher will intervene in the social system, (3) a randomization strategy which maps individuals or edges to treatments, and (4) a measurement of an outcome of interest after treatment has been assigned.
We review experiments that demonstrate potential experimental designs and we evaluate their advantages and tradeoffs for answering different types of causal questions about social influence.
We show how randomization also provides a basis for statistical inference when analyzing these experiments.
Nested relational query languages have been explored extensively, and underlie industrial language-integrated query systems such as Microsoft's LINQ.
However, relational databases do not natively support nested collections in query results.
This can lead to major performance problems: if programmers write queries that yield nested results, then such systems typically either fail or generate a large number of queries.
We present a new approach to query shredding, which converts a query returning nested data to a fixed number of SQL queries.
Our approach, in contrast to prior work, handles multiset semantics, and generates an idiomatic SQL:1999 query directly from a normal form for nested queries.
We provide a detailed description of our translation and present experiments showing that it offers comparable or better performance than a recent alternative approach on a range of examples.
Suppose that Alice and Bob are given each an infinite string, and they want to decide whether their two strings are in a given relation.
How much communication do they need?
How can communication be even defined and measured for infinite strings?
In this article, we propose a formalism for a notion of infinite communication complexity, prove that it satisfies some natural properties and coincides, for relevant applications, with the classical notion of amortized communication complexity.
More-over, an application is given for tackling some conjecture about tilings and multidimensional sofic shifts.
The word E transformed everything is this world, as well as the whole globe itself.
To a great extend this helps for eco friendly green world.
In educational field, electronic medium has played a major role.
It influenced and changed almost every component of it to electronic medium like e-book, online courses, etc.
Throughout the world, leading universities are offering online courses voluntarily.
Generally we refer to it as Massive Online Open Courses (MOOCs).
There are many debates going on related to success and consequences of MOOCs.
Many are highlighting that these courses are self-paced, economical, and provide quality training to all irrespective of geographical constraints.
But many other academic people go against these points and keep listing many other disadvantages of MOOCs.
This paper explores the basics of MOOCs at the initial section.
Following section will deal with advantages and disadvantages of MOOCs in general.
We the researchers collected the details about the awareness of MOOCs among teachers and students in a higher education institution in Oman.
We have also collected the details about MOOCs implementation and usage within Oman educational society.
Based on the collected information, we have evaluated and presented the findings about MOOCs impact in Oman higher education.
We have felt that doing appropriate improvements in MOOCs may become an imperative medium in Oman educational institutions.
The suggestions are listed in the discussion and recommendation section.
An intriguing open question is whether measurements made on Big Data recording human activities can yield us high-fidelity proxies of socio-economic development and well-being.
Can we monitor and predict the socio-economic development of a territory just by observing the behavior of its inhabitants through the lens of Big Data?
In this paper, we design a data-driven analytical framework that uses mobility measures and social measures extracted from mobile phone data to estimate indicators for socio-economic development and well-being.
We discover that the diversity of mobility, defined in terms of entropy of the individual users' trajectories, exhibits (i) significant correlation with two different socio-economic indicators and (ii) the highest importance in predictive models built to predict the socio-economic indicators.
Our analytical framework opens an interesting perspective to study human behavior through the lens of Big Data by means of new statistical indicators that quantify and possibly "nowcast" the well-being and the socio-economic development of a territory.
An automated approach to text readability assessment is essential to a language and can be a powerful tool for improving the understandability of texts written and published in that language.
However, the Persian language, which is spoken by over 110 million speakers, lacks such a system.
Unlike other languages such as English, French, and Chinese, very limited research studies have been carried out to build an accurate and reliable text readability assessment system for the Persian language.
In the present research, the first Persian dataset for text readability assessment was gathered and the first model for Persian text readability assessment using machine learning was introduced.
The experiments showed that this model was accurate and could assess the readability of Persian texts with a high degree of confidence.
The results of this study can be used in a number of applications such as medical and educational text readability evaluation and have the potential to be the cornerstone of future studies in Persian text readability assessment.
Deep Learning NLP domain lacks procedures for the analysis of model robustness.
In this paper we propose a framework which validates robustness of any Question Answering model through model explainers.
We propose that a robust model should transgress the initial notion of semantic similarity induced by word embeddings to learn a more human-like understanding of meaning.
We test this property by manipulating questions in two ways: swapping important question word for 1) its semantically correct synonym and 2) for word vector that is close in embedding space.
We estimate importance of words in asked questions with Locally Interpretable Model Agnostic Explanations method (LIME).
With these two steps we compare state-of-the-art Q&A models.
We show that although accuracy of state-of-the-art models is high, they are very fragile to changes in the input.
Moreover, we propose 2 adversarial training scenarios which raise model sensitivity to true synonyms by up to 7% accuracy measure.
Our findings help to understand which models are more stable and how they can be improved.
In addition, we have created and published a new dataset that may be used for validation of robustness of a Q&A model.
In this report, some cosmological correlation functions are used to evaluate the differential performance between C2075 and P100 GPU cards.
In the past, the correlation functions used in this work have been widely studied and exploited on some previous GPU architectures.
The analysis of the performance indicates that a speedup in the range from 13 to 15 is achieved without any additional optimization process for the P100 card.
We analyse a quantum-like Bayesian Network that puts together cause/effect relationships and semantic similarities between events.
These semantic similarities constitute acausal connections according to the Synchronicity principle and provide new relationships to quantum like probabilistic graphical models.
As a consequence, beliefs (or any other event) can be represented in vector spaces, in which quantum parameters are determined by the similarities that these vectors share between them.
Events attached by a semantic meaning do not need to have an explanation in terms of cause and effect.
An adversarial example is an example that has been adjusted to produce a wrong label when presented to a system at test time.
To date, adversarial example constructions have been demonstrated for classifiers, but not for detectors.
If adversarial examples that could fool a detector exist, they could be used to (for example) maliciously create security hazards on roads populated with smart vehicles.
In this paper, we demonstrate a construction that successfully fools two standard detectors, Faster RCNN and YOLO.
The existence of such examples is surprising, as attacking a classifier is very different from attacking a detector, and that the structure of detectors - which must search for their own bounding box, and which cannot estimate that box very accurately - makes it quite likely that adversarial patterns are strongly disrupted.
We show that our construction produces adversarial examples that generalize well across sequences digitally, even though large perturbations are needed.
We also show that our construction yields physical objects that are adversarial.
The renewable energies prediction and particularly global radiation forecasting is a challenge studied by a growing number of research teams.
This paper proposes an original technique to model the insolation time series based on combining Artificial Neural Network (ANN) and Auto-Regressive and Moving Average (ARMA) model.
While ANN by its non-linear nature is effective to predict cloudy days, ARMA techniques are more dedicated to sunny days without cloud occurrences.
Thus, three hybrids models are suggested: the first proposes simply to use ARMA for 6 months in spring and summer and to use an optimized ANN for the other part of the year; the second model is equivalent to the first but with a seasonal learning; the last model depends on the error occurred the previous hour.
These models were used to forecast the hourly global radiation for five places in Mediterranean area.
The forecasting performance was compared among several models: the 3 above mentioned models, the best ANN and ARMA for each location.
In the best configuration, the coupling of ANN and ARMA allows an improvement of more than 1%, with a maximum in autumn (3.4%) and a minimum in winter (0.9%) where ANN alone is the best.
Performance evaluation is a key issue for designers and users of Database Management Systems (DBMSs).
Performance is generally assessed with software benchmarks that help, e.g., test architectural choices, compare different technologies or tune a system.
In the particular context of data warehousing and On-Line Analytical Processing (OLAP), although the Transaction Processing Performance Council (TPC) aims at issuing standard decision-support benchmarks, few benchmarks do actually exist.
We present in this chapter the Data Warehouse Engineering Benchmark (DWEB), which allows generating various ad-hoc synthetic data warehouses and workloads.
DWEB is fully parameterized to fulfill various data warehouse design needs.
However, two levels of parameterization keep it relatively easy to tune.
We also expand on our previous work on DWEB by presenting its new Extract, Transform, and Load (ETL) feature as well as its new execution protocol.
A Java implementation of DWEB is freely available on-line, which can be interfaced with most existing relational DMBSs.
To the best of our knowledge, DWEB is the only easily available, up-to-date benchmark for data warehouses.
We propose an efficient and scalable method for incrementally building a dense, semantically annotated 3D map in real-time.
The proposed method assigns class probabilities to each region, not each element (e.g., surfel and voxel), of the 3D map which is built up through a robust SLAM framework and incrementally segmented with a geometric-based segmentation method.
Differently from all other approaches, our method has a capability of running at over 30Hz while performing all processing components, including SLAM, segmentation, 2D recognition, and updating class probabilities of each segmentation label at every incoming frame, thanks to the high efficiency that characterizes the computationally intensive stages of our framework.
By utilizing a specifically designed CNN to improve the frame-wise segmentation result, we can also achieve high accuracy.
We validate our method on the NYUv2 dataset by comparing with the state of the art in terms of accuracy and computational efficiency, and by means of an analysis in terms of time and space complexity.
Guided troubleshooting is an inherent task in the domain of technical support services.
When a customer experiences an issue with the functioning of a technical service or a product, an expert user helps guide the customer through a set of steps comprising a troubleshooting procedure.
The objective is to identify the source of the problem through a set of diagnostic steps and observations, and arrive at a resolution.
Procedures containing these set of diagnostic steps and observations in response to different problems are common artifacts in the body of technical support documentation.
The ability to use machine learning and linguistics to understand and leverage these procedures for applications like intelligent chatbots or robotic process automation, is crucial.
Existing research on question answering or intelligent chatbots does not look within procedures or deep-understand them.
In this paper, we outline a system for mining procedures from technical support documents.
We create models for solving important subproblems like extraction of procedures, identifying decision points within procedures, identifying blocks of instructions corresponding to these decision points and mapping instructions within a decision block.
We also release a dataset containing our manual annotations on publicly available support documents, to promote further research on the problem.
The paper presents three self-stabilizing protocols for basic fair and reliable link communication primitives.
We assume a link-register communication model under read/write atomicity, where every process can read from but cannot write into its neighbours' registers.
The first primitive guarantees that any process writes a new value in its register(s) only after all its neighbours have read the previous value, whatever the initial scheduling of processes' actions.
The second primitive implements a "weak rendezvous" communication mechanism by using an alternating bit protocol: whenever a process consecutively writes n values (possibly the same ones) in a register, each neighbour is guaranteed to read each value from the register at least once.
On the basis of the previous protocol, the third primitive implements a "quasi rendezvous": in words, this primitive ensures furthermore that there exists exactly one reading between two writing operations All protocols are self-stabilizing and run in asynchronous arbitrary networks.
The goal of the paper is in handling each primitive by a separate procedure, which can be used as a "black box" in more involved self-stabilizing protocols.
This short text summarizes the work in biology proposed in our book, Perspectives on Organisms, where we analyse the unity proper to organisms by looking at it from different viewpoints.
We discuss the theoretical roles of biological time, complexity, theoretical symmetries, singularities and critical transitions.
We explicitly borrow from the conclusions in some key chapters and introduce them by a reflection on "incompleteness", also proposed in the book.
We consider that incompleteness is a fundamental notion to understand the way in which we construct knowledge.
Then we will introduce an approach to biological dynamics where randomness is central to the theoretical determination: randomness does not oppose biological stability but contributes to it by variability, adaptation, and diversity.
Then, evolutionary and ontogenetic trajectories are continual changes of coherence structures involving symmetry changes within an ever-changing global stability.
Email tracking allows email senders to collect fine-grained behavior and location data on email recipients, who are uniquely identifiable via their email address.
Such tracking invades user privacy in that email tracking techniques gather data without user consent or awareness.
Striving to increase privacy in email communication, this paper develops a detection engine to be the core of a selective tracking blocking mechanism in the form of three contributions.
First, a large collection of email newsletters is analyzed to show the wide usage of tracking over different countries, industries and time.
Second, we propose a set of features geared towards the identification of tracking images under real-world conditions.
Novel features are devised to be computationally feasible and efficient, generalizable and resilient towards changes in tracking infrastructure.
Third, we test the predictive power of these features in a benchmarking experiment using a selection of state- of-the-art classifiers to clarify the effectiveness of model-based tracking identification.
We evaluate the expected accuracy of the approach on out-of-sample data, over increasing periods of time, and when faced with unknown senders.
Conventional approaches to image de-fencing suffer from non-robust fence detection and are limited to processing images of static scenes.
In this position paper, we propose an automatic de-fencing algorithm for images of dynamic scenes.
We divide the problem of image de-fencing into the tasks of automated fence detection, motion estimation and fusion of data from multiple frames of a captured video of the dynamic scene.
Fences are detected automatically using two approaches, namely, employing Gabor filter and a machine learning method.
We cast the fence removal problem in an optimization framework, by modeling the formation of the degraded observations.
The inverse problem is solved using split Bregman technique assuming total variation of the de-fenced image as the regularization constraint.
In this paper, systems of linear differential equations with crisp real coefficients and with initial condition described by a vector of fuzzy numbers are studied.
A new method based on the geometric representations of linear transformations is proposed to find a solution.
The most important difference between this method and methods offered in previous papers is that the solution is considered to be a fuzzy set of real vector-functions rather than a fuzzy vector-function.
Each member of the set satisfies the given system with a certain possibility.
It is shown that at any time the solution constitutes a fuzzy region in the coordinate space, alfa-cuts of which are nested parallelepipeds.
Proposed method is illustrated on examples.
Biomimetic entirely soft robots with animal-like behavior and integrated artificial nervous systems will open up totally new perspectives and applications.
However, until now all presented studies on soft robots were limited to partly soft designs, since all designs at least needed conventional, stiff, electronics, to sense, process signals and activate actuators.
We present the first soft robot with integrated artificial nervous system entirely made of dielectric elastomers - and without any conventional stiff electronic parts.
Supplied with only one external DC voltage, the robot autonomously generates all signals necessary to drive its actuators, and translates an in-plane electromechanical oscillation into a crawling locomotion movement.
Thereby, all functional parts are made of polymer materials and carbon.
Besides the basic design of the world's first entirely soft robot we present prospects to control general behavior of such robots.
Topic modeling is a very powerful technique in data analysis and data mining but it is generally slow.
Many parallelization approaches have been proposed to speed up the learning process.
However, they are usually not very efficient because of the many kinds of overhead, especially the load-balancing problem.
We address this problem by proposing three partitioning algorithms, which either run more quickly or achieve better load balance than current partitioning algorithms.
These algorithms can easily be extended to improve parallelization efficiency on other topic models similar to LDA, e.g., Bag of Timestamps, which is an extension of LDA with time information.
We evaluate these algorithms on two popular datasets, NIPS and NYTimes.
We also build a dataset containing over 1,000,000 scientific publications in the computer science domain from 1951 to 2010 to experiment with Bag of Timestamps parallelization, which we design to demonstrate the proposed algorithms' extensibility.
The results strongly confirm the advantages of these algorithms.
We study the information rates of unipolar orthogonal frequency division multiplexing (OFDM) in discrete-time optical intensity channels (OIC) with Gaussian noise under average optical power constraint.
Several single-, double-, and multicomponent unipolar OFDM schemes are considered under the assumption that independent and identically distributed (i.i.d.)
Gaussian or complex Gaussian codebook ensemble and nearest neighbor decoding (minimum Euclidean distance decoding) are used.
We obtain an array of information rate result.
These results validate existing signal-to-noise-and-distortion-ratio (SNDR) based rate analysis, establish the equivalence of information rates of certain schemes, and demonstrate the evident benefits of using component-multiplexing at high signal-to-noise-ratio (SNR).
For double- and multi-component schemes, the component power allocation strategies that maximize the information rates are investigated.
In particular, by utilizing a power allocation strategy, we prove that several multi-component schemes approach the high SNR capacity of the discrete-time Gaussian OIC under average power constraint to within 0.07 bits.
3D objects (artefacts) are made to fulfill functions.
Designing an object often starts with defining a list of functionalities that it should provide, also known as functional requirements.
Today, the design of 3D object models is still a slow and largely artisanal activity, with few Computer-Aided Design (CAD) tools existing to aid the exploration of the design solution space.
To accelerate the design process, we introduce an algorithm for generating object shapes with desired functionalities.
Following the concept of form follows function, we assume that existing object shapes were rationally chosen to provide desired functionalities.
First, we use an artificial neural network to learn a function-to-form mapping by analysing a dataset of objects labeled with their functionalities.
Then, we combine forms providing one or more desired functions, generating an object shape that is expected to provide all of them.
Finally, we verify in simulation whether the generated object possesses the desired functionalities, by defining and executing functionality tests on it.
Evidence of signatures associated with cryptographic modes of operation is established.
Motivated by some analogies between cryptographic and dynamical systems, in particular with chaos theory, we propose an algorithm based on Lyapunov exponents of discrete dynamical systems to estimate the divergence among ciphertexts as the encryption algorithm is applied iteratively.
The results allow to distinguish among six modes of operation, namely ECB, CBC, OFB, CFB, CTR and PCBC using DES, IDEA, TEA and XTEA block ciphers of 64 bits, as well as AES, RC6, Twofish, Seed, Serpent and Camellia block ciphers of 128 bits.
Furthermore, the proposed methodology enables a classification of modes of operation of cryptographic systems according to their strength.
The class of Gaussian Process (GP) methods for Temporal Difference learning has shown promise for data-efficient model-free Reinforcement Learning.
In this paper, we consider a recent variant of the GP-SARSA algorithm, called Sparse Pseudo-input Gaussian Process SARSA (SPGP-SARSA), and derive recursive formulas for its predictive moments.
This extension promotes greater memory efficiency, since previous computations can be reused and, interestingly, it provides a technique for updating value estimates on a multiple timescales
In the field of mutation analysis, mutation is the systematic generation of mutated programs (i.e., mutants) from an original program.
The concept of mutation has been widely applied to various testing problems, including test set selection, fault localization, and program repair.
However, surprisingly little focus has been given to the theoretical foundation of mutation-based testing methods, making it difficult to understand, organize, and describe various mutation-based testing methods.
This paper aims to consider a theoretical framework for understanding mutation-based testing methods.
While there is a solid testing framework for general testing, this is incongruent with mutation-based testing methods, because it focuses on the correctness of a program for a test, while the essence of mutation-based testing concerns the differences between programs (including mutants) for a test.
In this paper, we begin the construction of our framework by defining a novel testing factor, called a test differentiator, to transform the paradigm of testing from the notion of correctness to the notion of difference.
We formally define behavioral differences of programs for a set of tests as a mathematical vector, called a d-vector.
We explore the multi-dimensional space represented by d-vectors, and provide a graphical model for describing the space.
Based on our framework and formalization, we interpret existing mutation-based fault localization methods and mutant set minimization as applications, and identify novel implications for future work.
Epistemic logic with non-standard knowledge operators, especially the "knowing-value" operator, has recently gathered much attention.
With the "knowing-value" operator, we can express knowledge of individual variables, but not of the relations between them in general.
In this paper, we propose a new operator Kf to express knowledge of the functional dependencies between variables.
The semantics of this Kf operator uses a function domain which imposes a constraint on what counts as a functional dependency relation.
By adjusting this function domain, different interesting logics arise, and in this paper we axiomatize three such logics in a single agent setting.
Then we show how these three logics can be unified by allowing the function domain to vary relative to different agents and possible worlds.
A multiagent axiomatization is given in this case.
Unpaired Image-to-Image translation aims to convert the image from one domain (input domain A) to another domain (target domain B), without providing paired examples for the training.
The state-of-the-art, Cycle-GAN demonstrated the power of Generative Adversarial Networks with Cycle-Consistency Loss.
While its results are promising, there is scope for optimization in the training process.
This paper introduces a new neural network architecture, which only learns the translation from domain A to B and eliminates the need for reverse mapping (B to A), by introducing a new Deviation-loss term.
Furthermore, few other improvements to the Cycle-GAN are found and utilized in this new architecture, contributing to significantly lesser training duration.
We present an algorithm for creating high resolution anatomically plausible images consistent with acquired clinical brain MRI scans with large inter-slice spacing.
Although large data sets of clinical images contain a wealth of information, time constraints during acquisition result in sparse scans that fail to capture much of the anatomy.
These characteristics often render computational analysis impractical as many image analysis algorithms tend to fail when applied to such images.
Highly specialized algorithms that explicitly handle sparse slice spacing do not generalize well across problem domains.
In contrast, we aim to enable application of existing algorithms that were originally developed for high resolution research scans to significantly undersampled scans.
We introduce a generative model that captures fine-scale anatomical structure across subjects in clinical image collections and derive an algorithm for filling in the missing data in scans with large inter-slice spacing.
Our experimental results demonstrate that the resulting method outperforms state-of-the-art upsampling super-resolution techniques, and promises to facilitate subsequent analysis not previously possible with scans of this quality.
Our implementation is freely available at https://github.com/adalca/papago .
Geospatial extensions of SPARQL like GeoSPARQL and stSPARQL have recently been defined and corresponding geospatial RDF stores have been implemented.
However, there is no widely used benchmark for evaluating geospatial RDF stores which takes into account recent advances to the state of the art in this area.
In this paper, we develop a benchmark, called Geographica, which uses both real-world and synthetic data to test the offered functionality and the performance of some prominent geospatial RDF stores.
In this paper, we present Watasense, an unsupervised system for word sense disambiguation.
Given a sentence, the system chooses the most relevant sense of each input word with respect to the semantic similarity between the given sentence and the synset constituting the sense of the target word.
Watasense has two modes of operation.
The sparse mode uses the traditional vector space model to estimate the most similar word sense corresponding to its context.
The dense mode, instead, uses synset embeddings to cope with the sparsity problem.
We describe the architecture of the present system and also conduct its evaluation on three different lexical semantic resources for Russian.
We found that the dense mode substantially outperforms the sparse one on all datasets according to the adjusted Rand index.
Numerous pattern recognition applications can be formed as learning from graph-structured data, including social network, protein-interaction network, the world wide web data, knowledge graph, etc.
While convolutional neural network (CNN) facilitates great advances in gridded image/video understanding tasks, very limited attention has been devoted to transform these successful network structures (including Inception net, Residual net, Dense net, etc.) to establish convolutional networks on graph, due to its irregularity and complexity geometric topologies (unordered vertices, unfixed number of adjacent edges/vertices).
In this paper, we aim to give a comprehensive analysis of when work matters by transforming different classical network structures to graph CNN, particularly in the basic graph recognition problem.
Specifically, we firstly review the general graph CNN methods, especially in its spectral filtering operation on the irregular graph data.
We then introduce the basic structures of ResNet, Inception and DenseNet into graph CNN and construct these network structures on graph, named as G_ResNet, G_Inception, G_DenseNet.
In particular, it seeks to help graph CNNs by shedding light on how these classical network structures work and providing guidelines for choosing appropriate graph network frameworks.
Finally, we comprehensively evaluate the performance of these different network structures on several public graph datasets (including social networks and bioinformatic datasets), and demonstrate how different network structures work on graph CNN in the graph recognition task.
In this paper we propose an end-to-end trainable deep neural network model for egocentric activity recognition.
Our model is built on the observation that egocentric activities are highly characterized by the objects and their locations in the video.
Based on this, we develop a spatial attention mechanism that enables the network to attend to regions containing objects that are correlated with the activity under consideration.
We learn highly specialized attention maps for each frame using class-specific activations from a CNN pre-trained for generic image recognition, and use them for spatio-temporal encoding of the video with a convolutional LSTM.
Our model is trained in a weakly supervised setting using raw video-level activity-class labels.
Nonetheless, on standard egocentric activity benchmarks our model surpasses by up to +6% points recognition accuracy the currently best performing method that leverages hand segmentation and object location strong supervision for training.
We visually analyze attention maps generated by the network, revealing that the network successfully identifies the relevant objects present in the video frames which may explain the strong recognition performance.
We also discuss an extensive ablation analysis regarding the design choices.
The recent advancement in computing technologies and resulting vision based applications have gives rise to a novel practice called telemedicine that requires patient diagnosis images or allied information to recommend or even perform diagnosis practices being located remotely.
However, to ensure accurate and optimal telemedicine there is the requirement of seamless or flawless biomedical information about patient.
On the contrary, medical data transmitted over insecure channel often remains prone to get manipulated or corrupted by attackers.
The existing cryptosystems alone are not sufficient to deal with these issues and hence in this paper a highly robust reversible image steganography model has been developed for secret information hiding.
Unlike traditional wavelet transform techniques, we incorporated Discrete Ripplet Transformation (DRT) technique for message embedding in the medical cover images.
In addition, to assure seamless communication over insecure channel, a dual cryptosystem model containing proposed steganography scheme and RSA cryptosystem has been developed.
One of the key novelties of the proposed research work is the use of adaptive genetic algorithm (AGA) for optimal pixel adjustment process (OPAP) that enriches data hiding capacity as well as imperceptibility features.
The performance assessment reveals that the proposed steganography model outperforms other wavelet transformation based approaches in terms of high PSNR, embedding capacity, imperceptibility etc.
Existing counting methods often adopt regression-based approaches and cannot precisely localize the target objects, which hinders the further analysis (e.g., high-level understanding and fine-grained classification).
In addition, most of prior work mainly focus on counting objects in static environments with fixed cameras.
Motivated by the advent of unmanned flying vehicles (i.e., drones), we are interested in detecting and counting objects in such dynamic environments.
We propose Layout Proposal Networks (LPNs) and spatial kernels to simultaneously count and localize target objects (e.g., cars) in videos recorded by the drone.
Different from the conventional region proposal methods, we leverage the spatial layout information (e.g., cars often park regularly) and introduce these spatially regularized constraints into our network to improve the localization accuracy.
To evaluate our counting method, we present a new large-scale car parking lot dataset (CARPK) that contains nearly 90,000 cars captured from different parking lots.
To the best of our knowledge, it is the first and the largest drone view dataset that supports object counting, and provides the bounding box annotations.
Soft-input soft-output (SISO) detection algorithms form the basis for iterative decoding.
The associated computational complexity often poses significant challenges for practical receiver implementations, in particular in the context of multiple-input multiple-output wireless systems.
In this paper, we present a low-complexity SISO sphere decoder which is based on the single tree search paradigm, proposed originally for soft-output detection in Studer et al., IEEE J-SAC, 2008.
The algorithm incorporates clipping of the extrinsic log-likelihood ratios in the tree search, which not only results in significant complexity savings, but also allows to cover a large performance/complexity trade-off region by adjusting a single parameter.
This article presents for the first time a global method for registering 3D curves with 3D surfaces without requiring an initialization.
The algorithm works with 2-tuples point+vector that consist in pairs of points augmented with the information of their tangents or normals.
A closed-form solution for determining the alignment transformation from a pair of matching 2-tuples is proposed.
In addition, the set of necessary conditions for two 2-tuples to match is derived.
This allows fast search of correspondences that are used in an hypothesise-and-test framework for accomplishing global registration.
Comparative experiments demonstrate that the proposed algorithm is the first effective solution for curve vs surface registration, with the method achieving accurate alignment in situations of small overlap and large percentage of outliers in a fraction of a second.
The proposed framework is extended to the cases of curve vs curve and surface vs surface registration, with the former being particularly relevant since it is also a largely unsolved problem.
Most existing methods determine relation types only after all the entities have been recognized, thus the interaction between relation types and entity mentions is not fully modeled.
This paper presents a novel paradigm to deal with relation extraction by regarding the related entities as the arguments of a relation.
We apply a hierarchical reinforcement learning (HRL) framework in this paradigm to enhance the interaction between entity mentions and relation types.
The whole extraction process is decomposed into a hierarchy of two-level RL policies for relation detection and entity extraction respectively, so that it is more feasible and natural to deal with overlapping relations.
Our model was evaluated on public datasets collected via distant supervision, and results show that it gains better performance than existing methods and is more powerful for extracting overlapping relations.
We have formerly introduced Deep Random Secrecy, a new cryptologic technique capable to ensure secrecy as close as desired from perfection against unlimited passive eavesdropping opponents.
We have also formerly introduced an extended protocol, based on Deep Random Secrecy, capable to resist to unlimited active MITM.
The main limitation of those protocols, in their initial presented version, is the important quantity of information that needs to be exchanged between the legitimate partners to distill secure digits.
We have defined and shown existence of an absolute constant, called Cryptologic Limit, which represents the upper-bound of Secrecy rate that can be reached by Deep Random Secrecy protocols.
At last, we have already presented practical algorithms to generate Deep Randomness from classical computing resources.
This article is presenting an optimization technique, based on recombination and reuse of random bits; this technique enables to dramatically increase the bandwidth performance of formerly introduced protocols, without jeopardizing the entropy of secret information.
That optimization enables to envision an implementation of Deep Random Secrecy at very reasonable cost.
The article also summarizes former results in the perspective of a comprehensive implementation.
The temporal component of videos provides an important clue for activity recognition, as a number of activities can be reliably recognized based on the motion information.
In view of that, this work proposes a novel temporal stream for two-stream convolutional networks based on images computed from the optical flow magnitude and orientation, named Magnitude-Orientation Stream (MOS), to learn the motion in a better and richer manner.
Our method applies simple nonlinear transformations on the vertical and horizontal components of the optical flow to generate input images for the temporal stream.
Experimental results, carried on two well-known datasets (HMDB51 and UCF101), demonstrate that using our proposed temporal stream as input to existing neural network architectures can improve their performance for activity recognition.
Results demonstrate that our temporal stream provides complementary information able to improve the classical two-stream methods, indicating the suitability of our approach to be used as a temporal video representation.
In this paper one presents new similarity, cardinality and entropy measures for bipolar fuzzy set and for its particular forms like intuitionistic, paraconsistent and fuzzy set.
All these are constructed in the framework of multi-valued representations and are based on a penta-valued logic that uses the following logical values: true, false, unknown, contradictory and ambiguous.
Also a new distance for bounded real interval was defined.
With the rapid development of economy in China over the past decade, air pollution has become an increasingly serious problem in major cities and caused grave public health concerns in China.
Recently, a number of studies have dealt with air quality and air pollution.
Among them, some attempt to predict and monitor the air quality from different sources of information, ranging from deployed physical sensors to social media.
These methods are either too expensive or unreliable, prompting us to search for a novel and effective way to sense the air quality.
In this study, we propose to employ the state of the art in computer vision techniques to analyze photos that can be easily acquired from online social media.
Next, we establish the correlation between the haze level computed directly from photos with the official PM 2.5 record of the taken city at the taken time.
Our experiments based on both synthetic and real photos have shown the promise of this image-based approach to estimating and monitoring air pollution.
Many videos depict people, and it is their interactions that inform us of their activities, relation to one another and the cultural and social setting.
With advances in human action recognition, researchers have begun to address the automated recognition of these human-human interactions from video.
The main challenges stem from dealing with the considerable variation in recording settings, the appearance of the people depicted and the performance of their interaction.
This survey provides a summary of these challenges and datasets, followed by an in-depth discussion of relevant vision-based recognition and detection methods.
We focus on recent, promising work based on convolutional neural networks (CNNs).
Finally, we outline directions to overcome the limitations of the current state-of-the-art.
We propose an interpretation of the first-order answer set programming (FOASP) in terms of intuitionistic proof theory.
It is obtained by two polynomial translations between FOASP and the bounded-arity fragment of the Sigma_1 level of the Mints hierarchy in first-order intuitionistic logic.
It follows that Sigma_1 formulas using predicates of fixed arity (in particular unary) is of the same strength as FOASP.
Our construction reveals a close similarity between constructive provability and stable entailment, or equivalently, between the construction of an answer set and an intuitionistic refutation.
This paper is under consideration for publication in Theory and Practice of Logic Programming
Using a human-oriented formal example proof of the (lim+) theorem, i.e. that the sum of limits is the limit of the sum, which is of value for reference on its own, we exhibit a non-permutability of beta-steps and delta+-steps (according to Smullyan's classification), which is not visible with non-liberalized delta-rules and not serious with further liberalized delta-rules, such as the delta++-rule.
Besides a careful presentation of the search for a proof of (lim+) with several pedagogical intentions, the main subject is to explain why the order of beta-steps plays such a practically important role in some calculi.
The reconstruction of a deterministic data field from binary-quantized noisy observations of sensors which are randomly deployed over the field domain is studied.
The study focuses on the extremes of lack of deterministic control in the sensor deployment, lack of knowledge of the noise distribution, and lack of sensing precision and reliability.
Such adverse conditions are motivated by possible real-world scenarios where a large collection of low-cost, crudely manufactured sensors are mass-deployed in an environment where little can be assumed about the ambient noise.
A simple estimator that reconstructs the entire data field from these unreliable, binary-quantized, noisy observations is proposed.
Technical conditions for the almost sure and integrated mean squared error (MSE) convergence of the estimate to the data field, as the number of sensors tends to infinity, are derived and their implications are discussed.
For finite-dimensional, bounded-variation, and Sobolev-differentiable function classes, specific integrated MSE decay rates are derived.
For the first and third function classes these rates are found to be minimax order optimal with respect to infinite precision sensing and known noise distribution.
Model precision in a classification task is highly dependent on the feature space that is used to train the model.
Moreover, whether the features are sequential or static will dictate which classification method can be applied as most of the machine learning algorithms are designed to deal with either one or another type of data.
In real-life scenarios, however, it is often the case that both static and dynamic features are present, or can be extracted from the data.
In this work, we demonstrate how generative models such as Hidden Markov Models (HMM) and Long Short-Term Memory (LSTM) artificial neural networks can be used to extract temporal information from the dynamic data.
We explore how the extracted information can be combined with the static features in order to improve the classification performance.
We evaluate the existing techniques and suggest a hybrid approach, which outperforms other methods on several public datasets.
Datalog has become a popular language for writing static analyses.
Because Datalog is very limited, some implementations of Datalog for static analysis have extended it with new language features.
However, even with these features it is hard or impossible to express a large class of analyses because they use logical formulae to represent program state.
FormuLog fills this gap by extending Datalog to represent, manipulate, and reason about logical formulae.
We have used FormuLog to implement declarative versions of symbolic execution and abstract model checking, analyses previously out of the scope of Datalog-based languages.
While this paper focuses on the design of FormuLog and one of the analyses we have implemented in it, it also touches on a prototype implementation of the language and identifies performance optimizations that we believe will be necessary to scale FormuLog to real-world static analysis problems.
In large and active software projects, it becomes impractical for a developer to stay aware of all project activity.
While it might not be necessary to know about each commit or issue, it is arguably important to know about the ones that are unusual.
To investigate this hypothesis, we identified unusual events in 200 GitHub projects using a comprehensive list of ways in which an artifact can be unusual and asked 140 developers responsible for or affected by these events to comment on the usefulness of the corresponding information.
Based on 2,096 answers, we identify the subset of unusual events that developers consider particularly useful, including large code modifications and unusual amounts of reviewing activity, along with qualitative evidence on the reasons behind these answers.
Our findings provide a means for reducing the amount of information that developers need to parse in order to stay up to date with development activity in their projects.
The privacy implications of third-party tracking is a well-studied problem.
Recent research has shown that besides data aggregators and behavioral advertisers, online social networks also act as trackers via social widgets.
Existing cookie policies are not enough to solve these problems, pushing users to employ blacklist-based browser extensions to prevent such tracking.
Unfortunately, such approaches require maintaining and distributing blacklists, which are often too general and adversely affect non-tracking services for advertisements and analytics.
In this paper, we propose and advocate for a general third-party cookie policy that prevents third-party tracking with cookies and preserves the functionality of social widgets without requiring a blacklist and adversely affecting non-tracking services.
We implemented a proof-of-concept of our policy as browser extensions for Mozilla Firefox and Google Chrome.
To date, our extensions have been downloaded about 11.8K times and have over 2.8K daily users combined.
We introduce style augmentation, a new form of data augmentation based on random style transfer, for improving the robustness of convolutional neural networks (CNN) over both classification and regression based tasks.
During training, our style augmentation randomizes texture, contrast and color, while preserving shape and semantic content.
This is accomplished by adapting an arbitrary style transfer network to perform style randomization, by sampling input style embeddings from a multivariate normal distribution instead of inferring them from a style image.
In addition to standard classification experiments, we investigate the effect of style augmentation (and data augmentation generally) on domain transfer tasks.
We find that data augmentation significantly improves robustness to domain shift, and can be used as a simple, domain agnostic alternative to domain adaptation.
Comparing style augmentation against a mix of seven traditional augmentation techniques, we find that it can be readily combined with them to improve network performance.
We validate the efficacy of our technique with domain transfer experiments in classification and monocular depth estimation, illustrating consistent improvements in generalization.
The Personalization of information has taken recommender systems at a very high level.
With personalization these systems can generate user specific recommendations accurately and efficiently.
User profiling helps personalization, where information retrieval is done to personalize a scenario which maintains a separate user profile for individual user.
The main objective of this paper is to explore this field of personalization in context of user profiling, to help researchers make aware of the user profiling.
Various trends, techniques and Applications have been discussed in paper which will fulfill this motto.
Software architecture (SA) is celebrating 25 years.
This is so if we consider the seminal papers establishing SA as a distinct discipline and scientific publications that have identified cornerstones of both research and practice, like architecture views, architecture description languages, and architecture evaluation.
With the pervasive use of cloud provisioning, the dynamic integration of multi-party distributed services, and the steep increase in the digitalization of business and society, making sound design decisions encompasses an increasingly-large and complex problem space.
The role of SA is essential as never before, so much so that no organization undertakes `serious' projects without the support of suitable architecture practices.
But, how did SA practice evolve in the past 25 years? and What are the challenges ahead?
There have been various attempts to summarize the state of research and practice of SA.
Still, we miss the practitioners' view on the questions above.
To fill this gap, we have first extracted the top-10 topics resulting from the analysis of 5,622 scientific papers.
Then, we have used such topics to design an online survey filled out by 57 SA practitioners with 5 to 20+ years of experience.
We present the results of the survey with a special focus on the SA topics that SA practitioners perceive, in the past, present and future, as the most impactful.
We finally use the results to draw preliminary takeaways.
The construction of a reference ontology for a large domain still remains an hard human task.
The process is sometimes assisted by software tools that facilitate the information extraction from a textual corpus.
Despite of the great use of XML Schema files on the internet and especially in the B2B domain, tools that offer a complete semantic analysis of XML schemas are really rare.
In this paper we introduce Janus, a tool for automatically building a reference knowledge base starting from XML Schema files.
Janus also provides different useful views to simplify B2B application integration.
An accurate modeling of skin effect inside conductors is of capital importance to solve transmission line and scattering problems.
This paper presents a surface-based formulation to model skin effect in conductors of arbitrary cross section, and compute the per-unit-length impedance of a multiconductor transmission line.
The proposed formulation is based on the Dirichlet-Neumann operator that relates the longitudinal electric field to the tangential magnetic field on the boundary of a conductor.
We demonstrate how the surface operator can be obtained through the contour integral method for conductors of arbitrary shape.
The proposed algorithm is simple to implement, efficient, and can handle arbitrary cross-sections, which is a main advantage over the existing approach based on eigenfunctions, which is available only for canonical conductor's shapes.
The versatility of the method is illustrated through a diverse set of examples, which includes transmission lines with trapezoidal, curved, and V-shaped conductors.
Numerical results demonstrate the accuracy, versatility, and efficiency of the proposed technique.
Cryptography is an important field in the area of data encryption.
There are different cryptographic techniques available varying from the simplest to complex.
One of the complex symmetric key cryptography techniques is using Data Encryption Standard Algorithm.
This paper explores a unique approach to generation of key using fingerprint.
The generated key is used as an input key to the DES Algorithm
The objective of Content-Based Image Retrieval (CBIR) methods is essentially to extract, from large (image) databases, a specified number of images similar in visual and semantic content to a so-called query image.
To bridge the semantic gap that exists between the representation of an image by low-level features (namely, colour, shape, texture) and its high-level semantic content as perceived by humans, CBIR systems typically make use of the relevance feedback (RF) mechanism.
RF iteratively incorporates user-given inputs regarding the relevance of retrieved images, to improve retrieval efficiency.
One approach is to vary the weights of the features dynamically via feature reweighting.
In this work, an attempt has been made to improve retrieval accuracy by enhancing a CBIR system based on color features alone, through implicit incorporation of shape information obtained through prior segmentation of the images.
Novel schemes for feature reweighting as well as for initialization of the relevant set for improved relevance feedback, have also been proposed for boosting performance of RF- based CBIR.
At the same time, new measures for evaluation of retrieval accuracy have been suggested, to overcome the limitations of existing measures in the RF context.
Results of extensive experiments have been presented to illustrate the effectiveness of the proposed approaches.
A heterogeneous resource, such as a land-estate, is already divided among several agents in an unfair way.
It should be re-divided among the agents in a way that balances fairness with ownership rights.
We present re-division protocols that attain various trade-off points between fairness and ownership rights, in various settings differing in the geometric constraints on the allotments: (a) no geometric constraints; (b) connectivity --- the cake is a one-dimensional interval and each piece must be a contiguous interval; (c) rectangularity --- the cake is a two-dimensional rectangle or rectilinear polygon and the pieces should be rectangles; (d) convexity --- the cake is a two-dimensional convex polygon and the pieces should be convex.
Our re-division protocols have implications on another problem: the price-of-fairness --- the loss of social welfare caused by fairness requirements.
Each protocol implies an upper bound on the price-of-fairness with the respective geometric constraints.
In this paper, we report on experiments with the use of local measures for depth motion for visual action recognition from MPEG encoded RGBD video sequences.
We show that such measures can be combined with local space-time video descriptors for appearance to provide a computationally efficient method for recognition of actions.
Fisher vectors are used for encoding and concatenating a depth descriptor with existing RGB local descriptors.
We then employ a linear SVM for recognizing manipulation actions using such vectors.
We evaluate the effectiveness of such measures by comparison to the state-of-the-art using two recent datasets for action recognition in kitchen environments.
This paper reports the analysis of audio and visual features in predicting the continuous emotion dimensions under the seventh Audio/Visual Emotion Challenge (AVEC 2017), which was done as part of a B.Tech.2nd year internship project.
For visual features we used the HOG (Histogram of Gradients) features, Fisher encodings of SIFT (Scale-Invariant Feature Transform) features based on Gaussian mixture model (GMM) and some pretrained Convolutional Neural Network layers as features; all these extracted for each video clip.
For audio features we used the Bag-of-audio-words (BoAW) representation of the LLDs (low-level descriptors) generated by openXBOW provided by the organisers of the event.
Then we trained fully connected neural network regression model on the dataset for all these different modalities.
We applied multimodal fusion on the output models to get the Concordance correlation coefficient on Development set as well as Test set.
This paper proposes a joint segmentation and deconvolution Bayesian method for medical ultrasound (US) images.
Contrary to piecewise homogeneous images, US images exhibit heavy characteristic speckle patterns correlated with the tissue structures.
The generalized Gaussian distribution (GGD) has been shown to be one of the most relevant distributions for characterizing the speckle in US images.
Thus, we propose a GGD-Potts model defined by a label map coupling US image segmentation and deconvolution.
The Bayesian estimators of the unknown model parameters, including the US image, the label map and all the hyperparameters are difficult to be expressed in closed form.
Thus, we investigate a Gibbs sampler to generate samples distributed according to the posterior of interest.
These generated samples are finally used to compute the Bayesian estimators of the unknown parameters.
The performance of the proposed Bayesian model is compared with existing approaches via several experiments conducted on realistic synthetic data and in vivo US images.
Recent machine learning algorithms dedicated to solving semi-linear PDEs are improved by using different neural network architectures and different parameterizations.
These algorithms are compared to a new one that solves a fixed point problem by using deep learning techniques.
This new algorithm appears to be competitive in terms of accuracy with the best existing algorithms.
Liveliness detection acts as a safe guard against spoofing attacks.
Most of the researchers used vision based techniques to detect liveliness of the user, but they are highly sensitive to illumination effects.
Therefore it is very hard to design a system, which will work robustly under all circumstances.
Literature shows that most of the research utilize eye blink or mouth movement to detect the liveliness, while the other group used face texture to distinguish between real and imposter.
The classification results of all these approaches decreases drastically in variable light conditions.
Hence in this paper we are introducing fuzzy expert system which is sufficient enough to handle most of the cases comes in real time.
We have used two testing parameters, (a) under bad illumination and (b) less movement in eyes and mouth in case of real user to evaluate the performance of the system.
The system is behaving well in all, while in first case its False Rejection Rate (FRR) is 0.28, and in second case its FRR is 0.4.
The launch of Google Scholar (GS) marked the beginning of a revolution in the scientific information market.
This search engine, unlike traditional databases, automatically indexes information from the academic web.
Its ease of use, together with its wide coverage and fast indexing speed, have made it the first tool most scientists currently turn to when they need to carry out a literature search.
Additionally, the fact that its search results were accompanied from the beginning by citation counts, as well as the later development of secondary products which leverage this citation data (such as Google Scholar Metrics and Google Scholar Citations), made many scientists wonder about its potential as a source of data for bibliometric analyses.
The goal of this chapter is to lay the foundations for the use of GS as a supplementary source (and in some disciplines, arguably the best alternative) for scientific evaluation.
First, we present a general overview of how GS works.
Second, we present empirical evidences about its main characteristics (size, coverage, and growth rate).
Third, we carry out a systematic analysis of the main limitations this search engine presents as a tool for the evaluation of scientific performance.
Lastly, we discuss the main differences between GS and other more traditional bibliographic databases in light of the correlations found between their citation data.
We conclude that Google Scholar presents a broader view of the academic world because it has brought to light a great amount of sources that were not previously visible.
Multi-label classification (MLC) is a supervised learning problem in which, contrary to standard multiclass classification, an instance can be associated with several class labels simultaneously.
In this chapter, we advocate a rule-based approach to multi-label classification.
Rule learning algorithms are often employed when one is not only interested in accurate predictions, but also requires an interpretable theory that can be understood, analyzed, and qualitatively evaluated by domain experts.
Ideally, by revealing patterns and regularities contained in the data, a rule-based theory yields new insights in the application domain.
Recently, several authors have started to investigate how rule-based models can be used for modeling multi-label data.
Discussing this task in detail, we highlight some of the problems that make rule learning considerably more challenging for MLC than for conventional classification.
While mainly focusing on our own previous work, we also provide a short overview of related work in this area.
In this paper, we propose a convolutional neural network(CNN) with 3-D rank-1 filters which are composed by the outer product of 1-D filters.
After being trained, the 3-D rank-1 filters can be decomposed into 1-D filters in the test time for fast inference.
The reason that we train 3-D rank-1 filters in the training stage instead of consecutive 1-D filters is that a better gradient flow can be obtained with this setting, which makes the training possible even in the case where the network with consecutive 1-D filters cannot be trained.
The 3-D rank-1 filters are updated by both the gradient flow and the outer product of the 1-D filters in every epoch, where the gradient flow tries to obtain a solution which minimizes the loss function, while the outer product operation tries to make the parameters of the filter to live on a rank-1 sub-space.
Furthermore, we show that the convolution with the rank-1 filters results in low rank outputs, constraining the final output of the CNN also to live on a low dimensional subspace.
The question whether an ontology can safely be replaced by another, possibly simpler, one is fundamental for many ontology engineering and maintenance tasks.
It underpins, for example, ontology versioning, ontology modularization, forgetting, and knowledge exchange.
What safe replacement means depends on the intended application of the ontology.
If, for example, it is used to query data, then the answers to any relevant ontology-mediated query should be the same over any relevant data set; if, in contrast, the ontology is used for conceptual reasoning, then the entailed subsumptions between concept expressions should coincide.
This gives rise to different notions of ontology inseparability such as query inseparability and concept inseparability, which generalize corresponding notions of conservative extensions.
We survey results on various notions of inseparability in the context of description logic ontologies, discussing their applications, useful model-theoretic characterizations, algorithms for determining whether two ontologies are inseparable (and, sometimes, for computing the difference between them if they are not), and the computational complexity of this problem.
Many internal software metrics and external quality attributes of Java programs correlate strongly with program size.
This knowledge has been used pervasively in quantitative studies of software through practices such as normalization on size metrics.
This paper reports size-related super- and sublinear effects that have not been known before.
Findings obtained on a very large collection of Java programs -- 30,911 projects hosted at Google Code as of Summer 2011 -- unveils how certain characteristics of programs vary disproportionately with program size, sometimes even non-monotonically.
Many of the specific parameters of nonlinear relations are reported.
This result gives further insights for the differences of "programming in the small" vs. "programming in the large."
The reported findings carry important consequences for OO software metrics, and software research in general: metrics that have been known to correlate with size can now be properly normalized so that all the information that is left in them is size-independent.
The results obtained by analyzing signals with the Square Wave Method (SWM) introduced previously can be presented in the frequency domain clearly and precisely by using the Square Wave Transform (SWT) described here.
As an example, the SWT is used to analyze a sequence of samples (that is, of measured values) taken from an electroencephalographic recording.
We provide a framework for determining the centralities of agents in a broad family of random networks.
Current understanding of network centrality is largely restricted to deterministic settings, but practitioners frequently use random network models to accommodate data limitations or prove asymptotic results.
Our main theorems show that on large random networks, centrality measures are close to their expected values with high probability.
We illustrate the economic consequences of these results by presenting three applications: (1) In network formation models based on community structure (called stochastic block models), we show network segregation and differences in community size produce inequality.
Benefits from peer effects tend to accrue disproportionately to bigger and better-connected communities.
(2) When link probabilities depend on geography, we can compute and compare the centralities of agents in different locations.
(3) In models where connections depend on several independent characteristics, we give a formula that determines centralities 'characteristic-by-characteristic'.
The basic techniques from these applications, which use the main theorems to reduce questions about random networks to deterministic calculations, extend to many network games.
In this paper, we propose a normalized cut segmentation algorithm with spatial regularization priority and adaptive similarity matrix.
We integrate the well-known expectation-maximum(EM) method in statistics and the regularization technique in partial differential equation (PDE) method into normalized cut (Ncut).
The introduced EM technique makes our method can adaptively update the similarity matrix, which can help us to get a better classification criterion than the classical Ncut method.
While the regularization priority can guarantee the proposed algorithm has a robust performance under noise.
To unify the three totally different methods including EM, spatial regularization, and spectral graph clustering, we built a variational framework to combine them and get a general normalized cut segmentation algorithm.
The well-defined theory of the proposed model is also given in the paper.
Compared with some existing spectral clustering methods such as the traditional Ncut algorithm and the variational based Chan-Vese model, the numerical experiments show that our methods can achieve promising segmentation performance.
Decision making in modern large-scale and complex systems such as communication networks, smart electricity grids, and cyber-physical systems motivate novel game-theoretic approaches.
This paper investigates big strategic (non-cooperative) games where a finite number of individual players each have a large number of continuous decision variables and input data points.
Such high-dimensional decision spaces and big data sets lead to computational challenges, relating to efforts in non-linear optimization scaling up to large systems of variables.
In addition to these computational challenges, real-world players often have limited information about their preference parameters due to the prohibitive cost of identifying them or due to operating in dynamic online settings.
The challenge of limited information is exacerbated in high dimensions and big data sets.
Motivated by both computational and information limitations that constrain the direct solution of big strategic games, our investigation centers around reductions using linear transformations such as random projection methods and their effect on Nash equilibrium solutions.
Specific analytical results are presented for quadratic games and approximations.
In addition, an adversarial learning game is presented where random projection and sampling schemes are investigated.
I assume in this paper that the proposition "I cannot know your intentional states" is true.
I consider its consequences on the use of so-called "intentional concepts" for Requirements Engineering.
I argue that if you take this proposition to be true, then intentional concepts (e.g., goal, belief, desire, intention, etc.) start to look less relevant (though not irrelevant), despite being the focus of significant research attention over the past three decades.
I identify substantial problems that arise if you use instances of intentional concepts to reflect intentional states.
I sketch an approach to address these problems.
In it, intentional concepts have a less prominent role, while notions of time, uncertainty, prediction, observability, evidence, and learning are at the forefront.
Testing has become an indispensable activity of software development, yet writing good and relevant tests remains a quite challenging task.
One well-known problem is that it often is impossible or unrealistic to test for every outcome, as the input and/or output of a program component can represent incredbly large, unless infinite domains.
A common approach to tackle this issue it to only test classes of cases, and to assume that those classes cover all (or at least most) of the cases a component is susceptible to be exposed to.
Unfortunately, those kind of assumptions can prove wrong in many situations, causing a yet well-tested program to fail upon a particular input.
In this short paper, we propose to leverage formal verification, in particular model checking techniques, as a way to better identify cases for which the aforementioned assumptions do not hold, and ultimately strenghten the confidence one can have in a test suite.
The idea is to extract a formal specification of the data types of a program, in the form of a term rewriting system, and to check that specification against a set of properties specified by the programmer.
Cases for which tose properties do not hold can then be identified using model checking, and selected as test cases.
This work presents an in-depth analysis of the majority of the deep neural networks (DNNs) proposed in the state of the art for image recognition.
For each DNN multiple performance indices are observed, such as recognition accuracy, model complexity, computational complexity, memory usage, and inference time.
The behavior of such performance indices and some combinations of them are analyzed and discussed.
To measure the indices we experiment the use of DNNs on two different computer architectures, a workstation equipped with a NVIDIA Titan X Pascal and an embedded system based on a NVIDIA Jetson TX1 board.
This experimentation allows a direct comparison between DNNs running on machines with very different computational capacity.
This study is useful for researchers to have a complete view of what solutions have been explored so far and in which research directions are worth exploring in the future; and for practitioners to select the DNN architecture(s) that better fit the resource constraints of practical deployments and applications.
To complete this work, all the DNNs, as well as the software used for the analysis, are available online.
A formal theory based on a binary operator of directional associative relation is constructed in the article and an understanding of an associative normal form of image constructions is introduced.
A model of a commutative semigroup, which provides a presentation of a sentence as three components of an interrogative linguistic image construction, is considered.
Class prediction is an important application of microarray gene expression data analysis.
The high-dimensionality of microarray data, where number of genes (variables) is very large compared to the number of samples (obser- vations), makes the application of many prediction techniques (e.g., logistic regression, discriminant analysis) difficult.
An efficient way to solve this prob- lem is by using dimension reduction statistical techniques.
Increasingly used in psychology-related applications, Rasch model (RM) provides an appealing framework for handling high-dimensional microarray data.
In this paper, we study the potential of RM-based modeling in dimensionality reduction with binarized microarray gene expression data and investigate its prediction ac- curacy in the context of class prediction using linear discriminant analysis.
Two different publicly available microarray data sets are used to illustrate a general framework of the approach.
Performance of the proposed method is assessed by re-randomization scheme using principal component analysis (PCA) as a benchmark method.
Our results show that RM-based dimension reduction is as effective as PCA-based dimension reduction.
The method is general and can be applied to the other high-dimensional data problems.
Sparse code multiple access (SCMA) scheme is considered to be one promising non-orthogonal multiple access technology for the future fifth generation (5G) communications.
Due to the sparse nature, message passing algorithm (MPA) has been used as the receiver to achieve close to maximum likelihood (ML) detection performance with much lower complexity.
However, the complexity order of MPA is still exponential with the size of codebook and the degree of signal superposition on a given resource element.
In this paper, we propose a novel low complexity iterative receiver based on expectation propagation algorithm (EPA), which reduces the complexity order from exponential to linear.
Simulation results demonstrate that the proposed EPA receiver achieves nearly the same block error rate (BLER) performance as the conventional message passing algorithm (MPA) receiver with orders less complexity.
Reliable 4D aircraft trajectory prediction, whether in a real-time setting or for analysis of counterfactuals, is important to the efficiency of the aviation system.
Toward this end, we first propose a highly generalizable efficient tree-based matching algorithm to construct image-like feature maps from high-fidelity meteorological datasets - wind, temperature and convective weather.
We then model the track points on trajectories as conditional Gaussian mixtures with parameters to be learned from our proposed deep generative model, which is an end-to-end convolutional recurrent neural network that consists of a long short-term memory (LSTM) encoder network and a mixture density LSTM decoder network.
The encoder network embeds last-filed flight plan information into fixed-size hidden state variables and feeds the decoder network, which further learns the spatiotemporal correlations from the historical flight tracks and outputs the parameters of Gaussian mixtures.
Convolutional layers are integrated into the pipeline to learn representations from the high-dimension weather features.
During the inference process, beam search, adaptive Kalman filter, and Rauch-Tung-Striebel smoother algorithms are used to prune the variance of generated trajectories.
We provide a complete characterisation of the phenomenon of adversarial examples - inputs intentionally crafted to fool machine learning models.
We aim to cover all the important concerns in this field of study: (1) the conjectures on the existence of adversarial examples, (2) the security, safety and robustness implications, (3) the methods used to generate and (4) protect against adversarial examples and (5) the ability of adversarial examples to transfer between different machine learning models.
We provide ample background information in an effort to make this document self-contained.
Therefore, this document can be used as survey, tutorial or as a catalog of attacks and defences using adversarial examples.
In this paper, adaptive neural control (ANC) is investigated for a class of strict-feedback nonlinear stochastic systems with unknown parameters, unknown nonlinear functions and stochastic disturbances.
The new controller of adaptive neural network with state feedback is presented by using a universal approximation of radial basis function neural network and backstepping.
An adaptive neural network state-feedback controller is designed by constructing a suitable Lyapunov function.
Adaptive bounding design technique is used to deal with the unknown nonlinear functions and unknown parameters.
It is shown that, the global asymptotically stable in probability can be achieved for the closed-loop system.
The simulation results are presented to demonstrate the effectiveness of the proposed control strategy in the presence of unknown parameters, unknown nonlinear functions and stochastic disturbances.
Representing knowledge as high-dimensional vectors in a continuous semantic vector space can help overcome the brittleness and incompleteness of traditional knowledge bases.
We present a method for performing deductive reasoning directly in such a vector space, combining analogy, association, and deduction in a straightforward way at each step in a chain of reasoning, drawing on knowledge from diverse sources and ontologies.
We present PFDCMSS, a novel message-passing based parallel algorithm for mining time-faded heavy hitters.
The algorithm is a parallel version of the recently published FDCMSS sequential algorithm.
We formally prove its correctness by showing that the underlying data structure, a sketch augmented with a Space Saving stream summary holding exactly two counters, is mergeable.
Whilst mergeability of traditional sketches derives immediately from theory, we show that merging our augmented sketch is non trivial.
Nonetheless, the resulting parallel algorithm is fast and simple to implement.
To the best of our knowledge, PFDCMSS is the first parallel algorithm solving the problem of mining time-faded heavy hitters on message-passing parallel architectures.
Extensive experimental results confirm that PFDCMSS retains the extreme accuracy and error bound provided by FDCMSS whilst providing excellent parallel scalability.
We propose a version of the follow-the-perturbed-leader online prediction algorithm in which the cumulative losses are perturbed by independent symmetric random walks.
The forecaster is shown to achieve an expected regret of the optimal order O(sqrt(n log N)) where n is the time horizon and N is the number of experts.
More importantly, it is shown that the forecaster changes its prediction at most O(sqrt(n log N)) times, in expectation.
We also extend the analysis to online combinatorial optimization and show that even in this more general setting, the forecaster rarely switches between experts while having a regret of near-optimal order.
Social dilemmas, where mutual cooperation can lead to high payoffs but participants face incentives to cheat, are ubiquitous in multi-agent interaction.
We wish to construct agents that cooperate with pure cooperators, avoid exploitation by pure defectors, and incentivize cooperation from the rest.
However, often the actions taken by a partner are (partially) unobserved or the consequences of individual actions are hard to predict.
We show that in a large class of games good strategies can be constructed by conditioning one's behavior solely on outcomes (ie. one's past rewards).
We call this consequentialist conditional cooperation.
We show how to construct such strategies using deep reinforcement learning techniques and demonstrate, both analytically and experimentally, that they are effective in social dilemmas beyond simple matrix games.
We also show the limitations of relying purely on consequences and discuss the need for understanding both the consequences of and the intentions behind an action.
Automatic language identification is a natural language processing problem that tries to determine the natural language of a given content.
In this paper we present a statistical method for automatic language identification of written text using dictionaries containing stop words and diacritics.
We propose different approaches that combine the two dictionaries to accurately determine the language of textual corpora.
This method was chosen because stop words and diacritics are very specific to a language, although some languages have some similar words and special characters they are not all common.
The languages taken into account were romance languages because they are very similar and usually it is hard to distinguish between them from a computational point of view.
We have tested our method using a Twitter corpus and a news article corpus.
Both corpora consists of UTF-8 encoded text, so the diacritics could be taken into account, in the case that the text has no diacritics only the stop words are used to determine the language of the text.
The experimental results show that the proposed method has an accuracy of over 90% for small texts and over 99.8% for
Mobile devices gather the communication capabilities as no other gadget.
Plus, they now comprise a wider set of applications while still maintaining reduced size and weight.
They have started to include accessibility features that enable the inclusion of disabled people.
However, these inclusive efforts still fall short considering the possibilities of such devices.
This is mainly due to the lack of interoperability and extensibility of current mobile operating systems (OS).
In this paper, we present a case study of a multi-impaired person where access to basic mobile applications was provided in an applicational basis.
We outline the main flaws in current mobile OS and suggest how these could further empower developers to provide accessibility components.
These could then be compounded to provide system-wide inclusion to a wider range of (multi)-impairments.
We empirically investigate learning from partial feedback in neural machine translation (NMT), when partial feedback is collected by asking users to highlight a correct chunk of a translation.
We propose a simple and effective way of utilizing such feedback in NMT training.
We demonstrate how the common machine translation problem of domain mismatch between training and deployment can be reduced solely based on chunk-level user feedback.
We conduct a series of simulation experiments to test the effectiveness of the proposed method.
Our results show that chunk-level feedback outperforms sentence based feedback by up to 2.61% BLEU absolute.
This paper provides a case for using Bayesian data analysis (BDA) to make more grounded claims regarding practical significance of software engineering research.
We show that using BDA, here combined with cumulative prospect theory (CPT), is appropriate when a researcher or practitioner wants to make clearer connections between statistical findings and practical significance in empirical software engineering research.
To illustrate our point we provide an example case using previously published data.
We build a multilevel Bayesian model for this data, for which we compare the out of sample predictive power.
Finally, we use our model to make out of sample predictions while, ultimately, connecting this to practical significance using CPT.
Throughout the case that we present, we argue that a Bayesian approach is a natural, theoretically well-grounded, practical work-flow for data analysis in empirical software engineering.
By including prior beliefs, assuming parameters are drawn from a probability distribution, assuming the true value is a random variable for uncertainty intervals, using counter-factual plots for sanity checks, conducting predictive posterior checks, and out of sample predictions, we will better understand the phenomenon being studied, while at the same time avoid the obsession with p-values.
Recent studies have numerically demonstrated the possible advantages of the asynchronous non-orthogonal multiple access (ANOMA) over the conventional synchronous non-orthogonal multiple access (NOMA).
The ANOMA makes use of the oversampling technique by intentionally introducing a timing mismatch between symbols of different users.
Focusing on a two-user uplink system, for the first time, we analytically prove that the ANOMA with a sufficiently large frame length can always outperform the NOMA in terms of the sum throughput.
To this end, we derive the expression for the sum throughput of the ANOMA as a function of signal-to-noise ratio (SNR), frame length, and normalized timing mismatch.
Based on the derived expression, we find that users should transmit at full powers to maximize the sum throughput.
In addition, we obtain the optimal timing mismatch as the frame length goes to infinity.
Moreover, we comprehensively study the impact of timing error on the ANOMA throughput performance.
Two types of timing error, i.e., the synchronization timing error and the coordination timing error, are considered.
We derive the throughput loss incurred by both types of timing error and find that the synchronization timing error has a greater impact on the throughput performance compared to the coordination timing error.
The Bulk Synchronous Parallel(BSP) computational model has emerged as the dominant distributed framework to build large-scale iterative graph processing systems.
While its implementations(e.g., Pregel, Giraph, and Hama) achieve high scalability, frequent synchronization and communication among the workers can cause substantial parallel inefficiency.
To help address this critical concern, this paper introduces the GraphHP(Graph Hybrid Processing) platform which inherits the friendly vertex-centric BSP programming interface and optimizes its synchronization and communication overhead.
To achieve the goal, we first propose a hybrid execution model which differentiates between the computations within a graph partition and across the partitions, and decouples the computations within a partition from distributed synchronization and communication.
By implementing the computations within a partition by pseudo-superstep iteration in memory, the hybrid execution model can effectively reduce synchronization and communication overhead while not requiring heavy scheduling overhead or graph-centric sequential algorithms.
We then demonstrate how the hybrid execution model can be easily implemented within the BSP abstraction to preserve its simple programming interface.
Finally, we evaluate our implementation of the GraphHP platform on classical BSP applications and show that it performs significantly better than the state-of-the-art BSP implementations.
Our GraphHP implementation is based on Hama, but can easily generalize to other BSP platforms.
An important problem in the implementation of Markov Chain Monte Carlo algorithms is to determine the convergence time, or the number of iterations before the chain is close to stationarity.
For many Markov chains used in practice this time is not known.
Even in cases where the convergence time is known to be polynomial, the theoretical bounds are often too crude to be practical.
Thus, practitioners like to carry out some form of statistical analysis in order to assess convergence.
This has led to the development of a number of methods known as convergence diagnostics which attempt to diagnose whether the Markov chain is far from stationarity.
We study the problem of testing convergence in the following settings and prove that the problem is hard in a computational sense: Given a Markov chain that mixes rapidly, it is hard for Statistical Zero Knowledge (SZK-hard) to distinguish whether starting from a given state, the chain is close to stationarity by time t or far from stationarity at time ct for a constant c. We show the problem is in AM intersect coAM.
Second, given a Markov chain that mixes rapidly it is coNP-hard to distinguish whether it is close to stationarity by time t or far from stationarity at time ct for a constant c. The problem is in coAM.
Finally, it is PSPACE-complete to distinguish whether the Markov chain is close to stationarity by time t or far from being mixed at time ct for c at least 1.
Delay-coordinate reconstruction is a proven modeling strategy for building effective forecasts of nonlinear time series.
The first step in this process is the estimation of good values for two parameters, the time delay and the embedding dimension.
Many heuristics and strategies have been proposed in the literature for estimating these values.
Few, if any, of these methods were developed with forecasting in mind, however, and their results are not optimal for that purpose.
Even so, these heuristics---intended for other applications---are routinely used when building delay coordinate reconstruction-based forecast models.
In this paper, we propose a new strategy for choosing optimal parameter values for forecast methods that are based on delay-coordinate reconstructions.
The basic calculation involves maximizing the shared information between each delay vector and the future state of the system.
We illustrate the effectiveness of this method on several synthetic and experimental systems, showing that this metric can be calculated quickly and reliably from a relatively short time series, and that it provides a direct indication of how well a near-neighbor based forecasting method will work on a given delay reconstruction of that time series.
This allows a practitioner to choose reconstruction parameters that avoid any pathologies, regardless of the underlying mechanism, and maximize the predictive information contained in the reconstruction.
Advances in sequencing techniques have led to exponential growth in biological data, demanding the development of large-scale bioinformatics experiments.
Because these experiments are computation- and data-intensive, they require high-performance computing (HPC) techniques and can benefit from specialized technologies such as Scientific Workflow Management Systems (SWfMS) and databases.
In this work, we present BioWorkbench, a framework for managing and analyzing bioinformatics experiments.
This framework automatically collects provenance data, including both performance data from workflow execution and data from the scientific domain of the workflow application.
Provenance data can be analyzed through a web application that abstracts a set of queries to the provenance database, simplifying access to provenance information.
We evaluate BioWorkbench using three case studies: SwiftPhylo, a phylogenetic tree assembly workflow; SwiftGECKO, a comparative genomics workflow; and RASflow, a RASopathy analysis workflow.
We analyze each workflow from both computational and scientific domain perspectives, by using queries to a provenance and annotation database.
Some of these queries are available as a pre-built feature of the BioWorkbench web application.
Through the provenance data, we show that the framework is scalable and achieves high-performance, reducing up to 98% of the case studies execution time.
We also show how the application of machine learning techniques can enrich the analysis process.
Transportation processes, which play a prominent role in the life and social sciences, are typically described by discrete models on lattices.
For studying their dynamics a continuous formulation of the problem via partial differential equations (PDE) is employed.
In this paper we propose a symbolic computation approach to derive mean-field PDEs from a lattice-based model.
We start with the microscopic equations, which state the probability to find a particle at a given lattice site.
Then the PDEs are formally derived by Taylor expansions of the probability densities and by passing to an appropriate limit as the time steps and the distances between lattice sites tend to zero.
We present an implementation in a computer algebra system that performs this transition for a general class of models.
In order to rewrite the mean-field PDEs in a conservative formulation, we adapt and implement symbolic integration methods that can handle unspecified functions in several variables.
To illustrate our approach, we consider an application in crowd motion analysis where the dynamics of bidirectional flows are studied.
However, the presented approach can be applied to various transportation processes of multiple species with variable size in any dimension, for example, to confirm several proposed mean-field models for cell motility.
In social networks, link prediction predicts missing links in current networks and new or dissolution links in future networks, is important for mining and analyzing the evolution of social networks.
In the past decade, many works have been done about the link prediction in social networks.
The goal of this paper is to comprehensively review, analyze and discuss the state-of-the-art of the link prediction in social networks.
A systematical category for link prediction techniques and problems is presented.
Then link prediction techniques and problems are analyzed and discussed.
Typical applications of link prediction are also addressed.
Achievements and roadmaps of some active research groups are introduced.
Finally, some future challenges of the link prediction in social networks are discussed.
In this paper, is used the Lagrangian classical mechanics for modeling the dynamics of an underactuated system, specifically a rotary inverted pendulum that will have two equations of motion.
A basic design of the system is proposed in SOLIDWORKS 3D CAD software, which based on the material and dimensions of the model provides some physical variables necessary for modeling.
In order to verify the results obtained, a comparison the CAD model simulated in the environment SimMechanics of MATLAB software with the mathematical model who was consisting of Euler Lagrange's equations implemented in Simulink MATLAB, solved with the ODE23tb method, included in the MATLAB libraries for the solution of systems of equations of the type and order obtained.
This article also has a topological analysis of pendulum trajectories through a phase space diagram, which allows the identification of stable and unstable regions of the system.
We provide a solution for elementary science test using instructional materials.
We posit that there is a hidden structure that explains the correctness of an answer given the question and instructional materials and present a unified max-margin framework that learns to find these hidden structures (given a corpus of question-answer pairs and instructional materials), and uses what it learns to answer novel elementary science questions.
Our evaluation shows that our framework outperforms several strong baselines.
In IEEE 802.11 networks, selfish stations can pursue a better quality of service (QoS) through selfish MAC-layer attacks.
Such attacks are easy to perform, secure routing protocols do not prevent them, and their detection may be complex.
Two-hop relay topologies allow a new angle of attack: a selfish relay can tamper with either source traffic, transit traffic, or both.
We consider the applicability of selfish attacks and their variants in the two-hop relay topology, quantify their impact, and study defense measures.
The age of the root of the Indo-European language family has received much attention since the application of Bayesian phylogenetic methods by Gray and Atkinson(2003).
The root age of the Indo-European family has tended to decrease from an age that supported the Anatolian origin hypothesis to an age that supports the Steppe origin hypothesis with the application of new models (Chang et al., 2015).
However, none of the published work in the Indo-European phylogenetics studied the effect of tree priors on phylogenetic analyses of the Indo-European family.
In this paper, I intend to fill this gap by exploring the effect of tree priors on different aspects of the Indo-European family's phylogenetic inference.
I apply three tree priors---Uniform, Fossilized Birth-Death (FBD), and Coalescent---to five publicly available datasets of the Indo-European language family.
I evaluate the posterior distribution of the trees from the Bayesian analysis using Bayes Factor, and find that there is support for the Steppe origin hypothesis in the case of two tree priors.
I report the median and 95% highest posterior density (HPD) interval of the root ages for all the three tree priors.
A model comparison suggested that either Uniform prior or FBD prior is more suitable than the Coalescent prior to the datasets belonging to the Indo-European language family.
We propose a language-agnostic way of automatically generating sets of semantically similar clusters of entities along with sets of "outlier" elements, which may then be used to perform an intrinsic evaluation of word embeddings in the outlier detection task.
We used our methodology to create a gold-standard dataset, which we call WikiSem500, and evaluated multiple state-of-the-art embeddings.
The results show a correlation between performance on this dataset and performance on sentiment analysis.
The discriminative power of modern deep learning models for 3D human action recognition is growing ever so potent.
In conjunction with the recent resurgence of 3D human action representation with 3D skeletons, the quality and the pace of recent progress have been significant.
However, the inner workings of state-of-the-art learning based methods in 3D human action recognition still remain mostly black-box.
In this work, we propose to use a new class of models known as Temporal Convolutional Neural Networks (TCN) for 3D human action recognition.
Compared to popular LSTM-based Recurrent Neural Network models, given interpretable input such as 3D skeletons, TCN provides us a way to explicitly learn readily interpretable spatio-temporal representations for 3D human action recognition.
We provide our strategy in re-designing the TCN with interpretability in mind and how such characteristics of the model is leveraged to construct a powerful 3D activity recognition method.
Through this work, we wish to take a step towards a spatio-temporal model that is easier to understand, explain and interpret.
The resulting model, Res-TCN, achieves state-of-the-art results on the largest 3D human action recognition dataset, NTU-RGBD.
Numerous variants of Self-Organizing Maps (SOMs) have been proposed in the literature, including those which also possess an underlying structure, and in some cases, this structure itself can be defined by the user Although the concepts of growing the SOM and updating it have been studied, the whole issue of using a self-organizing Adaptive Data Structure (ADS) to further enhance the properties of the underlying SOM, has been unexplored.
In an earlier work, we impose an arbitrary, user-defined, tree-like topology onto the codebooks, which consequently enforced a neighborhood phenomenon and the so-called tree-based Bubble of Activity.
In this paper, we consider how the underlying tree itself can be rendered dynamic and adaptively transformed.
To do this, we present methods by which a SOM with an underlying Binary Search Tree (BST) structure can be adaptively re-structured using Conditional Rotations (CONROT).
These rotations on the nodes of the tree are local, can be done in constant time, and performed so as to decrease the Weighted Path Length (WPL) of the entire tree.
In doing this, we introduce the pioneering concept referred to as Neural Promotion, where neurons gain prominence in the Neural Network (NN) as their significance increases.
We are not aware of any research which deals with the issue of Neural Promotion.
The advantages of such a scheme is that the user need not be aware of any of the topological peculiarities of the stochastic data distribution.
Rather, the algorithm, referred to as the TTOSOM with Conditional Rotations (TTOCONROT), converges in such a manner that the neurons are ultimately placed in the input space so as to represent its stochastic distribution, and additionally, the neighborhood properties of the neurons suit the best BST that represents the data.
These properties have been confirmed by our experimental results on a variety of data sets.
Conceived in the early 1990s, Experience Replay (ER) has been shown to be a successful mechanism to allow online learning algorithms to reuse past experiences.
Traditionally, ER can be applied to all machine learning paradigms (i.e., unsupervised, supervised, and reinforcement learning).
Recently, ER has contributed to improving the performance of deep reinforcement learning.
Yet, its application to many practical settings is still limited by the memory requirements of ER, necessary to explicitly store previous observations.
To remedy this issue, we explore a novel approach, Online Contrastive Divergence with Generative Replay (OCD_GR), which uses the generative capability of Restricted Boltzmann Machines (RBMs) instead of recorded past experiences.
The RBM is trained online, and does not require the system to store any of the observed data points.
We compare OCD_GR to ER on 9 real-world datasets, considering a worst-case scenario (data points arriving in sorted order) as well as a more realistic one (sequential random-order data points).
Our results show that in 64.28% of the cases OCD_GR outperforms ER and in the remaining 35.72% it has an almost equal performance, while having a considerably reduced space complexity (i.e., memory usage) at a comparable time complexity.
Conventional surveillance systems for monitoring infectious diseases, such as influenza, face challenges due to shortage of skilled healthcare professionals, remoteness of communities and absence of communication infrastructures.
Internet-based approaches for surveillance are appealing logistically as well as economically.
Search engine queries and Twitter have been the primarily used data sources in such approaches.
The aim of this study is to assess the predictive power of an alternative data source, Instagram.
By using 317 weeks of publicly available data from Instagram, we trained several machine learning algorithms to both nowcast and forecast the number of official influenza-like illness incidents in Finland where population-wide official statistics about the weekly incidents are available.
In addition to date and hashtag count features of online posts, we were able to utilize also the visual content of the posted images with the help of deep convolutional neural networks.
Our best nowcasting model reached a mean absolute error of 11.33 incidents per week and a correlation coefficient of 0.963 on the test data.
Forecasting models for predicting 1 week and 2 weeks ahead showed statistical significance as well by reaching correlation coefficients of 0.903 and 0.862, respectively.
This study demonstrates how social media and in particular, digital photographs shared in them, can be a valuable source of information for the field of infodemiology.
This paper investigates reversibility properties of 1-dimensional 3-neighborhood d-state finite cellular automata (CAs) of length n under periodic boundary condition.
A tool named reachability tree has been developed from de Bruijn graph which represents all possible reachable configurations of an n-cell CA.
This tool has been used to test reversibility of CAs.
We have identified a large set of reversible CAs using this tool by following some greedy strategies.
Coupled natural systems are generally modeled at multiple abstraction levels.
Both structural scale and behavioral complexity of these models are determinants in the kinds of questions that can be posed and answered.
As scale and complexity of models increase, simulation efficiency must increase to resolve tradeoffs between model resolution and simulation time.
From this vantage point, we will show some problems and solutions by using as example a vegetation-landscape model where individual plants belonging to different species are represented as collectives that undergo growth and decline cycles spanning hundreds of years.
Collective plant entities are assigned to cells of a static, two-dimensional grid.
This coarse-grain model, guided by homomorphic modeling ideas, is derived from a fine-grain model representing plants as individual objects.
These models are developed using Python and GRASS tools.
A set of experiments is devised to reveal some barriers in modeling and simulating this class of systems.
A wireless network is realized by mobile devices which communicate over radio channels.
Since, experiments of real life problem with real devices are very difficult, simulation is used very often.
Among many other important properties that have to be defined for simulative experiments, the mobility model and the radio propagation model have to be selected carefully.
Both have strong impact on the performance of mobile wireless networks, e.g., the performance of routing protocols varies with these models.
There are many mobility and radio propagation models proposed in literature.
Each of them was developed with different objectives and is not suited for every physical scenario.
The radio propagation models used in common wireless network simulators, in general researcher consider simple radio propagation models and neglect obstacles in the propagation environment.
In this paper, we study the performance of wireless networks simulation by consider different Radio propagation models with considering obstacles in the propagation environment.
In this paper we analyzed the performance of wireless networks by OPNET Modeler .In this paper we quantify the parameters such as throughput, packet received attenuation.
In the last time some papers were devoted to the study of the con- nections between binary block codes and BCK-algebras.
In this paper, we try to generalize these results to n-ary block codes, providing an algorithm which allows us to construct a BCK-algebra from a given n-ary block code.
The aim of this paper is to alter the abstract definition of the program of the theoretical programming model which has been developed at Eotvos Lorand University for many years in order to investigate methods that support designing correct programs.
The motivation of this modification was that the dynamic properties of programs appear in the model.
This new definition of the program gives a hand to extend the model with the concept of subprograms while the earlier results of the original programming model are preserved.
Model checking has been successfully used in many computer science fields, including artificial intelligence, theoretical computer science, and databases.
Most of the proposed solutions make use of classical, point-based temporal logics, while little work has been done in the interval temporal logic setting.
Recently, a non-elementary model checking algorithm for Halpern and Shoham's modal logic of time intervals HS over finite Kripke structures (under the homogeneity assumption) and an EXPSPACE model checking procedure for two meaningful fragments of it have been proposed.
In this paper, we show that more efficient model checking procedures can be developed for some expressive enough fragments of HS.
The newly released Orange D4D mobile phone data base provides new insights into the use of mobile technology in a developing country.
Here we perform a series of spatial data analyses that reveal important geographic aspects of mobile phone use in Cote d'Ivoire.
We first map the locations of base stations with respect to the population distribution and the number and duration of calls at each base station.
On this basis, we estimate the energy consumed by the mobile phone network.
Finally, we perform an analysis of inter-city mobility, and identify high-traffic roads in the country.
PL for SOA proposes, formally, a software engineering methodology, development techniques and support tools for the provision of service product lines.
We propose rigorous modeling techniques for the specification and verification of formal notations and languages for service computing with inclinations of variability.
Through these cutting-edge technologies, increased levels of flexibility and adaptivity can be achieved.
This will involve developing semantics of variability over behavioural models of services.
Such tools will assist organizations to plan, optimize and control the quality of software service provision, both at design and at run time by making it possible to develop flexible and cost-effective software systems that support high levels of reuse.
We tackle this challenge from two levels.
We use feature modeling from product line engineering and, from a services point of view, the orchestration language Orc.
We introduce the Smart Grid as the service product line to apply the techniques to.
Visual object tracking is a challenging computer vision task with numerous real-world applications.
Here we propose a simple but efficient Spectral Filter Tracking (SFT)method.
To characterize rotational and translation invariance of tracking targets, the candidate image region is models as a pixelwise grid graph.
Instead of the conventional graph matching, we convert the tracking into a plain least square regression problem to estimate the best center coordinate of the target.
But different from the holistic regression of correlation filter based methods, SFT can operate on localized surrounding regions of each pixel (i.e.,vertex) by using spectral graph filters, which thus is more robust to resist local variations and cluttered background.To bypass the eigenvalue decomposition problem of the graph Laplacian matrix L, we parameterize spectral graph filters as the polynomial of L by spectral graph theory, in which L k exactly encodes a k-hop local neighborhood of each vertex.
Finally, the filter parameters (i.e., polynomial coefficients) as well as feature projecting functions are jointly integrated into the regression model.
Compared with word embedding based on point representation, distribution-based word embedding shows more flexibility in expressing uncertainty and therefore embeds richer semantic information when representing words.
The Wasserstein distance provides a natural notion of dissimilarity with probability measures and has a closed-form solution when measuring the distance between two Gaussian distributions.
Therefore, with the aim of representing words in a highly efficient way, we propose to operate a Gaussian word embedding model with a loss function based on the Wasserstein distance.
Also, external information from ConceptNet will be used to semi-supervise the results of the Gaussian word embedding.
Thirteen datasets from the word similarity task, together with one from the word entailment task, and six datasets from the downstream document classification task will be evaluated in this paper to test our hypothesis.
Recently, merging signal processing techniques with information security services has found a lot of attention.
Steganography and steganalysis are among those trends.
Like their counterparts in cryptology, steganography and steganalysis are in a constant battle.
Steganography methods try to hide the presence of covert messages in innocuous-looking data, whereas steganalysis methods try to reveal existence of such messages and to break steganography methods.
The stream nature of audio signals, their popularity, and their wide spread usage make them very suitable media for steganography.
This has led to a very rich literature on both steganography and steganalysis of audio signals.
This paper intends to conduct a comprehensive review of audio steganalysis methods aggregated over near fifteen years.
Furthermore, we implement some of the most recent audio steganalysis methods and conduct a comparative analysis on their performances.
Finally, the paper provides some possible directions for future researches on audio steganalysis.
The Internet-of-things (IoT) is the paradigm where anything will be connected.
There are two main approaches to handle the surge in the uplink (UL) traffic the IoT is expected to generate, namely, Scheduled UL (SC-UL) and random access uplink (RA-UL) transmissions.
SC-UL is perceived as a viable tool to control Quality-of-Service (QoS) levels while entailing some overhead in the scheduling request prior to any UL transmission.
On the other hand, RA-UL is a simple single-phase transmission strategy.
While this obviously eliminates scheduling overheads, very little is known about how scalable RA-UL is.
At this critical junction, there is a dire need to analyze the scalability of these two paradigms.
To that end, this paper develops a spatiotemporal mathematical framework to analyze and assess the performance of SC-UL and RA-UL.
The developed paradigm jointly utilizes stochastic geometry and queueing theory.
Based on such a framework, we show that the answer to the "scheduling vs. random access paradox" actually depends on the operational scenario.
Particularly, RA-UL scheme offers low access delays but suffers from limited scalability, i.e., cannot support a large number of IoT devices.
On the other hand, SC-UL transmission is better suited for higher device intensities and traffic rates.
Generative models that learn disentangled representations for different factors of variation in an image can be very useful for targeted data augmentation.
By sampling from the disentangled latent subspace of interest, we can efficiently generate new data necessary for a particular task.
Learning disentangled representations is a challenging problem, especially when certain factors of variation are difficult to label.
In this paper, we introduce a novel architecture that disentangles the latent space into two complementary subspaces by using only weak supervision in form of pairwise similarity labels.
Inspired by the recent success of cycle-consistent adversarial architectures, we use cycle-consistency in a variational auto-encoder framework.
Our non-adversarial approach is in contrast with the recent works that combine adversarial training with auto-encoders to disentangle representations.
We show compelling results of disentangled latent subspaces on three datasets and compare with recent works that leverage adversarial training.
Natural Language Interfaces and tools such as spellcheckers and Web search in one's own language are known to be useful in ICT-mediated communication.
Most languages in Southern Africa are under-resourced, however.
Therefore, it would be very useful if both the generic and the few language-specific NLP tools could be reused or easily adapted across languages.
This depends on the notion, and extent, of similarity between the languages.
We assess this from the angle of orthography and corpora.
Twelve versions of the Universal Declaration of Human Rights (UDHR) are examined, showing clusters of languages, and which are thus more or less amenable to cross-language adaptation of NLP tools, which do not match with Guthrie zones.
To examine the generalisability of these results, we zoom in on isiZulu both quantitatively and qualitatively with four other corpora and texts in different genres.
The results show that the UDHR is a typical text document orthographically.
The results also provide insight into usability of typical measures such as lexical diversity and genre, and that the same statistic may mean different things in different documents.
While NLTK for Python could be used for basic analyses of text, it, and similar NLP tools, will need considerable customization.
Identifying (and fixing) homonymous and synonymous author profiles is one of the major tasks of curating personalized bibliographic metadata repositories like the dblp computer science bibliography.
In this paper, we present and evaluate a machine learning approach to identify homonymous author bibliographies using a simple multilayer perceptron setup.
We train our model on a novel gold-standard data set derived from the past years of active, manual curation at the dblp computer science bibliography.
The problem of landmark recognition has achieved excellent results in small-scale datasets.
When dealing with large-scale retrieval, issues that were irrelevant with small amount of data, quickly become fundamental for an efficient retrieval phase.
In particular, computational time needs to be kept as low as possible, whilst the retrieval accuracy has to be preserved as much as possible.
In this paper we propose a novel multi-index hashing method called Bag of Indexes (BoI) for Approximate Nearest Neighbors (ANN) search.
It allows to drastically reduce the query time and outperforms the accuracy results compared to the state-of-the-art methods for large-scale landmark recognition.
It has been demonstrated that this family of algorithms can be applied on different embedding techniques like VLAD and R-MAC obtaining excellent results in very short times on different public datasets: Holidays+Flickr1M, Oxford105k and Paris106k.
This paper presents a framework for exact discovery of the top-k sequential patterns under Leverage.
It combines (1) a novel definition of the expected support for a sequential pattern - a concept on which most interestingness measures directly rely - with (2) SkOPUS: a new branch-and-bound algorithm for the exact discovery of top-k sequential patterns under a given measure of interest.
Our interestingness measure employs the partition approach.
A pattern is interesting to the extent that it is more frequent than can be explained by assuming independence between any of the pairs of patterns from which it can be composed.
The larger the support compared to the expectation under independence, the more interesting is the pattern.
We build on these two elements to exactly extract the k sequential patterns with highest leverage, consistent with our definition of expected support.
We conduct experiments on both synthetic data with known patterns and real-world datasets; both experiments confirm the consistency and relevance of our approach with regard to the state of the art.
This article was published in Data Mining and Knowledge Discovery and is accessible at http://dx.doi.org/10.1007/s10618-016-0467-9.
Content-based routing (CBR) is a powerful model that supports scalable asynchronous communication among large sets of geographically distributed nodes.
Yet, preserving privacy represents a major limitation for the wide adoption of CBR, notably when the routers are located in public clouds.
Indeed, a CBR router must see the content of the messages sent by data producers, as well as the filters (or subscriptions) registered by data consumers.
This represents a major deterrent for companies for which data is a key asset, as for instance in the case of financial markets or to conduct sensitive business-to-business transactions.
While there exists some techniques for privacy-preserving computation, they are either prohibitively slow or too limited to be usable in real systems.
In this paper, we follow a different strategy by taking advantage of trusted hardware extensions that have just been introduced in off-the-shelf processors and provide a trusted execution environment.
We exploit Intel's new software guard extensions (SGX) to implement a CBR engine in a secure enclave.
Thanks to the hardware-based trusted execution environment (TEE), the compute-intensive CBR operations can operate on decrypted data shielded by the enclave and leverage efficient matching algorithms.
Extensive experimental evaluation shows that SGX adds only limited overhead to insecure plaintext matching outside secure enclaves while providing much better performance and more powerful filtering capabilities than alternative software-only solutions.
To the best of our knowledge, this work is the first to demonstrate the practical benefits of SGX for privacy-preserving CBR.
Recurrent neural networks are strong dynamic systems, but they are very sensitive to their hyper-parameter configuration.
Moreover, training properly a recurrent neural network is a tough task, therefore selecting an appropriate configuration is critical.
There have been proposed varied strategies to tackle this issue, however most of them are still impractical because of the time/resources needed.
In this study, we propose a low computational cost model to evaluate the expected performance of a given architecture based on the distribution of the error of random samples.
We validate empirically our proposal using three use case.
The min-rank of a digraph was shown by Bar-Yossef et al.(2006) to represent the length of an optimal scalar linear solution of the corresponding instance of the Index Coding with Side Information (ICSI) problem.
In this work, the graphs and digraphs of near-extreme min-ranks are characterized.
Those graphs and digraphs correspond to the ICSI instances having near-extreme transmission rates when using optimal scalar linear index codes.
In particular, it is shown that the decision problem whether a digraph has min-rank two is NP-complete.
By contrast, the same question for graphs can be answered in polynomial time.
Additionally, a new upper bound on the min-rank of a digraph, the circuit-packing bound, is presented.
This bound is often tighter than the previously known bounds.
By employing this new bound, we present several families of digraphs whose min-ranks can be found in polynomial time.
High-quality video streaming, either in form of Video-On-Demand (VOD) or live streaming, usually requires converting (ie, transcoding) video streams to match the characteristics of viewers' devices (eg, in terms of spatial resolution or supported formats).
Considering the computational cost of the transcoding operation and the surge in video streaming demands, Streaming Service Providers (SSPs) are becoming reliant on cloud services to guarantee Quality of Service (QoS) of streaming for their viewers.
Cloud providers offer heterogeneous computational services in form of different types of Virtual Machines (VMs) with diverse prices.
Effective utilization of cloud services for video transcoding requires detailed performance analysis of different video transcoding operations on the heterogeneous cloud VMs.
In this research, for the first time, we provide a thorough analysis of the performance of the video stream transcoding on heterogeneous cloud VMs.
Providing such analysis is crucial for efficient prediction of transcoding time on heterogeneous VMs and for the functionality of any scheduling methods tailored for video transcoding.
Based upon the findings of this analysis and by considering the cost difference of heterogeneous cloud VMs, in this research, we also provide a model to quantify the degree of suitability of each cloud VM type for various transcoding tasks.
The provided model can supply resource (VM) provisioning methods with accurate performance and cost trade-offs to efficiently utilize cloud services for video streaming.
We propose an approach to index raster images of dictionary pages which in turn would require very little manual effort to enable direct access to the appropriate pages of the dictionary for lookup.
Accessibility is further improved by feedback and crowdsourcing that enables highlighting of the specific location on the page where the lookup word is found, annotation, digitization, and fielded searching.
This approach is equally applicable on simple scripts as well as complex writing systems.
Using our proposed approach, we have built a Web application called "Dictionary Explorer" which supports word indexes in various languages and every language can have multiple dictionaries associated with it.
Word lookup gives direct access to appropriate pages of all the dictionaries of that language simultaneously.
The application has exploration features like searching, pagination, and navigating the word index through a tree-like interface.
The application also supports feedback, annotation, and digitization features.
Apart from the scanned images, "Dictionary Explorer" aggregates results from various sources and user contributions in Unicode.
We have evaluated the time required for indexing dictionaries of different sizes and complexities in the Urdu language and examined various trade-offs in our implementation.
Using our approach, a single person can make a dictionary of 1,000 pages searchable in less than an hour.
Literature analysis is a key step in obtaining background information in biomedical research.
However, it is difficult for researchers to obtain knowledge of their interests in an efficient manner because of the massive amount of the published biomedical literature.
Therefore, efficient and systematic search strategies are required, which allow ready access to the substantial amount of literature.
In this paper, we propose a novel search system, named Co-Occurrence based on Co-Operational Formation with Advanced Method(COCOFAM) which is suitable for the large-scale literature analysis.
COCOFAM is based on integrating both Spark for local clusters and a global job scheduler to gather crowdsourced co-occurrence data on global clusters.
It will allow users to obtain information of their interests from the substantial amount of literature.
Robot-Assisted Therapy (RAT) has successfully been used in HRI research by including social robots in health-care interventions by virtue of their ability to engage human users both social and emotional dimensions.
Research projects on this topic exist all over the globe in the USA, Europe, and Asia.
All of these projects have the overall ambitious goal to increase the well-being of a vulnerable population.
Typical work in RAT is performed using remote controlled robots; a technique called Wizard-of-Oz (WoZ).
The robot is usually controlled, unbeknownst to the patient, by a human operator.
However, WoZ has been demonstrated to not be a sustainable technique in the long-term.
Providing the robots with autonomy (while remaining under the supervision of the therapist) has the potential to lighten the therapists burden, not only in the therapeutic session itself but also in longer-term diagnostic tasks.
Therefore, there is a need for exploring several degrees of autonomy in social robots used in therapy.
Increasing the autonomy of robots might also bring about a new set of challenges.
In particular, there will be a need to answer new ethical questions regarding the use of robots with a vulnerable population, as well as a need to ensure ethically-compliant robot behaviours.
Therefore, in this workshop we want to gather findings and explore which degree of autonomy might help to improve health-care interventions and how we can overcome the ethical challenges inherent to it.
Multi-threaded programs have traditionally fallen into one of two domains: cooperative and competitive.
These two domains have traditionally remained mostly disjoint, with cooperative threading used for increasing throughput in compute-intensive applications such as scientific workloads and cooperative threading used for increasing responsiveness in interactive applications such as GUIs and games.
As multicore hardware becomes increasingly mainstream, there is a need for bridging these two disjoint worlds, because many applications mix interaction and computation and would benefit from both cooperative and competitive threading.
In this paper, we present techniques for programming and reasoning about parallel interactive applications that can use both cooperative and competitive threading.
Our techniques enable the programmer to write rich parallel interactive programs by creating and synchronizing with threads as needed, and by assigning threads user-defined and partially ordered priorities.
To ensure important responsiveness properties, we present a modal type system analogous to S4 modal logic that precludes low-priority threads from delaying high-priority threads, thereby statically preventing a crucial set of priority-inversion bugs.
We then present a cost model that allows reasoning about responsiveness and completion time of well-typed programs.
The cost model extends the traditional work-span model for cooperative threading to account for competitive scheduling decisions needed to ensure responsiveness.
Finally, we show that our proposed techniques are realistic by implementing them as an extension to the Standard ML language.
Fractional Repetition (FR) codes are well known class of Distributed Replication-based Simple Storage (Dress) codes for the Distributed Storage Systems (DSSs).
In such systems, the replicas of data packets encoded by Maximum Distance Separable (MDS) code, are stored on distributed nodes.
Most of the available constructions for the FR codes are based on combinatorial designs and Graph theory.
In this work, we propose an elegant sequence based approach for the construction of the FR code.
In particular, we propose a beautiful class of codes known as Flower codes and study its basic properties.
In manufacture, steel and other metals are mainly cut and shaped during the fabrication process by computer numerical control (CNC) machines.
To keep high productivity and efficiency of the fabrication process, engineers need to monitor the real-time process of CNC machines, and the lifetime management of machine tools.
In a real manufacturing process, breakage of machine tools usually happens without any indication, this problem seriously affects the fabrication process for many years.
Previous studies suggested many different approaches for monitoring and detecting the breakage of machine tools.
However, there still exists a big gap between academic experiments and the complex real fabrication processes such as the high demands of real-time detections, the difficulty in data acquisition and transmission.
In this work, we use the spindle current approach to detect the breakage of machine tools, which has the high performance of real-time monitoring, low cost, and easy to install.
We analyze the features of the current of a milling machine spindle through tools wearing processes, and then we predict the status of tool breakage by a convolutional neural network(CNN).
In addition, we use a BP neural network to understand the reliability of the CNN.
The results show that our CNN approach can detect tool breakage with an accuracy of 93%, while the best performance of BP is 80%.
Some recent works revealed that deep neural networks (DNNs) are vulnerable to so-called adversarial attacks where input examples are intentionally perturbed to fool DNNs.
In this work, we revisit the DNN training process that includes adversarial examples into the training dataset so as to improve DNN's resilience to adversarial attacks, namely, adversarial training.
Our experiments show that different adversarial strengths, i.e., perturbation levels of adversarial examples, have different working zones to resist the attack.
Based on the observation, we propose a multi-strength adversarial training method (MAT) that combines the adversarial training examples with different adversarial strengths to defend adversarial attacks.
Two training structures - mixed MAT and parallel MAT - are developed to facilitate the tradeoffs between training time and memory occupation.
Our results show that MAT can substantially minimize the accuracy degradation of deep learning systems to adversarial attacks on MNIST, CIFAR-10, CIFAR-100, and SVHN.
The paper describes the design, the implementation of a neural controller used in an automatic daylight control system.
The automatic lighting control system (ALCS) attempt to maintain constant the illuminance at the desired level on working plane even if the daylight contribution is variable.
Therefore, the daylight will represent the perturbation signal for the ALCS.
The mathematical model of process is unknown.
The applied structure of control need the inverse model of process.
For this purpose it was used other artificial neural network (ANN) which identify the inverse model of process in an on-line manner.
In fact, this ANN identify the inverse model of process + the perturbation signal.
In this way the learning signal for neural controller has a better accuracy for the present application.
A wide range of numerical methods exists for computing polynomial approximations of solutions of ordinary differential equations based on Chebyshev series expansions or Chebyshev interpolation polynomials.
We consider the application of such methods in the context of rigorous computing (where we need guarantees on the accuracy of the result), and from the complexity point of view.
It is well-known that the order-n truncation of the Chebyshev expansion of a function over a given interval is a near-best uniform polynomial approximation of the function on that interval.
In the case of solutions of linear differential equations with polynomial coefficients, the coefficients of the expansions obey linear recurrence relations with polynomial coefficients.
Unfortunately, these recurrences do not lend themselves to a direct recursive computation of the coefficients, owing among other things to a lack of initial conditions.
We show how they can nevertheless be used, as part of a validated process, to compute good uniform approximations of D-finite functions together with rigorous error bounds, and we study the complexity of the resulting algorithms.
Our approach is based on a new view of a classical numerical method going back to Clenshaw, combined with a functional enclosure method.
Network traffic model is a critical problem for urban applications, mainly because of its diversity and node density.
As wireless sensor network is highly concerned with the development of smart cities, careful consideration to traffic model helps choose appropriate protocols and adapt network parameters to reach best performances on energy-latency tradeoffs.
In this paper, we compare the performance of two off-the-shelf medium access control protocols on two different kinds of traffic models, and then evaluate their application-end information delay and energy consumption while varying traffic parameters and network density.
From the simulation results, we highlight some limits induced by network density and occurrence frequency of event-driven applications.
When it comes to realtime urban services, a protocol selection shall be taken into account - even dynamically - with a special attention to energy-delay tradeoff.
To this end, we provide several insights on parking sensor networks.
We revisit two NP-hard geometric partitioning problems - convex decomposition and surface approximation.
Building on recent developments in geometric separators, we present quasi-polynomial time algorithms for these problems with improved approximation guarantees.
Human motion prediction model has applications in various fields of computer vision.
Without taking into account the inherent stochasticity in the prediction of future pose dynamics, such methods often converges to a deterministic undesired mean of multiple probable outcomes.
Devoid of this, we propose a novel probabilistic generative approach called Bidirectional Human motion prediction GAN, or BiHMP-GAN.
To be able to generate multiple probable human-pose sequences, conditioned on a given starting sequence, we introduce a random extrinsic factor r, drawn from a predefined prior distribution.
Furthermore, to enforce a direct content loss on the predicted motion sequence and also to avoid mode-collapse, a novel bidirectional framework is incorporated by modifying the usual discriminator architecture.
The discriminator is trained also to regress this extrinsic factor r, which is used alongside with the intrinsic factor (encoded starting pose sequence) to generate a particular pose sequence.
To further regularize the training, we introduce a novel recursive prediction strategy.
In spite of being in a probabilistic framework, the enhanced discriminator architecture allows predictions of an intermediate part of pose sequence to be used as a conditioning for prediction of the latter part of the sequence.
The bidirectional setup also provides a new direction to evaluate the prediction quality against a given test sequence.
For a fair assessment of BiHMP-GAN, we report performance of the generated motion sequence using (i) a critic model trained to discriminate between real and fake motion sequence, and (ii) an action classifier trained on real human motion dynamics.
Outcomes of both qualitative and quantitative evaluations, on the probabilistic generations of the model, demonstrate the superiority of BiHMP-GAN over previously available methods.
We present a novel layerwise optimization algorithm for the learning objective of Piecewise-Linear Convolutional Neural Networks (PL-CNNs), a large class of convolutional neural networks.
Specifically, PL-CNNs employ piecewise linear non-linearities such as the commonly used ReLU and max-pool, and an SVM classifier as the final layer.
The key observation of our approach is that the problem corresponding to the parameter estimation of a layer can be formulated as a difference-of-convex (DC) program, which happens to be a latent structured SVM.
We optimize the DC program using the concave-convex procedure, which requires us to iteratively solve a structured SVM problem.
This allows to design an optimization algorithm with an optimal learning rate that does not require any tuning.
Using the MNIST, CIFAR and ImageNet data sets, we show that our approach always improves over the state of the art variants of backpropagation and scales to large data and large network settings.
We study the lobby index (l-index for short) as a local node centrality measure for complex networks.
The l-inde is compared with degree (a local measure), betweenness and Eigenvector centralities (two global measures) in the case of biological network (Yeast interaction protein-protein network) and a linguistic network (Moby Thesaurus II).
In both networks, the l-index has poor correlation with betweenness but correlates with degree and Eigenvector.
Being a local measure, one can take advantage by using the l-index because it carries more information about its neighbors when compared with degree centrality, indeed it requires less time to compute when compared with Eigenvector centrality.
Results suggests that l-index produces better results than degree and Eigenvector measures for ranking purposes, becoming suitable as a tool to perform this task.
Supervised learning tends to produce more accurate classifiers than unsupervised learning in general.
This implies that training data is preferred with annotations.
When addressing visual perception challenges, such as localizing certain object classes within an image, the learning of the involved classifiers turns out to be a practical bottleneck.
The reason is that, at least, we have to frame object examples with bounding boxes in thousands of images.
A priori, the more complex the model is regarding its number of parameters, the more annotated examples are required.
This annotation task is performed by human oracles, which ends up in inaccuracies and errors in the annotations (aka ground truth) since the task is inherently very cumbersome and sometimes ambiguous.
As an alternative we have pioneered the use of virtual worlds for collecting such annotations automatically and with high precision.
However, since the models learned with virtual data must operate in the real world, we still need to perform domain adaptation (DA).
In this chapter we revisit the DA of a deformable part-based model (DPM) as an exemplifying case of virtual- to-real-world DA.
As a use case, we address the challenge of vehicle detection for driver assistance, using different publicly available virtual-world data.
While doing so, we investigate questions such as: how does the domain gap behave due to virtual-vs-real data with respect to dominant object appearance per domain, as well as the role of photo-realism in the virtual world.
Generative adversarial networks (GANs) are a recent approach to train generative models of data, which have been shown to work particularly well on image data.
In the current paper we introduce a new model for texture synthesis based on GAN learning.
By extending the input noise distribution space from a single vector to a whole spatial tensor, we create an architecture with properties well suited to the task of texture synthesis, which we call spatial GAN (SGAN).
To our knowledge, this is the first successful completely data-driven texture synthesis method based on GANs.
Our method has the following features which make it a state of the art algorithm for texture synthesis: high image quality of the generated textures, very high scalability w.r.t. the output texture size, fast real-time forward generation, the ability to fuse multiple diverse source images in complex textures.
To illustrate these capabilities we present multiple experiments with different classes of texture images and use cases.
We also discuss some limitations of our method with respect to the types of texture images it can synthesize, and compare it to other neural techniques for texture generation.
Structured illumination microscopy (SIM) is a very important super-resolution microscopy technique, which provides high speed super-resolution with about two-fold spatial resolution enhancement.
Several attempts aimed at improving the performance of SIM reconstruction algorithm have been reported.
However, most of these highlight only one specific aspect of the SIM reconstruction -- such as the determination of the illumination pattern phase shift accurately -- whereas other key elements -- such as determination of modulation factor, estimation of object power spectrum, Wiener filtering frequency components with inclusion of object power spectrum information, translocating and the merging of the overlapping frequency components -- are usually glossed over superficially.
In addition, most of the work reported lie scattered throughout the literature and a comprehensive review of the theoretical background is found lacking.
The purpose of the present work is two-fold: 1) to collect the essential theoretical details of SIM algorithm at one place, thereby making them readily accessible to readers for the first time; and 2) to provide an open source SIM reconstruction code (named OpenSIM), which enables users to interactively vary the code parameters and study it's effect on reconstructed SIM image.
Over the past few years, many black-hat marketplaces have emerged that facilitate access to reputation manipulation services such as fake Facebook likes, fraudulent search engine optimization (SEO), or bogus Amazon reviews.
In order to deploy effective technical and legal countermeasures, it is important to understand how these black-hat marketplaces operate, shedding light on the services they offer, who is selling, who is buying, what are they buying, who is more successful, why are they successful, etc.
Toward this goal, in this paper, we present a detailed micro-economic analysis of a popular online black-hat marketplace, namely, SEOClerks.com.
As the site provides non-anonymized transaction information, we set to analyze selling and buying behavior of individual users, propose a strategy to identify key users, and study their tactics as compared to other (non-key) users.
We find that key users: (1) are mostly located in Asian countries, (2) are focused more on selling black-hat SEO services, (3) tend to list more lower priced services, and (4) sometimes buy services from other sellers and then sell at higher prices.
Finally, we discuss the implications of our analysis with respect to devising effective economic and legal intervention strategies against marketplace operators and key users.
A classical theorem of Erdos, Lovasz and Spencer asserts that the densities of connected subgraphs in large graphs are independent.
We prove an analogue of this theorem for permutations and we then apply the methods used in the proof to give an example of a finitely approximable permutation parameter that is not finitely forcible.
The latter answers a question posed by two of the authors and Moreira and Sampaio.
Products of Hidden Markov Models(PoHMMs) are an interesting class of generative models which have received little attention since their introduction.
This maybe in part due to their more computationally expensive gradient-based learning algorithm,and the intractability of computing the log likelihood of sequences under the model.
In this paper, we demonstrate how the partition function can be estimated reliably via Annealed Importance Sampling.
We perform experiments using contrastive divergence learning on rainfall data and data captured from pairs of people dancing.
Our results suggest that advances in learning and evaluation for undirected graphical models and recent increases in available computing power make PoHMMs worth considering for complex time-series modeling tasks.
Due to the rapidly rising popularity of Massive Open Online Courses (MOOCs), there is a growing demand for scalable automated support technologies for student learning.
Transferring traditional educational resources to online contexts has become an increasingly relevant problem in recent years.
For learning science theories to be applicable, educators need a way to identify learning behaviors of students which contribute to learning outcomes, and use them to design and provide personalized intervention support to the students.
Click logs are an important source of information about students' learning behaviors, however current literature has limited understanding of how these behaviors are represented within click logs.
In this project, we have exploited the temporal dynamics of student behaviors both to do behavior modeling via graphical modeling approaches and to do performance prediction via recurrent neural network approaches in order to first identify student behaviors and then use them to predict their final outcome in the course.
Our experiments showed that the long short-term memory (LSTM) model is capable of learning long-term dependencies in a sequence and outperforms other strong baselines in the prediction task.
Further, these sequential approaches to click log analysis can be successfully imported to other courses when used with results obtained from graphical model behavior modeling.
While Kolmogorov complexity is the accepted absolute measure of information content in an individual finite object, a similarly absolute notion is needed for the information distance between two individual objects, for example, two pictures.
We give several natural definitions of a universal information metric, based on length of shortest programs for either ordinary computations or reversible (dissipationless) computations.
It turns out that these definitions are equivalent up to an additive logarithmic term.
We show that the information distance is a universal cognitive similarity distance.
We investigate the maximal correlation of the shortest programs involved, the maximal uncorrelation of programs (a generalization of the Slepian-Wolf theorem of classical information theory), and the density properties of the discrete metric spaces induced by the information distances.
A related distance measures the amount of nonreversibility of a computation.
Using the physical theory of reversible computation, we give an appropriate (universal, anti-symmetric, and transitive) measure of the thermodynamic work required to transform one object in another object by the most efficient process.
Information distance between individual objects is needed in pattern recognition where one wants to express effective notions of "pattern similarity" or "cognitive similarity" between individual objects and in thermodynamics of computation where one wants to analyse the energy dissipation of a computation from a particular input to a particular output.
Coreference resolution is an intermediate step for text understanding.
It is used in tasks and domains for which we do not necessarily have coreference annotated corpora.
Therefore, generalization is of special importance for coreference resolution.
However, while recent coreference resolvers have notable improvements on the CoNLL dataset, they struggle to generalize properly to new domains or datasets.
In this paper, we investigate the role of linguistic features in building more generalizable coreference resolvers.
We show that generalization improves only slightly by merely using a set of additional linguistic features.
However, employing features and subsets of their values that are informative for coreference resolution, considerably improves generalization.
Thanks to better generalization, our system achieves state-of-the-art results in out-of-domain evaluations, e.g., on WikiCoref, our system, which is trained on CoNLL, achieves on-par performance with a system designed for this dataset.
The vast majority of today's mobile malware targets Android devices.
This has pushed the research effort in Android malware analysis in the last years.
An important task of malware analysis is the classification of malware samples into known families.
Static malware analysis is known to fall short against techniques that change static characteristics of the malware (e.g. code obfuscation), while dynamic analysis has proven effective against such techniques.
To the best of our knowledge, the most notable work on Android malware family classification purely based on dynamic analysis is DroidScribe.
With respect to DroidScribe, our approach is easier to reproduce.
Our methodology only employs publicly available tools, does not require any modification to the emulated environment or Android OS, and can collect data from physical devices.
The latter is a key factor, since modern mobile malware can detect the emulated environment and hide their malicious behavior.
Our approach relies on resource consumption metrics available from the proc file system.
Features are extracted through detrended fluctuation analysis and correlation.
Finally, a SVM is employed to classify malware into families.
We provide an experimental evaluation on malware samples from the Drebin dataset, where we obtain a classification accuracy of 82%, proving that our methodology achieves an accuracy comparable to that of DroidScribe.
Furthermore, we make the software we developed publicly available, to ease the reproducibility of our results.
It has been suggested that changes in physiological arousal precede potentially dangerous aggressive behavior in youth with autism spectrum disorder (ASD) who are minimally verbal (MV-ASD).
The current work tests this hypothesis through time-series analyses on biosignals acquired prior to proximal aggression onset.
We implement ridge-regularized logistic regression models on physiological biosensor data wirelessly recorded from 15 MV-ASD youth over 64 independent naturalistic observations in a hospital inpatient unit.
Our results demonstrate proof-of-concept, feasibility, and incipient validity predicting aggression onset 1 minute before it occurs using global, person-dependent, and hybrid classifier models.
We introduce a novel multimodal machine translation model that utilizes parallel visual and textual information.
Our model jointly optimizes the learning of a shared visual-language embedding and a translator.
The model leverages a visual attention grounding mechanism that links the visual semantics with the corresponding textual semantics.
Our approach achieves competitive state-of-the-art results on the Multi30K and the Ambiguous COCO datasets.
We also collected a new multilingual multimodal product description dataset to simulate a real-world international online shopping scenario.
On this dataset, our visual attention grounding model outperforms other methods by a large margin.
Having more followers has become a norm in recent social media and micro-blogging communities.
This battle has been taking shape from the early days of Twitter.
Despite this strong competition for followers, many Twitter users are continuously losing their followers.
This work addresses the problem of identifying the reasons behind the drop of followers of users in Twitter.
As a first step, we extract various features by analyzing the content of the posts made by the Twitter users who lose followers consistently.
We then leverage these features to early detect follower loss.
We propose various models and yield an overall accuracy of 73% with high precision and recall.
Our model outperforms baseline model by 19.67% (w.r.t accuracy), 33.8% (w.r.t precision) and 14.3% (w.r.t recall).
Continuous integration (CI) tools integrate code changes by automatically compiling, building, and executing test cases upon submission of code changes.
Use of CI tools is getting increasingly popular, yet how proprietary projects reap the benefits of CI remains unknown.
To investigate the influence of CI on software development, we analyze 150 open source software (OSS) projects, and 123 proprietary projects.
For OSS projects, we observe the expected benefits after CI adoption, e.g., improvements in bug and issue resolution.
However, for the proprietary projects, we cannot make similar observations.
Our findings indicate that only adoption of CI might not be enough to the improve software development process.
CI can be effective for software development if practitioners use CI's feedback mechanism efficiently, by applying the practice of making frequent commits.
For our set of proprietary projects we observe practitioners commit less frequently, and hence not use CI effectively for obtaining feedback on the submitted code changes.
Based on our findings we recommend industry practitioners to adopt the best practices of CI to reap the benefits of CI tools for example, making frequent commits.
DynamicGEM is an open-source Python library for learning node representations of dynamic graphs.
It consists of state-of-the-art algorithms for defining embeddings of nodes whose connections evolve over time.
The library also contains the evaluation framework for four downstream tasks on the network: graph reconstruction, static and temporal link prediction, node classification, and temporal visualization.
We have implemented various metrics to evaluate the state-of-the-art methods, and examples of evolving networks from various domains.
We have easy-to-use functions to call and evaluate the methods and have extensive usage documentation.
Furthermore, DynamicGEM provides a template to add new algorithms with ease to facilitate further research on the topic.
Videos are one of the best documentation options for a rich and effective communication.
They allow experiencing the overall context of a situation by representing concrete realizations of certain requirements.
Despite 35 years of research on integrating videos in requirements engineering (RE), videos are not an established documentation option in terms of RE best practices.
Several approaches use videos but omit the details about how to produce them.
Software professionals lack knowledge on how to communicate visually with videos since they are not directors.
Therefore, they do not necessarily have the required skills neither to produce good videos in general nor to deduce what constitutes a good video for an existing approach.
The discipline of video production provides numerous generic guidelines that represent best practices on how to produce a good video with specific characteristics.
We propose to analyze this existing know-how to learn what constitutes a good video for visual communication.
As a plan of action, we suggest a literature study of video production guidelines.
We expect to identify quality characteristics of good videos in order to derive a quality model.
Software professionals may use such a quality model for videos as an orientation for planning, shooting, post-processing, and viewing a video.
Thus, we want to encourage and enable software professionals to produce good videos at moderate costs, yet sufficient quality.
The "Smart City" (SC) concept revolves around the idea of embodying cutting-edge ICT solutions in the very fabric of future cities, in order to offer new and better services to citizens while lowering the city management costs, both in monetary, social, and environmental terms.
In this framework, communication technologies are perceived as subservient to the SC services, providing the means to collect and process the data needed to make the services function.
In this paper, we propose a new vision in which technology and SC services are designed to take advantage of each other in a symbiotic manner.
According to this new paradigm, which we call "SymbioCity", SC services can indeed be exploited to improve the performance of the same communication systems that provide them with data.
Suggestive examples of this symbiotic ecosystem are discussed in the paper.
The dissertation is then substantiated in a proof-of-concept case study, where we show how the traffic monitoring service provided by the London Smart City initiative can be used to predict the density of users in a certain zone and optimize the cellular service in that area.
Seeking a general framework for reasoning about and comparing programming languages, we derive a new view of Milner's CCS.
We construct a category E of 'plays', and a subcategory V of 'views'.
We argue that presheaves on V adequately represent 'innocent' strategies, in the sense of game semantics.
We equip innocent strategies with a simple notion of interaction.
We then prove decomposition results for innocent strategies, and, restricting to presheaves of finite ordinals, prove that innocent strategies are a final coalgebra for a polynomial functor derived from the game.
This leads to a translation of CCS with recursive equations.
Finally, we propose a notion of 'interactive equivalence' for innocent strategies, which is close in spirit to Beffara's interpretation of testing equivalences in concurrency theory.
In this framework, we consider analogues of fair testing and must testing.
We show that must testing is strictly finer in our model than in CCS, since it avoids what we call 'spatial unfairness'.
Still, it differs from fair testing, and we show that it coincides with a relaxed form of fair testing.
In this paper, we present an unsupervised learning framework for analyzing activities and interactions in surveillance videos.
In our framework, three levels of video events are connected by Hierarchical Dirichlet Process (HDP) model: low-level visual features, simple atomic activities, and multi-agent interactions.
Atomic activities are represented as distribution of low-level features, while complicated interactions are represented as distribution of atomic activities.
This learning process is unsupervised.
Given a training video sequence, low-level visual features are extracted based on optic flow and then clustered into different atomic activities and video clips are clustered into different interactions.
The HDP model automatically decide the number of clusters, i.e. the categories of atomic activities and interactions.
Based on the learned atomic activities and interactions, a training dataset is generated to train the Gaussian Process (GP) classifier.
Then the trained GP models work in newly captured video to classify interactions and detect abnormal events in real time.
Furthermore, the temporal dependencies between video events learned by HDP-Hidden Markov Models (HMM) are effectively integrated into GP classifier to enhance the accuracy of the classification in newly captured videos.
Our framework couples the benefits of the generative model (HDP) with the discriminant model (GP).
We provide detailed experiments showing that our framework enjoys favorable performance in video event classification in real-time in a crowded traffic scene.
We propose an automatic algorithm, named SDI, for the segmentation of skin lesions in dermoscopic images, articulated into three main steps: selection of the image ROI, selection of the segmentation band, and segmentation.
We present extensive experimental results achieved by the SDI algorithm on the lesion segmentation dataset made available for the ISIC 2017 challenge on Skin Lesion Analysis Towards Melanoma Detection, highlighting its advantages and disadvantages.
Evolutionary algorithms based on modeling the statistical dependencies (interactions) between the variables have been proposed to solve a wide range of complex problems.
These algorithms learn and sample probabilistic graphical models able to encode and exploit the regularities of the problem.
This paper investigates the effect of using probabilistic modeling techniques as a way to enhance the behavior of MOEA/D framework.
MOEA/D is a decomposition based evolutionary algorithm that decomposes a multi-objective optimization problem (MOP) in a number of scalar single-objective subproblems and optimizes them in a collaborative manner.
MOEA/D framework has been widely used to solve several MOPs.
The proposed algorithm, MOEA/D using probabilistic Graphical Models (MOEA/D-GM) is able to instantiate both univariate and multi-variate probabilistic models for each subproblem.
To validate the introduced framework algorithm, an experimental study is conducted on a multi-objective version of the deceptive function Trap5.
The results show that the variant of the framework (MOEA/D-Tree), where tree models are learned from the matrices of the mutual information between the variables, is able to capture the structure of the problem.
MOEA/D-Tree is able to achieve significantly better results than both MOEA/D using genetic operators and MOEA/D using univariate probability models, in terms of the approximation to the true Pareto front.
The variance component tests used in genomewide association studies of thousands of individuals become computationally exhaustive when multiple traits are analysed in the context of omics studies.
We introduce two high-throughput algorithms -- CLAK-CHOL and CLAK-EIG -- for single and multiple phenotype genome-wide association studies (GWAS).
The algorithms, generated with the help of an expert system, reduce the computational complexity to the point that thousands of traits can be analyzed for association with millions of polymorphisms in a course of days on a standard workstation.
By taking advantage of problem specific knowledge, CLAK-CHOL and CLAK-EIG significantly outperform the current state-of-the-art tools in both single and multiple trait analysis.
This paper addresses the sensitivity of neural image caption generators to their visual input.
A sensitivity analysis and omission analysis based on image foils is reported, showing that the extent to which image captioning architectures retain and are sensitive to visual information varies depending on the type of word being generated and the position in the caption as a whole.
We motivate this work in the context of broader goals in the field to achieve more explainability in AI.
This paper proposes to perform authorship analysis using the Fast Compression Distance (FCD), a similarity measure based on compression with dictionaries directly extracted from the written texts.
The FCD computes a similarity between two documents through an effective binary search on the intersection set between the two related dictionaries.
In the reported experiments the proposed method is applied to documents which are heterogeneous in style, written in five different languages and coming from different historical periods.
Results are comparable to the state of the art and outperform traditional compression-based methods.
Robust estimators, like the median of a point set, are important for data analysis in the presence of outliers.
We study robust estimators for locationally uncertain points with discrete distributions.
That is, each point in a data set has a discrete probability distribution describing its location.
The probabilistic nature of uncertain data makes it challenging to compute such estimators, since the true value of the estimator is now described by a distribution rather than a single point.
We show how to construct and estimate the distribution of the median of a point set.
Building the approximate support of the distribution takes near-linear time, and assigning probability to that support takes quadratic time.
We also develop a general approximation technique for distributions of robust estimators with respect to ranges with bounded VC dimension.
This includes the geometric median for high dimensions and the Siegel estimator for linear regression.
We show that the mechanisms used in the name data networking (NDN) and the original content centric networking (CCN) architectures may not detect Interest loops, even if the network in which they operate is static and no faults occur.
Furthermore, we show that no correct Interest forwarding strategy can be defined that allows Interest aggregation and attempts to detect Interest looping by identifying Interests uniquely.
We introduce SIFAH (Strategy for Interest Forwarding and Aggregation with Hop-Counts), the first Interest forwarding strategy shown to be correct under any operational conditions of a content centric network.
SIFAH operates by having forwarding information bases (FIBs) store the next hops and number of hops to named content, and by having each Interest state the name of the requested content and the hop count from the router forwarding an Interest to the content.
We present the results of simulation experiments using the ndnSIM simulator comparing CCN and NDN with SIFAH.
The results of these experiments illustrate the negative impact of undetected Interest looping when Interests are aggregated in CCN and NDN, and the performance advantages of using SIFAH.
A novel framework is presented for the analysis of multi-level coding that takes into account degrees of freedom attended and ignored by the different levels of analysis.
It can be shown that for a multi-level coding system, skipped or incomplete error correction at many levels can save energy and provide equally good results to perfect correction.
This is the case for both discrete and continuous cases.
This has relevance to approximate computing, and also to deep learning networks, which can readily be construed as multiple levels of inadequate error correction reacting to some input signal, but which are typically considered beyond analysis by traditional information theoretical methods.
The finding also has significance in natural systems, e.g. neuronal signaling, vision, and molecular genetics, which can be characterized as relying on multiple layers of inadequate error correction.
In the context of Noisy Multi-Objective Optimization, dealing with uncertainties requires the decision maker to define some preferences about how to handle them, through some statistics (e.g., mean, median) to be used to evaluate the qualities of the solutions, and define the corresponding Pareto set.
Approximating these statistics requires repeated samplings of the population, drastically increasing the overall computational cost.
To tackle this issue, this paper proposes to directly estimate the probability of each individual to be selected, using some Hoeffding races to dynamically assign the estimation budget during the selection step.
The proposed racing approach is validated against static budget approaches with NSGA-II on noisy versions of the ZDT benchmark functions.
Egocentric spatial memory (ESM) defines a memory system with encoding, storing, recognizing and recalling the spatial information about the environment from an egocentric perspective.
We introduce an integrated deep neural network architecture for modeling ESM.
It learns to estimate the occupancy state of the world and progressively construct top-down 2D global maps from egocentric views in a spatially extended environment.
During the exploration, our proposed ESM model updates belief of the global map based on local observations using a recurrent neural network.
It also augments the local mapping with a novel external memory to encode and store latent representations of the visited places over long-term exploration in large environments which enables agents to perform place recognition and hence, loop closure.
Our proposed ESM network contributes in the following aspects: (1) without feature engineering, our model predicts free space based on egocentric views efficiently in an end-to-end manner; (2) different from other deep learning-based mapping system, ESMN deals with continuous actions and states which is vitally important for robotic control in real applications.
In the experiments, we demonstrate its accurate and robust global mapping capacities in 3D virtual mazes and realistic indoor environments by comparing with several competitive baselines.
Mathematical theorems are human knowledge able to be accumulated in the form of symbolic representation, and proving theorems has been considered intelligent behavior.
Based on the BHK interpretation and the Curry-Howard isomorphism, proof assistants, software capable of interacting with human for constructing formal proofs, have been developed in the past several decades.
Since proofs can be considered and expressed as programs, proof assistants simplify and verify a proof by computationally evaluating the program corresponding to the proof.
Thanks to the transformation from logic to computation, it is now possible to generate or search for formal proofs directly in the realm of computation.
Evolutionary algorithms, known to be flexible and versatile, have been successfully applied to handle a variety of scientific and engineering problems in numerous disciplines for also several decades.
Examining the feasibility of establishing the link between evolutionary algorithms, as the program generator, and proof assistants, as the proof verifier, in order to automatically find formal proofs to a given logic sentence is the primary goal of this study.
In the article, we describe in detail our first, ad-hoc attempt to fully automatically prove theorems as well as the preliminary results.
Ten simple theorems from various branches of mathematics were proven, and most of these theorems cannot be proven by using the tactic auto alone in Coq, the adopted proof assistant.
The implication and potential influence of this study are discussed, and the developed source code with the obtained experimental results are released as open source.
This paper introduces a network for volumetric segmentation that learns from sparsely annotated volumetric images.
We outline two attractive use cases of this method: (1) In a semi-automated setup, the user annotates some slices in the volume to be segmented.
The network learns from these sparse annotations and provides a dense 3D segmentation.
(2) In a fully-automated setup, we assume that a representative, sparsely annotated training set exists.
Trained on this data set, the network densely segments new volumetric images.
The proposed network extends the previous u-net architecture from Ronneberger et al. by replacing all 2D operations with their 3D counterparts.
The implementation performs on-the-fly elastic deformations for efficient data augmentation during training.
It is trained end-to-end from scratch, i.e., no pre-trained network is required.
We test the performance of the proposed method on a complex, highly variable 3D structure, the Xenopus kidney, and achieve good results for both use cases.
Energy storage systems (EES) are expected to be an indispensable resource for mitigating the effects on networks of high penetrations of distributed generation in the near future.
This paper analyzes the benefits of EES in unbalanced low voltage (LV) networks regarding three aspects, namely, power losses, the hosting capacity and network unbalance.
For doing so, a mixed integer quadratic programmming model (MIQP) is developed to minimize annual energy losses and determine the sizing and placement of ESS, while satisfying voltage constraints.
A real unbalanced LV UK grid is adopted to examine the effects of ESS under two scenarios: the installation of one community ESS (CESS) and multiple distributed ESSs (DESSs).
The results illustrate that both scenarios present high performance in accomplishing the above tasks, while DESSs, with the same aggregated size, are slightly better.
This margin is expected to be amplified as the aggregated size of DESSs increases.
Separate selling of two independent goods is shown to yield at least 62% of the optimal revenue, and at least 73% when the goods satisfy the Myerson regularity condition.
This improves the 50% result of Hart and Nisan (2017, originally circulated in 2012).
The implementation of smart building technology in the form of smart infrastructure applications has great potential to improve sustainability and energy efficiency by leveraging humans-in-the-loop strategy.
However, human preference in regard to living conditions is usually unknown and heterogeneous in its manifestation as control inputs to a building.
Furthermore, the occupants of a building typically lack the independent motivation necessary to contribute to and play a key role in the control of smart building infrastructure.
Moreover, true human actions and their integration with sensing/actuation platforms remains unknown to the decision maker tasked with improving operational efficiency.
By modeling user interaction as a sequential discrete game between non-cooperative players, we introduce a gamification approach for supporting user engagement and integration in a human-centric cyber-physical system.
We propose the design and implementation of a large-scale network game with the goal of improving the energy efficiency of a building through the utilization of cutting-edge Internet of Things (IoT) sensors and cyber-physical systems sensing/actuation platforms.
A benchmark utility learning framework that employs robust estimations for classical discrete choice models provided for the derived high dimensional imbalanced data.
To improve forecasting performance, we extend the benchmark utility learning scheme by leveraging Deep Learning end-to-end training with Deep bi-directional Recurrent Neural Networks.
We apply the proposed methods to high dimensional data from a social game experiment designed to encourage energy efficient behavior among smart building occupants in Nanyang Technological University (NTU) residential housing.
Using occupant-retrieved actions for resources such as lighting and A/C, we simulate the game defined by the estimated utility functions.
Earthquake signal detection is at the core of observational seismology.
A good detection algorithm should be sensitive to small and weak events with a variety of waveform shapes, robust to background noise and non-earthquake signals, and efficient for processing large data volumes.
Here, we introduce the Cnn-Rnn Earthquake Detector (CRED), a detector based on deep neural networks.
The network uses a combination of convolutional layers and bi-directional long-short-term memory units in a residual structure.
It learns the time-frequency characteristics of the dominant phases in an earthquake signal from three component data recorded on a single station.
We train the network using 500,000 seismograms (250k associated with tectonic earthquakes and 250k identified as noise) recorded in Northern California and tested it with an F-score of 99.95.
The robustness of the trained model with respect to the noise level and non-earthquake signals is shown by applying it to a set of semi-synthetic signals.
The model is applied to one month of continuous data recorded at Central Arkansas to demonstrate its efficiency, generalization, and sensitivity.
Our model is able to detect more than 700 microearthquakes as small as -1.3 ML induced during hydraulic fracturing far away than the training region.
The performance of the model is compared with STA/LTA, template matching, and FAST algorithms.
Our results indicate an efficient and reliable performance of CRED.
This framework holds great promise in lowering the detection threshold while minimizing false positive detection rates.
Multi-view image-based rendering consists in generating a novel view of a scene from a set of source views.
In general, this works by first doing a coarse 3D reconstruction of the scene, and then using this reconstruction to establish correspondences between source and target views, followed by blending the warped views to get the final image.
Unfortunately, discontinuities in the blending weights, due to scene geometry or camera placement, result in artifacts in the target view.
In this paper, we show how to avoid these artifacts by imposing additional constraints on the image gradients of the novel view.
We propose a variational framework in which an energy functional is derived and optimized by iteratively solving a linear system.
We demonstrate this method on several structured and unstructured multi-view datasets, and show that it numerically outperforms state-of-the-art methods, and eliminates artifacts that result from visibility discontinuities
The U.S Government has been the target for cyber-attacks from all over the world.
Just recently, former President Obama accused the Russian government of the leaking emails to Wikileaks and declared that the U.S. might be forced to respond.
While Russia denied involvement, it is clear that the U.S. has to take some defensive measures to protect its data infrastructure.
Insider threats have been the cause of other sensitive information leaks too, including the infamous Edward Snowden incident.
Most of the recent leaks were in the form of text.
Due to the nature of text data, security classifications are assigned manually.
In an adversarial environment, insiders can leak texts through E-mail, printers, or any untrusted channels.
The optimal defense is to automatically detect the unstructured text security class and enforce the appropriate protection mechanism without degrading services or daily tasks.
Unfortunately, existing Data Leak Prevention (DLP) systems are not well suited for detecting unstructured texts.
In this paper, we compare two recent approaches in the literature for text security classification, evaluating them on actual sensitive text data from the WikiLeaks dataset.
In conversational speech, the acoustic signal provides cues that help listeners disambiguate difficult parses.
For automatically parsing spoken utterances, we introduce a model that integrates transcribed text and acoustic-prosodic features using a convolutional neural network over energy and pitch trajectories coupled with an attention-based recurrent neural network that accepts text and prosodic features.
We find that different types of acoustic-prosodic features are individually helpful, and together give statistically significant improvements in parse and disfluency detection F1 scores over a strong text-only baseline.
For this study with known sentence boundaries, error analyses show that the main benefit of acoustic-prosodic features is in sentences with disfluencies, attachment decisions are most improved, and transcription errors obscure gains from prosody.
In this paper, we show the evaluation of the spectral radius for node degree as the basis to analyze the variation in the node degrees during the evolution of scale-free networks and small-world networks.
Spectral radius is the principal eigenvalue of the adjacency matrix of a network graph and spectral radius ratio for node degree is the ratio of the spectral radius and the average node degree.
We observe a very high positive correlation between the spectral radius ratio for node degree and the coefficient of variation of node degree (ratio of the standard deviation of node degree and average node degree).
We show how the spectral radius ratio for node degree can be used as the basis to tune the operating parameters of the evolution models for scale-free networks and small-world networks as well as evaluate the impact of the number of links added per node introduced during the evolution of a scale-free network and evaluate the impact of the probability of rewiring during the evolution of a small-world network from a regular network.
End-to-end networks trained for task-oriented dialog, such as for recommending restaurants to a user, suffer from out-of-vocabulary (OOV) problem -- the entities in the Knowledge Base (KB) may not be seen by the network at training time, making it hard to use them in dialog.
We propose a novel Hierarchical Pointer Generator Memory Network (HyP-MN), in which the next word may be generated from the decode vocabulary or copied from a hierarchical memory maintaining KB results and previous utterances.
This hierarchical memory layout along with a novel KB dropout helps to alleviate the OOV problem.
Evaluating over the dialog bAbI tasks, we find that HyP-MN outperforms state-of-the-art results, with considerable improvements (10% on OOV test set).
HyP-MN also achieves competitive performances on various real-world datasets such as CamRest676 and In-car assistant dataset.
Currently, various hardware and software companies are developing augmented reality devices, most prominently Microsoft with its Hololens.
Besides gaming, such devices can be used for serious pervasive applications, like interactive mobile simulations to support engineers in the field.
Interactive simulations have high demands on resources, which the mobile device alone is unable to satisfy.
Therefore, we propose a framework to support mobile simulations by distributing the computation between the mobile device and a remote server based on the reduced basis method.
Evaluations show that we can speed-up the numerical computation by over 131 times while using 73 times less energy.
We present a system for identifying conceptual shifts between visual categories, which will form the basis for a co-creative drawing system to help users draw more creative sketches.
The system recognizes human sketches and matches them to structurally similar sketches from categories to which they do not belong.
This would allow a co-creative drawing system to produce an ambiguous sketch that blends features from both categories.
Data mining practitioners are facing challenges from data with network structure.
In this paper, we address a specific class of global-state networks which comprises of a set of network instances sharing a similar structure yet having different values at local nodes.
Each instance is associated with a global state which indicates the occurrence of an event.
The objective is to uncover a small set of discriminative subnetworks that can optimally classify global network values.
Unlike most existing studies which explore an exponential subnetwork space, we address this difficult problem by adopting a space transformation approach.
Specifically, we present an algorithm that optimizes a constrained dual-objective function to learn a low-dimensional subspace that is capable of discriminating networks labelled by different global states, while reconciling with common network topology sharing across instances.
Our algorithm takes an appealing approach from spectral graph learning and we show that the globally optimum solution can be achieved via matrix eigen-decomposition.
This paper presents an angle-based approach for distributed formation shape stabilization of multi-agent systems in the plane.
We develop an angle rigidity theory to study whether a planar framework can be determined by angles between segments uniquely up to translations, rotations, scalings and reflections.
The proposed angle rigidity theory is applied to the formation stabilization problem, where multiple single-integrator modeled agents cooperatively achieve an angle-constrained formation.
During the formation process, the global coordinate system is unknown for each agent and wireless communications between agents are not required.
Moreover, by utilizing the advantage of high degrees of freedom, we propose a distributed control law for agents to stabilize a desired formation shape with desired orientation and scale.
Two simulation examples are performed for illustrating effectiveness of the proposed control strategies.
In this paper we proposed an ultimate theory to solve the multi-target control problem through its introduction to the machine learning framework in automatic driving, which explored the implementation of excellent drivers' knowledge acquisition.
Nowadays there exist some core problems that have not been fully realized by the researchers in automatic driving, such as the optimal way to control the multi-target objective functions of energy saving, safe driving, headway distance control and comfort driving, as well as the resolvability of the networks that automatic driving relied on and the high-performance chips like GPU on the complex driving environments.
According to these problems, we developed a new theory to map multitarget objective functions in different spaces into the same one and thus introduced a machine learning framework of SDL(Super Deep Learning) for optimal multi-targetcontrol based on knowledge acquisition.
We will present in this paper the optimal multi-target control by combining the fuzzy relationship of each multi-target objective function and the implementation of excellent drivers' knowledge acquired by machine learning.
Theoretically, the impact of this method will exceed that of the fuzzy control method used in automatic train.
Previous approaches to model and analyze facial expression analysis use three different techniques: facial action units, geometric features and graph based modelling.
However, previous approaches have treated these technique separately.
There is an interrelationship between these techniques.
The facial expression analysis is significantly improved by utilizing these mappings between major geometric features involved in facial expressions and the subset of facial action units whose presence or absence are unique to a facial expression.
This paper combines dimension reduction techniques and image classification with search space pruning achieved by this unique subset of facial action units to significantly prune the search space.
The performance results on the publicly facial expression database shows an improvement in performance by 70% over time while maintaining the emotion recognition correctness.
We present a survey on maritime object detection and tracking approaches, which are essential for the development of a navigational system for autonomous ships.
The electro-optical (EO) sensor considered here is a video camera that operates in the visible or the infrared spectra, which conventionally complement radar and sonar and have demonstrated effectiveness for situational awareness at sea has demonstrated its effectiveness over the last few years.
This paper provides a comprehensive overview of various approaches of video processing for object detection and tracking in the maritime environment.
We follow an approach-based taxonomy wherein the advantages and limitations of each approach are compared.
The object detection system consists of the following modules: horizon detection, static background subtraction and foreground segmentation.
Each of these has been studied extensively in maritime situations and has been shown to be challenging due to the presence of background motion especially due to waves and wakes.
The main processes involved in object tracking include video frame registration, dynamic background subtraction, and the object tracking algorithm itself.
The challenges for robust tracking arise due to camera motion, dynamic background and low contrast of tracked object, possibly due to environmental degradation.
The survey also discusses multisensor approaches and commercial maritime systems that use EO sensors.
The survey also highlights methods from computer vision research which hold promise to perform well in maritime EO data processing.
Performance of several maritime and computer vision techniques is evaluated on newly proposed Singapore Maritime Dataset.
In this paper we extend the classical notion of strong and weak backdoor sets for SAT and CSP by allowing that different instantiations of the backdoor variables result in instances that belong to different base classes; the union of the base classes forms a heterogeneous base class.
Backdoor sets to heterogeneous base classes can be much smaller than backdoor sets to homogeneous ones, hence they are much more desirable but possibly harder to find.
We draw a detailed complexity landscape for the problem of detecting strong and weak backdoor sets into heterogeneous base classes for SAT and CSP.
We present a novel cross-view classification algorithm where the gallery and probe data come from different views.
A popular approach to tackle this problem is the multi-view subspace learning (MvSL) that aims to learn a latent subspace shared by multi-view data.
Despite promising results obtained on some applications, the performance of existing methods deteriorates dramatically when the multi-view data is sampled from nonlinear manifolds or suffers from heavy outliers.
To circumvent this drawback, motivated by the Divide-and-Conquer strategy, we propose Multi-view Hybrid Embedding (MvHE), a unique method of dividing the problem of cross-view classification into three subproblems and building one model for each subproblem.
Specifically, the first model is designed to remove view discrepancy, whereas the second and third models attempt to discover the intrinsic nonlinear structure and to increase discriminability in intra-view and inter-view samples respectively.
The kernel extension is conducted to further boost the representation power of MvHE.
Extensive experiments are conducted on four benchmark datasets.
Our methods demonstrate overwhelming advantages against the state-of-the-art MvSL based cross-view classification approaches in terms of classification accuracy and robustness.
Depth from focus (DFF) is one of the classical ill-posed inverse problems in computer vision.
Most approaches recover the depth at each pixel based on the focal setting which exhibits maximal sharpness.
Yet, it is not obvious how to reliably estimate the sharpness level, particularly in low-textured areas.
In this paper, we propose `Deep Depth From Focus (DDFF)' as the first end-to-end learning approach to this problem.
One of the main challenges we face is the hunger for data of deep neural networks.
In order to obtain a significant amount of focal stacks with corresponding groundtruth depth, we propose to leverage a light-field camera with a co-calibrated RGB-D sensor.
This allows us to digitally create focal stacks of varying sizes.
Compared to existing benchmarks our dataset is 25 times larger, enabling the use of machine learning for this inverse problem.
We compare our results with state-of-the-art DFF methods and we also analyze the effect of several key deep architectural components.
These experiments show that our proposed method `DDFFNet' achieves state-of-the-art performance in all scenes, reducing depth error by more than 75% compared to the classical DFF methods.
In light of the tremendous amount of data produced by social media, a large body of research have revisited the relevance estimation of the users' generated content.
Most of the studies have stressed the multidimensional nature of relevance and proved the effectiveness of combining the different criteria that it embodies.
Traditional relevance estimates combination methods are often based on linear combination schemes.
However, despite being effective, those aggregation mechanisms are not effective in real-life applications since they heavily rely on the non-realistic independence property of the relevance dimensions.
In this paper, we propose to tackle this issue through the design of a novel fuzzy-based document ranking model.
We also propose an automated methodology to capture the importance of relevance dimensions, as well as information about their interaction.
This model, based on the Choquet Integral, allows to optimize the aggregated documents relevance scores using any target information retrieval relevance metric.
Experiments within the TREC Microblog task and a social personalized information retrieval task highlighted that our model significantly outperforms a wide range of state-of-the-art aggregation operators, as well as a representative learning to rank methods.
According to E.T.Jaynes and E.P.Wigner, entropy is an anthropomorphic concept in the sense that in a physical system correspond many thermodynamic systems.
The physical system can be examined from many points of view each time examining different variables and calculating entropy differently.
In this paper we discuss how this concept may be applied in information entropy; how Shannon's definition of entropy can fit in Jayne's and Wigner's statement.
This is achieved by generalizing Shannon's notion of information entropy and this is the main contribution of the paper.
Then we discuss how entropy under these considerations may be used for the comparison of password complexity and as a measure of diversity useful in the analysis of the behavior of genetic algorithms.
The paper focuses on the problem of vision-based obstacle detection and tracking for unmanned aerial vehicle navigation.
A real-time object localization and tracking strategy from monocular image sequences is developed by effectively integrating the object detection and tracking into a dynamic Kalman model.
At the detection stage, the object of interest is automatically detected and localized from a saliency map computed via the image background connectivity cue at each frame; at the tracking stage, a Kalman filter is employed to provide a coarse prediction of the object state, which is further refined via a local detector incorporating the saliency map and the temporal information between two consecutive frames.
Compared to existing methods, the proposed approach does not require any manual initialization for tracking, runs much faster than the state-of-the-art trackers of its kind, and achieves competitive tracking performance on a large number of image sequences.
Extensive experiments demonstrate the effectiveness and superior performance of the proposed approach.
Recent captioning models are limited in their ability to scale and describe concepts unseen in paired image-text corpora.
We propose the Novel Object Captioner (NOC), a deep visual semantic captioning model that can describe a large number of object categories not present in existing image-caption datasets.
Our model takes advantage of external sources -- labeled images from object recognition datasets, and semantic knowledge extracted from unannotated text.
We propose minimizing a joint objective which can learn from these diverse data sources and leverage distributional semantic embeddings, enabling the model to generalize and describe novel objects outside of image-caption datasets.
We demonstrate that our model exploits semantic information to generate captions for hundreds of object categories in the ImageNet object recognition dataset that are not observed in MSCOCO image-caption training data, as well as many categories that are observed very rarely.
Both automatic evaluations and human judgements show that our model considerably outperforms prior work in being able to describe many more categories of objects.
In this demo, we present PackageBuilder, a system that extends database systems to support package queries.
A package is a collection of tuples that individually satisfy base constraints and collectively satisfy global constraints.
The need for package support arises in a variety of scenarios: For example, in the creation of meal plans, users are not only interested in the nutritional content of individual meals (base constraints), but also care to specify daily consumption limits and control the balance of the entire plan (global constraints).
We introduce PaQL, a declarative SQL-based package query language, and the interface abstractions which allow users to interactively specify package queries and easily navigate through their results.
To efficiently evaluate queries, the system employs pruning and heuristics, as well as state-of-the-art constraint optimization solvers.
We demonstrate PackageBuilder by allowing attendees to interact with the system's interface, to define PaQL queries and to observe how query evaluation is performed.
Environment perception is an important task with great practical value and bird view is an essential part for creating panoramas of surrounding environment.
Due to the large gap and severe deformation between the frontal view and bird view, generating a bird view image from a single frontal view is challenging.
To tackle this problem, we propose the BridgeGAN, i.e., a novel generative model for bird view synthesis.
First, an intermediate view, i.e., homography view, is introduced to bridge the large gap.
Next, conditioned on the three views (frontal view, homography view and bird view) in our task, a multi-GAN based model is proposed to learn the challenging cross-view translation.
Extensive experiments conducted on a synthetic dataset have demonstrated that the images generated by our model are much better than those generated by existing methods, with more consistent global appearance and sharper details.
Ablation studies and discussions show its reliability and robustness in some challenging cases.
Using different methods for laying out a graph can lead to very different visual appearances, with which the viewer perceives different information.
Selecting a "good" layout method is thus important for visualizing a graph.
The selection can be highly subjective and dependent on the given task.
A common approach to selecting a good layout is to use aesthetic criteria and visual inspection.
However, fully calculating various layouts and their associated aesthetic metrics is computationally expensive.
In this paper, we present a machine learning approach to large graph visualization based on computing the topological similarity of graphs using graph kernels.
For a given graph, our approach can show what the graph would look like in different layouts and estimate their corresponding aesthetic metrics.
An important contribution of our work is the development of a new framework to design graph kernels.
Our experimental study shows that our estimation calculation is considerably faster than computing the actual layouts and their aesthetic metrics.
Also, our graph kernels outperform the state-of-the-art ones in both time and accuracy.
In addition, we conducted a user study to demonstrate that the topological similarity computed with our graph kernel matches perceptual similarity assessed by human users.
Estimating the Worst-Case Execution Time (WCET) of an application is an essential task in the context of developing real-time or safety-critical software, but it is also a complex and error-prone process.
Conventional approaches require at least some manual inputs from the user, such as loop bounds and infeasible path information, which are hard to obtain and can lead to unsafe results if they are incorrect.
This is aggravated by the lack of a comprehensive explanation of the WCET estimate, i.e., a specific trace showing how WCET was reached.
It is therefore hard to spot incorrect inputs and hard to improve the worst-case timing of the application.
Meanwhile, modern processors have reached a complexity that refutes analysis and puts more and more burden on the practitioner.
In this article we show how all of these issues can be significantly mitigated or even solved, if we use processors that are amenable to WCET analysis.
We define and identify such processors, and then we propose an automated tool set which estimates a precise WCET without unsafe manual inputs, and also reconstructs a maximum-detail view of the WCET path that can be examined in a debugger environment.
Our approach is based on Model Checking, which however is known to scale badly with growing application size.
We address this issue by shifting the analysis to source code level, where source code transformations can be applied that retain the timing behavior, but reduce the complexity.
Our experiments show that fast and precise estimates can be achieved with Model Checking, that its scalability can even exceed current approaches, and that new opportunities arise in the context of "timing debugging".
In visual recognition, the key to the performance improvement of ResNet is the success in establishing the stack of deep sequential convolutional layers using identical mapping by a shortcut connection.
It results in multiple paths of data flow under a network and the paths are merged with the equal weights.
However, it is questionable whether it is correct to use the fixed and predefined weights at the mapping units of all paths.
In this paper, we introduce the active weighted mapping method which infers proper weight values based on the characteristic of input data on the fly.
The weight values of each mapping unit are not fixed but changed as the input image is changed, and the most proper weight values for each mapping unit are derived according to the input image.
For this purpose, channel-wise information is embedded from both the shortcut connection and convolutional block, and then the fully connected layers are used to estimate the weight values for the mapping units.
We train the backbone network and the proposed module alternately for a more stable learning of the proposed method.
Results of the extensive experiments show that the proposed method works successfully on the various backbone architectures from ResNet to DenseNet.
We also verify the superiority and generality of the proposed method on various datasets in comparison with the baseline.
In this paper, we propose a robust visual tracking method which exploits the relationships of targets in adjacent frames using patchwise joint sparse representation.
Two sets of overlapping patches with different sizes are extracted from target candidates to construct two dictionaries with consideration of joint sparse representation.
By applying this representation into structural sparse appearance model, we can take two-fold advantages.
First, the correlation of target patches over time is considered.
Second, using this local appearance model with different patch sizes takes into account local features of target thoroughly.
Furthermore, the position of candidate patches and their occlusion levels are utilized simultaneously to obtain the final likelihood of target candidates.
Evaluations on recent challenging benchmark show that our tracking method outperforms the state-of-the-art trackers.
Re-speaking is a mechanism for obtaining high quality subtitles for use in live broadcast and other public events.
Because it relies on humans performing the actual re-speaking, the task of estimating the quality of the results is non-trivial.
Most organisations rely on humans to perform the actual quality assessment, but purely automatic methods have been developed for other similar problems, like Machine Translation.
This paper will try to compare several of these methods: BLEU, EBLEU, NIST, METEOR, METEOR-PL, TER and RIBES.
These will then be matched to the human-derived NER metric, commonly used in re-speaking.
While recent advances in deep reinforcement learning have allowed autonomous learning agents to succeed at a variety of complex tasks, existing algorithms generally require a lot of training data.
One way to increase the speed at which agents are able to learn to perform tasks is by leveraging the input of human trainers.
Although such input can take many forms, real-time, scalar-valued feedback is especially useful in situations where it proves difficult or impossible for humans to provide expert demonstrations.
Previous approaches have shown the usefulness of human input provided in this fashion (e.g., the TAMER framework), but they have thus far not considered high-dimensional state spaces or employed the use of deep learning.
In this paper, we do both: we propose Deep TAMER, an extension of the TAMER framework that leverages the representational power of deep neural networks in order to learn complex tasks in just a short amount of time with a human trainer.
We demonstrate Deep TAMER's success by using it and just 15 minutes of human-provided feedback to train an agent that performs better than humans on the Atari game of Bowling - a task that has proven difficult for even state-of-the-art reinforcement learning methods.
The abstraction tasks are challenging for multi- modal sequences as they require a deeper semantic understanding and a novel text generation for the data.
Although the recurrent neural networks (RNN) can be used to model the context of the time-sequences, in most cases the long-term dependencies of multi-modal data make the back-propagation through time training of RNN tend to vanish in the time domain.
Recently, inspired from Multiple Time-scale Recurrent Neural Network (MTRNN), an extension of Gated Recurrent Unit (GRU), called Multiple Time-scale Gated Recurrent Unit (MTGRU), has been proposed to learn the long-term dependencies in natural language processing.
Particularly it is also able to accomplish the abstraction task for paragraphs given that the time constants are well defined.
In this paper, we compare the MTRNN and MTGRU in terms of its learning performances as well as their abstraction representation on higher level (with a slower neural activation).
This was done by conducting two studies based on a smaller data- set (two-dimension time sequences from non-linear functions) and a relatively large data-set (43-dimension time sequences from iCub manipulation tasks with multi-modal data).
We conclude that gated recurrent mechanisms may be necessary for learning long-term dependencies in large dimension multi-modal data-sets (e.g. learning of robot manipulation), even when natural language commands was not involved.
But for smaller learning tasks with simple time-sequences, generic version of recurrent models, such as MTRNN, were sufficient to accomplish the abstraction task.
How many copies of a parallelepiped are needed to ensure that for every point in the parallelepiped a copy of each other point exists, such that the distance between them equals the distance of the pair of points when the opposite sites of the parallelepiped are identified?
This question is answered in Euclidean space by constructing the smallest domain that fulfills the above condition.
We also describe how to obtain all primitive cells of a lattice (i.e., closures of fundamental domains) that realise the smallest number of copies needed and give them explicitly in 2D and 3D.
Logic programs with aggregates (LPA) are one of the major linguistic extensions to Logic Programming (LP).
In this work, we propose a generalization of the notions of unfounded set and well-founded semantics for programs with monotone and antimonotone aggregates (LPAma programs).
In particular, we present a new notion of unfounded set for LPAma programs, which is a sound generalization of the original definition for standard (aggregate-free) LP.
On this basis, we define a well-founded operator for LPAma programs, the fixpoint of which is called well-founded model (or well-founded semantics) for LPAma programs.
The most important properties of unfounded sets and the well-founded semantics for standard LP are retained by this generalization, notably existence and uniqueness of the well-founded model, together with a strong relationship to the answer set semantics for LPAma programs.
We show that one of the D-well-founded semantics, defined by Pelov, Denecker, and Bruynooghe for a broader class of aggregates using approximating operators, coincides with the well-founded model as defined in this work on LPAma programs.
We also discuss some complexity issues, most importantly we give a formal proof of tractable computation of the well-founded model for LPA programs.
Moreover, we prove that for general LPA programs, which may contain aggregates that are neither monotone nor antimonotone, deciding satisfaction of aggregate expressions with respect to partial interpretations is coNP-complete.
As a consequence, a well-founded semantics for general LPA programs that allows for tractable computation is unlikely to exist, which justifies the restriction on LPAma programs.
Finally, we present a prototype system extending DLV, which supports the well-founded semantics for LPAma programs, at the time of writing the only implemented system that does so.
Experiments with this prototype show significant computational advantages of aggregate constructs over equivalent aggregate-free encodings.
Increasing data traffic demands over wireless spectrum have necessitated spectrum sharing and coexistence between heterogeneous systems such as radar and cellular communications systems.
In this context, we specifically investigate the co-channel coexistence between an air traffic control (ATC) radar and a wide area cellular communication (comms) system.
We present a comprehensive characterization and analysis of interference caused by the comms system on the ATC radar with respect to multiple parameters such as radar range, protection radius around the radar, and radar antenna elevation angle.
The analysis suggests that maintaining a protection radius of 50 km around the radar will ensure the required INR protection criterion of -10 dB at the radar receiver with ~0.9 probability, even when the radar beam is in the same horizon as the comms BS.
Detailed evaluations of the radar target detection performance provide a framework to choose appropriate protection radii around the radar to meet specific performance requirements.
This paper explores the use of Pyramid Vector Quantization (PVQ) to reduce the computational cost for a variety of neural networks (NNs) while, at the same time, compressing the weights that describe them.
This is based on the fact that the dot product between an N dimensional vector of real numbers and an N dimensional PVQ vector can be calculated with only additions and subtractions and one multiplication.
This is advantageous since tensor products, commonly used in NNs, can be re-conduced to a dot product or a set of dot products.
Finally, it is stressed that any NN architecture that is based on an operation that can be re-conduced to a dot product can benefit from the techniques described here.
We apply cross-lingual Latent Semantic Indexing to the Bilingual Document Alignment Task at WMT16.
Reduced-rank singular value decomposition of a bilingual term-document matrix derived from known English/French page pairs in the training data allows us to map monolingual documents into a joint semantic space.
Two variants of cosine similarity between the vectors that place each document into the joint semantic space are combined with a measure of string similarity between corresponding URLs to produce 1:1 alignments of English/French web pages in a variety of domains.
The system achieves a recall of ca.88% if no in-domain data is used for building the latent semantic model, and 93% if such data is included.
Analysing the system's errors on the training data, we argue that evaluating aligner performance based on exact URL matches under-estimates their true performance and propose an alternative that is able to account for duplicates and near-duplicates in the underlying data.
Modern computer threats are far more complicated than those seen in the past.
They are constantly evolving, altering their appearance, perpetually changing disguise.
Under such circumstances, detecting known threats, a fortiori zero-day attacks, requires new tools, which are able to capture the essence of their behavior, rather than some fixed signatures.
In this work, we propose novel universal anomaly detection algorithms, which are able to learn the normal behavior of systems and alert for abnormalities, without any prior knowledge on the system model, nor any knowledge on the characteristics of the attack.
The suggested method utilizes the Lempel-Ziv universal compression algorithm in order to optimally give probability assignments for normal behavior (during learning), then estimate the likelihood of new data (during operation) and classify it accordingly.
The suggested technique is generic, and can be applied to different scenarios.
Indeed, we apply it to key problems in computer security.
The first is detecting Botnets Command and Control (C&C) channels.
A Botnet is a logical network of compromised machines which are remotely controlled by an attacker using a C&C infrastructure, in order to perform malicious activities.
We derive a detection algorithm based on timing data, which can be collected without deep inspection, from open as well as encrypted flows.
We evaluate the algorithm on real-world network traces, showing how a universal, low complexity C&C identification system can be built, with high detection rates and low false-alarm probabilities.
Further applications include malicious tools detection via system calls monitoring and data leakage identification.
Multi-label learning is concerned with the classification of data with multiple class labels.
This is in contrast to the traditional classification problem where every data instance has a single label.
Due to the exponential size of output space, exploiting intrinsic information in feature and label spaces has been the major thrust of research in recent years and use of parametrization and embedding have been the prime focus.
Researchers have studied several aspects of embedding which include label embedding, input embedding, dimensionality reduction and feature selection.
These approaches differ from one another in their capability to capture other intrinsic properties such as label correlation, local invariance etc.
We assume here that the input data form groups and as a result, the label matrix exhibits a sparsity pattern and hence the labels corresponding to objects in the same group have similar sparsity.
In this paper, we study the embedding of labels together with the group information with an objective to build an efficient multi-label classification.
We assume the existence of a low-dimensional space onto which the feature vectors and label vectors can be embedded.
In order to achieve this, we address three sub-problems namely; (1) Identification of groups of labels; (2) Embedding of label vectors to a low rank-space so that the sparsity characteristic of individual groups remains invariant; and (3) Determining a linear mapping that embeds the feature vectors onto the same set of points, as in stage 2, in the low-dimensional space.
We compare our method with seven well-known algorithms on twelve benchmark data sets.
Our experimental analysis manifests the superiority of our proposed method over state-of-art algorithms for multi-label learning.
This paper proposes a model of information cascades as directed spanning trees (DSTs) over observed documents.
In addition, we propose a contrastive training procedure that exploits partial temporal ordering of node infections in lieu of labeled training links.
This combination of model and unsupervised training makes it possible to improve on models that use infection times alone and to exploit arbitrary features of the nodes and of the text content of messages in information cascades.
With only basic node and time lag features similar to previous models, the DST model achieves performance with unsupervised training comparable to strong baselines on a blog network inference task.
Unsupervised training with additional content features achieves significantly better results, reaching half the accuracy of a fully supervised model.
Obesity treatment requires obese patients to record all food intakes per day.
Computer vision has been introduced to estimate calories from food images.
In order to increase accuracy of detection and reduce the error of volume estimation in food calorie estimation, we present our calorie estimation method in this paper.
To estimate calorie of food, a top view and side view is needed.
Faster R-CNN is used to detect the food and calibration object.
GrabCut algorithm is used to get each food's contour.
Then the volume is estimated with the food and corresponding object.
Finally we estimate each food's calorie.
And the experiment results show our estimation method is effective.
Zero automata are a probabilistic extension of parity automata on infinite trees.
The satisfiability of a certain probabilistic variant of mso, called tmso + zero, reduces to the emptiness problem for zero automata.
We introduce a variant of zero automata called nonzero automata.
We prove that for every zero automaton there is an equivalent nonzero automaton of quadratic size and the emptiness problem of nonzero automata is decidable and both in NP and in coNP.
These results imply that tmso + zero has decidable satisfiability.
Human communication typically has an underlying structure.
This is reflected in the fact that in many user generated videos, a starting point, ending, and certain objective steps between these two can be identified.
In this paper, we propose a method for parsing a video into such semantic steps in an unsupervised way.
The proposed method is capable of providing a semantic "storyline" of the video composed of its objective steps.
We accomplish this using both visual and language cues in a joint generative model.
The proposed method can also provide a textual description for each of the identified semantic steps and video segments.
We evaluate this method on a large number of complex YouTube videos and show results of unprecedented quality for this intricate and impactful problem.
This paper studies the structure of a parabolic partial differential equation on graphs and digital n-dimensional manifolds, which are digital models of continuous n-manifolds.
Conditions for the existence of solutions of equations are determined and investigated.
Numerical solutions of the equation on a Klein bottle, a projective plane, a 4D sphere and a Moebius strip are presented.
The detection and localization of a target from samples of its generated field is a problem of interest in a broad range of applications.
Often, the target field admits structural properties that enable the design of lower sample detection strategies with good performance.
This paper designs a sampling and localization strategy which exploits separability and unimodality in target fields and theoretically analyzes the trade-off achieved between sampling density, noise level and convergence rate of localization.
In particular, the strategy adopts an exploration-exploitation approach to target detection and utilizes the theory of low-rank matrix completion, coupled with unimodal regression, on decaying and approximately separable target fields.
The assumptions on the field are fairly generic and are applicable to many decay profiles since no specific knowledge of the field is necessary, besides its admittance of an approximately rank-one representation.
Extensive numerical experiments and comparisons are performed to test the efficacy and robustness of the presented approach.
Numerical results suggest that the proposed strategy outperforms algorithms based on mean-shift clustering, surface interpolation and naive low-rank matrix completion with peak detection, under low sampling density.
Graph is a useful data structure to model various real life aspects like email communications, co-authorship among researchers, interactions among chemical compounds, and so on.
Supporting such real life interactions produce a knowledge rich massive repository of data.
However, efficiently understanding underlying trends and patterns is hard due to large size of the graph.
Therefore, this paper presents a scalable compression solution to compute summary of a weighted graph.
All the aforementioned interactions from various domains are represented as edge weights in a graph.
Therefore, creating a summary graph while considering this vital aspect is necessary to learn insights of different communication patterns.
By experimenting the proposed method on two real world and publically available datasets against a state of the art technique, we obtain order of magnitude performance gain and better summarization accuracy.
In many real world networks, a vertex is usually associated with a transaction database that comprehensively describes the behaviour of the vertex.
A typical example is the social network, where the behaviour of every user is depicted by a transaction database that stores his daily posted contents.
A transaction database is a set of transactions, where a transaction is a set of items.
Every path of the network is a sequence of vertices that induces multiple sequences of transactions.
The sequences of transactions induced by all of the paths in the network forms an extremely large sequence database.
Finding frequent sequential patterns from such sequence database discovers interesting subsequences that frequently appear in many paths of the network.
However, it is a challenging task, since the sequence database induced by a database graph is too large to be explicitly induced and stored.
In this paper, we propose the novel notion of database graph, which naturally models a wide spectrum of real world networks by associating each vertex with a transaction database.
Our goal is to find the top-k frequent sequential patterns in the sequence database induced from a database graph.
We prove that this problem is #P-hard.
To tackle this problem, we propose an efficient two-step sampling algorithm that approximates the top-k frequent sequential patterns with provable quality guarantee.
Extensive experimental results on synthetic and real-world data sets demonstrate the effectiveness and efficiency of our method.
Parallel coordinate plots (PCPs) are among the most useful techniques for the visualization and exploration of high-dimensional data spaces.
They are especially useful for the representation of correlations among the dimensions, which identify relationships and interdependencies between variables.
However, within these high-dimensional spaces, PCPs face difficulties in displaying the correlation between combinations of dimensions and generally require additional display space as the number of dimensions increases.
In this paper, we present a new technique for high-dimensional data visualization in which a set of low-dimensional PCPs are interactively constructed by sampling user-selected subsets of the high-dimensional data space.
In our technique, we first construct a graph visualization of sets of well-correlated dimensions.
Users observe this graph and are able to interactively select the dimensions by sampling from its cliques, thereby dynamically specifying the most relevant lower dimensional data to be used for the construction of focused PCPs.
Our interactive sampling overcomes the shortcomings of the PCPs by enabling the visualization of the most meaningful dimensions (i.e., the most relevant information) from high-dimensional spaces.
We demonstrate the effectiveness of our technique through two case studies, where we show that the proposed interactive low-dimensional space constructions were pivotal for visualizing the high-dimensional data and discovering new patterns.
Recent renewed interest in optimizing and analyzing floating-point programs has lead to a diverse array of new tools for numerical programs.
These tools are often complementary, each focusing on a distinct aspect of numerical programming.
Building reliable floating point applications typically requires addressing several of these aspects, which makes easy composition essential.
This paper describes the composition of two recent floating-point tools: Herbie, which performs accuracy optimization, and Daisy, which performs accuracy verification.
We find that the combination provides numerous benefits to users, such as being able to use Daisy to check whether Herbie's unsound optimizations improved the worst-case roundoff error, as well as benefits to tool authors, including uncovering a number of bugs in both tools.
The combination also allowed us to compare the different program rewriting techniques implemented by these tools for the first time.
The paper lays out a road map for combining other floating-point tools and for surmounting common challenges.
Visual representation is crucial for a visual tracking method's performances.
Conventionally, visual representations adopted in visual tracking rely on hand-crafted computer vision descriptors.
These descriptors were developed generically without considering tracking-specific information.
In this paper, we propose to learn complex-valued invariant representations from tracked sequential image patches, via strong temporal slowness constraint and stacked convolutional autoencoders.
The deep slow local representations are learned offline on unlabeled data and transferred to the observational model of our proposed tracker.
The proposed observational model retains old training samples to alleviate drift, and collect negative samples which are coherent with target's motion pattern for better discriminative tracking.
With the learned representation and online training samples, a logistic regression classifier is adopted to distinguish target from background, and retrained online to adapt to appearance changes.
Subsequently, the observational model is integrated into a particle filter framework to peform visual tracking.
Experimental results on various challenging benchmark sequences demonstrate that the proposed tracker performs favourably against several state-of-the-art trackers.
To solve the text-based question and answering task that requires relational reasoning, it is necessary to memorize a large amount of information and find out the question relevant information from the memory.
Most approaches were based on external memory and four components proposed by Memory Network.
The distinctive component among them was the way of finding the necessary information and it contributes to the performance.
Recently, a simple but powerful neural network module for reasoning called Relation Network (RN) has been introduced.
We analyzed RN from the view of Memory Network, and realized that its MLP component is able to reveal the complicate relation between question and object pair.
Motivated from it, we introduce which uses MLP to find out relevant information on Memory Network architecture.
It shows new state-of-the-art results in jointly trained bAbI-10k story-based question answering tasks and bAbI dialog-based question answering tasks.
Classification and clustering algorithms have been proved to be successful individually in different contexts.
Both of them have their own advantages and limitations.
For instance, although classification algorithms are more powerful than clustering methods in predicting class labels of objects, they do not perform well when there is a lack of sufficient manually labeled reliable data.
On the other hand, although clustering algorithms do not produce label information for objects, they provide supplementary constraints (e.g., if two objects are clustered together, it is more likely that the same label is assigned to both of them) that one can leverage for label prediction of a set of unknown objects.
Therefore, systematic utilization of both these types of algorithms together can lead to better prediction performance.
In this paper, We propose a novel algorithm, called EC3 that merges classification and clustering together in order to support both binary and multi-class classification.
EC3 is based on a principled combination of multiple classification and multiple clustering methods using an optimization function.
We theoretically show the convexity and optimality of the problem and solve it by block coordinate descent method.
We additionally propose iEC3, a variant of EC3 that handles imbalanced training data.
We perform an extensive experimental analysis by comparing EC3 and iEC3 with 14 baseline methods (7 well-known standalone classifiers, 5 ensemble classifiers, and 2 existing methods that merge classification and clustering) on 13 standard benchmark datasets.
We show that our methods outperform other baselines for every single dataset, achieving at most 10% higher AUC.
Moreover our methods are faster (1.21 times faster than the best baseline), more resilient to noise and class imbalance than the best baseline method.
Given n red and n blue points in general position in the plane, it is well-known that there is a perfect matching formed by non-crossing line segments.
We characterize the bichromatic point sets which admit exactly one non-crossing matching.
We give several geometric descriptions of such sets, and find an O(nlogn) algorithm that checks whether a given bichromatic set has this property.
In this paper we give an exponential lower bound for Cunningham's least recently considered (round-robin) rule as applied to parity games, Markhov decision processes and linear programs.
This improves a recent subexponential bound of Friedmann for this rule on these problems.
The round-robin rule fixes a cyclical order of the variables and chooses the next pivot variable starting from the previously chosen variable and proceeding in the given circular order.
It is perhaps the simplest example from the class of history-based pivot rules.
Our results are based on a new lower bound construction for parity games.
Due to the nature of the construction we are also able to obtain an exponential lower bound for the round-robin rule applied to acyclic unique sink orientations of hypercubes (AUSOs).
Furthermore these AUSOs are realizable as polytopes.
We believe these are the first such results for history based rules for AUSOs, realizable or not.
The paper is self-contained and requires no previous knowledge of parity games.
The mood of a text and the intention of the writer can be reflected in the typeface.
However, in designing a typeface, it is difficult to keep the style of various characters consistent, especially for languages with lots of morphological variations such as Chinese.
In this paper, we propose a Typeface Completion Network (TCN) which takes one character as an input, and automatically completes the entire set of characters in the same style as the input characters.
Unlike existing models proposed for image-to-image translation, TCN embeds a character image into two separate vectors representing typeface and content.
Combined with a reconstruction loss from the latent space, and with other various losses, TCN overcomes the inherent difficulty in designing a typeface.
Also, compared to previous image-to-image translation models, TCN generates high quality character images of the same typeface with a much smaller number of model parameters.
We validate our proposed model on the Chinese and English character datasets, which is paired data, and the CelebA dataset, which is unpaired data.
In these datasets, TCN outperforms recently proposed state-of-the-art models for image-to-image translation.
The source code of our model is available at https://github.com/yongqyu/TCN.
We study the application of active learning techniques to the translation of unbounded data streams via interactive neural machine translation.
The main idea is to select, from an unbounded stream of source sentences, those worth to be supervised by a human agent.
The user will interactively translate those samples.
Once validated, these data is useful for adapting the neural machine translation model.
We propose two novel methods for selecting the samples to be validated.
We exploit the information from the attention mechanism of a neural machine translation system.
Our experiments show that the inclusion of active learning techniques into this pipeline allows to reduce the effort required during the process, while increasing the quality of the translation system.
Moreover, it enables to balance the human effort required for achieving a certain translation quality.
Moreover, our neural system outperforms classical approaches by a large margin.
In this paper, second-order hidden Markov model (HMM2) has been used and implemented to improve the recognition performance of text-dependent speaker identification systems under neutral talking condition.
Our results show that HMM2 improves the recognition performance under neutral talking condition compared to the first-order hidden Markov model (HMM1).
The recognition performance has been improved by 9%.
Browsing privacy solutions face an uphill battle to deployment.
Many operate counter to the economic objectives of popular online services (e.g., by completely blocking ads) and do not provide enough incentive for users who may be subject to performance degradation for deploying them.
In this study, we take a step towards realizing a system for online privacy that is mutually beneficial to users and online advertisers: an information market.
This system not only maintains economic viability for online services, but also provides users with financial compensation to encourage them to participate.
We prototype and evaluate an information market that provides privacy and revenue to users while preserving and sometimes improving their Web performance.
We evaluate feasibility of the market via a one month field study with 63 users and find that users are indeed willing to sell their browsing information.
We also use Web traces of millions of users to drive a simulation study to evaluate the system at scale.
We find that the system can indeed be profitable to both users and online advertisers.
In this paper, we present a consensus-based framework for decentralized estimation of deterministic parameters in wireless sensor networks (WSNs).
In particular, we propose an optimization algorithm to design (possibly complex) sensor gains in order to achieve an estimate of the parameter of interest that is as accurate as possible.
The proposed design algorithm employs a cyclic approach capable of handling various sensor gain constraints.
In addition, each iteration of the proposed design framework is comprised of the Gram-Schmidt process and power-method like iterations, and as a result, enjoys a low-computational cost.
With the increasing availability of large databases of 3D CAD models, depth-based recognition methods can be trained on an uncountable number of synthetically rendered images.
However, discrepancies with the real data acquired from various depth sensors still noticeably impede progress.
Previous works adopted unsupervised approaches to generate more realistic depth data, but they all require real scans for training, even if unlabeled.
This still represents a strong requirement, especially when considering real-life/industrial settings where real training images are hard or impossible to acquire, but texture-less 3D models are available.
We thus propose a novel approach leveraging only CAD models to bridge the realism gap.
Purely trained on synthetic data, playing against an extensive augmentation pipeline in an unsupervised manner, our generative adversarial network learns to effectively segment depth images and recover the clean synthetic-looking depth information even from partial occlusions.
As our solution is not only fully decoupled from the real domains but also from the task-specific analytics, the pre-processed scans can be handed to any kind and number of recognition methods also trained on synthetic data.
Through various experiments, we demonstrate how this simplifies their training and consistently enhances their performance, with results on par with the same methods trained on real data, and better than usual approaches doing the reverse mapping.
For medical volume visualization, one of the most important tasks is to reveal clinically relevant details from the 3D scan (CT, MRI ...), e.g. the coronary arteries, without obscuring them with less significant parts.
These volume datasets contain different materials which are difficult to extract and visualize with 1D transfer functions based solely on the attenuation coefficient.
Multi-dimensional transfer functions allow a much more precise classification of data which makes it easier to separate different surfaces from each other.
Unfortunately, setting up multi-dimensional transfer functions can become a fairly complex task, generally accomplished by trial and error.
This paper explains neural networks, and then presents an efficient way to speed up visualization process by semi-automatic transfer function generation.
We describe how to use neural networks to detect distinctive features shown in the 2D histogram of the volume data and how to use this information for data classification.
We present a distributed algorithm for a swarm of active particles to camouflage in an environment.
Each particle is equipped with sensing, computation and communication, allowing the system to take color and gradient information from the environment and self-organize into an appropriate pattern.
Current artificial camouflage systems are either limited to static patterns, which are adapted for specific environments, or rely on back-projection, which depend on the viewer's point of view.
Inspired by the camouflage abilities of the cuttlefish, we propose a distributed estimation and pattern formation algorithm that allows to quickly adapt to different environments.
We present convergence results both in simulation as well as on a swarm of miniature robots "Droplets" for a variety of patterns.
We introduce a problem called the Minimum Shared-Power Edge Cut (MSPEC).
The input to the problem is an undirected edge-weighted graph with distinguished vertices s and t, and the goal is to find an s-t cut by assigning "powers" at the vertices and removing an edge if the sum of the powers at its endpoints is at least its weight.
The objective is to minimize the sum of the assigned powers.
MSPEC is a graph generalization of a barrier coverage problem in a wireless sensor network: given a set of unit disks with centers in a rectangle, what is the minimum total amount by which we must shrink the disks to permit an intruder to cross the rectangle undetected, i.e. without entering any disc.
This is a more sophisticated measure of barrier coverage than the minimum number of disks whose removal breaks the barrier.
We develop a fully polynomial time approximation scheme (FPTAS) for MSPEC.
We give polynomial time algorithms for the special cases where the edge weights are uniform, or the power values are restricted to a bounded set.
Although MSPEC is related to network flow and matching problems, its computational complexity (in P or NP-hard) remains open.
Domain Name System (DNS), one of the important infrastructure in the Internet, was vulnerable to attacks, for the DNS designer didn't take security issues into consideration at the beginning.
The defects of DNS may lead to users' failure of access to the websites, what's worse, users might suffer a huge economic loss.
In order to correct the DNS wrong resource records, we propose a Self-Feedback Correction System for DNS (SFCSD), which can find and track a large number of common websites' domain name and IP address correct correspondences to provide users with a real-time auto-updated correct (IP, Domain) binary tuple list.
By matching specific strings with SSL, DNS and HTTP traffic passively, filtering with the CDN CNAME and non-homepage URL feature strings, verifying with webpage fingerprint algorithm, SFCSD obtains a large number of highly possibly correct IP addresses to make an active manual correction in the end.
Its self-feedback mechanism can expand search range and improve performance.
Experiments show that, SFCSD can achieve 94.3% precision and 93.07% recall rate with the optimal threshold selection in the test dataset.
It has 8Gbps processing speed stand-alone to find almost 1000 possibly correct (IP, Domain) per day for the each specific string and to correct almost 200.
Anthropomimetic robots are robots that sense, behave, interact and feel like humans.
By this definition, anthropomimetic robots require human-like physical hardware and actuation, but also brain-like control and sensing.
The most self-evident realization to meet those requirements would be a human-like musculoskeletal robot with a brain-like neural controller.
While both musculoskeletal robotic hardware and neural control software have existed for decades, a scalable approach that could be used to build and control an anthropomimetic human-scale robot has not been demonstrated yet.
Combining Myorobotics, a framework for musculoskeletal robot development, with SpiNNaker, a neuromorphic computing platform, we present the proof-of-principle of a system that can scale to dozens of neurally-controlled, physically compliant joints.
At its core, it implements a closed-loop cerebellar model which provides real-time low-level neural control at minimal power consumption and maximal extensibility: higher-order (e.g., cortical) neural networks and neuromorphic sensors like silicon-retinae or -cochleae can naturally be incorporated.
Vehicular Ad Hoc Networks (VANET) is a very promising research venue that can offers many useful and critical applications including the safety applications.
Most of these applications require that each vehicle knows precisely its current position in real time.
GPS is the most common positioning technique for VANET.
However, it is not accurate.
Moreover, the GPS signals cannot be received in the tunnels, undergrounds, or near tall buildings.
Thus, no positioning service can be obtained in these locations.
Even if the Deferential GPS (DGPS) can provide high accuracy, but still no GPS converge in these locations.
In this paper, we provide positioning techniques for VANET that can provide accurate positioning service in the areas where GPS signals are hindered by the obstacles.
Experimental results show significant improvement in the accuracy.
This allows when combined with DGPS the continuity of a precise positioning service that can be used by most of the VANET applications.
Recent empirical studies show that the performance of GenProg is not satisfactory, particularly for Java.
In this paper, we propose ARJA, a new GP based repair approach for automated repair of Java programs.
To be specific, we present a novel lower-granularity patch representation that properly decouples the search subspaces of likely-buggy locations, operation types and potential fix ingredients, enabling GP to explore the search space more effectively.
Based on this new representation, we formulate automated program repair as a multi-objective search problem and use NSGA-II to look for simpler repairs.
To reduce the computational effort and search space, we introduce a test filtering procedure that can speed up the fitness evaluation of GP and three types of rules that can be applied to avoid unnecessary manipulations of the code.
Moreover, we also propose a type matching strategy that can create new potential fix ingredients by exploiting the syntactic patterns of the existing statements.
We conduct a large-scale empirical evaluation of ARJA along with its variants on both seeded bugs and real-world bugs in comparison with several state-of-the-art repair approaches.
Our results verify the effectiveness and efficiency of the search mechanisms employed in ARJA and also show its superiority over the other approaches.
In particular, compared to jGenProg (an implementation of GenProg for Java), an ARJA version fully following the redundancy assumption can generate a test-suite adequate patch for more than twice the number of bugs (from 27 to 59), and a correct patch for nearly four times of the number (from 5 to 18), on 224 real-world bugs considered in Defects4J.
Furthermore, ARJA is able to correctly fix several real multi-location bugs that are hard to be repaired by most of the existing repair approaches.
We explore the use of segments learnt using Byte Pair Encoding (referred to as BPE units) as basic units for statistical machine translation between related languages and compare it with orthographic syllables, which are currently the best performing basic units for this translation task.
BPE identifies the most frequent character sequences as basic units, while orthographic syllables are linguistically motivated pseudo-syllables.
We show that BPE units modestly outperform orthographic syllables as units of translation, showing up to 11% increase in BLEU score.
While orthographic syllables can be used only for languages whose writing systems use vowel representations, BPE is writing system independent and we show that BPE outperforms other units for non-vowel writing systems too.
Our results are supported by extensive experimentation spanning multiple language families and writing systems.
Access control is a crucial part of a system's security, restricting what actions users can perform on resources.
Therefore, access control is a core component when dealing with e-Health data and resources, discriminating which is available for a certain party.
We consider that current systems that attempt to assure the share of policies between facilities are prone to system's and network's faults and do not assure the integrity of policies lifecycle.
By approaching this problem with the use of a distributed ledger, namely a consortium blockchain, where the operations are stored as transactions, we ensure that the different facilities have knowledge about all the parties that can act over the e-Health resources while maintaining integrity, auditability, authenticity, and scalability.
Deep networks have recently been shown to be vulnerable to universal perturbations: there exist very small image-agnostic perturbations that cause most natural images to be misclassified by such classifiers.
In this paper, we propose the first quantitative analysis of the robustness of classifiers to universal perturbations, and draw a formal link between the robustness to universal perturbations, and the geometry of the decision boundary.
Specifically, we establish theoretical bounds on the robustness of classifiers under two decision boundary models (flat and curved models).
We show in particular that the robustness of deep networks to universal perturbations is driven by a key property of their curvature: there exists shared directions along which the decision boundary of deep networks is systematically positively curved.
Under such conditions, we prove the existence of small universal perturbations.
Our analysis further provides a novel geometric method for computing universal perturbations, in addition to explaining their properties.
The Rate Control Protocol (RCP) is a congestion control protocol that relies on explicit feedback from routers.
RCP estimates the flow rate using two forms of feedback: rate mismatch and queue size.
However, it remains an open design question whether queue size feedback in RCP is useful, given the presence of rate mismatch.
The model we consider has RCP flows operating over a single bottleneck, with heterogeneous time delays.
We first derive a sufficient condition for global stability, and then highlight how this condition favors the design choice of having only rate mismatch in the protocol definition.
Indexing highly repetitive collections has become a relevant problem with the emergence of large repositories of versioned documents, among other applications.
These collections may reach huge sizes, but are formed mostly of documents that are near-copies of others.
Traditional techniques for indexing these collections fail to properly exploit their regularities in order to reduce space.
We introduce new techniques for compressing inverted indexes that exploit this near-copy regularity.
They are based on run-length, Lempel-Ziv, or grammar compression of the differential inverted lists, instead of the usual practice of gap-encoding them.
We show that, in this highly repetitive setting, our compression methods significantly reduce the space obtained with classical techniques, at the price of moderate slowdowns.
Moreover, our best methods are universal, that is, they do not need to know the versioning structure of the collection, nor that a clear versioning structure even exists.
We also introduce compressed self-indexes in the comparison.
These are designed for general strings (not only natural language texts) and represent the text collection plus the index structure (not an inverted index) in integrated form.
We show that these techniques can compress much further, using a small fraction of the space required by our new inverted indexes.
Yet, they are orders of magnitude slower.
Developing an appropriate design process for a conceptual model is a stepping stone toward designing car bodies.
This paper presents a methodology to design a lightweight and modular space frame chassis for a sedan electric car.
The dual phase high strength steel with improved mechanical properties is employed to reduce the weight of the car body.
Utilizing the finite element analysis yields two models in order to predict the performance of each component.
The first model is a beam structure with a rapid response in structural stiffness simulation.
This model is used for performing the static tests including modal frequency, bending stiffens and torsional stiffness evaluation.
Whereas the second model, i.e., a shell model, is proposed to illustrate every module's mechanical behavior as well as its crashworthiness efficiency.
In order to perform the crashworthiness analysis, the explicit nonlinear dynamic solver provided by ABAQUS, a commercial finite element software, is used.
The results of finite element beam and shell models are in line with the concept design specifications.
Implementation of this procedure leads to generate a lightweight and modular concept for an electric car.
Over the last 25 years four million e-mail addresses have accumulated in the PGP web of trust.
In a study each of them was tested for vitality with the result of 40% being unreachable.
Of the mailboxes proven to be reachable, 46.77% turn out to be operated by one of three organizations.
In this article, the authors share their results and challenges during the study.
Over the last five years, methods based on Deep Convolutional Neural Networks (DCNNs) have shown impressive performance improvements for object detection and recognition problems.
This has been made possible due to the availability of large annotated datasets, a better understanding of the non-linear mapping between input images and class labels as well as the affordability of GPUs.
In this paper, we present the design details of a deep learning system for unconstrained face recognition, including modules for face detection, association, alignment and face verification.
The quantitative performance evaluation is conducted using the IARPA Janus Benchmark A (IJB-A), the JANUS Challenge Set 2 (JANUS CS2), and the LFW dataset.
The IJB-A dataset includes real-world unconstrained faces of 500 subjects with significant pose and illumination variations which are much harder than the Labeled Faces in the Wild (LFW) and Youtube Face (YTF) datasets.
JANUS CS2 is the extended version of IJB-A which contains not only all the images/frames of IJB-A but also includes the original videos for evaluating the video-based face verification system.
Some open issues regarding DCNNs for face verification problems are then discussed.
Two channels are equivalent if their maximum likelihood (ML) decoders coincide for every code.
We show that this equivalence relation partitions the space of channels into a generalized hyperplane arrangement.
With this, we define a coding distance between channels in terms of their ML-decoders which is meaningful from the decoding point of view, in the sense that the closer two channels are, the larger is the probability of them sharing the same ML-decoder.
We give explicit formulas for these probabilities.
Feature selection has been studied widely in the literature.
However, the efficacy of the selection criteria for low sample size applications is neglected in most cases.
Most of the existing feature selection criteria are based on the sample similarity.
However, the distance measures become insignificant for high dimensional low sample size (HDLSS) data.
Moreover, the variance of a feature with a few samples is pointless unless it represents the data distribution efficiently.
Instead of looking at the samples in groups, we evaluate their efficiency based on pairwise fashion.
In our investigation, we noticed that considering a pair of samples at a time and selecting the features that bring them closer or put them far away is a better choice for feature selection.
Experimental results on benchmark data sets demonstrate the effectiveness of the proposed method with low sample size, which outperforms many other state-of-the-art feature selection methods.
Word embedding methods revolve around learning continuous distributed vector representations of words with neural networks, which can capture semantic and/or syntactic cues, and in turn be used to induce similarity measures among words, sentences and documents in context.
Celebrated methods can be categorized as prediction-based and count-based methods according to the training objectives and model architectures.
Their pros and cons have been extensively analyzed and evaluated in recent studies, but there is relatively less work continuing the line of research to develop an enhanced learning method that brings together the advantages of the two model families.
In addition, the interpretation of the learned word representations still remains somewhat opaque.
Motivated by the observations and considering the pressing need, this paper presents a novel method for learning the word representations, which not only inherits the advantages of classic word embedding methods but also offers a clearer and more rigorous interpretation of the learned word representations.
Built upon the proposed word embedding method, we further formulate a translation-based language modeling framework for the extractive speech summarization task.
A series of empirical evaluations demonstrate the effectiveness of the proposed word representation learning and language modeling techniques in extractive speech summarization.
It is well known that normality (all factors of given length appear in an infinite sequence with the same frequency) can be described as incompressibility via finite automata.
Still the statement and proof of this result as given by Becher and Heiber in terms of "lossless finite-state compressors" do not follow the standard scheme of Kolmogorov complexity definition (the automaton is used for compression, not decompression).
We modify this approach to make it more similar to the traditional Kolmogorov complexity theory (and simpler) by explicitly defining the notion of automatic Kolmogorov complexity and using its simple properties.
Other known notions (Shallit--Wang, Calude--Salomaa--Roblot) of description complexity related to finite automata are discussed (see the last section).
As a byproduct, we obtain simple proofs of classical results about normality (equivalence of definitions with aligned occurences and all occurencies, Wall's theorem saying that a normal number remains normal when multiplied by a rational number, and Agafonov's result saying that normality is preserved by automatic selection rules).
Many natural language processing tasks can be modeled into structured prediction and solved as a search problem.
In this paper, we distill an ensemble of multiple models trained with different initialization into a single model.
In addition to learning to match the ensemble's probability output on the reference states, we also use the ensemble to explore the search space and learn from the encountered states in the exploration.
Experimental results on two typical search-based structured prediction tasks -- transition-based dependency parsing and neural machine translation show that distillation can effectively improve the single model's performance and the final model achieves improvements of 1.32 in LAS and 2.65 in BLEU score on these two tasks respectively over strong baselines and it outperforms the greedy structured prediction models in previous literatures.
We present a self-training approach to unsupervised dependency parsing that reuses existing supervised and unsupervised parsing algorithms.
Our approach, called `iterated reranking' (IR), starts with dependency trees generated by an unsupervised parser, and iteratively improves these trees using the richer probability models used in supervised parsing that are in turn trained on these trees.
Our system achieves 1.8% accuracy higher than the state-of-the-part parser of Spitkovsky et al.(2013) on the WSJ corpus.
The design of flow control systems remains a challenge due to the nonlinear nature of the equations that govern fluid flow.
However, recent advances in computational fluid dynamics (CFD) have enabled the simulation of complex fluid flows with high accuracy, opening the possibility of using learning-based approaches to facilitate controller design.
We present a method for learning the forced and unforced dynamics of airflow over a cylinder directly from CFD data.
The proposed approach, grounded in Koopman theory, is shown to produce stable dynamical models that can predict the time evolution of the cylinder system over extended time horizons.
Finally, by performing model predictive control with the learned dynamical models, we are able to find a straightforward, interpretable control law for suppressing vortex shedding in the wake of the cylinder.
In this paper, we propose an ad-hoc on-demand distance vector routing algorithm for mobile ad-hoc networks taking into account node mobility.
Changeable topology of such mobile ad-hoc networks provokes overhead messages in order to search available routes and maintain found routes.
The overhead messages impede data delivery from sources to destination and deteriorate network performance.
To overcome such a challenge, our proposed algorithm estimates link duration based neighboring node mobility and chooses the most reliable route.
The proposed algorithm also applies the estimate for route maintenance to lessen the number of overhead messages.
Via simulations, the proposed algorithm is verified in various mobile environments.
In the low mobility environment, by reducing route maintenance messages, the proposed algorithm significantly improves network performance such as packet data rate and end-to-end delay.
In the high mobility environment, the reduction of route discovery message enhances network performance since the proposed algorithm provides more reliable routes.
We explore several oversampling techniques for an imbalanced multi-label classification problem, a setting often encountered when developing models for Computer-Aided Diagnosis (CADx) systems.
While most CADx systems aim to optimize classifiers for overall accuracy without considering the relative distribution of each class, we look into using synthetic sampling to increase per-class performance when predicting the degree of malignancy.
Using low-level image features and a random forest classifier, we show that using synthetic oversampling techniques increases the sensitivity of the minority classes by an average of 7.22% points, with as much as a 19.88% point increase in sensitivity for a particular minority class.
Furthermore, the analysis of low-level image feature distributions for the synthetic nodules reveals that these nodules can provide insights on how to preprocess image data for better classification performance or how to supplement the original datasets when more data acquisition is feasible.
Accurate localization of other traffic participants is a vital task in autonomous driving systems.
State-of-the-art systems employ a combination of sensing modalities such as RGB cameras and LiDARs for localizing traffic participants, but most such demonstrations have been confined to plain roads.
We demonstrate, to the best of our knowledge, the first results for monocular object localization and shape estimation on surfaces that do not share the same plane with the moving monocular camera.
We approximate road surfaces by local planar patches and use semantic cues from vehicles in the scene to initialize a local bundle-adjustment like procedure that simultaneously estimates the pose and shape of the vehicles, and the orientation of the local ground plane on which the vehicle stands as well.
We evaluate the proposed approach on the KITTI and SYNTHIA-SF benchmarks, for a variety of road plane configurations.
The proposed approach significantly improves the state-of-the-art for monocular object localization on arbitrarily-shaped roads.
The Discontinuous Reception (DRX) mechanism is commonly employed in current LTE networks to improve energy efficiency of user equipment (UE).
DRX allows UEs to monitor the physical downlink control channel (PDCCH) discontinuously when there is no downlink traffic for them, thus reducing their energy consumption.
However, DRX power savings are achieved at the expense of some increase in packet delay since downlink traffic transmission must be deferred until the UEs resume listening to the PDCCH.
In this paper, we present a promising mechanism that reduces energy consumption of UEs using DRX while simultaneously maintaining average packet delay around a desired target.
Furthermore, our proposal is able to achieve significant power savings without either increasing signaling overhead or requiring any changes to deployed wireless protocols.
Support Vector Data Description (SVDD) is a popular outlier detection technique which constructs a flexible description of the input data.
SVDD computation time is high for large training datasets which limits its use in big-data process-monitoring applications.
We propose a new iterative sampling-based method for SVDD training.
The method incrementally learns the training data description at each iteration by computing SVDD on an independent random sample selected with replacement from the training data set.
The experimental results indicate that the proposed method is extremely fast and provides a good data description .
In this paper, we propose a new deep learning approach, called neural association model (NAM), for probabilistic reasoning in artificial intelligence.
We propose to use neural networks to model association between any two events in a domain.
Neural networks take one event as input and compute a conditional probability of the other event to model how likely these two events are to be associated.
The actual meaning of the conditional probabilities varies between applications and depends on how the models are trained.
In this work, as two case studies, we have investigated two NAM structures, namely deep neural networks (DNN) and relation-modulated neural nets (RMNN), on several probabilistic reasoning tasks in AI, including recognizing textual entailment, triple classification in multi-relational knowledge bases and commonsense reasoning.
Experimental results on several popular datasets derived from WordNet, FreeBase and ConceptNet have all demonstrated that both DNNs and RMNNs perform equally well and they can significantly outperform the conventional methods available for these reasoning tasks.
Moreover, compared with DNNs, RMNNs are superior in knowledge transfer, where a pre-trained model can be quickly extended to an unseen relation after observing only a few training samples.
To further prove the effectiveness of the proposed models, in this work, we have applied NAMs to solving challenging Winograd Schema (WS) problems.
Experiments conducted on a set of WS problems prove that the proposed models have the potential for commonsense reasoning.
Wireless Sensor Network (WSN) consists of large number of low-cost, resource-constrained sensor nodes.
The constraints of the wireless sensor node is their characteristics which include low memory, low computation power, they are deployed in hostile area and left unattended, small range of communication capability and low energy capabilities.
Base on those characteristics makes this network vulnerable to several attacks, such as sinkhole attack.
Sinkhole attack is a type of attack were compromised node tries to attract network traffic by advertise its fake routing update.
One of the impacts of sinkhole attack is that, it can be used to launch other attacks like selective forwarding attack, acknowledge spoofing attack and drops or altered routing information.
It can also used to send bogus information to base station.
This paper is focus on exploring and analyzing the existing solutions which used to detect and identify sinkhole attack in wireless sensor network.
The analysis is based on advantages and limitation of the proposed solutions.
We consider the problem of detecting data races in program traces that have been compressed using straight line programs (SLP), which are special context-free grammars that generate exactly one string, namely the trace that they represent.
We consider two classical approaches to race detection --- using the happens-before relation and the lockset discipline.
We present algorithms for both these methods that run in time that is linear in the size of the compressed, SLP representation.
Typical program executions almost always exhibit patterns that lead to significant compression.
Thus, our algorithms are expected to result in large speedups when compared with analyzing the uncompressed trace.
Our experimental evaluation of these new algorithms on standard benchmarks confirms this observation.
Sentence specificity quantifies the level of detail in a sentence, characterizing the organization of information in discourse.
While this information is useful for many downstream applications, specificity prediction systems predict very coarse labels (binary or ternary) and are trained on and tailored toward specific domains (e.g., news).
The goal of this work is to generalize specificity prediction to domains where no labeled data is available and output more nuanced real-valued specificity ratings.
We present an unsupervised domain adaptation system for sentence specificity prediction, specifically designed to output real-valued estimates from binary training labels.
To calibrate the values of these predictions appropriately, we regularize the posterior distribution of the labels towards a reference distribution.
We show that our framework generalizes well to three different domains with 50%~68% mean absolute error reduction than the current state-of-the-art system trained for news sentence specificity.
We also demonstrate the potential of our work in improving the quality and informativeness of dialogue generation systems.
The participatory Web has enabled the ubiquitous and pervasive access of information, accompanied by an increase of speed and reach in information sharing.
Data dissemination services such as news aggregators are expected to provide up-to-date, real-time information to the end users.
News aggregators are in essence recommendation systems that filter and rank news stories in order to select the few that will appear on the users front screen at any time.
One of the main challenges in such systems is to address the recency and latency problems, that is, to identify as soon as possible how important a news story is.
In this work we propose an integrated framework that aims at predicting the importance of news items upon their publication with a focus on recent and highly popular news, employing resampling strategies, and at translating the result into concrete news rankings.
We perform an extensive experimental evaluation using real-life datasets of the proposed framework as both a stand-alone system and when applied to news recommendations from Google News.
Additionally, we propose and evaluate a combinatorial solution to the augmentation of official media recommendations with social information.
Results show that the proposed approach complements and enhances the news rankings generated by state-of-the-art systems.
The advancement of mobile technologies and the proliferation of map-based applications have enabled a user to access a wide variety of services that range from information queries to navigation systems.
Due to the popularity of map-based applications among the users, the service provider often requires to answer a large number of simultaneous queries.
Thus, processing queries efficiently on spatial networks (i.e., road networks) have become an important research area in recent years.
In this paper, we focus on path queries that find the shortest path between a source and a destination of the user.
In particular, we address the problem of finding the shortest paths for a large number of simultaneous path queries in road networks.
Traditional systems that consider one query at a time are not suitable for many applications due to high computational and service costs.
These systems cannot guarantee required response time in high load conditions.
We propose an efficient group based approach that provides a practical solution with reduced cost.
The key concept for our approach is to group queries that share a common travel path and then compute the shortest path for the group.
Experimental results show that our approach is on an average ten times faster than the traditional approach in return of sacrificing the accuracy by 0.5% in the worst case, which is acceptable for most of the users.
Single individual haplotyping is an NP-hard problem that emerges when attempting to reconstruct an organism's inherited genetic variations using data typically generated by high-throughput DNA sequencing platforms.
Genomes of diploid organisms, including humans, are organized into homologous pairs of chromosomes that differ from each other in a relatively small number of variant positions.
Haplotypes are ordered sequences of the nucleotides in the variant positions of the chromosomes in a homologous pair; for diploids, haplotypes associated with a pair of chromosomes may be conveniently represented by means of complementary binary sequences.
In this paper, we consider a binary matrix factorization formulation of the single individual haplotyping problem and efficiently solve it by means of alternating minimization.
We analyze the convergence properties of the alternating minimization algorithm and establish theoretical bounds for the achievable haplotype reconstruction error.
The proposed technique is shown to outperform existing methods when applied to synthetic as well as real-world Fosmid-based HapMap NA12878 datasets.
Scalable user- and application-aware resource allocation for heterogeneous applications sharing an enterprise network is still an unresolved problem.
The main challenges are: (i) How to define user- and application-aware shares of resources?
(ii) How to determine an allocation of shares of network resources to applications?
(iii) How to allocate the shares per application in heterogeneous networks at scale?
In this paper we propose solutions to the three challenges and introduce a system design for enterprise deployment.
Defining the necessary resource shares per application is hard, as the intended use case and user's preferences influence the resource demand.
Utility functions based on user experience enable a mapping of network resources in terms of throughput and latency budget to a common user-level utility scale.
A multi-objective MILP is formulated to solve the throughput- and delay-aware embedding of each utility function for a max-min fairness criteria.
The allocation of resources in traditional networks with policing and scheduling cannot distinguish large numbers of classes.
We propose a resource allocation system design for enterprise networks based on Software-Defined Networking principles to achieve delay-constrained routing in the network and application pacing at the end-hosts.
The system design is evaluated against best effort networks with applications competing for the throughput of a constrained link.
The competing applications belong to the five application classes web browsing, file download, remote terminal work, video streaming, and Voice-over-IP.
The results show that the proposed methodology improves the minimum and total utility, minimizes packet loss and queuing delay at bottlenecks, establishes fairness in terms of utility between applications, and achieves predictable application performance at high link utilization.
We consider the multi-agent reinforcement learning setting with imperfect information in which each agent is trying to maximize its own utility.
The reward function depends on the hidden state (or goal) of both agents, so the agents must infer the other players' hidden goals from their observed behavior in order to solve the tasks.
We propose a new approach for learning in these domains: Self Other-Modeling (SOM), in which an agent uses its own policy to predict the other agent's actions and update its belief of their hidden state in an online manner.
We evaluate this approach on three different tasks and show that the agents are able to learn better policies using their estimate of the other players' hidden states, in both cooperative and adversarial settings.
Robot learning from demonstration (LfD) is a research paradigm that can play an important role in addressing the issue of scaling up robot learning.
Since this type of approach enables non-robotics experts can teach robots new knowledge without any professional background of mechanical engineering or computer programming skills, robots can appear in the real world even if it does not have any prior knowledge for any tasks like a new born baby.
There is a growing body of literature that employ LfD approach for training robots.
In this paper, I present a survey of recent research in this area while focusing on studies for human-robot collaborative tasks.
Since there are different aspects between stand-alone tasks and collaborative tasks, researchers should consider these differences to design collaborative robots for more effective and natural human-robot collaboration (HRC).
In this regard, many researchers have shown an increased interest in to make better communication framework between robots and humans because communication is a key issue to apply LfD paradigm for human-robot collaboration.
I thus review some recent works that focus on designing better communication channels/methods at the first, then deal with another interesting research method, Interactive/Active learning, after that I finally present other recent approaches tackle a more challenging problem, learning of complex tasks, in the last of the paper.
Detecting epileptic seizure through analysis of the electroencephalography (EEG) signal becomes a standard method for the diagnosis of epilepsy.
In a manual way, monitoring of long term EEG is tedious and error prone.
Therefore, a reliable automatic seizure detection method is desirable.
A critical challenge to automatic seizure detection is that seizure morphologies exhibit considerable variabilities.
In order to capture essential seizure patterns, this paper leverages an attention mechanism and a bidirectional long short-term memory (BiLSTM) model to exploit both spatially and temporally discriminating features and account for seizure variabilities.
The attention mechanism is to capture spatial features more effectively according to the contributions of brain areas to seizures.
The BiLSTM model is to extract more discriminating temporal features in the forward and the backward directions.
By accounting for both spatial and temporal variations of seizures, the proposed method is more robust across subjects.
The testing results over the noisy real data of CHB-MIT show that the proposed method outperforms the current state-of-the-art methods.
In both mixing-patients and cross-patient experiments, the average sensitivity and specificity are both higher while their corresponding standard deviations are lower than the methods in comparison.
Energy optimization has become a crucial issue in the realm of ICT.
This paper addresses the problem of energy consumption in a Metro Ethernet network.
Ethernet technology deployments have been increasing tremendously because of their simplicity and low cost.
However, much research remains to be conducted to address energy efficiency in Ethernet networks.
In this paper, we propose a novel Energy Aware Forwarding Strategy for Metro Ethernet networks based on a modification of the Internet Energy Aware Routing (EAR) algorithm.
Our contribution identifies the set of links to turn off and maintain links with minimum energy impact on the active state.
Our proposed algorithm could be a superior choice for use in networks with low saturation, as it involves a tradeoff between maintaining good network performance and minimizing the active links in the network.
Performance evaluation shows that, at medium load traffic, energy savings of 60% can be achieved.
At high loads, energy savings of 40% can be achieved without affecting the network performance.
In this paper, we give precise mathematical form to the idea of a structure whose data and axioms are faithfully represented by a graphical calculus; some prominent examples are operads, polycategories, properads, and PROPs.
Building on the established presentation of such structures as algebras for monads on presheaf categories, we describe a characteristic property of the associated monads---the shapeliness of the title---which says that "any two operations of the same shape agree".
An important part of this work is the study of analytic functors between presheaf categories, which are a common generalisation of Joyal's analytic endofunctors on sets and of the parametric right adjoint functors on presheaf categories introduced by Diers and studied by Carboni--Johnstone, Leinster and Weber.
Our shapely monads will be found among the analytic endofunctors, and may be characterised as the submonads of a universal analytic monad with "exactly one operation of each shape".
In fact, shapeliness also gives a way to define the data and axioms of a structure directly from its graphical calculus, by generating a free shapely monad on the basic operations of the calculus.
In this paper we do this for some of the examples listed above; in future work, we intend to do so for graphical calculi such as Milner's bigraphs, Lafont's interaction nets, or Girard's multiplicative proof nets, thereby obtaining canonical notions of denotational model.
Clone-and-own approach is a natural way of source code reuse for software developers.
To assess how known bugs and security vulnerabilities of a cloned component affect an application, developers and security analysts need to identify an original version of the component and understand how the cloned component is different from the original one.
Although developers may record the original version information in a version control system and/or directory names, such information is often either unavailable or incomplete.
In this research, we propose a code search method that takes as input a set of source files and extracts all the components including similar files from a software ecosystem (i.e., a collection of existing versions of software packages).
Our method employs an efficient file similarity computation using b-bit minwise hashing technique.
We use an aggregated file similarity for ranking components.
To evaluate the effectiveness of this tool, we analyzed 75 cloned components in Firefox and Android source code.
The tool took about two hours to report the original components from 10 million files in Debian GNU/Linux packages.
Recall of the top-five components in the extracted lists is 0.907, while recall of a baseline using SHA-1 file hash is 0.773, according to the ground truth recorded in the source code repositories.
Recently the widely used multi-view learning model, Canonical Correlation Analysis (CCA) has been generalised to the non-linear setting via deep neural networks.
Existing deep CCA models typically first decorrelate the feature dimensions of each view before the different views are maximally correlated in a common latent space.
This feature decorrelation is achieved by enforcing an exact decorrelation constraint; these models are thus computationally expensive due to the matrix inversion or SVD operations required for exact decorrelation at each training iteration.
Furthermore, the decorrelation step is often separated from the gradient descent based optimisation, resulting in sub-optimal solutions.
We propose a novel deep CCA model Soft CCA to overcome these problems.
Specifically, exact decorrelation is replaced by soft decorrelation via a mini-batch based Stochastic Decorrelation Loss (SDL) to be optimised jointly with the other training objectives.
Extensive experiments show that the proposed soft CCA is more effective and efficient than existing deep CCA models.
In addition, our SDL loss can be applied to other deep models beyond multi-view learning, and obtains superior performance compared to existing decorrelation losses.
Multimedia content delivery over the Internet is predominantly using the Hypertext Transfer Protocol (HTTP) as its primary protocol and multiple proprietary solutions exits.
The MPEG standard Dynamic Adaptive Streaming over HTTP (DASH) provides an interoperable solution and in recent years various adaptation logics/algorithms have been proposed.
However, to the best of our knowledge, there is no comprehensive evaluation of the various logics/algorithms.
Therefore, this paper provides a comprehensive evaluation of ten different adaptation logics/algorithms, which have been proposed in the past years.
The evaluation is done both objectively and subjectively.
The former is using a predefined bandwidth trajectory within a controlled environment and the latter is done in a real-world environment adopting crowdsourcing.
The results shall provide insights about which strategy can be adopted in actual deployment scenarios.
Additionally, the evaluation methodology described in this paper can be used to evaluate any other/new adaptation logic and to compare it directly with the results reported here.
Multi-label image classification is a fundamental but challenging task towards general visual understanding.
Existing methods found the region-level cues (e.g., features from RoIs) can facilitate multi-label classification.
Nevertheless, such methods usually require laborious object-level annotations (i.e., object labels and bounding boxes) for effective learning of the object-level visual features.
In this paper, we propose a novel and efficient deep framework to boost multi-label classification by distilling knowledge from weakly-supervised detection task without bounding box annotations.
Specifically, given the image-level annotations, (1) we first develop a weakly-supervised detection (WSD) model, and then (2) construct an end-to-end multi-label image classification framework augmented by a knowledge distillation module that guides the classification model by the WSD model according to the class-level predictions for the whole image and the object-level visual features for object RoIs.
The WSD model is the teacher model and the classification model is the student model.
After this cross-task knowledge distillation, the performance of the classification model is significantly improved and the efficiency is maintained since the WSD model can be safely discarded in the test phase.
Extensive experiments on two large-scale datasets (MS-COCO and NUS-WIDE) show that our framework achieves superior performances over the state-of-the-art methods on both performance and efficiency.
This paper explains genetic algorithm for novice in this field.
Basic philosophy of genetic algorithm and its flowchart are described.
Step by step numerical computation of genetic algorithm for solving simple mathematical equality problem will be briefly explained
One of the classical approaches for estimating the frequencies and damping factors in a spectrally sparse signal is the MUltiple SIgnal Classification (MUSIC) algorithm, which exploits the low-rank structure of an autocorrelation matrix.
Low-rank matrices have also received considerable attention recently in the context of optimization algorithms with partial observations.
In this work, we offer a novel optimization-based perspective on the classical MUSIC algorithm that could lead to future developments and understanding.
In particular, we propose an algorithm for spectral estimation that involves searching for the peaks of the dual polynomial corresponding to a certain nuclear norm minimization (NNM) problem, and we show that this algorithm is in fact equivalent to MUSIC itself.
Building on this connection, we also extend the classical MUSIC algorithm to the missing data case.
We provide exact recovery guarantees for our proposed algorithms and quantify how the sample complexity depends on the true spectral parameters.
Simulation results also indicate that the proposed algorithms significantly outperform some relevant existing methods in frequency estimation of damped exponentials.
In recent years, the decoding algorithms in communication networks are becoming increasingly complex aiming to achieve high reliability in correctly decoding received messages.
These decoding algorithms involve computationally complex operations requiring high performance computing hardware, which are generally expensive.
A cost-effective solution is to enhance the Instruction Set Architecture (ISA) of the processors by creating new custom instructions for the computational parts of the decoding algorithms.
In this paper, we propose to utilize the custom instruction approach to efficiently implement the widely used Viterbi decoding algorithm by adding the assembly language instructions to the ISA of DLX, PicoJava II and NIOS II processors, which represent RISC, stack and FPGA-based soft-core processor architectures, respectively.
By using the custom instruction approach, the execution time of the Viterbi algorithm is significantly improved by approximately 3 times for DLX and PicoJava II, and by 2 times for NIOS II.
We present a uniform method for translating an arbitrary nondeterministic finite automaton (NFA) into a deterministic mass action input/output chemical reaction network (I/O CRN) that simulates it.
The I/O CRN receives its input as a continuous time signal consisting of concentrations of chemical species that vary to represent the NFA's input string in a natural way.
The I/O CRN exploits the inherent parallelism of chemical kinetics to simulate the NFA in real time with a number of chemical species that is linear in the size of the NFA.
We prove that the simulation is correct and that it is robust with respect to perturbations of the input signal, the initial concentrations of species, the output (decision), and the rate constants of the reactions of the I/O CRN.
We give a new algorithm to construct optimal alphabetic ternary trees, where every internal node has at most three children.
This algorithm generalizes the classic Hu-Tucker algorithm, though the overall computational complexity has yet to be determined.
Development of reliable methods for optimised energy storage and generation is one of the most imminent challenges in moder power systems.
In this paper an adaptive approach to load leveling problem using novel dynamic models based on the Volterra integral equations of the first kind with piecewise continuous kernels.
These integral equations efficiently solve such inverse problem taking into account both the time dependent efficiencies and the availability of generation/storage of each energy storage technology.
In this analysis a direct numerical method is employed to find the least-cost dispatch of available storages.
The proposed collocation type numerical method has second order accuracy and enjoys self-regularization properties, which is associated with confidence levels of system demand.
This adaptive approach is suitable for energy storage optimisation in real time.
The efficiency of the proposed methodology is demonstrated on the Single Electricity Market of Republic of Ireland and Sakhalin island in the Russian Far East.
In this paper the problem of image restoration (denoising and inpainting) is approached using sparse approximation of local image blocks.
The local image blocks are extracted by sliding square windows over the image.
An adaptive block size selection procedure for local sparse approximation is proposed, which affects the global recovery of underlying image.
Ideally the adaptive local block selection yields the minimum mean square error (MMSE) in recovered image.
This framework gives us a clustered image based on the selected block size, then each cluster is restored separately using sparse approximation.
The results obtained using the proposed framework are very much comparable with the recently proposed image restoration techniques.
This paper proposes a hybrid technique for secured optimal power flow coupled with enhancing voltage stability with FACTS device installation.
The hybrid approach of Improved Gravitational Search algorithm (IGSA) and Firefly algorithm (FA) performance is analyzed by optimally placing TCSC controller.
The algorithm is implemented in MATLAB working platform and the power flow security and voltage stability is evaluated with IEEE 30 bus transmission systems.
The optimal results generated are compared with those available in literature and the superior performance of algorithm is depicted as minimum generation cost, reduced real power losses along with sustaining voltage stability.
Bipedal locomotion skills are challenging to develop.
Control strategies often use local linearization of the dynamics in conjunction with reduced-order abstractions to yield tractable solutions.
In these model-based control strategies, the controller is often not fully aware of many details, including torque limits, joint limits, and other non-linearities that are necessarily excluded from the control computations for simplicity.
Deep reinforcement learning (DRL) offers a promising model-free approach for controlling bipedal locomotion which can more fully exploit the dynamics.
However, current results in the machine learning literature are often based on ad-hoc simulation models that are not based on corresponding hardware.
Thus it remains unclear how well DRL will succeed on realizable bipedal robots.
In this paper, we demonstrate the effectiveness of DRL using a realistic model of Cassie, a bipedal robot.
By formulating a feedback control problem as finding the optimal policy for a Markov Decision Process, we are able to learn robust walking controllers that imitate a reference motion with DRL.
Controllers for different walking speeds are learned by imitating simple time-scaled versions of the original reference motion.
Controller robustness is demonstrated through several challenging tests, including sensory delay, walking blindly on irregular terrain and unexpected pushes at the pelvis.
We also show we can interpolate between individual policies and that robustness can be improved with an interpolated policy.
This paper is concerned with the design of cooperative distributed Model Predictive Control (MPC) for linear systems.
Motivated by the special structure of the distributed models in some existing literature, we propose to apply a state transformation to the original system and global cost function.
This has major implications on the closed-loop stability analysis and the mechanism of the resultant cooperative framework.
It turns out that the proposed framework can be implemented without cooperative iterations being performed in the local optimizations, thus allowing one to compute the local inputs in parallel and independently from each other while requiring only partial plant-wide state information.
The proposed framework can also be realized with cooperative iterations, thereby keeping the advantages of the technique in the former reference.
Under certain conditions, closed-loop stability for both implementation procedures can be guaranteed a priori by appropriate selections of the original local cost functions.
The strengths and benefits of the proposed method are highlighted by means of two numerical examples.
Recognizing fonts has become an important task in document analysis, due to the increasing number of available digital documents in different fonts and emphases.
A generic font-recognition system independent of language, script and content is desirable for processing various types of documents.
At the same time, categorizing calligraphy styles in handwritten manuscripts is important for palaeographic analysis, but has not been studied sufficiently in the literature.
We address the font-recognition problem as analysis and categorization of textures.
We extract features using complex wavelet transform and use support vector machines for classification.
Extensive experimental evaluations on different datasets in four languages and comparisons with state-of-the-art studies show that our proposed method achieves higher recognition accuracy while being computationally simpler.
Furthermore, on a new dataset generated from Ottoman manuscripts, we show that the proposed method can also be used for categorizing Ottoman calligraphy with high accuracy.
Workflow provenance typically assumes that each module is a "black-box", so that each output depends on all inputs (coarse-grained dependencies).
Furthermore, it does not model the internal state of a module, which can change between repeated executions.
In practice, however, an output may depend on only a small subset of the inputs (fine-grained dependencies) as well as on the internal state of the module.
We present a novel provenance framework that marries database-style and workflow-style provenance, by using Pig Latin to expose the functionality of modules, thus capturing internal state and fine-grained dependencies.
A critical ingredient in our solution is the use of a novel form of provenance graph that models module invocations and yields a compact representation of fine-grained workflow provenance.
It also enables a number of novel graph transformation operations, allowing to choose the desired level of granularity in provenance querying (ZoomIn and ZoomOut), and supporting "what-if" workflow analytic queries.
We implemented our approach in the Lipstick system and developed a benchmark in support of a systematic performance evaluation.
Our results demonstrate the feasibility of tracking and querying fine-grained workflow provenance.
In asynchronous physical-layer network coding (APNC) systems, the symbols from multiple transmitters to a common receiver may be misaligned.
The knowledge of the amount of symbol misalignment, hence its estimation, is important to PNC decoding.
This paper addresses the problem of symbol-misalignment estimation and the problem of optimal PNC decoding given the misalignment estimate, assuming the APNC system uses the root-raised-cosine pulse to carry signals (RRC-APNC).
First, we put forth an optimal symbol-misalignment estimator that makes use of double baud-rate samples.
Then, we devise optimal decoders for RRC-APNC in the presence of inaccurate symbol-misalignment estimates.
In particular, we present a new whitening transformation to whiten the noise of the double baud-rate samples.
Finally, we investigate the decoding performance of various estimation-and-decoding schemes for RRC-APNC.
Extensive simulations show that: (i) Our double baud-rate estimator yields substantially more accurate symbol-misalignment estimates than the baud-rate estimator does.
The mean-square-error (MSE) gains are up to 8 dB.
(ii) An overall estimation-and-decoding scheme in which both estimation and decoding are based on double baud-rate samples yields much better performance than other schemes.
Compared with a scheme in which both estimation and decoding are based on baud-rate samples), the double baud-rate sampling scheme yields 4.5 dB gains on symbol error rate (SER) performance in an AWGN channel, and 2 dB gains on packet error rate (PER) performance in a Rayleigh fading channel.
Recent studies in social media spam and automation provide anecdotal argumentation of the rise of a new generation of spambots, so-called social spambots.
Here, for the first time, we extensively study this novel phenomenon on Twitter and we provide quantitative evidence that a paradigm-shift exists in spambot design.
First, we measure current Twitter's capabilities of detecting the new social spambots.
Later, we assess the human performance in discriminating between genuine accounts, social spambots, and traditional spambots.
Then, we benchmark several state-of-the-art techniques proposed by the academic literature.
Results show that neither Twitter, nor humans, nor cutting-edge applications are currently capable of accurately detecting the new social spambots.
Our results call for new approaches capable of turning the tide in the fight against this raising phenomenon.
We conclude by reviewing the latest literature on spambots detection and we highlight an emerging common research trend based on the analysis of collective behaviors.
Insights derived from both our extensive experimental campaign and survey shed light on the most promising directions of research and lay the foundations for the arms race against the novel social spambots.
Finally, to foster research on this novel phenomenon, we make publicly available to the scientific community all the datasets used in this study.
The file system provides the mechanism for online storage and access to file contents, including data and programs.
This paper covers the high-level details of file systems, as well as related topics such as the disk cache, the file system interface to the kernel, and the user-level APIs that use the features of the file system.
It will give you a thorough understanding of how a file system works in general.
The main component of the operating system is the file system.
It is used to create, manipulate, store, and retrieve data.
At the highest level, a file system is a way to manage information on a secondary storage medium.
There are so many layers under and above the file system.
All the layers are to be fully described here.
This paper will give the explanatory knowledge of the file system designers and the researchers in the area.
The complete path from the user process to secondary storage device is to be mentioned.
File system is the area where the researchers are doing lot of job and there is always a need to do more work.
The work is going on for the efficient, secure, energy saving techniques for the file systems.
As we know that the hardware is going to be fast in performance and low-priced day by day.
The software is not built to comeback with the hardware technology.
So there is a need to do research in this area to bridge the technology gap.
We study opportunistic scheduling and the sum capacity of cellular networks with a full-duplex multi-antenna base station and a large number of single-antenna half-duplex users.
Simultaneous uplink and downlink over the same band results in uplink-to-downlink interference, degrading performance.
We present a simple opportunistic joint uplink-downlink scheduling algorithm that exploits multiuser diversity and treats interference as noise.
We show that in homogeneous networks, our algorithm achieves the same sum capacity as what would have been achieved if there was no uplink-to-downlink interference, asymptotically in the number of users.
The algorithm does not require interference CSI at the base station or uplink users.
It is also shown that for a simple class of heterogeneous networks without sufficient channel diversity, it is not possible to achieve the corresponding interference-free system capacity.
We discuss the potential for using device-to-device side-channels to overcome this limitation in heterogeneous networks.
In this paper a new distributed asynchronous algorithm is proposed for time synchronization in networks with random communication delays, measurement noise and communication dropouts.
Three different types of the drift correction algorithm are introduced, based on different kinds of local time increments.
Under nonrestrictive conditions concerning network properties, it is proved that all the algorithm types provide convergence in the mean square sense and with probability one (w.p.1) of the corrected drifts of all the nodes to the same value (consensus).
An estimate of the convergence rate of these algorithms is derived.
For offset correction, a new algorithm is proposed containing a compensation parameter coping with the influence of random delays and special terms taking care of the influence of both linearly increasing time and drift correction.
It is proved that the corrected offsets of all the nodes converge in the mean square sense and w.p.1.
An efficient offset correction algorithm based on consensus on local compensation parameters is also proposed.
It is shown that the overall time synchronization algorithm can also be implemented as a flooding algorithm with one reference node.
It is proved that it is possible to achieve bounded error between local corrected clocks in the mean square sense and w.p.1.
Simulation results provide an additional practical insight into the algorithm properties and show its advantage over the existing methods.
The impression of free will is the feeling according to which our choices are neither imposed from our inside nor from outside.
It is the sense we are the ultimate cause of our acts.
In direct opposition with the universal determinism, the existence of free will continues to be discussed.
In this paper, free will is linked to a decisional mechanism: an agent is provided with free will if having performed a predictable choice Cp, it can immediately perform another choice Cr in a random way.
The intangible feeling of free will is replaced by a decision-making process including a predictable decision-making process immediately followed by an unpredictable decisional one.
This paper examines fundamental error characteristics for a general class of matrix completion problems, where the matrix of interest is a product of two a priori unknown matrices, one of which is sparse, and the observations are noisy.
Our main contributions come in the form of minimax lower bounds for the expected per-element squared error for this problem under under several common noise models.
Specifically, we analyze scenarios where the corruptions are characterized by additive Gaussian noise or additive heavier-tailed (Laplace) noise, Poisson-distributed observations, and highly-quantized (e.g., one-bit) observations, as instances of our general result.
Our results establish that the error bounds derived in (Soni et al., 2016) for complexity-regularized maximum likelihood estimators achieve, up to multiplicative constants and logarithmic factors, the minimax error rates in each of these noise scenarios, provided that the nominal number of observations is large enough, and the sparse factor has (on an average) at least one non-zero per column.
Benefiting from its succinctness and robustness, skeleton-based human action recognition has recently attracted much attention.
Most existing methods utilize local networks, such as recurrent networks, convolutional neural networks, and graph convolutional networks, to extract spatio-temporal dynamics hierarchically.
As a consequence, the local and non-local dependencies, which respectively contain more details and semantics, are asynchronously captured in different level of layers.
Moreover, limited to the spatio-temporal domain, these methods ignored patterns in the frequency domain.
To better extract information from multi-domains, we propose a residual frequency attention (rFA) to focus on discriminative patterns in the frequency domain, and a synchronous local and non-local (SLnL) block to simultaneously capture the details and semantics in the spatio-temporal domain.
To optimize the whole process, we also propose a soft-margin focal loss (SMFL), which can automatically conducts adaptive data selection and encourages intrinsic margins in classifiers.
Extensive experiments are performed on several large-scale action recognition datasets and our approach significantly outperforms other state-of-the-art methods.
Deep reinforcement learning algorithms can learn complex behavioral skills, but real-world application of these methods requires a large amount of experience to be collected by the agent.
In practical settings, such as robotics, this involves repeatedly attempting a task, resetting the environment between each attempt.
However, not all tasks are easily or automatically reversible.
In practice, this learning process requires extensive human intervention.
In this work, we propose an autonomous method for safe and efficient reinforcement learning that simultaneously learns a forward and reset policy, with the reset policy resetting the environment for a subsequent attempt.
By learning a value function for the reset policy, we can automatically determine when the forward policy is about to enter a non-reversible state, providing for uncertainty-aware safety aborts.
Our experiments illustrate that proper use of the reset policy can greatly reduce the number of manual resets required to learn a task, can reduce the number of unsafe actions that lead to non-reversible states, and can automatically induce a curriculum.
The 2011 Grand Challenge in Service conference aimed to explore, analyse and evaluate complex service systems, utilising a case scenario of delivering on improved perception of safety in the London Borough of Sutton, which provided a common context to link the contributions.
The key themes that emerged included value co-creation, systems and networks, ICT and complexity, for which we summarise the contributions.
Contributions on value co-creation are based mainly on empirical research and provide a variety of insights including the importance of better understanding collaboration within value co-creation.
Contributions on the systems perspective, considered to arise from networks of value co-creation, include efforts to understand the implications of the interactions within service systems, as well as their interactions with social systems, to co-create value.
Contributions within the technological sphere, providing ever greater connectivity between entities, focus on the creation of new value constellations and new demand being fulfilled through hybrid offerings of physical assets, information and people.
Contributions on complexity, arising from the value co- creation networks of technology enabled services systems, focus on the challenges in understanding, managing and analysing these complex service systems.
The theory and applications all show the importance of understanding service for the future.
Tor is vulnerable to network-level adversaries who can observe both ends of the communication to deanonymize users.
Recent work has shown that Tor is susceptible to the previously unknown active BGP routing attacks, called RAPTOR attacks, which expose Tor users to more network-level adversaries.
In this paper, we aim to mitigate and detect such active routing attacks against Tor.
First, we present a new measurement study on the resilience of the Tor network to active BGP prefix attacks.
We show that ASes with high Tor bandwidth can be less resilient to attacks than other ASes.
Second, we present a new Tor guard relay selection algorithm that incorporates resilience of relays into consideration to proactively mitigate such attacks.
We show that the algorithm successfully improves the security for Tor clients by up to 36% on average (up to 166% for certain clients).
Finally, we build a live BGP monitoring system that can detect routing anomalies on the Tor network in real time by performing an AS origin check and novel detection analytics.
Our monitoring system successfully detects simulated attacks that are modeled after multiple known attack types as well as a real-world hijack attack (performed by us), while having low false positive rates.
This paper describes our system designed for the NLPCC 2016 shared task on word segmentation on micro-blog texts.
Network densification has always been an important factor to cope with the ever increasing capacity demand.
Deploying more base stations (BSs) improves the spatial frequency utilization, which increases the network capacity.
However, such improvement comes at the expense of shrinking the BSs' footprints, which increases the handover (HO) rate and may diminish the foreseen capacity gains.
In this paper, we propose a cooperative HO management scheme to mitigate the HO effect on throughput gains achieved via cellular network densification.
The proposed HO scheme relies on skipping HO to the nearest BS at some instances along the user's trajectory while enabling cooperative BS service during HO execution at other instances.
To this end, we develop a mathematical model, via stochastic geometry, to quantify the performance of the proposed HO scheme in terms of coverage probability and user throughput.
The results show that the proposed cooperative HO scheme outperforms the always best connected based association at high mobility.
Also, the value of BS cooperation along with handover skipping is quantified with respect to the HO skipping only that has recently appeared in the literature.
Particularly, the proposed cooperative HO scheme shows throughput gains of 12% to 27% and 17% on average, when compared to the always best connected and HO skipping only schemes at user velocity ranging from 80 km/h to 160 Km/h, respectively.
Perfect tracking control for real-world Euler-Lagrange systems is challenging due to uncertainties in the system model and external disturbances.
The magnitude of the tracking error can be reduced either by increasing the feedback gains or improving the model of the system.
The latter is clearly preferable as it allows to maintain good tracking performance at low feedback gains.
However, accurate models are often difficult to obtain.
In this article, we address the problem of stable high-performance tracking control for unknown Euler-Lagrange systems.
In particular, we employ Gaussian Process regression to obtain a data-driven model that is used for the feed-forward compensation of unknown dynamics of the system.
The model fidelity is used to adapt the feedback gains allowing low feedback gains in state space regions of high model confidence.
The proposed control law guarantees a globally bounded tracking error with a specific probability.
Simulation studies demonstrate the superiority over state of the art tracking control approaches.
In the era of Big Data and Deep Learning, there is a common view that machine learning approaches are the only way to cope with the robust and scalable information extraction and summarization.
It has been recently proposed that the CNL approach could be scaled up, building on the concept of embedded CNL and, thus, allowing for CNL-based information extraction from e.g. normative or medical texts that are rather controlled by nature but still infringe the boundaries of CNL.
Although it is arguable if CNL can be exploited to approach the robust wide-coverage semantic parsing for use cases like media monitoring, its potential becomes much more obvious in the opposite direction: generation of story highlights from the summarized AMR graphs, which is in the focus of this position paper.
This paper focuses on preserving the privacy of sensitive patterns when inducing decision trees.
We adopt a record augmentation approach for hiding sensitive classification rules in binary datasets.
Such a hiding methodology is preferred over other heuristic solutions like output perturbation or cryptographic techniques - which restrict the usability of the data - since the raw data itself is readily available for public use.
We show some key lemmas which are related to the hiding process and we also demonstrate the methodology with an example and an indicative experiment using a prototype hiding tool.
Recognition of low resolution face images is a challenging problem in many practical face recognition systems.
Methods have been proposed in the face recognition literature for the problem which assume that the probe is low resolution, but a high resolution gallery is available for recognition.
These attempts have been aimed at modifying the probe image such that the resultant image provides better discrimination.
We formulate the problem differently by leveraging the information available in the high resolution gallery image and propose a dictionary learning approach for classifying the low-resolution probe image.
An important feature of our algorithm is that it can handle resolution change along with illumination variations.
Furthermore, we also kernelize the algorithm to handle non-linearity in data and present a joint dictionary learning technique for robust recognition at low resolutions.
The effectiveness of the proposed method is demonstrated using standard datasets and a challenging outdoor face dataset.
It is shown that our method is efficient and can perform significantly better than many competitive low resolution face recognition algorithms.
Interpretable classification models are built with the purpose of providing a comprehensible description of the decision logic to an external oversight agent.
When considered in isolation, a decision tree, a set of classification rules, or a linear model, are widely recognized as human-interpretable.
However, such models are generated as part of a larger analytical process, which, in particular, comprises data collection and filtering.
Selection bias in data collection or in data pre-processing may affect the model learned.
Although model induction algorithms are designed to learn to generalize, they pursue optimization of predictive accuracy.
It remains unclear how interpretability is instead impacted.
We conduct an experimental analysis to investigate whether interpretable models are able to cope with data selection bias as far as interpretability is concerned.
Neural networks have proven effective at solving difficult problems but designing their architectures can be challenging, even for image classification problems alone.
Our goal is to minimize human participation, so we employ evolutionary algorithms to discover such networks automatically.
Despite significant computational requirements, we show that it is now possible to evolve models with accuracies within the range of those published in the last year.
Specifically, we employ simple evolutionary techniques at unprecedented scales to discover models for the CIFAR-10 and CIFAR-100 datasets, starting from trivial initial conditions and reaching accuracies of 94.6% (95.6% for ensemble) and 77.0%, respectively.
To do this, we use novel and intuitive mutation operators that navigate large search spaces; we stress that no human participation is required once evolution starts and that the output is a fully-trained model.
Throughout this work, we place special emphasis on the repeatability of results, the variability in the outcomes and the computational requirements.
Networks observed in real world like social networks, collaboration networks etc., exhibit temporal dynamics, i.e.nodes and edges appear and/or disappear over time.
In this paper, we propose a generative, latent space based, statistical model for such networks (called dynamic networks).
We consider the case where the number of nodes is fixed, but the presence of edges can vary over time.
Our model allows the number of communities in the network to be different at different time steps.
We use a neural network based methodology to perform approximate inference in the proposed model and its simplified version.
Experiments done on synthetic and real world networks for the task of community detection and link prediction demonstrate the utility and effectiveness of our model as compared to other similar existing approaches.
While autonomous multirotor micro aerial vehicles (MAVs) are uniquely well suited for certain types of missions benefiting from stationary flight capabilities, their more widespread usage still faces many hurdles, due in particular to their limited range and the difficulty of fully automating their deployment and retrieval.
In this paper we address these issues by solving the problem of the automated landing of a quadcopter on a ground vehicle moving at relatively high speed.
We present our system architecture, including the structure of our Kalman filter for the estimation of the relative position and velocity between the quadcopter and the landing pad, as well as our controller design for the full rendezvous and landing maneuvers.
The system is experimentally validated by successfully landing in multiple trials a commercial quadcopter on the roof of a car moving at speeds of up to 50 km/h.
Minimum vertex cover problem is an NP-Hard problem with the aim of finding minimum number of vertices to cover graph.
In this paper, a learning automaton based algorithm is proposed to find minimum vertex cover in graph.
In the proposed algorithm, each vertex of graph is equipped with a learning automaton that has two actions in the candidate or non-candidate of the corresponding vertex cover set.
Due to characteristics of learning automata, this algorithm significantly reduces the number of covering vertices of graph.
The proposed algorithm based on learning automata iteratively minimize the candidate vertex cover through the update its action probability.
As the proposed algorithm proceeds, a candidate solution nears to optimal solution of the minimum vertex cover problem.
In order to evaluate the proposed algorithm, several experiments conducted on DIMACS dataset which compared to conventional methods.
Experimental results show the major superiority of the proposed algorithm over the other methods.
Optimizing for long term value is desirable in many practical applications, e.g.recommender systems.
The most common approach for long term value optimization is supervised learning using long term value as the target.
Unfortunately, long term metrics take a long time to measure (e.g., will customers finish reading an ebook?), and vanilla forecasters cannot learn from examples until the outcome is observed.
In practical systems where new items arrive frequently, such delay can increase the training-serving skew, thereby negatively affecting the model's predictions for new products.
We argue that intermediate observations (e.g., if customers read a third of the book in 24 hours) can improve a model's predictions.
We formalize the problem as a semi-stochastic model, where instances are selected by an adversary but, given an instance, the intermediate observation and the outcome are sampled from a factored joint distribution.
We propose an algorithm that exploits intermediate observations and theoretically quantify how much it can outperform any prediction method that ignores the intermediate observations.
Motivated by the theoretical analysis, we propose two neural network architectures: Factored Forecaster (FF) which is ideal if our assumptions are satisfied, and Residual Factored Forecaster (RFF) that is more robust to model mis-specification.
Experiments on two real world datasets, a dataset derived from GitHub repositories and another dataset from a popular marketplace, show that RFF outperforms both FF as well as an algorithm that ignores intermediate observations.
Impulsive dynamical systems is a well-established area of dynamical systems theory, and it is used in this work to analyze several basic properties of reset control systems: existence and uniqueness of solutions, and continuous dependence on the initial condition (well-posedness).
The work scope is about reset control systems with a linear and time-invariant base system, and a zero-crossing resetting law.
A necessary and sufficient condition for existence and uniqueness of solutions, based on the well-posedness of reset instants, is developed.
As a result, it is shown that reset control systems (with strictly proper plants) do no have Zeno solutions.
It is also shown that full reset and partial reset (with a special structure) always produce well-posed reset instants.
Moreover, a definition of continuous dependence on the initial condition is developed, and also a sufficient condition for reset control systems to satisfy that property.
Finally, this property is used to analyze sensitivity of reset control systems to sensor noise.
This work also includes a number of illustrative examples motivating the key concepts and main results.
Leveraging large historical data in electronic health record (EHR), we developed Doctor AI, a generic predictive model that covers observed medical conditions and medication uses.
Doctor AI is a temporal model using recurrent neural networks (RNN) and was developed and applied to longitudinal time stamped EHR data from 260K patients over 8 years.
Encounter records (e.g. diagnosis codes, medication codes or procedure codes) were input to RNN to predict (all) the diagnosis and medication categories for a subsequent visit.
Doctor AI assesses the history of patients to make multilabel predictions (one label for each diagnosis or medication category).
Based on separate blind test set evaluation, Doctor AI can perform differential diagnosis with up to 79% recall@30, significantly higher than several baselines.
Moreover, we demonstrate great generalizability of Doctor AI by adapting the resulting models from one institution to another without losing substantial accuracy.
The predictive processing (PP) hypothesizes that the predictive inference of our sensorimotor system is encoded implicitly in the regularities between perception and action.
We propose a neural architecture in which such regularities of active inference are encoded hierarchically.
We further suggest that this encoding emerges during the embodied learning process when the appropriate action is selected to minimize the prediction error in perception.
Therefore, this predictive stream in the sensorimotor loop is generated in a top-down manner.
Specifically, it is constantly modulated by the motor actions and is updated by the bottom-up prediction error signals.
In this way, the top-down prediction originally comes from the prior experience from both perception and action representing the higher levels of this hierarchical cognition.
In our proposed embodied model, we extend the PredNet Network, a hierarchical predictive coding network, with the motor action units implemented by a multi-layer perceptron network (MLP) to modulate the network top-down prediction.
Two experiments, a minimalistic world experiment, and a mobile robot experiment are conducted to evaluate the proposed model in a qualitative way.
In the neural representation, it can be observed that the causal inference of predictive percept from motor actions can be also observed while the agent is interacting with the environment.
Lifelogging cameras capture everyday life from a first-person perspective, but generate so much data that it is hard for users to browse and organize their image collections effectively.
In this paper, we propose to use automatic image captioning algorithms to generate textual representations of these collections.
We develop and explore novel techniques based on deep learning to generate captions for both individual images and image streams, using temporal consistency constraints to create summaries that are both more compact and less noisy.
We evaluate our techniques with quantitative and qualitative results, and apply captioning to an image retrieval application for finding potentially private images.
Our results suggest that our automatic captioning algorithms, while imperfect, may work well enough to help users manage lifelogging photo collections.
Software is a field of rapid changes: the best technology today becomes obsolete in the near future.
If we review the graduate attributes of any of the software engineering programs across the world, life-long learning is one of them.
The social and psychological aspects of professional development is linked with rewards.
In organizations, where people are provided with learning opportunities and there is a culture that rewards learning, people embrace changes easily.
However, the software industry tends to be short-sighted and its primary focus is more on current project success; it usually ignores the capacity building of the individual or team.
It is hoped that our software engineering colleagues will be motivated to conduct more research into the area of software psychology so as to understand more completely the possibilities for increased effectiveness and personal fulfillment among software engineers working alone and in teams.
In meetings where important decisions get made, what items receive more attention may influence the outcome.
We examine how different types of rhetorical (de-)emphasis -- including hedges, superlatives, and contrastive conjunctions -- correlate with what gets revisited later, controlling for item frequency and speaker.
Our data consists of transcripts of recurring meetings of the Federal Reserve's Open Market Committee (FOMC), where important aspects of U.S. monetary policy are decided on.
Surprisingly, we find that words appearing in the context of hedging, which is usually considered a way to express uncertainty, are more likely to be repeated in subsequent meetings, while strong emphasis indicated by superlatives has a slightly negative effect on word recurrence in subsequent meetings.
We also observe interesting patterns in how these effects vary depending on social factors such as status and gender of the speaker.
For instance, the positive effects of hedging are more pronounced for female speakers than for male speakers.
The data underlying scientific papers should be accessible to researchers both now and in the future, but how best can we ensure that these data are available?
Here we examine the effectiveness of four approaches to data archiving: no stated archiving policy, recommending (but not requiring) archiving, and two versions of mandating data deposition at acceptance.
We control for differences between data types by trying to obtain data from papers that use a single, widespread population genetic analysis, STRUCTURE.
At one extreme, we found that mandated data archiving policies that require the inclusion of a data availability statement in the manuscript improve the odds of finding the data online almost a thousand-fold compared to having no policy.
However, archiving rates at journals with less stringent policies were only very slightly higher than those with no policy at all.
At one extreme, we found that mandated data archiving policies that require the inclusion of a data availability statement in the manuscript improve the odds of finding the data online almost a thousand fold compared to having no policy.
However, archiving rates at journals with less stringent policies were only very slightly higher than those with no policy at all.
We also assessed the effectiveness of asking for data directly from authors and obtained over half of the requested datasets, albeit with about 8 days delay and some disagreement with authors.
Given the long term benefits of data accessibility to the academic community, we believe that journal based mandatory data archiving policies and mandatory data availability statements should be more widely adopted.
The Python--elsA user interface of the elsA cfd (Computational Fluid Dynamics) software has been developed to allow users to specify simulations with confidence, through a global context of description objects grouped inside scripts.
The software main features are generated documentation, context checking and completion, and helpful error management.
Further developments have used this foundation as a coupling framework, allowing (thanks to the descriptive approach) the coupling of external algorithms with the cfd solver in a simple and abstract way, leading to more success in complex simulations.
Along with the description of the technical part of the interface, we try to gather the salient points pertaining to the psychological viewpoint of user experience (ux).
We point out the differences between user interfaces and pure data management systems such as cgns.
Various moral conundrums plague population ethics: The Non-Identity Problem, The Procreation Asymmetry, The Repugnant Conclusion, and more.
I argue that the aforementioned moral conundrums have a structure neatly accounted for, and solved by, some ideas in computability theory.
I introduce a mathematical model based on computability theory and show how previous arguments pertaining to these conundrums fit into the model.
This paper proceeds as follows.
First, I do a very brief survey of the history of computability theory in moral philosophy.
Second, I follow various papers, and show how their arguments fit into, or don't fit into, our model.
Third, I discuss the implications of our model to the question why the human race should or should not continue to exist.
Finally, I show that our model ineluctably leads us to a Confucian moral principle.
We evaluated the effectiveness of an automated bird sound identification system in a situation that emulates a realistic, typical application.
We trained classification algorithms on a crowd-sourced collection of bird audio recording data and restricted our training methods to be completely free of manual intervention.
The approach is hence directly applicable to the analysis of multiple species collections, with labelling provided by crowd-sourced collection.
We evaluated the performance of the bird sound recognition system on a realistic number of candidate classes, corresponding to real conditions.
We investigated the use of two canonical classification methods, chosen due to their widespread use and ease of interpretation, namely a k Nearest Neighbour (kNN) classifier with histogram-based features and a Support Vector Machine (SVM) with time-summarisation features.
We further investigated the use of a certainty measure, derived from the output probabilities of the classifiers, to enhance the interpretability and reliability of the class decisions.
Our results demonstrate that both identification methods achieved similar performance, but we argue that the use of the kNN classifier offers somewhat more flexibility.
Furthermore, we show that employing an outcome certainty measure provides a valuable and consistent indicator of the reliability of classification results.
Our use of generic training data and our investigation of probabilistic classification methodologies that can flexibly address the variable number of candidate species/classes that are expected to be encountered in the field, directly contribute to the development of a practical bird sound identification system with potentially global application.
Further, we show that certainty measures associated with identification outcomes can significantly contribute to the practical usability of the overall system.
We address the problem of activity detection in continuous, untrimmed video streams.
This is a difficult task that requires extracting meaningful spatio-temporal features to capture activities, accurately localizing the start and end times of each activity.
We introduce a new model, Region Convolutional 3D Network (R-C3D), which encodes the video streams using a three-dimensional fully convolutional network, then generates candidate temporal regions containing activities, and finally classifies selected regions into specific activities.
Computation is saved due to the sharing of convolutional features between the proposal and the classification pipelines.
The entire model is trained end-to-end with jointly optimized localization and classification losses.
R-C3D is faster than existing methods (569 frames per second on a single Titan X Maxwell GPU) and achieves state-of-the-art results on THUMOS'14.
We further demonstrate that our model is a general activity detection framework that does not rely on assumptions about particular dataset properties by evaluating our approach on ActivityNet and Charades.
Our code is available at http://ai.bu.edu/r-c3d/.
We propose fast probabilistic algorithms with low (i.e., sublinear in the input size) communication volume to check the correctness of operations in Big Data processing frameworks and distributed databases.
Our checkers cover many of the commonly used operations, including sum, average, median, and minimum aggregation, as well as sorting, union, merge, and zip.
An experimental evaluation of our implementation in Thrill (Bingmann et al., 2016) confirms the low overhead and high failure detection rate predicted by theoretical analysis.
Nowadays impact factor is the significant indicator for journal evaluation.
In impact factor calculation is used number of all citations to journal, regardless of the prestige of cited journals, however, scientific units (paper, researcher, journal or scientific organization) cited by journals with high impact factor or researchers with high Hirsch index are more important than objects cited by journals without impact factor or unknown researcher.
In this paper was offered weighted impact factor for getting more accurate rankings for journals, which consider not only quantity of citations, but also quality of citing journals.
Correlation coefficients among different indicators for journal evaluation: impact factors by Thomson Scientific, weighted impact factors offered by different researchers, average and medians of all citing journals impact factors and 5-year impact factors were analysed.
We present the first generative adversarial network (GAN) for natural image matting.
Our novel generator network is trained to predict visually appealing alphas with the addition of the adversarial loss from the discriminator that is trained to classify well-composited images.
Further, we improve existing encoder-decoder architectures to better deal with the spatial localization issues inherited in convolutional neural networks (CNN) by using dilated convolutions to capture global context information without downscaling feature maps and losing spatial information.
We present state-of-the-art results on the alphamatting online benchmark for the gradient error and give comparable results in others.
Our method is particularly well suited for fine structures like hair, which is of great importance in practical matting applications, e.g. in film/TV production.
In the following paper we present a new semantics for the well-known strategic logic ATL.
It is based on adding roles to concurrent game structures, that is at every state, each agent belongs to exactly one role, and the role specifies what actions are available to him at that state.
We show advantages of the new semantics, provide motivating examples based on sensor networks, and analyze model checking complexity.
Efficiently scheduling data processing jobs on distributed compute clusters requires complex algorithms.
Current systems, however, use simple generalized heuristics and ignore workload characteristics, since developing and tuning a scheduling policy for each workload is infeasible.
In this paper, we show that modern machine learning techniques can generate highly-efficient policies automatically.
Decima uses reinforcement learning (RL) and neural networks to learn workload-specific scheduling algorithms without any human instruction beyond a high-level objective such as minimizing average job completion time.
Off-the-shelf RL techniques, however, cannot handle the complexity and scale of the scheduling problem.
To build Decima, we had to develop new representations for jobs' dependency graphs, design scalable RL models, and invent RL training methods for dealing with continuous stochastic job arrivals.
Our prototype integration with Spark on a 25-node cluster shows that Decima improves the average job completion time over hand-tuned scheduling heuristics by at least 21%, achieving up to 2x improvement during periods of high cluster load.
Convolution Neural Networks, known as ConvNets exceptionally perform well in many complex machine learning tasks.
The architecture of ConvNets demands the huge and rich amount of data and involves with a vast number of parameters that leads the learning takes to be computationally expensive, slow convergence towards the global minima, trap in local minima with poor predictions.
In some cases, architecture overfits the data and make the architecture difficult to generalise for new samples that were not in the training set samples.
To address these limitations, many regularization and optimization strategies are developed for the past few years.
Also, studies suggested that these techniques significantly increase the performance of the networks as well as reducing the computational cost.
In implementing these techniques, one must thoroughly understand the theoretical concept of how this technique works in increasing the expressive power of the networks.
This article is intended to provide the theoretical concepts and mathematical formulation of the most commonly used strategies in developing a ConvNet architecture.
Typical spoken language understanding systems provide narrow semantic parses using a domain-specific ontology.
The parses contain intents and slots that are directly consumed by downstream domain applications.
In this work we discuss expanding such systems to handle compound entities and intents by introducing a domain-agnostic shallow parser that handles linguistic coordination.
We show that our model for parsing coordination learns domain-independent and slot-independent features and is able to segment conjunct boundaries of many different phrasal categories.
We also show that using adversarial training can be effective for improving generalization across different slot types for coordination parsing.
Deconvolutional layers have been widely used in a variety of deep models for up-sampling, including encoder-decoder networks for semantic segmentation and deep generative models for unsupervised learning.
One of the key limitations of deconvolutional operations is that they result in the so-called checkerboard problem.
This is caused by the fact that no direct relationship exists among adjacent pixels on the output feature map.
To address this problem, we propose the pixel deconvolutional layer (PixelDCL) to establish direct relationships among adjacent pixels on the up-sampled feature map.
Our method is based on a fresh interpretation of the regular deconvolution operation.
The resulting PixelDCL can be used to replace any deconvolutional layer in a plug-and-play manner without compromising the fully trainable capabilities of original models.
The proposed PixelDCL may result in slight decrease in efficiency, but this can be overcome by an implementation trick.
Experimental results on semantic segmentation demonstrate that PixelDCL can consider spatial features such as edges and shapes and yields more accurate segmentation outputs than deconvolutional layers.
When used in image generation tasks, our PixelDCL can largely overcome the checkerboard problem suffered by regular deconvolution operations.
Machine learning applications in medical imaging are frequently limited by the lack of quality labeled data.
In this paper, we explore the self training method, a form of semi-supervised learning, to address the labeling burden.
By integrating reinforcement learning, we were able to expand the application of self training to complex segmentation networks without any further human annotation.
The proposed approach, reinforced self training (ReST), fine tunes a semantic segmentation networks by introducing a policy network that learns to generate pseudolabels.
We incorporate an expert demonstration network, based on inverse reinforcement learning, to enhance clinical validity and convergence of the policy network.
The model was tested on a pulmonary nodule segmentation task in chest X-rays and achieved the performance of a standard U-Net while using only 50% of the labeled data, by exploiting unlabeled data.
When the same number of labeled data was used, a moderate to significant cross validation accuracy improvement was achieved depending on the absolute number of labels used.
This paper presents the Axon AI's solution to the 2nd YouTube-8M Video Understanding Challenge, achieving the final global average precision (GAP) of 88.733% on the private test set (ranked 3rd among 394 teams, not considering the model size constraint), and 87.287% using a model that meets size requirement.
Two sets of 7 individual models belonging to 3 different families were trained separately.
Then, the inference results on a training data were aggregated from these multiple models and fed to train a compact model that meets the model size requirement.
In order to further improve performance we explored and employed data over/sub-sampling in feature space, an additional regularization term during training exploiting label relationship, and learned weights for ensembling different individual models.
A consistent query answer in an inconsistent database is an answer obtained in every (minimal) repair.
The repairs are obtained by resolving all conflicts in all possible ways.
Often, however, the user is able to provide a preference on how conflicts should be resolved.
We investigate here the framework of preferred consistent query answers, in which user preferences are used to narrow down the set of repairs to a set of preferred repairs.
We axiomatize desirable properties of preferred repairs.
We present three different families of preferred repairs and study their mutual relationships.
Finally, we investigate the complexity of preferred repairing and computing preferred consistent query answers.
We address the problem of deploying a reinforcement learning (RL) agent on a physical system such as a datacenter cooling unit or robot, where critical constraints must never be violated.
We show how to exploit the typically smooth dynamics of these systems and enable RL algorithms to never violate constraints during learning.
Our technique is to directly add to the policy a safety layer that analytically solves an action correction formulation per each state.
The novelty of obtaining an elegant closed-form solution is attained due to a linearized model, learned on past trajectories consisting of arbitrary actions.
This is to mimic the real-world circumstances where data logs were generated with a behavior policy that is implausible to describe mathematically; such cases render the known safety-aware off-policy methods inapplicable.
We demonstrate the efficacy of our approach on new representative physics-based environments, and prevail where reward shaping fails by maintaining zero constraint violations.
Building on the Ethernet Passive Optical Network (EPON) and Gigabit PON (GPON) standards, Next-Generation (NG) PONs (i) provide increased data rates, split ratios, wavelengths counts, and fiber lengths, as well as (ii) allow for all-optical integration of access and metro networks.
In this paper we provide a comprehensive probabilistic analysis of the capacity (maximum mean packet throughput) and packet delay of subnetworks that can be used to form NG-PONs.
Our analysis can cover a wide range of NG-PONs through taking the minimum capacity of the subnetworks making up the NG-PON and weighing the packet delays of the subnetworks.
Our numerical and simulation results indicate that our analysis quite accurately characterizes the throughput-delay performance of EPON/GPON tree networks, including networks upgraded with higher data rates and wavelength counts.
Our analysis also characterizes the trade-offs and bottlenecks when integrating EPON/GPON tree networks across a metro area with a ring, a Passive Star Coupler (PSC), or an Arrayed Waveguide Grating (AWG) for uniform and non-uniform traffic.
To the best of our knowledge, the presented analysis is the first to consider multiple PONs interconnected via a metro network.
We consider contractive systems whose trajectories evolve on a compact and convex state-space.
It is well-known that if the time-varying vector field of the system is periodic then the system admits a unique globally asymptotically stable periodic solution.
Obtaining explicit information on this periodic solution and its dependence on various parameters is important both theoretically and in numerous applications.
We develop an approach for approximating such a periodic trajectory using the periodic trajectory of a simpler system (e.g. an LTI system).
Our approximation includes an error bound that is based on the input-to-state stability property of contractive systems.
We show that in some cases this error bound can be computed explicitly.
We also use the bound to derive a new theoretical result, namely, that a contractive system with an additive periodic input behaves like a low pass filter.
We demonstrate our results using several examples from systems biology.
We provide code that produces beautiful poetry.
Our sonnet-generation algorithm includes several novel elements that improve over the state-of-the-art, leading to rhythmic and inspiring poems.
The work discussed here is the winner of the 2018 PoetiX Literary Turing Test Award for computer-generated poetry.
Non-availability of reliable and sustainable electric power is a major problem in the developing world.
Renewable energy sources like solar are not very lucrative in the current stage due to various uncertainties like weather, storage, land use among others.
There also exists various other issues like mis-commitment of power, absence of intelligent fault analysis, congestion, etc.
In this paper, we propose a novel deep learning-based system for predicting faults and selecting power generators optimally so as to reduce costs and ensure higher reliability in solar power systems.
The results are highly encouraging and they suggest that the approaches proposed in this paper have the potential to be applied successfully in the developing world.
The goal of this paper is to analyze the behavior and intent of recent types of privacy invasive Android adware.
There are two recent trends in this area: more financial motives instead of ego motives, and the development of more dynamic analysis tools.
This paper starts with a review of Android mobile operating system security, and also addresses the pros and cons of open source operating system security.
Static analysis of malware provides high quality results and leads to a good understanding as shown in this paper.
However, as malware grows in number and complexity, there have been recent efforts to automate the detection mechanisms and many of the static tasks.
As Android's market share is rapidly growing around the world.
Android security will be a crucial area of research for IT security professionals and their academic counterparts.
The upside of the current situation is that malware is being quickly exposed, thanks to open source software development tools.
This cooperation is important in curbing the widespread theft of personal information with monetary value.
With the emergence of the Hospital Readmission Reduction Program of the Center for Medicare and Medicaid Services on October 1, 2012, forecasting unplanned patient readmission risk became crucial to the healthcare domain.
There are tangible works in the literature emphasizing on developing readmission risk prediction models; However, the models are not accurate enough to be deployed in an actual clinical setting.
Our study considers patient readmission risk as the objective for optimization and develops a useful risk prediction model to address unplanned readmissions.
Furthermore, Genetic Algorithm and Greedy Ensemble is used to optimize the developed model constraints.
With the Internet of Things (IoT) becoming a major component of our daily life, understanding how to improve the quality of service (QoS) for IoT applications through fog computing is becoming an important problem.
In this paper, we introduce a general framework for IoT-fog-cloud applications, and propose a delay-minimizing collaboration and offloading policy for fog-capable devices that aims to reduce the service delay for IoT applications.
We then develop an analytical model to evaluate our policy and show how the proposed framework helps to reduce IoT service delay.
Hazy images are common in real scenarios and many dehazing methods have been developed to automatically remove the haze from images.
Typically, the goal of image dehazing is to produce clearer images from which human vision can better identify the object and structural details present in the images.
When the ground-truth haze-free image is available for a hazy image, quantitative evaluation of image dehazing is usually based on objective metrics, such as Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity (SSIM).
However, in many applications, large-scale images are collected not for visual examination by human.
Instead, they are used for many high-level vision tasks, such as automatic classification, recognition and categorization.
One fundamental problem here is whether various dehazing methods can produce clearer images that can help improve the performance of the high-level tasks.
In this paper, we empirically study this problem in the important task of image classification by using both synthetic and real hazy image datasets.
From the experimental results, we find that the existing image-dehazing methods cannot improve much the image-classification performance and sometimes even reduce the image-classification performance.
We investigate underwater acoustic (UWA) channel equalization and introduce hierarchical and adaptive nonlinear channel equalization algorithms that are highly efficient and provide significantly improved bit error rate (BER) performance.
Due to the high complexity of nonlinear equalizers and poor performance of linear ones, to equalize highly difficult underwater acoustic channels, we employ piecewise linear equalizers.
However, in order to achieve the performance of the best piecewise linear model, we use a tree structure to hierarchically partition the space of the received signal.
Furthermore, the equalization algorithm should be completely adaptive, since due to the highly non-stationary nature of the underwater medium, the optimal MSE equalizer as well as the best piecewise linear equalizer changes in time.
To this end, we introduce an adaptive piecewise linear equalization algorithm that not only adapts the linear equalizer at each region but also learns the complete hierarchical structure with a computational complexity only polynomial in the number of nodes of the tree.
Furthermore, our algorithm is constructed to directly minimize the final squared error without introducing any ad-hoc parameters.
We demonstrate the performance of our algorithms through highly realistic experiments performed on accurately simulated underwater acoustic channels.
The ever increasing adoption of mobile devices with limited energy storage capacity, on the one hand, and more awareness of the environmental impact of massive data centres and server pools, on the other hand, have both led to an increased interest in energy management algorithms.
The main contribution of this paper is to present several new constant factor approximation algorithms for energy aware scheduling problems where the objective is to minimize weighted completion time plus the cost of the energy consumed, in the one machine non-preemptive setting, while allowing release dates and deadlines.Unlike previous known algorithms these new algorithms can handle general job-dependent energy cost functions, extending the application of these algorithms to settings outside the typical CPU-energy one.
These new settings include problems where in addition, or instead, of energy costs we also have maintenance costs, wear and tear, replacement costs, etc., which in general depend on the speed at which the machine runs but also depend on the types of jobs processed.
Our algorithms also extend to approximating weighted tardiness plus energy cost, an inherently more difficult problem that has not been addressed in the literature.
The Internet is a ubiquitous and affordable communications network suited for e-commerce and medical image communications.
Security has become a major issue as data communication channels can be intruded by intruders during transmission.
Though, different methods have been proposed and used to protect the transmission of data from illegal and unauthorized access, code breakers have come up with various methods to crack them.
DNA based Cryptography brings forward a new hope for unbreakable algorithms.
This paper outlines an encryption scheme with DNA technology and JPEG Zigzag Coding for Secure Transmission of Images.
Kinetic approaches, i.e., methods based on the lattice Boltzmann equations, have long been recognized as an appealing alternative for solving incompressible Navier-Stokes equations in computational fluid dynamics.
However, such approaches have not been widely adopted in graphics mainly due to the underlying inaccuracy, instability and inflexibility.
In this paper, we try to tackle these problems in order to make kinetic approaches practical for graphical applications.
To achieve more accurate and stable simulations, we propose to employ the non-orthogonal central-moment-relaxation model, where we develop a novel adaptive relaxation method to retain both stability and accuracy in turbulent flows.
To achieve flexibility, we propose a novel continuous-scale formulation that enables samples at arbitrary resolutions to easily communicate with each other in a more continuous sense and with loose geometrical constraints, which allows efficient and adaptive sample construction to better match the physical scale.
Such a capability directly leads to an automatic sample construction which generates static and dynamic scales at initialization and during simulation, respectively.
This effectively makes our method suitable for simulating turbulent flows with arbitrary geometrical boundaries.
Our simulation results with applications to smoke animations show the benefits of our method, with comparisons for justification and verification.
This paper describes how to obtain accurate 3D body models and texture of arbitrary people from a single, monocular video in which a person is moving.
Based on a parametric body model, we present a robust processing pipeline achieving 3D model fits with 5mm accuracy also for clothed people.
Our main contribution is a method to nonrigidly deform the silhouette cones corresponding to the dynamic human silhouettes, resulting in a visual hull in a common reference frame that enables surface reconstruction.
This enables efficient estimation of a consensus 3D shape, texture and implanted animation skeleton based on a large number of frames.
We present evaluation results for a number of test subjects and analyze overall performance.
Requiring only a smartphone or webcam, our method enables everyone to create their own fully animatable digital double, e.g., for social VR applications or virtual try-on for online fashion shopping.
In this paper, attempt is made to solve a few problems using the Polynomial Point Collocation Method (PPCM), the Radial Point Collocation Method (RPCM), Smoothed Particle Hydrodynamics (SPH), and the Finite Point Method (FPM).
A few observations on the accuracy of these methods are recorded.
All the simulations in this paper are three dimensional linear elastostatic simulations, without accounting for body forces.
Analyzing database access logs is a key part of performance tuning, intrusion detection, benchmark development, and many other database administration tasks.
Unfortunately, it is common for production databases to deal with millions or even more queries each day, so these logs must be summarized before they can be used.
Designing an appropriate summary encoding requires trading off between conciseness and information content.
For example: simple workload sampling may miss rare, but high impact queries.
In this paper, we present LogR, a lossy log compression scheme suitable use for many automated log analytics tools, as well as for human inspection.
We formalize and analyze the space/fidelity trade-off in the context of a broader family of "pattern" and "pattern mixture" log encodings to which LogR belongs.
We show through a series of experiments that LogR compressed encodings can be created efficiently, come with provable information-theoretic bounds on their accuracy, and outperform state-of-art log summarization strategies.
This paper discusses the conceptual design and proof-of-concept flight demonstration of a novel variable pitch quadrotor biplane Unmanned Aerial Vehicle concept for payload delivery.
The proposed design combines vertical takeoff and landing (VTOL), precise hover capabilities of a quadrotor helicopter and high range, endurance and high forward cruise speed characteristics of a fixed wing aircraft.
The proposed UAV is designed for a mission requirement of carrying and delivering 6 kg payload to a destination at 16 km from the point of origin.
First, the design of proprotors is carried out using a physics based modified Blade Element Momentum Theory (BEMT) analysis, which is validated using experimental data generated for the purpose.
Proprotors have conflicting requirement for optimal hover and forward flight performance.
Next, the biplane wings are designed using simple lifting line theory.
The airframe design is followed by power plant selection and transmission design.
Finally, weight estimation is carried out to complete the design process.
The proprotor design with 24 deg preset angle and -24 deg twist is designed based on 70% weightage to forward flight and 30% weightage to hovering flight conditions.
The operating RPM of the proprotors is reduced from 3200 during hover to 2000 during forward flight to ensure optimal performance during cruise flight.
The estimated power consumption during forward flight mode is 64% less than that required for hover, establishing the benefit of this hybrid concept.
A proof-of-concept scaled prototype is fabricated using commercial-off-the-shelf parts.
A PID controller is developed and implemented on the PixHawk board to enable stable hovering flight and attitude tracking.
Nowadays, millimeter-wave communication centered at the 60 GHz radio frequency band is increasingly the preferred technology for near-field communication since it provides transmission bandwidth that is several GHz wide.
The IEEE 802.11ad standard has been developed for commercial wireless local area networks in the 60 GHz transmission environment.
Receivers designed to process IEEE 802.11ad waveforms employ very high rate analog-to-digital converters, and therefore, reducing the receiver sampling rate can be useful.
In this work, we study the problem of low-rate channel estimation over the IEEE 802.11ad 60 GHz communication link by harnessing sparsity in the channel impulse response.
In particular, we focus on single carrier modulation and exploit the special structure of the 802.11ad waveform embedded in the channel estimation field of its single carrier physical layer frame.
We examine various sub-Nyquist sampling methods for this problem and recover the channel using compressed sensing techniques.
Our numerical experiments show feasibility of our procedures up to one-seventh of the Nyquist rates with minimal performance deterioration.
This paper presents Verisig, a hybrid system approach to verifying safety properties of closed-loop systems using neural networks as controllers.
Although techniques exist for verifying input/output properties of the neural network itself, these methods cannot be used to verify properties of the closed-loop system (since they work with piecewise-linear constraints that do not capture non-linear plant dynamics).
To overcome this challenge, we focus on sigmoid-based networks and exploit the fact that the sigmoid is the solution to a quadratic differential equation, which allows us to transform the neural network into an equivalent hybrid system.
By composing the network's hybrid system with the plant's, we transform the problem into a hybrid system verification problem which can be solved using state-of-the-art reachability tools.
We show that reachability is decidable for networks with one hidden layer and decidable for general networks if Schanuel's conjecture is true.
We evaluate the applicability and scalability of Verisig in two case studies, one from reinforcement learning and one in which the neural network is used to approximate a model predictive controller.
Specialized hardware architectures promise a major step in performance and energy efficiency over the traditional load/store devices currently employed in large scale computing systems.
The adoption of high-level synthesis (HLS) from languages such as C/C++ and OpenCL has greatly increased programmer productivity when designing for such platforms.
While this has enabled a wider audience to target specialized hardware, the optimization principles known from software design are no longer sufficient to implement high-performance codes, due to fundamental differences between software and hardware architectures.
In this work, we propose a set of optimizing transformations for HLS, targeting scalable and efficient architectures for high-performance computing (HPC) applications.
We show how these can be used to efficiently exploit pipelining, on-chip distributed fast memory, and on-chip streaming dataflow, allowing for massively parallel architectures with little off-chip data movement.
To quantify the effect of our transformations, we use them to optimize a set of high-throughput FPGA kernels, demonstrating that they are sufficient to scale up parallelism within the hardware constraints of the target device.
With the transformations covered, we hope to establish a common framework for performance engineers, compiler developers, and hardware developers, to tap into the performance potential offered by specialized hardware architectures using HLS.
The polarization process of polar codes over a ternary alphabet is studied.
Recently it has been shown that the scaling of the blocklength of polar codes with prime alphabet size scales polynomially with respect to the inverse of the gap between code rate and channel capacity.
However, except for the binary case, the degree of the polynomial in the bound is extremely large.
In this work, it is shown that a much lower degree polynomial can be computed numerically for the ternary case.
Similar results are conjectured for the general case of prime alphabet size.
Last mile link is often a bottleneck for end user.
However, users typically have multiple ways of accessing the Internet (cellular, ADSL, public Wifi).
This observation led to creation of protocols like mTCP or R-MTP.
Current bandwidth aggregation protocols are packet based.
However, this is not always practical - for example, non-TCP protocols are often blocked on firewalls.
Moreover, a lot of effort was devoted over the years into making single-path TCP work well over various types of links.
In this paper we introduce protocol which uses multiple TCP streams to establish single reliable connection attempting to maximize bandwidth and minimize latency.
We present an approach for the verification and validation (V&V) of robot assistants in the context of human-robot interactions (HRI), to demonstrate their trustworthiness through corroborative evidence of their safety and functional correctness.
Key challenges include the complex and unpredictable nature of the real world in which assistant and service robots operate, the limitations on available V&V techniques when used individually, and the consequent lack of confidence in the V&V results.
Our approach, called corroborative V&V, addresses these challenges by combining several different V&V techniques; in this paper we use formal verification (model checking), simulation-based testing, and user validation in experiments with a real robot.
We demonstrate our corroborative V&V approach through a handover task, the most critical part of a complex cooperative manufacturing scenario, for which we propose some safety and liveness requirements to verify and validate.
We construct formal models, simulations and an experimental test rig for the HRI.
To capture requirements we use temporal logic properties, assertion checkers and textual descriptions.
This combination of approaches allows V&V of the HRI task at different levels of modelling detail and thoroughness of exploration, thus overcoming the individual limitations of each technique.
Should the resulting V&V evidence present discrepancies, an iterative process between the different V&V techniques takes place until corroboration between the V&V techniques is gained from refining and improving the assets (i.e., system and requirement models) to represent the HRI task in a more truthful manner.
Therefore, corroborative V&V affords a systematic approach to 'meta-V&V,' in which different V&V techniques can be used to corroborate and check one another, increasing the level of certainty in the results of V&V.
Multispectral image analysis is a relatively promising field of research with applications in several areas, such as medical imaging and satellite monitoring.
A considerable number of current methods of analysis are based on parametric statistics.
Alternatively, some methods in Computational Intelligence are inspired by biology and other sciences.
Here we claim that Philosophy can be also considered as a source of inspiration.
This work proposes the Objective Dialectical Method (ODM): a method for classification based on the Philosophy of Praxis.
ODM is instrumental in assembling evolvable mathematical tools to analyze multispectral images.
In the case study described in this paper, multispectral images are composed of diffusion-weighted (DW) magnetic resonance (MR) images.
The results are compared to ground-truth images produced by polynomial networks using a morphological similarity index.
The classification results are used to improve the usual analysis of the apparent diffusion coefficient map.
Such results proved that gray and white matter can be distinguished in DW-MR multispectral analysis and, consequently, DW-MR images can also be used to furnish anatomical information.
We tested 14 very different classification algorithms (random forest, gradient boosting machines, SVM - linear, polynomial, and RBF - 1-hidden-layer neural nets, extreme learning machines, k-nearest neighbors and a bagging of knn, naive Bayes, learning vector quantization, elastic net logistic regression, sparse linear discriminant analysis, and a boosting of linear classifiers) on 115 real life binary datasets.
We followed the Demsar analysis and found that the three best classifiers (random forest, gbm and RBF SVM) are not significantly different from each other.
We also discuss that a change of less then 0.0112 in the error rate should be considered as an irrelevant change, and used a Bayesian ANOVA analysis to conclude that with high probability the differences between these three classifiers is not of practical consequence.
We also verified the execution time of "standard implementations" of these algorithms and concluded that RBF SVM is the fastest (significantly so) both in training time and in training plus testing time.
Most existing video summarisation methods are based on either supervised or unsupervised learning.
In this paper, we propose a reinforcement learning-based weakly supervised method that exploits easy-to-obtain, video-level category labels and encourages summaries to contain category-related information and maintain category recognisability.
Specifically, We formulate video summarisation as a sequential decision-making process and train a summarisation network with deep Q-learning (DQSN).
A companion classification network is also trained to provide rewards for training the DQSN.
With the classification network, we develop a global recognisability reward based on the classification result.
Critically, a novel dense ranking-based reward is also proposed in order to cope with the temporally delayed and sparse reward problems for long sequence reinforcement learning.
Extensive experiments on two benchmark datasets show that the proposed approach achieves state-of-the-art performance.
In this paper we present a new routing paradigm that generalizes opportunistic routing for wireless multihop networks.
In multirate anypath routing, each node uses both a set of next hops and a selected transmission rate to reach a destination.
Using this rate, a packet is broadcast to the nodes in the set and one of them forwards the packet on to the destination.
To date, there has been no theory capable of jointly optimizing both the set of next hops and the transmission rate used by each node.
We solve this by introducing two polynomial-time routing algorithms and provide the proof of their optimality.
The proposed algorithms run in roughly the same running time as regular shortest-path algorithms, and are therefore suitable for deployment in routing protocols.
We conducted measurements in an 802.11b testbed network, and our trace-driven analysis shows that multirate anypath routing performs on average 80% and up to 6.4 times better than anypath routing with a fixed rate of 11 Mbps.
If the rate is fixed at 1 Mbps instead, performance improves by up to one order of magnitude.
In discussions hosted on discussion forums for MOOCs, references to online learning resources are often of central importance.
They contextualize the discussion, anchoring the discussion participants' presentation of the issues and their understanding.
However they are usually mentioned in free text, without appropriate hyperlinking to their associated resource.
Automated learning resource mention hyperlinking and categorization will facilitate discussion and searching within MOOC forums, and also benefit the contextualization of such resources across disparate views.
We propose the novel problem of learning resource mention identification in MOOC forums.
As this is a novel task with no publicly available data, we first contribute a large-scale labeled dataset, dubbed the Forum Resource Mention (FoRM) dataset, to facilitate our current research and future research on this task.
We then formulate this task as a sequence tagging problem and investigate solution architectures to address the problem.
Importantly, we identify two major challenges that hinder the application of sequence tagging models to the task: (1) the diversity of resource mention expression, and (2) long-range contextual dependencies.
We address these challenges by incorporating character-level and thread context information into a LSTM-CRF model.
First, we incorporate a character encoder to address the out-of-vocabulary problem caused by the diversity of mention expressions.
Second, to address the context dependency challenge, we encode thread contexts using an RNN-based context encoder, and apply the attention mechanism to selectively leverage useful context information during sequence tagging.
Experiments on FoRM show that the proposed method improves the baseline deep sequence tagging models notably, significantly bettering performance on instances that exemplify the two challenges.
The practice of scientific research is often thought of as individuals and small teams striving for disciplinary advances.
Yet as a whole, this endeavor more closely resembles a complex system of natural computation, in which information is obtained, generated, and disseminated more effectively than would be possible by individuals acting in isolation.
Currently, the structure of this integrated and innovative landscape of scientific ideas is not well understood.
Here we use tools from network science to map the landscape of interconnected research topics covered in the multidisciplinary journal PNAS since 2000.
We construct networks in which nodes represent topics of study and edges give the degree to which topics occur in the same papers.
The network displays small-world architecture, with dense connectivity within scientific clusters and sparse connectivity between clusters.
Notably, clusters tend not to align with assigned article classifications, but instead contain topics from various disciplines.
Using a temporal graph, we find that small-worldness has increased over time, suggesting growing efficiency and integration of ideas.
Finally, we define a novel measure of interdisciplinarity, which is positively associated with PNAS's impact factor.
Broadly, this work suggests that complex and dynamic patterns of knowledge emerge from scientific research, and that structures reflecting intellectual integration may be beneficial for obtaining scientific insight.
This paper introduces a method, based on deep reinforcement learning, for automatically generating a general purpose decision making function.
A Deep Q-Network agent was trained in a simulated environment to handle speed and lane change decisions for a truck-trailer combination.
In a highway driving case, it is shown that the method produced an agent that matched or surpassed the performance of a commonly used reference model.
To demonstrate the generality of the method, the exact same algorithm was also tested by training it for an overtaking case on a road with oncoming traffic.
Furthermore, a novel way of applying a convolutional neural network to high level input that represents interchangeable objects is also introduced.
Missing data has a ubiquitous presence in real-life applications of machine learning techniques.
Imputation methods are algorithms conceived for restoring missing values in the data, based on other entries in the database.
The choice of the imputation method has an influence on the performance of the machine learning technique, e.g., it influences the accuracy of the classification algorithm applied to the data.
Therefore, selecting and applying the right imputation method is important and usually requires a substantial amount of human intervention.
In this paper we propose the use of genetic programming techniques to search for the right combination of imputation and classification algorithms.
We build our work on the recently introduced Python-based TPOT library, and incorporate a heterogeneous set of imputation algorithms as part of the machine learning pipeline search.
We show that genetic programming can automatically find increasingly better pipelines that include the most effective combinations of imputation methods, feature pre-processing, and classifiers for a variety of classification problems with missing data.
Voice disguise, purposeful modification of one's speaker identity with the aim of avoiding being identified as oneself, is a low-effort way to fool speaker recognition, whether performed by a human or an automatic speaker verification (ASV) system.
We present an evaluation of the effectiveness of age stereotypes as a voice disguise strategy, as a follow up to our recent work where 60 native Finnish speakers attempted to sound like an elderly and like a child.
In that study, we presented evidence that both ASV and human observers could easily miss the target speaker but we did not address how believable the presented vocal age stereotypes were; this study serves to fill that gap.
The interesting cases would be speakers who succeed in being missed by the ASV system, and which a typical listener cannot detect as being a disguise.
We carry out a perceptual test to study the quality of the disguised speech samples.
The listening test was carried out both locally and with the help of Amazon's Mechanical Turk (MT) crowd-workers.
A total of 91 listeners participated in the test and were instructed to estimate both the speaker's chronological and intended age.
The results indicate that age estimations for the intended old and child voices for female speakers were towards the target age groups, while for male speakers, the age estimations corresponded to the direction of the target voice only for elderly voices.
In the case of intended child's voice, listeners estimated the age of male speakers to be older than their chronological age for most of the speakers and not the intended target age.
Text segmentation, the task of dividing a document into contiguous segments based on its semantic structure, is a longstanding challenge in language understanding.
Previous work on text segmentation focused on unsupervised methods such as clustering or graph search, due to the paucity in labeled data.
In this work, we formulate text segmentation as a supervised learning problem, and present a large new dataset for text segmentation that is automatically extracted and labeled from Wikipedia.
Moreover, we develop a segmentation model based on this dataset and show that it generalizes well to unseen natural text.
We propose two novel techniques for overcoming load-imbalance encountered when implementing so-called look-ahead mechanisms in relevant dense matrix factorizations for the solution of linear systems.
Both techniques target the scenario where two thread teams are created/activated during the factorization, with each team in charge of performing an independent task/branch of execution.
The first technique promotes worker sharing (WS) between the two tasks, allowing the threads of the task that completes first to be reallocated for use by the costlier task.
The second technique allows a fast task to alert the slower task of completion, enforcing the early termination (ET) of the second task, and a smooth transition of the factorization procedure into the next iteration.
The two mechanisms are instantiated via a new malleable thread-level implementation of the Basic Linear Algebra Subprograms (BLAS), and their benefits are illustrated via an implementation of the LU factorization with partial pivoting enhanced with look-ahead.
Concretely, our experimental results on a six core Intel-Xeon processor show the benefits of combining WS+ET, reporting competitive performance in comparison with a task-parallel runtime-based solution.
The dominant object detection approaches treat the recognition of each region separately and overlook crucial semantic correlations between objects in one scene.
This paradigm leads to substantial performance drop when facing heavy long-tail problems, where very few samples are available for rare classes and plenty of confusing categories exists.
We exploit diverse human commonsense knowledge for reasoning over large-scale object categories and reaching semantic coherency within one image.
Particularly, we present Hybrid Knowledge Routed Modules (HKRM) that incorporates the reasoning routed by two kinds of knowledge forms: an explicit knowledge module for structured constraints that are summarized with linguistic knowledge (e.g. shared attributes, relationships) about concepts; and an implicit knowledge module that depicts some implicit constraints (e.g. common spatial layouts).
By functioning over a region-to-region graph, both modules can be individualized and adapted to coordinate with visual patterns in each image, guided by specific knowledge forms.
HKRM are light-weight, general-purpose and extensible by easily incorporating multiple knowledge to endow any detection networks the ability of global semantic reasoning.
Experiments on large-scale object detection benchmarks show HKRM obtains around 34.5% improvement on VisualGenome (1000 categories) and 30.4% on ADE in terms of mAP.
Codes and trained model can be found in https://github.com/chanyn/HKRM.
We present a novel application of LSTM recurrent neural networks to multilabel classification of diagnoses given variable-length time series of clinical measurements.
Our method outperforms a strong baseline on a variety of metrics.
Neural Machine Translation (NMT) can be improved by including document-level contextual information.
For this purpose, we propose a hierarchical attention model to capture the context in a structured and dynamic manner.
The model is integrated in the original NMT architecture as another level of abstraction, conditioning on the NMT model's own previous hidden states.
Experiments show that hierarchical attention significantly improves the BLEU score over a strong NMT baseline with the state-of-the-art in context-aware methods, and that both the encoder and decoder benefit from context in complementary ways.
Very often features come with their own vectorial descriptions which provide detailed information about their properties.
We refer to these vectorial descriptions as feature side-information.
In the standard learning scenario, input is represented as a vector of features and the feature side-information is most often ignored or used only for feature selection prior to model fitting.
We believe that feature side-information which carries information about features intrinsic property will help improve model prediction if used in a proper way during learning process.
In this paper, we propose a framework that allows for the incorporation of the feature side-information during the learning of very general model families to improve the prediction performance.
We control the structures of the learned models so that they reflect features similarities as these are defined on the basis of the side-information.
We perform experiments on a number of benchmark datasets which show significant predictive performance gains, over a number of baselines, as a result of the exploitation of the side-information.
Traditional methods for assessing illness severity and predicting in-hospital mortality among critically ill patients require time-consuming, error-prone calculations using static variable thresholds.
These methods do not capitalize on the emerging availability of streaming electronic health record data or capture time-sensitive individual physiological patterns, a critical task in the intensive care unit.
We propose a novel acuity score framework (DeepSOFA) that leverages temporal measurements and interpretable deep learning models to assess illness severity at any point during an ICU stay.
We compare DeepSOFA with SOFA (Sequential Organ Failure Assessment) baseline models using the same model inputs and find that at any point during an ICU admission, DeepSOFA yields significantly more accurate predictions of in-hospital mortality.
A DeepSOFA model developed in a public database and validated in a single institutional cohort had a mean AUC for the entire ICU stay of 0.90 (95% CI 0.90-0.91) compared with baseline SOFA models with mean AUC 0.79 (95% CI 0.79-0.80) and 0.85 (95% CI 0.85-0.86).
Deep models are well-suited to identify ICU patients in need of life-saving interventions prior to the occurrence of an unexpected adverse event and inform shared decision-making processes among patients, providers, and families regarding goals of care and optimal resource utilization.
Controllers for autonomous robotic systems can be specified using state machines.
However, these are typically developed in an ad hoc manner without formal semantics, which makes it difficult to analyse the controller.
Simulations are often used during the development, but a rigorous connection between the designed controller and the implementation is often overlooked.
This paper presents a state-machine based notation, RoboChart, together with a tool to automatically create code from the state machines, establishing a rigorous connection between specification and implementation.
In RoboChart, a robot's controller is specified either graphically or using a textual description language.
The controller code for simulation is automatically generated through a direct mapping from the specification.
We demonstrate our approach using two case studies (self-organized aggregation and swarm taxis) in swarm robotics.
The simulations are presented using two different simulators showing the general applicability of our approach.
Predicting both the time and the location of human movements is valuable but challenging for a variety of applications.
To address this problem, we propose an approach considering both the periodicity and the sociality of human movements.
We first define a new concept, Social Spatial-Temporal Event (SSTE), to represent social interactions among people.
For the time prediction, we characterise the temporal dynamics of SSTEs with an ARMA (AutoRegressive Moving Average) model.
To dynamically capture the SSTE kinetics, we propose a Kalman Filter based learning algorithm to learn and incrementally update the ARMA model as a new observation becomes available.
For the location prediction, we propose a ranking model where the periodicity and the sociality of human movements are simultaneously taken into consideration for improving the prediction accuracy.
Extensive experiments conducted on real data sets validate our proposed approach.
Knowledge workers face an ever increasing flood of information in their daily lives.
To counter this and provide better support for information management and knowledge work in general, we have been investigating solutions inspired by human forgetting since 2013.
These solutions are based on Semantic Desktop (SD) and Managed Forgetting (MF) technology.
A key concept of the latter is the so-called Memory Buoyancy (MB), which is intended to represent an information item's current value for the user and allows to employ forgetting mechanisms.
The SD thus continuously performs information value assessment updating MB and triggering respective MF measures.
We extended an SD-based organizational memory system, which we have been using in daily work for over seven years now, with MF mechanisms directly embedding them in daily activities, too, and enabling us to test and optimize them in real-world scenarios.
In this paper, we first present our initial version of MB and discuss success and failure stories we have been experiencing with it during three years of practical usage.
We learned from cognitive psychology that our previous research on context can be beneficial for MF.
Thus, we created an advanced MB version especially taking user context, and in particular context switches, into account.
These enhancements as well as a first prototypical implementation are presented, too.
This paper studies the problem of passive grasp stability under an external disturbance, that is, the ability of a grasp to resist a disturbance through passive responses at the contacts.
To obtain physically consistent results, such a model must account for friction phenomena at each contact; the difficulty is that friction forces depend in non-linear fashion on contact behavior (stick or slip).
We develop the first polynomial-time algorithm which either solves such complex equilibrium constraints for two-dimensional grasps, or otherwise concludes that no solution exists.
To achieve this, we show that the number of possible `slip states' (where each contact is labeled as either sticking or slipping) that must be considered is polynomial (in fact quadratic) in the number of contacts, and not exponential as previously thought.
Our algorithm captures passive response behaviors at each contact, while accounting for constraints on friction forces such as the maximum dissipation principle.
The most commonly used weighted least square state estimator in power industry is nonlinear and formulated by using conventional measurements such as line flow and injection measurements.
PMUs (Phasor Measurement Units) are gradually adding them to improve the state estimation process.
In this paper the way of corporation the PMU data to the conventional measurements and a linear formulation of the state estimation using only PMU measured data are investigated.
Six cases are tested while gradually increasing the number of PMUs which are added to the measurement set and the effect of PMUs on the accuracy of variables are illustrated and compared by applying them on IEEE 14, 30 test systems.
Driven by successes in deep learning, computer vision research has begun to move beyond object detection and image classification to more sophisticated tasks like image captioning or visual question answering.
Motivating such endeavors is the desire for models to capture not only objects present in an image, but more fine-grained aspects of a scene such as relationships between objects and their attributes.
Scene graphs provide a formal construct for capturing these aspects of an image.
Despite this, there have been only a few recent efforts to generate scene graphs from imagery.
Previous works limit themselves to settings where bounding box information is available at train time and do not attempt to generate scene graphs with attributes.
In this paper we propose a method, based on recent advancements in Generative Adversarial Networks, to overcome these deficiencies.
We take the approach of first generating small subgraphs, each describing a single statement about a scene from a specific region of the input image chosen using an attention mechanism.
By doing so, our method is able to produce portions of the scene graphs with attribute information without the need for bounding box labels.
Then, the complete scene graph is constructed from these subgraphs.
We show that our model improves upon prior work in scene graph generation on state-of-the-art data sets and accepted metrics.
Further, we demonstrate that our model is capable of handling a larger vocabulary size than prior work has attempted.
Many problems in machine learning and related application areas are fundamentally variants of conditional modeling and sampling across multi-aspect data, either multi-view, multi-modal, or simply multi-group.
For example, sampling from the distribution of English sentences conditioned on a given French sentence or sampling audio waveforms conditioned on a given piece of text.
Central to many of these problems is the issue of missing data: we can observe many English, French, or German sentences individually but only occasionally do we have data for a sentence pair.
Motivated by these applications and inspired by recent progress in variational autoencoders for grouped data, we develop factVAE, a deep generative model capable of handling multi-aspect data, robust to missing observations, and with a prior that encourages disentanglement between the groups and the latent dimensions.
The effectiveness of factVAE is demonstrated on a variety of rich real-world datasets, including motion capture poses and pictures of faces captured from varying poses and perspectives.
In this paper we present a unified framework for solving a general class of problems arising in the context of set-membership estimation/identification theory.
More precisely, the paper aims at providing an original approach for the computation of optimal conditional and robust projection estimates in a nonlinear estimation setting where the operator relating the data and the parameter to be estimated is assumed to be a generic multivariate polynomial function and the uncertainties affecting the data are assumed to belong to semialgebraic sets.
By noticing that the computation of both the conditional and the robust projection optimal estimators requires the solution to min-max optimization problems that share the same structure, we propose a unified two-stage approach based on semidefinite-relaxation techniques for solving such estimation problems.
The key idea of the proposed procedure is to recognize that the optimal functional of the inner optimization problems can be approximated to any desired precision by a multivariate polynomial function by suitably exploiting recently proposed results in the field of parametric optimization.
Two simulation examples are reported to show the effectiveness of the proposed approach.
We consider the single image super-resolution problem in a more general case that the low-/high-resolution pairs and the down-sampling process are unavailable.
Different from traditional super-resolution formulation, the low-resolution input is further degraded by noises and blurring.
This complicated setting makes supervised learning and accurate kernel estimation impossible.
To solve this problem, we resort to unsupervised learning without paired data, inspired by the recent successful image-to-image translation applications.
With generative adversarial networks (GAN) as the basic component, we propose a Cycle-in-Cycle network structure to tackle the problem within three steps.
First, the noisy and blurry input is mapped to a noise-free low-resolution space.
Then the intermediate image is up-sampled with a pre-trained deep model.
Finally, we fine-tune the two modules in an end-to-end manner to get the high-resolution output.
Experiments on NTIRE2018 datasets demonstrate that the proposed unsupervised method achieves comparable results as the state-of-the-art supervised models.
A broad class of software engineering problems can be generalized as the "total recall problem".
This short paper claims that identifying and exploring total recall language processing problems in software engineering is an important task with wide applicability.
To make that case, we show that by applying and adapting the state of the art active learning and text mining, solutions of the total recall problem, can help solve two important software engineering tasks: (a) supporting large literature reviews and (b) identifying software security vulnerabilities.
Furthermore, we conjecture that (c) test case prioritization and (d) static warning identification can also be categorized as the total recall problem.
The widespread applicability of "total recall" to software engineering suggests that there exists some underlying framework that encompasses not just natural language processing, but a wide range of important software engineering tasks.
The next generation of PaaS technology accomplishes the true promise of object-oriented and 4GLs development with less effort.
Now PaaS is becoming one of the core technical services for application development organizations.
PaaS offers a resourceful and agile approach to develop, operate and deploy applications in a cost-effective manner.
It is now turning out to be one of the preferred choices throughout the world, especially for globally distributed development environment.
However it still lacks the scale of popularity and acceptance which Software-as-a-Service (SaaS) and Infrastructure-as-a-Service (IaaS) have attained.
PaaS offers a promising future with novel technology architecture and evolutionary development approach.
In this article, we identify the strengths, weaknesses, opportunities and threats for the PaaS industry.
We then identify the various issues that will affect the different stakeholders of PaaS industry.
This research will outline a set of recommendations for the PaaS practitioners to better manage this technology.
For PaaS technology researchers, we also outline the number of research areas that need attention in coming future.
Finally, we also included an online survey to outline PaaS technology market leaders.
This will facilitate PaaS technology practitioners to have a more deep insight into market trends and technologies.
This paper analyzes how the distortion created by hardware impairments in a multiple-antenna base station affects the uplink spectral efficiency (SE), with focus on Massive MIMO.
This distortion is correlated across the antennas, but has been often approximated as uncorrelated to facilitate (tractable) SE analysis.
To determine when this approximation is accurate, basic properties of distortion correlation are first uncovered.
Then, we separately analyze the distortion correlation caused by third-order non-linearities and by quantization.
Finally, we study the SE numerically and show that the distortion correlation can be safely neglected in Massive MIMO when there are sufficiently many users.
Under i.i.d.Rayleigh fading and equal signal-to-noise ratios (SNRs), this occurs for more than five transmitting users.
Other channel models and SNR variations have only minor impact on the accuracy.
We also demonstrate the importance of taking the distortion characteristics into account in the receive combining.
Multiple Sclerosis (MS) is an autoimmune disease that leads to lesions in the central nervous system.
Magnetic resonance (MR) images provide sufficient imaging contrast to visualize and detect lesions, particularly those in the white matter.
Quantitative measures based on various features of lesions have been shown to be useful in clinical trials for evaluating therapies.
Therefore robust and accurate segmentation of white matter lesions from MR images can provide important information about the disease status and progression.
In this paper, we propose a fully convolutional neural network (CNN) based method to segment white matter lesions from multi-contrast MR images.
The proposed CNN based method contains two convolutional pathways.
The first pathway consists of multiple parallel convolutional filter banks catering to multiple MR modalities.
In the second pathway, the outputs of the first one are concatenated and another set of convolutional filters are applied.
The output of this last pathway produces a membership function for lesions that may be thresholded to obtain a binary segmentation.
The proposed method is evaluated on a dataset of 100 MS patients, as well as the ISBI 2015 challenge data consisting of 14 patients.
The comparison is performed against four publicly available MS lesion segmentation methods.
Significant improvement in segmentation quality over the competing methods is demonstrated on various metrics, such as Dice and false positive ratio.
While evaluating on the ISBI 2015 challenge data, our method produces a score of 90.48, where a score of 90 is considered to be comparable to a human rater.
Computationally efficient classification system architecture is proposed.
It utilizes fast tensor-vector multiplication algorithm to apply linear operators upon input signals .
The approach is applicable to wide variety of recognition system architectures ranging from single stage matched filter bank classifiers to complex neural networks with unlimited number of hidden layers.
We introduce the class of synchronous subsequential relations, a subclass of the synchronous relations which embodies some properties of subsequential relations.
If we take relations of this class as forming the possible transitions of an infinite automaton, then most decision problems (apart from membership) still remain undecidable (as they are for synchronous and subsequential rational relations), but on the positive side, they can be approximated in a meaningful way we make precise in this paper.
This might make the class useful for some applications, and might serve to establish an intermediate position in the trade-off between issues of expressivity and (un)decidability.
In this paper we present and start analyzing the iCub World data-set, an object recognition data-set, we acquired using a Human-Robot Interaction (HRI) scheme and the iCub humanoid robot platform.
Our set up allows for rapid acquisition and annotation of data with corresponding ground truth.
While more constrained in its scopes -- the iCub world is essentially a robotics research lab -- we demonstrate how the proposed data-set poses challenges to current recognition systems.
The iCubWorld data-set is publicly available.
The data-set can be downloaded from: http://www.iit.it/en/projects/data-sets.html.
We are concerned with robust and accurate forecasting of multiphase flow rates in wells and pipelines during oil and gas production.
In practice, the possibility to physically measure the rates is often limited; besides, it is desirable to estimate future values of multiphase rates based on the previous behavior of the system.
In this work, we demonstrate that a Long Short-Term Memory (LSTM) recurrent artificial network is able not only to accurately estimate the multiphase rates at current time (i.e., act as a virtual flow meter), but also to forecast the rates for a sequence of future time instants.
For a synthetic severe slugging case, LSTM forecasts compare favorably with the results of hydrodynamical modeling.
LSTM results for a realistic noizy dataset of a variable rate well test show that the model can also successfully forecast multiphase rates for a system with changing flow patterns.
Recently, it was shown that if multiplicative weights are assigned to the edges of a Tanner graph used in belief propagation decoding, it is possible to use deep learning techniques to find values for the weights which improve the error-correction performance of the decoder.
Unfortunately, this approach requires many multiplications, which are generally expensive operations.
In this paper, we suggest a more hardware-friendly approach in which offset min-sum decoding is augmented with learnable offset parameters.
Our method uses no multiplications and has a parameter count less than half that of the multiplicative algorithm.
This both speeds up training and provides a feasible path to hardware architectures.
After describing our method, we compare the performance of the two neural decoding algorithms and show that our method achieves error-correction performance within 0.1 dB of the multiplicative approach and as much as 1 dB better than traditional belief propagation for the codes under consideration.
In recent years, the number of Internet of Things (IoT) devices/sensors has increased to a great extent.
To support the computational demand of real-time latency-sensitive applications of largely geo-distributed IoT devices/sensors, a new computing paradigm named "Fog computing" has been introduced.
Generally, Fog computing resides closer to the IoT devices/sensors and extends the Cloud-based computing, storage and networking facilities.
In this chapter, we comprehensively analyse the challenges in Fogs acting as an intermediate layer between IoT devices/ sensors and Cloud datacentres and review the current developments in this field.
We present a taxonomy of Fog computing according to the identified challenges and its key features.We also map the existing works to the taxonomy in order to identify current research gaps in the area of Fog computing.
Moreover, based on the observations, we propose future directions for research.
We present Deeply Supervised Object Detector (DSOD), a framework that can learn object detectors from scratch.
State-of-the-art object objectors rely heavily on the off-the-shelf networks pre-trained on large-scale classification datasets like ImageNet, which incurs learning bias due to the difference on both the loss functions and the category distributions between classification and detection tasks.
Model fine-tuning for the detection task could alleviate this bias to some extent but not fundamentally.
Besides, transferring pre-trained models from classification to detection between discrepant domains is even more difficult (e.g.RGB to depth images).
A better solution to tackle these two critical problems is to train object detectors from scratch, which motivates our proposed DSOD.
Previous efforts in this direction mostly failed due to much more complicated loss functions and limited training data in object detection.
In DSOD, we contribute a set of design principles for training object detectors from scratch.
One of the key findings is that deep supervision, enabled by dense layer-wise connections, plays a critical role in learning a good detector.
Combining with several other principles, we develop DSOD following the single-shot detection (SSD) framework.
Experiments on PASCAL VOC 2007, 2012 and MS COCO datasets demonstrate that DSOD can achieve better results than the state-of-the-art solutions with much more compact models.
For instance, DSOD outperforms SSD on all three benchmarks with real-time detection speed, while requires only 1/2 parameters to SSD and 1/10 parameters to Faster RCNN.
Our code and models are available at: https://github.com/szq0214/DSOD .
Traditional information retrieval (such as that offered by web search engines) impedes users with information overload from extensive result pages and the need to manually locate the desired information therein.
Conversely, question-answering systems change how humans interact with information systems: users can now ask specific questions and obtain a tailored answer - both conveniently in natural language.
Despite obvious benefits, their use is often limited to an academic context, largely because of expensive domain customizations, which means that the performance in domain-specific applications often fails to meet expectations.
This paper proposes cost-efficient remedies: (i) we leverage metadata through a filtering mechanism, which increases the precision of document retrieval, and (ii) we develop a novel fuse-and-oversample approach for transfer learning in order to improve the performance of answer extraction.
Here knowledge is inductively transferred from a related, yet different, tasks to the domain-specific application, while accounting for potential differences in the sample sizes across both tasks.
The resulting performance is demonstrated with actual use cases from a finance company and the film industry, where fewer than 400 question-answer pairs had to be annotated in order to yield significant performance gains.
As a direct implication to management, this presents a promising path to better leveraging of knowledge stored in information systems.
In this paper, we propose a combination of pedestrian data collection and analysis and modeling that may yield higher competitive advantage in the business environment.
The data collection is only based on simple inventory and questionnaire surveys on a hypermarket to obtain trajectory path of pedestrian movement.
Though the data has limitation by using static trajectories, our techniques showed that it is possible to obtain aggregation of flow pattern and alley attractiveness similar to the result of aggregation using dynamic trajectory.
A case study of a real hypermarket demonstrates that daily necessity products are closely related to higher flow pattern.
Using the proposed method, we are also able to quantify pedestrian behavior that shoppers tend to walk about 7 times higher than the ideal shortest path
This paper deals with a new type of warehousing system, Robotic Mobile Fulfillment Systems (RMFS).
In such systems, robots are sent to carry storage units, so-called "pods", from the inventory and bring them to human operators working at stations.
At the stations, the items are picked according to customers' orders.
There exist new decision problems in such systems, for example, the reallocation of pods after their visits at work stations or the selection of pods to fulfill orders.
In order to analyze decision strategies for these decision problems and relations between them, we develop a simulation framework called "RAWSim-O" in this paper.
Moreover, we show a real-world application of our simulation framework by integrating simple robot prototypes based on vacuum cleaning robots.
Intelligent Transportation Systems (ITSs) require ultra-low end-to-end delays and multi-gigabit-per-second data transmission.
Millimetre Waves (mmWaves) communications can fulfil these requirements.
However, the increased mobility of Connected and Autonomous Vehicles (CAVs), requires frequent beamforming - thus introducing increased overhead.
In this paper, a new beamforming algorithm is proposed able to achieve overhead-free beamforming training.
Leveraging from the CAVs sensory data, broadcast with Dedicated Short Range Communications (DSRC) beacons, the position and the motion of a CAV can be estimated and beamform accordingly.
To minimise the position errors, an analysis of the distinct error components was presented.
The network performance is further enhanced by adapting the antenna beamwidth with respect to the position error.
Our algorithm outperforms the legacy IEEE 802.11ad approach proving it a viable solution for the future ITS applications and services.
A natural language interface (NLI) to structured query is intriguing due to its wide industrial applications and high economical values.
In this work, we tackle the problem of domain adaptation for NLI with limited data on target domain.
Two important approaches are considered: (a) effective general-knowledge-learning on source domain semantic parsing, and (b) data augmentation on target domain.
We present a Structured Query Inference Network (SQIN) to enhance learning for domain adaptation, by separating schema information from NL and decoding SQL in a more structural-aware manner; we also propose a GAN-based augmentation technique (AugmentGAN) to mitigate the issue of lacking target domain data.
We report solid results on GeoQuery, Overnight, and WikiSQL to demonstrate state-of-the-art performances for both in-domain and domain-transfer tasks.
This paper proposes models of learning process in teams of individuals who collectively execute a sequence of tasks and whose actions are determined by individual skill levels and networks of interpersonal appraisals and influence.
The closely-related proposed models have increasing complexity, starting with a centralized manager-based assignment and learning model, and finishing with a social model of interpersonal appraisal, assignments, learning, and influences.
We show how rational optimal behavior arises along the task sequence for each model, and discuss conditions of suboptimality.
Our models are grounded in replicator dynamics from evolutionary games, influence networks from mathematical sociology, and transactive memory systems from organization science.
Variational Bayesian neural nets combine the flexibility of deep learning with Bayesian uncertainty estimation.
Unfortunately, there is a tradeoff between cheap but simple variational families (e.g.~fully factorized) or expensive and complicated inference procedures.
We show that natural gradient ascent with adaptive weight noise implicitly fits a variational posterior to maximize the evidence lower bound (ELBO).
This insight allows us to train full-covariance, fully factorized, or matrix-variate Gaussian variational posteriors using noisy versions of natural gradient, Adam, and K-FAC, respectively, making it possible to scale up to modern-size ConvNets.
On standard regression benchmarks, our noisy K-FAC algorithm makes better predictions and matches Hamiltonian Monte Carlo's predictive variances better than existing methods.
Its improved uncertainty estimates lead to more efficient exploration in active learning, and intrinsic motivation for reinforcement learning.
In the last decade, an active area of research has been devoted to design novel activation functions that are able to help deep neural networks to converge, obtaining better performance.
The training procedure of these architectures usually involves optimization of the weights of their layers only, while non-linearities are generally pre-specified and their (possible) parameters are usually considered as hyper-parameters to be tuned manually.
In this paper, we introduce two approaches to automatically learn different combinations of base activation functions (such as the identity function, ReLU, and tanh) during the training phase.
We present a thorough comparison of our novel approaches with well-known architectures (such as LeNet-5, AlexNet, and ResNet-56) on three standard datasets (Fashion-MNIST, CIFAR-10, and ILSVRC-2012), showing substantial improvements in the overall performance, such as an increase in the top-1 accuracy for AlexNet on ILSVRC-2012 of 3.01 percentage points.
Natural disasters are a large threat for people especially in developing countries such as Laos.
ICT-based disaster management systems aim at supporting disaster warning and response efforts.
However, the ability to directly communicate in both directions between local and administrative level is often not supported, and a tight integration into administrative workflows is missing.
In this paper, we present the smartphone-based disaster and reporting system Mobile4D.
It allows for bi-directional communication while being fully involved in administrative processes.
We present the system setup and discuss integration into administrative structures in Lao PDR.
This work is dedicated to introducing, executing, and assessing a three-stage speaker verification framework to enhance the degraded speaker verification performance in emotional talking environments.
Our framework is comprised of three cascaded stages: gender identification stage followed by an emotion identification stage followed by a speaker verification stage.
The proposed framework has been assessed on two distinct and independent emotional speech datasets: our collected dataset and Emotional Prosody Speech and Transcripts dataset.
Our results demonstrate that speaker verification based on both gender cues and emotion cues is superior to each of speaker verification based on gender cues only, emotion cues only, and neither gender cues nor emotion cues.
The achieved average speaker verification performance based on the suggested methodology is very similar to that attained in subjective assessment by human listeners.
Critical to evaluating the capacity, scalability, and availability of web systems are realistic web traffic generators.
Web traffic generation is a classic research problem, no generator accounts for the characteristics of web robots or crawlers that are now the dominant source of traffic to a web server.
Administrators are thus unable to test, stress, and evaluate how their systems perform in the face of ever increasing levels of web robot traffic.
To resolve this problem, this paper introduces a novel approach to generate synthetic web robot traffic with high fidelity.
It generates traffic that accounts for both the temporal and behavioral qualities of robot traffic by statistical and Bayesian models that are fitted to the properties of robot traffic seen in web logs from North America and Europe.
We evaluate our traffic generator by comparing the characteristics of generated traffic to those of the original data.
We look at session arrival rates, inter-arrival times and session lengths, comparing and contrasting them between generated and real traffic.
Finally, we show that our generated traffic affects cache performance similarly to actual traffic, using the common LRU and LFU eviction policies.
We empirically study sorting in the evolving data model.
In this model, a sorting algorithm maintains an approximation to the sorted order of a list of data items while simultaneously, with each comparison made by the algorithm, an adversary randomly swaps the order of adjacent items in the true sorted order.
Previous work studies only two versions of quicksort, and has a gap between the lower bound of Omega(n) and the best upper bound of O(n log log n).
The experiments we perform in this paper provide empirical evidence that some quadratic-time algorithms such as insertion sort and bubble sort are asymptotically optimal for any constant rate of random swaps.
In fact, these algorithms perform as well as or better than algorithms such as quicksort that are more efficient in the traditional algorithm analysis model.
As microblogging services like Twitter are becoming more and more influential in today's globalised world, its facets like sentiment analysis are being extensively studied.
We are no longer constrained by our own opinion.
Others opinions and sentiments play a huge role in shaping our perspective.
In this paper, we build on previous works on Twitter sentiment analysis using Distant Supervision.
The existing approach requires huge computation resource for analysing large number of tweets.
In this paper, we propose techniques to speed up the computation process for sentiment analysis.
We use tweet subjectivity to select the right training samples.
We also introduce the concept of EFWS (Effective Word Score) of a tweet that is derived from polarity scores of frequently used words, which is an additional heuristic that can be used to speed up the sentiment classification with standard machine learning algorithms.
We performed our experiments using 1.6 million tweets.
Experimental evaluations show that our proposed technique is more efficient and has higher accuracy compared to previously proposed methods.
We achieve overall accuracies of around 80% (EFWS heuristic gives an accuracy around 85%) on a training dataset of 100K tweets, which is half the size of the dataset used for the baseline model.
The accuracy of our proposed model is 2-3% higher than the baseline model, and the model effectively trains at twice the speed of the baseline model.
As a data-centric cache-enabled architecture, Named Data Networking (NDN) is considered to be an appropriate alternative to the current host-centric IP-based Internet infrastructure.
Leveraging in-network caching, name-based routing, and receiver-driven sessions, NDN can greatly enhance the way Internet resources are being used.
A critical issue in NDN is the procedure of cache allocation and management.
Our main contribution in this research is the analysis of memory requirements to allocate suitable Content-Store size to NDN routers, with respect to combined impacts of long-term centrality-based metric and Exponential Weighted Moving Average (EWMA) of short-term parameters such as users behaviors and outgoing traffic.
To determine correlations in such large data sets, data mining methods can prove valuable to researchers.
In this paper, we apply a data-fusion approach, namely Principal Component Analysis (PCA), to discover relations from short- and long-term parameters of the router.
The output of PCA, exploited to mine out raw data sets, is used to allocate a proper cache size to the router.
Evaluation results show an increase in the hit ratio of Content-Stores in sources, and NDN routers.
Moreover, for the proposed cache size allocation scheme, the number of unsatisfied and pending Interests in NDN routers is smaller than the Degree-Centrality cache size scheme.
Research into the classification of time series has made enormous progress in the last decade.
The UCR time series archive has played a significant role in challenging and guiding the development of new learners for time series classification.
The largest dataset in the UCR archive holds 10 thousand time series only; which may explain why the primary research focus has been in creating algorithms that have high accuracy on relatively small datasets.
This paper introduces Proximity Forest, an algorithm that learns accurate models from datasets with millions of time series, and classifies a time series in milliseconds.
The models are ensembles of highly randomized Proximity Trees.
Whereas conventional decision trees branch on attribute values (and usually perform poorly on time series), Proximity Trees branch on the proximity of time series to one exemplar time series or another; allowing us to leverage the decades of work into developing relevant measures for time series.
Proximity Forest gains both efficiency and accuracy by stochastic selection of both exemplars and similarity measures.
Our work is motivated by recent time series applications that provide orders of magnitude more time series than the UCR benchmarks.
Our experiments demonstrate that Proximity Forest is highly competitive on the UCR archive: it ranks among the most accurate classifiers while being significantly faster.
We demonstrate on a 1M time series Earth observation dataset that Proximity Forest retains this accuracy on datasets that are many orders of magnitude greater than those in the UCR repository, while learning its models at least 100,000 times faster than current state of the art models Elastic Ensemble and COTE.
Current applications have produced graphs on the order of hundreds of thousands of nodes and millions of edges.
To take advantage of such graphs, one must be able to find patterns, outliers and communities.
These tasks are better performed in an interactive environment, where human expertise can guide the process.
For large graphs, though, there are some challenges: the excessive processing requirements are prohibitive, and drawing hundred-thousand nodes results in cluttered images hard to comprehend.
To cope with these problems, we propose an innovative framework suited for any kind of tree-like graph visual design.
GMine integrates (a) a representation for graphs organized as hierarchies of partitions - the concepts of SuperGraph and Graph-Tree; and (b) a graph summarization methodology - CEPS.
Our graph representation deals with the problem of tracing the connection aspects of a graph hierarchy with sub linear complexity, allowing one to grasp the neighborhood of a single node or of a group of nodes in a single click.
As a proof of concept, the visual environment of GMine is instantiated as a system in which large graphs can be investigated globally and locally.
In this article we propose a method for measuring internet connection stability which is fast and has negligible overhead for the process of its complexity.
This method finds a relative value for representing the stability of internet connections and can also be extended for aggregated internet connections.
The method is documented with help of a real time implementation and results are shared.
This proposed measurement scheme uses HTTP GET method for each connections.
The normalized responses to identified sites like gateways of ISPs, google.com etc are used for calculating current link stability.
The novelty of the approach is that historic values are used to calculate overall link stability.
In this discussion, we also document a method to use the calculated values as a dynamic threshold metric.
This is used in routing decisions and for load-balancing each of the connections in an aggregated bandwidth pipe.
This scheme is a very popular practice in aggregated internet connections.
Synthetic biology is a rapidly emerging research area, with expected wide-ranging impact in biology, nanofabrication, and medicine.
A key technical challenge lies in embedding computation in molecular contexts where electronic micro-controllers cannot be inserted.
This necessitates effective representation of computation using molecular components.
While previous work established the Turing-completeness of chemical reactions, defining representations that are faithful, efficient, and practical remains challenging.
This paper introduces CRN++, a new language for programming deterministic (mass-action) chemical kinetics to perform computation.
We present its syntax and semantics, and build a compiler translating CRN++ programs into chemical reactions, thereby laying the foundation of a comprehensive framework for molecular programming.
Our language addresses the key challenge of embedding familiar imperative constructs into a set of chemical reactions happening simultaneously and manipulating real-valued concentrations.
Although some deviation from ideal output value cannot be avoided, we develop methods to minimize the error, and implement error analysis tools.
We demonstrate the feasibility of using CRN++ on a suite of well-known algorithms for discrete and real-valued computation.
CRN++ can be easily extended to support new commands or chemical reaction implementations, and thus provides a foundation for developing more robust and practical molecular programs.
The design of the precoder the maximizes the mutual information in linear vector Gaussian channels with an arbitrary input distribution is studied.
Precisely, the precoder optimal left singular vectors and singular values are derived.
The characterization of the right singular vectors is left, in general, as an open problem whose computational complexity is then studied in three cases: Gaussian signaling, low SNR, and high SNR.
For the Gaussian signaling case and the low SNR regime, the dependence of the mutual information on the right singular vectors vanishes, making the optimal precoder design problem easy to solve.
In the high SNR regime, however, the dependence on the right singular vectors cannot be avoided and we show the difficulty of computing the optimal precoder through an NP-hardness analysis.
This is the preprint version of our paper on 2015 International Conference on Virtual Rehabilitation (ICVR2015).
In this paper, we described the imagination scenarios of a touch-less interaction technology for hemiplegia, which can support either hand or foot interaction with the smartphone or head mounted device (HMD).
The computer vision interaction technology is implemented in our previous work, which provides a core support for gesture interaction by accurately detecting and tracking the hand or foot gesture.
The patients interact with the application using hand/foot gesture motion in the camera view.
The use of programming languages such as Java and C in Open Source Software (OSS) has been well studied.
However, many other popular languages such as XSL or XML have received minor attention.
In this paper, we discuss some trends in OSS development that we observed when considering multiple programming language evolution of OSS.
Based on the revision data of 22 OSS projects, we tracked the evolution of language usage and other artefacts such as documentation files, binaries and graphics files.
In these systems several different languages and artefact types including C/C++, Java, XML, XSL, Makefile, Groovy, HTML, Shell scripts, CSS, Graphics files, JavaScript, JSP, Ruby, Phyton, XQuery, OpenDocument files, PHP, etc. have been used.
We found that the amount of code written in different languages differs substantially.
Some of our findings can be summarized as follows: (1) JavaScript and CSS files most often co-evolve with XSL; (2) Most Java developers but only every second C/C++ developer work with XML; (3) and more generally, we observed a significant increase of usage of XML and XSL during recent years and found that Java or C are hardly ever the only language used by a developer.
In fact, a developer works with more than 5 different artefact types (or 4 different languages) in a project on average.
As the fundamental phrase of collecting and analyzing data, data integration is used in many applications, such as data cleaning, bioinformatics and pattern recognition.
In big data era, one of the major problems of data integration is to obtain the global schema of data sources since the global schema could be hardly derived from massive data sources directly.
In this paper, we attempt to solve such schema integration problem.
For different scenarios, we develop batch and incremental schema integration algorithms.
We consider the representation difference of attribute names in various data sources and propose ED Join and Semantic Join algorithms to integrate attributes with different representations.
Extensive experimental results demonstrate that the proposed algorithms could integrate schemas efficiently and effectively.
Thanks to recent advances in CNNs, solid improvements have been made in semantic segmentation of high resolution remote sensing imagery.
However, most of the previous works have not fully taken into account the specific difficulties that exist in remote sensing tasks.
One of such difficulties is that objects are small and crowded in remote sensing imagery.
To tackle with this challenging task we have proposed a novel architecture called local feature extraction (LFE) module attached on top of dilated front-end module.
The LFE module is based on our findings that aggressively increasing dilation factors fails to aggregate local features due to sparsity of the kernel, and detrimental to small objects.
The proposed LFE module solves this problem by aggregating local features with decreasing dilation factor.
We tested our network on three remote sensing datasets and acquired remarkably good results for all datasets especially for small objects.
This paper considers the problem of completing assemblies of passive objects in nonconvex environments, cluttered with convex obstacles of unknown position, shape and size that satisfy a specific separation assumption.
A differential drive robot equipped with a gripper and a LIDAR sensor, capable of perceiving its environment only locally, is used to position the passive objects in a desired configuration.
The method combines the virtues of a deliberative planner generating high-level, symbolic commands, with the formal guarantees of convergence and obstacle avoidance of a reactive planner that requires little onboard computation and is used online.
The validity of the proposed method is verified both with formal proofs and numerical simulations.
Reversible interactions model different scenarios, like biochemical systems and human as well as automatic negotiations.
We abstract interactions via multiparty sessions enriched with named checkpoints.
Computations can either go forward or roll back to some checkpoints, where possibly different choices may be taken.
In this way communications can be undone and different conversations may be tried.
Interactions are typed with global types, which control also rollbacks.
Typeability of session participants in agreement with global types ensures session fidelity and progress of reversible communications.
We study the computational power of deciding whether a given truth-table can be described by a circuit of a given size (the Minimum Circuit Size Problem, or MCSP for short), and of the variant denoted as MKTP where circuit size is replaced by a polynomially-related Kolmogorov measure.
All prior reductions from supposedly-intractable problems to MCSP / MKTP hinged on the power of MCSP / MKTP to distinguish random distributions from distributions produced by hardness-based pseudorandom generator constructions.
We develop a fundamentally different approach inspired by the well-known interactive proof system for the complement of Graph Isomorphism (GI).
It yields a randomized reduction with zero-sided error from GI to MKTP.
We generalize the result and show that GI can be replaced by any isomorphism problem for which the underlying group satisfies some elementary properties.
Instantiations include Linear Code Equivalence, Permutation Group Conjugacy, and Matrix Subspace Conjugacy.
Along the way we develop encodings of isomorphism classes that are efficiently decodable and achieve compression that is at or near the information-theoretic optimum; those encodings may be of independent interest.
Digital Rights Management (DRM) prevents end-users from using content in a manner inconsistent with its creator's wishes.
The license describing these use-conditions typically accompanies the content as its metadata.
A resulting problem is that the license and the content can get separated and lose track of each other.
The best metadata have two distinct qualities--they are created automatically without user intervention, and they are embedded within the data that they describe.
If licenses are also created and transported this way, data will always have licenses, and the licenses will be readily examinable.
When two or more datasets are combined, a new dataset, and with it a new license, are created.
This new license is a function of the licenses of the component datasets and any additional conditions that the person combining the datasets might want to impose.
Following the notion of a data-purpose algebra, we model this phenomenon by interpreting the transfer and conjunction of data as inducing an algebraic operation on the corresponding licenses.
When a dataset passes from one source to the next its license is transformed in a deterministic way, and similarly when datasets are combined the associated licenses are combined in a non-trivial algebraic manner.
Modern, computer-savvy, licensing regimes such as Creative Commons allow writing the license in a special kind of language called Creative Commons Rights Expression Language (ccREL). ccREL allows creating and embedding the license using RDFa utilizing XHTML.
This is preferred over DRM which includes the rights in a binary file completely opaque to nearly all users.
The colocation of metadata with human-visible XHTML makes the license more transparent.
In this paper we describe a methodology for creating and embedding licenses in geographic data utilizing ccREL, and programmatically examining embedded licenses in component data...
We present an algorithmic framework for learning multiple related tasks.
Our framework exploits a form of prior knowledge that relates the output spaces of these tasks.
We present PAC learning results that analyze the conditions under which such learning is possible.
We present results on learning a shallow parser and named-entity recognition system that exploits our framework, showing consistent improvements over baseline methods.
Contemporary Deep Neural Network (DNN) contains millions of synaptic connections with tens to hundreds of layers.
The large computation and memory requirements pose a challenge to the hardware design.
In this work, we leverage the intrinsic activation sparsity of DNN to substantially reduce the execution cycles and the energy consumption.
An end-to-end training algorithm is proposed to develop a lightweight run-time predictor for the output activation sparsity on the fly.
From our experimental results, the computation overhead of the prediction phase can be reduced to less than 5% of the original feedforward phase with negligible accuracy loss.
Furthermore, an energy-efficient hardware architecture, SparseNN, is proposed to exploit both the input and output sparsity.
SparseNN is a scalable architecture with distributed memories and processing elements connected through a dedicated on-chip network.
Compared with the state-of-the-art accelerators which only exploit the input sparsity, SparseNN can achieve a 10%-70% improvement in throughput and a power reduction of around 50%.
A heuristic procedure based on novel recursive formulation of sinusoid (RFS) and on regression with predictive least-squares (LS) enables to decompose both uniformly and nonuniformly sampled 1-d signals into a sparse set of sinusoids (SSS).
An optimal SSS is found by Levenberg-Marquardt (LM) optimization of RFS parameters of near-optimal sinusoids combined with common criteria for the estimation of the number of sinusoids embedded in noise.
The procedure estimates both the cardinality and the parameters of SSS.
The proposed algorithm enables to identify the RFS parameters of a sinusoid from a data sequence containing only a fraction of its cycle.
In extreme cases when the frequency of a sinusoid approaches zero the algorithm is able to detect a linear trend in data.
Also, an irregular sampling pattern enables the algorithm to correctly reconstruct the under-sampled sinusoid.
Parsimonious nature of the obtaining models opens the possibilities of using the proposed method in machine learning and in expert and intelligent systems needing analysis and simple representation of 1-d signals.
The properties of the proposed algorithm are evaluated on examples of irregularly sampled artificial signals in noise and are compared with high accuracy frequency estimation algorithms based on linear prediction (LP) approach, particularly with respect to Cramer-Rao Bound (CRB).
We consider problems originating in economics that may be solved automatically using mathematical software.
We present and make freely available a new benchmark set of such problems.
The problems have been shown to fall within the framework of non-linear real arithmetic, and so are in theory soluble via Quantifier Elimination (QE) technology as usually implemented in computer algebra systems.
Further, they all can be phrased in prenex normal form with only existential quantifiers and so are also admissible to those Satisfiability Module Theory (SMT) solvers that support the QF_NRA.
There is a great body of work considering QE and SMT application in science and engineering, but we demonstrate here that there is potential for this technology also in the social sciences.
This paper explores the use of adversarial examples in training speech recognition systems to increase robustness of deep neural network acoustic models.
During training, the fast gradient sign method is used to generate adversarial examples augmenting the original training data.
Different from conventional data augmentation based on data transformations, the examples are dynamically generated based on current acoustic model parameters.
We assess the impact of adversarial data augmentation in experiments on the Aurora-4 and CHiME-4 single-channel tasks, showing improved robustness against noise and channel variation.
Further improvement is obtained when combining adversarial examples with teacher/student training, leading to a 23% relative word error rate reduction on Aurora-4.
We explore multitask models for neural translation of speech, augmenting them in order to reflect two intuitive notions.
First, we introduce a model where the second task decoder receives information from the decoder of the first task, since higher-level intermediate representations should provide useful information.
Second, we apply regularization that encourages transitivity and invertibility.
We show that the application of these notions on jointly trained models improves performance on the tasks of low-resource speech transcription and translation.
It also leads to better performance when using attention information for word discovery over unsegmented input.
For the problem of multi-class linear classification and feature selection, we propose approximate message passing approaches to sparse multinomial logistic regression (MLR).
First, we propose two algorithms based on the Hybrid Generalized Approximate Message Passing (HyGAMP) framework: one finds the maximum a posteriori (MAP) linear classifier and the other finds an approximation of the test-error-rate minimizing linear classifier.
Then we design computationally simplified variants of these two algorithms.
Next, we detail methods to tune the hyperparameters of their assumed statistical models using Stein's unbiased risk estimate (SURE) and expectation-maximization (EM), respectively.
Finally, using both synthetic and real-world datasets, we demonstrate improved error-rate and runtime performance relative to existing state-of-the-art approaches to sparse MLR.
Peridynamics is a non-local generalization of continuum mechanics tailored to address discontinuous displacement fields arising in fracture mechanics.
As many non-local approaches, peridynamics requires considerable computing resources to solve practical problems.
Several implementations of peridynamics utilizing CUDA, OpenCL, and MPI were developed to address this important issue.
On modern supercomputers, asynchronous many task systems are emerging to address the new architecture of computational nodes.
This paper presents a peridynamics EMU nodal discretization implementation with the C++ Standard Library for Concurrency and Parallelism (HPX), an open source asynchronous many task run time system.
The code is designed for modular expandability, so as to simplify it to extend with new material models or discretizations.
The code is convergent for implicit time integration and recovers theoretical solutions.
Explicit time integration, convergence results are presented to showcase the agreement of results with theoretical claims in previous works.
Two benchmark tests on code scalability are applied demonstrating agreement between this code's scalability and theoretical estimations.
In the Internet-of-Things, the number of connected devices is expected to be extremely huge, i.e., more than a couple of ten billion.
It is however well-known that the security for the Internet-of-Things is still open problem.
In particular, it is difficult to certify the identification of connected devices and to prevent the illegal spoofing.
It is because the conventional security technologies have advanced for mainly protecting logical network and not for physical network like the Internet-of-Things.
In order to protect the Internet-of-Things with advanced security technologies, we propose a new concept (datachain layer) which is a well-designed combination of physical chip identification and blockchain.
With a proposed solution of the physical chip identification, the physical addresses of connected devices are uniquely connected to the logical addresses to be protected by blockchain.
Determining the programming language of a source code file has been considered in the research community; it has been shown that Machine Learning (ML) and Natural Language Processing (NLP) algorithms can be effective in identifying the programming language of source code files.
However, determining the programming language of a code snippet or a few lines of source code is still a challenging task.
Online forums such as Stack Overflow and code repositories such as GitHub contain a large number of code snippets.
In this paper, we describe Source Code Classification (SCC), a classifier that can identify the programming language of code snippets written in 21 different programming languages.
A Multinomial Naive Bayes (MNB) classifier is employed which is trained using Stack Overflow posts.
It is shown to achieve an accuracy of 75% which is higher than that with Programming Languages Identification (PLI a proprietary online classifier of snippets) whose accuracy is only 55.5%.
The average score for precision, recall and the F1 score with the proposed tool are 0.76, 0.75 and 0.75, respectively.
In addition, it can distinguish between code snippets from a family of programming languages such as C, C++ and C#, and can also identify the programming language version such as C# 3.0, C# 4.0 and C# 5.0.
The algorithm-to-hardware High-level synthesis (HLS) tools today are purported to produce hardware comparable in quality to handcrafted designs, particularly with user directive driven or domains specific HLS.
However, HLS tools are not readily equipped for when an application/algorithm needs to scale.
We present a (work-in-progress) semi-automated framework to map applications over a packet-switched network of modules (single FPGA) and then to seamlessly partition such a network over multiple FPGAs over quasi-serial links.
We illustrate the framework through three application case studies: LDPC Decoding, Particle Filter based Object Tracking, and Matrix Vector Multiplication over GF(2).
Starting with high-level representations of each case application, we first express them in an intermediate message passing formulation, a model of communicating processing elements.
Once the processing elements are identified, these are either handcrafted or realized using HLS.
The rest of the flow is automated where the processing elements are plugged on to a configurable network-on-chip (CONNECT) topology of choice, followed by partitioning the 'on-chip' links to work seamlessly across chips/FPGAs.
One significant challenge in cognitive radio networks is to design a framework in which the selfish secondary users are obliged to interact with each other truthfully.
Moreover, due to the vulnerability of these networks against jamming attacks, designing anti-jamming defense mechanisms is equally important.
%providing the security defense is also of great importance.
In this paper, we propose a truthful mechanism, robust against the jamming, for a dynamic stochastic cognitive radio network consisting of several selfish secondary users and a malicious user.
In this model, each secondary user participates in an auction and wish to use the unjammed spectrum, and the malicious user aims at jamming a channel by corrupting the communication link.
A truthful auction mechanism is designed among the secondary users.
Furthermore, a zero-sum game is formulated between the set of secondary users and the malicious user.
This joint problem is then cast as a randomized two-level auctions in which the first auction allocates the vacant channels, and then the second one assigns the remaining unallocated channels.
We have also changed this solution to a trustful distributed scheme.
Simulation results show that the distributed algorithm can achieve a performance that is close to the centralized algorithm, without the added overhead and complexity.
Modern multiprocessor system-on-chips (SoCs) integrate multiple heterogeneous cores to achieve high energy efficiency.
The power consumption of each core contributes to an increase in the temperature across the chip floorplan.
In turn, higher temperature increases the leakage power exponentially, and leads to a positive feedback with nonlinear dynamics.
This paper presents a power-temperature stability and safety analysis technique for multiprocessor systems.
This analysis reveals the conditions under which the power-temperature trajectory converges to a stable fixed point.
We also present a simple formula to compute the stable fixed point and maximum thermally-safe power consumption at runtime.
Hardware measurements on a state-of-the-art mobile processor show that our analytical formulation can predict the stable fixed point with an average error of 2.6%.
Hence, our approach can be used at runtime to ensure thermally safe operation and guard against thermal threats.
Facial attribute recognition is conventionally computed from a single image.
In practice, each subject may have multiple face images.
Taking the eye size as an example, it should not change, but it may have different estimation in multiple images, which would make a negative impact on face recognition.
Thus, how to compute these attributes corresponding to each subject rather than each single image is a profound work.
To address this question, we deploy deep training for facial attributes prediction, and we explore the inconsistency issue among the attributes computed from each single image.
Then, we develop two approaches to address the inconsistency issue.
Experimental results show that the proposed methods can handle facial attribute estimation on either multiple still images or video frames, and can correct the incorrectly annotated labels.
The experiments are conducted on two large public databases with annotations of facial attributes.
In this paper, we propose and investigate a new neural network architecture called Neural Random Access Machine.
It can manipulate and dereference pointers to an external variable-size random-access memory.
The model is trained from pure input-output examples using backpropagation.
We evaluate the new model on a number of simple algorithmic tasks whose solutions require pointer manipulation and dereferencing.
Our results show that the proposed model can learn to solve algorithmic tasks of such type and is capable of operating on simple data structures like linked-lists and binary trees.
For easier tasks, the learned solutions generalize to sequences of arbitrary length.
Moreover, memory access during inference can be done in a constant time under some assumptions.
Although traditionally used in the machine translation field, the encoder-decoder framework has been recently applied for the generation of video and image descriptions.
The combination of Convolutional and Recurrent Neural Networks in these models has proven to outperform the previous state of the art, obtaining more accurate video descriptions.
In this work we propose pushing further this model by introducing two contributions into the encoding stage.
First, producing richer image representations by combining object and location information from Convolutional Neural Networks and second, introducing Bidirectional Recurrent Neural Networks for capturing both forward and backward temporal relationships in the input frames.
Logic Programming is a Turing complete language.
As a consequence, designing algorithms that decide termination and non-termination of programs or decide inductive/coinductive soundness of formulae is a challenging task.
For example, the existing state-of-the-art algorithms can only semi-decide coinductive soundness of queries in logic programming for regular formulae.
Another, less famous, but equally fundamental and important undecidable property is productivity.
If a derivation is infinite and coinductively sound, we may ask whether the computed answer it determines actually computes an infinite formula.
If it does, the infinite computation is productive.
This intuition was first expressed under the name of computations at infinity in the 80s.
In modern days of the Internet and stream processing, its importance lies in connection to infinite data structure processing.
Recently, an algorithm was presented that semi-decides a weaker property -- of productivity of logic programs.
A logic program is productive if it can give rise to productive derivations.
In this paper we strengthen these recent results.
We propose a method that semi-decides productivity of individual derivations for regular formulae.
Thus we at last give an algorithmic counterpart to the notion of productivity of derivations in logic programming.
This is the first algorithmic solution to the problem since it was raised more than 30 years ago.
We also present an implementation of this algorithm.
Enabling fully automated testing of mobile applications has recently become an important topic of study for both researchers and practitioners.
A plethora of tools and approaches have been proposed to aid mobile developers both by augmenting manual testing practices and by automating various parts of the testing process.
However, current approaches for automated testing fall short in convincing developers about their benefits, leading to a majority of mobile testing being performed manually.
With the goal of helping researchers and practitioners - who design approaches supporting mobile testing - to understand developer's needs, we analyzed survey responses from 102 open source contributors to Android projects about their practices when performing testing.
The survey focused on questions regarding practices and preferences of developers/testers in-the-wild for (i) designing and generating test cases, (ii) automated testing practices, and (iii) perceptions of quality metrics such as code coverage for determining test quality.
Analyzing the information gleaned from this survey, we compile a body of knowledge to help guide researchers and professionals toward tailoring new automated testing approaches to the need of a diverse set of open source developers.
This document is the first part of the author's habilitation thesis (HDR), defended on June 4, 2018 at the University of Bordeaux.
Given the nature of this document, the contributions that involve the author have been emphasized; however, these four chapters were specifically written for distribution to a larger audience.
We hope they can serve as a broad introduction to the domain of highly dynamic networks, with a focus on temporal graph concepts and their interaction with distributed computing.
High triangle density -- the graph property stating that a constant fraction of two-hop paths belong to a triangle -- is a common signature of social networks.
This paper studies triangle-dense graphs from a structural perspective.
We prove constructively that significant portions of a triangle-dense graph are contained in a disjoint union of dense, radius 2 subgraphs.
This result quantifies the extent to which triangle-dense graphs resemble unions of cliques.
We also show that our algorithm recovers planted clusterings in approximation-stable k-median instances.
The behavior of heterogeneous multi-agent systems is studied when the coupling matrices are possibly all different and/or singular (that is, its rank is less than the system dimension).
Rank-deficient coupling allows exchange of limited state information, which is suitable for study of output coupling in multi-agent systems.
We present a coordinate change that transforms the heterogeneous multi-agent system into a singularly perturbed form.
The slow dynamics is still a reduced-order multi-agent system consisting of a weighted average of the vector fields of all agents, and some sub-dynamics of agents.
The weighted average is an emergent dynamics, which we call a blended dynamics.
By analyzing or synthesizing the blended dynamics, one can predict or design the behavior of heterogeneous multi-agent system when the coupling gain is sufficiently large.
For this result, stability of the blended dynamics is required.
Since stability of individual agent is not asked, stability of the blended dynamics is the outcome of trading stability among the agents.
It can be seen that, under stability of the blended dynamics, the initial conditions of individual agents are forgotten as time goes on, and thus, the behavior of the synthesized multi-agent system are initialization-free and suitable for plug-and-play operation.
As a showcase, we apply the proposed tool to two application problems; distributed state estimation for linear systems, and practical synchronization of heterogeneous Van der Pol oscillators (for which phase cohesiveness is achieved).
We also present underlying intuition for two more applications; estimation of the number of nodes in a network, and a problem of distributed optimization.
Brain mapping research in most neuroanatomical laboratories relies on conventional processing techniques, which often introduce histological artifacts such as tissue tears and tissue loss.
In this paper we present techniques and algorithms for automatic registration and 3D reconstruction of conventionally produced mouse brain slices in a standardized atlas space.
This is achieved first by constructing a virtual 3D mouse brain model from annotated slices of Allen Reference Atlas (ARA).
Virtual re-slicing of the reconstructed model generates ARA-based slice images corresponding to the microscopic images of histological brain sections.
These image pairs are aligned using a geometric approach through contour images.
Histological artifacts in the microscopic images are detected and removed using Constrained Delaunay Triangulation before performing global alignment.
Finally, non-linear registration is performed by solving Laplace's equation with Dirichlet boundary conditions.
Our methods provide significant improvements over previously reported registration techniques for the tested slices in 3D space, especially on slices with significant histological artifacts.
Further, as an application we count the number of neurons in various anatomical regions using a dataset of 51 microscopic slices from a single mouse brain.
This work represents a significant contribution to this subfield of neuroscience as it provides tools to neuroanatomist for analyzing and processing histological data.
In cellular networks, the locations of the radio access network (RAN) elements are determined mainly based on the long-term traffic behaviour.
However, when the random and hard-to-predict spatio-temporal distribution of the traffic (load,demand) does not fully match the fixed locations of the RAN elements (supply), some performance degradation becomes inevitable.
The concept of multi-tier cells (heterogeneous networks, HetNets) has been introduced in 4G networks to alleviate this mismatch.
However, as the traffic distribution deviates more and more from the long-term average, even the HetNet architecture will have difficulty in coping up with the erratic supply-demand mismatch, unless the RAN is grossly over-engineered (which is a financially non-viable solution).
In this article, we study the opportunistic utilization of low-altitude unmanned aerial platforms equipped with base stations (BSs), i.e., drone-BSs, in 5G networks.
In particular, we envisage a multi-tier drone-cell network complementing the terrestrial HetNets.
The variety of equipment, and non-rigid placement options allow utilizing multitier drone-cell networks to serve diversified demands.
Hence, drone-cells bring the supply to where the demand is, which sets new frontiers for the heterogeneity in 5G networks.
We investigate the advancements promised by drone-cells, and discuss the challenges associated with their operation and management.
We propose a drone-cell management framework (DMF) benefiting from the synergy among software defined networking (SDN), network functions virtualization (NFV), and cloud-computing.
We demonstrate DMF mechanisms via a case study, and numerically show that it can reduce the cost of utilizing drone-cells in multitenancy cellular networks.
Though the ability of human beings to deal with probabilities has been put into question, the assessment of rarity is a crucial competence underlying much of human decision-making and is pervasive in spontaneous narrative behaviour.
This paper proposes a new model of rarity and randomness assessment, designed to be cognitively plausible.
Intuitive randomness is defined as a function of structural complexity.
It is thus possible to assign probability to events without being obliged to consider the set of alternatives.
The model is tested on Lottery sequences and compared with subjects' preferences.
The steered response power phase transform (SRP-PHAT) is a beamformer method very attractive in acoustic localization applications due to its robustness in reverberant environments.
This paper presents a spatial grid design procedure, called the geometrically sampled grid (GSG), which aims at computing the spatial grid by taking into account the discrete sampling of time difference of arrival (TDOA) functions and the desired spatial resolution.
A new SRP-PHAT localization algorithm based on the GSG method is also introduced.
The proposed method exploits the intersections of the discrete hyperboloids representing the TDOA information domain of the sensor array, and projects the whole TDOA information on the space search grid.
The GSG method thus allows to design the sampled spatial grid which represents the best search grid for a given sensor array, it allows to perform a sensitivity analysis of the array and to characterize its spatial localization accuracy, and it may assist the system designer in the reconfiguration of the array.
Experimental results using both simulated data and real recordings show that the localization accuracy is substantially improved both for high and for low spatial resolution, and that it is closely related to the proposed power response sensitivity measure.
Small variance asymptotics is emerging as a useful technique for inference in large scale Bayesian non-parametric mixture models.
This paper analyses the online learning of robot manipulation tasks with Bayesian non-parametric mixture models under small variance asymptotics.
The analysis yields a scalable online sequence clustering (SOSC) algorithm that is non-parametric in the number of clusters and the subspace dimension of each cluster.
SOSC groups the new datapoint in its low dimensional subspace by online inference in a non-parametric mixture of probabilistic principal component analyzers (MPPCA) based on Dirichlet process, and captures the state transition and state duration information online in a hidden semi-Markov model (HSMM) based on hierarchical Dirichlet process.
A task-parameterized formulation of our approach autonomously adapts the model to changing environmental situations during manipulation.
We apply the algorithm in a teleoperation setting to recognize the intention of the operator and remotely adjust the movement of the robot using the learned model.
The generative model is used to synthesize both time-independent and time-dependent behaviours by relying on the principles of shared and autonomous control.
Experiments with the Baxter robot yield parsimonious clusters that adapt online with new demonstrations and assist the operator in performing remote manipulation tasks.
A binary tanglegram is a pair <S,T> of binary trees whose leaf sets are in one-to-one correspondence; matching leaves are connected by inter-tree edges.
For applications, for example in phylogenetics or software engineering, it is required that the individual trees are drawn crossing-free.
A natural optimization problem, denoted tanglegram layout problem, is thus to minimize the number of crossings between inter-tree edges.
The tanglegram layout problem is NP-hard and is currently considered both in application domains and theory.
In this paper we present an experimental comparison of a recursive algorithm of Buchin et al., our variant of their algorithm, the algorithm hierarchy sort of Holten and van Wijk, and an integer quadratic program that yields optimal solutions.
Even though it is unrealistic to expect citizens to pinpoint the policy implementation that they prefer from the set of alternatives, it is still possible to infer such information through an exercise of ranking the importance of policy objectives according to their opinion.
Assuming that the mapping between policy options and objective evaluations is a priori known (through models and simulations), this can be achieved either implicitly through appropriate analysis of social media content related to the policy objective in question or explicitly through the direct feedback provided in the frame of a game.
This document focuses on the presentation of a policy model, which reduces the policy to a multi-objective optimization problem and mitigates the shortcoming of the lack of social objective functions (public opinion models) with a black-box, games-for-crowds approach.
Music summarization allows for higher efficiency in processing, storage, and sharing of datasets.
Machine-oriented approaches, being agnostic to human consumption, optimize these aspects even further.
Such summaries have already been successfully validated in some MIR tasks.
We now generalize previous conclusions by evaluating the impact of generic summarization of music from a probabilistic perspective.
We estimate Gaussian distributions for original and summarized songs and compute their relative entropy, in order to measure information loss incurred by summarization.
Our results suggest that relative entropy is a good predictor of summarization performance in the context of tasks relying on a bag-of-features model.
Based on this observation, we further propose a straightforward yet expressive summarizer, which minimizes relative entropy with respect to the original song, that objectively outperforms previous methods and is better suited to avoid potential copyright issues.
We show how to combine Bayes nets and game theory to predict the behavior of hybrid systems involving both humans and automated components.
We call this novel framework "Semi Network-Form Games," and illustrate it by predicting aircraft pilot behavior in potential near mid-air collisions.
At present, at the beginning of such potential collisions, a collision avoidance system in the aircraft cockpit advises the pilots what to do to avoid the collision.
However studies of mid-air encounters have found wide variability in pilot responses to avoidance system advisories.
In particular, pilots rarely perfectly execute the recommended maneuvers, despite the fact that the collision avoidance system's effectiveness relies on their doing so.
Rather pilots decide their actions based on all information available to them (advisory, instrument readings, visual observations).
We show how to build this aspect into a semi network-form game model of the encounter and then present computational simulations of the resultant model.
Cyclic redundancy check (CRC) codes check if a codeword is correctly received.
This paper presents an algorithm to design CRC codes that are optimized for the code-specific error behavior of a specified feedforward convolutional code.
The algorithm utilizes two distinct approaches to computing undetected error probability of a CRC code used with a specific convolutional code.
The first approach enumerates the error patterns of the convolutional code and tests if each of them is detectable.
The second approach reduces complexity significantly by exploiting the equivalence of the undetected error probability to the frame error rate of an equivalent catastrophic convolutional code.
The error events of the equivalent convolutional code are exactly the undetectable errors for the original concatenation of CRC and convolutional codes.
This simplifies the computation because error patterns do not need to be individually checked for detectability.
As an example, we optimize CRC codes for a commonly used 64-state convolutional code for information length k=1024 demonstrating significant reduction in undetected error probability compared to the existing CRC codes with the same degrees.
For a fixed target undetected error probability, the optimized CRC codes typically require 2 fewer bits.
With the growing usage of Bitcoin and other cryptocurrencies, many scalability challenges have emerged.
A promising scaling solution, exemplified by the Lightning Network, uses a network of bidirectional payment channels that allows fast transactions between two parties.
However, routing payments on these networks efficiently is non-trivial, since payments require finding paths with sufficient funds, and channels can become unidirectional over time blocking further transactions through them.
Today's payment channel networks exacerbate these problems by attempting to deliver all payments atomically.
In this paper, we present the Spider network, a new packet-switched architecture for payment channel networks.
Spider splits payments into transaction units and transmits them over time across different paths.
Spider uses congestion control, payment scheduling, and imbalance-aware routing to optimize delivery of payments.
Our results show that Spider improves the volume and number of successful payments on the network by 10-45% and 5-40% respectively compared to state-of-the-art approaches.
Shifting to a lexicalized grammar reduces the number of parsing errors and improves application results.
However, such an operation affects a syntactic parser in all its aspects.
One of our research objectives is to design a realistic model for grammar lexicalization.
We carried out experiments for which we used a grammar with a very simple content and formalism, and a very informative syntactic lexicon, the lexicon-grammar of French elaborated by the LADL.
Lexicalization was performed by applying the parameterized-graph approach.
Our results tend to show that most information in the lexicon-grammar can be transferred into a grammar and exploited successfully for the syntactic parsing of sentences.
One approach to achieving artificial general intelligence (AGI) is through the emergence of complex structures and dynamic properties arising from decentralized networks of interacting artificial intelligence (AI) agents.
Understanding the principles of consensus in societies and finding ways to make consensus more reliable becomes critically important as connectivity and interaction speed increase in modern distributed systems of hybrid collective intelligences, which include both humans and computer systems.
We propose a new form of reputation-based consensus with greater resistance to reputation gaming than current systems have.
We discuss options for its implementation, and provide initial practical results.
Prior investigations have offered contrasting results on a troubling question: whether the alphabetical ordering of bylines confers citation advantages on those authors whose surnames put them first in the list.
The previous studies analyzed the surname effect at publication level, i.e. whether papers with the first author early in the alphabet trigger more citations than papers with a first author late in the alphabet.
We adopt instead a different approach, by analyzing the surname effect on citability at the individual level, i.e. whether authors with alphabetically earlier surnames result as being more cited.
Examining the question at both the overall and discipline levels, the analysis finds no evidence whatsoever that alphabetically earlier surnames gain advantage.
The same lack of evidence occurs for the subpopulation of scientists with very high publication rates, where alphabetical advantage might gain more ground.
The field of observation consists of 14,467 scientists in the sciences.
A plain well-trained deep learning model often does not have the ability to learn new knowledge without forgetting the previously learned knowledge, which is known as catastrophic forgetting.
Here we propose a novel method, SupportNet, to efficiently and effectively solve the catastrophic forgetting problem in the class incremental learning scenario.
SupportNet combines the strength of deep learning and support vector machine (SVM), where SVM is used to identify the support data from the old data, which are fed to the deep learning model together with the new data for further training so that the model can review the essential information of the old data when learning the new information.
Two powerful consolidation regularizers are applied to stabilize the learned representation and ensure the robustness of the learned model.
We validate our method with comprehensive experiments on various tasks, which show that SupportNet drastically outperforms the state-of-the-art incremental learning methods and even reaches similar performance as the deep learning model trained from scratch on both old and new data.
Our program is accessible at: https://github.com/lykaust15/SupportNet
In this paper, we consider the network power minimization problem in a downlink cloud radio access network (C-RAN), taking into account the power consumed at the baseband unit (BBU) for computation and the power consumed at the remote radio heads and fronthaul links for transmission.
The power minimization problem for transmission is a fast time-scale issue whereas the power minimization problem for computation is a slow time-scale issue.
Therefore, the joint network power minimization problem is a mixed time-scale problem.
To tackle the time-scale challenge, we introduce large system analysis to turn the original fast time-scale problem into a slow time-scale one that only depends on the statistical channel information.
In addition, we propose a bound improving branch-and-bound algorithm and a combinational algorithm to find the optimal and suboptimal solutions to the power minimization problem for computation, respectively, and propose an iterative coordinate descent algorithm to find the solutions to the power minimization problem for transmission.
Finally, a distributed algorithm based on hierarchical decomposition is proposed to solve the joint network power minimization problem.
In summary, this work provides a framework to investigate how execution efficiency and computing capability at BBU as well as delay constraint of tasks can affect the network power minimization problem in C-RANs.
Data mining techniques have been widely used to mine knowledgeable information from medical data bases.
In data mining classification is a supervised learning that can be used to design models describing important data classes, where class attribute is involved in the construction of the classifier.
Nearest neighbor (KNN) is very simple, most popular, highly efficient and effective algorithm for pattern recognition.KNN is a straight forward classifier, where samples are classified based on the class of their nearest neighbor.
Medical data bases are high volume in nature.
If the data set contains redundant and irrelevant attributes, classification may produce less accurate result.
Heart disease is the leading cause of death in INDIA.
In Andhra Pradesh heart disease was the leading cause of mortality accounting for 32%of all deaths, a rate as high as Canada (35%) and USA.Hence there is a need to define a decision support system that helps clinicians decide to take precautionary steps.
In this paper we propose a new algorithm which combines KNN with genetic algorithm for effective classification.
Genetic algorithms perform global search in complex large and multimodal landscapes and provide optimal solution.
Experimental results shows that our algorithm enhance the accuracy in diagnosis of heart disease.
In this paper, we propose a probabilistic parsing model, which defines a proper conditional probability distribution over non-projective dependency trees for a given sentence, using neural representations as inputs.
The neural network architecture is based on bi-directional LSTM-CNNs which benefits from both word- and character-level representations automatically, by using combination of bidirectional LSTM and CNN.
On top of the neural network, we introduce a probabilistic structured layer, defining a conditional log-linear model over non-projective trees.
We evaluate our model on 17 different datasets, across 14 different languages.
By exploiting Kirchhoff's Matrix-Tree Theorem (Tutte, 1984), the partition functions and marginals can be computed efficiently, leading to a straight-forward end-to-end model training procedure via back-propagation.
Our parser achieves state-of-the-art parsing performance on nine datasets.
A point of a digital space is called simple if it can be deleted from the space without altering topology.
This paper introduces the notion simple set of points of a digital space.
The definition is based on contractible spaces and contractible transformations.
A set of points in a digital space is called simple if it can be contracted to a point without changing topology of the space.
It is shown that contracting a simple set of points does not change the homotopy type of a digital space, and the number of points in a digital space without simple points can be reduces by contracting simple sets.
Using the process of contracting, we can substantially compress a digital space while preserving the topology.
The paper proposes a method for thinning a digital space which shows that this approach can contribute to computer science such as medical imaging, computer graphics and pattern analysis.
The concept of h-index has been proposed to easily assess a researcher's performance with a single number.
However, by using only this number, we lose significant information about the distribution of citations per article in an author's publication list.
In this article, we study an author's citation curve and we define two new areas related to this curve.
We call these "penalty areas", since the greater they are, the more an author's performance is penalized.
We exploit these areas to establish new indices, namely PI and XPI, aiming at categorizing researchers in two distinct categories: "influentials" and "mass producers"; the former category produces articles which are (almost all) with high impact, and the latter category produces a lot of articles with moderate or no impact at all.
Using data from Microsoft Academic Service, we evaluate the merits mainly of PI as a useful tool for scientometric studies.
We establish its effectiveness into separating the scientists into influentials and mass producers; we demonstrate its robustness against self-citations, and its uncorrelation to traditional indices.
Finally, we apply PI to rank prominent scientists in the areas of databases, networks and multimedia, exhibiting the strength of the index in fulfilling its design goal.
libact is a Python package designed to make active learning easier for general users.
The package not only implements several popular active learning strategies, but also features the active-learning-by-learning meta-algorithm that assists the users to automatically select the best strategy on the fly.
Furthermore, the package provides a unified interface for implementing more strategies, models and application-specific labelers.
The package is open-source on Github, and can be easily installed from Python Package Index repository.
Disparity estimation is a difficult problem in stereo vision because the correspondence technique fails in images with textureless and repetitive regions.
Recent body of work using deep convolutional neural networks (CNN) overcomes this problem with semantics.
Most CNN implementations use an autoencoder method; stereo images are encoded, merged and finally decoded to predict the disparity map.
In this paper, we present a CNN implementation inspired by dense networks to reduce the number of parameters.
Furthermore, our approach takes into account semantic reasoning in disparity estimation.
Our proposed network, called DenseMapNet, is compact, fast and can be trained end-to-end.
DenseMapNet requires 290k parameters only and runs at 30Hz or faster on color stereo images in full resolution.
Experimental results show that DenseMapNet accuracy is comparable with other significantly bigger CNN-based methods.
We present a new back propagation based training algorithm for discrete-time spiking neural networks (SNN).
Inspired by recent deep learning algorithms on binarized neural networks, binary activation with a straight-through gradient estimator is used to model the leaky integrate-fire spiking neuron, overcoming the difficulty in training SNNs using back propagation.
Two SNN training algorithms are proposed: (1) SNN with discontinuous integration, which is suitable for rate-coded input spikes, and (2) SNN with continuous integration, which is more general and can handle input spikes with temporal information.
Neuromorphic hardware designed in 40nm CMOS exploits the spike sparsity and demonstrates high classification accuracy (>98% on MNIST) and low energy (48.4-773 nJ/image).
This paper describes the Pressure Ulcers Online Website, which is a first step solution towards a new and innovative platform for helping people to detect, understand and manage pressure ulcers.
It outlines the reasons why the project has been developed and provides a central point of contact for pressure ulcer analysis and ongoing research.
Using state-of-the-art technologies in convolutional neural networks and transfer learning along with end-to-end web technologies, this platform allows pressure ulcers to be analysed and findings to be reported.
As the system evolves through collaborative partnerships, future versions will provide decision support functions to describe the complex characteristics of pressure ulcers along with information on wound care across multiple user boundaries.
This project is therefore intended to raise awareness and support for people suffering with or providing care for pressure ulcers.
Despite being a relatively new communication technology, Low-Power Wide Area Networks (LPWANs) have shown their suitability to empower a major part of Internet of Things applications.
Nonetheless, most LPWAN solutions are built on star topology (or single-hop) networks, often causing lifetime shortening in stations located far from the gateway.
In this respect, recent studies show that multi-hop routing for uplink communications can reduce LPWANs' energy consumption significantly.
However, it is a troublesome task to identify such energetically optimal routings through trial-and-error brute-force approaches because of time and, especially, energy consumption constraints.
In this work we show the benefits of facing this exploration/exploitation problem by running centralized variations of the multi-arm bandit's epsilon-greedy, a well-known online decision-making method that combines best known action selection and knowledge expansion.
Important energy savings are achieved when proper randomness parameters are set, which are often improved when conveniently applying similarity, a concept introduced in this work that allows harnessing the gathered knowledge by sporadically selecting unexplored routing combinations akin to the best known one.
Frequent itemset mining leads to the discovery of associations and correlations among items in large transactional databases.
Apriori is a classical frequent itemset mining algorithm, which employs iterative passes over database combining with generation of candidate itemsets based on frequent itemsets found at the previous iteration, and pruning of clearly infrequent itemsets.
The Dynamic Itemset Counting (DIC) algorithm is a variation of Apriori, which tries to reduce the number of passes made over a transactional database while keeping the number of itemsets counted in a pass relatively low.
In this paper, we address the problem of accelerating DIC on the Intel Xeon Phi many-core system for the case when the transactional database fits in main memory.
Intel Xeon Phi provides a large number of small compute cores with vector processing units.
The paper presents a parallel implementation of DIC based on OpenMP technology and thread-level parallelism.
We exploit the bit-based internal layout for transactions and itemsets.
This technique reduces the memory space for storing the transactional database, simplifies the support count via logical bitwise operation, and allows for vectorization of such a step.
Experimental evaluation on the platforms of the Intel Xeon CPU and the Intel Xeon Phi coprocessor with large synthetic and real databases showed good performance and scalability of the proposed algorithm.
This paper considers a cellular system with a full-duplex base station and half-duplex users.
The base station can activate one user in uplink or downlink (half-duplex mode), or two different users one in each direction simultaneously (full-duplex mode).
Simultaneous transmissions in uplink and downlink causes self-interference at the base station and uplink-to-downlink interference at the downlink user.
Although uplink-to-downlink interference is typically treated as noise, it is shown that successive interference decoding and cancellation (SIC mode) can lead to significant improvement in network utility, especially when user distribution is concentrated around a few hotspots.
The proposed temporal fair user scheduling algorithm and corresponding power optimization utilizes full-duplex and SIC modes as well as half-duplex transmissions based on their impact on network utility.
Simulation results reveal that the proposed strategy can achieve up to 95% average cell throughput improvement in typical indoor scenarios with respect to a conventional network in which the base station is half-duplex.
In this paper, we give a distributed joint source channel coding scheme for arbitrary correlated sources for arbitrary point in the Slepian-Wolf rate region, and arbitrary link capacities using LDPC codes.
We consider the Slepian-Wolf setting of two sources and one destination, with one of the sources derived from the other source by some correlation model known at the decoder.
Distributed encoding and separate decoding is used for the two sources.
We also give a distributed source coding scheme when the source correlation has memory to achieve any point in the Slepian-Wolf rate achievable region.
In this setting, we perform separate encoding but joint decoding.
Co-localization is the problem of localizing objects of the same class using only the set of images that contain them.
This is a challenging task because the object detector must be built without negative examples that can lead to more informative supervision signals.
The main idea of our method is to cluster the feature space of a generically pre-trained CNN, to find a set of CNN features that are consistently and highly activated for an object category, which we call category-consistent CNN features.
Then, we propagate their combined activation map using superpixel geodesic distances for co-localization.
In our first set of experiments, we show that the proposed method achieves state-of-the-art performance on three related benchmarks: PASCAL 2007, PASCAL-2012, and the Object Discovery dataset.
We also show that our method is able to detect and localize truly unseen categories, on six held-out ImageNet categories with accuracy that is significantly higher than previous state-of-the-art.
Our intuitive approach achieves this success without any region proposals or object detectors, and can be based on a CNN that was pre-trained purely on image classification tasks without further fine-tuning.
Designing high performance channel assignment schemes to harness the potential of multi-radio multi-channel deployments in wireless mesh networks (WMNs) is an active research domain.
A pragmatic channel assignment approach strives to maximize network capacity by restraining the endemic interference and mitigating its adverse impact on network performance.
Interference prevalent in WMNs is multi-faceted, radio co-location interference (RCI) being a crucial aspect that is seldom addressed in research endeavors.
In this effort, we propose a set of intelligent channel assignment algorithms, which focus primarily on alleviating the RCI.
These graph theoretic schemes are structurally inspired by the spatio-statistical characteristics of interference.
We present the theoretical design foundations for each of the proposed algorithms, and demonstrate their potential to significantly enhance network capacity in comparison to some well-known existing schemes.
We also demonstrate the adverse impact of radio co- location interference on the network, and the efficacy of the proposed schemes in successfully mitigating it.
The experimental results to validate the proposed theoretical notions were obtained by running an exhaustive set of ns-3 simulations in IEEE 802.11g/n environments.
The Remote-PHY (R-PHY) modular cable network for Data over Cable Service Interface Specification (DOCSIS) service conducts the physical layer processing for the transmissions over the broadcast cable in a remote node.
In contrast, the cloud radio access network (CRAN) for Long-Term Evolution (LTE) cellular wireless services conducts all baseband physical layer processing in a central baseband unit and the remaining physical layer processing steps towards radio frequency (RF) transmission in remote nodes.
Both DOCSIS and LTE are based on Orthogonal Frequency Division Multiplexing (OFDM) physical layer processing.
We propose to unify cable and wireless cellular access networks by utilizing the hybrid fiber-coax (HFC) cable network infrastructure as fiber fronthaul network for cellular wireless services.
For efficient operation of such a unified access network, we propose a novel Remote-FFT (R-FFT) node that conducts the physical layer processing from the Fast-Fourier Transform (FFT) module towards the RF transmission, whereby DOCSIS and LTE share a common FFT module.
The frequency domain in-phase and quadrature (I/Q) symbols for both DOCSIS and LTE are transmitted over the fiber between remote node and cable headend, where the remaining physical layer processing is conducted.
We further propose to cache repetitive quadrature amplitude modulation (QAM) symbols in the R-FFT node to reduce the fronthaul bitrate requirements and enable statistical multiplexing.
We evaluate the fronthaul bitrate reductions achieved by R-FFT node caching, the fronthaul transmission bitrates arising from the unified DOCSIS and LTE service, and illustrate the delay implications of moving part of the cable R-PHY remote node physical layer processing to the headend.
Automated brain lesions detection is an important and very challenging clinical diagnostic task because the lesions have different sizes, shapes, contrasts, and locations.
Deep Learning recently has shown promising progress in many application fields, which motivates us to apply this technology for such important problem.
In this paper, we propose a novel and end-to-end trainable approach for brain lesions classification and detection by using deep Convolutional Neural Network (CNN).
In order to investigate the applicability, we applied our approach on several brain diseases including high and low-grade glioma tumor, ischemic stroke, Alzheimer diseases, by which the brain Magnetic Resonance Images (MRI) have been applied as an input for the analysis.
We proposed a new operating unit which receives features from several projections of a subset units of the bottom layer and computes a normalized l2-norm for next layer.
We evaluated the proposed approach on two different CNN architectures and number of popular benchmark datasets.
The experimental results demonstrate the superior ability of the proposed approach.
Fixed-point optimization of deep neural networks plays an important role in hardware based design and low-power implementations.
Many deep neural networks show fairly good performance even with 2- or 3-bit precision when quantized weights are fine-tuned by retraining.
We propose an improved fixedpoint optimization algorithm that estimates the quantization step size dynamically during the retraining.
In addition, a gradual quantization scheme is also tested, which sequentially applies fixed-point optimizations from high- to low-precision.
The experiments are conducted for feed-forward deep neural networks (FFDNNs), convolutional neural networks (CNNs), and recurrent neural networks (RNNs).
Avionics is one kind of domain where prevention prevails.
Nonetheless fails occur.
Sometimes due to pilot misreacting, flooded in information.
Sometimes information itself would be better verified than trusted.
To avoid some kind of failure, it has been thought to add,in midst of the ARINC664 aircraft data network, a new kind of monitoring.
During the past years, psychological diseases related to unhealthy work environments, such as burnouts, have drawn more and more public attention.
One of the known causes of these affective problems is time pressure.
In order to form a theoretical background for time pressure detection in software repositories, this paper combines interdisciplinary knowledge by analyzing 1270 papers found on Scopus database and containing terms related to time pressure.
By clustering those papers based on their abstract, we show that time pressure has been widely studied across different fields, but relatively little in software engineering.
From a literature review of the most relevant papers, we infer a list of testable hypotheses that we want to verify in future studies in order to assess the impact of time pressures on software developers mental health.
Given the increasing number of devices that is going to get connected to wireless networks with the advent of Internet of Things, spectrum scarcity will present a major challenge.
Application of opportunistic spectrum access mechanisms to IoT networks will become increasingly important to solve this.
In this paper, we present a cognitive radio network architecture which uses multi-stage online learning techniques for spectrum assignment to devices, with the aim of improving the throughput and energy efficiency of the IoT devices.
In the first stage, we use an AI technique to learn the quality of a user-channel pairing.
The next stage utilizes a non-parametric Bayesian learning algorithm to estimate the Primary User OFF time in each channel.
The third stage augments the Bayesian learner with implicit exploration to accelerate the learning procedure.
The proposed method leads to significant improvement in throughput and energy efficiency of the IoT devices while keeping the interference to the primary users minimal.
We provide comprehensive empirical validation of the method with other learning based approaches.
This paper provides a methodology to study the PHY layer vulnerability of wireless protocols in hostile radio environments.
Our approach is based on testing the vulnerabilities of a system by analyzing the individual subsystems.
By targeting an individual subsystem or a combination of subsystems at a time, we can infer the weakest part and revise it to improve the overall system performance.
We apply our methodology to 4G LTE downlink by considering each control channel as a subsystem.
We also develop open-source software enabling research and education using software-defined radios.
We present experimental results with open-source LTE systems and shows how the different subsystems behave under targeted interference.
The analysis for the LTE downlink shows that the synchronization signals (PSS/SSS) are very resilient to interference, whereas the downlink pilots or Cell-Specific Reference signals (CRS) are the most susceptible to a synchronized protocol-aware interferer.
We also analyze the severity of control channel attacks for different LTE configurations.
Our methodology and tools allow rapid evaluation of the PHY layer reliability in harsh signaling environments, which is an asset to improve current standards and develop new robust wireless protocols.
This paper discusses the controllability problem of complex networks.
It is shown that almost any weighted complex network with noise on the strength of communication links is controllable in the sense of Kalman controllability.
The concept of almost controllability is elaborated by both theoretical discussions and experimental verifications.
In this paper, efficient resource allocation for the uplink transmission of wireless powered IoT networks is investigated.
We adopt LoRa technology as an example in the IoT network, but this work is still suitable for other communication technologies.
Allocating limited resources, like spectrum and energy resources, among a massive number of users faces critical challenges.
We consider grouping wireless powered IoT users into available channels first and then investigate power allocation for users grouped in the same channel to improve the network throughput.
Specifically, the user grouping problem is formulated as a many to one matching game.
It is achieved by considering IoT users and channels as selfish players which belong to two disjoint sets.
Both selfish players focus on maximizing their own utilities.
Then we propose an efficient channel allocation algorithm (ECAA) with low complexity for user grouping.
Additionally, a Markov Decision Process (MDP) is used to model unpredictable energy arrival and channel conditions uncertainty at each user, and a power allocation algorithm is proposed to maximize the accumulative network throughput over a finite-horizon of time slots.
By doing so, we can distribute the channel access and dynamic power allocation local to IoT users.
Numerical results demonstrate that our proposed ECAA algorithm achieves near-optimal performance and is superior to random channel assignment, but has much lower computational complexity.
Moreover, simulations show that the distributed power allocation policy for each user is obtained with better performance than a centralized offline scheme.
The CFR+ algorithm for solving imperfect information games is a variant of the popular CFR algorithm, with faster empirical performance on a range of problems.
It was introduced with a theoretical upper bound on solution error, but subsequent work showed an error in one step of the proof.
We provide updated proofs to recover the original bound.
The Internet of Things (IoT) represents a comprehensive environment that consists of a large number of smart devices interconnecting heterogeneous physical objects to the Internet.
Many domains such as logistics, manufacturing, agriculture, urban computing, home automation, ambient assisted living and various ubiquitous computing applications have utilised IoT technologies.
Meanwhile, Business Process Management Systems (BPMS) have become a successful and efficient solution for coordinated management and optimised utilisation of resources/entities.
However, past BPMS have not considered many issues they will face in managing large scale connected heterogeneous IoT entities.
Without fully understanding the behaviour, capability and state of the IoT entities, the BPMS can fail to manage the IoT integrated information systems.
In this paper, we analyse existing BPMS for IoT and identify the limitations and their drawbacks based on Mobile Cloud Computing perspective.
Later, we discuss a number of open challenges in BPMS for IoT.
Deep learning hyper-parameter optimization is a tough task.
Finding an appropriate network configuration is a key to success, however most of the times this labor is roughly done.
In this work we introduce a novel library to tackle this problem, the Deep Learning Optimization Library: DLOPT.
We briefly describe its architecture and present a set of use examples.
This is an open source project developed under the GNU GPL v3 license and it is freely available at https://github.com/acamero/dlopt
This is the preprint version of our paper on 2015 International Conference on Virtual Rehabilitation (ICVR2015).
The purpose of this work is designing and implementing a rehabilitation software for dysphonic patients.
Constant training is a key factor for this type of therapy.
The patient can play the game as well as conduct the voice training simultaneously guided by therapists at clinic or exercise independently at home.
The voice information can be recorded and extracted for evaluating the long-time rehabilitation progress.
Data management has always been a multi-domain problem even in the simplest cases.
It involves, quality of service, security, resource management, cost management, incident identification, disaster avoidance and/or recovery, as well as many other concerns.
In our case, this situation gets ever more complicated because of the divergent nature of a cloud federation like BASMATI.
In this federation, the BASMATI Unified Data Management Framework (BUDaMaF), tries to create an automated uniform way of managing all the data transactions, as well as the data stores themselves, in a polyglot multi-cloud, consisting of a plethora of different machines and data store systems.
Classification of social media data is an important approach in understanding user behavior on the Web.
Although information on social media can be of different modalities such as texts, images, audio or videos, traditional approaches in classification usually leverage only one prominent modality.
Techniques that are able to leverage multiple modalities are often complex and susceptible to the absence of some modalities.
In this paper, we present simple models that combine information from different modalities to classify social media content and are able to handle the above problems with existing techniques.
Our models combine information from different modalities using a pooling layer and an auxiliary learning task is used to learn a common feature space.
We demonstrate the performance of our models and their robustness to the missing of some modalities in the emotion classification domain.
Our approaches, although being simple, can not only achieve significantly higher accuracies than traditional fusion approaches but also have comparable results when only one modality is available.
Distributed parameter estimation for large-scale systems is an active research problem.
The goal is to derive a distributed algorithm in which each agent obtains a local estimate of its own subset of the global parameter vector, based on local measurements as well as information received from its neighbours.
A recent algorithm has been proposed, which yields the optimal solution (i.e., the one that would be obtained using a centralized method) in finite time, provided the communication network forms an acyclic graph.
If instead, the graph is cyclic, the only available alternative algorithm, which is based on iterative matrix inversion, achieving the optimal solution, does so asymptotically.
However, it is also known that, in the cyclic case, the algorithm designed for acyclic graphs produces a solution which, although non optimal, is highly accurate.
In this paper we do a theoretical study of the accuracy of this algorithm, in communication networks forming cyclic graphs.
To this end, we provide bounds for the sub-optimality of the estimation error and the estimation error covariance, for a class of systems whose topological sparsity and signal-to-noise ratio satisfy certain condition.
Our results show that, at each node, the accuracy improves exponentially with the so-called loop-free depth.
Also, although the algorithm no longer converges in finite time in the case of cyclic graphs, simulation results show that the convergence is significantly faster than that of methods based on iterative matrix inversion.
Our results suggest that, depending on the loop-free depth, the studied algorithm may be the preferred option even in applications with cyclic communication graphs.
Cyber data attacks are the worst-case interacting bad data to power system state estimation and cannot be detected by existing bad data detectors.
In this paper, we for the first time analyze the likelihood of cyber data attacks by characterizing the actions of a malicious intruder.
We propose to use Markov decision process to model an intruder's strategy, where the objective is to maximize the cumulative reward across time.
Linear programming method is employed to find the optimal attack policy from the intruder's perspective.
Numerical experiments are conducted to study the intruder's attack strategy in test power systems.
Generalized linear models (GLMs) arise in high-dimensional machine learning, statistics, communications and signal processing.
In this paper we analyze GLMs when the data matrix is random, as relevant in problems such as compressed sensing, error-correcting codes or benchmark models in neural networks.
We evaluate the mutual information (or "free entropy") from which we deduce the Bayes-optimal estimation and generalization errors.
Our analysis applies to the high-dimensional limit where both the number of samples and the dimension are large and their ratio is fixed.
Non-rigorous predictions for the optimal errors existed for special cases of GLMs, e.g. for the perceptron, in the field of statistical physics based on the so-called replica method.
Our present paper rigorously establishes those decades old conjectures and brings forward their algorithmic interpretation in terms of performance of the generalized approximate message-passing algorithm.
Furthermore, we tightly characterize, for many learning problems, regions of parameters for which this algorithm achieves the optimal performance, and locate the associated sharp phase transitions separating learnable and non-learnable regions.
We believe that this random version of GLMs can serve as a challenging benchmark for multi-purpose algorithms.
This paper is divided in two parts that can be read independently: The first part (main part) presents the model and main results, discusses some applications and sketches the main ideas of the proof.
The second part (supplementary informations) is much more detailed and provides more examples as well as all the proofs.
Did the demise of the Soviet Union in 1991 influence the scientific performance of the researchers in Eastern European countries?
Did this historical event affect international collaboration by researchers from the Eastern European countries with those of Western countries?
Did it also change international collaboration among researchers from the Eastern European countries?
Trying to answer these questions, this study aims to shed light on international collaboration by researchers from the Eastern European countries (Russia, Ukraine, Belarus, Moldova, Bulgaria, the Czech Republic, Hungary, Poland, Romania and Slovakia).
The number of publications and normalized citation impact values are compared for these countries based on InCites (Thomson Reuters), from 1981 up to 2011.
The international collaboration by researchers affiliated to institutions in Eastern European countries at the time points of 1990, 2000 and 2011 was studied with the help of Pajek and VOSviewer software, based on data from the Science Citation Index (Thomson Reuters).
Our results show that the breakdown of the communist regime did not lead, on average, to a huge improvement in the publication performance of the Eastern European countries and that the increase in international co-authorship relations by the researchers affiliated to institutions in these countries was smaller than expected.
Most of the Eastern European countries are still subject to changes and are still awaiting their boost in scientific development.
In many image processing applications, such as segmentation and classification, the selection of robust features descriptors is crucial to improve the discrimination capabilities in real world scenarios.
In particular, it is well known that image textures constitute power visual cues for feature extraction and classification.
In the past few years the local binary pattern (LBP) approach, a texture descriptor method proposed by Ojala et al., has gained increased acceptance due to its computational simplicity and more importantly for encoding a powerful signature for describing textures.
However, the original algorithm presents some limitations such as noise sensitivity and its lack of rotational invariance which have led to many proposals or extensions in order to overcome such limitations.
In this paper we performed a quantitative study of the Ojala's original LBP proposal together with other recently proposed LBP extensions in the presence of rotational, illumination and noisy changes.
In the experiments we have considered two different databases: Brodatz and CUReT for different sizes of LBP masks.
Experimental results demonstrated the effectiveness and robustness of the described texture descriptors for images that are subjected to geometric or radiometric changes.
The paper deals with the modelling and control of a double winded induction generator.
The controlled process is an induction generator with distinct excitation winding.
At the generator terminal is connected a load (electrical consumer).
There are presented the results obtained by using a minimum variance adaptive control system.
The main goal of the control structure is to keep the generator output (terminal voltage) constant by controlling the excitation voltage from the distinct winding.
The study cases in the paper are for the validation of the reduced order model of induction generator (5th order model) used only to design the adaptive controller.
There is also validated the control structure.
There were considered variations of the mechanical torque.
Deep neural networks have achieved increasingly accurate results on a wide variety of complex tasks.
However, much of this improvement is due to the growing use and availability of computational resources (e.g use of GPUs, more layers, more parameters, etc).
Most state-of-the-art deep networks, despite performing well, over-parameterize approximate functions and take a significant amount of time to train.
With increased focus on deploying deep neural networks on resource constrained devices like smart phones, there has been a push to evaluate why these models are so resource hungry and how they can be made more efficient.
This work evaluates and compares three distinct methods for deep model compression and acceleration: weight pruning, low rank factorization, and knowledge distillation.
Comparisons on VGG nets trained on CIFAR10 show that each of the models on their own are effective, but that the true power lies in combining them.
We show that by combining pruning and knowledge distillation methods we can create a compressed network 85 times smaller than the original, all while retaining 96% of the original model's accuracy.
Many network optimization problems can be formulated as stochastic network design problems in which edges are present or absent stochastically.
Furthermore, protective actions can guarantee that edges will remain present.
We consider the problem of finding the optimal protection strategy under a budget limit in order to maximize some connectivity measurements of the network.
Previous approaches rely on the assumption that edges are independent.
In this paper, we consider a more realistic setting where multiple edges are not independent due to natural disasters or regional events that make the states of multiple edges stochastically correlated.
We use Markov Random Fields to model the correlation and define a new stochastic network design framework.
We provide a novel algorithm based on Sample Average Approximation (SAA) coupled with a Gibbs or XOR sampler.
The experimental results on real road network data show that the policies produced by SAA with the XOR sampler have higher quality and lower variance compared to SAA with Gibbs sampler.
In this paper, we report on our efforts for using Deep Learning for classifying artifacts and their features in digital visuals as a part of the Neoclassica framework.
It was conceived to provide scholars with new methods for analyzing and classifying artifacts and aesthetic forms from the era of Classicism.
The framework accommodates both traditional knowledge representation as a formal ontology and data-driven knowledge discovery, where cultural patterns will be identified by means of algorithms in statistical analysis and machine learning.
We created a Deep Learning approach trained on photographs to classify the objects inside these photographs.
In a next step, we will apply a different Deep Learning approach.
It is capable of locating multiple objects inside an image and classifying them with a high accuracy.
Fence instructions are fundamental primitives that ensure consistency in a weakly consistent shared memory multi-core processor.
The execution cost of these instructions is significant and adds a non-trivial overhead to parallel programs.
In a naive architecture implementation, we track the ordering constraints imposed by a fence by its entry in the reorder buffer and its execution overhead entails stalling the processor's pipeline until the store buffer is drained and also conservatively invalidating speculative loads.
These actions create a cascading effect of increased overhead on the execution of the following instructions in the program.
We find these actions to be overly restrictive and that they can be further relaxed thereby allowing aggressive optimizations.
The current work proposes a lightweight mechanism in which we assign ordering tags, called versions, to load and store instructions when they reside in the load/store queues and the write buffer.
The version assigned to a memory access allows us to fully exploit the relaxation allowed by the weak consistency model and restricts its execution in such a way that the ordering constraints by the model are satisfied.
We utilize the information captured through the assigned versions to reduce stalls caused by waiting for the store buffer to drain and to avoid unnecessary squashing of speculative loads, thereby minimizing the re-execution penalty.
This method is particularly effective for the release consistency model that employs uni-directional fence instructions.
We show that this mechanism reduces the ordering instruction latency by 39.6% and improves program performance by 11% on average over the baseline implementation.
Human nonverbal emotional communication in dyadic dialogs is a process of mutual influence and adaptation.
Identifying the direction of influence, or cause-effect relation between participants is a challenging task, due to two main obstacles.
First, distinct emotions might not be clearly visible.
Second, participants cause-effect relation is transient and variant over time.
In this paper, we address these difficulties by using facial expressions that can be present even when strong distinct facial emotions are not visible.
We also propose to apply a relevant interval selection approach prior to causal inference to identify those transient intervals where adaptation process occurs.
To identify the direction of influence, we apply the concept of Granger causality to the time series of facial expressions on the set of relevant intervals.
We tested our approach on synthetic data and then applied it to newly, experimentally obtained data.
Here, we were able to show that a more sensitive facial expression detection algorithm and a relevant interval detection approach is most promising to reveal the cause-effect pattern for dyadic communication in various instructed interaction conditions.
Automated Computer Aided diagnostic tools can be used for the early detection of glaucoma to prevent irreversible vision loss.
In this work, we present a Multi-task Convolutional Neural Network (CNN) that jointly segments the Optic Disc (OD), Optic Cup (OC) and predicts the presence of glaucoma in color fundus images.
The CNN utilizes a combination of image appearance features and structural features obtained from the OD-OC segmentation to obtain a robust prediction.
The use of fewer network parameters and the sharing of the CNN features for multiple related tasks ensures the good generalizability of the architecture, allowing it to be trained on small training sets.
The cross-testing performance of the proposed method on an independent validation set acquired using a different camera and image resolution was found to be good with an average dice score of 0.92 for OD, 0.84 for OC and AUC of 0.95 on the task of glaucoma classification illustrating its potential as a mass screening tool for the early detection of glaucoma.
During the last few years, there has been plenty of research for reducing energy consumption in telecommunication infrastructure.
However, many of the proposals remain unim-plemented due to the lack of flexibility in legacy networks.
In this paper we demonstrate how the software defined networking (SDN) capabilities of current networking equipment can be used to implement some of these energy saving algorithms.
In particular, we developed an ONOS application to realize an energy-aware traffic scheduler to a bundle link made up of Energy Efficient Ethernet (EEE) links between two SDN switches.
We show how our application is able to dynamically adapt to the traffic characteristics and save energy by concentrating the traffic on as few ports as possible.
This way, unused ports remain in Low Power Idle (LPI) state most of the time, saving energy.
Data-driven saliency has recently gained a lot of attention thanks to the use of Convolutional Neural Networks for predicting gaze fixations.
In this paper we go beyond standard approaches to saliency prediction, in which gaze maps are computed with a feed-forward network, and present a novel model which can predict accurate saliency maps by incorporating neural attentive mechanisms.
The core of our solution is a Convolutional LSTM that focuses on the most salient regions of the input image to iteratively refine the predicted saliency map.
Additionally, to tackle the center bias typical of human eye fixations, our model can learn a set of prior maps generated with Gaussian functions.
We show, through an extensive evaluation, that the proposed architecture outperforms the current state of the art on public saliency prediction datasets.
We further study the contribution of each key component to demonstrate their robustness on different scenarios.
This paper presents a real-time face recognition system using kinect sensor.
The algorithm is implemented on GPU using opencl and significant speed improvements are observed.
We use kinect depth image to increase the robustness and reduce computational cost of conventional LBP based face recognition.
The main objective of this paper was to perform robust, high speed fusion based face recognition and tracking.
The algorithm is mainly composed of three steps.
First step is to detect all faces in the video using viola jones algorithm.
The second step is online database generation using a tracking window on the face.
A modified LBP feature vector is calculated using fusion information from depth and greyscale image on GPU.
This feature vector is used to train a svm classifier.
Third step involves recognition of multiple faces based on our modified feature vector.
Region-based memory management (RBMM) is a form of compile time memory management, well-known from the functional programming world.
In this paper we describe our work on implementing RBMM for the logic programming language Mercury.
One interesting point about Mercury is that it is designed with strong type, mode, and determinism systems.
These systems not only provide Mercury programmers with several direct software engineering benefits, such as self-documenting code and clear program logic, but also give language implementors a large amount of information that is useful for program analyses.
In this work, we make use of this information to develop program analyses that determine the distribution of data into regions and transform Mercury programs by inserting into them the necessary region operations.
We prove the correctness of our program analyses and transformation.
To execute the annotated programs, we have implemented runtime support that tackles the two main challenges posed by backtracking.
First, backtracking can require regions removed during forward execution to be "resurrected"; and second, any memory allocated during a computation that has been backtracked over must be recovered promptly and without waiting for the regions involved to come to the end of their life.
We describe in detail our solution of both these problems.
We study in detail how our RBMM system performs on a selection of benchmark programs, including some well-known difficult cases for RBMM.
Even with these difficult cases, our RBMM-enabled Mercury system obtains clearly faster runtimes for 15 out of 18 benchmarks compared to the base Mercury system with its Boehm runtime garbage collector, with an average runtime speedup of 24%, and an average reduction in memory requirements of 95%.
In fact, our system achieves optimal memory consumption in some programs.
We study one-head machines through symbolic and topological dynamics.
In particular, a subshift is associated to the subshift, and we are interested in its complexity in terms of realtime recognition.
We emphasize the class of one-head machines whose subshift can be recognized by a deterministic pushdown automaton.
We prove that this class corresponds to particular restrictions on the head movement, and to equicontinuity in associated dynamical systems.
Vehicle safety depends on (a) the range of identified hazards and (b) the operational situations for which mitigations of these hazards are acceptably decreasing risk.
Moreover, with an increasing degree of autonomy, risk ownership is likely to increase for vendors towards regulatory certification.
Hence, highly automated vehicles have to be equipped with verified controllers capable of reliably identifying and mitigating hazards in all possible operational situations.
To this end, available methods for the design and verification of automated vehicle controllers have to be supported by models for hazard analysis and mitigation.
In this paper, we describe (1) a framework for the analysis and design of planners (i.e., high-level controllers) capable of run-time hazard identification and mitigation, (2) an incremental algorithm for constructing planning models from hazard analysis, and (3) an exemplary application to the design of a fail-operational controller based on a given control system architecture.
Our approach equips the safety engineer with concepts and steps to (2a) elaborate scenarios of endangerment and (2b) design operational strategies for mitigating such scenarios.
Visual segmentation is a key perceptual function that partitions visual space and allows for detection, recognition and discrimination of objects in complex environments.
The processes underlying human segmentation of natural images are still poorly understood.
Existing datasets rely on manual labeling that conflate perceptual, motor, and cognitive factors.
In part, this is because we lack an ideal observer model of segmentation to guide constrained experiments.
On the other hand, despite recent progress in machine learning, modern algorithms still fall short of human segmentation performance.
Our goal here is two-fold (i) propose a model to probe human visual segmentation mechanisms and (ii) develop an efficient algorithm for image segmentation.
To this aim, we propose a novel probabilistic generative model of visual segmentation that for the first time combines 1) knowledge about the sensitivity of neurons in the visual cortex to statistical regularities in natural images; and 2) non-parametric Bayesian priors over segmentation maps (ie partitions of the visual space).
We provide an algorithm for learning and inference, validate it on synthetic data, and illustrate how the two components of our model improve segmentation of natural images.
We then show that the posterior distribution over segmentations captures well the variability across human subjects, indicating that our model provides a viable approach to probe human visual segmentation.
Natural language generation lies at the core of generative dialogue systems and conversational agents.
We describe an ensemble neural language generator, and present several novel methods for data representation and augmentation that yield improved results in our model.
We test the model on three datasets in the restaurant, TV and laptop domains, and report both objective and subjective evaluations of our best model.
Using a range of automatic metrics, as well as human evaluators, we show that our approach achieves better results than state-of-the-art models on the same datasets.
In many scientific fields, the order of coauthors on a paper conveys information about each individual's contribution to a piece of joint work.
We argue that in prior network analyses of coauthorship networks, the information on ordering has been insufficiently considered because ties between authors are typically symmetrized.
This is basically the same as assuming that each co-author has contributed equally to a paper.
We introduce a solution to this problem by adopting a coauthorship credit allocation model proposed by Kim and Diesner (2014), which in its core conceptualizes co-authoring as a directed, weighted, and self-looped network.
We test and validate our application of the adopted framework based on a sample data of 861 authors who have published in the journal Psychometrika.
Results suggest that this novel sociometric approach can complement traditional measures based on undirected networks and expand insights into coauthoring patterns such as the hierarchy of collaboration among scholars.
As another form of validation, we also show how our approach accurately detects prominent scholars in the Psychometric Society affiliated with the journal.
This paper deals with Low-Density Construction-A (LDA) lattices, which are obtained via Construction A from non-binary Low-Density Parity-Check codes.
More precisely, a proof is provided that Voronoi constellations of LDA lattices achieve the capacity of the AWGN channel under lattice encoding and decoding.
This is obtained after showing the same result for more general Construction-A lattice constellations.
The theoretical analysis is carried out in a way that allows to describe how the prime number underlying Construction A behaves as a function of the lattice dimension.
Moreover, no dithering is required in the transmission scheme, simplifying some previous solutions of the problem.
Remarkably, capacity is achievable with LDA lattice codes whose parity-check matrices have constant row and column Hamming weights.
Some expansion properties of random bipartite graphs constitute an extremely important tool for dealing with sparse matrices and allow to find a lower bound of the minimum Euclidean distance of LDA lattices in our ensemble.
Salient object detection is a fundamental problem and has been received a great deal of attentions in computer vision.
Recently deep learning model became a powerful tool for image feature extraction.
In this paper, we propose a multi-scale deep neural network (MSDNN) for salient object detection.
The proposed model first extracts global high-level features and context information over the whole source image with recurrent convolutional neural network (RCNN).
Then several stacked deconvolutional layers are adopted to get the multi-scale feature representation and obtain a series of saliency maps.
Finally, we investigate a fusion convolution module (FCM) to build a final pixel level saliency map.
The proposed model is extensively evaluated on four salient object detection benchmark datasets.
Results show that our deep model significantly outperforms other 12 state-of-the-art approaches.
The past few years have seen a surge of interest in the field of probabilistic logic learning and statistical relational learning.
In this endeavor, many probabilistic logics have been developed.
ProbLog is a recent probabilistic extension of Prolog motivated by the mining of large biological networks.
In ProbLog, facts can be labeled with probabilities.
These facts are treated as mutually independent random variables that indicate whether these facts belong to a randomly sampled program.
Different kinds of queries can be posed to ProbLog programs.
We introduce algorithms that allow the efficient execution of these queries, discuss their implementation on top of the YAP-Prolog system, and evaluate their performance in the context of large networks of biological entities.
Detecting and evaluating regions of brain under various circumstances is one of the most interesting topics in computational neuroscience.
However, the majority of the studies on detecting communities of a functional connectivity network of the brain is done on networks obtained from coherency attributes, and not from correlation.
This lack of studies, in part, is due to the fact that many common methods for clustering graphs require the nodes of the network to be `positively' linked together, a property that is guaranteed by a coherency matrix, by definition.
However, correlation matrices reveal more information regarding how each pair of nodes are linked together.
In this study, for the first time we simultaneously examine four inherently different network clustering methods (spectral, heuristic, and optimization methods) applied to the functional connectivity networks of the CA1 region of the hippocampus of an anaesthetized rat during pre-ictal and post-ictal states.
The networks are obtained from correlation matrices, and its results are compared with the ones obtained by applying the same methods to coherency matrices.
The correlation matrices show a much finer community structure compared to the coherency matrices.
Furthermore, we examine the potential smoothing effect of choosing various window sizes for computing the correlation/coherency matrices.
Latent tree learning models represent sentences by composing their words according to an induced parse tree, all based on a downstream task.
These models often outperform baselines which use (externally provided) syntax trees to drive the composition order.
This work contributes (a) a new latent tree learning model based on shift-reduce parsing, with competitive downstream performance and non-trivial induced trees, and (b) an analysis of the trees learned by our shift-reduce model and by a chart-based model.
There are now several large scale deployments of differential privacy used to collect statistical information about users.
However, these deployments periodically recollect the data and recompute the statistics using algorithms designed for a single use.
As a result, these systems do not provide meaningful privacy guarantees over long time scales.
Moreover, existing techniques to mitigate this effect do not apply in the "local model" of differential privacy that these systems use.
In this paper, we introduce a new technique for local differential privacy that makes it possible to maintain up-to-date statistics over time, with privacy guarantees that degrade only in the number of changes in the underlying distribution rather than the number of collection periods.
We use our technique for tracking a changing statistic in the setting where users are partitioned into an unknown collection of groups, and at every time period each user draws a single bit from a common (but changing) group-specific distribution.
We also provide an application to frequency and heavy-hitter estimation.
Compositionality of semantic concepts in image synthesis and analysis is appealing as it can help in decomposing known and generatively recomposing unknown data.
For instance, we may learn concepts of changing illumination, geometry or albedo of a scene, and try to recombine them to generate physically meaningful, but unseen data for training and testing.
In practice however we often do not have samples from the joint concept space available: We may have data on illumination change in one data set and on geometric change in another one without complete overlap.
We pose the following question: How can we learn two or more concepts jointly from different data sets with mutual consistency where we do not have samples from the full joint space?
We present a novel answer in this paper based on cyclic consistency over multiple concepts, represented individually by generative adversarial networks (GANs).
Our method, ConceptGAN, can be understood as a drop in for data augmentation to improve resilience for real world applications.
Qualitative and quantitative evaluations demonstrate its efficacy in generating semantically meaningful images, as well as one shot face verification as an example application.
Authentication and authorization are two key elements of a software application.
In modern day, OAuth 2.0 framework and OpenID Connect protocol are widely adopted standards fulfilling these requirements.
These protocols are implemented into authorization servers.
It is common to call these authorization servers as identity servers or identity providers since they hold user identity information.
Applications registered to an identity provider can use OpenID Connect to retrieve ID token for authentication.
Access token obtained along with ID token allows the application to consume OAuth 2.0 protected resources.
In this approach, the client application is bound to a single identity provider.
If the client needs to consume a protected resource from a different domain, which only accepts tokens of a defined identity provider, then the client must again follow OpenID Connect protocol to obtain new tokens.
This requires user identity details to be stored in the second identity provider as well.
This paper proposes an extension to OpenID Connect protocol to overcome this issue.
It proposes a client-centric mechanism to exchange identity information as token grants against a trusted identity provider.
Once a grant is accepted, resulting token response contains an access token, which is good enough to access protected resources from token issuing identity provider's domain.
In this paper, we present a spectral graph wavelet approach for shape analysis of carpal bones of human wrist.
We apply a metric called global spectral graph wavelet signature for representation of cortical surface of the carpal bone based on eigensystem of Laplace-Beltrami operator.
Furthermore, we propose a heuristic and efficient way of aggregating local descriptors of a carpal bone surface to global descriptor.
The resultant global descriptor is not only isometric invariant, but also much more efficient and requires less memory storage.
We perform experiments on shape of the carpal bones of ten women and ten men from a publicly-available database.
Experimental results show the excellency of the proposed GSGW compared to recent proposed GPS embedding approach for comparing shapes of the carpal bones across populations.
In the last decade, computer-aided early diagnostics of Alzheimer's Disease (AD) and its prodromal form, Mild Cognitive Impairment (MCI), has been the subject of extensive research.
Some recent studies have shown promising results in the AD and MCI determination using structural and functional Magnetic Resonance Imaging (sMRI, fMRI), Positron Emission Tomography (PET) and Diffusion Tensor Imaging (DTI) modalities.
Furthermore, fusion of imaging modalities in a supervised machine learning framework has shown promising direction of research.
In this paper we first review major trends in automatic classification methods such as feature extraction based methods as well as deep learning approaches in medical image analysis applied to the field of Alzheimer's Disease diagnostics.
Then we propose our own design of a 3D Inception-based Convolutional Neural Network (CNN) for Alzheimer's Disease diagnostics.
The network is designed with an emphasis on the interior resource utilization and uses sMRI and DTI modalities fusion on hippocampal ROI.
The comparison with the conventional AlexNet-based network using data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset (http://adni.loni.usc.edu) demonstrates significantly better performance of the proposed 3D Inception-based CNN.
Named Entity Disambiguation (NED) is the task of linking a named-entity mention to an instance in a knowledge-base, typically Wikipedia.
This task is closely related to word-sense disambiguation (WSD), where the supervised word-expert approach has prevailed.
In this work we present the results of the word-expert approach to NED, where one classifier is built for each target entity mention string.
The resources necessary to build the system, a dictionary and a set of training instances, have been automatically derived from Wikipedia.
We provide empirical evidence of the value of this approach, as well as a study of the differences between WSD and NED, including ambiguity and synonymy statistics.
MeshFace photos have been widely used in many Chinese business organizations to protect ID face photos from being misused.
The occlusions incurred by random meshes severely degenerate the performance of face verification systems, which raises the MeshFace verification problem between MeshFace and daily photos.
Previous methods cast this problem as a typical low-level vision problem, i.e. blind inpainting.
They recover perceptually pleasing clear ID photos from MeshFaces by enforcing pixel level similarity between the recovered ID images and the ground-truth clear ID images and then perform face verification on them.
Essentially, face verification is conducted on a compact feature space rather than the image pixel space.
Therefore, this paper argues that pixel level similarity and feature level similarity jointly offer the key to improve the verification performance.
Based on this insight, we offer a novel feature oriented blind face inpainting framework.
Specifically, we implement this by establishing a novel DeMeshNet, which consists of three parts.
The first part addresses blind inpainting of the MeshFaces by implicitly exploiting extra supervision from the occlusion position to enforce pixel level similarity.
The second part explicitly enforces a feature level similarity in the compact feature space, which can explore informative supervision from the feature space to produce better inpainting results for verification.
The last part copes with face alignment within the net via a customized spatial transformer module when extracting deep facial features.
All the three parts are implemented within an end-to-end network that facilitates efficient optimization.
Extensive experiments on two MeshFace datasets demonstrate the effectiveness of the proposed DeMeshNet as well as the insight of this paper.
In this paper we will attempt to classify Lindenmayer systems based on properties of sets of rules and the kind of strings those rules generate.
This classification will be referred to as a parametrization of the L-space: the L-space is the phase space in which all possible L-developments are represented.
This space is infinite, because there is no halting algorithm for L-grammars; but it is also subjected to hard conditions, because there are grammars and developments which are not possible states of an L-system: a very well-known example is the space of normal grammars.
Just as the space of normal grammars is parametrized into Regular, Context-Free, Context-Sensitive, and Unrestricted (with proper containment relations holding among them; see Chomsky, 1959: Theorem 1), we contend here that the L-space is a very rich landscape of grammars which cluster into kinds that are not mutually translatable.
With the increasing scale of deployment of Internet of Things (IoT), concerns about IoT security have become more urgent.
In particular, memory corruption attacks play a predominant role as they allow remote compromise of IoT devices.
Control-flow integrity (CFI) is a promising and generic defense technique against these attacks.
However, given the nature of IoT deployments, existing protection mechanisms for traditional computing environments (including CFI) need to be adapted to the IoT setting.
In this paper, we describe the challenges of enabling CFI on microcontroller (MCU) based IoT devices.
We then present CaRE, the first interrupt-aware CFI scheme for low-end MCUs.
CaRE uses a novel way of protecting the CFI metadata by leveraging TrustZone-M security extensions introduced in the ARMv8-M architecture.
Its binary instrumentation approach preserves the memory layout of the target MCU software, allowing pre-built bare-metal binary code to be protected by CaRE.
We describe our implementation on a Cortex-M Prototyping System and demonstrate that CaRE is secure while imposing acceptable performance and memory impact.
In the past few years, several case studies have illustrated that the use of occupancy information in buildings leads to energy-efficient and low-cost HVAC operation.
The widely presented techniques for occupancy estimation include temperature, humidity, CO2 concentration, image camera, motion sensor and passive infrared (PIR) sensor.
So far little studies have been reported in literature to utilize audio and speech processing as indoor occupancy prediction technique.
With rapid advances of audio and speech processing technologies, nowadays it is more feasible and attractive to integrate audio-based signal processing component into smart buildings.
In this work, we propose to utilize audio processing techniques (i.e., speaker recognition and background audio energy estimation) to estimate room occupancy (i.e., the number of people inside a room).
Theoretical analysis and simulation results demonstrate the accuracy and effectiveness of this proposed occupancy estimation technique.
Based on the occupancy estimation, smart buildings will adjust the thermostat setups and HVAC operations, thus, achieving greater quality of service and drastic cost savings.
For many biological image segmentation tasks, including topological knowledge, such as the nesting of classes, can greatly improve results.
However, most `out-of-the-box' CNN models are still blind to such prior information.
In this paper, we propose a novel approach to encode this information, through a multi-level activation layer and three compatible losses.
We benchmark all of them on nuclei segmentation in bright-field microscopy cell images from the 2018 Data Science Bowl challenge, offering an exemplary segmentation task with cells and nested subcellular structures.
Our scheme greatly speeds up learning, and outperforms standard multi-class classification with soft-max activation and a previously proposed method stemming from it, improving the Dice score significantly (p-values<0.007).
Our approach is conceptually simple, easy to implement and can be integrated in any CNN architecture.
It can be generalized to a higher number of classes, with or without further relations of containment.
Resilience is widely recognized as an important design goal though it is one that seems to escape a general and consensual understanding.
Often mixed up with other system attributes; traditionally used with different meanings in as many different disciplines; sought or applied through diverse approaches in various application domains, resilience in fact is a multi-attribute property that implies a number of constitutive abilities.
To further complicate the matter, resilience is not an absolute property but rather it is the result of the match between a system, its current condition, and the environment it is set to operate in.
In this paper we discuss this problem and provide a definition of resilience as a property measurable as a system-environment fit.
This brings to the foreground the dynamic nature of resilience as well as its hard dependence on the context.
A major problem becomes then that, being a dynamic figure, resilience cannot be assessed in absolute terms.
As a way to partially overcome this obstacle, in this paper we provide a number of indicators of the quality of resilience.
Our focus here is that of collective systems, namely those systems resulting from the union of multiple individual parts, sub-systems, or organs.
Through several examples of such systems we observe how our indicators provide insight, at least in the cases at hand, on design flaws potentially affecting the efficiency of the resilience strategies.
A number of conjectures are finally put forward to associate our indicators with factors affecting the quality of resilience.
Coreference resolution is one of the first stages in deep language understanding and its importance has been well recognized in the natural language processing community.
In this paper, we propose a generative, unsupervised ranking model for entity coreference resolution by introducing resolution mode variables.
Our unsupervised system achieves 58.44% F1 score of the CoNLL metric on the English data from the CoNLL-2012 shared task (Pradhan et al., 2012), outperforming the Stanford deterministic system (Lee et al., 2013) by 3.01%.
In this work we propose a new method for the rhythm classification of short single-lead ECG records, using a set of high-level and clinically meaningful features provided by the abductive interpretation of the records.
These features include morphological and rhythm-related features that are used to build two classifiers: one that evaluates the record globally, using aggregated values for each feature; and another one that evaluates the record as a sequence, using a Recurrent Neural Network fed with the individual features for each detected heartbeat.
The two classifiers are finally combined using the stacking technique, providing an answer by means of four target classes: Normal sinus rhythm, Atrial fibrillation, Other anomaly, and Noisy.
The approach has been validated against the 2017 Physionet/CinC Challenge dataset, obtaining a final score of 0.83 and ranking first in the competition.
A three-point monotone difference scheme is proposed for solving a one-dimensional non-stationary convection-diffusion-reaction equation with variable coefficients.
The scheme is based on a parabolic spline and allows to linearly reproduce the numerical solution of the boundary value problem over the integral segment in the form of the function which continuous with its first derivative.
The constructed difference scheme give a highly effective tool for solving problems with a small parameter at the older derivative in a wide range of output data of the problem.
In the test case, numerical and exact solutions of the problem are compared with the significant dominance of the convective term of the equation over the diffusion.
Numerous calculations showed the high efficiency of the new monotonous scheme developed.
In this paper, a solution to the problem of Active Authentication using trace histories is addressed.
Specifically, the task is to perform user verification on mobile devices using historical location traces of the user as a function of time.
Considering the movement of a human as a Markovian motion, a modified Hidden Markov Model (HMM)-based solution is proposed.
The proposed method, namely the Marginally Smoothed HMM (MSHMM), utilizes the marginal probabilities of location and timing information of the observations to smooth-out the emission probabilities while training.
Hence, it can efficiently handle unforeseen observations during the test phase.
The verification performance of this method is compared to a sequence matching (SM) method , a Markov Chain-based method (MC) and an HMM with basic Laplace Smoothing (HMM-lap).
Experimental results using the location information of the UMD Active Authentication Dataset-02 (UMDAA02) and the GeoLife dataset are presented.
The proposed MSHMM method outperforms the compared methods in terms of equal error rate (EER).
Additionally, the effects of different parameters on the proposed method are discussed.
The categorization of emotion names, i.e., the grouping of emotion words that have similar emotional connotations together, is a key tool of Social Psychology used to explore people's knowledge about emotions.
Without exception, the studies following that research line were based on the gauging of the perceived similarity between emotion names by the participants of the experiments.
Here we propose and examine a new approach to study the categories of emotion names - the similarities between target emotion names are obtained by comparing the contexts in which they appear in texts retrieved from the World Wide Web.
This comparison does not account for any explicit semantic information; it simply counts the number of common words or lexical items used in the contexts.
This procedure allows us to write the entries of the similarity matrix as dot products in a linear vector space of contexts.
The properties of this matrix were then explored using Multidimensional Scaling Analysis and Hierarchical Clustering.
Our main findings, namely, the underlying dimension of the emotion space and the categories of emotion names, were consistent with those based on people's judgments of emotion names similarities.
Workflow graphs extend classical flow charts with concurrent fork and join nodes.
They constitute the core of business processing languages such as BPMN or UML Activity Diagrams.
The activities of a workflow graph are executed by humans or machines, generically called resources.
If concurrent activities cannot be executed in parallel by lack of resources, the time needed to execute the workflow increases.
We study the problem of computing the minimal number of resources necessary to fully exploit the concurrency of a given workflow, and execute it as fast as possible (i.e., as fast as with unlimited resources).
We model this problem using free-choice Petri nets, which are known to be equivalent to workflow graphs.
We analyze the computational complexity of two versions of the problem: computing the resource and concurrency thresholds.
We use the results to design an algorithm to approximate the concurrency threshold, and evaluate it on a benchmark suite of 642 industrial examples.
We show that it performs very well in practice: It always provides the exact value, and never takes more than 30 milliseconds for any workflow, even for those with a huge number of reachable markings.
Active communication between robots and humans is essential for effective human-robot interaction.
To accomplish this objective, Cloud Robotics (CR) was introduced to make robots enhance their capabilities.
It enables robots to perform extensive computations in the cloud by sharing their outcomes.
Outcomes include maps, images, processing power, data, activities, and other robot resources.
But due to the colossal growth of data and traffic, CR suffers from serious latency issues.
Therefore, it is unlikely to scale a large number of robots particularly in human-robot interaction scenarios, where responsiveness is paramount.
Furthermore, other issues related to security such as privacy breaches and ransomware attacks can increase.
To address these problems, in this paper, we have envisioned the next generation of social robotic architectures based on Fog Robotics (FR) that inherits the strengths of Fog Computing to augment the future social robotic systems.
These new architectures can escalate the dexterity of robots by shoving the data closer to the robot.
Additionally, they can ensure that human-robot interaction is more responsive by resolving the problems of CR.
Moreover, experimental results are further discussed by considering a scenario of FR and latency as a primary factor comparing to CR models.
Choreographic Programming is a programming paradigm for building concurrent programs that are deadlock-free by construction, as a result of programming communications declaratively and then synthesising process implementations automatically.
Despite strong interest on choreographies, a foundational model that explains which computations can be performed with the hallmark constructs of choreographies is still missing.
In this work, we introduce Core Choreographies (CC), a model that includes only the core primitives of choreographic programming.
Every computable function can be implemented as a choreography in CC, from which we can synthesise a process implementation where independent computations run in parallel.
We discuss the design of CC and argue that it constitutes a canonical model for choreographic programming.
Neural network training process takes long time when the size of training data is huge, without the large set of training values the neural network is unable to learn features.
This dilemma between time and size of data is often solved using fast GPUs, but we present a better solution for a subset of those problems.
To reduce the time for training a regression model using neural network we introduce a loss function called Nth Absolute Root Mean Error (NARME).
It helps to train regression models much faster compared to other existing loss functions.
Experiments show that in most use cases NARME reduces the required number of epochs to almost one-tenth of that required by other commonly used loss functions, and also achieves great accuracy in the small amount of time in which it was trained.
We analyze the AI alignment problem.
This is the problem of aligning an AI's objective function with human preferences.
This problem has been argued to be critical to AI safety, especially in the long run.
But it has also been argued that solving it robustly is extremely challenging, especially in highly complex environments like the Internet.
It seems crucial to accelerate research in this direction.
To this end, we propose a preliminary research program.
Our roadmap aims to decompose alignment into numerous more tractable subproblems.
Our hope is that this will help scholars, engineers and decision-makers to better grasp the upcoming difficulties, and to foresee how they can best contribute to the global effort.
Defeasible logics provide several linguistic features to support the expression of defeasible knowledge.
There is also a wide variety of such logics, expressing different intuitions about defeasible reasoning.
However, the logics can only combine in trivial ways.
This limits their usefulness in contexts where different intuitions are at play in different aspects of a problem.
In particular, in some legal settings, different actors have different burdens of proof, which might be expressed as reasoning in different defeasible logics.
In this paper, we introduce annotated defeasible logic as a flexible formalism permitting multiple forms of defeasibility, and establish some properties of the formalism.
This paper is under consideration for acceptance in Theory and Practice of Logic Programming.
In line with the sensorimotor contingency theory, we investigate the problem of the perception of space from a fundamental sensorimotor perspective.
Despite its pervasive nature in our perception of the world, the origin of the concept of space remains largely mysterious.
For example in the context of artificial perception, this issue is usually circumvented by having engineers pre-define the spatial structure of the problem the agent has to face.
We here show that the structure of space can be autonomously discovered by a naive agent in the form of sensorimotor regularities, that correspond to so called compensable sensory experiences: these are experiences that can be generated either by the agent or its environment.
By detecting such compensable experiences the agent can infer the topological and metric structure of the external space in which its body is moving.
We propose a theoretical description of the nature of these regularities and illustrate the approach on a simulated robotic arm equipped with an eye-like sensor, and which interacts with an object.
Finally we show how these regularities can be used to build an internal representation of the sensor's external spatial configuration.
We resolve in the affirmative conjectures of Repovs and A. Skopenkov (1998), and M. Skopenkov (2003) generalizing the classical Hanani-Tutte theorem to the setting of approximating maps of graphs on 2-dimensional surfaces by embeddings.
Our proof of this result is constructive and almost immediately implies an efficient algorithm for testing if a given piecewise linear map of a graph in a surface is approximable by an embedding.
More precisely, an instance of this problem consists of (i) a graph G whose vertices are partitioned into clusters and whose inter-cluster edges are partitioned into bundles, and (ii) a region R of a 2-dimensional compact surface M given as the union of a set of pairwise disjoint discs corresponding to the clusters and a set of pairwise non-intersecting "pipes" corresponding to the bundles, connecting certain pairs of these discs.
We are to decide whether G can be embedded inside M so that the vertices in every cluster are drawn in the corresponding disc, the edges in every bundle pass only through its corresponding pipe, and every edge crosses the boundary of each disc at most once.
The proof of the theorem concerning to the inverse cyclotomic Discrete Fourier Transform algorithm over finite field is provided.
The discrimination and simplicity of features are very important for effective and efficient pedestrian detection.
However, most state-of-the-art methods are unable to achieve good tradeoff between accuracy and efficiency.
Inspired by some simple inherent attributes of pedestrians (i.e., appearance constancy and shape symmetry), we propose two new types of non-neighboring features (NNF): side-inner difference features (SIDF) and symmetrical similarity features (SSF).
SIDF can characterize the difference between the background and pedestrian and the difference between the pedestrian contour and its inner part.
SSF can capture the symmetrical similarity of pedestrian shape.
However, it's difficult for neighboring features to have such above characterization abilities.
Finally, we propose to combine both non-neighboring and neighboring features for pedestrian detection.
It's found that non-neighboring features can further decrease the average miss rate by 4.44%.
Experimental results on INRIA and Caltech pedestrian datasets demonstrate the effectiveness and efficiency of the proposed method.
Compared to the state-of-the-art methods without using CNN, our method achieves the best detection performance on Caltech, outperforming the second best method (i.e., Checkboards) by 1.63%.
Finding the diameter of a dataset in multidimensional Euclidean space is a well-established problem, with well-known algorithms.
However, most of the algorithms found in the literature do not scale well with large values of data dimension, so the time complexity grows exponentially in most cases, which makes these algorithms impractical.
Therefore, we implemented 4 simple greedy algorithms to be used for approximating the diameter of a multidimensional dataset; these are based on minimum/maximum l2 norms, hill climbing search, Tabu search and Beam search approaches, respectively.
The time complexity of the implemented algorithms is near-linear, as they scale near-linearly with data size and its dimensions.
The results of the experiments (conducted on different machine learning data sets) prove the efficiency of the implemented algorithms and can therefore be recommended for finding the diameter to be used by different machine learning applications when needed.
This paper introduces CuCoTrack, a cuckoo hash based data structure designed to efficiently implement connection tracking.
The proposed scheme exploits the fact that queries always match one existing connection to compress the 5-tuple that identifies the connection.
This reduces significantly the amount of memory needed to store the connections and also the memory bandwidth needed for lookups.
CuCoTrack uses a dynamic fingerprint to avoid collisions thus ensuring that queries are completed in at most two memory accesses and facilitating a hardware implementation.
The proposed scheme has been analyzed theoretically and validated by simulation.
The results show that using 16 bits for the fingerprint is enough to avoid collisions in practical configurations.
To bridge the gap between the capabilities of the state-of-the-art in factoid question answering (QA) and what real users ask, we need large datasets of real user questions that capture the various question phenomena users are interested in, and the diverse ways in which these questions are formulated.
We introduce ComQA, a large dataset of real user questions that exhibit different challenging aspects such as temporal reasoning, compositionality, etc.
ComQA questions come from the WikiAnswers community QA platform.
Through a large crowdsourcing effort, we clean the question dataset, group questions into paraphrase clusters, and annotate clusters with their answers.
ComQA contains 11,214 questions grouped into 4,834 paraphrase clusters.
We detail the process of constructing ComQA, including the measures taken to ensure its high quality while making effective use of crowdsourcing.
We also present an extensive analysis of the dataset and the results achieved by state-of-the-art systems on ComQA, demonstrating that our dataset can be a driver of future research on QA.
We propose a novel and flexible rank-breaking-then-composite-marginal-likelihood (RBCML) framework for learning random utility models (RUMs), which include the Plackett-Luce model.
We characterize conditions for the objective function of RBCML to be strictly log-concave by proving that strict log-concavity is preserved under convolution and marginalization.
We characterize necessary and sufficient conditions for RBCML to satisfy consistency and asymptotic normality.
Experiments on synthetic data show that RBCML for Gaussian RUMs achieves better statistical efficiency and computational efficiency than the state-of-the-art algorithm and our RBCML for the Plackett-Luce model provides flexible tradeoffs between running time and statistical efficiency.
High altitude platform (HAP) drones can provide broadband wireless connectivity to ground users in rural areas by establishing line-of-sight (LoS) links and exploiting effective beamforming techniques.
However, at high altitudes, acquiring the channel state information (CSI) for HAPs, which is a key component to perform beamforming, is challenging.
In this paper, by exploiting an interference alignment (IA) technique, a novel method for achieving the maximum sum-rate in HAP-based communications without CSI is proposed.
In particular, to realize IA, a multiple-antenna tethered balloon is used as a relay between multiple HAP drones and ground stations (GSs).
Here, a multiple-input multiple-output X network system is considered.
The capacity of the considered M*N X network with a tethered balloon relay is derived in closed-form.
Simulation results corroborate the theoretical findings and show that the proposed approach yields the maximum sum-rate in multiple HAPs-GSs communications in absence of CSI.
The results also show the existence of an optimal balloon's altitude for which the sum-rate is maximized.
For autonomous agents to successfully operate in the real world, anticipation of future events and states of their environment is a key competence.
This problem has been formalized as a sequence extrapolation problem, where a number of observations are used to predict the sequence into the future.
Real-world scenarios demand a model of uncertainty of such predictions, as predictions become increasingly uncertain -- in particular on long time horizons.
While impressive results have been shown on point estimates, scenarios that induce multi-modal distributions over future sequences remain challenging.
Our work addresses these challenges in a Gaussian Latent Variable model for sequence prediction.
Our core contribution is a "Best of Many" sample objective that leads to more accurate and more diverse predictions that better capture the true variations in real-world sequence data.
Beyond our analysis of improved model fit, our models also empirically outperform prior work on three diverse tasks ranging from traffic scenes to weather data.
Grouping problems aim to partition a set of items into multiple mutually disjoint subsets according to some specific criterion and constraints.
Grouping problems cover a large class of important combinatorial optimization problems that are generally computationally difficult.
In this paper, we propose a general solution approach for grouping problems, i.e., reinforcement learning based local search (RLS), which combines reinforcement learning techniques with descent-based local search.
The viability of the proposed approach is verified on a well-known representative grouping problem (graph coloring) where a very simple descent-based coloring algorithm is applied.
Experimental studies on popular DIMACS and COLOR02 benchmark graphs indicate that RLS achieves competitive performances compared to a number of well-known coloring algorithms.
A fundamental property of complex networks is the tendency for edges to cluster.
The extent of the clustering is typically quantified by the clustering coefficient, which is the probability that a length-2 path is closed, i.e., induces a triangle in the network.
However, higher-order cliques beyond triangles are crucial to understanding complex networks, and the clustering behavior with respect to such higher-order network structures is not well understood.
Here we introduce higher-order clustering coefficients that measure the closure probability of higher-order network cliques and provide a more comprehensive view of how the edges of complex networks cluster.
Our higher-order clustering coefficients are a natural generalization of the traditional clustering coefficient.
We derive several properties about higher-order clustering coefficients and analyze them under common random graph models.
Finally, we use higher-order clustering coefficients to gain new insights into the structure of real-world networks from several domains.
Over the last five years Deep Neural Nets have offered more accurate solutions to many problems in speech recognition, and computer vision, and these solutions have surpassed a threshold of acceptability for many applications.
As a result, Deep Neural Networks have supplanted other approaches to solving problems in these areas, and enabled many new applications.
While the design of Deep Neural Nets is still something of an art form, in our work we have found basic principles of design space exploration used to develop embedded microprocessor architectures to be highly applicable to the design of Deep Neural Net architectures.
In particular, we have used these design principles to create a novel Deep Neural Net called SqueezeNet that requires as little as 480KB of storage for its model parameters.
We have further integrated all these experiences to develop something of a playbook for creating small Deep Neural Nets for embedded systems.
The fact that the results for 2-receiver broadcast channels (BCs) are not generalized to the 3-receiver ones is of information theoretical importance.
In this paper we study two classes of discrete memoryless BCs with non-causal side information (SI), i.e. multilevel BC (MBC) and 3-receiver less noisy BC.
First, we obtain an achievable rate region and a capacity outer bound for the MBC.
Second, we prove a special capacity region for the 3-receiver less noisy BC.
Third, the obtained special capacity region for the 3-receiver less noisy BC is extended to continuous alphabet fading Gaussian version.
It is worth mentioning that the previous works are special cases of our works.
In diffusion-based molecular communications, messages can be conveyed via the variation in the concentration of molecules in the medium.
In this paper, we intend to analyze the achievable capacity in transmission of information from one node to another in a diffusion channel.
We observe that because of the molecular diffusion in the medium, the channel possesses memory.
We then model the memory of the channel by a two-step Markov chain and obtain the equations describing the capacity of the diffusion channel.
By performing a numerical analysis, we obtain the maximum achievable rate for different levels of the transmitter power, i.e., the molecule production rate.
In this work, our objective is to find out how topological and algebraic properties of unrooted Gaussian tree models determine their security robustness, which is measured by our proposed max-min information (MaMI) metric.
Such metric quantifies the amount of common randomness extractable through public discussion between two legitimate nodes under an eavesdropper attack.
We show some general topological properties that the desired max-min solutions shall satisfy.
Under such properties, we develop conditions under which comparable trees are put together to form partially ordered sets (posets).
Each poset contains the most favorable structure as the poset leader, and the least favorable structure.
Then, we compute the Tutte-like polynomial for each tree in a poset in order to assign a polynomial to any tree in a poset.
Moreover, we propose a novel method, based on restricted integer partitions, to effectively enumerate all poset leaders.
The results not only help us understand the security strength of different Gaussian trees, which is critical when we evaluate the information leakage issues for various jointly Gaussian distributed measurements in networks, but also provide us both an algebraic and a topological perspective in grasping some fundamental properties of such models.
Millimeter wave (mmWave) signals are much more sensitive to blockage, which results in a significant increase of the outage probability, especially for the users at the edge of the cells.
In this paper, we exploit the technique of base station (BS) cooperation to improve the performance of the cell-edge users in the downlink transmission of mmWave cellular networks.
We design two cooperative schemes, which are referred to as fixed-number BS cooperation (FNC) scheme and fixed-region BS cooperation (FRC) scheme, respectively.
In FNC scheme, the cooperative BSs consist of the M nearest BSs around the served cell-edge users, and in FRC scheme, the cooperative BSs include all the BSs located within a given region.
We derive the expressions for the average rate and outage probability of a typical cell-edge user located at the origin based on the stochastic geometry framework.
To reduce the computational complexity of our analytical results for the outage probability, we further propose a Gamma approximation based method to provide approximations with satisfying accuracy.
Our analytical results incorporate the critical characteristics of mmWave channels, i.e., the blockage effects, the different path loss of LOS and NLOS links and the highly directional antenna arrays.
Simulation results show that the performance of the cell-edge users is greatly improved when mmWave networks are combined with the technique of BS cooperation.
Multipath transport protocols like MPTCP transfer data across multiple routes in parallel and deliver it in order at the receiver.
When the delay on one or more of the paths is variable, as is commonly the case, out of order arrivals are frequent and head of line blocking leads to high latency.
This is exacerbated when packet loss, which is also common with wireless links, is tackled using ARQ.
This paper introduces Stochastic Earliest Delivery Path First (S-EDPF), a resilient low delay packet scheduler for multipath transport protocols.
S-EDPF takes explicit account of the stochastic nature of paths and uses this to minimise in-order delivery delay.
S-EDPF also takes account of FEC, jointly scheduling transmission of information and coded packets and in this way allows lossy links to reduce delay and improve resiliency, rather than degrading performance as usually occurs with existing multipath systems.
We implement S-EDPF as a multi-platform application that does not require administration privileges nor modifications to the operating system and has negligible impact on energy consumption.
We present a thorough experimental evaluation in both controlled environments and into the wild, revealing dramatic gains in delay performance compared to existing approaches.
Traditional employment usually provides mechanisms for workers to improve their skills to access better opportunities.
However, crowd work platforms like Amazon Mechanical Turk (AMT) generally do not support skill development (i.e., becoming faster and better at work).
While researchers have started to tackle this problem, most solutions are dependent on experts or requesters willing to help.
However, requesters generally lack the necessary knowledge, and experts are rare and expensive.
To further facilitate crowd workers' skill growth, we present Crowd Coach, a system that enables workers to receive peer coaching while on the job.
We conduct a field experiment and real world deployment to study Crowd Coach in the wild.
Hundreds of workers used Crowd Coach in a variety of tasks, including writing, doing surveys, and labeling images.
We find that Crowd Coach enhances workers' speed without sacrificing their work quality, especially in audio transcription tasks.
We posit that peer coaching systems hold potential for better supporting crowd workers' skill development while on the job.
We finish with design implications from our research.
We consider secret key generation for a "pairwise independent network" model in which every pair of terminals observes correlated sources that are independent of sources observed by all other pairs of terminals.
The terminals are then allowed to communicate publicly with all such communication being observed by all the terminals.
The objective is to generate a secret key shared by a given subset of terminals at the largest rate possible, with the cooperation of any remaining terminals.
Secrecy is required from an eavesdropper that has access to the public interterminal communication.
A (single-letter) formula for secret key capacity brings out a natural connection between the problem of secret key generation and a combinatorial problem of maximal packing of Steiner trees in an associated multigraph.
An explicit algorithm is proposed for secret key generation based on a maximal packing of Steiner trees in a multigraph; the corresponding maximum rate of Steiner tree packing is thus a lower bound for the secret key capacity.
When only two of the terminals or when all the terminals seek to share a secret key, the mentioned algorithm achieves secret key capacity in which case the bound is tight.
This paper proposes a novel deep learning framework named bidirectional-convolutional long short term memory (Bi-CLSTM) network to automatically learn the spectral-spatial feature from hyperspectral images (HSIs).
In the network, the issue of spectral feature extraction is considered as a sequence learning problem, and a recurrent connection operator across the spectral domain is used to address it.
Meanwhile, inspired from the widely used convolutional neural network (CNN), a convolution operator across the spatial domain is incorporated into the network to extract the spatial feature.
Besides, to sufficiently capture the spectral information, a bidirectional recurrent connection is proposed.
In the classification phase, the learned features are concatenated into a vector and fed to a softmax classifier via a fully-connected operator.
To validate the effectiveness of the proposed Bi-CLSTM framework, we compare it with several state-of-the-art methods, including the CNN framework, on three widely used HSIs.
The obtained results show that Bi-CLSTM can improve the classification performance as compared to other methods.
Multi-group multicast beamforming in wireless systems with large antenna arrays and massive audience is investigated in this paper.
Multicast beamforming design is a well-known non-convex quadratically constrained quadratic programming (QCQP) problem.
A conventional method to tackle this problem is to approximate it as a semi-definite programming problem via semi-definite relaxation, whose performance, however, deteriorates considerably as the number of per-group users goes large.
A recent attempt is to apply convex-concave procedure (CCP) to find a stationary solution by treating it as a difference of convex programming problem, whose complexity, however, increases dramatically as the problem size increases.
In this paper, we propose a low-complexity high-performance algorithm for multi-group multicast beamforming design in large-scale wireless systems by leveraging the alternating direction method of multipliers (ADMM) together with CCP.
In specific, the original non-convex QCQP problem is first approximated as a sequence of convex subproblems via CCP.
Each convex subproblem is then reformulated as a novel ADMM form.
Our ADMM reformulation enables that each updating step is performed by solving multiple small-size subproblems with closed-form solutions in parallel.
Numerical results show that our fast algorithm maintains the same favorable performance as state-of-the-art algorithms but reduces the complexity by orders of magnitude.
Understanding emerging areas of a multidisciplinary research field is crucial for researchers,policymakers and other stakeholders.
For them a knowledge structure based on longitudinal bibliographic data can be an effective instrument.
But with the vast amount of available online information it is often hard to understand the knowledge structure for data.
In this paper, we present a novel approach for retrieving online bibliographic data and propose a framework for exploring knowledge structure.
We also present several longitudinal analyses to interpret and visualize the last 20 years of published obesity research data.
In recent years, the mathematical and algorithmic aspects of the phase retrieval problem have received considerable attention.
Many papers in this area mention crystallography as a principal application.
In crystallography, the signal to be recovered is periodic and comprised of atomic distributions arranged homogeneously in the unit cell of the crystal.
The crystallographic problem is both the leading application and one of the hardest forms of phase retrieval.
We have constructed a graded set of benchmark problems for evaluating algorithms that perform this type of phase retrieval.
The data, publicly available online, is provided in an easily interpretable format.
We also propose a simple and unambiguous success/failure criterion based on the actual needs in crystallography.
Baseline runtimes were obtained with an iterative algorithm that is similar but more transparent than those used in crystallography.
Empirically, the runtimes grow exponentially with respect to a new hardness parameter: the sparsity of the signal autocorrelation.
We also review the algorithms used by the leading software packages.
This set of benchmark problems, we hope, will encourage the development of new algorithms for the phase retrieval problem in general, and crystallography in particular.
Scientific Computing typically requires large computational needs which have been addressed with High Performance Distributed Computing.
It is essential to efficiently deploy a number of complex scientific applications, which have different characteristics, and so require distinct computational resources too.
However, in many research laboratories, this high performance architecture is not dedicated.
So, the architecture must be shared to execute a set of scientific applications, with so many different execution times and relative importance to research.
Also, the high performance architectures have different characteristics and costs.
When a new infrastructure has to be acquired to meet the needs of this scenario, the decision-making is hard and complex.
In this work, we present a Gain Function as a model of an utility function, with which it is possible a decision-making with confidence.
With the function is possible to evaluate the best architectural option taking into account aspects of applications and architectures, including the executions time, cost of architecture, the relative importance of each application and also the relative importance of performance and cost on the final evaluation.
This paper presents the Gain Function, examples, and a real case showing their applicabilities.
Summaries of meetings are very important as they convey the essential content of discussions in a concise form.
Generally, it is time consuming to read and understand the whole documents.
Therefore, summaries play an important role as the readers are interested in only the important context of discussions.
In this work, we address the task of meeting document summarization.
Automatic summarization systems on meeting conversations developed so far have been primarily extractive, resulting in unacceptable summaries that are hard to read.
The extracted utterances contain disfluencies that affect the quality of the extractive summaries.
To make summaries much more readable, we propose an approach to generating abstractive summaries by fusing important content from several utterances.
We first separate meeting transcripts into various topic segments, and then identify the important utterances in each segment using a supervised learning approach.
The important utterances are then combined together to generate a one-sentence summary.
In the text generation step, the dependency parses of the utterances in each segment are combined together to create a directed graph.
The most informative and well-formed sub-graph obtained by integer linear programming (ILP) is selected to generate a one-sentence summary for each topic segment.
The ILP formulation reduces disfluencies by leveraging grammatical relations that are more prominent in non-conversational style of text, and therefore generates summaries that is comparable to human-written abstractive summaries.
Experimental results show that our method can generate more informative summaries than the baselines.
In addition, readability assessments by human judges as well as log-likelihood estimates obtained from the dependency parser show that our generated summaries are significantly readable and well-formed.
We consider the problem of representing collective behavior of large populations and predicting the evolution of a population distribution over a discrete state space.
A discrete time mean field game (MFG) is motivated as an interpretable model founded on game theory for understanding the aggregate effect of individual actions and predicting the temporal evolution of population distributions.
We achieve a synthesis of MFG and Markov decision processes (MDP) by showing that a special MFG is reducible to an MDP.
This enables us to broaden the scope of mean field game theory and infer MFG models of large real-world systems via deep inverse reinforcement learning.
Our method learns both the reward function and forward dynamics of an MFG from real data, and we report the first empirical test of a mean field game model of a real-world social media population.
Resource usage data, collected using tools such as TACC Stats, capture the resource utilization by nodes within a high performance computing system.
We present methods to analyze the resource usage data to understand the system performance and identify performance anomalies.
The core idea is to model the data as a three-way tensor corresponding to the compute nodes, usage metrics, and time.
Using the reconstruction error between the original tensor and the tensor reconstructed from a low rank tensor decomposition, as a scalar performance metric, enables us to monitor the performance of the system in an online fashion.
This error statistic is then used for anomaly detection that relies on the assumption that the normal/routine behavior of the system can be captured using a low rank approx- imation of the original tensor.
We evaluate the performance of the algorithm using information gathered from system logs and show that the performance anomalies identified by the proposed method correlates with critical errors reported in the system logs.
Results are shown for data collected for 2013 from the Lonestar4 system at the Texas Advanced Computing Center (TACC)
Hyperspectral signature classification is a quantitative analysis approach for hyperspectral imagery which performs detection and classification of the constituent materials at the pixel level in the scene.
The classification procedure can be operated directly on hyperspectral data or performed by using some features extracted from the corresponding hyperspectral signatures containing information like the signature's energy or shape.
In this paper, we describe a technique that applies non-homogeneous hidden Markov chain (NHMC) models to hyperspectral signature classification.
The basic idea is to use statistical models (such as NHMC) to characterize wavelet coefficients which capture the spectrum semantics (i.e., structural information) at multiple levels.
Experimental results show that the approach based on NHMC models can outperform existing approaches relevant in classification tasks.
Unsupervised neural machine translation (NMT) is a recently proposed approach for machine translation which aims to train the model without using any labeled data.
The models proposed for unsupervised NMT often use only one shared encoder to map the pairs of sentences from different languages to a shared-latent space, which is weak in keeping the unique and internal characteristics of each language, such as the style, terminology, and sentence structure.
To address this issue, we introduce an extension by utilizing two independent encoders but sharing some partial weights which are responsible for extracting high-level representations of the input sentences.
Besides, two different generative adversarial networks (GANs), namely the local GAN and global GAN, are proposed to enhance the cross-language translation.
With this new approach, we achieve significant improvements on English-German, English-French and Chinese-to-English translation tasks.
Machine learning algorithms have reached mainstream status and are widely deployed in many applications.
The accuracy of such algorithms depends significantly on the size of the underlying training dataset; in reality a small or medium sized organization often does not have the necessary data to train a reasonably accurate model.
For such organizations, a realistic solution is to train their machine learning models based on their joint dataset (which is a union of the individual ones).
Unfortunately, privacy concerns prevent them from straightforwardly doing so.
While a number of privacy-preserving solutions exist for collaborating organizations to securely aggregate the parameters in the process of training the models, we are not aware of any work that provides a rational framework for the participants to precisely balance the privacy loss and accuracy gain in their collaboration.
In this paper, by focusing on a two-player setting, we model the collaborative training process as a two-player game where each player aims to achieve higher accuracy while preserving the privacy of its own dataset.
We introduce the notion of Price of Privacy, a novel approach for measuring the impact of privacy protection on the accuracy in the proposed framework.
Furthermore, we develop a game-theoretical model for different player types, and then either find or prove the existence of a Nash Equilibrium with regard to the strength of privacy protection for each player.
Using recommendation systems as our main use case, we demonstrate how two players can make practical use of the proposed theoretical framework, including setting up the parameters and approximating the non-trivial Nash Equilibrium.
We investigate different approaches for dialect identification in Arabic broadcast speech, using phonetic, lexical features obtained from a speech recognition system, and acoustic features using the i-vector framework.
We studied both generative and discriminate classifiers, and we combined these features using a multi-class Support Vector Machine (SVM).
We validated our results on an Arabic/English language identification task, with an accuracy of 100%.
We used these features in a binary classifier to discriminate between Modern Standard Arabic (MSA) and Dialectal Arabic, with an accuracy of 100%.
We further report results using the proposed method to discriminate between the five most widely used dialects of Arabic: namely Egyptian, Gulf, Levantine, North African, and MSA, with an accuracy of 52%.
We discuss dialect identification errors in the context of dialect code-switching between Dialectal Arabic and MSA, and compare the error pattern between manually labeled data, and the output from our classifier.
We also release the train and test data as standard corpus for dialect identification.
Spectral inference provides fast algorithms and provable optimality for latent topic analysis.
But for real data these algorithms require additional ad-hoc heuristics, and even then often produce unusable results.
We explain this poor performance by casting the problem of topic inference in the framework of Joint Stochastic Matrix Factorization (JSMF) and showing that previous methods violate the theoretical conditions necessary for a good solution to exist.
We then propose a novel rectification method that learns high quality topics and their interactions even on small, noisy data.
This method achieves results comparable to probabilistic techniques in several domains while maintaining scalability and provable optimality.
In this paper we propose a new family of RRT based algorithms, named RRT+ , that are able to find faster solutions in high-dimensional configuration spaces compared to other existing RRT variants by finding paths in lower dimensional subspaces of the configuration space.
The method can be easily applied to complex hyper-redundant systems and can be adapted by other RRT based planners.
We introduce RRT+ and develop some variants, called PrioritizedRRT+ , PrioritizedRRT+-Connect, and PrioritizedBidirectionalT-RRT+ , that use the new sampling technique and we show that our method provides faster results than the corresponding original algorithms.
Experiments using the state-of-the-art planners available in OMPL show superior performance of RRT+ for high-dimensional motion planning problems.
Understanding the world around us and making decisions about the future is a critical component to human intelligence.
As autonomous systems continue to develop, their ability to reason about the future will be the key to their success.
Semantic anticipation is a relatively under-explored area for which autonomous vehicles could take advantage of (e.g., forecasting pedestrian trajectories).
Motivated by the need for real-time prediction in autonomous systems, we propose to decompose the challenging semantic forecasting task into two subtasks: current frame segmentation and future optical flow prediction.
Through this decomposition, we built an efficient, effective, low overhead model with three main components: flow prediction network, feature-flow aggregation LSTM, and end-to-end learnable warp layer.
Our proposed method achieves state-of-the-art accuracy on short-term and moving objects semantic forecasting while simultaneously reducing model parameters by up to 95% and increasing efficiency by greater than 40x.
A new scheme to sample signals defined in the nodes of a graph is proposed.
The underlying assumption is that such signals admit a sparse representation in a frequency domain related to the structure of the graph, which is captured by the so-called graph-shift operator.
Most of the works that have looked at this problem have focused on using the value of the signal observed at a subset of nodes to recover the signal in the entire graph.
Differently, the sampling scheme proposed here uses as input observations taken at a single node.
The observations correspond to sequential applications of the graph-shift operator, which are linear combinations of the information gathered by the neighbors of the node.
When the graph corresponds to a directed cycle (which is the support of time-varying signals), our method is equivalent to the classical sampling in the time domain.
When the graph is more general, we show that the Vandermonde structure of the sampling matrix, which is critical to guarantee recovery when sampling time-varying signals, is preserved.
Sampling and interpolation are analyzed first in the absence of noise and then noise is considered.
We then study the recovery of the sampled signal when the specific set of frequencies that is active is not known.
Moreover, we present a more general sampling scheme, under which, either our aggregation approach or the alternative approach of sampling a graph signal by observing the value of the signal at a subset of nodes can be both viewed as particular cases.
The last part of the paper presents numerical experiments that illustrate the results developed through both synthetic graph signals and a real-world graph of the economy of the United States.
Motivated by applications in databases, this paper considers various fragments of the calculus of binary relations.
The fragments are obtained by leaving out, or keeping in, some of the standard operators, along with some derived operators such as set difference, projection, coprojection, and residuation.
For each considered fragment, a characterization is obtained for when two given binary relational structures are indistinguishable by expressions in that fragment.
The characterizations are based on appropriately adapted notions of simulation and bisimulation.
Darknet technology such as Tor has been used by various threat actors for organising illegal activities and data exfiltration.
As such, there is a case for organisations to block such traffic, or to try and identify when it is used and for what purposes.
However, anonymity in cyberspace has always been a domain of conflicting interests.
While it gives enough power to nefarious actors to masquerade their illegal activities, it is also the cornerstone to facilitate freedom of speech and privacy.
We present a proof of concept for a novel algorithm that could form the fundamental pillar of a darknet-capable Cyber Threat Intelligence platform.
The solution can reduce anonymity of users of Tor, and considers the existing visibility of network traffic before optionally initiating targeted or widespread BGP interception.
In combination with server HTTP response manipulation, the algorithm attempts to reduce the candidate data set to eliminate client-side traffic that is most unlikely to be responsible for server-side connections of interest.
Our test results show that MITM manipulated server responses lead to expected changes received by the Tor client.
Using simulation data generated by shadow, we show that the detection scheme is effective with false positive rate of 0.001, while sensitivity detecting non-targets was 0.016+-0.127.
Our algorithm could assist collaborating organisations willing to share their threat intelligence or cooperate during investigations.
There has been a growing interest for Wireless Distributed Computing (WDC), which leverages collaborative computing over multiple wireless devices.
WDC enables complex applications that a single device cannot support individually.
However, the problem of assigning tasks over multiple devices becomes challenging in the dynamic environments encountered in real-world settings, considering that the resource availability and channel conditions change over time in unpredictable ways due to mobility and other factors.
In this paper, we formulate a task assignment problem as an online learning problem using an adversarial multi-armed bandit framework.
We propose MABSTA, a novel online learning algorithm that learns the performance of unknown devices and channel qualities continually through exploratory probing and makes task assignment decisions by exploiting the gained knowledge.
For maximal adaptability, MABSTA is designed to make no stochastic assumption about the environment.
We analyze it mathematically and provide a worst-case performance guarantee for any dynamic environment.
We also compare it with the optimal offline policy as well as other baselines via emulations on trace-data obtained from a wireless IoT testbed, and show that it offers competitive and robust performance in all cases.
To the best of our knowledge, MABSTA is the first online algorithm in this domain of task assignment problems and provides provable performance guarantee.
The paper presents a deep learning-aided iterative detection algorithm for massive overloaded MIMO systems.
Since the proposed algorithm is based on the projected gradient descent method with trainable parameters, it is named as trainable projected descent-detector (TPG-detector).
The trainable internal parameters can be optimized with standard deep learning techniques such as back propagation and stochastic gradient descent algorithms.
This approach referred to as data-driven tuning brings notable advantages of the proposed scheme such as fast convergence.
The numerical experiments show that TPG-detector achieves comparable detection performance to those of the known algorithms for massive overloaded MIMO channels with lower computation cost.
Experience replay is one of the most commonly used approaches to improve the sample efficiency of reinforcement learning algorithms.
In this work, we propose an approach to select and replay sequences of transitions in order to accelerate the learning of a reinforcement learning agent in an off-policy setting.
In addition to selecting appropriate sequences, we also artificially construct transition sequences using information gathered from previous agent-environment interactions.
These sequences, when replayed, allow value function information to trickle down to larger sections of the state/state-action space, thereby making the most of the agent's experience.
We demonstrate our approach on modified versions of standard reinforcement learning tasks such as the mountain car and puddle world problems and empirically show that it enables better learning of value functions as compared to other forms of experience replay.
Further, we briefly discuss some of the possible extensions to this work, as well as applications and situations where this approach could be particularly useful.
Designing a logo is a long, complicated, and expensive process for any designer.
However, recent advancements in generative algorithms provide models that could offer a possible solution.
Logos are multi-modal, have very few categorical properties, and do not have a continuous latent space.
Yet, conditional generative adversarial networks can be used to generate logos that could help designers in their creative process.
We propose LoGAN: an improved auxiliary classifier Wasserstein generative adversarial neural network (with gradient penalty) that is able to generate logos conditioned on twelve different colors.
In 768 generated instances (12 classes and 64 logos per class), when looking at the most prominent color, the conditional generation part of the model has an overall precision and recall of 0.8 and 0.7 respectively.
LoGAN's results offer a first glance at how artificial intelligence can be used to assist designers in their creative process and open promising future directions, such as including more descriptive labels which will provide a more exhaustive and easy-to-use system.
Network virtualization and softwarizing network functions are trends aiming at higher network efficiency, cost reduction and agility.
They are driven by the evolution in Software Defined Networking (SDN) and Network Function Virtualization (NFV).
This shows that software will play an increasingly important role within telecommunication services, which were previously dominated by hardware appliances.
Service providers can benefit from this, as it enables faster introduction of new telecom services, combined with an agile set of possibilities to optimize and fine-tune their operations.
However, the provided telecom services can only evolve if the adequate software tools are available.
In this article, we explain how the development, deployment and maintenance of such an SDN/NFV-based telecom service puts specific requirements on the platform providing it.
A Software Development Kit (SDK) is introduced, allowing service providers to adequately design, test and evaluate services before they are deployed in production and also update them during their lifetime.
This continuous cycle between development and operations, a concept known as DevOps, is a well known strategy in software development.
To extend its context further to SDN/NFV-based services, the functionalities provided by traditional cloud platforms are not yet sufficient.
By giving an overview of the currently available tools and their limitations, the gaps in DevOps for SDN/NFV services are highlighted.
The benefit of such an SDK is illustrated by a secure content delivery network service (enhanced with deep packet inspection and elastic routing capabilities).
With this use-case, the dynamics between developing and deploying a service are further illustrated.
Overfitting is one of the most critical challenges in deep neural networks, and there are various types of regularization methods to improve generalization performance.
Injecting noises to hidden units during training, e.g., dropout, is known as a successful regularizer, but it is still not clear enough why such training techniques work well in practice and how we can maximize their benefit in the presence of two conflicting objectives---optimizing to true data distribution and preventing overfitting by regularization.
This paper addresses the above issues by 1) interpreting that the conventional training methods with regularization by noise injection optimize the lower bound of the true objective and 2) proposing a technique to achieve a tighter lower bound using multiple noise samples per training example in a stochastic gradient descent iteration.
We demonstrate the effectiveness of our idea in several computer vision applications.
The reassembly of a broken archaeological ceramic pottery is an open and complex problem, which remains a scientific process of extreme interest for the archaeological community.
Usually, the solutions suggested by various research groups and universities depend on various aspects such as the matching process of the broken surfaces, the outline of sherds or their colors and geometric characteris-tics, their axis of symmetry, the corners of their contour, the theme portrayed on the surface, the concentric circular rills that are left during the base construction in the inner pottery side by the fingers of the potter artist etc.
In this work the reassembly process is based on a different and more secure idea, since it is based on the thick-ness profile, which is appropriately identified in every fragment.
Specifically, our approach is based on information encapsulated in the inner part of the sherd (i.e. thickness), which is not -or at least not heavily- affected by the presence of harsh environmental conditions, but is safely kept within the sherd itself.
Our method is verified in various use case experiments, using cutting edge technologies such as 3D representations and precise measurements on surfaces from the acquired 3D models.
Motion planning problems have been studied by both the robotics and the controls research communities for a long time, and many algorithms have been developed for their solution.
Among them, incremental sampling-based motion planning algorithms, such as the Rapidly-exploring Random Trees (RRTs), and the Probabilistic Road Maps (PRMs) have become very popular recently, owing to their implementation simplicity and their advantages in handling high-dimensional problems.
Although these algorithms work very well in practice, the quality of the computed solution is often not good, i.e., the solution can be far from the optimal one.
A recent variation of RRT, namely the RRT* algorithm, bypasses this drawback of the traditional RRT algorithm, by ensuring asymptotic optimality as the number of samples tends to infinity.
Nonetheless, the convergence rate to the optimal solution may still be slow.
This paper presents a new incremental sampling-based motion planning algorithm based on Rapidly-exploring Random Graphs (RRG), denoted RRT# (RRT "sharp") which also guarantees asymptotic optimality but, in addition, it also ensures that the constructed spanning tree of the geometric graph is consistent after each iteration.
In consistent trees, the vertices which have the potential to be part of the optimal solution have the minimum cost-come-value.
This implies that the best possible solution is readily computed if there are some vertices in the current graph that are already in the goal region.
Numerical results compare with the RRT* algorithm.
This paper explores the problem of sockpuppet detection in deceptive opinion spam using authorship attribution and verification approaches.
Two methods are explored.
The first is a feature subsampling scheme that uses the KL-Divergence on stylistic language models of an author to find discriminative features.
The second is a transduction scheme, spy induction that leverages the diversity of authors in the unlabeled test set by sending a set of spies (positive samples) from the training set to retrieve hidden samples in the unlabeled test set using nearest and farthest neighbors.
Experiments using ground truth sockpuppet data show the effectiveness of the proposed schemes.
The Apache Spark framework for distributed computation is popular in the data analytics community due to its ease of use, but its MapReduce-style programming model can incur significant overheads when performing computations that do not map directly onto this model.
One way to mitigate these costs is to off-load computations onto MPI codes.
In recent work, we introduced Alchemist, a system for the analysis of large-scale data sets.
Alchemist calls MPI-based libraries from within Spark applications, and it has minimal coding, communication, and memory overheads.
In particular, Alchemist allows users to retain the productivity benefits of working within the Spark software ecosystem without sacrificing performance efficiency in linear algebra, machine learning, and other related computations.
In this paper, we discuss the motivation behind the development of Alchemist, and we provide a detailed overview its design and usage.
We also demonstrate the efficiency of our approach on medium-to-large data sets, using some standard linear algebra operations, namely matrix multiplication and the truncated singular value decomposition of a dense matrix, and we compare the performance of Spark with that of Spark+Alchemist.
These computations are run on the NERSC supercomputer Cori Phase 1, a Cray XC40.
We introduce the Free Music Archive (FMA), an open and easily accessible dataset suitable for evaluating several tasks in MIR, a field concerned with browsing, searching, and organizing large music collections.
The community's growing interest in feature and end-to-end learning is however restrained by the limited availability of large audio datasets.
The FMA aims to overcome this hurdle by providing 917 GiB and 343 days of Creative Commons-licensed audio from 106,574 tracks from 16,341 artists and 14,854 albums, arranged in a hierarchical taxonomy of 161 genres.
It provides full-length and high-quality audio, pre-computed features, together with track- and user-level metadata, tags, and free-form text such as biographies.
We here describe the dataset and how it was created, propose a train/validation/test split and three subsets, discuss some suitable MIR tasks, and evaluate some baselines for genre recognition.
Code, data, and usage examples are available at https://github.com/mdeff/fma
Existing methods for interactive image retrieval have demonstrated the merit of integrating user feedback, improving retrieval results.
However, most current systems rely on restricted forms of user feedback, such as binary relevance responses, or feedback based on a fixed set of relative attributes, which limits their impact.
In this paper, we introduce a new approach to interactive image search that enables users to provide feedback via natural language, allowing for more natural and effective interaction.
We formulate the task of dialog-based interactive image retrieval as a reinforcement learning problem, and reward the dialog system for improving the rank of the target image during each dialog turn.
To mitigate the cumbersome and costly process of collecting human-machine conversations as the dialog system learns, we train our system with a user simulator, which is itself trained to describe the differences between target and candidate images.
The efficacy of our approach is demonstrated in a footwear retrieval application.
Experiments on both simulated and real-world data show that 1) our proposed learning framework achieves better accuracy than other supervised and reinforcement learning baselines and 2) user feedback based on natural language rather than pre-specified attributes leads to more effective retrieval results, and a more natural and expressive communication interface.
Interest in emergent communication has recently surged in Machine Learning.
The focus of this interest has largely been either on investigating the properties of the learned protocol or on utilizing emergent communication to better solve problems that already have a viable solution.
Here, we consider self-driving cars coordinating with each other and focus on how communication influences the agents' collective behavior.
Our main result is that communication helps (most) with adverse conditions.
Recent progress on many imaging and vision tasks has been driven by the use of deep feed-forward neural networks, which are trained by propagating gradients of a loss defined on the final output, back through the network up to the first layer that operates directly on the image.
We propose back-propagating one step further---to learn camera sensor designs jointly with networks that carry out inference on the images they capture.
In this paper, we specifically consider the design and inference problems in a typical color camera---where the sensor is able to measure only one color channel at each pixel location, and computational inference is required to reconstruct a full color image.
We learn the camera sensor's color multiplexing pattern by encoding it as layer whose learnable weights determine which color channel, from among a fixed set, will be measured at each location.
These weights are jointly trained with those of a reconstruction network that operates on the corresponding sensor measurements to produce a full color image.
Our network achieves significant improvements in accuracy over the traditional Bayer pattern used in most color cameras.
It automatically learns to employ a sparse color measurement approach similar to that of a recent design, and moreover, improves upon that design by learning an optimal layout for these measurements.
This paper presents the Computoser hybrid probability/rule based algorithm for music composition (http://computoser.com) and provides a reference implementation.
It addresses the issues of unpleasantness and lack of variation exhibited by many existing approaches by combining the two methods (basing the parameters of the rules on data obtained from preliminary analysis).
A sample of 500+ musical pieces was analyzed to derive probabilities for musical characteristics and events (e.g. scale, tempo, intervals).
The algorithm was constructed to produce musical pieces using the derived probabilities combined with a large set of composition rules, which were obtained and structured after studying established composition practices.
Generated pieces were published on the Computoser website where evaluation was performed by listeners.
The feedback was positive (58.4% approval), asserting the merits of the undertaken approach.
The paper compares this hybrid approach to other approaches to algorithmic composition and presents a survey of the pleasantness of the resulting music.
Despite computation becomes much complex on data with an unprecedented scale, we argue computers or smart devices should and will consistently provide information and knowledge to human being in the order of a few tens milliseconds.
We coin a new term 10-millisecond computing to call attention to this class of workloads.
10-millisecond computing raises many challenges for both software and hardware stacks.
In this paper, using a typical workload-memcached on a 40-core server (a main-stream server in near future), we quantitatively measure 10-ms computing's challenges to conventional operating systems.
For better communication, we propose a simple metric-outlier proportion to measure quality of service: for N completed requests or jobs, if M jobs or requests' latencies exceed the outlier threshold t, the outlier proportion is M/N .
For a 1K-scale system running Linux (version 2.6.32), LXC (version 0.7.5) or XEN (version 4.0.0), respectively, we surprisingly find that so as to reduce the service outlier proportion to 10% (10% users will feel QoS degradation), the outlier proportion of a single server has to be reduced by 871X, 2372X, 2372X accordingly.
Also, we discuss the possible design spaces of 10-ms computing systems from perspectives of datacenter architectures, networking, OS and scheduling, and benchmarking.
The use of preferences in query answering, both in traditional databases and in ontology-based data access, has recently received much attention, due to its many real-world applications.
In this paper, we tackle the problem of top-k query answering in Datalog+/- ontologies subject to the querying user's preferences and a collection of (subjective) reports of other users.
Here, each report consists of scores for a list of features, its author's preferences among the features, as well as other information.
Theses pieces of information of every report are then combined, along with the querying user's preferences and his/her trust into each report, to rank the query results.
We present two alternative such rankings, along with algorithms for top-k (atomic) query answering under these rankings.
We also show that, under suitable assumptions, these algorithms run in polynomial time in the data complexity.
We finally present more general reports, which are associated with sets of atoms rather than single atoms.
A key enabler for the emerging autonomous and cooperative driving services is high-throughput and reliable Vehicle-to-Network (V2N) communication.
In this respect, the millimeter wave (mmWave) frequencies hold great promises because of the large available bandwidth which may provide the required link capacity.
However, this potential is hindered by the challenging propagation characteristics of high-frequency channels and the dynamic topology of the vehicular scenarios, which affect the reliability of the connection.
Moreover, mmWave transmissions typically leverage beamforming gain to compensate for the increased path loss experienced at high frequencies.
This, however, requires fine alignment of the transmitting and receiving beams, which may be difficult in vehicular scenarios.
Those limitations may undermine the performance of V2N communications and pose new challenges for proper vehicular communication design.
In this paper, we study by simulation the practical feasibility of some mmWave-aware strategies to support V2N, in comparison to the traditional LTE connectivity below 6 GHz.
The results show that the orchestration among different radios represents a viable solution to enable both high-capacity and robust V2N communications.
We propose an innovative meteorological radar, which uses reduced number of spatiotemporal samples without compromising the accuracy of target information.
Our approach extends recent research on compressed sensing (CS) for radar remote sensing of hard point scatterers to volumetric targets.
The previously published CS-based radar techniques are not applicable for sampling weather since the precipitation echoes lack sparsity in both range-time and Doppler domains.
We propose an alternative approach by adopting the latest advances in matrix completion algorithms to demonstrate the sparse sensing of weather echoes.
We use Iowa X-band Polarimetric (XPOL) radar data to test and illustrate our algorithms.
Using a dataset of over 1.9 million messages posted on Twitter by about 25,000 ISIS members, we explore how ISIS makes use of social media to spread its propaganda and to recruit militants from the Arab world and across the globe.
By distinguishing between violence-driven, theological, and sectarian content, we trace the connection between online rhetoric and key events on the ground.
To the best of our knowledge, ours is one of the first studies to focus on Arabic content, while most literature focuses on English content.
Our findings yield new important insights about how social media is used by radical militant groups to target the Arab-speaking world, and reveal important patterns in their propaganda efforts.
Nowadays, providing higher data rate is a momentous goal for wireless communications systems.
Interference is one of the important obstacles to reach this purpose.
Interference alignment is a management technique that align interference from other transmitters in the least possible dimension subspace at each receiver and as a result, provide the remaining dimensions for free interference signal.
An uncoordinated interference is an example of interference which cannot be aligned coordinately with interference from coordinated part and consequently, the performance of interference alignment approaches is degraded.
In this paper, we propose two rank minimization methods to enhance the performance of interference alignment in the presence of uncoordinated interference sources.
Firstly, a new objective function is chosen then, a new class of convex relaxation is proposed with respect to the uncoordinated interference which leads to decrease the optimal value of our optimization problem.
Moreover, we use schatten-p-norm as surrogate of rank function and we implement iteratively reweighted algorithm to solve optimization problem.
In addition, we apply our proposed methods to mitigate interference in relay-aided MIMO interference channel, and propose a weighted-sum method to improve the performance of interference alignment in the amplify-and-forward relay-aided MIMO system based on the rank minimization approach.
Finally, our simulation results show that our proposed methods can obtain considerably higher multiplexing gain and sum rate than other approaches in the interference alignment framework and the performance of interference alignment is improved.
This paper proposes a novel framework for the use of eye movement patterns for biometric applications.
Eye movements contain abundant information about cognitive brain functions, neural pathways, etc.
In the proposed method, eye movement data is classified into fixations and saccades.
Features extracted from fixations and saccades are used by a Gaussian Radial Basis Function Network (GRBFN) based method for biometric authentication.
A score fusion approach is adopted to classify the data in the output layer.
In the evaluation stage, the algorithm has been tested using two types of stimuli: random dot following on a screen and text reading.
The results indicate the strength of eye movement pattern as a biometric modality.
The algorithm has been evaluated on BioEye 2015 database and found to outperform all the other methods.
Eye movements are generated by a complex oculomotor plant which is very hard to spoof by mechanical replicas.
Use of eye movement dynamics along with iris recognition technology may lead to a robust counterfeit-resistant person identification system.
In this paper, we consider a time-optimal control problem with uncertainties.
Dynamics of controlled object is expressed by crisp linear system of differential equations with fuzzy initial and final states.
We introduce a notion of fuzzy optimal time and reduce its calculation to two crisp optimal control problems.
We examine the proposed approach on an example.
We propose a hypothesis only baseline for diagnosing Natural Language Inference (NLI).
Especially when an NLI dataset assumes inference is occurring based purely on the relationship between a context and a hypothesis, it follows that assessing entailment relations while ignoring the provided context is a degenerate solution.
Yet, through experiments on ten distinct NLI datasets, we find that this approach, which we refer to as a hypothesis-only model, is able to significantly outperform a majority class baseline across a number of NLI datasets.
Our analysis suggests that statistical irregularities may allow a model to perform NLI in some datasets beyond what should be achievable without access to the context.
In this paper, we present a joint compression and classification approach of EEG and EMG signals using a deep learning approach.
Specifically, we build our system based on the deep autoencoder architecture which is designed not only to extract discriminant features in the multimodal data representation but also to reconstruct the data from the latent representation using encoder-decoder layers.
Since autoencoder can be seen as a compression approach, we extend it to handle multimodal data at the encoder layer, reconstructed and retrieved at the decoder layer.
We show through experimental results, that exploiting both multimodal data intercorellation and intracorellation 1) Significantly reduces signal distortion particularly for high compression levels 2) Achieves better accuracy in classifying EEG and EMG signals recorded and labeled according to the sentiments of the volunteer.
Several recent papers use image denoising with a Fields of Experts prior to benchmark discrete optimization methods.
We show that a non-linear least squares solver significantly outperforms all known discrete methods on this problem.
Dynamic ensemble selection (DES) techniques work by estimating the level of competence of each classifier from a pool of classifiers.
Only the most competent ones are selected to classify a given test sample.
Hence, the key issue in DES is the criterion used to estimate the level of competence of the classifiers in predicting the label of a given test sample.
In order to perform a more robust ensemble selection, we proposed the META-DES framework using meta-learning, where multiple criteria are encoded as meta-features and are passed down to a meta-classifier that is trained to estimate the competence level of a given classifier.
In this technical report, we present a step-by-step analysis of each phase of the framework during training and test.
We show how each set of meta-features is extracted as well as their impact on the estimation of the competence level of the base classifier.
Moreover, an analysis of the impact of several factors in the system performance, such as the number of classifiers in the pool, the use of different linear base classifiers, as well as the size of the validation data.
We show that using the dynamic selection of linear classifiers through the META-DES framework, we can solve complex non-linear classification problems where other combination techniques such as AdaBoost cannot.
Exploiting dependencies between labels is considered to be crucial for multi-label classification.
Rules are able to expose label dependencies such as implications, subsumptions or exclusions in a human-comprehensible and interpretable manner.
However, the induction of rules with multiple labels in the head is particularly challenging, as the number of label combinations which must be taken into account for each rule grows exponentially with the number of available labels.
To overcome this limitation, algorithms for exhaustive rule mining typically use properties such as anti-monotonicity or decomposability in order to prune the search space.
In the present paper, we examine whether commonly used multi-label evaluation metrics satisfy these properties and therefore are suited to prune the search space for multi-label heads.
Blockchains have recently been under the spotlight due to the boom of cryptocurrencies and decentralized applications.
There is an increasing demand for querying the data stored in a blockchain database.
To ensure query integrity, the user can maintain the entire blockchain database and query the data locally.
However, this approach is not economic, if not infeasible, because of the blockchain's huge data size and considerable maintenance costs.
In this paper, we take the first step toward investigating the problem of verifiable query processing over blockchain databases.
We propose a novel framework, called vChain, that alleviates the storage and computing costs of the user and employs verifiable queries to guarantee the results' integrity.
To support verifiable Boolean range queries, we propose an accumulator-based authenticated data structure that enables dynamic aggregation over arbitrary query attributes.
Two new indexes are further developed to aggregate intra-block and inter-block data records for efficient query verification.
We also propose an inverted prefix tree structure to accelerate the processing of a large number of subscription queries simultaneously.
Security analysis and empirical study validate the robustness and practicality of the proposed techniques.
Software developers create and share code online to demonstrate programming language concepts and programming tasks.
Code snippets can be a useful way to explain and demonstrate a programming concept, but may not always be directly executable.
A code snippet can contain parse errors, or fail to execute if the environment contains unmet dependencies.
This paper presents an empirical analysis of the executable status of Python code snippets shared through the GitHub gist system, and the ability of developers familiar with software configuration to correctly configure and run them.
We find that 75.6% of gists require non-trivial configuration to overcome missing dependencies, configuration files, reliance on a specific operating system, or some other environment configuration.
Our study also suggests the natural assumption developers make about resource names when resolving configuration errors is correct less than half the time.
We also present Gistable, a database and extensible framework built on GitHub's gist system, which provides executable code snippets to enable reproducible studies in software engineering.
Gistable contains 10,259 code snippets, approximately 5,000 with a Dockerfile to configure and execute them without import error.
Gistable is publicly available at https://github.com/gistable/gistable.
Large-scale datasets for natural language inference are created by presenting crowd workers with a sentence (premise), and asking them to generate three new sentences (hypotheses) that it entails, contradicts, or is logically neutral with respect to.
We show that, in a significant portion of such data, this protocol leaves clues that make it possible to identify the label by looking only at the hypothesis, without observing the premise.
Specifically, we show that a simple text categorization model can correctly classify the hypothesis alone in about 67% of SNLI (Bowman et. al, 2015) and 53% of MultiNLI (Williams et. al, 2017).
Our analysis reveals that specific linguistic phenomena such as negation and vagueness are highly correlated with certain inference classes.
Our findings suggest that the success of natural language inference models to date has been overestimated, and that the task remains a hard open problem.
This paper describes LIUM submissions to WMT17 News Translation Task for English-German, English-Turkish, English-Czech and English-Latvian language pairs.
We train BPE-based attentive Neural Machine Translation systems with and without factored outputs using the open source nmtpy framework.
Competitive scores were obtained by ensembling various systems and exploiting the availability of target monolingual corpora for back-translation.
The impact of back-translation quantity and quality is also analyzed for English-Turkish where our post-deadline submission surpassed the best entry by +1.6 BLEU.
Over the last decade, the process of automatic image colorization has been of significant interest for several application areas including restoration of aged or degraded images.
This problem is highly ill-posed due to the large degrees of freedom during the assignment of color information.
Many of the recent developments in automatic colorization involve images that contain a common theme or require highly processed data such as semantic maps as input.
In our approach, we attempt to fully generalize the colorization procedure using a conditional Deep Convolutional Generative Adversarial Network (DCGAN), extend current methods to high-resolution images and suggest training strategies that speed up the process and greatly stabilize it.
The network is trained over datasets that are publicly available such as CIFAR-10 and Places365.
The results of the generative model and traditional deep neural networks are compared.
We propose a data-driven framework for optimizing privacy-preserving data release mechanisms toward the information-theoretically optimal tradeoff between minimizing distortion of useful data and concealing sensitive information.
Our approach employs adversarially-trained neural networks to implement randomized mechanisms and to perform a variational approximation of mutual information privacy.
We empirically validate our Privacy-Preserving Adversarial Networks (PPAN) framework with experiments conducted on discrete and continuous synthetic data, as well as the MNIST handwritten digits dataset.
With the synthetic data, we find that our model-agnostic PPAN approach achieves tradeoff points very close to the optimal tradeoffs that are analytically-derived from model knowledge.
In experiments with the MNIST data, we visually demonstrate a learned tradeoff between minimizing the pixel-level distortion versus concealing the written digit.
Scheduling and managing queues with bounded buffers are among the most fundamental problems in computer networking.
Traditionally, it is often assumed that all the proper- ties of each packet are known immediately upon arrival.
However, as traffic becomes increasingly heterogeneous and complex, such assumptions are in many cases invalid.
In particular, in various scenarios information about packet characteristics becomes avail- able only after the packet has undergone some initial processing.
In this work, we study the problem of managing queues with limited knowledge.
We start by showing lower bounds on the competitive ratio of any algorithm in such settings.
Next, we use the insight obtained from these bounds to identify several algorithmic concepts appropriate for the problem, and use these guidelines to design a concrete algorithmic framework.
We analyze the performance of our proposed algorithm, and further show how it can be implemented in various settings, which differ by the type and nature of the unknown information.
We further validate our results and algorithmic approach by a simulation study that provides further insights as to our algorithmic design principles in face of limited knowledge.
The Cloud radio access network (C-RAN) offers a revolutionary approach to cellular network deployment, management and evolution.
Advances in software-defined radio (SDR) and networking technology, moreover, enable delivering software-defined everything through the Cloud.
Resources will be pooled and dynamically allocated leveraging abstraction, virtualization, and consolidation techniques; processes will be automated using common application programming interfaces; and network functions and services will be programmatically provided through an orchestrator.
OOCRAN, oocran.dynu.com, is a software framework that is based on the NFV MANO architecture proposed by ETSI.
It provides an orchestration layer for the entire wireless infrastructure, including hardware, software, spectrum, fronthaul and backhaul.
OOCRAN extends existing NFV management frameworks by incorporating the radio communications layers and their management dependencies.
The wireless infrastructure provider can then dynamically provision virtualized wireless networks to wireless service providers.
The testbed's physical infrastructure is built around a computing cluster that executes open-source SDR libraries and connects to SDR-based remote radio heads.
We demonstrate the operation of OOCRAN and discuss the temporal implications of dynamic LTE small cell network deployments.
Optical backbone networks carry a huge amount of bandwidth and serve as a key enabling technology to provide telecommunication connectivity across the world.
Hence, in events of network component (node/link) failures, communication networks may suffer from huge amount of bandwidth loss and service disruptions.
Natural disasters such as earthquakes, hurricanes, tornadoes, etc., occur at different places around the world, causing severe communication service disruptions due to network component failures.
Most of the previous works on optical network survivability assume that the failures are going to occur in future, and the network is made survivable to ensure connectivity in events of failures.
With the advancements in seismology, the predictions of earthquakes are becoming more accurate.
Earthquakes have been a major cause of telecommunication service disruption in the past.
Hence, the information provided by the meteorological departments and other similar agencies of different countries may be helpful in designing networks that are more robust against earthquakes.
In this work, we consider the actual information provided by the Indian meteorological department (IMD) on seismic zones, and earthquakes occurred in the past in India, and propose a scheme to improve the survivability of the existing Indian optical network through minute changes in network topology.
Simulations show significant improvement in the network survivability can be achieved using the proposed scheme in events of earthquakes.
Evolution of deep learning shows that some algorithmic tricks are more durable , while others are not.
To the best of our knowledge, we firstly summarize 5 more durable and complete deep learning components for vision, that is, WARSHIP.
Moreover, we give a biological overview of WARSHIP, emphasizing brain-inspired computing of WARSHIP.
As a step towards WARSHIP, our case study of image super resolution combines 3 components of RSH to deploy a CNN model of WARSHIP-XZNet, which performs a happy medium between speed and performance.
In the modal mu-calculus, a formula is well-formed if each recursive variable occurs underneath an even number of negations.
By means of De Morgan's laws, it is easy to transform any well-formed formula into an equivalent formula without negations -- its negation normal form.
Moreover, if the formula is of size n, its negation normal form of is of the same size O(n).
The full modal mu-calculus and the negation normal form fragment are thus equally expressive and concise.
In this paper we extend this result to the higher-order modal fixed point logic (HFL), an extension of the modal mu-calculus with higher-order recursive predicate transformers.
We present a procedure that converts a formula into an equivalent formula without negations of quadratic size in the worst case and of linear size when the number of variables of the formula is fixed.
The sensitivity of networks regarding the removal of vertices has been studied extensively within the last 15 years.
A common approach to measure this sensitivity is (i) removing successively vertices by following a specific removal strategy and (ii) comparing the original and the modified network using a specific comparison method.
In this paper we apply a wide range of removal strategies and comparison methods in order to study the sensitivity of medium-sized networks from real world and randomly generated networks.
In the first part of our study we observe that social networks and web graphs differ in sensitivity.
When removing vertices, social networks are robust, web graphs are not.
This effect is conclusive with the work of Boldi et al. who analyzed very large networks.
For similarly generated random graphs we find that the sensitivity highly depends on the comparison method.
The choice of the removal strategy has surprisingly marginal impact on the sensitivity as long as we consider removal strategies implied by common centrality measures.
However, it has a strong effect when removing the vertices in random order.
Variance-based logic (VBL) uses the fluctuations or the variance in the state of a particle or a physical quantity to represent different logic levels.
In this letter we show that compared to the traditional bi-stable logic representation the variance-based representation can theoretically achieve a superior performance trade-off (in terms of energy dissipation and information capacity) when operating at fundamental limits imposed by thermal-noise.
We show that for a bi-stable logic device the lower limit on energy dissipated per bit is 4.35KT/bit, whereas under similar operating conditions, a VBL device could achieve a lower limit of sub-KT/bit.
These theoretical results are general enough to be applicable to different instantiations and variants of VBL ranging from digital processors based on energy-scavenging or to processors based on the emerging valleytronic devices.
Most Semantic Role Labeling (SRL) approaches are supervised methods which require a significant amount of annotated corpus, and the annotation requires linguistic expertise.
In this paper, we propose a Multi-Task Active Learning framework for Semantic Role Labeling with Entity Recognition (ER) as the auxiliary task to alleviate the need for extensive data and use additional information from ER to help SRL.
We evaluate our approach on Indonesian conversational dataset.
Our experiments show that multi-task active learning can outperform single-task active learning method and standard multi-task learning.
According to our results, active learning is more efficient by using 12% less of training data compared to passive learning in both single-task and multi-task setting.
We also introduce a new dataset for SRL in Indonesian conversational domain to encourage further research in this area.
Iterative decoding and linear programming decoding are guaranteed to converge to the maximum-likelihood codeword when the underlying Tanner graph is cycle-free.
Therefore, cycles are usually seen as the culprit of low-density parity-check (LDPC) codes.
In this paper, we argue in the context of graph cover pseudocodeword that, for a code that permits a cycle-free Tanner graph, cycles have no effect on error performance as long as they are a part of redundant rows.
Specifically, we characterize all parity-check matrices that are pseudocodeword-free for such class of codes.
Deep neural networks trained on large supervised datasets have led to impressive results in image classification and other tasks.
However, well-annotated datasets can be time-consuming and expensive to collect, lending increased interest to larger but noisy datasets that are more easily obtained.
In this paper, we show that deep neural networks are capable of generalizing from training data for which true labels are massively outnumbered by incorrect labels.
We demonstrate remarkably high test performance after training on corrupted data from MNIST, CIFAR, and ImageNet.
For example, on MNIST we obtain test accuracy above 90 percent even after each clean training example has been diluted with 100 randomly-labeled examples.
Such behavior holds across multiple patterns of label noise, even when erroneous labels are biased towards confusing classes.
We show that training in this regime requires a significant but manageable increase in dataset size that is related to the factor by which correct labels have been diluted.
Finally, we provide an analysis of our results that shows how increasing noise decreases the effective batch size.
Modern Graphics Processing Units (GPUs) are now considered accelerators for general purpose computation.
A tight interaction between the GPU and the interconnection network is the strategy to express the full potential on capability computing of a multi-GPU system on large HPC clusters; that is the reason why an efficient and scalable interconnect is a key technology to finally deliver GPUs for scientific HPC.
In this paper we show the latest architectural and performance improvement of the APEnet+ network fabric, a FPGA-based PCIe board with 6 fully bidirectional off-board links with 34 Gbps of raw bandwidth per direction, and X8 Gen2 bandwidth towards the host PC.
The board implements a Remote Direct Memory Access (RDMA) protocol that leverages upon peer-to-peer (P2P) capabilities of Fermi- and Kepler-class NVIDIA GPUs to obtain real zero-copy, low-latency GPU-to-GPU transfers.
Finally, we report on the development activities for 2013 focusing on the adoption of the latest generation 28 nm FPGAs and the preliminary tests performed on this new platform.
People usually get involved in multiple social networks to enjoy new services or to fulfill their needs.
Many new social networks try to attract users of other existing networks to increase the number of their users.
Once a user (called source user) of a social network (called source network) joins a new social network (called target network), a new inter-network link (called anchor link) is formed between the source and target networks.
In this paper, we concentrated on predicting the formation of such anchor links between heterogeneous social networks.
Unlike conventional link prediction problems in which the formation of a link between two existing users within a single network is predicted, in anchor link prediction, the target user is missing and will be added to the target network once the anchor link is created.
To solve this problem, we use meta-paths as a powerful tool for utilizing heterogeneous information in both the source and target networks.
To this end, we propose an effective general meta-path-based approach called Connector and Recursive Meta-Paths (CRMP).
By using those two different categories of meta-paths, we model different aspects of social factors that may affect a source user to join the target network, resulting in the formation of a new anchor link.
Extensive experiments on real-world heterogeneous social networks demonstrate the effectiveness of the proposed method against the recent methods.
We consider Markov Decision Problems defined over continuous state and action spaces, where an autonomous agent seeks to learn a map from its states to actions so as to maximize its long-term discounted accumulation of rewards.
We address this problem by considering Bellman's optimality equation defined over action-value functions, which we reformulate into a nested non-convex stochastic optimization problem defined over a Reproducing Kernel Hilbert Space (RKHS).
We develop a functional generalization of stochastic quasi-gradient method to solve it, which, owing to the structure of the RKHS, admits a parameterization in terms of scalar weights and past state-action pairs which grows proportionately with the algorithm iteration index.
To ameliorate this complexity explosion, we apply Kernel Orthogonal Matching Pursuit to the sequence of kernel weights and dictionaries, which yields a controllable error in the descent direction of the underlying optimization method.
We prove that the resulting algorithm, called KQ-Learning, converges with probability 1 to a stationary point of this problem, yielding a fixed point of the Bellman optimality operator under the hypothesis that it belongs to the RKHS.
Under constant learning rates, we further obtain convergence to a small Bellman error that depends on the chosen learning rates.
Numerical evaluation on the Continuous Mountain Car and Inverted Pendulum tasks yields convergent parsimonious learned action-value functions, policies that are competitive with the state of the art, and exhibit reliable, reproducible learning behavior.
The present study applies a novel two-dimensional learning framework (2D-UPSO) based on particle swarms for structure selection of polynomial nonlinear auto-regressive with exogenous inputs (NARX) models.
This learning approach explicitly incorporates the information about the cardinality (i.e., the number of terms) into the structure selection process.
Initially, the effectiveness of the proposed approach was compared against the classical genetic algorithm (GA) based approach and it was demonstrated that the 2D-UPSO is superior.
Further, since the performance of any meta-heuristic search algorithm is critically dependent on the choice of the fitness function, the efficacy of the proposed approach was investigated using two distinct information theoretic criteria such as Akaike and Bayesian information criterion.
The robustness of this approach against various levels of measurement noise is also studied.
Simulation results on various nonlinear systems demonstrate that the proposed algorithm could accurately determine the structure of the polynomial NARX model even under the influence of measurement noise.
Programming is a valuable skill in the labor market, making the underrepresentation of women in computing an increasingly important issue.
Online question and answer platforms serve a dual purpose in this field: they form a body of knowledge useful as a reference and learning tool, and they provide opportunities for individuals to demonstrate credible, verifiable expertise.
Issues, such as male-oriented site design or overrepresentation of men among the site's elite may therefore compound the issue of women's underrepresentation in IT.
In this paper we audit the differences in behavior and outcomes between men and women on Stack Overflow, the most popular of these Q&A sites.
We observe significant differences in how men and women participate in the platform and how successful they are.
For example, the average woman has roughly half of the reputation points, the primary measure of success on the site, of the average man.
Using an Oaxaca-Blinder decomposition, an econometric technique commonly applied to analyze differences in wages between groups, we find that most of the gap in success between men and women can be explained by differences in their activity on the site and differences in how these activities are rewarded.
Specifically, 1) men give more answers than women and 2) are rewarded more for their answers on average, even when controlling for possible confounders such as tenure or buy-in to the site.
Women ask more questions and gain more reward per question.
We conclude with a hypothetical redesign of the site's scoring system based on these behavioral differences, cutting the reputation gap in half.
We make an important connection to existing results in econometrics to describe an alternative formulation of inverse reinforcement learning (IRL).
In particular, we describe an algorithm using Conditional Choice Probabilities (CCP), which are maximum likelihood estimates of the policy estimated from expert demonstrations, to solve the IRL problem.
Using the language of structural econometrics, we re-frame the optimal decision problem and introduce an alternative representation of value functions due to (Hotz and Miller 1993).
In addition to presenting the theoretical connections that bridge the IRL literature between Economics and Robotics, the use of CCPs also has the practical benefit of reducing the computational cost of solving the IRL problem.
Specifically, under the CCP representation, we show how one can avoid repeated calls to the dynamic programming subroutine typically used in IRL.
We show via extensive experimentation on standard IRL benchmarks that CCP-IRL is able to outperform MaxEnt-IRL, with as much as a 5x speedup and without compromising on the quality of the recovered reward function.
Scientific collaborations are among the main enablers of development in small national science systems.
Although analysing scientific collaborations is a well-established subject in scientometrics, evaluations of scientific collaborations within a country remain speculative with studies based on a limited number of fields or using data too inadequate to be representative of collaborations at a national level.
This study represents a unique view on the collaborative aspect of scientific activities in New Zealand.
We perform a quantitative study based on all Scopus publications in all subjects for more than 1500 New Zealand institutions over a period of 6 years to generate an extensive mapping of scientific collaboration at a national level.
The comparative results reveal the level of collaboration between New Zealand institutions and business enterprises, government institutions, higher education providers, and private not for profit organisations in 2010-2015.
Constructing a collaboration network of institutions, we observe a power-law distribution indicating that a small number of New Zealand institutions account for a large proportion of national collaborations.
Network centrality concepts are deployed to identify the most central institutions of the country in terms of collaboration.
We also provide comparative results on 15 universities and Crown research institutes based on 27 subject classifications.
The focus of this paper is to quantify measures of aggregate fluctuations for a class of consensus-seeking multiagent networks subject to exogenous noise with alpha-stable distributions.
This type of noise is generated by a class of random measures with heavy-tailed probability distributions.
We define a cumulative scale parameter using scale parameters of probability distributions of the output variables, as a measure of aggregate fluctuation.
Although this class of measures can be characterized implicitly in closed-form in steady-state, finding their explicit forms in terms of network parameters is, in general, almost impossible.
We obtain several tractable upper bounds in terms of Laplacian spectrum and statistics of the input noise.
Our results suggest that relying on Gaussian-based optimal design algorithms will result in non-optimal solutions for networks that are driven by non-Gaussian noise inputs with alpha-stable distributions.
The manuscript has been submitted for publication to IEEE Transactions on Control of Network Systems.
It is the extended version of preliminary paper included in the proceedings of the 2018 American Control Conference.
Real-world machine learning applications may require functions that are fast-to-evaluate and interpretable.
In particular, guaranteed monotonicity of the learned function can be critical to user trust.
We propose meeting these goals for low-dimensional machine learning problems by learning flexible, monotonic functions using calibrated interpolated look-up tables.
We extend the structural risk minimization framework of lattice regression to train monotonic look-up tables by solving a convex problem with appropriate linear inequality constraints.
In addition, we propose jointly learning interpretable calibrations of each feature to normalize continuous features and handle categorical or missing data, at the cost of making the objective non-convex.
We address large-scale learning through parallelization, mini-batching, and propose random sampling of additive regularizer terms.
Case studies with real-world problems with five to sixteen features and thousands to millions of training samples demonstrate the proposed monotonic functions can achieve state-of-the-art accuracy on practical problems while providing greater transparency to users.
We study adaptive data-dependent dimensionality reduction in the context of supervised learning in general metric spaces.
Our main statistical contribution is a generalization bound for Lipschitz functions in metric spaces that are doubling, or nearly doubling.
On the algorithmic front, we describe an analogue of PCA for metric spaces: namely an efficient procedure that approximates the data's intrinsic dimension, which is often much lower than the ambient dimension.
Our approach thus leverages the dual benefits of low dimensionality: (1) more efficient algorithms, e.g., for proximity search, and (2) more optimistic generalization bounds.
Generating high-resolution, photo-realistic images has been a long-standing goal in machine learning.
Recently, Nguyen et al.(2016) showed one interesting way to synthesize novel images by performing gradient ascent in the latent space of a generator network to maximize the activations of one or multiple neurons in a separate classifier network.
In this paper we extend this method by introducing an additional prior on the latent code, improving both sample quality and sample diversity, leading to a state-of-the-art generative model that produces high quality images at higher resolutions (227x227) than previous generative models, and does so for all 1000 ImageNet categories.
In addition, we provide a unified probabilistic interpretation of related activation maximization methods and call the general class of models "Plug and Play Generative Networks".
PPGNs are composed of 1) a generator network G that is capable of drawing a wide range of image types and 2) a replaceable "condition" network C that tells the generator what to draw.
We demonstrate the generation of images conditioned on a class (when C is an ImageNet or MIT Places classification network) and also conditioned on a caption (when C is an image captioning network).
Our method also improves the state of the art of Multifaceted Feature Visualization, which generates the set of synthetic inputs that activate a neuron in order to better understand how deep neural networks operate.
Finally, we show that our model performs reasonably well at the task of image inpainting.
While image models are used in this paper, the approach is modality-agnostic and can be applied to many types of data.
Many recent works on knowledge distillation have provided ways to transfer the knowledge of a trained network for improving the learning process of a new one, but finding a good technique for knowledge distillation is still an open problem.
In this paper, we provide a new perspective based on a decision boundary, which is one of the most important component of a classifier.
The generalization performance of a classifier is closely related to the adequacy of its decision boundary, so a good classifier bears a good decision boundary.
Therefore, transferring information closely related to the decision boundary can be a good attempt for knowledge distillation.
To realize this goal, we utilize an adversarial attack to discover samples supporting a decision boundary.
Based on this idea, to transfer more accurate information about the decision boundary, the proposed algorithm trains a student classifier based on the adversarial samples supporting the decision boundary.
Experiments show that the proposed method indeed improves knowledge distillation and achieves the state-of-the-arts performance.
Mammography is the most effective and available tool for breast cancer screening.
However, the low positive predictive value of breast biopsy resulting from mammogram interpretation leads to approximately 70% unnecessary biopsies with benign outcomes.
Data mining algorithms could be used to help physicians in their decisions to perform a breast biopsy on a suspicious lesion seen in a mammogram image or to perform a short term follow-up examination instead.
In this research paper data mining classification algorithms; Decision Tree (DT), Artificial Neural Network (ANN), and Support Vector Machine (SVM) are analyzed on mammographic masses data set.
The purpose of this study is to increase the ability of physicians to determine the severity (benign or malignant) of a mammographic mass lesion from BI-RADS attributes and the patient,s age.
The whole data set is divided for training the models and test them by the ratio of 70:30% respectively and the performances of classification algorithms are compared through three statistical measures; sensitivity, specificity, and classification accuracy.
Accuracy of DT, ANN and SVM are 78.12%, 80.56% and 81.25% of test samples respectively.
Our analysis shows that out of these three classification models SVM predicts severity of breast cancer with least error rate and highest accuracy.
There is a vast body of theoretical research on lifted inference in probabilistic graphical models (PGMs).
However, few demonstrations exist where lifting is applied in conjunction with top of the line applied algorithms.
We pursue the applicability of lifted inference for computer vision (CV), with the insight that a globally optimal (MAP) labeling will likely have the same label for two symmetric pixels.
The success of our approach lies in efficiently handling a distinct unary potential on every node (pixel), typical of CV applications.
This allows us to lift the large class of algorithms that model a CV problem via PGM inference.
We propose a generic template for coarse-to-fine (C2F) inference in CV, which progressively refines an initial coarsely lifted PGM for varying quality-time trade-offs.
We demonstrate the performance of C2F inference by developing lifted versions of two near state-of-the-art CV algorithms for stereo vision and interactive image segmentation.
We find that, against flat algorithms, the lifted versions have a much superior anytime performance, without any loss in final solution quality.
One of the most straightforward, direct and efficient approaches to Image Segmentation is Image Thresholding.
Multi-level Image Thresholding is an essential viewpoint in many image processing and Pattern Recognition based real-time applications which can effectively and efficiently classify the pixels into various groups denoting multiple regions in an Image.
Thresholding based Image Segmentation using fuzzy entropy combined with intelligent optimization approaches are commonly used direct methods to properly identify the thresholds so that they can be used to segment an Image accurately.
In this paper a novel approach for multi-level image thresholding is proposed using Type II Fuzzy sets combined with Adaptive Plant Propagation Algorithm (APPA).
Obtaining the optimal thresholds for an image by maximizing the entropy is extremely tedious and time consuming with increase in the number of thresholds.
Hence, Adaptive Plant Propagation Algorithm (APPA), a memetic algorithm based on plant intelligence, is used for fast and efficient selection of optimal thresholds.
This fact is reasonably justified by comparing the accuracy of the outcomes and computational time consumed by other modern state-of-the-art algorithms such as Particle Swarm Optimization (PSO), Gravitational Search Algorithm (GSA) and Genetic Algorithm (GA).
Kernel methods play a critical role in many dimensionality reduction algorithms.
They are useful in manifold learning, classification, clustering and other machine learning tasks.
Setting the kernel's scale parameter, also referred as the kernel's bandwidth, highly affects the extracted low-dimensional representation.
We propose to set a scale parameter that is tailored to the desired application such as classification and manifold learning.
The scale computation for the manifold learning task enables that the dimension of the extracted embedding equals the intrinsic dimension estimation.
Three methods are proposed for scale computation in a classification task.
The proposed frameworks are simulated on artificial and real datasets.
The results show a high correlation between optimal classification rates and the computed scaling.
Finding heavy-elements (heavy-hitters) in streaming data is one of the central, and well-understood tasks.
Despite the importance of this problem, when considering the sliding windows model of streaming (where elements eventually expire) the problem of finding L_2-heavy elements has remained completely open despite multiple papers and considerable success in finding L_1-heavy elements.
In this paper, we develop the first poly-logarithmic-memory algorithm for finding L_2-heavy elements in sliding window model.
Since L_2 heavy elements play a central role for many fundamental streaming problems (such as frequency moments), we believe our method would be extremely useful for many sliding-windows algorithms and applications.
For example, our technique allows us not only to find L_2-heavy elements, but also heavy elements with respect to any L_p for 0<p<2 on sliding windows.
Thus, our paper completely resolves the question of finding L_p-heavy elements for sliding windows with poly-logarithmic memory for all values of p since it is well known that for p>2 this task is impossible.
Our method may have other applications as well.
We demonstrate a broader applicability of our novel yet simple method on two additional examples: we show how to obtain a sliding window approximation of other properties such as the similarity of two streams, or the fraction of elements that appear exactly a specified number of times within the window (the rarity problem).
In these two illustrative examples of our method, we replace the current expected memory bounds with worst case bounds.
A novel tag completion algorithm is proposed in this paper, which is designed with the following features: 1) Low-rank and error s-parsity: the incomplete initial tagging matrix D is decomposed into the complete tagging matrix A and a sparse error matrix E. However, instead of minimizing its nuclear norm, A is further factor-ized into a basis matrix U and a sparse coefficient matrix V, i.e.D=UV+E.
This low-rank formulation encapsulating sparse coding enables our algorithm to recover latent structures from noisy initial data and avoid performing too much denoising; 2) Local reconstruction structure consistency: to steer the completion of D, the local linear reconstruction structures in feature space and tag space are obtained and preserved by U and V respectively.
Such a scheme could alleviate the negative effect of distances measured by low-level features and incomplete tags.
Thus, we can seek a balance between exploiting as much information and not being mislead to suboptimal performance.
Experiments conducted on Corel5k dataset and the newly issued Flickr30Concepts dataset demonstrate the effectiveness and efficiency of the proposed method.
Correct operation of many critical systems is dependent on the data consistency and integrity properties of underlying databases.
Therefore, a verifiable and rigorous database design process is highly desirable.
This research aims to investigate and deliver a comprehensive and practical approach for modelling databases in formal methods through layered refinements.
The methodology is being guided by a number of case studies, using abstraction and refinement in UML-B and verification with the Rodin tool.
UML-B is a graphical representation of the Event-B formalism and the Rodin tool supports verification for Event-B and UML-B.
Our method guides developers to model relational databases in UML-B through layered refinement and to specify the necessary constraints and operations on the database.
We introduce the Visual Data Management System (VDMS), which enables faster access to big-visual-data and adds support to visual analytics.
This is achieved by searching for relevant visual data via metadata stored as a graph, and enabling faster access to visual data through new machine-friendly storage formats.
VDMS differs from existing large scale photo serving, video streaming, and textual big-data management systems due to its primary focus on supporting machine learning and data analytics pipelines that use visual data (images, videos, and feature vectors), treating these as first class entities.
We describe how to use VDMS via its user friendly interface and how it enables rich and efficient vision analytics through a machine learning pipeline for processing medical images.
We show the improved performance of 2x in complex queries over a comparable set-up.
Retaining players over an extended period of time is a long-standing challenge in game industry.
Significant effort has been paid to understanding what motivates players enjoy games.
While individuals may have varying reasons to play or abandon a game at different stages within the game, previous studies have looked at the retention problem from a snapshot view.
This study, by analyzing in-game logs of 51,104 distinct individuals in an online multiplayer game, uniquely offers a multifaceted view of the retention problem over the players' virtual life phases.
We find that key indicators of longevity change with the game level.
Achievement features are important for players at the initial to the advanced phases, yet social features become the most predictive of longevity once players reach the highest level offered by the game.
These findings have theoretical and practical implications for designing online games that are adaptive to meeting the players' needs.
Facial aging and facial rejuvenation analyze a given face photograph to predict a future look or estimate a past look of the person.
To achieve this, it is critical to preserve human identity and the corresponding aging progression and regression with high accuracy.
However, existing methods cannot simultaneously handle these two objectives well.
We propose a novel generative adversarial network based approach, named the Conditional Multi-Adversarial AutoEncoder with Ordinal Regression (CMAAE-OR).
It utilizes an age estimation technique to control the aging accuracy and takes a high-level feature representation to preserve personalized identity.
Specifically, the face is first mapped to a latent vector through a convolutional encoder.
The latent vector is then projected onto the face manifold conditional on the age through a deconvolutional generator.
The latent vector preserves personalized face features and the age controls facial aging and rejuvenation.
A discriminator and an ordinal regression are imposed on the encoder and the generator in tandem, making the generated face images to be more photorealistic while simultaneously exhibiting desirable aging effects.
Besides, a high-level feature representation is utilized to preserve personalized identity of the generated face.
Experiments on two benchmark datasets demonstrate appealing performance of the proposed method over the state-of-the-art.
This paper investigates generation of a secret key from a reciprocal wireless channel.
In particular we consider wireless channels that exhibit sparse structure in the wideband regime and the impact of the sparsity on the secret key capacity.
We explore this problem in two steps.
First, we study key generation from a state-dependent discrete memoryless multiple source.
The state of source captures the effect of channel sparsity.
Secondly, we consider a wireless channel model that captures channel sparsity and correlation between the legitimate users' channel and the eavesdropper's channel.
Such dependency can significantly reduce the secret key capacity.
According to system delay requirements, two performance measures are considered: (i) ergodic secret key capacity and (ii) outage probability.
We show that in the wideband regime when a white sounding sequence is adopted, a sparser channel can achieve a higher ergodic secret key rate than a richer channel can.
For outage performance, we show that if the users generate secret keys at a fraction of the ergodic capacity, the outage probability will decay exponentially in signal bandwidth.
Moreover, a larger exponent is achieved by a richer channel.
In this paper, the class of random irregular block-hierarchical networks is defined and algorithms for generation and calculation of network properties are described.
The algorithms presented for this class of networks are more efficient than known algorithms both in computation time and memory usage and can be used to analyze topological properties of such networks.
The algorithms are implemented in the system created by the authors for the study of topological and statistical properties of random networks.
Word embeddings are effective intermediate representations for capturing semantic regularities between words, when learning the representations of text sequences.
We propose to view text classification as a label-word joint embedding problem: each label is embedded in the same space with the word vectors.
We introduce an attention framework that measures the compatibility of embeddings between text sequences and labels.
The attention is learned on a training set of labeled samples to ensure that, given a text sequence, the relevant words are weighted higher than the irrelevant ones.
Our method maintains the interpretability of word embeddings, and enjoys a built-in ability to leverage alternative sources of information, in addition to input text sequences.
Extensive results on the several large text datasets show that the proposed framework outperforms the state-of-the-art methods by a large margin, in terms of both accuracy and speed.
TCP is the most widely used transport protocol in the internet.
However, it offers suboptimal performance when operating over high bandwidth mmWave links.
The main issues introduced by communications at such high frequencies are (i) the sensitivity to blockage and (ii) the high bandwidth fluctuations due to Line of Sight (LOS) to Non Line of Sight (NLOS) transitions and vice versa.
In particular, TCP has an abstract view of the end-to-end connection, which does not properly capture the dynamics of the wireless mmWave link.
The consequence is a suboptimal utilization of the available resources.
In this paper we propose a TCP proxy architecture that improves the performance of TCP flows without any modification at the remote sender side.
The proxy is installed in the Radio Access Network, and exploits information available at the gNB in order to maximize throughput and minimize latency.
We propose and prove a theorem that allows the calculation of a class of functionals on Poisson point processes that have the form of expected values of sum-products of functions.
In proving the theorem, we present a variant of the Campbell-Mecke theorem from stochastic geometry.
We proceed to apply our result in the calculation of expected values involving interference in wireless Poisson networks.
Based on this, we derive outage probabilities for transmissions in a Poisson network with Nakagami fading.
Our results extend the stochastic geometry toolbox used for the mathematical analysis of interference-limited wireless networks.
Over the last years many technological advances were introduced in Internet television to meet user needs and expectations.
However due to an overwhelming bandwidth requirements traditional IP-based television service based on simple client-server approach remains restricted to small group of clients.
In such situation the use of the peer-to-peer overlay paradigm to deliver live television on the Internet is gaining increasing attention.
Unfortunately the current Internet infrastructure provides only best effort services for this kind of applications and do not offer quality of service.
This paper is a research proposition which presents potential solutions for efficient IPTV streaming over P2P networks.
We assume that the solutions will not directly modify existing P2P IPTV protocols but rather will be dedicated for a network engineer or an Internet service provider which will be able to introduce and configure the proposed mechanisms in network routers.
The League Championship Algorithm (LCA) is sport-inspired optimization algorithm that was introduced by Ali Husseinzadeh Kashan in the year 2009.
It has since drawn enormous interest among the researchers because of its potential efficiency in solving many optimization problems and real-world applications.
The LCA has also shown great potentials in solving non-deterministic polynomial time (NP-complete) problems.
This survey presents a brief synopsis of the LCA literatures in peer-reviewed journals, conferences and book chapters.
These research articles are then categorized according to indexing in the major academic databases (Web of Science, Scopus, IEEE Xplore and the Google Scholar).
The analysis was also done to explore the prospects and the challenges of the algorithm and its acceptability among researchers.
This systematic categorization can be used as a basis for future studies.
Network operators are reluctant to share traffic data due to security and privacy concerns.
Consequently, there is a lack of publicly available traces for validating and generalizing the latest results in network and security research.
Anonymization is a possible solution in this context; however, it is unclear how the sanitization of data preserves characteristics important for traffic analysis.
In addition, the privacy-preserving property of state-of-the-art IP address anonymization techniques has come into question by recent attacks that successfully identified a large number of hosts in anonymized traces.
In this paper, we examine the tradeoff between data utility for anomaly detection and the risk of host identification for IP address truncation.
Specifically, we analyze three weeks of unsampled and non-anonymized network traces from a medium-sized backbone network to assess data utility.
The risk of de-anonymizing individual IP addresses is formally evaluated, using a metric based on conditional entropy.
Our results indicate that truncation effectively prevents host identification but degrades the utility of data for anomaly detection.
However, the degree of degradation depends on the metric used and whether network-internal or external addresses are considered.
Entropy metrics are more resistant to truncation than unique counts and the detection quality of anomalies degrades much faster in internal addresses than in external addresses.
In particular, the usefulness of internal address counts is lost even for truncation of only 4 bits whereas utility of external address entropy is virtually unchanged even for truncation of 20 bits.
ESA operates the Sentinel-1 satellites, which provides Synthetic Aperture Radar (SAR) data of Earth.
Recorded Sentinel-1 data have shown a potential for remotely observing and monitoring local conditions on broad acre fields.
Remote sensing using Sentinel-1 have the potential to provide daily updates on the current conditions in the individual fields and at the same time give an overview of the agricultural areas in the region.
Research depends on the ability of independent validation of the presented results.
In the case of the Sentinel-1 satellites, every researcher has access to the same base dataset, and therefore independent validation is possible.
Well documented research performed with Sentinel-1 allow other research the ability to redo the experiments and either validate or falsify presented findings.
Based on current state-of-art research we have chosen to provide a service for researchers in the agricultural domain.
The service allows researchers the ability to monitor local conditions by using the Sentinel-1 information combined with a priori knowledge from broad acre fields.
Correlating processed Sentinel-1 to the actual conditions is still a task the individual researchers must perform to benefit from the service.
In this paper, we presented our methodology in translating sentinel-1 data to a level that is more accessible to researchers in the agricultural field.
The goal here was to make the data more easily available, so the primary focus can be on correlating and comparing to measurements collected in the broadacre fields.
We illustrate the value of the service with three examples of the possible application areas.
The presented application examples are all based on Denmark, where we have processed all sentinel-1 scan from since 2016.
Electric vehicles play a key role in the sustainability of the Smart Cities as they contribute to the reduction of carbon emissions, the preservation of natural resources and the overall quality of life of citizens.
However, when the Smart Grid powers the charging of electric vehicles, high energy costs and power peaks challenge system reliability with risks of blackouts.
This is especially the case when the Smart Grid has to moderate additional uncertainties such as the penetration of renewable energy resources or energy market dynamics.
In addition, social dynamics such as the participation in demand-response programs, the discomfort experienced by an alternative suggested usage of the electric vehicles and even the fairness in terms of how equally discomfort is experienced among the participating citizens perplex even further the operation and regulation of the Smart Grid.
This paper introduces a fully decentralized and privacy-preserving learning mechanism for charging control of electric vehicles that regulates three Smart Grid socio-technical aspects: (i) reliability, (ii) discomfort and (iii) fairness.
By exclusively using local knowledge, an autonomous software agent generates energy demand plans for its vehicle that encode different charging regimes for the battery.
Agents interact to learn and make collective decisions of which plan to execute so that power peaks and energy cost are reduced.
The impact of improving reliability on discomfort and fairness is empirically shown using real-world data under a varied participation level of electric vehicles in the optimization process.
Recently, the demand for faster and more reliable data transmission has brought up complex communications systems.
As a result, it has become more difficult to carry out closed-form solutions that can provide insight about performance levels.
In this paper, different from the existing research, we study a cognitive radio system that employs hybrid-automatic-repeat-request (HARQ) protocols under quality-of-service (QoS) constraints.
We assume that the secondary users access the spectrum by utilizing a strategy that is a combination of underlay and interweave access techniques.
Considering that the secondary users imperfectly perform channel sensing in order to detect the active primary users and that there is a transmission deadline for each data packet at the secondary transmitter buffer, we formulate the state-transition model of the system.
Then, we obtain the state-transition probabilities when HARQ-chase combining is adopted.
Subsequently, we provide the packet-loss rate in the channel and achieve the effective capacity.
Finally, we substantiate our analytical derivations with numerical results.
Typical blur from camera shake often deviates from the standard uniform convolutional script, in part because of problematic rotations which create greater blurring away from some unknown center point.
Consequently, successful blind deconvolution requires the estimation of a spatially-varying or non-uniform blur operator.
Using ideas from Bayesian inference and convex analysis, this paper derives a non-uniform blind deblurring algorithm with several desirable, yet previously-unexplored attributes.
The underlying objective function includes a spatially adaptive penalty which couples the latent sharp image, non-uniform blur operator, and noise level together.
This coupling allows the penalty to automatically adjust its shape based on the estimated degree of local blur and image structure such that regions with large blur or few prominent edges are discounted.
Remaining regions with modest blur and revealing edges therefore dominate the overall estimation process without explicitly incorporating structure-selection heuristics.
The algorithm can be implemented using a majorization-minimization strategy that is virtually parameter free.
Detailed theoretical analysis and empirical validation on real images serve to validate the proposed method.
Verification of concurrent data structures is one of the most challenging tasks in software verification.
The topic has received considerable attention over the course of the last decade.
Nevertheless, human-driven techniques remain cumbersome and notoriously difficult while automated approaches suffer from limited applicability.
The main obstacle for automation is the complexity of concurrent data structures.
This is particularly true in the absence of garbage collection.
The intricacy of lock-free memory management paired with the complexity of concurrent data structures makes automated verification prohibitive.
In this work we present a method for verifying concurrent data structures and their memory management separately.
We suggest two simpler verification tasks that imply the correctness of the data structure.
The first task establishes an over-approximation of the reclamation behavior of the memory management.
The second task exploits this over-approximation to verify the data structure without the need to consider the implementation of the memory management itself.
To make the resulting verification tasks tractable for automated techniques, we establish a second result.
We show that a verification tool needs to consider only executions where a single memory location is reused.
We implemented our approach and were able to verify linearizability of Michael&Scott's queue and the DGLM queue for both hazard pointers and epoch-based reclamation.
To the best of our knowledge, we are the first to verify such implementations fully automatically.
Stylistic variation is critical to render the utterances generated by conversational agents natural and engaging.
In this paper, we focus on sequence-to-sequence models for open-domain dialogue response generation and propose a new method to evaluate the extent to which such models are able to generate responses that reflect different personality traits.
Most density based stream clustering algorithms separate the clustering process into an online and offline component.
Exact summarized statistics are being employed for defining micro-clusters or grid cells during the online stage followed by macro-clustering during the offline stage.
This paper proposes a novel alternative to the traditional two phase stream clustering scheme, introducing sketch-based data structures for assessing both stream density and cluster membership with probabilistic accuracy guarantees.
A count-min sketch using a damped window model estimates stream density.
Bloom filters employing a variation of active-active buffering estimate cluster membership.
Instances of both types of sketches share the same set of hash functions.
The resulting stream clustering algorithm is capable of detecting arbitrarily shaped clusters while correctly handling outliers and making no assumption on the total number of clusters.
Experimental results over a number of real and synthetic datasets illustrate the proposed algorithm quality and efficiency.
Software is everywhere, from mission critical systems such as industrial power stations, pacemakers and even household appliances.
This growing dependence on technology and the increasing complexity software has serious security implications as it means we are potentially surrounded by software that contain exploitable vulnerabilities.
These challenges have made binary analysis an important area of research in computer science and has emphasized the need for building automated analysis systems that can operate at scale, speed and efficacy; all while performing with the skill of a human expert.
Though great progress has been made in this area of research, there remains limitations and open challenges to be addressed.
Recognizing this need, DARPA sponsored the Cyber Grand Challenge (CGC), a competition to showcase the current state of the art in systems that perform; automated vulnerability detection, exploit generation and software patching.
This paper is a survey of the vulnerability detection and exploit generation techniques, underlying technologies and related works of two of the winning systems Mayhem and Mechanical Phish.
Programs with dynamic allocation are able to create and use an unbounded number of fresh resources, such as references, objects, files, etc.
We propose History-Register Automata (HRA), a new automata-theoretic formalism for modelling such programs.
HRAs extend the expressiveness of previous approaches and bring us to the limits of decidability for reachability checks.
The distinctive feature of our machines is their use of unbounded memory sets (histories) where input symbols can be selectively stored and compared with symbols to follow.
In addition, stored symbols can be consumed or deleted by reset.
We show that the combination of consumption and reset capabilities renders the automata powerful enough to imitate counter machines, and yields closure under all regular operations apart from complementation.
We moreover examine weaker notions of HRAs which strike different balances between expressiveness and effectiveness.
Gray Level Co-occurrence Matrices (GLCM) are one of the earliest techniques used for image texture analysis.
In this paper we defined a new feature called trace extracted from the GLCM and its implications in texture analysis are discussed in the context of Content Based Image Retrieval (CBIR).
The theoretical extension of GLCM to n-dimensional gray scale images are also discussed.
The results indicate that trace features outperform Haralick features when applied to CBIR.
In this paper, we aim at developing scalable neural network-type learning systems.
Motivated by the idea of "constructive neural networks" in approximation theory, we focus on "constructing" rather than "training" feed-forward neural networks (FNNs) for learning, and propose a novel FNNs learning system called the constructive feed-forward neural network (CFN).
Theoretically, we prove that the proposed method not only overcomes the classical saturation problem for FNN approximation, but also reaches the optimal learning rate when the regression function is smooth, while the state-of-the-art learning rates established for traditional FNNs are only near optimal (up to a logarithmic factor).
A series of numerical simulations are provided to show the efficiency and feasibility of CFN via comparing with the well-known regularized least squares (RLS) with Gaussian kernel and extreme learning machine (ELM).
Distributed representations of sentences have been developed recently to represent their meaning as real-valued vectors.
However, it is not clear how much information such representations retain about the polarity of sentences.
To study this question, we decode sentiment from unsupervised sentence representations learned with different architectures (sensitive to the order of words, the order of sentences, or none) in 9 typologically diverse languages.
Sentiment results from the (recursive) composition of lexical items and grammatical strategies such as negation and concession.
The results are manifold: we show that there is no `one-size-fits-all' representation architecture outperforming the others across the board.
Rather, the top-ranking architectures depend on the language and data at hand.
Moreover, we find that in several cases the additive composition model based on skip-gram word vectors may surpass supervised state-of-art architectures such as bidirectional LSTMs.
Finally, we provide a possible explanation of the observed variation based on the type of negative constructions in each language.
Tagging is a popular feature that supports several collaborative tasks, including search, as tags produced by one user can help others finding relevant content.
However, task performance depends on the existence of 'good' tags.
A first step towards creating incentives for users to produce 'good' tags is the quantification of their value in the first place.
This work fills this gap by combining qualitative and quantitative research methods.
In particular, using contextual interviews, we first determine aspects that influence users' perception of tags' value for exploratory search.
Next, we formalize some of the identified aspects and propose an information-theoretical method with provable properties that quantifies the two most important aspects (according to the qualitative analysis) that influence the perception of tag value: the ability of a tag to reduce the search space while retrieving relevant items to the user.
The evaluation on real data shows that our method is accurate: tags that users consider more important have higher value than tags users have not expressed interest.
Evaluating conjunctive queries and solving constraint satisfaction problems are fundamental problems in database theory and artificial intelligence, respectively.
These problems are NP-hard, so that several research efforts have been made in the literature for identifying tractable classes, known as islands of tractability, as well as for devising clever heuristics for solving efficiently real-world instances.
Many heuristic approaches are based on enforcing on the given instance a property called local consistency, where (in database terms) each tuple in every query atom matches at least one tuple in every other query atom.
Interestingly, it turns out that, for many well-known classes of queries, such as for the acyclic queries, enforcing local consistency is even sufficient to solve the given instance correctly.
However, the precise power of such a procedure was unclear, but for some very restricted cases.
The paper provides full answers to the long-standing questions about the precise power of algorithms based on enforcing local consistency.
The classes of instances where enforcing local consistency turns out to be a correct query-answering procedure are however not efficiently recognizable.
In fact, the paper finally focuses on certain subclasses defined in terms of the novel notion of greedy tree projections.
These latter classes are shown to be efficiently recognizable and strictly larger than most islands of tractability known so far, both in the general case of tree projections and for specific structural decomposition methods.
Android applications are frequently plagiarized or repackaged, and software obfuscation is a recommended protection against these practices.
However, there is very little data on the overall rates of app obfuscation, the techniques used, or factors that lead to developers to choose to obfuscate their apps.
In this paper, we present the first comprehensive analysis of the use of and challenges to software obfuscation in Android applications.
We analyzed 1.7 million free Android apps from Google Play to detect various obfuscation techniques, finding that only 24.92% of apps are obfuscated by the developer.
To better understand this rate of obfuscation, we surveyed 308 Google Play developers about their experiences and attitudes about obfuscation.
We found that while developers feel that apps in general are at risk of plagiarism, they do not fear theft of their own apps.
Developers also self-report difficulties applying obfuscation for their own apps.
To better understand this, we conducted a follow-up study where the vast majority of 70 participants failed to obfuscate a realistic sample app even while many mistakenly believed they had been successful.
Our findings show that more work is needed to make obfuscation tools more usable, to educate developers on the risk of their apps being reverse engineered, their intellectual property stolen, their apps being repackaged and redistributed as malware and to improve the health of the overall Android ecosystem.
Human-robot collaboration including close physical human-robot interaction (pHRI) is a current trend in industry and also science.
The safety guidelines prescribe two modes of safety: (i) power and force limitation and (ii) speed and separation monitoring.
We examine the potential of robots equipped with artificial sensitive skin and a protective safety zone around it (peripersonal space) to safe pHRI.
Automatically recognizing and localizing wide ranges of human actions has crucial importance for video understanding.
Towards this goal, the THUMOS challenge was introduced in 2013 to serve as a benchmark for action recognition.
Until then, video action recognition, including THUMOS challenge, had focused primarily on the classification of pre-segmented (i.e., trimmed) videos, which is an artificial task.
In THUMOS 2014, we elevated action recognition to a more practical level by introducing temporally untrimmed videos.
These also include `background videos' which share similar scenes and backgrounds as action videos, but are devoid of the specific actions.
The three editions of the challenge organized in 2013--2015 have made THUMOS a common benchmark for action classification and detection and the annual challenge is widely attended by teams from around the world.
In this paper we describe the THUMOS benchmark in detail and give an overview of data collection and annotation procedures.
We present the evaluation protocols used to quantify results in the two THUMOS tasks of action classification and temporal detection.
We also present results of submissions to the THUMOS 2015 challenge and review the participating approaches.
Additionally, we include a comprehensive empirical study evaluating the differences in action recognition between trimmed and untrimmed videos, and how well methods trained on trimmed videos generalize to untrimmed videos.
We conclude by proposing several directions and improvements for future THUMOS challenges.
The task of sentiment analysis of reviews is carried out using manually built / automatically generated lexicon resources of their own with which terms are matched with lexicon to compute the term count for positive and negative polarity.
On the other hand the Sentiwordnet, which is quite different from other lexicon resources that gives scores (weights) of the positive and negative polarity for each word.
The polarity of a word namely positive, negative and neutral have the score ranging between 0 to 1 indicates the strength/weight of the word with that sentiment orientation.
In this paper, we show that using the Sentiwordnet, how we could enhance the performance of the classification at both sentence and document level.
We present a new AI task -- Embodied Question Answering (EmbodiedQA) -- where an agent is spawned at a random location in a 3D environment and asked a question ("What color is the car?").
In order to answer, the agent must first intelligently navigate to explore the environment, gather information through first-person (egocentric) vision, and then answer the question ("orange").
This challenging task requires a range of AI skills -- active perception, language understanding, goal-driven navigation, commonsense reasoning, and grounding of language into actions.
In this work, we develop the environments, end-to-end-trained reinforcement learning agents, and evaluation protocols for EmbodiedQA.
Deep neural networks with discrete latent variables offer the promise of better symbolic reasoning, and learning abstractions that are more useful to new tasks.
There has been a surge in interest in discrete latent variable models, however, despite several recent improvements, the training of discrete latent variable models has remained challenging and their performance has mostly failed to match their continuous counterparts.
Recent work on vector quantized autoencoders (VQ-VAE) has made substantial progress in this direction, with its perplexity almost matching that of a VAE on datasets such as CIFAR-10.
In this work, we investigate an alternate training technique for VQ-VAE, inspired by its connection to the Expectation Maximization (EM) algorithm.
Training the discrete bottleneck with EM helps us achieve better image generation results on CIFAR-10, and together with knowledge distillation, allows us to develop a non-autoregressive machine translation model whose accuracy almost matches a strong greedy autoregressive baseline Transformer, while being 3.3 times faster at inference.
We study the query complexity of Weak Parity: the problem of computing the parity of an n-bit input string, where one only has to succeed on a 1/2+eps fraction of input strings, but must do so with high probability on those inputs where one does succeed.
It is well-known that n randomized queries and n/2 quantum queries are needed to compute parity on all inputs.
But surprisingly, we give a randomized algorithm for Weak Parity that makes only O(n/log^0.246(1/eps)) queries, as well as a quantum algorithm that makes only O(n/sqrt(log(1/eps))) queries.
We also prove a lower bound of Omega(n/log(1/eps)) in both cases; and using extremal combinatorics, prove lower bounds of Omega(log n) in the randomized case and Omega(sqrt(log n)) in the quantum case for any eps>0.
We show that improving our lower bounds is intimately related to two longstanding open problems about Boolean functions: the Sensitivity Conjecture, and the relationships between query complexity and polynomial degree.
Decide Madrid is the civic technology of Madrid City Council which allows users to create and support online petitions.
Despite the initial success, the platform is encountering problems with the growth of petition signing because petitions are far from the minimum number of supporting votes they must gather.
Previous analyses have suggested that this problem is produced by the interface: a paginated list of petitions which applies a non-optimal ranking algorithm.
For this reason, we present an interactive system for the discovery of topics and petitions.
This approach leads us to reflect on the usefulness of data visualization techniques to address relevant societal challenges.
Availability of large amount of clinical data is opening up new research avenues in a number of fields.
An exciting field in this respect is healthcare, where secondary use of healthcare data is beginning to revolutionize healthcare.
Except for availability of Big Data, both medical data from healthcare institutions (such as EMR data) and data generated from health and wellbeing devices (such as personal trackers), a significant contribution to this trend is also being made by recent advances on machine learning, specifically deep learning algorithms.
Supervised multi-channel audio source separation requires extracting useful spectral, temporal, and spatial features from the mixed signals.
The success of many existing systems is therefore largely dependent on the choice of features used for training.
In this work, we introduce a novel multi-channel, multi-resolution convolutional auto-encoder neural network that works on raw time-domain signals to determine appropriate multi-resolution features for separating the singing-voice from stereo music.
Our experimental results show that the proposed method can achieve multi-channel audio source separation without the need for hand-crafted features or any pre- or post-processing.
The multivariate probit model (MVP) is a popular classic model for studying binary responses of multiple entities.
Nevertheless, the computational challenge of learning the MVP model, given that its likelihood involves integrating over a multidimensional constrained space of latent variables, significantly limits its application in practice.
We propose a flexible deep generalization of the classic MVP, the Deep Multivariate Probit Model (DMVP), which is an end-to-end learning scheme that uses an efficient parallel sampling process of the multivariate probit model to exploit GPU-boosted deep neural networks.
We present both theoretical and empirical analysis of the convergence behavior of DMVP's sampling process with respect to the resolution of the correlation structure.
We provide convergence guarantees for DMVP and our empirical analysis demonstrates the advantages of DMVP's sampling compared with standard MCMC-based methods.
We also show that when applied to multi-entity modelling problems, which are natural DMVP applications, DMVP trains faster than classical MVP, by at least an order of magnitude, captures rich correlations among entities, and further improves the joint likelihood of entities compared with several competitive models.
The recovery type error estimators introduced by Zienkiewicz and Zhu use a recovered stress field evaluated from the Finite Element (FE) solution.
Their accuracy depends on the quality of the recovered field.
In this sense, accurate results are obtained using recovery procedures based on the Superconvergent Patch recovery technique (SPR).
These error estimators can be easily implemented and provide accurate estimates.
Another important feature is that the recovered solution is of a better quality than the FE solution and can therefore be used as an enhanced solution.
We have developed an SPR-type recovery technique that considers equilibrium and displacements constraints to obtain a very accurate recovered displacements field from which a recovered stress field can also be evaluated.
We propose the use of these recovered fields as the standard output of the FE code instead of the raw FE solution.
Techniques to quantify the error of the recovered solution are therefore needed.
In this report we present an error estimation technique that accurately evaluates the error of the recovered solution both at global and local levels in the FEM and XFEM frameworks.
We have also developed an h-adaptive mesh refinement strategy based on the error of the recovered solution.
As the converge rate of the error of the recovered solution is higher than that of the FE one, the computational cost required to obtain a solution with a prescribed accuracy is smaller than for traditional h-adaptive processes.
A software element defined in one place is typically used in many places.
When it is changed, all its occurrences may need to be changed too, which can severely hinder software evolution.
This has led to the support of encapsulation in modern programming languages.
Unfortunately, as is shown in this paper, this is not enough to express all the constraints that are needed to decouple programming elements that evolve at different paces.
In this paper we show that: a language can be defined to easily express very general coupling constraints; violations to these constraints can be detected automatically.
We then demonstrate several places where the need for coupling constraints arose in open-source Java projects.
These constraints were expressed in comments when explicit constraints would have enabled automatic treatment.
Convolutional Siamese neural networks have been recently used to track objects using deep features.
Siamese architecture can achieve real time speed, however it is still difficult to find a Siamese architecture that maintains the generalization capability, high accuracy and speed while decreasing the number of shared parameters especially when it is very deep.
Furthermore, a conventional Siamese architecture usually processes one local neighborhood at a time, which makes the appearance model local and non-robust to appearance changes.
To overcome these two problems, this paper proposes DensSiam, a novel convolutional Siamese architecture, which uses the concept of dense layers and connects each dense layer to all layers in a feed-forward fashion with a similarity-learning function.
DensSiam also includes a Self-Attention mechanism to force the network to pay more attention to the non-local features during offline training.
Extensive experiments are performed on four tracking benchmarks: OTB2013 and OTB2015 for validation set; and VOT2015, VOT2016 and VOT2017 for testing set.
The obtained results show that DensSiam achieves superior results on these benchmarks compared to other current state-of-the-art methods.
Micro-blogging services such as Twitter allow anyone to publish anything, anytime.
Needless to say, many of the available contents can be diminished as babble or spam.
However, given the number and diversity of users, some valuable pieces of information should arise from the stream of tweets.
Thus, such services can develop into valuable sources of up-to-date information (the so-called real-time web) provided a way to find the most relevant/trustworthy/authoritative users is available.
Hence, this makes a highly pertinent question for which graph centrality methods can provide an answer.
In this paper the author offers a comprehensive survey of feasible algorithms for ranking users in social networks, he examines their vulnerabilities to linking malpractice in such networks, and suggests an objective criterion against which to compare such algorithms.
Additionally, he suggests a first step towards "desensitizing" prestige algorithms against cheating by spammers and other abusive users.
"Fake news" is a recent phenomenon, but misinformation and propaganda are not.
Our new communication technologies make it easy for us to be exposed to high volumes of true, false, irrelevant, and unprovable information.
Future AI is expected to amplify the problem even more.
At the same time, our brains are reaching their limits in handling information.
How should we respond to propaganda?
Technology can help, but relying on it alone will not suffice in the long term.
We also need ethical policies, laws, regulations, and trusted authorities, including fact-checkers.
However, we will not solve the problem without the active engagement of the educated citizen.
Epistemological education, recognition of self biases and protection of our channels of communication and trusted networks are all needed to overcome the problem and continue our progress as democratic societies.
Bridge is among the zero-sum games for which artificial intelligence has not yet outperformed expert human players.
The main difficulty lies in the bidding phase of bridge, which requires cooperative decision making under partial information.
Existing artificial intelligence systems for bridge bidding rely on and are thus restricted by human-designed bidding systems or features.
In this work, we propose a pioneering bridge bidding system without the aid of human domain knowledge.
The system is based on a novel deep reinforcement learning model, which extracts sophisticated features and learns to bid automatically based on raw card data.
The model includes an upper-confidence-bound algorithm and additional techniques to achieve a balance between exploration and exploitation.
Our experiments validate the promising performance of our proposed model.
In particular, the model advances from having no knowledge about bidding to achieving superior performance when compared with a champion-winning computer bridge program that implements a human-designed bidding system.
In metabolomics, small molecules are structurally elucidated using tandem mass spectrometry (MS/MS); this resulted in the computational Maximum Colorful Subtree problem, which is NP-hard.
Unfortunately, data from a single metabolite requires us to solve hundreds or thousands of instances of this problem; and in a single Liquid Chromatography MS/MS run, hundreds or thousands of metabolites are measured.
Here, we comprehensively evaluate the performance of several heuristic algorithms for the problem against an exact algorithm.
We put particular emphasis on whether a heuristic is able to rank candidates such that the correct solution is ranked highly.
We propose this "intermediate" evaluation because evaluating the approximating quality of heuristics is misleading: Even a slightly suboptimal solution can be structurally very different from the true solution.
On the other hand, we cannot structurally evaluate against the ground truth, as this is unknown.
We find that one particular heuristic consistently ranks the correct solution in a top position, allowing us to speed up computations about 100-fold.
We also find that scores of the best heuristic solutions are very close to the optimal score; in contrast, the structure of the solutions can deviate significantly from the optimal structures.
As a promising downlink multiple access scheme, Rate-Splitting Multiple Access (RSMA) has been shown to achieve superior spectral and energy efficiencies compared with Space-Division Multiple Access (SDMA) and Non-Orthogonal Multiple Access (NOMA) in downlink single-cell systems.
By relying on linearly precoded rate-splitting at the transmitter and successive interference cancellation at the receivers, RSMA has the capability of partially decoding the interference and partially treating the interference as noise, and therefore copes with a wide range of user deployments and network loads.
In this work, we further study RSMA in downlink Coordinated Multi-Point (CoMP) Joint Transmission (JT) networks by investigating the optimal beamformer design to maximize the Weighted Sum-Rate (WSR) of all users subject to individual Quality of Service (QoS) rate constraints and per base station power constraints.
Numerical results show that, in CoMP JT, RSMA achieves significant WSR improvement over SDMA and NOMA in a wide range of inter-user and inter-cell channel strength disparities.
Specifically, SDMA (resp.NOMA) is more suited to deployments with little (resp. large) inter-user channel strength disparity and large (resp. little) inter-cell channel disparity, while RSMA is suited to any deployment.
We conclude that RSMA provides rate, robustness and QoS enhancements over SDMA and NOMA in CoMP JT networks.
This paper seeks to combine differential game theory with the actor-critic-identifier architecture to determine forward-in-time, approximate optimal controllers for formation tracking in multi-agent systems, where the agents have uncertain heterogeneous nonlinear dynamics.
A continuous control strategy is proposed, using communication feedback from extended neighbors on a communication topology that has a spanning tree.
A model-based reinforcement learning technique is developed to cooperatively control a group of agents to track a trajectory in a desired formation.
Simulation results are presented to demonstrate the performance of the developed technique.
This paper presents a new approach for training artificial neural networks using techniques for solving the constraint satisfaction problem (CSP).
The quotient gradient system (QGS) is a trajectory-based method for solving the CSP.
This study converts the training set of a neural network into a CSP and uses the QGS to find its solutions.
The QGS finds the global minimum of the optimization problem by tracking trajectories of a nonlinear dynamical system and does not stop at a local minimum of the optimization problem.
Lyapunov theory is used to prove the asymptotic stability of the solutions with and without the presence of measurement errors.
Numerical examples illustrate the effectiveness of the proposed methodology and compare it to a genetic algorithm and error backpropagation.
Investigation of divisibility properties of natural numbers is one of the most important themes in the theory of numbers.
Various tools have been developed over the centuries to discover and study the various patterns in the sequence of natural numbers in the context of divisibility.
In the present paper, we study the divisibility of natural numbers using the framework of a growing complex network.
In particular, using tools from the field of statistical inference, we show that the network is scale-free but has a non-stationary degree distribution.
Along with this, we report a new kind of similarity pattern for the local clustering, which we call "stretching similarity", in this network.
We also show that the various characteristics like average degree, global clustering coefficient and assortativity coefficient of the network vary smoothly with the size of the network.
Using analytical arguments we estimate the asymptotic behavior of global clustering and average degree which is validated using numerical analysis.
Clinical medical data, especially in the intensive care unit (ICU), consist of multivariate time series of observations.
For each patient visit (or episode), sensor data and lab test results are recorded in the patient's Electronic Health Record (EHR).
While potentially containing a wealth of insights, the data is difficult to mine effectively, owing to varying length, irregular sampling and missing data.
Recurrent Neural Networks (RNNs), particularly those using Long Short-Term Memory (LSTM) hidden units, are powerful and increasingly popular models for learning from sequence data.
They effectively model varying length sequences and capture long range dependencies.
We present the first study to empirically evaluate the ability of LSTMs to recognize patterns in multivariate time series of clinical measurements.
Specifically, we consider multilabel classification of diagnoses, training a model to classify 128 diagnoses given 13 frequently but irregularly sampled clinical measurements.
First, we establish the effectiveness of a simple LSTM network for modeling clinical data.
Then we demonstrate a straightforward and effective training strategy in which we replicate targets at each sequence step.
Trained only on raw time series, our models outperform several strong baselines, including a multilayer perceptron trained on hand-engineered features.
In this contribution, we investigate a coarsely quantized Multi-User (MU)-Multiple Input Single Output (MISO) downlink communication system, where we assume 1-Bit Digital-to-Analog Converters (DACs) at the Base Station (BS) antennas.
First, we analyze the achievable sum rate lower-bound using the Bussgang decomposition.
In the presence of the non-linear quanization, our analysis indicates the potential merit of reconsidering traditional signal processing techniques in coarsely quantized systems, i.e., reconsidering transmit covariance matrices whose rank is equal to the rank of the channel.
Furthermore, in the second part of this paper, we propose a linear precoder design which achieves the predicted increase in performance compared with a state of the art linear precoder design.
Moreover, our linear signal processing algorithm allows for higher-order modulation schemes to be employed.
We analyze cooperative Cournot games with boundedly rational firms.
Due to cogni- tive constraints, the members of a coalition cannot accurately predict the coalitional structure of the non-members.
Thus, they compute their value using simple heuris- tics.
In particular, they assign various non-equilibrium probability distributions over the outsiders' set of partitions.
We construct the characteristic function of a coalition in such an environment and we analyze the core of the corresponding games.
We show that the core is non-empty provided the number of firms in the market is sufficiently large.
Moreover, we show that if two distributions over the set of partitions are related via first-order dominance, then the core of the game under the dominated distribution is a subset of the core under the dominant distribution.
This paper proposes a deep learning architecture based on Residual Network that dynamically adjusts the number of executed layers for the regions of the image.
This architecture is end-to-end trainable, deterministic and problem-agnostic.
It is therefore applicable without any modifications to a wide range of computer vision problems such as image classification, object detection and image segmentation.
We present experimental results showing that this model improves the computational efficiency of Residual Networks on the challenging ImageNet classification and COCO object detection datasets.
Additionally, we evaluate the computation time maps on the visual saliency dataset cat2000 and find that they correlate surprisingly well with human eye fixation positions.
The paper proposes an approach to modeling users of large Web sites based on combining different data sources: access logs and content of the accessed pages are combined with semantic information about the Web pages, the users and the accesses of the users to the Web site.
The assumption is that we are dealing with a large Web site providing content to a large number of users accessing the site.
The proposed approach represents each user by a set of features derived from the different data sources, where some feature values may be missing for some users.
It further enables user modeling based on the provided characteristics of the targeted user subset.
The approach is evaluated on real-world data where we compare performance of the automatic assignment of a user to a predefined user segment when different data sources are used to represent the users.
Event recognition in still images is an intriguing problem and has potential for real applications.
This paper addresses the problem of event recognition by proposing a convolutional neural network that exploits knowledge of objects and scenes for event classification (OS2E-CNN).
Intuitively, it stands to reason that there exists a correlation among the concepts of objects, scenes, and events.
We empirically demonstrate that the recognition of objects and scenes substantially contributes to the recognition of events.
Meanwhile, we propose an iterative selection method to identify a subset of object and scene classes, which help to more efficiently and effectively transfer their deep representations to event recognition.
Specifically, we develop three types of transferring techniques: (1) initialization-based transferring, (2) knowledge-based transferring, and (3) data-based transferring.
These newly designed transferring techniques exploit multi-task learning frameworks to incorporate extra knowledge from other networks and additional datasets into the training procedure of event CNNs.
These multi-task learning frameworks turn out to be effective in reducing the effect of over-fitting and improving the generalization ability of the learned CNNs.
With OS2E-CNN, we design a multi-ratio and multi-scale cropping strategy, and propose an end-to-end event recognition pipeline.
We perform experiments on three event recognition benchmarks: the ChaLearn Cultural Event Recognition dataset, the Web Image Dataset for Event Recognition (WIDER), and the UIUC Sports Event dataset.
The experimental results show that our proposed algorithm successfully adapts object and scene representations towards the event dataset and that it achieves the current state-of-the-art performance on these challenging datasets.
Exploiting the fact that natural languages are complex systems, the present exploratory article proposes a direct method based on frequency distributions that may be useful when making a decision on the status of problematic phonemes, an open problem in linguistics.
The main notion is that natural languages, which can be considered from a complex outlook as information processing machines, and which somehow manage to set appropriate levels of redundancy, already "made the choice" whether a linguistic unit is a phoneme or not, and this would be reflected in a greater smoothness in a frequency versus rank graph.
For the particular case we chose to study, we conclude that it is reasonable to consider the Spanish semiconsonant /w/ as a separate phoneme from its vowel counterpart /u/, on the one hand, and possibly also the semiconsonant /j/ as a separate phoneme from its vowel counterpart /i/, on the other.
As language has been so central a topic in the study of complexity, this discussion grants us, in addition, an opportunity to gain insight into emerging properties in the broader complex systems debate.
In this paper, we theoretically investigate a new technique for simultaneous information and power transfer (SWIPT) in multiple-input multiple-output (MIMO) point-to-point with radio frequency energy harvesting capabilities.
The proposed technique exploits the spatial decomposition of the MIMO channel and uses the eigenchannels either to convey information or to transfer energy.
In order to generalize our study, we consider channel estimation error in the decomposition process and the interference between the eigenchannels.
An optimization problem that minimizes the total transmitted power subject to maximum power per eigenchannel, information and energy constraints is formulated as a mixed-integer nonlinear program and solved to optimality using mixed-integer second-order cone programming.
A near-optimal mixed-integer linear programming solution is also developed with robust computational performance.
A polynomial complexity algorithm is further proposed for the optimal solution of the problem when no maximum power per eigenchannel constraints are imposed.
In addition, a low polynomial complexity algorithm is developed for the power allocation problem with a given eigenchannel assignment, as well as a low-complexity heuristic for solving the eigenchannel assignment problem.
Many real world tasks such as reasoning and physical interaction require identification and manipulation of conceptual entities.
A first step towards solving these tasks is the automated discovery of distributed symbol-like representations.
In this paper, we explicitly formalize this problem as inference in a spatial mixture model where each component is parametrized by a neural network.
Based on the Expectation Maximization framework we then derive a differentiable clustering method that simultaneously learns how to group and represent individual entities.
We evaluate our method on the (sequential) perceptual grouping task and find that it is able to accurately recover the constituent objects.
We demonstrate that the learned representations are useful for next-step prediction.
The rapidly increasing number of mobile devices, voluminous data, and higher data rate are pushing to rethink the current generation of the cellular mobile communication.
The next or fifth generation (5G) cellular networks are expected to meet high-end requirements.
The 5G networks are broadly characterized by three unique features: ubiquitous connectivity, extremely low latency, and very high-speed data transfer.
The 5G networks would provide novel architectures and technologies beyond state-of-the-art architectures and technologies.
In this paper, our intent is to find an answer to the question: "what will be done by 5G and how?"
We investigate and discuss serious limitations of the fourth generation (4G) cellular networks and corresponding new features of 5G networks.
We identify challenges in 5G networks, new technologies for 5G networks, and present a comparative study of the proposed architectures that can be categorized on the basis of energy-efficiency, network hierarchy, and network types.
Interestingly, the implementation issues, e.g., interference, QoS, handoff, security-privacy, channel access, and load balancing, hugely effect the realization of 5G networks.
Furthermore, our illustrations highlight the feasibility of these models through an evaluation of existing real-experiments and testbeds.
Complex applications implemented as Systems on Chip (SoCs) demand extensive use of system level modeling and validation.
Their implementation gathers a large number of complex IP cores and advanced interconnection schemes, such as hierarchical bus architectures or networks on chip (NoCs).
Modeling applications involves capturing its computation and communication characteristics.
Previously proposed communication weighted models (CWM) consider only the application communication aspects.
This work proposes a communication dependence and computation model (CDCM) that can simultaneously consider both aspects of an application.
It presents a solution to the problem of mapping applications on regular NoCs while considering execution time and energy consumption.
The use of CDCM is shown to provide estimated average reductions of 40% in execution time, and 20% in energy consumption, for current technologies.
Network algorithms always prefer low memory cost and fast packet processing speed.
Forwarding information base (FIB), as a typical network processing component, requires a scalable and memory-efficient algorithm to support fast lookups.
In this paper, we present a new network algorithm, Othello Hashing, and its application of a FIB design called Concise, which uses very little memory to support ultra-fast lookups of network names.
Othello Hashing and Concise make use of minimal perfect hashing and relies on the programmable network framework to support dynamic updates.
Our conceptual contribution of Concise is to optimize the memory efficiency and query speed in the data plane and move the relatively complex construction and update components to the resource-rich control plane.
We implemented Concise on three platforms.
Experimental results show that Concise uses significantly smaller memory to achieve much faster query speed compared to existing solutions of network name lookups.
Partial differential equations are central to describing many physical phenomena.
In many applications these phenomena are observed through a sensor network, with the aim of inferring their underlying properties.
Leveraging from certain results in sampling and approximation theory, we present a new framework for solving a class of inverse source problems for physical fields governed by linear partial differential equations.
Specifically, we demonstrate that the unknown field sources can be recovered from a sequence of, so called, generalised measurements by using multidimensional frequency estimation techniques.
Next we show that---for physics-driven fields---this sequence of generalised measurements can be estimated by computing a linear weighted-sum of the sensor measurements; whereby the exact weights (of the sums) correspond to those that reproduce multidimensional exponentials, when used to linearly combine translates of a particular prototype function related to the Green's function of our underlying field.
Explicit formulae are then derived for the sequence of weights, that map sensor samples to the exact sequence of generalised measurements when the Green's function satisfies the generalised Strang-Fix condition.
Otherwise, the same mapping yields a close approximation of the generalised measurements.
Based on this new framework we develop practical, noise robust, sensor network strategies for solving the inverse source problem, and then present numerical simulation results to verify their performance.
It is common for cloud data centers meeting unexpected loads like request bursts, which may lead to overloaded situation and performance degradation.
Dynamic Voltage Frequency Scaling and VM consolidation have been proved effective to manage overloads.
However, they cannot function when the whole data center is overloaded.
Brownout provides a promising direction to avoid overloads through configuring applications to temporarily degrade user experience.
Additionally, brownout can also be applied to reduce data center energy consumption.
As a complementary option for Dynamic Voltage Frequency Scaling and VM consolidation, our combined brownout approach reduces energy consumption through selectively and dynamically deactivating application optional components, which can also be applied to self-contained microservices.
The results show that our approach can save more than 20% energy consumption and there are trade-offs between energy saving and discount offered to users.
Many academics have called for increasing attention to theory in software engineering.
Consequently, this paper empirically evaluates two dissimilar software development process theories - one expressing a more traditional, methodical view (FBS) and one expressing an alternative, more improvisational view (SCI).
A primarily quantitative survey of more than 1300 software developers is combined with four qualitative case studies to achieve a simultaneously broad and deep empirical evaluation.
Case data analysis using a closed-ended, a priori coding scheme based on the two theories strongly supports SCI, as does analysis of questionnaire response distributions (p<0.001; chi-square goodness of fit test).
Furthermore, case-questionnaire triangulation found no evidence that support for SCI varied by participants' gender, education, experience, nationality or the size or nature of their projects.
This suggests that instead of iteration between weakly-coupled phases (analysis, design, coding, testing), it is more accurate and useful to conceptualize development as ad hoc oscillation between organizing perceptions of the project context (Sensemaking), simultaneously improving mental pictures of the context and design artifact (Coevolution) and constructing, debugging and deploying software artifacts (Implementation).
One way to reduce the power consumption in large-scale multiple-input multiple-output (MIMO) systems is to employ low-resolution analog-to-digital converters (ADCs).
In this paper, we investigate antenna selection for large-scale MIMO receivers with low-resolution ADCs, thereby providing more flexibility in resolution and number of ADCs.
To incorporate quantization effects, we generalize an existing objective function for a greedy capacity-maximization antenna selection approach.
The derived objective function offers an opportunity to select an antenna with the best tradeoff between the additional channel gain and increase in quantization error.
Using the generalized objective function, we propose an antenna selection algorithm based on a conventional antenna selection algorithm without an increase in overall complexity.
Simulation results show that the proposed algorithm outperforms the conventional algorithm in achievable capacity for the same number of antennas.
In this paper we are proposing a new sorting algorithm, List Sort algorithm, is based on the dynamic memory allocation.
In this research study we have also shown the comparison of various efficient sorting techniques with List sort.
Due the dynamic nature of the List sort, it becomes much more fast than some conventional comparison sorting techniques and comparable to Quick Sort and Merge Sort.
List sort takes the advantage of the data which is already sorted either in ascending order or in descending order.
V1 is a declarative visual query language for schema-based property graphs.
V1 supports property graphs with mixed (both directed and undirected) edges and half-edges, with multivalued and composite properties, and with empty property values.
V1 supports temporal data types, operators, and functions, and can be extended to support additional data types, operators, and functions (one spatiotemporal model is presented).
V1 is generic, concise, has rich expressive power, and is highly receptive and productive.
Evidence in the literature from several business sectors shows that exploratory and exploitative innovation strategies are complementarily important for competitiveness.
Our empirical findings reinforced those evidences in the context of software development companies.
The innovative behaviour of individuals is an essential ingredient to success in both types of innovations strategies and leaders can have a big influence on this behaviour.
Adopting a leadership style that combines transactional and transformational practices is more likely to produce effective results in supporting innovative behaviour.
In software development, project managers and other group leaders should be stimulated and supported in adopting such practices to create the conditions for innovative behaviour to thrive.
We study the outcome of deferred acceptance when prospective medical residents can only apply to a limited set of hospitals.
This limitation requires residents to make a strategic choice about the quality of hospitals they apply to.
Through a mix of theoretical and experimental results, we study the effect of this strategic choice on the preferences submitted by participants, as well as on the overall welfare.
We find that residents' choices in our model mimic the behavior observed in real systems where individuals apply to a mix of positions consisting mostly of places where they are reasonably likely to get accepted, as well as a few "reach" applications to hospitals of very high quality, and a few "safe" applications to hospitals of lower than their expected level.
Surprisingly, the number of such "safe" applications is not monotone in the number of allowed applications.
We also find that selfish behavior can hurt social welfare, but the deterioration of overall welfare is very minimal.
This paper presents a new optimal filter namely past observation-based extended Kalman filter for the problem of localization of Internet-based mobile robot in which the control input and the feedback measurement suffer from communication delay.
The filter operates through two phases: the time update and the data correction.
The time update predicts the robot position by reformulating the kinematics model to be non-memoryless.
The correction step corrects the prediction by extrapolating the delayed measurement to the present and then incorporating it to the being estimate as there is no delay.
The optimality of the incorporation is ensured by the derivation of a multiplier that reflects the relevance of past observations to the present.
Simulations in MATLAB and experiments in a real networked robot system confirm the validity of the proposed approach.
This paper concerns automated vehicles negotiating with other vehicles, typically human driven, in crossings with the goal to find a decision algorithm by learning typical behaviors of other vehicles.
The vehicle observes distance and speed of vehicles on the intersecting road and use a policy that adapts its speed along its pre-defined trajectory to pass the crossing efficiently.
Deep Q-learning is used on simulated traffic with different predefined driver behaviors and intentions.
The results show a policy that is able to cross the intersection avoiding collision with other vehicles 98% of the time, while at the same time not being too passive.
Moreover, inferring information over time is important to distinguish between different intentions and is shown by comparing the collision rate between a Deep Recurrent Q-Network at 0.85% and a Deep Q-learning at 1.75%.
This paper presents a new dataset called HUMBI - a large corpus of high fidelity models of behavioral signals in 3D from a diverse population measured by a massive multi-camera system.
With our novel design of a portable imaging system (consists of 107 HD cameras), we collect human behaviors from 164 subjects across gender, ethnicity, age, and physical condition at a public venue.
Using the multiview image streams, we reconstruct high fidelity models of five elementary parts: gaze, face, hands, body, and cloth.
As a byproduct, the 3D model provides geometrically consistent image annotation via 2D projection, e.g., body part segmentation.
This dataset is a significant departure from the existing human datasets that suffers from subject diversity.
We hope the HUMBI opens up a new opportunity for the development for behavioral imaging.
We report results on benchmarking Open Information Extraction (OIE) systems using RelVis, a toolkit for benchmarking Open Information Extraction systems.
Our comprehensive benchmark contains three data sets from the news domain and one data set from Wikipedia with overall 4522 labeled sentences and 11243 binary or n-ary OIE relations.
In our analysis on these data sets we compared the performance of four popular OIE systems, ClausIE, OpenIE 4.2, Stanford OpenIE and PredPatt.
In addition, we evaluated the impact of five common error classes on a subset of 749 n-ary tuples.
From our deep analysis we unreveal important research directions for a next generation of OIE systems.
A novel Mathematical Random Number Generator (MRNG) is presented here.
In this case, "mathematical" refers to the fact that to construct that generator it is not necessary to resort to a physical phenomenon, such as the thermal noise of an electronic device, but rather to a mathematical procedure.
The MRNG generates binary strings - in principle, as long as desired - which may be considered genuinely random in the sense that they pass the statistical tests currently accepted to evaluate the randomness of those strings.
From those strings, the MRNG also generates random numbers expressed in base 10.
An MRNG has been installed as a facility on the following web page: http://www.appliedmathgroup.org.
This generator may be used for applications in tasks in: a) computational simulation of probabilistic-type systems, and b) the random selection of samples of different populations.
Users interested in applications in cryptography can build another MRNG, but they would have to withhold information - specified in section 5 - from people who are not authorized to decode messages encrypted using that resource.
In recent years, deep convolutional neural networks (CNNs) have shown record-shattering performance in a variety of computer vision problems, such as visual object recognition, detection and segmentation.
These methods have also been utilised in medical image analysis domain for lesion segmentation, anatomical segmentation and classification.
We present an extensive literature review of CNN techniques applied in brain magnetic resonance imaging (MRI) analysis, focusing on the architectures, pre-processing, data-preparation and post-processing strategies available in these works.
The aim of this study is three-fold.
Our primary goal is to report how different CNN architectures have evolved, discuss state-of-the-art strategies, condense their results obtained using public datasets and examine their pros and cons.
Second, this paper is intended to be a detailed reference of the research activity in deep CNN for brain MRI analysis.
Finally, we present a perspective on the future of CNNs in which we hint some of the research directions in subsequent years.
This paper addresses the problem of video summarization.
Given an input video, the goal is to select a subset of the frames to create a summary video that optimally captures the important information of the input video.
With the large amount of videos available online, video summarization provides a useful tool that assists video search, retrieval, browsing, etc.
In this paper, we formulate video summarization as a sequence labeling problem.
Unlike existing approaches that use recurrent models, we propose fully convolutional sequence models to solve video summarization.
We firstly establish a novel connection between semantic segmentation and video summarization, and then adapt popular semantic segmentation networks for video summarization.
Extensive experiments and analysis on two benchmark datasets demonstrate the effectiveness of our models.
Recently, the millimeter wave (mmWave) band has been investigated as a means to support the foreseen extreme data rate demands of emerging automotive applications, which go beyond the capabilities of existing technologies for vehicular communications.
However, this potential is hindered by the severe isotropic path loss and the harsh propagation of high-frequency channels.
Moreover, mmWave signals are typically directional, to benefit from beamforming gain, and require frequent realignment of the beams to maintain connectivity.
These limitations are particularly challenging when considering vehicle-to-vehicle (V2V) transmissions, because of the highly mobile nature of the vehicular scenarios, and pose new challenges for proper vehicular communication design.
In this paper, we conduct simulations to compare the performance of IEEE 802.11p and the mmWave technology to support V2V networking, aiming at providing insights on how both technologies can complement each other to meet the requirements of future automotive services.
The results show that mmWave-based strategies support ultra-high transmission speeds, and IEEE 802.11p systems have the ability to guarantee reliable and robust communications.
This paper describes the monomodal and multimodal Neural Machine Translation systems developed by LIUM and CVC for WMT17 Shared Task on Multimodal Translation.
We mainly explored two multimodal architectures where either global visual features or convolutional feature maps are integrated in order to benefit from visual context.
Our final systems ranked first for both En-De and En-Fr language pairs according to the automatic evaluation metrics METEOR and BLEU.
This paper presents a short and simple proof of the Four-Color Theorem, which can be utterly checkable by human mathematicians, without computer assistance.
The new key idea that has permitted it is presented in the Introduction.
One promising trend in digital system integration consists of boosting on-chip communication performance by means of silicon photonics, thus materializing the so-called Optical Networks-on-Chip (ONoCs).
Among them, wavelength routing can be used to route a signal to destination by univocally associating a routing path to the wavelength of the optical carrier.
Such wavelengths should be chosen so to minimize interferences among optical channels and to avoid routing faults.
As a result, physical parameter selection of such networks requires the solution of complex constrained optimization problems.
In previous work, published in the proceedings of the International Conference on Computer-Aided Design, we proposed and solved the problem of computing the maximum parallelism obtainable in the communication between any two endpoints while avoiding misrouting of optical signals.
The underlying technology, only quickly mentioned in that paper, is Answer Set Programming (ASP).
In this work, we detail the ASP approach we used to solve such problem.
Another important design issue is to select the wavelengths of optical carriers such that they are spread across the available spectrum, in order to reduce the likelihood that, due to imperfections in the manufacturing process, unintended routing faults arise.
We show how to address such problem in Constraint Logic Programming on Finite Domains (CLP(FD)).
This paper is under consideration for possible publication on Theory and Practice of Logic Programming.
Due to the increasing number of mobile robots including domestic robots for cleaning and maintenance in developed countries, human activity recognition is inevitable for congruent human-robot interaction.
Needless to say that this is indeed a challenging task for robots, it is expedient to learn human activities for autonomous mobile robots (AMR) for navigating in an uncontrolled environment without any guidance.
Building a correct classifier for complex human action is non-trivial since simple actions can be combined to recognize a complex human activity.
In this paper, we trained a model for human activity recognition using convolutional neural network.
We trained and validated the model using the Vicon physical action dataset and also tested the model on our generated dataset (VMCUHK).
Our experiment shows that our method performs with high accuracy, human activity recognition task both on the Vicon physical action dataset and VMCUHK dataset.
In this work we continue the syntactic study of completeness that began with the works of Immerman and Medina.
In particular, we take a conjecture raised by Medina in his dissertation that says if a conjunction of a second-order and a first-order sentences defines an NP-complete problems via fops, then it must be the case that the second-order conjoint alone also defines a NP-complete problem.
Although this claim looks very plausible and intuitive, currently we cannot provide a definite answer for it.
However, we can solve in the affirmative a weaker claim that says that all ``consistent'' universal first-order sentences can be safely eliminated without the fear of losing completeness.
Our methods are quite general and can be applied to complexity classes other than NP (in this paper: to NLSPACE, PTIME, and coNP), provided the class has a complete problem satisfying a certain combinatorial property.
Aspects of the properties, enumeration and construction of points on diagonal and Hermitian surfaces have been considered extensively in the literature and are further considered here.
The zeta function of diagonal surfaces is given as a direct result of the work of Wolfmann.
Recursive construction techniques for the set of rational points of Hermitian surfaces are of interest.
The relationship of these techniques here to the construction of codes on surfaces is briefly noted.
Online graph problems are considered in models where the irrevocability requirement is relaxed.
Motivated by practical examples where, for example, there is a cost associated with building a facility and no extra cost associated with doing it later, we consider the Late Accept model, where a request can be accepted at a later point, but any acceptance is irrevocable.
Similarly, we also consider a Late Reject model, where an accepted request can later be rejected, but any rejection is irrevocable (this is sometimes called preemption).
Finally, we consider the Late Accept/Reject model, where late accepts and rejects are both allowed, but any late reject is irrevocable.
For Independent Set, the Late Accept/Reject model is necessary to obtain a constant competitive ratio, but for Vertex Cover the Late Accept model is sufficient and for Minimum Spanning Forest the Late Reject model is sufficient.
The Matching problem has a competitive ratio of 2, but in the Late Accept/Reject model, its competitive ratio is 3/2.
Detection of protein-protein interactions (PPIs) plays a vital role in molecular biology.
Particularly, infections are caused by the interactions of host and pathogen proteins.
It is important to identify host-pathogen interactions (HPIs) to discover new drugs to counter infectious diseases.
Conventional wet lab PPI prediction techniques have limitations in terms of large scale application and budget.
Hence, computational approaches are developed to predict PPIs.
This study aims to develop large margin machine learning models to predict interspecies PPIs with a special interest in host-pathogen protein interactions (HPIs).
Especially, we focus on seeking answers to three queries that arise while developing an HPI predictor.
1) How should we select negative samples?
2) What should be the size of negative samples as compared to the positive samples?
3) What type of margin violation penalty should be used to train the predictor?
We compare two available methods for negative sampling.
Moreover, we propose a new method of assigning weights to each training example in weighted SVM depending on the distance of the negative examples from the positive examples.
We have also developed a web server for our HPI predictor called HoPItor (Host Pathogen Interaction predicTOR) that can predict interactions between human and viral proteins.
This webserver can be accessed at the URL: http://faculty.pieas.edu.pk/fayyaz/software.html#HoPItor.
In this project we outline a modularized, scalable system for comparing Amazon products in an interactive and informative way using efficient latent variable models and dynamic visualization.
We demonstrate how our system can build on the structure and rich review information of Amazon products in order to provide a fast, multifaceted, and intuitive comparison.
By providing a condensed per-topic comparison visualization to the user, we are able to display aggregate information from the entire set of reviews while providing an interface that is at least as compact as the "most helpful reviews" currently displayed by Amazon, yet far more informative.
Medical imaging is widely used in clinical practice for diagnosis and treatment.
Report-writing can be error-prone for unexperienced physicians, and time- consuming and tedious for experienced physicians.
To address these issues, we study the automatic generation of medical imaging reports.
This task presents several challenges.
First, a complete report contains multiple heterogeneous forms of information, including findings and tags.
Second, abnormal regions in medical images are difficult to identify.
Third, the re- ports are typically long, containing multiple sentences.
To cope with these challenges, we (1) build a multi-task learning framework which jointly performs the pre- diction of tags and the generation of para- graphs, (2) propose a co-attention mechanism to localize regions containing abnormalities and generate narrations for them, (3) develop a hierarchical LSTM model to generate long paragraphs.
We demonstrate the effectiveness of the proposed methods on two publicly available datasets.
During the 1990s, one of us developed a series of freeware routines (http://www.leydesdorff.net/indicators) that enable the user to organize downloads from the Web-of-Science (Thomson Reuters) into a relational database, and then to export matrices for further analysis in various formats (for example, for co-author analysis).
The basic format of the matrices displays each document as a case in a row that can be attributed different variables in the columns.
One limitation to this approach was hitherto that relational databases typically have an upper limit for the number of variables, such as 256 or 1024.
In this brief communication, we report on a way to circumvent this limitation by using txt2Pajek.exe, available as freeware from http://www.pfeffer.at/txt2pajek/.
We present a formal measure-theoretical theory of neural networks (NN) built on probability coupling theory.
Our main contributions are summarized as follows.
* Built on the formalism of probability coupling theory, we derive an algorithm framework, named Hierarchical Measure Group and Approximate System (HMGAS), nicknamed S-System, that is designed to learn the complex hierarchical, statistical dependency in the physical world.
* We show that NNs are special cases of S-System when the probability kernels assume certain exponential family distributions.
Activation Functions are derived formally.
We further endow geometry on NNs through information geometry, show that intermediate feature spaces of NNs are stochastic manifolds, and prove that "distance" between samples is contracted as layers stack up.
* S-System shows NNs are inherently stochastic, and under a set of realistic boundedness and diversity conditions, it enables us to prove that for large size nonlinear deep NNs with a class of losses, including the hinge loss, all local minima are global minima with zero loss errors, and regions around the minima are flat basins where all eigenvalues of Hessians are concentrated around zero, using tools and ideas from mean field theory, random matrix theory, and nonlinear operator equations.
* S-System, the information-geometry structure and the optimization behaviors combined completes the analog between Renormalization Group (RG) and NNs.
It shows that a NN is a complex adaptive system that estimates the statistic dependency of microscopic object, e.g., pixels, in multiple scales.
Unlike clear-cut physical quantity produced by RG in physics, e.g., temperature, NNs renormalize/recompose manifolds emerging through learning/optimization that divide the sample space into highly semantically meaningful groups that are dictated by supervised labels (in supervised NNs).
We present a palette-based framework for color composition for visual applications.
Color composition is a critical aspect of visual applications in art, design, and visualization.
The color wheel is often used to explain pleasing color combinations in geometric terms, and, in digital design, to provide a user interface to visualize and manipulate colors.
We abstract relationships between palette colors as a compact set of axes describing harmonic templates over perceptually uniform color wheels.
Our framework provides a basis for a variety of color-aware image operations, such as color harmonization and color transfer, and can be applied to videos.
To enable our approach, we introduce an extremely scalable and efficient yet simple palette-based image decomposition algorithm.
Our approach is based on the geometry of images in RGBXY-space.
This new geometric approach is orders of magnitude more efficient than previous work and requires no numerical optimization.
We demonstrate a real-time layer decomposition tool.
After preprocessing, our algorithm can decompose 6 MP images into layers in 20 milliseconds.
We also conducted three large-scale, wide-ranging perceptual studies on the perception of harmonic colors and harmonization algorithms.
The serious privacy and security problems related to online social networks (OSNs) are what fueled two complementary studies as part of this thesis.
In the first study, we developed a general algorithm for the mining of data of targeted organizations by using Facebook (currently the most popular OSN) and socialbots.
By friending employees in a targeted organization, our active socialbots were able to find new employees and informal organizational links that we could not find by crawling with passive socialbots.
We evaluated our method on the Facebook OSN and were able to reconstruct the social networks of employees in three distinct, actual organizations.
Furthermore, in the crawling process with our active socialbots we discovered up to 13.55% more employees and 22.27% more informal organizational links in contrast to the crawling process that was performed by passive socialbots with no company associations as friends.
In our second study, we developed a general algorithm for reaching specific OSN users who declared themselves to be employees of targeted organizations, using the topologies of organizational social networks and utilizing socialbots.
We evaluated the proposed method on targeted users from three actual organizations on Facebook, and two actual organizations on the Xing OSN (another popular OSN platform).
Eventually, our socialbots were able to reach specific users with a success rate of up to 70% on Facebook, and up to 60% on Xing.
Determining semantic similarity between academic documents is crucial to many tasks such as plagiarism detection, automatic technical survey and semantic search.
Current studies mostly focus on semantic similarity between concepts, sentences and short text fragments.
However, document-level semantic matching is still based on statistical information in surface level, neglecting article structures and global semantic meanings, which may cause the deviation in document understanding.
In this paper, we focus on the document-level semantic similarity issue for academic literatures with a novel method.
We represent academic articles with topic events that utilize multiple information profiles, such as research purposes, methodologies and domains to integrally describe the research work, and calculate the similarity between topic events based on the domain ontology to acquire the semantic similarity between articles.
Experiments show that our approach achieves significant performance compared to state-of-the-art methods.
Lately, the problem of code-switching has gained a lot of attention and has emerged as an active area of research.
In bilingual communities, the speakers commonly embed the words and phrases of a non-native language into the syntax of a native language in their day-to-day communications.
The code-switching is a global phenomenon among multilingual communities, still very limited acoustic and linguistic resources are available as yet.
For developing effective speech based applications, the ability of the existing language technologies to deal with the code-switched data can not be over emphasized.
The code-switching is broadly classified into two modes: inter-sentential and intra-sentential code-switching.
In this work, we have studied the intra-sentential problem in the context of code-switching language modeling task.
The salient contributions of this paper includes: (i) the creation of Hindi-English code-switching text corpus by crawling a few blogging sites educating about the usage of the Internet (ii) the exploration of the parts-of-speech features towards more effective modeling of Hindi-English code-switched data by the monolingual language model (LM) trained on native (Hindi) language data, and (iii) the proposal of a novel textual factor referred to as the code-switch factor (CS-factor), which allows the LM to predict the code-switching instances.
In the context of recognition of the code-switching data, the substantial reduction in the PPL is achieved with the use of POS factors and also the proposed CS-factor provides independent as well as additive gain in the PPL.
A Bernoulli Mixture Model (BMM) is a finite mixture of random binary vectors with independent Bernoulli dimensions.
The problem of clustering BMM data arises in a variety of real-world applications, ranging from population genetics to activity analysis in social networks.
In this paper, we have analyzed the information-theoretic PAC-learnability of BMMs, when the number of clusters is unknown.
In particular, we stipulate certain conditions on both sample complexity and the dimension of the model in order to guarantee the Probably Approximately Correct (PAC)-clusterability of a given dataset.
To the best of our knowledge, these findings are the first non-asymptotic (PAC) bounds on the sample complexity of learning BMMs.
Quantitative extraction of high-dimensional mineable data from medical images is a process known as radiomics.
Radiomics is foreseen as an essential prognostic tool for cancer risk assessment and the quantification of intratumoural heterogeneity.
In this work, 1615 radiomic features (quantifying tumour image intensity, shape, texture) extracted from pre-treatment FDG-PET and CT images of 300 patients from four different cohorts were analyzed for the risk assessment of locoregional recurrences (LR) and distant metastases (DM) in head-and-neck cancer.
Prediction models combining radiomic and clinical variables were constructed via random forests and imbalance-adjustment strategies using two of the four cohorts.
Independent validation of the prediction and prognostic performance of the models was carried out on the other two cohorts (LR: AUC = 0.69 and CI = 0.67; DM: AUC = 0.86 and CI = 0.88).
Furthermore, the results obtained via Kaplan-Meier analysis demonstrated the potential of radiomics for assessing the risk of specific tumour outcomes using multiple stratification groups.
This could have important clinical impact, notably by allowing for a better personalization of chemo-radiation treatments for head-and-neck cancer patients from different risk groups.
Hidden Markov model based various phoneme recognition methods for Bengali language is reviewed.
Automatic phoneme recognition for Bengali language using multilayer neural network is reviewed.
Usefulness of multilayer neural network over single layer neural network is discussed.
Bangla phonetic feature table construction and enhancement for Bengali speech recognition is also discussed.
Comparison among these methods is discussed.
Classification of multivariate time series (MTS) has been tackled with a large variety of methodologies and applied to a wide range of scenarios.
Among the existing approaches, reservoir computing (RC) techniques, which implement a fixed and high-dimensional recurrent network to process sequential data, are computationally efficient tools to generate a vectorial, fixed-size representation of the MTS that can be further processed by standard classifiers.
Despite their unrivaled training speed, MTS classifiers based on a standard RC architecture fail to achieve the same accuracy of other classifiers, such as those exploiting fully trainable recurrent networks.
In this paper we introduce the reservoir model space, an RC approach to learn vectorial representations of MTS in an unsupervised fashion.
Each MTS is encoded within the parameters of a linear model trained to predict a low-dimensional embedding of the reservoir dynamics.
Our model space yields a powerful representation of the MTS and, thanks to an intermediate dimensionality reduction procedure, attains computational performance comparable to other RC methods.
As a second contribution we propose a modular RC framework for MTS classification, with an associated open source Python library.
By combining the different modules it is possible to seamlessly implement advanced RC architectures, including our proposed unsupervised representation, bidirectional reservoirs, and non-linear readouts, such as deep neural networks with both fixed and flexible activation functions.
Results obtained on benchmark and real-world MTS datasets show that RC classifiers are dramatically faster and, when implemented using our proposed representation, also achieve superior classification accuracy.
We evaluate 8 different word embedding models on their usefulness for predicting the neural activation patterns associated with concrete nouns.
The models we consider include an experiential model, based on crowd-sourced association data, several popular neural and distributional models, and a model that reflects the syntactic context of words (based on dependency parses).
Our goal is to assess the cognitive plausibility of these various embedding models, and understand how we can further improve our methods for interpreting brain imaging data.
We show that neural word embedding models exhibit superior performance on the tasks we consider, beating experiential word representation model.
The syntactically informed model gives the overall best performance when predicting brain activation patterns from word embeddings; whereas the GloVe distributional method gives the overall best performance when predicting in the reverse direction (words vectors from brain images).
Interestingly, however, the error patterns of these different models are markedly different.
This may support the idea that the brain uses different systems for processing different kinds of words.
Moreover, we suggest that taking the relative strengths of different embedding models into account will lead to better models of the brain activity associated with words.
Simulation frameworks are important tools for the analysis and design of communication networks and protocols, but they can result extremely costly and/or complex (for the case of very specialized tools), or too naive and lacking proper features and support (for the case of ad-hoc tools).
In this paper, we present an analysis of three 5G scenarios using 'simmer', a recent R package for discrete-event simulation that sits between the above two paradigms.
As our results show, it provides a simple yet very powerful syntax, supporting the efficient simulation of relatively complex scenarios at a low implementation cost.
We elaborate on the recently proposed orthogonal time frequency space (OTFS) modulation technique, which provides significant advantages over orthogonal frequency division multiplexing (OFDM) in Doppler channels.
We first derive the input--output relation describing OTFS modulation and demodulation (mod/demod) for delay--Doppler channels with arbitrary number of paths, with given delay and Doppler values.
We then propose a low-complexity message passing (MP) detection algorithm, which is suitable for large-scale OTFS taking advantage of the inherent channel sparsity.
Since the fractional Doppler paths (i.e., not exactly aligned with the Doppler taps) produce the inter Doppler interference (IDI), we adapt the MP detection algorithm to compensate for the effect of IDI in order to further improve performance.
Simulations results illustrate the superior performance gains of OTFS over OFDM under various channel conditions.
Most current single image camera calibration methods rely on specific image features or user input, and cannot be applied to natural images captured in uncontrolled settings.
We propose directly inferring camera calibration parameters from a single image using a deep convolutional neural network.
This network is trained using automatically generated samples from a large-scale panorama dataset, and considerably outperforms other methods, including recent deep learning-based approaches, in terms of standard L2 error.
However, we argue that in many cases it is more important to consider how humans perceive errors in camera estimation.
To this end, we conduct a large-scale human perception study where we ask users to judge the realism of 3D objects composited with and without ground truth camera calibration.
Based on this study, we develop a new perceptual measure for camera calibration, and demonstrate that our deep calibration network outperforms other methods on this measure.
Finally, we demonstrate the use of our calibration network for a number of applications including virtual object insertion, image retrieval and compositing.
In a reversible language, any forward computation can be undone by a finite sequence of backward steps.
Reversible computing has been studied in the context of different programming languages and formalisms, where it has been used for debugging and for enforcing fault-tolerance, among others.
In this paper, we consider a subset of Erlang, a concurrent language based on the actor model.
We formally introduce a reversible semantics for this language.
To the best of our knowledge, this is the first attempt to define a reversible semantics for Erlang.
Whether teaching in a classroom or a Massive Online Open Course it is crucial to present the material in a way that benefits the audience as a whole.
We identify two important tasks to solve towards this objective, 1 group students so that they can maximally benefit from peer interaction and 2 find an optimal schedule of the educational material for each group.
Thus, in this paper, we solve the problem of team formation and content scheduling for education.
Given a time frame d, a set of students S with their required need to learn different activities T and given k as the number of desired groups, we study the problem of finding k group of students.
The goal is to teach students within time frame d such that their potential for learning is maximized and find the best schedule for each group.
We show this problem to be NP-hard and develop a polynomial algorithm for it.
We show our algorithm to be effective both on synthetic as well as a real data set.
For our experiments, we use real data on students' grades in a Computer Science department.
As part of our contribution, we release a semi-synthetic dataset that mimics the properties of the real data.
This paper proposes CAESAR, a novel multi-leader Generalized Consensus protocol for geographically replicated sites.
The main goal of CAESAR is to overcome one of the major limitations of existing approaches, which is the significant performance degradation when application workload produces conflicting requests.
CAESAR does that by changing the way a fast decision is taken: its ordering protocol does not reject a fast decision for a client request if a quorum of nodes reply with different dependency sets for that request.
The effectiveness of CAESAR is demonstrated through an evaluation study performed on Amazon's EC2 infrastructure using 5 geo-replicated sites.
CAESAR outperforms other multi-leader (e.g., EPaxos) competitors by as much as 1.7x in the presence of 30% conflicting requests, and single-leader (e.g., Multi-Paxos) by up to 3.5x.
While service-dominant logic proposes that all "Goods are a distribution mechanism for service provision" (FP3), there is a need to understand when and why a firm would utilise direct or indirect (goods) service provision, and the interactions between them, to co-create value with the customer.
Three longitudinal case studies in B2B equipment-based 'complex service' systems were analysed to gain an understanding of customers' co-creation activities to achieve outcomes.
We found the nature of value, degree of contextual variety and the firm's legacy viability to be viability threats.
To counter this, the firm uses (a) Direct Service Provision for Scalability and Replicability, (b) Indirect Service Provision for variety absorption and co-creating emotional value and customer experience and (c) designing direct and indirect provision for Scalability and Absorptive Resources of the customer.
The co-creation of complex multidimensional value could be delivered through different value propositions of the firm.
The research proposes a value-centric way of understanding the interactions between direct and indirect service provision in the design of the firm's value proposition and proposes a viable systems approach towards reorganising the firm.
The study provides a way for managers to understand the effectiveness (rather than efficiency) of the firm in co-creating value as a major issue in the design of complex socio-technical systems.
Goods are often designed within the domain of engineering and product design, often placing human activity as a supporting role to the equipment.
Through an SDLogic lens, this study considers the design of both equipment and human activity on an equal footing for value co-creation with the customer, and it yielded interesting results on when direct provisioning (goods) should be redesigned, considering all activities equally.
Obstacle detection plays an important role in unmanned surface vehicles (USV).
The USVs operate in highly diverse environments in which an obstacle may be a floating piece of wood, a scuba diver, a pier, or a part of a shoreline, which presents a significant challenge to continuous detection from images taken onboard.
This paper addresses the problem of online detection by constrained unsupervised segmentation.
To this end, a new graphical model is proposed that affords a fast and continuous obstacle image-map estimation from a single video stream captured onboard a USV.
The model accounts for the semantic structure of marine environment as observed from USV by imposing weak structural constraints.
A Markov random field framework is adopted and a highly efficient algorithm for simultaneous optimization of model parameters and segmentation mask estimation is derived.
Our approach does not require computationally intensive extraction of texture features and comfortably runs in real-time.
The algorithm is tested on a new, challenging, dataset for segmentation and obstacle detection in marine environments, which is the largest annotated dataset of its kind.
Results on this dataset show that our model outperforms the related approaches, while requiring a fraction of computational effort.
This paper develops a novel framework for sharing secret keys using the Automatic Repeat reQuest (ARQ) protocol.
We first characterize the underlying information theoretic limits, under different assumptions on the channel spatial and temporal correlation function.
Our analysis reveals a novel role of "dumb antennas" in overcoming the negative impact of spatial correlation on the achievable secrecy rates.
We further develop an adaptive rate allocation policy, which achieves higher secrecy rates in temporally correlated channels, and explicit constructions for ARQ secrecy coding that enjoy low implementation complexity.
Building on this theoretical foundation, we propose a unified framework for ARQ-based secrecy in Wi-Fi networks.
By exploiting the existing ARQ mechanism in the IEEE 802.11 standard, we develop security overlays that offer strong security guarantees at the expense of only minor modifications in the medium access layer.
Our numerical results establish the achievability of non-zero secrecy rates even when the eavesdropper channel is less noisy, on the average, than the legitimate channel, while our linux-based prototype demonstrates the efficiency of our ARQ overlays in mitigating all known, passive and active, Wi-Fi attacks at the expense of a minimal increase in the link setup time and a small loss in throughput.
Recurrent neural networks (RNNs) are an effective representation of control policies for a wide range of reinforcement and imitation learning problems.
RNN policies, however, are particularly difficult to explain, understand, and analyze due to their use of continuous-valued memory vectors and observation features.
In this paper, we introduce a new technique, Quantized Bottleneck Insertion, to learn finite representations of these vectors and features.
The result is a quantized representation of the RNN that can be analyzed to improve our understanding of memory use and general behavior.
We present results of this approach on synthetic environments and six Atari games.
The resulting finite representations are surprisingly small in some cases, using as few as 3 discrete memory states and 10 observations for a perfect Pong policy.
We also show that these finite policy representations lead to improved interpretability.
In this work we propose a multi-task spatio-temporal network, called SUSiNet, that can jointly tackle the spatio-temporal problems of saliency estimation, action recognition and video summarization.
Our approach employs a single network that is jointly end-to-end trained for all tasks with multiple and diverse datasets related to the exploring tasks.
The proposed network uses a unified architecture that includes global and task specific layer and produces multiple output types, i.e., saliency maps or classification labels, by employing the same video input.
Moreover, one additional contribution is that the proposed network can be deeply supervised through an attention module that is related to human attention as it is expressed by eye-tracking data.
From the extensive evaluation, on seven different datasets, we have observed that the multi-task network performs as well as the state-of-the-art single-task methods (or in some cases better), while it requires less computational budget than having one independent network per each task.
Convolutional neural networks (CNNs) have achieved great success on grid-like data such as images, but face tremendous challenges in learning from more generic data such as graphs.
In CNNs, the trainable local filters enable the automatic extraction of high-level features.
The computation with filters requires a fixed number of ordered units in the receptive fields.
However, the number of neighboring units is neither fixed nor are they ordered in generic graphs, thereby hindering the applications of convolutional operations.
Here, we address these challenges by proposing the learnable graph convolutional layer (LGCL).
LGCL automatically selects a fixed number of neighboring nodes for each feature based on value ranking in order to transform graph data into grid-like structures in 1-D format, thereby enabling the use of regular convolutional operations on generic graphs.
To enable model training on large-scale graphs, we propose a sub-graph training method to reduce the excessive memory and computational resource requirements suffered by prior methods on graph convolutions.
Our experimental results on node classification tasks in both transductive and inductive learning settings demonstrate that our methods can achieve consistently better performance on the Cora, Citeseer, Pubmed citation network, and protein-protein interaction network datasets.
Our results also indicate that the proposed methods using sub-graph training strategy are more efficient as compared to prior approaches.
Soon after its introduction in 2009, Bitcoin has been adopted by cyber-criminals, which rely on its pseudonymity to implement virtually untraceable scams.
One of the typical scams that operate on Bitcoin are the so-called Ponzi schemes.
These are fraudulent investments which repay users with the funds invested by new users that join the scheme, and implode when it is no longer possible to find new investments.
Despite being illegal in many countries, Ponzi schemes are now proliferating on Bitcoin, and they keep alluring new victims, who are plundered of millions of dollars.
We apply data mining techniques to detect Bitcoin addresses related to Ponzi schemes.
Our starting point is a dataset of features of real-world Ponzi schemes, that we construct by analysing, on the Bitcoin blockchain, the transactions used to perform the scams.
We use this dataset to experiment with various machine learning algorithms, and we assess their effectiveness through standard validation protocols and performance metrics.
The best of the classifiers we have experimented can identify most of the Ponzi schemes in the dataset, with a low number of false positives.
Neural networks are known to be vulnerable to adversarial examples.
Carefully chosen perturbations to real images, while imperceptible to humans, induce misclassification and threaten the reliability of deep learning systems in the wild.
To guard against adversarial examples, we take inspiration from game theory and cast the problem as a minimax zero-sum game between the adversary and the model.
In general, for such games, the optimal strategy for both players requires a stochastic policy, also known as a mixed strategy.
In this light, we propose Stochastic Activation Pruning (SAP), a mixed strategy for adversarial defense.
SAP prunes a random subset of activations (preferentially pruning those with smaller magnitude) and scales up the survivors to compensate.
We can apply SAP to pretrained networks, including adversarially trained models, without fine-tuning, providing robustness against adversarial examples.
Experiments demonstrate that SAP confers robustness against attacks, increasing accuracy and preserving calibration.
Incorporating graphs in the analysis of multivariate signals is becoming a standard way to understand the interdependency of activity recorded at different sites.
The new research frontier in this direction includes the important problem of how to assess dynamic changes of signal activity.
We address this problem in a novel way by defining the graph-variate signal alongside methods for its analysis.
Essentially, graph-variate signal analysis leverages graphs of reliable connectivity information to filter instantaneous bivariate functions of the multivariate signal.
This opens up a new and robust approach to analyse joint signal and network dynamics at sample resolution.
Furthermore, our method can be formulated as instantaneous networks on which standard network analysis can be implemented.
When graph connectivity is estimated from the multivariate signal itself, the appropriate consideration of instantaneous graph signal functions allows for a novel dynamic connectivity measure-- graphvariate dynamic (GVD) connectivity-- which is robust to spurious short-term dependencies.
Particularly, we present appropriate functions for three pertinent connectivity metrics-- correlation, coherence and the phase-lag index.
We show that our approach can determine signals with a single correlated couple against wholly uncorrelated data of up to 128 nodes in signal size (1 out of 8128 weighted edges).
GVD connectivity is also shown to be more robust than i) other GSP approaches at detecting a randomly traveling spheroid on a 3D grid and ii) standard dynamic connectivity in determining differences in EEG restingstate and task-related activity.
We also demonstrate its use in revealing hidden depth correlations from geophysical gamma ray data.
We expect that the methods and framework presented will provide new approaches to data analysis in a variety of applied settings.
Air pollution poses a serious threat to human health as well as economic development around the world.
To meet the increasing demand for accurate predictions for air pollutions, we proposed a Deep Inferential Spatial-Temporal Network to deal with the complicated non-linear spatial and temporal correlations.
We forecast three air pollutants (i.e., PM2.5, PM10 and O3) of monitoring stations over the next 48 hours, using a hybrid deep learning model consists of inferential predictor (inference for regions without air pollution readings), spatial predictor (capturing spatial correlations using CNN) and temporal predictor (capturing temporal relationship using sequence-to-sequence model with simplified attention mechanism).
Our proposed model considers historical air pollution records and historical meteorological data.
We evaluate our model on a large-scale dataset containing air pollution records of 35 monitoring stations and grid meteorological data in Beijing, China.
Our model outperforms other state-of-art methods in terms of SMAPE and RMSE.
We present LDAExplore, a tool to visualize topic distributions in a given document corpus that are generated using Topic Modeling methods.
Latent Dirichlet Allocation (LDA) is one of the basic methods that is predominantly used to generate topics.
One of the problems with methods like LDA is that users who apply them may not understand the topics that are generated.
Also, users may find it difficult to search correlated topics and correlated documents.
LDAExplore, tries to alleviate these problems by visualizing topic and word distributions generated from the document corpus and allowing the user to interact with them.
The system is designed for users, who have minimal knowledge of LDA or Topic Modelling methods.
To evaluate our design, we run a pilot study which uses the abstracts of 322 Information Visualization papers, where every abstract is considered a document.
The topics generated are then explored by users.
The results show that users are able to find correlated documents and group them based on topics that are similar.
Symmetry is an important factor in human perception in general, as well as in the visualization of graphs in particular.
There are three main types of symmetry: reflective, translational, and rotational.
We report the results of a human subjects experiment to determine what types of symmetries are more salient in drawings of graphs.
We found statistically significant evidence that vertical reflective symmetry is the most dominant (when selecting among vertical reflective, horizontal reflective, and translational).
We also found statistically significant evidence that rotational symmetry is affected by the number of radial axes (the more, the better), with a notable exception at four axes.
Material attributes have been shown to provide a discriminative intermediate representation for recognizing materials, especially for the challenging task of recognition from local material appearance (i.e., regardless of object and scene context).
In the past, however, material attributes have been recognized separately preceding category recognition.
In contrast, neuroscience studies on material perception and computer vision research on object and place recognition have shown that attributes are produced as a by-product during the category recognition process.
Does the same hold true for material attribute and category recognition?
In this paper, we introduce a novel material category recognition network architecture to show that perceptual attributes can, in fact, be automatically discovered inside a local material recognition framework.
The novel material-attribute-category convolutional neural network (MAC-CNN) produces perceptual material attributes from the intermediate pooling layers of an end-to-end trained category recognition network using an auxiliary loss function that encodes human material perception.
To train this model, we introduce a novel large-scale database of local material appearance organized under a canonical material category taxonomy and careful image patch extraction that avoids unwanted object and scene context.
We show that the discovered attributes correspond well with semantically-meaningful visual material traits via Boolean algebra, and enable recognition of previously unseen material categories given only a few examples.
These results have strong implications in how perceptually meaningful attributes can be learned in other recognition tasks.
Pain is a personal, subjective experience that is commonly evaluated through visual analog scales (VAS).
While this is often convenient and useful, automatic pain detection systems can reduce pain score acquisition efforts in large-scale studies by estimating it directly from the participants' facial expressions.
In this paper, we propose a novel two-stage learning approach for VAS estimation: first, our algorithm employs Recurrent Neural Networks (RNNs) to automatically estimate Prkachin and Solomon Pain Intensity (PSPI) levels from face images.
The estimated scores are then fed into the personalized Hidden Conditional Random Fields (HCRFs), used to estimate the VAS, provided by each person.
Personalization of the model is performed using a newly introduced facial expressiveness score, unique for each person.
To the best of our knowledge, this is the first approach to automatically estimate VAS from face images.
We show the benefits of the proposed personalized over traditional non-personalized approach on a benchmark dataset for pain analysis from face images.
Visual media are powerful means of expressing emotions and sentiments.
The constant generation of new content in social networks highlights the need of automated visual sentiment analysis tools.
While Convolutional Neural Networks (CNNs) have established a new state-of-the-art in several vision problems, their application to the task of sentiment analysis is mostly unexplored and there are few studies regarding how to design CNNs for this purpose.
In this work, we study the suitability of fine-tuning a CNN for visual sentiment prediction as well as explore performance boosting techniques within this deep learning setting.
Finally, we provide a deep-dive analysis into a benchmark, state-of-the-art network architecture to gain insight about how to design patterns for CNNs on the task of visual sentiment prediction.
We propose deterministic sampling strategies for compressive imaging based on Delsarte-Goethals frames.
We show that these sampling strategies result in multi-scale measurements which can be related to the 2D Haar wavelet transform.
We demonstrate the effectiveness of our proposed strategies through numerical experiments.
In this paper we present a new efficient algorithm for factoring the RSA and the Rabin moduli in the particular case when the difference between their two prime factors is bounded.
As an extension, we also give some theoretical results on factoring integers.
We introduce a general framework for visual forecasting, which directly imitates visual sequences without additional supervision.
As a result, our model can be applied at several semantic levels and does not require any domain knowledge or handcrafted features.
We achieve this by formulating visual forecasting as an inverse reinforcement learning (IRL) problem, and directly imitate the dynamics in natural sequences from their raw pixel values.
The key challenge is the high-dimensional and continuous state-action space that prohibits the application of previous IRL algorithms.
We address this computational bottleneck by extending recent progress in model-free imitation with trainable deep feature representations, which (1) bypasses the exhaustive state-action pair visits in dynamic programming by using a dual formulation and (2) avoids explicit state sampling at gradient computation using a deep feature reparametrization.
This allows us to apply IRL at scale and directly imitate the dynamics in high-dimensional continuous visual sequences from the raw pixel values.
We evaluate our approach at three different level-of-abstraction, from low level pixels to higher level semantics: future frame generation, action anticipation, visual story forecasting.
At all levels, our approach outperforms existing methods.
This is the preprint version of our paper on IEEE Virtual Reality Conference 2015.
A touch-less interaction technology on vision based wearable device is designed and evaluated.
Users interact with the application with dynamic hands/feet gestures in front of the camera.
Several proof-of-concept prototypes with eleven dynamic gestures are developed based on the touch-less interaction.
At last, a comparing user study evaluation is proposed to demonstrate the usability of the touch-less approach, as well as the impact on user's emotion, running on a wearable framework or Google Glass.
During imagery motor movements tasks, the so called mu and beta event related desynchronization (ERD) and synchronization (ERS) are taking place, allowing us to determine human patient imagery movement.
However, initial recordings of electroencephalography (EEG) signals contain system and environmental noise as well as interference that must be ejected in order to separate the ERS/ERD events from the rest of the signal.
This paper presents a new technique based on a reworked Second Order Blind Identification (SOBI) algorithm for noise removal while imagery movement classification is implemented using Support Vector Machine (SVM) technique.
Efforts to automate the reconstruction of neural circuits from 3D electron microscopic (EM) brain images are critical for the field of connectomics.
An important computation for reconstruction is the detection of neuronal boundaries.
Images acquired by serial section EM, a leading 3D EM technique, are highly anisotropic, with inferior quality along the third dimension.
For such images, the 2D max-pooling convolutional network has set the standard for performance at boundary detection.
Here we achieve a substantial gain in accuracy through three innovations.
Following the trend towards deeper networks for object recognition, we use a much deeper network than previously employed for boundary detection.
Second, we incorporate 3D as well as 2D filters, to enable computations that use 3D context.
Finally, we adopt a recursively trained architecture in which a first network generates a preliminary boundary map that is provided as input along with the original image to a second network that generates a final boundary map.
Backpropagation training is accelerated by ZNN, a new implementation of 3D convolutional networks that uses multicore CPU parallelism for speed.
Our hybrid 2D-3D architecture could be more generally applicable to other types of anisotropic 3D images, including video, and our recursive framework for any image labeling problem.
Action recognition and human pose estimation are closely related but both problems are generally handled as distinct tasks in the literature.
In this work, we propose a multitask framework for jointly 2D and 3D pose estimation from still images and human action recognition from video sequences.
We show that a single architecture can be used to solve the two problems in an efficient way and still achieves state-of-the-art results.
Additionally, we demonstrate that optimization from end-to-end leads to significantly higher accuracy than separated learning.
The proposed architecture can be trained with data from different categories simultaneously in a seamlessly way.
The reported results on four datasets (MPII, Human3.6M, Penn Action and NTU) demonstrate the effectiveness of our method on the targeted tasks.
This paper introduces a fast algorithm for simultaneous inversion and determinant computation of small sized matrices in the context of fully Polarimetric Synthetic Aperture Radar (PolSAR) image processing and analysis.
The proposed fast algorithm is based on the computation of the adjoint matrix and the symmetry of the input matrix.
The algorithm is implemented in a general purpose graphical processing unit (GPGPU) and compared to the usual approach based on Cholesky factorization.
The assessment with simulated observations and data from an actual PolSAR sensor show a speedup factor of about two when compared to the usual Cholesky factorization.
Moreover, the expressions provided here can be implemented in any platform.
The research on hashing techniques for visual data is gaining increased attention in recent years due to the need for compact representations supporting efficient search/retrieval in large-scale databases such as online images.
Among many possibilities, Mean Average Precision(mAP) has emerged as the dominant performance metric for hashing-based retrieval.
One glaring shortcoming of mAP is its inability in balancing retrieval accuracy and utilization of hash codes: pushing a system to attain higher mAP will inevitably lead to poorer utilization of the hash codes.
Poor utilization of the hash codes hinders good retrieval because of increased collision of samples in the hash space.
This means that a model giving a higher mAP values does not necessarily do a better job in retrieval.
In this paper, we introduce a new metric named Mean Local Group Average Precision (mLGAP) for better evaluation of the performance of hashing-based retrieval.
The new metric provides a retrieval performance measure that also reconciles the utilization of hash codes, leading to a more practically meaningful performance metric than conventional ones like mAP.
To this end, we start by mathematical analysis of the deficiencies of mAP for hashing-based retrieval.
We then propose mLGAP and show why it is more appropriate for hashing-based retrieval.
Experiments on image retrieval are used to demonstrate the effectiveness of the proposed metric.
We define a compilation scheme for a constructor-based, strongly-sequential, graph rewriting system which shortcuts some needed steps.
The object code is another constructor-based graph rewriting system.
This system is normalizing for the original system when using an innermost strategy.
Consequently, the object code can be easily implemented by eager functions in a variety of programming languages.
We modify this object code in a way that avoids total or partial construction of the contracta of some needed steps of a computation.
When computing normal forms in this way, both memory consumption and execution time are reduced compared to ordinary rewriting computations in the original system.
Partial mutual exclusion is the drinking philosophers problem for complete graphs.
It is the problem that a process may enter a critical section CS of its code only when some finite set nbh of other processes are not in their critical sections.
For each execution of CS, the set nbh can be given by the environment.
We present a starvation free solution of this problem in a setting with infinitely many processes, each with finite memory, that communicate by asynchronous messages.
The solution has the property of first-come first-served, in so far as this can be guaranteed by asynchronous messages.
For every execution of CS and every process in nbh, between three and six messages are needed.
The correctness of the solution is argued with invariants and temporal logic.
It has been verified with the proof assistant PVS.
This paper investigates exploration strategies of Deep Reinforcement Learning (DRL) methods to learn navigation policies for mobile robots.
In particular, we augment the normal external reward for training DRL algorithms with intrinsic reward signals measured by curiosity.
We test our approach in a mapless navigation setting, where the autonomous agent is required to navigate without the occupancy map of the environment, to targets whose relative locations can be easily acquired through low-cost solutions (e.g., visible light localization, Wi-Fi signal localization).
We validate that the intrinsic motivation is crucial for improving DRL performance in tasks with challenging exploration requirements.
Our experimental results show that our proposed method is able to more effectively learn navigation policies, and has better generalization capabilities in previously unseen environments.
A video of our experimental results can be found at https://goo.gl/pWbpcF.
This paper investigates the fully distributed cooperation scheme for networked nonholonomic mobile manipulators.
To achieve cooperative task allocation in a distributed way, an adaptation-based estimation law is established for each robotic agent to estimate the desired local trajectory.
In addition, wrench synthesis is analyzed in detail to lay a solid foundation for tight cooperation task.
Together with the estimated task, a set of distributed adaptive control is proposed to achieve motion synchronization of the mobile manipulator ensemble over a directed graph with a spanning tree irrespective of the kinematic and dynamic uncertainties in both the mobile manipulators and the tightly grasped object.
The controlled synchronization alleviates the performance degradation caused by the estimation/tracking discrepancy during transient phase.
Persistent excitation condition and noisy Cartesian-space velocities are totally avoided.
Furthermore, the proposed scheme is independent from the object's center of mass by employing formation-based task allocation and task-oriented strategy.
These attractive attributes facilitate its practical application.
It is theoretically proved that convergence of the cooperative task tracking error is guaranteed.
Simulation results validate the efficacy and demonstrates the expected performance of the proposed scheme.
For a given set of intervals on the real line, we consider the problem of ordering the intervals with the goal of minimizing an objective function that depends on the exposed interval pieces (that is, the pieces that are not covered by earlier intervals in the ordering).
This problem is motivated by an application in molecular biology that concerns the determination of the structure of the backbone of a protein.
We present polynomial-time algorithms for several natural special cases of the problem that cover the situation where the interval boundaries are agreeably ordered and the situation where the interval set is laminar.
Also the bottleneck variant of the problem is shown to be solvable in polynomial time.
Finally we prove that the general problem is NP-hard, and that the existence of a constant-factor-approximation algorithm is unlikely.
Convolutional neural networks (CNN) have recently achieved remarkable performance in a wide range of applications.
In this research, we equip convolutional sequence-to-sequence (seq2seq) model with an efficient graph linearization technique for abstract meaning representation parsing.
Our linearization method is better than the prior method at signaling the turn of graph traveling.
Additionally, convolutional seq2seq model is more appropriate and considerably faster than the recurrent neural network models in this task.
Our method outperforms previous methods by a large margin on both the standard dataset LDC2014T12.
Our result indicates that future works still have a room for improving parsing model using graph linearization approach.
This paper introduces an innovative approach for handling 2D compound hypotheses within the Belief Function Theory framework.
We propose a polygon-based generic rep- resentation which relies on polygon clipping operators.
This approach allows us to account in the computational cost for the precision of the representation independently of the cardinality of the discernment frame.
For the BBA combination and decision making, we propose efficient algorithms which rely on hashes for fast lookup, and on a topological ordering of the focal elements within a directed acyclic graph encoding their interconnections.
Additionally, an implementation of the functionalities proposed in this paper is provided as an open source library.
Experimental results on a pedestrian localization problem are reported.
The experiments show that the solution is accurate and that it fully benefits from the scalability of the 2D search space granularity provided by our representation.
For ergodic fading, a lattice coding and decoding strategy is proposed and its performance is analyzed for the single-input single-output (SISO) and multiple-input multiple-output (MIMO) point-to-point channel as well as the multiple-access channel (MAC), with channel state information available only at the receiver (CSIR).
At the decoder a novel strategy is proposed consisting of a time-varying equalization matrix followed by decision regions that depend only on channel statistics, not individual realizations.
Our encoder has a similar structure to that of Erez and Zamir.
For the SISO channel, the gap to capacity is bounded by a constant under a wide range of fading distributions.
For the MIMO channel under Rayleigh fading, the rate achieved is within a gap to capacity that does not depend on the signal-to-noise ratio (SNR), and diminishes with the number of receive antennas.
The analysis is extended to the K-user MAC where similar results hold.
Achieving a small gap to capacity while limiting the use of CSIR to the equalizer highlights the scope for efficient decoder implementations, since decision regions are fixed, i.e., independent of channel realizations.
In an organization, individuals prefer to form various formal and informal groups for mutual interactions.
Therefore, ubiquitous identification of such groups and understanding their dynamics are important to monitor activities, behaviours and well-being of the individuals.
In this paper, we develop a lightweight, yet near-accurate, methodology, called MeetSense, to identify various interacting groups based on collective sensing through users' smartphones.
Group detection from sensor signals is not straightforward because users in proximity may not always be under the same group.
Therefore, we use acoustic context extracted from audio signals to infer interaction pattern among the subjects in proximity.
We have developed an unsupervised and lightweight mechanism for user group detection by taking cues from network science and measuring the cohesivity of the detected groups in terms of modularity.
Taking modularity into consideration, MeetSense can efficiently eliminate incorrect groups, as well as adapt the mechanism depending on the role played by the proximity and the acoustic context in a specific scenario.
The proposed method has been implemented and tested under many real-life scenarios in an academic institute environment, and we observe that MeetSense can identify user groups with close to 90% accuracy even in a noisy environment.
Recognizing textual entailment is a fundamental task in a variety of text mining or natural language processing applications.
This paper proposes a simple neural model for RTE problem.
It first matches each word in the hypothesis with its most-similar word in the premise, producing an augmented representation of the hypothesis conditioned on the premise as a sequence of word pairs.
The LSTM model is then used to model this augmented sequence, and the final output from the LSTM is fed into a softmax layer to make the prediction.
Besides the base model, in order to enhance its performance, we also proposed three techniques: the integration of multiple word-embedding library, bi-way integration, and ensemble based on model averaging.
Experimental results on the SNLI dataset have shown that the three techniques are effective in boosting the predicative accuracy and that our method outperforms several state-of-the-state ones.
One of the most influential recent results in network analysis is that many natural networks exhibit a power-law or log-normal degree distribution.
This has inspired numerous generative models that match this property.
However, more recent work has shown that while these generative models do have the right degree distribution, they are not good models for real life networks due to their differences on other important metrics like conductance.
We believe this is, in part, because many of these real-world networks have very different joint degree distributions, i.e. the probability that a randomly selected edge will be between nodes of degree k and l. Assortativity is a sufficient statistic of the joint degree distribution, and it has been previously noted that social networks tend to be assortative, while biological and technological networks tend to be disassortative.
We suggest understanding the relationship between network structure and the joint degree distribution of graphs is an interesting avenue of further research.
An important tool for such studies are algorithms that can generate random instances of graphs with the same joint degree distribution.
This is the main topic of this paper and we study the problem from both a theoretical and practical perspective.
We provide an algorithm for constructing simple graphs from a given joint degree distribution, and a Monte Carlo Markov Chain method for sampling them.
We also show that the state space of simple graphs with a fixed degree distribution is connected via end point switches.
We empirically evaluate the mixing time of this Markov Chain by using experiments based on the autocorrelation of each edge.
These experiments show that our Markov Chain mixes quickly on real graphs, allowing for utilization of our techniques in practice.
Neglecting the effects of rolling-shutter cameras for visual odometry (VO) severely degrades accuracy and robustness.
In this paper, we propose a novel direct monocular VO method that incorporates a rolling-shutter model.
Our approach extends direct sparse odometry which performs direct bundle adjustment of a set of recent keyframe poses and the depths of a sparse set of image points.
We estimate the velocity at each keyframe and impose a constant-velocity prior for the optimization.
In this way, we obtain a near real-time, accurate direct VO method.
Our approach achieves improved results on challenging rolling-shutter sequences over state-of-the-art global-shutter VO.
The most efficient algorithms for finding maximum independent sets in both theory and practice use reduction rules to obtain a much smaller problem instance called a kernel.
The kernel can then be solved quickly using exact or heuristic algorithms - or by repeatedly kernelizing recursively in the branch-and-reduce paradigm.
It is of critical importance for these algorithms that kernelization is fast and returns a small kernel.
Current algorithms are either slow but produce a small kernel, or fast and give a large kernel.
We attempt to accomplish both of these goals simultaneously, by giving an efficient parallel kernelization algorithm based on graph partitioning and parallel bipartite maximum matching.
We combine our parallelization techniques with two techniques to accelerate kernelization further: dependency checking that prunes reductions that cannot be applied, and reduction tracking that allows us to stop kernelization when reductions become less fruitful.
Our algorithm produces kernels that are orders of magnitude smaller than the fastest kernelization methods, while having a similar execution time.
Furthermore, our algorithm is able to compute kernels with size comparable to the smallest known kernels, but up to two orders of magnitude faster than previously possible.
Finally, we show that our kernelization algorithm can be used to accelerate existing state-of-the-art heuristic algorithms, allowing us to find larger independent sets faster on large real-world networks and synthetic instances.
Classical control and management plane for computer networks is addressing individual parameters of protocol layers within an individual wireless network device.
We argue that this is not sufficient in phase of increasing deployment of highly re-configurable systems, as well as heterogeneous wireless systems co-existing in the same radio spectrum which demand harmonized, frequently even coordinated adaptation of multiple parameters in different protocol layers (cross-layer) in multiple network devices (cross-node).
We propose UniFlex, a framework enabling unified and flexible radio and network control.
It provides an API enabling coordinated cross-layer control and management operation over multiple network nodes.
The controller logic may be implemented either in a centralized or distributed manner.
This allows to place time-sensitive control functions close to the controlled device (i.e., local control application), off-load more resource hungry network application to compute servers and make them work together to control entire network.
The UniFlex framework was prototypically implemented and provided to the research community as open-source.
We evaluated the the framework in a number of use-cases, what proved its usability.
This paper investigates the offline packet-delay-minimization problem for an energy harvesting transmitter.
To overcome the non-convexity of the problem, we propose a C2-diffeomorphic transformation and provide the necessary and sufficient condition for the transformed problem to a standard convex optimization problem.
Based on this condition, a simple choice of the transformation is determined which allows an analytically tractable solution of the original non-convex problem to be easily obtained once the transformed convex problem is solved.
We further study the structure of the optimal transmission policy in a special case and find it to follow a weighted-directional-water-filling structure.
In particular, the optimal policy tends to allocate more power in earlier time slots and less power in later time slots.
Our analytical insight is verified by simulation results.
Software-defined networking (SDN) is a new paradigm that allows developing more flexible network applications.
SDN controller, which represents a centralized controlling point, is responsible for running various network applications as well as maintaining different network services and functionalities.
Choosing an efficient intrusion detection system helps in reducing the overhead of the running controller and creates a more secure network.
In this study, we investigate the performance of the well-known anomaly-based intrusion detection approaches in terms of accuracy, false alarm rate, precision, recall, f1-measure, area under ROC curve, execution time and Mc Nemar's test.
Precisely, we focus on supervised machine-learning approaches where we use the following classifiers: Decision Trees (DT), Extreme Learning Machine (ELM), Naive Bayes (NB), Linear Discriminant Analysis (LDA), Neural Networks (NN), Support Vector Machines (SVM), Random Forest (RT), K Nearest-Neighbour (KNN), AdaBoost, RUSBoost, LogitBoost and BaggingTrees where we employ the well-known NSL-KDD benchmark dataset to compare the performance of each one of these classifiers.
A simulation model is presented to analyze and evaluate the performance of VoIP based integrated wireless LAN/WAN with taking into account various voice encoding schemes.
The network model was simulated using OPNET Modeler software.
Different parameters that indicate the QoS like MOS, jitter, end to end delay, traffic send and traffic received are calculated and analyzed in Wireless LAN/WAN scenarios.
Depending on this evaluation, Selection codecs G.729A consider the best choice for VoIP.
A method based on the classical principal component analysis leads to demonstrate that the role of co-authors should give a h-index measure to a group leader higher than usually accepted.
The method rather easily gives what is usually searched for, i.e. an estimate of the role (or "weight") of co-authors, as the additional value to an author papers' popularity.
The construction of the co-authorship popularity H-matrix is exemplified and the role of eigenvalues and the main eigenvector component are discussed.
An example illustrates the points and serves as the basis for suggesting a generally practical application of the concept.
In this paper we present a biorealistic model for the first part of the early vision processing by incorporating memristive nanodevices.
The architecture of the proposed network is based on the organisation and functioning of the outer plexiform layer (OPL) in the vertebrate retina.
We demonstrate that memristive devices are indeed a valuable building block for neuromorphic architectures, as their highly non-linear and adaptive response could be exploited for establishing ultra-dense networks with similar dynamics to their biological counterparts.
We particularly show that hexagonal memristive grids can be employed for faithfully emulating the smoothing-effect occurring at the OPL for enhancing the dynamic range of the system.
In addition, we employ a memristor-based thresholding scheme for detecting the edges of grayscale images, while the proposed system is also evaluated for its adaptation and fault tolerance capacity against different light or noise conditions as well as distinct device yields.
Extraction of local feature descriptors is a vital stage in the solution pipelines for numerous computer vision tasks.
Learning-based approaches improve performance in certain tasks, but still cannot replace handcrafted features in general.
In this paper, we improve the learning of local feature descriptors by optimizing the performance of descriptor matching, which is a common stage that follows descriptor extraction in local feature based pipelines, and can be formulated as nearest neighbor retrieval.
Specifically, we directly optimize a ranking-based retrieval performance metric, Average Precision, using deep neural networks.
This general-purpose solution can also be viewed as a listwise learning to rank approach, which is advantageous compared to recent local ranking approaches.
On standard benchmarks, descriptors learned with our formulation achieve state-of-the-art results in patch verification, patch retrieval, and image matching.
In this paper, we define the general framework to describe the diffusion operators associated to a positive matrix.
We define the equations associated to diffusion operators and present some general properties of their state vectors.
We show how this can be applied to prove and improve the convergence of a fixed point problem associated to the matrix iteration scheme, including for distributed computation framework.
The approach can be understood as a decomposition of the matrix-vector product operation in elementary operations at the vector entry level.
Recently virtual platforms and virtual prototyping techniques have been widely applied for accelerating software development in electronics companies.
It has been proved that these techniques can greatly shorten time-to-market and improve product quality.
One challenge is how to test and validate a virtual prototype.
In this paper, we present how to conduct regression testing of virtual prototypes in different versions using symbolic execution.
Suppose we have old and new versions of a virtual prototype, we first apply symbolic execution to the new version and collect all path constraints.
Then the collected path constraints are used for guiding the symbolic execution of the old version.
For each path explored, we compare the device states between two versions to check if they behave the same.
We have applied this approach to a widely-used virtual prototype and detected numerous differences.
The experimental results show that our approach is useful and efficient.
We explore the concept of co-design in the context of neural network verification.
Specifically, we aim to train deep neural networks that not only are robust to adversarial perturbations but also whose robustness can be verified more easily.
To this end, we identify two properties of network models - weight sparsity and so-called ReLU stability - that turn out to significantly impact the complexity of the corresponding verification task.
We demonstrate that improving weight sparsity alone already enables us to turn computationally intractable verification problems into tractable ones.
Then, improving ReLU stability leads to an additional 4-13x speedup in verification times.
An important feature of our methodology is its "universality," in the sense that it can be used with a broad range of training procedures and verification approaches.
This paper presents a super-efficient spatially adaptive contrast enhancement algorithm for enhancing infrared (IR) radiation based superficial vein images in real-time.
The super-efficiency permits the algorithm to run in consumer-grade handheld devices, which ultimately reduces the cost of vein imaging equipment.
The proposed method utilizes the response from the low-frequency range of the IR image signal to adjust the boundaries of the reference dynamic range in a linear contrast stretching process with a tunable contrast enhancement parameter, as opposed to traditional approaches which use costly adaptive histogram equalization based methods.
The algorithm has been implemented and deployed in a consumer grade Android-based mobile device to evaluate the performance.
The results revealed that the proposed algorithm can process IR images of veins in real-time on low-performance computers.
It was compared with several well-performed traditional methods and the results revealed that the new algorithm stands out with several beneficial features, namely, the fastest processing, the ability to enhance the desired details, the excellent illumination normalization capability and the ability to enhance details where the traditional methods failed.
For an artificial creative agent, an essential driver of the search for novelty is a value function which is often provided by the system designer or users.
We argue that an important barrier for progress in creativity research is the inability of these systems to develop their own notion of value for novelty.
We propose a notion of knowledge-driven creativity that circumvent the need for an externally imposed value function, allowing the system to explore based on what it has learned from a set of referential objects.
The concept is illustrated by a specific knowledge model provided by a deep generative autoencoder.
Using the described system, we train a knowledge model on a set of digit images and we use the same model to build coherent sets of new digits that do not belong to known digit types.
The piggybacking framework for designing erasure codes for distributed storage has empirically proven to be very useful, and has been used to design codes with desirable properties, such as low repair bandwidth and complexity.
However, the theoretical properties of this framework remain largely unexplored.
We address this by adapting a general characterization of repair schemes (previously used for Reed Solomon codes) to analyze piggybacking codes with low substriping.
With this characterization, we establish a separation between piggybacking and general erasure codes, and several impossibility results for subcategories of piggybacking codes; for certain parameters, we also present explicit, optimal constructions of piggybacking codes.
The effort devoted to hand-crafting neural network image classifiers has motivated the use of architecture search to discover them automatically.
Although evolutionary algorithms have been repeatedly applied to neural network topologies, the image classifiers thus discovered have remained inferior to human-crafted ones.
Here, we evolve an image classifier---AmoebaNet-A---that surpasses hand-designs for the first time.
To do this, we modify the tournament selection evolutionary algorithm by introducing an age property to favor the younger genotypes.
Matching size, AmoebaNet-A has comparable accuracy to current state-of-the-art ImageNet models discovered with more complex architecture-search methods.
Scaled to larger size, AmoebaNet-A sets a new state-of-the-art 83.9% / 96.6% top-5 ImageNet accuracy.
In a controlled comparison against a well known reinforcement learning algorithm, we give evidence that evolution can obtain results faster with the same hardware, especially at the earlier stages of the search.
This is relevant when fewer compute resources are available.
Evolution is, thus, a simple method to effectively discover high-quality architectures.
Portmanteaus are a word formation phenomenon where two words are combined to form a new word.
We propose character-level neural sequence-to-sequence (S2S) methods for the task of portmanteau generation that are end-to-end-trainable, language independent, and do not explicitly use additional phonetic information.
We propose a noisy-channel-style model, which allows for the incorporation of unsupervised word lists, improving performance over a standard source-to-target model.
This model is made possible by an exhaustive candidate generation strategy specifically enabled by the features of the portmanteau task.
Experiments find our approach superior to a state-of-the-art FST-based baseline with respect to ground truth accuracy and human evaluation.
There is a common need to search of molecular databases for compounds resembling some shape, what suggests having similar biological activity while searching for new drugs.
The large size of the databases requires fast methods for such initial screening, for example based on feature vectors constructed to fulfill the requirement that similar molecules should correspond to close vectors.
Ultrafast Shape Recognition (USR) is a popular approach of this type.
It uses vectors of 12 real number as 3 first moments of distances from 4 emphasized points.
These coordinates might contain unnecessary correlations and does not allow to reconstruct the approximated shape.
In contrast, spherical harmonic (SH) decomposition uses orthogonal coordinates, suggesting their independence and so lager informational content of the feature vector.
There is usually considered rotationally invariant SH descriptors, what means discarding of some essential information.
This article discusses framework for descriptors with normalized rotation, for example by using principal component analysis (PCA-SH).
As one of the most interesting are ligands which have to slide into a protein, we will introduce descriptors optimized for such flat elongated shapes.
Bent deformed cylinder (BDC) describes the molecule as a cylinder which was first bent, then deformed such that its cross-sections became ellipses of evolving shape.
Legendre polynomials are used to describe the central axis of such bent cylinder.
Additional polynomials are used to define evolution of such elliptic cross-section along the main axis.
There will be also discussed bent cylindrical harmonics (BCH), which uses cross-sections described by cylindrical harmonics instead of ellipses.
All these normalized rotation descriptors allow to reconstruct (decode) the approximated representation of the shape, hence can be also used for lossy compression purposes.
Application-specific integrated circuit (ASIC) implementations for Deep Neural Networks (DNNs) have been adopted in many systems because of their higher classification speed.
However, although they may be characterized by better accuracy, larger DNNs require significant energy and area, thereby limiting their wide adoption.
The energy consumption of DNNs is driven by both memory accesses and computation.
Binarized Neural Networks (BNNs), as a trade-off between accuracy and energy consumption, can achieve great energy reduction, and have good accuracy for large DNNs due to its regularization effect.
However, BNNs show poor accuracy when a smaller DNN configuration is adopted.
In this paper, we propose a new DNN model, LightNN, which replaces the multiplications to one shift or a constrained number of shifts and adds.
For a fixed DNN configuration, LightNNs have better accuracy at a slight energy increase than BNNs, yet are more energy efficient with only slightly less accuracy than conventional DNNs.
Therefore, LightNNs provide more options for hardware designers to make trade-offs between accuracy and energy.
Moreover, for large DNN configurations, LightNNs have a regularization effect, making them better in accuracy than conventional DNNs.
These conclusions are verified by experiment using the MNIST and CIFAR-10 datasets for different DNN configurations.
Recently the influence maximization problem has received much attention for its applications on viral marketing and product promotions.
However, such influence maximization problems have not taken into account the monetary effect on the purchasing decision of individuals.
To fulfill this gap, in this paper, we aim for maximizing the revenue by considering the quantity constraint on the promoted commodity.
For this problem, we not only identify a proper small group of individuals as seeds for promotion but also determine the pricing of the commodity.
To tackle the revenue maximization problem, we first introduce a strategic searching algorithm, referred to as Algorithm PRUB, which is able to derive the optimal solutions.
After that, we further modify PRUB to propose a heuristic, Algorithm PRUB+IF, for obtaining feasible solutions more effciently on larger instances.
Experiments on real social networks with different valuation distributions demonstrate the effectiveness of PRUB and PRUB+IF.
The Honey-Bee game is a two-player board game that is played on a connected hexagonal colored grid or (in a generalized setting) on a connected graph with colored nodes.
In a single move, a player calls a color and thereby conquers all the nodes of that color that are adjacent to his own current territory.
Both players want to conquer the majority of the nodes.
We show that winning the game is PSPACE-hard in general, NP-hard on series-parallel graphs, but easy on outerplanar graphs.
In the solitaire version, the goal of the single player is to conquer the entire graph with the minimum number of moves.
The solitaire version is NP-hard on trees and split graphs, but can be solved in polynomial time on co-comparability graphs.
Archetypal scenarios for change detection generally consider two images acquired through sensors of the same modality.
However, in some specific cases such as emergency situations, the only images available may be those acquired through different kinds of sensors.
More precisely, this paper addresses the problem of detecting changes between two multi-band optical images characterized by different spatial and spectral resolutions.
This sensor dissimilarity introduces additional issues in the context of operational change detection.
To alleviate these issues, classical change detection methods are applied after independent preprocessing steps (e.g., resampling) used to get the same spatial and spectral resolutions for the pair of observed images.
Nevertheless, these preprocessing steps tend to throw away relevant information.
Conversely, in this paper, we propose a method that more effectively uses the available information by modeling the two observed images as spatial and spectral versions of two (unobserved) latent images characterized by the same high spatial and high spectral resolutions.
As they cover the same scene, these latent images are expected to be globally similar except for possible changes in sparse spatial locations.
Thus, the change detection task is envisioned through a robust multi-band image fusion method which enforces the differences between the estimated latent images to be spatially sparse.
This robust fusion problem is formulated as an inverse problem which is iteratively solved using an efficient block-coordinate descent algorithm.
The proposed method is applied to real panchormatic/multispectral and hyperspectral images with simulated realistic changes.
A comparison with state-of-the-art change detection methods evidences the accuracy of the proposed strategy.
Learning speaker turn embeddings has shown considerable improvement in situations where conventional speaker modeling approaches fail.
However, this improvement is relatively limited when compared to the gain observed in face embedding learning, which has been proven very successful for face verification and clustering tasks.
Assuming that face and voices from the same identities share some latent properties (like age, gender, ethnicity), we propose three transfer learning approaches to leverage the knowledge from the face domain (learned from thousands of images and identities) for tasks in the speaker domain.
These approaches, namely target embedding transfer, relative distance transfer, and clustering structure transfer, utilize the structure of the source face embedding space at different granularities to regularize the target speaker turn embedding space as optimizing terms.
Our methods are evaluated on two public broadcast corpora and yield promising advances over competitive baselines in verification and audio clustering tasks, especially when dealing with short speaker utterances.
The analysis of the results also gives insight into characteristics of the embedding spaces and shows their potential applications.
Transmission of information reliably and efficiently across channels is one of the fundamental goals of coding and information theory.
In this respect, efficiently decodable deterministic coding schemes which achieve capacity provably have been elusive until as recent as 2008, even though schemes which come close to it in practice existed.
This survey tries to give the interested reader an overview of the area.
Erdal Arikan came up with his landmark polar coding shemes which achieve capacity on symmetric channels subject to the constraint that the input codewords are equiprobable.
His idea is to convert any B-DMC into efficiently encodable-decodable channels which have rates 0 and 1, while conserving capacity in this transformation.
An exponentially decreasing probability of error which independent of code rate is achieved for all rates lesser than the symmetric capacity.
These codes perform well in practice since encoding and decoding complexity is O(N log N).
Guruswami et al. improved the above results by showing that error probability can be made to decrease doubly exponentially in the block length.
We also study recent results by Urbanke et al. which show that 2-transitive codes also achieve capacity on erasure channels under MAP decoding.
Urbanke and his group use complexity theoretic results in boolean function analysis to prove that EXIT functions, which capture the error probability, have a sharp threshold at 1-R, thus proving that capacity is achieved.
One of the oldest and most widely used codes - Reed Muller codes are 2-transitive.
Polar codes are 2-transitive too and we thus have a different proof of the fact that they achieve capacity, though the rate of polarization would be better as found out by Guruswami.
For many years, we have observed industry struggling in defining a high quality requirements engineering (RE) and researchers trying to understand industrial expectations and problems.
Although we are investigating the discipline with a plethora of empirical studies, they still do not allow for empirical generalisations.
To lay an empirical and externally valid foundation about the state of the practice in RE, we aim at a series of open and reproducible surveys that allow us to steer future research in a problem-driven manner.
We designed a globally distributed family of surveys in joint collaborations with different researchers and completed the first run in Germany.
The instrument is based on a theory in the form of a set of hypotheses inferred from our experiences and available studies.
We test each hypothesis in our theory and identify further candidates to extend the theory by correlation and Grounded Theory analysis.
In this article, we report on the design of the family of surveys, its underlying theory, and the full results obtained from Germany with participants from 58 companies.
The results reveal, for example, a tendency to improve RE via internally defined qualitative methods rather than relying on normative approaches like CMMI.
We also discovered various RE problems that are statistically significant in practice.
For instance, we could corroborate communication flaws or moving targets as problems in practice.
Our results are not yet fully representative but already give first insights into current practices and problems in RE, and they allow us to draw lessons learnt for future replications.
Our results obtained from this first run in Germany make us confident that the survey design and instrument are well-suited to be replicated and, thereby, to create a generalisable empirical basis of RE in practice.
In this paper, we propose a novel progressive parameter pruning method for Convolutional Neural Network acceleration, named Structured Probabilistic Pruning (SPP), which effectively prunes weights of convolutional layers in a probabilistic manner.
Unlike existing deterministic pruning approaches, where unimportant weights are permanently eliminated, SPP introduces a pruning probability for each weight, and pruning is guided by sampling from the pruning probabilities.
A mechanism is designed to increase and decrease pruning probabilities based on importance criteria in the training process.
Experiments show that, with 4x speedup, SPP can accelerate AlexNet with only 0.3% loss of top-5 accuracy and VGG-16 with 0.8% loss of top-5 accuracy in ImageNet classification.
Moreover, SPP can be directly applied to accelerate multi-branch CNN networks, such as ResNet, without specific adaptations.
Our 2x speedup ResNet-50 only suffers 0.8% loss of top-5 accuracy on ImageNet.
We further show the effectiveness of SPP on transfer learning tasks.
The recently proposed self-ensembling methods have achieved promising results in deep semi-supervised learning, which penalize inconsistent predictions of unlabeled data under different perturbations.
However, they only consider adding perturbations to each single data point, while ignoring the connections between data samples.
In this paper, we propose a novel method, called Smooth Neighbors on Teacher Graphs (SNTG).
In SNTG, a graph is constructed based on the predictions of the teacher model, i.e., the implicit self-ensemble of models.
Then the graph serves as a similarity measure with respect to which the representations of "similar" neighboring points are learned to be smooth on the low-dimensional manifold.
We achieve state-of-the-art results on semi-supervised learning benchmarks.
The error rates are 9.89%, 3.99% for CIFAR-10 with 4000 labels, SVHN with 500 labels, respectively.
In particular, the improvements are significant when the labels are fewer.
For the non-augmented MNIST with only 20 labels, the error rate is reduced from previous 4.81% to 1.36%.
Our method also shows robustness to noisy labels.
Virtualization is generally adopted in server and desktop environments to provide for fault tolerance, resource management, and energy efficiency.
Virtualization enables parallel execution of multiple operating systems (OSs) while sharing the hardware resources.
Virtualization was previously not deemed as feasible technology for mobile and embedded devices due to their limited processing and memory resource.
However, the enterprises are advocating Bring Your Own Device (BYOD) applications that enable co-existence of heterogeneous OSs on a single mobile device.
Moreover, embedded device require virtualization for logical isolation of secure and general purpose OSs on a single device.
In this paper, we investigate the processor architectures in the mobile and embedded space while examining their formal visualizability.
We also compare the virtualization solutions enabling coexistence of multiple OSs in Multicore Processor System-on-Chip (MPSoC) mobile and embedded systems.
We advocate that virtualization is necessary to manage resource in MPSoC designs and to enable BYOD, security, and logical isolation use cases.
We consider deletion correcting codes over a q-ary alphabet.
It is well known that any code capable of correcting s deletions can also correct any combination of s total insertions and deletions.
To obtain asymptotic upper bounds on code size, we apply a packing argument to channels that perform different mixtures of insertions and deletions.
Even though the set of codes is identical for all of these channels, the bounds that we obtain vary.
Prior to this work, only the bounds corresponding to the all insertion case and the all deletion case were known.
We recover these as special cases.
The bound from the all deletion case, due to Levenshtein, has been the best known for more than forty five years.
Our generalized bound is better than Levenshtein's bound whenever the number of deletions to be corrected is larger than the alphabet size.
The BLEBeacon dataset is a collection of Bluetooth Low Energy (BLE) advertisement packets/traces generated from BLE beacons carried by people following their daily routine inside a university building.
A network of Raspberry Pi 3 (RPi)-based edge devices were deployed inside a multi-floor facility continuously gathering BLE advertisement packets and storing them in a cloud-based environment.
The data were collected during an IRB (Institutional Review Board forhe Protection of Human Subjects in Research) approved one-month trial.
Each facility occupant/participant was handed a BLE beacon to carry with him at all times.
The focus is on presenting a real-life realization of a location-aware sensing infrastructure, that can provide insights for smart sensing platforms, crowd-based applications, building management, and user-localization frameworks.
This work describes and documents the published BLEBeacon dataset.
Evolutionary algorithms (EAs), a large class of general purpose optimization algorithms inspired from the natural phenomena, are widely used in various industrial optimizations and often show excellent performance.
This paper presents an attempt towards revealing their general power from a statistical view of EAs.
By summarizing a large range of EAs into the sampling-and-learning framework, we show that the framework directly admits a general analysis on the probable-absolute-approximate (PAA) query complexity.
We particularly focus on the framework with the learning subroutine being restricted as a binary classification, which results in the sampling-and-classification (SAC) algorithms.
With the help of the learning theory, we obtain a general upper bound on the PAA query complexity of SAC algorithms.
We further compare SAC algorithms with the uniform search in different situations.
Under the error-target independence condition, we show that SAC algorithms can achieve polynomial speedup to the uniform search, but not super-polynomial speedup.
Under the one-side-error condition, we show that super-polynomial speedup can be achieved.
This work only touches the surface of the framework.
Its power under other conditions is still open.
Comments of online articles provide extended views and improve user engagement.
Automatically making comments thus become a valuable functionality for online forums, intelligent chatbots, etc.
This paper proposes the new task of automatic article commenting, and introduces a large-scale Chinese dataset with millions of real comments and a human-annotated subset characterizing the comments' varying quality.
Incorporating the human bias of comment quality, we further develop automatic metrics that generalize a broad set of popular reference-based metrics and exhibit greatly improved correlations with human evaluations.
We define a plane curve to be threadable if it can rigidly pass through a point-hole in a line L without otherwise touching L. Threadable curves are in a sense generalizations of monotone curves.
We have two main results.
The first is a linear-time algorithm for deciding whether a polygonal curve is threadable---O(n) for a curve of n vertices---and if threadable, finding a sequence of rigid motions to thread it through a hole.
We also sketch an argument that shows that the threadability of algebraic curves can be decided in time polynomial in the degree of the curve.
The second main result is an O(n polylog n)-time algorithm for deciding whether a 3D polygonal curve can thread through hole in a plane in R^3, and if so, providing a description of the rigid motions that achieve the threading.
In this paper we introduce a mathematical model that captures some of the salient features of recommender systems that are based on popularity and that try to exploit social ties among the users.
We show that, under very general conditions, the market always converges to a steady state, for which we are able to give an explicit form.
Thanks to this we can tell rather precisely how much a market is altered by a recommendation system, and determine the power of users to influence others.
Our theoretical results are complemented by experiments with real world social networks showing that social graphs prevent large market distortions in spite of the presence of highly influential users.
Covert aspects of ongoing user mental states provide key context information for user-aware human computer interactions.
In this paper, we focus on the problem of estimating the vigilance of users using EEG and EOG signals.
To improve the feasibility and wearability of vigilance estimation devices for real-world applications, we adopt a novel electrode placement for forehead EOG and extract various eye movement features, which contain the principal information of traditional EOG.
We explore the effects of EEG from different brain areas and combine EEG and forehead EOG to leverage their complementary characteristics for vigilance estimation.
Considering that the vigilance of users is a dynamic changing process because the intrinsic mental states of users involve temporal evolution, we introduce continuous conditional neural field and continuous conditional random field models to capture dynamic temporal dependency.
We propose a multimodal approach to estimating vigilance by combining EEG and forehead EOG and incorporating the temporal dependency of vigilance into model training.
The experimental results demonstrate that modality fusion can improve the performance compared with a single modality, EOG and EEG contain complementary information for vigilance estimation, and the temporal dependency-based models can enhance the performance of vigilance estimation.
From the experimental results, we observe that theta and alpha frequency activities are increased, while gamma frequency activities are decreased in drowsy states in contrast to awake states.
The forehead setup allows for the simultaneous collection of EEG and EOG and achieves comparative performance using only four shared electrodes in comparison with the temporal and posterior sites.
An efficient speech to text converter for mobile application is presented in this work.
The prime motive is to formulate a system which would give optimum performance in terms of complexity, accuracy, delay and memory requirements for mobile environment.
The speech to text converter consists of two stages namely front-end analysis and pattern recognition.
The front end analysis involves preprocessing and feature extraction.
The traditional voice activity detection algorithms which track only energy cannot successfully identify potential speech from input because the unwanted part of the speech also has some energy and appears to be speech.
In the proposed system, VAD that calculates energy of high frequency part separately as zero crossing rate to differentiate noise from speech is used.
Mel Frequency Cepstral Coefficient (MFCC) is used as feature extraction method and Generalized Regression Neural Network is used as recognizer.
MFCC provides low word error rate and better feature extraction.
Neural Network improves the accuracy.
Thus a small database containing all possible syllable pronunciation of the user is sufficient to give recognition accuracy closer to 100%.
Thus the proposed technique entertains realization of real time speaker independent applications like mobile phones, PDAs etc.
We present techniques to prove termination of cycle rewriting, that is, string rewriting on cycles, which are strings in which the start and end are connected.
Our main technique is to transform cycle rewriting into string rewriting and then apply state of the art techniques to prove termination of the string rewrite system.
We present three such transformations, and prove for all of them that they are sound and complete.
In this way not only termination of string rewriting of the transformed system implies termination of the original cycle rewrite system, a similar conclusion can be drawn for non-termination.
Apart from this transformational approach, we present a uniform framework of matrix interpretations, covering most of the earlier approaches to automatically proving termination of cycle rewriting.
All our techniques serve both for proving termination and relative termination.
We present several experiments showing the power of our techniques.
Capturability analysis of the linear inverted pendulum (LIP) model enabled walking with constrained height based on the capture point.
We generalize this analysis to the variable-height inverted pendulum (VHIP) and show how it enables 3D walking over uneven terrains based on capture inputs.
Thanks to a tailored optimization scheme, we can compute these inputs fast enough for real-time model predictive control.
We implement this approach as open-source software and demonstrate it in dynamic simulations.
Background: Mobile phone sensor technology has great potential in providing behavioral markers of mental health.
However, this promise has not yet been brought to fruition.
Objective: The objective of our study was to examine challenges involved in developing an app to extract behavioral markers of mental health from passive sensor data.
Methods: Both technical challenges and acceptability of passive data collection for mental health research were assessed based on literature review and results obtained from a feasibility study.
Socialise, a mobile phone app developed at the Black Dog Institute, was used to collect sensor data (Bluetooth, global positioning system, and battery status) and investigate views and experiences of a group of people with lived experience of mental health challenges (N=32).
Results: On average, sensor data were obtained for 55% (Android) and 45% (iPhone OS) of scheduled scans.
Battery life was reduced from 21.3 hours to 18.8 hours when scanning every 5 minutes with a reduction of 2.5 hours or 12%.
Despite this relatively small reduction, most participants reported that the app had a noticeable effect on their battery life.
In addition to battery life, the purpose of data collection, trust in the organization that collects data, and perceived impact on privacy were identified as main factors for acceptability.
Conclusions: Based on the findings of the feasibility study and literature review, we recommend a commitment to open science and transparent reporting and stronger partnerships and communication with users.
Sensing technology has the potential to greatly enhance the delivery and impact of mental health care.
Realizing this requires all aspects of mobile phone sensor technology to be rigorously assessed.
Normalized graph cut (NGC) has become a popular research topic due to its wide applications in a large variety of areas like machine learning and very large scale integration (VLSI) circuit design.
Most of traditional NGC methods are based on pairwise relationships (similarities).
However, in real-world applications relationships among the vertices (objects) may be more complex than pairwise, which are typically represented as hyperedges in hypergraphs.
Thus, normalized hypergraph cut (NHC) has attracted more and more attention.
Existing NHC methods cannot achieve satisfactory performance in real applications.
In this paper, we propose a novel relaxation approach, which is called relaxed NHC (RNHC), to solve the NHC problem.
Our model is defined as an optimization problem on the Stiefel manifold.
To solve this problem, we resort to the Cayley transformation to devise a feasible learning algorithm.
Experimental results on a set of large hypergraph benchmarks for clustering and partitioning in VLSI domain show that RNHC can outperform the state-of-the-art methods.
Single image dehazing is a challenging ill-posed restoration problem.
Various prior-based and learning-based methods have been proposed.
Most of them follow a classic atmospheric scattering model which is an elegant simplified physical model based on the assumption of single-scattering and homogeneous atmospheric medium.
The formulation of haze in realistic environment is more complicated.
In this paper, we propose to take its essential mechanism as "black box", and focus on learning an input-adaptive trainable end-to-end dehazing model.
An U-Net like encoder-decoder deep network via progressive feature fusions has been proposed to directly learn highly nonlinear transformation function from observed hazy image to haze-free ground-truth.
The proposed network is evaluated on two public image dehazing benchmarks.
The experiments demonstrate that it can achieve superior performance when compared with popular state-of-the-art methods.
With efficient GPU memory usage, it can satisfactorily recover ultra high definition hazed image up to 4K resolution, which is unaffordable by many deep learning based dehazing algorithms.
In the face of scarcity in detailed training annotations, the ability to perform object localization tasks in real-time with weak-supervision is very valuable.
However, the computational cost of generating and evaluating region proposals is heavy.
We adapt the concept of Class Activation Maps (CAM) into the very first weakly-supervised 'single-shot' detector that does not require the use of region proposals.
To facilitate this, we propose a novel global pooling technique called Spatial Pyramid Averaged Max (SPAM) pooling for training this CAM-based network for object extent localisation with only weak image-level supervision.
We show this global pooling layer possesses a near ideal flow of gradients for extent localization, that offers a good trade-off between the extremes of max and average pooling.
Our approach only requires a single network pass and uses a fast-backprojection technique, completely omitting any region proposal steps.
To the best of our knowledge, this is the first approach to do so.
Due to this, we are able to perform inference in real-time at 35fps, which is an order of magnitude faster than all previous weakly supervised object localization frameworks.
A programmable optical computer has remained an elusive concept.
To construct a practical computing primitive equivalent to an electronic Boolean logic, one should find a nonlinear phenomenon that overcomes weaknesses present in many optical processing schemes.
Ideally, the nonlinearity should provide a functionally complete set of logic operations, enable ultrafast all-optical programmability, and allow cascaded operations without a change in the operating wavelength or in the signal encoding format.
Here we demonstrate a programmable logic gate using an injection-locked Vertical-Cavity Surface-Emitting Laser (VCSEL).
The gate program is switched between the AND and the OR operations at the rate of 1 GHz with Bit Error Ratio (BER) of 10e-6 without changes in the wavelength or in the signal encoding format.
The scheme is based on nonlinearity of normalization operations, which can be used to construct any continuous complex function or operation, Boolean or otherwise.
Bias is a common problem in today's media, appearing frequently in text and in visual imagery.
Users on social media websites such as Twitter need better methods for identifying bias.
Additionally, activists --those who are motivated to effect change related to some topic, need better methods to identify and counteract bias that is contrary to their mission.
With both of these use cases in mind, in this paper we propose a novel tool called UnbiasedCrowd that supports identification of, and action on bias in visual news media.
In particular, it addresses the following key challenges (1) identification of bias; (2) aggregation and presentation of evidence to users; (3) enabling activists to inform the public of bias and take action by engaging people in conversation with bots.
We describe a preliminary study on the Twitter platform that explores the impressions that activists had of our tool, and how people reacted and engaged with online bots that exposed visual bias.
We conclude by discussing design and implication of our findings for creating future systems to identify and counteract the effects of news bias.
The KE inference system is a tableau method developed by Marco Mondadori which was presented as an improvement, in the computational efficiency sense, over Analytic Tableaux.
In the literature, there is no description of a theorem prover based on the KE method for the C1 paraconsistent logic.
Paraconsistent logics have several applications, such as in robot control and medicine.
These applications could benefit from the existence of such a prover.
We present a sound and complete KE system for C1, an informal specification of a strategy for the C1 prover as well as problem families that can be used to evaluate provers for C1.
The C1 KE system and the strategy described in this paper will be used to implement a KE based prover for C1, which will be useful for those who study and apply paraconsistent logics.
Cross-correlation is a popular signal processing technique used in numerous location tracking systems for obtaining reliable range information.
However, its efficient design and practical implementation has not yet been achieved on mote platforms that are typical in wireless sensor network due to resource constrains.
In this paper, we propose SparseS-XCorr: cross-correlation via structured sparse representation, a new computing framework for ranging based on L1-minimization and structured sparsity.
The key idea is to compress the ranging signal samples on the mote by efficient random projections and transfer them to a central device; where a convex optimization process estimates the range by exploiting the sparse signal structure in the proposed correlation dictionary.
Through theoretical validation, extensive empirical studies and experiments on an end-to-end acoustic ranging system implemented on resource limited off-the-shelf sensor nodes, we show that the proposed framework can achieve up to two orders of magnitude better performance compared to other approaches such as working on DCT domain and downsampling.
Compared to the standard cross-correlation, it is able to obtain range estimates with a bias of 2-6cm with 30% and approximately 100cm with 5% compressed measurements.
Its structured sparsity model is able to improve the ranging accuracy by 40% under challenging recovery conditions (such as high compression factor and low signal-to-noise ratio) by overcoming limitations due to dictionary coherence.
The segmentation of liver lesions is crucial for detection, diagnosis and monitoring progression of liver cancer.
However, design of accurate automated methods remains challenging due to high noise in CT scans, low contrast between liver and lesions, as well as large lesion variability.
We propose a 3D automatic, unsupervised method for liver lesions segmentation using a phase separation approach.
It is assumed that liver is a mixture of two phases: healthy liver and lesions, represented by different image intensities polluted by noise.
The Cahn-Hilliard equation is used to remove the noise and separate the mixture into two distinct phases with well-defined interfaces.
This simplifies the lesion detection and segmentation task drastically and enables to segment liver lesions by thresholding the Cahn-Hilliard solution.
The method was tested on 3Dircadb and LITS dataset.
Big data sets must be carefully partitioned into statistically similar data subsets that can be used as representative samples for big data analysis tasks.
In this paper, we propose the random sample partition (RSP) data model to represent a big data set as a set of non-overlapping data subsets, called RSP data blocks, where each RSP data block has a probability distribution similar to the whole big data set.
Under this data model, efficient block level sampling is used to randomly select RSP data blocks, replacing expensive record level sampling to select sample data from a big distributed data set on a computing cluster.
We show how RSP data blocks can be employed to estimate statistics of a big data set and build models which are equivalent to those built from the whole big data set.
In this approach, analysis of a big data set becomes analysis of few RSP data blocks which have been generated in advance on the computing cluster.
Therefore, the new method for data analysis based on RSP data blocks is scalable to big data.
Tool-assisted refactoring transformations must be trustworthy if programmers are to be confident in applying them on arbitrarily extensive and complex code in order to improve style or efficiency.
We propose a simple, high-level but rigorous, notation for defining refactoring transformations in Erlang, and show that this notation provides an extensible, verifiable and executable specification language for refactoring.
To demonstrate the applicability of our approach, we show how to define and verify a number of example refactorings in the system.
Annual Average Daily Traffic (AADT) is an important parameter used in traffic engineering analysis.
Departments of Transportation (DOTs) continually collect traffic count using both permanent count stations (i.e., Automatic Traffic Recorders or ATRs) and temporary short-term count stations.
In South Carolina, 87% of the ATRs are located on interstates and arterial highways.
For most secondary highways (i.e., collectors and local roads), AADT is estimated based on short-term counts.
This paper develops AADT estimation models for different roadway functional classes with two machine learning techniques: Artificial Neural Network (ANN) and Support Vector Regression (SVR).
The models aim to predict AADT from short-term counts.
The results are first compared against each other to identify the best model.
Then, the results of the best model are compared against a regression method and factor-based method.
The comparison reveals the superiority of SVR for AADT estimation for different roadway functional classes over all other methods.
Among all developed models for different functional roadway classes, the SVR-based model shows a minimum root mean square error (RMSE) of 0.22 and a mean absolute percentage error (MAPE) of 11.3% for the interstate/expressway functional class.
This model also shows a higher R-squared value compared to the traditional factor-based model and regression model.
SVR models are validated for each roadway functional class using the 2016 ATR data and selected short-term count data collected by the South Carolina Department of Transportation (SCDOT).
The validation results show that the SVR-based AADT estimation models can be used by the SCDOT as a reliable option to predict AADT from the short-term counts.
The research described in this paper concerns automatic cyberbullying detection in social media.
There are two goals to achieve: building a gold standard cyberbullying detection dataset and measuring the performance of the Samurai cyberbullying detection system.
The Formspring dataset provided in a Kaggle competition was re-annotated as a part of the research.
The annotation procedure is described in detail and, unlike many other recent data annotation initiatives, does not use Mechanical Turk for finding people willing to perform the annotation.
The new annotation compared to the old one seems to be more coherent since all tested cyberbullying detection system performed better on the former.
The performance of the Samurai system is compared with 5 commercial systems and one well-known machine learning algorithm, used for classifying textual content, namely Fasttext.
It turns out that Samurai scores the best in all measures (accuracy, precision and recall), while Fasttext is the second-best performing algorithm.
Research interest in rapid structured-light imaging has grown increasingly for the modeling of moving objects, and a number of methods have been suggested for the range capture in a single video frame.
The imaging area of a 3D object using a single projector is restricted since the structured light is projected only onto a limited area of the object surface.
Employing additional projectors to broaden the imaging area is a challenging problem since simultaneous projection of multiple patterns results in their superposition in the light-intersected areas and the recognition of original patterns is by no means trivial.
This paper presents a novel method of multi-projector color structured-light vision based on projector-camera triangulation.
By analyzing the behavior of superposed-light colors in a chromaticity domain, we show that the original light colors cannot be properly extracted by the conventional direct estimation.
We disambiguate multiple projectors by multiplexing the orientations of projector patterns so that the superposed patterns can be separated by explicit derivative computations.
Experimental studies are carried out to demonstrate the validity of the presented method.
The proposed method increases the efficiency of range acquisition compared to conventional active stereo using multiple projectors.
In an incoherent dictionary, most signals that admit a sparse representation admit a unique sparse representation.
In other words, there is no way to express the signal without using strictly more atoms.
This work demonstrates that sparse signals typically enjoy a higher privilege: each nonoptimal representation of the signal requires far more atoms than the sparsest representation-unless it contains many of the same atoms as the sparsest representation.
One impact of this finding is to confer a certain degree of legitimacy on the particular atoms that appear in a sparse representation.
This result can also be viewed as an uncertainty principle for random sparse signals over an incoherent dictionary.
Introduced by Dal Lago and Hofmann, quantitative realizability is a technique used to define models for logics based on Multiplicative Linear Logic.
A particularity is that functions are interpreted as bounded time computable functions.
It has been used to give new and uniform proofs of soundness of several type systems with respect to certain time complexity classes.
We propose a reformulation of their ideas in the setting of Krivine's classical realizability.
The framework obtained generalizes Dal Lago and Hofmann's realizability, and reveals deep connections between quantitative realizability and a linear variant of Cohen's forcing.
We introduce TextWorld, a sandbox learning environment for the training and evaluation of RL agents on text-based games.
TextWorld is a Python library that handles interactive play-through of text games, as well as backend functions like state tracking and reward assignment.
It comes with a curated list of games whose features and challenges we have analyzed.
More significantly, it enables users to handcraft or automatically generate new games.
Its generative mechanisms give precise control over the difficulty, scope, and language of constructed games, and can be used to relax challenges inherent to commercial text games like partial observability and sparse rewards.
By generating sets of varied but similar games, TextWorld can also be used to study generalization and transfer learning.
We cast text-based games in the Reinforcement Learning formalism, use our framework to develop a set of benchmark games, and evaluate several baseline agents on this set and the curated list.
Continuous-time signals are well known for not being perfectly localized in both time and frequency domains.
Conversely, a signal defined over the vertices of a graph can be perfectly localized in both vertex and frequency domains.
We derive the conditions ensuring the validity of this property and then, building on this theory, we provide the conditions for perfect reconstruction of a graph signal from its samples.
Next, we provide a finite step algorithm for the reconstruction of a band-limited signal from its samples and then we show the effect of sampling a non perfectly band-limited signal and show how to select the bandwidth that minimizes the mean square reconstruction error.
We present a corpus of 5,000 richly annotated abstracts of medical articles describing clinical randomized controlled trials.
Annotations include demarcations of text spans that describe the Patient population enrolled, the Interventions studied and to what they were Compared, and the Outcomes measured (the `PICO' elements).
These spans are further annotated at a more granular level, e.g., individual interventions within them are marked and mapped onto a structured medical vocabulary.
We acquired annotations from a diverse set of workers with varying levels of expertise and cost.
We describe our data collection process and the corpus itself in detail.
We then outline a set of challenging NLP tasks that would aid searching of the medical literature and the practice of evidence-based medicine.
Deep neural network models for Chinese zero pronoun resolution learn semantic information for zero pronoun and candidate antecedents, but tend to be short-sighted---they often make local decisions.
They typically predict coreference chains between the zero pronoun and one single candidate antecedent one link at a time, while overlooking their long-term influence on future decisions.
Ideally, modeling useful information of preceding potential antecedents is critical when later predicting zero pronoun-candidate antecedent pairs.
In this study, we show how to integrate local and global decision-making by exploiting deep reinforcement learning models.
With the help of the reinforcement learning agent, our model learns the policy of selecting antecedents in a sequential manner, where useful information provided by earlier predicted antecedents could be utilized for making later coreference decisions.
Experimental results on OntoNotes 5.0 dataset show that our technique surpasses the state-of-the-art models.
We introduce here a fully automated convolutional neural network-based method for brain image processing to Detect Neurons in different brain Regions during Development (DeNeRD).
Our method takes a developing mouse brain as input and i) registers the brain sections against a developing mouse reference atlas, ii) detects various types of neurons, and iii) quantifies the neural density in many unique brain regions at different postnatal (P) time points.
Our method is invariant to the shape, size and expression of neurons and by using DeNeRD, we compare the brain-wide neural density of all GABAergic neurons in developing brains of ages P4, P14 and P56.
We discover and report 6 different clusters of regions in the mouse brain in which GABAergic neurons develop in a differential manner from early age (P4) to adulthood (P56).
These clusters reveal key steps of GABAergic cell development that seem to track with the functional development of diverse brain regions as the mouse transitions from a passive receiver of sensory information (<P14) to an active seeker (>P14).
The X-problem of number 3 for one dimension and related observations are discussed
Scholars and practitioners across domains are increasingly concerned with algorithmic transparency and opacity, interrogating the values and assumptions embedded in automated, black-boxed systems, particularly in user-generated content platforms.
I report from an ethnography of infrastructure in Wikipedia to discuss an often understudied aspect of this topic: the local, contextual, learned expertise involved in participating in a highly automated social-technical environment.
Today, the organizational culture of Wikipedia is deeply intertwined with various data-driven algorithmic systems, which Wikipedians rely on to help manage and govern the "anyone can edit" encyclopedia at a massive scale.
These bots, scripts, tools, plugins, and dashboards make Wikipedia more efficient for those who know how to work with them, but like all organizational culture, newcomers must learn them if they want to fully participate.
I illustrate how cultural and organizational expertise is enacted around algorithmic agents by discussing two autoethnographic vignettes, which relate my personal experience as a veteran in Wikipedia.
I present thick descriptions of how governance and gatekeeping practices are articulated through and in alignment with these automated infrastructures.
Over the past 15 years, Wikipedian veterans and administrators have made specific decisions to support administrative and editorial workflows with automation in particular ways and not others.
I use these cases of Wikipedia's bot-supported bureaucracy to discuss several issues in the fields of critical algorithms studies, critical data studies, and fairness, accountability, and transparency in machine learning -- most principally arguing that scholarship and practice must go beyond trying to "open up the black box" of such systems and also examine sociocultural processes like newcomer socialization.
If an interarea oscillatory mode has insufficient damping, generator redispatch can be used to improve its damping.
We explain and apply a new analytic formula for the modal sensitivity to rank the best pairs of generators to redispatch.
The formula requires some dynamic power system data and we show how to obtain that data from synchrophasor measurements.
The application of the formula to damp interarea modes is explained and illustrated with interarea modes of the New England 10-machine power system.
In this paper we propose a novel texture descriptor called Fractal Weighted Local Binary Pattern (FWLBP).
The fractal dimension (FD) measure is relatively invariant to scale-changes, and presents a good correlation with human viewpoint of surface roughness.
We have utilized this property to construct a scale-invariant descriptor.
Here, the input image is sampled using an augmented form of the local binary pattern (LBP) over three different radii, and then used an indexing operation to assign FD weights to the collected samples.
The final histogram of the descriptor has its features calculated using LBP, and its weights computed from the FD image.
The proposed descriptor is scale invariant, and is also robust in rotation or reflection, and partially tolerant to noise and illumination changes.
In addition, the local fractal dimension is relatively insensitive to the bi-Lipschitz transformations, whereas its extension is adequate to precisely discriminate the fundamental of texture primitives.
Experiment results carried out on standard texture databases show that the proposed descriptor achieved better classification rates compared to the state-of-the-art descriptors.
Datacenter applications demand both low latency and high throughput; while interactive applications (e.g., Web Search) demand low tail latency for their short messages due to their partition-aggregate software architecture, many data-intensive applications (e.g., Map-Reduce) require high throughput for long flows as they move vast amounts of data across the network.
Recent proposals improve latency of short flows and throughput of long flows by addressing the shortcomings of existing packet scheduling and congestion control algorithms, respectively.
We make the key observation that long tails in the Flow Completion Times (FCT) of short flows result from packets that suffer congestion at more than one switch along their paths in the network.
Our proposal, Slytherin, specifically targets packets that suffered from congestion at multiple points and prioritizes them in the network.
Slytherin leverages ECN mechanism which is widely used in existing datacenters to identify such tail packets and dynamically prioritizes them using existing priority queues.
As compared to existing state-of-the-art packet scheduling proposals, Slytherin achieves 18.6% lower 99th percentile flow completion times for short flows without any loss of throughput.
Further, Slytherin drastically reduces 99th percentile queue length in switches by a factor of about 2x on average.
Nowadays, the ubiquity of various sensors enables the collection of voluminous datasets of car trajectories.
Such datasets enable analysts to make sense of driving patterns and behaviors: in order to understand the behavior of drivers, one approach is to break a trajectory into its underlying patterns and then analyze that trajectory in terms of derived patterns.
The process of trajectory segmentation is a function of various resources including a set of ground truth trajectories with their driving patterns.
To the best of our knowledge, no such ground-truth dataset exists in the literature.
In this paper, we describe a trajectory annotation framework and report our results to annotate a dataset of personal car trajectories.
Our annotation methodology consists of a crowd-sourcing task followed by a precise process of aggregation.
Our annotation process consists of two granularity levels, one to specify the annotation (segment border) and the other one to describe the type of the segment (e.g. speed-up, turn, merge, etc.).
The output of our project, Dataset of Annotated Car Trajectories (DACT), is available online at https://figshare.com/articles/dact_dataset_of_annotated_car_trajectories/5005289 .
Novelty attracts attention like popularity.
Hence predicting novelty is as important as popularity.
Novelty is the side effect of competition and aging in evolving systems.
Recent behavior or recent link gain in networks plays an important role in emergence or trend.
We exploited this wisdom and came up with two models considering different scenarios and systems.
Where recent behavior dominates over total behavior (total link gain) in the first one, and recent behavior is as important as total behavior for future link gain in second one.
It suppose that random walker walks on a network and can jump to any node, the probablity of jumping or making connection to other node is based on which node is recently more active or receiving more links.
In our assumption random walker can also jump to node which is already popular but recently not popular.
We are able to predict rising novelties or popular nodes which is generally suppressed under preferential attachment effect.
To show performance of our model we have conducted experiments on four real data sets namely, MovieLens, Netflix, Facebook and Arxiv High Energy Physics paper citation.
For testing our model we used four information retrieval indices namely Precision, Novelty, Area Under Receiving Operating Characteristic(AUC) and Kendal's rank correlation coefficient.
We have used four benchmark models for validating our proposed models.
Although our model doesn't perform better in all the cases but, it has theoretical significance in working better for recent behavior dominant systems.
Thanks to the advances in the technology of low-cost digital cameras and the popularity of the self-recording culture, the amount of visual data on the Internet is going to the opposite side of the available time and patience of the users.
Thus, most of the uploaded videos are doomed to be forgotten and unwatched in a computer folder or website.
In this work, we address the problem of creating smooth fast-forward videos without losing the relevant content.
We present a new adaptive frame selection formulated as a weighted minimum reconstruction problem, which combined with a smoothing frame transition method accelerates first-person videos emphasizing the relevant segments and avoids visual discontinuities.
The experiments show that our method is able to fast-forward videos to retain as much relevant information and smoothness as the state-of-the-art techniques in less time.
We also present a new 80-hour multimodal (RGB-D, IMU, and GPS) dataset of first-person videos with annotations for recorder profile, frame scene, activities, interaction, and attention.
Navigating safely in urban environments remains a challenging problem for autonomous vehicles.
Occlusion and limited sensor range can pose significant challenges to safely navigate among pedestrians and other vehicles in the environment.
Enabling vehicles to quantify the risk posed by unseen regions allows them to anticipate future possibilities, resulting in increased safety and ride comfort.
This paper proposes an algorithm that takes advantage of the known road layouts to forecast, quantify, and aggregate risk associated with occlusions and limited sensor range.
This allows us to make predictions of risk induced by unobserved vehicles even in heavily occluded urban environments.
The risk can then be used either by a low-level planning algorithm to generate better trajectories, or by a high-level one to plan a better route.
The proposed algorithm is evaluated on intersection layouts from real-world map data with up to five other vehicles in the scene, and verified to reduce collision rates by 4.8x comparing to a baseline method while improving driving comfort.
While imitation learning is becoming common practice in robotics, this approach often suffers from data mismatch and compounding errors.
DAgger is an iterative algorithm that addresses these issues by continually aggregating training data from both the expert and novice policies, but does not consider the impact of safety.
We present a probabilistic extension to DAgger, which uses the distribution over actions provided by the novice policy, for a given observation.
Our method, which we call DropoutDAgger, uses dropout to train the novice as a Bayesian neural network that provides insight to its confidence.
Using the distribution over the novice's actions, we estimate a probabilistic measure of safety with respect to the expert action, tuned to balance exploration and exploitation.
The utility of this approach is evaluated on the MuJoCo HalfCheetah and in a simple driving experiment, demonstrating improved performance and safety compared to other DAgger variants and classic imitation learning.
Collaborative filtering (CF) is a powerful recommender system that generates a list of recommended items for an active user based on the ratings of similar users.
This paper presents a novel approach to CF by first finding the set of users similar to the active user by adopting self-organizing maps (SOM), followed by k-means clustering.
Then, the ratings for each item in the cluster closest to the active user are mapped to the frequency domain using the Discrete Fourier Transform (DFT).
The power spectra of the mapped ratings are generated, and a new similarity measure based on the coherence of these power spectra is calculated.
The proposed similarity measure is more time efficient than current state-of-the-art measures.
Moreover, it can capture the global similarity between the profiles of users.
Experimental results show that the proposed approach overcomes the major problems in existing CF algorithms as follows: First, it mitigates the scalability problem by creating clusters of similar users and applying the time-efficient similarity measure.
Second, its frequency-based similarity measure is less sensitive to sparsity problems because the DFT performs efficiently even with sparse data.
Third, it outperforms standard similarity measures in terms of accuracy.
Consider the problem in which n jobs that are classified into k types are to be scheduled on m identical machines without preemption.
A machine requires a proper setup taking s time units before processing jobs of a given type.
The objective is to minimize the makespan of the resulting schedule.
We design and analyze an approximation algorithm that runs in time polynomial in n, m and k and computes a solution with an approximation factor that can be made arbitrarily close to 3/2.
Colours are everywhere.
They embody a significant part of human visual perception.
In this paper, we explore the paradigm of hallucinating colours from a given gray-scale image.
The problem of colourization has been dealt in previous literature but mostly in a supervised manner involving user-interference.
With the emergence of Deep Learning methods numerous tasks related to computer vision and pattern recognition have been automatized and carried in an end-to-end fashion due to the availability of large data-sets and high-power computing systems.
We investigate and build upon the recent success of Conditional Generative Adversarial Networks (cGANs) for Image-to-Image translations.
In addition to using the training scheme in the basic cGAN, we propose an encoder-decoder generator network which utilizes the class-specific cross-entropy loss as well as the perceptual loss in addition to the original objective function of cGAN.
We train our model on a large-scale dataset and present illustrative qualitative and quantitative analysis of our results.
Our results vividly display the versatility and proficiency of our methods through life-like colourization outcomes.
Over the past decade, contextual bandit algorithms have been gaining in popularity due to their effectiveness and flexibility in solving sequential decision problems---from online advertising and finance to clinical trial design and personalized medicine.
At the same time, there are, as of yet, surprisingly few options that enable researchers and practitioners to simulate and compare the wealth of new and existing bandit algorithms in a standardized way.
To help close this gap between analytical research and empirical evaluation the current paper introduces the object-oriented R package "contextual": a user-friendly and, through its object-oriented structure, easily extensible framework that facilitates parallelized comparison of contextual and context-free bandit policies through both simulation and offline analysis.
We consider the general problem of matching a subspace to a signal in R^N that has been observed indirectly (compressed) through a random projection.
We are interested in the case where the collection of K-dimensional subspaces is continuously parameterized, i.e. naturally indexed by an interval from the real line, or more generally a region of R^D.
Our main results show that if the dimension of the random projection is on the order of K times a geometrical constant that describes the complexity of the collection, then the match obtained from the compressed observation is nearly as good as one obtained from a full observation of the signal.
We give multiple concrete examples of collections of subspaces for which this geometrical constant can be estimated, and discuss the relevance of the results to the general problems of template matching and source localization.
We address the problem of 3D human pose estimation from 2D input images using only weakly supervised training data.
Despite showing considerable success for 2D pose estimation, the application of supervised machine learning to 3D pose estimation in real world images is currently hampered by the lack of varied training images with corresponding 3D poses.
Most existing 3D pose estimation algorithms train on data that has either been collected in carefully controlled studio settings or has been generated synthetically.
Instead, we take a different approach, and propose a 3D human pose estimation algorithm that only requires relative estimates of depth at training time.
Such training signal, although noisy, can be easily collected from crowd annotators, and is of sufficient quality for enabling successful training and evaluation of 3D pose algorithms.
Our results are competitive with fully supervised regression based approaches on the Human3.6M dataset, despite using significantly weaker training data.
Our proposed algorithm opens the door to using existing widespread 2D datasets for 3D pose estimation by allowing fine-tuning with noisy relative constraints, resulting in more accurate 3D poses.
The pedagogy of teaching and learning has changed with the proliferation of communication technology and it is necessary to develop interactive learning materials for children that may improve their learning, catching, and memorizing capabilities.
Perhaps, one of the most important innovations in the age of technology is multimedia and its application.
It is imperative to create high quality and realistic learning environment for children.
Interactive learning materials can be easier to understand and deal with their first learning.
We developed some interactive learning materials in the form of a video for Playgroup using multimedia application tools.
This study investigated the impact of students' abilities to acquire new knowledge or skills through interactive learning materials.
We visited one kindergartens (Nursery schools), interviewed class teachers about their teaching methods and level of students' ability of recognizing English alphabets, pictures, etc.
The course teachers were provided interactive learning materials to show their playgroups for a number of sessions.
The video included English alphabets with related words and pictures, and motivational fun.
We noticed that almost all children were very interested to interact with their leaning video.
The students were assessed individually and asked to recognize the alphabets, and pictures.
The students adapted with their first alphabets very quickly.
However, there were individual differences in their cognitive development.
This interactive multimedia can be an alternative to traditional pedagogy for teaching playgroups.
The Sentinel-2 satellite mission delivers multi-spectral imagery with 13 spectral bands, acquired at three different spatial resolutions.
The aim of this research is to super-resolve the lower-resolution (20 m and 60 m Ground Sampling Distance - GSD) bands to 10 m GSD, so as to obtain a complete data cube at the maximal sensor resolution.
We employ a state-of-the-art convolutional neural network (CNN) to perform end-to-end upsampling, which is trained with data at lower resolution, i.e., from 40->20 m, respectively 360->60 m GSD.
In this way, one has access to a virtually infinite amount of training data, by downsampling real Sentinel-2 images.
We use data sampled globally over a wide range of geographical locations, to obtain a network that generalises across different climate zones and land-cover types, and can super-resolve arbitrary Sentinel-2 images without the need of retraining.
In quantitative evaluations (at lower scale, where ground truth is available), our network, which we call DSen2, outperforms the best competing approach by almost 50% in RMSE, while better preserving the spectral characteristics.
It also delivers visually convincing results at the full 10 m GSD.
The code is available at https://github.com/lanha/DSen2
An important way to resolve games of conflict (snowdrift, hawk-dove, chicken) involves adopting a convention: a correlated equilibrium that avoids any conflict between aggressive strategies.
Dynamic networks allow individuals to resolve conflict via their network connections rather than changing their strategy.
Exploring how behavioral strategies coevolve with social networks reveals new dynamics that can help explain the origins and robustness of conventions.
Here we model the emergence of conventions as correlated equilibria in dynamic networks.
Our results show that networks have the tendency to break the symmetry between the two conventional solutions in a strongly biased way.
Rather than the correlated equilibrium associated with ownership norms (play aggressive at home, not away), we usually see the opposite host-guest norm (play aggressive away, not at home) evolve on dynamic networks, a phenomenon common to human interaction.
We also show that learning to avoid conflict can produce realistic network structures in a way different than preferential attachment models.
The motor control problem involves determining the time-varying muscle activation trajectories required to accomplish a given movement.
Muscle redundancy makes motor control a challenging task: there are many possible activation trajectories that accomplish the same movement.
Despite this redundancy, most movements are accomplished in highly stereotypical ways.
For example, point-to-point reaching movements are almost universally performed with very similar smooth trajectories.
Optimization methods are commonly used to predict muscle forces for measured movements.
However, these approaches require computationally expensive simulations and are sensitive to the chosen optimality criteria and regularization.
In this work, we investigate deep autoencoders for the prediction of muscle activation trajectories for point-to-point reaching movements.
We evaluate our DNN predictions with simulated reaches and two methods to generate the muscle activations: inverse dynamics (ID) and optimal control (OC) criteria.
We also investigate optimal network parameters and training criteria to improve the accuracy of the predictions.
Non-frontal lip views contain useful information which can be used to enhance the performance of frontal view lipreading.
However, the vast majority of recent lipreading works, including the deep learning approaches which significantly outperform traditional approaches, have focused on frontal mouth images.
As a consequence, research on joint learning of visual features and speech classification from multiple views is limited.
In this work, we present an end-to-end multi-view lipreading system based on Bidirectional Long-Short Memory (BLSTM) networks.
To the best of our knowledge, this is the first model which simultaneously learns to extract features directly from the pixels and performs visual speech classification from multiple views and also achieves state-of-the-art performance.
The model consists of multiple identical streams, one for each view, which extract features directly from different poses of mouth images.
The temporal dynamics in each stream/view are modelled by a BLSTM and the fusion of multiple streams/views takes place via another BLSTM.
An absolute average improvement of 3% and 3.8% over the frontal view performance is reported on the OuluVS2 database when the best two (frontal and profile) and three views (frontal, profile, 45) are combined, respectively.
The best three-view model results in a 10.5% absolute improvement over the current multi-view state-of-the-art performance on OuluVS2, without using external databases for training, achieving a maximum classification accuracy of 96.9%.
Pinterest is an image-based online social network, which was launched in the year 2010 and has gained a lot of traction, ever since.
Within 3 years, Pinterest has attained 48.7 million unique users.
This stupendous growth makes it interesting to study Pinterest, and gives rise to multiple questions about it's users, and content.
We characterized Pinterest on the basis of large scale crawls of 3.3 million user profiles, and 58.8 million pins.
In particular, we explored various attributes of users, pins, boards, pin sources, and user locations, in detail and performed topical analysis of user generated textual content.
The characterization revealed most prominent topics among users and pins, top image sources, and geographical distribution of users on Pinterest.
We then investigated this social network from a privacy and security standpoint, and found traces of malware in the form of pin sources.
Instances of Personally Identifiable Information (PII) leakage were also discovered in the form of phone numbers, BBM (Blackberry Messenger) pins, and email addresses.
Further, our analysis demonstrated how Pinterest is a potential venue for copyright infringement, by showing that almost half of the images shared on Pinterest go uncredited.
To the best of our knowledge, this is the first attempt to characterize Pinterest at such a large scale.
We completely determine the complexity status of the dominating set problem for hereditary graph classes defined by forbidden induced subgraphs with at most five vertices.
Preventing traffic congestion by forecasting near time traffic flows is an important problem as it leads to effective use of transport resources.
Social network provides information about activities of humans and social events.
Thus, with the help of social network, we can extract which humans will attend a particular event (in near time) and can estimate flow of traffic based on it.
This opens up a wide area of research which poses need to have a framework for traffic management that can capture essential parameters of real-life behaviour and provide a way to iterate upon and evaluate new ideas.
In this paper, we present building blocks of a framework and a system to simulate a city with its transport system, humans and their social network.
We emphasize on relevant parameters selected and modular design of the framework.
Our framework defines metrics to evaluate congestion avoidance strategies.
To show utility of the framework, we present experimental studies of few strategies on a public transport system.
Journal impact factor (IF) as a gauge of influence and impact of a particular journal comparing with other journals in the same area of research, reports the mean number of citations to the published articles in particular journal.
Although, IF attracts more attention and being used more frequently than other measures, it has been subjected to criticisms, which overcome the advantages of IF.
Critically, extensive use of IF may result in destroying editorial and researchers behaviour, which could compromise the quality of scientific articles.
Therefore, it is the time of the timeliness and importance of a new invention of journal ranking techniques beyond the journal impact factor.
What are the key-features that enable an information diffusion model to explain the inherent dynamic, and often competitive, nature of real-world propagation phenomena?
In this paper we aim to answer this question by proposing a novel class of diffusion models, inspired by the classic Linear Threshold model, and built around the following aspects: trust/distrust in the user relationships, which is leveraged to model different effects of social influence on the decisions taken by an individual; changes in adopting one or alternative information items; hesitation towards adopting an information item over time; latency in the propagation; time horizon for the unfolding of the diffusion process; and multiple cascades of information that might occur competitively.
To the best of our knowledge, the above aspects have never been unified into the same LT-based diffusion model.
We also define different strategies for the selection of the initial influencers to simulate non-competitive and competitive diffusion scenarios, particularly related to the problem of limitation of misinformation spread.
Results on publicly available networks have shown the meaningfulness and uniqueness of our models.
We propose a novel system which can transform a recipe into any selected regional style (e.g., Japanese, Mediterranean, or Italian).
This system has two characteristics.
First the system can identify the degree of regional cuisine style mixture of any selected recipe and visualize such regional cuisine style mixtures using barycentric Newton diagrams.
Second, the system can suggest ingredient substitutions through an extended word2vec model, such that a recipe becomes more authentic for any selected regional cuisine style.
Drawing on a large number of recipes from Yummly, an example shows how the proposed system can transform a traditional Japanese recipe, Sukiyaki, into French style.
Despite initiatives to improve the quality of scientific codes, there still is a large presence of legacy code.
Such code often needs to implement a lot of functionality under time constrains, sacrificing quality.
Additionally, quality is rarely improved by optimizations for new architectures.
This development model leads to code that is increasingly difficult to work with.
Our suggested solution includes complexity-reducing refactoring and hardware abstraction.
We focus on the AIREBO potential from LAMMPS, where the challenge is that any potential kernel is rather large and complex, hindering systematic optimization.
This issue is common to codes that model multiple physical phenomena.
We present our journey from the C++ port of a previous Fortran code to performance-portable, KNC-hybrid, vectorized, scalable, optimized code supporting full and reduced precision.
The journey includes extensive testing that fixed bugs in the original code.
Large-scale, full-precision runs sustain speedups of more than 4x (KNL) and 3x (Skylake).
Satisficing is a relaxation of maximizing and allows for less risky decision making in the face of uncertainty.
We propose two sets of satisficing objectives for the multi-armed bandit problem, where the objective is to achieve reward-based decision-making performance above a given threshold.
We show that these new problems are equivalent to various standard multi-armed bandit problems with maximizing objectives and use the equivalence to find bounds on performance.
The different objectives can result in qualitatively different behavior; for example, agents explore their options continually in one case and only a finite number of times in another.
For the case of Gaussian rewards we show an additional equivalence between the two sets of satisficing objectives that allows algorithms developed for one set to be applied to the other.
We then develop variants of the Upper Credible Limit (UCL) algorithm that solve the problems with satisficing objectives and show that these modified UCL algorithms achieve efficient satisficing performance.
We prove that in the geometric complexity theory program the vanishing of rectangular Kronecker coefficients cannot be used to prove superpolynomial determinantal complexity lower bounds for the permanent polynomial.
Moreover, we prove the positivity of rectangular Kronecker coefficients for a large class of partitions where the side lengths of the rectangle are at least quadratic in the length of the partition.
We also compare rectangular Kronecker coefficients with their corresponding plethysm coefficients, which leads to a new lower bound for rectangular Kronecker coefficients.
Moreover, we prove that the saturation of the rectangular Kronecker semigroup is trivial, we show that the rectangular Kronecker positivity stretching factor is 2 for a long first row, and we completely classify the positivity of rectangular limit Kronecker coefficients that were introduced by Manivel in 2011.
High-energy physics experiments rely on reconstruction of the trajectories of particles produced at the interaction point.
This is a challenging task, especially in the high track multiplicity environment generated by p-p collisions at the LHC energies.
A typical event includes hundreds of signal examples (interesting decays) and a significant amount of noise (uninteresting examples).
This work describes a modification of the Artificial Retina algorithm for fast track finding: numerical optimization methods were adopted for fast local track search.
This approach allows for considerable reduction of the total computational time per event.
Test results on simplified simulated model of LHCb VELO (VErtex LOcator) detector are presented.
Also this approach is well-suited for implementation of paralleled computations as GPGPU which look very attractive in the context of upcoming detector upgrades.
Starting from an unsolved problem of information retrieval this paper presents an ontology-based model for indexing and retrieval.
The model combines the methods and experiences of cognitive-to-interpret indexing languages with the strengths and possibilities of formal knowledge representation.
The core component of the model uses inferences along the paths of typed relations between the entities of a knowledge representation for enabling the determination of hit quantities in the context of retrieval processes.
The entities are arranged in aspect-oriented facets to ensure a consistent hierarchical structure.
The possible consequences for indexing and retrieval are discussed.
In this paper, we report on the practical application of a novel approach for validating the knowledge of WordNet using Adimen-SUMO.
In particular, this paper focuses on cross-checking the WordNet meronymy relations against the knowledge encoded in Adimen-SUMO.
Our validation approach tests a large set of competency questions (CQs), which are derived (semi)-automatically from the knowledge encoded in WordNet, SUMO and their mapping, by applying efficient first-order logic automated theorem provers.
Unfortunately, despite of being created manually, these knowledge resources are not free of errors and discrepancies.
In consequence, some of the resulting CQs are not plausible according to the knowledge included in Adimen-SUMO.
Thus, first we focus on (semi)-automatically improving the alignment between these knowledge resources, and second, we perform a minimal set of corrections in the ontology.
Our aim is to minimize the manual effort required for an extensive validation process.
We report on the strategies followed, the changes made, the effort needed and its impact when validating the WordNet meronymy relations using improved versions of the mapping and the ontology.
Based on the new results, we discuss the implications of the appropriate corrections and the need of future enhancements.
Parking sensor network is rapidly deploying around the world and is regarded as one of the first implemented urban services in smart cities.
To provide the best network performance, the MAC protocol shall be adaptive enough in order to satisfy the traffic intensity and variation of parking sensors.
In this paper, we study the heavy-tailed parking and vacant time models from SmartSantander, and then we apply the traffic model in the simulation with four different kinds of MAC protocols, that is, contention-based, schedule-based and two hybrid versions of them.
The result shows that the packet interarrival time is no longer heavy-tailed while collecting a group of parking sensors, and then choosing an appropriate MAC protocol highly depends on the network configuration.
Also, the information delay is bounded by traffic and MAC parameters which are important criteria while the timely message is required.
Brandt et al.(2013) have recently disproved a conjecture by Schwartz (1990) by non-constructively showing the existence of a counterexample with about 10^136 alternatives.
We provide a concrete counterexample for Schwartz's conjecture with only 24 alternatives.
This paper addresses the problem of reducing the delivery time of data messages to cellular users using instantly decodable network coding (IDNC) with physical-layer rate awareness.
While most of the existing literature on IDNC does not consider any physical layer complications and abstract the model as equally slotted time for all users, this paper proposes a cross-layer scheme that incorporates the different channel rates of the various users in the decision process of both the transmitted message combinations and the rates with which they are transmitted.
The consideration of asymmetric rates for receivers reflects more practical application scenarios and introduces a new trade-off between the choice of coding combinations for various receivers and the broadcasting rate for achieving shorter completion time.
The completion time minimization problem in such scenario is first shown to be intractable.
The problem is, thus, approximated by reducing, at each transmission, the increase of an anticipated version of the completion time.
The paper solves the problem by formulating it as a maximum weight clique problem over a newly designed rate aware IDNC (RA-IDNC) graph.
The highest weight clique in the created graph being potentially not unique, the paper further suggests a multi-layer version of the proposed solution to improve the obtained results from the employed completion time approximation.
Simulation results indicate that the cross-layer design largely outperforms the uncoded transmissions strategies and the classical IDNC scheme.
This paper presents a procedural generation method that creates visually attractive levels for the Angry Birds game.
Besides being an immensely popular mobile game, Angry Birds has recently become a test bed for various artificial intelligence technologies.
We propose a new approach for procedurally generating Angry Birds levels using Chinese style and Japanese style building structures.
A conducted experiment confirms the effectiveness of our approach with statistical significance.
In a labeling scheme the vertices of a given graph from a particular class are assigned short labels such that adjacency can be algorithmically determined from these labels.
A representation of a graph from that class is given by the set of its vertex labels.
Due to the shortness constraint on the labels such schemes provide space-efficient representations for various graph classes, such as planar or interval graphs.
We consider what graph classes cannot be represented by labeling schemes when the algorithm which determines adjacency is subjected to computational constraints.
We consider the problem of answering queries about a sensitive dataset subject to differential privacy.
The queries may be chosen adversarially from a larger set Q of allowable queries in one of three ways, which we list in order from easiest to hardest to answer:   Offline: The queries are chosen all at once and the differentially private mechanism answers the queries in a single batch.
Online: The queries are chosen all at once, but the mechanism only receives the queries in a streaming fashion and must answer each query before seeing the next query.
Adaptive: The queries are chosen one at a time and the mechanism must answer each query before the next query is chosen.
In particular, each query may depend on the answers given to previous queries.
Many differentially private mechanisms are just as efficient in the adaptive model as they are in the offline model.
Meanwhile, most lower bounds for differential privacy hold in the offline setting.
This suggests that the three models may be equivalent.
We prove that these models are all, in fact, distinct.
Specifically, we show that there is a family of statistical queries such that exponentially more queries from this family can be answered in the offline model than in the online model.
We also exhibit a family of search queries such that exponentially more queries from this family can be answered in the online model than in the adaptive model.
We also investigate whether such separations might hold for simple queries like threshold queries over the real line.
We present a semantic vector space model for capturing complex polyphonic musical context.
A word2vec model based on a skip-gram representation with negative sampling was used to model slices of music from a dataset of Beethoven's piano sonatas.
A visualization of the reduced vector space using t-distributed stochastic neighbor embedding shows that the resulting embedded vector space captures tonal relationships, even without any explicit information about the musical contents of the slices.
Secondly, an excerpt of the Moonlight Sonata from Beethoven was altered by replacing slices based on context similarity.
The resulting music shows that the selected slice based on similar word2vec context also has a relatively short tonal distance from the original slice.
Immersive social interactions of mobile users are soon to be enabled within a virtual space, by means of virtual reality (VR) technologies and wireless cellular systems.
In a VR mobile social network, the states of all interacting users should be updated synchronously and with low latency via two-way communications with edge computing servers.
The resulting end-to-end latency depends on the relationship between the virtual and physical locations of the wireless VR users and of the edge servers.
In this work, the problem of analyzing and optimizing the end-to-end latency is investigated for a simple network topology, yielding important insights into the interplay between physical and virtual geometries.
Neural network models have shown promising results for text classification.
However, these solutions are limited by their dependence on the availability of annotated data.
The prospect of leveraging resource-rich languages to enhance the text classification of resource-poor languages is fascinating.
The performance on resource-poor languages can significantly improve if the resource availability constraints can be offset.
To this end, we present a twin Bidirectional Long Short Term Memory (Bi-LSTM) network with shared parameters consolidated by a contrastive loss function (based on a similarity metric).
The model learns the representation of resource-poor and resource-rich sentences in a common space by using the similarity between their assigned annotation tags.
Hence, the model projects sentences with similar tags closer and those with different tags farther from each other.
We evaluated our model on the classification tasks of sentiment analysis and emoji prediction for resource-poor languages - Hindi and Telugu and resource-rich languages - English and Spanish.
Our model significantly outperforms the state-of-the-art approaches in both the tasks across all metrics.
The present work provides a new approach to evolve ligand structures which represent possible drug to be docked to the active site of the target protein.
The structure is represented as a tree where each non-empty node represents a functional group.
It is assumed that the active site configuration of the target protein is known with position of the essential residues.
In this paper the interaction energy of the ligands with the protein target is minimized.
Moreover, the size of the tree is difficult to obtain and it will be different for different active sites.
To overcome the difficulty, a variable tree size configuration is used for designing ligands.
The optimization is done using a novel Neighbourhood Based Genetic Algorithm (NBGA) which uses dynamic neighbourhood topology.
To get variable tree size, a variable-length version of the above algorithm is devised.
To judge the merit of the algorithm, it is initially applied on the well known Travelling Salesman Problem (TSP).
Convolutional neural networks have achieved astonishing results in different application areas.
Various methods which allow us to use these models on mobile and embedded devices have been proposed.
Especially binary neural networks seem to be a promising approach for these devices with low computational power.
However, understanding binary neural networks and training accurate models for practical applications remains a challenge.
In our work, we focus on increasing our understanding of the training process and making it accessible to everyone.
We publish our code and models based on BMXNet for everyone to use.
Within this framework, we systematically evaluated different network architectures and hyperparameters to provide useful insights on how to train a binary neural network.
Further, we present how we improved accuracy by increasing the number of connections in the network.
The rapid developments of Artificial Intelligence in the last decade are influencing Aerospace Engineering to a great extent and research in this context is proliferating.
We share our observations on the recent developments in the area of Spacecraft Guidance Dynamics and Control, giving selected examples on success stories that have been motivated by mission designs.
Our focus is on evolutionary optimisation, tree searches and machine learning, including deep learning and reinforcement learning as the key technologies and drivers for current and future research in the field.
From a high-level perspective, we survey various scenarios for which these approaches have been successfully applied or are under strong scientific investigation.
Whenever possible, we highlight the relations and synergies that can be obtained by combining different techniques and projects towards future domains for which newly emerging artificial intelligence techniques are expected to become game changers.
The fields of neural computation and artificial neural networks have developed much in the last decades.
Most of the works in these fields focus on implementing and/or learning discrete functions or behavior.
However, technical, physical, and also cognitive processes evolve continuously in time.
This cannot be described directly with standard architectures of artificial neural networks such as multi-layer feed-forward perceptrons.
Therefore, in this paper, we will argue that neural networks modeling continuous time are needed explicitly for this purpose, because with them the synthesis and analysis of continuous and possibly periodic processes in time are possible (e.g. for robot behavior) besides computing discrete classification functions (e.g. for logical reasoning).
We will relate possible neural network architectures with (hybrid) automata models that allow to express continuous processes.
This paper introduces some foundations of wavelets over Galois fields.
Standard orthogonal finite-field wavelets (FF-Wavelets) including FF-Haar and FF-Daubechies are derived.
Non-orthogonal FF-wavelets such as B-spline over GF(p) are also considered.
A few examples of multiresolution analysis over Finite fields are presented showing how to perform Laplacian pyramid filtering of finite block lengths sequences.
An application of FF-wavelets to design spread-spectrum sequences is presented.
Network pruning is widely used for reducing the heavy computational cost of deep models.
A typical pruning algorithm is a three-stage pipeline, i.e., training (a large model), pruning and fine-tuning.
During pruning, according to a certain criterion, redundant weights are pruned and important weights are kept to best preserve the accuracy.
In this work, we make several surprising observations which contradict common beliefs.
For all the six state-of-the-art pruning algorithms we examined, fine-tuning a pruned model only gives comparable or even worse performance than training that model with randomly initialized weights.
For pruning algorithms which assume a predefined target network architecture, one can get rid of the full pipeline and directly train the target network from scratch.
Our observations are consistent for a wide variety of pruning algorithms with multiple network architectures, datasets, and tasks.
Our results have several implications: 1) training a large, over-parameterized model is not necessary to obtain an efficient final model, 2) learned "important" weights of the large model are not necessarily useful for the small pruned model, 3) the pruned architecture itself, rather than a set of inherited "important" weights, is what leads to the efficiency benefit in the final model, which suggests that some pruning algorithms could be seen as performing network architecture search.
Journal of the History of Biology provides a fifty-year long record for examining the evolution of the history of biology as a scholarly discipline.
In this paper, we present a new dataset and preliminary quantitative analysis of the thematic content of JHB from the perspectives of geography, organisms, and thematic fields.
The geographic diversity of authors whose work appears in JHB has increased steadily since 1968, but the geographic coverage of the content of JHB articles remains strongly lopsided toward the United States, United Kingdom, and western Europe and has diversified much less dramatically over time.
The taxonomic diversity of organisms discussed in JHB increased steadily between 1968 and the late 1990s but declined in later years, mirroring broader patterns of diversification previously reported in the biomedical research literature.
Finally, we used a combination of topic modeling and nonlinear dimensionality reduction techniques to develop a model of multi-article fields within JHB.
We found evidence for directional changes in the representation of fields on multiple scales.
The diversity of JHB with regard to the representation of thematic fields has increased overall, with most of that diversification occurring in recent years.
Drawing on the dataset generated in the course of this analysis, as well as web services in the emerging digital history and philosophy of science ecosystem, we have developed an interactive web platform for exploring the content of JHB, and we provide a brief overview of the platform in this article.
As a whole, the data and analyses presented here provide a starting-place for further critical reflection on the evolution of the history of biology over the past half-century.
Recently, deep learning has been playing a central role in machine learning research and applications.
Since AlexNet, increasingly more advanced networks have achieved state-of-the-art performance in computer vision, speech recognition, language processing, game playing, medical imaging, and so on.
In our previous studies, we proposed quadratic/second-order neurons and deep quadratic neural networks.
In a quadratic neuron, the inner product of a vector of data and the corresponding weights in a conventional neuron is replaced with a quadratic function.
The resultant second-order neuron enjoys an enhanced expressive capability over the conventional neuron.
However, how quadratic neurons improve the expressing capability of a deep quadratic network has not been studied up to now, preferably in relation to that of a conventional neural network.
In this paper, we ask three basic questions regarding the expressive capability of a quadratic network: (1) for the one-hidden-layer network structure, is there any function that a quadratic network can approximate much more efficiently than a conventional network?
(2) for the same multi-layer network structure, is there any function that can be expressed by a quadratic network but cannot be expressed with conventional neurons in the same structure?
(3) Does a quadratic network give a new insight into universal approximation?
Our main contributions are the three theorems shedding light upon these three questions and demonstrating the merits of a quadratic network in terms of expressive efficiency, unique capability, and compact architecture respectively.
For complexity of the heterogeneous minimum spanning forest problem has not been determined, we reduce 3-SAT which is NP-complete to 2-heterogeneous minimum spanning forest problem to prove this problem is NP-hard and spread result to general problem, which determines complexity of this problem.
It provides a theoretical basis for the future designing of approximation algorithms for the problem.
In online internet advertising, machine learning models are widely used to compute the likelihood of a user engaging with product related advertisements.
However, the performance of traditional machine learning models is often impacted due to variations in user and advertiser behavior.
For example, search engine traffic for florists usually tends to peak around Valentine's day, Mother's day, etc.
To overcome, this challenge, in this manuscript we propose three models which are able to incorporate the effects arising due to variations in product demand.
The proposed models are a combination of product demand features, specialized data sampling methodologies and ensemble techniques.
We demonstrate the performance of our proposed models on datasets obtained from a real-world setting.
Our results show that the proposed models more accurately predict the outcome of users interactions with product related advertisements while simultaneously being robust to fluctuations in user and advertiser behaviors.
Coaching technology, wearables and exergames can provide quantitative feedback based on measured activity, but there is little evidence of qualitative feedback to aid technique improvement.
To achieve personalised qualitative feedback, we demonstrated a proof-of-concept prototype combining kinesiology and computational intelligence that could help improving tennis swing technique utilising three-dimensional tennis motion data acquired from multi-camera video.
Expert data labelling relied on virtual 3D stick figure replay.
Diverse assessment criteria for novice to intermediate skill levels and configurable coaching scenarios matched with a variety of tennis swings (22 backhands and 21 forehands), included good technique and common errors.
A set of selected coaching rules was transferred to adaptive assessment modules able to learn from data, evolve their internal structures and produce autonomous personalised feedback including verbal cues over virtual camera 3D replay and an end-of-session progress report.
The prototype demonstrated autonomous assessment on future data based on learning from prior examples, aligned with skill level, flexible coaching scenarios and coaching rules.
The generated intuitive diagnostic feedback consisted of elements of safety and performance for tennis swing technique, where each swing sample was compared with the expert.
For safety aspects of the relative swing width, the prototype showed improved assessment ...
Within the framework of linear vector Gaussian channels with arbitrary signaling, closed-form expressions for the Jacobian of the minimum mean square error and Fisher information matrices with respect to arbitrary parameters of the system are calculated in this paper.
Capitalizing on prior research where the minimum mean square error and Fisher information matrices were linked to information-theoretic quantities through differentiation, closed-form expressions for the Hessian of the mutual information and the differential entropy are derived.
These expressions are then used to assess the concavity properties of mutual information and differential entropy under different channel conditions and also to derive a multivariate version of the entropy power inequality due to Costa.
In this paper, an underlay cognitive radio (CR) multicast network, consisting of a cognitive base station (CBS) and multiple multicast groups of secondary users (SUs), is considered.
All SUs, belonging to a particular multicast group, are served by the CBS using a common primary user (PU) channel.
The goal is to maximize the energy efficiency (EE) of the system, through dynamic adaptation of target rate and transmit power for each multicast group, under the PUs' individual interference constraints.
The optimization problem formulated for this is proved to be non quasi-concave with respect to the joint variation of the CBS's transmit power and target rate.
An efficient iterative algorithm for EE maximization is proposed along with its complexity analysis.
Simulation results illustrate the performance gain of our proposed scheme.
In this paper, we study a large-scale distributed coordination problem and propose efficient adaptive strategies to solve the problem.
The basic problem is to allocate finite number of resources to individual agents such that there is as little congestion as possible and the fraction of unutilized resources is reduced as far as possible.
In the absence of a central planner and global information, agents can employ adaptive strategies that uses only finite knowledge about the competitors.
In this paper, we show that a combination of finite information sets and reinforcement learning can increase the utilization rate of resources substantially.
The authors introduce a new vision for providing computing services for connected devices.
It is based on the key concept that future computing resources will be coupled with communication resources, for enhancing user experience of the connected users, and also for optimising resources in the providers' infrastructures.
Such coupling is achieved by Joint/Cooperative resource allocation algorithms, by integrating computing and communication services and by integrating hardware in networks.
Such type of computing, by which computing services are not delivered independently but dependent of networking services, is named Aqua Computing.
The authors see Aqua Computing as a novel approach for delivering computing resources to end devices, where computing power of the devices are enhanced automatically once they are connected to an Aqua Computing enabled network.
The process of resource coupling is named computation dissolving.
Then, an Aqua Computing architecture is proposed for mobile edge networks, in which computing and wireless networking resources are allocated jointly or cooperatively by a Mobile Cloud Controller, for the benefit of the end-users and/or for the benefit of the service providers.
Finally, a working prototype of the system is shown and the gathered results show the performance of the Aqua Computing prototype.
Drawing from research on computational models of argumentation (particularly the Carneades Argumentation System), we explore the graphical representation of arguments in a dispute; then, comparing two different traditions on the limits of the justification of decisions, and devising an intermediate, semi-formal, model, we also show that it can shed light on the theory of dispute resolution.
We conclude our paper with an observation on the usefulness of highly constrained reasoning for Online Dispute Resolution systems.
Restricting the search space of arguments exclusively to reasons proposed by the parties (vetoing the introduction of new arguments by the human or artificial arbitrator) is the only way to introduce some kind of decidability -- together with foreseeability -- in the argumentation system.
Local misalignment caused by global homography is a common issue in image stitching task.
Content-Preserving Warping (CPW) is a typical method to deal with this issue, in which geometric and photometric constraints are imposed to guide the warping process.
One of its essential condition however, is colour consistency, and an elusive goal in real world applications.
In this paper, we propose a Generalized Content-Preserving Warping (GCPW) method to alleviate this problem.
GCPW extends the original CPW by applying a colour model that expresses the colour transformation between images locally, thus meeting the photometric constraint requirements for effective image stitching.
We combine the photometric and geometric constraints and jointly estimate the colour transformation and the warped mesh vertexes, simultaneously.
We align images locally with an optimal grid mesh generated by our GCPW method.
Experiments on both synthetic and real images demonstrate that our new method is robust to colour variations, outperforming other state-of-the-art CPW-based image stitching methods.
Advertising options have been recently studied as a special type of guaranteed contracts in online advertising, which are an alternative sales mechanism to real-time auctions.
An advertising option is a contract which gives its buyer a right but not obligation to enter into transactions to purchase page views or link clicks at one or multiple pre-specified prices in a specific future period.
Different from typical guaranteed contracts, the option buyer pays a lower upfront fee but can have greater flexibility and more control of advertising.
Many studies on advertising options so far have been restricted to the situations where the option payoff is determined by the underlying spot market price at a specific time point and the price evolution over time is assumed to be continuous.
The former leads to a biased calculation of option payoff and the latter is invalid empirically for many online advertising slots.
This paper addresses these two limitations by proposing a new advertising option pricing framework.
First, the option payoff is calculated based on an average price over a specific future period.
Therefore, the option becomes path-dependent.
The average price is measured by the power mean, which contains several existing option payoff functions as its special cases.
Second, jump-diffusion stochastic models are used to describe the movement of the underlying spot market price, which incorporate several important statistical properties including jumps and spikes, non-normality, and absence of autocorrelations.
A general option pricing algorithm is obtained based on Monte Carlo simulation.
In addition, an explicit pricing formula is derived for the case when the option payoff is based on the geometric mean.
This pricing formula is also a generalized version of several other option pricing models discussed in related studies.
The Container Relocation Problem (CRP) is concerned with finding a sequence of moves of containers that minimizes the number of relocations needed to retrieve all containers, while respecting a given order of retrieval.
However, the assumption of knowing the full retrieval order of containers is particularly unrealistic in real operations.
This paper studies the stochastic CRP (SCRP), which relaxes this assumption.
A new multi-stage stochastic model, called the batch model, is introduced, motivated, and compared with an existing model (the online model).
The two main contributions are an optimal algorithm called Pruning-Best-First-Search (PBFS) and a randomized approximate algorithm called PBFS-Approximate with a bounded average error.
Both algorithms, applicable in the batch and online models, are based on a new family of lower bounds for which we show some theoretical properties.
Moreover, we introduce two new heuristics outperforming the best existing heuristics.
Algorithms, bounds and heuristics are tested in an extensive computational section.
Finally, based on strong computational evidence, we conjecture the optimality of the "Leveling" heuristic in a special "no information" case, where at any retrieval stage, any of the remaining containers is equally likely to be retrieved next.
We present a novel architectural enhancement of Channel Boosting in deep convolutional neural network (CNN).
This idea of Channel Boosting exploits both the channel dimension of CNN (learning from multiple input channels) and Transfer learning (TL).
TL is utilized at two different stages; channel generation and channel exploitation.
In the proposed methodology, a deep CNN is boosted by various channels available through TL from already trained Deep Neural Networks, in addition to its own original channel.
The deep architecture of CNN then exploits the original and boosted channels down the stream for learning discriminative patterns.
Churn prediction in telecom is a challenging task due to high dimensionality and imbalanced nature of the data and it is therefore used to evaluate the performance of the proposed Channel Boosted CNN (CB CNN).
In the first phase, discriminative informative features are being extracted using a staked autoencoder, and then in the second phase, these features are combined with the original features to form Channel Boosted images.
Finally, the knowledge gained by a pre trained CNN is exploited by employing TL.
The results are promising and show the ability of the Channel Boosting concept in learning complex classification problem by discerning even minute differences in churners and non churners.
The proposed work validates the concept observed from the evolution of recent CNN architectures that the innovative restructuring of a CNN architecture may increase the representative capacity of the network.
We present a new approach to rigid-body motion segmentation from two views.
We use a previously developed nonlinear embedding of two-view point correspondences into a 9-dimensional space and identify the different motions by segmenting lower-dimensional subspaces.
In order to overcome nonuniform distributions along the subspaces, whose dimensions are unknown, we suggest the novel concept of global dimension and its minimization for clustering subspaces with some theoretical motivation.
We propose a fast projected gradient algorithm for minimizing global dimension and thus segmenting motions from 2-views.
We develop an outlier detection framework around the proposed method, and we present state-of-the-art results on outlier-free and outlier-corrupted two-view data for segmenting motion.
Why did only we humans evolve Turing completeness?
Turing completeness is the maximum computing power, and we are Turing complete because we can calculate whatever any Turing machine can compute.
Thus we can learn any natural or artificial language, and it seems that no other species can, so we are the only Turing complete species.
The evolutionary advantage of Turing completeness is full problem solving, and not syntactic proficiency, but the expression of problems requires a syntax because separate words are not enough, and only our ancestors evolved a protolanguage, and then a syntax, and finally Turing completeness.
Besides these results, the introduction of Turing completeness and problem solving to explain the evolution of syntax should help us to fit the evolution of language within the evolution of cognition, giving us some new clues to understand the elusive relation between language and thinking.
Deep networks are successfully used as classification models yielding state-of-the-art results when trained on a large number of labeled samples.
These models, however, are usually much less suited for semi-supervised problems because of their tendency to overfit easily when trained on small amounts of data.
In this work we will explore a new training objective that is targeting a semi-supervised regime with only a small subset of labeled data.
This criterion is based on a deep metric embedding over distance relations within the set of labeled samples, together with constraints over the embeddings of the unlabeled set.
The final learned representations are discriminative in euclidean space, and hence can be used with subsequent nearest-neighbor classification using the labeled samples.
Spectrum sensing is a fundamental operation in cognitive radio environment.
It gives information about spectrum availability by scanning the bands.
Usually a fixed amount of time is given to scan individual bands.
Most of the times, historical information about the traffic in the spectrum bands is not used.
But this information gives the idea, how busy a specific band is.
Therefore, instead of scanning a band for a fixed amount of time, more time can be given to less occupied bands and less time to heavily occupied ones.
In this paper we have formulated the time assignment problem as integer linear programming and source coding problems.
The time assignment problem is solved using the associated stochastic optimization problem.
This paper considers the problem of finite dimensional output feedback H-infinity control for a class of nonlinear spatially distributed processes (SDPs) described by highly dissipative partial differential equations (PDEs), whose state is observed by a sensor network (SN) with a given topology.
A highly dissipative PDE system typically involves a spatial differential operator with eigenspectrum that can be partitioned into a finite-dimensional slow one and an infinite-dimensional stable fast complement.
Motivated by this fact, the modal decomposition and singular perturbation techniques are initially applied to the PDE system to derive a finite dimensional ordinary differential equation model, which accurately captures the dynamics of the slow modes of the PDE system.
Subsequently, based on the slow system and the topology of the SN, a set of finite dimensional distributed consensus observers are constructed to estimate the state of the slow system.
Then, a centralized control scheme, which only uses the available estimates from a specified group of SN nodes, is proposed for the PDE system.
An H-infinity control design method is developed in terms of bilinear matrix inequality (BMI), such that the original closed-loop PDE system is exponentially stable and a prescribed level of disturbance attenuation is satisfied for the slow system.
Furthermore, a suboptimal H-infinity controller is also provided to make the attenuation level as small as possible, which can be obtained via a local optimization algorithm that treats the BMI as double linear matrix inequality.
Finally, the proposed method is applied to the control of one dimensional Kuramoto-Sivashinsky equation (KSE) system.
The task of voxel resolution for a space curve in video memory of 3D display is set.
Furthermore, an approach solution of voxel resolution of arbitrary space curve, given in parametric form, is studied.
Numerous numbers of intensive experiments are conducted and interesting results with significant recommendations are presented.
We study the underlying structure of data (approximately) generated from a union of independent subspaces.
Traditional methods learn only one subspace, failing to discover the multi-subspace structure, while state-of-the-art methods analyze the multi-subspace structure using data themselves as the dictionary, which cannot offer the explicit basis to span each subspace and are sensitive to errors via an indirect representation.
Additionally, they also suffer from a high computational complexity, being quadratic or cubic to the sample size.
To tackle all these problems, we propose a method, called Matrix Factorization with Column L0-norm constraint (MFC0), that can simultaneously learn the basis for each subspace, generate a direct sparse representation for each data sample, as well as removing errors in the data in an efficient way.
Furthermore, we develop a first-order alternating direction algorithm, whose computational complexity is linear to the sample size, to stably and effectively solve the nonconvex objective function and non- smooth l0-norm constraint of MFC0.
Experimental results on both synthetic and real-world datasets demonstrate that besides the superiority over traditional and state-of-the-art methods for subspace clustering, data reconstruction, error correction, MFC0 also shows its uniqueness for multi-subspace basis learning and direct sparse representation.
Verification problems of programs written in various paradigms (such as imperative, logic, concurrent, functional, and object-oriented ones) can be reduced to problems of solving Horn clause constraints on predicate variables that represent unknown inductive invariants.
This paper presents a novel Horn constraint solving method based on inductive theorem proving: the method reduces Horn constraint solving to validity checking of first-order formulas with inductively defined predicates, which are then checked by induction on the derivation of the predicates.
To automate inductive proofs, we introduce a novel proof system tailored to Horn constraint solving and use an SMT solver to discharge proof obligations arising in the proof search.
The main advantage of the proposed method is that it can verify relational specifications across programs in various paradigms where multiple function calls need to be analyzed simultaneously.
The class of specifications includes practically important ones such as functional equivalence, associativity, commutativity, distributivity, monotonicity, idempotency, and non-interference.
Furthermore, our novel combination of Horn clause constraints with inductive theorem proving enables us to naturally and automatically axiomatize recursive functions that are possibly non-terminating, non-deterministic, higher-order, exception-raising, and over non-inductively defined data types.
We have implemented a relational verification tool for the OCaml functional language based on the proposed method and obtained promising results in preliminary experiments.
Many artificial intelligences (AIs) are randomized.
One can be lucky or unlucky with the random seed; we quantify this effect and show that, maybe contrarily to intuition, this is far from being negligible.
Then, we apply two different existing algorithms for selecting good seeds and good probability distributions over seeds.
This mainly leads to learning an opening book.
We apply this to Phantom Go, which, as all phantom games, is hard for opening book learning.
We improve the winning rate from 50% to 70% in 5x5 against the same AI, and from approximately 0% to 40% in 5x5, 7x7 and 9x9 against a stronger (learning) opponent.
Deep learning has become the state-of-art tool in many applications, but the evaluation and training of deep models can be time-consuming and computationally expensive.
The conditional computation approach has been proposed to tackle this problem (Bengio et al., 2013; Davis & Arel, 2013).
It operates by selectively activating only parts of the network at a time.
In this paper, we use reinforcement learning as a tool to optimize conditional computation policies.
More specifically, we cast the problem of learning activation-dependent policies for dropping out blocks of units as a reinforcement learning problem.
We propose a learning scheme motivated by computation speed, capturing the idea of wanting to have parsimonious activations while maintaining prediction accuracy.
We apply a policy gradient algorithm for learning policies that optimize this loss function and propose a regularization mechanism that encourages diversification of the dropout policy.
We present encouraging empirical results showing that this approach improves the speed of computation without impacting the quality of the approximation.
The self-organizational ability of ad-hoc Wireless Sensor Networks (WSNs) has led them to be the most popular choice in ubiquitous computing.
Clustering sensor nodes organizing them hierarchically have proven to be an effective method to provide better data aggregation and scalability for the sensor network while conserving limited energy.
It has some limitation in energy and mobility of nodes.
In this paper we propose a mobility prediction technique which tries overcoming above mentioned problems and improves the life time of the network.
The technique used here is Exponential Moving Average for online updates of nodal contact probability in cluster based network.
Flow-Aware Multi-Topology Adaptive Routing (FAMTAR) is a new approach to multipath and adaptive routing in IP networks which enables automatic use of alternative paths when the primary one becomes congested.
It provides more efficient network resource utilization and higher quality of transmission compared to standard IP routing.
However, thus far it has only been evaluated through simulations.
In this paper we share our experiences from building a real-time FAMTAR router and present results of its tests in a physical network.
The results are in line with those obtained previously through simulations and they open the way to implementation of a production grade FAMTAR router.
The original Pascaline was a mechanical calculator able to sum and subtract integers.
It encodes information in the angles of mechanical wheels and through a set of gears, and aided by gravity, could perform the calculations.
Here, we show that such a concept can be realized in electronics using memory elements such as memristive systems.
By using memristive emulators we have demonstrated experimentally the memcomputing version of the mechanical Pascaline, capable of processing and storing the numerical results in the multiple levels of each memristive element.
Our result is the first experimental demonstration of multidigit arithmetics with multi-level memory devices that further emphasizes the versatility and potential of memristive systems for future massively-parallel high-density computing architectures.
We study the existence of asymptotically stable periodic trajectories induced by reset feedback.
The analysis is developed for a planar system.
Casting the problem into the hybrid setting, we show that a periodic orbit arises from the balance between the energy dissipated during flows and the energy restored by resets, at jumps.
The stability of the periodic orbit is studied with hybrid Lyapunov tools.
The satisfaction of the so-called hybrid basic conditions ensures the robustness of the asymptotic stability.
Extensions of the approach to more general mechanical systems are discussed.
Analysis and prediction of stock market time series data has attracted considerable interest from the research community over the last decade.
Rapid development and evolution of sophisticated algorithms for statistical analysis of time series data, and availability of high-performance hardware has made it possible to process and analyze high volume stock market time series data effectively, in real-time.
Among many other important characteristics and behavior of such data, forecasting is an area which has witnessed considerable focus.
In this work, we have used time series of the index values of the Auto sector in India during January 2010 to December 2015 for a deeper understanding of the behavior of its three constituent components, e.g., the trend, the seasonal component, and the random component.
Based on this structural analysis, we have also designed five approaches for forecasting and also computed their accuracy in prediction using suitably chosen training and test data sets.
Extensive results are presented to demonstrate the effectiveness of our proposed decomposition approaches of time series and the efficiency of our forecasting techniques, even in presence of a random component and a sharply changing trend component in the time-series.
This document summarizes the major milestones in mobile Augmented Reality between 1968 and 2014.
Major parts of the list were compiled by the member of the Christian Doppler Laboratory for Handheld Augmented Reality in 2010 (author list in alphabetical order) for the ISMAR society.
Later in 2013 it was updated, and more recent work was added during preparation of this report.
Permission is granted to copy and modify.
In recent years identity-vector (i-vector) based speaker verification (SV) systems have become very successful.
Nevertheless, environmental noise and speech duration variability still have a significant effect on degrading the performance of these systems.
In many real-life applications, duration of recordings are very short; as a result, extracted i-vectors cannot reliably represent the attributes of the speaker.
Here, we investigate the effect of speech duration on the performance of three state-of-the-art speaker recognition systems.
In addition, using a variety of available score fusion methods, we investigate the effect of score fusion for those speaker verification techniques to benefit from the performance difference of different methods under different enrollment and test speech duration conditions.
This technique performed significantly better than the baseline score fusion methods.
It is well known that for some tasks, labeled data sets may be hard to gather.
Therefore, we wished to tackle here the problem of having insufficient training data.
We examined learning methods from unlabeled data after an initial training on a limited labeled data set.
The suggested approach can be used as an online learning method on the unlabeled test set.
In the general classification task, whenever we predict a label with high enough confidence, we treat it as a true label and train the data accordingly.
For the semantic segmentation task, a classic example for an expensive data labeling process, we do so pixel-wise.
Our suggested approaches were applied on the MNIST data-set as a proof of concept for a vision classification task and on the ADE20K data-set in order to tackle the semi-supervised semantic segmentation problem.
We evaluate the secrecy performance of a multiple access cooperative network where the destination node is wiretapped by a malicious and passive eavesdropper.
We propose the application of the network coding technique as an alternative to increase the secrecy at the destination node, on the top of improving the error performance of the legitimate communication, already demonstrated in the literature.
Network coding is leveraged by assuming that the legitime cooperative nodes are able to perform non-binary linear combinations of different frames before the transmission.
Different scenarios with and without channel state information (CSI) at the transmitter side are evaluated.
The effectiveness of the proposed schemes is evaluated in terms of secrecy outage probability through theoretic and numerical analyses.
It is shown that, even when the legitimate transmitters do not have any CSI, the secrecy can be increased through the use of network coding when compared to the direct transmission and traditional cooperative techniques.
The use of annotations, referred to as assertions or contracts, to describe program properties for which run-time tests are to be generated, has become frequent in dynamic programing languages.
However, the frameworks proposed to support such run-time testing generally incur high time and/or space overheads over standard program execution.
We present an approach for reducing this overhead that is based on the use of memoization to cache intermediate results of check evaluation, avoiding repeated checking of previously verified properties.
Compared to approaches that reduce checking frequency, our proposal has the advantage of being exhaustive (i.e., all tests are checked at all points) while still being much more efficient than standard run-time checking.
Compared to the limited previous work on memoization, it performs the task without requiring modifications to data structure representation or checking code.
While the approach is general and system-independent, we present it for concreteness in the context of the Ciao run-time checking framework, which allows us to provide an operational semantics with checks and caching.
We also report on a prototype implementation and provide some experimental results that support that using a relatively small cache leads to significant decreases in run-time checking overhead.
Message digest algorithms are one of the underlying building blocks of blockchain platforms such as Ethereum.
This paper analyses situations in which the message digest collision resistance property can be exploited by attackers.
Two mitigations for possible attacks are described: longer message digest sizes make attacks more difficult; and, including timeliness properties limits the amount of time an attacker has to determine a hash collision.
Many fundamental problems in natural language processing rely on determining what entities appear in a given text.
Commonly referenced as entity linking, this step is a fundamental component of many NLP tasks such as text understanding, automatic summarization, semantic search or machine translation.
Name ambiguity, word polysemy, context dependencies and a heavy-tailed distribution of entities contribute to the complexity of this problem.
We here propose a probabilistic approach that makes use of an effective graphical model to perform collective entity disambiguation.
Input mentions (i.e.,~linkable token spans) are disambiguated jointly across an entire document by combining a document-level prior of entity co-occurrences with local information captured from mentions and their surrounding context.
The model is based on simple sufficient statistics extracted from data, thus relying on few parameters to be learned.
Our method does not require extensive feature engineering, nor an expensive training procedure.
We use loopy belief propagation to perform approximate inference.
The low complexity of our model makes this step sufficiently fast for real-time usage.
We demonstrate the accuracy of our approach on a wide range of benchmark datasets, showing that it matches, and in many cases outperforms, existing state-of-the-art methods.
For many large undirected models that arise in real-world applications, exact maximumlikelihood training is intractable, because it requires computing marginal distributions of the model.
Conditional training is even more difficult, because the partition function depends not only on the parameters, but also on the observed input, requiring repeated inference over each training example.
An appealing idea for such models is to independently train a local undirected classifier over each clique, afterwards combining the learned weights into a single global model.
In this paper, we show that this piecewise method can be justified as minimizing a new family of upper bounds on the log partition function.
On three natural-language data sets, piecewise training is more accurate than pseudolikelihood, and often performs comparably to global training using belief propagation.
We present an approach to automate the process of discovering optimization methods, with a focus on deep learning architectures.
We train a Recurrent Neural Network controller to generate a string in a domain specific language that describes a mathematical update equation based on a list of primitive functions, such as the gradient, running average of the gradient, etc.
The controller is trained with Reinforcement Learning to maximize the performance of a model after a few epochs.
On CIFAR-10, our method discovers several update rules that are better than many commonly used optimizers, such as Adam, RMSProp, or SGD with and without Momentum on a ConvNet model.
We introduce two new optimizers, named PowerSign and AddSign, which we show transfer well and improve training on a variety of different tasks and architectures, including ImageNet classification and Google's neural machine translation system.
An essential part of building a data-driven organization is the ability to handle and process continuous streams of data to discover actionable insights.
The explosive growth of interconnected devices and the social Web has led to a large volume of data being generated on a continuous basis.
Streaming data sources such as stock quotes, credit card transactions, trending news, traffic conditions, time-sensitive patients data are not only very common but can rapidly depreciate if not processed quickly.
The ever-increasing volume and highly irregular nature of data rates pose new challenges to data stream processing systems.
One such challenging but important task is how to accurately ingest and integrate data streams from various sources and locations into an analytics platform.
These challenges demand new strategies and systems that can offer the desired degree of scalability and robustness in handling failures.
This paper investigates the fundamental requirements and the state of the art of existing data stream ingestion systems, propose a scalable and fault-tolerant data stream ingestion and integration framework that can serve as a reusable component across many feeds of structured and unstructured input data in a given platform, and demonstrate the utility of the framework in a real-world data stream processing case study that integrates Apache NiFi and Kafka for processing high velocity news articles from across the globe.
The study also identifies best practices and gaps for future research in developing large-scale data stream processing infrastructure.
We introduce a weighted version of the ranking algorithm by Karp et al.(STOC 1990), and prove a competitive ratio of 0.6534 for the vertex-weighted online bipartite matching problem when online vertices arrive in random order.
Our result shows that random arrivals help beating the 1-1/e barrier even in the vertex-weighted case.
We build on the randomized primal-dual framework by Devanur et al.(SODA 2013) and design a two dimensional gain sharing function, which depends not only on the rank of the offline vertex, but also on the arrival time of the online vertex.
To our knowledge, this is the first competitive ratio strictly larger than 1-1/e for an online bipartite matching problem achieved under the randomized primal-dual framework.
Our algorithm has a natural interpretation that offline vertices offer a larger portion of their weights to the online vertices as time goes by, and each online vertex matches the neighbor with the highest offer at its arrival.
This paper builds on altruistic locking which is an extension of 2PL.
It allows more relaxed rules as compared to 2PL.
But altruistic locking too enforces some rules which disallow some valid schedules (present in VSR and CSR) to be passed by AL.
This paper proposes a multiversion variant of AL which solves this problem.
The report also discusses the relationship or comparison between different protocols such as MAL and MV2PL, MAL and AL, MAL and 2PL and so on.
This paper also discusses the caveats involved in MAL and where it lies in the Venn diagram of multiversion serializable schedule protocols.
Finally, the possible use of MAL in hybrid protocols and the parameters involved in making MAL successful are discussed.
Andrew Tanenbaum and his textbooks -- e.g. on Operating Systems, Computer Networks, Structured Computer Organization and Distributed Systems, to name but a few -- have had a tremendous impact on generations of computer science students (and teachers at the same time).
Given this, it is striking to observe that this comprehensive body of work apparently does not provide a single line on a research topic that seems to be intimately related with his name (at least in German), i.e.
Xmas Research (XR).
Hence, the goal of this paper is to fill this gap and provide insight into a number of paradigmatic XR research questions, for instance: Can we today still count on Santa Claus?
Or at least on Xmas trees?
And does this depend on basic tree structures, or can we rather find solutions on the level of programming languages?
By addressing such basic open issues, we aim at providing a solid technical foundation for future steps towards the imminent evolution of Xmas 4.0.
The proposed Earth observation (EO) based value adding system (EO VAS), hereafter identified as AutoCloud+, consists of an innovative EO image understanding system (EO IUS) design and implementation capable of automatic spatial context sensitive cloud/cloud shadow detection in multi source multi spectral (MS) EO imagery, whether or not radiometrically calibrated, acquired by multiple platforms, either spaceborne or airborne, including unmanned aerial vehicles (UAVs).
It is worth mentioning that the same EO IUS architecture is suitable for a large variety of EO based value adding products and services, including: (i) low level image enhancement applications, such as automatic MS image topographic correction, co registration, mosaicking and compositing, (ii) high level MS image land cover (LC) and LC change (LCC) classification and (iii) content based image storage/retrieval in massive multi source EO image databases (big data mining).
Square grids are commonly used in robotics and game development as spatial models and well known in AI community heuristic search algorithms (such as A*, JPS, Theta* etc.) are widely used for path planning on grids.
A lot of research is concentrated on finding the shortest (in geometrical sense) paths while in many applications finding smooth paths (rather than the shortest ones but containing sharp turns) is preferable.
In this paper we study the problem of generating smooth paths and concentrate on angle constrained path planning.
We put angle-constrained path planning problem formally and present a new algorithm tailored to solve it - LIAN.
We examine LIAN both theoretically and empirically.
We show that it is sound and complete (under some restrictions).
We also show that LIAN outperforms the analogues when solving numerous path planning tasks within urban outdoor navigation scenarios.
We present a probabilistic model for learning from dynamic relational data, wherein the observed interactions among networked nodes are modeled via the Bernoulli Poisson link function, and the underlying network structure are characterized by nonnegative latent node-group memberships, which are assumed to be gamma distributed.
The latent memberships evolve according to Markov processes.
The optimal number of latent groups can be determined by data itself.
The computational complexity of our method scales with the number of non-zero links, which makes it scalable to large sparse dynamic relational data.
We present batch and online Gibbs sampling algorithms to perform model inference.
Finally, we demonstrate the model's performance on both synthetic and real-world datasets compared to state-of-the-art methods.
Remote job submission and execution is fundamental requirement of distributed computing done using Cluster computing.
However, Cluster computing limits usage within a single organization.
Grid computing environment can allow use of resources for remote job execution that are available in other organizations.
This paper discusses concepts of batch-job execution using LRM and using Grid.
The paper discusses two ways of preparing test Grid computing environment that we use for experimental testing of concepts.
This paper presents experimental testing of remote job submission and execution mechanisms through LRM specific way and Grid computing ways.
Moreover, the paper also discusses various problems faced while working with Grid computing environment and discusses their trouble-shootings.
The understanding and experimental testing presented in this paper would become very useful to researchers who are new to the field of job management in Grid.
With the growing interest on Network Analysis, Relational Data Mining is becoming an emphasized domain of Data Mining.
This paper addresses the problem of extracting representative elements from a relational dataset.
After defining the notion of degree of representativeness, computed using the Borda aggregation procedure, we present the extraction of exemplars which are the representative elements of the dataset.
We use these concepts to build a network on the dataset.
We expose the main properties of these notions and we propose two typical applications of our framework.
The first application consists in resuming and structuring a set of binary images and the second in mining co-authoring relation in a research team.
Detection of transitions between broad phonetic classes in a speech signal is an important problem which has applications such as landmark detection and segmentation.
The proposed hierarchical method detects silence to non-silence transitions, high amplitude (mostly sonorants) to low ampli- tude (mostly fricatives/affricates/stop bursts) transitions and vice-versa.
A subset of the extremum (minimum or maximum) samples between every pair of successive zero-crossings is selected above a second pass threshold, from each bandpass filtered speech signal frame.
Relative to the mid-point (reference) of a frame, locations of the first and the last extrema lie on either side, if the speech signal belongs to a homogeneous segment; else, both these locations lie on the left or the right side of the reference, indicating a transition frame.
When tested on the entire TIMIT database, of the transitions detected, 93.6% are within a tolerance of 20 ms from the hand labeled boundaries.
Sonorant, unvoiced non-sonorant and silence classes and their respective onsets are detected with an accuracy of about 83.5% for the same tolerance.
The results are as good as, and in some respects better than the state-of-the-art methods for similar tasks.
With the rapid advances of microarray technologies, large amounts of high-dimensional gene expression data are being generated, which poses significant computational challenges.
A first step towards addressing this challenge is the use of clustering techniques, which is essential in the data mining process to reveal natural structures and identify interesting patterns in the underlying data.
A robust gene expression clustering approach to minimize undesirable clustering is proposed.
In this paper, Penalized Fuzzy C-Means (PFCM) Clustering algorithm is described and compared with the most representative off-line clustering techniques: K-Means Clustering, Rough K-Means Clustering and Fuzzy C-Means clustering.
These techniques are implemented and tested for a Brain Tumor gene expression Dataset.
Analysis of the performance of the proposed approach is presented through qualitative validation experiments.
From experimental results, it can be observed that Penalized Fuzzy C-Means algorithm shows a much higher usability than the other projected clustering algorithms used in our comparison study.
Significant and promising clustering results are presented using Brain Tumor Gene expression dataset.
Thus patterns seen in genome-wide expression experiments can be interpreted as indications of the status of cellular processes.
In these clustering results, we find that Penalized Fuzzy C-Means algorithm provides useful information as an aid to diagnosis in oncology.
Dense video captioning is a fine-grained video understanding task that involves two sub-problems: localizing distinct events in a long video stream, and generating captions for the localized events.
We propose the Joint Event Detection and Description Network (JEDDi-Net), which solves the dense video captioning task in an end-to-end fashion.
Our model continuously encodes the input video stream with three-dimensional convolutional layers, proposes variable-length temporal events based on pooled features, and generates their captions.
Proposal features are extracted within each proposal segment through 3D Segment-of-Interest pooling from shared video feature encoding.
In order to explicitly model temporal relationships between visual events and their captions in a single video, we also propose a two-level hierarchical captioning module that keeps track of context.
On the large-scale ActivityNet Captions dataset, JEDDi-Net demonstrates improved results as measured by standard metrics.
We also present the first dense captioning results on the TACoS-MultiLevel dataset.
Recent research on problem formulations based on decomposition into low-rank plus sparse matrices shows a suitable framework to separate moving objects from the background.
The most representative problem formulation is the Robust Principal Component Analysis (RPCA) solved via Principal Component Pursuit (PCP) which decomposes a data matrix in a low-rank matrix and a sparse matrix.
However, similar robust implicit or explicit decompositions can be made in the following problem formulations: Robust Non-negative Matrix Factorization (RNMF), Robust Matrix Completion (RMC), Robust Subspace Recovery (RSR), Robust Subspace Tracking (RST) and Robust Low-Rank Minimization (RLRM).
The main goal of these similar problem formulations is to obtain explicitly or implicitly a decomposition into low-rank matrix plus additive matrices.
In this context, this work aims to initiate a rigorous and comprehensive review of the similar problem formulations in robust subspace learning and tracking based on decomposition into low-rank plus additive matrices for testing and ranking existing algorithms for background/foreground separation.
For this, we first provide a preliminary review of the recent developments in the different problem formulations which allows us to define a unified view that we called Decomposition into Low-rank plus Additive Matrices (DLAM).
Then, we examine carefully each method in each robust subspace learning/tracking frameworks with their decomposition, their loss functions, their optimization problem and their solvers.
Furthermore, we investigate if incremental algorithms and real-time implementations can be achieved for background/foreground separation.
Finally, experimental results on a large-scale dataset called Background Models Challenge (BMC 2012) show the comparative performance of 32 different robust subspace learning/tracking methods.
Hosting platforms for software projects can form collaborative social networks and a prime example of this is GitHub which is arguably the most popular platform of this kind.
An open source project recommendation system could be a major feature for a platform like GitHub, enabling its users to find relevant projects in a fast and simple manner.
We perform network analysis on a constructed graph based on GitHub data and present a recommendation system that uses link prediction.
In video super-resolution, the spatio-temporal coherence between, and among the frames must be exploited appropriately for accurate prediction of the high resolution frames.
Although 2D convolutional neural networks (CNNs) are powerful in modelling images, 3D-CNNs are more suitable for spatio-temporal feature extraction as they can preserve temporal information.
To this end, we propose an effective 3D-CNN for video super-resolution, called the 3DSRnet that does not require motion alignment as preprocessing.
Our 3DSRnet maintains the temporal depth of spatio-temporal feature maps to maximally capture the temporally nonlinear characteristics between low and high resolution frames, and adopts residual learning in conjunction with the sub-pixel outputs.
It outperforms the most state-of-the-art method with average 0.45 and 0.36 dB higher in PSNR for scales 3 and 4, respectively, in the Vidset4 benchmark.
Our 3DSRnet first deals with the performance drop due to scene change, which is important in practice but has not been previously considered.
The computational complexity of solving nonlinear support vector machine (SVM) is prohibitive on large-scale data.
In particular, this issue becomes very sensitive when the data represents additional difficulties such as highly imbalanced class sizes.
Typically, nonlinear kernels produce significantly higher classification quality to linear kernels but introduce extra kernel and model parameters which computationally expensive fitting.
This increases the quality but also reduces the performance dramatically.
We introduce a generalized fast multilevel framework for regular and weighted SVM and discuss several versions of its algorithmic components that lead to a good trade-off between quality and time.
Our framework is implemented using PETSc which allows an easy integration with scientific computing tasks.
The experimental results demonstrate significant speedup compared to the state-of-the-art nonlinear SVM libraries.
We study Doob's martingale convergence theorem for computable continuous time martingales on Brownian motion, in the context of algorithmic randomness.
A characterization of the class of sample points for which the theorem holds is given.
Such points are given the name of Doob random points.
It is shown that a point is Doob random if its tail is computably random in a certain sense.
Moreover, Doob randomness is strictly weaker than computable randomness and is incomparable with Schnorr randomness.
We develop a new algorithm for fitting circles that does not have drawbacks commonly found in existing circle fits.
Our fit achieves ultimate accuracy (to machine precision), avoids divergence, and is numerically stable even when fitting circles get arbitrary large.
Lastly, our algorithm takes less than 10 iterations to converge, on average.
The conventional model for online planning under uncertainty assumes that an agent can stop and plan without incurring costs for the time spent planning.
However, planning time is not free in most real-world settings.
For example, an autonomous drone is subject to nature's forces, like gravity, even while it thinks, and must either pay a price for counteracting these forces to stay in place, or grapple with the state change caused by acquiescing to them.
Policy optimization in these settings requires metareasoning---a process that trades off the cost of planning and the potential policy improvement that can be achieved.
We formalize and analyze the metareasoning problem for Markov Decision Processes (MDPs).
Our work subsumes previously studied special cases of metareasoning and shows that in the general case, metareasoning is at most polynomially harder than solving MDPs with any given algorithm that disregards the cost of thinking.
For reasons we discuss, optimal general metareasoning turns out to be impractical, motivating approximations.
We present approximate metareasoning procedures which rely on special properties of the BRTDP planning algorithm and explore the effectiveness of our methods on a variety of problems.
General human action recognition requires understanding of various visual cues.
In this paper, we propose a network architecture that computes and integrates the most important visual cues for action recognition: pose, motion, and the raw images.
For the integration, we introduce a Markov chain model which adds cues successively.
The resulting approach is efficient and applicable to action classification as well as to spatial and temporal action localization.
The two contributions clearly improve the performance over respective baselines.
The overall approach achieves state-of-the-art action classification performance on HMDB51, J-HMDB and NTU RGB+D datasets.
Moreover, it yields state-of-the-art spatio-temporal action localization results on UCF101 and J-HMDB.
We construct custom regularization functions for use in supervised training of deep neural networks.
Our technique is applicable when the ground-truth labels themselves exhibit internal structure; we derive a regularizer by learning an autoencoder over the set of annotations.
Training thereby becomes a two-phase procedure.
The first phase models labels with an autoencoder.
The second phase trains the actual network of interest by attaching an auxiliary branch that must predict output via a hidden layer of the autoencoder.
After training, we discard this auxiliary branch.
We experiment in the context of semantic segmentation, demonstrating this regularization strategy leads to consistent accuracy boosts over baselines, both when training from scratch, or in combination with ImageNet pretraining.
Gains are also consistent over different choices of convolutional network architecture.
As our regularizer is discarded after training, our method has zero cost at test time; the performance improvements are essentially free.
We are simply able to learn better network weights by building an abstract model of the label space, and then training the network to understand this abstraction alongside the original task.
Learning by contrasting positive and negative samples is a general strategy adopted by many methods.
Noise contrastive estimation (NCE) for word embeddings and translating embeddings for knowledge graphs are examples in NLP employing this approach.
In this work, we view contrastive learning as an abstraction of all such methods and augment the negative sampler into a mixture distribution containing an adversarially learned sampler.
The resulting adaptive sampler finds harder negative examples, which forces the main model to learn a better representation of the data.
We evaluate our proposal on learning word embeddings, order embeddings and knowledge graph embeddings and observe both faster convergence and improved results on multiple metrics.
We propose a method for visual question answering which combines an internal representation of the content of an image with information extracted from a general knowledge base to answer a broad range of image-based questions.
This allows more complex questions to be answered using the predominant neural network-based approach than has previously been possible.
It particularly allows questions to be asked about the contents of an image, even when the image itself does not contain the whole answer.
The method constructs a textual representation of the semantic content of an image, and merges it with textual information sourced from a knowledge base, to develop a deeper understanding of the scene viewed.
Priming a recurrent neural network with this combined information, and the submitted question, leads to a very flexible visual question answering approach.
We are specifically able to answer questions posed in natural language, that refer to information not contained in the image.
We demonstrate the effectiveness of our model on two publicly available datasets, Toronto COCO-QA and MS COCO-VQA and show that it produces the best reported results in both cases.
A novel decentralised trajectory generation algorithm for Multi Agent systems is presented.
Multi-robot systems have the capacity to transform lives in a variety of fields.
But, trajectory generation for multi-robot systems is still in its nascent stage and limited to heavily controlled environments.
To overcome that, an online trajectory optimization algorithm that generates collision-free trajectories for robots, when given initial state and desired end pose, is proposed.
It utilizes a simple method for obstacle detection, local shape based maps for obstacles and communication of robots' current states.
Using the local maps, safe regions are formulated.
Based upon the communicated data, trajectories are predicted for other robots and incorporated for collision-avoidance by resizing the regions of free space that the robot can be in without colliding.
A trajectory is then optimized constraining the robot to remain within the safe region with the trajectories represented by piecewise polynomials parameterized by time.
The algorithm is implemented using a receding horizon principle.
The proposed algorithm is extensively tested in simulations on Gazebo using ROS with fourth order differentially flat aerial robots and non-holonomic second order wheeled robots in structured and unstructured environments.
Fire disasters are man-made disasters, which cause ecological, social, and economic damage.
To minimize these losses, early detection of fire and an autonomous response are important and helpful to disaster management systems.
Therefore, in this article, we propose an early fire detection framework using fine-tuned convolutional neural networks for CCTV surveillance cameras, which can detect fire in varying indoor and outdoor environments.
To ensure the autonomous response, we propose an adaptive prioritization mechanism for cameras in the surveillance system.
Finally, we propose a dynamic channel selection algorithm for cameras based on cognitive radio networks, ensuring reliable data dissemination.
Experimental results verify the higher accuracy of our fire detection scheme compared to state-of-the-art methods and validate the applicability of our framework for effective fire disaster management.
In recent years, there has been an increasing interest in extending traditional stream processing engines with logical, rule-based, reasoning capabilities.
This poses significant theoretical and practical challenges since rules can derive new information and propagate it both towards past and future time points; as a result, streamed query answers can depend on data that has not yet been received, as well as on data that arrived far in the past.
Stream reasoning algorithms, however, must be able to stream out query answers as soon as possible, and can only keep a limited number of previous input facts in memory.
In this paper, we propose novel reasoning problems to deal with these challenges, and study their computational properties on Datalog extended with a temporal sort and the successor function (a core rule-based language for stream reasoning applications).
PageRank is a fundamental link analysis algorithm that also functions as a key representative of the performance of Sparse Matrix-Vector (SpMV) multiplication.
The traditional PageRank implementation generates fine granularity random memory accesses resulting in large amount of wasteful DRAM traffic and poor bandwidth utilization.
In this paper, we present a novel Partition-Centric Processing Methodology (PCPM) to compute PageRank, that drastically reduces the amount of DRAM communication while achieving high sustained memory bandwidth.
PCPM uses a Partition-centric abstraction coupled with the Gather-Apply-Scatter (GAS) programming model.
By carefully examining how a PCPM based implementation impacts communication characteristics of the algorithm, we propose several system optimizations that improve the execution time substantially.
More specifically, we develop (1) a new data layout that significantly reduces communication and random DRAM accesses, and (2) branch avoidance mechanisms to get rid of unpredictable data-dependent branches.
We perform detailed analytical and experimental evaluation of our approach using 6 large graphs and demonstrate an average 2.7x speedup in execution time and 1.7x reduction in communication volume, compared to the state-of-the-art.
We also show that unlike other GAS based implementations, PCPM is able to further reduce main memory traffic by taking advantage of intelligent node labeling that enhances locality.
Although we use PageRank as the target application in this paper, our approach can be applied to generic SpMV computation.
In one-way quantum computation (1WQC) model, universal quantum computations are performed using measurements to designated qubits in a highly entangled state.
The choices of bases for these measurements as well as the structure of the entanglements specify a quantum algorithm.
As scalable and reliable quantum computers have not been implemented yet, quantum computation simulators are the only widely available tools to design and test quantum algorithms.
However, simulating the quantum computations on a standard classical computer in most cases requires exponential memory and time.
In this paper, a general direct simulator for 1WQC, called OWQS, is presented.
Some techniques such as qubit elimination, pattern reordering and implicit simulation of actions are used to considerably reduce the time and memory needed for the simulations.
Moreover, our simulator is adjusted to simulate the measurement patterns with a generalized flow without calculating the measurement probabilities which is called extended one-way quantum computation simulator (EOWQS).
Experimental results validate the feasibility of the proposed simulators and that OWQS and EOWQS are faster as compared with the well-known quantum circuit simulators, i.e., QuIDDPro and libquantum for simulating 1WQC model.
Grid computing has attracted many researchers over a few years, and as a result many new protocols have emerged and also evolved since its inception a decade ago.
Grid protocols play major role in implementing services that facilitate coordinated resource sharing across diverse organizations.
In this paper, we provide comprehensive coverage of different core Grid protocols that can be used in Global Grid Computing.
We establish the classification of core Grid protocols into i) Grid network communication and Grid data transfer protocols, ii) Grid information security protocols, iii) Grid resource information protocols, iv) Grid management protocols, and v) Grid interface protocols, depending upon the kind of activities handled by these protocols.
All the classified protocols are also organized into layers of the Hourglass model of Grid architecture to understand dependency among these protocols.
We also present the characteristics of each protocol.
For better understanding of these protocols, we also discuss applied protocols as examples from either Globus toolkit or other popular Grid middleware projects.
We believe that our classification and characterization of Grid protocols will enable better understanding of core Grid protocols and will motivate further research in the area of Global Grid Computing.
Organizations and teams collect and acquire data from various sources, such as social interactions, financial transactions, sensor data, and genome sequencers.
Different teams in an organization as well as different data scientists within a team are interested in extracting a variety of insights which require combining and collaboratively analyzing datasets in diverse ways.
DataHub is a system that aims to provide robust version control and provenance management for such a scenario.
To be truly useful for collaborative data science, one also needs the ability to specify queries and analysis tasks over the versioning and the provenance information in a unified manner.
In this paper, we present an initial design of our query language, called VQuel, that aims to support such unified querying over both types of information, as well as the intermediate and final results of analyses.
We also discuss some of the key language design and implementation challenges moving forward.
This article reports on an exploratory case study conducted to examine the viability of Second Life (SL) as an environment for physical simulations and microworlds.
It begins by discussing specific features of the SL environment relevant to its use as a support for microworlds and simulations as well as a few differences found between SL and traditional simulators such as Modellus, along with their implications to simulations, as a support for subsequent analysis.
Afterwards, we will use Narayanasamy et al. and Johnston and Whitehead criteria to analyze the SL environment and determine into which of training simulators, games, simulation games, or serious games categories SL fits best.
We conclude that SL shows itself as a huge and sophisticated simulator of an entire Earthlike world used by thousands of users to simulate real life in some sense and a viable and flexible platform for microworlds and simulations.
Eigenvector localization refers to the situation when most of the components of an eigenvector are zero or near-zero.
This phenomenon has been observed on eigenvectors associated with extremal eigenvalues, and in many of those cases it can be meaningfully interpreted in terms of "structural heterogeneities" in the data.
For example, the largest eigenvectors of adjacency matrices of large complex networks often have most of their mass localized on high-degree nodes; and the smallest eigenvectors of the Laplacians of such networks are often localized on small but meaningful community-like sets of nodes.
Here, we describe localization associated with low-order eigenvectors, i.e., eigenvectors corresponding to eigenvalues that are not extremal but that are "buried" further down in the spectrum.
Although we have observed it in several unrelated applications, this phenomenon of low-order eigenvector localization defies common intuitions and simple explanations, and it creates serious difficulties for the applicability of popular eigenvector-based machine learning and data analysis tools.
After describing two examples where low-order eigenvector localization arises, we present a very simple model that qualitatively reproduces several of the empirically-observed results.
This model suggests certain coarse structural similarities among the seemingly-unrelated applications where we have observed low-order eigenvector localization, and it may be used as a diagnostic tool to help extract insight from data graphs when such low-order eigenvector localization is present.
This paper introduces deep neural networks (DNNs) as add-on blocks to baseline feedback control systems to enhance tracking performance of arbitrary desired trajectories.
The DNNs are trained to adapt the reference signals to the feedback control loop.
The goal is to achieve a unity map between the desired and the actual outputs.
In previous work, the efficacy of this approach was demonstrated on quadrotors; on 30 unseen test trajectories, the proposed DNN approach achieved an average impromptu tracking error reduction of 43% as compared to the baseline feedback controller.
Motivated by these results, this work aims to provide platform-independent design guidelines for the proposed DNN-enhanced control architecture.
In particular, we provide specific guidelines for the DNN feature selection, derive conditions for when the proposed approach is effective, and show in which cases the training efficiency can be further increased.
An important disadvantage of the h-index is that typically it cannot take into account the specific field of research of a researcher.
Usually sample point estimates of the average and median h-index values for the various fields are reported that are highly variable and dependent of the specific samples and it would be useful to provide confidence intervals of prediction accuracy.
In this paper we apply the non-parametric bootstrap technique for constructing confidence intervals for the h-index for different fields of research.
In this way no specific assumptions about the distribution of the empirical hindex are required as well as no large samples since that the methodology is based on resampling from the initial sample.
The results of the analysis showed important differences between the various fields.
The performance of the bootstrap intervals for the mean and median h-index for most fields seems to be rather satisfactory as revealed by the performed simulation.
Random fields are useful mathematical objects in the characterization of non-deterministic complex systems.
A fundamental issue in the evolution of dynamical systems is how intrinsic properties of such structures change in time.
In this paper, we propose to quantify how changes in the spatial dependence structure affect the Riemannian metric tensor that equips the model's parametric space.
Defining Fisher curves, we measure the variations in each component of the metric tensor when visiting different entropic states of the system.
Simulations show that the geometric deformations induced by the metric tensor in case of a decrease in the inverse temperature are not reversible for an increase of the same amount, provided there is significant variation in the system entropy: the process of taking a system from a lower entropy state A to a higher entropy state B and then bringing it back to A, induces a natural intrinsic one-way direction of evolution.
In this context, Fisher curves resemble mathematical models of hysteresis in which the natural orientation is pointed by an arrow of time.
We present 3DTouch, a novel 3D wearable input device worn on the fingertip for 3D manipulation tasks.
3DTouch is designed to fill the missing gap of a 3D input device that is self-contained, mobile, and universally working across various 3D platforms.
This paper presents a low-cost solution to designing and implementing such a device.
Our approach relies on relative positioning technique using an optical laser sensor and a 9-DOF inertial measurement unit.
3DTouch is self-contained, and designed to universally work on various 3D platforms.
The device employs touch input for the benefits of passive haptic feedback, and movement stability.
On the other hand, with touch interaction, 3DTouch is conceptually less fatiguing to use over many hours than 3D spatial input devices.
We propose a set of 3D interaction techniques including selection, translation, and rotation using 3DTouch.
An evaluation also demonstrates the device's tracking accuracy of 1.10 mm and 2.33 degrees for subtle touch interaction in 3D space.
Modular solutions like 3DTouch opens up a whole new design space for interaction techniques to further develop on.
Deep Linking is the process of referring to a specific piece of web content.
Although users can browse their files in desktop environments, they are unable to directly traverse deeper into their content using deep links.
In order to solve this issue, we demonstrate "DeepLinker", a tool which generates and interprets deep links to desktop resources, thus enabling the reference to a certain location within a file using a simple hyperlink.
By default, the service responds with an HTML representation of the resource along with further links to follow.
Additionally, we allow the use of RDF to interlink our deep links with other resources.
Well known in the theory of network flows, Braess paradox states that in a congested network, it may happen that adding a new path between destinations can increase the level of congestion.
In transportation networks the phenomenon results from the decisions of network participants who selfishly seek to optimize their own performance metrics.
In an electric power distribution network, an analogous increase in congestion can arise as a consequence Kirchhoff's laws.
Even for the simplest linear network of resistors and voltage sources, the sudden appearance of congestion due to an additional conductive line is a nonlinear phenomenon that results in a discontinuous change in the network state.
It is argued that the phenomenon can occur in almost any grid in which they are loops, and with the increasing penetration of small-scale distributed generation it suggests challenges ahead in the operation of microgrids.
This paper is devoted to the online dominating set problem and its variants.
We believe the paper represents the first systematic study of the effect of two limitations of online algorithms: making irrevocable decisions while not knowing the future, and being incremental, i.e., having to maintain solutions to all prefixes of the input.
This is quantified through competitive analyses of online algorithms against two optimal algorithms, both knowing the entire input, but only one having to be incremental.
We also consider the competitive ratio of the weaker of the two optimal algorithms against the other.
We consider important graph classes, distinguishing between connected and not necessarily connected graphs.
For the classic graph classes of trees, bipartite, planar, and general graphs, we obtain tight results in almost all cases.
We also derive upper and lower bounds for the class of bounded-degree graphs.
From these analyses, we get detailed information regarding the significance of the necessary requirement that online algorithms be incremental.
In some cases, having to be incremental fully accounts for the online algorithm's disadvantage.
In this paper we consider cryptographic applications of the arithmetic on the hyperoctahedral group.
On an appropriate subgroup of the latter, we particularly propose to construct public key cryptosystems based on the discrete logarithm.
The fact that the group of signed permutations has rich properties provides fast and easy implementation and makes these systems resistant to attacks like the Pohlig-Hellman algorithm.
The only negative point is that storing and transmitting permutations need large memory.
Using together the hyperoctahedral enumeration system and what is called subexceedant functions, we define a one-to-one correspondance between natural numbers and signed permutations with which we label the message units.
Robot manipulation is increasingly poised to interact with humans in co-shared workspaces.
Despite increasingly robust manipulation and control algorithms, failure modes continue to exist whenever models do not capture the dynamics of the unstructured environment.
To obtain longer-term horizons in robot automation, robots must develop introspection and recovery abilities.
We contribute a set of recovery policies to deal with anomalies produced by external disturbances as well as anomaly classification through the use of non-parametric statistics with memoized variational inference with scalable adaptation.
A recovery critic stands atop of a tightly-integrated, graph-based online motion-generation and introspection system that resolves a wide range of anomalous situations.
Policies, skills, and introspection models are learned incrementally and contextually in a task.
Two task-level recovery policies: re-enactment and adaptation resolve accidental and persistent anomalies respectively.
The introspection system uses non-parametric priors along with Markov jump linear systems and memoized variational inference with scalable adaptation to learn a model from the data.
Extensive real-robot experimentation with various strenuous anomalous conditions is induced and resolved at different phases of a task and in different combinations.
The system executes around-the-clock introspection and recovery and even elicited self-recovery when misclassifications occurred.
This paper proposes three simple, compact yet effective representations of depth sequences, referred to respectively as Dynamic Depth Images (DDI), Dynamic Depth Normal Images (DDNI) and Dynamic Depth Motion Normal Images (DDMNI), for both isolated and continuous action recognition.
These dynamic images are constructed from a segmented sequence of depth maps using hierarchical bidirectional rank pooling to effectively capture the spatial-temporal information.
Specifically, DDI exploits the dynamics of postures over time and DDNI and DDMNI exploit the 3D structural information captured by depth maps.
Upon the proposed representations, a ConvNet based method is developed for action recognition.
The image-based representations enable us to fine-tune the existing Convolutional Neural Network (ConvNet) models trained on image data without training a large number of parameters from scratch.
The proposed method achieved the state-of-art results on three large datasets, namely, the Large-scale Continuous Gesture Recognition Dataset (means Jaccard index 0.4109), the Large-scale Isolated Gesture Recognition Dataset (59.21%), and the NTU RGB+D Dataset (87.08% cross-subject and 84.22% cross-view) even though only the depth modality was used.
Within the Semantic Web community, SPARQL is one of the predominant languages to query and update RDF knowledge.
However, the complexity of SPARQL, the underlying graph structure and various encodings are common sources of confusion for Semantic Web novices.
In this paper we present a general purpose approach to convert any given SPARQL endpoint into a simple to use REST API.
To lower the initial hurdle, we represent the underlying graph as an interlinked view of nested JSON objects that can be traversed by the API path.
Neural network-based Open-ended conversational agents automatically generate responses based on predictive models learned from a large number of pairs of utterances.
The generated responses are typically acceptable as a sentence but are often dull, generic, and certainly devoid of any emotion.
In this paper, we present neural models that learn to express a given emotion in the generated response.
We propose four models and evaluate them against 3 baselines.
An encoder-decoder framework-based model with multiple attention layers provides the best overall performance in terms of expressing the required emotion.
While it does not outperform other models on all emotions, it presents promising results in most cases.
A lot of progress has been made to solve the depth estimation problem in stereo vision.
Though, a very satisfactory performance is observed by utilizing the deep learning in supervised manner for depth estimation.
This approach needs huge amount of ground truth training data as well as depth maps which is very laborious to prepare and many times it is not available in real scenario.
Thus, the unsupervised depth estimation is the recent trend by utilizing the binocular stereo images to get rid of depth map ground truth.
In unsupervised depth computation, the disparity images are generated by training the CNN with an image reconstruction loss based on the epipolar geometry constraints.
The effective way of using CNN as well as investigating the better losses for the said problem needs to be addressed.
In this paper, a dual CNN based model is presented for unsupervised depth estimation with 6 losses (DNM6) with individual CNN for each view to generate the corresponding disparity map.
The proposed dual CNN model is also extended with 12 losses (DNM12) by utilizing the cross disparities.
The presented DNM6 and DNM12 models are experimented over KITTI driving and Cityscapes urban database and compared with the recent state-of-the-art result of unsupervised depth estimation.
We explore recurrent encoder multi-decoder neural network architectures for semi-supervised sequence classification and reconstruction.
We find that the use of multiple reconstruction modules helps models generalize in a classification task when only a small amount of labeled data is available, which is often the case in practice.
Such models provide useful high-level representations of motions allowing clustering, searching and faster labeling of new sequences.
We also propose a new, realistic partitioning of a well-known, high quality motion-capture dataset for better evaluations.
We further explore a novel formulation for future-predicting decoders based on conditional recurrent generative adversarial networks, for which we propose both soft and hard constraints for transition generation derived from desired physical properties of synthesized future movements and desired animation goals.
We find that using such constraints allow to stabilize the training of recurrent adversarial architectures for animation generation.
We study a general class of dynamic multi-agent decision problems with asymmetric information and non-strategic agents, which includes dynamic teams as a special case.
When agents are non-strategic, an agent's strategy is known to the other agents.
Nevertheless, the agents' strategy choices and beliefs are interdependent over times, a phenomenon known as signaling.
We introduce the notions of private information that effectively compresses the agents' information in a mutually consistent manner.
Based on the notions of sufficient information, we propose an information state for each agent that is sufficient for decision making purposes.
We present instances of dynamic multi-agent decision problems where we can determine an information state with a time-invariant domain for each agent.
Furthermore, we present a generalization of the policy-independence property of belief in Partially Observed Markov Decision Processes (POMDP) to dynamic multi-agent decision problems.
Within the context of dynamic teams with asymmetric information, the proposed set of information states leads to a sequential decomposition that decouples the interdependence between the agents' strategies and beliefs over time, and enables us to formulate a dynamic program to determine a globally optimal policy via backward induction.
An enduring issue in higher education is student retention to successful graduation.
National statistics indicate that most higher education institutions have four-year degree completion rates around 50 percent, or just half of their student populations.
While there are prediction models which illuminate what factors assist with college student success, interventions that support course selections on a semester-to-semester basis have yet to be deeply understood.
To further this goal, we develop a system to predict students' grades in the courses they will enroll in during the next enrollment term by learning patterns from historical transcript data coupled with additional information about students, courses and the instructors teaching them.
We explore a variety of classic and state-of-the-art techniques which have proven effective for recommendation tasks in the e-commerce domain.
In our experiments, Factorization Machines (FM), Random Forests (RF), and the Personalized Multi-Linear Regression model achieve the lowest prediction error.
Application of a novel feature selection technique is key to the predictive success and interpretability of the FM.
By comparing feature importance across populations and across models, we uncover strong connections between instructor characteristics and student performance.
We also discover key differences between transfer and non-transfer students.
Ultimately we find that a hybrid FM-RF method can be used to accurately predict grades for both new and returning students taking both new and existing courses.
Application of these techniques holds promise for student degree planning, instructor interventions, and personalized advising, all of which could improve retention and academic performance.
Sentences with gapping, such as Paul likes coffee and Mary tea, lack an overt predicate to indicate the relation between two or more arguments.
Surface syntax representations of such sentences are often produced poorly by parsers, and even if correct, not well suited to downstream natural language understanding tasks such as relation extraction that are typically designed to extract information from sentences with canonical clause structure.
In this paper, we present two methods for parsing to a Universal Dependencies graph representation that explicitly encodes the elided material with additional nodes and edges.
We find that both methods can reconstruct elided material from dependency trees with high accuracy when the parser correctly predicts the existence of a gap.
We further demonstrate that one of our methods can be applied to other languages based on a case study on Swedish.
Recently, deep convolutional neural network (DCNN) achieved increasingly remarkable success and rapidly developed in the field of natural image recognition.
Compared with the natural image, the scale of remote sensing image is larger and the scene and the object it represents are more macroscopic.
This study inquires whether remote sensing scene and natural scene recognitions differ and raises the following questions: What are the key factors in remote sensing scene recognition?
Is the DCNN recognition mechanism centered on object recognition still applicable to the scenarios of remote sensing scene understanding?
We performed several experiments to explore the influence of the DCNN structure and the scale of remote sensing scene understanding from the perspective of scene complexity.
Our experiment shows that understanding a complex scene depends on an in-depth network and multiple-scale perception.
Using a visualization method, we qualitatively and quantitatively analyze the recognition mechanism in a complex remote sensing scene and demonstrate the importance of multi-objective joint semantic support.
Our paper is research in progress that is research investigating the use of games technology to enhance the learning of a physical skill.
The Microsoft Kinect is a system designed for gaming with the capability to track the movement of users.
Our research explored whether such a system could be used to provide feedback when teaching sign vocabulary.
Whilst there are technologies available for teaching sign language, currently none provide feedback on the accuracy of the users' attempts at making signs.
In this paper we report how the three-dimensional dsplay capability of the technology can enhance the users' experience.
Also, when using tracking to identify errors in physical movements, how and when should feedback be given.
A design science approach was undertaken to find a solution to this real world problem.
The design and implementation of the solution provides interesting insights into how technology can not only emulate but also improve upon traditional learning of physical skills.
With the success of deep learning techniques in a broad range of application domains, many deep learning software frameworks have been developed and are being updated frequently to adapt to new hardware features and software libraries, which bring a big challenge for end users and system administrators.
To address this problem, container techniques are widely used to simplify the deployment and management of deep learning software.
However, it remains unknown whether container techniques bring any performance penalty to deep learning applications.
The purpose of this work is to systematically evaluate the impact of docker container on the performance of deep learning applications.
We first benchmark the performance of system components (IO, CPU and GPU) in a docker container and the host system and compare the results to see if there's any difference.
According to our results, we find that computational intensive jobs, either running on CPU or GPU, have small overhead indicating docker containers can be applied to deep learning programs.
Then we evaluate the performance of some popular deep learning tools deployed in a docker container and the host system.
It turns out that the docker container will not cause noticeable drawbacks while running those deep learning tools.
So encapsulating deep learning tool in a container is a feasible solution.
Most learning algorithms require the practitioner to manually set the values of many hyperparameters before the learning process can begin.
However, with modern algorithms, the evaluation of a given hyperparameter setting can take a considerable amount of time and the search space is often very high-dimensional.
We suggest using a lower-dimensional representation of the original data to quickly identify promising areas in the hyperparameter space.
This information can then be used to initialize the optimization algorithm for the original, higher-dimensional data.
We compare this approach with the standard procedure of optimizing the hyperparameters only on the original input.
We perform experiments with various state-of-the-art hyperparameter optimization algorithms such as random search, the tree of parzen estimators (TPEs), sequential model-based algorithm configuration (SMAC), and a genetic algorithm (GA).
Our experiments indicate that it is possible to speed up the optimization process by using lower-dimensional data representations at the beginning, while increasing the dimensionality of the input later in the optimization process.
This is independent of the underlying optimization procedure, making the approach promising for many existing hyperparameter optimization algorithms.
Learning controllers for bipedal robots is a challenging problem, often requiring expert knowledge and extensive tuning of parameters that vary in different situations.
Recently, deep reinforcement learning has shown promise at automatically learning controllers for complex systems in simulation.
This has been followed by a push towards learning controllers that can be transferred between simulation and hardware, primarily with the use of domain randomization.
However, domain randomization can make the problem of finding stable controllers even more challenging, especially for underactuated bipedal robots.
In this work, we explore whether policies learned in simulation can be transferred to hardware with the use of high-fidelity simulators and structured controllers.
We learn a neural network policy which is a part of a more structured controller.
While the neural network is learned in simulation, the rest of the controller stays fixed, and can be tuned by the expert as needed.
We show that using this approach can greatly speed up the rate of learning in simulation, as well as enable transfer of policies between simulation and hardware.
We present our results on an ATRIAS robot and explore the effect of action spaces and cost functions on the rate of transfer between simulation and hardware.
Our results show that structured policies can indeed be learned in simulation and implemented on hardware successfully.
This has several advantages, as the structure preserves the intuitive nature of the policy, and the neural network improves the performance of the hand-designed policy.
In this way, we propose a way of using neural networks to improve expert designed controllers, while maintaining ease of understanding.
Spectrum sensing is the challenge for cognitive radio design and implementation, which allows the secondary user to access the primary bands without interference with primary users.
Cognitive radios should decide on the best spectrum band to meet the Quality of service requirements over all available spectrum bands.
This paper investigates the integrated centralized spectrum sensing techniques in multipath fading environment and the performance was analyzed with energy detection and wavelet based sensing techniques for unknown signal.
Keywords: Cognitive Radio, Spectrum Sensing, Signal Detection, Primary User, Secondary User
We propose a regularized zero-forcing transmit precoding (RZF-TPC) aided and distance-based adaptive coding and modulation (ACM) scheme to support aeronautical communication applications, by exploiting the high spectral efficiency of large-scale antenna arrays and link adaption.
Our RZF-TPC aided and distance-based ACM scheme switches its mode according to the distance between the communicating aircraft.
We derive the closed-form asymptotic signal-to-interference-plus-noise ratio (SINR) expression of the RZF-TPC for the aeronautical channel, which is Rician, relying on a non-centered channel matrix that is dominated by the deterministic line-of-sight component.
The effects of both realistic channel estimation errors and of the co-channel interference are considered in the derivation of this approximate closed-form SINR formula.
Furthermore, we derive the analytical expression of the optimal regularization parameter that minimizes the mean square detection error.
The achievable throughput expression based on our asymptotic approximate SINR formula is then utilized as the design metric for the proposed RZF-TPC aided and distance-based ACM scheme.
Monte-Carlo simulation results are presented for validating our theoretical analysis as well as for investigating the impact of the key system parameters.
The simulation results closely match the theoretical results.
In the specific example that two communicating aircraft fly at a typical cruising speed of 920 km/h, heading in opposite direction over the distance up to 740 km taking a period of about 24 minutes, the RZF-TPC aided and distance-based ACM is capable of transmitting a total of 77 Gigabyte of data with the aid of 64 transmit antennas and 4 receive antennas, which is significantly higher than that of our previous eigen-beamforming transmit precoding aided and distance-based ACM benchmark.
Dense word embeddings, which encode semantic meanings of words to low dimensional vector spaces have become very popular in natural language processing (NLP) research due to their state-of-the-art performances in many NLP tasks.
Word embeddings are substantially successful in capturing semantic relations among words, so a meaningful semantic structure must be present in the respective vector spaces.
However, in many cases, this semantic structure is broadly and heterogeneously distributed across the embedding dimensions, which makes interpretation a big challenge.
In this study, we propose a statistical method to uncover the latent semantic structure in the dense word embeddings.
To perform our analysis we introduce a new dataset (SEMCAT) that contains more than 6500 words semantically grouped under 110 categories.
We further propose a method to quantify the interpretability of the word embeddings; the proposed method is a practical alternative to the classical word intrusion test that requires human intervention.
This paper introduces the Java Software Evolution Tracker, a visualization and analysis tool that provides practitioners the means to examine the evolution of a software system from a top to bottom perspective, starting with changes in the graphical user interface all the way to source code modifications.
Hierarchical Task Network (HTN) planning uses task decomposition to plan for an executable sequence of actions as a solution to a problem.
In order to reason effectively, an HTN planner needs expressive domain knowledge.
For instance, a simplified HTN planning system such as JSHOP2 uses such expressivity and avoids some task interactions due to the increased complexity of the planning process.
We address the possibility of simplifying the domain representation needed for an HTN planner to find good solutions, especially in real-world domains describing home and building automation environments.
We extend the JSHOP2 planner to reason about task interaction that happens when task's effects are already achieved by other tasks.
The planner then prunes some of the redundant searches that can occur due to the planning process's interleaving nature.
We evaluate the original and our improved planner on two benchmark domains.
We show that our planner behaves better by using simplified domain knowledge and outperforms JSHOP2 in a number of relevant cases.
Many species dream, yet there remain many open research questions in the study of dreams.
The symbolism of dreams and their interpretation is present in cultures throughout history.
Analysis of online data sources for dream interpretation using network science leads to understanding symbolism in dreams and their associated meaning.
In this study, we introduce dream interpretation networks for English, Chinese and Arabic that represent different cultures from various parts of the world.
We analyze communities in these networks, finding that symbols within a community are semantically related.
The central nodes in communities give insight about cultures and symbols in dreams.
The community structure of different networks highlights cultural similarities and differences.
Interconnections between different networks are also identified by translating symbols from different languages into English.
Structural correlations across networks point out relationships between cultures.
Similarities between network communities are also investigated by analysis of sentiment in symbol interpretations.
We find that interpretations within a community tend to have similar sentiment.
Furthermore, we cluster communities based on their sentiment, yielding three main categories of positive, negative, and neutral dream symbols.
We consider peer review in a conference setting where there is typically an overlap between the set of reviewers and the set of authors.
This overlap can incentivize strategic reviews to influence the final ranking of one's own papers.
In this work, we address this problem through the lens of social choice, and present a theoretical framework for strategyproof and efficient peer review.
We first present and analyze an algorithm for reviewer-assignment and aggregation that guarantees strategyproofness and a natural efficiency property called unanimity, when the authorship graph satisfies a simple property.
Our algorithm is based on the so-called partitioning method, and can be thought as a generalization of this method to conference peer review settings.
We then empirically show that the requisite property on the authorship graph is indeed satisfied in the ICLR-17 submission data, and further demonstrate a simple trick to make the partitioning method more practically appealing for conference peer review.
Finally, we complement our positive results with negative theoretical results where we prove that under various ways of strengthening the requirements, it is impossible for any algorithm to be strategyproof and efficient.
We consider the problem of inference in discrete probabilistic models, that is, distributions over subsets of a finite ground set.
These encompass a range of well-known models in machine learning, such as determinantal point processes and Ising models.
Locally-moving Markov chain Monte Carlo algorithms, such as the Gibbs sampler, are commonly used for inference in such models, but their convergence is, at times, prohibitively slow.
This is often caused by state-space bottlenecks that greatly hinder the movement of such samplers.
We propose a novel sampling strategy that uses a specific mixture of product distributions to propose global moves and, thus, accelerate convergence.
Furthermore, we show how to construct such a mixture using semigradient information.
We illustrate the effectiveness of combining our sampler with existing ones, both theoretically on an example model, as well as practically on three models learned from real-world data sets.
A multiple instance dictionary learning method using functions of multiple instances (DL-FUMI) is proposed to address target detection and two-class classification problems with inaccurate training labels.
Given inaccurate training labels, DL-FUMI learns a set of target dictionary atoms that describe the most distinctive and representative features of the true positive class as well as a set of nontarget dictionary atoms that account for the shared information found in both the positive and negative instances.
Experimental results show that the estimated target dictionary atoms found by DL-FUMI are more representative prototypes and identify better discriminative features of the true positive class than existing methods in the literature.
DL-FUMI is shown to have significantly better performance on several target detection and classification problems as compared to other multiple instance learning (MIL) dictionary learning algorithms on a variety of MIL problems.
In this paper technological solutions for improving the quality of video transfer along wireless networks are investigated.
Tools have been developed to allow packets to be duplicated with key frames data.
In the paper we tested video streams with duplication of all frames, with duplication of key frames, and without duplication.
The experiments showed that the best results are obtained by duplication of packages which contain key frames.
The paper also provides an overview of the coefficients describing the dependence of video quality on packet loss and delay variation (network jitter).
We propose a novel scene graph generation model called Graph R-CNN, that is both effective and efficient at detecting objects and their relations in images.
Our model contains a Relation Proposal Network (RePN) that efficiently deals with the quadratic number of potential relations between objects in an image.
We also propose an attentional Graph Convolutional Network (aGCN) that effectively captures contextual information between objects and relations.
Finally, we introduce a new evaluation metric that is more holistic and realistic than existing metrics.
We report state-of-the-art performance on scene graph generation as evaluated using both existing and our proposed metrics.
We consider the previously defined notion of finite-state independence and we focus specifically on normal words.
We characterize finite-state independence of normal words in three different ways, using three different kinds of asynchronous deterministic finite automata with two input tapes containing infinite words.
Based on one of the characterizations we give an algorithm to construct a pair of finite-state independent normal words.
Super point is a special host in network which communicates with lots of other hosts in a certain time period.
The number of hosts contacting with a super point is called as its cardinality.
Cardinality estimating plays important roles in network management and security.
All of existing works focus on how to estimate super point's cardinality under discrete time window.
But discrete time window causes great delay and the accuracy of estimating result is subject to the starting of the window. sliding time window, moving forwarding a small slice every time, offers a more accuracy and timely scale to monitor super point's cardinality.
On the other hand, super point's cardinality estimating under sliding time window is more difficult because it requires an algorithm to record the cardinality incrementally and report them immediately at the end of the sliding duration.
This paper firstly solves this problem by devising a sliding time window available algorithm SRLA.
SRLA records hosts cardinality by a novel structure which could be updated incrementally.
In order to reduce the cardinality estimating time at the end of every sliding time window, SRLA generates a super point candidate list while scanning packets and calculates the cardinality of hosts in the candidate list only.
It also has the ability to run parallel to deal with high speed network in line speed.
This paper gives the way to deploy SRLA on a common GPU.
Experiments on real world traffics which have 40 GB/s bandwidth show that SRLA successfully estimates super point's cardinality within 100 milliseconds under sliding time window when running on a low cost Nvidia GPU, GTX650 with 1 GB memory.
The estimating time of SRLA is much smaller than that of other algorithms which consumes more than 2000 milliseconds under discrete time window.
An important component of achieving language understanding is mastering the composition of sentence meaning, but an immediate challenge to solving this problem is the opacity of sentence vector representations produced by current neural sentence composition models.
We present a method to address this challenge, developing tasks that directly target compositional meaning information in sentence vector representations with a high degree of precision and control.
To enable the creation of these controlled tasks, we introduce a specialized sentence generation system that produces large, annotated sentence sets meeting specified syntactic, semantic and lexical constraints.
We describe the details of the method and generation system, and then present results of experiments applying our method to probe for compositional information in embeddings from a number of existing sentence composition models.
We find that the method is able to extract useful information about the differing capacities of these models, and we discuss the implications of our results with respect to these systems' capturing of sentence information.
We make available for public use the datasets used for these experiments, as well as the generation system.
In fairly elementary terms this paper presents how the theory of preordered fuzzy sets, more precisely quantale-valued preorders on quantale-valued fuzzy sets, is established under the guidance of enriched category theory.
Motivated by several key results from the theory of quantaloid-enriched categories, this paper develops all needed ingredients purely in order-theoretic languages for the readership of fuzzy set theorists, with particular attention paid to fuzzy Galois connections between preordered fuzzy sets.
This paper proposes a simple, yet very effective method to localize dominant foreground objects in an image, to pixel-level precision.
The proposed method 'MASON' (Model-AgnoStic ObjectNess) uses a deep convolutional network to generate category-independent and model-agnostic heat maps for any image.
The network is not explicitly trained for the task, and hence, can be used off-the-shelf in tandem with any other network or task.
We show that this framework scales to a wide variety of images, and illustrate the effectiveness of MASON in three varied application contexts.
The null vector method, based on a simple linear algebraic concept, is proposed as a solution to the phase retrieval problem.
In the case with complex Gaussian random measurement matrices, a non-asymptotic error bound is derived, yielding an asymptotic regime of accurate approximation comparable to that for the spectral vector method.
The number of applications on online mobile application stores is increasing at a rapid rate.
Smart-phones are used by a wide range of people varying in age, and also in the ability to use a smart phone.
With the increasing dependency on smart-phones, the paper aims to determine whether the popular applications on Google Play, the official store for Android applications, can be used by people with vision impairment.
The accessibility of the applications was tested using an external keyboard, and TalkBack, an accessibility tool developed by Google.
It was found that several popular applications on the store were not designed keeping accessibility in mind.
It was observed that there exists a weak positive relationship between the popularity of the application and its accessibility.
A framework is proposed that can be used by developers to improve the accessibility of an application.
The paper also discusses the programming aspects to be considered while developing an Android application, so that the application can be used by sighted as well as visually impaired users.
The abundance of poorly optimized mobile applications coupled with their increasing centrality in our digital lives make a framework for mobile app optimization an imperative.
While tuning strategies for desktop and server applications have a long history, it is difficult to adapt them for use on mobile phones.
Reference inputs which trigger behavior similar to a mobile application's typical are hard to construct.
For many classes of applications the very concept of typical behavior is nonexistent, each user interacting with the application in very different ways.
In contexts like this, optimization strategies need to evaluate their effectiveness against real user input, but doing so online runs the risk of user dissatisfaction when suboptimal optimizations are evaluated.
In this paper we present an iterative compiler which employs a novel capture and replay technique in order to collect real user input and use it later to evaluate different transformations offline.
The proposed mechanism identifies and stores only the set of memory pages needed to replay the most heavily used functions of the application.
At idle periods, this minimal state is combined with different binaries of the application, each one build with different optimizations enabled.
Replaying the targeted functions allows us to evaluate the effectiveness of each set of optimizations for the actual way the user interacts with the application.
For the BEEBS benchmark suite, our approach was able to improve performance by up to 57%, while keeping the slowdown experienced by the user on average at 0.8%.
By focusing only on heavily used functions, we are able to conserve storage space by between two and three orders of magnitude compared to typical capture and replay implementations.
This paper develops efficient algorithms for distributed average consensus with quantized communication using the alternating direction method of multipliers (ADMM).
We first study the effects of probabilistic and deterministic quantizations on a distributed ADMM algorithm.
With probabilistic quantization, this algorithm yields linear convergence to the desired average in the mean sense with a bounded variance.
When deterministic quantization is employed, the distributed ADMM either converges to a consensus or cycles with a finite period after a finite-time iteration.
In the cyclic case, local quantized variables have the same mean over one period and hence each node can also reach a consensus.
We then obtain an upper bound on the consensus error which depends only on the quantization resolution and the average degree of the network.
Finally, we propose a two-stage algorithm which combines both probabilistic and deterministic quantizations.
Simulations show that the two-stage algorithm, without picking small algorithm parameter, has consensus errors that are typically less than one quantization resolution for all connected networks where agents' data can be of arbitrary magnitudes.
This paper proposes a text summarization approach for factual reports using a deep learning model.
This approach consists of three phases: feature extraction, feature enhancement, and summary generation, which work together to assimilate core information and generate a coherent, understandable summary.
We are exploring various features to improve the set of sentences selected for the summary, and are using a Restricted Boltzmann Machine to enhance and abstract those features to improve resultant accuracy without losing any important information.
The sentences are scored based on those enhanced features and an extractive summary is constructed.
Experimentation carried out on several articles demonstrates the effectiveness of the proposed approach.
Source code available at: https://github.com/vagisha-nidhi/TextSummarizer
Sounds are essential to how humans perceive and interact with the world and are captured in recordings and shared on the Internet on a minute-by-minute basis.
These recordings, which are predominantly videos, constitute the largest archive of sounds we know.
However, most of these recordings have undescribed content making necessary methods for automatic sound analysis, indexing and retrieval.
These methods have to address multiple challenges, such as the relation between sounds and language, numerous and diverse sound classes, and large-scale evaluation.
We propose a system that continuously learns from the web relations between sounds and language, improves sound recognition models over time and evaluates its learning competency in the large-scale without references.
We introduce the Never-Ending Learner of Sounds (NELS), a project for continuously learning of sounds and their associated knowledge, available on line in nels.cs.cmu.edu
Now-a-days, speech-based biometric systems such as automatic speaker verification (ASV) are highly prone to spoofing attacks by an imposture.
With recent development in various voice conversion (VC) and speech synthesis (SS) algorithms, these spoofing attacks can pose a serious potential threat to the current state-of-the-art ASV systems.
To impede such attacks and enhance the security of the ASV systems, the development of efficient anti-spoofing algorithms is essential that can differentiate synthetic or converted speech from natural or human speech.
In this paper, we propose a set of novel speech features for detecting spoofing attacks.
The proposed features are computed using alternative frequency-warping technique and formant-specific block transformation of filter bank log energies.
We have evaluated existing and proposed features against several kinds of synthetic speech data from ASVspoof 2015 corpora.
The results show that the proposed techniques outperform existing approaches for various spoofing attack detection task.
The techniques investigated in this paper can also accurately classify natural and synthetic speech as equal error rates (EERs) of 0% have been achieved.
As networks expand in size and complexity, they pose greater administrative and management challenges.
Software Defined Networks (SDN) offer a promising approach to meeting some of these challenges.
In this paper, we propose a policy driven security architecture for securing end to end services across multiple SDN domains.
We develop a language based approach to design security policies that are relevant for securing SDN services and communications.
We describe the policy language and its use in specifying security policies to control the flow of information in a multi-domain SDN.
We demonstrate the specification of fine grained security policies based on a variety of attributes such as parameters associated with users and devices/switches, context information such as location and routing information, and services accessed in SDN as well as security attributes associated with the switches and Controllers in different domains.
An important feature of our architecture is its ability to specify path and flow based security policies, which are significant for securing end to end services in SDNs.
We describe the design and the implementation of our proposed policy based security architecture and demonstrate its use in scenarios involving both intra and inter-domain communications with multiple SDN Controllers.
We analyse the performance characteristics of our architecture as well as discuss how our architecture is able to counteract various security attacks.
The dynamic security policy based approach and the distribution of corresponding security capabilities intelligently as a service layer that enable flow based security enforcement and protection of multitude of network devices against attacks are important contributions of this paper.
Macro-management is an important problem in StarCraft, which has been studied for a long time.
Various datasets together with assorted methods have been proposed in the last few years.
But these datasets have some defects for boosting the academic and industrial research: 1) There're neither standard preprocessing, parsing and feature extraction procedures nor predefined training, validation and test set in some datasets.
2) Some datasets are only specified for certain tasks in macro-management.
3) Some datasets are either too small or don't have enough labeled data for modern machine learning algorithms such as deep neural networks.
So most previous methods are trained with various features, evaluated on different test sets from the same or different datasets, making it difficult to be compared directly.
To boost the research of macro-management in StarCraft, we release a new dataset MSC based on the platform SC2LE.
MSC consists of well-designed feature vectors, pre-defined high-level actions and final result of each match.
We also split MSC into training, validation and test set for the convenience of evaluation and comparison.
Besides the dataset, we propose a baseline model and present initial baseline results for global state evaluation and build order prediction, which are two of the key tasks in macro-management.
Various downstream tasks and analyses of the dataset are also described for the sake of research on macro-management in StarCraft II.
Homepage: https://github.com/wuhuikai/MSC.
Cyberbullying has emerged as an important and growing social problem, wherein people use online social networks and mobile phones to bully victims with offensive text, images, audio and video on a 247 basis.
This paper studies negative user behavior in the Ask.fm social network, a popular new site that has led to many cases of cyberbullying, some leading to suicidal behavior.We examine the occurrence of negative words in Ask.fms question+answer profiles along with the social network of likes of questions+answers.
We also examine properties of users with cutting behavior in this social network.
Defining and measuring internationality as a function of influence diffusion of scientific journals is an open problem.
There exists no metric to rank journals based on the extent or scale of internationality.
Measuring internationality is qualitative, vague, open to interpretation and is limited by vested interests.
With the tremendous increase in the number of journals in various fields and the unflinching desire of academics across the globe to publish in "international" journals, it has become an absolute necessity to evaluate, rank and categorize journals based on internationality.
Authors, in the current work have defined internationality as a measure of influence that transcends across geographic boundaries.
There are concerns raised by the authors about unethical practices reflected in the process of journal publication whereby scholarly influence of a select few are artificially boosted, primarily by resorting to editorial maneuvres.
To counter the impact of such tactics, authors have come up with a new method that defines and measures internationality by eliminating such local effects when computing the influence of journals.
A new metric, Non-Local Influence Quotient(NLIQ) is proposed as one such parameter for internationality computation along with another novel metric, Other-Citation Quotient as the complement of the ratio of self-citation and total citation.
In addition, SNIP and International Collaboration Ratio are used as two other parameters.
We describe in this paper Hydra, an ensemble of convolutional neural networks (CNN) for geospatial land classification.
The idea behind Hydra is to create an initial CNN that is coarsely optimized but provides a good starting pointing for further optimization, which will serve as the Hydra's body.
Then, the obtained weights are fine tuned multiple times to form an ensemble of CNNs that represent the Hydra's heads.
By doing so, we were able to reduce the training time while maintaining the classification performance of the ensemble.
We created ensembles using two state-of-the-art CNN architectures, ResNet and DenseNet, to participate in the Functional Map of the World challenge.
With this approach, we finished the competition in third place.
We also applied the proposed framework to the NWPU-RESISC45 database and achieved the best reported performance so far.
Code and CNN models are available at https://github.com/maups/hydra-fmow
In distributed detection, there does not exist an automatic way of generating optimal decision strategies for non-affine decision functions.
Consequently, in a detection problem based on a non-affine decision function, establishing optimality of a given decision strategy, such as a generalized likelihood ratio test, is often difficult or even impossible.
In this thesis we develop a novel detection network optimization technique that can be used to determine necessary and sufficient conditions for optimality in distributed detection for which the underlying objective function is monotonic and convex in probabilistic decision strategies.
Our developed approach leverages on basic concepts of optimization and statistical inference which are provided in sufficient detail.
These basic concepts are combined to form the basis of an optimal inference technique for signal detection.
We prove a central theorem that characterizes optimality in a variety of distributed detection architectures.
We discuss three applications of this result in distributed signal detection.
These applications include interactive distributed detection, optimal tandem fusion architecture, and distributed detection by acyclic graph networks.
In the conclusion we indicate several future research directions, which include possible generalizations of our optimization method and new research problems arising from each of the three applications considered.
Online social networks (OSN) contain extensive amount of information about the underlying society that is yet to be explored.
One of the most feasible technique to fetch information from OSN, crawling through Application Programming Interface (API) requests, poses serious concerns over the the guarantees of the estimates.
In this work, we focus on making reliable statistical inference with limited API crawls.
Based on regenerative properties of the random walks, we propose an unbiased estimator for the aggregated sum of functions over edges and proved the connection between variance of the estimator and spectral gap.
In order to facilitate Bayesian inference on the true value of the estimator, we derive the approximate posterior distribution of the estimate.
Later the proposed ideas are validated with numerical experiments on inference problems in real-world networks.
For pattern recognition like image recognition, it has become clear that each machine-learning dictionary data actually became data in probability space belonging to Euclidean space.
However, the distances in the Euclidean space and the distances in the probability space are separated and ununified when machine learning is introduced in the pattern recognition.
There is still a problem that it is impossible to directly calculate an accurate matching relation between the sampling data of the read image and the learned dictionary data.
In this research, we focused on the reason why the distance is changed and the extent of change when passing through the probability space from the original Euclidean distance among data belonging to multiple probability spaces containing Euclidean space.
By finding the reason of the cause of the distance error and finding the formula expressing the error quantitatively, a possible distance formula to unify Euclidean space and probability space is found.
Based on the results of this research, the relationship between machine-learning dictionary data and sampling data was clearly understood for pattern recognition.
As a result, the calculation of collation among data and machine-learning to compete mutually between data are cleared, and complicated calculations became unnecessary.
Finally, using actual pattern recognition data, experimental demonstration of a possible distance formula to unify Euclidean space and probability space discovered by this research was carried out, and the effectiveness of the result was confirmed.
This paper analyzes irrelevance and independence relations in graphical models associated with convex sets of probability distributions (called Quasi-Bayesian networks).
The basic question in Quasi-Bayesian networks is, How can irrelevance/independence relations in Quasi-Bayesian networks be detected, enforced and exploited?
This paper addresses these questions through Walley's definitions of irrelevance and independence.
Novel algorithms and results are presented for inferences with the so-called natural extensions using fractional linear programming, and the properties of the so-called type-1 extensions are clarified through a new generalization of d-separation.
We present a real-time method for synthesizing highly complex human motions using a novel training regime we call the auto-conditioned Recurrent Neural Network (acRNN).
Recently, researchers have attempted to synthesize new motion by using autoregressive techniques, but existing methods tend to freeze or diverge after a couple of seconds due to an accumulation of errors that are fed back into the network.
Furthermore, such methods have only been shown to be reliable for relatively simple human motions, such as walking or running.
In contrast, our approach can synthesize arbitrary motions with highly complex styles, including dances or martial arts in addition to locomotion.
The acRNN is able to accomplish this by explicitly accommodating for autoregressive noise accumulation during training.
Our work is the first to our knowledge that demonstrates the ability to generate over 18,000 continuous frames (300 seconds) of new complex human motion w.r.t. different styles.
A regular Hilberg process is a stationary process that satisfies both a hyperlogarithmic growth of maximal repetition and a power-law growth of topological entropy, which are a kind of dual conditions.
The hyperlogarithmic growth of maximal repetition has been experimentally observed for texts in natural language, whereas the power-law growth of topological entropy implies a vanishing Shannon entropy rate and thus probably does not hold for natural language.
In this paper, we provide a constructive example of regular Hilberg processes, which we call random hierarchical association (RHA) processes.
Our construction does not apply the standard cutting and stacking method.
For the constructed RHA processes, we demonstrate that the expected length of any uniquely decodable code is orders of magnitude larger than the Shannon block entropy of the ergodic component of the RHA process.
Our proposition supplements the classical result by Shields concerning nonexistence of universal redundancy rates.
Euphonic conjunctions (sandhis) form a very important aspect of Sanskrit morphology and phonology.
The traditional and modern methods of studying about euphonic conjunctions in Sanskrit follow different methodologies.
The former involves a rigorous study of the Paninian system embodied in Panini's Ashtadhyayi, while the latter usually involves the study of a few important sandhi rules with the use of examples.
The former is not suitable for beginners, and the latter, not sufficient to gain a comprehensive understanding of the operation of sandhi rules.
This is so since there are not only numerous sandhi rules and exceptions, but also complex precedence rules involved.
The need for a new ontology for sandhi-tutoring was hence felt.
This work presents a comprehensive ontology designed to enable a student-user to learn in stages all about euphonic conjunctions and the relevant aphorisms of Sanskrit grammar and to test and evaluate the progress of the student-user.
The ontology forms the basis of a multimedia sandhi tutor that was given to different categories of users including Sanskrit scholars for extensive and rigorous testing.
Peer assessment is an efficient and effective learning assessment method that has been used widely in diverse fields in higher education.
Despite its many benefits, a fundamental problem in peer assessment is that participants lack the motivation to assess others' work faithfully and fairly.
Non-consensus is a common challenge that makes the reliability of peer assessment a primary concern in practices.
This research proposes a motivation model that uses review deviation and radicalization to identify non-consensus in peer assessment.
The proposed model is implemented as a software module in a peer code review system called EduPCR4.
EduPCR4 is able to monitor this measure and trigger teacher's arbitration when it detects possible non-consensus.
An empirical study conducted in a university-level C programming course showed that the proposed model and its implementation helped to improve the peer assessment practices in many aspects.
Detecting community structures in social networks has gained considerable attention in recent years.
However, lack of prior knowledge about the number of communities, and their overlapping nature have made community detection a challenging problem.
Moreover, many of the existing methods only consider static networks, while most of real world networks are dynamic and evolve over time.
Hence, finding consistent overlapping communities in dynamic networks without any prior knowledge about the number of communities is still an interesting open research problem.
In this paper, we present an overlapping community detection method for dynamic networks called Dynamic Bayesian Overlapping Community Detector (DBOCD).
DBOCD assumes that in every snapshot of network, overlapping parts of communities are dense areas and utilizes link communities instead of common node communities.
Using Recurrent Chinese Restaurant Process and community structure of the network in the last snapshot, DBOCD simultaneously extracts the number of communities and soft community memberships of nodes while maintaining the consistency of communities over time.
We evaluated DBOCD on both synthetic and real dynamic data-sets to assess its ability to find overlapping communities in different types of network evolution.
The results show that DBOCD outperforms the recent state of the art dynamic community detection methods.
ViDaExpert is a tool for visualization and analysis of multidimensional vectorial data.
ViDaExpert is able to work with data tables of "object-feature" type that might contain numerical feature values as well as textual labels for rows (objects) and columns (features).
ViDaExpert implements several statistical methods such as standard and weighted Principal Component Analysis (PCA) and the method of elastic maps (non-linear version of PCA), Linear Discriminant Analysis (LDA), multilinear regression, K-Means clustering, a variant of decision tree construction algorithm.
Equipped with several user-friendly dialogs for configuring data point representations (size, shape, color) and fast 3D viewer, ViDaExpert is a handy tool allowing to construct an interactive 3D-scene representing a table of data in multidimensional space and perform its quick and insightfull statistical analysis, from basic to advanced methods.
Human-human joint-action in short-cycle repetitive handover tasks was investigated for a bottle handover task using a three-fold approach: work-methods field studies in multiple supermarkets, simulation analysis using an ergonomics software package and by conducting an in-house lab experiment on human-human collaboration by re-creating the environment and conditions of a supermarket.
Evaluation included both objective and subjective measures.
Subjective evaluation was done taking a psychological perspective and showcases among other things, the differences in the way a common joint-action is being perceived by individual team partners depending upon their role (giver or receiver).
The proposed approach can provide a systematic method to analyze similar tasks.
Combining the results of all the three analyses, this research gives insight into the science of joint-action for short-cycle repetitive tasks and its implications for human-robot collaborative system design.
The use of drug combinations, termed polypharmacy, is common to treat patients with complex diseases and co-existing conditions.
However, a major consequence of polypharmacy is a much higher risk of adverse side effects for the patient.
Polypharmacy side effects emerge because of drug-drug interactions, in which activity of one drug may change if taken with another drug.
The knowledge of drug interactions is limited because these complex relationships are rare, and are usually not observed in relatively small clinical testing.
Discovering polypharmacy side effects thus remains an important challenge with significant implications for patient mortality.
Here, we present Decagon, an approach for modeling polypharmacy side effects.
The approach constructs a multimodal graph of protein-protein interactions, drug-protein target interactions, and the polypharmacy side effects, which are represented as drug-drug interactions, where each side effect is an edge of a different type.
Decagon is developed specifically to handle such multimodal graphs with a large number of edge types.
Our approach develops a new graph convolutional neural network for multirelational link prediction in multimodal networks.
Decagon predicts the exact side effect, if any, through which a given drug combination manifests clinically.
Decagon accurately predicts polypharmacy side effects, outperforming baselines by up to 69%.
We find that it automatically learns representations of side effects indicative of co-occurrence of polypharmacy in patients.
Furthermore, Decagon models particularly well side effects with a strong molecular basis, while on predominantly non-molecular side effects, it achieves good performance because of effective sharing of model parameters across edge types.
Decagon creates opportunities to use large pharmacogenomic and patient data to flag and prioritize side effects for follow-up analysis.
A major challenge in cyber-threat analysis is combining information from different sources to find the person or the group responsible for the cyber-attack.
It is one of the most important technical and policy challenges in cyber-security.
The lack of ground truth for an individual responsible for an attack has limited previous studies.
In this paper, we take a first step towards overcoming this limitation by building a dataset from the capture-the-flag event held at DEFCON, and propose an argumentation model based on a formal reasoning framework called DeLP (Defeasible Logic Programming) designed to aid an analyst in attributing a cyber-attack.
We build models from latent variables to reduce the search space of culprits (attackers), and show that this reduction significantly improves the performance of classification-based approaches from 37% to 62% in identifying the attacker.
We devise a new formulation for the vertex coloring problem.
Different from other formulations, decision variables are associated with the pairs of vertices.
Consequently, colors will be distinguishable.
Although the objective function is fractional, it can be replaced by a piece-wise linear convex function.
Numerical experiments show that our formulation has significantly good performance for dense graphs.
We present an algorithm for graph based saliency computation that utilizes the underlying dense subgraphs in finding visually salient regions in an image.
To compute the salient regions, the model first obtains a saliency map using random walks on a Markov chain.
Next, k-dense subgraphs are detected to further enhance the salient regions in the image.
Dense subgraphs convey more information about local graph structure than simple centrality measures.
To generate the Markov chain, intensity and color features of an image in addition to region compactness is used.
For evaluating the proposed model, we do extensive experiments on benchmark image data sets.
The proposed method performs comparable to well-known algorithms in salient region detection.
We study the task of image inpainting, which is to fill in the missing region of an incomplete image with plausible contents.
To this end, we propose a learning-based approach to generate visually coherent completion given a high-resolution image with missing components.
In order to overcome the difficulty to directly learn the distribution of high-dimensional image data, we divide the task into inference and translation as two separate steps and model each step with a deep neural network.
We also use simple heuristics to guide the propagation of local textures from the boundary to the hole.
We show that, by using such techniques, inpainting reduces to the problem of learning two image-feature translation functions in much smaller space and hence easier to train.
We evaluate our method on several public datasets and show that we generate results of better visual quality than previous state-of-the-art methods.
Music genre classification is one example of content-based analysis of music signals.
Traditionally, human-engineered features were used to automatize this task and 61% accuracy has been achieved in the 10-genre classification.
However, it's still below the 70% accuracy that humans could achieve in the same task.
Here, we propose a new method that combines knowledge of human perception study in music genre classification and the neurophysiology of the auditory system.
The method works by training a simple convolutional neural network (CNN) to classify a short segment of the music signal.
Then, the genre of a music is determined by splitting it into short segments and then combining CNN's predictions from all short segments.
After training, this method achieves human-level (70%) accuracy and the filters learned in the CNN resemble the spectrotemporal receptive field (STRF) in the auditory system.
The preceding paper constructed tangle machines as diagrammatic models, and illustrated their utility with a number of examples.
The information content of a tangle machine is contained in characteristic quantities associated to equivalence classes of tangle machines, which are called invariants.
This paper constructs invariants of tangle machines.
Chief among these are the prime factorizations of a machine, which are essentially unique.
This is proven using low dimensional topology, through representing a colour-suppressed machine as a diagram for a network of jointly embedded spheres and intervals in 4-space.
The complexity of a tangle machine is defined as its number of prime factors.
Many natural language processing applications use language models to generate text.
These models are typically trained to predict the next word in a sequence, given the previous words and some context such as an image.
However, at test time the model is expected to generate the entire sequence from scratch.
This discrepancy makes generation brittle, as errors may accumulate along the way.
We address this issue by proposing a novel sequence level training algorithm that directly optimizes the metric used at test time, such as BLEU or ROUGE.
On three different tasks, our approach outperforms several strong baselines for greedy generation.
The method is also competitive when these baselines employ beam search, while being several times faster.
Detecting the occlusion from stereo images or video frames is important to many computer vision applications.
Previous efforts focus on bundling it with the computation of disparity or optical flow, leading to a chicken-and-egg problem.
In this paper, we leverage convolutional neural network to liberate the occlusion detection task from the interleaved, traditional calculation framework.
We propose a Symmetric Network (SymmNet) to directly exploit information from an image pair, without estimating disparity or motion in advance.
The proposed network is structurally left-right symmetric to learn the binocular occlusion simultaneously, aimed at jointly improving both results.
The comprehensive experiments show that our model achieves state-of-the-art results on detecting the stereo and motion occlusion.
Synthetic image translation has significant potentials in autonomous transportation systems.
That is due to the expense of data collection and annotation as well as the unmanageable diversity of real-words situations.
The main issue with unpaired image-to-image translation is the ill-posed nature of the problem.
In this work, we propose a novel method for constraining the output space of unpaired image-to-image translation.
We make the assumption that the environment of the source domain is known (e.g. synthetically generated), and we propose to explicitly enforce preservation of the ground-truth labels on the translated images.
We experiment on preserving ground-truth information such as semantic segmentation, disparity, and instance segmentation.
We show significant evidence that our method achieves improved performance over the state-of-the-art model of UNIT for translating images from SYNTHIA to Cityscapes.
The generated images are perceived as more realistic in human surveys and outperforms UNIT when used in a domain adaptation scenario for semantic segmentation.
The problem of distributed rate maximization in multi-channel ALOHA networks is considered.
First, we study the problem of constrained distributed rate maximization, where user rates are subject to total transmission probability constraints.
We propose a best-response algorithm, where each user updates its strategy to increase its rate according to the channel state information and the current channel utilization.
We prove the convergence of the algorithm to a Nash equilibrium in both homogeneous and heterogeneous networks using the theory of potential games.
The performance of the best-response dynamic is analyzed and compared to a simple transmission scheme, where users transmit over the channel with the highest collision-free utility.
Then, we consider the case where users are not restricted by transmission probability constraints.
Distributed rate maximization under uncertainty is considered to achieve both efficiency and fairness among users.
We propose a distributed scheme where users adjust their transmission probability to maximize their rates according to the current network state, while maintaining the desired load on the channels.
We show that our approach plays an important role in achieving the Nash bargaining solution among users.
Sequential and parallel algorithms are proposed to achieve the target solution in a distributed manner.
The efficiencies of the algorithms are demonstrated through both theoretical and simulation results.
System development often involves decisions about how a high-level design is to be implemented using primitives from a low-level platform.
Certain decisions, however, may introduce undesirable behavior into the resulting implementation, possibly leading to a violation of a desired property that has already been established at the design level.
In this paper, we introduce the problem of synthesizing a property-preserving platform mapping: A set of implementation decisions ensuring that a desired property is preserved from a high-level design into a low-level platform implementation.
We provide a formalization of the synthesis problem and propose a technique for synthesizing a mapping based on symbolic constraint search.
We describe our prototype implementation, and a real-world case study demonstrating the application of our technique to synthesizing secure mappings for the popular web authorization protocols OAuth 1.0 and 2.0.
Data-driven workflows, of which IBM's Business Artifacts are a prime exponent, have been successfully deployed in practice, adopted in industrial standards, and have spawned a rich body of research in academia, focused primarily on static analysis.
In previous work, we obtained theoretical results on the verification of a rich model incorporating core elements of IBM's successful Guard-Stage-Milestone (GSM) artifact model.
The results showed decidability of verification of temporal properties of a large class of GSM workflows and established its complexity.
Following up on these results, the present paper reports on the implementation of SpinArt, a practical verifier based on the classical model-checking tool Spin.
The implementation includes nontrivial optimizations and achieves good performance on real-world business process examples.
Our results shed light on the capabilities and limitations of off-the-shelf verifiers in the context of data-driven workflows.
A growing number of applications users daily interact with have to operate in (near) real-time: chatbots, digital companions, knowledge work support systems -- just to name a few.
To perform the services desired by the user, these systems have to analyze user activity logs or explicit user input extremely fast.
In particular, text content (e.g. in form of text snippets) needs to be processed in an information extraction task.
Regarding the aforementioned temporal requirements, this has to be accomplished in just a few milliseconds, which limits the number of methods that can be applied.
Practically, only very fast methods remain, which on the other hand deliver worse results than slower but more sophisticated Natural Language Processing (NLP) pipelines.
In this paper, we investigate and propose methods for real-time capable Named Entity Recognition (NER).
As a first improvement step we address are word variations induced by inflection, for example present in the German language.
Our approach is ontology-based and makes use of several language information sources like Wiktionary.
We evaluated it using the German Wikipedia (about 9.4B characters), for which the whole NER process took considerably less than an hour.
Since precision and recall are higher than with comparably fast methods, we conclude that the quality gap between high speed methods and sophisticated NLP pipelines can be narrowed a bit more without losing too much runtime performance.
This paper studies the problem of reproducible research in remote photoplethysmography (rPPG).
Most of the work published in this domain is assessed on privately-owned databases, making it difficult to evaluate proposed algorithms in a standard and principled manner.
As a consequence, we present a new, publicly available database containing a relatively large number of subjects recorded under two different lighting conditions.
Also, three state-of-the-art rPPG algorithms from the literature were selected, implemented and released as open source free software.
After a thorough, unbiased experimental evaluation in various settings, it is shown that none of the selected algorithms is precise enough to be used in a real-world scenario.
Modern autonomous underwater vehicles (AUVs) have advanced sensing capabilities including sonar, cameras, acoustic communication, and diverse bio-sensors.
Instead of just sensing its environment and storing the data for post-Mission inspection, an AUV could use the collected information to gain an understanding of its environment, and based on this understanding autonomously adapt its behavior to enhance the overall effectiveness of its mission.
Many such tasks are highly computation intensive.
This paper presents the results of a case study that illustrates the effectiveness of an energy-aware, many-core computing architecture to perform on-board path planning within a batteryoperated AUV.
A previously published path planning algorithm was ported onto the SCC, an experimental 48 core single-chip system developed by Intel.
The performance, power, and energy consumption of the application were measured for different numbers of cores and other system parameters.
This case study shows that computation intensive tasks can be executed within an AUV that relies mainly on battery power.
Future plans include the deployment and testing of an SCC system within a Teledyne Webb Research Slocum glider.
Extreme learning machine (ELM), proposed by Huang et al., has been shown a promising learning algorithm for single-hidden layer feedforward neural networks (SLFNs).
Nevertheless, because of the random choice of input weights and biases, the ELM algorithm sometimes makes the hidden layer output matrix H of SLFN not full column rank, which lowers the effectiveness of ELM.
This paper discusses the effectiveness of ELM and proposes an improved algorithm called EELM that makes a proper selection of the input weights and bias before calculating the output weights, which ensures the full column rank of H in theory.
This improves to some extend the learning rate (testing accuracy, prediction accuracy, learning time) and the robustness property of the networks.
The experimental results based on both the benchmark function approximation and real-world problems including classification and regression applications show the good performances of EELM.
Reconstruction of skilled humans sensation and control system often leads to a development of robust control for the robots.
We are developing an unscrewing robot for the automated disassembly which requires a comprehensive control system, but unscrewing experiments with robots are often limited to several conditions.
On the contrary, humans typically have a broad range of screwing experiences and sensations throughout their lives, and we conducted an experiment to find these haptic patterns.
Results show that people apply axial force to the screws to avoid screwdriver slippage (cam-outs), which is one of the key problems during screwing and unscrewing, and this axial force is proportional to the torque which is required for screwing.
We have found that type of the screw head influences the amount of axial force applied.
Using this knowledge an unscrewing robot for the smart disassembly factory RecyBot is developed, and experiments confirm the optimality of the strategy, used by humans.
Finally, a methodology for robust unscrewing algorithm design is presented as a generalization of the findings.
It can seriously speed up the development of the screwing and unscrewing robots and tools.
Topic modeling enables exploration and compact representation of a corpus.
The CaringBridge (CB) dataset is a massive collection of journals written by patients and caregivers during a health crisis.
Topic modeling on the CB dataset, however, is challenging due to the asynchronous nature of multiple authors writing about their health journeys.
To overcome this challenge we introduce the Dynamic Author-Persona topic model (DAP), a probabilistic graphical model designed for temporal corpora with multiple authors.
The novelty of the DAP model lies in its representation of authors by a persona --- where personas capture the propensity to write about certain topics over time.
Further, we present a regularized variational inference algorithm, which we use to encourage the DAP model's personas to be distinct.
Our results show significant improvements over competing topic models --- particularly after regularization, and highlight the DAP model's unique ability to capture common journeys shared by different authors.
In this paper, we address the problem of inferring the layout of complex road scenes given a single camera as input.
To achieve that, we first propose a novel parameterized model of road layouts in a top-view representation, which is not only intuitive for human visualization but also provides an interpretable interface for higher-level decision making.
Moreover, the design of our top-view scene model allows for efficient sampling and thus generation of large-scale simulated data, which we leverage to train a deep neural network to infer our scene model's parameters.
Specifically, our proposed training procedure uses supervised domain-adaptation techniques to incorporate both simulated as well as manually annotated data.
Finally, we design a Conditional Random Field (CRF) that enforces coherent predictions for a single frame and encourages temporal smoothness among video frames.
Experiments on two public data sets show that: (1) Our parametric top-view model is representative enough to describe complex road scenes, (2) The proposed method outperforms baselines trained on manually-annotated or simulated data only, thus getting the best of both, (3) Our CRF is able to generate temporally smoothed while semantically meaningful results.
Deep Learning methods employ multiple processing layers to learn hierarchial representations of data.
They have already been deployed in a humongous number of applications and have produced state-of-the-art results.
Recently with the growth in processing power of computers to be able to do high dimensional tensor calculations, Natural Language Processing (NLP) applications have been given a significant boost in terms of efficiency as well as accuracy.
In this paper, we will take a look at various signal processing techniques and then application of them to produce a speech-to-text system using Deep Recurrent Neural Networks.
Anomaly detection problems (also called change-point detection problems) have been studied in data mining, statistics and computer science over the last several decades (mostly in non-network context) in applications such as medical condition monitoring, weather change detection and speech recognition.
In recent days, however, anomaly detection problems have become increasing more relevant in the context of network science since useful insights for many complex systems in biology, finance and social science are often obtained by representing them via networks.
Notions of local and non-local curvatures of higher-dimensional geometric shapes and topological spaces play a fundamental role in physics and mathematics in characterizing anomalous behaviours of these higher dimensional entities.
However, using curvature measures to detect anomalies in networks is not yet very common.
To this end, a main goal in this paper to formulate and analyze curvature analysis methods to provide the foundations of systematic approaches to find critical components and detect anomalies in networks.
For this purpose, we use two measures of network curvatures which depend on non-trivial global properties, such as distributions of geodesics and higher-order correlations among nodes, of the given network.
Based on these measures, we precisely formulate several computational problems related to anomaly detection in static or dynamic networks, and provide non-trivial computational complexity results for these problems.
This paper must not be viewed as delivering the final word on appropriateness and suitability of specific curvature measures.
Instead, it is our hope that this paper will stimulate and motivate further theoretical or empirical research concerning the exciting interplay between notions of curvatures from network and non-network domains, a much desired goal in our opinion.
Deep Neural Networks (DNNs) are becoming an important tool in modern computing applications.
Accelerating their training is a major challenge and techniques range from distributed algorithms to low-level circuit design.
In this survey, we describe the problem from a theoretical perspective, followed by approaches for its parallelization.
We present trends in DNN architectures and the resulting implications on parallelization strategies.
We then review and model the different types of concurrency in DNNs: from the single operator, through parallelism in network inference and training, to distributed deep learning.
We discuss asynchronous stochastic optimization, distributed system architectures, communication schemes, and neural architecture search.
Based on those approaches, we extrapolate potential directions for parallelism in deep learning.
The abundance of unlicensed spectrum in the 60 GHz band makes it an attractive alternative for future wireless communication systems.
Such systems are expected to provide data transmission rates in the order of multi-gigabits per second in order to satisfy the ever-increasing demand for high rate data communication.
Unfortunately, 60 GHz radio is subject to severe path loss which limits its usability for long-range outdoor communication.
In this work, we propose a multi-hop 60 GHz wireless network for outdoor communication where multiple full-duplex buffered relays are used to extend the communication range while providing end-to-end performance guarantees to the traffic traversing the network.
We provide a cumulative service process characterization for the 60 GHz outdoor propagation channel with self-interference in terms of the moment generating function (MGF) of its channel capacity.
We then use this characterization to compute probabilistic upper bounds on the overall network performance, i.e., total backlog and end-to-end delay.
Furthermore, we study the effect of self-interference on the network performance and propose an optimal power allocation scheme to mitigate its impact in order to enhance network performance.
Finally, we investigate the relation between relay density and network performance under a total power budget constraint.
We show that increasing relay density may have adverse effects on network performance unless self-interference can be kept sufficiently small.
Convolutional neural networks (CNNs) tend to become a standard approach to solve a wide array of computer vision problems.
Besides important theoretical and practical advances in their design, their success is built on the existence of manually labeled visual resources, such as ImageNet.
The creation of such datasets is cumbersome and here we focus on alternatives to manual labeling.
We hypothesize that new resources are of uttermost importance in domains which are not or weakly covered by ImageNet, such as tourism photographs.
We first collect noisy Flickr images for tourist points of interest and apply automatic or weakly-supervised reranking techniques to reduce noise.
Then, we learn domain adapted models with a standard CNN architecture and compare them to a generic model obtained from ImageNet.
Experimental validation is conducted with publicly available datasets, including Oxford5k, INRIA Holidays and Div150Cred.
Results show that low-cost domain adaptation improves results compared to the use of generic models but also compared to strong non-CNN baselines such as triangulation embedding.
While recurrent neural networks have found success in a variety of natural language processing applications, they are general models of sequential data.
We investigate how the properties of natural language data affect an LSTM's ability to learn a nonlinguistic task: recalling elements from its input.
We find that models trained on natural language data are able to recall tokens from much longer sequences than models trained on non-language sequential data.
Furthermore, we show that the LSTM learns to solve the memorization task by explicitly using a subset of its neurons to count timesteps in the input.
We hypothesize that the patterns and structure in natural language data enable LSTMs to learn by providing approximate ways of reducing loss, but understanding the effect of different training data on the learnability of LSTMs remains an open question.
An instance of the maximum mixed graph orientation problem consists of a mixed graph and a collection of source-target vertex pairs.
The objective is to orient the undirected edges of the graph so as to maximize the number of pairs that admit a directed source-target path.
This problem has recently arisen in the study of biological networks, and it also has applications in communication networks.
In this paper, we identify an interesting local-to-global orientation property.
This property enables us to modify the best known algorithms for maximum mixed graph orientation and some of its special structured instances, due to Elberfeld et al.(CPM '11), and obtain improved approximation ratios.
We further proceed by developing an algorithm that achieves an even better approximation guarantee for the general setting of the problem.
Finally, we study several well-motivated variants of this orientation problem.
Authentication of individuals via palmprint based biometric system is becoming very popular due to its reliability as it contains unique and stable features.
In this paper, we present a novel approach for palmprint recognition and its representation.
To extract the palm lines, local thresholding technique Niblack binarization algorithm is adopted.
The endpoints of these lines are determined and a connection is created among them using the Delaunay triangulation thereby generating a distinct topological structure of each palmprint.
Next, we extract different geometric as well as quantitative features from the triangles of the Delaunay triangulation that assist in identifying different individuals.
To ensure that the proposed approach is invariant to rotation and scaling, features were made relative to topological and geometrical structure of the palmprint.
The similarity of the two palmprints is computed using the weighted sum approach and compared with the k-nearest neighbor.
The experimental results obtained reflect the effectiveness of the proposed approach to discriminate between different palmprint images and thus achieved a recognition rate of 90% over large databases.
In this paper, we investigate the use of discourse-aware rewards with reinforcement learning to guide a model to generate long, coherent text.
In particular, we propose to learn neural rewards to model cross-sentence ordering as a means to approximate desired discourse structure.
Empirical results demonstrate that a generator trained with the learned reward produces more coherent and less repetitive text than models trained with cross-entropy or with reinforcement learning with commonly used scores as rewards.
In spite of the theoretical and algorithmic developments for system synthesis in recent years, little effort has been dedicated to quantifying the quality of the specifications used for synthesis.
When dealing with unrealizable specifications, finding the weakest environment assumptions that would ensure realizability is typically a desirable property; in such context the weakness of the assumptions is a major quality parameter.
The question of whether one assumption is weaker than another is commonly interpreted using implication or, equivalently, language inclusion.
However, this interpretation does not provide any further insight into the weakness of assumptions when implication does not hold.
To our knowledge, the only measure that is capable of comparing two formulae in this case is entropy, but even it fails to provide a sufficiently refined notion of weakness in case of GR(1) formulae, a subset of linear temporal logic formulae which is of particular interest in controller synthesis.
In this paper we propose a more refined measure of weakness based on the Hausdorff dimension, a concept that captures the notion of size of the omega-language satisfying a linear temporal logic formula.
We identify the conditions under which this measure is guaranteed to distinguish between weaker and stronger GR(1) formulae.
We evaluate our proposed weakness measure in the context of computing GR(1) assumptions refinements.
Traditional frameworks for dynamic graphs have relied on processing only the stream of edges added into or deleted from an evolving graph, but not any additional related information such as the degrees or neighbor lists of nodes incident to the edges.
In this paper, we propose a new edge sampling framework for big-graph analytics in dynamic graphs which enhances the traditional model by enabling the use of additional related information.
To demonstrate the advantages of this framework, we present a new sampling algorithm, called Edge Sample and Discard (ESD).
It generates an unbiased estimate of the total number of triangles, which can be continuously updated in response to both edge additions and deletions.
We provide a comparative analysis of the performance of ESD against two current state-of-the-art algorithms in terms of accuracy and complexity.
The results of the experiments performed on real graphs show that, with the help of the neighborhood information of the sampled edges, the accuracy achieved by our algorithm is substantially better.
We also characterize the impact of properties of the graph on the performance of our algorithm by testing on several Barabasi-Albert graphs.
When performing data classification over a stream of continuously occurring instances, a key challenge is to develop an open-world classifier that anticipates instances from an unknown class.
Studies addressing this problem, typically called novel class detection, have considered classification methods that reactively adapt to such changes along the stream.
Importantly, they rely on the property of cohesion and separation among instances in feature space.
Instances belonging to the same class are assumed to be closer to each other (cohesion) than those belonging to different classes (separation).
Unfortunately, this assumption may not have large support when dealing with high dimensional data such as images.
In this paper, we address this key challenge by proposing a semisupervised multi-task learning framework called CSIM which aims to intrinsically search for a latent space suitable for detecting labels of instances from both known and unknown classes.
Particularly, we utilize a convolution neural network layer that aids in the learning of a latent feature space suitable for novel class detection.
We empirically measure the performance of CSIM over multiple realworld image datasets and demonstrate its superiority by comparing its performance with existing semi-supervised methods.
We propose a new order preserving bilinear framework that exploits low-resolution video for person detection in a multi-modal setting using deep neural networks.
In this setting cameras are strategically placed such that less robust sensors, e.g. geophones that monitor seismic activity, are located within the field of views (FOVs) of cameras.
The primary challenge is being able to leverage sufficient information from videos where there are less than 40 pixels on targets, while also taking advantage of less discriminative information from other modalities, e.g. seismic.
Unlike state-of-the-art methods, our bilinear framework retains spatio-temporal order when computing the vector outer products between pairs of features.
Despite the high dimensionality of these outer products, we demonstrate that our order preserving bilinear framework yields better performance than recent orderless bilinear models and alternative fusion methods.
Three-dimensional geometric data offer an excellent domain for studying representation learning and generative modeling.
In this paper, we look at geometric data represented as point clouds.
We introduce a deep AutoEncoder (AE) network with state-of-the-art reconstruction quality and generalization ability.
The learned representations outperform existing methods on 3D recognition tasks and enable shape editing via simple algebraic manipulations, such as semantic part editing, shape analogies and shape interpolation, as well as shape completion.
We perform a thorough study of different generative models including GANs operating on the raw point clouds, significantly improved GANs trained in the fixed latent space of our AEs, and Gaussian Mixture Models (GMMs).
To quantitatively evaluate generative models we introduce measures of sample fidelity and diversity based on matchings between sets of point clouds.
Interestingly, our evaluation of generalization, fidelity and diversity reveals that GMMs trained in the latent space of our AEs yield the best results overall.
Mp3 is a very popular audio format and hence it can be a good host for carrying hidden messages.
Therefore, different steganography methods have been proposed for mp3 hosts.
But, current literature has only focused on steganalysis of mp3stego.
In this paper we mention some of the limitations of mp3stego and argue that UnderMp3Cover (Ump3c) does not have those limitations.
Ump3c makes subtle changes only to the global gain of bitstream and keeps the rest of bitstream intact.
Therefore, its detection is much harder than mp3stego.
To address this, joint distributions between global gain and other fields of mp3 bit stream are used.
The changes are detected by measuring the mutual information from those joint distributions.
Furthermore, we show that different mp3 encoders have dissimilar performances.
Consequently, a novel multi-layer architecture for steganalysis of Ump3c is proposed.
In this manner, the first layer detects the encoder and the second layer performs the steganalysis job.
One of advantages of this architecture is that feature extraction and feature selection can be optimized for each encoder separately.
We show this multi-layer architecture outperforms the conventional single-layer methods.
Comparing results of the proposed method with other works shows an improvement of 20.4% in the accuracy of steganalysis.
Natural language generation (NLG) is a critical component in spoken dialogue systems.
Classic NLG can be divided into two phases: (1) sentence planning: deciding on the overall sentence structure, (2) surface realization: determining specific word forms and flattening the sentence structure into a string.
Many simple NLG models are based on recurrent neural networks (RNN) and sequence-to-sequence (seq2seq) model, which basically contains an encoder-decoder structure; these NLG models generate sentences from scratch by jointly optimizing sentence planning and surface realization using a simple cross entropy loss training criterion.
However, the simple encoder-decoder architecture usually suffers from generating complex and long sentences, because the decoder has to learn all grammar and diction knowledge.
This paper introduces a hierarchical decoding NLG model based on linguistic patterns in different levels, and shows that the proposed method outperforms the traditional one with a smaller model size.
Furthermore, the design of the hierarchical decoding is flexible and easily-extensible in various NLG systems.
We motivate and give semantics to theory presentation combinators as the foundational building blocks for a scalable library of theories.
The key observation is that the category of contexts and fibered categories are the ideal theoretical tools for this purpose.
This paper uses a spatial Aloha model to describe a distributed autonomous wireless network in which a group of transmit-receive pairs (users) shares a common collision channel via slotted-Aloha-like random access.
The objective of this study is to develop an intelligent algorithm to be embedded into the transceivers so that all users know how to self-tune their medium access probability (MAP) to achieve overall Pareto optimality in terms of network throughput under spatial reuse while maintaining network stability.
While the optimal solution requires each user to have complete information about the network, our proposed algorithm only requires users to have local information.
The fundamental of our algorithm is that the users will first self-organize into a number of non-overlapping neighborhoods, and the user with the maximum node degree in each neighborhood is elected as the local leader (LL).
Each LL then adjusts its MAP according to a parameter R which indicates the radio intensity level in its neighboring region, whereas the remaining users in the neighborhood simply follow the same MAP value.
We show that by ensuring R less than or equal to 2 at the LLs, the stability of the entire network can be assured even when each user only has partial network information.
For practical implementation, we propose each LL to use R=2 as the constant reference signal to its built-in proportional and integral controller.
The settings of the control parameters are discussed and we validate through simulations that the proposed method is able to achieve close-to-Pareto-front throughput.
In this paper, we present our approach for the 2018 Medico Task classifying diseases in the gastrointestinal tract.
We have proposed a system based on global features and deep neural networks.
The best approach combines two neural networks, and the reproducible experimental results signify the efficiency of the proposed model with an accuracy rate of 95.80%, a precision of 95.87%, and an F1-score of 95.80%.
Recurrent neural networks (RNNs) are the state of the art in sequence modeling for natural language.
However, it remains poorly understood what grammatical characteristics of natural language they implicitly learn and represent as a consequence of optimizing the language modeling objective.
Here we deploy the methods of controlled psycholinguistic experimentation to shed light on to what extent RNN behavior reflects incremental syntactic state and grammatical dependency representations known to characterize human linguistic behavior.
We broadly test two publicly available long short-term memory (LSTM) English sequence models, and learn and test a new Japanese LSTM.
We demonstrate that these models represent and maintain incremental syntactic state, but that they do not always generalize in the same way as humans.
Furthermore, none of our models learn the appropriate grammatical dependency configurations licensing reflexive pronouns or negative polarity items.
Sampling above the Nyquist rate is at the heart of sigma-delta modulation, where the increase in sampling rate is translated to a reduction in the overall (mean-squared-error) reconstruction distortion.
This is attained by using a feedback filter at the encoder, in conjunction with a low-pass filter at the decoder.
The goal of this work is to characterize the optimal trade-off between the per-sample quantization rate and the resulting mean-squared-error distortion, under various restrictions on the feedback filter.
To this end, we establish a duality relation between the performance of sigma-delta modulation, and that of differential pulse-code modulation when applied to (discrete-time) band-limited inputs.
As the optimal trade-off for the latter scheme is fully understood, the full characterization for sigma-delta modulation, as well as the optimal feedback filters, immediately follow.
Learning intents and slot labels from user utterances is a fundamental step in all spoken language understanding (SLU) and dialog systems.
State-of-the-art neural network based methods, after deployment, often suffer from performance degradation on encountering paraphrased utterances, and out-of-vocabulary words, rarely observed in their training set.
We address this challenging problem by introducing a novel paraphrasing based SLU model which can be integrated with any existing SLU model in order to improve their overall performance.
We propose two new paraphrase generators using RNN and sequence-to-sequence based neural networks, which are suitable for our application.
Our experiments on existing benchmark and in house datasets demonstrate the robustness of our models to rare and complex paraphrased utterances, even under adversarial test distributions.
We introduce a saliency-based distortion layer for convolutional neural networks that helps to improve the spatial sampling of input data for a given task.
Our differentiable layer can be added as a preprocessing block to existing task networks and trained altogether in an end-to-end fashion.
The effect of the layer is to efficiently estimate how to sample from the original data in order to boost task performance.
For example, for an image classification task in which the original data might range in size up to several megapixels, but where the desired input images to the task network are much smaller, our layer learns how best to sample from the underlying high resolution data in a manner which preserves task-relevant information better than uniform downsampling.
This has the effect of creating distorted, caricature-like intermediate images, in which idiosyncratic elements of the image that improve task performance are zoomed and exaggerated.
Unlike alternative approaches such as spatial transformer networks, our proposed layer is inspired by image saliency, computed efficiently from uniformly downsampled data, and degrades gracefully to a uniform sampling strategy under uncertainty.
We apply our layer to improve existing networks for the tasks of human gaze estimation and fine-grained object classification.
Code for our method is available in: http://github.com/recasens/Saliency-Sampler
While game theory is widely used to model strategic interactions, a natural question is where do the game representations come from?
One answer is to learn the representations from data.
If one wants to learn both the payoffs and the players' strategies, a naive approach is to learn them both directly from the data.
This approach ignores the fact the players might be playing reasonably good strategies, so there is a connection between the strategies and the data.
The main contribution of this paper is to make this connection while learning.
We formulate the learning problem as a weighted constraint satisfaction problem, including constraints both for the fit of the payoffs and strategies to the data and the fit of the strategies to the payoffs.
We use quantal response equilibrium as our notion of rationality for quantifying the latter fit.
Our results show that incorporating rationality constraints can improve learning when the amount of data is limited.
In this paper, we propose a conceptually simple and geometrically interpretable objective function, i.e. additive margin Softmax (AM-Softmax), for deep face verification.
In general, the face verification task can be viewed as a metric learning problem, so learning large-margin face features whose intra-class variation is small and inter-class difference is large is of great importance in order to achieve good performance.
Recently, Large-margin Softmax and Angular Softmax have been proposed to incorporate the angular margin in a multiplicative manner.
In this work, we introduce a novel additive angular margin for the Softmax loss, which is intuitively appealing and more interpretable than the existing works.
We also emphasize and discuss the importance of feature normalization in the paper.
Most importantly, our experiments on LFW BLUFR and MegaFace show that our additive margin softmax loss consistently performs better than the current state-of-the-art methods using the same network architecture and training dataset.
Our code has also been made available at https://github.com/happynear/AMSoftmax
The secrecy capacity of the type II wiretap channel (WTC II) with a noisy main channel is currently an open problem.
Herein its secrecy-capacity is derived and shown to be equal to its semantic-security (SS) capacity.
In this setting, the legitimate users communicate via a discrete-memoryless (DM) channel in the presence of an eavesdropper that has perfect access to a subset of its choosing of the transmitted symbols, constrained to a fixed fraction of the blocklength.
The secrecy criterion is achieved simultaneously for all possible eavesdropper subset choices.
The SS criterion demands negligible mutual information between the message and the eavesdropper's observations even when maximized over all message distributions.
A key tool for the achievability proof is a novel and stronger version of Wyner's soft covering lemma.
Specifically, a random codebook is shown to achieve the soft-covering phenomenon with high probability.
The probability of failure is doubly-exponentially small in the blocklength.
Since the combined number of messages and subsets grows only exponentially with the blocklength, SS for the WTC II is established by using the union bound and invoking the stronger soft-covering lemma.
The direct proof shows that rates up to the weak-secrecy capacity of the classic WTC with a DM erasure channel (EC) to the eavesdropper are achievable.
The converse follows by establishing the capacity of this DM wiretap EC as an upper bound for the WTC II.
From a broader perspective, the stronger soft-covering lemma constitutes a tool for showing the existence of codebooks that satisfy exponentially many constraints, a beneficial ability for many other applications in information theoretic security.
In this paper we propose a novel approach to tracking by detection that can exploit both cameras as well as LIDAR data to produce very accurate 3D trajectories.
Towards this goal, we formulate the problem as a linear program that can be solved exactly, and learn convolutional networks for detection as well as matching in an end-to-end manner.
We evaluate our model in the challenging KITTI dataset and show very competitive results.
Two-step predictor/corrector methods are provided to solve three classes of problems that present themselves as systems of ordinary differential equations (ODEs).
In the first class, velocities are given from which displacements are to be solved.
In the second class, velocities and accelerations are given from which displacements are to be solved.
And in the third class, accelerations are given from which velocities and displacements are to be solved.
Two-step methods are not self starting, so compatible one-step methods are provided to take that first step with.
An algorithm is presented for controlling the step size so that the local truncation error does not exceed a specified tolerance.
This paper introduces a novel deep learning framework for image animation.
Given an input image with a target object and a driving video sequence depicting a moving object, our framework generates a video in which the target object is animated according to the driving sequence.
This is achieved through a deep architecture that decouples appearance and motion information.
Our framework consists of three main modules: (i) a Keypoint Detector unsupervisely trained to extract object keypoints, (ii) a Dense Motion prediction network for generating dense heatmaps from sparse keypoints, in order to better encode motion information and (iii) a Motion Transfer Network, which uses the motion heatmaps and appearance information extracted from the input image to synthesize the output frames.
We demonstrate the effectiveness of our method on several benchmark datasets, spanning a wide variety of object appearances, and show that our approach outperforms state-of-the-art image animation and video generation methods.
Energy consumption is an important concern in modern multicore processors.
The energy consumed during the execution of an application can be minimized by tuning the hardware state utilizing knobs such as frequency, voltage etc.
The existing theoretical work on energy mini- mization using Global DVFS (Dynamic Voltage and Frequency Scaling), despite being thorough, ignores the energy consumed by the CPU on memory accesses and the dynamic energy consumed by the idle cores.
This article presents an analytical model for the performance and the overall energy consumed by the CPU chip on CPU instructions as well as the memory accesses without ignoring the dynamic energy consumed by the idle cores.
We present an analytical framework around our energy-performance model to predict the operating frequencies for global DVFS that minimize the overall CPU energy consumption within a performance budget.
Finally, we suggest a scheduling criteria for energy aware scheduling of memory intensive parallel applications.
Changes in technology have resulted in new ways for bankers to deliver their services to costumers.
Electronic banking systems in various forms are the evidence of such advancement.
However, information security threats also evolving along this trend.
This paper proposes the application of Analytic Hierarchy Process (AHP) methodology to guide decision makers in banking industries to deal with information security policy.
The model is structured according aspects of information security policy in conjunction with information security elements.
We found that cultural aspect is valued on the top priority among other security aspects, while confidentiality is considered as the most important factor in terms of information security elements.
Cooperative transmission of data fosters rapid accumulation of knowledge by efficiently combining experiences across learners.
Although well studied in human learning and increasingly in machine learning, we lack formal frameworks through which we may reason about the benefits and limitations of cooperative inference.
We present such a framework.
We introduce novel indices for measuring the effectiveness of probabilistic and cooperative information transmission.
We relate our indices to the well-known Teaching Dimension in deterministic settings.
We prove conditions under which optimal cooperative inference can be achieved, including a representation theorem that constrains the form of inductive biases for learners optimized for cooperative inference.
We conclude by demonstrating how these principles may inform the design of machine learning algorithms and discuss implications for human and machine learning.
Acquisition of labeled training samples for affective computing is usually costly and time-consuming, as affects are intrinsically subjective, subtle and uncertain, and hence multiple human assessors are needed to evaluate each affective sample.
Particularly, for affect estimation in the 3D space of valence, arousal and dominance, each assessor has to perform the evaluations in three dimensions, which makes the labeling problem even more challenging.
Many sophisticated machine learning approaches have been proposed to reduce the data labeling requirement in various other domains, but so far few have considered affective computing.
This paper proposes two multi-task active learning for regression approaches, which select the most beneficial samples to label, by considering the three affect primitives simultaneously.
Experimental results on the VAM corpus demonstrated that our optimal sample selection approaches can result in better estimation performance than random selection and several traditional single-task active learning approaches.
Thus, they can help alleviate the data labeling problem in affective computing, i.e., better estimation performance can be obtained from fewer labeling queries.
Symbolic and logic computation systems ranging from computer algebra systems to theorem provers are finding their way into science, technology, mathematics and engineering.
But such systems rely on explicitly or implicitly represented mathematical knowledge that needs to be managed to use such systems effectively.
While mathematical knowledge management (MKM) "in the small" is well-studied, scaling up to large, highly interconnected corpora remains difficult.
We hold that in order to realize MKM "in the large", we need representation languages and software architectures that are designed systematically with large-scale processing in mind.
Therefore, we have designed and implemented the MMT language -- a module system for mathematical theories.
MMT is designed as the simplest possible language that combines a module system, a foundationally uncommitted formal semantics, and web-scalable implementations.
Due to a careful choice of representational primitives, MMT allows us to integrate existing representation languages for formal mathematical knowledge in a simple, scalable formalism.
In particular, MMT abstracts from the underlying mathematical and logical foundations so that it can serve as a standardized representation format for a formal digital library.
Moreover, MMT systematically separates logic-dependent and logic-independent concerns so that it can serve as an interface layer between computation systems and MKM systems.
Neural Style Transfer based on Convolutional Neural Networks (CNN) aims to synthesize a new image that retains the high-level structure of a content image, rendered in the low-level texture of a style image.
This is achieved by constraining the new image to have high-level CNN features similar to the content image, and lower-level CNN features similar to the style image.
However in the traditional optimization objective, low-level features of the content image are absent, and the low-level features of the style image dominate the low-level detail structures of the new image.
Hence in the synthesized image, many details of the content image are lost, and a lot of inconsistent and unpleasing artifacts appear.
As a remedy, we propose to steer image synthesis with a novel loss function: the Laplacian loss.
The Laplacian matrix ("Laplacian" in short), produced by a Laplacian operator, is widely used in computer vision to detect edges and contours.
The Laplacian loss measures the difference of the Laplacians, and correspondingly the difference of the detail structures, between the content image and a new image.
It is flexible and compatible with the traditional style transfer constraints.
By incorporating the Laplacian loss, we obtain a new optimization objective for neural style transfer named Lapstyle.
Minimizing this objective will produce a stylized image that better preserves the detail structures of the content image and eliminates the artifacts.
Experiments show that Lapstyle produces more appealing stylized images with less artifacts, without compromising their "stylishness".
How an information spreads throughout a social network is a valuable knowledge sought by many groups such as marketing enterprises and political parties.
If they can somehow predict the impact of a given message or manipulate it in order to amplify how long it will spread, it would give them a huge advantage over their competitors.
Intuitively, it is expected that two factors contribute to make an information becoming viral: how influential the person who spreads is inside its network and the content of the message.
The former should have a more important role, since people will not just blindly share any content, or will they?
In this work it is found that the degree of a node alone is capable of accurately predicting how many followers of the seed user will spread the information through a simple linear regression.
The analysis was performed with five different messages from Twitter network that was shared with different degrees along the users.
The results show evidences that no matter the content, the number of affected neighbors is predictable.
The role of the content of the messages of a user is likely to influence the network formation and the path the message will follow through the network.
As people become more concerned with the need to conserve their power consumption we need to find ways to inform them of how electricity is being consumed within the home.
There are a number of devices that have been designed using different forms, sizes, and technologies.
We are interested in large ambient displays that can be read at a glance and from a distance as informative art.
However, from these objectives come a number of questions that need to be explored and answered.
To what degree might lifestyle factors influence the design of eco-visualizations?
To answer this we need to ask how people with varying lifestyle factors perceive the utility of such devices and their placement within a home.
We explore these questions by creating four ambient display prototypes.
We take our prototypes and subject them to a user study to gain insight as to the questions posed above.
This paper discusses our prototypes in detail and the results and findings of our user study.
In this note we study the connection between the existence of a projective reconstruction and the existence of a fundamental matrix satisfying the epipolar constraints.
Image cropping aims at improving the aesthetic quality of images by adjusting their composition.
Most weakly supervised cropping methods (without bounding box supervision) rely on the sliding window mechanism.
The sliding window mechanism requires fixed aspect ratios and limits the cropping region with arbitrary size.
Moreover, the sliding window method usually produces tens of thousands of windows on the input image which is very time-consuming.
Motivated by these challenges, we firstly formulate the aesthetic image cropping as a sequential decision-making process and propose a weakly supervised Aesthetics Aware Reinforcement Learning (A2-RL) framework to address this problem.
Particularly, the proposed method develops an aesthetics aware reward function which especially benefits image cropping.
Similar to human's decision making, we use a comprehensive state representation including both the current observation and the historical experience.
We train the agent using the actor-critic architecture in an end-to-end manner.
The agent is evaluated on several popular unseen cropping datasets.
Experiment results show that our method achieves the state-of-the-art performance with much fewer candidate windows and much less time compared with previous weakly supervised methods.
A new transform over finite fields, the finite field Hartley transform (FFHT), was recently introduced and a number of promising applications on the design of efficient multiple access systems and multilevel spread spectrum sequences were proposed.
The FFHT exhibits interesting symmetries, which are exploited to derive tailored fast transform algorithms.
The proposed fast algorithms are based on successive decompositions of the FFHT by means of Hadamard-Walsh transforms (HWT).
The introduced decompositions meet the lower bound on the multiplicative complexity for all the cases investigated.
The complexity of the new algorithms is compared with that of traditional algorithms.
E-commerce users may expect different products even for the same query, due to their diverse personal preferences.
It is well-known that there are two types of preferences: long-term ones and short-term ones.
The former refers to user' inherent purchasing bias and evolves slowly.
By contrast, the latter reflects users' purchasing inclination in a relatively short period.
They both affect users' current purchasing intentions.
However, few research efforts have been dedicated to jointly model them for the personalized product search.
To this end, we propose a novel Attentive Long Short-Term Preference model, dubbed as ALSTP, for personalized product search.
Our model adopts the neural networks approach to learn and integrate the long- and short-term user preferences with the current query for the personalized product search.
In particular, two attention networks are designed to distinguish which factors in the short-term as well as long-term user preferences are more relevant to the current query.
This unique design enables our model to capture users' current search intentions more accurately.
Our work is the first to apply attention mechanisms to integrate both long- and short-term user preferences with the given query for the personalized search.
Extensive experiments over four Amazon product datasets show that our model significantly outperforms several state-of-the-art product search methods in terms of different evaluation metrics.
The Multi-Carrier Code Division Multiple Access (MC-CDMA) is becoming a very significant downlink multiple access technique for high-rate data transmission in the fourth generation wireless communication systems.
By means of efficient resource allocation higher data rate i.e. throughput can be achieved.
This paper evaluates the performance of criteria used for group (subchannel) allocation employed in downlink transmission, which results in throughput maximization.
Proposed algorithm gives the modified technique of sub channel allocation in the downlink transmission of MC-CDMA systems.
Simulation are carried out for all the three combining schemes, results shows that for the given power and BER proposed algorithm comparatively gives far better results
Complex systems are increasingly being viewed as distributed information processing systems, particularly in the domains of computational neuroscience, bioinformatics and Artificial Life.
This trend has resulted in a strong uptake in the use of (Shannon) information-theoretic measures to analyse the dynamics of complex systems in these fields.
We introduce the Java Information Dynamics Toolkit (JIDT): a Google code project which provides a standalone, (GNU GPL v3 licensed) open-source code implementation for empirical estimation of information-theoretic measures from time-series data.
While the toolkit provides classic information-theoretic measures (e.g. entropy, mutual information, conditional mutual information), it ultimately focusses on implementing higher-level measures for information dynamics.
That is, JIDT focusses on quantifying information storage, transfer and modification, and the dynamics of these operations in space and time.
For this purpose, it includes implementations of the transfer entropy and active information storage, their multivariate extensions and local or pointwise variants.
JIDT provides implementations for both discrete and continuous-valued data for each measure, including various types of estimator for continuous data (e.g.Gaussian, box-kernel and Kraskov-Stoegbauer-Grassberger) which can be swapped at run-time due to Java's object-oriented polymorphism.
Furthermore, while written in Java, the toolkit can be used directly in MATLAB, GNU Octave, Python and other environments.
We present the principles behind the code design, and provide several examples to guide users.
This article discusses how the automation of tensor algorithms, based on A Mathematics of Arrays and Psi Calculus, and a new way to represent numbers, Unum Arithmetic, enables mechanically provable, scalable, portable, and more numerically accurate software.
Recurrent Neural Networks (RNNs) continue to show outstanding performance in sequence modeling tasks.
However, training RNNs on long sequences often face challenges like slow inference, vanishing gradients and difficulty in capturing long term dependencies.
In backpropagation through time settings, these issues are tightly coupled with the large, sequential computational graph resulting from unfolding the RNN in time.
We introduce the Skip RNN model which extends existing RNN models by learning to skip state updates and shortens the effective size of the computational graph.
This model can also be encouraged to perform fewer state updates through a budget constraint.
We evaluate the proposed model on various tasks and show how it can reduce the number of required RNN updates while preserving, and sometimes even improving, the performance of the baseline RNN models.
Source code is publicly available at https://imatge-upc.github.io/skiprnn-2017-telecombcn/ .
We address the problem of executing tool-using manipulation skills in scenarios where the objects to be used may vary.
We assume that point clouds of the tool and target object can be obtained, but no interpretation or further knowledge about these objects is provided.
The system must interpret the point clouds and decide how to use the tool to complete a manipulation task with a target object; this means it must adjust motion trajectories appropriately to complete the task.
We tackle three everyday manipulations: scraping material from a tool into a container, cutting, and scooping from a container.
Our solution encodes these manipulation skills in a generic way, with parameters that can be filled in at run-time via queries to a robot perception module; the perception module abstracts the functional parts for the tool and extracts key parameters that are needed for the task.
The approach is evaluated in simulation and with selected examples on a PR2 robot.
Modern websites include various types of third-party content such as JavaScript, images, stylesheets, and Flash objects in order to create interactive user interfaces.
In addition to explicit inclusion of third-party content by website publishers, ISPs and browser extensions are hijacking web browsing sessions with increasing frequency to inject third-party content (e.g., ads).
However, third-party content can also introduce security risks to users of these websites, unbeknownst to both website operators and users.
Because of the often highly dynamic nature of these inclusions as well as the use of advanced cloaking techniques in contemporary malware, it is exceedingly difficult to preemptively recognize and block inclusions of malicious third-party content before it has the chance to attack the user's system.
In this paper, we propose a novel approach to achieving the goal of preemptive blocking of malicious third-party content inclusion through an analysis of inclusion sequences on the Web.
We implemented our approach, called Excision, as a set of modifications to the Chromium browser that protects users from malicious inclusions while web pages load.
Our analysis suggests that by adopting our in-browser approach, users can avoid a significant portion of malicious third-party content on the Web.
Our evaluation shows that Excision effectively identifies malicious content while introducing a low false positive rate.
Our experiments also demonstrate that our approach does not negatively impact a user's browsing experience when browsing popular websites drawn from the Alexa Top 500.
The majority of existing color naming methods focuses on the eleven basic color terms of the English language.
However, in many applications, different sets of color names are used for the accurate description of objects.
Labeling data to learn these domain-specific color names is an expensive and laborious task.
Therefore, in this article we aim to learn color names from weakly labeled data.
For this purpose, we add an attention branch to the color naming network.
The attention branch is used to modulate the pixel-wise color naming predictions of the network.
In experiments, we illustrate that the attention branch correctly identifies the relevant regions.
Furthermore, we show that our method obtains state-of-the-art results for pixel-wise and image-wise classification on the EBAY dataset and is able to learn color names for various domains.
In light of the quick proliferation of Internet of things (IoT) devices and applications, fog radio access network (Fog-RAN) has been recently proposed for fifth generation (5G) wireless communications to assure the requirements of ultra-reliable low-latency communication (URLLC) for the IoT applications which cannot accommodate large delays.
Hence, fog nodes (FNs) are equipped with computing, signal processing and storage capabilities to extend the inherent operations and services of the cloud to the edge.
We consider the problem of sequentially allocating the FN's limited resources to the IoT applications of heterogeneous latency requirements.
For each access request from an IoT user, the FN needs to decide whether to serve it locally utilizing its own resources or to refer it to the cloud to conserve its valuable resources for future users of potentially higher utility to the system (i.e., lower latency requirement).
We formulate the Fog-RAN resource allocation problem in the form of a Markov decision process (MDP), and employ several reinforcement learning (RL) methods, namely Q-learning, SARSA, Expected SARSA, and Monte Carlo, for solving the MDP problem by learning the optimum decision-making policies.
We verify the performance and adaptivity of the RL methods and compare it with the performance of a fixed-threshold-based algorithm.
Extensive simulation results considering 19 IoT environments of heterogeneous latency requirements corroborate that RL methods always achieve the best possible performance regardless of the IoT environment.
In this work, we consider a 1-bit quantized massive MIMO channel with superimposed pilot (SP) scheme, dubbed QSP.
With linear minimum mean square error (LMMSE) channel estimator and maximum ratio combining (MRC) receiver at the BS, we derive an approximate lower bound on the achievable rate.
When optimizing pilot and data powers, the optimal power allocation maximizing the data rate is obtained in a closed-form solution.
Although there is a performance gap between the quantized and unquantized systems, it is shown that this gap diminishes as the number of BS antennas is asymptotically large.
Moreover, we show that pilot removal from the received signal by using the channel estimate doesn't result in a significant increase in information, especially in the cases of low signal-to-noise ratio (SNR) and a large number of users.
We present some numerical results to corroborate our analytical findings and insights are provided for further exploration of the quantized systems with SP.
In order to avoid the state space explosion problem encountered in the quantitative analysis of large scale PEPA models, a fluid approximation approach has recently been proposed, which results in a set of ordinary differential equations (ODEs) to approximate the underlying continuous time Markov chain (CTMC).
This paper presents a mapping semantics from PEPA to ODEs based on a numerical representation scheme, which extends the class of PEPA models that can be subjected to fluid approximation.
Furthermore, we have established the fundamental characteristics of the derived ODEs, such as the existence, uniqueness, boundedness and nonnegativeness of the solution.
The convergence of the solution as time tends to infinity for several classes of PEPA models, has been proved under some mild conditions.
For general PEPA models, the convergence is proved under a particular condition, which has been revealed to relate to some famous constants of Markov chains such as the spectral gap and the Log-Sobolev constant.
This thesis has established the consistency between the fluid approximation and the underlying CTMCs for PEPA, i.e. the limit of the solution is consistent with the equilibrium probability distribution corresponding to a family of underlying density dependent CTMCs.
We focus on sorting, which is the building block of many machine learning algorithms, and propose a novel distributed sorting algorithm, named Coded TeraSort, which substantially improves the execution time of the TeraSort benchmark in Hadoop MapReduce.
The key idea of Coded TeraSort is to impose structured redundancy in data, in order to enable in-network coding opportunities that overcome the data shuffling bottleneck of TeraSort.
We empirically evaluate the performance of CodedTeraSort algorithm on Amazon EC2 clusters, and demonstrate that it achieves 1.97x - 3.39x speedup, compared with TeraSort, for typical settings of interest.
Robots capable of participating in complex social interactions have shown great potential in a variety of applications.
As these robots grow more popular, it is essential to continuously evaluate the dynamics of the human-robot relationship.
One factor shown to have potential impacts on this critical relationship is the human projection of stereotypes onto social robots, a practice that is implicitly known to effect both developers and users of this technology.
As such, in this research, we wished to investigate the difference in participants' perceptions of the robot interaction if we removed stereotype priming.
This has not yet been a common practice in similar studies.
Given the stereotypes of emotions among ethnic groups, especially in the U.S., this study specifically sought to investigate the impact that robot "skin color" could potentially have on the human perception of a robot's emotional expressive behavior.
A between-subject experiment with 198 individuals was conducted.
The results showed no significant differences in the overall emotion classification or intensity ratings for the different robot skin colors.
These results lend credence to our hypothesis that when individuals are not primed with information related to human stereotypes, robots are evaluated based on functional attributes versus stereotypical attributes.
This provides some confidence that robots, if designed correctly, can potentially be used as a tool to override stereotype-based biases associated with human behavior.
Symmetry is an important composition feature by investigating similar sides inside an image plane.
It has a crucial effect to recognize man-made or nature objects within the universe.
Recent symmetry detection approaches used a smoothing kernel over different voting maps in the polar coordinate system to detect symmetry peaks, which split the regions of symmetry axis candidates in inefficient way.
We propose a reliable voting representation based on weighted linear-directional kernel density estimation, to detect multiple symmetries over challenging real-world and synthetic images.
Experimental evaluation on two public datasets demonstrates the superior performance of the proposed algorithm to detect global symmetry axes respect to the major image shapes.
Enhancement and detection of 3D vessel-like structures has long been an open problem as most existing image processing methods fail in many aspects, including a lack of uniform enhancement between vessels of different radii and a lack of enhancement at the junctions.
Here, we propose a method based on mathematical morphology to enhance 3D vessel-like structures in biomedical images.
The proposed method, 3D bowler-hat transform, combines sphere and line structuring elements to enhance vessel-like structures.
The proposed method is validated on synthetic and real data and compared with state-of-the-art methods.
Our results show that the proposed method achieves a high-quality vessel-like structures enhancement in both synthetic and real biomedical images, and is able to cope with variations in vessels thickness throughout vascular networks while remaining robust at junctions.
Cooperative networking brings performance improvement to most of the issues in wireless networks, such as fading or delay due to slow stations.
However, due to cooperation when data is relayed via other nodes, there network is more prone to attacks.
Since, channel access is very important for cooperation, most of the attacks happens at MAC.
One of the most critical attack is denial of service, which is reason of cooperation failure.
Therefore, the cooperative network as well as simple wireless LAN must be defensive against DOS attacks.
In this article we analyzed all possible of DoS attacks that can happen at MAC layer of WLAN.
The cooperative protocols must consider defense against these attacks.
This article also provided survey of available solutions to these attacks.
At the end it described its damages and cost as well as how to handle these attacks while devising cooperative MAC.
Salient object detection is a problem that has been considered in detail and many solutions proposed.
In this paper, we argue that work to date has addressed a problem that is relatively ill-posed.
Specifically, there is not universal agreement about what constitutes a salient object when multiple observers are queried.
This implies that some objects are more likely to be judged salient than others, and implies a relative rank exists on salient objects.
Initially, we present a novel deep learning solution based on a hierarchical representation of relative saliency and stage-wise refinement.
Furthermore, we present data, analysis and benchmark baseline results towards addressing the problem of salient object ranking.
Methods for deriving suitable ranked salient object instances are presented, along with metrics suitable to measuring algorithm performance.
In addition, we show how a derived dataset can be successively refined to provide cleaned results that correlate well with pristine ground truth.
Finally, we provide a comparison among prevailing algorithms that address salient object ranking or detection to establish initial baselines.
As digital goods and services become an integral part of modern day society, the demand for a standardized and ubiquitous form of digital currency increases.
And it is not just about digital goods; the adoption of electronic and mobile commerce has not reached its expected level at all parts of the globe as expected.
One of the main reasons behind that is the lack of a universal digital as well as virtual currency.
Many countries in the world have failed to realize the potential of e-commerce, let alone m-commerce, because of rigid financial regulations and apparent disorientation & gap between monetary stakeholders across borders and continents.
Digital currency which is internet-based, non-banks issued and circulated within a certain range of networks has brought a significant impact on the development of e-commerce.
The research and analysis of this paper would focus on the feasibility of the operation of a digital currency and its economic implications.
Problems of the switching parallel system designing provided spatial switching of packets from random time are discussed.
Results of modeling of switching system as systems of mass service are resulted.
One problem with load test quality, almost always overlooked, is the potential for the load generator's user thread pool to sync up and dispatch queries in bunches rather than independently from each other like real users initiate their requests.
A spiky launch pattern misrepresents workload flow as well as yields erroneous application response time statistics.
This paper describes what a real user request timing pattern looks like, illustrates how to identify it in the load generation environment, and exercises a free downloadable tool which measures how well the load generator is mimicking the timing pattern of real web user requests.
Virtual machine live migration in cloud environments aims at reducing energy costs and increasing resource utilization.
However, its potential has not been fully explored because of simultaneous migrations that may cause user application performance degradation and network congestion.
Research efforts on live migration orchestration policies still mostly rely on system level metrics.
This work introduces an Application-aware Live Migration Architecture (ALMA) that selects suitable moments for migrations using application characterization data.
This characterization consists in recognizing resource usage cycles via Fast Fourier Transform.
From our experiments, live migration times were reduced by up to 74% for benchmarks and by up to 67% for real applications, when compared to migration policies with no application workload analysis.
Network data transfer during the live migration was reduced by up to 62%.
The robustness and security of the biometric watermarking approach can be improved by using a multiple watermarking.
This multiple watermarking proposed for improving security of biometric features and data.
When the imposter tries to create the spoofed biometric feature, the invisible biometric watermark features can provide appropriate protection to multimedia data.
In this paper, a biometric watermarking technique with multiple biometric watermarks are proposed in which biometric features of fingerprint, face, iris and signature is embedded in the image.
Before embedding, fingerprint, iris, face and signature features are extracted using Shen-Castan edge detection and Principal Component Analysis.
These all biometric watermark features are embedded into various mid band frequency curvelet coefficients of host image.
All four fingerprint features, iris features, facial features and signature features are the biometric characteristics of the individual and they are used for cross verification and copyright protection if any manipulation occurs.
The proposed technique is fragile enough; features cannot be extracted from the watermarked image when an imposter tries to remove watermark features illegally.
It can use for multiple copyright authentication and verification.
Teens are using mobile devices for an increasing number of activities.
Smartphones and a variety of mobile apps for communication, entertainment, and productivity have become an integral part of their lives.
This mobile phone use has evolved rapidly as technology has changed and thus studies from even 2 or 3 years ago may not reflect new patterns and practices as smartphones have become more sophisticated.
In order to understand current teen's practices around smartphone use, we conducted a two week, mixed-methods study with 14 diverse teens.
Through voicemail diaries, interviews, and real world usage data from a logging application installed on their smartphones, we developed an understanding of the types of apps used by teens, when they use these apps, and their reasons for using specific apps in particular situations.
We found that the teens in our study used their smartphones for an average of almost 3 hours per day and that two-thirds of all app use involved interacting with an average of almost 10 distinct communications applications.
From our study data, we highlight key implications for the design of future mobile apps or services, specifically new social and communications-related applications that allow teens to maintain desired levels of privacy and permanence on the content that they share.
SAML assertions are becoming popular method for passing authentication and authorisation information between identity providers and consumers using various single sign-on protocols.
However their practical security strongly depends on correct implementation, especially on the consumer side.
Somorovsky and others have demonstrated a number of XML signature related vulnerabilities in SAML assertion validation frameworks.
This article demonstrates how bad library documentation and examples can lead to vulnerable consumer code and how this can be avoided.
We propose a Visual-SLAM based localization and navigation system for service robots.
Our system is built on top of the ORB-SLAM monocular system but extended by the inclusion of wheel odometry in the estimation procedures.
As a case study, the proposed system is validated using the Pepper robot, whose short-range LIDARs and RGB-D camera do not allow the robot to self-localize in large environments.
The localization system is tested in navigation tasks using Pepper in two different environments: a medium-size laboratory, and a large-size hall.
Software-Defined Networking (SDN) is a novel networking paradigm that provides enhanced programming abilities, which can be used to solve traditional security challenges on the basis of more efficient approaches.
The most important element in the SDN paradigm is the controller, which is responsible for managing the flows of each correspondence forwarding element (switch or router).
Flow statistics provided by the controller are considered to be useful information that can be used to develop a network-based intrusion detection system.
Therefore, in this paper, we propose a 5-level hybrid classification system based on flow statistics in order to attain an improvement in the overall accuracy of the system.
For the first level, we employ the k-Nearest Neighbor approach (kNN); for the second level, we use the Extreme Learning Machine (ELM); and for the remaining levels, we utilize the Hierarchical Extreme Learning Machine (H-ELM) approach.
In comparison with conventional supervised machine learning algorithms based on the NSL-KDD benchmark dataset, the experimental study showed that our system achieves the highest level of accuracy (84.29%).
Therefore, our approach presents an efficient approach for intrusion detection in SDNs.
Machine Learning has been a big success story during the AI resurgence.
One particular stand out success relates to learning from a massive amount of data.
In spite of early assertions of the unreasonable effectiveness of data, there is increasing recognition for utilizing knowledge whenever it is available or can be created purposefully.
In this paper, we discuss the indispensable role of knowledge for deeper understanding of content where (i) large amounts of training data are unavailable, (ii) the objects to be recognized are complex, (e.g., implicit entities and highly subjective content), and (iii) applications need to use complementary or related data in multiple modalities/media.
What brings us to the cusp of rapid progress is our ability to (a) create relevant and reliable knowledge and (b) carefully exploit knowledge to enhance ML/NLP techniques.
Using diverse examples, we seek to foretell unprecedented progress in our ability for deeper understanding and exploitation of multimodal data and continued incorporation of knowledge in learning techniques.
We present a visually-grounded language understanding model based on a study of how people verbally describe objects in scenes.
The emphasis of the model is on the combination of individual word meanings to produce meanings for complex referring expressions.
The model has been implemented, and it is able to understand a broad range of spatial referring expressions.
We describe our implementation of word level visually-grounded semantics and their embedding in a compositional parsing framework.
The implemented system selects the correct referents in response to natural language expressions for a large percentage of test cases.
In an analysis of the system's successes and failures we reveal how visual context influences the semantics of utterances and propose future extensions to the model that take such context into account.
We examine the supervised learning problem in its continuous setting and give a general optimality condition through techniques of functional analysis and the calculus of variations.
This enables us to solve the optimality condition for the desired function u numerically and make several comparisons with other widely utilized supervised learning models.
We employ the accuracy and area under the receiver operating characteristic curve as metrics of the performance.
Finally, 3 analyses are conducted based on these two mentioned metrics where we compare the models and make conclusions to determine whether or not our method is competitive.
As our ground transportation infrastructure modernizes, the large amount of data being measured, transmitted, and stored motivates an analysis of the privacy aspect of these emerging cyber-physical technologies.
In this paper, we consider privacy in the routing game, where the origins and destinations of drivers are considered private.
This is motivated by the fact that this spatiotemporal information can easily be used as the basis for inferences for a person's activities.
More specifically, we consider the differential privacy of the mapping from the amount of flow for each origin-destination pair to the traffic flow measurements on each link of a traffic network.
We use a stochastic online learning framework for the population dynamics, which is known to converge to the Nash equilibrium of the routing game.
We analyze the sensitivity of this process and provide theoretical guarantees on the convergence rates as well as differential privacy values for these models.
We confirm these with simulations on a small example.
Recognizing an object's material can inform a robot on the object's fragility or appropriate use.
To estimate an object's material during manipulation, many prior works have explored the use of haptic sensing.
In this paper, we explore a technique for robots to estimate the materials of objects using spectroscopy.
We demonstrate that spectrometers provide several benefits for material recognition, including fast response times and accurate measurements with low noise.
Furthermore, spectrometers do not require direct contact with an object.
To explore this, we collected a dataset of spectral measurements from two commercially available spectrometers during which a robotic platform interacted with 50 flat material objects, and we show that a neural network model can accurately analyze these measurements.
Due to the similarity between consecutive spectral measurements, our model achieved a material classification accuracy of 94.6% when given only one spectral sample per object.
Similar to prior works with haptic sensors, we found that generalizing material recognition to new objects posed a greater challenge, for which we achieved an accuracy of 79.1% via leave-one-object-out cross-validation.
Finally, we demonstrate how a PR2 robot can leverage spectrometers to estimate the materials of everyday objects found in the home.
From this work, we find that spectroscopy poses a promising approach for material classification during robotic manipulation.
This paper presents a multidisciplinary task approach for assessing the impact of artificial intelligence on the future of work.
We provide definitions of a task from two main perspectives: socio-economic and computational.
We propose to explore ways in which we can integrate or map these perspectives, and link them with the skills or capabilities required by them, for humans and AI systems.
Finally, we argue that in order to understand the dynamics of tasks, we have to explore the relevance of autonomy and generality of AI systems for the automation or alteration of the workplace.
We describe computer algorithms that produce the complete set of isohedral tilings by n-omino or n-iamond tiles in which the tiles are fundamental domains and the tilings have 3-, 4-, or 6-fold rotational symmetry.
The symmetry groups of such tilings are of types p3, p31m, p4, p4g, and p6.
There are no isohedral tilings with symmetry groups p3m1, p4m, or p6m that have polyominoes or polyiamonds as fundamental domains.
We display the algorithms' output and give enumeration tables for small values of n. This expands on our earlier works (Fukuda et al 2006, 2008).
Automatic storytelling is challenging since it requires generating long, coherent natural language to describes a sensible sequence of events.
Despite considerable efforts on automatic story generation in the past, prior work either is restricted in plot planning, or can only generate stories in a narrow domain.
In this paper, we explore open-domain story generation that writes stories given a title (topic) as input.
We propose a plan-and-write hierarchical generation framework that first plans a storyline, and then generates a story based on the storyline.
We compare two planning strategies.
The dynamic schema interweaves story planning and its surface realization in text, while the static schema plans out the entire storyline before generating stories.
Experiments show that with explicit storyline planning, the generated stories are more diverse, coherent, and on topic than those generated without creating a full plan, according to both automatic and human evaluations.
Identifying user's identity is a key problem in many data mining applications, such as product recommendation, customized content delivery and criminal identification.
Given a set of accounts from the same or different social network platforms, user identification attempts to identify all accounts belonging to the same person.
A commonly used solution is to build the relationship among different accounts by exploring their collective patterns, e.g., user profile, writing style, similar comments.
However, this kind of method doesn't work well in many practical scenarios, since the information posted explicitly by users may be false due to various reasons.
In this paper, we re-inspect the user identification problem from a novel perspective, i.e., identifying user's identity by matching his/her cameras.
The underlying assumption is that multiple accounts belonging to the same person contain the same or similar camera fingerprint information.
The proposed framework, called User Camera Identification (UCI), is based on camera fingerprints, which takes fully into account the problems of multiple cameras and reposting behaviors.
The dynamic software development organizations optimize the usage of resources to deliver the products in the specified time with the fulfilled requirements.
This requires prevention or repairing of the faults as quick as possible.
In this paper an approach for predicting the run-time errors in java is introduced.
The paper is concerned with faults due to inheritance and violation of java constraints.
The proposed fault prediction model is designed to separate the faulty classes in the field of software testing.
Separated faulty classes are classified according to the fault occurring in the specific class.
The results are papered by clustering the faults in the class.
This model can be used for predicting software reliability.
Reducing network latency in mobile applications is an effective way of improving the mobile user experience and has tangible economic benefits.
This paper presents PALOMA, a novel client-centric technique for reducing the network latency by prefetching HTTP requests in Android apps.
Our work leverages string analysis and callback control-flow analysis to automatically instrument apps using PALOMA's rigorous formulation of scenarios that address "what" and "when" to prefetch.
PALOMA has been shown to incur significant runtime savings (several hundred milliseconds per prefetchable HTTP request), both when applied on a reusable evaluation benchmark we have developed and on real applications
This work studies the problem of content-based image retrieval, specifically, texture retrieval.
It focuses on feature extraction and similarity measure for texture images.
Our approach employs a recently developed method, the so-called Scattering transform, for the process of feature extraction in texture retrieval.
It shares a distinctive property of providing a robust representation, which is stable with respect to spatial deformations.
Recent work has demonstrated its capability for texture classification, and hence as a promising candidate for the problem of texture retrieval.
Moreover, we adopt a common approach of measuring the similarity of textures by comparing the subband histograms of a filterbank transform.
To this end we derive a similarity measure based on the popular Bhattacharyya Kernel.
Despite the popularity of describing histograms using parametrized probability density functions, such as the Generalized Gaussian Distribution, it is unfortunately not applicable for describing most of the Scattering transform subbands, due to the complex modulus performed on each one of them.
In this work, we propose to use the Weibull distribution to model the Scattering subbands of descendant layers.
Our numerical experiments demonstrated the effectiveness of the proposed approach, in comparison with several state of the arts.
Grounding textual phrases in visual content is a meaningful yet challenging problem with various potential applications such as image-text inference or text-driven multimedia interaction.
Most of the current existing methods adopt the supervised learning mechanism which requires ground-truth at pixel level during training.
However, fine-grained level ground-truth annotation is quite time-consuming and severely narrows the scope for more general applications.
In this extended abstract, we explore methods to localize flexibly image regions from the top-down signal (in a form of one-hot label or natural languages) with a weakly supervised attention learning mechanism.
In our model, two types of modules are utilized: a backbone module for visual feature capturing, and an attentive module generating maps based on regularized bilinear pooling.
We construct the model in an end-to-end fashion which is trained by encouraging the spatial attentive map to shift and focus on the region that consists of the best matched visual features with the top-down signal.
We demonstrate the preliminary yet promising results on a testbed that is synthesized with multi-label MNIST data.
We introduce CCN-RAMP (Routing to Anchors Matching Prefixes), a new approach to content-centric networking.
CCN-RAMP offers all the advantages of the Named Data Networking (NDN) and Content-Centric Networking (CCNx) but eliminates the need to either use Pending Interest Tables (PIT) or lookup large Forwarding Information Bases (FIB) listing name prefixes in order to forward Interests.
CCN-RAMP uses small forwarding tables listing anonymous sources of Interests and the locations of name prefixes.
Such tables are immune to Interest-flooding attacks and are smaller than the FIBs used to list IP address ranges in the Internet.
We show that no forwarding loops can occur with CCN-RAMP, and that Interests flow over the same routes that NDN and CCNx would maintain using large FIBs.
The results of simulation experiments comparing NDN with CCN-RAMP based on ndnSIM show that CCN-RAMP requires forwarding state that is orders of magnitude smaller than what NDN requires, and attains even better performance.
We demonstrate an FPGA implementation of a parallel and reconfigurable architecture for sparse neural networks, capable of on-chip training and inference.
The network connectivity uses pre-determined, structured sparsity to significantly reduce complexity by lowering memory and computational requirements.
The architecture uses a notion of edge-processing, leading to efficient pipelining and parallelization.
Moreover, the device can be reconfigured to trade off resource utilization with training time to fit networks and datasets of varying sizes.
The combined effects of complexity reduction and easy reconfigurability enable significantly greater exploration of network hyperparameters and structures on-chip.
As proof of concept, we show implementation results on an Artix-7 FPGA.
This work focuses on building language models (LMs) for code-switched text.
We propose two techniques that significantly improve these LMs: 1) A novel recurrent neural network unit with dual components that focus on each language in the code-switched text separately 2) Pretraining the LM using synthetic text from a generative model estimated using the training data.
We demonstrate the effectiveness of our proposed techniques by reporting perplexities on a Mandarin-English task and derive significant reductions in perplexity.
Activity detection is a fundamental problem in computer vision.
Detecting activities of different temporal scales is particularly challenging.
In this paper, we propose the contextual multi-scale region convolutional 3D network (CMS-RC3D) for activity detection.
To deal with the inherent temporal scale variability of activity instances, the temporal feature pyramid is used to represent activities of different temporal scales.
On each level of the temporal feature pyramid, an activity proposal detector and an activity classifier are learned to detect activities of specific temporal scales.
Temporal contextual information is fused into activity classifiers for better recognition.
More importantly, the entire model at all levels can be trained end-to-end.
Our CMS-RC3D detector can deal with activities at all temporal scale ranges with only a single pass through the backbone network.
We test our detector on two public activity detection benchmarks, THUMOS14 and ActivityNet.
Extensive experiments show that the proposed CMS-RC3D detector outperforms state-of-the-art methods on THUMOS14 by a substantial margin and achieves comparable results on ActivityNet despite using a shallow feature extractor.
We address the problem of using hand-drawn sketches to create exaggerated deformations to faces in videos, such as enlarging the shape or modifying the position of eyes or mouth.
This task is formulated as a 3D face model reconstruction and deformation problem.
We first recover the facial identity and expressions from the video by fitting a face morphable model for each frame.
At the same time, user's editing intention is recognized from input sketches as a set of facial modifications.
Then a novel identity deformation algorithm is proposed to transfer these facial deformations from 2D space to the 3D facial identity directly while preserving the facial expressions.
After an optional stage for further refining the 3D face model, these changes are propagated to the whole video with the modified identity.
Both the user study and experimental results demonstrate that our sketching framework can help users effectively edit facial identities in videos, while high consistency and fidelity are ensured at the same time.
This article extends the Generalized Asypmtotic Equipartition Property of Networked Data Structures to cover the Wireless Sensor Network modelled as coloured geometric random graph (CGRG).
The main techniques used to prove this result remains large deviation principles for properly defined empirical measures on CGRGs.
As a motivation for this article, we apply our results to some data from Wireless Sensor Network for Monitoring Water Quality from a Lake..
We consider generation and comprehension of natural language referring expression for objects in an image.
Unlike generic "image captioning" which lacks natural standard evaluation criteria, quality of a referring expression may be measured by the receiver's ability to correctly infer which object is being described.
Following this intuition, we propose two approaches to utilize models trained for comprehension task to generate better expressions.
First, we use a comprehension module trained on human-generated expressions, as a "critic" of referring expression generator.
The comprehension module serves as a differentiable proxy of human evaluation, providing training signal to the generation module.
Second, we use the comprehension module in a generate-and-rerank pipeline, which chooses from candidate expressions generated by a model according to their performance on the comprehension task.
We show that both approaches lead to improved referring expression generation on multiple benchmark datasets.
The task of classifying videos of natural dynamic scenes into appropriate classes has gained lot of attention in recent years.
The problem especially becomes challenging when the camera used to capture the video is dynamic.
In this paper, we analyse the performance of statistical aggregation (SA) techniques on various pre-trained convolutional neural network(CNN) models to address this problem.
The proposed approach works by extracting CNN activation features for a number of frames in a video and then uses an aggregation scheme in order to obtain a robust feature descriptor for the video.
We show through results that the proposed approach performs better than the-state-of-the arts for the Maryland and YUPenn dataset.
The final descriptor obtained is powerful enough to distinguish among dynamic scenes and is even capable of addressing the scenario where the camera motion is dominant and the scene dynamics are complex.
Further, this paper shows an extensive study on the performance of various aggregation methods and their combinations.
We compare the proposed approach with other dynamic scene classification algorithms on two publicly available datasets - Maryland and YUPenn to demonstrate the superior performance of the proposed approach.
A domain adaptation method for urban scene segmentation is proposed in this work.
We develop a fully convolutional tri-branch network, where two branches assign pseudo labels to images in the unlabeled target domain while the third branch is trained with supervision based on images in the pseudo-labeled target domain.
The re-labeling and re-training processes alternate.
With this design, the tri-branch network learns target-specific discriminative representations progressively and, as a result, the cross-domain capability of the segmenter improves.
We evaluate the proposed network on large-scale domain adaptation experiments using both synthetic (GTA) and real (Cityscapes) images.
It is shown that our solution achieves the state-of-the-art performance and it outperforms previous methods by a significant margin.
Quality of service (QoS) provisioning in next-generation mobile communications systems entails a deep understanding of the delay performance.
The delay in wireless networks is strongly affected by the traffic arrival process and the service process, which in turn depends on the medium access protocol and the signal-to-interference-plus-noise ratio (SINR) distribution.
In this work, we characterize the conditional distribution of the service process given the point process in Poisson bipolar networks.
We then provide an upper bound on the delay violation probability combining tools from stochastic network calculus and stochastic geometry.
Furthermore, we analyze the delay performance under statistical queueing constraints using the effective capacity formulation.
The impact of QoS requirements, network geometry and link distance on the delay performance is identified.
Our results provide useful insights for guaranteeing stringent delay requirements in large wireless networks.
The web provides a rich, open-domain environment with textual, structural, and spatial properties.
We propose a new task for grounding language in this environment: given a natural language command (e.g., "click on the second article"), choose the correct element on the web page (e.g., a hyperlink or text box).
We collected a dataset of over 50,000 commands that capture various phenomena such as functional references (e.g."find who made this site"), relational reasoning (e.g."article by john"), and visual reasoning (e.g."top-most article").
We also implemented and analyzed three baseline models that capture different phenomena present in the dataset.
Many real-world optimization problems require significant resources for objective function evaluations.
This is a challenge to evolutionary algorithms, as it limits the number of available evaluations.
One solution are surrogate models, which replace the expensive objective.
A particular issue in this context are hierarchical variables.
Hierarchical variables only influence the objective function if other variables satisfy some condition.
We study how this kind of hierarchical structure can be integrated into the model based optimization framework.
We discuss an existing kernel and propose alternatives.
An artificial test function is used to investigate how different kernels and assumptions affect model quality and search performance.
Active range sensing using structured-light is the most accurate and reliable method for obtaining 3D information.
However, most of the work has been limited to range sensing of static objects, and range sensing of dynamic (moving or deforming) objects has been investigated recently only by a few researchers.
Sinusoidal structured-light is one of the well-known optical methods for 3D measurement.
In this paper, we present a novel method for rapid high-resolution range imaging using color sinusoidal pattern.
We consider the real-world problem of nonlinearity and color-band crosstalk in the color light projector and color camera, and present methods for accurate recovery of color-phase.
For high-resolution ranging, we use high-frequency patterns and describe new unwrapping algorithms for reliable range recovery.
The experimental results demonstrate the effectiveness of our methods.
The upcoming big data era is likely to demand tremendous computation and storage resources for communications.
By pushing computation and storage to network edges, fog radio access networks (Fog-RAN) can effectively increase network throughput and reduce transmission latency.
Furthermore, we can exploit the benefits of cache enabled architecture in Fog-RAN to deliver contents with low latency.
Radio access units (RAUs) need content delivery from fog servers through wireline links whereas multiple mobile devices acquire contents from RAUs wirelessly.
This work proposes a unified low-rank matrix completion (LRMC) approach to solving the content delivery problem in both wireline and wireless parts of Fog-RAN.
To attain a low caching latency, we present a high precision approach with Riemannian trust-region method to solve the challenging LRMC problem by exploiting the quotient manifold geometry of fixed-rank matrices.
Numerical results show that the new approach has a faster convergence rate, is able to achieve optimal results, and outperforms other state-of-art algorithms.
This work presents an efficient method to solve a class of continuous-time, continuous-space stochastic optimal control problems of robot motion in a cluttered environment.
The method builds upon a path integral representation of the stochastic optimal control problem that allows computation of the optimal solution through sampling and estimation process.
As this sampling process often leads to a local minimum especially when the state space is highly non-convex due to the obstacle field, we present an efficient method to alleviate this issue by devising a proposed topological motion planning algorithm.
Combined with a receding-horizon scheme in execution of the optimal control solution, the proposed method can generate a dynamically feasible and collision-free trajectory while reducing concern about local optima.
Illustrative numerical examples are presented to demonstrate the applicability and validity of the proposed approach.
The Software Defined Networking (SDN) paradigm decouples control and data planes, offering high programmability and a global view of the network.
However, it is a challenge not only provide security in these next generation networks as well as allow that network attacks could be subjected to an incident and forensic treatment procedure.
This paper proposes the implementation of flexible mechanisms of monitoring and treatment of security events categorized per type of attack and associated with whitelist and blacklist resources by means of the SDN controller programmability.
The resources to perform intrusion and attack analysis are validated by means of a real SDN/OpenFlow testbed.
In this paper, we propose a useful replacement for quicksort-style utility functions.
The replacement is called Symmetry Partition Sort, which has essentially the same principle as Proportion Extend Sort.
The maximal difference between them is that the new algorithm always places already partially sorted inputs (used as a basis for the proportional extension) on both ends when entering the partition routine.
This is advantageous to speeding up the partition routine.
The library function based on the new algorithm is more attractive than Psort which is a library function introduced in 2004.
Its implementation mechanism is simple.
The source code is clearer.
The speed is faster, with O(n log n) performance guarantee.
Both the robustness and adaptivity are better.
As a library function, it is competitive.
Attacks against the control processor of a power-grid system, especially zero-day attacks, can be catastrophic.
Earlier detection of the attacks can prevent further damage.
However, detecting zero-day attacks can be challenging because they have no known code and have unknown behavior.
In order to address the zero-day attack problem, we propose a data-driven defense by training a temporal deep learning model, using only normal data from legitimate processes that run daily in these power-grid systems, to model the normal behavior of the power-grid controller.
Then, we can quickly find malicious codes running on the processor, by estimating deviations from the normal behavior with a statistical test.
Experimental results on a real power-grid controller show that we can detect anomalous behavior with over 99.9% accuracy and nearly zero false positives.
Deep neural networks excel at function approximation, yet they are typically trained from scratch for each new function.
On the other hand, Bayesian methods, such as Gaussian Processes (GPs), exploit prior knowledge to quickly infer the shape of a new function at test time.
Yet GPs are computationally expensive, and it can be hard to design appropriate priors.
In this paper we propose a family of neural models, Conditional Neural Processes (CNPs), that combine the benefits of both.
CNPs are inspired by the flexibility of stochastic processes such as GPs, but are structured as neural networks and trained via gradient descent.
CNPs make accurate predictions after observing only a handful of training data points, yet scale to complex functions and large datasets.
We demonstrate the performance and versatility of the approach on a range of canonical machine learning tasks, including regression, classification and image completion.
Multiple-antenna "based" transmitter (TX) cooperation has been established as a promising tool towards avoiding, aligning, or shaping the interference resulting from aggressive spectral reuse.
The price paid in the form of feedback and exchanging channel state information (CSI) between cooperating devices in most existing methods is often underestimated however.
In reality, feedback and information overhead threatens the practicality and scalability of TX cooperation approaches in dense networks.
Hereby we addresses a "Who needs to know what?" problem, when it comes to CSI at cooperating transmitters.
A comprehensive answer to this question remains beyond our reach and the scope of this paper.
Nevertheless, recent results in this area suggest that CSI overhead can be contained for even large networks provided the allocation of feedback to TXs is made non-uniform and to properly depend on the network's topology.
This paper provides a few hints toward solving the problem.
Humans have an unparalleled visual intelligence and can overcome visual ambiguities that machines currently cannot.
Recent works have shown that incorporating guidance from humans during inference for monocular viewpoint-estimation can help overcome difficult cases in which the computer-alone would have otherwise failed.
These hybrid intelligence approaches are hence gaining traction.
However, deciding what question to ask the human at inference time remains an unknown for these problems.
We address this question by formulating it as an Adviser Problem: can we learn a mapping from the input to a specific question to ask the human to maximize the expected positive impact to the overall task?
We formulate a solution to the adviser problem for viewpoint estimation using a deep network where the question asks for the location of a keypoint in the input image.
We show that by using the Adviser Network's recommendations, the model and the human outperforms the previous hybrid-intelligence state-of-the-art by 3.7%, and the computer-only state-of-the-art by 5.28% absolute.
In many multirobot applications, planning trajectories in a way to guarantee that the collective behavior of the robots satisfies a certain high-level specification is crucial.
Motivated by this problem, we introduce counting temporal logics---formal languages that enable concise expression of multirobot task specifications over possibly infinite horizons.
We first introduce a general logic called counting linear temporal logic plus (cLTL+), and propose an optimization-based method that generates individual trajectories such that satisfaction of a given cLTL+ formula is guaranteed when these trajectories are synchronously executed.
We then introduce a fragment of cLTL+, called counting linear temporal logic (cLTL), and show that a solution to planning problem with cLTL constraints can be obtained more efficiently if all robots have identical dynamics.
In the second part of the paper, we relax the synchrony assumption and discuss how to generate trajectories that can be asynchronously executed, while preserving the satisfaction of the desired cLTL+ specification.
In particular, we show that when the asynchrony between robots is bounded, the method presented in this paper can be modified to generate robust trajectories.
We demonstrate these ideas with an experiment and provide numerical results that showcase the scalability of the method.
Discovering automatically the semantic structure of tagged visual data (e.g. web videos and images) is important for visual data analysis and interpretation, enabling the machine intelligence for effectively processing the fast-growing amount of multi-media data.
However, this is non-trivial due to the need for jointly learning underlying correlations between heterogeneous visual and tag data.
The task is made more challenging by inherently sparse and incomplete tags.
In this work, we develop a method for modelling the inherent visual data concept structures based on a novel Hierarchical-Multi-Label Random Forest model capable of correlating structured visual and tag information so as to more accurately interpret the visual semantics, e.g. disclosing meaningful visual groups with similar high-level concepts, and recovering missing tags for individual visual data samples.
Specifically, our model exploits hierarchically structured tags of different semantic abstractness and multiple tag statistical correlations in addition to modelling visual and tag interactions.
As a result, our model is able to discover more accurate semantic correlation between textual tags and visual features, and finally providing favourable visual semantics interpretation even with highly sparse and incomplete tags.
We demonstrate the advantages of our proposed approach in two fundamental applications, visual data clustering and missing tag completion, on benchmarking video (i.e.TRECVID MED 2011) and image (i.e.NUS-WIDE) datasets.
As ontologies proliferate and automatic reasoners become more powerful, the problem of protecting sensitive information becomes more serious.
In particular, as facts can be inferred from other facts, it becomes increasingly likely that information included in an ontology, while not itself deemed sensitive, may be able to be used to infer other sensitive information.
We first consider the problem of testing an ontology for safeness defined as its not being able to be used to derive any sensitive facts using a given collection of inference rules.
We then consider the problem of optimizing an ontology based on the criterion of making as much useful information as possible available without revealing any sensitive facts.
CT protocol design and quality control would benefit from automated tools to estimate the quality of generated CT images.
These tools could be used to identify erroneous CT acquisitions or refine protocols to achieve certain signal to noise characteristics.
This paper investigates blind estimation methods to determine global signal strength and noise levels in chest CT images.
Methods: We propose novel performance metrics corresponding to the accuracy of noise and signal estimation.
We implement and evaluate the noise estimation performance of six spatial- and frequency- based methods, derived from conventional image filtering algorithms.
Algorithms were tested on patient data sets from whole-body repeat CT acquisitions performed with a higher and lower dose technique over the same scan region.
Results: The proposed performance metrics can evaluate the relative tradeoff of filter parameters and noise estimation performance.
The proposed automated methods tend to underestimate CT image noise at low-flux levels.
Initial application of methodology suggests that anisotropic diffusion and Wavelet-transform based filters provide optimal estimates of noise.
Furthermore, methodology does not provide accurate estimates of absolute noise levels, but can provide estimates of relative change and/or trends in noise levels.
Ad hoc network is a collection of wireless mobile nodes that dynamically form a temporary network without the use of any existing network infrastructure or centralized administration.
A cognitive radio is a radio that can change its transmitter parameters based on interaction with the environment in which it operates.
The basic idea of cognitive radio networks is that the unlicensed devices (cognitive radio users or secondary users) need to vacate the spectrum band once the licensed device (primary user) is detected.
Cognitive capability and reconfigurability are the key characteristics of cognitive radio.
Routing is an important issue in Mobile Cognitive Radio Ad Hoc Networks (MCRAHNs).
In this paper, a survey of routing protocols for mobile cognitive radio ad networks is discussed.
The continuing expansion of Internet media consumption has increased traffic volumes, and hence congestion, on access links.
In response, both mobile and wireline ISPs must either increase capacity or perform traffic engineering over existing resources.
Unfortunately, provisioning timescales are long, the process is costly, and single-homing means operators cannot balance across the last mile.
Inspired by energy and transport networks, we propose demand-side management of users to reduce the impact caused by consumption patterns out-pacing that of edge network provision.
By directly affecting user behaviour through a range of incentives, our techniques enable resource management over shorter timescales than is possible in conventional networks.
Using survey data from 100 participants we explore the feasibility of introducing the principles of demand-side management in today's networks.
The Good is Blondie, a wandering gunman with a strong personal sense of honor.
The Bad is Angel Eyes, a sadistic hitman who always hits his mark.
The Ugly is Tuco, a Mexican bandit who's always only looking out for himself.
Against the backdrop of the BOWS contest, they search for a watermark in gold buried in three images.
Each knows only a portion of the gold's exact location, so for the moment they're dependent on each other.
However, none are particularly inclined to share...
Writing concurrent programs for shared memory multiprocessor systems is a nightmare.
This hinders users to exploit the full potential of multiprocessors.
STM (Software Transactional Memory) is a promising concurrent programming paradigm which addresses woes of programming for multiprocessor systems.
In this paper, we implement BTO (Basic Timestamp Ordering), SGT (Serialization Graph Testing) and MVTO(Multi-Version Time-Stamp Ordering) concurrency control protocols and build an STM(Software Transactional Memory) library to evaluate the performance of these protocols.
The deferred write approach is followed to implement the STM.
A SET data structure is implemented using the transactions of our STM library.
And this transactional SET is used as a test application to evaluate the STM.
The performance of the protocols is rigorously compared against the linked-list module of the Synchrobench benchmark.
Linked list module implements SET data structure using lazy-list, lock-free list, lock-coupling list and ESTM (Elastic Software Transactional Memory).
Our analysis shows that for a number of threads greater than 60 and update rate 70%, BTO takes (17% to 29%) and (6% to 24%) less CPU time per thread when compared against lazy-list and lock-coupling list respectively.
MVTO takes (13% to 24%) and (3% to 24%) less CPU time per thread when compared against lazy-list and lock-coupling list respectively.
BTO and MVTO have similar per thread CPU time.
BTO and MVTO outperform SGT by 9% to 36%.
The multi-criteria decision making, which is possible with the advent of skyline queries, has been applied in many areas.
Though most of the existing research is concerned with only a single relation, several real world applications require finding the skyline set of records over multiple relations.
Consequently, the join operation over skylines where the preferences are local to each relation, has been proposed.
In many of those cases, however, the join often involves performing aggregate operations among some of the attributes from the different relations.
In this paper, we introduce such queries as "aggregate skyline join queries".
Since the naive algorithm is impractical, we propose three algorithms to efficiently process such queries.
The algorithms utilize certain properties of skyline sets, and processes the skylines as much as possible locally before computing the join.
Experiments with real and synthetic datasets exhibit the practicality and scalability of the algorithms with respect to the cardinality and dimensionality of the relations.
We investigate the problem of inconsistency measurement on large knowledge bases by considering stream-based inconsistency measurement, i.e., we investigate inconsistency measures that cannot consider a knowledge base as a whole but process it within a stream.
For that, we present, first, a novel inconsistency measure that is apt to be applied to the streaming case and, second, stream-based approximations for the new and some existing inconsistency measures.
We conduct an extensive empirical analysis on the behavior of these inconsistency measures on large knowledge bases, in terms of runtime, accuracy, and scalability.
We conclude that for two of these measures, the approximation of the new inconsistency measure and an approximation of the contension inconsistency measure, large-scale inconsistency measurement is feasible.
In this paper, we establish the matroid structures corresponding to data-local and local maximally recoverable codes (MRC).
The matroid structures of these codes can be used to determine the associated Tutte polynomial.
Greene proved that the weight enumerators of any code can be determined from its associated Tutte polynomial.
We will use this result to derive explicit expressions for the weight enumerators of data-local and local MRC.
Also, Britz proved that the higher support weights of any code can be determined from its associated Tutte polynomial.
We will use this result to derive expressions for the higher support weights of data-local and local MRC with two local codes.
We address the problem of bootstrapping language acquisition for an artificial system similarly to what is observed in experiments with human infants.
Our method works by associating meanings to words in manipulation tasks, as a robot interacts with objects and listens to verbal descriptions of the interactions.
The model is based on an affordance network, i.e., a mapping between robot actions, robot perceptions, and the perceived effects of these actions upon objects.
We extend the affordance model to incorporate spoken words, which allows us to ground the verbal symbols to the execution of actions and the perception of the environment.
The model takes verbal descriptions of a task as the input and uses temporal co-occurrence to create links between speech utterances and the involved objects, actions, and effects.
We show that the robot is able form useful word-to-meaning associations, even without considering grammatical structure in the learning process and in the presence of recognition errors.
These word-to-meaning associations are embedded in the robot's own understanding of its actions.
Thus, they can be directly used to instruct the robot to perform tasks and also allow to incorporate context in the speech recognition task.
We believe that the encouraging results with our approach may afford robots with a capacity to acquire language descriptors in their operation's environment as well as to shed some light as to how this challenging process develops with human infants.
In this paper, we propose a learning rule based on a back-propagation (BP) algorithm that can be applied to a hardware-based deep neural network (HW-DNN) using electronic devices that exhibit discrete and limited conductance characteristics.
This adaptive learning rule, which enables forward, backward propagation, as well as weight updates in hardware, is helpful during the implementation of power-efficient and high-speed deep neural networks.
In simulations using a three-layer perceptron network, we evaluate the learning performance according to various conductance responses of electronic synapse devices and weight-updating methods.
It is shown that the learning accuracy is comparable to that obtained when using a software-based BP algorithm when the electronic synapse device has a linear conductance response with a high dynamic range.
Furthermore, the proposed unidirectional weight-updating method is suitable for electronic synapse devices which have nonlinear and finite conductance responses.
Because this weight-updating method can compensate the demerit of asymmetric weight updates, we can obtain better accuracy compared to other methods.
This adaptive learning rule, which can be applied to full hardware implementation, can also compensate the degradation of learning accuracy due to the probable device-to-device variation in an actual electronic synapse device.
A simple proof is given for the monotonicity of entropy and Fisher information associated to sums of i.i.d. random variables.
The proof relies on a characterization of maximal correlation for partial sums due to Dembo, Kagan and Shepp.
Despite huge success of artificial intelligence, hardware systems running these algorithms consume orders of magnitude higher energy compared to the human brain, mainly due to heavy data movements between the memory unit and the computation cores.
Spiking neural networks (SNNs) built using bio-plausible neuron and synaptic models have emerged as the power-efficient choice for designing cognitive applications.
These algorithms involve several lookup-table (LUT) based function evaluations such as high-order polynomials and transcendental functions for solving complex neuro-synaptic models, that typically require additional storage.
To that effect, we propose `SPARE' - an in-memory, distributed processing architecture built on ROM-embedded RAM technology, for accelerating SNNs.
ROM-embedded RAMs allow storage of LUTs, embedded within a typical memory array, without additional area overhead.
Our proposed architecture consists of a 2-D array of Processing Elements (PEs).
Since most of the computations are done locally within each PE, unnecessary data transfers are restricted, thereby alleviating the von-Neumann bottleneck.
We evaluate SPARE for two different ROM-Embedded RAM structures - CMOS based ROM-Embedded SRAMs (R-SRAMs) and STT-MRAM based ROM-Embedded MRAMs (R-MRAMs).
Moreover, we analyze trade-offs in terms of energy, area and performance, for using the two technologies on a range of image classification benchmarks.
Furthermore, we leverage the additional storage density to implement complex neuro-synaptic functionalities.
This enhances the utility of the proposed architecture by provisioning implementation of any neuron/synaptic behavior as necessitated by the application.
Our results show up-to 1.75x, 1.95x and 1.95x improvement in energy, iso-storage area, and iso-area performance, respectively, by using neural network accelerators built on ROM-embedded RAM primitives.
Subgraph discovery in a single data graph---finding subsets of vertices and edges satisfying a user-specified criteria---is an essential and general graph analytics operation with a wide spectrum of applications.
Depending on the criteria, subgraphs of interest may correspond to cliques of friends in social networks, interconnected entities in RDF data, or frequent patterns in protein interaction networks to name a few.
Existing systems usually examine a large number of subgraphs while employing many computers and often produce an enormous result set of subgraphs.
How can we enable fast discovery of only the most relevant subgraphs while minimizing the computational requirements?
We present Nuri, a general subgraph discovery system that allows users to succinctly specify subgraphs of interest and criteria for ranking them.
Given such specifications, Nuri efficiently finds the k most relevant subgraphs using only a single computer.
It prioritizes (i.e., expands earlier than others) subgraphs that are more likely to expand into the desired subgraphs (prioritized subgraph expansion) and proactively discards irrelevant subgraphs from which the desired subgraphs cannot be constructed (pruning).
Nuri can also efficiently store and retrieve a large number of subgraphs on disk without being limited by the size of main memory.
We demonstrate using both real and synthetic datasets that Nuri on a single core outperforms the closest alternative distributed system consuming 40 times more computational resources by more than 2 orders of magnitude for clique discovery and 1 order of magnitude for subgraph isomorphism and pattern mining.
We propose a simple, yet powerful regularization technique that can be used to significantly improve both the pairwise and triplet losses in learning local feature descriptors.
The idea is that in order to fully utilize the expressive power of the descriptor space, good local feature descriptors should be sufficiently "spread-out" over the space.
In this work, we propose a regularization term to maximize the spread in feature descriptor inspired by the property of uniform distribution.
We show that the proposed regularization with triplet loss outperforms existing Euclidean distance based descriptor learning techniques by a large margin.
As an extension, the proposed regularization technique can also be used to improve image-level deep feature embedding.
While there are many approaches for automatically proving termination of term rewrite systems, up to now there exist only few techniques to disprove their termination automatically.
Almost all of these techniques try to find loops, where the existence of a loop implies non-termination of the rewrite system.
However, most programming languages use specific evaluation strategies, whereas loop detection techniques usually do not take strategies into account.
So even if a rewrite system has a loop, it may still be terminating under certain strategies.
Therefore, our goal is to develop decision procedures which can determine whether a given loop is also a loop under the respective evaluation strategy.
In earlier work, such procedures were presented for the strategies of innermost, outermost, and context-sensitive evaluation.
In the current paper, we build upon this work and develop such decision procedures for important strategies like leftmost-innermost, leftmost-outermost, (max-)parallel-innermost, (max-)parallel-outermost, and forbidden patterns (which generalize innermost, outermost, and context-sensitive strategies).
In this way, we obtain the first approach to disprove termination under these strategies automatically.
Generative adversarial networks (GANs) are a class of deep generative models which aim to learn a target distribution in an unsupervised fashion.
While they were successfully applied to many problems, training a GAN is a notoriously challenging task and requires a significant amount of hyperparameter tuning, neural architecture engineering, and a non-trivial amount of "tricks".
The success in many practical applications coupled with the lack of a measure to quantify the failure modes of GANs resulted in a plethora of proposed losses, regularization and normalization schemes, and neural architectures.
In this work we take a sober view of the current state of GANs from a practical perspective.
We reproduce the current state of the art and go beyond fairly exploring the GAN landscape.
We discuss common pitfalls and reproducibility issues, open-source our code on Github, and provide pre-trained models on TensorFlow Hub.
Spreadsheets provide a flexible and easy to use software development environment, but that leads to error proneness.
Work has been done to prevent errors in spreadsheets, including using models to specify distinct parts of a spreadsheet as it is done with model-driven software development.
Previous model languages for spreadsheets offer a limited expressiveness, and cannot model several features present in most real world spreadsheets.
In this paper, the modeling language Tabula is introduced.
It extends previous spreadsheet models with features like type constraints and nested classes with repetitions.
Tabula is not only more expressive than other models but it can also be extended with more features.
Moreover, Tabula includes a bidirectional transformation engine that guarantees synchronization after an update either in the model or spreadsheet.
Deep learning has demonstrated abilities to learn complex structures, but they can be restricted by available data.
Recently, Consensus Networks (CNs) were proposed to alleviate data sparsity by utilizing features from multiple modalities, but they too have been limited by the size of labeled data.
In this paper, we extend CN to Transductive Consensus Networks (TCNs), suitable for semi-supervised learning.
In TCNs, different modalities of input are compressed into latent representations, which we encourage to become indistinguishable during iterative adversarial training.
To understand TCNs two mechanisms, consensus and classification, we put forward its three variants in ablation studies on these mechanisms.
To further investigate TCN models, we treat the latent representations as probability distributions and measure their similarities as the negative relative Jensen-Shannon divergences.
We show that a consensus state beneficial for classification desires a stable but imperfect similarity between the representations.
Overall, TCNs outperform or align with the best benchmark algorithms given 20 to 200 labeled samples on the Bank Marketing and the DementiaBank datasets.
A generalized Nonlinear Fourier Transform (GNFT), which includes eigenvalues of higher multiplicity, is considered for information transmission over fiber optic channels.
Numerical algorithms are developed to compute the direct and inverse GNFTs.
For closely-spaced eigenvalues, examples suggest that the GNFT is more robust than the NFT to the practical impairments of truncation, discretization, attenuation and noise.
Communication using a soliton with one double eigenvalue is numerically demonstrated, and its information rates are compared to solitons with one and two simple eigenvalues.
We present a novel solution for Channel Assignment Problem (CAP) in Device-to-Device (D2D) wireless networks that takes into account the throughput estimation noise.
CAP is known to be NP-hard in the literature and there is no practical optimal learning algorithm that takes into account the estimation noise.
In this paper, we first formulate the CAP as a stochastic optimization problem to maximize the expected sum data rate.
To capture the estimation noise, CAP is modeled as a noisy potential game, a novel notion we introduce in this paper.
Then, we propose a distributed Binary Log-linear Learning Algorithm (BLLA) that converges to the optimal channel assignments.
Convergence of BLLA is proved for bounded and unbounded noise.
Proofs for fixed and decreasing temperature parameter of BLLA are provided.
A sufficient number of estimation samples is given that guarantees the convergence to the optimal state.
We assess the performance of BLLA by extensive simulations, which show that the sum data rate increases with the number of channels and users.
Contrary to the better response algorithm, the proposed algorithm achieves the optimal channel assignments distributively even in presence of estimation noise.
This paper defines The Dead Cryptographers Society Problem - DCS (where several great cryptographers created many polynomial-time Deterministic Turing Machines (DTMs) of a specific type, ran them on their proper descriptions concatenated with some arbitrary strings, deleted them and left only the results from those running, after they died: if those DTMs only permute and sometimes invert the bits on input, is it possible to decide the language formed by such resulting strings within polynomial time?
), proves some facts about its computational complexity, and discusses some possible uses on Cryptography, such as into distance keys distribution, online reverse auction and secure communication.
Receiver-initiated medium access control protocols for wireless sensor networks are theoretically able to adapt to changing network conditions in a distributed manner.
However, existing algorithms rely on fixed beacon rates at each receiver.
We present a new received initiated MAC protocol that adapts the beacon rate at each receiver to its actual traffic load.
Our proposal uses a computationally inexpensive formula for calculating the optimum beacon rate that minimizes network energy consumption and, so, it can be easily adopted by receivers.
Simulation results show that our proposal reduces collisions and diminishes delivery time maintaining a low duty cycle.
Online harassment has been a problem to a greater or lesser extent since the early days of the internet.
Previous work has applied anti-spam techniques like machine-learning based text classification (Reynolds, 2011) to detecting harassing messages.
However, existing public datasets are limited in size, with labels of varying quality.
The #HackHarassment initiative (an alliance of 1 tech companies and NGOs devoted to fighting bullying on the internet) has begun to address this issue by creating a new dataset superior to its predecssors in terms of both size and quality.
As we (#HackHarassment) complete further rounds of labelling, later iterations of this dataset will increase the available samples by at least an order of magnitude, enabling corresponding improvements in the quality of machine learning models for harassment detection.
In this paper, we introduce the first models built on the #HackHarassment dataset v1.0 (a new open dataset, which we are delighted to share with any interested researcherss) as a benchmark for future research.
Unambiguous non-deterministic finite automata have intermediate expressive power and succinctness between deterministic and non-deterministic automata.
It has been conjectured that every unambiguous non-deterministic one-way finite automaton (1UFA) recognizing some language L can be converted into a 1UFA recognizing the complement of the original language L with polynomial increase in the number of states.
We disprove this conjecture by presenting a family of 1UFAs on a single-letter alphabet such that recognizing the complements of the corresponding languages requires superpolynomial increase in the number of states even for generic non-deterministic one-way finite automata.
We also note that both the languages and their complements can be recognized by sweeping deterministic automata with a linear increase in the number of states.
Source code is rarely written in isolation.
It depends significantly on the programmatic context, such as the class that the code would reside in.
To study this phenomenon, we introduce the task of generating class member functions given English documentation and the programmatic context provided by the rest of the class.
This task is challenging because the desired code can vary greatly depending on the functionality the class provides (e.g., a sort function may or may not be available when we are asked to "return the smallest element" in a particular member variable list).
We introduce CONCODE, a new large dataset with over 100,000 examples consisting of Java classes from online code repositories, and develop a new encoder-decoder architecture that models the interaction between the method documentation and the class environment.
We also present a detailed error analysis suggesting that there is significant room for future work on this task.
In the field of digital image processing, JPEG image compression technique has been widely applied.
And numerous image processing software suppose this.
It is likely for the images undergoing double JPEG compression to be tampered.
Therefore, double JPEG compression detection schemes can provide an important clue for image forgery detection.
In this paper, we propose an effective algorithm to detect double JPEG compression with different quality factors.
Firstly, the quantized DCT coefficients with same frequency are extracted to build the new data matrices.
Then, considering the direction effect on the correlation between the adjacent positions in DCT domain, twelve kinds of high-pass filter templates with different directions are executed and the translation probability matrix is calculated for each filtered data.
Furthermore, principal component analysis and support vector machine technique are applied to reduce the feature dimension and train a classifier, respectively.
Experimental results have demonstrated that the proposed method is effective and has comparable performance.
Semantic NLP applications often rely on dependency trees to recognize major elements of the proposition structure of sentences.
Yet, while much semantic structure is indeed expressed by syntax, many phenomena are not easily read out of dependency trees, often leading to further ad-hoc heuristic post-processing or to information loss.
To directly address the needs of semantic applications, we present PropS -- an output representation designed to explicitly and uniformly express much of the proposition structure which is implied from syntax, and an associated tool for extracting it from dependency trees.
Instance-level human parsing towards real-world human analysis scenarios is still under-explored due to the absence of sufficient data resources and technical difficulty in parsing multiple instances in a single pass.
Several related works all follow the "parsing-by-detection" pipeline that heavily relies on separately trained detection models to localize instances and then performs human parsing for each instance sequentially.
Nonetheless, two discrepant optimization targets of detection and parsing lead to suboptimal representation learning and error accumulation for final results.
In this work, we make the first attempt to explore a detection-free Part Grouping Network (PGN) for efficiently parsing multiple people in an image in a single pass.
Our PGN reformulates instance-level human parsing as two twinned sub-tasks that can be jointly learned and mutually refined via a unified network: 1) semantic part segmentation for assigning each pixel as a human part (e.g., face, arms); 2) instance-aware edge detection to group semantic parts into distinct person instances.
Thus the shared intermediate representation would be endowed with capabilities in both characterizing fine-grained parts and inferring instance belongings of each part.
Finally, a simple instance partition process is employed to get final results during inference.
We conducted experiments on PASCAL-Person-Part dataset and our PGN outperforms all state-of-the-art methods.
Furthermore, we show its superiority on a newly collected multi-person parsing dataset (CIHP) including 38,280 diverse images, which is the largest dataset so far and can facilitate more advanced human analysis.
The CIHP benchmark and our source code are available at http://sysu-hcp.net/lip/.
We use reinforcement learning to learn tree-structured neural networks for computing representations of natural language sentences.
In contrast with prior work on tree-structured models in which the trees are either provided as input or predicted using supervision from explicit treebank annotations, the tree structures in this work are optimized to improve performance on a downstream task.
Experiments demonstrate the benefit of learning task-specific composition orders, outperforming both sequential encoders and recursive encoders based on treebank annotations.
We analyze the induced trees and show that while they discover some linguistically intuitive structures (e.g., noun phrases, simple verb phrases), they are different than conventional English syntactic structures.
Recurrent neural networks are a powerful means to cope with time series.
We show how a type of linearly activated recurrent neural networks can approximate any time-dependent function f(t) given by a number of function values.
The approximation can effectively be learned by simply solving a linear equation system; no backpropagation or similar methods are needed.
Furthermore, the network size can be reduced by taking only the most relevant components of the network.
Thus, in contrast to others, our approach not only learns network weights but also the network architecture.
The networks have interesting properties: They end up in ellipse trajectories in the long run and allow the prediction of further values and compact representations of functions.
We demonstrate this by several experiments, among them multiple superimposed oscillators (MSO) and robotic soccer.
Predictive neural networks outperform the previous state-of-the-art for the MSO task with a minimal number of units.
A constantly growing amount of information is available through the web.
Unfortunately, extracting useful content from this massive amount of data still remains an open issue.
The lack of standard data models and structures forces developers to create adhoc solutions from the scratch.
The figure of the expert is still needed in many situations where developers do not have the correct background knowledge.
This forces developers to spend time acquiring the needed background from the expert.
In other directions, there are promising solutions employing machine learning techniques.
However, increasing accuracy requires an increase in system complexity that cannot be endured in many projects.
In this work, we approach the web knowledge extraction problem using an expertcentric methodology.
This methodology defines a set of configurable, extendible and independent components that permit the reutilisation of large pieces of code among projects.
Our methodology differs from similar solutions in its expert-driven design.
This design, makes it possible for subject-matter expert to drive the knowledge extraction for a given set of documents.
Additionally, we propose the utilization of machine assisted solutions that guide the expert during this process.
To demonstrate the capabilities of our methodology, we present a real use case scenario in which public procurement data is extracted from the web-based repositories of several public institutions across Europe.
We provide insightful details about the challenges we had to deal with in this use case and additional discussions about how to apply our methodology.
In this paper, we consider the problem of allocating cache resources among multiple content providers.
The cache can be partitioned into slices and each partition can be dedicated to a particular content provider, or shared among a number of them.
It is assumed that each partition employs the LRU policy for managing content.
We propose utility-driven partitioning, where we associate with each content provider a utility that is a function of the hit rate observed by the content provider.
We consider two scenarios: i)~content providers serve disjoint sets of files, ii)~there is some overlap in the content served by multiple content providers.
In the first case, we prove that cache partitioning outperforms cache sharing as cache size and numbers of contents served by providers go to infinity.
In the second case, It can be beneficial to have separate partitions for overlapped content.
In the case of two providers, it is usually always beneficial to allocate a cache partition to serve all overlapped content and separate partitions to serve the non-overlapped contents of both providers.
We establish conditions when this is true asymptotically but also present an example where it is not true asymptotically.
We develop online algorithms that dynamically adjust partition sizes in order to maximize the overall utility and prove that they converge to optimal solutions, and through numerical evaluations, we show they are effective.
Nowadays, Tehran Urban and Suburban Railway System (TUSRS) is going to be completed by eight lines and 149 stations.
This complex transportation system contains 168 links between each station pairs and 20 cross-section and Y-branch stations among all eight lines.
In this study, we considered TUSRS as a complex network and undertook several analyzes based on graph theory.
Examining e.g. centrality measures, we identified central stations within TUSRS.
This analysis could be useful for redistributing strategy of the overcrowded stations and improving the organization of maintaining system.
These findings are also promising for better designing the systems of tomorrow in other metropolitan areas in Iran.
A recent study reported development of Muscorian, a generic text processing tool for extracting protein-protein interactions from text that achieved comparable performance to biomedical-specific text processing tools.
This result was unexpected since potential errors from a series of text analysis processes is likely to adversely affect the outcome of the entire process.
Most biomedical entity relationship extraction tools have used biomedical-specific parts-of-speech (POS) tagger as errors in POS tagging and are likely to affect subsequent semantic analysis of the text, such as shallow parsing.
This study aims to evaluate the parts-of-speech (POS) tagging accuracy and attempts to explore whether a comparable performance is obtained when a generic POS tagger, MontyTagger, was used in place of MedPost, a tagger trained in biomedical text.
Our results demonstrated that MontyTagger, Muscorian's POS tagger, has a POS tagging accuracy of 83.1% when tested on biomedical text.
Replacing MontyTagger with MedPost did not result in a significant improvement in entity relationship extraction from text; precision of 55.6% from MontyTagger versus 56.8% from MedPost on directional relationships and 86.1% from MontyTagger compared to 81.8% from MedPost on nondirectional relationships.
This is unexpected as the potential for poor POS tagging by MontyTagger is likely to affect the outcome of the information extraction.
An analysis of POS tagging errors demonstrated that 78.5% of tagging errors are being compensated by shallow parsing.
Thus, despite 83.1% tagging accuracy, MontyTagger has a functional tagging accuracy of 94.6%.
Millimeter wave (mm-wave) and massive MIMO have been proposed for next generation wireless systems.
However, there are many open problems for the implementation of those technologies.
In particular, beamforming is necessary in mm-wave systems in order to counter high propagation losses.
However, conventional beamsteering is not always appropriate in rich scattering multipath channels with frequency selective fading, such as those found in indoor environments.
In this context, time-reversal (TR) is considered a promising beamforming technique for such mm-wave massive MIMO systems.
In this paper, we analyze a baseband TR beamforming system for mm-wave multi-user massive MIMO.
We verify that, as the number of antennas increases, TR yields good equalization and interference mitigation properties, but inter-user interference (IUI) remains a main impairment.
Thus, we propose a novel technique called interference-nulling TR (INTR) to minimize IUI.
We evaluate numerically the performance of INTR and compare it with conventional TR and equalized TR beamforming.
We use a 60 GHz MIMO channel model with spatial correlation based on the IEEE 802.11ad SISO NLoS model.
We demonstrate that INTR outperforms conventional TR with respect to average BER per user and achievable sum rate under diverse conditions, providing both diversity and multiplexing gains simultaneously.
Virtualization technology allows currently any application run any application complex and expensive computational (the scientific applications are a good example) on heterogeneous distributed systems, which make regular use of Grid and Cloud technologies, enabling significant savings in computing time.
This model is particularly interesting for the mass execution of scientific simulations and calculations, allowing parallel execution of applications using the same execution environment (unchanged) used by the scientist as usual.
However, the use and distribution of large virtual images can be a problem (up to tens of GBytes), which is aggravated when attempting a mass mailing on a large number of distributed computers.
This work has as main objective to present an analysis of how implementation and a proposal for the improvement (reduction in size) of the virtual images pretending reduce distribution time in distributed systems.
This analysis is done very specific requirements that need an operating system (guest OS) on some aspects of its execution.
Moments capture a huge part of our lives.
Accurate recognition of these moments is challenging due to the diverse and complex interpretation of the moments.
Action recognition refers to the act of classifying the desired action/activity present in a given video.
In this work, we perform experiments on Moments in Time dataset to recognize accurately activities occurring in 3 second clips.
We use state of the art techniques for visual, auditory and spatio temporal localization and develop method to accurately classify the activity in the Moments in Time dataset.
Our novel approach of using Visual Based Textual features and fusion techniques performs well providing an overall 89.23 % Top - 5 accuracy on the 20 classes - a significant improvement over the Baseline TRN model.
Estimating statistical models within sensor networks requires distributed algorithms, in which both data and computation are distributed across the nodes of the network.
We propose a general approach for distributed learning based on combining local estimators defined by pseudo-likelihood components, encompassing a number of combination methods, and provide both theoretical and experimental analysis.
We show that simple linear combination or max-voting methods, when combined with second-order information, are statistically competitive with more advanced and costly joint optimization.
Our algorithms have many attractive properties including low communication and computational cost and "any-time" behavior.
This work deals with non-native children's speech and investigates both multi-task and transfer learning approaches to adapt a multi-language Deep Neural Network (DNN) to speakers, specifically children, learning a foreign language.
The application scenario is characterized by young students learning English and German and reading sentences in these second-languages, as well as in their mother language.
The paper analyzes and discusses techniques for training effective DNN-based acoustic models starting from children native speech and performing adaptation with limited non-native audio material.
A multi-lingual model is adopted as baseline, where a common phonetic lexicon, defined in terms of the units of the International Phonetic Alphabet (IPA), is shared across the three languages at hand (Italian, German and English); DNN adaptation methods based on transfer learning are evaluated on significant non-native evaluation sets.
Results show that the resulting non-native models allow a significant improvement with respect to a mono-lingual system adapted to speakers of the target language.
A body of literature has demonstrated that users' mental health conditions, such as depression and anxiety, can be predicted from their social media language.
There is still a gap in the scientific understanding of how psychological stress is expressed on social media.
Stress is one of the primary underlying causes and correlates of chronic physical illnesses and mental health conditions.
In this paper, we explore the language of psychological stress with a dataset of 601 social media users, who answered the Perceived Stress Scale questionnaire and also consented to share their Facebook and Twitter data.
Firstly, we find that stressed users post about exhaustion, losing control, increased self-focus and physical pain as compared to posts about breakfast, family-time, and travel by users who are not stressed.
Secondly, we find that Facebook language is more predictive of stress than Twitter language.
Thirdly, we demonstrate how the language based models thus developed can be adapted and be scaled to measure county-level trends.
Since county-level language is easily available on Twitter using the Streaming API, we explore multiple domain adaptation algorithms to adapt user-level Facebook models to Twitter language.
We find that domain-adapted and scaled social media-based measurements of stress outperform sociodemographic variables (age, gender, race, education, and income), against ground-truth survey-based stress measurements, both at the user- and the county-level in the U.S. Twitter language that scores higher in stress is also predictive of poorer health, less access to facilities and lower socioeconomic status in counties.
We conclude with a discussion of the implications of using social media as a new tool for monitoring stress levels of both individuals and counties.
Context: Surveys constitute an valuable tool to capture a large-scale snapshot of the state of the practice.
Apparently trivial to adopt, surveys hide, however, several pitfalls that might hinder rendering the result valid and, thus, useful.
Goal: We aim at providing an overview of main pitfalls in software engineering surveys and report on practical ways to deal with them.
Method: We build on the experiences we collected in conducting many studies and distill the main lessons learnt.
Results: The eight lessons learnt we report cover different aspects of the survey process ranging from the design of initial research objectives to the design of a questionnaire.
Conclusions: Our hope is that by sharing our lessons learnt, combined with a disciplined application of the general survey theory, we contribute to improving the quality of the research results achievable by employing software engineering surveys.
Infrastructures are not inherently durable or fragile, yet all are fragile over the long term.
Durability requires care and maintenance of individual components and the links between them.
Astronomy is an ideal domain in which to study knowledge infrastructures, due to its long history, transparency, and accumulation of observational data over a period of centuries.
Research reported here draws upon a long-term study of scientific data practices to ask questions about the durability and fragility of infrastructures for data in astronomy.
Methods include interviews, ethnography, and document analysis.
As astronomy has become a digital science, the community has invested in shared instruments, data standards, digital archives, metadata and discovery services, and other relatively durable infrastructure components.
Several features of data practices in astronomy contribute to the fragility of that infrastructure.
These include different archiving practices between ground- and space-based missions, between sky surveys and investigator-led projects, and between observational and simulated data.
Infrastructure components are tightly coupled, based on international agreements.
However, the durability of these infrastructures relies on much invisible work - cataloging, metadata, and other labor conducted by information professionals.
Continual investments in care and maintenance of the human and technical components of these infrastructures are necessary for sustainability.
Indices and materialized views are physical structures that accelerate data access in data warehouses.
However, these data structures generate some maintenance overhead.
They also share the same storage space.
The existing studies about index and materialized view selection consider these structures separately.
In this paper, we adopt the opposite stance and couple index and materialized view selection to take into account the interactions between them and achieve an efficient storage space sharing.
We develop cost models that evaluate the respective benefit of indexing and view materialization.
These cost models are then exploited by a greedy algorithm to select a relevant configuration of indices and materialized views.
Experimental results show that our strategy performs better than the independent selection of indices and materialized views.
We present a generic and automated approach to re-identifying nodes in anonymized social networks which enables novel anonymization techniques to be quickly evaluated.
It uses machine learning (decision forests) to matching pairs of nodes in disparate anonymized sub-graphs.
The technique uncovers artefacts and invariants of any black-box anonymization scheme from a small set of examples.
Despite a high degree of automation, classification succeeds with significant true positive rates even when small false positive rates are sought.
Our evaluation uses publicly available real world datasets to study the performance of our approach against real-world anonymization strategies, namely the schemes used to protect datasets of The Data for Development (D4D) Challenge.
We show that the technique is effective even when only small numbers of samples are used for training.
Further, since it detects weaknesses in the black-box anonymization scheme it can re-identify nodes in one social network when trained on another.
Generic word embeddings are trained on large-scale generic corpora; Domain Specific (DS) word embeddings are trained only on data from a domain of interest.
This paper proposes a method to combine the breadth of generic embeddings with the specificity of domain specific embeddings.
The resulting embeddings, called Domain Adapted (DA) word embeddings, are formed by aligning corresponding word vectors using Canonical Correlation Analysis (CCA) or the related nonlinear Kernel CCA.
Evaluation results on sentiment classification tasks show that the DA embeddings substantially outperform both generic and DS embeddings when used as input features to standard or state-of-the-art sentence encoding algorithms for classification.
This paper proposes a method based on signal injection to obtain the saturated current-flux relations of a PMSM from locked-rotor experiments.
With respect to the classical method based on time integration, it has the main advantage of being completely independent of the stator resistance; moreover, it is less sensitive to voltage biases due to the power inverter, as the injected signal may be fairly large.
Expressive efficiency refers to the relation between two architectures A and B, whereby any function realized by B could be replicated by A, but there exists functions realized by A, which cannot be replicated by B unless its size grows significantly larger.
For example, it is known that deep networks are exponentially efficient with respect to shallow networks, in the sense that a shallow network must grow exponentially large in order to approximate the functions represented by a deep network of polynomial size.
In this work, we extend the study of expressive efficiency to the attribute of network connectivity and in particular to the effect of "overlaps" in the convolutional process, i.e., when the stride of the convolution is smaller than its filter size (receptive field).
To theoretically analyze this aspect of network's design, we focus on a well-established surrogate for ConvNets called Convolutional Arithmetic Circuits (ConvACs), and then demonstrate empirically that our results hold for standard ConvNets as well.
Specifically, our analysis shows that having overlapping local receptive fields, and more broadly denser connectivity, results in an exponential increase in the expressive capacity of neural networks.
Moreover, while denser connectivity can increase the expressive capacity, we show that the most common types of modern architectures already exhibit exponential increase in expressivity, without relying on fully-connected layers.
Deep neural networks have shown promising results in image inpainting even if the missing area is relatively large.
However, most of the existing inpainting networks introduce undesired artifacts and noise to the repaired regions.
To solve this problem, we present a novel framework which consists of two stacked convolutional neural networks that inpaint the image and remove the artifacts, respectively.
The first network considers the global structure of the damaged image and coarsely fills the blank area.
Then the second network modifies the repaired image to cancel the noise introduced by the first network.
The proposed framework splits the problem into two distinct partitions that can be optimized separately, therefore it can be applied to any inpainting algorithm by changing the first network.
Second stage in our framework which aims at polishing the inpainted images can be treated as a denoising problem where a wide range of algorithms can be employed.
Our results demonstrate that the proposed framework achieves significant improvement on both visual and quantitative evaluations.
Visual query systems (VQSs) empower users to interactively search for line charts with desired visual patterns typically specified using intuitive sketch-based interfaces.
Despite their potential in accelerating data exploration, more than a decade of past work on VQSs has not been translated to adoption in practice.
Through a year-long collaboration with experts from three diverse domains, we examine the role of VQSs in real data exploration workflows, enhance an existing VQS to support these workflows via a participatory design process, and evaluate how VQS components are used in practice.
Via these observations, we formalize a taxonomy of key capabilities for VQSs, organized by three sensemaking processes.
Perhaps somewhat surprisingly, we find that ad-hoc sketch-based querying is not commonly used during data exploration, since analysts are often unable to precisely articulate the patterns they are interested in.
We find that there is a spectrum of VQS-centric data exploration workflows, depending on the application domain, and that many of these workflows are not effectively supported in present-day VQSs.
Our insights can pave the way for next-generation VQSs to be adopted in a variety of real-world applications.
In this paper, we address the problem of unsupervised video summarization that automatically extracts key-shots from an input video.
Specifically, we tackle two critical issues based on our empirical observations: (i) Ineffective feature learning due to flat distributions of output importance scores for each frame, and (ii) training difficulty when dealing with long-length video inputs.
To alleviate the first problem, we propose a simple yet effective regularization loss term called variance loss.
The proposed variance loss allows a network to predict output scores for each frame with high discrepancy which enables effective feature learning and significantly improves model performance.
For the second problem, we design a novel two-stream network named Chunk and Stride Network (CSNet) that utilizes local (chunk) and global (stride) temporal view on the video features.
Our CSNet gives better summarization results for long-length videos compared to the existing methods.
In addition, we introduce an attention mechanism to handle the dynamic information in videos.
We demonstrate the effectiveness of the proposed methods by conducting extensive ablation studies and show that our final model achieves new state-of-the-art results on two benchmark datasets.
Elastic distortion of fingerprints has a negative effect on the performance of fingerprint recognition systems.
This negative effect brings inconvenience to users in authentication applications.
However, in the negative recognition scenario where users may intentionally distort their fingerprints, this can be a serious problem since distortion will prevent recognition system from identifying malicious users.
Current methods aimed at addressing this problem still have limitations.
They are often not accurate because they estimate distortion parameters based on the ridge frequency map and orientation map of input samples, which are not reliable due to distortion.
Secondly, they are not efficient and requiring significant computation time to rectify samples.
In this paper, we develop a rectification model based on a Deep Convolutional Neural Network (DCNN) to accurately estimate distortion parameters from the input image.
Using a comprehensive database of synthetic distorted samples, the DCNN learns to accurately estimate distortion bases ten times faster than the dictionary search methods used in the previous approaches.
Evaluating the proposed method on public databases of distorted samples shows that it can significantly improve the matching performance of distorted samples.
The training of deep neural nets is expensive.
We present a predictor- corrector method for the training of deep neural nets.
It alternates a predictor pass with a corrector pass using stochastic gradient descent with backpropagation such that there is no loss in validation accuracy.
No special modifications to SGD with backpropagation is required by this methodology.
Our experiments showed a time improvement of 9% on the CIFAR-10 dataset.
Concolic testing combines program execution and symbolic analysis to explore the execution paths of a software program.
This paper presents the first concolic testing approach for Deep Neural Networks (DNNs).
More specifically, we formalise coverage criteria for DNNs that have been studied in the literature, and then develop a coherent method for performing concolic testing to increase test coverage.
Our experimental results show the effectiveness of the concolic testing approach in both achieving high coverage and finding adversarial examples.
Photometric stereo is a method that seeks to reconstruct the normal vectors of an object from a set of images of the object illuminated under different light sources.
While effective in some situations, classical photometric stereo relies on a diffuse surface model that cannot handle objects with complex reflectance patterns, and it is sensitive to non-idealities in the images.
In this work, we propose a novel approach to photometric stereo that relies on dictionary learning to produce robust normal vector reconstructions.
Specifically, we develop two formulations for applying dictionary learning to photometric stereo.
We propose a model that applies dictionary learning to regularize and reconstruct the normal vectors from the images under the classic Lambertian reflectance model.
We then generalize this model to explicitly model non-Lambertian objects.
We investigate both approaches through extensive experimentation on synthetic and real benchmark datasets and observe state-of-the-art performance compared to existing robust photometric stereo methods.
Preconditioned gradient methods are among the most general and powerful tools in optimization.
However, preconditioning requires storing and manipulating prohibitively large matrices.
We describe and analyze a new structure-aware preconditioning algorithm, called Shampoo, for stochastic optimization over tensor spaces.
Shampoo maintains a set of preconditioning matrices, each of which operates on a single dimension, contracting over the remaining dimensions.
We establish convergence guarantees in the stochastic convex setting, the proof of which builds upon matrix trace inequalities.
Our experiments with state-of-the-art deep learning models show that Shampoo is capable of converging considerably faster than commonly used optimizers.
Although it involves a more complex update rule, Shampoo's runtime per step is comparable to that of simple gradient methods such as SGD, AdaGrad, and Adam.
Structure, functionality, parameters and organization of the computing Grid in Poland is described, mainly from the perspective of high-energy particle physics community, currently its largest consumer and developer.
It represents distributed Tier-2 in the worldwide Grid infrastructure.
It also provides services and resources for data-intensive applications in other sciences.
We present a method to incorporate global orientation information from the sun into a visual odometry pipeline using only the existing image stream, where the sun is typically not visible.
We leverage recent advances in Bayesian Convolutional Neural Networks to train and implement a sun detection model that infers a three-dimensional sun direction vector from a single RGB image.
Crucially, our method also computes a principled uncertainty associated with each prediction, using a Monte Carlo dropout scheme.
We incorporate this uncertainty into a sliding window stereo visual odometry pipeline where accurate uncertainty estimates are critical for optimal data fusion.
Our Bayesian sun detection model achieves a median error of approximately 12 degrees on the KITTI odometry benchmark training set, and yields improvements of up to 42% in translational ARMSE and 32% in rotational ARMSE compared to standard VO.
An open source implementation of our Bayesian CNN sun estimator (Sun-BCNN) using Caffe is available at https://github. com/utiasSTARS/sun-bcnn-vo
In this paper, we have proposed a novel technique for cache replacement in Ad-hoc Network based on the mining of Association Rules
Polar codes are the first class of error correcting codes that provably achieve the channel capacity at infinite code length.
They were selected for use in the fifth generation of cellular mobile communications (5G).
In practical scenarios such as 5G, a cyclic redundancy check (CRC) is concatenated with polar codes to improve their finite length performance.
This is mostly beneficial for sequential successive-cancellation list decoders.
However, for parallel iterative belief propagation (BP) decoders, CRC is only used as an early stopping criterion with incremental error-correction performance improvement.
In this paper, we first propose a CRC-polar BP (CPBP) decoder by exchanging the extrinsic information between the factor graph of the polar code and that of the CRC.
We then propose a neural CPBP (NCPBP) algorithm which improves the CPBP decoder by introducing trainable normalizing weights on the concatenated factor graph.
Our results on a 5G polar code of length 128 show that at the frame error rate of 10^(-5) and with a maximum of 30 iterations, the error-correction performance of CPBP and NCPBP are approximately 0.25 dB and 0.5 dB better than that of the conventional CRC-aided BP decoder, respectively, while introducing almost no latency overhead.
Learning representations of data, and in particular learning features for a subsequent prediction task, has been a fruitful area of research delivering impressive empirical results in recent years.
However, relatively little is understood about what makes a representation `good'.
We propose the idea of a risk gap induced by representation learning for a given prediction context, which measures the difference in the risk of some learner using the learned features as compared to the original inputs.
We describe a set of sufficient conditions for unsupervised representation learning to provide a benefit, as measured by this risk gap.
These conditions decompose the problem of when representation learning works into its constituent parts, which can be separately evaluated using an unlabeled sample, suitable domain-specific assumptions about the joint distribution, and analysis of the feature learner and subsequent supervised learner.
We provide two examples of such conditions in the context of specific properties of the unlabeled distribution, namely when the data lies close to a low-dimensional manifold and when it forms clusters.
We compare our approach to a recently proposed analysis of semi-supervised learning.
A simple model of MNIST handwritten digit recognition is presented here.
The model is an adaptation of a previous theory of face recognition.
It realizes translation and rotation invariance in a principled way instead of being based on extensive learning from large masses of sample data.
The presented recognition rates fall short of other publications, but due to its inspectability and conceptual and numerical simplicity, our system commends itself as a basis for further development.
A key problem of robotic environmental sensing and monitoring is that of active sensing: How can a team of robots plan the most informative observation paths to minimize the uncertainty in modeling and predicting an environmental phenomenon?
This paper presents two principled approaches to efficient information-theoretic path planning based on entropy and mutual information criteria for in situ active sensing of an important broad class of widely-occurring environmental phenomena called anisotropic fields.
Our proposed algorithms are novel in addressing a trade-off between active sensing performance and time efficiency.
An important practical consequence is that our algorithms can exploit the spatial correlation structure of Gaussian process-based anisotropic fields to improve time efficiency while preserving near-optimal active sensing performance.
We analyze the time complexity of our algorithms and prove analytically that they scale better than state-of-the-art algorithms with increasing planning horizon length.
We provide theoretical guarantees on the active sensing performance of our algorithms for a class of exploration tasks called transect sampling, which, in particular, can be improved with longer planning time and/or lower spatial correlation along the transect.
Empirical evaluation on real-world anisotropic field data shows that our algorithms can perform better or at least as well as the state-of-the-art algorithms while often incurring a few orders of magnitude less computational time, even when the field conditions are less favorable.
This paper presents SgxPectre Attacks that exploit the recently disclosed CPU bugs to subvert the confidentiality and integrity of SGX enclaves.
Particularly, we show that when branch prediction of the enclave code can be influenced by programs outside the enclave, the control flow of the enclave program can be temporarily altered to execute instructions that lead to observable cache-state changes.
An adversary observing such changes can learn secrets inside the enclave memory or its internal registers, thus completely defeating the confidentiality guarantee offered by SGX.
To demonstrate the practicality of our SgxPectre Attacks, we have systematically explored the possible attack vectors of branch target injection, approaches to win the race condition during enclave's speculative execution, and techniques to automatically search for code patterns required for launching the attacks.
Our study suggests that any enclave program could be vulnerable to SgxPectre Attacks since the desired code patterns are available in most SGX runtimes (e.g., Intel SGX SDK, Rust-SGX, and Graphene-SGX).
Most importantly, we have applied SgxPectre Attacks to steal seal keys and attestation keys from Intel signed quoting enclaves.
The seal key can be used to decrypt sealed storage outside the enclaves and forge valid sealed data; the attestation key can be used to forge attestation signatures.
For these reasons, SgxPectre Attacks practically defeat SGX's security protection.
This paper also systematically evaluates Intel's existing countermeasures against SgxPectre Attacks and discusses the security implications.
Different from traditional action recognition based on video segments, online action recognition aims to recognize actions from unsegmented streams of data in a continuous manner.
One way for online recognition is based on the evidence accumulation over time to make predictions from stream videos.
This paper presents a fast yet effective method to recognize actions from stream of noisy skeleton data, and a novel weighted covariance descriptor is adopted to accumulate evidence.
In particular, a fast incremental updating method for the weighted covariance descriptor is developed for accumulation of temporal information and online prediction.
The weighted covariance descriptor takes the following principles into consideration: past frames have less contribution for recognition and recent and informative frames such as key frames contribute more to the recognition.
The online recognition is achieved using a simple nearest neighbor search against a set of offline trained action models.
Experimental results on MSC-12 Kinect Gesture dataset and our newly constructed online action recognition dataset have demonstrated the efficacy of the proposed method.
Learning semantic attributes for person re-identification and description-based person search has gained increasing interest due to attributes' great potential as a pose and view-invariant representation.
However, existing attribute-centric approaches have thus far underperformed state-of-the-art conventional approaches.
This is due to their non-scalable need for extensive domain (camera) specific annotation.
In this paper we present a new semantic attribute learning approach for person re-identification and search.
Our model is trained on existing fashion photography datasets -- either weakly or strongly labelled.
It can then be transferred and adapted to provide a powerful semantic description of surveillance person detections, without requiring any surveillance domain supervision.
The resulting representation is useful for both unsupervised and supervised person re-identification, achieving state-of-the-art and near state-of-the-art performance respectively.
Furthermore, as a semantic representation it allows description-based person search to be integrated within the same framework.
Small wind projects encounter difficulties to be efficiently deployed, partly because wrong way data and information are managed.
Ontologies can overcome the drawbacks of partially available, noisy, inconsistent, and heterogeneous data sources, by providing a semantic middleware between low level data and more general knowledge.
In this paper, we engineer an ontology for the wind energy domain using description logic as technical instrumentation.
We aim to integrate corpus of heterogeneous knowledge, both digital and human, in order to help the interested user to speed-up the initialization of a small-scale wind project.
We exemplify one use case scenario of our ontology, that consists of automatically checking whether a planned wind project is compliant or not with the active regulations.
Field failures, that is, failures caused by faults that escape the testing phase leading to failures in the field, are unavoidable.
Improving verification and validation activities before deployment can identify and timely remove many but not all faults, and users may still experience a number of annoying problems while using their software systems.
This paper investigates the nature of field failures, to understand to what extent further improving in-house verification and validation activities can reduce the number of failures in the field, and frames the need of new approaches that operate in the field.
We report the results of the analysis of the bug reports of five applications belonging to three different ecosystems, propose a taxonomy of field failures, and discuss the reasons why failures belonging to the identified classes cannot be detected at design time but shall be addressed at runtime.
We observe that many faults (70%) are intrinsically hard to detect at design-time.
We present a method for generating robust chaos.
It is based on the search algorithm weak symmetry violation in the reconstructed attractor.
On its basis the smooth functions in the form of a system of finite-difference equations.
To ensure robust chaos generator introduced piecewise continuous member.
The simulation results are given in the report.
The problem of time-constrained multi-agent task scheduling and control synthesis is addressed.
We assume the existence of a high level plan which consists of a sequence of cooperative tasks, each of which is associated with a deadline and several Quality-of-Service levels.
By taking into account the reward and cost of satisfying each task, a novel scheduling problem is formulated and a path synthesis algorithm is proposed.
Based on the obtained plan, a distributed hybrid control law is further designed for each agent.
Under the condition that only a subset of the agents are aware of the high level plan, it is shown that the proposed controller guarantees the satisfaction of time constraints for each task.
A simulation example is given to verify the theoretical results.
Femtocells have been considered by the wireless industry as a cost-effective solution not only to improve indoor service providing, but also to unload traffic from already overburdened macro networks.
Due to spectrum availability and network infrastructure considerations, a macro network may have to share spectrum with overlaid femtocells.
In spectrum-sharing macro and femto networks, inter-cell interference caused by different transmission powers of macrocell base stations (MBS) and femtocell access points (FAP), in conjunction with potentially densely deployed femtocells, may create dead spots where reliable services cannot be guaranteed to either macro or femto users.
In this paper, based on a thorough analysis of downlink (DL) outage probabilities (OP) of collocated spectrum-sharing orthogonal frequency division multiple access (OFDMA) based macro and femto networks, we devise a decentralized strategy for an FAP to self-regulate its transmission power level and usage of radio resources depending on its distance from the closest MBS.
Simulation results show that the derived closed-form lower bounds of DL OPs are tight, and the proposed decentralized femtocell self-regulation strategy is able to guarantee reliable DL services in targeted macro and femto service areas while providing superior spatial reuse, for even a large number of spectrum-sharing femtocells deployed per cell site.
Querying graph databases has recently received much attention.
We propose a new approach to this problem, which balances competing goals of expressive power, language clarity and computational complexity.
A distinctive feature of our approach is the ability to express properties of minimal (e.g. shortest) and maximal (e.g. most valuable) paths satisfying given criteria.
To express complex properties in a modular way, we introduce labelling-generating ontologies.
The resulting formalism is computationally attractive -- queries can be answered in non-deterministic logarithmic space in the size of the database.
Camouflaging data by generating fake information is a well-known obfuscation technique for protecting data privacy.
In this paper, we focus on a very sensitive and increasingly exposed type of data: location data.
There are two main scenarios in which fake traces are of extreme value to preserve location privacy: publishing datasets of location trajectories, and using location-based services.
Despite advances in protecting (location) data privacy, there is no quantitative method to evaluate how realistic a synthetic trace is, and how much utility and privacy it provides in each scenario.
Also, the lack of a methodology to generate privacy-preserving fake traces is evident.
In this paper, we fill this gap and propose the first statistical metric and model to generate fake location traces such that both the utility of data and the privacy of users are preserved.
We build upon the fact that, although geographically they visit distinct locations, people have strongly semantically similar mobility patterns, for example, their transition pattern across activities (e.g., working, driving, staying at home) is similar.
We define a statistical metric and propose an algorithm that automatically discovers the hidden semantic similarities between locations from a bag of real location traces as seeds, without requiring any initial semantic annotations.
We guarantee that fake traces are geographically dissimilar to their seeds, so they do not leak sensitive location information.
We also protect contributors to seed traces against membership attacks.
Interleaving fake traces with mobile users' traces is a prominent location privacy defense mechanism.
We quantitatively show the effectiveness of our methodology in protecting against localization inference attacks while preserving utility of sharing/publishing traces.
The ability to model search in a constraint solver can be an essential asset for solving combinatorial problems.
However, existing infrastructure for defining search heuristics is often inadequate.
Either modeling capabilities are extremely limited or users are faced with a general-purpose programming language whose features are not tailored towards writing search heuristics.
As a result, major improvements in performance may remain unexplored.
This article introduces search combinators, a lightweight and solver-independent method that bridges the gap between a conceptually simple modeling language for search (high-level, functional and naturally compositional) and an efficient implementation (low-level, imperative and highly non-modular).
By allowing the user to define application-tailored search strategies from a small set of primitives, search combinators effectively provide a rich domain-specific language (DSL) for modeling search to the user.
Remarkably, this DSL comes at a low implementation cost to the developer of a constraint solver.
The article discusses two modular implementation approaches and shows, by empirical evaluation, that search combinators can be implemented without overhead compared to a native, direct implementation in a constraint solver.
A network covert channel is created that uses resource names such as addresses to convey information, and that approximates typical user behavior in order to blend in with its environment.
The channel correlates available resource names with a user defined code-space, and transmits its covert message by selectively accessing resources associated with the message codes.
In this paper we focus on an implementation of the channel using the Hypertext Transfer Protocol (HTTP) with Uniform Resource Locators (URLs) as the message names, though the system can be used in conjunction with a variety of protocols.
The covert channel does not modify expected protocol structure as might be detected by simple inspection, and our HTTP implementation emulates transaction level web user behavior in order to avoid detection by statistical or behavioral analysis.
We calculate asymptotic expansions for the moments of number of comparisons used by the randomized quick sort algorithm using the singularity analysis of certain generating functions.
We propose graph-based predictable feature analysis (GPFA), a new method for unsupervised learning of predictable features from high-dimensional time series, where high predictability is understood very generically as low variance in the distribution of the next data point given the previous ones.
We show how this measure of predictability can be understood in terms of graph embedding as well as how it relates to the information-theoretic measure of predictive information in special cases.
We confirm the effectiveness of GPFA on different datasets, comparing it to three existing algorithms with similar objectives---namely slow feature analysis, forecastable component analysis, and predictable feature analysis---to which GPFA shows very competitive results.
Neural models have become ubiquitous in automatic speech recognition systems.
While neural networks are typically used as acoustic models in more complex systems, recent studies have explored end-to-end speech recognition systems based on neural networks, which can be trained to directly predict text from input acoustic features.
Although such systems are conceptually elegant and simpler than traditional systems, it is less obvious how to interpret the trained models.
In this work, we analyze the speech representations learned by a deep end-to-end model that is based on convolutional and recurrent layers, and trained with a connectionist temporal classification (CTC) loss.
We use a pre-trained model to generate frame-level features which are given to a classifier that is trained on frame classification into phones.
We evaluate representations from different layers of the deep model and compare their quality for predicting phone labels.
Our experiments shed light on important aspects of the end-to-end model such as layer depth, model complexity, and other design choices.
Recent work has suggested reducing electricity generation cost by cutting the peak to average ratio (PAR) without reducing the total amount of the loads.
However, most of these proposals rely on consumer's willingness to act.
In this paper, we propose an approach to cut PAR explicitly from the supply side.
The resulting cut loads are then distributed among consumers by the means of a multiunit auction which is done by an intelligent agent on behalf of the consumer.
This approach is also in line with the future vision of the smart grid to have the demand side matched with the supply side.
Experiments suggest that our approach reduces overall system cost and gives benefit to both consumers and the energy provider.
This paper discusses opportunities to parallelize graph based path planning algorithms in a time varying environment.
Parallel architectures have become commonplace, requiring algorithm to be parallelized for efficient execution.
An additional focal point of this paper is the inclusion of inaccuracies in path planning as a result of forecast error variance, accuracy of calculation in the cost functions and a different observed vehicle speed in the real mission than planned.
In this context, robust path planning algorithms will be described.
These algorithms are equally applicable to land based, aerial, or underwater mobile autonomous systems.
The results presented here provide the basis for a future Research project in which the parallelized algorithms will be evaluated on multi and many core systems such as the dual core ARM Panda board and the 48 core Single-chip Cloud Computer (SCC).
Modern multi and many core processors support a wide range of performance vs. energy tradeoffs that can be exploited in energyconstrained environments such as battery operated autonomous underwater vehicles.
For this evaluation, the boards will be deployed within the Slocum glider, a commercially available, buoyancy driven autonomous underwater vehicle (AUV).
This manuscript uses machine learning techniques to exploit baseball pitchers' decision making, so-called "Baseball IQ," by modeling the at-bat information, pitch selection and counts, as a Markov Decision Process (MDP).
Each state of the MDP models the pitcher's current pitch selection in a Markovian fashion, conditional on the information immediately prior to making the current pitch.
This includes the count prior to the previous pitch, his ensuing pitch selection, the batter's ensuing action and the result of the pitch.
Intent classification has been widely researched on English data with deep learning approaches that are based on neural networks and word embeddings.
The challenge for Chinese intent classification stems from the fact that, unlike English where most words are made up of 26 phonologic alphabet letters, Chinese is logographic, where a Chinese character is a more basic semantic unit that can be informative and its meaning does not vary too much in contexts.
Chinese word embeddings alone can be inadequate for representing words, and pre-trained embeddings can suffer from not aligning well with the task at hand.
To account for the inadequacy and leverage Chinese character information, we propose a low-effort and generic way to dynamically integrate character embedding based feature maps with word embedding based inputs, whose resulting word-character embeddings are stacked with a contextual information extraction module to further incorporate context information for predictions.
On top of the proposed model, we employ an ensemble method to combine single models and obtain the final result.
The approach is data-independent without relying on external sources like pre-trained word embeddings.
The proposed model outperforms baseline models and existing methods.
Multispectral (MS) image panchromatic (PAN) sharpening algorithms proposed to the remote sensing community are ever increasing in number and variety.
Their aim is to sharpen a coarse spatial resolution MS image with a fine spatial resolution PAN image acquired simultaneously by a spaceborne or airborne Earth observation (EO) optical imaging sensor pair.
Unfortunately, to date, no standard evaluation procedure for MS image PAN sharpening outcome and process is community agreed upon, in contrast with the Quality Assurance Framework for Earth Observation (QA4EO) guidelines proposed by the intergovernmental Group on Earth Observations (GEO).
In general, process is easier to measure, outcome is more important.
The original contribution of the present study is fourfold.
First, existing procedures for quantitative quality assessment (Q2A) of the (sole) PAN sharpened MS product are critically reviewed.
Their conceptual and implementation drawbacks are highlighted to be overcome for quality improvement.
Second, a novel (to the best of these authors' knowledge, the first) protocol for Q2A of MS image PAN sharpening product and process is designed, implemented and validated by independent means.
Third, within this protocol, an innovative categorization of spectral and spatial image quality indicators and metrics is presented.
Fourth, according to this new taxonomy, an original third order isotropic multi scale gray level co occurrence matrix (TIMS GLCM) calculator and a TIMS GLCM texture feature extractor are proposed to replace popular second order GLCMs.
Visual recognition of material boundaries in transparent vessels is valuable for numerous applications.
Such recognition is essential for estimation of fill-level, volume and phase-boundaries as well as for tracking of such chemical processes as precipitation, crystallization, condensation, evaporation and phase-separation.
The problem of material boundary recognition in images is particularly complex for materials with non-flat surfaces, i.e., solids, powders and viscous fluids, in which the material interfaces have unpredictable shapes.
This work demonstrates a general method for finding the boundaries of materials inside transparent containers in images.
The method uses an image of the transparent vessel containing the material and the boundary of the vessel in this image.
The recognition is based on the assumption that the material boundary appears in the image in the form of a curve (with various constraints) whose endpoints are both positioned on the vessel contour.
The probability that a curve matches the material boundary in the image is evaluated using a cost function based on some image properties along this curve.
Several image properties were examined as indicators for the material boundary.
The optimal boundary curve was found using Dijkstra's algorithm.
The method was successfully examined for recognition of various types of phase-boundaries, including liquid-air, solid-air and solid-liquid interfaces, as well as for various types of glassware containers from everyday life and the chemistry laboratory (i.e., bottles, beakers, flasks, jars, columns, vials and separation-funnels).
In addition, the method can be easily extended to materials carried on top of carrier vessels (i.e., plates, spoons, spatulas).
Pose-Graph optimization is a crucial component of many modern SLAM systems.
Most prominent state of the art systems address this problem by iterative non-linear least squares.
Both number of iterations and convergence basin of these approaches depend on the error functions used to describe the problem.
The smoother and more convex the error function with respect to perturbations of the state variables, the better the least-squares solver will perform.
In this paper we propose an alternative error function obtained by removing some non-linearities from the standard used one - i.e. the geodesic error function.
Comparative experiments conducted on common benchmarking datasets confirm that our function is more robust to noise that affects the rotational component of the pose measurements and, thus, exhibits a larger convergence basin than the geodesic.
Furthermore, its implementation is relatively easy compared to the geodesic distance.
This property leads to rather simple derivatives and nice numerical properties of the Jacobians resulting from the effective computation of the quadratic approximation used by Gauss-Newton algorithm.
We study the question of reconstructing a weighted, directed network up to isomorphism from its motifs.
In order to tackle this question we first relax the usual (strong) notion of graph isomorphism to obtain a relaxation that we call weak isomorphism.
Then we identify a definition of distance on the space of all networks that is compatible with weak isomorphism.
This global approach comes equipped with notions such as completeness, compactness, curves, and geodesics, which we explore throughout this paper.
Furthermore, it admits global-to-local inference in the following sense: we prove that two networks are weakly isomorphic if and only if all their motif sets are identical, thus answering the network reconstruction question.
Further exploiting the additional structure imposed by our network distance, we prove that two networks are weakly isomorphic if and only if certain essential associated structures---the skeleta of the respective networks---are strongly isomorphic.
In computer science, divide and conquer (D&C) is an algorithm design paradigm based on multi-branched recursion.
A D&C algorithm works by recursively and monotonically breaking down a problem into sub problems of the same (or a related) type, until these become simple enough to be solved directly.
The solutions to the sub problems are then combined to give a solution to the original problem.
The present work identifies D&C algorithms assumed within contemporary syntactic theory, and discusses the limits of their applicability in the realms of the syntax semantics and syntax morphophonology interfaces.
We will propose that D&C algorithms, while valid for some processes, fall short on flexibility given a mixed approach to the structure of linguistic phrase markers.
Arguments in favour of a computationally mixed approach to linguistic structure will be presented as an alternative that offers advantages to uniform D&C approaches.
We propose a stepsize adaptation scheme for stochastic gradient descent.
It operates directly with the loss function and rescales the gradient in order to make fixed predicted progress on the loss.
We demonstrate its capabilities by conclusively improving the performance of Adam and Momentum optimizers.
The enhanced optimizers with default hyperparameters consistently outperform their constant stepsize counterparts, even the best ones, without a measurable increase in computational cost.
The performance is validated on multiple architectures including dense nets, CNNs, ResNets, and the recurrent Differential Neural Computer on classical datasets MNIST, fashion MNIST, CIFAR10 and others.
This article is devoted to the stabilization of two underactuated planar systems, the well-known straight beam-and-ball system and an original circular beam-and-ball system.
The feedback control for each system is designed, using the Jordan form of its model, linearized near the unstable equilibrium.
The limits on the voltage, fed to the motor, are taken into account explicitly.
The straight beam-and-ball system has one unstable mode in the motion near the equilibrium point.
The proposed control law ensures that the basin of attraction coincides with the controllability domain.
The circular beam-and-ball system has two unstable modes near the equilibrium point.
Therefore, this device, never considered in the past, is much more difficult to control than the straight beam-and-ball system.
The main contribution is to propose a simple new control law, which ensures by adjusting its gain parameters that the basin of attraction arbitrarily can approach the controllability domain for the linear case.
For both nonlinear systems, simulation results are presented to illustrate the efficiency of the designed nonlinear control laws and to determine the basin of attraction.
The possibility of flexibly assigning spectrum resources with channels of different sizes greatly improves the spectral efficiency of optical networks, but can also lead to unwanted spectrum fragmentation.We study this problem in a scenario where traffic demands are categorised in two types (low or high bit-rate) by assessing the performance of three allocation policies.
Our first contribution consists of exact Markov chain models for these allocation policies, which allow us to numerically compute the relevant performance measures.
However, these exact models do not scale to large systems, in the sense that the computations required to determine the blocking probabilities---which measure the performance of the allocation policies---become intractable.
In order to address this, we first extend an approximate reduced-state Markov chain model that is available in the literature to the three considered allocation policies.
These reduced-state Markov chain models allow us to tractably compute approximations of the blocking probabilities, but the accuracy of these approximations cannot be easily verified.
Our main contribution then is the introduction of reduced-state imprecise Markov chain models that allow us to derive guaranteed lower and upper bounds on blocking probabilities, for the three allocation policies separately or for all possible allocation policies simultaneously.
The paper proposes a combination of the subdomain deflation method and local algebraic multigrid as a scalable distributed memory preconditioner that is able to solve large linear systems of equations.
The implementation of the algorithm is made available for the community as part of an open source AMGCL library.
The solution targets both homogeneous (CPU-only) and heterogeneous (CPU/GPU) systems, employing hybrid MPI/OpenMP approach in the former and a combination of MPI, OpenMP, and CUDA in the latter cases.
The use of OpenMP minimizes the number of MPI processes, thus reducing the communication overhead of the deflation method and improving both weak and strong scalability of the preconditioner.
The examples of scalar, Poisson-like, systems as well as non-scalar problems, stemming out of the discretization of the Navier-Stokes equations, are considered in order to estimate performance of the implemented algorithm.
A comparison with a traditional global AMG preconditioner based on a well-established Trilinos ML package is provided.
Generating natural questions from an image is a semantic task that requires using visual and language modality to learn multimodal representations.
Images can have multiple visual and language contexts that are relevant for generating questions namely places, captions, and tags.
In this paper, we propose the use of exemplars for obtaining the relevant context.
We obtain this by using a Multimodal Differential Network to produce natural and engaging questions.
The generated questions show a remarkable similarity to the natural questions as validated by a human study.
Further, we observe that the proposed approach substantially improves over state-of-the-art benchmarks on the quantitative metrics (BLEU, METEOR, ROUGE, and CIDEr).
In this paper, we propose and evaluate rate-maximizing pilot configurations for Unmanned Aerial Vehicle (UAV) communications employing OFDM waveforms.
OFDM relies on pilot symbols for effective communications.
We formulate a rate-maximization problem in which the pilot spacing (in the time-frequency resource grid) and power is varied as a function of the time-varying channel statistics.
The receiver solves this rate-maximization problem, and the optimal pilot spacing and power are explicitly fed back to the transmitter to adapt to the time-varying channel statistics in an air-to-ground (A2G) environment.
We show the enhanced throughput performance of this scheme for UAV communications in sub-6 GHz bands.
These performance gains are achieved at the cost of very low computational complexity and feedback requirements, making it attractive for A2G UAV communications in 5G.
We propose an efficient Stereographic Projection Neural Network (SPNet) for learning representations of 3D objects.
We first transform a 3D input volume into a 2D planar image using stereographic projection.
We then present a shallow 2D convolutional neural network (CNN) to estimate the object category followed by view ensemble, which combines the responses from multiple views of the object to further enhance the predictions.
Specifically, the proposed approach consists of four stages: (1) Stereographic projection of a 3D object, (2) view-specific feature learning, (3) view selection and (4) view ensemble.
The proposed approach performs comparably to the state-of-the-art methods while having substantially lower GPU memory as well as network parameters.
Despite its lightness, the experiments on 3D object classification and shape retrievals demonstrate the high performance of the proposed method.
Whenever multibeam satellite systems target very aggressive frequency reuse in their coverage area, inter-beam interference becomes the major obstacle for increasing the overall system throughput.
As a matter of fact, users located at the beam edges suffer from a very large interference for even a moderately aggressive planning of reuse-2.
Although solutions for inter-beam interference management have been investigated at the satellite terminal, it turns out that the performance improvement does not justify the increased terminal complexity and cost.
In this article, we pay attention to interference mitigation techniques that take place at the transmitter (i.e. the gateway).
Based on this understanding, we provide our vision on advanced precoding techniques and user clustering methods for multibeam broadband fixed satellite communications.
We also discuss practical challenges to deploy precoding schemes and the support introduced in the recently published DVB-S2X standard.
Future challenges for novel configurations employing precoding are also provided.
Identification of falls while performing normal activities of daily living (ADL) is important to ensure personal safety and well-being.
However, falling is a short term activity that occurs infrequently.
This poses a challenge to traditional classification algorithms, because there may be very little training data for falls (or none at all).
This paper proposes an approach for the identification of falls using a wearable device in the absence of training data for falls but with plentiful data for normal ADL.
We propose three `X-Factor' Hidden Markov Model (XHMMs) approaches.
The XHMMs model unseen falls using "inflated" output covariances (observation models).
To estimate the inflated covariances, we propose a novel cross validation method to remove "outliers" from the normal ADL that serve as proxies for the unseen falls and allow learning the XHMMs using only normal activities.
We tested the proposed XHMM approaches on two activity recognition datasets and show high detection rates for falls in the absence of fall-specific training data.
We show that the traditional method of choosing a threshold based on maximum of negative of log-likelihood to identify unseen falls is ill-posed for this problem.
We also show that supervised classification methods perform poorly when very limited fall data are available during the training phase.
We present an information-theoretic interpretation of quantum formalism based on a Bayesian framework and free of any additional axiom or principle.
Quantum information is merely construed as a technique of statistical estimation for analyzing a logical system subject to classical constraints, regardless of the specific variables used.
The problem is initially formulated in a standard Boolean algebra involving a particular set of working variables.
Statistical estimation is to express the truth table in terms of likelihood probability instead of the variables themselves.
The constraints are thus converted into a Bayesian prior.
This method leads to solving a linear programming problem in a real-valued probability space.
The complete set of alternative Boolean variables is introduced afterwards by transcribing the probability space into a Hilbert space, thanks to Gleason's theorem.
This allows to completely recover standard quantum information and provides an information-theoretic rationale to its technical rules.
The model offers a natural answer to the major puzzles that base quantum mechanics: Why is the theory linear?
Why is the theory probabilistic?
Where does the Hilbert space come from?
Also, most of the paradoxes, such as entanglement, contextuality, nonsignaling correlation, measurement problem, etc., find a quite trivial explanation, while the concept of information conveyed by a wave vector is clarified.
We conclude that quantum information, although dramatically expanding the scope of classical information, is not different from the information itself and is therefore a universal tool of reasoning.
We introduce Graph-Structured Sum-Product Networks (GraphSPNs), a probabilistic approach to structured prediction for problems where dependencies between latent variables are expressed in terms of arbitrary, dynamic graphs.
While many approaches to structured prediction place strict constraints on the interactions between inferred variables, many real-world problems can be only characterized using complex graph structures of varying size, often contaminated with noise when obtained from real data.
Here, we focus on one such problem in the domain of robotics.
We demonstrate how GraphSPNs can be used to bolster inference about semantic, conceptual place descriptions using noisy topological relations discovered by a robot exploring large-scale office spaces.
Through experiments, we show that GraphSPNs consistently outperform the traditional approach based on undirected graphical models, successfully disambiguating information in global semantic maps built from uncertain, noisy local evidence.
We further exploit the probabilistic nature of the model to infer marginal distributions over semantic descriptions of as yet unexplored places and detect spatial environment configurations that are novel and incongruent with the known evidence.
Uncertainty of decisions in safety-critical engineering applications can be estimated on the basis of the Bayesian Markov Chain Monte Carlo (MCMC) technique of averaging over decision models.
The use of decision tree (DT) models assists experts to interpret causal relations and find factors of the uncertainty.
Bayesian averaging also allows experts to estimate the uncertainty accurately when a priori information on the favored structure of DTs is available.
Then an expert can select a single DT model, typically the Maximum a Posteriori model, for interpretation purposes.
Unfortunately, a priori information on favored structure of DTs is not always available.
For this reason, we suggest a new prior on DTs for the Bayesian MCMC technique.
We also suggest a new procedure of selecting a single DT and describe an application scenario.
In our experiments on the Short-Term Conflict Alert data our technique outperforms the existing Bayesian techniques in predictive accuracy of the selected single DTs.
Automatic skin lesion segmentation on dermoscopic images is an essential component in computer-aided diagnosis of melanoma.
Recently, many fully supervised deep learning based methods have been proposed for automatic skin lesion segmentation.
However, these approaches require massive pixel-wise annotation from experienced dermatologists, which is very costly and time-consuming.
In this paper, we present a novel semi-supervised method for skin lesion segmentation by leveraging both labeled and unlabeled data.
The network is optimized by the weighted combination of a common supervised loss for labeled inputs only and a regularization loss for both labeled and unlabeled data.
In this paper, we present a novel semi-supervised method for skin lesion segmentation, where the network is optimized by the weighted combination of a common supervised loss for labeled inputs only and a regularization loss for both labeled and unlabeled data.
Our method encourages a consistent prediction for unlabeled images using the outputs of the network-in-training under different regularizations, so that it can utilize the unlabeled data.
To utilize the unlabeled data, our method encourages the consistent predictions of the network-in-training for the same input under different regularizations.
Aiming for the semi-supervised segmentation problem, we enhance the effect of regularization for pixel-level predictions by introducing a transformation, including rotation and flipping, consistent scheme in our self-ensembling model.
With only 300 labeled training samples, our method sets a new record on the benchmark of the International Skin Imaging Collaboration (ISIC) 2017 skin lesion segmentation challenge.
Such a result clearly surpasses fully-supervised state-of-the-arts that are trained with 2000 labeled data.
As the practical use of answer set programming (ASP) has grown with the development of efficient solvers, we expect a growing interest in extensions of ASP as their semantics stabilize and solvers supporting them mature.
Epistemic Specifications, which adds modal operators K and M to the language of ASP, is one such extension.
We call a program in this language an epistemic logic program (ELP).
Solvers have thus far been practical for only the simplest ELPs due to exponential growth of the search space.
We describe a solver that is able to solve harder problems better (e.g., without exponentially-growing memory needs w.r.t.K and M occurrences) and faster than any other known ELP solver.
At computer modeling of process of training it is usually supposed that all elements of a training material are forgotten with an identical speed.
But in practice that knowledge which are included in educational activity of the pupil are remembered much more strongly and forgotten more slowly then knowledge which he doesn't use.
For the purpose of more exact research of didactic systems is offered the model of training, in which consider that in case increasing the number of applications of this element of a learning material: 1) duration of its use by the pupil decreases; 2) the coefficient of forgetting decreases.
The computer model is considered, programs in the Pascal language are submitted, results of modeling are given and analyzed.
Keywords: didactics, information and cybernetic approach, computer modeling of process of training.
"How much energy is consumed for an inference made by a convolutional neural network (CNN)?"
With the increased popularity of CNNs deployed on the wide-spectrum of platforms (from mobile devices to workstations), the answer to this question has drawn significant attention.
From lengthening battery life of mobile devices to reducing the energy bill of a datacenter, it is important to understand the energy efficiency of CNNs during serving for making an inference, before actually training the model.
In this work, we propose NeuralPower: a layer-wise predictive framework based on sparse polynomial regression, for predicting the serving energy consumption of a CNN deployed on any GPU platform.
Given the architecture of a CNN, NeuralPower provides an accurate prediction and breakdown for power and runtime across all layers in the whole network, helping machine learners quickly identify the power, runtime, or energy bottlenecks.
We also propose the "energy-precision ratio" (EPR) metric to guide machine learners in selecting an energy-efficient CNN architecture that better trades off the energy consumption and prediction accuracy.
The experimental results show that the prediction accuracy of the proposed NeuralPower outperforms the best published model to date, yielding an improvement in accuracy of up to 68.5%.
We also assess the accuracy of predictions at the network level, by predicting the runtime, power, and energy of state-of-the-art CNN architectures, achieving an average accuracy of 88.24% in runtime, 88.34% in power, and 97.21% in energy.
We comprehensively corroborate the effectiveness of NeuralPower as a powerful framework for machine learners by testing it on different GPU platforms and Deep Learning software tools.
Autonomous robots need to interact with unknown, unstructured and changing environments, constantly facing novel challenges.
Therefore, continuous online adaptation for lifelong-learning and the need of sample-efficient mechanisms to adapt to changes in the environment, the constraints, the tasks, or the robot itself are crucial.
In this work, we propose a novel framework for probabilistic online motion planning with online adaptation based on a bio-inspired stochastic recurrent neural network.
By using learning signals which mimic the intrinsic motivation signalcognitive dissonance in addition with a mental replay strategy to intensify experiences, the stochastic recurrent network can learn from few physical interactions and adapts to novel environments in seconds.
We evaluate our online planning and adaptation framework on an anthropomorphic KUKA LWR arm.
The rapid online adaptation is shown by learning unknown workspace constraints sample-efficiently from few physical interactions while following given way points.
We initiate a general study of what we call orientation completion problems.
For a fixed class C of oriented graphs, the orientation completion problem asks whether a given partially oriented graph P can be completed to an oriented graph in C by orienting the (non-oriented) edges in P. Orien- tation completion problems commonly generalize several existing problems including recognition of certain classes of graphs and digraphs as well as extending representations of certain geometrically representable graphs.
We study orientation completion problems for various classes of oriented graphs, including k-arc- strong oriented graphs, k-strong oriented graphs, quasi-transitive oriented graphs, local tournament, acyclic local tournaments, locally transitive tournaments, locally transitive local tournaments, in- tournaments, and oriented graphs which have directed cycle factors.
We show that the orientation completion problem for each of these classes is either polynomial time solvable or NP-complete.
We also show that some of the NP-complete problems become polynomial time solvable when the input oriented graphs satisfy certain extra conditions.
Our results imply that the representation extension problems for proper interval graphs and for proper circular arc graphs are polynomial time solvable, which generalize a previous result.
As the volume of medicinal information stored electronically increase, so do the need to enhance how it is secured.
The inaccessibility to patient record at the ideal time can prompt death toll and also well degrade the level of health care services rendered by the medicinal professionals.
Criminal assaults in social insurance have expanded by 125% since 2010 and are now the leading cause of medical data breaches.
This study therefore presents the combination of 3DES and LSB to improve security measure applied on medical data.
Java programming language was used to develop a simulation program for the experiment.
The result shows medical data can be stored, shared, and managed in a reliable and secure manner using the combined model.
The use of satellite imagery has become increasingly popular for disaster monitoring and response.
After a disaster, it is important to prioritize rescue operations, disaster response and coordinate relief efforts.
These have to be carried out in a fast and efficient manner since resources are often limited in disaster-affected areas and it's extremely important to identify the areas of maximum damage.
However, most of the existing disaster mapping efforts are manual which is time-consuming and often leads to erroneous results.
In order to address these issues, we propose a framework for change detection using Convolutional Neural Networks (CNN) on satellite images which can then be thresholded and clustered together into grids to find areas which have been most severely affected by a disaster.
We also present a novel metric called Disaster Impact Index (DII) and use it to quantify the impact of two natural disasters - the Hurricane Harvey flood and the Santa Rosa fire.
Our framework achieves a top F1 score of 81.2% on the gridded flood dataset and 83.5% on the gridded fire dataset.
In this paper, we propose a novel fully convolutional two-stream fusion network (FCTSFN) for interactive image segmentation.
The proposed network includes two sub-networks: a two-stream late fusion network (TSLFN) that predicts the foreground at a reduced resolution, and a multi-scale refining network (MSRN) that refines the foreground at full resolution.
The TSLFN includes two distinct deep streams followed by a fusion network.
The intuition is that, since user interactions are more direct information on foreground/background than the image itself, the two-stream structure of the TSLFN reduces the number of layers between the pure user interaction features and the network output, allowing the user interactions to have a more direct impact on the segmentation result.
The MSRN fuses the features from different layers of TSLFN with different scales, in order to seek the local to global information on the foreground to refine the segmentation result at full resolution.
We conduct comprehensive experiments on four benchmark datasets.
The results show that the proposed network achieves competitive performance compared to current state-of-the-art interactive image segmentation methods
We propose a novel way of computing surface folding maps via solving a linear PDE.
This framework is a generalization to the existing quasiconformal methods and allows manipulation of the geometry of folding.
Moreover, the crucial quantity that characterizes the geometry occurs as the coefficient of the equation, namely the Beltrami coefficient.
This allows us to solve an inverse problem of parametrizing the folded surface given only partial data but with known folding topology.
Various interesting applications such as fold sculpting on 3D models and self-occlusion reasoning are demonstrated to show the effectiveness of our method.
The use of functional brain imaging for research and diagnosis has benefitted greatly from the recent advancements in neuroimaging technologies, as well as the explosive growth in size and availability of fMRI data.
While it has been shown in literature that using multiple and large scale fMRI datasets can improve reproducibility and lead to new discoveries, the computational and informatics systems supporting the analysis and visualization of such fMRI big data are extremely limited and largely under-discussed.
We propose to address these shortcomings in this work, based on previous success in using dictionary learning method for functional network decomposition studies on fMRI data.
We presented a distributed dictionary learning framework based on rank-1 matrix decomposition with sparseness constraint (D-r1DL framework).
The framework was implemented using the Spark distributed computing engine and deployed on three different processing units: an in-house server, in-house high performance clusters, and the Amazon Elastic Compute Cloud (EC2) service.
The whole analysis pipeline was integrated with our neuroinformatics system for data management, user input/output, and real-time visualization.
Performance and accuracy of D-r1DL on both individual and group-wise fMRI Human Connectome Project (HCP) dataset shows that the proposed framework is highly scalable.
The resulting group-wise functional network decompositions are highly accurate, and the fast processing time confirm this claim.
In addition, D-r1DL can provide real-time user feedback and results visualization which are vital for large-scale data analysis.
Most existing methods for automatic bilingual dictionary induction rely on prior alignments between the source and target languages, such as parallel corpora or seed dictionaries.
For many language pairs, such supervised alignments are not readily available.
We propose an unsupervised approach for learning a bilingual dictionary for a pair of languages given their independently-learned monolingual word embeddings.
The proposed method exploits local and global structures in monolingual vector spaces to align them such that similar words are mapped to each other.
We show empirically that the performance of bilingual correspondents learned using our proposed unsupervised method is comparable to that of using supervised bilingual correspondents from a seed dictionary.
We formulate problems of statistical recognition and learning in a common framework of complex hypothesis testing.
Based on arguments from multi-criteria optimization, we identify strategies that are improper for solving these problems and derive a common form of the remaining strategies.
We show that some widely used approaches to recognition and learning are improper in this sense.
We then propose a generalized formulation of the recognition and learning problem which embraces the whole range of sizes of the learning sample, including the zero size.
Learning becomes a special case of recognition without learning.
We define the concept of closest to optimal strategy, being a solution to the formulated problem, and describe a technique for finding such a strategy.
On several illustrative cases, the strategy is shown to be superior to the widely used learning methods based on maximal likelihood estimation.
Thanks to the low operational cost and large storage capacity of smartphones and wearable devices, people are recording many hours of daily activities, sport actions and home videos.
These videos, also known as egocentric videos, are generally long-running streams with unedited content, which make them boring and visually unpalatable, bringing up the challenge to make egocentric videos more appealing.
In this work we propose a novel methodology to compose the new fast-forward video by selecting frames based on semantic information extracted from images.
The experiments show that our approach outperforms the state-of-the-art as far as semantic information is concerned and that it is also able to produce videos that are more pleasant to be watched.
A latent-variable model is introduced for text matching, inferring sentence representations by jointly optimizing generative and discriminative objectives.
To alleviate typical optimization challenges in latent-variable models for text, we employ deconvolutional networks as the sequence decoder (generator), providing learned latent codes with more semantic information and better generalization.
Our model, trained in an unsupervised manner, yields stronger empirical predictive performance than a decoder based on Long Short-Term Memory (LSTM), with less parameters and considerably faster training.
Further, we apply it to text sequence-matching problems.
The proposed model significantly outperforms several strong sentence-encoding baselines, especially in the semi-supervised setting.
Deep learning has recently become one of the most popular sub-fields of machine learning owing to its distributed data representation with multiple levels of abstraction.
A diverse range of deep learning algorithms are being employed to solve conventional artificial intelligence problems.
This paper gives an overview of some of the most widely used deep learning algorithms applied in the field of computer vision.
It first inspects the various approaches of deep learning algorithms, followed by a description of their applications in image classification, object identification, image extraction and semantic segmentation in the presence of noise.
The paper concludes with the discussion of the future scope and challenges for construction and training of deep neural networks.
Detection of indoor and outdoor scenarios is an important resource for many types of activities such as multisensor navigation and location-based services.
This research presents the use of NMEA data provided by GPS receivers to characterize different types of scenarios automatically.
A set of static tests was performed to evaluate metrics such as number of satellites, positioning solution geometry and carrier-to-receiver noise-density ratio values to detect possible patterns to determine indoor and outdoor scenarios.
Subsequently, validation tests are applied to verify that parameters obtained are adequate.
Network embedding (NE) is playing a principal role in network mining, due to its ability to map nodes into efficient low-dimensional embedding vectors.
However, two major limitations exist in state-of-the-art NE methods: structure preservation and uncertainty modeling.
Almost all previous methods represent a node into a point in space and focus on the local structural information, i.e., neighborhood information.
However, neighborhood information does not capture the global structural information and point vector representation fails in modeling the uncertainty of node representations.
In this paper, we propose a new NE framework, struc2gauss, which learns node representations in the space of Gaussian distributions and performs network embedding based on global structural information.
struc2gauss first employs a given node similarity metric to measure the global structural information, then generates structural context for nodes and finally learns node representations via Gaussian embedding.
Different structural similarity measures of networks and energy functions of Gaussian embedding are investigated.
Experiments conducted on both synthetic and real-world data sets demonstrate that struc2gauss effectively captures the global structural information while state-of-the-art network embedding methods fails to, outperforms other methods on the structure-based clustering task and provides more information on uncertainties of node representations.
Zero-shot learning has received increasing interest as a means to alleviate the often prohibitive expense of annotating training data for large scale recognition problems.
These methods have achieved great success via learning intermediate semantic representations in the form of attributes and more recently, semantic word vectors.
However, they have thus far been constrained to the single-label case, in contrast to the growing popularity and importance of more realistic multi-label data.
In this paper, for the first time, we investigate and formalise a general framework for multi-label zero-shot learning, addressing the unique challenge therein: how to exploit multi-label correlation at test time with no training data for those classes?
In particular, we propose (1) a multi-output deep regression model to project an image into a semantic word space, which explicitly exploits the correlations in the intermediate semantic layer of word vectors; (2) a novel zero-shot learning algorithm for multi-label data that exploits the unique compositionality property of semantic word vector representations; and (3) a transductive learning strategy to enable the regression model learned from seen classes to generalise well to unseen classes.
Our zero-shot learning experiments on a number of standard multi-label datasets demonstrate that our method outperforms a variety of baselines.
The paper reports our participation in the shared task on word sense induction and disambiguation for the Russian language (RUSSE-2018).
Our team was ranked 2nd for the wiki-wiki dataset (containing mostly homonyms) and 5th for the bts-rnc and active-dict datasets (containing mostly polysemous words) among all 19 participants.
The method we employed was extremely naive.
It implied representing contexts of ambiguous words as averaged word embedding vectors, using off-the-shelf pre-trained distributional models.
Then, these vector representations were clustered with mainstream clustering techniques, thus producing the groups corresponding to the ambiguous word senses.
As a side result, we show that word embedding models trained on small but balanced corpora can be superior to those trained on large but noisy data - not only in intrinsic evaluation, but also in downstream tasks like word sense induction.
This paper studies change point detection on networks with community structures.
It proposes a framework that can detect both local and global changes in networks efficiently.
Importantly, it can clearly distinguish the two types of changes.
The framework design is generic and as such several state-of-the-art change point detection algorithms can fit in this design.
Experiments on both synthetic and real-world networks show that this framework can accurately detect changes while achieving up to 800X speedup.
The time domain inter-cell interference coordination techniques specified in LTE Rel.
10 standard improves the throughput of picocell-edge users by protecting them from macrocell interference.
On the other hand, it also degrades the aggregate capacity in macrocell because the macro base station (MBS) does not transmit data during certain subframes known as almost blank subframes.
The MBS data transmission using reduced power subframes was standardized in LTE Rel.11, which can improve the capacity in macrocell while not causing high interference to the nearby picocells.
In order to get maximum benefit from the reduced power subframes, setting the key system parameters, such as the amount of power reduction, carries critical importance.
Using stochastic geometry, this paper lays down a theoretical foundation for the performance evaluation of heterogeneous networks with reduced power subframes and range expansion bias.
The analytic expressions for average capacity and 5th percentile throughput are derived as a function of transmit powers, node densities, and interference coordination parameters in a heterogeneous network scenario, and are validated through Monte Carlo simulations.
Joint optimization of range expansion bias, power reduction factor, scheduling thresholds, and duty cycle of reduced power subframes are performed to study the trade-offs between aggregate capacity of a cell and fairness among the users.
To validate our analysis, we also compare the stochastic geometry based theoretical results with the real MBS deployment (in the city of London) and the hexagonal-grid model.
Our analysis shows that with optimum parameter settings, the LTE Rel.11 with reduced power subframes can provide substantially better performance than the LTE Rel.10 with almost blank subframes, in terms of both aggregate capacity and fairness.
Mining dense subgraphs on multi-layer graphs is an interesting problem, which has witnessed lots of applications in practice.
To overcome the limitations of the quasi-clique-based approach, we propose d-coherent core (d-CC), a new notion of dense subgraph on multi-layer graphs, which has several elegant properties.
We formalize the diversified coherent core search (DCCS) problem, which finds k d-CCs that can cover the largest number of vertices.
We propose a greedy algorithm with an approximation ratio of 1 - 1/e and two search algorithms with an approximation ratio of 1/4.
The experiments verify that the search algorithms are faster than the greedy algorithm and produce comparably good results as the greedy algorithm in practice.
As opposed to the quasi-clique-based approach, our DCCS algorithms can fast detect larger dense subgraphs that cover most of the quasi-clique-based results.
The design of robotic systems is largely dictated by our purely human intuition about how we perceive the world.
This intuition has been proven incorrect with regard to a number of critical issues, such as visual change blindness.
In order to develop truly autonomous robots, we must step away from this intuition and let robotic agents develop their own way of perceiving.
The robot should start from scratch and gradually develop perceptual notions, under no prior assumptions, exclusively by looking into its sensorimotor experience and identifying repetitive patterns and invariants.
One of the most fundamental perceptual notions, space, cannot be an exception to this requirement.
In this paper we look into the prerequisites for the emergence of simplified spatial notions on the basis of a robot's sensorimotor flow.
We show that the notion of space as environment-independent cannot be deduced solely from exteroceptive information, which is highly variable and is mainly determined by the contents of the environment.
The environment-independent definition of space can be approached by looking into the functions that link the motor commands to changes in exteroceptive inputs.
In a sufficiently rich environment, the kernels of these functions correspond uniquely to the spatial configuration of the agent's exteroceptors.
We simulate a redundant robotic arm with a retina installed at its end-point and show how this agent can learn the configuration space of its retina.
The resulting manifold has the topology of the Cartesian product of a plane and a circle, and corresponds to the planar position and orientation of the retina.
We present a comprehensive study and evaluation of existing single image dehazing algorithms, using a new large-scale benchmark consisting of both synthetic and real-world hazy images, called REalistic Single Image DEhazing (RESIDE).
RESIDE highlights diverse data sources and image contents, and is divided into five subsets, each serving different training or evaluation purposes.
We further provide a rich variety of criteria for dehazing algorithm evaluation, ranging from full-reference metrics, to no-reference metrics, to subjective evaluation and the novel task-driven evaluation.
Experiments on RESIDE shed light on the comparisons and limitations of state-of-the-art dehazing algorithms, and suggest promising future directions.
Trajectory prediction (TP) is of great importance for a wide range of location-based applications in intelligent transport systems such as location-based advertising, route planning, traffic management, and early warning systems.
In the last few years, the widespread use of GPS navigation systems and wireless communication technology enabled vehicles has resulted in huge volumes of trajectory data.
The task of utilizing this data employing spatio-temporal techniques for trajectory prediction in an efficient and accurate manner is an ongoing research problem.
Existing TP approaches are limited to short-term predictions.
Moreover, they cannot handle a large volume of trajectory data for long-term prediction.
To address these limitations, we propose a scalable clustering and Markov chain based hybrid framework, called Traj-clusiVAT-based TP, for both short-term and long-term trajectory prediction, which can handle a large number of overlapping trajectories in a dense road network.
Traj-clusiVAT can also determine the number of clusters, which represent different movement behaviours in input trajectory data.
In our experiments, we compare our proposed approach with a mixed Markov model (MMM)-based scheme, and a trajectory clustering, NETSCAN-based TP method for both short- and long-term trajectory predictions.
We performed our experiments on two real, vehicle trajectory datasets, including a large-scale trajectory dataset consisting of 3.28 million trajectories obtained from 15,061 taxis in Singapore over a period of one month.
Experimental results on two real trajectory datasets show that our proposed approach outperforms the existing approaches in terms of both short- and long-term prediction performances, based on prediction accuracy and distance error (in km).
The human heart is enclosed in the pericardial cavity.
The pericardium consists of a layered thin sac and is separated from the myocardium by a thin film of fluid.
It provides a fixture in space and frictionless sliding of the myocardium.
The influence of the pericardium is essential for predictive mechanical simulations of the heart.
However, there is no consensus on physiologically correct and computationally tractable pericardial boundary conditions.
Here we propose to model the pericardial influence as a parallel spring and dashpot acting in normal direction to the epicardium.
Using a four-chamber geometry, we compare a model with pericardial boundary conditions to a model with fixated apex.
The influence of pericardial stiffness is demonstrated in a parametric study.
Comparing simulation results to measurements from cine magnetic resonance imaging reveals that adding pericardial boundary conditions yields a better approximation with respect to atrioventricular plane displacement, atrial filling, and overall spatial approximation error.
We demonstrate that this simple model of pericardial-myocardial interaction can correctly predict the pumping mechanisms of the heart as previously assessed in clinical studies.
Utilizing a pericardial model can not only provide much more realistic cardiac mechanics simulations but also allows new insights into pericardial-myocardial interaction which cannot be assessed in clinical measurements yet.
The excessive use of digital devices such as cameras and smartphones in smart cities has produced huge data repositories that require automatic tools for efficient browsing, searching, and management.
Data prioritization (DP) is a technique that produces a condensed form of the original data by analyzing its contents.
Current DP studies are either concerned with data collected through stable capturing devices or focused on prioritization of data of a certain type such as surveillance, sports, or industry.
This necessitates the need for DP tools that intelligently and cost-effectively prioritize a large variety of data for detecting abnormal events and hence effectively manage them, thereby making the current smart cities greener.
In this article, we first carry out an in-depth investigation of the recent approaches and trends of DP for data of different natures, genres, and domains of two decades in green smart cities.
Next, we propose an energy-efficient DP framework by intelligent integration of the Internet of Things, artificial intelligence, and big data analytics.
Experimental evaluation on real-world surveillance data verifies the energy efficiency and applicability of this framework in green smart cities.
Finally, this article highlights the key challenges of DP, its future requirements, and propositions for integration into green smart cities.
We present a rational analysis of curiosity, proposing that people's curiosity is driven by seeking stimuli that maximize their ability to make appropriate responses in the future.
This perspective offers a way to unify previous theories of curiosity into a single framework.
Experimental results confirm our model's predictions, showing how the relationship between curiosity and confidence can change significantly depending on the nature of the environment.
In this technical report we present novel results of the dopamine neuromodulation inspired modulation of a polyaniline (PANI) memristive device excitatory learning STDP.
Results presented in this work are of two experiments setup computer simulation and physical prototype experiments.
We present physical prototype of inhibitory learning or iSTDP as well as the results of iSTDP learning.
Relational queries, and in particular join queries, often generate large output results when executed over a huge dataset.
In such cases, it is often infeasible to store the whole materialized output if we plan to reuse it further down a data processing pipeline.
Motivated by this problem, we study the construction of space-efficient compressed representations of the output of conjunctive queries, with the goal of supporting the efficient access of the intermediate compressed result for a given access pattern.
In particular, we initiate the study of an important tradeoff: minimizing the space necessary to store the compressed result, versus minimizing the answer time and delay for an access request over the result.
Our main contribution is a novel parameterized data structure, which can be tuned to trade off space for answer time.
The tradeoff allows us to control the space requirement of the data structure precisely, and depends both on the structure of the query and the access pattern.
We show how we can use the data structure in conjunction with query decomposition techniques, in order to efficiently represent the outputs for several classes of conjunctive queries.
We propose an end-to-end deep learning model for translating free-form natural language instructions to a high-level plan for behavioral robot navigation.
We use attention models to connect information from both the user instructions and a topological representation of the environment.
We evaluate our model's performance on a new dataset containing 10,050 pairs of navigation instructions.
Our model significantly outperforms baseline approaches.
Furthermore, our results suggest that it is possible to leverage the environment map as a relevant knowledge base to facilitate the translation of free-form navigational instruction.
We introduce the persistent homotopy type distance dHT to compare real valued functions defined on possibly different homotopy equivalent topological spaces.
The underlying idea in the definition of dHT is to measure the minimal shift that is necessary to apply to one of the two functions in order that the sublevel sets of the two functions become homotopically equivalent.
This distance is interesting in connection with persistent homology.
Indeed, our main result states that dHT still provides an upper bound for the bottleneck distance between the persistence diagrams of the intervening functions.
Moreover, because homotopy equivalences are weaker than homeomorphisms, this implies a lifting of the standard stability results provided by the L-infty distance and the natural pseudo-distance dNP.
From a different standpoint, we prove that dHT extends the L-infty distance and dNP in two ways.
First, we show that, appropriately restricting the category of objects to which dHT applies, it can be made to coincide with the other two distances.
Finally, we show that dHT has an interpretation in terms of interleavings that naturally places it in the family of distances used in persistence theory.
In this paper, we demonstrate an end-to-end spatiotemporal gesture learning approach for 3D point cloud data using a new gestures dataset of point clouds acquired from a 3D sensor.
Nine classes of gestures were learned from gestures sample data.
We mapped point cloud data into dense occupancy grids, then time steps of the occupancy grids are used as inputs into a 3D convolutional neural network which learns the spatiotemporal features in the data without explicit modeling of gesture dynamics.
We also introduced a 3D region of interest jittering approach for point cloud data augmentation.
This resulted in an increased classification accuracy of up to 10% when the augmented data is added to the original training data.
The developed model is able to classify gestures from the dataset with 84.44% accuracy.
We propose that point cloud data will be a more viable data type for scene understanding and motion recognition, as 3D sensors become ubiquitous in years to come.
Fostering technological innovation is intimately related to knowledge creation and recombination.
Here, we map research efforts in Greece within the domain of renewable energy technology and its intersections with the domains of nanoscience and nanotechnology with focus on materials, and electrical engineering and computer science by means of a combined statistical and network-based approach to studying collaboration in scientific authorship.
We specifically examine the content, organizational make-up and geographic trace of scientific collaboration, how these have evolved over the sixteen-year period 2000-2015, and we attempt to illuminate the processes underlying knowledge creation and diversification.
Our findings collectively provide insights into the collaboration structure and evolution of energy-related research activity in Greece and can be used to inform research, development and innovation policy for energy technology.
We argue that hierarchical methods can become the key for modular robots achieving reconfigurability.
We present a hierarchical approach for modular robots that allows a robot to simultaneously learn multiple tasks.
Our evaluation results present an environment composed of two different modular robot configurations, namely 3 degrees-of-freedom (DoF) and 4DoF with two corresponding targets.
During the training, we switch between configurations and targets aiming to evaluate the possibility of training a neural network that is able to select appropriate motor primitives and robot configuration to achieve the target.
The trained neural network is then transferred and executed on a real robot with 3DoF and 4DoF configurations.
We demonstrate how this technique generalizes to robots with different configurations and tasks.
Inversion and PDE-constrained optimization problems often rely on solving the adjoint problem to calculate the gradient of the objec- tive function.
This requires storing large amounts of intermediate data, setting a limit to the largest problem that might be solved with a given amount of memory available.
Checkpointing is an approach that can reduce the amount of memory required by redoing parts of the computation instead of storing intermediate results.
The Revolve checkpointing algorithm o ers an optimal schedule that trades computational cost for smaller memory footprints.
Integrat- ing Revolve into a modern python HPC code and combining it with code generation is not straightforward.
We present an API that makes checkpointing accessible from a DSL-based code generation environment along with some initial performance gures with a focus on seismic applications.
Automatic melody generation has been a long-time aspiration for both AI researchers and musicians.
However, learning to generate euphonious melodies has turned out to be highly challenging.
This paper introduces 1) a new variant of variational autoencoder (VAE), where the model structure is designed in a modularized manner in order to model polyphonic and dynamic music with domain knowledge, and 2) a hierarchical encoding/decoding strategy, which explicitly models the dependency between melodic features.
The proposed framework is capable of generating distinct melodies that sounds natural, and the experiments for evaluating generated music clips show that the proposed model outperforms the baselines in human evaluation.
Due to computational and storage efficiencies of compact binary codes, hashing has been widely used for large-scale similarity search.
Unfortunately, many existing hashing methods based on observed keyword features are not effective for short texts due to the sparseness and shortness.
Recently, some researchers try to utilize latent topics of certain granularity to preserve semantic similarity in hash codes beyond keyword matching.
However, topics of certain granularity are not adequate to represent the intrinsic semantic information.
In this paper, we present a novel unified approach for short text Hashing using Multi-granularity Topics and Tags, dubbed HMTT.
In particular, we propose a selection method to choose the optimal multi-granularity topics depending on the type of dataset, and design two distinct hashing strategies to incorporate multi-granularity topics.
We also propose a simple and effective method to exploit tags to enhance the similarity of related texts.
We carry out extensive experiments on one short text dataset as well as on one normal text dataset.
The results demonstrate that our approach is effective and significantly outperforms baselines on several evaluation metrics.
End-to-end task-oriented dialog systems usually suffer from the challenge of incorporating knowledge bases.
In this paper, we propose a novel yet simple end-to-end differentiable model called memory-to-sequence (Mem2Seq) to address this issue.
Mem2Seq is the first neural generative model that combines the multi-hop attention over memories with the idea of pointer network.
We empirically show how Mem2Seq controls each generation step, and how its multi-hop attention mechanism helps in learning correlations between memories.
In addition, our model is quite general without complicated task-specific designs.
As a result, we show that Mem2Seq can be trained faster and attain the state-of-the-art performance on three different task-oriented dialog datasets.
Millimeter-wave (mmWave) communications have been considered as a key technology for future 5G wireless networks because of the orders-of-magnitude wider bandwidth than current cellular bands.
In this paper, we consider the problem of codebook-based joint analog-digital hybrid precoder and combiner design for spatial multiplexing transmission in a mmWave multiple-input multiple-output (MIMO) system.
We propose to jointly select analog precoder and combiner pair for each data stream successively aiming at maximizing the channel gain while suppressing the interference between different data streams.
After all analog precoder/combiner pairs have been determined, we can obtain the effective baseband channel.
Then, the digital precoder and combiner are computed based on the obtained effective baseband channel to further mitigate the interference and maximize the sum-rate.
Simulation results demonstrate that our proposed algorithm exhibits prominent advantages in combating interference between different data streams and offer satisfactory performance improvement compared to the existing codebook-based hybrid beamforming schemes.
This paper proposes a new scheduler applying the concept of non-uniform laxity to Earliest deadline first (EDF) approach for aperiodic tasks.
This scheduler improves task utilisation (Execution time / deadline) and also increases the number of tasks that are being scheduled.
Laxity is a measure of the spare time permitted for the task before it misses its deadline, and is computed using the expression (deadline - (current time + execution time)).
Weight decides the priority of the task and is defined by the expression (quantum slice time / allocated time)*total core time for the task.
Quantum slice time is the time actually used, allocated time is the time allocated by the scheduler, and total core time is the time actually reserved by the core for execution of one quantum of the task.
Non-uniform laxity enables scheduling of tasks that have higher priority before the normal execution of other tasks and is computed by multiplying the weight of the task with its laxity.
The algorithm presented in the paper has been simulated on Cheddar, a real time scheduling tool and also on SESC, an architectural simulator for multicore platforms, for upto 5000 random task sets, and upto 5000 cores.
This scheduler improves task utilisation by 35% and the number of tasks being scheduled by 36%, compared to conventional EDF.
The non-stationarity characteristic of the solar power renders traditional point forecasting methods to be less useful due to large prediction errors.
This results in increased uncertainties in the grid operation, thereby negatively affecting the reliability and increased cost of operation.
This research paper proposes a unified architecture for multi-time-horizon predictions for short and long-term solar forecasting using Recurrent Neural Networks (RNN).
The paper describes an end-to-end pipeline to implement the architecture along with the methods to test and validate the performance of the prediction model.
The results demonstrate that the proposed method based on the unified architecture is effective for multi-horizon solar forecasting and achieves a lower root-mean-squared prediction error compared to the previous best-performing methods which use one model for each time-horizon.
The proposed method enables multi-horizon forecasts with real-time inputs, which have a high potential for practical applications in the evolving smart grid.
Supervised machine learning models boast remarkable predictive capabilities.
But can you trust your model?
Will it work in deployment?
What else can it tell you about the world?
We want models to be not only good, but interpretable.
And yet the task of interpretation appears underspecified.
Papers provide diverse and sometimes non-overlapping motivations for interpretability, and offer myriad notions of what attributes render models interpretable.
Despite this ambiguity, many papers proclaim interpretability axiomatically, absent further explanation.
In this paper, we seek to refine the discourse on interpretability.
First, we examine the motivations underlying interest in interpretability, finding them to be diverse and occasionally discordant.
Then, we address model properties and techniques thought to confer interpretability, identifying transparency to humans and post-hoc explanations as competing notions.
Throughout, we discuss the feasibility and desirability of different notions, and question the oft-made assertions that linear models are interpretable and that deep neural networks are not.
The need for higher agricultural productivity has demanded the intensive use of pesticides.
However, their correct use depends on assessment methods that can accurately predict how well the pesticides' spraying covered the intended crop region.
Some methods have been proposed in the literature, but their high cost and low portability harm their widespread use.
This paper proposes and experimentally evaluates a new methodology based on the use of a smartphone-based mobile application, named DropLeaf.
Experiments performed using DropLeaf showed that, in addition to its versatility, it can predict with high accuracy the pesticide spraying.
DropLeaf is a five-fold image-processing methodology based on: (i) color space conversion, (ii) threshold noise removal, (iii) convolutional operations of dilation and erosion, (iv) detection of contour markers in the water-sensitive card, and, (v) identification of droplets via the marker-controlled watershed transformation.
The authors performed successful experiments over two case studies, the first using a set of synthetic cards and the second using a real-world crop.
The proposed tool can be broadly used by farmers equipped with conventional mobile phones, improving the use of pesticides with health, environmental and financial benefits.
Face deidentification is an active topic amongst privacy and security researchers.
Early deidentification methods relying on image blurring or pixelization were replaced in recent years with techniques based on formal anonymity models that provide privacy guaranties and at the same time aim at retaining certain characteristics of the data even after deidentification.
The latter aspect is particularly important, as it allows to exploit the deidentified data in applications for which identity information is irrelevant.
In this work we present a novel face deidentification pipeline, which ensures anonymity by synthesizing artificial surrogate faces using generative neural networks (GNNs).
The generated faces are used to deidentify subjects in images or video, while preserving non-identity-related aspects of the data and consequently enabling data utilization.
Since generative networks are very adaptive and can utilize a diverse set of parameters (pertaining to the appearance of the generated output in terms of facial expressions, gender, race, etc.
), they represent a natural choice for the problem of face deidentification.
To demonstrate the feasibility of our approach, we perform experiments using automated recognition tools and human annotators.
Our results show that the recognition performance on deidentified images is close to chance, suggesting that the deidentification process based on GNNs is highly effective.
Being a matter of cognition, user interests should be apt to classification independent of the language of users, social network and content of interest itself.
To prove it, we analyze a collection of English and Russian Twitter and Vkontakte community pages by interests of their followers.
First, we create a model of Major Interests (MaIs) with the help of expert analysis and then classify a set of pages using machine learning algorithms (SVM, Neural Network, Naive Bayes, and some other).
We take three interest domains that are typical of both English and Russian-speaking communities: football, rock music, vegetarianism.
The results of classification show a greater correlation between Russian-Vkontakte and Russian-Twitter pages while English-Twitterpages appear to provide the highest score.
The line-of-sight (LoS) air-to-ground channel brings both opportunities and challenges in cellular-connected unmanned aerial vehicle (UAV) communications.
On one hand, the LoS channels make more cellular base stations (BSs) visible to a UAV as compared to the ground users, which leads to a higher macro-diversity gain for UAV-BS communications.
On the other hand, they also render the UAV to impose/suffer more severe uplink/downlink interference to/from the BSs, thus requiring more sophisticated inter-cell interference coordination (ICIC) techniques with more BSs involved.
In this paper, we consider the uplink transmission from a UAV to cellular BSs, under spectrum sharing with the existing ground users.
To investigate the optimal ICIC design and air-ground performance trade-off, we maximize the weighted sum-rate of the UAV and existing ground users by jointly optimizing the UAV's uplink cell associations and power allocations over multiple resource blocks.
However, this problem is non-convex and difficult to be solved optimally.
We first propose a centralized ICIC design to obtain a locally optimal solution based on the successive convex approximation (SCA) method.
As the centralized ICIC requires global information of the network and substantial information exchange among an excessively large number of BSs, we further propose a decentralized ICIC scheme of significantly lower complexity and signaling overhead for implementation, by dividing the cellular BSs into small-size clusters and exploiting the LoS macro-diversity for exchanging information between the UAV and cluster-head BSs only.
Numerical results show that the proposed centralized and decentralized ICIC schemes both achieve a near-optimal performance, and draw important design insights based on practical system setups.
In this paper we provide a survey of various libraries for homomorphic encryption.
We describe key features and trade-offs that should be considered while choosing the right approach for secure computation.
We then present a comparison of six commonly available Homomorphic Encryption libraries - SEAL, HElib, TFHE, Paillier, ELGamal and RSA across these identified features.
Support for different languages and real-life applications are also elucidated.
We study a classification problem where each feature can be acquired for a cost and the goal is to optimize a trade-off between the expected classification error and the feature cost.
We revisit a former approach that has framed the problem as a sequential decision-making problem and solved it by Q-learning with a linear approximation, where individual actions are either requests for feature values or terminate the episode by providing a classification decision.
On a set of eight problems, we demonstrate that by replacing the linear approximation with neural networks the approach becomes comparable to the state-of-the-art algorithms developed specifically for this problem.
The approach is flexible, as it can be improved with any new reinforcement learning enhancement, it allows inclusion of pre-trained high-performance classifier, and unlike prior art, its performance is robust across all evaluated datasets.
We have developed a system that automatically detects online jihadist hate speech with over 80% accuracy, by using techniques from Natural Language Processing and Machine Learning.
The system is trained on a corpus of 45,000 subversive Twitter messages collected from October 2014 to December 2016.
We present a qualitative and quantitative analysis of the jihadist rhetoric in the corpus, examine the network of Twitter users, outline the technical procedure used to train the system, and discuss examples of use.
The Moral Foundations Dictionary (MFD) is a useful tool for applying the conceptual framework developed in Moral Foundations Theory and quantifying the moral meanings implicated in the linguistic information people convey.
However, the applicability of the MFD is limited because it is available only in English.
Translated versions of the MFD are therefore needed to study morality across various cultures, including non-Western cultures.
The contribution of this paper is two-fold.
We developed the first Japanese version of the MFD (referred to as the J-MFD) by introducing a semi-automated method---this serves as a reference when translating the MFD into other languages.
We next tested the validity of the J-MFD by analyzing open-ended written texts about the situations that Japanese participants thought followed and violated the five moral foundations.
We found that the J-MFD correctly categorized the Japanese participants' descriptions into the corresponding moral foundations, and that the Moral Foundations Questionnaire (MFQ) scores were correlated with the frequency of situations, of total words, and of J-MFD words in the participants' descriptions for the Harm and Fairness foundations.
The J-MFD can be used to study morality unique to the Japanese and cultural differences in moral behavior.
Bounded rationality investigates utility-optimizing decision-makers with limited information-processing power.
In particular, information theoretic bounded rationality models formalize resource constraints abstractly in terms of relative Shannon information, namely the Kullback-Leibler Divergence between the agents' prior and posterior policy.
Between prior and posterior lies an anytime deliberation process that can be instantiated by sample-based evaluations of the utility function through Markov Chain Monte Carlo (MCMC) optimization.
The most simple model assumes a fixed prior and can relate abstract information-theoretic processing costs to the number of sample evaluations.
However, more advanced models would also address the question of learning, that is how the prior is adapted over time such that generated prior proposals become more efficient.
In this work we investigate generative neural networks as priors that are optimized concurrently with anytime sample-based decision-making processes such as MCMC.
We evaluate this approach on toy examples.
In this paper we present the Creative Invention Benchmark (CrIB), a 2000-problem benchmark for evaluating a particular facet of computational creativity.
Specifically, we address combinational p-creativity, the creativity at play when someone combines existing knowledge to achieve a solution novel to that individual.
We present generation strategies for the five problem categories of the benchmark and a set of initial baselines.
Computational research and data analytics increasingly relies on complex ecosystems of open source software (OSS) "libraries" -- curated collections of reusable code that programmers import to perform a specific task.
Software documentation for these libraries is crucial in helping programmers/analysts know what libraries are available and how to use them.
Yet documentation for open source software libraries is widely considered low-quality.
This article is a collaboration between CSCW researchers and contributors to data analytics OSS libraries, based on ethnographic fieldwork and qualitative interviews.
We examine several issues around the formats, practices, and challenges around documentation in these largely volunteer-based projects.
There are many different kinds and formats of documentation that exist around such libraries, which play a variety of educational, promotional, and organizational roles.
The work behind documentation is similarly multifaceted, including writing, reviewing, maintaining, and organizing documentation.
Different aspects of documentation work require contributors to have different sets of skills and overcome various social and technical barriers.
Finally, most of our interviewees do not report high levels of intrinsic enjoyment for doing documentation work (compared to writing code).
Their motivation is affected by personal and project-specific factors, such as the perceived level of credit for doing documentation work versus more "technical" tasks like adding new features or fixing bugs.
In studying documentation work for data analytics OSS libraries, we gain a new window into the changing practices of data-intensive research, as well as help practitioners better understand how to support this often invisible and infrastructural work in their projects.
To save time and money, businesses and individuals have begun outsourcing their data and computations to cloud computing services.
These entities would, however, like to ensure that the queries they request from the cloud services are being computed correctly.
In this paper, we use the principles of economics and competition to vastly reduce the complexity of query verification on outsourced data.
We consider two cases: First, we consider the scenario where multiple non-colluding data outsourcing services exist, and then we consider the case where only a single outsourcing service exists.
Using a game theoretic model, we show that given the proper incentive structure, we can effectively deter dishonest behavior on the part of the data outsourcing services with very few computational and monetary resources.
We prove that the incentive for an outsourcing service to cheat can be reduced to zero.
Finally, we show that a simple verification method can achieve this reduction through extensive experimental evaluation.
Face alignment, which is the task of finding the locations of a set of facial landmark points in an image of a face, is useful in widespread application areas.
Face alignment is particularly challenging when there are large variations in pose (in-plane and out-of-plane rotations) and facial expression.
To address this issue, we propose a cascade in which each stage consists of a mixture of regression experts.
Each expert learns a customized regression model that is specialized to a different subset of the joint space of pose and expressions.
The system is invariant to a predefined class of transformations (e.g., affine), because the input is transformed to match each expert's prototype shape before the regression is applied.
We also present a method to include deformation constraints within the discriminative alignment framework, which makes our algorithm more robust.
Our algorithm significantly outperforms previous methods on publicly available face alignment datasets.
This paper presents a practical approach to rapidly introduce new dataplane functionality into networks: End-hosts embed tiny programs into packets to actively query and manipulate a network's internal state.
We show how this "tiny packet program" (TPP) interface gives end-hosts unprecedented visibility into network behavior, enabling them to work with the network to achieve a common goal.
Our design leverages what each component does best: (a) switches forward and execute tiny packet programs (at most 5 instructions) at line rate, and (b) end-hosts perform arbitrary computation on network state, which are easy to evolve.
Using a hardware prototype on a NetFPGA, we show our design is feasible, at a reasonable cost.
By implementing three different research proposals, we show that TPPs are also useful.
And finally, we present an architecture in which they can be made secure.
Person Re-identification (ReID) is to identify the same person across different cameras.
It is a challenging task due to the large variations in person pose, occlusion, background clutter, etc How to extract powerful features is a fundamental problem in ReID and is still an open problem today.
In this paper, we design a Multi-Scale Context-Aware Network (MSCAN) to learn powerful features over full body and body parts, which can well capture the local context knowledge by stacking multi-scale convolutions in each layer.
Moreover, instead of using predefined rigid parts, we propose to learn and localize deformable pedestrian parts using Spatial Transformer Networks (STN) with novel spatial constraints.
The learned body parts can release some difficulties, eg pose variations and background clutters, in part-based representation.
Finally, we integrate the representation learning processes of full body and body parts into a unified framework for person ReID through multi-class person identification tasks.
Extensive evaluations on current challenging large-scale person ReID datasets, including the image-based Market1501, CUHK03 and sequence-based MARS datasets, show that the proposed method achieves the state-of-the-art results.
The DLVHEX system implements the HEX-semantics, which integrates answer set programming (ASP) with arbitrary external sources.
Since its first release ten years ago, significant advancements were achieved.
Most importantly, the exploitation of properties of external sources led to efficiency improvements and flexibility enhancements of the language, and technical improvements on the system side increased user's convenience.
In this paper, we present the current status of the system and point out the most important recent enhancements over early versions.
While existing literature focuses on theoretical aspects and specific components, a bird's eye view of the overall system is missing.
In order to promote the system for real-world applications, we further present applications which were already successfully realized on top of DLVHEX.
This paper is under consideration for acceptance in Theory and Practice of Logic Programming.
To avoid the foreseeable spectrum crunch, LTE operators have started to explore the option to directly use 5 GHz unlicensed spectrum band being used by IEEE 802.11 (WiFi).
However, as LTE is not designed with shared spectrum access in mind, there is a major issue of coexistence with WiFi networks.
Current coexistence schemes to be deployed at the LTE-U BS create coexistence gaps only in one domain (e.g., time, frequency, or space) and can provide only incremental gains due to the lack of coordination among the coexisting WiFi and LTE-U networks.
Therefore, we propose a coordinated coexistence scheme which relies on cooperation between neighboring LTE-U and WiFi networks.
Our proposal suggests that LTE-U BSs equipped with multiple antennas can create coexistence gaps in space domain in addition to the time domain gaps by means of cross-technology interference nulling towards WiFi nodes in the interference range.
In return, LTE-U can increase its own airtime utilization while trading off slightly its antenna diversity.
The cooperation offers benefits to both LTE-U and WiFi in terms of improved throughput and decreased channel access delay.
More specifically, system-level simulations reveal a throughput gain up to 221% for LTE-U network and 44% for WiFi network depending on the setting, e.g., the distance between the two cell, number of LTE antennas, and WiFi users in the LTE-U BS neighborhood.
Our proposal provides significant benefits especially for moderate separation distances between LTE-U/WiFi cells where interference from a neighboring network might be severe due to the hidden network problem.
We explore a collaborative multi-agent reinforcement learning setting where a team of agents attempts to solve cooperative tasks in partially-observable environments.
In this scenario, learning an effective communication protocol is key.
We propose a communication architecture that allows for targeted communication, where agents learn both what messages to send and who to send them to, solely from downstream task-specific reward without any communication supervision.
Additionally, we introduce a multi-stage communication approach where the agents co-ordinate via multiple rounds of communication before taking actions in the environment.
We evaluate our approach on a diverse set of cooperative multi-agent tasks, of varying difficulties, with varying number of agents, in a variety of environments ranging from 2D grid layouts of shapes and simulated traffic junctions to complex 3D indoor environments.
We demonstrate the benefits of targeted as well as multi-stage communication.
Moreover, we show that the targeted communication strategies learned by agents are both interpretable and intuitive.
In last decade, data analytics have rapidly progressed from traditional disk-based processing to modern in-memory processing.
However, little effort has been devoted at enhancing performance at micro-architecture level.
This paper characterizes the performance of in-memory data analytics using Apache Spark framework.
We use a single node NUMA machine and identify the bottlenecks hampering the scalability of workloads.
We also quantify the inefficiencies at micro-architecture level for various data analysis workloads.
Through empirical evaluation, we show that spark workloads do not scale linearly beyond twelve threads, due to work time inflation and thread level load imbalance.
Further, at the micro-architecture level, we observe memory bound latency to be the major cause of work time inflation.
Measuring science is based on comparing articles to similar others.
However, keyword-based groups of thematically similar articles are dominantly small.
These small sizes keep the statistical errors of comparisons high.
With the growing availability of bibliographic data such statistical errors can be reduced by merging methods of thematic grouping, citation networks and keyword co-usage.
Wireless Network-on-Chip (WNoC) appears as a promising alternative to conventional interconnect fabrics for chip-scale communications.
The WNoC paradigm has been extensively analyzed from the physical, network and architecture perspectives assuming mmWave band operation.
However, there has not been a comprehensive study at this band for realistic chip packages and, thus, the characteristics of such wireless channel remain not fully understood.
This work addresses this issue by accurately modeling a flip-chip package and investigating the wave propagation inside it.
Through parametric studies, a locally optimal configuration for 60 GHz WNoC is obtained, showing that chip-wide attenuation below 32.6 dB could be achieved with standard processes.
Finally, the applicability of the methodology is discussed for higher bands and other integrated environments such as a Software-Defined Metamaterial (SDM).
Although transfer learning has been shown to be successful for tasks like object and speech recognition, its applicability to question answering (QA) has yet to be well-studied.
In this paper, we conduct extensive experiments to investigate the transferability of knowledge learned from a source QA dataset to a target dataset using two QA models.
The performance of both models on a TOEFL listening comprehension test (Tseng et al., 2016) and MCTest (Richardson et al., 2013) is significantly improved via a simple transfer learning technique from MovieQA (Tapaswi et al., 2016).
In particular, one of the models achieves the state-of-the-art on all target datasets; for the TOEFL listening comprehension test, it outperforms the previous best model by 7%.
Finally, we show that transfer learning is helpful even in unsupervised scenarios when correct answers for target QA dataset examples are not available.
It is proposed a new code for contours of plane images.
This code was applied for optical character recognition of printed and handwritten characters.
One can apply it to recognition of any visual images.
It has long been known that certain superquantum nonlocal correlations collapse communication complexity, and it is conjectured that a statement like "communication complexity is not trivial" may provide an intuitive information-theoretic axiom for quantum mechanics.
With the goal of addressing this conjecture, we take aim at collapsing communication complexity using weaker nonlocal correlations, and present a no-go theorem for a broad class of approaches.
To achieve this, we investigate fault-tolerant computation by noisy circuits in a new light.
Our main technical result is that, perhaps surprisingly, noiseless XOR gates are not more helpful than noisy ones in read-once formulas that have noisy AND gates for the task of building amplifiers.
We also formalize a connection between fault-tolerant computation and amplification, and highlight new directions and open questions in fault-tolerant computation with noisy circuits.
Our results inform the relationship between superquantum nonlocality and the collapse of communication complexity.
The Keystroke Level Model (KLM) and Fitts Law constitute core teaching subjects in most HCI courses, as well as many courses on software design and evaluation.
The KLM Form Analyzer (KLM_FA) has been introduced as a practitioner s tool to facilitate web form design and evaluation, based on these established HCI predictive models.
It was also hypothesized that KLMFA can also be used for educational purposes, since it provides step by step tracing of the KLM modeling for any web form filling task, according to various interaction strategies or users characteristics.
In our previous work, we found that KLM-FA supports teaching and learning of HCI modeling in the context of distance education.
This paper reports a study investigating the learning effectiveness of KLM-FA in the context of campus-based higher education.
Students of a software quality course completed a knowledge test after the lecture- based instruction (pre-test condition) and after being involved in a KLMFA mediated learning activity (post-test condition).
They also provided posttest ratings for their educational experience and the tool s usability.
Results showed that KLM-FA can significantly improve learning of the HCI modeling.
In addition, participating students rated their perceived educational experience as very satisfactory and the perceived usability of KLM-FA as good to excellent.
Studies estimate that there will be 266,120 new cases of invasive breast cancer and 40,920 breast cancer induced deaths in the year of 2018 alone.
Despite the pervasiveness of this affliction, the current process to obtain an accurate breast cancer prognosis is tedious and time consuming, requiring a trained pathologist to manually examine histopathological images in order to identify the features that characterize various cancer severity levels.
We propose MITOS-RCNN: a novel region based convolutional neural network (RCNN) geared for small object detection to accurately grade one of the three factors that characterize tumor belligerence described by the Nottingham Grading System: mitotic count.
Other computational approaches to mitotic figure counting and detection do not demonstrate ample recall or precision to be clinically viable.
Our models outperformed all previous participants in the ICPR 2012 challenge, the AMIDA 2013 challenge and the MITOS-ATYPIA-14 challenge along with recently published works.
Our model achieved an F-measure score of 0.955, a 6.11% improvement in accuracy from the most accurate of the previously proposed models.
This article presents an anatomy of PhD programmes in Hellenic universities' departments of computer science/engineering from the perspective of research productivity and impact.
The study aims at showing the dynamics of research conducted in computer science/engineering departments, and after recognizing weaknesses, to motivate the stakeholders to take actions that will improve competition and excellence.
Beneficiaries of this investigation are the following entities: a) the departments themselves can assess their performance relative to that of other departments and then set strategic goals and design procedures to achieve them, b) supervisors can assess the part of their research conducted with PhDs and set their own goals, c) former PhDs who can identify their relative success, and finally d) prospective PhD students who can consider the efficacy of departments and supervisors in conducting high-impact research as one more significant factor in designing the doctoral studies they will follow.
Highly regularized LSTMs achieve impressive results on several benchmark datasets in language modeling.
We propose a new regularization method based on decoding the last token in the context using the predicted distribution of the next token.
This biases the model towards retaining more contextual information, in turn improving its ability to predict the next token.
With negligible overhead in the number of parameters and training time, our Past Decode Regularization (PDR) method achieves a word level perplexity of 55.6 on the Penn Treebank and 63.5 on the WikiText-2 datasets using a single softmax.
We also show gains by using PDR in combination with a mixture-of-softmaxes, achieving a word level perplexity of 53.8 and 60.5 on these datasets.
In addition, our method achieves 1.169 bits-per-character on the Penn Treebank Character dataset for character level language modeling.
These results constitute a new state-of-the-art in their respective settings.
Research on sound event detection (SED) with weak labeling has mostly focused on presence/absence labeling, which provides no temporal information at all about the event occurrences.
In this paper, we consider SED with sequential labeling, which specifies the temporal order of the event boundaries.
The conventional connectionist temporal classification (CTC) framework, when applied to SED with sequential labeling, does not localize long events well due to a "peak clustering" problem.
We adapt the CTC framework and propose connectionist temporal localization (CTL), which successfully solves the problem.
Evaluation on a subset of Audio Set shows that CTL closes a third of the gap between presence/ absence labeling and strong labeling, demonstrating the usefulness of the extra temporal information in sequential labeling.
CTL also makes it easy to combine sequential labeling with presence/absence labeling and strong labeling.
In this work, we investigate various methods to deal with semantic labeling of very high resolution multi-modal remote sensing data.
Especially, we study how deep fully convolutional networks can be adapted to deal with multi-modal and multi-scale remote sensing data for semantic labeling.
Our contributions are threefold: a) we present an efficient multi-scale approach to leverage both a large spatial context and the high resolution data, b) we investigate early and late fusion of Lidar and multispectral data, c) we validate our methods on two public datasets with state-of-the-art results.
Our results indicate that late fusion make it possible to recover errors steaming from ambiguous data, while early fusion allows for better joint-feature learning but at the cost of higher sensitivity to missing data.
In the field of generic object tracking numerous attempts have been made to exploit deep features.
Despite all expectations, deep trackers are yet to reach an outstanding level of performance compared to methods solely based on handcrafted features.
In this paper, we investigate this key issue and propose an approach to unlock the true potential of deep features for tracking.
We systematically study the characteristics of both deep and shallow features, and their relation to tracking accuracy and robustness.
We identify the limited data and low spatial resolution as the main challenges, and propose strategies to counter these issues when integrating deep features for tracking.
Furthermore, we propose a novel adaptive fusion approach that leverages the complementary properties of deep and shallow features to improve both robustness and accuracy.
Extensive experiments are performed on four challenging datasets.
On VOT2017, our approach significantly outperforms the top performing tracker from the challenge with a relative gain of 17% in EAO.
The groundbreaking experiment of Travers and Milgram demonstrated the so-called "six degrees of separation" phenomenon, by which any individual in the world is able to contact an arbitrary, hitherto-unknown, individual by means of a short chain of social ties.
Despite the large number of empirical and theoretical studies to explain the Travers-Milgram experiment, some fundamental questions are still open: why some individuals are more likely than others to discover short friend-of-a-friend communication chains?
Can we rank individuals on the basis of their ability to discover short chains?
To answer these questions, we extend the concept of potential gain, originally defined in the context of Web analysis, to social networks and we define a novel index, called "the navigability score," that ranks nodes in a network on the basis of how their position facilitates the discover of short chains that connect to arbitrary target nodes in the network.
We define two variants of potential gain, called the geometric and the exponential potential gain, and present fast algorithms to compute them.
Our theoretical and experimental analysis proves that the computation of the geometric and exponential gain are affordable even on large real-life graphs.
The rise of social media provides a great opportunity for people to reach out to their social connections to satisfy their information needs.
However, generic social media platforms are not explicitly designed to assist information seeking of users.
In this paper, we propose a novel framework to identify the social connections of a user able to satisfy his information needs.
The information need of a social media user is subjective and personal, and we investigate the utility of his social context to identify people able to satisfy it.
We present questions users post on Twitter as instances of information seeking activities in social media.
We infer soft community memberships of the asker and his social connections by integrating network and content information.
Drawing concepts from the social foci theory, we identify answerers who share communities with the asker w.r.t. the question.
Our experiments demonstrate that the framework is effective in identifying answerers to social media questions.
A worldwide movement towards the publication of Open Government Data is taking place, and budget data is one of the key elements pushing this trend.
Its importance is mostly related to transparency, but publishing budget data, combined with other actions, can also improve democratic participation, allow comparative analysis of governments and boost data-driven business.
However, the lack of standards and common evaluation criteria still hinders the development of appropriate tools and the materialization of the appointed benefits.
In this paper, we present a model to analyse government initiatives to publish budget data.
We identify the main features of these initiatives with a double objective: (i) to drive a structured analysis, relating some dimensions to their possible impacts, and (ii) to derive characterization attributes to compare initiatives based on each dimension.
We define use perspectives and analyse some initiatives using this model.
We conclude that, in order to favour use perspectives, special attention must be given to user feedback, semantics standards and linking possibilities.
Anticipating future actions is a key component of intelligence, specifically when it applies to real-time systems, such as robots or autonomous cars.
While recent works have addressed prediction of raw RGB pixel values, we focus on anticipating the motion evolution in future video frames.
To this end, we construct dynamic images (DIs) by summarising moving pixels through a sequence of future frames.
We train a convolutional LSTMs to predict the next DIs based on an unsupervised learning process, and then recognise the activity associated with the predicted DI.
We demonstrate the effectiveness of our approach on 3 benchmark action datasets showing that despite running on videos with complex activities, our approach is able to anticipate the next human action with high accuracy and obtain better results than the state-of-the-art methods.
Software analytics has been widely used in software engineering for many tasks such as generating effort estimates for software projects.
One of the "black arts" of software analytics is tuning the parameters controlling a data mining algorithm.
Such hyperparameter optimization has been widely studied in other software analytics domains (e.g.defect prediction and text mining) but, so far, has not been extensively explored for effort estimation.
Accordingly, this paper seeks simple, automatic, effective and fast methods for finding good tunings for automatic software effort estimation.
We introduce a hyperparameter optimization architecture called OIL (Optimized Inductive Learning).
We test OIL on a wide range of hyperparameter optimizers using data from 945 software projects.
After tuning, large improvements in effort estimation accuracy were observed (measured in terms of standardized accuracy).
From those results, we recommend using regression trees (CART) tuned by different evolution combine with default analogy-based estimator.
This particular combination of learner and optimizers often achieves in a few hours what other optimizers need days to weeks of CPU time to accomplish.
An important part of this analysis is its reproducibility and refutability.
All our scripts and data are on-line.
It is hoped that this paper will prompt and enable much more research on better methods to tune software effort estimators.
The impressive success of Generative Adversarial Networks (GANs) is often overshadowed by the difficulties in their training.
Despite the continuous efforts and improvements, there are still open issues regarding their convergence properties.
In this paper, we propose a simple training variation where suitable weights are defined and assist the training of the Generator.
We provide theoretical arguments why the proposed algorithm is better than the baseline training in the sense of speeding up the training process and of creating a stronger Generator.
Performance results showed that the new algorithm is more accurate in both synthetic and image datasets resulting in improvements ranging between 5% and 50%.
Crowdsourcing relies on people's contributions to meet product- or system-level objectives.
Crowdsourcing-based methods have been implemented in various cyber-physical systems and realtime markets.
This paper explores a framework for Crowdsourced Energy Systems (CES), where small-scale energy generation or energy trading is crowdsourced from distributed energy resources, electric vehicles, and shapable loads.
The merits/pillars of energy crowdsourcing are discussed.
Then, an operational model for CESs in distribution networks with different types of crowdsourcees is proposed.
The model yields a market equilibrium depicting traditional and distributed generator and load setpoints.
Given these setpoints, crowdsourcing incentives are designed to steer crowdsourcees to the equilibrium.
As the number of crowdsourcees and energy trading transactions scales up, a secure energy trading platform is required.
To that end, the presented framework is integrated with a lightweight Blockchain implementation and smart contracts.
Numerical tests are provided to showcase the overall implementation.
Learning parameters from voluminous data can be prohibitive in terms of memory and computational requirements.
We propose a "compressive learning" framework where we estimate model parameters from a sketch of the training data.
This sketch is a collection of generalized moments of the underlying probability distribution of the data.
It can be computed in a single pass on the training set, and is easily computable on streams or distributed datasets.
The proposed framework shares similarities with compressive sensing, which aims at drastically reducing the dimension of high-dimensional signals while preserving the ability to reconstruct them.
To perform the estimation task, we derive an iterative algorithm analogous to sparse reconstruction algorithms in the context of linear inverse problems.
We exemplify our framework with the compressive estimation of a Gaussian Mixture Model (GMM), providing heuristics on the choice of the sketching procedure and theoretical guarantees of reconstruction.
We experimentally show on synthetic data that the proposed algorithm yields results comparable to the classical Expectation-Maximization (EM) technique while requiring significantly less memory and fewer computations when the number of database elements is large.
We further demonstrate the potential of the approach on real large-scale data (over 10 8 training samples) for the task of model-based speaker verification.
Finally, we draw some connections between the proposed framework and approximate Hilbert space embedding of probability distributions using random features.
We show that the proposed sketching operator can be seen as an innovative method to design translation-invariant kernels adapted to the analysis of GMMs.
We also use this theoretical framework to derive information preservation guarantees, in the spirit of infinite-dimensional compressive sensing.
Sparse Bayesian learning is a state-of-the-art supervised learning algorithm that can choose a subset of relevant samples from the input data and make reliable probabilistic predictions.
However, in the presence of high-dimensional data with irrelevant features, traditional sparse Bayesian classifiers suffer from performance degradation and low efficiency by failing to eliminate irrelevant features.
To tackle this problem, we propose a novel sparse Bayesian embedded feature selection method that adopts truncated Gaussian distributions as both sample and feature priors.
The proposed method, called probabilistic feature selection and classification vector machine (PFCVMLP ), is able to simultaneously select relevant features and samples for classification tasks.
In order to derive the analytical solutions, Laplace approximation is applied to compute approximate posteriors and marginal likelihoods.
Finally, parameters and hyperparameters are optimized by the type-II maximum likelihood method.
Experiments on three datasets validate the performance of PFCVMLP along two dimensions: classification performance and effectiveness for feature selection.
Finally, we analyze the generalization performance and derive a generalization error bound for PFCVMLP .
By tightening the bound, the importance of feature selection is demonstrated.
Discrete energy minimization is a ubiquitous task in computer vision, yet is NP-hard in most cases.
In this work we propose a multiscale framework for coping with the NP-hardness of discrete optimization.
Our approach utilizes algebraic multiscale principles to efficiently explore the discrete solution space, yielding improved results on challenging, non-submodular energies for which current methods provide unsatisfactory approximations.
In contrast to popular multiscale methods in computer vision, that builds an image pyramid, our framework acts directly on the energy to construct an energy pyramid.
Deriving a multiscale scheme from the energy itself makes our framework application independent and widely applicable.
Our framework gives rise to two complementary energy coarsening strategies: one in which coarser scales involve fewer variables, and a more revolutionary one in which the coarser scales involve fewer discrete labels.
We empirically evaluated our unified framework on a variety of both non-submodular and submodular energies, including energies from Middlebury benchmark.
Channel estimation at millimeter wave (mmWave) is challenging when large antenna arrays are used.
Prior work has leveraged the sparse nature of mmWave channels via compressed sensing based algorithms for channel estimation.
Most of these algorithms, though, assume perfect synchronization and are vulnerable to phase errors that arise due to carrier frequency offset (CFO) and phase noise.
Recently sparsity-aware, non-coherent beamforming algorithms that are robust to phase errors were proposed for narrowband phased array systems with full resolution analog-to-digital converters (ADCs).
Such energy based algorithms, however, are not robust to heavy quantization at the receiver.
In this paper, we develop a joint CFO and wideband channel estimation algorithm that is scalable across different mmWave architectures.
Our method exploits the sparsity of mmWave MIMO channel in the angle-delay domain, in addition to compressibility of the phase error vector.
We formulate the joint estimation as a sparse bilinear optimization problem and then use message passing for recovery.
We also give an efficient implementation of a generalized bilinear message passing algorithm for the joint estimation in mmWave systems with one-bit ADCs.
Simulation results show that our method is able to recover the CFO and the channel compressively, even in the presence of phase noise.
We describe opportunities and challenges with wireless robotic materials.
Robotic materials are multi-functional composites that tightly integrate sensing, actuation, computation and communication to create smart composites that can sense their environment and change their physical properties in an arbitrary programmable manner.
Computation and communication in such materials are based on miniature, possibly wireless, devices that are scattered in the material and interface with sensors and actuators inside the material.
Whereas routing and processing of information within the material build upon results from the field of sensor networks, robotic materials are pushing the limits of sensor networks in both size (down to the order of microns) and numbers of devices (up to the order of millions).
In order to solve the algorithmic and systems challenges of such an approach, which will involve not only computer scientists, but also roboticists, chemists and material scientists, the community requires a common platform - much like the "Mote" that bootstrapped the widespread adoption of the field of sensor networks - that is small, provides ample of computation, is equipped with basic networking functionalities, and preferably can be powered wirelessly.
To better understand the effectiveness of continued training, we analyze the major components of a neural machine translation system (the encoder, decoder, and each embedding space) and consider each component's contribution to, and capacity for, domain adaptation.
We find that freezing any single component during continued training has minimal impact on performance, and that performance is surprisingly good when a single component is adapted while holding the rest of the model fixed.
We also find that continued training does not move the model very far from the out-of-domain model, compared to a sensitivity analysis metric, suggesting that the out-of-domain model can provide a good generic initialization for the new domain.
Plain recurrent networks greatly suffer from the vanishing gradient problem while Gated Neural Networks (GNNs) such as Long-short Term Memory (LSTM) and Gated Recurrent Unit (GRU) deliver promising results in many sequence learning tasks through sophisticated network designs.
This paper shows how we can address this problem in a plain recurrent network by analyzing the gating mechanisms in GNNs.
We propose a novel network called the Recurrent Identity Network (RIN) which allows a plain recurrent network to overcome the vanishing gradient problem while training very deep models without the use of gates.
We compare this model with IRNNs and LSTMs on multiple sequence modeling benchmarks.
The RINs demonstrate competitive performance and converge faster in all tasks.
Notably, small RIN models produce 12%--67% higher accuracy on the Sequential and Permuted MNIST datasets and reach state-of-the-art performance on the bAbI question answering dataset.
Estimation of Distribution Algorithms (EDAs) require flexible probability models that can be efficiently learned and sampled.
Generative Adversarial Networks (GAN) are generative neural networks which can be trained to implicitly model the probability distribution of given data, and it is possible to sample this distribution.
We integrate a GAN into an EDA and evaluate the performance of this system when solving combinatorial optimization problems with a single objective.
We use several standard benchmark problems and compare the results to state-of-the-art multivariate EDAs.
GAN-EDA doe not yield competitive results - the GAN lacks the ability to quickly learn a good approximation of the probability distribution.
A key reason seems to be the large amount of noise present in the first EDA generations.
We here study the behavior of political party members aiming at identifying how ideological communities are created and evolve over time in diverse (fragmented and non-fragmented) party systems.
Using public voting data of both Brazil and the US, we propose a methodology to identify and characterize ideological communities, their member polarization, and how such communities evolve over time, covering a 15-year period.
Our results reveal very distinct patterns across the two case studies, in terms of both structural and dynamic properties.
Machine translation (MT) was developed as one of the hottest research topics in the natural language processing (NLP) literature.
One important issue in MT is that how to evaluate the MT system reasonably and tell us whether the translation system makes an improvement or not.
The traditional manual judgment methods are expensive, time-consuming, unrepeatable, and sometimes with low agreement.
On the other hand, the popular automatic MT evaluation methods have some weaknesses.
Firstly, they tend to perform well on the language pairs with English as the target language, but weak when English is used as source.
Secondly, some methods rely on many additional linguistic features to achieve good performance, which makes the metric unable to replicate and apply to other language pairs easily.
Thirdly, some popular metrics utilize incomprehensive factors, which result in low performance on some practical tasks.
In this thesis, to address the existing problems, we design novel MT evaluation methods and investigate their performances on different languages.
Firstly, we design augmented factors to yield highly accurate evaluation.Secondly, we design a tunable evaluation model where weighting of factors can be optimised according to the characteristics of languages.
Thirdly, in the enhanced version of our methods, we design concise linguistic feature using POS to show that our methods can yield even higher performance when using some external linguistic resources.
Finally, we introduce the practical performance of our metrics in the ACL-WMT workshop shared tasks, which show that the proposed methods are robust across different languages.
We give a mathematical formalization of `generalized data parallel' operations, a concept that covers such common scientific kernels as matrix-vector multiplication, multi-grid coarsening, load distribution, and many more.
We show that from a compact specification such computational aspects as MPI messages or task dependencies can be automatically derived.
Predictive models of student success in Massive Open Online Courses (MOOCs) are a critical component of effective content personalization and adaptive interventions.
In this article we review the state of the art in predictive models of student success in MOOCs and present a categorization of MOOC research according to the predictors (features), prediction (outcomes), and underlying theoretical model.
We critically survey work across each category, providing data on the raw data source, feature engineering, statistical model, evaluation method, prediction architecture, and other aspects of these experiments.
Such a review is particularly useful given the rapid expansion of predictive modeling research in MOOCs since the emergence of major MOOC platforms in 2012.
This survey reveals several key methodological gaps, which include extensive filtering of experimental subpopulations, ineffective student model evaluation, and the use of experimental data which would be unavailable for real-world student success prediction and intervention, which is the ultimate goal of such models.
Finally, we highlight opportunities for future research, which include temporal modeling, research bridging predictive and explanatory student models, work which contributes to learning theory, and evaluating long-term learner success in MOOCs.
We present a novel unsupervised approach for multilingual sentiment analysis driven by compositional syntax-based rules.
On the one hand, we exploit some of the main advantages of unsupervised algorithms: (1) the interpretability of their output, in contrast with most supervised models, which behave as a black box and (2) their robustness across different corpora and domains.
On the other hand, by introducing the concept of compositional operations and exploiting syntactic information in the form of universal dependencies, we tackle one of their main drawbacks: their rigidity on data that are structured differently depending on the language concerned.
Experiments show an improvement both over existing unsupervised methods, and over state-of-the-art supervised models when evaluating outside their corpus of origin.
Experiments also show how the same compositional operations can be shared across languages.
The system is available at http://www.grupolys.org/software/UUUSA/
Text Proposals have emerged as a class-dependent version of object proposals - efficient approaches to reduce the search space of possible text object locations in an image.
Combined with strong word classifiers, text proposals currently yield top state of the art results in end-to-end scene text recognition.
In this paper we propose an improvement over the original Text Proposals algorithm of Gomez and Karatzas (2016), combining it with Fully Convolutional Networks to improve the ranking of proposals.
Results on the ICDAR RRC and the COCO-text datasets show superior performance over current state-of-the-art.
Good user experience with interactive cloud-based multimedia applications, such as cloud gaming and cloud-based VR, requires low end-to-end latency and large amounts of downstream network bandwidth at the same time.
In this paper, we present a foveated video streaming system for cloud gaming.
The system adapts video stream quality by adjusting the encoding parameters on the fly to match the player's gaze position.
We conduct measurements with a prototype that we developed for a cloud gaming system in conjunction with eye tracker hardware.
Evaluation results suggest that such foveated streaming can reduce bandwidth requirements by even more than 50% depending on parametrization of the foveated video coding and that it is feasible from the latency perspective.
In this paper, we conduct extensive simulations to understand the properties of the overlay generated by BitTorrent.
We start by analyzing how the overlay properties impact the efficiency of BitTorrent.
We focus on the average peer set size (i.e., average number of neighbors), the time for a peer to reach its maximum peer set size, and the diameter of the overlay.
In particular, we show that the later a peer arrives in a torrent, the longer it takes to reach its maximum peer set size.
Then, we evaluate the impact of the maximum peer set size, the maximum number of outgoing connections per peer, and the number of NATed peers on the overlay properties.
We show that BitTorrent generates a robust overlay, but that this overlay is not a random graph.
In particular, the connectivity of a peer to its neighbors depends on its arriving order in the torrent.
We also show that a large number of NATed peers significantly compromise the robustness of the overlay to attacks.
Finally, we evaluate the impact of peer exchange on the overlay properties, and we show that it generates a chain-like overlay with a large diameter, which will adversely impact the efficiency of large torrents.
The Gilbert type bound for codes in the title is reviewed, both for small and large alphabets.
Constructive lower bounds better than these existential bounds are derived from geometric codes, either over Fp or Fp2 ; or over even degree extensions of Fp: In the latter case the approach is concatena- tion with a good code for the Hamming metric as outer code and a short code for the Lee metric as an inner code.
In the former case lower bounds on the minimum Lee distance are derived by algebraic geometric arguments inspired by results of Wu, Kuijper, Udaya (2007).
A recommender system is an information filtering technology which can be used to predict preference ratings of items (products, services, movies, etc) and/or to output a ranking of items that are likely to be of interest to the user.
Context-aware recommender systems (CARS) learn and predict the tastes and preferences of users by incorporating available contextual information in the recommendation process.
One of the major challenges in context-aware recommender systems research is the lack of automatic methods to obtain contextual information for these systems.
Considering this scenario, in this paper, we propose to use contextual information from topic hierarchies of the items (web pages) to improve the performance of context-aware recommender systems.
The topic hierarchies are constructed by an extension of the LUPI-based Incremental Hierarchical Clustering method that considers three types of information: traditional bag-of-words (technical information), and the combination of named entities (privileged information I) with domain terms (privileged information II).
We evaluated the contextual information in four context-aware recommender systems.
Different weights were assigned to each type of information.
The empirical results demonstrated that topic hierarchies with the combination of the two kinds of privileged information can provide better recommendations.
We propose an algorithm to locate the most critical nodes to network robustness.
Such critical nodes may be thought of as those most related to the notion of network centrality.
Our proposal relies only on a localized spectral analysis of a limited subnetwork centered at each node in the network.
We also present a procedure allowing the navigation from any node towards a critical node following only local information computed by the proposed algorithm.
Experimental results confirm the effectiveness of our proposal considering networks of different scales and topological characteristics.
Visual recognition algorithms are required today to exhibit adaptive abilities.
Given a deep model trained on a specific, given task, it would be highly desirable to be able to adapt incrementally to new tasks, preserving scalability as the number of new tasks increases, while at the same time avoiding catastrophic forgetting issues.
Recent work has shown that masking the internal weights of a given original conv-net through learned binary variables is a promising strategy.
We build upon this intuition and take into account more elaborated affine transformations of the convolutional weights that include learned binary masks.
We show that with our generalization it is possible to achieve significantly higher levels of adaptation to new tasks, enabling the approach to compete with fine tuning strategies by requiring slightly more than 1 bit per network parameter per additional task.
Experiments on two popular benchmarks showcase the power of our approach, that achieves the new state of the art on the Visual Decathlon Challenge.
This paper introduces a method for predicting the likely behaviors of continuous nonlinear systems in equilibrium in which the input values can vary.
The method uses a parameterized equation model and a lower bound on the input joint density to bound the likelihood that some behavior will occur, such as a state variable being inside a given numeric range.
Using a bound on the density instead of the density itself is desirable because often the input density's parameters and shape are not exactly known.
The new method is called SAB after its basic operations: split the input value space into smaller regions, and then bound those regions' possible behaviors and the probability of being in them.
SAB finds rough bounds at first, and then refines them as more time is given.
In contrast to other researchers' methods, SAB can (1) find all the possible system behaviors, and indicate how likely they are, (2) does not approximate the distribution of possible outcomes without some measure of the error magnitude, (3) does not use discretized variable values, which limit the events one can find probability bounds for, (4) can handle density bounds, and (5) can handle such criteria as two state variables both being inside a numeric range.
In large-scale distributed learning, security issues have become increasingly important.
Particularly in a decentralized environment, some computing units may behave abnormally, or even exhibit Byzantine failures---arbitrary and potentially adversarial behavior.
In this paper, we develop distributed learning algorithms that are provably robust against such failures, with a focus on achieving optimal statistical performance.
A main result of this work is a sharp analysis of two robust distributed gradient descent algorithms based on median and trimmed mean operations, respectively.
We prove statistical error rates for three kinds of population loss functions: strongly convex, non-strongly convex, and smooth non-convex.
In particular, these algorithms are shown to achieve order-optimal statistical error rates for strongly convex losses.
To achieve better communication efficiency, we further propose a median-based distributed algorithm that is provably robust, and uses only one communication round.
For strongly convex quadratic loss, we show that this algorithm achieves the same optimal error rate as the robust distributed gradient descent algorithms.
The ability to build a model on a source task and subsequently adapt such model on a new target task is a pervasive need in many astronomical applications.
The problem is generally known as transfer learning in machine learning, where domain adaptation is a popular scenario.
An example is to build a predictive model on spectroscopic data to identify Supernovae IA, while subsequently trying to adapt such model on photometric data.
In this paper we propose a new general approach to domain adaptation that does not rely on the proximity of source and target distributions.
Instead we simply assume a strong similarity in model complexity across domains, and use active learning to mitigate the dependency on source examples.
Our work leads to a new formulation for the likelihood as a function of empirical error using a theoretical learning bound; the result is a novel mapping from generalization error to a likelihood estimation.
Results using two real astronomical problems, Supernova Ia classification and identification of Mars landforms, show two main advantages with our approach: increased accuracy performance and substantial savings in computational cost.
The utilization of web mapping becomes increasingly important in the domain of cartography.
Users want access to spatial data on the web specific to their needs.
For this reason, different approaches were appeared for generating on-the-fly the maps demanded by users, but those not suffice for guide a flexible and efficient process.
Thus, new approach must be developed for improving this process according to the user needs.
This work focuses on defining a new strategy which improves on-the-fly map generalization process and resolves the spatial conflicts.
This approach uses the multiple representation and cartographic generalization.
The map generalization process is based on the implementation of multi- agent system where each agent was equipped with a genetic patrimony.
We consider the transmission of packets across a lossy end-to-end network path so as to achieve low in-order delivery delay.
This can be formulated as a decision problem, namely deciding whether the next packet to send should be an information packet or a coded packet.
Importantly, this decision is made based on delayed feedback from the receiver.
While an exact solution to this decision problem is challenging, we exploit ideas from queueing theory to derive scheduling policies based on prediction of a receiver queue length that, while suboptimal, can be efficiently implemented and offer substantially better performance than state of the art approaches.
We obtain a number of useful analytic bounds that help characterise design trade-offs and our analysis highlights that the use of prediction plays a key role in achieving good performance in the presence of significant feedback delay.
Our approach readily generalises to networks of paths and we illustrate this by application to multipath transport scheduler design.
Humans are able to understand and perform complex tasks by strategically structuring the tasks into incremental steps or subgoals.
For a robot attempting to learn to perform a sequential task with critical subgoal states, such states can provide a natural opportunity for interaction with a human expert.
This paper analyzes the benefit of incorporating a notion of subgoals into Inverse Reinforcement Learning (IRL) with a Human-In-The-Loop (HITL) framework.
The learning process is interactive, with a human expert first providing input in the form of full demonstrations along with some subgoal states.
These subgoal states define a set of subtasks for the learning agent to complete in order to achieve the final goal.
The learning agent queries for partial demonstrations corresponding to each subtask as needed when the agent struggles with the subtask.
The proposed Human Interactive IRL (HI-IRL) framework is evaluated on several discrete path-planning tasks.
We demonstrate that subgoal-based interactive structuring of the learning task results in significantly more efficient learning, requiring only a fraction of the demonstration data needed for learning the underlying reward function with the baseline IRL model.
The problem of finding a finite state symbolic model which is bisimilar to a hybrid dynamical system (HDS) and has the minimum number of states is considered.
The considered class of HDS allows for discrete-valued inputs that only affect the jumps (events) of the HDS.
Representation of the HDS in the form of a transition system is revisited in comparison with prior works.
An algorithm is proposed for solving the problem which gives the bisimulation with the minimum number of states if it already exists and also a parameter of the algorithm is properly tuned.
There is no need for stability assumptions and no time discretization is applied.
The results are applied to an example
The well-known dictionary-based algorithms of the Lempel-Ziv (LZ) 77 family are the basis of several universal lossless compression techniques.
These algorithms are asymmetric regarding encoding/decoding time and memory requirements, with the former being much more demanding.
In the past years, considerable attention has been devoted to the problem of finding efficient data structures to support these searches, aiming at optimizing the encoders in terms of speed and memory.
Hash tables, binary search trees and suffix trees have been widely used for this purpose, as they allow fast search at the expense of memory.
Some recent research has focused on suffix arrays (SA), due to their low memory requirements and linear construction algorithms.
Previous work has shown how the LZ77 decomposition can be computed using a single SA or an SA with an auxiliary array with the longest common prefix information.
The SA-based algorithms use less memory than the tree-based encoders, allocating the strictly necessary amount of memory, regardless of the contents of the text to search/encode.
In this paper, we improve on previous work by proposing faster SA-based algorithms for LZ77 encoding and sub-string search, keeping their low memory requirements.
For some compression settings, on a large set of benchmark files, our low-memory SA-based encoders are also faster than tree-based encoders.
This provides time and memory efficient LZ77 encoding, being a possible replacement for trees on well known encoders like LZMA.
Our algorithm is also suited for text classification, because it provides a compact way to describe text in a bag-of-words representation, as well as a fast indexing mechanism that allows to quickly find all the sets of words that start with a given symbol, over a static dictionary.
We present Caffe con Troll (CcT), a fully compatible end-to-end version of the popular framework Caffe with rebuilt internals.
We built CcT to examine the performance characteristics of training and deploying general-purpose convolutional neural networks across different hardware architectures.
We find that, by employing standard batching optimizations for CPU training, we achieve a 4.5x throughput improvement over Caffe on popular networks like CaffeNet.
Moreover, with these improvements, the end-to-end training time for CNNs is directly proportional to the FLOPS delivered by the CPU, which enables us to efficiently train hybrid CPU-GPU systems for CNNs.
HIV/AIDS spread depends upon complex patterns of interaction among various sub-sets emerging at population level.
This added complexity makes it difficult to study and model AIDS and its dynamics.
AIDS is therefore a natural candidate to be modeled using agent-based modeling, a paradigm well-known for modeling Complex Adaptive Systems (CAS).
While agent-based models are also well-known to effectively model CAS, often times models can tend to be ambiguous and the use of purely text-based specifications (such as ODD) can make models difficult to be replicated.
Previous work has shown how formal specification may be used in conjunction with agent-based modeling to develop models of various CAS.
However, to the best of our knowledge, no such model has been developed in conjunction with AIDS.
In this paper, we present a Formal Agent-Based Simulation modeling framework (FABS-AIDS) for an AIDS-based CAS.
FABS-AIDS employs the use of a formal specification model in conjunction with an agent-based model to reduce ambiguity as well as improve clarity in the model definition.
The proposed model demonstrates the effectiveness of using formal specification in conjunction with agent-based simulation for developing models of CAS in general and, social network-based agent-based models, in particular.
In this paper, we introduce the notion of Plausible Deniability in an information theoretic framework.
We consider a scenario where an entity that eavesdrops through a broadcast channel summons one of the parties in a communication protocol to reveal their message (or signal vector).
It is desirable that the summoned party have enough freedom to produce a fake output that is likely plausible given the eavesdropper's observation.
We examine three variants of this problem -- Message Deniability, Transmitter Deniability, and Receiver Deniability.
In the first setting, the message sender is summoned to produce the sent message.
Similarly, in the second and third settings, the transmitter and the receiver are required to produce the transmitted codeword, and the received vector respectively.
For each of these settings, we examine the maximum communication rate that allows a given minimum rate of plausible fake outputs.
For the Message and Transmitter Deniability problems, we fully characterise the capacity region for general broadcast channels, while for the Receiver Deniability problem, we give an achievable rate region for physically degraded broadcast channels.
This paper proposes a generic selective-candidate framework with similarity selection rule (SCSS) for performance enhancement of well-established evolutionary optimization algorithms.
It is done by using a more efficient selective searching direction.
In the SCSS framework, M (M > 1) candidates are generated from each current solution by M independent reproduction procedures.
The winner is then determined by employing a similarity selection rule that achieves a balance between exploitation and exploration.
This computationally light rule simultaneously considers the evolution status (fitness ranking information) of the current solution as well as its Euclidian distances to each of the M candidates.
The SCSS framework can be easily applied to evolutionary algorithms or swarm intelligences.
Experiments conducted with 60 benchmark functions show the superiority of SCSS in three classic, four state-of-the-art and four up-to-date algorithms.
The Industrial Internet market is targeted to grow by trillions of US dollars by the year 2030, driven by adoption, deployment and integration of billions of intelligent devices and their associated data.
This digital expansion faces a number of significant challenges, including reliable data management, security and privacy.
Realizing the benefits from this evolution is made more difficult because a typical industrial plant includes multiple vendors and legacy technology stacks.
Aggregating all the raw data to a single data center before performing analysis increases response times, raising performance concerns in traditional markets and requiring a compromise between data duplication and data access performance.
Similar to the way microservices can integrate disparate information technologies without imposing monolithic cross-cutting architecture impacts, we propose microdatabases to manage the data heterogeneity of the Industrial Internet while allowing records to be captured and secured close to the industrial processes, but also be made available near the applications that can benefit from the data.
A microdatabase is an abstraction of a data store that standardizes and protects the interactions between distributed data sources, providers and consumers.
It integrates an information model with discoverable object types that can be browsed interactively and programmatically, and supports repository instances that evolve with their own lifecycles.
The microdatabase abstraction is independent of technology choice and was designed based on solicitation and review of industry stakeholder concerns.
This paper studies the challenging problem of fingerprint image denoising and inpainting.
To tackle the challenge of suppressing complicated artifacts (blur, brightness, contrast, elastic transformation, occlusion, scratch, resolution, rotation, and so on) while preserving fine textures, we develop a multi-scale convolutional network, termed U- Finger.
Based on the domain expertise, we show that the usage of dilated convolutions as well as the removal of padding have important positive impacts on the final restoration performance, in addition to multi-scale cascaded feature modules.
Our model achieves the overall ranking of No.2 in the ECCV 2018 Chalearn LAP Inpainting Competition Track 3 (Fingerprint Denoising and Inpainting).
Among all participating teams, we obtain the MSE of 0.0231 (rank 2), PSNR 16.9688 dB (rank 2), and SSIM 0.8093 (rank 3) on the hold-out testing set.
Volatility is a quantity of measurement for the price movements of stocks or options which indicates the uncertainty within financial markets.
As an indicator of the level of risk or the degree of variation, volatility is important to analyse the financial market, and it is taken into consideration in various decision-making processes in financial activities.
On the other hand, recent advancement in deep learning techniques has shown strong capabilities in modelling sequential data, such as speech and natural language.
In this paper, we empirically study the applicability of the latest deep structures with respect to the volatility modelling problem, through which we aim to provide an empirical guidance for the theoretical analysis of the marriage between deep learning techniques and financial applications in the future.
We examine both the traditional approaches and the deep sequential models on the task of volatility prediction, including the most recent variants of convolutional and recurrent networks, such as the dilated architecture.
Accordingly, experiments with real-world stock price datasets are performed on a set of 1314 daily stock series for 2018 days of transaction.
The evaluation and comparison are based on the negative log likelihood (NLL) of real-world stock price time series.
The result shows that the dilated neural models, including dilated CNN and Dilated RNN, produce most accurate estimation and prediction, outperforming various widely-used deterministic models in the GARCH family and several recently proposed stochastic models.
In addition, the high flexibility and rich expressive power are validated in this study.
Tactile sensing is a key enabling technology to develop complex behaviours for robots interacting with humans or the environment.
This paper discusses computational aspects playing a significant role when extracting information about contact events.
Considering a large-scale, capacitance-based robot skin technology we developed in the past few years, we analyse the classical Boussinesq-Cerruti's solution and the Love's approach for solving a distributed inverse contact problem, both from a qualitative and a computational perspective.
Our contribution is the characterisation of algorithms performance using a freely available dataset and data originating from surfaces provided with robot skin.
Differentiating intrinsic language words from transliterable words is a key step aiding text processing tasks involving different natural languages.
We consider the problem of unsupervised separation of transliterable words from native words for text in Malayalam language.
Outlining a key observation on the diversity of characters beyond the word stem, we develop an optimization method to score words based on their nativeness.
Our method relies on the usage of probability distributions over character n-grams that are refined in step with the nativeness scorings in an iterative optimization formulation.
Using an empirical evaluation, we illustrate that our method, DTIM, provides significant improvements in nativeness scoring for Malayalam, establishing DTIM as the preferred method for the task.
In recent years, Log-Structured Merge-trees (LSM-trees) have been widely adopted for use in the storage layer of modern NoSQL systems.
Because of this, there have been a large number of research efforts, from both the database community and the systems community, that try to improve various aspects of LSM-trees.
In this paper, we provide a survey of recent LSM efforts so that readers can learn the state of the art in LSM-based storage techniques.
We provide a general taxonomy to classify the literature of LSM improvements, survey the efforts in detail, and discuss their strengths and trade-offs.
We further survey several representative LSM-based open-source NoSQL systems and we discuss some potential future research directions resulting from the survey.
This comment recalls a previously proposed encoding scheme involving two synchronized random number generators (RNGs) to compress the transmission message.
It is also claimed that the recently proposed random number modulation (RNM) scheme suffers considerably from the severe error propagation, and that, in general, the overall energy consumption is minimized when all information bits are transmitted as fast as possible with the minimum latency.
The aim of this article is to present an overview of the existing biomedical data warehouses and to discuss the issues and future trends in this area.
We illustrate this topic by presenting the design of an innovative, complex data warehouse for personal, anticipative medicine.
Ponzi schemes are financial frauds where, under the promise of high profits, users put their money, recovering their investment and interests only if enough users after them continue to invest money.
Originated in the offline world 150 years ago, Ponzi schemes have since then migrated to the digital world, approaching first on the Web, and more recently hanging over cryptocurrencies like Bitcoin.
Smart contract platforms like Ethereum have provided a new opportunity for scammers, who have now the possibility of creating "trustworthy" frauds that still make users lose money, but at least are guaranteed to execute "correctly".
We present a comprehensive survey of Ponzi schemes on Ethereum, analysing their behaviour and their impact from various viewpoints.
Perhaps surprisingly, we identify a remarkably high number of Ponzi schemes, despite the hosting platform has been operating for less than two years.
The Jordan center of a graph is defined as a vertex whose maximum distance to other nodes in the graph is minimal, and it finds applications in facility location and source detection problems.
We study properties of the Jordan Center in the case of random growing trees.
In particular, we consider a regular tree graph on which an infection starts from a root node and then spreads along the edges of the graph according to various random spread models.
For the Independent Cascade (IC) model and the discrete Susceptible Infected (SI) model, both of which are discrete time models, we show that as the infected subgraph grows with time, the Jordan center persists on a single vertex after a finite number of timesteps.
Finally, we also study the continuous time version of the SI model and bound the maximum distance between the Jordan center and the root node at any time.
Let D be a set of n disks in the plane.
We present a data structure of size O(n) that can compute, for any query point q, the largest disk in D that contains q, in O(log n) time.
The structure can be constructed in O(n log^3 n) time.
The optimal storage and query time of the structure improve several recent solutions by Augustine et al. and by Kaplan and Sharir.
In this paper, we study the interactions among interconnected autonomous microgrids, and propose a joint energy trading and scheduling strategy.
Each interconnected microgrid not only schedules its local power supply and demand, but also trades energy with other microgrids in a distribution network.
Specifically, microgrids with excessive renewable generations can trade with other microgrids in deficit of power supplies for mutual benefits.
Since interconnected microgrids operate autonomously, they aim to optimize their own performance and expect to gain benefits through energy trading.
We design an incentive mechanism using Nash bargaining theory to encourage proactive energy trading and fair benefit sharing.
We solve the bargaining problem by decomposing it into two sequential problems on social cost minimization and trading benefit sharing, respectively.
For practical implementation, we propose a decentralized solution method with minimum information exchange overhead.
Numerical studies based on realistic data demonstrate that the total cost of the interconnected-microgrids operation can be reduced by up to 13.2% through energy trading, and an individual participating microgrid can achieve up to 29.4% reduction in its cost through energy trading.
Cities across the United States are undergoing great transformation and urban growth.
Data and data analysis has become an essential element of urban planning as cities use data to plan land use and development.
One great challenge is to use the tools of data science to promote equity along with growth.
The city of Atlanta is an example site of large-scale urban renewal that aims to engage in development without displacement.
On the Westside of downtown Atlanta, the construction of the new Mercedes-Benz Stadium and the conversion of an underutilized rail-line into a multi-use trail may result in increased property values.
In response to community residents' concerns and a commitment to development without displacement, the city and philanthropic partners announced an Anti-Displacement Tax Fund to subsidize future property tax increases of owner occupants for the next twenty years.
To achieve greater transparency, accountability, and impact, residents expressed a desire for a tool that would help them determine eligibility and quantify this commitment.
In support of this goal, we use machine learning techniques to analyze historical tax assessment and predict future tax assessments.
We then apply eligibility estimates to our predictions to estimate the total cost for the first seven years of the program.
These forecasts are also incorporated into an interactive tool for community residents to determine their eligibility for the fund and the expected increase in their home value over the next seven years.
There is overwhelming evidence that human intelligence is a product of Darwinian evolution.
Investigating the consequences of self-modification, and more precisely, the consequences of utility function self-modification, leads to the stronger claim that not only human, but any form of intelligence is ultimately only possible within evolutionary processes.
Human-designed artificial intelligences can only remain stable until they discover how to manipulate their own utility function.
By definition, a human designer cannot prevent a superhuman intelligence from modifying itself, even if protection mechanisms against this action are put in place.
Without evolutionary pressure, sufficiently advanced artificial intelligences become inert by simplifying their own utility function.
Within evolutionary processes, the implicit utility function is always reducible to persistence, and the control of superhuman intelligences embedded in evolutionary processes is not possible.
Mechanisms against utility function self-modification are ultimately futile.
Instead, scientific effort toward the mitigation of existential risks from the development of superintelligences should be in two directions: understanding consciousness, and the complex dynamics of evolutionary systems.
One of the most important problems of data processing in high energy and nuclear physics is the event reconstruction.
Its main part is the track reconstruction procedure which consists in looking for all tracks that elementary particles leave when they pass through a detector among a huge number of points, so-called hits, produced when flying particles fire detector coordinate planes.
Unfortunately, the tracking is seriously impeded by the famous shortcoming of multiwired, strip and GEM detectors due to appearance in them a lot of fake hits caused by extra spurious crossings of fired strips.
Since the number of those fakes is several orders of magnitude greater than for true hits, one faces with the quite serious difficulty to unravel possible track-candidates via true hits ignoring fakes.
We introduce a renewed method that is a significant improvement of our previous two-stage approach based on hit preprocessing using directed K-d tree search followed a deep neural classifier.
We combine these two stages in one by applying recurrent neural network that simultaneously determines whether a set of points belongs to a true track or not and predicts where to look for the next point of track on the next coordinate plane of the detector.
We show that proposed deep network is more accurate, faster and does not require any special preprocessing stage.
Preliminary results of our approach for simulated events of the BM@N GEM detector are presented.
We study policy iteration for infinite-horizon Markov decision processes.
It has recently been shown policy iteration style algorithms have exponential lower bounds in a two player game setting.
We extend these lower bounds to Markov decision processes with the total reward and average-reward optimality criteria.
While current deep learning systems excel at tasks such as object classification, language processing, and gameplay, few can construct or modify a complex system such as a tower of blocks.
We hypothesize that what these systems lack is a "relational inductive bias": a capacity for reasoning about inter-object relations and making choices over a structured description of a scene.
To test this hypothesis, we focus on a task that involves gluing pairs of blocks together to stabilize a tower, and quantify how well humans perform.
We then introduce a deep reinforcement learning agent which uses object- and relation-centric scene and policy representations and apply it to the task.
Our results show that these structured representations allow the agent to outperform both humans and more naive approaches, suggesting that relational inductive bias is an important component in solving structured reasoning problems and for building more intelligent, flexible machines.
Many convolutional neural networks (CNNs) have a feed-forward structure.
In this paper, a linear program that estimates the Lipschitz bound of such CNNs is proposed.
Several CNNs, including the scattering networks, the AlexNet and the GoogleNet, are studied numerically and compared to the theoretical bounds.
Next, concentration inequalities of the output distribution to a stationary random input signal expressed in terms of the Lipschitz bound are established.
The Lipschitz bound is further used to establish a nonlinear discriminant analysis designed to measure the separation between features of different classes.
Diacritical marks play a crucial role in meeting the criteria of usability of typographic text, such as: homogeneity, clarity and legibility.
To change the diacritic of a letter in a word could completely change its semantic.
The situation is very complicated with multilingual text.
Indeed, the problem of design becomes more difficult by the presence of diacritics that come from various scripts; they are used for different purposes, and are controlled by various typographic rules.
It is quite challenging to adapt rules from one script to another.
This paper aims to study the placement and sizing of diacritical marks in Arabic script, with a comparison with the Latin's case.
The Arabic script is cursive and runs from right-to-left; its criteria and rules are quite distinct from those of the Latin script.
In the beginning, we compare the difficulty of processing diacritics in both scripts.
After, we will study the limits of Latin resolution strategies when applied to Arabic.
At the end, we propose an approach to resolve the problem for positioning and resizing diacritics.
This strategy includes creating an Arabic font, designed in OpenType format, along with suitable justification in TEX.
Most recent approaches use the sequence-to-sequence model for paraphrase generation.
The existing sequence-to-sequence model tends to memorize the words and the patterns in the training dataset instead of learning the meaning of the words.
Therefore, the generated sentences are often grammatically correct but semantically improper.
In this work, we introduce a novel model based on the encoder-decoder framework, called Word Embedding Attention Network (WEAN).
Our proposed model generates the words by querying distributed word representations (i.e.neural word embeddings), hoping to capturing the meaning of the according words.
Following previous work, we evaluate our model on two paraphrase-oriented tasks, namely text simplification and short text abstractive summarization.
Experimental results show that our model outperforms the sequence-to-sequence baseline by the BLEU score of 6.3 and 5.5 on two English text simplification datasets, and the ROUGE-2 F1 score of 5.7 on a Chinese summarization dataset.
Moreover, our model achieves state-of-the-art performances on these three benchmark datasets.
Cyber attacks and malware are now more prevalent than ever and the trend is ever upward.
There have been several approaches to attack detection including resident software applications at the root or user level, e.g., virus detection, and modifications to the OS, e.g., encryption, application signing, etc.
Some approaches have moved to lower level detection and preven- tion, e.g., Data Execution Prevention.
An emerging approach in countermeasure development is the use of hardware performance counters existing in the micro-architecture of modern processors.
These are at the lowest level, implemented in processor hardware, and the wealth of data collected by these counters affords some very promising countermeasures with minimal overhead as well as protection from being sabotaged themselves by attackers.
Here, we conduct a survey of recent techniques in realizing effective countermeasures for cyber attack detection from these hardware performance counters.
Evacuation is one of the main disaster management solutions to reduce the impact of man-made and natural threats on building occupants.
To date, several modern technologies and gamification concepts, e.g. immersive virtual reality and serious games, have been used to enhance building evacuation preparedness and effectiveness.
Those tools have been used both to investigate human behavior during building emergencies and to train building occupants on how to cope with building evacuations.
Augmented Reality (AR) is novel technology that can enhance this process providing building occupants with virtual contents to improve their evacuation performance.
This work aims at reviewing existing AR applications developed for building evacuation.
This review identifies the disasters and types of building those tools have been applied for.
Moreover, the application goals, hardware and evacuation stages affected by AR are also investigated in the review.
Finally, this review aims at identifying the challenges to face for further development of AR evacuation tools.
We formulate and solve the energy minimization problem for a clustered device-to-device (D2D) network with cache-enabled mobile devices.
Devices are distributed according to a Poisson cluster process (PCP) and are assumed to have a surplus memory which is exploited to proactively cache files from a library.
Devices can retrieve the requested files from their caches, from neighboring devices in their proximity (cluster), or from the base station as a last resort.
We minimize the energy consumption of the proposed network under a random prob- abilistic caching scheme, where files are independently cached according to a specific probability distribution.
A closed-form expression for the D2D coverage probability is obtained.
The energy consumption problem is then formulated as a function of the caching distribution, and the optimal probabilistic caching distribution is obtained.
Results reveal that the proposed caching distribution reduces energy consumption up to 33% as compared to caching popular files scheme.
Modern large-scale computing deployments consist of complex applications running over machine clusters.
An important issue in these is the offering of elasticity, i.e., the dynamic allocation of resources to applications to meet fluctuating workload demands.
Threshold based approaches are typically employed, yet they are difficult to configure and optimize.
Approaches based on reinforcement learning have been proposed, but they require a large number of states in order to model complex application behavior.
Methods that adaptively partition the state space have been proposed, but their partitioning criteria and strategies are sub-optimal.
In this work we present MDP_DT, a novel full-model based reinforcement learning algorithm for elastic resource management that employs adaptive state space partitioning.
We propose two novel statistical criteria and three strategies and we experimentally prove that they correctly decide both where and when to partition, outperforming existing approaches.
We experimentally evaluate MDP_DT in a real large scale cluster over variable not-encountered workloads and we show that it takes more informed decisions compared to static and model-free approaches, while requiring a minimal amount of training data.
We introduce the Densely Segmented Supermarket (D2S) dataset, a novel benchmark for instance-aware semantic segmentation in an industrial domain.
It contains 21,000 high-resolution images with pixel-wise labels of all object instances.
The objects comprise groceries and everyday products from 60 categories.
The benchmark is designed such that it resembles the real-world setting of an automatic checkout, inventory, or warehouse system.
The training images only contain objects of a single class on a homogeneous background, while the validation and test sets are much more complex and diverse.
To further benchmark the robustness of instance segmentation methods, the scenes are acquired with different lightings, rotations, and backgrounds.
We ensure that there are no ambiguities in the labels and that every instance is labeled comprehensively.
The annotations are pixel-precise and allow using crops of single instances for articial data augmentation.
The dataset covers several challenges highly relevant in the field, such as a limited amount of training data and a high diversity in the test and validation sets.
The evaluation of state-of-the-art object detection and instance segmentation methods on D2S reveals significant room for improvement.
Optimizing deep neural networks (DNNs) often suffers from the ill-conditioned problem.
We observe that the scaling-based weight space symmetry property in rectified nonlinear network will cause this negative effect.
Therefore, we propose to constrain the incoming weights of each neuron to be unit-norm, which is formulated as an optimization problem over Oblique manifold.
A simple yet efficient method referred to as projection based weight normalization (PBWN) is also developed to solve this problem.
PBWN executes standard gradient updates, followed by projecting the updated weight back to Oblique manifold.
This proposed method has the property of regularization and collaborates well with the commonly used batch normalization technique.
We conduct comprehensive experiments on several widely-used image datasets including CIFAR-10, CIFAR-100, SVHN and ImageNet for supervised learning over the state-of-the-art convolutional neural networks, such as Inception, VGG and residual networks.
The results show that our method is able to improve the performance of DNNs with different architectures consistently.
We also apply our method to Ladder network for semi-supervised learning on permutation invariant MNIST dataset, and our method outperforms the state-of-the-art methods: we obtain test errors as 2.52%, 1.06%, and 0.91% with only 20, 50, and 100 labeled samples, respectively.
In this paper,a new design of wireless sensor network (WSN)node is discussed which is based on components with ultra low power.We ha e de eloped a Low cost and low power WSN Node using MSP430 and nRF24L01.The architectural circuit details are presented.This architecture fulfils the requirements like low cost,low power,compact size and self organization.Various tests are carried out to test the performance of the nRF24L01 module.The packet loss,free Space loss (FSL)and battery lifetime calculations are described.These test results will help the researchers to build new applications using abo e node and to work efficiently with nRF24L01.
Multiple Kernel Learning, or MKL, extends (kernelized) SVM by attempting to learn not only a classifier/regressor but also the best kernel for the training task, usually from a combination of existing kernel functions.
Most MKL methods seek the combined kernel that performs best over every training example, sacrificing performance in some areas to seek a global optimum.
Localized kernel learning (LKL) overcomes this limitation by allowing the training algorithm to match a component kernel to the examples that can exploit it best.
Several approaches to the localized kernel learning problem have been explored in the last several years.
We unify many of these approaches under one simple system and design a new algorithm with improved performance.
We also develop enhanced versions of existing algorithms, with an eye on scalability and performance.
The number of references per paper, perhaps the best single index of a journal's scholarliness, has been studied in different disciplines and periods.
In this paper we present a four decade study of eight engineering journals.
A data set of over 70000 references was generated after automatic data gathering and manual inspection for errors.
Results show a significant increase in the number of references per paper, the average rises from 8 in 1972 to 25 in 2013.
This growth presents an acceleration around the year 2000, consistent with a much easier access to search engines and documents produced by the generalization of the Internet.
Tissue texture is known to exhibit a heterogeneous or non-stationary nature, therefore using a single resolution approach for optimum classification might not suffice.
A clinical decision support system that exploits the subband textural fractal characteristics for best bases selection of meningioma brain histopathological image classification is proposed.
Each subband is analysed using its fractal dimension instead of energy, which has the advantage of being less sensitive to image intensity and abrupt changes in tissue texture.
The most significant subband that best identifies texture discontinuities will be chosen for further decomposition, and its fractal characteristics would represent the optimal feature vector for classification.
The performance was tested using the support vector machine (SVM), Bayesian and k-nearest neighbour (kNN) classifiers and a leave-one-patient-out method was employed for validation.
Our method outperformed the classical energy based selection approaches, achieving for SVM, Bayesian and kNN classifiers an overall classification accuracy of 94.12%, 92.50% and 79.70%, as compared to 86.31%, 83.19% and 51.63% for the co-occurrence matrix, and 76.01%, 73.50% and 50.69% for the energy texture signatures, respectively.
These results indicate the potential usefulness as a decision support system that could complement radiologists diagnostic capability to discriminate higher order statistical textural information, for which it would be otherwise difficult via ordinary human vision.
We propose a novel part-based method for tracking an arbitrary object in challenging video sequences, focusing on robustly tracking under the effects of camera motion and object motion change.
Each of a group of tracked image patches on the target is represented by pairs of RGB pixel samples and counts of how many pixels in the patch are similar to them.
This empirically characterises the underlying colour distribution of the patches and allows for matching using the Bhattacharyya distance.
Candidate patch locations are generated by applying non-shearing affine transformations to the patches' previous locations, followed by local optimisation.
Experiments using the VOT2016 dataset show that our tracker out-performs all other part-based trackers in terms of robustness to camera motion and object motion change.
In this paper, we present the design process of a novel solution for enabling the collaboration between OpenStack cloud systems in SAML federations with standalone attribute authorities, such as national research and education federations or eduGAIN.
The software solution that realizes the integration of systems serves as a case study to show how abstract desirable engineering properties fixed at the beginning of the design process can be implemented during the development phase.
An analysis of earlier generations of OpenStack-related developments trying to tackle the same problem is given.
Many aspects of this software integration can be generalized to serve as a template for federative cloud access.
Judgment aggregation is a general framework for collective decision making that can be used to model many different settings.
Due to its general nature, the worst case complexity of essentially all relevant problems in this framework is very high.
However, these intractability results are mainly due to the fact that the language to represent the aggregation domain is overly expressive.
We initiate an investigation of representation languages for judgment aggregation that strike a balance between (1) being limited enough to yield computational tractability results and (2) being expressive enough to model relevant applications.
In particular, we consider the languages of Krom formulas, (definite) Horn formulas, and Boolean circuits in decomposable negation normal form (DNNF).
We illustrate the use of the positive complexity results that we obtain for these languages with a concrete application: voting on how to spend a budget (i.e., participatory budgeting).
Wrist-wearables such as smartwatches and fitness bands are equipped with a variety of high-precision sensors that support novel contextual and activity-based applications.
The presence of a diverse set of on-board sensors, however, also expose an additional attack surface which, if not adequately protected, could be potentially exploited to leak private user information.
In this paper, we investigate the feasibility of a new attack that takes advantage of a wrist-wearable's motion sensors to infer input on mechanical devices typically used to secure physical access, for example, combination locks.
We outline an inference framework that attempts to infer a lock's unlock combination from the wrist motion captured by a smartwatch's gyroscope sensor, and uses a probabilistic model to produce a ranked list of likely unlock combinations.
We conduct a thorough empirical evaluation of the proposed framework by employing unlocking-related motion data collected from human subject participants in a variety of controlled and realistic settings.
Evaluation results from these experiments demonstrate that motion data from wrist-wearables can be effectively employed as a side-channel to significantly reduce the unlock combination search-space of commonly found combination locks, thus compromising the physical security provided by these locks.
The relationship of scientific knowledge development to technological development is widely recognized as one of the most important and complex aspects of technological evolution.
This paper adds to our understanding of the relationship through use of a more rigorous structure for differentiating among technologies based upon technological domains (defined as consisting of the artifacts over time that fulfill a specific generic function using a specific body of technical knowledge).
The main contribution of this paper is a simple semi-supervised pipeline that only uses the original training set without collecting extra data.
It is challenging in 1) how to obtain more training data only from the training set and 2) how to use the newly generated data.
In this work, the generative adversarial network (GAN) is used to generate unlabeled samples.
We propose the label smoothing regularization for outliers (LSRO).
This method assigns a uniform label distribution to the unlabeled images, which regularizes the supervised model and improves the baseline.
We verify the proposed method on a practical problem: person re-identification (re-ID).
This task aims to retrieve a query person from other cameras.
We adopt the deep convolutional generative adversarial network (DCGAN) for sample generation, and a baseline convolutional neural network (CNN) for representation learning.
Experiments show that adding the GAN-generated data effectively improves the discriminative ability of learned CNN embeddings.
On three large-scale datasets, Market-1501, CUHK03 and DukeMTMC-reID, we obtain +4.37%, +1.6% and +2.46% improvement in rank-1 precision over the baseline CNN, respectively.
We additionally apply the proposed method to fine-grained bird recognition and achieve a +0.6% improvement over a strong baseline.
The code is available at https://github.com/layumi/Person-reID_GAN.
In this paper, we introduce a new uplink visible light indoor positioning system that estimates the position of the users in the network-side of a visible light communications (VLC) system.
This technique takes advantage of the diffuse components of the uplink channel impulse response for positioning, which has been considered as a destructive noise in existing visible light communication positioning literature.
Exploiting the line of sight (LOS) component, the most significant diffusive component of the channel (the second power peak (SPP)), and the delay time between LOS and SPP, we present a proof of concept analysis for positioning using fixed reference points, i.e. uplink photodetectors (PDs).
Simulation results show the root mean square (RMS) positioning accuracy of 25 cm and 5 cm for one and 4 PDs scenarios, respectively.
Cyber-Physical Systems (CPS) are systems composed by a physical component that is controlled or monitored by a cyber-component, a computer-based algorithm.
Advances in CPS technologies and science are enabling capability, adaptability, scalability, resiliency, safety, security, and usability that will far exceed the simple embedded systems of today.
CPS technologies are transforming the way people interact with engineered systems.
New smart CPS are driving innovation in various sectors such as agriculture, energy, transportation, healthcare, and manufacturing.
They are leading the 4-th Industrial Revolution (Industry 4.0) that is having benefits thanks to the high flexibility of production.
The Industry 4.0 production paradigm is characterized by high intercommunicating properties of its production elements in all the manufacturing processes.
This is the reason it is a core concept how the systems should be structurally optimized to have the adequate level of redundancy to be satisfactorily resilient.
This goal can benefit from formal methods well known in various scientific domains such as artificial intelligence.
So, the current research concerns the proposal of a CPS meta-model and its instantiation.
In this way it lists all kind of relationships that may occur between the CPSs themselves and between their (cyber-and physical-) components.
Using the CPS meta-model formalization, with an adaptation of the Formal Concept Analysis (FCA) formal approach, this paper presents a way to optimize the modelling of CPS systems emphasizing their redundancy and their resiliency.
Can health entities collaboratively train deep learning models without sharing sensitive raw data?
This paper proposes several configurations of a distributed deep learning method called SplitNN to facilitate such collaborations.
SplitNN does not share raw data or model details with collaborating institutions.
The proposed configurations of splitNN cater to practical settings of i) entities holding different modalities of patient data, ii) centralized and local health entities collaborating on multiple tasks and iii) learning without sharing labels.
We compare performance and resource efficiency trade-offs of splitNN and other distributed deep learning methods like federated learning, large batch synchronous stochastic gradient descent and show highly encouraging results for splitNN.
We provide a formula for the number of edges of the Hasse diagram of the independent subsets of the h-th power of a path ordered by inclusion.
For h=1 such a value is the number of edges of a Fibonacci cube.
We show that, in general, the number of edges of the diagram is obtained by convolution of a Fibonacci-like sequence with itself.
The impact of soiling on solar panels is an important and well-studied problem in renewable energy sector.
In this paper, we present the first convolutional neural network (CNN) based approach for solar panel soiling and defect analysis.
Our approach takes an RGB image of solar panel and environmental factors as inputs to predict power loss, soiling localization, and soiling type.
In computer vision, localization is a complex task which typically requires manually labeled training data such as bounding boxes or segmentation masks.
Our proposed approach consists of specialized four stages which completely avoids localization ground truth and only needs panel images with power loss labels for training.
The region of impact area obtained from the predicted localization masks are classified into soiling types using the webly supervised learning.
For improving localization capabilities of CNNs, we introduce a novel bi-directional input-aware fusion (BiDIAF) block that reinforces the input at different levels of CNN to learn input-specific feature maps.
Our empirical study shows that BiDIAF improves the power loss prediction accuracy by about 3% and localization accuracy by about 4%.
Our end-to-end model yields further improvement of about 24% on localization when learned in a weakly supervised manner.
Our approach is generalizable and showed promising results on web crawled solar panel images.
Our system has a frame rate of 22 fps (including all steps) on a NVIDIA TitanX GPU.
Additionally, we collected first of it's kind dataset for solar panel image analysis consisting 45,000+ images.
We present the MAC network, a novel fully differentiable neural network architecture, designed to facilitate explicit and expressive reasoning.
MAC moves away from monolithic black-box neural architectures towards a design that encourages both transparency and versatility.
The model approaches problems by decomposing them into a series of attention-based reasoning steps, each performed by a novel recurrent Memory, Attention, and Composition (MAC) cell that maintains a separation between control and memory.
By stringing the cells together and imposing structural constraints that regulate their interaction, MAC effectively learns to perform iterative reasoning processes that are directly inferred from the data in an end-to-end approach.
We demonstrate the model's strength, robustness and interpretability on the challenging CLEVR dataset for visual reasoning, achieving a new state-of-the-art 98.9% accuracy, halving the error rate of the previous best model.
More importantly, we show that the model is computationally-efficient and data-efficient, in particular requiring 5x less data than existing models to achieve strong results.
Replacing a portion of current light duty vehicles (LDV) with plug-in hybrid electric vehicles (PHEVs) offers the possibility to reduce the dependence on petroleum fuels together with environmental and economic benefits.
The charging activity of PHEVs will certainly introduce new load to the power grid.
In the framework of the development of a smarter grid, the primary focus of the present study is to propose a model for the electrical daily demand in presence of PHEVs charging.
Expected PHEV demand is modeled by the PHEV charging time and the starting time of charge according to real world data.
A normal distribution for starting time of charge is assumed.
Several distributions for charging time are considered: uniform distribution, Gaussian with positive support, Rician distribution and a non-uniform distribution coming from driving patterns in real-world data.
We generate daily demand profiles by using real-world residential profiles throughout 2014 in the presence of different expected PHEV demand models.
Support vector machines (SVMs), a set of supervised machine learning models, are employed in order to find the best model to fit the data.
SVMs with radial basis function (RBF) and polynomial kernels were tested.
Model performances are evaluated by means of mean squared error (MSE) and mean absolute percentage error (MAPE).
Best results are obtained with RBF kernel: maximum (worst) values for MSE and MAPE were about 2.89 10-8 and 0.023, respectively.
This study investigates wireless information and energy transfer for dual-hop amplify-and-forward full-duplex relaying systems.
By forming energy efficiency (EE) maximization problem into a concave fractional program of transmission power, three relay control schemes are separately designed to enable energy harvesting and full-duplex information relaying.
With Rician fading modeled residual self-interference channel, analytical expressions of outage probability and ergodic capacity are presented for the maximum relay, signal-to-interference-plus-noise-ratio (SINR) relay, and target relay.
It has shown that EE maximization problem of the maximum relay is concave for time switching factor, so that bisection method has been applied to obtain the optimized value.
By incorporating instantaneous channel information, the SINR relay with collateral time switching factor achieves an improved EE over the maximum relay in delay-limited and delay-tolerant transmissions.
Without requiring channel information for the second-hop, the target relay ensures a competitive performance for outage probability, ergodic capacity, and EE.
Comparing to the direct source-destination transmission, numerical results show that the proposed relaying scheme is beneficial in achieving a comparable EE for low-rate delay-limited transmission.
Automated synthesis of reactive systems from specifications has been a topic of research for decades.
Recently, a variety of approaches have been proposed to extend synthesis of reactive systems from proposi- tional specifications towards specifications over rich theories.
We propose a novel, completely automated approach to program synthesis which reduces the problem to deciding the validity of a set of forall-exists formulas.
In spirit of IC3 / PDR, our problem space is recursively refined by blocking out regions of unsafe states, aiming to discover a fixpoint that describes safe reactions.
If such a fixpoint is found, we construct a witness that is directly translated into an implementation.
We implemented the algorithm on top of the JKind model checker, and exercised it against contracts written using the Lustre specification language.
Experimental results show how the new algorithm outperforms JKinds already existing synthesis procedure based on k-induction and addresses soundness issues in the k-inductive approach with respect to unrealizable results.
Over the last decade, Convolutional Neural Network (CNN) models have been highly successful in solving complex vision problems.
However, these deep models are perceived as "black box" methods considering the lack of understanding of their internal functioning.
There has been a significant recent interest in developing explainable deep learning models, and this paper is an effort in this direction.
Building on a recently proposed method called Grad-CAM, we propose a generalized method called Grad-CAM++ that can provide better visual explanations of CNN model predictions, in terms of better object localization as well as explaining occurrences of multiple object instances in a single image, when compared to state-of-the-art.
We provide a mathematical derivation for the proposed method, which uses a weighted combination of the positive partial derivatives of the last convolutional layer feature maps with respect to a specific class score as weights to generate a visual explanation for the corresponding class label.
Our extensive experiments and evaluations, both subjective and objective, on standard datasets showed that Grad-CAM++ provides promising human-interpretable visual explanations for a given CNN architecture across multiple tasks including classification, image caption generation and 3D action recognition; as well as in new settings such as knowledge distillation.
Low-rank modeling has a lot of important applications in machine learning, computer vision and social network analysis.
While the matrix rank is often approximated by the convex nuclear norm, the use of nonconvex low-rank regularizers has demonstrated better recovery performance.
However, the resultant optimization problem is much more challenging.
A very recent state-of-the-art is based on the proximal gradient algorithm.
However, it requires an expensive full SVD in each proximal step.
In this paper, we show that for many commonly-used nonconvex low-rank regularizers, a cutoff can be derived to automatically threshold the singular values obtained from the proximal operator.
This allows the use of power method to approximate the SVD efficiently.
Besides, the proximal operator can be reduced to that of a much smaller matrix projected onto this leading subspace.
Convergence, with a rate of O(1/T) where T is the number of iterations, can be guaranteed.
Extensive experiments are performed on matrix completion and robust principal component analysis.
The proposed method achieves significant speedup over the state-of-the-art.
Moreover, the matrix solution obtained is more accurate and has a lower rank than that of the traditional nuclear norm regularizer.
When using active learning, smaller batch sizes are typically more efficient from a learning efficiency perspective.
However, in practice due to speed and human annotator considerations, the use of larger batch sizes is necessary.
While past work has shown that larger batch sizes decrease learning efficiency from a learning curve perspective, it remains an open question how batch size impacts methods for stopping active learning.
We find that large batch sizes degrade the performance of a leading stopping method over and above the degradation that results from reduced learning efficiency.
We analyze this degradation and find that it can be mitigated by changing the window size parameter of how many past iterations of learning are taken into account when making the stopping decision.
We find that when using larger batch sizes, stopping methods are more effective when smaller window sizes are used.
There is an increasing demand for goal-oriented conversation systems which can assist users in various day-to-day activities such as booking tickets, restaurant reservations, shopping, etc.
Most of the existing datasets for building such conversation systems focus on monolingual conversations and there is hardly any work on multilingual and/or code-mixed conversations.
Such datasets and systems thus do not cater to the multilingual regions of the world, such as India, where it is very common for people to speak more than one language and seamlessly switch between them resulting in code-mixed conversations.
For example, a Hindi speaking user looking to book a restaurant would typically ask, "Kya tum is restaurant mein ek table book karne mein meri help karoge?"
("Can you help me in booking a table at this restaurant?").
To facilitate the development of such code-mixed conversation models, we build a goal-oriented dialog dataset containing code-mixed conversations.
Specifically, we take the text from the DSTC2 restaurant reservation dataset and create code-mixed versions of it in Hindi-English, Bengali-English, Gujarati-English and Tamil-English.
We also establish initial baselines on this dataset using existing state of the art models.
This dataset along with our baseline implementations is made publicly available for research purposes.
Islamophobic hate speech on social media inflicts considerable harm on both targeted individuals and wider society, and also risks reputational damage for the host platforms.
Accordingly, there is a pressing need for robust tools to detect and classify Islamophobic hate speech at scale.
Previous research has largely approached the detection of Islamophobic hate speech on social media as a binary task.
However, the varied nature of Islamophobia means that this is often inappropriate for both theoretically-informed social science and effectively monitoring social media.
Drawing on in-depth conceptual work we build a multi-class classifier which distinguishes between non-Islamophobic, weak Islamophobic and strong Islamophobic content.
Accuracy is 77.6% and balanced accuracy is 83%.
We apply the classifier to a dataset of 109,488 tweets produced by far right Twitter accounts during 2017.
Whilst most tweets are not Islamophobic, weak Islamophobia is considerably more prevalent (36,963 tweets) than strong (14,895 tweets).
Our main input feature is a gloVe word embeddings model trained on a newly collected corpus of 140 million tweets.
It outperforms a generic word embeddings model by 5.9 percentage points, demonstrating the importan4ce of context.
Unexpectedly, we also find that a one-against-one multi class SVM outperforms a deep learning algorithm.
The emergence of low-cost personal mobiles devices and wearable cameras and the increasing storage capacity of video-sharing websites have pushed forward a growing interest towards first-person videos.
Since most of the recorded videos compose long-running streams with unedited content, they are tedious and unpleasant to watch.
The fast-forward state-of-the-art methods are facing challenges of balancing the smoothness of the video and the emphasis in the relevant frames given a speed-up rate.
In this work, we present a methodology capable of summarizing and stabilizing egocentric videos by extracting the semantic information from the frames.
This paper also describes a dataset collection with several semantically labeled videos and introduces a new smoothness evaluation metric for egocentric videos that is used to test our method.
Graph algorithms applied in many applications, including social networks, communication networks, VLSI design, graphics, and several others, require dynamic modifications -- addition and removal of vertices and/or edges -- in the graph.
This paper presents a novel concurrent non-blocking algorithm to implement a dynamic unbounded directed graph in a shared-memory machine.
The addition and removal operations of vertices and edges are lock-free.
For a finite sized graph, the lookup operations are wait-free.
Most significant component of the presented algorithm is the reachability query in a concurrent graph.
The reachability queries in our algorithm are obstruction-free and thus impose minimal additional synchronization cost over other operations.
We prove that each of the data structure operations are linearizable.
We extensively evaluate a sample C/C++ implementation of the algorithm through a number of micro-benchmarks.
The experimental results show that the proposed algorithm scales well with the number of threads and on an average provides 5 to 7x performance improvement over a concurrent graph implementation using coarse-grained locking.
We propose a conceptual model of software development that encompasses all approaches: traditional or agile, light and heavy, for large and small development efforts.
The model identifies both the common aspects in all software development, i.e., elements found in some form or another in each and every software development project (Intent, Product, People, Work, Time, Quality, Risk, Cost, Value), as well as the variable part, i.e., the main factors that cause the very wide variations we can find in the software development world (Size, Age, Criticality, Architecture stability, Business model, Governance, Rate of change, Geographic distribution).
We show how the model can be used as an explanatory theory of software development, as a tool for analysis of practices, techniques, processes, as the basis for curriculum design or for software process adoption and improvement, and to support empirical research on software development methods.
This model is also proposed as a way to depolarize the debate on agile methods versus the rest-of-the-world: a unified model.
This paper introduces analogical and deductive methodologies for the design medical processor units (MPUs).
From the study of evolution of numerous earlier processors, we derive the basis for the architecture of MPUs.
These specialized processors perform unique medical functions encoded as medical operational codes (mopcs).
From a pragmatic perspective, MPUs function very close to CPUs.
Both processors have unique operation codes that command the hardware to perform a distinct chain of subprocesses upon operands and generate a specific result unique to the opcode and the operand(s).
In medical environments, MPU decodes the mopcs and executes a series of medical sub-processes and sends out secondary commands to the medical machine.
Whereas operands in a typical computer system are numerical and logical entities, the operands in medical machine are objects such as such as patients, blood samples, tissues, operating rooms, medical staff, medical bills, patient payments, etc.
We follow the functional overlap between the two processes and evolve the design of medical computer systems and networks.
One of the most attractive features of untyped languages is the flexibility in term creation and manipulation.
However, with such power comes the responsibility of ensuring the correctness of these operations.
A solution is adding run-time checks to the program via assertions, but this can introduce overheads that are in many cases impractical.
While static analysis can greatly reduce such overheads, the gains depend strongly on the quality of the information inferred.
Reusable libraries, i.e., library modules that are pre-compiled independently of the client, pose special challenges in this context.
We propose a technique which takes advantage of module systems which can hide a selected set of functor symbols to significantly enrich the shape information that can be inferred for reusable libraries, as well as an improved run-time checking approach that leverages the proposed mechanisms to achieve large reductions in overhead, closer to those of static languages, even in the reusable-library context.
While the approach is general and system-independent, we present it for concreteness in the context of the Ciao assertion language and combined static/dynamic checking framework.
Our method maintains the full expressiveness of the assertion language in this context.
In contrast to other approaches it does not introduce the need to switch the language to a (static) type system, which is known to change the semantics in languages like Prolog.
We also study the approach experimentally and evaluate the overhead reduction achieved in the run-time checks.
The paper presents a parallel implementation of existing image fusion methods on a graphical cluster.
Parallel implementations of methods based on discrete wavelet transformation (Haars and Daubechies discrete wavelet transform) are developed.
Experiments were performed on a cluster using GPU and CPU and performance gains were estimated for the use of the developed parallel implementations to process satellite images from satellite Landsat 7.
The implementation on a graphic cluster provides performance improvement from 2 to 18 times.
The quality of the considered methods was evaluated by ERGAS and QNR metrics.
The results show performance gains and retaining of quality with the cluster of GPU compared to the results obtained by the authors and other researchers for a CPU and single GPU.
3D models provide a common ground for different representations of human bodies.
In turn, robust 2D estimation has proven to be a powerful tool to obtain 3D fits "in-the- wild".
However, depending on the level of detail, it can be hard to impossible to acquire labeled data for training 2D estimators on large scale.
We propose a hybrid approach to this problem: with an extended version of the recently introduced SMPLify method, we obtain high quality 3D body model fits for multiple human pose datasets.
Human annotators solely sort good and bad fits.
This procedure leads to an initial dataset, UP-3D, with rich annotations.
With a comprehensive set of experiments, we show how this data can be used to train discriminative models that produce results with an unprecedented level of detail: our models predict 31 segments and 91 landmark locations on the body.
Using the 91 landmark pose estimator, we present state-of-the art results for 3D human pose and shape estimation using an order of magnitude less training data and without assumptions about gender or pose in the fitting procedure.
We show that UP-3D can be enhanced with these improved fits to grow in quantity and quality, which makes the system deployable on large scale.
The data, code and models are available for research purposes.
In this paper, we propose a novel sparse learning based feature selection method that directly optimizes a large margin linear classification model sparsity with l_(2,p)-norm (0 < p < 1)subject to data-fitting constraints, rather than using the sparsity as a regularization term.
To solve the direct sparsity optimization problem that is non-smooth and non-convex when 0<p<1, we provide an efficient iterative algorithm with proved convergence by converting it to a convex and smooth optimization problem at every iteration step.
The proposed algorithm has been evaluated based on publicly available datasets, and extensive comparison experiments have demonstrated that our algorithm could achieve feature selection performance competitive to state-of-the-art algorithms.
We study benefits of opportunistic routing in a large wireless ad hoc network by examining how the power, delay, and total throughput scale as the number of source- destination pairs increases up to the operating maximum.
Our opportunistic routing is novel in a sense that it is massively parallel, i.e., it is performed by many nodes simultaneously to maximize the opportunistic gain while controlling the inter-user interference.
The scaling behavior of conventional multi-hop transmission that does not employ opportunistic routing is also examined for comparison.
Our results indicate that our opportunistic routing can exhibit a net improvement in overall power--delay trade-off over the conventional routing by providing up to a logarithmic boost in the scaling law.
Such a gain is possible since the receivers can tolerate more interference due to the increased received signal power provided by the multi-user diversity gain, which means that having more simultaneous transmissions is possible.
Over the past three years Pinterest has experimented with several visual search and recommendation services, including Related Pins (2014), Similar Looks (2015), Flashlight (2016) and Lens (2017).
This paper presents an overview of our visual discovery engine powering these services, and shares the rationales behind our technical and product decisions such as the use of object detection and interactive user interfaces.
We conclude that this visual discovery engine significantly improves engagement in both search and recommendation tasks.
Being able to soundly estimate roundoff errors of finite-precision computations is important for many applications in embedded systems and scientific computing.
Due to the discrepancy between continuous reals and discrete finite-precision values, automated static analysis tools are highly valuable to estimate roundoff errors.
The results, however, are only as correct as the implementations of the static analysis tools.
This paper presents a formally verified and modular tool which fully automatically checks the correctness of finite-precision roundoff error bounds encoded in a certificate.
We present implementations of certificate generation and checking for both Coq and HOL4 and evaluate it on a number of examples from the literature.
The experiments use both in-logic evaluation of Coq and HOL4, and execution of extracted code outside of the logics: we benchmark Coq extracted unverified OCaml code and a CakeML-generated verified binary.
Online advertising is progressively moving towards a programmatic model in which ads are matched to actual interests of individuals collected as they browse the web.
Letting the huge debate around privacy aside, a very important question in this area, for which little is known, is: How much do advertisers pay to reach an individual?
In this study, we develop a first of its kind methodology for computing exactly that -- the price paid for a web user by the ad ecosystem -- and we do that in real time.
Our approach is based on tapping on the Real Time Bidding (RTB) protocol to collect cleartext and encrypted prices for winning bids paid by advertisers in order to place targeted ads.
Our main technical contribution is a method for tallying winning bids even when they are encrypted.
We achieve this by training a model using as ground truth prices obtained by running our own "probe" ad-campaigns.
We design our methodology through a browser extension and a back-end server that provides it with fresh models for encrypted bids.
We validate our methodology using a one year long trace of 1600 mobile users and demonstrate that it can estimate a user's advertising worth with more than 82% accuracy.
In this paper, we propose an inertial forward backward splitting algorithm to compute a zero of the sum of two monotone operators, with one of the two operators being co-coercive.
The algorithm is inspired by the accelerated gradient method of Nesterov, but can be applied to a much larger class of problems including convex-concave saddle point problems and general monotone inclusions.
We prove convergence of the algorithm in a Hilbert space setting and show that several recently proposed first-order methods can be obtained as special cases of the general algorithm.
Numerical results show that the proposed algorithm converges faster than existing methods, while keeping the computational cost of each iteration basically unchanged.
Map construction in large scale outdoor environment is of importance for robots to robustly fulfill their tasks.
Massive sessions of data should be merged to distinguish low dynamics in the map, which otherwise might debase the performance of localization and navigation algorithms.
In this paper we propose a method for multi-session map construction in large scale outdoor environment using 3D LiDAR.
To efficiently align the maps from different sessions, a laser-based loop closure detection method is integrated and the sequential information within the submaps is utilized for higher robustness.
Furthermore, a dynamic detection method is proposed to detect dynamics in the overlapping areas among sessions of maps.
We test the method in the real-world environment with a VLP-16 Velodyne LiDAR and the experimental results prove the validity and robustness of the proposed method.
We propose three private information retrieval (PIR) protocols for distributed storage systems (DSSs) where data is stored using an arbitrary linear code.
The first two protocols, named Protocol 1 and Protocol 2, achieve privacy for the scenario with noncolluding nodes.
Protocol 1 requires a file size that is exponential in the number of files in the system, while Protocol 2 requires a file size that is independent of the number of files and is hence simpler.
We prove that, for certain linear codes, Protocol 1 achieves the maximum distance separable (MDS) PIR capacity, i.e., the maximum PIR rate (the ratio of the amount of retrieved stored data per unit of downloaded data) for a DSS that uses an MDS code to store any given (finite and infinite) number of files, and Protocol 2 achieves the asymptotic MDS-PIR capacity (with infinitely large number of files in the DSS).
In particular, we provide a necessary and a sufficient condition for a code to achieve the MDS-PIR capacity with Protocols 1 and 2 and prove that cyclic codes, Reed-Muller (RM) codes, and a class of distance-optimal local reconstruction codes achieve both the finite MDS-PIR capacity (i.e., with any given number of files) and the asymptotic MDS-PIR capacity with Protocols 1 and 2, respectively.
Furthermore, we present a third protocol, Protocol 3, for the scenario with multiple colluding nodes, which can be seen as an improvement of a protocol recently introduced by Freij-Hollanti et al..
Similar to the noncolluding case, we provide a necessary and a sufficient condition to achieve the maximum possible PIR rate of Protocol 3.
Moreover, we provide a particular class of codes that is suitable for this protocol and show that RM codes achieve the maximum possible PIR rate for the protocol.
For all three protocols, we present an algorithm to optimize their PIR rates.
We introduce a theory-driven mechanism for learning a neural network model that performs generative topology design in one shot given a problem setting, circumventing the conventional iterative process that computational design tasks usually entail.
The proposed mechanism can lead to machines that quickly response to new design requirements based on its knowledge accumulated through past experiences of design generation.
Achieving such a mechanism through supervised learning would require an impractically large amount of problem-solution pairs for training, due to the known limitation of deep neural networks in knowledge generalization.
To this end, we introduce an interaction between a student (the neural network) and a teacher (the optimality conditions underlying topology optimization): The student learns from existing data and is tested on unseen problems.
Deviation of the student's solutions from the optimality conditions is quantified, and used for choosing new data points to learn from.
We call this learning mechanism "theory-driven", as it explicitly uses domain-specific theories to guide the learning, thus distinguishing itself from purely data-driven supervised learning.
We show through a compliance minimization problem that the proposed learning mechanism leads to topology generation with near-optimal structural compliance, much improved from standard supervised learning under the same computational budget.
Automated story generation is the problem of automatically selecting a sequence of events, actions, or words that can be told as a story.
We seek to develop a system that can generate stories by learning everything it needs to know from textual story corpora.
To date, recurrent neural networks that learn language models at character, word, or sentence levels have had little success generating coherent stories.
We explore the question of event representations that provide a mid-level of abstraction between words and sentences in order to retain the semantic information of the original data while minimizing event sparsity.
We present a technique for preprocessing textual story data into event sequences.
We then present a technique for automated story generation whereby we decompose the problem into the generation of successive events (event2event) and the generation of natural language sentences from events (event2sentence).
We give empirical results comparing different event representations and their effects on event successor generation and the translation of events to natural language.
The aim of fine-grained recognition is to identify sub-ordinate categories in images like different species of birds.
Existing works have confirmed that, in order to capture the subtle differences across the categories, automatic localization of objects and parts is critical.
Most approaches for object and part localization relied on the bottom-up pipeline, where thousands of region proposals are generated and then filtered by pre-trained object/part models.
This is computationally expensive and not scalable once the number of objects/parts becomes large.
In this paper, we propose a nonparametric data-driven method for object and part localization.
Given an unlabeled test image, our approach transfers annotations from a few similar images retrieved in the training set.
In particular, we propose an iterative transfer strategy that gradually refine the predicted bounding boxes.
Based on the located objects and parts, deep convolutional features are extracted for recognition.
We evaluate our approach on the widely-used CUB200-2011 dataset and a new and large dataset called Birdsnap.
On both datasets, we achieve better results than many state-of-the-art approaches, including a few using oracle (manually annotated) bounding boxes in the test images.
Data augmentation is usually used by supervised learning approaches for offline writer identification, but such approaches require extra training data and potentially lead to overfitting errors.
In this study, a semi-supervised feature learning pipeline was proposed to improve the performance of writer identification by training with extra unlabeled data and the original labeled data simultaneously.
Specifically, we proposed a weighted label smoothing regularization (WLSR) method for data augmentation, which assigned the weighted uniform label distribution to the extra unlabeled data.
The WLSR method could regularize the convolutional neural network (CNN) baseline to allow more discriminative features to be learned to represent the properties of different writing styles.
The experimental results on well-known benchmark datasets (ICDAR2013 and CVL) showed that our proposed semi-supervised feature learning approach could significantly improve the baseline measurement and perform competitively with existing writer identification approaches.
Our findings provide new insights into offline write identification.
This research introduces a new constraint domain for reasoning about data with uncertainty.
It extends convex modeling with the notion of p-box to gain additional quantifiable information on the data whereabouts.
Unlike existing approaches, the p-box envelops an unknown probability instead of approximating its representation.
The p-box bounds are uniform cumulative distribution functions (cdf) in order to employ linear computations in the probabilistic domain.
The reasoning by means of p-box cdf-intervals is an interval computation which is exerted on the real domain then it is projected onto the cdf domain.
This operation conveys additional knowledge represented by the obtained probabilistic bounds.
Empirical evaluation shows that, with minimal overhead, the output solution set realizes a full enclosure of the data along with tighter bounds on its probabilistic distributions.
This paper discusses online algorithms for inverse dynamics modelling in robotics.
Several model classes including rigid body dynamics (RBD) models, data-driven models and semiparametric models (which are a combination of the previous two classes) are placed in a common framework.
While model classes used in the literature typically exploit joint velocities and accelerations, which need to be approximated resorting to numerical differentiation schemes, in this paper a new `derivative-free' framework is proposed that does not require this preprocessing step.
An extensive experimental study with real data from the right arm of the iCub robot is presented, comparing different model classes and estimation procedures, showing that the proposed `derivative-free' methods outperform existing methodologies.
The detection of overlapping communities is a challenging problem which is gaining increasing interest in recent years because of the natural attitude of individuals, observed in real-world networks, to participate in multiple groups at the same time.
This review gives a description of the main proposals in the field.
Besides the methods designed for static networks, some new approaches that deal with the detection of overlapping communities in networks that change over time, are described.
Methods are classified with respect to the underlying principles guiding them to obtain a network division in groups sharing part of their nodes.
For each of them we also report, when available, computational complexity and web site address from which it is possible to download the software implementing the method.
In the presence of great social diversity in India, it is difficult to change the social background of students, parents and their economical conditions.
Therefore the only option left for us is to provide uniform or standardize teaching learning resources or methods.
For high quality education throughout India there must be some nation-wide network, which provides equal quality education to all students, including the student from the rural areas and villages.
The one and only simple solution to this is Web Based e-Learning.
In this paper we try to give some innovative ideas to spread the Web Based e-Learning (WBeL) concept in to the minds of young India along with various approaches taken or to be taken, associated to it till date besides of instructional design models, different course developmental models, the role of technical writing and merit-demerit of WBeL till date.
We study the problem of building models that disentangle independent factors of variation.
Such models could be used to encode features that can efficiently be used for classification and to transfer attributes between different images in image synthesis.
As data we use a weakly labeled training set.
Our weak labels indicate what single factor has changed between two data samples, although the relative value of the change is unknown.
This labeling is of particular interest as it may be readily available without annotation costs.
To make use of weak labels we introduce an autoencoder model and train it through constraints on image pairs and triplets.
We formally prove that without additional knowledge there is no guarantee that two images with the same factor of variation will be mapped to the same feature.
We call this issue the reference ambiguity.
Moreover, we show the role of the feature dimensionality and adversarial training.
We demonstrate experimentally that the proposed model can successfully transfer attributes on several datasets, but show also cases when the reference ambiguity occurs.
Meta-learning is a powerful tool that builds on multi-task learning to learn how to quickly adapt a model to new tasks.
In the context of reinforcement learning, meta-learning algorithms can acquire reinforcement learning procedures to solve new problems more efficiently by meta-learning prior tasks.
The performance of meta-learning algorithms critically depends on the tasks available for meta-training: in the same way that supervised learning algorithms generalize best to test points drawn from the same distribution as the training points, meta-learning methods generalize best to tasks from the same distribution as the meta-training tasks.
In effect, meta-reinforcement learning offloads the design burden from algorithm design to task design.
If we can automate the process of task design as well, we can devise a meta-learning algorithm that is truly automated.
In this work, we take a step in this direction, proposing a family of unsupervised meta-learning algorithms for reinforcement learning.
We describe a general recipe for unsupervised meta-reinforcement learning, and describe an effective instantiation of this approach based on a recently proposed unsupervised exploration technique and model-agnostic meta-learning.
We also discuss practical and conceptual considerations for developing unsupervised meta-learning methods.
Our experimental results demonstrate that unsupervised meta-reinforcement learning effectively acquires accelerated reinforcement learning procedures without the need for manual task design, significantly exceeds the performance of learning from scratch, and even matches performance of meta-learning methods that use hand-specified task distributions.
This paper studies the distributed state estimation problem for a class of discrete-time stochastic systems with nonlinear uncertain dynamics over time-varying topologies of sensor networks.
An extended state vector consisting of the original state and the nonlinear dynamics is constructed.
By analyzing the extended system, we provide a design method for the filtering gain and fusion matrices, leading to the extended state distributed Kalman filter.
It is shown that the proposed filter can provide the upper bound of estimation covariance in real time, which means the estimation accuracy can be evaluated online.It is proven that the estimation covariance of the filter is bounded under rather mild assumptions, i.e., collective observability of the system and jointly strong connectedness of network topologies.
Numerical simulation shows the effectiveness of the proposed filter.
We propose a methodology for clustering financial time series of stocks' returns, and a graphical set-up to quantify and visualise the evolution of these clusters through time.
The proposed graphical representation allows for the application of well known algorithms for solving classical combinatorial graph problems, which can be interpreted as problems relevant to portfolio design and investment strategies.
We illustrate this graph representation of the evolution of clusters in time and its use on real data from the Madrid Stock Exchange market.
Inserting an end of a rope through a loop is a common and important action that is required for creating most types of knots.
To perform this action, we need to pass the end of the rope through an area that is enclosed by another segment of rope.
As for all knotting actions, the robot must for this exercise control over a semi-compliant and flexible body whose complex 3d shape is difficult to perceive and follow.
Additionally, the target loop often deforms during the insertion.
We address this problem by defining a virtual magnetic field through the loop's interior and use the Biot Savart law to guide the robotic manipulator that holds the end of the rope.
This approach directly defines, for any manipulator position, a motion vector that results in a path that passes through the loop.
The motion vector is directly derived from the position of the loop and changes as soon as it moves or deforms.
In simulation, we test the insertion action against dynamic loop deformation of different intensity.
We also combine insertion with grasp and release actions, coordinated by a hybrid control system, to tie knots in simulation and with a NAO robot.
By executing jobs serially rather than in parallel, size-based scheduling policies can shorten time needed to complete jobs; however, major obstacles to their applicability are fairness guarantees and the fact that job sizes are rarely known exactly a-priori.
Here, we introduce the Pri family of size-based scheduling policies; Pri simulates any reference scheduler and executes jobs in the order of their simulated completion: we show that these schedulers give strong fairness guarantees, since no job completes later in Pri than in the reference policy.
In addition, we introduce PSBS, a practical implementation of such a scheduler: it works online (i.e., without needing knowledge of jobs submitted in the future), it has an efficient O(log n) implementation and it allows setting priorities to jobs.
Most importantly, unlike earlier size-based policies, the performance of PSBS degrades gracefully with errors, leading to performances that are close to optimal in a variety of realistic use cases.
The introduction of lung cancer screening programs will produce an unprecedented amount of chest CT scans in the near future, which radiologists will have to read in order to decide on a patient follow-up strategy.
According to the current guidelines, the workup of screen-detected nodules strongly relies on nodule size and nodule type.
In this paper, we present a deep learning system based on multi-stream multi-scale convolutional networks, which automatically classifies all nodule types relevant for nodule workup.
The system processes raw CT data containing a nodule without the need for any additional information such as nodule segmentation or nodule size and learns a representation of 3D data by analyzing an arbitrary number of 2D views of a given nodule.
The deep learning system was trained with data from the Italian MILD screening trial and validated on an independent set of data from the Danish DLCST screening trial.
We analyze the advantage of processing nodules at multiple scales with a multi-stream convolutional network architecture, and we show that the proposed deep learning system achieves performance at classifying nodule type that surpasses the one of classical machine learning approaches and is within the inter-observer variability among four experienced human observers.
Most geometric approaches to monocular Visual Odometry (VO) provide robust pose estimates, but sparse or semi-dense depth estimates.
Off late, deep methods have shown good performance in generating dense depths and VO from monocular images by optimizing the photometric consistency between images.
Despite being intuitive, a naive photometric loss does not ensure proper pixel correspondences between two views, which is the key factor for accurate depth and relative pose estimations.
It is a well known fact that simply minimizing such an error is prone to failures.
We propose a method using Epipolar constraints to make the learning more geometrically sound.
We use the Essential matrix, obtained using Nister's Five Point Algorithm, for enforcing meaningful geometric constraints on the loss, rather than using it as labels for training.
Our method, although simplistic but more geometrically meaningful, using lesser number of parameters, gives a comparable performance to state-of-the-art methods which use complex losses and large networks showing the effectiveness of using epipolar constraints.
Such a geometrically constrained learning method performs successfully even in cases where simply minimizing the photometric error would fail.
The identification of authorship in disputed documents still requires human expertise, which is now unfeasible for many tasks owing to the large volumes of text and authors in practical applications.
In this study, we introduce a methodology based on the dynamics of word co-occurrence networks representing written texts to classify a corpus of 80 texts by 8 authors.
The texts were divided into sections with equal number of linguistic tokens, from which time series were created for 12 topological metrics.
The series were proven to be stationary (p-value>0.05), which permits to use distribution moments as learning attributes.
With an optimized supervised learning procedure using a Radial Basis Function Network, 68 out of 80 texts were correctly classified, i.e. a remarkable 85% author matching success rate.
Therefore, fluctuations in purely dynamic network metrics were found to characterize authorship, thus opening the way for the description of texts in terms of small evolving networks.
Moreover, the approach introduced allows for comparison of texts with diverse characteristics in a simple, fast fashion.
This article explores a relationship between inconsistency in the pairwise comparisons method and conditions of order preservation.
A pairwise comparisons matrix with elements from an alo-group is investigated.
This approach allows for a generalization of previous results.
Sufficient conditions for order preservation based on the properties of elements of pairwise comparisons matrix are derived.
A numerical example is presented.
While Monte Carlo Tree Search and closely related methods have dominated General Video Game Playing, recent research has demonstrated the promise of Rolling Horizon Evolutionary Algorithms as an interesting alternative.
However, there is little attention paid to population initialization techniques in the setting of general real-time video games.
Therefore, this paper proposes the use of population seeding to improve the performance of Rolling Horizon Evolution and presents the results of two methods, One Step Look Ahead and Monte Carlo Tree Search, tested on 20 games of the General Video Game AI corpus with multiple evolution parameter values (population size and individual length).
An in-depth analysis is carried out between the results of the seeding methods and the vanilla Rolling Horizon Evolution.
In addition, the paper presents a comparison to a Monte Carlo Tree Search algorithm.
The results are promising, with seeding able to boost performance significantly over baseline evolution and even match the high level of play obtained by the Monte Carlo Tree Search.
Prior efforts to create an autonomous computer system capable of predicting what a human being is thinking or feeling from facial expression data have been largely based on outdated, inaccurate models of how emotions work that rely on many scientifically questionable assumptions.
In our research, we are creating an empathetic system that incorporates the latest provable scientific understanding of emotions: that they are constructs of the human mind, rather than universal expressions of distinct internal states.
Thus, our system uses a user-dependent method of analysis and relies heavily on contextual information to make predictions about what subjects are experiencing.
Our system's accuracy and therefore usefulness are built on provable ground truths that prohibit the drawing of inaccurate conclusions that other systems could too easily make.
"Concentrated differential privacy" was recently introduced by Dwork and Rothblum as a relaxation of differential privacy, which permits sharper analyses of many privacy-preserving computations.
We present an alternative formulation of the concept of concentrated differential privacy in terms of the Renyi divergence between the distributions obtained by running an algorithm on neighboring inputs.
With this reformulation in hand, we prove sharper quantitative results, establish lower bounds, and raise a few new questions.
We also unify this approach with approximate differential privacy by giving an appropriate definition of "approximate concentrated differential privacy."
Public Good Software's products match journalistic articles and other narrative content to relevant charitable causes and nonprofit organizations so that readers can take action on the issues raised by the articles' publishers.
Previously an expensive and labor-intensive process, application of machine learning and other automated textual analyses now allow us to scale this matching process to the volume of content produced daily by multiple large national media outlets.
This paper describes the development of a layered system of tactics working across a general news model that minimizes the need for human curation while maintaining the particular focus of concern for each individual publication.
We present a number of general strategies for categorizing heterogenous texts, and suggest editorial and operational tactics for publishers to make their publications and individual content items more efficiently analyzed by automated systems.
Interference alignment is a signaling technique that provides high multiplexing gain in the interference channel.
It can be extended to multi-hop interference channels, where relays aid transmission between sources and destinations.
In addition to coverage extension and capacity enhancement, relays increase the multiplexing gain in the interference channel.
In this paper, three cooperative algorithms are proposed for a multiple-antenna amplify-and-forward (AF) relay interference channel.
The algorithms design the transmitters and relays so that interference at the receivers can be aligned and canceled.
The first algorithm minimizes the sum power of enhanced noise from the relays and interference at the receivers.
The second and third algorithms rely on a connection between mean square error and mutual information to solve the end-to-end sum-rate maximization problem with either equality or inequality power constraints via matrix-weighted sum mean square error minimization.
The resulting iterative algorithms converge to stationary points of the corresponding optimization problems.
Simulations show that the proposed algorithms achieve higher end-to-end sum-rates and multiplexing gains that existing strategies for AF relays, decode-and-forward relays, and direct transmission.
The first algorithm outperforms the other algorithms at high signal-to-noise ratio (SNR) but performs worse than them at low SNR.
Thanks to power control, the third algorithm outperforms the second algorithm at the cost of overhead.
This paper is mainly a semi-tutorial introduction to elementary algebraic topology and its applications to Ising-type models of statistical physics, using graphical models of linear and group codes.
It contains new material on systematic (n,k) group codes and their information sets; normal realizations of homology and cohomology spaces; dual and hybrid models; and connections with system-theoretic concepts such as observability, controllability, and input/output realizations.
Sustainable and economical generation of electrical power is an essential and mandatory component of infrastructure in today's world.
Optimal generation (generator subset selection) of power requires a careful evaluation of various factors like type of source, generation, transmission & storage capacities, congestion among others which makes this a difficult task.
We created a grid to simulate various conditions including stimuli like generator supply, weather and load demand using Siemens PSS/E software and this data is trained using deep learning methods and subsequently tested.
The results are highly encouraging.
As per our knowledge, this is the first paper to propose a working and scalable deep learning model for this problem.
In this paper we present a blind deconvolution scheme based on statistical wavelet estimation.
We assume no prior knowledge of the wavelet, and do not select a reflector from the signal.
Instead, the wavelet (ultrasound pulse) is statistically estimated from the signal itself by a kurtosis-based metric.
This wavelet is then used to deconvolve the RF (radiofrequency) signal through Wiener filtering, and the resultant zero phase trace is subjected to spectral broadening by Autoregressive Spectral Extrapolation (ASE).
These steps increase the time resolution of diffraction techniques.
Results on synthetic and real cases show the robustness of the proposed method.
This paper investigates the problem of efficient displacement of random sensors where good communication within the network is provided, there is no interference between sensors and the movement cost is minimized in expectation.
There have been extensive efforts in government, academia, and industry to anticipate, forecast, and mitigate cyber attacks.
A common approach is time-series forecasting of cyber attacks based on data from network telescopes, honeypots, and automated intrusion detection/prevention systems.
This research has uncovered key insights such as systematicity in cyber attacks.
Here, we propose an alternate perspective of this problem by performing forecasting of attacks that are analyst-detected and -verified occurrences of malware.
We call these instances of malware cyber event data.
Specifically, our dataset was analyst-detected incidents from a large operational Computer Security Service Provider (CSSP) for the U.S. Department of Defense, which rarely relies only on automated systems.
Our data set consists of weekly counts of cyber events over approximately seven years.
Since all cyber events were validated by analysts, our dataset is unlikely to have false positives which are often endemic in other sources of data.
Further, the higher-quality data could be used for a number for resource allocation, estimation of security resources, and the development of effective risk-management strategies.
We used a Bayesian State Space Model for forecasting and found that events one week ahead could be predicted.
To quantify bursts, we used a Markov model.
Our findings of systematicity in analyst-detected cyber attacks are consistent with previous work using other sources.
The advanced information provided by a forecast may help with threat awareness by providing a probable value and range for future cyber events one week ahead.
Other potential applications for cyber event forecasting include proactive allocation of resources and capabilities for cyber defense (e.g., analyst staffing and sensor configuration) in CSSPs.
Enhanced threat awareness may improve cybersecurity.
GitHub is the largest source code repository in the world.
It provides a git-based source code management platform and also many features inspired by social networks.
For example, GitHub users can show appreciation to projects by adding stars to them.
Therefore, the number of stars of a repository is a direct measure of its popularity.
In this paper, we use multiple linear regressions to predict the number of stars of GitHub repositories.
These predictions are useful both to repository owners and clients, who usually want to know how their projects are performing in a competitive open source development market.
In a large-scale analysis, we show that the proposed models start to provide accurate predictions after being trained with the number of stars received in the last six months.
Furthermore, specific models---generated using data from repositories that share the same growth trends---are recommended for repositories with slow growth and/or for repositories with less stars.
Finally, we evaluate the ability to predict not the number of stars of a repository but its rank among the GitHub repositories.
We found a very strong correlation between predicted and real rankings (Spearman's rho greater than 0.95).
In this work, we introduce pose interpreter networks for 6-DoF object pose estimation.
In contrast to other CNN-based approaches to pose estimation that require expensively annotated object pose data, our pose interpreter network is trained entirely on synthetic pose data.
We use object masks as an intermediate representation to bridge real and synthetic.
We show that when combined with a segmentation model trained on RGB images, our synthetically trained pose interpreter network is able to generalize to real data.
Our end-to-end system for object pose estimation runs in real-time (20 Hz) on live RGB data, without using depth information or ICP refinement.
Due to recent technical and scientific advances, we have a wealth of information hidden in unstructured text data such as offline/online narratives, research articles, and clinical reports.
To mine these data properly, attributable to their innate ambiguity, a Word Sense Disambiguation (WSD) algorithm can avoid numbers of difficulties in Natural Language Processing (NLP) pipeline.
However, considering a large number of ambiguous words in one language or technical domain, we may encounter limiting constraints for proper deployment of existing WSD models.
This paper attempts to address the problem of one-classifier-per-one-word WSD algorithms by proposing a single Bidirectional Long Short-Term Memory (BLSTM) network which by considering senses and context sequences works on all ambiguous words collectively.
Evaluated on SensEval-3 benchmark, we show the result of our model is comparable with top-performing WSD algorithms.
We also discuss how applying additional modifications alleviates the model fault and the need for more training data.
The 55th Design Automation Conference (DAC) held its first System Design Contest (SDC) in 2018.
SDC'18 features a lower power object detection challenge (LPODC) on designing and implementing novel algorithms based object detection in images taken from unmanned aerial vehicles (UAV).
The dataset includes 95 categories and 150k images, and the hardware platforms include Nvidia's TX2 and Xilinx's PYNQ Z1.
DAC-SDC'18 attracted more than 110 entries from 12 countries.
This paper presents in detail the dataset and evaluation procedure.
It further discusses the methods developed by some of the entries as well as representative results.
The paper concludes with directions for future improvements.
We study the problem of distributed maximum computation in an open multi-agent system, where agents can leave and arrive during the execution of the algorithm.
The main challenge comes from the possibility that the agent holding the largest value leaves the system, which changes the value to be computed.
The algorithms must as a result be endowed with mechanisms allowing to forget outdated information.
The focus is on systems in which interactions are pairwise gossips between randomly selected agents.
We consider situations where leaving agents can send a last message, and situations where they cannot.
For both cases, we provide algorithms able to eventually compute the maximum of the values held by agents.
In the problem of matrix compressed sensing we aim to recover a low-rank matrix from few of its element-wise linear projections.
In this contribution we analyze the asymptotic performance of a Bayes-optimal inference procedure for a model where the matrix to be recovered is a product of random matrices.
The results that we obtain using the replica method describe the state evolution of the recently introduced P-BiG-AMP algorithm.
We show the existence of different types of phase transitions, their implications for the solvability of the problem, and we compare the results of the theoretical analysis to the performance reached by P-BiG-AMP.
Remarkably the asymptotic replica equations for matrix compressed sensing are the same as those for a related but formally different problem of matrix factorization.
The banking industry is very important for an economic cycle of each country and provides some quality of services for us.
With the advancement in technology and rapidly increasing of the complexity of the business environment, it has become more competitive than the past so that efficiency analysis in the banking industry attracts much attention in recent years.
From many aspects, such analyses at the branch level are more desirable.
Evaluating the branch performance with the purpose of eliminating deficiency can be a crucial issue for branch managers to measure branch efficiency.
This work not only can lead to a better understanding of bank branch performance but also give further information to enhance managerial decisions to recognize problematic areas.
To achieve this purpose, this study presents an integrated approach based on Data Envelopment Analysis (DEA), Clustering algorithms and Polynomial Pattern Classifier for constructing a classifier to identify a class of bank branches.
First, the efficiency estimates of individual branches are evaluated by using the DEA approach.
Next, when the range and number of classes were identified by experts, the number of clusters is identified by an agglomerative hierarchical clustering algorithm based on some statistical methods.
Next, we divide our raw data into k clusters By means of self-organizing map (SOM) neural networks.
Finally, all clusters are fed into the reduced multivariate polynomial model to predict the classes of data.
The need for countering Advanced Persistent Threat (APT) attacks has led to the solutions that ubiquitously monitor system activities in each host, and perform timely attack investigation over the monitoring data for analyzing attack provenance.
However, existing query systems based on relational databases and graph databases lack language constructs to express key properties of major attack behaviors, and often execute queries inefficiently since their semantics-agnostic design cannot exploit the properties of system monitoring data to speed up query execution.
To address this problem, we propose a novel query system built on top of existing monitoring tools and databases, which is designed with novel types of optimizations to support timely attack investigation.
Our system provides (1) domain-specific data model and storage for scaling the storage, (2) a domain-specific query language, Attack Investigation Query Language (AIQL) that integrates critical primitives for attack investigation, and (3) an optimized query engine based on the characteristics of the data and the semantics of the queries to efficiently schedule the query execution.
We deployed our system in NEC Labs America comprising 150 hosts and evaluated it using 857 GB of real system monitoring data (containing 2.5 billion events).
Our evaluations on a real-world APT attack and a broad set of attack behaviors show that our system surpasses existing systems in both efficiency (124x over PostgreSQL, 157x over Neo4j, and 16x over Greenplum) and conciseness (SQL, Neo4j Cypher, and Splunk SPL contain at least 2.4x more constraints than AIQL).
Clock synchronization is a widely discussed topic in the engineering literature.
Ensuring that individual clocks are closely aligned is important in network systems, since the correct timing of various events in a network is usually necessary for proper system implementation.
However, many existing clock synchronization algorithms update clock values abruptly, resulting in discontinuous clocks which have been shown to lead to undesirable behavior.
In this paper, we propose using the pulse-coupled oscillator model to guarantee clock continuity, demonstrating two general methods for achieving continuous phase evolution in any pulse-coupled oscillator network.
We provide rigorous mathematical proof that the pulse-coupled oscillator algorithm is able to converge to the synchronized state when the phase continuity methods are applied.
We provide simulation results supporting these proofs.
We further investigate the convergence behavior of other pulse-coupled oscillator synchronization algorithms using the proposed methods.
A software project has "Hero Developers" when 80% of contributions are delivered by 20% of the developers.
Are such heroes a good idea?
Are too many heroes bad for software quality?
Is it better to have more/less heroes for different kinds of projects?
To answer these questions, we studied 661 open source projects from Public open source software (OSS) Github and 171 projects from an Enterprise Github.
We find that hero projects are very common.
In fact, as projects grow in size, nearly all project become hero projects.
These findings motivated us to look more closely at the effects of heroes on software development.
Analysis shows that the frequency to close issues and bugs are not significantly affected by the presence of project type (Public or Enterprise).
Similarly, the time needed to resolve an issue/bug/enhancement is not affected by heroes or project type.
This is a surprising result since, before looking at the data, we expected that increasing heroes on a project will slow down howfast that project reacts to change.
However, we do find a statistically significant association between heroes, project types, and enhancement resolution rates.
Heroes do not affect enhancement resolution rates in Public projects.
However, in Enterprise projects, the more heroes increase the rate at which project complete enhancements.
In summary, our empirical results call for a revision of a long-held truism in software engineering.
Software heroes are far more common and valuable than suggested by the literature, particularly for medium to large Enterprise developments.
Organizations should reflect on better ways to find and retain more of these heroes
In this paper, an online adaptation algorithm for bipedal walking on uneven surfaces with height uncertainty is proposed.
In order to generate walking patterns on flat terrains, the trajectories in the task space are planned to satisfy the dynamic balance and slippage avoidance constraints, and also to guarantee smooth landing of the swing foot.
To ensure smooth landing of the swing foot on surfaces with height uncertainty, the preplanned trajectories in the task space should be adapted.
The proposed adaptation algorithm consists of two stages.
In the first stage, once the swing foot reaches its maximum height, the supervisory control is initiated until the touch is detected.
After the detection, the trajectories in the task space are modified to guarantee smooth landing.
In the second stage, this modification is preserved during the Double Support Phase (DSP), and released in the next Single Support Phase (SSP).
Effectiveness of the proposed online adaptation algorithm is experimentally verified through realization of the walking patterns on the SURENA III humanoid robot, designed and fabricated at CAST.
The walking is tested on a surface with various flat obstacles, where the swing foot is prone to either land on the ground soon or late.
This short paper provides a description of an architecture to acquisition and use of knowledge by intelligent agents over a restricted domain of the Internet Infrastructure.
The proposed architecture is added to an intelligent agent deployment model over a very useful server for Internet Autonomous System administrators.
Such servers, which are heavily dependent on arbitrary and eventual updates of human beings, become unreliable.
This is a position paper that proposes three research questions that are still in progress.
Virality of online content on social networking websites is an important but esoteric phenomenon often studied in fields like marketing, psychology and data mining.
In this paper we study viral images from a computer vision perspective.
We introduce three new image datasets from Reddit, and define a virality score using Reddit metadata.
We train classifiers with state-of-the-art image features to predict virality of individual images, relative virality in pairs of images, and the dominant topic of a viral image.
We also compare machine performance to human performance on these tasks.
We find that computers perform poorly with low level features, and high level information is critical for predicting virality.
We encode semantic information through relative attributes.
We identify the 5 key visual attributes that correlate with virality.
We create an attribute-based characterization of images that can predict relative virality with 68.10% accuracy (SVM+Deep Relative Attributes) -- better than humans at 60.12%.
Finally, we study how human prediction of image virality varies with different `contexts' in which the images are viewed, such as the influence of neighbouring images, images recently viewed, as well as the image title or caption.
This work is a first step in understanding the complex but important phenomenon of image virality.
Our datasets and annotations will be made publicly available.
This paper discusses models for dialogue state tracking using recurrent neural networks (RNN).
We present experiments on the standard dialogue state tracking (DST) dataset, DSTC2.
On the one hand, RNN models became the state of the art models in DST, on the other hand, most state-of-the-art models are only turn-based and require dataset-specific preprocessing (e.g.DSTC2-specific) in order to achieve such results.
We implemented two architectures which can be used in incremental settings and require almost no preprocessing.
We compare their performance to the benchmarks on DSTC2 and discuss their properties.
With only trivial preprocessing, the performance of our models is close to the state-of- the-art results.
Everyone knows that thousand of words are represented by a single image.
As a result image search has become a very popular mechanism for the Web searchers.
Image search means, the search results are produced by the search engine should be a set of images along with their Web page Unified Resource Locator.
Now Web searcher can perform two types of image search, they are Text to Image and Image to Image search.
In Text to Image search, search query should be a text.
Based on the input text data system will generate a set of images along with their Web page URL as an output.
On the other hand, in Image to Image search, search query should be an image and based on this image system will generate a set of images along with their Web page URL as an output.
According to the current scenarios, Text to Image search mechanism always not returns perfect result.
It matches the text data and then displays the corresponding images as an output, which is not always perfect.
To resolve this problem, Web researchers have introduced the Image to Image search mechanism.
In this paper, we have also proposed an alternate approach of Image to Image search mechanism using Histogram.
Several end-to-end deep learning approaches have been recently presented which extract either audio or visual features from the input images or audio signals and perform speech recognition.
However, research on end-to-end audiovisual models is very limited.
In this work, we present an end-to-end audiovisual model based on residual networks and Bidirectional Gated Recurrent Units (BGRUs).
To the best of our knowledge, this is the first audiovisual fusion model which simultaneously learns to extract features directly from the image pixels and audio waveforms and performs within-context word recognition on a large publicly available dataset (LRW).
The model consists of two streams, one for each modality, which extract features directly from mouth regions and raw waveforms.
The temporal dynamics in each stream/modality are modeled by a 2-layer BGRU and the fusion of multiple streams/modalities takes place via another 2-layer BGRU.
A slight improvement in the classification rate over an end-to-end audio-only and MFCC-based model is reported in clean audio conditions and low levels of noise.
In presence of high levels of noise, the end-to-end audiovisual model significantly outperforms both audio-only models.
A practical limitation of deep neural networks is their high degree of specialization to a single task and visual domain.
Recently, inspired by the successes of transfer learning, several authors have proposed to learn instead universal, fixed feature extractors that, used as the first stage of any deep network, work well for several tasks and domains simultaneously.
Nevertheless, such universal features are still somewhat inferior to specialized networks.
To overcome this limitation, in this paper we propose to consider instead universal parametric families of neural networks, which still contain specialized problem-specific models, but differing only by a small number of parameters.
We study different designs for such parametrizations, including series and parallel residual adapters, joint adapter compression, and parameter allocations, and empirically identify the ones that yield the highest compression.
We show that, in order to maximize performance, it is necessary to adapt both shallow and deep layers of a deep network, but the required changes are very small.
We also show that these universal parametrization are very effective for transfer learning, where they outperform traditional fine-tuning techniques.
This paper describes the deployment and implementation of a blockchain to improve the security, knowledge, intelligence and collaboration during the inter-agent communication processes in restrict domains of the Internet Infrastructure.
It is a work that proposes the application of a blockchain, platform independent, on a particular model of agents, but that can be used in similar proposals, once the results on the specific model were satisfactory.
The state-of-the-art performance of deep learning algorithms has led to a considerable increase in the utilization of machine learning in security-sensitive and critical applications.
However, it has recently been shown that a small and carefully crafted perturbation in the input space can completely fool a deep model.
In this study, we explore the extent to which face recognition systems are vulnerable to geometrically-perturbed adversarial faces.
We propose a fast landmark manipulation method for generating adversarial faces, which is approximately 200 times faster than the previous geometric attacks and obtains 99.86% success rate on the state-of-the-art face recognition models.
To further force the generated samples to be natural, we introduce a second attack constrained on the semantic structure of the face which has the half speed of the first attack with the success rate of 99.96%.
Both attacks are extremely robust against the state-of-the-art defense methods with the success rate of equal or greater than 53.59%.
Code is available at https://github.com/alldbi/FLM
The Multiple Instance Hybrid Estimator for discriminative target characterization from imprecisely labeled hyperspectral data is presented.
In many hyperspectral target detection problems, acquiring accurately labeled training data is difficult.
Furthermore, each pixel containing target is likely to be a mixture of both target and non-target signatures (i.e., sub-pixel targets), making extracting a pure prototype signature for the target class from the data extremely difficult.
The proposed approach addresses these problems by introducing a data mixing model and optimizing the response of the hybrid sub-pixel detector within a multiple instance learning framework.
The proposed approach iterates between estimating a set of discriminative target and non-target signatures and solving a sparse unmixing problem.
After learning target signatures, a signature based detector can then be applied on test data.
Both simulated and real hyperspectral target detection experiments show the proposed algorithm is effective at learning discriminative target signatures and achieves superior performance over state-of-the-art comparison algorithms.
Undetected errors are important for linear codes, which are the only type of errors after hard decision and automatic-repeat-request (ARQ), but do not receive much attention on their correction.
In concatenated channel coding, suboptimal source coding and joint source-channel coding, constrains among successive codewords may be utilized to improve decoding performance.
In this paper, list decoding is used to correct the undetected errors.
The benefit proportion of the correction is obviously improved especially on Hamming codes and Reed-Muller codes, which achieves about 40% in some cases.
But this improvement is significant only after the selection of final codewords from the lists based on the constrains among the successive transmitted codewords.
The selection algorithm is investigated here to complete the list decoding program in the application of Markov context model.
The performance of the algorithm is analysed and a lower bound of the correctly selected probability is derived to determine the proper context length.
The idea of a two wheel self-balancing robot has become very popular among control system researchers worldwide over the last decade.
This paper presents a one variant of the implementation of the self-balancing robot using the VEX Robotics Kit.
Convolutional neural networks (CNNs) are widely used in many image recognition tasks due to their extraordinary performance.
However, training a good CNN model can still be a challenging task.
In a training process, a CNN model typically learns a large number of parameters over time, which usually results in different performance.
Often, it is difficult to explore the relationships between the learned parameters and the model performance due to a large number of parameters and different random initializations.
In this paper, we present a visual analytics approach to compare two different snapshots of a trained CNN model taken after different numbers of epochs, so as to provide some insight into the design or the training of a better CNN model.
Our system compares snapshots by exploring the differences in operation parameters and the corresponding blob data at different levels.
A case study has been conducted to demonstrate the effectiveness of our system.
We present, GeniePath, a scalable approach for learning adaptive receptive fields of neural networks defined on permutation invariant graph data.
In GeniePath, we propose an adaptive path layer consists of two complementary functions designed for breadth and depth exploration respectively, where the former learns the importance of different sized neighborhoods, while the latter extracts and filters signals aggregated from neighbors of different hops away.
Our method works in both transductive and inductive settings, and extensive experiments compared with competitive methods show that our approaches yield state-of-the-art results on large graphs.
Objectives: The article provides an overview of current trends in personal sensor, signal and imaging informatics, that are based on emerging mobile computing and communications technologies enclosed in a smartphone and enabling the provision of personal, pervasive health informatics services.
Methods: The article reviews examples of these trends from the PubMed and Google scholar literature search engines, which, by no means claim to be complete, as the field is evolving and some recent advances may not be documented yet.
Results: There exist critical technological advances in the surveyed smartphone technologies, employed in provision and improvement of diagnosis, acute and chronic treatment and rehabilitation health services, as well as in education and training of healthcare practitioners.
However, the most emerging trend relates to a routine application of these technologies in a prevention/wellness sector, helping its users in self-care to stay healthy.
Conclusions: Smartphone-based personal health informatics services exist, but still have a long way to go to become an everyday, personalized healthcare-provisioning tool in the medical field and in a clinical practice.
Key main challenge for their widespread adoption involve lack of user acceptance striving from variable credibility and reliability of applications and solutions as they a) lack evidence-based approach; b) have low levels of medical professional involvement in their design and content; c) are provided in an unreliable way, influencing negatively its usability; and, in some cases, d) being industry-driven, hence exposing bias in information provided, for example towards particular types of treatment or intervention procedures.
Machine Learning on graph-structured data is an important and omnipresent task for a vast variety of applications including anomaly detection and dynamic network analysis.
In this paper, a deep generative model is introduced to capture continuous probability densities corresponding to the nodes of an arbitrary graph.
In contrast to all learning formulations in the area of discriminative pattern recognition, we propose a scalable generative optimization/algorithm theoretically proved to capture distributions at the nodes of a graph.
Our model is able to generate samples from the probability densities learned at each node.
This probabilistic data generation model, i.e. convolutional graph auto-encoder (CGAE), is devised based on the localized first-order approximation of spectral graph convolutions, deep learning, and the variational Bayesian inference.
We apply our CGAE to a new problem, the spatio-temporal probabilistic solar irradiance prediction.
Multiple solar radiation measurement sites in a wide area in northern states of the US are modeled as an undirected graph.
Using our proposed model, the distribution of future irradiance given historical radiation observations is estimated for every site/node.
Numerical results on the National Solar Radiation Database show state-of-the-art performance for probabilistic radiation prediction on geographically distributed irradiance data in terms of reliability, sharpness, and continuous ranked probability score.
There is surprisingly little known about agenda setting for international development in the United Nations (UN) despite it having a significant influence on the process and outcomes of development efforts.
This paper addresses this shortcoming using a novel approach that applies natural language processing techniques to countries' annual statements in the UN General Debate.
Every year UN member states deliver statements during the General Debate on their governments' perspective on major issues in world politics.
These speeches provide invaluable information on state preferences on a wide range of issues, including international development, but have largely been overlooked in the study of global politics.
This paper identifies the main international development topics that states raise in these speeches between 1970 and 2016, and examine the country-specific drivers of international development rhetoric.
Automatic feature extraction using neural networks has accomplished remarkable success for images, but for sound recognition, these models are usually modified to fit the nature of the multi-dimensional temporal representation of the audio signal in spectrograms.
This may not efficiently harness the time-frequency representation of the signal.
The ConditionaL Neural Network (CLNN) takes into consideration the interrelation between the temporal frames, and the Masked ConditionaL Neural Network (MCLNN) extends upon the CLNN by forcing a systematic sparseness over the network's weights using a binary mask.
The masking allows the network to learn about frequency bands rather than bins, mimicking a filterbank used in signal transformations such as MFCC.
Additionally, the Mask is designed to consider various combinations of features, which automates the feature hand-crafting process.
We applied the MCLNN for the Environmental Sound Recognition problem using the Urbansound8k, YorNoise, ESC-10 and ESC-50 datasets.
The MCLNN have achieved competitive performance compared to state-of-the-art Convolutional Neural Networks and hand-crafted attempts.
A new stereoscopic image quality assessment database rendered using the 2D-image-plus-depth source, called MCL-3D, is described and the performance benchmarking of several known 2D and 3D image quality metrics using the MCL-3D database is presented in this work.
Nine image-plus-depth sources are first selected, and a depth image-based rendering (DIBR) technique is used to render stereoscopic image pairs.
Distortions applied to either the texture image or the depth image before stereoscopic image rendering include: Gaussian blur, additive white noise, down-sampling blur, JPEG and JPEG-2000 (JP2K) compression and transmission error.
Furthermore, the distortion caused by imperfect rendering is also examined.
The MCL-3D database contains 693 stereoscopic image pairs, where one third of them are of resolution 1024x728 and two thirds are of resolution 1920x1080.
The pair-wise comparison was adopted in the subjective test for user friendliness, and the Mean Opinion Score (MOS) can be computed accordingly.
Finally, we evaluate the performance of several 2D and 3D image quality metrics applied to MCL-3D.
All texture images, depth images, rendered image pairs in MCL-3D and their MOS values obtained in the subjective test are available to the public (http://mcl.usc.edu/mcl-3d-database/) for future research and development.
With the ever increasing demands of cloud computing services, planning and management of cloud resources has become a more and more important issue which directed affects the resource utilization and SLA and customer satisfaction.
But before any management strategy is made, a good understanding of applications' workload in virtualized environment is the basic fact and principle to the resource management methods.
Unfortunately, little work has been focused on this area.
Lack of raw data could be one reason; another reason is that people still use the traditional models or methods shared under non-virtualized environment.
The study of applications' workload in virtualized environment should take on some of its peculiar features comparing to the non-virtualized environment.
In this paper, we are open to analyze the workload demands that reflect applications' behavior and the impact of virtualization.
The results are obtained from an experimental cloud testbed running web applications, specifically the RUBiS benchmark application.
We profile the workload dynamics on both virtualized and non-virtualized environments and compare the findings.
The experimental results are valuable for us to estimate the performance of applications on computer architectures, to predict SLA compliance or violation based on the projected application workload and to guide the decision making to support applications with the right hardware.
Functional Electrical Stimulation (FES) systems are successful in restoring motor function and supporting paralyzed users.
Commercially available FES products are open loop, meaning that the system is unable to adapt to changing conditions with the user and their muscles which results in muscle fatigue and poor stimulation protocols.
This is because it is difficult to close the loop between stimulation and monitoring of muscle contraction using adaptive stimulation.
FES causes electrical artefacts which make it challenging to monitor muscle contractions with traditional methods such as electromyography (EMG).
We look to overcome this limitation by combining FES with novel mechanomyographic (MMG) sensors to be able to monitor muscle activity during stimulation in real time.
To provide a meaningful task we built an FES cycling rig with a software interface that enabled us to perform adaptive recording and stimulation, and then combine this with sensors to record forces applied to the pedals using force sensitive resistors (FSRs), crank angle position using a magnetic incremental encoder and inputs from the user using switches and a potentiometer.
We illustrated this with a closed-loop stimulation algorithm that used the inputs from the sensors to control the output of a programmable RehaStim 1 FES stimulator (Hasomed) in real-time.
This recumbent bicycle rig was used as a testing platform for FES cycling.
The algorithm was designed to respond to a change in requested speed (RPM) from the user and change the stimulation power (% of maximum current mA) until this speed was achieved and then maintain it.
We describe a method to produce a network where current methods such as DeepFool have great difficulty producing adversarial samples.
Our construction suggests some insights into how deep networks work.
We provide a reasonable analyses that our construction is difficult to defeat, and show experimentally that our method is hard to defeat with both Type I and Type II attacks using several standard networks and datasets.
This SafetyNet architecture is used to an important and novel application SceneProof, which can reliably detect whether an image is a picture of a real scene or not.
SceneProof applies to images captured with depth maps (RGBD images) and checks if a pair of image and depth map is consistent.
It relies on the relative difficulty of producing naturalistic depth maps for images in post processing.
We demonstrate that our SafetyNet is robust to adversarial examples built from currently known attacking approaches.
Size-Change Termination is an increasingly-popular technique for verifying program termination.
These termination proofs are deduced from an abstract representation of the program in the form of "size-change graphs".
We present algorithms that, for certain classes of size-change graphs, deduce a global ranking function: an expression that ranks program states, and decreases on every transition.
A ranking function serves as a witness for a termination proof, and is therefore interesting for program certification.
The particular form of the ranking expressions that represent SCT termination proofs sheds light on the scope of the proof method.
The complexity of the expressions is also interesting, both practicaly and theoretically.
While deducing ranking functions from size-change graphs has already been shown possible, the constructions in this paper are simpler and more transparent than previously known.
They improve the upper bound on the size of the ranking expression from triply exponential down to singly exponential (for certain classes of instances).
We claim that this result is, in some sense, optimal.
To this end, we introduce a framework for lower bounds on the complexity of ranking expressions and prove exponential lower bounds.
We propose a novel convolutional neural network architecture for estimating geospatial functions such as population density, land cover, or land use.
In our approach, we combine overhead and ground-level images in an end-to-end trainable neural network, which uses kernel regression and density estimation to convert features extracted from the ground-level images into a dense feature map.
The output of this network is a dense estimate of the geospatial function in the form of a pixel-level labeling of the overhead image.
To evaluate our approach, we created a large dataset of overhead and ground-level images from a major urban area with three sets of labels: land use, building function, and building age.
We find that our approach is more accurate for all tasks, in some cases dramatically so.
There is a recent interest in developing statistical filtering methods for stochastic optimization (FSO) by leveraging a probabilistic perspective of incremental proximity methods (IPMs).
The existent FSO methods are derived based on the Kalman filter (KF) and extended KF (EKF).
Different with classical stochastic optimization methods such as the stochastic gradient descent (SGD) and typical IPMs, such KF-type algorithms possess a desirable property, namely they do not require pre-scheduling of the learning rate for convergence.
However, on the other side, they have inherent limitations inherited from the nature of KF mechanisms.
It is a consensus that the class of particle filters (PFs) outperforms the KF and its variants remarkably for nonlinear and/or non-Gaussian statistical filtering tasks.
Hence, it is natural to ask if the FSO methods can benefit from the PF theory to get around of the limitations of the KF-type IPMs.
We provide an affirmative answer to the aforementioned question by developing three PF based stochastic optimization (PFSO) algorithms.
For performance evaluation, we apply them to solve a least-square fitting problem using a simulated data set, and the empirical risk minimization (ERM) problem in binary classification using real data sets.
Experimental results demonstrate that our algorithms outperform remarkably existent methods in terms of numerical stability, convergence speed, and flexibility in handling different types of loss functions.
Relative worst-order analysis is a technique for assessing the relative quality of online algorithms.
We survey the most important results obtained with this technique and compare it with other quality measures.
Early software effort estimation is a hallmark of successful software project management.
Building a reliable effort estimation model usually requires historical data.
Unfortunately, since the information available at early stages of software development is scarce, it is recommended to use software size metrics as key cost factor of effort estimation.
Use Case Points (UCP) is a prominent size measure designed mainly for object-oriented projects.
Nevertheless, there are no established models that can translate UCP into its corresponding effort, therefore, most models use productivity as a second cost driver.
The productivity in those models is usually guessed by experts and does not depend on historical data, which makes it subject to uncertainty.
Thus, these models were not well examined using a large number of historical data.
In this paper, we designed a hybrid model that consists of classification and prediction stages using a support vector machine and radial basis neural networks.
The proposed model was constructed over a large number of observations collected from industrial and student projects.
The proposed model was compared against previous UCP prediction models.
The validation and empirical results demonstrated that the proposed model significantly surpasses these models on all datasets.
The main conclusion is that the environmental factors of UCP can be used to classify and estimate productivity.
The research explores and examines factors for supplier evaluation and its impact on process improvement particularly aiming on a steel pipe manufacturing firm in Gujarat, India.
Data was collected using in-depth interview.
The questionnaire primarily involves the perception of evaluation of supplier.
Factors influencing supplier evaluation and its influence on process improvement is also examined in this study.
The model testing and validation were done using partial least square method.
Outcomes signified that the factors that influence the evaluation of the supplier are quality, cost, delivery and supplier relationship management.
The study depicted that quality and cost factors for supplier evaluation are insignificant.
The delivery and supplier relationship management have the significant influence on the evaluation of the supplier.
The research also depicted that supplier evaluation has a significant influence on process improvement.
Many researchers have considered quality, cost and delivery as the factors for evaluating the suppliers.
But for a company, it is quintessential to have a good relationship with the supplier.
Hence, the factor, supplier relationship management is considered for the study.
Also, the case study company focused more on quality and cost factors for the supplier evaluation of the firm.
However, delivery and supplier relationship management are also equally important for a firm in evaluating the supplier.
In order to avoid unnecessary applications of Miller-Rabin algorithm to the number in question, we resort to trial division by a few initial prime numbers, since such a division take less time.
How far we should go with such a division is the that we are trying to answer in this paper?For the theory of the matter is fully resolved.
However, that in practice we do not have much use.
Therefore, we present a solution that is probably irrelevant to theorists, but it is very useful to people who have spent many nights to produce large (probably) prime numbers using its own software.
Skeleton based action recognition distinguishes human actions using the trajectories of skeleton joints, which provide a very good representation for describing actions.
Considering that recurrent neural networks (RNNs) with Long Short-Term Memory (LSTM) can learn feature representations and model long-term temporal dependencies automatically, we propose an end-to-end fully connected deep LSTM network for skeleton based action recognition.
Inspired by the observation that the co-occurrences of the joints intrinsically characterize human actions, we take the skeleton as the input at each time slot and introduce a novel regularization scheme to learn the co-occurrence features of skeleton joints.
To train the deep LSTM network effectively, we propose a new dropout algorithm which simultaneously operates on the gates, cells, and output responses of the LSTM neurons.
Experimental results on three human action recognition datasets consistently demonstrate the effectiveness of the proposed model.
The advancement in technology has brought a new era in terrorism where Online Social Networks have become a major platform of communication with wide range of usage from message channeling to propaganda and recruitment of new followers in terrorist groups.
Meanwhile, during the terrorist attacks people use social networks for information exchange, mobilizing and uniting and raising money for the victims.
This paper critically analyses the specific usage of social networks in the times of terrorism attacks in developing countries.
We characterize binary words that have exactly two unbordered conjugates and show that they can be expressed as a product of two palindromes.
The current study examines how adequate coordination among different cognitive processes including visual recognition, attention switching, action preparation and generation can be developed via learning of robots by introducing a novel model, the Visuo-Motor Deep Dynamic Neural Network (VMDNN).
The proposed model is built on coupling of a dynamic vision network, a motor generation network, and a higher level network allocated on top of these two.
The simulation experiments using the iCub simulator were conducted for cognitive tasks including visual object manipulation responding to human gestures.
The results showed that synergetic coordination can be developed via iterative learning through the whole network when spatio-temporal hierarchy and temporal one can be self-organized in the visual pathway and in the motor pathway, respectively, such that the higher level can manipulate them with abstraction.
Our Chapter in the upcoming Volume I: Computer Science and Software Engineering of Computing Handbook (Third edition), Allen Tucker, Teo Gonzales and Jorge L. Diaz-Herrera, editors, covers Algebraic Algorithms, both symbolic and numerical, for matrix computations and root-finding for polynomials and systems of polynomials equations.
We cover part of these large subjects and include basic bibliography for further study.
To meet space limitation we cite books, surveys, and comprehensive articles with pointers to further references, rather than including all the original technical papers.
Automatic segmentation of retinal blood vessels from fundus images plays an important role in the computer aided diagnosis of retinal diseases.
The task of blood vessel segmentation is challenging due to the extreme variations in morphology of the vessels against noisy background.
In this paper, we formulate the segmentation task as a multi-label inference task and utilize the implicit advantages of the combination of convolutional neural networks and structured prediction.
Our proposed convolutional neural network based model achieves strong performance and significantly outperforms the state-of-the-art for automatic retinal blood vessel segmentation on DRIVE dataset with 95.33% accuracy and 0.974 AUC score.
We investigate pivot-based translation between related languages in a low resource, phrase-based SMT setting.
We show that a subword-level pivot-based SMT model using a related pivot language is substantially better than word and morpheme-level pivot models.
It is also highly competitive with the best direct translation model, which is encouraging as no direct source-target training corpus is used.
We also show that combining multiple related language pivot models can rival a direct translation model.
Thus, the use of subwords as translation units coupled with multiple related pivot languages can compensate for the lack of a direct parallel corpus.
Understanding structural controllability of a complex network requires to identify a Minimum Input nodes Set (MIS) of the network.
It has been suggested that finding an MIS is equivalent to computing a maximum matching of the network, where the unmatched nodes constitute an MIS.
However, maximum matching of a network is often not unique, and finding all MISs may provide deep insights to the controllability of the network.
Finding all possible input nodes, which form the union of all MISs, is computationally challenging for large networks.
Here we present an efficient enumerative algorithm for the problem.
The main idea is to modify a maximum matching algorithm to make it efficient for finding all possible input nodes by computing only one MIS.
We rigorously proved the correctness of the new algorithm and evaluated its performance on synthetic and large real networks.
The experimental results showed that the new algorithm ran several orders of magnitude faster than the existing method on large real networks.
GFDM and WCP-COQAM are amongst the candidate physical layer modulation formats to be used in 5G, whose claimed lower out-of-band (OOB) emissions are important with respect to cognitive radio based dynamic spectrum access solutions.
In this study, we compare OFDM, GFDM and WCP-COQAM in terms of OOB emissions in a fair manner such that their spectral efficiencies are the same and OOB emission reduction techniques are applied to all of the modulation types.
Analytical PSD expressions are also correlated with the simulation based OOB emission results.
Maintaining the same spectral efficiency, carrier frequency offset immunities will also be compared.
Memristors are low-power memory-holding resistors thought to be useful for neuromophic computing, which can compute via spike-interactions mediated through the device's short-term memory.
Using interacting spikes, it is possible to build an AND gate that computes OR at the same time, similarly a full adder can be built that computes the arithmetical sum of its inputs.
Here we show how these gates can be understood by modelling the memristors as a novel type of perceptron: one which is sensitive to input order.
The memristor's memory can change the input weights for later inputs, and thus the memristor gates cannot be accurately described by a single perceptron, requiring either a network of time-invarient perceptrons or a complex time-varying self-reprogrammable perceptron.
This work demonstrates the high functionality of memristor logic gates, and also that the addition of theasholding could enable the creation of a standard perceptron in hardware, which may have use in building neural net chips.
We present a novel semantic light field (LF) refocusing technique that can achieve unprecedented see-through quality.
Different from prior art, our semantic see-through (SST) differentiates rays in their semantic meaning and depth.
Specifically, we combine deep learning and stereo matching to provide each ray a semantic label.
We then design tailored weighting schemes for blending the rays.
Although simple, our solution can effectively remove foreground residues when focusing on the background.
At the same time, SST maintains smooth transitions in varying focal depths.
Comprehensive experiments on synthetic and new real indoor and outdoor datasets demonstrate the effectiveness and usefulness of our technique.
This letter deals with the controllability issue of complex networks.
An index is chosen to quantitatively measure the extent of controllability of given network.
The effect of this index is analyzed based on empirical studies on various classes of network topologies, such as random network, small-world network, and scale-free network.
This article addresses an open problem in the area of cognitive systems and architectures: namely the problem of handling (in terms of processing and reasoning capabilities) complex knowledge structures that can be at least plausibly comparable, both in terms of size and of typology of the encoded information, to the knowledge that humans process daily for executing everyday activities.
Handling a huge amount of knowledge, and selectively retrieve it ac- cording to the needs emerging in different situational scenarios, is an important aspect of human intelligence.
For this task, in fact, humans adopt a wide range of heuristics (Gigerenzer and Todd) due to their bounded rationality (Simon, 1957).
In this perspective, one of the re- quirements that should be considered for the design, the realization and the evaluation of intelligent cognitively inspired systems should be represented by their ability of heuristically identify and retrieve, from the general knowledge stored in their artificial Long Term Memory (LTM), that one which is synthetically and contextually relevant.
This require- ment, however, is often neglected.
Currently, artificial cognitive systems and architectures are not able, de facto, to deal with complex knowledge structures that can be even slightly comparable to the knowledge heuris- tically managed by humans.
In this paper I will argue that this is not only a technological problem but also an epistemological one and I will briefly sketch a proposal for a possible solution.
This paper presents a summary of the first Workshop on Building Linguistically Generalizable Natural Language Processing Systems, and the associated Build It Break It, The Language Edition shared task.
The goal of this workshop was to bring together researchers in NLP and linguistics with a shared task aimed at testing the generalizability of NLP systems beyond the distributions of their training data.
We describe the motivation, setup, and participation of the shared task, provide discussion of some highlighted results, and discuss lessons learned.
We study supervised learning problems using clustering constraints to impose structure on either features or samples, seeking to help both prediction and interpretation.
The problem of clustering features arises naturally in text classification for instance, to reduce dimensionality by grouping words together and identify synonyms.
The sample clustering problem on the other hand, applies to multiclass problems where we are allowed to make multiple predictions and the performance of the best answer is recorded.
We derive a unified optimization formulation highlighting the common structure of these problems and produce algorithms whose core iteration complexity amounts to a k-means clustering step, which can be approximated efficiently.
We extend these results to combine sparsity and clustering constraints, and develop a new projection algorithm on the set of clustered sparse vectors.
We prove convergence of our algorithms on random instances, based on a union of subspaces interpretation of the clustering structure.
Finally, we test the robustness of our methods on artificial data sets as well as real data extracted from movie reviews.
Next generation multi-beam SatCom architectures will heavily exploit full frequency reuse schemes along with interference management techniques, e.g., precoding or multiuser detection, to drastically increase the system throughput.
In this framework, we address the problem of the user selection for multicast precoding by formulating it as a clustering problem.
By introducing a novel mathematical framework, we design fixed-/variable-size clustering algorithms that group users into simultaneously precoded and served clusters while maximising the system throughput.
Numerical simulations are used to validate the proposed algorithms and to identify the main system-level trade-offs.
With the rapid advancement of wireless network technology, usage of WSN in real time applications like military, forest monitoring etc. found increasing.
Generally WSN operate in an unattended environment and handles critical data.
Authenticating the user trying to access the sensor memory is one of the critical requirements.
Many researchers have proposed remote user authentication schemes focusing on various parameters.
In 2013, Li et al. proposed a temporal-credential-based mutual authentication and key agreement scheme for WSNs.
Li et al. claimed that their scheme is secure against all major cryptographic attacks and requires less computation cost due to usage of hash function instead encryption operations.
Unfortunately, in this paper we will show that their scheme is vulnerable to offline password guessing attack, stolen smart card attack, leakage of password etc. and failure to provide data privacy.
A heavy path in a weighted graph represents a notion of connectivity and ordering that goes beyond two nodes.
The heaviest path of length l in the graph, simply means a sequence of nodes with edges between them, such that the sum of edge weights is maximum among all paths of length l. It is trivial to state the heaviest edge in the graph is the heaviest path of length 1, that represents a heavy connection between (any) two existing nodes.
This can be generalized in many different ways for more than two nodes, one of which is finding the heavy weight paths in the graph.
In an influence network, this represents a highway for spreading information from a node to one of its indirect neighbors at distance l. Moreover, a heavy path implies an ordering of nodes.
For instance, we can discover which ordering of songs (tourist spots) on a playlist (travel itinerary) is more pleasant to a user or a group of users who enjoy all songs (tourist spots) on the playlist (itinerary).
This can also serve as a hard optimization problem, maximizing different types of quantities of a path such as score, flow, probability or surprise, defined as edge weight.
Therefore, if one can solve the Heavy Path Problem (HPP) efficiently, they can as well use HPP for modeling and reduce other complex problems to it.
Conventional approaches to image de-fencing use multiple adjacent frames for segmentation of fences in the reference image and are limited to restoring images of static scenes only.
In this paper, we propose a de-fencing algorithm for images of dynamic scenes using an occlusion-aware optical flow method.
We divide the problem of image de-fencing into the tasks of automated fence segmentation from a single image, motion estimation under known occlusions and fusion of data from multiple frames of a captured video of the scene.
Specifically, we use a pre-trained convolutional neural network to segment fence pixels from a single image.
The knowledge of spatial locations of fences is used to subsequently estimate optical flow in the occluded frames of the video for the final data fusion step.
We cast the fence removal problem in an optimization framework by modeling the formation of the degraded observations.
The inverse problem is solved using fast iterative shrinkage thresholding algorithm (FISTA).
Experimental results show the effectiveness of proposed algorithm.
The NUbots are an interdisciplinary RoboCup team from The University of Newcastle, Australia.
The team has a history of strong contributions in the areas of machine learning and computer vision.
The NUbots have participated in RoboCup leagues since 2002, placing first several times in the past.
In 2014 the NUbots also partnered with the University of Newcastle Mechatronics Laboratory to participate in the RobotX Marine Robotics Challenge, which resulted in several new ideas and improvements to the NUbots vision system for RoboCup.
This paper summarizes the history of the NUbots team, describes the roles and research of the team members, gives an overview of the NUbots' robots, their software system, and several associated research projects.
The automatic parking is being massively developed by car manufacturers and providers.
Until now, there are two problems with the automatic parking.
First, there is no openly-available segmentation labels of parking slot on panoramic surround view (PSV) dataset.
Second, how to detect parking slot and road structure robustly.
Therefore, in this paper, we build up a public PSV dataset.
At the same time, we proposed a highly fused convolutional network (HFCN) based segmentation method for parking slot and lane markings based on the PSV dataset.
A surround-view image is made of four calibrated images captured from four fisheye cameras.
We collect and label more than 4,200 surround view images for this task, which contain various illuminated scenes of different types of parking slots.
A VH-HFCN network is proposed, which adopts an HFCN as the base, with an extra efficient VH-stage for better segmenting various markings.
The VH-stage consists of two independent linear convolution paths with vertical and horizontal convolution kernels respectively.
This modification enables the network to robustly and precisely extract linear features.
We evaluated our model on the PSV dataset and the results showed outstanding performance in ground markings segmentation.
Based on the segmented markings, parking slots and lanes are acquired by skeletonization, hough line transform and line arrangement.
Formal Concept Analysis "FCA" is a data analysis method which enables to discover hidden knowledge existing in data.
A kind of hidden knowledge extracted from data is association rules.
Different quality measures were reported in the literature to extract only relevant association rules.
Given a dataset, the choice of a good quality measure remains a challenging task for a user.
Given a quality measures evaluation matrix according to semantic properties, this paper describes how FCA can highlight quality measures with similar behavior in order to help the user during his choice.
The aim of this article is the discovery of Interestingness Measures "IM" clusters, able to validate those found due to the hierarchical and partitioning clustering methods "AHC" and "k-means".
Then, based on the theoretical study of sixty one interestingness measures according to nineteen properties, proposed in a recent study, "FCA" describes several groups of measures.
We propose the residual expansion (RE) algorithm: a global (or near-global) optimization method for nonconvex least squares problems.
Unlike most existing nonconvex optimization techniques, the RE algorithm is not based on either stochastic or multi-point searches; therefore, it can achieve fast global optimization.
Moreover, the RE algorithm is easy to implement and successful in high-dimensional optimization.
The RE algorithm exhibits excellent empirical performance in terms of k-means clustering, point-set registration, optimized product quantization, and blind image deblurring.
In this paper some new experimental results about the statistical characterization of the non-line-of-sight (NLOS) bias affecting time-of-arrival (TOA) estimation in ultrawideband (UWB) wireless localization systems are illustrated.
Then, these results are exploited to assess the performance of various maximum-likelihood (ML) based algorithms for joint TOA localization and NLOS bias mitigation.
Our numerical results evidence that the accuracy of all the considered algorithms is appreciably influenced by the LOS/NLOS conditions of the propagation environment.
A large amount of research effort has been dedicated to adapting boosting for imbalanced classification.
However, boosting methods are yet to be satisfactorily immune to class imbalance, especially for multi-class problems.
This is because most of the existing solutions for handling class imbalance rely on expensive cost set tuning for determining the proper level of compensation.
We show that the assignment of weights to the component classifiers of a boosted ensemble can be thought of as a game of Tug of War between the classes in the margin space.
We then demonstrate how this insight can be used to attain a good compromise between the rare and abundant classes without having to resort to cost set tuning, which has long been the norm for imbalanced classification.
The solution is based on a lexicographic linear programming framework which requires two stages.
Initially, class-specific component weight combinations are found so as to minimize a hinge loss individually for each of the classes.
Subsequently, the final component weights are assigned so that the maximum deviation from the class-specific minimum loss values (obtained in the previous stage) is minimized.
Hence, the proposal is not only restricted to two-class situations, but is also readily applicable to multi-class problems.
Additionally,we also derive the dual formulation corresponding to the proposed framework.
Experiments conducted on artificial and real-world imbalanced datasets as well as on challenging applications such as hyperspectral image classification and ImageNet classification establish the efficacy of the proposal.
Unified Virtual Memory (UVM) was recently introduced on recent NVIDIA GPUs.
Through software and hardware support, UVM provides a coherent shared memory across the entire heterogeneous node, migrating data as appropriate.
The older CUDA programming style is akin to older large-memory UNIX applications which used to directly load and unload memory segments.
Newer CUDA programs have started taking advantage of UVM for the same reasons of superior programmability that UNIX applications long ago switched to assuming the presence of virtual memory.
Therefore, checkpointing of UVM will become increasingly important, especially as NVIDIA CUDA continues to gain wider popularity: 87 of the top 500 supercomputers in the latest listings are GPU-accelerated, with a current trend of ten additional GPU-based supercomputers each year.
A new scalable checkpointing mechanism, CRUM (Checkpoint-Restart for Unified Memory), is demonstrated for hybrid CUDA/MPI computations across multiple computer nodes.
CRUM supports a fast, forked checkpointing, which mostly overlaps the CUDA computation with storage of the checkpoint image in stable storage.
The runtime overhead of using CRUM is 6% on average, and the time for forked checkpointing is seen to be a factor of up to 40 times less than traditional, synchronous checkpointing.
Patterns of interdisciplinarity in science can be quantified through diverse complementary dimensions.
This paper studies as a case study the scientific environment of a generalist journal in Geography, Cybergeo, in order to introduce a novel methodology combining citation network analysis and semantic analysis.
We collect a large corpus of around 200,000 articles with their abstracts and the corresponding citation network that provides a first citation classification.
Relevant keywords are extracted for each article through text-mining, allowing us to construct a semantic classification.
We study the qualitative patterns of relations between endogenous disciplines within each classification, and finally show the complementarity of classifications and of their associated interdisciplinarity measures.
The tools we develop accordingly are open and reusable for similar large scale studies of scientific environments.
Humor is an integral part of human lives.
Despite being tremendously impactful, it is perhaps surprising that we do not have a detailed understanding of humor yet.
As interactions between humans and AI systems increase, it is imperative that these systems are taught to understand subtleties of human expressions such as humor.
In this work, we are interested in the question - what content in a scene causes it to be funny?
As a first step towards understanding visual humor, we analyze the humor manifested in abstract scenes and design computational models for them.
We collect two datasets of abstract scenes that facilitate the study of humor at both the scene-level and the object-level.
We analyze the funny scenes and explore the different types of humor depicted in them via human studies.
We model two tasks that we believe demonstrate an understanding of some aspects of visual humor.
The tasks involve predicting the funniness of a scene and altering the funniness of a scene.
We show that our models perform well quantitatively, and qualitatively through human studies.
Our datasets are publicly available.
LTE-Unlicensed (LTE-U) has recently attracted worldwide interest to meet the explosion in cellular traffic data.
By using carrier aggregation (CA), licensed and unlicensed bands are integrated to enhance transmission capacity while maintaining reliable and predictable performance.
As there may exist other conventional unlicensed band users, such as Wi-Fi users, LTE-U users have to share the same unlicensed bands with them.
Thus, an optimized resource allocation scheme to ensure the fairness between LTE-U users and conventional unlicensed band users is critical for the deployment of LTE-U networks.
In this paper, we investigate an energy efficient resource allocation problem in LTE-U coexisting with other wireless networks, which aims at guaranteeing fairness among the users of different radio access networks (RANs).
We formulate the problem as a multi-objective optimization problem and propose a semi-distributed matching framework with a partial information-based algorithm to solve it.
We demonstrate our contributions with simulations in which various network densities and traffic load levels are considered.
Existing methods for arterial blood pressure (BP) estimation directly map the input physiological signals to output BP values without explicitly modeling the underlying temporal dependencies in BP dynamics.
As a result, these models suffer from accuracy decay over a long time and thus require frequent calibration.
In this work, we address this issue by formulating BP estimation as a sequence prediction problem in which both the input and target are temporal sequences.
We propose a novel deep recurrent neural network (RNN) consisting of multilayered Long Short-Term Memory (LSTM) networks, which are incorporated with (1) a bidirectional structure to access larger-scale context information of input sequence, and (2) residual connections to allow gradients in deep RNN to propagate more effectively.
The proposed deep RNN model was tested on a static BP dataset, and it achieved root mean square error (RMSE) of 3.90 and 2.66 mmHg for systolic BP (SBP) and diastolic BP (DBP) prediction respectively, surpassing the accuracy of traditional BP prediction models.
On a multi-day BP dataset, the deep RNN achieved RMSE of 3.84, 5.25, 5.80 and 5.81 mmHg for the 1st day, 2nd day, 4th day and 6th month after the 1st day SBP prediction, and 1.80, 4.78, 5.0, 5.21 mmHg for corresponding DBP prediction, respectively, which outperforms all previous models with notable improvement.
The experimental results suggest that modeling the temporal dependencies in BP dynamics significantly improves the long-term BP prediction accuracy.
Artificial Neural Networks (ANNs) have found widespread applications in tasks such as pattern recognition and image classification.
However, hardware implementations of ANNs using conventional binary arithmetic units are computationally expensive, energy-intensive and have large area overheads.
Stochastic Computing (SC) is an emerging paradigm which replaces these conventional units with simple logic circuits and is particularly suitable for fault-tolerant applications.
Spintronic devices, such as Magnetic Tunnel Junctions (MTJs), are capable of replacing CMOS in memory and logic circuits.
In this work, we propose an energy-efficient use of MTJs, which exhibit probabilistic switching behavior, as Stochastic Number Generators (SNGs), which forms the basis of our NN implementation in the SC domain.
Further, error resilient target applications of NNs allow us to introduce Approximate Computing, a framework wherein accuracy of computations is traded-off for substantial reductions in power consumption.
We propose approximating the synaptic weights in our MTJ-based NN implementation, in ways brought about by properties of our MTJ-SNG, to achieve energy-efficiency.
We design an algorithm that can perform such approximations within a given error tolerance in a single-layer NN in an optimal way owing to the convexity of the problem formulation.
We then use this algorithm and develop a heuristic approach for approximating multi-layer NNs.
To give a perspective of the effectiveness of our approach, a 43% reduction in power consumption was obtained with less than 1% accuracy loss on a standard classification problem, with 26% being brought about by the proposed algorithm.
Estimation, recognition, and near-future prediction of 3D trajectories based on their two dimensional projections available from one camera source is an exceptionally difficult problem due to uncertainty in the trajectories and environment, high dimensionality of the specific trajectory states, lack of enough labeled data and so on.
In this article, we propose a solution to solve this problem based on a novel deep learning model dubbed Disjunctive Factored Four-Way Conditional Restricted Boltzmann Machine (DFFW-CRBM).
Our method improves state-of-the-art deep learning techniques for high dimensional time-series modeling by introducing a novel tensor factorization capable of driving forth order Boltzmann machines to considerably lower energy levels, at no computational costs.
DFFW-CRBMs are capable of accurately estimating, recognizing, and performing near-future prediction of three-dimensional trajectories from their 2D projections while requiring limited amount of labeled data.
We evaluate our method on both simulated and real-world data, showing its effectiveness in predicting and classifying complex ball trajectories and human activities.
Traditional approaches for complementary product recommendations rely on behavioral and non-visual data such as customer co-views or co-buys.
However, certain domains such as fashion are primarily visual.
We propose a framework that harnesses visual cues in an unsupervised manner to learn the distribution of co-occurring complementary items in real world images.
Our model learns a non-linear transformation between the two manifolds of source and target complementary item categories (e.g., tops and bottoms in outfits).
Given a large dataset of images containing instances of co-occurring object categories, we train a generative transformer network directly on the feature representation space by casting it as an adversarial optimization problem.
Such a conditional generative model can produce multiple novel samples of complementary items (in the feature space) for a given query item.
The final recommendations are selected from the closest real world examples to the synthesized complementary features.
We apply our framework to the task of recommending complementary tops for a given bottom clothing item.
The recommendations made by our system are diverse, and are favored by human experts over the baseline approaches.
Generative models with an encoding component such as autoencoders currently receive great interest.
However, training of autoencoders is typically complicated by the need to train a separate encoder and decoder model that have to be enforced to be reciprocal to each other.
To overcome this problem, by-design reversible neural networks (RevNets) had been previously used as generative models either directly optimizing the likelihood of the data under the model or using an adversarial approach on the generated data.
Here, we instead investigate their performance using an adversary on the latent space in the adversarial autoencoder framework.
We investigate the generative performance of RevNets on the CelebA dataset, showing that generative RevNets can generate coherent faces with similar quality as Variational Autoencoders.
This first attempt to use RevNets inside the adversarial autoencoder framework slightly underperformed relative to recent advanced generative models using an autoencoder component on CelebA, but this gap may diminish with further optimization of the training setup of generative RevNets.
In addition to the experiments on CelebA, we show a proof-of-principle experiment on the MNIST dataset suggesting that adversary-free trained RevNets can discover meaningful latent dimensions without pre-specifying the number of dimensions of the latent sampling distribution.
In summary, this study shows that RevNets can be employed in different generative training settings.
Source code for this study is at https://github.com/robintibor/generative-reversible
Localization performance in wireless networks has been traditionally benchmarked using the Cramer-Rao lower bound (CRLB), given a fixed geometry of anchor nodes and a target.
However, by endowing the target and anchor locations with distributions, this paper recasts this traditional, scalar benchmark as a random variable.
The goal of this work is to derive an analytical expression for the distribution of this now random CRLB, in the context of Time-of-Arrival-based positioning.
To derive this distribution, this work first analyzes how the CRLB is affected by the order statistics of the angles between consecutive participating anchors (i.e., internodal angles).
This analysis reveals an intimate connection between the second largest internodal angle and the CRLB, which leads to an accurate approximation of the CRLB.
Using this approximation, a closed-form expression for the distribution of the CRLB, conditioned on the number of participating anchors, is obtained.
Next, this conditioning is eliminated to derive an analytical expression for the marginal CRLB distribution.
Since this marginal distribution accounts for all target and anchor positions, across all numbers of participating anchors, it therefore statistically characterizes localization error throughout an entire wireless network.
This paper concludes with a comprehensive analysis of this new network-wide-CRLB paradigm.
We took part in the YouTube-8M Video Understanding Challenge hosted on Kaggle, and achieved the 10th place within less than one month's time.
In this paper, we present an extensive analysis and solution to the underlying machine-learning problem based on frame-level data, where major challenges are identified and corresponding preliminary methods are proposed.
It's noteworthy that, with merely the proposed strategies and uniformly-averaging multi-crop ensemble was it sufficient for us to reach our ranking.
We also report the methods we believe to be promising but didn't have enough time to train to convergence.
We hope this paper could serve, to some extent, as a review and guideline of the YouTube-8M multi-label video classification benchmark, inspiring future attempts and research.
In computer simulation of the learning process is usually assumed that all elements of the training material are assimilated equally durable.
But in practice, the knowledge, which a student uses in its operations, are remembered much better.
For a more precise study of didactic systems the multi component model of learning are proposed.
It takes into account: 1) the transition of weak knowledge in trustworthy knowledge; 2) the difference in the rate of forgetting the trustworthy and weak knowledge.
It is assumed that the rate of increase of student's knowledge is proportional to: 1) the difference between the level of the requirements of teachers and the number of learned knowledge; 2) the amount of learned knowledge, raised to some power.
Examples of the use of a multi component model for the study of situations in the learning process are considered, the resulting graphs of the student's level of knowledge of the time are presented.
A generalized model of learning, which allows to take into account the complexity of the various elements of the educational material are proposed.
The possibility of creating a training program for the training of students of pedagogical institutes are considered.
A tower is a sequence of words alternating between two languages in such a way that every word is a subsequence of the following word.
The height of the tower is the number of words in the sequence.
If there is no infinite tower (a tower of infinite height), then the height of all towers between the languages is bounded.
We study upper and lower bounds on the height of maximal finite towers with respect to the size of the NFA (the DFA) representation of the languages.
We show that the upper bound is polynomial in the number of states and exponential in the size of the alphabet, and that it is asymptotically tight if the size of the alphabet is fixed.
If the alphabet may grow, then, using an alphabet of size approximately the number of states of the automata, the lower bound on the height of towers is exponential with respect to that number.
In this case, there is a gap between the lower and upper bound, and the asymptotically optimal bound remains an open problem.
Since, in many cases, the constructed towers are sequences of prefixes, we also study towers of prefixes.
Tabular notations, in particular SCR specifications, have proved to be a useful means for formally describing complex requirements.
The SCR method offers a powerful family of analysis tools, known as the SCR Toolset, but its availability is restricted by the Naval Research Laboratory of the USA.
This toolset applies different kinds of analysis considering the whole set of behaviours associated with a requirements specification.
In this paper we present a tool for describing and analyzing SCR requirements descriptions, that complements the SCR Toolset in two aspects.
First, its use is not limited by any institution, and resorts to a standard model checking tool for analysis; and second, it allows to concentrate the analysis to particular sets of behaviours (subsets of the whole specifications), that correspond to particular scenarios explicitly mentioned in the specification.
We take an operational notation that allows the engineer to describe behavioural "scenarios" by means of programs, and provide a translation into Promela to perform the analysis via Spin, an efficient off-the-shelf model checker freely available.
In addition, we apply the SCR method to a Pacemaker system and we use its tabular specification as a running example of this article.
We define the task of salient structure (SS) detection to unify the saliency-related tasks like fixation prediction, salient object detection, and other detection of structures of interest.
In this study, we propose a unified framework for SS detection by modeling the two-pathway-based guided search strategy of biological vision.
Firstly, context-based spatial prior (CBSP) is extracted based on the layout of edges in the given scene along a fast visual pathway, called non-selective pathway.
This is a rough and non-selective estimation of the locations where the potential SSs present.
Secondly, another flow of local feature extraction is executed in parallel along the selective pathway.
Finally, Bayesian inference is used to integrate local cues guided by CBSP, and to predict the exact locations of SSs in the input scene.
The proposed model is invariant to size and features of objects.
Experimental results on four datasets (two fixation prediction datasets and two salient object datasets) demonstrate that our system achieves competitive performance for SS detection (i.e., both the tasks of fixation prediction and salient object detection) comparing to the state-of-the-art methods.
Differential privacy is a promising approach to privacy preserving data analysis with a well-developed theory for functions.
Despite recent work on implementing systems that aim to provide differential privacy, the problem of formally verifying that these systems have differential privacy has not been adequately addressed.
This paper presents the first results towards automated verification of source code for differentially private interactive systems.
We develop a formal probabilistic automaton model of differential privacy for systems by adapting prior work on differential privacy for functions.
The main technical result of the paper is a sound proof technique based on a form of probabilistic bisimulation relation for proving that a system modeled as a probabilistic automaton satisfies differential privacy.
The novelty lies in the way we track quantitative privacy leakage bounds using a relation family instead of a single relation.
We illustrate the proof technique on a representative automaton motivated by PINQ, an implemented system that is intended to provide differential privacy.
To make our proof technique easier to apply to realistic systems, we prove a form of refinement theorem and apply it to show that a refinement of the abstract PINQ automaton also satisfies our differential privacy definition.
Finally, we begin the process of automating our proof technique by providing an algorithm for mechanically checking a restricted class of relations from the proof technique.
Features that capture well the textural patterns of a certain class of images are crucial for the performance of texture segmentation methods.
The manual selection of features or designing new ones can be a tedious task.
Therefore, it is desirable to automatically adapt the features to a certain image or class of images.
Typically, this requires a large set of training images with similar textures and ground truth segmentation.
In this work, we propose a framework to learn features for texture segmentation when no such training data is available.
The cost function for our learning process is constructed to match a commonly used segmentation model, the piecewise constant Mumford-Shah model.
This means that the features are learned such that they provide an approximately piecewise constant feature image with a small jump set.
Based on this idea, we develop a two-stage algorithm which first learns suitable convolutional features and then performs a segmentation.
We note that the features can be learned from a small set of images, from a single image, or even from image patches.
The proposed method achieves a competitive rank in the Prague texture segmentation benchmark, and it is effective for segmenting histological images.
Continual data collection and widespread deployment of machine learning algorithms, particularly the distributed variants, have raised new privacy challenges.
In a distributed machine learning scenario, the dataset is stored among several machines and they solve a distributed optimization problem to collectively learn the underlying model.
We present a secure multi-party computation inspired privacy preserving distributed algorithm for optimizing a convex function consisting of several possibly non-convex functions.
Each individual objective function is privately stored with an agent while the agents communicate model parameters with neighbor machines connected in a network.
We show that our algorithm can correctly optimize the overall objective function and learn the underlying model accurately.
We further prove that under a vertex connectivity condition on the topology, our algorithm preserves privacy of individual objective functions.
We establish limits on the what a coalition of adversaries can learn by observing the messages and states shared over a network.
This study analyzes how web audiences flow across online digital features.
We construct a directed network of user flows based on sequential user clickstreams for all popular websites (n=1761), using traffic data obtained from a panel of a million web users in the United States.
We analyze these data to identify constellations of websites that are frequently browsed together in temporal sequences, both by similar user groups in different browsing sessions as well as by disparate users.
Our analyses thus render visible previously hidden online collectives and generate insight into the varied roles that curatorial infrastructures may play in shaping audience fragmentation on the web.
As long as human beings exist on this earth, there will be confidential images intended for limited audience.
These images have to be transmitted in such a way that no unauthorized person gets knowledge of them.
DNA sequences play a vital role in modern cryptography and DNA sequence based cryptography renders a helping hand for transmission of such confidential images over a public insecure channel as the intended recipient alone can decipher them.
This paper outlines an integrated encryption scheme based on DNA sequences and scrambling according to magic square of doubly even order pattern.
Since there is negligible correlation between the original and encrypted image this method is robust against any type of crypt attack.
Face recognition has achieved great progress owing to the fast development of the deep neural network in the past a few years.
As an important part of deep neural networks, a number of the loss functions have been proposed which significantly improve the state-of-the-art methods.
In this paper, we proposed a new loss function called Minimum Margin Loss (MML) which aims at enlarging the margin of those overclose class centre pairs so as to enhance the discriminative ability of the deep features.
MML supervises the training process together with the Softmax Loss and the Centre Loss, and also makes up the defect of Softmax + Centre Loss.
The experimental results on MegaFace, LFW and YTF datasets show that the proposed method achieves the state-of-the-art performance, which demonstrates the effectiveness of the proposed MML.
As computer vision before, remote sensing has been radically changed by the introduction of Convolution Neural Networks.
Land cover use, object detection and scene understanding in aerial images rely more and more on deep learning to achieve new state-of-the-art results.
Recent architectures such as Fully Convolutional Networks (Long et al., 2015) can even produce pixel level annotations for semantic mapping.
In this work, we show how to use such deep networks to detect, segment and classify different varieties of wheeled vehicles in aerial images from the ISPRS Potsdam dataset.
This allows us to tackle object detection and classification on a complex dataset made up of visually similar classes, and to demonstrate the relevance of such a subclass modeling approach.
Especially, we want to show that deep learning is also suitable for object-oriented analysis of Earth Observation data.
First, we train a FCN variant on the ISPRS Potsdam dataset and show how the learnt semantic maps can be used to extract precise segmentation of vehicles, which allow us studying the repartition of vehicles in the city.
Second, we train a CNN to perform vehicle classification on the VEDAI (Razakarivony and Jurie, 2016) dataset, and transfer its knowledge to classify candidate segmented vehicles on the Potsdam dataset.
A major challenge in consumer credit risk portfolio management is to classify households according to their risk profile.
In order to build such risk profiles it is necessary to employ an approach that analyses data systematically in order to detect important relationships, interactions, dependencies and associations amongst the available continuous and categorical variables altogether and accurately generate profiles of most interesting household segments according to their credit risk.
The objective of this work is to employ a knowledge discovery from database process to identify groups of indebted households and describe their profiles using a database collected by the Consumer Credit Counselling Service (CCCS) in the UK.
Employing a framework that allows the usage of both categorical and continuous data altogether to find hidden structures in unlabelled data it was established the ideal number of clusters and such clusters were described in order to identify the households who exhibit a high propensity of excessive debt levels.
The rise of social media is enabling people to freely express their opinions about products and services.
The aim of sentiment analysis is to automatically determine subject's sentiment (e.g., positive, negative, or neutral) towards a particular aspect such as topic, product, movie, news etc.
Deep learning has recently emerged as a powerful machine learning technique to tackle a growing demand of accurate sentiment analysis.
However, limited work has been conducted to apply deep learning algorithms to languages other than English, such as Persian.
In this work, two deep learning models (deep autoencoders and deep convolutional neural networks (CNNs)) are developed and applied to a novel Persian movie reviews dataset.
The proposed deep learning models are analyzed and compared with the state-of-the-art shallow multilayer perceptron (MLP) based machine learning model.
Simulation results demonstrate the enhanced performance of deep learning over state-of-the-art MLP.
The Information Flow Framework (IFF) is a descriptive category metatheory currently under development, which is being offered as the structural aspect of the Standard Upper Ontology (SUO).
The architecture of the IFF is composed of metalevels, namespaces and meta-ontologies.
The main application of the IFF is institutional: the notion of institutions and their morphisms are being axiomatized in the upper metalevels of the IFF, and the lower metalevel of the IFF has axiomatized various institutions in which semantic integration has a natural expression as the colimit of theories.
Recent studies indicate the feasibility of full-duplex (FD) bidirectional wireless communications.
Due to its potential to increase the capacity, analyzing the performance of a cellular network that contains full-duplex devices is crucial.
In this paper, we consider maximizing the weighted sum-rate of downlink and uplink of an FD heterogeneous OFDMA network where each cell consists of an imperfect FD base-station (BS) and a mixture of half-duplex and imperfect full-duplex mobile users.
To this end, first, the joint problem of sub-channel assignment and power allocation for a single cell network is investigated.
Then, the proposed algorithms are extended for solving the optimization problem for an FD heterogeneous network in which intra-cell and inter-cell interferences are taken into account.
Simulation results demonstrate that in a single cell network, when all the users and the BSs are perfect FD nodes, the network throughput could be doubled.
Otherwise, the performance improvement is limited by the inter-cell interference, inter-node interference, and self-interference.
We also investigate the effect of the percentage of FD users on the network performance in both indoor and outdoor scenarios, and analyze the effect of the self-interference cancellation capability of the FD nodes on the network performance.
For a closed-loop system, which has a contention-based multiple access network on its sensor link, the Medium Access Controller (MAC) may discard some packets when the traffic on the link is high.
We use a local state-based scheduler to select a few critical data packets to send to the MAC.
In this paper, we analyze the impact of such a scheduler on the closed-loop system in the presence of traffic, and show that there is a dual effect with state-based scheduling.
In general, this makes the optimal scheduler and controller hard to find.
However, by removing past controls from the scheduling criterion, we find that certainty equivalence holds.
This condition is related to the classical result of Bar-Shalom and Tse, and it leads to the design of a scheduler with a certainty equivalent controller.
This design, however, does not result in an equivalent system to the original problem, in the sense of Witsenhausen.
Computing the estimate is difficult, but can be simplified by introducing a symmetry constraint on the scheduler.
Based on these findings, we propose a dual predictor architecture for the closed-loop system, which ensures separation between scheduler, observer and controller.
We present an example of this architecture, which illustrates a network-aware event-triggering mechanism.
A number of differences have emerged between modern and classic approaches to constituency parsing in recent years, with structural components like grammars and feature-rich lexicons becoming less central while recurrent neural network representations rise in popularity.
The goal of this work is to analyze the extent to which information provided directly by the model structure in classical systems is still being captured by neural methods.
To this end, we propose a high-performance neural model (92.08 F1 on PTB) that is representative of recent work and perform a series of investigative experiments.
We find that our model implicitly learns to encode much of the same information that was explicitly provided by grammars and lexicons in the past, indicating that this scaffolding can largely be subsumed by powerful general-purpose neural machinery.
This paper provides guidance to an analyst who wants to extract insight from a spreadsheet model.
It discusses the terminology of spreadsheet analytics, how to prepare a spreadsheet model for analysis, and a hierarchy of analytical techniques.
These techniques include sensitivity analysis, tornado charts,and backsolving (or goal-seeking).
This paper presents native-Excel approaches for automating these techniques, and discusses add-ins that are even more efficient.
Spreadsheet optimization and spreadsheet Monte Carlo simulation are briefly discussed.
The paper concludes by calling for empirical research, and describing desired features spreadsheet sensitivity analysis and spreadsheet optimization add-ins.
Rare diseases affect a relatively small number of people, which limits investment in research for treatments and cures.
Developing an efficient method for rare disease detection is a crucial first step towards subsequent clinical research.
In this paper, we present a semi-supervised learning framework for rare disease detection using generative adversarial networks.
Our method takes advantage of the large amount of unlabeled data for disease detection and achieves the best results in terms of precision-recall score compared to baseline techniques.
Recent developments in quaternion-valued widely linear processing have established that the exploitation of complete second-order statistics requires consideration of both the standard covariance and the three complementary covariance matrices.
Although such matrices have a tremendous amount of structure and their decomposition is a powerful tool in a variety of applications, the non-commutative nature of the quaternion product has been prohibitive to the development of quaternion uncorrelating transforms.
To this end, we introduce novel techniques for a simultaneous decomposition of the covariance and complementary covariance matrices in the quaternion domain, whereby the quaternion version of the Takagi factorisation is explored to diagonalise symmetric quaternion-valued matrices.
This gives new insights into the quaternion uncorrelating transform (QUT) and forms a basis for the proposed quaternion approximate uncorrelating transform (QAUT) which simultaneously diagonalises all four covariance matrices associated with improper quaternion signals.
The effectiveness of the proposed uncorrelating transforms is validated by simulations on both synthetic and real-world quaternion-valued signals.
Virtual reality allows to create situations which can be experimented under the control of the user, without risks, in a very flexible way.
This allows to develop skills and to have confidence to work in real conditions with real equipment.
VR is then widely used as a training and learning tool.
More recently, VR has also showed its potential in rehabilitation and therapy fields because it provides users with the ability of repeat their actions several times and to progress at their own pace.
In this communication, we present our work in the development of a wheelchair simulator designed to allow children with multiple disabilities to familiarize themselves with the wheelchair.
This paper focuses on a multimodal language understanding method for carry-and-place tasks with domestic service robots.
We address the case of ambiguous instructions, that is, when the target area is not specified.
For instance "put away the milk and cereal" is a natural instruction where there is ambiguity regarding the target area, considering environments in daily life.
Conventionally, this instruction can be disambiguated from a dialogue system, but at the cost of time and cumbersome interaction.
Instead, we propose a multimodal approach, in which the instructions are disambiguated using the robot's state and environment context.
We develop the Multi-Modal Classifier Generative Adversarial Network (MMC-GAN) to predict the likelihood of different target areas considering the robot's physical limitation and the target clutter.
Our approach, MMC-GAN, significantly improves accuracy compared with baseline methods that use instructions only or simple deep neural networks.
Semantically understanding complex drivers' encountering behavior, wherein two or multiple vehicles are spatially close to each other, does potentially benefit autonomous car's decision-making design.
This paper presents a framework of analyzing various encountering behaviors through decomposing driving encounter data into small building blocks, called driving primitives, using nonparametric Bayesian learning (NPBL) approaches, which offers a flexible way to gain an insight into the complex driving encounters without any prerequisite knowledge.
The effectiveness of our proposed primitive-based framework is validated based on 976 naturalistic driving encounters, from which more than 4000 driving primitives are learned using NPBL - a sticky HDP-HMM, combined a hidden Markov model (HMM) with a hierarchical Dirichlet process (HDP).
After that, a dynamic time warping method integrated with k-means clustering is then developed to cluster all these extracted driving primitives into groups.
Experimental results find that there exist 20 kinds of driving primitives capable of representing the basic components of driving encounters in our database.
This primitive-based analysis methodology potentially reveals underlying information of vehicle-vehicle encounters for self-driving applications.
Suicide is an important but often misunderstood problem, one that researchers are now seeking to better understand through social media.
Due in large part to the fuzzy nature of what constitutes suicidal risks, most supervised approaches for learning to automatically detect suicide-related activity in social media require a great deal of human labor to train.
However, humans themselves have diverse or conflicting views on what constitutes suicidal thoughts.
So how to obtain reliable gold standard labels is fundamentally challenging and, we hypothesize, depends largely on what is asked of the annotators and what slice of the data they label.
We conducted multiple rounds of data labeling and collected annotations from crowdsourcing workers and domain experts.
We aggregated the resulting labels in various ways to train a series of supervised models.
Our preliminary evaluations show that using unanimously agreed labels from multiple annotators is helpful to achieve robust machine models.
Semantic parsing aims at mapping natural language to machine interpretable meaning representations.
Traditional approaches rely on high-quality lexicons, manually-built templates, and linguistic features which are either domain- or representation-specific.
In this paper we present a general method based on an attention-enhanced encoder-decoder model.
We encode input utterances into vector representations, and generate their logical forms by conditioning the output sequences or trees on the encoding vectors.
Experimental results on four datasets show that our approach performs competitively without using hand-engineered features and is easy to adapt across domains and meaning representations.
Software Defined Networking (SDN) can effectively improve the performance of traffic engineering and has promising application foreground in backbone networks.
Therefore, new energy saving schemes must take SDN into account, which is extremely important considering the rapidly increasing energy consumption from Telecom and ISP networks.
At the same time, the introduction of SDN in a current network must be incremental in most cases, for both technical and economic reasons.
During this period, operators have to manage hybrid networks, where SDN and traditional protocols coexist.
In this paper, we study the energy efficient traffic engineering problem in hybrid SDN/IP networks.
We first formulate the mathematic optimization model considering SDN/IP hybrid routing mode.
As the problem is NP-hard, we propose the fast heuristic algorithm named HEATE (Hybrid Energy-Aware Traffic Engineering).
In our proposed HEATE algorithm, the IP routers perform the shortest path routing using the distribute OSPF link weight optimization.
The SDNs perform the multi-path routing with traffic flow splitting by the global SDN controller.
The HEATE algorithm finds the optimal setting of OSPF link weight and splitting ratio of SDNs.
Thus traffic flow is aggregated onto partial links and the underutilized links can be turned off to save energy.
By computer simulation results, we show that our algorithm has a significant improvement in energy efficiency in hybrid SDN/IP networks.
Blockchain platforms, such as Ethereum, allow a set of actors to maintain a ledger of transactions without relying on a central authority and to deploy scripts, called smart contracts, that are executed whenever certain transactions occur.
These features can be used as basic building blocks for executing collaborative business processes between mutually untrusting parties.
However, implementing business processes using the low-level primitives provided by blockchain platforms is cumbersome and error-prone.
In contrast, established business process management systems, such as those based on the standard Business Process Model and Notation (BPMN), provide convenient abstractions for rapid development of process-oriented applications.
This article demonstrates how to combine the advantages of a business process management system with those of a blockchain platform.
The article introduces a blockchain-based BPMN execution engine, namely Caterpillar.
Like any BPMN execution engine, Caterpillar supports the creation of instances of a process model and allows users to monitor the state of process instances and to execute tasks thereof.
The specificity of Caterpillar is that the state of each process instance is maintained on the (Ethereum) blockchain and the workflow routing is performed by smart contracts generated by a BPMN-to-Solidity compiler.
The Caterpillar compiler supports a large array of BPMN constructs, including subprocesses, multi-instances activities and event handlers.
The paper describes the architecture of Caterpillar, and the interfaces it provides to support the monitoring of process instances, the allocation and execution of work items, and the execution of service tasks.
With the increasing usage of smartphones, there is a corresponding increase in the phone metadata generated by individuals using these devices.
Managing the privacy of personal information on these devices can be a complex task.
Recent research has suggested the use of social and behavioral data for automatically recommending privacy settings.
This paper is the first effort to connect users' phone use metadata with their privacy attitudes.
Based on a 10-week long field study involving phone metadata collection via an app, and a survey on privacy attitudes, we report that an analysis of cell phone metadata may reveal vital clues to a person's privacy attitudes.
Specifically, a predictive model based on phone usage metadata significantly outperforms a comparable personality features-based model in predicting individual privacy attitudes.
The results motivate a newer direction of automatically inferring a user's privacy attitudes by looking at their phone usage characteristics.
The Turing Test (TT) checks for human intelligence, rather than any putative general intelligence.
It involves repeated interaction requiring learning in the form of adaption to the human conversation partner.
It is a macro-level post-hoc test in contrast to the definition of a Turing Machine (TM), which is a prior micro-level definition.
This raises the question of whether learning is just another computational process, i.e. can be implemented as a TM.
Here we argue that learning or adaption is fundamentally different from computation, though it does involve processes that can be seen as computations.
To illustrate this difference we compare (a) designing a TM and (b) learning a TM, defining them for the purpose of the argument.
We show that there is a well-defined sequence of problems which are not effectively designable but are learnable, in the form of the bounded halting problem.
Some characteristics of human intelligence are reviewed including it's: interactive nature, learning abilities, imitative tendencies, linguistic ability and context-dependency.
A story that explains some of these is the Social Intelligence Hypothesis.
If this is broadly correct, this points to the necessity of a considerable period of acculturation (social learning in context) if an artificial intelligence is to pass the TT.
Whilst it is always possible to 'compile' the results of learning into a TM, this would not be a designed TM and would not be able to continually adapt (pass future TTs).
We conclude three things, namely that: a purely "designed" TM will never pass the TT; that there is no such thing as a general intelligence since it necessary involves learning; and that learning/adaption and computation should be clearly distinguished.
In this paper a novel Quantum Double Delta Swarm (QDDS) algorithm modeled after the mechanism of convergence to the center of attractive potential field generated within a single well in a double Dirac delta well setup has been put forward and the preliminaries discussed.
Theoretical foundations and experimental illustrations have been incorporated to provide a first basis for further development, specifically in refinement of solutions and applicability to problems in high dimensional spaces.
Simulations are carried out over varying dimensionality on four benchmark functions, viz.Rosenbrock, Rastrigrin, Griewank and Sphere as well as the multidimensional Finite Impulse Response (FIR) Filter design problem with different population sizes.
Test results illustrate the algorithm yields superior results to some related reports in the literature while reinforcing the need of substantial future work to deliver near-optimal results consistently, especially if dimensionality scales up.
We extend the notion of the distance to a measure from Euclidean space to probability measures on general metric spaces as a way to do topological data analysis in a way that is robust to noise and outliers.
We then give an efficient way to approximate the sub-level sets of this function by a union of metric balls and extend previous results on sparse Rips filtrations to this setting.
This robust and efficient approach to topological data analysis is illustrated with several examples from an implementation.
An overwhelming number of true and false news stories are posted and shared in social networks, and users diffuse the stories based on multiple factors.
Diffusion of news stories from one user to another depends not only on the stories' content and the genuineness but also on the alignment of the topical interests between the users.
In this paper, we propose a novel Bayesian nonparametric model that incorporates homogeneity of news stories as the key component that regulates the topical similarity between the posting and sharing users' topical interests.
Our model extends hierarchical Dirichlet process to model the topics of the news stories and incorporates Bayesian Gaussian process latent variable model to discover the homogeneity values.
We train our model on a real-world social network dataset and find homogeneity values of news stories that strongly relate to their labels of genuineness and their contents.
Finally, we show that the supervised version of our model predicts the labels of news stories better than the state-of-the-art neural network and Bayesian models.
arXiv is a popular pre-print server focusing on natural science disciplines (e.g.physics, computer science, quantitative biology).
As a platform with focus on easy publishing services it does not provide enhanced search functionality -- but offers programming interfaces which allow external parties to add these services.
This paper presents extensions of the open source framework arXiv Sanity Preserver (SP).
With respect to the original framework, it derestricts the topical focus and allows for text-based search and visualisation of all papers in arXiv.
To this end, all papers are stored in a unified back-end; the extension provides enhanced search and ranking facilities and allows the exploration of arXiv papers by a novel user interface.
This paper is concerned with how to make efficient use of social information to improve recommendations.
Most existing social recommender systems assume people share similar preferences with their social friends.
Which, however, may not hold true due to various motivations of making online friends and dynamics of online social networks.
Inspired by recent causal process based recommendations that first model user exposures towards items and then use these exposures to guide rating prediction, we utilize social information to capture user exposures rather than user preferences.
We assume that people get information of products from their online friends and they do not have to share similar preferences, which is less restrictive and seems closer to reality.
Under this new assumption, in this paper, we present a novel recommendation approach (named SERec) to integrate social exposure into collaborative filtering.
We propose two methods to implement SERec, namely social regularization and social boosting, each with different ways to construct social exposures.
Experiments on four real-world datasets demonstrate that our methods outperform the state-of-the-art methods on top-N recommendations.
Further study compares the robustness and scalability of the two proposed methods.
We now advocate a novel physical layer security solution that is unique to our previously proposed GPSM scheme with the aid of the proposed antenna scrambling.
The novelty and contribution of our paper lies in three aspects: 1/ principle: we introduce a `security key' generated at Alice that is unknown to both Bob and Eve, where the design goal is that the publicly unknown security key only imposes barrier for Eve.
2/ approach: we achieve it by conveying useful information only through the activation of RA indices, which is in turn concealed by the unknown security key in terms of the randomly scrambled symbols used in place of the conventional modulated symbols in GPSM scheme.
3/ design: we consider both Circular Antenna Scrambling (CAS) and Gaussian Antenna Scrambling (GAS) in detail and the resultant security capacity of both designs are quantified and compared.
Provenance, or information about the sources, derivation, custody or history of data, has been studied recently in a number of contexts, including databases, scientific workflows and the Semantic Web.
Many provenance mechanisms have been developed, motivated by informal notions such as influence, dependence, explanation and causality.
However, there has been little study of whether these mechanisms formally satisfy appropriate policies or even how to formalize relevant motivating concepts such as causality.
We contend that mathematical models of these concepts are needed to justify and compare provenance techniques.
In this paper we review a theory of causality based on structural models that has been developed in artificial intelligence, and describe work in progress on using causality to give a semantics to provenance graphs.
Pulmonary vein isolation (PVI) is a common procedure for the treatment of atrial fibrillation (AF).
A successful isolation produces a continuous lesion (scar) completely encircling the veins that stops activation waves from propagating to the atrial body.
Unfortunately, the encircling lesion is often incomplete, becoming a combination of scar and gaps of healthy tissue.
These gaps are potential causes of AF recurrence, which requires a redo of the isolation procedure.
Late-gadolinium enhanced cardiac magnetic resonance (LGE-CMR) is a non-invasive method that may also be used to detect gaps, but it is currently a time-consuming process, prone to high inter-observer variability.
In this paper, we present a method to semi-automatically identify and quantify ablation gaps.
Gap quantification is performed through minimum path search in a graph where every node is a scar patch and the edges are the geodesic distances between patches.
We propose the Relative Gap Measure (RGM) to estimate the percentage of gap around a vein, which is defined as the ratio of the overall gap length and the total length of the path that encircles the vein.
Additionally, an advanced version of the RGM has been developed to integrate gap quantification estimates from different scar segmentation techniques into a single figure-of-merit.
Population-based statistical and regional analysis of gap distribution was performed using a standardised parcellation of the left atrium.
We have evaluated our method on synthetic and clinical data from 50 AF patients who underwent PVI with radiofrequency ablation.
The population-based analysis concluded that the left superior PV is more prone to lesion gaps while the left inferior PV tends to have less gaps (p<0.05 in both cases), in the processed data.
This type of information can be very useful for the optimization and objective assessment of PVI interventions.
Resources for the non-English languages are scarce and this paper addresses this problem in the context of machine translation, by automatically extracting parallel sentence pairs from the multilingual articles available on the Internet.
In this paper, we have used an end-to-end Siamese bidirectional recurrent neural network to generate parallel sentences from comparable multilingual articles in Wikipedia.
Subsequently, we have showed that using the harvested dataset improved BLEU scores on both NMT and phrase-based SMT systems for the low-resource language pairs: English--Hindi and English--Tamil, when compared to training exclusively on the limited bilingual corpora collected for these language pairs.
The deluge of date rate in today's networks imposes a cost burden on the backhaul network design.
Developing cost efficient backhaul solutions becomes an exciting, yet challenging, problem.
Traditional technologies for backhaul networks include either radio-frequency backhauls (RF) or optical fibers (OF).
While RF is a cost-effective solution as compared to OF, it supports lower data rate requirements.
Another promising backhaul solution is the free-space optics (FSO) as it offers both a high data rate and a relatively low cost.
FSO, however, is sensitive to nature conditions, e.g., rain, fog, line-of-sight.
This paper combines both RF and FSO advantages and proposes a hybrid RF/FSO backhaul solution.
It considers the problem of minimizing the cost of the backhaul network by choosing either OF or hybrid RF/FSO backhaul links between the base-stations (BS) so as to satisfy data rate, connectivity, and reliability constraints.
It shows that under a specified realistic assumption about the cost of OF and hybrid RF/FSO links, the problem is equivalent to a maximum weight clique problem, which can be solved with moderate complexity.
Simulation results show that the proposed solution shows a close-to-optimal performance, especially for practical prices of the hybrid RF/FSO links.
The search engine is tightly coupled with social networks and is primarily designed for users to acquire interested information.
Specifically, the search engine assists the information dissemination for social networks, i.e., enabling users to access interested contents with keywords-searching and promoting the process of contents-transferring from the source users directly to potential interested users.
Accompanying such processes, the social network evolves as new links emerge between users with common interests.
However, there is no clear understanding of such a "chicken-and-egg" problem, namely, new links encourage more social interactions, and vice versa.
In this paper, we aim to quantitatively characterize the social network evolution phenomenon driven by a search engine.
First, we propose a search network model for social network evolution.
Second, we adopt two performance metrics, namely, degree distribution and network diameter.
Theoretically, we prove that the degree distribution follows an intensified power-law, and the network diameter shrinks.
Third, we quantitatively show that the search engine accelerates the rumor propagation in social networks.
Finally, based on four real-world data sets (i.e., CDBLP, Facebook, Weibo Tweets, P2P), we verify our theoretical findings.
Furthermore, we find that the search engine dramatically increases the speed of rumor propagation.
Graph processing is becoming increasingly prevalent across many application domains.
In spite of this prevalence, there is little research about how graphs are actually used in practice.
We conducted an online survey aimed at understanding: (i) the types of graphs users have; (ii) the graph computations users run; (iii) the types of graph software users use; and (iv) the major challenges users face when processing their graphs.
We describe the participants' responses to our questions highlighting common patterns and challenges.
We further reviewed user feedback in the mailing lists, bug reports, and feature requests in the source repositories of a large suite of software products for processing graphs.
Through our review, we were able to answer some new questions that were raised by participants' responses and identify specific challenges that users face when using different classes of graph software.
The participants' responses and data we obtained revealed surprising facts about graph processing in practice.
In particular, real-world graphs represent a very diverse range of entities and are often very large, and scalability and visualization are undeniably the most pressing challenges faced by participants.
We hope these findings can guide future research.
Computational modeling of visual saliency has become an important research problem in recent years, with applications in video quality estimation, video compression, object tracking, retargeting, summarization, and so on.
While most visual saliency models for dynamic scenes operate on raw video, several models have been developed for use with compressed-domain information such as motion vectors and transform coefficients.
This paper presents a comparative study of eleven such models as well as two high-performing pixel-domain saliency models on two eye-tracking datasets using several comparison metrics.
The results indicate that highly accurate saliency estimation is possible based only on a partially decoded video bitstream.
The strategies that have shown success in compressed-domain saliency modeling are highlighted, and certain challenges are identified as potential avenues for further improvement.
The World Wide Web (WWW) allows the people to share the information (data) from the large database repositories globally.
The amount of information grows billions of databases.
We need to search the information will specialize tools known generically search engine.
There are many of search engines available today, retrieving meaningful information is difficult.
However to overcome this problem in search engines to retrieve meaningful information intelligently, semantic web technologies are playing a major role.
In this paper we present survey on the search engine generations and the role of search engines in intelligent web and semantic search technologies.
Computer algebra systems are a great help for mathematical research but sometimes unexpected errors in the software can also badly affect it.
As an example, we show how we have detected an error of Mathematica computing determinants of matrices of integer numbers: not only it computes the determinants wrongly, but also it produces different results if one evaluates the same determinant twice.
Recognition of surgical gesture is crucial for surgical skill assessment and efficient surgery training.
Prior works on this task are based on either variant graphical models such as HMMs and CRFs, or deep learning models such as Recurrent Neural Networks and Temporal Convolutional Networks.
Most of the current approaches usually suffer from over-segmentation and therefore low segment-level edit scores.
In contrast, we present an essentially different methodology by modeling the task as a sequential decision-making process.
An intelligent agent is trained using reinforcement learning with hierarchical features from a deep model.
Temporal consistency is integrated into our action design and reward mechanism to reduce over-segmentation errors.
Experiments on JIGSAWS dataset demonstrate that the proposed method performs better than state-of-the-art methods in terms of the edit score and on par in frame-wise accuracy.
Our code will be released later.
In this paper, we study the generation of maximal Poisson-disk sets with varying radii.
First, we present a geometric analysis of gaps in such disk sets.
This analysis is the basis for maximal and adaptive sampling in Euclidean space and on manifolds.
Second, we propose efficient algorithms and data structures to detect gaps and update gaps when disks are inserted, deleted, moved, or have their radius changed.
We build on the concepts of the regular triangulation and the power diagram.
Third, we will show how our analysis can make a contribution to the state-of-the-art in surface remeshing.
Aspect Term Extraction (ATE), a key sub-task in Aspect-Based Sentiment Analysis, aims to extract explicit aspect expressions from online user reviews.
We present a new framework for tackling ATE.
It can exploit two useful clues, namely opinion summary and aspect detection history.
Opinion summary is distilled from the whole input sentence, conditioned on each current token for aspect prediction, and thus the tailor-made summary can help aspect prediction on this token.
Another clue is the information of aspect detection history, and it is distilled from the previous aspect predictions so as to leverage the coordinate structure and tagging schema constraints to upgrade the aspect prediction.
Experimental results over four benchmark datasets clearly demonstrate that our framework can outperform all state-of-the-art methods.
Test functions are important to validate and compare the performance of optimization algorithms.
There have been many test or benchmark functions reported in the literature; however, there is no standard list or set of benchmark functions.
Ideally, test functions should have diverse properties so that can be truly useful to test new algorithms in an unbiased way.
For this purpose, we have reviewed and compiled a rich set of 175 benchmark functions for unconstrained optimization problems with diverse properties in terms of modality, separability, and valley landscape.
This is by far the most complete set of functions so far in the literature, and tt can be expected this complete set of functions can be used for validation of new optimization in the future.
The vast parallelism, exceptional energy efficiency and extraordinary information inherent in DNA molecules are being explored for computing, data storage and cryptography.
DNA cryptography is a emerging field of cryptography.
In this paper a novel encryption algorithm is devised based on number conversion, DNA digital coding, PCR amplification, which can effectively prevent attack.
Data treatment is used to transform the plain text into cipher text which provides excellent security
The present survey aims at presenting the current machine learning techniques employed in security games domains.
Specifically, we focused on papers and works developed by the Teamcore of University of Southern California, which deepened different directions in this field.
After a brief introduction on Stackelberg Security Games (SSGs) and the poaching setting, the rest of the work presents how to model a boundedly rational attacker taking into account her human behavior, then describes how to face the problem of having attacker's payoffs not defined and how to estimate them and, finally, presents how online learning techniques have been exploited to learn a model of the attacker.
In the past few years, deep reinforcement learning has been proven to solve problems which have complex states like video games or board games.
The next step of intelligent agents would be able to generalize between tasks, and using prior experience to pick up new skills more quickly.
However, most reinforcement learning algorithms for now are often suffering from catastrophic forgetting even when facing a very similar target task.
Our approach enables the agents to generalize knowledge from a single source task, and boost the learning progress with a semisupervised learning method when facing a new task.
We evaluate this approach on Atari games, which is a popular reinforcement learning benchmark, and show that it outperforms common baselines based on pre-training and fine-tuning.
This paper introduces a novel framework for modeling interacting humans in a multi-stage game.
This "iterated semi network-form game" framework has the following desirable characteristics: (1) Bounded rational players, (2) strategic players (i.e., players account for one another's reward functions when predicting one another's behavior), and (3) computational tractability even on real-world systems.
We achieve these benefits by combining concepts from game theory and reinforcement learning.
To be precise, we extend the bounded rational "level-K reasoning" model to apply to games over multiple stages.
Our extension allows the decomposition of the overall modeling problem into a series of smaller ones, each of which can be solved by standard reinforcement learning algorithms.
We call this hybrid approach "level-K reinforcement learning".
We investigate these ideas in a cyber battle scenario over a smart power grid and discuss the relationship between the behavior predicted by our model and what one might expect of real human defenders and attackers.
Neural program embeddings have shown much promise recently for a variety of program analysis tasks, including program synthesis, program repair, fault localization, etc.
However, most existing program embeddings are based on syntactic features of programs, such as raw token sequences or abstract syntax trees.
Unlike images and text, a program has an unambiguous semantic meaning that can be difficult to capture by only considering its syntax (i.e. syntactically similar pro- grams can exhibit vastly different run-time behavior), which makes syntax-based program embeddings fundamentally limited.
This paper proposes a novel semantic program embedding that is learned from program execution traces.
Our key insight is that program states expressed as sequential tuples of live variable values not only captures program semantics more precisely, but also offer a more natural fit for Recurrent Neural Networks to model.
We evaluate different syntactic and semantic program embeddings on predicting the types of errors that students make in their submissions to an introductory programming class and two exercises on the CodeHunt education platform.
Evaluation results show that our new semantic program embedding significantly outperforms the syntactic program embeddings based on token sequences and abstract syntax trees.
In addition, we augment a search-based program repair system with the predictions obtained from our se- mantic embedding, and show that search efficiency is also significantly improved.
A convex network can be defined as a network such that every connected induced subgraph includes all the shortest paths between its nodes.
Fully convex network would therefore be a collection of cliques stitched together in a tree.
In this paper, we study the largest high-convexity part of empirical networks obtained by removing the least number of edges, which we call a convex skeleton.
A convex skeleton is a generalisation of a network spanning tree in which each edge can be replaced by a clique of arbitrary size.
We present different approaches for extracting convex skeletons and apply them to social collaboration and protein interactions networks, autonomous systems graphs and food webs.
We show that the extracted convex skeletons retain the degree distribution, clustering, connectivity, distances, node position and also community structure, while making the shortest paths between the nodes largely unique.
Moreover, in the Slovenian computer scientists coauthorship network, a convex skeleton retains the strongest ties between the authors, differently from a spanning tree or high-betweenness backbone and high-salience skeleton.
A convex skeleton thus represents a simple definition of a network backbone with applications in coauthorship and other social collaboration networks.
High-resolution depth maps can be inferred from low-resolution depth measurements and an additional high-resolution intensity image of the same scene.
To that end, we introduce a bimodal co-sparse analysis model, which is able to capture the interdependency of registered intensity and depth information.
This model is based on the assumption that the co-supports of corresponding bimodal image structures are aligned when computed by a suitable pair of analysis operators.
No analytic form of such operators exist and we propose a method for learning them from a set of registered training signals.
This learning process is done offline and returns a bimodal analysis operator that is universally applicable to natural scenes.
We use this to exploit the bimodal co-sparse analysis model as a prior for solving inverse problems, which leads to an efficient algorithm for depth map super-resolution.
Biological plastic neural networks are systems of extraordinary computational capabilities shaped by evolution, development, and lifetime learning.
The interplay of these elements leads to the emergence of adaptive behavior and intelligence.
Inspired by such intricate natural phenomena, Evolved Plastic Artificial Neural Networks (EPANNs) use simulated evolution in-silico to breed plastic neural networks with a large variety of dynamics, architectures, and plasticity rules: these artificial systems are composed of inputs, outputs, and plastic components that change in response to experiences in an environment.
These systems may autonomously discover novel adaptive algorithms, and lead to hypotheses on the emergence of biological adaptation.
EPANNs have seen considerable progress over the last two decades.
Current scientific and technological advances in artificial neural networks are now setting the conditions for radically new approaches and results.
In particular, the limitations of hand-designed networks could be overcome by more flexible and innovative solutions.
This paper brings together a variety of inspiring ideas that define the field of EPANNs.
The main methods and results are reviewed.
Finally, new opportunities and developments are presented.
Community detection is a key data analysis problem across different fields.
During the past decades, numerous algorithms have been proposed to address this issue.
However, most work on community detection does not address the issue of statistical significance.
Although some research efforts have been made towards mining statistically significant communities, deriving an analytical solution of p-value for one community under the configuration model is still a challenging mission that remains unsolved.
To partially fulfill this void, we present a tight upper bound on the p-value of a single community under the configuration model, which can be used for quantifying the statistical significance of each community analytically.
Meanwhile, we present a local search method to detect statistically significant communities in an iterative manner.
Experimental results demonstrate that our method is comparable with the competing methods on detecting statistically significant communities.
Convolutional Neural Networks (CNNs) is one of successful method in many areas such as image classification tasks.
However, the amount of memory and computational cost needed for CNNs inference obstructs them to run efficiently in mobile devices because of memory and computational ability limitation.
One of the method to compress CNNs is compressing the layers iteratively, i.e. by layer-by-layer compression and fine-tuning, with CP-decomposition in convolutional layers.
To compress with CP-decomposition, rank selection is important.
In the previous approach rank selection that is based on sensitivity of each layer, the average rank of the network was still arbitrarily selected.
Additionally, the rank of all layers were decided before whole process of iterative compression, while the rank of a layer can be changed after fine-tuning.
Therefore, this paper proposes selecting rank of each layer using Variational Bayesian Matrix Factorization (VBMF) which is more systematic than arbitrary approach.
Furthermore, to consider the change of each layer's rank after fine-tuning of previous iteration, the method is applied just before compressing the target layer, i.e. after fine-tuning of the previous iteration.
The results show better accuracy while also having more compression rate in AlexNet's convolutional layers compression.
The article describes the prospects of model base management system design automation for decision support systems and suggests the toolbox scheme for design automation based on intelligent technologies.
We here summarize our experience running a challenge with open data for musical genre recognition.
Those notes motivate the task and the challenge design, show some statistics about the submissions, and present the results.
Full-reference image quality assessment (FR-IQA) techniques compare a reference and a distorted/test image and predict the perceptual quality of the test image in terms of a scalar value representing an objective score.
The evaluation of FR-IQA techniques is carried out by comparing the objective scores from the techniques with the subjective scores (obtained from human observers) provided in the image databases used for the IQA.
Hence, we reasonably assume that the goal of a human observer is to rate the distortion present in the test image.
The goal oriented tasks are processed by the human visual system (HVS) through top-down processing which actively searches for local distortions driven by the goal.
Therefore local distortion measures in an image are important for the top-down processing.
At the same time, bottom-up processing also takes place signifying spontaneous visual functions in the HVS.
To account for this, global perceptual features can be used.
Therefore, we hypothesize that the resulting objective score for an image can be derived from the combination of local and global distortion measures calculated from the reference and test images.
We calculate the local distortion by measuring the local correlation differences from the gradient and contrast information.
For global distortion, dissimilarity of the saliency maps computed from a bottom-up model of saliency is used.
The motivation behind the proposed approach has been thoroughly discussed, accompanied by an intuitive analysis.
Finally, experiments are conducted in six benchmark databases suggesting the effectiveness of the proposed approach that achieves competitive performance with the state-of-the-art methods providing an improvement in the overall performance.
People are rated and ranked, towards algorithmic decision making in an increasing number of applications, typically based on machine learning.
Research on how to incorporate fairness into such tasks has prevalently pursued the paradigm of group fairness: giving adequate success rates to specifically protected groups.
In contrast, the alternative paradigm of individual fairness has received relatively little attention, and this paper advances this less explored direction.
The paper introduces a method for probabilistically mapping user records into a low-rank representation that reconciles individual fairness and the utility of classifiers and rankings in downstream applications.
Our notion of individual fairness requires that users who are similar in all task-relevant attributes such as job qualification, and disregarding all potentially discriminating attributes such as gender, should have similar outcomes.
We demonstrate the versatility of our method by applying it to classification and learning-to-rank tasks on a variety of real-world datasets.
Our experiments show substantial improvements over the best prior work for this setting.
Recently, arithmetic coding has attracted the attention of many scholars because of its high compression capability.
Accordingly, in this paper a method which adds secrecy to this well-known source code is proposed.
Finite state arithmetic code (FSAC) is used as source code to add security.
Its finite state machine (FSM) characteristic is exploited to insert some random jumps during source coding process.
In addition, a Huffman code is designed for each state to make decoding possible even in jumps.
Being Prefix free, Huffman codes are useful in tracking correct states for an authorized user when s/he decodes with correct symmetric pseudo random key.
The robustness of our proposed scheme is further reinforced by adding another extra uncertainty by swapping outputs of Huffman codes in each state.
Several test images are used for inspecting the validity of the proposed Huffman Finite State Arithmetic Coding (HFSAC).
The results of several experimental, key space analyses, statistical analysis, key sensitivity and plaintext sensitivity tests show that HFSAC with a little effect on compression efficiency for image cryptosystem provides an efficient and secure way for real-time image encryption and transmission.
Nowadays with the help of advanced technology, modern vehicles are not only made up of mechanical devices but also consist of highly complex electronic devices and connections to the outside world.
There are around 70 Electronic Control Units (ECUs) in modern vehicle which are communicating with each other over the standard communication protocol known as Controller Area Network (CAN-Bus) that provides the communication rate up to 1Mbps.
There are different types of in-vehicle network protocol and bus system namely Controlled Area Network (CAN), Local Interconnected Network (LIN), Media Oriented System Transport (MOST), and FlexRay.
Even though CAN-Bus is considered as de-facto standard for in-vehicle network communication, it inherently lacks the fundamental security features by design like message authentication.
This security limitation has paved the way for adversaries to penetrate into the vehicle network and do malicious activities which can pose a dangerous situation for both driver and passengers.
In particular, nowadays vehicular networks are not only closed systems, but also they are open to different external interfaces namely Bluetooth, GPS, to the outside world.
Therefore, it creates new opportunities for attackers to remotely take full control of the vehicle.
The objective of this research is to survey the current limitations of CAN-Bus protocol in terms of secure communication and different solutions that researchers in the society of automotive have provided to overcome the CAN-Bus limitation on different layers.
In this paper, the tracking control problem of a class of uncertain Euler-Lagrange systems subjected to unknown input delay and bounded disturbances is addressed.
To this front, a novel delay dependent control law, referred as Adaptive Robust Outer Loop Control (AROLC) is proposed.
Compared to the conventional predictor based approaches, the proposed controller is capable of negotiating any input delay, within a stipulated range, without knowing the delay or its variation.
The maximum allowable input delay is computed through Razumikhin-type stability analysis.
AROLC also provides robustness against the disturbances due to input delay, parametric variations and unmodelled dynamics through switching control law.
The novel adaptive law allows the switching gain to modify itself online in accordance with the tracking error without any prerequisite of the uncertainties.
The uncertain system, employing AROLC, is shown to be Uniformly Ultimately Bounded (UUB).
As a proof of concept, experimentation is carried out on a nonholonomic wheeled mobile robot with various time varying as well as fixed input delay, and better tracking accuracy of the proposed controller is noted compared to predictor based methodology.
For many people suffering from motor disabilities, assistive devices controlled with only brain activity are the only way to interact with their environment.
Natural tasks often require different kinds of interactions, involving different controllers the user should be able to select in a self-paced way.
We developed a Brain-Computer Interface (BCI) allowing users to switch between four control modes in a self-paced way in real-time.
Since the system is devised to be used in domestic environments in a user-friendly way, we selected non-invasive electroencephalographic (EEG) signals and convolutional neural networks (CNNs), known for their ability to find the optimal features in classification tasks.
We tested our system using the Cybathlon BCI computer game, which embodies all the challenges inherent to real-time control.
Our preliminary results show that an efficient architecture (SmallNet), with only one convolutional layer, can classify 4 mental activities chosen by the user.
The BCI system is run and validated online.
It is kept up-to-date through the use of newly collected signals along playing, reaching an online accuracy of 47.6% where most approaches only report results obtained offline.
We found that models trained with data collected online better predicted the behaviour of the system in real-time.
This suggests that similar (CNN based) offline classifying methods found in the literature might experience a drop in performance when applied online.
Compared to our previous decoder of physiological signals relying on blinks, we increased by a factor 2 the amount of states among which the user can transit, bringing the opportunity for finer control of specific subtasks composing natural grasping in a self-paced way.
Our results are comparable to those shown at the Cybathlon's BCI Race but further improvements on accuracy are required.
Recently deep learning based recommendation systems have been actively explored to solve the cold-start problem using a hybrid approach.
However, the majority of previous studies proposed a hybrid model where collaborative filtering and content-based filtering modules are independently trained.
The end-to-end approach that takes different modality data as input and jointly trains the model can provide better optimization but it has not been fully explored yet.
In this work, we propose deep content-user embedding model, a simple and intuitive architecture that combines the user-item interaction and music audio content.
We evaluate the model on music recommendation and music auto-tagging tasks.
The results show that the proposed model significantly outperforms the previous work.
We also discuss various directions to improve the proposed model further.
We examine volume computation of general-dimensional polytopes and more general convex bodies, defined as the intersection of a simplex by a family of parallel hyperplanes, and another family of parallel hyperplanes or a family of concentric ellipsoids.
Such convex bodies appear in modeling and predicting financial crises.
The impact of crises on the economy (labor, income, etc.)makes its detection of prime interest.
Certain features of dependencies in the markets clearly identify times of turmoil.
We describe the relationship between asset characteristics by means of a copula; each characteristic is either a linear or quadratic form of the portfolio components, hence the copula can be constructed by computing volumes of convex bodies.
We design and implement practical algorithms in the exact and approximate setting, we experimentally juxtapose them and study the tradeoff of exactness and accuracy for speed.
We analyze the following methods in order of increasing generality: rejection sampling relying on uniformly sampling the simplex, which is the fastest approach, but inaccurate for small volumes; exact formulae based on the computation of integrals of probability distribution functions; an optimized Lawrence sign decomposition method, since the polytopes at hand are shown to be simple; Markov chain Monte Carlo algorithms using random walks based on the hit-and-run paradigm generalized to nonlinear convex bodies and relying on new methods for computing a ball enclosed; the latter is experimentally extended to non-convex bodies with very encouraging results.
Our C++ software, based on CGAL and Eigen and available on github, is shown to be very effective in up to 100 dimensions.
Our results offer novel, effective means of computing portfolio dependencies and an indicator of financial crises, which is shown to correctly identify past crises.
We present a novel method for obtaining high-quality, domain-targeted multiple choice questions from crowd workers.
Generating these questions can be difficult without trading away originality, relevance or diversity in the answer options.
Our method addresses these problems by leveraging a large corpus of domain-specific text and a small set of existing questions.
It produces model suggestions for document selection and answer distractor choice which aid the human question generation process.
With this method we have assembled SciQ, a dataset of 13.7K multiple choice science exam questions (Dataset available at http://allenai.org/data.html).
We demonstrate that the method produces in-domain questions by providing an analysis of this new dataset and by showing that humans cannot distinguish the crowdsourced questions from original questions.
When using SciQ as additional training data to existing questions, we observe accuracy improvements on real science exams.
In this paper, we propose a unified framework and an algorithm for the problem of group recommendation where a fixed number of items or alternatives can be recommended to a group of users.
The problem of group recommendation arises naturally in many real world contexts, and is closely related to the budgeted social choice problem studied in economics.
We frame the group recommendation problem as choosing a subgraph with the largest group consensus score in a completely connected graph defined over the item affinity matrix.
We propose a fast greedy algorithm with strong theoretical guarantees, and show that the proposed algorithm compares favorably to the state-of-the-art group recommendation algorithms according to commonly used relevance and coverage performance measures on benchmark dataset.
Multiple-input multiple-output (MIMO) millimeter wave (mmWave) systems are vulnerable to hardware impairments due to operating at high frequencies and employing a large number of radio- frequency (RF) hardware components.
In particular, nonlinear power amplifiers (PAs) employed at the transmitter distort the signal when operated close to saturation due to energy efficiency considerations.
In this paper, we study the performance of a MIMO mmWave hybrid beamforming scheme in the presence of nonlinear PAs.
First, we develop a statistical model for the transmitted signal in such systems and show that the spatial direction of the inband distortion is shaped by the beamforming filter.
This suggests that even in the large antenna regime, where narrow beams can be steered toward the receiver, the impact of nonlinear PAs should not be ignored.
Then, by employing a realistic power consumption model for the PAs, we investigate the trade-off between spectral and energy efficiency in such systems.
Our results show that increasing the transmit power level when the number of transmit antennas grows large can be counter-effective in terms of energy efficiency.
Furthermore, using numerical simulation, we show that when the transmit power is large, analog beamforming leads to higher spectral and energy efficiency compared to digital and hybrid beamforming schemes.
This paper deals with a method for generating realistic labeled masses.
Recently, there have been many attempts to apply deep learning to various bio-image computing fields including computer-aided detection and diagnosis.
In order to learn deep network model to be well-behaved in bio-image computing fields, a lot of labeled data is required.
However, in many bioimaging fields, the large-size of labeled dataset is scarcely available.
Although a few researches have been dedicated to solving this problem through generative model, there are some problems as follows: 1) The generated bio-image does not seem realistic; 2) the variation of generated bio-image is limited; and 3) additional label annotation task is needed.
In this study, we propose a realistic labeled bio-image generation method through visual feature processing in latent space.
Experimental results have shown that mass images generated by the proposed method were realistic and had wide expression range of targeted mass characteristics.
Inter-domain routing is a crucial part of the Internet designed for arbitrary policies, economical models, and topologies.
This versatility translates into a substantially complex system that is hard to comprehend.
Monitoring the inter-domain routing infrastructure is however essential for understanding the current state of the Internet and improving it.
In this paper we design a methodology to answer two simple questions: Which are the common transit networks used to reach a certain AS?
How much does this AS depends on these transit networks?
To answer these questions we digest AS paths advertised with the Border Gateway Protocol (BGP) into AS graphs and measure node centrality, that is the likelihood of an AS to lie on paths between two other ASes.
Our proposal relies solely on the AS hegemony metric, a new way to quantify node centrality while taking into account the bias towards the partial view offered by BGP.
Our analysis using 14 years of BGP data refines our knowledge on Internet flattening but also exhibits the consolidated position of tier-1 networks in today's IPv4 and IPv6 Internet.
We also study the connectivity to two content providers (Google and Akamai) and investigate the AS dependency of networks hosting DNS root servers.
These case studies emphasize the benefits of the proposed method to assist ISPs in planning and assessing infrastructure deployment.
Understanding the cognitive evolution of researchers as they progress in the academia is an important but complex problem, a problem belonging to a class of problems, which often require the development of models for gaining further understanding in the intricacies of the domain.
The research question that we address in this paper is how to effectively model this temporal cognitive mental development of prolific researchers.
Our proposed solution to this problem is based on noting that the academic progression and notability of a researcher are linked with a progressive increase in the citation count for the scholar's refereed publications quantified using indices such as the Hirsch index.
In other words, we propose the use of yearly cognitive increment of a scholar's cognition to be quantifiable using a function of the scholar's citation index, thereby considering the index as an indicator of the discrete approximation of the scholar's cognitive development.
Using validated agent-based modeling, a paradigm presented as part of our previous work i.e. Cognitive Agent-based Computing framework, we present both formal as well as visual agent-based complex network representations for this cognitive evolution in the form of a Temporal Cognitive Level Network (TCLN) model.
As a proof of the effectiveness of this approach, we demonstrate validation of the model using historic data of citations.
In general the problem of finding a miminum spanning tree for a weighted directed graph is difficult but solvable.
There are a lot of differences between problems for directed and undirected graphs, therefore the algorithms for undirected graphs cannot usually be applied to the directed case.
In this paper we examine the kind of weights such that the problems are equivalent and a minimum spanning tree of a directed graph may be found by a simple algorithm for an undirected graph.
In this paper, we present a learning based approach to depth fusion, i.e., dense 3D reconstruction from multiple depth images.
The most common approach to depth fusion is based on averaging truncated signed distance functions, which was originally proposed by Curless and Levoy in 1996.
While this method is simple and provides great results, it is not able to reconstruct (partially) occluded surfaces and requires a large number frames to filter out sensor noise and outliers.
Motivated by the availability of large 3D model repositories and recent advances in deep learning, we present a novel 3D CNN architecture that learns to predict an implicit surface representation from the input depth maps.
Our learning based method significantly outperforms the traditional volumetric fusion approach in terms of noise reduction and outlier suppression.
By learning the structure of real world 3D objects and scenes, our approach is further able to reconstruct occluded regions and to fill in gaps in the reconstruction.
We demonstrate that our learning based approach outperforms both vanilla TSDF fusion as well as TV-L1 fusion on the task of volumetric fusion.
Further, we demonstrate state-of-the-art 3D shape completion results.
It has been believed that stochastic feedforward neural networks (SFNNs) have several advantages beyond deterministic deep neural networks (DNNs): they have more expressive power allowing multi-modal mappings and regularize better due to their stochastic nature.
However, training large-scale SFNN is notoriously harder.
In this paper, we aim at developing efficient training methods for SFNN, in particular using known architectures and pre-trained parameters of DNN.
To this end, we propose a new intermediate stochastic model, called Simplified-SFNN, which can be built upon any baseline DNNand approximates certain SFNN by simplifying its upper latent units above stochastic ones.
The main novelty of our approach is in establishing the connection between three models, i.e., DNN->Simplified-SFNN->SFNN, which naturally leads to an efficient training procedure of the stochastic models utilizing pre-trained parameters of DNN.
Using several popular DNNs, we show how they can be effectively transferred to the corresponding stochastic models for both multi-modal and classification tasks on MNIST, TFD, CASIA, CIFAR-10, CIFAR-100 and SVHN datasets.
In particular, we train a stochastic model of 28 layers and 36 million parameters, where training such a large-scale stochastic network is significantly challenging without using Simplified-SFNN
Semantic segmentation of motion capture sequences plays a key part in many data-driven motion synthesis frameworks.
It is a preprocessing step in which long recordings of motion capture sequences are partitioned into smaller segments.
Afterwards, additional methods like statistical modeling can be applied to each group of structurally-similar segments to learn an abstract motion manifold.
The segmentation task however often remains a manual task, which increases the effort and cost of generating large-scale motion databases.
We therefore propose an automatic framework for semantic segmentation of motion capture data using a dilated temporal fully-convolutional network.
Our model outperforms a state-of-the-art model in action segmentation, as well as three networks for sequence modeling.
We further show our model is robust against high noisy training labels.
The predominant use of wireless access networks is for media streaming applications, which are only gaining popularity as ever more devices become available for this purpose.
However, current access networks treat all packets identically, and lack the agility to determine which clients are most in need of service at a given time.
Software reconfigurability of networking devices has seen wide adoption, and this in turn implies that agile control policies can be now instantiated on access networks.
The goal of this work is to design, develop and demonstrate FlowBazaar, an market-based approach to create a value chain from the application on one side, to algorithms operating over reconfigurable infrastructure on the other, so that applications are able to obtain necessary resources for optimal performance.
Using YouTube video streaming as an example, we illustrate how FlowBazaar is able to adaptively provide such resources and attain a high QoE for all clients at a wireless access point.
This paper presents a method for adding multiple tasks to a single deep neural network while avoiding catastrophic forgetting.
Inspired by network pruning techniques, we exploit redundancies in large deep networks to free up parameters that can then be employed to learn new tasks.
By performing iterative pruning and network re-training, we are able to sequentially "pack" multiple tasks into a single network while ensuring minimal drop in performance and minimal storage overhead.
Unlike prior work that uses proxy losses to maintain accuracy on older tasks, we always optimize for the task at hand.
We perform extensive experiments on a variety of network architectures and large-scale datasets, and observe much better robustness against catastrophic forgetting than prior work.
In particular, we are able to add three fine-grained classification tasks to a single ImageNet-trained VGG-16 network and achieve accuracies close to those of separately trained networks for each task.
Code available at https://github.com/arunmallya/packnet
We present a methodology for fast prototyping of morphologies and controllers for robot locomotion.
Going beyond simulation-based approaches, we argue that the form and function of a robot, as well as their interplay with real-world environmental conditions are critical.
Hence, fast design and learning cycles are necessary to adapt robot shape and behavior to their environment.
To this end, we present a combination of laminate robot manufacturing and sample-efficient reinforcement learning.
We leverage this methodology to conduct an extensive robot learning experiment.
Inspired by locomotion in sea turtles, we design a low-cost crawling robot with variable, interchangeable fins.
Learning is performed using both bio-inspired and original fin designs in an artificial indoor environment as well as a natural environment in the Arizona desert.
The findings of this study show that static policies developed in the laboratory do not translate to effective locomotion strategies in natural environments.
In contrast to that, sample-efficient reinforcement learning can help to rapidly accommodate changes in the environment or the robot.
This paper presents a memory efficient architecture that implements the Multi-Scale Line Detector (MSLD) algorithm for real-time retinal blood vessel detection in fundus images on a Zynq FPGA.
This implementation benefits from the FPGA parallelism to drastically reduce the memory requirements of the MSLD from two images to a few values.
The architecture is optimized in terms of resource utilization by reusing the computations and optimizing the bit-width.
The throughput is increased by designing fully pipelined functional units.
The architecture is capable of achieving a comparable accuracy to its software implementation but 70x faster for low resolution images.
For high resolution images, it achieves an acceleration by a factor of 323x.
A human computation system can be viewed as a distributed system in which the processors are humans, called workers.
Such systems harness the cognitive power of a group of workers connected to the Internet to execute relatively simple tasks, whose solutions, once grouped, solve a problem that systems equipped with only machines could not solve satisfactorily.
Examples of such systems are Amazon Mechanical Turk and the Zooniverse platform.
A human computation application comprises a group of tasks, each of them can be performed by one worker.
Tasks might have dependencies among each other.
In this study, we propose a theoretical framework to analyze such type of application from a distributed systems point of view.
Our framework is established on three dimensions that represent different perspectives in which human computation applications can be approached: quality-of-service requirements, design and management strategies, and human aspects.
By using this framework, we review human computation in the perspective of programmers seeking to improve the design of human computation applications and managers seeking to increase the effectiveness of human computation infrastructures in running such applications.
In doing so, besides integrating and organizing what has been done in this direction, we also put into perspective the fact that the human aspects of the workers in such systems introduce new challenges in terms of, for example, task assignment, dependency management, and fault prevention and tolerance.
We discuss how they are related to distributed systems and other areas of knowledge.
In this paper we consider the uplink of a massive MIMO communication system using 5G New Radio-compliant multiple access, which is to co-exist with a radar system using the same frequency band.
We propose a system model taking into account the reverberation (clutter) produced by the radar system at the massive MIMO receiver.
Then, we propose several linear receivers for uplink data-detection, ranging by the simple channel-matched beamformer to the zero-forcing and linear minimum mean square error receivers for clutter disturbance rejection.
Our results show that the clutter may have a strong effect on the performance of the cellular communication system, but the use of large-scale antenna arrays at the base station is key to provide increased robustness against it, at least as far as data-detection is concerned.
Data-driven analysis of complex networks has been in the focus of research for decades.
An important question is to discover the relation between various network characteristics in real-world networks and how these relationships vary across network domains.
A related research question is to study how well the network models can capture the observed relations between the graph metrics.
In this paper, we apply statistical and machine learning techniques to answer the aforementioned questions.
We study 400 real-world networks along with 2400 networks generated by five frequently used network models with previously fitted parameters to make the generated graphs as similar to the real network as possible.
We find that the correlation profiles of the structural measures significantly differ across network domains and the domain can be efficiently determined using a small selection of graph metrics.
The goodness-of-fit of the network models and the best performing models themselves highly depend on the domains.
Using machine learning techniques, it turned out to be relatively easy to decide if a network is real or model-generated.
We also investigate what structural properties make it possible to achieve a good accuracy, i.e. what features the network models cannot capture.
Deep neuroevolution and deep reinforcement learning (deep RL) algorithms are two popular approaches to policy search.
The former is widely applicable and rather stable, but suffers from low sample efficiency.
By contrast, the latter is more sample efficient, but the most sample efficient variants are also rather unstable and highly sensitive to hyper-parameter setting.
So far, these families of methods have mostly been compared as competing tools.
However, an emerging approach consists in combining them so as to get the best of both worlds.
Two previously existing combinations use either an ad hoc evolutionary algorithm or a goal exploration process together with the Deep Deterministic Policy Gradient (DDPG) algorithm, a sample efficient off-policy deep RL algorithm.
In this paper, we propose a different combination scheme using the simple cross-entropy method (CEM) and Twin Delayed Deep Deterministic policy gradient (td3), another off-policy deep RL algorithm which improves over ddpg.
We evaluate the resulting method, cem-rl, on a set of benchmarks classically used in deep RL.
We show that cem-rl benefits from several advantages over its competitors and offers a satisfactory trade-off between performance and sample efficiency.
Motion planning under differential constraints, kinodynamic motion planning, is one of the canonical problems in robotics.
Currently, state-of-the-art methods evolve around kinodynamic variants of popular sampling-based algorithms, such as Rapidly-exploring Random Trees (RRTs).
However, there are still challenges remaining, for example, how to include complex dynamics while guaranteeing optimality.
If the open-loop dynamics are unstable, exploration by random sampling in control space becomes inefficient.
We describe a new sampling-based algorithm, called CL-RRT#, which leverages ideas from the RRT# algorithm and a variant of the RRT algorithm that generates trajectories using closed-loop prediction.
The idea of planning with closed-loop prediction allows us to handle complex unstable dynamics and avoids the need to find computationally hard steering procedures.
The search technique presented in the RRT# algorithm allows us to improve the solution quality by searching over alternative reference trajectories.
Numerical simulations using a nonholonomic system demonstrate the benefits of the proposed approach.
A number of methods have been proposed over the last decade for encoding information using deoxyribonucleic acid (DNA), giving rise to the emerging area of DNA data embedding.
Since a DNA sequence is conceptually equivalent to a sequence of quaternary symbols (bases), DNA data embedding (diversely called DNA watermarking or DNA steganography) can be seen as a digital communications problem where channel errors are tantamount to mutations of DNA bases.
Depending on the use of coding or noncoding DNA hosts, which, respectively, denote DNA segments that can or cannot be translated into proteins, DNA data embedding is essentially a problem of communications with or without side information at the encoder.
In this paper the Shannon capacity of DNA data embedding is obtained for the case in which DNA sequences are subject to substitution mutations modelled using the Kimura model from molecular evolution studies.
Inferences are also drawn with respect to the biological implications of some of the results presented.
Given a graph where every node has certain attributes associated with it and some nodes have labels associated with them, Collective Classification (CC) is the task of assigning labels to every unlabeled node using information from the node as well as its neighbors.
It is often the case that a node is not only influenced by its immediate neighbors but also by higher order neighbors, multiple hops away.
Recent state-of-the-art models for CC learn end-to-end differentiable variations of Weisfeiler-Lehman (WL) kernels to aggregate multi-hop neighborhood information.
In this work, we propose a Higher Order Propagation Framework, HOPF, which provides an iterative inference mechanism for these powerful differentiable kernels.
Such a combination of classical iterative inference mechanism with recent differentiable kernels allows the framework to learn graph convolutional filters that simultaneously exploit the attribute and label information available in the neighborhood.
Further, these iterative differentiable kernels can scale to larger hops beyond the memory limitations of existing differentiable kernels.
We also show that existing WL kernel-based models suffer from the problem of Node Information Morphing where the information of the node is morphed or overwhelmed by the information of its neighbors when considering multiple hops.
To address this, we propose a specific instantiation of HOPF, called the NIP models, which preserves the node information at every propagation step.
The iterative formulation of NIP models further helps in incorporating distant hop information concisely as summaries of the inferred labels.
We do an extensive evaluation across 11 datasets from different domains.
We show that existing CC models do not provide consistent performance across datasets, while the proposed NIP model with iterative inference is more robust.
This paper deals with a very renowned website (that is Book-Crossing) from two angles: The first angle focuses on the direct relations between users and books.
Many things can be inferred from this part of analysis such as who is more interested in book reading than others and why?
Which books are most popular and which users are most active and why?
The task requires the use of certain social network analysis measures (e.g. degree centrality).
What does it mean when two users like the same book?
Is it the same when other two users have one thousand books in common?
Who is more likely to be a friend of whom and why?
Are there specific people in the community who are more qualified to establish large circles of social relations?
These questions (and of course others) were answered through the other part of the analysis, which will take us to probe the potential social relations between users in this community.
Although these relationships do not exist explicitly, they can be inferred with the help of affiliation network analysis and techniques such as m-slice.
Spreadsheets are used to develop application software that is distributed to users.
Unfortunately, the users often have the ability to change the programming statements ("source code") of the spreadsheet application.
This causes a host of problems.
By critically examining the suitability of spreadsheet computer programming languages for application development, six "application development features" are identified, with source code protection being the most important.
We investigate the status of these features and discuss how they might be implemented in the dominant Microsoft Excel spreadsheet and in the new Google Spreadsheet.
Although Google Spreadsheet currently provides no source code control, its web-centric delivery model offers technical advantages for future provision of a rich set of features.
Excel has a number of tools that can be combined to provide "pretty good protection" of source code, but weak passwords reduce its robustness.
User access to Excel source code must be considered a programmer choice rather than an attribute of the spreadsheet.
Convolutional LDPC ensembles, introduced by Felstrom and Zigangirov, have excellent thresholds and these thresholds are rapidly increasing as a function of the average degree.
Several variations on the basic theme have been proposed to date, all of which share the good performance characteristics of convolutional LDPC ensembles.
We describe the fundamental mechanism which explains why "convolutional-like" or "spatially coupled" codes perform so well.
In essence, the spatial coupling of the individual code structure has the effect of increasing the belief-propagation (BP) threshold of the new ensemble to its maximum possible value, namely the maximum-a-posteriori (MAP) threshold of the underlying ensemble.
For this reason we call this phenomenon "threshold saturation."
This gives an entirely new way of approaching capacity.
One significant advantage of such a construction is that one can create capacity-approaching ensembles with an error correcting radius which is increasing in the blocklength.
Our proof makes use of the area theorem of the BP-EXIT curve and the connection between the MAP and BP threshold recently pointed out by Measson, Montanari, Richardson, and Urbanke.
Although we prove the connection between the MAP and the BP threshold only for a very specific ensemble and only for the binary erasure channel, empirically a threshold saturation phenomenon occurs for a wide class of ensembles and channels.
More generally, we conjecture that for a large range of graphical systems a similar saturation of the "dynamical" threshold occurs once individual components are coupled sufficiently strongly.
This might give rise to improved algorithms as well as to new techniques for analysis.
Health related social media mining is a valuable apparatus for the early recognition of the diverse antagonistic medicinal conditions.
Mostly, the existing methods are based on machine learning with knowledge-based learning.
This working note presents the Recurrent neural network (RNN) and Long short-term memory (LSTM) based embedding for automatic health text classification in the social media mining.
For each task, two systems are built and that classify the tweet at the tweet level.
RNN and LSTM are used for extracting features and non-linear activation function at the last layer facilitates to distinguish the tweets of different categories.
The experiments are conducted on 2nd Social Media Mining for Health Applications Shared Task at AMIA 2017.
The experiment results are considerable; however the proposed method is appropriate for the health text classification.
This is primarily due to the reason that, it doesn't rely on any feature engineering mechanisms.
For many real-life Bayesian networks, common knowledge dictates that the output established for the main variable of interest increases with higher values for the observable variables.
We define two concepts of monotonicity to capture this type of knowledge.
We say that a network is isotone in distribution if the probability distribution computed for the output variable given specific observations is stochastically dominated by any such distribution given higher-ordered observations; a network is isotone in mode if a probability distribution given higher observations has a higher mode.
We show that establishing whether a network exhibits any of these properties of monotonicity is coNPPP-complete in general, and remains coNP-complete for polytrees.
We present an approximate algorithm for deciding whether a network is monotone in distribution and illustrate its application to a real-life network in oncology.
Explainability and interpretability are two critical aspects of decision support systems.
Within computer vision, they are critical in certain tasks related to human behavior analysis such as in health care applications.
Despite their importance, it is only recently that researchers are starting to explore these aspects.
This paper provides an introduction to explainability and interpretability in the context of computer vision with an emphasis on looking at people tasks.
Specifically, we review and study those mechanisms in the context of first impressions analysis.
To the best of our knowledge, this is the first effort in this direction.
Additionally, we describe a challenge we organized on explainability in first impressions analysis from video.
We analyze in detail the newly introduced data set, the evaluation protocol, and summarize the results of the challenge.
Finally, derived from our study, we outline research opportunities that we foresee will be decisive in the near future for the development of the explainable computer vision field.
Secure multiparty computation (MPC) allows joint privacy-preserving computations on data of multiple parties.
Although MPC has been studied substantially, building solutions that are practical in terms of computation and communication cost is still a major challenge.
In this paper, we investigate the practical usefulness of MPC for multi-domain network security and monitoring.
We first optimize MPC comparison operations for processing high volume data in near real-time.
We then design privacy-preserving protocols for event correlation and aggregation of network traffic statistics, such as addition of volume metrics, computation of feature entropy, and distinct item count.
Optimizing performance of parallel invocations, we implement our protocols along with a complete set of basic operations in a library called SEPIA.
We evaluate the running time and bandwidth requirements of our protocols in realistic settings on a local cluster as well as on PlanetLab and show that they work in near real-time for up to 140 input providers and 9 computation nodes.
Compared to implementations using existing general-purpose MPC frameworks, our protocols are significantly faster, requiring, for example, 3 minutes for a task that takes 2 days with general-purpose frameworks.
This improvement paves the way for new applications of MPC in the area of networking.
Finally, we run SEPIA's protocols on real traffic traces of 17 networks and show how they provide new possibilities for distributed troubleshooting and early anomaly detection.
Peer review, evaluation, and selection is a fundamental aspect of modern science.
Funding bodies the world over employ experts to review and select the best proposals of those submitted for funding.
The problem of peer selection, however, is much more general: a professional society may want to give a subset of its members awards based on the opinions of all members; an instructor for a MOOC or online course may want to crowdsource grading; or a marketing company may select ideas from group brainstorming sessions based on peer evaluation.
We make three fundamental contributions to the study of procedures or mechanisms for peer selection, a specific type of group decision-making problem, studied in computer science, economics, and political science.
First, we propose a novel mechanism that is strategyproof, i.e., agents cannot benefit by reporting insincere valuations.
Second, we demonstrate the effectiveness of our mechanism by a comprehensive simulation-based comparison with a suite of mechanisms found in the literature.
Finally, our mechanism employs a randomized rounding technique that is of independent interest, as it solves the apportionment problem that arises in various settings where discrete resources such as parliamentary representation slots need to be divided proportionally.
Wit is a form of rich interaction that is often grounded in a specific situation (e.g., a comment in response to an event).
In this work, we attempt to build computational models that can produce witty descriptions for a given image.
Inspired by a cognitive account of humor appreciation, we employ linguistic wordplay, specifically puns, in image descriptions.
We develop two approaches which involve retrieving witty descriptions for a given image from a large corpus of sentences, or generating them via an encoder-decoder neural network architecture.
We compare our approach against meaningful baseline approaches via human studies and show substantial improvements.
We find that when a human is subject to similar constraints as the model regarding word usage and style, people vote the image descriptions generated by our model to be slightly wittier than human-written witty descriptions.
Unsurprisingly, humans are almost always wittier than the model when they are free to choose the vocabulary, style, etc.
This paper attempts to explain consequences of the relational calculus not allowing relations to be domains of relations, and to suggest a solution for the issue.
On the example of SQL we describe the consequent problem of the multitude of different representations for relations; analyze in detail the disadvantages of the notions "TABLE" and "FOREIGN KEY"; and propose a complex solution which includes brand new data language, abandonment of tables as a representation for relations, and relatively small yet very significant alteration of the data storage concept, called "multitable index".
We establish exact recovery for the Least Unsquared Deviations (LUD) algorithm of Ozyesil and Singer.
More precisely, we show that for sufficiently many cameras with given corrupted pairwise directions, where both camera locations and pairwise directions are generated by a special probabilistic model, the LUD algorithm exactly recovers the camera locations with high probability.
A similar exact recovery guarantee was established for the ShapeFit algorithm by Hand, Lee and Voroninski, but with typically less corruption.
This paper proposes a design for a hybrid, city-wide urban navigation system for moving agents demanding dedicated assistance.
The hybrid system combines GPS and vehicle-to-vehicle communication from an ad-hoc network of parked cars, and RFID from fixed infrastructure -such as smart traffic lights- to enable a safely navigable city.
Applications for such a system include high-speed drone navigation and directing visually impaired pedestrians.
The Internet provides students with a unique opportunity to connect and maintain social ties with peers from other schools, irrespective of how far they are from each other.
However, little is known about the real structure of such online relationships.
In this paper, we investigate the structure of interschool friendship on a popular social networking site.
We use data from 36,951 students from 590 schools of a large European city.
We find that the probability of a friendship tie between students from neighboring schools is high and that it decreases with the distance between schools following the power law.
We also find that students are more likely to be connected if the educational outcomes of their schools are similar.
We show that this fact is not a consequence of residential segregation.
While high- and low-performing schools are evenly distributed across the city, this is not the case for the digital space, where schools turn out to be segregated by educational outcomes.
There is no significant correlation between the educational outcomes of a school and its geographical neighbors; however, there is a strong correlation between the educational outcomes of a school and its digital neighbors.
These results challenge the common assumption that the Internet is a borderless space, and may have important implications for the understanding of educational inequality in the digital age.
Mixed-criticality systems combine real-time components of different levels of criticality, i.e. severity of failure, on the same processor, in order to obtain good resource utilisation.
They must guarantee deadlines of highly-critical tasks at the expense of lower-criticality ones in the case of overload.
Present operating systems provide inadequate support for this kind of system, which is of growing importance in avionics and other verticals.
We present an approach that provides the required asymmetric integrity and its implementation in the high-assurance seL4 microkernel.
The infamous Facebook emotion contagion experiment is one of the most prominent and best-known online experiments based on the concept of what we here call "living labs".
In these kinds of experiments, real-world applications such as social web platforms trigger experimental switches inside their system to present experimental changes to their users - most of the time without the users being aware of their role as virtual guinea pigs.
In the Facebook example the researches changed the way users' personal timeline was compiled to test the influence on the users' moods and feelings.
The reactions to these experiments showed the inherent ethical issues such living labs settings bring up, mainly the study's lack of informed consent procedures, as well as a more general critique of the flaws in the experimental design.
In this chapter, we describe additional use cases: The so-called living labs that focus on experimentation with information systems such as search engines and wikis and especially on their real-world usage.
The living labs paradigm allows researchers to conduct research in real-world environments or systems.
In the field of information science and especially information retrieval - which is the scientific discipline that is concerned with the research of search engines, information systems, and search related algorithms and techniques - it is still common practice to perform in vitro or offline evaluations using static test collections.
Living labs are widely unknown or unavailable to academic researchers in these fields.
A main benefit of living labs is their potential to offer new ways and possibilities to experiment with information systems and especially their users, but on the other hand they introduce a whole set of ethical issues that we would like to address in this chapter.
One of the most fundamental problems when designing controllers for dynamic systems is the tuning of the controller parameters.
Typically, a model of the system is used to obtain an initial controller, but ultimately the controller parameters must be tuned manually on the real system to achieve the best performance.
To avoid this manual tuning step, methods from machine learning, such as Bayesian optimization, have been used.
However, as these methods evaluate different controller parameters on the real system, safety-critical system failures may happen.
In this paper, we overcome this problem by applying, for the first time, a recently developed safe optimization algorithm, SafeOpt, to the problem of automatic controller parameter tuning.
Given an initial, low-performance controller, SafeOpt automatically optimizes the parameters of a control law while guaranteeing safety.
It models the underlying performance measure as a Gaussian process and only explores new controller parameters whose performance lies above a safe performance threshold with high probability.
Experimental results on a quadrotor vehicle indicate that the proposed method enables fast, automatic, and safe optimization of controller parameters without human intervention.
Grammatical Evolution (GE) is a population-based evolutionary algorithm, where a formal grammar is used in the genotype to phenotype mapping process.
PonyGE2 is an open source implementation of GE in Python, developed at UCD's Natural Computing Research and Applications group.
It is intended as an advertisement and a starting-point for those new to GE, a reference for students and researchers, a rapid-prototyping medium for our own experiments, and a Python workout.
As well as providing the characteristic genotype to phenotype mapping of GE, a search algorithm engine is also provided.
A number of sample problems and tutorials on how to use and adapt PonyGE2 have been developed.
Handwritten recognition (HWR) is the ability of a computer to receive and interpret intelligible handwritten input from source such as paper documents, photographs, touch-screens and other devices.
In this paper we will using three (3) classification t o re cognize the handwritten which is SVM, KNN and Neural Network.
Next Point-of-Interest (POI) recommendation is of great value for both location-based service providers and users.
Recently Recurrent Neural Networks (RNNs) have been proved to be effective on sequential recommendation tasks.
However, existing RNN solutions rarely consider the spatio-temporal intervals between neighbor check-ins, which are essential for modeling user check-in behaviors in next POI recommendation.
In this paper, we propose a new variant of LSTM, named STLSTM, which implements time gates and distance gates into LSTM to capture the spatio-temporal relation between successive check-ins.
Specifically, one-time gate and one distance gate are designed to control short-term interest update, and another time gate and distance gate are designed to control long-term interest update.
Furthermore, to reduce the number of parameters and improve efficiency, we further integrate coupled input and forget gates with our proposed model.
Finally, we evaluate the proposed model using four real-world datasets from various location-based social networks.
Our experimental results show that our model significantly outperforms the state-of-the-art approaches for next POI recommendation.
Many imaging tasks require global information about all pixels in an image.
Conventional bottom-up classification networks globalize information by decreasing resolution; features are pooled and downsampled into a single output.
But for semantic segmentation and object detection tasks, a network must provide higher-resolution pixel-level outputs.
To globalize information while preserving resolution, many researchers propose the inclusion of sophisticated auxiliary blocks, but these come at the cost of a considerable increase in network size and computational cost.
This paper proposes stacked u-nets (SUNets), which iteratively combine features from different resolution scales while maintaining resolution.
SUNets leverage the information globalization power of u-nets in a deeper network architectures that is capable of handling the complexity of natural images.
SUNets perform extremely well on semantic segmentation tasks using a small number of parameters.
The safety, mobility, environmental, energy, and economic benefits of transportation systems, which are the focus of recent Connected Vehicles (CVs) programs, are potentially dramatic.
However, realization of these benefits largely hinges on the timely integration of the digital technology into the existing transportation infrastructure.
CVs must be enabled to broadcast and receive data to and from other CVs (Vehicle-to-Vehicle, or V2V communication), to and from infrastructure (Vehicle-to-Infrastructure, or V2I, communication) and to and from other road users, such as bicyclists or pedestrians (Vehicle-to-Other road users communication).
Further, for V2I-focused applications, the infrastructure and the transportation agencies that manage it must be able to collect, process, distribute, and archive these data quickly, reliably, and securely.
This paper focuses V2I applications, and studies current digital roadway infrastructure initiatives.
It highlights the importance of including digital infrastructure investment alongside investment in more traditional transportation infrastructure to keep up with the auto industrys push towards connecting vehicles to other vehicles.
By studying the current CV testbeds and Smart City initiatives, this paper identifies digital infrastructure components being used by public agencies.
It also examines public agencies limited budgeting for digital infrastructure, and finds current expenditure is inadequate for realizing the potential benefits of V2I applications.
Finally, the paper presents a set of recommendations, based on a review of current practices and future needs, designed to guide agencies responsible for transportation infrastructure.
Techniques for dense semantic correspondence have provided limited ability to deal with the geometric variations that commonly exist between semantically similar images.
While variations due to scale and rotation have been examined, there lack practical solutions for more complex deformations such as affine transformations because of the tremendous size of the associated solution space.
To address this problem, we present a discrete-continuous transformation matching (DCTM) framework where dense affine transformation fields are inferred through a discrete label optimization in which the labels are iteratively updated via continuous regularization.
In this way, our approach draws solutions from the continuous space of affine transformations in a manner that can be computed efficiently through constant-time edge-aware filtering and a proposed affine-varying CNN-based descriptor.
Experimental results show that this model outperforms the state-of-the-art methods for dense semantic correspondence on various benchmarks.
Regular Path Queries (RPQs) are a type of graph query where answers are pairs of nodes connected by a sequence of edges matching a regular expression.
We study the techniques to process such queries on a distributed graph of data.
While many techniques assume the location of each data element (node or edge) is known, when the components of the distributed system are autonomous, the data will be arbitrarily distributed.
As the different query processing strategies are equivalently costly in the worst case, we isolate query-dependent cost factors and present a method to choose between strategies, using new query cost estimation techniques.
We evaluate our techniques using meaningful queries on biomedical data.
Sketching is an important activity for understanding, designing, and communicating different aspects of software systems such as their requirements or architecture.
Often, sketches start on paper or whiteboards, are revised, and may evolve into a digital version.
Users may then print a revised sketch, change it on paper, and digitize it again.
Existing tools focus on a paperless workflow, i.e., archiving analog documents, or rely on special hardware - they do not focus on integrating digital versions into the analog-focused workflow that many users follow.
In this paper, we present the conceptual design and a prototype of LivelySketches, a tool that supports the "round-trip" lifecycle of sketches from analog to digital and back.
The proposed workflow includes capturing both analog and digital sketches as well as relevant context information.
In addition, users can link sketches to other related sketches or documents.
They may access the linked artifacts and captured information using digital as well as augmented analog versions of the sketches.
We further present results from a formative user study with four students and outline possible directions for future work.
Cloud computing has emerged as a powerful and elastic platform for internet service hosting, yet it also draws concerns of the unpredictable performance of cloud-based services due to network congestion.
To offer predictable performance, the virtual cluster abstraction of cloud services has been proposed, which enables allocation and performance isolation regarding both computing resources and network bandwidth in a simplified virtual network model.
One issue arisen in virtual cluster allocation is the survivability of tenant services against physical failures.
Existing works have studied virtual cluster backup provisioning with fixed primary embeddings, but have not considered the impact of primary embeddings on backup resource consumption.
To address this issue, in this paper we study how to embed virtual clusters survivably in the cloud data center, by jointly optimizing primary and backup embeddings of the virtual clusters.
We formally define the survivable virtual cluster embedding problem.
We then propose a novel algorithm, which computes the most resource-efficient embedding given a tenant request.
Since the optimal algorithm has high time complexity, we further propose a faster heuristic algorithm, which is several orders faster than the optimal solution, yet able to achieve similar performance.
Besides theoretical analysis, we evaluate our algorithms via extensive simulations.
A bicolored rectangular family BRF is a collection of all axis-parallel rectangles contained in a given region Z of the plane formed by selecting a bottom-left corner from a set A and an upper-right corner from a set B.
We prove that the maximum independent set and the minimum hitting set of a BRF have the same cardinality and devise polynomial time algorithms to compute both.
As a direct consequence, we obtain the first polynomial time algorithm to compute minimum biclique covers, maximum cross-free matchings and jump numbers in a class of bipartite graphs that significantly extends convex bipartite graphs and interval bigraphs.
We also establish several connections between our work and other seemingly unrelated problems.
Furthermore, when the bicolored rectangular family is weighted, we show that the problem of finding the maximum weight of an independent set is NP-hard, and provide efficient algorithms to solve it on certain subclasses.
Unravelings are transformations from a conditional term rewriting system (CTRS, for short) over an original signature into an unconditional term rewriting systems (TRS, for short) over an extended signature.
They are not sound w.r.t. reduction for every CTRS, while they are complete w.r.t. reduction.
Here, soundness w.r.t. reduction means that every reduction sequence of the corresponding unraveled TRS, of which the initial and end terms are over the original signature, can be simulated by the reduction of the original CTRS.
In this paper, we show that an optimized variant of Ohlebusch's unraveling for a deterministic CTRS is sound w.r.t. reduction if the corresponding unraveled TRS is left-linear or both right-linear and non-erasing.
We also show that soundness of the variant implies that of Ohlebusch's unraveling.
Finally, we show that soundness of Ohlebusch's unraveling is the weakest in soundness of the other unravelings and a transformation, proposed by Serbanuta and Rosu, for (normal) deterministic CTRSs, i.e., soundness of them respectively implies that of Ohlebusch's unraveling.
Deep image translation methods have recently shown excellent results, outputting high-quality images covering multiple modes of the data distribution.
There has also been increased interest in disentangling the internal representations learned by deep methods to further improve their performance and achieve a finer control.
In this paper, we bridge these two objectives and introduce the concept of cross-domain disentanglement.
We aim to separate the internal representation into three parts.
The shared part contains information for both domains.
The exclusive parts, on the other hand, contain only factors of variation that are particular to each domain.
We achieve this through bidirectional image translation based on Generative Adversarial Networks and cross-domain autoencoders, a novel network component.
Our model offers multiple advantages.
We can output diverse samples covering multiple modes of the distributions of both domains, perform domain-specific image transfer and interpolation, and cross-domain retrieval without the need of labeled data, only paired images.
We compare our model to the state-of-the-art in multi-modal image translation and achieve better results for translation on challenging datasets as well as for cross-domain retrieval on realistic datasets.
This paper proposes a double-layered framework (or form of network) to integrate two mechanisms, termed consensus and conservation, achieving distributed solution of a linear equation.
The multi-agent framework considered in the paper is composed of clusters (which serve as a form of aggregating agent) and each cluster consists of a sub-network of agents.
By achieving consensus and conservation through agent-agent communications in the same cluster and cluster-cluster communications, distributed algorithms are devised for agents to cooperatively achieve a solution to the overall linear equation.
These algorithms outperform existing consensus-based algorithms, including but not limited to the following aspects: first, each agent does not have to know as much as a complete row or column of the overall equation; second, each agent only needs to control as few as two scalar states when the number of clusters and the number of agents are sufficiently large; third, the dimensions of agents' states in the proposed algorithms do not have to be the same (while in contrast, algorithms based on the idea of standard consensus inherently require all agents' states to be of the same dimension).
Both analytical proof and simulation results are provided to validate exponential convergence of the proposed distributed algorithms in solving linear equations.
This paper presents a double jaw hand for industrial assembly.
The hand comprises two orthogonal parallel grippers with different mechanisms.
The inner gripper is made of a crank-slider mechanism which is compact and able to firmly hold objects like shafts.
The outer gripper is made of a parallelogram that has large stroke to hold big objects like pulleys.
The two grippers are connected by a prismatic joint along the hand's approaching vector.
The hand is able to hold two objects and perform in-hand manipulation like pull-in (insertion) and push-out (ejection).
This paper presents the detailed design and implementation of the hand, and demonstrates the advantages by performing experiments on two sets of peg-in-multi-hole assembly tasks as parts of the World Robot Challenge (WRC) 2018 using a bimanual robot.
Dense local descriptors and machine learning have been used with success in several applications, like classification of textures, steganalysis, and forgery detection.
We develop a new image forgery detector building upon some descriptors recently proposed in the steganalysis field suitably merging some of such descriptors, and optimizing a SVM classifier on the available training set.
Despite the very good performance, very small forgeries are hardly ever detected because they contribute very little to the descriptors.
Therefore we also develop a simple, but extremely specific, copy-move detector based on region matching and fuse decisions so as to reduce the missing detection rate.
Overall results appear to be extremely encouraging.
Software Defined Networks (SDN) provide vital benefits to network administrators by offering global visibility and network-wide control over the switching infrastructure of the network.
It is rather much difficult to obtain the same benefits in the presence of middleboxes (MBs), due to (i) lack of a proper topology discovery mechanism in environments with a mix of forwarding devices and middleboxes.
(ii) lack of generic APIs to abstract and gain control on these rigid and heterogeneous third-party middleboxes (iii) lack of a generic network infrastructure framework to monitor and verify any specific device or path connectivity status in the network.
These limitations make automation of network operations such as, network-wide monitoring, policy enforcement and rule-placement much difficult to handle.
Hence, there is a greater urge even from middlebox vendors, to better handle the control and visibility aspects of the network in presence of middleboxes.
In this paper, we propose a Unified network infrastructure framework for gaining global network visibility, by discovering the network topology in the presence of middleboxes, along with a framework to support the end-to-end path connectivity verification, independent of SDN.
We have also addressed security aspects and provided necessary APIs to support our framework.
This paper presents a novel method to predict future human activities from partially observed RGB-D videos.
Human activity prediction is generally difficult due to its non-Markovian property and the rich context between human and environments.
We use a stochastic grammar model to capture the compositional structure of events, integrating human actions, objects, and their affordances.
We represent the event by a spatial-temporal And-Or graph (ST-AOG).
The ST-AOG is composed of a temporal stochastic grammar defined on sub-activities, and spatial graphs representing sub-activities that consist of human actions, objects, and their affordances.
Future sub-activities are predicted using the temporal grammar and Earley parsing algorithm.
The corresponding action, object, and affordance labels are then inferred accordingly.
Extensive experiments are conducted to show the effectiveness of our model on both semantic event parsing and future activity prediction.
The LLVM compiler framework supports a selection of loop transformations such as vectorization, distribution and unrolling.
Each transformation is carried-out by specialized passes that have been developed independently.
In this paper we propose an integrated approach to loop optimizations: A single dedicated pass that mutates a Loop Structure DAG.
Each transformation can make use of a common infrastructure such as dependency analysis, transformation preconditions, etc.
Multilingual topic models enable crosslingual tasks by extracting consistent topics from multilingual corpora.
Most models require parallel or comparable training corpora, which limits their ability to generalize.
In this paper, we first demystify the knowledge transfer mechanism behind multilingual topic models by defining an alternative but equivalent formulation.
Based on this analysis, we then relax the assumption of training data required by most existing models, creating a model that only requires a dictionary for training.
Experiments show that our new method effectively learns coherent multilingual topics from partially and fully incomparable corpora with limited amounts of dictionary resources.
Web intelligence can be considered as a subset of Artificial Intelligence.
It uses existing data in web to produce new data, knowledge and wisdom to support decision making and new predictions for web users.
Artificial Intelligence is ever changing and evolving field of computer science and it is extensively used in wide array of web based business applications.
Although it is used substantially in web based systems in developed countries, it is not examined whether it is being substantially used in Sri Lanka.
Every Sri Lankan citizen depends on Public Service more or less throughout his/ her life time and at least more than 3 times: at birth, marriage and death.
So providing most of these services to its citizen, Sri Lankan Government uses more or less of its country web portal.
This paper presents a model to evaluate web intelligence capability based on weight to key functionalities with respect to web intelligence.
The government websites were checked by the proposed criteria to show the potential of using web intelligent technology to provide website based services.
The result indicates that the use of web intelligence techniques openly and publicly to provide web based services through government web portal to its citizens is not satisfactory.
It also indicates that lack of using the technologies pertaining to web intelligence in the public service web hinders the most of the advantages that citizen and government can gain from such technological involvement.
In many advanced video based applications background modeling is a pre-processing step to eliminate redundant data, for instance in tracking or video surveillance applications.
Over the past years background subtraction is usually based on low level or hand-crafted features such as raw color components, gradients, or local binary patterns.
The background subtraction algorithms performance suffer in the presence of various challenges such as dynamic backgrounds, photometric variations, camera jitters, and shadows.
To handle these challenges for the purpose of accurate background modeling we propose a unified framework based on the algorithm of image inpainting.
It is an unsupervised visual feature learning hybrid Generative Adversarial algorithm based on context prediction.
We have also presented the solution of random region inpainting by the fusion of center region inpaiting and random region inpainting with the help of poisson blending technique.
Furthermore we also evaluated foreground object detection with the fusion of our proposed method and morphological operations.
The comparison of our proposed method with 12 state-of-the-art methods shows its stability in the application of background estimation and foreground detection.
Automatic Speech Recognition (ASR) by machine is an attractive research topic in signal processing domain and has attracted many researchers to contribute in this area.
In recent year, there have been many advances in automatic speech reading system with the inclusion of audio and visual speech features to recognize words under noisy conditions.
The objective of audio-visual speech recognition system is to improve recognition accuracy.
In this paper we computed visual features using Zernike moments and audio feature using Mel Frequency Cepstral Coefficients (MFCC) on vVISWa (Visual Vocabulary of Independent Standard Words) dataset which contains collection of isolated set of city names of 10 speakers.
The visual features were normalized and dimension of features set was reduced by Principal Component Analysis (PCA) in order to recognize the isolated word utterance on PCA space.The performance of recognition of isolated words based on visual only and audio only features results in 63.88 and 100 respectively.
Recently, topic modeling has been widely used to discover the abstract topics in text corpora.
Most of the existing topic models are based on the assumption of three-layer hierarchical Bayesian structure, i.e. each document is modeled as a probability distribution over topics, and each topic is a probability distribution over words.
However, the assumption is not optimal.
Intuitively, it's more reasonable to assume that each topic is a probability distribution over concepts, and then each concept is a probability distribution over words, i.e. adding a latent concept layer between topic layer and word layer in traditional three-layer assumption.
In this paper, we verify the proposed assumption by incorporating the new assumption in two representative topic models, and obtain two novel topic models.
Extensive experiments were conducted among the proposed models and corresponding baselines, and the results show that the proposed models significantly outperform the baselines in terms of case study and perplexity, which means the new assumption is more reasonable than traditional one.
We describe a simple but effective method for cross-lingual syntactic transfer of dependency parsers, in the scenario where a large amount of translation data is not available.
The method makes use of three steps: 1) a method for deriving cross-lingual word clusters, which can then be used in a multilingual parser; 2) a method for transferring lexical information from a target language to source language treebanks; 3) a method for integrating these steps with the density-driven annotation projection method of Rasooli and Collins (2015).
Experiments show improvements over the state-of-the-art in several languages used in previous work, in a setting where the only source of translation data is the Bible, a considerably smaller corpus than the Europarl corpus used in previous work.
Results using the Europarl corpus as a source of translation data show additional improvements over the results of Rasooli and Collins (2015).
We conclude with results on 38 datasets from the Universal Dependencies corpora.
This note explores the relation between the boxicity of undirected graphs and the Ferrers dimension of digraphs.
The unmanned air-vehicle (UAV) or mini-drones equipped with sensors are becoming increasingly popular for various commercial, industrial, and public-safety applications.
However, drones with uncontrolled deployment poses challenges for highly security-sensitive areas such as President house, nuclear plants, and commercial areas because they can be used unlawfully.
In this article, to cope with security-sensitive challenges, we propose point-to-point and flying ad-hoc network (FANET) architectures to assist the efficient deployment of monitoring drones (MDr).
To capture amateur drone (ADr), MDr must have the capability to efficiently and timely detect, track, jam, and hunt the ADr.
We discuss the capabilities of the existing detection, tracking, localization, and routing schemes and also present the limitations in these schemes as further research challenges.
Moreover, the future challenges related to co-channel interference, channel model design, and cooperative schemes are discussed.
Our findings indicate that MDr deployment is necessary for caring of ADr, and intensive research and development is required to fill the gaps in the existing technologies.
Compressive sensing (CS) has recently emerged as a powerful framework for acquiring sparse signals.
The bulk of the CS literature has focused on the case where the acquired signal has a sparse or compressible representation in an orthonormal basis.
In practice, however, there are many signals that cannot be sparsely represented or approximated using an orthonormal basis, but that do have sparse representations in a redundant dictionary.
Standard results in CS can sometimes be extended to handle this case provided that the dictionary is sufficiently incoherent or well-conditioned, but these approaches fail to address the case of a truly redundant or overcomplete dictionary.
In this paper we describe a variant of the iterative recovery algorithm CoSaMP for this more challenging setting.
We utilize the D-RIP, a condition on the sensing matrix analogous to the well-known restricted isometry property.
In contrast to prior work, the method and analysis are "signal-focused"; that is, they are oriented around recovering the signal rather than its dictionary coefficients.
Under the assumption that we have a near-optimal scheme for projecting vectors in signal space onto the model family of candidate sparse signals, we provide provable recovery guarantees.
Developing a practical algorithm that can provably compute the required near-optimal projections remains a significant open problem, but we include simulation results using various heuristics that empirically exhibit superior performance to traditional recovery algorithms.
We examine some variants of computation with closed timelike curves (CTCs), where various restrictions are imposed on the memory of the computer, and the information carrying capacity and range of the CTC.
We give full characterizations of the classes of languages recognized by polynomial time probabilistic and quantum computers that can send a single classical bit to their own past.
Such narrow CTCs are demonstrated to add the power of limited nondeterminism to deterministic computers, and lead to exponential speedup in constant-space probabilistic and quantum computation.
We show that, given a time machine with constant negative delay, one can implement CTC-based computations without the need to know about the runtime beforehand.
Learning to optimize - the idea that we can learn from data algorithms that optimize a numerical criterion - has recently been at the heart of a growing number of research efforts.
One of the most challenging issues within this approach is to learn a policy that is able to optimize over classes of functions that are fairly different from the ones that it was trained on.
We propose a novel way of framing learning to optimize as a problem of learning a good navigation policy on a partially observable loss surface.
To this end, we develop Rover Descent, a solution that allows us to learn a fairly broad optimization policy from training on a small set of prototypical two-dimensional surfaces that encompasses the classically hard cases such as valleys, plateaus, cliffs and saddles and by using strictly zero-order information.
We show that, without having access to gradient or curvature information, we achieve state-of-the-art convergence speed on optimization problems not presented at training time such as the Rosenbrock function and other hard cases in two dimensions.
We extend our framework to optimize over high dimensional landscapes, while still handling only two-dimensional local landscape information and show good preliminary results.
We have employed a recent implementation of genetic algorithms to study a range of standard benchmark functions for global optimization.
It turns out that some of them are not very useful as challenging test functions, since they neither allow for a discrimination between different variants of genetic operators nor exhibit a dimensionality scaling resembling that of real-world problems, for example that of global structure optimization of atomic and molecular clusters.
The latter properties seem to be simulated better by two other types of benchmark functions.
One type is designed to be deceptive, exemplified here by Lunacek's function.
The other type offers additional advantages of markedly increased complexity and of broad tunability in search space characteristics.
For the latter type, we use an implementation based on randomly distributed Gaussians.
We advocate the use of the latter types of test functions for algorithm development and benchmarking.
Motivated by recently derived fundamental limits on total (transmit + decoding) power for coded communication with VLSI decoders, this paper investigates the scaling behavior of the minimum total power needed to communicate over AWGN channels as the target bit-error-probability tends to zero.
We focus on regular-LDPC codes and iterative message-passing decoders.
We analyze scaling behavior under two VLSI complexity models of decoding.
One model abstracts power consumed in processing elements ("node model"), and another abstracts power consumed in wires which connect the processing elements ("wire model").
We prove that a coding strategy using regular-LDPC codes with Gallager-B decoding achieves order-optimal scaling of total power under the node model.
However, we also prove that regular-LDPC codes and iterative message-passing decoders cannot meet existing fundamental limits on total power under the wire model.
Further, if the transmit energy-per-bit is bounded, total power grows at a rate that is worse than uncoded transmission.
Complementing our theoretical results, we develop detailed physical models of decoding implementations using post-layout circuit simulations.
Our theoretical and numerical results show that approaching fundamental limits on total power requires increasing the complexity of both the code design and the corresponding decoding algorithm as communication distance is increased or error-probability is lowered.
In the context of 3D mapping, larger and larger point clouds are acquired with LIDAR sensors.
The Iterative Closest Point (ICP) algorithm is used to align these point clouds.
However, its complexity is directly dependent of the number of points to process.
Several strategies exist to address this problem by reducing the number of points.
However, they tend to underperform with non-uniform density, large sensor noise, spurious measurements, and large-scale point clouds, which is the case in mobile robotics.
This paper presents a novel sampling algorithm for registration in ICP algorithm based on spectral decomposition analysis and called Spectral Decomposition Filter (SpDF).
It preserves geometric information along the topology of point clouds and is able to scale to large environments with non-uniform density.
The effectiveness of our method is validated and illustrated by quantitative and qualitative experiments on various environments.
We describe a method for attaching persistent metadata to an image.
The method can be interpreted as a template-based blind watermarking scheme, robust to common editing operations, namely: cropping, rotation, scaling, stretching, shearing, compression, printing, scanning, noise, and color removal.
Robustness is achieved through the reciprocity of the embedding and detection invariants.
The embedded patterns are real onedimensional Mellin monomial patterns distributed over two-dimensions.
The embedded patterns are scale invariant and can be directly embedded in an image by simple pixel addition.
Detection achieves rotation and general affine invariance by signal projection using implicit Radon transformation.
Embedded signals contract to one-dimension in the two-dimensional Fourier polar domain.
The real signals are detected by correlation with complex Mellin monomial templates.
Using a unique template of 4 chirp patterns we detect the affine signature with exquisite sensitivity and moderate security.
The practical implementation achieves efficiencies through fast Fourier transform (FFT) correspondences such as the projection-slice theorem, the FFT correlation relation, and fast resampling via the chirp-z transform.
The overall method utilizes orthodox spread spectrum patterns for the payload and performs well in terms of the classic robustness-capacity-visibility performance triangle.
Tags are entirely imperceptible with a mean SSIM greater than 0.988 in all cases tested.
Watermarked images survive almost all Stirmark attacks.
The method is ideal for attaching metadata robustly to both digital and analogue images.
The process of designing neural architectures requires expert knowledge and extensive trial and error.
While automated architecture search may simplify these requirements, the recurrent neural network (RNN) architectures generated by existing methods are limited in both flexibility and components.
We propose a domain-specific language (DSL) for use in automated architecture search which can produce novel RNNs of arbitrary depth and width.
The DSL is flexible enough to define standard architectures such as the Gated Recurrent Unit and Long Short Term Memory and allows the introduction of non-standard RNN components such as trigonometric curves and layer normalization.
Using two different candidate generation techniques, random search with a ranking function and reinforcement learning, we explore the novel architectures produced by the RNN DSL for language modeling and machine translation domains.
The resulting architectures do not follow human intuition yet perform well on their targeted tasks, suggesting the space of usable RNN architectures is far larger than previously assumed.
Optimization on manifolds is a rapidly developing branch of nonlinear optimization.
Its focus is on problems where the smooth geometry of the search space can be leveraged to design efficient numerical algorithms.
In particular, optimization on manifolds is well-suited to deal with rank and orthogonality constraints.
Such structured constraints appear pervasively in machine learning applications, including low-rank matrix completion, sensor network localization, camera network registration, independent component analysis, metric learning, dimensionality reduction and so on.
The Manopt toolbox, available at www.manopt.org, is a user-friendly, documented piece of software dedicated to simplify experimenting with state of the art Riemannian optimization algorithms.
We aim particularly at reaching practitioners outside our field.
Special scattered subwords, in which the gaps are of length from a given set, are defined.
The scattered subword complexity, which is the number of such scattered subwords, is computed for rainbow words.
This paper presents a wp-style calculus for obtaining expectations on the outcomes of (mutually) recursive probabilistic programs.
We provide several proof rules to derive one-- and two--sided bounds for such expectations, and show the soundness of our wp-calculus with respect to a probabilistic pushdown automaton semantics.
We also give a wp-style calculus for obtaining bounds on the expected runtime of recursive programs that can be used to determine the (possibly infinite) time until termination of such programs.
The problem of state reconstruction and estimation is considered for a class of switched dynamical systems whose subsystems are modeled using linear differential-algebraic equations (DAEs).
Since this system class imposes time-varying dynamic and static (in the form of algebraic constraints) relations on the evolution of state trajectories, an appropriate notion of observability is presented which accommodates these phenomena.
Based on this notion, we first derive a formula for the reconstruction of the state of the system where we explicitly obtain an injective mapping from the output to the state.
In practice, such a mapping may be difficult to realize numerically and hence a class of estimators is proposed which ensures that the state estimate converges asymptotically to the real state of the system.
Real-world multi-agent planning problems cannot be solved using decision-theoretic planning methods due to the exponential complexity.
We approximate firefighting in rescue simulation as a spatially distributed task and model with multi-agent Markov decision process.
We use recent approximation methods for spatial task problems to reduce the model complexity.
Our approximations are single-agent, static task, shortest path pruning, dynamic planning horizon, and task clustering.
We create scenarios from RoboCup Rescue Simulation maps and evaluate our methods on these graph worlds.
The results show that our approach is faster and better than comparable methods and has negligible performance loss compared to the optimal policy.
We also show that our method has a similar performance as DCOP methods on example RCRS scenarios.
In this paper, we will demonstrate how Manhattan structure can be exploited to transform the Simultaneous Localization and Mapping (SLAM) problem, which is typically solved by a nonlinear optimization over feature positions, into a model selection problem solved by a convex optimization over higher order layout structures, namely walls, floors, and ceilings.
Furthermore, we show how our novel formulation leads to an optimization procedure that automatically performs data association and loop closure and which ultimately produces the simplest model of the environment that is consistent with the available measurements.
We verify our method on real world data sets collected with various sensing modalities.
The paper presents original approach to concurrent optimization of the transmitting and receiving parts of adaptive communication systems (CS) with feedback channels.
The results of research show a possibility and the way of designing the systems transmitting the signals with a bit rate equal to the capacity of the forward channel under given bit-error rate (BER).
The results of work can be used for design of different classes of high-efficient low energy/size/cost CS, as well as allow further development and extension.
Although the CSP (constraint satisfaction problem) is NP-complete, even in the case when all constraints are binary, certain classes of instances are tractable.
We study classes of instances defined by excluding subproblems.
This approach has recently led to the discovery of novel tractable classes.
The complete characterisation of all tractable classes defined by forbidding patterns (where a pattern is simply a compact representation of a set of subproblems) is a challenging problem.
We demonstrate a dichotomy in the case of forbidden patterns consisting of either one or two constraints.
This has allowed us to discover new tractable classes including, for example, a novel generalisation of 2SAT.
We investigate weak recognizability of deterministic languages of infinite trees.
We prove that for deterministic languages the Borel hierarchy and the weak index hierarchy coincide.
Furthermore, we propose a procedure computing for a deterministic automaton an equivalent minimal index weak automaton with a quadratic number of states.
The algorithm works within the time of solving the emptiness problem.
In this work, we propose a novel framework for privacy-preserving client-distributed machine learning.
It is motivated by the desire to achieve differential privacy guarantees in the local model of privacy in a way that satisfies all systems constraints using asynchronous client-server communication and provides attractive model learning properties.
We call it "Draw and Discard" because it relies on random sampling of models for load distribution (scalability), which also provides additional server-side privacy protections and improved model quality through averaging.
We present the mechanics of client and server components of "Draw and Discard" and demonstrate how the framework can be applied to learning Generalized Linear models.
We then analyze the privacy guarantees provided by our approach against several types of adversaries and showcase experimental results that provide evidence for the framework's viability in practical deployments.
We present a language independent, unsupervised approach for transforming word embeddings from source language to target language using a transformation matrix.
Our model handles the problem of data scarcity which is faced by many languages in the world and yields improved word embeddings for words in the target language by relying on transformed embeddings of words of the source language.
We initially evaluate our approach via word similarity tasks on a similar language pair - Hindi as source and Urdu as the target language, while we also evaluate our method on French and German as target languages and English as source language.
Our approach improves the current state of the art results - by 13% for French and 19% for German.
For Urdu, we saw an increment of 16% over our initial baseline score.
We further explore the prospects of our approach by applying it on multiple models of the same language and transferring words between the two models, thus solving the problem of missing words in a model.
We evaluate this on word similarity and word analogy tasks.
Image Forensics has already achieved great results for the source camera identification task on images.
Standard approaches for data coming from Social Network Platforms cannot be applied due to different processes involved (e.g., scaling, compression, etc.).
Over 1 billion images are shared each day on the Internet and obtaining information about their history from the moment they were acquired could be exploited for investigation purposes.
In this paper, a classification engine for the reconstruction of the history of an image, is presented.
Specifically, exploiting K-NN and decision trees classifiers and a-priori knowledge acquired through image analysis, we propose an automatic approach that can understand which Social Network Platform has processed an image and the software application used to perform the image upload.
The engine makes use of proper alterations introduced by each platform as features.
Results, in terms of global accuracy on a dataset of 2720 images, confirm the effectiveness of the proposed strategy.
Mined Semantic Analysis (MSA) is a novel concept space model which employs unsupervised learning to generate semantic representations of text.
MSA represents textual structures (terms, phrases, documents) as a Bag of Concepts (BoC) where concepts are derived from concept rich encyclopedic corpora.
Traditional concept space models exploit only target corpus content to construct the concept space.
MSA, alternatively, uncovers implicit relations between concepts by mining for their associations (e.g., mining Wikipedia's "See also" link graph).
We evaluate MSA's performance on benchmark datasets for measuring semantic relatedness of words and sentences.
Empirical results show competitive performance of MSA compared to prior state-of-the-art methods.
Additionally, we introduce the first analytical study to examine statistical significance of results reported by different semantic relatedness methods.
Our study shows that, the nuances of results across top performing methods could be statistically insignificant.
The study positions MSA as one of state-of-the-art methods for measuring semantic relatedness, besides the inherent interpretability and simplicity of the generated semantic representation.
This paper presents a deep-learning based framework for addressing the problem of accurate cloud detection in remote sensing images.
This framework benefits from a Fully Convolutional Neural Network (FCN), which is capable of pixel-level labeling of cloud regions in a Landsat 8 image.
Also, a gradient-based identification approach is proposed to identify and exclude regions of snow/ice in the ground truths of the training set.
We show that using the hybrid of the two methods (threshold-based and deep-learning) improves the performance of the cloud identification process without the need to manually correct automatically generated ground truths.
In average the Jaccard index and recall measure are improved by 4.36% and 3.62%, respectively.
Standardized corpora of undeciphered scripts, a necessary starting point for computational epigraphy, requires laborious human effort for their preparation from raw archaeological records.
Automating this process through machine learning algorithms can be of significant aid to epigraphical research.
Here, we take the first steps in this direction and present a deep learning pipeline that takes as input images of the undeciphered Indus script, as found in archaeological artifacts, and returns as output a string of graphemes, suitable for inclusion in a standard corpus.
The image is first decomposed into regions using Selective Search and these regions are classified as containing textual and/or graphical information using a convolutional neural network.
Regions classified as potentially containing text are hierarchically merged and trimmed to remove non-textual information.
The remaining textual part of the image is segmented using standard image processing techniques to isolate individual graphemes.
This set is finally passed to a second convolutional neural network to classify the graphemes, based on a standard corpus.
The classifier can identify the presence or absence of the most frequent Indus grapheme, the "jar" sign, with an accuracy of 92%.
Our results demonstrate the great potential of deep learning approaches in computational epigraphy and, more generally, in the digital humanities.
Rollating walkers are popular mobility aids used by older adults to improve balance control.
There is a need to automatically recognize the activities performed by walker users to better understand activity patterns, mobility issues and the context in which falls are more likely to happen.
We design and compare several techniques to recognize walker related activities.
A comprehensive evaluation with control subjects and walker users from a retirement community is presented.
Event-based state estimation can achieve estimation quality comparable to traditional time-triggered methods, but with a significantly lower number of samples.
In networked estimation problems, this reduction in sampling instants does, however, not necessarily translate into better usage of the shared communication resource.
Because typical event-based approaches decide instantaneously whether communication is needed or not, free slots cannot be reallocated immediately, and hence remain unused.
In this paper, novel predictive and self triggering protocols are proposed, which give the communication system time to adapt and reallocate freed resources.
From a unified Bayesian decision framework, two schemes are developed: self-triggers that predict, at the current triggering instant, the next one; and predictive triggers that indicate, at every time step, whether communication will be needed at a given prediction horizon.
The effectiveness of the proposed triggers in trading off estimation quality for communication reduction is compared in numerical simulations.
We propose a novel video object segmentation algorithm based on pixel-level matching using Convolutional Neural Networks (CNN).
Our network aims to distinguish the target area from the background on the basis of the pixel-level similarity between two object units.
The proposed network represents a target object using features from different depth layers in order to take advantage of both the spatial details and the category-level semantic information.
Furthermore, we propose a feature compression technique that drastically reduces the memory requirements while maintaining the capability of feature representation.
Two-stage training (pre-training and fine-tuning) allows our network to handle any target object regardless of its category (even if the object's type does not belong to the pre-training data) or of variations in its appearance through a video sequence.
Experiments on large datasets demonstrate the effectiveness of our model - against related methods - in terms of accuracy, speed, and stability.
Finally, we introduce the transferability of our network to different domains, such as the infrared data domain.
Monolingual data have been demonstrated to be helpful in improving translation quality of both statistical machine translation (SMT) systems and neural machine translation (NMT) systems, especially in resource-poor or domain adaptation tasks where parallel data are not rich enough.
In this paper, we propose a novel approach to better leveraging monolingual data for neural machine translation by jointly learning source-to-target and target-to-source NMT models for a language pair with a joint EM optimization method.
The training process starts with two initial NMT models pre-trained on parallel data for each direction, and these two models are iteratively updated by incrementally decreasing translation losses on training data.
In each iteration step, both NMT models are first used to translate monolingual data from one language to the other, forming pseudo-training data of the other NMT model.
Then two new NMT models are learnt from parallel data together with the pseudo training data.
Both NMT models are expected to be improved and better pseudo-training data can be generated in next step.
Experiment results on Chinese-English and English-German translation tasks show that our approach can simultaneously improve translation quality of source-to-target and target-to-source models, significantly outperforming strong baseline systems which are enhanced with monolingual data for model training including back-translation.
While deep learning is an exciting new technique, the benefits of this method need to be assessed with respect to its computational cost.
This is particularly important for deep learning since these learners need hours (to weeks) to train the model.
Such long training time limits the ability of (a) a researcher to test the stability of their conclusion via repeated runs with different random seeds; and (b) other researchers to repeat, improve, or even refute that original work.
For example, recently, deep learning was used to find which questions in the Stack Overflow programmer discussion forum can be linked together.
That deep learning system took 14 hours to execute.
We show here that applying a very simple optimizer called DE to fine tune SVM, it can achieve similar (and sometimes better) results.
The DE approach terminated in 10 minutes; i.e.84 times faster hours than deep learning method.
We offer these results as a cautionary tale to the software analytics community and suggest that not every new innovation should be applied without critical analysis.
If researchers deploy some new and expensive process, that work should be baselined against some simpler and faster alternatives.
YouTube draws large number of users who contribute actively by uploading videos or commenting on existing videos.
However, being a crowd sourced and large content pushed onto it, there is limited control over the content.
This makes malicious users push content (videos and comments) which is inappropriate (unsafe), particularly when such content is placed around cartoon videos which are typically watched by kids.
In this paper, we focus on presence of unsafe content for children and users who promote it.
For detection of child unsafe content and its promoters, we perform two approaches, one based on supervised classification which uses an extensive set of video-level, user-level and comment-level features and another based Convolutional Neural Network using video frames.
Detection accuracy of 85.7% is achieved which can be leveraged to build a system to provide a safe YouTube experience for kids.
Through detailed characterization studies, we are able to successfully conclude that unsafe content promoters are less popular and engage less as compared with other users.
Finally, using a network of unsafe content promoters and other users based on their engagements (likes, subscription and playlist addition) and other factors, we find that unsafe content is present very close to safe content and unsafe content promoters form very close knit communities with other users, thereby further increasing the likelihood of a child getting getting exposed to unsafe content.
Facial expression recognition has been an active area in computer vision with application areas including animation, social robots, personalized banking, etc.
In this study, we explore the problem of image classification for detecting facial expressions based on features extracted from pre-trained convolutional neural networks trained on ImageNet database.
Features are extracted and transferred to a Linear Support Vector Machine for classification.
All experiments are performed on two publicly available datasets such as JAFFE and CK+ database.
The results show that representations learned from pre-trained networks for a task such as object recognition can be transferred, and used for facial expression recognition.
Furthermore, for a small dataset, using features from earlier layers of the VGG19 network provides better classification accuracy.
Accuracies of 92.26% and 92.86% were achieved for the CK+ and JAFFE datasets respectively.
This is the preprint version of our paper on JOMS.
In this paper, two mHealth applications are introduced, which can be employed as the terminals of bigdata based health service to collect information for electronic medical records (EMRs).
The first one is a hybrid system for improving the user experience in the hyperbaric oxygen chamber by 3D stereoscopic virtual reality glasses and immersive perception.
Several HMDs have been tested and compared.
The second application is a voice interactive serious game as a likely solution for providing assistive rehabilitation tool for therapists.
The recorder of the voice of patients could be analysed to evaluate the long-time rehabilitation results and further to predict the rehabilitation process.
Person re-identification (Re-ID) aims at recognizing the same person from images taken across different cameras.
To address this task, one typically requires a large amount labeled data for training an effective Re-ID model, which might not be practical for real-world applications.
To alleviate this limitation, we choose to exploit a sufficient amount of pre-existing labeled data from a different (auxiliary) dataset.
By jointly considering such an auxiliary dataset and the dataset of interest (but without label information), our proposed adaptation and re-identification network (ARN) performs unsupervised domain adaptation, which leverages information across datasets and derives domain-invariant features for Re-ID purposes.
In our experiments, we verify that our network performs favorably against state-of-the-art unsupervised Re-ID approaches, and even outperforms a number of baseline Re-ID methods which require fully supervised data for training.
End-to-end trained Recurrent Neural Networks (RNNs) have been successfully applied to numerous problems that require processing sequences, such as image captioning, machine translation, and text recognition.
However, RNNs often struggle to generalise to sequences longer than the ones encountered during training.
In this work, we propose to optimise neural networks explicitly for induction.
The idea is to first decompose the problem in a sequence of inductive steps and then to explicitly train the RNN to reproduce such steps.
Generalisation is achieved as the RNN is not allowed to learn an arbitrary internal state; instead, it is tasked with mimicking the evolution of a valid state.
In particular, the state is restricted to a spatial memory map that tracks parts of the input image which have been accounted for in previous steps.
The RNN is trained for single inductive steps, where it produces updates to the memory in addition to the desired output.
We evaluate our method on two different visual recognition problems involving visual sequences: (1) text spotting, i.e. joint localisation and reading of text in images containing multiple lines (or a block) of text, and (2) sequential counting of objects in aerial images.
We show that inductive training of recurrent models enhances their generalisation ability on challenging image datasets.
In order to generate prime implicants for a given cube (minterm), most of minimization methods increase the dimension of this cube by removing one literal from it at a time.
But there are two problems of exponential complexity.
One of them is the selection of the order in which the literals are to be removed from the implicant at hand.
The latter is the mechanism that checks whether a tentative literal removal is acceptable.
The reduced Offset concept has been developed to avoid of these problems.
This concept is based on positional-cube representation where each cube is represented by two n-bit strings.
We show that each reduced Off-cube may be represented by a single n-bit string and propose a set of bitwise operations to be performed on such strings.
The experiments on single-output benchmarks show that this approach can significantly speed up the minimization process, improve the quality of its results and reduce the amount of memory required for this aim.
The goal of compressive sensing is efficient reconstruction of data from few measurements, sometimes leading to a categorical decision.
If only classification is required, reconstruction can be circumvented and the measurements needed are orders-of-magnitude sparser still.
We define enhanced sparsity as the reduction in number of measurements required for classification over reconstruction.
In this work, we exploit enhanced sparsity and learn spatial sensor locations that optimally inform a categorical decision.
The algorithm solves an l1-minimization to find the fewest entries of the full measurement vector that exactly reconstruct the discriminant vector in feature space.
Once the sensor locations have been identified from the training data, subsequent test samples are classified with remarkable efficiency, achieving performance comparable to that obtained by discrimination using the full image.
Sensor locations may be learned from full images, or from a random subsample of pixels.
For classification between more than two categories, we introduce a coupling parameter whose value tunes the number of sensors selected, trading accuracy for economy.
We demonstrate the algorithm on example datasets from image recognition using PCA for feature extraction and LDA for discrimination; however, the method can be broadly applied to non-image data and adapted to work with other methods for feature extraction and discrimination.
In this paper, the author explore the challenges with respect to the security aspect in MANETs and propose a new approach which makes use of a bio-inspired methodology.
This paper elaborates various attacks which can be perpetrated on MANETs and current solutions to the aforementioned problems, and then it describes a Bio-Inspired Method which could be a possible solution to security issues in MANETs.
Internet of Things (IoT) systems have aroused enthusiasm and concerns.
Enthusiasm comes from their utilities in people daily life, and concerns may be associated with privacy issues.
By using two IoT systems as case-studies, we examine users' privacy beliefs, concerns and attitudes.
We focus on four major dimensions: the collection of personal data, the inference of new information, the exchange of information to third parties, and the risk-utility trade-off posed by the features of the system.
Altogether, 113 Brazilian individuals answered a survey about such dimensions.
Although their perceptions seem to be dependent on the context, there are recurrent patterns.
Our results suggest that IoT users can be classified into unconcerned, fundamentalists and pragmatists.
Most of them exhibit a pragmatist profile and believe in privacy as a right guaranteed by law.
One of the most privacy concerning aspect is the exchange of personal information to third parties.
Individuals' perceived risk is negatively correlated with their perceived utility in the features of the system.
We discuss practical implications of these results and suggest heuristics to cope with privacy concerns when designing IoT systems.
Economical and environmental concerns necessitate network engineers to focus on energy-efficient access network design.
The optical network units (ONUs), being predominantly responsible for the energy consumption of Ethernet Passive Optical Network (EPON), motivates us towards designing a novel protocol for saving energy at ONU.
The proposed protocol exploits different low power modes (LPM) and opts for the suitable one using traffic prediction.
This scheme provides a significant improvement of energy-efficiency especially at high load (~ 40%) over existing protocols.
A better understanding of the performance and a deeper insight into several design aspects can only be addressed through a detailed mathematical analysis.
The proposed protocol involves traffic prediction which infringes Markovian property.
However, some pragmatic assumptions along with a proper selection of observation instances and state descriptions allow us to form a Discrete Time Markov Chain (DTMC) of the proposed algorithm.
Thus, the primary objective of this paper is to propose a novel scheme for achieving energy-efficiency at ONU and mathematically analyze the performance of it with the help of a DTMC.
The analysis reveals that the energy-efficiency is more sensitive to the power consumption of doze mode as compared to other LPM while the effect of sleep-to-wake-up time is minor.
Unlike classification, position labels cannot be assigned manually by humans.
For this reason, generating supervision for precise object localization is a hard task.
This paper details a method to create large datasets for 3D object localization, with real world images, using an industrial robot to generate position labels.
By knowledge of the geometry of the robot, we are able to automatically synchronize the images of the two cameras and the object 3D position.
We applied it to generate a screw-driver localization dataset with stereo images, using a KUKA LBR iiwa robot.
This dataset could then be used to train a CNN regressor to learn end-to-end stereo object localization from a set of two standard uncalibrated cameras.
We investigate the fundamental capacity limits of space-time journeys of information in mobile and Delay Tolerant Networks (DTNs), where information is either transmitted or carried by mobile nodes, using store-carry-forward routing.
We define the capacity of a journey (i.e., a path in space and time, from a source to a destination) as the maximum amount of data that can be transferred from the source to the destination in the given journey.
Combining a stochastic model (conveying all possible journeys) and an analysis of the durations of the nodes' encounters, we study the properties of journeys that maximize the space-time information propagation capacity, in bit-meters per second.
More specifically, we provide theoretical lower and upper bounds on the information propagation speed, as a function of the journey capacity.
In the particular case of random way-point-like models (i.e., when nodes move for a distance of the order of the network domain size before changing direction), we show that, for relatively large journey capacities, the information propagation speed is of the same order as the mobile node speed.
This implies that, surprisingly, in sparse but large-scale mobile DTNs, the space-time information propagation capacity in bit-meters per second remains proportional to the mobile node speed and to the size of the transported data bundles, when the bundles are relatively large.
We also verify that all our analytical bounds are accurate in several simulation scenarios.
We present the University at Buffalo's Airborne Networking and Communications Testbed (UB-ANC Drone).
UB-ANC Drone is an open software/hardware platform that aims to facilitate rapid testing and repeatable comparative evaluation of airborne networking and communications protocols at different layers of the protocol stack.
It combines quadcopters capable of autonomous flight with sophisticated command and control capabilities and embedded software-defined radios (SDRs), which enable flexible deployment of novel communications and networking protocols.
This is in contrast to existing airborne network testbeds, which rely on standard inflexible wireless technologies, e.g., Wi-Fi or Zigbee.
UB-ANC Drone is designed with emphasis on modularity and extensibility, and is built around popular open-source projects and standards developed by the research and hobby communities.
This makes UB-ANC Drone highly customizable, while also simplifying its adoption.
In this paper, we describe UB-ANC Drone's hardware and software architecture.
This paper presents the IMS contribution to the PolEval 2018 Shared Task.
We submitted systems for both of the Subtasks of Task 1.
In Subtask (A), which was about dependency parsing, we used our ensemble system from the CoNLL 2017 UD Shared Task.
The system first preprocesses the sentences with a CRF POS/morphological tagger and predicts supertags with a neural tagger.
Then, it employs multiple instances of three different parsers and merges their outputs by applying blending.
The system achieved the second place out of four participating teams.
In this paper we show which components of the system were the most responsible for its final performance.
The goal of Subtask (B) was to predict enhanced graphs.
Our approach consisted of two steps: parsing the sentences with our ensemble system from Subtask (A), and applying 12 simple rules to obtain the final dependency graphs.
The rules introduce additional enhanced arcs only for tokens with "conj" heads (conjuncts).
They do not predict semantic relations at all.
The system ranked first out of three participating teams.
In this paper we show examples of rules we designed and analyze the relation between the quality of automatically parsed trees and the accuracy of the enhanced graphs.
The paper presents a technique to improve human detection in still images using deep learning.
Our novel method, ViS-HuD, computes visual saliency map from the image.
Then the input image is multiplied by the map and product is fed to the Convolutional Neural Network (CNN) which detects humans in the image.
A visual saliency map is generated using ML-Net and human detection is carried out using DetectNet.
ML-Net is pre-trained on SALICON while, DetectNet is pre-trained on ImageNet database for visual saliency detection and image classification respectively.
The CNNs of ViS-HuD were trained on two challenging databases - Penn Fudan and TUD-Brussels Benchmark.
Experimental results demonstrate that the proposed method achieves state-of-the-art performance on Penn Fudan Dataset with 91.4% human detection accuracy and it achieves average miss-rate of 53% on the TUDBrussels benchmark.
We present a framework for efficient inference in structured image models that explicitly reason about objects.
We achieve this by performing probabilistic inference using a recurrent neural network that attends to scene elements and processes them one at a time.
Crucially, the model itself learns to choose the appropriate number of inference steps.
We use this scheme to learn to perform inference in partially specified 2D models (variable-sized variational auto-encoders) and fully specified 3D models (probabilistic renderers).
We show that such models learn to identify multiple objects - counting, locating and classifying the elements of a scene - without any supervision, e.g., decomposing 3D images with various numbers of objects in a single forward pass of a neural network.
We further show that the networks produce accurate inferences when compared to supervised counterparts, and that their structure leads to improved generalization.
The SINTAGMA information integration system is an infrastructure for accessing several different information sources together.
Besides providing a uniform interface to the information sources (databases, web services, web sites, RDF resources, XML files), semantic integration is also needed.
Semantic integration is carried out by providing a high-level model and the mappings to the models of the sources.
When executing a query of the high level model, a query is transformed to a low-level query plan, which is a piece of Prolog code that answers the high-level query.
This transformation is done in two phases.
First, the Query Planner produces a plan as a logic formula expressing the low-level query.
Next, the Query Optimizer transforms this formula to executable Prolog code and optimizes it according to structural and statistical information about the information sources.
This article discusses the main ideas of the optimization algorithm and its implementation.
Hash-based message authentication codes are an extremely simple yet hugely effective construction for producing keyed message digests using shared secrets.
HMACs have seen widespread use as ad-hoc digital signatures in many Internet applications.
While messages signed with an HMAC are secure against sender impersonation and tampering in transit, if used alone they are susceptible to replay attacks.
We propose a construction that extends HMACs to produce a keyed message digest that has a finite validity period.
We then propose a message signature scheme that uses this time-dependent MAC along with an unique message identifier to calculate a set of authentication factors using which a recipient can readily detect and ignore replayed messages, thus providing perfect resistance against replay attacks.
We further analyse time-based message authentication codes and show that they provide stronger security guarantees than plain HMACs, even when used independently of the aforementioned replay attack resistant message signature scheme.
In many cases, government data is still "locked" in several "data silos", even within the boundaries of a single (inter-)national public organization with disparate and distributed organizational units and departments spread across multiple sites.
Opening data and enabling its unified querying from a single site in an efficient and effective way is a semantic application integration and open government data challenge.
This paper describes how NARA is using Semantic Web technology to implement an application integration approach within the boundaries of its organization via opening and querying multiple governmental data sources from a single site.
The generic approach proposed, namely S3-AI, provides support to answering unified, ontology-mediated, federated queries to data produced and exploited by disparate applications, while these are being located in different organizational sites.
S3-AI preserves ownership, autonomy and independency of applications and data.
The paper extensively demonstrates S3-AI, using the D2RQ and Fuseki technologies, for addressing the needs of a governmental "IT helpdesk support" case.
The governing equations for electromagneto-thermomechanical systems are well established and thoroughly derived in the literature, but have been limited to small deformations.
This assumption provides an "ease" in the formulation: electromagnetic fields are governed in a Eulerian frame, whereby the thermomechanics is solved in a Lagrangean frame.
It is possible to map the Eulerian frame to the current placement of the matter and the Lagrangean frame to a reference placement.
The assumption of small deformations eliminates the distinction between current and initial placement such that electromagnetism and thermomechanics are formulated in the same frame.
We present a rigorous and thermodynamically consistent derivation of governing equations for fully coupled electromagneto-thermomechanical systems properly handling finite deformations.
A clear separation of the different frames is necessary.
In this work, we solve thermomechanics in the Lagrangean frame and electromagnetism in the Eulerian frame and manage the interaction between the fields.
The approach is similar to its analog in fluid structure interaction, but additionally challenging because the electromagnetic governing equations must also be solved within the solid body while following their own different set of transformation rules.
We further present a mesh-morphing algorithm necessary to accommodate finite deformations to solve the electromagnetic fields outside of the material body.
We illustrate the use of the new formulation by developing an open-source implementation using the FEniCS package and applying this implementation to several engineering problems in electromagnetic structure interaction undergoing large deformations.
The task of drug-target interaction prediction holds significant importance in pharmacology and therapeutic drug design.
In this paper, we present FRnet-DTI, an auto encoder and a convolutional classifier for feature manipulation and drug target interaction prediction.
Two convolutional neural neworks are proposed where one model is used for feature manipulation and the other one for classification.
Using the first method FRnet-1, we generate 4096 features for each of the instances in each of the datasets and use the second method, FRnet-2, to identify interaction probability employing those features.
We have tested our method on four gold standard datasets exhaustively used by other researchers.
Experimental results shows that our method significantly improves over the state-of-the-art method on three of the four drug-target interaction gold standard datasets on both area under curve for Receiver Operating Characteristic(auROC) and area under Precision Recall curve(auPR) metric.
We also introduce twenty new potential drug-target pairs for interaction based on high prediction scores.
Codes Available: https: // github. com/ farshidrayhanuiu/ FRnet-DTI/ Web Implementation: http: // farshidrayhan. pythonanywhere. com/ FRnet-DTI/
Intelligent Transportation Systems (ITS) use data and information technology to improve the operation of our transportation network.
ITS contributes to sustainable development by using technology to make the transportation system more efficient; improving our environment by reducing emissions, reducing the need for new construction and improving our daily lives through reduced congestion.
A key component of ITS is traveler information.
The Oregon Department of Transportation (ODOT) recently implemented a new traveler information system on selected freeways to provide drivers with travel time estimates that allow them to make more informed decisions about routing to their destinations.
The ODOT project aims to improve traffic flow and promote efficient traffic movement, which can reduce emissions rates and improve air quality.
The new ODOT system is based on travel data collected from a recently-increased set of sensors installed on its freeways.
Our current project investigates novel data cleaning methodologies and the integration of those methodologies into the prediction of travel times.
We use machine learning techniques on our archive to identify suspect data, and calculate revised travel times excluding this suspect data.
We compare the resulting travel time predictions to ground-truth data, and to predictions based on simple, rule-based data cleaning.
We report on the results of our study using qualitative and quantitative methods.
The term Big Data is usually used to describe huge amount of data that is generated by humans from digital media such as cameras, internet, phones, sensors etc.
By building advanced analytics on the top of big data, one can predict many things about the user such as behavior, interest etc.
However before one can use the data, one has to address many issues for big data storage.
Two main issues are the need of large storage devices and the cost associated with it.
Synthetic DNA storage seems to be an appropriate solution to address these issues of the big data.
Recently in 2013, Goldman and his collegues from European Bioinformatics Institute demonstrated the use of the DNA as storage medium with capacity of storing 2.2 peta bytes of information on one gram of DNA and retrived the data successfully with low error rate.
This significant step shows a promise for synthetic DNA storage as a useful technology for the future data storage.
Motivated by this, we have developed a software called DNACloud which makes it easy to store the data on the DNA.
In this work, we present detailed description of the software.
The effects of molecularly targeted drug perturbations on cellular activities and fates are difficult to predict using intuition alone because of the complex behaviors of cellular regulatory networks.
An approach to overcoming this problem is to develop mathematical models for predicting drug effects.
Such an approach beckons for co-development of computational methods for extracting insights useful for guiding therapy selection and optimizing drug scheduling.
Here, we present and evaluate a generalizable strategy for identifying drug dosing schedules that minimize the amount of drug needed to achieve sustained suppression or elevation of an important cellular activity/process, the recycling of cytoplasmic contents through (macro)autophagy.
Therapeutic targeting of autophagy is currently being evaluated in diverse clinical trials but without the benefit of a control engineering perspective.
Using a nonlinear ordinary differential equation (ODE) model that accounts for activating and inhibiting influences among protein and lipid kinases that regulate autophagy (MTORC1, ULK1, AMPK and VPS34) and methods guaranteed to find locally optimal control strategies, we find optimal drug dosing schedules (open-loop controllers) for each of six classes of drugs and drug pairs.
Our approach is generalizable to designing monotherapy and multi therapy drug schedules that affect different cell signaling networks of interest.
We present a method for performing hierarchical object detection in images guided by a deep reinforcement learning agent.
The key idea is to focus on those parts of the image that contain richer information and zoom on them.
We train an intelligent agent that, given an image window, is capable of deciding where to focus the attention among five different predefined region candidates (smaller windows).
This procedure is iterated providing a hierarchical image analysis.We compare two different candidate proposal strategies to guide the object search: with and without overlap.
Moreover, our work compares two different strategies to extract features from a convolutional neural network for each region proposal: a first one that computes new feature maps for each region proposal, and a second one that computes the feature maps for the whole image to later generate crops for each region proposal.
Experiments indicate better results for the overlapping candidate proposal strategy and a loss of performance for the cropped image features due to the loss of spatial resolution.
We argue that, while this loss seems unavoidable when working with large amounts of object candidates, the much more reduced amount of region proposals generated by our reinforcement learning agent allows considering to extract features for each location without sharing convolutional computation among regions.
The number of optimization techniques in the combinatorial domain is large and diversified.
Nevertheless, real-world based benchmarks for testing algorithms are few.
This work creates an extensible real-world mail delivery benchmark to the Vehicle Routing Problem (VRP) in a planar graph embedded in the 2D Euclidean space.
Such problem is multi-objective on a roadmap with up to 25 vehicles and 30,000 deliveries per day.
Each instance models one generic day of mail delivery, allowing both comparison and validation of optimization algorithms for routing problems.
The benchmark may be extended to model other scenarios.
Herein, the problem of simultaneous localization of two sources given a modest number of samples is examined.
In particular, the strategy does not require knowledge of the target signatures of the sources a priori, nor does it exploit classical methods based on a particular decay rate of the energy emitted from the sources as a function of range.
General structural properties of the signatures such as unimodality are exploited.
The algorithm localizes targets based on the rotated eigenstructure of a reconstructed observation matrix.
In particular, the optimal rotation can be found by maximizing the ratio of the dominant singular value of the observation matrix over the nuclear norm of the optimally rotated observation matrix.
It is shown that this ratio has a unique local maximum leading to computationally efficient search algorithms.
Moreover, analytical results are developed to show that the squared localization error decreases at a rate faster than the baseline scheme.
Women are dramatically underrepresented in computer science at all levels in academia and account for just 15% of tenure-track faculty.
Understanding the causes of this gender imbalance would inform both policies intended to rectify it and employment decisions by departments and individuals.
Progress in this direction, however, is complicated by the complexity and decentralized nature of faculty hiring and the non-independence of hires.
Using comprehensive data on both hiring outcomes and scholarly productivity for 2659 tenure-track faculty across 205 Ph.D.-granting departments in North America, we investigate the multi-dimensional nature of gender inequality in computer science faculty hiring through a network model of the hiring process.
Overall, we find that hiring outcomes are most directly affected by (i) the relative prestige between hiring and placing institutions and (ii) the scholarly productivity of the candidates.
After including these, and other features, the addition of gender did not significantly reduce modeling error.
However, gender differences do exist, e.g., in scholarly productivity, postdoctoral training rates, and in career movements up the rankings of universities, suggesting that the effects of gender are indirectly incorporated into hiring decisions through gender's covariates.
Furthermore, we find evidence that more highly ranked departments recruit female faculty at higher than expected rates, which appears to inhibit similar efforts by lower ranked departments.
These findings illustrate the subtle nature of gender inequality in faculty hiring networks and provide new insights to the underrepresentation of women in computer science.
In this paper we present the state of advancement of the French ANR WebStand project.
The objective of this project is to construct a customizable XML based warehouse platform to acquire, transform, analyze, store, query and export data from the web, in particular mailing lists, with the final intension of using this data to perform sociological studies focused on social groups of World Wide Web, with a specific emphasis on the temporal aspects of this data.
We are currently using this system to analyze the standardization process of the W3C, through its social network of standard setters.
We have witnessed the discovery of many techniques for network representation learning in recent years, ranging from encoding the context in random walks to embedding the lower order connections, to finding latent space representations with auto-encoders.
However, existing techniques are looking mostly into the local structures in a network, while higher-level properties such as global community structures are often neglected.
We propose a novel network representations learning model framework called RUM (network Representation learning throUgh Multi-level structural information preservation).
In RUM, we incorporate three essential aspects of a node that capture a network's characteristics in multiple levels: a node's affiliated local triads, its neighborhood relationships, and its global community affiliations.
Therefore the framework explicitly and comprehensively preserves the structural information of a network, extending the encoding process both to the local end of the structural information spectrum and to the global end.
The framework is also flexible enough to take various community discovery algorithms as its preprocessor.
Empirical results show that the representations learned by RUM have demonstrated substantial performance advantages in real-life tasks.
State-of-the-art slot filling models for goal-oriented human/machine conversational language understanding systems rely on deep learning methods.
While multi-task training of such models alleviates the need for large in-domain annotated datasets, bootstrapping a semantic parsing model for a new domain using only the semantic frame, such as the back-end API or knowledge graph schema, is still one of the holy grail tasks of language understanding for dialogue systems.
This paper proposes a deep learning based approach that can utilize only the slot description in context without the need for any labeled or unlabeled in-domain examples, to quickly bootstrap a new domain.
The main idea of this paper is to leverage the encoding of the slot names and descriptions within a multi-task deep learned slot filling model, to implicitly align slots across domains.
The proposed approach is promising for solving the domain scaling problem and eliminating the need for any manually annotated data or explicit schema alignment.
Furthermore, our experiments on multiple domains show that this approach results in significantly better slot-filling performance when compared to using only in-domain data, especially in the low data regime.
Real-time semantic segmentation plays an important role in practical applications such as self-driving and robots.
Most semantic segmentation research work focuses on improving estimation accuracy with little consideration on the efficiency.
Several previous studies that emphasize high-speed inference often cannot produce high-accuracy segmentation results.
In this paper, we propose a novel convolutional network named Efficient Dense modules with Asymmetric convolution (EDANet), which employs an asymmetric convolution structure and incorporates the dilated convolution and the dense connectivity to achieve high efficiency at low computational cost and model size.
EDANet is 2.7 times faster than the existing fast segmentation network ICNet, while it achieves a similar mIoU score without any additional context module, post-processing scheme, and pretrained model.
We evaluate EDANet on Cityscapes and CamVid datasets and compare it with the other state-of-art systems.
Our network can run with the high-resolution inputs at the speed of 108 FPS on a single GTX 1080Ti card.
We propose a neural embedding algorithm called Network Vector, which learns distributed representations of nodes and the entire networks simultaneously.
By embedding networks in a low-dimensional space, the algorithm allows us to compare networks in terms of structural similarity and to solve outstanding predictive problems.
Unlike alternative approaches that focus on node level features, we learn a continuous global vector that captures each node's global context by maximizing the predictive likelihood of random walk paths in the network.
Our algorithm is scalable to real world graphs with many nodes.
We evaluate our algorithm on datasets from diverse domains, and compare it with state-of-the-art techniques in node classification, role discovery and concept analogy tasks.
The empirical results show the effectiveness and the efficiency of our algorithm.
Compute-and-forward (CAF) relaying is effective to increase bandwidth efficiency of wireless two-way relay channels.
In a CAF scheme, a relay is designed to decode a linear combination composed of transmitted messages from other terminals or relays.
Design for error-correcting codes and its decoding algorithms suitable for CAF relaying schemes remain as an important issue to be studied.
As described in this paper, we will present an asymptotic performance analysis of LDPC codes over two-way relay channels based on density evolution (DE).
Because of the asymmetric characteristics of the channel, we use the population dynamics DE combined with DE formulas for asymmetric channels to obtain BP thresholds.
Additionally, we also evaluate the asymptotic performance of spatially coupled LDPC codes for two-way relay channels.
The results indicate that the spatial coupling codes yield improvements in the BP threshold compared with corresponding uncoupled codes for two-way relay channels.
Finally, we will compare the mutual information rate and rate achievability between the CAF scheme and the MAC separation decoding scheme.
We demonstrate the possibility that the CAF scheme has higher reliability in the high-rate region.
﻿Introduction
Since the beginning of humanity the female breast has been synonymous with the idea of femininity. 
The “ideal” size, however, always depended on whatever was in vogue at the time, and any appropriate changes were made on illustrations. 
The ?rst references to surgical interventions to increase the size of the female breast date back to the end of the nineteenth century. 
There are reports of treatments ranging from fat transplants to paraf?n injections, from creams and various synthetic materials to silicone injections, and, as one can imagine, these had disastrous consequences. 
It was not until the 1960s that it became possible to develop usable silicone gel implants. 
The further development of these has continued until the present day and has given rise to a safe method of breast augmentation. 
This is due above all to the viscosity of silicone gel, which enables the implant to be as natural as possible. 
There are also saline-?lled implants on the market, but these have inherent disadvantages. 
The saline can diffuse more easily through the outer silicone layer, which ?rstly may produce a loss and unevenness in size, and secondly may give rise to noises.
The advantages of titanium-coated hydrogel breast implants and others need to be demonstrated in long-term studies. 
For decades we have been using roughened implants with silicone gel (INAMED Aesthetics, Santa Barbara, CA; D?sseldorf, Germany [formerly McGhan Medical]) without complications and with a low capsular ?brosis rate.
Breast enlargement is a very frequently desired operation.
This book presents the most simple,clear technique in order to ensure that the novice has a basic idea of how to introduce breast implants and to avoid risks.
The simplest,safest access is by means of a 3- to 4-cm-long incision in the inframammary fold which,if it is made precisely,if an atraumatic suture technique is used and if there is good postoperative treatment,is hardly visible after 3months.
The access described in the manual is very clear and easily understandable and also produces good aesthetic results.
Of course,a breast implant may also be introduced via the nipple and via the axilla.
This requires the person carrying out the operation to have appropriate experience. 
In some cases it will be indicated.
Any breast implant,however,may be introduced without any problems by means of the access described in the manual.
It is then up to the young aesthetic surgeon to build on this knowledge.
Once the question of access has been resolved,the second-most-important decision is whether the implant is going to be placed above the pectoralis major or underneath it.
Here,too,the manual gives clear and easily understood instructions,namely,that generally,if there is good skin and gland coverage,the implant is positioned above the muscle,between muscle and gland.
The operation is carried out macroscopically and the dissected pocket is monitored by means of an endoscope so that any bleeding is seen,all strands of connective tissue are cleanly cut through, and the implant pocket is prepared in an anatomically clean manner.
In a clinical study of 500 patients followed up at the Bodenseeklinik,the ?brosis rate was not signi?cantly lower with submuscular access than with supramuscular access (<4%).
Submuscular access is and must be carried out if,following pregnancy or dramatic weight loss,only a very thin ?ap of ptotic skin is present, meaning that the covering is very weak.
Otherwise an impression of the implant and a rippling phenomenon is unavoidable.
In this case the implant must be placed under the muscle.
This intervention is more laborious and causes more bleeding.
The pectoralis major must be separated while in view,including by endoscope,at its lower margin up to the midline using an electric scalpel,cutting through its points of attachment on the relevant costal arches,directly from the rib.
Subsequently, the muscular pocket can generally be dissected bluntly.
A disadvantage of this method may be that the implant slips and that the muscle contracts and changes,which means that when the implant is in the submuscular position,there may be later cosmetic problems and changes if the submuscular pocket is not dissected completely cleanly.
Consequently,a novice in the area of aesthetic breast surgery should send patients who have dif?cult skin and ptotic breasts to an experienced breast surgeon.
As a novice,one does not do oneself any favors by carrying out complicated breast operations.
In this book,therefore, we have only selected subglandular breast implants with access via the inframammary fold since this intervention can be easily learned, is standardized,and is associated with low risk.
When implants are used,these should only be implants that have been licensed by the health authorities.
Similarly,to start with one should not use implants that are too large (not over 350g) since these are associated with signi?cantly more postoperative complications and a signi?cantly greater desire for subsequent operations than is the case ofsmaller implants.
Breast Implants 
Every day,women in Germany ful?ll their dreams of having well-formed breasts.
While the round shapes – which are obviously implants – are still preferred in the USA,German women want their surgically enlarged breasts to have a natural appearance:the ‘tear shape’.
Yet,it is not only important to have a natural appearance;the implanted material should also feel as natural as possible.
Tear-shaped implants are made entirely of silicone – with good reason.
Other materials used for breast implants have proved to be extremely disadvantageous for patients.Sodium chloride is certainly safe as regards patients’health but it has drawbacks: the implants gurgle and the material has nothing in common with the surrounding breast tissue.
For this reason,only McGhan silicone gel breast implants from Inamed Aesthetics* are used at Professor Mang’s Bodenseeklinik.
As the sole manufacturer,Inamed Aesthetics has experience with these implants stretching back more than 25 years. 
This is an important point as the quality and safety of the implant play an important role in the result of the breast operation.
Publications throughout the world con?rm the fact that these implants have the lowest complication rate,which is in line with the high quality and safety requirements at the Bodenseeklinik.
A standardized quality mark for breast implants has been in existence throughout the entire European Union for three years.
This guarantees that the implants will not harm patients’health.
The silicone implants used today are ?lled with cross-linked (cohesive) silicone and therefore cannot leak.
If such an implant is cut open,the contents appear as ?rm as a wine gum.
The surface has also been made rough which ensures that the implant meets completely naturally with the tissue.
Inamed Aesthetics guarantees the safety of the implants with the INAMEDPlus guarantee program.
This program covers every McGhan silicone gel breast implant which has been explanted due to unexplained damage to the implant shell and the resultant rupture of the shell.
The decisive factor is that it was implanted after March 31 2004. 
Even in such highly unusual cases, only a cosmetic correction is required – the patient’s health has not been put at risk because of this at any time.
If the explanted product is no longer being produced,it will be replaced by a current and equivalent breast implant.
And what is more,patients affected by this will receive a ?nancial contribution of up to 1,000 Euro for explantation required as a result of damage to the implant shell,as long as this is carried out within 10 years after implantation.
More detailed information on this exclusive guarantee program can be obtained directly from the Bodenseeklinik on request.
Duplicate Patient Information 
The patient is ?rst given comprehensive information about the objectives and risks of the procedure on the day of the ?rst consultation. 
A written record is kept of this.
One day before the surgical procedure,the patient is again given comprehensive information on two separate occasions:once by the surgeon and once by the surgical resident.
All the risks are set down in writing at this time.
In addition to general operative risks such as wound infection,impairment to wound healing,injuries to blood vessels and nerves,scar formation,subsequent bleeding,thrombosis and embolism,in the case of breast enlargement it is also necessary to give information to the patient about circulatory disturbances and sensitivity disturbances relating to the nipple,impairment of ability to breast-feed,necrosis of the skin, glands,and adipose tissue,asymmetry (especially if this exists already before the operation) and speci?cally about capsular ?brosis,prosthetic defects,and possible displacement of the prosthesis.
Surgical Planning
The operation is performed under general anesthesia achieved with endotracheal intubation or laryngeal mask ventilation.
The day before the operation the doctor carrying out the operation discusses with the patient in detail what changes the latter desires and how these can be achieved by the surgeon.
The patient must be warned about having unrealistic expectations,and the patient must be informed in detail about postoperative behavior.
There should be intra-operative singleshot infection prophylaxis with 2g cefaclor.
The surgical planning must incorporate information about the skin condition,muscle thickness,a mammogram,or ultrasound of the breast. 
It must also cover the shape of the chest,the current size of the breast, circumference of the thorax,and the weight and stature of the patient.
A novice in breast augmentation surgery should start with implants that are not too large (no larger than 320g) and begin by using the safest access.
This is access in the inframammary fold (3).
This access (approximately 3–4cm) is free from problems,can be clearly seen,and is easy to learn.
It ensures safe dissection in view and low-risk introduction of the implants.
Wound closure without tension using a 4.0 poliglecaprone 25 (Monocryl) intracutaneous suture ensures that the scar is as good as invisible if there is normal wound healing and good care is taken of the scar.
Often this scar is less unsightly than the scar that is produced with axillary axis (1). 
With regard to the latter,patients often complain that they have an unsightly scar when naked.
Periareolar access (2) is very rarely indicated. 
This may lead,in addition to visible scar formation,to sensitivity disturbances in the nipple area.
Both forms of access (1 and 2) should be in the repertoire of a well-trained aesthetic breast surgeon.
Since this manual is primarily intended to convey basic knowledge,the video and text will give detailed information about submammary,i.e.,supramuscular, access.
As an appendix to this chapter,reference is made to submuscular access,which is indicated if the skin is poor,in order to ensure better coverage of the implant and to avoid the phenomenon of rippling.
Submuscular access is more invasive since the pectoralis major has to be completely separated at its caudal and medial point of attachment. 
Detachment of the muscle makes the breast more susceptible to subsequent deformation.
The nipple is not lifted upwards to such a large extent,and on the basis of our studies (comparison of 100 patients) the ?brosis rate does not differ signi?cantly between the submuscular and supramuscular position (<4%).
In most cases the implant may be placed above the muscle (in over two-thirds of our patient group).
Incision Line in the Case of Submammary Access 
■ The incision lines are marked on the evening before surgery or on the morning of the operation with the patient standing upright.
First the jugulum is marked,then the midline down to the xiphoid and the navel. 
Next the cranial boundaries of the mammary glands are marked by pushing the breast upwards with the palm of the hand.
Depending on the intended degree of augmentation,the incision line is made either at the level of the inframammary fold or a corresponding 1–3cm lower.
The medial incision boundary should not extend beyond the medial boundary of the nipple.
The incision line is normally 3–4cm and runs,swinging slightly,precisely in the line that will subsequently be the inframammary fold.
It may be positioned slightly higher,but should never be too low,i.e.,underneath the inframammary fold,since the incision could be seen when wearing a bikini. 
■ From the medial margin of the nipple,a line is drawn in a caudal direction.
This vertical marking line may not be exceeded by the incision in a medial direction,since this scar region may be visible.
De?nition of the Subsequent Breast or Implant Size by Establishing the Distance Between the Lower Margin of the Nipple and the Subsequent Inframammary Fold 
■ The distance from the nipple to the inframammary fold is measured. 
By pushing the breast upwards with the palm of the hand in a medial, cranial,and lateral direction,the existing boundaries of the mammary glands are marked.
Depending on the ?ndings and on the patient’s wishes,the surgeon carrying out the operation draws the extension of the breast boundary in a medial direction (shrinking the intermammary distance),or in a lateral and caudal direction,according to the desired enlargement and change in the form of the breast. 
■ It is particularly important to be aware of the inframammary fold,which with appropriate enlargement of the breast must be moved in a caudal direction so that it does not,after the operation,come to rest on the lower breast pole but in the new inframammary fold that is in a lower position. 
■ The submammary incision is drawn in the submammary fold,or parallel to this but lower,beginning medially from the vertical extension of the medial areolar boundary to the intended submammary fold.
The length in the lateral direction is usually approximately 4cm and therefore enables the usual types of implants to be introduced easily.
The distance from the lower pole of the nipple to the incision in the subsequent fold enveloping the breast is dependent on the desired breast size (B,C,D) and therefore on the size of the implant.
The larger the implant,the greater the distance.A rule of thumb is that:
Size B:approximately 4.5–5.5cm
Size C:approximately 5.5–6.5cm
Size D:approximately 6.5–7.5cm
Positioning of the Patient,Disinfection of the Operating Area [0.9% NaCl 500ml,1% prilocaine 250mg (equivalent to 25ml), epinephrine 0.5mg,8.4% NaHCO3 5mEq] 
■ Following intubation (if the patient wishes,the operation may also be carried out under general anesthesia with laryngeal mask ventilation or by means of tumescent local anesthesia),the patient should be positioned on her back with a slightly raised upper body (30%–40%).
The arms are approximately 75% abducted.Attention must be paid here to any tension or pressure.
We recommend using soft silicone cushions under the entire arm so that there is no nerve damage to the brachial plexus.
Similarly,when operating it must be ensured that neither the operating surgeon nor the assistant leans on the arms. 
■ Disinfection is carried out carefully using the colored disinfectant solution Cutasept.
The sterile draping is applied in such a way that the operating area is protected from the head/neck or anesthesia area.
Tumescence The advantages of prior tumescence (manually or mechanically using a pump) are impressive.
There is less bleeding.The gland is lifted from the fascia of pectoralis major.
As a result,dissection is simple because the correct layer is easily located and time is saved.
Wound healing is faster.
Initially,the incision area is in?ltrated to deep into the muscle fascia. 
Then by pulling up the mammary gland with the left hand,tumescence is continued in the prefascial,parasternal,and lateral regions in the whole of the dissection area.
As an operating surgeon,one detects how the gland becomes detached from the muscle fascia and can predissect the subsequent dissection boundaries and layers with the tumescence needle.
As a result,much time is saved in the dissection process since this dissection can generally be carried out using the ?nger as a blunt instrument,in the correct layer and completely without bleeding.
For this reason it is important that the surgeon performing the operation carries out the tumescence him/herself and does not leave it to his assistant.
■ Approximately 100ml of tumescence solution is in?ltrated on each side, depending on the size of the breast.
In the process,the complete mammary gland is lifted up from the pectoral muscle. 
On the basis of a clinical trial,which involved the introduction of breast implants with and without tumescence (n=100),we have shown that postoperative swelling and pain are reduced and that the healing process is accelerated.
Note: Liberal preoperative tumescence of the operation site may be confusing for inexperienced surgeons since it results in an increase in the breast volume.
Submammary Incision 
■ The breast is lifted up by the assistant using his/her right hand,and the operating surgeon makes an incision precisely at the position previously marked.
It should be ensured here that the incision from the medial to the lateral level is performed in a slight arch shape that matches the intended inframammary fold,since this makes the subsequent scar as inconspicuous as possible.
The incision is made fully into the subcutaneous adipose tissue.
Preparation,Step 1 
■ Using his/her right hand,the assistant holds the sharp four-pronged retractor under traction on the upper incision margin in such a way that the operating surgeon can carry out the dissection along the mammary gland in the direction of the pectoralis major fascia cleanly and without bleeding using surgical tweezers and Metzenbaum dissecting scissors. 
Owing to the tumescence this is largely free from bleeding.
The excess tumescence solution ?ows back out again.
If there are small amounts of bleeding,the sites can be coagulated using bipolar tweezers.
Preparation,Step 2:Precise Demonstration of the Caudal, Medial and Lateral Borders of Pectoralis Major 
The use of an illuminated retractor or a forehead lamp enables one to get an overview of the operation site.
Strong strands of connective tissue are dealt with by sharp dissection in the lateral and medial directions.
In view,the entire lower part of the breast muscle can be demonstrated very well.
Tearing of the fascia or muscle should be avoided.
Strong strands of connective tissue generally lie medially in the direction of the sternum. 
These must be dissected cleanly and sharply,in view.
Bleeding from the vessels that perforate the fascia should be stopped carefully using bipolar tweezers since this is often the cause of postoperative bleeding. 
■ In this dissection step,the sharp four-pronged retractor is exchanged for a Roux retractor.
The assistant holds this up under traction so that despite the small cut,there is a good clear view of the operation site.
One must leave oneself time for this dissection step.
When the correct layer has been found,i.e.,when one is exactly on the fascia,further dissection of the whole implant pocket can generally be carried out using just the right middle or index ?nger as a blunt instrument.
Deep,Blunt Dissection 
■ When forming the pocket for the subsequent implant,it is important to dissect suf?ciently in the cranial and medial directions,in order to obtain a soft,inconspicuous transition of the implant margin.
In the medial direction,one should ensure that there is no connection between the sites of the left and right implants.
There should be a safety margin of at least 3cm,since otherwise there will not be a good aesthetic result. 
■ In addition,when carrying out blunt dissection,one should ensure that the pocket is not taken too far in the lateral direction,since this could cause the bed of the implant to be too big laterally,producing separation of the breasts,i.e.,they slide to the side and the result is not good.
It is important that the breast “stands”and that in the lower neckline,the medial,cranial margin is well positioned,without the implant or the margin of the implant being discernible.
The art of implant surgery is to create an implant site that constitutes an optimum precondition for the implant.
As a result of the prior tumescence,detachment of the gland from the fascia is trouble-free.
The boundaries must be smooth in all directions and they must be suf?ciently extensive so as not to cause later creasing of the implant.
If the dissection using the middle or index ?nger as a blunt instrument in the medial and cranial part is not completely successful,this dissection may be completed using Metzenbaum dissecting scissors with the illuminated retractor.
Pushing to the side can easily be performed bluntly. 
■ The implant compartments must be identical to the marking lines that were drawn before the operation.
It should be ensured that the two implant pockets are symmetrical and the same size.
Consequently,before incorporating implants,both the implant sites should be examined very precisely and hemostasis should be carried out twice using the bipolar tweezers.
Wound Revision and Hemostasis Using the Illuminated Retractor and Bipolar Tweezers 
■ Hemostasis is carried out by means of electrocoagulation and with the assistance of an endoscope or an illuminated retractor.
This hemostasis is carried out twice.
One always begins with the right breast.
After dissection and hemostasis have been completed,a damp compress is applied. 
When the left side has been dissected,a second hemostasis is carried out before incorporation of the implant.
Until now,we have not seen any postoperative bleeding in patients where this second hemostasis has been carried out.c
The patient,however,must not get up for 24h after the operation,during which time her blood pressure is monitored,and the patient is supine with the upper body raised and with a light compression bandage.
Determining the Size and Shape of the Implant 
The shape and size of the implant depend on the individual.
The operating surgeon must have a stock of implants that comprises all common sizes and shapes (anatomical,round,etc.).
The size is dictated by the skin and muscle conditions.
If,as an operating surgeon,one is faced with the decision of using a larger or smaller implant,then as a novice one should choose the smaller implant.
At the beginning the selection of the shape of the implant is not so crucial.
This is also something one can talk about with the patient before the operation using implant samples.
It is de?nitely not wrong to begin with round low-pro?le implants*.Later one can then incorporate other shapes into one’s repertoire.
The sizers produced by implant manufacturers are helpful when it comes to determining the implant to be used.
Implants of any size can be simulated.
The incorporation of the sizer is trouble-free and involves the assistant holding open the operative access using a Langenbeck retractor.
The sizer is ?lled up until the agreed breast size is obtained.
The same procedure is repeated on the opposite side.
The sizer also enables one to balance out differences in the size of the breasts very well.
Together with INAMED we are also developing sizers of different shapes, which means that it is possible,intraoperatively,not only to determine the size,but also the selection of the shape.
As a result one can determine more easily what shape of implant is best for each type of breast.
Fitting the Final Implant 
■ After removal of the sizer and after hemostasis has been carried out again,the implant,which has been immersed in betadine (Betaisadona), can be ?tted.
To do this the assistant holds the skin and gland tissue using a medium-sized Langenbeck retractor demonstrating the apex with strong traction in a cranial direction.
The operating surgeon ?xes the implant at the opening with his or her index ?nger and uses the other hand to prevent the implant from sliding out.
Through alternating movements of both index ?ngers,the implant is introduced through the small opening.
In doing this the correct position of the prosthesis must be checked,and it must not be allowed to unfold.
The incorporated prosthesis is then smoothed out both above and below the implant using the ?nger. 
■ In the center of the base,i.e.,on the underside of the implant,there is a small nipple.
After incorporation of the implant,this should be positioned approximately at the height of the actual nipple.
The implant must be free over its whole base and without folds and ideally ?ll out the entire dissection boundaries,without causing impressions,particularly in the cranial and medial part (bulging);if this is the case,the dissection of the implant site has not been suf?cient.
In these circumstances a smaller implant must be used or the implant pocket must be enlarged. 
This cannot happen if the shape and size of the implant have previously been correctly determined using the sizer.
Exact Positioning of the Implant 
■ If the desired implant is in the correct position and has an optimum ?t,it is immersed once more in betadine solution and is implanted in the way described.
In the process it must be possible to feel the small marking in the central region of the base of the implant using one’s middle ?nger to ensure that the positioning of the latter is correct.
The implant is smoothed above and below using the middle and index ?nger.
A check is carried out to ensure the implant ?ts the implant pocket exactly.
Insertion of the Redon Drain (size 10) 
■ A size 10 Redon drain is used for wound drainage.
It empties laterally and is ?xed using one suture.
One should ensure that the implant is not damaged and that the drain is positioned in such a way that between the implant and muscle fascia it extends from the edge of the incision to the medial dissection margin.
Deep Wound Closure 
■ Three concealed 2.0 Monocryl interrupted sutures are used as deep ?xation sutures,which connect the lower dermis with the fascia.
This suturing is important to achieve a stable,lower boundary to the inframammary fold and to avoid a later sinking or slipping of the implant in a caudal direction.
Consequently,the sutures must be deep and complete, protecting the implant,so that no dehiscence can occur.
The Redon drain should not lie under the suturing but rather should,prior to this,be pushed under the implant in the direction of the sternum.
For suturing the assistant should hold the implant away in the cranial direction using a Langenbeck retractor to ensure that there is no accidental puncturing of the prosthesis by the sutures.
Two-Layer,Atraumatic Wound Closure Using 4.0 Monocryl 
■ Following subcutaneous closure with 2.0 Monocryl interrupted sutures there is already good,tension-free wound closure of the skin.
Subsequently,the skin is closed continuously,intracutaneously using 4.0 Monocryl.
One concealed knot is made at the beginning and the end of the suture so that this thread does not have to be pulled out later.
Since we have been using only Monocryl sutures in breast surgery,we have not observed any problems with healing,thread granulomas or poor scar healing.
If good care is taken of the scar,the incision generally heals without any problems and is virtually invisible.
Dressing 
■ After wound closure,the incisions are closed using Steri-Strips that the patient may remove herself after 8days.
In addition to the dressing, small pieces of gauze and 10?15cm Cutiplast plasters are applied to the nipples,and a 10?10cm folded pressure dressing and 10?15 cm Cutiplast plaster are applied to the submammary incision.
Subsequently,a bandage is used for postoperative compression.
After 2days this is changed to a sports bra of the appropriate size.
Aftercare 
■ After the operation the patient is monitored for 24h.
The blood pressure is monitored and it ought not to be above 120mg systolic.
During this time the patient must stay in bed on her back with the upper body raised by 30°.
The ?rst day after the operation the Redon drain is removed and the bandage is changed.
If the course of the recovery is without problems,the patient is given a well-?tting sports bra.
This is adjusted in the clinic and the patient must wear it at home for 4weeks.
Where the implant is beneath the muscle,we recommend that the patients wear a ‘Stuttgart belt’.
This reduces muscle swelling,produces a supple connective tissue site (long-term study from the USA) and accelerates the contouring process. 
For the ?rst 8 days after the operation,the patient receives an antibiotic (cefaclor) and triamcinolone acetonide tablets,8mg per day.
After 8 days,the patient can remove the Steri-Strips herself.
The incisions must be taken care of for 2 weeks using dexpanthenol ointment,and after 4weeks silicone gel must be used or a plaster applied for 2 months.
Four weeks after the operation it is possible to do heavy physical work and sport.
Social activities and work do not pose a problem after 8days. 
Patients are requested to go to the breast clinic immediately if there are any problems.
After 12months there will be a ?nal check with precise photo documentation.
Appendix:Breast Augmentation Submuscular Access 
Submuscular access is more dif?cult,involves more bleeding and is more time-consuming.
This operation technique is indicated when the skin and glandular tissue are unfavorable and too thin:if the implants were incorporated above the muscle,the skin covering would be too thin and rippling and the impression of an implant would be inevitable.
In this case the implant must go underneath the muscle.
It cannot be said that one method is better than the other;the operating surgeon should,based on his or her experience,decide in each individual case whether the implant should be placed above or below the muscle.
A submuscular implant is appropriate if the following conditions exist: 
? Glandular hypoplasia,with thin covering of soft tissue 
? Postpartum involution atrophy with moderate surplus of thin soft tissue 
? Glandular aplasia 
? Previous subcutaneous mastectomy 
? Recurrent capsular ?brosis 
? Pressure atrophy of the breast where an implant is already in place 
In over two-thirds of the cases it will be ?ne to position the implant above the muscle.
This is why there is more detail about this method in the manual. 
■ If the implant is positioned underneath the muscle,then the points of attachment of pectoralis major are detached along the rib from the intermediate area medially in the direction of the sternum,where one should always ensure that there is a distance of 3cm or two ?ngers from the right to the left breast between the two implant pockets.
The implant pockets must not communicate.
■ The dissection may be carried out using a scalpel or Metzenbaum scissors.
It is recommended,however,that the electric scalpel be used since this produces less bleeding.
One should always use the rib for orientation.
When carrying out the dissection,one should ensure that no perforations are produced in the intercostal spaces.
A good light source is required for submuscular dissection,so that one can also stop bleeding in the deeper regions without dif?culty.
■ Like the supramuscular access,the beginning of the dissection is very important,as this is when the correct layer is demonstrated.
Then, even when carrying out dissection under the muscle,much of the detachment can be carried out using the middle or index ?nger as a blunt instrument.
In the medial and caudal part it may be necessary to cut through the connective tissue and strong muscle ?bers using a sharp instrument,either with Metzenbaum scissors or the electric scalpel.
If the electric scalpel is used,care must be taken to avoid perforations.
This diagram illustrates how the pectoralis major is inserted on the sternum caudally.
This part of the insertion,depicted with the dotted line,is detached up to the height of the nipple using scissors or the electric scalpel.
The process of completely cutting through it can be monitored using the adipose tissue behind as a guide,as this will become visible because the muscle retracts.
Meticulous hemostasis must be carried out twice in order to avoid postoperative bleeding,which always occurs during the ?rst 24h.
These two ?gures show the position of the implant underneath the muscle and above the muscle,respectively.
One can see that as a result of the traction and ?tting of the implant,the muscle retracts after being detached and this ensures good coverage over two-thirds of the implant.
The three-layer wound closure is the same for both access methods. 
The concealed ?xation suture of the muscle fascia with 2.0 Monocryl is important since this ensures that the inframammary fold is de?ned and a stable counter-position to the implant is created.
Results 
Patient I: This is a 24-year-old patient whose breasts did not make her feel like a woman.She wanted her breasts enlarged to a size 75 B. 
For this patient,access via the inframammary fold was used to insert a 230-g round,low-pro?le implant produced by Inamed.
The implant was placed above the muscle.
Patient I: Twelve Months After the Implant Normal wound healing, no scar formation, no ?brosis. 
The breast has an anatomical shape with a round implant.
Patient II: This is a 45-year-old patient who had given birth twice.
She expressed a desire to have breasts like she had before the births,now that she had ?nished having children.
A supramuscular implantation with a 260-g round,low-pro?le implant was carried out using the supramuscular access on this patient.
Twelve months after the operation:The breast shows no scar formation and has a natural shape.
An outsider would not notice that breast augmentation had taken place.
Patient III:32-year old patient with breast hypoplasia and thin skin covering with prominent sternum.
Implant 290,submuscular.
It is up to the experienced surgeon to decide whether a submuscular implant is appropriate.
If there is any doubt,then the submuscular implant is the safe option.
There has been no de?nitive scienti?c clari?cation relating to the incidence of capsular ?brosis.
Introduction 
Many patients who wish to improve the shape of their upper arms have a considerable surplus of skin.
The cause can be,for example,massive weight loss,but the process of skin aging can also leave such signs.
In this case only excision can produce the desired improvement in the contour.
Even the most careful upper-arm tightening,however,will result in a scar on the medial side of the arm,starting in the armpit and stretching as far as the elbow.
The patient must therefore be informed accordingly because most patients want this procedure in order to be able to show their arms in public again.
Upper-Arm Tightening 
Upper-arm tightening is requested increasingly by women over the age of 60.
It is often surprising that women of this age do not have a facelift; instead they are more bothered by their ?abby upper arms when they want to wear a bathing suit or sleeveless clothes.
The only way of eliminating the surplus skin and the wrinkles in the long term is cutaneous excision.
The art of the surgeon in doing this is to position the incision in such a way that it is on the medial side of the upper arm and to ensure that the resection of the skin is carried out so generously that the entire upper-arm region is tightened.
Upper-arm tightening is not technically dif?cult.
The thick skin/fat ?aps are dissected off the fascia,protecting the nerves and vessels,following exact marking of the incision line.
The same basic principle applies to all operations to tighten the skin,namely,that the ?ap is mobilized and, following appropriate measurement,is then ?xed in place in stages with key sutures so that neither too much nor too little skin is removed. 
Mang’s principle always applies:I can measure ten times but only cut once.
This should always be kept in mind so that each resection border is measured precisely.
The resection border will then be sutured without tension and no surplus.
As cutting too far towards the olecranon process during upper-arm tightening often causes problems in patients with poor healing,we developed the “?sh-mouth”incision in our department.
This means that an incision in the shape of a ?sh mouth is made in the axilla,stretching to the middle of the medial side of the upper arm.
This leads to a scar in the axilla and in about the upper third of the medial side of the upper arm,which is not so obvious.
Furthermore,the ?sh-mouth incision also achieves tangential tightening in the axilla and vertical tightening in the upper-arm area,so the troublesome surplus skin and the folds of skin in the axilla and upper third of the upper arm when wearing sleeveless clothes are eliminated.
Every patient must be informed of the possibility of scarring as a result of this operation.
Aftercare is also very important.
Subsequently,the scars are treated with ointment and silicone dressing.
Duplicate Patient Information
The patient is ?rst given comprehensive information about the objectives and risks of the procedure on the day of the ?rst consultation. 
A written record is kept of this.
One day before the surgical procedure,the patient is again given comprehensive information on two separate occasions:once by the surgeon and once by the surgical resident.
All the risks are set down in writing at this time.
Preliminary Examinations
Current preoperative routine laboratory examinations,ECG,chest X-ray, clinical examination.
Photographic Documentation 
■ Frontal view of the arms with the patient standing. 
■ Arms abducted by 30°–45°at the shoulder joint.
Surgical Planning 
The procedure is carried out under tumescent local anesthesia or under general anesthesia with endotracheal intubation.
On the day before the operation the surgeon discusses in detail with the patient which changes he or she wants and how the surgeon can achieve this.
The patient must be warned about unrealistic expectations and must be fully informed about postoperative behavior,in particular about how to care for the scar.
Intraoperative single-shot injection prophylaxis with cefaclor 2g. 
Compression dressing,patient is monitored for 24h.
Preliminary Marking of Incision Lines 
■ Before the operation,the areas of surplus skin are marked with the patient standing with his/her arms slightly abducted and bent at the elbow joint to 70°.
Optimum preoperative marking is extremely important for brachioplasty.
The surgeon must take his/her time and position the incision in such a way that it cannot be seen either from the front or the back when the patient’s upper arm is hanging down.
In order to do this the surgeon holds the surplus skin together between the thumb and index ?nger of his/her left hand and marks the outer resection border with a pen.
In general,markings are made for the upper longitudinal incision about two ?nger widths above the sulcus bicipitalis medialis. 
The exact course of the lower incision is only de?ned during the operation.
To achieve a symmetrical result,however,the incision is marked approximately before the operation.
If appropriate,the spindlelike resection in the axilla can be extended in an axillary direction by Z-plasty or a vertical ellipse.
In all aesthetic resections of cutaneous/fatty ?aps in the head,neck,or body the ?nal resection is carried out in stages in order to ensure that neither too much nor too little is removed,as in both these cases the result would be unsatisfactory.
The skill of the aesthetic surgeon is to have a feeling for the tissue,to be able to think in three dimensions,and to be able to ful?ll the patient’s wishes with a rigorous explanation of the procedure.
An aesthetic surgeon can only be successful in the long term if he or she does this.
Mang’s Fish-Mouth Technique 
In appropriate cases,i.e.,when the folds of skin do not extend a long way into the elbow region,a variation of the incision,without extension beyond the cranial third of the upper arm,can be successful.
With this incision not only vertical tightening in the upper arm area is achieved, but also tangential tightening in the axilla.
The advantage of this incision is that the scar is barely visible,and skin folds in the axilla and upper third of the upper arm can be eliminated very effectively.
The scar can hardly be seen at all when sleeveless clothes are worn.
Positioning,Disinfection 
■ The patient’s arms are abducted by 90°before the operation.
Care should be taken to position them correctly so that there is no pressure or traction in order avoid damaging the brachial plexus.
Disinfection with Cutasept is carried out to the edges and to the breast region.
Tumescence 
■ Tumescence is then performed without blurring the marked borders. 
Approximately 200ml of tumescence solution without the addition of triamcinolone acetonide is injected manually per side (0.9% NaCl, 500ml,1% prilocaine 250mg=25ml,epinephrine 0.5mg, 8.4% NaHCO3 5mEq). 
■ Tumescence is carried out in the layer where dissection will later be done, i.e.,on the fascia of the upper arm,so that the skin/fat ?ap is separated from the fascia by the injection itself.
During tumescence the surgeon can feel the thickness of the ?ap and can therefore carry out dissection quickly and with almost no bleeding.
The tumescence also predetermines the level of dissection,so that no deeper vessels or nerves are damaged.
Incision 
■ The incision starts at the marked line above the sulcus bicipitalis medialis.
The incision made with a size 15 scalpel should be wedge-shaped (30°),so that when the wound is closed later (equilateral triangle with the deepest point on the upper arm fascia),an inverted scar is not produced.
Super?cial Preparation 
■ After the upper,tangential (30°) incision has been made,the assistant inserts a sharp retractor and pulls it forward gently so that dissection can be done more easily with a scalpel.
It should be ensured that the medial brachial cutaneous nerve is not damaged.
Deep Preparation,Hemostasis 
■ The skin ?ap is best dissected by pulling it upward with two fourpronged retractors.
During this procedure the assistant should ensure that the retractors are pulled forward gently.
At the same time the assistant can carry out hemostasis with bipolar tweezers.
As a result of the tumescence in?ltration,the surgical area is clearly visible and not covered with blood.
This enables dissection from the fascia of the upper arm to be carried out quickly.
The surgeon can do this with either scissors or a scalpel.
Incision of the Dissected Dermofat Flap in Stages 
■ Once the skin/fat ?ap along the fascia of the upper arm has been dissected to deep within the marked resection border,Backhaus clamps are attached to both ends and rotated gently in a cranial direction.
Resection of surplus fatty tissue,in particular at the cranial and caudal incision borders,is carried out appropriately.
Incision of the dermofat ?ap at marked sites is then done while monitoring the tension.
When doing this it is important that the incisions are made under slight tension stage by stage in line with the cranial incision line to prevent too little skin from being excised,resulting in an unsatisfactory result,or too much skin being excised,resulting in the scar being placed under too much tension (risk of hypotrophic scarring).
Fixing of the Skin Flap with 3.0 Monocryl Key Sutures 
■ 3.0 Monocryl key sutures are placed at the incisions of the marked points.
In doing this the correctness of the extent of the incision and later resection can be checked once again.
After ?xing the skin ?ap,the surplus sections of skin and possibly of fatty tissue can be seen;these must be removed before skin resection.
Resection in Stages 
■ Resection is carried out in stages while keeping an eye on the resulting skin tension.
Following resection,subcutaneous tissue remains on the fascia without undermining.
As a result,no wound cavity is created, which would promote seroma formation.
Redon drains are not required here. 
■ Resection is carried out in stages with a size 15 scalpel,and the assistant holds the sections of the ?ap to be resected upwards under tension with two Backhaus clamps in order to achieve a clean resection border.
Two-Layer Skin Closure 
■ The skin edges are closed with concealed subcutaneous interrupted 3.0 Monocryl sutures.
Each successive suture bisects the wound length;this prevents “dog ears”at the end of the sutures.
It is best if the sutures are started at the distal end and progress to the middle.
Suturing can then be started at the proximal end (axilla) and continued to the middle. 
■ Complete wound closure is then carried out with two-layer 4.0 Monocryl interrupted sutures.
The wound is closed,therefore,with so little tension that the cutaneous suturing (running or intracutaneous) then only plays a minor role.
Cutaneous Sutures:Running or Intracutaneous 4.0 Monocryl 
■ In general,we carry out all cutaneous suturing intracutaneously with 4.0 Monocryl.
This suturing method has proved to be the best,as it does not need to be removed and does not cause granulomas.
It produces optimum healing of the suture line. 
Running sutures should also be mentioned in this manual.
A study (n=25) comparing running sutures with intracutaneous sutures showed that results were similar.
Running sutures are removed after 8days.
Dressing 
■ Steri-Strips are ?rst applied as a dressing to relieve the tension on the cutaneous sutures.
Afterwards,sterile cotton is wound around the Cutiplast wound dressing.
In addition,the arm is then loosely wrapped with elastic bandages from the wrist to the shoulder.
Aftercare 
The operation can be carried out either on an inpatient or outpatient basis. 
■ The dressing is removed on the ?rst postoperative day.
The Steri-Strips are left in place for 8days and can be removed by the patient.
During this time there should be antibiotic prophylaxis and the arm should be elevated.
The patient should avoid physical exertion for a period of 2–3weeks in order to permit undisturbed wound healing.
To prevent congestion of the lymphatics,lymph drainage can be carried out from the 8th postoperative day.
After removing the Steri-Strips,the patient should treat the scar with dexapanthenol ointment for 2weeks and then with silicone ointment for a further 2months.
If after 2months it can be seen that scarring is disturbed,it can be treated,as with all scars,with intralesional injections of triamcinolone crystal suspension 40mg.
With any scar this treatment should be carried out as soon as possible,as these injections improve erythema and bulging scars considerably in the ?rst few months. 
In extreme cases hypertrophic scars must be excised after a period of 12 months and treated with stimulating radiation in divided doses for several days immediately after excision.
Cooperation with an experienced radiologist is necessary for this.
Note: It is possible to insert a Redon drain to drain off wound secretions. 
In most cases,this may be removed as early as the ?rst day after the operation.
Results 
Patient I:This is a 64-year-old patient with skin folds owing to her age in the entire axilla and upper arm region,extending to the elbow.
In this case a longitudinal,spindlelike excision was carried out.
Patient I:Twelve Months After the Operation Twelve months after the operation there is no noticeable scarring, and the skin folds have been eliminated as far as the elbow area.
Patient II:This is a 59-year-old patient with folds of skin in the upper third of the upper arm,extending to the axilla. 
The “?sh-mouth technique”was used for this patient,i.e.,the incision was only in the axilla and the upper third of the upper arm.
This results in a shorter operation time and less scarring.
Patient II:Twelve Months After the Operation 
After eliminating the folds,the volume was also reduced.
The incision in the axilla and the upper medial part of the upper arm is not visible.
Introduction
If the result of liposuction in the abdominal area is inadequate or there is an excessive overhang of abdominal skin and subcutaneous adipose tissue,it may be bene?cial to perform an abdominoplasty to improve the functional and aesthetic result.
In the abdominoplasty,the surplus section of abdominal skin is removed with the attached subcutaneous adipose tissue.
In a few cases,resection of the infraumbilical surplus tissue will be suf?cient,but usually a complete abdominoplasty with umbilical translocation must be performed to achieve optimal results.
In this procedure,tightening of the periumbilical area is also extremely signi?cant, for example,with extreme fold formation following pregnancies.
Rarely, there is also slackening of the abdominal muscles.
This should be treated prior to tightening of the abdominal wall (e.g.,by physiotherapy).
The patient’s skin type and age play an important part in this operation. 
In many cases,it is not possible to remove all the folds and striae and this must be explained to the patient.
Furthermore,female patients must avoid pregnancy in the foreseeable future.
It is not necessary to achieve a speci?c weight for this procedure,but a few conditions relating to this should be ful?lled.
The body weight should have stabilized several months before the procedure,and this should be at a level the patient can maintain after the procedure.
Tightening of the Abdominal Wall
An experienced aesthetic surgeon must look carefully at the indications for liposuction and for tightening of the abdominal wall.
At present, unfortunately,a decision is taken to carry out liposuction too often,and the patient is later disappointed if the skin then hangs down loosely. 
Frequently,tightening of the abdominal wall is requested by patients who have increased skin accumulation around the umbilical area and a slack lower abdominal wall following pregnancies
.It is also frequently requested by patients who have lost a lot of weight (20–40kg) and by older patients who have a slack abdominal wall.
If performed correctly,the operation itself will be successful in the longterm and satisfactory for the patient.
In relation to the surgical technique,in addition to precise dissection of the abdominal fascia with immediate hemostasis,the incision line in the bikini area must be marked carefully and the repositioning and reconstruction of the navel must be performed well so that the result is satisfactory for the patient.
It must be ensured that there are no umbilical or abdominal wall hernias.
For reconstruction of the navel,we have described the method that we ?nd the easiest and most comprehensible and has provided the best results.
When making the incision in the bikini area,it should be ensured that no “dog-ears”are formed at the side and that,following complete dissection as far as the costal arch with the upper body slightly angled, resection of the skin is carried out in stages with key sutures in such a way that the skin ?ap is resected precisely,section by section,and without any signi?cant tension so that necrosis is avoided.
The video shows that the fat is resected obliquely,also stage by stage,to avoid any postoperative retraction of the ?ap.
Immediate hemostasis is important so that the Hb value does not fall below 8mg/dl.
It is recommended that obese patients give an autologous donation of blood 4weeks before the operation.
Patients must also be given thrombosis prophylaxis and infection prophylaxis intra-operatively and for 10days after the operation.
Duplicate Patient Information
The patient is ?rst given comprehensive information about the objectives and risks of the procedure on the day of the ?rst consultation. 
A written record is kept of this. 
One day before the surgical procedure,the patient is again given comprehensive information on two separate occasions:once by the surgeon and once by the surgical resident.
All the risks are set down in writing at this time. 
Severe blood loss requiring a transfusion of blood or blood components occurs rarely.
An autologous blood donation may be very sensible for obese patients and for extensive reconstructions of the abdominal wall.
It is possible to avoid damaging the internal abdominal organs by carrying out an ultrasound examination before the operation and by ruling out hernias.
Otherwise,if there is an umbilical hernia,the abdominal cavity may be opened up during the dissection of the navel.
As the wound surface is large,the patient must be made aware that postoperative bleeding,hematomas,and wound-healing disturbances may occur following the operation.
Therefore,the operation must be performed in the hospital,careful postoperative wound checks must be carried out, and thrombosis and antibiotic prophylaxis must be given.
If the scars are taut,they may enlarge and this may result in thick, distended,discolored,painful scars.
Preliminary Examinations 
■ Current preoperative routine laboratory tests,ECG,chest X-ray 
■ Clinical examination of the patient with ultrasound ?ndings to rule out hernias 
■ Possibly two autologous blood donations
Photographic Documentation 
■ Patient in standing position from the front 
■ Patient in standing position from two sides 
■ Patient in standing position from behind
Surgical Planning
Tightening of the abdominal wall is indicated if the skin no longer shrinks following substantial weight loss,or after a pregnancy that has overstretched the abdominal skin and,as a result of this,the elastic ?bers of the skin have been destroyed (cellulite) or the abdominal muscles have been strained and have moved away from one another in the center, which has resulted in divari?cation with a midline hernia.
Retracted and painful scars following a gynecological operation (caesarian section) can also be a reason for tightening the abdominal wall.
If the patient is severely overweight,weight loss before the operation is necessary.
In rare cases,tightening of the abdominal wall may be combined with liposuction.
The operation is performed under general anesthesia.
The type of incision depends on the type and amount of surplus skin.
On the day before the operation,the surgeon has a discussion with the patient about the changes requested by him/her and the performance of the operation itself.
The incision is marked precisely on the patient,who should be in a standing position.
When doing this,it should be ensured that a median line runs from the xiphoid process over the navel to the mons pubis and that there are no differences in the sides when drawing the line.
A vertical incision is to be avoided.
If there is not too much surplus skin,it is better to site the incision slightly more cranially.
The incision line is usually to be marked through the layer of fat and the surplus skin.
A good estimation of how high the incision must be to avoid the necessity of a vertical incision can be made before the operation.
This is the surgeon’s art. 
Whether the incision line is horizontal or W-shaped is not important. 
The important factor is the patient’s individual anatomical characteristics,and the individual incision line should be adapted to these.
Thrombosis prophylaxis with s.c.fractionated heparin given once daily should be started the day before the operation.
This thrombosis prophylaxis should be continued for 10days after the operation,as one of the main risks in tightening of the abdominal wall is the danger of thrombosis and embolism.
Intraoperative infection prophylaxis with cefaclor 2g.
Postoperative Treatment
In order to relieve the pressure on the sutures,it is necessary to position the bed in a speci?c way for the ?rst 3days after the operation.
The knees should be at an angle and the upper body slightly raised.
The patient should be mobilized as early as the ?rst day after the operation to prevent blood clots forming.
Initially,there should be no extension of the upper body,so that wound healing is not impaired.Frequent movement of the legs is good,as this promotes the return blood ?ow.
On the second day after the operation,the Redon drains are removed,the dressing is changed,and a special compression girdle is ?tted.
Thrombosis prophylaxis (fractionated heparin s.c.) and antibiotic protection (oral cefaclor) should be carried out for 10days after the operation.
The compression girdle should be worn for 4weeks;then intensive care should be taken of the scar with silicone gel and/or silicone plasters.
It is possible to resume sporting activities after 8weeks.
Typical Findings:Indications for Tightening the Abdominal Wall 
The limit of the indications for liposuction in the area of the abdomen/hips is exceeded if either the skin is slack and cracked (severe cellulite) following pregnancies or all the skin of the lower abdomen is slackened as a result of the aging process or extreme weight loss. 
The incision line is marked through the surplus skin and should not be extended beyond this laterally and cranially in the bikini region.
Marking the Individual Incision Line 
■ Before the operation,the midline from the xiphoid process to the mons pubis and the W-shaped or arched horizontal incision line will be marked on the patient,who should be standing.
The horizontal incision line should be marked in the pubic hair boundary to approximately 3–4cm caudal to the anterior superior iliac spine on both sides or steeper/straighter according to the requirements and the patient’s characteristics.
Therefore,the most wide-ranging incision variations are possible,depending on the individual ?ndings for the patient.
It is important that the incision line is marked in the relaxed skin tension lines,preferably does not extend beyond the bikini region,and is selected in such a way that a vertical incision is not required.
It may also be useful to mark the course of the costal arch for orientation.
Positioning,Disinfection 
■ The operation is performed with the patient in a supine position.
The upper body is raised by 30°and the hips and knee are slightly ?exed.
It should be ensured that the extremities are well padded and positioned. 
An indwelling catheter is inserted that should be left in place for 24h.
Tumescence 
■ Following disinfection and sterile draping,the incision is tume?ed with 500ml tumescence solution (0.9% NaCl 500ml,1% prilocaine 250mg=25ml,epinephrine 0.5mg,8.4% NaHCO3 5mEq).
The tumescence solution (500ml) should not be injected more than twice,and this will not be necessary.
Larger quantities of tumescence solution given under general anesthesia may increase the danger of thrombosis and cause hypervolemia and even pulmonary edema.
Incision 
■ Following the individually marked incision line,a sharp incision is made with the size 10 scalpel as far as the rectus fascia.
The scalpel should be introduced at an angle of 30°so that the resection edges can be brought together later,section by section,without the formation of cavities below and depressions above.
The subsequent scar is a sign of a wellperformed abdominoplasty.
Preparation of the Lower Abdomen 
■ After the abdominal fascia has been identi?ed,the cutaneous/fatty ?ap is dissected cranially along the super?cial fascia.
The correct layer can be easily dissected with both sharp and blunt instruments.
The perforating vessels are electrocoagulated. 
■ Dissection must be performed with careful hemostasis,as otherwise there may be a drop in the Hb value later owing to the large wound surface.
The abdominal fascia must be handled carefully and perforations must be avoided.
Purse-string suturing can also be carried out if there is more severe bleeding.
If the fascia is damaged,this must be closed immediately with 3.0 Vicryl interrupted sutures.
Incision Around the Navel 
■ If dissection is performed in the lower abdomen as far as the level of the navel,a circular incision should be made around the navel.
The assistant holds the cranial and caudal areas of the region taut with two singlepronged retractors so that the incision can be made easily.
Mobilization and Dissection of the Navel 
■ Here the dermofat ?ap is mobilized away from the navel.
The assistant then holds the dissection area taut with the two single-pronged retractors and using surgical tweezers.
In the further dissection with the Metzenbaum dissection scissors it must be ensured that the umbilical stalk is suf?ciently thick and that a wide base is created during the dissection to prevent later perfusion disorders of the navel. 
Bleeding should be stopped carefully with the bipolar tweezers.
Vertical Splitting of the Dermofat Flap as Far as the Base of the Navel 
■ To facilitate further cranial dissection,the dermofat ?ap is incised longitudinally in the median line from the edge of the wound to the navel.
The assistant pulls the edges of the wound upwards with two Backhaus clamps.
A large wound retractor can also be used for obese patients. 
■ The length of the median line between the two points a and b is precisely such that later,after resection of the skin,the edges of the wound meet section by section without a vertical incision being necessary.
In relation to this,point a,the border of the incision edge,varies depending on the surplus skin,i.e.,the more surplus skin that is present,the deeper point a is located.
If there is less surplus skin,this point (a) must be correspondingly higher so that later there will be only a horizontal scar.
Complete Mobilization of the Umbilical Stalk 
■ By vertically dividing the dermofat ?ap,it is easy to dissect the umbilical stalk cleanly and with a broad base while it is in view.
The supplying vessels must be retained at the base.
If an umbilical hernia or hernias of the abdominal wall have been diagnosed before the operation,these should be treated appropriately during the operation.
The wound surfaces and the navel should be covered during the procedure with moist, warm compresses.
Dissection of the Upper Abdomen
■ Following mobilization of the cutaneous/fatty ?ap,dissection is continued in a lateral direction as far as the xiphoid process and the costal arch (forming the waist). 
■ The lateral dissection can be performed deeply and bluntly.
To do this,a moist compress should be wrapped around the right middle and index ?ngers and the entire lateral section,from the lateral costal arch to the iliac crest,can thus be pushed away bluntly.
■ The assistant must ensure immediate hemostasis at all times by using the bipolar or monopolar tweezers.
Depending on the surgeon’s preference,sharp dissection can be done with the size 10 scalpel blade or with large dissection scissors. 
It may be useful to use an illuminated retractor in the vicinity of the xiphoid process and at the base of the ribs.
There is an increased possibility of bleeding with dissection of the xiphoid process in the area of the costal arch using a sharp instrument.
The bleeding must be controlled by immediate and rapid coagulation or purse-string suturing.
Doubling of the Rectus Abdominis Fascia 
In patients who have lost a lot of weight after being severely overweight, there is sometimes overstretching of the abdominal muscles so that these move away from one another in the center.
In extreme cases,a midline hernia occurs.
Appropriate surgical treatment should then be carried out for these. 
■ In order to achieve a good result for the tightening of the abdominal wall,doubling of the fascia longitudinally is routinely carried out with 0 Vicryl sutures (interrupted mattress sutures) and,depending on the ?ndings,doubling obliquely.
This doubling of the fascia must be based on the individual ?ndings.
This allows a good base to be created for the later skin/fat tightening. 
■ For body contouring,suction can also be carried out in the area of the hips during the operation via the open abdominal wall.
There are many variations in aesthetic/plastic surgery for optimizing the result.
However, a basic requirement is good basic knowledge and mastery of standard operations.
De?ning the Resection Boundaries with Upper Body Flexed at 30° 
■ Following prior precise wound revision and hemostasis,the entire cutaneous/fatty ?ap is pulled down under traction with the upper body ?exed (30°) to de?ne the boundaries for later resection.
In an ideal case,point b will meet point a.
This ensures that a vertical excision will not be necessary and therefore no troubling scar will occur.
If the abdominal wall is very slack,the distance may be greater.
In such cases,it is important that the later scar is formed section by section,without tension and that it is not retracted and there is no surplus skin with a distended overhang.
Repositioning of the Navel Using a V-shaped Incision 
■ To ensure the scar is aesthetically pleasing a V-shaped incision is made at the new insertion site of the navel following prior con?rmation of the correct position.
The easiest method of doing this is for the surgeon to feel the umbilical stalk beneath the dermofat ?ap with his middle ?nger, to determine the position by exerting slight pressure in a cranial direction with the middle ?nger and by marking a point corresponding to the tip of the ?nger with a marking pen using the other hand.
Pulling the Navel Out of the V-Shaped Incision with Curved Forceps 
■ With the aid of long curved forceps the navel is gripped at the holding sutures and pulled upward.
Positioning the Navel 
■ The navel is positioned outwardly and ?ts into the correct position in the external cutaneous incision without tension.
Trimming of the Skin of the Navel and Adaptation to the V-shaped Incision 
■ To interrupt a circular navel scar line the lower third of the navel is removed to correspond with the V-shaped incision in the abdominal wall.
This simple and effective method of reconstruction of the navel prevents disturbances to wound healing,necrosis of the navel,and cosmetically unpleasant changes in the area of the navel.
The navel thus has a natural appearance.
Closure of the Navel in Three Layers 
■ In order to avoid later disturbances to healing and necrosis,the navel must be ?xed in place in three layers.
At the base,this is with deep ?xation with absorbable suture material of strength 3.0.
To allow further perfusion and stabilization of the navel using the peri-umbilical adipose tissue,5.0 Monocryl interrupted sutures are then inserted.
The skin is adapted with continuous intracutaneous suturing with 4.0 Monocryl. 
This ensures that the navel is well stabilized,has contact with the dermofat ?ap on all sides and that no serous swellings can form in spaces.
Fixation of the Surplus Sections of Skin with 2.0 Monocryl Key Sutures 
■ The surplus skin is pulled down under slight traction to de?ne the resection boundaries.
2.0 Monocryl key sutures are placed at equal intervals, and this allows the surgeon to identify as early as this stage of the operation how far the resection must be taken laterally if“dog-ears”are to be avoided.
The incision can be extended in a lateral direction at this stage of the operation,depending on this.
The trick for all tightening operations is that the amount of skin that must be removed can be de?ned exactly prior to resection by positioning key sutures.
This ensures that the later result will be good and the scar pleasing. 
■ The individual key sutures are placed one after the other so that individual corrections can be made at any time.
Resection of the Skin in Stages 
■ Resection of the skin is performed with regular checks on the tension of the remaining skin.
The skin/fat resection should be performed at an angle of 70°so that the lower border of the incision of 30°meets the upper border of the incision section by section with no retraction or bulging. 
■ After the resection has been completed,particles of fat and surplus skin that spoil the result should be removed.
The lateral edges of the incision should also be checked and any “dog-ears”must be evened out.
Insertion of Redon Drains 
■ Two size-12 Redon drains leading out onto the shaved mons pubis are inserted into two sections of the lower abdomen before the skin is closed.
The Redon drains are removed after the second postoperative day and the catheter is removed after 24h.
Wound Closure in Three Layers 
■ Skin closure is carried out layer by layer,?rst with concealed 2.0 Monocryl interrupted sutures,then with concealed 3.0 Monocryl subcutaneous interrupted sutures.
Finally,the wound is closed with running 4.0 Monocryl sutures.
For this,it is important that suturing begins at the lateral ends on both sides so that the two sutures meet at point a.
This prevents the skin being uneven in the lateral area and produces the desired traction in a medial direction.
Dressing 
■ The navel is packed with a ?ne gauze soaked in betadine (Beta-isadona) ointment and covered with Cutiplast.
The other incisions are closed with adhesive Steri-Strip dressings.
These dressings can be removed after 8days when the wound is checked.
The Tensoplast adhesive dressing remains in place until the Redon drains are removed.
A special compression girdle is then ?tted that should be worn for 6 weeks.
The fresh scars are treated with dexapanthenol ointment for 14 days after the operation, then with silicone ointment or silicone plasters for 2months.
Fitting the Abdominal Belt 
■ In addition to a Tensoplast bandage,an abdominal belt is also used until the Redon drains are removed.
This ensures good compression on the detached wound surfaces,which prevents serous swellings and bleeding. 
During this time,the patient should have bed rest in a slightly angled supine position with the upper body raised.
■ The abdominal belt should be ?tted with traction.
It should be loosened if the patient has dif?culty breathing.
Thrombosis and infection prophylaxis must be carried out during the patient’s stay in the hospital.
Note: For safe dissection of the navel,it is important to ensure that the tissue is fully supplied with blood.
However,if too much adipose tissue is left,this may cause elevated pressure on the repositioned navel.
In addition,the ‘steal phenomenon’may result,since the adipose tissue left behind may require part of the blood supply. 
Compression of the abdominal wall using the abdominal bandage should not be too severe,as this may cause necroses of the distal end of the ?ap (“most poorly perfused area”).
The distal end of the wound must never be undermined! 
Deep ?xation of the navel requires precise localisation of the navel opening.
A two-layer wound closure may be used if desired.
Results 
Patient I:This is a 46-year-old female patient after three pregnancies with divari?cation of the recti and fat ?ap.
Doubling of the fascia was carried out in addition to the skin/fat resection and repositioning of the navel. 
Twelve months after the operation.Normal wound healing, good contouring of the abdomen and hips.
Patient II:This is a 49-year-old female patient following substantial weight loss (40kg). 
Twelve months after the operation.Healthy scar.
Introduction 
This is a delicate and often unsatisfactory area of aesthetic surgery.
The operation is requested by women over 50,and usually the patients expect too much.
In horizontal and inguinal tightening of the thigh,the traction component is often so high that it later results in an unsightly scar,and after only a few months the inner side of the thigh develops creases.
In the case of skin that has signi?cant cellulite and slackness of the thighs as far as the knee area,it may be possible to carry out tightening in a vertical direction in a similar way to upper arm tightening,and this can be discussed with the patient.
It should be made clear to the patient, however,that this may produce an unsightly scar.
The technique in an inner thigh lift is similar to that used with the upper arm,namely,a deep,subcutaneous dissection on the fascia and a stepby-step skin resection that has previously been drawn precisely.
The crucial point when it comes to horizontal,inguinal thigh lifts is the strong traction forces.
It is important in this operation that the thigh ?aps are “hung”at two points in order to reduce the traction forces on the skin.
First,this subcutaneous cutaneous/fatty ?ap is ?xed to the periosteum of the pubic bone with a nylon suture.
Laterally the inguinal ligament must be visualized.
This is where the second anchorage takes place in order to prevent dehiscence and subsequent descent of the scar. 
Yet despite hanging at these two points,long-term results are often unsatisfactory.
Patients should be told this when they are given information about the operation.
Nevertheless.the distress of patients is often so great that they are prepared to put up with these disadvantages and therefore often still want the operation.
When tightening the inside of the thigh,after ?xing the two anchoring sutures with the upper body slightly ?exed,the excess cutaneous/fatty ?ap is resected without tension and without steps,so that after the operation a tension-free wound in the bikini area is produced,which must be treated appropriately postoperatively using ointments and silicone plasters.
For an additional 3 weeks,antibiotic protection must also be given and the patients must wear a specially adapted girdle.
The same applies to the buttock lift.
In this operation the problem is the incision line and the visible scar.
With a buttock lift,the incision line should not be much beyond the buttock crease,since this scar is very unsightly.
Similarly,the resection must be carried out in a wedge shape in the form of an equilateral triangle,so that the deepest point,the socalled zero point of the fascia corresponds exactly to the changed crease and the resulting suture lies in what will be the new buttock fold.
Otherwise there is problematic scar formation that is very dif?cult to correct.
In general,one should not combine liposuction with lifting operations, since this may impair the healing process and increase the risk of thrombosis and embolism.
When the skin is still young and elastic,it is possible to remove smaller limited deposits of fat by means of isolated liposuction.
If skin has lost its elasticity through aging or major weight loss,a lifting operation is recommended to achieve cosmetic improvements.
Often,the loose skin on the inner side of the thigh is operated on together with the loose skin on the buttock,since this is a cosmetic unit.
This operation,which is frequently requested,is also demonstrated in the video.
Generally,the operation is largely without complications.
Nevertheless, there may be isolated cases of complications during or after the surgical intervention,despite taking the greatest care.
More severe bleeding is stopped immediately during the operation.
Pressure damage on nerves and soft tissues resulting from incorrect positioning should be avoided. 
These injuries recede,however,after a few days in most cases.
This also applies to skin damage resulting from disinfectant.
After the operation there may be pain and tension that can sometimes last for a lengthy period.
There is also sometimes swelling in the area of the joints,which may last for up to 6 months and can be treated easily by lymph drainage.
The risk of thrombosis is extremely rare since bloodthinning measures are used,surgical stockings are worn,and there is early mobilization.
The main complication is permanent scar formation as a result of impaired wound healing.
Occasionally,if there is a predisposition to this, thick,bulging,discolored,and painful scars are produced (scar proliferation;hypertrophic scars).
With prompt treatment of the scar changes using injections of 40mg triamcinolone,a corrective operation can be avoided.
Duplicate Patient Information
The patient is ?rst given comprehensive information about the objectives and risks of the procedure on the day of the ?rst consultation.
A written record is kept of this. 
One day before the surgical procedure,the patient is again given comprehensive information on two separate occasions:once by the surgeon and once by the surgical resident.
All the risks are set down in writing at this time. 
Although one tries to achieve symmetry before the intervention by precisely drawing the areas of skin that are to be removed,after the operation there may still be small differences between the sides.
If this is very unsightly,it is possible to compensate by making a small extra intervention under local anesthesia without a need to admit the patient.
During the ?rst few weeks after the operation,the scars frequently move caudally. 
If the patient is also given a buttock lift,he/she must be made aware that the shape is primarily determined by the musculature and cannot be substantially changed by the intervention.
Preliminary Examinations 
■ Current preoperative routine laboratory tests,ECG,chest X-ray if the patient is over 50. 
■ Clinical examination of the patient.
Photographic Documentation 
■ Frontal view of patient standing,torso and legs 
■ Side view of patient standing 
■ Rear view of patient standing
Surgical Planning
The operation is performed under general anesthesia with endotracheal intubation.
Before the intervention the affected area is shaved.
The day before the operation the surgeon carrying out the operation discusses with the patient in detail what he/she wants in terms of changes and how the surgeon can achieve this,and draws the incision lines and resection boundaries precisely.
The patient must be warned about having unrealistic expectations and be given detailed information about postoperative measures in order to avoid scar formation as far as possible.
Intraoperative single-shot infection prophylaxis with cefaclor 2g,treatment in the hospital,Steri-Strip dressing,thrombosis (fractionated heparin 1 ampule i.m.preoperatively and 3 days postoperatively) and embolism prophylaxis,special girdle.
If the loss of elasticity and slackness of the skin is con?ned to the upper third of the thigh,the operation may be carried out in a half-moon shaped skin/fat resection in this area (a).
The scar is then located in the groin and runs into the buttock crease.
There is no scar on the inner side of the thigh.
This is the operation that is wanted most frequently and is also presented in detail in the video.
If the overstretched and therefore loose skin stretches over the whole inner side of the thigh as far as the knee,then it is necessary to carry out additional vertical removal of skin/fat,depending on the extent of the skin on the inner side of the thigh (b).
The scar is then located in the groin and on the inner side of the thigh,depending on how far the slackness of the skin extends,to just above the knee.
If there is also pronounced wrinkling of skin on the buttock,then a skin/fat resection must be carried out here as well.
The scar then will be in the buttock crease and runs forwards into the inguinal region.
The resection lines are drawn before the operation with the patient in a standing position.
It must be kept in mind here that the incision line in the area of the inguinal fold should be relatively high (two ?ngerwidths in the cranial direction) since the scars always descend slightly over time and then could be visible in the upper leg area.
The incision in the groin,which is at the height of the pubic hair boundary laterally,generally runs above the inguinal fold to the thigh-perineal crease and ?nishes at the innermost part of the buttock crease,which is lengthened accordingly if there is also a buttock lift.
If only a buttock lift is carried out,then only the resection in the area of the buttocks is drawn according to the extent desired.
Positioning,Disinfection 
■ For the operation,the patient is placed on the operating table in a supine position with the knees as far apart as the shoulders and the hips ?exed at an angle of 30°.
If extensive removal of skin is required, for example,if there has been extreme weight loss,then it may be necessary to use a urinary catheter both during and shortly after the operation.
Tumescence 
■ After shaving and careful disinfection of the whole operating area,the tumescence solution (0.9% NaCl 500ml,1% prilocaine 250mg=25ml, epinephrine 0.5mg,8.4% NaHCO3 5mEq) is in?ltrated into the skin area to be resected along the predrawn incision and dissection boundaries. 
For each side,depending on the extent of the ?abby skin,one needs between 250 and 500ml of tumescence solution.
The tumescence solution is pumped in manually,until a taut elastic skin tension and the typical blanching effect are noted.
The resection area (a) is,as in all tightening operations,only established when,following dissection (b),the exact super?uous skin has been ?xed using key sutures.
Consequently,the same basic principle always applies that before the skin ?ap is resected one makes the incision on the resection line that has been pulled over and only then carries out the whole dissection.
Dissection boundary is dependent on how far the loose skin extends.
Incision of the Skin 
■ The incision is made according to the marks made preoperatively.
Note that the incision line runs for approximately two ?ngerwidths to the cranial side of the groin,since the scar moves caudally owing to the later traction. 
■ The incision is made using a size 10 scalpel,radically,as far as the subcutaneous adipose tissue and may,without repositioning the patient,be continued as far as the middle third of the buttock region.
If no buttock lift is indicated,the incision should be as far as possible into the buttock region so that the posterior part of the thigh is also tightened and modeled.
Preparation 
■ When dissecting away the cutaneous/fatty ?ap,the assistant uses two sharp retractors and holds these under tension so that the dissection using sharp instruments can be carried out without any problems.
It is rigorously dissected off as low down the thigh as the extent of the slackness demands.
Deep Dissection and Hemostasis 
■ Deep dissection is carried out using the Metzenbaum dissecting scissors by pulling the cutaneous/fatty ?ap caudally.
Bleeding is stopped using bipolar or monopolar tweezers.
If dissection is carried out in the correct layer,precisely above the thigh fascia,no vessel ligatures are required. 
■ Deep dissection is taken as far as was drawn on the day before the operation (dissection area) and discussed with the patient. 
Only in rare cases do we carry out a vertical incision in addition to the inguinal incision,since most patients when they are given detailed information about the operation have problems with the prospect of what is usually a visible scar.
If,however,there is very loose skin as far down as the knee,this incision line cannot be avoided.
De?nition of Resection Boundaries 
■ The assistant pushes the area of skin that has been dissected away in a cranial direction.
Similarly,the cutaneous ?ap is pulled upwards with a sharp retractor and surgical tweezers in a cranial direction.
The thigh is rotated inwards by the assistant so as to achieve as straight a position as possible,as is it would be in the standing position.
The skin incisions are made precisely in these positions so that step by step the incision points (a) correspond with the cranial inguinal incision.
Skin Resection 
■ Skin resection is performed with precise monitoring of the resulting tension on the cutaneous suture.
The resection boundaries are dictated by the positions of the key sutures,which are taken out again after the resection,because the cutaneous ?ap – and this is the most important part of the operation – needs to be anchored deeply at two points with permanent sutures in order to achieve a satisfactory long-term result. 
■ After the skin resection residual areas of fat are removed.
In the process it should be noted that subcutaneous fat is removed in the shape of a wedge,so that later joining can be step by step without any excess material.
For all lifts concerning skin and extremities,it is important to have wedge-shaped joining in the form of an equilateral triangle.
This prevents formation of seromas,promotes good wound healing,and therefore scar healing.
Fixation Suture on the Pubic Bone with 2.0 Monocryl 
■ Following skin resection the inguinal ligament is dissected deeply using dissecting scissors.
The same applies to the pubic bone further caudally. 
The periosteum of the pubic bone can be felt easily.
Suturing to connect the subcutaneous fascia and adipose tissue of the cutaneous ?ap with the periosteum of the pubic bone may be carried out using a 2.0Monocryl suture.
We have the best experience with Monocryl and there has never been any impairment to wound healing.
Owing to the long absorption rate,Monocryl has the same life as a mono?lament cutaneous suture.
Second Fixation Suture on the Inguinal Ligament 
■ After the deep demonstration of the inguinal ligament has taken place over its whole length using dissecting scissors,the subcutaneous adipose sheath with the subcutaneous fascia is anchored to the inguinal ligament using 2.0 Monocryl interrupted sutures,to distribute the main weight, avoid secondary descent of the scars and divari?cation of the labia majora.
Deep Wound Closure and Insertion of a Redon Drain (no.10) 
■ Before deep wound closure,the ?ap is trimmed and the excess fatty tissue is resected.
After dissection of the subcutaneous fascia (Scarpa’s fascia) this is closed by means of deeply concealed 3.0 Monocryl interrupted sutures.
This suture and the two ?xation sutures on the inguinal ligament and the periosteum of the pubic bone ensure that little tension is placed on the ?nal subcutaneous and cutaneous suture.
This is important to ensure good healing of the scar later on.Redon drains size 10 are inserted.
Intracutaneous Skin Closure 
■ After subcutaneous adaptation using 3.0 Monocryl interrupted sutures, the skin is closed without tension with a running intracutaneous 4.0 Monocryl suture.
Dog-ears should be avoided and if they are present, they should be corrected at the caudal end of the incision line running into the buttock crease. 
■ If,following a thigh lift,a buttock lift is carried out,the patient is turned onto his/her stomach,but in the same position. 
■ The intervention is completed with a 4.0 running intracutaneous Monocryl suture.*
Dressing 
■ Dressing is with Steri-Strips that are removed after 8days once the wound has been checked.
For the ?rst 2 days after the operation compression dressings and Cutiplast with special girdles are used.
The Redon drain can be removed on the ?rst or second day after the operation,depending on the results.
Antibiotics and thrombosis prophylaxis should be given.
Note: If the ?xation shown here appears to be too static,then it is possible to suture the thigh fascia to the deep pubic fascia for dynamic anchoring instead of ?xation to the periosteum of the pubic bone.
Buttock Lift:Positioning,Disinfection 
■ It is crucial,as with all lifting operations,to ensure that the preoperative drawing is correct,so that the wedge-shaped resection later produces a scar that lies exactly in the buttock crease.
This means that later on it will be scarcely visible.
Tumescence Tumescent pretreatment of the tissue is the norm in aesthetic operations today.
When buttock lifts are carried out,approximately 200ml of the tumescence solution (0.9%NaCl 500ml,1% prilocaine 200mg=20ml, epinephrine 0.5mg,8.4% NaHCO3 5mEq) is suf?cient.
If the buttock lift is combined with a thigh lift,which is something frequently wanted upwards of a certain age,both the incision lines are joined in such a way as to achieve a homogeneous radial tightening of the whole thigh and buttock area.
Unfortunately,patients often have too high expectations and forget that the shape of the buttock is primarily determined by the shape of the musculature and not by fatty accumulations and loose skin.
Owing to the tumescent in?ltration,the operating area is free from blood,thereby ensuring safe,simple,and quick dissection.
Incision Line 
■ Preoperatively the incision line is drawn precisely with the patient in a standing position.
The intended line of the subsequent buttock crease is marked,and the distances to the caudal and cranial incision margin are measured to ensure they are of the same length.
By pinching together the buttock crease with both hands the extent of resection can be determined.
It is recommended that one should always be conservative at the beginning.
Over time one must explore the limits as an aesthetic surgeon,but one should always bear in mind that it is easier to correct excesses than it is to correct de?cits.
Incision 
■ Using a no.10 scalpel a wedge shape is cut at an angle of approximately 70°downwards as far as the fascia of gluteus maximus.
The deepest point of the wedge excision on the muscle fascia should be at the level of what will be the new buttock crease.
The resection boundaries should be equivalent to an equilateral triangle,which later,following buttock lift and closure,produces the new buttock crease.
Wedge-Shaped Dermolipectomy 
■ After the deep fat layer has been cut through in the shape of a wedge with a 70°angle using the no.10 scalpel,the cutaneous/fatty ?ap is deeply dissected away in full using dissecting scissors from the fascia of the gluteus maximus.
ransverse Incision Through the Resected Area 
■ The deepest point of the wedge excision must be on the buttock crease that is to be de?ned later.
The resection boundaries correspond to an equilateral triangle (a–b=a–c) that after tightening and closure produce the new buttock crease.
Hemostasis,Deep Wound Closure,Insertion of a Redon Drain 
■ Following precise hemostasis and insertion of a no.10 Redon drain,the ?rst deep 3.0 Monocryl sutures are positioned in order to ?x the buttock crease.
Two-Layer,Tension-Free Wound Closure 
■ 3.0 Monocryl subcutaneous interrupted sutures are used.
This removes the tension from the ?nal,running,intracutaneous 4.0 Monocryl suture. 
This suture material has the major advantage that it heals without irritation,hardly produces any suture granulomas,does not need to be removed,has a long life,and produces excellent aesthetic results.
Dressing 
■ A Steri-Strip dressing is applied directly to the wound.
Eight days after the operation,this is removed once the wound has been checked.
For days,Cutiplast and compression bandages are used to prevent edema and thrombosis.
Postoperatively,a special girdle that has been made to measure prior to the operation should be worn for 4weeks.
Infection prophylaxis should be 2g cefaclor.
Note: The method of buttock lift shown here is indicated where there is surplus skin coverage.
If,however,there is little surplus skin and the rede?ning of the buttock crease is a priority,then a more timeconsuming surgical technique is required.
In this case,the epithelium is removed from the skin area which has been marked out and the skin area is separated at the level of the new buttock crease as far as the gluteal fascia. 
After thinning out the caudal and cranial dermofat ?aps,these are anchored to the gluteal fascia thus de?ning the new buttock crease.
Postoperative Treatment: Course of Action After the Operation;Precautionary Measures 
■ Correct postoperative treatment after thigh and buttock lifts is very important to ensure long-term satisfactory results. 
■ For 24h after the operation there should be bed rest and monitoring on the ward.
Careful mobilization aided by a nurse starts the day after the operation.
Moving the thighs apart should be avoided so as to prevent unnecessary traction on the wound.
So as not to impair wound healing, any tension on the sutures should be avoided when the patient stands up. 
Legs should be moved regularly to promote the return blood ?ow. 
■ The Redon drains are removed painlessly after 1–2days.
While the patient is on the ward,lymph drainage and physiotherapy are recommended. 
■ The fresh scars are treated postoperatively for 3 weeks using dexpenthanol ointment and subsequently treated with silicone plasters for 2 months.
The made-to-measure compression girdle should be worn for 4 weeks after the operation to prevent swelling and edema.
After this one can resume sport activities. 
■ Antibiotic prophylaxis with cefaclor and embolism prophylaxis using low-molecular-weight heparin is only indicated during the stay on the ward.
Results 
Patient I:This is a 62-year-old female patient with typical wrinkling in the upper third of the medial thigh.
In this case inguinal tightening has been carried out without a vertical incision.
The cutaneous ?ap was anchored on the periosteum and the inguinal ligament.
Intensive scar treatment was carried out for 6months.
Twelve months after the operation.
Weight gain of the patient and sports activities in the gym.
Healthy scars owing to good wound healing and intensive treatment of the scar.
Introduction
In 1921,a French surgeon carried out curettage on the knee area of one of his patients in order to achieve an improvement in the shape.
This procedure was later combined with suction,until in the end curettage was abandoned.Prof.
Fournier introduced the cross technique in 1987, achieving impressive results with regard to the evenness of the skin.
The shape and size of the cannulas used for liposuction have continued to change and develop.
The pioneers of liposuction were Ilouz,Fournier, and Klein.
An ultrasound-assisted method was ?rst introduced in 1982. 
Another technique that protects the tissue by using vibrating cannulas was introduced by an American,W.P.Coleman,in 2000.
As this book is intended to impart basic knowledge,the tumescence technique demonstrated in the accompanying video is manual liposuction,as this is most suitable for learning the new technique of liposuction from the beginning.
Admittedly,this technique is time-consuming, but it achieves good results and can be learned reliably.
Of all the additional instruments used at the Bodenseeklinik,the best when it comes to large areas of liposuction has proved to be the MicroAire (MicroAire Surgical Instruments,Charlottesville,VA,see ?gure on p.162) system (tissue-sparing;suction without much bleeding; comfortable for the surgeon to operate;almost pain-free suction;timesaving).
The size of cannulas varies between 2 and 4mm;at the beginning of suction 3mm cannulas should be used (extremities,saddle area). 
In very corpulent patients,4mm cannulas can be used in the abdominal area.
For delicate modeling in the neck,buttock,knee,and ankle areas, 2mm cannulas are suf?cient.
The protective technique of tumescent liposuction has considerably reduced the high risks of dry suction under general anesthetic (thrombosis,blood loss,embolism,infection,scarring,skin unevenness, hematoma).
If the tumescence solution containing local anesthetic as well as vasoconstricters is injected beforehand general anesthesia is not necessary.
The patient receives only sedation and intraoperative monitoring (IV access,pulse,blood pressure,O2 saturation,and ECG monitoring).
Adding adrenaline to the solution as a vasoconstricter reduces the risk of the patient losing a large amount of blood and prevents large hematomas from developing.
In addition,the incidence of complications can be drastically reduced by perioperative thrombosis and embolism prophylaxis [single-shot cefaclor 2g,Mono Embolex IM (low molecular weight heparin) before, during,and after the operation].
In a study carried out by the American Society of Dermatologic Surgery there were no cases of embolism, thrombosis,or infection in 15,336 patients treated with tumescent liposuction.
Problems can result,however,from the use of too much tumescence solution,which can place considerable strain on the circulation.
The decisive factor is the tumescence solution used.
The ?rst tumescent local anesthesia with lidocaine was described and documented by Klein as a local anesthetic solution.
Mang’s solution uses prilocaine as a local anesthetic in an even smaller dose (the smallest dose allowing almost painless suction was determined in a clinical study),as it exhibits the least toxicity.
The prilocaine plasma levels were considerably below those for lidocaine.
Duplicate Patient Information
The patient is ?rst given comprehensive information about the objectives and risks of the procedure on the day of the ?rst consultation.
A written record is kept of this.
All the risks are set down in writing at this time.
It should be made clear that the patient may experience pain during the operation and that occasionally pressure damage may occur to the nerves and soft tissue.
This will subside again in the space of a few weeks.
The loss of a large amount of blood necessitating blood transfusions does not normally occur when the tumescence technique is used. 
Bloody effusions and a feeling of numbness in the operation site can occur after the procedure.
Dimpling and the limits of the possibilities of liposuction must also be explained to the patient,as must the risk of thrombosis and embolism as well as the small scars that will occur at the insertion sites.
In rare cases allergic reactions can occur in the skin, mucous membranes,heart,circulation,kidneys,or nerves.
For this reason liposuction should be carried out on an inpatient basis with standby and monitoring.
If there are considerable irregularities in contour,the patient should be advised to have a corrective operation.
Preliminary Examination 
■ Current,preoperative routine laboratory tests with APC resistance and glucose-6-phosphate dehydrogenase.
ECG and chest X-ray if the patient is 49 years old or over.
The patient should undergo a clinical examination, in particular to identify hernias in the abdominal region and varicose veins,congestion of the lymphatics,etc.in the lower extremities. 
■ Photographic documentation according to the problem zone:images taken from the front,side and from behind with the patient standing.
Surgical Planning
The operation enables deposits of fat to be reduced in a de?ned area of the body surface that cannot be reduced by dietary measures or sporting activity alone.
Surplus fat is removed by suction in order to reduce the thickness of the fatty layer of the skin.
The amount of fat removed is limited by the loss of body ?uids and blood.
For this reason liposuction is not a procedure for reducing general obesity.
On the day before the operation the surgeon discusses with the patient in detail which changes he or she wants and how this will be achieved.
The areas to be removed by suction and the tumescence borders are marked exactly.
In order to keep the operation risk as low as possible the patient should be made aware that he or she should not take any anticoagulants such as acetylsalicylic acid before the operation.
The patient should also not smoke before the operation as this causes a reduction in perfusion. 
The risk of blood clots forming in the body also increases if the patient is taking contraceptives.
In such cases the patient should stop smoking 2weeks before the procedure and for the duration of the wound-healing period at the very least.
Intraoperative single-shot infection prophylaxis should be carried out with 2g cefaclor and inpatient treatment and thrombosis/embolism prophylaxis before the operation and for 1day after the operation with one ampule of fractionated heparin s.c.
A special girdle should be ?tted.
Anatomy of Liposuction of the Abdomen,Hips,Thighs 
Liposuction of the abdominal/hip region is the most frequently requested procedure,particularly by men.
Only individual zones should ever be treated with liposuction,i.e.,abdomen/hips or outer and inner thighs and buttock region (saddle area),as ?rst the amount of tumescence that can be injected is limited (maximum 6l) and second the procedure would be too stressful for the patient.
As the navel region is particularly sensitive,a lot of tumescence must be used here.
Liposuction of both hips or the upper and lower abdomen is carried out in a fan shape with the patient frequently changing position.
Liposuction should be carried out carefully in the upper abdominal area,and a thin layer of fat should be left below the skin,as otherwise dimples will form and there can be loose skin.
Anatomy of Liposuction of the Hips,Back,Thighs,Buttocks (Body Contouring)
Liposuction of the so-called saddle area is the procedure requested most by women (body contouring of the hips,the lateral and medial sides of the thighs,buttocks).
After the fat deposits have been marked precisely, an aesthetic result is achieved by carrying out liposuction homogeneously through 360°without the formation of dimples,by changing the patient’s position on the operating table,and by checking again at the end of the operation,with the patient standing up,whether the contours have been suctioned well.
The more experienced a surgeon is,the more he or she can remove.
Novices must be very cautious and restrained,as dimples are more dif?cult to correct than residual deposits of fat,which can be removed without any problems after 6 months.
Successful liposuction of the back can only be achieved if tunneling is carried out cautiously using a low-level vacuum (maximum 0.4 at), leaving a layer of fat on the subcutaneous tissue,and through a ?brotic/tightening effect being achieved by the tunneling.
Caution must also be exercised when carrying out liposuction of the buttock region since if too many fat cells are removed,dimples can form and there can be loose skin.
Modeling of the hips and the medial and lateral sides of the thighs can be achieved very successfully using liposuction,as the skin here generally produces a good tightening effect.
Anatomy of Liposuction of the Axilla,Chest,Hips,Lateral Side of the Thighs
In principle,liposuction can be carried out in any area of the body where there are aesthetically intrusive deposits of fat.
This is the main advantage of the tumescence technique.
Because of the anatomical situation of the axilla,the surgeon has to be very careful.
It is better to leave out the axillar region to prevent injuries.
Anatomy of Liposuction of the Medial/Lateral Side of the Thighs,Knee
The contours of the lower extremities are very well suited to liposuction, in particular the deposits of fat on the lateral and medial sides of the thighs and the knees.
Before the operation varicosity of the long and short saphenous veins and any lymphatic diseases must be taken into account.
The thighs must not be skeletonized,i.e.,all the fat must not be removed, as this leads to a very poor cosmetic result.
A suf?cient subcutaneous layer of fat must be left.
As long as pure fat is appearing;the procedure can be continued without risk.
When the fat becomes mixed with tumescence solution and ?nally only tumescence solution appears in the tube;the procedure should be ended in order to prevent skeletonization and the formation of dimples.
Liposuction in this region is shown in detail on the DVD.
Anatomy of Liposuction of the Medial Part of the Thighs,the Knees, Calves,Ankles
Liposuction of the lower extremities is usually carried out as two separate procedures:?rst the lateral and medial sides of the thighs and the knee area,then the calf and ankle region.
If only the medial side is to be altered,the medial side of the ankle,calf,knee,and thigh can be treated in one procedure;particular attention must be paid to thrombosis and embolism prophylaxis during this procedure.
The patient should be mobilized immediately after the operation.
Anatomy of Liposuction of the Calf and Ankles
Unfortunately,fat calves often result from muscular hypertrophy.
When performing liposuction of the calves the surgeon must have a lot of experience and be very careful to avoid causing dimples.
For this reason caution must be exercised during liposuction of the calves and ankles. 
A 2-mm cannula with a vacuum that is not too high must be used (no higher than 0.6 at).
After the operation immediate mobilization and the ?tting of a compression girdle are advisable.
Anatomy of Liposuction of the Breast,Axilla,Upper Arms
Gynecomastia in men can be treated very well by means of tumescent liposuction.
Preoperative investigation of the breast area by means of mammography or ultrasound is necessary.
The entire breast area can be removed by suction by means of two small incisions that are not visible.
Axillary fat can be removed during the same operation if required.
Anatomy of Liposuction of a Double Chin
Two small submental incisions and a retroauricular incision are made. 
With a quantity of tumescence solution of 300–500ml the entire submental region extending deep into the neck area can be removed by liposuction.
If required,the lateral cheek areas can also be removed. 
The procedure is often combined with a facelift.
After the operation a compression dressing is worn for approximately 1week so that the submental skin that has been detached in the neck area can adapt after liposuction to the areas where fat has been removed. 
Just by tunneling with the 2mm cannula,scar contractions occur,which result in a tightening effect.
Mechanical and Manual Tumescence
Tumescence solution can be applied either manually with an injection syringe or mechanically with a pump.
In the manual technique the injection syringe is connected to the tumescence solution via a one-way cock. 
The manual technique requires a lot of time and effort and has the same results as the mechanical injection of solution via a pump.
In this technique the pump is connected with the system via a three-way or six-way cock so that the tumescence solution can be applied evenly and homogeneously via three or six cannulas,also saving time.
For liposuction in the abdominal/hip area about 6l of tumescence solution is needed.
Manual application of the solution takes 90min;application using the pump takes 45min.
After applying all the tumescence solution,it should be given at least 30min to take effect.
During this time disinfection and sterile draping of the patient are carried out.
Mang’s tumescence solution (0.9% NaCl 3,000ml,1% Prilocaine 1,500mg=150ml,epinephrine 3mg,Na HCO3 8.4% 30mEq,triamcinolone acetonide 30mg) should still be limited to 6l for patients weighing up to 80kg.
If the patient weighs more than this and is in good general condition,the amount of tumescence can be increased to 7l.
The best temperature for the solution is 30°C (warm cabinet). 
The prepared tumescence solution should be injected within 1h of preparation.
The solution must only be prepared (sterile preparation) by a quali?ed person supervised by a doctor.
The surgeon must apply the tumescence himself/herself,as in so doing he/she can see exactly how much tumescence solution ?ows into each fat deposit.
He/she can therefore already begin to estimate during tumescence from which regions the most fat cells will need to be removed. 
Tumescence solution that has been opened must not be reused under any circumstances.
Location of the Incision Sites from the Rear 
■ Shoulders:Three incisions.
At the lateral,medial and caudal ends of the collection of fat. 
■ Buttocks:Three incisions.
One in the upper quadrant and two in the lateral and medial parts of the gluteal fold. 
■ When treating a problem zone a general rule of thumb is that at least three incisions are necessary,one of which should be at the lowest point to allow the tumescence solution to drain.
This prevents congestion as well as prolonged swelling and infection.
If it becomes apparent during liposuction that another incision is necessary,this can be made without problem,as these are microincisions that will not be visible after 6months.
Instead of sutures,Steri-Strips are applied to the incisions for 8days.
Location of the Incision Sites from the Front 
■ Submental region:Four incisions – two in the submental area,two on the earlobes. 
■ Upper arms:Lateral condyle and ventral muscle belly ofthe biceps muscle. 
■ Breast:At three o’clock laterally,at six o’clock caudally where the collections of fat protrude. 
■ Upper abdomen:Three ?ngerwidths caudal to the costal margin on each side. 
■ Hips:Four incisions divided between the individual quadrants. 
■ Lower abdomen:Four incisions,two in the bikini area and two at the level of the navel half-way between the iliac crest and the navel. 
■ Lateral side of the thighs:Three incisions,one below the trochanter,one at the lowest point of the collection of fat and one in the gluteal fold. 
■ Medial side of the thighs:Two incisions,one incision midway between the inguinal region and the knee at the lowest point of the collection of fat and one dorsally in the gluteal fold. 
■ Knees:Two incisions located cranially and caudally to the fat deposit. 
■ Calves:Four incisions.Two lateral,two medial. 
■ Ankles:Three incisions.
Two dorsal (caution:Achilles tendon!),one ventral at the area of attachment of the tibialis anterior tendon.
Schematic Diagram of Mang’s Tumescent Liposuction Technique
Cross-Section of the Skin Before Tumescence
The diagram shows normal fat cells,embedded in the infrastructural connective tissue (ICT).
In dry liposuction under anesthesia,these connective tissue structures are largely destroyed,causing blood loss, hematomas,and the formation of dimples under the skin.
This is avoided by using the tumescence technique.
Cross-Section of the Skin Following 
Tumescence Tumescent local anesthesia (TLA) refers to the in?ltration of the skin and subcutis with a large quantity of very diluted local anesthetic (below 0.1%) with adrenalin (less than 1mg/l) and NaHCO3 until the tissue swells suf?ciently. 
TLA results in good anesthesia and hemostasis and means that the patient is responsive and mobile.
Using TLA the procedure can be carried out without additional anesthesia. 
Removing the tissue that is full of tumescence solution during liposuction does not cause any blood loss and,in particular, preserves the surrounding tissue.
Cross-Section of the Liposuction Technique 
Liposuction is carried out with 2.0–4.0mm cannulas.
Because the fat cells are hygroscopic they are softened by tumescence and can therefore be removed by suction atraumatically and selectively without damaging the surrounding tissue (infrastructural supporting tissue).
With a movement similar to that of a violin bow,moving constantly in a 180°radius and never stopping in one place,the entire area of fat is removed by suction,starting at the bottom and working upwards towards the epidermis.
The skill is in leaving a thin layer of fat below the epidermis so that dimples are not formed later and a good tightening effect is achieved.
Cross-Section of the Tissue 6Months After Liposuction with Preservation of the Infrastructural Connective Tissue (ICT) 
Six months after liposuction using the tumescence technique and cannulas less than 4.0mm in size you can see that the infrastructural connective tissue has been preserved.
The endoscopic image shows the intraoperative ?ndings.
The most important point when carrying out liposuction is that a so-called fat ?lm is preserved in the upper section and that the connective tissue below it is preserved.
This causes the “chewing gum”effect whereby the undamaged connective tissue septa contract after liposuction,tightening the skin. 
You can see from the aspirate,which contains almost entirely fat with no blood,that only a small amount of tissue has been destroyed.
Technique 
■ The full extent of each fat deposit is marked precisely on the standing patient.
The problem zones must be marked with small circles,increasing in size until they reach the edges.
By doing this you can start to plan before the operation where the largest quantities need to be removed.
Disinfection 
■ Disinfection is carried out before tumescence.
Manual Liposuction 
■ The injection into the adipose tissue can be done by hand,in which case the quantity ofsolution used must be constantly checked and attention must be paid to achieving an even distribution.
This method takes about 1.5h.
Mechanical Liposuction 
■ The injections can also be carried out with an electric pump.
When doing this,you must always ensure that the cannulas are in the correct position.
The pump transfers the tumescence solution via a distribution system (3–6 connectors);it must always be ensured that the solution is injected evenly and not too rapidly.
As the patient is responsive and mobile,tumescence/liposuction of any part of the body is possible.
Tumescence can be discontinued when the areas to be treated show the so-called blanching effect,i.e.,are white and elastic.
A maximum of 6l of solution should be injected in order to avoid cardiac or neurological irritation.
The process lasts approximately 45min.
Tumescence and liposuction should be carried out with anesthesiology monitoring and stand-by.
Procedure 
■ After thorough disinfection,again an incision is made with a size 11 scalpel.
This incision is not sutured later and cannot be seen.
This process is completely free of pain because of tumescence.
The liposuction cannulas can be inserted without much pressure,and the openings should point towards the subcutis.
At the beginning of the procedure the cannula should not be more than 4.0mm.
At the end of the procedure, after the majority of the fat has been removed,a 2.0–3.0mm cannula is used for delicate modeling.
The tumescence technique allows the procedure to be carried out with almost no bleeding. 
■ Tumescence allows the tissue to be tunneled without much effort. 
Novices should not initially use the assisted system,but should carry out liposuction manually in order to get a feel for the tissue.
In order to achieve an even result,the same amount must be removed from all sides at angles of 90,180 and 360°.
The fat should be removed using smooth, constant,forward and backward movements,similar to the movement of a violin bow.
The fat should always be removed from within the predetermined level and in a fan shape.
Several incisions are necessary to reach the problem zones well,and one of these should always be at the lowest point of the problem area to allow the tumescence to drain.
As long as pure fat appears the procedure can be continued without risk.
When the fat becomes mixed with tumescence solution and then only tumescence solution appears,the procedure should be ended in order to avoid skeletonization and the formation of dimples.
Ideally,a “fat ?lm”should be left directly under the skin during liposuction.
Liposuction should therefore always be carried out from the deepest layers to the upper ones. 
■ Because the procedure is carried out under local anesthesia it is possible for the patient to roll over;therefore,all areas can be reached easily and evenly. 
This is a particular advantage for achieving homogeneous liposuction,as it brings about a tightening effect without the formation ofdimples. 
■ Because the patient is mobile all problem areas on the face and the body can be treated.
It should be ensured that suction is carried out evenly and in one plane in order to avoid contour irregularities.
This is harder to even out than residual persistent deposits,which can be corrected without any problems. 
■ To make sure the wound is well drained,an incision must be positioned at the lower pole of the area to be removed during liposuction.
Contouring can also be carried out from here.
If the patient experiences pain, a strong,fast-acting analgesic can be given via the venous cannula. Synthetic opioids,e.g.,piritramide (Dipidolor?),have proved effective in these circumstances. 
■ Liposuction should be carried out on an inpatient basis and requires a lot of experience.
An experienced surgeon will preserve a thin layer of fat below the skin. 
■ When using the aspirator it is important that there is a constant vacuum of about 0.8 (Atmos Medizintechnik aspirator*).
Dressing 
■ After liposuction,Steri-Strips are applied to the insertion sites.
The wounds are not closed further because of the desired drainage effect. 
The Steri-Strips can be removed by the patient after 8days.
The dressing is applied with the patient standing up.
Absorbent pads take up the ?uid produced in the ?rst few days after the procedure.
A compression girdle is worn for a few weeks after the operation.
Antibiotic cover and thrombosis prophylaxis should be given.
Aftercare 
■ The patient is monitored for 24h after the operation,during which time he or she should move about as much as possible (1h lying down, 20min walking up and down in the room so that the tumescent ?uid drains). 
■ On the 1st postoperative day the entire dressing is changed and a compression girdle is ?tted before counteracts swelling and pain and to help adapt the skin to the changed contours of the body.
This compression girdle also encourages the skin to tighten and should be worn for at least 4weeks after the operation. 
■ Two weeks after the operation the skin can be treated with moisturizing body lotion,massaged gently on a daily basis into the areas of skin treated.
Physical exertion,sport,and exposure to direct sunlight are permitted after 4weeks.
We recommend training in the gym after liposuction.
A “top body”or “washboard stomach”can normally only be achieved by liposuction in combination with strenuous physical training,not by liposuction alone.
With the help of liposuction,fat cells are permanently removed.
Since the fat cells do not grow back,liposuction treatment produces a permanent effect.
However,further changes to the shape of the body are possible. 
The results of the operation are dependent on the patient’s general health,the condition of the skin,the patient’s age and weight,and the hormonal content of the body,among other things.
In particular,signi?cant weight gain caused by nutrition will result in the layer of adipose tissue increasing again even in the treated area,as the remaining fat cells will ?ll out.
Occasionally,wavelike unevenness or dimples become visible on the surface of the skin,but these usually reduce again within 6months.
As with all aesthetic procedures,corrective operations may be necessary if the results of the treatment do not meet the patient’s expectations or if an unsatisfactory aesthetic result is produced because of wound-healing disturbances, infections, postoperative bleeding, etc.
Results 
Patient I:Liposuction in the submental region of a 38-year old patient. 
Side view 12 months later.
Patient II:A major problem for men predominantly aged over 45 is deposits of fat in the chest area.
Good,long-lasting results are obtained using the tumescence technique presented here.
 Side view 12 months later.
Patient III:Saddle area before and 6 months after treatment.
Patient IV:A 39-year-old patient with collections of fat in the hip and abdominal areas,and 12 months after tumescent liposuction of abdomen,hips and mons pubis.
Patient V:Patient with collections of fat around the hips,lateral and medial sides of the thighs and the buttock region,and view after modeling of the abdomen,hips and buttocks,12 months after the operation.
Introduction 
Hair transplantation has been requested by many men since it is known that new methods (micropunch technique,slit technique,laser-assisted) do not leave any visible scars.
The hairs that are transplanted from the back of the head rarely fall out,and the procedure is atraumatic and virtually painless.
A special team is necessary for hair transplantations. 
This is made up of a surgeon and at least two trained assistants who prepare the hair follicles.
Besides precise preparation of the hair follicles, correct insertion of the hair follicles at the correct angle using either the micropunch or the slit technique is extremely important.
This is the only way to achieve a natural result;it is the art of the hair transplant surgeon. 
For this reason we have a dedicated hair transplantation team at the Bodenseeklinik who carry out only hair transplantations.
The only way to produce good,lasting results is practice,experience,and the precise preparation and insertion of the hair follicles.
In the hair transplantation chapter a clear overview is given of what must be done to achieve successful hair transplantation.
In addition to the precise harvesting of an appropriate donor strip from the back of the head with atraumatic closure,successful hair transplantation involves microscopic preparation of the hair follicles and insertion of the implants either by the micropunch technique or the slit technique,using either one or more follicles in either a manual or a laser technique.
The precise insertion technique is determined individually for each patient and each area.
Beauty ideals vary a great deal,but thick,shiny hair is desirable in all cultures because it is a symbol of health and youth.
Even the ancient Egyptians saw it as a catastrophe if someone’s hair became thinner and thinner.
In our society as well,where a youthful appearance plays a very important role,thick,healthy hair is a great advantage.
In the Western world roughly every second man is affected by hair loss.
The most common form of hair loss is so-called androgenetic alopecia,masculine type hair loss.
The hormone dihydrotestosterone plays a key role in androgenetic alopecia.
This hormone is formed from the male sex hormone testosterone under the in?uence of a particular enzyme.
Dihydrotestosterone causes hair to become thinner and thinner in particular places such as the brow,temples and the crown and ?nally to fall out.
The decisive factor when it comes to hair transplantation is that hair on the back of the head (coronal hair),facial hair and body hair are immune to the hormone dihydrotestosterone.
This explains why hairs taken from the back of the head and transplanted to bald patches do not then fall out.
They continue growing and produce healthy hair,which can be washed,blow dried,and dyed normally.
Transplantation of a patient’s own hair is a skillful redistribution of healthy hair follicles to bald patches and,with the new methods available,results in a natural appearance.
Considerations before hair transplantation:The patient’s hair should be allowed to grow as long as possible so that the harvest area can be covered with the remaining hair and is not visible.
The patient should not take any anticoagulants. 
The procedure is carried out under local anesthesia. 
After the operation a loose-?tting hat (e.g.,baseball cap) should be worn.
Preparation of the Patient,Hairline Design
Donor Area 
■ The donor area should not be more than 2cm above an imaginary line connecting the tips of the patient’s ears behind the head.
Be careful not to harvest an overly large skin strip so that you will not have to discard hair follicles later. 
■ When determining the size of the donor area,keep the preparation capacity of your transplantation team in mind! 
Only shorten hairs whose follicles are to be dissected later.
Leave the remaining hairs as long as possible so that they will cover the donor site after transplantation. 
■ Measure follicle group density,i.e.,follicular units per square centimeter. 
■ With this ?gure,the number of follicular units to be transplanted can be calculated from the total area of the donor strip.
Local Anesthesia,Tumescence 
■ Local anesthesia with articaine and adrenaline (e.g.,B.Ultracain DSforte,Septanest with adrenaline 1/100,000) is administered in the form of a ring block below the harvest site,using an intradermal injection technique. 
■ This is followed by intradermal in?ltration anesthesia using 0.5% prilocaine with adrenaline. 
■ Injection of a 0.9% saline solution is employed to achieve tumescence of the donor area. 
■ Caution:Inject the tumescence solution intradermally and subdermally; subgaleal injection is contraindicated! 
This precaution prevents injury to major nerves and blood vessels during the subsequent skin incision.
Donor Strip Harvesting 
■ Remove a trapzoidal donor strip with the base of the trapezoid in a caudal position! 
■ Avoid transection of the hair follicles by making an incision at an angle of about 45°and cutting exactly parallel to the direction of hair growth. 
■ The upper incision angle can vary.
Use a magnifying device with a power of 2?.
Multiple incisions at the same location cause transection,and thus destruction,of the hair follicle. 
■ Cautiously excise the strips;pull gently to detach them below the hair roots in the fatty layer. 
■ Do not injure the vascular-neural bundle.
To avoid injuring the galea at all costs,the best policy is:hands off the galea! 
■ Place the harvested strip into a sterile cooled 0.9% saline solution immediately. 
■ No mobilization.
No opening of the galea. 
■ Hemostasis should be carried out on the galea only and not near the hair follicle. 
■ Pull the edges of the wound together over the donor site using mono?lament absorbable sutures (2?0 or 0),e.g.,Monocryl. 
■ Insert the needle into the skin and out again below the hair roots;use a concealed knot.
With this technique,the wound edges are already optimally adapted;smaller hemorrhages are automatically compressed.
Skin Closure with Continuous Sutures 
■ Perform skin closure with running sutures;use non-absorbable mono?lament sutures (e.g.,Prolene or Resolon 4?0). 
■ Make sure that the cutaneous sutures are not under tension and that the needle is inserted super?cially.
Inserting the needle too deeply may result in hair follicle necrosis and ultimately scar-tissue alopecia. 
■ When harvesting,dissecting or transplanting hair follicles,avoid doing anything that will result in trauma or reduced perfusion.
Follicular Unit Preparation (Minigrafts,Micrografts,Single Hairs) 
■ The donor strip is placed on a non-slip sterile wooden board and sliced into small segments.
Work with magnifying spectacles or a binocular microscope. 
■ Avoid transection.
Fix the skin ?rmly.
Avoid multiple incisions. 
■ The segments are divided further into strips;the follicular units are now arranged in a row on a piece of gauze. 
■ As part of the preparation work,the units are separated and sorted into single-hair units or units containing 2–4 hairs. 
■ For larger numbers of hair transplants,two to three trained surgical assistants are required for the preparation work. 
■ Replace scalpel blades frequently.
Do not crush the hair follicles! 
■ The dissected follicular units are sorted into rows of 10 units each. 
■ A total of 10 rows per gauze strip and Petri dish equals 100 follicular units or grafts.
Cool the saline solution suf?ciently before use.
Keep the transplants moist at all times!
Recipient Area,Holes and Slits 
■ Ring blockade with articaine and adrenaline (e.g.,Ultracain DS-forteor Septanestwith adrenaline 1/100,000).
Be careful to use an intradermal injection technique and avoid subgaleal in?ltration. 
■ In?ltration with prilocaine 0.5% with adrenaline in the treatment area. 
■ In addition,inject 0.9% saline solution to achieve intradermal and subdermal tumescence.
Allow 10–15min for the solution to take effect. 
■ Be careful to work in the direction of hair growth.
The use of a magnifying device with a power of 2–4?is recommended.
Following the hairline design,punch out 0.8 mm holes for transplants containing 1–2 hairs. 
■ After punching between 5 and 10 holes,make a test transplant to determine whether the transplants can be inserted without any problems.
For example,check whether the size and depth of the holes are suf?cient. 
■ Never transplant hair only along the marked line,as this results in an unsightly “pearl necklace effect”.
A feathered hairline is the effect you want to achieve:“irregular regularity”is the key word here! 
Use the laser for bald areas;switch to cold steel methods in areas still covered by dense hair.
Make continual test transplants to check the suitability of the holes.
If necessary,change the laser setting.
Use slender angled tweezers.
Transplantation Channels:Micropunches (a), Microdrills (b) and Erbium YAG-Laser (c) 
■ Use micropunches with a diameter of 0.8mm,1.0mm or,in rare cases, 1.2mm to avoid an unaesthetic tufted “doll’s head”effect. 
■ Be sure to select micropunches that permit lateral skin ejection and have an internal ground surface. 
■ The distance between hairs is increased by tumescence.
The microholes are placed between healthy hair roots. 
■ In patients with very dense remaining hair,employ a slit technique using chisel blades or 15°,30°or 45°Sharpoint blades. 
■ Measure the number of holes or slits per square centimeter for the documentation. 
■ The holes or slits must be counted consecutively to guarantee correspondence with the number of prepared follicular units. 
■ Transplantation of follicular units with a sharp angled microtweezers (e.g.,Micro 2000 made by Medicon).
Perform non-traumatic implantation with no crushing of hair roots.
The follicular units are placed on moist gauze strips draped over the back of the surgeon’s left hand;they are picked up individually with the microtweezers and then transplanted. 
■ Keep the follicular units moist!
Transplantation 
■ Use swabs to keep the transplantation area clean and free of blood. 
Crusted dried blood prevents a clear overview of the surgical area. 
During hair transplantation,a systematic approach is vital! 
■ When placing transplants in holes,the end of the transplant should be ?ush with the skin surface. 
■ When placing transplants in slits,the transplants should project 0.5–1.0mm above skin level. 
Never insert the transplants too deep since cysts are likely to form in 2–3 months in patients with deep transplants. 
Since the effect of adrenaline and tumescence wears off after 2–3h,stay within the time limits for the transplantation procedure.
Aftercare
Postoperative Precautions
No bandage is necessary with modern surgical methods.
There is no permanent visible scarring.
The same criteria apply,however,after a hair transplantation as after any other operation in the facial area. 
■ Infection prophylaxis is given for 3days after the operation.
From the 3rd day the patient can wash his or her hair with a mild chamomile shampoo.
The hair can then be washed daily.
The hair transplants are ?xed securely and ?rmly. 
■ After a maximum of 2weeks all crusts should have disintegrated with washing;crusts delay wound healing.
Rough manipulation should be avoided,particularly in the 1st postoperative week,as there is a risk of postoperative bleeding.
The patient can be professionally and socially active again 1week after the operation.
After 6weeks vasodilating hair lotion should be used.
The crusts disintegrate quickly with regular washing.
Result 
A 50-year-old patient with Norwood type V hair loss,and 12 months after the operation,following two procedures with a total of 3,120follicular units.
Introduction
Adjuvant therapies should be included in the repertoire of every aesthetic surgeon.
It would exceed the scope of this manual to describe all adjuvant therapies in detail.
Anyone who wishes to undertake further training in this ?eld can ?nd detailed information primarily in dermatological textbooks.
Therefore,a few important adjuvant therapies will be dealt with only brie?y in this volume.
Please refer to the texts in Volume I of the manual for the basic information.
Dermabrasion,chemical peeling,and erbium-YAG laser treatment are examined methodically,but only very brie?y to provide an understanding of the basic principles.
Adjuvant therapies are very often combined with surgery,and an experienced aesthetic surgeon will choose appropriate treatments,depending on the types of wrinkles and skin type.
We do not use injectable alloplastic materials,as damage may occur that is extremely dif?cult to correct and,in a few cases,even irreparable.
The use of autologous fat injections (Mang’s spacelift) and biological implants,such as collagen and hyaluronic acid,is preferred.
The decision to use botulinum toxin injections must be based on stringent criteria. 
The results for forehead wrinkles are good and the treatment can be repeated at intervals of 6months.
The euphoria generated by laser therapy in the early 1990s has not entirely satis?ed expectations for the treatment of the “aging face.”
The laser is not a “miracle weapon,”but has now attained an established place in the ?eld of adjuvant therapies.
We primarily use the ultrapulse CO2 laser for skin resurfacing.
This has already been described in detail in the audiovisual aids in Volume I.
The surgeon must decide whether to perform dermabrasion,chemical peeling,or laser therapy for wrinkles in the perioral region on the basis of his/her experience and his/her own judgment.
Dermabrasion with a diamond cylinder gives good long-term results with no scarring or abnormal pigmentation for moderately deep lip wrinkles in younger patients.
Chemical peeling (e.g.,trichloroacetic acid 35%) may be useful for older patients with deeper wrinkles.
Erbium-YAG laser provides the best results for wrinkles in the perioral region and particularly the area of the lower eyelids.
Local Anesthesia
Nerve Exit Points,Supraorbital Nerve,Infraorbital Nerve,Mental Nerve 
If adjuvant therapies are not combined with operations (e.g.,a facelift), they are performed under local anesthesia and as day-case treatment. 
Nerve block anesthesia with Ultracain 1% (articaine) and additional adrenaline have proved to be successful.
When treating the entire face by laser or chemical peeling,light sedation also can be induced with Dormicum (midazolam) with anesthesiology stand-by.
With all operations carried out as day cases,a venous line and,if necessary,antibiotic prophylaxis are recommended.
Biological Implants*
Only endogenous (bone,cartilage,fascia,connective and adipose tissue, etc.) and biosynthetic materials (collagen,hyaluronic acid) are used at our clinic.
We do not use alloplastic materials (e.g.,silicone and paraf?n oils, PMMA,etc.) since they can cause unpredictable and sometimes irreparable damage.
The most important principle in aesthetic surgery is health before beauty.
Avoid all new materials that have not undergone long-term testing.
This applies not only to injectable materials but also to breast implants and suture material.
Injectable collagen is an ultrapuri?ed bovine collagen of type I.
Depending on the concentration (35–65mg/ml),this material is available in various ready-to-use ampules with a local anesthetic.
The injection of the dermal ?ller substance has been given correctly if the aesthetically disturbing area is overcorrected and the skin becomes white (blanching effect).
This technique can be used to treat all wrinkles in the facial area (glabella,eyes,nasolabial folds,lips),acne and accident scars,and also to augment cheeks and lips.
If collagen is to be used,a test must be performed with 0.2ml of collagen on the inside of the forearm 4weeks prior to treatment to rule out allergies.
This test is not necessary if hyaluronic acid is used.
This substance is also fully biodegradable,but it is a polysaccharide,not a protein compound. 
This means there is nearly no allergenic potential and testing in advance is unnecessary.
The indications are the same as for collagen,although hyaluronic acid is slightly more viscous to inject.
Hyaluronic acid is also available in various concentrations,so ?ne creases in the area of the eyes can be treated with material with a lower concentration and nasolabial folds and lips can be augmented with material with a higher concentration.
Injection Technique
The liquid collagen is injected intradermally at an angle of 30°,resulting in over-correction and the “blanching effect.” 
The injection must be strictly intradermal,and it is essential that it is not subcutaneous,as otherwise it will be ineffective and the material will be absorbed immediately.
If the correct injection technique is used,the result will last for 6–8months.
Maintenance injections can then be given.
Report – Technique
Collagen and Hyaluronic Acid 
■ The skin must be thoroughly disinfected prior to the injection.
Nerve block anesthesia with 1% Ultracain and additional adrenaline can be used in patients who are particularly sensitive to pain and when treating large areas.
Surface anesthesia with the anesthesia ointment EMLA? (lidocaine-prilocaine cream) may be given at the patient’s request. 
■ Hyaluronic acid and collagen are injected directly into the wrinkle using Mang’s serial point-by-point technique with overcorrection.
The injection should be made at an angle of 30°.
The patient should be lying down and the doctor carrying out the treatment should be sitting.
Overcorrection can be up to 100%.
If the injections are placed correctly,raising of the skin and a blanching effect will be visible immediately. 
■ The injection should be as close as possible to the surface.
The point-bypoint technique is used to remove forehead wrinkles (glabella),nasolabial folds and lip wrinkles.
Fine eye wrinkles are treated with linear injections. 
The needle is inserted super?cially along the eye wrinkle and pushed forwards;when it is retracted,the material ?ows like water into a riverbed. 
The wrinkle is then massaged immediately to prevent nodules forming.
In principle,all wrinkles in the face and neck area can be treated with these two techniques.
The skin is always pretensioned by applying mild traction. 
■ Fine corrections can be made at the end of the procedure with the aid of a magnifying glass.
All nodules and necklacelike structures should be smoothed and massaged.
The injectable material should spread out, almost as if in a riverbed,if an optimal result is to be achieved. 
■ A high-concentration collagen is used to enlarge the lips.
When carrying out lip augmentation for the ?rst time,it is advisable to begin ?lling-in at the margin of the lip,i.e.,at the transition of the lip from red to white. 
The needle should be inserted along the edge of the lip at an angle of 10–20°,starting at the corner of the mouth and working toward the center.
Ideally,the material should be distributed along the vermilion border,thus rede?ning the contours.
Up to 4ml of collagen may be injected per session,depending on the extent of lip augmentation. 
■ After the treatment is completed,dexpanthenol ointment should be applied evenly to the injection sites and the treated areas should be compressed under slight pressure for approx.15min.
Avoid sun and alcohol for 24h.
Make-up can be worn again 1day after the operation. 
The patient is also able to return to work 1day after the operation.
This is a 38-year-old female patient with a deep nasolabial fold. 
Injection of 1ml collagen on each side. 
Follow-up after four months with smoothed nasolabial fold.
This is a 38-year-old female patient with a deep nasolabial fold. 
Injection of 1ml collagen on each side. 
Follow-up after four months with smoothed nasolabial fold.
Crystalline Polylactic Acid 
Polylactic acid is available as a lyophilisate that is dissolved with water for injections.
In addition to microspheres,polylactic acid contains the products carboxymethylcellulose and mannitol.
Poly-L-lactic acid is biocompatible,immunologically inactive,and biologically absorbable. 
Synthetic production is used;therefore skin testing is not necessary*. 
■ Indication:deep folds,to provide contours and to build up volume,e.g., nasolabial folds,marionette folds,and cheeks,possibly chin,scars,and upper lip.
Also to build up the cheeks in cases of lipoatrophy. 
■ Mechanism:after Sculptra? has been injected,the wrinkle is mechanically ?lled with the injected volumes.
The water contained in the suspension is,however,absorbed by the body within a few days and the wrinkle returns.
A gradual and natural build-up of volume is achieved only after this as a result of the formation of new collagen ?bers.
This provides a lasting effect which,in a good case,may last for more than 2years. 
Induration may sometimes occur. 
■ Contraindications:allergy to one of the components;acute or chronic skin diseases:injections in the vermilion of the lips. 
■ Explanation of procedure:a written declaration of consent must be obtained from the patient regarding possible complications such as hematomas,swellings,reddening of the skin and formation of nodules. 
? Injection depth:deep dermis to the border to the subcutis. 
? Materials required: poly-L-lactic acid (Sculptra?) Water for injections Possibly local anesthetic 
? Storage:at room temperature (not above 30°C)
Usage: 
■ Reconstitute the lyophilisate with 5ml water for injection (note:it can also be dissolved with 4ml water and 1ml local anesthetic).
Add the water to the bottle slowly and allow to stand for at least 2 h so that the water can penetrate the lyophilisate 
■ roduce photographic documentation prior to treatment. 
■ Possibly local anesthesia (cream or regional anesthesia). 
■ Disinfect skin. 
■ Shake the bottle well until the suspension is homogeneous,immediately before use Shake again before opening the bottle in every case! 
■ Use a 26-Ga needle for injection. 
■ Check that the injection needle is unobstructed before every injection is given. 
■ Linear injection technique:?rst insert the full length of the cannula,then inject with a slight punching pressure when withdrawing the needle. 
Inject only small quantities (0.1–0.2ml per injection). 
■ Then massage the area of the face treated (preferably with cream to reduce the friction) and cool if necessary to reduce the swelling. 
■ Aftercare:cooling until the swelling has reduced.
Massage the areas of the face treated for a few minutes over several days.
Contouring Using the Mang Method 
This involves combined treatment with NewFill? for the deeper layers of skin (subcutaneous linear injection technique) and Viscontour? for the super?cial wrinkles (epidermal point-by-point injection technique). 
Our experience has shown that combined treatment with the lactic acid product Sculptra? and the hyaluronic acid product Viscontour? produces good results in the long term although neither material is alloplastic.
Botulinum Toxin
Horizontal lines and glabella wrinkles are often dif?cult to remove surgically.
The forehead is made up of numerous mimicry muscles that cannot be entirely smoothed-out even following a brow lift (endoscopic, coronal,or hairline cut).
Botox is therefore an important resource for removing wrinkles in the forehead region.
Patients are amazed at the results and even accept that the injections will have to be repeated after 4–6months if they want to have a smooth forehead.
Botulinum toxin must be injected by an experienced doctor under sterile conditions in the clinic,with the treatment carried out as day-case surgery.
Otherwise,signi?cant complications may occur,including paralysis of the eyes.
The preoperative marking of the injection sites is particularly important if adverse side effects are to be avoided.
The patient should frown so that it is possible to see the area of maximum muscle activity.
Particular care should be taken in the supraorbital region and lateral to the pupillary boundary (illustrations).
No more than 1.5ml botulinum toxin,corresponding to 60U of Botox, should be injected per session.
Treatment should be repeated after 4months at the earliest.It is safe to give three injections per year.
As the ampules supplied by the company contain 2.5ml botulinum toxin, which is dissolved in non-preserved saline solution,it is advisable to inject 1.2ml per session.
To avoid wasting of the material it is always advisable to treat two patients at the same time.
It is possible to treat periorbital wrinkles,perioral wrinkles,a drooping corner of the mouth,and wrinkles in the chin and neck (platysma),as well as forehead wrinkles,with botulinum toxin.
The platysma can extend over the thorax as far as beyond the second rib and is above the fascia here.
Diagonal neck wrinkles can be treated via 6–12 injection sites.
These should be positioned in the shape of an upside-down triangle and 4U of Botox should be injected at each site,at intervals of 1cm with the needle at an angle of 45°.
This treatment can also be combined with a facelift,but we recommend that botulinum toxin is not be given intraoperatively while the patient is under anesthesia.
Botulinum toxin should not be given until the second day after the operation for medicolegal reasons.
Report – Technique 
■ Little material is required for botulinum toxin injections.
The ampule contents are dissolved in 2.5ml of a non-preserved saline solution.
The suction of the syringe plunger is evidence of the vacuum inside the ampule. 
■ For the injection,we use a convention insulin syringe with appropriately ?ne graduations (4U of botulinum toxin correspond to 0.1ml).
It is recommended that the novice use syringes with a volume of 0.3ml so that the dosage of the injections can be even more accurate. 
■ The injection is made directly into the center of the muscle with a 30-Ga cannula.
In the forehead and glabella regions,it is recommended that the injection be made at an angle of 90°,vertical to the periosteum.
The syringe is then pulled back slightly until the center of the muscle is reached.
The material,usually 0.1ml,is then injected.
The injection quantity is lower/fewer units are injected in the perioral and periorbital areas,i.e.,2–3U. 
■ The marking of the injection sites prior to the operation is particularly important if adverse side effects are to be avoided.
The injection must be made under sterile conditions following prior careful disinfection. 
■ In women with highly arched eyebrows,the muscles of the forehead are not highly developed.
They have a lower mass and therefore only a small amount of Botox is required for paralysis.
The ?xed points for the injection in such cases are the midline between the two eyebrows,and on the vertical line from the inner canthus to the upper margin of the osseous orbit as well as 1cm cranial to this. 
■ In women with more horizontal eyebrows,the muscles are more highly developed,and a slightly larger quantity of botulinum toxin is therefore required.
Additional injections can be made 1cm above the osseous margin of the orbit in a line running from the middle of the pupil in a cranial direction.
There is a danger of ptosis if material is injected lower than this. 
■ In patients with pronounced horizontal wrinkles caused by the activity of the frontalis muscle,the injections are made along an imaginary horizontal line between the eyebrows and hairline in the vertical line running from the pupil in a cranial direction.
Further injections are made between these two points.
Additional sites can be de?ned individually depending on muscle activity and the depth of the wrinkles in the forehead area. 
■ Eyebrows that appear too straight and droop at the sides can be lifted with injections.
In this procedure,botulinum toxin is injected into the upper lateral section of the orbicularis oculi muscle at a site close to the orbital margin,1–2cm above the lateral corner of the eyelid.
Applying a counterpull to the frontalis muscle causes slight raising of the lateral eyebrow. 
■ Crow’s feet are treated with one injection 1.5cm lateral to the canthus and two injections cranial and caudal to this point.
The osseous orbit serves as a point of orientation.
Tensioning of the orbicularis muscle can sometimes create a tense or bitter facial expression.
By injecting Botox into parts of the ring muscle,this can be modi?ed to give the patient a more friendly facial expression.
The injections are made directly below the edge of the lower eyelid in the mid-pupillary line.
During the injections,the patient should have his or her eyes open and be looking upwards. 
■ Depending on how vigorously the orbicularis oris muscle is contracted, 2–4 injection sites are distributed in a line along the lip margins,i.e.,one point lateral to the philtrum on the left margin of the lips and one on the right,and one further point. 
■ Furrows develop over the years as a result of the pull of the depressor anguli oris muscle and these run from the corner of the mouth in a caudal direction.
The injection is made into the center of the muscle, which can be identi?ed by palpation,approx.1cm lateral and 1cm caudal to the corner of the mouth. 
■ If the skin is highly elastotic,contraction of the mentalis muscle may result in the chin having a “cobblestone”appearance.
Botulinum toxin (0.1ml) is injected at two paramedial injection points,approx.0.5–1cm above the tip of the chin. 
■ The platysma can be inactivated by botulinum toxin so that the neck appears smooth when tensioned.
Treatment should be started with low doses.
The experienced doctor can then extend the injections to the entire face with the following units. 
■ Twenty units of botulinum toxin,injected into the procerus muscle and the middle of the corrugator supercilii muscle,divided into several individual doses,are suf?cient to smooth “anger wrinkles.”
To reduce the activity of the frontalis muscle,treatment should be with a total dose of around 16 units per session.
The treated areas are compressed brie?y after the injection.
The patient must then keep his/her head upright. 
■ Three units of botulinum toxin per injection site are used to smooth crow’s feet in the area of the eyes.
In the perioral area,1–2U are injected per injection site with the needle at an oblique angle,inserted only slightly and pointing in a cranial direction. 
■ To lift the corner of the mouth,3–5U are injected into the center of the depressor anguli oris muscle.
The center is identi?ed by palpation. 
■ In the chin region,3–5U injected at two injection sites in the area of the mentalis muscle will be suf?cient to achieve a smooth appearance.
The injection should be vertical and in the direction of the periosteum. 
■ When treating the submental region,the platysma should be contracted and held between the thumb and index ?nger (platysmal bands).
Four units of botulinum toxin are injected subcutaneously,directly into the platysma at intervals of 1cm with the needle at an angle of 45°. 
■ There are many indications for the use of botulinum toxin and the aesthetic surgeon must gradually push the boundaries to be able to achieve good results without risks.
A 37-year-old patient with pronounced forehead mimicry. 
Injection of 1.0ml botulinum toxin in the forehead area following prior marking of the injection site. 
Findings 3months after treatment.
Mang’s Spacelift
Introduction
The name spacelift was chosen by the author and protected by patent (no.30323891) as appropriately puri?ed and centrifugated,recycled fat droplets are injected into the entire face,as in a honeycomb,using microinjections.
The fat particles break down but,as a result of the contact with vessels (because they are not injected in large quantities in a bolus dose),they are able to form their own ?broblasts and the catabolized fat cells are augmented with ?broblasts and elastin ?bers.
Virtually no scars are formed and the face stabilizes as a result of the procedure. 
Naturally,injections can be made beneath other wrinkles in the forehead and nasolabial area using a conventional fat injection technique.
Lipotransfer is also recommended for lip augmentation.
Indications
As early as 1893,Neuber reported that adipose tissue transplant material could survive only in the smallest particles.
This is the most important condition for a successful fat transplant.
In 1922,Lexer stated that if the adipose tissue is not damaged by bleeding either when it is removed or when it is implanted,it can survive for 3years.
In 1950,Peer announced that up to 50% of transplanted fat survives if excessive negative pressure is not exerted on the fat during extraction by suction and excessive positive pressure is not exerted on the fat during injection.
Vascularization of the fat droplets takes place after 4days and until that time survival is guaranteed as a result of diffusion.
In 1986,Coleman reported that fat can only survive as a tissue compound and not as an individual cell.
Oil,blood,and local anesthetics must be separated from the structural fat by gentle centrifugation.
The individual particles of adipose tissue must be positioned close to the vessels to be fed to facilitate independent anchoring in the surrounding tissue.
Thus,all the criteria for a stable transplant would be ful?lled.
Indications: 
? To replace atrophied or wasted structures resulting from aging or the sequelae of in?ammatory skin diseases (e.g.,acne) 
? To strengthen existing structures 
? To create harmonious and aesthetically pleasing facial features by replacing wasted tissue with fan-shaped,three-dimensional implantation of autologous fat particles 
? Congenital or acquired deformities of the osseous and connective tissue structures (sequelae of burning, blunt soft-tissue injuries, facial fractures, cleft lips, midfacial hypoplasia, hemifacial atrophies, micrognathia)
■ The overall appearance of the face and the proportions can be improved by emphasizing speci?c facial structures (e.g.,the chin appears smaller when the lips and the margins of the lower jaw are augmented). 
■ The fat must be removed under sterile conditions in the operating room. 
■ Sites for fat removal are those where contours can be achieved without creating hollows (e.g.,double chin,lower abdomen,medial side of the thigh,knee). 
■ Following tumescent anesthesia,the fat is removed using low-vacuum liposuction (–0.2atm;this is approximately 20–30% of the vacuum used with normal liposuction) with a blunt 2ml suction cannula. 
■ The diameter of the cannula openings should correspond to a Luer-Lock so that the fat particles can pass through the equipment without being damaged further during the later transplantation = gentle curettage of the tissue with minimal vacuum.
Technique
■ If suction is performed using a conventional liposuction system,the fat is now transferred to 10ml syringes under sterile conditions.
The plungers are then removed from the syringes.
The syringes are placed in a centrifuge and spun at 3,000rpm for 4min. 
■ This separates the aspirate into three layers: 
? The top layer consists of oil and ruptured fat cells;this is drained and carefully dabbed away.
? The bottom layer consists of tumescence solution and blood;this is drained off. 
? The middle layer is made up of usable subcutaneous adipose tissue; using an adapter,this is transferred into a 1ml Luer-Lock syringe without traumatization.
Injection Technique 
■ The fat should be injected in a fan shape and in two to three layers.
The face is built up and stabilized with fat droplets using a three-dimensional technique,as if in a honeycomb. 
■ The supraorbital,infraorbital;and mental nerves can be blocked to provide anesthesia.T
he individual injection sites may also be treated with local anesthesia. 
■ The fat removed using the tumescence technique is spun at 3,000rpm for 4min so that only vital,puri?ed fat is used for the fat injection.
The fat is transferred into 1-ml Luer-Lock syringes using a special adapter. 
The globules of fat can be positioned,as if they are a string of pearls, using a 20- or 23-Gg needle.
This ensures surface contact with the surrounding capillaries and also allows the fat implants to become ?rmly anchored in the surrounding connective tissue.
■ Three-dimensional implantation of fat globules is particularly effective. 
With this technique,several channels are placed on top of one another in a fan-shaped pattern at various levels within the subcutaneous tissue.
It is best to begin with the deepest fan-shaped layer and then place the fanshaped layers on top of one another.
In addition,particularly pronounced mimicry wrinkles on the forehead and in the nasolabial area can be treated separately using the intracutaneous serial point-by-point technique with a ?ne needle,in a similar way to the point-by-point collagen technique. 
■ It is important that all the areas treated by injection are massaged with the ?nger when the treatment is completed so that there is no bulging and no nodules form.
This applies particularly to sites treated by injection in the lip,nasolabial,zygomatic arch,and forehead areas.
Lip modeling can be performed easily with this technique,but three injections will be required (at 0,6,and 12 months). 
■ The survival of the transplanted fat globules can only be guaranteed if the maximum distance to well-perfused host tissue is 1.5mm.
Otherwise, the fat transplant will die,it will be absorbed,or it will become calci?ed. 
■ First,a tunnel is created at the tip using the cannula and without exerting any pressure.
This is ?lled with puri?ed fat when the cannula is pulled back,by exerting slight,uniform pressure on the plunger.
A row of channels is then created with the cannula,and these are ?lled with fat when the cannula is pulled back. 
■ Compression bandages are only necessary if there is concern about possible displacement of the implant.
Areas with pronounced mimicry, e.g.,the glabella,are immobilized with Steri-Strips.Cooling for 2–3 days is advisable.
Antibiotic cover is given. 
■ Several sessions (up to three) are usually necessary,as the connective tissue septa of the subcutaneous tissue will only allow in a certain quantity of adipose tissue transplants.
Otherwise,the fat globules will be traumatized.
A certain amount of edema also always develops in the host area as a result of the in?ltration.
Possible complications: 
? Edema indicating repair processes in the many small channels created;possible for up to 4weeks. 
? Hematomas (owing to incorrect technique). 
? Overcorrection,undercorrection. 
? The formation of palpable and visible nodules,even in the tissue surrounding the defect,can be avoided if a fan-shaped implantation technique is used. 
? Fat necrosis occurs if too much fat has been implanted in a limited host area. 
Please note:the maximum distance permitted from the center of the fat droplet to the surrounding capillary tissue is 1.5mm.
Otherwise, fat necrosis and possibly calci?cation may occur. 
? Migration of the fat implant is possible if the injection is made into muscle or ?rm connective tissue. 
? Infections. 
? Nerve and vascular damage is virtually ruled out if blunt cannulas are used.
Advantages of lipotransfer: 
? The fat globules can be obtained easily using liposuction. 
? The transplant is autologous. 
? No immunological reactions/complications are to be expected. 
? Fat can be injected below all wrinkles and depressions if the correct technique is used. 
? It is possible to repeat the treatment without any problems. 
? The costs are comparatively low.
Surplus aspirated fat can be frozen (e.g.,2?10ml) and reinjected again in divided doses (at 0,6,and 12 months).
The fat should be stored at –18°C for no longer than 1year.
Remove the syringes ?lled with fat from the freezer 3h before reinjection.
Each fat transplant should be marked with the operation date and the patient’s name and date of birth. 
As a result of the divided and repeated injection of fat cells,increased ?brosis (booster effect) occurs and this ensures a longer-lasting effect.
Diagram of the Fat Injection 
Ideally,the fat will be injected in drops into the infrastructural connective tissue (ICT) in a three-dimensional way.
This ensures surface contact with the surrounding capillaries and allows the fat to become anchored in the surrounding connective tissue.
It is transformed into separate scar and connective tissue as a result of ?broblast activity,which ensures the facial skin is stabilized and acts as a prophylaxis against aging. 
A spacelift is not recommended if there are hanging areas of skin.
A spacelift can postpone the need for a facelift but is not a substitute for one.
Three-Dimensional Diagram of Fat Injection into Subcutaneous Tissue 
The fat droplets lodge themselves in the subcutaneous tissue.
If positioned correctly,and because they are not injected in a bolus dose,they become associated with the capillaries and consequently,following appropriate transformation,they help to stabilize the infrastructural connective tissue (ICT).
Increasing the Density of the Connective Tissue Following Breakdown of Fat Droplets 
The loss of elastin and collagen ?bers caused by aging can be partly offset with the breakdown/transformation of adipose tissue as a result of ?broblast activity.
The absorption rate for fat is different for every patient,so even this procedure must be carefully explained.
Even though this method does not offer eternal youth,the spacelift is a step forward towards the goal of biological anti-aging.
A 39-year-old female patient with drooping eyelids,nasolabial and lip wrinkles,and a tired facial expression. 
Findings 6 months after the operation following two fat injections (First session 15ml,second session 8ml).
Dermabrasion
Introduction 
Dermabrasion demands experience,skill,and concentration from the doctor carrying out the treatment.
If performed well,the results for dermabrasion are very good,particularly in the perioral region and the area of the lips.
The doctor’s experience will determine whether he/she chooses to use chemical peeling,laser treatment,or abrasion treatment. 
This depends on the patient’s age and skin type.
The abrasion head,which is coated with diamond dust,must be supported and held perpendicular to the plane of rotation.
The pressure on the burr should always be the same to avoid creating grooves.
Dermabrasion should not be performed below the level of the dermis.
After-care consists of placing a wound gauze soaked in antibiotic ointment on the wound.
This dressing is removed after 24h.
The face is treated with dexpanthenol ointment for a further 8days.
Make-up may be applied after epithelization of the wound surface.
It is necessary to protect the skin from the sun or even avoid the sun for 3months (pigment abnormalities).
Report – Technique 
■ Protective goggles should be worn during the procedure.
The operation site should be draped and disinfected carefully.
The skin should be tightened by an assistant.
Level/?at surfaces can be created by stretching the skin.
This facilitates abrasion and permits the application of treatment at an exact depth.
Skin tension must be maintained during the entire abrasion procedure.
The abrasion head,which is coated with extremely ?ne diamond dust,must always be kept perpendicular to the plane of rotation.
It should not be guided in the direction of rotation.
Instead,it should be moved over the surface of the skin against the rotation of the abrasion head at an angle of 90°and slight pressure should be applied. 
Punctiform,super?cial bleeding is the most reliable indicator that the grinding procedure has reached the optimal depth.
Abrasion should not be performed at a deeper level. 
■ Dermabrasion is complete when an even wound surface with ?ne,punctiform/diffuse bleeding has been created.
A wound gauze soaked in antibiotic ointment is then placed over the wound.
A 54-year-old patient with wrinkles in the perioral and upper lip areas. 
Findings 12 months after dermabrasion.
Chemical Peeling
Introduction
Chemical peeling falls into the dermatologist’s sphere and can be studied in full in textbooks covering this ?eld.
The same applies to all types of laser treatment.
These two procedures are therefore discussed only brie?y in this manual.
Chemical peeling includes various types of peeling,which differ in terms of the intensity (e.g.,fruit acid,glycolic acid,alpha hydroxy acid, trichloroacetic acid,phenol).
The application of the agents appears simple,but experience is vital and an expert assessment of the skin areas is essential.
The key to successful peeling is to apply the solution evenly and homogeneously,to provide accurate information about the risks (scarring and abnormal pigmentation),and to carry out the correct follow-up treatment.
Report – Technique 
■ The most practical,most effective,and safest method of chemical peeling for the novice is to use 30% trichloroacetic acid.
This removes the entire upper layer of skin,down to the reticular dermis.
Nerve block anesthesia of the infraorbital and/or mental nerves can be used in patients who are particularly sensitive to pain,but local anesthesia using an occlusive dressing is usually suf?cient. 
■ Following disinfection,the skin is treated with acetone to remove super?cial skin scales.
This allows better penetration of the acid into the skin. 
After the acid has been applied,the area to be treated is marked.
The acid is applied homogenously over the entire surface at a consistent pressure.
This is the true art of any type of peeling.
The application of the acid may be repeated several times,depending on the depth of the wrinkles,using slight pressure.
Each area of skin must be treated with the same intensity so that the skin relief does not vary later.
The typical blanching,which is a sign that the treatment has started to take effect (frost effect),begins before the treatment has been completed. 
■ Follow-up treatment is with Vaseline as this reduces the sensation of tautness.
Herpes prophylaxis with acyclovir 400mg three times daily is recommended for 5days as well as antibiotic cover.
Female patient with numerous wrinkles in the mouth region. 
Findings six months after chemical peeling with trichloroacetic acid.
Erbium-YAG Laser
Introduction
The euphoria generated by laser therapy in the 1990s has dissipated somewhat,as the long-term results of treatment for the aging face did not live up to all the expectations.
Laser surgery will develop further in the future and the repertoire of the aesthetic surgeon is unimaginable without it,but its use must be considered very carefully.
Skin resurfacing with pulsed CO2 laser treatment was explained in detail in Volume I of the manual,so only pulsed erbium-YAG laser treatment will be described brie?y here.
The advantage of using erbium-YAG laser treatment instead of CO2 laser treatment is that there is less necrosis and the treated area heals more rapidly because of the lower thermal impact on deeper tissue layers.
The lack of a coagulation effect,however,limits the treatment of wrinkles as it is presumed that the collagen structure will not change because the ablation is virtually non-thermal.
In principle,pulsed CO2 laser treatment can be used in all cases where erbium-YAG laser treatment is recommended,so an aesthetic surgeon should only purchase an erbium-YAG laser if his work focuses on antiaging surgery of the face.
Report – Technique 
■ Crow’s feet in the lower-lid area can be treated well with the erbium-YAG laser.
The advantage of this non-invasive procedure,which can be carried out on an outpatient basis,is the rapid healing of the treated sites. 
Following disinfection and anesthesia of the operation site (e.g.,blocking of the infraorbital nerve),the boundaries of the section to be treated by laser are ?rst de?ned.
The laser is then guided evenly,section by section,over the area to be ablated.
Slight overlapping will not be harmful.
The use of the erbium-YAG laser as an additional resource during plastic/aesthetic procedures,e.g.,facelift or blepharoplasty,is an elegant, non-invasive way of treating wrinkles and creases in aged and sundamaged facial skin quickly,especially around the mouth and eyes and on the forehead and cheeks.
When used correctly,possible risks such as abnormal pigmentation and scarring are virtually ruled out.
The effect achieved is good when smoothing super?cial and medium-depth skin wrinkles.
When treating deeper skin wrinkles,there is de?nitely an improvement in the overall appearance,but the wrinkles cannot be completely removed.
In this case,CO2 laser treatment is more effective. 
Wound discharge and crust formation are less pronounced following erbium-YAG laser treatment and do not persist for as long as with CO2 laser treatment.
There is also less postoperative skin reddening and this reduces more quickly.
■ Use of the erbium-YAG laser for patients who have aesthetically disturbing skin changes in the facial area that are not yet too severe can therefore enhance the spectrum of practical work performed by surgeons with an interest in skin surgery.
A 39-year-old female patient with deep lower-lid creases. 
Six weeks after erbium-YAG laser treatment. 
Hyal System
Introduction 
The Hyal System makes use of the fact that native hyaluronic acid has a high level of biointeractivity and can therefore increase ?broblast activity and the neosynthesis of endogenous hyaluronic acid,elastin and collagen.
Unlike highly cross-linked,chemically changed hyaluronic acids which are used exclusively as dermal ?llers,the Hyal System* injection technique attempts to create attractive tissue by building up the extracellular matrix of the skin areas in three dimensions using small droplets,a method similar to that used in a spacelift.
The desired effect is achieved in 6 weeks at the latest.It is intended more as prophylaxis against aging skin and can also be used in the area of the neck,chest and hands.
In 1934,Karl Meyer and John Palmer isolated hyaluronic acid (a glucosaminoglycan) from the vitreous body of a cow’s eye.
Hyaluronic acid, a linear polymer,is made up of the disaccharide units D-glucuronic acid and N-acetyl-glucosamine.
It occurs naturally in human eyes,in joint surfaces,and in the skin.
In the skin,it serves as a substrate of the cell structure and the extracellular matrix.
In the dermis,it is associated with the elasticity and hydration of the skin.
It also increases ?broblast activity and the neosynthesis of endogenous hyaluronic acid,elastin and collagen.
Until very recently,hyaluronic acid was considered to be only a space-?lling substance with a purely mechanical function.
We now know that hyaluronic acid speci?cally modulates biological processes in humans and animals via endogenous membrane receptors.
Depending on the area of application,we can therefore regard hyaluronic acid both as a medication with a long-term pharmacological effect and as a medical product when only the viscoelastic properties of this macromolecule are used.
In aesthetic medicine,it is used to reduce skin wrinkles,to increase regional volume,and to treat scarring,as well as to improve skin tautness and strength.
In general,it is possible to distinguish the two products. 
■ 1. Dermal ?ller 
? Highly cross-linked,chemically modi?ed hyaluronic acid 
? Molecule inertia 
? Static skin implant 
? Mechanical increase in volume 
■ 2. Hyal System 
? Native hyaluronic acid 
? High level of biointeractivity 
? Homogeneous distribution in the skin layers/surfaces 
? Biorevitalization with long-term effect
Hyal System is available in 1.1ml ready-to-use syringes.
This is a natural, chemically unchanged hyaluronic acid polymer (polysaccharide).
The solution is highly concentrated and has a low viscosity and therefore has good ?ow properties in comparison with dermal ?llers.
Report – Technique 
■ Following surface or nerve-block anesthesia (supraorbital,infraorbital, mental) and disinfection,injection of the Hyal System into the papillary dermis via a 30-guage cannula is started. 
■ The angle of insertion is normally 10–15°and the cannula should then be advanced parallel to the surface of the skin.
Blanching of the skin will be visible if the injection has been given correctly.
A cross-link,tunnel,or fan injection technique is used depending on the anatomical region. 
A serial point-by-point injection technique can also be used with appropriate indications (nasolabial).
In the cheek region,a cross-link injection technique is used,i.e.,following an imaginary,diagonal framework; injections are given either at every or at every second horizontal and vertical point of intersection,and the entire area to be treated is thus undermined.
The needle is inserted at an angle of 10–15°and then moved so that it is parallel to the surface of the skin.
This ensures the correct injection level (the papillary dermis) is reached.
The injections are ?rst made in a horizontal direction and the area being treated is then brie?y compressed.
The injections are then continued in a vertical direction. 
■ At the sides of the eyes,the tunnel technique is most suitable.
Injections are made into the upper dermis in parallel lines. 
■ The cross-link technique is most suitable for treating the glabella and the upper area of the forehead because of the large area coverage.
In modi?ed form,this technique can be applied laterally.
■ To achieve a rejuvenating effect in the upper perioral region,the Hyal System is injected parallel to the upper lip in droplets.
Similar injections are made parallel to the lower lip to complete this treatment. 
■ The Hyal System can also be used to tighten larger areas of the neck,the d?collet? region and the hands,and a modi?ed cross-link technique must be used in these areas,i.e.,systematic,even,and fan-shaped injections must be given over the entire area to achieve a satisfactory result. 
The aim is to establish ?broblast activity and neogenesis of endogenous hyaluronic acid,elastin and collagen. 
■ Depending on the size of the areas to be treated,multiple syringes of 1.1ml may be injected.
We use 2ampules per session. 
■ In young patients who still have ?rm skin tone,three injections at intervals of 4weeks will be necessary initially.
Subsequent injections should be repeated every 4–8months to maintain the result.
In older patients with atonic skin and insuf?cient elasticity,three injections should be given at fortnightly intervals and boosters should then be given every 3–6months.
Follow-up Treatment 
■ Following treatment,the undermined area should be compressed for approx.15min.
The Hyal System is an innovative method to restore better quality to the aging skin.
In the future,it is certain that there will be many useful developments in aesthetic surgery.
Prospects – What Is the Future of Aesthetic Surgery?
There has been a boom in aesthetic surgery all over the world and the rate of growth has doubled.
The age of patients ranges from 14 to 80 years and every ?fth cosmetic operation is now requested by a man. 
Research into new materials,implants,instruments and equipment,even robot-controlled operation modules,is important for the further development of aesthetic surgery,but these can never replace the skill of the aesthetic surgeon.
A ?rst-rate aesthetic surgeon must not only be welltrained;he must also be a psychologist and an artist if he wants good results.
The fundamental requirement,however,is correct training.
Aesthetic surgery is high-tech surgery.
It has a ?xed position in society and must establish itself as an independent,interdisciplinary specialty. 
Aesthetic surgery must no longer be taught as an appendage to the specialties of surgery,plastic surgery,ENT surgery or maxillofacial surgery, but must be taught over a 3-year advanced training period following high-quality surgical or plastic surgery training and acknowledged as a specialty with a recognized title.
This is my hope for the future,as only this will make it possible for us to achieve worldwide quality assurance and make aesthetic surgery a recognized specialist surgical discipline.
Aesthetic surgeons should work together with specialists in all disciplines whom they could learn from,and with whom they should exchange their knowledge at conferences throughout the world,never forgetting the Hippocratic oath.
Aesthetic surgery should not be “alteration surgery”but rather “well-being surgery.”
We have understood our profession correctly if we are able to make patients feel good.
As president of the International Society ofAesthetic Surgery (ISAS*),in the future I would like to give all young colleagues with an interest in this ?eld an opportunity to become members and make the specialty of aesthetic surgery accessible in a yearly “exchange of ideas.”
Only when the range of treatments is improved and developed further internationally,and there is a spirit of cooperation among surgeons,will we be able to gain better recognition within society for this ?eld.
I hope that this manual can play a part in this and my dream one day,of standardized training leading to the title “aesthetic surgeon,”will become a reality.
Aesthetic surgery is a specialty of the future.
Young doctors are extremely interested in this ?eld.
Doctors from all over the world visit Prof.Mang’s clinic every day.
The Manual ofAesthetic Surgery,Volumes I and II,forms the basis for comprehensive training in the ?eld of aesthetic surgery.
The Bodenseeklinik offers an opportunity for interested doctors to apply the knowledge described in the two volumes of the manual in practical aesthetic surgery.
A hospitality fee of 250 US dollars per day is charged for this.
This money will be used by the Prof.
Mang Foundation charity to help needy children.
The skin is composed of three layers: epidermis, dermis and subcutaneous tissue. 
The thickness of the layers varies with different anatomical regions. 
The epidermis is thickest on the palm and soles, and very thin on the eyelids, while the dermis is thickest on the back. 
Keratinocytes are the main component of the epidermis. 
Melanocytes are the cells located in the epidermis whose function it is to produce pigment. 
The ratio is about one in every ten basal keratinocytes. 
Differences in skin color according to race are explained by the number of melanosomes. 
Langerhans cells represent 3–5% of the cells of the stratum spinosum where they are situated between the keratinocytes. 
The dermis consists of a supporting matrix (ground substances) in which polysaccharides and proteins act to produce proteoglycans. 
The protein fibers inside the dermis are represented by collagen, elastin and other components, such as fibrillin and microfibril proteins. 
The blood supply to the skin comes from the deep plexuses located at the fascia and subcutaneous level. 
With aging, there is a decrease in total collagen content in the skin, an increased amount of type III collagen, decreased number and diameter of elastin fibers, and a lack of interaction between water and surrounding molecules which contribute to the dry and wrinkled aspect.
The skin is composed of three layers: epidermis, dermis and subcutaneous tissue. 
The epidermis is the outer layer and is formed mainly by keratinocytes whose main function is to synthesize keratin. 
The dermis is the middle layer and its main component is collagen. 
This layer lies on lobules of lipocytes. 
The thickness of the layers varies with different anatomical regions. 
The epidermis is thickest on the palm and soles, and very thin on the eyelids, while the dermis is thickest on the back.
The epidermis is the outer part of the skin and is composed of three basic cell types: keratinocytes, melanocytes and Langerhans cells. 
Merkel cells can be found on the palms and soles and are located directly above the basal membrane.
Keratinocytes are the main component of the epidermis. 
Their function is to produce keratin, a complex filamentous protein that forms the stratum corneum of the epidermis.
The epidermis is composed of several layers, beginning with the innermost as follows: basal layer, malpighian layer, granular layer and horny layers (stratum corneum). 
The palms and soles have also a clear layer called stratum lucidum (above the granular layer). 
The horny layer and granular layer are the thickest on the palms and soles and are almost absent on the flexor aspect of the forearms. 
Cycling stem cells, located at the basal layer, provide a pool for epidermal regeneration. 
As the basal cells divide, they flatten and move upward. 
The process of desquamation implies degradation of the lamellated lipid from the intercellular space and loss of desmosomal interconnections. 
The keratinocytes play an important role in the immune function of the skin.
Melanocytes are the cells located in the epidermis whose function it is to produce pigment. 
The ratio is about one in every ten basal keratinocytes. 
The face and genitalia have a greater amount of these cells. 
The melanocyte cell is a dendritic type, extending for long distances within the epidermis and in close contact with the keratinocytes. 
Together they form the “epidermal melanin unit”. 
Melanin is synthetized by melanocytes in the basal layer of the epidermis and transferred to surrounding keratinocytes in melanosomes. 
Differences in skin color according to race is explained by the number of melanosomes. 
People with fair skin have fewer melanosomes which are smaller and packaged within membrane complexes. 
People with darker skin have more melanosomes which are larger and not packed. 
Sun exposure stimulates melanocytes to produce larger melanosomes.
Langerhans cells represent 3–5% of the cells of the stratum spinosum where they are situated between the keratinocytes. 
They are responsible for the immunological response of the skin.
The dermoepidermal junction represents the junction between the epidermis and the dermis. 
It is located at the basement membrane zone and resembles a semipermeable filter which allows cells and fluids to travel between epidermis and dermis. 
It also serves as a structural support for the epidermis.
The eccrine and appocrine glands, ducts and pilosebaceous units constitute the skin adnexa. 
All have a role in epidermis regeneration (reepithelization). 
When an injury occurs, the keratinocytes from the adnexa migrate to the skin surface.
These glands have three main components: 
? The intraepidermal spinal ducts which open directly onto the skin surface 
? The straight dermal portion of the duct composed of cuboidal epithelial cells
? The secretory zone located in the superficial panniculus. 
In the back region, this zone is situated in the deep dermis
The role of these glands is also to produce sweat which is similar in composition to plasma with regard to the electrolytes. 
They are important in thermoregulatory function and are present in great amounts in the palms, soles and axillae. 
Some eccrine glands from the axillae have widely dilated secretory coils in patients with hyperhidrosis.
Appocrine glands develop on the infundibular upper portion of the hair follicle. 
They are intimally related to the pilar units. 
The coiled secretory gland is present at the junction of the dermis and subcutaneous fat. 
Appocrine secretion is odorless and episodic.
The appocrine units of the human body are generally confined to the axillae, areolae, genital region, ear canal and eyelids. 
The glands start to function after puberty.
Hair follicles develop in rows of three. 
Primary follicles are surrounded by the appearance of two secondary follicles. 
The amount of pilosebaceous units decreases throughout life mainly because of poor formation of secondary follicles. 
The hair follicle has three main components:
? The lower part beginning at the base of the follicle and extending to the insertion of the arrector pili muscle 
? The middle portion, also called the isthmus, from the arrector pili to the entrance of the sebaceous duct 
? The upper part, called the infundibulum, extends to the follicular orifice
The lower part of the hair follicle is also subdivided into five components: the dermal hair papilla; the hair matrix; the hair; the inner root sheath and the outer root sheath. 
The formation of the hair starts at the level of bulb, from the pluripotential cells. 
The melanin produced by the melanocytes is incorporated into the cells of the future hair through phagocytosis. 
At the level of the isthmus, the outer root sheath is no longer covered by the inner root. 
The outer root undergoes keratinization. 
The bulge cells posses stem cell properties, having the proliferative capacity to regenerate not only hair follicles but also sebaceous glands and epidermis.
The rate of hair growth depends on the mitotic activity of the cells of the bulb matrix. 
Hair growth is a cycle having three phases: anagen, catagen and telogen. 
The histological aspect of the hair follicle is different for each of the phases. 
The anagen is the growth phase, the catagen represents the regression phase, and the telogen is the rest phase. 
The hair follicle is the most susceptible to IPL treatment during the anagen phase. 
During the anagen phase, the stem cells differentiate into eight different cell types. 
From the bulge area, the stem cells ascend into the outer root sheath. 
Those which reach the hair germ transform into matrix keratinocytes to rebuild the hair shaft. 
The pigmentation and hair shaft synthesis take place in this phase. 
Three types of melanosomes are present in the hair. 
The erytomelanin granules are seen in red hair while the pheomelanin granules are found in blond and dark hair. 
In dark hair there are more melanosomes than in light hair.
In white or grey hair, the melanocytes of the hair matrix are much reduced and show degenerative changes. 
Melanin synthesis and pigment transfer to bulb keratinocytes depends on the precursors and their regulation is receptor dependent.
The transition from the anagen to the catagen phase varies from one skin region to another. 
There are several molecular regulators of this transition. 
The catagen phase consists of involution of the hair follicle, apoptosis and terminal differentiation. 
The first sign of catagen is the cessation of melanin production in the hair bulb. 
As the lower follicle recedes, a temporary structure, called the “epithelial strand”, forms and is considered to be unique to this phase.
After the catagen phase, the hair follicles enter into the telogen phase. 
In this phase, the follicle has a depigmented proximal hair shaft called “club hair”. 
This club hair most often remains in the hair canal. 
The transition from telogen to anagen occurs when a few stem cells at the base of the follicle near the dermal papilla are activated. 
The new follicle takes place adjacent to the old pocket. 
The hair cycle is a process influenced by many mediators and receptors. 
The same author suggested an inhibition disinhibition system that has the epithelial stem cells from the bulge region as the central pacemaker. 
It seems that the hair cycle clock is located in the dermal papilla.
The hair follicle has a strong influence on skin biology and plays an important role in the reparative process, especially in the outer root sheath which provides epithelial cells to cover wounds. 
The hair follicle has regenerative proprieties also. 
It has the ability to regenerate itself with the initiation of each cycle. 
The regenerative potential is demonstrated after massive damage during chemotherapy treatment.
It has been shown that the hair follicle influences the angiogenesis process. 
The dermis in the proximity of anagen follicles is more vascularized than that around telogen follicles. 
Blood vessel changes in the skin during the hair cycle are also controlled by the follicle.
The dermis consists of a supporting matrix (ground substances) in which polysaccharides and proteins act to produce proteoglycans. 
The protein fibers inside the dermis are represented by collagen, elastin and other components, such as fibrillin and microfibril proteins.
The collagen fibers within the dermis are 2–15?m wide. 
The thin, finely woven meshwork of collagen fibers is found in the papillary dermis. 
The collagen fibril diameter increases progressively with the depth of the dermis. 
The rest of the dermis, called the reticular dermis, has collagen fibers united into thick bundles. 
This part is composed primarily of type I collagen. There are several types of collagen. 
Type I collagen is predominant in the postfetal skin. 
Type III is found mainly in reticular fibers and is prevalent in early fetal life. 
In postfetal life, it is mainly located in the subepidermal area. 
Type IV collagen is present in the basement membrane. 
The fetus has predominantly type III collagen while the skin of the adult contains mainly type I collagen. 
Collagen is primarily responsible for the skin’s tensile strength. 
In young adults, collagen from the papillary dermis is organized as a meshwork of randomly oriented thin fibers and small bundles.
Elastin fibers are mixed collections of various distinctive glycoproteins which have a microfibrilare structure. 
They are thin in comparison with collagen bundles and measure from 1–3?m. 
The fibers are thickest in the lower portion of the dermis. 
At the level of the papillary dermis, they form an intermediate plexus of thinner elaunin.
During life, the elastic fibers undergo significant changes. 
In young children, the fibers are not fully mature, so the microfibrils predominate. 
With aging, there is gradual decrease in the number of peripheral microfibrils and the surface of elastic fibers appears irregular and granular. 
In very old people, some elastin fibers undergo fragmentation and disintegration.
The ground substance is an amorphous structure present between the collagen fibers and the collagen bundles.
It consists of glycosaminoglycans and mucopolysaccharides.
In healing wounds, the ground substance contains sulfated and nonsulfated acid mucopolysaccharides.
Smooth muscles are present as arrectores pilorum in the tunica dartes of the external genitalia and in the areolae of the breast. 
The muscle fibers of the arrectores pilorum start in the connective tissue and insert in the hair follicle in an obtuse angle below the sebaceous glands. 
By contraction, they pull the hair follicle into a vertical position. 
Aggregates of smooth muscle cells are present between the arterioles and the venules. 
They are called “glomus bodies” and serve to shunt blood from the arterioles to the venules.
 Most are located in the digits. 
Striated muscles are present in the skin of the neck as platysma and the skin of the face (superficial face muscles of expression). 
Their origin is the fascia or periostum and travel through the subcutaneous tissue into the lower dermis.
Skin, like any other organ, undergoes alterations with aging. 
Several changes have been proved. 
The collagen matrix starts to defragment although the cross-links prevent complete removal of collagen fragments. 
The fragments cannot be incorporated into new collagen fibrils and cause defects in the collagen matrix. 
The fibroblasts cannot attach to the fragmented collagen and the loss of attachments leads to collapse. 
This will produce less collagen and more collagen-degrading enzymes. 
In aged skin, the collagen networks appear to be increased but this is due to adherence to ground substance. 
Increased age is associated with decreased collagen content and straightening of collagen fibers organized in loose bundles. 
There is also an increase of type III collagen observed mainly in subjects over the age of 70. 
The elastin component starts to show degradation of fibers, resulting in decreased number and diameter. 
In photoexposed areas, there is an increase in abnormal elastin which is predominantly localized in the upper dermis. 
Increasing age does not alter the water structure of the skin. 
However, there is an increase in total water content in photoaged skin. 
This is paradoxical as aged skin seems dry. 
The lack of interaction between the water and the surrounding molecules in photoaged skin contributes to its characteristically dry and wrinkled appearance.
The blood supply to the skin comes from the deep plexuses located at the fascia and subcutaneous level. 
Once the vessels enter the space between the subcutaneous tissue and corium they branch out to various cutaneous appendages. 
The ascending arterioles supply a subpapillary plexus and form capillary loops in the papillary layer between the ridges. 
From these capillaries, the blood is drained by venules which descend to the plexuses. 
The blood flow through the superficial layer of the dermis is controlled by arteriovenous anastomoses which can act as shunts to short circuit the flow. 
These anastomoses are well demonstrated at the level of the fingers.
The peripheral nerves influence the pattern of blood vessel branching and differentiation by secreting the vascular endothelial growth factor.
The small arteries of the deep vascular plexus and the arterioles present in the dermis have three layers: (1) intima, composed of endothelial cells and internal elastic lamina; (2) media, with at least two layers of muscle cells in the small arteries and one layer of muscle cells in arterioles; and (3) adventitia of connective tissue. 
The capillaries located in the dermis have a layer of endothelial cells and a layer of pericytes. 
The walls of the veins are thinner than those of the arteries and do not have a clear structure of three layers. 
The postcapillary venule has endothelial cells, pericytes and a basement membrane. 
A special vascular structure called glomus is present within the reticular dermis of the nail beds, fingers and toes, ears, and face, and is important in thermal regulation. 
It represents a special arteriovenous shunt that connects the arterioles with the venules.
With aging, there is a dependent reduction in the total number of papillary loop microvessels, decreased thickness of microvessel basement membrane and decreased number of perivascular cells. 
These changes lead to decreased perfusion and increased capillary fragility. 
The clinical manifestation of these changes are purpura, teleangiectasia, pallor, angioma and venous lake formation. 
The function of the skin microvessels is affected by the aging process and leads to decreased vasoreactivity and impaired wound repair.
Dermal lymphatics are often hard to see in the normal skin because they do not have the well-developed walls that blood vessels have. 
They first appear at the subpapillary dermis. 
When they are seen in the dermal papillae, it is considered abnormal. 
The initial lymphatic vessels are cylindrical microtubules and are composed of attenuated endothelial cells. 
They form a mesh-like network of about 200–500?m in the human scalp. 
Occasional valves can be seen emerging from the endothelial lining. 
The dermal lymphatics are easily detected in conditions associated with increased lymphatic drainage, as occurs in urticaria or inflammations.
The skin is supplied by sensory nerves and autonomic nerves which permeate the entire dermis. 
The sensory nerves have a myelin sheath. 
The face and extremities have the highest density of sensory branches. 
These branches have two main endings: corpuscular, which embrace non-nervous elements, and free, which do not. 
Examples of corpuscular branches are: Pacinian, Golgi-Mazzoni, Krause or Meissner. 
In the Ruffini structures, (abundant in human digits) several expanded endings branch from a single myelinated afferent fibre. 
The “free nerve-endings” are located in the superficial dermis and in the overlying epidermis. 
In the dermis, they are arranged in a tuft-like manner. 
Hair follicles also have nerve terminals which run parallel to and encircle the hair follicles.
The thickness of the epidermis is variable. 
It is very thick on the palms, soles and other friction surfaces. 
These areas are more resistant to treatments using light sources. 
The thickness of the dermis is also variable. 
In the eyelid, the dermis is thinnest; on the back, it is the thickest. 
This variable is important when considering IPL treatment in different anatomical regions. 
People with fair skin have fewer melanosomes which are smaller and packed, while people with dark skin have more melanosomes which are larger and not packed. 
IPL has the best results in fair skinned people. 
The hair follicles and vascular dermal elements are not uniformly distributed at the same level. 
This is important to take into consideration when choosing IPL parameters.
In white and grey hair, the melanocytes of the hair matrix are much reduced and show degenerative changes. 
They are the most resistant to IPL hair removal. 
Hair follicles in the anagen phase are the most susceptible to IPL treatment. 
With aging, there is a decrease in total collagen content in the skin, an increased amount of type III collagen, decreased number and diameter of elastin fibers, and a lack of interaction between water and surrounding molecules which contribute to the dry and wrinkled aspect.
The face and the hands have the highest density of sensory nerves and are the most painful areas in IPL treatment.
The effects of light on skin are due to various degrees of absorption of electromagnetic radiation. 
The visible light spectrum has a 400–760 nm wavelength. 
The light-tissue interaction effects are due to absorption and excitation of photons. 
The Intense Pulse Light is situated in the visible light of the electromagnetic spectrum. 
Once the light reaches the skin, part of it is absorbed, part is reflected or scattered, and part is further transmitted. 
Selective photothermolysis is the basic principle of Intense Pulsed Light treatment. 
It consists of matching a specific wavelength and pulse duration to obtain optimal effect on a target tissue with minimal effect on the surrounding tissues. 
The structures of the tissue that absorb the photons are known as chromophores. 
They have different wavelengths of absorption. 
The most common chromophores encountered in the skin are: hemoglobin and its derivates, melanin, water and foreign pigmented tattoos. 
The main target structures for Intense Pulsed Light treatment are melanin and blood vessels. 
The fluence delivered to the chromophores must be high enough to destroy them. 
In order to enhance the photodynamic therapy effect which is based on selective phothermolysis, photosensistizers can be used as adjuvants.
The effects of light on skin are due to various degrees of absorption of electromagnetic radiation (EMR). 
The EMR represents the fundamental form of energy having wave and particle properties. 
According to Plancks law, long wavelength photons carry less energy than short wavelength photons. 
The EMR includes radiowaves, microwaves, infrared radiation, visible light, ultraviolet radiation and x-rays (Fig. 2.1). 
EMR is generally classified according to wavelength. 
The visible light spectrum has a 400–760 nm wavelength. 
The light-tissue interaction effects are due to absorption and excitation of photons. 
The Intense Pulse Light is situated in the visible light of the electromagnetic spectrum. 
To understand the effects of light on tissue, it is necessary to define some terms:
? Fluence (F) represents the amount of energy measured in Joules (J) per unit area, measured in cm2: F = J/cm2. 
? Power measured in watts (W) represents the amount of energy delivered over a certain period of time: W=J/s.
? Thermal relaxation time (TRT) is the time necessary for an object to cool down to 50% of its original temperature. 
TRT is further detailed in this chapter. 
? Wavelength influences selective light absorption by a certain target and also influences the depth of tissue penetration. 
The majority of light systems have different filters which allow certain wavelengths to enter the tissue, thus producing the selection of the desired light spectrum. 
? Footprint (device spot) size has an important role in light penetration into the tissue. 
When a small spot size is used for light emission, only a small part will reach the deep target structures. 
A larger footprint offers a more planar geo metry of light penetration and better efficacy. 
A spot size of about 7–10 mm is needed for maximal light penetration to the mid-dermal structures. 
The bigger the spot, the deeper the level of penetration). 
? Pulse duration. 
Light can be delivered in a pulsed or continuous wave. 
The intense pulsed light devices are based on pulsed delivery that allows more selective tissue damage. 
Pulse duration represents the time of exposure to the light beams. 
Laser and pulsed light systems enable the selection of pulse duration, which is influenced by the TRT of the target. 
? Pulse delay represents the time that allows the skin and blood vessels to cool down between pulses, while the heat is retained inside the targets. 
When the pulse is shorter than the thermal relaxation time (TRT), the heat will act mainly on the target structures. 
When the pulse is longer than the TRT, the heat will be conducted to the surrounding structures. 
It is recommended that the pulse timing be higher than the skin cooling time to avoid damage to the surrounding structures.
Heating is one of the effects induced by light ab sorption. 
It is not uniformly distributed inside the skin. 
This process is more representative around the target cells. 
The temperature is directly related to the excitation of molecules. 
As the temperature is raised, different changes take place at the molecular level. 
DNA, RNA and some proteins are affected by the heat which causes them to unwind or even melt at varying temperatures. 
The final result would be denaturation and coagulation of the above- mentioned structures. 
These effects are dependent on temperature and length of exposure. 
Depending on the target tissue, the light-tissue interactions will cause tissue necrosis, blood coagulation and structure alterations. 
Some of the heating effects are beneficial at the level of the target tissues but are dangerous to the surrounding tissue. 
This should always be kept in mind when choosing the treatment parameters.
The coagulation damage depends not only on the temperature but also on the exposure time. 
For instance, a high temperature and a short exposure can be less aggressive than a lower temperature with a longer period of exposure. 
The dermis, being rich in collagen and elastin, is more thermally stable than the epidermis, mainly due to elastin proprieties.
Once the light reaches the skin, part of it is absorbed, part is reflected or scattered, and part is further transmitted. 
The scattering process takes place when the photon particles change the direction of propagation. 
This phenomenon takes place inside the skin where different structures have different indices of refraction. 
The scattering effect makes the light spread out and limits the depth of light penetration. 
It seems that the dermal collagen is responsible for most of the scattering. 
The amount of scattering is inversely proportional to the wavelength of the light. 
Some (4–7%) of the light is reflected, this phenomenon being produced by a change in the air and stratum corneum refractive index. 
The amount of light that is reflected decreases with the decreasing angle of incidence. 
The least reflection occurs when the light is perpendicular to the tissue. 
A very small amount of light is further transmitted. 
It has been proved that transmission of the light varies according to the skin type. 
The white dermis transmits from about 50% at 400 nm to 90% at 1,200 nm, while the black epidermis transmits less than 40% at 400 nm and 90% at 1,200 nm. 
In general, there is a gradual increase in skin penetration at longer wavelengths.
Most of the light is absorbed by the skin. 
This phenomenon is responsible for the desired effects on the tissue. 
The structures of the tissue that absorb the photons are known as chromophores. 
They have different wavelengths of absorption. 
The most common chromophores encountered in the skin are: hemoglobin and its derivates, melanin, water and foreign pigmented tattoos. 
Once the light is absorbed, the chromophores become excitated. 
For wavelengths varying from 300–1,200 nm, melanin is the dominant absorbent.
Light-tissue effects can be grouped in:
? Photothermal – represented mainly by coagulation or vaporization of tissue based on absorption 
? Photomechanical – tissue disruption often encountered by pulsed laser beams 
? Photochemical – direct breakage of chemical tissue bonds or chemical interaction with an applied drug 
? Photobiostimulation – tissue stimulation with very low level laser light 
? Selective photothermolysis – The concept of photothermolysis was introduced for the first time by Parrish and Anderson in 1983. 
According to their description, three effects are necessary to produce selective photothermolysis 
? Absorption of a specific wavelength by the target structures 
? The exposure time should be less than or at least equal to the time of cooling of the target structures 
? There is a need for enough fluence to produce a damaging temperature within the target structures
The main target structures for Intense Pulsed Light treatment are melanin and blood vessels. 
To understand the relation between exposure time and extent of thermal damage, it is important to detail the “thermal relaxation time” (TRT). 
This represents the time required to cool a small target structure. 
The cooling is achieved by conduction, convection and radiation. 
Conduction is the main component of cooling. 
Smaller objects cool faster than larger objects. 
The TRT is proportional to the square of the size.
To allow enough time for the epidermis and other skin structures to cool down, the pulse duration should be shorter than the cooling time of the target but longer than the cooling time of the skin. 
This has clinical implications especially for hair removal. 
The hair follicles are grossly grouped as coarse and fine. 
They have different sizes and consequently different TRTs. 
An epidermal thickness of 0.1 mm has a TRT of about 1 ms while a vessel of 0.1 mm has a TRT of about 4 ms. 
A vessel three times bigger (0.3 mm) has a TRT of approximately 10 ms. 
Larger structure targets cool down slower and need increased delay time and multiple pulsing. 
Theoretically, most vessels smaller than 0.3 mm require only a single pulse. 
It is recommended that pulses be spaced at 10 ms or longer to accommodate normal epidermal TRT. 
Patients more prone to thermal injuries should have at least 20–30 ms of TRT. 
When the pulse width is greater than the TRT, nonspecific thermal damage occurs because of heat diffusion. 
The fluence delivered to the chromophores must be high enough to destroy them.
In order to enhance the photodynamic therapy effect which is based on selective phothermolysis, photosensitizers have been introduced as adjuvants. 
There are topical and systemic photosensitizers. 
The first generation of photosensitizers was developed about 30 years ago and belongs to the porphyrin family. 
5-aminolevulinic acid (ALA) and methyl aminolevulinate (MAL) are the most common sensitizers. 
Second generation photosensitizers have the advantage of having a limited effective period. 
ALA is not a photosensitizer by itself, but it is metabolized to photosensitizing protoporphyrin IX. 
The spectrum of absorption of protoporphyrin IX is in the visible spectrum. 
The peak of absorption is 405 nm. 
Systemic sensitizers are administered intravenously since they do not penetrate the skin. 
Hematoporphyrin and photofrin have been thoroughly studied with regard to their peak of absorption. 
Applications of these photosensitizers in association with Intense Pulse Light may increase the efficacy of the treatment. 
This concept of combining light with a photosensitizing agent known as photodynamic therapy has wider applications, including tumor treatment.
The human skin has several major ultraviolet radiation absorbing endogenous chromophores. 
Among them are urocanic acid, aminoacids, melanin and its precursors. 
The chromophore identification can be done by action spectroscopy. 
Theoretically, an action spectrum for a given photobiological endpoint will be the same as the absorption spectrum of its chromophore. 
The skin chromophores have an overlying spectra. 
From all chromophores present in the skin, the melanin and hemoglobin with its derivates are the most important regarding light pulsed treatment.
The term melanin is widely used to describe the skins red-brown pigment which resides in the epidermis. 
The biosynthesis of melanin within melanocytes is a complex process and is incompletely understood.
It is believed that they are polymers with multiplemonomer units linked by non-hydrolysable bonds. 
There are two major classes of natural malanins: the black-brown eumelanin and the yellow-red pheomelanin. 
They are differentiated by their molecular building blocks. 
Eumelanin is the dominant pigment.
Human skin coloration is dependent on spatial distribution of the melanin and haemoglobin chromophores. 
Eumelanin plays a fundamental role in skin appearance and photoprotection. 
A weak correlation was noticed between the scattering properties of skin and tissue type with the average scatter size higher in patients with higher melanin content. 
The skin has a multilayered structure. 
The two main chromophores in the skin, melanin and hemoglobin, are present in different layers, with the melanin found in the top layer (mainly epidermis) and the hemoglobin found in the bottom layer (vascular network of the dermis). 
To avoid skin damage, higher cut-off filters, multiple pulses and increased delay time should be chosen for darker skin types. 
The Fitzpatrick skin typing system from I to IV has different skin colors according to pigment intensity. 
Although it is a widely used scale, it has been criticized that human eye evaluation is subjective and confounded by the presence of hemoglobin. 
Although the human eye can distinguish adjacent brown and red colors, it is almost impossible to distinguish the relative contribution of melanin and hemoglobin when they overlay one another, as often happens in young and photoprotected skin.
There are elaborated methods which try to evaluate skin color objectively. 
These are based on spectrophotometric or colorimetric techniques. 
Although these methods are more objective, they still cannot completely separate the individual contributions of the chromophores.
Exogenous chromophores can be administered to the skin to prevent sunburn (exogenous chromophores from sunscreens) or in combination with ultraviolet radiation for therapeutic benefit.
The intense pulsed light is situated in the visible light of the electromagnetic spectrum. 
Heating is an important effect induced by light absorption. 
This often leads to cell necrosis, blood coagulation and structure alterations. 
The light interacts with the skin and part of it is absorbed, part reflected or scattered, and part is further transmitted. 
The absorption is responsible for the desired effect on the tissue. 
The two main skin chromophores present in the skin and responsible for the light effects are melanin and hemoglobin. 
Selective photothermolysis is the basic principle of Intense Pulsed Light treatment. 
It consists of matching a specific wavelength and pulse duration to obtain optimal effect on a target tissue with minimal effect on the surrounding tissues. 
Melanin is located within the top layer of the skin (epidermis) and hemoglobin is found in the bottom layer (vascular network of the dermis).
Light and laser devices have common considerations and include hazards both to the patient and to the medical staff. 
The operating manual of the IPL device should be read by all personnel manipulating the device. 
Personnel in the treatment room should have protection against accidental exposure to the IPL, either directly or indirectly from a reflecting device. 
The visual hazard seems to be the main danger. 
Inadvertent exposure of the eyes during treatment may damage some eye structures. 
During treatment, wearing specially designed protective eyeglasses is important not only for the patient but also for the staff present in the room. 
Due to inadvertent “advertising” of IPL or laser technologies and unrealistic expectations by the public, physicians may run the risk of being sued for the results. 
Avoiding malpractice lawsuits implies acting correspondingly and adapting the treatment to the patient’s needs. 
Informed consent for any treatment is a must. 
Although there are reports of informed verbal consent, written consent is mandatory in today’s litiginous society. 
Protection against lawsuits lies also in the ability of the physician to recognize problematic patients.
Light and laser devices have common considerations and include hazards both to the patient and to the medical staff. 
Intense Pulsed Light (IPL) is widely used in many medical and aesthetic centers. 
IPL wavelengths range from 550 to 1200nm. 
Most IPL devices have the ability to perform self-testing when the system is turned on. 
If an error occurs, the message is displayed on the screen. 
The newest devices automatically shut down when exposed to a light overdose. 
Some devices have an emergency shutoff knob. 
It is recommended that treatment be done by a trained physician or at least by a trained nurse supervised directly by a physician. 
The main responsibility belongs to the physician who should be the one to adjust the device parameters.
The operating manual of the IPL device should be read by all personnel manipulating the device. 
Personnel in the treatment room should have protection against accidental exposure to the IPL, either directly or indirectly from a reflecting device. 
Handling the treatment head should be done cautiously to avoid discharges into free space. 
Since some of the devices that emit IPL also have laser treatment heads, the warning and hazards should also be addressed to them. 
The devices should comply with international standards.
Treatment room. 
Treatments should be carried out in a specially designed room that respects the safety of light and laser radiation. 
The entrance should be clearly labeled with signs indicating high intensity light. 
The number of people in the treatment room should be limited and related to the procedure. 
The system should not be used in the presence of flammable materials. 
The doors of the treatment room should be closed when the device is in use and a special sign reading “Danger” should be placed outside the treatment room.
Optical safety. 
The visual hazard seems to be the main danger. 
Inadvertent exposure of the eyes during treatment may damage some eye structures. 
The light beam danger comes when the applicator is directed by mistake at the eye or is reflected off an instrument. 
The treatment head should be directed only to the treatment area. 
Reflective objects, such as watches, jewelry, and shiny instruments, should be kept away from the light beam. 
Do not look directly at the light emission head. 
During treatment, wearing specially designed protective eyeglasses is important not only for the patient but also for the staff present in the room. 
Usually, the patient wears goggles which allow the doctor to perform treatments in the periorbital area easily.
Electrical safety. 
Some devices produce high voltages which can be retained by some components after the power supply has been turned off. 
IPL devices need appropriate electrical safety precautions. 
Fluid containers should be kept away from the devices. 
Any repairs to the IPL device should be done by authorized and trained people. 
Keep the device covers closed during functioning. 
An emergency shutoff knob is often present and it bypasses the controlled power. 
When the machine is not in use, always turn the system off. 
Untrained personnel should not operate the IPL devices. 
Doctors, midlevel providers and technicians should work together to monitor the equipment, the patient and the environment for safety.
Theoretical potential risk of infection transmission is another hazard. 
To diminish this risk, in addition to wearing regular gloves, footprints are cleared using chlorhexidine solution between treatments. 
Although some authors raise the question about the long-term safety of lasers and IPL, including the possible risk of melanoma, no reports of this association can be found in the medical literature. 
While there are no reports on the use of IPL treatment in pregnant women, we do not recommend its use under these circumstances.
Due to inadvertent “advertising” of IPL or laser technologies and unrealistic expectations by the public, physicians may run the risk of being sued for the results. 
Avoiding malpractice lawsuits implies acting correspondingly and adapting the treatment to the patient’s needs. 
In the last decade, the cost of medical malpractice and lawsuits has been impressive and continuously increasing. 
Informed consent for any treatment is a must. 
Although there are reports of informed verbal consent, written consent is mandatory in today’s litiginous society. 
The goals are to include the patient in the decision-making process, to inform the patient of the various methods and instruments, and to inform the patient about the potential benefits and hazards of the treatment. 
Most informed consent dealing with aesthetic procedures includes the possibility of patient photography or videotaping. 
Photodocumentation as well as record storage are important for follow-up and possible liability problems. 
The majority of the devices are built to store this information. 
We advise introducing as much data as possible into the patient record (anesthetic type, reason for treatment, treatment parameters, cooling method, side effects, complications, recommendations, etc.).
Protection against lawsuits lies also in the ability of the physician to recognize problematic patients. 
These are patients with Body Dysmorphic Disorder (BDD) or “Beauty Hypochondriasis”. 
BDD represents preoccupation with an imagined defect in a normal appearing person or, if a mild physical anomaly is present, the concern is disproportional. 
“Beauty hypochondriasis” refers to a preoccupation centering usually on one part of the body that is experienced as repulsive and deformed. 
Both conditions lead to impairment in social and occupational activities and distress. 
These patients have also a feeling of inferiority, guilt, or altered body image. 
The incidence of BDD in the general population was found to be up to 2.4%, with a higher prevalence of 7–15% among patients seeking cosmetic surgery procedures. 
Since these patients have an emotional rather than a physical problem, they are rarely satisfied with cosmetic procedures.
The obligation of a doctor performing IPL treatments is to do his work in accordance with the standard of care. 
It is important to be on the safe side and avoid lawsuits. 
The ‘standard of care’ is often described by some as whatever an expert in the field says it is, and the jury believes that. 
For instance, in a case against an IPL procedure, the doctor performing this procedure must have the skills ordinarily possessed by a specialist in this field. 
The standard of care should be included in the patient’s record or informed consent form and refers to the explanations that a reasonable medical practitioner would provide patients. 
The term of ‘negligence’ requires fulfillment of four elements: duty, breach of duty, causation and damages. 
Duty refers to the treatment performed by another reasonable physician by the same method. 
Breach of duty refers to the fact that the negligent physician did not perform the same type of treatment in the same manner as another reasonable physician would. 
Causation refers to the relation between duty and damage. 
Damages can be economic or non-economic, such as emotional. 
In some instances, there can be two or more methods for treating the same pathology. 
In this situation, the doctor does not fall below the standard of care with any of the acceptable methods even if one is less effective than another. 
For instance, someone may be sued after using IPL for hair removal instead of Nd:Yag laser. 
Since both are accepted and recognized methods, the physician is within the standard of care.
Inevitably, IPL as a laser procedure has adverse effects and complications. 
These can be the trigger of malpractice lawsuits. 
Complications, such as skin thermal injury that can appear after IPL treatments, are not by definition medical malpractice. 
It often happens in these cases that the patients seek legal advice. 
In such a situation, the patient who likes the doctor and communicates easily with him is less likely to sue even when a complication occurs.
Professional errors can be related to deficient training, inadequate patient information, or inappropriate treatment. 
To diminish professional errors, guidelines and instructions are necessary. 
Training for IPL technology or laser procedures refers not only to device-handling and indications but also to a wider area such as: identifying possible skin lesions (malignancies) which should not be treated in this manner or skin conditions that need further attention (e.g., blisters, crusts, minor burns) after IPL. 
Greve underlines the importance of continuous medical education and board examinations.
Although there is an increase in the involvement of mid-level providers with these procedures (physician assistants, nurse practitioners), there are some concerns that this may decrease the overall quality of treatment as these people may have less experience. 
Without appropriate training and supervision, physician extenders can have a higher incidence of complications. 
When a physician extender is involved in performing treatments, he might be found liable for negligence according to the law of that area. 
It is mandatory to know beforehand if an extender is allowed to perform the treatments and under what circumstances. 
Nevertheless, when treatments are performed by physician extenders, this does not release the doctor from malpractice liability. 
In most cases, the physician is sued along with the extender.
Following is the informed consent that we suggest for IPL treatment.
Intense Pulsed Light Treatment. Informed Consent
The Intense Pulse Light Treatment is based on the light emitted by a flash lamp. 
Using different wavelengths, the device has proved to be useful in the treatment of vascular lesions, pigmentary lesions and hair removal. 
Partial skin rejuvenation can be obtained sometimes. 
More than one treatment may be needed in order to obtain the desired effect.
Patients should not be tanned at the treatment. 
If they are tanned, delaying the treatment for a few weeks is recommended to diminish the rate of complications. 
Immediately after the treatment, blue or red discoloration may appear. 
Usually this disappears within a few days. 
Most procedures do not necessitate anesthesia. 
Topical anesthetic creams can be used before the procedure. 
Eye protection will be used by the patient and the staff for the entire treatment period. 
Although no reaction on a developing fetus has been reported, the procedure is not recommended for pregnant women.
No guarantees can be made of the exact results from this treatment. 
Although the treatment is safe, some complications may appear:
? Pigmentary changes can be either of increased pigment (hyperpigmentation) or decreased pigment (hypopig-mentation). 
Most of the time, these color changes are temporary and resolve over several weeks to months. 
Permanent pigmentary changes may also appear. 
? Pain. 
The level of sensation during treatment varies from person to person. 
A warm or burning sensation can be reduced by using topical anesthetics before the procedure and ice packs after the treatment. 
? Excessive redness or swelling. 
In some instances, excessive redness or swelling can persist for up to a few days after treatment. 
In certain cases, mild topical steroids can be used to hasten recovery. 
? Infection is extremely rare as the technology does not break the skin. 
? Blisters can be encountered in certain people, especially in those with higher sensitivity.
? Scarring is possible. 
Normally, the IPL technology does not produce scarring. 
However, there are a few reports in the medical literature of scarring.
? Lack of satisfaction. 
Different patients respond differently to IPL treatment. 
Most people report significant improvement after a series of treatments. 
While positive changes can be expected, no changes may occur for reasons beyond the physicians control.
To obtain the best results, the skin should be thoroughly protected from sun exposure after the treatment, using sunscreens with SPF 30 or higher. 
There is no restriction on washing the treated area right after the procedure. 
I declare that the above treatment procedure has been explained to me, along with alternative methods of treatment and the risks of the procedure, and all my questions have been answered.
I consent to photographs of the treatment areas before and after in order to document the treatment process.
The visual hazard is the main danger when performing IPL treatments. 
Because of “inadvertent advertising” and unrealistic expectations by the public, physicians may run the risk of being sued for the results. 
Photodocumentation is as important as record storage. 
Written informed consent is mandatory in today’s litiginous society. 
Identify problematic patients (Body Dysmorphic Disorder, beauty hypochondriasis) – and stay away from them. 
Communication with the patient is essential.
A patient who likes the doctor and communicates easily with him is less likely to sue even when a complication occurs. 
When treatments are performed by mid-level providers, the doctor is not released from malpractice liability.
The prices for IPL equipment vary from a few thousand dollars to more than $100,000, depending on the producer and the properties of the device. 
Acquisition of the equipment can be made by purchase, lease or rental. 
In analyzing which method is most feasible for his/her office, one must take into consideration the capital investment, maintenance costs, costs of treatment, and an estimation of the number of patients expected. 
Before purchasing the device, the physician should compare vendors, extent of warranties and service availability. 
The room should be large enough to accommodate the treatment table, IPL device, stand tables, small stores cupboards, refrigerator and anesthesia equipment. 
Written informed consent, medical history, physical examination findings, and data on previous treatments should be recorded. 
Digital cameras replace in most cases 35 mm cameras for private offices. 
Although there are simulation programs to show the patient the possible result, these should be used cautiously since the result might not match the simulation.
The prices for IPL equipment vary from a few thousand dollars to more than $100,000, depending on the producer and the properties of the device. 
Acquisition of the equipment can be made by purchase, lease or rental. 
In analyzing which method is most feasible for his/her office, one must take into consideration the capital investment, maintenance costs, costs of treatment, and an estimation of the number of patients expected. 
If the number of treatments is not significant, one might consider rental of the equipment. 
Before purchasing the device, the physician should compare vendors, extent of warranties and service availability. 
Renting the device has the advantage of obtaining the latest technology while, after purchasing, it might not be worthwhile economically to renew the equipment.
Protective devices include ocular protection, gloves, other disposables and warning signs. 
Ocular protection is available as glasses or goggles. 
When several devices or lasers are available in the same room, attention should be paid not to mix the protective devices between them. 
Many goggles are wavelength specific and should be used only with the proper device. 
Several pairs of goggles or glasses should be available for the patient, physician, assistant and all other persons in the room. 
Plastic eye shields are recommended. 
These come in several sizes (small, medium and large) and are light-weight. 
They are used when eyelid treatment is performed. 
A topical corneal anesthetic should be used prior to eye shield insertion. 
Glossy metal eye shields, as other glossy instruments, should be avoided in the laser room as they might reflect the light. 
We strongly recommend plastic shields and non-reflecting surgical instruments.
Non-sterile gloves are suitable for performing the treatment. 
Warning signs should be posted inside the room as well as on the door outside. 
Doors should be locked when the device is in use.
The room should be large enough to accommodate the treatment table, IPL device, stand tables, a cupboard for small stores, refrigerator and anesthesia equipment when needed. 
Each country will have its own regulations regarding the size of the room that should be used. 
A minimum of 30 m2 is desired if general anesthesia is to be performed. 
If the treatment room has windows, drapes should be used to cover them to avoid IPL scattering or reflection. 
If there are mirrors in the room, they should be hidden from the light beam.
The treatment table should be placed so as to allow enough space around it for movement. 
A central position is better when a general anesthesia machine is used. 
Various types of tables are available, from simple to flexible. 
The more flexible the table, the greater the patient’s comfort. 
Manually controlled tables provide flexible articulated posturing of the patient. 
Some tables can be operated by a pedal shaft selector and foot pump.
Other equipment that is used such as trays and holding devices should be mobile so they can be located easily as needed. 
The anesthesia machine and additional monitors should be mobile as well, to adjust their position when treating the head or extremities. 
A recovery room with the necessary equipment should be available next to the treatment room. 
A specialized nurse is required to take care of the patient until he/she becomes ambulatory. 
A refrigerator is used to store the transparent gel, cooling gels and other medications. 
A powerful light should be mounted on top of the table for better lighting. 
A magnifier can be attached to the table side to enhance precision. 
Several cupboards for storage of disposable products should be available. 
Among the important items are:
? Razor blades for completing hair removal in cases where epilation was not properly done by the patient
? Gauze, sterile and non-sterile, in various sizes
? Cotton pads
? Make-up removal solutions
? Anesthetic creams
? Plastic sheaths to cover the anesthetic cream on the area prior to treatment 
? Rolls
? Gloves
? Spatula for spreading the anesthetic cream and the gel
? Transparent gel for application as interface between the device head and skin; using the gels provided by the company is advised
? Gel masks, usually made of propylene glycol and water with plastic coverings; there are different designs (eye mask, full face mask) which provide the cooling needed immediately after the treatment. 
Wet gauze or rolls placed in the refrigerator present a cheap alternative for cooling the treated area.
? Disposable panties, bras, bikini briefs
 Written informed consent as well as medical history, physical examination findings, and data on previous treatments should be kept. 
Except for the informed consent, all other information can be stored on a computer program. 
Treating children requires general anesthesia in most cases. 
A meeting with the anesthesiologist, blood tests and informed consent for general anesthesia should be considered for these cases.
A treatment report should be recorded either on a special chart or on the computer. 
Treatment parameters, such as fluence, pulse duration and delay, should be recorded. 
It is important to assess and correlate the parameters of the previous treatment with the result. 
The parameters should be adjusted according to the result obtained. 
Written instructions should be given to the patient after the procedure - verbal instructions are not always remembered.
A digital camera is mandatory. 
Digital cameras have replaced in most cases 35 mm cameras. 
The digital image can be immediately seen on a computer with an excellent view of the details. 
The higher the resolution of the digital camera, the better the view of the details. 
We recommend using at least 3 mega pixel digital cameras. 
A computer and monitor are necessary to process and view the digital images. 
Both laptop and desktop computers can be easily used.
Several software programs are available for accessing the pictures, arranging and editing them. 
They also have the advantage of adjusting brightness, contrast and gamma corrections. 
Annotation on pictures and measuring tools are also possible. 
Although there are simulation programs to show the patient the possible result, these should be used cautiously since the result might not match the simulation. 
A printer is helpful to obtain hard copies of the patient record, recommendations after treatment, and pictures before and after treatment.
The acquisition of IPL equipment can be made by purchase, lease or rental. 
One must analyze which method is best for his/her office. 
If the number of treatments is not significant, one might consider rental of the equipment. 
When several devices or lasers are available the same room, attention should be paid not to confuse their protective devices (goggles or glasses). 
Plastic eye shields are recommended when treating the periorbital area. 
The room should be large enough to accommodate the treatment table, IPL device, stand tables, small stores cupboards, refrigerator and anesthesia equipment. 
Written informed consent, medical history, physical examination findings, and data on previous treatments should be recorded. 
Digital cameras replace in most cases 35 mm cameras for private offices. 
Although there are simulation programs to show the patient the possible result, these should be used cautiously since the result might not match the simulation.
Patient selection is one of the most important parts of a successful aesthetic procedure. 
There are two main categories that make a patient an unlikely candidate for cosmetic procedures. 
One is anatomical unsuitability and the other is psychological unsuitability. 
Problematic patients are usually those with high expectations, excessive demands, indecisive or immature personalities, secretive or “surgiholics”, those with factitious diseases, and those with familial disapproval. 
Patient evaluation regarding health status should be done thoroughly and include a search for conditions that contraindicate IPL treatments. 
A pregnant woman or a patient with significant venous insufficiency and dilated veins might not be suitable for IPL treatment. 
The local area examination can also reveal conditions, such as skin malignancy, for which IPL would not be the proper treatment. 
Conversing with the patient and getting to know his/her expectations might be the key to avoiding liability. 
All patient data and photodocumentation are kept in a separate record for each patient.
Patient evaluation regarding health status should be done thoroughly and include a search for conditions that contraindicate IPL treatments. 
A pregnant woman or a patient with significant venous insufficiency and dilated veins might not be suitable for IPL treatment. 
The local area examination can also reveal conditions, such as skin malignancy, for which IPL would not be the proper treatment. 
Particular care should be paid to pigmentary changes. 
Dermatoscopy or skin biopsy can help in making the correct diagnosis before treatment. 
Sometimes patient expectations might be higher than the possible IPL results. 
A woman with severe rhytides is not a suitable candidate for IPL treatment if the expectation is to be rid of the wrinkles.
Conversing with the patient and getting to know his/her expectations might be the key to avoiding liability. 
All patient data and photodocumentation are kept in a separate record for each patient. 
The discussion with the patient should include explaining the diagnosis (e.g., why pigmented spots appear), the principle of IPL treatment, treatment alternatives (laser, peelings, surgeries), anesthesia type, length of treatment, recovery period, long-term results, possible side effects and complications, and expected costs. 
After the first consultation, the patient takes the informed consent home, reads it carefully, and completes it. 
Very rarely do we perform treatment the same day. 
We prefer to do a test and invite patient to return in one week for reevaluation. 
When larger areas (face, hands, legs) are planned for treatment, the test is mandatory. 
The device is set with the patient parameters and a single light pulse is applied to the area. 
The reason for doing this test is not to see the possible result but to evaluate the possible side-effects and complications. 
This test helps in preventing professional errors. 
In the presence of complications, the treatment parameters are adjusted (e.g., decrease the fluence; increase the pulse delay) and a second test is performed on a different area. 
Patients should understand that multiple treatments are needed and permanent results should not be expected even after multiple treatments. 
More precautions should be taken when treating patients with Fitzpatrick skin types IV–VI. 
The possibility of more complications should also be explained before treatment. 
Although topical anesthesia will be used before every treatment, patients should be told that some amount of pain can be expected during treatment. 
A written informed consent is obtained before any treatment.
It is important to maintain post-treatment communication. 
A patient who experiences a difficult posttreatment course or complications needs more contact with the physician. 
These patients are usually more demanding and the temptation to avoid them should be resisted.
The basic treatment protocol is herein described. 
Specific differences for hair removal, rejuvenation or vascular treatment will be described in the corresponding chapter.
Once the test result shows no sign of complications, the patient is accepted by the assisting nurse. 
The area that needs to be treated is cleaned with wet gauze. 
Special attention is paid to removing make-up from the face. 
Make-up can interfere with light transmission and absorption. 
Topical anesthetics are widely used for these procedures. 
Pain perception varies from individual to individual. 
We have encountered patients who did not need topical anesthetic before IPL treatment as well as patients who complained of pain after topical anesthetics were used. 
We have found that longer wavelengths and higher fluences are associated with more pain.
Topical anesthetics are typically constructed of three main components: an aromatic ring, an ester or amide linkage, and a tertiary amine. 
They prevent the initiation and transmission of nerve impulses by targeting free nerve endings in the dermis. 
Although there are many topical anesthetics, such as Betacaine-LA, Tetracaine, Topicaine, and S-Caine, EMLA (Astra Pharmaceuticals) and ELA-MAX (Ferndale Laboratories) are the most widely used for topical anesthesia. 
Although many topical anesthetics are effective in reducing pain associated with cutaneous procedures, many necessitate a prolonged application time (more than 1 h). 
ELA-MAX and EMLA have superior anesthetic effects 60 min after application when compared to Tetracaine and Betacaine-LA ointment. 
Invasive anesthesia methods, such as nerve blocks or intravenous sedation, is not needed for IPL treatment.
EMLA cream is a 5% mixture of lidocaine and prilocaine. 
It consists of 25mg/mL lidocaine and 25 mg/mL prilocaine in an oil-in-water emulsion cream. 
Most dermal anesthesia under occlusive dressing is obtained after 60 min. 
Inadequate analgesia after application for only 30 min has been reported. 
The analgesic effect of EMLA cream was shown to increase 15–30 min after its removal, probably due to continuous release from a reservoir of anesthetic located in the stratum corneum. 
ELA-MAX is made up of 4% lidocaine cream in a liposomal vehicle. 
No occlusion is required and the application time is 15–45 min. 
ELA-MAX is less expensive than ELA.
The use of topical anesthetic is helpful before the procedure and provides effective dermal anesthesia with rapid onset of action and minimal side effects. 
The most encountered side effects are erythema, edema and skin blanching. 
Among the patients who used EMLA, Alster found 10% erythema and 90% skin blanching. 
Among the most serious EMLA complications is the possibility of hemoglobin conversion to methemoglobin and consecutive tissue hypoxia.
It seems that this complication is more frequent in infants, patients with glucose-6phosphate dehydrogenase or methemoglobinemia inducing drugs.
Melanin and hemoglobin are the two dominant chromophores in the skin, both having a significant impact on the reflection spectra. 
Immediate posttreatment bluish appearance, perilesional erythema, blanching or “urticariform” reaction are signs of good response for vessels. 
The different forms of hemoglobin exhibit different characteristic absorption spectra. 
Any change in hemoglobin concentration will affect the absorption spectra. 
Arildsson postulated that there is an increase in perfusion in the deep vessels in anesthetized skin, compensating for the decrease in number of physiologically active capillaries. 
A different study demonstrated that the blood flow in EMLA analgesized skin increased through dilatation of larger deeper skin vessels. 
We have observed that EMLA works faster on the face than in other areas, most probably due to higher vascularity and greater absorption in this area.
Once the area is anesthesized, the patient is brought into the treatment room and laid on a special table to be comfortable during the treatment. 
The anesthetic cream is removed and the area cleaned with wet gauze. 
A thin layer (2–3 mm) of cold transparent gel is applied to the skin. 
Skin cooling during and/or after treatment helps to protect the epidermis from unwanted thermal injury. 
Contact skin cooling is sometimes used for anesthesia during dermatological procedures. 
The cooling process is important for most IPL applications. 
The epidermal temperature is decreased by the cooling method, while the chromophore temperature remains unchanged and effective for the treatment. 
The cooling method also allows the delivery of higher fluencies with fewer side effects.
There are three main methods of surface cooling during IPL or laser treatment: precooling, parallel cooling and postcooling. 
Precooling refers to decreasing the temperature of the epidermis immediately before the pulse. 
Parallel cooling takes place at the same time as the pulse. 
It is preferable for devices with a longer pulse duration. 
Postcooling refers to decreasing the temperature immediately after the treatment, usually done with ice packs. 
Spray and contact cooling are the main methods used for most light pulsed and laser devices. 
Both methods necessitate control of precooling time to achieve selectivity and to prevent the epidermis from frost injuries. 
The precooling time of the spray cannot be easily controlled. It seems that an external cooling medium around – 50 for 1 s of precooling is ideal to avoid frost injuries. 
The size, energy and discharge specifications of most IPL devices require a cooling circuit where water is pumped around the flash lamp to cool it.
Protective eyeglasses are used by the patient and medical staff for the entire period of the treatment. 
Particular attention should be paid to selecting proper parameters, taking into consideration the test result. 
Choosing too short treatment intervals can lead to more complications. 
At the end of the treatment, the area is wiped and cooled with ice packs for about 15 min. 
When larger areas are treated, the ice packs are placed immediately as each anatomic region is completed. 
For instance, when IPL is used for leg hair removal, the ice packs are applied as soon as one anatomic area is treated (i.e., anterior or posterior calf, anterior or posterior thigh). 
Cooling during and after treatment is essential for most procedures. 
When cooling is not performed, the thermal injury can affect not only the chromophores but also the surrounding tissue. 
Epidermal damage can be seen easily.
The patient is allowed to use make-up immediately. 
Avoiding sun exposure or photosensitizing medication is strongly recommended between treatments. 
We usually perform treatments 1 month apart but, in cases of complications, this interval is lengthened. 
The relatively short time for the procedure and the quick recovery have led IPL treatments to be considered a “weekend procedures”.
A well-motivated patient seems to have better satisfaction. 
Identifying the anatomical or psychological unsuitability of the patient is the key to patient selection. 
Problematic patients are usually those high expectations, excessive demands, indecisive or immature personalities, secretive or “surgiholics”, those with factitious disease, and those with familial disapproval.
Particular attention should be paid to ruling out skin malignancies in the area that needs to be treated. 
Conversation with the patient, getting known his/her expectations, might be the key to avoiding liabilities. 
A short test before starting the treatment provides details about skin reactivity and response. 
In the presence of side effects, the treatment parameters are adjusted. 
Posttreatment communication with the patient is also important in avoiding liabilities. 
EMLA and ELA-Max are the most used topical anesthetics for IPL procedures. 
Skin cooling during and/or after treatment helps to protect the epidermis from thermal injury. 
Particular attention should be paid to selecting proper parameters, taking into account the test result. 
Avoiding sun exposure or photosensitizing medication is strongly recommended between treatments.
The term ‘photorejuvenation’ describes the simultaneous improvement of various epidermal changes related to aging. 
Sun exposure and smoking are the main factors that induce premature skin aging. 
Rhytides are due to a decrease in facial skin elasticity causing accentuation of lines and wrinkles. 
There are three main mechanisms of non-ablative technology involved in skin rejuvenation:
? Heating that leads to fibroblast activation, remodeling of collagen and increased synthesis of pro- collagen III 
? Dermatologic regression, represented by displacement of photodamaged dermis and improvement of epidermal and dermal parameters 
? Endothelial disruption, cytokine activation and collagen remodeling.
Hemoglobin and melanin are the primary chromophores involved in skin rejuvenation. 
Type I photorejuvenation refers to vascular anomalies, pigmentary changes or pilosebaceous changes, while Type II is related to dermal and subcutaneous senescence. 
The main advantages of IPL skin rejuvenation are minimal downtime recovery, fast and easy to perform, minimal complications, minimal interference with lifestyle and long-term improvement.
The term ‘photorejuvenation’ describes the simultaneous improvement of various epidermal changes related to aging. 
Sun exposure and smoking are the main factors that induce premature skin aging. 
With age, there is increased sun exposure. 
Among the ultraviolet (Bernstein et al.) radiations, UVB is considered the most damaging. 
UVA can also produce burns if it is administered at high levels. 
The extent of sun damage is proportional to the amount of exposure. 
Thus, as one ages, areas such as face, neck, upper chest and hands are more prone to photoaging. 
These alterations are more frequent in persons who are fair-skinned.
Intrinsic skin aging refers to structural changes that are independent of environmental influences. 
Often the skin has a mottled appearance due to a decrease in melanocytes.
Over the age of 30, the melanocytic number decreases 10–20% per 10-years. 
However, there is an irregular melanosome distribution to the adjacent keratinocytes. 
Xerosis is typically found with aged skin and is due to decreased sebaceous and eccrine gland function. 
It is especially evident on the hands and face. 
The rough aspect of photoaged skin is due to changes in the stratum corneum and the amount of glycosaminoglycan in the dermis. 
Dermal changes include accumulation of fibrous material, especially in the superficial reticular dermal layer. 
These changes are known as elastosis due to the high uptake of the elastic tissue stains. 
Degenerative changes are evident in the collagen structure also. 
All these changes produce various clinical presentations represented by mottled pigmentation, teleangiectasia, wrinkled and dry skin. 
The changes in the epidermis are represented by irregular pigmentation, atrophy and cellular atypia. 
Both extrinsic and intrinsic aging is associated with the production of excessive amounts of free radicals.
The three main expressions of aged skin are rhytides, pigmented lesions and vascular lesions.
Rhytides are due to a decrease in facial skin elasticity causing accentuation of lines and wrinkles. 
Dermal elastic fibers initially thicken. 
Because the skin loses its elasticity, gravity leads to a sagging effect, especially visible on the neck and jaw line. 
Repeated facial muscle movements accentuate the lines of expression.
Lentigines are light-to-medium brown benign hyperpigmented macules. 
Lentigo simplex arises in childhood and does not have a predilection for sun-exposed areas. 
The pigment is uniformly distributed throughout the lesion. 
Solar lentigo is an acquired lesion present on sun-exposed areas such as face, chest, and hands. 
They usually appear in middle age and are due to an increased number of melanocytes and melanin deposition in the basal layer. 
Histologic examination reveals parakeratosis and epithelial acanthosis.
Caf?-au-lait macules are light tan-to-brown hypermelanotic flat lesions with a clear demarcation from surrounding skin. 
They can appear at birth or later, especially in the first 2 decades of life. 
Histology shows hypermelanosis in the basal layer of the epidermis including basal melanocytes and keratinocytes. 
These lesions do not have a malignant potential.
Poikiloderma of Civatte appears as a rusty-brown hyperpigmentation and telangiectasia. 
It is more frequently located on the neck, chest and lateral side of the face. 
Although its origin is suggested as a hormonal imbalance associated with menopause, its location on sun-exposed areas suggests that sun has an important contribution. 
Pulsed light or laser treatments have proved to be useful in treating these lesions.
Ephelides, commonly called freckles, are small tan-brown macules that occur on sun-exposed areas. 
Histology shows hypermelanization confined to the basal cell layer. 
The melanosomes and melanocytes appear to be enlarged.
Melasma is represented by irregular brown or grayish facial hypermelanosis and is more often present in women with Fitzpatrick skin type IV to VI. 
The condition is more evident in UV-exposed areas, worsening in the summer and improving in the winter. 
Genetic predisposition, oral contraceptives, pregnancy or endocrine dysfunction have been related to its appearance. 
When it is related to pregnancy, it usually resolves within a few months after delivery.
Nevus of Ota is a bluish-gray macular lesion present on the face, in the area innervate by the trigeminal nerve. 
It is more commonly seen in Asians and Blacks. 
The lesion often varies in color and the edges are usually not well-demarcated. 
Histology shows bipolar dermal melanocytes distributed largely in the upper part of the dermis. 
The epidermis and dermis are usually normal.
Nevus of Ito is a grayish-blue discoloration with histological aspects similar to nevus of Ota. 
It is frequently located on the shoulder or upper arm and is seen more often in Japanese.
Baker nevus is a light to medium brown patch, usually of a few centimeters, that appears frequently in childhood. 
The lesions are hyperkeratotic and covered by coarse hair. 
The histological aspect shows acanthosis, hyperkeratosis, rete ridge elongations, increased number of melanocytes and thickening of the dermis. 
This lesion is considered to be an organoid hamartoma.
Telangiectasia refers to superficial cutaneous vessels typically up to 1mm in diameter. 
The vessels can be of arteriole, capillary or venule origin. 
The arteriolar type is bright red and protrudes above the skin surface. 
The capillary type is red. 
These lesions are often located on the face in light skinned patients. 
They are mainly distributed on the nose and mid-cheeks. 
They are also associated with rosacea, pregnancy, steroids or actinic damage. 
There are four clinical types: simple or linear, arborizing, spider and papular. 
The red/blue linear and arborizing lesions are often present on the face and legs while the papular type is part of syndromes such as Osler-WeberRender or associated with collagen vascular diseases.
Venous lakes are dilated venules in the upper dermis. 
They are commonly located on the ears or lips. 
They appear as dark-blue soft nodules of a few millimeters size. 
They are present in severe solar elastosis when the stromal support is diminished.
Senile purpuras are purple-red echymoses due to the loss of subcutaneous tissue and a predisposition to vascular damage. 
Their fragility is due to flattening of the dermal-epidermal junction.
Antiaging treatment, such as lights, lasers and creams, stimulate the production of new collagen. 
It seems that the fibroblast attachment to the new collagen allows stretching and balances collagen production. 
Many methods of improving photoaged skin have been reported. 
Intense Pulsed Light is a non-coherent light produced by a flash lamp. 
This is a relatively recent technology used for skin rejuvenation.
The quality of aging skin can be improved by ablative or non-ablative treatments. 
The difference between these two methods is that the epidermis is not disrupted in non-ablative treatments, and the recovery time is incomparably faster than in ablative treatments. 
The appearance and quality of skin is improved by IPL through stimulating the body’s natural wound healing mechanism. 
The light targets the microvasculature and the pigmented lesions; and is responsible for initiation of the described process. 
Hemoglobin and melanin are primary chromophores involved in skin rejuvenation. 
The peak of hemoglobin absorption is around 580 nm, while for melanin it ranges from 400 to 750nm.
Photorejuvenation can be categorized in two types: type I refers to vascular anomalies, pigmentary changes or pilosebaceous changes, while type II is related to dermal and subcutaneous senescence. 
Type I may be categorized into three subtypes. Type 1a includes rosacea and telangiectasis, Type 1b refers to pore size and skin roughness, and Type 1c includes pigmentary changes. 
There are three main mechanisms of non-ablative technology involved in skin rejuvenation:
? Heating that leads to fibroblast activation, remodeling of collagen and increased synthesis of pro- collagen III 
? Dermatologic regression, represented by displacement of photodamaged dermis and improvement of epidermal and dermal parameters 
? Endothelial disruption, cytokine activation and collagen remodeling
Several authors have reported the histological changes after IPL treatment. 
Most reported collagen improvement. 
In an animal study (Kunning mouse model), it was found that non-ablative laser improved the thickness of the dermal layers and collagen fiber density. 
The amount of hydroxyproline content and collagen synthesis increased. 
Biopsies from three patients after five treatments showed significant deposition of collagen in the superficial layer of the dermis. 
Collagen type I and III were identified. 
The activation of intracellular fibroblast activity and collagen proliferation is initiated by thermal injury to the collagen fibers caused by heat conducted from the chromophores. 
The collagen fibers are also damaged by their light absorption and by the non-selective heating of the dermis. 
Negishi considered that wavelengths between 400 to 600nm are absorbed by the collagen fibers and cause the injury. 
An increase of procollagen type I, III collagenase, elastin and hyaluronate receptor has been noticed after IPL. 
New collagen formation was observed by Goldberg after treatment. 
One study examined the malar skin histologically one week after IPL treatment. 
The telangiectasia, inflammation, elastosis changes, epidermal atrophy, rete ridge flattening and basal cell necrosis were found to be improved. 
The epidermal thickness increased to a range from 0.01–0.03mm. 
The greatest improvement was observed in the degree of elastosis. 
The effect of IPL on the skin structure of five women was evaluated by Prieto. 
He analyzed skin punch biopsies before treatment, 1 week, 3 months and 12 months after five treatments. 
In three patients he found at least one follicle containing Demodex organisms and perifollicular lymphoid infiltrate but no significant perifolicular infiltrate 1 week later. 
Biopsies done later showed Demodex and mild perifolicular lymphoid infiltrate. 
The author concluded that IPL induces coagulation necrosis of Demodex organisms. 
It seems that Demodex contains some chromophore that makes the parasite more sensitive to IPL. 
On a fibroblast culture, it was shown that IPL inhibits the MMPs. 
MMPs are a family of zinc-containing proteases with different substrate specificities and inducibility.
The mechanism by which IPL improves pigment lesions was investigated by Yamashita. 
Solar lentigines were treated with three sessions of IPL and observed on consecutive days, using reflectance-made confocal microscopy and optical coherence tomography. 
It was found that the melanosomes from the epidermal basal layer migrated towards the skin surface. 
Electron microscopy of the desquamated crusts showed numerous melanosomes.
Skin rejuvenation should achieve reduction of visible pigmentation and vessels and improve skin texture. 
The physician must thoroughly explain to the patient the difference in the expected results between IPL technology and other ablative technologies (i.e., CO2 laser, chemical peelings). 
The fact that vascular and pigmented improvement will be noted a few months later and the fact that minimal wrinkle and skin texture improvement will be seen almost one year later should be emphasized during consultation. 
During the physical examination, special attention should be paid to ruling out skin malignancies. 
If the patient is taking retinoids (isotretinoin) or other photosensitizing medication, delaying treatment for at least 6 months is suggested. 
Pregnant women or the presence of active infectious disease are contraindications to treatment. 
Particular care must be paid to sun-tanned patients. 
Treatment is performed only if the patient is willing to avoid sun exposure or to use sunscreens for the entire treatment period. 
Patients with a history of herpes simplex infection require prophylaxis.
Most of our patients request facial rejuvenation. 
The area should be cleaned and free of make-up before starting the treatment. 
We are in favor of using topical anesthetics (EMLA or ELA-Max) before the treatment. 
The basic treatment protocol is followed, as detailed in another chapter. 
The first parameter introduced to the machine is the skin type according to Fitzpatrick classification. 
Then the SR parameter is set up. 
The treated area is covered by a single pass. 
The mode for dealing with vascular lesions can also be used in a different session to enhance the results. 
A single pass is performed during one session. 
Large footprints are used for large areas. 
Thus, the treatment is faster and the light distribution into the skin is more uniform with better effects. 
Small footprints are used when the anatomic region is curved, as on the eyelids, nose, upper lip or ears. 
When performing eyelid treatment, plastic eye shields are used as protectors. 
The patient is strongly encouraged to keep the eyes closed during the procedure. 
The footprint is always placed perpendicular to the skin. 
In areas with hyperpigmentation, slight pressure is used; in areas with predominant vascular abnormalities, no pressure is applied. 
In this way, the blood vessels are not emptied and the treatment is more effective.
When approaching the hair-bearing area on the face, such as the eyebrows, this is covered by white gauze and the IPL footprint is placed 2–3mm from the edge. 
Unintended hair removal in this area canappear if precautions are not taken. 
At times, uneven edges of the treated area can be noticed in the treatment of Poikiloderma of Civatte. 
Performing treatment of the entire cosmetic anatomical units is recommended in such a situation. 
Treatments are performed one-three months apart.
If almost no change in the pigmentation or vascular lesion is noted after the first treatment, the fluence is increased by 2–4J/cm2 for the next session. 
A good response can be seen immediately after treatment when blanching of the vessels, “urticaria” type reaction or slight darkening of the pigmented lesions appears. 
Treatment time varies according to the anatomical area, from a few minutes to more than 15 min for the neck and chest. 
Ice packs are always used at the end of the treatment and are intended to reduce the burning sensation and decrease the swelling. 
Almost all patients are able to resume normal activities after the procedure. 
Some patients experience a burning sensation that disappears within minutes. 
Erythema and edema are present in most patients and resolve within hours to 2–3 days. 
The reaction of the pigmented lesions usually causes their darkening for the next 7–8 days. 
We do not consider this as signs of complications unless the unwanted effect lasts for more than a few days. 
These are part of the normal skin response after IPL treatment.
The skin is not a uniform structure and lesions are present at different levels of the dermis. 
This is why we prefer to alternate the cutoff filters instead of using a single wavelength for every treatment. 
Regression of the results is a normal process that occurs after any cosmetic procedure. 
The regression is usually visible from one to a few years after treatment. 
Maintenance treatments every year are recommended to continue seeing positive effects.
Many studies have confirmed the effectiveness of photorejuvenation. 
Women seek this treatment more frequently. 
The request for IPL procedures from men is less than 10% of all requests. 
However, men are self-conscious and cautious. 
They are often concerned about a few lesions (i.e. lentigines) although they have diffuse dyschromias. 
Unlike women, they often prefer to have the lesions treated instead of having a fullface treatment. 
Men are less willing to return to work if significant swelling is present after treatment. 
Particular attention should be paid to hair-bearing areas (chin, cheeks, preauricular and perioral areas). 
A lesion located in this area can be treated with the price of hair removal. 
Race also has an influence on skin response to treatment. 
In Asians, pigmentary problems are more frequently encountered than wrinkling. 
Among pigmented lesions, nevus of Ota is more frequently present in Asians.
There are a variety of IPL devices on the market, and parameters vary widely from one device to another. 
Certain fluences which are safe for a particular application in one device may be dangerous with other IPL devices; modern IPL devices deliver a constant spectrum emission at low fluences. 
Former generations of IPL systems had variations of the beam as the pulse progressed, with the end of the pulse more in the red/infrared spectrum; modern IPL devices have a computer system that reduces this socalled “spectral jitter”.
Skin texture improvement has been reported by several authors. 
Non-ablative photorejuvenation is considered to “remodel” the dermis due to the thermal injury to the papillary and upper reticular dermis, sparing the epidermis. 
Collagen remodeling continues for up to one year after the end of IPL treatment. 
In a long-term follow- up study, Weiss reported skin textural improvement in 83% of patients. 
The evaluation was done four years after the IPL procedure, with a chart review of 80 randomly selected patients. 
The face responded slightly better than the chest or neck with a 90% texture improvement. 
After four IPL treatments, Bitter reported wrinkle improvement from a score of 5 (moderate) to 2.83 post-treatment (mild). 
The patients also reported some degree of improvement in skin laxity. 
The level of satisfaction was as high as 88%. 
A higher satisfaction rate after skin rejuvenation was reported by Fodor et al. with the 5-point Likert scale to evaluate the results; 93.2% of the patients felt they had good to very good results, with better improvement in pigmented and vascular lesions compared to skin texture. 
We have not seen significant skin texture improvement after IPL treatment. 
The lighter skin color obtained should not be confused with skin texture and rhytid improvement. 
During consultation, we strongly emphasize that significant skin texture improvement will not be achieved with this method.
If the patient understands this, the satisfaction level is high.
The association of ALA and IPL has been successfully used to improve skin texture. 
5-ALA is a photosensitizing agent often used to treat acne vulgaris, skin carcinomas, psoriasis or other dermatologic conditions. 
Its application was extended recently by using it for skin rejuvenation in combination with IPL. 
The topical application of ALA produces an accumulation of the endogenous photosensitizer protoporphyrin IX (PpIX). 
The maximum absorption of PpIX induced by 5-ALA is at 410, 630 and 690 nm. 
The free radicals resulting from ALA metabolisation have a selective action based on accumulation mainly in the pilosebaceous units and hyperproliferative keratinocytes. 
IPL decreases the amount of Propionibacterium acne and reduces the size of sebaceous glands and the amount of sebum production. 
When ALA is combined with IPL for photodynamic therapy, it is applied for a relatively short period of about one hour before the procedure. 
This short incubation time is enough to improve IPL results. 
The positive sideeffects of ALA and IPL combination were recorded after treatment of actinic keratosis. 
Improvement of skin elasticity, wrinkles and pigmentary changes have been noticed. 
Good results of rejuvenation after ALA and IPL have been recorded for patients with photoaging. 
A split-face comparison study of IPL alone or combined with 5-ALA for photorejuvenation was reported by Alster. 
Better results were observed after two treatments with the 5-ALA-IPL combination although desquamation was observed in these areas. 
The same combination was reported to be useful for treatment of acne vulgaris. 
It proved to be more efficient than IPL alone, although the level of improvement was 66.8%. 
A clearance rate of 71.8% for the same condition was reported in another study. 
There are no data on humans but Hedelund demonstrated that IPL has no carcinogenic potential in mice. 
The main advantage of combining 5-ALA with IPL is a reduced number of treatments and better clinical outcome. 
Photodynamic therapy with ALA is not widely approved and most countries restrict its use to the experimental level.
With its ability to emit a wide spectrum of wavelengths and adjustment of pulse duration, delay and fluences, IPL has proven to be useful for treating various vascular and pigmented lesions. 
There are several reports of vascular lesion improvement when performing skin rejuvenation. 
Most report on telangiectasia improvement. 
When using Vasculight or Quantum to treat telangiectasia, Goldman prefer to use a double pulse of about 2.4–4ms duration, with a delay time of 10ms in light skin and 20–40ms in darker skin. 
The fluences are usually between 28 and 35J/cm2.
Our experience with Vasculight for photorejuvenation shows that fluences between 25 and 45J/cm2, cutoff filters of 560 and 640nm, a pulse duration of 2.4–7ms, and a pulse delay of 15–75ms are the parameters most often employed. 
Using Lumenis One, the fluences delivered are less than with Vasculight. 
Negishi performed photorejuvenation on 73 patients using the original IPL or Quantum IPL. 
The fluences varied from 23 to 27J/cm2, pulse duration varied from 2.8 to 6ms and the pulse delay from 20 to 40ms. 
Excellent results for small red telangiectasias were obtained by using synchronized pulses with an initial short 2.4–3ms followed by a second longer 4–8ms pulse. 
Weiss reported 82% telangiectasia improvement. 
The evaluation was done four years after IPL treatment by reviewing the charts of 80 randomly selected patients. 
The face responded slightly better than the chest or neck with a 90% texture improvement.
There is limited data in the literature regarding treatment of rosacea with IPL. 
Rosacea is a common condition and includes stages such as facial flushing, erythema, edema or rhinophyma, but its exact etiology is not clear. 
Mark reported good results on a small number of patients, and a mean clearance of 77.8% was reported by Schroeter. 
The clearance time persisted for an average of 51.6 months. 
A recurrence rate of about 7% was observed 3 years posttreatment.
A higher cutoff filter, longer pulse duration and longer pulse delay has a better effect on deeper vessels with large diameters, while a shorter cutoff filter, shorter pulses and shorter delay has a greater effect on superficial dermal melanin and superficial small vessels.
Pigmentary improvement when performing skin rejuvenation is almost invariably reported. 
Melanin is the target chromophore of pigmentary lesions. 
The majority of the melanin is concentrated in the basal layer of the epidermis and has the highest absorption spectrum in the UV. 
The melanin pigment is packed within melanosomes which are found within the melanosytes. 
The melanosome has a Thermal Relaxation Time (TRT) of about 10–100ns. 
Watanabe found that melanosomal injury is independent of pulse width at 694, 630 or 532nm, if the pulse is below 1?s. 
He concluded that 1 ?sec is the effective TRT of the melanosome. 
The shorter the pulse width, the more localized the damage. 
The absorption coefficient of melanin decreases as the wavelength increases. 
Thus, greater energy for longer wavelengths is required to injure the melanosome. 
The repigmentation after treatment occurs from residual melanocytes from the adnexal structures or migration from non-treated areas.
When treating pigmentary lesions, the natural response includes the formation of tiny crusts that peel off within a few days. 
Performing an examination using Woods lamp to establish the depth of melanin pigmentation prior to treatment has been suggested. 
There are three histological types: epidermal, dermal and mixed. 
It is important to adjust the device parameters and alternate the cutoff filters and fluences for efficient treatment of resistant melasma. 
Lesions located at different levels respond differently. 
When treating melasma patients with IPL, we always ask the patient to discontinue birth control pills and avoid sun exposure. 
The results of pigmentary lesions are demonstrated in Table 6.1. 
Using a single IPL treatment, Bjerring obtained 96% pigment reduction with a higher clearance rate for lentigo solaris. 
Kawada reported solar lentigines and ephelides improvement after 3–5 sessions of IPL treatment. 
48% of patients reported more than 50% improvement and 20% had more than 75% improvement. 
Better response rate was noticed for small plaques. 
Excellent results are reported for lentigines and other pigmentary lesions. 
When treating pigmentary lesions (solar lentigines and ephelides) using Lumenis One, Konishi obtained clinical improvement by choosing low fluences (12–14J/cm2), double pulses of 4ms and a pulse delay of 20ms. 
He reported a decrease in the melanin index. 
Pigmentary changes were treated by Huang using fluences of 25–35J/cm2, 4ms single or double pulses, 20–40ms pulse delay and 550, 590nm cutoff filters. 
Freckling and lentigines were treated by Kawada using a Quantum IPL. 
He used smaller fluences of 20–24J/cm2, 2.6–5ms pulse duration, 20ms pulse delay and 560nm cutoff filter. 
He also noticed a better response for small lesions. Bitter treated 49 patients with Vasculight for photodamage. 
He used fluences from 30–50J/cm2, pulse durations of 2.4–4.7ms, pulse delays of 10–60ms and cutoff filters of 550 and 570nm. 
Although IPL requires more sessions to treat lentiges, it is associated with a lower risk of post-inflammatory hyperpigmentation. 
In general, patients with epidermal melasma have a better response than those with mixed-type melasma the superficial lesions (caf? au lait, ephelides, epidermal melasma) have a better response to IPL while deeper lesions (nevus of Baker, mixed melasma) are more resistant to the procedure. 
More treatments are needed for deeper pigmented lesions. 
Nevus spilus was successfully treated by Gold  using a 590nm cutoff filter. 
According to Huang, among all pigmentary lesions, freckles seems to respond the best to IPL treatment. 
Poikiloderma of Civatte is a combination of telangiectasia, atrophy and pigmentary changes. 
The recent discovery of familial cases of Poikiloderma of Civatte shows that it also has a genetic component transmitted as an autosomal dominant trait.
Several authors have reported Poikiloderma improvement with IPL treatment. 
Multiple sessions are usually necessary. 
Goldman recommends starting treatment with a 550 or 560nm filter to prevent too much epidermal absorption. 
In a different study, the same author reported good improvement in 42% of patients after an average of 2.8 treatments; fluences between 30 and 34J/cm2 were used. 
When 50% improvement was noticed on the previous treatment, either the same fluence was used or it was increased by 5%. 
Significant improvement of Poikiloderma of Civatte (grade 4: 75–100%) after IPL was seen in 82% of patients. 
Clearance of telangiectasia and hyperpigmentation was noted in more than 75% of patients. 
Paquet obtained an 80% clinical, histologic and spectrophotometric decrease in hypermelanosis in two patients following drug-induced toxic epidermal necrolysis. 
Although the literature is limited, IPL systems with an ability to adjust wavelength, pulse width and delay are useful for treating facial hypermelanosis.
The treatment intervals for skin rejuvenation are reported to vary from 2 weeks to 8 weeks. 
Most physicians perform treatments one month apart. 
We also prefer performing the treatment every month, although this interval is arbitrary. 
For people with posttreatment side effects or higher skin sensitivity, we extend the interval between treatments. 
In our experience, highest patient satisfaction is for pigmented or vascular lesions as a part of photodamaged skin. 
Shorter wavelengths are better for treatment of these lesions; longer wavelengths penetrate deeper and are better for wrinkle reduction and texture improvement.
Noninvasive methods for rejuvenation, such as IPL, need to compete with laser resurfacing, chemical peels and dermabrasion. 
Ablative procedures injure the epidermis and produce changes in the dermis followed by an inflammatory response that stimulates fibroblasts to produce scar collagen. 
In these situations, the skin is more sensitive, there is prolonged healing time and a need for wound care 
The main advantages of IPL skin rejuvenation are the minimal downtime recovery, fast and easy to perform, minimal complications, minimal interference with lifestyle and long-term improvement.
Sun exposure and smoking are the main fac- ? tors that induce premature skin aging. 
Over the age of 30, the melanocytic number decreases 10–20% per 10 years. 
Dermal changes in the aged skin are responsible for various clinical presentations, such as mottled pigmentation, telangiectasia, wrinkles and dryness. 
Hemoglobin and melanin are the primary chromophores involved in skin rejuvenation. 
Type I photorejuvenation refers to vascular anomalies, pigmentary changes or pilosebaceous changes, while Type II is related to dermal and subcutaneous senescence. 
Most histological studies show collagen improvement after IPL treatment. 
Skin rejuvenation is aimed at reducing visible pigmentary changes and blood vessels and at improving skin texture. 
The face is the most frequently treated area. 
Overlapping during treatment should be avoided. 
Large footprints are more efficient for treating large areas. 
The light distribution into the skin is more uniform, resulting in a better effect. 
Hair-bearing area should be protected during treatment. 
Special attention should be paid to men who have large hair-bearing areas.
A good response immediately after treatment is blanching of the vessels, “urticaria” type reaction or slight darkening of pigmented lesions. 
Erythema and edema are present in most patients and resolve within hours to 2–3 days. 
Adjusting wavelengths according to the type and depth of the lesion may improve the results. 
There is a variety of IPL devices on the market. 
The treatment parameters from one device do not fit other devices. 
In our experience, pigmented and vascular lesions respond better after IPL treatment compared with skin texture improvement. 
The association of ALA and IPL has been successfully reported to improve skin texture but it is not widely approved and most countries restrict its use to the experimental level. 
Superficial pigmented lesions, such as ephelides and epidermal melasma, have a better response to IPL while deeper lesions are more resistant. 
The main advantages of IPL skin rejuvenation are minimal downtime recovery, fast and easy to perform, minimal complications, minimal interference with lifestyle and long-term improvement.
Hirsutism is represented by excessive growth of the coarse hairs in women, distributed in a male-like pattern. 
Hypertrichosis is represented by excessive growth of coarser and longer hair than is normal for the age, sex and race of the person. 
The hair growth cycle has three phases: anagen, catagen and telogen. 
The anagen phase is the growth phase, the catagen phase is the regression phase and the telogen phase is the rest phase. 
The hair follicle is the most susceptible to IPL treatment during the anagen phase. 
The melanin is the target chromophore for hair removal. 
There are three types of melanosomes present in the hair. 
Erythromelanin granules are present in red hair while eumelanin and pheomelanin granules are found in varying proportions in blond and dark hair. 
The targets for hair removal are the dermal papilla and the bulge area. 
The heat-induced destruction of the hair shaft leads to hair “dropout”. 
The partial injury to the germinative zone leads to telogenshock response, prolonged telogen dropout, and development of dystrophic hairs which are thinner in texture and have variable pigmentation. 
Multiple IPL treatments are usually needed. If no improvement is obtained after 5–6 sessions, interrupting the treatment should be considered. 
The darker the skin and the brighter the hair, the less effective the treatment will be.
Hirsutism is represented by excessive growth of the coarse hairs in women, distributed in a male-like pattern. 
There are racial and ethnic differences in hair distribution. 
The most frequently used method to grade hirsutism is the Ferriman-Gallwey scoring scale. 
According to Ehrmann, 5% of women in the United States suffer from hirsutism. 
Age also influences hair distribution, and unwanted facial hair is more common in postmenopausal women. 
Endocrine disorders characterized by hyperandrogenemia are responsible for increased hair growth. 
The source of the endocrinological problem can be found in the pituitary gland (Cushing disease), the adrenal gland (hyperplasia or tumors) or in the ovaries (polycystic ovary disease, tumors). 
Exogenous anabolic steroids are also associated with hirsutism. 
The most common hormonal cause of hirsutism is polycystic ovary disease. 
Testing of elevated androgen levels in woman with moderate or severe hirsutism that appears suddenly and is rapidly progressive or associated with menstrual dysfunction or obesity is recommended prior to starting hair removal treatment. 
However, the severity of hirsutism is not well correlated with the androgen level. 
The response of the follicle to androgen excess varies among persons. 
Oral contraceptives and antiandrogen drugs are the most used pharmacological therapy. 
Hirsutism treatment in patients with polycystic ovary disease is difficult and there are reports showing 25% hair growth after 36 months of treatment.
Hypertrichosis is represented by excessive growth of coarser and longer hair than is normal for the age, sex and race of the person. 
Although there are described mechanisms of hypertrichosis, the triggers that initiate these mechanisms are unknown. 
The congenital forms of hypertrichosis include nevocellular nevus, hamartoma, hemihypertrophy, hypertrichosis cubiti, neurofibroma, hairy cutaneous malformations of palms and soles, spinal hypertrichosis, anterior cervical hypertrichosis and several congenital syndromes in which generalized hypertrichosis is a primary feature. 
The acquired disorders associated with hypertrichosis include Becker nevus, hypertrichosis of pinna, hypertrichosis associated with local inflammation, pharmacological hypertrichosis (cyclosporine, cortisone, streptomycin) and other acquired disorders associated with generalized hypertrichosis (dermatomyositis, hyperthyroidism, hypothyroidism).
Sometimes, hair removal can also have non-cosmetic applications. 
For instance, hair removal of flaps or treatment of areas with recurrent folliculitis can be of real benefit for the patient. 
Digestive reconstruction with a hair-bearing pectoralis flap can lead to disfagia and even halitosis. 
Urethral or vaginal reconstruction with scrotal or pudendal hairy flaps may obstruct urinary flow or increase the risk of infection.
Older methods of hair removal include shaving, plucking, waxing, depilatory creams and electrolysis. 
Galvanic, electrolysis, thermolysis and blend methods are three types of electrosurgical epilation. 
Most are temporary methods, relatively inexpensive. 
Among the common side effects encountered are:
? Shaving: dermatitis, minor cuts and pseudofolliculitis
? Waxing: pain, minor burns, irritation, folliculitis, post-inflammatory hyperpigmentation
? Electrolysis: edema, erythema, pain, scarring, post-inflammatory pigmentary changes
? Topical creams: acne, pseudofolliculitis, burning
There are three methods of permanent hair removal: electrolysis, IPL and laser treatment. 
Although widely used in the past, electrolysis is sometimes poorly tolerated by patients and has 15–50% permanent hair loss per treatment. 
The pulsed light and laser treatments seem to be more reliable and more frequently used than electrolysis recently.
Detailed histology and biology of the hair follicle was described in Chapt. 1 (Skin anatomy). 
Herein we emphasize the most important facts that influence treatment. 
There are three main components of the hair follicle: the infundibulum, the isthmus and the hair bulb with dermal papilla. 
The bulge area is located about 1–1.5mm below the skin surface near the follicle bulb. 
Recent evidence shows that follicular stem cells are located in the bulge and the outer root sheath. 
They have the capacity to regenerate not only the hair follicles but also sebaceous glands and epidermis. 
The follicle depth varies according to the anatomical area.
The hair growth cycle has three phases: anagen, catagen and telogen. 
The anagen phase is the growth phase, the catagen phase is the regression phase and the telogen phase is the rest phase. 
The hair follicle is the most susceptible to IPL treatment during the anagen phase. 
This phase is variable in duration and can last up to 6 years. 
The catagen phase is the relatively constant phase, usually lasting for about 3 weeks. 
Most follicles, most of the time, are in the anagen phase (80–85%) while the remaining follicles are either in the catagen (2%) or the telogen phase (10–15%). 
The transition from one hair follicle phase to another varies according to the anatomical region. 
The percentage of hair follicles in the telogen phase is about 15% in the scalp and 75% in the extremities. 
The anagen phase duration varies from 2 month to 1 year in the face, from 1 month to 6 months in the extremities. 
This is why more IPL treatments are needed for each area in order to catch the hair follicles in the anagen phase. 
Factors such as age, gender, anatomical region and hormones affect the duration of anagen phase.
During the hair cycle, there are also changes in vascularization. 
These changes seem to be related to the hair cycle regulation process. 
The hair follicle is well vascularized during the anagen phase, while vascularization is much reduced during the catagen phase.
The lights as lasers have a similar mechanism of acting on the chromophore. 
The melanin is the target chromophore for hair removal. 
For selective damaging of the hair follicle, the light energy is absorbed by the melanin (endogenous chromophore) present in the hair shaft, outer root sheath of the infundibulum and matrix area. 
There are three types of melanosomes present in the hair. 
Erythromelanin granules are present in red hair while eumelanin and pheomelanin granules are found in varying proportions in blond and dark hair. 
In white or grey hair, the melanocytes of the hair matrix are much reduced and show degenerative changes. 
Eumelanin and pheomelanin have different wavelength absorption peaks. 
It has been shown that the absorbance rate is 30 times lower at a wavelength of 694nm for pheomelanin compared to eumelanin. 
The light absorption of pheomelanin is very low at wavelengths from 750 to 800nm. 
Because blonde or whitegrey hair has a paucity of melanin, they are less susceptible to IPL treatment. 
The targets for hair removal are the dermal papilla and the bulge area. 
The heatinduced destruction of the hair shaft leads to hair “dropout”. 
The partial injury to the germinative zone leads to telogen-shock response, prolonged telogen dropout, and development of dystrophic hairs which are thinner in texture and have variable pigmentation. 
Dark-skinned people have a high content of melanin within the epidermis. 
This absorbs the energy, resulting in possible heating and damage of the surrounding skin. 
Extra care must be taken when treating patients with Fitzpatrick skin type V and VI.
A histological examination study performed on nine subjects after a single IPL treatment showed clumping of melanin, hair shaft follicles and coagulative necrosis of the hair shaft. 
At 48 h, half the follicles contained apoptotic keratinocytes and had perifollicular edema. 
Some hair follicles presented perifollicular hemorrhage.
At a longer posttreatment interval (2 weeks–20 months), many follicles had apoptotic keratinocytes, perifollicular fibrosis and melanophages.
During consultation, taking a detailed medical history can be of extreme importance. 
Specific questions to identify endocrinological problems leading to hirsutism should be asked. 
Obese people, those with polycystic ovary syndrome or other endocrinological disorders should be referred first to an endocrinologist. 
This does not mean that they cannot benefit from IPL treatment. 
Most authors refrain from using light or lasers for patients undergoing isotretinoin therapy. 
It is also our protocol to delay treatment from 6–12 month after stopping drug intake. 
The reasons for delaying treatment are seen in several reports that showed delayed healing and scarring. 
However, Khatri reported good results in six patients taking isotretinoin, and no complications were reported. 
It is our recommendation not to perform treatment in these patients until large studies demonstrate its safety. 
Other contraindications to treatment are patients with a history of keloids and connective tissue disorders.
The physical examination should be done carefully for the desired anatomic region. 
It is important to rule out skin malignancies and active skin infection. 
Particular attention should be paid to the presence of pigmented lesions or tattoos in the area. 
Treatment can alter the pigment. 
We recommend covering the lesions with a small white pad during treatment. 
Treatment to tanned people is delayed for a few weeks to diminish the chances of side effects, especially hypopigmentation. 
A careful analysis of the distribution of unwanted hair should be done. 
The quantity, color and quality of hair follicles should be compared with healthy people having normal hair distribution. 
All this information should be explained to the patient, as his hair distribution, perception or expectations can be disproportional.
People coming for epilation desire definitive hair removal. 
According to the FDA, “permanent hair removal” refers to a significant reduction of hair follicles, stable for a period of time longer than the complete growth cycle of the hair follicle. 
This should be explained to the patient, as most interpret the same sentence as no hair regrowth ever. 
Educating patients and explaining the expected outcomes and possible complications is very important. 
We explain to patients that multiple treatments are needed and even then, a permanent result should not be expected. 
The results of each treatment are marked on the chart. 
If no significant improvement is obtained after 7–8 treatments, we suggest stopping treatment.
Any method of hair removal except shaving should be stopped at least 2 months prior to treatment. 
Shaving is the only method which does not remove the hair bulb. 
With other methods, the target structures are removed and treatment is in vain. 
Two or 3 days before the treatment, the area should be shaved. 
Performing treatment on an unshaved area can lead to more complications. 
The long dark hair lying on the skin absorbs the energy and may burn the epidermis. 
For bikini area treatment, patients are told to wear white undergarments as black ones are more prone to reacting to the treatment. 
If small areas are treated, this can be done without topical anesthesia. 
The bikini and periareolar areas are the most sensitive. 
In these areas or other large areas, we always recommend an EMLA or ELAMax application one hour prior to treatment. 
When larger areas are treated, necessitating more time, breaks for ice pack cooling are taken. 
Cooling is continued for 15 min after finishing the treatment. 
A test is always performed before starting the procedure.
The IPL device is relatively easy to handle. 
The computer software provides suggested treatment parameters based on patient hair color, type, and skin type. 
The degree of contrast between skin and hair, the type of hair color and the amount of melanin content are important factors in the success of IPL hair removal. 
The light penetration depth is limited by irradiating areas of the skin that are too small. 
To avoid the effect of radial dissipation of energy, the spot size should be larger than the light penetration depth into the tissues, about 5–10mm.
The possibility of double or triple pulse distribution causes the hair follicle to heat up in a stepwise fashion. 
Lengthening the pulse duration carries a risk of epidermal damage. 
A pulse delay over 3ms is recommended to allow the epidermis to cool down. 
Longer wavelengths are preferred, as the chromophore is situated deep in the skin. 
The longer the wavelength, the deeper the light penetration into the skin. 
Shorter wavelengths are more effective for light and thin brown hairs. 
Applying slight pressure on the skin is recommended when performing the treatment. 
This will empty the blood vessels from underneath and minimize the absorption of light energy by hemoglobin.
Treatment parameters need to be adjusted according to the skin response from the anterior session. 
When side-effects or complications are encountered after one session, the fluence is decreased by about 2–4J/cm2 and the pulse delay is increased by 10%. 
Future treatment parameters are adjusted according to the previous response. 
We always recommend recording the patient evaluation and side-effects for the whole treatment period. 
The presence of certain side-effects as a paradoxical effect indicates interruption of the treatment. 
For further details, please see Chapter. 9 on Complications.
The timing of multiple treatments varies according to the hair growth cycle in that region and the hair type. 
In general, treatments to the face, neck, axilla and bikini area are done at 5–6 weeks intervals. 
The extremities and thorax are treated with a 7–8 weeks interval. 
Almost all patients experience edema and erythema for a short period of time after treatment, which is considered a normal response. 
Patients should be reminded that they will have hair growth in the days after treatment. 
This is a normal response and represents the extrusion of the damaged hair from the follicle. 
It should not be interpreted as failure of the treatment. 
Dark skin phenotypes remain problematic for IPL-assisted treatments. 
Sunscreens are essential to protect the skin from sun during the treatment period.
Haedersdal identified controlled clinical trials of hair removal between 1990 and 2004 and compared the results of hair removal using lasers and light by studying nine randomized controlled trials and 21 controlled trials, which included only two studies on IPL. 
It is difficult to integrate the data as there are many factors that can influence outcome: fluence, wavelengths, spot size, pulse duration, presence or not of skin cooling, and patient parameters. 
Many studies confirmed the long-term hair removal efficacy of the IPL system.
Most literature studies report on hair removal for patients with skin type I–IV. 
The clearance rate after IPL hair removal varies widely from 20–93.5%. 
As can be seen, various cutoff filters and a wide range of fluences are used by different authors. 
These vary also according to the IPL device. 
Performing two treatments with fluences of 40–42J/cm2, Weis noticed a 33% hair count reduction at 6 months. 
A reduction in the remaining hair follicles was also recorded. 
A relatively low hair reduction (27%) was reported by Goldberg after one to three treatments. 
The fluences used ranged from 6.25–6.45J/ cm2 with a pulse duration of 35ms. 
Using high fluences of up to 55J/cm2, Gold obtained a 60% hair reduction at 12 weeks post hair removal. 
80.2% hair clearance at 8 months post-treatment was obtained by Troilius. 
The parameters used were a cutoff filter of 600nm; mean fluence of 19.3J/cm2 and a pulse duration of 44.5ms.
No significant difference in hair loss after single (54% reduction) or multiple treatments (64% reduction) was observed by Sadick 6 months post-treatment. 
The fluence used varied from 40–42J/cm2 and cutoff filters used were 590nm for skin type I, 615nm for skin type II, 645nm for type III, and 695nm for type IV. 
In a different study, the same author reported 76% hair removal after a mean of 3.7 treatments. 
He used 615nm cutoff filters and 39–42J/cm2 for Fitzpatrick skin type II; 645 nm and 34–40J/cm2 for skin type III-IV, and 695nm and 38–40J/cm2 for skin type V. 
Maximal benefit of photoepilation was achieved from the initial 1–3 treatments.
The level of patient satisfaction is hard to anticipate. 
In a retrospective study, Lor evaluated the satisfaction level of 207 patients: 22% were very satisfied, 45% satisfied and 33% unsatisfied. 
Using fluences between 35–39J/cm2, 645 and 695nm cutoff filters and pulse delays <40ms (63.8% cases) and >40ms (36.2% cases), Fodor evaluated the satisfaction level of 80 treated patients. 
The patients who had fewer treatments were more satisfied than those who had more than seven treatments. 
The author’s clinical impression was that the best response was noticed after first few treatments, which explained the satisfaction level.
One of the limiting factors that prevent the physician from applying higher fluences to make the treatment more effective is pain. 
Shorter wavelengths are more painful, probably because the epidermis absorbs most of them. 
It has been shown on skin biopsies that light produced by “Photoderm” will reach a depth of 1.3mm.
There are recommendations to perform at least three treatments but there are no recommendations about when to stop the treatment. 
Usually we stop after 7–8 treatments, unless significant improvement is gained. 
When treating various body areas, the interval between treatments should be adjusted according to the resting period of the hair follicles. 
Most authors prefer to perform treatments at 4–6 weeks intervals.
Studies on IPL hair removal for dark skin types have been reported. 
Most IPL devices enable a wide range of wavelengths by choosing different cutoff filters, thereby sometimes being effective in dark skins. 
Johnson reported a 85–100% clearance in three patients with skin types V and VI. 
Long pulse delays (>80ms) were used. 
Temporary hyperpigmentation was encountered in one case. 
Lee evaluated the results after treating 28 Asian patients who have a higher epidermal melanin content than Caucasians. 
A higher clearance of axillary hair of 83.4% was observed for the group with higher cutoff filters (645–950nm). 
The average fluence for this group was 17.1J/cm2. 
For dark skinned patients, the pulse duration should be extended, thereby producing gradual heating and less damage to the epidermal layer. 
When the same fluence was distributed to the skin at a duration of 15ms compared to 30ms, it was found that the shorter duration had a 6°C higher temperature of the skin surface. 
Low fluences and longer pulse delays are recommended for dark skin types. 
A device combining the optical energy and radiofrequency was used in a study to perform hair removal in darker skin types. 
Although less optical energy was needed for treatment, only 46% hair removal was obtained 3 months after a single treatment.
There are only limited studies on this topic. At present, we recommend IPL hair removal without reservations for patients with skin types I–IV and fine or coarse black hair type. 
The darker the skin and the brighter the hair, the less effective the treatment will be. 
We do not perform IPL hair removal for skin types V or VI or for blond or white hair. 
For darker skin types, some authors prefer using Nd:Yag laser. 
Identification of patients with unrealistic expectations increases the satisfaction level.
Both IPL devices and lasers are currently used for hair removal. 
The effectiveness of different devices varies according to fluence, wavelength, pulse duration and delay. 
It is hard to compare the results of different studies as different devices, fluences and cutoff filters are used.
The efficacy of IPL, diode laser and Alexandrite laser was studied on 232 patients with skin types II–IV. 
At 6 months, optimal hair removal reduction was noticed with no significant differences between the sources (IPL: 66.9% clearance; Alexandrite: 68.7%; diode: 71.7%). 
Amin evaluated the results of epilation by comparing two IPL devices (Palomar/Starlux Rs, Palomar/ Starlux Y) a diode laser and an Alexandrite laser. 
The results were evaluated 210 days later by photographing the treated area. 
There was about 50% hair count reduction in all four areas but the Alexandrite laser had the highest pain score. 
Eleven patients were treated by Goh in a site-by-site manner using IPL and Nd:Yag laser. 
No significant differences were noticed in the results. The Nd:Yag laser was more painful than IPL. 
Bjerring evaluated side-by-side the IPL and the Ruby laser for hair removal. 
After three treatments, hair reduction was obtained by 93.55% of IPL treated patients and by 54.8% of Ruby laser treated patients. 
Additional IPL treatments resulted in only 6.6% further hair reduction. 
The pain level in the Ruby laser group was 3.5 times higher than with IPL treatments.
Having a wide spectrum of wavelengths (500– 1,200nm), the IPL device has better penetration than Alexandrite or Ruby lasers. 
Shorter wavelengths can be used to target red-brown hair in individuals with light skin type, although the result is not as good as for dark hair.
Most studies report hair removal from body areas that are most requested for treatment, such as the face, axilla and bikini. 
However, successful treatment with IPL of a relapsing hairy intradermal nevus after shave excision was reported by Moreno-Arias. 
IPL was also successfully used to correct improper hairline placement after hair transplantation; three treatment sessions were enough to correct the problem. 
IPL applications have been extended to treat hairy grafts and flaps. 
Four patients who needed facial or breast reconstruction with flaps after cancer excision were successfully treated for hair removal. 
The authors noticed simultaneous improvement of skin coarseness, pigmentation and erythema. 
Excellent results were reported by Schroeter, who obtained 90% hair removal in transsexual patients. 
The average follow-up period was 44 months. 
The same author reported a negative correlation between hair removal and age of patient. 
This seems to be the single study that reports a correlation between age and amount of hair clearance.
PTD implies the application of a photosensitizing drug (e.g., ALA) and appropriate selection of wavelengths to cause selective tissue destruction. 
More details about photosensitizing drugs are described in Chapter 2. applied the principles of PDT to treat 11 hirsute patients. 
The area was first epilated and 20% topical ALA was used. 
Three hours later, the area was treated (wavelength of 630nm; fluence of 100–200J/cm2). 
A 50% reduction in hair regrowth was obtained after 3 months.
Nahavandi evaluated the efficacy of VPL in the treatment of unwanted hair in 77 volunteers. 
VPL delivers a pulsed train of light, each train containing up to 15 micro pulses. 
More than 50% hair clearance was observed in 88.3% of patients.
Hirsutism is represented by excessive growth of coarse terminal hair in women and distributed in a male like pattern. 
Hypertrichosis is represented by excessive growth of coarser and longer hair than is normal for the age, gender and race of the person. 
The hair follicle is most susceptible to IPL treatment during the anagen phase. 
The transition from one hair follicle phase to another varies according to the anatomical area. 
The anagen phase varies according to the anatomical area. 
Melanin is the target chromophore for hair removal. 
In white or grey hair, the melanocytes of the hair matrix are much reduced and show degenerative changes. 
These types of hair are less susceptible to IPL treatment. 
The targets for hair removal are the dermal papilla and the bulge area. 
Identify endocrinological problems before treatment. 
Refrain from using IPL in patients currently taking isotretinoin. 
Treatment of tanned skin is delayed for a few weeks. 
Defining the term “permanent hair removal” before starting treatment might increase the satisfaction level. 
Multiple treatments are usually needed. 
If no improvement is obtained after 5–6 sessions, interrupting the treatment should be considered.
Shaving is the only recommended method before IPL treatment. 
Treatment parameters need to be adjusted according to the previous results. 
Most physicians perform treatments 4–6 weeks apart. 
Hair removal efficacy increases with the darkness of the hair color and with the amount of fluence. 
The darker the skin and the brighter the hair, the less effective the treatment will be.
According to the International Society of the Study of Vascular Anomalies, vascular anomalies can be classified as vascular tumors (origin in the endothelial hyperplasiate and vascular malformations (normal endothelial turnover). 
Hemangiomas are the most common benign vascular tumors of infancy. 
Portwine stains are present in 0.3–0.5% of newborns and are congenital malformations of the superficial dermal capillaries. 
The vascular lesions that most benefit from IPL treatment are: hemangiomas, PWS, angiomas, telangiectasias, leg veins, rosacea and Poikiloderma of Civatte. 
Angiofibroma, cutaneous lesions of Kaposi sarcoma, Blue Rubber Nevus syndrome, hereditary hemorrhagic telangiectasias and stria distensia may also have some benefit. 
The main chromophores are oxyhemoglobin and deoxyhemoglobin. 
Superficial red vascular lesions have a high amount of oxyhemoglobin. 
The pulse duration should be shorter than the thermal relaxation time of the chromophore to preferentially protect the surrounding tissue from heat damage. 
The need for multiple treatments should be emphasized to the patient. 
For deep hemangiomas, IPL treatment alone is not effective. 
Capillary malformations on the limbs have less response to treatment compared to those on the head and neck.
Vascular lesions have been reported as treated with technologies that emit green light (532 nm), yellow light (578–600 nm), red light (755–810 nm), near infrared light (1,064 1993nm), and broad-spectrum IPL (range 500–1200 nm). 
To understand the effect of IPL on vascular lesions, it is important first to become familiar with these conditions.
Hemangiomas are the most common benign vascular tumors of infancy. 
They are proliferative, have plump endothelium and can appear on the skin, mucosa or other soft tissues. 
Most are located in the head and neck areas and occur with a higher frequency in females. 
There are three types of hemangiomas: superficial, deep and mixed. 
Superficial hemangiomas are found in the papillary dermis and have a bright red color. 
Deep hemangiomas are present in the reticular dermis and subcutaneous fat and have a bluish appearance. 
The overlying skin might have a network of telangiectasias. 
At first, it can be difficult to distinguish hemangioma from vascular malformation. 
Hemangiomas are rarely present at birth. 
They appear after 3–4 weeks and have a rapid growth during the next few weeks. 
Vascular malformations are usually present at birth and increase in size as the child grows. 
Hemangiomas have a bright red color that deepens by the second half of the first year of life, while the hue aspect of a vascular malformation persists. 
Usually hemangiomas have a firm rubbery consistency while vascular malformations are soft and easily compressed.
Hemangiomas have usually three stages: a growing, rapid proliferative phase usually during the first six months of life; a stable period and an involution period. 
During the involution phase, there are color changes and superficial lesions have a flaccid waxy yellow skin. 
About 30% of involuting hemangiomas will leave some marks of atrophy, fibrosis or telangiectasia. 
It has been estimated that hemangiomas involute by 10% of their volume per year. 
Lesions on the nose and lips involute more slowly. 
If there are no signs of regression by age 6–8 years, they are not likely to completely regress. 
Adult hemangiomas consist of mature capillary-size vessels of about 100 ?m.
At times, hemangiomas may complicate. 
About 5–11% can ulcerate, become infected or bleed. 
Usually these complications appear during the proliferative phase. 
However, in certain circumstances, they can lead to significant morbidity, impairing vision (in the periorbital area), interfering with feeding or respiration (in the nose or mouth areas), impairing hearing (in the auditory canal) or can even be life threatening, necessitating early intervention. 
Skeletal distortion is rare but can appear as a mass effect of large hemangiomas. 
IPL or lasers (Argon; Pulsed Dye Laser- PDL; Nd:Yag) have been reported as treatment tools for hemangiomas.
Port-wine stains are present in 0.3–0.5% of newborns and are congenital malformations of the superficial dermal capillaries. 
The ectatic capillaries have different sizes and depths, most varying from 10–150 ?m in diameter and 300–600 ?m in depth. 
Videomicroscopy analysis of PWS shows three types of vascular ectasia located at the vertical loops of the papillary plexus, deeper horizontal vessels in the papillary plexus and combined vertical and horizontal vessels involvement. 
Clinically, they appear as pink macules which progressively dilate to become red. 
These lesions do not spontaneously resolve. 
They should be differentiated from other pink stains as nevus flammens or salmon patch; these lesions usually disappear within the first year of life. 
PWS are predominantly located in the head and neck area, are well delineated and commonly involve the distribution area of the trigeminal nerve. 
PWS lesions can be associated with other medical conditions. 
SturgeWeber syndrome is one of the most common. 
They also can be associated with varicose veins and skeletal tissue hypertrophy in Klippel-Trenaunay syndrome.
Telangiectases are characterized by permanent dilation of vessels with diameters ranging from 0.1–1 mm. 
Most originate in a dilated venule but capillaries and arterioles are sometimes affected. 
The arteriolar type has a bright red color and protrudes above the skin surface. 
The capillary type is red. 
These lesions can be seen in numerous conditions such as collagen vascular diseases, post-trauma, after sun damage or radiodermatitis. 
Most often they are present in the upper part of the body. 
There are four clinical types: simple or linear, arborizing, spider and popular. 
Entities in which telangiectasias are the main pathologic process are: unilateral nevoid telangiectasia, generalized essential telangiectasia, hereditary benign telangiectasia and cutaneous collagenous vascular diseases.
It has been shown that a red telangiectasia has a higher concentration of oxygenated hemoglobin while a blue venulectasia has a higher concentration of deoxygenated hemoglobin. 
Most telangiectases tend to become darker and larger with advancing age, due to the progressive vessel ectasia.
Cherry angiomas are often present in the trunk during adulthood. 
They are small red papules which do not change color on compression. 
They consist of dilated capillary blood vessels located within the superficial dermis.
Spider angiomas are present in about 10–15% of adults and children. 
They are predominantly located on the face, neck and upper trunk. 
Pregnant women or patients with liver diseases have a higher incidence of these lesions. 
Spider angiomas have a main vessel located in an arteriola, from where the blood flows to the peripheral capillaries. 
They have a central slightly elevated red punctum from which the blood vessels radiate (“spider legs”).
Rosacea is a chronic inflammatory eruption of the flushing areas of the face. 
In a histological study it was found that deranged connective tissue is secondary to damaged capillaries. 
The primary cause of the damage seems to be the environment, mainly the sun. 
Most often the lesion involves the nose and cheeks. 
In the mild form, it appears as slight flushing of the area but, as the process becomes more severe, the lesions are deeper (red-purple) with dilated superficial capillaries. 
In severe cases, pustules may develop.
Piogenic granuloma is an acquired vascular lesion, usually solitary and bright red colored. 
The lesion is considered to be a hyperplastic process. 
It grows rapidly, especially in places of trauma and can easily bleed. 
Cutaneous lesions are most often encountered but mucosal location is not rare.
Venous malformations are easily recognized due to their blue hue appearance and are easily compressible. 
Small venous malformations have a good response to IPL treatment.
Leg veins are caused by gravitational dilatation, reflux and incompetent valves. 
These veins are connected to larger reticular or a varicose “feeding” vein. 
Their structure is similar to telangiectasias or venous lakes. 
They have a thicker adventitia and increased basal lamina when compared to facial veins.
Venous lakes most often appear in the fourth or fifth decade of life. 
They are commonly located on the face, neck and oral mucosa. 
With time they tend to enlarge and may bleed. 
They are venous ectasia.
Scar telangiectasias.
Frequently, hypertrophic or keloid scars have telangiectasias on their surface. 
This is a result of neovascularization initiated by angiogenic stimuli. 
As the scar matures, the angiogenesis decreases. 
Sometimes the scar has a red color for a longer period of time due to persistence of stimuli or delayed regression of capillaries.
The vascular lesions that most benefit from IPL treatment are: hemangiomas, PWS, angiomas, telangiectasias, leg veins, rosacea and Poikiloderma of Civatte. 
Angiofibroma, cutaneous lesions of Kaposi sarcoma, Blue Rubber Nevus syndrome, hereditary hemorrhagic telangiectasias and stria distensia may also have some benefit.
The IPL system acts on the principle of selective photothermolysis and has proved to be useful in treating many vascular lesions. 
The main chromophores are oxyhemoglobin and deoxyhemoglobin. 
Superficial red vascular lesions have a high amount of oxyhemoglobin. 
The wavelength absorption peaks of oxyhemoglobin are: 418, 542 and 577 nm. 
Deoxyhemoglobin is predominantly located in deeper vascular lesions and mainly in the lower extremities. 
It has an absorption spectrum in the 600–750 nm range. 
However, oxyhemoglobin and deoxyhemoglobin are not the only chromophores present in the skin. 
The light absorption by the melanin causes some limitations in penetration depth. 
The “perfect” wavelength of different lasers used in the past (e.g., Argon 577 nm) were recently replaced by other lasers (e.g., Pulse dye, Alexandrite, 1,064 nm Nd:Yag) or IPL devices. 
Based on a mathematical model, Ross concluded that, with an optimal set of parameters, IPL devices and lasers are comparable in the treatment of vascular and pigmented lesions regarding the efficiency and safety. 
However, unlike lasers, IPL devices have a wide spectrum of wavelengths with different depth penetration, different absorption by the skin and more complex tissue response.
The pulse duration should be shorter than the TRT of the chromophore in order to preferentially protect the surrounding tissue from heat damage. 
The long delay between pulses and the long pulse durations offer enough cooling of the epidermis and small vessels without a significant decrease in temperature for large vessels. 
As a consequence, high fluences can be applied for heating large vessels without injuring the epidermis. 
For a vessel of 0.1 mm diameter, the TRT is about 4 ms, and for a 0.3 mm vessel the TRT is about 10 ms. 
This shows that larger vessels cool more slowly than epidermis for a single pulse. 
For these vessels, multiple pulses are preferred (longer than 10 ms).
Baumler elaborated a mathematical model for investigating the effect of IPL on blood vessels of 60, 150, 300 and 500 ?m. 
The two extremities of the IPL spectrum (near infrared and near visible range) were studied. 
Both provided homogenous heating of the blood vessels. 
Small vessels (<60 ?m) had only a moderate increase in temperature. 
The effective time interval to raise the temperature in larger vessels (>60 ?m) was shorter than the pulse duration. 
Effective coagulation was difficult to achieve at the bottom area of the vessels. 
This can explain the refractory of some lesions to treatment. 
It was postulated that coagulation of the blood occurs at temperatures higher than 70°C.
The initial consultation should establish the correct diagnosis of the vascular lesion. 
In case of a vascular malformation, parental education about the lesion, treatment options and possible results should be addressed. 
The need for intravenous sedation or general anesthesia for multiple treatments should also be emphasized. 
We always refer a child with a vascular malformation to a pediatric consultation. 
The possibility of associated abnormalities including neurological disorders should be ruled out first. 
In certain conditions, such as Sturge-Weber syndrome, patients may have seizures. 
Because the epilepsy can be initiated by the intense light, it is our protocol not to perform treatment in these situations unless an informed release from the neurologist is obtained. 
Patients with PWS distribution to the lower limbs should be evaluated for underlying Klippel Trenaunay syndrome. 
Patients with a history of oral conceptive use, pregnancy, recent thrombophlebitis, or lower extremities venous insufficiency are not good candidates for IPL treatment, and we refrain from performing it in any of these circumstances. 
Patients who are tanned, have a history of keloids or post inflammatory hyperpigmentation are also not good candidates for treatment. 
Although there are authors who recommend bleaching agents prior to treatment, we prefer delaying treatment for 4–6 weeks.
In most cases, the treatment of vascular lesions is mainly cosmetic. 
Only when the lesion interferes with the functioning of an organ (eye, mouth) does the IPL treatment have a functional correction component. 
It is important to correctly diagnose the vascular lesion and evaluate the suitability of the patient for IPL treatment. 
Special attention should be paid to patients with unrealistic expectations. 
They are not good candidates for IPL treatment. 
The success rate can also be affected by previous treatments, such as intralesional steroid injection for hemangiomas or previous irradiation. 
These produce fibrotic changes which make the lesion more resistant to treatment. 
Some patients have undergone previous treatments with lasers and have pigmentary alterations or scarring at the time of examination. 
These side effects should be mentioned and noted in the patient’s record, explaining that this may alter the efficacy of treatment in that area. 
There is no clear definition of when to start treating PWS lesions. 
We prefer starting treatment early in childhood when they are smaller and more superficially located. 
With age, they become thicker, darker and harder to treat.
Local anesthetics that produce vasoconstriction are to be avoided in order to enhance to the maximum the light distribution to the chromophores. 
In children, intravenous sedation or general anesthesia is employed. 
Most of the time, teleangiectasias and superficial reticular leg veins can be treated without anesthetics. 
We prefer to use only ELA-Max as a local anesthetic as it causes minimal vasoconstriction. 
Other factors that induce vasoconstriction, such as decreased room temperature, should be avoided prior to treatment. 
After entering into the IPL device the patient’s skin type and lesion type, the computer automatically selects the wavelength, pulse duration, delay and fluence. 
These parameters can be further adjusted from treatment to treatment. 
Sometimes it is difficult to categorize a certain lesion in a category (e.g., PWS can be superficial, medium or deep). 
Treatment parameters need to be adjusted to the clinical response. 
For superficial lesions, the transparent gel to use should not be cold to avoid vasospasm. 
Most IPL devices are equipped with a cooling facility to decrease the epidermal temperature during treatment. 
Applying cooling on small superficial vascular lesions may cause vasoconstriction and result in a less beneficial therapeutic effect. 
We do not use cooling in these situations but always use it immediately after treatment. 
The immediate appearance of local bruisingor a bluish aspect is a sign of a good clinical response to the treatment. 
Perilesional erythema, blanching or “urticariform” reactionor vein thrombosis are also signs of good response for linear vessels. 
When performing the treatment, the hand piece is placed in contact with the transparent gel without any pressure. 
Even small pressure can empty the vessels by diverting the blood into collaterals and causing the treatment to be less effective. 
Care is taken to avoid overlapping the treated areas. 
Larger spot sizes have deeper penetration into the lesion with a better response. 
The usual treatment interval is 4–6 weeks. 
Avoiding sun exposure between treatments is important. 
There is no restriction to using make-up immediately after treatment.
To be efficient in treating vascular lesions, the IPL device should produce a wavelength having the best absorption by the target (oxyhemoglobin, deoxyhemoglobin), have the ability to reach the depth of the blood vessels, produce enough energy to damage the vessels with limited harm to the surrounding tissue, and enough exposure to coagulate the vessel. 
Pulses that are shorter than the TRT of a vessel will not produce enough heating of the vessel to be therapeutically effective. 
On the other hand, excessively long pulse durations produce heat diffusion to the surrounding tissues. 
Patients with superficial vascular lesions, such as teleangiectasias, require a few treatments (usually up to three) to achieve result. 
Other lesions, such as PWS and hemangiomas, require many more treatments, often more than ten to achieve the desired result. 
The treatment period will take more than a year, and this should be emphasized to the patient before starting treatment. 
Larger and deeper vessels require higher fluences and usually have a poor result.
IPL devices with a wide range of wavelengths (500– 1200 nm) by using different cutoff filters are able to eliminate shorter wavelengths and allow deeper dermal penetration. 
By selecting multiple pulses and longer pulse durations, more heating is produced which is important for the treatment of large caliber vessels.
The literature is limited in describing the treatment of hem angiomas with IPL. 
Therapeutic tools for hemangiomas include pulsed light devices, pulse dye lasers, Nd:Yag lasers and other methods such as cryotherapy and local or systemic steroid injections. 
Although there is a debate between the “wait and see” strategy and the active method, there is no question about treating hemangiomas with functional and structural impairment. 
Some authors strongly recommend treating the hemangiomas at the very early stage (macular stain) in order to stop their growth. 
Treatment may be useful even during the active proliferative phase (month 3–9), when they show ulceration and bleeding. 
Two cases of ulcerated hemangiomas were treated by Jorge. 
Good results were obtained after two to four sessions. 
Complete epithelization was obtained at between 1 and 2 months.
Less efficient is treatment performed during the involution phase due to the altered fibrofatty structure. 
It is our belief that superficial hemangiomas, especially in the proliferative phase of growth, respond better to treatment. 
It is for this reason that we prefer to treat these lesions as soon as possible. 
For deep hemangiomas, IPL treatment alone is not effective. 
In this situation, we combine IPL, Nd:Yag laser and intralesional steroid injections.
The results of PWS treatment with IPL are various as reported in the literature. 
Most PWS lesions are located in the head and neck. 
These lesions do not regress over time and usually hypertrophy in adulthood, making treatment more difficult. 
Twentytwo patients with PWS were treated by Ho, five to seven times, using cutoff filters of 550, 570, and 590 nm and fluences from 35 to 75 J/cm2. 
Most patients (81.8%) obtained little to moderate improvement. 
Little improvement was also reported by Reynolds, who treated 12 subjects with IPL (Lumina device). 
At fluences lower than 26 J/cm2, there was no clinical response. 
The patients who failed to show any response had pink PWS. 
According to the author, the darker the PWS, the better the fading that was seen. 
Moderate improvement was reported in 47% of the patients treated by Ozdemir with IPL (Lumina, Lynton). 
Complete clearing was seen in only one patient of 12.
On a small number of patients with mature PWS treated with a 515 nm cutoff filter and fluences from 25 to 30 J/cm2, Cliff reported at least 50% improvement after three treatments. 
Some of the patients with PWS had previous laser treatment. 
Bjerring reported the results in a series of 15 patients with PWS resistant to PDL. 
After four treatments, about half the patients obtained clinical clearance. 
Eight patients had less than 25% clearance after IPL treatment. 
In these cases, the PWS was located in the central part of the face (second branch of the fifth cranial nerve).
It seems that the lesions in this area are more resistant to treatment. 
It was reported by the patients that IPL was less painful than PDL treatment. 
Raulin successfully treated with IPL a PWS resistant to PDL. 
Dealing with previously treated PWS is difficult because the lesions usually have hypertrophic scarring and skin textural changes. 
Higher energies are required to be efficient and the results cannot be anticipated.
Some PWS lesions or congenital vascular malformations may be resistant to therapy (Verkruysse et al. 2008). 
It seems that the depth and heterogeneity of lesions causes an unpredictable response. 
The deeper and smaller the capillary malformation, the more heterogenous, the harder to treat. 
It is believed that vessels smaller than 50 ?m are less suitable for treatment due to insufficient intravascular heat generation. 
The need to predict the results of treatment was behind the drive to find a device suitable for this task. 
Pulsed photothermal radiometry is a noncontact technique by which the skin is exposed to laser light. 
The temporal evaluation of the surface radiometric temperature is evaluated with an infrared detector. 
The laser light being absorbed by the lesion chromophores causes a spatial distribution of temperature. 
Although promising results have been reported, as with any diagnostic tool, it is recommended to have a realistic overview of its accuracy.
There are some reasons why PWS are resistant to treatment: inadequate depth of light penetration (most PDL have up to 1 mm penetration), inadequate conduction of heating from the chromophore to the vessel wall, inadequate blood volume (small diameter capillaries do not have enough hemoglobin) and inadequate fluence entering the capillary.
Capillary malformations on the limbs have a poor response to laser treatment compared to those located on the head and neck. 
There is little evidence to explain this phenomenon. 
However, it was proved that cutaneous blood flow of the lesions situated in the head and neck increased with the ambient temperature, unlike those present on the limbs. 
Those observations might suggest a reason for the better response of capillary malformations of the head and neck to light and laser therapy.
Pink PWS are more difficult to lighten than mature red PWS. 
Deep and nodular PWS are more resistant to treatment. 
Photodynamic therapy has also been used for treating PWS. 
The principle is to add an exogenous chromophore into the capillaries, either transcutaneous or systemically. 
Most chromophores used have porphyrin precursors. 
Porphyrins have a wide spectrum of absorption with large peaks in the blue and red spectrum and a smaller peak in the yellow range. 
The light acting on the capillaries generates a photochemical reaction. 
It was reported that capillary destruction is more efficient in this way.
The flashlamp PDL has been proved to be effective for facial vascular lesions, including telangiectasias. 
However, significant side effects have been reported, such as pronounced purpura and changes in pigmentation. 
Angermeier reported his experience in treating the following facial vascular lesions: 79 telangiectasias, 74 rosaceas and 45 hemangiomas. 
Facial telangiectasia was treated with double pulse, 550 nm cutoff filter and fluences of 36–45 J/cm2. 
Most patients required single or double treatments in order to achieve 75–100% clearance. 
Less aggressive parameters were correlated with a higher number of treatments.
One of the largest studies on facial telangiectasia treatment with IPL was reported by Clementoni. 
He evaluated 518 consecutive patients who had between one to nine treatments (average 1.6). 
Large facial veins were treated with triple pulses, 590 cutoff filter and a fluence rate of 50–56 J/cm2. 
Fine lesions were treated with double pulses, 570 nm cutoff filter and 40–43 J/cm2. 
Significant clearance (75–100%) was obtained in 87.6% of patients. 
In a different study, the same author evaluated the results after treating 1,000 consecutive patients with facial telangiectasias. 
89.7% had a clearance rate of 75–100%; 23.8% had lesion clearance after a single treatment, while 45.2% required two treatments to achieve the same result. 
According to the author, there was no correlation between the skin type and the clearance rate, but the result was influenced by the operator experience.
Other authors reported good results for telangiectasia. 
Raulin reported on the treatment of essential telangiectasia and Poikiloderma of Civatte with IPL. 
Ten of fourteen patients showed excellent results and three had good results. 
The single treatment of lower extremity telangiectasias is not enough, according to Green. 
Only 10% of lesions had complete clearing. 
The study was performed at the beginning of the IPL era. 
The same study showed a 21% scarring rate, which was not even close to the complication rate in other studies.
Among facial or leg telangiectasias, spider nevi, erythrosis interfollicularis and senile angioma treated with IPL, the highest clearance (90%) was observed for facial telangiectasias and erythrosis interfollicularis. 
Facial telangiectasia is easier to treat than leg veins. 
The forehead location when associated with rosacea had the best clearance (87%), as reported by Schroeter. 
According to Goldman, bright red lesions are better treated with 515 and 590 nm filters and blue lesions with 590 nm or higher filters.
Leg veins with different diameters respond differently. 
Most IPL studies were done on leg veins having a less than 3 mm diameter. 
The larger the vein, the lesser the response to treatment. 
Sclerotherapy is known to be effective in the treatment of vascular lesions located on the legs. 
However, some patients are fearful of needles, and there are patients with superficial vessels smaller than the diameter of a 30 gauge needle or are resistant to sclerotherapy and who would benefit from light or laser treatment. 
The pulse duration should mach the diameter of the vascular lesion and be about the same as the thermal relaxation time for the lesion diameter. 
If the pulse duration is too short, the heating will be located mainly in the upper part of the skin and might not reach deep lesions. 
Unlike PDL, the IPL system has the advantage of using longer pulses. 
In order to destroy blood vessels larger than 100 ?m in diameter, it has been calculated that the pulse duration should be in the 3–10 ms range Goldman reported 94% clearance of leg veins for more than 50% of his patients, and 79% had a high rate of clearance (75– 100%). 
The treated lesions and parameters used were as following: leg veins <0.4 mm: 550 nm cutoff filter and 50 ms delay; leg veins between 0.4–1 mm: 570 nm cutoff filter and 50–100 ms delay; leg veins between 1–3 mm: 590 nm cutoff filter and 150 ms delay. 
In all the lesions, the least responsive to treatment were leg veins with a 1–3 mm diameter. 
Shorter wavelengths (500–600 nm) are preferred for treating class I superficial red telangiectasias, while longer wavelengths (>750 nm) are effective for class II–III, deeper blue reticular veins. 
Superficial and small sized vessels are best treated with longer pulses. 
Triple pulses and higher fluences are also preferred.
Using a dual wavelength approach, Sadick obtained a 75–100% clearance of leg veins in 80% of patients. 
An average of 2.5 treatments were needed, using 550 nm (Photoderm) and 1,064 nm (Vasculight). 
The short wavelength was addressed to the red vascular lesions and the long wavelength was addressed to the blue vascular lesions. 
The overall satisfaction level was reported to be 76%. 
When leg veins do not respond well to IPL or have a deeper location or larger caliber, Nd:Yag laser can be of real benefit. 
Some IPL platforms are also equipped with Nd:Yag laser.
The results of treating Poikiloderma of Civatte and rosacea were presented in Chapter 6 (Skin rejuvenation). 
Treatment of tufted angioma is usually unsatisfactory. 
However, Chiu reported a case of tufted angioma that was successfully treated with IPL. 
Four treatments were performed at 3–4 weeks intervals. 
The fluence used was 40 J/cm2 with a 560 nm cutoff filter. 
Facial and neck erythrosis were treated by Terracina with IPL.
Complete clearance was obtained in 24 patients while another two had some improvement. 
When treating venous malformations with IPL, Raulin was able to obtain a 70–100% clearance in eight malformations smaller than 100 cm2. 
Three malformations larger than 100 cm2 needed more treatments (up to 18 sessions). 
A 590 nm cutoff filter and an average 80.4 J/cm2 were most often used parameters. 
Small venous malformations have a good response to IPL. 
Large lesions require sclerotherapy, embolization or surgical excision. 
Lym phatic malformations, and deep venous or venolymphatic lesions do not respond well to IPL treatment. 
We do not perform IPL treatment in these conditions.
Striae distensae on 15 women was treated by Hernandez-Perez by IPL. 
After five treatments spaced at 2 weeks intervals, a decrease in the number of striae from 117 to 94 was noted. 
Microscopic changes were found. 
Improvement of elastosis, edema and atrophy was reported. 
Inflammation and collagen fiber quality were also improved. 
From all parameters, the dermal thickness had the best improvement. 
We refrain from treating striae with IPL.
Red scars, and hypertrophic or keloid scars which often contain telangiectasias may benefit from IPL treatment. 
The concept is to reduce the number of containing blood vessels (neocapillaries) which can stop the growing process and improve the color. 
Bellew compared in a side-by-side manner the effect of PDL and IPL on hypertrophic scars after breast reduction and mastopexy. 
After two treatments, improvement was obtained in both groups with no significant differences between them. 
Erol treated with IPL 109 patients with hypertrophic scars after surgeries, trauma, acne and burns. 
Five patients had keloids. 
The average number of treatments was eight and they were performed at 2–4 weeks intervals. 
Overall clinical improvement was found in 92.5% of the patients, while 65% had good to excellent results.
Acne vulgaris is frequently encountered in young people. 
Several factors have been incriminated in its pathogenesis including; abnormal desquamation of the follicular keratinocytes and presence of bacterial infection caused mainly by Propionebacterium acnes. 
Acne scarring is produced by the destruction of the collagen fibers and subsequent fibrosis formation. 
Increased sebum production and inflammatory response are considered to be the main cause of the disease. 
The most vulnerable lesions for the IPL treatments are the red macules as reported by Chang. 
A side-by-side study was performed by Rojanamatin who compared the effect of the IPL alone or in combination with the ALA on inflammatory facial acne vulgaris. 
Although reduction in the number of acnea lesions was present on both sides, the combined ALA and IPL treatments had better results after three sessions compared to IPL alone. 
Several other authors reported good results using IPL-ALA for the management of acne vulgaris. 
Pretreatment of acne lesions with methyl aminolevulinate is also postulated to respond better than the IPL alone. 
On an evidence-based review of lasers and lights, Haeders - dal found that photodyna - mic therapy is superior to other methods for acnea treatment.
Inflammatory lesions are responding better than non-inflammatory lesions. 
It should be emphasized that Acne vulgaris responds well to systemic therapy using antibiotics and retinoids. 
The photodynamic therapy is a good alternative for the treatment of resistant cases of acnea or for patients who refuse to take systemic retinoids.
There is scant literature comparing the effect of IPL and laser on the same vascular lesions. 
A comparison of different devices - PDL, Alexandrite, KTP, Nd:Yag and IPL - on previously treated capillary malformations was made by McGill. 
The study was performed on 18 patients and the results were evaluated by videomicroscopy and color measurements. 
Four patients failed to respond to any technology. 
The Alexandrite laser had the largest mean improvement in color but it was associated with more complications (scarring and pigmentation) than the others. 
The IPL was better than KTP and Nd:Yag laser. 
On a split treatment of patients with dyschromia (vascular and pigmented lesion), it was found that KTP laser and IPL achieved marked improvement. 
At 1-month post-treatment, patient evaluation showed a 65.5% improvement for IPL and 60.8% for KTP laser. 
The KTP laser caused slightly more discomfort.
One of the challenges is treating vascular lesions in dark skinned patients. 
When the lesions are deep, the chance of success is minimal. 
On one side, skin with a high amount of melanin needs to be protected, but on the other side, low fluences or changes in pulse duration and delay reduce the efficacy of treatment. 
These patients have the highest risk of pigmentary alterations. 
Higher fluences are needed to produce similar effects in dark skinned patients but it should be used cautiously so as not to injure the epidermis. 
Longer pulse durations and longer pulse delays are preferred. 
Large footprints are preferred for large lesions for a more uniform light distribution. 
A decrease in the fluence by 10–20% is recommended when treating areas prone to scarring, such as the anterior chest.
There are very few studies on psychological scoring before and after treatment of vascular lesions. 
The psychological distress was significantly reduced after treatment of less severe facial vascular lesions (telangiectasia, spider vein, cherry angioma). 
Patient satisfaction after PWS treatment is rarely reported in the literature. 
Hansen carried out a satisfaction survey for these patients and found that most noted little or no change in texture or dimension after PDL treatments, although 62% had color improvement. 
Most patients were satisfied or neutral with regard to satisfaction with therapy. 
Men were more likely to be dissatisfied. 
Fodor reported that 72% of patients treated with IPL considered the results as mild to excellent. 
In the same study, when comparing side-by-side with Nd:Yag for the same lesion, higher satisfaction was reported by the patients for the laser treatment area. 
However, this was more painful than IPL. 
There are hybrid devices that combine the IPL and Nd:Yag laser treatment for a better result and a higher level of satisfaction.
Superficial red vascular lesions have a high amount of oxyhemoglobin.
Deoxyhemoglobin is predominantly located in deeper vascular lesions and mainly in the lower extremities.
The initial consultation should establish the correct diagnosis of the vascular lesion.
Do not hesitate to refer a child with vascular malformations (especially of the head) to a pediatrician for a thorough check-up. 
Associated abnormalities including neurological disorders are sometimes present.
The need for multiple treatments should be emphasized to the patient. In children, there is need for sedation or general anesthesia.
The pulse duration should be shorter than the TRT of the chromophore to preferentially protect the surrounding tissue from heat damage.
The success rate of the treatment can be altered by previous other treatments which produce fibrotic changes. 
Any factor that produces vasoconstriction should be avoided prior to treatment. 
The immediate occurrence of local bruising, a bluish aspect, perilesional erythema, blanching or “urticariform” reaction are signs of a good clinical response. 
No pressure should be placed on the hand piece during treatment. 
During the involution phase of hemangioma, treatment is less effective. 
For deep hemangiomas, IPL treatment alone is not effective. 
Capillary malformations on the limbs have less response to treatment compared to those on the head and neck. 
Deep and nodular PWS are more resistant to treatment. 
Most cherry angiomas and telangiectasias have excellent results from IPL treatment. 
Facial telangiectasia is more responsive to treatment when compared to leg veins. 
Superficial and small sized leg veins are best treated with shorter pulses. 
Deeper and large size vessels are best treated with longer pulses. 
In dark skinned patients, longer pulse durations and longer pulse delays are preferable.
As with any other medical technology, side effects and complications can occur after IPL treatment. 
Some complications can be prevented by knowing the principles of therapy and treatment strategies, and from having experience with the device. 
The complications caused by IPL treatments may be divided into two groups: those which are due to errors in handling of the device and those which are patient dependent. 
Improper training of operators and insufficient experience may lead to undesirable results. 
Not infrequently there are “profit motivated “cosmetic centers” where IPL technology is used by people with minimal training and background. 
The second type of complication which is related to the patient’s skin reactivity is harder to be anticipated. 
The major complications are permanent pigmentary changes, hair stimulation, paradoxical effect, leukotrichia, uveitis and iritis and scarring. 
The minor complications are erythema and purpura which last more than three days, blisters, temporary pigmentary changes and temporary hair discoloration.
As with any other medical technology, side effects and complications can occur after IPL treatment. 
Some complications can be prevented by knowing the principles of therapy and treatment strategies, and from having experience with the device. 
The following side effects and complications are the most often encountered after IPL treatments:
? Erythema, characterized by redness that usually appears a few minutes after the treatment, which may last for a variable period of time. 
It is the most common reaction after this treatment. 
? Edema is characterized by local swelling which appears within minutes after the treatment and usually is discrete. 
It is more visible in areas with lax connective tissue, such as the eyelids, and it subsides in a few days.
? Purpura/bruising is the appearance of a bluish discoloration in the treatment area. 
It turns yellow within days, as the hemoglobin metabolizes. 
It may take a few days up to 2 weeks until it disappears. 
? Hematoma is caused by a small amount of blood that accumulates in the subcutaneous tissue. 
It is a rare phenomenon, but may occur when treating vascular lesions, especially leg veins. 
It is self limited and takes days to weeks to resorb. 
? Blisters are the consequence of the accumulation of clear fluids at the dermo-epidermal level or within the dermis. 
? Crusts are the brown scaling tissues which may easily peel off from healthy skin. 
? Infection is characterized by redness, increasing local pain and edema, with or without fever. 
Reactivation of herpes simplex infection can be encountered in some patients. 
? Hyperpigmentation is a darkening of the skin, usually transitory, fading spontaneously within a few months. 
Patients with darker skin are more prone to this reaction. 
? Paradoxical effect is defined as growing of new fine hair in the proximity of the treated area. 
? Leukotrichia represents the growth of white hair following IPL treatment. 
This phenomenon is explained by the difference in thermal relaxation time of the melanocytes and the germinative cells. 
It can be temporary or permanent (Radmanesh et al. 2002). 
? Temporary hair discoloration is a very rare phenomenon and refers to a yellow appearance of black hair after IPL treatment. 
? Scarring is also a rare complication after IPL treatment. 
Although an exact explanation is not yet available, post-treatment blisters, infection or high energies may lead to scar healing.
The complications caused by IPL treatments may be divided into two groups: those which are due to errors in handling of the device and those which are patient-dependent. 
Improper training of operators and insufficient experience may lead to undesirable results. 
Not infrequently there are “profit motivated “cosmetic centers” where IPL technology is used by people with minimal training and background. 
Complications like the “zebra” appearance can easily be avoided by proper training of personnel. 
The second type of complication which is related to the patients’ skin reactivity is harder to be anticipated. 
It is obvious that patients with darker skin should be treated more cautiously. 
Another measure that should be adopted is readjusting the parameters of the device according to the response during the previous treatment.
Patient-related complications may also be subdivided into major and minor complications.
Most of the major complications may appear within weeks from the treatment and include: 
Permanent pigmentary changes. 
The incidence of pigmentary changes varies widely in the literature. 
These studies were not performed under standardized conditions and many parameters differ from one study to another. 
Most commonly, hyperpigmentation is caused by vessels destruction and, hence, hemosiderin deposits within the dermis. 
Dark-skinned patients are more prone to pigmentary changes; it seems that their melanocytes are more reactive to the heating stimulation.
Hair growing stimulation following IPL and laser treatment has been reported in 10.5% of patients coming for epilation. 
This phenomenon was mainly observed in areas with fine hair, such as the face and neck. 
The new growing hair appears to be thicker and darker. 
This reaction has also been reported to be induced when the IPL was used for the treatment of vascular lesions or tattoo removal.
Paradoxical effect. 
The exact cause of this phenomenon is not known but it is believed that the light energy activates the dormant hair follicles. 
The incidence of this very unpleasant side effect was reported to be up to 10.2% in one study. 
It seems that hormonal imbalance has an influence.
Leukotrichia: the incidence of this complication is reported to be up to 3.5% in patients treated with IPL for hair removal; 31% of the patients have this condition temporarily and return to their previous color within a few months.
Uveitis and iritis can be induced by treating lesions on the eyelids, or by avoiding the use of eye protection. 
It is more often encountered after long wavelength applications. 
Iris melanin absorption seems to be responsible for this effect. 
The treatment might be performed under the supervision of an ophthalmologist.
Scarring is seldom reported in the literature. 
There is only one study on dealing with telangiectases removal from the lower extremities which reported a 21% incidence of scarring. 
This study was performed in the beginning of the IPL era and newer literature fails to report such a high incidence of this annoying complication. 
The same study reported a 50% rate of hyperpigmentation which is much higher than other reports on this complication.
Most of the minor complications occur within minutes to days after the completion of the treatment. 
Pain or local discomfort during treatment is not considered to be a complication and depends on the pain threshold of each patient. 
It can be easily lowered by using local anesthetics.
Erythema and purpura: Some articles mention redness and purpura as side-effects. 
We prefer to consider them as side effects only when they persist for more than 3 days. 
Otherwise, it is a normal skin response to the IPL energy; purpura, or minimal bruising which are described as “urticariform” reactions, may be considered as signs of the effectiveness of treatment. 
Those signs are seen immediately during the treatment mainly after treating vascular lesions. 
Erythema and perifollicular edema are the most common side effects after hair removal. 
A shorter pulse duration has a higher risk of epidermal injury. 
Purpura is rarely encountered, and it is scattered and associated with short pulse duration.
Blisters: Studies have demonstrated the presence of subepidermal necrosis in areas of blistering induced by lasers. 
These histological findings are similar to those observed in burns.
Temporary pigmentary changes. 
The occurrence of hypopigmentation after IPL or laser treatment seems to be related to the suppression of melanogenesis and is not due to melanocytes destruction. 
This might explain the temporary character of the hypopigmentation. 
The mechanism of hyperpigmentation was described above. 
Often, this side effect is transitory and disappears once the hemoglobin and the hemosiderin are metabolized.
Temporary hair discoloration.
Only one case of temporary yellow discoloration of the hair after IPL treatment has been reported. 
The growing proximal part of the hair was yellow, which makes it difficult to differentiate from the distal hair bleaching induced by sun exposure. 
A decrease in eumelanin production and an increase in the pheomelanin amount seems to explain this result.
Other complications. 
Temporary alopecia is the result of IPL treatment in hair bearing areas.
With modern IPL devices, the incidence of side effects and complications is reduced. 
It is always better to be cautious and recognize those patients who are prone to develop complications. 
Dark skinned patients and those with a history of complications after IPL treatment belong in this category. 
Careful adjustments of the IPL parameters should be made, and this was described in previous chapters.
Most often, the erythema and the edema are selflimited to hours or days. 
In patients with a history of prolonged edema, topical steroid creams help to make the recovery shorter. 
The crusts after pigmentary lesions treatment or after blisters tend to peel off. 
Usually, there is no need for a topical moistener to hasten the process.
In the presence of perilesional erythema or oozing, we recommend topical antibiotics.
Antiviral prophylactic treatment is prescribed for patients with a past history of herpes. 
Treatment of the new hair growth is not easy but shorter cutoff filter may improve the result. 
Treatment of scars caused by the IPL is not well defined. 
Usually these scars are slightly depressed and hypopigmented. 
CO2 lasers, dermabrasion or chemical peelings can help to make the skin color uniform and improve the aesthetic result.
The “zebra” appearance is due to poor footprint application when overlapping is avoided and strips of untreated area are seen between the treated areas. 
This unpleasant appearance can be improved by treating the previous sparse area. 
The size of the footprint should be adjusted to match the area.
In the presence of complications, extending the time intervals between the treatments is recommended, to allow skin recovery. 
There are no clear recommendations in the literature for the optimal time interval to the next treatment after the occurrence of any complication event. 
We postpone the next treatment for a few weeks. 
The fluence and pulse delay are also adjusted to reduce the energy inflicted on the skin. 
More details can be found in previous chapters. 
The cooling methods and avoidance of sun exposure before and after treatment are important to avoiding side effects.
Practical Points There are two types of complications: one which is due to errors in handling of the device and another which is patient-related. 
The major complications are permanent pigmen- ? tary changes, hair stimulation, paradoxical effect, leukotrichia, uveitis and iritis and scarring. 
The minor complications are erythema and purpura which last more than 3 days, blisters, temporary pigmentary changes and temporary hair discoloration. 
Purpura, minimal bruising or “urticariform” reaction are signs of effective vascular lesions.
Dark skinned patients are more prone to suffer from side effects. 
In the presence of complications, the treatment parameters need to be adjusted.
The eyelids are highly specialized structures with peculiar anatomic components.
The ocular globes are allocated in two symmetrically bony cavities called orbits, consisting of seven bones that develop the orbital walls.
The roof is composed mostly of the orbital plate of the frontal bone and posteriorly of a minor part of the sphenoid bone.
The lateral wall comprises the orbital surface of the zygomatic bone and the sphenoid bone.
The floor is composed of the orbital plate of the max- illa anterolaterally of the zygomatic bone and pos- teriorly of the palatine bone.
The medial wall consists of the ethmoid, frontal, lacrimal, and sphenoid bone.
The der- mis contains elastic fibers, blood vessels, lym- phatics, and nerves.
The underlying fat is scant or not present in the subcutaneous tissue, where the hair follicles and pilosebaceous glands are located.
The apocrine glands of Moll are located near the lid margin, and the sebaceous glands of Zeiss are associated with the follicles of the eye- lashes.
The eyelids function to protect the eye globe from local and external injuries.
Furthermore, they regulate the light that reaches the eye and uniformly distribute the tear film, mucus, and oil during blinking, of great impor- tance for the health of the cornea.
The eyelids are divided into upper and lower eyelids, which are similar but with different characteristics mainly in the lid retractor arrangement.
The space between the open lids is known as the palpebral fissure, which measures 7–12 mm, while the normal excursion of the lids is 14–17 mm.
In the normal adult fissure, the highest point of the upper lid is just nasal to the center of the pupil, while the low- est point of the lower lid is just temporal to the center of the pupil.
In youths, the upper lid margin rests at the upper limbus, whereas in adults it rests 1.5 mm below the limbus.
The lower eyelid mar- gin rests at the level of the lower limbus.
The lateral canthal angle is 2 mm higher than the medial canthal angle in Europeans, but is 3 mm higher in Asians.
The distance from the medial canthus to the midline of the nose is approximately 15 mm.
The lateral canthus lies directly over the sclera, and the medial canthus is separated from the eye by the lacrimal lake and caruncle, a yellowish tis- sue containing sebaceous and sweat glands.
The lid margins are 2 mm wide and form the junction between the skin and the conjunctiva, the mucous membrane of the lids.
They meet at the gray line, near the posterior edge of the lid margin, the junc- tion of the anterior and posterior lamellae of the lids.
The eyelashes are located anteriorly and the openings of the meibomian glands posteriorly.
There are approximately 100–150 eyelashes on the upper lid and about 50–75 on the lower.
The follicular structure of eyelashes includes the 2 sebaceous (Zeiss) and sweat (Moll) glands, while the tarsal glands (Meibomian) open posteriorly to the lid margin.
The tears that appear at the tips of the small papillae are drained from the surface of the eyes through the openings by a pump mechanism.
The lacrimal secretory system con- trols the amount of tears and is divided into the basic and reflex secretors.
The basic secretor is composed of three sets of glands. (1) Conjunctival, tarsal, and limbal mucin-secreting goblet cells; the overlying aqueous layer is spread more uni- formly because of this inner layer (precorneal tear film). (2) The accessory lacrimal exocrine glands of Krause and Wolfring, located in the subcon- junctival tissue. (3) The oil-producing Meibomian glands and the palpebral glands of Zeiss and Moll.
The reflex secretor is divided into two parts by the lateral horn of the levator palpebrae superioris.
The first fold of the upper eyelid is represented by the superior palpebral sulcus, 9–10 mm (indi- vidual and racial variations) above the lid margin, and represents the junction of the levator palpe- brae superioris with the orbital septum and the fibrous insertion of the levator aponeurosis into the skin.
There is a thin fascial layer between the skin and the orbicularis oculi muscle, with no fat tissue.
The eyelid normally is located at the supe- rior border of the tarsus, and the skin below the lid is attached to the underlying tarsus with the levator aponeurosis, which has projections ante- riorly through the pretarsal orbicularis to the skin and posteriorly to the inferior portion of the anterior tarsus.
The skin of the upper eyelid is more freely movable because of the lack of supe- rior aponeurotic attachments and underlying orbital septum.
The second layer of the eyelid is the orbicularis oculi muscle, which is divided into orbital and pal- pebral parts that function independently.
The orbital part is a voluntary muscle while the palpe- bral part is both voluntary and involuntary.
The orbital portion extends in a wide, circular fashion around the orbit, interdigitating with other muscles of facial expression.
It has a curved origin from the medial orbital margin, being attached to the super- omedial orbital margin, maxillary process of the frontal bone, medial palpebral ligament, frontal C Rigoni process of the maxilla, and inferomedial orbital margin.
The palpebral portion is further subdi- vided into pretarsal and preseptal portions.
The preseptal orbicularis muscle covers the orbital sep- tum and originates medially from a superficial and deep head associated with the medial palpebral ligament.
The fibers from the upper and lower eye- lid join laterally to form the lateral palpebral raphe, which is attached to the overlying skin.
The pretar- sal portion lies anterior to the tarsus, with a super- ficial and deep head of origin intimately associated with the medial palpebral ligament.
Fibers run horizontally and laterally to extend deep to the lat- eral palpebral raphe, to insert in the lateral orbital tubercle through the intermediary of the lateral canthal tendon.
The peripheral fibers sweep across the eyelid over the orbital margin in a series of concentric loops, the more central ones forming almost complete rings, interdigitating with other muscles of facial expression.
In the upper lid the orbital part extends as far as the forehead, covers the corrugator supercilii muscle, and continues lat- erally over the anterior temporal fascia.
The third layer of the lids in the upper portion is the orbital septum, a fascial membrane that separates the eyelid structures from the deeper orbital structures, and attaches to the orbital mar- gin a thickening called the arcus marginalis, the point of confluence for the facial bone perios- teum and the periorbita.
With age, the septum weakens and bulging of the orbital fat pad becomes visible.
Its removal is important in blepharoplastic surgery.
The fourth layer of the upper lid is the post- septal fat pad, contained within the orbit by the orbital septum.
In the lower lid, the orbital part lies on the origins of the elevator muscles of the upper lip and nasal ala, and continues to cover partially the masseter muscle (Figs. 1.1 and 1.2).
Asians have different periorbital anatomic characteristics, the eyelid being one of the most prominent features of the face.
Moreover, there is also a wide variety of eyelid shapes, mostly with regard to the presence and location of the supra- tarsal fold and/or presence of an epicanthal fold.
The most obvious feature of Oriental eyelids is the absent or very low supratarsal fold with relatively "full"?periorbital tissues.
Only a very small percentage of Orientals have a manifest supratarsal fold.
In fact, at this location in other ethnic groups the levator aponeurosis sends fibers to the overlying skin, anchoring it down to the eyelid, creating the fold.
In Orientals this fusion is scarce, making the supratarsal fold closer to the eyelid edge.
Because the septal-levator fusion is so low on the eyelid, retroseptal fat descends in the fold and creates an impression of a fuller upper eyelid.
A submuscularis fibroadipose tissue layer and a more lowly positioned transverse ligament were recently identified and found exclusively in the Asian eye (Fig. 1.3).
The conjunctiva is a smooth, translucent mucous membrane of stratified columnar epithelium, situated on the inferior surface of the tarsus, from the mucocutaneous junction of the lid margin to the tarsal plate border.
The conjunctiva is reflected at the fornix on the globe as the bulbar conjunctiva.
Tarsal conjunctiva is adherent to the tarsus, while a submucosal lamina propria under- lies orbital palpebral conjunctiva and allows dis- section from the vascular M?ller muscle.
The ciliary muscles of Riolano are situated anterior to the tarsus and near the cilia.
The arteries of the eyelids develop from the internal carotid artery through the ophthalmic artery and the external carotid artery through the facial and superficial temporal branches.
The branches of the internal carotid are later- ally, the lacrimal artery and medially, the supratrochlear and medial palpebral branches of the ophthalmic artery.
The veins of the eyelids are called the pretar- sal and posttarsal veins.
The pretarsals are super- ficial and are connected medially to the angular vein and lateroposteriorly to the superficial tem- poral and lacrimal veins.
The posttarsals are deeper, and connect the orbital veins with the deeper branches of the anterior facial vein and the pterygoid plexus.
The lymphatics of the eyelids, like the veins, have pretarsal and posttarsal systems.
The lateral vessels drain the lateral areas of the lids and the deeper vessels the conjunctiva of the upper folds and lacrimal glands, which drain into the superfi- cial and deep preauricular nodes.
The temporal branch of the facial nerve inner- vates the upper region while the zygomatic branch of the facial nerve innervates the lower region.
Sensory innervation of the eyelids is sup- plied by terminal branches of the ophthalmic and maxillary divisions of the trigeminal nerve.
Within the superior orbit, the frontal branch of the ophthalmic division of the trigeminal nerve arrives anteriorly between the periorbita of the roof and the levator muscle.
Here, it divides into a larger supraorbital nerve and a smaller supratrochlear nerve.
Terminal branches of these nerves supply sensation to the upper eyelid and forehead.
The lips are subjected to numerous movements, so their aspect varies according to movement.
Furthermore, from their shape (motion) we can guess whether a person is happy or sad.
Their function, together with the mouth and the oral cavity, is supported by a complex system of structures and muscles to participate in the process of mastication, speaking, and especially nonverbal communication.
They are so pliable and elastic that they are capable of wide excursions of movement.
The lips form the mouth and surround the oral cavity.
They lie in the central portion of the inferior third of the face.
The upper lip corresponds superiorly to the inferior margin of the base of the nose and extends laterally to the nasolabial fold, and inferiorly to the free edge of the vermilion border.
The lower lip extends from the superior free vermilion edge superiorly, to the commis- sures laterally, and the mandible inferiorly.
The upper and lower lips join at the oral commissures.
Inferiorly the limit of the lips in the central region is the mentolabial sulcus, which intraorally cor- responds to the depth of the gingivolabial sulcus.
From the anatomic viewpoint, the philtrum and its pillars belong to the upper lip.
The phil- trum lies in the central region and extends from the base of the nose to the upper lip border.
It is a depression between two raised vertical columns of tissue known as the pillars.
The surface of the lips is composed of hairy skin, vermilion border, vermilion, and oral mucosa.
The shape of the lips varies with age and ethnicity.
The vermilion is the red part of the lips and is covered with a mod- ified mucous membrane, which continues with the oral mucosa of the gingivolabial sulcus, and is dry as it contains no salivary, sweat, or oil (sebaceous) glands.
The vermilion border is the edge of clearer skin that borders the vermilion.
The Cupid's bow is considered the contour of the line formed by the vermilion border in the central region of the upper lip.
The philtrum is formed by a combination of longitudinal collagen condensa- tions supported by a rich elastic tissue compo- nent and interdigitating orbicularis oris muscle fibers.
The oral mucosa consists of stratified squa- mous non-keratinized epithelium and covers the part inside the oral cavity of the lips.
The oral commissure represents the point at which the lateral borders of the vermilion of the upper and lower lips join.
The external surface of the lips is rich in seba- ceous glands, whose secretion prevents dryness and desquamation.
The labial glands are minor salivary glands situated between the mucous membrane and the orbicularis oris muscles around the orifice of the mouth.
The labial glands are circular in form and about the size of small peas; their ducts open by minute orifices on the mucous membrane The perioral orbicularis oris musculature, the intrinsic and circumferential muscle of the lip, provides the center of the coordination of muscular activity.
The orbicularis oris muscle, a voluntary, mimic striated muscle, has no bony attachment and is not supported by bone or cartilage, and has a sphinteric function.
Into this mus- cle insert the antagonistic and synergistic elevator, depressor, and retractor muscle groups that create coordination between contraction and relaxation of the movements of the buccinator, levator anguli oris, depressor anguli oris, zygomaticus major, and risorius that insert into the modiolus.
This is formed by several retractor muscles converging to act on the angle of the mouth.
Lip elevator muscles insert into the upper lip: levator labii superioris, levator anguli oris, levator labii superioris alaeque nasi, and zygomaticus minor and major.
The lip depressors are the: depressor labii inferioris, mentalis, and platysma.
The motor innervation to the perioral muscu- lature uniformly comes from the seventh cranial nerve, the facial nerve.
The buccal and marginal branches primarily supply innervation to the perioral musculature.
The fibers supply the majority of the muscles of the face from their undersurface.
The lips are abundantly provided with sensitive nerve endings.
Sensory innervation is supplied to the upper lip by the infraorbital branch of the maxillary division of the trigeminal nerve.
The nerve runs beneath the levator labii superioris and superficial to the levator anguli oris to supply the lateral nasal sidewall, ala, colu- mella, medial cheek, and upper lip.
The mental nerve, a branch of the mandibular nerve, innervates the lower lip.
The arterial vascularization of the lips, origi- nating in the external carotid system, is supplied by the superior and inferior labial arteries that arise from each facial artery lateral to the commissure.
Venous drainage occurs via the anterior facial vein and partly via the submental veins.
The lymphatic drainage of the lips occurs through submandibular and submental nodes.
Lymphatic drainage from the upper lip is unilateral except for the midline, where some drainage is available to the submental nodes.
The upper lip and lateral lower lip drain to the submandibular nodes.
The average age of the population is constantly rising all over the world, in particular in the industrialized nations.
Therefore, the geri- atric population represents the fastest growing segment of Western countries.
It has been esti- mated that the elderly will constitute up to 25 % of the US population by 2025 and up to 34 % of the European population by 2050 and that the average life span is expected to extend another 10 years by 2050 worldwide.
The effects of human aging are also primarily visible in the skin with alterations such as atrophy, increased wrinkling, sagging, skin laxity, and changes in skin pigmentation.
In this context, it is well known that humans have always been fascinated by conserving youth.
Indeed, there has been an extraordinary spreading of both surgical and nonsurgical aesthetic and cosmetic pro- cedures in the last two decades.
Particularly, according to statistics from the American Society for Aesthetic Plastic Surgery, since 1997, there has been an increase of 444% in the total number of cosmetic procedures in the United States with surgical and nonsurgical ones being increased by 119% and 726 %, respectively.
Therefore, understanding the mechanisms of skin aging is the key point in order to correctly and effectively counteract and reduce the time effects on the skin through the use of proper and safe intervention modalities.
We can perceive the age of people from the appearance of their face as this is the part of the body which is the most exposed to environmental factors such as ultraviolet (UV) radiation above all; therefore it is not surprising that it represents one of the body areas where the signs of skin aging initially appear.
For example, wrinkles, which constitute the most common and worrying sign of skin aging, are usually particularly concentrated around the eyes and lip.
As regards the eye area, it is well known that few of the first signs of aging appearing in the late 20s and early 30s are usually located around the eyes.
In this area, the skin undergoes numerous morphological and structural changes lead- ing to the typical aging alterations observed in the orbital region such as brow ptosis, dermatochalasis, blepharochalasis, periorbital wrinkles, fat pad, malar bags, etc..
The considerable enhancement in skin thinning is involved in the appearance of dynamic rhytids at the lateral canthi known as crow's feet, whereas increased laxity on the upper lid leads to hooding and occa- sionally pseudoptosis, a condition generally known as dermatochalasis.
Moreover, lower lid skin and orbital septum laxity are able to guide to the formation of bags which may also be favored by edema and skin stretching (malar bags).
When both the skin and the orbicularis muscle are involved, the presence of redundant folds of loose skin, muscle, fat, and interstitial edema which extend from beyond the lateral cheek often past the midpupillary line, or even from canthus to canthus, may develop defining a condition commonly known as festoons.
Apart from skin alterations, also muscle and sub- cutaneous tissue modifications contribute to the development of other noticeable signs of aging in the periorbital area.
For example, contraction of the orbicularis muscle drives to changes in the overlying skin supporting the formation of the condition known as crow's feet; changes in fat amount and position are also strictly linked to aging variations observed in eye surrounding area, whereas important transformations in the laxity of the connective tissue structures and the canthal tendons may lead to a smaller appearance of the eyes, scleral show, or even ectropion.
Apart from the periorbital area, lips, which are part of the aesthetic unit that involves the mouth and the perioral tissue, represent another face site particularly susceptible to manifest aging signs.
While during puberty the lips become fuller because of the hypertrophy of the orbicularis muscle and glandular components, they progressively lose definition as a person ages, tending to become flatter and presenting also upper lip elongation.
The oral commissures tend to descend and vertical wrinkles develop at or above the vermillion border due to skin thinning and orbicu- laris muscle atrophy.
Aging of this area is also characterized by perioral fine lines, mario- nette lines, and flattening of the cupid bow.
The dynamics of lip movement change with age too.
The smile, for instance, gets narrower vertically and wider transversely.
Moreover, the consequences of the aging process are also the most evident along the mandible area where loss of subcutaneous fat tends to create a prejowl sulcus between the chin and sagging lower cheek and anterior to the masseter muscle.
As already mentioned, aging affects the human face by provoking an array of microscopic and macroscopic complex volumetric changes.
These changes are exacerbated and/or accelerated by bad habits (e g , smoking) and environmental factors.
Therefore, both intrinsic and extrinsic factors are responsible for skin aging, together leading to reduced structural integrity and loss of physiological function.
Intrinsic aging is defined as the amount of corpo- ral changes that develop during the normal aging process affecting all body areas as a result of genetic factors.
As regards the skin, the intrinsic aging process leads to epidermal and dermal thinning; intrinsically aged skin appears to be thin, dry, and transparent, presenting with fine wrinkles and irregular hair growth and touring out to be unable to sweat sufficiently.
As a consequence, skin effectiveness to act as a first barrier against environmental and exter- nal factors gradually decreases.
Other cutaneous intrinsic alterations are linked to a reduction in the number of nerve endings and in the production of sex hormones which are responsible for decreased skin sensibility.
Concerning the histopathological modifications, general atrophy of the extracellular matrix (ECM) with decreased elastin and disintegration of elastic fibers represents the most common features of intrinsically aged skin.
All these events may vary in relation to body site, differing also per ethnic group .
Moreover, in intrinsically aged skin, there is a decrease in vessel size without a significant difference in the vascular density .
However, even in subjects living strictly indoor all their life, skin that is aged only by intrinsic factors does not exist.
Obviously, aged skin always reflects a variable impact of extrinsic aging, superimposed on the level of intrinsic aging.
One of the major determinants of intrinsic aging is represented by reactive oxygen species (ROS), which are continuously produced inside our body as a result of the aerobic metabolism in the mitochondria.
Indeed, in the skin, about 1.5–5 % of the consumed oxygen is converted into ROS by intrinsic processes, with keratinocytes and fibroblasts being the main cutaneous producers .
The reactive superoxide anion radical (?O2?) is the principal ROS type formed in mitochondria, being able to harm numerous different cellular functions leading to nuclear and mitochondrial DNA damage, telomere shortening, protein glycosylation, lipid and protein oxida- tion, collagen and elastin degradation, downregu- lation of collagen synthesis, increased expression of matrix metalloproteinases (MMPs), as well as neovascularization.
Moreover, not only ROS production increases with age, but also human skin cell ability to repair DNA damage steadily decreases over the years, potentiating ROS effects.
Apart from ROS production, other main factors which play an important role in intrinsic skin aging are the reduction in repli- cative ability of cells (cellular senescence) and the enhancement of ECM degradation.
The replicative capacity of human cells decreases with time, and in the skin, this is particularly true for keratinocytes, melanocytes, and fibroblasts.
Thus, senescent cells not able to undergo cellular division are found in higher levels in aged skin.
This is due to telomere shortening: with each cellular division, a small fragment of the telomere is definitively lost at the chromosome ends, and after 25–30 cellular divisions, they become so critically short that DNA loss during subsequent cell divisions leads to decline of somatic cell function, cell cycle arrest, and senescence.
Finally, an additional factor involved in intrinsic skin aging is represented by the increased expression of enzymes which act degrading ECM of the dermis; for example, an increase in MMP expression together with the reduction of the MMP inhibitors has been shown in aged fibroblast.
Notably, all these extracellular alterations of ECM may be triggered by ROS production.
Extrinsic aging is caused by external environmental factors such as solar radiation, cigarette smoking, pollutants, etc.
Particularly, UV exposure is believed to be the primary factor involved in extrinsic skin aging, through a process known as photoaging.
This is especially true for exposed body sites such as the face.
Indeed, about 80 % of facial aging is due to photoaging.
The rate and the intensity of UV radiation effects on skin aging are related to several factors such as frequency, duration, and intensity of solar expo- sure as well as the different phototypes, being more prominent in fair skin individuals (skin types I and II) and less noticeable in subjects with skin type III or higher.
Photoaging is a cumulative process which shows a wide range of effects on the skin.
Photoaged skin is commonly characterized by the presence of wrinkles, pigmented spots and pigmentation disorders, verrucous papules, dryness, telangiectasias, loss of elasticity, laxity, and rough-textured appearance.
Particularly, the formation of wrinkles and small brown pigmented sharply demarcated spots, known as lentigines, seems to be the most common hallmark of photoaging; for these rea- sons their development mechanisms are discussed in detail in the following subheading.
However, photoaging damage predominantly occurs in the connective tissue, also referred to as ECM whose most important and abundant structures being collagen, elastin, and glycosaminoglycans (GAGs), all essential to maintain the strength, the elastic- ity, and the hydration of the skin.
Indeed, regarding histopathological changes, progressive disorientation of dermal collagen and elastic fibers bundles is a common feature of photoaged skin.
A significant increase in space between fiber bundles, thinning of fibers, and increased disorga- nization of fiber proteins are also present.
Finally, photoaged skin is characterized by a loss of mature collagen, and basophilic degeneration of connective tissue, evidenced by denatured elastin fibers and collagen fibers.
Typical for a photoaged skin is the deposition of abnormal elastin with histological examina- tion revealing deranged and highly branched elastic fibers that form aggregates of elastotic material formed by a combination of UV- or ROS-induced degradation of elastin and biosynthesis of amorphous and dysfunctional elastin and fibrillin.
Moreover, an age-dependent decrease in the cutaneous vascularity of sun- exposed facial area together with a reduction in vessel size and vascular number compared to younger skin is also reported.
UV radiation is the main actor of extrinsic aging, being able to damage various cellular structures both directly and indirectly, thereby accelerating the aging process.
An important role is played by UVB which is mainly responsible for direct cell damage.
Indeed, even if a great amount of UVB is absorbed in the stratum corneum, attenuated UVB radiation also reaches viable epidermal cells, determining biological damage.
Particularly, the most dangerous and critical type of biological damage is represented by DNA alteration.
Actually, upon UVB cellular absorption, various DNA mutations may be set up through the formation of bonds between adja- cent pyrimidines which cause the development of cyclobutane–pyrimidine dimmers and pyrimi- dine–pyrimidone (6-4) photoproducts.
Thus, mutated DNA and RNA bases are able to affect cellular protein synthesis and accumulation of unrepaired mutations can cause cell cycle arrest and apoptosis.
However, at the same time, mutations can impair the cell apoptotic ability, enhanc- ing skin malignancies' development.
All these events are deeply influenced by the skin cell type, the cumulative UV dose, and the UV wavelength type which impact the final outcome.
Moreover, UV radiation is also able to induce biological damage and accelerate aging through indirect pathways with endogenous or exogenous photosensitizers that absorb UVA and even visi- ble (VIS) wavelengths from both the sun and arti- ficial sources.
As a result, ROS such as singlet oxygen or direct photochemical changes to biomolecules may be performed.
Therefore, even if UVA and VIS radiation are less absorbed by epidermal components and hence penetrate deeper into the dermis, they should not be ignored as potential source of photoaging.
In addition, aging modification is also indirectly caused by UV through ROS-induced damage which is able to stimulate the synthesis of MMPs.
Thus, other important factors of photodamage are also increased MMP overex- pression and activity, being responsible to the degradation of dermal connective tissue.
Particularly, MMP upregulation is able to occur after low UV exposure doses, less than one mini- mal erythema dose.
Therefore, even daily exposures to a low-dose solar UV radiation below sunburn are thought to be sufficient to induce MMP upregulation and their related photoaging consequences such as degradation of skin colla- gen and elastic fibers above all.
Notably, MMP production is not only induced by ROS but also by inflammatory cells (macrophages and neutro- phils) which infiltrate the skin after UV-induced inflammatory effects.
Apart from ECM degradation through MMPs, UV-induced ROS are also able to damage GAGs, important structures to give support, strength, and flexibility to the connective tissue and keep the tissue hydrated.
For example, the most known member of GAG family, hyaluronic acid, is strongly reduced in the dermis after chronic UVB exposure .
Furthermore, UV is also able to increase the expression of fibromodulin, a small leucine-rich repeat protein which interacts with type I and II collagen fibrils, thereby affecting ECM metabolism through the alteration of the balance between collagen synthesis and degradation, leading to collagen deficiency observed in photoaged skin .
Therefore, as described above, UV radiation plays a major role in extrinsic aging (photo- aging).
Particularly, wrinkles and lentigine formation represent the two most classical examples of the key role played by UV radiation in determining the skin effects of extrinsic aging (photoaging).
Indeed, regarding wrinkles, UV-induced degradation of skin collagen and elastic fibers through MMP activity is one of the main mechanisms involved in their formation.
Particularly, UVR-induced ROS are able to activate signaling kinases (activator protein-1 and MAPK signaling) which control the tran- scription of MMPs in epidermal keratinocytes and dermal fibroblasts.
Moreover, keratinocytes exposed to UVB radiation produce and secrete cytokines such as interleukin (IL)-1α, IL-6, and tumor necrosis factor (TNF-α), which stimulate epidermal keratinocytes and dermal fibroblasts and enhance MMP-1, MMP-2, MMP- 9, and MMP-12 levels leading to wrinkle formation through damage of dermal collagen and elastic fibers.
Furthermore, UVB radiation is also able to induce MMP-1, MMP-3, and MMP-9 in normal human epidermis, whereas UVA stimulates the expression of MMP-1, MMP-2, and MMP-3 in fibroblasts.
In addition, UVB radiation may also contribute to wrinkle formation by inducing fibroblast elas- tase via cytokines released by exposed keratinocytes .
Consequently, several mechanisms are con- sidered to be involved in wrinkle development such as the decrease of collagen and elastin fibers in dermis, the degradation of basement mem- brane at the dermal–epidermal junction, and a decrease in the three-dimensional organization of the ECM.
Hence, UV radiation has been implicated in wrinkle formation through its exacerbation of the decline in tensile strength and elasticity and its ability to cause the degradation of the supporting structural components of the dermal ECM.
The key role of UV radiation in extrinsic aging is also showed by their strong involvement in the development of solar lentigines, another classical marker of skin aging together with wrinkles.
These brown pigmented lesions may be induced by mutations of keratinocytes and melanocytes which both play a role in pigment formation and transfer.
In this context, UV radiation is considered the principal actor in their formation process.
Particularly, through its ability to induce mutations in cutaneous cells, UVB radiation is thought to be the extrinsic factor most responsi- ble for pigment spot development.
Mechanism Overlap Mechanisms of intrinsic aging and extrinsic aging share substantial overlap, both featuring DNA damage.
For example, critical shortening of telomeres, which cause cellular senescence and organism aging overall, is related to finite number of cell divisions, depending to passage of time in proliferative tissues which also characteristically increase after injury, including UV irradiation.
ROS production represents another common executor of both intrinsic and extrinsic aging, being strictly linked to DNA damage and senescence.
Indeed, it is well known that ROS can be produced by both intrinsic aerobic metab- olism and extrinsic UV exposure.
Moreover, when a cell enters senescence, p53 functions such as enhanced DNA repair capacity and stimulation of antioxidant defenses cease, leaving viable but nonproliferative cells (e g , dermal fibroblasts) in a state of chronic oxidative stress that promotes the pro-inflammatory environment characteristic of old skin, making them also more susceptible to UV radiation- induced damage.
As a result of these common alterations, it is not surprising that both intrinsic and extrinsic aging are able to determine some similar qualitative and quantitative changes in ECM, leading to loss of tensile strength and recoil capacity, wrinkle formation, dryness, impaired wound healing, and increased fragility .
Nevertheless, not all aging-related ECM modifications are analogous between intrinsic and extrinsic pathways; e.g., globally, intrinsically aged skin preferentially shows atrophy of dermal structures, whereas photoaged skin is predomi- nately characterized by the accumulation of aber- rant elastin fibers and GAGs, together with damaged and diminished collagen.
Patients often present with many reasons for wanting to obtain lip augmentation or restoration; invariably, the majority complains of an aging look or thinning to the perioral region and requests lip reshaping due to loss of volume.
The commonly encountered signs of perioral aging include decreased vermillion showing, blunting of the Cupid's bow, less visible white roll, verti- cal rhytids, marionette lines, formation of a men- talis crease, and deep nasolabial folds.
Younger patients present for lip augmentation in order to obtain the ever-changing look that is desirable at the moment.
It appears that atrophy and not descent accounts for most of the aging of the face.
There is a loss of glycosaminoglycans and pro- teoglycans in the dermis and a simultaneous decrease in the amount of collagen and elastic fibers.
The repeated contraction of facial muscles also leads to rhytid formation in the perioral region.
On the macrolevel as a person ages, the lips become flatter, the upper lip elongates, the Cupid's bow is lost, and the oral commissures descend.
Resorption of the facial bones with ensuing soft tissue repositioning has also been implicated as a cause of aging in the perioral region.
Injection of hyaluronic acid, a glycos- aminoglycan, acts to increase the water content of the lips for a plumper, fuller appearance.
Normal lip aging can be exacerbated by sun exposure and smoking.
Hyaluronic acid (HA) is a commonly used temporary filler for lip augmentation due to its hygroscopic properties and resulting natural appearance.
The effects last between 3 and 6 months.
Some patients may experience long- term results from HA fillers due to neocollagenesis induced by mechanical tension on fibroblasts from HA injection.
It is the author's opinion that the trauma induced by injection leads to an inflammatory process that also extends the effects of HA fillers.
They do not require skin testing unlike the collagen- based fillers.
HA fillers consist of an uncross- linked soluble HA phase and an insoluble cross-linked fraction of HA particles of a predetermined size.
HA is cross-linked in order to increase the half-life after injection.
The degree of cross-linking within the HA filler corresponds to the firmness of the resulting gel.
The firmness of HA fillers is measured by the elastic modulus (G’), and higher numbers cor- respondtofirmerproducts(Restylane > Juvederm Ultra > Belotero Balance).
Physicians must keep in mind the firmness of the product they are injecting in order to achieve natural feeling lips.
They have desirable safety profiles due to the reversibility by enzymatic degradation using hyalurodinase.
Currently, there are two FDA-approved HA fillers specifically for lip augmentation: Restylane Injectable Gel and Restylane Silk (Galderma S A , Lausanne, Switzerland).
However, off- label uses of Juvederm Ultra (Allergan, Inc., Irvine, CA), Captique (Inamed Corporation, Santa Barbara, CA), and Belotero Balance (Merz North America, Greensboro, NC) include lip aug- mentation.
Restylane, Captique, and Juvederm are bacterially derived from Streptococcus equi and thus have low risk of immunogenicity and allergic reaction.
Restylane is a cross-linked HA gel that is clas- sified as a nonanimal stabilized HA filler.
It can be used to treat nasolabial folds, marionette lines, and glabellar lines and to augment the lips.
Side effects of injection with Restylane can last up to 1 week and include redness, swelling, bruising, and induration.
It is firm filler that does not spread out after injection because of its viscosity and higher elastic modulus.
Captique replaced Hylaform and Hylaform Plus from the Inamed Corporation seeing as they are no longer used. Captique is also classified as a nonanimal stabilized HA.
Juvederm Ultra (classified as Hylacross HA) is a less viscous long-chain HA gel with a lower elastic modulus that tends to spread more after injection.
It is also more concentrated and hydrophilic than Restylane and thus will absorb more water from the surrounding tissues.
Belotero Balance (classified as cohesive polydensified matrix HA) is the newest HA  filler to come to market and has the lowest vis- cosity and elastic modulus between Restylane and Juvederm.
It has the greatest capability  to spread after injection, which is desirable  when performing lip augmentation in order to preserve pliability of the lips.
It’s especially effective as a superficial filler when used to restore areas of vertical rhytids to a more youth- ful appearance.
When preparing to inject a patient with any filler, the physician should bear in mind the important anatomy surrounding the perioral region (Fig. 3.2).
The lip is mainly composed of the orbicu- laris oris muscle with an overlying fascia.
However, many muscles insert into the angle of the mouth and should be considered when injecting.
The corresponding vermillion or cutaneous layers sit on top of the fascia.
The vascular supply of the perioral region consists of the facial artery and its direct branch into the lip, the labial artery.
The infraorbital and mental nerves provide this area with sensation and motor function.
The operator must keep in mind the aesthetics of lip augmentation, which is subject to constantly changing social pressure, in order to achieve high patient satisfaction and cosmetically acceptable results.
The lower lip should be fuller with more vermillion show than the upper lip.
In Caucasian women, it is advisable to follow the golden ratio of 1:1.618 in terms of volume in the upper and lower lip.
Black and Asian women may have proportions that approximate 1:1.
When viewing the patient in profile, the upper lip should project slightly farther than the lower lip.
The white roll separates the facial skin from the vermillion of the lip.
It must be continuous and smooth because a misalignment as small as 1 mm can be noticeable to observers.
The Glogau- Klein point (the elevation or ski slope in the upper lip where the skin turns into red vermillion at the arches) can be enhanced to project the upper lip and create the ski slope to look charac- teristic of younger lips.
There are natural prominences in the upper and lower lips that must be maintained to achieve the pouty look.
These include the two tubercles that lie lateral to the midline on both sides of the lower lip, the tubercle that lies in the midline of the upper lip, and the two tubercles that lie in the lateral corners of the upper lip.
When performing lip augmentation, it is essential that the doctor appropriately fill lips to satisfy patient expectations but not overfill them so as to create a "duckbill"?appearance.
It is preferable to treat again later if the desired cosmetic effect is not achieved.
In order for the patient to observe the chang- ing lip proportions, it is useful to have a hand mirror available and pre- and post-procedure photographs taken from the anterior and lateral view.
Swelling will not occur symmetrically after injection so the injector should treat each side of the lip for each step before moving onto the next step.
Additionally, injectors need to be cognizant of the amount of volume that has been used at each step of the procedure in order to avoid the unwanted consequence of running short of prod- uct before completing the augmentation.
In patients who only agree to pay for a certain amount of product, this amount must guide and limit the extent of augmentation.
Viscous fillers such as Restylane should be injected more deeply to avoid visible nodules and the Tyndall effect.
Less viscous fillers such as Juvederm and Belotero can be used more superficially to achieve natural results.
Patients should be educated and counseled on realistic expectations of results.
Visual scales are used to grade lip augmentation pre- and post- procedure such as the lip fullness scale.
Prior to any lip augmentation procedure, it is essential to understand the importance of facial proportions and anatomy.
While some studies have shown that facial symmetry is an important factor to define a more desirable face, it has also been shown that for a given individual there were statistically significant lower ratings of attractive- ness for perfectly symmetrical computer-generated left-left and right-right faces compared to natural faces.
Clearly, the goal of lip augmentation should not be complete symmetry.
Natural asymmetry makes a face and its expressions unique and attractive.
Injectors should bear this in mind when performing augmentation of facial features and exercise the best scientific and artistic skills that will lead to aesthetically pleasing results.
Firstly, it is vital to gauge the expectations of the patient prior to beginning injection.
Patients should be counseled to expect subtle improvement of their facial appearance with the help of lip augmentation within the boundaries of their individual natural features.
Patients presenting with a desire to completely change their appear- ance or to change their lips to resemble a celebrity's have unrealistic goals.
This should be discussed and clarified at the first visit.
Appropriate expectations help achieve good outcomes and satisfied patients.
Following a discussion on expectations, patient should be provided with a hand mirror to allow them to point to areas of concern.
This helps the clinician understand the exact areas that need augmentation in the patient's mind.
For example, a patient may suggest that her upper lip is too thin in the middle or the corners of the mouth are downturned.
Doing this exercise with the patient helps build good rapport, puts the doc- tor on the same page as the patient, and prevents dissatisfaction after the procedure.
The doctor should pay special attention to the elements of concern as the patient sees it.
Photographs must be taken from the front and side profiles under standard lighting and background before and after procedures.
This allows for appropriate follow-up, understanding and managing compli- cations, guiding future treatments, and providing documentation.
Once the patient is ready for the procedure, topical anesthesia is used to anesthetize the treatment site.
This author prefers topical anesthesia instead of nerve blocks as the former allows for maintenance of lip movement during injection, thus helping to assess the amount of product injected and its effect on appearance.
Nerve blocks cause loss of lip movement and affect real-time assessment of results.
The anesthetic of choice is 30 % lidocaine in petrolatum ointment for 30–40 min pre-procedure.
If the treatment involves the nasolabial folds, cheeks, and/or upper cutaneous lip, these should be augmented first followed by the lips, seeing as the lidocaine will spread from these areas toward the infraorbital nerve and help with anesthetizing the lips.
Once the area is anesthetized, the patient is seated on the treatment chair in a partially inclined, well-supported position, comfortable to the patient and the doctor.
This is necessary for a slow, well-controlled injection.
The needle is primed with a bolus of the product.
Anterograde injection with this drop helps to roll any vasculature out of the way of the needle instead of puncturing through them.
The eyesight and tip of needle are in one line with the lip for clear view of the injection and its immediate effect.
To enhance the vermillion border, the needle is inserted at the initial point such that it enters parallel into the natural tunnellike space between the white roll and vermillion with constant anterograde pressure.
The initial point will depend on the plan of augmentation for an individual patient and on their natural lip architecture.
Some patients may require a more lateral starting point while others can be started more medially.
The tunnel-like space is between the wet and dry sides of the lip.
It is important to deliver augmentation into the correct anatomical region to gain optimal aesthetic effects and avoid poor outcomes.
Injection is continued slowly with small volumes injected with steady pressure for even results.
If the overall lip volume is being augmented, this author's preferred method is to inject from the vermillion border with the needle angled parallel into the red pulp.
This leads to accentuation of the tubercles and less pain than experienced when injecting directly into the red pulp.
Alternatively, the injections can be continued in the pulp area of the lip in the same manner as above, layered underneath the first layer and using it as a support.
For a more pouty appearance (Paris lip), dermal filler is first placed along the border of the upper lip, adding definition.
Then the Cupid's bow is accentuated to create a dis- tinctive"V"?shape.
Afterward, the vertical arches between the upper lip and nose are built up.
This creates the distinctive peaks that are the signature of the Paris lip.
Finally, when augmenting the lower lip, focus on the middle third as the area of injection.
If you augment the entire lower lip, a rounded, duck-like appearance will result.
If the patient has a previous scar in the area of injection, the technique should be modified such that the scar area is bypassed.
The scar prevents proper flow of the product across the scar tissue.
In certain patients, onabotulinum toxin may be injected into the orbicularis oris in combination with lip fillers to produce elevation of the lip along with augmentation and reduction in perioral rhytids.
To prevent bruising, light pressure is applied to the injection sites.
The pressure should not be firm as this can cause displacement of the filler product.
Patient is also advised to hold an ice pack on the area for 15–20 min or longer if on a blood-thinning medication.
Patients should be instructed that HA absorbs water and causes some swelling in addition to that caused by the trauma of the injection itself.
This edema should subside by 10–15 % in 48–72h.
If bruising is noted on follow-up, pulsed dye laser may be used 48 h post-procedure to lighten the color of the bruise and speed up the healing process.
Hyaluronic acid fillers are well tolerated and rarely result in adverse reactions for patients due to their temporary nature and low immunogenicity .
Commonly experienced reactions due to injection include local inflammation, hyperemia, bruising, redness, warmth, tenderness, and ecchymosis.
Hypersensitivity and an angioedematous swelling can be seen in some patients.
Bruising and swelling usually abate within 7 days.
Erythema and telangiectasias may persist more than 2 weeks after injection and can be treated with hyaluro- dinase Infection from filler injection is also a cause for concern and can present as induration, erythema, itching, and tenderness.
The clinician should use an alcohol swab to prepare a sterile site for the injection, thus avoiding the risk of transdermally acquired bacterial infection from the presence of biofilms.
Patients with a his- tory of herpes simplex labialis can have reactivation of infection after injection and thus should be given prophylaxis with antiviral medications (acyclovir or valacyclovir) 2 days before their procedure and for 5 days after.
Painful, red nodules that present within 3–14 days after injec- tion are suspicious for infection and should be cultured.
Antibiotic treatment may be indicated depending on culture results, and steroids may be added depending on the amount of inflammation.
It is not recommended that injectors store leftover product due to increased infection risk.
Some complications arise from the inherent technique and expertise of the operator.
The Tyndall effect can occur with certain HA fillers when the filler is injected too superficially, and thus a bluish discoloration results at the injection site.
Vascular occlusion of the blood supply of the lips can result when injecting directly into an artery or vein or from compression.
The operator should take care to avoid the superior and inferior labial arteries when injecting the lips.
Hyalurodinase should be immediately injected locally with flooding of the area if severe localized pain or unusual blanching is noted.
Firm massage to the area is also indicated. 
Necrosis of the occluded area can occur if this is not recognized and treated promptly.
Immune-related adverse events include delayed-type hypersensitivity and the development of foreign body granulomas.
Foreign body granulomatous reactions are rare when using hyaluronic acid but lead to the presence of lumps or small nodules on the lips.
Differentiating between an immune process and a normal reaction has largely been based on the intensity of the reaction and clinician preference.
Hyaluronic acid fillers are an effective treatment option for patients who want to augment and revitalize their lips.
Injectors must keep in mind the natural aesthetics of the patient and patient preferences when providing this procedure.
Major adverse effects are rare, and thus HA fillers are a first-line treatment modal- ity for the nonpermanent augmentation of lips.
Lip wrinkles are fine or deep lines that can be observed around the mouth.
They appear as vertical lip lines perpendicular to the vermillion border and can be divided in static and dynamic wrinkles.
Static wrinkling can be caused by several factors, such as age, sun exposure, cigarette smoking, as well as unknown causes like genetics, gender dif- ferences, and intrinsic soft tissue characteristics.
Dynamic perioral wrinkles are caused by muscle contractions, which can be voluntary (e g , smoking or playing wind instruments) or involuntary (e g , smiling or grimacing).
Many aesthetic procedures for lip wrinkles are available: static wrinkles can be treated through facial skin resurfacing, laser, mechanical derm- abrasion, skin needling, chemical peels, and soft tissue fillers; for dynamic wrinkles, BOTOX? injections can be very useful.
This wide selection of techniques allows us to choose those with higher efficacy, minimal adverse effects, and quick healing time.
In particular skin needling and BOTOX? injections are the newest procedures that share all the advantages listed above.
A good knowledge of perioral region’s anatomy is essential for a careful approach to aesthetic procedures, in particular to provide the best treat- ment options for each patient and to avoid side effects.
The perioral muscles are arranged in interlac- ing and decussating bundles organized in several layers.
They can be classified into three groups based on insertion.
Group I muscles insert into the modiolus; they are orbicularis oris, buccinator, levator anguli oris, depressor anguli oris, zygomaticus major, and risorius.
Group II muscles insert into the upper lip; they are levator labii superioris, levator labii superioris alaeque nasi, and zygomaticus minor.
Group III muscles insert into the lower lip; they are depressor labii inferioris, mentalis, and platysma.
Skin needling, also called dermarolling, percutaneous collagen induction (PCI), or collagen induction, is an efficient technique used since 1995 in many aesthetic procedures.
To date, skin needling, alone or combined with other methods, has been proposed as an effective method to treat scars and wrinkles, but also to enhance penetration of topical sub- stances in dermatologic pathology such as melasma.
The device used is a drum-shaped roller stud- ded with a variable number of fine microneedles, which penetrate a depth of 0.5–3 mm into the skin creating thousands of micro-wounds without damaging the epidermidis.
The micro-holes, in fact, are about four cells in diameter so that the stratum corneum remains intact.
The needles must be rolled in four directions: vertically, horizontally, diagonally right to the left, and diagonally left to the right.
Needling should be performed maintaining a mild pressure over the affected area, for a total of 10–14 passes.
Skin needling is effective in minimizing scars (acne scars, burn scars) and wrinkles and by pro- moting the neocollagenogenesis and the wound healing process through a cascade of growth factors.
Despite ablative laser treatment, skin needling does not cause thermal damage, and no signs of dermabrasive reduction of epidermal thickness are evident 24 h after the procedure so that neither increased nor decreased the number of melanocytes.
For these reasons, it is a procedure suitable for all skin types, it can be repeated safely, and it is also applicable in some skin areas that are not suitable for laser treatments and deep peels.
Skin needling represents a useful tool for the treatment of perioral wrinkles, especially for ver- tical lines, also called "bar code"?and for perioral wrinkles, typical of smokers; it can also be an adjuvant treatment, in association with other techniques (such as filler injection), for the"mar- ionette"?wrinkles.
With repeated sessions, new collagen is gradually produced, plumping and filling, in a physiologic way, wrinkles, lines, and depressed scars.
It is a minimally invasive procedure with rapid healing and little downtime.
The epidermis is not damaged, so that it can be repeated.
???Risks of postinflammatory hypo- or hyperpig- mentation are minimal.
It is suitable for all skin types, even thin and previously lasered skin.
It can be performed on all areas of the face, neck, and body.
It costs less than laser treatments.
Botulinum toxin injection for treatment of facial wrinkles has been one of the most frequently performed aesthetic treatments in these last years.
Since botulism was first described in the eigh- teenth century, the botulinum toxin type A has been used first for skeletal muscle hyperactivity, and then it was being investigated for the treatment of many other conditions, until its approbation for temporary improvement of moderate to severe glabellar lines.
It was approved for human use under the name of Oculinum??? first and then after acquisition by Allergan, Inc., under the name of BOTOX?.
Botulinum toxin type A acts by inhibiting the exocytosis of acetylcholine on cholinergic nerve endings of motor nerves.
In fact after binding to the membrane of the nerve terminal, it is translocated into the neuronal cytosol where it cleaves one or more of the SNARE (soluble N ethylmaleimide-sensitive factor attachment protein receptor) proteins.
Since these proteins are necessary for vesicle docking and fusion, botulinum toxin type A reduces neurotransmitter release.
This induced weakness of the muscle starts after 3–7 days and lasts for a period of 3–4 months .
Botulinum toxin type A is most often injected into muscles that are overactive.
It results in decreased muscle activity.
For cosmetic use, the target of BOTOX? is the muscles of facial expression.
In fact, with repeated contraction of these muscles, the skin overlying the muscle can develop wrinkles.
It is very important to distinguish dynamic wrinkles, caused by muscle contraction, from static wrinkles, caused by photo- and chrono- aging, because the latter is not affected by treat- ment with BOTOX?.
To distinguish them, physician can ask patients to grimace.
Botox can be used, for example, to correct glabella wrinkles, frown lines in the forehead, perior- bital lines, hyperactivity in the muscles of the upper lip, and hypertrophy of the musculus masseter.
Many studies show that efficacy of botulinum toxin type A after several treatment sessions per- sists for many years without decrements in safety and without necessity of increased doses.
We can identify three important muscles to treat in the perioral region.
Its function is to close the mouth by approximat- ing the lips, to bring the lips together against the alveolar arch, and to protrude the lips.
For these reasons, its hyperactivity is in part responsible for perioral wrinkle formation.
Because of its circular shape, it is recommended to treat both the upper and lower por- tions to maintain balance.
Injections must be superficial, performing it in the lower dermis and no deeper than the dermo- subcutaneous junction.
The dilution of BOTOX? should be 100 U vial of BOTOX? with anywhere from 1 to 4 ml of normal saline.
In this way, it can spread over the superficial fibers of the orbicularis oris, treat- ing the multitude of vertical lines across the lips.
In this way we can use only 1 or 2 U for each quadrant, with a total treatment dose of 4–6 U for the whole area.
Injections can be performed either into the border between the pars peripheralis and pars marginalis or 3–5 mm above the vermillion bor- der into the lateral pars of the orbicularis oris, at least 1 cm from the mouth corner and avoiding the philtrum column area, for risk of flattening its lateral edges.
The needle should be injected parallel to the skin surface.
Its function is to pull the corners of the mouth downward, moving the marionette lines down.
Injections should be superficial in the lower third of this muscle, with the needle directed laterally.
The dilution of BOTOX? should be 100 U vial of BOTOX? with 1 ml of normal saline.
In this way we can avoid not only aesthetic adverse effects such as an asymmetric smile but also functional disturbances such as drooling, dribbling, or even dysarthria.
It is recommended a total treatment dose of 6 U which must be divided between two injection sites, one per side.
Injections should be per- formed in the projection of the muscle, 1 cm lat- eral and 1.5 cm below the oral commissure.
It is very important to avoid injection into neurovascular structures that lie in this area, such as the marginal mandibular nerve and facial artery and vein.
With this aim it could be useful to lift the skin and muscle with the nondominant hand before injecting BOTOX.
Its contraction raises the chin, elevates the skin of the lower lip upward, and protrudes and everts the lower lip during drinking.
Its hyperactivity can cause a deep wrinkle between the lower lip and the prominence of the mandible.
Moreover, with loss of collagen and subcutaneous fat that occurs with aging, it can appear as chin dimpling.
BOTOX treatment is effective in individuals who exhibit dynamic chin wrinkles.
As for the depressor anguli oris, also for men- talis, the dilution should be 100 U vial of BOTOX? with only 1 ml of normal saline, to avoid the induc- tion of weakness of the depressor labii inferioris, producing in this way muscular aberration of the mouth (inability to speak, drink, and eat).
It is recommended that a total dose of 6 U be divided equally between two injection sites, one per side.
Injections should be performed subcutaneously or intramuscularly at two symmetrical points located close to the chin midline, 1 cm above the lower edge of the jaw.
Even if the use of BOTOX?, according to current regulations, is allowed only for glabellar lines, analyzing the existing literature, we can find several scientific articles that show its use for other facial areas, including the perioral one.
BOTOX injection in the perioral area has two important advantages: it takes only about 20 min to be done; it is a noninvasive technique, with minimal adverse effects if performed by an expert physician.
?A good knowledge of periocular region's anat- omy is fundamental to aesthetic procedures approach of the eye wrinkles area.
This knowledge, including vascular supply, nerve position, and facial compartments, is necessary to provide the best treatment options for patients, manage complications appropriately, achieve optimal results, and avoid unwanted side effects.
First of all it is necessary to define anatomic confines of orbit: the superior margin is delimited by the frontal bone and sphenoid; the inferior margin is bordered by the maxilla, palatine, and zygo- matic; the medial margin is defined by the eth- moid, lacrimal bone, and frontal bone; and the lateral margin is delimited by the zygomatic and sphenoid.
In Table 5.1 we list the bones articulating to form the orbit.
Looking more superficially, the skin covering the orbital opening (mostly composed of eyelid skin) is the thinnest in the body, with minimal or no subcutaneous fat.
Immediately underlying it is the orbicularis oculi muscle.
It is divided into pre-tarsal, preseptal, and orbital components.
Deeper to the orbital part of the orbicularis, along the superior orbital rim, lies the corrugator supercilii muscle.
Orbital parts of the orbicularis and corrugator muscle are brow depressors.
The orbicularis muscle is the only active force that keeps the lower eyelid margin in its normal position.
Deeper to the orbicu- laris lies the orbital septum, which is a thin fibrous connective tissue layer extending from the orbital rim to the eyelid margin.
It is well known that the botulinum target is muscles: in Table 5.2 we summarize the anatomy and func- tion of periocular region muscles.
All muscles of the upper face contribute to brow position.
This must be considered for the aesthetic appearance of the upper face and must be balanced to achieve an acceptable and pleasing result.
All facial muscles of the upper face are innerved by facial nerve (VII cranial nerve).
The periocular region is vascularized by branches of the superficial temporal artery, arising from the external carotid artery: the zygomatic-orbital artery (collateral branch of the superficial temporalartery) and the frontal artery (terminal branch of artery temporal surface).
These anatomic basics are essential for a layered approach, a correct evaluation of the skin, fat, muscle, and bone to determine which procedure is best suited for each patient.
This chapter evaluates clinical application of two aesthetic procedures available for patients presenting for periorbital rejuvenation: Botox and skin needling.
The use of Botulinum toxin A (BoNT-A in cos- metic dermatology has increased in popularity due to the efficacy and relative safety of the treatment.
BoNT-A is a natural substance, made by Clostridium botulinum bacteria; it is a very powerful toxin, which causes a temporary paralysis of the muscle.
It works on the motor end plates of the skeletal muscle.
It also has an action on sympathetic smooth muscle and sweat glands.
Several medical companies have developed synthetic forms of the toxin, with the same effects of natural toxin, but safely in microdoses.
Some of the mainstream brand names include Botox?, Dysport?, Xeomin?, Vistabel?, and Neurobloc?; these are used to treat periocular wrinkles, brow ptosis, blepharospasm, hemifa- cial spasm, and facial palsy rehabilitation.
The short-term safety profile of BoNT-A in cosmetic nonsurgical procedures was confirmed for all the three commercial formulations.
The use of botu- linum toxin A (BoNT-A for aesthetic treatments is growing steadily, and new safety data have been reported in recently published studies.
Since botulism was first described in the eigh- teenth century, this neurotoxin has undergone a slow development to Botox which is now manufactured.
Voluntary muscle contraction is a response to stimulation by action potentials pass- ing along a nerve to the muscle end plate.
Once the action potentials reach a synapse at the neuromus- cular junction, they stimulate an influx of calcium into the cytoplasm of the nerve ending, and mobi- lization of acetylcholine toward the synapse occurs.
Acetylcholine fuses with the nerve ending membrane and then crosses the synapse to bind with receptors on the muscle fiber, which leads to contraction.
BoNT-A inhibits the discharge of ace- tylcholine into the synapse by bonding to the nerve at the neuromuscular ending.
The toxin is then internalized via receptor-mediated endocytosis, and a toxin-containing vesicle is formed within the nerve ending.
These internalized vesicles inhibit the acetylcholine protein (synaptosomal-associ- ated protein-25) that is located on the cell membrane.
This inhibits muscle contraction, which leads to reversible muscle atrophy.
Physiology and mechanism of action are emphasized because the only way to utilize any type of BoNT-A properly is to have an in-depth understanding of how to modify the normal movements of the mimetic muscles of the face.
When injections of BoNT-A are appropriately performed, desirable and reproducible results without adverse sequelae are created.
BoNT-A effect starts from between 3 to 7 days after injec- tion and lasts between 2 and 6 months (average 4 months).
The peak action is at 7–10 days after injection with complete paralysis of the muscle area treated, which then it gradually wears off.
Injections can be repeated.
As with any other types of treatment, before per- forming cosmetic procedures with BoNT-A both the patient and physician should discuss treat- ment expectations, to prevent disappointment.
The areas of dynamic motion, such as the gla- bella, frontal region, and periorbital lines, are the best application areas for BoNT-A because this procedure is ideal for reduction of mimetic effects of wrinkles and folds, while it is less suit- able for static wrinkles and very deep folds.
About the periocular region the best site of application are.
Corrugator supercilii muscle, to correct glabella lines, the vertical frown lines just between and above the two eyebrows.
Excellent results are achieved when injecting these lines with BoNT-A Patients are asked to frown before injection, while the largest mus- cle body of the procerus and corrugator muscles is palpated.
The corrugator supercilii muscle is injected 1 cm above the orbital rim.
With a distance less than 1 cm, diffusion of the material into the medial part of the eyebrow is possible and may lead to local eyebrow ptosis.
This effect is temporary and not treatable.
Female patients require 5 × 4 U (0.1 ml); most males require up to 5 × 6 U (0.15 ml) depending on the muscle tone.
Orbicularis oculi muscle, to correct “smile” wrinkles and lines at the outer corners of each eye.
Lateral canthal wrinkles are caused by the contraction of the lateral side of the orbital portion of the orbicularis oculi and therefore are referred to as dynamic wrinkles.
They are the result of infolding and pleating of the over- lying skin, which radiate away from the lateral canthus.
These wrinkles are perpendicular to the direction of the lateral muscle fibers of the orbital portion of the orbicularis oculi, which run mostly in a vertical direction around the lateral canthus.
Because crow's feet are enhanced during smiling or laughing, the con- traction of the risorius and zygomaticus major et minor also contributes to the formation of these lateral canthal wrinkles.
Consequently, when persons laugh, smile, or grin, they contract the risorius and zygomaticus major et minor, which also can accentuate the lower aspect of their crow's feet.
Three injection sites lateral to each eye are almost always sufficient to give relaxation to the part of the orbicularis oculi muscle that is responsible for crow's feet.
Each injection site receives 4 U (0.1 ml).
This gives adequate response rates up to 16 weeks postinjection.
By choosing the upper injection site just below the eye- brow, an aesthetically pleasing lift of the lat- eral eyebrow can be achieved as a beneficial extra effect of this treatment.
The caudal injection site is 1–2 cm below the medial one and stays away from the orbital rim.
This injection technique gives good aesthetic outcomes with slight elevation of the lateral eyebrow and clear reduction of periorbital lines.
We remember that the palpebral portion of the orbicularis oculi should not be treated with BoNT-A because it can cause loss of the volun- tary and involuntary functions of eyelid closure.
Administration of BoNT-A should be avoided during pregnancy and breastfeeding and in patients with disorders of the neuromuscular junction (such as myasthenia gravis, Lambert- Eaton syndrome) and neurodegenerative dis- eases such as amyotrophic lateral sclerosis.
Simultaneous use of aminoglycoside antibiotics (gentamycin, tobramycin) should be avoided because of their potentiating effect on BoNT-A Other theoretical drug interactions could occur with calcium channel blockers, cyclosporine, and cholinesterase inhibitors.
Highly frequent administration of BoNT-A (more than every 12 weeks) and repeated expo- sure can lead to formation of neutralizing anti- bodies against the toxin which lead to disappointing results.
Thorough preoperative evaluation with meticulous surgical planning to achieve facial aesthetic balance between the forehead, eyelids, and midface is imperative to avoid or decrease potential functional and/or cosmetic complications in cosmetic periocular surgery.
Before performing surgery, the physician should be aware of the patient's history of dry eyes, previous facial trauma, previous injection of Botox Cosmetic, history of previ- ous laser-assisted in situ keratomileusis, and past facial surgery.
Intraoperative and postoperative medical and surgical management of cosmetic periocular surgery complications focus on decreasing the risk of postoperative ptosis, lagophthalmos, lid retraction, and lid asymmetry, with special attention to limiting the risk of visual loss secondary to orbital hem- orrhage.
Skin needling is also called micro-needling therapy or collagen induction therapy.
It is a minimally invasive nonsurgical and nonablative procedure for facial rejuvenation that involves the use of a micro-needling device to create controlled skin injury.
Skin needling is able to treat wrinkles of the periocular and perilabial region, cheeks, neck, and d?collet?.
Other areas of the body can also be treated such as back of the hands and arms.
Skin needling can be used for skin rejuvena- tion: a variety of needle lengths can be used to treat different depths and therefore affect different concerns on the skin.
Rollers with longer needles are used on more difficult problems such as deep ingrained wrinkles around the mouth, whereas shorter needles are used for general rejuvenation.
In case of wrinkles associated with skin aging, one or two skin needling treatments are recommended every year.
The microneedles penetrate through the epidermis but do not remove it; the epidermis is only punctured and heals rapidly.
The needles seem to separate the cells from one another rather than cut through them, and thus many cells are spared.
Because the needles are set in a roller, every needle initially penetrates at an angle and then goes deeper as the roller turns.
Finally, the needle is extracted at a converse angle, therefore curving the tracts and reflecting the path of the needle as it rolls into and then out of the skin for about 0.5 mm into the dermis.
The epidermis, and particularly the stratum corneum, remains intact except for the minute holes, which are about four cells in diameter.
The controlled injury triggers the body to fill these micro- wounds by producing new collagen and elastin in the papillary dermis; in addition, new capillaries are formed.
This neovascularization and neocollagenesis following treatment leads to reduction of scars and skin rejuvenation, i.e., improved skin texture, firmness, and hydration.
Since 1995, this technique has been used to achieve percutaneous collagen induction in order to reduce skin imperfections.
However, to date, skin needling has mostly been proposed as an effective method of treating scars (including hypertrophic scars) caused by acne, surgery or thermal burns, stretch marks, perilabial and periorbital wrinkles, photoaging, and hyperpigmen- tation, e g , in melisma.
For wrinkles of periocular region, skin needling can be considered for the treatment of crown's feet wrinkles and glabella wrinkles with good results.
Generally three or four sessions are needed to obtain satisfactory results.
Skin needling can be combined at a later stage with other noninvasive procedures such as.
Skin needling is carried out by rolling a special device over the skin comprising a rolling barrel fitted with a variable number of microneedles.
There are various skin needling devices including Dermaroller? (Dermaroller GmbH), Dermapen? (Equipmed Pty Ltd; Australia), Derma-Stamps? (Dermaroller USA), and radial disks incorporating fine microneedles of various diameter and length, fabricated from a wide range of materials such as silicon, glass, metals, and polymers. The needles are up to 3 mm in length.
The skin needling procedure takes a few minutes up to an hour to complete, depending on the area to be treated and the severity of the problem. 
No lotions, makeup, or other topical products are applied on the treatment area on the day of the procedure.
The skin is punctured in a specific pattern using a skin needling device.
The device is rolled over the skin multiple times for best results.
As each fine needle punctures the skin, it creates a channel or micro- wound stimulating skin cell regeneration.
Application of local anesthetic cream can prevent procedure pain and help in performing the procedure properly.
A minimum of 6 weeks is recommended between two treatments as it takes that long for new natural collagen to form.
Skin needling is well tolerated by patients, but dryness, scaling, redness, and swelling may be seen after treat- ment, lasting for several days or longer, depending on the depth of penetration of the needles.
Sun protection for several weeks is recommended.
As the microholes close quickly, post- operative wound infection is rare.
Emollients or antibiotic creams may be prescribed, if considered necessary.
Rejuvenation of skin may be seen as soon as 2 weeks and as long as 6–8 months after the medical procedure.
The number of needling procedures depends on the individual skin condition.
Three to four treatments may be needed for moderate acne scars.
Contraindications and Adverse Effects Some clinical condition can be considered as absolute contraindication to skin needling procedure.
Treatment with Roaccutane within the last 3 months.
Presence of open wounds, cuts, or abrasions on the skin.
Radiation treatment within the last year.
A current outbreak of herpes simplex or any other infection or chronic skin condition in the area to be treated.
Areas of the skin that are numb or lack sensation.
Pregnancy or breastfeeding.
History of keloid or hypertrophic scars or poor wound healing.
The observation of all the pre- and postoperative precautions and respect of contraindication reduce the risk of adverse effects that are minimal with this type of treatment and typically include minor flak- ing or dryness of the skin, with scab formation in rare cases, milia, and hyperpigmentation which can occur only very rarely and usually resolves after a month.
Edema and erythema are the most frequent sequelae. Recovery may take 24 h or up to a few days.
Most patients are able to return to work the following day.
Recovery time depends on the treat- ment level and the length of the needles.
Botox and skin needling are two procedures that can give good aesthetic outcome in periorbital wrinkles correction.
Professional skin needling is considered to be one of the safest skin treatment procedures for this area.
Unlike chemical peels, dermabrasion, and laser treatments, skin needling causes minimal damage to the thin skin of the periocular region.
Botox has an ideal characteristic: it is the best treatment to correct dynamic wrinkles as periocular ones.
Moreover if a complication does arise, while not aesthetically acceptable and potentially untoward, it is time limited, and the anatomical area will eventually return to its pretreatment baseline status.
For these reasons, Botox and skin needling can be considered two useful tools that physicians can use for periocular wrinkle correction.
Chemical peeling, or chemoexfoliation, is a dermatological procedure commonly used for both skin rejuvenation and some cutaneous condi- tions.
It consists of the application of one or more chemical exfoliating compounds to the skin to remove and regenerate part of the epidermis and dermis.
This may result in the improvement of the physical appearance of the skin and a decrease in the number of wrinkles, pigmentations (e.g., melasma, lentigo), and inflammatory lesions (e.g., acne, rosacea).
Many chemical compounds may be used as peeling agents and their effects may differ, varying from light to medium and deep regeneration.
This review focuses on the use of peeling in the perioral and periocular region for the treatment of aging, photoaging, and melasma.
Although chemoexfoliation represents one of the oldest cosmetic procedures, described in ancient Egyptian writings and performed by Romans as well as Indian and Turkish women through rudimentary methods to smooth the skin, the concept of peeling acquired a scientific identification in the late 1800s, when phenol was described to lighten the skin.
Soon other peeling agents (e.g., salicylic acid, resorcinol, trichloroacetic acid) were identified, and in the mid-1900s peeling procedures were used for medical purposes.
However, it was only in the 1970s that chemical peeling became popular.
Before focusing on specific peeling for the lip and eye region, a brief classification of generic chemical peeling agents is listed based on of the depth of dermal penetration.
Very superficial (glycolic acid 30–50 %, Jessner solution applied in 1–3 coats, sali- cylic acid 25 % applied in 1 coat, resorcinol, 20 % applied briefly (5–10 min), trichloro- acetic acid (TCA), 10 % applied in 1 coat), in which the area to be treated is confined to the stratum corneum, with no alteration below it.
??Superficial (glycolic acid, 50–70 % applied for 3–10 min; salicylic acid 25 % applied in 4–10 coats, pyruvic acid 40 % applied in 4–5 coats, Jessner solution applied in 4–10 coats; resorcinol, 40 % applied for 30–60 min; TCA 20 %), when part or all of the epidermis is involved.
Medium (TCA 35 %, pyruvic acid 50–60 % applied in several coats, augmented TCA (gly- colic acid 70 %+ TCA 35 %, Jessner solu- tion+ TCA 35 %, salicylic acid+ TCA 35 %), involving both epidermidis and papillary dermis.
????Deep (TCA 50 %, phenol), involving the epi- dermis, papillary dermis, and reticular dermis.
Very superficial and superficial peelings involve the stratum corneum or the epidermis in toto and represent well-tolerated treatments with very low risk of side effects.
Medium-depth peelings involve epidermis and papillary dermis, causing denaturalization of proteins, clinically characterized by skin bleaching (frosting).
Histologic modifications in connective tissue with new deposition of collagen and elastic fibers may be observed after these procedures.
They require a posttreatment procedure and are associated with some side effects.
Deep peelings cause a significant dermal injury, involving the reticular layer.
They also cause a quick and intense frost, resulting in der- mal regeneration with new deposition of collagen and glycosaminoglycans.
Special care is required for this type of peeling since severe complications may occur.
The choice of the most appropriate peeling agent, keeping in mind the related depth of penetration in the skin, is crucial.
Both perioral and periocular regions represent very sensitive anatomic areas and, therefore, in general the use of soft peelings is recommended.
This chapter reviews the types of peeling indicated for these specific areas.
(HSV) infection, prior treatments such as oral isotretinoin, radiation, or laser skin resurfacing, and photosensitizing medications should also be carefully evaluated to avoid scarring or slow reepithelialization.
Skin type and phototype should be also carefully examined.
Thicker and oily skins, for instance, are more resistant to peeling and may require a deeper treatment than other skin types.
Fitzpatrick’s phototypes IV–VI are not recom- mended for medium to deep peeling because of the high risk for pigmentary dyschromias.
A pos- itive history for other skin disorders, such as atopic dermatitis, seborrheic dermatitis, psoria- sis, contact dermatitis, or rosacea must be investigated for their potential exacerbation during the postpeeling period.
Patients with a history of HSV should be treated with antiviral drugs from the prepeel period until complete reepithelializa- tion, especially when medium-depth or deep peelings are performed.
Patients with significant history or current evidence of any psychological disorder, or with immunocompromising diseases or allergies, should not be treated.
Skin priming with topical compounds (reti- noic acid, glycolic acid, pyruvic acid, and hydro- quinone) is usually suggested 2 weeks before the peeling to improve its performance.
Skin priming allows an easier and uniform penetration of the peeling agent, reducing the reepithelialization phase as well as the risk of posttreatment hyper- pigmentation.
However, when treating the periocular and/or perioral area, skin priming should be avoided as it may irritate the skin.
Finally, complications of peeling should be considered before performing a peel, keeping in mind the direct relationship between the frequency of complications and the peel's depth.
Before considering chemoexfoliation, a series of evaluations should be made.
First of all, a thorough patient evaluation including age, sex, skin type, aging, and photoaging severity, in addition to the presence of any psychological discomfort or other skin disorders, must be considered.
Moreover, the patient's history of abnormal or keloid scarring, perioral herpes simplex virus (deeper treatments lead to more complications).
The most frequent changes are pigmentary (hyperpigmentation and hypopigmentation).
Phototypes IV–V are at higher risk especially when medium-depth peeling is performed.
Early sun exposure and/or the use of oral contracep- tives are aggravating factors.
Scarring (atrophic or hypertrophic scars) represents a relevant complication for deep peelings.
Scars usually appear on the lower part of the face (perioral region), probably due to the mechanical stretch- ing occurring in this area during eating and speaking.
Lower eyelid ectropion has also been observed 3–6 months after phenol peeling.
HSV represents a frequent complication in patients with a history of HSV recurrence when undergoing a medium-depth peeling.
HSV prophylaxis is necessary when these procedures are performed but not for superficial peeling.
Bacterial infections are not common but can be observed, Pseudomonas infection is the most problematic.
Other possible pathogens include Staphylococcus, Streptococcus, and Candida.
Persistent erythema is considered a physiological event when skin remains erythematous for up to 3 weeks after the peel.
Erythema is caused by angiogenic factors stimulating vasodilatation.
When erythema persists for more than 3 weeks and is associated with pruritus, it could be indicative of scarring formation, requiring the use for a short period of potent topical corticosteroids or systemic steroids.
Silicone sheeting or pulsating dye laser represent other therapeutic options, especially in cases of evident thickening or scarring.
Milia may occur after a period of 8–16 weeks after a procedure, probably resulting from occlu- sive postpeeling treatments.
Acneiform eruption may be observed in a small percentage of patients during the reepithe- lialization phase or immediately after, owing to an exacerbation of preexisting acne-prone skin or the use of occlusive products on the skin during the postpeeling period.
Systemic antibiotics are usually administered to obtain satisfactory results.
Allergic reactions are relatively rare and most commonly associated with the use of resorcinol.
Allergic reactions may be misdiagnosed as the clinical presentation (erythema, pruritus, edema) resembles normal postpeeling reactions.
Antihistamines together with steroids may be used to manage these complications.
Cardiotoxicity is a potentially severe compli- cation that may occur during phenol peeling.
It has been demonstrated that phenol can be responsible for cardiac toxicity, including tachycardia (arrhythmia), premature ventricular beats, bigeminy, and atrial and ventricular tachycardia, in addition to liver and kidney side effects.
It is therefore important that a phenol peel is performed by qualified physicians in an operat- ing room with cardiopulmonary monitoring of the patient.
Alpha-hydroxy acids (AHA) are a group of car- boxylic acids characterized by a hydroxyl group attached to the alpha position of the carbon atom.
AHAs increase epidermal thickness and dermal glycosaminoglycan content and are used to treat photoaging, acne, pigmentary, and keratinization disorders.
Glycolic acid represents one of the most commonly used AHAs, used both in topical creams at low concentrations (5–20 %) and as a peeling agent at concentrations up to 70 %.
Because of its small molecular weight and size, it has high skin penetration.
It is considered a relatively safe, effective, and well tolerated peeling agent.
Glycolic acid causes superficial peeling with few complications, although dermal wounds similar to those caused by 40 % TCA have been reported with the use of higher concen- tration (70 %) or in cases of prolonged exposure.
Neutralization with any alkaline solution (generally sodium bicarbonate 8–15 %) is required after glycolic acid peeling to avoid any further penetration through the deeper skin layers.
Glycolic acid can be used for both perioral and periocular regions, avoiding deep penetration or long exposure and providing prompt neutral- ization with a solution of sodium bicarbonate as soon as erythema appears.
Particular care should be taken for vulnerable areas, such as nasal ala, lips, lateral canthus, and oral commissures, which can be protected with an ointment.
Treatment can be repeated every 3–4 weeks for a total of six treatments.
Later on, moisturizers, emollients, and sun- screens must be applied for 5–7 days during the healing process.
No other particular medication is required; in addition, cream containing AHAs should be avoided for 2–3 days following the procedure.
Mandelic acid is an AHA derived from almonds.
The large size of the molecule causes a slow and uniform penetration, making mandelic acid, at 30–50 %, the ideal treatment for sensitive skins.
Although it is not specific for perioral and peri- ocular areas, it can be used with no precautions or restriction in these areas. 
Tretinoin peel is a solution of tretinoin at a high concentration, varying from 1 to 5 %, in propylene glycol.
It is applied by gauze or a brush in one or more coats and left on the skin for 4–8 h after which it is removed with water.
In consideration of its potential irritative effects, this kind of peeling should be managed with particular care in the perioral area affected by melasma, restricting the time of exposition.
Because of its teratogenicity, this type of peeling should be avoided in women at any stage of pregnancy.?
Salicylic acid is an organic carboxylic acid with a hydroxyl group in the beta position.
Its lipophilic structure allows it to easily penetrate through sebaceous glands and corneous cells, with consequent destruction and exfoliation of the upper layers of the epidermis.
It is mainly indicated for superficial and medium acne scars, particularly for those with a remarkable hyper- chromic component; inflammatory acne, rosa- cea; melasma; and photoaging.
It is not a specifically designed peel for perioral and periocular areas, but can be used in these regions with some precautions.
Patients suffering from salicylate allergy cannot receive salicylic acid as a peeling treatment.
Yellow peel (YP) is a combination of retinoic acid with phytic acid, kojic acid, and azelaic acid, which block the synthesis of melanin at different levels.
Vitamin C bisabolol, and salicylic acid are also contained in this formulation.
The name of this peel originates from the particular yellow coloration of the skin after its application.
YP allows a sort of modulating peeling involving both superficial and medium epidermis.
Furthermore, it induces new epidermis regeneration with few risks for potential dyschromia.
Mainly indicated for melasma and hyperpigmentation in general, it can be used in perioral and periocular areas, using a gentle massage and leaving on the skin no longer than 15–30 min.
Resorcinol, or m hydroxybenzene, is a compound structurally and chemically similar to phenol.
It is a reducing agent, used in concentrations ranging from 10 to 50 %, and is able to break keratin bonds and induce both epidermal regeneration and dermal fibroblast proliferation.
Resorcinol is usually applied with a spatula and left on the skin for 25–60 min, increasing by 5 min each week, according to different authors.
After the paste has been removed, the skin appears burned and exfoliates for the following 7–10 days.
Postpeel care with antibiotic and corticosteroid creams together with sunscreen is important to prevent complications (pigmentary changes and allergic reactions).
The main indications for a resorcinol peel are acne, including comedonic acne, along with pigmented lesions, melasma, and superficial scars.
It can be used in the perioral area affected by melasma.
Jessner's solution (JS) is a combination of salicylic acid (14 g , resorcinol (14 g , lactic acid (85 %, 14 g and ethanol (95 %, up to 100 ml), which can be used either alone for superficial peeling or in combination with other agents to facilitate medium-depth procedures.
Its efficacy in the treatment of comedonic and inflammatory acne and dyschromias depends on both its keratolytic and anti-inflammatory activity.
JS causes keratinocyte dyscohesion as well as intrand intercellular edema.
JS is usually applied in two to three coats with wet gauze, sponges, or a brush.
The application of the solution is typically accompanied by mild erythema and an intense burning sensation, followed by a faint frost presenting as a whitening of the skin with a dust-like aspect.
Postpeel exfoliation usually occurs within few days and may persist for up to 8–10 days.
It can be used in the perioral area affected by melasma.
Among medium-deep peeling, trichloroacetic acid (TCA) represents a good option for perioral or periocular areas affected by advanced photo- aging.
Usually used at concentrations ranging from 10–20 % to 35–50 % for superficial and medium-depth peeling, respectively, the use of TCA in concentrations higher than 35 % is not suggested because of potential scarring.
The procedure may be painful.
TCA can be applied with cotton tips, brushes, or small gauzes.
Peeling depth is easily monitored by erythema and frosting degrees.
In particular, minimal erythema represents a very superficial peeling, involving mostly the stratum corneum; mild erythema with light frosting patches corresponds to superficial peeling, causing 2–4 days of exfoliation.
White frost with a background of erythema shows a medium-depth peel and solid white frost is indic- ative of a deep peel, extending down the papillary dermis.
When TCA is applied in several coats, a deeper peeling is obtained.
In such instances it is better to use lighter TCA concentrations.
An intense burning sensation is typical of TCA peelings and requires the use of wet cold compresses at the end of the procedure.
Afterward, a cream or ointment with 1 % hydro- cortisone may be applied to soothe the skin.
Sun exposure must be avoided for 4–5 months after the procedure.
Patients should be informed about darkening skin color and potential swelling.
The exfoliation usually begins 3–4 days after the peeling procedure.
During this period the skin must not be removed to avoid postinflammatory hyperpigmentation.
If erythema persists for 2 or 3 weeks after exfoliation, the use of light cortico- steroid or zinc oxide paste is suggested.
TCA is variably responsible for changes in epidermal thickness, epidermal and dermal protein denaturation, and coagulative necrosis, resulting in epidermis revitalization, an increase in both fibroblasts and collagen types I and III, and reduction of the elastic component.
TCA should be avoided in patients with phototype V–VI because of potential darkening and scarring.
An innovative formulation of TCA, 3.75 % combined with lactic acid 15 %, specifically designed for periocular and perioral areas, has recently become available.
This combination peel was successfully used to improve periorbital hyperpigmentation with very low risk.
Perioral and periocular peelings, in consideration of the anatomic areas characterized by thin skin and sensitive skin, should be soft and never aggressive.
Radiofrequency (RF) as used in the field of medi- cine refers to the use of electromagnetic waves at radio frequencies to produce heat.
RF has been used in surgical and non-surgical aesthetic medicine for more than 70 years.
Radio frequencies between 30 and 30 MHz are generally used to produce heat at various skin levels, and hence can be used in the treatment of skin laxity and cellulite.
RF treatment makes use of a fundamental concept in the theory of electricity, namely, if a given quantity of electric current encounters a resis- tance (for DC current) and/or impedance (for AC current), heat is produced in direct proportion to the current and the resistance and/or impedance.
If RF treatment is used to destroy tissue it is referred to as "ablative RF."?If it does not destroy tissue it is referred to as "non-ablative RF"?
RF ablation procedures most commonly employ a radio scalpel when it is necessary to destroy a limited volume of skin in a controlled and reproducible manner.
RF ablation therapies are used to treat neoplasms and hepatic, pancreatic, bone, and pulmonary metastasis by means of thermoablation.
They have also been successfully used in treating pain and cardiac arrhythmia.
It is noteworthy that the introduction of an RF-ablative probe directly into a blood vessel permits the closure and destruction of the target vessel in much the same way as does laser therapy.
This technique is recommended for treating rectilinear veins such as the great saphenous vein.
?Non-ablative RF procedures have many fields of application in aesthetic medicine, especially where the common denominator is the treatment of skin laxity.
In November 2001, the Food and Drug Administration (FDA) approved RF therapy as the preferred method for the treatment for skin depressions.
The so-called "aesthetic"?effects of RF ther- apy operate on thermally damaged collagen and elastin by breaking their intermolecular bonds, which in turn brings about a re-structuring of the dermal fibers over the following weeks and, hence, elasticizing of the skin.
Monopolar RF handsets apply RF energy generated by an RF unit to one point of the skin to be treated, and a metal plate, also connected to the RF generator, is placed at another point.
Until recently the handsets used in non- ablative therapies have been of two types, monopolar and bipolar, but tripolar and quadripolar handsets are now available for the application of RF therapy .
The RF monopolar handset requires a metal plate to be placed near the area to be treated by the handset.
The RF bipolar handset does not require the use of a metal plate, since both poles of the RF generator pulses are in the handset itself.
The impedance that an RF circuit encounters depends of the conductivity of the tissue to be treated, e g , the thickness of dermis, the quantity of fat, and the thickness and structure of the con- nective and glandular tissue.
The higher the impedance of the tissue to be treated, the greater are the heat produced and the thermal effects .
Furthermore, owing to the impedance of the skin, the deeper the penetration of the RF electro- magnetic wave, the more heat it produces.
The heat induced by the RF wave travels from the skin surface to the hypodermis producing tem- peratures that vary from 30 to 35 °C at the sur- face, and from 60 to 65 °C at 9 mm.
The increase in temperature causes an increase in blood flow through vessels, leading to drainage in the adipose tissue.
In addition, the heat generated by this increase in tempera- ture shrinks the collagen fibers, creating a progressive regenerating effect during the following weeks.
New collagen results from the heat produced by RF waves through a series of intermediary steps.
The RF heat produces mediating heat- shock proteins, which in turn stimulate the T lymphocytes and monocytes to produce cyto- kines and fibroblast growth factor 1, which in turn stimulates the fibroblasts to produce the new collagen.
Histological studies conducted 4 months after treatment showed an increase in collagen density and a reduction in volume of the sebaceous glands.
Numerous studies have shown that RF treatment can be safely used on both dark and light phototypes since it does not interfere with melanogenesis.
RF treatment is indicated for skin laxity of the face and body, and has recently been indicated for the treatment of cellulite and stretch marks.
In addition, more recent studies indicate that RF can reduce hyperhidrosis.
Contraindications are pregnancy, arrhythmia, use of a pacemaker, epilepsy, anticoagulant drug use, and skin infections.
The treatment protocol prescribes one or more passes over the area to be treated depending on the complexity of the case, and each therapy ses- sion can last from 15 to 35/45 min.
The treatment requires the application of a simple gel, or a gel to which either hyaluronic acid or other active ingredients have been added to enhance the anti-aging effect (Fig. 7.3).
The heat transmitted by RF therapy can provoke an unpleasant sensation depending on the intensity of the heat, the area treated, and the sensitivity of the patient.
Immediately after treatment a slight erythema may appear over the treated area (in 35 % of patients), which disappears after 2–3 h and allows the patient to continue with normal daily activity (Fig. 7.4).
It should be noted that there are now systems for the cooling of skin that limit the dispersion of heat toward the epidermis and hence reduce the risk of skin burn.
It is advisable to apply a lenitive mask after treatment and subsequently a repair cream together with a strong sunscreen.
Very rarely tiny vesicles form, which subsequently transform into crusts that exfoliate in 4–5 days.
To facilitate this process one can apply a hyaluronic acid-based cream.
It is important to note that the patient should not be exposed to the sun in the 2 weeks following treatment, and in any case should apply a strong sunscreen whenever outdoors.
Fractional RF, termed “fractional” in analogy with the fractional laser, produces short, intense electric pulses at adjustable frequencies that travel from one electrode to another on the hand- set, thus generating heat on/in the skin to be treated.
Bipolar RF is one of the most innovative, non- invasive treatments for the face and body in the field of aesthetic dermatology.
Skin applications of fractional RF have two principal effects.
Selective vaporization of superficial layers of skin less than 1 mm.
A series of microholes in the skin which, upon healing, produce tighter skin.
The distance between holes can be varied by varying the frequency of impulse repetitions; the size and depth of the holes can be varied by varying the energy generated and emitted by the handset.
There are many types of handset used in fractional RF, which can have from a minimum of 5 to a maximum of 225 microneedles.
Treatment with these microneedles causes a series of damaged micropoints on the skin, which initiate a regenerative process.
The needles are equipped with “shock absorbers,” which allow the technician to adapt to all facial and body contours while maintaining a constant and repeatable pressure.
Fractional RF treatment involves the deep layers of the dermis and epidermis.
The skin generates in a totally natural manner without pain, thus making an immediate return to a normal social life possible.
Indications for fractional RF are facial and body skin laxity, acne scars, wrinkles at all depths, and stretch marks.
The handset is applied to each area to be treated only once.
A few minutes after treatment an erythema appears over the treated area, which then disap- pears in the course of a few hours.
It is advisable to apply a lenitive mask after treatment followed by a repair cream together with a strong sunscreen.
Three or four treatments are advisable, with a 2-week period separating each treatment.
After the initial treatment cycle, monthly maintenance treatments are advisable.
Visible results are evident after 4 months.
Two treatment cycles per year are advisable.
Given that burn points that heal in 5–7 days are a possible collateral effect of fractional RF treatment, it is advisable to apply an antibiotic ointment and hyaluronic acid cream.
In addition, the application of a strong sunscreen is necessary for at least 1 month after treatment.
Techniques Biorejuvenation is a common term to indicate mesotherapy for skin rejuvenation (also called bio- revitalization or mesolift).
It's a technique used to rejuvenate and tone the skin by means of an injection in the superficial dermis of suitable products, perfectly biocompatible and totally absorbable.
The goal of this technique is to increase the biosynthetic capacity of fibroblasts, inducing the reconstruction of an optimal physiologic environment, facilitating interaction between cells, and increasing collagen, elastin, and hyaluronic acid (HA) production.
The desired final effect is a firm, bright, and moisturized skin.
Chronoaging is responsible of clinical and histologic changes because of the intrinsic aging, like alterations in skin texture, elasticity, pigmenta- tion, and modifications of subcutaneous tissue and the vascular system.
Clinically, aged skin is characterized as thin, dry, and pale, with noticeable wrinkles and decreased elasticity.
Histologically, the epidermis becomes atrophic; there's the accumulation of elastotic material in the papillary and mid-dermis, a process known a"solar elastosis"?and quantitative changes in collagen, which are reflected in a decline in biosyn- thesis and content.
The degree of changes is genetically determined and so different in each individual.
Chronoaging can be worsened by cumulative environmental damages, such as chronic UV exposure (photodamage), pollution, and smoking.
The effect of photodamage, termed "photoaging" is characterized by wrinkles, shallowness, laxity, patchy pigmentation, and rough- textured skin that histologically are signs of hyperplasia or atrophy.
Dermal features include elastosis, degeneration of collagen, anchoring fibrils, and dilated and twisted blood vessels.
UV exposure activates free radicals and matrix-degrading metalloproteinase enzymes, including collagenase.?
The mesotherapy can be performed in cases of mild to moderate chronoaging and photoaging, aging prevention, and preparation to sun exposure and smokers.
The treatment is indicated for both young skin, yet elastic and vital, to reduce the physiological aging process, because the used substances ensure deep hydration that delays the onset of age-related imperfections and counteracts oxidative damage that is caused by environmental factors and exposure to sunlight, and for mature skins, to reduce the signs of chronoaging by reactivating the cellular functionality.
Biorevitalization performs three main functions.
??Restructuring: it promotes cell turnover and the production of collagen, elastin, and hyaluronic acid.
????Antioxidant: it protects the skin from free radicals that are released as a result of environmental factors and solar radiation.
?????Moisturizing: it promotes the rapid recall of water in the tissues.
It allows to obtain substantial improvements in terms of (Fig. 8.2).
Mesotherapy injection is a minimally painful procedure and requires no anesthesia.
The sessions are performed in cycles and do not involve side effects except in rare cases, a slight temporary redness or a small bruise in the area due to the trauma of the needle insertion, which tends to disappear spontaneously in 2–3 days, and usual activities can be resumed immediately.
The main advantages/disadvantages and con- traindications are listed in Table 8.1.
The desired effect – firm, bright, moisturized skin – can be achieved by microinjections in the superficial dermis of products  containing only one active ingredient or “cocktails” of dif- ferent compounds that are biocompatible and absorbable.
The products available in mesotherapy for skin rejuvenation are Hyaluronic acid alone (1.35–3 %), Hyaluronic acid 0.2, 1, or 3 % plus other active ingredients like vitamins, amino acids, miner- als, coenzymes, nucleic acids, and β-glucan, Macromolecules,  Organic silicium, Autologous cultured fibroblasts, Growth factors, Homeopathic products.
The most frequently used substance is natural non-cross-linked hyaluronic acid (Fig. 8.4).
Chemically HA is the major nonsulfated glycosaminoglycan of the connective tissue scaffold, synthesized by fibroblasts within the cell membrane and then released in the extracellular space.
Interestingly, the injection of simple HA not only provides enrichment of one of the main ECM compounds and deep hydration of the skin but also stimulates fibroblasts, acting on specific receptors (CD44, RHAMM, and ICAM-1)4 to synthesize new scaffold compounds.
One gram of HA can bind up to 6 L of water.
This means that the higher the percentage of HA is in a composition (milligrams of HA per milliliter), the higher its capacity to retain water will be.
Derived either from rooster combs or from bacterial fermentation, HA has no species specificity, and the risk of a hypersensitivity reaction is so low that skin testing is unnecessary.
Hyaluronic acid used in mesotherapy is not cross-linked, it is not much stable, it is very fluid, and it has a short half- life, even shorter than the one used in fillers.
The great versatility of biorevitalization lies in the different biological effects of the injected active substances.
The synergy of different func- tional ingredients can treat skin in a more com- plete way, acting on various age-related marks caused by both intrinsic and extrinsic aging fac- tors, with a preventive and curative action.
In a mesotherapy cocktail, vitamins are the most important active component.
Vitamin A regulates the epidermis turnover and it is an antidrying agent.
The “vitamin B complex”—usually indicating a group of vitamins that includes vitamin B1 (thi- amine), B2 (riboflavin), B3 (niacin), B5 (panto- thenate), B6 (pyridoxine), B9 (folic acid), and B12 (cyancobalamin)— includes coenzymes involved in several metabolic processes that help the scavenging of free radicals.
Vitamin C is a well-known antioxidant and it induces collagen synthesis.
Vitamin E is an antioxidant and moisturizer.
Vitamin K has an effect on microcirculation.
Vitamin D, vitamin H (biotin), vitamin B10, and vitamin I (inositol) are important too.
The amino acids build polypeptides constituting the matrix of cell architecture.
Sodium, potassium, calcium, and magnesium act as catalysts in numerous cell functions.
Coenzymes are nonprotein organic components that help the enzymes in their catalytic function.
They are"activators"?of biochemical reactions and help the dermis turnover.
DNA and RNA are bound to proteins, and they give information for the regulation of protein synthesis.
β-Glucan acts as a free radical scavenger.
Polynucleotidic macromolecules favor skin hydration increasing water retention; they act as scavengers of free radicals, and they enhance the physiologic activity of fibroblasts.
There are different injection techniques in the superficial dermis (Fig. 8.5) that can be per- formed always keeping the needle with an incli- nation of 45°
Picotage: it’s more useful in younger people who want to prevent skin aging due to sun exposure and tanning sunbeds.
It consists of microinjections, is very superficial, is practi- cally painless, and is spaced at 2 mm, and the needle penetrates the treated area at 2–2.5 mm.
During the procedure the physician maintains a constant pressure on the plunger. The mostly cured areas are the face, neck, decollete, and less frequently hands (Fig. 8.6).
Cross-linking: it is recommended for the pre- vention and treatment of skin aging (patients with a more advanced stage of chronoaging, compared to the prior technique).
It consists in performing intradermal linear infiltrations with a complete penetration of needle verti- cally and horizontally, spaced at 1 cm, to form a grid.
The product is injected during the extraction of the needle from skin dermis.
Linear threading: either vertical or horizon- tal injections are performed.
Vertical injections are useful to prepare the nose-labial and glabellar wrinkles 10–15 days before injecting dermal fillers and botulinum toxin.
Horizontal injections are useful in treating neck wrinkles.
To reduce the burning sensation, the physician can apply anesthetic cream 1 h before the treatment.
It is recommended to avoid injecting products containing also vitamin C After the treatment, a gentle massage with a vitamin K cream can be given.
The procedure generally takes about 20 min, but it may vary depending on the treated area.
Sun and smoking avoidance are recommended for the next 48 h There is no downtime or recovery time with this procedure (Fig. 8.7).
Treatments should be done once every 2 weeks for 3–4 weeks, then once a month for 3–4 months.
The results are maintained by touch-up treatments once or twice a year.
This protocol may vary according to the patient's age, clinical presentation at first visit, and response to initial treatments.
Typically, 2–3 treatments are necessary to see some results, even if the bright- ness is visible after the first treatment, due to a vascular stimulus by the microinjections.
The number of treatments can vary from patient to patient and depends on the treated area and patient's expectations.
It is important to remem- ber that mesotherapy is not a filling technique, but it permits the rejuvenation of the skin by increas- ing its hydration and by reconstructing an optimal physiologic environment for the fibroblasts.
Sessions of biorevitalization can be performed in combination with other treatments, surgical or not, such as peeling, face lift, eyelid surgery, fillers, infiltration of botulinum toxin, and laser treatments.
Biorejuvenation can be used to prepare the skin 2 weeks before the injections of other products.
This is because biorevitalization works improving globally the skin unlike other treatments that act more locally.
Among these are: Peelings The purposes of chemical peelings are to erase shallower wrinkles, restore tone, give freshness and radiance to the face, eliminate dark spots and scars, etc.
Peelings represent an accelerated form of exfoliation induced and controlled by the use of one or more caustic chemicals applied to the skin.
Depending on the agent used, its concentration and the time of application, these preparations cause a partial or total programmed destruction of the epidermis.
The effect is the stimulation of cellular turn- over through the removal of the horny layer and, simultaneously, the induction of the synthesis of new collagen in the dermis.
The result is the replacement of old tissue with a healthier and less corrupt one.
There are three broad categories of peelings which act in different ways depending on the depth of penetration of exfoliating: the superficial peelings, middle, and deep ones.
Superficial peelings: they allow the immediate resumption of work and social activities.
The treatment is ambulatory and is composed of cycles with more spaced sessions (intervals of 7–15 days apart), also can be performed twice a year, for example, glycolic acid peels, salicylic acid peels, and retinoic acid peels.
Medium or deep peelings: they are suitable for individuals with badly damaged skin by the chrono- and photoaging or for skin that pres- ents wrinkles and rather deep furrows and spread discoloration.
This kind of peelings requires a recovery period at home during which skin exfoliation occurs and is much stronger than after a superficial peel.
An example is trichloroacetic acid peel that may be superficial, medium, or deep depending on the concentration of acid used.
The results depend on the type of peel made; for example, a deep one has a definite result and only one treatment is required, while the medium peel and the superficial ones are less invasive, but allow less lasting results and therefore need to be repeated regularly.
All types of peels require careful and meticulous aftercare treatment, consisting basically in no sun exposure until healing has occurred (or cycle ended) and the use of sunscreens, emollient creams, and products based on alphahydroxy acids for home use.
Fillers In cosmetic medicine "fillers" are materi- als that are injected into the dermis or subcutaneous tissue in order to fill a depression or increase the volume.
They may be transient, when their cosmetic-clinical effect ceases after some time, and permanent, remain where injected, for life.
Hyaluronic acid gel fillers are now the most used and safe and they are completely resorbable.
Another widely used material for particular anatomical areas is the calcium hydroxyapatite (this filler is usually composed for 30 % of micro- spheres of synthetic calcium hydroxylapatite (CaHA) and 70 % from an aqueous gel solution).
No one of the so-called "permanent"?fillers has been approved by the FDA.
The permanent fillers (non-resorbable) were basically created for the need to make a correction very durable.
Unfortunately, experience has shown that very often this intent is illusory, not because the substance does not remain long in the skin tissue but because it has its own weight and density that lead it slowly to migrate from the implant site following the force of gravity.
So there is a progressive appearance of the filler in a different region from the injection site.
More serious is the frequent appearance, years after the implantation, of very serious local inflammation, abscesses, and granulomas or thick areas of fibrotic tissue that follow for months and years tormenting the patient's appearance, his/her health, and his/her normal social life.
The best known is the liquid silicone, however, banned in Italy by ministerial decree in 1993.
Fillers can be used to treat facial wrinkles, after a restrictive and low-calorie diet or after a prolonged period of illness, when we see the area of the cheeks gaunt; very satisfactory is the instal- lation of fillers in the lips for young women and even older women to give volume and firmness to the face, to redesign the profile of the nose (rino- filler), for reconstructive purposes, depressed scars, results of acne and chicken pox, asymme- tries, and facial atrophies, and in all cases where we want to take care of ourselves from the aes- thetic point of view.
The hyaluronic acid gel is injected into the dermis with thin needles of different diameters and lengths depending on the viscosity of hyaluronic acid and the area to be treated or with ago cannulae.
The aesthetic result is very natural and the absorption of the substance is gradual.
The material is fully resorbable, and its duration is very satisfactory, generally 8–12 months or more, depending on the areas treated, the individual characteristics, and lifestyle.
After the injections mild redness is frequent, but disappears in a matter of hours without a trace, and variably, a slight swelling.
You could also have the formation of some bruisings or hematomas (caused by rupture of a small capillary during injection) which resolves spontane- ously and in a short time with the local application of creams based on vitamin lactoferrin.
It is not advisable to expose the treated area directly to the sun or sunlamps.
It is also advisable not to rub and massage the site for 24 h and not to apply any makeup for at least 3–4 h after.
Botulinum toxin The botulinum toxin is a protein produced by the bacterium Clostridium botu- linum that, by blocking the transmission of nerve impulses, reduces muscle contraction.
Only a fraction of the toxin (type A is used in aesthetic medicine, purified and diluted in saline.
After a careful study of facial expressions of the person, the physician practices many small injections in the chosen areas, paralyzing the underlying muscles producing a "lifting effect." The areas of choice of Botox are wrinkles around the eye and the eyebrow, but also the wrinkles of the forehead.
In other words, the Botox is suitable for correcting the wrinkles arising from facial muscle movements (facial expres- sion muscles).
The effect is temporary, 4–6 months, and starts a week after the infiltration.
The microinjections of botulinum toxin which, of course, are not toxic are not painful and do not cause swelling.
There is a theoretical risk of a hypersensitivity reaction to the product itself or to the additives contained in it, which in any case is directly pro- portional to the amount administered.
In particular, the current formulations of botulinum are contraindicated for people with allergies to milk, because they are used as a preservative albumin.
Furthermore, the botulinum toxin is not? recommended during pregnancy and lactation.
The "mesobotulino"?is one of the most interesting methods in the field of rejuvenation.
It consists of microinjections of botulinum toxin in very diluted form, with a cocktail of amino acids and vitamins that aim to correct the oval of the face, cheeks, and sagging chin and neck profile.
The action of vitamins, combined with the par- alyzing botulinum, smoothes the skin and gives a lifting effect.
The treatment is safe and provides only a slight annoyance given by injections.
Laser Fractional CO2 laser is the first choice for sun damage, wrinkles, and texture because it eliminates the superficial layers of the skin, stim- ulating at the same time, the contraction of the fibers of collagen, and elastin in the dermis.
The fractional CO2 laser can shrink the skin and reduce the appearance of fine wrinkles and large pores; it can act effectively even on acne scars and skin discoloration.
It practically eliminates the superficial layer of the skin and, at the same time, strongly stimu- lates the deep layers so as to have an intense process of rejuvenation or tissue repair.
The use of the fractionated scanner instead of the previous type "ablative"?greatly reduces the down time (time to return to social life after the treatment) because the microareas treated are spaced with free areas with the result of a more rapid healing.
Since Albert Einstein first developed the concept of laser radiation, physicians have used lasers along with other components of the electromag- netic spectrum in a variety of medical and cosmetic applications.
Appreciation of the physics behind lasers provides a foundation for understanding its applications.
The electromagnetic spectrum comprises radiation energy spanning short gamma waves to long radio waves, and in between it includes x rays, ultraviolet radiation, visible light, infrared light, and microwaves.
If sufficient electromagnetic radiation is absorbed by resting atoms, their electrons are stimulated to excited states.
When these electrons eventually return to resting states, the atom releases the same absorbed energy at its wavelength in a process known as"spontaneous emission."?Spontaneous emission may be hastened, or stimulated, when an excited atom is irradiated a second time with the same wavelength used to excite it originally.
The second hit may come from a new source of energy or from spontaneous emissions of nearby atoms.
As a result, if atoms are concentrated in a particular medium and confined within a reflective charged cavity, emissions may become markedly amplified because of the interaction of the spontaneous emissions and the surrounding stimulated atoms.
Maiman was the first to demonstrate Einstein's theories of stimulated emission using visible light .
This led to his coining the now familiar acronym LASER, which stands for light amplification by stimulated emission of radiation.
Although light technically refers to the visible spectrum, all laser emissions, whether in the visible spectrum or not, are generally referred to as laser light.
The wavelength of the laser light is dependent on the medium of the reflective charged cavity.
In the original Maiman study, ruby crystal made up the medium, but since that time several other mediums, such as alexandrite, potassium titanyl phos- phate (KTP), and others, have been used to generate other wavelengths in medicine.
Laser light is monochromatic, coherent, and collimated.
Monochromaticity results from its consisting of one wavelength.
Coherence refers to light waves that travel in phase, both in time and space, and collimation relates to the parallel nature and lack of divergence of the light waves.
Use of lasers by physicians was revolutionized by the concept of selective photothermolysis.
Essentially, selective photothermolysis takes advantage of the heterogeneous absorption spectra of anatomic structures, particularly chromophores . such as melanin, hemoglobin, and water.
Laser light energy absorbed by a target chromophore is converted primarily to heat, destroying the chro- mophore itself and the surrounding cell.
The heat created at the site of the target chromophore may dissipate to surrounding cells, causing their destruction.
The preferential absorption of these structures for different wavelengths permits their targeted ablation, coagulation, or thermal damage with important preservation of surrounding structures.
Successful laser use, however, relies on more than wavelength and target.
Training, experience, and management of settings such as fluence, spot size, and pulse width are critical to safe and effective clinical outcomes.
Fluence is a measure of the laser's energy in joules per centimeter squared.
Spot size is clinically important since larger spot sizes may cause peripheral damage around small targets.
It also results in deeper penetration of the laser's effects, but with more scatter.
Pulse width is a measure of laser exposure time and is clinically relevant because of its relationship to ther- mal relaxation time (TRT).
For a given tissue target, TRT is the time required to lose half of its heat.
If the pulse width is longer than the TRT, less ablation and more surrounding damage in the form of coagulative necrosis occurs.
The objective of this chapter is to review common applications of lasers for the treatment of periorbital concerns.
These applications are broad and include resurfacing as well as the elimination of unwanted vascular and pigmented lesions.
Although some of the technologies dis- cussed in this chapter now serve as newer tools in traditional surgery, such as lasers in place of scal- pels for making incisions, the following discus- sion will primarily concentrate on the role of the technologies when employed as the primary therapeutic intervention.
Noninvasive and minimally invasive treatments for periorbital photodamage and rejuvenation have grown markedly in recent years.
Today's strategies commonly employ resurfacing lasers in addition to the related technology of radio frequency, which is discussed in a separate chapter.
These technologies have shown effectiveness improving skin laxity, rhytides, scars, and more recently premalignant changes of the skin, namely, actinic keratoses.
Originally, resurfacing lasers were nonfractional and fully ablative carbon dioxide lasers.
Though they delivered impressive results, they came with substantial risks, particularly for scarring and hypopigmentation.
With regard to periorbital treatments, scarring could further lead to ectropion, entropion, andepiphora.
Nonfractional, non-ablative laser alternatives followed, which were safer, but delivered less impressive results.
With the advent of fractional lasers, meaningful rejuvenation became achievable with far less risk.
The seminal concept of fractional laser delivery was first described in 2004 and has since been applied to non-ablative and ablative devices.
In essence, a fractional system delivers laser in a pixilated pattern, creating zones of injury surrounded by areas of unaffected skin.
Evidence for the efficacy and safety of these systems for periorbital treatment is now supported throughout the literature.
A retrospective study of 31 patients treated with a non-ablative fractional resurfacing laser to the upper and lower eyelids was evaluated for changes in eyelid tightening and eyelid aperture.
The laser consisted of a fractionated 1,550 nm erbium-doped fiber laser and was delivered over 3–7 treatment sessions.
All patients achieved eye- lid tightening, without any concerning adverse effects or downtime.
Just over half, specifically 55.9 %, also achieved increase in eyelid aperture.
Improvement in eyelid tightening and aperture can be seen in Fig. 9.1.
Ablative systems have also been formally evaluated around the eye.
A pro- spective study of 15 patients evaluated the effect of an ablative fractional carbon dioxide laser resurfacing treatment for laxity of the eyelid and periorbital skin.
Investigators found a 53.1 % improvement in rhytides and 42.0 % improvement in skin redundancy.
The only adverse effects reported in the study were two patients experiencing post-inflammatory hyperpigmentation that resolved after 3 months with hydroquinone and sunscreen.
While that study did not report any serious adverse effects, ectropion has been 9 reported following ablative fractional carbon dioxide resurfacing on the lower eyelids.
Overall, improvement in skin laxity and fine rhyt- ides with fractional carbon dioxide resurfacing can be seen in Fig. 9.2.
Surgeons should notably be aware of the value of these rejuvenating laser systems for the improvement of surgical scars, since their efficacy has been shown for a variety of scar types.
One study objectively and quantifiably examined the effect of ablative fractional carbon dioxide laser resurfacing of 19 atro- phic scars resulting from surgery or trauma.
Subjects were treated 3 times and followed for 6 months.
Subjective assessment of treated scars both by investigators and patients found improvement in skin texture.
These findings were confirmed by optical tomographic analysis that quantifiably demonstrated a 38.0 % mean reduc- tion of volume and 35.6 % mean reduction of scar depth.
In addition to the cosmetic enhancements achievable with these technologies, the medical value should not be underestimated.
Periorbital skin cancers commonly challenge ophthalmic and dermatologic surgeons.
Laser resurfacing has recently been shown to be valuable against premalignant changes, namely, actinic keratoses, which may serve as precursors to squamous cell carcinoma.
The mechanism of therapy is not yet understood, but the clinical response is evident, as in Fig. 9.3.
The authors of this chapter frequently employ a non-ablative fractional thulium 1927 nm laser as field treatment to reduce actinic keratoses over the face, including periorbital skin.
Several pigmented concerns of the periorbital skin respond to laser therapy.
Commonly used lasers for pigment include the ruby, alexandrite, diode, and neodymium-doped yttrium aluminum garnet (Nd:YAG), as their wavelengths can target melanin.
These lasers effectively treat periorbital lesions such as ephelides, lentigines,caf? au lait spots, nevi of Ota, congenital melanocytic nevi, and tattoos.
Ephelides, also known as freckles, arise on sun-exposed areas as well-defined circular or oval hyperpigmented macules of just a few millimeters.
While not precancerous themselves, high concentrations of ephelides on the face have been associated with genetic variations in the melanocortin 1 receptor (MC1R).
MC1R gene variants are also associated with fair skin, red hair, and melanoma and nonmelanoma skin cancer.
On pathology, an ephelide demonstrates normal epidermal configuration, but tends to have larger melanocytes in the basal layer with additional dendritic branching.
Lentigines may be characterized as simple or solar and may involve mucosal surfaces, unlike ephelides.
Simple lentigines arise at an earlier age and in any location in contrast to solar lentigines, which arise in adulthood and in sun- exposed areas.
Solar lentigines appear with increasing age and are a sign of photodamage.
Lentigines are well-defined circular or oval hyperpigmented macules and tend to be slightly darker and larger.
Lentigines display elongated rete ridges on pathology, with more numerous melanocytes than typical skin.
The solar lentigo has rete ridges that are more uniform and clubbed in appearance when compared to the rete ridges of a simple lentigo.
Lentigines are associated with several genetic syndromes, including LEOPARD, which also demonstrates ocular hypertelorism, and Peutz-Jeghers syndrome, which often includes periorbital and conjunctival lentigines.
Ephelides and lentigines are treated similarly and effectively with lasers targeting pigment.
A study of 34 pigmented lesions, including lentigines, was performed using the Q switched ruby laser at settings of 694 nm, 40 ns pulse duration, and 4.5 and/or 7.5 J/cm2.
Substantial clearing was appreciated in the lentigines with just one treatment at either fluence.
Long-term follow-up reveals efficacy in the majority of patients with lentigines.
In another study of 10 patients with solar lentigines treated once or twice with the Q switched ruby laser, 77 % demonstrated continued response at 10–21 months follow-up.
Photopigmentation was also found to be effectively treated with a 1927 nm non-ablative fractionated thulium laser.
Treatment produced moderate to marked improvement in overall appearance and pigmentation with high patient satisfaction.
The response to treatment was main- tained at 1 and 3 months' follow-up.
Congential melanocytic nevi are collections of melanocytes presenting as flat or raised blue- brown lesions, with or without excess hair, and with an increased risk of melanoma when giant.
Intervention depends on risk of progression to melanoma, cosmetic disfigurement of the lesion, and complexity of removal.
Laser therapy is helpful, but is also controversial based on questions of dysplastic effects of lasers on nevi and an increased risk of melanoma in some congenital nevi.
In addition, recurrence of lesion and color is not uncommon.
One strategy for treatment is laser ablation.
In a study of 13 patients with medium-sized congenital nevi, as much tis- sue as possible was excised, followed by erbium/ YAG ablation of residua. 83 % of patients were rated as having good to excellent results by the physician global assessment scale, and 77 % of patients reported good to excellent results at 4 months after treatment.
Ablative lasers have also been successful in dark skin types.
Another approach involves pigment-specific lasers.
In a study, 9 patients with medium-sized congenital nevi on the face or upper limbs were treated on average 9.6 times with a Q switched ruby laser.
After treatment, 0–20 % of the lesions' color remained.
However, 8 demonstrated slight repigmentation that responded to additional treatment.
One lesion returned to its original color within a month of its final treatment and therefore was simply excised.
The periorbital pigment concerns in Asian skin can also pose a challenge in terms of correcting nevi.
In one study, 7 small congenital nevi in 24 Korean patients were treated with Er:YAG laser followed by long-pulsed alexandrite laser at 1-month intervals.
At 8 weeks after the final treatment, all treated nevi showed complete removal of pigmentation with only one recur- rence of pigment after 6 months.
A nevus of Ota is a blue-brown patch that usu- ally arises in infancy, around puberty, or in pregnancy.
A favored site is the periorbital skin.
Often the ipsilateral sclera shows blue-brown hyperpig- mentation as well.
Less commonly, other components of the eye can be affected, and, importantly, glaucoma may be seen in 10 % of those affected with nevus of Ota.
The pathology explains the blue hue to the skin, namely, a higher than normal concentration of dermal melanocytes.
Laser treatment is effective against the cutaneous periorbital features of nevus of Ota and has been demonstrated with numerous lasers, including the alexandrite, ruby, and Nd:YAG.
Notably, these same laser measures are not safe as therapy for scleral involvement of the pigment.
One study composed of 602 Chinese nevus of Ota patients found benefits with each additional treatment using a Q switched alexandrite laser.
The study also found poorer response on the eyelid skin, which is referred to by some as the "panda sign."?
They recommend discounting the traditional Tanino classification of nevus of Ota, which is based on clinical distribution, and instead adopting a system based on response to laser treatment.
In addition to the endogenous pigment con- cerns discussed above, exogenous pigment in the form of tattoos can be found periorbitally.
The steady rise in the use of tattooed makeup around the eyes has been accompanied by, not surpris- ingly, an equally steady rise in the number of patients seeking periorbital tattoo removal.
Another study of 119 nevus of Ota patients demonstrated a marked periorbital under-response.
Common challenges to periorbital tattoo removal are preservation of hair follicles, as these tattoos are typically placed along the eyelash and eye- brow lines, and the avoidance of red, white, and beige/brown tattoo pigment since these pigments paradoxically darken with Q switched laser treatment.
The phenomenon of paradoxical darkening is generally attributed to the reduction of ferric oxide (Fe3+) to ferrous oxide (Fe2+) in the pigment.
Because of both challenges, periorbital tattoo removal is often accomplished with the careful use of a Q switched Nd:YAG and/or an ablative laser, particularly a fractionated carbon dioxide laser.
The Q switched Nd:YAG allows for small spot sizes that better target fine eyelid tattoos and lessen risk of adjacent follicle damage.
An effective response to the Q switched Nd:YAG can be seen in Fig. 9.4.
Ablative lasers are incorporated when red, white, and beige/ brown pigments are present, since these lasers do not cause paradoxical darkening.
These ablative lasers effectively clear tattoos via superficial tissue vaporization and subsequent transepidermal elimination of unwanted tattoo pigment.
Another study found that the short- pulse erbium-doped yttrium aluminum garnet (SP Er:YAG) laser was superior to the Q switched Nd:YAG laser and Q switched alexandrite laser for removing cosmetic tattoos of white, flesh- colored, and brown inks.
With the Q switched lasers, all three pigments darkened initially and then resolved gradually requiring up to 20, 18, and 10 sessions to remove white, flesh-colored, and brown tattoos, respectively.
Only six sessions were required with the SP Er:YAG laser.
Numerous vascular concerns of the periorbital skin are effectively treated with lasers, specifi- cally the pulsed dye laser (PDL) or KTP laser, as their wavelengths effectively target hemoglobin.
Common examples include superficial infantile hemangiomas, capillary vascular malformations, venous malformations, spider angiomas, cherry angiomas, telangiectasias, reticular veins, pyogenic granulomas, and purpura.
Hemangiomas are benign proliferative vascular tumors of endothelial tissue that affect 2–3 % of newborns and up to 10 % of infants within the first year.
The majority affect the head and neck, with 16 % of facial hemangiomas involving the eyelid.
They may present as superficial, deep, or compound (superficial and deep) and display many months of a proliferative phase followed by spontaneous involution at rates of about 10 % per year.
Particular attention must be paid to deep and compound hemangiomas around the eye because of potential for amblyopia from anisometropia, strabismus, and obstruction, all of which can be exacerbated during the hemangioma's proliferative phase.
Despite involution, residual cosmetically undesirable effects are common in any form of hemangioma.
Some report textural changes in up to 50 % of heman- giomas after involution.
Long-term residua from hemangiomas are more common when involution occurs over a longer period of time, and unfortunately no methods of identifying rate of involution presently exist.
The hemangioma and potential residua are recognized as caus- ing psychological strain in children and family members.
In general, laser therapy is not the ideal choice for deep hemangiomas because of their limited depth of penetration.
However, for super- ficial hemangiomas, PDL is an excellent treatment option as it is safe and effective and minimizes extent of proliferation and residua if treated early.
A report of 22 patients highlights the value of early treatment of superficial eyelid hemangiomas with the 595 nm PDL.
These patients underwent 2–14 treatments, initiating therapy at 5–28 weeks of age. 
77.3 % received an improvement rating of excellent (7–100 % improvement) and 36 % demonstrated complete clearance.
No scarring, atrophy, hypopigmentation, infections, or ulcerations occurred during the study period, with the only side effect being hyperpigmentation in two subjects.
Catalyzing its resolution and presumably limiting the proliferative phase likely contributed to the patients having no hemangioma residua.
This report is in contrast to historical reports that resulted in side effects, particularly atrophy and hypopigmentation, but these side effects are attributable to the use of higher fluences, smaller spot sizes, absence of epidermal cooling, and different PDL wavelengths.
Two examples of the efficacy of treating superfi- cial infantile hemangiomas with the PDL on the eyelid are shown in Figs. 9.5 and 9.6.
Vascular malformations are localized defects of vascular morphogenesis, which is in contrast to the neoplastic nature of hemangiomas.
They are categorized by their anomalous vessels (e g , capillary, venous, arterial, lymphatic) and by whether they have a fast (arterial) or slow flow.
Capillary vascular malformations (CVMs), often referred to as portwine stains, are observed in 0.03 % of the population.
Facial CVMs classically course along the distribution of trigeminal nerve sensory branches, namely, V1 (ophthalmic), V2 (maxillary), and V3 (mandibu- lar) branches.
When present, especially around the eye, risks of coincident glaucoma and choroi- dal vascular malformations exist, as do concerns for syndromic capillary venous malformations such as Sturge-Weber syndrome, von Hippel- Lindau syndrome, and Bonnet-Dechaume syndrome.
Over years and without treatment, CVMs typically develop vessel ectasia, which corresponds to the thickening, darkening, and cobblestoning appearance in aged lesions.
Exuberant overgrowth can potentially lead to visual field obstruction of the eye or airway depending on location.
The PDL is an important therapy in the treatment of periorbital CVMs and should be considered a treatment of choice for flat or mildly hypertrophic lesions.
Other technologies are helpful, however, as intense pulsed light and the alexandrite lasers, among others, have demonstrated efficacy and even advantage in some situations.
Early treatment has been shown to be safe and more effective.
Although anatomic differences do exist in terms of response to laser and light treat- ments, periorbital CVMs tend to respond well.
Efficacy from treatment with the 595 nm PDL can be appreciated in Fig. 9.7.
Venous malformations are examples of abnormal venous morphogenesis.
While sclerotherapy with or without surgical excision is an important therapeutic option to consider, relatively long- wavelength lasers that penetrate more deeply into cutaneous veins may serve as an effective requiring medically necessary surgical interventions generally expect and accept purpura.
However, purpura from a cosmetic procedure is often more frustrating for patients, as many of these patients demand little downtime or choose to have the procedure before important social events.
Whatever the cause of purpura, the PDL can effectively accelerate the resolution.
This was demonstrated in a study of ten adults with far more rapid resolution of purpura after treatment with a 595 nm PDL at spot size of 10 mm, fluence of 7.5 J/cm2, and pulse duration of 6 ms. 
When faced with appropriate candidates in our practice, the authors of this chapter often utilize a long-pulsed 532 or 1064 nm KTP laser with effective results.
Other collections of superficial vessels, including spider angiomas, cherry angiomas, telangiec- tasias, reticular veins, and pyogenic granulomas, also respond well to laser therapy.
Xanthelasma, or xanthelasma palpebrarum, is a soft, yellow papule and plaque involving the periorbital skin.
Histologically, the lesions consist of foamy, lipidladen histiocytes.
For some, xanthe- lasma is a sign of hyperlipidemia and for a small minority a sign of familial hypercholesterolemia.
While most patients with xanthelasma are nor- molipidemic, there is new evidence that normo- lipidemic patients with xanthelasma have similar cardiovascular risk to hyperlipidemic patients and should therefore be fully investigated in order to allow detection and early management of such risk.
Benefits of diet or medical therapy to treat xanthelasma are minimal, leaving most patients to rely on surgical or laser intervention if removal is desired.
Traditionally destructive methods, such as cryotherapy, chemical peeling, scalpel surgery, and electrosurgery, have not delivered sustained results and bring substantial risks for scar, dyspigmentation, ectropion, and eyelid asymmetry.
Interestingly, several types of lasers have shown efficacy against xanthelasma, including those traditionally used for pigment, blood ves- sels, and resurfacing.
Ablative lasers were studied initially.
One study of 23 patients with a cumulative total of 52 xanthelasma lesions assessed efficacy of an ultrapulse carbon dioxide (CO2) laser with a follow-up period of 10 months .
One treatment cleared all lesions, although three patients developed recurrence and dyspigmentation was found in 17 %.
Importantly, no scarring was reported.
Another study investigated the use of the erbium/YAG laser in 15 patients with 33 xanthelasma lesions.
With one treatment, the authors report complete clearance with- out dyspigmentation or scarring.
Recently, in a case series, 20 lesions were reported to be removed after a single ultrapulse CO2 laser (10,600 nm) treatment with only two patients developing recurrence during the follow- up period of 9 months.
Both these patients had been treated earlier by different modalities in the past.
Side effects included only post-inflammatory hyperpigmentation in two patients.
A recent study of twenty patients compared the efficacy of ablative fractional CO2 laser to super-pulsed CO2 laser and found that downtime was significantly shorter for lesions treated by fractional CO2 compared with those treated by super-pulsed CO2 laser.
Patient satisfaction was also significantly higher for lesions treated by fractional CO2 laser, especially flat plaques of xanthelasma that occupied large surface areas, compared with those treated by super-pulsed CO2.
For giant xanthelasma palpebrarum, twelve patients were treated with ultrapulsed CO2 in three to four ses- sions at 15-day intervals with complete resolu- tion and only one recurrence at 6 months.
Despite success with ablative lasers, non- ablative alternatives are desirable to minimize risks even further as well as to bypass the need for wounding, injection of local anesthetic, and downtime.
Some claim benefits of the 1064 nm Nd:YAG.
However, a controversial subsequent report of 37 patients with 76 lesions found both the 1064 and 532 nm Nd:YAG inef- fective, even with more aggressive parameters.
However, another non-ablative alterna- tive, specifically the PDL, has shown greater promise.
In a study of 20 patients with 38 lesions, patients underwent 5 treatments with the 585 nm PDL at 2–3-week intervals.
About two- thirds demonstrated greater than 50 % improvement and one quarter demonstrated greater than 75 % improvement.
A novel use of the non- ablative 1,450 nm diode laser for the treatment of xanthelasma was reported with 12 (75 %) of the 16 patients achieving moderate to marked improvement.
Additionally, there is evidence that supports non-ablative fractional resurfacing as a means to remedy xanthelasma.
In a report, a 52-year-old woman with 4 years of xanthelasma was treated with the 1550 nm erbium-doped fractional laser.
After 7 treatments at 4–11-week intervals, the patient achieved near total improvement.
Periorbital adnexal structures, both normal and abnormal, can be removed with success using lasers.
These structures include unwanted normal hair, trichiasis, syringomas, and hidrocystomas.
Laser hair removal has become a practical and often permanent means to remove unwanted hair.
Removal generally relies on pigment- specific lasers that target melanin in the hair follicle, although intense pulsed light devices are sometimes used with good response.
Hair follicle elimination and destruction are observed clinically and histologically following laser treatment.
Because pigment serves as the target chromophore, blonde or white hairs are not as responsive to treatment.
Abnormal hair may also be targeted, so long as it is still pigmented.
One report demonstrated efficacy of periorbital laser hair removal in ten patients with eyelid trichiasis after treatment with the ruby laser.
At settings of 3 J and a 3.5 mm spot size, the ruby laser completely eliminated eyelid trichiasis in 6 patients after 1–3 sessions.
Another three patients achieved a partial response.
The tenth patient was lost to follow-up.
Importantly, there were no reported complications and the procedure was well tolerated.
Abnormal tumors of the adnexa, most commonly syringomas and hidrocystomas, are also treatable with lasers.
Syringomas and hidrocystomas are benign adnexal neoplasms that may be solitary, multiple, or eruptive lesions.
Essentially, effective treatment involves lesion destruction.
This could be achieved using excision, electrodesiccation, or dermabrasion, among other destructive methods, but these come with risks for scarring and dyspigmentation.
The benefit of laser resurfacing as a means of lesion destruction is the minimization of complications in tandem with efficacy.
Periorbital syringomas are a therapeutic challenge as one must take into account the number of lesions and skin type to determine which treat- ment is most appropriate for each patient.
In one study using an ablative carbon dioxide laser, ten patients with multiple periorbital syringomas were treated at 5 W 0.2 s scan time, and 3 mm spot size.
Two to four passes over 1–4 treat- ment sessions were performed resulting in elimination of syringomas in all patients over a median follow-up period of 16 months.
Adverse effects included transient erythema lasting 6–12 weeks in all patients and hyperpigmentation in a patient with type IV skin that resolved over 8–12 weeks.
A trial of fractional ablative CO2 laser was performed to treat 35 patients with periorbital syrin- gomas with two sessions of fractional ablative CO2 laser at 1-month intervals.
Laser fluences were delivered in two or three passes over the lower eyelids, using a pulse energy of 100 mJ and a density of 100 spots/cm2.
Clinical improvement after 2 months of treatment showed the majority of patients having some mild to moderate improvement with only three patients having greater than 75 % clearance.
This could be attributed to the nature of fractional devices when treating these lesions.
When employing fractional devices, the pinpoint injury is not wide enough to cause complete lesional destruction.
Therefore, multiple passes and consecutive treatments are needed to effectively reduce lesions.
Erbium laser ablation has shown efficacy against syringomas.
In a study of 104 patients with a variety of skin lesions, some with syringomas, the erbium/ YAG system successfully eliminated the lesions using a 0.350 ms pulse duration and 0.1–1.7 J. 
The syringomas were successfully vaporized with minimal peripheral thermal damage and good to excellent cosmetic outcome.
Other more inventive methods have been published attempting to treat these periorbital syrin- gomas.
One study employed a multiple-drilling method using CO2 laser for 11 patients with syringomas.
Rather than resurfacing or cutting the skin, the clinicians created several relatively deep holes with the ablative laser into the targeted lesions.
This strategy was taken in an attempt to reach the deep components of the adnexal structures.
Eleven patients were treated, 10 with periorbital lesions and one with vulvar lesions, over one to four treatment sessions.
All patients were found to achieve good to excellent clinical responses.
No serious complications were noted.
Another group cleverly integrated temporary tattooing into the treatment of patients' syringomas.
In this report, multiple periorbital syringomas had their surface epithelium removed with a carbon dioxide laser.
Afterward, droplets of black ink were laid on the syringomas and iontophoresis was performed to create a tattoo in the lesions.
Finally, the Q switched alexandrite laser was applied to the lesion with complete disappearance of the syringomas at the 1-week follow-up evaluation.
The only significant adverse effect was hyperpigmentation lasting more than 2 months in a patient with type V skin.
Hidrocystomas have also been treated effectively with lasers.
Surprisingly, some report successful treatment of hidrocystomas using PDL.
This is unexpected since the PDL's target chromophore in a hidrocystoma is not known.
In one report, a 585 nm pulsed dye laser was used at fluences of 7.0–7.5 J cm2 over 6- to 8-week intervals.
After 4 treatments, there was near total resolution of the lesions.
Other reports, however, have not had such success with the PDL, raising questions about the real effectiveness of this strategy.
As would be predicted, however, hidrocystomas can be successfully treated using ablative lasers, such as the carbon dioxide laser.
Conceptually, this makes sense, as destruction of the cyst wall itself could lead to resolution of the lesion.
Despite success with lasers for these adnexal lesions, some groups still rely and endorse electrosurgery and excision.
Certainly, these options are successful in some circumstances, such as cases of giant histiocytomas, and are relatively more accessible to clinicians.
However, the authors of this chapter encourage clinicians not to choose these alternatives simply because laser systems may not be available in their immediate practice.
The use of lasers around the eyes raises a number of serious safety concerns.
Physicians must be fluent in these concerns and know the appropriate measures to protect themselves, their staff, and the patients.
Ocular damage from inadvertent laser exposure is always a risk with lasers.
Ocular melanin and vasculature are at particular risk when using lasers that target those chromophores.
The cornea and sclera are at particular risk when using resurfacing lasers because of the high water content of these structures.
In practice, if any reasonable risk exists to the eyes, everyone must have protective eyewear.
For the physician and staff, wraparound goggles should be worn that are rated as having an optical density (OD) of 4 or greater.
OD is calculated as log (1/T where T is the transmittance of light through the eyewear.
The particular OD for each pair of goggles differs based on wavelength and should be specified directly on the glasses.
One should not rely on the color of the protective goggles alone as a determinant of which pair to wear.
For patients, external or internal eye shields may be used.
When the laser is not in or directed at the immediate eyelid area, external opaque shields should be adequate.
Otherwise, internal shields are required.
When choosing internal eye shields, non-reflective metal shields should be used.
Internal plastic shields used by some surgeons during non-laser procedures do not adequately protect against most lasers, such as the carbon dioxide laser, since they may penetrate the shield.
Pretreatment with ophthalmic anesthetic drops may alleviate patient discomfort, and internal eye shields are generally well tolerated.
Despite available safety protocols, complications from periorbital laser use are reported, especially when the appropriate precautions are not met.
Complications include iris atrophy, posterior synechiae, iris pigment dispersion, anterior uveitis, ectropion, and blindness.
In most reports of these cases, the patient simply closed their eyes, covered their eyes with their own fingers, or inadequately covered the eyes with displaced external shields.
Several of the reports stem from laser hair removal of the lower aspect of the eyebrow.
Often external eye shields were displaced or removed to allow a bulky laser tip to treat the target area.
The laser's proximity in combination with Bell's phenomenon puts the patient's eyes at substantial risk when lasing the lower eyebrow.
Despite proper shielding, patients may still appreciate a flash of light concurrent with each periorbital laser pulse.
The pulse is thought to somehow trigger the retinal photoreceptors.
Safety concerns have been raised but the evi- dence does not show any harmful effects.
In one study, five patients undergoing diode laser hair removal for severe trichiasis were evaluated with pre- and posttreatment ophthalmic exams.
These exams included slitlamp, pupillary, fun- duscopic, and objective retinal electroretinogram studies.
Although 3 of the 5 patients experienced the sensation of flashing lights during treatment, there was no detectable change in any of the listed exams after treatment.
Aside from ophthalmic risks, lasers carry intrinsic safety concerns for fires and burns, par- ticularly when flammable materials such as paper drapes, alcohol, or supplied oxygen are used.
Therefore, flammable materials should be removed from the treatment area.
Additionally, when a sedated patient requires concentrated oxygen and/or nitrous oxygen, use of a laryngeal mask or endotracheal intubation limits release of the flammable gas.
Moist surgical drapes contribute to fire hazard safety and may even be wrapped around the portion of a laryngeal mask or endotracheal tube exiting the mouth.
Aerosolization of infectious agents, like viruses, and tissue particles are also concerns with laser treatment.
These risks are still being clarified, but appropriate ventilation, consistent vacuum use, gloves, and masks may assist in preventing consequences from these risks.
Lasers are invaluable for medical and cosmetic concerns around the eyes.
Rejuvenation and the elimination of pigment, vascular lesions, dark circles, xanthelasma, and adnexal tumors are all possible with the appropriate use of lasers.
With advances of existing technologies and the development of newer technologies on the horizon, periorbital concerns will continue to be more effectively and safely treated.
Platelet-rich plasma (PRP) is an autologous concentration of human platelets contained in a small volume of plasma.
Platelets can be likened to cell reservoirs that produce, store, and, finally, release numerous growth factors capable of stimulating the proliferation of stem cells and the replication of mesenchymal cells, fibroblasts, osteoblasts, and endothelial cells.
PRP is composed of several different growth factors, platelet-derived growth factor (PDGF), transforming growth factor a (TGF-a , vascular endothelial growth factor (VEGF), insulin-like growth factor 1 (IGF-1), epidermal growth factor (EGF), basic fibroblast growth factor (bFGF), transforming growth factor b1 (TGF-b1), and platelet-activating factor (PAF), that are released through degranulation and stimulate bone and soft tissue healing.
The secretion of these growth factors begins within 10 min after clotting, and more than 95 % of the presynthesized growth factors are secreted within 1 h (Fig. 10.1).
The addition of thrombin and calcium chloride activates platelets in PRP and induces the release of factors from alpha granules.
Adult mesenchymal stem cells, osteoblasts, fibroblasts, endothelial cells, and epidermal cells typically express cell membrane receptors to growth factors present in PRP so these ones bind their transmembrane receptors, inducing an activation of internal signal protein.
These processes cause cellular proliferation, matrix formation, osteoid production, collagen synthesis, etc.
It is important to underline that the PRP growth factors are not mutagenic because they don't enter in the cell or nucleus; therefore, PRP does not induce tumor formation.
The phases of the working procedure of platelet gel therapy are collection into a test tube, cell enrichment, activation, quality control test, and record.
PRP is obtained from a sample of patients’ blood drawn at the time of treatment. A 40 cc venous blood draw will yield 7–9 cc of PRP depending on the baseline platelet count of an individual, the device used, and the technique employed.
The blood draw occurs with the addition of an anticoagulant, such as acid citrate dextrose A (ACD), to prevent platelet activation prior to its use.
PRP is prepared by a process known as differential centrifugation.
In differential centrifugation, acceleration force is adjusted to sediment certain cellular constituents based on different specific gravity.
There are many ways of preparing PRP.
It can be prepared by the PRP method.
In the PRP method, an initial centrifugation to separate red blood cells (RBCs) is followed by a second centrifugation to concentrate platelets, which are suspended in the smallest final plasma volume.
WB (whole blood) is initially collected in tubes that contain anticoagulants.
The first spin step is performed at constant acceleration to separate RBCs from the remaining WB volume.
After the first spin step, the WB separates into three layers: an upper layer that contains mostly platelets and WBC, an intermediate thin layer that is known as the buffy coat and that is rich in WBCs, and a bottom layer that consists mostly of RBCs.
For the production of pure PRP (P PRP), upper layer and superficial buffy coat are transferred to an empty sterile tube.
For the production of leukocyte-rich PRP (L PRP), the entire layer of buffy coat and few RBCs are transferred.
The second spin step is then performed.
"g"?for second spin should be just adequate to aid in formation of soft pellets (erythrocyte-platelet) at the bottom of the tube.
The upper portion of the volume that is composed mostly of PPP (platelet-poor plasma) is removed.
Pellets are homogenized in 5 ml of plasma to create the PRP (platelet-rich plasma) high concentration of leukocytes.
Obtain WB by venipuncture in acid citrate dextrose (ACD) tubes.
Do not chill the blood at any time before or during platelet separation.
Centrifuge the blood using a “soft” spin.
Transfer the supernatant plasma containing platelets into another sterile tube (without anticoagulant).
Centrifuge tube at a higher speed (a hard spin) to obtain a platelet concentrate.
The lower 1/3 is PRP and upper 2/3 is platelet- poor plasma (PPP). 
At the bottom of the tube, platelet pellets are formed.
Remove PPP and suspend the platelet pellets in a minimum quantity of plasma (5–7 mL) by gently shaking the tube.
WB should be stored at 20–24 °C before centrifugation.
Centrifuge WB at a “high” speed.
Three layers are formed because of its density: the bottom layer consisting of RBCs, the middle layer consisting of platelets and WBCs, and the top PPP layer.
Remove supernatant plasma from the top of the container.
Transfer the buffy coat layer to another sterile tube.
Centrifuge at low speed to separate WBCs or use leukocyte filtration filter.
There are many PRP systems commercially mar- keted, which facilitate the preparation of ready to apply platelet-rich suspensions in a reproducible manner.
All operate on a small volume of drawn blood (20–60 mL) and on the principle of centrifugation.
These systems differ widely in their ability to collect and concentrate platelets depending on the method and time of its centrifugation.
As a result, suspensions of different concentrations of platelets and leukocytes are obtained.
Differences in the concentrations in platelets and WBCs influence the diversity of growth factor concentration.
It is difficult to assess which kit for PRP preparation is better and which is worse.
PRP devices can be usually divided into lower (2.5–3 times baseline concentration) and higher (5–9 times baseline concentration) systems.
The high-yielding devices include Biomet GPS II and III (platelet count 3–8×), Harvest SmartPrep 2 APC+ (4–6×), and Arteriocyte-Medtronic Magellan (3–7×).
The lower concentration sys- tems include Arthrex ACP (2–3×), Cascade PRP therapy (1–1.5×), and PRGF by Biotech Institute, Vitoria, Spain (2–3×), and Regen PRP (Regen Laboratory, Mollens, Switzerland).
In our experience, in order to prepare a gel that is a homogeneous mass of an adequate volume and yet remains manageable, the platelet concentra- tion needs to be 750,000–1,000,000/μL.
With this concentration of platelets, the gel forms in about 5–7 min.
Once the PRP has been obtained, a full blood count is performed, and on the basis of the platelet count, the PRP is diluted or concentrated under sterile conditions.
The production of autologous thrombin, used as the activator, involves the following steps: collec- tion of another blood sample (in ACD or sodium citrate), centrifugation of the sample for 10 min at 3000 rpm, collection of the plasma supernatant in a new test tube (under sterile conditions), addi- tion of 0.2 mL of calcium gluconate for every 1 mL of plasma, incubation at 37 °C for 15–30 min, collection of the supernatant containing the precursors of thrombin (under sterile conditions), and freezing and storage at 30 °C until needed.
In order to produce the gel, the platelet concentrate is placed in a sterile plate and then the activators are added, i.e., 1 mL of autologous thrombin and 1 mL of calcium gluconate for every 10 mL of PRP.
At this point, the mixture is left to incubate at room temperature.
If the coagulation process takes longer than expected, the preparation can be incubated for about 5 min at 37 °C to facilitate the reaction.
The platelet concentrate must be sterile.
The blood components must be prepared according to the principles of good manufacturing practices.
Each procedure must undergo quality control tests including determination of the volume, platelet count, count of contaminating white blood cells, and assay of fibrinogen levels.
The final product must carry a label indicating the surname and name of the patient, his or her date of birth, type of product, and the date of its preparation.
The patient's personal data and the characteristics of the component are also recorded in the related files stored in the archives of the transfusion center.
Complete physical examination and an analysis of the following clinical information should be obtained: general conditions of hygiene, lifestyle (smoking, alcohol), availability of family support, ability to walk, presence of occlusive arterial disease, past his- tory of deep vein thrombosis, and presence of pain while walking and/or at rest (standing and/or lying).
Thanks to its autogenous preparation, PRP is safe and therefore free from transmissible diseases (HIV, HBV, HCV, West Nile fever, Creutzfeldt- Jakob disease); so, it is well accepted by patients.
Since the gel is homemade, it is probably a cheaper source of growth factors than the indus- trially produced ones and also provides growth factors not otherwise available for clinical use.
The patients show good compliance toward the product and the procedures necessary for  its production.
Angiogenic; antibacterial properties; stimulating the formation of connective and epithelial tissue; osteogenesis stimulators; security (219/05 Law, DD. MM. 3/3/05); nontoxic tissue injury; can be autologous; quick and easy preparation.
Patient thrombocytopenic; vascular access compromise; septic patient; very large lesion; patient too small; patient positive for HBV, HCV, and HIV; emergencies. 
There are two types of contra- indications to treatment with platelet gel: (1) those potentially harmful to the patient, such as hemodynamic instability, pregnancy, malignan- cies, infections, and/or osteomyelitis at the site of application, and (2) those making the autologous product difficult to obtain or of poor quality, such as thrombocytopenia, platelet disorders, and treatment with drugs affecting platelet function and/or coagulation (e.g., oral anticoagulants, heparin, nonsteroidal anti-inflammatory drugs).
After centrifugation, the platelet and fibrin com- ponent of the blood (the top layer) is extracted and reinjected into the area of concern (Fig. 10.2).
In dermatology and cosmetic medicine, PRP has been used to treat.
Venous and arterial leg ulcers.
Diabetic foot ulcers.
Pressure ulcers (bedsores).
Skin graft donor sites.
First- and second-degree thermal burns.
Superficial injuries, cuts, abrasions, and surgical wounds.
Hair loss disorders – PRP has been shown to reinvigorate dormant hair follicles and stimu- late new hair growth.
Posttraumatic scars – PRP combined with centrifuged fat tissue and fractional laser resurfacing improve cosmetic appearance of scars.
Facial rejuvenation– PRP injections can treat wrinkles, photodamage, and discoloration in conjunction together with other treatment modalities.
In the chrono-aging processes, dermal fibro- blasts play a key role, thanks to their interactions with keratinocytes, adipocytes, and mast cells.
Besides, they are also the source of extracellular matrix, proteins, glycoproteins, adhesive molecules, and various cytokines and increase the activation of the fibroblast-keratinocyte-endothelium axis, maintaining skin integrity.
PRP, increasing the length of the dermo- epidermal junction, the amount of collagen, and the number of fibroblasts, can be considered an effective therapy for skin rejuvenation: PRP in fact induces keratinocyte and fibroblast prolifera- tion and typically collagen production amplifica- tion, increasing dermal elasticity.
It is also useful for tightening around the eyes (for thin crepe-like skin and fine lines) (Fig. 10.3) and in the areas as cheeks and midface, thinning skin on the neck, jawline and submalar regions, back of hands,d?collet?, and others (e g , knees, elbows, and upper arms, as well as for post- pregnancy skin laxity).
Besides platelet-rich plasma (PRP) can be used for enhancing, reshaping, and volumizing the lips; it is often used to improve very fine lines around the lips, helping to restore skin hydration and elasticity.
A topical anesthetic or a nerve block will be used for pain management.
There are some additional effects using PRP combined with other aesthetic procedures as fractional laser or lipostructure.
PRP in associa- tion with fractional laser increased skin elasticity and decreased the erythema index; keratinocyte and fibroblast proliferation and collagen produc- tion can explain these capacities.
The use of PRP mixed with purified fat graft has several advantages: PRP increased fat cell survival rate and stem cell differentiation.
This combination has been used for recon- structing the three-dimensional projection of the face contour in patients affected by facial aging characterized by atrophy of subcutaneous and soft tissue with loss of volume and elasticity, restoring the superficial density of facial tissue.
PRP is an easily accessible source of growth factors for supporting bone and soft tissue healing.
PRP can be enriched with the presence of a fibrin matrix (PRFM): fibrin matrices, in fact, enhanced the delivery of platelet growth factors.
It consists of weak thrombin concentrations which entail equilateral junctions.
These connected junctions permit the formation of a fine and flexible fibrin network capable of supporting cytokines and cellular migration that occurs.
This results in an increase in the half-life of these cytokines as their release and use will occur at the time of initial scarring matrix remodeling.
Thus, the cytokines are made available for a mandatory period required by the cells to initiate the healing.
Fibrin meshwork in PRF differs from that in PRP.
In PRP, there are bilateral junctions resulting in a rigid network that does not honor the cytokine enmeshment and cellular migration.
The increased thrombin required for rapid setting of the PRP leads to a rigid polymerized material.
PRFM has been proposed and effectively used in several facial plastic surgery settings: as PRFM can induce dermal augmentation, it can be used for treatment of dermal and subdermal tissues of the nasolabial folds, acne scars, and lip augmentation.
PRFM can be mixed with autologous fat ex vivo and the composite graft injected.
The fibrin matrix associated with platelet-released growth factors should promote better graft take.
This technique has been used for lip augmentation, with good results.
Barbed suture lifting is a minimally invasive surgical technique for facial rejuvenation.
Aging of the face and neck results in ptosis of soft tissues and the appearance of more prominent facial lines.
For correction of these changes, surgeons are devising more procedures with fewer inci- sions and shorter postoperative recovery periods.
Many of these procedures use absorbable and nonabsorbable sutures in the dermis and subcutis to lift lax skin.
Limitations of these implants have included the protrusion of sutures through the skin and asymmetry of the cosmetic effect, often requiring correction with additional sutures, and limited durability of effects.
The treatment by absorbable threads, known as balance lift or nonsurgical face lifting, is an innovative technique used in aesthetic medicine that is useful for supporting and stretching the face and body tissues.
The suspension threads are used to improve eyelid ptosis and the perior- bicular groove, with a significant improvement of the shape of the eyebrows.
They are used also to reduce ptosis of the neck, and middle and lower face.
Aging induces a scaffolding of dermal facial skin as well as a progressively decreasing fat compo- nent, attributable to thinner connective tissue and collapse of elastic fibers.
The affected areas generally include the cheeks, eyebrows, mandibular area, and neck.
Dermatochalasis of the facial and neck soft tissues, including the superficial muscular aponeurotic system (SMAS) and the muscular tissue, is the cause of the distinctive aging signs on the face.
The profile of the mandibular margin becomes unclear, displaying its deterioration (descent of the aging jaw line); the forehead has horizontal wrinkles where other vertical ones are added to the glabellar area; the zygomatic malar region (middle face) displays a downward trend; a lachrymal furrow appears and the nasobuccal and buccomandibular areas deepen; the skin of the eyelid becomes flabby and protrudes in correspondence with the lower eyelid, owing to production of adipose bubbles; and finally, a plasmatic parcel and cutaneous flabbiness appear on the neck.
The facelift to correct facial aging has evolved into an elaborate and complicated procedure requiring a lengthy recovery time.
The recent introduction of absorbable barbed sutures producing a lifting action for this type of aging offers a good alternative to more invasive procedures.
The plugs present on the surface of the wires allow the combination with other nonsurgical rejuvenation procedures, such as botulinum toxin or substances with a transient and volumizing filler effect.
The implant can be performed in an outpatient setting under local anesthesia.
The surgeon first establishes the degree and direction of the desired tightening.
This determines the course and number of sutures that have to be placed to achieve the best result.
Infiltration of local anes- thesia is limited to these lines and the insertion points of the straight needle.
For lifting of the brow and middle and lower face, 3- to 4-mm incisions for insertion of the straight needle are made posterior to the frontal and temporal hair- line.
For lifting of the neck, incisions are made posterior to the sternocleidomastoid muscle of the lateral neck.
To place an individual thread, the surgeon guides the straight needle through the incision and into the subcutaneous plane.
For some anatomic locations it is advantageous to bend the needle to more easily allow it to follow the dynamic face lines.
The needle is advanced in this plane in a zig-zag movement along the marked trajectory.
Once anchored, this zig-zag placement of the suture limits retrograde motion along the suture and results in an implanted suture that is longer than the drawn trajectory.
This maximizes the number of barbs in the subcutis and theoretically provides greater stability of the translocated skin.
Movement of the needle and suture through the subcutis is generally well tolerated by patients.
If the straight needle moves superficially to this plane, it is immediately apparent as linear dimpling of the overlying skin.
If the needle enters into the deep subcutis or approaches the muscle fascia or periosteum, the patient will report the sensation of pain or pressure.
At any point, the straight needle may be partially or completely removed and reposi- tioned.
The straight needle exits the skin inferior to the eyebrow or near the medial face or neck.
It is then cut from the thread after pulling the attached suture through the skin.
This leaves the barbed portion of the thread buried in the subcu- tis with the free ends extending from the proximal insertion point and the distal medial face exit point.
The curved needle on the proximal end of the suture may then be used to anchor the suture near its insertion to the underlying fascia or periosteum.
A 3- to 4-mm incision, 1–2 cm posterior to the insertion points, serves as an exit point for the curved needle after deep suturing to the fascia or periosteum.
Greater security of this anchor point is achieved by tying this suture at its proximal end with a paired suture running a similar parallel course in the skin.
The resulting knot can be seated in this posterior incision by gentle traction on the distal ends of the paired sutures.
When all planned sutures have been placed and anchored, the patient returns to the seated position.
Holding the distal end of the suture protruding from the medial face, brow, or neck with one hand, the surgeon uses the other hand to push the lax skin overlying the suture toward the anchoring point.
The unidirectional barbs catch on the fibrous septae of the subcutis, preventing retrograde movement.
Together, the surgeon and patient decide the degree of tight- ening along any given suture.
The distal end of the suture extending from the medial face, brow, or neck is cut at its exit point and retracts under the skin.
Incisions used for insertion of the straight needle and anchoring heal rapidly by secondary intention.
The translocation of the skin along the suture can cause lax skin folds in the hairline and lateral neck that can be quickly and completely remodeled or redistributed to the scalp and neck in several days or weeks.
Mild complications such as swelling, bruising, and subjective feelings of "tightness"?usually resolve within 1–3 weeks.
Transient neuropathy of the greater auricular nerve has occurred in several patients when using the sternocleido- mastoid muscle fascia as an anchoring point on the lateral neck.
Because this technique may be released with intense pressure, patients must initially avoid strong exercise or movements that could dislodge the tightened skin from the hundreds of barbs along the sutures.
Non-peer- reviewed data from the manufacturer demon- strate that in laboratory rats these sutures develop a fibrous capsule that becomes well integrated into the dermis and subcutaneous tissue over several months.
A similar process in human skin can lead to a long-lasting cosmetic effect.
The actual long-term durability of the tightening effects of these sutures is unknown.
Early adopters of this procedure have demon- strated maintenance of cosmetic effects at 6 months.
The technique will lead to a true firming of the skin affected by laxity if the thread is placed along the traction lines.
The threads are located in a direction perpendicular or at an obtuse angle with respect to the traction lines of the skin, to obtain a tightened effect.
The number of wires implanted may vary according to the material of the thread used and the form chosen by the operator.
A worldwide variety of barbs, with differences in costs and types of materials, are available.
Here we list our experi- ence of the main ones: 1. PDO – Polydioxanone 2. Polylactic acid – Poly(L-lactide)-ε- caprolactone copolymer.
The application of suspension threads can be used at the face level in the following cases: Loose and heavier jaw line Soft tissues descending to cheekbones and cheeks level, with a consequent loss of natural volume and fatigue of the face Presence of pronounced furrows and wrinkles; Breakdown of the skin in the neck area, mainly in the central zone Lifting the eyebrow arch.
Suspension threads can be used at the body level in the following cases: Tissue laxity in the inner arms, Firming the abdomen, Skin breakdown inside the thigh, Sagging skin in the upper area of the knees; Loss of form at the navel due to partial cover- age by the abdominal skin, Mild ptosis of the breasts, Soft ptosis of the buttocks.
PDO (polydioxanone) is a polymer of polylactic caprolactone, a bioabsorbable material and antimicrobial already used in surgery and biocompatible with the dermis.
The indication is for sagging and relaxation, or when preventive treatment is needed.
Biocompatible, antimicrobial, Absorption by hydrolytic action within 6–8 months, suitable for medical use and surgery This material is already used in general and specialized surgery as sutures; it is slowly absorb- able, biocompatible, and antimicrobial.
The threads are implanted in the skin.
In the preorbicular area, they are inserted in a direction along the front lines and/or along the brow for lifting, to improve periocular dermatochalasis.
They are inserted obliquely by hand at the side of the eye down to within 1 cm of the medial canthus.
At the level of the labia, they are inserted to follow along the labia for lip contouring, along the vermilion for improving the firmness of the lips, and at the corners of the lips in a direction perpendicular to the wrinkles to enhance the face expression.
Generally speaking, the shift of the skin tissue through the thread represents a fundamental mechanism in cutaneous tissue repair processes and tissue regeneration.
The cell is stimulated by the presence of several transduction systems mechanically placed at the level of the cell membrane, the best known of which is integrin.
The mechanical stimulus exerted on the outer extracellular matrix determines via integrin the intracellular biological changes that can activate specific genes.
The fibroblasts are particularly sensitive to mechanical stimuli, and when subjected to mechanical stimulation they activate genes for the production of collagen and other proteins.
The biostimulant PDO threads implanted in the dermis are able to stimulate the fibroblasts, activating an increased synthesis of collagen.
The PDO absorbable threads (thickness 0.05–0:19 mm and length 3–16 cm) are positioned inside a needle (26, 29, or 30 gauge).
After a preliminary visit the clini- cian must accurately mark with a dermographic pen the face areas, neck, and d?collet?  that must be treated, according to the situation and the demands of the patient.
Insertion of the needles (29–31 gauge) with PDO threads can then take place without trauma and without any need for local anesthesia.
The needle tips used in this technique are shaped to reduce pain.
The surface of the needle also has a double coating that makes it homogeneous.
The number of threads released into tissues during treatment varies depending on several factors such as patient's age, degree of skin aging, degree of failure of the tissues, and extent of the area to be treated.
Usually 20–60 threads are used.
The needle is inserted fully and advanced, leaving a thread inserted and implanted in the tissues.
Anesthesia is not necessary; cooling the treated area with dry ice will be sufficient.
In the case of more than 10 implants, we recommended a bandage (e.g., Tensoplast) to restrict movement in the first 24 h postoperatively.
Rapid lifting of the treated area is visible immediately after the end of treatment.
The patient can restart daily activities at the end of treatment.
The thread is inserted in directions orthogonal to each other to form a grid that counteracts the gravita- tional vectors that lead to relaxation, behaving like a foreign body coated by collagen, which forms the true support structure desired.
PDO has been used for decades in surgery for the execution of absorbable sutures.
There is a marked improvement after 1–2 months. 
The skin tone also improves, as does the appear- ance of wrinkles.
An innovative technique in the field of aesthetic medicine, with very little pain, which improves the skin tone and its aspect.
After approximately 6–8 months, the threads in the PDO have been completely reabsorbed by hydrolytic action in a totally natural and  harmless  manner,  but  the  biostimulation and lifting effect continues the mechanical support and remains stable for 6–8 months, and generates a significant stimulation of endoge- nous cells whose benefits will last much longer.
Edema and erythema for 24–48 h after treatment, hematomas at implant, hardening small transients.
High sensitivity in the treated area, which usually disappears within 1–3 days following treatment.
In extreme cases the threads can be extracted, within 20 days after treatment, by a small incision and extraction using forceps.
Skin infections, recurrent herpes simplex (requires antiviral prophylaxis), collagen, predisposition to form keloids, pregnancy, lactation, coagulation disorders, exagger- ated expectations of cosmetic surgery.
The Screw is the latest innovation in the field of non-invasive minilifting of the face and body.
Thanks to its special shape the microthread support "screw"?produces greater vascularity with improved macrophage response that makes more effective the biostimulative response, especially for fibroblasts, which become more "active"?and more"flexible"?in the healing process.
The combination between the microthread, a PDO monofilament, and the screw grants significant synergy, which results in better toning action on the connective tissue, as the thread screw stimulates mostly the “cascade effect” of the systems of healing and restructuring  tissue.
Effective in activating the process of restructur- ing the dermoepidermal junction, the fine thread contour technique reshapes the body.
Barbed Threads Barbed bidirectional threads are useful to create a "mini-invasive suspension surgery"?in a treatment that allows lifting and repositioning of tis- sues with greater modeling.
The barbed bidirectional threads bring tissue to the opposite direction through bidirectional pinning, with minimum risk of displacement or migration, thus facilitating the anchorage in the tissue and enhancing the lifting effect.
Multidirectional threads are useful for all ptosis tissues located at the lateral third of the eyebrow, and all applicants requiring a traditional facelift and surgical and aesthetic facial improvement, as well as fillers and lipofilling. 
Nowadays micropipes can be used as an alternative to microneedles and offer greater advantages.
The micropipes must be used carefully, and sometimes have been used to make microtunnels with a fine pipe under local anesthesia or products containing connective micrografts.
Micropipes are recommended only for selected areas and methods.
The micropipe Cogs multidi- rectional (21 gauge × 60–90 mm) are typically indicated for the frontal area as well as the neck.
The lower eyelid (PDO 30 gauge × 27 mm) is very useful with this micropipe, allowing the placement of microthreads in the lower area of the eyelid.
The Short pipe for nose and neck (19 gauge × 40 mm 4D thread) is used for correction of the silhouette of the nose in selected cases, by pulling on the tip and reducing mild disharmonies, creating an interesting volumetric effect.
The 4D screw micropipes allow thread cutting in four dimensions.
In this way they are able to optimize the lifting effect and minimize the damage to tissues.
These threads are biocompatible and fully resorbable.
Polylactic acid and caprolactone also have a revitalizing action and are reported to be long lasting.
Professor Marlen Sulamanidze, a specialist in reconstructive plastic surgery and aesthetics, is recognized by the worldwide medical community as the inventor in 1995 of the first wire for lifting tissue ptosis.
In 2008 he launched on the world market the three types of resorbable wire Nano, Excellence, and Light Lift.
He invented a noninvasive lifting technique using permanent traction threads in polypropylene.
The technique has evolved over 8 years of experience with non- resorbable threads.
Patent Pending and Safety 16 patents over the years.
This is a surgical outpatient treatment carried out with or without local anesthesia according to the medical evaluation of the case.
Threads are inserted subcutaneously through a microhole, along precise lines of skin tension, by means of a thin needle or a needle-pipe blunt tip (tip shape reduces the local trauma), exerting a slight traction to lift the relaxed tissues.
The threads adhere to the skin owing to the presence of special anchors (plugs).
The effect is achieved because the lift-thread insertion follows geometric traction where nothing is left to chance, and in fact the treatment requires, in addition to a certain manual skill, a flawless knowledge of anatomy.
The caprolactone allows the gradual absorption and uniformity of polylactic acid while ensur- ing the mechanical strength and the elasticity of the thread in time; also, the capacity of bios- timulation is associated in time with the effect of traction of these threads in restoring luminosity and color.
The microcirculation capillaries of the proximal threads increase in number compared with the peripheral circulation, and the vessel lumen is more dilated.
Studies have shown that for the entire period after insertion the vessels remain dilated, with constant hyperemia maintaining a trophism of the treated area with formation of new collagen, fibrin, and elastin.
The fibroblasts of the fibrotic tissue created with the placing of the threads are functionally active, observable from increased spread of chromatin in the nucleus and cytoplasm.
The connective tissue layer in which the thread is implanted, treated with the blue dye toluidine, contains an increased number of mast cells, and at the same time show an increased concentration in the vascular channels of the microcirculation.
The granules of mast cells contain hyaluronic acid, a polysaccharide complex, which is a structural component of the papillary dermis granular layer of the epidermis and is also apparent in the superficial vessels of the skin.
There is evidence that reduction of the amount of hyaluronic acid affects the immune status of the skin, and that its inner dermal injec- tion improves the structure of the skin.
The reabsorption process begins after about 180 days after implantation and is completed after more than a year.
The unrolling of sutures takes placed within 3 weeks from the implant, since the thread is composed of two strands.
The process of opening the thread by imbibition in the dermal tissue leads to an affection tensor characteristic unique to this type of thread, i.e., ready-to-use,"pre-filled"?braided and sutured, 4 cm long, 23 gauge.
Anchoring in the medium/deep dermis, on both the face and body, are soon remarkable. 
The results are apparent after 3 months with a progressive improvement in the following months.
This thread is composed of a needle-pipe pre- loaded suture, characterized by a multidirectional microanchor of 12 cm with an indication for treatment of ptotic skin areas more pronounced in various areas of the face.
It is best indicated for the lifting of the cheekbone.
Rubber thread with a double needle without a lifting skin retraction with a microanchored convergence.
Not take food, hot liquids, and solids for 3 days.
Avoid alcohol for 2–3 weeks.
Limit mimicry activity for 7 days.
Limit gym, sauna, swimming, and exposure to direct sunlight for 3–5 weeks.
Use antibiotics for 3–5 days, in the case of lowered immunity or if using more packs of sutures for surgery.
Sleep supine or side to side with a pillow in the case of face, neck, and abdomen surgery.
Hypertension II and III.
Inflammation or cancer in the target area.
Propensity of keloids and hypertrophy.
None of the complications presented herein has generated the appearance of long-term functional disorders and no visible and permanent effects.
Moreover, none of the complications presented required the need for prolonged treatment.
Innovations in operative techniques generally contribute to enhanced results, greater patient happiness, and a decrease in operative morbidity.
The immediate effect is the lifting of the tissue, owing to the mechani- cal action produced by the thread, which con- trasts with the falling of the area treated (Figs. 11.3a–c and 11.4).
This is possible because of the arrangement of thethreads' barbs, disposed in two directions (divergent and opposite), compared with the middle point of the thread.
Once positioned in the subcutaneous tissue, the threads will continue to exert their sustaining action on the tissues.
Therefore, it is possible to claim that the lifting effect is guaranteed and fortified by the cutaneous reaction (fibrosis) that appears along the length of the thread, which remains effective and steady even when the thread is completely reabsorbed (after about a year).
The reabsorption occurs as a result of the action of the histiocytic-reticule system, which concretizes a selective hydrolysis action of the reabsorbable thread from the periphery toward the center.
The most impor- tant limits of this technique are that it is indicated for moderate cutaneous descent.
For overabundant tissue, the prescription remains traditional lifting.
In cases of more advanced and evident signs of aging, patients must opt for traditional surgical options that are more invasive and direct.
Therefore, strict selection criteria must be adopted when selecting patients to be treated with this technique.
Reabsorbable Happy Lift Revitalizing thread constitutes an efficient and safe procedure of mid-face lifting and rejuvenation of the supe- rior cervical region of the face and neck.
It is also possible to combine this with other methods that allow optimization of the facial rejuvenating effect, such as botulinum toxin, fillers, chemical peelers, photorejuvenation with a pulsed light, and lip filling.
These threading procedures do not require general anesthesia, are virtually free of bleeding or pain, and do not produce intra- and postopera- tional scars that are visible on the skin, nor do they require long postoperative recovery times.
The technique is practicable in day surgery, and the patient may immediately return everyday activities shortly following the procedure.
Hyaluronic acid dermal fillers have become a mainstay for soft-tissue augmentation while providing numerous advances in the area of cosmetic surgery.
HA fillers are primarily used for the treatment of facial changes associated with aging, which include thinning of the epidermis, loss of skin elasticity, subcutaneous fat and bony changes, and atrophy of muscle, all of which can result in a loss of volume.
HA fillers are longer lasting and less immunogenic, making them the most common of the tem- porary fillers on the market.
The vast majority of treatments are efficacious and patient satisfaction is generally high.
Despite having a low overall side effect profile, early and delayed complications, ranging from minor to severe, have been reported following HA filler injection.
The most common potential sequelae following HA filler injection result from the injection site reactions and include ecchymosis, edema, erythema, and pain.
Other less common adverse events include nodules (both inflammatory and noninflammatory), hyperpigmentation, telangiectasia, and dyschromia.
More serious adverse events, while rare, include vascular compromise that can result in tissue necrosis and acute vision loss.
Some degree of pain is expected with needle puncture, with thicker gauge needles expected to cause a greater degree of pain due to more extensive tissue injury.
Where the injection is placed can also determine how much pain is experienced, as more sensitive areas tend to be more painful such as injections of the lip, injections of the periocular skin, and perioral injections.
Injection site pain is minimized by the formulation of hyaluronic acid fillers to include lidocaine.
If the HA filler does not include an anesthetic, a topical anesthetic ointment can be applied before treatment.
The topical anesthetic should be applied 20–30 min before injection.
In addition, cold compresses can be applied just before injection to numb the area to diminish pain sensation.
Erythema (i.e., redness) is frequently observed immediately after injection with HA fillers.
Erythema is a local effect due to puncture trauma and associated inflammation.
Erythema is best managed by applying cold compresses postinjection for 5–10 min to reduce inflammation.
After the procedure, patients can be advised to use ice packs at home every few hours on the day of the injection.
Caution must be emphasized to avoid prolonged ice pack usage to reduce the risk of cold injury to the skin.
In addition, vitamin K cream can be useful in accelerating resolution of erythema.
Furthermore, redness can be reduced using a prudent injection technique that will minimize the number of skin punctures during the injection process, thus limiting trauma to the injected area.
Such techniques include placing the filler with a serial injection, using the fanning technique, or using linear tunneling with threading.
The erythema will generally last for a few hours and may persist for a few days.
If the erythema persists longer than the expected duration of a few days, then a hypersensitivity reaction is suspected.
Effective treatments for this hypersensitivity reaction include oral tetracycline or isotretinoin.
For persistent erythema, a medium-strength topical steroid is warranted.
High-potency steroids should not be used as they increase the risk of atrophy and telangiectasia.
Of note, patients with rosacea have a higher risk of developing postinjection erythema and should be warned of this risk prior to commencing treatment.
While not as common as redness and swelling, ecchymosis (bruising) is an adverse effect that can occur in patients after receiving a hyaluronic acid filler injection.
Bruising is caused by the perforation of vessels during filler injection, typi- cally the dermal veins.
Additionally, pressure of the injected material can cause injury to proximal blood vessels, causing bruising.
Common locations for bruising are the upper third of the naso- labial fold, the upper lip, the lateral edge of the lower lip, and the perioral region.
In particular, injections to the lower eyelid often result in bruise formation.
Bruising is frequently observed after injection into the dermal and immediate subdermal planes using fanning and threading techniques.
Bruising may develop soon after the injection, but often is delayed, most notably in those patients who are on anticoagulation therapy.
Therefore, patients should be counseled to discontinue any unnecessary anticoagulation medications or products 1 week prior to treatment to potentially reduce the severity of bruising.
The blood-thinning products to be avoided include aspirin, non- steroidal anti-inflammatory drugs (NSAIDs), warfarin, clopidogrel, dipyridamole, garlic tablets, ginkgo biloba, ginseng, fish oil, St.John's wort, and vitamin E supplements.
Bruising can be mitigated by applying cold compresses and firm pressure to the affected area before and after the procedure.
Furthermore, vitamin K cream can be useful in accelerating the healing of bruising, just as it is for erythema.
Laser treatment may also accelerate the elimination of a bruise.
Furthermore, postinjection bruising may be limited in extent by the incorporation of epinephrine in the filler which causes vasoconstriction and dampens the activity of eosinophils that can cause bruising.
Other recommendations to limit bruising include using the smallest gauge needle possible that can effectively deliver the filler, delivering small aliquots of product utilizing a slow injection technique, using the depot tech- nique at the preperiosteal level and limiting the number of transcutaneous puncture sites.
The use of blunt cannulas may also limit bruising.
The typical time course for resolution of a bruise is approximately 1 week and can range from 5 to 10 days.
Concerned patients should be instructed that the bruise may progress to a darker discoloration for 1–3 days posttreatment before it slowly resolves over 5–10 days.
Patients should be made aware that the development of a bruise will not interfere with the treatment outcome.?
Telangiectasia is an abnormal aggregation of arterioles, capillaries, or venules.
This neovascularization process is an adverse outcome that may occur at the HA filler injection sight.
The proliferation of these vessels is caused by trauma to the tissue due to the product causing tissue expansion.
Telangiectasia can appear within days or weeks following the procedure.
Left untreated, they typically resolve within 3–12 months.
Telangiectasias following dermal filler injection have been successfully treated using a 532-nm laser (532 nm KTP and the 532 nm diode copper vapor) or 1,064 nm laser.
Other forms of effective laser therapy for telangiectasias include intense pulsed light (IPL) and 585 nm pulsed dye laser.
In addition to laser treatment, telangiectasias can also be treated with hyaluronidase.?
The trauma induced by dermal filler procedures, including HA dermal injection, can cause post-inflammatory hyperpigmentation.
Post- inflammatory hyperpigmentation is more common in patients of color as darker colored skin has a greater tendency to hyperpigment following needle trauma.
Hyperpigmentation is particularly seen in individuals with Fitzpatrick skin types IV, V and VI.
Treatment for persistent post-inflammatory hyperpigmentation that occurs following HA dermal filler injection should include the application of topical bleaching agents such as topical hydroquinone (2-8 %) and Retin-A (tretinoin) .
In addition to bleaching agents, consistent daily sunscreen usage must be adhered to.
If the hyperpigmentation is resistant to this first line of treatment, chemical peels can be used .
If these treatments are unsuccessful, then laser treatment should be initiated.
Laser choice will depend on skin type.
IPL is effective in the treatment of Fitzpatrick skin types I–IV, while the Nd:YAG 1,064 nm laser has been effective for treating darker skin tones.
Bluish discoloration under the skin is an adverse reaction seen particularly with hyaluronic acid fillers, usually the result of an improper injection technique whereby the filler in injected too super- ficially (or migrates superficially).
Note that the color observed in patients has also been described as a grayish tint.
The dermal hue change has been explained by the Tyndall effect, in which an optical phenomenon occurs as light is scattered as it passes through colloidal particles in solution.
Since blue light, with a wave- length of 400 nm, scatters more readily than longer wavelengths, this is the predominant color seen when HA filler particles scatter light.
While the Tyndall effect is the commonly accepted explanation for this particular dermal hue change, alternative explanations have been proposed to explain this discoloration.
While the Tyndall effect is known to be caused by a variety of hyal- uronic acid derivatives, BeloteroR, a monophasic, highly cross-linked hyaluronic acid dermal filler, is reported not to cause the Tyndall effect.
This cause of dyschromia can usually be avoided if the product is injected at the correct dermal level.
The more superficial the placement of the HA filler material, the longer the discolor- ation may last.
To correct Tyndall effect discolor- ation due to HA, hyaluronidase is used.
In addition, if necessary, surgical excision can be employed using a surgical scalpel (#11 blade) and then extruding the unwanted filler material.
The Nd:YAG 1064 nm laser has also been used successfully to treat this adverse reaction.
Noninflammatory nodules are small palpable lumps that are oftentimes visible under the skin.
These single, isolated lumps manifest at the injection site a few weeks following filler injection.
Small nodule formation is an adverse effect mainly due to technical error and is commonly seen with superficial injection of HA fillers.
Nodules tend to occur around the mouth and eyes when dermal fillers are injected superficially.
Thus, nodule formation is commonly due to improper superficial injection technique, as is dyschromia due to Tyndall effect discussed prior.
In addition, noninflammatory nodules result from overcorrection, whereby an excessive amount of material has accumulated in the tissue.
Furthermore, nodules may occur due to poor placement within highly mobile areas, such as the lips.
Nodules are less commonly seen with hyaluronic acid usage when compared to the particulate fillers calcium hydroxylapatite (CaHA) and poly-l lactic acid (PLLA).
Proper injection technique is paramount to minimize the formation of nodules.
In the event of HA filler-induced nodules, hyaluronidase is the treatment of choice used to eradicate the subcutaneous nodule and to mitigate overcorrection.
Hyaluronidase is an enzyme that dissolves hyaluronic acid in the skin and is employed to reverse the effects of HA filler injections.
Before treating with hyaluronidase, a skin test must be performed to ensure there is no allergic response to hyaluronidase.
Anaphylaxis is a potential side effect of hyaluronidase, so it is important to ensure a negative allergic response test prior to administration of hyaluronidase.
In contrast to noninflammatory nodules, foreign body granulomas are inflammatory nodules caused by a nonallergic chronic inflammatory reaction.
The resulting inflammatory lesion is predominantly composed of multinucleated giant cells and is caused by granulomatous inflammation after the aggregation of macrophages in response to large foreign bodies that cannot be phagocytosed by macrophages.
Filler- related foreign body granulomas typically occur 6–24 months after filler injection.
Foreign body granulomas are rare, with a reported inci- dence of foreign body granulomas after injection of hyaluronic acid of 0.02–0.4 %, with a peak estimated incidence of 1.0 %.
Clinically, foreign body granulomas caused by hyaluronic acid mainly appear as cystic granu- lomas and can be accompanied by edema and erythema.
Development of a sterile abscess results from a process of encapsulation that prevents the absorption of the injected material into the surrounding tissues.
Characteristic histological findings include palisaded granulomatous tissue composed primarily of giant cells and macrophages.
A differential diagnosis should be performed to distinguish granulomas from noninflammatory nodules.
Filler-induced granulomas are differentiated from nodules in that the size of the granuloma becomes larger than the volume that was injected, and granulomas develop simultaneously at multiple sites of injection.
Since foreign body granulomas are not allergic reactions and are often triggered by a systemic bacterial infec- tion, it is currently not possible to predict which patients are at risk for developing granulomas.
Left untreated, they may remain virtually unchanged for some years and then resolve spontaneously.
The primary treatment of foreign body granulomas caused by HA fillers is intralesional corticosteroid injections (betamethasone, prednisone, or triamcinolone).
The local injection of corticosteroids disrupts the activities of fibroblasts, giant cells, and macrophages.
Depending on the severity of the reaction, 5–10 mg/cc of corticosteroid should be used.
If necessary, repeat the corticosteroid injection 4–6 weeks later.
For intralesional injections it is recommended that a 0.5 or 1.0 mL insulin syringe with a 30 gauge needle be used.
A smaller diameter syringe is advantageous as it allows the resistance of the granuloma to be sensed, which helps prevent corticosteroid- induced dermal atrophy.
As granulomas tend to spread into the surrounding tissue in a finger- like pattern, the preferred technique is to inject a small amount of drugs gradually, moving from the periphery to the central area.
To prevent recurrence, it is preferable to inject a high dose of triamcinolone mixed with lidocaine when performing intralesional steroid injections.
As an alternative treatment for granulomatous reactions, an injection containing bleomycin may worksuccessfully.
Inaddition, 5-fluorouracil, an antimitotic agent, has been used in intrale- sional injections to treat granulomas.
Furthermore, granulomatous reactions to HA fillers have been treated with hyaluronidase.
Finally, systemic oral steroid therapy can be used for recurring foreign body granulomas.
The use of oral prednisone at a starting dose of 30 mg/ day and a maintenance dose and 60 mg/day can prevent the recurrence of granulomas.
Minocycline combined with oral or intralesional steroids is effective in treating widespread inflammatory granulomas.
The excision of foreign body granulomas is not a therapy of first choice because the complete removal of a granuloma is often not possible in many cases as granulomas are invasive and have non-confined borders with the surrounding tissue.
However, in the case of an obvious sterile abscess, an effective treatment is incision and drainage of the abscess.
Edema is a common adverse reaction subsequent to an HA filler injection.
Hyaluronic acid derivatives are particularly hydrophilic and can be associated with localized edema.
Just as with erythema, edema is due to puncture trauma and associated inflammation.
The swelling can be expected to persist for a similar duration as the erythema, sometimes slightly longer.
Depending on the injection site, such as lip injection, swelling can be more profound and last longer, with an expected duration of 2–3 days.
Swelling is managed by applying cold compresses postinjection for 5–10 min to reduce inflammation.
After the procedure, patients can be advised to use ice packs at home every few hours on the day of the injection.
Caution must be emphasized to avoid prolonged ice pack usage to reduce the risk of cold injury to the skin.
Furthermore, swelling can be reduced using a prudent injection technique that will minimize the number of skin punctures during the injection process, thus limiting trauma to the injected area.
Such techniques include placing the filler with a serial injection, using the fanning technique, injecting at the preperiosteal level, or using linear tunneling with threading.
Care must be taken when injecting HA fillers into regions such as the lower eyelids and lips where there is a greater likelihood of undesirable excessive visible edema.
Additional volume may develop in these areas because of excessive water sequestration caused by hyaluronic acid derivatives.
Thus, conservative treatment should be undertaken when injecting the lower eyelids and lips.
Facial angioedema is an adverse event that can occur following HA filler injection.
Facial angioedema results from a hypersensitivity reaction, which is an allergic reaction mediated by T lymphocytes.
This allergic reaction is thought to be related to protein contaminants present in the filler material.
Hypersensitivity reactions related to der- mal fillers are an infrequent complication.
Immune- mediated angioedema is rare, with an estimated incidence of less than one to five in 10,000.
Angioedema normally manifests within approximately 2 weeks posttreatment.
Angioedema is more commonly seen with superficially placed hyaluronic acid derivatives.
A particular area of concern is the lip when injected superficially with HA fillers.
In the event of angioedema, the allergen (hyaluronic acid deriva- tive) which is the inciting factor must be removed.
This is accomplished by injecting hyaluronidase locally.
If necessary, the symptoms of angioedema can be treated with oral prednisone.
Herpes simplex virus reactivation has been reported following HA dermal filler injections, likely associated with the inherent skin irritation caused by injection.
Common sites of reactivation are the perioral area, nasal mucosa, and mucosa of the hard palate.
These case reports are anecdotal and no definitive evidence-based data has implicated fillers in the causation of recurrent herpes infection.
However, for those patients with a history of cold sores, especially after prior filler injections, an antiherpes prophylaxis regimen may prove beneficial.
Prophylactic treatment with valacyclovir should be initiated prior to injection to reduce herpetic reactivation, with a dosage of 500 mg twice daily for 3–5 days.
If a herpetic reactivation occurs in the absence of prophylactic treatment, then 2 g of valacyclovir twice daily for 1 day should be given to tamper the infectious outbreak.
Vascular compromise following dermal filler injection is a rare but very serious potential adverse event, with an incidence estimate of 0.001 % for hyaluronic acid fillers.
Vascular compromise results from vessel injury, compression, or occlusion following dermal filler placement.
Oftentimes vascular compromise results when the intravascular injection of material into an artery occurs, causing an embolism that impedes blood flow.
Vascular injury can cause tissue necrosis and acute vision loss.
To limit the risk of injection procedures, it is imperative that administrators of dermal fillers have a thorough understanding of facial anatomy.
Vascular compromise is an emergent condition that requires prompt action to avoid catastrophic consequences.
Vascular-mediated events may result in skin necrosis following hyaluronic acid filler injection.
Impending necrosis following filler injection is a major, early-onset complication that is likely the result of vascular injury, compression, or obstruction of the facial artery, angular artery, lateral nasal artery, supratrochlear artery, or their branches.
During the injection procedure, filler material may inadvertently be injected into vessels and flow antegrade or retrograde through the vasculature, causing an occlusion leading to local or distal tissue necrosis.
In a review study of necrotic events following dermal filler injection, the most common injection site for necrosis was the nose (33.3 %), followed by the nasolabial fold (31.2 %).
Necrosis can also occur due to vessel injury and compression secondary to the local edema caused by the hydrophilic properties of hyaluronic acid fillers.
The anatomic regions most susceptible to ischemic necrosis are the glabella and the nasolabial fold.
These are regions where the blood sup- ply is poor or is predominantly dependent on a single arterial branch.
The glabella region is supplied by the supratrochlear and supraorbital arteries, terminal branches of the ophthalmic artery.
Retinal artery occlusion can be caused by injections to the glabella, leading to vision impairment and complete vision loss.
The nasola- bial fold is supplied by the angular artery.
Alar necrosis has been reported following injection to the nasolabial fold, likely due to compression of the angular artery or its branches.
To prevent these serious adverse vascular events, extra caution must be taken when injecting into these areas.
Aspiration prior to injection is recommended to help prevent accidental placement of the filling agent within a vessel.
It is important to watch for the signs of vascular compromise which are severe pain (more than what is expected for a dermal filler injection) and an area of blanching.
If these symptoms occur, swift and aggressive treatment is necessary to prevent tissue necrosis.
In the event of hyaluronic acid-induced vascular compromise and impending necrosis, immediately discontinue the injection.
Next, administer a cutaneous injection of hyaluronidase in the site of filler placement.
Then apply a 2 % nitroglyc- erin paste to the skin.
Nitroglycerin paste has a vasodilatory effect on small-caliber arteri- oles, thus improving flow within the dermal vasculature.
Apply the paste cyclically for 12 h on and 12 h off until clinical improvement.
To further increase vasodilatation to the affected area, apply warm compresses and massage the area.
In addition, aspirin 325 mg daily should be given to prevent clot formation.
Furthermore, methylprednisolone (Medrol dose pack) should be prescribed along with prophylactic antibiotic ther- apy such as levofloxacin.
Along with these measures, application of topical oxygen infusion cream (Dermacyte Oxygen Concentrate, Oxygen Biotherapeutics) twice daily has been reported effective.
Low molecular weight heparin has also been used in the management of patients with filler-induced vascular occlusion.
Factors that increase the possibility of vessel occlusion and resulting vascular compromise include high-pressure injections (anterograde flow more likely), large-volume bolus injections, a stationary rather than moving needle, and a deep plane of injection (larger vessels are found beneath the dermis in the subcutaneous fat).
The glabella is a high-risk anatomic location for ischemic necrosis.
Accidental injection of the supratrochlear or supraorbital arteries in the glabellar region can cause a central retinal artery embolism that impedes blood flow to the retina resulting in visual impairment as a result of retro- grade flow of the filler material into the central retinal artery.
Precautions that can be taken to minimize the risk of central retinal artery embo- lism and iatrogenic blindness include aspirating before injection to detect accidental entry into a vessel; using needles and cannulas of small size as opposed to larger ones, and blunt flexible nee- dles and microcannulas when possible; perform- ing low-pressure injections with the release of the least amount of substance possible rather than bolus injections; and avoiding injection into traumatized tissue.
If visual impairment results after filler injection, an ophthalmologist should be consulted immediately.
First approved in 1989 for the treatment of various neuromuscular disorders, it was not until 2002 that the US Food and Drug Administration (FDA) approved botulinum toxin for its first der- matologic application: enhanced cosmesis of glabellar lines.
By 2004, the FDA extended its approval of onabotulinumtoxin A or Botox?, to the treatment of primary axillary hyperhidrosis refractory to treatment with topical agents.
At present, injection of botulinum toxin is one of the most commonly employed modalities for facial cosmetic enhancement in the United States.
According to the American Society for Aesthetic Plastic Surgery, neurotoxin injection was the most commonly performed nonsurgical cosmetic procedure in the year 2013: of the near 15.1 mil- lion cosmetic procedures performed that year, approximately 42 % (6.3 million) were botulinum toxin injections.
Furthermore, axillary and palmar hyperhidrosis, which affect approximately 1–3 % of the population, had historically proven difficult to treat prior to the approval of botulinum toxin injections.
These findings demonstrate both the clinical and economic impact of botulinum toxin injections in modern medicine, as well as their essential place in the armamentarium of the procedural dermatologist.
While the side effect profile of botulinum toxin formulations is generally favorable, it is important for clinicians to be aware of the com- plications associated with their use.
In this chapter, we explore several of these common and rare adverse effects, with focus on their typical clinical presentation and indications for management.
Furthermore, we briefly discuss the emergence of patients presenting with complications following injection of illicit botulinum toxin-containing compounds in the hands of untrained or nonmedical personnel.
Produced by Clostridium botulinum, an anaerobic, spore-forming bacterium, botulinum toxin is a zinc-containing neurotoxic enzyme that exerts its effect within the synaptic bouton of the neuromuscular junction.
Through the hydrolysis of the proteins synaptobrevin (also referred to as neuronal vesicle-associated membrane protein or VAMP), SNAP25, and syntaxin, botulinum toxin inhibits the release of the neurotransmitter acetyl- choline, thereby inducing flaccid paralysis in affected muscles.
Pathologically, this effect is best demonstrated by the disease manifestations of botulism.
Following ingestion and/or inhalation of clostridial spores, there is reactivation of the bacte- rial life cycle, with resultant production of massive amounts of botulinum toxin.
Ultimately, the systemic release of this toxin load results in a clinical entity characterized by descending flaccid paraly- sis, respiratory arrest, and possibly death.
The strength of botulinum toxin is recorded as a measure of its paralytic activity in mouse species.
The standard unit of injection, the unit (U , is described as the lethal dose for 50 % of mouse models, or LD50, following intraperitoneal injec- tion into the mouse abdomen.
In humans, this dose has been estimated in the range of 3000 U. 
In 1989, the FDA approved the use of botulinum toxin type A – produced from the A subtype of Clostridium botulinum – as a local treatment for disorders ranging from blepharospasm and strabismus to various chronic facial spasm disorders.
It was not until 2002 that botulinum toxin was approved for use in the management of moderate to severe glabellar lines.
As with the neuro- muscular disorders, its therapeutic effect was mediated by the induction of flaccid paralysis in muscles underlying the skin in which rhytides were present.
Following the release of tension within these muscles– which typically takes about 14 days to reach maximal effect– there is a general flattening of the overlying skin that lasts for a period of 3–6 months.
At this point, the recycling and regeneration of new neuromuscular junctions results in the reappearance of the original rhytides.
Despite its effects at the neuromuscular junction, botulinum toxin does not appear to induce any reactive changes within myocytes themselves.
In a clinicopathologic series performed on patients who received botulinum toxin type A injections in doses up to five times greater than those typically used for enhanced cosmesis, histologic examination did not demonstrate any chronic changes in muscle tissue, including scar- ring, fibrosis, or atrophy.
At present, there are three botulinum toxin- containing agents in use in the United States: onabotulinumtoxin A abobotulinumtoxin A and incobotulinumtoxin A All three are derived from Clostridium botulinum serotype A and they each possess their own unique clinical indications.
Among the most commonly used botulinum toxin formulations on the market today, onabotu- linumtoxin A – better known by its trade name, Botox? – is commonly used in cosmetic derma- tology for the release of glabellar lines, hyperki- netic frontal lines, and lines of the lateral canthus (“crow’s feet”).
Marketed under the trade name Dysport?, abob- otulinumtoxin A is also used in clinical practice for facial cosmetic enhancement.
However, it is important for clinicians to note that the relative potency of Dysport? does not equal that of Botox?: several studies have reported conversion factors ranging from 1:3 to 1:5 (Botox? vs. Dysport?). 
While the clinical effects after appropriate dosing are often similar between both agents, these findings are of considerable impor- tance for clinicians or practices in which both toxin formulations are used interchangeably.
Appearing more recently on the market than the other two formulations, incobotulinumtoxin A (trade name Xeomin?) has been approved for the treatment of cervical dystonia, blepharospasm, and glabellar lines.
In comparison to onabotulinum- toxin A and abobotulinumtoxin A containing formulations, Xeomin features a lower total load of protein; therefore, this compound was originally marketed as a potentially safer option due to a theoretically lower chance of inducing host immunologic response.
However, one random- ized, double-blinded trial comparing incobotu- linumtoxin A to onabotulinumtoxin A failed to demonstrate any measurable difference in safety or neutralizing antibody generation between the two arms.?
A myriad of studies have demonstrated the relative safety and low side effect profile of all of the above formulations of botulinum toxin for local injection.
Typically, the common adverse effects experienced by patients are similar for all three agents: these include reactions at the injection site (erythema, pruritus, hematoma formation, or transient rash), focal muscle weakness, and headache.
The risk of complication is related to both the dose administered and the site of injection.
In one meta-analysis on onabotulinumtoxin A's safety in facial injections, it was found that adverse effect rates were much higher in onabotulinumtoxin A groups than in corresponding placebo groups.
Additionally, injections at the glabella carried a higher risk of complications than those at the lateral canthus.
Interestingly, the most commonly reported adverse effect at both sites was transient headache, which may have been an artifact of injection rather than the toxin itself.
While many of the above complications are transient and do not require further management, several rare complications associated with botulinum toxin injection have been reported.
As these manifestations may require further management, it is important for clinicians to be aware of their typical presentations.
The muscles injected during the treatment of glabellar and canthal lines lie in close proximity to key muscles of ocular movement.
Accordingly, it is important for dermatologists to remain cognizant of the ophthalmic complications associated with injection of botulinum toxin.
Among these, the most commonly reported ocular adverse effect is eyelid ptosis.
Typically occurring following injection of the procerus muscle, the proposed mechanism of effect is lateral spreading of injected botulinum toxin through the orbital septum.
This places the levator palpebrae superioris muscle of the upper eye- lid at risk of paralysis, with resultant eyelid ptosis that can manifest within the first 2 weeks following injection.
Clinically, this may manifest as a 1–2 mm depression of the affected eyelid; diag- nosis may be made by comparison of the affected eye with the contralateral lid, as well as an obscuring of the upper pupillary rim on the affected side.
While most cases of ptosis are mild and tend to resolve within 2–4 weeks of injection, evaluation with an ophthalmologist may be indicated in cases of severe visual impairment.
In these cases, the use of mydriatic eyedrops may induce enough upper eyelid contraction to overcome the degree of induced ptosis.
Clinicians may lower the risk of inducing eyelid ptosis by using concentrated solutions of botulinum toxin; this will diminish the migratory potential of a large bolus of dilute solution.
Additionally, while gentle massage is advised to increase in-plane spread of toxin, avoidance of overaggressive horizontal massage will prevent the risk of toxin reaching the medial orbital septum.
Another ocular complication associated with botulinum toxin injection is diplopia, or the visual perception of a doubled image secondary to impaired extraocular muscle function.
These findings are typically due to infiltration of injected botulinum toxin into the nearby extraocular mus- cles and often occur following injection of a large bolus of botulinum toxin or injection at the hands of untrained personnel.
The typical patient presentation is an individual complaining of distorted vision 1–2 weeks following botulinum toxin injection.
Paralysis of the lateral rectus muscle is among the most commonly reported complica- tions; this may occur secondary to the regularity of the lateral canthus as a site of injection for enhanced cosmesis, as well as its close proximity to the lateral rectus muscle.
However, excessive injection of the procerus or nasalis muscles may result in paralysis of the medial rectus muscle.
Depending on the severity of diplopia, referral to ophthalmology may be warranted; however, clini- cians can assure patients that this effect will reverse following regeneration of the neuromus- cular junctions within the affected extraocular muscle.
A more severe manifestation of extraocular dysfunction is strabismus, or unilateral deviation of the affected globe secondary to the loss of function of an extraocular muscle.
If suspected, urgent evaluation by an ophthalmologist is war- ranted: left unmanaged, these patients may experience long-term visual dysfunction.
Ophthalmologists may choose to employ unilateral eye patching or treatment with visual glass prisms throughout the 3–6-month window until the effects of the toxin fade.
Unlike eyelid ptosis, eyebrow ptosis typically occurs following injection of the frontalis muscle for the treatment of hyperkinetic frontal lines.
This scenario often arises secondary to asym- metrical injection of botulinum toxin, injecting a large bolus of dilute solution in the frontal zone, or overaggressive horizontal massage following injection.
It can be avoided by careful preparation in the pre-procedural window: using the smallest amount of concentrated injection material possi- ble, as well as mapping injection sites prior to treatment, may prove helpful for clinicians.
Similar in presentation to eyebrow ptosis is an entity known as pseudoptosis.
In the presence of redundant frontal skin– common in older patients or those with photodamaged skin– injection of the frontalis muscle may result in skin and subcu- taneous tissue folding over the superior aspect of the brow.
Counseling patients on the risks associ- ated with frontalis muscle injection, as well as careful patient selection for injection at this site, may mitigate brow ptosis or pseudoptosis.
Patients with this degree of cutaneous elasticity should also be advised that tissue edema follow- ing injection is common; this tends to resolve 24–48 h following injection.
Exaggerated elevation of the brow tail may occur in the setting of overaggressive treatment of the procerus muscle in comparison to the frontalis muscle.
These patients will develop pronounced elevation of the lateral brow in comparison to the medial brow, which alters resting facial appear- ance and may interfere with normal expressions of emotion.
This complication highlights a clinical pearl for botulinum toxin injection at any site: treatment of a muscle group (i e , elevators) without concomitant injection of its antagonist group (i.e., depressors) may result in unfavorable cosmesis or distortion of resting facial structure.
The superolateral aspect of the bony orbit hosts the lacrimal fossa, within which the lacrimal gland–responsible for tear production – is situated.
When botulinum toxin injectables were first approved for the treatment of lateral canthal lines, there was concern among clinicians that their proximity to this gland could pose a theoretical risk of iatrogenic xerophthalmia.
However, contrasting reports on the effects of injection on tear production exist in the literature: while one prospective cohort of 26 crow's feet areas injected with botulinum toxin type A demonstrated no statistical difference in tear production (as measured by the Schirmer test), one recent study demonstrated that injection of the lateral canthal folds decreased both tear production and tear film stability.
In this latter study, the severity of xerophthalmia was directly related to increasing patient age and increasing dose of botulinum toxin administered.
Despite these findings, numerous case reports have described complications related to tear production following injection for lateral canthal rhytides.
The generally accepted mechanisms of pathogenesis include direct toxin-mediated effects at the lacrimal gland, as well as orbicularis oculi muscle dysfunction secondary to toxin injection.
The typical presentation mirrors that of xerophthalmia: patients report conjunctival injection, a sensation of "sand-like dryness"?or gen- eral eye irritation in the weeks following botulinum toxin injection.
If treatment is indicated, it is generally conservative and limited to management of the adverse effects experienced; patients can also be assured that their symptoms will improve as the effects of the botulinum toxin begin to fade.
However, in cases where there is a significant degree of lagophthalmos (inability to close the eyelids), patients may present with ectropion (eversion of the lower eyelid), which places them at an increased risk of developing chronic keratitis and/or corneal ulcerations.
In cases where this degree of lid dysfunction is suspected, urgent ophthalmological evaluation is warranted.
Avoiding a large bolus of injection at the lateral canthus, as well as avoiding injection within one centimeter of the orbital ridge, can decrease the risk of botulinum toxin-induced xerophthalmia.?
Many patients undergoing botulinum toxin injection may desire treatment of lower facial rhytides.
Treatment in this region poses unique challenges to clinicians: a complex network of eleven separate levator and depressor muscles control lip movement, each with functions rang- ing from speech and eating to subtleties of facial expression.
Therefore, improper treatment in this area may result in notable impairment for patients.
Ptosis of the upper lip may arise following either injection in the infraorbital or perizygo- matic area, as well as injections affecting the superior aspect of the orbicularis oris muscle itself.
For the former, resultant paralysis of lip elevators found in this region–including the zygomaticus major and minor, levator labii superioris, levator labii superioris alaeque nasi, and levator anguli oris–may result in asymmetrical drooping of the upper lip.
Beyond the resultant unfavorable cosmesis, a significant degree of ptosis may interfere with normal speech, chewing, and facial expressions.
Conversely, ptosis of the lower lip may occur with improper injection in the region of the oral depressors, as well as migration of a large bolus into this region.
Ptosis at this site may result in symmetric protrusion of the lower lip or may cause downward bowing of one oral commissure in comparison to the contralateral side.
Beyond unfavorable cosmesis, such drooping of the oral commissures may interfere with drinking, eating, or speaking; if severe, patients may even experience spontaneous dripping of saliva from the affected oral commissure.
Injections in the area surrounding the chin are becoming increasingly popular, especially among male patients.
Typically, patients present for evaluation of "chin furrowing"?or corrugation of the skin overlying the chin secondary to a hypertrophic mentalis muscle, as well as relaxation of a "chin dimple"?or midline cleft overlying the chin.
Chemical denervation with botulinum toxin may ameliorate both conditions, but strict adherence to injections in the midline– as well as injecting at a safe distance from the orbicularis oris muscle – is essential at this site.
Improper injection lateral to the mentalis may result in paralysis of the lower lip depressors, with resultant protrusion of the lower lip or ptosis.
Additionally, migration upward toward the inferior rim of the orbicularis oris may prevent lip pursing and interfere with speech.
Chemodenervation of the platysma provides patients with a safe, nonsurgical method for relaxing vertical neck bands.
Nevertheless, the anterior neck is replete with neurovascular structures at potential risk of disruption, and the large surface area of the platysma often inclines clinicians to inject a high number of units of botulinum toxin (upwards of 100–200U in a single session has been reported by some).
It is advised that clinicians with limited experience in injecting the platysma refer these patients to a procedural dermatologist or someone with greater familiarity of administering botulinum toxin in this area.
Furthermore, the use of no greater than 50U of botulinum toxin is advised when injecting in the anterior neck.
Following injection of the platysma, common adverse effects include those related to the injection process itself: pain, bruising, neck weakness, and generalized anterior neck discomfort are often seen, and patients can be reassured that these symptoms will fade within several days.
Although rarer, more alarming complications may arise following injections of the anterior neck: impaired neck flexion, hoarseness of the voice, and dysphagia have all been reported in the literature.
Their mechanism is likely related to improperly injecting botulinum toxin deeply into the neck, as well as migration of a large bolus toward the deeper musculature of the neck.
To avoid these potential complications, clinicians should use the smallest bolus of concentrated botulinum toxin possible, and they should inject superficially in a horizontal plane.
Having the patient lie supine with their neck slightly flexed during injection may aid in preventing deep injections; additionally, following penetration of the skin, lifting up gently on the syringe to dem- onstrate a superficial location of the needle bevel will aid clinicians in administering the bolus in plane with the platysma.
Historically, hyperhidrotic disorders have posed clinicians with challenges for long-term management.
While the etiology of these disorders ranges from congenital to acquired, they are all believed to result from hyperactive eccrine gland secretion secondary to excessive stimulation by cholinergic sympathetic nerves.
Accordingly, body areas with a high volume of eccrine glands – such as the axillae, palms, and soles– are typically the most affected in these disorders.
In 2004, the FDA approved use of onabotulinum- toxin A for use in the treatment of focal axillary hyperhidrosis; however, both onabotulinumtoxin A and rimabotulinumtoxin B (trade name Myobloc; a botulinum toxin derived from Clostridium botulinum subtype B are often used in clinical practice for the management of focal hyperhidrosis of other sites, including the soles and craniofacial areas.
Several complications following treatment of hyperhidrotic disorders with botulinum toxin have been reported in the literature.
Ona- botulinumtoxin A has been reported as having a radial diffusion capacity of up to 1.5 cm within axillary skin, which makes mapping of the affected axillary skin essential prior to injection.
The starch-iodine test may prove useful in this regard, as it provides clinicians with a demonstrable area of involvement, which can serve as a guide for botulinum toxin injection.
If improperly injected, patients may experience minimal clinical benefit and/or injection site reactions secondary to material migration.
Although exceedingly rare, there is one case report in the literature of a patient who developed superficial thrombophlebitis (Mondor's disease) of the anterior chest veins following injection of botulinum toxin subtype A for treatment of axillary hyperhidrosis.
These findings suggest that while botulinum toxin has proven effective in the management of axillary hyperhidrosis, administration of these injectable formulations is not without associated risks.
The most common complication of botulinum toxin injection for palmar hyperhidrosis is hand weakness: patients may report a general loss of dexterity that improves over the following 3–6 months.
In order to minimize these complications, physicians should first map the injectable area and then administer the injection with a goal of distributing toxin within the superficial dermis.
Many complications following injection of botulinum toxin have been reported in the literature, although cases are sporadic and often contested between studies.
However, two of these entities are worth mentioning: superficial temporal artery pseudoaneurysm and systemic manifestations following local injection.
The superficial temporal artery is one of the terminal branches of the external carotid artery.
In its course through the lateral face, the superficial temporal artery runs along the posterior aspect of the neck of the mandible and ascends ~1–2 cm anterior to the tragus in the preauricular area.
It ultimately splits into two prominent branches – a frontal and parietal branch – both of which can be palpated for pulses or may be visible in certain individuals.
Due to its superficial nature, the superficial temporal artery and its associated branches are at increased risk of trauma during both surgical and nonsurgical procedures of the lateral face.
Several cases of pseudoaneurysm of these vessels have been reported several months following injection of botulinum toxin: patients typically presented with nontender, pulsating, or non-pulsating frontal and/or temporal masses that corresponded with the site of injection.
Occasionally, a bruit may be auscultated over the mass.
Diagnosis can be confirmed with Doppler ultrasound, which demonstrates blood flow with an outpouching mass in connection with the affected vessel.
Depending on the resources available in one's community, prompt evaluation and management by a vascular surgeon or interven- tional radiologist is indicated.
Although exceedingly rare, physicians should remain aware of the risk of systemic manifestations following local botulinum toxin injection.
There are several reports in the literature– occur- ring in both the cosmetic and facial spasmodic disorder settings– of injections of botulinum toxin inducing systemic myasthenic crises.
These findings highlight the importance of obtaining a good clinical history– including a personal or family history of myasthenia gravis or other motor neuron disorders– in all patients before botulinum toxin injection.
Accordingly, clinicians should not administer botulinum toxin injections to any patient with a personal history of disorders involving the motor neuron unit.
At present, administration of botulinum toxin for enhanced facial cosmesis is still the most com- monly performed nonsurgical cosmetic procedure in the United States.
The relatively low adverse effect profile associated with these compounds, coupled with their ubiquitous presence through- out the United States, has resulted in the public's perception of botulinum toxin-containing agents as a safe option for enhancing facial cosmesis.
Although it is strongly suggested that those seeking botulinum toxin injections consult a dermatologist, individuals throughout the medical community– including physicians of all special- ties, nurse practitioners, physician's assistants, dentists, and registered nurses– can pursue certification and provide Botox? injection as a regular part of their clinical practice.
These training certifications, which generally consist of a single-day 8-h course, may not provide healthcare providers with enough time to hone their skills in injecting botulinum toxin; accordingly, the risk of the above complications is generally higher.
Alarmingly, it has been the experience of the authors that there has been an increase in the number of patients presenting for evaluation of complications following botulinum toxin in the nonmedical/illicit setting and/or outside of the United States.
Many of the complications experienced by these patients have been detailed above, with the most common complaint generally being asymmetrical facial tone and resultant lack of cosmetic benefit.
The setting in which these injections are provided also poses a challenge for managing clinicians: as many of these patients are unaware of the type of substance they received as an injection, there is a chance that they have received non- FDA-approved botulinum toxin agents and/or formulations containing many different compounds.
Clues to the latter include induration or prolonged erythema at the injection site, local tissue necrosis, and/or soft tissue hardening as a result of chronic inflammation and fibrosis.
While granuloma formation following injection of onabotulinumtoxin A has been reported in the literature, these cases are the subject of controversy among clinicians; in the presence of a positive his- tory of facial injections performed outside of the medical setting in the United States, the presence of a granuloma strongly suggests injection of a material other than botulinum toxin .
In cases of unclear etiology, pathologic assessment of a biopsy specimen can reveal the presence of foreign bodies, including silicone globules or other foreign injection materials.
While medical management with immunosuppressive agents such as cyclosporine or oral steroids may prove useful in uncomplicated cases of granuloma formation, surgical management– including excision and local tissue debridement – is most likely warranted.
When administered by trained medical professionals, botulinum toxin injections provide patients with a safe, nonsurgical method for both cosmetic and medical conditions alike.
Nevertheless, it is important for clinicians to remain mindful that these agents are not inert: as a neurotoxic compound, botulinum toxin can pose significant morbidity to patients when injected improperly.
Awareness of both the common and rare side effects associated with botulinum toxin injection encourages best practice standards whenever botulinum toxin is injected and also facilitates prompt evaluation and management in the event that a patient experiences any of these described complications (Figs. 13.1, 13.2, and 13.3).
Targeted and innovative techniques and protocols are increasingly used in noninvasive eye and lip rejuvenation with the aim to obtain the best results and reduce side effects.
Understanding the anatomy of the eyelids, lips, and surrounding structures is important to achieve the best results and avoid potential complications.
Palpebral area is very delicate consisting of three layers: cutaneous, muscular, and fibrous layers.
In particular, palpebral skin is thinner com- pared to other skin districts; its hydrolipidic film and skin barrier function are weaker.
The dermis is poorly represented and less rich in collagen and elastic fibers.
The hypodermis is almost absent and blood and lymphatic circulations are slow.
Eyelid skin shows vascular fragility and increased vulnerability to actinic damage.
The use of lasers to treat the eyelids is often limited by longer postoperative wounding, erythema, and the potential risk for hypopigmentation and ectropion.
The upper lip extends from the base of the nose superiorly to the nasolabial folds laterally and to the free edge of the vermilion border inferiorly.
The lower lip extends from the superior free vermilion edge superiorly, to the commissures laterally, and to the mandible inferiorly.
From superficial to deep, the layers of the upper and lower lips include the epidermis, subcutaneous tissue, orbicularis oris muscle fibers, and mucosa.
Fractionated laser technology has allowed physicians to minimize downtime and complica- tions increasing the number of treatments with lower rate of complications than non-fractionated laser treatment.
While ablative fractional devices allow for quicker recovery than traditional fully ablative devices, when compared with their non-ablative counterparts, downtime can be considerably longer, in average 5~7 days.
Unfortunately, adverse effects can still occur even with the best technology and physician care.
Non-ablative fractional lasers (NAFL) are more gentle than the ablative and require a moderate amount of downtime as they induce limited tissue damage and melanocyte stimulation.
In general, NAFR has fewer complications than traditional ablative lasers.
Most complications can be easily managed and are self-limited.
With regard to any side effect, early identification and treatment will improve outcome.
Ablative fractionated lasers (AFL) reduce the tissue trauma decreasing downtime while retaining resurfacing action.
These lasers are significantly safer than their non-fractionated counterpart, but they still maintain high risk of potential damage and complications.
Complication prevention, detection, and treatment are an important part of a physician's ability to provide the best results when treating a patient with fractionated laser.
Fractional lasers perform a pixelated pattern photothermolysis.
This technology makes it possible to obtain microareas of thermal damage surrounded by healthy tissue.
These microcolumns of damage stimulate the healing and skin restruc- turing processes with the production of new collagen and elastin, similar to those achieved with massive treatment of the entire surface, but instead limited to dots of a diameter of 70–150 μm separated by bridges of untouched skin.
It has been estimated that the thermal damage induced by these microcolumns reaches a depth of between 300 and 400 μm in the dermis.
Histological studies by Hantash et al. demonstrated that areas of epidermal and dermal necrosis are visible in the skin immediately after treatment that rapidly heal within 24 h showing keratinocyte migration and elimination of the necrotic epidermal columns through exfoliation of the stratum corneum.
Changes in cell mor-phology have also been observed in the deeper portions of the "columns".?
Specifically, stationary cuboidal phenotypes and even spindle cell migration are visible.
These cells are considered to be responsible for the rapid healing and reepithelialization phenomena after fractional laser treatment.
Fractional CO2 laser combines the"concept"?of fractional photothermolysis with an ablative wavelength of 10,600 mm, successfully treating photoaging, acne scars, and skin flabbiness with minimized postoperative risks and discomfort.
Fractional CO2 laser treatment does not require general anesthesia; however, a cooling system is implemented and topical anesthetics can be applied beforehand.
Erythema may appear and lasts 5–7 days only, with only minimal risks of post-inflammatory hyperpigmentation and superinfections.
The treatment requires more sessions than normal CO2 laser treatment and the results are slower; however, patients prefer fractional laser treatment since it ensures faster heal- ing times without any restrictions to their daily activities.
The non-ablative fractionated lasers combine the gentle and safe aspects of fractionated and non-ablative technologies aiming to improve tex- ture, mild to moderate wrinkles, and acne scarring.
In general, NAFR has fewer complications than traditional ablative lasers.
Most complications can be easily managed and are self-limited.
As with any side effect, early identification and treatment will improve outcome.?
Despite concerns for erythema, it should be kept in mind that erythema is the clinical end point of fractional resurfacing and is an expected, transient side effect.
In the case of non-ablative or ablative fractional resurfacing, redness may persist for 3–7 days.
For NAFR, prolonged erythema is defined as posttreatment redness that persists longer than 4 days.
It has been reported in less than 1 % of patients treated with NAFR.
Ablative fractional resurfacing erythema has a longer duration.
Usually post-resurfacing erythema fades gradually over time.
Prolonged erythema (Fig. 14.1a, b can be caused by inappropriate laser settings, infections, and contact dermatitis.
Patients can be started on a topical steroid (hydrocortisone 2 %) to reduce inflammation.
Transient erythema after non-resurfacing procedures could be covered with cover-up makeup.
After laser resurfacing mild edema could appear together with erythema, remissioning in automatic.
Especially at the level of the eyelids, laser treatment produces marked edema, which can be notable for several days.
The edema of the eye- lids after laser resurfacing can get worse for 1–2 days after the procedure before it starts to reduce because it tends to congregate at eyelid levels.
When needed edema can be treated with oral corticosteroids such as methylprednisolone in a brief course of 5 days (60 mg daily).
Immediate posttreatment urticaria is an expected consequence of fractionated laser skin resurfacing that usually resolves within 3–4 days.
Cold- induced urticaria has been described after fractional carbon dioxide laser resurfacing of the face associated with cooling systems used during the procedure.
A complete medical history before starting treatment could be useful for prevention.
Petechiae or purpura can occur immediately or days after treatment and can take 1–2 weeks to resolve.
Postprocedure bruising can be minimized by avoidance of anticoagulants and other medication that may predispose (e g , aspi- rin, vitamin E ginkgo biloba, etc.).
Intense bruising could resolve leaving post- inflammatory hyperpigmentation, especially in photodamaged individuals and darker skin types.
Focal crusting and erosions may frequently occur during a non-ablative procedure.
Erosions or crusts lasting more than 2–3 days should be treated with a brief course of topical steroids.
Persisting lesions should indicate other causes, such as infections, inappropriate laser settings, or picking behavior.
After AFR larger areas of disepithelialization could be commonly seen and resolve in about a week.
Treatment of the eyelids with AFR in particu- lar can cause erythema, edema, and focal erosions, visible for 3–5 days after treatment.
Posttreatment with abundant emollients can accelerate healing.
Widespread small vesicles may be a reactive phenomenon after fractional laser treatments, especially in eyelid regions.
These lesions resolve within a day or two helped by topical corticosteroid application.
The thin skin of the eyelids is particularly sensitive to irritants and allergens and is thus prone to develop contact dermatitis due to the irritant and/or allergic potential of pre- and posttreatment topical agents (Fig. 14.2).
It is recognized that a wide variety of creams, ointments, cleansers, and other skin care products may cause contact dermatitis after laser resurfacing.
Contact with the same trigger may not lead to a rash on other areas of the skin.
Gentle skin care and topical corticosteroids are recommended if needed.
Acneiform eruption incidence has been reduced by fractional technology comparing to traditional laser resurfacing.
After NAFR treatments the rates of acneiform eruptions range from 2 to 10 %; milia can occur in up to 19 % of treated patients.
AFR treatments also show lower risk of developing acneiform eruptions or acne exacerbation and milia, which may be due to occlusive mois- turizer application in the postoperative period.
Acne and milia often resolve without additional intervention as the healing processes.
Nonocclusive and noncomedogenic moisturizers may help in reducing their incidence.
The most common infection after fractional laser skin resurfacing is related to the herpes simplex virus (HSV), with reported rates ranging from 0.3 to 2.76%.
The incidence of bacterial infection after NAFR appears extremely low with 0.1 % of all treated cases documented to develop impetigo.
The infection rates with traditional ablative laser resurfacing were much higher, with 2–7 % of cases developing HSV reactivation.
Herpes simplex reactivation could be very common without prophylaxis.
Patients may not present with classic herpetiform vesicopustules, but instead may demonstrate only superficial ero- sions that develop during the first week after treatment.
To minimize the risk of HSV reactivation with fractional resurfacing, antiviral prophy- laxis should be administered when a prior his- tory of facial HSV is documented or if full-face ablative laser procedures are performed.
Prophylactic therapy is important even in those without a history of herpetic infections.
All patients should be placed on antiviral prophy- laxis, starting the day before the procedure in those without history of herpetic infections and 3 days before in those with history of herpetic infection.
Antiviral therapy should be continued for a total of 10 days.
The most common causes of skin infections after fractional resurfacing include Staphylococcus aureus (Fig. 14.3), Pseudomonas, Klebsiella, and Enterobacter.
Persistent pruritus and prolonged erythema may be associated with candidiasis.
Atypical mycobacterial infection has also been reported.
For this reason, many practitioners prescribe oral antibiotics and anti- virals before starting the procedure continuing until skin reepithelialization is almost complete.
Even if prophylactic antibiotics and antivirals have been used, in suspicion of skin infection, microbiologic culture testing should be con- ducted to identify the organism and its sensitivity to treatment.
Hyperpigmentation is one of the more common side effects of cutaneous laser resurfacing and may be expected to some degree in all patients with darker skin tones.
Post-inflammatory hyperpigmentation (Fig. 14.4) is much less frequent with fractional laser skin resurfacing than with their non-fractional counterparts.
Even though it is observed in 1–32% of patients depending on the device used, setting parameters, and Fitzpatrick skin phototype.
The reaction is transient, but its resolution may be hastened with the postoperative use of a variety of topi- cal agents, including hydroquinone and retinoic, azelaic, and glycolic acid.
Darker skin phototypes (Fitzpatrick III–VI) have higher susceptibility for developing hyperpigmentation after AFR.
NAFR are associated with very low rates of post-inflamma- tory hyperpigmentation, darker skin phototypes being more prone to develop it.
In general fractional laser treatments of darker skin should use higher fluencies, lower densities, and longer intervals between treatments.
Regular sunscreen use is also important during the healing process to prevent further skin darkening.
Hypopigmentation is not a common complication of AFR.
Postoperative hypopigmentation is often not observed for several months and is particularly difficult because of its tendency to be not responsive to treatment .
When the treatment is performed on a single part of an anatomic site, like perioral regions or eye- lids, it can result in smoothness consistency of the treated side versus the surrounding skin.
In general it resolves spontaneously.
Delayed reepithelialization may occur following the application of resurfacing lasers.
When it doesn't occur in about a week, other causes should be investigated; the most frequent is infection.
It is extremely important to manage this uncommon complication because the longer the skin repairs, the higher is the risk of scarring.
Although the risk of scarring has been significantly reduced with the newer pulsed systems (compared with the continuous wave lasers), 122 inadvertent pulse stacking or scan overlapping, poor technique, as well as incomplete removal of desiccated tissue between laser passes can cause excessive thermal injury that could increase the development of fibrosis.
The most common cause of scarring is postoperative infection.
Focal areas of bright erythema, with pruritus, may signal impending scar formation.
Ultrapotent topical corticosteroid preparations should be applied to decrease the inflammatory response.
A pulsed dye laser (PDL) can also be used to improve the appearance and symptoms of laser-induced burn scars.
The periorbital and mandibular regions are scar-prone anatomic locations that require more conservative treatment protocols.
Eye damages caused by laser procedures are not very common complications secondary to the use of inappropriate safety measures.
Ocular injuries reported during laser use include coloboma and corneal, vitreous, and retinal damage.
Before the laser is turned on, in ready mode patients' eyes should be closed or covered with opaque goggles or eye shields.
The operator and other personnel in the room should wear filter glasses that selec- tively exclude wavelengths emitted by the laser.
Laser-protective eyewear is a well-recognized precaution and includes wraparound glasses and goggles, which are rated by optical density (OD) at various wavelengths.
Ectropion of the lower eyelid after periorbital frational laser is rarely seen.
It is more frequent in patients who have had previous lower blepharoplasty or other surgical manipulations of the periorbital region.
Preoperative clinical evaluation is important to determine eyelid skin laxity and elasticity.
Lower fluences and fewer laser passes should be performed in the periorbital area to decrease the risk of lid eversion.
When ectropion occurs, it usually requires surgical correction.?
Koebnerization Laser-induced trauma may initiate a koebnerizing dermatosis, including diseases such as vitiligo and psoriasis.
Eruptive keratoacanthomas have been reported, most likely secondary to koebnerization.
Laser safety includes the use of protective eye- wear and eye shields, laser signage, control of surgical smoke, tissue splatter and plume, and attention to non-beam and beam hazards.
Treatment setting regulation is important to prevent side effects.
In particular, when treating eyelids laser settings should be lower than those for non-eyelid skin because of the thinness of eye- lid skin.
In particular treatment with fractional lasers should be approached with lower fluence, lower density, and shorter pulse duration settings.
Appropriate precooling, cooling during the procedure, and postcooling should also be con- sidered to provide an extra measure of epidermal protection.
In addition, a detailed disclosure of potential side effects protects not only the patient but also the provider.
In preventing fractional laser complications while treating eyelids, the lips and perioral region are also important to perform correct pre- and post-laser skin care.
Regular use of sunscreens and avoidance of tanning should be started on a preoperative regimen about a full month in advance continuing sun avoidance as skin care practice after the resurfacing.
As regards to post-laser treatment skin care, for patients undergoing NAFL resurfacing, the care is minimal.
The use of a mild, fragrance-free cleanser and moisturizer could be recommended resuming regular skin care regimen after about 1 week.
For patients undergoing AFL, some surgeons recommend cleaning process with only tap water and gentle gauze followed by application of a light lubricating ointment.
Others add the use of local antibiotics and/or antifungal.
When the resurface is complete (between days 4 and 6 after the procedure) but redness has not faded, fragrance-free cleanser and moisturizer should be used.
Makeup is allowed once resurfacing is complete preferring mineral makeup.
It is good news that silicone gel breast implants are returning to the market after an absence of 11 years.
Compared with saline-filled implants, they are easier to use, have no valve or filler mechanism and are prefilled.
Saline-filled implants, as everyone knows, show a ripple effect but there is less foreign material in the body. 
Gel-filled implants feel more natural.
I consider myself a breast implant customer, and there are two things I would like to know when purchasing a breast implant:
1. How thick is the outer membrane of the implant?
I need to know this to make an informed choice about long term rupture rates of the implant.
I would tend to choose thicker rather than thinner walled implants.
2. How thick is the gel inside the implant?
A highly viscous gel might not feel as natural but should be less likely to migrate outside a ruptured capsule surrounding the implant.
Long term follow-up
Because breast implants are inserted for many years, we need long term follow-up.
This should be easy, because women should be highly motivated to be interested in their health; both their breast health and systemic health, but it isn’t that easy.
The new breast shape becomes incorporated into the patient’s body image, and sometimes the powerful defence mechanism of denial takes effect.
Women occasionally do not tell a new partner they have had surgery and the result can be so good that the partner does not suspect.
The implants become a secret from a past life. 
Imagine receiving by mail annual recheck reminder cards for implant and breast re-examinations in those circumstances.
Add to that name changes and moving from city to city, even other provinces, states and countries, and the problem of follow- up assumes new proportions.
Ideally, follow-up for a breast implant patient should be for the rest of a woman’s life.
Who should do this follow-up?
It can be any physician or surgeon.
A national registry for breast implant patients should be set up in Ottawa so that patients can notify the registry of problems such as further operations for pain, scarring, capsule surgery or implant removal or replacement.
This is being done in Alberta and it should be done for the whole country, as it is in Denmark.
In future studies, I think we need to compare apples with oranges.
Not all breast implants are the same.
We need to compare same with same and we need to have considerable technical information from manufacturers so that we can compare implants.
Silicone implants Since 1992, there has been considerable debate as to whether silicone causes or exacerbates immune diseases. 
A number of women have syndromes which are hard to diagnose, in which their biochemical tests are normal for the tests we have available to us now.
As silicone gel implants come back on the market, it might be of value to consider not doing breast augmentations with these implants on women who already have collagen diseases, or syndromes such as chronic fatigue syndrome, fibromyalgia, or those with a strong family history of a collagen disease such as rheumatoid arthritis.
Prospective patients might object, saying they have a right to an operation. 
That might be so, but declining certain patients might be the best thing to do. 
Double lumen implants Consider the double lumen implant. 
It might be one of the best ideas in beast implants. 
This implant, available in the 1980s, was a silicone gel implant, surrounded by a second membrane, forming an outer pocket filled with saline.
It combined the best of both ideas.
If the inner membrane ruptured, gel would be contained in the outer compartment.
If the outer compartment ruptured, it was saline-filled and small volume so there would be little change in implant volume.
But there was a rub.
It was more complicated, and more difficult to make, so it was more expensive.
Sometimes though, it might be best to consider these principles over price when considering a breast implant, even though there is much to be said for simplicity.
Plastic surgeons have sometimes promoted the newest implant with the assumption that the newest idea is the best. 
Those of us who have followed the debate for a longer period have sometimes seen the reverse.
When thin-walled implants replaced thick-walled implants the change, though logical, was not an advance.
Sometimes ideas with alonger track record eventually prove to be better.
Diagnosis of ruptured implants It can be difficult to clinically diagnose a ruptured silicone gel implant.
Scans are good diagnostic tools, but are not accurate in very small ruptures and no one is suggesting we routinely diagnose rupture by surgery.
Perhaps after a certain time period, breast implant patients should have their implants replaced, but who would suggest operating on a happy patient with no complications? 
Long term follow-up will cost something but the expense is worthwhile and necessary.
It will also be disclosed to consumers from time to time as the data is analysed and verified.
This is as it should be and we should contribute to finding out all we can know about these useful devices.
Abstract Purpose – The purpose of this paper is to help understand the extent of regulation of aesthetic medicine in various developed countries and to discuss the current pitfalls and potential strategies in regulating this area of healthcare.
Design/methodology/approach – A range of published articles and press reports from bound and internet sources on aesthetic medicine in the recent five to six years were obtained to allow a better understanding of existing practices and regulatory climate.
Reports from relevant authorities in various countries were also referred to for information on proposed regulatory regimes and future regulatory directions.
Findings – The practice of aesthetic medicine has been marginally regulated, even in more highly developed countries.
The main regulatory concern appears to be the practice of minimally invasive aesthetic surgery by general practitioners.
Professional voluntary self-regulation would probably not be effective in view of the peculiar nature of aesthetic medicine vis-a` -vis conventional medicine.
Practical implications – There is a need for health regulatory bodies across the world to brace themselves for potentially more widespread health and social risks posed by aesthetic medicine.
Statutory governance is needed to maintain safe practice standards and to manage the supply and demand of aesthetic services.
In less developed countries, there is a need for better public education and empowerment to enable patients to make better-informed decisions and assume greater responsibility for the aesthetic services that they seek.
Originality/value – This paper discusses regulatory issues concerning aesthetic medicine which are rarely featured in academic journals. 
It offers some strategies for better regulation of aesthetic medicine which health authorities in certain countries may find useful.
Introduction Aesthetic medicine is a term that has been used by medical professionals to describe surgical procedures (viz. aesthetic/cosmetic surgery) and medical treatments that aim to improve a person’s appearance or subjective well-being.
Indeed, it is a practice of “medicalised” beauty therapy. 
In a society where beauty is increasingly seen as an essential ingredient of health (Moosa, 2002), more doctors are providing aesthetic services as part of their medical practice.
The line dividing between conventional and aesthetic medicine is blurring as more people begin to regard aesthetic medicine as a form of medical science (Peng et al., 1995) and as part of conventional health services.
 In the UK, some hospitals in the National Health Service (NHS) even incorporated beauty therapy in managing certain groups of patients (Sharma and Black, 2001).
Aesthetic surgery should be distinguished from reconstructive surgery as the latter depicts surgery that aims to restore function and form ravaged by disease.
This distinction has been made historically since the mid nineteenth century by the late German facial surgeon, Johann Friedrich Dieffenbach (1792-1847), who is regarded as the “father of plastic surgery” (Gilman, 1999). 
Today, the term “aesthetic surgery” has been widely replaced by “cosmetic surgery”, as evidenced by the greater number of citations using the latter term.
The rise of aesthetic medicine Many modern aesthetic procedures date back to the 1880s and 1890s.
The rise in the number of aesthetic surgery patients and procedures has been remarkable only in the last few decades (BBC, 2006; Cullen, 2002).
For example, between 1997 and 2005, there has been an increase of 444 percent in the total number of cosmetic procedures performed in the USA (ASAPS, 2005).
The rise in the popularity of aesthetic surgery has fostered the expansion of minimally invasive aesthetic procedures such as Botox injections, mesotherapy, laser treatments and intense pulsed light (IPL) therapy (Klatsky, 2001).
These are mainly office-based procedures that require minimal or no local anaesthesia.
The popularity of such aesthetic procedures emanated to neighbouring European countries (Cosmetic design.com, 2005; Bell, 2001) and the UK (Batty, 2005), and later to the USA and South America.
Currently, such procedures are also widely performed in Asian countries like China, South Korea, Thailand and even Cambodia (Weaver, 2003; Bae, 2005; Han, 2005; Choi, 2004; Smith, 1999).
In 2003, nearly 20 million hair removal, skin rejuvenation, tattoo removal, acne reduction and photodynamic therapy treatments were performed.
These earned almost US$6.5 billion for practitioners and US$372 million in revenue for equipment manufacturers (Medical Insight Inc., 2005).
In the same year, four million Botox procedures were performed worldwide, generating over US$2.1 billion in procedure fees.
A new hybrid of services known as “medical spas” also emerged (NCEA, 2005).
These are co-locations of conventional medical services with spa facilities providing minimally invasive aesthetic services, which make up the fastest growing segment of the whole spa industry.
In the USA, a 109 percent rise in the number of such medical spas was noted during the period 2002 to 2004 (ISPA, 2004).
Regulation of aesthetic medicine in the UK, Australia and Canada A brief global scan of health regulatory systems revealed that the practice of aesthetic medicine has been marginally regulated, even in the more developed countries.
However, there are rising concerns that existing health regulations may not be adequate or appropriate to ensure safe practice in aesthetic medicine (BBC, 2005; NSW Health, 2005; Fu, 2004; Enriquez, 2003).
There is less concern over aesthetic procedures performed by registered specialists like dermatologists and plastic surgeons as compared to general practitioners (GPs) because specialists spend more time on training and are generally regarded by the public as more highly competent than GPs.
The need for stricter regulation therefore appears more dire as the number of GPs performing such procedures increases.
UK In the UK, doctors who perform aesthetic (or cosmetic) medicine are primarily those who dropped out of specialist training to work in the private sector.
These doctors are not required to undergo any special training in aesthetic medicine, even though they are free to offer such treatments.
There is no specialist register for aesthetic medicine and doctors from any medical specialty can provide such services.
The Healthcare Commission of the UK, which regulates and inspects all registered establishments carrying out cosmetic surgery, recently estimated that 63 out of 784 aesthetic surgery practitioners were not registered as specialists (Commission for Healthcare Audit and Inspection, 2005).
In 2000, the Care Standard Act was enacted to better regulate private practitioners of aesthetic medicine.
Under the Act, these practitioners were required either to be on the specialist register or to have undertaken specialist training relevant to the procedures they provide if they were already practising prior to 1 April 2002.
Private practitioners were also required to provide the Healthcare Commission with patientssrecords and patient satisfaction surveys.
Australia Cosmetic procedures such as surgical facelifts, chemical peels, botox injections and laser hair removal are highly popular in Australia.
Of the estimated 500 doctors who provide aesthetic procedures in Australia, about 25 per cent are general practitioners (Cornwell, 2000).
The aesthetic practitioners often work in small practices or as employees in specialised clinics.
The industry is highly competitive with very little published research for fear of disclosure of trade secrets.
This probably explains the lack of professional guidance and peer review in aesthetic practice.
Clinics that provide aesthetic services requiring only light sedation do not need to be licensed.
There is no regulation of the quantities or combinations of drugs used, or the qualifications of staff performing the procedures.
Medical practitioners with only basic medical qualifications can proclaim themselves as aesthetic medicine experts, since aesthetic medicine is not a registrable (or legally protected) specialty.
Canada Minimally invasive aesthetic procedures like Botox, aesthetic filler injections, non-surgical facelifts and laser hair removal are highly popular in Canada (Medicard, 2003).
As in Australia, there are no specific regulations in Canada governing the practice of cosmetic procedures, albeit devices such as lasers are regulated under certain legislation, e g the Radiation Emitting Devices Act and the Medical Devices Regulations.
– Amended to O Reg. 
14/95) to regulate medical clinics (CPSO, 2002a).
Nevertheless, this is only limited to clinics that use anaesthesia.
It is noted that under the IHF Act, licensees have to ensure that service providers have qualifications relevant to the services they provide.
The College has also formulated standards on aesthetic surgery to help providers comply with the law.
Despite such regulations, a doctor in Ontario can still delegate aesthetic procedures to non-qualified personnel under his/her supervision (Harvey, 1998).
Even with the Advertising Regulations (CPSO, 2002b), doctors are still falsely proclaiming themselves as aesthetic medicine experts.
Such doctors are also providing patients with misleading information about their qualifications, experience and the aesthetic services they provide.
The need for more effective regulation The gaps in the regulation of aesthetic medicine illustrated in the above countries strongly suggest the need for more effective regulation.
The primary objective of health regulation is to ensure public safety and thereby preserve patients’ trust in the healthcare system and healthcare professionals.
In the presence of regulation, the regulatory body, be it internal (i.e. self) or external, becomes accountable for the safety and quality of the healthcare practices and services rendered to the public. 
Regulatory methods like licensing, certification and accreditation have a common purpose of ensuring that only sufficiently competent practitioners are allowed to practise.
Every procedure in aesthetic medicine is meant to alter some part(s of the body to achieve a more pleasant appearance.
Such alteration would carry a certain risk of harm.
If the principle of health regulation is to prevent harm to patients, then logically aesthetic medicine should be regulated like conventional medicine.
Why then are many countries still hesitant to regulate aesthetic medicine?
Is it a question of cost-effectiveness of regulation?
Or are there political considerations?
One possible reason for the lack of effective regulation is the perception that aesthetic medicine is consumer-driven and therefore the industry should be self-regulated.
Furthermore, many regulatory authorities foresee fiscal and administrative limitations in regulating the industry and are reluctant to be accountable for the quality and safety of aesthetic services.
This reluctance is aggravated by the potential political sensitiveness of disrupting the livelihood of existing aesthetic practitioners by introducing regulation.
Scientific literature has clearly demonstrated the risk of complications from minimally invasive aesthetic procedures, while the efficacy of such procedures remains controversial.
Lasers and IPL have been shown to cause burns and pigmentation problems (Dijkema and Van der Lei, 2005; Roberts et al., 1996) while Botox injections can lead to bruising and blepharoptosis (Caruthers, 2002).
The side effects of aesthetic filler injections range from allergic reactions and permanent lumps to serious complications like blindness and cerebral artery embolism (Medical News Today, 2004; Silva and Curi, 2004; Egido et al., 1993; Andre et al., 2005).
Even simple aesthetic procedures like chemical peeling can lead to bacterial skin infections, herpes simplex reactivations and delayed wound healing (Monheit, 2004).
The most controversial of all aesthetic procedures is probably mesotherapy.
Originally invented in France for treating tinnitus, vascular and lymphatic disorders, mesotherapy has evolved into a body-contouring procedure and a form of therapy for skin ageing and male-pattern baldness (Kalil et al., 2005).
Despite its growing popularity, there is insufficient scientific evidence that establishes its safety and efficacy.
Incidences of urticarial skin reactions and mycobacterium infections have been reported (Bessis et al., 2004; Urbani, 1994; Nagore et al., 2001).
There are currently no standard mesotherapy formulations.
In the USA, no pharmaceutical preparation is licensed or approved by the US Food & Drug Administration (FDA) for use in mesotherapy to achieve anti-aging or adipose tissue reduction purposes.
In fact, phosphatidylcholine (also known as Lipostabil) was prohibited by the Brazilian National Agency of Health in 2003 due to the lack of scientific data supporting its use for the reduction of fat (Hexsel et al., 2003).
The continual rise in the popularity of aesthetic medicine will create unnecessary public demand and may impact negatively on the cultural and moral values of people in the society.
Societal attitudes towards the concept of beauty may become perversed, and people may be encouraged to resort to aesthetic treatments to resist age-related physical changes (Kanter et al., 2001).
This is already seen in certain countries, for example South Korea, where even high school students are placing increasing emphasis on appearances (Park, 2004).
Furthermore, aesthetic medicine may distort the traditional doctor-patient relationship, raise patientssexpectations and increase the risk of medical malpractice litigation (Cullen, 2002).
An unregulated aesthetic medicine industry may also have an adverse impact on professional and ethical standards, as medical professionals may be more likely to sacrifice ethical and moral principles to achieve their profit-driven goals (Ring, 2002).
As aesthetic medicine is a relatively new field, the asymmetry of knowledge between practitioners and patients is more marked and therefore may result in greater likelihood of supply-induced demand.
The latter will create a vicious cycle of escalating public demand, increasing supply and more widespread risks.
Why voluntary self-regulation will not work Health and medical regulation involves a whole range of methods from incentive-based to coercive in nature.
Regulation may be carried out by professionals themselves (internal/self) or by persons outside of the profession (external).
The medical profession is traditionally self-regulated by a representative body of senior doctors.
The regulatory activities are usually backed by legislation specific to registration, practice ethics, discipline and continuous professional development.
This model of regulation, known as statutory self-regulation, is adopted in many countries where governance is by medical councils.
Self-regulation can also be voluntary, which means that professional bodies voluntarily promulgate practice and ethical guidelines for the medical profession.
The activities of such professional bodies are usually not supported by any legislation.
Unlike conventional medical services and providers, there are certain factors inherent in the field of aesthetic medicine that are likely to render voluntary self-regulation ineffective. 
These factors are: Doctors who enter the field of aesthetic medicine do so primarily for its lucrativeness.
General practitioners are turning to aesthetic medicine as an alternative source of income because the intense competition in the private sector and emerging managed care payment schemes have, to a certain extent, reduced the profitability of “bread-and-butter” medical services. 
For livelihood reasons, many aesthetic practitioners would not normally undergo lengthy specialist training and therefore may not be adequately qualified to spearhead self-regulation.
The aesthetic medicine industry is highly competitive and therefore, there is no incentive for any successful practitioner to share his/her experience and skills with others.
This could explain the current lack of published research, clinical guidelines and training courses for providers. 
Such a problem however is not as critical in the US, where professional aesthetic medicine organisations tend to be more highly active in promulgating practice standards and organising relevant training courses (AAAM, 2005).
Voluntary self-regulation often involves quality improvement activities where medical practitioners participate in peer reviews and learn from observed practice deficiencies.
This would probably not happen in the aesthetic industry where practitioners are unlikely to reveal or acknowledge their own shortcomings.
Many medical practitioners are divided on the issue of regulating aesthetic medicine. 
This is because they belong to different stakeholder groups and therefore may perceive regulation in the narrow context of their own agendas. 
It is possible that specialists may adopt a protectionist attitude and favour stricter regulation while GPs who practise aesthetic medicine would prefer minimal regulation and a level-playing field.
Doctors who are not in the aesthetic business or who do not intend to enter the industry may either support regulation or be ambivalent. 
This situation makes voluntary self-regulation difficult.
Voluntary self-regulation often involves the accreditation of training programmes to improve practice standards. 
There are however very few, if any, independent and impartial accrediting bodies that have the expertise to accredit aesthetic medicine training programmes.
Due to the individualistic and non evidence-based nature of the practice, there is little information on the required standards of practice. 
Without such information, it would not be possible to assess the quality of training programmes.
This situation encourages the proliferation of sub-standard, “fly by night” programmes organised by commercial companies.
Voluntary self-regulation may not work in a traditional Asian society that is less litigious than most western societies, and where the socio-cultural concept of “face” prevails (Roth, 2005).
Asian patients are less likely to sue doctors when aesthetic procedures go awry because of the fear of stigmatisation and embarrassment of making public one’s vanity. 
Consequently, there is minimal public pressure on aesthetic practitioners to maintain good practice standards, let alone start voluntary self-regulation.
How should aesthetic medicine be regulated?
Statutory governance of the aesthetic medicine industry may be necessary at the stage where voluntary self-regulation is either not forthcoming or is expected to be ineffective. 
A comprehensive and logical approach to regulating aesthetic medicine should consider the following aspects as outlined below.
Regulating aesthetic medicine facilities In many countries, medical clinics are required to be licensed by health regulatory authorities or accredited by third-party organizations (Teerawattananon et al., 2003; DoH, 2004; AHCA, 2003).
Clinics that house minimally invasive aesthetic procedures may be separately licensed/accredited and monitored if the procedures are deemed to be of higher risk.
In the UK, the Expert Group on the Regulation of Cosmetic Surgery had proposed that all facilities providing aesthetic procedures such as Botox and aesthetic filler injections be licensed with the Healthcare Commission (DoH, 2005).
The aim of licensing/accrediting clinics is to ensure that aesthetic procedures are carried out safely in hygienic and well-equipped premises and by qualified practitioners.
Regular patient record audits and clinic inspections should be conducted to ensure compliance with licensing/accreditation requirements.
Regulating aesthetic equipment, devices and drugs In most countries, the use of medicines and health-related devices (e.g. Therapeutic Goods Administration in Australia and US Food and Drug Administration) are already regulated.
Regulators should continue to keep abreast with new information on emerging products and devices, in particular their safety profile. 
While it may be impossible to test every product or device that enters the market, some audit and quality assurance framework should be put in place to monitor patients who are treated using such products and devices.
A major concern is the use of lasers in aesthetic medicine, which are more likely to cause serious adverse effects than other aesthetic devices.
The Committee of Inquiry in Australia had recommended that Class 3b and 4 lasers be regulated under the Radiation Control Act, which means that users would need to be licensed (HCCC, 1999).
It may not be adequate to just restrict the use of lasers to registered medical practitioners, as most doctors would not be competent enough to operate laser equipment, given that such skills are normally not taught during basic medical training.
Only practitioners who have undergone specialist or formal laser training should be allowed to use laser equipment.
Hence, it had been proposed in NSW that the Radiation Advisory Council issue the user licence only after it had reviewed the quality and appropriateness of the practitioner’s training in lasers.
Where the use of pharmaceutical products in aesthetic medicine is concerned, manufacturers of such products should be required by law to provide detailed product information on the indications, contraindications, possible side effects and constituents of the products. 
This is to ensure accountability from the manufacturers and to prevent harmful products from entering the market.
Regulating practitioners and training The medical practitioner’s level of competency and training in aesthetic procedures has always been a major concern.
Aesthetic medicine is not traditionally taught in medical schools, and is only included in formal specialist training programmes in certain countries like the USA (Pu et al., 1999, 2006).
More commonly, doctors learn aesthetic procedural skills in workshops organised by manufacturers/suppliers of aesthetic pharmaceutical products or devices.
Hence, there is a need for formal structured training in aesthetic procedures to be included in the training curriculum of medical students, general practitioners and specialist trainees.
Learning should be restricted to procedures that are scientifically proven to be effective (e g laser depigmentation, Botox, etc.).
Training programmes should be properly accredited by established and reputable professional organisations.
In Australia, both the Australasian College of Dermatologists and Australasian College of Surgeons have included laser training as part of specialist training in dermatology and plastic surgery respectively (HCCC, 1999).
In addition, laser training and accreditation programmes have been started by a few professional bodies.
Training should be mandated by law (e g the Care Standard Act in UK) otherwise medical practitioners may simply boycott the training programmes.
Continuing aesthetic medicine training and education should be made compulsory and linked to medical registration.
Regulating the actual practice There are two aspects of aesthetic practice that should be regulated: the manner of practice and the technical aspect. 
The manner of practice encompasses advertising, obtaining consent and risk communication, and reflects the professional conduct of the practitioner. 
There should be legal controls to prevent false claims and professional misrepresentation. 
Physicians should not proclaim themselves as specialists in areas that they have not received formal training or which have not been legally recognised as medical specialties. 
Notwithstanding the fact that lay persons may proclaim themselves as experts in dubious areas of aesthetic practice, medical practitioners by virtue of their well-respected profession should ideally not associate themselves with non medical-related practices.
As part of good surgical practice, consent for any aesthetic procedure should be sought from the patient only after s he has been informed of the diagnosis, treatment approaches, expected outcomes, potential side effects and treatment alternatives (McGillis and Stanton-Hicks, 1998).
These essentials in risk communication may be expressed as statutory guidelines to ensure compliance (Peter et al., 1995).
In Australia, it was proposed that a Cosmetic Surgery Credentialing Council be formed to oversee, among other things, media promotions related to the cosmetic surgery industry, including preparing a Code of Conduct for practitioners on advertising, informed consent, communication and patient selection as well as guidelines on patient examination and assessment (Cornwell, 2000).
In France, it was proposed that patient information as part of informed consent be crafted according to guidelines set by the National Agency for Accreditation and Evaluation in Health (ANAES) and endorsed by the French Society of Plastic Reconstructive and Aesthetic Surgery (Flageul et al., 2003).
Educating the public There should be an avenue for the public to obtain information on aesthetic practitioners and services, so that they can make informed decisions concerning aesthetic treatments. 
The information should preferably be provided by a neutral professional body and should include a listing of qualified practitioners as well as indications, effectiveness and potential adverse effects of various treatments based on scientific evidence. 
The mode of delivery of such information may be through the internet or other mass media such as newspapers, magazines, television and radio. 
The setting up of consumer watchdogs like those in Canada (e.g. Health Canada, Canadian Quackery Watch) should also be encouraged to raise public awareness of the aesthetic industry.
Managing the media The media, while useful to regulatory authorities as an avenue to educate the public, may be responsible for creating unnecessary public demand in aesthetic medicine.
It is recognised that media advertising is not just a source of “purchase-related information”, but also a means of social communication (Leiss et al., 1990).
Advertising actually influences how the public behaves (Lysonski and Pollay, 1990).
Past research has demonstrated that advertising can impact on consumersslives by misleading and deceiving consumers (Davis, 1994).
The social risk of over-publicising aesthetic medicine is that it may create perverse public perceptions on the concept of beauty.
The time and money spent on aestheticism may increase at the expense of more important societal obligations such as childhood education, disease prevention and community development.
These arguments support a greater governmental role in regulating the media and moderating the social impact of publicity in aesthetic medicine.
All media material concerning aesthetic medicine should be subject to regulatory scrutiny prior to their public release.
Media messages that might reinforce existing devaluative attitudes of society toward what is considered physically repugnant should be prohibited, since research has shown that the demand for aesthetic medicine arises in response to such societal attitudes (McGregor, 1967).
Regulating the demand and supply In developing or newly-developed countries where the population’s general educational level and spending power are still moderate, consumers may be more price-sensitive and therefore seek cheaper aesthetic services provided by poorly-trained practitioners instead of bona fide specialists.
The less-educated consumer is also less proactive in seeking information about the industry and is more dependent on health authorities to provide such information.
Such deficient “buyers beware” culture should be taken into account when planning a regulatory framework for the industry. 
 In such situations, there is a need to inculcate in the public a greater sense of individual responsibility for aesthetic services used and their clinical outcomes (Fogli, 2003).
Greater control on the supply of aesthetic services may be necessary to keep the demand in check.
A professional register may be set up to limit entry to only well-trained and competent practitioners. 
As the public becomes more mature and knowledgeable, the rules may be gradually relaxed to make way for a more market-based industry.
It may also be necessary to “tame” public demand for aesthetic services by de-emphasising the importance of physical beauty and redefining social values.
This would help avert spurious growth in aesthetic medicine services. 
Overall, it may be easier to regulate aesthetic medicine by controlling supply rather than demand as the latter tends to be more resistant to change or influence, even that of the economy (Duncan et al., 2004).
Regulation versus facilitation Health authorities in most countries are charged with the responsibility for ensuring public safety in the context of healthcare.
Likewise, the role of clinical governance in aesthetic medicine would be to ensure safe practice standards.
However, being a non essential commodity, aesthetic services are distinct from conventional medical services in that the former have higher potential for generating foreign revenue and boosting the economy of the country (Atkinson, 2002; Teh and Chu, 2005).
Although most governments in western countries leave the onus of spearheading sectoral development to the private sector, asian governments are proactive in facilitating medical tourism and developing aesthetic services (Teh and Chu, 2005; SingaporeMedicine, 2004).
The conflicting goals of sector regulation vis-a -vis facilitation thus create further problems in the regulation of aesthetic medicine.
Hence, health authorities need to be clear about their agenda when regulating aesthetic services, and should avoid adopting conflicting roles in governance.
Conclusion While aesthetic medicine may be a booming industry that has promising financial returns, health regulators and policymakers should bear in mind that the physical and socio-psychological well-being of the public may be at stake if the industry grows out of control.
Facilitation of health sector development should never be done at the expense of public safety and social well-being. 
Unlike conventional medicine, voluntary self-regulation is unlikely to be effective in preventing the ethical and social ills of the aesthetic industry.
Depending on the maturity and awareness of the public, greater clinical governance may be needed to better manage the supply and demand of aesthetic services, and ensure that aesthetic practice remains safe and scientifically substantiated.
Continuing public education is necessary to empower patients and consumers to take greater responsibility for their own health and aesthetic care decisions.
Talk about a lunch hour well spent—New Yorkers can now add quick-fix Botox treatments to their list of mid-afternoon errands.
The Upper East Side recently welcomed Smoothmed, an expert Botox on the go retail shop that requires no appointments and promises to banish fine lines in 30 minutes or less.
The walk-in concept, started by two plastic surgeons, takes the battle against aging to a newly accessible level.
The social fabric is changing, Dr Andrew Elkwood, co-owner of the store, said.
Men are more willing to have Botox, and women are more willing to talk about it.
The treatments are administered by physicians who have been trained in the Smoothmed method, and each patient's procedure history is kept on file to ensure the most efficient treatments.
And if a quick trip to New York is not in the future, ELLE.com has found the most effective anti-aging treatments, no needles required.
At 38, Juliet*, a New Yorker whose fashion sensibility falls somewhere distinctly south of the uptown/downtown divide, is simply far too cool—not to mention too young—to be a candidate for the F word.
Or so she thought.
But last year, just before Christmas, the self-described last person on earth anyone would suspect of having cosmetic surgery checked into an exclusive Upper East Side clinic for top-secret eyelid-boosting blepharoplasty, laser skin resurfacing, and the very lift (albeit, she hastens to point out, a minilift) she had previously feared.
For years, you see yourself in pictures and stare at the loose jawline and the tired-looking eyes, and you know that inside, your personality is so much younger and fresher and more joyful than that, Juliet says.
After a while, it's just like, `Screw it!
I m taking care of this.'
Not so long ago, with a tidal wave of miracle shots and high-tech lasers flooding doctors' offices, face-lifts seemed destined to land somewhere between perms and corsets in the dusty annals of beauty history.
But now, the same snip that was once seen as a last resort is back in vogue, thanks to two recent realizations: (a No matter how wash-and-wear they sound, most minor, quick-fix solutions tend to have relatively minor, short-lived results, and (b the dreaded wind tunnel look is all but a thing of the past.
Everyone's face-lift nightmare is the Joker, with those fish lips pulled back into a permanent smile.
It's awful!
Juliet says, letting out a small, smug chuckle.
Honestly, I look like I ve had a three-month vacation.
Wendy Lewis, a New York City cosmetic personal shopper of sorts who charges $350 per hour to help clients navigate the bewildering world of scalpels and injections, says at least 50 percent of her clients are still adamant surgery-phobes who either fear the downtime, price tag, or anesthesia associated with going under the knife, or who simply haven't made that particular mental leap (having a face-lift involves a moment of reckoning, like acknowledging that you're going through menopause, Lewis says).
However, the other half are increasingly warming up to the idea of having lesser surgeries relatively early on so that they won't require a major lift later, Lewis says.
Besides, in an era of $1,200-a pop injections that require regular refills, surgery makes a new kind of sense.
I know women who spend more per year on maintenance than they'd spend on a face-lift, Lewis says.
Sometimes, surgery can get you more bang for your buck.
THE NAME GAME Given the number of catchy-sounding lifts—some of which aren't in fact lifts at all—on the market, it's no wonder Lewis is more overbooked than ever.
There's nothing wrong with the plasma face-lift offered by Beverly Hills cosmetic surgeons Daniel Yamini, MD, and Steven Svehlak, MD, which uses nitrogen plasma energy to resurface the skin—but it's not surgery.
Similarly, the trademarked RestyLift, aka the Volumalift, touted on websites as a 30-minute injectable face-lift, is really just generous shots of the hyaluronic-based filler Restylane.
On the other hand, the one hour, no downtime Lifestyle Lift, which local TV news crews have heralded as a breakthrough in fast-recovery mini-nips, strikes me as a misleading marketing scheme, Lewis says.
As far as I can tell, it's a real face-lift, real surgery, but the terminology makes it sound like nothing.
That said, perusing the surgical menu at A list doctors' offices isn't necessarily any easier.
It practically takes a medical degree to choose between mini-lifts, S lifts, and the L I F T (limited incision face-lift technique).
These are nebulous terms that don't tell you a lot, says Park Avenue plastic surgeon David Hidalgo, MD, a renowned plastic surgery naturalist who was once chief of plastic surgery at New York City's Memorial Sloan Kettering Cancer Center.
Hidalgo simplifies things considerably, explaining that all face-lifts can be classified two ways: by length of incision and by how they manipulate the tissues underneath the skin.
Traditional face-lifts have a full-pattern scar that goes in front of the ear and then extends into the crease behind the ear and into the hairline, he says.
The procedures mentioned above, on the other hand, are variations on the short-scar technique, in which the incision stops before going into the hairline; its popularity grew in the 1990s, after minimizing downtime became a priority and surgeons began to realize they were over operating, removing too much valuable facial fat and pulling too tight.
Hidalgo says a short-scar procedure may sound less invasive, but it can be 90 percent as major as the traditional method.
But plastic surgeon Malcolm Paul, MD, of Newport Beach, California, says that while a short-scar procedure is perfectly adequate for a small amount of looseness in the jowls and cheeks, if a neck needs to be corrected from the jawline down to the clavicle, you need a longer incision to get to it.
Both doctors agree that whether a face-lift is mini or not is primarily determined by the treatment of the SMAS, a fibrous layer that lies between the skin and the muscle and can be tightened in three basic ways.
Least invasive is the SMAS-ectomy, which Hidalgo performs on 90 percent of his patients.
Here, we take a bite out of the SMAS through the incision, tightening the cheeks and pulling up the jowls, he says.
The much more involved Extended-SMAS procedure is like peeling an onion, layer by layer, he says; first the skin is pulled back, and then the entire SMAS is lifted and tightened separately.
The least common SMAS technique is the deep-plane face-lift, in which, he says, you go down to the deepest level and lift everything sandwiched together.
A Belgian surgeon named Patrick Tonnard, MD, came up with the latest way to handle this tricky middle layer: the relatively quick, less-invasive MACS (minimal access cranial suspension lift).
In this version, skin is elevated just a bit, allowing the doctor to weave a stitch through the SMAS and the fat of the cheek, hoisting them up like a purse string.
Manhattan plastic surgeon Lawrence Reed, MD, says the MACS has become his most popular procedure because of its limited undermining (the separation of skin from underlying tissues) and the fact that it leaves just a small scar in front of the ear.
People heal and look good in four to five days, Reed says.
Still, the greatest advance in plastic surgery wasn't technological, it was people realizing they wanted to look natural, Reed says.
We've had the ability to do a natural-looking face-lift for years.
Believe it or not, a lot of people used to like that tight look悠 ve been performing the short-scar lift since I opened my practice in 1976, but it took a long time for anyone to want it.
Lewis agrees.
The technique doesn't matter as much as the amount and direction of the pull, which is a matter of taste that varies from doctor to doctor, she says.
These are the questions you need to ask: How much tissue is being removed?
What areas will be affected - are you going to get my neck?
And where will the scar be?
WATERSHED MOMENT Water—or, more precisely, saline solution—is playing a major role in a new breed of lifts that employ tumescence, a technique borrowed from liposuction, in which large amounts of saline-diluted topical painkillers are injected just under the skin to manage pain with less anesthesia and blood loss during surgery, making it easier to operate.
 A six-year study of 678 face-lifts published in 2004 found that tumescence significantly reduced post-op swelling and bruising, improved scar quality, and minimized other complications such as skin necrosis and hair loss.
Plastic surgeon Zachary Gerut, MD, of Hewlett, New York, is at the deep end of the tumescence pool.
His Rapid Recovery Face-lift begins with the injection of nearly a liter of a diluted Novocaine/adrenaline/saline cocktail into the fatty tissues right under the skin.
Just before I start the surgery, the face becomes taut.
If you tap on it, it sounds like you're tapping a flexed muscle, Gerut says.
Patients look like Jabba the Hut.
It may sound unattractive, not to mention painful, but Gerut says the trick reduces the bruising and swelling associated with anesthesia and eliminates the need for drains and heavy-duty bandages.
(Other surgeons point out that post-op dressings are a matter of personal preference, no matter which technique is practiced.)
Santa Monica's Patrick Abergel, MD—a maverick whose new procedures are often viewed as risky by the competition—has devised a water lift, in which a syringe injector inserts a mixture of saline, lidocaine, and epinephrine that helps liberate the skin from its underlying structures.
He inserts the Abergel Skin Elevator, a custom-made tool not unlike a slim, blunt-ended knitting needle, through a small puncture wound and uses it to elevate the skin further, and then pulls back one to two inches of the loosened skin through a small one-inch incision tucked against the ear.
For those who don't like the idea of all those painkillers being injected, Hidalgo says tumescence doesn't necessarily mean a face-lift will be more hazardous (the drugs are too diluted to risk overdose); its main benefit is making the procedure easier on the surgeon, not producing a better-looking lift.
What I really don't like about it is that all the swelling doesn't go away when you're ready to put things back together, Hidalgo says.
You can't see the elasticity or tone in the skin.
But Paul, who uses what's known as a superwet technique—halfway between the standard amount of local anesthesia and tumescence—in all of his face-lifts, says it makes the SMAS lift off like an orange peel.
If separating the tissues is easier, surgery is less traumatic, there's less risk of nerve damage, and recovery is faster.
HANGING BY A THREAD Cautious optimism is still the prevailing attitude toward what is arguably the biggest development in facial surgery in the past decade: implanted suspension devices, particularly barbed threads—long, thin polypropylene strips that are inserted through small puncture openings near the hairline and swoop down under the skin, grabbing errant fat pads and yanking them back up to their youthful positions.
The most popular, Contour Threads, arrived on the scene two years ago, billed as scar-free, speedy (they take less than an hour to implant), and less pricey than a real lift, with very little anesthesia required.
The technique has proven to be trickier than promised.
While most doctors agree that Contour Threads weren't faulty in the way they originally suspected (they're don't slice through tissue with normal facial movements, and they're not usually difficult to remove), the end result varies widely.
Hidalgo has seen puckering and irregularities and, almost worse—no result at all.
Most of the time, when surgeons who use them show their results at national meetings, everyone looks at each other and says, `Is it me, or do you not see much difference between the before and after, either?' 
he says.
You cannot get anywhere near the results of a traditional, albeit more invasive, face-lift procedure.
Even those who do use the threads say their tensile strength is disappointing; the lift that was originally said to require a total of four threads can actually take up to 12, which means extra swelling and bruising, a downtime of up to 10 days, and, perhaps most disturbing, an increased amount of foreign material lodged permanently under the skin—all for something that might not last long.
Reed's have persisted as little as four months; Lewis tells her clients not to hope for more than two years.
A regular face-lift with cutting, sewing, and suturing is more invasive but has less bruising and swelling and looks normal much sooner than the Contour Threadlift, Reed says, though he finds the sutures useful for patients who are on blood thinners or are undergoing chemotherapy, for whom more invasive procedures are out of the question.
However, nearly every doctor interviewed for this story says future generations of the threads will hold more promise.
First up, an absorbable version of Contour Threads that consist of polydioxanone (the longest lasting absorbable suture to date), which has received FDA approval and is tentatively scheduled to hit the market in the next few months.
Absorbable sutures are said to have substantial tensile strength at six weeks, to have less at three months, and to be completely gone within six months.
What's the point of an absorbable thread if the current ones don't last long enough?
They make sense for a new generation of hybrid techniques, in which threads are getting their best results yet by working in tandem with other procedures.
Facial aging is a combination of gravitational changes and loss of volume, so in many patients, it's not enough to just reposition things.
You have to add volume, too, whether through a cheek implant, fat injections, or other fillers such as Juv?derm, Paul says.
When you take threads and add filler, plus Botox to quiet down the muscles around the brow, one plus one plus one doesn't equal three—it equals 10.
To wit: Paul devised a lift that combines ultrasonic liposuction on the neck and jawline, which emulsifies fat, making it easier to extract, and causes skin to tighten, with just two threads on each side to hold up the remaining lax skin.
Ironically, threads are also proving useful as an adjunct to full-blown surgery.
Tighter skin shouldn't be the only thing holding up a face-lift—you need internal support, says Stephen Bosniak, MD, a plastic surgeon who works in New York City and Rio de Janeiro.
His short-scar face-lift now has the addition of a few deep-reaching Contour Threads, which, he says, tack the SMAS up higher and give extra support to the lower jowls without the aesthetic problems of more superficially placed threads (plus, since the tissues are being undermined, they only need the thread to be in place until they re-adhere post surgery).
Last November, Paul published a paper on using barbed sutures in open mid-facelifts in Aesthetic Surgery Journal.
Part of the problem with a traditional face-lift is that skin, muscle—everything—gets pulled in the same direction.
Barbed sutures give you more control, in that you can pull deeper tissues vertically, and pull the skin in a different direction, he says.
When you put the barbed suture at a deep layer, it works like a shish kebab, catching multiple tissues and stacking them on top of each other to get volume over the cheekbone.
If you did that with a single, normal suture, you'd just catch the tissue in one place, and wherever the needle goes in, that's where you anchor it.
According to Paul, barbed sutures, which he leaves poking out of the surface of the skin for two days after surgery, also provide a revolutionary opportunity for post-op fine-tuning.
When the patient comes in two days later, she looks in the mirror, and I can say, `Okay, I think if your left cheek was up another millimeter, the result would be even more spectacular,' Paul says.
Nothing else has offered that level of precision.
Maria Siemionow, the Cleveland microsurgeon who, last December, led a team of seven male doctors in a 22-hour operation to complete the first facial transplant in the United States, explains how they replaced 80 percent of a patient's face.
"As a hand surgeon, I worked with a lot of children who were burn victims—they'd been holding up their hands to protect their faces from the fire. 
I thought there must be a way to use the techniques in hand surgery to help their faces.
But the skin that was used in facial reconstructive surgery doesn't have the pliability and vascularity that facial tissue demands.
And you also need functional units—a nose, lip, eyelids—we don't tend to have extras.
"When I received approval in 2004, I organized a team of more than 30 specialists to help screen and work with candidates who included patients who had burn injuries or suffered from some kind of congenital errors. 
These are people whose goal isn't to be beautiful, but to have a nose to take a shower—if you don't the water goes in the hole and floods the throat.
"In selecting a donor, we needed someone who had no major diseases, was the same gender—a woman can't worry about shaving her face!
—and matched for approximate age and skin color.
"There were eight surgeons in two teams. 
I was in both the donor and recipient rooms, overseeing the work and assisting surgeons as needed. 
One team did a skin graft to remove all tissues that were damaged.
Then we closed the vessels—you are trying to have a bloodless field. 
hen you are looking at the arteries, veins, and nerves—which will be connected to the arteries, veins, and nerves of the donor.
"The other team removed the donor's face, which took more than nine hours.
First, we lined up the transplant with the patient's face and reconstructed it structure by structure. 
Once you have matched all the vessels, you suture them together.
"You are trying to keep the surgery within the `critical ischemia time,' which means the window that you can cut off blood supply from the graft.
For the skin, this can be about 10 hours, in which time you should connect the arteries and the veins so that blood flow can return and the transplant can `pink up.
I don't remember any nervousness in the room because we were all too busy concentrating.
"Overall, the surgery went even better than expected. 
The patient will participate in physical therapy until she regains control of her jaw and cheeks.
We don't know how long that will take because this has never been done before."
Contrary to what you might assume, face fillers—Botox, Radiesse, Juv?derm, etc.—cost about the same as a face-lift: The pricing doesn't come out to be that different, says dermatologist Doris Day, MD, author of Forget the Facelift: Turn Back the Clock with a Revolutionary Program for Ageless Skin.
With face fillers, you pay less at a time, but since the results aren't lasting, your procedures will add up to about the same cost of a face-lift in the long run.
Yet despite comparable costs, the American Society for Aesthetic Plastic Surgery reports that while plastic surgery rates are dropping, nonsurgical treatments are greatly increasing.
What gives?
Day points to improved technology such as the new MiXto laser treatment and the growing range of face fillers on the market.
Dysport, a Botox competitor, just got FDA approval—and Sculptra, an injectible that lasts for up to two years instead of the usual six to nine months, isn't far behind.
There's really something for everyone—now we have fillers that go deeper, that aren't as superficial, and that come in a variety of consistencies and strengths, she says, adding, Sometimes I ll inject more than one product in the same person—I ll use an off-brand form of Sculptra to create some volume, and then I ll use Radiesse, Juv?derm, Restylane, or Evolence to fill in what I need to fill in afterward.
There are ways to mix them together to get the best results.
Though injectable fillers have made huge strides in the past several years, there are some situations where plastic surgery is the best option.
Face fillers can treat certain lines and wrinkles.
But their results expire and they can't tighten the skin or reposition the deep tissues, says plastic surgeon William Boss, MD.
And in cases where, for example, someone has a tremendous amount of excess skin in the neck area, they're going to need a full face-lift procedure.
For her part, Day agrees: If your neck is really sagging, there's nothing else to be done—it needs to be lifted and to have some skin removed.
Also, if your upper eyelids are drooping over and you can't see, you'll need an eye lift.
Those are the two main things I send people to plastic surgeons for—the neck and eyes.
Age can also determine what route you take.
We take it on a case-by-case basis, but for people in their thirties or early forties, I generally look for noninvasive alternatives such as injectables, says Boss.
And even as you age and become a potential face-lift candidate, you don't necessarily have to go all the way.
Boss, whose practice is based in New Jersey, has developed what he calls the Cool Lift—a procedure that he's been performing for just over two years and which mixes an internal laser treatment with suspension sutures.
Having seen how successful the laser was in shrinking the skin along the inner thighs, stomach, and arms, I incorporated it into a face-lift, says Boss.
The laser is utilized to melt fat, tighten the skin, and recontour.
Then suspension sutures are placed into the deep tissues to lift, and skin is removed.
According to Boss, his procedure differs from a full-fledged face-lift in that minimal skin is elevated, it's done under local anesthesia, and it has a shorter recuperation period—most patients can go out to dinner or back to work within five to seven days afterward.
Even though technology and doctors are making it progressively easier to get a quick fix (note NYC's SmoothMed, a walk-in Botox store owned by plastic surgeons), Day says that preventative steps and topical treatments come first.
She recommends investing in a good SPF 15 or higher sunscreen or makeup, and looking for eye and face creams that contain retinols: Anybody who can tolerate retinoic acid should use it, she says.
Niacinamide is also a good ingredient—it's an anti-inflammatory and antioxidant.
Day's favorites?
For face cream she likes Aveeno Positively Radiant Anti-Wrinkle Cream, Neutrogena Healthy Skin Anti-Wrinkle Cream, and Olay Regenerist Daily Regenerating Serum; for eye cream, she recommends Olay Professional Pro-X Eye Restoration Complex or SkinMedica TNS Illuminating Eye Cream.
Botox has many surprising applications—from disabling sweat glands to preventing migraines—but when you think of major facial contouring, it isn't the first injectable that springs to mind.
However, a recent study by Sydney-based plastic surgeon Steven Liew, MD, published in Dermatology Times showed that the drug can be used to narrow the lower face with, well, jaw-dropping results.
When injected into the masseter (chewing) muscle, botulinum toxin inhibits movement; over time, this causes the muscle to atrophy and shrink.
In Liew's study, which involved 82 female test subjects, all of the patients showed a measurable slimming of the jaw beginning two to four weeks posttreatment, reaching a plateau after three months and lasting up to a year.
The masseter is what we call a `stupid muscle,' meaning it has only one function, which is to chew, Liew says.
It has fewer nerve endings than the areas we inject for frown or smile lines, so the Botox doesn't dissipate as quickly.
Another cosmetic benefit is that diminishing the jaw creates the illusion of a hollowing underneath the cheekbones, which causes them to appear more chiseled.
Many patients say they don't need to wear blush anymore, Liew says.
Enlargement of the muscle may be genetic (in Southeast Asia, where Botox jaw reduction was first done in 1994, 20 percent of the population is affected by masseter hypertrophy, which Liew has dubbed "square-face syndrome"), or it can be caused by chronic teeth grinding and temporomandibular joint disorder (TMJ), issues that Botox also alleviates, along with the attendant pain.
Any notion that the condition can be brought on by an addiction to Bubble Yum, however, is unsubstantiated.
You'd have to chew a lot of gum to build up that muscle, says New York City and Miami dermatologist Fredric Brandt, MD.
That's not something anyone should worry about.
In cases where the breadth of the lower face is due to bone mass, Botox can still be used, but its effects will be less dramatic.
You can check to see what's causing most of the enlargement using a CT scan, Brandt says.
Some people are still very happy to have this done rather than go through jaw reduction surgery, which involves breaking and removing part of the bone.
Liew believes that the uptick in patients asking for this procedure is because an oval jaw is currently considered optimally attractive for women, as opposed to the '80s, when a stronger, more Sigourney Weaver look was in.
But anyone wishing to soften her facial silhouette should proceed with caution.
This procedure is best for younger patients, in their twenties and thirties, without laxity of the skin, Brandt says, because reducing the muscle is likely to cause more jowl formation in the neck.
When you get older, you actually want some of that muscle to hold skin taut.
Studies have also shown a slight decrease in chewing pressure posttreatment (it might be a little harder getting through a steak at first, Liew says), and only experienced derms should be trusted to wield the needle.
If the Botox is injected too far forward on the face, Brandt warns, you might get some muscle weakness in the lip area, which can affect the smile.
Dreading a lifetime of costly Botox injections?
Some eager surgeons are encouraging women to just cut it out (the muscle, that is).
A onetime procedure that costs about the same as two years of Botox will remove the corrugator muscle that creates vertical wrinkles between the brows.
It's usually done in conjunction with upper-eyelid surgery, but Kirkland, Washington–based plastic surgeon Gavin Dry, MD, says more patients are asking for it as an isolated procedure to prevent the brow from wrinkling in the first place.
According to Manhattan plastic surgeon David Hidalgo, MD, though, patients should keep in mind the change is permanent.
I wouldn't say, `Well, you're going to need Botox for the rest of your life, so let's go to the operating table,' Hidalgo notes.
That's not reason enough for surgery.
Besides, he warns that you may end up going back to the needle anyway: Removing the entire corrugator could cause nerve damage, so there's usually residual muscle function.
Rhinoplasty: One Woman's Plastic Surgery Nightmare On a bitter night in February 1992, Hope, a 19-year-old aspiring actor, and her boyfriend, an early Internet entrepreneur, headed out for some sinus-clearing chili dishes and tangy margaritas at a Mexican restaurant on Manhattan's Upper West Side.
It should have been a celebratory occasion: Six weeks earlier, Hope—whose pillowy lips, high, rounded cheekbones, big, bright smile, and wild auburn mane drew comparisons to Julia Roberts in Pretty Woman—had undergone rhinoplasty, commonly known as a nose job.
She had long viewed her olfactory organ as oversize, practically begging for reduction and refinement so as to harmonize with, rather than from, her otherwise comely features.
A month and a half post-op, Hope's nasal anatomy was still in a highly delicate state, but the black-and-blue bruising around her eyes had faded, and the nose itself was finally pain-free.
However, as she entered the restaurant on that dim winter evening, Hope smashed her schnoz smack into a plate-glass door that she failed to see.
Blood spattered everywhere; she lost consciousness for a time and ended up in the emergency room.
She had shattered her septum, twisting her nose to the right, narrowing and raising her right nostril in Picasso-esque fashion, and leaving her with a permanent asymmetry in the middle of her face.
The truth is, Hope already hated her nose job and even wished she had her old honker back.
He gave me a too-thin nose with a pinched tip, she said bitterly about her plastic surgeon.
I looked like I d had a nose job.
It was the Diamond Nose.
The term, once uttered worshipfully but now muttered in denigration, refers to the handiwork of Howard Diamond, MD, a Manhattan plastic surgeon wildly popular in the '60s and '70s who, irrespective of patients' individual features, churned out cookie-cutter conks instantly recognizable by their uniformly scooped-out bridges and turned-up ends.
Beholding her new reflection served only as a cringing reminder to Hope of her adolescent self-loathing and desperate approval-seeking.
It was an impulsive decision, she says.
My mother encouraged it.
A lot of my friends had their noses done.
I wanted to fit in.
At the end of the day, I didn't like me, and that's why I had the surgery.
Now, when I look at pictures taken before, I ask, `Why did I do this?'
There was nothing wrong with me.
Hope learned a classic plastic-surgery lesson the hard way: Cosmetic procedures, especially rhinoplasty, are not to be indulged in lightly.
The operation, surgeons say, may be the most challenging and difficult of all cosmetic surgeries, and yet, according to the most recent statistics from the American Society for Aesthetic Plastic Surgery, we now undergo some 150,000 rhinoplasties per year, and the American Academy of Facial Plastic and Reconstructive Surgery estimates there are around 40,000 do-overs annually.
A certain percentage require revisions, including small tweaking as well as big operations, says Jack Gunter, MD, professor of plastic surgery at University of Texas Southwestern Medical Center in Dallas.
The statistic that's reported is 10 to 15 percent overall.
The nose is a diminutive structure, bely its internal complexity, which includes a valve and several bony structures that control the dynamics and temperature of airflow and 5 million scent receptors that detect odors and transmit the smells to the olfactory bulbs in the brain.
And rhinoplasty is notorious for its vagaries.
Nasal tissues can be wayward, the cartilage refusing to bend to the surgeon's will or the skin too thick to drape smoothly and evenly over the underlying structure.
Furthermore, an alteration such as the removal of a hump from the bridge can change the shape and position of adjacent parts of the nose, prompting a cosmetic domino effect.
But the real wild card, Upper East Side plastic surgeon David Hidalgo, MD, says, is how a patient heals.
It is not unusual for a result that looks great on the table to be less stellar when healing is complete.
This can be due to scar-tissue formation that masks subtle aesthetic nuances in shape or to soft-tissue contraction during healing, which can collapse cartilage that has been weakened as a side effect of improving shape.
Given the fickleness of the nose and the protean nature of the procedure, many plastic surgeons think rhinoplasty is best left to the relatively few who specialize in it.
There are perhaps only a dozen surgeons who do more than 250 rhinoplasties annually, says Rollin Daniel, MD, a nose-job-only surgeon in Newport Beach, California, and cofounder of the Rhinoplasty Society, an elite professional group.
And secondaries, Gunter says, are the most difficult in cosmetic surgery.
With primaries, we have to reshape the nose.
With secondaries, there's very little normal anatomy.
The cartilage is distorted or missing.
You have to rebuild the nose.
There's scar tissue, making the dissection more difficult.
The skin is harder to drape properly.
Mallets and chisels aside, cosmetic surgeons also agree that, emotionally and psychologically, repeat rhinoplasty patients are often emotionally fraught (and justifiably so!)
, ratcheting up the pressure of an already demanding procedure.
Hope certainly fit the profile of a secondary rhinoplasty candidate.
Not only was she unhappy about her nose, but its malfunctioning physiology caused a round-the-clock deluge of mucus that led to countless indignities, from chronic sinus infections to boyfriends' complaints about her snoring.
Yet putting up with such unpleasantries still seemed preferable to another agonizing round of surgery—until 1996, when Hope bumped into a high school classmate she hadn't seen in many years.
The woman stared at her for several unforgiving seconds before blurting, What happened to you?! That was it.
The lash of those words provoked Hope to throw down the gauntlet, or, in her case, the Breathe Right strips.
No longer a self-pitying, scalpel-shy victim, she became a disgruntled cosmetic-surgery consumer entitled to the Rolls-Royce of reconstructive nose jobs.
Jetting between New York and Los Angeles to meet with a who's who of surgeons—15 or so in all—she racked up thousands of frequent-flier miles and shelled out sky-high consultation fees.
Her timing was propitious, coinciding with a revolution in rhinoplasty that transformed and refined the procedure, enabling surgeons to achieve more exacting results.
Its leading practitioners rejected the homogenizing reduction of noses and innovated a radically new technique.
In the old-school closed method, all incisions were made inside the nose, which limited the possibility of making dramatic changes or correcting extreme anomalies.
In the new open method, a single, minimally scarring incision is made across the columella, the strip of skin separating the nostrils, which, like popping the hood of a car, releases the nose's outer covering of skin.
Peeling it back, the surgeon exposes the entire inner hub of nasal organs and can repair, rearrange, and reconstruct any damaged, off-kilter, or missing pieces with precise cartilage grafts.
An open rhinoplasty is like opening your closet door wide to see everything hanging on the rod together, Hidalgo analogizes.
This allows you to select, edit, and rearrange freely.
Closed rhinoplasty only opens the door a crack, so you have to reach in and pull items out one at a time.
It's dark in there, and you never see everything at once.
Open rhinoplasty is especially useful in secondary operations, where often all the clothes have been pulled off the hangers and lie in a crumpled heap inside.
Although this evolution of the procedure should have made it a snap for Hope to land a specialist, remarkably, she found herself out of options in 2002.
No doctor would agree to operate, she says.
They didn't think they could improve on my nose.
They'd say, `You'll never be happy.
You're a pretty girl.
Leave well enough alone.'
But in 2008, long after Hope had forsaken her quest, the possibility of a reprieve presented itself when she came across an article on using injectable fillers to camouflage nasal deformities and finesse flawed rhinoplasty.
Coincidentally, her best friend had just recommended a registered-nurse-cum-aesthetician named Robin Hillary who, working from an examining room at an Upper East Side plastic surgeon's office, could do wonders with such needlework.
Hope quickly became a devotee of Hillary's no-knife nose job, which entailed endless injections of Juv?derm, a hyaluronic-acid-gel formulation that was squirted into Hope's ragged nasal bridge to smooth its surface irregularities.
Though impermanent, the effect was a clear improvement.
However, when Hillary tried to inflate Hope's collapsed right nostril with filler, the treatment backfired.
It further compressed the already squashed nasal opening, almost sealing it shut.
After about six months of giving Hope's nose her best shots, Hillary, recognizing that her syringes had their limitations, suggested that Hope meet with the plastic surgeon working away in the very next room.
Other than the $250 consultation fee, what did Hope have to lose?
So in December 2008, Hope met with plastic surgeon Craig Foster, MD.
You've got the stigmata of a rhinoplasty.
The tip is pinched, Foster noted, ticking off a litany of problems and speculating about their causes and possible solutions.
The goal is to make your nose look less operated-on, straighten it, and open up your airway, he said.
On January 13, 2009, Hope returned to undergo secondary rhinoplasty surgery, a $10,000 operation ($1,000 more than a primary) in Foster's state-of-the-art facility. 
(Many top surgeons outfit their private offices with customized operating rooms.)
In preparation, Hope changed into a burgundy medical gown and matching booties and took a seat in a private waiting area, where I kept her company.
For almost 20 years, I ve lived with a crooked nose, she said, her voice cracking and her eyes welling.
All I ve done is look at other people's noses.
I think my twisted nose is the first thing people see, and they think something really bad happened, like I had a coke problem.
Now I m nervous, but I m excited.
I can't believe I m finally going to do this.
But it was fate.
Those were Hope's parting words as a nurse appeared, escorting her to the operating room as I trailed along.
Climbing on the operating table, Hope lay back, offering up her delicate wrist for the anesthesiologist's fentanyl-laced IV drip and immediately conking out.
Foster daintily sliced across her columella with a small blade and retracted the outer layer of skin, keeping it in place with surgical hooks, his access to the subcutaneous nasal skeleton of bone and cartilage now unfettered.
Foster's first order of business was Hope's septum.
Though crooked, it was surprisingly unmolested, as he put it, and thus was an excellent donor site for grafting.
(In some cases when a nose has already been operated on, there may be no excess septal cartilage to spare for grafting, in which case an ear or a rib provides the source.)
Foster pared and shaped translucent cuttings from it for implanting in the defective areas.
To counter the depression in the cartilage on the left side of Hope's nose, he sewed a spreader graft to her septum, straightening out its contour and widening the middle of her bridge to ease her breathing and stop her unremitting spigot of mucus.
Everybody's nose runs all the time, Foster said while doing so.
The usual problem in an operated nose is that scars inside get in the way of the flow.
It pools and runs out of the nose instead of going back down the throat.
To remarry the tip and the bridge of Hope's nose, as Foster put it, he added a strut to stabilize its pinched end and pre-empt drooping.
And to flesh it out and make it broader and rounder—at Hope's request—he sutured to each side a lateral graft.
Finally, to fix her flattened right nostril, he sliced an opening along its outer edge, into which he inserted a rim graft, much as you might cut open a tiny pita-bread pocket and fill it with your stuffing of choice.
It had been an hour since Foster began.
He had already re-draped the skin, stitching it to the columella.
But then, without looking at his hands but rather keeping his eyes straight ahead, he started running his fingers up and down the length of it, as if playing the piano, relying on touch to determine the symmetry and linearity of forms.
He must have hit a wrong note, because his fingertips hesitated, sensing a deficiency on the left side, which meant he wasn't done yet.
Although he had already stitched the columella closed, Foster cut the suture and lifted the skin once again, adding another small graft to the tip.
Let me just noodle this a bit, he said, focusing on Hope's nostrils before finishing up.
Her rims are in a better position—not perfect.
Five to 7 percent of my rhinoplasties aren't perfect.
I have to futz around with them six to nine months later.
But Hope was immediately over the moon with her new nose.
Right away, despite her post-op condition—which she described as like being hit by a Mack truck, her nose so blocked that it felt as if she'd snorted a brick, the dried blood caked onto the sutures tugging at her tender columella, not to mention her inability to sleep upright as dictated by the aftercare instructions—she was able to pierce through an addling haze of Vicodin to behold, in the blimp of swollen tissue throbbing in the center of her face, an inchoate vision of nasal pulchritude.
All the wrongs that had been plaguing Hope for years had finally been made right.
Not only was her ski-jump bridge gone, but so were the pinched tip and the broken septal bone poking out on her right side.
And—finally!
—she had two open and functional nostrils.
As Foster had counseled Hope to be prepared for extended swelling—nine to 12 months for a secondary rhinoplasty (as compared to six to nine months for a primary)—she took it in stride, her spirits buoyed at every milestone in the healing process.
Among the most memorable was Day 34, the first time she sneezed, the blast shooting out any remaining temporary stitches.
Oh, my God, it felt so good! 
she recounted.
It was better than an orgasm!
As the months passed, her days of snoring and sinus infections became distant memories, and her new nose brought unexpected positive changes, like a newly vibrant sense of smell, as well as livelier taste buds.
And because Hope could now breathe out of both nostrils, her oxygen intake was bolstered and her skin more radiant, eliciting compliments over its healthy glow.
In April, at a follow-up appointment with Foster, Hope reported the reactions that she had been receiving about her nose.
People are saying, `You look great.' 
But they can't figure out what it is.
A good sign of surgery is when you can't pinpoint where the change comes from, the surgeon replied.
I love my new nose, she said, breaking into her Julia Roberts grin.
It's still swollen, but I know it's just right—not too small, not too big, not too wide, and not too skinny.
This is the nose I wish I d gotten from the get-go.
It's a botched job gone good.
DEFINITION OF AESTHETIC PRACTICE There is currently no internationally accepted definition of aesthetic practice.
The UK Cosmetic Surgery Interspecialty Committee has defined cosmetic surgery as an area of practice involving "Operations and other procedures that revise or change the appearance, colour, texture, structure, or position of bodily features, which most would consider otherwise to be within the broad range of 'normal' for that person".
The American Board of Cosmetic Surgery has defined cosmetic surgery as "a subspeciality of medicine and surgery that uniquely restricts itself to the enhancement of appearance through surgical and medical techniques.
It is specifically concerned with maintaining normal appearance, restoring it, or enhancing it beyond the average level toward some aesthetic ideal".
STATISTICS ON COSMETIC SURGERY According to statistics from the American Society for Aesthetic Plastic Surgery (ASAPS) in 2005, there were nearly 11.5 million surgical and nonsurgical procedures performed in the United States.
[2] Surgical procedures accounted for nearly 19% of the total, with nonsurgical procedures making up 81% of the total.
Since 1997, there has been an increase of 444 percent in the total number of cosmetic procedures.
Surgical procedures have increased by 119 percent and nonsurgical procedures by 726 percent.
he top five nonsurgical cosmetic procedures in 2005 were:
5. Chemical peels (556,172) Women had nearly 10,500,000 cosmetic procedures performed in 2005, accounting for 91.4% of the total.
Men had 985,000 procedures, approximately 9% of the total.
People between the ages of 35 and 50 years accounted for the majority of procedures (accounting for 5.3 million (47%) procedures.
Those between 51 and 64 years accounted for 24%, patients aged between 19 and 34 years accounted for 24%, those 65 years and older accounted for 5%, and those aged 18 years and under accounted for 1.5%.
The most common procedures for those aged 18 years and under were laser hair removal, microdermabrasion, rhinoplasty (nose reshaping), otoplasty (cosmetic ear surgery), and chemical peel.
The majority (48%) of the cosmetic procedures were performed in an office facility, 28% in a free-standing surgi-center, and 24% in a hospital.
Americans spent approximately $12.4 billion on cosmetic procedures in 2008.[2]
THE SOCIAL IMPLICATIONS OF AESTHETIC MEDICINE Due to the increasing demand for aesthetic procedures, it is not uncommon for patients to encounter a menu of aesthetic supplies and procedures.
These range from skin care products, skin rejuvenation (tightening, pore reduction, blemish removal, smoothening and tightening the skin), anti-wrinkle treatment, acne scar treatment, pigment removal, stretch mark removal, neck lifting, hair restoration, hair removal, breast firming/enlargement, skin whitening, cellulite removal, lip enhancement, tattoo removal, broken capillary treatment, square jaw treatment, nonsurgical facelift, fat removal, anti-aging medicine, hormonal therapy to mesotherapy.
Numerous skin rejuvenation treatment programs (topical creams, skin care products), skin rejuvenation procedures (such as filler and cosmetic botulinum toxin injections, lasers, light devices, radiofrequency devices, and surgical procedures) have been introduced by dermatologists and plastic surgeons and subsequently, by diverse medical specialities.
Nonmedical practitioners, e g , the beauticians and spa operators, have jumped onto the bandwagon to provide such services.
Many of these aesthetic treatments claim to rejuvenate the skin but are not supported by good scientific evidence.
Services and procedures that are unproven in efficacy by medical practitioners are often provided at significant cost to patients, which is considered by many medical practitioners to be a deviation from the normal practice of modern medicine.
Many medical practitioners have perceived such deviations to be a growing problem that needs to be addressed as it undermines the trust in and professionalism of the medical fraternity.
Concerns have been raised regarding safety issues as well as the quality of such services by the medical profession and the public.
Some patients even sustain injuries and complications from these procedures.
THE MANY CAUSES FOR INCREASED DEMAND FOR AESTHETIC PROCEDURES Several factors can be attributed to the increasing demand for aesthetic dermatology procedures, namely, (1) the secular consumer culture among the population at large to prolong youthfulness and self-image, (2) economic abundance, in particular, among well paid executives during good economic climates and the large proportion of retiring baby boomers who have accumulated sufficient savings as they reach their retirement age, (3) technological and medical advances whereby new cosmeceutics and devices have been invented to treat cosmetic disorders with minimal downtime and complications, (4) media-driven demand and hype promoted by some beauticians, medical practitioners, and the cosmeceutical and medical devices industries, (5) high-pressure advertising by various media, (6), a breakdown of institutions and cultural constraints, philosophy, policies etc and, (7) a lack of regulatory control that would help to differentiate evidence from nonevidence-based aesthetic procedures and appropriate training and accreditation regulations.
 All these factors lead to consumer-driven medicine.
Consumer-driven medicine is often industry-driven medicine with a strong motivation for profit which, in turn, leads to questionable practices and infringement of medical ethics.
It is often media-driven medicine because the media benefits from consumption by serving as the vehicle of advertising.
Advertising encourages needless consumption that may lead to unethical promotions.
EFFECTS OF INCREASED DEMAND FOR AESTHETIC PROCEDURES Increased demand for aesthetic procedures has resulted in an ever-increasing number of beauty parlour and charlatans providing scientifically unproven aesthetic services and making unproven claims.
There is also an increased interest among medical/dental practitioners offering aesthetic procedures for which they were never trained during their medical training, and a confused public who will not be able to distinguish a medically trained aesthetic physician from one who is not.
This confused public is beginning to lose confidence and trust in the medical profession because some practitioners are offering nonevidence-based aesthetic treatments and merge their treatments with those offered by beauticians and participate with media to promote their treatments, thereby trivializing and commercializing medical services.
These commercialized services tend to falsely promote such procedures as having low/no risk, omitting side effects, exaggerating benefits, exaggerating the indications rather than than the limitations of the treatments, overstating good results and hiding complications and poor responses, and tending to make unsubstantiated claims of superiority.
This has been referred to as the invisible hand of the marketing department.
UPHOLDING THE INTEGRITY AND RESPONSIBILITIES OF THE MEDICAL PROFESSION IN AESTHETIC MEDICINE The medical fraternity has been traditionally regarded as a credible and trustworthy profession that has typically abided by the time - honored Hippocratic Oath.
The advice and services offered by medical professionals are often taken seriously with the belief that doctors will always provide scientifically proven and effective procedures. 
Their medical training and government regulations in most countries often ensure that the profession maintains a high standard of practice.
The public generally trusts medical practitioners to carry out aesthetic procedures rather than to leave it to an untrained beautician.
Thus, medical practitioners are attracted by the fairly lucrative and easy way to make a living than to practice conventional medicine.
These factors have inevitably driven medical practitioners of diverse specialities into providing aesthetic services.
We know that aesthetic medicine, as it is promoted today, generally does not have strong evidence-based rigor in many of the procedures offered.
There is a large gap between evidence and practice.
There is a tendency for practitioners of aesthetic medicine to move away from science to quackery.
This has led to loss of objectivity and of the conventional professional pursuit of excellence which is expected in the practice of medicine.
The problem is further compounded by the fact that, at the moment, there are no hard-and-fast rules governing the way aesthetic procedures like botulinum toxin injections are carried out or promoted. 
Patient safety is left to the discretion of individual doctors.
In many countries, there is no proper accreditation process to regulate the practice of aesthetic medicine.
Many aesthetic practitioners are not adequately trained to carry out aesthetic procedures.
This has resulted in patients complaining against medical practitioners.
The practice of aesthetic medicine should not be exempted from the need for structured training and accreditation.
This ultimately serves to protect the public from unproven and unsafe treatments.
Leaving the aesthetic medicine industry to regulate itself is not a feasible solution as professional and ethical standards in an unregulated industry might take a hit in this lucrative business.
In Singapore, the Singapore Medical Council (SMC) Ethical Code and Ethical Guidelines require doctors to treat patients according to generally accepted methods.
A doctor shall not offer to patients, management plans or remedies that are not generally accepted by the profession, except in the context of a formal and approved clinical trial.
The guiding principles in any medical treatment must be that it is effective and that due cognizance will be given to patient safety.
In the context of aesthetic practice, it must go beyond the Do No Harm principle and must be seen to benefit the patient positively.
There are many examples to quote to confirm that this principle is often not practiced in aesthetic medicine.
For example, one report on the treatment of cellulite with noninvasive devices including massage, radiofrequency, mesotherapy, carboxy therapy etc concluded that … no treatment is completely successful as none are more than mildly and temporarily effective.
However, despite the lack of evidence to support efficacy, treatment options continue to proliferate for treating cellulite.
Another report on mesotherapy stated …despite the increasing interest in mesotherapy as an alternative method for body contouring, there are few reports of its safety, efficacy, and mechanism of action.
Their study on the efficacy of mesotherapy for body contouring concluded that mesotherapy is not an effective alternative treatment modality for body contouring.
NEED FOR ETHICAL GUIDELINES FOR THE PRACTICE OF AESTHETIC MEDICINE FOR THE MEDICAL PROFESSION Evidence-based medicine/practice There is a need to implement guiding principles on the practice of aesthetic medicine by the medical profession.
Evidence-based practice is probably the best approach.
As per the SMC Ethical Code and Ethical Guidelines, doctors are responsible for ensuring that they are competent and adequately trained before performing any treatment or procedure on a patient.
He or she should keep abreast of medical knowledge relevant to practice and ensure that clinical and technical skills are maintained.
WHAT IS EVIDENCE-BASED MEDICINE?
Evidence-based medicine (EBM) is defined as "the conscientious, explicit and judicious use of current best evidence about the care of individual patients".
The keywords in the definition are conscientious which signifies an active process which requires learning, practice, and reflection; explicit which describes it as a transparent process used to practice EBM; current reflecting being up to date, and best which signifies that one should seek the most reliable evidence source to inform practice.
EBM is a way of thinking and working with the sole objective of ensuring improved health of our patients.
The term, evidence-based practice is often used instead of EBM and is defined as integrating one's clinical expertise with the best external evidence from systematic research.
It should be noted that therapeutic guidelines are not the same as EBM.
Many dermatology guidelines now incorporate a grading system that describes the quality of evidence used to make recommendations and describe their strength.
Searching for relevant information for your patients frequently opens up more rather than fewer treatment options.
It is estimated that to keep up with the best evidence available; a general physician would have to examine many journal articles daily and throughout his/her life.
The trick is to know how to find information efficiently, appraise it critically, and use it well.
The techniques and skills needed to find, critically appraise, and use the best evidence available for the care of individual patients, have been developed over two centuries.
These techniques and skills are currently best known as EBM.
Two filters need to be applied if one is to keep practicing EBM: The first is to discard irrelevant information, and the second is to spend more time looking at a few high-quality papers, as per the concept of hierarchy of evidence.
Suzanne Fletcher and Dave Sackett described "levels of evidence" for ranking the validity of evidence about the value of preventive maneuvers, and then assigned them as "grades of recommendations".
Modifications of the above system have been proposed over the last few years. 
But basically, all utilize levels of evidence and grades of recommendation (www.cebm.net/levels_of_evidence.asp).
Levels of evidence are based on study design and the methodological quality of individual studies. 
Grades of recommendation are based on the strength of supporting evidence, taking into account its overall level and the considered judgment of the guideline developers.
THE COCHRANE SKIN GROUP The Cochrane Collaboration (www.cochrane.org), an international voluntary group of reviewers and researchers from a range of professional backgrounds dedicated to producing systematic reviews, was established in 1992
In December 1997, a Cochrane Skin Group was registered with the Cochrane Collaboration to prepare, maintain, and disseminate reviews on the effects of health care for people with dermatological conditions.
The Group has to produce the best evidence about the effects (good or harm) of health care interventions for dermatological diseases.
The scope of the Group includes any dermatological problem that leads a person to seek help from a health care practitioner.
The Group seeks to find and analyze all evidence on the effectiveness of preventive, medical, and surgical interventions and of different models of health care provision and management of dermatological diseases. 
This includes evidence about dermatological treatments that are sold over-the-counter or are widely available.
The Cochrane review is systematic, structured, and painstakingly assembled; it minimizes bias and ensures quality. 
Whenever possible, Cochrane reviews are based only on RCTs because of the major biases associated with other study designs for assessing treatment effects. 
After approval according to the internal and external refereeing procedures of the Group, the review is published in the Cochrane Library (www.cochranelibrary.com).
A useful source for evidence-based dermatology reports can be found in the Cochrane Skin Group review wherein specific reports on treatment and other studies are collated and analyzed based on the strengths of the reports.
Examples of such Cochrane reports are: Laser and photoepilation for unwanted hair growth reported by M. Haedersdal and P.C. G?tzsche.
The authors' conclusion was, "some treatments lead to temporary short-term hair removal.
High quality research is needed on the effect of laser and photoepilation".
Laser resurfacing for facial acne scars reported by Jordan R.E., Cummins C.L., Burls A.J.E., Seukeran D.C. 
The authors' conclusions were, "the lack of good-quality evidence does not enable any conclusions to be drawn about the effectiveness of lasers for treating atrophic or ice-pick acne scars. 
Well designed, randomized, controlled comparisons of carbon dioxide versus Erbium:YAG laser are urgently needed."
WHAT ARE WE TO DO WHEN THE IRRESISTIBLE FORCE OF THE NEED TO OFFER CLINICAL ADVICE MEETS WITH THE IMMOVABLE OBJECT OF FLAWED EVIDENCE?
All we can do is our best: give the advice, but alert the advisees to the flaws in the evidence on which it is based.
APPLICATION OF EVIDENCE-BASED PRACTICE IN AESTHETIC MEDICINE IN SINGAPORE On 1 November 2008, the SMC introduced its guidelines on the practice of aesthetic procedures for Singapore medical practitioners.
Aesthetic procedures under List B are currently regarded as having low or very low level of evidence and are not considered as being well-established. 
Medical practitioners who wish to perform List B aesthetic procedures should list themselves with the SMC's APOC using a prescribed List B notification form before carrying out any List B aesthetic procedure. 
Doctors may be subject to audit and should comply with requirements set by the SMC's APOC and the Ministry of Health. 
Proper documentation of the indications and outcomes of the treatments and procedures are of utmost importance.
Medical practitioners who wish to perform procedures that fall within the definition of Aesthetic Practice in the guidelines but which are not found in either List A or List B, will also have to list themselves with the SMC's APOC. T
he APOC may then decide on the classification of the procedure or further dictate how the doctor should proceed. 
It is recommended that medical practitioners should not practice such procedures until they have been classified by the SMC's APOC.
List A aesthetic procedures This list reflects the aesthetic treatments and procedures that are supported by moderate to high level of scientific evidence and/or have local medical expert consensus that the procedures are well-established and acceptable. 
They are grouped into noninvasive, minimally invasive, and invasive.
List B aesthetic procedures List B contains aesthetic treatments and procedures that are currently regarded as having low/very low level of evidence and/or being neither well established nor acceptable currently.
Having satisfied all the above circumstances and documentation, it is still required of doctors to practise List B aesthetic procedures only under highly monitored conditions that enable the efficacy, or lack thereof, of such procedures to be objectively demonstrated. 
The objectives, methodology, analysis, and findings obtained through such treatments must be of sufficient scientific validity to establish efficacy or otherwise. 
In addition, patient response should be documented and retained alongside all case records of such treatments. 
In the event that the procedure yields adverse or neutral outcomes, the practice of the procedure(s) must be terminated.
The patients must not be charged highly profitable fees for such procedures of low evidence, but a fair fee representing the cost of the procedures plus the cost of providing and administering them. 
Financial documents relating to these procedures must also be retained for the purpose of audit when required. 
No medical practitioner shall advertise that he or she is performing aesthetic procedures in List B. COMPLIANCE WITH SINGAPORE MEDICAL COUNCIL AESTHETIC PROCEDURE GUIDELINES Any medical practitioner who performs any aesthetic procedure that is not in accordance with these guidelines or with any requirements set by the SMC or MOH will be deemed by the medical profession as being unethical and bringing disrepute to the profession. 
Such a doctor may be liable for disciplinary action by the SMC.
"You would be so pretty if you got your nose fixed," my mother said.
Fixed. 
Like I was damaged.
She introduced me to friends with a disclaimer: "My daughter wasn't supposed to look like this."
 She'd chosen my father for breeding because of his handsome face and long fingers, and she wanted her daughter to be a button-nosed pianist. 
My nose was huge, and I didn't play the piano, and my parents are now divorced. 
Just saying.
My beak was big, without a doubt— lumpy, crooked, and, I dare say, stereotypically Jewish amid the sea of wealthy Protestants in our Illinois town (voted, in 1997, one of the best in the nation in which to raise children).
"Pain for beauty," was my mother's motto, as she warped my long hair into scalp-pinching braids. 
A former actress and model, she appeared in a few episodes of All My Children before I was born. 
When I was growing up, she couldn't leave the house without asking, "Do I look beautiful? 
You're not looking—look. 
Beautiful?"
My mom had convinced her frugal mother to pay for her own nose job when she was a teen; later, she had it tweaked twice to get it right.
 So when a salesclerk or waitress recognized us as mother and daughter, she was insulted. 
Her surgeries were supposed to have saved her from looking like me.
At her best, my mother has always been generous, fun, witty, and deeply concerned about me, and, in her way, she was trying to help. 
To her, a smaller nose would be one less obstacle to a happy future. 
But when she asked again and again if she looked beautiful, I'd burst into tears, desperate for her to tell me I was beautiful too.
And yet, oddly, when I wasn't crying, I was also totally into my nose.
I was convinced that it made me funny, approachable, quirky, Jewish; that my personality, my identity, and my relationship with my mother were all in there. 
Depending on the day, my haircut, the bathroom lighting, or whether a boy had asked me to a dance (and they did), I ping-ponged between feeling exotic and like a total freak. 
Still, I rejected a "happy puberty" nose job at 13 and a "sweet 16" rhinoplasty, too.
Yet by 2006, when I was 24, single, and working in film production in New York City, it became clear that my mother wasn't the only one who reacted negatively to my nose. 
People stopped me on the street to tell me I looked like Barbra Streisand. 
Japanese tourists wanted to take pictures with me. 
Hairstylists told me I needed to play down my nose—"no offense." 
I started writing down the unprovoked remarks in a journal; in one year, I recorded 59.
2006/9/23 Gay man at a party in Williamsburg: "Damn, girl, why you never got that thing taken off?"
A woman overhearing: "I like it, don't get it fixed."
Her friend: "You're Jewish, right?"
The nose hadn't stopped me from dating, getting jobs, or making friends. 
But it was beginning to feel like some silly outfit I'd put on to annoy my mother. 
After years of defiantly building an identity around it, was I finally going to admit that I hated it too?
I went to see a plastic surgeon, who created a digital projection of the nose he could give me: scooped, small, with a slight upturn. 
The picture looked like another girl. 
And she was so pretty.
But I was worried. 
Would changing my nose change my identity? 
Would I lose the thing that gave me character? 
The doctor seemed amused. 
"Do you really think you are your nose?" 
he asked.
Well, yes. 
And this, I realized, was the real problem: My nose overshadowed what I loved about myself. 
After years of struggling not to be my mother's mirror, it was time to be happy with my own reflection.
My surgery was in November 2007. 
My mother flew to New York and booked a hotel for us near the doctor's office, where she drew me baths and read to me for the first few days of my recovery—the kind of mothering I had longed for. 
"Relax," she told me as she replaced the bandages. 
"Soon you'll be prettier than me."
My new nose isn't tiny, but it's smaller, more sculpted. 
As one colleague said, "Now I can see your eyes, cheeks, and smile, too." 
I hate to say it, but my mother is right: It's the nose I was meant to have.
I don't have to make excuses for it or cover it with my hair. 
I can just be.
Asked why she used to be so cruel about my nose, my mother has explained that her own mother made her feel worthless. 
While that sad cycle helps me to understand her, it doesn't excuse our past. 
Now each time she tells me I'm lovely, I feel the full force of its opposite—the stinging comments of my youth. 
But at least I believe her. 
I hate to admit how much that matters to me.
In the post-ski-jump era, the best nose job is one that looks like no nose job at all.
According to NYC plastic surgeon Steven Pearlman, MD, who specializes in revision rhinoplasty (i e , do-overs), spreader grafts—pieces of the patient's own cartilage he inserts into the middle third of the nose—create a smoother curve from the brow to the tip, with no 'drop-off' in the middle.
For patients with very thin skin, who were previously prone to the bony-looking shrink-wrap effect, Pearlman also adds ultrathin grafts of cartilage or tissue taken from the temple to the tip of the nose, a trick he refers to as carpet padding.
Scalpel-phobic?
Injections of Botox and fillers temporarily smooth bumps, lift drooping tips, and add definition, says L A –based cosmetic surgeon Alexander Rivkin, MD.
He perfected the profile below in minutes with two vials of the filler Radiesse, which lasts 10 to 12 months.
For a long-term fix, Rivkin recently began offering the same treatment using ArteFill, a filler made of Lucite spheres suspended in collagen, which is shown to last up to 15 years.
Botox has become synonymous with many things — vanity, self-esteem, the search for eternal youth — but before it became a fixture in high-end dermatologists' offices, it had a lot of non-cosmetic medical applications. 
(Well, after scientists learned how to process Botulinum toxin so that it no longer killed us immediately.)
This past Friday, Botox was approved by the FDA for use in the treatment of chronic migraines. 
In a story in the New York Times, a company spokesperson predicted that Botox sales for medical use would soon surpass sales for cosmetic use.
 Botox, which is produced by Allergan, was first approved for use in 1989 for various eye muscle disorders.
 It wasn't approved for cosmetic use and wrinkle reduction until 2002, after doctors noted the rather fortuitous effects that the injections produced in the skin around the treated areas. 
And, eureka, a "miraculous" and contentious potion was born.
Even more recent studies show that Botox provides a moderate, yet statistically significant decrease in the number of days chronic migraine sufferers experienced headaches. 
The FDA defines "chronic" as more than 15 days per month. 
Ouch. 
If you've ever had a migraine and all the accompanying symptoms — visual disturbances, nausea, sensitivity to light — imagine how debilitating it would be to have them that frequently.
We spoke to Dr. Alexander Mauskop, who is the Director and founder of the New York Headache Center, and is board-certified in Neurology with a sub-specialty in Headache Medicine. 
Dr.Mauskop has been using Botox off-label (it's legal, but not covered by insurance) for chronic migraine treatment for 15 years; he estimates that he's treated about 5000 patients.
ELLE: So will Botox become a first-line therapy for chronic migraines, or is it better suited as an adjunct to other medical treatments?
Alexander Mauskop: If money was not an issue, I would use it as first line preventive therapy in my patients.
Traditional migraine drugs that we use have a lot of side effects. 
I'm also a big advocate of alternative therapies. 
You have to see what works in each individual case.
ELLE: Speaking of side effects, doesn't Botox have some scary ones?
AM: It's very dependent on the experience of the physician. 
I would make sure that your physician has done these types of injections a few hundred times, at least. 
Side effects can include droopy eyelids, neck weakness, headache (it takes about a week for the Botox to take effect), and neck pain. 
And of course — no wrinkles.
ELLE: How many injections and how often?
AM: It requires about 20-30 injections every three months. 
We use a "follow the pain" approach and inject the forehead, jaw muscles, neck muscles, back of the skull, and sometimes even the shoulders.
ELLE: How exactly does it work? 
Don't migraines originate in the brain?
AM: Everyone was skeptical at first, but when we were faced with difficult patients we thought it was worth a try. 
It worked for many patients. 
Then the research finally caught up with clinical observation. 
In migraines you can have some muscle spasm of the cranial muscles; Botox can block those muscle contractions and the neurotransmitters that send pain messages to the brain.
It's all a feedback mechanism.
ELLE: How much does the treatment cost?
AM: It can cost between $1,000 and $2,000 per treatment. 
But now that it's FDA approved for this, insurance companies will have to start picking up some of the cost.
All of which means that this could be a promising treatment for more people with chronic migraines. 
There are obviously a lot of cost-benefit considerations. 
Hate needles? 
Probably not the right therapy. 
Hate taking tons of pills? 
Perhaps Botox would work for you. 
But please, as always, educate yourself and speak to a trusted healthcare provider before undertaking any kind of medical treatment.
Sailing up New York's Sprain Brook Parkway in my father's beige, ocean-liner-size Cadillac, I declared from the backseat: I want a nose job.
A few girls in my ninth-grade class had returned from winter vacation with snazzy new snouts, and I was resolved to do the same.
I had, after all, inherited my father's nose, complete with its protruding bump and long, beakish tip.
A cuter, daintier model, I argued, might soften my boyish face.
Both of my parents wholeheartedly agreed.
I wish I had a recording of the conversation so that I could replay the long sigh of relief that discharged from my mother's mouth.
My father married her later in life; by the time I was born, he was 50. 
Raised in a poor immigrant neighborhood in East New York, Brooklyn, he had cultivated a distinguished look and a self-styled British accent that gave him the enchanting air of Cary Grant. 
In all things, I sought his approval. 
At 10, I toiled alongside him in his workshop in our basement; later I took up his passions of writing and painting. 
He taught me to love Laurel & Hardy. 
Just as Hardy would lock eyes with the camera, expressing his irritation with Laurel's antics, my dad looked to me for understanding.
On a trip to California when I was 16, we left my mother and sister at the hotel; in the rental car, he told me we were on our way to an important meeting in Hollywood to discuss the possibility of producing his screenplay. 
It was a journey that could change his fate, and I was his chosen copilot.
Despite this bond, by the time I hit puberty, it was clear that, unlike my younger sister, Sophia—who was popular, well accessorized, and boy-crazy—I was not living up to his definition of womanhood. 
My father was born in 1928, the same year as Mickey Mouse, and possessed an old-fashioned, Disneyfied view of women: We should be soft, feminine, gentle—and constantly on a quest for our prince. 
Next to the label-conscious, salon-styled girls at my private school, I resembled a prepubescent boy, both aesthetically and spiritually. 
Rail thin, breasts hidden under oversize flannel shirts, I spent weekends wolfing down pizza and playing video games.
And then there was my nose—his nose—which grew more exaggerated at the onset of puberty. 
It became the focus of my self-loathing, a manifestation of all my shortcomings as a girl. 
Altering it was one way, at least, that I could become more feminine.
So, a few days after high school graduation, I finally got my nose job. 
The surgery flattened the bridge of my nose but left it with a lengthy tip and asymmetrical nostrils.
A second procedure shaved down the tip and reshaped the nostrils. 
As promised, it made my face softer. 
Less self-conscious, I began to put more care into the way I dressed and even wore a little makeup.
But the anxious, tugging sensation in my chest was still there. 
Surgery eliminated the one problem that had so preoccupied me, but it forced me to acknowledge another, bigger issue—my sexuality—that would make me a failed woman in my father's eyes.
With time, I was able to crack my former self-perception.
In college, I entered my first relationship with a woman and began to feel attractive and desirable—another first.
I spent a semester in Florence, suspended in what seemed like a whirling dream of bicurious girls trying to bed me. 
Would that have been possible with my old nose?
I had spent my first postcollege year in San Francisco, but my father asked me to return to New York because of his waning health. 
Within a few weeks, I met a woman and one night accidentally fell asleep at her apartment, waking at 5 a.m. to discover 16 messages from my mother on my cell phone.
I rushed home and tearfully came clean. 
My mother lambasted me for deceit and pronounced my sexuality a "phase."
From then on, my father was polite to me at family occasions, but increasingly distant. 
One Father's Day, I had a trophy made for him, which he received with indifference. 
When I bought tickets to "Seeing Debussy, Hearing Monet" at Carnegie Hall for his birthday, he declined, blaming his health.
My mother filled the gap, vilifying me and my "choices" even after I moved in with my girlfriend. 
She told me the news had aggravated my father's health, that he couldn't sleep at night because he was so distraught.
Until age 79, he spent his semiretired days in his Brooklyn law office, curled up with copies of The New Yorker and the New York Post, listening to a crackling radio and trading dirty jokes with the guys down the hall. 
But last November, he was hospitalized after a fall, and it fell to me to pack up the office where he'd worked for more than 50 years. 
I uncovered unsold screenplays and short stories, illegible phone messages, and, at the very bottom of the pile, a stack of pictures. 
I flipped through them, snickering at the '70s sideburns and wide-collar shirts, until one gave me pause. 
It was my father's high school graduation portrait.
Staring out from this glossy black-and-white photo was my own face: my closed-mouth smile; my dark, deep-set eyes. 
And there it was: my old nose. 
Despite everything the surgery did for me—and it did a lot—I wish I could undo it. 
At 31, I've come to appreciate the things I inherited from my father: his humor, his love of ketchup and Mondrian paintings. 
When he is gone, how will I reconcile my decision to erase something we once shared? 
I stared at the image, tears running down my cheeks.
Soon afterward, my father entered hospice care. 
Twice, his lungs filled with fluid, and he narrowly escaped death. 
But while his body hangs on precariously, his mind remains remarkably intact.
I've spent more time with him this year than over the previous 10, our relationship reduced to its purest form: a father and daughter who have a lot in common and care for one another. 
We talk or watch Laurel & Hardy while he keeps the nurses laughing with puns and one-liners. 
It's both a gift and some kind of cosmic joke.
In the end, this is what we're left with: an aging parent wasting away in a hospital room, muscle and fat disappearing from his body. 
When I visit him each week, I scan his face: eyes, cheeks, mouth, chin. 
My gaze stops at the nose, and I bring my right hand to my face and run my index finger along the bridge of my own.
I was a fat kid, born fat to my petite European mother, who had been a fit model in the '60s. 
My chubbiness worked for me. 
I could fall down in front of our apartment building on NYC's ultraskinny Fifth Avenue and bounce right up, unharmed. 
Compared to my older brother, I was treated with kid gloves: I never had to do chores, wash dishes, babysit, because my parents thought I already had enough to deal with, what with my gigantic size. 
Most obsessed of all was my mother. 
When I was 13, she got me my own private aerobics instructor. 
But by 15, at 5'8", I was an adult size 16.
I didn't mind it so much, really. 
I didn't want to deal with boys or sex or going to parties and all that. 
I was really happy playing video games, reading, baking brownies with my best friend, and watching movies. 
The only problems occurred when I was out in public, and then it was the men who were the cruelest, not the girls. 
Grown men. 
Once, on my way to school, two guys walked toward me, and, from about 30 feet away, I could hear their soft, high-pitched singsong: "Fatty! 
Faaaaatty!"
By 16, I'd had enough, and I spent a year and a half weighing every single thing I put in my mouth, eating salads with no dressing, climbing the StairMaster for an hour a day, doing countless sit-ups. 
I hit 155 pounds, and considered myself normal looking—except for my stomach. 
While the rest of my body shrunk down, my waist remained a stubborn inner tube of flesh. 
I felt deformed. 
And I still hadn't kissed a boy. 
The thought of talking to one absolutely terrified me: I was fat; boys made fun of me—that was the way it was.
Senior year of high school, a solution presented itself. 
It was creative and unorthodox, but also totally insane: I fashioned a girdle out of duct tape and old panty hose. 
Yes! 
Sometimes when I pulled the tape off at the end of the day, skin would come with it, leaving scars that stuck to my hips and rib cage for years. 
But at last I could wear a T-shirt in public. 
I had a figure—a nice one.
My mother, terrified that I was going to hurt myself (not an unfounded fear, considering I passed out once or twice), made an appointment with a plastic surgeon. 
I'd been dreaming about liposuction ever since I read about it in Jackie Collins' Hollywood Wives, my favorite book at age 10.
Yep, that's not going anywhere, the doctor said when I lifted my shirt to display the inner tube.
A week later, I was under anesthesia and on the operating table.
This was the '90s, the stone age of lipo; surgeons flooded the area with saline, then sucked out the fat through a large cannula.
Recovery was awful.
I woke up swaddled in my constrictive bodysuit, and in a really bad mood—a common side effect of anesthesia.
But a nurse stared me straight in the face and said, Wow, you're a really good-looking girl.
The way she said it—seriously, almost to herself more than to me—made me think she meant it.
It was the first time anyone had ever complimented my looks.
After three weeks of intense pain and constant fantasies about my new body, I unzipped my bodysuit and got an unpleasant shock. 
My stomach looked almost exactly the same—a little slimmer, maybe—save for one thing: a huge dent, like a dog bite, right beneath my belly button. 
My mother confessed that she had run into the operating room as I was being wheeled in to tell the doctor "not to do too much." 
From the looks of it, the doctor just stuck the cannula in at that one spot and pulled it out. 
I played on my mom's guilt for a full year, and then she allowed me to get a second operation.
My new doctor was French, handsome, and blunt.
I got the exact same Yep, that's not going anywhere line, but in a French accent.
Plus, What a mess he made!
What endearing honesty.
At least he said he could fix it, which was all I cared about.
This time, lipo worked.
(We sucked a lot of fat away, the surgeon said.)
The next day, my face broke out in a violent, lumpy red rash which my derm diagnosed as rosacea: It happens sometimes after the body's had a major trauma to it, like a car crash.
It had never occurred to me to wonder what exactly was actually occurring during surgery, while I lay unconscious.
Later, I d see a video on TV of the same tumescent liposuction procedure.
The violence of it—the cannula shoving violently in and out of a stomach swollen with liquid??—was horrible.
But it didn't stop me from getting my arms done the next year, when I turned 20.
I don't remember much about this one—how I got my mother to agree to it, who the doctor was, what happened before or after.
I do remember the recovery was easier than with my stomach.
Three lipos in three years, all before I had even graduated college.
I felt like I looked okay—not the freak I once thought I was.
But weight was still a constant issue, as was my wonky-looking stomach.
The divet was still very much there, and on top of that, one side of my waist indented more than the other.
Exercise did nothing to balance it out.
And while I might have looked great in clothes, I never wore a bikini, and I never let a boy see me naked with the lights on.
Over the next eight years, I did everything from endermologie (a deep-tissue, machine-based massage) to mesotherapy (a big, painful, $3,000 mess of injections of a so-called fat-dissolving compound, which peppered my stomach with odd, circular bruises and did absolutely nothing to the fat).
I tried seaweed body wraps, a roster of famous diet doctors, trendy detoxes, and celebrity-trainer workouts.
By 29, I was a size 4 and 136 pounds.
But my stomach, that stomach, was still there: a misshapen reminder both of who I had once been, and who I still couldn't be.
A bad breakup triggered the next surgery.
The difference between lipo circa 1998 and lipo circa 2009 was shocking; the procedure was cheaper, faster, better.
And most important I didn't need my mother's permission.
I plonked down my entire savings, $10,000 (a lump sum I got as an advance for a book I was ghostwriting) for Manhattan-based surgeon Bruce Katz, MD.
When I showed him my stomach, he just smiled kindly and said, Yes, I think I can help you with that.
Katz's specialty, laser-assisted lipo uses a cannula with a laser at its tip: The beam of light ruptures fat cells, the contents of which are then easily vacuumed out through the cannula.
Also, I was awake for the entire procedure.
Katz would ask me to get up from the table every so often to see how everything hangs.
Up.
Then down.
Then up.
Then down.
Impulse in check, though, the first appointment I made this time around was with my therapist—not a surgeon—in search of approval or absolution.
Am I crazy for even considering this again?
I think in your case, when you had it done when you were younger, it wasn't in your control, and that must have been so frustrating, she says.
So now that you're older and you can do it the way you want to do it, without any sort of familial repercussions—no guilt or shame—but privately, of course that's tempting.
That's when I mention to her it's my thighs I want done.
But your legs are tiny! 
she says, surprised.
Plastic surgery can become incredibly addictive.
I don't think you're crazy, but I definitely think getting lipo on your thighs is crazy.
I make another appointment with Katz.
He pinches my legs and says, Nope.
Nothing I can do about this.
The fat is under the muscle.
The only way to get rid of it is with cardio.
For a second, I don't understand.
What does he mean, No?
But his refusal—the first I ve heard over the course of four operations in a dozen years—gives me pause.
The surgeries on my waistline solved a problem that had no other clear solution, but it's startling to realize how accustomed I ve become to throwing money and a cannula at my every body issue (an attitude I may have inherited from my mother, who, despite discouraging my own revisions, has a surgery count nearing the double digits).
Now, after a few weeks of daily visits to the gym, my developing muscles remind me that change can be achieved by myself, all on my own?
—on this particular problem area, at least.
Fifteen times a week on average, Robert Schwarcz, MD, a New York City–based cosmetic surgeon, injects patients with Botox.
For certain individuals he also writes down a phone number on a piece of paper and tells them to make an appointment.
It's not for a dermatologist or a colorist with a flair for youthful-looking highlights.
The number is for Angela Kulangi, a facialist at Total Skin, a day spa that specializes in electric facials that deliver, via small wet sponges, low levels of microcurrent—1/1,000,000 of an amp (a light bulb runs on less than one ampere)—to stimulate the muscles of the face and neck.
If the patient has been using neurotoxins for more than three years, and if she has genetically thin skin and slim facial musculature, I ll make a gentle suggestion for her to see Angela, says Schwarcz.
I like the idea of providing a plumpness to a nonactive muscle and generating controlled muscular activity.
This same youthful fullness is what everyone who opens a jar of hyaluronic acid cream or books a filler session is attempting to retain or replicate.
And it's not that the botulinum toxins—Botox, Dysport, and the recently FDA-approved Xeomin—are in direct opposition to that end.
In fact, the toxins do not act directly on muscles—they bind to neurotransmitters, preventing them from signaling muscles to contract.
Initial medical use for the toxins wasn't even related to wrinkles or anti-aging.
In 1980, doctors began using it to quiet uncontrollable blinking and relax muscles that cause eyes to cross.
The cosmetic neurotoxin revolution began in 1987, when two Vancouver-based doctors discovered the neurotoxin's smoothing effect on the elevens, the frown lines between the eyebrows.
Derms and nonderms alike promptly took it one better, using injections to create lift.
When a neurotoxin is shot into a muscle that pulls downward, say, in the brow area, the antagonist muscle that pulls upward is left unopposed to dominate.
Add to that carefully placed injections to relax the frontalis muscle, which creates the worry lines, those horizontal ones across the forehead, and doctors could mimic the effect of a brow lift without picking up a scalpel.
If a muscle is immobilized, even temporarily, it will use less energy and have a tendency to atrophy, says skin physiologist Peter Pugliese, MD, author of the textbook Physiology of the Skin, who notes that researchers soon figured out how to make this atrophy yield short-term aesthetic benefits.
Dermatologist Fredric Brandt, MD, whose New York and Florida–based practice is the largest user of Botox in the world, explains that one can, like a sculptor, dramatically slim the jawline by injecting a large amount of a neurotoxin into the masseter, the primary chewing muscle that runs along the side of the face.
It is reversible, Brandt says.
But one treatment will last for a year.
However, atrophy can have a downside—which is where, for some doctors, electric facials come in.
These doctors believe that, in the wrong hands over time, neurotoxins could cause the face to lose desired fullness, and so they are prescribing microcurrent as a noninvasive companion to neurotoxin injections to diminish any loss in muscle tone.
In fact, dermatologist Nicholas Perricone, MD, steers his patients away from using neurotoxins at all, believing microcurrent, plus the right diet and topicals, to be the best anti?wrinkle strategy.
Electric facials, whether done at home or in a spa, he argues, help build convexities in the face.
Convexities are what make you youthful, he says.
That is critical.
If you look at the cheekbones, the forehead, the temples, the jawline of someone young, they come out in an arc away from the face.
They bulge out.
Around the age of 40 to the midfifties, the convexities go flat.
From 60 up, they can go concave.
Electrostim keeps the muscles plump and active, preventing or correcting loss of the convexities.
The idea of using electric current to stimulate muscles sounds both high-tech and barbaric, but in truth it has been in practice for hundreds of years.
For that we can thank Jean Jallabert, a professor in Geneva, Switzerland, for credibly reporting in 1748 that he alleviated paralysis in a locksmith's right arm by using a 90-minute series of electric shock sessions over the course of several months.
In 1982, researcher Ngok Cheng, MD, at the Catholic University of Leuven, Belgium, led a study that provided hard evidence of microcurrent's role in cellular vitality by proving that microcurrent increased levels of ATP—the fuel a cell needs to function—in lab-rat skin cells by 500 percent.
Orthopedic surgeon Robert Becker, MD, compiled multiple studies in his 1985 tome The Body Electric, citing the role of electricity in cell regeneration.
For decades, microcurrent has been used in different frequencies and waveforms to treat everything from wounds to migraines to chronic pain.
Professional athletes and anyone who has had physical therapy have often experienced an electrostim machine, as orthopedists routinely prescribe microcurrent to aid in the repair of ligaments and muscles.
On a muscular level, the microcurrent acts like a personal trainer to tone and shorten muscle fibers.
On a dermal level, as Pugliese, the skin physiologist, notes, there is serious anti-aging action going on.
Pugliese has spent more than five years analyzing microcurrent's effect on fibroblasts by biopsying skin before and in between microcurrent treatments, and has found a statistically significant increase not only in the production of collagen and elastin, the skin's main structural proteins, which degrade with age, but in that of glycosaminoglycans, or GAGs, the viscous material in which the proteins are embedded.
When you see a nice plump cheek like a baby's and you pinch it and it feels very good and snappy, he says, that's GAGs.
And, according to Perricone, the long-term benefits are more than skin-deep: If you have a microstimulation machine, you don't have to have perfect genes, Perricone says.
When I first started working with celebrities, I assumed they were genetically gifted and had perfect symmetry.
But now he knows that symmetry can be made: Not only can we use electrostim to increase our muscle mass, we can accentuate one side of the face by working it harder than the other to give a more symmetrical appearance.
Electric facials are on the menu everywhere from Perricone's New York flagship spa to Four Seasons hotels to Elizabeth Arden's Red Door salons.
Professional-grade microcurrent machines emit a positive and a negative current via two wands, probes, or sponges.
When the probes are placed a few inches apart on the face, a circuit of current travels from one point to the other and stimulates the tissue in between, Perricone says.
The current is subsensory, which means all one feels is the gliding of the rods and perhaps a slight tingle.
Customers often fall asleep midfacial.
The other option is to DIY with an at-home device.
Suzanne Somers teamed up with engineer Rodger Mohme, who previously led the team at Apple to shrink a desktop computer down to laptop size, to create the FaceMaster, a vanity-table version of a large in-spa machine.
The only handheld microcurrent device with FDA approval is the NuFACE, created by Carol Cole, a SoCal facialist who got tired of lugging her gigantic machine up into the Hollywood Hills.
It emits the same level of current as a pro machine (you can get a 30-minute poolside NuFACE treatment at the Four Seasons Maui for $125), but the micro-amps deliver via two fixed metal probes.
ELLE editors tested both the FaceMaster and NuFACE in our offices and found they instantly increased circulation for that glowy, plump-but-not-puffy look that lasted for a few hours.
But, in our untrained hands, the DIY could not provide microcurrents' more sophisticated, bespoke effects.
With the right expertise, microcurrent can be used to dramatically, if temporarily, shape the face.
It's no wonder celebrities have become insatiable consumers of electric facials, especially during awards season.
The pop lasts for about five hours, says facialist Melanie Simon, whose skin-care company, Circ-Cell, is partially backed by Lynn Harless, aka Justin Timberlake's mom.
Madonna and Kate Winslet are outspoken fans of Tracie Martyn's trademarked Red Carpet Facial, a proprietary treatment that incorporates mild electrical current.
Regular microcurrent sessions were rumored to be Princess Di's beauty secret.
And according to an industry source, J Lo just spent $22,900 on her own professional-grade CACI Ultra (no word on whether she's administering them herself).
Depending on where the probes are placed, either above the origin or insertion point of a muscle, and how many seconds they're held there, users can smooth a furrowed area by stretching the muscle or add lift by shortening the muscle.
If you lift from the cheekbones toward the hairline, it will make your eyes more almond shaped, says makeup artist Kristin Hilton, who travels between New York and L A to work on clients including Uma Thurman and Milla Jovovich.
You can even create an arch in the eyebrows.
Hilton keeps NuFACE in her makeup kit so she can sculpt and lift before she applies a client's makeup.
I m a skeptical person, Hilton says.
For me to like something like this is unusual.
But I use it for five minutes on each side, pulling upward.
Everything's tighter.
You look more awake.
People know something's different, but they don't know what.
Usually they say, `Did you get your hair cut?'
The exact protocol for combining Botox and microcurrent has yet to be written, but most proponents agree to wait a few weeks post-injection before getting a facial.
According to Charles Boyd, MD, a plastic surgeon with practices in Michigan and New York, In the first 24 hours after an injection, you could potentially move the Botox from a muscle where you injected it into a muscle you did not intend, he says.
That doesn't mean it's going to move from your forehead to your neck, but maybe from your eyebrow to your upper eyelid.
Simon's clients wait two weeks post-Botox for an electric facial, then return for monthly follow-ups (per skin's renewal cycle, which is 28 days).
Botox and electric facials are great companions.
I could spend hours smoothing lines out and then my clients will walk out the door and make the expression that caused the wrinkle 1,000 times that night, Simon says.
Botox is very efficient at knocking out expression wrinkles.
Electric current fixes everything else?
—it's the cherry on top.
Designers have been showing Birkenstock- and Teva-inspired shoes on the runway for several seasons now, and certain fashion editors have even vowed to retire their heels completely. 
But some women may not have gotten the memo, if an increasingly popular procedure is any indication.
Plumping fillers—the same kind that doctors inject into the face to add volume back—can help high heel wearers who just can't lay off the stilettos and have chronic foot pain as a result. 
According to Dr. Mitchell Chasin, the founder and medical director of Reflections Center for Skin and Body in New Jersey, requests for a so-called "stiletto lift" have increased over the last year.
Normally when you walk, your weight gets evenly distributed over the entire sole of the foot.
But when you strap on a heel, suddenly that weight all comes down over the ball of the foot.
The ball of the foot has a pad of fat that's a shock absorber between the bone and the outside world, Dr. Chasin said. 
"As people wear high heels, that pad of fat gets pushed out of the way and what happens then is that bone doesn't really have a cushion and it becomes irritated." 
Your 125mm Louboutins then become torture devices as a result.
Dr Chasin offers a procedure in which he injects a filler called Radiesse into the sole of the foot.
Radiesse acts in two different ways to help restore volume and cushioning to the ball of the foot.
In the weeks immediately following the injection, it forms a sort of lattice over the bone, which acts as a cushion.
Then over the longer term, it actually stimulates your own body to create collagen, which further increases the cushioning.
Dr Chasin said that most women experience immediate relief after the injections.
But first, you have to get through the experience of having multiple injections in the bottom of your foot.
A typical patient requires one to two syringes of the filler to treat the feet completely.
Before Dr Chasin injects them, he numbs the bottom of the feet first with a topical numbing cream, then with injectable lidocaine, a stronger numbing agent.
He also mixes lidocaine into the filler syringes.
Still sound horrifying?
People have so much pain there to begin with that sometimes the lidocaine is a relief, Dr Chasin countered.
While the stiletto lift has been around for about five years, Dr Chasin has been seeing an increase in requests for it over the last six to 10 months.
He currently does about 10 of them per month.
The cost varies from $750 to $1,500 depending how many syringes of filler the woman requires, and results generally last for about a year.
Professional women between the ages of 35 and 45 make up the bulk of those requesting the procedure.
We also get people who are going out more, who are dancing, who are more active socially.
They're active professionals, Dr Chasin said.
But getting the injections doesn't mean you should run around in six-inchers everyday.
Use heels in moderation and not all the time, Dr Chasin advised.
That's what got you there in the first place.
He also said that the skinniness of the heel matters as much or more than the height.
So try to opt for chunkier heels and save the pin-thin stilettos for special occasions.
The answer isn't to fill it and continue to abuse it, he said.
But do the women he treats actually take this advice?
Not at all!
They're like, 'Okay!' 
then they come in to see me while wearing their heels, Dr Chasin laughed.
We can only try, right?
Application of Microautologous Fat Transplantation in the Correction of Sunken Upper Eyelid Background: Although fat grafting has been clinically applied by surgeons in esthetic and reconstructive surgery, it has widely evolved in processes such as harvesting, processing, and placement of fat, using the fat-grafting procedure, which dates back over 100 years. 
Surgeons frequently use fat grafting to recontour, augment, or fill soft-tissue defects, facial wrinkles, or skin problems such as depressions or scars. 
However, fat grafting has not been thoroughly understood and has not been conclusively standardized to ensure superior clinical results. 
Methods: This study was intended to determine the role of microautologous fat transplantation (MAFT) under evidence-based medicine, particularly in accurate delivery of small fat parcels. 
The research method involved the conceptualization of MAFT and the development of an innovative surgical instrument for fat placement. 
Clinically, 168 patients with sunken upper eyelids with multiple folds underwent this procedure. 
Results: The major findings suggested that MAFT exhibits promising clinical results and offers a superior guideline for fat placement. 
Details of the technique and theoretical implications are also discussed. 
Conclusions: The therapeutic effects of MAFT and the long-term clinical results of patients with sunken upper eyelids with multiple folds indicated satisfactory outcomes. 
Based on the results, MAFT offers an alternative option to surgeons for performing fat grafting and provides a more favorable option for the benefit and welfare of patients by reducing the potential complications.
In Asian people, the superior sulcus in the upper eyelid frequently becomes visible because of aging or overzealous removal of the orbital fat through blepharoplasty or other unknown etiologies. 
The hollow or sunken appearance is often accompanied by multiple eyelid folds, which result from variable insertion levels of levator palpebrae superioris, with consequent thinning of the skin and orbicularis oculi muscle and/or preaponeurotic fat atrophy. 
The coalescence of both phenomena results in a tired, weary, and even exhausted appearance.
Certain strategies, such as autologous fat grafting, dermal fat grafting, or allogenous dermal grafting, have been described and detailed in the literature for correcting the aforementioned problems.
Currently, numerous soft-tissue fillers are widely used despite their short duration of efficacy.
A reliable strategy with favorable long-term effects that leads to higher patient satisfaction and higher confidence of surgeons has not yet been developed.
Fat grafting was initially addressed over a century ago by Neuber.
Thereafter, fat-grafting techniques, methods, and protocols have been enhanced numerous times to improve the fat survival or fat retention rate.
Numerous surgical mentors in this field have investigated innovative methods for potentially improving fat grafting.
In this article, we advocate a new concept of microautologous fat transplantation (MAFT) and its application in correcting sunken upper eyelids with multiple folds.
The results indicated that MAFT facilitates reduced morbidity, higher patient satisfaction, and favorable long-term follow-up results.
MATERIALS AND METHODS Patient Demography A total of 168 patients (2 men and 166 women) received fat grafting for correction of sunken upper eyelids with multiple folds from September 2007 to September 2010 at the Charming Institute of Aesthetic and Regenerative Surgery, Kaohsiung, Taiwan.
These patients were regularly followed up at the outpatient clinic.
Preoperative and postoperative photographs taken at each visit were compared.
In addition, complications such as calcification, fibrosis, nodulation, uneven skin (irregular surface), and cyst formation were meticulously recorded.
Anesthetization Unless accompanied with other major adjunct procedures, all patients were anesthetized using total intravenous anesthesia for the entire MAFT procedure.
Concurrently, local anesthesia was administered at the incision sites (donor and recipient sites) by infiltrating 2% Xylocaine (Lidocaine Hydrochloride 20mg/ml, Oriental Co., Taiwan) with epinephrine (1:1000).
The fat-harvesting area, primarily the lower abdomen, was preinfiltrated with a tumescent solution prepared at a ratio of 2% Xylocaine:Lactate Ringer solution:epinephrine (1:1000) = 10 mL:30 mL:0.2 mL.
Fat Harvesting The donor area was preinfiltrated with a tumescent solution after the incisional site was anesthetized.
Approximately 10 to 15 min after the tumescent solution was administered, a blunt-tip cannula (diameter, 3 mm) was used to harvest the fat, and the lipoaspirated volume was the same as that of the infiltrated tumescent solution to achieve a high proportion of purified fat after centrifugation.
To ensure minimal damage to the lipoaspirate, the plunger of a 10-mL Luer-Lok syringe was pulled back for 2 to 3 mL and maintained so that, while it was connected to the liposuction cannula, the reactive aspirating negative pressure was maintained between 270 and 330 mm Hg.
Fat Processing and Refinement For fat processing, various methods, such as the sieve method, multiple-layer gauze filtration, and centrifugation, have been proposed in the literature.
The internationally accepted Coleman’s technique was used for processing the lipoaspirate through centrifugation because of its advantages of less environmental exposure and lower manual manipulation in the aseptic procedure.
A standard centrifugation of 3000 rpm, which was approximately 1200g for 3 minutes, was applied to process (purify) the fat.
Fat Transfer The purified fat was carefully transferred into a 1-mL Luer Slip syringe by using a transducer and was prepared for transplantation.
Supplemental Digital Content 1, which displays the microautologous fat transplantation for sunken upper eyelids.
After the purified fat was transferred, the fatfilled syringe was loaded into the MAFT-Gun instrument.
The predetermined volume of the fat parcel to be injected during each triggering was adjusted by rotating the dial with labeled numbers depicting the total injection frequencies per 1 mL of fat graft.
An 18-G blunt cannula was used to inject fat while withdrawing the MAFT-Gun.
Each delivered fat volume was set at 1/240 mL and meticulously transplanted in 3 to 4 layers: a deep layer above the inferior orbital rim; a middle layer, the sub–orbicularis oculi muscle (deep in the muscle); and superficial layer, the supraorbicularis oculi muscle (just beneath the dermis of the eyelid).
Postoperative care was provided regularly and without any special dressings or massage.
Oral antibiotic and nonsteroid anti-inflammatory drugs were administered for 3 days, as required.
All patients were regularly photographed at each follow-up visit, and the preoperative and postoperative photographs of each patient were compared.
RESULTS The average age of the patients was 35.5 years (range, 26–52 years), and the total injection volume of fat was 1.8 mL for the right side (range, 1.2–2.3 mL) and 1.7 mL for the left side (range, 0.8–2.4 mL).
The average operation time was 34 min unless combined with other adjunctive procedures such as upper or lower blepharoplasty or fat grafting of other areas.
No major complications were encountered except 2 cases of prolonged swelling for > 2 weeks.
All patients were satisfied with the results except one who requested secondary fat grafting.
DISCUSSION Literature Review For over a century, surgeons have struggled to apply autologous fat grafting in plastic, reconstructive, and esthetic surgery with variable results.
In 1893, the grand surgeon, Neuber, became the first to reconstruct a facial defect.
Thereafter, several reports have described fat grafting: Kanavel stated that “fat cells are the best friend of the surgeon,” Peer described an approximately 55% fat-graft retention rate, and Bames9 reported convincing results regarding fat grafting for breast augmentation.
In 1977, Illouz10 reported on “liposuction” and developed the related medical instruments.
Subsequently, fat graft ing was fine-tuned and applied in plastic surgery; for example, Fournier used fat grafting to fill involuted facial tissues, and Chajchir and Benzaquen used fat grafting for rejuvenation of facial wrinkles and treatment of hemifacial atrophy.
In addition, various endeavors have been attempted for recipient site preparation; for example, Asken performed subcision to prepare a pocket for fat grafting, and Nguyen et al reported muscle as the optimal recipient site for fat grafting.
In the past 2 decades, prominent surgeons have illustrated numerous principal theories.
Carpaneda and Ribeiro postulated higher fat graft survival, and in 1993 and 1994, they experimentally proved that the graft survival is higher when the grafting is within 1 to 2 mm from the margin.
In 1994, Coleman presented the structure fat graft method and emphasized that in special locations, such as periorbital areas, each fat parcel must be between 1/30 and 1/50 mL.
Based on the review of the aforementioned literature, experts and scientists in the field have demonstrated various fatgrafting techniques; however, no conclusive strategy has yet been developed.
Evidence-based Medicine in Fat Grafting Evidence-based medicine applies the most reliable evidence gained from scientific methods to clinical decision making. 
In autologous fat grafting, 2 theories were proposed by Carpaneda and Coleman, which demonstrate the importance of evidencebased medicine.
Theory by Carpaneda Carpaneda and Ribeiro demonstrated only 40% graft survival at 1.5 ± 0.5 mm peripheral to the graft margin.  
Furthermore, they reported that thickness and geometrical shape are the keys to successful fat transplantation and concluded that the diameter of the fat graft (either spherical or cylindroid shaped) should be < 3 mm to achieve higher graft survival rates.
Coleman’s Theory Coleman proposed the concept of structure fat grafting and emphasized that the fat parcels should be manually arranged in layers with a volume of < 1/10 mL per injection for each parcel; in special sites such as periorbital areas, each fat parcel should be 1/30–1/50 mL. 
Moreover, the complications and morbidities can be minimized by avoiding the central necrosis of a fat graft that can be induced through overinjection when each parcel is placed.
Disadvantages of Commercialized Medical Devices: Ratchet Guns For several years, commercially available ratchet guns have been clinically applied in fat grafting with variable results. 
The advantages of ratchet guns include the accurate control of fat parcels by pulling of the trigger, and the ergonomic design.
However, the disadvantages are the relatively large volume injected per triggering (1/10 mL, 1/2 mL, and up to 1 mL) and the potential exposure of the fat graft inside the syringe to ambient air because of repeated manipulation of the plunger. 
Although some surgeons have reported favorable results, most surgeons are hesitant in using these instruments. 
Accordingly, in addition to accuracy, fat grafting also necessitates delicate, precise, and consistent control of the placement of each fat parcel.
The aforementioned evidence-based medicine indicates the necessity for precise, accurate, and consistent delivery of each parcel, as insisted by Coleman and Carpaneda. 
However, commercial devices have not yet satisfied all the requirements for precise delivery.
Moreover, manual operation is not only physically difficult; it is also scientifically impossible to deliver each fat parcel at minute volumes between 1/30 and 1/50 mL when using this method.
Myths Regarding Overinjection with Massage Previous studies have emphasized the requirements for optimal fat grafting at recipient sites, such as the periorbital area, wherein a fat droplet should be as minute as 1/30 to 1/50 mL. 
The delicate placement of fat graft relies on the surgeon’s manual skills; however, even an experienced surgeon cannot expect to inject each minute fat parcel with accuracy and consistency, particularly in the case of tissue resistance inside the donor area. 
Therefore, instances of overinjection or abrupt placement of fat are frequently observed and unavoidable.
Based on a study conducted by Peer that indicated approximately 55% graft survival after fat grafting, some surgeons prefer to perform fat grafting through overinjection at the recipient site, followed by vigorous manual massage exerted by the operator to even out and flatten the skin surface. 
However, as illustrated in Figure 7, overinjection can eventfully induce several complications as described previously.
Concept of MAFT Similar to the acceptance of skin grafts, a fat graft regains its blood circulation 48 h after implantation (neovascularization formation). 
However, the inflow of nutrients and outflow of metabolites (adipocytes) depend on the initial diffusion and plasmatic imbibition after grafting. 
Carpaneda and Ribeiro demonstrated that the central area eventually necrotizes and only the marginal zone survives at a rate of approximately 40% at 1.5 ± 0.5 mm from the grafted margin, regardless of the shape of the fat graft (spherical or cylindrical).
Previous studies complications associated with the large size of an implanted fat parcel, including absorption, cyst formation, fibrosis, calcification, ossification, and asymmetry. 
This phenomenon of “central necrosis” induces a cascade of aggressive chain reactions, leading to numerous unavoidable complications including unpredictable graft survival and fat retention. 
Therefore, it is highly recommended that the size of the implanted fat droplets be as small as possible.
Based on the theory postulated by Carpaneda, the mathematical formula for determining the optimal volume of fat parcels (here, the fat graft is presumed to be spherical in shape) is calculated as follows: the volume of a globe is (4/3)πr3, where r is the global radius and π is the ratio of any circle’s circumference to its diameter in Euclidean space. 
Table 1 presents the information regarding the volume of each fat parcel at the radii of 1, 1.5, and 2 mm.
We concluded that the injection frequency of 1-mL fat parcels for a spherical graft with a 2-mm radius can be calculated by dividing 1000 mm3 (1 mL) by (4/3) π (2 mm)3. 
Therefore, a minimal injection frequency of 30 was set for each 1 mL fat-graft parcel to achieve superior graft survival rates.
What MAFT Emphasizes and How It Works Precise, Accurate, and Consistent Placement The subcutaneous tissue overlying the skin where fat grafts are primarily placed possesses a strong longitudinal adherence to the fibrous septa, with strong fascia or ligaments. 
For placement of minute parcels, a tunneling maneuver of the injection needle is required by moving it back and forth to loosen the tough subcutaneous tissues and place as small a par cel as possible. 
Such a maneuver in fat placement results in more tissue injury and necessitates a longer healing time. 
Moreover, severe ecchymosis and swelling in the first 2 weeks after grafting embarrassed and frustrated the patients with an unattractive or bruised appearance. 
Some swelling persisted even 16 weeks after grafting.
Alternative to Overinjection with Manual Massage As observed in Figure 7, an irregular depression was observed as a soft-tissue defect (mimicking soft-tissue depression or grooves). 
Surgeons often overinjected the sunken areas, followed by vigorous massage to remold and flatten them evenly.
However, after vigorous massage, the fat parcels formed a confluent mass. 
Although the skin surface appeared smooth and full after operation for days to weeks, the remolding processes including absorption and fibrosis continued and evolved because of central necrosis. 
Eventually, as shown in Figure 7E, irregular skin and uneven surface over the graft area appeared as unavoidable morbidities.
An Instrument to Reflect the Concept of MAFT The innovative transmission system of the MAFTGun was designed to minimize the injecting volume to 1/240 mL per parcel. 
This microdelivery mechanism ensured that the radius of each injected droplet was approximately 1 mm, which has been documented in the literature as essential to decreasing the severity of the inevitable central necrosis of the fat parcel. 
Therefore, the graft survival rate improved when the fat droplet resided in the tissue with no potential central necrosis, which was the primary reason for impairing the transmission of nutrients inside and the metabolites outside the adipocyte (or preadipocyte).
No Excess Swelling and Long Healing Time Compared with Other Traditional Modalities Even when performed by the most experienced surgeons, postoperative swelling and edema are unavoidable, which often frustrates patients. 
Severe bruising and swelling develop in the recipient areas in the first week after fat grafting, and this is primarily attributed to the to-and-fro movements of the injection cannula during the injection procedure. 
However, the strong and tough adherent subcutaneous tissue in some recipient areas needs to be loosened by using such a maneuver to place the fine fat parcel and avoid the dislodgement of huge droplets by abrupt overinjection. 
The patented microcontrolling system of MAFT-Gun ensured that the injection volume of each parcel was predetermined and thus controlled in the procedure. 
A steady and accurate volume was transmitted at each trigger pull, regardless of the strength exerted by surgeons or the tempo of the injection. 
Therefore, by reducing the frequency of the back-and-forth tunneling movements used to loosen the recipient area, postoperative healing time was reduced, and the swelling and edematous appearance was minimal when compared with traditional techniques.
Enhanced Performance of Surgeons Surgeons have to turn their hands often because the fixed pinhole of commercial injection needles requires changing for various grafting sites. 
The inconstant and labor-intensive turning of the surgeons' hands impedes efficacious performance during the grafting procedure. 
A clear marking with 360° multirange adjustability of the MAFT instrument provided precise control of the direction of injection. 
With this innovation, surgeons were able to change the pinhole of the injection needle freely, comfortably, and accurately.
The user-friendly trigger system based on the pulling maneuver enabled a predetermined volume of each parcel to be precisely delivered. 
Right- or left-handed use of the handpiece was set in advance, which is therefore adaptable for use by all surgeons.
Innovation of MAFT-Gun The MAFT-Gun provided an innovative operating system to deliver fat grafts with a flexible volume of 1/60, 1/90, 1/120, 1/150, 1/180, or 1/240 mL per injection (by rotating the adjustable dial to 60, 90, 120, 150, 180, or 240, respectively), which was suitable for the needs of the surgeons performing the grafting procedure at various areas. 
Ease of use and ergonomics in application defined the characteristics of this state-of-the-art device as a preferential assisting device.
Problems with Sunken Upper Eyelids Sunken upper eyelids, particularly in Oriental people, are a common occurrence and often present a weak and tired appearance.
Based on racial and physiognomic considerations, Asian people might wish to enhance the facial appearance caused by sunken upper eyelids, not only for beautification but also for luck.
Several operative strategies, including fascia, dermal grafts, and derma-fat-fascia grafts, have been documented in the literature.
However, no satisfactory long-term results have been reported.
Commercial soft-tissue fillers such as hyaluronic acid, collagen, or other synthetic biocompatible/degradable materials are popular in the cosmetic market.
However, because of the high cost, short duration, and risk of allergies and other complications, an ideal filler is yet to be defined regarding persistency, no allergy reactions, and lower morbidity.
In this study, the sunken upper eyelids were recontoured using the MAFT concept and by employing the MAFTGun, which enabled an accurate and precise transplantation of each fat droplet.
In addition to the recontouring of the hollow-looking eyes, most patients observed that after MAFT, their skin texture and appearance was more youthful and rejuvenated, which implied the existence of stem cells or stromal vascular fractions in the transplanted fat.
Because fat grafts play an essential role as “more than a permanent filler,” as advocated by Coleman, they occupy the future stage in regenerative medicine.
Therefore, the success of completion of a fat grafting procedure has promising applications not only in cosmetic surgery but also in reconstructive surgery.
CONCLUSIONS The evolutionary era of fat grafting is the result of the continuous endeavors of various plastic surgeons.
All the strategies of fat grafting, including harvesting, processing, refinement, and transplantation, exhibit promising progress.
MAFT is introduced as a novel approach in fat grafting, and its execution can be accomplished using the innovative MAFT-Gun instrument.
Using this technique, sunken upper eyelids with multiple folds, which present as hollow eyes, can be reconstructed with favorable long-term follow-up results.
Diffusion and Perfusion: The Keys to Fat Grafting Abstract Background: Fat grafting is now widely used in plastic surgery. 
Long-term graft retention can be unpredictable. 
Fat grafts must obtain oxygen via diffusion until neovascularization occurs, so oxygen delivery may be the overarching variable in graft retention.
Methods: We studied the peer-reviewed literature to determine which aspects of a fat graft and the microenvironment surrounding a fat graft affect oxygen delivery and created 3 models relating distinct variables to oxygen delivery and graft retention.
Results: Our models confirm that thin microribbons of fat maximize oxygen transport when injected into a large, compliant, well-vascularized recipient site. 
The “Microribbon Model” predicts that, in a typical human, fat injections larger than 0.16 cm in radius will have a region of central necrosis. 
Our “Fluid Accommodation Model” predicts that once grafted tissues approach a critical interstitial fluid pressure of 9 mm Hg, any additional fluid will drastically increase interstitial fluid pressure and reduce capillary perfusion and oxygen delivery. 
Our “External Volume Expansion Effect Model” predicts the effect of vascular changes induced by preoperative external volume expansion that allow for greater volumes of fat to be successfully grafted.
Conclusions: These models confirm that initial fat grafting survival is limited by oxygen diffusion. 
Preoperative expansion increases oxygen diffusion capacity allowing for additional graft retention. 
These models provide a scientific framework for testing the current fat grafting theories.
Autologous fat grafting is increasingly used for breast augmentation and reconstruction. 
Fat grafts are easily available, biocompatible, cause low donor-site morbidity, and give grafted sites a natural appearance. 
However, fat grafting is generally considered an unpredictable procedure, with long-term retention commonly varying between 10% and 80%. 
Much work has been done to optimize this procedure; however, the clinical practice has advanced faster than the supporting science. 
Fat grafting can be considered in phases: harvesting, processing, reinjecting, and managing the recipient site. 
To determine the optimal surgical methods for harvesting, processing, and reinjecting, Gametal completed an extensive literature review. 
Their results show the variability of current surgical techniques with current literature only supporting general principles and not any specific technique. 
Furthermore, no experimental or theoretical studies have analyzed how the different surgical methods alter the microenvironment surrounding the graft or how the microenvironment affects graft retention.
In this context, we define a model as a conceptual and mathematical representation of a phenomenon that has occurred in the past in an attempt to predict how it will occur in the future as specific variables change. 
Modeling has been critical for many advances in plastic surgery. 
Our group has modeled both skin expansion and the mechanical forces involved in vacuum-assisted closure devices. 
These models have helped enhance our understanding of the biological effects and medical uses of mechanical forces. 
Modeling provides the theoretical framework for testing theories. 
This study aims to identify the role of the recipient site in fat grafting and create a model to serve as a scientific basis to analyze the variables related to the recipient site and graft retention.
Grafted fat initially lacks vascular support and must receive oxygen and nutrients by diffusion from nearby capillaries until neovascularization occurs.18 Oxygen seems to be the critical molecule required for cell survival.
Low oxygen partial pressures in the center of the graft can lead to cell necrosis.
Attempts to improve graft retention have largely been based on the “cell survival theory,” which states that long-term graft volume consists primarily of grafted adipocytes that have survived the entire procedure.
This theory has generally been accepted and has directed most efforts to maintaining adipocytes viability through improved harvesting, processing, and reinjecting techniques.
Studies supporting the cell survival theory claim that the “viable zone” (40% adipocyte survival) reaches as far down as 0.2 cm from the periphery of the grafted tissue.
These conclusions are based mostly on morphological observations with H E staining.
However, judging adipocyte health by shape or nuclear appearance can be misleading, and histologic sections are too thin to show most nuclei of healthy adipocytes.
Moreover, the “cell survival theory” may fail to get to the complex phenomena occurring in fat grafting.
Fat grafts are not pure aggregates of adipocytes, but a mixture of adipocytes, preadipocytes, endothelial cells, pericytes, stem cells, fibroblasts, inflammatory cells, and matrix.
Using immunostain for perilipin, a method for determining adipocyte viability, Etoetal in the Yoshimura Lab tested the cell survival theory and concluded that a dynamic remodeling of grafted adipose tissue (AT) occurs.
In hypoxic cell cultures, they demonstrated that adipocytes cannot survive more than 1 day of severe ischemia-mimicking conditions (1% O2 with no serum), whereas adipose-derived stromal cells (ASCs) remained viable for up to 72 hours.
In a second experiment, the inguinal fat pad of a mouse was grafted to the scalp.
Only the peripheral area (surviving zone; <0.03 cm from the edge) of the graft had a high survival rate of both adipocytes and ASCs.
In a deeper (regenerating) zone, most adipocytes did not survive more than 1 day, but ASCs survived and eventually provided new adipocytes.
By day 3, the number of proliferating cells increased, and by day 7, they found an increased thickness of the zone with viable adipocytes.
At the center of the graft, no AT survived, and macrophages removed the dead cells; this was named the “necrotic zone.”
Recent studies support this “host replacement theory.”
Rigamonti et al26 suggest that 4.8% of preadipocytes are replicating at any time, and 1–5% of adipocytes are replaced each day.
When mouse AT oxygenation reaches less than 65% of baseline, adipocytes undergo apoptosis in 24 hours; however, ASCs can survive for multiple days in severe hypoxia.
Hypoxia is known to enhance ASC proliferation.
Injured adipocytes release fibroblast growth factor-2, which stimulates ASC proliferation and hepatocyte growth factor, contributing to the regeneration of AT.
The retention of grafted fat largely depends on the distance metabolites must travel to reach the center of the graft and on the depths of the surviving and regenerating zones.
MATERIALS AND METHODS We searched Pubmed for original articles with the term “fat grafting” in the title or abstract to determine which aspects of the microenvironment surrounding fat grafts affect retention. 
We modeled the relationship between oxygen consumption by metabolizing fat tissue and oxygen delivery via diffusion for grafted fat cylinders of varying radii. 
We called this the “Microribbon Model.”
In a steady state, oxygen delivery matches oxygen consumption, and oxygen must diffuse off erythrocytes, through the capillary walls, through the extracellular space, and into the grafted cells. 
To optimize the potential for oxygen delivery, the vascular perfusion must be functioning properly, and several articles propose that an increased interstitial fluid pressure (IFP) during fat grafting might restrict perfusion. 
To determine the physiological plausibility of this hypothesis, we modeled the relationship between fluid accumulation, IFP, and perfusion. 
We called this the “Fluid Accommodation Model.”
To study how the information from the first 2 models can be used to optimize the fat grafting procedure, we explored which existing fat grafting procedures were attempting to optimize the variables in our models. 
Although several articles emphasized a biological approach with cytokines and stem cells, only external volume expansion (EVE) emphasized enhancing oxygen delivery. 
Therefore, we modeled how EVE could prepare the recipient site to allow the critical variables in the first 2 models to be optimized during fat grafting. 
We called this the “EVE Effect Model.” 
The information from these models comes from our calculations derived from established equations, relationships, and constants.
RESULTS The literature to date concludes that oxygen delivery seems to be the most crucial molecule for fat graft survival. 
The thickness of the exterior rim of viable cells in multicell spheroids increases linearly with the theoretical oxygen diffusion distance. 
Oxygen diffusion is the limiting variable in determining cell survival in spheroids. 
Therefore, it seems that the core principle of fat graft survival is that oxygen concentration at any point in a graft is a function of the oxygen concentration of the surrounding capillaries, the diffusion rate of oxygen to reach that point in the tissue, the distance from the oxygen source, and the metabolic rate. 
In other words, at every point within a fat graft, there is a race between the rate at which oxygen is needed by the cells and the rate at which oxygen can be delivered by the capillaries and diffused through the AT.
According to standard principles in physiology, the metabolic rate of a given section of AT is directly proportional to its volume (V). 
However, the diffusion rate of any substance is directly proportional to the surface area (SA) over which diffusion takes place, and the SA:V ratio of any interior section of a cylinder is (2/radius). 
Therefore, as the radius of a cylindrical injection of AT increases, the SA:V ratio decreases, and oxygen’s diffusion rate cannot meet the tissue’s metabolic needs. 
In addition, diffusion is related to the square of the distance between the oxygen source and where it is consumed. 
When the distance is increased by a factor of 2, the delivery of oxygen is decreased by a factor of 4.
Using diffusion and metabolism equations and biological and physical constants,31–37 we modeled the theoretical borders between the surviving, regenerating, and necrotic zones for fat grafts of different radii (Appendix 1). 
According to the Microribbon Model in standard human conditions, the largest fat microribbon with no necrotic zone would have a radius of 0.16 cm. 
Such a graft would have a surviving zone of 0.03 cm and a regenerating zone of 0.13 cm. 
As graft radius increases beyond this point, the necrotic zone grows rapidly.
This Microribbon Model correlates well with experimental data. 
When multicell spheroids were cultured in excess medium, the spheroids ceased to expand at a radius of 0.15 cm. 
Carpaneda and Ribeiro suggest that, in humans, only the region 0.15 cm from the edge of a fat graft retains a significant percent of its volume. 
Current successful method descriptions suggest a 0.13-cm limit for the radius of reinjected fat.
Multiple studies report negative correlation between fat graft particle width and retention percentage. 
Long-term fat graft retention requires small volumes of fat to be diffusely distributed into a well-vascularized recipient site through well-separated tunnels. 
If the microinjections are not diffusely distributed in the recipient site, they will coalesce, forming particles too wide to survive.
Fat graft survival is also largely dependent on the vasculature’s ability to delivery oxygen blood through the capillaries surrounding the graft. 
Several surgeons have suggested that injecting too much fat into a small recipient site can increase IFP enough to constrict capillaries, inducing ischemia in the grafted tissues. 
Guyton demonstrated that, for up to a certain volume, interstitial fluid can accumulate in a tissue without significant IFP increase, but beyond that range, any additional fluid causes drastic IFP increases, quickly reaching levels associated with compartment syndrome. 
Milosevic et al also demonstrated that capillary perfusion decreases with increasing IFP. 
Therefore, it is recognized that fluid accumulation can lead to increased IFP and that increased IFP can lead to decreased capillary perfusion.
Using these relationships, we modeled the change in relative capillary perfusion as a function of IFP and interstitial fluid accumulation to determine if increased IFP during fat graft is enough to limit capillary perfusion and oxygen delivery. 
According to this Fluid Accommodation Model, a given tissue compartment can accommodate about 60% of its weight in interstitial fluid before reaching a critical IFP (IFPC) of 9 mm Hg, beyond which, any additional fluid causes a drastic IFP increase and capillary perfusion decrease. 
It is important to recognize that this 60% accommodation is a generalized estimation, which will change with differences in the compliance of tissues; therefore, the IFP could be monitored intraoperatively if excess fat injections could be a possibility. 
This IFPC closely correlates with recent suggestions of IFP-based fat grafting stop points of 9–10 mm Hg.
Increased IFP may also inhibit retention of grafted cells by mechanical compression, which induces apoptosis and regulates cytokine release.
Therefore, interstitial volume, compliance, and vascularity determine how many microribbons of fat can be dispersed before increasing IFP enough to significantly reduce perfusion and cell survival.
Mechanical forces can induce angiogenesis, adipogenesis, and increased subcutaneous tissue thickness and compliance. 
Our group previously studied the mechanism behind EVE’s effects and found that the macroscopic swelling is likely due to deformation of the extracellular matrix, which induces tension on the cells anchored to extracellular matrix fibers. 
This micromechanical strain is transferred to the cytoskeleton, where it acts as a gate-control signal to induce proliferation.
EVE-induced ischemia activates the hypoxia inducible factor -1α/vascular endothelial growth factor pathway to induce vascular remodeling, angiogenesis, and cell proliferation. 
Adipogenesis can be induced by lymphedema or water-rich empty proteinaceous matrices. 
Inflammation promotes these processes.
Khouri et al developed a suction-based EVE bra (BRAVA) that noninvasively induces long-term breast growth in humans.
It uses cyclical forces, which have a greater effect than continuous forces. 
Daily BRAVA-use induces temporary edema and angiogenesis and sustained increases in subcutaneous tissue thickness and compliance. 
Once fat grafting to the breast was more accepted and understood, BRAVA was proposed for application in preparation for fat grafting. 
Pre-expansion and resultant augmentation had a strong linear correlation (R2=0.87), and pre-expansion allowed significantly more AT to be grafted and retained than what was reported in a meta-analysis of 6 other published reports on fat graft breast augmentation without pre-expansion (P < 0.00001). 
Preoperative expansion also has several clinical applications in breast augmentation and reconstruction (Khouri et al, unpublished data, 2014).
To understand the effectiveness of EVE devices for fat grafting, we must consider the ratio of grafted fat to recipient site volume. 
If the original recipient site is 100 mL and noncompliant, and 30 mL of AT are to be grafted, there is no need for pre-expansion because, with a 30% increase, the AT can be diffusely microinjected. 
If the original recipient site is 100 mL, and the volume of AT to be grafted is 90 mL, this 90% increase cannot be done without overcrowding, which would cause coalescence, increased IFP, reduced perfusion and oxygen delivery, thinner surviving and regenerating zones, and significant volume loss. 
However, our EVE Effect Model predicts that a tight 100-mL recipient site can be transformed into a compliant 300-mL site and, according to the Starling equation, cause edema influx. 
Because the fat microribbons can be diffusely dispersed into the pre-expanded tissue, less coalescence occurs and more AT can be grafted before reaching IFPC. 
As IFP increases, the Starling equation dictates that interstitial fluid is reabsorbed, allowing IFP to quickly return to baseline levels.
Our EVE Effect Model also predicts that the preoperative cyclical negative pressure treatment increases the host vascular density and diameter, increasing total oxygenated blood delivery, decreasing the mean distance each oxygen molecule must diffuse to reach the center of a grafted microribbon, and accelerating graft revascularization: a major determinant of volume retention.
DISCUSSION We have presented 3 models to predict how recipient site vascularity, volume, and compliance determine fat graft retention by regulating oxygen delivery via perfusion and diffusion. 
To the best of our knowledge, this is the first study to mathematically model the essential variables relating to oxygen delivery and graft retention. 
The information from these models comes from our calculations derived from established equations, relationships, and constants. 
Our Microribbon Model suggests that any fat injection with a radius greater than 0.16 cm in standard human conditions will have a zone of central necrosis. 
Our Fluid Accommodation Model suggests that interstitial fluid injections that cause IFP to increase past IFPC will restrict perfusion. 
Our EVE Effect Model explains how the information from the first 2 models can be used to optimize the fat grafting procedure. 
It predicts that preoperative EVE increases recipient site vascularity, volume, and compliance, allowing more AT to be successfully grafted. 
For grafting small volumes of fat into large, compliant, highly vascularized recipient sites, the recipient bed will likely accept the graft even if the surgeon does not specifically consider each of the variables in these models. 
However, for grafting large volumes of fat into small, restricted, poorly vascularized recipient sites for procedures such as total breast reconstruction, or irradiated tissues, surgeons must optimize each of the variables related to graft retention. 
None of these models can predict fat graft retention on their own, but taken together, they enhance our understanding of the biological events occurring during fat grafting and allow us harness this knowledge to enhance surgical outcomes.
Modeling provides the theoretical framework for testing theories, so these models require experimental data to be further accepted. 
To test the Microribbon Model, experiments similar to the ones performed by Eto et al would have to be reproduced, but pO2 would have to be measured at various depths within grafts or cylindrical cell cultures of various radii. 
To test the Fluid Accommodation Model, studies would have to measure IFP, perfusion, and pO2 as fluid is injected into living tissue. 
To test the EVE Effect Model, a complex set of experiments would be required. 
More importantly, to truly test the effects of EVE in fat grafting, a large prospective randomized controlled trial should be performed. 
It is hoped that these studies will lead to long-term prospective studies in humans.
Conclusion Our models predict the following: that fat injections larger than 0.16 cm in radius will have an area of central necrosis; that, in fat grafting, IFPs greater than 9 mmHg will cause decreased capillary perfusion; and that EVE can enhance tissue volume, vascularity, and compliance, allowing more AT to be successfully grafted.
Platelet rich plasma is a promising therapy in dermatology and aesthetic medicine. 
In this article we will discuss the pros and cons of platelet rich plasma (PRP) and the usage of PRP in aesthetics. 
PRP is especially used for conditions like facial and neck rejuvenation, fine lines and wrinkles, abdominal striae and facial scarring.
Usage of platelet rich plasma (PRP) in aesthetic medicine is a new concept.
In dermatology and cosmetic medicine, PRP has been used to treat acne, scarring, and alopecia (especially in women).
It is also effective for skin rejuvenation and tightening around the eyes.
Before injecting PRP to treat hair loss, a tiny scalp roller with spikes is used to stimulate the thinning areas.
The rationale is that this sends a message to the hair follicles to start the healing process.
Then, PRP is injected over the affected area to further stimulate stem cells in the follicle.
Platelet-rich plasma is injected by multiple tiny punctures under the dermis, with or without topical local anesthesia.
The process is painless if sufficient topical anesthesia is applied.
When PRP is injected into the damaged area, it stimulates the tissue, causing mild inflammation that triggers the healing cascade.
As a result, new collagen begins to develop.
As this collagen matures, it begins to shrink and tightens and strengthens the skin.
Improvement in skin texture and tone is noticeable within 3 weeks.
Full collagen regeneration requires 3 months.
The PRP treatments can be used on all skin types and tones.
Minimal swelling, bruising, and redness for the initial 12 to 24 hours are expected.
A bruise at the needlestick site may be visible for 2 to 3 days.
Swelling from the fluid is what the patient will notice first.
During several weeks, the platelets stimulate growth factors, which assists in more collagen stimulation.
Treatment results vary but last up to 18 months in most patients.
In PRP, activated platelets release many other bioactive proteins responsible for attracting macrophages and mesenchymal stem cells.
Inside the platelet are two types of granules, namely, alpha granules and dense bodies.
Alpha granules contain the clotting and growth factors that are released in the healing process.
Normally at the resting state, platelets require a trigger to activate and become a participant in wound healing and hemostasis.
Growth factors and other cytokines in platelets include the following: platelet-derived growth factor, transforming growth factor, fibroblast growth factor, insulinlike growth factor 1, insulin like growth factor 2, vascular endothelial growth factor, epidermal growth factor, interleukin 8, keratinocyte growth factor, and connective tissue growth factor.
The platelets secrete growth factors, including platelet-derived growth factor and vascular endothelial growth factors.
Platelet-derived growth factor is one of numerous growth factors or proteins that regulate cell growth and division.
In particular, it has a significant role in the formation of blood vessels (angiogenesis) and the growth of blood vessels from already existing blood vessel tissue.
Vascular endothelial growth factor is a chemical signal produced by cells that stimulates the growth of new blood vessels.
It is part of the system that restores the oxygen supply to tissues when blood circulation is inadequate.
Advantages of using PRP for aesthetic medicine include the following: tissue regeneration and rejuvenation, induction of cell differentiation, extracellular matrix formation, recruitment of other cells to the site of injury, and an increase in collagen production, which can increase skin thickness and overall skin health. 
In addition, PRP is nonallergenic, is an autologous physiological product, eliminates donor transmissible infections, and is a biological glue for tissue adhesion, especially in skin flaps, bone grafts, and trauma. 
Although PRP is a promising therapy for most patients, the practitioner must take into account some considerations during the initial assessment before suggesting this treatment.
Contraindications include the following: sepsis, cancer, chemotherapy, platelet dysfunction syndrome, critical thrombocytopenia, hypofibrinogenemia, hemodynamic instability, anticoagulation therapy, acute and chronic infections, chronic pathological conditions of the liver, severe metabolic and systemic disorders, and skin disease (systemic lupus erythematosus, porphyria, and allergies), as well as heavy nicotine, drug, and alcohol consumption.
Adverse effects of PRP treatment may occur, some of which are significant.
The most common adverse effects are infection, skin discoloration and bruising, pain in the injected area, allergic reaction (a rare occurrence), and blood clot (because PRP therapy uses a needle, a vein could be damaged).
Certain factors (eg, smoking and alcohol intake) diminish stem cell release.
Avoiding these will increase the success of the PRP procedure.
The platelets work by causing an inflammatory reaction.
If this inflammatory reaction is diminished, the clinical outcome is significantly compromised.
For this reason, the use of anti-inflammatory drugs is not recommended.
This restriction should be in place for about 1 to 2 weeks.
Platelet concentration is a rich source of various cytokines and growth factors, which are activated after its injection into the target tissue.
Platelets are activated endogenously by coagulation factors (in some methods of preparing PRP, the activated PRP is injected to the tissue).
Following their attachment to special receptors on the cell surfaces, some intracellular processes are activated, that facilitate extracellular matrix (ECM) accumulation and improve cell proliferation and differentiation.
Tissue regeneration is resulted from cell proliferation, angiogenesis and cell migration.
Matrix metaloproteinas proteins (MMP) are involved in aging process by degradation of collagen and other extracellular matrix (ECM) proteins, this characteristic can be used to benefit rejuvenation.
They can help regeneration of dermis through omission of collagen fragments that are harmful to the dermal connective tissue, and so, provide an appropriate foundation for new collagen deposition.
In some studies aPRP (activated PRP) increases the expression of MMP-1 and MMP-3 protein.
Thus, aPRP may cause ECM remodeling through stimulating the removal of photo-damaged ECM components and inducing the synthesis of new collagen by fibroblasts, which are in turn proliferated by their stimulation.
Another mechanism of PRP for skin rejuvenization, is through acceleration of hyaluronic acid production.
Hyaluronic acid absorbs water and makes hyaluronic acid matrix swelled which increases skin volume and turgor.
It also promotes cell proliferation, extracellular matrix synthesis and helps to the adjustment of the collagen fibers diameter.
Overall, it could enhance skin elasticity.
All these processes and some other unknown ones contribute to tissue rejuvenation through PRP.
Platelet Rich Plasma (PRP) is used for stimulation of both superficial and deep dermis layers.
For superficial stimulation, the injection must be done in the superficial dermis.
The PRP must be injected into the deep dermis or subdermal tissues when using as filler.
The superficial injection might be done just like mesotherapy technique in order to improve the skin texture, volume and hydration.
The technique is easy to be performed and has no important side-effects.
Side-effects might appear from mild bruising and occasional swelling to rarely infections.
Compared with other skin rejuvenation therapies, the clinical experience using PRP can result in skin rejuvenation and global facial volumisation.
PRP is a form of bio-stimulator that is safe and creates an immediate, long lasting volumetric effect with natural looking results.
To prepare PRP, a small amount of blood is drawn from the patient’s arm.
The blood is then placed in a centrifuge that spins at high speed and separates the platelets from the rest of the blood components.
The typical baseline blood platelet count is approximately 200 000 per microliter; therapeutic PRP centrifuges concentrate the platelets by roughly 5-fold.
However, broad variability exists in the production of PRP by various concentrating equipment and techniques.
The platelets collected in PRP are activated by the addition of thrombin and/or calcium gluconate, which induces the release of these factors from alpha granules.
The entire process takes less than 15 minutes and increases the concentration of platelets and growth factors up to 600%, along with an inherent rise in human stem cell proliferation due to exposure to concentrated platelets up to 10 times above native levels.
The concentrated PRP is then injected into and around the affected area, jump-starting and significantly strengthening the body’s natural healing signals.
Injections of PRP heal the area over time, during 1 to 3 months.
Because the patient’s blood is used, there is no risk of a transmissible infection and a low risk of allergic reaction.
Aging of the skin, dermal components, and cells means that the skin texture and appearance deteriorate and have been damaged.
Aging affects the hands and soft tissue of the face, neck, and decollete.
This is characterized by sagging jowls, thinning of the skin, puffiness, age spots, and wrinkling.
In dermatology and cosmetic medicine, PRP has been used to treat acne, scarring, and alopecia (especially in women).
It is also effective for skin rejuvenation and tightening around the eyes (for thin crepe-like skin and fine lines) and in the following areas: cheeks and midface, thinning skin on the neck, jawline and submalar regions, back of hands, decollete, and others (eg, knees, elbows, and upper arms, as well as for postpregnancy skin laxity).
Platelet-rich plasma is injected by multiple tiny punctures under the dermis, with or without topical local anesthesia.
The process is painless if sufficient topical anesthesia is applied.
When PRP is injected into the damaged area, it stimulates the tissue, causing mild inflammation that triggers the healing cascade.
As a result, new collagen begins to develop.
As this collagen matures, it begins to shrink and tightens and strengthens the skin, as well as the tendons and ligaments of the damaged area when it is injected at that level.
Improvement in skin texture and tone is noticeable within 3 weeks.
Full collagen regeneration requires 3 months.
Topical skin care and light therapies can enhance these results.
Advanced wrinkling cannot be reversed, and severe scarring may not respond to treatment.
In my experience, surgical scars respond well cosmetically.
The PRP treatments can be used on all skin types and tones.
Minimal swelling, bruising, and redness for the initial 12 to 24 hours are expected.
A bruise at the needlestick site may be visible for 2 to 3 days.
Swelling from the fluid is what the patient will notice first.
During several weeks, the platelets stimulate growth factors, which assists in more collagen stimulation.
Treatment results vary but last up to 18 months in most patients.
Biannual touch-up treatments will maintain the results.
As an initial treatment strategy, up to 3 injections may be given within a 6-month time frame.
These are usually performed 2 to 3 weeks apart.
Certain factors (eg, smoking and alcohol intake) diminish stem cell release.
Avoiding these will increase the success of the PRP procedure.
The platelets work by causing an inflammatory reaction.
If this inflammatory reaction is diminished, the clinical outcome is significantly compromised.
For this reason, the use of anti-inflammatory drugs is not recommended.
This restriction should be in place for about 1 to 2 weeks.
Proponents of PRP therapy argue that negative clinical results are associated with poor-quality PRP harvest or concentration by inadequate devices.
The specification that gathering devices capture a percentage of a given thrombocyte count is a marketing bias because significant individual variability exists in the platelet concentration of human plasma.
More is not necessarily better in this case.
Variability in platelet concentrating techniques may alter platelet degranulation characteristics, which could affect clinical results.
PRP has made the most significant progress in the facial area. 
Platelet Rich Plasma (PRP) with fat transfer is the surgical combination of injecting a patient’s own plasma containing growth factors along with their own purified fat to augment areas of lost volume and wrinkles on the face. 
Containing beneficial growth factors, PRP may additionally be used with fat transfer or subcision to re-plump areas of lost volume or depressed scarring from acne or trauma. 
Subcision surgically releases the pulled down portion of the scar from within, inducing the body’s healing response to create blemish free skin cells. 
Combined with fat transfer, PRP softens the appearance of depressed, roling scars. 
The latest facial rejuvenation procedure is the face lift which combines the power of new PRP technology and facial fillers to minimize the signs of facial aging.
This non- surgical procedure promotes new tissue growth to improve overall facial skin tone for a more youthful appearance.
The PRP is combined with a facial filler and then re- injected into areas of concern around the face.
Patients benefit from this procedure as there is minimal downtime and results can last for over a year.
The true “lift” effect is achievable with a combination of fillers, layered with the PRP serum.
The fillers provide an instant fill or volume correction and the PRP – injected above the filler – immediately kick-starts a skin regeneration process.
Patients can see and feel the effects within minutes as their skin becomes tauter and smoother.
The use of PRP with fillers not only enhances the skin tone and texture, but prolongs the effective filler correction for 3 to 6 months longer than when fillers are used alone.
Monthly intradermal injections of PRP in 3 sessions have shown satisfactory results in face and neck rejuvenation and scar attenuation.
A study showed that a combination of fractional non-ablative (erbium glass) laser therapy with topical application of PRP, resulted in objective improvement in skin elasticity, a lower erythema index and an increase in collagen density as well.
Histological examination showed an increase in length of dermoepidermal junction, amount of collagen and fibroblasts in the treated skin.
Patients who don’t want or need fillers can benefit from PRP. 
The activated PRP serum can be injected just under the skin surface to stimulate the body to make a small amount of its own ‘filler’. 
Although this will not approximate the same results as one gets from a gel filler, some improvement in textural changes can be seen.
PRP in combination with fractional ablative lasers (carbon dioxide) for deep wrinkles and severe photodamaged skin, has also been shown to reduce commonly encountered, transient adverse effects and decrease the downtime. 
Fractional laser treatments are known for their ability to retexture skin. 
Adding PRP takes laser resurfacing to a new level by accelerating healing and increasing desired new collagen formation. 
Following your laser treatment, activated PRP serum is applied to skin that is ideally suited to accept the wound-healing platelet serum. 
PRP can also be used as ‘PRP Facial’ which consists of PRP applied to skin that has been prepared by an automatic microneedle. 
These micro-needling makes tiny “wounds” in the skin which accept the PRP serum and begins the process of collagen creation along with the tissue enhancement from growth factors found in the plasma serum. 
The micro needling-based procedure is also producing great results in terms of minimizing the appearance of both scarring and stretch marks.
In scarring, the micro needling is used to break up the fibrous tissues of the scar and the PRP spurs the growth of healthy tissue.
For stretch marks, micro needling creates damage over the thinned skin of the stretch mark. 
PRP then promotes growth of thicker skin.
As with all therapies, adequate training and experience are paramount. 
The beauty of the PRP technique, especially in dermatology and as an adjunctive tool in practice, is that it can be used as part of a multifaceted or layered approach. 
Significant clinical outcomes can be obtained with concomitant use of light therapies, fillers, and mesotherapy. 
Due to limited studies on clinical efficacy and safety, further studies are required to investigate the mechanism of action behind the therapeutic effects of these products and their long-term safety.
Still, the PRP has certain limitations as there is no standardization in PRP preparation and specific quality parameters in PRP preparation are still lacking.
The research reveals the impact of a belief in god and god’s Providence on the happiness and quality of life of patients bene?ting from aesthetic medicine treatments in Poland (country where over 90 % of society declare to be deeply devout). 
The work also examines age and sex of the patients bene?ting from beauty treatments (botulinum toxin, ?
llers, medical peels and needle mesotherapy), their quality of life and also the impact of various factors, including God and Divine Providence on their happiness. 
The research shows the analysis of factors in?uencing the successes or failures in the past year and presents the comparison of patients who have bene?ted from the aesthetic medicine treatments (cosmetic medicine) to the common average Polish citizens.
The concept of God is not an innate one, but it is gradually formed in the psyche of a humankind. 
It is usually being shaped from an early childhood in the process of socialization, and it is modi?ed on the basis of one’s own cognitive and emotional experiences, primarily associated with the structure of one’s family life. 
Providence is in as god’s interest in everything that He has created especially a man, leading him to the full perfection and to the ultimate goal.
There is no speci?c, biblical prohibition of changing the appearance of our face, our bodies. 
The belief is that God gives us the freedom of choice, and our conscience must lead us and guide through the behaviours that are not explicitly prohibited for us. 
For this reason, one cannot ?
nd any justi?cation for believing that aesthetic medicine treatments (cosmetic medicine) are clearly immoral; however, one must be careful not to overuse them. 
People are worthy, as they are made in the image of God. 
A person’s worth does not depend on and does not change with changes in the appearance of one’s face or body because I have rejected him…,because man sees the appearance but the Lord looks into the heart. 
It is not our appearance that makes us worthy, but a belief in God whose image we have been made. 
Regardless of how one looks, this should not be forgotten.
The following work reveals what kind of in?uence of a belief in God and God’s Providence has on the happiness and quality of life of patients bene?ting from aesthetic medicine treatments in Poland (country where over 90 % of society declare to be deeply devout). 
The work examines age and sex of the patients bene?ting from beauty treatments, their quality of life and also the impact of various factors, a belief in God on their happiness.
The research shows the analysis of factors in?uencing the successes or failures in the past year and compares the patients who have bene?ted from the aesthetic medicine treatments to the common average Polish citizens.
In the research, 603 respondents of both sexes took part; they were aged from 21 to 61 years, bene?ting from the cosmetic treatments in aesthetic medicine clinics in Poland (botulinum toxin, ?
llers, medical peels, needle mesotherapy), randomly selected to take part in the test.
Scheffeg’s and Bonferroni’s methods and expert system were used for multiple comparisons. 
In the social studies, an assessment fraction error (1/2 con?dence interval) is possible to be below 4 %, and in this test, it was at 3.99 %. 
The formula for the minimum sample size for dichotomous variables (it is a vast majority in the study) is given by equation: where n—sample size; Z—statistics for the desired level of con?dence; p—an estimate of the expected proportion of the variable of interest in the population; d—half the width of the desired interval.
The sample size n is 603 patients. 
For a 95 % con?dence level, from a standard normal distribution, Z = 1.96. 
The value d is at the level of 3.99 %, assuming that the position of the variables of interest is approaching unity (assumed p = 99 %).
The following issues regarding patients bene?ting from the aesthetic treatments have been examined: age distribution, taking gender into account; evaluation of the factors essential to a happy life, including the in?uence of God and God’s Providence on patients’ happiness; evaluation of the quality of life of patients bene?ting from the aesthetic medicine treatments (cosmetic medicine); causal attribution style, that is, who or what did the successes or failures in the past year depend on.
Age distribution of people bene?ting from aesthetic medicine treatments, taking gender into account. 
The average age of patients bene?ting from the aesthetic medicine treatments (cosmetic medicine) in Poland is 38 years; most often, they are people from 30 to 49 years of age (74 %). 
Women signi?cantly earlier begin to use such treatments. 
In a group of men, the greatest interest in the use of aesthetic medicine treatments begins before 40 years of age (which is probably associated with the midlife crisis); however, from 45 years, it steadily decreases. 
Ageing women much less frequently resign from the treatments. 
Nevertheless, men and women over 55 years of age signi?cantly lose interest in the aesthetic medicine treatments.
Evaluation of factors essential to a happy life of patients bene?ting from aesthetic medicine treatments in Poland. 
People perceive happiness in many different ways.
For some, the condition for happiness will be owning a lot of money, for others enjoying a good health, and for others, family and a successful marriage or appropriate education and work. 
Patients were asked to classify, in the order of importance (1—the least important, 7—the most important), the individual factors that could bring a sense of happiness.
For patients bene?ting from the aesthetic medicine treatments (cosmetic medicine), the most important prerequisite for happiness is health (91 %).
The second is cheerfulness and optimism (86 %). 
Family; children rank also very high (84 %), followed by successful marriage (83 %). 
The least important condition for happiness is the Providence of God that appears in 36 % of evaluations in the lower half of the scale. 
The next is education (18 %), a strong character (16 %) and friends (15 %).
Evaluation of the quality of life of patients bene?ting from the aesthetic medicine treatments. 
Evaluation of the ‘‘level of happiness’’ and joy of life is virtually not possible other than the subjective evaluation and declaration. 
Therefore, respondents were asked how much they enjoy their lives.
It is easy to notice that all aesthetic medicine patients enjoy life to some extent. 
The vast majority of them say that they really enjoy their life (91 %).
Causal attribution style. 
The causal attribution scale used in the study is supposed to provide answers to the question: who or what do the aesthetic medicine patients make responsible for the quality of their own lives: themselves, the authorities, friends, strangers, fate/Providence of God or aesthetic medicine treatments (cosmetic medicine).
Aesthetic medicine patients more often (16 % p.p.) see their impact on whether the past year was successful or not, and 4.5 times more often indicate a signi?cant impact of the authorities on their life than the average Pole does. 
In a much lower degree (14 % p.p.) than average Poles, aesthetic medicine patients recognize the impact of fate and Divine Providence on the past year, similar to the impact of other people (5 % less p.p.).
First of all, it is worth noting that all patients using aesthetic medicine treatments (cosmetic medicine) very highly evaluated their own impact on whether last year was successful or not.
Even in the case of an unsuccessful year, assessment of one’s own impact on this situation has increased.
Strangers have had a limited and permanent in?uence on the aesthetic medicine patients which does not change in respect of whether the year was successful or not.
In case of ‘the average Pole’’, it looks different, the in?uence of strangers on the past year has been indicated, and their impact increases in the situations where the last year was unsuccessful.
However, patients bene?ting from aesthetic medicine treatments, in case of unsuccessful year, are more likely to blame their friends for it.
Unfortunately, the study lacks data from ‘Social Diagnosis’’.
When it comes to fate and God’s Providence then again, aesthetic medicine patients much less often point out that this factor is an indicator of the past year’s success or failure comparing to the average Poles, but in both cases, the frequency of the indications on the fate and Providence of God as the cause increased if the past year was unsuccessful.
Aesthetic medicine patients are much more aware of the impact of the authorities on whether their year was successful or not than the average Pole. 
Also, in case of an unsuccessful year, the tendency to indicate the authorities as the reasons for such state does not increase so dramatically as in case of the average Pole. 
In general, it is possible to notice an increased desire to indicate the individual factors that could, in subjects’ opinion, have an impact on whether the past year was successful or not. 
But there is one exception. 
A lot of patients are more likely to point to the aesthetic medicine treatments (cosmetic medicine) as one of the reasons that the past year was successful.
The study carried out by psychologists has shown that people who consider themselves as attractive rate higher their mental health and are more extraverted and happier. 
Their quality of life is signi?cantly improved. 
Langlois in his research notes similarly that the attractive people, in accordance with the stereotype ‘‘beautiful is good’’, are considered to be (in addition to greater intelligence, more assertiveness, more con?dence) happier than the average ones. 
This is related to the fact that beautiful people, since birth experience more smile, are milder judged by environment and a greater capacity is seen in them. 
Such person becomes more con?dent and enjoys the world and the environment. 
Aesthetic medicine patients are also satis?ed and happy people.
In this work, all the aesthetic medicine patients enjoy the life to some extent; none of the interviewed replied that they do not enjoy the life. 
Up to 91 % of them say that they highly enjoy it. 
This percentage is signi?cant, compared to other social groups, for instance, to patients in the perimenopausal period. 
Here, Polish women evaluate their quality of life as good in 41 %, while Greek women only in 17.6 %. 
It is de?nitely better among American women, as it has been shown that 51 % of them during menopause and after menopause are very satis?ed and experience the happiest period in their life; however, patients after aesthetic medicine treatments (cosmetic medicine) evaluate their quality of life the highest.
For patients bene?ting from the aesthetic medicine treatments (cosmetic medicine), the most important prerequisite for happiness is their health (91 %). 
The second is cheerfulness and optimism (86 %). 
Family; children rank also very high (84 %) and successful marriage (83 %). 
The least important condition of happiness is the Providence of God that appears in 36 % of evaluations in the lower half of the scale. 
The next is education (18 %), a strong character (16 %) and friends (15 %).
The style of the causal attribution is a human tendency to search for reasons for one’s own state of behaviour and effects of the actions or state of behaviours and the effects of other people’s actions in certain factors.
The causal attribution scale used in the study provided an answer to the question whether the past year has been successful or not and who or what do the aesthetic medicine patients make responsible for the quality of their own lives: themselves, the authorities, friends, strangers, fate/ Providence of God or aesthetic medicine treatments.
In many studies, this question is related to a con?rmed attributive inclination in the service of ego (‘‘whatever good—it is me, whatever wrong—it’s not me’’) and the theory of social ingratitude, which says that the social reception of changes at the macro-level is asymmetrical: those who from the beginning are gaining on these changes show little appreciation to their creators, they mainly look for causes of improvement of their own life in themselves, and the change for better, they experience quite poorly.
Those who claim to be harmed as a result of the implementation of reforms blame the authors of the reforms for the deterioration of the conditions of their life and experience changes for worse much stronger.
Therefore, who the responsibility is assigned to depends on the direction of the perceived change in the quality of one’s own life.
It is noticed that in comparison with the results of the "Social Diagnosis", which has an impact on the entire Polish population, the results obtained in the study of aesthetic medicine patients considerably differ from them.
Aesthetic medicine patients can more often (about 16 p p ) see their impact on whether the past year was successful or not.
This is probably the greater awareness and social status of patients bene?ting from such treatments, because the average aesthetic medicine and anti-ageing patient in Poland is a young woman, well established, educated and happy with the life.
This can be seen especially in the impact of authorities on the past yearAesthetic medicine patients 4.5 times more often show a signi?cant impact of the authority on their life than does the average Pole, because the average Pole much more frequently blame fate, Providence and other people for deterioration in the quality of his life, while not recognizing the impact of the authority on such situation.
Even if the year was relatively quiet a calm year in the politics, the aesthetic medicine patients, as a better-situated social group, can detect not only simple political events, but also the less noticeable to the average Pole, such as, tax system affecting the economy.
One can notice that patients bene?ting from aesthetic medicine treatments (cosmetic medicine) are much more aware and feel more responsible for their fate than the average Pole.
Aesthetic medicine patients differ from the average people; this is indicated by the causal attribution style, that is, who will the responsibility for the speci?c changes in the quality of life be assigned to.
In addition, over 90 % of the population declare that is deeply devout and 41.6 % of adults are reported to systematically participate in church services and other religious ceremonie.
In such case, the response in this study should be different, because of the deep faith, but only 36 % of patients responded that their happiness depends on God and Providence.
In addition, according to the "Social Diagnosis", the impact of religious practices on the mental well-being of Poles deteriorates yearly that would explain the low results in this work.
Most likely, such data result from the fact that patients strongly tend to think about God, in the toughest moments of their lives, when they are faced with a disease (oncology, surgery, etc.), while the aesthetic medicine is the only ?
eld of medicine that deals with a healthy patient.
Patients bene?t from treatments to improve their appearance, in the aesthetic sense.
Keep in mind that a typical aesthetic medicine patient in Poland is a young woman, well established and educated, while most of the religious groups in Poland include: women above the age of 60, primarily an inhabitant of the village, with basic education, mostly annuitants and retired, who rarely or never use aesthetic medicine from the ?
nancial reasons and their beliefs.
Therefore, patients after aesthetic medicine treatments (cosmetic medicine) in Poland are a group of people who are highly assessing their quality of life and who are happy; however, to a small extent, they attribute these reasons to God or Divine Providence, even in such a Catholic country as Poland.
The average age of patients bene?ting from the aesthetic medicine treatments (cosmetic medicine) in Poland is 38 years. 
91 % of patients bene?ting from the aesthetic medicine treatments (cosmetic medicine) claim that they strongly enjoy life. 
For patients using the aesthetic medicine treatments (cosmetic medicine) in Poland, God and Divine Providence (36 %) and education (18 %), a strong character (16 %) and friends (15 %) are the least important conditions for happiness. 
The most important prerequisite for happiness for them is health (91 %), cheerfulness and optimism (86 %) and family; children (84 %) and successful marriage (83 %). 
Aesthetic medicine patients much less (about 14 % p.p.) than the average Poles tended to be in?uenced by fate and Providence of God in the past year. 
Aesthetic medicine patients more often (about 16 % p.p.) notice their own impact on whether the past year was successful or not, and 4.5 times more often indicate a signi?cant impact of the authority on their life than does the average Pole.
The Royal College of Surgeons (RCS) has today published information about a new system of certification that will be introduced in cosmetic surgery next year. 
It will help distinguish highly qualified, competent surgeons who perform cosmetic surgery from those who lack experience and training.
Ever since Sir Bruce Keogh’s Review of the Regulation of Cosmetic Interventions in 2013, the RCS has led work to make cosmetic surgery safer for patients. 
This includes developing robust standards of training and practice for surgeons undertaking cosmetic surgery, which have been developed by the Cosmetic Surgery Interspecialty Committee (CSIC) in a number of surgical areas.
I was involved in the development of the cosmetic surgery standards as the deputy lead in aesthetic facial surgery for the British Association of Oral & Maxillofacial Surgery (BAOMS) and have been a SAC RCS Aesthetic Surgery committee member for a number of years. 
Even though I am a trained Consultant Oral and Maxillofacial Surgeon (OMFS), with more than 20 years’ experience of working in the NHS and private sector, I plan to apply for certification.
This is because, at the moment, it can be very difficult for a patient to assess a surgeon’s training and experience. 
Prospective patients are often advised to look for a record of surgical training and for the national surgical association they may belong to. 
I believe we need a more robust system to protect patients and one which they will understand.
Cosmetic facial surgery is very much a part of the surgical role and experience of OMFS. 
In the NHS, I treat patients with cancer of the mouth, head and neck. 
I also perform reconstructive surgery of the face, jaw, skin and mouth.
The patients I treat for cosmetic surgery are predominantly female and they are primarily concerned with the effects of ageing. 
They may come to see me if they are contemplating having a face lift or skin tightened around their eyes, face, neck or jaw line.
I also treat younger patients who may have concerns about the look of their nose, ears, or the size of their chin.  
For my patients, entrusting someone with how their face looks is an enormous and potentially life-changing decision.
I know from treating patients who have come to see me for corrective surgery following treatment elsewhere, how devastating it can be when things go wrong.
One case that springs to mind involved a woman who had travelled to North Africa for a face lift. 
Her wounds had become infected and she had necrosis, or dead skin, around her face, when she returned home. 
I repaired the wounds and treated the infection but the experience was very unpleasant and distressing for her.
Another case involved a patient who had threads inserted from her temple into the middle of her face, to tighten the skin. 
The procedure had been performed by an aesthetic doctor, not a surgeon.
She could feel the stitches through her skin and asked me to remove them and to perform a face lift.
You may ask how a voluntary system of certification would protect these patients. 
Those of us who have studied, trained and worked as surgeons, know how much training and experience is required to perform an operation successfully. 
Informing and educating patients about this is an important part of the process.
Under the proposed new system patients will be able to search for a certified surgeon on a register on the RCS website removing some of the hurdles they have to get through to find an appropriate surgeon.
Surgeons will also only be able to apply for certification in one or more groups of closely related procedures if they are on the GMC specialist register in the area, in which they wish to practice. 
This will improve patient safety - we cannot all be specialists in every region of the body and a surgeon should only practice cosmetic surgery in the field they are trained in. 
But for patients’ protection, it is also important that surgeons only undertake procedures in an area in which they have the appropriate training, qualifications and experience. 
Applicants for certification will have to demonstrate evidence of their personal experience and training, in particular body areas. 
The well trained surgeon should not be concerned by this, and frankly, we should not support complaints from surgeons who are ineligible to apply because they only undertake one or two procedures a year. 
Certification should also help protect patients from unscrupulous ‘fly in and fly out’ surgeons who are not appropriately qualified and do not have proper insurance. 
If these individuals wish to be certified they will have to provide evidence of how they meet all the requirements, including providing evidence of their qualifications, training, experience and that they have insurance to practice cosmetic surgery in the UK.
Collecting information about a patient’s outcome of their cosmetic surgery is also important.
Cosmetic surgery cannot record cure or survival rates, and it is very subjective, but it is important that surgeons are able to show they can deliver the outcomes the patient would hope for. 
This will help the surgeon to establish the importance of understanding the patient’s expectations before they agree to operate.
It is as true in cosmetic surgery as other branches of surgery that a good surgeon is the surgeon who knows when not to operate.
I hope you will join me in preparing and applying for certification. 
Let’s protect patients and help them to identify appropriately qualified and experienced surgeons from individuals who should not be operating.
Mr Tim Mellor is a Consultant and Oral & Maxillofacial Surgeon.
We partnered with Google to find out our beloved country's most-asked beauty questions, and then turned to the nation's top skin, makeup, and hair experts for the answers.
HOW OLD DO I NEED TO BE BEFORE I CAN GET BOTOX? 
(MOST SEARCHED IN SOUTH CAROLINA) According to dermatologist Fredric Brandt, MD, who was one of the first practitioners to use Botox in the '90s, "it's not a chronological age, it's a biological age." 
You'll know you're a candidate "if you have lines and wrinkles around your eyes or on your brow that are caused by expression movements" and that remain visible when your face is at rest.
HOW DO YOU KEEP NECK SKIN TIGHT? 
(MOST SEARCHED IN CALIFORNIA AND MARYLAND) Celeb-beloved Beverly Hills dermatologist Harold Lancer, MD, says that "the neck is one of the most asked-about areas of the body for women.
" It's also notoriously one of the hardest to treat once signs of aging, such as sagging or wrinkling, begin to show. 
In addition to scrupulous use of SPF, Lancer suggests extending your facial skin care regimen—including serums, exfoliating cleansers, and retinoids—down to the bra line. "
Many people make the mistake of using body lotions on the neck and de?collete?
," he says, "but the skin there is thinner and more delicate than the rest of the body." 
To address skin on the neck and chest that has already become lax, derms rely on collagen-stimulating fractionated radio-frequency and ultrasound devices, which typically deliver tightening results within three to six months posttreatment. 
For a quicker fix, a strategic Botox injection technique known as the Nefertiti lift—in which small amounts of the neurotoxin are distributed along the jawline—can relax the muscles that pull the neck downward.
HOW CAN YOU HIDE WRINKLES? 
(MOST SEARCHED IN COLORADO, DELAWARE, AND FLORIDA) Wrinkles provide a coverage challenge because they have a different texture and color from surrounding skin—they appear darker since the valley of the wrinkle doesn't reflect light.
Makeup artist Taylor Chang-Babaian treats unevenness with a hyaluronic acid–based serum that acts as "a filler to plump up the wrinkles." 
She then eliminates darkness with either a sheer foundation or a pigment-rich concealer, depending on the zone of the face.
"The skin on the forehead is really thin, so you need less makeup there," she says. 
For creases around the eyes, "I apply the concealer at a 45-degree upward angle, which will cancel out the wrinkles that point downward, as well as draw the eyes up and change the angles of the face," Chang-Babaian says. 
"You want all the angles to go up."
WHICH FOODS INCREASE SKIN'S ELASTICITY? 
(MOST SEARCHED IN CONNECTICUT) Packed with omega-3 essential fatty acids, cold-water fish—wild salmon, sardines, herring, trout—help boost skin's bounce. 
For the seafood averse, oral supplements or citrus fruits, leafy vegetables, and flaxseed are also proven to keep things tight.
The bee story begins in Asia, with SangMi Han, MD, a research biologist, at the National Academy of Agricultural Science in South Korea.
Following the observation that Korean beekeepers had flawless hands, Han began to investigate the beneficial effects of bee venom on the skin. 
She found that the venom was a potent combination of enzymes, peptides, and amino acids that worked on all levels of the skin to encourage cell regeneration and collagen formation and reverse sun damage by increasing epidermal growth factor (EGF).
EGF repairs the skin, fades freckles, and wrecks wrinkles. 
A beauty triple threat. 
The research also showed the venom to decrease matrix metalloproteinases (MMPs), which are destructive proteins created by ultraviolet light. 
Abundant MMPs equal a one-way ticket to sagsville. 
But the purification process is the pi?ce de r?sistance of this whole tale. 
Han created a purification process that removed any unwanted particles from the venom after it had been collected from the hive, guaranteeing its purity and potency. 
Best of all, the process was safe, ensuring the bee's life-span, well-being, and performance were not affected.
But let's not forget beautifying manuka honey, created by bees who feed on the manuka bush, which is native to New Zealand. 
Honey, an amazing humectant, keeps the skin hydrated, soft, and vibrant. 
It helps in the fight against MMPs as well, helping to preserve a youthful look. 
Oh, and did we mention that in addition to reversing wrinkles, both the honey and the venom have antibacterial properties that can zap zits? 
Pretty damn cool. 
So what happens when you mix purified bee venom and manuka honey? 
You get the closest thing to Botox in a bottle. 
Manuka Doctor, which harnesses the power of natural ingredients from the hive into scientifically enhanced skin-care formulations that are refined, concentrated and 100 percent bee friendly. 
Manuka Doctor is the only skin-care range to combine the potency of patented purified bee venom with the healing benefits of certified manuka honey for authentic solutions that work to combat everything from blemishes to signs of aging. 
The brand is made up of serums, oils, creams, face masks, targeted wrinkle fillers, lip plumpers, and primers so you can easily pick your, er, poison?
Reading the title of this makes me instantly dislike the author. 
Me. 
I sound self-obsessed, superficial and vapid.
I know I'm none of those things, which is why I'm sharing my story. 
Because I am surprisingly comfortable in my own skin.
I started one of the first beauty blogs in the world: Meg's Makeup. 
My job was to test potions and powders. 
I flew around the country and filmed national TV shows talking about serums and creams. 
I was always honest. 
When anyone asked what the best way to get rid of lines were, I would tell them the truth: Botox. 
Plastic surgery isn't for everyone, but I've never been against surgical enhancements. 
The boobs I envisioned myself having never came naturally so I bought some. 
I have thin hair, so my luscious mane is all extensions. 
My top lip is skinny, so I get it injected to appear fuller. 
I also rescue dogs, give money to the less fortunate and hand out sandwiches to the homeless to combat my fear of being judged. 
And the fear of being judged is why every celebrity in Hollywood denies having had work done. 
It's just their "lucky genes" and "eating clean" that has kept them perpetually young and thin. 
I live here. 
I know that is complete B.S. Growing up, I weighed 90 pounds soaking wet. 
My body type isn't apple or pear, it's spider: I have spindly, long chicken legs and arms and a really round stomach. 
I buy my clothes in a size zero, but I can't wear anything that's tight in the middle. 
When I turned 38, I quit smoking and a sad consequence of that is weight gain.
In my case, the weight concentrated even more around my belly and everywhere I went—the nail salon, the dry cleaners—I found myself being congratulated on a baby I was not carrying. 
I followed the typical course of action: I subscribed to a whole foods delivery service, hired a personal trainer and went to the gym four days a week for five months. 
My arms became sculpted.
My butt was toned. 
My legs were great. 
My stomach? 
The same.
I began to get discouraged, so I decided to call in the big guns. 
They say to be successful in life, you should have a good lawyer, mechanic and accountant on speed dial. 
If you live in Los Angeles, you have to replace those with colorist, dermatologist and plastic surgeon. 
Look, I know a lot of plastic surgeons. 
I've seen their before and after photos, I've interviewed them for magazines and TV, I've even been in a relationship with one.
I'm the friend everyone calls when they're looking for a recommendation. 
Dr. Marc Mani is the real deal. 
He's like a nightclub with no sign out front; you have to audition to get him to work on you and he has sent more than a few friends out the door if he didn't approve of their decision to get work done.
(A close girlfriend told me that Dr. Mani matter-of-factly informed her that if her boobs were any larger, she would look ridiculous.) 
Other surgeons are just happy for the business, but I knew he would tell me the truth.
I walked into his office and changed into a gown. 
He stared at my stomach, then he looked me straight in the eye and said, "Go to the gym. 
If it doesn't go away after that, then come back." 
That was that.
I went to back to working out for a few months, but when nothing changed, I made another appointment to see Dr. Mani.
This time, he gave me a different response: "Yes, that stomach is hereditary, part of getting older." 
He paused and then pinched under my chin. 
"This is also fat you can't exercise away. 
I'll remove that as well." 
My God, I hadn't even noticed my chin before!
"The smoking hollowed out your face, so I'm going to take the fat from your stomach and put it into your cheeks to fill your face out," he said. 
Then he touched the area under my eyes. 
"And while you're under, I'm going to give you a lower bleph." 
I nodded my head as though I knew what a "lower bleph" was (turns out, it stands for blephoraplasty—the removal of bags under the eyes). 
I was glad he pointed out these other areas that needed tuning up a mere two days before the stomach surgery because it gave me less time to obsess about it. 
Anyway, there was no question about having him do it—he does the most natural work in town.
Most people keep their plastic surgery a secret. 
Not me. 
I announced it on Facebook and made a YouTube video about it. 
I am in the beauty industry and have always believed in full transparency. 
Plastic surgery is a subject that a lot of women are curious about and I don't think wanting to look better should be a taboo subject. 
Sure, I got a few snotty comments but more than that, I got tons of questions and I replied honestly to all of them.
It's been six weeks since the removal of Gus (what I nicknamed my stomach), and everything else. 
I wore a bandage around my stomach and chin for 24 hours a day for the first week, half that time for the following week. 
It was uncomfortable, but not painful. 
I was up and walking around by the second day but ideally, it takes a week at least to fully recover.
Was my decision extreme? 
Yes, of course it was. 
Could I have lived a perfectly fine life without it? 
Without a doubt. 
Do I feel like a new person? 
No, but I do feel like someone who can go out confidently in a bathing suit. 
Is it expensive? 
Yes, it starts at around 35k, but I don't have children so I won't be paying for college any time soon. 
Was it worth it?
Look at my pictures and decide.
And here's the trailer I made to announce my decision and poke a little fun at myself.
Until recently, surgery with anesthesia was the best option for anyone looking to achieve the Holy Grail of lower-face #goals: a sharper, more chiseled chin. 
Enter a game-changing new injectable solution called Kybella. 
The first injectable substance that the FDA has approved to treat submental fat, Kybella is designed to dissolve stubborn under-the-chin fat. 
Kybella is deoxycholic acid, a fat-absorbing substance found in our bodies naturally. 
When injected, it destroys fat cells under the chin, leaving the skin substantially tighter and the jaw more contoured. 
More than 20 studies, with thousands of patients, have showed that almost 70 percent noted both physical and emotional improvement after treatment.
For best results, patients should commit to about two to six treatments, spaced a month apart, with anywhere from 12 to 20 little injections per visit, says Kavita Mariwalla, MD, a dermatologist in West Islip, New York. 
That might sound like hours of pin-prickly pain, but the treatment is actually quicker than a manicure, at about 20 minutes per session. 
Plus, only a teaspoon of substance is needed to see a difference. 
Unlike Botox, which yields results that last for up to six months in as few as four days, Kybella results are noticeable at four to six weeks and last for years, which means that touch-ups shouldn't be needed. 
As for side effects, expect swelling and bruising that start to fade within three days of treatment. 
So why not inject Kybella all over? 
As of right now, it's indicated only for smaller areas. 
But stay tuned; a similar substance may be in the pipeline for use below the neck. 
Until recently, those wishing to erase a double chin had only one truly effective quick-fix option: surgical liposuction. 
Now, however, streamlining the jaw requires nothing more than a trip to the dermatologist. 
The same molecule used by our digestive system to break down dietary fat is being injected into double chins to melt away neck fat. 
The long-awaited ATX-101, now known as Kybella, has finally been FDA approved.
"It destroys fat cells and increases local collagen production," New York–based dermatologist Francesca Fusco, MD, says in the March 2015 issue of ELLE, making loose flesh—a side effect of some fat-removal methods—less of a worry.
"It's simply an injection, so the ease of use is a great thing," Fusco says of the jawline sculptor. 
Using a synthetic version of the same molecule, deoxycholic acid, that our digestive system uses to process dietary lipids, Kybella shrinks the small pockets of fat that cause double chins. 
In clinical trials, patients experienced significant loss in submental fat as well as an increase in skin firmness following a series of up to six injections spaced four weeks apart.
But the procedure isn't entirely flawless. 
In one clinical study, over 20 percent of patients experienced moderate postshot pain and some experienced bruising and swelling that can last two to five days after each treatment.
About six years ago, a friend looked at my forehead with as much worry as her well-Botoxed brow could muster. 
Her eyebrows endeavored to meet, like the fingers of Adam and God on the ceiling of the Sistine Chapel, sending ever-so-gentle undulations across her forehead. 
What's wrong? 
I asked, frowning and no doubt animating the San Andreas-like fault line between my own brows.
 "You overuse your forehead muscles. 
Your brow is very active," she told me. 
You need Botox.
At 33, this was a first: I had never been accused of hyperactivity. 
While the rest of my body had long demonstrated a gift for leisure, apparently my histrionic brow had been busy in a compensatory frenzy of activity.
Initially, I decided to reject my "friend's" suggestion. 
After all, my frown lines and crow's feet had taken decades of smiling and weeping and laughing and stressing to build. 
We should be proud that we've survived this long in the world, but on the other hand, we don't need to look dejected and angry when we aren't, says Vancouver-based ophthalmologist and cosmetic surgeon Jean Carruthers, MD, aka the mother of Botox.
 In the late '80s, she had been using botulinum toxin to treat ophthalmic issues, such as eye spasms, when she happened upon the injectable's smoothing benefits. 
She's been partaking in her own discovery ever since. 
I haven't frowned since 1987, she tells me cheerily over the phone. 
To Carruthers, the magic of this "penicillin for your self-esteem" is how using it changes people's perceptions of you. 
Think about the Greek masks. 
If you're wearing a sad mask all the time, that's how people read you. 
Are you an energetic, happy person, or are you a frustrated wretch?
 If you get rid of that hostile-looking frown, you're not going to look angry and you're not going to look sad. 
Isn't that better?
I finally experienced this for myself five years ago, when a couple of married plastic-surgeon friends called me.
It was a sunny Sunday afternoon, they had an extra vial of bo' they were hoping to polish off, and they asked me to join them—as if it were an invitation to share a bottle of French ros?. 
It turns out that most of my reservations were financial, because free Botox I did not even try to resist. 
A week later, the skin on my forehead was as taut and smooth as a Gala apple.
Without those fine lines and wrinkles, as Carruthers foretold, I not only looked better, I felt better: As a delightfully unforeseen bonus, the treatment eradicated my tension headaches.
I was also potentially enjoying some long-term antiaging benefits: A 2012 South Korean study concluded that Botox improves the quality of our skin's existing collagen, and peer-reviewed research published in July 2015 by the Journal of the American Medical Association Facial Plastic Surgery revealed that just a single session of Botox improves skin's elasticity in the treated area. 
"It looks like Botox remodels collagen in a more organized fashion and also spurs the production of new collagen and elastin—the fibers that give skin its recoil, its bounce and buoyancy," says NYC-based dermatologist Robert Anolik, who notes that the benefits are cumulative. 
"We're still trying to figure out the how and the why." 
Botox also may improve overall skin texture by impeding oil production.
"It's believed that Botox can trigger a reduction in the size of the oil gland. 
As a consequence, the skin may look smoother and pores should look smaller," Anolik says. 
Another theory gaining traction in academic circles: "Botox might serve as an antioxidant, preventing inflammatory damage on the surrounding collagen and elastin."
I definitely was a return customer, visiting my derm for the occasional top-up. 
Then last year I got pregnant and had to stop cold turkey.
(Allergan, the maker of Botox, recommends that pregnant or breastfeeding mothers avoid the use of neurotoxins.) 
Despite Botox's potential preventative powers, I'm sorry to report that those once-slumbering dynamic lines and wrinkles, the ones not even a natural disaster could have summoned into action, made an aggressive comeback. 
Still nursing, and with time—and REM sleep—in short supply, I decided to look for the next best thing, testing an assortment of topicals, products, and devices, a sort of alt-tox regimen.
To be clear: There isn't anything that can effectively target the dynamic facial lines (those activated by movement) and inhibit facial muscle activity like an injectable neurotoxin. 
But that by no means dissuades skin-care brands from marketing products claiming Botox-like effects. 
(Biopharmaceutical company Revance is busy developing a topical version of Botox, to be administered by derms. 
The cream, purportedly as effective as the injectable but tailored to target crow's feet specifically, is currently in phase three of FDA testing and years away from availability.) 
There's Erasa XEP-30, which contains a patented neuropeptide designed to mimic the paralyzing effects of the venom of the Australian cone snail. 
And you thought a toxin derived from botulism was exotic!
For my needle-less approach, I opt to begin, appropriately, with Dr. Brandt Needles No More. 
Miami-based dermatologist Joely Kaufman, MD, who worked with the late Dr. Brandt in designing the quick-fix wrinkle-relaxing cream, says the key ingredient, "designed to mimic the effects we see with botulinum toxin injections," is a peptide blend that, when absorbed, blocks the signals between nerves and muscle fibers that cause contractions. 
The muscle-relaxing mineral magnesium was added to the cocktail to further enervate muscle movements. 
In an in-house peer-reviewed study, an impressive 100 percent of the test subjects reported that their brow crinkles were significantly visibly smoother in just one hour. 
I apply the light, vaguely minty serum liberally, and identify a satisfying wrinkle-blurring effect. 
Over the next few weeks, I find myself squinting and frowning in my bathroom mirror, strenuously appraising my vitalized new look—probably not the most productive wrinkle-reduction strategy.
While most dermatologists consider Botox the gold-standard short-term wrinkle eraser, there is another school of thought. 
For decades, Connecticut-based dermatologist Nicholas Perricone, MD, has been preaching the doctrine that wrinkles aren't what make us look old. 
"Youthfulness comes from convexities. 
When we get to our forties, those convexities start becoming flat, and then as we get really old, they become concave, Perricone says. "
When I started working with celebrities, I always assumed that they were genetically gifted because they had this beautiful symmetry. 
But I got up close and it wasn't just symmetry." 
Instead, his star clients all had "more convexity in the face than the average person," meaning plump, full cheeks, foreheads and temples, a plush roundness that comes by grace of toned, healthy muscles. 
To him, Botox is counterintuitive: We shouldn't be paralyzing the muscles in our face, we should be pumping them up. "
It's not the muscles that are the problem. 
It's the lack of muscles," says Perricone, who recommends aerobicizing facial muscles with electric stimulation devices.
At the Hotel Bel-Air, I once enjoyed a 90-minute electric facial with a NuFACE device. 
The handheld gizmo stimulates muscle contractions with microcurrent energy delivered via two metal attachments. 
I remember floating out of the spa, my skin feeling as fresh and petal-soft as the peonies blooming in the hotel's gardens.
"Electrostimu-lation promotes the production of glycosaminoglycans, which [bind with] proteins floating around in the extracellular matrix," says Pennsylvania-based skin physiologist Peter Pugliese, MD. 
Dosing the skin with electricity, he says, also works on a cellular level to jump-start the creation of ATP (adenosine triphosphate, a molecule essential for cellular energy) as well as collagen and elastin, and, over time, will reduce visible crinkles while enhancing muscle tone.
I acquire my very own NuFACE, and dutifully, for five minutes a day, sweep the device in an upward motion across my cheek.
It does make my face look a bit fuller, fresher, smoother—brighter, even. 
Though it turns out that performing this in my bathroom while the baby naps does not prove quite as restorative as enjoying a 90-minute spa treatment at the Hotel Bel-Air.
There is one more stop on the anti-wrinkle express, and for that I skip from high tech to low tech—very low—and score a pack of Frownies facial patches. 
The cult product was dreamed up in 1889 by a housewife, Margaret Kroesen, for her daughter, a concert pianist afflicted with frown lines from years of concentrated playing. 
The paper and adhesive patches pull skin into place, smooth and flat, while you sleep. 
Gloria Swanson wore them in Sunset Blvd.; Raquel Welch praised their powers in her book Raquel: Beyond the Cleavage.
Some people wear negligees, I think as I tuck into bed.
Me?
Flesh-toned facial Post-its. 
But the next morning, I wake to find that my brow looks astonishingly well-rested (even if the rest of me is not).
Used in concert, my new arsenal of treatments has made me look somewhat more alert, vaguely less exhausted; my cheeks are more plumped up, maybe even a little more convex. 
I behold my napping nine-month-old, his pillowy cheeks pink from sleep, and marvel at that bounty of collagen and elastin and glycosaminoglycans, that efficient ATP, those energetic fibroblasts not yet lethargic from age. 
But what I marvel at most is that he doesn't know about any of this, doesn't know from wrinkles and lines, and doesn't care—he has other things to laugh, and frown, about.
Living in L.A. for the past two decades, I've become immune to many a visual WTF.
Post-rhinoplasty teens with bandaged noses and bruised eyes cruising $5,000 bags at Barneys no longer shock. 
Prides of taut moms with the exact same shade of buttery highlights and identical plump pouts don't raise a brow. 
Even matrons with pulled-taffy faces and staple scars behind their ears barely move me. 
But lately, I've noticed a phenomenon far subtler than these, and yet more disturbing: young women, sometimes very young, their lips suspiciously full, cheekbones hyper-defined, skin seemingly airbrushed, like filtered selfies come to life.
Now, I'm no crusader against cosmetic fairy dust. 
My inaugural Botox injections, at age 33, erased the two exclamation points between my eyebrows. 
(I noticed them one afternoon in my rearview mirror at a traffic light and actually rubbed at them with a wet thumb as if they were smudges.) 
ut on the day I got those shots, that was all I got; I left the doctor's office with my laugh lines and the faint crinkles around my eyes intact.
"They want these cheekbones. 
They want those lips. 
That chin. 
We're living in a world of immediate gratification" These days, however, a subtle tweak equals a missed opportunity, at least among a certain demographic. 
And dermatological weapons that originated to replace lost facial fat and smooth lined skin—i.e., to address aging—are augmenting faces that haven't had a chance to age at all or, in some cases, even to mature. 
New York City–based dermatologist Dennis Gross, MD, says that women barely past college age have begun to come in with a laundry list. 
"They want these cheekbones. 
They want those lips. 
That chin. 
We're living in a world of immediate gratification," he says. 
"I tell them, 'You don't have to do everything at once.'" 
L.A. derm Jessica Wu, MD, sees a fixation with every line: "It used to be people waited until their thirties, forties, and fifties to treat smile lines or crow's-feet. 
Now it seems like filler is an accessory."
But as I stare (and stare) at these creatures— many of whom have undoubtedly been rendered lusher, more symmetrical, more empirically beautiful by such fixes—I wonder: Are these toxins and fillers actually doing the opposite of what their users hope and making young women look years older? 
What is the long game for a woman who started customizing her features in her twenties?
What will "youthful" look like in her forties and beyond?
"What is the long game for a woman who started customizing her features in her twenties? 
What will 'youthful' look like in her forties and beyond?"
According to the American Society of Plastic Surgeons, use of soft-tissue fillers among 20- to 29-year-olds was up to more than 67,000 procedures in 2015, an almost 33 percent jump from 2010; the same age group had more than 100,000 Botox treatments. 
(To be clear, that's still only a fraction of what 30- to 39-year-olds had done in 2015: 270,054 fillers, 1.2 million Botox sessions.)
On the brutally honest cosmetic enhancement forum RealSelf.com—visited by some 77 million people last year—the demographic most interested in dermal fillers so far this year is 25 to 34, and that interest has risen 37 percent since 2015. 
Among that age group, "there's no more stigma to having fillers or Botox," notes RealSelf spokesperson Jennifer Moses. 
"The mind-set is more like, 'It's my body.
It's my choice. 
It's my money.'"
Amen to that.
But even my five-year-old's devilish grin makes her eyes crinkle.
That's not aging, it's looking human.
When we see a 24-year-old with no discernible demarcations of life—no fissures, nary a freckle—the brain gets addled.
It's hard to process because it looks odd, abnormal, says S Jay Olshansky, PhD, a researcher at the University of Chicago's Center on Aging.
He calls the effect an extreme dissonance: It makes us stare at the face longer, trying to figure out what's wrong with it.
In 2014, Olshansky helped launch the website Face My Age.
It's a fascinating way to bolster or blindside your self-esteem in mere minutes—and, interestingly, two-thirds of its users are between 18 and 34.
Plug in your gender and birth date and upload an unsmiling, no-makeup selfie, and, based on wrinkles, age spots, and hair color, the program will assess your age.
(It rated me six years younger than my actual age—thank you, Botox.)
The algorithm attempts to take plastic surgery into account—it's one of the questions asked—though when Olshansky ran a picture of 81-year-old Joan Rivers, the software clocked her at 56.
Rivers would have loved that.
But as the TV-watching public knows, the human brain is not as easily fooled.
An altered face reads to the eye as ageless, says Olshansky, but not necessarily in a good way.
"They'd rather look like techno-humans with no shadows on their faces than look even slightly aged."
Think about it: What assumptions do you make about women who have had "work done"? 
Even if you can't put your finger on it, even if the work is good, by and large, the brain categorizes them as older. 
Which, according to Gross, would be the fate young patients fear most. 
"They'd rather look like techno-humans with no shadows on their faces" than look even slightly aged, he says. 
He's actually been told, "I'd rather look weird than old."
In 2013, Carolyn Black Becker, PhD, a psychology professor at Trinity University, co-authored a study entitled, I m Not Just Fat, I m Old: Has the Study of Body Image Talk Overlooked 'Old Talk'?
Polling 914 women ages 18 to 87, she found that 66 percent grouse about signs of aging.
What really struck me is that 85 percent of 18- to 29-year-olds are engaging in old talk, Becker says with a sigh.
I heard comments like, 'I use fillers because I m seeing the bare hint of a line.
I wouldn't want to have any wrinkles or any lines on my face.'
Almost every expert I asked about the filler 'er up era name-checked the Kardashian-Jenners.
That big a cultural shift can't, and shouldn't, be blamed on a single family, even a very well-documented one. 
But almost every expert I asked about the filler 'er up era name-checked the Kardashian-Jenners. 
Kylie, whose extraterrestrial beauty could pass for anything from 16 to 34 (actual age: 18), is probably the most influential shape shifter of all, with her 68 million–plus Instagram fans.
In an April interview in Paper, Jenner said the biggest misconception people have about her is, "Probably that I'm just super fake and that at 16/17, I got my full face reconstructed...."I do want to sympathize, but a review of pictures since 2010 shows an incredible transformation, even given the forces of wealth, puberty, and contouring makeup. 
Never mind her much-discussed lips—gone is her cute cinnamon sprinkle of freckles, faint laugh lines, and the normal hollows beneath her eyes.
Simon Ourian, MD, the Beverly Hills cosmetic derm to the whole clan, insists that Kylie "hasn't done as much as people think" but agrees that overfilling and over smoothing is making twentysomethings look "older" and "cartoony." 
As a policy, Ourian won't treat girls younger than 18, but he regularly receives calls from 12- and 14-year-olds looking to inflate their lips. 
"At 18, they can make a decision for themselves without the influence of other people," he says. 
"That doesn't mean that they make the right decision."
"So perhaps it's not a bad idea to wait to change one's looks until the brain, too, has been allowed to mature a bit."
You can't buy a drink until you're 21, or in some cases rent a car until you're 25. 
Research into "emerging adulthood"—roughly ages 18 to 29—has revealed that well into our twenties, the brain is still a work in progress. 
So perhaps it's not a bad idea to wait to change one's looks until the brain, too, has been allowed to mature a bit. (How many of us feel truly great about the tattoo we got at 2 A.M. on our 20th birthday? And chances are it's not on your face.)
(How many of us feel truly great about the tattoo we got at 2 A.M. on our 20th birthday? 
And chances are it's not on your face.)
A few weeks ago, I had dinner with an 18-year-old movie star who told me everyone asks if her plump pout is inflated. 
It's not, she swore, and pictures of her as a child with a big pucker are proof. 
But as we walked through Beverly Hills on a warm night, she went on to tell me she could name three actresses her age who use fillers in their lips and cheeks. 
It's crazy, she said with a shrug. 
"They're already hooked."
Asians increasingly seek non-surgical facial esthetic treatments, especially at younger ages. 
Published recommendations and clinical evidence mostly reference Western populations, but Asians differ from them in terms of attitudes to beauty, structural facial anatomy, and signs and rates of aging. 
A thorough knowledge of the key esthetic concerns and requirements for the Asian face is required to strategize appropriate facial esthetic treatments with botulinum toxin and hyaluronic acid (HA) fillers.
The Asian Facial Aesthetics Expert Consensus Group met to develop consensus statements on concepts of facial beauty, key esthetic concerns, facial anatomy, and aging in Southeastern and Eastern Asians, as a prelude to developing consensus opinions on the cosmetic facial use of botulinum toxin and HA fillers in these populations.
Beautiful and esthetically attractive people of all races share similarities in appearance while retaining distinct ethnic features. 
Asians between the third and sixth decades age well compared with age-matched Caucasians. 
Younger Asians’ increasing requests for injectable treatments to improve facial shape and three-dimensionality often reflect a desire to correct underlying facial structural deficiencies or weaknesses that detract from ideals of facial beauty.
Facial esthetic treatments in Asians are not aimed at Westernization, but rather the optimization of intrinsic Asian ethnic features, or correction of specific underlying structural features that are perceived as deficiencies. 
Thus, overall facial attractiveness is enhanced while retaining esthetic characteristics of Asian ethnicity. 
Because Asian patients age differently than Western patients, different management and treatment planning strategies are utilized.
In Asia, the past decade has witnessed a tremendous increase in the number of patients who request and receive facial injectable treatments, non-ablative skin resurfacing and other non-surgical procedures, compared with the number of patients undergoing esthetic facial surgery. 
This is probably because the public has a much greater awareness of the treatment options available to them. 
Reasons for this include technological advances and improved results achievable with injectable treatments such as botulinum toxin and hyaluronic acid (HA) fillers, the increasing social acceptability of enhancing one’s appearance, increasing affordability and accessibility of injectable treatments, and the rise of the middle class in Asia.
Most Asian esthetic patients, whether young or old, prefer to avoid surgery wherever possible, and they seek natural-looking results.
Therefore, Asian physicians have had to respond to their patients’ expectations, study, and then innovate procedures and management strategies to address the Asian esthetic, including facial shape, structure and proportion, and impact of the aging process on Asian faces.
To date, most studies and published recommendations on the use of facial injectable treatments (especially their use in combination) reference Western populations. 
However, ethnic Asians differ from them in both facial appearance and baseline structural facial anatomy. 
The signs and rate of onset of facial aging are also different in Asians. 
Existing published recommendations cannot be applied directly to Asians. 
Furthermore, relatively few published papers cited in PubMed describe the use of botulinum toxin and HA fillers in Asians, and only one paper describes their combined use in the Asian face. 
Unfortunately, data from clinical trials are often not relevant to real-world practice because typically only one standardized treatment intervention is studied in one facial area; however, esthetic treatment is usually multimodal and individualized. 
Therefore, there is a need for expert guidance on facial esthetic treatment of Asians.
To this end, the Asian Facial Aesthetics Expert Consensus Group, which comprised an anatomist, plastic surgeons, and dermatologists from 11 Asia–Pacific countries, met to discuss current practices regarding the use of non-invasive esthetic treatments in Asians. 
As a prelude to developing consensus opinions on the use of botulinum toxin and HA fillers in Asians, the group discussed concepts of facial beauty and attractiveness, as well as key esthetic concerns, facial anatomy, and aging in Southeastern and Eastern Asians. 
The Expert Group’s goal was to identify esthetic treatments and outcomes that Asian patients most commonly require, and to develop consensus opinions on how these can best be provided.
Proceedings of this meeting are intended to offer guidance to physicians who provide surgical and non-invasive facial esthetic treatment to Asian patients, in the absence of published clinical evidence. 
In this, the first of two papers, attitudes to facial beauty in Asia are described. 
Given that a thorough knowledge of the patient’s facial anatomy and aging process is required to inform facial esthetic treatment, those factors specific to Asian population groups are also discussed. 
Asians are defined here as the diverse groups of ethnicities from East Asia (e.g., China, Korea, Japan, Hong Kong, Taiwan) and Southeast Asia (e.g., Thailand, Singapore, Indonesia, Philippines); those from the Indian subcontinent are not included.
To determine the key trends in the type of Asian patients who present for facial esthetic treatment, the patients’ key facial esthetic concerns, and the most commonly used facial esthetic treatments for each age group, 25 members of the Expert Group completed a pre-meeting online survey developed by Dr. Steven Liew. 
Twenty-one Expert Group members then attended a consensus meeting in Seoul, Korea held on June 4 to June 5, 2014.
The members of this Expert Group have a mean 17 years of specialized experience in the field of facial esthetics (range 7–30 years) and treat Asian patients from China, Hong Kong, India, Indonesia, Japan, Korea, the Philippines, Singapore, Taiwan, Thailand, and Australia.
The process used to develop the consensus statements presented here was based on agreed statements created following discussions around survey outcomes, peer-reviewed literature, and clinical experience. 
Final versions of the statements were approved by all authors after being suggested and debated by the experts during the meeting, and modified, if necessary, while drafting the manuscript.
This article does not contain any studies with human participants or animals performed by any of the authors.
The points presented here are a summary of the outcomes of discussions that took place at the Expert Consensus Group meeting and thus reflect the consensus expert opinions of all participants.
Attractive and beautiful people of all races have distinct ethnic features, which reflect harmony, symmetry, and balance. 
However, when comparing the most attractive and beautiful people with their counterparts in other parts of the world, they share remarkable similarity with respect to facial shape.
Facial shape is the essential key to facial beauty, with an oval face considered attractive (and youthful) by people of all racial backgrounds. 
An oval face in this context refers to a smooth egg-shaped curve outlining the perimeter of the face, with a smooth transition from the forehead through the temples, around the outside of the cheeks, preauricular region, angle of the jaw, and jawline through to the chin, without indentations or projections in the line. 
A well-projected nose and chin is also considered attractive. 
Asians of all ethnicities and ages highly prize clear, unblemished, fair, and youthful skin.
Beauty is universal. 
Every ethnic group has its esthetically strong and weak points, but on the whole, the most beautiful and attractive people of each and all races tend to look similar in terms of face shape, and harmonious delicacy of features, balance, and symmetry. 
As faces become less attractive, they display more distinct ethnic features. 
Caucasian faces generally have more pronounced three-dimensionality with larger, more deeply set eyes, greater anterior projection of the brow, nose, maxilla, and chin. 
Caucasians also tend to have narrower faces and greater vertical height. 
Asians tend to have a wider face with shorter vertical height, which is flat or concave in the medial maxilla and has a lack of brow, nasal, and chin projection. 
On the other hand, they possess greater infraorbital volume, fuller lips, and superior skin qualities compared to Caucasians, which enables them to resist environmental insults and delay physiological and anatomical signs of aging.
Mindful of this concept of universal beauty, regardless of race or ethnicity, physicians in Asia seek to enhance “deficient” features and improve esthetic balance. 
In Asians, attractiveness is achieved by aiming to create an oval facial shape, by narrowing the lower face and increasing vertical height of the face. 
The anterior projection of the brow, medial cheek, nose, and chin is increased to improve the three-dimensionality of the face, and the appearance of the eyes is enlarged.
Globalization has enhanced our ability to recognize, study, and create beauty in all ethnic groups. 
In Asians, it is now common to improve anterior projection and three-dimensionality (double eyelid, nose/cheek/forehead augmentation), increase vertical height (chin augmentation), and reduce lower facial width (masseter reduction). 
In the past, these practices may have been perceived as an attempt to “Westernize” the Asian face. 
However, this is now understood to be the optimization of facial esthetic appearance within the individual’s own ethnicity.
Beauty is influenced by customs, traditions, and trends, and the current ideals for the female Asian face include a smooth, convex forehead, large eyes, a petite nose with a raised bridge, full but not prominent lips that are proportionally balanced, and an oval, egg-shaped face with a neat “v-shaped” jawline.
Beautiful people of all races show similarity in facial characteristics while retaining distinct ethnic features. 
Asians are not a homogeneous group but rather comprise many varied ethnic origins, with each group having its own unique facial characteristics. 
Treatment to achieve esthetic changes in Asians should not be viewed as an attempt at Westernization, but rather the optimization of Asian ethnic features, in the same way that Westerners who receive lip enhancement, lateral malar enhancement, or skin tanning are not trying to “Easternize” their appearance as they attempt to make up for their intrinsic ethnicity-associated structural weaknesses.
The desire for facial esthetic improvement has always existed, but uptake was previously limited due to the expense of plastic surgery and the limited number of skilled practitioners.
The results of the Expert Group’s pre-meeting survey showed that most Asian facial esthetics patients are female, but the proportion of males seeking treatment has increased during the last decade, from 12 % in 2005–2009 to 19 % in 2010–2014.
The proportion of younger Asian patients (i e , aged 18–40 years) who present with esthetic concerns has increased slightly in the past 5 years: from 44 % in 2005–2009 to 48 % in 2010–2014.
This may be the result of an increased sense of self identity and pride, and younger patients having more economic power, aspiration, and social independence.
Other reasons for this increase include (i understanding that early use of esthetic treatment may prevent or reduce progress of aging; (ii) increasing awareness of esthetic procedures and treatments received by their peers and “public” figures in social media; (iii) increasing accessibility and affordability of products and treatments; (iv) increasing numbers of trained esthetic physicians; and (v the safety of injectable products that has emerged over the past 5–10 years.
The Expert Group’s pre-meeting survey responses regarding the top three structural esthetic concerns among Asians of different age groups is shown in Table 1—rated according to physicianssopinions of what patients needed to have treated and according to what physicians thought were the patientssopinions regarding their most pressing concerns.
There was generally agreement between the two sets of opinions.
The survey results showed that younger patients (aged ?
40 years) most commonly request the esthetic treatments that improve facial shape and three-dimensionality.
While these patients believe that they are merely seeking esthetic improvements, physicians recognize that requested treatments result from the underlying facial structural features common to Asians that can contribute to a negative esthetic impact.
As patients age, their treatment needs and preferences evolve to address issues associated with aging.
Those aged older than 40 years are more likely to request treatments and procedures that improve volume loss, sagging, and wrinkles.
The available treatment options, as well as awareness of new treatments and procedures, have increased over the past decade.
The proportion of younger patients in Asia who present with esthetic concerns has increased over the past 5 years.
The most common treatment concerns among younger patients (excluding skin concerns) are the result of underlying structural features that can contribute to a negative esthetic impact or relative weakness.
In Asia, patients seek treatment at a relatively young age to address the perceived undesirable facial features that correlate with the underlying characteristic structural anatomical features detailed in Table 2.
A thorough knowledge of the patient’s facial anatomy and age-related processes is required to inform facial esthetic treatment.
The physical features of the Asian face are related to specific skeletal and morphological features that differ from those of Caucasians.
Although there is great diversity due to the ethnic variations that exist among Asian populations, typical facial features can still be identified.
Asians tend to have a wide and short face.
In profile, the face typically appears flat or, in some cases, even concave.
Compared with the Caucasian face, the Asian face is characterized by greater intercanthal width, epicanthal folds, smaller eye fissure length, hooding of the upper eyelid/lateral brow (creating a “puffy” eyelid appearance), smaller oral width, greater mandibular width and a square lower face, and retruded chin.
The nose has a flat dorsum, wider base, and less tip projection The lips of Asians tend to be fuller than those of Caucasians, with the upper lip often being more prominent.
Ethnic Asians have a thicker soft tissue layer next to the most lateral point of the ala nasi, compared with Caucasians. 
These factors, together with retrusion of the pyriform margin of the bony structure in Asians, correspond with the hidden columella, wider alar base, and flatter nose characteristically observed in Asians.
Skin aging differs between Asians and Caucasians in several aspects.
Pigmentary problems such as lentigines and seborrheic keratosis are particularly common among Asians, but wrinkles tend to manifest 1–2 decades later in Asians than in age-matched Caucasians.
A comparison of skin aging in Chinese and French populations living under similar climate conditions indicated that in the Chinese population, wrinkle development followed a biphasic trend, with a slow increase until the ages of 40–50 years, followed by a rapid increase thereafter.
By the age of 60 years, the wrinkle intensity in both French and Chinese populations appeared similar.
This is likely due to the increased melanin in Asian skin affording a sun protection factor (SPF) of approximately 7, compared with a SPF of 3.4 in Caucasians.
In another study that compared facial wrinkles among Japanese, Chinese, and Thai women, the wrinkle intensity was greatest among Thais, followed by Chinese, and then Japanese women.
With Thailand being the most tropical country of the three and having a higher ultraviolet light exposure, photoaging was the most obvious reason for these findings, but other factors, including language and facial expression, also contributed to differences in wrinkle score between Chinese and Japanese women.
Factors other than SPF differences that may also contribute to the reduced skin aging observed between Asians and age-matched Caucasians include skin structure and thickness, diets high in antioxidants (e g , green tea and omega-3 and -6 fatty acids), and smoking rates.
Sociocultural aspects, such as skincare practices and muscle use, during language articulation and facial expressions may also contribute to the differential development of dynamic wrinkles between Asians and Western populations.
In Asians, the physiological facial aging process involves the same dynamic and complex three-dimensional interplay between the overlying soft tissue and its underlying skeletal structures as in Western populations. 
Facial fat and soft tissue volume loss, deflation and descent, and bony remodeling all give rise to the same common signs of aging. 
Retaining ligaments also play a role: as the ligamentous system attenuates, facial fat descends.
Despite the similarities in the physiological processes and characteristics of facial aging among all races, differences in skeletal structural support and in the propensities of facial soft tissue to sag result in slower rates of facial aging in Asians than in Caucasians.
In most Asian patients, the dense fat and fibrous connection between the superficial muscular aponeurotic system and the deep fascia reduce midfacial sagging for longer, and the combination of increased superficial fat and thickened dermis lessen the incidence of superficial rhytids.
Eventually, however, due to the loss of dermal support (despite the initially thicker skin), the heavier malar fat pad and weaker skeletal support in the Asian face contribute to tissue descent that manifests as facial sagging with aging.
Nevertheless, overall, the Asian face retains its youthful appearance for longer due to delayed signs of skin aging and sagging, compared with age-matched Caucasians.
Asians between the third and sixth decades age well compared with age-matched Caucasians.
Even within the population described as “Asian” in this manuscript, significant facial morphological differences are seen, which may underlie the scarcity of published treatment recommendations targeted at “Asians” as a group.
Asians have facial anatomical features that may contribute to what they perceive as an esthetically undesirable appearance.
Increasing numbers of younger patients in Asia are seeking cosmetic treatment specifically to address these structural issues, as evidenced by the types of treatment that are in highest demand by different age groups.
Treatments to achieve esthetic changes in Asians are not aimed at the Westernization, but rather the optimization and beautification of their ethnic features, via the correction of underlying structural characteristics that can contribute to a negative esthetic impact.
In Asians, facial aging manifests differently compared with Caucasians in terms of the time course and observed changes, and Asians who present for anti-aging treatments require different strategies and management techniques from Caucasian patients.
The intrinsic sun protection afforded by the pigment in Asian skin delays photoaging, which appears to be an important factor contributing to the perception that Asians age well.
Although Asians age better extrinsically, in particular those who present for first treatment at a later age require treatment to address not only the underlying structural features that can contribute to a negative esthetic impact but also cumulative age-related changes.
These patients will often require combination treatment and a treatment planning strategy that recognizes the complexity of the interplay between the underlying anatomical state and skin aging.
Background: Direct-to-implant breast reconstruction can be achieved more easily by means of soft-tissue replacement devices such as dermal matrices and synthetic meshes. 
The feasibility of a subcutaneous approach has been recently investigated by some studies with different devices functioning as implant support. 
Aim of this study is to analyze the long-term results, both objective and subjective, of a previous nonrandomized trial comparing prepectoral (subcutaneous) and retropectoral breast reconstructions. 
Methods: Patients enrolled in a nonrandomized prospective trial, comparing the standard retropectoral reconstruction and the prepectoral subcutaneous approach, using a titanium-coated mesh in both techniques, were followed up and evaluated for long-term results. 
Cases were compared in terms of the causes and rate of reinterventions, of the postoperative BREAST-Q questionnaire results, and of an objective surgical evaluation. 
Results: The subcutaneous group had a rate of implant failure and removal of 5.1% when compared with 0% in the retropectoral group. 
Aesthetic outcome was significantly better for the subcutaneous group both at a subjective and at an objective evaluation. 
Capsular contracture rate was 0% in the subcutaneous group. 
Conclusions: A higher rate of implant failure and removal, although not significant, always because of skin flaps and wound problems, should be taken into account for a careful patients selection. 
The subcutaneous breast reconstruction shows good long-term results. 
A coherent subjective and objective cosmetic advantage of this approach emerges. 
Moreover, no capsular contracture is evident, albeit in a relatively limited number of cases.
An implant-based breast reconstruction (IBBR) is by far the preferred way of restoring a female breast after mastectomy and a 2-stage breast reconstruction, by means of a tissue expander, accounts for approximately 70% of all reconstructions according to the American Society of Plastic Surgeons statistics.
Nonetheless, the opportunity of a direct-to-implant breast reconstruction in breast surgical oncology is very fascinating and tempting for surgeons and for women as well.
Whenever surgical conditions, tumor stage and adjuvant treatments allow this option, it is definitely worth considering the avoidance of a temporary tissue expander and its discomfort.
A 1-stage procedure is quite demanding in technical terms.
A full muscular pocket, to cover the prosthesis, allows the use of small to medium size implants and sometimes does not let the surgeon recreate a good lower pole shape and inframammary fold contour.
The introduction of soft-tissue replacement devices in IBBR dramatically expands this field in breast surgery.
Acellular dermal matrices (ADMs) are by far the most frequently used worldwide.
A lot of data are present in literature for ADMs use in breast reconstruction.Synthetic meshes are used as well, as an alternative to ADMs.
A titanium-coated polypropylene mesh (TCPM), TiLoop Bra (pfm medical, Cologne, Germany), is approved for use in breast surgery in Europe since 2008.
There are studies showing its safety and effectiveness in IBBR, with promising results in terms of capsular contracture.
Soft-tissue replacement devices are traditionally used as an inferolateral extension of pectoralis major muscle. 
ADMs or synthetic meshes function as a hammock to adjust an implant after its placement in a retropectoral position and after muscle detachment from its inferior aspect. 
Recently, a novel approach, with a prepectoral, subcutaneous, muscle-sparing implant positioning, has been described. 
In a previous study, we described a prospective nonrandomized clinical trial designed to compare a muscle-sparing method of using TCPM, as a complete coverage for a prepectoral implant, with the standard retropectoral muscular mesh implant coverage. 
Results are limited to a shortterm follow-up, with surgical complications and implant loss showing no difference between the 2 groups.
Aim of this study is to analyze and evaluate longterm results on the same patients enrolled in the aforementioned trial, with 25 months of median follow-up. 
To ascertain reliability and quality of the subcutaneous reconstructions performed in the previous study, the analysis is focused on long-term surgical complications, requiring reintervention, along with objective parameters such as rippling, capsular contracture, and cosmetic outcome. 
Another primary endpoint of present evaluation is women’s quality of life (QOL), analyzing functional and aesthetic subjective parameters.
In 2011, a prospective nonrandomized clinical study was started to compare the use of TCPM in direct-to-implant reconstructions either in the standard muscular mesh implant coverage (group 1, G 1, breast implants in partial retropectoral position, with synthetic TCPM placed as a hammock-like elongation of the muscle over the inferolateral pole) or in a different muscle-sparing technique (group 2, G 2, totally subcutaneous, prepectoral implant adjustment, wrapped in a TCPM bag).
Enrollment ended in January 2014.
Study design and surgical details were previously described.
Briefly, inclusion criteria were age less than 80 years, normal body mass index (BMI; range, 18.5–24.9), small-to-medium size breasts, and ptosis grade of the first and second degree according to the 3-tier Regnault ptosis scale.
Exclusion criteria were previous breast surgery, T4 and metastatic cancers, refusal to sign the consent, comorbidities (diabetes, renal failure, congestive heart failure, pulmonary diseases, hypertension, chronic hepatic diseases, and metabolic diseases), smoking, and previous radiotherapy to the chest wall.
Cases baseline characteristics and oncological data of the 2 groups are shown in Table 1.
In May 2015, with a minimum follow-up of 16 months from the last enrolled case, we proceeded to a surgical, functional, and aesthetic analysis of all cases.
Patients were called and invited to participate in a long-term clinic evaluation.
All patients signed a consent to accept the visit/questionnaire within a standard out-patient clinic scheduled activity.
No ethical committee approval was required.
All further surgical procedures and postoperative radiation therapies, between the first reconstruction and last follow-up, were investigated and registered.
Furthermore, both an objective and a subjective evaluation were conducted.
All the evaluations started and were completed within May 2015.
The subjective evaluation was conducted using the postoperative section of BREAST-Q (Memorial Sloan-Kettering Cancer Center and The University of British Columbia ? 
2006, all rights reserved).
According to a recent study on long-term patient-reported QOL after breast implant reconstruction,26 the BREAST-Q reconstruction module was divided into multiple independent scales: satisfaction with breasts (16 items), satisfaction with outcome (7 items), psychosocial well being (10 items), physical well being (16 items), and sexual well being (6 items).
For each scale, item responses were summed and transformed into a score, ranging from 0 to 100.
Higher scores indicate greater satisfaction or QOL.
On the other hand, the objective evaluation was performed by 2 surgeons simultaneously.
The evaluating surgeons were staff members of the Breast Unit but not those who operated on the patients of the study.
A 5-item form was fulfilled for every reconstruction case.
The form was structured as follows: a capsular contracture evaluation, using the 4-grade Baker classification; b registration of signs of rippling, using a 5-grade Likert scale (score: 1, strongly disagree; 2, disagree; 3, undecided; 4, agree; 5, strongly agree) to judge the statement “this case has no signs at all of rippling”; c registration of signs of implant’s profile visibility, using a 5-grade Likert scale to judge the statement “this case has no signs at all of a visible implant”; d registration of signs of implant borders tangibility at smooth touch, using a 5-grade Likert scale to judge the statement “this case has no signs at all of palpable implant borders”; e aesthetic result, judging the statement “this case has an excellent aesthetic outcome,” still using a 5-grade Likert scale.
This evaluation was conducted by 2 different surgeons at the same time, giving a single agreed score for every item.
Subjective evaluation was conducted by every single patient, whereas the objective evaluation was performed on every single operated breast, therefore, for both reconstructions in bilateral cases.
Statistical Analysis Comparison between muscular mesh pocket and mesh bag pocket patients was made by Wilcoxon’s rank sum test for continuous variables and χ2 test or Fisher’s exact test for categorical variables. 
BREASTQ scores were compared as a continuous variable. 
For each scale, we reported median, range, mean, and standard deviation of the score. 
All statistical analyses were performed by STATA 12.1 (StataCorp. 2011, Stata Statistical Software: Release 12, StataCorp LP, College Station, Tex.)
Twenty-nine patients had been initially enrolled in G 1 and 34 patients in G 2; there were 5 bilateral reconstructions in each group, meaning that an overall number of 34 cases in G 1 and 39 cases in G 2 had been performed.
Cases from the 2 groups were not significantly different in terms of type of intervention (nipple-sparing mastectomy vs skin-sparing mastectomy), concurrent axillary lymph nodes dissection (ALND), preoperative chemotherapy, and postoperative radiation therapy (Table 1).
One patient of G 2 died before this study and was not evaluated.
Therefore, 29 patients from G 1 and 33 patients from G 2 were invited and visited as for the purposes of this analysis.
One patient in G 2 had her implant removed in the early postoperative course because of a large skin-flap necrosis (as reported in the previous study on short-term complications).
Two more patients in G 2 had their implant removed later on because of wound dehiscence during chemotherapy in one case and ipsilateral chest wall local recurrence in the other.
Because all these 3 cases had a different type of reconstruction, with autologous flaps, they were not submitted to present study objective and subjective functional and aesthetic evaluation.
Eventually, 29 (49%) patients, 34 (49%) cases, were evaluated from G 1 and 30 (51%) patients, 35 (51%) cases, from G 2, with 26 and 25 months of median follow-up, respectively.
Early surgical complications from a previous study and long-term results with data analyses from this study are shown in Table 2.
Breast reconstruction is nowadays a goal that can be achieved more easily and with a much greater cosmetic satisfaction for both women and surgeons. 
An implant reconstruction is the preferred way of achieving such a result, and a 2-stage procedure is the commonest solution. 
A single-stage procedure is an interesting option whenever anatomical and oncological characteristics allow it. 
In a recent study, the single-stage and 2-stage procedures are compared in terms of surgical complications and women’s satisfaction. 
Results show no differences in terms of surgical complications, but the singlestage approach is associated with higher sexual well-being satisfaction, even though more than 80% of patients required reinterventions with additional surgical revisions.
Even in terms of costs, a 2013 study shows a slight advantage for the 1-stage procedure, although only during the first 18 months and without statistical significance. 
A 1-stage technique with a direct-toimplant approach is, therefore, more often adapted particularly after the introduction of soft-tissue replacement devices such as ADMs and synthetic meshes. 
The standard use of these entails their placement as an elongation of the pectoralis major muscle, previously detached from its inferior aspect. 
The implant is, hence, covered by the muscle on the medial-upper pole and by such devices on the inferolateral pole to better define the lower profile and inframammary fold contour. 
Based on the rationale that prosthetic devices are actually subcutaneous under the mastectomy flaps in all the inferolateral reconstructed breast, a complete subcutaneous approach can possibly be used in selected cases. 
The direct-to-implant subcutaneous reconstruction with a muscle-sparing technique, wrapping the implant entirely within a TCPM bag, and placing it underneath skin flaps, is described in a previous study, which constitutes the background of this long-term analysis. 
The subcutaneous procedure means shifting the position of the implant from a retropectoral site to a prepectoral one, as schematically shown in Figs. 4, 5.
The subcutaneous approach is described, shortly after our study, also in 2 more papers, although in a smaller number of cases and using ADMs as coverage for the implant.
Results of our previously published prospective nonrandomized clinical study are limited to short-term complications and show that there are no differences in terms of surgical complications between the 2 groups of patients.
Studies on the subcutaneous approach conclude that it is safe and feasible.
Unfortunately, so far, none of them present long-term results.
Moreover, few article deal with the QOL in terms of cosmetic outcomes of breast cancer survivors submitted to any type of reconstruction, as highlighted in a recent study.
This study faces the issue of subjective cosmetic outcome in the restricted sample of breast cancer survivors enrolled in the previous trial comparing the prepectoral and retropectoral breast reconstructions.
In addition to the cosmetic aspect, a subjective functional evaluation (as part of BREAST-Q and an objective assessment, from surgeonssperspective, are considered too.
Results show that in terms of long-term outcomes the musclesparing technique has an encouraging performance.
First, it should be acknowledged that 1 late implant failure and removal, because of poor incision borders healing during chemotherapy, was recorded in G 2.
This, summed with 1 early implant loss from the short-term evaluation, makes a surgical failure rate for the subcutaneous technique of 2 out of 39 cases (5.1%) versus 0% in the standard muscular mesh pocket group.
The other registered implant removal in G 2 was because of a locoregional chest wall recurrence.
Notwithstanding this difference in surgical failure, due in both cases to skin flaps and wound problems, the implant change rate for functional and aesthetic reasons definitely favors the subcutaneous technique with 4 cases (12%) and 0 cases (0%) for G-1 and G-2, respectively. 
A similar number of fat grafts over the implant procedures, to ameliorate the cosmetic outcome, were performed in the 2 groups. 
In terms of subjective QOL parameters, the BREAST-Q evaluation gives a statistically significant difference in the “satisfaction with outcome” group of items, with the subcutaneous technique favored over the retropectoral one. 
In our opinion, this excellent result in the subjective satisfaction with reconstructed breast is because of the more natural appearance that a subcutaneous implant entails. 
This approach, in fact, naturally recreates the innate ptosis of the breast, being the implant in the original anatomical position of the breast gland.
Among the objective evaluation parameters, a significant difference is exhibited for the capsular contracture rate and for the aesthetic outcome.
In both cases, the subcutaneous technique results are superior to the retropectoral standard procedure.
As for the capsular contracture rate, a III–IV grade contracture is recorded in 4 (12%) G 1 cases, compared with 0% in G 2.
A theoretical explanation for such a good result in capsular contracture is that the subcutaneous approach avoids any mechanical stress over the implant and over its capsule as opposed to a retropectoral technique.
This hypothesis is corroborated by a recent article showing a significant difference in capsular thickness between tissue expanders placed subcutaneously and those placed in the standard submuscular position.
Interestingly, no grade III–IV capsular contractures were encountered in G 2 even considering that 9 (26%) cases in this group received a postoperative radiation therapy.
Furthermore, the chemically and biologically inert nature of the titanium-coated mesh might play a role in this as hypothesized in a recent study.
Synthetic mesh constitutes an interface between implant and skin flaps, recreating a new fascia and supporting the implant itself to prevent rotations in the early postoperative course.
The same goal can be achieved by ADMs as described in the aforementioned studies.
A TCPM has a good flexibility, which helps in placement.
Its loose knitwork helps in fluids drainage, avoiding closed space.
Moreover, the new fascia created by mesh integration within tissues appears very thin and soft when exposed.
A formal histologic analysis of TCPM integration within tissues was performed in 1 study, with good results in terms of fibrosis and subsequent capsular contracture.
Besides, costs are definitely lower for TCPM when compared with ADMs overall.
Nonetheless, a subcutaneous reconstruction can be done, as reported in the cited articles from different authors with TCPM or ADMs alternatively and seemingly with good results in both cases.
The surgeons’ aesthetic outcome judgment is deemed excellent in 91% of G-2 cases versus 65% of G-1, and this is coherent with the women subjective evaluation, with a significant advantage of the subcutaneous approach. 
A retropectoral implant has the disadvantage of the muscle presence, which can retract or somehow flatten the implant itself, with high ridden breast appearance and unpleasant “animated” implants. 
A subcutaneous reconstruction, instead, allows a more natural ptosis and appearance of the breast (See video, See Supplemental Digital Content 1, which displays cosmetic long-term result of a bilateral subcutaneous breast reconstruction, bilateral mastectomy with direct-to-implant subcutaneous breast reconstruction in a skinny woman, and appearance at 24 month follow-up, in a patient submitted to systemic adjuvant chemotherapy and postoperative radiation therapy on the right side.
On the other hand, it is important to highlight that a careful patients selection is mandatory for a subcutaneous approach. 
The absence of muscle coverage over the implant can expose the medial aspect of the reconstructed breast to more visible implant borders and signs of rippling. 
These drawbacks can be corrected with a fat graft procedure over the implant capsule, as occurred in 3 cases (9%) in G-2. 
The surgeon choice of the subcutaneous technique over the submuscular one can be made intraoperatively on the basis of skin flaps viability and thickness. 
Also the reconstructed breast medial-upper aspect and sternal border contour is deemed important in the decision of using or sparing the muscle. 
A BMI lower than 18.5 was an exclusion criterion in patients enrollment. 
Nonetheless, even with normal BMI patients, in case of skinny flaps on the upper/medial pole of the reconstructed breast, a submuscular approach might be chosen if implant borders visibility and some ripples are predictable. 
Some oncological aspects are of utmost importance as well, in the surgical planning beforehand. 
In case, if a postoperative radiation, and a chemotherapy as well, is anticipated, which could jeopardize a correct wound and skin flaps healing, then a subcutaneous approach and maybe the entire directto-implant reconstruction strategy would be better changed. 
All results are limited by the number of cases and by the nonrandomization nature of the previous study upon which this long-term analysis was conducted. 
Further analysis and larger numbers should be used to confirm present results. 
Nonetheless, this is, to our knowledge, the first study evaluating the subcutaneous approach with a median follow-up of 25 months. 
In conclusion, subcutaneous breast reconstruction exhibits encouraging results in terms of aesthetic outcome and capsular contracture over a long-term period of evaluation. 
Skin flaps viability and wound healing are of utmost importance for its successful performance. 
Present results might lead to a consideration of the muscle-sparing subcutaneous approach as a valid alternative to the standard retropectoral technique. 
As much as conservative, mastectomies have changed the breast surgical oncology scenario, a “conservative reconstruction” paradigm is worth considering.
Gone are the days when a consistently radiant glow is chalked up to good genes alone. 
In this era of multi skin-care regimens, what everyone really wants to know when they see a lustrous complexion is, "What are you using and who is your dermatologist?" 
Enter Manhattan dermatologist Dennis Gross, MD, and his eponymous skin-care line. 
A favorite of the perfectly complected Naomi Campbell and January Jones, it includes the award-winning Alpha Beta Peel, a must-have in ELLE editors' routines. 
Dedicated to making industry secrets available beyond his Upper East Side offices, Gross is tackling new frontiers in antiaging and developing accessible treatments and products to help smooth and luminize your skin.
WHAT'S THE MOST COMMON QUESTION YOU GET FROM PATIENTS? 
The phrase "tired-looking skin" has grown organically among my patient population. 
Dull, less vibrant, not responding like it used to. 
The skin gets exhausted and runs out of energy.
It can be from stress or pollution or lifestyle. 
Even the amount of heavy metals from hard water alone starts taking its toll on the skin and makes every problem you have look worse.
WHAT'S THE BEST SOLUTION? 
It's a rather quick fix. 
Skin needs an influx of energy from the mitochondria, which is like the battery within skin cells. 
This can come from a topical ingredient like vitamin C, the most powerful and important part of any antiaging routine. 
It's an antioxidant, which means it both increases collagen levels and protects the collagen you already have. 
It also works on uneven skin tones—an incredible combo. 
Vitamin C does lose some potency when exposed to air, so look for products with airtight valves, as well as amber bottles to help protect from light degradation.
WHAT ARE THE BIGGEST CHANGES IN ANTIAGING TECHNOLOGY YOU'VE SEEN IN THE PAST FEW YEARS? 
Now we can do things that don't have a downtime. 
Treatments like Fraxel, with its significant recovery time and pain, are being phased out in favor of noninvasive lasers that smooth and firm without destroying or injuring the skin. 
My 3D laser treatments, for instance, are a combination, a cocktailing, of two lasers that stimulate collagen, remove uneven skin colors, work on redness, and firm skin on the face and neck. 
Another advantage of this approach is its reach: The cells that make collagen lie at multiple levels, but previous lasers have worked on only one skin layer. 
The GentleMax Pro uses dual-wavelength technology to heat skin to 45 degrees Celsius and encourages the fibroblast cells to stimulate collagen; the Vbeam laser follows to eliminate redness and continue stimulation of collagen production. 
Noninvasive lasers can even work preventively, meaning whatever stage you're in—a fine line, a wrinkle, drooping—if you have the budget, you can start doing treatments immediately to stop the clock. 
It's all based on not letting your skin's collagen production diminish. 
In your midthirties, when you start to see the texture of your skin change, you might start thinking about it.
Also, five years ago, no one thought that heat could destroy fat. 
We had the idea of freezing fat, maybe, and trying to break it up with ultrasound, but now there's SculpSure, which is a breakthrough, revolutionary treatment to reduce body fat with noninvasive lasers. 
The laser safely raises the temperature of body fat to destroy fat cells [adipose tissue] beneath the skin, which are then naturally eliminated by the body and don't return.
If there is excess fat in certain pockets that have been resistant to diet and exercise, it's now possible to get rid of them. 
I think SculpSure will replace liposuction.
ARE ANTIAGING SUPPLEMENTS EFFECTIVE? 
Supplements are an important part of healthy living. 
My motto is, anything good for the heart is good for the skin.
I'm a big fan of omega-3 fatty acids, fish oils, resveratrol, and vitamin D. I believe they will slow the aging process to keep skin looking younger. 
WHERE IS THE FUTURE OF ANTIAGING GOING? 
LED light increases energy in fibroblast cells, stimulating collagen production. 
And it's completely nonirritating. 
You want to be sure, with any LED device, to look at the instructions and the claims. 
Government regulations require backup for statements that devices produce collagen; they require photos of people who have gotten results. 
LED light is the next frontier.
1
Dr Dennis Gross Skincare Spectralite Eyecare Pro ($159, drdennisgross.com),which utilizes red light therapy, can result in the reduction of fine lines if used for three minutes daily over eight weeks.
2
Lips have no ability to tan, so be sure to use a hydrating lip product with SPF, like Palmer's Cocoa Butter formula Swivel Stick ultimate moisture ($2.19, target.com).
3
For irritated, dry skin, Gross recommends Aveeno Eczema Therapy Moisturizing Cream ($9.68, amazon.com), rich in soothing collodial oatmeal.
4
His C+ Collagen Brighten + Firm Vitamin C Serum ($78, sephora.com) "is approaching equal popularity" to his coveted peel.
5
 Gross describes the advent of his at-home Alpha Beta Daily Peel pads ($16, sephora.com) as "one of the crowning moments of my career."
Facial lines and wrinkles are strongly influenced by individual differences in anatomy and muscle activity and no single injection protocol will suit all patients. 
However, there is only limited information in the published literature on how to develop a tailored approach to botulinum toxin treatment.
An expert panel of physicians was convened to establish a consensus on developing an individualized approach to treatment of the forehead with incobotulinumtoxinA. 
Separate treatment protocols were developed for men and women and subdivided by background level of muscle activity: kinetic, hyperkinetic, and hypertonic. 
Each muscle tone category was then further subdivided to take account of individual characteristics that can influence treatment.
Consensus members describe how to perform a dynamic assessment to optimize the dose and injection technique for each patient. 
A tailored treatment protocol is described for men and women with a wide range of forehead presentations. 
For each presentation, units of toxin as well as the precise location of injection points were defined by creating a 12-zone map of the forehead.
These recommendations depart from traditional consensus documents by providing detailed incobotulinumtoxinA injection protocols for the forehead based on the major parameters that differ between patients, including muscular anatomy, size, and tone.
It is expected that the use of this document will lead to more satisfactory, natural, and individualized aesthetic outcomes for patients.
All share the same mode of action, with differences between products occurring because of proprietary manufacturing processes, purification methods, and inactive ingredients in the formulation. 
INCO is the only product free from complexing proteins or neurotoxin-associated proteins. 
These play no role in the neuronal mode of action of the toxin complex and have no effect on product diffusion. 
Stability is unaffected by their absence, with INCO having a shelf-life of 3–4 years at room temperature, compared with 2–3 years for ONA and ABO and a requirement for refrigeration. 
A lack of complexing proteins also reduces the potential antigenicity of a product and thus the risk of developing secondary neutralizing antibodies.
All three toxins are US Food and Drug Administration (FDA)-approved for the treatment of moderate-to-severe glabellar frown lines. 
ONA and INCO are also approved for the treatment of lateral periorbital lines (crow’s feet) in the EU. 
For INCO only, European regulatory authorities reached a consensus in 2016 on approvability for the combined treatment of upper facial lines (glabellar frown lines, lateral periorbital lines, and horizontal forehead lines). 
Results from head-to-head clinical trials have demonstrated that INCO and ONA have similar efficacy and tolerability when used in a 1:1 dose conversion ratio for the treatment of glabellar frown lines and lateral periorbital lines. 
A recent consensus review suggests that a conversion ratio of 1:2.5 (INCO/ONA:ABO) may be assumed in aesthetic indications. 
In addition to the above-mentioned approved indications, all the products are also widely used off-label for a number of other aesthetic indications, including lifting and reshaping the eyebrows, softening perioral lines, treating platysmal bands, and correcting facial asymmetry.
The most important goal of botulinum toxin treatment in aesthetic medicine is to achieve a balance between dynamic wrinkles caused by hyperactive muscles while maintaining natural facial animation. 
This is influenced by a number of factors including individual anatomy, structure, action and mass of the muscles, and personal aesthetic preferences. 
Tailored treatment taking into account all these factors is required for optimal results and consequently patient satisfaction with treatment and their physician.
Why is this expert consensus required in aesthetics? 
Clinical data and consensus articles provide helpful guidance on aesthetic indications, but their consideration of individual patient differences is more limited. 
Patients differ enormously in their facial anatomy both within and between genders, and treating all patients at the same injection points and with the same doses will leave many with less than optimal results. 
The current document was put together to help physicians develop an individualized approach to botulinum toxin treatment of the forehead.
Twelve experts in the fields of aesthetic medicine, dermatology, and plastic surgery convened four times between July 2014 and February 2015 in Madrid, Spain, to develop independent, consensus-based recommendations for the use of INCO for aesthetic indications in patients with varying degrees of muscular activity and wrinkle severity. 
During these meetings, the group developed a series of recommendations covering the clinical history and physical evaluation of the patient as well as a muscular map of each treatment area illustrating the points of injection. 
The following text summarizes the recommendations for the treatment of the forehead. 
The content reflects the opinions of the authors only. 
The recommendations of the group for the treatment of other facial muscles will be published in a separate paper.
A number of patient characteristics and anatomical features help define their suitability for botulinum toxin injection. 
The positions, strength, and insertion points of the facial muscles can be determined by inspecting them at rest, by observing their movements while the patient makes varying facial expressions, and by palpating them. 
Signs for areas of stronger contraction include greater dynamic movement, deeper lines, and larger apparent mass during use.
Consensus members classified patients into three groups based on their facial muscle contractions and line severity prior to treatment: kinetic, hyperkinetic, and hypertonic. 
Kinetic patients are those with regular muscle contraction and wrinkles during active expression but not at rest. 
Hyperkinetic patients have more excessive muscle contraction and may require more frequent treatment and higher doses to achieve the desired effect. 
Finally, hypertonic patients are those with an inability to relax specific muscles and with visible wrinkles at rest. 
They may still be candidates for treatment, but should be advised that while botulinum toxin treatment may result in some improvements, the wrinkles will not completely disappear and additional use of an injectable dermal filler may be necessary. 
Deep static lines due to loss of skin elasticity are not suitable for botulinum toxin injection.
The aim of treatment is to eliminate lines when the patient is at rest, but to leave the ability for some movement and minimal wrinkling when the patient is animated or actively expressing emotion.
The dosing units described in this document are applicable to INCO and ONA and use standard reconstitution volumes. 
The units of ABO are different, but a conversion ratio of INCO/ONA:ABO of 1:2.5 or 1:3 is generally assumed in aesthetic indications.
Horizontal forehead lines are caused by the contraction of the frontalis, a large pair of muscles whose fibers are oriented vertically and whose medial fibers are joined in the glabellar region, where they intersect with the procerus. 
The central and lateral fibers merge with the corrugator supercilii and the inner part of the orbicularis oculi. 
Contraction of the frontalis raises the eyebrows and the upper eyelid, wrinkling the forehead in the process. 
The corrugator supercilii work antagonistically as depressors of the brow, and the forehead should not undergo treatment without treatment of the corrugator. 
The simultaneous treatment of all upper facial lines is an approved indication for INCO and is effective and commonly performed in aesthetic practice.
Before beginning the treatment, the physician should evaluate the patient for expressivity, muscle mass, symmetry, lateral versus medial movement, compensation for brow ptosis, and brow width and height. 
The intensity of contractions along the height of the frontalis can differ substantially from individual to individual, and variations in muscle function should be taken into account when deciding on the dose of botulium toxin and where the injections will be placed. 
This can be detected by light palpation over the area while the patient actively raises and lowers the eyebrows.
Treatment of the frontalis muscles can not only reduce horizontal forehead lines but can also affect eyebrow shape and height. 
Brow shape is influenced by the complex interplay between the frontalis and the lateral (lateral orbicularis oculi) and medial (procerus, corrugator supercilii, medial orbicularis oculi) brow depressors, and the use of botulinum toxin treatments to shape the brow is not considered in this document. 
To precisely define the location of injection points for individual patients, the consensus group divided the forehead into 12 zones positioned 1.5?
2.0 cm above the eyebrow to avoid any risk of brow ptosis.
Injection points and units vary between men and women due to differences in anatomy and patient preferences, and they were considered separately when designing a treatment protocol for the forehead. 
The point of divergence of the two frontalis muscles is generally lower in men than women, which has implications for injection sites. 
Men typically also have greater muscle mass and a larger forehead surface area than women and require higher doses. 
Eyebrows are naturally positioned lower in men, and excessive relaxation of the lower frontalis can result in brow ptosis. 
Men and women were then subdivided by muscle tone prior to treatment (kinetic, hyperkinetic, and hypertonic). 
Each muscle tone category was further subdivided to take account of individual characteristics that can influence treatment. 
In this manner, a tailored treatment protocol was developed for women and men with a wide range of forehead presentations.
In women with an average size forehead and dynamic forehead lines, the group recommends intramuscular injection at four injection points across the midline of the forehead (F5 to F8) with 1?
2 U of botulinum toxin per point depending on the strength of the frontalis. 
The injection points should be ~1.5?
2.0 cm apart and placed on alternating sides of the targeted horizontal lines.
Women with a high forehead and dynamic wrinkles are treated as mentioned previously with 1?
2 U at each of injection points F5 to F8. 
If required, a second line of injections can be placed above the first with the addition of two injection points at F2 and F3, and subcutaneous injection of 1 U of botulinum toxin at each point.
The so-called Mephisto sign occurs in some patients when lateral movement of the frontalis remains after treatment and produces visible wrinkles. 
It is more common when treatment of the forehead is restricted to the area between the midpupillary lines. 
Women with strong lateral frontalis fibers should receive 1 U intramuscularly at F6 and F7 and 1 U subcutaneously at points F9 and F12.
Upper eyelid ptosis may occur when treatment of the frontalis muscle unmasks subtle pre-existing weakness of the levator palpebrae superioris muscle. 
Botulinum toxin product labels recommend evaluation of the upper eyelid, especially in patients with a history of glabellar trauma or surgery, for the presence of levator palpebra muscle separation or weakness. 
These patients are treated by intramuscular injection of 1?
2 U at points F5 to F8, but with the addition of 1 U of botulinum toxin subcutaneously at points F6 and F7.
The frontalis may form either a uniform band across the forehead or be V-shaped with a relative absence of fibers medially. 
Women with the latter presentation are treated by subcutaneous injection of 1 U of botulinum toxin on the midline between F6 and F7 in addition to intramuscular injection of 1?
2 U at points F5 and F8.
Treatment is with the same pattern of intramuscular injection as for women with a kinetic frontalis, but using the higher end of the dose range (2 U) per injection point.
In the first row of injections, treatment is with 2 U intra-muscularly at each of points F5 to F8. 
Women with a high forehead can receive a second line of injections placed above the first. 
However, in those with a hyperkinetic frontalis, the two additional injection points at F2 and F3 are injected intramuscularly rather than subcutaneously with 1 U of botulinum toxin at each point.
Treatment is with 2 U of toxin injected intramuscularly at injection sites F6 and F7. 
Two lateral injections are also placed on each side of the forehead by injecting 1 U of toxin subcutaneously at the lateral limit of zones F1/F5 and F5/F9, and on the other side of the forehead 1 U at the lateral limit of zones F4/F8 and F8/F12.
These women should receive 1 U of botulinum toxin subcutaneously at each of two sites defined as the midline of F5/F6 and F7/F8 in addition to the standard F5–F8 injection points.
Treatment is with intramuscular injection of 2 U of botulinum toxin at each of points F5 to F8 and with 1 U subcutaneously on the midline of F6/F7.
Hypertonic patients are difficult to treat, and the limitations of botulinum toxin treatment should be explained beforehand. 
The recommended treatment protocol is 1 U of botulinum toxin subcutaneously at sites F5 and F8, and 1 U intramuscularly at sites F6 and F7. 
Hypertonic patients are particularly susceptible to brow ptosis, and injection in the lateral limit of the frontalis should be avoided.
Treatment is as for women with an average-size forehead with the recommendation that patients are evaluated after several days at the follow-up appointment to determine if further treatment is necessary.
These are uncommon in women with a hypertonic frontalis, and the details of their treatment are presented in Table 1.
For men with regular frontalis contractions, dynamic forehead wrinkles, and an average size forehead, the group recommends intramuscular injection at four injection points across the midline of the forehead (F5 to F8) with 2 U of botulinum toxin per point. 
As in women, the injection points should be ~1.5–2.0 cm apart and placed on alternating sides of the targeted horizontal lines.
It is important not to accentuate a prominent forehead due to a receding hairline or shaved head. 
Smoothing out the entire forehead or leaving muscle activity above the level of the normal hairline would draw attention to the upper third of the face. 
In addition to intramuscular injection of 2 U at points F5 to F8, these men can also be treated with additional intramuscular injection of 1 U at points F1 and F4 (1 cm below the muscle attachment).
In addition to intramuscular injection of 2 U at points F5 to F8, 1 U of toxin can be injected intramuscularly at each of injection sites F9 and F12 coinciding with the point of maximum contraction.
Men with palpebral weakness should receive intramuscular injection of 2 U at points F5 and F8 plus 3 U at points F6 and F7.
Standard treatment is with intramuscular injection of 2 U at points F5 to F8 plus subcutaneous injection of 1 U of botulinum toxin on the midline of F6/F7.
Treatment should be restricted to 1 U intramuscularly at points F6 and F7 and 1 U subcutaneously at points F9 and F12.
The group recommends the same pattern of intramuscular injection as for men with a kinetic frontalis (F5 to F8) with the addition of two further intramuscular injections of 1U each on the midline of points F1/F2 and F3/F4.
A second line of injections can be placed above the first as in men with a kinetic frontalis, but a higher dose may be used. 
In addition to 2 U intramuscularly in each of points F5 to F8, they are treated with 2 U intramuscularly in the midline of points F1/F2 and 2 U intramuscularly in the midline of points F3/F4.
In addition to intramuscular injection of 2 U at points F5 to F8, 1 U of toxin can be injected intramuscularly at each of injection sites F9 and F12 (coinciding with the point of maximum contraction) plus 1 U intramuscularly on the midline of points F1/F2 and points F3/F4.
The group recommends intramuscular injection of 1 U at points F5 to F8 plus 2 U of botulinum toxin intramuscularly on the midline of points F1/F2 and points F3/F4.
Standard treatment is with intramuscular injection of 2 U at points F5 to F8. 
In addition, these patients can receive intra-muscular injection of 1 U of botulinum toxin on the midline between points F1/F2 and F3/F4 as well as 1 U subcutaneously at each of points F6 and F7.
Men with a short forehead and hyperkinetic frontalis receive the standard treatment of 2 U intramuscularly at each of points F5 to F8.
Prominent lateral superciliary wrinkles will require additional injections in the lateral frontalis. 
In addition to intramuscular injection of 2 U at points F5 to F8, 1 U can be injected intra-muscularly on the midline of points F1/F2 and F3/F4, and 1 U subcutaneously in the lateral limit of points F9 and F12.
The limitations of botulinum toxin treatment in these patients should be explained before starting the treatment to avoid disappointment. 
The recommended treatment protocol is 1 U of botulinum toxin intramuscularly at sites F5 to F8.
Two rows of injection are used. 
In the first row, toxin is administered intramuscularly as 1 U at each of points F5 to F8.
In the second row, 1 U is administered intramuscularly at the midline of points F1/F2 and F3/F4.
One unit of toxin may be injected intramuscularly at the midline of points F1/F5, F2/F6, F3/F7, and F4/F8. 
When performing this treatment, the brow depressor muscles (corrugator supercilii, procerus, depressor supercilii, and superolateral portion of the orbicularis oculi) should be treated at the same time.
Men with a short forehead and hypertonic frontalis should be treated with intramuscular injection of 1 U at each of points F5 to F8.
The Mephisto sign and V-shaped frontalis are rare in men with a hypertonic frontalis, and the details of their treatment are presented in Table 2.
Patient satisfaction with botulinum toxin treatment depends on the physician’s ability to visualize and recommend a treatment plan that meets the patient’s goals and needs. 
A number of consensus documents have been published to assist physicians on the use of botulinum toxin in aesthetic medicine. 
However, to the authors’ knowledge, this is the first document to provide an individualized approach to treatment with detailed injection protocols for the forehead based on the major parameters that differ between patients, including muscular anatomy, size, and tone.
The recommendations were developed on the premise that no single injection protocol can suit all patients. 
However, few studies in the literature account for differences in facial anatomy and muscle tone when evaluating treatment with botulinum toxin. 
For example, a review on the use of botulinum toxin in men found only two studies that accounted for gender in either the study design or subgroup analysis and only one dose-ranging study. 
As the number of male patients seeking treatment has been increasing, physicians need to account for gender when evaluating and treating their cosmetic patients. 
Sexual dimorphism in facial anatomy and cutaneous physiology is well documented, yet these differences are rarely accounted for in clinical practice. 
Men and women also differ in a number of other facial features including the size of the forehead, the position and shape of the eyebrows, and the shape of the jaw. 
Such anatomical variations between genders result in differences in aging and consequently in how individuals should be treated.
For an optimal aesthetic outcome, each patient should undergo a static and dynamic assessment of muscle position, mass, and functional status prior to treatment. 
In the current consensus, muscle tone is divided into kinetic, hyperkinetic, and hypertonic, and each must be treated differently for optimal results. 
Careful observation of the extent of dynamic movement of the skin will identify areas of stronger or weaker muscle contraction. 
In this way, the physician can determine why certain wrinkles are formed and which muscles are creating them. 
This information is needed to balance the effects of opposing muscles and minimize the risk of unwanted outcomes. 
The static and dynamic evaluation may also identify other more subtle variations in facial musculature that should be considered during the planning of an effective botulinum toxin treatment regimen, such as palpebral weakness, compensatory muscle use, and facial asymmetry. 
The dynamic assessment is therefore essential to optimize the dose and injection technique for each patient.
It is hoped that the current consensus document will be of use to a wide range of aesthetic physicians from beginners to experts. 
It departs from the single template of dosing and injection points described in most consensus guidelines by tailoring treatment protocols to individual patients, which will lead to more satisfactory, natural, and individualized aesthetic outcomes.
When people go under the knife, they're focusing more and more on fat, fillers, and facelifts to achieve the perfect selfie. 
The American Society of Plastic Surgeons released data that shows upticks in facelifts, fat transplants, and injectables—and Instagram filters might be the reason.
Doctors performed almost 1.8 million cosmetic surgeries in 2016. 
The five most popular: 1. 
Breast augmentation (290,467 procedures, up 4 percent from 2015) 2. 
Liposuction (235,237 procedures, up 6 percent from 2015) 3. 
Nose reshaping (223,018 procedures, up 2 percent from 2015) 4. 
Eyelid surgery (209,020 procedures, up 2 percent from 2015 )5. 
Facelifts (131,106 procedures, up 4 percent from 2015).
Plus, a whopping 15.5 million minimally invasive cosmetic procedures were performed last year. 
The most popular varieties: 1. 
Botox (7 million procedures, up 4 percent from 2015) 2. 
Soft tissue fillers (2.6 million procedures, up 2 percent from 2015) 3. 
Chemical peel (1.36 million procedures, up 4 percent since 2015) 4. 
Laser hair removal (1.1 million procedures, down 1 percent from 2015) 5. 
Microdermabrasion (775,000 procedures, down 3 percent from 2015).
The study found several fascinating trends in 2016's data. 
First of all, the facelift had previously fallen from the top five (bested by the tummy tuck), but came back in 2016, and surgeons point to Instagram. 
"Patients are captivated by instant improvements to the face," Dr. Debra Johnson, president of the American Society of Plastic Surgeons, said in a statement. 
"It's evident in the popularity of apps and filters that change how we can shape and shade our faces."
Second, patients are getting more procedures that harvest their own fat to deposit elsewhere in the body. 
There were increases in cosmetic fat injections that target specific areas, that "freeze" fat without surgery, and that tighten sagging areas. 
The number of butt and breast augmentations that use fat grafting also increased.
Third, and perhaps most striking, is that the ASPS's data covered labiaplasty for the first time ever. 
The surgery, which lifts or injects fat and fillers into the labia, was performed 12,000 times in 2016, a 39 percent uptick from 2015.
Beyond the ASPS data, surgeons are reporting anecdotally that patients' specific requests have been changing, according to CNN. 
One doctor said his patients are requesting smaller sizes when they get breast implants. 
Another said his patients want fewer procedures that remove extra skin and are opting for fillers instead. 
Fillers are also popular because of the Kylie Jenner effect; the reality star has admitted she uses them to plump her famous lips.
But the Guardian also reports that more people are actually requesting lip reduction procedures than ever before. 
There were 28,430 lip augmentation procedures last year, but there were also 3,547 lip reduction surgeries, with the latter skyrocketing 283 percent.
"A decade ago plastic surgeons might have seen a patient every seven to ten years when they needed a major procedure like a facelift or tummy tuck," Dr. Johnson said in the ASPS statement. 
"Now patients have ongoing relationships with their plastic surgeons and feel more comfortable discussing all areas of their body that they may be interested in rejuvenating."
Hollywood's proclivity for Botox, fillers, and other under-the-needle procedures yielding magically smooth, line-free complexions is at an all time high. 
And I'm not afraid to admit that as a beauty editor, and pop-culture-devouring woman in 2017, it's fed a beautiful dark-twisted fantasy of staying wrinkle-free forever. 
I mean, I have yet to see any semblance of crow's feet on a Kardashian. 
I have nothing against cosmetic surgery. 
In fact, by nature of the job, I'm up on every cutting-edge procedure and toy with the idea of them daily. 
But as of now, I'm looking to topical solutions to ward off the early signs of aging I'm dealing with.
While my regimen used to be comprised of a myriad of different products, lately I've been looking to one: La Prairie's new Line Interception Power Duo. 
Two weeks in, it's worked better than anything else so far and I've got the litmus test to prove it—my very first wrinkle! 
Or rather expression line, which is one of the three types of wrinkles one can have. 
The other two are sun-induced and gravity lines. 
The latter being vertical and the hardest to counter. 
Thankfully, the combination of a day and night cream found in the Power Duo address all three in Botox-like fashion—but *sans* needles.
"The day cream focuses on expression lines," explains Dr. Jacqueline Hill, director of innovation and science at La Prairie.
"The three peptides we use in our day cream work on expression lines by interfering with the signaling process that leads to muscle contraction at three different levels."
To put it in perspective, Botox injections cause muscles to refrain from contracting by inhibiting the signaling pathway a bit later in the game (the second level, as opposed to the first). 
The day cream acts earlier on and in more complementary ways in gentler fashion—i.e. no weird facial expressions.
In addition to collagen-stimulating peptides and moisturizing hyaluronic acid, the formula also contains SPF 30, offering broad spectrum protection with UVA and UVB filters that prevent future damage from sun exposure.
Good News: At night, the skin is in a regenerative mode, meaning skin renewal is accelerated and repair processes take place. 
Bad News: These processes slow down with age. 
The silver lining? 
The night cream helps to compensate for this decline by regenerating the skin at several levels, counteracting the impact of daily aggressions.
"It helps stimulate the production of collagen, elastin, and hyaluronic acid," says Dr. Hill. 
"Thus, helps to optimize skin renewal to fill wrinkles from within and strengthen the extracellular matrix. 
The appearance of wrinkles, even vertical, is visibly reduced."
The biggest claim of the La Prairie's groundbreaking duo is that it can visibly decrease the appearance of wrinkles after 14 days and 14 nights. 
Because I look at my face day in and day out, I knew the only way I'd be able to really tell is with the receipts. 
i.e. photographs of the expression line on my forehead in question. 
So I snapped a photo of myself pre-Line Interception, then after I used the day cream and night cream daily, along with my normal routine of cleansing, toning, and exfoliating for 14 days. 
As you can see in the before and after, my fine line is *technically* still there. 
However, it's far less perceptible in the hi-res shot, let alone with the bare eye. 
To me, that's a win.
A few other notes: Despite being packed with SPF, the day cream is ultra-lightweight and melts into the skin, making it imperceptible beneath my makeup, which cannot be said for most moisturizers with sun protection. 
The night cream is equally lovely to slather on before bed. 
Another thing I love about the duo is that it's two different creams housed in one airless—no oxidation—carry-on-friendly tube that just makes life easy. 
I can take it anywhere.
As far as the price tag, $350 is a precious amount of change, but when you consider that it's a non-invasive alternative to pricey procedures, as well as something that can complement and even prolong the need for cosmetic procedures like Botox, it's a worthwhile AM/PM investment for the woman that doesn't mess around when it comes to skincare.
It goes without saying that no one wants to look done. 
Still, we've all seen it, or maybe it's even happened to us: the brow too high, too low, too frozen; the balloonish lips or Angelina-Jolie-in-Maleficent cheekbones that give the game away. 
Here is a person, these all-too-obvious needle- assisted interventions say, who hoped to look younger, prettier—someone who strove to arrest aging, ? 
la Blake Lively's magical imperviousness to time in The Age of Adaline, or even to reverse it, like Benjamin Button—and overshot the mark.
Thankfully, as dermatologists have grown more sophisticated in their methods and the array of fillers, neurotoxins, and fat dissolvers like Kybella has become more diverse and specialized, patently obvious nonsurgical work is becoming the exception rather than the rule. 
(And surely, if the 9 million-plus injections done in the United States in 2015 had made us a nation of funny- faced freaks, we'd know it.)
The best needle wielders now recognize that the most natural-looking effects are achieved incrementally, with tiny, almost imperceptible adjustments.
"I'm a big believer in 'You don't fill up the gas tank in one try,' " says Los Angeles-based dermatologist Annie Chiu, MD. 
"Softly adjusting gives the most beautiful results, and budget-wise it's more reasonable as well. 
You can always add, but it's harder to take away. 
Hyaluronic acid fillers are reversible, but you obviously don't want to do that unless absolutely necessary."
"Icall them little tweak-bits," says New York-based derm Dendy Engelman, MD. 
"This is the secret behind all the celebrities who the layperson thinks are just genetic phenoms. 
They are able to age beautifully because they're not doing major overhauls. 
They're not changing their faces, adding tons of volume, or erasing their expressions. 
They're just focusing on tiny changes that really fly under the radar. 
They're not so perceptible that it's like, 'Oh, she went and got her eyes done' or 'She's changed her lips.' 
Nobody can tell."
With injectables, small hits can have a big impact—and not necessarily in the places one might expect. 
Engelman, for example, sometimes uses "a tiny bit of Botox at the base of the columella, which is that divider between the nostrils," to lift the tip of the nose. 
"There are a lot of small physiological changes that people don't really notice as signs of aging, which we can address," she says. 
Another trick: making the eyes look bigger by injecting a baby dose of neurotoxin just underneath the eye. 
"If you just put one unit of Botox there," Engelman says, "it drops the lower eyelid about one or two millimeters and opens up the aperture of the eye. 
So you look a little more awake, a little younger or prettier—but not noticeably different."
In more traditionally treated areas, derms tend to stay with standard doses of Botox and fillers—"I believe that if you use too little between the eyebrows, you're not going to prevent those etched lines from getting deeper over time,"says New York–based dermatologist Whitney Bowe, MD.
"And I find that I need to put in .1 to .2 ccs—the more traditional doses of filler—along the cheekbone in order to get the lifting effect I'm after." 
But for the rest of the face, Bowe says, "I've completely changed my injection technique."
To address crow's feet, for example, Bowe "wraps" microdoses of neurotoxin—delivered with an ultrathin tuberculin needle—around the eye, starting from the tail of the eyebrow and finishing under the lower eyelid.
"Instead of hitting that area with just three injections on each side, which is what was studied during FDA trials, I actually do a series of about six or seven injection sites," she says. 
"That way, I get a very gentle, natural, widespread effect that opens up the eye and lightens up heavy lids. 
It also changes the texture of the skin in a way that traditional deeper injections don't, because I'm actually affecting only the very superficial muscle fibers. 
So when people complain that they have crepey or cigarette-paper skin around the eye, it helps to smooth that out."
Similarly, Bowe uses minuscule doses of hyaluronic acid fillers in marionette lines, smile lines, and nasolabial folds, placing them shallowly into the dermis "to gently hydrate the skin from beneath the surface." 
This imparts an immediate dewy glow but also, she says, galvanizes a longer-term benefit: "It triggers your own body to make more collagen.
I'm deliberately wounding the skin in tiny points down and along those lines in order to tell your body to start healing itself. 
I find that by doing this injection technique, I'm able to get a much more powerful preventive effect from the filler, because I'm creating ten- sion on the fibroblast cells, making them create more collagen. 
Again, it's very off-label, but if I see people every three months and I use very low doses distributed in a lot of different areas, I'm able to get healthier-looking skin over time. 
So even after the enzymes in our bodies break down that hyaluronic acid filler, the skin looks tighter and firmer."
"This is the secret behind all the celebrities," Engelman says.
The goal, always, should be natural movement (nothing should "stop you from communicating, emoting, showing sympathy or empathy or interest," Bowe says)—even if that means leaving a few wrinkles unsmoothed and not going full throttle on a particular area, which could create, say, an overlarge lip that's disharmonious with the rest of the face. 
Indeed, with cruel irony, anything too heavy-handed can actually backfire and make someone's face read as being older, rather than younger. 
"It's always that fine line between doing just enough but never teetering over into too much," Engelman says. 
"I think that although Kylie Jenner has had good work, she looks about 15 years older than her real age. 
All these young girls are doing way too much, way too early. 
I always say to my young patients, 'The one thing I can't give you is your actual youth, so you need to ride that out as long as you can. 
When that starts to break down—and it will—we can start to do things. 
But in the meantime, don't go messing with it. 
If you start monkeying with it too early, it knocks you into an older-looking category.'"
When we do see overfilled or disconcertingly immobile faces, there can be several factors to blame, but it nearly always involves either an unskilled injector or an unscrupulous one who will acquiesce to patients who want—and are willing to pay for—something they don't need. 
"Every time I look at celebrities who have crossed over to the dark side of doing too much, it's not that I'm upset with them; I'm upset with the doctor who did it to them," Engelman says. 
"We all know the right aesthetic."
It's important, therefore, to find a board-certified dermatologist or plastic surgeon who will work with you to strike the right balance—and say no to you when necessary. 
When Bowe encounters "millennials who come in with Instagram pictures of enormous lips," she says, "I have to counsel them extensively about how we have to maintain the proper ratios and proportions. 
I can put in only a little bit of product but focus on the pillows of the lips, and give you a beautiful, sexy smirk when you're at rest. 
I can turn up the corners or make the Cupid's bow pop. 
It's not about pump- ing lips full of product and giving you two big sausages." 
Even when a patient does have naturally thin lips and desires a fuller pout, Bowe takes it slow: "I'd rather do a series of treatments using very small injections at a time. 
Someone might need two syringes to get to the point where she's going to be happy, yes, but I'll do one syringe, and then I'll have the patient come back in a month or two to do the second. 
I like to give the tissues a chance to recover, and then evaluate."
In general, derms are breaking away from a one-size-fits-all approach and tackling individual faces with an eye toward modest interventions that preserve idiosyncrasies and asymmetries; the goal is to make us look like better versions of ourselves, not like everyone else. 
"I really believe injectables are an art," Chiu says. 
"Every single face is different, and there are vast differences, even culturally, in how you approach someone. 
It's about enhancing—not changing."
"When I talk to patients, new or established, I'm actually analyzing their expressions and balance and beauty," says New York dermatologist and Mount Sinai Medical Center associate clinical professor Ellen Marmur, MD. 
"By the time we catch up on our news or introductions, I already have an idea of what I might like to offer. 
I draw out a master plan with each patient, even using an iPad painting tool on a photo of the patient. 
We start with baby steps and give touch-ups until we've achieved a uniquely personalized map of what and where to inject."
Although it may seem counterintuitive, or even wasteful, to invest money in something so deliberately invisible, Marmur says, "economic analysis has proven that routine, under-the-radar procedures pay off over time more than the big, dramatic antiaging procedures." 
No one will know how we've managed to sail through time so remarkably unravaged. 
And isn't it better to leave them guessing?
If you thought boob jobs came in just two speeds—big and extra big—think again. 
According to a survey conducted by The Plastic Surgery Group, 30 percent of patients are now requesting smaller nipples with their regular breast augmentation programming.
One hundred thirty-one participants were asked to rate nipples from one to five in order of attractiveness and to appraise the diameter of the areola, Goldilocks style, as "too big," "too small," or "just right."
"We found that patients with smaller nipples rated higher in attractiveness than those with larger nipples," consultant plastic surgeon Mo Akhavani said in a press release. 
The nipples that ranked most attractive and as "just right" in size occupied 25 to 30 percent of the breast when viewed head on.
On the flip side, nips that occupied more than 50 percent of the breast in the same view earned a sweeping "too big" from 92 percent of respondents. 
Small nipples RULE. 
Got it. 
But not too small. 
Seventy-eight percent of respondents rated areolas that occupied less than 15 percent of the breast width "too small."
"Clearly the results of our survey demonstrate that a single nipple size is not appropriate for all women and the nipple diameter should be adjusted so that it is 25 to 30 percent of the breast width. 
There is also a modern trend towards smaller nipples," Akhavani added.
Choose your own proportionately sized nipple adventure.
Aesthetic outcome in patients’ demand for cosmetic treatments have been influenced by some parameters such as clinical improvement after procedures, risk and potential complications, and downtime periods. 
These parameters could validate plasma technology as a new regenerative modality.
A basic understanding of plasma physics and histopathological aspects of plasma is required to understand the impact of plasma technology on aesthetic medicine. 
Plasma is the fourth state of matter, which comprises ionized atoms. 
Almost the entire universe comprises this matter. 
When sufficient energy is delivered to gas atoms, ionization occurs and the atoms acquire a positive charge after the electrons escape.
In terms of pressure, plasma can be categorized into low-pressure, atmospheric-pressure, and high-pressure plasma. 
Furthermore, in terms of relative temperatures, it can be classified into thermal and non-thermal plasma. 
Plasma can also be produced artificially.
For artificial plasma production, using an ultra-high-frequency radiofrequency generator, the energy applied to the gas atoms is emitted in a millisecond pulse to the skin via a handpiece. 
Thermal-air-based electrical discharge plasma is used in cosmetic regeneration.
Plasma action’s mechanism on the skin involves two steps: immediate tissue contraction and thermal disruption. 
Denaturation of collagen and other proteins in the dermis following the thermal effect of plasma induces clinically observed, immediate tissue contraction. 
Cascade of neo-collagenization has been stimulated through thermal disruption of dermal solar elastosis, fibroblasts activation, and migration from the deeper dermis and cytokine release.
Plasma is more uniform than ablative resurfacing lasers, including carbon dioxide (CO2) resurfacing laser, since it does not depend on the interaction with a specific target.
Regarding the level of energy of plasma and tissue shedding, high-energy plasma induces shedding in epidermis and upper dermis, whereas low-energy plasma induces shedding in only the upper part of tum corneum.
An experimental study on comparing the histopathological effect of plasma skin regeneration (PSR) and CO2 laser in terms of tissue and plasma interaction was conducted in 2008. 
The results of this animal study demonstrated that high-fluence CO2 laser generated a greater thickness of thermal damage zone than any energy level of PSR. 
Furthermore, it revealed that plasma produced equal thermal damage zone from the low- and medium-fluence CO2 laser.
Epidermal necrosis using low-energy settings PSR were less than or similar to epidermal changes in the lowest fluence in CO2 laser.
PSR technology helps in the treatment of facial rhytides, solar keratosis, seborrhoeic keratosis, and warts. 
Some studies have considered the clinical effects of PSR technology on periorbital rejuvenation.
More than 90% improvement with conventional blepharoplasty and approximately 20% and 30% improvement using PSR in the tightening of upper eyelid and peri-orbital wrinkles were achieved, respectively. 
Approximately 40% improvement in facial acne scars after six months was achieved after a single treatment of PSR.
PSR has been used in the rejuvenation of non-facial skin, including the chest, neck, and dorsum of hands. 
Furthermore, it can be used to treat traumatic scars, benign familial pemphigus, and porokeratosis.
Although no major side effects in clinical studies have been reported to date, side effects could occur due to the heat delivered to the skin, including erythema, edema, scaling, crusting, scarring, and skin pigmentation.
Plasma application has great potential in dermatology; however, further clinical and histopathological studies are required to support previous findings and to address some issues and questions about safety and efficacy.
Technological advances in medicine have given the sick and the disabled a chance of making a full recovery. 
However, contemporary trends show that medicine goes far beyond its restorative functions. 
The increasing popularity of plastic surgery raises many questions. 
Is medicine beginning a new era of its development as a response to the commercialisation of the human body? 
Does not correcting nature lead to people’s segregation into the better and the worse ones?
The development of medical technologies has accelerated the commercialisation of the body by treating it as a package that one can “redecorate” to be more attractive in the market of social relations. 
Plastic surgery is trying to solve the identity crisis and psychological problems of people. 
New forms of medicalisation are emerging. 
A lack of success is seen as a symptom of a disease that can be cured with a scalpel.
Plastic surgery can be a tool of reconstruction of one’s identity but only under strict circumstances. 
By posturing as a solution to people’s psychological and sociological problems, medicine must reckon with a moral/ethical critique.
The fact that humans have bodies is undeniable. 
However, the answer to the question of where the boundary between the physical and social body lies is not so obvious. 
If it is possible to function with artificial organs, support life with an apparatus or change the appearance through surgery, where does the “pure” or “natural” man end?
The world is becoming increasingly uncertain; during the last two decades expressions such as the culture of fear or the risk society have emerged. 
Uncertainty concerns not only the condition of the natural environment or the state of world security but also what the human body is. 
Being a subject of medical interest for many centuries, it has become one of the most medicalised elements of the contemporary world. 
Technological advances in medicine have created enormous possibilities, giving hope for fitness and recovery to the sick, handicapped and injured in accidents, allowing for early diagnosis that saves lives of millions of people in the world, and enabling us to look inside the human body and remove defects that formerly excluded man from functioning in society.
However, currently observed trends indicate that medicine has gone beyond curative and restorative functions understood as providing help to those in need. 
Today, medical activities often concern “repairing normality”. 
The growing popularity of plastic surgery, compounded by the media, makes us ask whether medicine is entering a new phase associated with the technological progress as a response to the commercialisation of the body. 
Does not correcting nature lead to segregation of people into the better ones and the worse ones? 
And consequently, does aesthetic medicine, whose aim is to eliminate uncertainty about one’s body, not give birth to this uncertainty anew? 
In the author’s opinion, technological advances in medicine have accelerated the commercialisation of the human body, making it into a package that can be modernised and improved, thus increasing an individual’s attractiveness in the labour market. 
The body becomes a product, an investment that determines the position of a human being in the arena of interpersonal relations. 
Aesthetic medicine seeks to solve the identity crisis and psychological problems of contemporary people. 
Further manifestations of medicalisation are emerging. 
A healthy person is a person improved by surgical procedures that change not only his or her appearance but also personality. 
A lack of success and self-confidence or an unsuccessful marital life are symptoms of a disease that can be cured with a scalpel.
The presented considerations draw theoretical inspirations from the sociology of the body and the sociology of health, disease and medicine. 
Within the first of the mentioned sub-disciplines of sociology, three main approaches can be distinguished: issues related to the social regulation of the body, the ontology of the body and the experiencing of the body.
The social regulation of the body. 
This perspective focuses on the influence of social institutions on the regulation, control, monitoring and use of the human body. 
Our bodies are subjected to careful “treatment” from the moment of birth the aim of which is to suppress and channel natural instincts to build the so-called social body. 
The constant conflict between nature and culture, between reason and emotions, resurfaces here. 
Many authors are interested in the way institutions of religion, law or medicine regulate the human body. 
The most evident influence of these three institutions can be seen at the birth and death of the human body, but there is also debate over topics such as abortion, euthanasia or organ transplants. 
It is worth noting here that in this perspective feminist movements, which have made the issue of the regulation of the female body by all kinds of institutions one of their central issues, have found their place. 
The control of the human body, manifested in a variety of forms, is to serve primarily to maintain social order. 
The theorists of this trend indicate that throughout the whole life of man the human body is subject to two types of regulations: the first one consists in “training” the human body to specific behaviours, and the main mechanism in this respect is socialisation whose task is to encourage people to hide their natural instincts and behaviours under the mask of socialised conduct. 
We learn to wear a special type of clothing, how to behave in specific situations, and what should not be done or must not be done. 
The other type of regulations is based on continuous control of the effects obtained in the first stage. 
Institutions established for this purpose have a set of penalties and rewards by means of which they effectively enforce the required behaviour. 
Individuals whose bodies do not follow such regulations are isolated and go to hospitals or prisons.
The lived body. 
According to this trend, the human body determines the way in which one explores and responds to the world. 
The perception and experiencing of the world are rooted in the senses of sight, touch, smell, hearing and taste, i.e. the elements that undoubtedly belong to human corporeality. 
Through this corporeality, the outer world appears to us and our reactions to it are born. 
Usually, we are unaware of this fact, because we are also ignorant of the existence of our own body. 
We feel it only when its dysfunction appears—a cold, fracture, contusion, or haemorrhage. 
A disease often forces us to rebuild our consciousness and how we perceive our body. 
A great deal of considerations is also devoted to new tasks and possibilities that stand before the body and the embodied self of a sick person. 
This perspective has appeared as a response to the dualism of the soul and body, nature and culture, reason and emotions popularised in the literature. 
Proponents of this approach argue that it is impossible to separate these two entities from each other, as there is constant interaction between them. 
The body and the soul are a unity that manifests itself most fully in a situation of illness, suffering and pain. 
Somatic complaints are accompanied by specific emotions—anger, sadness, or a sense of powerlessness.
The ontology of the body. 
This last perspective is the most important here, as it focuses on the dilemmas of aesthetic medicine. 
The growing uncertainty of the surrounding world means that the human body has begun to be perceived as fragile, delicate, unreliable, and therefore requiring constant improvements and enhancements. 
At the same time, people have begun to see that the human body is the seat of the self, the place where the human soul is hidden. 
This has additionally intensified the willingness to care for the packaging of such noble content. 
The human body has started to be perceived as a process, it has become a “project” that is developed and carried out throughout an individual’s life. 
Body projects, as this phenomenon is defined in the discussed perspective, have become the basis for an individual’s self-identification. 
Genetic engineering, a healthy diet, physical activity, cosmetic procedures and plastic surgery are just some examples of such “projects”. 
It turns out, however, that what was to contribute to the rebuilding of self-confidence creates an even greater sense of uncertainty. 
The boundary between the body and its image is becoming blurred. 
Increasingly, the reflection, the image of the body itself—the ECG, X-ray or computed tomography—becomes more reliable than the actual person. 
The image of the body becomes the basis for its transformation. 
Nowadays, aesthetic medicine more often creates the image of a human being than simply eliminates existing defects. 
In many television stations, one can watch programmes in which people, mainly women, undergo painful and risky surgical procedures in the name of beauty, self-confidence and success. 
It would not be possible if it were not for medical technological advances that can form human bodies like the sculptor’s hands create a statue. 
However, this is associated with several threats that will be discussed further on.
The other sub-discipline—the sociology of health, disease and medicine— focuses on the social construction of health and disease concepts, a reflection on medicine itself, its organisation and medical professions as well as the relationship between medical science and social sciences. 
In its context, one can speak about the social consequences of changes in medicine. 
Medical advances and health education have changed the structure of diseases from acute to chronic, have caused the emergence of a holistic model of health and disease as a response to the biomedical model, and have increased health awareness of modern societies as well as the importance of the concept of health and disease as social constructs, not only objective products of evidence-based medicine. 
Especially since health has become a commercialised, commoditised category, it deserves consideration. 
There has been a transition from “being healthy” to “having health”, making it a commodity, changing the definition of the disease, and thus blurring the already fluid boundary between the common understanding of health and disease.
Corporeality is associated mainly with the physical dimension, is proof of our existence, and the permanent absence of the body indicates the end of our biological life. 
Ever since ancient times, however, the human body has also had other dimensions—the psychological one, associated with experiencing one’s organism and its perception by an individual, as well as the social one, whose essence is related to other people and how they perceive the body of an individual. 
Therefore, since the beginning of time, the body has been subjected to rituals, customs and treatments aimed at embellishing, modifying, displaying, decorating or covering its certain parts. 
The most common procedure is to hide the body under layers of clothing. 
Nudity, in the Bible, is associated with the feeling of shame and is inseparable from the original sin. 
Having committed the sin, the first people “realised that they were naked”. 
Nowadays deprived of clothes, we feel naked, exposed, but not only in the physical dimension but above all in the spiritual dimension. 
Clothing conceals the imperfections of our beauty, but also of our conscience. 
In addition, without clothing, we become extremely like each other, ordinary, average. 
Clothes are something that sets us apart in a way, makes us richer or poorer, having better or worse taste, while in the face of nakedness we only differ in terms of sex.
Nowadays, the external appearance has become the most important trademark of a person which determines his or her economic, professional, family and social position. 
The ways of dressing, doing one’s hair and makeup, the complexion, the body shape and body structure promoted in the media show at the same time the ideals of a woman and a man that are desirable and appropriate. 
The external appearance determines inclusion or exclusion from a given social group. 
Newspapers, magazines, television, cinema, and the music industry define patterns of appearance that are fashionable. 
The way these patterns are presented means that they also become desirable. 
As in the past, the advertisement showed that a person could become someone better, more valuable, less frustrated, happier or more attractive thanks to the acquisition of a specific product, today television shows that to achieve it one only needs to undergo plastic surgery.
The human body has ceased to be treated as permanent and immutable. 
This change in perception has an ontological meaning, as it forces us to revisit the answer to the question of what the human body is. 
For centuries, it was treated as permanent, immutable and, among others, because of that fact, it was not an object of interest in social sciences. 
In sociology, it appeared incidentally as the basis for social activities, but not as an object of a separate scientific reflection. 
Anthropology was the first to make the body the centre of study, showing its diversity, dissimilarity of treatment and use. 
In sociology, the body appeared through the sociology of sport, followed by the sociology of the body and medicine. 
The body has ceased to be something that “is”, nowadays one “has”, “possessed”, “modifies”, and “creates” the body. 
The body is treated as a process and as a “project”. 
It has become a long-term investment that is supposed to provide benefits, prestige, and respect. 
Thus, the flexible body is subjected to many modifications throughout the entire human life, depending on the needs or desires of a given individual. 
At the same time, it is perceived as the most important element of human personality. 
Jan Szczepa?ski’s classic concept distinguishes biogenic elements (of which the body is part) as well as psychogenic and sociogenic personalities. 
The body is also an important element of Ralph Turner’s self-image and self-conception. 
The selfimage is a “photograph” of our “self” which we see at a specific moment and which changes from moment to moment. 
The self-conception is a permanent “picture” of our “self”, the concept of who we really are. 
If the body of an individual ceases to be permanent and becomes “fluid”, it also has consequences for such lasting constructs as the self-conception. 
Interest in body makeover programmes, in which ordinary people decide to “remodel” their bodies, shows not only the economic dimensions of the play between demand and supply of commercial medical services but also the social transformation of orientation towards hedonistic and consumerist values. 
According to Wolfgang Welsch, referred to by Agnieszka Maj, today’s man is homo aestheticus, i.e., an “educated hedonist”—a sensitive, conscious person with a sublime taste, ready to shape and stylise his or her soul and body.
As the programmes show, many people deciding to undergo plastic surgery believe that with their new noses or breasts their well-being will improve, their self-confidence will increase and, therefore, the quality of their lives will be enhanced. 
The first programmes on this subject did not take into consideration possible side effects, which might suggest that the procedures were a safe technique for improving one’s appearance. 
Currently, information on side effects appears in the programmes, the convalescence process is also shown, which somewhat balances the optimistic and pleasant image of plastic surgery. 
Moreover, there are also programmes showing the side effects of plastic surgery. 
Many formats also show people for whom aesthetic medicine is the last chance for a normal life and full social activity (victims of accidents, diseases, people with congenital malformations and deformities). 
In this way, two separate types of medicine are presented—restorative medicine and medicine that fulfils desires. 
The former gives a chance for a normal life, restoring a healthy look and self-confidence, counteracting social exclusion and stigmatisation. 
The latter makes life better, easier, more beautiful; pleasant medicine that makes an individual’s life better and an individual him or herself healthier. 
This division is equally unfavourable for patients as well as medicine itself. 
On the one hand, it somewhat alleviates the hedonistic orientation of aesthetic medicine itself, but on the other hand, the confrontation of the healthy and the sick that avail of its offer falls to the disadvantage of the former, accused of vanity.
The contemporary image of the body contradicts somewhat demographic trends. 
On the one hand, we are dealing with the so-called greying population, which is a result of the decline in the number of births and the extension of the length of human life. 
On the other hand, there is no place for old age in modern societies. 
It is associated with illness, suffering, powerlessness and social uselessness, and above all with a deformed body, which becomes unattractive, unsightly, wrinkled, and distorted. 
Technological advances have contributed to a significant extension of human existence, and now attempts are being made to mask this achievement with the help of plastic surgery. 
Medicine has become a cure and a weapon at the same time. 
Old age has been medicalised and is treated like a disease. 
Death begins to be perceived as an “accident at work” made by a doctor which soon can be prevented. 
And elderly people as a social category are marginalised, sidelined, even though in many countries they constitute a huge percentage of the population. 
Old age is unfashionable and unwanted, and modern generations are no different from the main character in The Picture of Dorian Gray who wanted to preserve his youth at all costs. 
According to surveys conducted by the Centre for Public Opinion Research (CBOS) from 2017, as much as 87% of Poles attach great importance to their own appearance. 
According to the same report, the look is of great importance both in personal life and professional career. 
As far as the personal sphere is concerned, as much as 38% of the respondents believe that the appearance has a large impact on the success of a person in life, while for 32% the appearance determines the success of a person in life. 
In the professional sphere, the responses were 37% and 33%, respectively. 
According to the GfK Beauty report from 2016, the external appearance reflects well-being for 35% of the respondents. 
The determinants of beauty in women are now symmetry and proportionality of the figure and face, well-groomed appearance, smooth and radiant skin without discoloration, wrinkles and pimples, unnecessary hair, and the body structure— from a slim figure to anorexic thinness. 
The determinants of male beauty are also proportionality and symmetry, as well as a muscular body structure, clean skin and neat nails, and increasingly often a lack of characteristic male hair. 
Added to this is a radiant smile with white teeth and elegant fashionable attire for both sexes.
Old age has no place in this scenario; if someone wants to age with dignity, they must cover up the signs of entering the golden years. 
Only in this way will they not be excluded from society, the labour market, and the social relations market.
Aesthetic medicine is a branch of medical aesthetics understood as a subdiscipline of medicine dealing with the prevention of skin aging and improving the physical attractiveness of the patient (by restoring or improving the natural appearance).
Treatments aimed at modifying the human body have been known for millennia. 
In the 6th century BC in India, reconstructions of the nose, ears and mouth were carried out, while in China since the 10th century AD women’s feet were bound because the ideal was a woman with small feet. 
In the Turkish literature of the 11th century, descriptions of operations of drooping eyelids and gynecomastia procedures appeared. 
Pioneering facelifts, often ending in patients’ death due to the materials used—a solution of arsenic and lead—were carried out in the 19th century in Great Britain. 
In the 20th century, surgical procedures became even more popular, and at the same time more invasive. 
In the 1920s, women had their ribs removed to give them the slim wasp waist. 
Such a procedure was allegedly performed on Pola Negri. 
Marilyn Monroe also owed her face and image to plastic surgery—to make her face shapelier, a sponge implant was sewn into her chin. 
It was reportedly replaced every five years. 
The first plastic surgery clinic was opened in the United States in 1921 and was founded by a Polish surgeon, Jacek Maliniak.
The 1960s were a period of popularisation of silicone breast implants. 
In 2000, Botox was invented, which became a commonly used means for smoothing wrinkles, and in 2004, operations rejuvenating voice were initiated. 
Michael Jackson and Cher, who since 1988 have underwent numerous plastic surgeries and became the icons of plastic surgery. 
In 2004, 12 million plastic surgeries were performed in the United States.
Currently, plastic surgeries are a fashionable Holy Communion gift in Brazil and are very popular among the Chinese families with adolescent daughters. 
In the 2003 CBOS study, only 1% of the respondents admitted to having undergone a beautifying treatment, in 2009 it was 2%. 
How many people really want to do this is evidenced by the number of applications for the Polish edition of the “Make Me Beautiful” programme. 
As many as 100,000 Polish women are ready to change their body to find a better job, a partner, to repair marital relations, or to get rid of complexes. 
Breast enlargement or liposuction become a remedy for many problems of a psychological or social nature— these treatments are aimed at solving financial problems (finding a job) or even supplementing intellectual deficiencies (a more beautiful and well-groomed person is also a wiser, better-educated person). 
Individuals are becoming increasingly insecure about themselves and their abilities. 
To increase chances in the labour market or social relations, a person undergoes treatments believing that they will contribute to this goal. 
Medicine today meets social expectations. 
Thanks to technological advances, medicine can save lives even in the most hopeless situations. 
However, it is becoming a threat to many spheres of human functioning by medicalising them. 
Work, professional success, social life, sexual life or earnings cease to be dependent on personality traits, intellect, education, and become dependent on the offer of aesthetic medicine.
Progress in medicine has two faces. 
On the one hand, achievements in this field have given hope for health and life to many millions of sick, handicapped and disabled people. 
Organ transplants or artificial organs prolong the lives of many people with dysfunction of specific parts of the body, and restore mobility to handicapped people, artificial insemination gives hope to many married couples who have difficulty with having children, specialist equipment sustains vital functions, monitors body parameters and is used for complicated procedures, while plastic surgery enables the removal of congenital defects and injuries or damage resulting from accidents. 
On the other hand, medicine has contributed to the acceleration of the commercialisation of the body. 
It has become a commodity with a clearly defined price and market value, depending on the investments made. 
This second face of medicine becomes morally ambiguous, for example, because it contributes to the segregation of people into the better and worse ones, more and less valuable. 
Economic disparities are becoming more pronounced as not everyone can afford these aesthetic medical services. 
The cost of liposuction is approx. 
PLN 3,900, abdominoplasty—PLN 4,900, breast enlargement—PLN 3,500, breast reduction—PLN 4,900, cheek lifting—PLN 3,900, and eyelid surgery—PLN 2,900. 
Added to this are dental procedures (one implant costs approx. 
PLN 8,000), dermatological treatments (Botox—PLN 1,000), beautician’s and hairdresser’s services as well as designers’ consultations usually combined with the change of wardrobe. 
The prices vary significantly and depend on the prestige of the clinic, as well as the medical procedure used (equipment, the form of treatment, materials, etc.). 
Nevertheless, these are not cheap procedures. 
Therefore, those who can afford the costs are made more beautiful, becoming even more attractive, desirable or rich. 
Most people are only left frustrated, and this frustration is the cumulative effect of dissatisfaction with one’s own body and economic constraints preventing its change. 
Such people are left only with the possibility of participating in television programmes in which they make an exhibitionist act of having their bodies publicly improved.
Medicine, manipulating the human body, also manipulates the mind and personality of man. 
Plastic surgery carries a huge risk of psychological problems for both patients and their immediate environment. 
For many people, it seems that after the surgery not only their appearance will change, but also their character, personality, and financial, marital as well as social problems will disappear. 
Reality after the surgery verifies this illusion quite quickly, leaving scars on the body and soul. 
Very often, plastic surgery is a threat to the youngest members of the patient’s family, as children do not recognise a new face of their parent or relative after surgery. 
This can have very serious negative consequences in the relationship between the person who has undergone the procedure and the child.
What is the purpose of contemporary aesthetic medicine—repairing what has been damaged, or creating something new, better, based on what was old and good? 
In other words, does the doctor have the right to model the patient’s body according to the preferences of the latter, despite a lack of health reasons, or should medicine help only when the patient’s life and health are in danger? 
This question does not concern only plastic surgery, but also genetic engineering that enables manipulation of the human genome or prosthetics, i.e. implantation of artificial organs. 
The answer should consider the medical, psychological, social and economic aspects, as it is impossible to talk about these issues and not to mention their commercial dimension. 
This question about the purpose of aesthetic medicine can be extended to medicine in general. 
Not only has the body becomes a commodity, health in general has become commoditised, and the state of one’s depends not on the biological conditions but on one’s financial resources.
Aesthetic medicine is commercial medicine, market-oriented, which has little to do with social service or conscience. 
Each doctor has the right to refuse to perform a medical procedure citing the clause of conscience. 
Nobody has heard of such a behaviour among plastic surgeons or dentists, but it is quite common, for example, among gynaecologists. 
Of significance is probably the fact that aesthetic medicine is basically a completely private activity, while gynaecological services are performed both privately and state-run institutions. 
A private practice often has completely different rules and a system of values. 
As it is a commercial activity, it is subject to market regulations, not moral regulations.
Improvement and beautification become more important than medical treatment.
A serious consequence of medicalisation of old age and popularisation of plastic surgery is the commercialisation, and thus the commoditisation, of the human body. 
Medical specialisations have already introduced a significant fragmentation of man into individual organs, treated by many doctors narrowly specialised in the pathology of a specific organ. 
The patient is dehumanised and ceases to be an actor or a partner and is perceived as a sick pancreas or a badly prognostic cancer. 
Aesthetic medicine further deepens this depersonalisation, assigning a material value to the human body, just as the price of a car and its individual parts are established. 
A facelift begins to resemble the straightening of a car’s body after an accident. 
Advice by stylists and make-up artists is comparable to needing a new paint job, and dental implants—a set of new tires. 
This “repaired” man “leaves” the beauty salon and enters the world where the cult of youth and success dictates the rules.
General medicine is currently generating many discussions about its achievements, discoveries and activities, as well as the effects of these activities. 
The definition of health comprises an overall well-being including physical, mental and social dimensions instead of using the absence of illness or disability as the criteria. 
However, ways to achieve this well-being are changing, and technological advances are being widely used. 
On the one hand, medicine is focused on saving human life.
Its basic principle is “do no harm”. 
It should be emphasised, however, that this does not refer only to a purely medical dimension, in the form of iatrogenic errors, but also to mental and social harm that is far more serious in its consequences. 
On the other hand, medicine is increasingly commercialised, becoming one of many service-provided companies. 
Its goal ceases to be the change from the “bad” state to the “good” state, but increasingly often the goal is the change from the “good” to the “better” state. 
This is particularly visible in the field of aesthetic medicine, which is extremely commercialised nowadays. 
This fact has several negative consequences, such as: psychological effects of cosmetic procedures and plastic surgery, not always in line with patients’ expectations; an increase in social inequalities and progressive polarisation between the very rich and very poor, the beautiful and ugly, the improved and damaged; financial consequences associated with the need to repeat some treatments; social consequences related to new fashions and new ideals of beauty and success redefine interpersonal relationships that were once based on knowledge, experience, opinions or interests, and are replaced by the external appearance; making the external appearance a value in the labour market equal to knowledge and skills; the fluidity of “self”, instability of the self-conception, and consequent mental imbalance leading to emotional and personality disorders; moral consequences related to blurring the boundary between what is real and natural, and what is artificial and created.
Aesthetic medicine can be a tool for rebuilding one’s identity, but only in justified cases (genetic defects or personal injuries due to accidents). 
However, by offering its services as a key to solving personal and professional problems, medicine can lead to disturbances in the sphere of self-image and self-conception, and therefore, must reckon with moral objections.
The most hazardous adverse reactions following hyaluronic acid injections in aesthetic medicine involve vascular complications, known as the Nicolau Syndrome. 
This article presents a vascular complication in the area of the upper part of the nasolabial fold following subcutaneous administration of 0.5 ml of hyaluronic acid. 
At the time of the injection, paling occurred, which was followed by livedo racemosa appearing an hour later. 
Upon the lapse of a week, an ulceration appeared. 
It was not until the tenth day after the hyaluronic acid injection that hyaluronidase was administered. 
After 15 hyperbaric oxygen exposures, the ulcer was completely healed.
Currently, aesthetic medicine uses 160 fillers produced by 50 producers to correct wrinkles and furrows. 
The most popular filler is hyaluronic acid, which was first used in September 1996. 
It was a cross-linked derivative of hyaluronic acid obtained from Streptococcus bacteria.
Adverse reactions following the use of hyaluronic acid constitute an important problem in aesthetic medicine. 
A misdiagnosis may involve a longlasting ineffective therapy, whereas proper diagnosis results in a quick healing process. 
Adverse reactions after the injection of hyaluronic acid may be divided into early and delayed reactions.
Early adverse reactions (erythema, oedema, callosity) usually disappear within a few days following an injection of hyaluronic acid and depend mainly on the level of proteins and bacterial endotoxins.
Delayed reactions occur in about 1% of cases in the form of papules, granulomas, ulcers and biofilms. 
They may occur several days, weeks or months, or even years following administration of hyaluronic acid.
However, the most dangerous adverse reactions following the injections with hyaluronic acid are vascular complications, i.e. the Nicolau Syndrome, which typically occurs during the injection or a few hours after its administration.
Below we present a case of an occurrence of vascular complications following the administration of hyaluronic acid.
A beautician administered to a 45-year-old woman a subcutaneous dose of 0.5 ml of hyaluronic acid under the upper part of the nasolabial fold. 
Immediately after the administration of the filler, paling and pain occurred at the injection site. 
One hour after the injection, retinoblastoma (livedo racemosa) occurred as well as pain radiating to the left eye and a watery secretion from the left nasal passage and tearing of the left eye. 
Also, a sore which had developed in the locality of the application of the hyaluronic acid intensified. 
The next day, the beautician diagnosed herpes and recommended local treatment.
Three days later, the woman appeared for consultation at the Emergency Ward in a hospital. 
An antibiotic ointment was prescribed. 
Oedema and pain in the locality in which the filler was administered had increased, and an ulcer appeared within a week of the treatment.
It was not until the tenth day following the injection of hyaluronic acid in the upper part of the nasolabial fold that 100 U of hyaluronidase was administered to the patient for the purpose of dissolving the filler. 
In addition, an antibiotic therapy (ciprofloxacin and clarithromycin) and prednisone were used.
As a result of this, by the next day, the pain subsided and the swelling decreased. 
To accelerate the treatment of the ulcer, the use of hyperbaric oxygen (HBO) was recommended. 
Each HBO session at 2.5 ATA pressure lasted 90 minutes during which the patient breathed oxygen with 5-minute breaks during which she breathed air. 
During HBO treatment, the ulcer was gradually reduced and after 15 exposures the ulcer was completely healed. 
At present, there is mild scarring at the place of ulceration.
In recent years, aesthetic medicine procedures are frequently performed by unqualified persons, e.g. beauticians. 
The patient was not informed by the beautician about the possibility of an occurrence of adverse reactions. 
Already during the administration of the filler, skin paling and pain appeared at the injection site. 
The beautician disregarded the symptom as she was not familiar with the possible adverse reactions following an injection of hyaluronic acid. 
Upon the lapse of just a few hours, livedo racemosa occurred around the apex of the nose and the upper left side of the nasolabial furrow. 
The beautician diagnosed herpes and recommended local treatment. 
On the third day the patient arrived at the emergency ward at a hospital where only a bacterial infection was diagnosed and an antibiotic ointment prescribed. 
On the seventh day following the injection, an ulcer occurred around the upper left nasolabial furrow. 
It was not until the tenth day after the application of the hyaluronic acid that the right treatment was applied. 
Following an intradermal test, 100 U of hyaluronidase was administered to the upper nasolabial furrow region. 
Already a few minutes after the injection of hyaluronidase, the pain subsided and the swelling decreased. 
In order to accelerate the treatment of ulcers, the use of hyperbaric oxygenation was recommended.
Before administering hyaluronidase, an allergic test should be performed – in rare cases, urticaria or angioedema may occur (in 0.1% of patients) following the administration of hyaluronidase. 
Hyaluronidase is of an animal origin (bovine or ovine) and the occurring allergy may be of type I (immediate) and IV (delayed).
In the described case, only a compression of the facial artery by hyaluronic acid occurred. 
The symptoms included paling and pain at the injection site. 
In such an event it is required to interrupt the procedure and immediately administer hyaluronidase for the purpose of dissolving the hyaluronic acid. 
If ulceration occurs, it is necessary to apply hyperbaric oxygenation.
Intravascular injections are much more serious as the administration of as little as 0.05 ml of hyaluronic acid may result in blindness. 
Anatomic changes in the facial vessels make the procedure unpredictable. 
Injections with hyaluronic acid in the nasal region are particularly hazardous. 
The dorsal nasal artery, which constitutes the terminal branch of the ophthalmic artery, may be single or double. 
The size of a single nasal dorsal artery may reach as much as 1 mm.
Another hazardous area is the nasolabial fold through which the facial artery passes and then transfers into the angular artery that connects with the ophthalmic artery. 
In the described case, vascular symptoms occurred as a result of compression on the facial artery with hyaluronic acid.
The augmentation of wrinkles and furrows should only be performed by physicians who know the anatomy and rules of conduct in the case of an occurrence of adverse reactions. 
Vascular complications are among the most serious complications in aesthetic medicine. 
In the event of an occurrence of skin paling and pain while administering hyaluronic acid, it is required to immediately stop the treatment and administer hyaluronidase to dissolve the filler. 
Hyperbaric oxygenation may play an important role in the treatment of vascular complications of aesthetic medicine.
Anyone who’s had a rough week knows the look: puffy eyes, dark circles, the sudden appearance of fine lines. 
“We call it stress aging,” says Manhattan-based dermatologist and psychiatrist Amy Wechsler, MD. 
“Daily stressors such as a demanding job, a lack of sleep, and an unhealthy lifestyle can manifest as pallid patches, pimples, and wrinkles, which can add three to six years to your skin.”
As dermatologist Whitney Bowe, MD, author of The Beauty of Dirty Skin, explains, “Stress slows down digestion, which creates a shift in bacteria that can compromise the integrity of your gut lining. 
This can cause inflammation throughout the body and lead to acne flare-ups as well as premature aging.” 
Furthermore, the stress hormone cortisol is no friend to skin. 
“It’s infamous for breaking down collagen,” Bowe says.
Thankfully, much of this damage can be mitigated by focusing on daily moments of self-care. 
“When you trigger what’s called the relaxation response, it can stop psychological stress from being translated into inflammation,” Bowe says. 
“I have patients download the meditation app Breathe—five minutes a day can make a world of difference to skin.”
Regular exercise (and sex!) 
is also key. 
“Both increase levels of beta-endorphins, which fight the effects of cortisol,” Wechsler says. 
Even if you’re not feeling stressed, your skin may be. 
Environmental aggressors can also make your complexion freak out. 
“Pollutants cause the formation of free radicals, which damage DNA, resulting in skin aging,” explains Miami-based dermatologist Loretta Ciraldo, MD. 
“Irritants like dust mites and airborne allergens activate enzymes that break down collagen to worsen wrinkles.” 
Look for products with skin barrier–protecting ceramides, anti-inflammatory ingredients such as rose, and antioxidants like vitamin C and green tea. 
Topical and oral probiotics “send calming signals to skin,” Bowe says, and cannabidiol (CBD), a non-psychoactive component of cannabis, has shown anti-inflammatory and anti-acne benefits when applied topically.
Ingested in tinctures or gummies (like celeb-favorite brand Lord Jones, which will debut a skin-care line this month), CBD can diminish anxiety and summon a stressbusting snooze.
Can Botox make you happy?
Botox has moved far beyond its primary use as a cosmetic wrinkle reducer to become an FDA-approved wonder drug used to prevent migraines, excessive sweating, and overactivebladder issues. 
Next, it might supplant SSRIs as a treatment for clinical depression. 
The neurotoxin’s effects on mood are well documented—three randomized, double-blind, placebo-controlled trials have shown it can lessen symptoms of depression when injected in the “11 lines” between the brows—and now Botox manufacturer Allergan has begun the tests needed to clear it for widespread use. 
How does it work? 
One hypothesis is that by paralyzing the muscles that are engaged when you frown, Botox interrupts a biofeedback loop to the brain: “Facial expressions can directly influence emotions—smiling may cause people to feel happy, and frowning makes people feel sad,” says New York dermatologist and psychiatrist Evan Rieder, MD. 
But it’s not a balm for all. 
People who weren’t depressed, Rieder points out, “did not experience an elevation in mood in any of the studies.”
So, you're thinking about getting Botox? 
The popular injectible of botulinum toxin eliminates wrinkles by temporarily paralyzing muscles in the face—and if that doesn’t sound terrifying to you then did you even read the words “paralyzing muscles”? 
In spite of that, Botox was still one of the most popular aesthetic procedures in 2018—namely because the results are excellent, the downtime minimal, and the side effects (if done correctly) nearly non-existent. 
Even so, there are undoubtedly a lot of questions swirling in your mind before going under the needle. 
Here's what you need to know before Botox.
1
Be aware of where your injectable came from Make sure your doctor is an official vendor for any substance you're having injected. 
Allergan, Merz, and Galderma are three of the top manufacturers of neurotoxins and fillers—like Botox, Vistabel, Bocouture, Xeoxin, Azzalure, and Dysport—and Allergan also makes the fat-dissolving Kybella. 
To reduce the risk of getting a expired, contaminated, or potentially dangerous product, some manufacturers' websites offer a tool to search by zip code for every licensed physician who's obtained their product legally.
2
Bin the bargains If the price is questionably low for Botox or filler, you may be getting a diluted dosage, says New York–based dermatologist Kavita Mariwalla, MD. 
Another possibility is that your doctor purchased the product from a supplier in a country such as Canada or the United Kingdom, where government price controls keep pharmaceutical prices substantially lower than those in the United States. 
Not only is it illegal (with very few exceptions) for doctors to intentionally purchase medications outside the country for use on patients within the U.S., manufacturers also say that unauthorized suppliers may compromise the effectiveness and safety of injectables by, for example, not storing them at the proper temperature or even offering counterfeit products.
That said, prices for in-office treatments tend to be higher in metropolitan regions, such as New York, Chicago, and Dallas, where there's a greater demand for cosmetic procedures. 
To find out the price range in your area, call around. 
New York-based dermatologist Elizabeth Hale, MD, adds that you're usually better off with a doctor who bases his or her fee on how many units of product are used, rather than how many different zones of the face are injected. 
"All the muscles in the face are intertwined, and even when I treat, say, just the '11' lines between the brows, I always put a tiny bit in the forehead to balance things out—I don't count that as two [separate] zones."
3
Blood-thinning meds aren't the only thing to avoid pre-injection Most Botox and filler veterans know to lay off anticoagulants such as aspirin and ibuprofen before treatment, since those types of drugs hinder blood clotting and increase the risk of bruising should the needle nick a blood vessel. 
But Manhattan dermatologist Patricia Wexler, MD, has a longer list of things to forgo, including some seemingly innocuous pantry staples. 
"No fish oil, multivitamins, green tea, cinnamon, ginger, and red wine a full week before treatment," she says. 
"Antioxidants, though not all of them, can increase the fragility of blood vessels and prevent clotting." 
Ask your MD at least two weeks ahead of time for a full list of what to avoid.
"No fish oil, multi-vitamins, green tea, cinnamon, ginger, and red wine a full week before treatment," Wexler says.
4
A consultation is crucial"The person performing the injection should have you smile and frown and raise your eyebrows," Hale says. 
"An experienced professional is carefully evaluating you that whole time to see how different areas of your face naturally move, so that he or she can keep you looking refreshed instead of expressionless." 
Some derms like to ask patients to talk about something they're passionate about to gauge facial movement. 
You should also be given a thorough health assessment prior to the injection. 
Certain antibiotics, specifically in the aminoglycoside category, like gentamicin (prescribed for bacterial infections), can increase the potency of neurotoxins. 
(To avoid risk, don't receive treatment for the duration of your antibiotic prescription.) 
Worst-case scenario: You end up with a droopy lid, according to Mariwalla.
5
Bruises can be undone Neurotoxins generally require finer needles and are usually placed more superficially than fillers, but any injection could potentially hit a vessel, causing blood to pool beneath the skin and form an unattractive black-and-blue blotch. 
Fortunately, many dermatology practices, including Hale's, offer a next- day complimentary vascular laser treatment, which breaks down pooled blood into smaller particles, thereby greatly diminishing bruises within 24 hours. 
"It's a good idea to ask up front if whoever you're going to offers it," Hale says. 
"Our patients take a lot of comfort in knowing they can come back for that."
6
Not all fillers are created equal "Never get silicone. 
It's the one filler we see the most complications from," says Mariwalla of one injectable that's occasionally used—but not FDA approved—to fill wrinkles in the face. 
Unlike malleable hyaluronic acid–based fillers, which can be absorbed by the body and will eventually break down, silicone is a synthetic material that can't be metabolized and can harden over time, creating unsightly, uneven bulges. 
"It's permanent, and it does not age well with you," Mariwalla says.
7
Be prepared to take a minute For several hours after your shots, be prepared to avoid putting makeup, washing your hair, exercising, lying down, or messing with the injected zones. 
"You do not want to spread the toxin to muscles or weaken the injection," explains Wexler. 
You're also not going to want to fly for several hours after Botox, as there is some concern that change in cabin pressure will affect the spread of the toxin to muscles you do not want affected. 
Stay grounded (literally and figuratively) for a few days.
8
The Botox buzz Botox given between the brows (the 11’s), can give a temporary sensation of dizziness or headache, which Wexler calls a Botox buzz. 
"This is very transient, and is usually from slight swelling in the area from the fluid injected, and resolves within ten to 15 minutes," she says.
9
Early bird gets...the best results Preventative Botox is a term used when treating younger patients—say, those in their late twenties or early thirties—when expression lines are visible at rest and during movement. 
Starting early will not only prevent worsening, but can typically reverse these first blush lines and wrinkles. 
But, proceed with caution. 
"If you start using the toxin when no lines are visible, you will be using it for 50 years!" 
explains Wexler cautions.
I like to grind. 
Work-wise, dance-wise—let’s grind it out. 
But there’s an area I don’t appreciate grinding, and that’s my mouth. 
I remember when I first realized I was mercilessly grinding my teeth at night: I was on a shoot, getting ready to interview celebrity cosmetic dentist Bill Dorfman about teeth whitening. 
Before filming, he inspected my teeth to make sure everything was good to go.
“Your teeth are beautiful, but do you experience jaw pain?”
Thinking about it, yes, I did. 
My face and head felt sore all the time. 
I had more headaches in a month than I recalled having...ever (not related to politics at that point. 
Those were the days.) 
How did he know that? 
Turns out, my masseter muscle—more on this later—and teeth were a dead giveaway, specifically, my top incisors (the middle two teeth) and my cuspids (the canine teeth) of both my upper and lower jaw.
My cuspids had been sanded down from pointy to flat and straight-edged, and my front two teeth were uneven in length. 
These were the visual red flags that I had been getting my grind on. 
The masseter muscle is the muscle that helps you chew—it’s connected to your lower jaw and cheekbone. 
Because of my grinding, it’s like that muscle had been pumping iron: the constant chewing motion caused the muscle to become enlarged, altering the shape of my face. 
Dr. Dorfman cosmetically sanded my front two teeth so they were the same length and suggested I get a mouthguard to prevent future damage, but also suggested I look into the cause of this: TMJ.
What is TMJ? 
“The TMJ is actually an acronym for temporomandibular joint,” said Dr. Lawrence Fung, DDS of Silicon Beach Dental in Culver City, California. 
“The TMJ consists of structures that connect the lower jaw to the upper jaw and are involved in everyday chewing. 
It is within the TMJ structures that make a substantial contribution to the production of maxillofacial—jaw—pain.”
“A lot of times patients will come in having self-diagnosed TMJ issues,” said Dr. Victoria Veytsman, DDS of Cosmetic Dental Studios in New York City. 
“This presents pain in the jaw, headaches, tightness, difficulty chewing, earaches, limited opening, locking and sometimes clicking. 
When you see your dentist, they may choose to do imaging to evaluate any sort of damage to the joint as well. 
It can cause pain anywhere in the face, jaw and neck.”
Sounds about right. 
Aesthetically, wearing down of the teeth is the most common clue that you’re grinding them, and can ultimately speed up the appearance of aging. 
“Teeth shorten in length over time, with grinding giving a more aged appearance to the smile and entire face,” said Dr. Veytsman. 
“Shorter teeth mean reduced lower third of the face proportionately and gives the illusion of an aging face much faster.”
Once I got my teeth to a good place aesthetically, I was desperate to find out how to ease the pain and protect them long-term. 
First and foremost, I got a nightguard moulded to my mouth at the dentist’s office. 
At the very least, I didn’t want to grind my teeth down anymore than I already had. 
Now, I wear mine religiously. 
I don’t see stress as something that will ever totally disappear from my life—unless winning the lottery is in my future—but taking the time for breathing exercises and meditation before bed has helped me relax and prepare myself for a peaceful sleep. 
A CBD pen from Beboe also currently helps.
I’ve also been offered several practical tips to help calm my jaw inflammation, like avoiding chewy foods and keeping Motrin on standby to help with pain. 
I even tested out acupuncture (yes, on my face) and myofacial massage—in which fingers are used to massage the inside of the mouth—but nothing truly alleviated the aching I experienced long-term except for Botox.
How Does Botox Help With Teeth Grinding? 
Botox has several benefits. 
Of course, we know it as an injectable that helps with aging, but it can help lessen the aching of your jaw by stopping you from grinding your teeth.
“Botox correctly administered can be a great tool to help alleviate pain from grinding as it will help relax the muscles that are in pain,” said Dr. Fung. 
Officially, Botox is approved to relax the muscles in the face to prevent wrinkles from forming.
In this case, it relaxes the masseter muscle and prevents it from overworking.
I had noticed that my face was starting to look wider, and it was because I was building up those masseter muscles. 
Botox helps to lessen the appearance of this muscle—almost deflating it—so in addition to alleviating the clenching, it also slims your jawline. 
A great cosmetic bonus, in my humble opinion.
What Can Go Wrong? 
I’ve been getting this done for two years now, and there’s a few things I’ve learned over time. 
First, I should note that getting Botox injections in the masseter muscle are not an on-label usage for the substance, so make sure you find a professional well-versed in the anatomy of the face. 
You can discuss with your cosmetic dermatologist or a preferred ENT physician, neurosurgeon or dentist. 
(On-label uses for Botox include treating “the look of moderate to severe forehead lines, crow’s feet lines, and frown lines between the eyebrows in adults,” according to the brand website.) 
Overall, I get these injections every four to six months, about 30 units in each masseter muscle. 
There is such a thing as getting too much Botox—if you watch Bravo, you’ve probably been exposed to this—so beware. 
Make sure that you consult your injector prior to getting these injections and ask them how long they’ve been administering injectables like Botox specifically for the masseter muscle.
“Ask [your provider] what can go wrong. 
If you ask a lot of questions about the procedure, then you know that that person has an understanding of the right technique and potential complications,” said Dr. Nancy Samolitis of Facile Dermatology and Boutique in Los Angeles. 
“If you ask what could go wrong and they say, ‘nothing!’ 
that’s a little worrisome.”
Like Dr. Samolitis said, research is critical in finding someone to administer these injections, or any injection. 
You want to make sure they have a good history of performing this treatment or else you could end up like I did at one point, with a crooked smile. 
“The masseter muscle is located in close proximity to the muscles that make you smile,” Dr. Samolitis said. 
“The smile muscles are just a little more superficial. 
It’s very important that the person injecting you understands facial anatomy, because if the wrong muscle is injected or the Botox spreads to the smile muscle, you may not be able to smile for three to four months.”
This happened to me.
(I’ll spare you photographic evidence.) 
As someone who works on-camera for a living, this was a costly mistake—we ended up not airing segments I had shot because of my obviously asymmetrical smile. 
Unfortunately, this isn’t an immediately fixable problem. 
“Sometimes it only happens on one side. 
Do you go and paralyze the other side? 
It’s not something you’re able to fix. 
You just have to wait it out,” said Dr. Samolitis.
The beauty industry is rife with over the top claims about "miracle" skincare products and services, but one too-good-to-be-true innovation I can confidently get behind is the microcurrent facial. 
It involves using a low-grade electrical current to "train" your facial muscles to appear more lifted, tightened, and firm. 
In fact, its nickname is the "non-invasive facelift."
You know it actually works because it has been used medically since the 1980s, approved by the FDA as a muscle stimulator to treat Bell's palsy and muscle paralysis. 
After noticing improved results in patients with atrophied, sagging facial muscles, microcurrent was then adopted as an anti-aging tool. 
Top facialists like Joanna Vargas, Ildi Pekar, and Shamara Bondaroff swear by it, while at-home tools like NuFace and Ziip have become increasingly popular in everyday skin routines.
I experienced the magic of a microcurrent facial myself while visiting the Carillon Miami Wellness Resort. 
Feeling extra dry, dull, and puffy from flying and drinking beachside cocktails to my heart's content (how could you not?)
, I was looking for a treatment to contour my face and give it its glow back. 
Before getting into the facial, I gave myself half an hour to indulge in the massive spa's hydrotherapy circuit: I jumped back and forth from massaging waters, saunas, and steam rooms. 
By the end of it, my muscles felt melted like butter and all the steam had my face ready for Carillon esthetician Nerys Rodriguez to start my 80-minute microcurrent facial.
I walked away from the treatment room with visibly plumper-looking skin, major lift around my brows, more of a defined jawline, and more prominent cheekbones. 
I felt so confident that I totally skipped the usual contouring I do when I applied makeup later that day.
Impressed by my results, I chatted with the esthetician about microcurrent facials, how they work, and why people love them so much they're getting them weekly.
Microcurrent facials are like a gym workout for your face.
"The muscles on the face start going south, just like everything else. 
We have to keep it fit. 
So, we use current to stimulate the muscle, starting low then increasing gradually until you have the firmness you would like," Rodriguez explains. 
While Rodriguez says having a microcurrent facial once a month is sufficient, she adds that clients of hers at Carillon—the wellness resort is also residential—come weekly.
She adds that a microcurrent facial also doubles as an lymphatic drainage massage.
"That's why you were less puffy afterward," she tells me.
"We're hitting a lot of the pressure points on the face."
Microcurrent facials keep skin firm.
"Results are the eyes will be lifted, the forehead gets tighter, and you'll see more of an 'awakened' look," Rodriguez says. 
"It also stimulates collagen, so you'll have a fuller look as well. 
Collagen is the main protein the body has to build muscle. 
As we age, we lose collagen." 
As Rodriguez explains, microcurrent has been shown to encourage the production of ATP (Adenosine triphosphate), which leads to the creation of structural proteins like elastin and collagen.
The procedure starts out like a regular facial.
The entire process included cleansing, LED light therapy, exfoliation, serums, and masking. 
Rodriguez applied a thin film of a conductive gel on me (you can use any water-based gel) then used a microcurrent machine, which had two wands that the electrical current ran through.
I didn't feel any discomfort nor pain, just the cool metal wands lifting sections of my face and staying put for a few seconds and repeating before moving to the next section. 
"It's sending a signal for the muscle that this is where it belongs," Rodriguez says of "training" the facial tissue by going over different parts of your face multiple times.
Inside of the Carillon spa’s thermal experience.
Microcurrent is really safe for most people, but there are a few exceptions.
"People with heart issues, like if they have a pace maker, are not advised to get a microcurrent facial because it stimulates the blood," Rodriguez says. 
She also doesn't recommend microcurrent facials for those with severe acne, adding, "If it's a pimple here or there we can do it. 
If it's aggravated, I would not recommend it.
There's a lot of inflammation going on, so we don't really want to stimulate that." 
Pregnant women in their first trimester are also cautioned against doing it.
If you've had fillers or Botox, you should wait two weeks for it to settle before getting a facial. 
"We don't want to alter their results," Rodriguez says. 
After that period, however, "[botox and fillers] actually work better when you get microcurrent, because it'll make your procedure last longer. 
I actually recommend it, and I've seen great results where it lasts even longer, like the fillers and the Botox."
I noticed higher, tighter cheeks and a more defined jawline after my microcurrent facial.
At-home microcurrent tools work, but are not as strong as professional tools.
Rodriguez says younger clients can get away with using at-home microcurrent tools for maintenance. 
"You don't have to be so dedicated to a professional microcurrent service unless you really want to avoid sagging at all times," she says. 
"If someone's 50 and they're seeing signs of aging, you need something stronger." 
She does advise all clients to pair professional microcurrent facials with at-home tools for maximum results. 
"It only takes five minutes. 
And, I swear, I'm 45 and I use it and I see great results. 
I don't always have to use the professional machine from work, but I just use the NuFace and it's great too," Rodriguez adds.
There’s an unspoken rule on dating apps that says if you don’t meet the person you’re talking to within a week, it’s never going to happen. 
Someone’s leaving tomorrow to go solo backpacking for the month but will hit you up when they’re back? 
Forget about it. 
You have to “figure out your work schedule” before confirming Wednesday? 
No, you’re just not that interested. 
These things move quickly, so when you do find a hot filmmaker who lives in your neighborhood and is passionate about climate change, you have to shoot your shot—even if you have a standing lip plumping appointment on the books.
The good news is that the most popular cosmetic procedures of the moment are non-invasive, meaning you can go in on your lunch break and downtime is minimal or even non-existent. 
With a skilled professional and a great concealer, you can have your Botox and your Hinge date all in one night, whereas in other cases, it’s best to give your skin the buffer of a couple days off.
Below, Gabby Garritano, PA, founder of medical aesthetics clinic JECT NYC, and dermatologist Shereene Idriss, MD, of Union Square Laser Dermatology, break down the recovery windows for the most common beauty treatments. 
Because if you can avoid a guy knocking your newly injected cheekbones when he goes in for the kiss, trust me, you should.
What It Is: a fractionated, resurfacing laser with non-ablative (targets the tissue, not the skin’s surface) or ablative (removes the top layer of skin and creates open wounds) devices to address scarring, pigmentation, sun damage, wrinkles, and more.
Book Your Date: 1 week after. 
According to Dr. Idriss, it’ll feel like you have a bad sunburn for up to two days post-treatment, then over the course of the week, you’ll experience changes in pigmentation as the brown spots flake off. 
In addition to moisturizing regularly, the most important thing you can do is not pick.
What It Is: an injectable neurotoxin that smooths fine lines, wrinkles in the forehead, and crow’s feet by temporarily paralyzing muscles.
Book Your Date: the same day. 
Both Garritano and Dr. Idriss agree that bruising from Botox injections is unlikely and since you won’t see results in terms of facial movements for about a week, you are in the clear to go out immediately after your appointment. 
Garritano recommends icing any bumps that may occur at the injection sites, then touching up with concealer before leaving the office.
What It Is: an injectable hyaluronic acid substance that temporarily increases volume and definition in the lips.
Book Your Date: 2-3 days after. 
“The main side effects are bruising, swelling, and soreness, but that will subside several days after injections,” says Garritano.
“Take arnica, avoid drinking and taking aspirin products for 24 hours before and after the procedure, and ice the area.”
What It Is: an injectable hyaluronic acid substance that temporarily increases volume and definition in the cheeks. 
The main difference between injectables for the lips versus cheeks or smile lines is the density of the hyaluronic acid gel particles.
Book Your Date: 1-2 days after. 
The potential side effects are the same for fillers across the board, but less drastic here. 
Chances are, swelling and bruising will be minor, but you will be sore for a couple of days, so plan the date for a time you can smile fully without wincing.
What It Is: PRP facials, also known as “vampire facials,” take platelet rich plasma from the patient’s own blood and deliver it back into the skin via microneedling. 
Platelets contain high levels of growth factors, which stimulate cell turnover.
Book Your Date: 3-5 days after. 
“Immediately after, you’ll be red and may have some soreness similar to a sunburn, but that’ll typically resolve in 72 hours. 
More sensitive patients may have up to a week of a mild sunburn,” says Garritano. 
For the first week, you’ll want to avoid any retinoids or exfoliating products and apply minimal makeup.
What It Is: a skin-rejuvenating procedure that involves puncturing the skin with microneedles ranging in length from .5-2mm. 
It creates controlled injuries to the skin, which puts collagen production into overdrive, resulting in less visible scarring and increased plumpness and radiance.
Book Your Date: It depends on your skin.
Many people walk out of a microneedling procedure with an out-of-this world glow and wake up the next day looking great, while others have redness that lasts up to five days. 
If it’s your first time, the pros say to give yourself three days of downtime to be safe. 
The more you get microneedling (every four to six weeks is recommended), the less reactive your skin will be.
What It Is: a chemical solution applied to the skin that treats sun spots, uneven texture, fine lines, and acne. 
There are varying levels of chemical peels: Light, superficial ones use glycolic, lactic, or alpha hydroxy acids, while the deepest use tricholoracetic acid (TCA) or phenol and often require intensive aftercare practices.
Book Your Date: It depends on the strength of your peel. 
“Light peels have immediate redness with minimal downtime and you can expect to look your best within 24 hours, but stronger peels can have up to a week of heavy peeling and redness,” says Garritano, who notes that days two and three are usually when the dry, flakiness really starts to show. 
If you do go out, make sure your skin is heavily moisturized (Garritano loves Glytone Soothing Lipid Recovery Cream) and you’re using an SPF of 30 or higher.
What It Is: a minimally invasive facial that uses tiny crystals to exfoliate the superficial layer of dull, uneven skin and help stimulate collagen. 
Over time, it can reduce the appearance of dark spots, discoloration, and sun damage.
Book Your Date: 1 day after. 
Dr. Idriss says that even though microdermabrasion is gentle if done correctly and most people walk out with smoother, more radiant skin, there is always a chance you’ll be red post-treatment for a couple of hours to a day.
What It Is: a technique commonly used to remove hair from the root on the brows and upper lip.
Book Your Date: 1-2 days after. 
Redness and reactive acne are possible side effects, which will be made worse if you’re using retinol products (avoid them the week leading up to your appointment). 
Dr. Idriss recommends waiting a couple of days before dating for your skin to calm down post-treatment and keeping the area well-moisturized in the meantime.
What It Is: an injectable synthetic deoxycholic acid product that permanently destroys fat cells in the submental facial area and may require up to a maximum of six treatments.
Book Your Date: 2 weeks after. 
“Swelling and tenderness and numbness in the area is to be expected and lasts anywhere from one to two weeks,” says Dr. Idriss. 
She adds that you feel nodules post-treatment, which will gradually disappear, and you can massage the area if you can tolerate the pain.
Fuzz Buster: Laser Hair Removal What It Is: Laser hair removal isn’t new, but the technology has dramatically improved. 
The theory is simple: Pulses of laser light target the pigment in a hair follicle; the pigment absorbs the light and in the process damages the follicle, which prevents hair from growing back. 
“Someone with dark hair will get the best results,” explains Christian Karavolas, owner of Romeo & Juliette Laser Hair Removal in New York.
Cost: $500 to $700 per session for full legs; six treatments are usually recommended, spaced six to eight weeks apart.
What I Expected: I can certainly take a little pain in the name of vanity, but the idea of a laser systematically zapping the entire surface area of my legs had me in a cold sweat.
What It’s Actually Like: I’d chosen Romeo & Juliette because I had heard the name whispered in fashion circles—particularly in connection to Victoria’s Secret Angels, who I figure must know a thing or two about the subject. 
Lo and behold, the first person I see in the waiting room looks like an off-duty Angel. 
I take that as a good sign. 
I fill out a de- tailed questionnaire about my skin and health history, and then I’m led to a treatment room. 
The technician evaluates my skin, hands me a pair of safety goggles, and uses a red marker to divide my legs into quadrants from thigh to ankle. 
“Are you ready?” 
she asks sweetly before turning on the laser (the Synchro REPLA:Y Excellium 3.4). 
The first zaps feel hot and shocking, but once I realize it isn’t all that bad, I zone out. 
The technician asks me every couple of minutes if I’m okay, and honestly, I am. 
The whole thing is over in 25 minutes. 
When I sit up and look at my legs, though, my stomach drops: They are completely hairless (yay!)
, but they are also covered with bright red splotches. 
The tech assures me this is perfectly normal. 
She’s right: Within the hour, my legs look fine. 
And did I mention hairless?
The Results: Even after one session, I see a significant decrease in hair growth. 
After a few more, I stop shaving altogether. 
To be honest, I feel jubilant about the whole thing. 
Freedom from my razor! 
I can’t believe that I waited this long to do it. 
—Alexandra Parnass.
The Millennial Laser: Clear + Brilliant What It Is: An intro-level skin-resurfacing laser that works on all skin tones. 
Clear + Brilliant uses heat to poke fractional, invisible columns into your face. 
These micro-injuries stimulate new collagen, which treats early signs of aging—including fine lines, sunspots, enlarged pores, and dullness. 
“It emits the same energy as alternative treatments, but in a smaller, more shallow dose, so there’s less recovery time,” says Anne Chapas, MD, the New York City dermatologist who performed my treatment.
Cost: $300 to $500 per session, depending on your provider.
What I Expected: I went in wanting to even out my skin tone and restore the glow I’d lost from age, stress, and polluted Manhattan air. 
I’ve had laser hair removal done, so I was anticipating that familiar rubber band– snap feeling (spoiler alert! 
I was wrong).
What It’s Actually Like: The appointment lasts 30 minutes, but half of that time is spent getting frosted with numbing cream and waiting for the effects to kick in. 
The laser treatment itself takes less than five minutes, as Chapas makes quick passes over each section of my face. 
The sensation is hot, somewhere between getting poked by a tattoo gun and accidentally tapped by a curling iron.
I leave with a face that feels and looks as though I fell asleep on the beach sans SPF. 
It takes about two hours for the intense redness to fade, and 24 for the swelling to go down. 
Over the next few days, my skin has the texture of low-grit sandpaper.
The Results: On day five, the magic hap- pens. 
I wake up as smooth as a baby seal, with a radiance I’ve only ever been able to fake with makeup. 
My pores even look smaller. 
As for my dark spots, I don’t notice any improvement, but I was told that pigmentation issues typically require four to six treatments to resolve. 
—Maddie Aberman.
Boss Brows: Microblading What It Is: A semi-permanent tattooing process that creates the look of full brows. 
The practitioner, wielding a disposable microblade, etches fine, superficial cuts into the skin, then deposits pigment to mimic the appearance of real hairs. 
The effects last between one and three years—a dream for a natural blonde like me, who’s been spending 10 minutes each morning filling in my light, sparse arches.
Cost: Starts at about $700. 
At her New York City studio, Piret Aava (eyebrowdoctor.com) charges $1,500, which includes a touch-up.
What I Expected: I’m ink-experienced—I have 13 tats total, and I’m not too proud to admit that every single one of them hurt. 
But I had no idea what it would feel like to get a tattoo so close to my eyes. 
And I was worried about the possibility of going from patchy brows to boxy, asymmetrical ones.
What It’s Actually Like: The session is a lot like going to the dentist: You’re reclining in a comfortable chair with a bright light shining on your face, and you’re experiencing an entirely tolerable amount of pain. 
First, Aava mixes several samples of taupe pigment—lighter or darker, cooler or warmer—then paints a streak of each above my right brow. 
Mirror in hand, I decide with Aava which hue meshes best with my hair color and skin tone. 
After outlining my brows with pencil to map out the most flattering shape for my face—and dousing the area with numbing cream—Aava picks up her blade. 
And...phew! 
It’s a scratch-like sensation that’s no more uncomfortable than threading. 
For the next 45 minutes, my eyes water only two or three times as she etches tiny incisions along my brows and inserts the pigment.
The Results: Turns out, cutting your face without scabbing isn’t a thing. 
Mine is minimal, though for about a week, my look is more Groucho Marx than Lily Collins. 
But now, thanks to my microblading fairy godmother, I wake up with the full, neat brows of my dreams—no pencils or pomades required. 
—Kate Foster.
Line Limiter: Botox What It Is: An injectable neurotoxin (Botulinum toxin type A) that temporarily paralyzes muscles to reduce the appearance of wrinkles.
Cost: Depends on the doctor and the number of locations treated, but anticipate spending at least $600 for a small area, like crow’s-feet, and around $1,000 for a large one, such as the neck.
What I Expected: When I’m not smiling or laughing, I look like Grumpy Cat. 
I have what are called “11 lines” between my brows and several horizontal fore- head creases. 
I’m hoping a few shots of Botox will make me appear less like a feline meme.
What It’s Actually Like: “You definitely have some forehead lines and 11s,” confirms NYC dermatologist Macrene Alexiades, MD, PhD.
“How do you feel about your crow’s-feet?” 
“Do it all!” 
I tell her. 
The nurse applies some numbing cream. 
Alexiades tells me to hold the nurse’s hand, and prick, prick, prick goes the needle into my eyebrow muscles. 
I feel just a little pinch and drop the hand-holding. 
As Alexiades works, she explains that she administers Botox in two sessions, since it takes about a week for results to fully take effect. 
I’ll be returning a week later for a follow-up, when she can determine the areas that need more attention (which helps results last up to six months, instead of the usual four). 
The nurse tells me not to exercise, bend over, or lie down for the next four hours to help the Botox settle into the treated area. 
I’m so diligent about keeping my head up that I trip on a step while leaving Alexiades’s office.
The Results: I didn’t want an ice-block forehead, so that means I still have some lines, though they’re much more faint. 
I can still move my brows up and down. 
I was expecting my face to feel tight and stiff, but it feels basically the same as before. 
The overall effect is very natural—I look more relaxed, refreshed, and happier. 
Good-bye, Grumpy Cat. 
—Carol Luz.
In the past, when people complimented me on my skin, I would credit my glowing complexion to my relentless commitment to skincare (three steps: cleanse, hydrate, and protect) and sleep (minimum of seven hours per night). 
However, there was another “secret” that I wasn’t so keen to share: my bi-annual Botox appointment. 
As a woman of color, the topic of injectables wasn’t something that came up at brunch on a regular basis. 
I figured that everyone else who looked like me just wasn’t that into it—or not doing it at all.
According to Carlos Charles of Derma di Colore in New York City, he finds that roughly five to 10 percent of his clientele are requesting injectables, but notes they skew older and with lighter complexions.
“This is mainly because of the differences in the pattern of aging in light versus darker skin tones,” he explains.
“In lighter complexions, signs of aging such is fine lines and volume loss can occur earlier, since these are likely due to an increased susceptibility to the damaging effects of ultraviolet light. 
Also, the pattern of aging in darker skin is typically characterized less by fine lines or wrinkles, but more by overall volume loss later in life.”
"Women of color typically start neuromodulators [like Botox] a bit later than Caucasian patients."
As a beauty editor, I have written about injectables in the past, however, it started to feel weird to promote something that I actually never tried. 
After years of avoidance, and now in my early 30s, I finally buckled after seeing a male friend undergo the treatment and come out looking like a new man. 
After two appointments, I found myself loving the results. 
I looked like a better, more well-rested version of myself sans crow's feet or a wrinkled forehead (which happens when my stress level climbs).
So, why aren't more women of color talking about getting injectables? 
Ahead, we hear from dermatologists on the stigma of getting botox or fillers, the procedures themselves, and more answers to burning questions.
Who is the average patient getting injectables? 
Michelle Henry, a dermatologist and dermatologic surgeon based in New York City, estimates that roughly 50 percent of her clients are of color, and of that pool, about 25 to 30 percent of them are requesting injectables. 
“Women of color typically start neuromodulators a bit later than Caucasian patients, because melanin is protective against UV damage that contributes to the formation of fine lines and wrinkles,” she says. 
Henry adds that she most of her patients still fall within the late 20s and early 30s regardless of ethnicity.
What areas are people getting fillers/Botox? 
For those not super familiar with the filler-game, there are a ton of options available, but it can be confusing to understand what they do. 
To brush up, I caught up with Melissa Doft, a New York City-based, board-certified plastic surgeon to give me the run down on which injectable does what. 
Consider this your new cheat sheet the next time you go to the derm—or even plastic surgeon.
“Hyaluronic acid-thicker versions (like Voluma or Restalift) are great for adding volume to the cheeks, jawline or chin. 
Medium thickness injectables like Restylane, Juvederm or Belotero are excellent for around the mouth to fill in the laugh lines and nose for a liquid rhinoplasty,” Doft explains. 
“More common ‘thinner versions’ like Vobella or Refyne, work well for lips, ear lobes or tear trough—the groove between your lower eyelid and the cheek."
Fran E. Cook-Bolden, a New York City-based dermatologist, cosmetic surgeon and assistant clinical professor at Mount Sinai Health Center says that the areas most commonly treated among women of color include: "the forehead, the area between the eyebrows (known as the glabellar area), the area around our eyes (known as crow’s feet), particularly the area beneath our eyes after years of joyful smiles or squinching to read cell phone messages and lastly, the chin to correct dimpling or a cleft in the middle of the chin."
Will people notice that I got fillers or Botox? 
Naturally, when I had my first Botox appointment, I was nervous about what the outcome would be. 
Despite knowing my administer’s background, I was fearful that I’d look like a weird, altered version of myself versus a more, youthful refreshed one. 
And, turns out I wasn’t that far off to wonder if people “knew” what I had done.
According to Harold Lancer, a board-certified dermatologist based in Beverly Hills, who counts clients including Beyonc?
, Kim Kardashian West and Jennifer Lopez as clients (note: Lancer did not confirm any of the women above have undergone cosmetic procedures), there is a chance that the human mind can subconsciously detect irregularities. 
“If you look at someone, any body part, and it doesn’t match the rest of the surrounding environment you know something is wrong,” he explains.
“When using injectables, the anti-aging goal is subtle changes that leave patients looking refreshed and relaxed—as if they have just returned from a relaxing retreat or vacation,” Cook-Bolden adds.
Do women of color need to approach injectables differently from other patients? 
While most fillers are created equal, those with melanin-rich skin do need to be a bit more mindful about how things are administered. 
Women of color are often warned they're prone to hyperpigmentation—is it true? 
“Hyperpigmentation with injections is a function of the artistry and the expertise of the person doing it,” explains Lancer. 
“It’s not the material, it’s the method in which it’s done. 
Hyperpigmentation is a result of bruising causing pigment from the blood flow in the skin. 
Everyone is prone to that; however, it is more difficult to repair in darker skin types because you have competing melanin interfering with treatment protocols,” he explains.
Henry cautions that bruising can also occur on occasion. 
“If a component of blood called hemosiderin is deposited in the skin, it can leave long-term discoloration. 
This is much harder to resolve in darker complexions versus those with lighter skin tones.”
Is there a stigma toward injectables in communities of color? 
Beyond the logistics of fillers—why is everyone so hush-hush about getting them? 
You and I both know many of our faves over 40+ who looks suspiciously amazing. 
Is it their skin care regimen? 
Does black really not crack? 
Or do they have a not-so-secret secret that they simply aren’t disclosing?
“I think there is still a lot of stigma around cosmetic procedures in communities of color,” shares Henry. 
“There is still a sense that in changing one’s appearance, they are rejecting their ethnicity or heritage. 
However, this is starting to change and women of color are starting to benefit from cosmetic procedures with less guilt and stigma.”
“There is still a sense that in changing one’s appearance, they are rejecting their ethnicity or heritage.”
David Shafer, board-certified plastic surgeon and founder of Shafer Plastic Surgery & Laser Center in New York City notes that celebrities are more keen to share who their dermatologist is but not reveal who their plastic surgeon actually is. 
“Injectables are so popular right not that it seems like everyone—young and old—are requesting treatments,” he explains. 
"Social media certainly plays a role as well as the improving economy as people have more discretionary spending. 
Sometimes it can be frustrating as I would love if my celebrity and VIP patients would be more vocal about their treatments and procedures, but I respect their privacy and I think that is why they keep returning for more treatments with me,” he reveals.
What should you be looking for in a practitioner? 
Make sure whoever you're going to for fillers has a very customized approach for each patient. 
Subtle changes versus those that look “over-corrected” is a big concern for all patients, but is often a result of a cookie-cutter approach. 
“To achieve the most natural result, fillers and neurotoxins must be individualized to meet each patients need,” Cook-Bolden explains.
“Just like the practice of medicine is an art and a science, the use of injectables should be defined as a fine art. 
Opposed to just filling in spaces, we focus on the landscape or palette that we’re working with to restore volume and correct hollowing supporting and restoring ones natural, more youthful bony structure.”
Looking for a doctor who understands ethnic variations in beauty is key.
Henry agrees that looking for a doctor who understands ethnic variations in beauty is key. 
“For instance, the ‘perfect’ proportions for a Caucasian lip is 1:1.6, which allows for a slightly larger bottom lip. 
On the other hand, in Black and Asian women, we often see 1:1 ratio of the upper to lower lip, which is more common and considered more desirable in women of color.”
Are injectables worth their high price? 
While Botox and fillers can be costly—anywhere from $400 to over $1,000 in New York City, for example—the age-old saying “you get what you pay for” comes to mind. 
“There are too many cosmetic practitioners—and people—looking for price as the determining factor as to who does what. 
People are looking for a bargain versus a trained professional,” adds Lancer.
Part of what you're paying for are results that visibly flatter, but don't look obvious. 
“Great work should not be apparent in any ethnicity. 
The patient should look more youthful but not ‘worked on’—if it is noticeable, it is not good work, in my opinion,” emphasizes Henry. 
“Most injectors have a consistent aesthetic. 
Viewing their before and after photos will give you a sense of if their aesthetic is more subtle or natural.”
Bottom line: Aim to look like an enhanced-yet-natural version of yourself, prompting people to ask you “where did you vacation?” 
instead of “what did you get done?”
Flaccidity is closely related to age.
Currently, the fight against the passage of time and its impact on the body results in a continuous i d race for products and procedures aimed at mitigating the visible signs of aging.
Cutaneous flaccidity is a complex problem that involves intrinsic and extrinsic factors.
The loss of volume, excess of skin pigmentation and the low or irregular reflectance of light are among the intrinsic factors1.
On the other hand, the most relevant extrinsic factor is sun exposure, also known as photo-aging.
One of the first events in skin aging is flaccidity.
For example in the face, a descent of the middle and lower thirds occur.
Flaccidity can also be seen in other areas of the body, such as the abdomen or brachial area.
In this dynamic process, aging, as well as soft tissue and bone structures are involved1.
Not only is there a decrease in collagen, skin thinning and fat loss lead to flaccidity2,3.
In addition, there is a subtle interaction between bone resorption, fat atrophy, the thinning of collagen and elastic fibers1, and an evident decrease in cell turnover.
All these factors together added to the effect of gravity on the loose tissue, ultimately conduct to the formation of folds, wrinkles and the fine lines4.
Very frequently, the correction of these aging signs has been approached with surgical techniques and other invasive rejuvenation procedures, such as sutures5, or other suspension systems, like threads.
But these invasive procedures are not the desired choice for many patients.
Skin rejuvenation therapies should be oriented towards the mitigation of the damage and the restoration of the original structure of the tissues, in order to recover their mechanical properties.
Procedures should be as minimally invasive, safe and effective as possible, and deliver natural and more lasting, focusing on the needs of each patient individually4.
The objective of this study was to evaluate the safety and efficacy of AFLAX MLT?, a new multilevel, minimally invasive treatment to recover the elasticity and firmness of the skin.
Study design Prospective, non-randomized, single-center study, carried out on women, at the GMC Clinic center in Buenos Aires (Argentina), during February 2018.
The study was conducted in accordance with the principles established in the current revised version of the Declaration of Helsinki, with Good Clinical Practice (BPC).
Inclusion criteria: Women from 29 to 62 years old, with signs of abdominal flaccidity.
Exclusion criteria: i systemic pathologies, ii) under daily medication, and iii) have received any aesthetic treatment one month before or less of the first session.
Patients were consecutively recruited.
Patients received 3 treatment sessions with AFLAX MLT? in the abdomen, over a month, according to the product specifications.
A follow-up visit was made 30 days after the third treatment.
The treatment consisted of the administration of three products, two of them injectable and a third topical:  Vial 1: injectable for subcutaneous action.
Assets specially designed to act in depth, nourish and protect the tissues that offer trophic support and provide structural basis for the upper layers.
Active principles: glycine, proline, lysine, leucine, alanin, hialuronic acid, decapeptide-4, oligopeptide-24, lipoic acid, thiamin, cyanocobalamin, adenine.  Vial 2: intradermal injectable product specifically designed to act where proteins such as collagen, fibrin or elastin are synthesized.
Improves skin mechanical properties.
Active principles: centroxifenoxine, ascorbic acid, sodium lactate, copper gluconate, zinc gluconate, condroitin sulphate, tripeptide-6.  Vial 3: cosmetic topical product.
This cream on the most superficial layers of the skin (epidermis) producing a tensor effect, immediate and lasting.
Active principles: glutapeptide, cafesilane C2, celutrat, raffermine, sesaflash, lecithin.
Aflax MLT? protocol must be administered by a physician.
Before starting the treatment, skin mechanical properties were evaluated with a cutometer /MPA 580, Courage + Khazaka Electronic GmbH, Cologne, Germany).
The Cutometer? measures the elasticity of the skin by means of pressure (suction) that deforms the skin.
The resistance of the skin to the negative pressure is related to the firmness and its ability to return to the initial position.
All parameters are shown as curves in real time during the measurement.
This device allows the obtainment of information about the elastic and mechanical properties of the skin and to quantify objectively its efficiency.
Measurements were taken at a pressure of 450 mbar, through a 2 mm probe hole.
Cutometry parameters were: suction 2 seconds (on), 10 repetitions and 2 seconds between suction (off).
The treatment protocol included 3 sessions of AFLAX MLT? multilevel therapy in the abdomen, in a period of 1 month.
Each session lasted approximately 20 minutes and was performed by the same physician and in the same facilities.
Every session, included the application of the 3 AFLAX MLT? products (vial 1, vial 2 and cream): i vial 1 was injected at a depth of 5-6 mm through a slow, retrograde, fan technique, with a 27G / 40mm needle; ii) vial 2 was injected intradermally (3 mm), with a 30G1/2 needle: iii) and vial 3 was applied topically in the consultation.
The patient was given the tube of cream for a home-based application during the following 10 days, in the same area and once a day, until the next session.
After each treatment, pain was assessed with a visual analog scale (VAS), classifying it from 0 to 10 (0: no pain and 10: maximum pain that the patient was able to imagine). 30 days after the third therapeutic session, patient and physician satisfaction was evaluated, using a subjective 1 to 5 scale (1: not satisfied, 2: I am not sure, 3: a little bit satisfied, 4: satisfied and 5: very satisfied). 30 days after the third therapeutic session cutometry was performed again.
R0, R2, R5 and R7 variables were re-evaluated with the Cutometer?MPA 580.
At each treatment and follow-up visit, any adverse effects and its characteristics were recorded.
Analyzed variables were: age, patient and researcher satisfaction (outcome), pain and cutometry variables (R0, R2, R5 and R7).
R0: Parameter that shows the maximum amplitude of the curve and represents the passive response of the skin to force (firmness).
R2: represents the gross elasticity, which is the resistance versus return capacity
R5: represents the net elasticity: elastic portion of the curve.
R7: Assesses the portion of the elastic curve compared to the complete curve.
The closer is to 1 (100%), the more elastic the curve.
Side effects were recorded at every visit.
Unless otherwise indicated, quantitative variables are described as the mean followed by the standard deviation (SD) between brackets, while categorical variables are described as a percentage.
Statistical analysis included appropriate measures for statistical significance (Student?€?s paired two-sample t test) using the standard cutoff for significance of P 0.05 via Microsoft Excel.
he study included 39 female patients, with an average age of 45.80 (10.09).
Before treatment, the mean results of skin cutometry were: R0 = 0.28 (0.05) mm, R2 = 0.65 (0.09) mm, R5 = 0.42 (0.07) mm and R7 = 0.33 (0.04) mm.
After 30 days of the third treatment, the results were: R0 = 0.34 (0.05) mm, R2 = 0.76 (0.07) mm, R5 = 0.48 (0.07) mm and R7 = 0.33 (0.04) mm.
The differences between the pre- and post-treatment values were statistically significant for all variables R0 p <0.0001, R2 p <0.0001, R5 p = 0.0003 and R7 p <0.0001 (Figure 1).
The percentages of improvement of the characteristics of the skin at 30 days after the third treatment were: 22.91% for R0, 16.82% for R2, 14.40% for R5 and 20.73% for R7.
R0: Parameter that shows the maximum amplitude of the curve and represents the passive response of the skin to force (firmness).
R2: represents the gross elasticity, which is the resistance versus return capacity.
R5: represents the net elasticity: elastic portion of the curve.
R7: Assesses the portion of the elastic curve compared to the complete curve.
The closer is to 1 (100%), the more elastic the curve.
The subjective evaluation of the treatment by the patients, 30 days after the third treatment with AFLAX MLT? was: 35.90%  5 points “very satisfied”, 53.85%    4points “satisfied”, 10.26% 3 points “a little satisfied”, 0% 2 “I’m not sure”, 0% 1 “not satisfied”.
The subjective evaluation of the treatment by the researcher, 30 days after the third treatment with AFLAX MLT? was: 28.21%  5 “very satisfied”, 53.85%    4“satisfied”, 12.82% 3 “a little satisfied”, 5.13% 2 “I’m not sure”, 0% 1 “not satisfied”.
The average of the subjective assessment by the patients included in the study was 4.26 ± 0.64 and 4.05± 0.79 by the researcher. The differences between the two assessments were not statistically significant (p = 0.2010).
The average value of pain assessment by the patients after the application of each product of AFLAX MLT? treatment, by a VAS  scale: was: 2.33 ± 1.44 for  vial   1;2.77 ± 1.51 for vial 2 and 0.03 ± 0.16 for cream. The differences between vial 1 and vial 2, both administered by injection with 27G and 30G1/2 needle respectively, were not statistically significant (Table 1).
The only adverse effects observed were the usual mild inflammatory signs after a puncture. All resolved within few days.
The results of the study show that AFLAX MLT?? multilevel therapy improves the mechanical properties of the skin, significantly increasing firmness and elasticity.
Both the patients and the researcher evaluated positively the results.
The treatment was well tolerated by the patients, without observing adverse effects.
AFLAX MLT?? multi-level treatment is designed for each product to act at the proper level and help replenish the original structure of the tissue that has deteriorated over time.
Each product contains different actives and its action targets a specific depth of the skin.
This approach is based on the multi-factorial physiopathology of flaccidity.
The results obtained are good and patients?? appearance is natural.
This treatment is indicated when the first symptoms of skin aging appear, so that it could delay the onset of the visible signs of flaccidity and restore the damage.
These statements can be made after reviewing the results of the cutometry, a validated technique that analyzes the mechanical behavior of the skin and thus, the repercussion of age-related changes and photo- aging.
R parameters recorded an important increment in skin firmness compared to the basal values.
Other treatments, such as injected conditioned autologous serum, have also reported an improvement in skin characteristics, but the results have not been as good as the ones reported in this study, with AFLAX MLT??.
Increments of 10.38% in R0, 16.59% in R2, 11.21% in R5 and 16.16% in R7 were reported.
Other techniques such as non-invasive treatment with ultrasound, have also reported the amelioration of the mechanical properties of the skin.
A study assessed the improvement of normal skin after the application of ultrasound, obtaining an improvement in firmness (R = 0) of 15.95%, and 5.52% in the elastic component (R2).
The multi-level treatment AFLAX MLT? offers a minimally invasive option, with no important side effects and with significant results that improve the mechanical characteristics of the skin in a natural way.
However, more studies would be needed with a greater number of patients, in which men were included and with longer follow-up.
Likewise, the duration of the treatment effect should be evaluated and assessed to see if it is possible to improve the result by administering a greater number of sessions and analyze whether it offers the same benefits in other areas of the body.
As it is known the leading and prompting role of involutionary skin changes genesis is caused by abnormal microcirculation based on endothelial and immune dysfunction as well as structural and functional alterations in intercellular matrix (ICM), represented by the consequences of mainly quantitative and functional interruptions in systemic interaction of connective tissue and immune cells 1-4.
Along with that, objective characteristics of involutionary skin changes with well-known clinical signs can be achieved using instrumental skin assessment to make it more specific.
Nowadays, autofibroblasts have proven their efficiency in correcting facial contour, various folds, wrinkles, and atrophic scarring.
Satisfactory clinical effect was observed after three transplantation procedures and lasted for some months5-6.
It is convenient that the biopsy can be repeated many times, cells can be obtained at early passages with the possibility of cryobanking until the next time application.
Safety and efficiency of dermal fibroblasts autotransplantation have been proven by the results of many multi-central randomized placebo-controlled double-blind clinical studies7.
Until now, the most well-known and officially recognized technology of dermal autofibroblasts application was developed in the USA, LAVIV (azficel-T by Fibrocell Science company.
In 2011, FDA (Food and Drug Administration) authorized Fibrocell Science with the right to use LAVIV (azficel-T for nasolabial folds correction7.
However, despite the quite convincing results of fibroblasts autotransplantation, the obtained effect is not always satisfactory neither regarding the clinical manifestations nor the duration of action.
Therefore, currently, new approaches are being developed to the method, sometimes combining it with transplantation of cells with different tissue origin.
A new way of using platelets properties in the course of their co-transplantation with fibroblasts is currently being developed.
However, there has yet to be presented convincing evidence of such an approach's effectiveness in cosmetology at this point.
Nowadays platelet functions have demonstrated a fundamentally new, unexpected side.
As it turned out, the platelets actively participate in inducing inflammation, necessary to prompt the immune reflex, renewal, and formation of immunological response via the cells of native and adaptive immunity (containing TLR-2, TLR-4, TLR-7 ?– TLR-9)8.
They produce lots of growth factors as well as other biologically active substances.
According to the available data, platelet granules contain 827 proteins and their secretion provides cross-coupling of platelets, immune and stromal cells.
It has been shown that fibroblasts stimulated by cytokines respond with the synthesis of collagen and non-collagen proteins10.
Administration of platelets or their products prior to dermal fibroblasts leads to the limited, moderate inflammation.
It may create the conditions for adequate influence upon the transplanted fibroblasts of inflammatory cytokines which in their turn, provide a positive selection of young cells and stimulate their activity.
In this way an increase of cellular efficiency of autofibroblasts can be achieved, transplanted following the platelets, and therefore, we can expect an increased number of young skin fibroblasts because of their high remodeling activity.
In the view of the above said and considering the main function of fibroblasts, at the heart of the method of involutionary changes correction development called neofibrolifting lies the idea of autotransplantation of exactly these cells.
Based on the information regarding the fibroblasts properties in ex vivo cultures as well as currently known powerful stimulatory action, we designed the method which implies prearrangement of moderate and positive inflammation using the platelet- based product.
As a result we influence fibroblasts and inflammatory immune system cells with the following transplantation of autofibroblasts in the initially prepared area.
Throughout this article we would like to present the results of practical use of neofibroblifting method, which is based on stimulation of connective tissue and immune cells by means of growth factors and cytokines followed by administration of autologous dermal fibroblasts.
The study involved female patients who turned to the Institute of Plastic Surgery “Virtus” for cosmetological assistance, with concerns regarding the process of involutionary skin changes.
They were divided into 4 age groups: 25-35 y.o. (n=13), 36-45 y.o. (n=16), 46-55 y.o. (n=18), 56 and older (n=13).
Prior to participation in the research program all patients were examined by a dermatologist, physician, surgeon, endocrinologist and clinical immunologist.
In cases of existing pathology that required treatment, patients received necessary recommendations  and were excluded from the program of involutionary skin changes correction.
The material for obtaining and culturing dermal fibroblasts was obtained from the postauricular area punch-bioptate using the 3.5 mm punch-needles.
Following the mechanical morcellization, the achieved tissue fragments were transferred to the Petri dish into the warm (37,0±0,5°?) growth medium DMEM/F12 with L-glutamin (CTS? GlutaMAX?-I Supplement, Gibco), 1% non-essential aminoacids (MEM Non-Essential Amino Acids Solution, 100×, Gibco), 9 nmol main fibroblasts growth factor (FGF-Basic (AA 1-155) Recombinant Human Protein, Gibco), 15%  fetal bovine  serum  (Fetal Bovine Serum, Gibco) and 0.5% antibiotics (Penicillin- Streptomycin, Gibco). Then the dishes with the material were placed into CO2-incubator.
The culture medium was changed every 3-4 days.
Plasma rich platelets (PRP) were extracted out of 20 ml of the patients' whole venous blood.
For that purpose, a Harvest Smart PReP2 centrifuge (USA) was used.
For structural skin changes evaluation an Ultrasound dermal scanning method by means of mobile high-frequency US device ?DUB - Digital Ultraschall Bildsystem-tpm? with Software DUB-SkinScan ver.3.2 (Germany) was used.
Epidermal hydration level was evaluated using corneometry, based on the measurement of electric capacity of dielectric medium.
Examination of the epidermal barrier function was carried out by measuring skin surface moisture evaporation, transepidermal water loss (TEWL).
The studies employed the diagnostic system Multi Skin Test Center? MC 1000 (Courage+Khazaka electronic GmbH, Germany).
For blood flow testing an ultrasound Doppler scanning (device Minnimax-Dopler-K? St.Pete, Russia) was used.
Blood flow rate in microcirculatory bloodstream was measured using the sensor with emission frequency of 25 mHz.
Additionally, volumetric blood flow rate skin control, forehead and mental area was performed (Qas in ml/sec/cm).
For the interpretation of the results the critical value of significance level was considered 0.05.
The obtained results were processed using the variational statistics methods and Excell (MS Office XP).
As a means of descriptive statistics for quantitative measure, the mean (M value with standard deviation (±SD) was used as well as the Student parametric statistics (t .
Neofibrolifting technique was performed the following way: PRP was administered intradermally at the amount of 14 ml.
After 2 weeks, the same area was treated with intradermal transplantation of 60 mln autofibroblasts.
Bioptates harvesting for the research was performed prior to the treatment, 2 weeks after PRP administration and then 2 weeks afterwards, on the 6th and 12 months after the fibroblasts autotransplantation.
On certain dates, clinical lab and instrumental exams were performed.
As shown in tables 1-7, neofibrolifting resulted in the essential improvement of structural and functional skin parameters (Table 1).
Table 2 shows that the dermal thickness essentially increased in the youngest group as a result of PRP action and remained thick up to six months after the administration of fibroblasts, having normalized after twelve months.
Such an easy enhancement might be the evidence of the so-called reserve of regeneration mechanisms at a relatively young age.
In three other older groups, a considerable increase of dermal thickness took place just six months after autotransplantation and continued increasing up to the twelfth month following the treatment.
Table 3, shows the acoustic skin density also increases as a result of neofibrolifting.
In two young groups this result took place after six and twelve months following the fibroblasts administration, while in other older groups the skin acoustic density increased already after the PRP administration and remained increased up to the end of the studies and even grew thicker in the 2nd and 3rd groups after twelve months.
Analyzing the changes of corneometric anova parametric values in female patients’ skin of different age groups in the dynamics of fibrolifting treatment (Tables 4 and 5), it was discovered that the level of skin hydration in all studied groups increased following the treatment, while TEWL gradually decreased.
We can observe pronounced tendency of increasing corneometric indicators after PRP administration in all the studied groups, while considerable increase was shown only in group 3.
After fibroblasts administration skin hydration became considerably high in all groups, however the high and level in 12 months was recorded only in the 2nd and 3rd groups.
TEWL indicators influenced by neofibrolifting dropped considerably after 6-12 months following autofibroblasts implantation, except for the older group of patients, where the changes after 12 months period seemed uncertain (Tables 6 and 7).
In patients of 56 years of age and older, the increased VBF in the forehead area was observed just after PRP administration; proved stimulation in the cheek area was registered after fibroblasts autotransplantation.
Involutionary skin changes process was determined by progressively decreased epidermal, dermal thickness, acoustic skin density, its hydration, increased transepidermal loss of water (TWEL) and slow blood flow rate, in the forehead and cheek area that possibly is a proving evidence of the influence of microcirculation disorder that leads to structural and functional skin disorganization.
Our studies proved that after the administration of the fibroblasts the epidermal thickness clearly increased in all age groups, while in group 46-55 y o , it had a demonstratively positive and considerable reaction to the PRP administration (Table 1).
In the 1st, 2nd and 4th groups the effect was achieved following autofibroblast administration.
Positive effect of the procedure related to the epidermal thickness was observed after six and twelve months only in the 46- 55 y o age group.
Increased skin acoustic density as a result of neofibrolifting influence can be explained by the enhanced synthesis of collagen fibers, which represents the key elements, that reflect ultrasound waves in the organized state [Jasaitiene D et al, 2011].
Therefore, according to the structural and functional indicators skin conditions were substantially improved based on such indicators as epidermal and dermal thickness, acoustic density, corneometric and evaporimetric data practically in all age groups, most frequently following the fibroblast administration.
The achieved effect lasted for 6-12 months in most cases after autotransplantation It can be assumed that positive skin changes in the process of neofibrolifting treatment can basically be explained by the considerable stimulating therapeutic effect on the volumetric blood flow rate (Tables 6 and 7).
As it is demonstrated in the presented tables, the volumetric blood flow (VBF) in the course of neofibrolifting increased in the forehead and cheek area.
However, in the 1st and the youngest group of patients (25-35 y o ) the increase in the forehead area was observed only in terms of pronounced tendency and it was only in the cheek area that the increased blood flow was real at the end of observation period.
In the group of patients of 36-45 y o , VBF increased considerably in the forehead area for six months following the fibroblasts treatment and lasted for twelve months.
Increased VBF in the cheek area in this group took place immediately after fibroblasts administration and as well lasted until the end of observation.
In the group of 46-55 y o , the real increase of VBF in both areas took place immediately after autotransplantation and lasted for twelve months of observation, however in the forehead area the increased indicators during the observation period had only pronounced tendency nature.
It is important to note that VBF stimulation at the level of pronounced tendency in both areas in all groups of patients was registered just after PRP administration.
Such an obvious consistency even despite the large range of measured values, allows one to assume that PRP administration practically always promotes those necessary fundamental changes that provide further development of transplanted autofibroblasts effect.
Therefore, as a result of the neofibrolifting treatment we can observe pronounced stimulation of age-related structural and functional skin indicators such as: epidermal and dermal thickness, acoustic density, hydration of epidermis and TEWL, as well as VBF in forehead and cheek areas.
The obtained results indicate that aging skin goes through serious structural and functional changes that involve both epidermis and dermis.
They have complex and complicated nature and result in the abnormalities of different levels of regulatory mechanisms.
That is why dermal fibroblasts stop receiving enough metabolic microcirculatory support, and are negatively influenced by endothelial dysfunction, which is largely developed by immunologic mechanisms.
Apparently, understanding the immunopathology process of skin aging mechanism and its influence over the development of involutionary changes has become the key issue in the aging skin regeneration approaches.
As a result of neofibrolifting, VBF rate essentially increases under the influence of fibroblast autotransplantation.
PRP administration can strongly influence indicators mainly in the forehead area and only in the group of patients at the age of 56 and older.
However, in all cases, obvious regenerative tendency can be observed. 2.
All structural and functional aging skin indicators can be largely normalized by using neofibrolifting.
PRP administration in some cases could lead to positive results, however, the "complete"?neofibrolifting, that is PRP combined with autofibroblasts led to the promotion of regeneration and normalization of functional and structural indicators.
Most studies which demonstrated a regeneration of aging skin were observed over the whole period following the treatment of twelve months.
Cellulite is a very common syndrome, universally characterized by the accumulation of localized adiposity and increased body weight1,2.
The condition is so prevalent (especially in women) that many studies have sought to determine whether it is a pathologic occurrence or should even be considered a chronic disease precisely because it is so common.
Although it is very difficult to define a condition that is not considered pathologic, if a condition affects quality of life, it becomes pathologic.
Scientific research tells us that although the genesis of so-called cellulitis is linked to causal, hormonal, genetic, circulatory, and lymphatic factors, it also depends on the subjec's lifestyle.
In the last few years, different modalities have become available for the noninvasive reduction of adipose tissue, including radiofrequency and low-energy laser procedures, high intensity focused ultrasonography, and cryolipolysis3.
In March 2018, an American Society of Plastic Surgeons Report showed a rise in body shaping and non-invasive procedures; the statistics also reveal Americans are turning to new and innovative ways to shape their bodies, as minimally invasive cosmetic procedures have increased nearly 200% since 2000.
More people are choosing to shape different parts of their bodies using ultrasound, radio frequency, infrared light, vacuum massage and injectable medication to reduce fat cells.
Non-invasive procedures to eliminate fat and tighten the skin are gaining popularity, with the fastest growing procedure - cellulite treatments - up nearly 20% over last year 2017.
The term cryolipolysis refers to the gradual and noninvasive cooling of adipose tissue to induce a process called lipolysis, or the breakdown of lipids.
Among these technologies, cryolipolysis has been studied most often, both in in vitro animal models and in randomized controlled trials involving humans.
Scientific studies have shown that under conditions of prolonged exposure to temperatures close to freezing, fat cells are more vulnerable to the effects of cold than surrounding tissues are7.
Other scientific articles have demonstrated that exposure to cold induces the apoptosis of fat cells and the production of cytokines and other mediators of inflammation that gradually eliminate the cells involved8.
In the weeks after treatment, macrophages steadily digest the fat cells exposed to cooling, thus reducing the thickness of the treated adipose layer.
The lipids derived from the cells are slowly released and transported by the lymphatic system for processing and elimination, as happens with fats derived from food.
Although inflammatory reactions and the in situ recall of cells responsible for the elimination of particles are triggered by cryolipolysis, the therapy does not alter blood chemistry values.
This finding indicates that the technology is noninvasive compared with other techniques.
The low rate of adverse effects associated with cryolipolysis is, in fact, the main reason doctorsand patients prefer this technology over others.
The first target of a body remodeling treatment is certainly to guarantee the best result, one that is tangible, lasting, and maximally safe in terms of collateral risks10 and ineffective outcomes.
In this study, we combined the destruction of adipose cells with the simultaneous emission of modulated currents (50-Hz pulses) to sculpt the dermal-epidermal profile (cutaneous), while improving the elasticity of the skin and preserving uninvolved tissues.
The use of cold temperatures combined with a vacuum exploits the principles of cryogenesis and cryocyanogenesis, which act on areas with excess fat, facilitating its disposal.
As stated previously, after treatment, an apoptotic process is triggered in the adipocytes, which leads to a natural, physiological death.
The cells of the immune system determine the natural disposal of damaged adipocytes.
Our approach to this retrospective study was to demonstrate the irrefutable and certain validity of cryoliposculpt treatment through the analysis of data and presentation of the results.
A total of 40 patients were involved in our retrospective study, divided into two cohorts.
Inclusion criteria included an age between 20 and 66 years, presence of localized adiposity and so-called Cellulite (PEFS - Edematofibrosclerotic Panniculopathy), in diet treatment by weight loss with personalized diet.
Exclusion criteria: patients in pregnancy / lactation, renal / hepatic insufficiency, previous cardiac pathologies and / or in pharmacological treatment.
For this study, recruitment of patients was ultimately based on choice of anatomic area and thickness of the fat layer and recognition that cryolipolysis is not recommended for everyone (ie, it is indicated for localized adiposity rather than for obese patients and is most suitable for"body sculpture"?.
All subjects were evaluated after undergoing a medical examination, an anthropoplicometric examination, ultrasonography of the panniculus adiposus, and blood tests.
Patients were assigned to one of two groups: patients who followed a balanced low-calorie diet only or patients who followed a balanced low-calorie diet and underwent a body rehabilitation protocol with cryolipolysis combined with bioactive currents in different areas (Cryoliposculpt).
Every 15 days the weight was checked and the measures of waist, hip, abdominal line, buttocks circumference, thigh root and thigh median were evaluated.
Photographs were taken before and after the evaluation period because it has been shown that cryoliposculpt also induces action on fibroblasts over time11.
The evaluations of our retrospective and observational study were conducted at T0 (baseline) and at T1 (ie at 8 weeks from the beginning of the therapeutic program) as inspected by other studies in the literature.
All the raw data collected, of the anthropometric measurements already specified, have been elaborated and produced using statistically relevant graphs.
The cryolipolysis device used in this study is called Cryoliposculpt.
It has unique technical characteristics that guarantee the efficacy and safety of the treatment while preserving cellular structures and their functionality.
The applicator generates aspiration through an active vacuum, which sucks the treated area inside a cavity, where it comes into contact with two cooling elements.
These cooling elements reduce the temperature by 8°C to 10°C A contact sensor constantly monitors the surface temperature of the skin to ensure safety and efficacy throughout the treatment (Figure 1).
The active non-inertial vacuum, which is continuous and customizable (with respect to the mechanical resistance of the tissues), preserves local microcirculation.
In addition to an inertial vacuum, the device allows delivery of a cycle of modulated microcurrents emitted in succession (spikes of current at 50 Hz) in a random way that does not induce adaptation in the cells.
The microcurrents act on the extracellular interstitium, the microcirculation, and the remodeling and orientation of the collagen fibers without inducing a joule heating in contact with the tissues12.
During treatment, the applicators were positioned on the area of the body where the patient hoped to reduce fat.
An inverse thermal shock was applied to the underlying adipose tissue, cooling it to freezing, while avoiding other tissues.
Although the tissues were under a negative pressure of about 30 mm Hg for 50 minutes during standard treatment, the constant mobilization of the tissue ensured there was no vascular damage or atrophy of the microcirculation; thus, no post-treatment massage was necessary13.
The other Cryolipolysis machines on the market cause the formation of the “stick of butter” and, therefore, to avoid problems are matched as a result of the treatment or manual massages14 or shock  waves  to  accelerate the process of ‘restitutio ad integrum’ of the treated tissues.
We enrolled a total of 40 patients with localized fat and cellulite (average age, 43 years).
Of these, 20 patients were treated with a personalized balanced low-calorie diet and cryoliposculpt and 20 patients followed only a personalized balanced diet.
After measuring the previously indicated areas (waist, hips, abdominal line, buttocks, thigh root and thigh median) at T0 and T1, in this retrospective study we observed in the cohort that performed both the diet and the cryoliposculpt, better results compared to the cohort that only performed the diet.
All 40 patients performed a similar personalized diet.
We observed that the cohort patients who did both thedietandthecryoliposculpt, weremuchmoreadherent and precise in following the dietary indications achieving better weight loss results.
From the measurements measured at T0 and T1, in the areas already specified, we observed a quantitative improvement in localized fat deposits and treated cellulite (Figure 3).
In patients treated with cryoliposculpt, we observed a marked improvement in the dermoepidermal tonicity of the treated areas.
We verified with these patients the results, using a verbal questionnaire concerning the result obtained on a scale from 0 to 3 (0 = null result, 1 = discrete, 2 = good, 3 = excellent), which confirmed our observation.
These observations were also confirmed by the photographic documentation carried out at T0 and T1, with frontal, rear, right lateral and left lateral views, using a standardized grid for the position of the feet.
The evaluation of the photos was made by us doctors and by the patients themselves.
Figure 3 shows the average measurements reported between T0 and T1 (ie, 8 weeks from the beginning of the therapeutic path), the period necessary to determine real results of the treatment7.
We managed to reduce the average waist circumference by 3.65 cm with combined cryolipolysis and diet versus a 1.65-cm reduction with diet alone, a greater than twofold difference between the groups.
The most striking and significant result was evident in the side area, with an average reduction of 4.55 cm in the combined cryolipolysis and diet group compared with 0.275 cm with diet alone, an approximately 16- fold difference between the groups.
In the abdominal area, average reductions were approximately 6 cm and 1.7 cm, respectively.
In the buttocks, average reductions were 7.9 cm and 1.4 cm, respectively.
In the thighs, reductions of 3.35 and 2.625 cm were observed in the cryoliposculpt and diet group compared with reductions of 1.05 cm and 1.275 cm in the diet only group (Figure 4 and Figure 5).
We therefore deduce that a therapeutic treatment program that includes cryoliposculpt and a balanced and controlled diet facilitated reductions in circumference in different areas of the body that were approximately 4 times greater than those produced by diet alone.
The results of the present study suggest an important role of cryoliposculpt in the so-called cellulite, as association of cryolipolysis and active microcurrents for the improvement of the tone and texture of the treated tissue.
This new technique was shown to be a good and safe alternative to invasive treatments of adipose tissue16, even if it remains the gold standard.
Overall, the study demonstrated a reduction in different anatomic areas that was approximately four times greater than that obtained with diet alone.
The safety of all results obtained and efficacy of treatments, inaprotocoltestedworldwide, letsconvalited non-invasive alternatives to body remodeling.
No adverse collateral effects were shown.
Damage to and destruction of adipose cells was achieved without adverse effects to nearby tissues and vascular vessels while preserving all cellular functions of the treated tissues that were under mechanical stress and thermal shock inverted.
Nerves and bones were also unaffected, and no changes were observed in the main organs of the body17.
Gradual improvements over time in the thickness of adipose tissues, illustrating the concept of systemic body remodeling, induced physiological - but nontraumatic - reactions in the body.
Overall, we observed greater improvements in areas with a large quantity of adipose tissue; in addition, the biological inflammatory process removed adipocytes over time and reduced the adipose layer.
Thus, adipose tissue freezing offers a potential new option for many people by remodeling the body without any invasive side effects.
Adiposis dolorosa (AD) was first described in 1892 by the physician, philosopher, neurologist, scientist, Francis Xavier Dercum1-4, from a case in the Philadelphia Hospital.
In this original paper, Dercum described 3 cases of the disease with the gross pathological findings of 2 cases, both of which showed abnormal thyroid glands, thus leading the neurologist to believe that the disease was a clinical entity on the basis of a"disthiroydia"?
This article was preceded by a case report by Dercum himself in 18886, as a 51 year-old woman of Irish heritage with severe pain and enlarged subcutaneous adipose tissue on her arms and back.
He wrote: "Evidently the disease is not simple obesity.
If so, how are we to dispose of the nervous elements present?
Equally plain is it that we have not myxoedema to deal with.
All of these cases lack the peculiar physiognomy, the spade-like hands, the infiltrated skin, the peculiar slowing of speech, and the host of other symptoms found in myxoedema.
It would seem then, that we have here to deal with a connective tissue dystrophy, a fatty metamorphosis of various stages of completeness, occurring in separate regions, or at best unevenly distributed and associated with symptoms suggestive of an irregular and fugitive irritation of nerve-trunks - possibly a neuritis...
Inasmuch as fatty swelling and pain are the most prominent features of the disease, I propose for it the name Adiposis Dolorosa"?
Dercum regarded the disease as a clinical entity and named it adiposis dolorosa (AD) because of its most characteristic symptom, painful fat.
In 1899 White7 described an interesting case of AD as follows: "My patient shrieks when she is gripped...my patient can hardly walk,...My patient goes out of her mind temporarily.
Headache is a common symptom.
Herpes, hematemesis, epistaxis, early menopause, slight pigmentation of the skin, atrophy of the muscles of the hand, and reaction of degeneration of them have all been described as occasional symptoms. ...In my case administration of thyroid did no good....She has been in several hospitals but all with no benefit"?
The first clinical classification system for AD (also named Dercum's Disease, DD) was developed in 1900 by Giudiceandrea as follows: I Nodular type.
A form with painful lipomas, most commonly on the arms or the legs or on the back or thorax.
Sometimes the lipomas occur on multiple locations and occasionally the lipomas form a confluent mass.
The nodules are variable in size and painful on palpation.
A form with diffusely painful adipose tissue.
The pain is symmetric.
A form with diffusely painful adipose tissue and with painful nodular masses.
This classification was then revised in 1901 by Roux and Vitaut9 which proposed four cardinal symptoms of DD, used as diagnostic criteria for several years.
What was reported by Burr in 1900, was then confirmed in 1902 by Dercum19 who described two other cases of AD and considered the most interesting histological finding to be interstitial inflammation of the nerves in the adipose tissue of the painful sites.
In the same year, Dercum and MacCarthy20 published a case of AD with complete autopsy findings, the main pathological lesion being an"adenocarcinoma"?of the pituitary body, while the thyroid appeared regular.
Next, several cases were described, many of which showed abnormalities of the pituitary gland.
DD was also defined as a disorder of the "?haemolymph"?system by Dercum and McCarthy themselves20 and "a general disease of the lymphatic system"?by Mills, suggesting that dysfunction in the hemovascular and/or lymphatic systems may contribute to the development of lipomas.
As early as 1910, Stern noted that neuropsychiatric disturbances and asthenia did not accompany every case.
Cushing in 191227 first questioned the rationale of calling the disease a clinical entity, stating that, in his opinion, many cases reported as AD, "are actually examples of disturbed metabolism secondary to disease of the ductless glands"?
In his later articles, Dercum appeared to be of the same opinion.
In sections from DD adipose tissue increased levels of connective tissue were described by Myers in 1923.
In 1924 Purves-Stewart29 classified the disease among the thropho-neuroses, probably due to disturbed activity of the thyroid and the posterior lobe of the pituitary body.
Winkelman and Eckel in 1925 reported that the disease could be considered as a polyglandular disorder with a consequent altered fat metabolism.
In the first decades of the 1900s several further cases of AD were described.
Moreover, Foot et al in 1926 described a case of AD with necropsy: "The body is that of an extraordinarily adipose negress. "The necropsy findings coincide very accurately with those in undoubted cases of AD.
The very definite lesions in practically all the endocrine glands are striking: pituitary sclerosis and hyperplasia, with a tumor; sclerosis and changes in the colloid content of the thyroid; persistent and well preserved thymic rests; adenoma of both suprarenals, with hyperplasia; ovarian sclerosis and atrophy; and definite, though slight, changes in the pancreas.
Besides these, we see changes in the cranial bones, with exostoses and definite cerebral atrophy, with some generalized thickening of the dura."?.
It is justifiable, however, to ascribe the pathologic findings in this case to a profound disturbance in the endocrine system, probably arising as a result of one of the lesions found in the hypophysis cerebri"?
At the same time, Labb? and Boulin40 reported a case of AD with psychic and nervous disorders which they could not attribute to any one thing which could at the same time cause obesity.
These Authors questioned whether the weakness and susceptibility to fatigue and psychiatric manifestations should be classified as cardinal symptoms.
They argued that obesity per se can induce asthenia, and that it is unclear whether mental disturbances should be included as cardinal symptoms.
Gram in 1930 described a high incidence of obesity with tender subcutaneous infiltrations,"?deforming arthritis"?of the knee, and arterial hypertension in women around and after the climacteric age.
Newburgh in 1931 pointed out that painful areas of fat could disappear just by regulating diet.
According to Wilson43 the disease could be considered as"really a syndrome of symptoms in obese people"?and "AD could not be a clinical entity since there have been no findings consistent in all the cases reported in literature"?
He considered more reasonable to assume that the condition is one of either simple obesity or lipomatosis associated with neurosis or neurasthenia, and that the pathological conditions that had been found in these cases that have come to autopsy were incidental.
A report by Boller in 1934 showed that intralesional injections of procaine relieved pain in six cases.
Kling in 1937 reported on 112 cases of juxta- articular AD, their significance and relation to DD and osteoarthritis.
Since then, four cases of juxta-articular DD in association with seropositive rheumatoid arthritis were reported.
Furthermore, Kling came up with the theory that adipose tissue deposits around the knees might interfere by pressure on the joint with the blood supply and resulted in the development of painful osteoarthritis.
In 1952 Steiger et al expressed their doubts on the pluriglandular involvement in DD.
Hovesen in 1953 reported the inflammatory signs in the DD adipose tissue, i e infiltration of leukocytes and plasma cells.
The painful lipomas could appear in any location and, even if several adipose tissue diseases may present similarly, the pain of DD is specifically associated with fatty nodules.
The absence of pain of the adipose masses should indeed distinguish DD from Cushing syndrome, multiple symmetric lipomatosis, familiar multiple lipomatosis and lipedema as well as cutaneous malignant metastases.
In 2005 DD was unrelated with malignancy by Wortham and Tomlinson.
Gastrointestinal symptoms were also found to be associated in some DD patients as well as metabolic complications including obesity, diabetes, hypertension, dyslipidemia, and nonalcoholic fatty liver disease.
Hereditary factors in DD have been reported by some Authors; however, most reported cases of familiar occurrence of the condition was considered to be sporadic.
DD has been suggested to be an expression of familial multiple lipomas, which is an autosomal dominant disease characterized by multiple asymptomatic lipomas63.
This observation was derived by studying the family patterns of 2 siblings with DD; findings suggested that the disease segregates in an autosomal dominant fashion with variable phenotypic expressivity, ranging from totally asymptomatic to extremely painful lipomas.
Mutational analysis excluded the 8344A→G mitochondrial mutation seen in other patients with multiple lipomas.
The A→G transition at position 8344 in the tRNAlys gene of mitochondrial DNA has been described in the syndrome myoclonic epilepsy and ragged-red fibers (MERRF).
The presence of multiple lipomas resembling those of multiple symmetrical lipomatosis had been described in some members of pedigrees with MERRF harboring the 8344 tRNA mutation64.
An inflammatory etiology has been proposed for DD.
However, laboratory markers for inflammation markers, such as erythrocyte sedimentation rate (ESR) and C reactive protein (CRP), were reported by some authors as normal in most patients.
On the other hand, a few studies revealed elevated levels of CRP and ESR, even if some patients were also affected by an autoimmune disease.
Commonly, markers for autoimmune disease, such as autoantibodies, are negative in DD.
A review of the histopathologic findings of DD showed no consistent histologic abnormality in the adipose tissue that might distinguish these tumors from common sporadic lipomas.
The involvement of hormones and neuropeptides as well as a low level chronic inflammation and vascular factors was discussed by Hansson et al in 2011.
In theory, the sudden appearance of the disease together with the incidence of a slight increase in the number of inflammatory cells in the fat pointed toward the disease being, in part, an immune defense reaction.
Herbst et al in 2009 reported that inflammation and excess collagen may contribute to lower relative resting energy expenditure in patients with AD.
The authors observed significantly higher IL-6 as well as mononuclear giant cell levels in AD compared with control adipose tissue.
The study on adipokines indicated that there was no difference in the levels of tumor necrosis factor (TNF)-±, leptin, adiponectin, plasminogen activator inhibitor-1, interleukin (IL)-1, IL-8, IL-10, macrophage inflammatory protein (MIP)-1±, and monocyte chemotactic protein (MCP) compared to controls.
Nonetheless,  significantly  lower  MIP- 1β  expression and a trend toward higher levels of    IL-13 (interleukin-13) were reported.
In addition, lower levels of fractalkine, also known as chemokine (C X3-C motif) ligand 1, were seen.
The authors concluded that the lowered fractalkine levels were logical, since with prolonged release of fractalkine as seen in neuropathic pain, the receptors to which fractalkine binds are upregulated.
This suggests that there is shift from fractalkine release to receptor-bound fractalkine.
The lower levels of fractalkine found in DD could thus suggest that the substance is receptor-bound.
When receptors are occupied by fractalkine, pain and resistance to opioid analgesia are promoted.
Rasmusssen et al discovered an abnormal lymphatic phenotype in three patients with the disease compared with four female controls using near-infrared fluorescence (NIRF) lymphatic imaging.
The lymphatics in the participants with DD were intact and dilated but could not readily clear lymph when compared with lymphatics in four control patients.
Further NIRF imaging revealed masses of fluorescent tissue within the painful nodules, suggesting a lymphovascular etiology.
Kawale et al85 presented a DD patient with painful thickening of the scalp in bilateral parieto- occipital areas and vertex for more than a year.
The pain in the scalp caused headaches and disturbed sleep and daily activities.
CT and MRI revealed diffuse thickening of the scalp tissue, but no evidence for other anomalies.
Tsang et al noted a case of DD that caused weight loss failure after Roux-en-Y gastric bypass.
Eighteen months after the operation the patient was unable to lose weight, despite adherence to behavioral and dietary guidance.
Endoscopy performed 15 months after the operation excluded that any complications had occurred.
Dercum patients often report that their obesity is refractory to diet and exercise intervention.
Nonetheless, this has never been studied.
Hao et al (2018)87 have recently described an interesting case of a 39-year old man with trauma induced DD.
The authors in their report highlighted the rare nature of painful adipose deposits and the diagnostic challenges.
On histopathology, the fat deposition in DD was notable for mature adult fatty tissue and sometimes, a number of blood vessels suggesting an angiolipoma.
According to some reports, ultrasonography and magnetic resonance imaging (MRI) may aid in the diagnosis of DD74,88,89.
In the study by Tins et al88 on 13 patients with DD, lesions of the condition were found to be markedly hyperechoic on ultrasound, superficial in location, and distinct from characteristic lipomas.
Further, when validated on more than 6000 MRIs, they appeared as ill-defined, nodular, "blush-like"?subcutaneous fat on unenhanced MRI with a decreased T1-weighted signal.
No case of DD was without these features in the study, and the authors concluded that these findings, along with multiple subcutaneous fatty lesions, is ?€?very suggestive and possibly pathognomonic?€?for the condition.
In regards to the pain treatment in DD, some improvement was reported after systemic or intralesional treatment with corticosteroids47,80,90,91, whereas others experienced worsening of the pain92.
According to Taniguchi et al93, the alterations of fat metabolism induced by corticosteroid excess could play a role in the development of this syndrome.
An earlier study suggested that a defect in the synthesis of monounsaturated fatty acids may play a role in its development.
Further studies are needed to support this hypothesis and to identify a specific biochemical defect.
Dalziel94 suggested that the autonomous nervous system mediates pain in DD.
Vasoconstrictor response could be normalized by lidocaine infusion that is thought to decrease the local or central sympathetic vasoconstrictor tone.
Nonetheless, any substantial evidence of nervous system dysfunction has never been found in DD and is hence merely a theory.
Gonciarz et al95 reported in 1997 that interferon (INF)- alfa-2b induced long-term relief of pain in 2 patients with AD and chronic hepatitis C The analgesic effect of IFN therapy occurred 3 weeks after treatment for 6 months.
Whether the mechanism of pain relief with IFN is related to its antiviral effect, to the production of endogenous substances, or to the interference of INF with cytokines involved in cutaneous hyperalgesias, i e interleukin 1 and tumor necrosis factor-alpha, remains still undefined.
Two DD case reports have described pain relief with daily intake of mexiletine, an antiarrhythmic.
Traditional analgesics, such as nonsteroidal anti-inflammatory drugs (NSAIDs), had been thought to have a poor effect, with the pain in DD often refractory to analgesics and to non-steroidal anti- inflammatory drugs (NSAIDs)44,46,68,77-79,91-100.
However, in their extensive article published in 2007, Herbst and Asare-Bediako concluded that 89% achieved relief when treated with an NSAID, as did 97% when treated with an opiate58.
In the same year, Singal et al101 reported improvement of a DD patient on infliximab, with and without methotrexate.
In 2008, Desai et al102 reported on successful treatment with a lidocaine (5%) patch, and Lange et al69 on one with pregabalin associated to manual lymphatic drainage.
Metformin was used with success for AD associated pain by Labuzek et al.
It was hypothesized that the drug could favorably alter the cytokine profile, impacting on tumor necrosis factor, interleukin“1, and leptin104,105.
The pilot study of Herbst and Rutledge105 suggested that rapid cycling hypobaric pressure might reduce pain in patients with DD.
Nonpharmacological approaches for DD may be used as adjuncts to pharmacologic treatments.
Some of these include acupuncture, cognitive behavioral therapy, hypnosis, and biofeedback.
Several liposuction treated patients were reported by Hansson et al in 2011.
According to Dalziel the mechanism behind pain relief following liposuction was nerve plexus destruction within the adipose tissue94.
However, Hansson et al retained unlikely that direct nerve destruction alone explained the pain reduction seen following liposuction.
Liposuction is regarded as a supportive treatment for DD.
Any skeletal pain is not affected.
A significant initial reduction of pain and an improved quality of life is seen but these effects decrease over time.
In an extensive review published in 2012 based on literature data and studies concerning 111 DD patients, Hansson et al described the classification, symptoms and diagnosis, as well as, the epidemiology, etiology, genetic counselling, treatment and prognosis of the disease.
They discussed which symptoms were cardinal and which were associated and promoted a “minimal definition” of AD which including the following signs:
Most often generalized overweight or obesity
 Chronically painful adipose tissue (>3 months)
These authors also suggested the following classification system:
Type I: Generalized diffuse form; generalized, widespread painful adipose tissue in the absence of discreet lipomas 
Type II: Generalized nodular form; widespread painful  adipose tissue with concomitant intense pain in and around multiple discreet lipomas.
Type III: Localized nodular form; pain in and  around ? multiple discreet lipomas
Type IV: Juxta-articular form; discreet deposits of excess fat in specific locations, including the medial aspect of the knee, the hips, and, rarely, the upper arm.
Hanssen et al, by retracing many cases described in the literature, analyzed the consistency between the clinical signs reported and the minimum criteria for the diagnosis of DD.
With the exception of a few cases, according to the authors most of the analyzed literature cases, were not fully consistent with the minimal diagnostic criteria.
Since the original description of DD, in addition to the painful nodular fatty deposits (which are often unaffected by weight loss), the clinical spectrum has changed to include to various degrees other components of DD58 i e general obesity, easy fatigability and weakness (asthenia), and a wide variety of unexplained emotional disturbances, such as depression, confusion, and dementia.
This observation is why DD has been proposed to be relabeled as "Dercum syndrome".
DD has been classified by the World Health Organization (WHO) as a distinct entity and listed as a rare disease by the Orphanet115 and by the National Organization for Rare Disorders (NORD)116.
According to the latter "Dercum Disease is a rare disorder in which there are fatty deposits which apply pressure to the nerves, resulting in weakness and pain.
Various areas of the body may swell for no apparent reason.
The swelling may disappear without treatment, leaving hardened tissue or pendulous skin folds"?
Steiner et al70 referred to DD as a frequently overlooked disease and considered its assignment to the neuropathic pain syndromes to be justified.
Traditional management of DD relying on weight reduction and surgical excision of particularly troublesome lesions has been largely unsatisfactory.
Even at the present time, no known drug can change the course of the disease, and available treatments are only symptomatic.
Originally, Dercum5 attributed the disease to an endocrine dysfunction, as he found atrophy of the thyroid gland.
Similarly, Waldorp proposed that the disease is caused by hypophyseal dysfunction.
However, endocrine involvement was ruled out as early as in 1952.
In addition, more actual approaches have not revealed any endocrine abnormalities.
So, an endocrine dysfunction as the etiology of DD has little support in the modern literature.
Moreover, there are no uniform findings pointing to an inflammatory etiology in DD.
In conclusion, the findings on DD pathophysiology are still inconclusive and the clinical significance of some reports is unclear.
Based on literature data and personal experience, the perception is that this complex condition, which often takes on the contours of a real syndrome, is much more frequent than one might think.
Specific research aimed at defining its pathophysiological aspects could undoubtedly allow better clinical results and therefore a strong effort by the scientific community is warranted to make the diagnosis more accurate and develop targeted therapies against such complex pathological condition which, despite being devastating for patients, is not always recognized and, too often, either underestimated or even neglected.
?Basal Cell Carcinoma (BCC) is the most common skin cancer worldwide, with an incidence of 146-422 cases/ year/100,000 persons in the US, depending on latitude1.
It is more frequent in men and in elderly, but its incidence amongst people younger than 40 is increasing, particularly in women1.
The main cause is the exposure to UV rays of the sun (intermittent intense, rather than cumulative)1,2,3, which is why it is more common in of therapy, a monthly follow-up was performed for the first six months; then quarterly, in the next six months; subsequent monitoring was done every six months. equatorial regions, in the most sun-exposed areas of skin and in fair skin types (tendency to burn, rather than tan)1,3.
The sites most affected are the photo- exposed areas and in 90% of cases it is localized to the head, preferring cheeks, nasolabial folds, forehead and eyelids1.
The periocular region is interested in 20% of cases.
Regarding the clinical features of BCC, six overall subtypes have been identified, of which three are more frequent and three are quite rare, but more aggressive (high risk).
Nodular Basal Cell Carcinoma is the most common subtype (50-79%) of all basal cell carcinomas1 (Table 1).
Basal Cell Carcinoma may be treated with different therapeutic procedures, even with high rates of healing1, but the surgical removal of the tumor remains the preferred method, due to its greater therapeutic efficacy1,5.
Despite surgical treatment being the best method, there are cases in which surgery is not feasible and alternative methods must be used.
Over time, several alternative topical therapies have been utilized, some of which are now obsolete, others are rarely used, and others, more recent, appear promising.
Imiquimod (IMQ), an immune-modulator recently introduced, was effective in the medical treatment of certain skin diseases of viral and neoplastic origin7.
Currently, many studies are underway in order to evaluate its effectiveness in several skin diseases and neoplasms, while the preliminary results are already very promising1,7,8.
Objectives of this study are: first, to evaluate Imiquimod effectiveness in the treatment of subeyelid Nodular Basal Cell Carcinoma; second, to evaluate its ease and safety in delicate areas of the face; third, to evaluate the aesthetic and functional results, in an area of the face where surgery is not always indicated, because of possible permanent sequelae.
A 95 year old woman, with fair skin, light brown hair and blue eyes (Fitzpatrick skin-type 3), for two years had a Nodular Basal Cell Carcinoma in the left subeyelid region.
From six months the lesion ulcerated (rodent ulcer), with a crater-like appearance and hardened edges (Figure 1).
The patient was treated with topical application of 5% Imiquimod cream with the following protocol: 3 applications a week (Mon- Wed-Fri) for a duration of 7 weeks.
The cream was applied in the morning, by covering with a thin layer across the neoplasm, including an annular area of healthy skin around the lesion, for a width of 2 mm.
The cream was left to act for 8 hours, and then it was washed with warm water and mild detergent.
Care was taken to anamnestic and clinical evaluation, also with photographic documentation, at the start of each week,before applying the cream.
After finishing the course.
In the first two weeks, the treatment produced a progressive erythema, which affected the tumor, the upper portion of the cheek and the lower eyelid.
In the next week, it established a growing edema, with a mild serous oozing and some crusts.
Meanwhile, in the upper cheek, growing itching appeared, and sometimes burning with desquamation.
From the fourth week a progressive regression of the tumor was observed, while intense erythema persisted associated with edema.
At the end of treatment the tumor had disappeared; the residual edema resolved within two weeks, while the erythema showed a progressive reduction and disappeared altogether during the third month (Figure 2).
In the following controls we never observed redness or swelling, discolored or atrophic outcomes, and we did not find any other sign of aesthetic damage.
At 36 months, there was no evidence of tumor recurrence and no functional impairment of the involved eyelid; cheeks and eyelids were perfectly symmetrical, with excellent aesthetic results and high satisfaction of the patient (Figure 3).
Surgical removal is the best treatment method for all Basal Cell Carcinoma, due to its greater therapeutic efficacy.
Currently, three main surgical techniques may be used, all of which are effective in high percentage of cases.
Electrodesiccation and Curettage is effective in 95.1% of cases, but it may exit in discolored scar1; Standard Surgical Excision is effective in 95.2% of cases, but it may give unacceptable aesthetic and functional outcomes1,3,10.
Mohs Micrographic Surgery, a tissue sparing method, gives the best results, with efficacy in 98.6% of cases1,3,4; however, this technique is not always feasible, for the frequent lack of specific infrastructures3.
Despite surgical removal remaining the best therapeutic method for Basal Cell Carcinoma, there are cases in which surgery is not possible2,4.
There is no indication for surgery in cases of large or multiple lesions, in difficult anatomical sites, and in high surgical risk patients (elderly, comorbidity, anticoagulants); in other cases there is a consistent risk of unacceptable aesthetic and functional outcome or the patient refuses surgery4,5,8.
Moreover, as in the case we observed, the tumor also may involve a part of the lower eyelid, exposing to the risk of functional damage in case of surgical removal, with possible ectropion, as well as unpredictable cosmetic damage.
When surgery is not feasible, there are several alternative topical therapies(Table 2), of which Imiquimod 5% topic cream, is the most recent and the most promising, because it was effective in the medical treatment of several skin diseases of viral and neoplastic origin.
At present, Imiquimod is approved by the FDA only for the treatment of Anogenital Warts, Actinic Keratosis and Superficial-BCC.
Consequently, the use of IMQ in the Nodular subtype of BCC, currently, must be considered off label.
However, as some recent studies show, it may also be effective in the Nodular-BCC4,5,9, as in other skin tumors.
Imiquimod acts as a potent immune response modifier: it has, firstly, a direct action, with induction of apoptosis in tumor cell lines, by up-regulating pro-apoptotic proteins8; secondly, an indirect action, by release of modulatory cytokines (ILs, IFNa-g which increase the cytotoxic T cells and natural Killer-cells7,8,11.
The main advantages are: high effectiveness with low costs, easy home use, and useful alternative for subjects who cannot be treated surgically7,10.
The local side effects of IMQ are, generally, modest and tolerable, consisting of: erythema, edema, itching, burning, erosion, scabbing, crusting1,2,3.
The systemic adverse effects are very rare and may consist of: flu-like symptoms, nausea, headache, myalgia, fatigue and fever5.
For the use of Imiquimod, a standardized protocol does not exist3.
The most used application is provided 5 times a week, for 6 weeks3,5,8,9; but there are other protocols with application 2-7 times a week, for 4-12 weeks2,4,5,10,11,12.
The effectiveness of the treatment varies from 78,4 to 93,4%, in relation to different variables, and in some works success rates up to 100% have been reported4,5,10,11.
The method we used, with topical application of 5% Imiquimod cream, 3 times a week for 7 weeks, was fully effective, leading to the complete disappearance of the tumor, with no evidence of recurrence at 36 months (Figure 3).
Also from a functional point of view, there were good results, as no static or dynamic alteration of the eyelid function was observed, which can happen with surgical treatment4.
Technically, the method was easy to play; the procedure was done at home, without the need for hospitalization.
The treatment was well tolerated, with erythema, edema, crusting, and only a mild itching or, sometimes, a burning sensation.
In addition, the method did not require anesthesia, removal of tissue, suture, reconstruction or other surgical traumatism.
With regard to aesthetic aspects, there was no scar, no discoloration, no atrophy or fibrosis, and no other type of cosmetic damage.
Cheeks and eyelids appeared perfectly symmetrical, without any anatomical alteration to the lower left eyelid.
The hypo-pigmented area, under the medial canthus, appearing in figure 3, was not caused by this treatment, because it was pre- existing, as seen in figure 1, and was due to photo- chrono-aging, like other discolorations of the face.
Effectiveness: Imiquimod shows full effectiveness also in the Nodular-BCC, with complete disappearance of the tumor and no recurrence at 36 months.
Easy and safe: home treatment may be done, without need for hospitalization; IMQ use is easy and safe in difficult sites and in certain patients, without need for anesthesia, tissue removal, sutures or reconstruction.
Aesthetic results: IMQ gives excellent aesthetic results, as well as functional, without scar, discoloration or atrophy, and without functional damage.
This recent therapeutic method appears fully effective and easily achievable.
The procedure could become the first choice for this particular site and could also find broad indication in other delicate areas of the face.
Age-related facial changes recognize several etiologic factors involving changes of the skin, attrition of the facial septa, and craniofacial resorption, with the significant contribution of external factors, such as body mass index, hormones, alcohol consumption, cigarette smoking, and unprotected sun exposure.
In developed countries, improving the quality of aging has become a major target, also involving aesthetic dermatology.
Over the last decade, minimally invasive procedures have expanded exponentially due to the increasing availability of suitable products and the growing preference of patients for non-surgical methods, with consequent increase in the development of injectable materials for soft-tissue augmentation.
Nasolabial folds may become very marked with aging and can be corrected through dermal filler injections which fill the skin from the inside, restoring the lost facial volume and reducing the depth of these creases.
Multiple injectable materials are available, such as various hyaluronic acid products, calcium hydroxyapatite, and a few others.
For deep facial wrinkles, dermal fillers, compared to botulinum toxin, have the unique property of augmenting tissue volume.
Hyaluronic acid (HA) is commonly used in aesthetic medicine as a filling material (filler), to reduce both superficial and medium-deep wrinkles, or as a bio-revitalizing product to slow down the skin aging process; in fact, thanks to its natural hydrating and stimulating properties, HA reduces the signs of aging while improving skin turgor and elasticity.
HA dermal fillers vary widely in their physical and chemical characteristics and many variables contribute to their overall performance; however, they overall fulfill the request for a safe, quick, and effective procedure, and have a lower risk of allergic reactions compared to previously used collagen fillers.
The main disadvantage to overcome remains the procedure-associated pain and a limited duration of action.
In order to reduce the discomfort/pain sensation perceived by the subject during and after the injection procedure, lidocaine is often mixed with injectable dermal fillers.
Several experiences have been reported on the addition of lidocaine to HA products within the clinical settings with positive outcomes in terms of pain reduction.
However, this technique still raises some concerns relating to the possible impairment of quality and efficacy, as well as the volume and flow characteristics of the injections.
Products containing pre-incorporated, preservative- free lidocaine, which generally preserve volume, concentration, consistency and flow characteristics may seem safer, and associated with less procedural pain.
There are only a few studies directly comparing pure HA fillers and fillers containing HA and lidocaine, whose results reveal in general no significant differences in the safety, efficacy, and longevity of the two treatments, with less procedural pain associated with lidocaine addition.
However, the possibility of a true lidocaine allergy should always be considered since cases, though rare, have been reported, including type I –“ immediate hypersensitivity reactions and severe angioedema, or type IV –“ delayed reactions.
Aliaxin EV Essential Volume (IBSA Farmaceutici Italia SrL) is a resorbable medical device, consisting of a physiological, non-pyrogenic sterile gel that contains cross-linked HA (CLHA) of non-animal origin, produced by bacterial fermentation, used as a filler to correct deep facial cutaneous sagging and to increase facial volume.
It is known that chemico-physical and biological characterization of HA-based dermal fillers is of key importance to differentiate between the numerous available products and to optimize their use.
The different Aliaxin formulations have been tested for their content in soluble HA, water uptake capacity, rheological behavior, stability to enzymatic degradation, and in vitro capacity to stimulate the production of extracellular matrix components, and the different formulations were found to be equivalent to each other regarding insoluble hydrogel concentration.
The results obtained support the product claims of different clinical indications, and its classification regarding hydro-, lift- action.
Furthermore, the biological outcomes also support the efficacy of the product in restoring skin structure.
In the clinical setting, Aliaxin? showed that it can be safely and effectively used, either alone or in combination with a non-cross-linked HA complexed with vitamins, antioxidants, amino acids and minerals, to improve nasolabial fold hydration, trans-epidermal water loss and wrinkle aesthetic appearance.
This study was aimed at comparing, in women aged 40-65 years, Aliaxin EV without and with the addition of lidocaine 0.3% (extemporaneous mixture) in terms of filler on nasolabial folds and of discomfort/pain sensation perceived by the subject during and after the injection procedure.
The investigational product, Aliaxin EV, was compared within subjects with Aliaxin EV with lidocaine 0.3% (split-face method); the two injective products were assigned to the right or left face side of each subject according to a previously defined randomization list.
This study was performed in agreement with the Declaration of Helsinki.
Before the screening, all subjects gave written informed consent.
A final version of the study protocol and appendices were submitted to an Independent Ethic Committee (I E C at DERMING S r l , Clinical Research and Bioengineering Institute.
The President of I.E.C. was Prof. Demetrio Neri.
On 10th March 2017, the clinical trial obtained the I E C approval with protocol number E0817.
The study was registered on ClinicalTrials.gov public registry with the ID NCT03273556.
This was a comparative, randomized, single blind (subjects did not know on which hemiface lidocaine was used), single-center investigator-initiated trial of 4-week duration.
The investigator was an experienced dermatologist.
The study was conducted on 27 female volunteers, aged 40-65 years, who met the inclusion/exclusion criteria detailed below.
Inclusion criteria were: a Wrinkle Severity Rating Scale (WSRS) score between 2-4; asking for nasolabial folds correction; having received lidocaine, at least once before, as local anesthetic; agreeing to maintain their normal habits regarding food, physical activity, make- up use, facial cosmetic and cleansing products, and to arrive at each study visit without make-up; accepting to avoid facial exposure to strong UV irradiation (UV sessions, or sun baths) during the study.
Main exclusion criteria were: pregnant or lactating women, smokers, alcohol or drug abusers, women having received skin treatments for aesthetic correction, such as biomaterials implants, face lifting, botox injections, laser, chemical peeling, in the 6 months prior to the study start, or any permanent filler in the past, women with dermatitis, or other clinically relevant skin conditions or any skin lesions in the treated area, women affected with systemic diseases.
Use of anticoagulants and antiplatelet drugs, anti-histamines, topical and systemic corticosteroids, narcotics, antidepressants, immunosuppressive drugs (with the exception of contraceptive or hormonal treatments starting more than 1 year ago), or of any drugs able to influence the test results in the investigator's opinion was not permitted during the study period. 3 hours before the visit the volunteers were asked not to drink coffee or alcohol, and no cosmetic product to be applied on the skin test areas in the 2 hours preceding each visit.
Study procedures included 2 visits: one baseline visit (T0), when clinical and instrumental evaluations were performed, followed by the injection procedure and thereafter by skin discomfort/pain subjects' self-assessment (immediately and 2 hours after the procedure); and one final visit after 4 weeks (T4) for clinical and instrumental evaluations.
The investigational aesthetic procedure consisted in injecting a maximum of 0.5 mL of Aliaxin EV per hemiface with and without lidocaine 0.3%.
Two injection techniques were used in combination: (I a single-bolus injection with a 27G x 19 mm needle in the periosteal level of the nasal base; (II) linear retrograde injection with a 27G x 13 mm needle in the deep dermis; the needle was inserted along the length of the skin depression and the product was slowly deposited in the fold during needle extraction.
Efficacy evaluation included qualitative (clinical) and quantitative (instrumental) assessments performed at both study visits.
Filling activity on the nasolabial folds was rated by the investigator by means of the WSRS (Table 1)31, while the aesthetic performance was assessed by the Global Aesthetic Improvement Scale (GAIS, Table 2).
At the end of the study (T4), each volunteer was asked to express a self-assessment on the perceived efficacy of the study treatments on the nasolabial folds in terms of filler results (very marked; marked; medium; slight; absent).
Non-invasive instrumental evaluations included 3D nasolabial fold pictures taken with a Primos compact portable device (GFMesstechnik).
The Primos software is able to elaborate 3D representations of skin wrinkles as well as measure the main skin profilometric parameters in vivo or on skin replicas, according to the law DIN EN ISO 4228; moreover, the software directly compares the different images obtained at the study visits before and after treatment.
As a measuring method, Primos compact uses a digital stripe projection based on micro-mirrors which allows for fast and highly precise measuring data acquisition (the speed <70 ms for measuring data admission provides perfect results).
An assortment of different measuring fields, realized by means of different precise recording optics, was used to ensure a wide spectrum of measuring possibilities with ranges up to micrometers.
Following the injection procedure, the investigator assessed product tolerability noting any immediate local events/reactions (swelling, pain, erythema, bruising) and asking for any other adverse event, local or systemic, occurred during the study.
Sensations of stinging, itching, tightening, burning, discomfort and pain were scored by each study subject separately for each hemiface immediately and 2 hours after the injection procedure on a 10 unit visual analogue scale (VAS), where 0 means no sensation and 10 very strong sensation.
The VAS scores were expressed separately for each of the two treated sides.
The volunteers were also asked to express an overall tolerability self-assessment at the end of the study, as bad, poor, good or excellent.
Efficacy variables expressed in absolute values were compared versus baseline and between the two study products.
The data processing was performed by descriptive and inferential analysis: clinical data, VAS and GAIS score by non-parametric test (Wilcoxon test); instrumental data by non- parametric test (Wilcoxon test), when the normality hypothesis was rejected by the Shapiro-Wilk normality test (threshold at 5%), or by parametric test (paired t test), when the normality hypothesis was confirmed.
Twenty-seven women were included in the study: mean age was 55 years, mean body weight 62.5 kg and mean BMI 27.7.
One woman dropped out from the study without a final assessment visit for personal reasons.
The statistical analysis was thus performed on the 26 cases who completed the study as per protocol.
Both study products induced a very significant reduction in wrinkles severity (mean reduction 33.3%, Wilcoxon test p<0.001 T4 vs T0 for both products) with a decrease ?1 grade of the WSRS photographic reference scale observed in 100% of the included subjects.
 The GAIS scores in terms of aesthetic performance, in all 52 hemifaces of the 26 subjects were rated as improved compared to baseline; in particular the investigator rated 8 hemifaces (15% of the subjects) as “improved” (5 with Aliaxin EV alone and 3 with Aliaxin EV plus lidocaine);  26  (50%)  as  “much  improved”  (12  and 14 respectively); 18 (35%) (9 and 9 respectively) as “very much improved”.
There was no significant difference between the two products. The final (T4) GAIS mean value was 1.8 for both treatments. The results of the self-assessment questionnaire on  treatment  efficacy at T4 are summarized in Table 3.
The results of the self-assessment questionnaire on treatment efficacy at T4 are summarized in Table 3.
All but one volunteer expressed a positive judgment on the global aesthetic performance of the products: 3 reported a slight improvement, 2 a medium improvement and the remaining 21 judged their improvement from marked to very marked; none perceived a difference in overall aesthetic performance between the two treatments.
Skin profilometry data showed statistically significant reductions versus baseline in average roughness of the analyzed profile (Ra: -36% with Aliaxin EV alone and -32% with the lidocaine combination; Wilcoxon test p 0.001 T4 vs T0 for both products), in wrinkles total height (Rt: -83% and -69% respectively; Wilcoxon test p 0.001 T4 vs T0 for both products), and in wrinkle maximum depth (Figure 1; Rv: -41% and -31% respectively; Wilcoxon/ paired t test p 0.001 T4 vs T0 for both products).
No statistically significant difference between the 2 treatments was detectable (Paired t test T4 Aliaxin vs T4 Aliaxin + lidocaine p 0.05); however, a trend in favor of Aliaxin alone was observed for all parameters.
Primos photographic documentation of both hemifaces of subject 21, each treated with a different study product is reported in Figure 2.
Slight bruising occurred in 10 volunteers at one injection site, which was attributed to the injection procedure and had disappeared at T4; the women reported that the bruises had actually disappeared within 5-10 days.
At the volunteers' self-assessment of pain/discomfort sensations, the differences between products were statistically significant immediately after the injection procedure (T0A), losing statistical significance at the 2-hour assessment (T02h).
Overall only 3 subjects (12%) scored a VAS magnitude >5.
The investigator judged both products overall tolerability good or excellent in 100% of subjects, as did all volunteers.
The injection procedure with addition of lidocaine was judged more comfortable by 85% of women, while 15% preferred the procedure with Aliaxin EV alone.
An optimal dermal filler should be safe, effective, durable and fully satisfy the patient’s aesthetic demand.
HA-based injectable products are the most extensively used fillers and may vary greatly in terms of HA concentration, particle size, cross-linking agent used, cross-linking degree, percentage of cross-linked and free unmodified HA.
A reliable tool to assess the efficacy of HA-based compounds is the analysis of elastic fibers and collagen in the skin.
Indeed, injection of cross-linked HA (CLHA) into dermal-equivalent cultures was shown to induce elongation of fibroblasts and type I collagen synthesis due to up regulation of the transforming growth factor beta (TGF-β) pathway.
HA injections also increase the synthesis of neo-collagen I and pro-collagen I and III mRNA compared to baseline.
Aliaxin is a recently developed line of HA-based gel medical devices for intradermal treatment, available in prefilled syringes for local injection.
All Aliaxin products contain a highly purified cross-linked HA sodium salt, with a mix of molecular weight ranging from 500 to 2000 kDa, and the addition of sodium phosphate and water for injectable preparations.
Aliaxin products have a good safety and quality profile, as documented by in vitro and in vivo tests, and clinical studies.
Aliaxin EV - Essential Volume is a well-characterized resorbable medical device that, compared to other dermal fillers, showed a particularly high hydro-action with a prolonged aesthetic effect, as well as an easier deliverability due to lower rigidity and viscosit.
Its ability to promote restoration of skin structures was demonstrated in vitro.
The addition of lidocaine to HA products is rather common in clinical practice, with the aim of reducing pain and discomfort associated with the injection procedure, although questions about quality, efficacy, injection characteristics, and safety of the combination have not been fully answered.
This is why this study was aimed at comparing Aliaxin EV with and without lidocaine addition in terms of efficacy, safety and tolerability.
In this randomized, split-face study, Aliaxin EV with and without lidocaine showed to be effective in reducing wrinkle depth as measured by the investigator with specific grading scales, by skin profilometry and as judged by volunteers' self-assessment.
The two injection techniques used were chosen because the first allows a better performance of the injected product, contributing to the volumetric correction of the compartment without useless dispersion in other anatomical areas, and the second, because it allows a better filling effect of nasolabial folds.
The addition of lidocaine to Aliaxin EV reduced pain and stinging and made the injection more comfortable in the judgment of the majority of study subjects, though it has to be remarked that the intensity of such symptoms was rated by very few women higher than half the VAS scale for both products.
The overall tolerability of both treatments was judged from good to excellent by the investigator and by all the study subjects.
This seems to indicate that the injection procedure was not considered really painful or distressing by the volunteers, even without the addition of lidocaine.
Indeed, only 12% of patients scored a significant pain sensation.
In our opinion, this suggests refraining from a systematic use of lidocaine, but rather consulting with the patient about his/her pain threshold and evaluate on an individual basis the best anesthetic procedure, also taking into consideration the opportunity of performing a local anesthesia before the procedure instead of using a lidocaine-containing product or an extemporaneous mixture.
Though the efficacy of the two products, in terms of nasolabial fold reduction, showed no statistical difference, we think it is worth commenting that the instrumental measurements demonstrated a slight but consistent trend toward a superiority of Aliaxin EV alone for all parameters.
Once again, these results do not convince us that the addition of lidocaine to HA dermal fillers impairs their effectiveness.
Previous papers published in the literature and a recent metanalysis by Wang generally report that the addition of lidocaine to HA dermal fillers is safe and inert, and associated with procedural pain reduction.
Our results are not in contrast, however, Aliaxin alone showed a somewhat greater efficacy and seemed to not induce clinically significant pain.
Moreover, most of the previous studies compared different products, and only relatively few studies, including a limited number of subjects, directly compared the same product with and without lidocaine.
We feel that the availability of a minimally invasive procedure not conceived as overly painful by women could save the use of an anesthetic with its potential to increase the risk for adverse events, and to impact on product administration and aesthetic outcome.
In conclusion, Aliaxin EV proved to be effective in reducing nasolabial folds, giving a satisfying aesthetic performance and resulting very well tolerated, both with and without the addition of lidocaine.
Injection correction of tear trough and palpebromalar groove with hyaluronic acid-based fillers has become part of a practice of aesthetic medicine doctor.
This procedure can be carried out by a needle or blunt cannula1.The lower eyelid zone is the most challenging area to treat with hyaluronic acid\1.
Kane described the correction of the tear trough as a difficult problem with multiple causes.
He showed the technique of correction of the tear trough and lower lid using injection of the filler by a needle.
Lambros stressed that when correcting tear trough, it is important to evaluate the following factors: skin quality, definition of the hollow, the orbital fat, and the color of the overlying skin.
Lambros described the microbolus supraperiostal injection technique of the hyaluronic acid by a needle.
He noted that the best patients for the treatment are those with young, thick skin and a definite hollow.
In some cases, after correcting tear trough with a good immediate aesthetic result, adverse effects may appear after a while.
Such delayed adverse effects include: accumulation of the filler below the tear trough (Figure 1), enlargement in the hernia of lower eyelids, and periorbital puffiness.
To prevent these adverse effects, it is very important to consider the anatomical features of each patient.
Hirmand proposed a classification system of the tear trough deformity based on clinical evaluation.
Sadick et al. developed the tear trough rating scale by objectively and subjectively evaluating the clinical appearance of the tear trough with regard to depth of the trough, hyperpigmentation, volume of prolapsed fat, and skin rhytidosis.
Zhivokova and Krasnoselskikh described the principles of the selection of patients for correction of the periorbital zone by fillers depending on eye prominence, presence and prominence of orbital fat, and location and strength of the orbital ligament.
Classification of prominent orbital fat of the lower eyelids is difficult, since the aging of the lower eyelid zone is associated not so much with the increase in fat, but also with the aging of the entire middle third of the face as a whole.
However, to facilitate the selection of patients for correction of the periorbital zone by fillers, I apply my original classification of the lower eyelid zone11.
I identified 3 clinical types of the lower eyelid zone, depending on the presence and prominence of orbital fat: Type I no prominent orbital fat (Figure 2.1) Type II: prominent orbital fat in the medial part of the orbit and visualization of the bony orbital margin in the lateral part (Figure 2.2) Type III: prominent orbital fat visible in both medial and lateral parts of the orbit (Figure 2.3).
For patients with the second and especially the third clinical type of the lower eyelid area, I recommend surgical treatment of hernia of the lower eyelid.
However, in the presence of contraindications to the operation, injection camouflage of hernias is possible.
It should be borne in mind that the target area where the filler will be injected depends on the anatomical features of the periorbital zone, that is, the clinical type of the lower eyelid zone.
To reduce the risk of adverse effects, the filler should be injected precisely into the target space, regardless of the instrument used (needle or cannula).
In all cases of augmentation of the periorbital zone by the filler, submuscular (supraperiosteal) level of product placement is used.
Before the procedure, the marking of dangerous areas is necessary to prevent their traumatization: the zone of angular vessels (the inner corner of the eye and the centimeter lateral to it) and a zone of an infraorbital neurovascular fascicle13.
I recommend to avoid injections into the marked dangerous areas.
From February 2016 to January 2018 118 female patients were treated, aged between 28 and 56 years old.
Patients were divided into 3 groups, depending on the presence and prominence of orbital fat.
Treatment was carried out on: 47 type I (37 by needle 10 by blunt cannula), 66 type II (53 by needle 13 by blunt cannula), 5 type III (4 by needle, 1 by blunt cannula).
In all cases, a monophasic hyaluronic acid-based filler of average reticulation was used for treatment.
Correction of the lower eyelid zone was carried out according to the principles outlined below.
The result was determined by the patient through a visual assessment of photographs based on the criteria for the presence of tear trough and palpebromalar groove.
Patients noted good satisfaction with the results of the correction of the lower eyelid zone.
Most of the patients in this study were able to resume their normal activities after 1 to 2 days with makeup.
In all patients, the aesthetic result lasted for more than one year.
Only six patients were dissatisfied with their results because of hypercorrection.
For the treatment of this effect, hyaluronidase was used (Table 1).
The first clinical type is associated with a loss of volume throughout the lower eyelid area.
More often these are young patients complaining of dark circles under the eyes.
When correcting this type, the filler should be injected under the orbicularis oculi muscle into the space bounded from the top (cranial) by septum and from below (caudally) by the orbital ligament (ORL)14 (Figure 3.1).
Injection of the filler below the ORL ligament can lead to the accumulation of the filler below the tear trough in the deferred perspective.
The marking points between which the injections will be made are: the projection of the bony orbital margin (upper border) and the eyelid-cheek junction (lower border).
When correcting with a needle, the filler is injected bolus or microbolus between the upper and lower boundaries under the orbicularis oculi muscle with a preliminary marking of the dangerous zones.
When correcting with cannula, the filler is also injected under the orbicularis oculi muscle between the boundaries described above.
The preferred entry point for the cannula at the level of lateral canthus is between the projection of the bony orbital margin and the eyelid- cheek junction (lateral lid entry point).
When using the cheek entry point in some cases, the cannula does not pass through the ORL, but pushes it away.
As a result, augmentation tear trough using the cannula from the cheek entry point gives a good immediate aesthetic effect, but delayed may result in the accumulation of the filler below the tear trough.
This type is manifested by hernia of the lower eyelid, visible in the medial part of the orbit.
And simultaneous visualization of the bony orbital margin of the lateral part.
Thus, in the medial part of the lower eyelid the excess volume in the form of a hernia, and in the lateral, loss of volume.
The injection of a filler into a prominent orbital fat is dangerous because of the risk of infection complications and adverse effects in the form of long edema of the periorbital zone and increase in the hernia volume; it should be avoided.
When correcting this type, the filler should be injected submuscular (Figure 3.2), but in the medial part below (caudal) of the tear trough to prevent the filler from getting into the hernia.
And in the lateral part - between the projections of the bony orbital margin and the eyelid- cheek junction.
When using the cannula, the medial part is augmented from the cheek entry point, and the lateral one from the lateral lid entry point.
In this type hernias are visible in the medial and lateral part of the orbit.
As I mentioned above, the injection of the filler into the prominent orbital fat is unacceptable.
Therefore, when correcting this type, the filler is injected only below (caudal) of the tear trough and palpebromal groove9,11 (Figure 3.3).
For the injection, you can use both the needle and the cannula: cheek entry point (Figure 4.3).
An incomplete correction is recommended to avoid a "pillow face" (Figure 5.3).
Observance of the anatomical features of each patient, including the existence and expression of prominent orbital fat, is necessary to obtain the optimal result of injection correction of the periorbital zone.
Correction of the lower eyelid zone, taking into account the principles outlined in this article, gives a good aesthetic result with a low risk of adverse effects.
If the level and target zone of augmentation are observed, post-procedure puffiness is minimal or absent, and a good aesthetic result lasts more than a year.
Androgenetic alopecia is a common form of scalp hair loss that affects up to 50% of males between 18 and 40 years old.
It is a disorder that also affects the female sex and is characterized by a polymorphic clinical presentation.
Even though this condition is a paraphysiological condition, the loss of hair leads to stressful events for the patients with considerable psychosocial consequences.
Genetic and hormone factors play a major role in the pathogenesis of the disease but, given the variety of phenotypes, other etiological factors could divide the disorder into different subgroups.
Hair follicle is a skin appendage and exhibits hair cycle that is divided into three phases: anagen, catagen and telogen4.
The relative duration of these phases varies with a lot of physiologic and pathologic factors as well as hormone factors, body site, age and nutritional status.
Many biochemical substances have been investigated pathologically for studying hair growth and its cycle8,9, but the regulatory mechanism of the hair cycle has not yet been fully understood.
Two leukocyte populations reside in the epidermal compartment of normal skin in adult mice: Langerhans cells, which are a skin-specific member of the dendritic cell family of antigen- presenting cells10, and dendritic epidermal T cells, which are tissue resident γδ T cells11.
The dermal compartment also contains several resident leukocyte populations, including dermal dendritic cells, macrophages, and mast cells.
Keratinocytes are the major cellular component forming the interfollicular epidermis and the intrafollicular epidermis known as the outer root sheath.
Interestingly, Langerhans cells and dendritic epidermal T cells are frequently identified in the outer root sheath of hair follicles, and macrophages and mast cells often show perifollicular distributions in the dermal extracellular matrix.
Mast cells release a number of important signalling molecules, among which histamine has particularly potent pro-inflammatory activities.
Many authors have observed that both the mast cell population and the total histamine content reveal quantitative fluctuations during each cycle.
Botchkarev et al. for example, have reported pathologically that the number and granulation status of the mast cells change dramatically during the murine hair cycle and the mast cells product antagonists and the mast cell secretagogues significantly alter hair cycling in mice.
Moretti et al. demonstrated the fluctuations of the skin mast cell population and histamine content which occur during the hair cycles of rats.
The mast cell population revealed a rise during each telogen, to reach a peak within the first days of anagen, followed by a decline.
Histamine fluctuations were also fairly regular, hence the amine content steadily increased over, approximately, the first 7-11 days of each anagen, continuously decreased during telogen and then increased again.
There was a steep rise of histamine in the first month, already observed by Hardwick25 and Parratt26 and referred by the last author to a temporary influence on the skin of unknown factors, perhaps extracutaneous and "related to the stress involved in weaning"?
During subsequent cycles, the pattern of each fluctuation was remarkably similar and the mast cells and the amine's quantitative changes, instead, probably depended on the hair cycle.
With the aging of the animals, both mast cell and histamine fluctuations manifested a progressive tendency to level out and disappear.
On this ground the continuous decline of the amine while the rats are growing older could indicate a progressive hair's mantle involution.
A close relationship between hair's mantle involution and aging has been noted, in fact, by some authors.
There is no doubt, on the other hand, that the flattening out of all observed fluctuations is a function of aging.
This is in agreement with the fall of skin histamine typical of that process in many species.
Thus, the follicle would produce the histamine which would cause the involution of the follicle itself.
Kumamoto et al. stated that hair follicles might also serve as local reservoirs of precursors for one or more of the skin resident leukocyte populations.
To test this concept, they isolated vibrissal follicles from adult mice and cultured them in the presence of stem cell factor, interleukin 3, interleukin 7, granulocyte-macrophage colony-stimulating factor and Flt3 ligand, which are known to promote the growth and differentiation of Langerhans cells, dendritic epidermal T cells, macrophages, and mast cells.
They reported that relatively large numbers of CD45+/lineage- negative (Lin- )/c kit+/ Fc??RI+ leukocytes with characteristic features of mast cells emerge from the hair follicles under these culture conditions.
Unfractionated hair follicle cultures (containing 40%-70% CD45+ cells) released significant amounts of histamine on ligation of surface IgE receptors, as well as in response to substance P or compound 48/80.
They have demonstrated in this study that relatively large numbers of CD45+/Lin-/c kit+ leukocytes can be readily propagated ex vivo from hair follicle specimens in the presence of 5 added growth factors (stem cell factor, interleukin 3, interleukin 7, granulocyte-macrophage colony-stimulating factor and Flt3 ligand).
These hair follicle derived leukocytes exhibited characteristic features of mast cells, including inclusion of metachromatic granules, surface expression of Fc??RI, proliferative responsiveness to stem cell factor, and histamine release on ligation of surface IgE.
Mast cells are present in normal skin, and increased numbers of mast cells are regularly observed in the skin of patients with atopic dermatitis even before the onset of inflammation.
Unfortunately, in literature, there are no studies that show the histamine activity on follicular keratinocytes but, although with proper precautions and expanding the search area, we can introduce the topic referring to studies conducted on patients with atopic dermatitis.
Mast cells release a number of important signaling molecules, among which histamine has particularly potent pro-inflammatory activities.
After mast cell degranulation, histamine concentrations within the tissue can rise to 10– 1000 μM, and his tamine levels have been reported for lesioned and nonlesioned skin of patients with atopic dermatitis.
A role of endogenous histamine in the modulation of keratinocyte maturation has been suggested by Ashida et al.48 based on the observation that antihistamines have a beneficial effect on skin barrier recovery after tape striping in normal mouse skin.
Gschwandtner et al.49 addressed the impact of histamine on the differentiation of human keratinocytes and their findings show that histamine prevents the expression of late differentiation antigens in keratinocytes and strongly decreases the expression of tight junction and desmosomal proteins, leading to the formation of a defective skin barrier.
To investigate which of the four known histamine receptors (H1R–H4R) is targeted by histamine to induce its effect on keratinocytes, selective agonists and antagonists for the different receptors were applied to keratinocytes in the presence or absence of histamine.
The expression of differentiation proteins was inhibited by the histamine receptor-1 (H1R) agonist 2-pyridylethylamine, while agonists of the other three histamine receptors did not change the expression of keratinocyte differentiation markers.
Accordingly, preincubation of keratinocytes with the H1R antagonist, cetirizine, suppressed the histamine effect.
Cetirizine is a safe and selective, second-generation histamine H1 receptor antagonist, widely used in daily practice.
Charlesworth et al.50 showed that cetirizine causes a significant reduction in both the inflammatory cell infiltrate and PGD2 production and these effects are not related to its anti-H1 activity.
Studies on H1 receptor binding have demonstrated that compared with many other commonly used second-generation H1 antihistamines, cetirizine has a relatively higher and more favorable affinity and selectivity for H1 receptors, which confers a more potent, faster onset and longer duration of action.
Studies investigating the anti-inflammatory/ anti-allergic effects of cetirizine have indicated that it may exhibit anti-inflammatory properties independent of its H1 effects.
Rossi et al.3 carried out a pilot study to evaluate the efficacy of topical cetirizine in patients with AGA.
This study arises from the assumption that prostaglandins would have an important role in the hair growth.
Their action is variable depending on the class they belong to: PGE and PGF2?± play a generally positive role on the hair growth, while PGD2 an inhibitory role on the hair growth.
In this report the efficacy and tolerability of a galenic lotion based on cetirizine 1% once a day on the scalp in a sample of 85 patients was evaluated.
On the basis of hypertrichosis observed in patients treated with analogues of prostaglandin PGF2?±, the supposed mechanism of action concerns the probable ability of Cetirizine to influence the prostaglandin activity.
Nevertheless, it is undeniable that cetirizine is an antihistamine drug.
The data available in literature, therefore, lead us to think that histamine can play a central role in the regulation of the hair cycle.
Histamine can be responsible for the onset of some pathological models that we now call “androgenetic” but which may actually have different etiology.
These disorders could be managed differently from classical androgenetic alopecia, for example using an oral antihistamine drug, such as cetirizine, at an early stage.
Thus, in order to prevent thinning.
They are well known: - The perifollicular inflammation that accompanies hair miniaturization in androgenetic alopecia; - The active phases of the disease in which the patient reports scalp pain and itching, accompanied by an intense hair fall; - And the "fibrous streamers"?located below the follicles in advanced miniaturization, along which it is possible to observe the "Arao-Perkins bodies"? a small residue of anagen follicles of previous cycles.
All these considerations would seem to suggest the presence of a chronic inflammatory disease that presents episodes of remission and exacerbation and that it should be treated as such.
Our knowledge is still very limited and further studies are required to explore the topic.
In the literature, different articles show that several leukocyte populations normally reside in mouse skin and they are frequently identified within or around hair follicles.
These hair follicle derived leukocytes exhibited characteristic features of mast cells.
Mast cells release a number of important signaling molecules, among which histamine has particularly potent pro-inflammatory activities.
Both the mast cell population and the total histamine content revealed quantitative fluctuations during each cycle.
Histamine prevents the expression of late differentiation antigens in keratinocytes and strongly decreases the expression of tight junction and desmosomal proteins, leading to the formation of a defective skin barrier thus, the follicle would produce the histamine which would cause the involution of the follicle itself.
Preincubation of keratinocytes with the H1R antagonist, cetirizine, infact, suppressed the histamine effect.
Cetirizine is a safe and selective, second-generation histamine H1 receptor antagonist, widely used in daily practice.
Studies investigating the anti-inflammatory/ anti-allergic effects of cetirizine have indicated that it may exhibit anti-inflammatory properties independent of its H1 effects.
The data available, therefore, leads us to think that histamine can play a central role in the regulation of the hair cycle.
Histamine can be responsible for the onset of some pathological models that we now call "androgenetic"?but which may actually have a different etiology.
These disorders could be managed differently from classical androgenetic alopecia, for example using an oral antihistamine drug, such as cetirizine, at an early stage in order to prevent thinning.
Three major pitfalls of this review must be noted.
Firstly, there are currently no studies that refer to the influence of histamine on follicular keratinocytes.
The author was inspired by work on patients suffering from other diseases, trying to broaden the field of research.
Secondly, to date, histamine has been considered as a factor that positively influences hair growth.
According to the author, the opposite is true.
Lastly, most of the studies in the literature refer to experiments on mice.
The data available are still too few.
Perhaps in the future, we will be able to talk about "histaminergic alopecia"?but further studies are needed to clarify this important issue.
Cryolipolysis was first described by Rox Anderson and Dieter Manstein in 2008.
It is a unique non invasive method for the selective reduction of fat cells with controlled, localized cooling.
It can selectively damage subcutaneous fat without causing damage to the overlying skin.
Three days after a cryolipolysis session, histological analysis reveals an apoptosis of the adipocytes and a phagocytosis of fat by macrophages, that is present 14-30-days post- treatment.
Histopathologic evidence of inflammation becomes apparent at 10 days and declines 90 days after treatment.
Clinically, cryolipolysis results in localized panniculitis and modulation of fat.
The decrease of fat thickness occurs gradually over the first 3 months following the treatment, and is most pronounced in patients with limited, discrete fat bulges.
Erythema of the skin, bruising, and temporary numbness at the treatment site are commonly observed.
Paradoxical adipose hyperplasia4 and skin necrosis are rare side effects of cryolipolysis.
The aim of this case report is to share CT-scan pictures of a patient revealing subcutaneous fat hyperdensity 3 months after a cryolipolysis session.
Report A 58-year-old woman with no known medical or surgical history came to our clinic with complaints of abdominal fat.
After reviewing available options, we decided to perform a cryolipolysis session.
The cryolipolysis device was a Cristal? (Deleo, France).
An aspirated and refrigerated handpiece set named Ruby by the Deleo company was used at -7C and with a vacuum level 4 on the sub-umbilical area.
A non aspirated and refrigerated handpiece set named Ametysth was used at -7°C without vacuum on the supra-umbilical area.
The session was performed in August 2017 lasting 60 minutes by a French plastic surgeon (L L ).
The 2 areas (sub and supra- umbilical) were treated simultaneously.
The size of the treated area was around 450cm2.
No issues were noted during or immediately after the procedure.
Three months after treatment, the patient visited a gastroenterologist for belly pain without any other symptoms.
The blood test was normal (CBC, ALT, AST, GGT, and PROTEIN C .
An abdominal CT-scan was performed as suggested by the gastroenterologist and discovered a hyperdensity of the subcutaneous fat limited to the cryolipolysis treated areas (Figure 1A - 1B).
No organic cause was noticed and the belly pain disappeared spontaneously without any treatment within a month.
Four months after the procedure, the patient had a 1kg weight loss (66kg to 65kg), a sub- umbilical circumference loss of 4cm (105cm to 101cm) and a supra- umbilical circumference loss of 3cm (77cm to 74cm) (Figure 2A - 2B).
The pinch test decreased from 52mm to 40mm on the sub-umbilical area and from 52mm to 48mm on the supra-umbilical area.
Cryolipolysis is a safe and effective procedure for the reduction of fat in isolated pockets of excess adipose tissue.
Biologically, no changes in lipid levels or liver function tests have been linked.
In our case, the CT- scan was performed 3 months after treatment.
To date, no case describes the radiological signs of cryolipolysis on CT-scan.
The hyperdensity of the subcutaneous fat found in imaging confirms the inflammatory reaction of cryolipolysis but also proves its safety on the overlying tissues.
It has been shown histologically in previous studies that inflammation and loss of adipose tissue are well correlated, and the maximum lobular panniculitis is approximately 4 weeks after cold exposure and resolves about 3 months after.
About this patient, this would seem to support the conclusion that the sequelae of cryolipolysis was responsible for the pain.
The inflammatory reaction is still present even after 3 months, suggesting that some people are more sensitive than others.
The reaction of cold exposure seems to be more important and having a prolonged effect in time.
The CT- scan had also shown that the tissue's inflammation was deeper on the area treated with a handpiece using vacuum compared to a no-vacuum handpiece (maximum supra-umbilical fat hypersignal depth: 0.8cm versus maximum sub-umbilical fat hypersignal depth: 1.6cm).
This suggests that, when the skin laxity allows it, the handpieces with vacuum are more efficient than the handpieces without.
Cryolipolysis induces inflammation related to cold exposure.
At the time of maximum effectiveness, the subcutaneous fat becomes a panniculitis.
This phenomenon is observed on CT scan by a hyperdensity area.
The physiology and efficacy of cryolipolysis have been largely described in the literature.
But for the first time, a case report correlates known clinical and histological data with radiological evidence of inflammation related to the destruction of adipocytes.
Currently, the demand for cosmetic procedures has grown exponentially.
Dental procedures, as well as medical ones, besides working to obtain the principle of health promotion, accompany this trend by the search for aesthetics and welfare.
Facial aesthetic harmony correlates directly with the smile which, in turn, is formed by the union of three components: teeth, gum and lips.
The smile becomes aesthetically pleasing when these elements are disposed in suitable proportion, and exposure of the gingival tissue is limited to 3 mm.
When the gingival exposure is greater than 3 mm, it is characterized the non-aesthetic condition called gummy smile, which affects some patients psychologically.
The gummy smile is often found in women.
The predominance of females can be explained by the fact that male patients present a lower smile line.
Several therapeutic modalities have been proposed for the correction of gummy smile, among them: gingivectomy or gingivoplasty, myectomy and orthognathic surgery; and the last two procedures are more invasive and associated with high morbidity.
In contrast, the use of botulinum toxin can be considered as a therapeutic option to surgery, because it is a more conservative method, more effective, faster and safer.
Currently, botulinum toxin has been shown effective in the treatment of gummy smile in patients with hyperfunction of the muscles involved in smiling, as well as in patients with other stomatological disorders such as bruxism, clenching and orofacial pain.
The purpose of this article is to present the therapeutic association between gingivoplasty and the application of botulinum toxin in managing gummy smile.
Caucasian female, 26-years-old, attended the private clinic with complaints of gummy smile (Figure 1).
Clinically, the patient had an anatomic discrepancy between the length of the upper anterior teeth, and evident gingival exposure greater than 3 mm, featuring the gummy smile (Figure 2).
Initially, gingivoplasty was proposed.
Subsequently, after the clinical results, the application of botulinum toxin was suggested for the correction of gummy smile.
However, the patient was oriented about the recurrence of gingival smile after 6 months of application, because of temporary results.
Under local infiltrative anesthesia, the gingivoplasty was performed with the electrocautery (BE 3000TM, KVN, S?o Paulo, Brazil).
The length of the teeth was increased, characterizing the dental zenith.
The patient reported no complaints or complications after surgery.
After 30 days, satisfactory tissue repair (Figure 4) and the persistence of complaint of gummy smile reported by the patient (Figure 5) were observed.
In the same consultation, botulinum toxin was applied.
Prior to application of botulinum toxin, the surface of the skin was disinfected with ethyl alcohol 70% and the oils from the area were removed, in order to avoid local infection.
The points of application were marked, beside each nostril.
Then, local anesthetic (EmlaTM, Astra, S??o Paulo, Brazil) was applied with the aim of promoting comfort during the procedure.
Botulinum toxin type A (BotoxTM 200 units, Allergan Pharmaceuticals, Westport, Ireland) was diluted in 2 ml of saline, according to the manufacturer's instructions, and injected 2 units in the recommended site, laterally to each nostril.
After application, the patient was advised not to bow their head during the first four hours and not to engage in physical activity during the first 24 hours after the procedure.
After 10 days, the patient was examined.
She presented a uniform dehiscence of the upper lip (Figure 6).
Side effects or complaints were not reported.
Several etiologies have been suggested to gummy smile, as vertical maxillary exces, delayed passive eruption, hyperfunction of the muscles involved in smiling and reduced length of the clinical crown of the teeth, which can occur separately or together, and determine the type of treatment to be used.
In gummy smile caused by overactive muscle, botulinum toxin was indicated.
It is the first treatment of choice for the ease and security of applications, fast effect, besides being a more conservative approach when compared to surgical procedures (myectomy or Le Fort I osteotomy).
The activity of the smile is determined by several facial muscles, such as the elevator of the upper lip and wing of the nose, the zygomatic major and minor, the angle of the mouth, orbicularis oris and risorius.
Among them, the first three ones play higher function and determine the amount of lip elevation and, therefore, should be the muscles affected by the injection of the toxin.
The fibers of these muscles converge to the same area, and they form a triangle, which suggests that the point of adequate election comprehend the three muscles in a single injection.
The toxin, when injected, can spread in an area of 10 to 30 mm, which allows the effective extent.
The proposed site of injection was laterally to the wing of the nose.
After being injected into predetermined locations, the toxin decreases the contraction of the muscles responsible for the elevation of the upper lip, and this reduces gum exposure.
In this report, the result was satisfactory to the harmony of the smile of the patient by association of treatments - gingivoplasty and application of botulinum toxin type A The institution of isolated treatments could not culminate in the excellence of the earned results.
Initially, the creation of the new dental zenith during the course of gingivoplasty promoted the new dental architecture (Figures 2 and 4), favoring harmony gingival-dental- facial for the patient (Figures 1 and 5).
Subsequently, the application of botulinum toxin type A softened the gummy smile, by the uniform dehiscence itself of the upper lip, still promoting smoothness to facial lines of the smile, as can be seen in the nasolabial folds, adjacent to the nostrils, comparing Figures 1 and 6.
The application of botulinum toxin is an alternative; it is less invasive, faster, safer, more effective and it produces harmonics and pleasing results when applied in target muscles, respecting the appropriate dose and type of smile.
It is therefore, a useful adjunct in the aesthetic improvement of the smile and provides better results when combined with gingivoplasty.
Polycystic ovary syndrome (PCOS) is a heterogeneous endocrine disorder with prevalence rates ranging from 5% to 13.9% in women of reproductive age.
PCOS is mainly characterized by chronic anovulation, polycystic ovary morphology, and hyperandrogenism1,2,3.
PCOS is frequently associated with obesity, insulin resistance (IR), diabetes, hypertension and dyslipidemia, conditions conducive to increased cardiovascular disease (CVD) risk.
The percentage of insulin resistance in PCOS has been reported at between 44 to 70%7,8 using surrogate markers.
There is a general agreement that obese women with PCOS are insulin resistant, but IR should be assessed in all PCOS women, both lean and overweight subjects.
Weight reduction in PCOS improves metabolic health10, although, the optimal diet composition is unclear.
High-protein diets cause11 greater satiation and have a greater thermogenic effect.
They are recommended for the reduction of the loss of lean tissue mass during weight loss, for the decrease of serum lipid profile, and for postprandial insulin sensitivity.
Data from several recent studies has promoted also high-fat16, low carbohydrate diets, up to ketogenic diets for their beneficial effects on glucose homeostasis and reduction of body weight and hyperinsulinemia.
Dietary omega-3 polyunsaturated fatty acids (PUFAs) are known to reduce triglyceride and cholesterol levels, and to ameliorate hyperinsulinemia in PCOS patients18.
However, their impact on reduction in androgens is controversial.
The concept of reducing dietary glycemic index (GI) or glycemic load (GL) in the treatment of PCOS has also received considerable interest.
In the general population, this approach can optimally improve the metabolic profile.
At present, data on the effect of modulation of GI or GL on insulin sensitivity, acute satiety, and long-term weight loss and weight-loss maintenance are conflicting.
A low glycemic load diet contains carbohydrates that minimize changes in post prandial glucose levels and leads to a sustained reduction in hyperinsulinemia, in fasting glucose levels and in serum lipids in PCOS.
However, the realization of any long term benefits requires compliance to the low glycemic load diet.
Currently, few studies evidence the beneficial effects of a low glycemic load diet on metabolic profiles of PCOS, regardless of weight reduction.
The aim of this study was to evaluate the effects of two isocaloric and isoenergetic diets differing in glycemic load (GL) on metabolic patterns PCOS syndrome: diet A with a moderately low glycemic load (GL=79-105) versus diet B with a moderately high glycemic load (GL=123- 134) in order to broaden the results of our previous preliminary study.
PCOS patients were selected at the Department of Clinical Medicine and Surgery, University “Federico II”, Naples (Italy) and they all met the Rotterdam criteria of ESHRE/ASRM PCOS Consensus Workshop Group (presence of two out of the following three features: menstrual irregularity; clinical or biochemical hyperandrogenism; positive  ultrasound  presentation of polycystic ovaries by scan).
Only 15 patients, age- matched, with a similar socio-economic background (age: 24.2±3.1 y; BMI: 28.9±5.1 kg/m2) duly completed the protocol.
Exclusion criteria were the following: pregnancy, endocrine disorders, and the use of oral contraceptive or insulin-sensitizing agents.
Patients participating to regular physical activity were also excluded.
Patients were randomly provided with, according to a cross“over design, an isocaloric and isoenergetic diet with either a moderately low glycemic load or a moderately high glycemic load for 3 months, respectively.
The Glycemic Index (GI) is a quantitative assessment of foods based on postprandial blood glucose response, which is often expressedasapercentageoftheresponsetoanequivalent carbohydrate portion of a reference food (white bread or glucose)23.
The glycemic load, which assesses the total glycemic effect of the diet and has proved very useful in epidemiologic studies, is the product of the dietary GI and total dietary carbohydrate: GL= [GI x CHO(g ] /100.
The GI was determined by using the Brand-Miller tables23,24; such tables adopt glucose as a parameter for carbohydrate.
Average daily GI was calculated as [(grams of carbohydrate from food item/total daily grams of carbohydrate) — GI value of the food item].
 Low GI foods are defined as having a value < 56; medium GI foods a value of 56-69, and high GI foods a value ? 70.
Dietary GL was calculated by multiplying the daily GI of each food by the amount of carbohydrate consumed and dividing the product into 100.
To determine energy requirement and consequentially the prescription of the relative diet, the Harris Benedict equation was used so to predict basal metabolic rate adjusted for physical activity level; Table 1 reports composition of 1500-1800 kcal diets with low or moderately high Glycemic Load.
The nutrition education protocol was developed by dietitians.
A dietitian initially instructed the subjects on quantification and recording of their daily food intakes.
Follow-up counseling occurred every 6 days until the end of the study.
At this visit, diet prescription and nutrition goals were reviewed.
The calculation of the Homa index was performed on the parameters of fasting plasma glucose and insulin concentrations, using the following formula: HOMA index = fasting glucose (mmol/L * fasting insulin (mU/L /22.5.
The cut- off point was set to more than 2.5.
A complete physical examination was performed on each patient, at baseline and after 3 and 6 months, including a hirsutism exam using the Ferriman and Gallwey score.
Each body area was visually scored on a scale of zero to four; a score of zero indicated no terminal hair growth, while a score of four indicated full male pattern terminal hair growth.
The evaluation of acne included clinical examination, grading and lesion counting.
Additionally, height, weight, and age, were noted, and body mass index (BMI) was calculated for each patient.
At baseline and after 3 and 6 months, all subjects underwent pelvic ultrasonography and venous blood sampling to evaluate metabolic parameters (total cholesterol, HDL cholesterol, triglycerides, fibrinogen, serum glucose, C peptide, and insulin levels, fasting and two hours after breakfast).
All participants provided data with written health questionnaires and menstrual cycle calendars.
Patients were advised not to perform additional physical exercise compared to the standard physical activity recommendations (150-minute per week of moderate exercise regimen), to avoid interference or bias with the prescribed dietary program.
Data is expressed as mean ± SD and was log transformed before analysis when skewed. Two-tailed analysis was performed by using SPSS13 for Windows (SPSS, Inc., Chicago, IL, USA).
Statistical significance was set at p< 0.05.
Two-tailed analysis was performed by using SPSS13 for Windows (SPSS, Inc., Chicago, IL, USA).
Statistical significance was set at p 0.05.
Baseline parametric data was assessed by using a one- way ANOVA.
For comparison between time points, a repeated measure of ANOVA was used for parametric data.
Bonferroni adjustments were performed on multiple comparisons.
Both diets were well tolerated.
There was an improvement in menstrual cycle in 9 out of the 15 subjects (60%) during the low glycemic load diet (diet A and in 3 patients (20%) during the high glycemic load diet (diet B (p 0.032).
Examination findings reveal no improvement of hirsutism and acne after both diets.
Change of metabolic variables after Diet A and B in the 15 PCOS patients are shown in Table 2.
The mean body weight and BMI changes from baseline to 3 months after both diets were not statistically significant (p 0.784 after diet A vs p 0,914 after diet B (Table 2).
Mean fasting HOMA-R changes from baseline to 3 months after both diets were not statistically significant (p 0.204 after diet A vs p 0,089 after diet B .
As compared with baseline values, three  months  after diet A, there were statistically significant reductions of 2h-after  breakfast  serum  glucose  (from  89.4±8.4 to 78.1±4.2, p<0.011) and 2h-after breakfast-insulin (from 47.2±25.3 to 21.3±7.9, p=<0.001) (Figures 1 and 2). 
Hyperinsulinemia contributes to the hyperandrogenism of PCOS by stimulating ovarian androgen production and decreasing serum sex hormone-binding globulin (SHBG) concentrations.
Following three months of diet B a trend towards a reduction in circulating in serum glucose and 2h-after breakfast-insulin levels was observed, but in none of the cases had it reached statistical significance (Table 2).
It is thought that patients affected by PCOS have a genetic predisposition to insulin resistance of skeletal muscle leading to an elevation in insulin secretion that stimulates testosterone production from the ovaries, which remain sensitive to insulin action.
Infertility, hirsutism, and obesity are characteristics of the disorder.
In addition, women with PCOS are at an elevated risk of developing both T2D and cardiovascular disease, presumably due to their insulin resistance and hyperinsulinism.
Although treatment with oral contraceptives and other drugs that alter the reproductive-endocrine axis can alleviate symptoms, there is a need for nonpharmacological treatment options.
That diet modification through carbohydrate restriction could alleviate symptoms by lowering insulin secretion is a possibility worth pursuing.
Weight reduction through energy restriction has been shown to exert positive influences on both metabolic and hormonal aspects of this condition.
To date, nutritional studies in PCOS patients have focused on the effects of energy restriction and weight loss.
The effects of dietary composition, alterations in carbohydrate amount and type have also been investigated, and more recently, dietary fatty acids, with a particular emphasis on PUFA, have been shown to decrease insulin resistance, prevent excess insulin secretion, and, consequently, decrease the androgen excess and improve gonadal function in PCOS11-15.
Previous studies have reported that women on high protein diets experienced a significant weight loss and a decrease in their serum glucose level.
Some studies have shown that protein-enriched diets can lead to greater weight loss and improvements in biomarkers of metabolic syndrome than standard protein diets.
On the contrary, some high protein diets do not produce significant weight loss, causing a high percentage of adults to stop the diet after a short period of time and return to their original weight.
Other findings have reported that a low-GI hypocaloric diet enhanced insulin sensitivity in a range of subjects, including individuals with type 2 diabetes and PCOS patients.
Interestingly, some authors have reported a significant interaction between diet and metformin use such that the combination of a low-GI diet + metformin use was associated with the greatest improvements in insulin sensitivity32.
In diabetic patients, evidence from studies has suggested that a low-GI hypocaloric diet improved glycemic control reduced waist circumference and reduced weight by increasing satiety and by promoting fat oxidation at the expense of carbohydrate oxidation33,34.
The metabolic profile well known in women with PCOS is equivalent to the insulin resistance syndrome, a clustering within an individual with hyperinsulinemia, mild glucose intolerance, dyslipidemia, and hypertension.
The insulin resistance syndrome has been recognized as a risk factor for developing type 2 diabetes and CVD not only in obese and overweight patients, but also in normal weight women with PCOS where weight loss is not a choice for their management.
Moreover, recent studies have suggested that compared with either aerobic or resistance training alone, combined aerobic-resistance training is more efficacious for improving insulin sensitivity and reducing abdominal fat in a range of obese patient groups.
The improvement of insulin resistance and the decrease of insulin concentration and action can be achieved in different ways: if overweight or if obesity is present, by reducing body weight with lifestyle modifications; by using insulin- sensitizing agents, or by using antiandrogens.
In fact, it has been demonstrated that long-term treatment with antiandrogens may improve insulin sensitivity in both normal weight and obese PCOS women presenting an insulin-resistant state, even without changes in body weight.
This updated study confirms the potential benefits of an isocaloric and isoenergetic diet with a low GI, also related to a relatively high content of unsaturated lipids (see table 1), for the treatment of PCOS, regardless of weight loss and physical activity.
Our sample is low due to the high difficulties in carrying out all the steps contemplated by the protocol.
In summary, our study has shown that an isocaloric and isoenergetic diet with a low GI and a moderately high content of unsaturated lipids leads to a significant reduction in serum glucose and insulin levels two hours after breakfast and improved menstrual regularity in PCOS women over a three-month period, compared with a conventional, relatively high GI, isocaloric diet.
In our hypothesis improved insulin resistance per se, independent of weight loss, may positively affect menstrual cycle in PCOS, as suggested by the higher prevalence of improved menstrual cycles after diet A versus diet B In recent years, there has been an increased focus on the potential beneficial effects of specific dietary fatty acids for chronic diseases including insulin resistance and cardiovascular disease.
It has been shown that replacement of dietary carbohydrate with PUFA in a reduced energy diet may offer additional health benefits in the management of PCOS.
The adoption of healthy dietary patterns should be encouraged among women with PCOS, as they are rich in dietary fiber, antioxidants and anti-inflammatory nutrients, leading to greater satiety, and anti-hyperlipidemic, antihypertensive and antidiabetic properties16.
Some authors have proposed that n-3 PUFAs may improve insulin sensitivity by decreasing the production of inflammatory cytokines including tumor necrosis factor alpha (TNF-α), interleukin 6 (IL-6) and increasing secretion of the anti- inflammatory hormone adiponectin. 
Fatty acids and their derivatives also have a role to play in various aspects of reproduction, and are involved in oocyte fertilization, as well as fetal and infant development.
Ongoing work within our research group has suggested that supplementing the diet of women with PCOS with LC n-3 PUFA may have an anti-androgenic effect, mediated by a decrease in the plasma n-6:n-3 ratio rather than a direct functional effect of n-3 PUFA.
In addition to the antiandrogen effect of PUFAs, lifestyle modification, such as diet re-calibration and increased physical activity, is considered as the first- line treatment for PCOS women: a modest weight loss in obese PCOS women of only 5% of initial body weight can result in pregnancy, while a weight loss of 5–10% can reduce hyperandrogenism and insulin levels, mostly in obese PCOS women.
To date, the composition of the optimal diet for women with PCOS is not yet known, but such a diet must not only assist short term with weight management, symptoms and fertility, but also specifically target the long-term risks of type 2 diabetes and CVD.
With insulin resistance and compensatory hyperinsulinemia now recognized as a key factor in the pathogenesis of PCOS, it has become clear that reducing insulin levels and improving insulin sensitivity are an essential part of management.
Diet plays a significant role in the regulation of blood glucose and insulin levels, yet research into the dietary management of PCOS is lacking and most studies have focused on energy restriction rather than dietary composition per se.
Our data has shown that a moderately low glycemic load is recommended for managing PCOS.
This diet is sufficient to significantly improve hyperinsulinemia, restore ovulation and improve menstrual regularity.
The metabolic effects of this dietary program resulted independent of pharmacological therapy, weight loss and exercise and this represents an important tool, considering the chronic nature of PCOS and its high prevalence in young age.
Realization of any long term benefits such as metabolic syndrome and CVD prevention in PCOS would require compliance to the dietary program.
This study suggests that compliance to a moderately low glycemic load diet is possible over a three month period.
The benefits of lifestyle intervention in people with PCOS show that it is possible for dietary modification to ameliorate metabolic parameters and the risk of long term health problems linked with insulin resistance and it is important that this data has been confirmed on a larger number of patients with PCOS in this updated study.
In recent years there has been a growing interest in non-surgical procedures for facial rejuvenation.
Hyaluronic acid (HA) and calciumhydroxyapatite (CHA) are currently the most widely used dermal filler for full facial treatments1.
These provide a high level of comfort in the treatment (both for the practitioner and for the patient) and a long-lasting effect.
In addition, these are safe substances with regard to their compatibility and local resistance.
In addition, the proven safety of Agarose gel has also been a factor in their increased use.
Although only rarely complications are reported.
Agarose is in principle not a completely new material in medicine.
It has been used in the dental field for more than a decade.
The substance class is a neutral polysaccharide, it is completely biocompatible and thus degradable6.
The aim of this article is to show agarose gel as at least equivalent to those for full-facial treatment.
The following overview describes the possibilities of this treatment method.
Detailed knowledge of the facial anatomy with the knowledge of changes in the age are necessary to achieve balanced and natural results after the injection.
It is also essential to be familiar with the character of the dermal filler to be used.
Each face region has its own perfect dermal filler for its purpose.
Complete biodegradability, local resistance and good tolerability as well as perfect biocompatibility are indispensable properties of a suitable dermal filler.
The results of the filler injection are extremely technically and material dependent.
It is necessary to place a 3-dimensional lattice of injected material below the surface of the skin to add volume, alter surface area and thicken skin or subcutaneous tissue.
The degree of correction and the result depends heavily on the injection technique used and the material used hence the required volume.
In the past, surgical techniques dominated the field of facial rejuvenation.
However, the importance of a three- dimensional volume restoration using dermal fillers has become increasingly recognized in recent years and has increasingly gained precedence over a two-dimensional lift by the scalpel.
Hyaluronic acid is very hydrophilic, therefore a change in the treated region after injection is expected.
That makes the substance perfect for a more superficial correction.
It also hydrates the skin, so that wrinkled skin is better glazed.
However, to correct for bone resorption at depth, a substance with less hydrophilic property is needed.
No part of the human organism reflects aging like the facial area.
The face consists of skin, subcutaneous fat, muscle and bone structure.
These individual components age independently, they have their own laws of aging.
In old age, not only changes in the surface of the skin are noted.
Aging processes also occur in various deep structures of the human face especially in the area of the maxillary and mandibular; most notably fat loss and bone resorption.
Striking is the sinking of soft tissue such as eyebrows and cheeks.
Nasal manifestations of the nose result from degradation of the bony and cartilaginous framework.
Therefore, the sinking of the tip of the nose and a shortening of the columella arise.
Through this downward movement, the nose appears longer and larger.
The nose is now larger and the nasolabial angle can be sharpened.
In addition, the elderly patient usually has excessively inelastic skin, which makes an injection more difficult.
However, there are differences, especially depending on race, genetic factors and individual aspects (some earlier, later).
The upper third of the face consists mainly of the forehead.
Here, dominates the glabella and horizontal folds.
These are usually dynamic wrinkles.
Here, a treatment with botulinum toxin is recommended.
Therapeutically significant is the midface, especially with respect to the following changes e g slackening, sinking of the cheek fat, reduction of hypodermic fat, strengthening nasolabial folds, slackening of the lower eyelids with optical extension.
In addition, there is an aging of the nose (extension of the nose, rarification of the nasal skeleton, loss of hypodermic fat in the nasal area, broadening of the nose and convex nose bridge).
Overall, the midface in old age tends to change to hollow-cheeked, flat, empty, and narrow.
The nose as the central area in the middle face of the human being is one of the first visible structures for the opposite and the mirror image.
It is a complex, three- dimensional, trapezoidal organ protruding from the face.
Due to its three-dimensionality, however, the nose causes many people an increased aesthetic distress12.
While wrinkles are often only perceived as two- dimensional disturbing strokes, the cosmetic problems and desires of the patients with regard to the appearance of the nose are much more complex.
Surgical rhinoplasty is therefore one of the most common aesthetic surgical procedures.
As with all surgical interventions, there is a certain amount of rhinoplasty risk of complications with corresponding convalescence time and downtime.
In addition, the operation is associated with high costs.
Increasingly, therefore, many patients develop the desire to avoid surgical intervention.
Still, most have never heard of a nonsurgical nose job.
The lower third of the face extends from the subnasal area to the chin seat.
Displacement of the cheek fat caudally.
This also applies to the mimic muscles, the zygomatic muscle, major and minor, and the m risorius.
Bone atrophy of the maxilla in anterior-posterior direction and congenital low-grade retrognathia.
Due to the sinking cheek fat, the impression of a deepened nasolabial fold is intensified.
By increasing the cheek fat above the nasolabial fold this impression is emphasized.
The cutaneous and subcutaneous shrinkage of fat or bone resorption can be temporarily compensated by appropriate injections of collagen, autologous fat, hyaluronic acid or even agarose gel6,16,17.
Agarose is in principle not a completely new material in medicine.
It has been used in the dental field for more than ten years6.
The substance class is a polysaccharide of D galactose and 3,6-anhydro L galactose, which are glycosidically linked.
Thus it represents a main component of the agar7. 100% based on natural polysaccharides, it is completely biocompatible and thus degradable.
Agarose gel is a sterile, biodegradable, viscoelastic, isotonic, transparent injectable gel implant16,18.
Agarose is broken down in the human organism.
For this purpose, agarose is first transported by the action of macrophages from the site of application and then degraded enzymatically by means of galactosidase.
Agarose is metabolized in the pentose cycle at macrophage, platelet and endothelial reticulum levels6,16.
Because of its biocompatibility, agarose is widely used in clinical trials.
Therefore, the substrate is used in biocompatible tests, for example with regard to cytotoxicity, genotoxicity, mutagenesis, sensitivity, and subcutaneous implants.
In addition, the gel is used in biotechnology for three- dimensional tissue growth and as a controlled release substrate for pharmacological substances.
For the preparation of the treatment a superficial anesthesia with a topical anesthetic is recommended.
In some cases, a local anesthetic injection should also be considered.
Treatment should be as painless as possible.
In addition to topical anesthesia, the use of very thin cannulas also serves this purpose.
It can also be mixed local anesthetic with the gel.
Agarose itself is because of its isotonic properties, as mentioned above, an almost painless injectable.
The injection should be done very slowly.
Only when stretching out of the tissue does it burn.
It is essential to have an extended massage of the injected area to disperse the imported substance with the surrounding tissue.
Agarose gel transforms into a hydrocolloid after injection into the tissue.
This creates a natural and harmonious look.
According to the principle “What you see is what you get”, the result visible immediately after the injection is also the final result.
An additional advantage is the use in patients who have demonstrated intolerances to hyaluronic acid or other ingredients in previous treatments.
In our practical everyday life we inject a variety of facial areas.
The agarose gel used in this study contained 2.5% agarose and 97.5% saline solution for nasolabial fold (Figure. 1) and 3,5% agarose and 96,5% saline solution for rhinoplasty and jaw angle (Figure. 2 & 3).
Patients with acute or chronic skin pathologies or direct involvement in or around the area to be treated were excluded.
Pregnancy, lactation and hyaluronic acid treatment less than 3 months earlier were also excluded criteria.
After discussing patient in-depth information and written consent, discussing the risks and benefits of the procedure, the risks and benefits of alternatives, and answering all questions, the written consent form outlined possible complications such as bruising, swelling and hematoma or pain.
To reduce bruising, patients were asked not to take salicylates in the last 2 weeks before treatment.
In total 27 patients were treated (14 non-surgical nose jobs, four augmentation of jaw angle and nine nasolabial folds).
The patients were between 32 and 68 years old.
All patients were female.
Nobody had a treatment with permanent fillers before. 5 previously had an injection with hyaluronic acid in the area of the nasolabial fold.
For a better comfort, a superficial anesthetic cream was applied.
The agarose was mixed with 0.1 mL of lidocaine 0.1% to be as painless as possible during the injection.
For the injection of the nose and nasolabial fold 30 gauge needles should be used with a length of 13 mm, for the jaw angle 27 gauge needles.
The esthetic evaluation was done after 14 days, and 1, 3 and 6 months after the injection.
While a volume of 1.4 mL agarose gel 2.5% was sufficient for the nasolabial fold (Figure. 1), 2.8 mL were necessary for the jaw angle of agarose gel 3.5% (Figure. 2).
However, for the rhinoplasty, only 0.3 mL of 3.5% agarose gel was sufficient (Figure. 3).
Direct finger compression with cotton gauze and mild cooling were used to reduce bruising and swelling.
Further, no special instructions were required and the patients immediately returned to work.
The only adverse events described were hematoma, redness, bruising and swelling.
All adverse events lasted for a maximum of 4 days.
Patients were asked to re-present 14 days and one month after injection for follow-up and possible reinjection to correct for asymmetry or lack of desired fullness.
These repairs were usually made after 14 or 30 days, with 0.1 mL agarose in the area of the nose (by only three patients), 0.2-0.3 mL at the nasolabial folds (six patients), and a maximum of 0.4 mL at the jaw angle area (two patients).
Such improvements were not necessary in these three patients (see below in Figures 1-3).
Therefore, we asked for an additional follow-up after 3 and 6 months.
All results remained after 6 months.
The agarose-based filler is a great new option for modeling and aesthetic correction in nonsurgical rhinoplasty and complete face treatment.
Especially for patients who want to avoid surgery.
But even for the practitioner, it offers a low-complication possibility of a nose correction with relatively little effort compared to an operative procedure.
So far we have had several very good substances (such as hyaluron and calcium hydroxyapatite) available.
Now, with agarose gel, another substance enriches the dermal filler range with an interesting option.
This popularity of a substance such as agarose will continue to increase in the future as the aging population seeks viable options to correct the signs of aging without surgery.
The utilization of these fillers by trained professionals provides an effective and safe therapy for the management of the aging face.
Polylactic acid (PLLA) is used since long time to achieve the indirect volume augmentation of soft tissues.
This substance was approved in Europe in 1999 and in 2004 FDA has also approved the use of this substance in the treatment of facial lipoatrophy in HIV-positive patients.
In 2009 FDA has also approved the use of this substance in the treatment of facial wrinkles in Aesthetic Medicine.
Polylactic acid has an indirect effect: the volume augmentation is not due to the space occupied by the substance.
It is due to the local reaction caused by polylactic acid and subsequent collagen production by fibroblasts.
It is a completely resorbable filler.
It decomposes into H2O and CO2 in a year.
Some publications show that the stimulation in collagen production could help in the treatment of skin laxity in inner arm region, above all in post menopause women.
Authors will show their experience in the treatment of skin laxity in inner arm region with superficial infiltration of hyper-diluited polylactic acid.
30 female patients were treated, aged between 37 and 64 years old.
Dismorfism was classifyed according to Eric A Appelt and staff new classification system.
Polylactic acid was diluted with 9cc of sterile distilled water plus 1cc of lidocaine 20mg/10ml 12 hours before treatment.
The area of the arms was infiltrated with 1 fl of polylactic acid for a total of 10 cc of solution per side.
The PLLA was injected into the deep dermis, in retrograde direction, in opposite and intersecting fan micropunctures, using 2.5 cc for each injection and a vigorous massage was performed after each infiltration.
The Result was defined by the patients through a visual assessment of the photographs based on criteria of skin colour, presence of wrinkles, shape and volume.
In order to provide objective validation of the result, we performed patients' satisfaction test with the help of photographs as well as ultrasound examination.
After the third application (from 12 to 24 weeks), one was able to observe an improvement in the treated area with good and very food results in groups IIa and IIb and unexpected results from very good to excellent in type IIIa patients at T120.
The skin of the medial region of the arms is extremely thin and delicate, which is why filler hyper-dilution has been employed, associated to vigorous massage which has led to brilliant results where the right therapeutic indication has been considered.
The upper third region of the arms is the one most subject to the modifications arising from weight loss and skin ageing processes: particularly the skin of the medial arm region, which is described by Avelar as the thinnest and most delicate of the whole body.
Glanz and Gonzales-Ulloa have shown that, with age, the lower curvature of the arms' soft tissues increases and a there is a loss of connective support structures with consequent onset of increasingly worsening ptosis up to the occurrence of what is referred to as the ?€?bat wings?€?deformity8.
From the histological point of view the skin of this region features skin about 0.1mm thick, whose appearance is always sinuous and, in some cases, with scarce papillae.
The collagen of young skin is dense and even in the papillary dermis, arranged in parallel bands in the reticular dermis.
In aged skin collagen decreases and its appearance is heterogeneous and disordered.
Although the corrective approach to arm imperfections is very often surgical and depends on the severity of the dysmorphic feature that mainly affects the skin and the subcutaneous adipose component, many patients reject the procedure and seek less invasive methods.
A number of aesthetic medicine techniques have been employed in recent years, including fillers, in order to restore volume to the arm contour and improve the signs of skin ageing in this region.
Among the contemplated fillers, the rationale for using polylactic acid especially lies with its stimulating action for the production of dermal neocollagen.
The use of polylactic acid has gained extensive recognition and commendations: in 1999 in Europe for the correction of scars and wrinkles, in 2004 the FDA approved for treatment of HIV-related lipoatrophy and in 2009 for filling nasolabial folds for aesthetic purposes.
Polylactic acid (PLLA) belongs to the family of alpha hydroxy acids, is biodegradable and bio-compatible.
The injection form consists of a biodegradable synthetic polymer consisting of PLLA micro-particles and associated to sodium carboxymethyl cellulose and apyrogenic mannitol, the latter having an emulsifying effect increasing the moisturising effect of carboxymethyl cellulose.
After injecting, polylactic acid is hydrolysed into lactic acid monomers, which induce a local inflammatory response by drawing in monocytes, macrophages and fibroblasts.
After fibroblasts appear, neocollagenesis occurs of type I and type III collagen fibres with thickening of the dermis.
For the aim of seeking the best corrective approach for this anatomical region, a number of classification systems regarding its aesthetic modifications have been described.
The exact therapeutic indication is therefore related to the degree of dysmorphism of this anatomical region.
Specifically, Eric A. Appelt and staff put forth a new classification system - modifying the one described by Rohrich and Kenkel - entailing seven clinical groups and taking into account the degree of cutaneous excess relating to ptosis, the amount of fat in the subcutaneous tissue and their anatomical distribution area. 
Classification according to Rohrich  R.J., Kenkel J.M.: “Back and arms”. 
In“Ultrasound-assisted liposuction”, 1st Ed. St. Louis, Mo: Quality Medical Publishing, 1998. Pp 231-252. Modified by Appelt et al. in “An algorithmic approach to upper arm contouring. Plast Reconstr Surg. 2006; 118(1):237-46. 
Taking into account this classification and some patients’ wish not to undergo surgery, we have selected the cases to be treated with PLLA infiltration. The results obtained were assessed with photos and ultrasound examination with 22 MHZ transducer.
?From January 2015 to December 2016, 30 patients were treated, aged between 37 and 64 years old (average age: 48.43).
Treatment was carried out on: 12 type IIa, 10 type IIb and 8 type IIIa patients.
The patients were all female, average age 48.4 but with high variability (9 years), due to the fact that, apart from a peak around the age of 40, patients are almost evenly distributed between 40 and 65 years of age.
No significant age differences are observed among classes IIa, IIb and IIIc (p 0.598, NS) Table 2b.
The following exclusion criteria were considered: pregnancy; breastfeeding; a history of allergy with episodes of anaphylactic reactions and/or hypersensitivity to local anaesthetics, latex and silicon; coagulation disorders; cardiological and metabolic diseases; keloid diathesis9.
Polylactic acid was diluted with 9cc of sterile distilled water plus 1cc of lidocaine 20mg/10ml 12 hours before treatment12.
The area of the arms to be treated was identified with the patients?€? arms hanging down naturally.
The medial area of the arms was considered from the axillary column to the medial condyle of the humerus.
Each area was infiltrated with 1 fl of polylactic acid for a total of 10 cc of solution per side.
A 25G x 15 cm spinal needle was used for the infiltration.
The PLLA was injected into the deep dermis, in retrograde direction, in opposite and intersecting fan micropunctures, using 2.5 cc for each injection.
The patients signed an informed treatment consent form.
Three treatments were performed, with thirty-day intervals: T30, T60, T905.
Vigorous massage was performed after each infiltration.
Patients were then prescribed to massage the treated areas five times a day for five minutes for the five days following infiltration6.
Photos were taken of the patients, and ultrasound examination with 22 MHZ transducer was carried out at: T0, T30 (4 weeks), T60 (8 weeks), T90 (12 weeks) and T120 (16 weeks).
The patients were asked to view the photographs at T30, T60 and T120 and to assess the result according to the Global Aesthetic Improvement Scale (Gais), in an assessment scale taking into consideration five categories (poor; absent; good; very good; excellent) on the basis of some features such as: colour changes of the skin surface, the presence of wrinkles, the shape of the arm contour and the subcutaneous volume change.
After the first session (T30: four weeks) it was not possible to observe any improvement upon clinical examination, except a slight improvement of the skin texture, while the sonogram already showed a slight thickening of the deep dermis.
After the second treatment (T60: eight weeks), both the patients and the operator observed an improvement of the cutaneous surface with greater superficial tension and brighter colour, especially in the type IIa and IIb group of patients (Figures 1a,1b and Figures 2a, 2b).
With the third application (from 12 to 24 weeks), one was able to observe an improvement in the treated area with good and very food results in groups IIa and IIb and unexpected results from very good to excellent in type IIIa patients at T120 (Table 4, Figures 3a, 3b, 3c and Figures 4a, 4b).
As regards the ultrasound analysis of the treated areas, it was possible to observe greater thickening and greater echogenicity at deep dermis level already from time T30.
Echogenicity and thickness continued increasing slowly and progressively up to time T120, although it was not possible to assess the numerical extent (Figures 5a, 5b and Figures 6a, 6b).
No clinically significant adverse events occurred. 20 cases presented with widespread ecchymosis after treatment and 8 cases experienced pain in the injection area.
When comparing progress among the initial classes, a significant difference is observed between the initially most severe group (IIIa) and the others.
The difference is due to the fact that at first intermediate assessment (T60) the score improvement in group IIIa is lower than the other two classes (p=0.006), at the second intermediate assessment group IIIa achieves a good level of satisfaction by the patients, with no difference from the other groups (p=0.896) and at final assessment group IIIa reports an even higher level of satisfaction (p=0.003), achieving a very high level (4.6, considering that 5 corresponds to the statement “Excellent result consistent with the patient’s expectations”), while the other two groups on average stop at a lower level (3.6).
Although treatment of the dysmorphism and skin ageing of the arms is often surgical, for those patients who do not wish to undergo surgery, and in whom the aesthetic changes are not excessively significant, an aesthetic me- dical approach may be taken into consideration in order to improve the clinical situation.
The use of polylactic acid has been recently put forth in order to address the loss of skin density and thickness due to ageing proces- ses3,4.
The dermis thickening action of this filler has been pro- ven in the literature, thanks to its ability to trigger a lo- cal inflammatory response that results in neocollagene- sis11,14,15.
Through histological observations on biopsies, Lemperle and staff have observed the presence of a thin extrace- llular matrix around the hydrolysed PLLA microspheres and infiltrate of macrophages and lymphocytes already three months after the polylactic acid infiltration.
Six months later, biopsy examinations have shown the per- sistence of macrophages and giant cells around the PLLA microspheres, whereas nine months later there was no longer any trace, signifying complete degradation of the injected product and neocollagenesis in the dermis.
Vle- ggaar and Bauer have conducted biopsies in a 55-year- old patient treated with PLLA infiltration to correct the nasolabial folds, showing collagen thickening after 30 months7,13,24.
Philip Stein et al. report that the increase in thickness induced by PLLA is due to the formation of the capsule induced by macrophages, myofibroblasts and neo-for- mation of type I and II collagen fibres22.
With regards to dilution, in our experience we have em- ployed 10cc hyper-dilution, 9 cc of which of sterile dis- tilled water and 1 cc of 20mg/10ml lidocaine, in order to obtain a more fluid, hence easier to handle solution that would spread more easily.
This dilution has allowed us to prevent the possible formation of subcutaneous lumps that often represent a complication for this filler and which might have occurred easily in this anatomical area, also in connection to the thinness of the skin19.
In order to prevent possible complications, for each in- filtration, 10ml of PLLA were injected per side into the deep dermis (preferable) or subcutaneously and vigorous massage was performed on the whole area, followed by massage at home according to the 5-5-5 scheme21.
In order to provide objective validation of the result, we performed patients?€? satisfaction test with the help of photographs as well as ultrasound examination.
The latter showed a thickening effect, although minimal, of the deep dermis, especially in the cases where clinical improvement was most conspicuous, particularly in type IIIa patients.
Specifically, these patients had a better represented subcutaneous adipose tissue compared to the other two classes considered.
This likely further affected the patients?€? opinion of an improvement.
However, the pro- blem of the control group remains, as it was not carried out and would have further validated the results of per- sonal experience.
Our experience did not point to complications.
Side effects such as stinging and pain were reported, which spontaneously subsided in few hours and benefited from the vigorous massage.
?Treatment of ageing in the medial region of the arms may represent a challenge for the aesthetic doctor, espe- cially in patients who reject surgery.
Among the various methods, the use of fillers has a rationale due to their sti- mulating effect for collagen production and thickening of the deep dermis.
Polylactic acid has recently been approved by the FDA for aesthetic use, particularly for correcting the nasola- bial folds.
In view of its ease of handling and scarcity of side effects, when used properly, it has started being used off-label in other body regions as well.
The skin of the medial region of the arms is extremely thin and delicate, which is why filler hyper-dilution has been employed, associated to vigorous massage which has led to brilliant results where the right therapeutic indication has been considered.
Restructuring treatment to reverse aging of the periorbital region is one of the challenges faced by plastic surgeons.
Although there is no single treatment for different skin types, it is nonetheless important to evaluate which treatments are most suitable.
Our study compares amino acids injection treatments with treatments combining percutaneously injected dermaroller and amino acids.
We considered parameters like patient's discomfort during treatment and post- treatment satisfaction.
Such data were analyzed with the Students' t-test.
Infiltration with amino acids proved to be more effective in the long term than the combined approach.
The periorbital region includes lower and upper eyelid and the external area formed by zygomatic and frontal bone structures.
This region is frequently affected by early signs of aging, often requiring intervention in order to improve wrinkles, abnormal pigmentation, skin relaxation, and brow ptosis, albeit, without altering the facial expression1.
In order to determine the best treatment, it is necessary to define a global approach to the affected area, since most aesthetic defects are often associated.
There are two approaches; on the one hand, the surgical route (lower blepharoplasty, upper blepharoplasty, eyebrow lifting and cantopexy, cheeckbone lift and lipofilling)2-4; on the other, the medical-aesthetic solution, which presents the most used and standardized options, like botulinum or hyaluronic acid injections, peeling treatments and the use dermarollers5.
Both approaches are often integrated with each other in order to obtain comprehensive results to the subcutaneous-muscular structure, as well as to the skin proper (Figure 1).
Our study was performed on 10 patients who were treated at our office in 2015.
Our aim was to verify the effectiveness of the bio-revitalizing treatment by comparing it with the use of dermapen and bio- revitalizing products.
Our female patients had a mean age of 53 years (39-73) and no relevant associated diseases.
All patients present a light to moderate facial aging (0-2 Lemperle scale).
Each patient was treated in one eye contour area with bio-revitalizing micro-particles (amino acids + noncross-linked hyaluronic acid).
The injected mix was 1 cc of noncross-linked hyaluronic acid + 100 mg of freeze dried amino acid (50 mg of Glycine, 37.6 mg of L Proline, 5.4 mg of L Lysine, 7 mg of L leucine).
The other eye contour was treated with a 1.5 mmdermapen with controlled mechanical and biorevitalizing damage seeping through small incisions (Figure 4).
Patients were visited 1 day and 7 days after treatment, respectively.
Lastly, we assessed patient's satisfaction with the results of treatment, as well as mild-moderate complications, if any, and the discomfort of both treatments, which we determined on the VAS scale and compared with the Students' t-test to define the statistical significance of averages.
During follow-up, we identified a standard trend, both in terms of satisfaction and the onset of complications; on the other hand, we also took account of non- uniformity of assessment, with regard to the pain of both treatments.
In terms of the onset of complications, on the first day, 8 in 10 patients had greater swelling in the area treated with bio-revitalizing concentrate injection, whilst 2 out of 10 women did not present any difference with the area treated with dermapen. 2 out of 10 patients had bruising in the area treated with bio-revitalizing concentrate injection.Notwithstanding initial swelling-related discomfort, 7 days after treatment, patients reported greater satisfaction about the area treated with bio-revitalizing concentrate injection, with an average VAS score of 8.1, compared with a 6.7 scored by the area treated with dermapen (Tables 1 and 2).
A comparison between our data with Students?€? t test yielded a statistically significant average variance (p <0.012).
Finally, we evaluated patient discomfort.
This datum did not demonstrate uniformity in the sample.
Patients evaluated discomfort at the time of treatment with bio-revitalizing concentrate injection with a mean of 5.4, while the average VAS score was 5.5. in the area treated with dermapen.
The difference between the two data is not statistically significant (p <0.87).
?Proper treatment of the periorbital area continues to be challenging for plastic surgeons.
For a better approach to treatment, it is necessary to fully understand the structures involved in this area's aging process and leverage or counteract their features in order to improve aesthetic results.
Starting from the deepest structure, it is important to identify the bone surface that can function as support and be used for anchors, suspensions or the implantation of prostheses7-8.
Next, we reach the orbicular muscle, whose sphincter movement leads, over the years, to the formation of so-called "crow's feet"? the action of this muscle can be countered in a simple and effective way with the use of botulinum toxin.
However, although the intent is to counteract the muscle movement and to prevent the formation of additional wrinkles, existing wrinkles must also be treated.
The eyelid skin adheres loosely to the orbicular muscle and becomes more evident with age, when subcutaneous tissue and collagen component (which are already very scarce in this area) tend to disappear.
What is enhanced, therefore, is thin skin, which is often loose, non-elastic and with dark spots caused by sun damage.
This is, in our experience, the most difficult area to treat.
The use of long-chain hyaluronic acid is to be discouraged, since it is often perceptible to the touch in areas where the skin is so thin, and because it will likely migrate due to the action of the underlying muscle9-10.
The combined use of botulinum toxin, bio-revitalizing concentrate injection and chemical peeling acts at different levels, starting from the muscle up to the horny layer, and considerably improves skin elasticity, texture and brightness.
The addition of dermaroller with its controlled mechanical action on the dermis creates a further action of a different type.
Our patient sample showed greater satisfaction with the side treated only with bio-revitalizing concentrate.
This result means for us that, although non-crosslinked hyaluronic acid and amino acids have a relatively low molecular weight, percutaneous penetration, even if facilitated, of the dermaroller, is not satisfactory.
Conversely, direct injection of bio-revitalizing concentrate, though at first produces greater trauma and greater liquid retention (swelling), eventually minimizes complications and becomes more effecting within one week from treatment.
Amino acids in the bio-revitalizing concentrate, indeed, stimulate collagen synthesis and growth factors.
Ultimately, the action is to recreate the normal structure of the dermis, improve tissue nourishment and activate metabolic processes, while also slowing down catabolic reactions.
In our experience, therefore, the best non- surgical treatment against aging of the periorbital area is bio-revitalizing concentrate infiltration following botulinum toxin treatment, to be completed with appropriate peeling.
Treatment of the periocular area with a combined approach including surgery and aesthetic medicine is always advisable.
In our experience, in fact, this area has strong aesthetic and emotional relevance for patients, and effective remedy to aging leads to great satisfaction.
Although it requires excellent knowledge in both fields, the dual approach is essential and useful even in cases of small surgical complications, which can be improved through cosmetic medicine measures.
Nowadays, the non-surgical treatment of loose skin on body contours is one of the main requests from our patients.
The decline in demand for major surgery worldwide has stimulated research into a technique that could allow an effective result, on an outpatient basis, without the need for any convalescence period.
The experience we have gained of implanting polylactic acid suspension wires in the face was the starting point for developing a new technique, aimed exclusively at correcting slackness in body skin.
We have reconsidered the anatomical know-how in our possession and, by making the necessary changes, have created a much more suitable implant model, particularly for the abdomen, inner arm and inner thigh areas.
Between May 2013 and May 2016, we treated the following patients: 32 on the abdomen, 22 on the arms and 16 on the thighs.
The patients were all female, and 37 to 62 years of age.
The procedure was found to last 30-40 minutes and was well-tolerated, with daily activities being resumed as soon as the procedure ended.
We found no major or minor complications either during or after the treatment.
Even immediately post-treatment, it was possible to note a marked improvement in the tautness of the skin.
After about 60-90 days there was a stable improvement in skin tone and the tissue laxity induced. 6 months later, the results had remained stable, whilst after 1 year an initial decline was observed.
This technique can therefore be considered a safe and effective method.
Experience, respect for anatomy and patient selection are fundamental aspects in obtaining the best results in each individual case.
Over recent years, there has been an increase in requests for the correction of skin slackness in different areas of the body.
Next to the face, which remains the main request, the demand for treatment of slack skin on the front abdominal wall and the inner arms has increased.
Major surgery (surgical facelift) has undergone a decline over recent years, in favour of less invasive techniques that ensure a much shorter post-operative decursus with the immediate recovery of daily activities1,2.
For example, new non-invasive instrument technologies, such as radio-frequency and micro-focalised ultrasound, have been introduced in order to activate neo-collagenesis and the production of new elastic fibres, thereby reducing skin slackness3-8.
Re-absorbable suspension threads were introduced, which, in addition to creating an immediate lifting effect, stimulate the fibroblasts over time to produce fibroblasts, collagen (types I and III) and elastic fibres6-13.
These new techniques are vastly different from a surgical facelift, which remains a more definitive, but far more invasive intervention, since these activate more tissue stimulation and regeneration, which allows a ?€?softer?€?lifting effect, which is less immediate, structurally different, less long lasting, but which is repeatable, less invasive and less expensive.
Based on our accumulated experience of using resorbable threads for facial slackness, we have dealt with treating skin slackness in other areas of the body, in particular, the anterior abdominal wall, the inner arms and the inner thigh regions, using wires fitted with bi-directional cones.
After some initial experiences which were characterised by poor results, we reconsidered our own anatomical ideas and, by making the necessary changes, created a model implant that is far more suitable for the abdomen, inner arms and inner thighs.
The presence in the skin of lines that correspond to the orientation of natural tension vectors in the skin with a lower tension was first described by Langer in 1861.
In 1942 Cox, and later Stark in 1977 demonstrated that the direction of Langer's lines remains, even after skin is removed from the body and the tension to which has been subjected is resolved, and concluded that these lines had an anatomical basis14-16.
By analysing the direction of the collagen fibres in the dermis, Cox and Stark showed that there was a correlation between the direction of the collagen fibres in the dermis and the direction of Langer lines.
Therefore, the presence and orientation of Langer lines (Figure 1), in our opinion, should always be considered when suspension sutures are used to reduce skin slackness in the various areas of the body.
The sutures we use are made of a resorbable material (polylactic acid) and are fitted with re-absorbable cones (made from polylactic acid and glycolic acid).
Each suture has two sets of cones: these two series have the same number of cones, and are oriented in opposite directions towards the two ends of the suture.
The cones are inserted into the suture and held between two nodes which are placed 0.5 cm or 0.8 cm from one another.
The central part of the suture measures 2 cm and is devoid of cones.
Three different lengths are available for this type of sutures: 30 cm (8 cones), 27.5 cm (12 cones) and 26.7 (16 cones).
The suture material (polylactic acid) is completely re-absorbed in 18-24 months: it acts in the subcutis, stimulating the fibroblasts to produce fibres and collagen, which helps to gradually increase the volume of the area.
The cone material (82% L lactic acid and 18% glycolic copolymer) is reabsorbed entirely in 8-12 months17.
The cones have a (smooth) 360° surface anchorage to the subcutaneous tissue, which allows an instant and solid fixation.
As foreign bodies stimulate the inflammatory reaction, they slowly degrades them, and a fibrous capsule is formed around them: this fibrous reaction consolidates support of the adipose tissue, and prevents the risks of migration or extrusion13.
The technique provides for the insertion of 2 polylactic acid wires with 12 or 16 cones, longitudinally in correspondence with the alba line, using a narrow V technique (with an opening oriented towards the navel), both above and below the position of the umbilicus, put in place with a moderate tension (to prevent the wires sliding, which could tear the subcutaneous tissue).
This allows the right abdominal quadrants to be divided from the left, due to stiffening of the tissue at the midline.
These are the lines of Langer, with their various orientations and directions between the supra-umbilical and sub-umbilical regions, which allow the abdomen to be divided into four separate quadrants.
Hence this technique is defined as the'4-Technique' (Figure 2).
At this point, following the direction of the Langer Lines, for each quadrant, a 12 or 16 cones wire is implanted (depending on the size of the patient), using a U shaped technique, with a tension direction distributed from the medial area to the side area.
Own maximum expression precisely corresponding to its medial area, 1-2 wires are implanted with 12 or 16 cones using the C technique, on the axis of the Langer lines, with traction vectors from the medial region to the lateral region.
The shape that the implant of these wires takes, which resembles a Tau cross, dictated the name of this as the Tau-Technique.
The surface of the inner thigh region is separated into an anterior to a posterior area, again taking the distribution of the Langer lines into account.
To avoid cancelling the opposing tensile forces that the suspension wires can generate, we decided to separate the surface of the arm in an anterior and a posterior area, taking the distribution of the Langer lines into account at all times.
The technique therefore provides for an initial implantation of 1 or 2 wires with 16 cones by a linear technique in correspondence to the medial region of the arm.
This allows resistance to the opposed sliding of the wires to be created, simulating separation of the circular surface of the arm into two semi-circular areas.
The technique therefore provides for the initial implantation of 1 or 2 wires with 16 cones using a linear technique, in correspondence to the medial region of the thigh.
This allows a resistance to the opposed sliding of the wires to be created, simulating the separation of the circular surface of the thigh into two semi-circular areas.
At this point, 1-2 wires are implanted with 12 or 16 cones using a C technique, in axis with the Langer lines, with traction vectors from the medial region towards the lateral one.
It is important to identify (using a Doppler or echo- colour-Doppler device) and mark the path of the great saphenous vein before starting the procedure to position the threads, in order to avoid an accidental puncture and to create a safe area during the implantation.
Between May 2013 and May 2016, we treated the following patients: 32 abdominal patients, 22 inner arm patients and 16 inner thigh patients.
The patients were all female, and from 37 to 62 years of age.
The patients selected had a slackness measured by the pinch-test of between 1 and 2 cm; lower values were excluded due to the risk the cones would be visible on the surface; higher values were excluded because the adipose tissue present could have compromised the result.
The patients were all monitored at 15 days, 2 months, 3 months, 6 months and 12 months.
In choosing the type of wires to be used, measurements taken of the surface to be treated on the patient played a fundamental role.
As a rule, 16-cone threads were used for all areas of the body; when the morphological characteristics demanded it (limited surfaces in the regions being treated), 12-cone wires were used.
Depending on the size of the various body areas, 12 or 16 cones were implanted. 8-cone wires were never been used because they have a tensile strength that is too low for the body districts in question.
Normally, 6 threads are used to treat the abdomen; where there is a limited treated surface, 4 wires are used (2 vertically positioned with the linear technique and 2 positioned laterally with the低”  technique) .
Normally, 3 wires are used for the arm; where there is an increase in the dimensions of the treated area, up to 6 wires (2 longitudinally positioned with the linear technique and 4 positioned transversely with the “C” ?technique) are used .
For the inner thigh, 4 wires were normally used, with a maximum of 6 wires for large surfaces (2 positioned longitudinally with the linear technique and 4 positioned transversely with the “C” ?technique).
All of the treatments were performed with the patient correctly positioned on an operating table.
The treatments lasted 30-40 years and were well tolerated by the patients, who were able to resume their daily activities immediately after the procedure (apart from suspending sporting activities for 15 days).
We did not detect any major complications; the only minor complications were bruising and some irregularities of the skin surface.
In rare cases, bruising may occur (especially after treating the arms) which resolves spontaneously in about 15 days.
Immediately post-treatment, it is very common to have irregularities of the skin surface, which resolve spontaneously within 7-10 days.
Approximately 60-90 days after implantation, a stable improvement in skin tone and tissue firmness can be observed, probably induced by the neo-collagenesis process due to the initial polylactic acid resorption process.
At the check-up 6 months after implantation, the results were still stable, whilst after 1 year, the beginning of a descent was observable.
The use of polylactic acid suspension wires for treating slackness of the abdominal wall, the inner arms and inner thigh regions arose from the experiences gained from using them on the face.
However, the anatomical differences between the various body districts demands a careful assessment of Langer?€?s lines and the adoption of implantation techniques that are specifically designed to successfully treat large flat areas of skin (such as the anterior abdominal wall) or semi-circumferential areas (such as the inside of the arms and thighs).
One needs to be well-acquainted with the techniques for implanting the suspension wires, and above all, to use the correct anatomical plane for inserting the threads themselves.
To facilitate wire implantation, we recommend the use of an operating bed and correctly positioning the patient.
During implantation, the fact that the patient complains of intense pain can be attributed to the needle reaching a layer that is too deep and puncturing the muscle fascia.
This should be assiduously avoided to prevent complications in the post-treatment period.
Careful selection of patients is also important for avoiding those in which the results will be poor, i e those with excess adipose tissue (in which the threads will not be able to reduce the slackness) and those with skin and subcutaneous tissue that is too thin (in which the cones will be excessively visible on the surface for several months).
We believe this method offers considerable advantages.
First of all, the correction of skin laxity of the body with this method is carried out in a surgical outpatient's clinic, without any patient admission, under local anaesthesia.
The implant usually takes only 30-40 minutes and allows almost immediate recovery of daily activities, even if with some limitations on sporting activities in the first 15 days.
This method has also proved to be easily repeatable over time, with results that improve with subsequent implants.
Sub-dividing the areas to be treated into quadrants also means that interventions can be performed in several steps, both to consolidate the result over time, and in response to the patien's requirements (dividing the treatment into several phases also spreads out the cost).
Last but not least, this method is considerably less expensive, and does not require any period of convalescence post- treatment, compared to a surgical face lift, even if the results are less long-lasting.
The use of polylactic acid suspension wires in the treatment of slackness of the abdominal wall, and the inner regions of the arms and thighs can be considered to be a safe and effective method.
Experience, respect for anatomy and patient selection are fundamental aspects in obtaining the best results in each individual case.
Only recently have we come to a freedom in terms of our body management; one that finally gets a new role that is no longer a docile body but a flexible one, which changes through personal choice and action.
It is up to each individual to take responsibility for their own body design because, through the body, we define ourselves and our identity making the body become a visible and personal identity.
It is a thoughtful project, in which the lifestyle, guided by the regimes, is essential: "the appearance of the body, its behaviour, its own sensuality, the ways of undoing basic needs, food, and clothing"?
New body management techniques, the ones called reflexive, are also very important: their main task is to work on the body to modify it; trying to keep it or adapt it in the most appropriate and responsive ways depends on the individual.
Amongst all of these techniques, there is also the aesthetic surgery.
The purpose of this work is to deeply study the motivations that induce people to undergo cosmetic medicine treatments.
With this type of research I want to show all the reasons behind something that can be seen as an apparent aesthetic need.
There was a research that focused on the evaluation of the relationship that links sexuality to beauty and the motivations that push a woman to undergo to aesthetic medicine treatments.
The aim of this work is to analyze the reasons that lead people to undergo aesthetic medicine treatments.
This kind of research shows what can be hidden behind an illusory purely aesthetic necessity.
Underlying this work was a research that focused on the analysis of the relationship between sexuality and beauty, and the reasons that lead women to undergo aesthetic medicine treatments.
Furthermore, the research has examined in depth how the necessity of changing something about themselves, for real problems or for aesthetic wishes, grows and becomes real.
The method that has been used to gather the declarations about this kind of research is a qualitative method, because this method allows the achievement of the predetermined goals through the collection of both stories and in-depth interviews.
Because, as Patton asserts ?qualitative methods are highly personal and interpersonal, because naturalistic inquiry takes the researcher into the real world where people live and work, and because in-depth interviewing opens up what is inside people.
More specifically, it is a biographical type of interview, a research technique that focuses on the biographic study and the reconstruction of the past of the person interviewed, thanks to the confidentiality and the freedom of expression given to the subject.
The biographical interview is a specific application of in- depth interview; it involves unstructured interviews and open-ended questions.
The peculiarity of the unstructured interview, also known as depth interview, resides in the individuality of the topics and in the itinerary of the interview.
The interviewer has only one job: to speak about the topics he wants to speak about; then, the interviewer will let the interviewee develop his way; keeping the initiative of the conversation, stimulating him or leading him to in-depth analysis when there is a connection with interesting topics.
The interviewer has to encourage and stimulate the interviewee and he also has to control the conversation, containing the digression and avoiding off topic comments2-4.
Furthermore, I have chosen to use the qualitative interview because the actions of listening and mediation of the interviewer carry out a function of care towards the interviewed5-8.
In qualitative research, without listening to the other one, there cannot be the comprehension of his point of view, through that process of identification, from the interviewer to the interviewed, essential for the collection of data9.
In-depth interviews were 15, pointed towards women exclusively, and aged between 30 and 60, integrated with phone interviews.
Each interview has been anonymous and confidential.
Indeed, each person who joined this research required anonymity and the names used are fictitious names.
As the interviewer, I have always respected these tasks.
I have always kept the feedback with the interviewee under control after the primary question because, as Rogers says: ?An essential part of the interviewer’s job is the use of techniques in order to focus on and to control the interaction after the primary question, in this way the aims will be achieved properly?.
A typical intervention is represented by the use of the probing questions: these are not proper questions, these are neutral stimuli that have the function to encourage the interviewed to move forward, to lower his defensive barriers, to examine in depth the topic, to give more details11-12.
This is useful to fix the common problem: to receive incomplete, inaccurate and unsuitable answers.
I have tried to keep the topic unchanged, avoiding the use of sentences like “cut to the chase”, “summarize”, etc… therefore, I have tried to accept, respectfully, the interviewed point of view without judgement, neither positive or negative.
Sometimes, it was hard to keep an impartial attitude, because of the sensitivity of the topics13.
Furthermore, during the interviews, I always tried to take advantage of the moments of silence of the interviewed, using them to encourage her to examine in depth a specific topic and I forced myself to focus on that topic without thinking about the next question to ask.
The social and physical place in which the interviews have been realized, has been chosen by the interviewed themselves, so they could feel comfortable and free to express themselves in the most spontaneous way.
This work is developed on an investigation about"the beauty's world"?which focuses on the necessity of a detailed study about the sexual dimension inside itself.
The theoretical propositions and the conceptual tools that I chose for the interpretation of “the beauty’s world”? have concerned many authors that approached those concepts in different ways.
More specifically, Bourdieu sees the human being as a thinking body and he asserts: I am the body that I have.
We learn through our body.
The social order enrolls inside the bodies through this enduring comparison, more or less dramatic, but that always leaves space to the affectivity and, more specifically, to the affective transactions with the social place.
Indeed, Remaury, regarding the relationship between health and beauty, thinks that the body fulfillment is based on the youth-beauty-health triad: beauty corresponds with youth, gained by health.
Therefore, health shows the beauty, the biggest expectation about the present body's cult and the first duty about the self- care.
The pluralization of the current social context highlights how much, nowadays, there is a freedom of managing our own body that accepts a new role, not more a docile body but a flexible one, target of choices and actions that are personal: each one, then, has the responsibility of his own design because, through it, his identity can be determined, the body becomes a visible messenger of identity itself19.
Furthermore, new techniques for the management of the body, called reflexive, should be considered important: their main aim is to work on the body in order to alter it, preserve it or adapt it in the most suitable way, depending on the subject?€?s necessity.
Aesthetic surgery is one of these techniques.
The body-project includes another innovation: the privatization of the body, or better, the privatization of the agencies of social production of the body, the demonstration of the ruling uncertainty in modernity20.
Current societies are, just like yesterday, somatic societies: inside of them, body becomes political and a cultural field of battles21.
According to Juvin, the body becomes bank and market, it is a standard of truth, because it is the only one that remains, as a constant, in our past.
The body is the center of the power, object of all the expectations.
According to Juvin’s theory, if in the past the soul reigned, in this wellness civilization, the body is the ruler.
Therefore, in a society like the current one, where the communication, being multimedia, is able to reach a lot of interlocutors instantaneously (normally, women are more involved than men), it is easy to impose beauty’s models and ideals and so Remuary asserts that ?This augmentation of the beauty’s models is caused by the media (journalists and advertisers): their aim is to sell female beauty’s models repeated a lot of times, whose intensity - despite the attacks of which women are victims - not only doesn’t reduce but it continues to grow during the years.
Today, the image of the realized body goes with a higher and higher number of moments of our lives – from the grains we eat at breakfast, to the time we spend at the cinema?.
One of the reasons that convinces women to resort to aesthetic medicine is the desire of an improvement in order to like themselves more and to be liked by the others.
Indeed, social desirability influences the choices of the social actors.
Giovanna, 31, explains how her problem influenced herself in her social relationships: ?This problem affected my self-esteem and my relationship with others.
It affected me so much it limited my routine.
In the morning, for example, I rarely put my make-up on… because I don’t have time, desire, because in the past I didn’t need to do it; now it became a complex, to show myself to the others, without my make-up on is awful… it leads me to bend, to walk with my head down… I would rather appear rude not saying hi to someone than to show myself with no make-up on.
Basically, I organize it: if I have to pop in to the Garden [a famous meeting place in the city of Catanzaro] to buy cigarettes, I put my make-up on or I?€?d rather not stop by, there is no other option.
I care about the judgement of the people who know me… of course, if I go in a new place it is normal that I feel more sorry if a guy, maybe a nice one, sees me in that way… in the places that I already know it is the same… man or woman, it is the same! […] The idea that the others have about you influences you anyway.
Everyone's opinion.
All that we are is the result of what the others want (us to be)?.
Maria, 40, regarding how the social conditioning can bring a woman to do aesthetic medicine treatments, says:
?Some women decide to have a makeover because they are influenced by what surrounds them, they look for the way to find themselves, they want to find self-confidence in an outward change.
There are also women that seem like fools to me, that do it only because they are influenced by a stereotype to which they want to conform to?.
Lucia, 60, married and mother of three, resorted to aesthetic medicine after a family crisis which involved the relationship with her husband.
About the way in which sexuality changes during the years, so she says: ?Now it is important for me to like myself, before pleasure was important for me.
Unfortunately, sexuality with my husband has changed, he has got a problem, a prostate cancer, for this reason we no longer have sex, but before his operation sex was different, it has changed over time because it is normal… when you are self-confident you have a different attitude, you dare more?.
Francesca, 55, says that she resorted to aesthetic medicine because she needed to be “re-born” after a difficult period and after an abortion.
Regarding the relationship between sexuality and beauty she says that:
?It is true that, when you like yourself, you are more liked by the others… even when you have sex but most of all in what precedes sex… you are more self-confident and, as a consequence of that, you transmit your self-confidence to your partner. I always liked myself, but it is obvious that, with a little help, everything becomes easier?.
Laura, 50, wife and mother, had breast surgery twice and speaking about the relationship with sex, she says:
?The first time I had breast augmentation I did it not for myself, because I could have worn a push-up bra, the problem is with the other sex, if you have to take off your clothes in front of a man and you don’t like yourself, you feel uncomfortable.
You can have toned legs but if you have two awful boobs, what does it matter?
I live only for my job, I don’t care about anything else… it sounds bad but also my friends, my family, my daughter, everything is subsequent to my gym… my life is my job so it is normal that my job has influenced and will always influence my life choices.
During the years, I noticed physical changes but also a very strong change about sex, I used to be a panther… I trained as much as I wanted to have sex… now it is not the same, after the pregnancy everything has changed… my lust is equal to zero.
I don’t have arousal.
I don’t have the desire to make love, even though my husband is five years younger than me and he goes through everything.
Honestly, my relationship with him is less passionate than before, but we grew up a lot.
The rhythm and the frequency decrease because we both have chaotic lives and, honestly, when I come back home I set up, I reorganize and then I just want to rest, holding my husband but I want to relax?.
And then, about this topic, she asserts that: ?Even in sex… come on, you know that a man doesn’t notice your foot’s nail, but we… do!
We must be perfect… light turned off or suffused in order to hide as much as possible our flaws… but the thing is… in that moment a man just wants to reach the orgasm, he doesn’t care about your nail, he doesn’t care about your extra pound!?.
After the declarations of the interviewed it is clear that an essential role in the decisional process, which moved these women to undergo aesthetic medicine treatments, has been played by Doctor Catizzone.
Her way to approach her patients, the comprehension and the reassurances she gave to them, can be considered as shared and decisive factors for all the women interviewed.
Giovanna, 31, says that: ?Of course, if I hadn’t known about the Doctor’s “reputation”, I would have relied on no one else… if I hadn’t had the certainty that the Doctor had the right competences, I would have entrusted my face to her care… because an arm is an arm, but a face is a face?.
Barbara, a 50 years old interviewee, says that for a long time she had the desire to undergo aesthetic operations but only the meeting with the Doctor gave her the right confidence, so she declares: ?I have been thinking of doing them for a long time but I made my decision only when I met the Doctor.
 I was scared, you never know who you can find… then I met the Doctor and my experience started.
I rely on her entirely. This plan is good for now, then we’ll see… when I get old we will decide what to do?.
Lucia, 60, asserts that in the figure of the Doctor she found, first of all, a person inclined to listen, so she says: ?I was wasted, my weight was 48 kgs, my face was marked… I would have changed everything about me… luckily, I met Rosanna who didn’t permit it and she proceeded step by step… I’ve been using botulin and hyaluronic acid for a year… once a year, but then I continue with a vitamin cycle.
The doctor, who is a very honest person, has suggested to me to proceed slowly and to start with the face revitalization.
Since then, I never come here saying “do this to me” but I say to her: “Rosanna, how do I look to you?
What can we do?”.
I totally trust her.
I want to say it again, when I came here for the first time I would have done everything, I would have spent millions!?.
Serena, 40, says that she does annual mesotherapy treatments and, as a consequence of a brutal accident, she had to do a facial plastic, 20 years earlier and, regarding the relationship between doctor and patient, she asserts: ?I went to the Doctor and I trusted her because she doesn’t want to take advantage of her activity; if you go to her, asking her to do lips augmentation, she says no…and before doing something she examines the reasons, she is a kind of psychologist!?.
Giovanna, 31, resorted to aesthetic medicine after a true problem, more than an aesthetic one, so she speaks about what for a  woman should be considered  the strength of her own body: ?In my opinion, a woman is beautiful if she has a beautiful face, her weight can also be 200 kgs but she should have a beautiful face… perfection is hard to be reached, everyone has her own flaws (and women are very good at finding them!) but for me the most important thing is to have a not big enough nose, expressive eyes, whatever color, plump lips… and teeth… teeth are always important… I watch everyone’s teeth.
The body impresses me less, both in women and men. The more a man is toned, the less I like him?.
Lucia, 60, about what captures the attention of a man regarding a woman, says: ?We should understand which is the most important thing also for men… I mean, a young lady could have the will to do breast augmentation in order to be more attractive.
For a man, for example, the breasts, the buttocks, the lips are more important.
I think that for a woman the face is more important… also the breasts, but the buttocks less, I think! I mean, b-side is the first thing a man looks at?.
Bianca, 38, who had some problems on her face because of a violent cystic acne, about her idea of beauty asserts: ?I am an aesthete, I like beauty! I know that I am not beautiful, I can be appreciated or not, I know which are my strengths and my weaknesses. 
Beauty, even though it could be a superficial concept, is aesthetic. There are well - defined aesthetic standards: a tall, slim, wiry woman with a beautiful breast, not a 4th size or a 5th size of bra, but that fits with her body… that a woman is “curvy” is not a problem for me but she also has to have a beautiful face.
The thing is: if you have a beautiful body, the face (which is important anyway), can be less important than the body; if you have a curved body, you should have a beautiful face inevitably… I find beauty there and for this reason it overshadows the body.
The man, indeed, has totally different standards of beauty.
It depends on the man, if he is a man with capital “M”, he would agree with me, but it is not a common situation, I have a very bad consideration about men and I feel more sorry because I have a son… but I will do my best to help him grow up also with a female point of view, so he can see everything from every perspective.
Anyway, I think that there are men, not a lot, that appreciate women beyond their aesthetic beauty?.
Marta, 40, regarding her idea about a beautiful woman and how a man considers a beautiful woman, expresses her point of view:
?In  my  opinion,  a  beautiful  woman  should have a beautiful face, that’s all
A man looks at a beautiful bottom, beautiful boobs and he no longer understands.
A man, honestly, thinks with everything but his head.
I am sure that, no man likes a too plumped lip.
He could be more attracted by the body.
I think that, if a woman has a toned body and beautiful breasts, she is ok with that and it is normal that a man likes her, but a deformed face, because of surgery, no, I think that nobody likes it?.
Laura, 50, admits that she lives the research of beauty as an extreme love for her own body, so she asserts, regarding her idea of aesthetic beauty: ?In my opinion, the physique is more important than the face… I mean physique in all senses. I don’t agree with medicine, I don’t agree with something that could hurt our body.
I don’t agree with medicine, I don’t agree with something that could hurt our body.
I always say to my daughter “you don’t have the right to use drugs, not because drugs are illegal, but because you are in good health and you don’t have the right to take off your health when there are people that are born and live in a wheelchair.
If you have life and beauty you can’t ruin them, you don’t have the right to do that, on the contrary, you have to defend them”.
In my opinion, beauty and health are on the same road.
As for beauty, I think that women and men have different standards.
Women do everything for being thin, toned, we do a lot of sacrifices and then, maybe, men look at the one with a big bottom, a shapely woman.
I like when a woman is not too shapely.
I don’t like too slim women and neither do men… they can’t stimulate a sexual desire, they can make men feel tenderness and we can envy them for the way in which they can wear a dress.
We actually create a lot of problems… we personally let men notice our own flaws because otherwise it is not clear how some women, objectively ugly, could be liked by men?.
This research has put in evidence the strong collaboration between two disciplines very different between them, sociology and medicine.
This research focused on the evaluation of the relationship that links sexuality to beauty and the motivations that push a woman to undergo aesthetic medicine treatments.
Techniques of skin rejuvenation most commonly remain the ablative and exfoliative types of treatments such as peelings, lasers and micro-dermabrasions.
In contrast, the revitalizing treatments focus on replenishment and nourishment to reverse the cutaneous aging signs.
This concept, unique to the technique of skin rejuvenation mesotherapy or revitalization, focuses on replacement and nourishment of the vital components of the skin with mostly poly-vitamin solutions and especially non- cross-linked hyaluronic acid.
Also, soft tissue filler injections have evolved to unique formulations adapted to various depths of injections by altering the rheology of the hyaluronic acid (HA) fillers with various concentrations, cross-linking degree and chain lengths to adapt product injection to unique depths and indications.
HA fillers are being used for skin rejuvenation purposes by utilizing the low density, low cross-linked HA products suited for dermal injections and not purely sub-dermal as with past and traditional HA fillers used mostly for contouring and volumizing.
Many doctors will perform skin rejuvenation with either fine line filler products (minimal cross-linked HA) placed dermally or just sub-dermal by manual or device injection or some may prefer to treat with poly-vitamin products with mesotherapy techniques of injection.
Only recently have we realized the importance of combining both techniques for optimal results.
The technique, which I have named "Bio-skin-gineering"? focuses on treating each individual layer of the skin, with knowledge of exact depths and angle of injection with various products according to the suitability of the product for the specific layer.
The rationale behind this concept is that each layer from epidermal, DEJ, dermal to subdermal needs to be treated individually in order to reduce aging changes in the layers individually and to optimize results.
This protocol is a natural, safe and effective therapeutic intervention to improve the external cutaneous signs  of solar elastosis and photo-aging of the face, neck, d?collet? region and for the hands.
The protocol is especially suitable for patients with:-?? Thin or dehydrated skin -?? Fine wrinkling (crepe paper appearance) -?? Superficial lines or skin coarseness -?? Textural skin aging (due to solar elastosis or poor lifestyle habits) -Need for preventative intervention.
The specific indications that can benefit from this treatment includes: Peri-orbital loose skin and wrinkling  Peri-oral wrinkles Cheek wrinkles Forehead lines Neck and d?collet? skin aging Skin aging on hands.
The  proposed  protocol  focuses  on  replenishment of the decrease in HA (free HA) within the layers; supplementation of the necessary anti-oxidants, vitamins, minerals and agents that will stimulate cell renewal and reduce oxidative aging process; and lastly to replace lost volume within the deep dermal and sub-dermal layers with minimally cross-linked HA to obtain a cushioning effect.
The bio-skin-gineering technique opinion aims to emphasize the importance of knowledge of the precise depths of each layer of the skin and how to accurately reach each layer to optimize treatment outcomes.
The technique is performed in 4 steps, all done in the same treatment session.
Epidermal rejuvenation (0.05 - 0.1mm thick on face to d?collet?) .
The epidermic technique, as described in mesotherapy practice, is performed with a 30G needle of 12mm length at an angle of 10° or less to the skin, with only the bevel entering this superficial layer9-13 (Figures 1A and B .
The movement is rapid and almost"tremor - like" to ensure that the needle does not enter too deep.
Soft pressure on the syringe plunger will ensure droplets of the product are deposited on the surface and reach the epidermis.
Alternatively, the technique of epidermal skin needling with a device containing short 0.5mm - 1.0mm needles and very slight pressure (minimal bleeding) can be used to rejuvenate the epidermis.
The product is applied to the skin before and during the rolling technique on the skin (Figures 1C and D .
Rejuvenation of the epidermis is performed using a sterile injectable poly-vitamin solution with only free hyaluronic acid (not cross-linked at all) that is suitable for epidermal level of injection or needling.
An example of a suitable product for this technique is NCTF135HA?, due to its optimal suitability within the epidermis and dermis, and due to the numerous scientific safety and efficacy data.
Role of epidermal technique: enhance cell turnover, improve moisture balance, nourishment of the avascular epidermis and intense cutaneous stimulation.
The epidermic technique contributes greatly to the radiance that patients notice from the 1st session.
This technique has been described in mesotherapy practice as the papule technique.
This layer can only be reached with a manual injection with a 30G - 32G needle of about 4mm length.
Separating the epidermis from the dermis, with a papule forming, is the aim of the technique.
A typical wheel or papule has to be visible during injection, avoiding too deep injection as this will reach deeper dermis or subdermal level.
The technique is focused on problem areas such as inside wrinkles, scars, eyelid skin or areas where maximal lifting is required.
The same product as mentioned in epidermal technique and not at all with a cross-linked HA, as the papule will remain.
Product choice is essential and should be water soluble, neutral pH and sterile for injection purposes to minimize pain and complications.
Role of papule technique in DEJ: optimizes transfer of nutrients, enhanced microcirculation and improvement of the upper part of the papillary dermis (DEJ).
The dermis is most often treated with aesthetic treatments.
Low viscosity and minimal  cross-linked  HA fillers have become popular for dermal injection to enhance collagen quality and quantity.
Non cross-linked HA solutions are often used for intra- dermal injection. 
The purpose of dermal injections: enhancing collagen content and quality, extracellular matrix enhancement and also improvement of fibroblast and elastin quality.
The objective is to nourish the dermis and the fibroblasts with essential vitamins and minerals, to replace free HA within the dermis and to create multiple punctures into the dermis for microcirculatory improvement, growth factor release and to create a favorable environment for dermal fibroblast cell function.
A quality product containing an ideal combination of essential nutrients, vitamins, minerals, anti-oxidants and free HA will further ensure the bio-revitalising effect of the treatment.
The technique used is referred to as a multi-puncture technique (Figures 3A, B and C .
Ideally performed with a 30G or 32G needle of 4 - 6mm length at an angle of 45° to the skin.
Multiple, fast and repetitive punctures into the dermis is used.
Free (non cross-linked) HA integrates in the dermis and will not cause any complications of long-term nodules or "Tyndell effect".
This will replenish the ECM, induce deep hydration and stimulate fibroblast activity to increase production of the scaffolding components within the ECM.
Dermal rejuvenation with cross-linked HA fillers (low degree cross-linking) are done with a different technique to avoid nodules and other possible complications.
Very small amounts of product should be injected per point.
The techniques that I have found to cause the least amount of side effects for the patients are not papules, but rather very fine ‘worms’ or lines of product in a mesh/ grid pattern.
Alternatively a micro-cannula (25 - 27G) can be used intradermal or immediately sub-dermal which is associated with more resistance due to the density of the dermis and more pain compared to sub-dermal.
Role of cross-linked HA in dermis: maximizes collagen stimulation, support and enhancement of a thin and dehydrated dermis.
The deepest injection of this protocol could either be performed last or one could also start with this layer, especially when using a product that has lidocaine incorporated within the product.
This would minimize the discomfort of the previous techniques in a sensitive patient.
The sub-dermal layer, which is the superficial fat pad directly below the dermis, forms a"cushioning" effect to plump a thin and fragile skin.
The depth ranges from 1 - 1.5 mm in facial skin and is reached with at least 45° angle of injection.
Use a product containing low degree cross-linked HA and also with free HA that is suitable for superficial injection and should not leave nodule formation or uneven lumps or bumps.
The use of low viscosity hyaluronic acid fillers as a cushioning effect and enhancement of the superficial fat pads under the dermis have increased in popularity over the past few years (Figure 4A).
In high-risk regions such as peri-orbital and forehead region, the use of a 25G cannula is vital to reduce the risk of intravascular placement.
When using a needle technique, it is recommended to perform aspiration on the syringe before injection of a cross-linked HA filler.
Most side effects are transient, mild and technique dependent.
These may include mild erythema, bruising and swelling.
Using exclusively HA soft tissue fillers for this directly subdermal level will ensure safety, reversibility and reduced lumpiness, especially with a very low viscosity product.
This technique is ideal to use in patients with loss of sub-dermal fat pads and a skinny or skeletal face.
The loss of superficial as well as deep fat pads leads to more excessive wrinkling and dehydration of the skin and dermis.
Subdermal cross-linked HA injections will give an instant plumping of wrinkling, as well as continued stimulation of collagen and elastin production.
Though, the revitalizing, hydrating and long-term improvement of the dermis and epidermis is only seen when combined with the prior techniques.
The technique used can be either with a micro-cannula (27G or 30G) with a fanned technique (less bruising and reduced risk for vascular placement) or with sub-dermal multipuncture technique with needles (Figures 5A, B and C .
The technique choice varies according to the physician's comfort in skills and according to the product used (Figure 6).
Prior treatment with anaesthetic cream improves patient comfort
In practice, I sometimes start from the deepest technique and end with the most superficial technique.
This reduces pain for sensitive patients because the fine line filler contains lidocaine, which then makes the following techniques almost pain free.
Finish the treatment with an intensive massage with a suitable post procedure product.
The bio-skin-gineering technique can be scheduled in   a course of treatments ranging from 2 - 3 treatments depending on the severity of cutaneous aging of the patient.
The treatments can be spaced apart from 4 - 6 weeks apart with ideally 3 sessions per year.
Clinical results are visible from the first session, which includes improved hydration, smoothness and plumpness (Figures 7A and B .
Continued improvement from 6 weeks to 6 months includes wrinkle reduction and firmness, this can be explained by understanding the wound healing phases of neocollagenesis starting from 4 to 6 weeks only (Figures 8A, B and C .
In a clinical practice it is not possible for physicians to confirm histological improvement on each of our patients following procedures, but we can rely on the studies performed by the manufacturers for efficacy and safety.
In general we understand from clinical data that HA soft tissue fillers have a good safety profile, especially when performed with quality products and meticulously safe and sterile techniques.
Various manufacturers of HA fillers have further shown the improvement of collagen quality and quantity following sub- and intra-dermal placement.
Gathering from these studies, we understand that this protocol will therefore improve collagen quantity and quality.
Injectable polyrevitalizing solutions used with good mesotherapy techniques have also clinically proven the benefits of treatment with quality products on skin hydration, glow, evenness and further collagen stimulation.
Therefore, we could assume that the combined technique would give us the combined the histological improvement within the skin.
Results are most visible in patients with dehydrated, wrinkled or dull skin requiring an intensive boost.
Results are specifically evident on areas with thinner skin such as peri-orbital, neck and d?collet? regions.
Each layer has specific purposes of functionality and contributing towards the skin’s external appearance (Tables 1 and 2).
Histological Changes seen in Skin Photoaging or solar elastosis.
The use of injectable HA to rejuvenate the skin, rehydrate the dermis and reduce wrinkles are based of the role it plays within the human skin.
Some of the functions of HA within human skin :Cushioning and plumping effect (anti-wrinkle) Hydrating (retaining water) Keeps collagen and elastin moist – youthful skinOptimizing wound healingImmune functionUV radiation protectionImproves collagen synthesis and normal skin functionIndispensable for the visco-elastic balance of epidermis and dermisRole player in keratinization in epidermis.
The BIO-SKIN-GINEERING protocol is a combination of techniques to ensure rejuvenation from the deepest layer to the most superficial layer to improve the total rejuvenation of the skin and long-term patient satisfaction.
Ideal for wrinkled, dehydrated or dull skin requiring an intensive boost.
The protocol is most suitable for peri- orbital, peri-oral, neck and d?collet?, or other areas.
Side effects may include some small bruises depending on technique, but other than this no significant risks are involved when using high quality products.
The benefits for the patients include instant radiance and hydration with textural improvement from 4 weeks following the treatment is visible, especially when using high quality products with supporting data.
This technique aims to improve the total rejuvenation of the skin and long-term patient satisfaction.
Physicians should be familiar with the techniques to reach each layer separately and have knowledge of the average depth of the layers in facial skin especially.
High quality polyrevitalising solution with non-crosslinked hyaluronic acid is used for the epidermal, DEJ and dermal layers.
Low degree crosslinking HA fillers are used for the deep dermal injections and sub-dermal placement.
Ensure that products used for this technique has sufficient safety and efficacy data.
The combined techniques and products ensure combined histological benefits and combined clinically evident benefits for both the patient and the physician.
Although scientific data exists for the use of low viscosity HA fillers and also for the use of certain mesotherapy or revitalizing solutions, this combined technique shows promising results and more research in the combination of these techniques and products would be beneficial.
In recent years, the increasing demand for minimally aggressive techniques for body contouring, using noninvasive devices have been developed.
The improvement of body contouring using non-invasive liposuction approaches in place of surgical liposuction, is associated with safer procedures, quicker recovery, fewer side effects, and less discomfort, representing one of the most stimulating areas of plastic and aesthetic surgery today.
In order to improve the efficacy of liposuction method, several technical updates have been introduced, such as ultrasound-assisted or laser- assisted liposuction.
These improvements have increased the selectivity of instruments for adipose tissue, enlarging the total amount of lipoaspirate and reducing total blood loss.
In the literature, several papers have been reported on these surgical techniques, analyzing, in particular, how they work as well as the aesthetic outcome, but no one has given an evidence-based comparison of them.
It is known that the advantages of laser treatment include a less bleeding, dermal shrinking and an earlier recovery time5 and, also, that it does not adversely affect the platelet levels6 but a limitation of these external laser devices is that they cannot use high energy because it causes thermal burns.
The advantages of ultrasound methods are the selective rupture of adipocytes membranes, leaving the blood vessels, connective tissue or nerves undamaged showing no effects on surrounding tissues as damage is restricted to a small point, the tip of the cannula.
As known, the ultrasonic waves can penetrate through tissue and during the way they lose energy for reflecting, scattering, or absorbing by tissues.
It is also reported that ultrasound energy produces molecular vibrations causing a temperature increase above 56° C and the subsequent disruption of the cell membrane and collagen denaturation.
Our group has previously investigated the ex vivo effect of both external and surgical ultrasound-irradiation on human adipose tissue specimens by evaluating the weight change and lipid release over time, the histological architecture as well as adipocytolysis and apoptotic cell death induced by treatment.
In particular, after both external and surgical ultrasound treatment, we observed a significant weight loss, a triglycerides and cholesterol release, a clear alteration of adipose tissue architecture, collagen fiber damaged and a down- modulation of procaspase-9 and an increased level of caspase-310.
In the present work, we report the results of an experimental comparative study between two surgical devices, one based on a low frequency high intensity ultrasound supplied with a harmonic cannula and the second equipped with a Nd:YAG laser cannula, performing ex vivo surgical procedures on samples of human adipose tissue.
Sample weight variation as well as amounts of oily fractions released after treatment as well as the relative neutral lipid profile were evaluated at different time intervals.
The effect of both treatments on the histology of adipose tissue was also analyzed.
Neutral lipids: cholesterol (C), oleic acid (OA), 1,3-dipalmitoyl-2-oleoylglycerol(POP),phosphomolybdic acid hydrate (Cat. 221856) and haematoxylin/eosin, solvents for TLC assay (hexane, diethyleter, acetic acid) were purchased from Sigma Chemical Co. (St. Louis, MO, USA).
Aluminium silica plates were acquired from Merck, Darmstadt, Germany.
Formaldehyde was purchased from J T Baker (Phillipsburg NJ, USA).
Sterile catheter tip syringes were acquired from BD Plastipak (Franklin Lakes, NJ USA 07417). 10% neutral buffered formalin ready to use, dehyol absolute, dehyole 95, and paraffin were purchased from Bio-Optica (Milan, Italy).
Advanced smart processor (ASP300), automatic stainer (5010 Autostainer XL), and rotative microtome (RM2135) were purchased from Leica Microsystems (Nussloch, Germany).
Direct light microscopy (Eclipse 50i, Nikon Corporation, Japan) and inverted optical microscope (Nikon Eclipse TS100, Nikon Corporation, Japan) were from Zeiss (Jena, Germany).
The ultrasound device “Micro lipocavitation” was purchased by Lain Electronic S.r.l., Milan, Italy (Technosinergye Medical Technology, Pomezia - Rome - Italy), composed of a high frequency generator, a radiofrequency transmission cable, and probe with piezoelectric crystal.
The device uses modern microprocessors, able to monitor the session peak cavitation, to manage the enormous amount of energy produced by low frequency ultrasound (US) (37.2-42.2 kHz).
The pulsed 1,064 nm Nd:YAG laser “SmartLipo” (DEKA MELA, Calenzano, Florence, Italy), was composed by a  laser  generator that works in ultra-pulsed mode (pulse time 150 microseconds; frequency 10-40Hz), a transmission cable made in optical fibers (300 micron) and a handpiece equipped with cannula (diameter 1mm) to introduce the optical fiber in tissue thickness.
Subcutaneous tissue specimens of white human fat were obtained from one patient, a 44 year old woman with massive weight loss  history (BMI at  operation=29 kg/m2) referred to the Plastic and Reconstructive Surgery Unit (Director: Prof. Maurizio Giuliani), Casa di Cura Di Lorenzo, Avezzano,  L’Aquila  - Italy,  requiring  a body contouring and undergoing abdominoplasty.
Before treatment, the patient, who gave written informed consent, had an accurate clinical examination to evaluate the general health condition.
Patient was normal.
In order to obtain a good quality of ex vivo adipose tissue sample, reducing the tissue injury, no adrenaline-based solution was administered before skin incision, and the flap was raised with blade; small vessels were coagulated using a bipolar forceps whereas larger perforators were tied with absorbable braided suture.
Immediately after surgery, the sample was kept sterile in saline buffer (NaCl 0,9%) (SB) and sent to the laboratory for ex vivo laser and ultrasound treatment.
Fresh human adipose tissue explant was divided into 6 portions of the same size and weight as far as possible (range: 232-235 g taking care to preserve the integrity of the structural continuity and the junction between the skin layer and subcutaneous fat.
Two tissue portions were used as control (untreated), two were treated with the US device and two were treated with the Nd:YAG laser, as described below.
In order to faithfully reproduce the surgical procedure, adipose tissue specimens were infiltrated by syringe with a volume of saline solution equal to sample weight.
The amount of SB spontaneously released by the specimens was evaluated.
Samples were further weighed at 20 minutes and 2 hours from the end of treatment with US or Nd:YAG laser device for 10 minutes.
Control samples (untreated) were also weighed at the same time intervals.
As previously described10, US treatment was carried out by a surgeon in our group, with a surgical probe equipped with a harmonic cannula (diameter: 2mm; length: 15cm).
The default setting of the ultrasound generator for surgical procedure was chosen to perform the treatment: power 100% (??19-21 W cm2); frequency 37.2-42.2 kHz; the single exposure time was 10 minutes.
To find the optimal resonance frequency, prior to the procedure, the auto-best frequency test was performed according to the manufacturer's instructions.
The treatment was implemented by introducing the cannula directly into fat thickness without skin incision.
The single Nd:YAG laser treatment was performed in parallel by a second surgeon of our group on ex vivo adipose tissue samples in the same environmental conditions of US treatment described above.
The default setting of the laser generator chosen to execute the treatment was: power 10 Watt, 40 Hz and 10 minutes of exposure time.
In order to achieve a uniform energy supply, the optical fiber tip was perpendicularly cut before treatment.
The trocar cannula was introduced into fat thickness without skin incision and then the optical fiber was driven 2 mm beyond the trocar cannula tip.
Both procedures were performed within the standards of clinical practice on ex vivo adipose tissue, administering the energy with slow fan-shape movements from the subcutaneous layer moving to the deeper one (Figure 1).
In the control group, samples were processed in the same manner without US or Nd:YAG laser irradiation.
The experiments were performed at room temperature (~20°C .
Lipids were extracted from the fluid portions released and collected at 2 hours after US and Nd:YAG laser treatments by the sequential addition of 200 μl methanol/HCl, 500 μl chloroform, and 200 μl methanol.
The extraction and centrifugation steps were repeated twice.
The organic phases, obtained from different extraction steps, were collected, dried under nitrogen, and then applied to TLC silica gel plates (20x20 cm).
Neutral lipids were then resolved using the mixture of hexane/diethyleter/acetic acid (70:25:3 v/v) as solvent.
The solvent was allowed to ascend to 1 cm from the top of the plate, and then the plate was removed, air-dried and stained.
Cholesterol (C , oleic acid (OA), 1,3-dipalmitoyl-2-oleoyl glycerol (POP), the latter representative of triacylglycerols (TAG), were used as standard lipids.
TLC staining was obtained by vaporizing 10% phosphomolybdic acid solution on plates.
To perform the plates staining a phosphomolybdic acid solution was prepared by dissolving 10 g in 100 ml ethanol.
Dried silica plates were put in the preheated oven for 10 minutes at 80?°C and acquired by densitometer (UVItec Limited BTS -20M, Cambridge UK).
The densitometric analysis was performed using ImageJ software.
The data obtained from control, US- and laser- treated specimens were compared.
Untreated, US- and laser-treated human adipose tissue samples were fixed in 10% neutral buffered formalin at room temperature for 3 days.
The samples were washed under running water for 2 hours, and dehydrated in the ethanol ascendant series with an automatic processor (Leica ASP 300) and then manually embedded in paraffin.
Tick sections of 4μm were gained with a rotative microtome and stained with haematoxylin and eosin with an automatic stainer (Leica 5010 Autostainer XL).
Glasses were analyzed independently by two pathologists with an Axioskop optical microscope purchased from Zeiss, Germany at 4X magnification.
Statistical analysis of data was performed using two- way ANOVA followed by Bonferroni post-hoc test (Prism 5.0 GraphPad Software, San Diego,  Ca).  Data was expressed as mean ± SD. P<0.05 was considered statistically significant.
The study looked at comparing the ability of low frequency high intensity US and Nd:YAG laser devices to reduce the subcutaneous adipose tissue stores, ex vivo adipose tissue samples, previously infiltrated with SB, and treated for 10 minutes with a single US (Figure 1 upper panel) or laser exposure (Figure 1 lower panel).
The sample weight was evaluated immediately after treatment (T0) and at different time points (20 minutes and 2 hours) from the end of treatment and the relative results are shown in Figure 2.
The spontaneously released fluid from untreated samples (control) was collected and measured at each time points, generating a relative basic sample weight loss (~3% at T0, ~5% at 20 minutes, ~9% at 2 hours).
Both US and laser treatment, either at 20 minutes or 2 hours, led to a significant sample weight loss when compared to untreated samples, with US treatment more relevant than laser exposure in particular at 2 hours.
Of note, US treatment caused a sample weight loss significantly greater than the Nd:YAG laser-induced effect already at 20 minutes post-exposure (P< 0.001) (Figure 2).
All fluid portions released from untreated, US- or Nd:YAG laser-treated specimens were collected and measured either at 20 minutes or 2 hours from the end of treatment (Table 1).
In accordance with the results on weight loss, both US and Nd:YAG laser exposures induced a release of fluid significantly greater than observed in untreated samples either at 20 minutes or 2 hours following treatment (P 0.001).
On the other hand, US treatment induced release of a higher fluid volume as compared to laser exposure (P 0.05) (Table 1).
Moreover, the fluid released by US-treated samples appeared also visually thicker when compared to those collected from the laser-treated samples.
In particular, the amount of the oily fraction in the fluid portions released after US treatment at 2 hours appeared objectively greater in comparison to laser treatment at the same time interval.
The representative images of fluid portions collected after 20 minutes or 2 hours from treatments are shown in Figure 3.
To evaluate the effectiveness of low frequency high intensity US treatment and Nd:YAG laser to injure the adipocyte membranes of ex vivo adipose tissue samples, the analysis of neutral lipids?€? levels in the fluid portions released from US- or laser-treated or untreated samples at both 20 minutes or 2 hours was performed by TLC.
In particular, the levels of cholesterol (CO), oleic acid (OA) and triacylglycerols (TAG) were analyzed after 2 hours from the end of treatment (Figure 4).
Both US and Nd:YAG laser treatments were able to cause an increase of lipid released when compared to untreated samples (control) with different levels of statistical significance, and the effect of US irradiation was more relevant.
Indeed, the treatment with US caused a release of lipid fraction significantly higher when compared to Nd:YAG laser exposure (P 0.001 for all analyzed lipids) (Figures 4 A C .
A representative TLC of neutral lipids released in untreated sample (control) and in US- and Nd:YAG laser-treated samples after 20 minutes or 2 hours from exposure is shown in Figures 4 D E and shows the TLC chromatography of lipid standards: C (Rf=0.38), OA (Rf= 0.46), and POP (Rf= 0.82).
Histologically, the effect caused by US- and Nd:YAG laser treatments to the adipocyte membranes was also verified.
Figure 5 shows representative images of the histology of adipose tissue sections from samples after 2 hours from either US or Nd:YAG laser treatment ex vivo.
Sections stained with haematoxylin-eosin displayed a normal lobular architecture with regular and undamaged adipocyte membranes in untreated samples (Figure 5A).
The alterations of adipocyte morphology of US treated samples associated with cell membrane appeared unbundled and broken compared to the histology of Nd:YAG laser-treated sample (Figure 5B and C respectively).
Even if the effectiveness of both US-assisted and laser- assisted liposuction has been extensively investigated, comparative studies of these aesthetical surgical procedures have not been reported on and should be required.
Indeed, the previous studies have been mainly focused on comparing either US-assisted lipolysis or laser-assisted lipolysis versus conventional liposuction alone.
To our knowledge, the present study is the first to reproduce in a controlled environment, surgical procedures of clinical practice which demonstrates if both methodologies give overlapping results.
In terms of weight reduction and amount of lipid released, US irradiation provided more significant effects within 20 minutes after the exposure.
Moreover, the assessment of the released oily fractions following each treatment showed that the US exposure induced a significant increase of neutral lipid levels (C OA and TAG) when compared to the Nd:YAG laser-treated samples.
In addition, US irradiation caused a more effective adipocytolytic effect, which was also supported by microscopic evaluation of adipose tissue sections revealing a high number of injured cell in US treated samples.
However, all these results should be confirmed by additional experiments and clinical studies conducted on larger series of patients.
Our results highlight that ultrasound-assisted liposuction is more effective to reduce the adipose tissue weight probably due to physical phenomena generated into adipose thickness.
As known, an ultrasound device, during lipoaspiration procedure, generates compression and depression cycles of ultrasonic waves at appropriate frequency.
This action produces a negative pressure in the adipose tissue thickness leading to cavitation that allows a selective tissue lipolysis with adipocyte rupture and triglyceride release from the lipid vacuole to the extracellular space.
Of interest, Bani et al demonstrated that, after ultrasound treatment, signs of interstitial inflammation were absent and the cavitation-induced effects seem to be restricted only to adipocytes, without injury to skin, vessels, nerves, or connective tissue.
Moreover, it has been demonstrated that ultrasound-assisted lipoaspiration treatment does not impair the proliferative ability and osteogenic potential of Adipose Stem Cells (ASCs) normally present in adipose tissue.
On the contrary, a different physical process is generated into adipose tissue using the laser-assisted liposuction.
In general, laser device releases a beam that is converted to heat energy through the adipose tissue, connective tissue, and blood vessels.
The 1,064 nm Nd:YAG laser exerts a lipolytic action melting the subcutaneous adipose tissue and, also, the septae that connects the dermal and muscle layers are divided.
Anyway, the use of surgical 1,064 nm Nd:YAG laser, even if it is less traumatic than traditional liposuction methods20, has the disadvantage to provoke a thermal injury; it is known that thermal energy or heating induces cell morphology and tissue texture alterations when the laser cannula reaches the temperature ranged between 47°C - 52°C.
Even if preliminary, all the results from our experimental study could represent a stimulus for appropriate clinical studies designed to comparatively evaluate the efficacy of these two surgical approaches by examining tissue in vivo.
Since the second half of the twentieth century, stretch marks have increased exponentially among the female gender and has gradually started to affect young boys as well, becoming probably the most widespread imperfection in the new generations.
Biodermogenesi? has shown remarkable effectiveness in the regeneration of stretch marks1 and post- surgical scars and burns, favoring the production of collagen and elastic fibers.
The aim of this study is to verify the outcomes of the new synergy developed by Biodermogenesi? on a group of female patients, all burdened by very old stretch marks that are white and opaque in color with a deep and rigid structure.
The new synergy, which combines electromagnetic fields with electron flow and vacuum, provides a number of sessions drastically reduced compared to the previous Bi-one? technology, to regenerate a twenty-year stria; before it required at least 20 sessions.
Stretchmarks that are more than twenty years old are more difficult to work on to obtain a significant improvement.
The existing literature has recently focused on the analysis of the results obtained with various types of lasers, facing some consolidated limits: the greater efficacy documented regarding only the red stretchmarks, and therefore newly formed stretch marks.
However, we saw that at the end of laser treatment programs the results were varying from moderate to none and were not replicable on all the patients, especially on SA.
Furthermore the laser procedure caused the constant presence of edema and PIH and a recurrent pain detected by patients.
Briefly analyzing the existing literature in support of laser therapy, we note various evaluations of the results obtained.
The results obtained with a non-ablative fractional laser in the experience of Katz et al.3, limited to only two young patients (12 and 13 years) with striae dated 3 and 10 months, appear positive.
Other experiences speak instead of appreciable results on a part of the patients treated, compared to others whose outcomes have been minimal or none.
In this sense, we recall the experiences of Bach and his colleagues4: "six of the 22 patients (27%) showed good to excellent clinical improvement from baseline, whereas the other 16 (63%) showed various degrees of improvement".
This is confirmed also by Stotland and his colleagues who confirm that: "photographs of 8 randomly selected patients showed an overall improvement of 26% to 50% in 63% (5 of 8 patients)"?
Even the experience of Tretti Clementoni has obtained greater uniformity of results6, he confirms that some patients treated with non-ablative fractional laser do not show significant outcomes: "the volume of SD depressions improved by more than 50% (mean improvement 58%) in the majority of patients (11 of 12 patients) and the color of the lesions improved by more than 50% (mean improvement 54%) in 83,33% of patients (10/12)"?
Apart from Katz's experience3, the other researchers4,5,6 do not mention the visual appearance of stretch marks (white or red) and their dating.
The experience of De Angelis et al. is based on 51 patients treated with 1540-nm fractional nonablative Er: Glass laser and the evaluation was done both by researchers and blinded, always with positive outcomes, noting a reduction in striae generally greater than 50%.
With the same technology, Farhad Malekzad and coll.22 evaluated the efficacy on patients with skin phototype between II and V burdened by SA with different outcomes.
We note that after 3 sessions, 2 out of 10 patients left the study, 3 out of 10 patients declared no results (no improvement) and 5 poor results (poor); the same patients, after a follow-up of three months, 2 out of 10 declared no results and 6 out of 10 little result.
In addition to the controversial outcomes found by the authors, the non-ablative fractional laser is characterized by moderate pain during therapy, micro-crusting, recurrent edema and hyperpigmentation generally reabsorbed in the course of 5/10 days.
De Angelis and coll. recommend to patients a prophylaxis in the month before the treatments and in the following six months.
Particular attention of the present study is reserved to the hypochromia of the dated striae and to the possibility to recover the faculty of tanning by the same ones.
This aspect has already been studied in the past, with a therapy based on excimer lasers.
Goldberg treated patients with 8/9 sessions per patient, with an attenuation in 60 cases, while in 15 there was no improvement.
Alexiades-Armenakas et al. have always performed 9 sessions per patient and have documented a 68% improvement in hypopigmentation but only stable between 1 and 6 months.
At the end of the 6 months no residual outcome was found.
The practically zero stabilization of the improvements obtained has limited the spread of this therapy, leaving the problem of hypopigmentation unresolved.
?We analyzed a group of 20 patients aged between 34 and 66 years, all with SA aged between 20 and 35 years, and treated them with a cycle of 9 sessions of Biodermogenesi?? on a weekly basis.
The patients were all healthy and did not have any preconditions for being excluded from the trial.
The exclusion criteria are as follows: Pace-Maker users; cancer therapy in progress or during the last 5 years; epilepsy; vascular alterations such as varices, phlebitis and thrombophlebitis; pregnancy or breastfeeding; alterations and hormonal therapies manifested during the last 6 months; anti-coagulant therapy; phenomena of anorexia or bulimia during the last 2 years.
Biodermogenesi? treatment was performed with an electro-medical device called Bi-one? 2.0 MD, combined with three synergistic active ingredients.
Treated stretch marks were present on the breasts, arms, abdomen, hips, buttocks, thighs, calves; for the cases in question we treated a single area on each patient, as required by the official protocols.
The treatment was performed with a non-invasive electro-medical device :Bi-one? 2.0 MD"?and protected by some international patents (Expo Italia S r l , via Segantini, 34, Firenze, Italy).
The apparatus was equipped with a generator of electromagnetic fields, an electron flow generator, a pair of vacuum pumps and a series of handpieces.
The generator of electromagnetic fields emits a capacitive shielded signal at a variable frequency, ranging from 0.5 to 1 MHz ± 10%, and variable intensity up to a maximum value of 6W on a 500 Ohm resistance.
The device is equipped with a bio-feedback system that allows to change independently the intensity and frequency of the signal delivered according to the different biological characteristics of each individual patient, increasing the temperature of the treated area between 39° C and 40° C The electron flow generator emits a 5 Hz square wave signal with a maximum intensity of 0.36 mA on a 500 Ohm load.
Generators of the electromagnetic field and of the electron flow are separated mechanically and galvanically from each other and towards the network plant.
The brushless vacuum pumps allow delivering negative pressure with absolute precision and stability, with a maximum value of - 0.35 atmospheres.
The treatment procedure was divided into two distinct phases, during which several forms of energies are present for different biological actions.
The first phase was about a light mechanical peeling.
For this phase, we used a single-use abrasive pad placed inside the PEELING handpiece.
PEELING handpiece works with a gentle vacuum action, designed to lift the tissue with the stretchmarks, bringing the hollow area of the imperfection outwards.
The complete deconstruction of the elastic fibers that characterizes very old striae1,7 allows the striae to extend outwards, bringing the dense and compact corneous layer in relief, favoring a selective reduction.
The second phase, called ACTIVE PLUS, provides the synergistic action of vacuum and biocompatible electromagnetic field generated thanks to a capacitive radiofrequency with variable frequency and intensity.
The combination of these forms of energy activates a greater action by the arterial capillaries, it increases the caliber and brings to the matrix oxygen and nutritional elements, stimulates the lymphatic microcirculation, and helps to drain part of the toxins present.
The simultaneous flow of electrons, object of the new technology, allows a reduction to the electrical resistance of the skin tissue, effectively amplifying the yield of the electromagnetic field.
As the effectiveness and the useful dose of the applied electromagnetic field is inversely proportional to the electrical resistance; the flow of electrons reduces this value and consequently increases the regenerative efficacy of the electromagnetic field.
At the same time we observe a strong pumping of the Na + / K +, able to increase the fibroblast activity, leading to the synthesis of collagen and elastic fibers and favoring a tissue repair.
The technology adopted for the present study provides a platform developed by NXP, a company owned by Philips, able to compare the acceleration obtained with sodium and potassium through the cellular barriers: Previous Bi-one?? technology from 300 to 450 mV New Bi-one?? 2.0 MD technology from 750 to 850 mV Documenting an effectiveness on average by double with respect to the previous technology.
The full treatment session takes about 25 minutes in total.
The protocol provides a preliminary evaluation system of stretch marks that determines the level of difficulty and therefore anticipates the patient what the result will be, how many sessions will be needed to obtain this result and how long the treatment cycle will take to be completed.
All the patients examined, respected the indications provided by the protocol.
The evaluation of the results of the treatment of stretch marks was carried out by using the VAS (Visual Analogue Scale) scales of the patient and the doctor.
Assessments were made before the start of treatment, during the preliminary visit (T0), after the last treatment (T1) and after a period between 6 and 12 months from the end of the sessions (T2).
The VAS scale asks the patient to make the most of the following parameters: Improvement of the stretch marks to the touch (depth and fibrosis) Improvement of the visual stretch marks (color and opacity) Increase in the faculty of the stretch mark to get a tan The values are expressed from 0 = 0% (no improvement), from 1 =  1%  to 20% (poor  improvement), from 2 = 21% to 40% (minimal improvement), from 3 = 41%  to 60%  (moderate  improvement), from 4 = 61%  to 80%(good improvement), from 5 = 81% to 100% (excellent improvement).
The results obtained are summarized in the following Tables 1, 2 (patient’s VAS scale) and Table 3 (doctor’s VAS scale).
The scales measure the perceived improvement on the treated stretch marks.
At the end of the treatment program (T1), 11 patients (55%) found an improvement between 41% and 60%, while 9 patients (45%) found an improvement between 61% and 80%.
The perception of the improvement obtained was increased when the follow-up (T2) was performed after more than 6 months from the end of the treatment program: 1 patient (5%) found an improvement between 41% and 60%, 6 patients (30%) found an   improvement between 61% and 80%, while 13 patients (65%) found an improvement between 81% and 100%.
At the end of the treatment program (T1) the doctors in one case (5%) evaluated an improvement between 21% and 40%, on another patient (5%), between 41% and 60%, on 15 patients (75%), between 61% and 80% and on 3 patients, between 81% and 100%.
Also the evaluation of the improvements obtained was increased by the doctors during the follow-up (T2): in one patient (5%) they evaluated an improvement between 41% and 60%, on 8 patients (40%) between 61% and 80% and on 11 patients (55%) between 81% and 100%.
Analyzing the documented results, a noticeable overall improvement of the treated stretch marks is evident, both in the evaluation of the patients and the doctor.
In the T1 test, we notice an overall improvement of the SA, which tends to increase in the months after the treatment.
The progression of the improvement is due to the activation of virtuous reactions, which we have also found with the previous version of the technology.
The reactivation of the sodium and potassium pump allows restoring a better activity of the fibroblast, which physiologically manifests itself in the course of a few weeks after the treatments, during when the maximum regenerative response is obtained by the treated tissue.
Another aspect that the patients have greatly appreciated is given by the newfound ability of striae to tan with the sun exposure another aspect that we had found with the previous technology.
Early experiences, first of Dr Artigiani and coll.1 had documented a recovery of the ability to tan by the stretch marks treated with Biodermogenesi?.
This aspect has allowed patients to expose themselves to ultraviolet also during the treatments, highlighting a progressive tanning of the striae.
Of course, for the patients living in Sanremo and Palermo who were part of this study, it was much easier to obtain the tanning of the stria as both of the cities are known for their beach attractions.
The stabilization and progression of the outcomes, as shown by the conclusions made in T2, where the appreciation of patients and doctors is consolidated, is confirmed by Bacci, who performed a follow-up after more than 5 years from the end treatment with Biodermogenesi?.
In his study, Bacci highlighted a general improvement of the results previously achieved, without any regression of the outcomes obtained on the patients.
Unlike what was found with other technologies, the treatment of striae with Biodermogenesi?? did not cause pain, discomfort, or any side effects, even minimal, at the end of each treatment session.
Patients were able to regain their lifestyle immediately without any limitation.
The choice of evaluating the results obtained with the present therapy by means of the VAS scale exposes to the risk of subjectivity that would not occur with instrumental or bioptic tests.
However, we believe that the type of result obtained, which is the filling of the striae, eveniftheyarepresentformorethantwentyyears, and their subsequent tanning makes this assessment acceptable.
In fact, the filling and the rediscovered capability of tanning of the stretched skin derives exclusively from a reorganization of the epidermis and the dermis, a restored basal membrane, a correct positioning of the melanocytes and from an adequate dermal vascularization.
Basically, to completely tan the striae, it is essential to fully regenerate the skin tissue.
In our opinion, the results obtained on all patients adopting t the VAS scale is certainly subjective, but in the specific case it is not questionable.
Biodermogenesi? opens up a new perspective in the treatment of SA by applying for the first time a non- invasive method that is not based on damage and subsequent repair.
We know that collagen fibers change between 52° and 55° C and contract at 65° C and come to denature between 60° and 70° C.
The thermal effect induced by Biodermogenesi? stabilizes the dermis temperature between 39° and 40° C and therefore the variation of collagen and elastic fibers documented bioptically by Bacci6 and by Artigiani et all. 1 gives us a curiosity about the induced regenerative mechanism, presumably related to the Vant Hoff law.
In the case of Biodermogenesi?, it is believed that the thermal effect is the consequence of the increased activity of Na+ / K across the membranes, favored by the applied electromagnetic field 18,19, it determines this reaction for mere friction.
The regenerative faculty of the tissues subject to greater activity by these carriers is amply demonstrated by the literature in sports medicine, in the field of recovery of muscle injuries.
However, the evident improvement of the treated striae is obtained in total absence of side effects.
Biodermogenesi? can be used successfully in the treatment of SA, even if they are very old (more than twenty years), favoring both an aesthetic result and an effective regeneration of dermis and epidermis, as evidently demonstrated by the renewed ability to tan by the striae as a result of correct skin reorganization.
All patients treated in accordance with the protocols have noticed a significant improvement in the imperfection, also confirmed by the doctors, with no side effects and without limitations to a normal lifestyle.
Lips are one of the most injected areas on the human body.
Hyaluronic acid (HA) is a golden standard mostly used for this procedure1.
The main aspects in the treatment of lips are comfort (for both the practitioner and for the patient) and a long-lasting effect.
In addition, it is necessary to use safe substances with regard to their compatibility and local resistance.
Based of the proven safety of Agarose gel it has been a factor in its increased use.
Only rarely are complications reported.
Agarose is in principle not a completely new material in medicine.
It has been used in the dental field for more than a decade.
The substance class is a neutral polysaccharide, it is completely biocompatible and thus degradable.
The aim of this article is to show agarose gel as at least an equivalent to those for treatment of the lips.
The following overview describes the possibilities of this treatment method.
In the lower part of the face are the lips as a focal point, especially in the foreground of the interest.
Lip augmentation is a cosmetic procedure that can give you fuller, plumper lips.
These days, an injectable dermal filler is the most commonly used method of lip augmentation.
There are many types of dermal filler that can be injected into your lips and around the mouth.
But the most common fillers today are products that contain hyaluronic acid.
The goal of an ideal injection is to make, improve the appearance of lips by adding shape, structure, volume, but also a natural softness too, without causing a change in movement or in facial expression.
The best suited filler for the lip area is on one hand locally resistant, that means the injected substance remains in the addressed region and does not migrate in around tissue.
Other unwanted effects are also possible, such as pain, swelling and bruising, lip asymmetry, allergic reaction causing redness or itching around the lips.
For this reason, the knowledge of the diversity of different materials and techniques in aesthetic medicine in cases of lip augmentation, is essential to increases the quality of treatment and for the patient satisfaction.
A prerequisite for this process is a good cooperation between patient and doctor, and which techniques or products are best to be used.
As a new alternative to the previous substances a new filler is now available: Agarose-gel.
The agarose gel is available on the market fulfilling the expectations and requirements of the most demanding practitioners.
Agarose is a polysaccharide from D galactose and 3,6-anhydro-L galactose, which are glycosidically linked.
It puts the main component on the agarose.
As it is made up of 100% natural polysaccharides, it is complete biologically compatible and also degradable.
It contains for example no cross-linked synthetic chemicals BDDE (1,4-butanediol diglyceridyl ether).
The gel is sterile, very viscous and elastic, and clear and transparent.
Due to the isotonic property of the gel, this filler is almost painless when injected.
It is locally stable and it has very few side effects.
There is also an immediate result achievable without expected downtime.
The increase in volume is directly visible because no hydrophilic volume process has to be waited for.
The human organism has no specific enzyme, to break down agarose.
Compared to hyaluronic acid, which is degraded by hyaluronidase, agarose degradation is made slowly by macrophages, before it finally takes place in the pentose cycle, and it is eliminated in the endoplasmic reticulum.
Thereby agarose is a long lasting aesthetic effect expected to last.
Agarose has been the substance of choice in various studies regarding its biocompatibility and no cytotoxic and genotoxic properties.
Due to its biocompatible character agarose gel has been around for over 10 years already in frequent use in the field of dental medicine and in oral surgery.
It is described to be a soft tissue augmentation for the perioral region.
The occurrence of complications is extremely rare.
Another benefit of this new filler is the replacement of lost subcutaneous fat and remodeling of the upper and lower lip contours.
Subcutaneous filler is able to achieve a youthful and natural look by filling-out lines around the mouth, the nasolabial fold as well as chin augmentation (Figure 1).
Patients are trending to natural and biocompatible dermal fillers for a safe and effective solution to anti- aging.
Patients with acute or chronic skin pathologies or direct involvement in or around the lips to be treated were excluded.
Pregnancy, lactation and hyaluronic acid treatment less than 3 months earlier were also excluded criteria.
In total 11 patients were treated.
The patients were between 19 and 38 years old.
All patients were female.
Nobody had had a treatment with permanent fillers before.
Five patients had previously had an injection with hyaluronic acid in the lips.
It is recommended to use before treatment a local disinfection and also to apply anesthetic cream to the injection site to numb the area.
In some cases, there is also the need to consider a local injection with anesthetic (lip block).
The treatment should be as painless as possible for the clients.
For this, besides a topical anesthesia, also the application of a very thin cannula is recommended.
Agarose itself is an almost painless injectable because of its isotonic properties, as mentioned above.
Only in the expansion in the tissue does a burning symptom come.
Therefore, the agarose gel can be mixed with a local anesthetic, such as Lidocaine.
Due to the viscosity of the material generally it is possible to use the 30 gauge cannula for the lips.
The reduction of hematomas and swelling is avoided largely by the direct compression and cooling, for example with cool packs, for a few minutes after the injection.
This minimizes and closes the bleeding.
Patients should avoid for about half an hour hot drinks (Coffee and Tea) due to lip anesthesia.
A direct reintegration into social life is easily possible due to the fast convalescence.
For example, the treatment can also take place during lunch breaks or before important events (Weddings), and patients can return to work or participate in events after the treatment on the same day.
According to the principle "What you see is what you get"?the result is visible immediately after the injection as well the final result An additional benefit is the use for patients who have been previously demonstrated intolerance, incompatibility to hyaluronic acid or other ingredients.
The only adverse events described were hematoma, redness, bruising and swelling.
All adverse events lasted for a maximum of 7 days.
The sense of satisfaction by the patients were evaluated with the use of a subjective analog scale from 1 to 10.
The mean score of satisfaction of cosmetic result was 7-10 immediately after treatment, and the score decreased after some months (Table 3).
The results lasted 5 months with a gradual decline to baseline.
The injected Agarose gel was very well tolerated with only a few mild adverse reactions which resolved spontaneously after a few days only.
No major complications (e g infectious processes, palpable implants, nodularitiy, overcorrection, allergies) were observed.
Agarose gel is a safe, low-risk, easily applicable therapy option for practitioner and provides a particularly good alternative method for augmentation area of the lips.
The application of agarose shows through clinical studies and analysis a high safety.
This innovative filler is characterized by local stability and good compatibility.
Because of its biocompatible character, it does not matter what material was used previously in the injection area.
As this remedy is 100% natural and very viscous substance, patients will achieve a high degree of satisfaction, as their facial expressions remain very natural and harmonious even when they move.
Thus, a realistic satisfaction of the patient expectation can be achieved, with an excellent cosmetic effect.
In addition, agarose has a very fast convalescent period and subsequent changes in shape do not come after the injection.
In summary, treatment with agarose has many benefits.
Thanks to its properties, this filler represents an important and successful option in the modern aesthetic medicine.
Agarose Gel is available in four different strengths for very soft, moderate, mild and fairly deep wrinkles.
The ultra-sound evaluation of dermis, along with other instrumental examinations, medical history and traditional clinical examinations aimed at identifying the patien's request, is included in the aesthetic and medical checkup.
This non-invasive diagnostic technique is considered as a powerful tool in the diagnosis and management of dermatological conditions in the clinical practice, since it provides clinical data that would not be available at naked eye examination.
With regard to this, it is also important to highlight that ultra-sound evaluation with high-frequency probe (20- 100 MHz) can play a crucial role in the evaluation of age- related dermal change in the clinical practice.
Moreover, the presence and degree of a typical Subepidermal Low- Echogenic Band (SLEB) is strictly related to photoaging degree: the lower is SLEB echogenicity, the greater is photoaging.
The high-frequency probe helps evaluate the structural characteristics and dermal thickness.
The aim of the cutaneous ultra- sound examination is to provide a qualitative and quantitative evaluation of skin layers and surrounding structures, being an additional and reliable instrument in the diagnostic phase as well as in the evaluation of the activity and severity of cutaneous diseases.
Photoaging initially produces an increase in elastic fibers with consequent dermal hyperechogenicity, which then results in the loss of collagen and elastic fibers and a decrease in dermal thickness and echogenicity.
Several studies support the relationship between cutaneous thickness and skin aging.
A study by Gniadecka M et al 1998 showed the thickness and echogenicity of dermal layers by means of ultra-sound technique.
The author showed that changes in dermal layers differ according to which body region is evaluated.
In fact, in photo- exposed body regions, the most superficial dermal layers are characterized by a progressive reduction in echogenicity, while in the body regions that are not exposed to sun rays the author observed an increase in echogenicity.
The deepest layers of dermis have an increased echogenicity in all body regions.
The author concluded that although photoaging and chrono-aging increase and/or reduce skin thickness depending on the body region studied, no strict correlation was generally observed between skin thickness and age.
In a later study, Gniadecka M 2001 analyzed skin aging-related dermal change observed by means of ultra-sound evaluation with high-frequency probe.
The author showed that the presence of SLEB in photo-aging is strictly related firstly to the degeneration of elastic fibers in papillary dermis, secondly to the basophil degradation of collagen and thirdly to the accumulation of glycosaminoglycans (GAGs) and water in papillary dermis.
Based on these considerations, SLEB could be an ultra- sound manifestation of elastosis and edema in papillary dermis.
The correlation between age, SLEB thickness and echogenicity makes it possible to use these parameters to evaluate the level of cutaneous photoaging.
Based on a number of available scientific data, ultra-sound evaluation is a non-invasive diagnostic technique that supports anti-aging treatment monitoring and can be considered as a valid option for the future evaluation of the efficacy of tailored anti-aging injections and topical therapies.
All the above is supported by a clinical study conducted in 28 patients eligible for biostimulation treatment with a medical device (Sunekos 200) in injectable intradermal sterile solution, containing a functional complex of 6 amino acids (glycine, L proline, L lysine, L leucine, L valine, L alanine) in association with high-purity hyaluronic acid, of non- animal origin, at a concentration of 10 mg/ml.
It has been documented that the morphological structure of elastin is characterized by a prevalence of L alanine and L valine12-14 that make this product particularly active on the turnover of the proteins of the ExtraCellular Matrix in case of facial skin laxity.
According to the recommendations of the Italian Society of Mesotherapy (Societ? Italiana di Mesoterapia, SIM), the investigators administered one session for 1 month (4 infiltrations); one session every 15 days for 2 months (4 infiltrations) and one monthly maintenance session.
Monitoring by ultra-sound technique was performed just before the first administration, before the fifth administration and one month after the last maintenance administration, in the zygomatic and mandibular region on the right and left hemilates (Figure 1).
In the study group (Figure 1) an improvement was observed in the structure, as well as an increase in the dermal thickness, a hypoechogenicity of dermis and an improvement in SLEB with matrix reorganization, and these factors were maintained until the end of treatment.
The most important factor is matrix reorganization.
In conclusion, all subjects showed a reduction in the echogenicity of the dermis, associated with an increase in the thickness after only 4 weekly sessions; one month after the last treatment, an increase in the thickness was observed, as well as a normalization of echogenicity related to patient’s age, matrix reorganization and SLEB.
In summary, the ultra-sound evaluation of dermis, along with other instrumental examinations, medical history and traditional clinical examinations aimed at identifying the patient's request, is part of the aesthetic medical checkup.
This non-invasive diagnostic technique supports anti- aging treatment monitoring and can be considered as a valid option for the future evaluation of the efficacy of tailored anti-aging injections and topical therapies.
It is also an additional reliable instrument in the diagnosis and management of dermatological conditions in daily clinical practice.
Based on these considerations, in a recent clinical experience 28 patients were treated with a product containing hyaluronic acid and AA and showed a qualitative and quantitative improvement in most patients (Figure 1).
After 4 weekly administrations, the ultra-sound evaluation showed a reduction in echogenicity that could be related to the deposit of hyaluronic acid and an increase in edema in the acute phase.
At the end of treatment, the increase in thickness was maintained and echogenicity restored.
The evaluation performed in this undoubtedly positive experience should be further studied in a systematic clinical study, since the results could be extremely relevant.
Chrono-aging is the plethora of metabolic changes occurring in skin physiology as an effect of aging.
One well-known manifestation is skin laxity, which is due to both a decrease in collagen and elastin production and to changes in the extracellular matrix including reduction in hyaluronic acid (HA) synthesis.
The glycosaminoglycan HA is widely distributed in epithelial and connective tissues and is a chief component of the extracellular matrix.
Here it contributes to tissue hydrodynamics and viscoelasticity, protection from oxidative stress and, most crucially, tissue repair.
HA's regenerative action is due to its intrinsic anti-inflammatory and bio-stimulating properties, resulting in fibroblast proliferation and increased collagen production.
The aforementioned properties make HA a highly desirable dermal agent in the field of aesthetic medicine for the correction of soft tissue defects.
However, once injected in the dermis, native HA is rapidly degraded by hyaluronidase, making it non-viable for mid- to long-term results.
This led to chemically stabilizing HA via cross-linking, a process increasing the molecule's stability, rigidity and elasticity, but whose main drawback is the chemical alteration of HA's natural molecular structure.
This issue was bypassed by Profhilo?, a 2015 product developed by IBSA Pharmaceuticals, whose innovative thermal production process yields stable, cooperative hybrid HA complexes without the need for BDDE or other chemical agents.
The product formulation is a mixture of 32 mg of high molecular weight HA (110-1400 kDa) and 32 mg of low molecular weight HA (80-110 kDa), stabilized by a thermal process consisting of a high-temperature step followed by a low-temperature step.
Profhilo?’s unique characteristics include high HA concentration, excellent manageability, low viscosity, optimal tissue diffusion, a low tissue inflammatory response and a duration comparable to weakly cross-linked gel.
Profhilo?’seffectiveness has been proven in in vitro studies, where it demonstrated enhanced tissue repair, extra cellul are nvironmentre modeling and neofibrogenic and adipogenic properties, while maintaining optimal conditions for fibroblast, keratinocyte and adipocyte vitality.
Profhilo?’s clinical indications in the field of aesthetic medicine are in tissue remodeling and improvement of skin laxity of the face, neck and body, and its in vivo efficacy has been proven on 120 patients over the course of 4 independent published studies.
IBSA has furthermore developed the Bio Aesthetic Points (BAP) technique, a Profhilo?-specific injection procedure for the tissue remodeling of the malar and sub-malar areas.
The technique entails five 0.2 mL bolus injections in the superficial subcutaneous tissue compartment of each hemiface, localized in anatomically receptive facial areas identified for the lack of large vessels and nerve branches: the zygomatic protrusion, the nasal base, the tragus, the chin and the mandibular angles (Figure 1).
This minimizes the risks and maximizes the diffusion of the product in the lower third of the face.
The BAP technique allows for highly satisfactory results with only 2 treatments performed 4 weeks apart.
Previously published clinical experience on Profhilo?'s efficacy in tissue remodeling prompted the current study, which focuses on an ethnic subpopulation of the Slavs, i e the Central Eastern European group.
This ethnic group displays so-called Oriental mongoloid face features, which differ from the Caucasian craniofacial form in facial profile, shape of the orbits, cheekbones and mandibular angle.
Specifically, the oriental face is wide and round or square, with a flattened facial profile, shorter forehead, and broad nasal bridge with wide nasal wings.
The eyes are typically almond-shaped with an elongated intercanthal width, set in fuller upper eyelids with an absent supratarsal fold (so-called "single eyelid"?.
On the basis of gross histological findings, the single eyelid is due to the fusion of the levator palpebrae aponeurosis with the orbital septum closer to the eyelid margin than in non-Asians, hindering the aponeurotic fibers of the levator from reaching the subcutaneous tissues, which is responsible for the formation of a double eyelid crease.
Oriental mongoloid lips are fuller and more protuberant, and the chin more receded than in Caucasian individuals.
Overall, the skull morphotype is roughly rectangular, compared to the trapezoidal Caucasian morphotype.
Lastly, the mimetic- and chewing muscles are active and well developed: it has been posited that the peculiar Oriental structural features of the zygomatic area and the malaris muscle, which is inconsistent in Caucasian anatomy, prevents soft tissue ptosis, with an overall anti-aging effect of the midface.
Despite the numerical strength of the considered population, few studies have assessed facial skin aging in Asian populations.
One such study highlighted an increase in transepidermal water loss (TEWL), denoting a loss in stratum corneum barrier function, coupled with a decrease of sebum content, due to a decrease in estrogen-induced sebaceous gland activity.
Based on these observations, we investigated the efficacy of Profhilo? for facial skin rejuvenation in 10 individuals (9 females, 1 male) of Oriental appearance aged 26 to 62 (mean = 44 years).
We included only participants compliant to the following criteria: No rejuvenation procedures performed 6 months ago or later.
Relatively normal somatic health.
Not during pregnancy/lactation period.
No tendency to form cheloid scars.
Participants were requested to maintain the same habits on food, exercise, make-up, cosmetics, and detergent.
A comparative analysis was led between group 1 (5 participants), treated with Profhilo? following the BAP technique for injection points (29G needle) (Figure 1), and group 2 (5 participants), treated with diffuse injections of Profhilo?.
The latter technique consisted of diffuse 0.05 mL injections 1 cm2 apart in the subcutaneous layer of the right and left sumbalar areas (30G needle).
Both groups were treated in two sessions at 4-week intervals, and efficacy was evaluated pre- and post-treatment.
Comparison was performed via photographical evidence, Soft Plus and Antera 3D assessment systems.
The Callegari Soft Plus probe system was used to measure skin hydration (in terms of capacity measurement), elasticity (in terms of stress/deformation of the skin by suction application) and melanin (via a double wavelength reflectance photometer) at three points on the right side of the face: the center of the forehead, the outer corner of the eye (1 cm above the zygomatic arch and 2 cm laterally from the outer cantus) and the cheek (2 cm laterally from the labial commissure).
The Miravex Antera 3D macrophotography camera was employed for 3D topographical and chromophore analysis, specifically assessing overall wrinkle size, skin texture in terms of arithmetical mean roughness and average pigmentation concentration.
Again, three areas of the right side of the face were assessed: the glabellar area, the outer corner of the eye and the cheek area near the labial commissure27.
Statistical analysis was performed using Fisher's angular transformation (φ-method) to compare Group 1 (treated by diffuse injection) and Group 2 (treated by Bio- Esthetic Points).
Theφ-method estimates the statistical significance of difference between the percentages of two samples according to the null hypothesis (H 0): the percent of persons with apparent effect in sample 1 is no more than in sample 2.
Visual pre- and post-treatment comparisons showed a clear improvement in skin wrinkles and fine lines, with skin appearing brighter and more toned after completion of the procedure (Figure 2).
Visual pre- and post-treatment comparisons showed a clear improvement in skin wrinkles and fine lines with skin subjectively appearing brighter and more toned after completion of the procedure.
Though no objective measurements of volumetric changes or sebum changes were performed to support existing research, as this was not within the focus area of this case study.
The Soft Plus assessment outputs recorded a significant improvement (P value 0,008) in skin hydration with an average increase of 12,13 u c .
Skin elasticity and melanin levels also displayed an overall ameliorationof an average 0,59 u c and 2,7 u c respectively, albeit non-significant (P value 0,28 and 0,15 respectively) Macrophotographical evidence from the Antera 3D camera displays a clear improvement in topographical parameters facial atopic manifestations in wintertime such as skin reddening and peeling.
Likewise, after the first treatment sitting the patient noted a significant improvement in her skin condition and claimed complete remission of peeling after the second sitting.
Quantitatively, a significant improvement in wrinkle overall size (P value 0,01) was measured, with an average 8,5% size decrease (Figure 5).
Skin texture was also positively affected, with an average arithmetical mean roughness decrease of 9,2% (P value 0,002).
Lastly, a 4,5% average decrease in skin pigmentation was measured (P value 0,01).
Furthermore, two patients suffering from pre-existing dermatological conditions reported an improvement in their ailment after treatment.
Patient A (female, 26 years) (Diffuse Injections Group), who suffered from occasional eruptions of acne vulgaris, described a significant decrease in the rash after the second treatment sitting, and an overall reduction in the severity of stagnant post-acne stains.
Patient B (female, 36 years) (BAP Group), suffered from Regarding the injection technique, comparison between group 1, treated following the BAP technique, and group 2, treated with diffuse injections in the subcutaneous layer, revealed no significant difference in terms of efficacy in all the parameter measured.
No adverse events were reported during this study, except for some minor petechiae.
Facial aging is the product of cumulative effects of time on the skin, soft tissues and deep structural components of the face, and is a combined result of skin textural changes and loss of facial volume1,2.
Among the skin alterations, loss of tissue elasticity and skin laxity due to decrease in elastin, collagen and hyaluronic acid production strongly affects the phenotypic presentation of the face, causing superficial textural wrinkling and alterations of its 3D topography.
In recent years, there has been a steady increase in nonsurgical procedures for facial rejuvenation.
Factors which make the nonsurgical approach so appealing are the immediacy of the cosmetic result and a short recovery time.
Profhilo?? is an exclusive skin bioremodeling treatment designed by IBSA Pharmaceuticals to treat loss of facial volume and elasticity.
Profhilo?'s stabilized hybrid hyaluronic acid complexes stimulate the production of collagen and elastin, thus significantly improving the appearance of wrinkles and fine lines, while increasing tone and hydration across the face.
Previous published clinical experience has tested the efficacy and tolerability of Profhilo?? on 120 patients in 4 independent studies, with highly satisfactory quantitative results in terms of skin hydration, elasticity, trans-epidermal water loss (TEWL), validated clinical scales (WSRS, FVLS and Beagley and Gibson Scale) and patient and doctor satisfaction rates, with no relevant side effects.
Based on Profhilo?'s success in the Caucasian ethnicity, the present report aimed to test the treatment's efficacy on a different ethnic subpopulation, i e the Central Eastern European group, which exhibits so-called Asian mongoloid face features.
In this case report, 10 patients (9 female, 1 male) were treated with subcutaneous injections of Profhilo? 2,0 ml for facial skin rejuvenation.
Photographic pre- and post-treatment comparison revealed a smoothing and lightening of the skin, with macroscopic improvement of wrinkles and fine lines.
Quantitative analysis and topographical measurements further highlighted a significant increase in skin hydration and a slight improvement in skin elasticity, confirmed the improvement in skin texture and wrinkle severity, and recorded a decrease in skin pigmentation, probably due to the antioxidant activity of HA28.
Furthermore, two patients with pre-existing dermatological conditions, namely acne vulgaris and atopic dermatitis, achieved remission after treatment.
These results confirm the in vitro properties exhibited by Profhilo?: an increase in expression levels of collagen in fibroblasts and keratinocytes and of elastin in the extracellular matrix, combined with enhanced adipogenic differentiation and proliferation of adipose- derived stem cells (ASCs), resulting in excellent regenerative action.
The case report confirms Profhilo? as a unique product with polypotent properties, and as an effective and highly tolerable nonsurgical skin bioremodeling treatment in patients of diverse ethnicities.
Absorbable fillers have become increasingly popular to reverse signs of aging on the face.
Since bovine collagen fillers received Food and Drug Administration approval in 1981, these fillers gained popularity for more than a decade; however, bovine collagen had the potential for allergic reactions and required skin testing before the first treatment.
Since then, non-animal-based hyaluronic acid (HA), which had been used for intra- articular joint injection and ophthalmologic procedures for many years with a very good safety profile, was introduced and has become the most commonly used facial filler over the past several years.
HA fillers show excellent efficacy not only in correcting wrinkles but also in restoring tissue volume with minimal downtime.
These fillers are easy to use, allergy-free, and enzymatically degradable using an injection of hyaluronidase in case of a bad result1.
Botulinum toxin injection for treatment of facial wrinkles is the most frequently performed cosmetic procedure in the Treatment of frown lines and crow's feet, which are the cosmetic indications approved by the U S Food and Drug Administration, and horizontal forehead lines, offers predictable results, has few adverse effects, and is associated with high patient satisfaction.
Wrinkles are formed by dermal atrophy and repetitive contraction of underlying facial musculature.
Botulinum toxin is a potent neurotoxin that inhibits release of acetylcholine at the neuromuscular junction.
Injection of small quantities of botulinum toxin into specific overactive muscles causes localized muscle relaxation that smooths the overlying skin and reduces wrinkles.
Botulinum toxin effects take about two weeks to fully develop and last three to four months.
Dynamic wrinkles, seen during muscle contraction, yield more dramatic results than static wrinkles, which are visible at rest.
Minimally Invasive Procedures for Nasal Aesthetics rests its therapeutic base on two pillars: first, the control of the muscular activity at the base of the nose that provokes the rotation and the dropping of the tip, through the use of botulinum toxin A (BTxA), and second, the improvement of the nasal profile and ageing with the use of absorbable fillers.
This article describes the procedure and clinical outcomes and discusses the indications of the treatment and possible mechanisms of the long-lasting effects.
The author successfully performed a Minimally Invasive Procedures for Nasal Aesthetics has developed mini- invasive techniques using botulinum toxin A (BTxA) and absorbable fillers hyaluronic acid (HA) for the correction of nasal imperfections. 48 carefully selected patients, between September 2017 and October 2018, with an eight - to twelve - months follow-up.
Patient ages ranged from 21 to 39 years.
All of the patients elected not to undergo any aesthetic nasal surgery but were requesting a slight improvement of their nasal shape.
Monophasic Hyaluronic Acid (HA) gel with the presence of two different molecular weights: 1000 kDa and 2000 kDa (Figure 1).
This is an advantage both from a biological and mechanical point of view.
In fact, different molecular weights allow the physician to act on different HA receptors, putting a number of mechanisms in place that are involved in the regeneration of skin tissue.
Furthermore, there is a mechanical advantage; a high molecular weight HA fills larger spaces, while a low molecular weight HA fills smaller spaces, resulting in a complete filling action.
Additional important features of product are its safety and manageability.
For example, as with most volumising fillers, it is indicated for use at the supraperiosteal layer and deep tissue, but with Monophasic hyaluronic acid (HA) gel no problems arise if it is injected at the mid or deep dermal level (Figure 2).
Therefore, it can be used safely throughout the nose area.
The smoothness of the gel reduces the possibility of side-effects such as swelling and bruising, as well as the risk of over-correction.
Monophasic hyaluronic acid (HA) gel is indicated in all patients who wish to create or redefine facial contour, in those who have lost subcutaneous tissue, and for correcting deficits following injuries.
Surface Appearance: the external nose has a pyramidal shape (Figure 3).
The nasal root is located superiorly, and is continuous with the forehead.
The apex of the nose ends inferiorly in a rounded 'tip'.
Spanning between the root and apex is the dorsum of the nose.
Located immediately inferiorly to the apex are the nares; piriform openings into the vestibule of the nasal cavity.
The nares are bounded medially by the nasal septum, and laterally by the ala nasi (the lateral cartilaginous wings of the nose).
Skeletal Structure: the skeleton of the external nose is made of both bony and cartilaginous components: bony component - located superiorly, and is comprised of contributions from the nasal bones, maxillae and frontal bone (Figure 4).
Cartilaginous component - located inferiorly, and is comprised of the two lateral cartilages, two alar cartilages and one septal cartilage (Figure 5).
There are also some smaller alar cartilages present.
Whilst the skin over the bony part of the nose is thin, that overlying the cartilaginous part is thicker with many sebaceous glands.
This skin extends into the vestibule of the nose via the nares (Figure 6).
Here there are hairs which function to filter air as it enters the respiratory system (Figure 7).
The muscles involved in the rotation of the nasal tip towards the maxillary bone, are the depressor septi nasi and levator labii alaeque nasi.
Their treatment with BTxA is easy.
The depressor septi nasi muscle can be injected along both its insertion above the columella and in the nasal spine.
In this case, we use 4 Units Botulinum Toxin (Botox? Allergan).
If there is hypertonia of the levator labii alaeque nasi muscle with a clear lift of the nasal sides and rotation of the tip downwards, we can give the injection of 4 Units Botulinum Toxin (Botox? Allergan) (Figure 8).
To appreciate the result, it is necessary to wait for 7 to 15 days.
We always perform a retouch session after 15 days, both to evaluate the results and for possibly enhancing it with another injection of a few units.
It is important to be careful while treating the levator labii alaeque nasi muscle, since it is possible that the length of the upper lip can increase, getting ptosis.
The risk is lower in youmg women with gummy smile, short lips (less than 1.5 cm) than in older people (over 60 years of age) with long lips.
When the distance between the nasal spine and the apex of the Cupid's arch is more than 1.8 cm, the treatment is strictly contraindicated.
Normally use average cross-linked hyaluronic acids, and in particular: Aliaxin? EV.
The safety of materials is fundamental to obtain good results, and so the advice is to use well-known materials with a very high safety profile.
We always start with topical anaesthesia with an anaesthetic lotion for at least 30 min.
Before the procedure, the midline and the most prominent spot of the nasal domes were marked.
A Cannula guide needle was used through the nose tip to create the entry point (center of domes tip defining point) (Figure 9).
First Step: with the columella angle needs to be enlarged by moving the columella through the entry point and placing the Aliaxin? EV dermal filler into the nasal septal cartilage.
The cannula is not removed at all during the procedure.
Forward and backward maneuvering places the Aliaxin? EV dermal filler into the columella9.
Second Step: it is necessary to move along the nasal dorsum from the entrance point and to put the Aliaxin? EV dermal filler over the nasal periosteum and proceed to the glabella to lift the nasal bridge.
The cannula is not removed at all during the procedure.
Forward and backward maneuvering places the Aliaxin? EV dermal filler into the nasal dorsum9.
The amount injected is variable depending on the imperfection to correct.
If the Nasolabial angle is also reduced (less than 90?), we proceed with an injection at the level of the nasal spine to open this angle that should be possibly more that 90?. 
This injection opens the Nasofrontal angle, so it is obvious that the best indication remains the one with a reduced angle, less than 115?.
It is better not to exaggerate with the injection and reach the optimum result , We use fan technique and, again the best results are obtained step by step. 
Between September 2017 and October 2018, 48 carefully selected patients, with an eight - to twelve - months follow-up.
Patient ages ranged from 21 to 39 years.
The results were satisfactory in all but 42 of the 48 cases based on patient feedback (Table 1).
Six patients found the results inadequate and those patients underwent normal rhinoplasty afterward.
The operation duration was under 30 minutes in all of the cases.
Our longest follow-up was 12 months, during which we observed that the final outcome appeared after the third month and did not undergo any change afterward.
No complication related to the Aliaxin? EV dermal filler and Botulinum Toxin (Botox? Allergan).
Facial rejuvenation involves a spectrum of interventions, ranging from topical cosmetic products to surgical tissue manipulation.
Botulinum toxin and Dermal filler injections fall somewhere in the middle of this spectrum.
Used alone or in conjunction with other modalities, botulinum toxin and dermal filler products play an important role in achieving a youthful, aesthetically pleasing facial appearance.
As demonstrated throughout this article, nonsurgical pproaches to facial rejuvenation have become enormously popular among both patients and practitioners in recent years.
Facial rejuvenation is comprised of a spectrum of interventions ranging from topical cosmetics to surgical restoration.
Injectable products fall somewhere in the middle of the spectrum, offering dramatic aesthetic results for a moderate cost and require minimal posttreatment recovery time.
The present article serves as a general conceptual outline regarding the use of injectable products to achieve facial rejuvenation.
Certainly, every patient warrants treatment approaches tailored to their specific situation, and when questions arise, specific recommendations should be sought from one's more experienced colleagues11.
In the author's experience, injection of HA gel is a valuable tool for minimally invasive nasal reshaping.
Experienced plastic surgeons can use HA injection as an alternative/ complement to many indications for rhinoplasty because of its versatility.
In the author's opinion, this is infrequently considered by many surgeons.
Benefits with HA injection include a quick and noninvasive method to change nasal features without need for general anesthesia.
The procedure is associated with no/minimal downtime and with lower cost per treatment compared with rhinoplasty.
Minor and sometimes time-consuming and risky secondary surgical procedures can sometimes be avoided with HA injection.
In addition, HA gel injections are useful for preserving the height of the nose, which can be challenging with a surgical reshaping rhinoplasty.
The nonpermanent nature of HA and reversibility with hyaluronidase are also favorable properties.
Limitations include a relatively short duration of effect in some cases and thus need for retreatment.
Although use of HA in aesthetic facial treatments is well established for treatment of wrinkles and folds, most patients are unaware of nasal indications.
As a nonsurgical minimally invasive alternative to rhinoplasty, it would likely appeal to many patients who wish to modify the appearance of their nose.
HA treatment may also serve as a door opener to surgery for patients who are reluctant to undergo rhinoplasty.
Rhinoplasty is one of the most common cosmetic procedures performed by plastic surgeons.
However, non-surgical nose jobs with a dermal filler are becoming increasingly popular in the world.
Filler rhinoplasty has become an advantageous choice for patients that are afraid of surgery or general anesthesia.
It is a fast, safe, simple, and effective method when compared with surgical rhinoplasty.
On the other hand, HA filler rhinoplasty can be completely reversed with hyaluronidase when needed.
Signorini et al. recommend an injection of 10 to 20 U hyaluronidase for areas less than 2.5 mm and two to four injections of 10 to 20 U hyaluronidase for areas greater than 2.5 mm.
In some patients, the use of BOTOX? increases the distance between the columellar base and the vermilion border, creating the appearance of a fuller and voluminous lip.
It can also correct the gingival smile.
If the toxin diffuses laterally in the base of the columella, it can affect the levator labii superioris and the orbicularis oris, provoking an unaesthetic elongation of the superior lip, filtrum flattening, and labial sphincter incompetence when talking and drinking.
The use of high doses in the nasal tip can produce an exaggerated opening of the nostrils and a strong elevation of the tip, leaving an unattractive appearance in the frontal view.
The clinical effect in this area usually lasts for a shorter time than other parts of the face.
The first days after the injection, the patient can experience pain in the nasal tip.
Nasal aesthetic problems are one of the few fields in which we are not able to offer our patients an acceptable, minimally invasive alternative.
Furthermore, we have patients who are incapable of arranging their daily programs to accommodate the required recovery period or who do not wish to undergo such a significant operation because of their associated health problems or anxiety over an irreversible change in their facial characteristics.
The main objective of the technique we describe is to provide patients with a simple method for nose reshaping, which can be performed in the office under topical anesthesia in less than 30 minutes and is therefore with Botolinum toxin or Fillers in the patient?€?s mind.
For selected patients, however, our method can be proposed as a simple, office-based procedure that can be performed under topical anesthesia in a matter of minutes with virtually no downtime.
At the end of the session, we normally use a camphor cream to disinfect and reduce the oedema which is usually modest.
The patient can immediately resume his or her daily activities.
The main indication of these procedures is in all minor defects of appearance of the nose, particularly for the plunging tip.
A second important indication is the flat nose, frequently seen in black/brown and yellow skin people.
Another useful indication is the correction of many post-surgical imperfections, which will be difficult to treat otherwise.
Corrective surgery is not always so easy to perform.
The training of doctors, who want to engage with this easy technique that gives extraordinary results, is always necessary and essential.
Rules, written long time ago and well documented, remain the best way to achieve good results and reduce to the minimum the incidence of side effects.
Potential major complications of injection rhinoplasty include infection, ischaemic necrosis from arterial embolism, pressure necrosis from overinjection of nasal tip and osteophyte from periosteal injection.
These risks may be reduced, with effective nasal analysis, meticulous injection technique, and a good understanding of nasal cartilaginous and vascular anatomy.
Radix and upper nasal third injections should be medially placed to avoid the dorsal and lateral nasal arteries.
Pre-injection palpation may aid identification, and aspiration before injection is mandatory.
Intravascular filler injection can lead to arterial embolisation and subsequent skin necrosis or retinopathy.
Visual impairment following middle facial third filler injection mandates urgent opthalmological review to exclude retinal embolism.
Prompt anticoagulation and hyaluronidase injection may be a useful adjunct should complications arise.
Injection of HA gel is a valuable tool for plastic surgeons to consider for nasal reshaping.
Small corrective refinements offered to patients may help achieve higher patient satisfaction and have in many cases had a surprisingly long duration of effect.
The clinical experience gained with HA gel injections for nasal treatments over 15 years has also shown that HA gel can be used for correction of minor postrhinoplasty defects in appropriate patients.
Minimally invasive procedures for nasal aesthetics described herein is one of very few minimally invasive alternatives for aesthetic nasal surgery.
For selected patients, our method can be used as a simple, office- based procedure that can be performed under topical anesthesia without any significant morbidity, a very high patient satisfaction, and a recovery period of only two to three days.
The reversibility of the result, at least for a short period of time, is also appealing to patients who are uncertain about the outcome of nasal surgery.
Injection rhinoplasty is not a substitution for surgical rhinoplasty.
There are many indications where it will be insufficient to achieve the desired aesthetic outcome.
Noses that are significantly overprojected, or overrotated, have a shallow radix, and tension noses are better suited to surgical correction.
It is however a useful postoperative adjunct to surgery or in those patients contemplating rhinoplasty.
The non-permanence and minimal morbidity of associated with degradable fillers is especially beneficial to those patients who seek cosmetic rhinoplasty but are discouraged by the risks and convalescence of surgery.
The story of melatonin dates back to the beginning of the evolution process.
In fact, this tiny molecule, known as a biogenic amine or indole (its molar mass is 232.278 g Mol) appeared very early in living organisms and was then ?€?conserved?€?throughout evolution; thus, the melatonin present in current living humans is identical to that present in cyanobacteria that have existed on Earth for billions of years.
In 1958 it was officially discovered by Lerner and, since then, the interest in this substance has never faded and given rise to a huge amount of papers, trying to unravel the mystery of its meaning24.
Initially thought to be produced by the pineal gland only, it is now known to be produced in many, and probably all, cells of the body.
Its first- described actions linked melatonin to circadian (and circannual) rhythm regulation, however more recent studies validated the great number of functions of this molecule, which include actions at the molecular level that are able to modify the physiology of organs and organisms.
The initial idea that melatonin could be produced only in animals with the pineal gland, was lately modified since it was demonstrated that it could be synthesized in every living organism, including bacteria, unicellular organisms and plants.
As far as melatonin signal transduction is concerned, the indole works via well-defined membrane receptors (MT1 and MT2)8, and also nuclear receptors (RZR/RORalpha).
However, its actions far transcend these receptors, since binding sites have also been described in the cytosol and mitochondria.
In addition, some of melatonin actions receptor-independent, due to its ability to permeate all barriers acting as a potent radical scavenger.
The sense of the above is that probably no cell or function in plants and animal kingdoms escapes the impact of melatonin.
In fact, it has a pivotal role in a bunch of different physiological processes, and it also may have a significant role in the etiology of many disorders.
Recently it has been postulated that the initial and primary action of melatonin in photosynthetic cyanobacteria, which appeared on Earth 3.5-3.2 billion years ago, was as an antioxidant.
This is due to the fact that photosynthesis is associated with the generation of toxic free-radicals.
The other functions came about much later in the evolution process.
Oxygen is an essential element for aerobic organisms because oxidative metabolism represents the main energy source.
However, the partial reduction of O2, derived from the normal physiology of the organisms, results in Reactive Oxygen Species (ROS) formation.
These molecules include two major groups: free radicals such as the superoxide anion and hydroxyl radical, and molecules such as hydrogen peroxide.
As we know, oxidative stress occurs when an imbalance between pro-oxidant and anti-oxidant molecules takes place, due to an increase of ROS and of Reactive Nitrogen Species (RNS), and a decrease of the activity of the antioxidant defense mechanism.
Melatonin has a major role in the antioxidant defense mechanism, in fact this multifunctional molecule, whose amphiphilic nature enables it to penetrate all morphophysiological barriers and all subcellular compartments, protects cellular membranes, the electron transport chain and mitochondria from oxidative injury.
As far as the latter are concerned, the measurement of subcellular distribution of melatonin has shown that the concentration of the indole in the mitochondria greatly exceeds that in the blood.
Melatonin presumably enters mitochondria through oligopeptide transporters (PEPT1 and PEPT2) and it seems to function as an apex antioxidant.
In addition to being taken up from the circulation, melatonin may be produced in the mitochondria as well, according to recent data, providing on-site additional protection as a powerful antioxidant.
Moreover, melatonin increases the permeability of membranes and acts as lipoxygenase inhibitor, helping in maintaining the efficiency of the local antioxidant system.
Therefore, melatonin's high concentrations and multiple actions as an antioxidant provide a potent antioxidant protection to these organelles which are commonly exposed to abundant free radicals.
However, it is important to underline that the melatonin molecule, in order to display its own antioxidant activity, needs to be oxidized and cannot be reduced to its former state because it forms several stable products upon reacting with free radicals.
For that reason, it is also called "terminal"?or"suicidal"?antioxidant and its concentration in physiological fluids decreases, as the scavenging process progresses.
In addition, its metabolites also have antioxidant properties; thus, the protection exerted by melatonin against oxidative damage to cells and particularly to DNA is a continuous process.
Health maintenance is strongly dependent by a proper internal organization which should be synchronized to the daily light/dark cycle of the external environment.
This organization is provided by a complex mechanism that includes a master clock (located in the suprachiasmatic nucleus: SCN) that is able to demonstrate an autonomous circadian rhythm of a little more than 24 hours.
At the cellular level, the macromolecular transcription-based oscillator is formed by the clock and the clock-controlled genes, which contribute to the rhythmic functions of the organism.
However, as said, despite the fact that the cells have a circadian rhythm to ameliorate their ability to survive, it is essential that the rhythm is well synchronized to the external light/dark cycle, in order to align endogenous processes.
This task is performed by melatonin (N acetyl-5methoxytriptamine), the well- known tryptophan-derived indole of the pineal gland, that is produced and secreted according to a circadian rhythm that is connected to the light/dark cycle.
At least four enzymes are involved in the synthesis of melatonin.
Among them, arylalkylamine N Acetyl- Transferase (NAT) is considered the rate-limiting enzyme in the regulation of melatonin biosynthesis.
In fact, the NAT enzyme exhibits a robust daily rhythm, reaching concentrations that are 100 times higher during the dark phase, compared to daylight hours.
Sympathetic nerve endings in the superior cervical ganglion release norepinephrine (NE) in accordance with a circadian rhythm, which is related to the light/dark cycle in the environment, increasing its secretion during the dark phase.
NE induces melatonin biosynthesis from the pinealocyte and, in this view, tryptophan is first converted into 5-hydroxytryptophan by the enzyme tryptophan hydroxylase, which is then decarboxylated into serotonin.
Serotonin is acetylated into N acetylserotonin (by NAT), which is finally O methylated into melatonin by Hydroxyindole-O Methyl-Transferase (HIOMT).
Once released into the circulation, roughly 70% of melatonin is bound to albumin, and another 30% diffuses to the surrounding tissues.
The main metabolic pathway of melatonin occurs in the liver where it is hydroxylated to form 6-hydroxymelatonin, then conjugated with sulphate or glucoronate and finally excreted in the urine.
Daily light exposure that affects the retina, directly influences melatonin production, blunting its rhythm.
This is due to a direct neural connection between the eye and the pineal gland, through which it receives information about light (or dark) conditions of the environment.
In addition, melatonin travels throughout the body without limitations and, therefore, it is considered as a ubiquitous molecule.
Finally, the chemical conservation of melatonin in all tested species makes it a candidate for a universal time messenger.
Measures of melatonin are considered the best peripheral index of human circadian timing based on an internal 24-hours clock.
Plasma melatonin reflects the melatonin synthesized in the pineal gland, since no storage compartments for the pineal indole do exist.
In humans, melatonin secretion increases soon after the onset of darkness, with a peak in the middle of the night (between 2:00 and 4:00) followed by a gradual decrease during the second half of the dark phase.
Serum melatonin concentrations during nighttime also vary considerably according to age and among individuals, the highest amount being in the first years of life, falling immediately before puberty and then maintained throughout adulthood, followed by a progressive decrease during the aging process that leads to minimal levels with old age.
According to its circadian rhythm, melatonin is mostly secreted during the night, with lowest plasma levels during the day.
In this view, light is recognized as the most efficient stimulus to blunt melatonin secretion and, in fact, the exposure to light during the night determines chronodisruption that may have deleterious consequences on well-being and it is called light pollution (especially with short wavelength light in the 460- 480nm spectrum: blue light).
Unlike our ancestors, who lived in natural environments, the modern generations of people residing in developed Countries have self- selected their light-dark cycle.
The most important differences between these two lifestyles, with respect to light exposure, are: a progressive general decrease in light intensity and regularity; a modification in light timing, with delayed and reduced exposure during the day and increased light at night; and finally, a shift in the light spectrum towards artificial light sources with a strong blue component.
It has been demonstrated that light at night enriched with wavelength between 460- 480nm can cause toxic effects to the eye inasmuch they can penetrate the cells and their organelles, inducing the generation of ROS in retinal epithelium mitochondria and even apoptosis.
Thus, nocturnal lighting, and specifically that with a high short wavelength content (i e mobile phone and tablet screens), should be avoided also because blue light at night has a greater impact on retina cells, with respect to Sun-derived blue light during the day, due to retinal physiology changes between day and night.
Even though some applications have been recently developed and released to reduce the negative effects of the use of electronic devices at night by adjusting the display color temperature according to the natural light/dark cycle (namely, reducing the blue light content during nighttime and increasing it during light hours), attention should be paid because the melatonin-inhibiting activity of light can be initiated at extremely low lux levels.
However, in order to maintain a good health of our circadian system, appropriate lighting levels during the day should also be recommended.
Diurnal light should not be poor in short wavelength, since the maximum human circadian spectral sensitivity, in terms of melatonin suppression ability, occurs in this part of the spectrum.
There are several situations in which individuals are particularly exposed to a chronodisruptive illumination, with significant effects on human health.
Among them, shift work is one of the most frequent in modern population.
In fact, approximately 15 to 20% of workers in Europe and US participate in shift work, including work at night, and rises up to 30% when manufacturing, mining, transportation, health care, communications and hospitality sectors are considered.
Epidemiological studies have demonstrated an increased risk of some cancers, namely breast, prostate, colorectal and endometrial cancers.
The reason being the disruption of the circadian oscillator, with the consequence of melatonin circadian rhythm alteration, and the decrease of melatonin concentrations, due to light at night.
In this view, a recent paper suggests that women with hereditary breast cancer predispositions should avoid using light at night.
Since the circadian oscillator is involved in the regulation of cellular division pathway, its disruption may be linked to disturbances of the cell cycle control, with the consequence of an acceleration of malignant growth.
In addition, the concomitant decrease in melatonin concentrations may induce a decrease in its overall availability as antioxidant molecule, therefore increasing tumorigenesis and acceleration of malignant growth.
This assumption is in line with the repeatedly observed increases in lipid peroxidation and decreases in glutathione peroxidase and superoxide dismutase in pinealectomized animals.
In addition, the association of shift work with metabolic syndrome, cardiovascular diseases and type II diabetes has also been demonstrated.
In fact, altered food intake and obesity, eventually associated to high blood pressure, are shown to be induced, or aggravated by shift work which also acts causing sleep disturbances.
Sleep deficit and interruption are also known to be associated to changes in eating behavior and obesity, however the relationship between the increased body mass index related to eating at night and the aspects of circadian rhythmicity in nutrient uptake is not simple to demonstrate.
Recently, it has been shown that insulin resistance is promoted by circadian perturbance, under conditions of controlled sleep loss.
Importantly, the change in insulin sensitivity was associated with increases in inflammation markers in those subjects.
One of the predictable consequences of the concomitant nocturnal shutoff of melatonin blood concentrations by light at night is, therefore, an increased oxidative damage to biomolecules.
Moreover, circadian perturbations due to repeated phase shifts have also been shown to increase oxidative damage, to reduce lifespan in animals and to increase the amount of 8-hydroxydeoxyguanosine (a product resulting from free radical damage to DNA) in the DNA of shift workers.
Another frequent situation that is capable to disrupt our internal circadian rhythm is the so-called jet- lag that depends on a rapid travel across multiple time zones, associated to the fact that the change is considered by the organism too drastic to allow the circadian system to adapt smoothly.
In fact, melatonin rhythm is shifted and does not resemble the light/ dark cycle of the external environment.
Common symptoms are sleep impairment, anxiety, depressed mood, gastrointestinal complaints and dizziness.
In recent years, the fact that many individuals (especially the younger ones) shift their sleep and activity times by several hours between workdays and the weekends gave rise to what is now called "social jet-lag"? which is comparable to jet-lag.
In addition, as noted above, the internet, email, video games and television until late not only contribute to later bed times, but also induce a decrease of the physiological melatonin rise at nighttime, due to the exposure to modern LED screens which are known to blunt melatonin secretion because of their higher blue light content, with respect to white incandescent bulbs and compact fluorescent bulbs.
As a result, adolescents experience a misalignment between biological and social rhythms which, added to sleep loss, results in fatigue, daytime sleepiness, behavioral problems and poor academic achievement, also opening the door to future problems as obesity, metabolic syndrome, diabetes, increased cardiovascular risk and infertility.
Epidemiological studies demonstrated that children sleep approximately 1.2 hours less than their counterparts of a century ago.
In addition, the sleep of exposed adolescent becomes irregular, shortened and delayed in relation with later sleep onset and early waking time, which results in rhythm desynchronization.
It is noteworthy that, according to several studies, among adolescents 47.8% of high school students in India, 25% in Japan, 22.8% in the US, 16.1% in China and 9.9% in Spain suffer from insomnia due to many causes, but probably as the result of disturbed habits and irregular lifestyles.
In this view, the expanding use of leisure technology seems to have substantially contributed to this sleep deficiency.
Therefore, the permanent social jet-lag resulting in clock misalignment and melatonin rhythm decrease and disruption experienced by a high number of adolescents should be considered as a matter of public health.
In this view, very recent data obtained in preschool-age children indicate that this specific population is particular sensitive to evening light exposure, in terms of melatonin suppression, with the consequence of easily and rapidly disrupting the circadian rhythmicity1.
On the other hand, melatonin treatment of these patients may have beneficial effects on sleep disturbances.
It is well known that, inadequate timing, spectrum and intensity of retinal light input produced by nocturnal activities and sleep during daytime is a key factor to explain the incidence of chronodisruption, since it not only induces instability of the master internal pacemaker, but it also reduces melatonin synthesis.
As noted above, this reduction, due to repeated light exposure at nighttime (and the consequent decrease in melatonin production) may have an important role in the pathogenesis of a number of illnesses, such as breast cancer, cardiovascular problems, diabetes, cognitive dysfunctions, male and female reproductive problems, among others.
As far as the latter is concerned, a strict link between melatonin and female reproduction has been already established.
Since decades ago it has been demonstrated that melatonin is involved in the regulation of the reproductive system of both, males and females, and the most recent data of the literature confirms and ameliorates our knowledge in that field.
In fact, melatonin is able to control the reproductive axis through a quite complex mechanism which includes the regulation of the secretion of Gonadotrophin-Releasing Hormone (GnRH), and of the activity of the gonadotrophin release-inhibitory hormone (GnIH), which has been recently shown to have a role in the mechanism that regulates male and female reproduction, acting directly on GnIH neurons through its receptors to induce (birds) or to inhibit (mammals) the expression and release of GnIH55.
In this view, in mammals it is able to inhibit the activity of components of the Hypothalamic-Pituitary-Gonadal (HPG) axis, including a reduction of sexual behavior.
The GnIH content of the brain is influenced by changes in day length and, on the other side, melatonin stimulates the release of GnIH from the hypothalamus of birds and mammals.
It is noteworthy that GnIH neurons express melatonin receptors, thus suggesting a strict regulatory connection between the pineal hormone and the GnIH action within the brain.
In addition, melatonin stimulates the secretion of progesterone from granulosa cells.
Another important melatonin target tissue is the pituitary gland and, in adult mammals, the dominant pituitary site of melatonin action is the Pars Tuberalis (PT), a thin layer of the anterior pituitary that surrounds the pituitary stalk and extends rostrally along the ventral surface of the median eminence.
In fact, it is now believed that melatonin signal duration (long in winter and short in summer) drives the photoperiodic control over multiple aspects of neuroendocrine physiology, including the lactotrophic and reproductive axes, via the PT in adult mammals.
However, differently from seasonal breeders, human reproductive efficiency seems to be less dependent by seasonal day length variations, but this does not mean the melatonin has no effects on human reproductive organs.
In fact, data from the last two decades indicates that the pineal indole has multiple effects directly at the level of the gonads and their adnexa in the human and other mammals.
In particular, both stable circadian rhythm and cyclic melatonin availability are critical for optimal ovarian physiology, gestation and parturition.
In particular, as far as the latter is considered, light at night impedes regular uterine contractions in late term human pregnancy, reinforcing the importance of a correct melatonin rhythm in coordinating nocturnal myometrial contractions such that delivery of offspring more frequently occurs at night than during the day.
On the other hand, women with higher nocturnal melatonin surges display more vigorous and coordinated uterine contractions at parturition.
In addition, chronodisruption during pregnancy has also deleterious effects on the health of progeny, including metabolic, cardiovascular and cognitive dysfunctions.
In this view, since light exposure after darkness onset at night disrupts the master circadian clock and suppresses elevated nocturnal melatonin levels, leading to pathophysiology and/or diseases, light at night should be avoided.
Melatonin is also produced in the peripheral female reproductive organs, including granulosa cells, the cumulus oophorus, and the oocyte.
These cells, along with the blood, may contribute to follicular fluid melatonin content, which is higher than that in the blood by a three-fold factor.
The origin of melatonin in the follicular fluid was commonly thought to be the exclusive result of its uptake from the blood.
However, there is a bunch of evidence indicating other ovarian cells (as indicated above) that are able to synthesize melatonin which could be appropriately released into the follicular fluid.
The fact that follicular fluid melatonin concentrations in humans are two times higher in large follicles just prior ovulation, with respect to those in smaller antral, immature follicles induces to speculate that because melatonin is such a potent antioxidant, the elevated concentrations in the follicular fluid at the time of ovulation could be physiologically advantageous.
In this view, recent data demonstrates that there is a strong positive relationship between melatonin levels in the follicular fluid and the quality and quantity of oocytes and that melatonin concentrations in the follicular fluid can be considered as a marker of in vitro fertilization techniques and ovarian reserve.
In addition, melatonin is able to reduce granulosa cells oxidative damage, in an animal model.
The presence of the indole acting as a potent antioxidant molecule is so important because mammalian gametes and embryos are highly vulnerable to oxidative stress due to the presence of high lipid levels and the ovulatory processes have been linked to inflammation and high free radical production.
Briefly, the inflammatory-like process identified in the ovary at the time of ovulation include augmented synthesis of prostaglandins and cytokines, increased activation of proteolytic enzymes and elevated capillary permeability, which are all associated to a higher production of damaging reactive oxygen species.
In addition, it is known that macrophages, leucocytes and vascular endothelial cells residing in the vicinity of large follicles contribute to free radicals production at the time of follicle rupture.
Free radicals influence the balance between oxidation-reduction reactions, disturb the trans-bilayer-phospholipid symmetry of the plasma membrane and enhance lipid peroxidation.
Therefore, in order to protect the ovum from oxidative damage during the ovulation process, the presence of melatonin would ensure that it escapes molecular damage, with the consequence of a healthy embryo and fetus.
In this view, recent data indicates that melatonin is able to significantly improve the cytoplasmic maturation of bovine oocytes through the amelioration of organelles distribution, the increase of intracellular glutathione and ATP levels, the enhancement of antioxidant genes expression, and the modulation of the so-called fertilization-related events, all of which results in increased fertilization capacity and developmental ability.
Other Authors recently demonstrated that the experimental damage of mouse oocytes induced by the administration of Bisphenol A (BPA, which is a known potent disruptor of mammalian oocytes quality) may be reversed by melatonin administration in vivo, increasing the fertilization rate by restoring the BPA-induced defects of fertilization proteins and events through the reduction of ROS levels and inhibition of apoptosis.
Additionally, a recent paper indicates that melatonin may be considered as a promising pharmacologic agent in the prevention of reproductive toxicity caused by endocrine-disrupting chemicals, as BPA60.
Finally, melatonin has also been shown to modulate cell cytoskeleton, therefore ameliorating the physical cell resistance.
In this view, a recent paper demonstrated that melatonin could become an important tool in the management of ovarian and luteal diseases.
Female fertility inhumansisnotconstant throughout the reproductive period (from menarche to menopause), but it peaks at about 25 years and rapidly declines after 35 years.
Nowadays, due to current cultural and social trends, many women around the world decide to delay their "pregnancy project"?
In fact, in recent decades maternal age has progressively increased from about 5% of women who became pregnant when older than 30 years old in 1975, to about 26% in 2010 and, therefore, many of them become exposed to infertility when they decide to conceive, due to the ovarian aging process.
More recent data obtained by an Indian study group confirmed the beneficial role of younger age in reproduction, emphasizing its importance especially when in vitro fertilization techniques are concerned.
From a biological point of view, the ovarian aging process is characterized by a decline in mitochondria function, in the integrity of the cytoskeleton and especially in telomere length (which is considered as a biomarker of cellular senescence and is highly sensitive to oxidative events), oocyte reserve as well as an obvious increase in the number of low-quality oocytes.
In this view, mitochondria, which are the primary energy generators and are also the main source and target of free radicals, and mitochondrial oxidative stress in particular is considered a major factor in contributing to the aging process.
Recent data of the literature clearly indicates that melatonin is able to significantly delay the fertility decline associated to reproductive aging by improving both the quality and the quantity of oocytes.
In particular, melatonin is effective to either ameliorate mitochondria oxidative damage (preventing cardiolipin oxidation, which is known to be a key component of mitochondria membrane) and apoptosis, and to preserve optimal mitochondria function in the aging ovary, also contributing to maintain the length of the telomeres in aging mouse ovaries.
Therefore, acting on mitochondria seems to be an attractive perspective for health and lifespan, since rejuvenating aged mitochondria could be an interesting strategy to improve health.
In fact, a recent paper dealing with the aging process was able to demonstrate that melatonin may have beneficial effects at different levels of the anti-inflammatory network.
Since obesity in humans is associated to poor outcome across the reproductive spectrum, an obese animal model has been used to demonstrate that oral administration of melatonin is able to significantly reduce ROS generation and stimulate sirtuins, to prevent chromosome abnormalities and meiotic defects in oocytes, with the result of healthier embryos.
In addition, melatonin supplementation significantly improves oocytes mitochondria membrane potential, enhances their ATP production and induces a more uniform, granulated distribution of active mitochondria in maturing oocytes, which is an important index of oocyte quality.
Of particular interest is the recent data that suggests a role for melatonin in protecting the endoplasmic reticulum (ER) of the cells from oxidation and damage and, therefore in preserving the reproductive organs from premature aging.
For example, mice intraperitoneal lipopolysaccharide (LPS) injections during pregnancy retard intrauterine growth and induce fetal death.
In addition, the placenta of pregnant mice displays an important ER stress, which is almost completely alleviated by previous melatonin administration, ultimately protecting fetuses.
Poor oocyte quality is one of the major problems where Assisted Reproductive Techniques (ART) are concerned, even though the methodologies have greatly improved within the last two decades.
This is generally believed to be the result of oxidative damage of the gamete.
In this particular case, the presence of radical scavengers, as well as antioxidative enzymes which metabolize ROS to inactive products, is essential in protecting the ovum from oxidative damage.
Since melatonin is considered to be either a direct free radical scavenger, and a stimulator of gene expression and activities of antioxidant enzymes, it seems reasonable to consider this indole to have a utility in improving the quality of human ova, both in normal conditions or to be used for ART.
In fact, melatonin administration to infertile women who failed to become pregnant in previous ART trial, was able to induce a marked improvement of the fertilization rate, by reducing free radical damage and elevating the percent of oocytes that reached maturity.
In a more recent study, melatonin started before ART cycles and continued during pregnancy resulting in improved fertilization success (50% higher in melatonin treatment cycle, with respect to non-melatonin cycles), pregnancy rates and pregnancy outcomes.
In addition, recent data from a randomized, controlled, double blind study indicates that melatonin supplementation to PCOS patients undergoinginvitrofertilizationforpooroocytes quality is able to ameliorate oocyte and embryo quality, acting synergistically with inositol, which is also known to have an important role in reproduction.
More recent data, obtained in a PCOS animal model, demonstrated that melatonin has the potential to induce oocyte nuclear maturation and guarantees the fertilization potential.
In this view, studies designed to evaluate the effects of melatonin administration to young female patients undergoing ART for reduced ovarian reserve are now on the way to be completed.
Again in the PCOS field, a very recent paper demonstrated that melatonin supplementation to PCOS women for a 12 week period resulted in a significant amelioration of hirsutism, total testosterone concentrations, while reducing oxidative stress and inflammation biomarkers.
Once pregnancy is achieved, either naturally or through ART, maternal melatonin levels progressively increase until term, in order to transfer a high amount to the fetus, due to its important role in brain formation and differentiation.
In fact, serum melatonin concentrations reach a peak in the third trimester, with values that are more than the double of those in non-pregnant women, and rapidly decreasing to non-pregnant levels on the 2nd day postpartum.
On the other hand, in pathological pregnancies, melatonin levels are lower after 32 weeks of gestations.
Maternal melatonin provides the first circadian signal to the fetus and it is also important due to its well-known antioxidant protective effect.
Therefore, in presence of lower plasma melatonin levels or circadian rhythm disruption (i e light at night) during pregnancy an alteration of fetal brain programming may occur, with long-term effects.
The pathophysiological basis of this outcome, besides cycle disruption, may also be the induction of high oxidative stress in compromised pregnancies (those with gestational diabetes mellitus, intrauterine growth retardation, preeclampsia, maternal undernutrition, maternal stress).
In this view, suppression of maternal plasma melatonin circadian rhythm by continuous light exposure during the second half of gestation is able to induce several effects on fetal development as, among them, intrauterine growth retardation, altered a decreased corticosterone rhythm, altered brain development, in experimental animals.
The shift work while pregnant is associated with a higher risk of prematurity and/or low for gestational age babies, spontaneous abortion.
Finally, recent data indicates that the administration of melatonin to women with pregnancy disorders has been established as an efficient therapeutic approach against fetal brain injuries.
Antioxidant properties of melatonin may also be demonstrated in the placenta, where the indole is highly produced and protects against molecular damage and cellular dysfunction due to local oxidative stress.
In fact, recent studies demonstrated that placenta owns the machinery to produce melatonin throughout pregnancy (from week 7 to term), with a maximal expression around the third trimester.
In addition, the primary villous cytotrophoblast seems to be the main melatonin production site in the placenta and the in vitro melatonin treatment is able to induce an increase in human chorionic gonadotropin (hCG) secretion, probably mediated by MT1 and MT2 melatonin receptors, which contributes to the correct function of the placenta.
In this view, since a precise balance between formation and degeneration of the syncytiotrophoblast syncytium, which is derived by the proliferating cytotrophoblats, is necessary to prevent placental pathologies, melatonin may have a major influence in creating a stable villous cytotrophoblasts/syncytiotrophoblast homeostasis.
In fact, it is well known that the indole has a prominent regulatory effect on apoptosis, showing anti-apoptotic actions in normal cells, while being pro-apoptotic in cancerous cells and that these dual functions are believed to be utilized by the placenta cells to maintain the necessary balance between cyto- and syncytio- trophoblasts.
In a recent report, using a mid- to late- gestation animal model, the authors demonstrated that melatonin supplementation increased heifers umbilical arterial blood flow that was previously reduced by food restriction, suggesting that these specific responses on umbilical arterial hemodynamics and fetal development may be partially mediated through vascular melatonin receptors.
On the basis of the above considerations, it is easy to understand that melatonin can be used as a pharmacological agent, and in this view, the ability of exogenously administered melatonin to phase shift human circadian rhythms was firstly described in the mid ‘80s3.
If given before the natural rise of endogenous melatonin, phase advances in sleep, core body temperature have been observed.
On the other hand, if given in the early biological morning it can induce a phase delay in circadian timing.
This ability of the indole to advance or delay clock timing depending on the biological time of administration has been used in the treatment of circadian rhythm disorders in which the sleep/wake cycle is desynchronized from the circadian timing system.
In fact, appropriately timed melatonin (0.3-5mg p o ) has been shown to alleviate symptoms of jet-lag (social jet-lag included) and night shift work.
A great deal of effort has focused on trying to identify the optimal treatment regimen and, at the moment, it is widely accepted that melatonin, in order to synchronize the sleep/wake rhythm, should always be given at the same time, roughly at 22:00 and at a dosage that is dependent on the subject.
In particular, due to the fact that melatonin night peak physiologically decreases with advancing age, the amount needed to obtain the effect is higher in the older population, with respect to younger people who need lower amounts.
It is noteworthy that young and young/adults if treated with high amounts of melatonin may experience sleep disturbances like nightmares and hangover symptoms the next morning.
Therefore, it seems that an amount of melatonin as low as 1-2mg per night is the most suitable to obtain the results without unwanted side effects in younger populations.
Regarding the so called "social jet- lag"?is concerned, which mainly involves the young and young/adult population, it is important to underline that it is able to induce a circadian rhythm disruption and a decrease of melatonin plasma concentrations, with the risk of reproductive, metabolic, cardiovascular and cancer problems.
The administration of melatonin has been shown to ameliorate circadian rhythm synchronization, with the achievement, as far as reproduction is concerned, of higher fertility score.
The increase of melatonin plasma concentration may also be achieved by endogenous stimulation.
In fact, the overnight acupressure of the so-called wrist HT7 point resulted in an amelioration of sleep parameters in a number of insomniac patients.
Despite the efficacy of exogenously administered melatonin as a synchronizing agent in circadian rhythm disorders, its use to exploit the potent antioxidant ability opens wide scenarios, especially as far as the reproductive system is concerned.
In fact, it is quite evident that melatonin is an essential part of the mechanism that physiologically regulates the reproductive system, acting on every component and positively influencing them.
In addition, not all melatonin is pineal-derived, since there is clear evidence that peripheral reproductive structures have the machinery to actively produce melatonin for their own use.
The fact that mitochondria are able to produce melatonin and that, therefore there is no cell that does not synthetize this important indole, added to the knowledge that melatonin's functions, both in terms of its receptor-mediated and receptor-independent actions, are ubiquitous, leads to suggest that the indole may be critical not only to maintain reproductive health, but health in general.
Regular physiological rhythms are important to ensure a stable maternal environment which provides the fetus with a series of stimuli that facilitate prenatal perceptual learning and development of his/her internal and external environment.
As a consequence, the occurrence of circadian rhythms that are already present during fetus life as regular repetitions of identical sequences (i e light/dark cycles), may help the fetus to develop the ability to adapt to change in an environment characterized by high regularity.
In this view, melatonin, which plays a pivotal role in the regularity and synchronization of central and peripheral oscillators, allowing the development of harmonious internal functioning and adaptation of the internal milieu to the external environment, is generally considered to be the best peripheral biomarker of human circadian timing.
For that reason, its use, alone or as adjuvant therapy, is highly recommended not only to preserve good health in general, but also in particular cases such as when female reproductive problems are concerned, in young and older females.
As stated above, melatonin administration is able to induce a higher quality of oocytes and therefore to ameliorate ART outcomes, with the consequence of better embryos, fetuses and newborns.
In conclusion, the story of melatonin as a therapeutic agent is far from being completed.
On the contrary, it seems that the more the knowledge advances, the wider the clinical use of melatonin becomes, especially in the field of aging process and female reproduction where the main actors are the mother, the oocyte, the placenta and the embryo/fetus/newborn.
All of them may benefit from the presence of adequate amounts of melatonin, in order to correct either circadian rhythm disruptions, and/or increased oxidative damage of very sensitive structures.
In this view, the gold standard for the next years is to extend the use of melatonin in routine clinical practice, in the prevention and treatment of an increasing number of pathologies, considering that the utility of this essential biogenic amine is not only for reproductive well-being, but for the improved health of other tissues as well, and ensuring, in this view, that melatonin may be considered the most important endogenous compound that is able to display anti-aging activities, acting at different levels.
The oncological patient needs to adapt their cosmetic needs (hygiene, hydration, sun protection and make up) to their new situation.
Their skin, mucous membranes and extremities (hair and nails), can be subject to changes and adverse effects either directly (radiotherapy, surgery), or indirectly (chemotherapy, directed treatments, immunotherapy, hormonotherapy) produced by the treatments.
There exists evidence of the beneficial, and even therapeutic, benefits that cosmetics play in managing some of the adverse dermatological effects produced by antineoplastic therapies.
A cosmetic product could be a very useful contributory therapeutic to: i prepare and strengthen the skin before receiving a treatment, ii) reduce the skin toxicity produced by antineoplastic treatments (xerosis, itching, erythema, paronychia, fissures, rashes, hyperkeratosis, radiodermititis, photosensitivity), iii) increase the oncology treatment adherence and iv) allow image recovery and improve the quality of life.
The cosmetic needs of an oncological patient start from before the beginning of the treatment: to prepare the skin, avoid irritation, sensitivity and allergenic substances.
Later, during the treatment, it will be necessary to relieve the increased dryness, protect from the sun, and be covered by concealing make up.
Finally, after treatment the skin will need regeneration.
When choosing a cosmetic product, the oncological patient must not only take into account personal care use but also those uses carried out during professional treatments, whether they are aesthetical, for illness or physiotherapy.
The main aim of this work is to define the criteria that a cosmetic product must complete for its use by oncological patients, identifying both the suggested ingredients and those that aren't recommended.
S such we have focused our work on the following areas: Raise awareness of the importance of suitable oncological cosmetic products, both to medical professionals and to patients.
Create a list of non-recommended ingredients that allow for avoidance protocols to be carried out, identifying inappropriate agents for oncological patients.
Create a list of recommended ingredients, to maintain and improve skin qualities, and contribute to limit adverse dermatological effects caused by treatments.
Assess the producers of these products -?? Produce guides to therapeutic cosmetic products  that improve the choice of ingredients for cosmetic products based on their different uses.
Pub med, Cochrane, Ovid; III) filters: reviews, full text articles, from the last 5 years.
Compilation of the information from cosmetic care before, during and after oncological treatment, cit-  ed in: i) specialist studies in oncology and aesthetics (Masters in Quality of Life and Medical Aesthetic Care edition I and II) and ii) in health institutions and scien- tific societies such as: ASCO (American Society of Clin- ical Oncology), SEOM (Spanish Society of Oncological Medicine), SEOR (Spanish Society of Oncological Ra- diotherapy), OMS (World Health Organisation), AEDV (Spanish Academy of Dermatology and Venereology), ADD (American Academy of Dermatology), SKF (Skin Cancer Foundation).
A review and adjustment of algorithms an criteria of cosmetic care for other at-risk group (immunocom- promised, photosensitive, atopic, sensitive skin and al- tered dermal barrier patients).
A review of the profile of more than 500 ingredients: evaluation of their valuation, ratings and security analysed in the following databases: Scientific Com- mittee on Consumer Products (SCCS) of the European Commission, Cosmetic Ingredient Review (CIR), Envi- ronmental Working Group (EWG), Cosing (database of cosmetic ingredients proposed by the European Com- mission).
 Note: SCCS and CIR emit judgements that direct legal changes in Europe and North America, respectively. 
The ingredients have been selected from current cosmetic products catalogued by their pro- ducers as “for oncological use and other ailments”, such as sensitive and atopic skin and those suitable for children.
The three recommended cosmetic products with most consensus have been: Hydrating emulsions free of alcohol, perfumes  and hypoallergenics, ph neutral soaps, high photoprotection
Of the ingredients analysed, there are approximately 40% with studies that show potentially harmful effects to oncological patients: irritation, allergic reactions, endocrinic disruption, toxicity, photosensitivity, carcinogenicity, toxic effects on reproduction, mutagenic.
This paper gives the conclusions taken from analysis of more than 100 ingredients selected according to their beneficial properties, perjudicial effects and/or frequency of appearance in current European cosmetic products (Table 1).
The results of this complete work (analysis of 500 ingredients) will be published in the 1st edition of the Vadem?cum de Cosm?tica Oncol?gica, which will be presented at the 35th SEME Congress.
The antineoplastic treatments reduce the skin's tolerance to cosmetic products, and this has been attributed to an imbalance in the corneal layer (modifications in the proliferation/maturity of the keratinocytes or keratinization mechanisms) which affects the functioning of the dermal barrier.
The use of appropriate cosmetic products can control the seriousness of the symptoms derived from this disruption.
This still isn't an accepted definition of "barrier repair products?
It is believed that use of cream moisturiser is an excellent therapy to counteract the disruptions in altered or diseased skin.
The ingredients of a cosmetic product formulated to improve skin quality before, during and after oncological treatment must be: i active ingredients, ii) auxiliary ingredients (water, moisturisers, emollients, firming oil, emulsifiers, gelling agents, surfactants and conservatives), iii) gentle and free of alcohol (colorants, perfumes, aromas and essential oils), iv) non-sensitising or hypoallergenic and v non-blocking.
According to European Parliament regulations regarding cosmetic products, it is possible to guarantee the safety of finished cosmetic products on the basis of the security of the knowledge relating to the ingredients that they contain.
Various active ingredients with antimicrobiotic, anti- oxidant, cleansing, deodorant, antiperspirant, emollient, hair conditioning, moisturising, keratolytic, hy- drating, refreshing, skin conditioning, skin protecting, and calming cosmetic functions have been evaluated, such as those used in cosmetic products.
These ingredients can produce anti-inflammatory effects (alpha-bisabolol, vit.
E calendula, shea butter, etc.), antipruritic (dexpanthenol, niacinamide, evening primrose oil), restoratives (rose hip oil, dexpanthenol, alpha- bisabolol, vit.
E omega 3, omega 6, allantonin, Asiatic pennywort, marigold, niacinamide, vit F growth drivers, etc.), and hydrators (Aloe Vera, hyaluronic acid, Urea, etc.) but in some products irritants, sensitizers, endocrinic disruptors, CMR (carcinogens, mutagenic and toxic for the reproduction) and nanomaterials have been found that could be counterproductive in the oncological patient.
Fragrances are the most common cause of allergies in cosmetic products, followed by conservatives and hair dyes; but all of the components must be considered potentially sensitizing.
The sensitivity to fragrances refers to those both of a synthetic and natural origin.
According to the SCCS report on essential oils, it hasn't been demonstrated, in the scientific literature reviewed, that the compounds of natural fragrances are safer than synthetic ones.
Neither do they justify that it might be possible to establish the concentration in which it would be improbable that sensitivity is induced by the fragrance.
A recent study on the presence and distribution of conservatives in more than a thousand products advised to prompt measures that lead to a restriction in the use of problem conservatives, and they consider that compiling cosmetic ingredients allows the creation of"prohibited"?product lists for sensitive people.
The conservatives that are added to cosmetic products can cause the skin to become sensitized for the exposed user.
Cases of allergy to safe conservatives are increasingly frequent where they have made damaged skin more sensitive.
We must choose the least sensitizing conservatives with special attention to skin with an altered dermal barrier.
It should be highlighted that the term natural is not synonymous with innocuous, and that the extracts of many plants are chemically complex.
The cosmetic products are considered to be within the elements that can be exposed to humans and the endocrine disruptors.
These chemical substances are capable of altering the hormonal equilibrium and their exposition has been related to different endocrine disorders (obesity, diabetes, etc.), alterations in the reproductive functions and different types of cancer (breast, prostate, pancreas, brain).
The European Union REACH (Registration, Evaluation, Authorization, and Restriction of Chemicals) considers that chemicals of endocrine alteration are products of a similar risk as substances classed as "high concern"?or SVHC (Substances of Very High Concern).
In 2018 the UN published a list prepared by the commission of The International Panel on Chemical Pollution (IPCP) of 45 chemical substances that have been identified as chemical substances of endocrine alteration (EDC) or potential EDC (until the end of July 2017).
In it are found substances that could be present in cosmetics, such as: triclosan, parabens, phthalates, sun filters (benzophenone-3 (Oxybenzone), 4-Metyhlbenzylidene camphor (4-MBC), Ethylhexyl methylcinnamate (Octinoxate,OMC).
The intrinsic photoprotection mechanisms of the skin can become diminished and be insufficient to prevent photoaging and photo photocarcinogenesis23, making the skin of the oncological patient more vulnerable to sun exposure.
The increased photosensitivy owing to treatments requires that the protection adequately covers the UVA spectrum, which causes the majority of the photosensitivity mechanisms, and the visible and infrared light because of the risk of hyperpigmentation.
Chemical solar filters can have photodegrading problems because of the action of sunlight.
They can also cause the possibility of producing irritation and variable phototoxicity, presenting a higher risk of causing contact reactions compared with mineral screens.
For this reason there is a special risk of intolerance in sensitive skin after chemotherapy and/ or radiotherapy.
EU legislation on the regulation of cosmetics establishes that they must be produced to the standards of best practice, which include an evaluation of safety for human health of the finished cosmetic product, before it is launched on the market.
Even so, there are still concerns about the release and/or possible presence of trace contaminants during some manufacturing processes.
For example, the CIR emphasized that the polymerization in benzene of carbomer and other acrolytes must be avoided and limits the impurities of heavy metals present in zinc salts.
The SCCS recommends using amines for cosmetics that aren't easily nitro sated and/or give rise to non-carcinogenic nitrosamines.
Some publications have found that residues of ethylene oxide remain after the manufacture of some cosmetics.
Many compounds have received favourable reports from expert commissions on the grounds that they are manufactured in concentrations and dosages as "non- irritant"?
The manufacturer must provide information if they are produced as non-irritant.
There are contradictory reports and studies which has made it difficult to catalogue some ingredients.
Other ingredients have favourable reports when applied to healthy or intact skin or with sunburn, and these have been classed as apt.
However because of their characteristics, such as size, it is not recommended to apply these on skin with an altered dermal barrier (nano titanium dioxide, nano zinc oxide), because of the risk of percutaneous absorption.
The use of nano ZnO in cosmetic products must not imply a risk to the consumer in the absence of a substantial systemic exposure.
Oncological treatments can alter the functioning of the skin barrier, making it more permeable and sensitive to certain ingredients.
The terms of use of cosmetics can go further than hygiene purposes.
Many of the ingredients of cosmetic products which are left on can accumulate with time and contribute to long term toxic effects which are hard to evaluate.
The following composition recommendations are given for a cosmetic product for an oncological patient: - It must not contain more ingredients than are strictly necessary.
It must not contain substances (including impurities or traces) with the following properties: carcinogenic, mutagen, reproductively toxic properties (CMR), with disruptive endocrine activity, potentially allergic nor with criteria included in lists of substances subject to authorization.
The legislation advises this for vulnerable people (children under three years old, the elderly, pregnant or breastfeeding women and people with altered immune responses).
They must not contain substances under suspicion, which are included in credible lists such as VHCs (Very High Concern Chemicals) compiled by REACH.
They must not contain substances which are being studied, as The Scientific Committee on Consumer Safety can take up to five years to emit judgements.
Priority must be given to substances with reports compiled by expert commissions and which are supported in the scientific literature.
Cosmetics destined for patients with skin diseases must have been clinically proved and have demonstrated a good tolerance profile.
Studies are needed on absorption through skin with an altered dermal barrier.
The production methodology must be of maximum security.?
Many new ingredients show highly allergenic properties with use and over time.
For this reason, that the formulation of a cosmetic product aimed at altered skin should not only not contain potentially allergenic substances, but also contains those which have been proven to not be so.
The evaluation of ingredients and the creation of lists must be open, reviewable, updateable and be subject to modifications according to judgements and evaluations published by expert commissions.
The oncological cosmetic criteria proposed by the authors of this work are the following:
Free of sensitising substances allergenics, fragrances, perfumes, some sun screens, conservatives and colo- rants, etc.)
surfactants, acids, alcohols, formaldehydes, parabens, etc.)
(resorcinol, 4-cloro methylphenol, DEP or diethyl phthalate, benzophenone 1, Oxybenzone, 4-Methylben- zylidene Camphor, Octinoxate, Methylparaben, Butyl- paraben, Ethylparaben, Propylparaben, Triclosan, Ho- mosalate, etc)
(Titanium Dioxide, Zinc Oxide, Drometrizole trisilox- ane, etc.)
(Alpha bisabolol, Vit. E, calendula, shea butter, Dexpan- thenol, niacinamide, evening primrose oil, rosehip oil, Alpha- bisabolol, Vit. E, Omega 3, Omega 6, Allantoin, Asian pennywort, Calendula, Niacinamide, Vit F, Aloe Vera, hyaluronic acid, Urea, etc.)
(emulsions, lotions, pastes, ointments, cleansers with- out soap, etc.)
(good tolerance profile tested and clinically proven in patients with skin diseases and oncological patients)
(octamethylcyclotetrasiloxane, nitrosamines, boric acid, phthalates, formaldehyde, etc.)
Several studies have shown that skin can be stimulated by intradermal injection of biological substances that induce dermis revitalization, in order to prevent and reduce aging-related alterations1.
With regard to this, several products are used in Aesthetic Medicine to prevent and reduce skin aging.
Among these products, Hyaluronic Acid (HA), which is often used alone or in combination with other substances, certainly plays a key role thanks to its natural stimulating and hydrating properties.
However, it is necessary to clarify the criteria underlying the choice of the different products currently available.
Collagen is the main protein in human connective tissue accounting for 25% of the total mass and 6% of body weight.
The most stable molecular disposition and re- arrangement of collagen is proline-rich triple helix.
Collagen structural unit is represented by tropocollagen, a protein with a molecular mass of approximately 285 kilodaltons made of three left-handed polypeptide chains that join to form a right-handed triple helix.
For collagen Type I there are usually two alpha 1 chains and one alpha 2 chain, while collagen Type III consists of three alpha 1 chains.
As previously said, our body produces a number of different collagens, the types involved in the biostimulation process being Type I and Type III3, the structural differences of which are responsible for their effects on the skin.
Besides structural differences, the two types of collagen have a strong expression of receptor sites to attack metalloproteases.
Although collagen Type I (fibrotic) is responsible for biological aging and characterizes mature age, it has been previously highlighted that its increase leads to aesthetic improvement2,3; while the presence of collagen Type III (reticular) characterizes young skin.
Although there is no recognized correlation between molecular structure, fibrotic collagen and reticular collagen, a review of the available data shows a different structural role of collagen Type III, characterized, within its ?±2 chains, by the presence of an amino acid, cistein, which is absent in other chains and appears to prevent the action of metalloproteases (MMPs) in the degradation of the collagen produced.
Collagen Type I has been recognized to be a collagen of fibrotic nature; however, we should be noted that some studies have shown to argue this data, by recognizing that collagen Type I (fibrotic) is a fibrillar collagen with a different biochemical structure and does not necessarily result in biological aging.
Zigrino P et al 2016 highlighted the role of fibroblasts in activating metalloproteases and regulating collagen homeostasis in adult skin.
Fibroblasts produce several types of collagen based not only on patients' age but also on the stimulations they receive and the environment they live in (Extra Cellular Matrix-ECM).
The difference in response is to be found both at receptor level, in stimulated fibroblasts, and at environmental level where procollagen is released.
The reduced synthesis of collagen that characterizes adult age reflects two mechanisms:
Fibroblast aging, characterized by a reduction in their number.
Low mitotic activity, resulting in reduced activation and production of collagen fibers, hyaluronic acid and elastin.
Moreover, clinical studies show that the possible effect caused by mechanical stimulation of the skin and the alteration of cellular matrix would trigger the aging process with subsequent reduction in the production of collagen6,7.
The therapeutic use of hyaluronic acid in several clinical settings requires a few considerations on the possible interactions of this molecule with cells.
This is certainly a new era for biological studies that will improve understanding of HA functions, properties and roles.
The crucial steps have been the following.
Identifying cellular receptors.
Identifying HA-synthetase.
Identifying intracellular HA.
It has been demonstrated that the different types of HA receptors, hyaladerins, are located on the cellular membranes, on the extracellular (pericellular) matrix and inside the cells.
In particular, the membrane receptors are mainly located on the fibroblasts of isoform CD-44.
CD-44 receptors are stimulated by fragments of the dermal matrix, which suggests that this substance needs to be continuously formed, and by growth factors.
CD-44 binding to HA appears to affect cellular adhesion, angiogenesis, cellular proliferation, cellular migration and cell-to-cell adhesion.
Moreover, it has been shown that the stimulus induced on CD44 receptors would induce the neoformation of reticular collagen.
Conversely, some authors highlight that the stimulation of CD39 and CD40 receptors leads to the neoformation of fibrotic collagen.
Fibrosis causes an increase in fibrotic Type I collagen that, by exerting a tensive action on the dermis, is responsible for the lifting effects on skin.
This aesthetic improvement is actually caused by the implementation of an inflammatory process, responsible for the functional damage to tissue.
The international literature confirms that inflammation includes the production of Transforming Growth Factor Beta 1, which in turn stimulates the production of collagen Type I and dermal fibrosis8.
In a recent review, Kavasi RM et al have highlighted some HA properties depending on its molecular weight.
The analysis of the literature shows that the biostimulation treatment with free High Molecular Weight Hyaluronic Acid (HMWHA) is reported to stimulate the production of inflammatory cytokines.
The subsequent fragmentation of hyaluronic acid is reported to produce fragments that in turn would be able to stimulate CD44 receptors with the consequent production of reticular collagen.
This degradation process, resulting in the formation of fragments with increasingly low molecular weight, is reported to induce the production of inflammatory cytokines.
Therefore, the inflammatory reaction seems to be induced by any hyaluronic acids, regardless of whether their molecular weight is low or high.
Hyaluronic acid has been shown to be produced by several cell populations located both in the dermal layer and in the epidermis, such as keratinocytes.
Although the authors have recognized that HA plays a key role in these processes and that the effects produced in the tissues are determined by the size and concentration of HA, they concluded that the complexity of these processes makes it extremely necessary to further analyze these issues in additional studies.
Quan T et al.10 have highlighted that fibroblasts can maintain their functional activation capacity, but it is crucial to stimulate the extracellular matrix.
Although it is commonly thought that non-crosslinked, non- reticulated hyaluronic acid (HA) in concentrations ranging from 0.8 to 2%, with molecular weight (MW) > 1.000.000 Dalton produces the lowest number of inflammatory cytokines, the fragments produced by degradation seem to maintain an inflammatory action.
An important aspect to be highlighted concerns recommendations about contraindications to the biostimulation/biorevitalization treatment.
A product that contains an association of more substances in the same mixture is more likely to produce adverse reactions, in particular if there is nickel which excludes its use by allergic patients.
Biostimulation contraindications are: acute articular rheumatism; presence of previous non-resorbable fillers at the infiltration sites (this is a sufficient reason to avoid the use of other fillers); patients under anti-coagulant treatment (sodium warfarin); patients with cancer who receive chemo- and/or radiation therapy.
For the treatment of patients with previous cancer diseases, who are not currently receiving any pharmacological treatment and/or radiation therapy, it is appropriate to reach a multidisciplinary case-by-case consensus.
Several preparations indicated for biostimulation/ biorevitalization are available on the market with different characteristics in terms of molecular weight (MW), concentration and viscosity.
These products can contain: Free non-cross-linked HA, Weakly cross-linked or stabilized HA. 
HA associated to amino acids (AAs). In these formulations, HA can be full-sized or fragmented.
HA associated to amino acids and other substances.
These products require the activation of hyaluronidases and they undergo a prolonged degradation process; HA associated to glycerol, Glycerol has a protective action against HA and slows down the degradation process, HA associated to mannitol. Mannitol maintains homeostasis and allows HA to remain longer in the area where it has been injected.
One of the most important factors that plays a crucial role in the choice of a product for biostimulation treatment is HA concentration.
The different HA concentrations can drive a targeted use based on the area to be treated and the desired result: prevention of skin aging and restitution of tissue integrity on mature skin.
The available concentrations are (Figure 1): 0.8% concentration: Indicated in particularly thin skin areas (periocular and perioral regions, neck region) and in prevention and maintenance treatments.
It is advisable to inject the product into the superficial and medium dermis, possibly with the picotage technique.
The maintenance protocol includes monthly sessions.
1.4-1.6% concentration: Indicated for the treatment of face, neck, d?collet?, hands and body areas in prevention, restitution and maintenance treatments.
It is advisable to inject the product into the medium and deep dermis with linear technique and / or picotage technique.
The protocol includes 3 fortnightly sessions, followed by monthly maintenance treatment. 1.8-2% concentration: Indicated for the treatment of areas with thick skin, in restitution treatments and to obtain a temporary filler effect.
The protocol includes: 3 fortnightly sessions, followed by monthly maintenance treatment.
It is advisable to inject the product directly into the deep dermis/superficial subcutaneous layer with linear technique and/or picotage technique. 2% concentration: Indicated only for face treatments in the restitution phase.
It is advisable to inject the product into the medium and deep dermis / superficial subcutaneous layer with linear technique and / or picotage technique.
The protocol includes 3 fortnightly sessions, followed by monthly sessions.
The association of stable hybrid cooperative complexes of high HA (1.100-1.400 KDa) and low molecular weight HA (80-100 KDa) is characterized by a concentration of 32 mg H-HA + 32 mg L-HA in 2ml (pre-filled syringes).
The combined action of stable hybrid HA complexes allows to obtain a bioregeneration and remodeling action on the dermis.
The treatment pattern includes two treatments at 1-month interval followed by a maintenance treatment at 2 months depending on the aging level.
Among the hyaluronic acid-based products associated with amino acids there are several formulations.
A HA formulation with a concentration of 10 mg/ml, P.M. 200 – 400 Kd associated with lyophilized amino acids (glycine, l-proline, l-leucine, l-lysine) that are involved in the process of production of collagen is a Class 3 medical device and its administration is recommended in superficial and medium dermis of face, neck, d?collet?. 
The protocol includes 4 fortnightly sessions followed by monthly sessions, possibly with administration of non-crosslinked HA between sessions.
It is indicated in dehydrated skin with medium degree cutaneous relaxation.
In the formulations that include the association of fragmented HA with AA, HA is in micromolecular form (20-38 monomers) i.e. fragmented with MW from 1440 to 2736 kD; it is associated to amino acids (l-isoleucine, l-leucine,    l-lysine    hydrochloride,    l-proline,  l-valine, l-glycine, l-serine, l-alanine).
The protocol includes 4 weekly sessions, 2 fortnightly sessions, followed by monthly sessions.
It is advisable to inject the product in the superficial and medium dermis of the face, neck, d?collet?, back of hands.
It is indicated in dehydrated skin with mild-degree skin relaxation.
Finally, a particular association of HA + AA with concentration of 10 mg/ml (3 ml) PM 200 Kd is an association with lyophilized amino acids (glycine, l-proline, l-leucine, l-lysine, l-alanine, l-valine), with L-alanine and l-valine being the amino acid constituents of elastin, making this product particularly active on the turnover of ExtraCellular Matrix proteins and in case of skin laxity of the face where the biostimulation treatment is indicated.
The latter is a crucial element in the physiological promotion of neocollagenesis and elastogenesis through the migration of fibroblast in the injected area.
A recent in vitro study conducted on human dermal fibroblasts showed the efficacy  of  extracellular  matrix  proteins, in particular elastin, on biosynthesis; it has been demonstrated that by changing the quality and quantity of amino acids in the mix, it is possible to increase the expression of elastin, at gene and protein level, while maintaining collagen stimulation.
In summary, several products are used to prevent or reduce damage caused by skin aging.
Among these, the HA-based preparations play a crucial role.
Available data show that HA is often used either free or in combination with other substances, thanks to its natural stimulating and hydrating properties.
There are several HA-based preparations available for biostimulation that differ in terms of molecular weight, concentration and viscosity.
Although the available scientific evidence supports an increasingly important role of HA in skin biostimulation, recent findings allow to define bioregeneration as a process that physiologically promotes neocollagenesis and elastogenesis through the migration of fibroblast chemiotaxis in the injected area.
With regard to this, the involved factor would be the presence, in the injected area, of constituting amino acids such as L alanine and L valine that seem to regulate the biosynthesis of proteins of the extracellular matrix, especially elastin.
A final consideration, not to be forgotten, is that the wide range of available products allows to personalize the treatment based on the patients’ age and degree of skin aging.
Over the last few years, the search for aesthetic treatments aiming at improving image and self-esteem has increased.
Among the complaints presented in aesthetic clinics, localized abdominal fat is recurring in the daily routine of these centers, especially from the feminine public, who has greater difficulty in decreasing it.
In fact, women present a higher number of adipocytes compared to men, due to gestation and because the lipogenesis process is better favored in comparison to lipolysis, a phenomenon caused by a greater concentration of lipolysis inhibiting receptors in relation to lipolytics.
In order to treat this aesthetic illness, new technologies are being developed every year.
One of the technologies that has been increasingly used in the field of aesthetics is shock waves, which consist of the conversion of electric energy into mechanical energy which is generated by electromagnetism, electro-hydraulic methods, pneumatic methods, or through piezoelectric effect.
Such energy is capable of significantly altering a specific area without modifying the structures that surround it, and it can be used to decrease localized fat and improve the appearance of the gynoid lipodystrophy (GLD) acting upon the adipocytes and leading to collagen remodeling.
The most important mechanical effects of the shock waves are reflexes with pressure and tension forces on the thresholds of different impedances, the generation of cavitation and the formation of micro-jets that cause erosion and micro-perforations in vases and membranes.
Shock waves induce membrane hyperpolarization, the formation of free radicals and generate oxygen radicals that should perform a fundamental role in the translation of the mechanical energy of the shock waves into a biological effect that, when applied to the skin, promotes the formation of micro-bubbles in the fluids, whose flow is made difficult by the obstacle created by the interface areas.
This phenomenon prevents the micro-bubbles from collapsing in a symmetric manner, generating cavitations.
The lipid profile is comprised of triglycerides, HDL, LDL, total cholesterol, non HDL, VLDL and total lipids.
Based on these biochemical exams, it is possible to verify whether the patient in question presents dyslipidemias or not.
These changes may be due to the high concentration of circulating fatty acids generated in the liver of patients with excessive body fat.
The aspartate aminotransferase (AST) and alanine aminotransferase (ALT) are markers of liver lesions and, because lipid metabolism is performed in the liver, the change of its profile may be related to pathologies in this organ and, consequently, interfere with hepatic markers.
Therefore, lesions may occur in this organ when there is an increase in body fat.
Finally, C reactive protein (CRP) is a marker present in acute inflammatory processes, whether the latter are or not related to infectious frameworks.
Increased body fat also elevates the serum levels of CRP, because adipocytes release pro-inflammatory markers, the adipokines.
Taking into account the metabolic changes due to the action of adipokines secreted by adipocytes, such as their effect on hepatic functions, this study aimed at evaluating not only the efficiency of shock wave therapy on decreasing measures in the abdominal area, but also the influence of the action mechanism of the technique on both lipidic and hepatic metabolism.
In order to proceed with the survey, the project was submitted to the Research Ethics Committee at Universidade Positivo, the institution in which it was undertaken.
After the approval under the report number 2,658,312, a public notice was published for volunteers in social media and a selection was performed after an analysis, in which 11 volunteers complying with inclusion and exclusion criteria were selected.
The following criteria were applied: women between 30 and 50 years of age with a Body Mass Index (BMI) equal or above 30Kg/m2 and waist-hip ratio equal or above 0.80cm.
During the selection, the following exclusion criteria were applied: individuals presenting hypertension and/ or non-controlled diabetes; referred dislipidemies; liver, heart and/or kidney diseases; neoplasms; epilepsy; pregnant women; nursing women; individuals whose skin in the abdominal area was not whole; individuals who underwent surgeries in the area less than a year ago; individuals with copper IUD or metallic prosthesis in the area; smokers; individuals taking medicines: antihyperlipemic drugs, drugs controlled by legislation, phyto-therapeutic or nutricosmetics substances accelerating metabolism or weight loss, and individuals under treatment with restrictive diets.
These criteria were aimed at the maximum decrease of any external interference in the research.
The volunteers signed an Informed Consent allowing the use of anthropometric and biochemical data and photographic records and declared to be aware of the study's goals, indications, possible complications, the technique to be used, and number of sessions.
Blood collection (after an eight-hour fasting), the nutritional evaluation (comprised of bioimpedance), anthropometric measures (circumference and weight) and photographic records (front and profile) were carried out in two phases: at the beginning and at the end of the sessions, with the aim of verifying the changes in composition before and after the treatment, At the end of the treatment, the volunteer was asked to fill out a satisfaction survey.
Circumference measurement was done 5 centimeters above the umbilical scar (supra abdominal), right over it (waist), 5 centimeters below the scar (infra abdominal) and of the highest portion of the glutes (hip).
The equipment used in the research was Hygiapulse? provided by KLD Biosistemas?, which is registered at the Ag?ncia Nacional de Vigil?ncia Sanit?ria (ANVISA – National Hygiene Vigilance Agency) – register number 10245230022 –, with a 25mm radial transmitter and set for localized fat, a 100 mJ intensity, a 15 Hz frequency and an emission of 3000 discharges per area (10 X 15 cm).
In order to perform the applications, the abdomen was divided into six quadrants, four in the central portion and two on the sides of the abdomen.
Neutral carbopol gel was used as a conducting agent.
In order to guarantee the reproducibility of the application technique, the manipulation of the gauntlet was standardized: it was moved vertically, horizontally and diagonally during the emission of the shock waves, in a consecutive manner until the end of the 3,000 pulses.
?During the treatment, one of the volunteers gave up treatment for personal reasons.
After the end of the treatment of the 10 volunteers, a comparison was performed of the anthropometric measures and the biochemical parameters of each one.
In relation to circumference, it was observed that 9 patients presented an average decrease of 3.9 cm in the supra abdominal area, 7 volunteers decreased an average 4.14 cm on their waists, 8 models lost 4.06 cm on their infra abdominal portion, and 9 patients presented an average decrease of 5.1 cm on the circumference of their hips.
Volunteers 3, 4, 5, 6 and 8 showed decreased measures in the four areas, and it is important to emphasize the fact that patient 8, who did physical activities three times a week, presented the greatest decrease in measurements in the treatment area.
Model 7, who does not do any physical activities and has visceral fat, did not present any decrease in abdominal circumference (Table 1).
The parameters gauged by bioimpedance were the following: BMI, lean mass, percentage of body fat and weight (Table 2).
Of the 10 volunteers, 4 practice some type of physical activity (walking, body building) between one and three times a week and all have a normal diet.
Despite these features in common, patient 8 presented a decrease in BMI and weight, model 9 only in BMI, volunteer 2 only in percentage of fat, and patient 1 presented an increase in all of the parameters analyzed by the bioimpedance equipment.
The decrease in body fat percentage took place only in four patients, and only two presented decreased weight.
The only model who did not present changes in her BMI and weight, and presented gains in her lean mass and loss of body fat was patient 2, who, 31 years old, was the youngest of the group being studied.
From the results obtained in the biochemical analyses (Table 3), it can be observed that all the patients presented increased triglycerides and VLDL, some presented a decrease in serum levels of AST (7 volunteers) and ALT (6 volunteers), 8 patients presented increased HDL and 7 presented decreased LDL.
There was also an increase in serum levels of total cholesterol in 5 models and total lipids in 9, and CRP was detected as non-reagent on all of them before the treatment, turning into reagent in 6 volunteers after 10 sessions.
In the satisfaction survey based on the Likert scale that was filled by the volunteers, the following topics were verified: skin texture related to tissue flaccidity, decrease in localized fat, definition of body contour, bowel motility, diuresis, changes in menstrual flow and cramps, post-application discomfort, sensibility in the local of application, and positive and negative aspects in relation to the services provided.
In the skin texture item, 71.42% reported significant improvement in local tissue flaccidity, 14.28% did not notice any differences, and 14.28% reported worsening conditions.
In relation to decreased localized fat, 42.85% noticed decreases, the same percentage did not notice any differences, and 14.28% reported that it did not decrease. 57.14% of the participants reported an improvement in the definition of body contour, 28.57% did not notice any differences, and 14.28% reported worsening.
None of the models presented any changes in bowel motility, presented any post-application discomfort or had any sensitization that might be provoked by the conducing gel.
Regarding diuresis, 57.14% did not present any changes and 42.85% noticed an increase in urination frequency.
71.42% did not present any changes in their menstrual cycle nor menstrual cramps different from those they normally have, 14.28% presented some changes in their cycles and dysmenorrhea, and 14.28% use some type of contraceptive method so that they do not menstruate.
All of the volunteers classified the services during the period of the treatment as excellent.
Figures 1 and 2 show the result obtained as well as the improvement in skin texture.
In this study, the increase in lean mass presented by bioimpedance cannot be directly related to the practice of physical activities, because of the five volunteers that presented muscle gains, only one declared doing some physical activities.
The results pointing to weight and body fat gains, regardless of practicing physical activities or not may be related to the fact that energy spent by the volunteers is lower than the calories obtained through food, which is the most common mechanism of weight gain.
The 31-54 age range was adopted because after the age of 25 there is a significant progressive decrease in metabolism10.
The increase in VLDL and HDL lipoproteins indexes and the changes in AST and ALT hepatic markers remained within the normal parameters and suggest that, during the employment of the technique, triglycerides are metabolized by the liver and transported by blood circulation, but without compromising the organ in healthy individuals and producing mainly higher- density lipoproteins, which are more difficult to be found in higher levels in patients presenting cardiovascular diseases.
It is believed that VLDL, even though they are not the main lipoproteins associated to pathologies, were higher because they are the second lipoproteins that are produced by hepatocytes to transport triglycerides, chylomicrons being the first11.
The elevation in total lipids concentrations in all of the patients, in addition to the increase in total cholesterol in 5 of them, is also justified by the circulating triglycerides released by adipocytes12.
The fact that the C Reactive Protein went from non- reagent to reagent in 60% of the volunteers suggests that the extra-corporal therapy by shock waves provokes an inflammatory process in the adipose tissue through its mechanical action, as it is expected that every aesthetic procedure causes a controlled inflammatory reaction.
The results of the four patients who did not present this difference may be justified by the use of anti- inflammatory medication near the day or on the very day that blood was collected; by an anti-inflammatory diet, which is comprised of food that is able to inhibit or block the action of inflammatory agents; and by a greater resistance of the body to inflammatory processes.
The physiological effect of mechanical and thermal stress by the shock waves equipment entails tissue damages to the adipocytes which triggers off an inflammatory response and the release of important cytokines13.
In 2011, Ferraro et al.14 conducted a study with 50 patients associating shock wave extra- body therapy after the application of cryolipolysis.
In the study, the techniques were applied for the treatment of abdominal localized fat in 14 patients, five females and nine males.
The average decrease in abdominal circumference at the end of the treatment was of 6.86 cm, showing that when one associates shock waves to cryolipolysis there is an improvement in the results, especially in males, due to the body composition and metabolism that facilitates both lipolysis and apoptosis.
In his 2017 study, Diogo et al.15 analyzed the lipid profile of six male individuals before and after they were submitted to an ultra-cavitation procedure that, like the shock wave equipment, also promotes lipolysis and cellular apoptosis.
In the study, it was found that the serum levels of triglycerides and VLDL increased both in the active group, that practiced some type of physical activity, and in the sedentary group, so that in the first this increase doubled in comparison with the second group, having found out that lipolysis is more effective when there is an association of an aesthetic treatment and physical activity.
The decrease in abdominal circumference confirms the efficacy of the treatment mainly on the outlook of the volunteers.
The significant improvement in tissue flaccidity in the abdomen takes place because of the lesion caused by cavitation generated by the shock waves, which leads to the production of vascular endothelial growth factor (VEGF) and endothelial nitric oxide synthase, inducing the neocollagenesis process, improving the structure and quality of collagen and elastic fibers of the tissue16.
Because of its ability of tissue decompression and lipolysis through its mechanical and cavitational effects described in the review by Modena et al. 201717, the greater definition in body contour and the decrease in localized fat were noticed at a great scale by the models, in addition to being proved through measures and photographs.
Increased diuresis and dysmenorrhea reported by the majority of the patients may be due to the vibration provoked by the equipment, which stimulates the contraction of the smooth muscles, both in organs and vessels, increasing kidney overload and may cause accentuated menstrual cramps during the treatment period.
?This study showed that, when having an extra-corporeal shock wave therapy, the volunteers presented decreased abdominal located fat as well as improved body contour and skin appearance.
Through the analysis of the biochemical parameters obtained, it was observed that shock wave therapy does not appear to cause liver lesions and consequently leads to pathogenic metabolic changes.
It is suggested that a future survey is carried out with a greater number of participants, taking into account predisposing genetic factors and feeding habits that might interfere with the investigation, in addition to comparing the efficiency of the technique in patients who practice physical activities and sedentary ones.
Hyaluronic acid (HA) is a linear polysaccharide composed of repeating b 1,4-linked D glucuronic acid (GlcA) andb-1,3-linked N acetyl-D glucosamine(GlcNAc) disaccharide units, which is found ubiquitously in the extracellular matrix (ECM) of all vertebrate tissues, although in widely variable concentrations and bound to different partners.
It is also named ‘‘hyaluronate’’ referring to its salt form, or  ‘‘hyaluronan”,  a  term that includes all forms of the molecule1.
More than 50% of HA in our body is localized in the skin, where it is synthesized by fibroblasts, keratinocytes, and endothelial cells of the dermal microcirculation in many variants of different molecular weight2.
Under normal circumstances, HA is quite abundant in the dermis, where, due to its viscoelasticity and capacity to retain water, it plays a crucial role in controlling tissue hydration, keeping an appropriate tissue volume to protect skin cells from mechanical damage, and maintaining structural stability3.
In the epidermis, it carries out a trophometabolic activity, contributing to the preservation of cutaneous homeostasis.
HA is a non-immunogenic molecule, a polymer devoid of protein epitopes, and it also has the advantage that, regardless of the problem, it can be easily removed by digestion with hyaluronidase4.
HA plays important and different roles in the process of tissue repair.
Its interactions with specific signaling receptors maintain structural cell integrity and promote recovery from tissue injury1.
Since aesthetic medicine is nowadays not only aimed at alleviating (skin) diseases but also at improving one's perception of wellness, the repairing properties and the safety characteristics of HA have made it, in the last decades, the most commonly used material for soft tissue augmentation in aesthetic medicine4.
In fact, the marked reduction in HA content characteristic of the aged skin plays a crucial role in dermal thinning and wrinkles formation.
Therefore, injected HA-based fillers are largely used to"fill-up"?the dermis and improve facial wrinkles.
The different HA dermal fillers vary widely in their physical and chemical characteristics and the many variables affect their overall performance.
In general, they improve skin turgor and elasticity.
In recent years, different techniques have been developed with the aim of improving HA stability, thus slowing its degradation in tissues and allowing manufacturers to control the gel stiffness.
Interestingly, besides improvement of deep wrinkles and volume deficiency, new objectives are being pursued with HA-based fillers, such as deep skin hydration.
Intradermal injections of HA may be used to boost the water content in the extracellular matrix of the dermis, resulting in deep dermal hydration and improvement of skin surface roughness and fine wrinkles.
The studied product is a ready-to-use solution of stabilized, injectable HA which has unique rheological properties (Table 1), that give the product high deformability and low stiffness and viscosity.
For these reasons, the product offers a dual function, called "hydrostretching"? consisting of both a process of dermal hydration and tissue bio-restructuring, and a mechanical stretching action on superficial wrinkles.
The low stiffness and viscosity and the high plasticity also favor an optimal tissue integration and enable the product to be injected into different layers of the dermis, up to the most superficial ones, making it particularly effective on the most dynamic facial areas (perioral and periocular areas and the forehead).
The studied product is suitable to improve dry skin, with poor elasticity and/or poor skin texture, and to stretch superficial wrinkles, in particular mimic wrinkles, and to correct acne scars.
A recent study on 18 volunteers who had undergone two injections of the studied product 2 months apart and followed up for a further 3 months, demonstrated a significant improvement of wrinkles grade around the eyes and the lip, and wrinkles severity of nasolabial folds already after the first injection.
In addition to this there was an improvement of the aging/photoaging grade and surface microrelief after 2 months, following the second injection, and a parallel improvement of instrumental skin profilometry and optical colorimetry.
This study also confirmed the good tolerability profile of the product and the duration of its effect.
One hundred women with Glogau Wrinkle Scale grade 2-3, requiring deep hydration according to medical advice, have been consecutively enrolled from March to June 2018 by three Italian physicians within their patients.
Patients were asked to maintain their habits (food, physical activity, make-up use, facial cosmetics and cleansing products) and not to expose their face to strong UV irradiation without proper sun protection.
Patients with the following characteristics were excluded: pregnancy; lactation; smoking; alcohol or drug abuse; having received skin treatments for esthetic correction 6 months prior to the study treatment; having already performed permanent dermal fillers; dermatological diseases as well as general diseases (diabetes, endocrine disease, hepatic, renal, cardiac and pulmonary disorders, cancer, neurological diseases); drugallergy; inflammatoryand/orimmunosuppressive diseases.
All the patients signed an Informed Consent allowing the use of photographic record for scientific publications, in which they declared to be aware of the product, treatment choice, alternative treatments and alternative products, possible complications and number of sessions.
The studied product was administered with three different techniques.
A microlinear or microbolus technique (<0.05 ml each) in the areas of the face requiring deep hydration (malar/submalar areas).
A microbolus technique to stretch the dynamic facial wrinkles (microdroplet injections ?0.01 ml each along the path of the wrinkle).
A combined technique to stretch the static facial wrinkles (microlinear retrograde technique ?0.01 ml each below the path of the wrinkle, followed by microdroplet injections ?0.01 ml on the same wrinkle).
Two injections were administered to each  patients 60 days apart.
The most commonly treated areas were the periocular and perioral zones, which are highly dynamic and have thin skin, being quite difficult to treat.
In these areas 30G or 33G needles were preferred.
Subjects were asked to express their degree of satisfaction about the improvement  of  facial microroughness, by answering to the question “How satisfied are you with the improvement in you facial microroughness  following  the  treatment?”  on a 1 to 10 verbal rating scale (VRS), where  1  means “not at all satisfied” and 10 “completely satisfied”. 
Evaluations were requested twice: 2 months after the first injection (T1) immediately before performing the second injections and then 3 months after the second injection (T2). 
At the same timepoints, physicians as well were asked to grade their degree of satisfaction with the treatment on 1-10 VRS.
The mean score expressed by patients and physicians at the two study timepoints is reported in Table 2.
In particular, at the end of the study, the mean scores were 8.5 and 8 respectively.
Pictures of some example cases are reported in figures 1-4.
From a tolerability standpoint, only a few deposits were observed in some patients with particularly thin skin, that remained perceivable for 2 weeks before complete reabsorption.
No adverse events related to the product have been reported.
Approximatively 20% of the treated patents reported discomfort/unpleasant sensation, which is in line with a previously published study.
Viscoderm? Hydrobooster is a stabilized HA injectable preparation, characterized by low viscosity and stiffness and high deformability.
Its rheological properties favor tissue integration and allow to inject the product in different layers of the dermis, thus providing a dual function: deep hydration and tissue restructuring on one side, together with a mechanical action aimed at stretching the most superficial wrinkles on the other.
Clinical experience has shown a good tolerability and a high degree of satisfaction by both patients and physicians.
The skin is a sense organ that covers the body.
It regulates the body temperature, gets pale with sweating, serves as both a thermostat and a protection barrier.
Its thickness is between 1 mm and 4 mm.
It is the largest and heaviest organ of the body, constituting 16% of the total body weight.
The thinnest skin is to be found on the eyelids while the thickest on the sole of the foot.
The basic structure of the skin has three different layers: Epidermis: It is the outermost layer of the skin that acts as a protective layer.
The skin renewal takes place in this layer.
Dermis: It is the middle layer which is effective in the durability of the skin.
Hair follicles, sweat glands and sebaceous glands are found in this section.
Hypodermis: An inner layer of subcutaneous fat.
It provides energy to the skin and is responsible for the insulation function.
Poly-L Lactic Acid (PLLA) based filler was first introduced in Europe in 1999 under the trade name"New Fill"?and then in 2004 in the United States market under the trade name Sculptra L for use in HIV-related lipoatrophy cases.
Approximately 150,000 patients were treated in the 10-year period after use9,10.
Several studies have been conducted on PLLA safety, efficacy and persistence11-13.
PLLA is mostly used in the face but there are sources in the literature that show it can be used in other areas.
PLLA has been used as a suture material and absorbable screw for approximately 40 years in medicine.
Its biocompatibility and efficacy have been shown in previous studies.
In a study conducted in Brazil in 2008, it was reported that the injection to nasolabial folds still persists after 3 years.
PLLA creates a foreign tissue reaction at the site where it is applied, increasing the number of macrophages, mast cells and lymphocytes in that region, decreasing fibroblastic activity and increasing neocollagenesis slowly.
New collagen formation appears at 1 month, and until the 9th month, the formation is observed to increase.
In the previous studies the PLLA particles showed signs of disappearance in the 6th month and they are eliminated completely in the 9th month.
The experiment was performed on patients aged between 34 and 61 years old, some of whom had undergone medical aesthetic treatments, while the remaining patients had not.
The participants were divided into 2 groups.
There were 10 participants in each group.
All the patients signed an Informed Consent allowing the use of photographic record for scientific publications, in which they declared to be aware of the product, treatment choice, alternative treatments and alternative products, possible complications and number of sessions.
Information about the groups is given below: Group 1: In this group, 10 patients received APTOS brand PLLA/CL.
No other medical aesthetic procedures were performed at the same time and no other medical aesthetic procedure was performed for 3 months.
Group 2: In this group, 10 patients were treated with HiFu application and PLLA / CL were applied to the face during the same week.
No other medical aesthetic procedure was applied for 3 months.
After the applications, the participants were evaluated with VAS questionnaire from 0 to 10 points.
The questions were answered by both the practitioner and the participant before, during and after the application twice, first, 1 month after the application and then 3 months later.
The skin color and stains, skin moisture, fine lines, elasticity and sag (especially in the jaw line) were evaluated.
A statistical program and photographs were used to evaluate the results.
Statistical analyses were performed using SPSS software (version 25.5).
Basic descriptive statistics were assessed to describe the survey results as the means ± standard deviations.
The total VAS score was found to be 32.8±6.08 before the treatment and 53.0±5.92 after 3 months of treatment, and the healing rate was 62% according to doctor evaluation.
In the second group with HIFU and PLLA/PCL Aptos thread combined application, the total VAS score was found to be 30.8±11.9 before the treatment, 39.6±11.5 after the treatment, 46.2±9.97 1 month after the treatment and 54.2±8.48 3 months after the treatment.
The rate of recovery was 29% after treatment, 50% after 1 month of treatment and 76% after the 3 month- treatment.
In this group, the total VAS score was found to be 35.9 ± 9.82 before the treatment and 55.8±7.65 after the treatment and the recovery rate was found to be 55% according to doctor evaluation.
Table 2 shows the skin characteristics according to the patients.
In the evaluation of the study, the skin hydration was found to be 5.10 ± 1.79 before the treatment in the first group and 7.20 ± 1.31 the third month after the treatment.
In the second group, the mean pre-operative average was 4.5 ± 2.8, and the mean value after treatment was 3.40± 2.01.
Skin hydration increased by 42% in the first group and 65% in the second group.
Before the treatment the mean skin color was 4.90 ± 2.42 in the first group and 4.8 ± 2.29 in the second group.
In the first group, mean skin color was measured as 5.30 ± 2.00, 1 month after the treatment and as 6.01 ± 0.56, 3 months after the treatment.
In the second group, the mean skin color was 6.50 ± 2.41 after 1 month and 7.20 ± 2.20 after 3 months of treatment.
An improvement of the skin color was observed by 22% and 50% for the first group and the second group respectively.
The presence of fine lines was determined as 6.5 ± 2.71 in the first group and 5.2 ± 3.15 in the second group before the treatment.
At the end of the treatment, there was an improvement of 17% in the first group and 50% in the second group.
In the evaluation of the jaw line, the mean value was 3.30 ± 1.63 in the first group and 3.4 ± 1.57 in the second group before the treatment.
The evaluation on the third month following the treatment revealed that, the mean of the first group was 8.0 ± 0.94 and the second group was 8.30 ± 1.15.
The flatness and tension of the jaw line increased by 142% in the first group and 144% in the second group.
The recovery rate of the lines around the mouth was 112% in the first group and 142% in the second group.
The improvement in the lines around the eyes was recorded at 25% in the first group and 58% in the second group.
In the evaluation of skin elasticity, the mean pre- treatment in the first group was 3.30 ± 1.33, 5.01 ?± 0.05 1 month after the treatment and 6.90 ± 0.87 3 months after the treatment.
In the second group, the mean pre- treatment, 1 month after the treatment and 3 months after the treatment was 4.6 ± 1.83, 6.51 ± 0.26 and 7.60 ± 1.26 respectively.
The increase in skin elasticity was 109% in the first group and 65% in the second group.
During the treatment, the pain level was determined as 7.20 ± 2.04 in the first group and 9,6 ± 0,69 in the second group.
Table 3 shows the skin characteristics evaluations by the doctor.
The skin hydration was evaulated at 5.10 ± 1.79 before the treatment in the first group and 7.70±0.82 the third month after the treatment.
In the second group, the pre-treatment average was 5.1±1.83, and the mean value after 3 months of treatment was 7.80±1.81.
Skin hydration increased by 50% in the first group and by 53% in the second group.
The mean skin color was 4.8±2.52 in the first group and 5.50±2.22 in the second group.
In the first group, skin color after 1 month was measured as 5.40±2.01, and after 3 months of treatment as 6.40±1.57.
In the second group, the skin color was 7.30±1.88 after 1 month and 7.90±1.72 after 3 months of treatment.
An improvement of the skin color was observed at 33 % for the first group and 44 % for the second group.
The presence of fine lines was determined as 6.50±2.79 in the first group and 5.90±2.68 in the second group before the treatment.
After the end of the treatment, there was an improvement of 17% in the first group and 36 % in the second group.
In the evaluation of the jaw line, the mean value was 3.20±1.93 in the first group and 4.10±1.52 in the second group before the treatment. 3 months after treatment, the mean of the first group was 8.40±0.96 and the second group was 8.20±1.13.
The flatness and tension of the jaw line increased by 162% in the first group and by 100% in the second group.
The recovery rate of the lines around the mouth was 130% in the first group and 93% in the second group.
The lines around the eyes were improved by 33% in the first group and by 32% in the second group.
In the evaluation of skin elasticity, the mean pre-treatment in the first group was 3.10±1.37, 5.20±1.03 after 1 month of treatment and 7.30±1.15 after 3 months of treatment.
In the second group, the mean pre-treatment, after 1 month-treatment and after 3 month-treatment was 5.20±1.54, 6.90±1.37 and 8.0±0.81 respectively.
The increase in skin elasticity was 135% in the first group and 54 % in the second group.
During the treatment, the pain level was determined as 7.70±1.49 in the first group and 9.70±0.48 in the second group.
The main reason for patients trying an aesthetic treatment is to counteract the symptoms of ageing.
However, most facial treatments and methods offer relief only for some wrinkles or shrink-wrapped failing skin outwardly responding to the volume and forms of a fresh face.
Injectable poly-L lactic acid is a biodegradable artificial polymer for the improvement of lipoatrophy and is widely used in Europe.
Sculptra was famously applied for improvement of nasolabial folds, lack of medial and lower face volume, jawline slack, and another type of facial ageing.
Sculptra treatment is a minimally invasive and efficient method.
There are main directions for the application of PLLA: bone resorption, fat death and skin laxity; and at which layer of the face and at what level PLLA is determined depends on the patien's health.
Also, for skin laxity, the contrast happens“as PLLA needs to be practised just under the skin, into the subdermal level, with needles or cannulas, but with no fat in connecting tissues.
It is in this layer that the best skin quality results can be achieved, which is the idea of this study20,21.
Whenever PLLA is injected into a subdermal plane there is an improvement in skin quality of three types.
The first being clear glow-luminosity produced by the hydration of the treated skin, providing the effect of a healthy, young and well-ageing skin.
An addditional benefit is the reduction of skin atrophy associated with aging.
Moreover, it reduces skin laxity by increasing skin adherence to lower-level tissue.
This is normally due to the generation of collagen fibres resulting from the PLLA applications22.
In the study carried out by Avelar et al.23 three sessions were applied to the patients at intervals of 45-60 days.
Notwithstanding PLLA being supported for many uses - bone resorption, fat loss and skin laxity - there is a regular increase in skin quality after treatment.
However, not only is it necessary to know PLLA but also to define the level of injection.
In a previous study, a patient with poor skin quality was treated with a 12-week PLLA treatment.
Due to the poor quality of the skin, the first and second treatments were applied for four weeks, allowing sufficient time for collagen restoration and repair.
Four weeks after her initial treatment, the patient showed little or no cosmetic improvement.
After the second treatment, an improvement of between 20% and 30% in tissue quality was observed according to the comparison of the photos of the patient before and after the treatment and the patient?€?s opinion on the results of the treatment.
Eight weeks after the second treatment, a third treatment was performed to resume collagen repair.
No side effects related to treatment were observed.
After completing the treatment, visual inspection and skin quality improvement resulted in significant results in terms of elasticity.
In addition, as a result of the 12-week PLLA administration, an increase in fibrotic layer in the dermis and subdermal layer and skin shine were observed.
In addition, collagen restoration resulted in a healthier skin, reduced pores and a more youthful appearance.
No adverse effects were observed during the annual follow- up of the patient.
In another study, participants were treated with injectable PLLA or human collagen for 3 weeks.
There is a 3-week period between treatments.
For the members of the PLLA group, 3 injection sessions were completed.
Three weeks after the last treatment with injectable PLLA, an important development was recognised in the wrinkle assessment scores compared to the baseline.
Changes continued to appear until the 13-month evaluation period and were reported during the 19 and 25-month evaluation points.
After the injection of PLLA, the number of nodules and papules was 7% and 9%, respectively.
Further investigations can serve to maintain the advantage of injectable PLLA performance for aesthetic improvement of facial shape dysfunctions and help manage suitable patient choice criteria for treatment of this strategy.
In a similar study, 210 female participants were chosen to correct injectable (PLLA) age and disease-related facial volume deficits.
The questionnaire was sent to patients treated with PLLA 6 months earlier or more.
After the treatment, some of the patients had papules or nodules.
After treatment, some of the patients had papules or nodules.
One questionnaire was posted to 281 patients previously treated with PLLA for 6 months or more.
PLLA was reconstituted by adding 5 mL of sterile water before injection and 1 mL of 1% xylokine before injection.
Patients treated with PLLA had a recovery time of 24 months.
The maximum improvement was seen after several treatment sessions31.
In our study we evaluated the effect of PLLA.
Similar to the results reported in the literature, PLLA use increased skin collagen.
A skin increase of 42% was observed in the skin group with PLLA.
This rate was higher in the HIFU and PLLA group.
There was a moderate improvement in skin color.
In particular, there was a significant improvement in the jaw line, and both groups had close rates in skin flexibility only.
In the PLLA group, a higher rate than in the HIFU and PLLA group was found.
This can be considered as evidence that PLLA significantly increases skin elasticity and eliminates the signs of aging.
In this study, it was observed that there was little recovery immediately after application.
However, a high rate of improvement was achieved 1 month and 3 months after the treatment.
This is in line with the studies in the literature.
In this study, we investigated the effect of PLLA/PCL Aptos thread treatment on 20 female patients.
The treatment covers a period of 3 months.
No side effects were reported by the patients.
Evaluations were made through questionnaires and photographs.
Both the patient and the physician evaulated the results and similar data were obtained.
When the survey data were evaluated, an improvement of only 65% in the PLLA /PCL Aptos thread group and 76% in the combined treatment group was observed. 
These results showed close values in the evaluation of patients and doctors.
Combined treatment with skin hydration and skin color gave better results.
In the presence of fine lines, there was a moderate improvement in PLLA/PCL Aptos thread application.
A high improvement was observed in the jaw line and mouth lines. A smoother and taut image was obtained.
As for skin elasticity, only PLLA/PCL Aptos thread application showed a better result than combined application.
In general, when the results are evaluated, it can be said that PLLA/PCL Aptos thread application is effective on skin quality.
This effect depends on the time of the application and the patien's condition prior to the treatment.
A minimal improvement immediately after the application but a high recovery after 3 months emphasize the importance of application time.
Facial skin aging is a phenomenon involving a set of microscopic and macroscopic complex volumetric changes.
These changes can be explained by several factors, including deep three-dimensional structural support, subcutaneous fat redistribution, bad habits or environmental factors.
Skin aging is caused by a disruption in the vascular and connective tissue architecture of the dermal and hypodermal layers associated with a reduction in the number and activity of fibroblasts1.
It should be highlighted that skin aging is due to intrinsic factors, mainly responsible for physiological aging (intrinsic ageing)2,3,4,5 and extrinsic factors responsible for photo-induced aging (extrinsic ageing)2 elastic fibers, reduced number of fibroblasts, mastocytes and Langerhans cells.
With regard to connective tissue, physiological aging causes a reduction in proteoglycans and glycosaminoglycans, thickened elastic and collagen fibers as well as elastin structural changes.
Another important issue is photo-induced aging that is characterized by skin thickening, capillary dilation, sebaceous gland hyperplasia, collagen and elastin degradation, epidermal damage (on keratinocyte nuclei), and dermal damage with elastic fiber degeneration (solar elastosis).
This aspect is responsible for visually evident cutaneous manifestations (Figure 2), including deep and irregular wrinkles, thick texture, resulting from the anarchic production of abnormal elastic tissue (elastosis), collagen loss and degradation, impaired vessel regeneration5.
One of the most important aspects is physiological aging which includes, at the epidermal level, early keratinization, reduction in Langerhans cells, irregular distribution of melanocytes, reduction in dermal papillae and epithelial crests; at the dermal level, reduced thickness, disorganized  and  fragmented collagen and Kligman classification (Figure 3) distinguishes several types of wrinkles.
 Linear or expressional wrinkles: they are initially reversible, caused by the contraction of facial mimic muscles, run always perpendicular to the muscles; they are more marked in emotional and expressive people and are sub-divided into periocular wrinkles or “crow’s feet”, glabellar folds (frown lines), which run horizontally on the forehead, from the nose root in cranial-caudal and  medial-lateral  direction,  and peri-labial (“smoking”) wrinkles that are localized vertically on the upper lip and around the mouth.
Glyphic wrinkles: they are the clinical sign of actinic damage, caused by an accentuation of normal skin folding, run obliquely and perpendicularly to the other types of wrinkles, and are mostly localized on the chicks.
Sleeping lines (creases): they are created by prolonged facial positions, are initially reversible, run perpendicularly to linear lines, are  usually  localized on the forehead  and  chicks.
Ripples  (crinkles):  are small wrinkles found on the arms, thighs and gluteus muscles, related  to  physiological  aging,  due to collapse of subepidermal vertical elastic fibers (due to lack of adherence between dermis and epidermis); they are also  present  in  non-photo-exposed  regions in elderly people, but can be seen at all ages.
Naso- labial wrinkles are deep folds between the upper lip and the nose wings; they delimit the most important muscular masses of the face (especially the orbicular oris muscle and the masseter).
They are formed when the anterior fascial attachments between the skin and the Superficial Musculo-Aponeurotic System (SMAS) become weaker, which results in the collapse of excess skin.
Another important aspect that affects skin aging is a change in the structure of dermis, characterized by a reduction in the cellular component that is submerged in the extracellular matrix formed by the fundamental substance.
The fundamental, or amorphous, substance consists of glycosaminoglycans (GAGs) and proteoglycans which are glycosaminoglycans bound to large proteins (LP) and fibrillar proteins.
The GAGs, D glucosamine or D galactosamine are polysaccharides consisting of disaccharide units, each containing one amino hexose.
Commonly-known GAGs are hyaluronic acid and heparin, while the most abundant GAGs in the skin are hyaluronate and dermatan sulphate.
In young dermis, there is a prevalence of chondroitin-4-sulfate and chondroitin-6-sulfate, while in adult dermis, there is a prevalence of keratan sulfate with depletion of hyaluronic acid7.
It is of note that in dermis all GAGs, with the exception of hyaluronic acid, are bound in large amounts to fibrous proteins thus forming proteoglycans.
Proteoglycans are the intercellular "concrete" that fills the space between the cells of most tissues, including articular cartilage and dermis.
This aspect seems to be particularly important, as GAGs play a crucial role in the fundamental substance of dermis and articular cartilage thus optimizing its structure.
In fact, they are guarantors of isoionia, isoosmia, isotonia of the fundamental substance; thanks to their molecular structure they can fight the positive charges of lytic enzymes such as hyaluronidase, protease, elastase, glucuronidase.
The fundamental substance is the environment where fibroblasts, the cells for the synthesis of elastin, collagen and glycosaminoglycans are submerged.
With regard to this, skin aging involves the so-called "escape"?of glycosaminoglycans of dermis, including hyaluronic acid, with a reduction in cellular synthesis reactions and an increase in catabolic reactions with subsequent dehydration and impairment of the functions of the fundamental substance.
The last important aspect is the alteration of the micro-circle that causes a metabolic change due to which the high-energy producing aerobic glycolytic metabolism switches to a low-energy producing anaerobic metabolism.
The ATP availability is reduced with consequent impairment of anabolic metabolism and reduced biosynthesis of glycosaminoglycans and collagen.
Therefore, we observe a reduced synthesis of collagen Type I / collagen Type III typical of young age as well as a generalized extracellular atrophy in intrinsically aged skin; moreover, photo-aged skin is characterized by catabolic and anabolic remodeling events specific for the matrix components9.
The reduced bioavailability of ATP slows down cellular mitoses with subsequent reduction in skin thickness, while the reduced ability of GAGs to fix cations is responsible for the alteration of the micro-circulation and non-enzymatic glycosylation of collagen.
In Aesthetic Medicine, any type of flaws (wrinkles, so- called cellulitis, overweight, etc.) must be evaluated from the medical point of view with a diagnostic approach.
The first Aesthetic Medicine visit, consistently with the recommendations of the Italian Society of Aesthetic Medicine (SIME), includes medical history and traditional clinical examinations aimed at identifying the patient's request, and includes a number of morphological and functional evaluations, including psychological evaluation, morpho-anthropological and postural evaluation, phlebological evaluation of lower limbs, adipose tissue ultrasound, skin evaluation with cutaneous checkup according to C A Bartoletti-G Ramette?€?s method, and blood chemistry (Figure 4).
This approach allows to design a customized, preventive and site-specific treatment.
In particular, cutaneous checkup according to C A Bartoletti/G Ramette's method includes inspections at naked-eye, magnifying lens, natural light, cold light, Wood?€?s light, corneometry, sebometry, pHmetry, 15% lactic acid sensitivity test and evaluation of reactivity with dermographism.
The evaluation obtained by cutaneous checkup, in particular with instrumental examination, is crucial to formulate a correct diagnosis, to make a cosmetological prescription and to define appropriate treatments for the prevention and management of skin aging.
This procedure helps identify the skin biotype, classify the phototype, make a functional balance, determine the degree of skin aging and monitor skin values over time.
The diagnostic path allows to implement a tailored preventive and corrective program.
It is recommended to use official methods that have been implemented for at least two years and are supported by referenced scientific evidence, published in indexed journals and involving an appropriate number of patients.
According to SIME recommendations, the Board of experts agrees that the interventional approach to patient-reported flaws includes a diagnostic evaluation, a phase of normalization of abnormal parameters, a patient education program on the importance of daily skin care at home and at the clinician?€?s of preventive and corrective treatment based on a shared plan of action, of monitoring of results and the related corrective measures when any changes occur.
Based on these considerations, skin biostimulation is one of the medical-aesthetical procedures used to fight or slow down the process of skin aging whose manifestation is the onset of wrinkles and folds.
The word biostimulation (from the Greek “β?ο?”  (b?os), life and from the Latin “stimulate”, spur) refers to a technique, method or practice that can trigger a response in a living system through the application of a stimulus. 
It is a site-specific intradermal injection treatment (face, neck, d?collet?, hands, body) of substances aimed at stimulating fibroblasts, not only to produce elastin, collagen, hyaluronic acid, being therefore eligible to prevent, delay and affect chrono- and photo-aging.
The crucial aspect of biostimulation is the protection of the physiology of the patient's skin, especially to provide elements useful to cellular regeneration, starting from the improvement of the entire tissue and the restoration of the structures that the aging processes and diseases impoverish through biochemical and biophysical procedures.
Therefore, biostimulation carried out with a site-specific intradermal treatment allows to slow down skin aging by ensuring a better physiological skin brightness, elasticity and turgor.
In summary, before implementing any therapeutic techniques, it is important to collect medical history and carry out traditional clinical examination aimed at identifying what is the patient's request, through a number of morphological and functional evaluations, including morpho-anthropological and postural assessment, phlebological evaluation of lower limbs, hypodermal ultrasound, skin evaluation with cutaneous checkup according to Bartoletti-Ramette?€?s method, psychological evaluation and blood chemistry.
This approach allows to design a customized, preventive and site-specific treatment.
Several types of injectable materials have been used for facial rejuvenation and soft tissue augmentation.
The popularity of facial fillers has grown substantially worldwide due to its effectiveness and safety as a non-surgical procedure.
The ideal injectable material should offer good esthetic results with lasting effects, be safe, biocompatible and stable at the place of implantation, with minimal complications and no risk of migration1.
However, this kind of product has yet to be discovered.
All filling substances, to a greater or lesser extent, have some adverse effect2.
Depending on their permanence in the tissues, cosmetic fillers are classified in two categories: temporary or permanent.
All injectable fillers can produce unexpected adverse reactions, from small to large responses, with severe complications.
Temporary agents may induce serious complications, which usually resolve spontaneously over a variable period of time.
Permanent fillers can also generate minimal adverse reactions such as pain, swelling, erythema, ecchymosis; and large responses such as nodules or granulomas that do not resolve spontaneously4.
Liquid injectable silicone (LIS) is a product without odor or color, composed of polymerized dimethyl- siloxane chains.
Its popularity is based on the fact that it is permanent, economical, minimally antigenic and non-carcinogenic.
Although it is used as a facial filler material, it has not been approved by the US FDA for this application1.
In some cases, it has been associated with displacement or migration which can occur after many years, leading to an accumulation of particles and nodular granulomas at sites far removed from the points of injection7.
These granulomas are also called "siliconomas"? and were described for the first time in 1964.
The inflammation induced by LIS may occur as a response from the immune system to silica per se or its additives (or contaminants) such as platinum, an amorphous aggregate of silica, or fumed silica.
Previous studies support the fact that a small fraction of the granulomas formed in reaction to silicone injections are infectious9.
In addition, patients are not always aware of the material that has been injected or recall if they have previously received treatment with another compound10.
We present a case of "iatrogenic allogenosis"?
Its definition was established by Coiffman in 2012 as a term that defines the disastrous results produced by several permanent substances, months or years after being injected.
It is called "Allogenosis"? because it is caused by allogenic substances, that is, foreign to the organism; and "Iatrogenic"? because it is caused by medical intervention.
The goal of this article is to report a case of filler migration with foreign body granulomas (siliconomas) at a distant site, and to raise awareness of the late complications of soft tissue filler injections.
Autologous fat infiltration (lipofilling) was subsequently performed to correct the hypotrophic defect.
Four years after the first intervention, the patient presented a subcutaneous nodule in the right malar region and nasal root, of approximately 2 x 2 cm, with overlying erythema.
A new MRI was performed showing a collection of subcutaneous material in both malar/ zygomatic areas with left predominance (Figure 3).
Surgical removal of the right malar lesion was performed again, with good scarring.
Two years after the last intervention (16 years after being treated with silicone), the patient kept the small residual erythematous nodules described above and swelling, and began to develop new facial nodules, one at the level of the medial canthus of the right eye and nasal root, approximately 2 x 1.5 cm; and some about 5 mm in the right and left ?€?marionette lines?€?
These lesions did not have clinical signs of infection (Figure 4).
We performed a high frequency ultrasound (HUS) (Figure 5) in which we observed image in"snowstorm"? which suggested the presence of a permanent material, typical of silicone.
Since the lesions were distributed extensively in the dermis of almost the entire facial region, due to the esthetic consequences (scars) of an extirpation, a new surgery was not considered.
She started treatment with Minocycline 100mg / day.
Within 4 weeks of treatment, the swelling and erythema improved substantially.
During the following weeks, the nodules became softer and smaller; however, small nodules around the left eye were still visible.
Minocycline was continued and a follow-up visit 4 months after initiation of therapy showed a significant clinical improvement (Figure 6).
Complications due to injection of LIS are rare.
They include chronic cellulitis, nodules and subcutaneous plaques, foreign body reactions, and migration of silicone.
Treatment of silicone-induced granulomas has been based on case reports, proved difficult to manage and was in many cases unsuccessful.
The treatment must be individualized.
Erythema, swelling, induration, pain, deformity and hyperpigmentation may also appear.
Some authors propose that complications due to LIS are due to the use of adulterated silicone, large volume injections or because of administration by inexperienced/untrained professionals.
Subsequent to injection, silicone is encapsulated in the fibrous tissue due to the host inflammatory response, resulting in increased volume.
Histologically, silicone- induced granulomas contain multinucleated giant cells and histiocytes that are seen in the dermis and subcutaneous cellular tissue, together with polymorphic pseudocystic spaces representing LIS particles.
Our case presented a similar pattern.
In addition, another exogenous material different from silicone could also be observed in the histopathological exam of the patient, similar to what has been published by Wang et al, who suggest that granulomas can also be induced by the injection of adulterated silicone or when injected in conjunction with other substances.
A large volume of injection results in silicone migration thereby leading to granuloma appearance in areas distant from the site of infiltration.
Due to this, our patient presented granulomas in the zygomatic area and medial canthus of the eye, even though the silicone was injected into the "marionette lines".
The pathogenesis of granuloma formation is uncertain.
Granulomas may be caused by a generic response to a foreign body, or to an adulterant in the silicone or by an infectious process.
One of the proposed hypotheses is that liquid silicone can act as a niche for bacterial proliferation.
Non-tuberculous mycobacteria at the subcutaneous level have been reported in adulterated liquid silicone.
Additionally, bacteria can form a biofilm around the silicone.
Treatments range from surgical resection for localized granulomas to treatments with oral or systemic corticosteroid, minocycline, 5-fluorouracil or isotretinoin, among others.
In cases in which a surgical removal is necessary, the patient may present scars.
In addition, sometimes it is necessary to remove thick layers of tissue and the cosmetic result may not be satisfactory.
This happened to our patient, who presented a hypotrophic scar after surgical removal of the"siliconoma"? subsequently needing a new procedure to correct the defect.
Antibiotics, especially minocycline, have been used successfully due to their anti-inflammatory, immunomodulatory and antigranulomatous effects, as well as their coverage for mycobacteria.
Suchismita et al14, have reported a case treated with doxycycline (100 mg every 12h for 3 months) with improvement of granulomatous reactions at 3 months.
We report the successful treatment of multiple silicone granulomas of the face (siliconomas) with a low dose minocycline regime (100mg/day).
Current studies suggest that high frequency ultrasound (HUS) is an economical, useful and non-invasive diagnostic tool to determine the nature and type of material and to identify the injection site and quantity of injected filler.
Grippaudo et al23 demonstrated that the use of HUS helps to identify the place and quantity of silicone injected into the soft tissue.
In ultrasound, permanent fillers such as silicone show "snow storm" image with posterior acoustic shadow.
In this case HUS allowed to identify the actual location and confirm the type of filler injected in the soft tissue of the face.
Granuloma formation by silicone should be considered as a differential diagnosis in any patient with a history of cosmetic injections who develops facial swelling.
In our opinion, monotherapy with minocycline is a good alternative treatment for patients who present facial granuloma.
When EC law-makers decided to regulate the cosmetic sector with Regulation 1223/2009 to establish precise rules for cosmetic manufacturers, they certainly did not foresee how much and fast the web would change and the huge amount of information, sometimes incorrect, that goes around on it and which led to a rapidly growing trend: DIY homemade cosmetics.
The Cosmetic Regulation establishes strict norms - and sanctions for those who break them - aimed at protecting end users on three aspects: the ingredients used, the manufacturing process, the labelling describing the product.
Conversely, homemade cosmetics shirk the laws governing commercial cosmetic products and stand as a sector with no rules and which exposes the unaware users to a number of serious consequences.
Homemade cosmetics are the consequence of misleading and inaccurate information that has been spread over the web and the media on certain ingredients used in cosmetics, supposedly dangerous or anyway inappropriate for skin, thus unnecessarily demonised.
On the one hand, this generated mistrust for everything that appeared artificial and industrially processed or manufactured and, on the other hand, it led to the exasperation of the idea that everything that is natural is also healthy.
This new trend triggered a number of personal care enthusiasts, who unfortunately lack specific professional qualifications, to disseminate increasingly sophisticated recipes through the web, developed through home-based, unprofessional testing, in kitchens and with the little household tools available - a phenomenon that has rapidly gone viral.
The recipes of soaps, creams and deodorants, yet also nail polish and makeup, have spread rapidly over the web and entered the homes of ?€?DIY cosmetic makers?€?through Youtube and internet blogs managed by the pioneers of homemade cosmetics.
As it seems, the reliability and efficacy of the final product is basically given by the number of positive reviews that each recipe obtains, not by rigorous and scrupulous laboratory testing that guarantees product safety and efficacy, as it should be.
Therefore, self-manufactured cosmetics face a number of limitations.
Firstly, there is the purchase and imprudent use of ingredients, which can be harmful if not handled with the proper precautions and may not always meet the required quality standards.
The purchase of ingredients, especially by inexperienced cosmetic makers, on the internet from non-certified ingredient manufacturers or suppliers can indeed prove to be a very bad idea.
In addition, besides sourcing ingredients whose quality is impossible to verify, there is a high risk of inaccurately measuring and combining those ingredients to form the recipe, especially if using household tools, which are clearly inadequate ?€“ clearly, far too many risks that may compromise the quality of the final product.
Another aspect not to be underestimated is ensuring micro biological stability of the cosmetic product, which is definitely not in the ability of homemade cosmetic makers because safety tests cannot be carried out at home.
Finally, also the containers the final products are stored in can release substances that alter the final product, if the quality of such containers is not proven and certified.
If "DIY cosmetic makers"?use the cosmetics they makeon themselves, no particular law is being broken.
On the other hand, if homemade products are used by other people, given as gifts (as certain bloggers like to underline) or marketed through private stores, there may be some serious legal consequences.
In the event that the cosmetic product causes damage to the end user, for instance a bad allergic reaction, though regulation 1223/2009 cannot be enforced, civil law will apply instead - in Italy, it would be article 2043 of the Civil Code - on the grounds that anyone who causes damage to third parties is obliged to pay compensation.
Unfortunately, in this case the burden of proof lies with the injured party, with the consequent difficulty in identifying the culprit.
In addition, besides the combination of ingredients, damage to the user may also occur due to the poor quality of a single ingredient, which may have been purchased anywhere in the world, clearly making it even more difficult to trace back where it came from in the first place.
The hope for the future is to put the spotlight on the need to define the rules for DIY cosmetics, a trend that is silently but rapidly growing in popularity in Italy and all over Europe.